No title

Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

MICHAEL D. BEECHER (167), Departments of Psychology and Biology, University of Washington, Seattle, Washington 98195, USA ALISON M. BELL (227), School of Integrative Biology, University of Illinois, Urbana-Champaign, Urbana, Illinois 61801 ´ RIQUE DUBOIS (59), De´partement de Science Biologique, FRE´DE Universite´ de Montre´al Case Postale 6128, Succursale Centre-Ville Montre´al, Que´bec, Canada H3C 3J7 DIEGO GIL (337), Departamento de Ecologı´a Evolutiva, Museo Nacional de Ciencias Naturales (CSIC), Madrid, Spain LUC-ALAIN GIRALDEAU (59), De´partement des Sciences Biologiques, ` Montre´al, Case Postale 8888, Succursale Universite´ Du Que´bec A Centre-Ville, Montre´al, Que´bec, Canada H3C 3P8 CAMILLA A. HINDE (283), Department of Zoology, Cambridge CB2 3EJ, United Kingdom WILL HOPPITT (105), Center for Social Learning and Cognitive Evolution, School of Biology, University of St. Andrews, Bute Medical Building, Queen’s Terrace, St. Andrews, Fife KY16 9TS, Scotland ROBERT E. JOHNSTON (439), Department of Psychology, Cornell University, Ithaca, New York 14853-7601, USA MATTHIEU KELLER (399), Haras Nationaux, F-37380 Nouzilly, France and Universite´ de Tours, F-37041 Tours, France and CNRS, UMR6175, F-37380 Nouzilly, France and Inra, Umr85 Physiologie de La Reproduction et des Comportements, F-37380 Nouzilly, France REBECCA M. KILNER (283), Department of Zoology, Cambridge CB2 3EJ, United Kingdom KEVIN N. LALAND (105), Center for Social Learning and Cognitive Evolution, School of Biology, University of St. Andrews, Bute Medical Building, Queen’s Terrace, St. Andrews, Fife KY16 9TS, Scotland

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FRE´DE´RIC LE´VY (399), Haras Nationaux, F-37380 Nouzilly, France and Universite´ de Tours, F-37041 Tours, France and CNRS, UMR6175, F-37380 Nouzilly, France and Inra, Umr85 Physiologie de La Reproduction et des Comportements, F-37380 Nouzilly, France ANDREW SIH (227), Environmental Science and Policy, University of California, Davis, Davis, California 95616 BARBARA WEBB (1), Institute for Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom

Preface

Since 1965 Advances in the Study of Behavior has contributed to the ‘‘development of cooperation and communication among scientists’’ in our field and in neighboring fields by publishing critical reviews, theoretical syntheses, and reformulations of persistent problems and by highlighting especially penetrating research that introduces important new concepts. The current volume continues this tradition with a wide‐ranging collection of papers that span much of the modern study of animal behavior. We dedicate this volume to our colleague, friend, and fellow editor, Dr. Christopher J. Barnard (1952–2007). Chris spent his professional career at Nottingham University School of Biology, rising from lecturer to Professor of Animal Behaviour. Chris and I were officemates when he was a graduate student at Oxford studying under the direction of John Krebs. His research at that time was on the foraging behavior of house sparrows and how some individuals parasitize the efforts of conspecifics, research that set the stage for current ideas on alternative tactics and behavioral syndromes. Chris and his students used an integrative approach to the study of behavior, for example, in examining the relationship between behavior, hormones, and parasitic infections, and in the study of individual and kin recognition. He was also deeply interested in animal welfare and championed an animal‐centered approach. In addition to his distinguished and prolific research career (he wrote 120 papers and wrote or edited seven books), Chris was a devoted teacher who encouraged field experiences for students and in 2003 he received the Lord Dearing Award for ‘‘outstanding contributions to teaching and learning.’’ He was also generous to his colleagues serving as Executive Editor of Animal Behaviour (1997–2001), President of the Association for the Study of Animal Behaviour (2004–2007), and in the last two years as an editor of Advances in the Study of Behavior. Chris has left an indelible mark on the field of animal behavior. Chris Barnard’s eclectic interests and wonderful enthusiasm for diverse and interdisciplinary approaches to behavior are reflected in the chapters of this volume. Barbara Webb describes the increasing and varied use of robots to study animal behavior. Luc‐Alain Giraldeau and Fre´de´rique Dubois describe their research on social foraging and exploitative behavior, an approach that originated with some of Chris’ pioneering ideas. Learning and social behavior are also the themes of Will Hoppitt and Kevin N. Laland’s study on ‘‘Social Processes Influencing Learning in Animals’’ and the ‘‘Function and Mechanisms of Song Learning in the Song Sparrow’’ by Michael D. Beecher. Behavioral syndromes are discussed by Andrew Sih xi

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and Alison M. Bell. Parent–offspring interactions, maternal effects, and maternal behavior are the topics of papers by Rebecca M. Kilner and Camilla A. Hinde, Diego Gil, and Fre´de´ric Le´vy and Matthieu Keller. The last three chapters in this volume by Diego Gil, Fre´de´ric Le´vy and Matthieu Keller, and Robert Johnston reflect a broadly integrative approach to behavior by combining mechanisms and function to understand the effects of yolk hormones on bird behavior, the neurobiology of maternal behavior in sheep, and individual and kin recognition through chemical communication in rodents. These nine manuscripts reflect the diversity of approaches and ideas that make the study of animal behavior such an exciting field. H. JANE BROCKMANN

ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Using Robots to Understand Animal Behavior Barbara Webb institute for perception, action and behaviour, school of informatics, university of edinburgh, edinburgh eh8 9ab, united kingdom

I. INTRODUCTION What does it mean to have ‘‘understood’’ or ‘‘explained’’ animal behavior? Tinbergen’s (1963) four questions are often cited: What is the function, how did it evolve, how did it develop (in the animal’s lifetime), and what are the immediate internal and external causes? Of course, as Tinbergen himself realized, these questions are not independent. They can be paired in at least two ways, as concerning ultimate (functional and evolutionary) or proximate (developmental and immediate) causes, and as concerning historical (evolutionary and developmental) or mechanistic1 (functional and causal) accounts. More recently, the concept of mechanism has been more explicitly developed in the philosophy of science as a general account of the nature of explanation (Garber, 2002; Machamer et al., 2000). According to this view, a scientific explanation is the description of a mechanism, that is, of an actual physical system (rather than the more general sense of ‘‘mechanism’’ as some sequence of causal events) consisting of parts or components, their operations and their organization, which interact to produce the phenomena of interest. Importantly, this description can specify the function of the parts at different levels (i.e., not requiring a full reduction to physical mechanics) relative to the interests of the scientist. For example, a population biologist might describe a system made up of replicators with different survival rates, without being concerned precisely how replication comes about; a geneticist, on the other hand, might want to understand how the particular 1 ‘‘Mechanistic’’ explanation is sometimes identified with causal and contrasted to functional explanation. I am taking a mechanistic account to be an explanation of how (causally) a system produces some behavioural capability (function), irrespective of how or why it came to have that function.

1 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00001-6

Copyright 2008, Elsevier Inc. All rights reserved.

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replication mechanism of DNA operates. While both would consider the system they describe to be ultimately grounded in basic physical principles, it is not considered necessary to describe the system down to this level to have provided a scientific account of the phenomenon. Moreover, the explanation at the higher levels might be the same for systems that differ at lower levels—the same population dynamics can result from different replication mechanisms. Or, to take a more neuroethological example, the fact that vertebrate photoreceptors signal increases in light intensity by hyperpolarization, and invertebrate photoreceptors by depolarization, due to completely different transduction mechanisms, ‘‘appears to be trivial’’ (van Hateren and Snippe, 2006) from the point of view of understanding visual processing algorithms, since the response characteristics of the sensory cells to varying input (under daylight conditions at least) are sufficiently similar to be treated as equivalent. The idea of explanation as mechanism description implies that we could evaluate our explanations by building the machines so described and seeing if they produce the relevant phenomena. In this review, I will describe just such a literal approach to understand some of the mechanisms responsible for animal behavior. The discussion above suggests that we can attempt to replicate the relevant mechanisms, at least with respect to a certain level of explanation, without necessarily having to use the same fundamental material basis, for example, we can use novel electronic transduction mechanisms as the front end for vision to replicate some biological mechanism of visual processing. Of course, it remains a matter of hypothesis that the lower level mechanism is not essential to understand the higher level function. There may well be conditions of testing that reveal the difference, for example, in range, sensitivity, efficiency, adaptation, and recovery properties of photoreception. Nevertheless, if we can, for example, fabricate an electronic system that responds to visual motion by adjusting turning torque to successfully stabilize its trajectory (Harrison and Koch, 2000), then this can be considered a potential mechanistic explanation for the optomotor reflex seen in flies (Warzecha and Egelhaaf, 1996)—although, as with any explanation, it remains possible that the precise explanation encapsulated in this device is incorrect. Perhaps more importantly, if we think we have the correct explanation but, on building the described mechanism, find that it does not produce the expected phenomena, it is evident that our explanation is flawed or incomplete. Building machines that replicate animal capabilities as a means of understanding how they work is an old idea, with a history stretching back to the automata of the Greeks. However, until the last century, technological limitations severely restricted the scope of such devices. In 1912, Hammond and Miessner (cited in Cordeschi, 2002) designed and constructed an

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‘‘electric dog’’ which exhibited phototropism by connecting two light sensors via relays to a drive motor and a steering wheel. The design was explicitly influenced by Loeb’s descriptions of tropisms in animals, and Loeb (1918/1973) wrote that: The best proof of the correctness of our view would consist in the fact that machines could be built showing the same type of volition or instinct as an animal going to the light . . . the actual construction of a heliotropic machine not only supports the mechanistic conceptions of the volitional and instinctive actions of animals but also the writer’s theory of heliotropism, since this theory served as the basis in the construction of the machine (pp. 68–69).

A fascinating account of similar early machines is provided by Cordeschi (2002). An important motivation for these machines was to demonstrate that ‘‘biological’’ capabilities such as goal directedness, learning, variety of response, and intelligence could be replicated (and hence accounted for) mechanistically, and did not require some unique or vitalist force. Hull (1943), for example, explicitly outlined a ‘‘robot approach’’: ‘‘Regard . . . the behaving organism as a complex self‐maintaining robot [that could be] constructed of materials as unlike ourselves as may be . . .’’ and argued for the development of ‘‘psychic’’ machines to illustrate that the principles of learning and goal directed behavior could be mechanized. Hull’s ideas inspired the robot rat of Ross (1935), which was built to illustrate that: it may be possible to test the various psychological hypotheses as to the nature of thought by constructing machines in accordance with the principles that these hypotheses involve and comparing the behavior of the machine with that of intelligent creatures (Ross, 1935, p. 387).

But note that this work was (perhaps of necessity) imitation at a high level— this synthetic method is not intended to give any indication as to the nature of the mechanical structures of physical functions of the brain itself, but only to determine as closely as may be the type of function that may take place between ‘stimulus’ and ‘response’ (ibid).

More recently, interest in building machines that reproduce specific behaviors of animals has revived, this time often aimed at replication at a deeper level of similarity, including the ‘‘nature . . . of physical functions of the brain itself.’’ This has been motivated by the recognition that we are still unable to build machines that have the capability and flexibility of animals to interact intelligently with real environments, despite huge advances in

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technology, particularly computational power. With potential robotic applications in mind, it is believed that imitating biological systems could be a good way to discover effective solutions (Ayers et al., 2002; Beer et al., 1997; Paulson, 2004). However, a problem quickly revealed when trying to imitate biology to build better robots is that our understanding of the underlying mechanisms of the biological systems is rarely good enough to enable a direct translation into hardware and software. The problem is more fundamental than just continuing limitations in the available technology for implemention. It is frequently found, when replication is attempted, that supposedly ‘‘complete’’ descriptions of a biological system turn out to be missing essential components. From a technological perspective, it is not important if the solution finally implemented and working on a robot is in fact the same as that operating in the animal. An implemented and working solution has passed a strong test of plausibility as a potential explanation. Nevertheless, if we want to claim that this working mechanism is also an explanation of the biological exemplar, we must apply a number of other criteria for assessing the likelihood of this being a correct explanation of the animal’s behavior. How similar in detail is the actual performance under comparable conditions? How consistent are the implemented mechanisms with what is known of the internal components of the animal? If precise emulation is not possible, is this simply due to technical limitations, or are there more fundamental problems with the proposed mechanism? In previous articles, I have discussed at greater length the issues involved in deciding to what degree a particular implemented mechanism on a robot might be considered a good explanation in biology, and how the approach of building a physical robot compares to other modeling approaches such as computer simulation or mathematical models (Webb, 2000, 2001, 2006b). Here, I intend instead to review in more practical terms the process and outcomes of adopting this methodological approach, which could be summarized as ‘‘mechanistic explanation by replication.’’ This review will describe what could be considered a series of steps toward building a ‘‘complete robot cricket’’ (Fig. 1). This choice of animal is somewhat arbitrary, but seems a reasonable aim in terms of behavioral capabilities that are not (at first sight) obviously beyond modern technology. Nevertheless, if we could reproduce cricket behavior, we would have solved some interesting problems—such as localization, recognition, navigation, motor control, multimodality, and learning. There is substantial biological data on each of these issues that the robot can incorporate, and where data is lacking the robot can provide indications of the possible form of future data, that is, make predictions to guide experimentation. Of course, some other interesting problems, such as high level reasoning, will

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Fig. 1. Three robot platforms used to test models of cricket behavior: front, a ‘‘Khepera’’ (Mondada et al., 1994), which performs phonotaxis; middle, a ‘‘Koala’’ (K‐Team, 2001) that combines auditory and visual behaviors; rear, a ‘‘Whegs’’ (Horchler et al., 2004) that emulates insect walking.

be neglected, as indeed will be a number of cricket capabilities that are not viable to copy, such as digestion, reproduction, and development. While the cricket will act as a unifying theme in what follows, the work discussed will also be drawn from research on other insects that have similar capabilities to the cricket; for example, in visual control or six‐legged walking, where knowledge of these systems in the cricket is less advanced. Nevertheless, a ‘‘complete robot cricket’’ remains the goal of this research in several important senses. First, it is considered essential to this approach that the complete loop of behavior, from environment to sensors, central processing, actuation, and subsequent effect on and feedback from the environment is modeled; in particular with full consideration of the physical interactions and how they contribute to the behavioral capabilities. Second, the implemented mechanisms must, of necessity, be completely and precisely specified—there is no room for loose definitions, approximate specifications, or unexplained black boxes. This can result in the filling of gaps by speculation that, to a conventional biologist, may seem unjustifiably beyond the available evidence. Nevertheless, as I shall argue further below, this ungrounded speculation can often usefully complement the more cautious approach, if only by vividly exposing limitations in the conventional account. Finally, ‘‘complete’’ is intended in the sense that more than one behavior or sensorimotor system should be implemented on the same

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robot, to address directly the issue of how different behaviors interact and what mechanisms are required to organize them into a system that more fully resembles a whole organism. It is worth noting at this point that there are other researchers pursuing a similar target of implementing robotic mechanisms aimed at a ‘‘complete’’ animal, although more commonly, work in this area focuses on reproducing just one biological capability, such as coordinated walking (e.g., Delcomyn, 2004; Espenschied et al., 1996), specific visual reflexes (e.g., Srinivasan et al., 1999), or collective interactions (e.g., Floreano et al., 2007; Melhuish et al., 1999) (see many further examples in the following sections). To give just three examples here of more ‘‘complete’’ approaches: Rana computatrix (Arbib, 1982, 1987; Arbib and Liaw, 1995; Corbacho et al., 2005) is intended to embody a number of different visual control mechanisms identified in frogs, and explore their interactions in closed loop behavior. Although largely tested in simulation, the mechanisms have also been explored in robot implementations (e.g., Weitzenfeld, 2004). The Psiharpax project (Meyer et al., 2005) ‘‘aims at endowing a robot with a sensori‐motor equipment and a neural control architecture that will afford some of the capacities of autonomy and adaptation that are exhibited by real rats.’’ It includes models of hippocampus place cells, basal ganglia action selection (based on proposal of Gurney et al., 2004; implemented on a robot by Prescott et al., 2006), and associative and reinforcement learning. Ayers and Witting (2007) describe an underwater robot that aims to reproduce a wide range of sensory, motor, and behavioral capabilities of the lobster. Crickets have been studied in behavioral biology for many years, particularly for their conspicuous communication behavior (Huber et al., 1989; Pollack, 2001). Rather than taking a traditional approach to explaining their behavior, the following has been organized to reflect the three themes of completeness outlined above. What is learnt by approaching mechanisms of cricket behavior in terms of complete physical loops through the environment? What has resulted from the attempt to fill in all the black boxes? How has the consideration of interaction between behaviors informed the research? The intention is to illustrate key aspects of the biorobotic methodology.

II. BEHAVIOR AND THE PHYSICAL INTERFACE A fundamental difference of robotic implementations from computer simulations of animal behavior (or hypotheses expressed verbally or mathematically) is that the problem of physical interaction with the environment has to be solved. That is, there must be explicit means of transducing

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relevant signals and of materially affecting the surroundings. While these processes can in principle be included in simulation, in practice they are often finessed, for example, by assuming the animal has a ‘‘mate’’ detector, or that intended motion in a particular direction or speed actually results in such motion.2 A real robot has to act within a world of real physics, whereas in simulation the environment must also be modeled, in a way that is bound to include simplification and very likely distortion. While the robotic interaction with its environment might introduce its own distortions—for example in scale—and solving the problems of real physical interactions may sometimes seem a distraction, the solution can help emphasize the direct role of physical constraints in shaping behavioral capabilities. It is obvious that animals cannot respond to signals for which they have no sensors. In some conspicuous examples, sensors have evolved to serve highly specialized functions (e.g., a chemical receptor pathway dedicated to a specific pheromone as found in moths; Matsumoto and Hildebrand, 1981; Sanes and Hildebrand, 1976), so their direct role in determining the behavior is clear. Nevertheless, in attempts to explain animal behavior, the extent to which the form of the physical coupling might substantially simplify the subsequent processing needed to control the behavior is not always appreciated (Wehner, 1987). For example, there are many ways to obtain depth information from vision, but one of the simplest is to assume a flat topography and use elevation (Collett and Udin, 1988). Crabs, which live in such a flat habitat, appear to be specifically tuned to this cue by systematic variance in resolution along the vertical dimension of the eye, mapping equal depth changes to equal numbers of ommatidia spanned (Zeil and Hemmi, 2006). Understanding the physics of these interactions can similarly be critical for actuation (i.e., the means by which animals cause effects on the world— principally to move themselves in relation to it, or to move parts of it in relation to themselves). Again, this is sometimes obvious, particularly where there have evolved specific actuators to perform specialized tasks, such as the long ovipositor enabling the female cricket to lay eggs deeper within a substrate (Masaki, 1986). But sometimes it is more subtle, such as the potential contribution of compliance to stabilization in locomotion (Kubow and Full, 1999). A robotic perspective, by forcing consideration of the physical, can be a useful way to highlight these contributions, as the following examples further illustrate. 2 It is true that the problem is also sometimes finessed in robotics by choosing some arbitrary but easily detected stimulus (such as uniquely coloured objects) to represent the natural stimulus; and equally true that some simulations may include realistic details of transduction processes.

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A. DIRECTIONAL HEARING IN CRICKETS Cricket males produce calling songs, attractive to females, by moving their wings so as to rub a comb on one against a plectrum on the other, creating a vibration that is amplified by a resonant area on the wing (Bennett‐Clark, 1989). The carrier frequency of the song, around 4–5 kHz for most cricket species, is a consequence of the rate at which the teeth of the comb pass over the plectrum. This carrier frequency is one of the cues that female crickets use to discriminate conspecifics, that is, they approach more directly songs that are nearer the correct carrier frequency (Oldfield, 1980; Thorson et al., 1982). Sounds at higher frequencies (more than 15 kHz) produce an avoidance reaction as they usually signify bat ultrasound (Pollack and Hoy, 1981). The wavelength of the calling song (around 6–7 cm) compared to the separation of the crickets ears (1–2 cm) produces a problem for localizing the sound to which evolution has provided an elegant solution, one that was independently discovered by engineers and called a pressure difference receiver (Autrum, 1941). The cricket’s ears consist of a pair of tympani, on each front leg, and associated vibration receptors that appear to have evolved from proprioceptive chordontonal organs (Yack, 2004). The cricket’s body provides no significant sound shadowing for sounds of the wavelength of the calling song (Boyd and Lewis, 1983), so there is little external amplitude difference between the ears, and the time difference in the arrival of sound is only a few microseconds. The tympani are connected to each other and to a pair of spiracles on either side of the prothorax by a set of tracheal tubes (Fig. 2A) (Larsen and Michelsen, 1978). Sound thus reaches both the external and the internal surface of the tympani, the latter after delay and filtering in the tracheal tubes. The vibrations of the tympani are thus determined by the combination of filtered delayed and direct sounds (Michelsen et al., 1994). We have mimicked this auditory morphology in an electronic circuit used on a robot (Lund et al., 1997). In a simplified approach, which illustrates the principle, two microphone inputs are used.3 Each input is delayed (representing time for sound to travel through the tracheal tube) and then subtracted from the other (representing sound on opposite sides of a tympanum) to form a composite response (corresponding to the combined sound vibrating the tympanum). In our circuit, the distance between the

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In Torben‐Nielsen et al. (2005), we describe a more elaborate four input system using the delays and propagation amplitudes as measured between the tympani and spiracles for the cricket. However, this turns out not significantly to affect the behavioural results for the robot.

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two microphones was set at 18 mm, that is,
¼ of the wavelength of the Gryllus bimaculatus song carrier frequency of 4.7 kHz. The delay was set to 53 ms, the time for sound to propagate the distance between the microphones. Thus, the direct and delayed inputs to the sum were 180 out of phase if the sound was on the same side as the direct input; and if sound was on the opposite side, the direct and delayed inputs were in phase. The relative phase varied between these two extremes as the sound direction changed. By physically summing these inputs that are in or out of phase, the electronic analog of the tympani responded at an amplitude that corresponded to the direction of the sound source (see Fig. 2B). In the cricket, the length of the delays is fixed by the morphology of the auditory system. This appears to involve not just the tracheal tube length and shape, but also the contribution of the dividing membrane, which enhances the phase shift, particularly for the carrier frequency (Michelsen and Lo¨he, 1995). As a consequence, for both the cricket and the robot, the range of frequencies for which accurate directional information is available is limited by these physical factors. But this might directly contribute to the apparent selectivity of female crickets for a particular carrier frequency in the song (Fig. 3). When two simultaneous songs with different carrier frequencies are presented to our robot, it consistently ‘‘prefers’’ the 4.7 kHz song (Lund et al., 1997). Songs of other frequencies are equally audible to the robot, but less localizable, that is, this selectivity is achieved without any explicit process to filter for the sound frequency. Does this fully account for carrier frequency selectivity in the cricket? There are several caveats. The propagation of sound within the trachea is complex and several mechanisms contribute to shaping the delay and amplitude of the signal reaching the inside surface of the tympanum. Some of these processes appear to be frequency dependent (Michelsen et al., 1994) and so might contribute to selectivity. The phase delay tuning is unlikely to be precise due to natural variation in cricket size; movement of the legs while walking and of the spiracles while breathing are also likely to change the exact properties. However, it is not essential that the cricket has precise directional accuracy—in the robot experiments, a small mislocation of the sound direction only resulted in slightly more curved trajectories to a sound source rather than missing it entirely. Finally, there is also frequency tuning in the auditory receptors of the cricket (Esch et al., 1980; Imaizumi and Pollack, 1999; Mason and Faure, 2004; Oldfield et al., 1986) with a large proportion of the sensory cells responding best around the natural carrier frequency. As yet the precise transduction mechanism that produces this tuning, and the extent of its contribution to the behavioral preference, is unknown.

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Fig. 3. Tracks of the robot when played simultaneous calling songs at different carrier frequencies (4.7 kHz vs 6.7 kHz). The robot ‘‘prefers’’ the cricket carrier frequency (bottom speaker) because of the tuned delay in the auditory periphery, rather than explicit frequency filtering. Left, each trial is started from the same location; right, each trial is started from a different location.

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B. VISUAL STABILIZATION Several behaviors observed in crickets (e.g., Bo¨hm et al., 1991) but more thoroughly studied in other insects involve the use of visual motion to control heading. These include the optomotor response (Fermi and Reichardt, 1963; Goodman, 1965; Go¨tz, 1975; Reichardt, 1965) already referred to in the introduction, and also centering responses, balancing optic flow on each side (Srinivasan and Zhang, 1997; Srinivasan et al., 1991), a tendency to approach dark bars (Atkins et al., 1987; Horn and Wehner, 1975; Robert, 1988; Wallace, 1958), chasing of small visual objects (Land and Collett, 1974; Srinivasan and Bernard, 1977), and responding to visual expansion by avoidance or landing manouvres (Judge and Rind, 1997; Tammero and Dickinson, 2002). Inspired by these behaviors, there has been much interest in devising suitable sensory arrays and processing algorithms to extract visual motion for use in robot control (e.g., Franceschini et al., 1992; Srinivasan et al., 1999; Thakoor et al., 2004). One interesting line of research is the development of visual sensors that combine photoreceptors with signal processing in analog electronic hardware, to obtain rapid and continuous motion information that can be used directly for controlling robot motion (e.g., Harrison and Koch, 1999; Higgins, 2001; Liu and Usseglio‐Viretta, 2001; Yakovleff et al., 1995). An important aspect of this technology is that it is potentially much more power efficient than conventional digital computation (Mead, 1989; although a combined approach may be the optimum, Sarpeshkar, 1998). This reflects increasing recent attention paid to the issue of energy efficiency as a constraint on sensory processing in biology (e.g., Laughlin et al., 1998; Niven et al., 2007). There has been a particular interest in using this technology on flying robots (e.g., Zufferey and Floreano, 2006), which are strongly constrained in the weight of the power source they can carry (Ellington, 1999). Typically, these sensors have much lower resolution than conventional cameras, but neither animals nor robots want more powerful sensors if the cost in energy to transport, maintain, and operate them is too high. For most of these tasks, it is not necessary that the processing recovers the actual motion, but rather that it recovers ecologically relevant aspects of the motion, as required for appropriate motor responses. This may involve a system highly tuned to just one motion field, as in the locust lobular giant motion detector (LGMD) (Gabbiani et al., 1999, 2002; Rind and Simmons, 1997), which has a systematic dendritic layout making it selective for motion on a collision course with the animal. This system has been successfully copied in several robot implementations (e.g., Blanchard et al., 2000), most recently by Yue and Rind (2006) who introduced a convolution interaction between inputs that weakened the relative input of isolated

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motion compared to locally coherent motion. Whether this additional process, which substantially enhanced the ability of the robot to deal with complex backgrounds and varying light levels, has a counterpart in the locust visual system is as yet unknown. Other identified neurons that appear tuned to specific motion fields corresponding to self motion (Borst and Haag, 2002; Egelhaaf et al., 2002; Krapp, 2000; Krapp and Hengstenberg, 1996) have been shown to be sufficient for extracting egomotion information using a very simple and computationally efficient linear estimator, tested on a gantry robot (Franz et al., 2004). Franceschini et al. (2007) have shown how a minimalist (two photoreceptor) optic flow sensor based on insect elementary motion detectors (Pudas et al., 2007) can be used on a robotic helicopter to address the long‐standing issue of how a flying insect controls its height above the ground, for example in take off, landing and maintaining a constant height above varying terrain. The insect could attempt to measure altitude, or derive it from the relationship of ventral optic flow (VOF) to groundspeed—basically, the higher it is, the slower will be the apparent motion of the ground directly below it. Instead, it appears that the directly available cue—VOF—is used in a feedback control loop, with the insect altering its lift to maintain a set‐ point VOF, and thus a constant groundspeed:height ratio. This simple mechanism has a number of desirable properties. If the animal increases its forward speed, it will automatically increase its height as it takes off. If it gradually decreases speed, it will gradually decrease height and thus land smoothly. If the terrain rises, the VOF will increase and the insect will compensate by increasing its height. If the insect is slowed by a headwind, it will descend; this is a strategy likely to reduce the headwind, or even to lead the insect to land if it cannot make any progress against the headwind. All these features could be demonstrated on the robotic implementation and compared to reports of insect flight in these different conditions (Franceschini et al., 2007). Visual stabilization of trajectories, in insects generally, and crickets specifically, is not only based on optical flow information. Crickets, like a number of other insects, have a distinct visual area at the top of the eye, the dorsal rim, specialized for polarized light vision (Labhart and Meyer, 1999). It has been shown that crickets will maintain a consistent walking direction with respect to the plane of polarization (Brunner and Labhart, 1987). Each ommatidia has orthoganally oriented receptors, and polarized light sensitive interneurons in the medulla exhibit an opponent response (Labhart, 1988), that is, to the difference between the orthogonal receptor responses, thus eliminating the effects of background illumination levels. These POL1 interneurons have three basic orientations, at 10 , 60 , and 130 with respect to the body orientation (Labhart, 1988). They also show

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wide field spatial integration, which improves estimates of the sky polarization under cloudy conditions and increase overall sensitivity (Labhart et al., 2001). This sensory system has been copied using photodiodes and linear polarized filters on the Sahabot robot (Lambrinos et al., 1997) with three polarization opposition units at the same orientations as the animal. The signal was shown to be effective as a compass input for accurate path integration, as has been studied in detail in desert ants (Wehner, 1994; some evidence that crickets use polarized skylight for path integration is provided by Beugnon and Campan, 1989). Lambrinos et al. (2000) also noted that the compass direction could be obtained from the polarization pattern in several different ways: by finding the direction providing the maximum response, by pre‐scanning the full range of directions and storing the response in a look‐up table, or by deriving the direction analytically. The last is perhaps unlikely for the animal, but does provide a measure of what is the best possible information that might be obtained from the sensor array, which could be used in comparisons with the neural and behavioral response. Recent results from locusts (Heinze and Homberg, 2007) have suggested that there is a systematic encoding of e‐vector information in the central complex (for related results in crickets, see Sakura and Labhart, 2005). Another result from the Sahabot robot, and its testing in the real desert environment of ants, was the observation that reliable use of the polarization pattern was dependent on stable tilt and pitch position of the sensors with respect to the sky. This problem of stabilization relative to the horizon might be solved by the additional visual sensors, found in many insects including crickets, known as ocelli (Goodman, 1981). These single lens sensors, separate from the main eye, occur as three dorsal units on the head in most flying insects, one on each side and one centrally at the front. As a consequence of their low resolution and simple optics, these sensors are thought to provide only coarse information about the light level in their respective pointing directions (but see Berry et al., 2007). Their positioning makes them ideal for sensing the change in light level that occurs when deviation in roll or pitch takes the sensor above or below the horizon (Stange, 1981; Taylor, 1981; Wilson, 1978). Several robotic implementations have demonstrated the effectiveness of such a sensor system for maintaining a steady pose. Chahl et al. (2003) describe a sensor based on ocelli in the dragonfly. This is designed to detect correlated changes in illumination (e.g., one side decreases as the other increases) which are likely to be due to self motion rather than external changes. These cause a reactive motor response to attempt to rebalance the light levels. By using UV/green spectral opponency (Chappell and DeVoe, 1975), the system also eliminates the effects of

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sun and varying sky color. Embedded in a small aircraft, it can be used to apply simple proportional control for roll and pitch to stabilize flight relative to the ocelli cues. There is still much to be discovered about how the various visual—and nonvisual—stabilization mechanisms in insects may be integrated. For example, Parsons et al. (2006) have identified a lobular tangential cell, sensitive to large‐field visual rotation from the compound eyes that also responds to rotation signaled by the ocelli. Reiser and Dickinson (2003) have developed a hybrid computational/mechanical model for testing different models of fly flight that combine visual information with proprioceptive information from the halteres (gyroscopic sensors) under appropriate conditions of dynamic feedback. C. MECHANORECEPTORS AND AVOIDANCE Most insects are covered with a variety of mechanoreceptors, which play a role in many behaviors, particularly providing proprioceptive signals. One nonproprioceptive function is wall‐following behavior, during which the mechanical response of the antennae is used to maintain or modulate the distance from the wall (Camhi and Johnson, 1999). Real antennae have a large and complex range of tactile responses. There are campaniform sensilla, marginal sensilla, and terminal pore hairs found on each of the 150–170 segments of the cockroach flagellum; and mechanoreceptive hairs, chordontonal organs, and Johnston’s organ in the base segments (Seelinger and Tobin, 1981). However, it is unclear how much of this complexity is needed for behaviors such as wall following. We have used IR proximity sensors on a robot (Chapman and Webb, 2006) to mimic lightweight, low inertia antennae held in a fixed position relative to the body, as observed in running cockroaches (Camhi and Johnson, 1999). A minimal ‘‘distance’’ map, distinguishing stimuli at the tip versus the base of the virtual antenna, was found to be sufficient as input to a neural network that controlled both fast wall‐following‐ and escape behaviors (see Section III.B). For example, the robot could track a zig‐zag shaped wall at a speed of over 20 cm/s. It was unable to follow walls closely if the only sensing was at the antennae base, suggesting that the animals are using contact distance information from the antennae in performing this behavior, not just simple contact. Lee et al. (2008) used sensors designed to emulate different mechanical properties of cockroach antennae for wall following. A tapered tube of polyurethane emulated decreasing stiffness along the length of the antennae. The tube contained several flex sensors, from which an estimate of the bending of the antennae and hence the distance from the wall could be obtained. This was mounted on a robot and used to test a simple model for

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continuous angular adjustment using proportional‐derivative (PD) feedback control (i.e., turning rate proportional to the distance and to the rate of change in distance). This model had already been shown to fit cockroach behavioral data (Cowan et al., 2006), and neurophysiological data from the antennal nerve suggested that sensors can provide the required distance and velocity information (Lee et al., 2008). Testing on the robot showed that this model could suffice under more realistic dynamic conditions such as friction between wall and antennae and forward speed dynamics. Although a wheeled robot was used, the model was also elaborated by connecting it to an established model of leg dynamics, showing that it is consistent with the control parameters needed for six‐legged running. The aim was ‘‘to address increasingly refined questions about the underlying biological system’’ at appropriate levels (Lee et al., 2008). Another well‐studied mechanoreceptor system in crickets and cockroaches are the cerci, a pair of appendages on the rear of the abdomen covered in sensory hairs specialized for detecting air currents (Jacobs, 1995). One behaviorally significant source of these signals is the air movement created by predators, such as wasps (Gnatzy, 1996) or wolf spiders (Dangles et al., 2006). The animal can produce a very rapid (<100 ms) escape response, consisting of an initial turn to orient away from the wind direction, followed by burst of rapid running. In some circumstances, a jump response might be produced (Tauber and Camhi, 1995), and crickets have also been observed to freeze or kick (Gnatzy, 1996). The hairs on the cerci are mechanically constrained to oscillate in a preferred direction (Edwards and Palka, 1974) functioning as an array to cover the full range of possible attack directions (Landolfa and Jacobs, 1995). We devised hair sensors for a robot that preserved this characteristic directional responsiveness (Chapman, 2001). Each consisted of a small coil of wire (taken from a light‐bulb filament) with an extended straight portion at the end forming a hair of around 10 mm in length. The coil surrounded an insulated wire with the insulation scraped off on one side. Wind would cause the hair to vibrate and the coil to touch the central wire, acting as a switch. Only vibration in one direction would close the circuit. Despite the simple nature of this device, it provided a surprisingly effective (if not highly robust) sensor. Each closing of the switch could be treated as a spike in a sensory neuron, and a burst of wind would produce a spike train. Four sensors were mounted, facing different directions, on each of two ‘‘cerci’’ extensions at the rear of a small‐wheeled robot (Fig. 4). This proved sufficient to allow the robot to make a directed escape comparable to the cricket. One obvious question raised is why, if eight hairs appear sufficient for the robot, the adult cricket has close to 300

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Fig. 4. Top left, a robot with wind‐sensitive hairs mounted on rear cerci. Lower left, a close up of the hairs, which are constructed from light‐bulb filament wire. Right, The neural circuit controlling the escape behavior.

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filiform hairs on the cerci. It seems unlikely that they are contributing only to escape behaviors, but may in fact be involved in more sophisticated detection of environmental features signaled by wind flow. One limitation in this work is that the scale of the robot hair sensors differs by several orders of magnitude from those on the cricket. Consequently the transduction of the stimulus will differ, for example, in the extent to which the hair projects from the boundary layer of airflow around the cerci. We have recently been investigating the design of a micro‐ electromechanical system (MEMS) version of the hair sensors (Argyrakis et al., 2007). A similar aim but rather different design is described by Krijnen et al. (2006). These sensors should be of comparable scale to cricket filiform hairs, although it will be difficult to obtain comparable sensitivity, as cercal hair sensors have been estimated to be the most sensitive receptors of any animal system (Shimozawa et al., 2003). Designs exploiting the mechanics of silicon, as for designs exploiting subthreshold electrical properties such as the analog electronic visual sensors discussed in Section II.B, are much more subject to variation, distortion, and breakage than conventional mechanical and electronic design. In this there is a clear resemblance to natural systems, leading us to explore how sensory interfaces and subsequent processing can best deal with this level of variation. Redundancy, robustness to noise, and/or adaptivity become essential elements of the problems to be explored. We have recently proposed a mechanism for homeostatic tuning of receptors and shown how it might allow a robot to adjust to the variation in its sensor properties (Gonos and Webb, 2008). D. ACTIVE SENSING The pattern of movement made by the sensor itself can extract specific kinds of signals. Du¨rr and Krause (2001) describe how stick insects make continuous cyclic movement patterns with their antennae, coordinated to the stepping cycle. The motion is well suited to detect obstacles of a height that requires alteration of the stepping pattern. They describe this as using a ‘‘leg‐ like sensor to guide a leg.’’ Kaneko et al. (1998) have studied antennae‐ inspired artificial sensors and shown, for example, how the contact point of an object on a flexible antennae (i.e., distance of an obstacle) can be recovered by measuring the rotational compliance at the base when it is actively driven. In Du¨rr et al. (2007), contact distance is instead derived from the frequency of oscillation measured at the tip after active contact with an obstacle, using a bionic sensor based on stick insect antennae morphology. In addition, the damping characteristics of the vibration can be used to rapidly and reliably differentiate materials with different compliance. In each case, the useful signal is generated by the active movement of the sensor.

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Movement can often disambiguate a sensory signal: A good example is that of a two‐input auditory system which, for any sound difference at the ears indicates a cone of possible locations in three‐dimensional space, sometimes called the ‘‘cone of confusion’’ (Blauert, 1997). But each possible position would predict a different change in the sound at each ear for a particular movement (Wallach, 1940). In an animal or robot doing phonotaxis, equal strength of signals in both ears could mean the sound is directly behind rather than directly in front. One might expect this to result in the robot moving away from sound as often as toward it. However, as soon as there is deviation from exactly 180 (direct retreat from the sound), the resulting ear difference will induce further turning away from 180 and toward the sound. A noise‐free simulation might get stuck moving in exactly the wrong direction, but this does not happen with the robot. Note that as the cricket has its ears on its legs, simply stepping will produce the required deviation. Active vision has been a very important approach in robotics. It has often been noted, for example, that depth information can be extracted from the relative visual motion of surroundings caused by an animal’s or robot’s self motion, and it has been shown that insects can use such information (Lehrer et al., 1988). Kral (1998) reviews research on how the praying mantis moves its head to obtain depth from motion parallax before making a jump or strike. Katsman and Rivlin (2003) have built a ‘‘mantis head camera’’ that mimics these movements, and they show that the accuracy that can be algorithmically expected from this system (and occurs in the implementation) is closely comparable to that reported for the animal. It has been suggested that the saccadic flight motion (straight segments followed by sharp turns) in the fly allows it to separate translatory and rotational visual motion (Land and Collett, 1997) so that the former can be used for depth information; this principle has been used successfully on robots such as the fly‐vision inspired robot described in Franceschini et al. (1992). Kern et al. (2005) recorded from large‐field motion detectors in the fly using a replay of the natural stimuli sequence created in free flight, and found strong responses to translatory optic flow. They note that: ‘‘Our conclusions obtained with behaviorally generated optic flow do not match previous conclusions based on conventional stimuli exclusively defined by the experimenter.’’ E. SIX‐LEGGED WALKING As for sensing, sometimes the problems of locomotion can be solved by simple physics. Cockroaches and other insects, at least during straight walking on flat surfaces, typically use a tripod gait (Delcomyn, 1985;

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Graham, 1985; Wilson, 1966) in which the front and rear legs on one side of the body move in phase with the middle leg on the other side, providing three points of support at all times. In principle, this gait pattern can be very easily reproduced. The robot RHex (Saranli et al., 2001) uses a single motor per leg, which rotates quickly while the leg is in the air to emulate the swing phase of a step, and more slowly while on the ground to emulate the stance phase. The pattern can be generated by a clock signal to produce a basic tripod gait, which along with the natural compliance of the leg allows the robot to negotiate uneven terrain at a rapid speed. In Spagna et al. (2007), the agility of this robot is improved by two further mechanical adjustments: changing the leg orientation to have a broader ‘‘foot’’ contact with the substrate; and adding spines to the sides of the leg. The demonstration, using this robot, that no explicit adaptation of the gait is necessary for running over uneven surfaces has led to tests on the cockroach itself of the amount of variation in muscle activation during escape runs over flat versus uneven terrain. It was found (Sponberg and Full, 2008) that despite substantial perturbation of the body axes on rough terrain, it could be traversed almost as rapidly as flat terrain and there was no evidence of adjustment of the muscle action potentials; suggesting that distributed mechanical feedback is the main mechanism for stabilization in this situation. Another simple hexapod robot design (Quinn et al., 2001) uses one drive motor for all six legs to produce a tripod pattern of footfalls by replacing the swing of individual legs with the rotation of three‐legged wheels (called ‘‘Whegs,’’ see Fig. 1). The rotating legs also mechanically imitate two other features of cockroach behavior when surmounting obstacles (Watson et al., 2002): that they swing their front leg high, and if encountering large barriers, change gait to move contralateral legs in phase. In the robot, torsional compliance in the axles means that if one leg is unable to raise the body over an obstacle, the other leg will rotate to come in phase and thus help surmount the obstacle, that is, variation in the gait pattern is obtained purely through the mechanics. Because of the efficiency and reliability of the Whegs design, we have used it to implement a version of the cricket robot that can operate over uneven surfaces, such as outdoor terrain normally encountered by the insect (Horchler et al., 2004). While this was successful in practice in showing the sound source could be located under such conditions, the limitations of this robot platform raised several new issues for understanding cricket behavior (Reeve et al., 2005). One is a simple issue of self‐ generated noise—from the motor in the case of this robot, from vibration due to foot‐falls in the cricket (Schildberger et al., 1988b). Another is that both robots (RHex and Whegs) described above are very limited compared

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to real insect walking mechanisms. An important advantage of having three or more degrees of freedom per leg, as insects do, is that this permits motion at any time in an arbitrary direction. The Whegs robot, which is steered essentially like a car, has a large turning circle that makes it effectively impossible to replicate cricket‐like paths toward the sound source. The RHex robot is able to turn on the spot by separate control of the rotation of legs on each side, but cannot manouver sideways, and is effectively no more ‘‘insect‐like’’ in the movement of its body than a dual drive wheeled robot. While this might be sufficient (with the additional effects of compliance and springiness in the legs) for replicating fast running in cockroaches, it is probably not adequate for reproducing realistic turning control when modeling slower moving insects that are altering their heading direction toward an attractive stimulus. With the aim of understanding how a robot with a more insect‐like leg morphology might best be able to control the complexity that results from having three joints on each of six legs, we have studied and modeled turning behavior in stick insects (Rosano and Webb, 2007). The underlying coordination mechanism of the model uses a set of distributed inter‐leg influences to create a walking pattern, rather than a central generator, as described by Cruse (1991), which has been successfully implemented in a number of robots (e.g., Espenschied et al., 1996; Ferrell, 1995; Kindermann, 2001; Weidemann et al., 1994). An interesting addition made by Cruse to his control model is that coordination between the legs can be aided by a feed‐forward principle (Cruse et al., 1996, 2007). If one joint is pulling in the desired direction of motion, and there is some passive compliance in all joints, then the deviation in each joint created by the pulling leg is in fact exactly the motion required for that joint to act in coordination with the others. By using a positive feedback of this motion, each joint can make the right response for globally coordinated action, without any explicit top–down calculation or control. This is another example where the physics (the fact that the legs are mechanically linked via the body and the ground) has simplified the problem to be solved. However, actually implementing such a control scheme in a dynamic walking device is still nontrivial. For example, to avoid the system responding to any external force (such as gravity), there must also be negative feedback. Also for it to change its behavior (such as turning toward a stimulus), there must be some active control, not just passive responsiveness. From close analysis of the leg movements of freely turning stick insects, generated in response to an attractive visual stimulus (Rosano and Webb, 2007), we observed that turning motions could be well described by the following assumptions:

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 The front legs attempt to follow a trajectory that pulls the prothorax

(and hence the rest of the body) in a straight line toward the target.  Rotation is aided by a sideways movement of the middle legs and

differential forward motion of the rear legs (but without explicit targeting in either segment). Note that this improves tightness of turns but is not in fact necessary to produce a turn (Rosano and Webb, 2006).  Each leg joint also responds to the passive motion caused by the movement of the other legs, so that the actual movement is a combination of feedback and feed‐forward influences (a similar control approach, also inspired by the stick insect, has been developed independently by Schneider et al., 2006). Implementing this control on a dynamic simulation of a six‐legged robot, it was possible to produce very effective and insect‐like turns (Rosano and Webb, 2007). Suitable coordination of the legs also emerged (without having been pre‐programmed) for several other situations such as walking on tilted surfaces.

F. SUMMARY The examples discussed in this section illustrate how aspects of physical embodiment can be critical to the mechanisms of behavior, often simplifying the problem to be solved. Replicating these physical details can be very informative. Indeed, independently of attempts to replicate the behavior, a robot equipped with sensors or actuators that resemble those of the animal can be usefully employed to characterize the nature of the problem in the real (or experimental) environment of the animal. For example, Grasso et al. (1996) used a lobster‐like robot to understand odor‐dispersal patterns in turbulent water. Labhart (1999) used an opto‐electronic model of the cricket’s POL1 neuron to measure the effect of aperture size (effective spatial field) on polarization estimates under clear and cloudy skies. The chemical plume that attracts moths to a mate is characterized using robot‐ mounted sensors by Pyk et al. (2006), showing that the temporal structure (patchiness) decreases with increased flow rates (wind speed), and there is an optimum match of the sensor temporal dynamics and the flow velocity. Ideally, to best explore and exploit these kinds of physical interactions, a robotic model of a biological system would use sensors and actuators as similar as possible to those of the real animal. However, the approach is still limited by the difference in capabilities between artificial and biological sensors and actuators. For example, in chemical sensing, most available sensors are far less sensitive and also much more sluggish in response than

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biological chemoreceptors, prompting some researchers to use the actual biological sensors, dissected from a silkworm moth and interfaced to electronics on a robot (Kuwana et al., 1999). Another plausible strategy is to attempt to preserve the mapping from the stimulus to some higher stage of processing rather than precisely mimicking every stage of the sensory processing. For example, the precise neural wiring of the insect vision system laminar is unknown, and hence a direct replication of the mechanisms of motion detection is not possible; yet many systems have been built that aim to replicate the response properties of the lobular plate large‐field cells which integrate this motion, and use these in closed loop behavior (see Section II.B). Artificial replication of biological sensor properties, though limited, is still substantially more advanced than replication of actuation. We lack any technology with the properties of muscle: able to act simultaneously as a motor, brake, shock absorber, spring, and strut (Kornbluh et al., 2002); with comparable energy efficiency, response time, stroke, force, velocity characteristics, accuracy, repeatability, reliability, and so on. There is active research into possible alternatives that may reproduce some of these characteristics, such as electroactive polymers (Bar‐Cohen, 2006). Often, failure to replicate the behavioral capabilities of animals has made more apparent the role that these physical factors play in achieving such capabilities. Abstracting away from physics and believing that a successful account of a behavioral mechanism has been provided is shaky ground. Pfeifer et al. (2007) discuss how convergent ideas on the critical importance of embodiment have emerged from biorobotic research. They note that physical constraints, by shaping the dynamics, not only can be used to obtain stability and efficiency, but also to induce regularities, such as time‐locked correlations in feedback from actions, that enhance more complex information processing such as learning. While a variety of new and yet to be developed technologies are needed to replicate the physical interface of animals to their environment, it is generally assumed that the internal neural processes connecting sensors to actuators can be adequately replicated with electronic computation. This may turn out not to be true. Perhaps there are explicit properties and capabilities that can only be obtained by chemically identical processes. In modeling, the internal processes, lower‐level properties (which are often not included in standard neural network computation) can turn out to be important. For example, song pattern recognition in bushcrickets can be elegantly accounted for by including subthreshold oscillation properties in a single model neuron (Webb et al., 2007). Again, attempts to replicate the behavior using an alternative medium (silicon vs neurons) should help us discover which properties of the substrate are in fact critical to reproduce.

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III. COMPLETING THE MECHANISM DESCRIPTION While the nature of physical interaction importantly shapes behavior, it is nevertheless true that most interesting behaviors of animals such as crickets are also dependent on nontrivial neural processing that connects sensory and motor systems. There is rarely a simple linear relationship between input and output. There are also very few, if any, examples of animal systems where the complete sensorimotor pathway is well understood. Even for classic model systems, such as escape responses, there is not a full description of neural connectivity let alone functionality. How then to proceed? The robotic approach makes it explicit that the mapping to be explained is how to produce the right motor output for the given input and context. This point needs to be emphasized because the problem of understanding internal processing mechanisms is often approached by assuming that it is necessary to ‘‘decode’’ the sensory signals to reconstruct the input stimulus. But this may not be necessary as a stage toward producing the required output (Webb, 2006a). As Horridge (2005) argues ‘‘the generally accepted view that insects actually see things was in some ways a block to progress.’’ For example, the resolution of the insect eye is not a fixed quantity but depends on the task, and ‘‘perhaps all insect vision is economically explained by innate detectors of cues that are quite insufficient to reassemble the image’’ (ibid). One consequence of this perspective is that sometimes surprisingly simple mechanisms turn out to be adequate for the specific task, even when the behavior appears quite complex. As early biorobots showed (e.g., Walter, 1950, whose Machina speculatrix used only two relays for ‘‘computation’’), we do not need to introduce many internal complexities to get seemingly complex behavior. An obvious counterpoint is that in analyzing observed behavior, we need to be careful not to assume that external complexity reflects real internal complexity. The strategy, then, in much of the following work is to start by building very simple internal mechanisms (but still embodied in complete devices), examining what they can and cannot do in real world tests, and using these results to guide the introduction of more detail where it seems necessary. Sometimes this means that known details, for example, of the neural pathways in the animal, may not be included in initial models. The assumption is that is best to use such prior knowledge as an additional constraint, rather than blindly copying the circuits without understanding their functional role. A. PHONOTAXIS IN CRICKETS Section II.B described the physical mechanisms that result in the cricket having a directionally dependent difference in tympanal vibration amplitude for sounds of the calling song frequency. However, this does not itself

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suffice to explain how female crickets find mates. The obvious way to use this input to control the behavior is for the female to turn in the direction of higher tympanal vibration amplitude—or as Weber and Thorson (1988) put it: she follows the simple rule ‘turn towards the ear more strongly stimulated’. We use the word ‘simple’ because a two‐eared robot programmed to obey this rule (if suitable noise were incorporated) could be made to track a sound source in a manner like that of the female.

But how exactly does this work? For example, could there be a direct connection of ear stimulation to the stepping speed on the opposite side of the cricket, as in a Braitenberg (1986) vehicle? The discussion in Section II.F of insect walking and turning already suggests this is unlikely, as six‐ legged turning requires more subtle coordination between the legs. Are the ear inputs compared in some way, and does this produce a fixed size or rate of turn or a graded response, relative to the angle of the sound source? Is the comparison instantaneous or does the cricket integrate the input over some time interval before producing the response? Is the response continuous? Despite the large number of studies of this behavior (e.g., Hedwig and Poulet, 2005; Murphey and Zaretsky, 1972; Oldfield, 1980; Popov and Shuvalov, 1977; Schmitz et al., 1982; Stout et al., 1983; Weber, 1990; Weber et al., 1981), there is surprisingly little consensus on any of these issues, yet understanding the neural ‘‘encoding’’ of the auditory system depends critically on what ‘‘decoding’’ the motor system is required to do. Moreover, the mate‐finding behavior of females is not simply to approach the loudest sound but to selectively approach sounds that have the right temporal pattern. In 1982, what was known about this selectivity was summarized by Thorson et al. as the ‘‘30 Hz rule’’: crickets (or more specifically, Gryllus campestris) would approach a sound pattern that repeated at 30 Hz, which corresponds to the rate at which the male closes and opens its wings, producing sound only on the downstroke. Female crickets appeared less sensitive to the duty cycle or to higher order pattern features such as chirps (groups of several sound bursts separated by pauses) although some studies have shown that these can influence taxis in some cricket species (e.g., Doherty, 1985; Hedrick, 1988; Hedwig and Poulet, 2005; Stout and McGhee, 1988; Wendler, 1990). This led to the assumption that the critical neural mechanism to discover was the neural filter for 30 Hz (Huber and Thorson, 1985) particularly as the known thoracic ganglion interneurons did not appear to show such filtering (Schildberger et al., 1989; Wohlers and Huber, 1981). Significant in this context were the results of Schildberger (1984a), which showed brain neuron (BN) responses that appeared to correlate to highpass, lowpass, and bandpass filtering of the pattern.

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Assuming there is filtering to recognize the pattern, and comparison of left–right stimulation to determine the turning direction, a third issue arising is how the recognition and localization processes interact. Does recognition somehow gate the localization response—and if so, at which stage of processing, and how? Several behavioral studies seemed to suggest that this interaction is a serial process, that is, rather than turning to the difference in raw sound amplitude, the insect first filters the sound for the correct pattern and then turns in the direction in which the pattern is most clearly represented (Pollack, 1986; Stabel and Wendler, 1989). But recent evidence has called this into question as discussed below. 1. A Simple Model Our first approach in building a robot model of this behavior was to consider whether the fact that crickets only approach sounds of a particular pattern could be used to simplify the required control. The basic idea is as follows: If the animal needs to compare some parameter of its neural encoding of the sound to decide in which direction to turn, then perhaps it can exploit the fact that the relevant sound has a pattern by using the onsets of the sound as the significant cue. Onsets encode directionality in the relative latency of firing.4 If the system is sensitive only to onsets, there are two constraints that would limit its response to patterns of varying syllable repetition frequency. The first is that the time constants of neural responses make them inherently act as low‐pass filters, smoothing out rapid changes in a signal. The second is that responses can only occur as fast as the pattern frequency, so the slower this becomes, the more ineffective the response, up to the limit where no pattern (continuous sound) produces no response. Thus with no further filtering process, there will be an effective bandpass of frequencies to which the system produces the optimum response. This idea can be embodied in a simple neural circuit as shown in Fig. 5 (top). Each neuron is modeled as a single compartment integrate and fire unit. The time constants for the neurons are determined by the required filter properties. In the case of the auditory neurons, this acts as a low‐pass smoothing of the signal pattern for syllable rates faster than 40 Hz. The synaptic connection from auditory neurons to motor neurons is modeled to exhibit short‐term depression, that is, successive spikes from the presynaptic neuron produce successively smaller increases in the postsynaptic 4 The small (microsecond) phase difference in arrival at the ears results, via the pressure difference mechanism, in a large amplitude difference in vibration, which is neurally represented in a relatively large (millisecond) difference in the onset of firing of the sensory and interneurons.

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Fig. 5. Top, a simple circuit for controlling phonotaxis. Bottom, from left to right, response to different syllable repetition intervals (SRI); lower graphs show sound input, middle the AN response, top the MN response. The neural time constants result in a bandpass preference (greater firing in motor neurons) for SRI corresponding to cricket song.

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current, unless there is a gap in spiking allowing the synapse to recover. Consequently, the motor neuron response is bandpass with respect to the syllable rate (Fig. 5, bottom). Implemented on a robot equipped with the electronic auditory system described in Section II.A, this control mechanism was able to reproduce a number of the behavioral experiments on crickets (Webb and Scutt, 2000). It could reliably approach sound, or in an analog of the Kramer treadmill paradigm (e.g., as used by Schmitz et al., 1982) orient toward and oscillate about the sound direction. It was suitably selective for the 30‐Hz pattern in the calling song, and could even ‘‘choose’’ this rate over alternative rates. Moreover, when faced with split songs and combinations of nondirectional sound from above and sound from the side, the behavior resembled that of the cricket (Stabel and Wendler, 1989) in moving toward the side with the clearer pattern rather than the higher amplitude of input. 2. Does This Explain Cricket Phonotaxis? Given that this simple model appears to reproduce many aspects of the cricket’s behavior, an obvious question is whether it can be directly mapped to the neural network supporting this behavior in the cricket. Note that this reflects a general issue for mechanistic models: demonstration that a mechanism produces the behavior of interest does not suffice to conclude that the same mechanism underlies the behavior of the natural system. In the absence of alternative models, or other evidence, we might provisionally conclude that we have the best current explanation. But in the case of the cricket, there are details known about the real neural circuit that the simple circuit described in Section III.A.1 contradicts. At the most basic level, the use of both excitatory and inhibitory synapses from a single neuron is already not consistent with neurobiology. Also, one prediction from this model, that crickets played simultaneous songs with slightly varying onset times should choose the leading song, has not been subsequently supported in behavioral tests (Hedwig and Poulet, 2005). One approach we could take to improve the robot model would be to incorporate all the known neurophysiology. For example, there are around 50–60 sensory units innervating the tympanal organ, which have varied thresholds and amplitude ranges (Esch et al., 1980). In the prothoracic ganglion, a number of auditory interneurons have been identified, the most prominent being the pairs of ‘‘omega neurons’’ (ON1 and ON2) that make inhibitory cross connections to contralateral auditory neurons (Horseman and Huber, 1994; Wohlers and Huber, 1981). Two pairs of ascending neurons (AN1 and AN2) have been relatively well studied. Ablation (Atkins et al., 1984) or hyperpolarization (Schildberger et al., 1988a) of AN1 has been shown to strongly affect the phonotaxis response,

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whereas AN2 appears to be critical in the escape response to bat ultrasound (Nolen and Hoy, 1984). As already mentioned, Schildberger (1984a) has described several brain neurons (BN) that respond to sound, and which appear to have different filtering properties for the sound patterns. It is assumed from anatomy that ascending neurons such as AN1 directly synapse onto the BN1 described by Schildberger, which then connect to BN2. Staudacher and Schildberger (1998) and Staudacher (2001) have described properties of some descending neurons, many of which show a response to sound. The response to calling songs is typically ‘‘gated’’ by whether or not the animal is walking. One of these neurons has a firing rate that correlates with the angular velocity of the animal, and another seems to be necessary and sufficient for the onset of walking. However, the evidence is not sufficient to determine with any clarity the output circuitry for phonotaxis. An obvious problem here is that despite all these details, knowledge of the network is incomplete. Should we then refrain from further model building until all the connections and neural properties have been uncovered? While obviously it is important to continue using standard (and newly emerging) neurophysiological techniques to discover more about this system, nevertheless it is still useful to build models that incorporate what we currently know, and to speculate about the unknown connections. Moreover, such models need not include every known detail but rather should include features that have some hypothesized role in the mechanism. The robotic implementation provides a functional context for assessing the potential role of identified neurons and highlighting the most critical ‘‘missing parts’’ that could be filled in by suitable experimentation. One insight gained from attempting the robotic implementation is the importance of understanding how the cricket can determine its response from the current or recent input. Reporting the neural response as the mean number of spikes per sound burst can be rather misleading regarding the information actually available in the spike trains. This is improved by using measures of instantaneous spike rates, which show for example, a peak in firing with the onset of sound bursts (Nabatiyan et al., 2003) but may still mislead if the temporal response is averaged over many trials, thus producing features in the data that may not in fact be reliable cues in an ongoing process. The dynamics of the turning response itself were equally under explored when the first robot models were built. There was little indication of how long sound is integrated before a turn is produced, how large a turn is made, and how often. Significant results have emerged in recent cricket experiments that use a much higher time resolution to analyze the behavior (Hedwig and Poulet, 2004). It was observed that there is a small but significant steering response to every pulse of sound within the song, within a short time interval (around 50 ms). This strongly suggests that there is a

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turning reflex that does not include a stage for filtering the temporal pattern in the song, as the pattern (the syllable rate) cannot be detected from the first sound burst alone. Rather, it appears that the turning reflex is modulated, over a much longer time scale (2–5 s), by an independent process that filters for the temporal pattern (Poulet and Hedwig, 2005). Our most recent experiments on robot phonotaxis incorporate a corresponding change to the basic hypothesis about the mechanisms in the cricket based on this data. We suggest that there is a relatively direct, or ‘‘fast’’ connection from the thoracic AN1 neurons to the motor control of turning. This connection is modulated by disinhibition via the BNs, which filter for the temporal pattern in the sound. An interesting issue here is whether the fast turning is effectively a direct reflex, with the circuitry either within the thoracic ganglion or with at most one or two connections within the brain. A local circuit is made more plausible by consideration of the mechanisms described in Section II.E for controlling six‐legged turning behavior, in which modulation of the foreleg trajectories alone is sufficient to generate coordinated turning toward a target (Rosano and Webb, 2006). B. ESCAPE As for phonotaxis, the neural mechanisms underlying the escape response to wind stimulation of cercal hair sensors have been the subject of substantial investigation. The sensory neurons project to the terminal abdominal ganglia (Bacon and Murphy, 1984; Edwards and Palka, 1974) where there is a ‘‘map’’‐like organization of dendritic terminals according to the directional sensitivity of the hair sensors (Jacobs and Theunissen, 2000; Paydar et al., 1999). A number of giant interneurons (GIs) have been identified in this ganglia (Kanou, 1996; Mendenhall and Murphey, 1974; Miller and Jacobs, 1984), which provide rapid connection to the thoracic ganglia where the motor response is generated. Additional local interneurons and ascending and descending connections (Baba et al., 1991, 1995) are also part of the underlying circuitry, although their precise contribution to the behavior is still unclear. For the robot with simple hair sensors described in Section II.C, we programmed a neural circuit as shown in Fig. 4, using simplified integrate and fire neurons which could be simulated in real time on the robot’s processor. The aim was to incorporate key properties of the known circuit identified from the research literature into a minimalist model that would suffice to produce the observed escape responses (a more detailed mathematical model of the cockroach escape circuit has been implemented by Ezrachi, 2003). Accordingly, the circuit had separate pathways for triggering the escape response and for controlling the direction of movement,

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corresponding to observed distinction of these roles in cricket terminal abdominal ganglion (Paydar et al., 1999) and thoracic ganglion (Gras and Ho¨rner, 1992; Ho¨rner, 1992). The trigger pathway receives input from acceleration sensitive sensory neurons, and the directional pathway from velocity sensitive sensory neurons. For each, there were four hair sensor inputs, oriented in four directions, and four thoracic neurons, representing the GIs; specifically, the trigger pathway modeled the ventral LGI and MGI neuron pairs, and the directional pathway the two largest dorsal GIs, conventionally named 10‐2 and 10‐3 (Kohstall‐Schnell and Gras, 1994; Miller and Jacobs, 1984). The pattern of weights connecting the sensory neurons to the GIs produced tuning curves in the latter’s response matched to those observed in neurophysiological studies. Local interneurons in the terminal abdominal ganglion and less well‐characterized GIs were not included in the model. Data on which to base the thoracic stage of the neural model were sparse, and its design was driven rather by the functions required: integration of sensory information, bilateral comparison, and generation of motor commands. Temporal integration is done by a set of four neurons connected to the trigger GIs. These will respond to a single strong stimulus or successive weak stimuli; they can also potentially receive input from other sensory modalities (see Section IV.A). They activate a four‐neuron motor network, which produces a sustained but self‐limiting burst of activity. Depending on the neuron first activated in this network, this will either drive the robot forward, or initially reverse it and then drive it forward, depending on whether the stimulus was predominantly to the rear or front. Bilateral directional comparison involves two neurons receiving input from the directional GIs (and potentially other sensory systems) corresponding to the right or left side stimuli. These neurons are mutually inhibitory, so that the first to start firing will determine the turning direction. They control the robot’s motion by inhibiting the motor output on the corresponding side. The results are reported in detail in Chapman (2001), and include successful reproduction of the directionality of escape, including the emergence of different ‘‘turn types’’ to front and rear stimuli (Ritzmann, 1993).

C. HOMING Visual homing is a short‐range navigation method that has been well documented in wasps and bees (summarized in Faurier and Campan, 2004) and ants (Collett et al., 1992; Wehner, 1992), and is possibly used by other insects—we have recently shown that it occurs in crickets (see below). Essentially it involves using the difference between the current visual

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scene and the remembered scene at the home location to determine movement in a direction that will bring the animal closer to home. In general, it is assumed that the home location (e.g., a nest entrance) is not itself immediately visible (otherwise the animal could use beacon aiming or visual taxis to locate it) but rather that the surrounding visual scenery is used as a guide to its location. A classic experiment was performed by Tinbergen and Kruyt (1938), who showed that a wasp would search for its nest in the location indicated by a surrounding circle of pine cones, and the search could be affected by various manipulations of these cues. The importance of navigation capabilities for robots has inspired a number of researchers to imitate these capabilities in robot‐based models. These models vary in the extent to which they aim to replicate and explain the animal behavior, versus obtaining the most effective or efficient solution. The ‘‘snapshot’’ model proposed by Cartwright and Collett (1983) to explain insect homing, based on comparison of the bearing and size of landmarks, has been implemented on a robot several times (e.g., Hong et al., 1991; Weber et al., 1999), in particular by Lambrinos et al. (2000), who tested the behavior in the same conditions (a desert environment with artificial landmarks) as those used to study the desert ant Catyglyphis (Wehner, 1994). They also proposed a simplified model that stores and compares average landmark vectors (by summing over the bearings of individual landmarks) rather than storing a landmark view. This provides a very efficient process (e.g., implemented in analog hardware by Mo¨ller, 2000; used for building longer linked routes by Smith et al., 2007) but has not been shown to work reliably in realistic environments. By contrast, methods based on direct comparison of images (not extracting landmarks) have been shown to be effective in realistic scenes (e.g., the image warping approach of Franz et al., 1998) but seem to require intensive computation, beyond what seems plausible for an insect brain. Vardy and Mo¨ller (2005) have proposed a variant of this method that uses optical flow algorithms to calculate the displacement of the image, which has proved relatively robust to feature mismatch while being significantly more efficient to compute. Zeil et al. (2003) used a robotic gantry in natural surroundings to record the change in the image that would be seen by a wasp as it approached its nest. They showed that a simple calculation—the root mean square (RMS) difference between the current image and the home image—produces a monotonic function leading to the home position for natural images. Homing can thus be regarded as a gradient descent problem, equivalent to phonotaxis, of moving in the direction that reduces the RMS. One difference, however, is that an insect or robot can only sense the RMS value at one location at a time. Consequently, the strategy required could be

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compared to that for chemotaxis in an animal such as C. elegans which moves up a chemical gradient using single location sensing. This behavior has been modeled (Feree and Lockery, 1999) as: dy ¼ Obias þ z0 CðtÞ þ z1 ðCðtÞ  Cðt  1ÞÞ dt

ð1Þ

where dy/dt is the turning rate (i.e., the angle turned at a time step), bias, z0, and z1 are constant parameters, C(t) is the input at time t, and C(t)–C (t–1) is the temporal gradient. After turning, a agent moves forward by the distance UxC(t), where U is a constant. We have implemented such a strategy in a robotic implementation of visual homing (Zampoglou et al., 2006). The robot’s camera points at a parabolic mirror to obtain a panoramic view. The current grey level image is first rotated according to the heading of the robot and then subtracted (pixel by pixel) from an image stored at the home location to obtain the input C(t). An evolutionary algorithm was used to tune the parameters of the equation. The robot was able to home successfully, in a normal indoor environment using this approach, but it was not without some limitations. Using the RMS difference is more robust than image warping to changes in the scene such as movement of some objects, although it tends to get caught in local minima if one nearby object dominates the image. It is also not robust to changes in the lighting conditions. We have explored alternative image comparison statistics that are more robust, but possibly less plausible for the animal (Szenher, 2007). Some further issues arising from the robot investigations that might be interesting to explore in the animal are whether the animal tries to adopt the same orientation (rather than ‘‘mentally rotating’’ the image) and whether and how it keeps its visual sensors level. An interesting point to note about visual homing is that this mechanism could also account for the behavior of animals in some classic ‘‘place memory’’ paradigms. In particular, the Morris water maze (Morris, 1984) used for rats requires the rat to locate a submerged (and hence invisible) platform using surrounding visual cues. We were interested to see if insects might be capable of learning an equivalent task, that is, using visual homing in an arbitrary situation to escape punishment, not only for finding a nest site. Experiments by Mizunami et al. (1998b) had suggested that cockroaches could learn to relocate a cool spot in a heated arena by using surrounding visual cues. In recent experiments (Wessnitzer et al., 2008), we have demonstrated that crickets can also learn this task (Fig. 6), and, for example, will search in the position corresponding to the visual cues if these are rotated. We also found that performance was substantially enhanced when the surrounding cues were a natural scene rather than simple black landmarks, which would be consistent with the use of an image‐based rather

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Trial1

Trial2

Trial3

Trial4

Trial5

Trial6

Trial7

Trial8

Trial9

Fig. 6. Recorded tracks of a cricket in a heated arena over nine trials. It learns to relocate the invisible cool spot (small circle) from different starting positions (crosses) using visual cues on the walls of the arena (not shown).

than a landmark‐based algorithm to find the home target. We are currently developing a robot model to try to account for the acquisition of this behavior (see Section on learning below).

D. SUMMARY The work described above provides several examples of a general methodology, in which the main physical constraints of the system have been built in mechanical and electronic hardware, and the controller—either at the level of individual neurons, or at a higher level—has been programmed so that the robot response can be tested in real world situations. In several cases, the same modeling infrastructure has been used to test several

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different control hypotheses (Webb, 2006b). Sometimes the same controller has been tested on different robot bases (Reeve and Webb, 2002 vs Horchler et al., 2004) or with modifications to the sensory system (Webb, 1995 vs Lund et al., 1997). Some parts of the processing have been implemented both in software and in analog hardware (Reeve et al., 2005). Such strategies help to determine which aspects of the implementation are actually critical to replicate the behavior. One feature of this methodology that tends to differ from more conventional computational neuroscience is that, while the models may become increasingly elaborate in the included details, at each iteration the aim is to have a complete closed loop system. It is important to have a real behavioral output that affects the subsequent input. This can help to avoid making assumptions about the required internal ‘‘decoding’’ by always providing a context in which to evaluate what information is actually needed to guide the response, rather than assuming that veridical reconstruction of the stimulus is the aim of the neural processing. Similarly, the actual production of the behavior must be addressed, rather than simply interpreting the firing of some ‘‘output’’ neuron as equivalent to behavior. While it can be useful at times to know what information in principle can be obtained from a sensory spike train, without the right task context, the effectiveness and efficiency of the system may be incorrectly estimated. Note also that iterations of the model need not always be in the direction of including more detail. It may be possible through this methodology to determine that imitating the functional role of certain processes, without copying the lower level details, is quite sufficient to replicate the capabilities. A critical issue that remains, however, is how much it is safe to conclude from either successful replication or a failure to replicate the observed behavior of an animal with a particular implemented mechanism (Webb, 2006b). There are a number of factors, other than the core hypothesis we wish to test, which may determine the outcome. These include distortions of the hypothesis through necessary simplifications or compromises in the implementation, differences in the conditions of testing for the robot and the animal, free parameters in the model that allow it to be tuned to match the data, or even the possibility that the original observations of the animal behavior were flawed or incomplete. As above, we can attempt to limit these problems by using several different implementations (so results are not dependent on one interpretation), by testing under a wider range of experimental conditions (keeping these as similar to the animal as possible) and by including a wider range of data. Ideally, the robot model should go beyond accounting for the existing data to predict new results for animal experiments.

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IV. TOWARD THE COMPLETE CRICKET Many robots are constructed to do a single task but there is much interest in making them more behaviorally flexible. Some tasks inherently require the integration of a number of systems, such as the ‘‘Grand Challenges’’ of autonomous driving in desert or urban environments (DARPA, 2004, 2007). So far the biorobotic examples discussed are behaviors that involve one sensory modality and can be described as a fairly direct transformation (albeit with some filtering and adaptive processing) from the input stimulus to the behavior. Such behaviors have, not surprisingly, received extensive study as it is relatively easy to isolate the inputs and map the connectivity. But even insects (and simpler animals) are capable of combining different sensorimotor behaviors in an adaptive way, and many ‘‘simple’’ behaviors require such integration. For example, olfactory search in moths involves casting across the odor plume and surging up it, where these behaviors are triggered by chemical sensing but guided by wind sensing and vision (Arbas et al., 1993; Willis et al., 2002). A recent robot implementation of this behavior by Pyk et al. (2006) found that a ground‐based robot could locate the odor source using odor and wind sensing alone, but that a flying robot (a blimp) also required the visual stabilization mechanisms. They also note that successful behavior was possible with very limited resolution in the sensory system (essentially having one threshold for detection of odor) and motor outputs (using a fixed forward speed and a few fixed rotation angles). In recent research, we have used the robotic approach to try to gain insight into some of the mechanisms that enable the integration of behaviors. As this is still a very open problem in biology, the implemented systems are necessarily more speculative. A popular approach is to describe the problem as one of action selection (Bryson, 2007; Prescott et al., 2007) and to implement mechanisms based on competing drives. Prescott et al. (2006) demonstrate a mechanism for successful coordination of several behaviors in robots based on the neural architecture of the basal ganglia of rats. Ayers and Witting (2007) describe an alternative approach used for their robotic lobster, with behavior organized at two levels: sensors modulate state variables to either trigger immediate adaptive responses or to release stored sequences of behaviors (derived from ethograms). We have investigated several mechanisms for behavioral integration based on insect neurophysiology. A. CONTEXTUAL ESCAPE The escape response in cockroaches and crickets, described in Sections II.C and III.B in response to wind stimuli, can also be elicited by sudden changes in the environment, such as switching on a light, or the onset of

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loud sound (Ritzmann et al., 1991). In addition, the likelihood for triggering escape to wind stimuli can be modulated by the environmental conditions, with a lower escape response threshold observed in bright environments (Ritzmann, 1993). We have included such effects in our robot model of escape (described in Section III.B) using a simple microphone and a light sensor as auditory and visual inputs. These are integrated in a ‘‘context’’ neuron, which connects into the thoracic trigger circuit. Sufficient activation of either sensor will trigger a nondirected escape run, and a subthreshold activation will sensitize the trigger neurons so they are more likely to respond to input via the wind sensors. The ‘‘antennal’’ system discussed in Section II.C is also integrated into the same circuit, and interacts with it in two ways. Stimulation of the base of the antennae is fed via a pair of interneurons (modeling descending mechanosensory interneurons described by Burdohan and Comer, 1996 and Ye and Comer, 1996) to the trigger neurons, in this case, to those that produce a reverse before the forward escape run. Both the tip and base of the antennae are connected via directional interneurons to the bilateral directional comparison neurons that control turning. The base neurons drive a turn away from the antennal stimulus but the tip neurons drive a turn toward it; this is necessary for producing wall‐following behavior. To allow this circuit to control both the original wall‐following behavior and escape behavior, it was necessary to modulate these connections according to whether the robot was stationary (in which case antennal input is a predator and should produce an escape response) or moving (in which case antennal input is more likely to be self generated by running into an obstacle). The modulation was produced by connecting the forward drive neurons from the motor circuit to the appropriate synapses from sensors to interneurons (Chapman and Webb, 2006). Note this is a form of corollary discharge (Poulet and Hedwig, 2007), that is ‘‘signals generated in motor regions that target circuits engaged in sensory processing.’’ The entire circuit is shown in Fig. 7. One interesting aspect of this multimodal mechanism for escape is that habituation is included in the synaptic connections from each modality to the trigger neurons, rather than in the trigger neurons themselves. This has the advantage of allowing the habituation to be appropriately tuned for the different kinds of sensory inputs; but more importantly has the effect that habituation of escape to one stimulus does not prevent escape to a novel stimulus in another modality. This should be testable in the animal. B. COMBINING AUDITORY AND VISUAL SYSTEMS Bo¨hm et al. (1991) investigated the interaction between cricket phonotaxis (sound‐localizing) behavior and the cricket’s response to visual stimuli, including the optomotor response. The results led them to conclude

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Motor

MC68332

Thoracic direction

Thoracic CPG

Excitation Inhibition Facilition Depression Combined

Thoracic integrator

LGI

MGI

Cercal SN trigger

TAG GI trigger

DMIb-1 Context

Auditory SN Photic SN

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Antennal SN

TAG GI direction

Cercal SN direction

Fig. 7. Neural circuit for multimodal escape and wall following. Note the ‘‘context’’ neuron which modulates the thoracic integration, and also corollary discharge connections from the CPG that modulate the response of tip versus base antennae sensing.

that the ‘‘turning tendency [of the cricket to both stimuli] can be explained as the weighted sum of the turning tendencies evoked by the two individual stimuli.’’ It seemed straightforward, therefore, to extend the robot model of phonotaxis described in Section III.B by adding an optomotor response based on the neuromorphic visual motion sensor circuits described in Section II.C. However, we immediately found (Webb and Harrison, 2000) that while adding the two turning tendencies would work in open‐loop (e.g., for the cricket fixed on a treadmill in the Bo¨hm et al. experiments), it was not a satisfactory solution in the more realistic closed‐loop situation (e.g., the robot or the cricket moving in a normal environment). Essentially, the problem was that each turn toward the sound would produce a clear optomotor stimulus, which would cause the robot to correct itself and turn away from the sound again. This unsatisfactory result was an empirical demonstration of the problem theoretically formulated by von Holst and Mittelstaedt (1950): How can an animal with an optomotor reflex makes intentional turns without automatically correcting (and thus negating) them?

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One obvious and easily implemented solution to this problem is to have the intentional turning response inhibit the optomotor response. This kind of switching behavior has been shown in several animal systems, for example, suppression of the optomotor reflex in response to escape signals in the locust (Robert and Rowell, 1992) and during pursuit turns in the housefly (Srinivasan and Bernard, 1977). Nevertheless, there are alternative solutions, which also have some biological support. For example, the relative gain of the phonotaxis and optomotor turning tendencies could be adjusted to account for the additional optomotor stimulation caused by the phonotaxis turns. However, any solution using constant gains will only work if the dynamics of the two behaviors are precisely matched (Collett, 1980). In fact there is a continuum of possibilities, from entire suppression of the response to optomotor stimuli (gain set to zero, i.e., switching as above) to adjusting the gain to compensate precisely in both timing and magnitude for the expected visual feedback caused by the phonotaxis turn. In between these extremes, the compensation could be more or less approximate, for example, suppressing any response to visual input in the expected direction but not the opposite direction. To adjust the gain (in direction, magnitude, and time course) implies the existence not only of corollary discharge (the system must ‘‘know’’ when a turn has been internally generated, that is, some signal from the motor system must be sent internally to interact with the sensory processing) but also of a predictive forward model that is able to calculate (though perhaps only approximately) the expected visual input (Webb, 2004). These ideas developed over several different implementions on robots. Initially, we interfaced the analog VLSI sensor of Harrison and Koch (1999) to the existing cricket robot. The measured optomotor stimulus was added to the left and subtracted from the right motor command sent to the robot wheels. However, this influence was gated by the output from the simulated neural circuit that controlled phonotaxis, inhibiting any optomotor response for the duration of each turn triggered toward the sound. The resulting response enabled the robot to compensate for a large bias in its motor output using the optomotor signal while still successfully approaching the sound source (Webb and Harrison, 2000). When tested in open‐loop conditions, it reproduced the results of Bo¨hm et al. (1991) and in fact more closely resembled their measured cricket behavior than the additive scheme they proposed (Webb and Reeve, 2003). We then considered a neural implementation of the interaction (described in detail in Reeve and Webb, 2002 and in an alternative version in Russo et al., 2005). In this case, the optomotor sensor is used to produce spike trains, summed in two optomotor interneurons, OC and OA (optomotor clockwise and anticlockwise), which act as a temporal integration stage. These interneurons mutually inhibit one another. The

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output from the interneurons, in the absence of other sensory signals, steers the robot in the appropriate direction by excitatory inputs to the left or right motor neurons, for example, clockwise visual rotation will result in rightward rotation of the robot. Inhibitory synaptic connections are added from phonotaxis interneurons to the optomotor interneuron corresponding to the expected turn direction. Note that this does not simply suppress all optomotor response (as in the previous gating mechanism) but only the response to stimulus in the expected direction. Moreover, the amount and timing of the inhibition can be adjusted so as to compensate more or less precisely for the expected input. In Russo et al. (2005), we used a genetic algorithm to find the right synaptic parameters to produce inhibitory currents that cancelled the reafferent signals. An interesting issue raised by this work is whether an insect can actually learn from experience to generate the precise inhibitory signals needed to compensate for reafferent stimuli. Suggestive of this possibility is the observation that after damage to their cerci, crickets are only able to regain appropriate directional escape responses if they are allowed to walk freely during the recovery period (Kanou and Kondoh, 2004; Kanou et al., 1999, 2002). We have speculated that the mushroom body neuropil in insect brains may serve such a function, and recently have begun to build simulation and robot models of associative learning in this neural structure. C. LEARNING Insect behavior has been described so far in this paper as a collection of highly specialized, domain‐specific, sensorimotor loops; each presenting a particular solution to an ecologically relevant problem. It has been suggested that peripheral sensory or motor physical characteristics may be tuned to the task to simplify internal processing, and that this processing, in turn, may be simpler than expected if considered in the task domain. The neural control pathways for these behaviors may or may not pass through the brain. Some sensors (e.g., visual and antennae) feed directly to the brain but may still connect to descending control neurons via short pathways within it. Other sensory systems (e.g., cercal escape) connect to motor outputs directly through peripheral ganglia, but also appear to send some processes to the brain. For some, such as phonotaxis, there are clearly ascending and descending pathways to the brain involved in the behavior, but may also be direct connections within the thoracic ganglion. If these reflex loops form the basis of behavioral control, there are various simple ways in which they could interact, such as summing several sensory inputs to determine the motor output, or one pathway inhibiting another, as described in Sections IV.A and IV.B. But such interactions are

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not sufficient to account for some of the observed capabilities of insects, particularly their ability to learn novel multimodal associations (Homberg, 2005) and show context‐specific modulation of behavior (e.g., Liu et al., 1999). Moreover, insect brain anatomy reveals certain structures that appear to function as centers for multimodal coordination, association and learning (reviewed, from a robot control perspective, in Wessnitzer and Webb, 2006). In particular, the mushroom bodies, a pair of large and distinctively shaped neuropils in the insect brain, appear to receive inputs from a variety of modalities (Mobbs, 1982; Nishikawa et al., 1998), usually as part of a secondary pathway parallel to more direct sensorimotor loops (Fahrbach, 2006). These inputs diverge onto a large number of Kenyon cells which form parallel pathways, bifurcating into output lobes (Schuermann, 1974; Strausfeld et al., 1998). Extrinsic neurons in the output regions typically exhibit multimodal responses (e.g., in the cricket, responding to auditory, visual and wind stimuli; Schildberger, 1984b) and also interesting motor related activity (Okada et al., 1999). It is thought that these features suggest roles in pattern recognition (Heisenberg, 1994), possibly through sparse coding (Olshausen and Field, 2004), and in integrating sensory and motor signals (Okada et al., 1999), possibly to discriminate self‐stimulation from environmental stimulation (Martin et al., 1998; Mizunami et al., 1998a). But the best explored function of this neural area is associative learning (the evidence is reviewed for bees in Menzel, 2001; for Drosophila in Gerber et al., 2004). As yet there are only a few attempts to model this pathway (e.g., Nowotny et al., 2005). We have focussed on doing so in the context of closed loop tasks, that is, ones where the response of the animal or robot alters the subsequent sensory input. For example, imitating a fly in a flight simulator (Liu et al., 1999), we have simulated the association of visual patterns with heat punishment such that the system can learn to make an anticipatory avoidance response to visual stimuli that predict it is drifting into a punishment area (Wessnitzer et al., 2007). The use of a mushroom body like neural architecture allows this system to make nonelemental associations (Giurfa, 2003) for example, to learn to approach A and B but avoid the compound stimulus AB. This model uses spike timing dependent plasticity (STDP) as a learning mechanism, which appears to occur in insect mushroom bodies (Cassenaer and Laurent, 2007). In a second task (Wessnitzer and Webb, 2007), we have looked at the association of visual and auditory stimuli as reported by van Helversen and Wendler (2000) in bushcrickets. These animals can learn to maintain a fixed heading relative to a visual cue which they have associated with approach to a sound source. We have shown that this association can be easily learnt in an MB circuit and are currently testing the system in real robots.

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D. FINAL COMMENTS The application of learning mechanisms to robotics is a huge field, which there is not space here to review. It provides many interesting examples in which biologically‐derived mechanisms, such as reinforcement learning, have been formalized and extensively developed and tested in real scenarios (Sutton and Barto, 1998). Sometimes methods developed from formal principles in control have been shown to have plausible explanatory value for biological systems (e.g., Wo¨rgo¨tter and Porr, 2005). Alternative biologically‐inspired adaptive methods such as evolutionary algorithms have also been explored widely in robotics (Nolfi and Floreano, 2004). V. CONCLUSIONS The attempt to build robots that replicate animal behaviors has a number of motivations. As discussed in the introduction, one is to demonstrate that a mechanistic account of animal behavior is possible. Of course, our understanding of what a ‘‘mere machine’’ can do has changed radically over the last half century with the huge developments in the field of computing. However, there has perhaps been a consequent over‐emphasis on computational explanations, which have ignored or underplayed the role of direct physical interactions in determining the behavioral capabilities of animals. This is one issue that robotic models have made more concrete (Pfeifer et al., 2007). Recent developments in this field are often viewed as driven largely by engineering motives. Animals excel in certain areas—such as adaptive interaction with complex environments—where current technology struggles. Adopting solutions to sensorimotor control that have been refined by millions of years of evolution is, on the face of it, a plausible strategy. However, there are many reasons why the most effective or efficient solutions for engineering may differ from biological systems. The fact that such solutions are not constrained to be incremental improvements but can be redesigned from the ground up opens up a far wider space of possible implementations. Similarly, there is no necessity for engineered systems to be limited by the need for self‐replication and self‐development, or even necessarily self‐sufficiency for obtaining energy or maintaining survival, although all these characteristics might have some advantage if machines could be developed that exhibit them. Nevertheless, it is still valuable to consider what can be learnt from imitating animals. Learning from an animal requires that we actually understand how the animal works, which is by no means frequently the case. Perhaps as a consequence, it is evident that much work in this area is now being done

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with a primarily biological motivation, driven by animal behavior researchers who see robotics as a tool that allows problems to be explored from a novel perspective. It has long been recognized that mathematical and computational modeling are important means to enforce theoretical rigor, both in defining terms and mechanisms explicitly, and in formally demonstrating the conclusions that follow (sometimes counter‐intuitively) from certain hypotheses. Robotic implementations of models have similar benefits while adding the advantage of bypassing the need to simulate (or mathematically represent) the interaction with the environment, instead using real interaction to represent itself. While simulation of such interactions is improving (e.g., virtual reality), it is still limited for many physical factors, can be expensive to develop and run, and can be potentially misleading with the risk of overlooking critical real world factors. However, one should remain aware that any specific robot implementation will introduce its own distortions in representing the interaction, due to inevitable differences in the characteristics of sensors, actuators, size, materials, and so on. As well as acting as models of mechanisms of animal behavior, biorobots can interact with animal behavior studies in other interesting ways. It has already been mentioned how robotic systems might be used to gather data about the ecological conditions of a task, by providing naturalistic movement of a sensor through the environment. Balch et al. (2006) discuss how techniques developed within robotics and artificial intelligence, such as tracking, automatic behavior labeling, and learning executable models (e.g., stochastic Markov processes) of behavior from examples can be applied to analyze social insect behaviors. Another relatively new approach is to have the robot directly interact with the animal, as in the project reported by Halloy et al. (2007) in which the robots acted as ‘‘interactive decoys’’ to explore the self‐organizing dynamics of cockroaches choosing shelters. The robots both embodied a mathematical model of the behavior—adapting the time spent under one shelter according to the number of surrounding animals—and could be used to influence and explore the response of the animals. Another approach is the development of ‘‘cyborgs,’’ that is, systems that combine a real animal with robotic control devices (e.g., Bozkurt et al., 2008; Sato et al., 2008). As discussed above, robotic implementations force the modeler to fill in gaps, and this can motivate a wider exploration of possible mechanisms than occurs in conventional study. For example, the range of alternative algorithms that have been developed to account for insect visual homing (Vardy and Mo¨ller, 2005) are largely due to robotic explorations of this problem. A robot might also provide a better exploration of the boundary conditions on proposed mechanisms, such as whether a particular homing

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mechanism requires accurate compass information and some way for the animal to internally ‘‘rotate’’ an image. Awareness of these conditions can drive the search for an additional mechanism that ensures the conditions are met, or the attempt to devise an alternative mechanism that is not subject to the same conditions, for example, homing algorithms that do not require a compass. The robot may also help to identify at what level some generality of mechanism occurs so that explanations can be transferred to different animals. Nevertheless, there are important caveats as to the efficacy of the biorobotic approach to animal behavior. The intended mapping between the biological and robotic mechanisms, and which aspects of the robot are critical versus mere implementation details is not always made explicit (Datteri and Tamburrini, 2007). The engineering background of many researchers can lead to insufficient attention to rigorously evaluating the explanatory power of the implemented system. It is important to know precisely how well it replicates the behavior, how plausible are the proposed mechanisms for a biological instantiation, and whether it generates new predictions that can be tested by conventional experiments. Characteristics of the technology will tend to favor some solutions over others, so that success or failure of replication in robotics might be misleading about the true mechanism in the animal. Clearly the biorobotic approach is not ideal for every problem. The areas where it is likely to be most productive are those where a complete behavioral loop can be closed, that is, with some knowledge or plausible hypotheses about the nature of each of the intervening mechanisms. This tends to suggest that simpler behaviors (such reflexes) and simpler animals are still the best focus of study. It is also desirable that current technology allows comparable sensory and motor interfaces to be built. This does not necessarily mean low‐level physical identity but rather that at an appropriate level there are comparable characteristics, for example, the signal and amplitude range of the sensors and the dynamics of motion through the environment. By preference it should be possible to test the robot and the animal under the same or very similar conditions. Another consideration is the quality of the available data, and perhaps even more importantly, the scope for carrying out future experiments (behavioral or physiological) that may be suggested by the robot results. It is almost trivially easy in robotics to simultaneously record all stimulus, neural and motor parameters during behavior, but formidably difficult in animals. Note that these constraints may work against one another: Insects are potentially simpler systems, but their small size may limit our ability to replicate the physics of their sensory and motor interactions.

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Webb, B. (2006a). Transformation, encoding and representation. Curr. Biol. 6, R184–R185. Webb, B. (2006b). Validating biorobotic models. J. Neural Eng. 3, R25–R35. Webb, B., and Harrison, R. (2000). Integrating sensorimotor systems in a robot model of cricket behaviour. In ‘‘Sensor Fusion and Decentralized Control in Robotic Systems III’’ (G. T. McKee and P. S. Schenker, Eds.), pp. 113–124. Proceedings of the Society of Photo‐Optical Instrumentation Engineers (SPIE)4196. Webb, B., and Reeve, R. (2003). Reafferent or redundant: How should a robot cricket use an optomotor reflex. Adapt. Behav. 11, 137–158. Webb, B., and Scutt, T. (2000). A simple latency dependent spiking neuron model of cricket phonotaxis. Biol. Cybern. 82, 247–269. Webb, B., Wessnitzer, J., Bush, S., Schul, J., Buchli, J., and Ijspeert, A. (2007). Resonant neurons and bushcricket behaviour. J. Comp. Physiol. A 193, 285–288. Weber, T. (1990). Phonotaxis and visual orientation in Gryllus campestris L: Behavioural experiments. In ‘‘Sensory Systems and Communication in Arthropods’’ (F. G. Gribakin, K. Wiese, and A. V. Popov, Eds.), pp. 377–386. Birkhauser Verlag, Basel. Weber, T., and Thorson, J. (1988). Auditory behaviour in the cricket IV. Interaction of direction of tracking with perceived temporal pattern in split‐song paradigms. J. Comp. Physiol. A 163, 13–22. Weber, T., Thorson, J., and Huber, F. (1981). Auditory behaviour of the cricket. I. Dynamics of compensated walking and discrimination paradigms on the Kramer treadmill. J. Comp. Physiol. A 141, 215–232. Weber, K., Venkatesh, S., and Srinivasan, M. (1999). Insect‐inspired robotic homing. Adapt. Behav. 7, 65–97. Wehner, R. (1987). Matched filters—neural models of the external world. J. Comp. Physiol. A 161, 511–531. Wehner, R. (1992). Arthropods. In ‘‘Animal Homing’’ (F. Papi, Ed.), pp. 45–144. Chapman and Hall, London. Wehner, R. (1994). The polarization‐vision project: Championing organismic biology. In ‘‘Neural Basis of Behavioural Adaptation’’ (K. Schildberger and N. Elsner, Eds.), pp. 103–143. Fischer, Stuttgart. Weidemann, H.‐J., Pfeiffer, F., and Eltze, J. (1994). The six‐legged TUM walking robot In ‘‘Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems ‘94. Advanced Robotic Systems and the Real World,’’pp. 1026–1033. Weitzenfeld, A. (2004). A multilevel approach to biomimetic robots: From schemas to neural networks. Tech. Rep. ITAM. Wendler, G. (1990). Pattern recognition and localization in cricket phonotaxis. In ‘‘Sensory Systems and Communication in Arthropods’’ (F. G. Gribakin, K. Wiese, and A. V. Popov, Eds.), pp. 387–394. Birkhauser Verlag, Basel. Wessnitzer, J., and Webb, B. (2006). Multimodal sensory integration in insects—Towards insect brain control architectures. Bioinspir. Biomim. 1, 63–75. Wessnitzer, J., and Webb, B. (2007). A neural model of cross‐modal association in insects. In ‘‘Proceedings of the European Symposium on Artificial Neural Networks’’(M. Verleysen, Ed.), 415–421. D-side, Bruges. Wessnitzer, J., Webb, B., and Smith, D. (2007). A model of non‐elemental associative learning in the Mushroom body neuropil of the insect brain. In ‘‘Proceedings of the International Conference on Adaptive and Natural Computing Algorithms.’’LNCS 4431, 4432. Wessnitzer, J., Mangan, M., and Webb, B. (2008). Place memory in crickets. Proc. R. Soc. B. 275, 915–921. Willis, M. A., Belanger, J., and Jouse, W. C. (2002). Olfactory orientation in animals: Hypothesis testing with a mobile robot. Soc. Neurosci. Abstr. 28, 465.5.

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Wilson, D. (1966). Insect walking. Annu. Rev. Entemol. 11, 103–122. Wilson, M. (1978). The functional organization of locust ocelli. J. Comp. Physiol. 124, 297–316. Wohlers, D. W., and Huber, F. (1981). Processing of sound signals by six types of neurons in the prothoracic ganglion of the cricket, Gryllus campestris L. J. Comp. Physiol. A 146, 161–173. Wo¨rgo¨tter, F., and Porr, B. (2005). Temporal sequence learning, prediction and control—A review of different models and their relation to biological mechanisms. Neural Comput. 17, 245–319. Yack, J. E. (2004). The structure and function of auditory chordotonal organs in insects. Microsc. Res. Tech. 63, 315–337. Yakovleff, A., Abbott, D., Nguyen, X. T., Eshraghian, K., Cantoni, V., Lombardi, L., Mosconi, M., Savini, M., and Setti, A. (1995). Obstacle avoidance and motion‐induced navigation. In ‘‘Computer Architectures for Machine Perception (CAMP’95)’’ p. 384. Ye, S., and Comer, C. (1996). Correspondance of escape‐turning behavior with activity of decending mechanosensory interneurons in the cockroach, Periplaneta americana. J. Neurosci. 16, 5844–5853. Yue, S., and Rind, F. C. A. (2006). Collision detection in complex dynamic scenes using an LGMD‐based visual neural network with feature enhancement. IEEE T. Neural Netw. 17, 705–716. Zampoglou, M., Szenher, M., and Webb, B. (2006). Adaptation of controllers for image‐based homing. Adapt. Behav. 14, 381–399. Zeil, J., and Hemmi, J. M. (2006). The visual ecology of fiddler crabs. J. Comp. Physiol. A 192, 1–25. Zeil, J., Hoffmann, M. I., and Chahl, J. S. (2003). Catchment areas of panoramic images in outdoor scenes. J. Opt. Soc. Am. A 20, 450–469. Zufferey, J.‐C., and Floreano, D. (2006). Fly‐inspired visual steering of an ultralight indoor aircraft. IEEE T. Robot. 22, 137–146.

ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Social Foraging and the Study of Exploitative Behavior Luc‐Alain Giraldeau* and Fre´de´rique Dubois{ *DE´PARTEMENT DES SCIENCES BIOLOGIQUES, UNIVERSITE´ DU QUE´BEC A`

{

MONTRE´AL, CASE POSTALE 8888, SUCCURSALE CENTRE‐VILLE, MONTRE´AL, QUE´BEC, CANADA H3C 3P8 ´ DE MONTRE´AL CASE POSTALE DE´PARTEMENT DE SCIENCE BIOLOGIQUE, UNIVERSITE ´ 6128, SUCCURSALE CENTRE‐VILLE MONTREAL, QUE´BEC, CANADA H3C 3J7

DEDICATION We wish to dedicate this chapter to the lasting memory of Christopher Barnard who passed away in 2007. Much of the work reported here, which has been the major thrust of our own research programs, originates from his pioneering ideas. He will be missed.

I. WHY STUDY FORAGING? In a noted review celebrating the field of behavioral ecology, an influential colleague once wrote: ‘‘Interest in foraging research has clearly waned. . ., perhaps because it has become difficult to expand beyond the successes of «classical» foraging theory’’ (Gross, 1994). At the same time, he noted that the studies of sexual behavior, sexual selection, and mate choice were on the rise. He reflected a general feeling of the time that the study of foraging was moribund and that its most exciting questions had been addressed and answered. The future clearly belonged to sexual selection. Despite the pessimistic augurs of 1990s, the study of foraging today is neither dead nor depleted as evidenced by the recent publication of an impressive synthesis of its development since Stephens and Krebs’ (1986) classic monograph (Stephens et al., 2007). The persistence of foraging research is perhaps due to the pivotal position it occupies in behavioral sciences linking questions that project down to the processes operating within individuals as well as up to population and community‐level phenomena. 59 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00002-8

Copyright 2008, Elsevier Inc. All rights reserved.

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A. PROJECTING DOWN TO THE INDIVIDUAL The field of foraging has changed from its initial guise of optimal foraging theory using behavioral systems as black boxes with inputs and expected outputs (Krebs et al., 1983; Pyke et al., 1977; Stephens and Krebs, 1986). Today, students of foraging theory clearly need to look into the black box and study the mechanisms of decision. In the process, foraging has grown to include some animal cognition (Giraldeau, 1997), a field that looks into the behavioral mechanisms involved in gathering and processing information as well as sensory ecology (Wehner, 1997) that looks at the sources of information available to individuals. It has led to the emergence of increasing numbers of studies of sampling behavior (Shettleworth et al., 1988; Stephens, 1987) and more recently the use of social information (Dall et al., 2005; Danchin et al., 2004). Behavioral ecologists working on sampling have found a great deal in common with experimental psychologists that study animal learning who for the most part use food as a convenient reward. This has led to increasing interplay between mechanisms and function. A number of foraging models now include neurocognitive constraints. For instance, Bateson and Kacelnik (1998) incorporate the scalar property of time estimation mechanisms (Gibbon et al., 1988) to account for the apparent risk‐sensitive choice of animals. The same scalar property in which longer intervals are estimated less accurately can also account for patch exploitation as well as preference for immediate over delayed rewards (Stephens, 2007). Stephens (2007) goes as far as mentioning that foraging questions have led to the need for a general theory that would predict conditions under which different cognitive traits such as declarative and procedural representation should be expected. At the same time, researchers from other fields have become increasingly interested in foraging behavior. For instance, psychologists and anthropologists interested in the origin of culture, the production of innovative behavior, and imitation have conducted research in a foraging context (Galef and Giraldeau, 2001; Lefebvre et al., 2004). Classic examples involve sweet potato washing in Japanese macaques, food handling techniques in chimpanzees, food extraction techniques (Whiten et al., 1999), and overcoming food neophobia in rats (Galef, 1990). These questions about mechanisms have led to evolutionary questions about the survival value of these learning skills (Giraldeau et al., 2002). As a result, predictions about the taxonomic distribution of the ability to imitate have now been formulated in terms of whether the animal’s foraging ecology makes it advantageous or not (Giraldeau et al., 2002). A large number of social interactions whether cooperative or exploitative revolve around foraging, whether social insects provisioning the communal

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nest or vampire bats regurgitating blood meals to a companion when needed. Research on animal cooperation, for instance, has often resorted to food rewards as a means of studying the behavioral rules that promote cooperation or defection (Stephens et al., 2002). B. PROJECTING UP TO POPULATIONS AND COMMUNITIES While foraging seems to be an important component of many studies into behavioral mechanisms, the behavior itself remains of central importance to ecologists. Many ecological problems at the population level involve foraging. The link between foraging and population phenomena is direct. For instance, predator prey interactions are really foraging, at least for one of the species involved, and so predicting the abundance of prey species may involve knowing something about the foraging decisions of its predators. The exploitation of food resources is of central importance to a number of problems in ecology. For instance, community structure is often dependent on trophic interactions among species. Habitat selection depends on the availability of food resources and the competitive interactions among the foragers exploiting them. Speciation, the very motor of the biodiversity we wish to preserve, often follows from problems of food exploitation, a prime example being the spectacular radiation of the Galapagos finches. Construction of food webs requires knowing something about foraging. All these questions in ecology can benefit from projections of knowledge of individual foraging behavior to population‐level phenomena.

II. THE ADVENT OF SOCIAL FORAGING THEORY A. FREQUENCY‐DEPENDENCE CHARACTERIZES SOCIAL FORAGING Many population‐level questions deal with animals that interact socially. So it seems important, if one wishes to address ecological problems that any foraging theory is able to deal with groups of foragers. Optimal foraging theory has relied heavily on simple optimality as a means to formalize foraging problems. This modeling approach does not deal effectively with cases where animals interact and affect each other’s payoffs. For instance, predicting the prey choice of a solitary forager requires calculating the payoffs of including and not including prey types and then predicting that the animal will choose a policy that provides the maximum payoff: the optimal diet. If, however, foragers are competing with others, the payoff obtained from choosing a diet will depend on the frequency with which

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other members of the population also choose that diet. When this happens, the payoffs are frequency‐dependent and simple optimality cannot be used to calculate the best diet policy. What is needed then is an evolutionary game‐theoretic approach that expects solutions to be the evolutionarily stable strategy (ESS; Maynard Smith, 1982). Foraging questions that require such a game‐theoretic approach have been ignored for some time. Yet, many of the population‐level phenomena that interest ecologists involve competition, a situation that is expected to generate frequency‐dependent payoffs that require analysis using evolutionary game theory. Whenever foraging payoffs are expected to be frequency‐ dependent, that is when the behavior of one individual can reasonably be assumed to affect the gains of others, we will need to turn to social foraging theory (Giraldeau and Caraco, 2000; Waite and Field, 2007). Although it often appears quite reasonable to assume that many social foraging systems are characterized by frequency‐dependent payoffs, empirical demonstrations of this frequency‐dependence remain surprisingly scarce. It would no doubt be important to document the instances where frequency‐dependence occurs instead of simply assuming it. However, until this is done, social foraging theory must continue to rely on the assumption. As a result, social foraging theory provides an entirely distinct framework from optimal foraging theory; it offers a method to investigate questions of group size and dispersion, foraging cooperation and reciprocity, food exploitation, and the complexities of prey selection under competitive conditions. B. DISTINCTIVE FORAGING QUESTIONS The field of social foraging theory is subdivided into four main categories: group membership, decisions within patches, descriptive models of phenotypic diversity, and producer–scrounger (PS) decisions (Giraldeau and Caraco, 2000). Each area is itself characterized by a number of distinct research programs. We briefly present an overview of each before devoting more attention to PS decisions, the area in which most progress has been recorded over recent years. 1. Group Membership Decisions A large part of social foraging concerns animals that forage in aggregations, whether these arise as a result of attraction to conspecifics or to local resource clumps. It is useful to recognize that the way the presence of others affects an animal’s fitness has resulted in two distinct research traditions. One research tradition, mostly interested in the evolutionary origin of groups, concerns itself mostly with cases where the presence of companions

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is hypothesized to enhance fitness, at least for some initial range of group sizes. These animals live in an ‘‘aggregation economy’’ (Giraldeau and Caraco, 2000) because the local economy favors aggregation, at least initially. The other research tradition, mostly interested in animal distributions, concerns itself with cases where the presence of a companion reduces the fitness of all other foragers through interference. This ‘‘dispersion economy’’ exerts a dispersive force on individuals because each would do best by dispersing away from others (Giraldeau and Caraco, 2000). 2. Decisions Within Patches Once foragers search for food in groups, they will inevitably encounter food clumps collectively. Traditional foraging theory has modeled the trade‐off between current patch exploitation versus searching for the next patch: the marginal value theorem (Charnov, 1976). However, when groups exploit patches, the trade‐off is slightly different. As Sjerps and Haccou (1994) indicated, leaving a patch too soon now leaves food behind for competitors to exploit. Moreover, the frequency‐dependence of the payoffs calls for a game‐theoretic analysis of the patch departure. Although Parker (Parker, 1978; Parker et al., 1993) has already developed several models of competitive patch exploitation, there are still few experimental studies of the problem. In one case, Livoreil and Giraldeau (1997) have shown that when three spice finches exploit the same depleting seed patch, at least one individual will need to forage beyond its optimal departure time if it is to remain synchronized with the foraging of its flock mates. Patch exploitation time will also likely depend on the foragers’ prey choices. Optimal prey models once again have been developed for single, solitary foragers but as we saw earlier, choosing prey in a group becomes a game whose solution can be rather involved (Heller, 1980; Mitchell, 1990). Once again research on prey choice in a social foraging context remains a poorly studied area. 3. Descriptive Models of Phenotypic Diversity In order to deal with the foraging behavior of grouped individuals, it is necessary to be able to describe quantitatively the behavioral diversity both within and among groups beforehand. Such models have been presented in Giraldeau and Caraco (2000), but published descriptions of phenotypic diversity remain rare. Under this category of social foraging, we can study the mechanisms that operate either to enhance or to reduce the extent of phenotypic diversity within groups. For instance, how does learning affect the diversity within

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groups; should learning promote diversity within or between groups? These issues have been of central importance in debates about the origin of culture within nonhuman primates (Whiten et al., 1999). 4. PS Decisions When a solitary animal requires a resource, it must invest some effort in searching it out. The same is not always true of a social forager who has the extra option of avoiding most search costs by simply waiting until some other individual discovers it. Once this happens, it can decide to approach and attempt to take some or all of the food, or wait further until a better opportunity arises. This possibility of exploiting the behavioral efforts of others is particularly widespread. It is known as ‘‘freeloading’’ in economics, ‘‘tolerated theft,’’ and ‘‘food‐sharing’’ in anthropology and under various names such as ‘‘joining,’’ ‘‘kleptoparasitism,’’ and ‘‘scrounging’’ (Barnard and Sibly, 1981; Brockmann and Barnard, 1979; Giraldeau and Caraco, 2000) in behavioral ecology. Traditionally, the food joining has been argued to provide some of the benefits of group foraging and so the basis of an aggregation economy. The argument supposes that all group members concurrently search for food and when one of them discovers a clump; all other group members rush in to obtain a share. This group foraging scenario has come to be known as ‘‘information‐sharing’’ because it assumes that information on the location of a discovered food patch is shared concurrently with all group members (Clark and Mangel, 1984; Ranta et al., 1993; Ruxton et al., 1995). The important assumption under information sharing (IS) is that all group members search for food concurrently without losing any ability to detect joining opportunities. Consequently, when a clump is discovered, the remaining individuals come in to join. If we had to predict the frequency of joining in a group of G individuals, it would simply be (G
1)/G. Barnard and Sibly (1981) were the first to picture the use of others’ food as a scramble between two mutually exclusive foraging strategies. They coined the expressions ‘‘Producer’’ and ‘‘Scrounger’’ to denote strategies that correspond to investing some effort in making a resource available and exploiting that effort, respectively. When they did this, they broke away from the traditional information‐sharing approach because now only those group members engaged in playing producer are searching for food; the others are waiting for discoveries to occur. They formalized the first PS game and applied it to the foraging interactions they observed within flocks of captive house sparrows (Passer domesticus). Their study established the game‐theoretic, alternative option game as a potential device for analyzing

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the economics of the joining decision. Since then, it has been found to apply to a potentially wide range of resource exploitation problems (Barnard, 1984). Reviewing the progress accomplished in all four areas of social foraging theory is clearly beyond the scope of this chapter. We choose therefore to concentrate on one area, the PS decision, to illustrate the extent to which social foraging theory offers increasing opportunities for integration from individual mechanisms to population‐level phenomena.

III. THE PS GAME A. GAME CHARACTERISTICS The characteristics of a PS game have been defined in detail by Parker (1984) and Giraldeau and Caraco (2000). In brief, the PS game involves two strategies (producer and scrounger) for which scrounging payoffs are negatively frequency‐dependent and scrounger does worse than producer when scrounger is common but better than producer when scrounger is rare. Three types of payoff functions to producer meet the criteria of a PS game: (1) the payoffs of playing producer can be reduced by increasing the frequencies of scrounger (Fig. 1A), (2) producer may be unaffected by the frequency of scrounger (Fig. 1B), or (3) payoffs to producer can be enhanced by the frequency of scroungers (Fig. 1C). When the game is symmetric, that is payoffs do not depend on who is using the strategy (e.g., a male or a female, a dominant or a subordinate, a young or an old individual), it predicts the existence of an evolutionarily stable mixture of the two strategies that is characterized by equal payoffs to each. Later, we will look into asymmetric or phenotype‐limited games where payoffs depend on individual characteristics such as age or dominance. B. EXPECTED SOLUTIONS TO PS GAMES 1. The Evolutionarily Stable Strategy Evolutionary game theory is increasingly applied to behavioral problems (Dugatkin and Reeve, 1998). Traditionally, in evolutionary game theory, acceptable solutions to behavioral games had to be evolutionarily stable in the sense that once the solution is reached by the population, natural selection could no longer allow the spread and invasion of any alternative (Maynard Smith, 1982). The argument is genetic, based on alternative alleles coding for alternative courses of action and natural selection is the

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A

Payoffs

B

C

Proportion scrounger FIG. 1. The payoff functions of the producer–scrounger game. The three panels give different possible effects of scroungers on the producers’ (thin line) and the scroungers’ payoffs (thick line). Panel A gives the classic producer–scrounger payoffs: producer and scrounger payoffs are depressed by increased proportion of playing scrounger but scroungers are affected more strongly. Panel B shows a case where the producers are unaffected by scroungers, whereas in panel C, producer payoffs seem to benefit from increased frequencies of scrounger.

mechanism regulating the frequency of alternatives in the population. An initial population of pure producer, for instance, could be invaded by alleles for scrounger because the initially rare scroungers would do much better than any of the individuals bearing producer alleles (Fig. 1A). Scrounger alleles could not go to fixation because a population made up of pure scroungers would be invaded by producers. So, neither producer nor scrounger alone can be evolutionarily stable. The ESS in this case is mixed, allowing the frequency of scrounger alleles to increase until the fitness of scroungers drops to the fitness of producers. 2. The Behaviorally Stable Strategy In most behavioral instances, however, animals reach game solutions by adjusting their use of strategies according to the conditions in which they are playing the game. The mechanism of adjustment in this case is not

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natural selection but some behavioral rule that takes the individuals to a point such as a Nash equilibrium where no player can do better by unilaterally switching to an alternative course of action. The mechanism that adjusts the frequency of alternatives in such a group is not selection acting on genetic alternatives and so describing it as evolutionarily stable, as is commonly done, can be misleading. Dawkins (1980) recognized the importance of this distinction early on and proposed to call such behavioral solutions ‘‘developmentally stable strategies’’ (Dawkins, 1980). The name did not really garner much use in the literature and calling the strategy developmentally stable remains ambiguous because it can refer either to alternative development histories, for instance hooknoses and jacks in salmon (Gross, 1991), or to learned alternative strategies. More recently, Mottley and Giraldeau (2000) proposed a mechanism‐free name applicable to all solutions, the stable equilibrium frequencies (SEFs). While the name can be applied to either evolutionarily or behaviorally stable strategies (BSSs), it does not indicate the mechanism through which stability is achieved. Because we feel the solution can depend on the mechanism through which it is reached (Hines and Bishop, 1983), we suggest referring to solutions attained by behavioral adjustment as a BSS rather than an ESS. 3. Why is It Important to Distinguish Between ESS and BSS? The relationship between BSSs and ESSs has been addressed by Harley (1981) who predicted that the individuals’ behavioral adjustment rules should evolve in such a way that the evolutionarily expected rule is the one that leads a population as quickly as possible to a state that is analogous to an otherwise ESS (Harley, 1981; but see Houston and Sumida, 1987). Harley’s prediction, and hence the commonly accepted concordance between behaviorally stable and ESSs, requires an extra unstated assumption we call the ‘‘behavioral gambit,’’ in reference to well‐known phenotypic gambit by Grafen (1991). The phenotypic gambit is openly discussed and assumes that adaptation can be studied at the level of the phenotype without the need to worry about its genetic substrate because selection acting on alternative phenotypes is equivalent to selection acting on alternative genotypes. Moreover, population geneticists have argued that constraints linked to genetic architecture need not interfere with the approach of studying the adaptation of phenotypes (e.g., Hammerstein, 1998). But behavior and decisions are phenotypes of the central nervous system and the cognitive systems it supports. Can adaptation be studied at the level of behavior without knowing how selection acting on alternative behavioral phenotypes feeds back to genes that do not code for behavior but rather the

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cognitive systems that produced them? While there is some population genetic support for the phenotypic gambit, there is much less for the behavioral gambit. Moreover, there is virtually no experimental investigation that compares the form of strategic and genetic solutions to games. For instance, the same game solution can take on different appearances: a given ESS mixture of producers and scroungers can involve pure individuals committed to one or the other strategy, a population of individuals who all use the same mixture of the two strategies, or a combination of the two as long as the ESS is maintained at the population level. While such alternative expressions of the same solution are equivalent and equally expected for ESS, is this also the case for BSS? For instance, because a BSS requires animals to adjust their behavior and hence sample alternatives, it may be more likely that BSS solutions will always end up with a population of individuals using both alternatives. It appears increasingly important, first, to recognize that we are using the behavioral gambit, second, to see whether it is justified, and, finally, if it is, to explore how populations that reach a BSS differ from those in which solutions are an ESS. The quantitative tools proposed by Giraldeau and Caraco (2000) as a means to partition diversity within and among populations may play an important role in exploring such a question. IV. RATE‐MAXIMIZING PS MODEL Barnard and Sibly’s original PS game lacks many of the details required to make quantitative predictions of producer and scrounger frequencies in a foraging context (Barnard and Sibly, 1981). In order to be able to make such predictions it is essential that the game be applied to a specific foraging scenario: that means making the game into a foraging model by specifying the constraints and the currency of fitness. Three types of foraging models have been developed. One is a deterministic rate‐maximizing model (Vickery et al., 1991), another is a static, stochastic, risk‐minimizing model (Caraco and Giraldeau, 1991), and the third a state‐dependent dynamic game (Barta and Giraldeau, 2000). We present each in turn and, when available, their associated empirical investigations. A. THE MODEL The model presented here is derived from that presented in Giraldeau and Caraco (2000), which was modified from a three‐strategy model that was presented first by Vickery et al. (1991). The model makes the following assumptions: animals are in a group of G individuals, any individual playing

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scrounger within the group can detect any of the group’s successful producers, the food clump contains F indivisible items, the finder of the clump always gets a items, the finder’s advantage, for its exclusive use and shares the remaining F–a items equally with the scroungers. The proportion of the group playing producer is p and the proportion playing scrounger is (1–p). Once a producer finds a food patch, the (1–p)G individuals playing the scrounger strategy arrive together and share with the individual playing producer the remaining F
a food items. We define the energy intake of a producer and a scrounger as Ip and Is, respectively, and calculate intake over some time interval T. If l gives a producer’s rate of encounter with food patches, then the producer’s intake after time T is   F
a Ip ¼ lT a þ ð1Þ 1 þ ð1
pÞG and the scrounger’s intake is:



Is ¼ lGTp

F
a 1 þ ð1
pÞG

 ð2Þ

Setting Ip equal to Is to find the point of equal payoffs and hence SEF gives: p∧ ¼

a 1 þ F G

ð3Þ

where p^ is the SEF for playing producer within the group for a < F and for G  2. The model therefore predicts that the frequency of scrounger in a group depends on the ratio a/F, the finder’s share, and G, the group size. The larger the finder’s share, the lower the equilibrium proportion of scroungers within a group. The larger the group, all else being equal, the more scroungers will occur at the equilibrium. Both these predictions have received some experimental support.

B. EMPIRICAL SUPPORT 1. Evidence for the Existence of a Finder’s Share As Eq. (3) demonstrates, the finder’s share (a/F) is a fundamental component of the rate‐maximizing game and its magnitude will affect the equilibrium frequency of scroungers within a group. It is therefore important to document whether a finder’s share actually occurs in group foraging situations. Early research on two species of estrildid finches tested in captivity, nutmeg mannikins (then called spice finches) (Lonchura

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punctulata), and zebra finches (Taeniopygia guttata) strongly hinted at the existence of a finder’s advantage (a) when flocks search for divisible clumps of seeds of different richness (F) (Giraldeau et al., 1990). In mannikins, the food finders were able to monopolize the first two or three seeds of a patch (a ¼ 2–3) before having to share the rest with the scroungers irrespective of the number of seeds in the patch (F). In the zebra finches, however, the finder’s advantage depends both on the animal’s social status and the patch’s richness; subordinates lose any finder’s advantage when they forage on poor patches. Field studies of a group of 24–26 tufted capuchin monkeys (Cebus apella) also report a finder’s advantage (Di Bitetti and Janson, 2001). Provisions of tangerines and bananas were placed on platforms distributed within Iguazu´ National Park, Argentina and observers noted the process of platform discovery. They report that the platform’s discoverer enjoyed a finder’s share, the magnitude of which depended on a number of factors, including dominance status of the discoverer, interindividual distances, the extent to which information concerning the discovery was broadcast to the rest of the group members and the total amount of fruit on the platform. 2. Evidence of Incompatible Search Modes The main difference between IS and PS games concerns the extent to which looking for joining opportunities excludes any search for finding opportunities. When individuals can search for both finding and joining opportunities concurrently, there is no point in choosing one option over the other. However, when there is a trade‐off such that searching for one excludes the other, then clearly animals must choose which to use at any given time. The evidence gathered so far in support of the PS approach suggests that, in the animals in which it was studied, searching for finding opportunities seriously hampers the search for joining opportunities. What kind of evidence supports such a trade‐off? In a laboratory study of flocks of nutmeg mannikins foraging for hidden clumps of seeds, Coolen et al. (2001) found that the bird’s posture could indicate whether it was currently investing in the producer or the scrounger alternative. They found that head position in reference to a projected line originating from an animal’s eye and passing through its nares while the bird was hopping on the grid was a statistically efficient predictor of whether the bird would obtain food as a producer or a scrounger. The head was down when the line pointed toward or below the horizon and up otherwise (Fig. 2). The more a bird hopped with the head pointed down, the more it was recorded to produce food (Fig. 2A). The more it hopped with the head pointed up, the more it was observed to scrounge, a result that was replicated in a second study (Wu and Giraldeau, 2005; Fig. 2B).

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Patches found

20

10

0 0

50

100

150

200

Hops head down 1.0

Proportion of patches joined

0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 Proportion of time searching head up FIG. 2. Results of experiments involving flocks of nutmeg mannikins (Lonchura punctulata) in which individuals are observed as they collectively search for food, hidden among wells on a wooden table. The birds’ head position is based on an imaginary line projecting from the birds’ eyes through its nares (right‐hand panels). When the line is horizontal or higher, the bird is hopping with the head up. Otherwise, the head is pointing down. The top left graph shows that as the frequency of a bird’s hopping with the head down increases so does the frequency with which it is reported to find food (produce). Results modified from Coolen et al. (2001). The bottom panel illustrates that the more a bird hops with its head pointed up, the more likely it is to obtain food by scrounging. Results modified from Wu and Giraldeau (2005). Illustrations of nutmeg mannikin heads are courtesy of Gi‐Mick Wu.

Coolen et al.’s multivariate analyses indicate that no other recorded behavior except head position while hopping would predict the outcome of their foraging (Coolen et al., 2001). If hopping with the head up leads to joining only and never producing, while hopping with the head down leads only to producing and never joining, then the alternatives are apparently incompatible. It is unclear, however, why hopping with the head down prevents birds from detecting joining opportunities or why hopping with

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the head up can cause a bird to hop over a well that contains food without detecting it. Clearly, the results have implications both for their ocular neuroanatomy as well as perhaps their cognitive ability to process concurrent information that would allow them to act on detected producer or scrounger opportunities, a problem of limited attention (Dukas, 1998). The result naturally requires generalization to other avian and nonavian species. Nonetheless, in the case of nutmeg mannikins, at least, evidence indicates that producing and scrounging are mutually exclusive alternatives as assumed by the game. 3. Using the Finder’s Share to Alter the BSS When, as was found to be the case in mannikins, the finder’s advantage changes very little with the size of the food clump, it becomes possible to alter the finder’s share simply by changing (F), the total number of seeds in a clump. The approach was used by Giraldeau and Livoreil (1998) who presented three flocks of five mannikins each with foraging habitats that contained clumps of 5, 10, or 20 seeds. The finder’s share is expected to be inversely proportional to the clump’s richness. Each flock experienced the patch types in a different sequence and the proportional use of scrounger is reported in Fig. 3. When compared with predictions drawn from Eq. (3), the results appear entirely consistent with the interpretation that the birds adjusted their individual use of producer and scrounger resulting in the

Proportion joining

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lnformation sharing

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Prediction based on PS game using finder's share

60 40 20 0

Low Medium High Finder's share (a/F)

FIG. 3. Results of an experiment conducted with three flocks of five nutmeg mannikins (Lonchura punctulata) that search for food together. Each flock was submitted to three seed distributions, each corresponding to a low, medium, or high finder’s share. Each flock foraged in one seed distribution for several days before being switched to another. All flocks experienced a different order of seed distributions. The histograms give the mean (þSEM) proportion of patches scrounged in the tree flocks for a given seed distribution. The thick line gives the predicted proportion of scrounging based on the rate‐maximizing model Eq. (3) while the thin line gives expectations for the naive information‐sharing model. Results modified from Giraldeau and Livoreil (1998).

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group converging upon the expected BSS of scrounger (Fig. 3). Comparing the same results to the predictions of an information‐sharing game in which all available group members join each discovery, we find that the results clearly do not match the predictions. However, they could be consistent with a version of the information‐sharing game in which joining depended on the number of seeds expected once the joiner arrives (the complement of the finder’s advantage). Such a model would also predict that joining would decline when patch richness declines. Up to now, however, the information‐ sharing model has not been developed to such a level of sophistication. Until such a development occurs, it remains nonetheless a plausible alternative. 4. Using Group Size to Alter the BSS Equation (3) also predicts that the frequency of scrounging at equilibrium should increase with increasing group size. However, the same qualitative effect is predicted by IS where joining frequency is given by (G
1)/G. Coolen (2002) tested the effect of increasing group size and patch richness on joining frequencies of nutmeg mannikins. She observed flocks of three and six birds as they foraged for poor or rich seed clumps. To distinguish between IS and PS, Coolen (2002) argued that as the frequency of joining increases, only a PS game approach predicts lower group rates of encounter with new food clumps because all those individuals engaged in playing scrounger do not search for food. In IS, by contrast, there is no incompatibility between the two search modes and so group patch encounter rates should not be affected by the frequency of joining. Coolen’s results support the PS game approach (Coolen, 2002). She reports an increase in the frequency of joining when group size increases. Even though the increase is also predicted on the basis of IS, the absolute levels of joining are always well below those expected by IS. Moreover, the discovery intervals between successive food patches increased at larger group size, as expected by the PS approach. An IS approach would likely have predicted no change in discovery intervals when going from small to large groups as the same number of patches were available per capita. The decline could be due to increased interference within the larger groups but that is unlikely given foraging areas were increased in proportion to group size and no overt aggressive displays or displacements were observed. The birds therefore appear to play a PS game with incompatible alternative search modes. 5. Altering the BSS by Changing the Costs of Producing and Scrounging Another way of testing the PS game consists of experimentally altering the costs of using one strategy or the other. For instance, by increasing the cost of playing producer one expects the equilibrium frequency of producers to decline. That is precisely what was done for flocks of nutmeg

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mannikins where small weights were either present or absent on the lids that producers had to lift to find food. When the lids were weighted, the relative frequency of producer decreased such that more scroungers were recorded (Giraldeau et al., 1994). Morand‐Ferron et al. (2007) also altered the costs of producing and scrounging with flocks of the carib grackle (Quiscalus lugubris). This bird is a gregarious opportunistic passerine that can readily feed on human‐ provisioned food. In Barbados where it was observed, some birds dipped pieces of dry food in a puddle before consuming it; a procedure called ‘‘dunking’’ that made it easier to swallow (Morand‐Ferron et al., 2004). These ‘‘dunked’’ food items therefore apparently become more profitable such that they are often kleptoparasitized by neighboring flock mates. In a recent study of kleptoparasitic interactions over dunked items, Morand‐ Ferron et al. (2007) independently altered the costs of either producing or stealing a dunked item. The cost of dunking was modified by adding a barrier that forced a dunking producer to adopt a longer and more circuitous route between the dry food and the puddle. Longer routes result in greater costs of producing. They modified the costs of scrounging the dunked item by changing the shape of the puddle while keeping its surface approximately equal. A rectangular puddle provides a longer perimeter and so should correspond to a greater distance between the dunking producer and a potential kleptoparasite, which reduced the efficiency of scrounging from 48% to 37%. The study shows that the payoffs to kleptoparasites were frequency dependent; the more scroungers that were present in a group, the lower their payoffs (estimated in latency to a successful kleptoparasitic act). When they experimentally manipulated the costs of scrounging (rectangular versus circular puddle), they found that the frequency of scrounging was high when its costs were low, and the frequency was low when its costs were high (Fig. 4). Experimental manipulation of the costs of producing (circuitous vs direct route) had the expected effect on the frequency of scrounging: when producing was cheap there were fewer scroungers, but the reverse was true when producing was costly. 6. Evidence That Groups Reach the Predicted Rate‐Maximizing BSS The previous studies establish that some bird species, tested in the laboratory and in the field, appear to have the behavioral flexibility required to adjust to changing BSS values. However, the evidence so far is purely qualitative and it would be reassuring if we could demonstrate that when animals are engaged in a PS type game, they actually converge on and reach the expected BSS values. This is precisely what the experiment by Mottley and Giraldeau (2000) proposes to do.

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A

Residual proportion of scroungers

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FIG. 4. Results of a field experiment involving carib grackles (Quiscalus lugubris) studied in Barbados. The birds are offered a pile of dry food and a puddle of water. The producer strategy consists of carrying the dry food to the puddle and dunking it in the water to make it easier to swallow. The scrounger strategy is a kleptoparasitic behavior that consists of waiting at the puddle and appropriating the items as they are being dunked by a producer. The experiment consisted in altering independently the costs of either the producer or the scrounger strategy by either increasing the travel distance from pile to puddle (producer cost) or increasing the density of competitors at the puddle (scrounger cost). The experiment was conducted in three phases where the costs to a strategy are low, then high, and then low again. Results report the residual proportion of scrounging observed during the different phases of the experiment that changed in all cases in the direction expected by the producer–scrounger game. Taken from Morand‐Ferron et al. (2007) with permission.

One difficulty with showing the frequency‐dependence of payoffs is precisely the flexibility that animals have, which allows them to avoid using unprofitable alternatives. It becomes necessary to find a way of constraining them to one role while submitting them to the different combinations of producers and scroungers. Mottley managed to do this

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by constructing a polarized food patch that can only be produced from one side or scrounged from the other (Fig. 5). By aligning 22 of these patches down the middle of an aviary, he created distinct producer and scrounger compartments within which he could test all possible combinations of producer and scrounger for three flocks of six nutmeg mannikins. He found that the device produced payoff functions (seeds/s) that corresponded exactly to the conditions of a PS game; scrounger does better than producer when rare, and worse than producer when common. He could even rig his apparatus to produce two distinct BSS values, one around 2–3 scroungers and another around a single scrounger. To know whether the birds could adjust their behavior in order to reach the expected BSS, he placed them in the same apparatus that was modified by removing the partition allowing the birds to go freely from one compartment to the other. He started with the apparatus rigged for a BSS of one scrounger and observed that after 3–4 days of trials, the flocks had reached and remained at the predicted BSS (Fig. 6). Then, when the apparatus setting was changed to make the expected BSS of scroungers between 2 and 3, the flocks readjusted within a day or two and reached once again the predicted BSS (Fig. 6). These results indicate that a group of birds placed in an apparatus that generates payoff functions corresponding to a PS game can adjust their behavior in such a way as to reach the expected BSS after a few days. Mottley and Giraldeau (2000) argue that the mechanism used by the birds to do so is likely to be some form of learning where animals sample both alternatives and alter their investment in each until they payoffs become equal. V. STOCHASTIC, RISK‐SENSITIVE MODELS Up to this point, we have considered only a strictly deterministic approach in the sense that, for a given population frequency of producer and scrounger, each strategy yields a given payoff with certainty. However, certainty is not common in the real world such that using scrounger or producer on the next play is more likely associated with a distribution of potential payoffs characterized by a mean and a variance. We know that foragers are sensitive to mean rewards but they should also be sensitive to the variance of an option’s payoffs (Caraco, 1981; Stephens, 1981). This is because an option’s variance, or uncertainty, can sometimes mean the difference between death and survival. The logic of risk‐sensitive foraging is captured by the energy‐budget rule of Stephens (1981). It can be illustrated using foragers facing two options with equal mean rewards but different variance. When the forager expects not to meet its energetic

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Producer side

A

Scrounger side Barrier B Producer side

Scrounger side Division Seed container

Collecting dish

String

Perch

FIG. 5. The apparatus used to confine small nutmeg mannikins (Lonchura punctulata) using either producer or scrounger foraging alternatives. The bottom panel shows a top view of a single polarized food patch. Birds may perch on the left side and obtain food by producing only. They can produce by pulling on a string that protrudes from an opaque partition. Once the string is pulled, seeds that had been held in place in a small vial on the right‐hand side are released into a square plastic container. The producer birds on the left can eat by stretching their neck through a small opening in the partition next to the seed dish. Birds on the right play only scrounger. They must wait until a bird in the producer compartment pulls the string in order to feed. They cannot have access to seeds without a producer. The top panel shows a plan view of a whole polarized aviary in which a line of 22 feeders are aligned in the same direction. The top compartment is the producer compartment, the bottom the scrounger. The experimenter can measure the payoffs to producer and scrounger by placing different combinations of birds in either compartment. Having done so, he or she can remove the top part of the divider between compartments to allow birds to choose where they wish to forage. Taken from Mottley and Giraldeau (2000) with permission.

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5 A 4 3 2 1 0

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FIG. 6. Results obtained for birds foraging in the apparatus depicted in Fig. 5. Birds can freely move from the producer and scrounger side. The apparatus can be rigged to generate two different behaviorally stable strategies (gray zones) to the producer–scrounger game. Results are for three flocks of six nutmeg mannikins (Lonchura punctulata) foraging for several days in one and then the other conditions. Taken from Mottley and Giraldeau (2000) with permission.

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requirement, it should prefer the more uncertain alternative and hence be risk‐prone. However, when it is doing well and expects not to fall short of its energetic requirement, it should avoid uncertainty and be risk‐averse (Fig. 7).

A. STATIC STOCHASTIC MODEL

Expected reward

Caraco and Giraldeau (1991) were the first to formulate a risk‐sensitive version of the PS game. They assume that producer provides a more uncertain payoff than scrounger because producer must rely on its own food discoveries and because of that, runs a greater chance of coming up with nothing. When it does find food, however, it enjoys some priority of access to the resources over the scroungers and so may obtain more food per patch than the scroungers. Scrounger, on the other hand, provides a more certain payoff because it relies on the discoveries of several individuals that play producer. Each patch provides less food than for the producer, but scroungers get to exploit more patches and so are less likely to end up with nothing. The model predicts that scrounger will be rare when the costs associated with its use are high or when the producer’s priority over the resources is high. All other factors being equal, the proportion of scrounger within a group is expected to increase as the expected encounter rate with resource clumps increases. The direction of the effect of physiological requirement,

A

B Options

FIG. 7. Graphical illustration of the importance of uncertainty for survival. The graph shows two options, A and B, each provides the same mean expected reward, but option B is more variable or uncertain. The dotted line shows the minimum reward threshold that is required to avoid some fitness cost. In this case, only option B offers a risk of being above this minimum threshold and so is the only rational choice.

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however, depends on group size and the extent of the producer’s priority over food resources. When producers have a strong competitive advantage over the clump’s resources, then the proportion of scrounger should decrease with increasing physiological requirement. When the producer’s priority is low, an increase in physiological requirement should lead to an increase in the use of scrounger despite its risk‐averse properties because the large difference in the mean rewards obtained by producer and scrounger far outweigh the effects of differences in their variance. B. EMPIRICAL EVIDENCE One way to test predictions of risk‐sensitive models has been to alter the rate at which foragers encounter food patches (Koops and Giraldeau, 1996). When encounter rates are high, foragers are more likely to meet their foraging requirements and so they should use scrounger more. Another way to test the model involves reducing the energy reserves of individuals such that the energy they require to regain their equilibrium metabolic balance is increased. Whether in such a case, animals should increase producer or scrounger depends on the extent to which the finder has some priority over the food it discovers (Caraco and Giraldeau, 1991). When producers have low priority, then increased energy requirements predict increased use of scrounger. These predictions were tested with mixed success by Koops and Giraldeau (1996) using wild‐caught flocks of starlings (Sturnus vulgaris) foraging on arrays of food clumps in the laboratory. They found, for instance, that as expected by the model, individual starlings increased their use of scrounger when the expected rate of clump encounter increased (Fig. 8A). However, the effect could also have been a spurious by‐product of increased proximity between food clumps rather than a behavioral response to encounter rate. They also found that producers had very low priority of access to food such that under those conditions increased food requirement would predict increased use of scrounger. Even though as expected, seven of the eight subjects tested increased their use of scrounger when their energy requirement was increased, the effect was rather weak and fell short of statistical significance and so it is difficult to be conclusive (Fig. 8B). Similar mixed results were obtained using an entirely different experimental approach with captive flocks of nutmeg mannikins (Wu and Giraldeau, 2005). In this case, the experimenters measured the number of seeds required daily by each bird to maintain a stable energy budget. Then they observed the birds as they foraged on grids presented in indoor aviaries. By measuring the time spent hopping with the head pointed up or down, Wu and Giraldeau could measure the temporal investment in both

Proportional use of scrounger in high food density

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FIG. 8. Evidence of risk‐sensitive use of producer and scrounger alternatives in European starlings (Sturnus vulgaris). Each dot represents the proportion of scrounging recorded for a given bird in both foraging conditions. In the top panel, birds forage in low and high food density such that expected encounter rate with patches is either low or high, respectively. The diagonal line gives the expectation if there were no effect of patch density. The risk‐sensitive model predicts an increase in the use of scrounging with increasing expected patch encounter. Almost all dots are above the diagonal indicating that the birds increased their use of scrounger with food density. The lower panel shows the results of a test where the food requirement R was either low or high. Although most of the points are above the diagonal, the effect is weak even if consistent. Results taken from Koops and Giraldeau (1996) with permission.

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foraging strategies. Using this approach, they showed that producer was indeed the more uncertain strategy as the coefficient of variation of intake rate while playing producer was almost double the coefficient of variation observed for the scrounger strategy. They also showed that while scrounger provided a lower chance of energy shortfall for low energetic requirements, producer was the strategy that lowered the chances of energy shortfall over a large range of larger intake requirements. The risk‐of‐shortfall differences, however, between producer and scrounger remained small and it is not surprising therefore that their results report consistent but near significant increases in producer use with increased food requirements.

VI. STATE‐DEPENDENT DYNAMIC PS GAME The previous stochastic model is static in the sense that it does not take into consideration the effect of time of day on the decision to use producer or scrounger. However, there is considerable evidence for the existence of a large number of daily behavioral routines such as avian dawn choruses (McNamara et al., 1987) or food storing and retrieving (McNamara et al., 1990). Such routines are expected to occur simply as a result of day–night alternation and quite independently of changes in external conditions such as food abundance. It is therefore possible to expect daily routines of producing and scrounging activity. Barta and Giraldeau (2000) developed a stochastic dynamic state‐ dependent model of the PS foraging game. The model converts all decisions into a survival probability and calculates the best possible sequence of decisions over the course of the day for individuals depending on their physiological state and the danger of predation. The model assumes that energy is acquired only by foraging that only occurs during daylight hours, while resting provides safety from predators but at some metabolic cost. The model predicts little effect from the danger of predation. However, daily PS routines are expected (Fig. 9). In essence, scrounging should peak early and late in the day while producing should be more prevalent during the midday. Moreover, low‐energy reserves favor scrounging early in the day but the same physiological conditions call for more producing later in the day (Fig. 9). Although it seems clear that an individual’s propensity to use producer or scrounger alternatives should be affected by its physiological state, there is no evidence up to now for the existence of daily routines in PS games even though such foraging routines have been found in other contexts (Dall and Witter, 1998; Lucas and Walter, 1991).

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FIG. 9. Results of a dynamic state‐dependent model of the producer–scrounger game. Panel A shows that when animals have high energy reserves they should not bother foraging at all, no matter what the time of day. However, when energy reserves are low, the animal should resort to scrounging early in the day and to producing late in the day. The consequence is shown in panel B where a daily producer–scrounger routine is expected where scrounging should be most frequent early and late in the day. Modified from Barta and Giraldeau (2000).

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VII. PS INFORMATION GAMES In many circumstances, animals must assess the quality of environmental parameters to make behavioral decisions that maximize fitness. To obtain this information, they can either interact with their environment, thereby obtaining personal information, or can use social information produced by the behavior of other individuals (Dall et al., 2005; Danchin et al., 2004). This social information could be broadcast voluntarily, in which case it would be considered communication. Alternatively, it can be provided inadvertently as a result of the other individuals’ exploitation of resources. This inadvertent information can consist of actual evidence of the resources’ quality in which case it is public information (Valone, 2007; Valone and Templeton, 2002) or it could simply be derived from the behavioral decision of others. Evidence for the use of public information exists in a variety of contexts including habitat assessment (e.g., Boulinier and Danchin, 1997), foraging (Galef and Giraldeau, 2001), opponent assessment (Freeman, 1987; Johnsson and Akerman, 1998; Oliveira et al., 1998), and mate choice (e.g., Dugatkin 1996; Galef and White, 2000; Gibson and Ho¨glund, 1992; Nordell and Valone, 1998). However, when it becomes impossible to collect both personal and social information concurrently, animals must choose between the two and the problem takes the form of a PS game (Giraldeau et al., 2002). In this version of the game individuals whose activity produces information inadvertently are producers, and those who are inactive and observe are scroungers. Basically, the more individuals engage in the producer alternative, then the more profitable it becomes to obtain information socially and hence scrounge. Alternatively, the more individuals engage in scrounging social information, the fewer producers there are to copy and the lower the value of being a scrounger. The incompatibility between the two modes of obtaining information can be due to either cognitive or sensory limitations (see Giraldeau et al., 2002). The payoff functions in PS information games can take different forms. For instance, in some cases, scrounging information has no effect on the fitness of the producers (Fig. 1B). This would be the case for instance when individuals acquire information about the relative fighting ability of potential opponents by observing aggressive interactions between others (Oliveira et al., 1998). In other cases, the game’s payoff structure may be more similar to a foraging game where scrounging information reduces the payoffs to producers (Fig. 1A). This may occur in the context of habitat selection where breeders frequently rely on their own success to assess the quality of their breeding site and decide whether or not to leave. This information, however, is unavailable for nonbreeders and juveniles.

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Thus, to select their future breeding site, individuals can use personal information and base their choice on ecological parameters that relate to habitat quality, such as resource density or predation risk. Alternatively they can rely on public information and base their choice on the presence or breeding success of conspecifics the previous year (Doligez et al., 2002; Nocera et al., 2006). The use of such strategies, however, tends to increase the density of individuals on high‐quality patches, leading to an increase in competition on these patches and hence a decrease in fitness. Frequency‐dependence of the payoffs may also occur in the context of information use in mate selection, when some individuals copy others’ decision to avoid the cost of mate sampling (Westneat et al., 2000). Many examples of mate copying come from lekking species where males provide only sperm to females, and females therefore are unlikely to suffer a reduction in their breeding success if others copy their mating decision. Recent laboratory studies (Doucet et al., 2004; Swaddle et al., 2005), however, revealed that copying may also occur in monogamous species where males provide parental care and females therefore have a lower breeding success if males have to divide their efforts among several females. Whatever the context in which public information is used, the incompatibility between the collection of private and public information leads to a paradoxical prediction in the case of information use (Giraldeau et al., 2002). If the collection of private information is costly and individuals are selected to use the most profitable source of information, then public information will provide a better alternative. However, as more and more individuals turn to use public information, fewer are left producing it. Public information will recruit until all but one group member are engaged in the use of public information. At this stage, the whole group acts on the information acquired by only one of its members and so it is difficult to argue that using social information is more profitable than its alternative.

VIII. PROJECTING DOWN TO INDIVIDUAL BEHAVIOR A. IMPLICATIONS FOR THE USE OF AGGRESSION Most PS games assume that the producer of a food patch shares the remaining resources with the scroungers without aggression (Giraldeau and Caraco, 2000; Vickery et al., 1991). However, in some species, individuals often behave aggressively to obtain the whole contested resource. Given that escalated fights may be costly in both time and energy, the economic defendability of food patches should affect not only the aggressiveness of foragers but also their decision to play producer or scrounger.

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To investigate the ecological circumstances under which aggressive behavior should occur and how it should influence the rate at which individuals join others’ food discoveries, Dubois and Giraldeau (2005) have developed an evolutionary game model that incorporates both the PS and hawk–dove games: individuals not only must decide at a given time to play producer or scrounger, but also can adopt either the aggressive hawk tactic or the nonfighting dove tactic. The model predicts that resource owners should always defend aggressively the food patch, whereas, under many circumstances, only a fraction of the individuals playing scrounger should attempt aggressive appropriation of the producer’s food. This arises because the producer gets the finder’s advantage and hence can afford to pay more for defense than the scroungers are willing to pay for appropriation. In addition, considering the fact that individuals can compete aggressively profoundly affects some of the predictions from earlier PS game models. Indeed, as in previous PS games, Dubois and Giraldeau’s model predicts that the expected frequency of scroungers should increase as the scroungers’ share (F
a) increases (Dubois and Giraldeau, 2005). On the other hand, when group foragers can use escalated aggressive tactics, there are conditions under which all group members should play producer, whereas the stable strategy necessarily corresponds to a mixture of both tactics when producers and scroungers use nonaggressive scramble foraging tactics. In addition, contrary to earlier PS games that predict an increase in the expected frequency of scroungers with increasing group size, Dubois and Giraldeau’s model predicts that individuals should reduce their use of the scrounger tactic when competitor number increases (Dubois and Giraldeau, 2005). Up to now, empirical tests of the PS games have applied mostly to situations where resources were not economically defendable and so there is no evidence yet that individuals modify their use of producer and scrounger in response to changes in their probability of successfully evicting competitors from a food patch. The model predicts that high levels of aggressiveness should be associated with high frequencies of scroungers at equilibrium. If this was so, prey that would adjust their spatial distribution in order to provide larger scrounger’s shares and hence higher frequencies of scrounger (i.e., lowered defendability; Robb and Grant, 1998) would benefit from having fewer producers seeking them out. B. PHENOTYPIC CONSTRAINTS ON THE USE OF PRODUCER AND SCROUNGER STRATEGIES Models of the PS game usually assume that individuals’ payoffs are independent of their phenotype and predict therefore that all group members at equilibrium should have equal payoffs regardless of the alternative

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they use (Caraco and Giraldeau, 1991; Vickery et al., 1991). However, many empirical studies of PS systems report that the strategy used differs for individuals with different phenotypes. Several laboratory studies, for instance, have found that zebra finches can show distinct tactical choices ranging from pure producer to pure scrounger (Beauchamp, 2001; Biondolillo et al., 1997), and that even though individuals are capable of adjusting levels of scrounging when tested under different conditions, they tend to use the same combination of tactics in different environments. These results suggest that tactic choice could be phenotypically limited (Beauchamp, 2001). Given that an animal’s dominance rank, its foraging efficiency as well as its spatial position within the foraging group can influence the payoffs it gets from being a scrounger or a producer, these attributes are thought to affect a group forager’s decision to use one or the other tactic. 1. The Effect of Social Dominance An individual’s competitive capacity is expected to play an important role in its decision to play producer or scrounger: because of their competitive superiority, dominants are expected to play mainly scrounger while subordinates on the contrary should play mainly producer. Accordingly, several empirical studies have reported that food discoverers are most often subordinate individuals that are frequently supplanted from patches by dominants (Bugnyar and Kotrschal, 2002; Lendvai et al., 2006; Liker and Barta, 2002; Rohwer and Ewald, 1981). On the other hand, others report no dominance effect on tactic use, even when individuals compete for food aggressively and form dominance hierarchies within foraging flocks (Beauchamp, 2006; Giraldeau and Lefebvre, 1986; Giraldeau et al., 1990; Robinette Ha and Ha, 2003). So perhaps the influence of dominance rank on tactic use depends on the species’ particular social or ecological conditions. Indeed, competitive asymmetries should affect the individuals’ payoffs only when the high‐ranking individuals can defend the food patches aggressively, leading to the exclusion of subordinate individuals. Hence, the effects of dominance on tactic use should be stronger and more detectable when individuals forage for defendable food patches (Grant, 1993). In accordance with this prediction, Theimer (1987) found that dominant dark‐eyed juncos (Junco hyemalis) consumed significantly more seeds than subordinates when resources were clumped, but this difference disappeared when food was dispersed and hence less defendable (Grant, 1993). Thus, this result suggests that all the parameters that directly affect the level of defendability of resources, including patch size or competitor number, should influence each individual’s decision to specialize exclusively or not

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on one role (producer or scrounger). The phenotype‐limited model developed by Barta and Giraldeau (1998) makes predictions that are consistent with these; it predicts that high‐ranking individuals that can obtain a larger share of resources in a patch should use mainly the scrounger tactic, while subordinates that are competitively inferior should use mainly the producer tactic, obtaining thereby lower payoffs. However, only strong differences in competitive ability are expected to influence the use of producing or scrounging tactics and the effect of dominance on tactic use should be detectable only in small groups (Barta and Giraldeau, 1998). Not only ecological but also social factors can modify the effect of dominance rank. Indeed, Robinette Ha and Ha (2003) found that northwestern crows (Corvus caurinus) that scrounged aggressively did select scrounging targets that were less related to them, while apparently nonaggressive food sharing among the producer and the scroungers occurred more frequently between closely related individuals. Thus, dominance would be important mostly in interactions involving nonrelatives.

2. Knowledge Limitation and Learning Ability The ability of foragers to detect food items can also constrain individuals to use one alternative more frequently than the other. Two competing hypotheses have been proposed concerning the origin of such differences in tactic use. The first considers that individuals differ with regard to traits that directly affect the rate at which they can discover food items. According to this hypothesis, foragers that are less efficient at obtaining food, because they are either less informed or less capable of learning the foraging techniques of discoverers, are expected to rely on the scrounger tactic to a greater extent and do so regardless of the number of foragers within the group or the richness of each food patch (Beauchamp, 2006). Conversely, the second hypothesis considers that individuals do not differ in either the quantity of information they have about the number and location of food patches or their learning ability. According to this hypothesis, some individuals would nonetheless fail to learn because they are reinforced for joining responses (Giraldeau, 1984). Indeed, if the probability that an animal learns to discover or join a food source depends on the frequency and performance of discovers already in the group, then only a small proportion of individuals will ever learn a food‐finding technique and hence be a producer for that food type. When the differences in tactic use result from this type of frequency‐dependent learning, then we should expect individuals to have flexible producing specializations that will change from one food type to the next. If this is so, reciprocity in producing and scrounging over the whole

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range of food types exploited by a group should lead to the establishment of a ‘‘skill pool,’’ a group of individuals that feed as generalists but enjoy the benefits of searching like specialists (Giraldeau, 1984). There is support for this skill pool hypothesis: Giraldeau and Lefebvre (1986) found that feral pigeons (Columba livia) switched roles in response to changes in both food patch type and flock composition. In addition, several experimental studies report that the presence of demonstrators can prevent other group foragers from learning the food‐finding behavior (Beauchamp and Kacelnik, 1991; Giraldeau and Lefebvre, 1986, 1987; Lefebvre, 1986). Under certain conditions, for instance, naive pigeons fail to perform the food‐finding task they observed performed by pretrained producers, even after the latter are removed from the flock. Although this result apparently demonstrates that social foraging can negatively affect learning, failures to perform a producing behavior do not necessarily mean that scroungers do not know the producer’s food‐finding technique (Morand‐Ferron et al., 2004). Furthermore, other mechanisms such as confusion or overshadowing could be responsible for the inhibition of social learning. For instance, experiments on pigeons have demonstrated that the efficiency of cultural transmission is affected by the number of naive scrounging individuals within the group (Lefebvre and Giraldeau, 1994; Lefebvre and Helder, 1997); Lefebvre and Helder (1997) notably found that a single scrounger could both join a single producer and learn its producer’s discovery technique through observational learning. Group scroungers, however, were incapable of learning the producing technique because they were apparently distracted or confused by the behavior of uninformed conspecifics that provided them with irrelevant information. Experiments with zebra finches also have demonstrated that the presence of a conspecific acting as a reliable indicator of feeding opportunities can overshadow signals needed to exploit new foraging situations on an individual basis (Beauchamp and Kacelnik, 1991). Additional experiments are required to investigate the relative contributions of these mechanisms on the inhibition of social learning. 3. Location Within a Group as a Constraint It is generally assumed that the spatial location of an individual within a foraging group should vary depending on whether it is a producer or a scrounger because the benefits associated with each are thought to depend on the position occupied within the group. Supporting this idea, genetic algorithm simulations of movement rules for producers and scroungers show that these rules evolve toward making scroungers occupy central positions while producers search for food at the group’s periphery

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(Barta et al., 1997). These distinct positions arise because the probability that an animal gets to eat from a scrounging opportunity increases the closer it is to its finder. Not knowing which individual will discover food next, the best place to be is as close as possible to all others: the center of the group. Producers, on the other hand, benefit from being as far from any potential scrounger as possible and that places them on the periphery of the group, a position where interference during search should also be minimized (Barta et al., 1997). Experiments conducted by Flynn and Giraldeau (2001) with nutmeg mannikins provide strong support for these predictions; they found that birds that had not been trained to find food, foraged closer to the center than constrained producers. Furthermore, flocks with many constrained scroungers were significantly more compact than flocks with fewer scroungers (Flynn and Giraldeau, 2001). Naturally, the risk of mortality from predation can also influence an individual’s choice of spatial position (Hamilton, 1971; Hirsh, 2007; Krause, 1994; McGowan et al., 2006). Because socially dominant individuals generally occupy safer central positions, they force subordinates to the more hazardous periphery. Once dominants are in the center and subordinates at the periphery, then the best foraging option is for central dominants to play scrounger and for peripheral subordinates to play producer. Whether dominance alone or spatial position alone acts as phenotypic constraints on PS use remains to be established. Perhaps both factors combine to generate phenotypically constrained use of producer and scrounger. What is needed at this stage are more detailed studies of how dominance and spatial position affect an individual’s use of one or the other tactic.

IX. IMPLICATIONS FOR POPULATION EFFECTS The presence of scrounging within a foraging group lowers the rate at which resources are consumed because only individuals playing the producer tactic search for food. This reduction in per capita searching efficiency can have two distinct effects. At the level of the prey, it reduces predation pressure because the group of its predators is now less efficient at finding them. It follows that prey should evolve characteristics that favor scrounging by its predators. We will explore how the prey’s cryptic coloration affects group forager’s foraging efficiency. Despite the apparent disadvantages that scrounging imposes on predators by lowering their corporate foraging efficiency, at the population dynamic level, scrounging can paradoxically help maintain higher equilibrium levels of predator and prey abundances. We further discuss this result below.

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A. THE EVOLUTION OF CRYPTIC COLORATION Prey species exploited by predators engaged in a PS game benefit from a reduced predation pressure and this advantage increases with the number of nonsearching scroungers within the group of predators. Among other parameters, the expected frequency of scroungers at equilibrium is predicted to increase when the cost associated with playing producer increases or the finder’s share decreases (Vickery et al., 1991). There is experimental support for this prediction: nutmeg mannikins tend to reduce their use of the producer tactic and increase their use of the scrounger tactic when the cost of producing increases (Giraldeau et al., 1994). Hence, by imposing foraging costs to individuals playing producer, the prey benefit from reducing the number of predators concurrently engaged in searching them out. Cryptic coloration generally reduces a forager’s ability at finding its food (Dukas and Kamil, 2000; Endler, 1978; Merilaita and Lind, 2005; Ruxton et al., 2004). When cryptic prey are exploited by predators that scrounge, their coloration can provide a further benefit by reducing the number of group members engaged in seeking them out. Experiments, again with nutmeg manikins, provide some support for this prediction (Barrette and Giraldeau, 2006). The birds did not reduce their antipredatory vigilance when foraging under cryptic conditions. However, both the number of prey detection errors and the proportion of scroungers were higher when the prey were cryptic rather than conspicuous (Barrette and Giraldeau, 2006). Hence, cryptic coloration, by imposing a producer‐specific cost, forced group foragers to increase their use of the scrounger tactic that in turn diminishes the rate at which prey are exploited. Most studies of the benefits of cryptic coloration have been conducted with solitary predators exploiting sequences of single cryptic prey (Pietrewicz and Kamil, 1981). The results obtained by Barrette and Giraldeau (2006) suggest that it may be worth reexploring the evolution of aposematic coloration when predators are social. Factors such as clumping that promote scrounging may provide extra benefits to the prey when predators can scrounge.

B. REGULATION OF POPULATION DYNAMICS It is commonly acknowledged that behavior such as territoriality, aggression, and habitat selection influence demographic parameters, and hence regulate populations through their effect on individuals’ mortality or reproductive rates. It has been suggested that scrounging could also affect demographic parameters and predator–prey population dynamics (Coolen, 2002).

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The reason why scrounging could have a regulatory effect on population size comes from the fact the foraging efficiency of the group depends on the number of scroungers, which varies with competitor number (Coolen, 2002). Indeed, when the number of foragers is small, the frequency of producing at equilibrium and hence the rate at which resources are made available should be high, and population size therefore should increase. However, as group size increases, so does the expected frequency of scrounging, and this frequency‐dependence of payoffs then should maintain stable population sizes. A recent theoretical analysis of the dynamics of prey and predator populations supports this view. Indeed, by comparing the dynamics of groups where scrounging was either absent or present, Coolen et al. (2007) found that the presence of scrounging contributes to the regulation of both predator and prey populations. Moreover, their model predicts that prey persist at higher densities when scrounging is present because the exploitation pressure is then reduced. Predators therefore can exploit a higher density of resources when scrounging is present and consequently they can also maintain a higher abundance under this condition. Thus, although scrounging negatively affects the growth rate of a population, the presence of nonsearching individuals would also contribute to maintaining higher numbers of both prey and predators. C. THE EVOLUTION OF BEHAVIORAL PLASTICITY It is clearly established that many animals are capable of adjusting their behavior to make the best of environmental and social conditions. A likely mechanism for such adjustment is learning: the acquisition and storage of information that influences behavioral decisions. Accordingly, a number of studies have investigated how learning can affect the way animals reach solutions to games, particularly in the context of cooperation (e.g., Stephens et al., 2002). One approach to the study of learning has been to evaluate the performance of so‐called ‘‘learning rules,’’ an algorithm that specifies how information is collected, processed, and then used in deciding among alternatives (Harley, 1981). While many studies have explored how different learning rules perform against each other in a game context (Beauchamp, 2000; Harley, 1981; Houston and Sumida, 1987), few have explored how these rules may evolve within a game context. From a theoretical point of view, this question has been recently addressed by Dubois et al. (MS) who asked whether flexible strategy use could evolve in a background of fixed strategists in a frequency‐dependent game such as the PS game. Their model’s assumptions concerning the game’s payoffs are the same as in the model by Vickery et al. (1991), except that the group is initially made of (G
1) inflexible individuals that play only one tactic each and a single

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flexible individual that can modify its use of both incompatible tactics to maximize its intake when conditions are modified. The simulation allows for strategy representation in the next generation to depend on its foraging payoffs in the current generation. Whatever the value of the parameters, the model predicts that the proportion of producers and scroungers rapidly reaches the equilibrium predicted by Vickery et al.’s (1991) model. However, although the flexible strategy can increase in representation within the population, in no condition does it ever reach fixation. In all cases, the population is made up of a combination of fixed producers and scroungers and flexible individuals. Moreover, the expected stable proportions of flexible individuals as well as their use of the producer and scrounger tactics depend on the extent to which their estimate of the producer’s expected gain is biased. Indeed, when flexible individuals use the optimal combination of tactics at each generation because either the gains are accurately estimated or the biases are symmetrically distributed around a mean of zero, flexible individuals end up always playing pure producer, regardless of initial conditions. Conversely, when flexible individuals have biased estimates of the expected payoffs to alternatives and systematically underestimate the expected payoffs, then flexible individuals at equilibrium are expected to use both tactics, and as the individuals increasingly underestimate the producer payoff, so does the use of the scrounger alternative. The results lead to interesting predictions. For instance, in most conditions, flexible individuals are expected to show little or no behavioral flexibility and to be mostly playing producer (when payoffs are accurately estimated or the biases are symmetrically distributed around a mean of zero) or scrounger (when flexible individuals underestimate the payoffs and the difference between the producer payoff and their estimate of the payoff is very large). Differences among individuals in their age or condition as well as costs to flexibility are generally considered as important factors that can generate consistent individual differences in behavior (Dall et al., 2004; McElreath and Strimling, 2006). Results from Dubois et al.’s (MS) model, however, suggest that frequency‐dependent selection alone could lead to such differences, and so even if the costs and benefits associated with each behavioral alternative is the same for all individuals. In addition, the expected proportion of flexible individuals at equilibrium should be the same irrespective of the degree of variability in the environment. This prediction widely differs from the commonly accepted argument that flexibility would be favored in variable environments (Luttbeg and Warner, 1999; Via and Lande, 1985). Perhaps environmental variability counts when individuals are solitary and their payoffs are not frequency dependent. However, the evolution of learning does not seem to depend on environmental variability for social animals that engage in games where payoffs are

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frequency dependent. When the payoffs are frequency‐dependent, individuals that respond flexibly to changes in local conditions affect not only their expected gain but also those of all other group members, including those that use fixed behavioral rules. As a consequence, even if flexible individuals can adjust their behavior to maximize their fitness when environmental conditions change, one of the two fixed tactics always does better than the flexible behavioral rule. X. RELEVANCE OF PS GAMES FOR NON‐FOOD RESOURCES Predictions from the PS game have been tested in a group foraging context but this game can also apply to many other situations where one or more individuals (scroungers) can reduce the costs of obtaining a limited resource by exploiting the investment of others (producers). Thus, scroungers can benefit from stealing not only food but also mating opportunities or parental care. A. MATING OPPORTUNITIES The costs of courting and mating include mainly a decreased foraging efficiency and an increased mortality rate due to predation. Indeed, in many species, males produce exaggerated, sexual displays that not only attract females but also make the males more conspicuous to predators. In the Tungara frog (Physalaemus pustulosus), for instance, females prefer males that produce complex songs with chucks, but these elements are costly as they also attract predators (Ryan, 1985). Females’ preferences for exaggerated traits favor the use of alternative tactics, in which males steal fertilizations and hence parasitize the efforts of others while avoiding the cost of courting. Such alternative mating tactics, on the other hand, vary considerably from one species to the other: male guppies (Poecilia reticulata), for instance, can adopt the sneaky tactic and attempt to circumvent female choice using forced copulations (e.g., Godin, 1995), while in many anuran amphibians, satellite males stay near a calling male to intercept females as they approach (Halliday and Tejedo, 1995). Several studies indicate that males using calling or satellite tactics play a PS game. Indeed males can employ both tactics interchangeably, but obviously only one at a time. In addition, although both tactics can lead to successful mating, calling males (producer) are more likely to achieve mating than satellite males (scrounger). Finally, the number of females that a calling male can attract, and hence his expected breeding success, is negatively frequency dependent on the number of courting males within the population (Walker and Cade, 2003).

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It would be of interest therefore to explore the determinants of what would be a producer’s advantage in such a system. Likely factors would involve the propensity of females of being tricked into accepting silent males and the caller’s ability to keep satellites at some distance from itself. Perhaps the structural characteristics of habitats will also be influential. Whatever the determinants, we predict that as the caller’s advantage increases, the frequency of satellites will decline. There is empirical support that the proportion of males adopting alternative tactics may depend in other species on social and ecological conditions, and that males are capable of adjusting their behavior in response to changes in the benefits associated with each tactic. For instance, the proportion of pupfish (Cyprinodon pecosensis) males adopting territorial, sneaker, and satellite breeding tactics depends on population density: as population density decreases, so does the benefit of maintaining a territory, leading to an increase in the proportion of sneakers and satellites (Kodric‐Brown, 1986). The cost of calling, however, especially in terms of increased predation danger, will also play an important role. As the cost increases, it should erode the caller’s advantage and hence increase the proportion of satellite males. We know that male Tungara frogs, for instance, curtail conspicuous activities and reduce the intensity of their mating calls in response to simulated attacks by model bats (Ryan, 1985). In such a case, it would not be too surprising to see in other species that the satellite strategy also responds to predation pressure and increases in frequency when predators are present. Experimental manipulation of the risk of predation resulted in the expected change in tactics use in guppies (Godin, 1995): males performed a lower proportion of sigmoid displays and increased their sneaky mating attempts when the risk of predation was increased. The cause of this change, however, remains unclear, as predation risk affects not only the costs and benefits associated with each alternative tactic but also the female preferences (Breden and Stoner, 1987). Indeed, females from high‐predation populations show a lower preference for brightly colored males than do females from low‐predation populations, and variation in female preference therefore could be the main factor influencing the relative use of alternative mating tactics in this species. If scrounging can have implications for population dynamics, as suggested above, it could also influence sexual selection when males have an elaborated sexual display involving both physical and behavioral traits (Morrell and Kokko, 2004). It would be the case particularly in bowerbirds, in which males build more or less ornamented bowers and then decorate them with colored objects collected from the forests. Females use bower quality and its number of decorations to assess the owner’s quality. Thus, the bower’s quality is an important determinant of the male’s mating

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success (Wojcieszek et al., 2007). Males can obtain decorations either by searching for new objects (producer) or by stealing decorations in other males’ burrows (scrounger). As in the context of foraging, individuals that play scrounger obtain direct benefits from stealing, as each stolen object increases the quality of its bower. Males also gain indirect benefits through stealing because it also decreases the quality of their rivals’ bowers. Males, should not, however, spend too much time stealing others’ decorations because they must spend time on their own bower to protect it from potential stealers (Wojcieszek et al., 2007) and because mating only occurs at one’s bower. So to predict how males should divide their time among these three mutually exclusive activities (i.e., staying on their burrow, searching for new objects, and stealing objects from rivals’ burrow), Morrell and Kokko (2004) developed a game model. Interestingly, as females become increasingly choosy, their model predicts that males should increase the time they spend searching for new objects, which is expected to lead to an increase and then a decrease in bower quality. B. PARENTAL CARE When successful rearing of offspring requires the assistance of one or both parents, females must decide either to invest all their effort in the production of a large number of young many of which will die from lack of parental care or to divide her effort in producing fewer young and providing them parental care that will increase their survival. The existence of such a trade‐off means that some females may profitably adopt scrounging tactics that parasitize the parental efforts of others. Indeed, one of the very first published instances of a frequency‐dependent behavioral evolutionary game concerns the parental investment decision of the great golden digger wasp (Sphex ichneumoneus) (Brockmann et al., 1979). The females of this species lay their egg in a burrow. They can either dig the burrow themselves; a strategy called ‘‘digging,’’ or use the burrow of another female, called ‘‘entering.’’ Digging is for all intents and purposes the producer strategy while entering corresponds to scrounger. Research with these insects has established that the frequency‐dependence of payoffs contributes to maintaining these alternative parental strategies (Brockmann et al., 1979). In most cases of conspecific brood parasitism, a female lays eggs in the nest of a conspecific to exploit the conspecific’s parental care. This type of conspecific brood parasitism occurs mainly in birds (Yom‐Tov, 2001), but also in fishes (e.g., Sato, 1986), amphibians (e.g., Summers and Amos, 1997), and insects (e.g., Zink, 2003). In addition to a reduced cost in parental care, it has been suggested that parasitic females could also benefit

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from spreading eggs among different nests, increasing the likelihood that at least one of their offspring will escape predation (Petrie and Møller, 1991). On the other hand, the hatching success of such parasitic eggs may be low when hosts can detect them and adopt countermeasures. In American coots (Fulica americana), for instance, parasitic eggs suffer a high mortality rate, mainly because of egg rejection by their hosts (Lyon, 1993). Because parasitic females are frequently floater females without a nest of their own, conspecific brood parasitism was traditionally considered to be a conditional strategy adopted by low‐quality females that are making the best of a bad job. Accordingly, in American coots, the breeding success of floater females is 16 times lower than that of nesting females (Lyon, 1993). However, not only condition‐dependence but also frequency‐dependence influences the fitness of both alternatives because parasitic females have to compete for a small number of nests when conspecific brood parasitism is common. Thus, in the treehopper insect (Publilia concava), Zink (2003) found that the time to find hosts increased as the frequency of the parasitic tactic increases.

XI. CONCLUSIONS Foraging behavior occupies a pivotal role that allows researchers to explore both the mechanisms of behavior operating within individuals and their population‐level consequences. Foraging, therefore, offers a diverse set of research questions that deserve further attention. In particular, the close association between social foraging and evolutionary game theory requires that we question the realism of the behavioral gambit: does the form of a game’s solution differ depending on whether it is an ESS or a BSS? The question is not trivial because the mechanisms of behavioral stability are almost never studied, except perhaps in the context of reciprocity and cooperation. The presumed superiority of plastic over fixed strategy is rarely questioned. Yet, simulation studies indicate this may not be the case in situations of payoff frequency‐dependence where evolution leads to only a fraction of flexible strategists. A number of recent studies of social foraging have been concerned with individual decisions to produce or scrounge. While a considerable number of experimental studies have supported the PS approach, models of its alternative, the information‐sharing approach, have remained few and elementary. It would be important to investigate the extent to which more realistic models of IS could account for the behavior that has been described so far.

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The PS approach has proven quite useful in exploring questions of resource defense. In particular, it provides a convenient means of investigating how the characteristics of a resource affect the extent to which it should be appropriated by others. Because there can be no defense without having others attempting to appropriate, the PS game provides a means to investigate the whole process of resource defense. Moreover, the PS game can be applied to a variety of resources, not just food. Doing so requires formulating specific games for each resource type, whether information or parental care or some other type. Such models are dearly needed. In addition, as PS games generally consider only two alternative tactics, future theoretical developments should integrate additional strategies to account for the diversity of phenotypes that is frequently observed. Simple models such as the rock‐scissors‐paper game would be certainly appropriate to investigate the factors that favor the evolution and maintenance of alternative phenotypes within populations. PS games are also important for population‐level phenomena. Specifically, they can be involved in the population regulation of predator and prey. However, they could also serve as the basis for investigating prey and predator coevolution. Prey would profit by adopting traits that favor the emergence of PS games among their predators. Traits such as clumped distributions and cryptic coloration would be especially effective because they contribute to favoring scrounging among the predators, thereby reducing the rate at which prey are exploited. In addition, as scrounging is likely to prevent the formation of search images when animals switch from one tactic to the other, PS games would favor generalism and hence prevent animals from improving their ability to detect cryptic prey with time. Exploring the predators’ countermeasures would also be particularly important. We conclude by arguing that foraging is far from depleted. Quite the opposite, recent advances in social foraging theory have opened up a new and exciting set of foraging questions that go well beyond the exploitation of food but concerns the use of a wide range of resources. We strongly encourage researchers to tackle these crucial questions.

Acknowledgments We thank H. Jane Brockmann for having invited us to write this chapter and Steven Hamblin for his useful comments on an earlier version of the manuscript. This chapter was written in part while L‐AG was on sabbatical leave at McGill University as well as at the Institut de Recherche en Biologie Ve´ge´tale of Universite´ de Montre´al. Much of the research reported here was supported by Discovery grants from Canada’s Natural Sciences and Engineering Research Council to L‐AG and FD. We are grateful and indeed fortunate that Canadian taxpayers support pure research.

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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Social Processes Influencing Learning in Animals: A Review of the Evidence Will Hoppitt and Kevin N. Laland center for social learning and cognitive evolution, school of biology, university of st. andrews, bute medical building, queen’s terrace, st. andrews, fife ky16 9ts, scotland

I. INTRODUCTION The topic of animal social learning has attracted much interest among ethologists, behavioral ecologists, anthropologists, and psychologists over last decades (Heyes, 1994; Heyes and Galef, 1996; Galef and Giraldeau, 2001; Shettleworth, 1998). Social learning can be defined broadly as ‘‘learning that is influenced by observation of, or interaction with, a conspecific, or its products’’ (Box, 1984; Heyes, 1994). However, social learning is also taken to include cross‐species learning, especially from humans (e.g., Hayes and Hayes, 1952; Moore, 1992; Whiten, 1998), so most researchers in the field would substitute ‘‘another individual’’ for ‘‘conspecific.’’ This definition is broad indeed and would include cases such as a young bird imprinting on its parents, or a honeybee communicating the location of flowers to fellow hive members (von Frisch, 1967). In fact, in most cases, one would be hard pressed to demonstrate that an instance of learning in a natural population was unaffected in any way by interaction with a conspecific. Accordingly, researchers in the field of social learning tend to focus on general processes thought to be widely important in promoting social learning, potentially across different contexts and modalities, rather than on specialized cases, such as the above. While social learning does not necessarily result in concordance between the observer’s and demonstrator’s behavior (e.g., Darby and Riopelle, 1959), social learning that results in matching behavior has attracted most attention. Such learning can result in the social transmission of information through a population (Galef, 1976), resulting in homogeneity of behavior that extends beyond the period of interaction (Galef, 1988). Examples 105 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00003-X

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include the spread of foraging skills or vocalizations through vertebrate populations (Lefebvre and Palameta, 1988). The possibility that these processes could result in simple animal ‘‘cultures’’ being maintained in natural populations has been a topic of major interest (Laland and Janik, 2006; see special editions of journals dedicated to this topic: Fleagle, 2003; Galef and Heyes, 2004). An area of debate in the field of social learning is the extent to which the social learning observed in nonhuman animals is homologous to the processes underlying culture in humans. For instance, it has often been argued that imitation and teaching have major roles to play in maintaining human, but not animal culture (Galef, 1992, 2004; Tomasello, 1994) though this is contentious (Heyes, 1993; Laland and Hoppitt, 2003; Whiten et al., 2004). Although social learning of matching behavior has been demonstrated numerous times in nonhuman animals, there are many routes by which this could occur besides imitation or teaching. Consequently, central to the resolution of debates over animal ‘‘cultures’’ is an understanding of those social learning processes that can generate findings in animals that resemble those resulting from imitation or teaching, but nonetheless are the result of different, perhaps simpler psychological mechanisms. Here we present a classification of social learning processes, building on earlier schemes (Galef, 1988; Heyes, 1994; Whiten and Ham, 1992), in which we specify a number of social processes that might influence learning. Our classification differs from earlier schemes in that we discuss the evidence required to detect the operation of each process, by exclusion or control of all the alternatives. For each process, we assess the evidence from the existing social learning literature of its operation in animals.

II. CLASSIFICATION OF PROCESSES INVOLVED IN SOCIAL LEARNING To attain a full understanding of social learning in nature, researchers need to comprehend the underlying psychological processes: that is, how does the demonstrator provide the cues required for social learning, and how do these cues affect the observer’s behavior to instigate learning? To this end, classification systems provide a useful service: they afford definitions of the plausible ways in which social learning might occur, and organize the data into categories, thereby assisting researchers to assess the evidence for each process, and to elucidate its significance. A number of authors have attempted classifications of social learning ‘‘mechanisms’’ (Galef, 1988; Heyes, 1994; Whiten and Ham, 1992; Zentall, 1996, 2001), but in practice, there is only partial consensus over terminology. Typically classifications have been devised, at least partly, with the goal of

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cataloguing all plausible imitation‐like mechanisms that must be ruled out if researchers are to recognize instances of ‘‘true’’ imitation. Consequently, although advances have been made in isolating imitation, there has been comparatively little progress in distinguishing nonimitative processes from each other. It is often assumed that the purported ‘‘simplest’’ social learning processes, such as local enhancement (Thorpe, 1956), are in operation in the wild, inferred by a process of parsimony (e.g., Galef and Giraldeau, 2001). However, in reality, several processes could account for most instances of animal social learning, including imitation, and it is a subjective enterprise to decide which is the ‘‘simplest’’ (Roitblatt, 1998). In order merely to validate the existence of a particular social learning process, researchers require data that can discriminate one process from another (Byrne, 2002). A further problem with some existing schemes is that some social learning processes are defined on criteria that are not directly observable or cannot be reliably detected (Heyes, 1994). Heyes (1994) attributes this to the fact that the social learning scheme uses the underlying learning mechanism as a basis for classification, and a learning mechanism is ‘‘something inside an individual.’’ Heyes contrasts the social learning scheme with the well‐established asocial learning classification (Rescorla, 1988), where the conditions of learning and type of behavior change are used as the basis for classification, which in turn acts as an indicator of the underlying mechanism. If researchers are to distinguish different social learning processes empirically, it would be advantageous to base classifications on directly observable criteria, preferably criteria that are amenable to measurement in the field, or to experimental manipulation in the laboratory. In a classification by Heyes (1994), social learning processes are classified as a subset of asocial learning phenomena, such as single‐stimulus or response‐reinforcer learning. This one‐to‐one alignment of social and asocial learning is justified as a hypothesis that the conditions for learning in each case are the same, as are the underlying mechanisms. Consequently, her classification is primarily concerned with social learning processes that result directly in learning. Here, we build on Heyes’ (1994) classification to include social influences that might lead indirectly to social learning. For example, if one individual had an established tendency to follow another, then the following act in itself might not involve further learning (though the tendency to follow maybe a result of learning). However, if the second individual had knowledge of a novel food source, it could lead the first individual to that source, at which point the first individual might also form an association between that location and food. The process by which the observer was brought to the food source in the first place is a matter of interest if one is to understand the social transmission of information. Here we are interested in defining processes that are important in animal social

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learning, whether they result directly in learning, or do so indirectly, but nonetheless in a manner that frequently leads to learning. We define social learning as any process through which one individual (‘‘the demonstrator’’) influences the behavior of another individual (‘‘the observer’’) in a manner that increases the probability that the observer learns. We discuss nine such processes, which are listed with definitions in Table I. Although we believe this classification scheme will prove useful to researchers interested in social learning, we recognize that it is far from perfect. For instance, our scheme, like others before it, exhibits overlapping and nonhierarchical categories (e.g., local vs stimulus enhancement), and some categories that can lead to learning by both direct and indirect means (e.g., stimulus enhancement). This reflects the history of the terms involved: if one were to devise a new classification scheme from scratch, conceivably such problems might be avoided. However, our primary goal here is not to provide a new classification scheme per se but rather to assess the evidence for different types of social learning processes, and the methods for distinguishing between them, using terminology that is both widely used and broadly understood. As far as possible, we use explanatory terms in a similar manner to the original definition, but introduce refinements in the interests of defining processes according to testable criteria. Some cases of social learning have hitherto been categorized according to functional, rather than mechanistic, criteria. Examples include ‘‘eavesdropping,’’ defined as extracting information from interactions between others (McGregor and Dabelsteen, 1996), and ‘‘mate‐choice copying’’ (Dugatkin, 1996). These terms describe the circumstances under which social learning is employed rather than the underlying psychological process. While we discuss these phenomena, we do not include such functional terms in our classification because such cases demand a complementary mechanistic explanation. Where the psychological processes have also been studied, we have included these cases in our discussion. For example, mate‐choice copying in quail (Galef and White, 1998) is a strong case for stimulus enhancement. Unfortunately, there has to date been little research into how other cases of eavesdropping occur, as a result of which we have difficulty in fitting many findings into our categorization. We make an exception in forming a separate category for ‘‘social enhancement of food preferences’’ (Galef, 1989), for which we feel there is strong evidence of a specialized mechanism of transmission. A. STIMULUS ENHANCEMENT Stimulus enhancement (Spence, 1937) occurs when a demonstrator’s behavior increases the probability that an observer is exposed to a stimulus, resulting in an increase in the observer’s rate of interaction with stimuli of

TABLE I Definitions Adopted for Different Social Learning Processes Social learning process Stimulus enhancementa,b Local enhancementb

Observational conditioninga

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Social enhancement of food preferencesa Response facilitationb

Social facilitationb Contextual imitationa

Production imitationa

Definition Stimulus enhancement occurs when observation of a demonstrator (or its products) exposes the observer to a single stimulus at time t1 and single stimulus exposure effects a change in the observer detected, in any behavior, at t2. Local enhancement occurs when, after, or during a demonstrator’s presence, or interaction with objects, at a particular location, an observer is more likely to visit or interact with objects at that location. Observational conditioning is a subset of stimulus–stimulus learning in which observation of a demonstrator exposes the observer to a relationship between stimuli at t1, and exposure to this relationship effects a change in the observer detected, in any behavior, at t2. Social enhancement of food preferences occurs when after being exposed to a demonstrator carrying cues associated with a particular diet, the observer becomes more likely to consume that diet. Response facilitation occurs when the presence of a demonstrator performing an act (often resulting in reward) increases the probability of an animal that sees it doing the same. Social facilitation occurs when the mere presence of a demonstrator affects the observer’s behavior. Contextual imitation occurs when directly through observing a demonstrator performing an action in a specific context, an observer becomes more likely to perform that action in the same context. Production imitation occurs when after observing a demonstrator performing a novel action, or novel sequence, or a combination of actions that is not in its own repertoire, an observer then becomes more likely to perform that same action or sequence of actions.

Source Heyes, 1994, p. 216

After Thorpe, 1963

Adapted from Heyes, 1994, p. 220

After Galef, 1989

Byrne, 1994, p. 237

After Zajonc, 1965 Adapted from Byrne, 2002, p. 82 After Byrne, 2002

(Continued)

TABLE I (Continued) Social learning process Observational R–S learninga 110

Emulationa

a

Definition Observational R–S learning is defined as ‘‘a subset of response‐reinforcer learning (R–S)’’ in which observation of a demonstrator exposes the observer to a relationship between a response and a reinforcer at t1, and exposure to this relationship effects a change in the observer detected, in any behavior, at t2. Emulation occurs when after observing a demonstrator interacting with objects in its environment, an observer becomes more likely to perform any actions that bring about a similar effect on those objects.

Source Heyes, 1994, p. 225 (‘‘Observational learning’’) After Tomasello, 1990 and Custance et al., 1999

Indicates that the process described leads directly to social learning. Indicates that the process described influences the observer in a way that might often lead indirectly to social learning. Stimulus enhancement, by definition, directly involves learning, but might also lead indirectly to further learning about the stimulus in question. b

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the same type. Heyes (1994) argued that the process could be regarded as a subset of single‐stimulus learning, where exposure to a stimulus results in a change in the subject’s responsiveness to that stimulus. Stimulus enhancement could be regarded as a case of sensitization, where subsequent responsiveness to the stimulus is increased, but there is no reason to suppose that there might not instead be habituation. We adopt definition given by Heyes (1994): Stimulus enhancement occurs when observation of a demonstrator (or its products) exposes the observer to a single stimulus at time t1 and single stimulus exposure effects a change in the observer detected, in any behaviour, at t2. (p. 216).

This definition does not specify the manner in which the observer is exposed to the stimulus, so it could have its attention drawn to the stimulus by the demonstrator, or the demonstrator could act in some other way to make the stimulus more accessible to the observer. An example of the latter is a mother bringing prey to her young. Critical to stimulus enhancement is that there is a degree of stimulus generalization that means the observer’s response to similar stimuli in different locations may be affected. Stimulus enhancement might also lead indirectly to further learning about the stimulus in question, through the observer’s subsequent interactions with that stimulus. B. LOCAL ENHANCEMENT The term ‘‘local enhancement’’ was introduced by Thorpe (1956), who later defined it as ‘‘apparent imitation resulting from directing the animal’s attention to a particular object or to a particular part of the environment’’ (Thorpe, 1963, p. 134). It has been suggested that local enhancement be regarded as a subset of stimulus enhancement (Galef, 1988; Heyes, 1994), where the stimulus in question is a particular location. However, stimulus enhancement, as defined above, requires that the animals exhibit single‐ stimulus learning, whereas here we stress that local enhancement can occur without learning. Such nonlearning processes are of interest to social learning researchers because they can indirectly lead to social learning. For example, a demonstrator may attract individuals to its location merely as a consequence of a tendency for individuals to aggregate, and the effect may not last once the demonstrator has moved. This would not constitute stimulus enhancement by Heyes’ (1994) definition. Rather than invent a new term, we suggest that local enhancement be retained to refer to all such location effects, irrespective of whether they result in learning. ‘‘Local

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enhancement’’ is used in this sense throughout much of the literature (e.g., Galef and Giraldeau, 2001). Hence: Local enhancement occurs when, after or during a demonstrator’s presence, or interaction with objects, at a particular location, an observer is more likely to visit or interact with objects at that location.

Our definition differs from Thorpe’s in that no reference is made to ‘‘attention,’’ a faculty that can be difficult to measure reliably (Heyes, 1994). In addition, our definition does not require that the interaction result in ‘‘apparent imitation’’; here local enhancement may or may not result in social learning. By our definition, a number of specific processes could result in local enhancement, including (1) stimulus enhancement of a specific location, (2) an aggregation effect (e.g., Waite, 1981), (3) a tendency for individuals to move around as a group, for instance, allowing knowledgeable individuals to lead naive individuals to a novel food source (Laland and Williams, 1997), and (4) the products of a demonstrator’s behavior (e.g., scent marks and feces) attracting other individuals to a location (e.g., Telle, 1966). We suggest that the definition of local enhancement be kept broad; once local enhancement has been detected, the precise mode of operation can be investigated. C. OBSERVATIONAL CONDITIONING Observational conditioning traditionally refers to Pavlovian conditioning in which an unconditioned response (UR) to a stimulus on the part of the demonstrator, such as a fear response, acts as an unconditioned stimulus (US) eliciting a matching response on the part of the observer. Consequently, the stimulus to which the demonstrator is responding becomes a conditioned stimulus (CS) for the observer, to which it will later respond in the same way (Cook et al., 1985). For example, a rhesus monkey (Macaca mulatta) that has had no contact with snakes will not show fear if presented with a snake. However, if it is exposed to another individual reacting fearfully to a snake, they will also display fear, and show fear when later presented with the snake. Heyes (1994) criticizes the original definition of observational conditioning on the grounds that is too restrictive, and suggests that the term should refer to any case where observation of a demonstrator’s behavior increases the probability that an observer will be exposed to an S–S relationship. We adopt Heyes’ (1994) definition: Observational conditioning is a subset of stimulus‐stimulus learning in which observation of a demonstrator exposes the observer to a relationship between stimuli at t1, and exposure to this relationship effects a change in the observer detected, in any behaviour, at t2.

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Our use of the term ‘‘observation’’ is not intended to restrict this process to visual cues. The type of S–S learning involved depends, as with asocial learning, on the relationship between the two stimuli (S1 and S2), which can be positively correlated (S1 predicts S2), resulting in excitatory conditioning, or negatively correlated (S1 predicts absence of S2), resulting in inhibitory conditioning. The nature of the second stimulus also affects the observed behavioral change, whether if it is appetitive (attractive to the animal) or aversive (Heyes, 1994). In the former case, observational conditioning could be excitatory‐appetitive, with the first stimulus becoming more likely to evoke a response, or inhibitory‐appetitive, with the first stimulus becoming less likely to evoke a response. For example, an observer might observe a demonstrator lifts the covers on blue cups to find food, resulting in excitatory‐appetitive observational conditioning and causing the observer subsequently to respond to the blue cups. The same observer might also observe a demonstrator lifts the covers on red cups for no reward, resulting in inhibitory‐appetitive observational conditioning and causing the observer not to respond to red cups. This offers a plausible process by which animals can learn socially where to find food (Galef and Giraldeau, 2001; Zentall, 2001). Here, the demonstrator reveals a spatial relationship between S1 (the blue cup) and S2 (the food) rather than a temporal relationship. There is a lack of evidence in the asocial learning literature that spatial contiguity alone can result in learning (Heyes, 1994; Rescorla, 1988). Nonetheless, experiments on social learning in chicks suggest that spatial contiguity might facilitate learning (Suboski, 1990). This might be because, as a direct consequence of the demonstrator’s actions, S1 is likely to be subject to the observer’s attention immediately prior to the revealing of the food, effectively resulting in a temporal relationship between the two in the observer’s perception (Heyes, 1994). Asocial learning theory suggests that observational conditioning could also be inhibitory‐aversive, in which case S1 becomes more likely to evoke a response, or excitatory‐aversive, in which case S1 becomes less likely to evoke a response (Heyes, 1994).

D. SOCIAL ENHANCEMENT OF FOOD PREFERENCES Perhaps the most studied of any social learning process is the social enhancement of food preferences in rats (e.g., Galef, 1996). Galef and Wigmore (1983) found that an observer rat (Rattus norvegicus) would prefer to eat a novel diet after having been exposed to a demonstrator conspecific that had recently eaten that food. They found that physical

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contact between the demonstrator and observer is not necessary for social enhancement to occur, but instead depends on odor cues on the demonstrator’s breath. Following Galef and coworkers (e.g., Galef, 1989; Galef and Durlach, 1993), we refer to this process as ‘‘social enhancement of food preferences’’: Social enhancement of food preferences occurs when, after being exposed to a demonstrator carrying cues associated with a particular diet, the observer becomes more likely to consume that diet.

We justify ‘‘social enhancement of food preferences’’ as a category of its own right on the grounds that it is taxonomically widespread, at least within mammals, yet there exists no compelling evidence that it can be accounted for any of the other processes in our classification (see Section III.D). Although odor cues seem most important for rats, we do not preclude the fact that other cues might predominate in other species.

E. RESPONSE FACILITATION Byrne (1994) introduced the term ‘‘response facilitation’’ to refer to instances when the presence of a demonstrator performing an act (often resulting in reward) increases the probability of an animal which sees it doing the same (p. 237).

It is generally assumed that such a process would have a transient effect on behavior, as it may be the product of priming (Byrne, 1994). Such priming could be accounted for by residual neural activity remaining for a short time after observation of the demonstrator’s actions. Alternatively, transience may be due to the fact that as other, mutually exclusive, actions are observed and the probability of their performance increases, the probability of the first action being performed will necessarily reduce. Several related terms must be considered. ‘‘Contagion’’ was used by Thorpe (1963) to refer to the unconditioned release of an instinctive behavior in one animal by the performance of the same behavior in another animal. Contagion can therefore be seen as a subset of response facilitation that requires no prior learning. ‘‘Social facilitation,’’ has been used in a similar manner. For instance, Visalberghi and Adessi (2000) define social facilitation as ‘‘the increased probability of performing a class of behaviors in the presence of a conspecific performing the same class of behaviors’’ (p. 69). One apparent difference between response facilitation and

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contagion or social facilitation (in the sense of Visalberghi and Adessi, 2000) is that the latter two are usually assumed to apply to whole classes of behavior such as feeding or running (Zentall, 1996), whereas response facilitation may affect specific individual actions (Byrne, 1994, 1999). However, response facilitation could operate at a number of different levels of ‘‘resolution,’’ much like imitation (see below). Aside from providing researchers with another explanation to be ruled out in tests of animal imitation, response facilitation is an interesting possibility in its own right, providing another plausible mechanism by which social learning might indirectly occur. By synchronizing behavior, response facilitation might act to inform animals when and where to perform certain actions. For example, a frugivore might learn that the fruit of a particular tree is good to eat if it is present in the tree with other individuals who are eating and is primed to eat as a consequence. In this manner, response facilitation could result in learning functionally equivalent to contextual imitation (see below), by a similar process to Suboski’s (1990) releaser‐induced recognition learning.

F. SOCIAL FACILITATION In addition to being regarded a synonym of ‘‘contagion,’’ the term ‘‘social facilitation’’ has been deployed in cases where the mere presence of another animal affects behavior (Zajonc, 1965). While Zajonc described this effect as mediated by an increase in arousal, we suggest that some types of behavior may be facilitated whereas others may be unaffected or inhibited by the presence of others. This leads us to the following definition: Social facilitation occurs when the mere presence of a demonstrator affects the observer’s behaviour.

This process could potentially lead indirectly to social learning if, for instance, an individual is more likely to exhibit exploratory behavior in the presence of other individuals, perhaps through a reduction in neophobia, allowing individuals to learn about novel objects.

G. IMITATION Imitation is among the most contentious of social learning processes, with seemingly little consensus on exactly what is ‘‘imitation’’ or ‘‘true imitation’’ and how it should be defined. There seem to be three main issues

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underlying differences of opinion (Byrne, 2002). First, some researchers (e.g., Tomasello, 1996) have suggested that imitation should be seen as evidence of a capacity for intentionality, and hence researchers should only consider cases of social learning to be imitation if there is evidence of intentional copying. However, intentionality is not directly observable, and although it may be inferred, it is not easy to study experimentally (Zentall, 1996). In addition, we see no reason why an animal should not be able to learn about behavior through observation without a capacity for intentionality (Byrne, 1999). Second, there is disagreement over how accurately the observer must copy the demonstrator to constitute a case of imitation (Nehaniv and Dautenhahn, 2002) However, there are a number of factors that might determine how accurate an imitative match will be. One reason why the match might not always be perfect is that imitation might be used in concert with other factors in skill learning such as unlearned predispositions and asocial learning mechanisms (Byrne, 2002). Nonetheless, if researchers can reliably detect a level of correspondence between the actions of the demonstrator and observer that is lacking in suitable control subjects, this could be taken as evidence of an imitative influence on the learning of behavior. Third, some researchers maintain that only novel acts, not already in the observer’s repertoire, can be imitated (Byrne and Tomasello, 1995). This requirement stems from the concern that if actions are already in the observer’s repertoire, then they might be subject to contagion, which could be mistaken for a general imitative capacity. While this may be a concern when assessing anecdotal reports of imitation from the field, in the laboratory other social learning processes can be controlled for experimentally. At the same time, the processes by which an individual acquires topographically novel behavior to its repertoire are of great interest to anyone investigating how an animal adapts to its environment. The problem of novelty is addressed neatly if we adopt Byrne’s (2002) definitions of two distinct types of imitation, contextual imitation and production imitation (stemming from a distinction made in the vocal learning literature; Janik and Slater, 2000). 1. Contextual Imitation Byrne (2002, p. 82) defines contextual imitation as ‘‘. . . learning [by observation] to employ an action already in the repertoire in different circumstances, not learning its form.’’ Here we adopt a variant definition that does not exclude the possibility that an animal might learn a novel behavior by production imitation while simultaneously learning the context in which to use it:

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Contextual imitation occurs when, directly through observing a demonstrator perform an action in a specific context, an observer becomes more likely to perform that action in the same context.

The effect of contextual imitation is ‘‘direct’’ rather than an indirect result of another social learning process, such as stimulus enhancement, followed by trial and error learning. For instance, an observer could learn through contextual imitation to perform an action in a specific location, at a specific time of day or in response to a specific stimulus, making it a potentially powerful means of learning. Contextual imitation can be regarded as a case of stimulus–response (S–R) learning. Although somewhat discredited for asocial cases of instrumental learning, S–R learning is gaining credibility in the field of social learning (e.g., McGregor et al., 2006; Saggerson et al., 2005). Our definition of contextual imitation differs from Heyes’ (1994) who defined imitation as a subset of response–reinforcer (R–S) learning. Here we recommend that researchers be open to the possibility that observers might imitate ‘‘blindly,’’ in the absence of demonstrator reward (Shettleworth, 1998). There is empirical support that ‘‘blind’’ imitation can occur (McGregor et al., 2006; Moore, 1992, 1996), and it has rarely been shown that the observer must see the demonstrator being rewarded for contextual imitation to occur [Akins and Zentall (1998) is an exception]. This mechanism is consistent with recent neural network models of contextual imitation, based on Hebbian and Stentian learning principles (Hoppitt, 2005; Laland and Bateson, 2001). The conditions for contextual imitation to occur are similar to those for observational conditioning, in so far as the observer is ‘‘presented’’ with two stimuli: the contextual stimulus and the experience of observing a conspecific perform an action. However, observational conditioning involves the formation of an S–S association, whereas contextual imitation involves the formation of an S–R association between the contextual stimulus and the observed response. Contextual imitation requires some kind of neural connection between the experience of observing an action and performance of that action. Contextual imitation differs from response facilitation in that the observer’s response is context‐specific. 2. Production Imitation For Byrne (2002), ‘‘production imitation’’ refers to imitation where the form of a novel action is learned through observation. This is different to R–S learning, where an individual learns about the consequences of a response. Production imitation involves learning by observation about how to perform the response itself, for example, learning how to hit a golf ball.

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To a degree, the requirement of novelty falls foul of our endeavor to define social learning processes according to observable criteria because one can rarely be sure which actions are already in an individual’s repertoire. Even if a complete history of the individual’s behavior is available, it is difficult to exclude the possibility that an individual has an unlearned predisposition for an action it has never performed. However, the problem seems unavoidable if researchers are to investigate the addition of novel actions to an individual’s repertoire, and in experimental contexts, the problem can be finessed by presenting animals with actions that they are unlikely to have previously performed. Novelty presents a further, paradoxical problem. Every action any individual performs is likely to be novel to some degree as it adapts its motor output to the exact environmental conditions and the orientations of the objects with which it is interacting (Schmidt, 1991). For example, a tennis player never plays exactly the same shot twice as they adapt their motor output to allow for differences in height, speed, and spin of the ball; their own position in the court; that of their opponent, etc. This, of course, does not mean the tennis player has to learn how to play every shot from scratch. Instead, a player has a limited repertoire of different shots, for example slice, lob, which they have learned to adjust to the circumstances. Paradoxically, it can be argued that no action is ever completely novel, as it is always built up of component actions that have been performed before. A solution would be to conceptualize production imitation as involving the learning of a novel sequence of simpler actions (termed action units by Heyes and Ray, 2000). Such actions need not themselves be novel because all novel actions can be seen as a sequence of familiar actions at some level (Byrne, 2002). A number of models and reviews have observed that novel skill learning appears to occur through sequence learning (Adams, 1984; Gazzaniga et al., 1998; Jordan and Rosenbaum, 1989; Keele, 1986; Lornez, 1965; Schmidt, 1991). Recent models of imitation reflect this, hypothesizing that production imitation occurs through the learning of novel sequences of actions or action units [string parsing model by Byrne (1999) and associative sequence learning model by Heyes and Ray (2000)]. In theory, novel actions could be learned as a combination of existing actions because an individual could learn to combine two action‐units simultaneously rather than sequentially (e.g., Moore, 1992). We propose the following definition to account for these recent conceptual advances: Production imitation occurs when, after observing a demonstrator perform a novel action, or novel sequence or combination of actions, that is not in its own repertoire, an observer then becomes more likely to perform that same action or sequence of actions.

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A number of qualifying statements are required. To be regarded as imitation, the novel action must be acquired to the observer’s repertoire directly through observation rather than through the indirect effects of other social processes followed by asocial learning. The requirement that an observer should become ‘‘more likely’’ to perform the novel action or sequence is necessary because there is always a chance that animals will spontaneously ‘‘invent’’ an action without first observing it. If production imitation does occur through the learning of novel sequences of action units, this has important implications for how accurately the observer imitates the demonstrator. The closeness of the match will depend critically on the ‘‘size’’ of the action units that make up the sequence being copied. It follows that imitation can occur at a number of levels (Byrne, 2002). For illustration, imagine an observer learning by production imitation how to pick fruit from a tree, after observing a demonstrator grasps a fruit, and then pull back to detach it from its stem. The novel action could be copied as a sequence of two simpler actions: (1) grasp fruit and (2) pull back; or it could be copied as a longer sequence of smaller actions, where each movement around each finger joint is copied for the grasping movement, followed by the movement around the elbow and shoulder joint for the pulling back movement. In the first case, the imitated fruit‐picking action might vary considerably from that of the demonstrator, if the observers ‘‘grasp’’ and ‘‘pull‐back’’ actions happen to differ from the demonstrator’s versions of the same action. Yet if every movement of each digit and the arm is copied then the imitated version will closely match that of the demonstrator. At this stage, researchers cannot be certain that production imitation does operate through sequence learning. However, following Heyes and Ray (2000) and Byrne (1999), we suggest that it is likely to prove worthwhile to conceptualize the process as the acquisition of novel sequences because this allows for the possibility that imitation might not occur at the exact‐movement level and allows researchers to quantify the probability that a given sequence will arise by chance (Whiten and Custance, 1996). Byrne and Russon (1998) claim that an imitator might not directly copy the actions of a demonstrator, but through repeated observation, extract and copy the underlying organization of the demonstrator’s behavior. They term this process program‐level imitation, as oppose to action‐level imitation. Program‐level imitation is not the same as merely imitating at a low level of resolution because it requires inferences concerning which aspects of a sequence are important, which parts of a sequence form different subroutines, and the rules by which those subroutines are applied (Byrne, 2002).

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In other words, the observer can extract the program for behavior from a demonstrator. This does not necessarily require any ability for mental‐state attribution, or mind reading (Dawkins and Krebs, 1978), where the observer understands the demonstrator’s goals, but could instead rely on system for detecting statistical regularities in demonstrators’ behavior, a method that Byrne (1999) calls ‘‘string parsing.’’ H. OBSERVATIONAL R–S LEARNING While our definitions of contextual and production imitation do not specify that the observer must observe the demonstrator being rewarded in order to imitate, it could conceivably be adaptively advantageous for an observer to be sensitive to demonstrator reinforcement, enabling it to bias copying toward advantageous actions. In addition, if an observer could learn R–S contingencies through observation, it could potentially learn what not to do in a specific context, as a result of watching other animals make mistakes (Heyes, 1994; Want and Harris, 1998). Such learning does not fulfill the definition for contextual imitation, but could perhaps be regarded as a case of observational R–S learning that overlaps with contextual imitation when the observer’s learned response matches that of the demonstrator. We avoid using the term observational learning favored by Heyes (1994), because it is a term that has been used in a number of different ways (e.g., Davey, 1981; Herbert and Harsh, 1944; Bugnyar and Kotrschal, 2002), but adopt her definition for the phenomenon (Heyes, 1994): Observational R–S learning is ‘a subset of response‐reinforcer learning (R–S) in which observation of a demonstrator exposes the observer to a relationship between a response and a reinforcer at t1, and exposure to this relationship effects a change in the observer detected, in any behaviour, at t2.’ (p. 225).

Observational R–S learning could potentially be combined with production imitation to ensure that an observer disproportionately acquires novel action sequences that it sees being rewarded. I. EMULATION The term emulation has acquired a number of different meanings because it was first introduced to animal learning (Tomasello, 1990). In general terms, it refers to instances where the observer copies the results of a demonstrator’s behavior rather than the demonstrator’s actions themselves. However, there are a number of ways in which this could occur. Goal emulation (Whiten and Ham, 1992) was initially used to refer to cases where the observer not only understands that the demonstrator’s

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behavior has certain consequences but also recognizes that it can achieve the same goal in a different way. However, this definition refers to unobservable mechanisms, that is the observer’s understanding of the demonstration, so has little utility in our classification. The process could be interpreted as a case of low‐resolution imitation (Whiten and Ham, 1992), with the observer substituting its own functionally equivalent action. Observational conditioning could result in a similar phenomenon. For example, if an observer observes a demonstrator interacting with a manipulandum for a food reward then it might form an association between the manipulandum and the food, causing it to find its own way of interacting with the manipulandum. There are other ways in which an observer could learn to copy the results of a demonstrator’s behavior; for one, an observer could try and recreate the movements of objects with which the demonstrator interacted, termed object movement reenactment (Custance et al., 1999). Alternatively, an observer could try and recreate the final state resulting from a demonstrator’s behavior, which Custance et al. (1999) call final state recreation, though in theory, an observer could recreate a number of intermediate states to emulate the demonstrator’s actions. According to our definition: Emulation occurs when, after observing a demonstrator interacting with objects in its environment, an observer becomes more likely to perform any actions that bring about a similar effect on those objects.

R–S learning could be implicated in emulation in the same way it is in imitation, if an observer disproportionately emulates demonstrators it observes being rewarded, but there is also the possibility that an observer will ‘‘blindly’’ emulate regardless of its consequences. It has been noted that if one considers the results of an individual’s behavior, such as the movement of objects being manipulated, as an extension of that behavior, there is little distinction between imitation and emulation (Whiten and Ham, 1992). In this light, it seems arbitrary to limit ‘‘behavior’’ to the movement of body parts and the line between imitation and emulation becomes somewhat fuzzy, especially given that an observer could potentially combine aspects of a demonstrator’s body movements and aspects of the movements of objects in its environment (or other results). This is especially likely if imitation occurs at the program level, when the organization of behavior may depend on monitoring the state of objects being manipulated (Byrne, 2002). Consequently, it might be more fruitful to consider body movements as just one type of action‐ dependent cue that an observer could use in order to imitate the demonstrator. Given that ‘‘clear‐cut’’ cases of imitation and emulation could

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theoretically be distinguished experimentally, we will maintain the distinction for now, and later return to the issue in light of the evidence. ‘‘Emulation’’ has acquired a slightly different meaning over recent years (Byrne, 2002), referring to instances where an observer learns about the ‘‘affordances’’ of objects (Tomasello, 1998), that is, what can be done with an object. However, such ‘‘affordance learning’’ (Custance et al., 1999) is well accounted for by other processes such as observational conditioning and contextual imitation. Byrne (2002) illustrates affordance learning with the example that seeing a chimpanzee smash open a nut between two stones reveals nuts are breakable and they contain edible kernels, properties that can be learned by an observer. However, learning the latter property, that nuts contain edible kernels, can be accounted for by observational conditioning, where the observer learns the S–S association between ‘‘nuts’’ and ‘‘food.’’ Learning the former property could be a case of contextual imitation, where the observer learns to direct a particular ‘‘smashing’’ action toward nuts, or it could be a case of emulation as it is defined above. Accordingly, our definition of emulation does not include reference to affordances.

III. EMPIRICAL EVIDENCE FOR SOCIAL LEARNING PROCESSES Here we (1) discuss the evidence that is needed to isolate each of the social learning processes defined above, (2) evaluate whether such evidence has been provided by research into animal social learning, and (3) suggest methods that might reveal such evidence. Of course, the above processes need not operate in isolation. In any given case, the demonstrator might have a number of influences on the observer that act in concert to result in social learning. While mutually exclusive or hierarchical categories would be preferable, the definitions above serve a useful purpose in defining explanatory processes that can be isolated experimentally. If researchers are to be sure that any one process operates they must isolate that process, by assessing the necessary and sufficient conditions for the relevant behavior change in the observer (Zentall, 1996). Here we suggest that the best way of doing this is through experimental manipulation of the demonstrator’s role. While anecdotal evidence may be suggestive of the operation of a particular process, it will rarely provide unambiguous evidence of a social learning process because there are not usually any suitable control subjects. Therefore, we focus on the experimental evidence necessary to demonstrate each social learning process. We include some examples of vocal learning in the appropriate categories to illustrate that vocal learning can also fit into our scheme. However, we do

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not intend to provide a complete review of the evidence for social influences on vocal learning, and instead refer the reader to Janik and Slater (1997, 2000).

A. STIMULUS ENHANCEMENT To show that stimulus enhancement is occurring, researchers need to establish that a demonstrator’s actions toward a particular stimulus caused single‐stimulus learning on the part of the observer. Usually, it will be of more interest to investigate whether the observer’s response rate was increased, as this could result in social transmission. To do so, researchers must rule out forms of local enhancement that are not a result of stimulus enhancement, such as a tendency to aggregate, or for a demonstrator’s products to attract observers to a particular location. Researchers could demonstrate stimulus generalization, where the observer is more likely to interact with an object of the same type as the one with which the demonstrator is interacting with, rather than the exact same object. Alternatively, researchers could show that the increase in responsiveness persists for more than a short period after the demonstrator has left the area, and all traces of its products, such as feces or odors, have been removed. Such cases of stimulus enhancement, where the attraction to a location lasts beyond the presence of individuals at that location, has been termed ‘‘delayed local enhancement’’ (Coolen et al., 2003). Stimulus enhancement has often been implicated when animals learn from other animals’ products. For example, naive chickadees (Parus atricapillus) learn more quickly to peck through milk bottle tops if they are first exposed to opened milk bottles (Sherry and Galef, 1984). Similarly, Galef and Beck (1985) found that if rats mark foods they have eaten, this increases the probability that a conspecific will eat the food. However, both cases might not be a case of single stimulus learning and may instead be manifestations of associative learning. There are countless cases of social learning following direct observation of a demonstrator that are consistent with a stimulus enhancement explanation, for example, acquisition of an observed discrimination (Edwards et al., 1976; Heyes et al., 2000; Kohn, 1976; Kohn and Dennis, 1972; Vanayan et al., 1985; Zentall, 1996). One such example is provided by Krebs et al. (1972) who showed that great tits (Parus major) were more likely to interact with a particular ‘‘type’’ of place for food, after having seen a conspecific find food in a similar location. McQuoid and Galef (1993) show a similar effect in the jungle fowl. However, such cases are almost always also consistent with other social learning processes such as observational

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conditioning. It could be that the observers have learned through observation to associate a particular location or stimulus with food. In order to demonstrate stimulus enhancement, researchers need to rule out observational conditioning and observational R–S learning, by ensuring that the observer does not associate the stimulus or a response with an appetitive stimulus. In cases where the demonstrator is not rewarded, observational conditioning is usually ruled out. For example, Warden and Jackson’s (1935) study of rhesus monkeys used a duplicate cage method, where the observer and demonstrator were provided with identical sets of objects. They showed that after the demonstrator had been allowed to select an object (without reward), the observer tended to choose one of the same type. In a similar experiment, Fiorito and Scotto (1992) found that an octopus, presented with a red and a white ball, would choose to attack the ball of the color it had previously seen a demonstrator attacking (for no reward). While these cases could be examples of stimulus enhancement, they could also be instances of contextual imitation, where the observer learns to direct a particular action at the object, e.g., ‘‘grasp’’ or ‘‘attack.’’ Some evidence is provided for stimulus enhancement where observers learn more rapidly to solve a task than control subjects, but are not more likely to use the actions used by the demonstrator. For example, Nagell et al. (1993) presented chimpanzees with a rake‐like object and food, which was out of the subject’s reach. Two groups of observers saw a demonstrator obtaining the food reward using the rake, with either the teeth or the edge of the rake facing down. A third group saw no demonstration. Those subjects who saw no demonstration used the rake less often to gain the food reward, indicating social learning of some kind among the observers. However, those that did observe a demonstration did not copy the exact method used by the demonstrator. Such results offer no direct evidence of contextual imitation, because the specific action used by the demonstrator is not copied, although it is plausible that the chimpanzees were imitating at a low level of resolution. These data could also be accounted for by observational conditioning. Some cases of ‘‘mate‐choice copying’’ (Dugatkin, 1996; White, 2004) provide among the clearest evidence for stimulus enhancement. For example, Galef and White found that female quail (Coturnix japonica) preferred to associate with males they have seen in proximity to another female (Galef and White, 1998; White and Galef, 1999). This preference is not location‐specific (White and Galef, 1999) and generalizes to other males with similar traits (White and Galef, 2000), ruling out local enhancement. Observational conditioning seems unlikely, because there are no clear S–S associations that could have been formed. Contextual imitation is ruled out because the effect depends only on observed female–male proximity, and

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not on the demonstrator’s behavior because a stuffed female demonstrator will achieve the same effect (Akins et al., 2002). A number of similar cases have been studied, notably in fish (reviewed by Witte, 2006). While such cases would fit with our adopted definition, they are perhaps indicative of a special mechanism that has evolved to allow females to choose the best mate, rather than of a general process underlying social learning. In conclusion, despite the widespread assumption that stimulus enhancement is common, there are surprisingly few cases where it has been shown unambiguously to be in operation. B. LOCAL ENHANCEMENT It has long been appreciated that many species of animal aggregate and move as a group, such as flocks of sheep, herds of deer, flocks of birds, shoals of fish, and swarms of insects (Armstrong, 1951; Galef and Giraldeau, 2001). Consequently, individuals of many species will be attracted to a location where conspecifics are present. On a large scale, individuals could be attracted to novel food patches in their environment, but perhaps forage independently once they arrive there (Krebs et al., 1972). In their review of the social influences on foraging, Galef and Giraldeau (2001) conclude that there is ‘‘unequivocal evidence that birds will travel to and feed in locations where they can see other individuals feeding’’ (p. 4). One well‐known example is given by numerous species of carrion‐feeding Old World vultures (e.g., Genus Gyps), which rely on sight to locate food. Vultures are attracted to a location by the sight of circling conspecifics, which usually indicates a carcass has been located. In this case, the observers seem to respond to the sight of individuals before they can see the carcass itself, so observational conditioning cannot account for the increase in the probability an individual will visit the location. However, the tendency to approach circling conspecifics may itself be a result of prior S–S learning, if individuals have learned to associate circling conspecifics with food. Large‐scale local enhancement is far from limited to birds. Mammal examples include the presence of a Norway rat at a feeding site, even an anesthetized individual, which attract conspecifics from a distance (Galef, 1981). Laland and Williams (1997) demonstrated that such a process can result in social learning in the guppy; a naive guppy will follow informed individuals to a food source and will consequently learn the route. In conclusion, there is a large body of evidence suggesting that local enhancement occurs on a relatively large scale. Hoppitt and Laland (In Press) investigated the scale at which local enhancement was occurring. He found that a chicken is more likely to initiate bouts of drinking from the same water bowl as a drinking

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conspecific than it is from an adjacent bowl, and this effect persists (for ~30 s) after the conspecific has left. In contrast, Hoppitt found that a chicken is no more likely to feed from the same bowl as a currently feeding conspecific than it is from an adjacent food bowl. However, for a short period after the conspecific has left the area (<10 s), it is more likely to feed from the specific bowl from which the conspecific fed. In this case, local enhancement seems to operate on a small scale, but the effect is offset against a spatial ‘‘repulsion’’ effect, which operates when the birds are feeding and are perhaps more likely to be aggressive (see Fig. 1). On an even finer scale, individuals could be attracted to a specific part of an object such as a lever that has to be pressed for food. Heyes and Saggerson (2002) found that budgerigars (Melopsittacus undulates) were more likely to remove stoppers of the same color that they had seen demonstrators remove in order to gain access to food inside a container, but only if the stoppers were kept in the same specific locations as they were for the demonstration. This suggests that there was a location effect of some kind. However, this could be explained as a case of observational conditioning, where the observers have formed an S–S association between the location and the food. Many such cases can be interpreted in this manner. Fritz et al. (2000) provide an example that cannot be accounted for by observational conditioning. These authors gave greylag geese (Anser anser)

B

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FIG. 1. Hypothetical probability distributions showing how an observer’s probability of interaction with objects at a given location might vary with (A) the location’s distance from a demonstrator and (B) the time since a demonstrator was interacting with objects at that location. Local enhancement attracts observers close to locations a demonstrator has interacted with recently, but there may be an interference effect, which means an observer is less likely to interact with exactly the same object at exactly the same time as a demonstrator. Such an interference effect is probable where dominance hierarchies exist and subordinates are likely to avoid going too close to a dominant individual.

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goslings a box covered by a gliding lid that could be opened to obtain food by pushing with the beak against a bar on the lid. During the test sessions, the ‘‘observer’’ group was exposed to a human demonstrator tapping at the bar on the lid with forefinger, whereas the control group received no demonstration. Fritz et al. found that the observers opened the box (always by pushing against the bar) more frequently than did the control group. Because the demonstration did not involve presentation of a reward, observational conditioning is ruled out, making this perhaps the clearest vertebrate case of visual local enhancement on a small scale. Leadbeater and Chittka (2007) provide an invertebrate example of local enhancement that cannot be accounted for by observational conditioning. In their experiment, bumblebees (Bombus terrestris) were presented with an arena containing ‘‘artificial flowers’’ of two types, blue unrewarding flowers and yellow rewarding flowers. Bees learned to reverse an initial unlearned preference for blue flowers, and did so faster in the presence of informed demonstrators that had learned the discrimination. Leadbeater and Chittka found that artificial model bees placed on yellow flowers were sufficient to increase the speed at which the preference was learned. A model bee cannot engage in feeding, and so cannot trigger feeding through response facilitation, nor act to reveal a reward to the observer, ruling out observational conditioning. Leadbeater and Chittka also found that dead bees would attract naive foragers to specific yellow flowers, and found no evidence that the effect generalized to unoccupied yellow flowers, suggesting that local enhancement is due to conspecific attraction rather than stimulus enhancement. In this case, it is clear that local enhancement acts to attract naive foragers to specific yellow flowers, causing them to learn the association with reward earlier. As far as we are aware, this is the only case where visual local enhancement directly involving a conspecific demonstrator has been shown to operate on such a small scale. There is more evidence that individuals can be attracted to a highly specific location by a demonstrator’s products, in the absence of a demonstrator. Many species of ant are well known to follow pheromone trails left by other individuals in the colony in order to find a food source (e.g., Denny et al., 2001). Adamo et al. (2000) showed that the wasp Vespula germanica is attracted to locations by the odor of conspecifics, and this cue is much more effective than the visual cue of a conspecific [in contrast to Leadbeater and Chittka (2007), who found the opposite for bumblebees]. Galef and Beck’s (1985) finding that rats prefer to eat foods that have been marked by conspecifics may be accounted for by local enhancement if rats are attracted to the food by scent marks. Alternatively, it could be that the rats were no more likely to visit locations marked by other individuals, but instead preferred to eat food that had been marked. Laland and Plotkin

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(1990) found that fecal and urine marks localized to both the food source and peripheral to it were necessary to affect food preference, suggesting that both processes may be important. Telle (1966) provides further evidence that rats are indeed attracted to locations by scent marks, and will follow scent trails left by other individuals. Campbell and Heyes (2002) provide evidence that odor cues can result in highly specific local enhancement in rats. They found that rats exposed to a lever which a conspecific had recently pushed either up or down while out of sight of the ‘‘observer’’ would prefer to push the lever in the same direction, due to the odor cues deposited on the lever. In conclusion, there is evidence of local enhancement through visual and olfactory cues on a large scale, and that products such as scent marks can attract individuals of some species to quite specific locations. However, due to the difficulties in isolating local enhancement from other social learning processes, there are only two studies (Fritz et al. 2000; Leadbeater and Chittka, 2007) conclusively showing that visual cues associated with a demonstrator can attract individuals to very specific locations in the environment, in the absence of contextual imitation or observational conditioning. C. OBSERVATIONAL CONDITIONING Observational conditioning occurs when observation of a demonstrator results in the observer forming an S–S association. There are numerous examples of social learning that could be accounted for by observational conditioning, for instance, any cases when an observer learns where to find food after observing a demonstrator foraging (e.g., Krebs et al., 1972; McQuoid and Galef, 1993). In this case, the observer could be learning to associate environmental cues with food. However, such examples are also consistent with other processes such as stimulus enhancement. One example where observational conditioning is likely to be involved (Heyes, 1994) is described by Palameta and Lefebvre (1985). They presented pigeons (Columba livia) with boxes of grain covered with a sheet of white paper, marked with red spot, and showed that birds that had seen a demonstrator peck through the paper for a reward of grain were more likely to do so than birds that saw a demonstrator peck through the paper for no reward. One explanation is that the birds that had observed the demonstrator being rewarded had formed an association between the red dots and food, and those that had seen the demonstrator not being rewarded had not formed the association. However, an alternative explanation is that the first group had their attention attracted to the cups more effectively than the control group because the sight of a feeding demonstrator might be a more effective cue for stimulus enhancement than the sight of a nonfeeding demonstrator.

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A number of studies have documented the ability of several species to make use of ‘‘public information’’ (Valone, 1989, Valone and Templeton, 2002), referring to socially obtained information about the quality of an environmental resource (e.g. Templeton and Giraldeau, 1995). While many such cases may not involve learning, exposure to public information can result in social learning consistent with observational conditioning. For instance, Coolen et al. (2003) allowed nine‐spined sticklebacks (Pungitius pungitius) to observe two equivalent‐sized shoals feeding at feeders with different rates of food delivery. When later allowed access to the two feeders (in the absence of demonstrators), the observers preferred the feeder that had a higher rate of delivery. A ‘‘simple’’ case of local enhancement is ruled out because observers were not responding to the mere presence of demonstrators but to rate of feeding itself. Nor were the demonstrators able to assess patch quality directly because the food items were not visible during the observation phase. One plausible explanation is that observers formed S–S associations of different strengths between each food patch and food, as a result of a previously formed association between the sight of a feeding conspecific and food. However, as with the study by Palameta and Lefebvre (1985), it could be that demonstrators feeding at a higher rate acted as more effective cues for local or stimulus enhancement. However, both accounts fail to explain why a closely related species with a similar ecology and life history, the three‐spined stickleback (Gasterosteus aculeatus) is unable to engage in this form of learning (Coolen et al., 2003). Conceivably, the nine‐spined are primed by prior experience or unlearned predispositions to pay particular attention to, or learn effectively from, feeding shoal mates. Such ambiguities could be resolved using a devaluation procedure similar to that used by Balleine and Dickinson (1998) to study asocial instrumental learning in rats. They trained rats to press a lever and pull a chain, with one action reinforced by presentation of a protein‐rich food pellet and the other by a starch solution. Animals were then prefed one of the foods to satiety, while being deprived of the alternative, which acted to ‘‘devalue’’ the former. When tested, the rats, being more strongly motivated to obtain the deprived diet, preferred the action that had yielded it in training. This is taken as evidence that the rats had formed an association between a response and the specific food type it yielded. We see utility in an observational analogue to this experiment, in which observers are exposed to demonstrators receiving different rewards at different locations. After devaluation of one of the rewards, observers could then be tested for a preference for one of the locations. If they prefer the location associated, during demonstration, with the nondevalued reward, it would indicate that they had formed an S–S association during the observation phase, and thus

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rule out stimulus enhancement. Alternative rewards would have to be distinguishable to the observer during the observation phase—perhaps food and water. This assumes that the animal is capable of ‘‘goal revaluation’’ (Dickinson and Balleine, 2000), but a positive result would rule out a stimulus enhancement explanation. It may be easier to show that an observer has formed an inhibitory‐ appetitive association through observation of a demonstrator. For example, Darby and Riopelle (1959) showed that rhesus monkeys that saw a demonstrator displacing one of two objects to find food were more likely to displace the same object themselves. In contrast, observers that saw a demonstrator displaces one of the two objects and not find food would usually displace the other object. While stimulus enhancement could account for the first group choosing to displace the object they had seen that demonstrator displaces, it cannot account for the second group choosing to displace the object they had not seen the demonstrator displaces. It is likely that this group had learned that the object the demonstrator displaced did not hide food. This example could also be accounted for by observational R–S learning; if the first group learned to direct a particular ‘‘displacement action’’ to one of the objects after seeing the demonstrator do so and be rewarded, whereas the second group learned not to direct that action to one of the objects after seeing the demonstrator do so for no reward, and so were more likely to direct that action to the other object. There are also cases suggestive of excitatory‐aversive observational conditioning (Hall, 1968; Klopfer, 1957; Lore et al., 1971; Suboski, 1990). For example, Mason (1988) showed that red‐winged blackbirds (Agelaius phoeniceus) would learn to avoid taking food from a container with a distinctive pattern, if they saw demonstrators feeding from the container and becoming ill. However, this again could be a case of observational R–S learning, with the observers learning not to direct a ‘‘feeding action’’ toward the distinctive container, rather than learning to associate the container itself with illness. It would seem to be a difficult task to rule out this alternative explanation, unless one could show that observers could not learn such associations for two different actions directed to the same object (see below). The paradigm cases of observational conditioning are the social acquisition of snake fear in monkeys (Cook et al., 1985) and studies of (Curio, 1988) social transmission of predator recognition in blackbirds (Turdus merula). In the former, naive rhesus monkeys acquired snake fear as a result of observing a demonstrator reacting fearfully to a snake. It was noted that the observer monkeys, while observing the demonstrator reacting fearfully to the snake, also showed an emotional fear response. This

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leads Mineka and Cook (1988) to hypothesize that the fear response of the demonstrator acts as a US releasing a matching UR in the observer. Since the US is paired with another stimulus, the snake, the second stimulus becomes a CS, which elicits a conditioned response, a fear response, through classical conditioning. The latter case, where blackbirds learn to recognize a predator after seeing a demonstrator mobbing an individual of that species, can be explained in a similar manner. In this case, observation of the demonstrator’s mobbing behavior releases matching mobbing behavior in the observer. These examples fit in with the general definition given for observational conditioning because the observer has been exposed to an S–S contingency, that is snakes are associated with an observed fearful response or predators with an observed mobbing response. The transmission of matching behavior in such cases is reliant on the fact that the demonstrator’s response releases matching behavior in the observer (i.e., they are reliant on response facilitation). In such cases, it may be that response facilitation is itself a result of prior learning. For example, the appropriate neural connections may have formed previously when individuals simultaneously reacted to predators in the same fearful manner (Heyes, 1994; Mineka and Cook, 1988). Similar cases of observational conditioning have been shown in several species of fish (Brown and Chivers, 2006) and New Zealand robins (Maloney and MacLean, 1995). Learning is not always reliant on visual cues. Curio (1988) found that playback of mobbing calls, from a conspecific or other species, was enough to elicit a mobbing response in the observer, and so result in social transmission. In fish, the modality of transmission is usually chemical; observer fish respond to alarm substances released by other fish in response to danger. Observational conditioning might also work in the opposite direction: allowing animals to learn to respond to an alarm signal appropriately. Pollack et al. (2003) found that if fathead minnows (Pimephales promelas) were exposed to stickleback (Culaea inconstans) alarm substances in combination with cues associated with a known predator, they would learn to respond appropriately to the heterospecific alarm cue. It seems plausible to us that many animals learn to respond appropriately to conspecific or heterospecific signals and vocalizations in such a manner (‘‘comprehension learning’’ Janik and Slater, 2000). In conclusion, there are many examples suggesting that observational conditioning might play an important role in the transmission of predator recognition and foraging information, although there are few clear‐cut cases that cannot be explained by other processes, or in the absence of other processes. In many situations, observational conditioning generates

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behavior similar to stimulus enhancement. It might also prove difficult to distinguish cases of observational conditioning from observational R–S learning and contextual imitation. D. SOCIAL ENHANCEMENT OF FOOD PREFERENCES We defined social enhancement of food preferences as occurring when, after being exposed to a demonstrator carrying cues associated with a particular diet, the observer becomes more likely to consume that diet. The classic case is that of the Norway rat: if rats smell the odor of a particular food type on a conspecific’s breath, they will show a subsequent preference for that food (Galef and Wigmore, 1983; Galef et al., 1984). Galef and Allen (1995) went on to show that an arbitrary diet preference could be transmitted and maintained in a laboratory population after the original ‘‘founder’’ population had been replaced with other individuals, suggesting that the process could maintain dietary traditions in the wild. Theoretically, stimulus enhancement could result in a process similar to social enhancement of food preferences, if the demonstrator acted merely to expose the observer to the odor of the food, thus sensitizing it to that odor. However, Galef et al. (1985) found that exposure to a food type in the absence of a demonstrator was not sufficient to induce a preference for that food. In addition, Galef et al. (1997) found that the mechanism could reverse a learned aversion to a particular food type. This suggests that exposure to a recently fed demonstrator acts to change the observer’s perception of the ‘‘palatability,’’ or some other property, of the food that the demonstrator has eaten, rather than acting to sensitize it to that stimulus. In another experiment, Galef et al. (1997) trained observer rats to find banana‐flavored pellets in one arm of a T‐maze and chow‐flavored pellets in the other arm. After exposure to a demonstrator that had recently eaten one of these foods, observers became more likely to search the arm of the maze where they knew that food could be located. It is unlikely the rats were responding to the odor of the pellets in the maze because inaccessible food pellets of both types were placed at the maze junction to mask the smell of the food in the arms. Galef et al. (1985) found that social enhancement of food preference was only evident if food‐related cues were presented in a social context, that is, if food‐related cues were presented with conspecific‐related cues. Galef et al. (1988) went on to show that if food‐related cues were paired with carbon disulfide, a compound present in rats’ breath, then this was sufficient to induce a comparable effect on diet preference. This leads to the possibility that social enhancement of food preferences is a result of observational conditioning. Perhaps, during demonstration, the food odor carried by the

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demonstrator acts as a CS that when experienced by an observer in contiguity with aspects of the demonstrator, such as carbon disulfide (US), results in the observer’s increased preference for the CS (Galef and Durlach, 1993). If this were the case, we would expect social enhancement of food preferences to be subject to the same properties as classical conditioning itself (Heyes, 1994). One such property, ‘‘overshadowing,’’ predicts that a CS (CS1), when paired with a US, will acquire less conditioned value if it is accompanied by another stimulus (CS2). A stronger effect, ‘‘blocking,’’ occurs if CS2 has previously been paired with the US. However, Galef and Durlach (1993) found that if a demonstrator had recently consumed a diet containing two flavors (CS1 and CS2), an observer would still prefer a diet containing just one of these flavors (CS1). This preference was as strong as subjects whose demonstrator had consumed only one flavor, indicating a lack of overshadowing. The same was true for observers who had already socially acquired a preference for the second flavor (CS2), indicating a lack of blocking. Galef and Durlach also found that social enhancement of food preferences occurred even when the food types were familiar to the observers, which is contrary to the predictions of another established property of S–S learning, ‘‘latent inhibition.’’ It is a moot point as to what extent the failure of the social enhancement of food preferences to conform to expected associationist phenomena, such as blocking and overshadowing, rules these data out as a case of observational conditioning. At this stage, little is known as to whether social learning exhibits the same patterns as asocial learning (Shettleworth, 2001). The alternative view, which we favor, is that rather than being the consequence of a generalized learning process (observational conditioning), it is more likely that social enhancement of food preferences is the result of an adaptive specialization, evolved to fulfill a particular function, namely, to implement the adaptive strategy ‘‘eat what others eat.’’ Moreover, there are indications that social enhancement of food preferences may be commonplace, with evidence of its occurrence in mice (Mus domesticus, Valsecchi and Galef, 1989), Mongolian gerbils (Meriones unguiculatus, Galef et al., 1998), dwarf‐hamsters (Phodopus campbelli, Lupfer et al., 2003), spiny mice (Acomys cahirinus, McFayden‐Ketchum and Porter, 1989), dogs (Canis familiaris, Lupfer‐Johnson and Ross, 2007), and bats (Carollia perspicillata, Ratcliffe and ter Hofstede (2005). E. RESPONSE FACILITATION In order to demonstrate response facilitation, researchers must first show that the rate of performance of a particular action is increased by exposure to other individuals performing the same behavior. There are numerous

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cases where groups of animals seem to be synchronized in their behavior, but behavioral synchrony does not have to be a product of response facilitation. If external stimuli or conditions are affecting a number of individuals in the same way then this could cause individuals to do the same things at the same time, in the absence of any social effect. For example, Armstrong (1951) notes that although individuals of many species of birds start singing at approximately the same time each morning, this could be explained by a threshold light intensity, triggering dawn song. It has been noted that in flocks of birds or herds of mammals, individuals seem to fly or run if they observe another individual doing so (Armstrong, 1951). Although response facilitation appears to be a highly plausible explanation, it is difficult to rule out the possibility that individuals are individually alarmed by external cues, which are perhaps too subtle to be detected by a human observer. Such behavior has been cited as an example of unlearned contagion (Zentall, 1996); however, it could be that individuals have learned to run when other individuals run because they have learned that it signals the arrival of a predator. Another example, cited as contagion, is synchronized courtship in some species of grebes (Nuechterlein and Storer, 1982; Zentall, 1996), where, as far as the human observer can tell, the birds are responding to one another rather than any external stimulus. There is evidence suggesting a response facilitation effect on feeding in some species. For example, Tolman (1967a,b) found that the rate of pecking in a domestic chick increased as a function of the pecking rate of its companion. Sherry (1977) found a similar effect in the red jungle fowl (Gallus gallus spadiceus). Visalberghi and Adessi (2000) provide strong evidence of a response facilitation effect on feeding in the capuchin monkey. They showed that individuals were more likely to eat a novel food if they were in the presence of conspecifics who were feeding on a different, familiar food than individuals who were alone, or those in the presence of nonfeeding conspecifics (social facilitation control). Because the demonstrators were not eating the novel food itself, it is unlikely that observation could have resulted in the formation of an S–S association between the novel food item and appetitive reinforcement, ruling out observational conditioning. Hoppitt et al. (2007) found evidence of a response facilitation effect on the rate at which domestic fowl initiated bouts of preening. The rate at which chickens initiated bouts of preening was more strongly related to the number of birds already preening in the same aviary than it was to the number of birds preening in an adjacent, visually obscured aviary. This rules out the possibility that any plausible external cues could be wholly responsible for the behavioral synchrony.

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In theory, behavioral synchrony could also be accounted for by local enhancement. For instance, if individuals move around together, they will experience the same locations in their environment together, and perhaps respond to stimuli at those locations in the same way. However, Hoppitt et al. found that although birds had preferred areas for preening, a local enhancement effect can be ruled out because the rate of preening was more strongly affected by the number of other birds preening in these areas than it was by the number of birds that were sitting there, suggesting the effect was action‐specific. Similar within‐aviary effects were found for ‘‘dustbathing’’ and ‘‘sitting down,’’ but a local enhancement explanation cannot be ruled out for these actions. A problem arises with this alternative‐action method if behavior is location‐specific; for instance, feeding might often be directed toward a specific location such as a food bowl, confounding the effects of response facilitation and local enhancement. We addressed this issue by using a ‘‘three‐bowl test’’ to investigate whether there is a response facilitation effect on feeding and drinking in the domestic fowl (Hoppitt and Laland, In Press). The experimental setup consisted of three identical bowls, placed adjacent to each other, two of which contained food, one of which contained water, or vice versa (see Fig. 2). We found that the rate at which one individual initiates bouts of drinking from a given water bowl is increased if another individual is already drinking from an adjacent bowl. Conversely, there was no evidence that an individual drinking increased the rate at which other individuals fed from the adjacent food bowl. This suggests the effect cannot be wholly accounted for by local enhancement

Water bowls

Food bowl

FIG. 2. Experimental apparatus for the ‘‘three‐bowl test’’ for response facilitation of drinking (Hoppitt and Laland, In Press). The apparatus for the three‐bowl test for response facilitation of feeding was identical except with two food bowls and one water bowl.

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because there was an action‐specific effect. We take this finding as evidence of a response facilitation effect on drinking. Conversely, there was no evidence of an action‐specific effect on feeding, suggesting that feeding was influenced only by local enhancement (see Section III.C). Convincing cases of response facilitation come from the literature on observational conditioning. Cook et al. (1985) showed that an observer rhesus monkey, when observing a demonstrator responding fearfully to a snake (characterized by piloerection, grimacing and vocalizations), would show similar behavior itself. Because the observers in pretests had not responded to snakes fearfully, it is highly unlikely that the observer and demonstrator were responding to an external cue in the same way, and indeed, the observers responded fearfully even when they could not see the snake. Similar effects are seen in blackbird mobbing (Curio, 1988). Byrne and Tanner’s (2006) experiments on a western lowland gorilla (Gorilla g. gorilla) suggest that response facilitation might operate on specific actions. They found that the subject was spontaneously more likely to execute a similar hand gesture to one she had observed the experimenter performing, during the period following the demonstration than during the control period preceding it. There is no question of external cues affecting demonstrator and observer in the same way because the demonstrator was a human experimenter, making arbitrary hand gestures. These findings might have serious implications for cases of apparent production imitation; for instance, Hayes and Hayes (1952) ‘‘do‐as‐I‐do’’ experiments could be accounted for by such an effect (see evidence for production imitation below). F. SOCIAL FACILITATION To demonstrate social facilitation one must demonstrate that the mere presence of a conspecific has an affect on behavior. A possible example is provided by Waas et al. (2000), who report evidence that playback of recorded colony sounds to wild royal penguins (Eudyptes schlegeli) during the breeding cycle, supplementing naturally occurring colony sounds, facilitated a number of agonistic and sexual behavior patterns. It seems plausible that the behaviors were facilitated by any cues associated with the mere presence of other individuals, which would make this a case of social facilitation. Alternatively, this effect could be explained by response facilitation if the penguins were responding to noises associated with particular actions, such as the ‘‘jab hiss’’ or ‘‘forward trumpet,’’ with a matching response. However, Waas et al. were more concerned with whether the phenomenon could account for reproductive synchrony, and did not make this distinction. Waas et al. (2005) report a similar phenomenon in zebra

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finches (Taeniopygia guttata), where females respond to playback of conspecific calls by laying earlier and with larger clutch sizes. Of most interest for our purposes is whether there is a social facilitation effect on task learning. In testing for such an effect, it is important that the demonstrator does not have access to the task itself or to a similar one, to rule out local and stimulus enhancement, observational conditioning, imitation, observational R–S learning, and emulation, nor have the opportunity to perform the action required to solve the task, to rule out response facilitation. If the demonstrator does have the opportunity to perform that action, researchers must show that learning of the task does not correlate with the demonstrator’s frequency of performance of that action. There are few cases where experimenters have attempted to isolate a social facilitation effect on task learning because researchers are usually interested in controlling for social facilitation in order to isolate another social learning process such as imitation. Visalberghi and Adessi (2000) showed that capuchin monkeys are more likely to learn to eat a novel food type in the presence of (nonfeeding) individuals than they are when they are alone. Tolman (1964) showed a similar effect on domestic chicks. In contrast, experiments on rats have shown that bar pressing acquisition of a social facilitation group is retarded compared to a group not exposed to a conspecific (Bankhart et al., 1974; Zentall and Levine, 1972; Zentall, 1996). The effect might depend on the exact conditions involved (e.g., which other individuals are present, familiar/nonfamiliar) and the type of actions required to solve the task. A further issue for investigation includes the exact mode by which the effect operates, for instance, is it a reduction in neophobia, an increase in general activity levels, or an increase in a subset of behavior patterns implicated in task solving? 1. Contextual Imitation To demonstrate contextual imitation, researchers must show that an observer has learned to use an action in a particular context as a result of observing the action demonstrated in that context. The ‘‘two‐action method’’ is widely regarded as the most successful method for testing imitative ability (Dawson and Foss, 1965). The experimental subjects must solve a task with two alternative solutions. Typically, half of the subjects observe a demonstrator solving the task in one way, the other half the alternative. Subjects are then tested to see which method they use, and if they disproportionately used the method that they observed, this is taken as evidence of imitation. Whether other social learning processes can be ruled out as an explanation for matching behavior depends on the nature of the actions demonstrated. If the two actions are directed to different parts of the experimental apparatus, then local enhancement cannot be ruled out.

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Likewise, if the actions are both directed to the same location, but there is no evidence that the action used by the demonstrator is being copied, this does not rule out local or stimulus enhancement as an explanation for task acquisition (e.g., Nagell et al., 1993). If the two actions result in different movements of the experimental apparatus (e.g., Bugnyar and Huber, 1997) then emulation cannot be ruled out, unless it can be shown that the movement is copied in egocentric space (relative to oneself), rather than allocentric space (relative to the external environment). The latter method was used by Heyes et al. (1992), who showed that a rat would push a joystick to its left or its right, as it had seen a demonstrator do, and this effect remains when the joystick is moved, so it is operated in a perpendicular plane to the demonstration. However, it was later shown that odor cues left on the joystick could act as a cue for the observers, which could result in a matching response through local enhancement (Campbell and Heyes, 2002; Mitchell et al., 1999). In Dawson and Foss’ (1965) original two‐action test, emulation is ruled out, as birds used their beak or feet to open food containers and the resulting movements of the experimental apparatus were very similar. However, Galef et al. (1986) replicated the experiment with larger sample sizes and found that the imitation effect seemed to be weaker and transitory. Others have attempted to control for emulation by providing a control group that observe a ‘‘disembodied’’ movement (Whiten and Ham, 1992) or ‘‘ghost’’ control, where the manipulandum moves by itself, which may be followed by a reward for an inactive demonstrator (e.g., Fawcett et al., 2002; Heyes et al., 1994). If the ‘‘disembodied‐movement’’ control group fails to move the manipulandum in the way they observed, but those subjects who saw a full demonstration do copy the movement, it is taken as evidence that the subjects are imitating and not emulating the demonstrator. However, Byrne (2002) notes that spontaneous movements of objects could be extrinsically of less interest to animals than the actions of a demonstrator, so one cannot rule out emulation on the strength of this evidence. Even if an animal does attend to the movement of a manipulandum followed by presentation of food, it may be more likely to learn that the spontaneous movement of the manipulandum signals food presentation (observational conditioning) than it is to try to reproduce those movements. Consequently, we do not regard a ‘‘ghost control’’ a satisfactory control for emulation. The two‐action test does not inherently test for production imitation because it does not show that the alternative actions are novel. However, it provides evidence consistent with contextual imitation, so long as the context–action combination is novel. The method has been widely used to test for an imitative ability in primates, rodents, and birds. Table II summarizes the findings for each taxonomic group.

TABLE II Our Interpretation of the Findings of Two‐Action Tests for Imitation Resulting in Behavioral Matching on the Part of the Observer

Taxonomic group Primates

Source Whiten and Custance, 1996 Whiten, 1998 Custance et al., 1999 Nagell et al., 1993

139

Call and Tomasello, 1995 Bugnyar and Huber, 1997 Voelkl and Huber, 2000

Rodents

Collins, 1988 Denny et al., 1983, 1988 Heyes and Dawson, 1990; Heyes et al., 1992; Mitchell et al., 1999

Species Chimpanzee (Pan troglodytes) Chimpanzee (Pan troglodytes) Capuchin monkey (Cebus apella) Chimpanzee (Pan troglodytes) Orangutan (Pongo pygmaeus) Marmosets (Callithrix jacchus) Marmosets (Callithrix jacchus) Mouse (Mus musculus) Rat (Rattus norvegicus) Rat (Rattus norvegicus)

Stimulus enhancement

Emulation

Response facilitation combined with stimulus enhancement

Contextual imitation

Noa

Yesa

Yesa

Yesa

Noa

Yesa

Yesa

Yesa

Noa

Yesa

Yesa

Yesa

Yes

No

Possiblyb

Possiblyb

Yes

No

Possiblyb

Possiblyb

Possiblyc

Yes

Yes

Yes

No

No

Yes

Yes

No No Yes

Yes Yes No

Yes No Yes

Yes No Yes

(Continued)

TABLE II (Continued)

Taxonomic group Birds

Source

Species

Dawson and Foss, 1965; Galef et al., 1986 Heyes and Saggerson, 2002 Lefebvre et al., 1997

Budgerigars (Melopsittacus undulates) Budgerigars (Melopsittacus undulates) Carib grackles (Quiscalus lugubris) Ravens (Corvus corax)

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Fritz and Kotraschal, 1999 Zentall et al., 1996; Kaiser et al., 1997 McGregor et al., 2006 Dorrance and Zentall, 2002; Saggerson et al., 2005 Akins and Zentall, 1996, 1998 Campbell et al., 1999 Fawcett et al., 2002

Stimulus enhancement

Emulation

Response facilitation combined with stimulus enhancement

Contextual imitation

No

No

Yes

Yes

No

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Pigeons (Columba livia)

No

No

Yes

Yes

Pigeons (Columba livia) Pigeons (Columba livia)

No No

No No

Yes No

Yes Yes

Japanese quail (Coturnix japonica) Starlings (Sturnus vulgaris) Starlings (Sturnus vulgaris)

No

No

Yes

Yes

No

Yes

Yes

Yes

No

Yesd

Yes

Yes

The columns ‘‘Stimulus enhancement,’’ ‘‘Emulation,’’ ‘‘Response facilitation combined with stimulus enhancement,’’ and ‘‘Contextual imitation’’ show whether each process can sufficiently account for behavioral matching found in the experiment, or whether it can be ruled out as a full explanation (see full text for criteria). For instance, ‘‘No’’ in the stimulus enhancement column means that stimulus enhancement alone cannot explain the findings of the study. a Some actions copied, others not. b No evidence of an action‐specific effect, but could be imitation or response facilitation at low level of resolution. c Weak or ambiguous evidence for an action‐specific effect. d Negative ‘‘ghost control’’ used, but not considered sufficient evidence against emulation. References not in text.

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Among the best evidence of contextual imitation is the work of Zentall et al. on quail and pigeons (Akins and Zentall, 1996; Kaiser et al., 1997; Zentall et al., 1996). Observers watched demonstrators step or peck on a treadle for a food reward, and were more likely to solve the task using the method they had seen. As both actions were directed to the same location and resulted in the same movement of the treadle, local enhancement and emulation are ruled out. McGregor et al. (2006) used a similar two‐action task, where pigeons observed a demonstrator either peck at or step on a horizontal bar, and were then tested in extinction (with no reward) for their own response to the task. However, in this case, the observers had already been trained both to peck at and step on the lever for the reward. Despite the fact that the birds had already learned how to solve the task, after the observation phase they still showed a tendency to copy the actions of the demonstrator. The effect was equally strong regardless of whether the demonstrators were unrewarded, frequently rewarded, or rarely rewarded. This supports the notion that contextual imitation can result in ‘‘blind’’ copying of a demonstrator’s reaction to a stimulus, regardless of the consequences. However, such a finding is surprising. Given evidence that pigeons can learn about the consequences of actions through observation (Saggerson et al., 2005; see Section III.I), why would they copy an unrewarded demonstrator? A possible explanation for these findings is provided by Byrne (1999, 2002), who suggests that response facilitation provides an alternative explanation for the data generated by two‐action tasks. For instance, this explanation may account for the finding of Zentall et al. (1996) that the pigeons who observed the demonstrator pecking the treadle also tended to peck, whereas those that observed the demonstrator step on the treadle were more likely to step on it. It may be that birds that saw a demonstrator peck were just more likely to peck when they were tested on the task, purely because of a transient increase in pecking rate, or probability of pecking, in relation to stepping, and vice versa for the other group. This ambiguity is exacerbated by the fact that in many two‐action tests, the observer is rewarded for its responses (of either action) during the test phase that would act to amplify any initial preference for a specific action, which might otherwise have proved to be transitory. Response facilitation provides an alternative explanation for McGregor et al.’s (2006) finding that pigeons seemed to ‘‘blindly’’ imitate a demonstrator’s actions, whether the demonstrator was rewarded or not, and despite the fact they had already learned how to solve the task. Perhaps the observers were subject to response facilitation, which acted to ‘‘drown out’’ their previous asocial learning, and caused them to use the same action as the demonstrator at test.

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If researchers are to be sure that they have a case of contextual imitation, rather than response facilitation, it must be shown that the observers have learned to use the target action in that context. One way of eliminating response facilitation as an explanation could be to introduce a delay between observation and exposure to the task, so as to let the possible effects of response facilitation wear off. However, it is difficult to know exactly how long a response facilitation effect could last. Alternatively researchers could introduce control subjects, who do not observe demonstrators solving the task, but instead observe demonstrators performing the same actions in a different context. Perhaps the most effective method for ruling out response facilitation is to show directly that an observer has learned to use an action in specific context during the observation phase. Campbell et al. (1999) used a two‐ object/two‐action test, where observer starlings observed a conspecific removing either red or black stoppers from a box containing food by either pushing it into the box with a closed beak or pulling it out with an open beak. Birds observed one of four possible combinations of red or black and up or down, and were more likely to choose to remove the same colored stoppers using the same action as the demonstrator. However, this might still be accounted for by response facilitation of the action, combined with stimulus enhancement of the stopper color. If birds had instead watched one action being directed toward one color stopper and the other action being directed toward another color stopper and had copied each action in its context, then this would have been strong evidence against response facilitation. Emulation is also not ruled out in Campbell et al.’s experiment. Dorrance and Zentall’s (2002) experiments on pigeons aimed to investigate whether imitative learning of a peck/step two‐action task is context‐ specific. In their first experiment, observers saw a demonstrator pecking at a treadle in response to a white light, and stepping on it in response to a green light (or vice versa) and receiving a food reward in each case. Observers then received conditional discriminative training that was either consistent with that they had seen demonstrated or the reverse. It was found that the consistent group did not learn the task more rapidly than the reverse group, suggesting that there was not a context‐specific imitative effect. Conversely, Dorrance and Zentall’s (2002) second experiment suggests there is. In this case, observers first received asocial conditional discrimination training, for example, peck in response to a white light and tread in response to a green light. One group of observers then observed a demonstrator successfully responding to the stimuli in manner consistent with their own training, whereas another group observed a demonstrator successfully performing the reverse discrimination. All observers then received conditional discrimination training that was a reversal of their initial asocial training. It was

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found that those birds that had observed demonstrators responding to the reverse discrimination learned the discrimination more rapidly themselves. The results of the second experiment suggest that there is a context‐specific imitative effect on learning that cannot be accounted for by response facilitation, and is consistent with our adopted definition of contextual imitation. It is not clear why the demonstrator’s actions were imitated in the second experiment, where the social information obtained from the demonstration contradicted the observer’s first‐hand experience, but not in the first experiment, where it did not. Nonetheless, these results seem robust, as Saggerson et al. (2005) were able to replicate the findings of both these experiments. In contrast to Dorrance and Zentall, Saggerson et al. tested observer birds in extinction, thus ruling out the possibility that any intial reversal of action preference was inflated by trial and error learning during the test phase. A number of cases suggestive of contextual imitation arise in the vocal learning literature. Several species of birds are subject to what has been termed ‘‘action‐based learning’’ (reviewed in Marler and Nelson, 1993). This occurs when an individual learns to restrict its usage of songs to a subset of those in its repertoire, through the influence of others. For example, white‐crowned sparrows (Zonotrichia leucophrys) start singing at 7–9 months of age, and for a short period, the ‘‘plastic song’’ phase, will sing a much greater variety of songs than later in life. Laboratory experiments found that individuals were more likely to retain a song type if they had heard that song type sung by another individual during the plastic song phase. It appears that individuals attempt to cut down their repertoire to match that of their neighbors, a finding that is supported by field observations of field sparrows (Spizella pusilla, Nelson, 1992). Cases of action‐based learning are consistent with contextual imitation, in so far as the observer is learning to use a song already in its repertoire, but it is not usually clear that observers learn the context in which specific songs or calls are to be used. However, there are a few cases that indicate that this can occur. Kroodsma (1988) reports that blue‐winged warblers (Vermivora pinus) sing two different songs types. One is sung at dawn at a high rate and the other is produced later in the day and sung at a slower rate. Kroodsma found that if young birds are exposed to the songs types with the time and speed of singing reversed, they would also produce the song types in the reversed contexts. Spector et al. (1989) made a similar finding for yellow warblers (Dendroica petechia). They found that young birds could be trained to use a song type, usually used at dawn, in the middle of the day after being presented with the song type at this time. More recent evidence is provided by Goodale and Kotagama’s (2006) field observations of the greater racket‐tailed drongo (Dicrurus paradiseus). Goodale and

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Kotagama found that this species not only mimics other species calls, but also the contexts in which the calls are used. They found strong evidence that the drongo will use mimicked alarm calls in the same circumstances in which it would usually use its own alarm calls, and the same was true for mimicked mobbing calls. This suggests that, at least in this case, vocal mimicry may involve both production imitation (but see Section III.G.2) and contextual imitation of the model species. However, we cannot rule out the possibility that the drongos’ interactions with other species in mixed flocks (Goodale and Kotagama, 2006) serve to shape their mimicry to the appropriate circumstances. A number of mammals have alarm calls that are used in response to a specific danger. For example, vervet monkeys (Cercopithecus aethiops) have various alarm calls, used in response to different predators (leopards, eagles, and snakes), which cause others to respond with the appropriate escape behavior (Seyfarth et al., 1980). While there appears to be some unlearned tendency to produce alarm calls in the appropriate circumstances (e.g., initially the eagle call is used in response to many flying objects), the ability to discriminate real threats improves with age (Seyfarth and Cheney, 1986). Juveniles could plausibly learn the contexts in which alarm calls are to be used through contextual imitation of adults. However, this is contentious as juveniles might instead learn which objects are dangerous after observing adult’s reactions to them, perhaps through observational conditioning. Also, as far as we are aware, a maturation effect on call usage has not yet been ruled out. A stronger case is provided by Reiss and McCowan’s (1993) experiments on two male bottlenose dolphins (Tursiops truncatus). The dolphins were provided with an underwater keyboard, on which they could press six keys labeled with different shapes. On pressing a specific key, a dolphin was either presented with a specific object with which it could interact (a fish, ball, ring, disk, or ringfloat) or in the case of one key, tactile contact from a trainer. Each object/activity was also signaled by a computer‐generated whistle, intended to be different to any vocalization in the subjects’ repertoire. After a number of sessions, the dolphins spontaneously began to mimic some of the computer‐generated sounds. Whistles associated with ‘‘ball,’’ ‘‘ring,’’ and ‘‘rub’’ were all eventually mimicked, and also tended to be mimicked in appropriate behavioral circumstances: that is, before pressing the appropriate key, while interacting with the appropriate object or in the case of the rub whistle, while engaged in tactile contact with a trainer. It seems unlikely that response facilitation could account for the findings because dolphins were not rewarded for producing whistles in the appropriate circumstances. These findings raise the interesting possibility that

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dolphins in the wild might learn the context in which to produce specific vocalizations through contextual imitation. There are, therefore, a few cases suggesting that at least some birds and mammals are capable of learning the context in which to use a vocalization after observing another individual doing so. However, in copying vocalizations made by others, an observer is not copying the demonstrator’s actions, but the results of those actions: the sounds produced. This would make cases of vocal ‘‘imitation’’ fall into the category for emulation (Byrne, 2002). This argument is discussed further in Section III.G.2. In conclusion, cases of vocal learning aside, to date there are only two published experimental studies, both on pigeons, that provide conclusive evidence of contextual imitation (Dorrance and Zentall, 2002; Saggerson et al., 2006). 2. Production Imitation To demonstrate production imitation, it must be shown that an observer has acquired a novel action or sequence of actions not already in its repertoire. Above we suggested that it is likely to prove of utility to view production imitation as the learning of novel sequences of action units (Heyes and Ray, 1999). This approach was utilized by Whiten (1998) in sequential imitation experiments on chimpanzees. Whiten investigated whether four chimpanzees could learn by imitation how to open an ‘‘artificial fruit,’’ that is, a box containing food, which could only be opened after disabling a number of ‘‘defenses.’’ The defenses consisted of a bolt, a pin, and a handle, each of which could be removed or disabled in two different ways (using one of two actions), and could removed in any order, giving six possible arbitrary sequences of removal. In Whiten’s experiment, each of four observers watched a human demonstrator performing a different sequence of behavior patterns to open the artificial fruit, and subsequently tested to see if it removed the defenses in the same sequence, and using the same actions, as the demonstrator. This is essentially a modified version of the two‐action test where the alternative actions are replaced with action sequences. Because each sequence is arbitrary, Whiten rules out the possibility that observers would inevitably converge on the demonstrated solution by trial and error learning. Such an approach also allows one to quantify the probability that an individual will hit on the same sequence as its demonstrator by chance, under the null hypothesis that observers do not imitate the observer. This approach differs from the two‐action tests described in Section III. G.1, in that it can test for production imitation. In Whiten’s experiment, though it is plausible that each component action is familiar to the observer, it is improbable that the precise sequence of actions it observes would already be present in its repertoire, if it has not previously had access to

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the experimental apparatus, and does not usually combine the actions in question. Whiten (1998) found that the order in which defenses were removed was transmitted to the four observers, but interestingly each sequence took time to emerge. Subjects were tested over three test trials, interspersed with demonstrations, and there was no evidence that the sequence had been imitated on the first two trials, but strong evidence on the third trial. However, this cannot be accounted for by trial and error learning because several arbitrary sequences could lead to reward. The findings could provide insight into the mechanism underlying production imitation; perhaps the chimpanzees formed a template of the demonstrator’s behavior and then modified their own behavior to match the template. The results of Whiten’s (1998) experiment constitute evidence that an arbitrary sequence of some kind had been learned through observation. However, subjects did not appear to have used exactly the same actions as the demonstrator in order to make the required manipulations. The chimpanzees only copied one pair of alternative action units used in the task (see also Whiten et al., 1996), poking a bolt versus pulling and twisting it, actions which result in different movements of the bolt itself, suggesting that emulation may be involved. This hypothesis is supported by the fact that one of the subjects was seen to copy the general form of the poking action unit, but showed variation in her performance of the action unit, varying the digit used to poke, and also using her knuckles and finger joints. An alternative explanation is that chimpanzees were imitating at a low level of resolution, using functionally equivalent actions to accomplish the same purpose. Overall, in this case, it is clear that observation has had a direct effect on skill learning, but the results could be interpreted as imitation or emulation. Sequential tasks of this kind have great potential to detect production imitation because if the demonstrator’s sequence is copied, most other processes are ruled out. In Whiten’s task, each action in the demonstrated sequence is directed to a different part of the apparatus. Nonetheless, a sequence match could not be explained by local or stimulus enhancement, at least not exclusively, because these processes operating in isolation could not affect the order in which the observer interacts with the different locations. It could be that the observers do not directly learn the sequence of actions, but instead learn the order in which different parts of the apparatus should be interacted with. They may also learn to direct each action unit to a particular location, in a manner analogous to contextual imitation. To rule out this possibility, one could use a sequence of action units that are directed to the same location, or are location independent, such as hand gestures (Custance et al., 1995; Hayes and Hayes, 1952).

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However, there is no reason why the former process should not be considered to be production imitation. Response facilitation is unlikely to account for imitation of the sequential structure of behavior, although it might increase the probability that each component action be performed, and so act to make an observer more likely to converge on the same sequence as the demonstrator than individuals that had seen no demonstration. However, the process is unlikely to account for different individuals using the same actions in a different order, as they saw demonstrated. One could argue that the actions seen earlier in a sequence might be primed more strongly than later actions, and therefore might be utilized first by the observer. However, if such a process increases the probability the observer will converge on the same sequence as the demonstrator, there is no reason not to label it ‘‘production imitation.’’ In fact, conversely, we would expect response facilitation to be strongest on the action seen most recently. A moot point is how accurate the sequence match must be for learning to be considered a case of production imitation. As was noted in Section II. G.2, accuracy depends in part on the level of resolution at which imitation occurs. One can regard a sequential task as being set at a certain level, and a positive result implies that the subject is imitating at a resolution equal to the level of the task or higher, whereas a negative result suggests that the subject is not imitating at a level of resolution high enough to be detected, or it is not imitating at all. There have been few experimental attempts to study whether observers can imitate novel action sequences, studies being limited to Whiten’s (1998) artificial fruit studies on chimpanzees and Call and Tomasello’s (1995) work on orangutans. In the latter study, Call and Tomasello tested whether orangutans could learn, by imitation, to combine a sequence of two actions to obtain a food reward. They found no evidence that the subjects tended to use the sequence of actions they had seen demonstrated, by either human or conspecific demonstrators. Byrne and Russon (1998) suggest that wild mountain gorillas (G. gorilla) might use production imitation to learn to feed effectively. Gorillas must learn a complex sequence of actions in order to process their plant food, for example, nettles must be processed in a particular way so that they do not sting their lips. Seemingly, here production imitation would be highly adaptive. The food‐processing methods are complex, and may take an individual a long time to master by trial and error. However, the sequences that the gorillas use to process their food are not arbitrary, but a logical sequence that must be followed correctly in order to attain the desired outcome (Whiten, 1998). Therefore, individuals learning to process foods by trial and error may converge on the same action sequence in the absence of imitative effects (Tomasello, 1998).

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There is other experimental evidence of sequence learning through observation. Will et al. (1974) showed that not only could rats learn to respond to a discriminative stimulus (a tone) with a bar‐press action, after seeing a demonstrator do so, but also there was a significant correlation between the strategy an observer used and that of the demonstrator. It was shown that rats were more likely to use multiple bar presses on presentation of the tone, rather than a single bar press, if they had seen the demonstrator doing so. This could be interpreted as production imitation to the extent that the rats can be regarded as learning a novel sequence. However, this interpretation is clearly contentious because the rats could merely be repeating an established behavior in a novel context. Alternatively, rats that observed a demonstrator performing a multiple‐press response had merely formed a stronger association between the context and the bar pressing response, and so responded more vigorously themselves. Hayes and Hayes’ (1952) famous ‘‘do‐as‐I‐do’’ experiment is often considered to be strong evidence of imitation. This method was later repeated in a more rigorous manner by Custance et al. (1995) and has been replicated in orangutans (Call, 2001). In the Custance et al. (1995) experiment, two chimpanzees were subjected to a three and a half month teaching phase, where they were trained to copy a human demonstrator performing 1 of 15 different actions in response to the command, ‘‘Do this!’’ Custance et al. then tested whether the subjects would imitate 48 arbitrary actions in response to the same command, and found that they were significantly more likely to perform an action similar to the demonstrated action than any other. Because the actions did not result in the movement of any objects, emulation is ruled out. These experiments are commonly interpreted as evidence that chimpanzees and orangutans are not only capable of production imitation (Whiten and Custance, 1996) but also able to form a generalized ‘‘concept’’ of imitation, which enables them to imitate on command. However, these conclusions seem premature. The notion that apes can learn to imitate on command is questionable on the grounds that there was no control condition to test whether subjects were less likely to imitate gestures spontaneously, when they had not received a command to do so. More critical to the former conclusion, that the subjects were capable of production imitation, is the fact that one cannot be sure that the actions were not already part of the subjects’ gestural repertoire. The problem is illustrated by Byrne and Tanner (2006), who ran a similar experiment on a western lowland gorilla, except they did not train the subject to respond to a ‘‘Do this!’’ command as in the previous cases. They found the gorilla spontaneously imitated four of seven arbitrary actions presented, in the absence of any command or reward. On analysis of videotapes

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documenting the gorilla’s past gestures, it was found that it had previously performed all of the actions that had been ‘‘imitated,’’ suggesting they were already part of its repertoire. Byrne and Tanner (2006, p.226) suggest that this offers an alternative explanation for the apparent production imitation in all three great ape species: after observing the test gesture, subjects became more likely to use whichever action in their repertoire ‘‘most resembled the demonstrator’s action,’’ making it a case of response facilitation. Similar experiments to Hayes and Hayes’ (1952) have been run on bottlenose dolphins (T. truncatus, reviewed by Herman, 2002). One experiment (Xitco, 1988, in Herman, 2002) utilized two dolphins, Ake and Phoenix, that had been trained to respond to specific gestures, made by their human trainer, with specific actions such as somersaulting underwater or leaping over an object. Each dolphin took turns at being the demonstrator or observer. During a training trial, the demonstrator responded to 1 of 10 gestures from its trainer with 1 of 10 actions. The observer, which could see the demonstrator dolphin but not the demonstrator’s trainer, received a ‘‘mimic’’ gesture from its own trainer, indicating that it should copy the demonstrator. The observer dolphin was rewarded for copying the action used by the demonstrator, and both Ake and Phoenix successfully learned to do so for all but one action. During transfer testing, the observer dolphin was tested to see if, on receiving the ‘‘mimic’’ sign, it would imitate the demonstrator dolphin performing 15 transfer actions not used in training. Some of the actions used by Xitco (1988) were directed at specific objects, but Herman (2002) argues convincingly that stimulus and local enhancement are insufficient explanations for the copying of such behavior, since there were a number of actions that could have been directed at these objects. Twelve of the actions were familiar to the dolphins and, of these, Phoenix successfully imitated 7 and Ake 6, following less than 10 demonstrations. Three of the demonstrated actions were described as ‘‘novel,’’ of which Phoenix imitated two and Ake one. It is these latter cases that are candidates for production imitation. However, all the imitated ‘‘novel’’ actions were directed at specific objects: press paddle with rostrum; pull rope and ring bell; and place ring on stick. Consequently, these cases are open to the same explanation as most cases of the two‐action test: the actions used by the observer may not have been novel, but rather were established behavior used in a novel context. Herman (2002) claims that Xitco’s experiment provides evidence that the dolphins had formed a concept of imitation because they were able to generalize their responses to the mimic gesture during the transfer test. If this were so, we could rule out response facilitation because imitation

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would be context specific. Unlike the experiments of gestural imitation in great apes (Call, 2001; Custance et al., 1995; Hayes and Hayes, 1952), the dolphins were provided with control ‘‘nonmimic’’ trials, during which the observer’s trainer did not give the mimic sign and imitation was not observed. However, these nonmimic trials do not satisfactorily rule out response facilitation, because the observer was given a gesture to perform an alternative control action. It could be that, during mimic trials, observers were subject to response facilitation, so were more likely to perform the demonstrated action, but during nonmimic trials this tendency was overridden by the alternative gesture given by the trainer. In another, similar experiment (Xitco, 1988, in Herman, 2002), the same observers were trained to imitate a demonstrator in response to a mimic sign, given after a delay of up to 80 s. Successful imitations were observed, but the success rate dropped dramatically (95% after 25 s; 74% after 60 s; 59% after 80 s), suggesting a transient effect that is consistent with a response facilitation explanation. To rule out response facilitation, we require nonmimic trials during which no cue, or, better still, a specific ‘‘nonmimic’’ gesture, is given by the trainer. Xitco (1988) reports evidence that two dolphins learned to perform a number of different actions demonstrated to them by a human. However, the apparent imitation arose over a number of demonstrations, and Herman (2002) suggests that some shaping of behavior may have occurred. In a similar study, Herman et al. (1999, in Herman, 2002) found that a dolphin, Elele, would adopt a posture similar to that demonstrated by her human trainer at the side of her pool. For example, if the trainer lay on her back with her leg in the air, the dolphin would turn onto its back and raise its tail out of the water, while if the trainer jumped, Elele would thrust upward out of the water. Equivalent results were obtained in experiments where the demonstrator was observed via a video screen (Herman et al., 1993). Herman (2002) interprets this as evidence that the dolphin can relate its body image to the human body plan, enabling it to imitate by analogy. However, again, it is not clear that the actions and postures being performed by Elele were novel. An alternative explanation is that the dolphin was exhibiting response facilitation, by becoming more likely to perform whichever action in her repertoire was closest to that demonstrated. This would still require the dolphin to generalize, for instance, a human leg as being a tail and an arm as a pectoral fin, but it would not be a case of production imitation. A strong case can be made to regard the ability of many birds and marine mammals to learn songs and calls from conspecifics to be considered production imitation [reviewed respectively in Catchpole and Slater (1995) and Janik and Slater (1997)]. In their review of the role of social learning in vocal communication, Janik and Slater (2000) distinguish cases where

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familiar elements or songs are recombined into a novel sequence from cases where a completely novel sound is learned, reserving the term ‘‘production learning’’ for the latter. They argue the most convincing cases of production learning come from vocal mimics that are able to copy the calls of other species or environmental sounds (Baylis, 1982). Sometimes the mimicked sounds may be very different from those in the species’ natural repertoire, providing a clear case where the observer has learned a novel vocalization (Janik and Slater, 1997). Famous avian examples include talking grey parrots (Psittacus erithacus,Pepperberg, 2000) and Australia’s superb lyrebird (Menura novaehollandiae, Robinson, 1975), which can mimic other species as well as anthropogenic sounds, such as the sounds of camera motor drives and chainsaws. Among marine mammals, bottlenose dolphins are able to imitate not only conspecifics but also humans and even arbitrary computer‐generated sounds (Richards et al., 1984). Similarly, Ralls et al. (1985) report how two male harbor seals (Phoca vitulina) spontaneously imitated phrases spoken by human visitors to their aquarium. Janik and Slater (2000) note that, in practice, it will often prove difficult to determine whether an apparently novel vocalization has arisen through a recombination of preexisting elements into a new sequence rather than as a completely novel sound. Indeed, we suspect that an extension of this distinction to nonvocal behavior, where ‘‘elements’’ of behavior are less readily identified, will prove wholly untenable (see Section II.G.2). Consequently, we do not follow Janik and Slater’s line, and by our definition, learning a novel combination of familiar elements also qualifies as production imitation. For us, production imitation could occur at a still higher level. One possible example is provided by Hultsch and Todt (1989), who presented nightingales (Luscinia megarhynchos) with a repeated sequence of songs on tape. They found that the nightingales tended to copy aspects of the song sequence presented to them: though they did not copy the exact sequence, successive songs tended to be sung in proximity, as a single ‘‘package.’’ However, the concept of emulation provides an alternative way to think about vocal ‘‘imitation.’’ Byrne (2002) points out that birds such as Mynah birds, which copy human speech, do so using a very difficult anatomical structure to the one humans use to speak. This means that the birds are not copying our actions, but the results of those actions (the sound). Indeed, even when vocal imitation is intraspecific and the anatomical structures involved are the same, it is clearly not the movements of those structures that are being imitated but again the results of those actions. By this reasoning, ‘‘vocal imitation,’’ and most cases of vocal learning, including in humans, is emulation and not imitation. However, one could argue that this merely serves to illustrate the arbitrary nature of the imitation/emulation dichotomy. We return to this issue in Section III.I.

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Possible evidence for production imitation comes from Moore’s (1992) experiments on a grey parrot (P. erithacus), named Okichoro. Okichoro’s keeper demonstrated a number of behavior patterns to him, each involving an action and a word or a sentence, for example, waving and saying ‘‘ciao,’’ or opening his mouth and saying ‘‘look at my tongue.’’ In time, Okichoro began to imitate each action (using his foot as an analogue of the human hand) with its accompanying verbal label for a total of eight combinations, without being rewarded for doing so, thus ruling out a shaping effect. Even assuming each word or sentence and each action was already in Okichoro’s repertoire, each behavior pattern can be thought of as a combination of two‐action units, and it is highly unlikely that the match could have occurred by chance. Thus, it does seem that the parrot learned novel combinations of action units. One problem with labeling this a case of production imitation is that, as noted before, the copying of sounds is a case of emulation rather than a case of imitation, because it is the results of the behavior rather than the behavior itself that is copied. This is illustrated well by Okichoro himself, who emulated many sounds in a nonvocal way, for example, that the sound of someone knocking on the door was emulated by a tapping of the beak on a perch (Moore, 1992). Overall there does not seem to be a clear‐cut case of production imitation documented in the literature. This is perhaps a result of the emulation/ imitation dichotomy that does not allow for the fact that an observer might use both action‐based and results‐based cues in acquiring novel skills (Call and Carpenter, 2002). Using an approach similar to that used by Whiten (1998), where one tests whether an observer can acquire a novel arbitrary sequence of predefined action units, appropriately controlled for other processes, is among the most promising methods with which to test for production imitation.

G. OBSERVATIONAL R–S LEARNING To demonstrate observational R–S learning, one must show that an observer has formed an association between the action it saw a demonstrator performs and the observed consequences of that action. There are numerous cases where it has been shown that social transmission will only occur (e.g., Palameta and Lefebvre, 1985), or is more effective (Giraldeau and Templeton, 1991), if the observer sees the demonstrator being rewarded. However, in most cases, it has not been shown that contextual imitation is occurring at all, so observation conditioning can account for most of these findings. Heyes et al. (1994) found that rats would only copy a demonstrator if the demonstrator was rewarded in a joystick pushing task,

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but odor cues left on the joystick affected the observer’s response, casting doubt on the finding that it was a case of contextual imitation (Mitchell et al., 1999). Some evidence for observational R–S learning comes from Akins and Zentall’s (1996, 1998) studies of quail (C. japonica). These authors found that, in a two‐action test, where the observer watches a demonstrator stepping on or pecking at a treadle, the observer will only imitate the demonstrator if it observes the demonstrator being rewarded for its actions. However, it may be that the observer has not learned an association between the demonstrator’s response and the reward (an R–S association), but may instead have learned an S–S association between the treadle and food, making it more likely to respond to the treadle. The demonstrator’s action could then have been copied by ‘‘blind’’ contextual imitation, or, as noted above, response facilitation. In principle, researchers could distinguish observational R–S learning from other processes using a modified version of the two‐action test, where the observer sees a demonstrator respond to the same contextual stimulus with two different actions, one of which is rewarded, the other of which is not. If observers then preferentially imitate the action they saw rewarded then this constitutes evidence they have formed an R–S association by observation. Neither blind contextual imitation nor observational conditioning could account for this result. An alternative approach was used by Saggerson et al. (2005), who used a devaluation procedure to test for the formation of R–S associations in the observer. Observers first observed demonstrators stepping on a treadle to receive a reward of grain, lit by a red light, and pecking at the same treadle to receive a reward of grain lit by a green light (or vice versa). Devaluation training consisted of separate presentations of each of the lights, red and green, one of which was paired with food, whereas the other, devalued stimulus was not. The intention was that the birds would value the devalued outcome less than the alternative because the former would be a less reliable indicator of food presentation. When observers were subsequently given access to the treadle, they preferentially responded with the observed action leading to the nondevalued outcome, suggesting they had indeed formed an association between an action and its outcome through observation. As noted above (Section III.G.1), this finding is at odds with McGregor et al. (2006), who found evidence that pigeons would ‘‘blindly’’ imitate an unrewarded demonstrator on a similar two‐action task. However, McGregor et al.’s findings are also consistent with response facilitation, whereas such effects are eliminated in Saggerson et al., because each observer saw both peck and step actions during the observation phase. Saggerson et al.’s findings constitute the most convincing evidence of observational R–S learning of a reward contingency.

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Mason (1988) provides a suggestive case where the observer may have learned a punishment contingency. In his experiment, red‐winged blackbirds learned not to feed from a distinctive container after having observed demonstrators doing so and becoming ill. However, this could be accounted for by excitatory‐aversive observation conditioning, where the observer learns an S–S association between the container and illness. There are a number of similar examples that can be accounted for in the same way (Johnston et al., 1998; Klopfer, 1957; Lore et al., 1971). H. EMULATION To show that an observer has acquired a novel skill through emulation, researchers need to show that it has recreated the results of a demonstrator’s actions, rather than reproduced those actions themselves. One way of testing for this is to expose the observer to the results of the demonstrator’s actions, without allowing them to observe the actions directly. If vocal imitation is viewed as a case of emulation (Byrne, 2002), there is probably more evidence for emulation than any other social learning process, with a wealth of evidence that birds and other animals can imitate sounds (Janik and Slater, 1997; Slater, 1986). However, here we focus on the evidence that animals can learn an action by recreating the movements of objects that they observe moving when the demonstrator acts on them. There are cases suggestive of emulation from two‐action test experiments, where an observer has recreated the movements of the experimental apparatus, after seeing a demonstrator manipulates that apparatus (e.g., Campbell et al., 1999). The most suggestive is provided by Whiten (1998), in his ‘‘artificial fruit’’ experiment, where chimpanzee subjects had to remove three ‘‘defenses’’ in order to get to the food inside the container, each of which could be removed using one of two different actions. The ‘‘bolts’’ could be removed by poking them through in one direction or by twisting them out in the other, two actions that resulted in different movements of the apparatus. Here, it was found that observers tended to use the same methods as the demonstrator. The ‘‘pin’’ could be removed by spinning it without gripping it with the fingers, or by gripping it and turning it. These actions resulted in the same movement of the apparatus, and suggestively, observers did not use the action they saw demonstrated more often than the alternative action, suggesting that observers were emulating the demonstrator. This hypothesis is supported by the fact that one of the subjects was seen to copy the general form of the poking action unit, but showed variation in her performance of the action unit, varying the digit used to poke and also using her knuckles and finger joints. Again, an alternative explanation is that chimpanzees were imitating at a low level of resolution.

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In such ‘‘suggestive’’ cases, emulation is practically indistinguishable from contextual imitation. One potential solution is to expose the observer to the ‘‘disembodied’’ or ‘‘ghost’’ movements of experimental apparatus (Whiten and Ham, 1992) and to test whether it recreates the movements it observed. Denny et al. (1983, 1988) showed that after observing a pendulum bar that moved to the right, followed by food presentation and to the left, followed by no food, or vice versa, rats would tend to push the bar in the direction they had seen followed by food. This is strong evidence of an emulation effect. Klein and Zentall (2003) made a similar finding for pigeons, but the effect has not been replicated in other species (e.g. Fawcett et al., 2002). However, as noted above, spontaneous movements of objects could be of little interest to animals (Byrne, 2002). Animals observe many spontaneous movements in their natural environment, for example, branches moving in the wind, which they do not try and recreate. It may be that animals do emulate object movements in general, but only when certain rules are met (e.g., when that object is touched by a conspecific). If that were the case, it would be unlikely that a disembodied movement task would yield positive results. An alternative approach is to manipulate the salience of the object movements involved in a task. For example, in a task involving manipulation of a pendulum, one could modify the thickness of the pendulum. If the observers are imitating the demonstrator, then this should have no effect, whereas if they were emulating, we would expect observers to be more likely to recreate the movements of the more salient, thicker pendulum. Another alternative would be to use a modified two‐action test, where experimental apparatus is devised that can be operated in four different ways, each of which results in one of only two possible movements of the apparatus. For example, one could provide a lever that can be twisted or pushed down in order to obtain a food reward, and each of these movements can be accomplished in two ways, using the teeth or using the hand. Observers would then observe one of four possible demonstrations: twist lever with hand, twist lever with teeth, push lever down with hand, and hold lever in teeth and push down. If observers copied the exact manner in which the demonstrator solved the task, this would be evidence that it was imitating the demonstrator. However, if only the movement was copied, this would be evidence that it was emulating the demonstrator. We are unaware of any studies in which the two‐action test has been modified to isolate action‐based cues and results‐based cues in this way. As a result, researchers are currently left with just two strong candidate cases for emulation of object movements, in rats (Denny, 1983, 1988) and pigeons (Klein and Zentall, 2003). We suggest it is worth considering at this stage whether the dichotomy between imitation and emulation is useful, at least with respect to action learning. It is typically assumed that imitation and emulation are the results

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of different underlying mechanisms. Imitation is seen as the result of a high‐ resolution mechanism that enables observers to translate the movements of a demonstrator to yield the motor program for executing matching movements. Emulation is viewed as the result of a mechanism by which the observer forms a template of the results of a demonstrator’s behavior, and attempts, by trial and error, to match that template with its own behavior. However, the latter mechanism could also be used to account for some cases of imitation because the observer could attempt, by trial and error, to match its own body movements to a remembered template of the demonstrator’s body movements. In addition, imitation may not occur at the movement level, but, as noted above, might involve learning a novel sequence of action units. If this were true, each action unit would be recognized in the demonstrator’s behavior by observable cues characteristic of that action unit, which could include both body movements and environmental effects. The emulation controls in the experiments described above only really act to isolate imitation at the movement level of resolution. If the above reasoning is correct, there is a strong case to be made that the imitation/emulation dichotomy be dropped. Instead, one could consider the actions and results of a demonstrator’s behavior as two different sources of information that can be used by an observer. Call and Carpenter (2002) suggest a similar framework where the observer can use three sources of information: actions, results, and goals. However, because an observer’s understanding of a demonstrator’s goals is not directly observable, we suggest it might prove profitable to concentrate our efforts on results and actions. In our suggested framework, production imitation can be based on action‐based cues, results‐based cues, or a combination of both. Vocal imitation is production imitation, based solely on results‐based cues; the sounds created by the demonstrator. When the observer also learns the context in which to use a specific vocalization (Kroodsma, 1988; Reiss and McCowan, 1993; Spector et al., 1989), it also qualifies as contextual imitation. Other cases, namely, Whiten’s (1998) artificial fruit experiment on chimpanzees and Moore’s (1992) studies of an African grey parrot are also production imitation, but are probably based on a combination of action‐ and results‐based cues.

IV. CONCLUSIONS To attain a full understanding of social learning in nature, researchers need to gain insight into the underlying psychological processes. We have attempted to facilitate this objective by providing a classification scheme of social learning processes using definitions based solely on observable

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criteria. In the light of this scheme, we have then assessed the evidence that each process is in operation. Despite extensive research, there seem to be few experimental studies of social learning where researchers can isolate exactly which type of social learning process is operating. In the case of the supposedly ‘‘simpler’’ social learning mechanisms, this is perhaps because little effort has gone into distinguishing local and stimulus enhancement from observational conditioning. It is often assumed that if an animal can learn where to find food through observation then it is a case of local enhancement, though observational conditioning could account equally well for the phenomenon. We encourage researchers to follow the example of recent studies (e.g., Leadbeater and Chittka, 2007) in taking steps to distinguish between these ‘‘simple’’ processes. Early efforts to detect animal imitation were hampered by disagreement over what imitation is. We feel a distinction between ‘‘contextual’’ and ‘‘production imitation’’ (Byrne, 2002) goes some way to resolving such conflicts. A major problem with isolating contextual imitation is that, until recently, investigations have usually ignored the possibility that a response facilitation effect could account for a positive result in the two‐action test. Detecting a clear‐cut case of production imitation has also proved problematic because in many cases, production imitation is likely to be indistinguishable from emulation. This problem is exacerbated by the fact that candidate cases appear to be a mixture of imitation and emulation. We advocate that the dichotomy between imitation and emulation be dropped with respect to action learning, and instead consider results and actions as two sources of information that can be used in imitative learning.

Acknowledgments WH was supported by a BBSRC postgraduate studentship and KNL by a Royal Society university research fellowship. We are grateful to Celia Heyes, Patrick Bateson, Dick Byrne, Thomas Bugnyar, and two anonymous reviewers for their helpful comments on an earlier manuscript, and Jeff Galef, Rob Honey, Patrice Adret, Peter Slater, and Vincent Janik for helpful discussion.

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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Function and Mechanisms of Song Learning in Song Sparrows* Michael D. Beecher departments of psychology and biology, university of washington, seattle, washington 98195, usa

DEDICATION *Dedicated to my colleagues in the song sparrow research over the years (in order of appearance): Phil Stoddard, Liz Campbell, Patti Loesche, Cindy Horning, John Burt, Michelle Elekonich, Cully Nordby, Adrian O’Loghlen, Chris Hill, Suzanne Bard, Brendan Reeves, Tim Billo, Chris Templeton, and Caglar Akcay.

I. INTRODUCTION A. BACKGROUND The use of elaborate acoustic vocalizations, or song, in intraspecific communication is common in a wide variety of animal groups (Searcy and Andersson, 1986). In the oscine passerines (songbirds), song has an additional, intriguing aspect: it is learned. By present estimates, vocal learning has evolved independently in the birds, cetaceans, bats, and primates, and within the birds, independently in the songbirds, hummingbirds, and parrots (Jarvis, 2004). From the evolutionary point of view, the songbirds are particularly interesting because of the amazing variety of song‐learning patterns that have been discovered within this group of 4000‐odd species (Beecher and Brenowitz, 2005; Kroodsma, 1988, 1996). Within the primates, on the other hand, vocal learning appears to be confined to a single species, humans, and its presence in our species is the second reason for the fascination with song learning in the songbirds: its many parallels with human language learning. The parallels first recognized were: an early sensitive period, a perceptual filtering mechanism tuned to species communication signals, the key role of auditory feedback in normal development, a 167 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00004-1

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temporal separation between sensory and motor learning, and a subsong or babbling stage (Marler, 1970a; Nottebohm, 1970). These parallels have helped stimulate the study of the songbird neural song control system, which has become a major vertebrate model system for the study of neural plasticity (Doupe and Kuhl, 1999; Jarvis, 2004; Konishi, 1985; Marler, 1991; Nottebohm, 1984). In most songbirds, song functions in the contexts of intrasexual competition and mate attraction. In most territorial temperate‐zone passerines, only males sing and the major intrasexual context is the defense of the territory (review in Catchpole and Slater, 1995) with song functioning as a long‐distance signal to ‘‘post’’ the territory and to communicate with neighbors in negotiating territorial boundaries. I will confine my discussion in this chapter to the case where only the male sings, but for more on cases where the female sings as well, see the recent review by Riebel (2003). Evolutionary questions about song learning in songbirds focused originally on the adaptive advantages of learning versus not learning song, that is, on the origin of song learning in the oscine line (Nottebohm, 1972). As comparative studies of songbirds have accumulated, however, the focus has shifted to the evolution of different song‐learning programs, the inferred genetic‐ developmental programs underlying particular patterns of song learning observed in a species, or a race or a population of a species (Beecher and Brenowitz, 2005; Kroodsma, 1978, 1983, 1996; Marler and Peters, 1988a; Nelson, 1999; Slater, 2003). The song‐learning program specifies how learning proceeds and the critical features of the learning environment, for example, how long the sensitive period stays open; how many songs the bird keeps for his final repertoire; whether the bird imitates tutor songs or improvises on them, or invents new songs; whether the bird requires early exposure to conspecific song; how constrained the bird is to copy only songs that fit species‐specific parameters; the key social variables to attend to; and so on. B. SOCIAL FACTORS IN SONG LEARNING In this chapter, I focus on social factors in song learning. Early studies of song learning in the songbirds explicitly excluded social factors. There were both theoretical and experimental reasons for doing so. The original theoretical conception of song learning was derived from the classical ethological concept of imprinting, translated into the song‐learning context by Thorpe (1958) and then fully developed in the experiments of Marler and his colleagues (e.g., Marler, 1970b). By analogy to the classical imprinting studies, it was supposed that the key stimuli for song learning would be very basic, processed by species‐specific filtering mechanisms, and that learning would occur during an early ‘‘sensitive period.’’ This view provided

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the rationale for the tape tutor experiment, in which all aspects of the species‐ and population‐typical song‐learning context were removed except song. Besides fitting the theoretical view, the tape tutor experiment also indisputably provided more experimental control than would be possible were actual birds the song tutors. From this spartan experimental paradigm have come many important generalizations about song learning, including the concept of the sensitive period for song memorization, and the species‐ specific stimulus filtering mechanism for species song (sometimes referred to as the ‘‘innate template’’). In his classic series of tape tutor experiments, Marler (1970b) showed that to develop normal song, a white‐crowned sparrow male must hear conspecific song during an early sensitive period (roughly the second month of life); the bird will reject heterospecific song heard during this period, as well as conspecific song heard after the sensitive period. The tape tutor paradigm has generated most of what we know about song learning, and has been particularly valuable in identifying the sensory mechanisms that guide and constrain it (e.g., Soha and Marler, 2000, 2001). Workers in the field became aware of the importance of social factors in song learning, however, with the discovery that birds learned more readily from live tutors than from tape‐recorded song (Baptista and Petrinovich, 1984, 1986; Chaiken et al., 1993; Clayton and Pro¨ve, 1989; Cunningham and Baker, 1983; Kroodsma and Pickert, 1984a,b; Kroodsma and Verner, 1978; Nicolai, 1959; Payne, 1981; Price, 1979; Rice and Thompson, 1968; Thielke, 1970; Waser and Marler, 1977). Moreover, some of the rules of song learning derived from tape tutor studies appeared to bend, if not break, when the song tutors were actual birds. For example, whereas tape tutor studies had indicated that the sensitive period for white‐crowned sparrows closes at
50 days, and that heterospecific songs are uniformly rejected (Marler, 1970b), Baptista and Petrinovich (1984, 1986) showed that if a young white‐crowned sparrow was exposed to a tape tutor through 50 days and thereafter exposed to a live tutor, the young bird would learn the song of the live tutor, and in some cases would do so even if he were a heterospecific tutor. Consider another example: In our study species, the song sparrow, young birds stop learning new songs from tape tutors in their natal summer by the time they are 3–4 months old (Marler and Peters, 1987), whereas they continue to learn new songs from live tutors into the fall and perhaps the following spring, when they are 5–9 months old (Nordby et al., 2001). This difference, like many of the conflicting results from live and tape tutor experiments, has a significant confound and thus an alternative interpretation; in this particular case, the critical confound may be with differences in the song‐learning programs of eastern and western song sparrows (discussed below in Section D). But despite such problems of interpretation and considerable debate (Baptista and Gaunt, 1997; Nelson, 1997, 1998),

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there is consensus on the point that ‘‘the social stimulus of a live adult is a more potent stimulus during song development than is the presentation of songs through loudspeakers alone’’ (Casey and Baker, 1993, p. 723). While the live versus tape tutor contrast inevitably suggested the importance of social factors, the results of the tape tutor experiments themselves, especially the studies of Marler and Peters on ‘‘overproduction’’ in swamp sparrows, have provided additional motivation to search for social factors. In particular, Marler and Peters’ (1981, 1982a,b) finding that a swamp sparrow memorizes (and subsequently sings during the plastic song phase) more songs than he keeps for his final repertoire (overproduction) highlights the question: How does the bird select his final songs from the many he has heard? And this leads naturally to the question concerning the natural song‐learning context: How does the bird choose his song tutors? Finally, field studies have also provided a major impetus to the study of social factors. Although field studies cannot provide the experimental control of a laboratory study, they naturally bring into focus the social variables that are controlled out of laboratory experiments. To the question of when song learning occurs, field studies have added the questions of where and from whom, and have given a new context for the questions of how many, which ones, and how accurately (Kroodsma, 1978). Researchers doing the first field studies on song learning noted that learning appeared to occur later than indicated by the classical tape tutor studies, post‐ rather than pre‐ dispersal, so that birds wound up learning songs not from their father and birds in the natal area, but from birds in the area where they would breed, often their neighbors of their first breeding season (e.g., Bewick’s wrens, Kroodsma, 1974; saddlebacks, Jenkins, 1978; white‐crowned sparrows, Baptista and Morton, 1982; indigo buntings, Payne, 1982). Although this interpretation has been disputed (for reviews of this dispute, see Kroodsma et al., 1984; Payne and Payne, 1997), these field studies stimulated attempts to incorporate social factors into accounts of song learning. The field studies of our group on song sparrows (Beecher et al., 1994b; Nordby et al., 1999, 2002, 2007) have also pointed to the importance of social variables, and will be considered in detail in Section III. Despite the problems raised by field studies and by experiments with social tutors, the basic findings of the classical tape tutor experiments have not yet been firmly contradicted in any species. In particular, although the sensitive period for song learning may extend much further into the first year for some species than was originally thought, for no species does it appear to be true that song learning is equally possible or equally likely at all points during the bird’s life. Moreover, even if a powerful heterospecific social tutor can overcome it, the preference for conspecific song invariably found in tape tutor experiments does suggest some form of tuning for

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conspecific song. Nevertheless, comparative studies of songbird species have revealed an amazing diversity in song‐learning patterns, both between species and between different populations of the same species, and this diversity should warn us not to take any particular pattern of song learning, for example, that shown by white‐crowned sparrows, as ‘‘typical’’ or ‘‘fundamental’’ (reviews in Beecher and Brenowitz, 2005; Catchpole and Slater, 1995; Kroodsma, 1978, 1983, 1988, 1996). For example, in contrast to the ‘‘classic’’ song‐learning pattern of the white‐crowned sparrow, song learning in some oscines occurs long after the first few months of life, and indeed a bird may add songs throughout the lifetime; individuals may learn heterospecific songs in some circumstances; song learning may consist more of invention and improvisation than of simple imitation; the memorization and production phases of song learning may overlap extensively; song may develop apparently normally in the absence of any song tutoring; and so on (Beecher and Brenowitz, 2005). Figure 1 summarizes some of the important dimensions of variation in the song‐learning programs of songbird species.

1. When song is learned

Natal summer

Throughout first year

2. How many songs a bird learns

Single song

Small to mod rep size

3. Need song tutoring?

Song abnormal w/o

4. Copying fidelity

Imitation (faithful copying)

5. Degree of canalization

Rejection of heterospecific material

Throughout lifetime

>100 songs

Song normal w/o Improvisation (using tutor material)

Invention (require tutoring?) Tendency to copy almost anything (mimicry)

FIG. 1. Major dimensions on which the song‐learning programs of songbird species differ. (1) When song is learned or how long the song repertoire is modified: from a sensitive period early in life to throughout the lifetime. (2) How many songs a bird learns: from a single song to over a thousand (with small to moderate repertoire sizes being the rule). (3) Effect of isolation from song in early life: from birds that produce normal species song to birds that produce grossly abnormal song. (4) Copying fidelity: from imitation (faithful copying of tutor song) to improvisation (using tutor material) to invention (which may or may not require song tutoring). (5) Degree of canalization: from rejection of heterospecific material to ability to learn almost anything (mimicry).

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A nice example of a difference in song‐learning programs was provided by a comparison of song learning in two closely related emberizine sparrows by Marler and Peters (1988a,b). Using the tape tutor method, they showed that song sparrows will sometimes copy heterospecific elements, especially if they are embedded in song sparrow like syntax, whereas the congeneric swamp sparrows will not, regardless of the syntactical context. This example is but one of many illustrating the fact that the different songbird species show many different patterns of song learning. Although we are a long way from a comprehensive account of song learning in the songbirds, in the end such an account will have to deal with this diversity.

II. STUDIES OF SOCIAL FACTORS IN SONG LEARNING Despite the gathering consensus on the importance of social factors in song learning, we have little understanding of how social variables shape song learning. The numerous comparisons of live versus tape tutors mentioned above are usually indirect (often across different studies), and only a very few studies have actually attempted to analyze social factors. As Nelson has pointed out, it is not at all clear what precise aspects of social stimulation influence song development, and indeed even whether the effects are ‘‘truly social’’ (Nelson, 1997). A major goal of our research group over the past 20 years has been to uncover and analyze the social factors in song learning in the song sparrow (Melospiza melodia). Before turning to that research, in this section, I consider other research on this topic, and in the following section, several new theories about how social factors may operate in song learning. Researchers have taken two rather different approaches to analyzing the role of social factors in song learning. In one approach, researchers manipulate large‐scale social context settings, typically in large aviaries. The best example of this kind of research comes from the West‐King group studying cowbirds, and the Eens and Hausberger groups studying starlings (e.g., Eens, 1997; King et al., 2005; Poirier et al., 2004). The usual manipulation consists of setting up different kinds of social groups, and then contrasting differences in song learning in these groups. For example, the comparison can be between subjects housed with adult males versus those housed without, or housed with females from the same population versus females from a different population (King et al., 2005; White et al., 2002). The conclusions that come from these studies tend to be rather broad‐brush, and often pertain to more general behavioral competencies. I do not attempt to summarize these studies in this chapter.

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The other approach has been more analytic, using simpler social situations and trying to isolate key variables. This approach has produced few conclusions to date, probably because there are only a small number of social variables one can actually manipulate when the song tutors are live birds. You can vary how close the tutor is to the subject, whether he is visible to the subject, whether the two can physically interact, and so forth, but you cannot manipulate the actual behavior of the tutor (including his singing) except in fairly gross ways (e.g., with a testosterone implant). To date, most of these studies have focused on one or another of the following four hypotheses. According to the Aggression hypothesis, young birds learn more from aggressive song tutors. Payne (1981), for example, found that 1‐year‐old captive indigo buntings were more likely to copy songs of an adult they could interact with directly than those of an adult they could hear and see but not interact with directly. However, while aggression was observed, it was not isolated from other variables relating to social contact. Similarly, Clayton (1987) housed male zebra finches with two adult males from 35 days post‐hatching on, and found that the subjects learned preferentially from the adult that showed more aggression toward them. On the other hand, Casey and Baker (1993) found that when aggressive adults were the only available song tutors, young white‐crowned sparrows not only failed to copy their songs but also developed abnormal songs. According to the Contingency hypothesis, the key element in social interactions is that the tutor song is contingent on some behavior of the young bird. Several studies have used a variation on the tape tutor design where an arbitrary response of the bird (e.g., a key peck) triggers song presentation. Despite some promising early studies, however, a recent replication study by Houx and ten Cate (1999) found no difference in the learning of contingent versus noncontingent song. According to the Visual Signal hypothesis, it is the live tutor’s visual presence that makes him more effective than the tape tutor. However, most of the studies that have actually manipulated visual exposure, while controlling other facets of social interaction, have failed to support this hypothesis. In the zebra finch, exposure to a visual model of an adult male paired with song playback does not enhance learning relative to exposure to playback alone (Bolhuis et al., 1999). Moreover, zebra finch fledglings prevented from seeing by eye patches still learned from a tutor in the same cage (Adret, 2004). In the next section, I discuss our experiments indicating that the visual component is not critical in song learning for song sparrows. Finally, considering human parallels, it is worth noting that blind children learn language with no difficulty. In summary, there is minimal support at the present time for any of these hypotheses about possible social factors in song learning, although the

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problem at this point is less an accumulation of negative evidence as it is a lack of evidence (negative or positive). In the next section, I consider three theories concerning social factors that are somewhat orthogonal to the hypotheses just considered.

III. DEVELOPING THEORIES OF SONG LEARNING The tape tutor versus live tutor controversy can be viewed from another, purely theoretical angle. Although this is rarely discussed, the tape tutor and live tutor paradigms implicitly suggest different models of the nature of the song‐learning process. The tape tutor paradigm implies that song learning is essentially a process of overhearing or simple eavesdropping on a singing adult. In contrast, the typical live tutor setup—with the young bird stationed close to a singing adult bird—implies that the fundamental process involves direct interaction of the older bird (song tutor) with the young bird. However, as Marler has noted (Marler and Peters, 1988b), both experimental setups are potentially ‘‘unnatural’’: We do not know if in nature the young bird learns from a song tutor who is singing solo and out of sight (as implied by the tape tutor design), or from a song tutor who is up close and interactive (as implied by the typical live tutor design), or, perhaps, in some other way altogether. The major attempt to reconcile the conflicting views generated by the tape tutor and live tutor paradigms has been a theory proposed by Nelson and Marler (1994). According to this theory, song learning occurs in two phases. In the first phase of song learning, the young bird memorizes many songs during the natal summer, many more songs than he will ultimately keep for his final repertoire. In the second, ‘‘action‐based’’ phase of song learning, typically occurring early in the following spring, the bird counter‐ sings with his new neighbors as he tries to establish a territory, and selects from his earlier‐ memorized songs those that best match the songs of the birds he is now interacting with. Thus, the Nelson–Marler theory incorporates the implicit models of both the tape tutor and live tutor paradigms: The early, memorization phase of song learning follows the ‘‘simple eavesdropping’’ model, while the later ‘‘action‐based’’ phase conforms to the ‘‘direct interaction’’ model. Although the Nelson–Marler theory is consistent with the results of a number of laboratory and field studies (e.g., Nelson, 1992; Nordby et al., 2007), to date, there is no direct field evidence concerning the nature of the social interactions that occur during the presumptive ‘‘action‐based’’ learning phase (or for that matter, of those that may or may not occur during the presumptive ‘‘memorization’’ phase). Here I propose a third model, based in part on recent research on social eavesdropping (Peake, 2005), which indicates an alternative way in which

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social interaction might affect song learning. The central idea is that the young bird learns by eavesdropping, not on solo singing, but on singing interactions between two or more birds. Recent field experiments on songbirds have shown that males base their decisions on whom to challenge and females base their decisions on whom to mate with on information about the dominance relationship of the singing males, information which they extract when eavesdropping on singing interactions (Mennill et al., 2002, 2003; Naguib and Todt, 1997; Naguib et al., 1999, 2004; Otter et al., 1999; Peake et al., 2001). In the species studied to date, song overlapping, song‐ type matching, and/or song leading seem to be the critical cues signaling dominance (Kunc et al., 2006; Mennill and Ratcliffe, 2004; Naguib, 1999; Peake et al., 2005). We hypothesize that young birds too may use information they extract from singing interactions they overhear to decide which songs to learn or retain; dominance would likely be one important dimension. The only field study to date that relates dominance to song learning is Payne’s field study of the African village indigobird (Payne, 1985). He found that village indigobirds typically copied the song of the dominant bird in the area. Also relevant is the ‘‘social modeling’’ theory, as developed by Pepperberg, which suggests that observation by the young bird of communication interactions between individuals who have mastered the communication system may be critical for vocal learning (Pepperberg, 1985). There is a second unique type of information a young bird could extract from the interactive singing (counter‐singing) of two adults that he could not extract from solo singing of these same birds: contextual information relating to singing rules concerning the appropriate replies to particular songs in particular contexts. We discuss singing rules we have observed in song sparrows in Section IV.B below. In the study of bird song learning, the focus has always been on the learning of particular songs rather than the learning of how to use them, but the two processes may be intertwined. This is the case for human language learning of course, and the notion that this might be true for a songbird as well provides additional rationale for testing the Social Eavesdropping hypothesis. A key prediction of the Social Eavesdropping hypothesis—true regardless of whether dominance relationships or song reply rules are the key factor—is that a young bird who needs to interact with a new neighbor may select for his final repertoire not just the songs of that individual, but songs the young bird has heard other birds singing to that individual as well (or instead). The previous discussion can be summarized in terms of three hypotheses about the social nature of song learning. These hypotheses are illustrated in Fig. 2. These hypotheses are not mutually exclusive, but they are eminently testable.

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3 Tutor 2

2

1

Tutor 1

Student FIG. 2. Schematic representation of the ways in which the young bird could extract information for song learning, illustrating three different hypotheses concerning the song‐learning process. Direction of arrowhead indicates the direction of singing. (1) Direct Interaction: The young bird learns songs of (or retains songs he memorized early that are most similar to those of) tutors he directly interacts with, counter‐singing between tutor and student being critical. According to the Nelson–Marler theory, such interaction, usually involving song matching, is characteristic of the late phase of song learning. (2) Simple Eavesdropping Hypothesis: The young bird learns songs simply by listening to a bird sing, and no interaction is required. According to the Nelson–Marler theory, this pertains only to the early phase of song learning. (3) Social Eavesdropping Hypothesis: The young bird preferentially learns songs he overhears in counter‐singing interactions between other birds. A young bird attending to both sides of the interaction can extract two unique types of information that he could not extract from solo singing of these same birds: contextual information relating to social dominance and song reply rules (see text).

IV. SONG FUNCTION AND SONG LEARNING IN SONG SPARROWS A. RESEARCH PROGRAM The goal of our research program has been to understand the role of song in the song sparrow’s natural ecology and the function and development of song learning in this species. Song learning has traditionally been investigated in the laboratory under tightly controlled conditions. As noted earlier, the bulk of what we know about song learning comes from studies in which young birds are isolated from conspecifics at or near to hatching, and tutored by tape‐recorded songs delivered from a loudspeaker. Clearly that approach is at the least incomplete if social factors play a role in song learning. On the other hand, our ability to understand the mechanisms of song learning is unlikely to be successful if we restrict ourselves to inference from field observations, no matter how good these observations may be. Because of the inevitable trade‐offs between analytical rigor and ecological

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validity, a complete picture can be gained only by combining the differing perspectives of field and lab, and of observation and experiment. For this reason, in our research program, we have taken a wide range of approaches, including field observation, field experiments, ‘‘semi‐natural’’ lab studies, and more controlled lab studies (Fig. 3). In the next sections, I summarize our major findings from these various studies. B. BACKGROUND

Analytic -------------------------------------- Observational

Our study species is the song sparrow (Melospiza melodia), a common species found throughout North America. A male song sparrow typically has 6–12 distinct song types in his song repertoire (Peters et al., 2000). A typical song repertoire in our population is shown in Fig. 4. Female song sparrows do not sing (except under rare circumstances, see Arcese et al., 1988). A song sparrow sings his song types with ‘‘eventual variety,’’ that is, he repeats songs of one type many times (a ‘‘song bout’’) before switching to a new type. In ‘‘free’’ singing (i.e., when the bird is singing solo, not interacting with another bird), a song sparrow uses the different types interchangeably and with approximately equal frequency, although his pattern of singing is different when counter‐singing with a neighbor (see

Long-term field observation: From whom/where/when do birds learn their songs? How does song affect reproductive success? Radio-tracking birds in their first year.

“Semi-natural”(roof) expts: From whom/where/when do birds learn their songs? Live tutor expts: What are the critical social variables?

Field playback expts: How do bird use their songs?

Virtual tutor expts: What are the critical social variables?

Field ------------------------------------------------------------------------ Laboratory

FIG. 3. A schematic representation of our research program. Note that field studies can be observational or experimental, and lab studies too can range from observational ‘‘semi‐natural’’ studies to more analytic experiments.

Frequency (kHz)

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8 6 4 2 1s Time

FIG. 4. The song repertoire of one male. Frequency (vertical) scale: 0–10 kHz, markers at 2 kHz intervals. Songs are 2–3 s long.

below). Within a bout of one song type, a song sparrow sings variations on the type, but this intra‐type variation is small compared to inter‐type variation, and a song type may be considered as a class of very similar songs (Fig. 5, Nowicki et al., 1994; Podos et al., 1992; Stoddard et al., 1988). A particular song X is classified as belonging to song type class A rather than song type class B if (1) it is structurally or perceptual more similar to A; (2) the bird sings X in bouts of A rather than in bouts of B; and (3) in response to playback of song X, the bird responds with a song from the A class rather the B class. Although the song types in an individual’s repertoire are as distinct from one another as are the song types of different birds, as just suggested, neighboring birds will often share some of their song types. As we will see, these similar song types can be traced to a history of song learning, that is, one bird having learned from the other, or both having learned from a third bird, or some other history of song learning (the more links in the chain, the less similar the songs will be). Examples of song sharing are shown in Fig. 6 (for two neighbors) and Fig. 7 (for three neighbors). The song sharing in Fig. 6 is close enough that, for the three shared types shown, one of the birds was likely the song tutor of the other bird. Figure 7 shows several songs where the sharing is not so close, and illustrates that the criteria the investigator uses

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kHz 8 B1

A1 2 1s

B2

B3

C1

B4

D1

B5

E1

B6

FIG. 5. A partial repertoire of one male illustrating the larger differences between types (contrast A through E) and the smaller differences among variations within a type (a natural sequence of 6 B variations is shown). Note that variations in the B’s are mostly in the latter part of the song (typical): The terminal trill is dropped in B2, and replaced with a different trill in B6, and the middle trill is replaced with a different trill in B3. Other small changes can be detected, for example, in the number of elements in a trill.

to declare two songs ‘‘shared’’ are necessarily arbitrary. The ultimate criterion for sharing, and one that can be applied only on occasion, is that the two birds behave as if they perceive the songs as shared, as when they ‘‘type‐ match’’ each other with their shared songs (discussed in the next section). Song sharing is very local, with birds more than four or five territories apart rarely sharing song types. This pattern of neighbor song sharing— which has been observed in many different songbird species (see Catchpole and Slater, 1995)—will occur when young birds learn the songs of the neighborhood into which they immigrate following natal dispersal. A young song sparrow usually begins singing subsong in the late summer or early fall, but does not sing adult‐like song until the following spring. He

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Frequency (kHz)

Bird A

Bird B

8 6 4 2 1s Time

FIG. 6. Partial song type repertoires of two song sparrow subjects. Birds A and B were neighbors and shared the first three songs in their nine‐song repertoires (33% sharing). The shared songs of birds A and B are shown in the top three rows, while six of their remaining unshared types are shown in the bottom three rows.

usually crystallizes his repertoire by early or mid March, shortly before the breeding season begins in earnest. He does not add or delete songs from his repertoire after his first breeding season (Nordby et al., 2002).

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yygm

bymp

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ppom

FIG. 7. Partial repertoires of three neighboring song sparrows: yygm, bymp, and ppom. Each row shows shared songs. To be considered shared, two songs had to match at least half of their component phrases. In borderline cases, we put more weight on the more invariant early portions of the song and less on later parts of the song. The number of elements in the phrase was generally disregarded, as this is a feature that the bird often varies from one rendition to another (e.g., see fourth shared song, middle phrase, following the buzz: in these renditions, yygm has five elements, bymp has three elements, but the phrase is considered the same because the component elements are the same). A borderline case of sharing is the sixth shared song: The two songs differ in terms of the initial paired elements and the end phrase. The middle three phrases (buzz, trill, and high sweep) are the same, so the song is considered more than half similar.

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C. HOW SONG SPARROWS USE THEIR SONGS 1. Background: Song Matching Many studies have shown that birds will, at least under some circumstances, reply to a shared song with the same song type: ‘‘song matching,’’ or more precisely, type matching (reviews in Krebs et al., 1981; McGregor, 1991; Smith, 1991). Most workers have viewed type matching as the bird’s way of directing his response at the bird who has just sung. A puzzling finding has been that higher rates of type matching occur when the stimulus song is the bird’s own song or a song of a stranger that is similar to one of the bird’s own songs, than when it is a shared neighbor song (song sparrows, McArthur, 1986; Stoddard et al. 1992a; western meadowlarks, Falls 1985; great tits, Falls et al. 1982). Song sparrows, for example, match neighbor song only at about chance (
10%) level (Stoddard et al., 1992a). We were able to shed some light on this finding in the course of a series of ‘‘playback’’ studies which I describe next. In our playback experiments, we simulate a neighbor singing to the subject from near their mutual territory boundary (Fig. 8). In a key study in this series, we tested established song sparrow neighbors (mostly birds who had been neighbors for several years) in the mid to late breeding season. While these birds did not type match the broadcast neighbor song, they did consistently reply with some other song type they shared with that neighbor (Beecher et al., 1996). That is, if the two birds shared three songs in their repertoires, say A, B, and C, but no others, then a bird would reply to a neighbor’s A with B or C rather than A or any of his unshared songs. We have called this pattern of song selection repertoire matching. We hypothesized that repertoire matching represents a response that is directed but less intense than type matching. Song sparrows, like most songbirds, respond more aggressively to a song of a stranger than to one of an established neighbor, at least when the song comes from within that neighbor’s territory (Stoddard, 1996; Stoddard et al., 1990, 1991). Thus it seemed plausible that a song sparrow might respond aggressively to a brand new neighbor, type matching shared songs, but as the neighbor became better established (‘‘Dear Enemies,’’ see Section V.C.2) the antagonism would diminish and type matching be succeeded by repertoire matching. To test this hypothesis, we compared a bird’s response to the song of a neighbor that was new that breeding season. We did this twice during the breeding season: early, in April, and again a month and a half later (Beecher et al., 2000a). Early in the breeding season, new neighbors will have only recently established their territorial boundary, which may still be in dispute, and territorial skirmishes will have occurred recently or may still be occurring. A new neighbor singing at the boundary early in the season

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Subject’s territory

FIG. 8. The goal of our typical playback experiment is to measure the response of the subject to songs of his neighbor. We simulate a neighbor singing by broadcasting his song into the subject’s territory from a directional speaker (fitted with a parabolic reflector). In all of the experiments discussed in this chapter, this ‘‘playback’’ speaker was placed just inside the neighbor’s territory, near the two birds’ mutual territory boundary. The neighbor generally does not hear the broadcast song (‘‘playback’’) unless he happens to be close, because the speaker is highly directional. However, he may hear the subject singing in response to the playback. For this reason, we either wait for the neighbor to be at the far back of his territory, or, better, one researcher lures the neighbor to that spot with low‐level playback of stranger song. Territories are big enough that birds interacting at one boundary generally will not hear interactions at the opposite boundary, especially if they are each interacting with their own (simulated) intruder. If, despite our precautions, the real neighbor intrudes during the 3‐min trial period, that trial is discarded and repeated on another day. We measure the intensity of the subject’s response to the playback and also what songs, if any, he sings. The song playback can be noncontingent, that is, begun at some point when both birds are relatively quiet, or it can be contingent on the subject’s response (‘‘interactive playback’’). In the latter case we might, for example, wait for the subject to sing a shared song, then present him with a type match (the neighbor’s version of the same song type), a repertoire match (a different neighbor song they share), or a song they do not share (we observe the subject’s song and compare it to his neighbor’s song repertoire on our field computer). Measures of response strength include closest approach to the playback speaker (generally the best measure), number of flights (these are generally short, as the subject flits about looking for the intruder), latency to respond, and number of threat displays (soft songs and wing waves). Number of (normal) songs is generally not correlated with other measures of response intensity, probably because highly aggressive birds generally stop singing or switch to soft song. Nevertheless, we are typically interested in which particular songs the subject sings in response to the playback.

thus represents a more serious challenge than a well‐established neighbor singing at that same boundary and we predicted higher levels of type matching on the earlier occasion. As predicted, early in the season, birds usually replied to a shared neighbor song with a type match, whereas a month and a half later they usually replied with a repertoire match (in this experiment they never responded with unshared song either early or late). These results are consistent with the hypothesis that type matching is a more aggressive or escalated response than repertoire matching.

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In two subsequent playback studies (Beecher and Campbell, 2005; Burt et al., 2001), we made additional discoveries about how song sparrow neighbors use their shared and unshared songs when interacting. First, type matching is a threat, that is, a bird responds more aggressively when type‐matched by a neighbor than he does when repertoire‐matched by him. Second, a bird that has been type‐matched will escalate by staying on type (rather than switching to a new type) and responding aggressively. Third, a song sparrow can de‐escalate an interaction by replying to a type‐matching neighbor with an unshared song: Birds depart the scene sooner if the simulated opponent sings an unshared song than if he sings a repertoire match. Fourth, a bird responds to neighbor song sooner if it is a shared song than if it is an unshared song, which suggests that shared song is a directed signal. The results of our playback studies taken together suggest that song sparrow neighbors (1) communicate preferentially with songs they share, (2) threaten by type matching, (3) send directed but less threatening signals by repertoire matching, and (4) de‐escalate maximally by replying with an unshared song (or by not singing and leaving the scene). Thus our results suggest that when neighbors share some song types and not others, they may use their shared songs to mediate their territorial and other interactions as described in the next section. 2. Singing Rules in the Intrasexual Context For a territorial species such as the song sparrow, the benefit of interaction by singing (counter‐singing) is that it can substitute for more costly forms of negotiating territory boundaries, such as fighting. For example, if two male song sparrows cannot resolve the issue at a distance with song, they approach one another, cease singing, and switch to visual displays and ‘‘soft song,’’ a low‐volume, structurally very different type of vocalization (Searcy et al., 2006), and then often to fighting. In species with song repertoires (about three‐quarters of all songbirds), the singer has a ‘‘choice’’ of which of his various song types to sing. How do they make these choices? As indicated in the previous section, we have used experimental ‘‘playback’’ studies in which we simulate a neighbor singing near the subject’s territorial boundary to develop a picture of the singing rules used by territorial male song sparrows which seem to play an important role in maintaining neighbor relations and territorial boundaries (Beecher and Campbell, 2005; Beecher et al., 1996, 2000a; Burt et al., 2001, 2002; Stoddard et al., 1988, 1990, 1991, 1992a). The key is how the birds use their shared songs. As noted above, song sparrows in our population typically share some but not all of their songs (typically they share two to five of their eight to nine songs), and they use the subset of shared types in a graded communication system. Some experience with one

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another is required for the two birds to learn the subset (a bird will share different subsets of his repertoire with different neighbors), but presumably they acquire this knowledge over the course of counter‐singing bouts. Our studies have established that song sparrows in our population communicate according to the following rules. Consider two hypothetical neighbors with 10 songs each who share three songs—A, B, and C—but no others. Bird 1 can ‘‘address’’ bird 2 by singing one of their shared types A, B, or C (toward bird 2, since other neighbors may also share some of the types). Let us say bird 1 sings A. Bird 2 then can ‘‘acknowledge’’ the signal by replying with B or C (repertoire match), or escalate by replying with A (type match), or de‐escalate by singing one of unshared types, or ignore by not singing at all. If bird 2 type matches bird 1 (sings A), bird 2 can escalate further by continuing to sing that song type, or he can de‐escalate by switching to another shared song (repertoire matching), or de‐escalate further by switching to an unshared type. Note that to type match requires no prior experience with your opponent—the bird simply replies with his most similar song, and generally the ‘‘match’’ will be perceptually obvious—but to reply with a shared song, or with an unshared song, the bird needs to have had some experience with his neighbor: The bird needs to know which songs they share and which they do not. These ‘‘singing rules’’ are summarized in Figs. 9 and 10.

A B C

A B C D E F G H

A

U W X Y Z

Type match Repertoire match Non-shared

FIG. 9. A diagram of two neighbors who share three of their eight songs each (shared song types indicated by the same letter). The bird on the left begins the interaction by singing one of their shared songs, A. The bird on the right can reply by singing A (a type match, which escalates the interaction), B or C (repertoire matches, which are directed but less likely to escalate the interaction), or any one of his five remaining unshared songs (which generally de‐ escalates the interaction). Note that the diagram is not to scale: The birds would be fighting, not singing, if they were actually this close!

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Bird 1:

Bird 2:

Bird 1:

De-escalate

Escalate

Stays on same type and responds aggressively

Sings shared song

Sings type match

Sings type match

Sings repertoire match

Switches to a different song

Sings unshared song

Switches to a different song

FIG. 10. Diagram of singing interactions between two neighbors who share some song types. Escalation is indicated by behaviors higher in the diagram, and de‐escalation by behaviors lower in the diagram. In this figure, the interaction begins when bird 1 sings a shared song type. Bird 2 can then type match bird 1 (escalate), repertoire match (a directed but neutral signal), or sing an unshared song (de‐escalate). If bird 1 is type matched, he may respond to the escalation by staying on the same type and responding aggressively (a further escalation) or de‐escalate by switching to another song type and not responding strongly. Aggressive response refers to searching for, threatening, or attacking the singer.

A similar pattern of singing has been observed in the banded wren (Molles and Vehrencamp, 2001), another territorial bird with neighbor song sharing. The hypothesis that having shared songs facilitates communication between territorial neighbors is one hypothesis for the function of a song‐learning program that leads to song sharing with neighbors. If the system of long‐distance communication described here is beneficial in resolving most disputes with minimal cost, then we should see birds with shared songs faring better than those without shared songs. In Section V of this chapter, I will consider this and several other hypotheses about the benefits of song sharing. 3. Intersexual Context As is the case for many songbirds, male song sparrows sing at a higher rate when they are unmated (unpublished data), and song clearly has a strong mate attraction role. Its role in close social interactions is more difficult to determine, and songbird researchers have generally resorted to

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using the copulation solicitation display as a preference assay (Searcy, 1992a). Searcy pioneered this technique with song sparrows, and showed that females preferred (gave more copulation solicitation displays) to playback of larger repertoires versus smaller repertoires (8 songs preferred over 4, 16 preferred over 8, Searcy, 1984). Searcy also found, however, that females showed no preference for larger repertoires in the field, when mate choice was measured by pairing date (Searcy, 1984). We have examined the role of song sharing in mate choice (O’Loghlen and Beecher, 1997, 1999). We tested responses of female song sparrows to songs of their mates and neighbors, as well as to songs that were similar to or different from the songs of their mates. Females gave more copulation solicitation displays to songs recorded from their mates, fewer to songs of neighbor males, and fewest to songs of ‘‘stranger’’ males (males several territories removed). Among the stranger songs, however, females responded more strongly to songs that were most similar structurally to types in their mates’ repertoires (matching songs). One interesting implication of the observed female preferences for neighbor over stranger song, and for matching stranger over non‐matching stranger song, is that any male with songs structurally similar to mate songs or even to non‐mate but local songs, might be at an advantage in sexual interactions with females in local neighborhoods. However, when we examined birds in our field population, we found no evidence that females preferred as extra‐pair partners males who had local‐shared songs (Hill et al., manuscript in preparation). Approximately 25% of song sparrow chicks in the sample were due to extra‐pair matings and the genetic father of the extra‐pair chicks was always a neighbor of the female and her social mate, but extra‐pair paternity was unrelated to repertoire size, the extent to which the extra‐pair mates shared songs with neighbors, or the extent to which the extra‐pair mates shared songs with the female’s social mate. These results are reminiscent of Searcy’s with eastern song sparrows, where laboratory preference tests suggested the importance of repertoire size but a field study failed to confirm this preference. D. FUNCTION OF SONG SHARING AND SONG REPERTOIRES Several studies on the correlates of song sharing and song repertoires have suggested that these traits may be advantageous for male song sparrows. In a longitudinal study of 45 song sparrows followed from their first year on territory, we found that the number of songs a bird shared with his neighborhood group in his first breeding season predicted his lifetime territory tenure (range 1–8, mean ¼ 2.82 years) but his repertoire size did not (Beecher et al., 2000b). We also found that song sharing increased with

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repertoire size up to but not beyond 8–9 song types, which are the most common repertoire sizes in the population (range in our sample, 5–13). This partial confound of song sharing and repertoire size may account for an earlier finding of territory tenure–repertoire size correlation in song sparrows (Hiebert et al., 1989), but see below. In another western population, this one in California, Wilson et al. (2000) found a positive correlation between the probability of a male song sparrow surviving and remaining on his territory from one year to the next and the number of songs the bird shared with adjacent neighbors. It has also been shown in this California population that song sparrows are less aggressive toward neighbors with whom they share songs (Wilson and Vehrencamp, 2001). In further studies of the Mandarte Island (B.C.) song sparrow population studied by Hiebert et al (1989), Reid et al (2004) found that first‐year males with larger repertoires were not more likely to acquire a territory, to acquire a larger territory, or to settle sooner. They were, however, more likely to mate, and their mates were more likely to begin laying earlier. Song sharing was not measured in this study, and because of the partial correlation of song sharing with repertoire size, we cannot disentangle these effects. One possible interpretation of the data on western song sparrows is that song sharing plays a key role in male–male competition while repertoire size plays a key role in mate choice. Later in this chapter, I discuss similar studies that have been carried out with eastern song sparrow populations and other species.

E. SONG LEARNING 1. Field Studies: Methods and Approach In hopes of identifying the key social variables in song learning for our study species, we began our investigations in the field (Beecher et al., 1994b; Nordby et al., 1999). We chose as our study population a sedentary (nonmigratory) population of song sparrows in an undeveloped 534‐acre park bordering Puget Sound in Seattle, Washington. We reasoned that if we color‐banded and recorded all (or nearly all) of the adult males in this population, we would then be able to identify the song tutors of all young birds entering the population in that year, provided of course that the young birds learned their songs after natal dispersal. Post‐dispersal learning is the typical pattern in songbirds (see review in Beecher et al., 1997). In our population, birds we have banded in the nest and subsequently recaptured within the population post‐dispersal (typically having dispersed some distance from the nest) sing song types of their post‐dispersal area rather than their natal area.

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Typically 100–150 song sparrow males are on territories in our study population in a given year. After a preliminary study with birds hatched in the years 1986–1990 (Beecher et al., 1994b), we carried out a full‐scale study of a cohort of 41 birds who hatched in 1992 and survived to song crystallization the following spring (Nordby et al., 1999). We considered as possible song tutors all older birds in the study population who were on territory in the subject’s hatching year. We identified the older bird with the most similar rendition of the type (complete with idiosyncratic features not seen in other renditions of the type) as the young bird’s ‘‘probable tutor’’ for that type. This judgment is rarely problematic, because song sparrow songs are complex and similar songs stand out on the background of the nearly infinite variety of possible song types. When two (or more) older birds had versions of a song that were highly similar to the young bird’s—not unusual in this population where neighbors share songs—they would both receive credit as the tutor for that song. We compared several different criteria, varying in strictness, for identifying a bird as a song tutor and happily they led to essentially the same conclusions, with the exception, of course, of the number of tutors identified (see discussion in Nordby et al., 1999). Although in recent years we have developed automatic, computerized methods for analyzing song similarity, these methods are faster but not as accurate as the judgments of human observers. Hence in our analysis of song learning, we have relied on the judgments of multiple experienced human observers. These judgments have been informed by our extensive field observations of singing in this species, by the results of our field playback studies (Beecher et al., 1996, 2000a; Burt et al., 2001, 2002; Stoddard et al., 1988, 1990, 1991, 1992a), and by our laboratory perceptual experiments (Beecher et al., 1994a; Horning et al., 1993; Stoddard et al., 1992b). These issues are discussed in detail in Beecher (1996). 2. Rules of Song Learning Inferred from Field Studies The results from our field studies are summarized below as ‘‘rules of song learning.’’ The bulk of these results come from Nordby et al. (1999), except those as indicated from Nordby et al. (2001, 2007) and Beecher et al. (1994b). Rule 1: Copy songs of conspecific singers. Song sparrows copy almost nothing but song sparrow song in the field, despite the occasional copy of a song or song element of a Bewick’s wren (personal observation) or white‐ crowned sparrow (Baptista, 1988). They will readily copy swamp sparrow song in the lab (Marler and Peters, 1988a), so it would appear their failure to do so in the field (except for on the odd occasion) implies a mechanism for selecting conspecific models.

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Rule 2: Complete song learning by first spring. As established in field studies below, song sparrows can memorize new songs at least into their first autumn (until they are 6 months old) and we suspect that they may be able to do so into their first spring. In the field, they at least continue to modify their repertoire (drop songs, rearrange elements in songs) into their first spring. But they do not appear to change their song repertoires after their first breeding season (Nordby et al., 2001). Rule 3: Copy song types completely and precisely. Young song sparrows often develop near‐perfect copies of the songs of their older neighbors. It is this fact that made us realize that we could trace song learning in the field. The song similarities can be striking, with the differences between tutor and student often being no greater than one normally sees in repetitions of the same song sung by one bird. These field results differ from the comparable laboratory findings of Marler and Peters (1987, 1988a) using tape tutors. In the laboratory setting, song sparrows copy song elements quite precisely, but they frequently combine elements from different songs to form what we will call ‘‘hybrid’’ song types—songs made up of parts of different song types. That is, they often copy song elements but use them to improvise new song types. Rule 4: Learn the songs of multiple birds. It typically takes three to five song tutors to account for the young bird’s entire repertoire of eight or nine song types. In Nordby et al. (1999), only 1 of the 41 subjects appeared to be a song ‘‘clone’’ of a single older bird. Rule 5: Learn from your neighbors. Invariably, a bird’s song tutors turn out to have been neighbors in the young bird’s hatching summer, and, if they survived the winter, the following spring (the young bird’s first breeding season) as well. The young bird usually establishes his territory within the territorial range of his song tutors, often replacing a tutor that died. One typical case is illustrated in Fig. 11. In the cases where the young bird does not establish his territory among his tutor‐neighbors, the evidence suggests that he did not because he could not—because none of his tutors had died and/or because other young birds moved into this area. The young bird appears to commence song learning shortly after he has dispersed from his natal area. Because adult males (potential song tutors) in our population typically will remain on their territories from one year to the next unless they die in the interim, it is essentially impossible for us to determine from field data when the young bird learns his songs. We originally thought that most or all of song memorization occurs in the traditional lab‐determined sensitive period, roughly the second and third months of life (Marler and Peters, 1987), but this was only a plausible guess and our lab studies have cast doubt on that assumption (see below).

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A

B

Natal Summer (1992) rmpy

rmpy

pmrg

ybym

brim

oogm

Spring (1993)

oogm

rwmo rmyy

pmrg

ybym

rmyy

pmyg

brim

pmyg

rwmo

airm ybrm bmrr

bibm

ybrm

oggm

oggm oimr = Tutor territory 50 m

bmrr

bibm oimr

= Subject territory = Gone by spring 1993

FIG. 11. (A) Territories of airm’s tutors in his natal summer (1992). Adult birds (potential tutors) are shown by their color bands (four‐letter codes) and their territories by dotted lines. The identified tutors of airm and their territories are shown by the dark hatching. (B) Territory of airm the following spring (1993), overlaid on the territories of summer 1992. Adult males who did not survive the winter are crossed out. Of the 13 adult birds shown, 8 out of 13 did not survive the winter; 4 out of airm’s 5 tutors did not survive the winter. (This is an unusually high mortality rate: overwinter survival is typically 60–70%.) Note that airm established his territory in an area overlapping the former territories of 3 out of the 4 deceased tutors and next to his one surviving tutor (oggm). The young bird shared songs with the surviving tutor, and with other young birds who moved into that area as they had similarly learned songs of the area.

Rule 6: Preferentially learn or retain song types of tutors surviving to your first breeding season. Birds often have song types that can be traced to tutors that were alive in the young bird’s natal summer but died before the next breeding season. Nevertheless, they generally retain more songs of tutors who survive into the next breeding season than of tutors who do not. We refer to this late learning as ‘‘late influence’’ because it may not be de novo learning: These songs could have been memorized in the natal summer and retained because the bird continues to hear them the following autumn and/ or spring. This would be the pattern hypothesized as typical by Nelson and Marler (Nelson, 1992; Nelson and Marler, 1994): The young bird memorizes songs during a sensitive period in the natal summer and the following spring, retains some of these songs and drops others on the basis of his social interactions with his territorial neighbors (‘‘selective attrition’’). We have recently compared the song repertoires of young song sparrows in the plastic song phase (late winter, early spring) and crystallized phases, and found that they do indeed retain songs that are more similar to those of their spring‐time territorial neighbors, while dropping some songs that are less similar (Nordby et al., 2007).

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Rule 7: Preferentially learn tutor‐shared songs. As noted earlier, in our field population neighbors typically share a portion of their song repertoires, on average about two to four of their eight to nine song types. We have found that the young bird preferentially learns (or retains) song types shared by two or more of his tutors (Beecher et al., 1994b). There are several possible reasons that shared song types might be particularly salient, including these: (1) they are heard more than unshared song types; (2) ‘‘the same song’’ is being sung by several birds; (3) they are heard more often in counter‐singing interactions than are unshared songs. The last possibility is considered further below. One interpretation of the function of the preference for tutor‐shared songs is that it represents a ‘‘bet‐hedging’’ strategy to guarantee that the young bird has song types he will share with his neighbors in his first breeding season. If instead the bird learned tutor‐unique songs, he would have songs ‘‘specialized’’ for these particular tutor‐neighbors (i.e., share these songs with one neighbor only). But these specialized songs would be good only until the tutor dies or moves, whereas a shared song is good until all the birds having it in the neighborhood die or move, and probably even longer because other young birds moving into the area also preferentially learn shared types. Rule 8: Individualize your song repertoire. The rules so far can be interpreted to fit the overall rule: Learn songs that you will share with your neighbors in your first breeding season. We have recently discovered, however, an important exception to that rule (Nordby et al., 2007). In the transition from plastic song to final crystallized song, the young bird often modifies a song so that it actually becomes a poorer match to the model song of the putative tutor and to similar songs of his present neighbors (who may or may not include the tutor). One example is shown in Fig. 12. We interpret this as the bird ‘‘individualizing’’ his song. The song may still be perceived by the birds as a shared song (even if it perhaps no longer meets our criteria for a ‘‘shared’’ song), while at the same time being perceived as his particular version of that song type. Thus the bird gets to have his cake and eat it too: to have songs that are both shared with his neighbors yet unique to him. Even should this interpretation prove correct, however, we have no hypothesis for why the bird individualizes some of his songs but not others. 3. Seminatural Lab Studies of Song Learning The next step after our field studies was ‘‘semi‐natural’’ laboratory studies, in which we retained some of the key features of the natural world—including multiple live tutors singing from spatially separated ‘‘territories’’—while we maintained some degree of experimental control, for

FUNCTION AND MECHANISMS OF SONG LEARNING IN SONG SPARROWS

Neighbor 10

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Subject Plastic

6 2 kHz Crystallized

FIG. 12. An example of a song ‘‘individualized’’ by a young bird. Plastic version (top right) and crystallized version (bottom right) of a song type that bird GEMO shared with his neighbor and probable tutor (top left). In the young bird’s plastic song (recorded in December and January), he has simplified the initial notes by dropping the upper part, but the rest of the song is a good imitation except for the wobbly pitch. Note that some of the differences between the prototype song of the adult and the plastic song of the young bird (top row) are within the normal range of variation on a type (e.g., three initial notes instead of two, one final note complex instead of two). Two small changes in the final, crystallized version, however, make the young bird’s song look fairly different from the adult’s song: GEMO has simplified the trill (left out one of the three notes in the repeated element) and has transposed the end segment to the middle of the song. It is likely that the two birds would nevertheless treat these songs as the same type (i.e., would type match or repertoire match as appropriate).

example, could move the young subjects nearer to or farther from particular tutors. We carried out two such studies (Nordby et al., 2000, 2001). In both of them, we used aviaries on the roof of our laboratory building to simulate natural field conditions. Four adult males who had been neighbors in the field, and who shared some of their songs, were stationed on ‘‘territories’’ at the four corners of the roof. The ‘‘territories’’ were small aviaries containing a potted tree and numerous additional perches. The males did in fact become quite territorial about these ‘‘territories’’ and counter‐sang with one another like song sparrows in the field. In both experiments, young birds were moved from territory to territory, as we believed they moved in the field (Arcese, 1987, 1989); we have recently begun radio‐tracking young song sparrows to get more direct information on their behavior in this phase. In our first experiment (Nordby et al., 2000), we hand‐raised eight young song sparrows and then put them through two phases of song learning. In Phase 1 (roughly June and July), they were moved from one tutor to

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another every few days, so that they got to see and hear each of the four tutors up close (one‐quarter of the time) and hear each of them at a distance as well (three‐quarters of the time). In Phase 2 (autumn and then again in the spring), the young subjects were split into one of two experimental conditions. For half of the subjects (‘‘stationary’’ group), a bird, when he was on the roof, was always next to a particular one of the four tutors. For the other half of the subjects (‘‘rotator’’ group), a bird, when he was on the roof, spent equal time next to each of the four tutors. Because the subjects themselves sing in this phase (unlike in the early phase), we had just four of them out at a time, with one each next to one of the four tutors. While one group of subjects was on the roof, the other group was in acoustic isolation. All subjects learned from multiple tutors, usually from three or all four. The pattern of learning between the two experimental groups was quite different however. Subjects who were next to just one of the tutors in the late phase preferentially retained songs of that tutor for their final repertoire (‘‘proximity’’ effect). In contrast, the subjects who continued to rotate between tutors in the late phase, all learned the most from one particular tutor of the four. That tutor was not the most popular tutor overall for the four ‘‘stationary’’ subjects, so the proximity effect trumped the tutor effect (whatever that tutor effect may have been). We cannot specify, of course, the key variable for the proximity effect: Did birds learn most from the tutor they were stationed next to because they were next to him, or because they could see him (the other tutors were 13–17 m away and could not be seen), or because they interacted with him more in some way, or for some other reason altogether? Our second experiment with live tutors (Nordby et al., 2001) examined the question of late song learning. We used the same basic layout on the roof, again with four adult tutors who shared some songs with one another, each stationed in his aviary ‘‘territory’’ in one corner of the roof; note that these were different tutors from the previous experiment. We again rotated the young birds among these tutors in the early phase (June and July). The major manipulation occurred in Phase 2 (fall and spring), which took up after the birds had had
2 months off (August and September, birds in isolation): We replaced two of the original tutors with two new tutors. Thus, there were six tutors from whom the young birds could learn songs: two were ‘‘permanent’’ (present in both phases), two were ‘‘early‐only’’ (Phase 1 only), and two were ‘‘late‐only’’ (Phase 2 only). The two ‘‘late‐only’’ tutors shared one song with each other but no songs with the two ‘‘early‐ only’’ tutors or the two ‘‘permanent’’ tutors. We expected the subjects to learn the most from the permanent tutors and least from the late tutors, though we also thought that the subjects might prune their repertoires as

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Marler and Nelson propose, retaining early memorized songs that were most similar to the songs of the late‐only tutors. For the late phase, the 12 subjects were divided into three groups (‘‘cohorts’’); while one cohort was on the roof with the tutors, the other two cohorts were in acoustic isolation. When a subject was on the roof, he was always next to the same tutor (like the ‘‘stationary’’ subjects of the previous experiment). The results of this experiment surprised us, and differed in several important ways from the results of the first experiment. Of the six tutors, one of the late‐only tutors was the most effective tutor, with over half the songs learned by the 12 subjects traced to him. Virtually no songs of the two early only tutors were retained. The effectiveness of the late‐only tutor occurred despite his late start (September 29), which is well past the end of classical critical period, and despite his never being close and visible to three‐ quarters of the subjects. Thus in this experiment, the tutor effect (whatever it may have been) trumped the proximity effect. This second seminatural experiment confirmed the results of our first one in showing that late experience (from autumn on) was critical in determining a bird’s repertoire. However much of the late learning in the second experiment was not mere selections of earlier‐memorized songs but in fact de novo learning of new songs. We have not pursued this question of how long de novo song learning is possible in song sparrows, and in particular, whether it can still occur in the spring, because we feel this question distracts from more interesting questions about song learning. But clearly the ‘‘late’’ (post‐summer) phase is a crucial time for song learning in song sparrows, and our subsequent experiments have typically been designed with the major experimental manipulation occurring in the late phase. This second study made two additional points. First, it showed that neither proximity nor visual contact was necessary for song learning. Second, it suggested that auditory interaction may be the critical variable in song learning. Although the experiment had not been set up to measure singing interactions, the song tutors were recorded for 1 h per day, and we observed that our supertutor—who accounted for
50% of all songs learned—was extremely interactive with other tutors, and perhaps with the young birds as well (our recordings were inadequate on the latter point). Although he also sang more than the other tutors, we had not found a song rate (‘‘dosage’’) effect on learning in the previous experiment, and so we speculated that the key factor may have been that he sang interactively: He was much more likely to reply (sing shortly after) another tutor’s song than were any of the other tutors. These conclusions—the first strong, the second admittedly speculative—were crucial to the design of our next experiments and to the development of our ‘‘virtual tutor’’ system.

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4. Analytic Live Tutor Studies Our next experiment with live tutors (Beecher et al., 2007) was less ‘‘natural’’ than our roof aviary experiments but we hoped that its stronger controls and experimental manipulations would permit stronger inferences than were possible from the roof experiments. We compared two types of song tutoring: that resulting from direct interaction with the song tutor, and that resulting from social eavesdropping, that is, overhearing the singing interactions of other birds. Again the major experimental manipulation occurred in Phase 2 of this experiment. Subjects were exposed to the songs of four tutors during the early ‘‘memorization’’ phase (Phase 1) of song learning and to just two of them again in the later ‘‘action‐based’’ learning phase (Phase 2). As noted above, our field studies and our roof aviary experiments both indicated that birds are more likely to retain songs for their adult repertoire that they heard in their natal summer if they are exposed to them again the following spring. Thus, we assumed that birds would learn more from the two tutors present during both phases than from the two tutors present only in the early phase. As in the previous experiments, our experimental manipulation was carried out in Phase 2. Of the two tutors returning in Phase 2, one became a subject’s interactive tutor, while the other became the subject’s overheard tutor, that is, was overheard interacting with another, yoked subject. This yoked design is illustrated in Fig. 13. That is, on day 1, subject 1 interacted with tutor BO, while subject 2 overheard their singing. On day 2, subject 2 interacted with tutor PP, while subject 1 overheard their singing. Similarly, on day 1, subject 3 interacted with tutor PP, while subject 4 overheard them, while on day 2, subject 4 interacted with tutor BO, while subject 3 overheard them. Subjects 1–4 were isolated for the next 2 days while subject pairs 5 and 6 and 7 and 8 went through this same sequence. We found that subjects learned (retained) more songs from their overheard tutor than from their interactive tutor (about twice as many on average). We ascertained that the subject learned songs of the overheard tutor, not of the overheard yoked subject, because the repertoire of a subject was no more similar to that of the yoked subject he overheard than it was to that of the non‐yoked subjects he never heard. Although many interpretations of this result are possible, we consider just two. First, it may be that birds learn more from eavesdropping on singing interactions (Social Eavesdropping Hypothesis, Section III) than participating in them themselves (Direct Interaction Hypothesis). Second, and this hypothesis seems likely to be complementary to the previous hypothesis, the overheard interactions may have been less threatening. The close, intense nature of the interactive tutor vis‐a`‐vis the subject may have been

FUNCTION AND MECHANISMS OF SONG LEARNING IN SONG SPARROWS

Subject 1

197

Subject 2

Day 1

BO

Day 2

PP

FIG. 13. Schematic representation of the yoked subject design (Beecher et al., 2007). In Phase 2, a subject was exposed to one tutor live, the interactive tutor, on day 1, and overheard a similar tutor–subject pair on day 2. For one‐half of the subjects, subject and interactive tutor were separated by a black cloth screen (not shown). Two birds, BO and PP, were used as tutors in this experiment. On days 3 and 4 (not shown), the young bird was returned to his home cage in a closed chamber. Note: The schematic representation does not show that the subject and interactive tutor were in their own separate cages within a larger sound‐insulated chamber.

intimidating, thus diverting his attention to the overheard, ‘‘more distant’’ songs. Of the overheard pair, the subject learned the songs of the tutor, the older and presumably more dominant member of the yoked pair, rather than of the young bird (who, at least in the beginning, was singing obviously immature, plastic songs). However, while the results of this experiment provide stronger support for the Social Eavesdropping hypothesis than the Direct Interaction hypothesis, it is far from a ‘‘strong inference’’ contrast of the two hypotheses. In the next section, we discuss our development of a methodology that will permit such tests. 5. Virtual Tutor Studies At the conclusion of our second seminatural (roof aviary) experiment (Nordby et al., 2001), we made the following proposal: We suggest that in future studies it may be profitable to try and simulate live tutors and key aspects of the natural social situation using tape tutors. The experiment could be set up so that the ‘‘tutors’’ interacted with one another from separate ‘‘territories’’ and, ideally, with the tutees during the plastic song phase as well (antiphonal singing, song matching, etc.). This simulation would capture some features of the natural conditions, including spatial separation of singing adult males, clear definition of song types via shared song types, and interactive singing . . .. We suggest that . . . how the tape tutor

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‘‘uses’’ its songs (e.g., to reply to, type match or overlap the tutee; to respond to some but not others of the tutee’s songs; to interact with the other tutors) will outweigh how often particular songs are played. (p. 844, Nordby et al., 2001).

In short, we envisioned a rapprochement of the tape tutor and live tutor paradigms, using recorded song in a sophisticated way to simulate live singing birds that could interact both with one another and with the subject as well. Digitized songs, computer programs, and powerful computers have made this possible, and we have dubbed the computer‐simulated song tutors we have developed ‘‘virtual tutors.’’ The virtual tutor method permits us to maintain the experimental rigor of the tape tutor paradigm, while capturing at least some of the key features of natural singing, especially interactive singing, as well as the ability to interact with the subject (Beecher and Burt, 2004). The virtual tutor software, designed by John Burt (www.syrinxpc.com), can be programmed to (1) sing solo, non‐interactively, that is, ‘‘posting mode,’’ similar to a tape tutor, or (2) sing interactively with the subject, or (3) sing interactively with another virtual tutor. In the interactive modes, it interacts as a live song sparrow would, using singing and song type selection rules that we have extracted from our field observations and playback studies (especially Beecher et al., 1996, 2000a; Burt et al., 2001, 2002), as discussed in Section IV.B.1 above. When interacting directly with the subject, the virtual tutor can identify what the subject is singing and reply appropriately. For example, if the subject sings a song that is similar to one of the virtual tutor’s, the virtual tutor can respond with that same song type (‘‘song matching,’’ see below). The repertoire of each virtual tutor is based on that of a real bird and consists of 8–10 song types, with 8–10 variations on each type (Podos et al., 1992; Stoddard et al., 1988). When more than one virtual tutor is used in an experiment, each virtual tutor’s songs are played from different loudspeakers in the subject’s chamber. Virtual tutors are fixed to particular loudspeakers to reinforce the impression that the subject is positioned between two neighbors. We realized that we might be able to capture—purely acoustically—the key features of normal singing interactions when the best tutor in our second roof aviary experiment taught songs to young birds who could hear him at a distance but not see him (Nordby et al., 2000, 2001). And in the live tutor experiment just described (Beecher et al., 2007), birds learned equally well from interactive tutors whether there was a blind between the young bird and the tutor or not (half the birds had the blind, half did not). Several other experiments with songbirds have also indicated that the tutor does not have to be seen to be effective (Adret, 2004; Bolhuis et al., 1999).

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Our first studies with the virtual tutor program were pilot studies to see how well it would work (Burt et al., manuscript in preparation). In the first study (Burt et al., unpublished), hand‐raised birds were exposed to virtual tutors only; the virtual tutors were interactive among themselves but did not attempt to match the subjects. The young subjects developed songs like birds in the field: they copied whole songs, copied multiple tutors, and preferred tutor‐shared songs. In a second study (Burt et al., unpublished), we collected birds from the field in August when they were 3–4 months old (exact age unknown) and had presumably had ample song tutoring in the wild (details of course unknown). Lab tutoring with four virtual tutors began in late October, when the birds were at least 5 months old. The singing of the virtual tutors was interactive, both with one another and with the subject (though they did not attempt to match the song). All subjects developed normal repertoires, with about half of the songs drawn from the late virtual tutors. This is impressive considering that the subjects did not hear the virtual tutor songs until they were 5 months old, and not until after several months of song tutoring by real birds in the wild. In another experiment (Burt et al., 2007), we used a hybrid design: In the early phase (early summer), young birds were exposed to two pairs of interacting live tutors on alternate days. In the second phase (January through March), the subjects were isolated and exposed to digitized songs from two of these tutors (i.e., they were now ‘‘virtual tutors’’), one tutor from each of the two original pairs. On alternate days, a subject heard songs from each of the two tutors; for each subject, one of the tutors was interactive, and the other was noninteractive (i.e., was just heard singing solo). The amount of song from each subject’s interactive and noninteractive tutors was equated. Subjects learned (or retained) more songs from the late interactive tutor than from the late noninteractive tutor. Interestingly, subjects also learned/ retained more songs from the early singing partner of the late interactive tutor. This implies that the young bird remembered the singing interactions he heard the previous summer and selected his songs from those he overheard sung to his present interactive tutor 6 months earlier. Thus these results, like the results of the live tutor experiment (Beecher et al., 2007) discussed in the previous section, also point to the importance of overheard singing interactions, though in this case the overheard interaction had occurred in the early phase rather than in late phase of learning. The results of these two experiments taken together suggest that social interaction may indeed be critical for song learning, but in both cases, it appears that the key social interaction was an overheard one. We are presently carrying out a virtual tutor experiment in which subjects are exposed to two sets of virtual tutors (each tutor is given his own loudspeaker, and apparent territory direction). One pair of tutors is

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interactive, interacting in accordance with the ‘‘western song sparrow’’ rules described above, and the other set is noninteractive (they sing the same number of songs as the other pair, but they never interact). This study will provide a strong test of the Social Eavesdropping hypothesis, according to which the subjects should learn more songs from the interactive pair than from the noninteractive pair.

V. DISCUSSION A. HYPOTHESES ON THE FUNCTION OF SONG AND SONG LEARNING In this chapter, I have focused on the song‐learning program of one particular species, but as noted in Section I, perhaps the most impressive aspect of the song‐learning programs of songbirds is the incredible interspecific diversity. One cannot discuss the function of a song‐learning program without some hypothesis about what it is supposed to accomplish, and in this section, I describe some of the many hypotheses of song function in the literature. Most functional hypotheses of song have focused on the adult song repertoire, and have not directly addressed the song‐learning strategy by which the bird reaches that final repertoire. That is, these hypotheses about song function have to be extrapolated to hypotheses about the function of the underlying song‐learning program. Moreover, most of these functional hypotheses have focused on either the intrasexual or the intersexual context and ignored the other (Beecher et al., 1994b; Kroodsma and Byers, 1991). In some cases this narrow focus may be justified. For example, in sedge warblers, males sing incessantly until they have attracted a mate, and then stop singing altogether (Catchpole, 1976), indicating that the intersexual function of song predominates. But in most species, the evidences suggest that song functions in both intrasexual and intersexual contexts. Most hypotheses of song function have had a narrow focus in a second respect, namely, in viewing song repertoires or song sharing to be the target of selection. For this reason, I will refer to these two classes of theories hereafter as the Repertoire and Sharing hypotheses. According to the Repertoire hypothesis, the song‐learning program functions to give the bird a large song repertoire, while according to the Sharing hypothesis, it functions to give the bird songs he shares with his neighbors or group members. Although these two goals are not incompatible, song sharing does not require a large repertoire, and in some cases may favor smaller repertoires (see below).

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1. The Repertoire Hypothesis There is considerable empirical support for the hypothesis that repertoire size is under strong directional sexual selection in some songbirds (reviews in Catchpole and Slater, 1995; Gil and Gahr, 2002; Searcy and Andersson, 1986; Searcy and Yasukawa, 1996), and in some cases the evidence suggests that repertoire size is driven by female choice. The sedge warbler, just mentioned, provides one example. In laboratory studies, Catchpole and colleagues have shown that females give more copulation solicitation displays to large repertoires than to small repertoires (Catchpole et al., 1984), and in field studies they have shown that females choose large‐repertoire males before small‐repertoire males (Buchanan and Catchpole, 1997). However in a recent study of this species, it was found that contrary to prediction, extra‐pair males had smaller song repertoires (and smaller territories) than the female’s social mate. Other studies have suggested that in the polygynous great reed warbler, large‐repertoire males get more mates than do smaller‐repertoire males (Catchpole, 1986) and more extra‐ pair matings as well (Hasselquist et al., 1996). However, a recent study and reanalysis of the earlier data (Forstmeier and Leisler, 2004) have indicated that the harem size–repertoire size correlation disappears when corrections are made for territory quality and male age (repertoire size increases with age), suggesting that when there is enough competition for territories, older, larger‐repertoire males get the better quality territories, and females choose mates on the basis of territory quality. Although song sharing also varies in this population, it has not been measured in these studies. In comparative studies of four species of warblers, Catchpole found that repertoire sizes are larger in monogamous species (including the sedge warbler) than in polygynous species, which he explains as due to more intense female choice in the monogamous species; he argues that females choose territories in the polygynous species and males in the monogamous species (Catchpole, 1980). In contrast, in a comparative analysis of eight species of North American wrens, Kroodsma found the reverse correlation between mating system and repertoire size: Polygynous species had larger song repertoires than did monogamous species (Kroodsma, 1977). Kroodsma suggests, however, that male–male competition, rather than female choice, may be the factor driving increases in repertoire size (Kroodsma, 1988). The generality and applicability of the Repertoire hypothesis is seriously limited, however, by the fact that the majority of songbird species have just one or a few songs. About 30% of species have single‐song repertoires, and at least another 50% have very small repertoires (fewer than five or so song types), hardly the acoustical equivalent of the peacock’s tail. Small repertoires can perhaps be explained as resulting from the high costs of large

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repertoires, with the costs generally hypothesized to relate to the demands on brain space made by repertoires and/or the effects of stress on the development of song repertoires (Garamszegi and Eens, 2004; Gil and Gahr, 2002; MacDonald et al., 2006; Nowicki et al., 1998; Pfaff et al., 2007). This hypothesis is supported by the finding by Buchanan et al. (2004) that brain centers involved in song learning are selectively affected by developmental stress in zebra finches. This finding also raises the question, however, of whether large‐repertoire species would be more affected by this cost than would small‐repertoire species such as the zebra finch. Finally, phylogenetic studies suggest that song repertoires have been lost in some lines, for example, in emberizine sparrows and blackbirds (Irwin, 1988, 1990). In some of these single‐song species, repertoires appear to be disfavored despite a female preference (measured by copulation solicitation display tests in the lab) for large (supernormal) song repertories (Searcy, 1992b). These questions, taken with the prevalence of small‐ repertoire (including one‐song) species, suggests that we should consider alternative hypotheses that posit selection pressures on some aspect of song other than repertoire size. 2. The Song‐Sharing Hypothesis One advantage of plasticity is that a bird can copy the songs of group mates or territorial neighbors, and thus ‘‘share’’ songs with them. Most studies that have examined neighbors for song sharing in repertoire species, or song similarity in single‐song species, have found greater similarity between neighbors than between non‐neighbors (e.g., marsh wrens, Verner, 1975; bobolinks, Avery and Oring, 1977; indigo buntings, Payne, 1982; village indigobirds, Payne, 1985; tufted titmice, Schroeder and Wiley, 1983; great tits, McGregor and Krebs, 1982; corn buntings, McGregor and Thompson, 1988; field sparrows, Nelson, 1992; Smith’s longspur, Briskie, 1999; yellow warbler, Beebee, 2002; and see reviews in Catchpole and Slater, 1995; Handley and Nelson, 2005). There are other populations, however, where birds share no more with neighbors than with other birds in the population (e.g., chaffinches, Slater and Ince, 1982; western meadowlarks, Horn and Falls, 1988; Kentucky warblers, Tsipoura and Morton, 1988; Gambel’s white‐crowned sparrows, Nelson, 1999; and of especial interest here, most eastern song sparrow populations, Hughes et al., 1998). Some of these exceptions to the rule may be Type‐II errors, that is, failures to detect a difference (neighbors songs are more similar than non‐neighbors songs) that is actually present. This kind of error is possible given the very conservative sharing criteria used by most researchers. As noted earlier, Nelson and Marler (1994) have argued that in many populations, especially migratory populations, birds may memorize songs from one set of birds in their

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natal summer and then, in the following spring, select from this large pool of songs those songs that best match the songs of the totally different birds who are now their neighbors. This process would produce populations where neighbor song sharing might not pass the song‐sharing threshold applied by our group or the Searcy–Nowicki group, for example, or indeed by most song researchers, but might be detectable by someone looking for these subtle similarities. An excellent example has been provided by Nelson (1992). Field sparrow males return to the breeding area with two or three songs; Nelson showed that they retained the one that most closely resembled the song of the most actively singing neighbor (although the song similarities might not have satisfied the usual song‐sharing criteria). In any case, the degree of song similarity between neighbors will surely be greater in populations where birds learn their songs directly from their neighbors than in cases where they follow the more indirect Nelson–Marler selection process. Song sharing is found in a variety of social contexts, not only in territorial neighbors (the most commonly studied context) but also in lekking species and in communal breeders (Brown and Farabaugh, 1997; Payne and Payne, 1997; Trainer, 1989). This ubiquity of song sharing has led some workers to suggest that song sharing may be the most general function of song learning; this idea has been stated most strongly by Brown and Farabaugh: ‘‘vocal learning has evolved to allow individuals to share vocalizations with a particular subset of conspecifics, such as territorial rivals or flock mates, rather than with any conspecific’’ (1997, p. 99). Although the Sharing hypothesis might seem to have a better chance at broad generality than the Repertoire hypothesis given that song sharing among neighbors has been found in the majority of species that have been carefully examined, a problem for the Sharing hypothesis is the rival, more parsimonious hypothesis that birds learn songs that are more similar to those of their neighbors or group members simply because these are the birds they happen to encounter in the song‐learning phase. If the cast of neighbors (or group members) remains reasonably stable after the song‐ learning phase, the young bird will wind up sharing songs with these neighbors (or group members). This more parsimonious hypothesis means one must identify additional design features or make additional predictions that distinguish between the hypothesis that song sharing is adaptive and the simpler hypothesis that is merely an incidental consequence of song learning in a stable neighborhood (or group) context. We return to this point below. The first proposed Sharing hypothesis was the Genetic Adaptation hypothesis (Baker, 1975; Nottebohm, 1970). According to this hypothesis, males learn songs and females develop their song preferences before

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dispersal. Thus the male’s song dialect is a reliable indicator of his natal area and the female uses the male’s song dialect as a means to choose a male from the same natal area as herself, thus presumably gaining a mate with the same local (genetic) adaptations as herself. This theory requires (1) an adaptive correlation between geography and population genetic structure, (2) a predictive correlation between dialect and population genetic structure, and (3) female choice of mates who sing the local dialect; a parallel hypothesis could be developed concerning male–male competition, although no one has done so to date. There is little direct support for the Genetic Adaptation hypothesis. Two recent studies of white‐crowned sparrows give conflicting results, with one group (MacDougall‐Shackleton and MacDougall‐Shackleton, 2001) finding a weak correlation of dialect and population genetic structure in California populations of Zonotrichia leucophrys oriantha and another group finding no such correlation in Oregon and Washington populations of Z. l. pugetensis (Soha et al., 2004). Finally, there seems to be an underlying logical problem with the Genetic Adaptation hypothesis: Would not a female do better to base her choice on a signal that more directly reflects the local genome, rather than on a learned signal which can be (and often is) learned far from the natal area? Nevertheless, it is possible that a learned signal could be a better predictor of geographic origin than a signal with a direct genetic basis (e.g., Boehm and Zufall, 2006), and with the increasing sophistication of genetic methods, better data sets relating to this hypothesis may be on the way. The other attraction of this hypothesis is that a female preference for local songs might be favored both for the direct benefits—a local male might be more competitive and a better parent (see Badge hypothesis below)—and for the indirect benefits (locally adapted genome). The Mimicry hypothesis of Craig and Jenkins (1982) focuses on the male–male competition context. According to this hypothesis, immigrant or first‐year birds entering the population mimic the songs of the established territory‐holding birds to gain an advantage in establishing their own territories. The advantage presumably arises from the newcomers being confused with established birds and thereby receiving reduced aggression. Craig and Jenkins also argued that a song repertoire is a counter‐adaptation to mimicry, that is, an established bird could maintain his individuality by singing more song types than the newcomer could mimic. The Mimicry hypothesis has received little support. It is inconsistent with the many experiments showing well‐developed individual recognition in songbird species with song sharing (review in Stoddard, 1996). Moreover, direct tests have failed to support the hypothesis (e.g., McGregor and Krebs, 1984; Payne, 1983). In a playback experiment that tests this hypothesis about as directly as can be done, Wilson and Vehrencamp (2001) compared

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the aggressive response of song sparrows to (1) neighbor songs, (2) stranger songs that were highly similar to (i.e., ‘‘mimicked’’) neighbor songs, and (3) non‐mimic stranger songs. The birds responded just as aggressively to mimic stranger songs as to non‐mimic stranger songs, and more aggressively to either than to neighbor songs. The Badge hypothesis has been formulated most explicitly by Rothstein and Fleisher (1987) and more informally in several other papers (Beecher et al., 1997; Brown and Farabaugh, 1997; Wilson and Vehrencamp, 2001). According to this hypothesis, shared songs serve as a badge of familiarity in groups and among territorial neighbors. In the case of territorial neighbors, the Badge hypothesis is closely associated with the Dear Enemy hypothesis (Fisher, 1954; Getty, 1987; Jaeger, 1981; Temeles, 1994; Ydenberg et al., 1988), which suggests that long‐term neighbors should be preferred to new neighbors because new neighbors are inherently expansionist, whereas old neighbors generally respect territory boundaries once they have been established. Beletsky and Orians (1989) have shown that male red‐winged blackbirds with familiar neighbors have greater breeding success than do males with unfamiliar neighbors. Neither preferring nor cooperating with familiar neighbors requires shared songs, of course, but shared songs are a reliable signal of familiarity or locality since they must be learned in the local neighborhood. Another variation on the Badge hypothesis has been offered recently by Lachlan et al. (2004). The companion version of the Badge hypothesis is that females prefer local males because of various advantages they have over immigrant males, and learned area songs are a reliable signal of a background in the local area. This hypothesis is similar to the Genetic Adaptation hypothesis, but more general as to the proposed mechanism of the local advantage. Familiarity with the local area is generally thought to provide numerous advantages to an individual (Davis and Stamps, 2004). Thus, it might benefit a female to choose a male singing local song (a reliable signal that it has been in the area for some time) over a male singing nonlocal song (reliably signaling that it has arrived only recently). The Badge hypothesis is also different from the Genetic Adaptation hypothesis in assuming that song is learned after dispersal from the natal area rather than before (though if dispersal is not far, this distinction could be irrelevant). 3. Are the Repertoire and Sharing Hypotheses Mutually Exclusive? A tendency among bird song researchers that has bedeviled the field has been to focus on one particular context (e.g., mate choice or male–male competition) or one particular song trait (e.g., song repertoires or song sharing) or one particular species. But multiple selection pressures may act on song and they may act on several different aspects of song. For

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example, in the case of western song sparrows, the results of the studies cited earlier might be taken to indicate—if some inconsistencies are ignored—that female choice selects for large song repertoires and male–male competition (in the context of territory acquisition and maintenance) selects for song sharing (Beecher et al., 2000b; Reid et al., 2004; Wilson et al., 2000). While this interpretation is probably still too simplistic, it at least is a step up from the viewing selection as acting on either repertoire size or on song sharing. The next step will be to consider other song traits that might be the target of selection. Song performance is one such candidate. Songs can be difficult or costly to sing, and the ability to perform songs well might be especially attractive or salient to receivers, possibly providing information as to the quality of the singer. An example that is likely relevant to song sparrows is the trade‐off between trill rate and frequency bandwidth, first pointed out by Podos (1997): Songs with simultaneously fast trill rates and wide bandwidths are difficult to sing. Although I do not consider this variable further in this chapter, interest in performance as a important characteristic of a bird’s song has increased in recent years (e.g., Ballentine et al., 2004; Beebee, 2004; Byers, 2007; Cramer and Price, 2007; Forstmeier et al., 2002) and seems likely to enter the fray as a serious hypothesis for explaining song evolution. Finally, these different selection pressures may compete, pushing song traits in different, and sometimes opposite directions. That selection for song sharing and selection for large song repertoires are at least partially contrary is a simple logical consequence of the fact that a song‐learning strategy cannot optimize both traits. Song learning designed to maximize the number of songs copied from a set of birds (e.g., present neighbors) cannot also maximize the percentage of songs shared with this or a similar set of birds (e.g., the future neighbors). The bird that learns just those songs shared by his tutor‐neighbors will necessarily have both a smaller repertoire and a higher sharing index than will the bird who learns all of their songs. If the function of the song‐learning program is the acquisition of a repertoire of songs shared with certain key individuals, it is generally true that this goal can be met with a relatively small repertoire. Thus, the Sharing hypothesis could explain the prevalence of small repertoire species, while specifying a counter‐force that might hold down repertoire size in species with intermediate‐sized repertoires. The Sharing hypothesis provides a novel perspective on the difference between birds that do not modify their repertoires after their first year (aged‐limited or closed‐ended learners) and birds that do (open‐ended learners). If the function of the song‐learning program is give the bird songs that

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are similar to those of his neighbors, then an open‐ended learner could add and drop songs each year so as to increase song sharing with his new neighbors. Such a pattern has been observed in several species so far examined (Lemon et al., 1994; Martens and Kessler, 2000; McGregor and Krebs, 1989; Rasmussen and Dabelsteen, 2002; Sorjonen, 1986; Trainer, 1989). A similar result has been found for birds that change their song in the beginning of their first or second breeding season (Briskie, 1999; O’Loghlen and Rothstein, 2002; Payne and Payne, 1997). Moreover, the optimal repertoire size should be smaller for open‐ended learners than for comparable closed‐ended learners, because open‐ended learners have the opportunity to replace non‐matching songs with matching songs. Contrariwise, a closed‐ ended learner does not have the ability to change his repertoire to increase sharing, but if he has more songs to begin with, he has a better chance of coming up with a suitable match. Supporting this prediction, open‐ended learners that replace songs so as to increase sharing have been found to have smaller repertoires than comparable closed‐ended learners (Griessmann and Naguib, 2002; Kipper et al., 2004; Lemon et al., 1994; McGregor and Krebs, 1989; Payne and Payne, 1997). If the most general prediction of the Sharing hypothesis is that the song‐ learning program should equip the birds with songs that he shares with his neighbors, then what is the best song‐learning strategy in populations where neighbors change within as well as between breeding seasons? Kroodsma (1996) has argued that for birds without long‐term neighbors, there is no advantage to shared songs, and so the development of generalized species‐ typical songs will be favored. The sedge wren (Cistothorus platensis) provides a test of this prediction. Northern populations of sedge wrens are migratory and during the breeding season they are seminomadic as well. Thus even in the breeding season, they have a constantly changing set of neighbors. These sedge wrens show a unique pattern of song learning in tape tutor experiments: They do not imitate tutor songs but rather improvise songs (different but derived from the tutor songs) or invent songs (totally new), all of them normal species songs (Kroodsma and Verner, 1978; Kroodsma et al., 1999a). Consequently, each bird winds up with a repertoire of unique songs, and two neighbors in the field (who probably will not be neighbors for long) will share no song types. In contrast, the closely related but sedentary marsh wrens faithfully copy tutor songs in comparable experiments, and in the field they share songs with their neighbors (Kroodsma and Pickert, 1984a; Verner, 1975). Furthermore, tropical populations of sedge wrens are sedentary, in contrast to the seminomadic northern populations, and this tropical sedge wrens show the common pattern of song sharing with neighbors that is generally taken to imply song learning from neighbors (Kroodsma et al., 1999b).

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B. THE SONG SPARROW SONG‐LEARNING PROGRAM: AN ADAPTIVE STRATEGY? We have made good progress in characterizing the song‐learning program of song sparrows in our study population, identifying eight ‘‘rules’’ that taken together might be considered an adaptive learning strategy (Section IV.D.2). These are: (1) Learn songs preferentially from conspecific singers. (2) Complete song learning in the first year. (3) Copy song types completely and precisely. (4) Learn the songs of multiple birds. (5) Learn from your neighbors. (6) Preferentially learn or retain song types of those tutors who survive into your first breeding season. (7) Preferentially learn tutor‐shared songs. (8) Individualize (at least some of the songs in) your song repertoire. Taken together, Rules 3–7 work to maximize the number of songs the young song sparrow will share with his neighbors, especially his near neighbors, in his first breeding season, while maintaining some individuality in his songs. They are consistent with the Sharing hypothesis that the underlying song‐learning program has been shaped by the advantages of sharing songs with neighbors. They are also consistent with our finding of a positive correlation between song sharing and survival (Beecher et al., 2000b). However, we should be cautious about classifying Rules 3–7 as adaptations of the song‐learning program. As Williams has admonished, ‘‘Adaptation is often recognized in purely fortuitous effects, and natural selection is invoked to resolve problems that do not exist’’ (p. 4, Williams, 1966). A skeptic could look at the evidence we have presented and argue that it is not necessary to call Rules 3–7 ‘‘adaptations.’’ The skeptic could argue that all we have observed could be explained with just Rules 1 and 2 (learn preferentially from conspecific singers in your first year of life) and two additional basic assumptions, that the bird is most likely to learn songs he hears most often (dosage effect), and songs of birds he is near most often (proximity effect). Dosage could explain why a bird is more likely to learn tutor‐shared songs than tutor‐unique songs (the former are heard more often since two or more tutors sing them) and why the bird is more likely to learn (retain) songs of tutors who survive the winter (their songs are heard more often), while proximity could explain learning from multiple neighbors (the bird is closer to neighbors than to non‐neighbors, and he has multiple neighbors). This dosage‐proximity hypothesis must be seriously considered in light of the finding, mentioned above, that eastern song sparrows typically do not share songs with their neighbors. Song sparrows are one of numerous songbird species that show marked population differences in singing (e.g., Canady et al., 1984; Ewert and Kroodsma, 1994; Kroodsma and Verner, 1978; Kroodsma et al., 1999a,b).

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While song sparrow populations show only modest variation in repertoire size (Peters et al., 2000), they show marked variation in song sharing among neighbors. In all western populations carefully surveyed so far (in Washington, California, and British Columbia), birds typically share two to four songs with a given immediate neighbor, but none with birds just a few territories removed (Beecher et al., 1994b; Cassidy, 1993; Hill et al., 1999; Nielsen and Vehrencamp, 1995; Reeves and Beecher, manuscript in preparation; Wilson et al., 2000). In eastern populations (Pennsylvania, Maine, Ontario), on the other hand, birds rarely share songs with neighbors, and share no more with neighbors than with non‐neighbors (Borror, 1965; Harris and Lemon, 1972, 1974; Hughes et al., 1998; Kramer and Lemon, 1983; but see Foote and Barber, 2007). This difference between western and eastern populations of song sparrows with respect to song sharing brings the question of song‐learning adaptations to a head. Perhaps the reason song sparrows in western populations wind up with neighbor‐shared songs while song sparrows in eastern populations do not, is simply because western birds have a stable set of tutor‐neighbors, while eastern birds do not. That is, song sharing may be simply an incidental consequence of neighborhood stability in sedentary western populations, rather than a selected feature of an adaptive song‐ learning strategy. Song sparrows in eastern migratory populations may be equipped with the same song‐learning program as their western counterparts, and fail to develop shared songs because neighbor turnover within and between breeding seasons is too great. In this respect, eastern song sparrows may resemble the northern sedge wrens described in the previous section. Hughes et al., however, propose a different hypothesis, suggesting that the difference in song sharing between the two populations is the result of a difference in the underlying song‐learning programs: ‘‘Washington and Pennsylvania song sparrows differ in how they learn song, in that Washington birds copy whole songs, while Pennsylvania birds appear to copy and recombine song segments, as has been found in laboratory studies of song learning. . . . Thus both song learning and the function of song repertoires differ between populations of song sparrows’’ (Hughes et al., 1998, p. 437). The Hughes et al. argument assumes that song sharing is adaptive in western populations but not in eastern populations. This assumption is plausible, given that, as Kroodsma has suggested for northern sedge wrens, song sharing with neighbors may be neither possible nor beneficial in populations where a bird does not have long‐term neighbors. Recently, Hughes and colleagues have gathered evidence suggesting that song sharing is indeed not advantageous in their Pennsylvania population: Unlike in our Washington population, they found no correlation between song sharing

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and survival (Hughes et al., 2007). It is not clear, however, whether such an advantage could be detected given the low level of sharing that exists in this population. C. THE COMPARATIVE METHOD AND THE ‘‘COMMON GARDEN’’ EXPERIMENT Population contrasts of the sort just described provide our best route to addressing functional questions. Specifically, they provide us a way to determine precisely which aspects of a song‐learning program can be considered adaptations. Identifying adaptive differences in the song traits of two closely related species, or populations of a single species, is not sufficient to conclude that there are differences in the song‐learning programs of the two, because the adaptive difference may simply be a facultative response to the differing environments. To illustrate this problem, consider two hypothetical closely related species or populations of a single species (for simplicity, I say ‘‘populations’’ throughout). Suppose that a male of population A typically has 10 or so songs in his song repertoire, whereas a male of population B typically has 100 or so. Suppose further that we have determined that this difference reflects an adaptive fit to their different environments: 10 is optimal for the typical environment of A and 100 is optimal for the typical environment of B. Perhaps this relates to differing population densities experienced by A and B which leads to more intense competition among males in population B. While this may be an adaptive difference, it does not necessarily reflect differences in the underlying song‐learning programs of population A and B, because the same song‐learning program could underlie both phenotypes, with repertoire size being a facultative response to the differing environments. For example, in the denser population, birds might interact with more adult song tutors during the song‐learning phase and thus learn more songs than birds in the less dense population. Alternatively, the two population could have evolved differing song‐learning programs, each of which leads to the mean optimum repertoire size for the population‐typical population density. For example, the window for song learning (the sensitive period) might remain open longer for birds in population B than those in population A, so that birds of population B learn more songs. Or more brain space for songs may be allotted for population B than for population A, so B males can learn more songs than A males (e.g., Canady et al., 1984). Thus in the first case above, the song repertoire difference is a proximate effect of the environmental difference—birds of population B learn more songs because there are more song tutors—and no difference in the underlying song‐learning program is needed to explain the difference in repertoire size. In the second case above, the song repertoire difference is an

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ultimate effect of the environmental difference, in that the difference in song characteristics can be traced to differences in the underlying song‐ learning programs. In either case, the fit of repertoire size to the local population density is adaptive, but it is only in the second case that we can say that the difference is due to an adaptation, an underlying difference in the song‐learning programs of the two populations. A method that we could use to determine whether song differences between two species (or two populations of one species) reflect differences in the underlying song‐learning programs, rather than facultative or plastic responses of a particular song‐learning program to environmental differences, is the common‐environment or reciprocal‐environment experiment (Kawecki and Ebert, 2004). In this method, individuals of the different species or populations are exposed to a common environment (or ‘‘common garden’’) or to both environments (where the interesting case is the ‘‘reciprocal’’ transplantation, that is, individuals of species A exposed to the environment typical of species B, and vice versa). Although this design has not been used as much as it should be in the study of song learning, there are several notable examples, including Kroodsma and Canady’s (1985) comparison of eastern and western populations of marsh wrens with respect to repertoire size, Marler and Peters’ (1988a,b) comparison of song sparrows and swamp sparrows with respect to repertoire size and song selectivity, and Nelson and colleagues’ comparison of different white‐crowned sparrow races with respect to several aspects of song development (Nelson, 1999; Nelson et al., 1995, 1996a,b). In each of these cases, clear differences between the two populations or species exposed to a common tutoring regime have been demonstrated, indicating differences in the genetic‐developmental program underlying song learning. For example, when measured in the field, the song repertoires of western marsh wrens are about twice as large as those of eastern marsh wrens, and this difference persists when young birds from both populations are raised in the laboratory under a common tutoring regime. Kroodsma suggests that the difference between eastern and western marsh wrens may be traced to the greater population densities of western marsh wrens, selecting for larger repertoire sizes due to increased male–male competition; this scenario is similar to the hypothetical example I gave above. In summary, a common garden comparison of eastern and western song sparrows provides a clear way to address the question of whether selection has acted on western song sparrows so as to increase the probability of song sharing with neighbors. Eastern and western birds exposed to a common tutoring regime might show the differences suggested by Hughes et al. in the quote above, or perhaps some other sort of difference. If no difference between eastern and western birds was found, that would be evidence for

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the simple dosage‐proximity hypothesis we have suggested as an alternative. Finally, we would argue that comparative studies of this sort are precisely what the field needs to analyze possible adaptations of the developmental mechanisms of song learning.

D. PHYLOGENETIC APPROACHES TO THE EVOLUTION OF SONG The other major approach to identifying adaptations is the comparative or phylogenetic method in which we superimpose song traits on a known phylogeny (Harvey and Purvis, 1991; and for a recent example, Raine et al., 2006). Several recent studies have correlated song traits with ecological and evolutionary variables that might be expected to affect song. These studies, though few in number, converge on the general conclusion that song traits are extremely labile in evolution. In two pioneering studies, Irwin (1988, 1990) found that variation in song repertoire size is not explained by directional sexual selection in either emberizine sparrows or New World blackbirds. In both groups, the phylogeny suggests that the common ancestor of the group was a repertoire species, thus indicating selection for smaller repertoires in several of the extant species. Price and Lanyon (2004) looked for a correlation between the intensity of sexual selection and song complexity in the oropendolas and caciques. They detected effects of sexual selection, but found that different aspects of song have been affected in different lineages. Handley and Nelson (2005) examined 65 populations in the family Fringillidae. They found that song sharing or ‘‘dialects’’ have evolved rapidly in response to local conditions, being responsive to whether the species is migratory or sedentary and to breeding latitude (higher song sharing for sedentary species and species breeding at low latitudes). Local song sharing was randomly distributed on the phylogeny. Repertoire size and song sharing were uncorrelated, consistent with my suggestion in Section V.A. that these two traits may be responsive to different selective forces. These phylogenetic studies suggest that song provides multiple potential targets for selection and thus different evolutionary patterns may have emerged in different lineages (Price and Lanyon, 2004). If this is generally true, it points to the importance of framing comparative analyses of song within a phylogenetic framework. A phylogenetic analysis might reveal, as these recent analyses suggest, that song‐learning programs have evolved along different trajectories in different lineages. For example, perhaps the same selection pressure, say female choice, has favored different responses in different lineages, for example, large song repertoires in one, high‐ performance song in another, lifetime song learning in another, and song

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mimicry in a fourth. The major roadblock to this phylogenetic approach is the paucity of comparative data on the details of song learning, compared to more easily measured song traits such as repertoire size. What accounts for the interspecific diversity of song and song learning in the songbirds? Although pure functional hypotheses provide a start, future hypotheses will almost surely need to be placed in a phylogenetic context. As we have argued elsewhere (Beecher and Brenowitz, 2005), the most difficult problem for functional hypotheses is presented by different songbird lineages responding to similar selection pressures with different modifications of the song‐learning program. For example, the song‐learning programs of sedge warblers and marsh wrens may have been driven by sexual selection to the same end of developing a large song repertoire. But the differences in the song‐learning programs of these two species—marsh wrens require external song models (Kroodsma and Pickert, 1980, 1984a), whereas sedge warblers do not (Leitner et al., 2002)—might best be explained not in terms of different selection pressures but in terms of different ancestries. E. CONCLUSIONS AND FUTURE DIRECTIONS In our research program, we switch between questions about proximate mechanisms (including developmental mechanisms) and questions about ultimate causes (function). We attempt to integrate these approaches because we believe they will prove to be synergistic. Proximate and ultimate questions, while logically independent, are intimately intertwined in any realistic evolutionary scenario. If particular mechanisms (including developmental ones) are the target of selection, then we cannot understand function without understanding mechanism, and vice versa. The goal of identifying the adaptations underlying song learning—in the strong sense of Williams (1966)—can be approached in a third way besides the common garden and comparative‐phylogenetic approaches I have suggested. If a putative adaptation can be characterized well enough, it may be more readily explained as serving a particular specific function than as an incidental consequence of some more basic set of adaptations. To illustrate this point, we believe we will be able to distinguish—albeit only partially, and indirectly—between the sharing and dosage‐proximity hypotheses with results from virtual tutor experiments. For example, we can have two virtual tutors sing one of their pairs of shared songs (call them A1 and A2) interactively, and another pair of their shared songs (B1 and B2) non‐ interactively. If the young bird hears these four songs, and other unshared songs of the virtual tutors, equally often, and yet shows a learning preference for A, we have eliminated dosage as the important variable. While a

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test of a proximate hypothesis does not directly address a functional hypothesis, some proximate mechanisms are more consistent with a particular functional hypothesis than others. This (hypothetical) finding would be evidence that a bird’s learning is shaped more by how his tutor‐neighbors use their shared songs when communicating, than by how often he hears the particular songs, and would be more consistent with the hypothesis that the shared song‐learning preference is an adaptation ‘‘for’’ sharing songs with neighbors. In short, we feel that our experiments can provide insight not only into questions concerning the proximate mechanisms of song learning but as well into functional questions concerning possible adaptations of song‐learning programs. As a final point, I would argue that future studies of social factors in song learning should focus on the three hypotheses outlined earlier in the chapter (see Fig. 2). Our lab studies so far have provided stronger support for the Social Eavesdropping hypothesis than for the Direct Interaction hypothesis, but this research is clearly just in the beginning stages. In addition to laboratory approaches, however, these hypotheses can be evaluated directly in the field (Fig. 3). The field approach is free of the problems of ecological validity that have beset lab studies of song learning (Beecher, 1996) and has become feasible with recent advances in radio telemetry permitting the radio‐tagging of small passerines (e.g., Norris and Stutchbury, 2001). Locating young songbirds in the field can be a challenge: They are often quite inconspicuous, and indeed in certain phases of their development, they may strive to be so. It is now possible to locate and track radio‐tagged young songbirds and to observe in the field the extent to which they directly interact with adults and/or eavesdrop on the adults’ solo or interactive singing. We are pursuing this direction at present (Templeton et al., unpublished) and we recommend it to the field at large.

VI. SUMMARY In this chapter, I examine song learning in the oscine passerines (songbirds) from several angles, with special attention to our study species, the song sparrow. I focus on social factors and suggest that previous research on song‐learning points to three different hypotheses about their role. According to the simple eavesdropping hypothesis, the young bird need only overhear an adult bird singing to learn song (this situation is mimicked by the classic ‘‘tape tutor’’ design). According to the direct interaction hypothesis, the young bird needs to interact with the song tutor to learn songs (this situation is mimicked by the early ‘‘live tutor’’ designs). And according to

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the social eavesdropping hypothesis, the young bird learns best by eavesdropping on singing interactions between adult tutors. Thus in this last case, social interaction is critical, but the young bird need not directly participate in it. These hypotheses are not mutually exclusive, but each makes some distinctive predictions, and we have argued that the hypotheses can be rigorously tested using our ‘‘virtual tutor’’ design, in which a computer simulates interacting song tutors and/or interacts directly (sings) with the young bird. We are presently carrying out these studies. I describe our field and laboratory studies of song sparrows. I give particular attention to how the birds use their songs in the normal territorial context because these interactions may be crucial to song learning, especially if the social eavesdropping hypothesis proves to be true. Song sparrows in our population typically share songs with their neighbors, and song sharing is a good predictor of a bird’s lifetime territory tenure. I summarize our field studies of song learning in terms of ‘‘rules’’ of song learning (e.g., learn the songs of several, neighboring males, preferentially retain songs these tutor‐neighbors share). Our roof‐top ‘‘semi‐natural’’ studies confirm many of these findings, and make several additional points. First, learning proceeds throughout the first year, right up to the bird’s first breeding season, and memorization of new songs can occur at least into the bird’s first fall, a much longer learning period than was previously thought. Second, counter‐singing between song tutors seems especially important. Third, a song tutor does not have to be seen to be effective. Our subsequent, more analytic lab studies suggest that eavesdropping on singing interactions may indeed be critical in song learning, and they have stimulated us to turn to the ‘‘virtual tutor’’ method to analyze social interaction factors more rigorously. I discuss the two most popular classes of hypotheses of song function—that song repertoire size or song sharing is the target of selection—and consider their relation to song‐learning programs. Finally, I return to the question of which aspects of the song sparrow song‐learning program—in particular those that seem to lead to song sharing among neighbors—can be considered evolved adaptations. The question is whether song sparrows learn shared songs because their song‐learning program, in some way, leads them in that direction (the ‘‘sharing hypothesis’’) or as an incidental consequence of their movements. Doubts about the sharing hypothesis arise from a notable population difference in song: neighbors in western song sparrows share songs, whereas neighbors in eastern song sparrows typically do not. This population difference raises both doubts about the adaptation hypothesis and an opportunity to test it. I suggest that a ‘‘common garden’’ experiment, in which young birds from both populations are raised under a common tutoring regime,

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would directly address this question. Finally, I suggest that our studies on the proximate mechanisms of song learning may also help us address this question, because when we have adequately characterized the mechanisms of song learning, we may find that they are most consistent with one particular set of hypothesized song‐learning adaptations.

Acknowledgments I thank my colleagues, listed in the dedication, without whom none of this work would have been accomplished; National Science Foundation for supporting our song sparrow research for 20‐plus years; Discovery Park for hosting our field research; Jane Brockmann, Peter Slater, and Don Kroodsma, for doing what they could to correct my errors; and Inger Mornestam Beecher for doing what she could to make this chapter readable and, most of all, for getting me started in this field many years ago!

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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Insights for Behavioral Ecology from Behavioral Syndromes Andrew Sih* and Alison M. Bell{ *environmental science and policy, university of california, davis, davis, california 95616 { school of integrative biology, university of illinois, urbana‐champaign, urbana, illinois 61801

I. INTRODUCTION A few years ago, we coauthored two papers outlining the concept of behavioral syndromes, a research approach that focuses on correlations and carryovers among behaviors that have historically been often studied separately (Sih et al., 2004a,b). A behavioral syndrome involves behavioral consistency, both within and between individuals. Within‐individual consistency occurs when individuals behave in a consistent way through time or across situations, that is individuals have a behavioral type. Between‐individual consistency occurs when individuals differ in their behavioral type, which would be reflected statistically as a behavioral correlation among individuals. An example of a behavioral syndrome is the positive correlation between boldness and aggressiveness that has been documented in several species (Bell, 2005; Bell and Sih, 2007; Dingemanse et al., 2007; Dochtermann and Jenkins, 2007; Duckworth and Badyaev, 2007; Huntingford, 1976a; Johnson and Sih, 2005; Kortet and Hedrick, 2007; Moretz et al., 2007; Reaney and Backwell, 2007). Individual animals that are more bold (than others) in the face of predation risk also tend to be more aggressive toward conspecifics. Our earlier papers emphasized evolutionary and ecological implications of behavioral syndromes as well as the potential for behavioral syndromes to serve as a conceptual bridge integrating proximate mechanisms (genetics, development, and neuroendocrine mechanisms) with the ecology and evolution of behavior. In recent years, there has been an explosion of interest and research activity examining behavioral syndromes and the closely related concepts of animal personality, temperament, and coping styles (Dingemanse and 227 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00005-3

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Re´ale, 2005; Gosling, 2001; Koolhaas et al., 1999, 2006; Overli et al., 2007; Reale et al., 2007). Several symposia discussed these concepts at major behavior conferences, including one asking ‘‘Do behavioral syndromes represent a paradigm shift in behavioral ecology?’’ (International Society for Behavioural Ecology meeting in Tours, France, 2006). The New York Times’ Magazine full‐length, cover article on animal personalities (January 22, 2006) was a testament to the appeal of the topic to the general public. This burst of activity generated numerous exciting new ideas and insights as well as criticism, controversy, and, in our view, some misunderstandings. Here, we present our suggested roadmap for the future study of behavioral syndromes. We first outline a brief history of the concept, and clarify some misunderstandings about the definition of a behavioral syndrome. While these points are not inherently forward thinking, we feel that they must be clarified before proceeding. Then, we describe exciting avenues of study that derive from the fact that in the last 5 years, enough studies have been done to show that for at least two main types of behavior—boldness and aggressiveness—we often see behavioral correlations, but sometimes we do not. Further, sometimes behavioral correlations are stable over time, but other times they are not. One major challenge for the next wave of studies should thus be to better understand the factors that influence when behaviors are clustered together as a behavioral syndrome, and when the behavioral correlations are decoupled. Here, we describe recent developments using both proximate and adaptive frameworks to explain patterns of variation in behavioral syndromes. We champion an approach that blends these two views. Finally, although boldness and aggressiveness (and associated coping styles) have received considerable attention, we note here several other, potentially important, behavioral syndromes that have not yet received much attention. These expand the scope of behavioral syndromes to cover a broader range of issues, including many that have rarely been addressed by behavioral ecologists. We close by summarizing some directions for future study.

II. A BRIEF HISTORY OF THE IDEA One criticism of the concept of behavioral syndromes has been that it is not new. We agree. Some behavioral ecologists have long emphasized the importance of individual variation in behavioral type. In addition, behavioral consistency is a major area of study in several other subfields of behavior (e.g., behavioral genetics, applied animal behavior, the study of personality in humans, and other animals). And, the importance of correlated traits has long been emphasized in evolutionary biology. Thus, the recent surge of interest in behavioral syndromes does not derive from it being a truly new

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idea, but comes instead from the possibility that it represents an opportunity for new insights to emerge from the melding of ideas and methods from several, interrelated, but somewhat disparate fields. Among behavioral ecologists, an effective tactic for studying the role of particular ecological circumstances in governing behavior has been to compare groups exposed to different treatments (e.g., with or without predation risk, high or low food) rather than focus on individuals. That is, a standard methodology is to use different individuals in different treatment groups, and to focus on mean‐level differences. Although this approach is effective at determining whether a specific factor is driving behavioral differences, an important alternative view is to regard individual variation as more than mere noise. Several prominent behavioral ecologists brought attention to the importance of individual behavioral variation in the 1980s and 1990s (Arnqvist and Henriksson, 1997; Clark and Ehlinger, 1987; Huntingford, 1976b; Magurran, 1993; Riechert and Hedrick, 1993; Slater, 1981; Stamps, 1991; Wilson, 1998; Wilson et al., 1994). In addition, discrete, bimodal behavioral types, such as alternative strategies (producer/scrounger, hawk/dove, defect/cooperate) have long been a mainstream area of study in behavioral ecology. Other familiar types of discrete behavioral variation include dominant/subordinate, territory holder/floater, or for that matter, male/female. Indeed, much of game theory is concerned with interactions that can be between individuals with different behavioral types, a point we develop later in the chapter. However, despite this tradition and precedent, most studies in behavioral ecology have not analyzed or emphasized individual variation. Similarly, until recently, most studies in behavioral ecology have focused on behavior within a given situation without looking to see if behavioral tendencies carry over to other situations. For example, while many have looked at alternative male strategies in mating contests, until recently, few have asked whether those strategies carry over to other contexts, such as aggressiveness toward females during courtship, to parental care behavior, to feeding voracity, or boldness in the presence of predators. Based on the reasoning that natural selection favors optimal behavior in every situation, most studies have focused on behavior in one situation. In contrast, the study of individual variation in behavioral type and carryovers across situations has been a central issue for numerous studies of proximate mechanisms underlying behavior. A focal question has been: What role do genes and neuroendocrine mechanisms play in explaining why some individuals are more aggressive or more anxious than others as a general coping style expressed in many situations? Indeed, the tradition of studying proximate mechanisms governing different coping styles in laboratory rodents (Benus et al., 1987, 1991; Koolhaas et al., 1999; Meaney, 2001), primates (Capitanio et al., 1998; Suomi, 1987), and farm animals

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(Hessing et al., 1993) played a major role in guiding recent studies on animal personalities, in particular, in Europe (Drent et al., 2003; Verbeek et al., 1994, 1996). Along similar lines, individual variation in behavioral tendencies across multiple situations is, of course, at the heart of the study of human personalities (Plomin and Dunn, 1986), and analogous work on animal personalities as conducted by psychologists (Gosling, 2001). While these fields have a history of studying behavioral consistency and behavioral correlations, to our knowledge, until recently, these studies were not on the radar for most behavioral ecologists. Another situation where individual variation has traditionally been quantified is where, perhaps due to logistical constraints, the standard methodology involves studying a relatively small number of individuals over a relatively long period. For example, primatologists have long noted that certain individuals have particular behavioral characteristics, being sociable or aggressive (Stevenson‐Hinde et al., 1980). We think that the extensive documentation of temperament in nonhuman primates does not mean that they have ‘‘more personality’’ than other animals. Instead, a more practical explanation for this bias is that the number of individuals available for study is generally more limited in primatology than in studies of other animals. Therefore, primatologists gathered a lot of data on the same individuals and were thus immediately confronted by the personalities of their subjects. Behavioral correlations are potentially important for the same reason that correlations, in general (in any field of science, logic, etc.), can be important. Essentially, it means that to understand one behavior, we need to consider other correlated behaviors. The idea that traits might be correlated and that trait correlations might be important has long been understood and studied by evolutionary biologists. For example, life history theory has long emphasized that to understand fundamental traits like deferred reproduction or senescence, it is crucial to consider trade‐offs generated by correlations across the life history (Roff, 1992). In addition, evolutionary biologists have a history of studying limited plasticity, an idea that is implied by within‐individual consistency in behavior (Schlichting and Pigliucci, 1998). Finally, evolutionary biologists have been studying the evolution of correlated traits, mostly with respect to morphological traits, long before they were drawing the attention of behavioral ecologists (Armbruster and Schwaegerle, 1996; Brodie, 1992; Lande and Arnold, 1983). A current area of excitement in evolutionary biology focuses on the interplay between selection and genetics in governing the evolution of integrated phenotypes, packages that could include morphological, physiological, life history, and behavioral traits (Pigliucci and Preston, 2004). In this context, studying behavioral syndromes is too narrow a view. When possible, we should further broaden our view to study how behavioral

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syndromes are integrated with other aspects of the organisms’ overall phenotype. Overall, progress in understanding behavioral syndromes clearly has been and should continue to be enhanced by drawing from analogous, well‐established fields of study.

III. CLARIFYING THE DEFINITION OF A BEHAVIORAL SYNDROME In discussions at recent symposia, in recent papers, and in anonymous reviews of our papers and grant proposals, we have often encountered several main misconceptions about the definition of a behavioral syndrome. In our earlier paper (Sih et al., 2004a,b), we defined a behavioral syndrome as ‘‘a suite of correlated behaviors across multiple (two or more) observations.’’ Others have only considered studies with more than two observations per individual as addressing a behavioral syndrome; however, we did not do that in our original definition. Our conceptual focus is on the importance of behavioral correlations per se. Thus, we do not see a basis or value for excluding the minimal situation (two observations, one correlation) from the bailiwick of behavioral syndromes. Because we define a behavioral syndrome as a correlation, the critical statistical test is whether a correlation between behaviors is significantly different from zero. Obviously, a stronger correlation ( judged by the correlation coefficient, not by the p value) reflects a tighter and potentially more important relationship between two behaviors. However, even a relatively weak correlation (r  0.2–0.3, which is the effect size observed in many studies), especially if it is a genetic correlation (Roff, 1995), can still have important ecological and evolutionary implications. For example, even a very low genetic correlation on the order of 0.1–0.2 can still produce biologically meaningful correlated response to selection on the unselected trait, depending on the intensity of selection and the heritabilities of the two traits (Falconer and Mackay, 1996). At this point, we do not see a compelling reason to draw precise boundaries on the situations or contexts that are worth examining in a behavioral syndromes framework, although correlations across seemingly unrelated contexts and that are long‐lasting might be particularly interesting. In fact, variation in diverse behavioral contexts (in addition to the well‐studied shy‐ bold or aggression axes) such as mating behavior, parental behavior, learning styles, coping styles, cooperative behavior, and information processing all are candidates for study from a behavioral syndromes perspective (see Section IV). In our original definition, we took a broad, inclusive view that a behavioral syndrome could involve: (1) different contexts at the same point in time (e.g., feeding vs mating activity in one set of conditions), (2) the

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same context but at different points in time (e.g., feeding activity in the presence vs absence of predators, or feeding voracity as a juvenile vs as an adult), or (3) different contexts at different points in time (e.g., aggression toward conspecifics in the absence of predators vs feeding activity in the presence of predators). To emphasize, a behavioral syndrome could involve behavioral consistency through time in either the same basic context, for example, voracity in juveniles and adults (Johnson, 2003), or across contexts, for example aggression in a parental and competitive context (Ketterson and Nolan, 1999). Below, we describe how, according to this very general definition of a behavioral syndrome, a behavioral syndrome does not have to: (1) be stable over a lifetime or even over a large proportion of a lifetime, (2) involve a genetic basis, (3) involve both multiple contexts and multiple situations, (4) be independent of social status or condition, (5) involve a dichotomy of behavioral types, and (6) be associated with suboptimal behavior. They certainly do not require animals to show little or no behavioral plasticity. While a behavioral syndrome might be more interesting or more important if it has a strong genetic basis, is stable over a lifetime, carries over across multiple contexts, and results in suboptimal behavior, these are not part of the definition of the concept. According to our definition, behavioral syndromes need not involve stability of behavioral types over an individual’s entire lifetime. Long‐term stability is more likely to represent a developmental constraint than short‐term stability if it means that an individual is ‘‘stuck’’ with a behavioral type throughout its entire lifetime. For example, behavioral correlations through ontogeny mean that selection on behavior at one age could have correlated effects later in life (Bell and Stamps, 2004). However, even short‐term behavioral consistency can be very important. For example, a short‐term carryover of aggressiveness into other contexts could make the difference between life and death if it means that a male that is pumped up on testosterone behaves inappropriately in the presence of a predator. Overall, we see no valid way or compelling reason to draw absolute cutoffs to define how stable a behavioral correlation needs to be in order to be interesting. Instead, we suggest that it would be more useful to focus on determining the causes and consequences of different degrees of stability. It is here, especially, that we think we have a lot to learn from the human personality literature, which suggests that some personality dimensions are more stable than others, and different periods of development are characterized by more or less change (Caspi et al., 2005; Roberts et al., 2006). Therefore, an interesting question is the relative durability or stability of a behavioral syndrome—is it stable throughout an organism’s entire lifetime, or more likely to change during particular developmental periods such

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as adolescence, or only in the presence of a social group? Determining whether an individual’s behavioral type is solidified only after critical period of time has elapsed, or following a major habitat shift, such as migration, is a promising task for future work. Furthermore, a behavioral syndrome need not have a genetic basis [note that we differ here from some definitions of personality (e.g., van Oers et al., 2005)]. We define a behavioral syndrome in a statistical sense—as a correlation between behaviors—without any underlying assumptions about its proximate cause or source. Although behavioral types appear to usually depend on genes [and on environmental experiences and a geneenvironment interaction (Bell, 2005; Bell and Stamps, 2004; Carere et al., 2001)], a genetic basis to an individual’s behavioral types is not part of the definition of a behavioral syndrome. Even if variation in behavioral types is entirely determined by differences in early experience (or maternal effects), we still consider this to be a behavioral syndrome. Similarly, even if behavioral types are largely regulated by social status or differences in condition (i.e., even if behavioral type is labile if status or condition changes), if individuals exhibit behavioral consistency and differ from one another, this is a behavioral syndrome. Some view behavioral syndromes that are primarily due to social status (e.g., dominants are more aggressive than subordinates) as less interesting; however, in our view, this is not relevant to the definition per se. By adopting a broad, inclusive view that conceptualizes a behavioral syndrome as a reaction norm, where an individual’s behavioral type is a product of genetic, environmental, and GE sources, we can avoid the person‐situation debate (i.e., is human personality more determined by the ‘‘person’’ or the ‘‘situation’’?), which preoccupied psychologists several decades ago (Mischel, 2004; Penke et al., 2007). Behavioral syndromes do not have to be associated with suboptimal behavior; they can be adaptive. Although the term ‘‘syndrome’’ has negative connotations in the clinical literature (e.g., chronic fatigue syndrome), and although some examples of behavioral syndromes have emphasized suboptimal outcomes (Johnson and Sih, 2005; Sih et al., 2003), our definition does not require either suboptimality or limited plasticity relative to the optimal. The term behavioral syndromes was coined because the term syndrome is used in other areas of evolutionary biology. For example, suites of covarying traits forming ‘‘pollination syndromes’’ (Johnson and Steiner, 2000), ‘‘migratory syndromes’’ (Dingle, 2001), or ‘‘life history syndromes’’ (Roff, 1992) are generally thought to be adaptive responses to selection which favors responses in multiple traits, not just one. Along these lines, behavioral syndromes are not, by definition, incompatible with adaptive behavioral plasticity. Some have suggested that behavioral syndromes imply little or no behavioral plasticity and that

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examples of adaptive behavioral plasticity are evidence against behavioral syndromes (Neff and Sherman, 2004). The definition of behavioral syndromes, however, is agnostic about the degree of behavioral plasticity. Even if all individuals are highly plastic (e.g., change their activity substantially in the presence versus absence of predators), if the rank‐order differences between individuals is maintained (i.e., individuals that are more active than others in the absence of predators continue to be more active than others in the presence of predators), then we have a behavioral syndrome. If individuals show limited plasticity (less than optimal plasticity) associated with their behavioral type (Duckworth, 2006; Johnson and Sih, 2005; Sih et al., 2003), this makes the behavioral syndrome particularly important in determining fitness; however, limited plasticity is not an inherent part of the definition of a behavioral syndrome. Finally, although some discussions of behavioral syndromes or coping styles talk about a dichotomy of behavioral types (e.g., proactive vs reactive or shy vs bold), the concept of a behavioral syndrome does not imply any particular frequency distribution of behavioral types. Populations often have a continuous distribution (perhaps a normal distribution) of behavioral types. The exception might be that when behavioral types are associated with discrete morphotypes (e.g., males vs females, or alternative male mating morphotypes), then they might also show a discrete behavioral dichotomy. However, to emphasize, although the distribution of behavioral types is a characteristic of a behavioral syndrome, it is not the part of the definition. The relationship between behavioral syndromes and the related concepts of temperament, personality, and coping style is described by Reale et al. (2007). We prefer the term behavioral syndrome for two primary reasons. First, the term is inclusive and general: unlike many definitions of temperament (Reale et al., 2007), for example, a behavioral syndrome does not have to be genetically based or a characteristic of juveniles or to be stable across the life course. We prefer a broad definition because we see behavioral syndromes as an important conceptual bridge with wide‐ranging implications for many topics in behavioral ecology. Second, because behavioral syndromes are defined as correlations, the study of behavioral syndromes fits squarely within the existing framework for studying suites of traits, covariation, syndromes, etc. in evolutionary ecology.

IV. UNDERSTANDING VARIATION IN BEHAVIORAL SYNDROMES In recent years, dozens of studies have tested for behavioral correlations in numerous taxa. Many studies have found significant behavioral correlations, but others have not. Clearly, the question is thus not—do they exist

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or not? Instead, a key issue now is to explain variation in behavioral syndromes. First, what are the patterns? For a set of behaviors over multiple contexts and situations, which behaviors are correlated and which are not? For behaviors that are sometimes correlated, how stable are the correlations over ontogeny, and over an evolutionary timescale? Or, conversely, when are behaviors decoupled? Second, what explains the observed patterns? Can they be best understood in terms of proximate constraints or adaptive (cost/benefit) considerations or a combination of the two? If proximate constraints are important, which types of constraints underlie which correlations? And, if adaptive approaches are useful, what role do ecological or social selection pressures play in shaping behavioral syndromes? At the most fundamental level, what conceptual framework should we use to explain and ideally predict patterns of variation in behavioral syndromes? These are very exciting, challenging questions that the field is just beginning to address. Before proceeding, we first clarify the distinction between variation in behavioral type and variation in behavioral syndromes. Behavioral ecologists have a long history of thinking about and quantifying population variation in mean behavioral type. For example, conventional wisdom suggests that we expect, and indeed often see, that animals are more bold (with or without predators present) if they have evolved in situations with low predation risk [e.g., islands or ponds without important predators (Cox and Lima, 2006; Giles and Huntingford, 1984; Magurran, 1986; Reznick, 1983)]. Or, animals are more aggressive if they have evolved in situations where resources are defensible [e.g., fish hatcheries as compared to wild fish (e.g., Sundstrom et al., 2004)]. These are statements about the mean value of boldness or aggressiveness for individuals in a population. The clear expectation is that cost/benefit considerations can often explain variations in behavioral type. In contrast, we are only beginning to quantify and think about variation in behavioral syndromes. Why is it that in some species and under some circumstances, behaviors cluster together into a correlated package, but in other cases, individual behavior is not consistent through time or across situations? For example, although it is clear that higher risk often favors reduced boldness (i.e., due to a higher cost of being bold), it is not clear how we might expect predation risk to influence the correlation between boldness and aggressiveness. Intriguingly, recent studies show that there is a connection between predation risk and the correlation between boldness and aggressiveness in sticklebacks (Bell, 2005, 2007; Dingemanse et al., 2004); however, we challenge the reader to decipher which way this relationship goes. Is a significant positive correlation between these behaviors found in populations with high risk or low risk? And, more importantly, why should it be that way? Indeed, what conceptual framework should we

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use to understand this pattern—a framework based on cost/benefit considerations (selection on the correlation), or one that emphasizes proximate mechanisms, or both? In human terms, the question is—what framework should we use to understand the structure of personality? While in what follows, we emphasize questions about behavioral syndromes, the same broad framework should ideally help to also understand issues about variation in behavioral types. Before we can offer an interesting biological explanation for variation in a behavioral syndrome, it is first necessary to determine whether the failure to find a significant correlation in some circumstances is being caused by lack of statistical power, for example, due to low sample sizes or lack of variance. That is, for detecting covariances or correlations, a key possible problem is low variance in one or both variables. Without variance in both variables, it is difficult to detect covariance. Thus, one reason why a behavioral correlation might be detected at some ages but not others could be that individuals are generally less behaviorally variable at one age, therefore, giving the impression that a link is no longer present. Alternatively, a correlation might appear to change simply due to a change in variance in one variable (Fig. 1). Changes in variance can prompt interesting biological questions about the cause of changes in covariance: one reason why individuals are not predictable from one context to the next is because they are all doing the same thing in one of the contexts under consideration. Therefore, we suggest that simply examining changes in the distribution of behaviors is a useful first step along the way to understand the causes of variable correlations. Also, some studies might overemphasize the importance of behavioral syndromes by focusing on the extreme behavioral types (the most bold vs most shy). If the distribution of behavioral scores does not really follow a clean bimodal distribution, then such a classification ignores possibly important intermediates. This is especially important when individuals at the extreme are qualitatively different from the intermediates.

A. PROXIMATE EXPLANATIONS FOR VARIATION IN BEHAVIORAL SYNDROMES Above and beyond issues of statistical power per se, an approach for explaining why behavioral carryovers and consistency might exist (and potentially when they might break down) invokes proximate mechanisms (i.e., hormones, physiology, and genetics) that underlie multiple behaviors. At heart, the logic is that: (1) behavioral consistency might be explained by proximate mechanisms that are less plastic (more stable) over time than behavior per se and (2) that behavioral correlations across multiple

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A

Y

Decreased variation in both X and Y B

X

Decreased variation in X C

Y Y

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FIG. 1. Changes in behavioral correlations can be caused by changes in variance. Each data point represents a different individual. The range of values of X is in black, and that in Y is in gray. The best‐fit regression line is shown. In (A), X and Y are positively correlated. In (B), variation in both X and Y has decreased, causing the correlation between X and Y to vanish. In (C), variation in X has decreased while that in Y is unchanged, causing the slope to increase.

contexts or situations might be due to proximate mechanisms that govern multiple behaviors (e.g., one hormone or one gene controls several behaviors). An obvious place to start looking for the proximate source of behavioral correlations is genetics, if suites of behaviors are affected by the same genes or hormones (pleiotropy) (Mackay, 2004). Indeed, much of the literature on coping styles examines how genetic variation in the hypothalamic pituitary adrenal axis might underlie variation in behavioral type (Boyce and Ellis, 2005; Koolhaas et al., 1999). However, it seems that the more we learn about genetic and neuroendocrine mechanisms underlying suites of behaviors, the more it becomes clear that most systems are very complex, full of interactions and feedback, and that the behaviors of interest to behavioral ecologists are often many steps away from a simple genetic source (Henderson, 1990; Kendler and Greenspan, 2006). This complexity builds in flexibility and offers multiple opportunities for selection to act to uncouple deleterious combinations. For example, the effect of any given hormone, for example corticosteroids, can

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depend on binding globulins, multiple receptors, receptor densities in different tissues, and interactions and feedbacks among multiple hormones (Sapolsky et al., 2000). In some cases, higher levels of the same neurochemical can be associated with either higher or lower levels of risk seeking or aggressiveness (Bell et al., 2007). Although it is tempting to offer casual causal statements such as high testosterone explains aggressiveness, other neurochemicals that have been suggested to relate to aggressiveness or boldness include vasopressin or AVT (e.g., Veenema et al., 2006), corticosteroids (e.g., Carere et al., 2003), and brain monoamines (e.g., Winberg and Nilsson, 1993). Indeed, the very fact that so many different neurochemicals can be associated with aggressiveness hints at the complexity of the overall behavioral system. We are only beginning to scratch the surface of the interactions (Veenema et al., 2006), but we suggest that this complexity holds the key to help explain variation in behavioral syndromes. For example, a candidate for explaining the correlation between boldness and aggressiveness might be a gene (e.g., monoamine oxidase) that regulates expression of other genes that control both pathways. Even if the genetic control of behavioral types is very complex (e.g., involves networks of many interacting genes), it remains plausible that variation in expression of some key genes could help explain the dynamics of the overall genetic network that underlies behavioral correlations. Modern genomic tools both empirical and theoretical (e.g., network theory) lend a sense of excitement to this developing field. So what sorts of systems are likely to generate stable behavioral types which vary among individuals? We propose, generally, that aspects of systems that are less plastic (e.g., receptor density as opposed to circulating hormone levels) or that are the product of hormonally regulated, organizational processes early in development (e.g., structural differences in the brain) could underlie behavioral syndromes that are stable over ontogeny. In contrast, aspects of behavior that are regulated by more plastic aspects of the neuroendocrine system (e.g., circulating hormone levels) should be less likely to be part of a stable behavioral syndrome (Bell, 2007). Circulating hormone levels can change almost as rapidly as behavior per se, thus while they could potentially explain short‐term carryovers, even across types of behavior (e.g., short‐term spillovers from aggressiveness toward male conspecifics to aggressiveness toward females or toward a predator), they would not appear to be a good candidate for explaining long‐term stability of behavioral types. Morphological mechanisms (e.g., organ size or brain structures) that can only change slowly are especially good candidates for explaining stable behavioral types and a stable behavioral syndrome. Given that key morphological traits might develop relatively early in life, these represent developmental or ontogenetic constraints on behavioral syndromes. Life history

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events that feature morphological restructuring (e.g., metamorphosis for insects or amphibians or onset of reproduction) might then be key cusps that cause changes in behavioral type and even behavioral syndromes. Furthermore, although relatively fixed, morphological mechanisms might result in stable behavioral types and syndromes within a lifetime, they need not constrain evolution of behavioral syndromes across generations. For example, the same proximate mechanisms that result in a positive correlation between boldness and aggressiveness among individuals within one population need not produce a positive correlation among populations (Bell, 2005). One proximate source that could anchor a behavioral type involves physiological mechanisms associated with variation in growth rate (Biro and Stamps, 2008; Careau et al., 2008; Stamps, 2007). Stamps (2007) proposed that animals might develop physiological mechanisms that favor a consistent, as opposed to a variable, growth rate. If there is a trade‐off between growth rate and mortality, such that some individuals grow fast but risk predator‐induced mortality while others play it safe by growing slowly [but both strategies have equal fitness (Mangel and Stamps, 2001)], then any behavior that contributes to the growth–mortality trade‐off will be variable among individuals. This framework not only explains variation in behavioral types but also provides a mechanism for explaining behavioral correlations. Boldness and aggressiveness should be positively correlated because they are both components of an overall high risk, high gain life history type (Stamps, 2007). The two, however, would not be correlated if some key aspect of the behavior–life history relationship is violated; for example, if boldness or aggressiveness do not result in more resources, or if boldness does not result in higher mortality (e.g., if predation risk is low). We discuss this idea in more detail below. A specific possibility is that variation in metabolic rates and associated physiological morphology underlies variation in feeding–growth strategies. High feeding and growth rates require high activity that, in turn, requires high metabolic rates. Most importantly, high‐energy intake rates require large organs to process food (e.g., large intestines), take in oxygen (large lungs), and remove wastes (large liver or kidneys). The fact that organ size is relatively fixed then determines a physiological type that governs behavior and life history types (Biro and Stamps, 2008; Careau et al., 2008). B. ULTIMATE (ADAPTIVE) EXPLANATIONS FOR VARIATION IN BEHAVIORAL SYNDROMES An alternative, complementary approach uses adaptive (cost/benefit) considerations to explain variation in behavioral syndromes. Recent theoretical papers have proposed adaptive hypotheses based on three main

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classes of mechanisms: (1) the benefits of specialization, (2) the benefits of consistency per se, what we call ‘‘status quo’’ selection, and (3) the social benefits of predictability. While these mechanisms have been discussed primarily in the context of explaining why individuals have consistent behavioral types, the hope is that these frameworks will also help understand the other issues about the structure of behavioral syndromes (see the list of issues at the beginning of Section III). Before proceeding, note that here, we follow the evolutionary ecology tradition of referring to a trait as being ‘‘adaptive’’ if it yields high‐current fitness. We are not addressing the alternative definition that an adaptation requires evolution of the trait for its current function (Gould and Vrba, 1982). Perhaps the most general adaptive reason for an individual to maintain a particular behavioral type is that there are benefits to specializing on that type. Sih et al. (2004a,b) discussed how existing theory on the evolution of specialists versus generalists, and related theory on the evolution of fixed versus plastic traits, might offer insights on conditions favoring the evolution of consistent behavioral types. In essence, the issue is that in a variable environment, why should individuals evolve a relatively fixed strategy (a consistent behavioral type) rather than be highly plastic to track environmental change over space or time? According to earlier theory [that was couched in terms of developmental plasticity, and not behavior or behavioral syndromes; see Sih et al. (2004a,b)], two key factors are the cost of switching traits and the ability of individuals to accurately and adaptively match their traits to the current environment. Individuals should exhibit highly plastic behavior if the cost of switching behavioral strategies is low, and if individuals can accurately assess the current environment and behave accordingly. Conversely, individuals might maintain a consistent behavioral type if it is costly to switch behavioral types, or if they are ineffective at matching their behavior to the current environment. One main reason why individuals might not be able to exhibit adaptive behavioral plasticity is if they lack precise information about the current environment. Consider, for example, the challenge of investing in the stock market. In order to buy and sell optimally, one needs useful information about different options and market conditions. Gathering that information takes time and energy (i.e., costly). Furthermore, even a well‐informed individual still experiences considerable uncertainty about market conditions. Given these costs and uncertainties, it might often be better to choose a portfolio and stick with it (i.e., low plasticity), rather than attempt to play the market actively. McElreath and Strimling (2006) explored this conjecture with a formal model [based on Sih (1992)] exploring fixed versus plastic prey responses to variation in predation risk. Prey with complete information should hide

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when predators are present, but forage actively when predators are absent. The problem is that prey might not know accurately if predators are currently present or not. McElreath and Strimling (2006) confirmed Sih’s result (1992) that if prey are uncertain about whether predators are present or not, then fixed behavior can be favored over inaccurate tracking of risk. This does not, however, explain why some individuals are bold while others are shy. McElreath and Strimling (2006) added the exciting insight that differences between individuals in a state variable (e.g., size, vigor) that determines the relative ability to be bold (or shy) can explain why some are bold while others are shy. The key is that state variables (size, vigor, condition, energy reserves, life history stage, information state, skill level) change relatively slowly over time. Thus, although behavior can, in theory, change very rapidly, if the optimal behavior is connected to a slower, more stable state variable, then this connection can explain behavioral consistency, and differences in state can explain differences in behavioral type. Critical issues that McElreath and Strimling (2006) did not address are how might individual differences in state arise, and most importantly, why should these differences be maintained? If variation in state explains variation in behavior, then the key question is – what maintains within‐ and between‐individual consistency in state? Wolf et al. (2007a) examined a specific mechanism for generating variation in state. They posited that individuals might vary in their tendency to explore their environment early in life. Individuals that are more exploratory have more information (an asset) that they can use to gain resources (that can be converted into fitness) later in life. Conversely, individuals that have explored less have lower assets. Following the asset protection principle (Clark, 1994), animals that have more assets (more to protect) should play it safe (be less bold and less aggressive) relative to animals that have fewer assets. As noted above, as long as assets do not change appreciably, individuals should maintain a stable behavioral type. That is, as long as differences in assets are maintained, this model can explain three key things about behavioral syndromes: (1) why individuals maintain a consistent behavioral type, (2) why individuals differ in behavioral type, and (3) why boldness and aggressiveness might be positively correlated. McElreath et al. (2007) noted, however, that the asset protection principle is inherently a negative feedback process that should not maintain differences in assets (or more generally, state). Individuals that have high assets should protect them by not taking risks (i.e., by being shy and unaggressive); however, assuming that being bold or aggressive is necessary to gain assets, over time, being shy and unaggressive should erode assets. In contrast, individuals with low assets should take risks to gain more assets. As long as they survive, their assets should increase. Thus, assets (state)

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should converge over time, and if differences in state underlie differences in behavior, then behavior should converge over time. Thus, the Wolf et al. (2007a) mechanism can only explain long‐term stability of behavioral syndromes if behavior has relatively little effect on state, for example, when individuals immediately (and in many cases, repeatedly) convert new assets into reproduction, rather than accumulate assets (Wolf et al., 2007b). The problem then is that in many, perhaps most, ecologically relevant situations, behavior should have important effects on state. In that case, to maintain stable differences in state (and thus in behavioral type), we need positive feedback between behavioral type and state. We (Sih, B. Luttbeg, and S. Fogarty) have explored a set of analytical and dynamic programming models to examine effects of positive feedback scenarios on behavioral syndromes. Here, we present a few main, intuitive reasonable points. Positive feedback can occur if higher state increases the tendency to be bold (and/or aggressive) that maintains high state (and vice versa for lower state and shyness). Some general scenarios that could produce this positive feedback are diagrammed in Fig. 2. One simple mechanism occurs when higher state directly reduces the risk of being bold. For example, higher

Increased energy gain per unit behavior Energy gain Behavior (e.g. boldness)

Asset protection reduced behavior

Build defensive machinery

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FIG. 2. The interplay between negative feedback (via asset protection) that tends to break down consistent behavioral types and several positive feedback loops that tend to maintain consistent behavioral types. The positive feedback loops (in italics) come through higher state either increasing the benefit or decreasing the cost of further bold behavior. See the text for a more detailed description.

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state could mean larger size, greater physical vigor, speed, or strength that result in higher escape success. Higher escape success translates into a lower probability of death (lower cost) while being bold. Alternatively, higher state could increase the benefit of being bold. For example, if larger, more vigorous individuals outcompete conspecifics for food, they stand to gain more resources per unit time spent being bold. In either case, the outcome is that high state individuals should continue to be bold, and to thus garner the resources required to maintain high state. To emphasize, this is in contrast to asset protection (Clark, 1994; Wolf et al., 2007a) where high state individuals should be less bold (to protect assets) and should thus decline in state over time. The benefits of high state could either be direct (i.e., high state per se directly affects the benefits and costs of being bold) or be mediated through alterations in morphology (essentially a second state variable besides size, or energy reserves per se). For example, individuals with high‐energy reserves could divert energy into building defensive morphologies (e.g., armor, spines), competitive morphologies (e.g., weapons for winning contests), or metabolic morphologies (e.g., larger liver) that increase the benefit or reduce the cost of being bold. Mechanisms that include induced morphological changes are important because they are slow, and often difficult to reverse, and might thus play a particularly strong role in locking an individual into a particular behavioral type. In the above scenarios, low state individuals are shy or unaggressive as a ‘‘best of a bad job’’ strategy. There is much to be gained from being bold or aggressive to gain more resources; however, if the costs of being bold or aggressive while in low state are high enough (e.g., if predation risk or costs of fighting are very high), these individuals might be stuck with playing it safe. In essence, losers stay as losers. Note that, as is often the case in dynamic programming models, the time until a time horizon makes a difference. Early in the season, it may pay for a low state individual to take chances to increase state because there is plenty of time left to reap the benefits of high state. In contrast, with less time remaining, the benefits of being bold or aggressive to increase state are reduced. In situations where the low state strategy (being shy and unaggressive) yields lower fitness, we might expect natural selection to weed out this strategy. Why should it persist? One simple possibility is that initial differences in state (which persist due to positive feedback mechanisms) are due largely to chance events early in life; that is, much of the observed variation in behavioral type might be environmentally induced rather than genetic. Behavioral types, however, are generally at least moderately heritable (Kendler and Greenspan, 2006; Penke et al., 2007; van Oers et al., 2005). Maintenance of genetic variation in personality types can be due to

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frequency‐dependence, or perhaps a mutation–selection balance (if behavioral types are influenced by many genes of small effect each of which can mutate; Penke et al., 2007). Positive feedback as outlined above can explain the existence and maintenance of two behavioral types—bold versus shy, or aggressive versus unaggressive. While some systems might indeed have a dichotomy of two behavioral types, many systems likely feature a continuous distribution of behavioral types. We suggest that a general mechanism to explain a continuous range of behavioral types is selection favoring behavioral consistency per se. We have termed this ‘‘individual status quo’’ selection (Sih et al., in press)— where individuals do best if they continue to do what they have been doing. One well‐known mechanism that can produce this effect is learning and increased skill with experience. In the context of behavioral syndromes, the notion is, for example, that bold individuals might learn, with experience, how to be effective at being bold, which should favor them continuing to be bold, which gives them more experience at being bold, and so on. Alternatively, shy individuals learn how to be shy, and intermediate shy–bold individuals learn how to be intermediate in their behavioral type, and so on. Stamps (2007) emphasized a fascinating form of ‘‘status quo’’ selection that involves selection favoring individuals maintaining a consistent growth rate (see references in Stamps, 2007). Highly fluctuating growth rates can result in various physiological problems including low‐quality tissues and disease (Arendt, 1997; Metcalfe and Monaghan, 2001; Stamps, 2007). In humans, in particular, low growth in utero followed by rapid compensatory growth afterward has been associated with subsequent heart disease, type‐2 diabetes, and hypertension (Bateson et al., 2004). Differences among individuals in preferred optimal growth rates (and thus in risks taken to achieve those growth rates) could then explain consistent differences in behavioral type. The central issue addressed by most extant theory on evolution of behavioral syndromes has been the maintenance of within‐individual and between‐ individual consistency in behavioral type. Another key challenge is to explain behavioral correlations across contexts. For example, why are boldness and aggressiveness positively correlated? Stamps (2007) and Wolf et al. (2007a) explained this positive correlation by noting that these two behavioral tendencies can represent alternative methods for gaining resources while taking risks. In that case, if selection favors one (e.g., boldness), it should favor the other (aggressiveness). In Stamps’ (2007) framework, if an individual has a high growth rate life history, then it should be both bold and aggressive, and vice versa if it has a low growth rate life history. In a model of Wolf et al. (2007a), individuals that have high assets should be both shy and unaggressive, and others with low assets should be bold and aggressive. As Stamps (2007) emphasized, this logic holds only if both behavioral tendencies are indeed

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associated with high growth rates. If, for example, aggressiveness is not associated with resource gain, then we do not expect aggressiveness and boldness to be positively correlated. The positive feedback framework championed here provides an additional mechanism explaining why boldness and aggressiveness might be positively correlated. These two behavioral tendencies represent not just two alternative ways of gaining assets, they provide synergistic benefits. Being bold brings in resources that result in increased resource holding potential (RHP). Increased RHP enhances the benefit of being aggressive. In turn, being aggressive and winning contests brings in energy that increases escape success, and then allows individuals to be more bold. Boldness and aggressiveness are, however, not always significantly correlated. In particular, a few empirical studies suggest that boldness and aggressiveness appear to be positively correlated only in populations experiencing high predation risk (Bell, 2005; Bell and Sih, 2007; Dingemanse et al., 2007). How can theory explain this pattern? Extant theories hinge on some variation of a growth/mortality trade‐off. Bold, aggressive individuals collect more resources (and grow faster) but suffer higher risks. If predation risk is indeed high, the synergy between boldness and aggressiveness favors a positive correlation between the two. If, however, risk is low, then the trade‐off is no longer important. All individuals should be bold when predators are absent, and if no one has built a defensive morphology, then when predators are present (e.g., in the experiment to evaluate boldness), all individuals should be apparently shy. In that case, there should be little variance among individuals in boldness and thus little opportunity for significant covariance of boldness with aggressiveness. While this theory predicts the observed relationship between risk and the correlation between boldness and aggressiveness, it does not explain the maintenance of variation in boldness in populations with low risk (e.g., Bell, 2005; Bell and Sih, 2007; Dingemanse et al., 2007). To us, this highlights the need for further theoretical work to better explain observed patterns. Note that many of the positive feedback and individual status quo scenarios discussed above involve a coupling of behavioral type with a physiological mechanism. Individuals exhibit a consistent behavioral type, and different individuals have different behavioral types because their behavioral type is anchored to a less plastic state variable. These less plastic state variables could be aspects of physiology or morphology that we discussed in the section on proximate mechanisms underlying variations in behavioral syndromes. For example, variation in boldness and aggressiveness (and other personality traits associated with resource acquisition under risk) might be connected to variation in metabolic rates that are ultimately

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anchored by organ size (e.g., liver, kidney, heart, or lung size) and other relatively stable aspects of metabolic machinery (Biro and Stamps, 2008; Careau et al., 2008). Or, individual differences in sensitivity, choosiness, or learning styles might be associated with variation in relatively stable aspects of brain morphology/physiology or sensory machinery (see Section IV). Some general integrative insights are as follows: (1) to explain stable differences in behavioral type, we should look for associated proximate mechanisms (e.g., metabolic or sensory machinery) that are less plastic than behavior; (2) these proximate mechanisms should not, however, be viewed simply as fixed constraints that determine behavioral type. Instead, they are part of a feedback loop with behavior where the optimal behavior depends on the proximate mechanism and adaptive plasticity in the proximate mechanism depends on behavior; finally, (3) positive or status quo feedback loops can enhance small, initial differences in individual traits (e.g., in metabolic machinery and associated boldness and aggressiveness) to produce long‐term, stable differences in behavioral type. This is a simple, adaptive explanation for why early experiences (early developmental time windows) might be particularly important in shaping both behavioral types and associated proximate mechanisms. To emphasize, the positive feedback and status quo mechanisms discussed above do not obviate the negative feedback inherent in the asset protection principle. Our view is thus that the maintenance of stable behavioral types emerges from an interplay between asset protection (negative feedback) tending to breakdown both within‐individual and between‐individual consistency, opposed by positive feedback and status quo mechanisms tending to maintain behavioral syndromes. The next wave of models on adaptive behavioral syndromes should aim to incorporate biologically specific mechanisms including both negative and positive feedbacks. A final class of explanations for behavioral consistency emphasizes the social benefits of being predictable (Dall et al., 2004; McNamara et al., 2008). In a social context, the problem with being consistent is that predictable individuals run the risk of being exploited. Individuals that are reliably cooperative can be easily cheated, and individuals that are predictably unaggressive doves can lose out to individuals that would otherwise also play dove. When is it beneficial to be predictable? Dall et al. (2004) suggest that it can be beneficial to be consistent if consistency allows one to manipulate the behavior of others via credible threats or promises. A threat to be highly aggressive can cause an opponent to back off rather than engage in a highly costly fight. However, this threat should only be taken seriously if it is actually credible, that is, if the individual is indeed reliable. Similarly, a promise to cooperate can induce a partner to trade favors, but only if the promise is reliable.

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McNamara et al. (2008) modeled the joint evolution of trustworthiness (being reliably cooperative) and social sensitivity about the trustworthiness of others. Socially sensitive individuals gain the benefits of recognizing cooperators from cheaters but accept sampling costs (i.e., the time and energy required to evaluate and remember who is a cooperator and who is a cheater) to gain that social information. Insensitive individuals save on sampling costs, but know less about the trustworthiness of their social partners. In the absence of variation in trustworthiness, there is no need to be socially sensitive. If, however, the population includes both cooperators and cheaters, then this favors the maintenance of variation in social sensitivity. Given that a population has some socially sensitive individuals, this favors the persistence of both cheaters and reliable, trustworthy cooperators. Cheaters exploit insensitive cooperators while reliable cooperators gain the trust of sensitive reciprocators. Interestingly, the models by Dall et al. (2004) and McNamara et al. (2008) predict that behavioral consistency should be more common or more developed in species with more social interaction. That is, if the social benefits of predictability are a major factor explaining the evolution of behavioral syndromes, then social species should clearly exhibit ‘‘more personality’’ than asocial congeners. In addition, if aggressive, competitive contests play an important role in driving the evolution of behavioral consistency, then again, species where aggressive interactions are more common or important should exhibit more clear‐cut behavioral syndromes. This hypothesis can be tested by comparing closely related species or populations that differ in sociality, ideally within a phylogenetic framework. To date, attempts to explain behavioral syndromes have focused primarily on why individuals exhibit behavioral consistency (why they have a behavioral type) and why different individuals have different behavioral types. Only a few have explicitly addressed why particular behaviors (e.g., boldness and aggressiveness) should be correlated, and even fewer have looked at how either proximate or adaptive factors might explain variation in these behavioral correlations. To us, this final issue is the most exciting one. Why should the correlation between boldness and aggressiveness be stronger under higher predation risk? What explains variation among systems in the stability of behavioral correlations over ontogeny and over evolutionary time? Can we predict a priori how males and females should differ in behavioral syndromes in species with different mating systems? Can we predict a priori how species with different ecologies or different population genetic structures should differ in their behavioral syndromes? Our hope is that the next decade will see the development of a unified theory of behavioral syndromes that will enhance our understanding on all of these exciting issues.

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V. BEYOND THE USUAL BEHAVIORAL SYNDROMES To date, much of the ecologically based work on behavioral syndromes has focused on variation in boldness, aggressiveness, or activity per se. These three are often interrelated (Bell, 2005; Huntingford, 1976a; Johnson and Sih, 2005; Riechert and Hedrick, 1993). Boldness is also associated with an individual’s exploratory tendency, another frequently invoked personality trait. Relative to low explorers, high explorers are bolder in novel situations, and perhaps generally bolder with risk and more aggressive. The proactive/reactive axis also embodies elements of boldness, aggressiveness, and activity, where proactive individuals tend to be more bold, active, and aggressive than reactive ones (Koolhaas et al., 1999). As noted by Stamps (2007), an ecologically important connection between these behavioral tendencies is that higher values for all of these often results in both higher resource intake and higher mortality risk. That is, they can be viewed as alternative ways of taking risks to gain rewards. While the above behavioral tendencies reflect major fields of study in behavioral ecology (e.g., predator–prey behavior, competition), other important areas of behavioral ecology (e.g., cooperation, mate choice, parental care, learning) focus on other aspects of behavior that have not yet received much attention from a behavioral syndrome view. We suggest that behavioral consistency likely appears and plays an important role in these other aspects of behavior. Thus, we next discuss several underexamined, potential behavioral syndromes that should benefit from more study. In particular, we focus on syndromes associated with: (1) environmental and social sensitivity, (2) learning, (3) choosiness, (4) mating, (5) parental styles, (6) cooperativeness, and (7) dispersal. En route, we note that many focal issues in behavioral ecology might involve the interplay of multiple behavioral syndromes. For example, mating success might be influenced by behavioral tendencies relative to aggressiveness, boldness, social sensitivity, choosiness, cooperativeness, and parental style. A. ENVIRONMENTAL AND SOCIAL SENSITIVITY The behavioral ecology approach implicitly assumes that animals respond to environmental variation, make adaptive choices (prefer high‐quality options over others), and often modify their behavior based on previous experiences. That is, individuals exhibit environmental sensitivity, adaptive choosiness, and learning. Here, we suggest that further study of individual variation in these three traits should prove highly insightful. By environmental sensitivity, we mean the tendency to alter behavior in response to environmental variation. The term ‘‘environmental responsiveness’’ might be more evocative; however,

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an existing literature (Boyce and Ellis, 2005; Koolhaas et al., 1999) uses the term ‘‘sensitivity.’’ In our terminology, sensitivity does not necessarily imply choosiness, learning, or the ability to make intelligent (adaptive) decisions. Learning involves alteration of future behavior following experiences. Thus, learning implies sensitivity to earlier experiences; however, sensitivity does not necessarily result in learning. Choosiness is the tendency to prefer some options over others. It requires sensitivity but could involve innate preferences that need not be learned. We next outline and discuss frameworks for thinking about syndromes of sensitivity, learning, and choosiness. To organize our thinking about sensitivity, we distinguish three sequential stages in ecologically important tasks, each of which could involve individual variation in sensitivity: (1) first, individuals choose a time and place for a given task; (2) they next choose (or avoid) particular ‘‘partners’’ (e.g., social partners or predators or prey); and (3) finally, they respond, often flexibly, to those ‘‘partners.’’ For example, predators evaluate (and choose) places to search for prey [optimal patch use (Stephens and Krebs, 1986); here, prey are ‘‘partners’’], evaluate different prey items (Sih and Christensen, 2001), and adjust their attack strategy to overcome prey escape and defense tactics (Curio, 1976). Prey do the opposite in each stage. In a mating context, males and females evaluate (and choose) places to search for mating partners, evaluate the quality of different potential mates, and then adjust courtship behavior in response to signals from a particular potential mate. In a cooperative situation, theory assumes that individuals can distinguish between cooperators and defectors and behave accordingly (McNamara et al., 2008). In a contest situation or a dominance hierarchy, individuals are assumed to evaluate relative RHP and/or dominance of different contestants (Maynard Smith, 1982), and to adjust their behavior as a contest unfolds (Riechert and Hammerstein, 1983). In each of these tasks and stages, individuals likely differ in their sensitivity. The literature on proactive/reactive coping styles emphasizes individual variation in environmental sensitivity (Benus et al., 1987, 1990; Koolhaas et al., 1999). Reactive individuals are highly sensitive to changes in their environment. In contrast, proactive individuals follow set behavioral routines and are relatively insensitive to environmental changes. These differences in coping style are associated with genetically based differences in neuroendocrine profiles (Koolhaas et al., 1999) and have ecological and evolutionary implications. Notably, the differences in sensitivity are also related to differences in response to environmental challenges. Sensitive, reactive individuals tend to be more fearful, whereas proactive individuals tend to be more bold and aggressive. Proactive individuals thus tend to dominate in stable environments; however, because proactive individuals are insensitive to environmental change, they do poorly in fluctuating

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environments (Benus et al., 1987, 1991; Dingemanse et al., 2004). Coping styles have been studied in some detail in a range of animals including laboratory rodents (Benus et al., 1987, 1991; Koolhaas et al., 1999), farm animals (Hessing et al., 1993), great tits (Drent et al., 2003; Verbeek et al., 1994, 1996), rainbow trout (Overli et al., 2007), and primates (Capitanio et al., 1998; Suomi, 1987). A parallel literature in humans (Aron, 1996; Boyce and Ellis, 2005; Jawer, 2005) notes that variation in sensitivity might be associated with variation in habitat and job choice (highly sensitive people avoid highly stimulating situations), in fine‐scale behavior (e.g., preferred volume level while listening to music), in other aspects of personality (e.g., creativity), and in mental and physical well‐being (e.g., extreme sensitivity might be associated with depression, migraine headaches, and suppressed immune systems). Although the recent growth of interest in animal personality has brought the literature on coping styles to the attention of behavioral ecologists, to date, with the exception of the work on great tits, few studies in behavioral ecology have quantified either individual variation in environmental or social sensitivity, or its effect on behavior or performance. One notable exception is the work by Patricelli et al. (2002, 2006) on bowerbirds. Male bowerbirds display for females in front of elaborate bowers. Patricelli et al. (2002, 2006) used a robot female that they could control to evaluate the relative ability of different males to adjust their courtship intensity to signals from the female. They found that males that displayed very intensely regardless of signals of interest (or not) from females tended to scare females away. Most notably, their quantitative analysis revealed that a large proportion of the variance in male mating success could be explained by the male’s sensitivity (and adjustment) to female signals (Patricelli et al., 2002, 2006); that is, social sensitivity could play a large role in sexual selection. A second example, again associated with mating success, involves hyperaggressive males in water striders (Sih and Watters, 2005). Males show individual variation in their response to females, males, and male–female pairs. Ideally, males should attempt to mate with females, but should not attempt to mate with males, and have almost no success at separating pairs in order to take over a female. Most males are sensitive to the nature of other water striders; that is, they attempt to mate with females, but not with males or pairs; however, some are hyperaggressive—they expend a great deal of effort toward trying to mate forcibly with not just females, but also males or pairs. Quantitative analyses showed that hyperaggressiveness in water striders has important negative effects on mating success. Does it exist in other species? Our view is that in numerous seminars over the years,

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we have heard anecdotes and often seen photographs of males attempting to mate (and in some cases, mating) with inappropriate partners (e.g., with males, females of other species, dead females, inanimate objects that, in some sense, resemble conspecific females). Our suggestion is that it would be useful to quantify individual variation in this aspect of social sensitivity in more systems. In both of the above examples, social sensitivity was associated with what can be termed ‘‘social skill,’’ the ability to adjust behavior adaptively to environmental variation. In the context of sexual selection, following the three‐stage view outlined earlier, mating success might depend on individual variation in skill in: choosing the right places and times to search for mates, efficient mate choice (e.g., Sih and Watters, 2005), and courtship and response to potential mates (e.g., Patricelli et al., 2002, 2006). Most studies of sexual selection focus on relatively static traits (e.g., male size, color, ornaments). Here, we hypothesize that unexplained variation in mating success might be due to individual variation in behavioral tendencies that underlie variation in multiple aspects of social skill. In each of the above mating examples, the emphasis was on one aspect of social sensitivity. In the syndrome context, an interesting issue is whether social sensitivity carries over across different tasks and contexts. For example: (1) within one stage of the mating context, for example, the mate choice stage, is ability to distinguish the correct species correlated to ability to distinguish the correct gender, and/or the ability to evaluate variation in mate quality within the correct gender? (2) across stages, though still all within the mating context, is sensitivity within the mate choice stage correlated to environmental sensitivity in choosing the time or place to search for mates, for example, sensitivity in choosing among social situations that might differ in density, sex ratio, and/or the mix of behavioral types present? Or, (3) is sensitivity in the mate choice stage correlated to sensitivity to subtle signals in the male–female interplay that results in successful mating? (4) Going beyond mating, is sensitivity in the mating context correlated to social sensitivity in other contexts, for example,, in partner choice and adjustments to social situations or partners in the context of cooperation or competition? And, (5) going beyond social situations, is social sensitivity in one or more social situations correlated to sensitivity relative to other fitness‐related options, for example, habitat choice or diet choice? Finally, is sensitivity correlated to other aspects of personality? The coping style literature and the work on water striders suggest that sensitivity is negatively related to boldness and aggressiveness, but is this a general feature of nature? Should we expect to generally see positive or negative correlations between aspects of sensitivity? If individuals vary along a general sensitivity index, then sensitivity should be positively correlated across different tasks

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or situations. Alternatively, if sensitivity draws on a finite pool of attention (Dukas, 1998), then we might expect negative correlations between sensitivity in different tasks; for example, sensitivity toward potential mates might draw attention away from, and thus reduce sensitivity toward food or predators. In addition, although our general syndrome‐based expectation might be that individuals that are more sensitive than others to the presence of conspecifics per se should also be relatively sensitive to differences among conspecifics in mate quality, this does not appear to hold for hyperaggressive individuals. They are highly sensitive to the presence of conspecifics but insensitive to variations in mate quality; they quickly orient toward, approach and attempt to mate with all conspecifics—male or female. Thus, variation in hyperaggressiveness can generate a negative correlation between sensitivity in different stages of the mating sequence. How might a sensitivity syndrome influence correlations between other behavioral axes? Because the response of a sensitive individual can either be fearful or aggressive, variation in sensitivity can generate either a positive or a negative correlation between boldness and aggressiveness. The proactive‐reactive literature on coping styles suggests that sensitive individuals are generally fearful; that is, they are shy and unaggressive. Variation in sensitivity then contributes to a positive correlation between boldness and aggressiveness. If, however, sensitive individuals respond by being aggressive (as opposed to insensitive individuals ignoring conspecifics), then the result should be a negative correlation between boldness and aggressiveness. Insensitive individuals ignore conspecifics (i.e., they are unaggressive) and predators (i.e., they are bold). Bell and Sih (2007) found that in stickleback fish, these bold, unaggressive animals tend to suffer high predation. In the above examples involving mating behavior, as well as in the literature on coping styles, there is ample evidence that individual variation in sensitivity influences components of fitness. A generality might be that extremes in sensitivity (extremely sensitive people, hyperaggressive water strider males) are selected against (but see Boyce and Ellis, 2005), but that for an intermediate range, selection on sensitivity depends on environmental conditions and environmental stability, generating stabilizing selection on sensitivity. In addition, social sensitivity might be under frequency‐ dependent selection. Finally, a key to understanding selection on sensitivity should be understanding how it relates to an overall, potentially broad behavioral syndrome. Our overall view is that enough examples exist (particularly in the coping styles literature) to suggest that individual variation in environmental and social sensitivity is common, potentially quite important and worthy of

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further study. In particular, from the behavioral syndrome view, we suggest the need for more study on correlations among different aspects of sensitivity and between sensitivity and other aspects of behavioral type (e.g., boldness, aggressiveness, and cooperativeness). B. LEARNING A major field in animal behavior that is related to sensitivity and has also not been well explored from a behavioral syndromes view involves individual differences in learning. For example, if individuals that are good at learning about one type of task are also good at learning about others, then there could be an important carryover across learning tasks. On the other hand, if there are trade‐offs between performance on different learning tasks, then what is gained by learning to solve one problem could come at the expense of learning something else. Another question is whether individual learning styles form part of an individual’s overall behavioral type: particularly exploratory individuals might have more opportunities to experience stimuli and to learn from them. Both of these questions are discussed below. If individual differences in learning and memory are correlated across contexts, then individuals that are good at learning about how to avoid predators, for example, might also be good at remembering where they stored food last year, or what are the reliable cues indicating a suitable nest site. On the other hand, if there are costs of learning, then what an individual has learned about one thing might come at the expense of something else. There are several different ways in which correlated individual differences in learning could be manifested. For example, if general process theory is correct (Shettleworth, 1998), then individual differences in the mechanisms underlying associative learning will generate consistent individual differences in performance on associative learning. Alternatively, there might be correlated individual differences in types of learning (operant and classical conditioning, habituation, sensitization, imprinting, and song learning), all of which involve different ecological contexts, cognitive, and perceptual systems. On the other hand, there might be correlated individual differences in learning that involve the same perceptual systems; for example, individuals that are good at learning how to associate a visual signal with food are also good at associating visual signals with predators, or mates. Finally, there might be correlated individual differences in learning about specific ecological tasks, for example, individuals that are good at associating a chemical cue with the presence of a predator also have good spatial memory about the location of particularly dangerous areas of habitat.

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Despite the rich literature on the mechanisms of learning, we know relatively little about correlated learning tasks in ecologically relevant contexts. A relevant body of literature is concerned with whether there is such a thing as general cognitive ability, which improves individual performance on a variety of learning tasks. If such a general learning syndrome exists, then individual differences in learning are really reflecting differences in ‘‘intelligence,’’ rather than differences in motivation or exploration. For example, studies on mice have shown that up to 38% of the variation in performance on a battery of learning tests assessing a variety of different cognitive tasks can be attributed to general cognitive ability, even when differences in exploration are accounted for (Galsworthy et al., 2002; Matzel et al., 2003, 2006). From a behavioral ecological point of view, the key question is whether there are carryovers or trade‐offs between abilities on different ecological tasks, not necessarily whether that reflects something about general cognitive ability. Despite the obvious ecological importance of such carryovers, correlated differences in learning have rarely been tackled from an ecological or evolutionary point of view. The most intuitive, adaptive expectation is that natural selection should favor general intelligence, but the growing literature on cognitive ecology is showing that animals are especially clever about the most ecologically pertinent challenges (Healy and Braithwaite, 2000; Real, 1993; Shettleworth, 1998). Measuring individual differences in learning in several different contexts across a wide range of ecologically relevant challenges is an obvious priority for future work. Implicit in the argument against general intelligence is that there are costs of learning (Stephens, 1991) that could impose trade‐offs between different forms of learning. For example, learning a new association of color with food caused bumblebees to perform errors in a previously learned task (interference) (Worden et al., 2005). Similarly, flies that had been selected for learning ability showed a trade‐off between short‐ and long‐term memory (Mery et al., 2007), a cost of long‐term memory in terms of stress resistance (Mery and Kawecki, 2005), productivity (egg laying rate) when subjected to nutritional stress (Mery and Kawecki, 2004), and larval competitive ability (Mery and Kawecki, 2003). Familiar behavioral axes such as shy–bold or aggressive–nonaggressive might also be correlated with differences in learning. For example, although proactive individuals perform consistently better than reactive individuals in a standard task, when faced with a change in the environment, reactive mice and great tits are more likely to change their search patterns and to adaptively modulate their behavior than proactive individuals (Benus et al., 1987; Verbeek et al., 1994). Such behavioral flexibility is not due to an intrinsic difference in learning ability between the two types of individuals because both types of individuals are equally capable of learning the task (Benus

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et al., 1987, 1990). Instead, the difference reflects a difference in the amount of exploration between the two groups; the reactive individuals acquire information about the environment that they use in subsequent behavior. Surprisingly few other studies have asked whether individual differences in learning reflect a personality ‘‘type.’’ Individual variation in innovation, or adopting a new behavior pattern when the environment changes, has been documented in several different species (Boogert et al., 2006; Godin et al., 2005; Pfeffer et al., 2002; Reader, 2003), but we know little about whether variation in innovation reflects general learning ability, sociality, boldness, or state dependence [reviewed in Laland and Reader (1999b)]. Some studies have suggested that innovators are likely to be those at the outskirts of social groups (Kummer and Goodall, 1985) and experimental work on guppies has shown that while state‐dependent factors such as sex and hunger level are partly responsible for foraging innovations, some individuals are consistently more likely to innovate than others (Laland and Reader, 1999a,b). Finally, in systems with social learning, there appear to be individual differences in tendency to rely on individual‐based learning (using personal, private information) as opposed to social learning (using public information (Galef and Giraldeau, 2001; Marchetti and Drent, 2000; Valone, 2007). Some of the variation in tendency to learn from others is associated with age; for example in mate copying, younger, inexperienced females tend to copy the mate preferences of older females rather than rely on their own assessments of mate quality (Amlacher and Dugatkin, 2005; Dugatkin and Godin, 1993). However, public versus private learning might also be related to behavioral type. An obvious hypothesis that, to our knowledge, has not been tested is whether individual variation in sociability or affiliativeness is positively correlated to tendency to rely on social learning. Presumably, more sociable individuals will, on average, be exposed to more opportunities for social learning. The question here is, even with equal opportunities for social learning, do more sociable individuals tend to rely more (than less sociable individuals) on public information, as opposed to personal experiences? Alternatively, the producer–scrounger literature suggests that more aggressive, dominant individuals might rely relatively more on public information (generated by subordinates) about resources (Liker and Barta, 2002). A strong reliance on public information (e.g., copying) can generate rapid swings in group preferences (‘‘fads’’) that can strongly favor particular types (Gladwell, 2000). This can have important effects on evolutionary dynamics (Kirkpatrick and Dugatkin, 1994). In addition, the relative use of public versus private information can have major impacts on the dynamics of how social groups respond to changing environments (Valone, 2007), a key issue in a heavily human‐altered modern world. If social learning style is correlated to personality, then selection on behavioral type influences social learning

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and vice versa. Thus, an important additional insight that can come from the behavioral syndrome view involves the dynamics of the joint evolution and ontogenetic development of social learning and personality. C. CHOOSINESS Choosiness is the strength of preference for some options over others. If individuals set a threshold criterion where options are only accepted if they are above the threshold in quality, then choosy individuals have a higher threshold. If options are evaluated sequentially, being choosy often involves evaluating more options before making a choice. Choice has been studied in many isolated contexts, for example, diet choice (Sih and Christensen, 2001), mate choice (Andersson and Simmons, 2006), or habitat choice (Stamps et al., 2005). Although studies of mate choice often assume that a given individual has fixed preferences, in other contexts (e.g., diet choice), the usual notion is that individuals alter their choosiness depending on the magnitude of differences in quality between different options, and on the availability of high‐quality options (Crowley et al., 1991; Stephens and Krebs, 1986). For example, in a simple scenario with only two types of options, A and B, where A is better than B, individuals should be more choosy if A is much better than B, and if A is highly abundant. In contrast, if A is only a bit better than B, or if A is scarce, then the same individuals should not be as choosy, they should accept either A or B. It is important to note the distinction between variation in choosiness and variation in preference (Jennions and Petrie, 1997). For the latter, females are known to vary, for example, in what sorts of male traits they prefer (Brooks and Endler, 2001; Cummings and Mollaghan, 2006; Forstmeier and Birkhead, 2004; Jang and Greenfield, 2000; Morris et al., 2003). Less is known about variation among females in choosiness per se, that is, in the strength of their preference (but see Reinhold et al., 2002). A choosiness syndrome can then be evaluated either within a given context or across contexts. In the diet choice context, for example, if the individual foragers that are most choosy when food is abundant are also the most choosy when food is scarce, then the result is a choosiness syndrome. In the diet choice literature, although hundreds of studies have documented average preferences and how mean choosiness varies with the abundance of different prey types (reviewed in Sih and Christensen, 2001), we know of no studies that explicitly tested for consistency in choosiness. In humans, we have the sense that some people are consistently choosier than others in what they are willing to eat, but we know of no data on this issue. In a mating context, a classic method for evaluating female choice involves offering the focal female an opportunity to interact with two males (e.g., on opposite sides of a partitioned aquarium). The usual goal

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is to test whether, on average, females prefer particular male phenotypes (e.g., larger males or more colorful males). Our impression is that a typical result might be to find that 16 of 20 females prefer the larger male, so the conclusion is that females prefer larger males. However, in many cases, the investigator might ‘‘toss out’’ females that showed no significant preference, and among the 20 that showed a preference, they varied substantially in the strength of their preference. Some strongly preferred the larger male, whereas others exhibited only a weak preference for the larger male. The point here is that substantial variation might exist in female choosiness; however, to date, the field generally has not focused on this variation. In the syndrome context, we are interested in whether females are repeatable in their choosiness across trials in the same basic situation, and whether they are consistent in their mate choosiness across different situations (e.g., different types of focal male traits or different male abundances). Across contexts, the issue is whether the same individuals that are choosier than others about their diets are also relatively choosy about their mates (and about other social partners, about aspects of habitat use etc). In humans, we know people who are particularly choosy about their diets and about their wines, or about their music, movies, or TV shows, or about their brands of clothing or electronic equipment, or about their mating partners. The question is: ‘‘Is choosiness correlated across these different situations?’’ Optimality theory identifies an optimal degree of choosiness in any given situation. Just as behavioral correlations in the shy–bold or aggressive–unaggressive syndromes can spillover to be associated with suboptimal behavior, a choosiness syndrome can result in suboptimal behavior. A behavioral syndrome hypothesis is that, if a choosiness syndrome exists, an individual that is generally very choosy across many situations will likely be too choosy in some situations. D. MATING BEHAVIOR Perhaps the most often studied subject in behavioral ecology is mating behavior and sexual selection. Despite the fact that behavioral tendencies such as aggressiveness and social sensitivity clearly influence mating tactics and mating success, to date, few studies have integrated the behavioral syndrome approach into studies of mating and sexual selection. That is, relatively few studies have quantified whether individual variation in mating tactics is correlated with behavior in other contexts. In many systems, males clearly exhibit individual variation in mating tactics. In some cases, males have alternative mating morphs, often involving large territorial males versus smaller, sneaky males (Emlen, 1997; Shuster, 1989; Sinervo and Lively, 1996; Watters, 2005). Although it

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seems obvious that males with very different morphologies (e.g., size, ornamentation) will also likely differ in their overall behavioral type (as expressed in various nonmating contexts—foraging, antipredator, dispersal etc), few have actually quantified how these alternative male types differ in behavior outside of the mating context. In other cases, males within a given population differ in mating tactics, but without major, obvious differences in morphology. For example, in the search phase, males can either be territorial (or simply, site‐faithful) or actively explore a large area across many territories. When females are encountered, males can either display to them, be sneaky, or attempt to coerce females to mate (Magellan and Magurran, 2007; Reichard et al., 2007). In socially monogamous systems, males can either be faithful to a female (and often, provide parental care) or be a philanderer who devotes considerable effort toward gaining extrapair copulations (Westneat and Sargent, 1996). In turn, other studies have documented individual variation among females in their preferences (Brooks and Endler, 2001; Cummings and Mollaghan, 2006; Forstmeier and Birkhead, 2004; Jang and Greenfield, 2000; Morris et al., 2003). For example, some females like symmetrical males while others like asymmetrical ones (Morris et al., 2006). Some females copy the preferences of other females while others rely on their own assessment of males. The obvious syndrome question is whether there is something else distinctive about females with different preferences, apart from obvious attributes such as size (Morris et al., 2006) or age. In the particular situation where females engage in sexual cannibalism, individual females differ in their tendency to attack males versus mate with them (Johnson and Sih, 2005). In the syndrome context, the question is: Do individual differences in mating tactics reflect differences in overall behavioral type? Are mating tactics part of a behavioral syndrome? If so, then this potentially introduces another form of interaction or even conflict between natural selection and sexual selection. For ornaments, observed traits are thought to be shaped by a trade‐off between sexual selection favoring exaggeration of the ornament versus natural selection preventing further elaboration (Endler, 1995; Kokko et al., 2006). For behavior, a similar trade‐off might often exist where sexual selection favors more highly active, aggressive, or bold behavioral types than is favored by natural selection in other contexts (e.g., in a parental care context or when predators are present). That is, selection favoring high aggressiveness in male–male competition for access to females might spillover to cause apparently inappropriate parental care behavior (Wingfield et al., 1990), or inappropriately bold responses to predation risk. Of course, conversely, it is also possible that selection favoring high aggressiveness in nonmating contexts could spillover to

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cause inappropriately aggressive behavior toward mates. An example of the latter involves sexual cannibalism in fishing spiders where selection favoring high voracity in a nonmating context (in a food‐limited environment) appears to have spilled over to contribute to excess sexual cannibalism (Johnson and Sih, 2005). Overall, a full understanding of selection on mating tactics (male and female) might require knowing how these tactics are part of a broader behavioral syndrome. If mating tactics are part of a broader behavioral syndrome, then this suggests the possibility of adaptive female choice based on the male’s behavioral type. Theory predicts that females should prefer male traits that are indicators that the male can provide either direct benefits or good genes. In humans, mate choice is often based not just on resources or good looks, but on personality. The notion that the same idea might apply in other animals has rarely been studied explicitly. Our suggestion here is that there are several ways that a male’s behavioral type (e.g., as revealed by his mating tactics or displays) might provide useful indicators for guiding female choice. One possibility is female choice for good genes, where the male’s behavioral type [which is typically heritable (Penke et al., 2007; Reale et al., 2007; van Oers et al., 2005)] provides an ecological or social mechanism for why some male genotypes might enhance offspring fitness. By definition, a male’s behavioral type provides information on how he copes with various environmental pressures. His boldness and aggressiveness influence his style and ability to cope with food limitation, competition, and predation risk. Boldness might also be associated with dispersal tendencies (Dingemanse et al., 2003; Fraser et al., 2001). Social sensitivity in mating displays might be correlated to social sensitivity in other contexts. If conditions are likely to be stable across generations, a female can prefer males that have a behavioral type that worked well in the present generation. If she can determine offspring environments [e.g., via maternally controlled habitat selection that might be followed by a tendency for offspring to prefer that habitat throughout their lives (Davis and Stamps, 2004)], then she can use her mate choice to provide her offspring with a suitable, adaptive behavioral type. Similarly, if offspring are likely to disperse on their own into new, different conditions, in principle, a female could choose a male with a behavioral type that fits the anticipated new conditions. Finally, if her offspring are likely to face unpredictable conditions, she could choose an environmentally sensitive male who can cope well with changing environments. Or, if success in social interactions inherently requires social sensitivity, then females might generally prefer males whose displays indicate high social sensitivity (e.g., Patricelli et al., 2002, 2006). This could, in part, explain the human female preference for males that are funny (Bressler and Balshine, 2006).

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Alternatively, female choice might be for direct benefits. The benefit of preferring highly aggressive males that have won male–male contests can either derive from immediate resources provided by those males, or from future benefits associated with males that can provide a superior territory. In some cases, females prefer less aggressive males, perhaps because they are less likely to engage in costly sexual coercion that either wastes the female’s time and energy, or can even injure females (Ophir et al., 2005). Most interestingly, females might use the male’s contest behavior or courtship displays to evaluate his future parental care or cooperation (variation in parental care is discussed below). This makes sense if his premating behavior is an honest indicator of his postmating behavior. An issue of general importance is: when a male ‘‘promises’’ to provide future benefits, why should he keep that promise? If males promise to provide good parental care, but then often renege on that promise, females should not trust the promise, and males should then not even bother to make the promise; that is, without honest signals, the system breaks down. The usual idea is that honesty is enforced by high signal costs (Zahavi, 1975). If the signal is costly (a handicap), then male production of the signal should be proportional to their ability to handle the cost. An alternative possibility is that the signal is an index—if there is a strong correlation between the male’s signal and either his direct (possibly, deferred) benefits, or his genes (LeBas et al., 2003; McGlothlin et al., 2005). A tight behavioral syndrome possibly provides that correlation. If there is a negative correlation between a male’s aggressiveness and paternal care (Wingfield et al., 1990), then a male’s aggressiveness during male–male competition or courtship displays might be a useful indicator of his future cooperation in parental care. E. PARENTAL STYLES How parents behave toward their offspring can strongly affect the fitness of their offspring. Still, within a given species, it is often reported that individuals differ in how they parent. Such individual variation might reflect state‐dependent differences in sex, age, condition, or in the trade‐off between current versus future reproduction. However, some individual birds consistently provide more parental care than others, that is, individual differences are repeatable (Schwagmeyer and Mock, 2003). A relatively unexplored area is whether such individual differences in parental behavior reflect part of an overall behavioral type. For example, Budaev et al. (1999) found that parental convict cichlids differed in how they behaved toward their offspring, and those differences were correlated with behavior in other contexts. Individuals that provided more parental care (food provisioning) were also more exploratory and less

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aggressive (Budaev et al., 1999). Other studies have also reported evidence of a trade‐off between parental behavior and aggression. One of the best examples of an aggressive spillover, in fact, is the image of a male pumped up on testosterone who does not behave as a good dad (Ketterson et al., 1992; Nunes et al., 2000; Ros et al., 2004; Van Duyse et al., 2000; Veiga et al., 2002). Another well‐studied trade‐off is between parental care and mate attraction: males that spend more time attracting mates or seeking EPCs provide less parental care (Bjelvenmark and Forsgren, 2003; Clark and Galef, 1999; De Ridder et al., 2000; Duckworth et al., 2003; Kokko, 1998; Magrath and Elgar, 1997; Magrath and Komdeur, 2003; Mitchell et al., 2007; Peters, 2002; Qvarnstrom, 1997; Smith, 1995). There are at least two possible mechanisms that could produce this negative relationship. More ornamented males might provide less parental care because they can achieve relatively greater reproductive success from seeking EPCs [trade‐off (Magrath and Komdeur, 2003)]. Or, females paired with attractive males provide more parental care in order to prevent the desertion of their attractive mates [differential allocation (Kokko, 1998)], and this, in turn, allows attractive males to provide less parental care. An alternative view, however, is that a positive relationship between ornament size and parental behavior could be adaptive if females use a male’s ornament as an indicator of his future behavior as a parent (‘‘the good parent’’ hypothesis) (Pampoulie et al., 2004). Female mate choice for ornamented males could really therefore reflect choice of a package of male traits that includes parental care (Schwagmeyer and Mock, 2003). This hypothesis has been formalized as the ‘‘sealed bid model,’’ where individuals behave as if they have committed to a certain level of parental care at the outset and do not modify their care in response to the partner’s effort (Schwagmeyer and Mock, 2003). This model is in contrast to the negotiation model, where individuals adjust their parental care facultatively in response to the efforts of their mate (McNamara et al., 1999). The sealed bid model is broadly consistent with a behavioral syndrome: males vary in the amount of paternal care they provide, males are consistent across broods or seasons, and a male’s parental type is indicated by an ornamental trait. However, the negotiation model also raises interesting syndrome questions related to individual variation in cooperation and social sensitivity, as discussed above. And what about females? Do females differ in the quality of parental care they provide to their offspring? Several studies have shown that male birds are more consistent in their parental behavior relative to females. For example, in several birds, male feeding rate is repeatable and heritable, whereas females are not repeatable (Freeman‐Gallant and Rothstein, 1999; MacColl and Hatchwell, 2003; Nakagawa et al., 2007; Schwagmeyer and Mock, 2003). This finding has been interpreted as reflecting greater responsiveness on the part of the female to the needs of her offspring and the

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behavior of her partner. On the other hand, female mice that had been artificially selected to be aggressive (low attack latency) actually engaged in more maternal behaviors such as nursing, licking, and grooming of her pups (Benus and Rondigs, 1996). So do individual differences in parental style really matter? A fascinating recent line of research on the mechanisms of parental effects in rats offers a resounding ‘‘yes’’ to this question. Like the mice mentioned above, mothering rats differ in the amount of arched back nursing and licking and grooming behavior they perform. Pups that receive more licking and grooming are less fearful and less stress responsive than pups from less attentive mothers (Storey et al., 2006). Differences in the offspring of high licking and grooming versus low licking and grooming mothers emerged early in life and were long term (but reversible) (Weaver et al., 2004). In fact, the offspring of high licking and grooming mothers ended up becoming high licking and grooming mothers themselves (Champagne and Meaney, 2006). Using cross fostering, Meaney et al. have convincingly demonstrated that the inheritance of parental styles is epigenetic and occurs via DNA methylation of the glucocorticoid receptor. There is converging evidence that something similar occurs in rhesus monkeys: some female monkeys are more ‘‘abusive’’ than others, as judged by differences in rates of maternal rejection and grooming. Variation among mothers influences their offspring’s anxiety and fearfulness, eventually influencing the way these offspring behave as parents (Maestripieri et al., 2006). Cross fostering experiments have shown that these effects are also nongenetic, probably mediated by serotonergic transmission (Maestripieri et al., 2007). Our understanding of the mechanisms linking the effects of mothers on their offspring has far outpaced our understanding of the evolutionary forces that could maintain variation among female rats and monkeys in maternal behaviors. One hypothesis is that it is adaptive: perhaps stressed, that is, low licking and grooming mothers ‘‘program’’ their kids to respond to adversity (Diorio and Meaney, 2007). Therefore, low licking and grooming mothers are favored in stressful environments. Alternatively, or in addition, perhaps a female’s maternal style is part of her overall behavioral type; perhaps mothers that engage in more abusive behavior as parents are also distinctive in other respects, which outweigh the costs of impaired maternal performance (Bennett et al., 2002; Champoux et al., 2002). F. COOPERATIVENESS Cooperation is the subject of a great deal of behavioral study in both behavioral ecology and human psychology. Here, we consider the possible role of behavioral syndromes in the study of cooperation. Simple theory on

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cooperation examines individuals that are either cooperators or defectors (or perhaps, follow a tit‐for‐tat, TFT, strategy). Few studies, however, have actually quantified individual variation in cooperativeness (Bergmuller et al., 2007; Wright, 2007). In the syndrome context, a key question is: do individual differences in tendency to cooperate carry over across multiple contexts? Are the same individuals cooperative or even altruistic in social foraging, group vigilance, resource sharing, and cooperative breeding or shared parental care? In some situations, our general view is that individuals cooperate due to kin selection (Hamilton, 1964), whereas in others, the notion is that cooperation reflects reciprocal altruism (Trivers, 1971). Does frequent cooperation with kin spillover to result in excess cooperation with nonkin or vice versa? Of course, theory does not assume that individuals should be unconditionally cooperative. Instead, if an individual is highly cooperative, then others should exploit that tendency by engaging in subtle cheating. This, in turn, favors the evolution of social sensitivity [to evaluate the trustworthiness of potential social partners (McNamara et al., 2008)]. Put another way, analyses of cooperation are usually best thought of as an interplay between cooperation and conflict or deception (Dugatkin and Reeve, 1998). This interplay suggests that behavior in cooperative scenarios might often reflect the intersection of several behavioral tendencies. Consider, for example, predator inspection. In several species of schooling fish (e.g., guppies, sticklebacks), individuals leave the school and approach predators apparently to gain information about the risk posed by the predator (Pitcher, 1992). Most interestingly, individuals often inspect in pairs. Several studies have examined whether these pairs might be reciprocal altruists where the lead individual (the one that is closer to the predator) at any given time is being altruistic—taking greater risks while generating benefits (information) for both members of the pair (Dugatkin and Alfieri, 1991; Milinski, 1987). The altruistic act is reciprocated repeatedly in one predator inspection bout when individuals take turns being the lead individual. In particular, investigators have been interested in whether the dynamics of predator inspection fit a simple TFT model, where individuals cooperate as long as their partner cooperates (inspects), but defect (stop inspecting) when the partner defects. In that context, a given individual’s behavior during predator inspection could depend on its: (1) cooperative tendency, (2) schooling tendency, (3) social sensitivity, and also (4) boldness per se. Boldness can be measured by the individual’s tendency to do predator inspection even when alone. Schooling tendency can be assayed by looking at group size preferences when offered a choice between groups of different size. Social sensitivity has at least two elements—individuals should reduce their tendency to inspect if the partner has recently defected, but also increase their tendency to inspect if the partner resumes being cooperative.

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Cooperativeness should be assessed after accounting statistically for these other behavioral tendencies. Overall behavior and interaction dynamics should depend on the interplay between these different behavioral tendencies for both individuals. Later in this chapter, we discuss quantitative methods for examining multiple behavioral tendencies in more detail. G. DISPERSAL Dispersal behavior can have critical effects on various ecological and evolutionary processes (Clobert et al., 2001). In particular, in modern habitats that are often fragmented, there is considerable interest in how dispersal and movements among patches affect metapopulation and metacommunity dynamics (Holyoak et al., 2005), as well as species invasions. Most theory in these aspects of spatial ecology emphasizes the importance of the amount and pattern of dispersal; however, few include much, if any, mechanistic details on the biology of dispersal. One potentially very important factor is individual variation in dispersal behavior (Benard and McCauley, 2008) and its relationship to a general behavioral type; that is, how dispersal is part of an overall behavioral syndrome. The dispersal process involves three stages each of which can be influenced by individual variation in behavioral type (Baguette and Van Dyck, 2007): (1) leaving a source patch, (2) moving through a matrix of unsuitable habitat, and (3) settling into a new patch. The relationship between behavioral type and the tendency to leave a source patch depends on the ecological and social pressures involved in inducing dispersal. If dispersal is not a direct response to stressful conditions in the source patch, but is instead active and ‘‘voluntary,’’ then dispersers might tend to be the more bold individuals, individuals with less fear of the unknown. Even when all or most individuals have strong incentives to leave, if the costs of dispersal are also high, we still might expect dispersers to be more bold than average. Indeed, some studies have found that dispersers tend to be more bold or exploratory than average (Dingemanse et al., 2003; Fraser et al., 2001; Rehage and Sih, 2004; Whybrow, 2005). In contrast, if individuals are driven to disperse by high predation risk, then it might be the more timid ones that opt to leave, dispersers might tend to be the less bold (more fearful) individuals. Alternatively, if dispersal is induced by interference competition and aggression, then dispersers might be the unaggressive, subordinate individuals that are driven out by more aggressive dominants. On the other hand, in marmots, it is the most aggressive (disagreeable) individuals that are forced to leave the social group (Armitage, 1986). Finally, even without aggression, at high density, asocial individuals (that avoid conspecifics) might be overrepresented among the dispersers (Cote and Clobert, 2007). The main points are

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that we expect nonrandom dispersal by behavioral type to be common and that the particular behavioral type that disperses more should be predictable given the ecological or social scenario. Recent work on the spread of western bluebirds in the United States highlights many of these points. As the range of Western bluebirds has expanded, they have displaced native Mountain bluebirds. Duckworth and Badyaev (2007) showed that aggressiveness is heritable in Western bluebirds and it is the especially aggressive Western bluebirds which disperse into new areas, outcompeting the Mountain bluebirds. However, over generations, the aggressiveness of Western bluebirds in their new range decreased rapidly in an evolutionary response to selection for reduced aggressiveness, probably because more aggressive males provide less parental care and therefore have lower reproductive success (Duckworth, 2006). This study shows that different behavioral types are favored at different stages of invasion: the aggressive Western bluebirds disperse, while less aggressive individuals are favored after establishment. Ecological selection pressures in the matrix habitat between patches can also represent a strong, selective filter that favors some behavioral types over others. And, settlement and successful establishment in a new patch can depend on behavioral type. Highly social individuals should be attracted to conspecifics and should thus be less likely to colonize empty habitats, as compared to asocial individuals. Asocial individuals might thus be particularly important in metapopulation dynamics and the spread of invasions. Finally, after settling, the new disperser’s behavioral type could play an important role in determining its establishment and impact on the colonized community. In order to establish in a new habitat, dispersers typically need to cope with novel selection pressures—often, new predators, competitors, or prey. The ability to cope with new challenges might require problem solving that is associated with low neophobia. Interestingly, broad, comparative analyses of birds suggest that invasive species tend to be non‐neophobic species that often discover new foraging innovations (Sol et al., 2002, 2005). Overall, we suggest that assays that document individual variation in boldness, aggressiveness (as compared to affiliative tendency), neophobia, and dispersal tendency could help understand major patterns in spatial ecology. VI. FUTURE PROSPECTS A. GAME THEORY AND EFFECTS OF SOCIAL GROUP COMPOSITION Game theory assumes that in social groups, the fitness of behavioral types (e.g., of hawks vs doves or of territorials vs satellites) is frequency‐ dependent; that is, it depends on the mix of behavioral types in the group

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(Dugatkin and Reeve, 1998; Maynard Smith, 1982; Sinervo and Calsbeek, 2006). This basic scenario holds for many theoretical behavioral dichotomies—for example, hawk/dove, producer/scrounger, cooperator/defector, as well as for more complex games like rock/scissors/paper. Although this is a fundamental tenet of game theory that has guided our thinking on social behavior for the last 35 years, surprisingly few studies have experimentally manipulated the frequency of behavioral types to examine actual effects on fitness or on behavioral dynamics (e.g., Beauchamp et al., 1997; Flynn and Giraldeau, 2001). The exception involves morphologically based alternative mating types (AMTs), for example, larger territorial individuals versus smaller satellites. Here, because behavioral types are easy to identify, studies have indeed examined frequency‐dependent fitnesses associated with the different types. But even here, few experimental studies have manipulated the relative frequency of these AMTs (Bleay et al., 2007; Warner and Hoffman, 1980). A possible explanation for this disconnect between decades of theory and empirical work is the fact that until recently, relatively few studies have quantified individual variation in behavioral type in order to identify ‘‘hawks’’ and ‘‘doves,’’ a prerequisite for experimentally creating groups with different mixes of hawks and doves. Now that behavioral syndromes are receiving more attention, a key issue should be to better understand how the behavior and fitness of different behavioral types depends on the group’s social composition [mix of behavioral types in the group (e.g., Sih and Watters, 2005)]. Social selection theory (Wolf et al., 1998) provides a quantitative framework for relating both individual traits and the group’s social composition to individual fitness. The basic idea extends the regression approach for quantifying natural selection and sexual selection on traits (e.g., Arnold and Wade, 1984a,b). Wolf et al. (1998) incorporated effects of the individual’s group social composition by adding the social group’s mean trait value as an independent variable in the regression equation of traits (individual and group) on fitness. The method partitions out natural and sexual selection gradients (relationships between the individual’s traits and fitness) from social selection gradients (the relationship between the interacting group’s mean trait value and individual fitness). Selection on a focal trait then also depends on social selection—the product of the social selection gradient and the covariance between the individual’s trait and the social group’s trait. In frequency‐dependent games, individual fitness should depend on the interaction between the individual’s trait and the group’s social composition. This is handled by adding an interaction term into the regression equation. Variation in social group composition (e.g., the mix of more vs less aggressive animals in a social group) likely affects not just the fitness of each behavioral type but also the actual behavior expressed by different

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individuals. When a group of highly aggressive individuals are put together, some will remain highly aggressive, while others will substantially reduce their aggressiveness, that is, individuals will likely vary in behavioral plasticity. The behavioral syndrome view suggests new ways of thinking about behavioral plasticity in a game context. Most simple game theory models assume that individuals have either pure behavioral types (e.g., no plasticity, pure hawks, or pure doves) or no behavioral types [i.e., all individuals follow the same optimal probabilistic or condition‐dependent ESS (Dugatkin and Reeve, 1998; Maynard Smith, 1982)]. In contrast, behavioral syndrome studies suggest that many real animals show some, but limited (less than optimal), plasticity; for example, both shy and bold individuals alter their boldness depending on the context (are less bold when predators are present), but within limits that allow us to identify some individuals as being consistently more shy versus more bold. Using a reaction norm framework, the simple behavioral syndrome approach posits that males differ in average behavior, but have similar behavioral plasticity (parallel reaction norms). In reality, animals appear to differ substantially in both mean behavioral type and behavioral plasticity (Koolhaas et al., 1999). Only a few studies have examined effects of the group’s social composition on behavioral plasticity within groups. Some found that individuals do not retain their behavioral types when they are placed in a social group (Mottley and Giraldeau, 2000). Other studies show that behavioral types are largely maintained (e.g., aggressive individuals stay relatively aggressive, or AMTs do not modify their behavior) regardless of the group’s social composition (Sih and Watters, 2005; van Erp‐van der Kooij et al., 2003). However, even in these studies, where most individuals maintained their behavioral type, some individuals showed substantial behavioral plasticity. For example, Sih and Watters, (2005) created groups of water striders that differed in average male aggressiveness. They found that although, in general, hyperaggressiveness was only seen in groups made up primarily of highly aggressive males, one hyperaggressive male emerged in a group that was created by putting together very unaggressive males. Apparently, one male that was unaggressive in a mixed social background became much more aggressive when it was surrounded by males that were all relatively passive. Clearly, more study is needed to better understand variation among behavioral types in their social plasticity in response to the group’s social composition. Finally, if the fitness of behavioral types depends on the group’s social composition, then individuals should choose group social compositions that favor them (i.e., they should exhibit adaptive social situation choice). Alternatively, individuals might exhibit nonadaptive social preferences; for example, through imprinting, individuals might prefer associating with

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their own behavioral type even when this is not adaptive. In any case, patterns of social situation choice should have important effects on selection and evolution. As noted above, social selection theory emphasizes that the covariance between individual traits and group traits (e.g., a tendency for aggressive individuals to interact with other aggressive individuals) is a key to how social selection influences selection on individual traits. Phenotype‐ dependent social situation choice is a likely mechanism generating this covariance. In general, adaptive situation choice can generate diversity both by driving the evolution of specialization (and ultimately, speciation) and by allowing the maintenance of variation (Wcislo, 1989; Wilson and Yoshimura, 1994). For example, if bold individuals do well in habitat X, but not Y, and vice versa for shy individuals, then both can do well and persist if they each prefer their optimal habitats. Social situation choice, however, is made more complicated by the fact that the sum of individual social situation choices determines the observed social compositions in different groups. Hawks that prefer to associate with doves might not be able to do so if doves avoid associating with hawks. Further study of behavioral type‐dependent social situation choice should prove insightful. B. BEHAVIOR AS THE OUTCOME OF MULTIPLE BEHAVIORAL SYNDROMES In the above discussion of cooperation, as with other behaviors, behavioral expression by each individual, and behavioral dynamics in an interaction, probably depend on multiple behavioral axes. This is, in fact, a familiar concept in human personality studies. Our behavior in any given situation is thought to reflect five main personality axes—the Big Five (extraversion, neuroticism, agreeableness, openness, and conscientiousness) with multiple subfactors within each of these five main factors (McCrae and Costa, 1999). The Big Five is a quantitative, statistical construct that emerged from factor analysis. Behavioral assays (often, questionnaires) assign a subject a score (from 0 to 100) on each of the five factors. Thus, in principle, it is possible to quantify the relative contribution of each of the Big Five to variation in actual behavior in a given context. Along similar lines, some detailed studies in behavioral genetics have partitioned out how behavior in a standardized laboratory assay (e.g., the open field assay) reflects multiple statistical factors [e.g., activity per se, exploratory tendency vs fear or anxiety (Henderson et al., 2004)]. Quantitative analysis of the role of multiple behavioral axes in explaining overall behavior and fitness outcomes should be an exciting future step for behavioral ecology. For example, in a mating context, one could quantify individual variation among a group of males in activity, aggressiveness, and social sensitivity (Fig. 3). Ideally, each of these axes would be assessed in

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Activity

Scramble competition for females

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Probability of mating per mating attempt

FIG. 3. Multiple pathways for how multiple behavioral tendencies might influence mating success. On the left are three behavioral axes: activity, social sensitivity, and aggressiveness. All three influence encounter rates with females. Social sensitivity and aggressiveness also affect variation among males in their efficiency of choosing suitable females to court, and probability of mating given a mating attempt. See the text for details.

multiple contexts, of which mating is only one. Each of these behavioral tendencies could then play a role in governing behavior and success in multiple stages that lead up to overall mating success. First, males must encounter females. Depending on the social system, either more active or more aggressive males might encounter females more. In systems with ‘‘scramble competition’’ for finding females, more active males should, on average, be better scramble competitors. In systems with interference competition for access to females, more aggressive males should outcompete other males in agonistic contests and might thus encounter more females. In general, males that have higher social sensitivity should exercise adaptive social situation choice that should enhance their encounter rates with females. For example, they might actively leave sites with an unfavorable sex ratio and prefer sites with more females per male. Social sensitivity should also help males to efficiently choose appropriate females to court (Sih and Watters, 2005). Inappropriate choices could include the wrong species, gender, or age class, or females that are of either low quality or too high quality (females that will very likely reject the focal male). Finally,

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social sensitivity might be associated with higher courtship ability that enhances the probability of mating per mating attempt (Patricelli et al., 2002, 2006). More aggressive displays could not only increase the probability of mating per mating attempt but also be associated with inappropriate mating attempts and unwanted sexual coercion (Ophir et al., 2005; Sih and Watters, 2005). Although, in theory, distinct behavioral axes should be uncorrelated (e.g., the Big Five in human personality study are orthogonal factors from a factor analysis), in reality, in any given sample, these axes might be correlated. For example, aggressiveness might be negatively correlated to social sensitivity (see earlier references). In principle, one could use path analysis (see Sih et al., 2002) to quantify correlations among behavioral axes and the relative effects of each of these behavioral axes in determining overall mating success via multiple pathways.

VII. SUMMARY After placing the study of behavioral syndromes into historical perspective and clarifying some misconceptions about the term, the aim of this chapter is to illustrate some of the important questions that come into focus when viewing animal behavior through ‘‘behavioral syndromes lenses.’’ In general, we see two particularly exciting research directions. One of these focuses on trying to understand variation in behavioral syndromes. The other applies the behavioral syndromes approach to topics of interest to behavioral ecologists that have not historically focused on individual variation: For any given behavior, do individuals behave consistently differently from each other? If so, are those differences correlated across contexts? The next major task in studies of behavioral syndromes themselves is to quantify and explain the patterns of variation in behavioral syndromes. As a first step, for example, we would like to know which behaviors tend to occur in clusters and which tend to be independent? When do correlations break down over ontogenetic and evolutionary time? Then, the challenge is to explain those patterns from both a proximate and ultimate perspective— how does selection act on differences in the lability of proximate mechanisms to produce variable correlations? At the same time, we expect that the next major wave of studies on behavioral syndromes will apply these ideas to understand topics of interest to behavioral ecologists, things like mate choice, cooperation, and group living. We described several relatively understudied axes of behavioral variation, for example environmental and social sensitivity, learning,

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choosiness, cooperativeness, etc, all of which could offer new insights into long‐standing questions. Along the way, we highlighted other priorities for research such as consideration of nonbehavioral traits such as physiology and morphology as part of an integrated phenotype and the inclusion of conceptual (e.g., dynamic programming, network theory, and path analysis) and empirical (e.g., genomics) tools. Acknowledgments This chapter was shaped by conversations with many people at many conferences, but in particular, by ongoing discussions with members of the Sih laboratory and by stimulating interactions with Cait McGraw. Thanks to Judy Stamps, Shala Hankison, and Ripan Malhi for advice. The work was funded, in part, by a grant from the National Science Foundation.

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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Information Warfare and Parent–Offspring Conflict Rebecca M. Kilner and Camilla A. Hinde department of zoology, cambridge cb2 3ej, united kingdom

If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle. Sun Tzu, The Art of War

I. INTRODUCTION Information warfare occurs when factions use and manage information to gain an advantage over their rivals. It is now commonplace in diverse contexts ranging from identity fraud to industrial espionage, from biotech war (Monbiot, 2002) to military campaigns (reviewed by Badsey, 1999). Here we suggest that information warfare plays an important, though usually overlooked, role in influencing the outcome of parent–offspring conflict, the evolutionary conflict of interest between parents and their young over the provision of parental investment. Information warfare can arise whenever one party has, or purports to have, access to information that their rivals are keen to know (e.g., Dawkins and Krebs, 1978; Krebs and Dawkins, 1984). In principle, the asymmetry in knowledge can counterbalance any asymmetry in physical prowess between rivals, and the more knowledgeable party might even gain the upperhand. For this reason, information warfare is aptly applied to the context of parent–offspring conflict, because young animals in conflict with their parents are often placed at a disadvantage by their relatively small size and weaker physical power (Alexander, 1974; Mock and Parker, 1997; Trivers, 1974). When information is discussed in this context, it is usually in relation to advertisements of offspring condition and their capacity to manipulate 283 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00006-5

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parents (e.g., Godfray, 1991; Godfray and Johnstone, 2000; Mock and Parker, 1997; Royle et al., 2002; Trivers, 1974, 1985; Wright and Leonard, 2002; Zahavi, 1977). But advertisement is only one of several ways in which information can be deployed to bring power. Information can also be withheld or only partially revealed, to the advantage of the signaler. There may be campaigns to spread disinformation and there can be competition for access to tactical information. Rivals may attempt to block the sending of key information or they may selectively leak information to malevolent third parties to engineer the downfall of their opponent. In the second part of this chapter, we show how each of these strategies influences interactions within families in the natural world. The hypotheses we formulate here are based on empirical examples and we hope that they will inspire more formal theoretical work in this area. By contrast, the literature to date on parent–offspring conflict has a strong theoretical bias, which has already been the subject of several excellent reviews (Godfray, 1995a, 1999; Godfray and Johnstone, 2000; Mock and Parker, 1997; Parker et al., 2002). We therefore start with just a brief introduction to the theory of parent–offspring conflict, before assessing in some detail the empirical evidence that it is a significant selective force in nature.

II. PARENT–OFFSPRING CONFLICT AS A SELECTIVE FORCE IN NATURE A. WHEN DO WE SEE PARENT–OFFSPRING CONFLICT? 1. Theoretical Explanations for Parent–Offspring Conflict The concept of parent–offspring conflict was developed by Trivers (1974), when he applied the revolutionary gene‐level analyses of social evolution pioneered by Hamilton (1963, 1964a,b) to his own ideas about parental investment (Trivers, 1972). Trivers showed that parent–offspring conflict can arise whenever parents reproduce sexually and when the supply of parental investment costs them their future fitness. The costs of care mean that parents are behaving altruistically when provisioning their current young because they are sacrificing their future fitness, while sexual reproduction means that parents are behaving altruistically toward individuals (offspring) to whom they are not genetically identical. Therefore, the supply of parental investment can be analyzed in exactly the same way as other cases of kin‐selected altruism (Dawkins, 1976; Hamilton, 1963). Trivers (1974) developed his idea of parent–offspring conflict by imagining the interactions between a single caribou calf and its mother, here explicitly framing the conflict as a battle over the division of resources between the current offspring and the next. This sort of parent–offspring

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conflict is often referred to as ‘‘interbrood conflict,’’ to distinguish it from ‘‘intrabrood conflict’’ in which offspring enter into conflict with parents over the division of resources among members of the current brood (Macnair and Parker, 1979; Mock and Parker, 1997). Both interbrood and intrabrood conflict arise because asymmetries in relatedness generate contrasting optimal levels of investment for parents and young, and these are illustrated in Fig. 1. Taking the parent’s perspective, natural selection favors parental strategies that strike the optimal balance between the benefits of supplying investment and the reduction in fitness they experience as a result (Fig. 1). From the offspring’s perspective, however, natural selection strikes a different optimum. Individual offspring inflate the benefits of parental investment by the inverse of the coefficient of relatedness (r) between them and their parents: for example, in a monogamous sexually reproducing family, individual offspring value the benefits of parental investment twice as highly as their parents (Lazurus and Inglis, 1986). The optimal level of investment from the offspring’s perspective thus exceeds that of its parents, and the disparity between the two optima generates conflict (Fig. 1). This is true whether we are contemplating interbrood conflict over the total level of parental investment supplied to current young or intrabrood conflict over the division of investment within the current brood. To engage

Fitness

2B

B C

Optimal PI for parent

Optimal PI for offspring

Parental investment Fig. 1. Parent–offspring conflict, after Fig. 1B in Lazurus and Inglis (1986). ‘‘B’’ denotes the fitness benefits gained from the provision of parental investment. ‘‘C’’ shows the fitness costs experienced by parents as they supply parental investment; ‘‘2B’’ shows the fitness benefits experienced by offspring as their parents supply investment (assuming a monogamous mating system in a diploid species). The optimal levels of investment for each party can be found at the point at which they experience greatest benefit for least cost. The horizontal arrow indicates the disparity between optima, which is the source of parent–offspring conflict.

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in the former type of conflict, offspring must somehow influence the total amount of parental investment supplied, by forcing parents to supply resources that they would prefer to withhold for reproduction in the future. Interbrood conflict therefore potentially influences the evolution of interactions between offspring and parents. To incite intrabrood conflict, however, offspring must attempt to corrupt the equitable distribution of resources among the brood favored by parents (all else being equal) that are equally related to each of their current young. Young animals may skew investment toward themselves and away from their siblings (in whom they have a lower genetic stake) either by appealing directly to parents or by outcompeting rival siblings. Intrabrood conflict can therefore influence the evolution of interactions among offspring as well as those between parents and their young. As a prelude to discussing ways of detecting parent–offspring conflict in nature, we should resolve some conceptual niggles that often follow the outline of the theory in its simplest terms. In some respects, it may seem counterintuitive that a conflict of interest can ever arise between real parents and offspring. After all, offspring surely constitute their parent’s fitness, so how can parents be in conflict with their own fitness? To see past, this obstacle involves thinking about natural selection on genes for strategies rather than on individuals, just as we do when thinking about other sorts of kin‐selected altruism. Conflict between parents and offspring may seem impossible when considered at the level of the individual but, taking the gene’s‐eye view, we can see that selfish behavior induced by genes in either parents or offspring can increase their chance of being transmitted to the next generation. What about the problem that one day offspring will themselves become parents and fall victim to the very same ploy they used as offspring to gain an advantage over their parents (Alexander, 1974)? Again, gene‐level thinking provides the answer. It is perfectly possible for the optimal strategy for offspring to be quite different from the optimal strategy for parents (Dawkins, 1976), even when both strategies are expressed by the same individual. Indeed, this has been shown to be the case in the formal population genetic models of parent–offspring conflict developed by Geoff Parker and others (Macnair and Parker, 1978, 1979; Metcalf et al., 1979; Parker and Macnair, 1978; Stamps et al., 1978). 2. Evolutionary Squabbles Versus Evolutionary Conflicts Although the concept of parent–offspring conflict is robustly supported by theoretical analyses (reviewed by Godfray, 1995a; Godfray and Johnstone, 2000; Mock and Parker, 1997; Parker et al., 2002), at least three influential reviews suggest that it may be a relatively unimportant selective force in nature because there is little direct evidence to show

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that it exists (Bateson, 1994; Mock and Forbes, 1992; Mock and Parker, 1997, pp. 254–257). However, as we show below, recent work argues against this view and this lays the foundation for our discussion of information warfare. Therefore, we start by assessing the current empirical evidence for parent–offspring conflict, addressing common criticisms leveled at tests of parent–offspring conflict and suggesting further techniques by which its effects might be detected in nature. At first sight, it might seem surprising that parent–offspring conflict is not considered to be rife among animal families. Bleats and squawks are a regular feature of our own family lives and casual observation suggests that they are just as commonplace in bird and mammal families, at least. However, Mock and Forbes (1992) suggest that these behavioral disputes are not themselves evidence of evolutionary conflict and are better referred to as ‘‘evolutionary squabbles.’’ Their argument is that the phenotypic disagreements expressed in these battles may have nothing to do with the genetic sources of conflict identified by parent–offspring conflict theory (Mock and Forbes, 1992). A dramatic example that nicely illustrates their point comes from the Gala´pagos fur seal (Arctocephalus galapagoensis; Trillmich and Wolf, 2007; Fig. 2). In this species, it can take up to two years to wean an offspring and to roughly 9% of females, a new pup may be born before weaning is complete. Older offspring can launch aggressive attacks on their new siblings and

Fig. 2. A female Gala´pagos fur seal Arctocephalus galapagoensis squabbles with her older offspring. The newborn pup is in front of her. Picture taken by Fritz Trillmich.

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mothers must intervene to protect the weaker, smaller juvenile. At first sight, this seems to be a case of overt parent–offspring conflict, in which mothers and their older offspring aggressively dispute the continued survival of the younger pup. However, more detailed analysis of these interactions suggests an alternative interpretation. A long‐term study has found that the extent of aggression, and pup reproductive value, varies with the level of marine productivity. When productivity is low, the survival of the younger pups is greatly reduced by both the aggression of the older sibling and its continued demands for lactation. But when productivity is high, older siblings can be weaned immediately after the birth of the second pup and there is then no aggression from the older pup toward its new sibling. Trillmich and Wolf (2007) suggest that the unpredictable nature of marine productivity forces mothers to hedge their bets, and optimistically produce a second offspring before weaning the first. After the birth, mothers then use aggression from their older offspring to measure marine productivity and choose to abandon newborn pups when attacks are severe in order to continue investing in the older offspring, who has the higher reproductive value in these conditions. According to this alternative view, then, the aggressive interactions seen between mothers and offspring are not caused by evolutionary conflict but instead are a source of information about marine productivity, enabling mothers to follow an optimal reproductive strategy. To qualify as an example of evolutionary conflict, the behavioral dispute we observe must have direct antagonistic fitness consequences for parents and offspring, causing offspring to gain fitness at their parents’ expense, for example. In the case of the Galapagos fur seal, and for many of the most obvious evolutionary squabbles such as weaning tantrums in mammals (e.g., Bateson, 1994) or loud begging in young birds (e.g., Briskie et al., 1994), these particular fitness consequences simply have not been measured. In addition, in primates at least, tantrums and squabbles at the time of weaning are often viewed as part of the process of behavioral development, with no root cause in evolutionary conflict whatsoever (Bateson, 1994). In a few cases, the fitness consequences of an evolutionary squabble have been determined by observation for parents and offspring. One such example comes from red deer Cervus elaphus (Clutton‐Brock et al., 1982). The continued suckling of the offspring prevents mothers from conceiving again, thus generating parent–offspring conflict over the duration of parental care. Among the red deer that live on Rum, calves suckle for at least 100 days after they are born and gain weight rapidly during this time, putting on more weight and enjoying greater survival prospects the longer they are allowed to suckle. Prolonged suckling therefore increases calf fitness, but

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this is achieved at some cost to the mother’s fitness. The longer her current calf suckles, the less likely it is that she will conceive during the autumn rut (Clutton‐Brock et al., 1982). These observations apparently indicate offspring ‘‘winning’’ parent–offspring conflict (Mock and Parker, 1997) because calves are gaining fitness at their mother’s expense. However, as we now elaborate, another interpretation is also possible. Gomendio (1991) studied the influence of suckling duration on subsequent conception in a captive colony of rhesus macaques Macacca mulatta. Just as in the red deer study (Clutton‐Brock et al., 1982), she found that the greater the suckling period, the less likely mothers were to conceive during the following mating season. However, Gomendio’s (1991) detailed observations showed that mothers controlled the duration of the suckling period and thus chose whether to terminate investment in the current offspring in favor of breeding again. Mothers that forwent reproduction and continued investment in their current young were not victims of their overdemanding babies, but females that had strategically chosen to continue to suckle, instead of breeding again (Gomendio, 1991). In the macaques, therefore, mothers apparently ‘‘win’’ parent–offspring conflict, or at least deprive their offspring of the opportunity to gain fitness at the expense of future young. Exactly the same reasoning could be applied to the data collected on red deer (Clutton‐Brock et al., 1982). Perhaps hinds that had poor prospects of breeding again successfully chose to continue feeding their current young instead. Observational data like those collected on the red deer therefore cannot tell us much about the outcome of parent–offspring conflict, even though they center on the fitness measures typically lacking from studies of parent–offspring interactions. In general, without knowledge of the cause and effect, we cannot determine whether investment decisions are driven by offspring or by parents that makes it impossible to say which party is ‘‘winning’’ any evolutionary conflict between them. 3. Evidence of Parent–Offspring Conflict To demonstrate evidence of parent–offspring conflict, therefore, we need to understand how interactions between parents and their young influence the provision of investment and to show that there are antagonistic fitness consequences for parents and offspring. There are at least two well‐ characterized contexts in which these criteria are met, and parent–offspring conflict theory successfully predicts behavior seen in nature. a. Sex ratio wars The first is the sex ratio wars of the social Hymenoptera, a special form of interbrood conflict waged between the queen and her worker offspring over the proportion of male versus female reproductives

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that should be produced (Bourke and Franks, 1995; Mock and Parker, 1997; Ratnieks et al., 2006; Su¨ndstrom and Boomsma, 2001; Trivers and Hare, 1976). In the Hymenoptera, sex is determined by haplodiploidy with fertilized eggs yielding daughters and unfertilized eggs producing sons. In the social Hymenoptera, queens are equally related to sons and daughters (r ¼ 0.5) but in colonies with one queen who is singly mated, the workers are three times as related to their sisters (r ¼ 0.75) as their brothers (r ¼ 0.25). Therefore, whereas queens would prefer an even ratio of daughters versus sons (1:1), the optimal sex allocation from the workers’ perspective is 3:1 (Trivers and Hare, 1976). The disparity in optima generates conflict between queens and workers over sex allocation, and workers can bias sex allocation in their favor by selectively destroying male eggs. Who wins this evolutionary conflict? It is rare for either party to achieve investment at their optimum and the conflict is more typically resolved at some intermediate point (Ratnieks et al., 2006). Importantly, either party can gain the upperhand. For example, in bumblebees Bombus terrestris, sex ratios are closer to the queen’s optimum than to the workers’ (Bourke, 1997; Ratnieks et al., 2006). By contrast, in the wood ant Formica truncorum, colonies with singly mated queens show female‐biased sex ratios that are close to the worker’s optimum (e.g., Su¨ndstrom and Ratnieks, 1998). b. IGF‐II and other examples of genomic imprinting Parent–offspring conflict shows itself in quite a different way in mice, humans, and other mammals. The majority of genes in these animals are expressed in a similar fashion, regardless of whether they are inherited from the mother or the father. By contrast, the so‐called ‘‘imprinted genes’’ bear the stamp of the mother, or father, from whence they came because their pattern of expression depends on which parent transmitted them (reviewed by Haig, 2004). Haig and Westoby (1989) hypothesize that this curious pattern of genetic inheritance has evolved as a consequence of parent–offspring conflict. More specifically, it results from conflict between maternally and paternally inherited genes within the offspring over the provision of (say) maternal investment. In species where females bear young by more than one father during the course of their lives, paternally derived genes are expected to demand more maternal investment than those that have been inherited from the mother herself (Haig and Westoby, 1989). The Igf 2 and Igf 2 genes in mice beautifully support the idea of Haig and Westoby (1989). Igf 2 is paternally imprinted and encodes IGF‐II, an insulin‐related polypeptide that plays a key role in extracting resources from the mother during pregnancy. When the paternal allele of Igf 2 is experimentally inactivated, offspring are 60% their normal size at birth.

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Inactivation of the maternal allele has no such effect on birth weight (Haig, 1997). Counteracting the effects of Igf 2 is the maternally imprinted Igf 2r that encodes a receptor that acts as a sink for IGF‐II, thus reducing its influence on resource transfer from mother to offspring (Haig and Graham, 1991). When the maternal allele of this gene is inactivated, offspring are 20–30% larger than normal at birth, while inactivation of the paternal allele leaves birth weight unchanged (Haig, 1997). The functioning alleles of Igf 2 and Igf 2r thus balance the resources given to the offspring against the resources that are retained by the mother and potentially given to other young (Haig and Graham, 1991). The paternally imprinted Igf 2 serves to tip the balance in favor of the offspring, whereas the maternally imprinted Igf 2r tips it back toward the mother. Although there is a clear evidence that parent–offspring conflict has caused the evolution of genomic imprinting of Igf 2 and Igf 2r, and other loci (Haig, 2000), the evidence is less clear‐cut for other imprinted genes and the generality of the idea of Haig and Westoby (1989) remains to be determined (Haig, 2004). B. WHY DON’T WE SEE PARENT–OFFSPRING CONFLICT IN OTHER CONTEXTS? It is clear from the preceding section that parent–offspring conflict is a robust evolutionary concept that can explain parent–offspring interactions seen in nature. The key problem to address next is to explain why its effects appear to be confined to relatively few contexts, such as the sex ratio conflicts seen in the social Hymenoptera and genomic imprinting in mammals. Does it mean that parent–offspring conflict has a very narrow evolutionary influence or are there other explanations (Alexander, 1974; Bateson, 1994; Godfray, 1995a; Mock and Forbes, 1992; Mock and Parker, 1997; Queller, 1994; Stamps et al., 1985)? 1. Critical Empirical Tests Are Hard to Conduct One of the earliest general explanations for the lack of empirical evidence for parent–offspring conflict is connected with the difficulty of testing the theory in nature. The theory (in its early formulations at least) yields few testable predictions (e.g., Clutton‐Brock, 1990; Stamps et al., 1985); parental investment or other appropriate fitness measures are hard to quantify empirically (e.g., Mock and Forbes, 1992; Mock and Parker, 1997); the hypothetical genotypic conflict bears little relationship with observed phenotypic reality (e.g., Bateson, 1994; Mock and Forbes, 1992). We can certainly sympathize with these complaints, having battled ourselves for several years with designing empirical tests of parent–offspring conflict! Nevertheless, it is interesting to observe that exactly the same

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difficulties beset the study of sexual conflict (Arnqvist and Rowe, 2005, pp. 40–43), yet this is a field that has recently been championed as a ‘‘new paradigm’’ (Tregenza et al., 2006), and where research activity in general is thriving (Figure 1 in Arnqvist and Rowe, 2005). Clearly, these problems are not insurmountable and the field of parent–offspring conflict has much to gain by adopting some of the methods that empiricists have recently been using for the study of sexual conflict (a suggestion that has been made before: Queller, 1994). For example, Mathias Ko¨lliker, Allen Moore and their colleagues have pioneered the use of genetic techniques in the study of parent–offspring conflict (Ko¨lliker and Richner, 2001; Ko¨lliker et al., 2000, 2005) and this approach might fruitfully be developed further in the laboratory, through experimental evolution [Queller, 1994; see e.g., Holland and Rice (1999) and Hosken et al. (2001) for use of this approach in sexual conflict], or in the field, by analysis of long‐term datasets for evidence of selection acting in opposing directions on parental and offspring traits [see e.g., Foerster et al. (2007) for use of an equivalent approach in sexual conflict]. In short, it is true that critical tests of parent–offspring conflict are difficult to stage, and this explains why we have relatively little evidence of this sort for its influence on the evolution of parent–offspring interactions. However, it is important to realize that the lack of evidence does not itself mean that parent–offspring conflict is an unimportant selective force in nature. We simply have to be a little more imaginative in the way we test these ideas. 2. The Zone of Conflict is too Small for Parent–Offspring Conflict to be Significant The second reason explaining the lack of evidence for parent–offspring conflict centers on the theory behind it (Mock and Forbes, 1992). Conflict arises due to a disparity between parents and offspring in their optimal investment strategies (Fig. 1). The smaller the disparity, the lower the intensity of conflict and, we can assume, the harder it becomes to detect evidence of parent–offspring conflict in nature (Mock and Forbes, 1992; Queller, 1994). As Mock and Forbes (1992) point out, the shape of the cost and benefit functions of parental investment greatly influences the intensity of conflict. For example, if the costs of care are relatively low (Fig. 3), then parents and offspring may be in broad agreement about the level of parental investment to be supplied. A similar situation arises when the benefits of investment are relatively steeply accelerating (Mock and Forbes, 1992). With such close agreement between parents and offspring in their optimal levels of investment, no wonder evidence of parent–offspring conflict is hard to find (Mock and Forbes, 1992).

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A

B 2B

2B

B

Fitness

Fitness

C

B C

Optimal PI for parent

Optimal PI for offspring

Optimal PI for parent

Optimal PI for offspring

Parental investment Fig. 3. A reduction in the cost of parental investment leads to a reduction in parent–offspring conflict intensity. ‘‘B’’ denotes the fitness benefits gained from the provision of parental investment. ‘‘C’’ shows the fitness costs experienced by parents as they supply parental investment; ‘‘2B’’ shows the fitness benefits experienced by offspring as their parents supply investment (assuming a monogamous mating system in a diploid species). The optimal levels of investment for each party can be found at the point at which they experience greatest benefit for least cost. The horizontal arrows indicate the disparity between optima, which is the source of parent–offspring conflict. The longer the arrow, the greater the intensity of conflict. Parent–offspring conflict intensity is therefore greater in (A) than in (B).

While this perspective is insightful and interesting, presumably it describes only some of the variation in conflict intensity that is seen in nature. Mock and Forbes (1992) make a valuable point by showing that some parent–offspring interactions will be relatively peaceable. The key point here is to recognize that the extent of parent–offspring conflict can vary among families. Sometimes, ecological circumstances will cause optimal levels of parental investment for parents and offspring to converge, but in other situations, they will be forced apart and conflict intensity will rise accordingly. For example, the situation in Fig. 3B might describe older parents, breeding for the last time, or parents that are semelparous or unlikely to survive to breed again (e.g., North American bird species; Martin et al., 2000), whereas Fig. 3A might more accurately portray conflict in a family with young, iteroparous parents (e.g., South American bird species; Martin et al., 2000). Future work on parent–offspring conflict could exploit this potential ecological variation in conflict intensity to test theory, just as studies of sexual conflict have done (e.g., Jormalainen et al., 2000; Rowe et al., 1994). The convergence of parent and offspring optima under some ecological circumstances therefore does not undermine the

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possibility that parent–offspring conflict could be a powerful selective force in nature. Rather, it offers an interesting opportunity for testing parent–offspring conflict theory with comparative techniques. 3. Parents Are too Powerful This was one of the first objections to the notion of parent–offspring conflict (Alexander, 1974), and it remains a forceful counterargument today (Mock and Forbes, 1992), at least for species where parents interact directly with their offspring. According to this view, true instances of parent–offspring conflict are confined to the sex ratio wars of the social Hymenoptera, and genomic imprinting in mammals, because these contexts offer a rare opportunity for parents and offspring to compete as equals, either as rival adults within the hymenopteran nest or as physiological manipulators battling for control within the mammalian womb. In virtually all other cases, parents so completely overwhelm offspring with their physical dominance, and their control over the provision of care, that offspring are incapable of contesting investment decisions and the hypothetical genotypic parent–offspring conflict never materializes phenotypically. There is no question that parents can physically dominate their young. For example, avian offspring cannot determine their sex, nor the extent to which their egg will be provisioned, nor the size of the family into which they will hatch, yet each of these will influence the level of investment the offspring obtains and hence its future fitness (de Kogel and Prijs, 1996; Nager et al., 2000). After hatching, offspring are often hampered in physical disputes with their parents by their relative inferiority. For example, coot Fulica atra parents physically overpower offspring seeking additional resources, grasping young by the neck, and ‘‘tousling’’ them briefly for a few seconds before releasing them. After repeated punishment of this sort, offspring seldom solicit food from that parent again (Horsfall, 1984). Parents can also use more insidious forms of control. By incubating eggs before the clutch is complete (reviewed by Magrath, 1990), or by spreading egg‐laying over a long period (e.g., Smiseth et al., 2006), they can establish a size hierarchy within the brood through hatching asynchrony and thus create offspring that vary in competitive ability. Competitive asymmetries among the offspring may also be established by differential prenatal exposure to steroid hormones. Small doses of maternal androgens added to the avian yolk before laying can influence the development of nestling behavior after hatching (reviewed by Groothuis et al., 2005), while the sex of neighboring embryos developing in the same uterine horn can influence the development of competitive behaviors in young mammals after birth because neighboring offspring are bathed in the same sex steroids (reviewed by Ryan and Vandenbergh, 2002). Parents can even influence the dynamics

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of sibling competition through the design of the nest. For example, shallow scrapes facilitate the eviction of younger siblings in the masked booby Sula dactylatra (Lougheed and Anderson, 1999), while cavity or domed nests with single sites of food delivery allow competitively dominant offspring to monopolize access to feeding parents (e.g., Kacelnik et al., 1995; Ostreiher, 1997). By contrast, the cellular compartmentalization of larvae in bee and wasp nests perhaps serves to prevent interference competition among rival offspring (Fig. 4). When parents have this much control over the extent of sibling rivalry, then how can interactions among competing offspring possibly have been shaped by parent–offspring conflict? This question has been addressed most extensively in the context of avian siblicide (Forbes and Mock, 2000; Mock and Forbes, 1992; Mock and Parker, 1997), and the same rationale more recently extended to cases of mammalian siblicide (e.g., Hofer and East, 2007; Trillmich and Wolf, 2007). O’Connor (1978) was the first to use analyses based on asymmetries in relatedness to suggest that siblicide might be a manifestation of parent–offspring conflict, in which dominant offspring prefer to force brood reduction sooner than might be optimal for parents. O’Connor’s calculations assume that offspring have equal reproductive value, but the existence of pronounced brood size hierarchies in species that show siblicide forces us to reevaluate this assumption (Mock

Fig. 4. The nest of the tropical paper wasp Polistes canadensis. Picture taken by Kathryn Booth.

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and Forbes, 1992; Mock and Parker, 1997). If offspring are of unequal value, then parents may choose to dispense with low‐quality offspring sooner than if they were of greater value (Haig, 1990; Temme, 1986), bringing the optimal brood reduction strategies for parents and dominant young closer together (Mock and Forbes, 1992), and thereby removing the potential for parent–offspring conflict (Mock and Forbes, 1992). Seen from this perspective, siblicide becomes an elaborate form of parentally controlled brood reduction. Parents may have laid more eggs than they ever intended should become dependent young as an insurance against hatching failure, or congenital defects, and siblicide offers them a means of dispensing with redundant young (Forbes and Mock, 2000; Forbes et al., 1997). Parents transfer the power of execution to their offspring, with whom they cooperate over the timing of brood reduction (Forbes and Mock, 2000; Forbes et al., 1997; Mock and Parker, 1997). A general conclusion from this body of work is that parent–offspring conflict is unlikely to influence interactions among rival offspring, not because parents enjoy an unchallenged position of power but because they have the ability to create distinct castes of doomed versus dominant offspring (Forbes and Mock, 2000; Mock and Parker, 1997). These phenotypes change the fitness of doomed offspring so greatly that optimal patterns of brood reduction for parents and dominant offspring are brought into alignment and conflict disappears. However, when the fate of offspring is not so clear‐cut, and especially when offspring have the autonomy to determine their own destiny without relying on parentally bestowed phenotypes alone (see Sections III and IV below), then we maintain that parent–offspring conflict can still influence the evolution of sibling rivalry. Parents may dominate their young physically but offspring can use other means to regain control of provisioning (see Sections III and IV below). 4. Conflict Resolution Conceals the Original Disparity in Optima A key step in understanding how parent–offspring conflict might be detectable as a selective force in nature has come from the work of Godfray (1991, 1995a, 1999). His contribution has been to divide theoretical treatments of parent–offspring conflict into ‘‘battleground models’’ that define the zone of conflict (Figs. 1 and 3 are such examples) and ‘‘resolution models’’ that seek to predict the outcome of conflict. The resolution models take account of the likely coevolutionary processes between parents and offspring that result from conflict, and that may have concealed the original disparity in optima described by the battleground models. Conflict outcomes can either be evolutionarily stable (e.g., Cheverud and Moore, 1994; Godfray, 1995a) or unstable stages in a continuing arms race (e.g., Haig, 1996a; Mock and Parker, 1997). The resolution models therefore

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make predictions about what we should expect to see in nature and are more likely to be successful in this endeavor than the battleground models (Godfray, 1995a, 1999). Godfray’s classification thus neatly dispenses with many of the empirical difficulties associated with earlier tests of parent–offspring conflict theory (described in Sections II.B.1 and II.B.2), which are derived from the battleground models. It also takes account of the objection raised in Section II.B.3 (that the scale of parental power prevents conflict from materializing) by classifying it as just one of many possible resolutions to parent–offspring conflict. a. Phenotypic resolution models Phenotypic resolution models have had some success in explaining offspring behavior, in contexts beyond the sex ratio conflicts of the social Hymenoptera or genomic imprinting, and therefore add further support to the contention that parent–offspring conflict is a significant selective force in nature. One example comes from the parasitoid wasps, whose offspring hatch on or in an insect host and draw nourishment from the insect’s body. As the parasitoid larvae grow and mature, their host slowly dies. In the so‐called ‘‘solitary’’ species, offspring are equipped with large mandibles, which they use to fight other larvae to the death. In the more ‘‘gregarious’’ species, however, larvae coexist within the same insect body (Godfray, 1994). Theoretical work suggests that selection is likely to favor the evolution of larval fights to the death when clutch sizes range from two to four eggs (Godfray, 1987; Rosenheim, 1993). Under these circumstances, mothers should concede defeat to their offspring and lay just a single egg per host, fewer than is otherwise optimal from her perspective. A key prediction, therefore, is that parasitoid clutch sizes should show a bimodal distribution with few species laying clutches of two to four eggs (Godfray, 1987). The prediction is accurate for one parasitoid taxon, Apanteles sensu lato (Le Masurier, 1987), an assemblage of several genera in which larval fighting is relatively common (Mayhew and Hardy, 1998). By contrast in the Bethylid parasitoid wasps, a taxon with similar species richness to A. sensu lato, ecological conditions appear not to have favored the evolution of siblicide, even in broods of two to four larvae. Here, there is no scope for phenotypic parent–offspring conflict and clutch sizes are continuously distributed (Mayhew and Hardy, 1998). A second example comes from work on birds. Several resolution models have analyzed the function of nestling begging behavior in resolving parent–offspring conflict (reviewed by Godfray, 1995a; Godfray and Johnstone, 2000). A prediction the models share (but see Bergstrom and Lachmann, 1998; Johnstone, 1999) is that an evolutionarily stable resolution to the conflict can be achieved, as long as begging nestlings suffer some fitness cost from soliciting care (Godfray, 1995a). Regardless of the

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conflict’s precise outcome, costly begging is therefore an evolutionary hallmark of resolved parent–offspring conflict. Greater costs are required to resolve conflict when relatedness between parents and young is low, and conflict is more intense (Godfray, 1991, 1995a,b). The prediction is empirically appealing because it explains the paradoxical exuberance and noisiness of nestling begging displays. Nevertheless, empirical tests have yielded mixed results (reviewed by Chappell and Bachman, 2002; Haskell, 2002; Leonard et al., 2003). In several cases, poor experimental design has complicated interpretation of data (reviewed by Chappell and Bachman, 2002; Haskell, 2002; Kilner, 2001). After allowing for this problem, there is some evidence that artificially elevated levels of begging can increase the risk of attracting predators to the nest (Haskell, 2002) or impede growth (Kilner, 2001; Rodrı´guez‐Girones et al., 2001). In addition, in species where parents punish overdemanding young, solicitation behaviors carry socially imposed costs that may function to enforce honesty [e.g., European coot (Horsfall, 1984; Galapa´gos fur seal, Trillmich and Wolf, 2007; Fig. 2)]. However, a general finding is that begging costs are relatively low and often sufficiently low that they cannot easily be detected (Chappell and Bachman, 2002; Kilner, 2001; Leonard et al., 2003). It remains unclear whether such small fitness costs of begging constitute evidence of resolved parent–offspring conflict. Nevertheless, begging costs apparently rise as the average relatedness between parents and offspring falls, just as theory predicts (Briskie et al., 1994). Phenotypic resolution models thus offer a promising technique for understanding how parent–offspring conflict has shaped the evolution of offspring behavior. However, their application is limited by the difficulty of describing the complexities of the natural world in relatively simple mathematical terms (Royle et al., 2002). This could explain their somewhat limited scope thus far, but these shortcomings might be fixable especially if models are built with specific empirical systems in mind. ‘‘Negotiation’’ models (McNamara et al., 1999), which incorporate real‐time flexibility in behavioral rules, have successfully predicted male–female interactions during offspring provisioning (Johnstone and Hinde, 2006; McNamara et al., 2003), and might just as fruitfully be employed to understand parent–offspring interactions. Models might further benefit by incorporating ecological information. The parasitoid wasp example suggests that the conditions for conflict are likely to be influenced by subtle variations in ecology, which so far have not been incorporated into many models. Conflict resolutions could therefore vary among families within populations, among populations within species, and among species in ways that have yet to be captured theoretically (Royle et al., 2002). This might even explain

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some of the variation in the begging cost measurements because successive experiments have mostly involved different species. Finally, theoretical attempts to understand interactions within the family are known to be sensitive to the number of individuals taking part (Godfray, 1999; Parker, 1985; Parker et al., 2002). Conflict resolutions are typically modeled as dyadic interactions between parents and offspring or between rival young, but the resolution of interbrood conflict might be strongly contingent on the outcome of intrabrood conflict (e.g., Kilner et al., 1999, 2004). To be more realistic, the phenotypic resolution models need to take these complexities into account (discussed by Parker et al., 2002). b. Quantitative genetic resolution models This class of resolution model considers how conflict has influenced the genetic architecture of traits involved in interactions between parents and offspring (Ko¨lliker et al., 2005) and it has had some success in predicting the coadaptation of maternal characters associated with dispensing care and offspring traits used for soliciting care (e.g., Agrawal et al., 2001; Lock et al., 2004, in press). A general prediction is that there should be a genetic correlation between parental provisioning and offspring solicitation behaviors (Ko¨lliker et al., 2005; Wolf and Brodie, 1998). When selection acts more strongly on offspring begging behaviors than on parental provisioning, and increased provisioning reduces offspring begging intensity, then a positive genetic correlation is predicted (Ko¨lliker et al., 2005). By contrast, when selection has a greater influence on the supply of care than on offspring demands, and parents provide more care to highly demanding young, then a negative genetic correlation is expected (Ko¨lliker et al., 2005; Wolf and Brodie, 1998). Genetic correlations in both directions have been found in nature. In great tits (Ko¨lliker et al., 2000), mice Mus musculus (Curley et al., 2004; Hager and Johnstone, 2003) and burying beetles Nicrophorus vespilloides (Lock et al., 2004, in press), the correlation is positive, whereas in the burrower bug, Sehirus cinctus, it is negative (Agrawal et al., 2001). Whether this diversity is the result of different selection regimes remains to be determined, however. c. Other coevolutionary hallmarks of conflict Both the phenotypic and quantitative genetic resolution models of conflict describe the behaviors and correlations between traits that we expect to see at an evolutionarily stable outcome of conflict. But it is unclear whether this point is ever reached in real families, at least in cases where both parents and offspring have the opportunity to influence the provision of investment (Haig, 1996a). If evolutionary equilibrium has not been reached, an alternative

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way to detect evidence of conflict is to search for hallmarks of an escalated coevolutionary arms race between parents and offspring (Elliot and Crespi, 2006; Haig, 1993, 1996a,b; Summers and Crespi, 2005). For example, Haig (1993) points out that placental hormones are typically produced at far greater levels than is necessary to elicit a response from the same receptors in nonpregnant females. This he interprets as evidence of coevolutionary warfare in which selection favors offspring that produce high levels of placental hormones but favors mothers who become increasingly resistant to the hormones’ effect. For example, after a meal, a mother and fetus must decide how to share the resulting increase in blood sugar and it is here that parent–offspring conflict potentially shows itself (Haig, 1993). The mother can keep most of the blood sugar if she quickly reduces her blood sugar levels after eating, by secreting high levels of insulin. The fetus counteracts these maternal tactics by secreting human placental lactogen (hPL) that increases the mother’s resistance to insulin and so keeps blood sugar levels elevated for longer, thereby allowing the fetus to take a greater portion. In response, the mother swamps the effect of hPL by increasing her levels of insulin production, so establishing a hormonal tug of war with the unborn baby over levels of glucose in the blood (Haig, 1993). The marked variation in placental morphology among mammalian species can similarly be viewed as evidence of different stages reached in the arms race between mothers and their young (Elliot and Crespi, 2006; Haig, 1993). Mothers appear to have retained the upperhand in pigs, horses, whales, and lemurs where extraembryonic tissues do not gain direct access to maternal circulation. But in rodents, lagomorphs, insectivores, and primates, offspring seem to have gained the advantage. In these taxa, extraembryonic tissues breach the mother’s blood vessels and the placenta gains direct access the maternal bloodstream (Haig, 1993). Genomic analyses also offer a promising means of detecting evidence of evolutionary conflict between parents and young (Crespi and Summers, 2006; Summers and Crespi, 2005). At the genetic level, the hallmark of conflict is a rapidly escalating arms race between key coding regions of genes mediating parent–offspring interactions, and the intensity of conflict can be measured through the rate of evolutionary change in these gene sequences. Relatively high rates of evolutionary change have been found in the cadherin genes that mediate interactions between maternal and fetal tissues during mammalian pregnancy (Summers and Crespi, 2005) and the angiogenin gene, which is essential for tissue vascularization of the developing placenta (Crespi and Summers, 2006), indicating that their evolution may have been driven by parent–offspring conflict (Crespi and Summers, 2006; Summers and Crespi, 2005).

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C. CONCLUSIONS We conclude from this survey of recent work that parent–offspring conflict is perhaps a more significant selective force in nature than earlier reviews could have appreciated (Bateson, 1994; Mock and Forbes, 1992; Mock and Parker, 1997). Two outcomes to parent–offspring conflict seem possible. The first is an evolutionarily stable resolution, and this seems most likely when there is a marked discrepancy between parents and young in their ability to control the provision of investment. Selection may have favored the evolution of behaviors to contest the decisions made by the more powerful party, but the imbalance in power has resulted in a stable impasse. Thus, the conspicuously antagonistic behaviors that initially drew empiricists to study parent–offspring conflict, such as the weaning tantrums of young mammals or the siblicidal behavior of birds, may have evolved initially as a consequence of parent–offspring conflict, but the conflict itself has long since been resolved, in these cases apparently in favor of the parents (Section II.B.2; Bateson, 1994; Gomendio, 1991). A similarly stable conflict resolution seems to have been reached in the A. sensu lato parasitoid wasps, this time in favor of the offspring. Mothers are powerless to challenge the siblicidal tendencies of their offspring after hatching because they do not attend the brood. They anticipate their offspring’s behavior by laying a suboptimal clutch size (Godfray, 1987; Le Masurier, 1987). An alternative outcome to parent–offspring conflict is an unstable arms race through evolutionary time, in which neither party retains the upperhand for long. This appears to be the likely outcome when parents and offspring compete as equals, for example, either as rival adults in the case of the hymenopteran queen and her workers or as equally powerful agents of physiological manipulation, in the case of mammalian mothers and their unborn young (Sections II.A.3.a and II.A.3.b). In some cases, ecological circumstances may temporarily favor one party (e.g., Chapuisat et al., 1997; Ratnieks et al., 2006), generating contrasting outcomes within species. In this regard, parent–offspring conflict closely resembles sexual conflict over mating, which similarly causes coevolutionary arms races (e.g., Holland and Rice, 1999) and whose outcome can be influenced by ecology (e.g., Davies, 1992; reviewed by Arnqvist and Rowe, 2005). The arms races initiated by parent–offspring conflict seem to have had a similarly profound evolutionary influence, driving the evolution of vertebrate reproductive mode (Crespi and Semeniuk, 2004), vulnerability to certain forms of cancer in humans (Crespi and Summers, 2006), and the propensity for speciation in the mammals (Elliot and Crespi, 2006). We conclude, therefore, that parent–offspring conflict is a significant selective force in nature.

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III. INFORMATION FROM OFFSPRING TO PARENTS In the second part of this chapter, we show how information warfare can influence parent–offspring conflict. Information warfare centers on the power that can be gained by access to key pieces of information, such as the benefits to be gained from parental investment. The party with the greater access to information can potentially gain the upperhand from the resulting asymmetry in knowledge (e.g., Charnov and Krebs, 1975). Unlike most of the examples of physical control we have considered so far, offspring and parents potentially compete as equals. Offspring may gain the upperhand because the relevant pieces of information are personal, pertaining to offspring condition, sex, or paternity, for example (Trivers, 1974). Or parents may be placed at an advantage because they have more opportunity to gather information, as is the case when they assess environmental food availability while provisioning young confined to a nest, for example. The control of information, in conjunction with the use of physical power, has for some time been recognized as a key influence on conflicts within hymenopteran societies (e.g., Beekman et al., 2003; Keller and Nonacs, 1993; Su¨ndstrom and Boomsma, 2001). We now show how information warfare can have an important influence on the outcome of parent–offspring conflict. A. ‘‘CHARMING’’ RESOURCES FROM PARENTS In setting out his theory of parent–offspring conflict, Trivers (1974) recognized that offspring are considerably disadvantaged in any conflict with their parents by their physical inferiority (‘‘an offspring cannot fling its mother to the ground at will’’) and suggested that instead offspring induce parents to provide additional investment by using psychological tactics. Offspring have private information about their quality or condition, which parents are keen to know and which, suggested Trivers (1974), offspring can exaggerate to their own advantage. Consistent with Trivers’ suggestion, there is now evidence from several taxa that young animals can convey otherwise cryptic aspects of their condition by visual, auditory, tactile, and chemical communication and that these displays can ‘‘charm’’ carers into supplying additional resources. Whether these behaviors succeed in manipulating parents to the offspring’s advantage remains uncertain, however. B. SOLICITATION DISPLAYS IN INSECTS, BIRDS, AND MAMMALS Perhaps the greatest diversity of offspring display comes from the insects. Chemical communication dominates social interactions among the insects, and new evidence suggests it is likely to play a key role in offspring–parent communication as well (Ko¨lliker et al., 2006). In the burrower bug, nymphs

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emit a blend of eight volatile compounds whose composition changes with their nutritional condition. Odors produced by offspring in poor condition elicit a greater provisioning response than those released by better nourished young (Ko¨lliker et al., 2006). Likewise, bumblebee B. terrestris workers select hungrier offspring for preferential provisioning by the chemical composition of their cuticles, which varies with larval condition (den Boer and Duchateau, 2006). Communication in other modalities is also important. Visual display seems to mediate the provision of care in the ponerine ant Gnamotogenys striatula, where workers preferentially feed larvae that raise their head and neck in a swaying movement toward them (Kaptein et al., 2005). Similar swaying and tactile cues also stimulate provisioning of burying beetle Vespilloides orbicollis larvae by parents (e.g., Rauter and Moore, 1999). In the paper wasp Vespa orientalis, workers provision offspring in response to auditory or vibrational cues generated by larvae rhythmically rubbing their mandibles against the cell wall (Ishay and Schwartz, 1973). Vibrational signals also coordinate the provision of maternal defense against predators in the thornbug treehopper Umbonia crassicornis. Nymphs that are disturbed by a predator start to tremble on their leaf stem, and this causes the mother to return to defend her young (Cocroft, 1996, 1999). Among the birds, visual and auditory displays predominate. Perhaps the most remarkable aspect of these displays is their lack of diversity. Whereas avian courtship displays can involve dazzling shows of color (e.g., superb fairy‐wren Malurus cyaneus, Mulder and Magrath, 1994) and ornamentation (e.g., peacock Pavo cristatus, Petrie and Halliday, 1994), an assortment of complex songs (e.g., sedge warbler Acrocephalus schoenobaenus, Buchanan and Catchpole, 1997) as well as dancing (e.g., long‐tailed manakin Chiroxiphia linearis, McDonald and Potts, 1994), prancing, and strutting (e.g., Lawes parotia Parotia lawesii, Frith and Beehler, 1998), the solicitation behaviors of their young typically involve the repeated calling of a single note and the display of a patch of red, orange, or yellow (Kilner, 2006; Fig. 5). Nevertheless, these displays are an effective means of procuring extra resources from parents and of outcompeting siblings for food. Parent great tits (Parus major) busy feeding young at the nest, for example, will hasten their return with food if exposed to taped begging calls broadcast from speakers hidden in their nests (Hinde, 2006), as will reed warblers Acrocephalus scirpaceus confronted with a greater area of brightly colored gape (Kilner et al., 1999). When the orange plumes adorning the head of young American coots Fulica americana were trimmed experimentally, they were less likely to be fed than intact young (Lyon et al., 1994), while domesticated

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Fig. 5. Great tit Parus major chicks begging in the nest. Picture taken by Camilla A. Hinde.

Canary Serinus canaria nestlings with artificially reddened gapes were favored by provisioning parents (Kilner, 1997). Extra calling can also divert resources to the louder chick in the brood (Leonard and Horn, 2001a). Whether they are displaying visually or vocally, selection has favored offspring that maximize their psychological power by standing out against background noise. For example, the brightly colored gapes displayed by passerine nestlings as they demand food are usually surrounded by a pale fleshy border, which reflects ultraviolet light particularly well (Hunt et al., 2003; Jourdie et al., 2004). Its brightness may allow chicks to stand out from the drab, brown nest against which they display (Hunt et al., 2003). It may also enhance detectability in a dim signaling environment because species that nest in poorly lit cavities have offspring with broader and paler colored fleshy borders than those raised in better lit nests (Ingram, 1920; Kilner and Davies, 1998). The vocal display is similarly designed to promote a nestling’s conspicuousness. Young tree swallows Tachycineta bicolor, for example, make longer, louder calls that cover a broader frequency range, when reared in nests exposed to high levels of ambient noise than when raised under quieter conditions (Leonard and Horn, 2005). For young mammals that are provisioned by lactation, offspring can increase the supply of investment by a combination of tactile stimulation and vocalization. Suckling induces milk ejection in the short term and stimulates the secretion of prolactin, which maintains milk production in

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the longer term (reviewed by Wells, 2003). Vocalizations serve to increase the opportunity for suckling by summoning back mothers that have wandered away from their young (e.g., Schleich and Busch, 2004; Smiseth and Lorentsen, 2001) or by informing the mother of missed opportunities for suckling caused by sibling competition for nipple access (Thomas et al., 2001). Vocalizations are also graded signals of offspring condition, with subtly differing effects on maternal response. For example, sows are more likely to respond to the calls made by colder hungrier piglets that are more frequent, higher pitched, and louder than the calls uttered by well‐fed, warm piglets (Weary et al., 1996). In humans, crying pitch is higher in unhealthy babies and is used by parents for assessing the healthiness of their children (Wells, 2003). Vocalizations continue to influence the provision of care after lactation has ceased in cooperatively breeding meerkats Suricata suricatta and banded mongooses Mungos mungo. In these species, persistent loud calling by pups stimulates helpers to provide invertebrates (e.g., Bell, 2007; Manser and Avey, 2000). C. BLACKMAILING RESOURCES FROM PARENTS It is impossible to tell from these observations of the natural world whether offspring are successful in using their begging displays to elicit a greater level of care than is preferred by the caregiver, as Trivers (1974) suggested. If offspring could manipulate parents through psychological means, it would initiate counterselection on parents for resistance to manipulation and then it becomes very difficult to see intuitively which party would eventually win the conflict. For this reason, several formal theoretical analyses have been developed to understand how the evolution of offspring begging displays might have influenced the outcome of parent–offspring conflict. We now take a brief detour to look at two of the more influential models, before returning to the question of whether Trivers (1974) was correct in suggesting that offspring might be able to win parent–offspring conflict by exaggerating their needs. In 1977, Zahavi suggested an altogether more Machiavellian interpretation of offspring solicitation displays than that advanced by Trivers (1974). Zahavi (1977) argued that offspring manipulate their parents into supplying more food not by exaggerating their true needs but by threatening parents with their own self‐destruction. According to this view, the conspicuous nature of offspring solicitation displays serves not to stimulate caregivers but to attract the attention of potential predators. Parents are thus forced to feed offspring to silence them and remove the threat of danger. Godfray (1995a) points out that this idea is similar to the logic underlying the first formal analyses to investigate how offspring solicitation displays might

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influence the outcome of parent–offspring conflict (Macnair and Parker, 1979; Mock and Parker, 1997; Parker, 1985, pp.165–184; Parker and Macnair, 1979). In these so‐called ‘‘pro rata’’ models, parents and offspring bargain over the level of investment to be provided, and the offspring’s bargaining power comes from the reduction in fitness caused by intense begging, which is effectively a more general form of blackmail (Godfray, 1995a) than that suggested by Zahavi (1977). Offspring start the bargaining process by demanding resources at some begging intensity of their choosing and parents respond by supplying investment. If the amount supplied is less than ‘‘expected’’ by offspring, then offspring increase their begging intensity. If they are given more, then begging levels are reduced. The precise outcome of the bargaining process varies according to the shape of the demand and supply functions (which are fixed and therefore may not be ESSs), whether the analysis is focused on intrabrood conflict or interbrood conflict, the particular mating system of the parents, and whether or not biparental care is involved (Mock and Parker, 1997; Parker, 1985, pp. 165–184). However, except under very specific conditions, neither party emerges from the bargaining process as an outright winner. The most common outcome is some form of compromise at which investment is allocated at a point in between the parent’s and offspring’s optima (Macnair and Parker, 1979; Mock and Parker, pp. 165–184; Parker, 1985; Parker and Macnair, 1979). Parker and Macnair’s (Macnair and Parker, 1979; Mock and Parker, pp. 165–184; Parker, 1985; Parker and Macnair, 1979) analyses of offspring begging behavior therefore capture the spirit of Zahavi’s (1977) verbal argument, but differ in their conclusion. In contrast to Zahavi (1977), they show that offspring are unlikely to ‘‘win’’ conflict outright by using begging displays to blackmail their parents. D. RESISTANCE TO MANIPULATION: SELECTION FOR HONEST COMMUNICATION Returning to Trivers’ (1974) original verbal argument, the first formal model to develop this idea was built on the signaling theory developed by Grafen (1990) and investigated how parents should best respond to signals of offspring need (Godfray, 1991). In this model, offspring begging intensity varies with condition, increasing as offspring become more needy, and becoming more costly as begging intensity rises. This time, parental response to begging is not fixed, as in the Parker and Macnair models (Macnair and Parker, 1979; Mock and Parker, pp. 165–184; Parker, 1985; Parker and Macnair, 1979), but allowed to evolve in relation to begging intensity. At equilibrium, offspring signal their true condition with costly displays and parents respond by supplying food in relation to the intensity of the begging display.

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A key feature of the Godfray (1991) signaling model is that it detects only separating equilibria, and it is this requirement that leads to the conclusion that begging must be costly in order to enforce honesty. In other words, when offspring signals of need and parental responses vary continuously, costly and honest signaling is the only stable outcome. However, Bergstrom and Lachmann (1998) have subsequently shown that models detecting pooling equilibria can also yield honest stable signaling, which is cost‐free. In practice, this means that when offspring signals of need, and parental responses, are defined by step functions then cheap signaling can be evolutionarily stable. In effect, begging costs are associated with the resolution of the information conveyed, rather than with honesty itself. High‐quality information about offspring condition is costly to advertise honestly (Brilot and Johnstone, 2002). It is possible that there are different species‐ specific trade‐offs between signal costs and the information displayed (Johnstone and Godfray, 2002; Kilner, 2002), but it remains to be determined which of these approaches best describes the solicitation behavior seen in nature. In general, however, the signaling models show that although there is selection on offspring to use their psychological power to procure resources at levels close to their optimum (Trivers, 1974), it is opposed by selection on parents to respond only to credible sources of information about offspring quality (Grafen, 1990, 1991, 1995b). According to these models, the psychological tricks used by offspring to procure food persist only because they provide parents with a valuable insight into the qualities of their young, by conveying accurate information about offspring condition that parents cannot otherwise divine. In contrast with Trivers’ (1974) verbal argument, these formal analyses show that, in general, offspring signals of condition are not faked, and this allows parents to ‘‘win’’ parent–offspring conflict. Nevertheless, there are exceptions to this rule that suggest that Trivers’ (1974) suggestions could apply in some cases. In cases of hatching asynchrony, for example, chicks in the same nest are likely to vary in both their ability to pay the cost of signaling and in the amount they stand to gain (Kilner and Johnstone, 1997). Where signalers vary in quality, stable signaling need only be honest on average (Johnstone and Grafen, 1993), creating the opportunity for stable deception among high‐quality individuals for whom the cost of exaggerated begging is relatively cheap (Kilner, 2001). It is tempting to suggest, therefore, that larger nestlings use their begging displays in exactly the manner predicted by Trivers (1974), exploiting parental uncertainty about the costs of begging each of their young sustains to exaggerate their true need (Kilner and Johnstone, 1997).

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E. INFORMATION USE AND THE OUTCOME OF CONFLICT The preceding summary of the ‘‘pro rata’’ and ‘‘signaling’’ analyses of the evolution of begging displays illustrates that the nature of the information advertised by offspring can have an important influence on the outcome of parent–offspring conflict. The information advertised in each case is given by the contrasting functions of the begging cost. In the ‘‘pro rata’’ models, offspring are in effect advertising the threat of their own self‐destruction and it is this threat that forces parents to invest more than is optimal for them (Mock and Parker, 1997, p. 189). In the ‘‘signaling models,’’ investment levels are restored to the parent’s optimum because offspring advertise their true condition. Here, begging costs function to maintain honest signaling by preventing cheats from exaggerating their true need. By choosing to advertise information, offspring can therefore direct the provision of parental care. By advertising the threat of their own self‐destruction, they can solicit greater levels of investment than parents would prefer to supply. In theory, then, offspring can regain some ground in parent–offspring conflict by advertising certain sorts of threat (Zahavi, 1977), rather than by advertising information that parents are keen to know (Trivers, 1974). Whether this ever happens in nature remains to be determined (but see Section IV.D).

F. DISINFORMATION Offspring may not advertise exaggerations of the truth as Trivers (1974) suggested, but they can peddle untruths and advertise disinformation to gain an advantage over their caregivers. This is the particular speciality of the obligate interspecific brood and social parasites, found among the birds and the insects, respectively, who grow up in the nest of a foreign species and succeed by masquerading as their victim’s own needy young. Their strategies of deception are successful because they are rare parasites of the usual system of communication between offspring and their parents. Some avian brood parasites, such as nestling shiny cowbirds Molothrus bonariensis, appear to possess no special adaptation for exploiting host parents (Lichtenstein, 2001) and simply by occupying a foreign nest they passively continue the deception begun by their mother when she parasitized the host clutch. Others actively advertise disinformation. For example, young screaming cowbirds Molothrus rufoaxillaris and great‐spotted cuckoos Clamator glandarius, which are often raised alongside host nestlings, beg in the manner of very hungry host offspring even when they have been recently fed (Lichtenstein, 2001; Redondo and Zun˜iga, 2002). Different techniques are employed by brood parasites that actively kill host young

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soon after hatching. The common cuckoo Cuculus canorus nestling solicits high levels of care by producing begging calls that sound like a host brood (Davies et al., 1998; Kilner et al., 1999), carefully tuning the pitch of its calls to most effectively exploit its various hosts (Madden and Davies, 2006). Nestling Horsfield’s hawk‐cuckoos Cuculus fugax similarly mimic the demands of a host brood, this time by displaying yellow wing‐patches as they gape for food to simulate the appearance of three hungry mouths in the host nest (Tanaka and Ueda, 2005; Fig. 6). Horsfield’s bronze‐cuckoo Chalcites basalis chicks advertise disinformation about their true identity by mimicking the structure of host superb fairy‐wren begging calls and this appears to reduce the risk that they will be rejected by their hosts. Shining bronze‐cuckoo nestlings Chalcites lucidus, which do not follow this strategy of disinformation but are otherwise very similar to Horsfield’s bronze‐cuckoo chicks, are routinely recognized and abandoned by superb fairy‐wrens (Langmore et al., 2003). Among the insects, offspring of the socially parasitic cuckoo butterfly Maculinea rebeli run a prolonged disinformation advertising campaign to ensure that they are first transported to the host’s ant Myrmica schencki nest, then integrated into the colony and nourished for 11–23 months (Akino et al., 1999). Fourth instar caterpillars masquerade as Myrmica brood by mimicking their characteristic cuticular hydrocarbons. The mimicry is

Fig. 6. Horsfield’s Hawk–cuckoo Cuculus fugax chick in the nest of a red‐flanked bluetail Tarsiger cyanurus. Picture taken from videotape filmed by Keita Tanaka.

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imperfect at this stage but nevertheless sufficient to encourage M. schenckii workers to pick up the caterpillars and transport them back to the nest. By acquiring further colony‐specific hydrocarbons once they have penetrated the colony, the caterpillars perfect the forgery of their identity signature so successfully that workers prefer to nourish imposters over kin larvae (Akino et al., 1999; Thomas et al., 1998) and even cut up their own brood to feed them to the alien in their nest (Elmes et al., 2004). G. WITHHOLDING INFORMATION There are other ways in which offspring can turn information into power, without using advertisement. By withholding key information from their parents or other caregivers (Johnstone, 1997; Keller, 1997), offspring can generate an information asymmetry that gives them the upperhand in parent–offspring conflict. Molecular techniques allow us to see the information that (we assume) natural selection has allowed offspring to keep hidden. For example, in many bird species, members of the same brood can be sired by more than one male (reviewed by Westneat and Stewart, 2003), but offspring do not advertise their paternity (Davies, 1992; Keller, 1997; Kempenaers and Sheldon, 1996). By concealing this information, a nestling can extract care from a male who would otherwise choose to provide none at all (Briskie et al., 1998; Davies, 1992; Kempenaers and Sheldon, 1996). With no knowledge of paternity, males tending nestlings are forced to feed them all or else risk depriving their own offspring of food. Likewise, avian offspring that were dumped as eggs in a conspecific’s nest by their mother (Yom‐Tov, 2001) conceal information about their maternity and so obtain care from their surrogate mother. The same rationale applies to the advertisement of offspring sex, a trait which avian offspring seldom reveal directly while in the nest, unless the species is so size dimorphic that it becomes impossible to hide (Kilner, 2006; Lessells, 2002). Offspring may choose to conceal this information when parents are inclined to favor one sex over another. A theoretical model by Lessells (2002) suggests that distinctive sexual signatures can evolve but only if parents favor sons, say, and daughters gain so much inclusive fitness through their brothers that this offsets the personal fitness they lose as a result of parental favoritism. The concealment of vital information may also play an important role in resolving the sex ratio conflicts observed in social Hymenoptera. We have already seen that conflicts of interest arise in monogynous colonies between a singly mated queen and her workers because asymmetries in relatedness lead the workers to favor a female‐biased sex ratio among the queen’s progeny when the queen herself favors equal investment in sons and daughters. To make strategic adjustments to colony sex allocation in their

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favor, the workers require accurate information about the sex of the developing young. Therefore, it would pay the queen and her sons to conspire in concealing offspring sex from workers for as long as possible (Chapuisat et al., 1997; Keller, 1997; Nonacs and Carlin, 1990; Su¨ndstrom and Boomsma, 2001). There is some indication that offspring sex is temporarily concealed in the carpenter ant Camponotus floridanus because workers cannot discriminate the sex of the larvae and can only tell males from females once offspring reach the pupal stage (Nonacs and Carlin, 1990). Similarly, the execution of male Formica exsecta offspring by workers is delayed until late in larval development (Chapuisat et al., 1997). In F. truncorum, genetic recognition cues are imperfectly encoded in the ants’ cuticular hydrocarbons and this limits the extent to which workers can bias sex investment ratios toward their optimum (Boomsma et al., 2003). In the ant Lasius niger, however, workers can readily distinguish the sex of even young larvae suggesting that the concealment of sexual identity is not a universal phenomenon (Jemielity and Keller, 2003).

H. WHO WINS? How does information warfare influence the outcome of interbrood conflict? We have seen that offspring can potentially gain back some power by advertising threats of their own self‐destruction (Section III.C), but these are empty threats if parents have superior knowledge of the presence of potential predators (see Section V.C). Advertising disinformation certainly gives brood parasitic offspring the means to exploit their foster parents (Section III.F), but in regular families, this strategy is more likely to be opposed by selection for credible advertisements, which can end in parents winning the conflict (Section III.D). This leaves two likely means by which strategic management of information might enable offspring to gain an edge over their parents. The first is by withholding key aspects of personal information (Section III.G). Here, offspring may be assisted to victory by one parent seeking to gain an advantage over the other. In dunnock Prunella modularis families, for example, mothers may conspire with their offspring to conceal paternity markers from provisioning males (Davies et al., 1992). The second is by exploiting parental uncertainty about their ability to sustain the cost of begging, when rivals vary subtly in their signaling costs (Section III.E). To be successful here, offspring would benefit by retaining high levels of control over their own growth rates, so that parents have little direct knowledge of aspects of their condition that are not advertised (like signaling costs); by being born into relatively large families, so that parents

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cannot easily track the progress of each offspring; and by being born in relatively quick succession, so that any size disparities among siblings are readily blurred.

IV. INTERACTIONS AMONG SIBLINGS Information warfare is not confined to communication between offspring and their parents. In this section, we show that it can also have an important influence on interactions between competing young. A. ADVERTISEMENT OF INFORMATION Rival siblings competing for resources from parents can advertise information to promote their chances of success. Sibling rivalry may drive greater levels of salesmanship (Briskie et al., 1994) and cause advertisements to become more salient or elaborate than when offspring solicit care alone (Godfray, 1995b; Hauber and Kilner, 2007; Horn and Leonard, 2002; Johnstone, 1999). For example, to distinguish themselves from their rivals in the competition for food (Horn and Leonard, 2002), individual nestlings sometimes call at a faster rate in a brood than when they beg alone (Leonard and Horn, 2001b; Price, 1996). Similarly, the most highly ornamented of all avian young occur in families where parents control provisioning to such an extent that competing offspring presumably must employ psychological tactics to secure care (Kilner, 2006), instead of the more usual techniques involving shoving, jostling, and physical aggression (Drummond, 2006; Mock and Parker, 1997). Nevertheless, theoretical analyses suggest that advertisements of information will persist only if they are honest, and then parents stand to gain the upperhand in intrabrood conflict (Godfray, 1995b; Godfray and Johnstone, 2000). Rival offspring may also increase their chances of success in the physical battle for parental resources by advertising their intention to compete to siblings, thereby negotiating a reduced level of competition before parents arrive at the nest with food (The Sibling Negotiation Hypothesis: Roulin, 2002). In theory, offspring less willing to engage in the competition for food can then withdraw and save their energies for contests they are more likely to win (Roulin and Johnstone, 2003). This idea is appealing because it provides an adaptive explanation for the common observation that offspring of many bird species perform begging displays in the absence of parents (Budden and Wright, 2001), and the behavior of barn owl Tyto alba nestlings is consistent with some of its predictions (Roulin, 2001, 2004; Roulin et al., 2000). Nevertheless, it is not an easy hypothesis to test

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experimentally and so it remains unclear whether offspring really can adjust their begging behavior solely in relation to the competitive intentions advertised by their siblings. When competing offspring are close relatives, selection will favor less intense rivalry than when nonrelatives compete (reviewed by Mock and Parker, 1997). Kin‐based nepotism in turn selects for the advertisement of kinship (reviewed by Sherman et al., 1997), providing that kin discrimination is on average beneficial for the signaler as well as the receiver (Johnstone, 1997). Here, advertisement of kinship might be regarded as a preemptive act of self‐defense, a white flag flown to deter rare but potentially fatal attacks by relatives. The parasitoid Aleochara bilineata provides one such example. In this species, adult females lay their eggs at sites where pupal host cabbage root flies are likely to aggregate, and first instar larvae must search for a host in which to complete their development. Since larvae fight to the death over host ownership, offspring would benefit by avoiding those already parasitized by their siblings. Kinship is advertised by means of an anal plug that seals the entrance hole made as larvae enter the host pupa, and larvae choose to superparasitize only those hosts that are not already occupied by siblings (Lize et al., 2006). In the Arizona tiger salamander Ambystoma trigrinum nebulosum, broadcast information about kinship also reduces the potential for siblings to harm each other, but in quite a different way. Larvae of this species can develop either into the ‘‘typical’’ morph, which eats mostly invertebrates, or into a ‘‘cannibal’’ morph, which is larger and has a specialized mouth that allows it to eat conspecifics and, potentially, siblings (Pfennig and Collins, 1993). However, development of the cannibal morph is influenced by sibship‐specific olfactory cues that impede or delay cannibal development if predominantly kin share the same pond. Therefore, when cannibals do develop, they are most likely to eat distant relatives (Pfennig and Collins, 1993; Pfennig et al., 1994). Kin‐mediated development of a larval fighting morph has also been found in the parasitoid wasp Copidosoma floridanum (Giron et al., 2004), but here it seems to serve the selfish interests of just some of the sibship (Gardner et al., 2007; Grbic et al., 1992). The female wasp typically lays one male and one female egg in a host moth egg and after parasitism, the moth egg hatches and the caterpillar develops to its final instar (Grbic et al., 1992). During this time, the wasp eggs undergo polyembryony yielding about 1200 clones from the female egg and 200 clones from the male egg. Of these, roughly 40 of the female larvae develop into the ‘‘precocious’’ morph that is characterized by more rapid growth than the reproductive morph, a lack of circulatory, respiratory, excretory, and reproductive systems, and death before reaching adulthood. Female precocious larvae seek

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out and destroy rivals, typically sparing their genetic sister clones (r ¼ 1) and instead targeting their more distantly related brothers (r ¼ 0.25) (Grbic et al., 1992). Just as in the Arizona tiger salamander, fewer precocious larvae develop when the relatedness of offspring sharing the same host is experimentally increased and the number of attacks on rivals decreases correspondingly (Giron et al., 2004). Kinship is advertised by variation in larval extraembryonic membrane proteins, whose high degree of variability is probably maintained at least in part from selection by the host’s immune system (Giron and Strand, 2004). Intriguingly, precocial larval attack is least likely when genes that are inherited by common paternal descent are shared with the potential victim. Whereas sister clones are highly unlikely to be targeted, brothers and unrelated females who each lack this key badge of kinship are equally vulnerable to much higher levels of attack behavior by the precocious larvae (Table 1 in Giron et al., 2004). In this case then, larval kinship advertisement is effectively a paternity marker and serves to protect the interests of genes inherited from the same father (see also Boomsma, 1996). A similar alliance between maternally inherited genes in the offspring and the mother can also form, particularly if a mechanism exists for their mutual recognition. The best evidence for an alliance like this comes from maternal–fetal interactions in mammals, in a phenomenon known as gestational drive (Haig, 1996b). Haig (1996b) suggests that the provision of care among successive mammalian offspring can be distorted from equality by rare alleles that the mother and offspring share in common. Offspring possessing the rare allele are favored by the same allele when it is expressed in the mother’s body and so acquire more investment. Here skewed patterns of investment result not from intrabrood conflict but from intragenomic conflict, in which one allele favors itself at the expense of other alleles at the same locus and potentially at a cost to other genes sharing the genome. Gestational drive is therefore a source intragenomic conflict and there should be counterselection to prevent it (Haig, 1996b). How might maternal and fetal alleles detect each other in order to effect gestational drive? One possibility is through the cell surface adhesion proteins that bind homophilically and mediate interactions between fetal and maternal tissues during pregnancy (Haig, 1996b). The cadherins are one such class of molecule and are known to play a key role in placentation and invasion of maternal tissue by the trophoblast (Haig, 1996b; Summers and Crespi, 2005). Two specific cadherin molecules involved in fetal–maternal interactions appear to have evolved under high levels of intragenomic conflict, which is consistent with them being instrumental in gestational drive (Summers and Crespi, 2005). Whereas paternally inherited badges of

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kinship in Copidosoma appear to give their owners the upperhand in intrabrood conflict, the greenbeard cadherin genes seem to have generated an unending coevolutionary arms race. B. DISINFORMATION The importance of advertising disinformation in determining the outcome of competition with siblings is most apparent in the context of worker policing in the social hymenoptera (Ratnieks, 1988). As a result of the haplodiploid sex determination mechanism, workers can produce sons without mating. When queens are singly mated, workers favor their sons (r ¼ 0.5) over their brothers (r ¼ 0.25). Queens, by contrast, favor production of their sons (r ¼ 0.5) over the workers’ sons, who are their grandsons (r ¼ 0.25). When the queen mates more than twice, however, the collective interests of the workers and the queen become aligned because workers are more closely related to the queen’s sons, their brothers (r ¼ 0.25), than to full or half nephews (r < 0.25). Worker policing is then favored, in which workers prevent each other from reproducing. However, the selfish interests of individual workers differ from those of the collective. Worker policing therefore yields a special case of sibling rivalry in which individual workers favor production of their own sons (r ¼ 0.5) over those produced by their sisters and half‐sisters (r < 0.25) (Ratnieks, 1988; Ratnieks et al., 2006). The outcome of this rivalry is influenced by access to information about the maternity of male offspring. Clearly, individual workers would benefit by concealing information that their sons are worker‐produced. But the queen benefits by overriding this strategy and she appears to win this particular battle in the information war. In the honey bee Apis mellifera and the ant C. floridanus queens mark their eggs with distinctive pheromones to facilitate worker policing (Ratnieks et al., 2006). Nevertheless, advertisement of disinformation in anarchistic Cape honey bee colonies enables worker‐laid male eggs to evade policing. The queen’s own eggs are identifiable by compounds from her Dufour gland (Ratnieks, 1995) and it seems that the eggs of anarchistic workers survive because they bear a counterfeit queenlaid signal (Oldroyd and Ratnieks, 2000). C. COMPETITION FOR TACTICAL INFORMATION In some cases, tactical information may be won through physical competition and monopolized to the benefit of the victor. Spadefoot toads (Spea spp.) reveal how such domination might be achieved. Toad eggs are laid into ephemeral pools of water, often in desert‐like habitat. The emerging tadpoles have a high degree of developmental plasticity and siblings may

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develop into one of two phenotypes: an omnivorous morph that feeds on pond detritus or a carnivorous cannibalistic morph, which has a large keratinized beak ideally suited for feeding on fellow tadpoles. The cannibal morph has higher fitness in ephemeral pools because then tadpoles can grow and develop quickly before the pond dries up. It does less well in longer‐lasting pools where the omnivore tadpoles flourish, developing slowly but storing supplies of lipids that promote survival after metamorphosis (Pfennig, 1990, 1992). The switch from omnivore to cannibal morph is triggered by consumption of fairy shrimp at any point during development. The concentration of fairy shrimp is an important source of information about pool longevity, increasing as the life span of the pool decreases (Pfennig, 1990). In effect, it advertises the profitability of pursuing either developmental trajectory. However, the information is monopolized by larger, competitively superior tadpoles, which dominate access to the shrimp (Frankino and Pfennig, 2001) and so gain fitness benefits at their rivals’ expense. D. LEAKING INFORMATION Offspring can potentially gain power by choosing to leak information to eavesdroppers, to the detriment of their rivals. Perhaps this explains the common observation that smaller nestlings beg more intensely than other members of the brood (e.g., Go¨ttlander, 1987; Lotem, 1998; Price and Ydenberg, 1995). By calling loudly, small nestlings could be blackmailing their more dominant siblings into conceding resources, by threatening to betray the location of the nest to a passing predator. Leaking information could also provide a means by which parasitoid wasp larvae can achieve victory in fights to the death. Endoparasitoid larvae wear a protective coat that shields them from recognition by the host’s immune system. In addition, they are armed with large mandibles, but the death of a competitor is not typically the direct result of combat. Instead, wounding disrupts the camouflage of the protective coat, reveals the identity of the parasitoid larva, and this leads to fatal attack by the hemocytes of the host’s immune system (Godfray, 1994). E. BLOCKING INFORMATION Offspring may attempt to corrupt or block the information sent by rivals, competing for the same resources. Horn and Leonard (2002) have drawn parallels between competing male frogs, chorusing for the attention of females and competing offspring calling loudly for parental attention. Chorusing is especially likely to evolve when receivers reward signalers

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by responding to the first call they hear (Greenfield et al., 1997), which might be the case in tree swallows T. bicolor, at least (Leonard and Horn, 2001b). Horn and Leonard (2002) suggest that just as individual frogs may attempt to jam their rivals’ vocal displays with loud, long rhythmic calls, so may individual nestlings try to block the calls of their siblings (Horn and Leonard, 2002). Perhaps this accounts for the lack of diversity in avian begging calls, in particular their loud and repetitive nature (Greenfield et al., 1997; Horn and Leonard, 2002, Leonard and Horn, 2001b). It would be interesting to test this idea further with comparative analyses of call structure in relation to brood size. F. COLLECTIVE BARGAINING There are times when rivalries between offspring will be relaxed because by cooperating as a collective they can gain greater fitness than would be possible by following individual strategies of selfishness (Forbes, 1993, 2007). This is particularly so when offspring function as a collective to advertise information. A nice example is provided by the flightless blister beetle Meloe franciscanus, which parasitizes the nest of the solitary bee Habropoda pallida (Saul‐Gershenz and Millar, 2006). The beetle has an extraordinary means of dispersal between the widely distributed host nests. Aggregations of beetle larvae cooperate to hoodwink male bees into transporting them first onto the body of a female bee and thence back to the nest (Fig. 7). The process begins as female beetles lay their eggs at the base of plants that provide nectar for the host bee. The larvae hatch and immediately aggregate on the tips of the plants in clusters of between 120 and 2000 individuals where collectively they mimic a female bee, partly in appearance but principally through chemical mimicry of a sex pheromone, which lures in unsuspecting males. When a male approaches and attempts to mate, some larvae fling themselves at him and attach themselves. The male then transfers them on to a female bee during his next copulation. The female bee carries the beetle larvae back to the nest, where they complete their development, emerge, and mate (Saul‐Gershenz and Millar, 2006). Presumably, there comes a moment as the male bee approaches the collective to mate when the selfish interests of some larvae override the collective interests of the brood, and sibling rivalry flares up as a contest to board the male’s body. It would be interesting to determine the larval characteristics that allow some offspring to complete their life cycle, but doom others to stay behind as part of an increasingly unconvincing simulation of a female bee. A similar tension between sibling cooperation and conflict comes from a detailed study of the vibrational signals made by thornbug treehopper offspring in response to the appearance of a wasp predator, which summon

Fig. 7. Three steps in the Habropoda‐Meloe aggressive mimicry system. (A) A typical aggregation of Meloe franciscanus triungulins on a grass blade. (B) A male Habropoda pallida bee inspecting an M. franciscanus triungulin aggregation on the branch tip of the dune plant Astragalus lentiginosus var. borreganus. (C) A male bee covered with M. franciscanus triungulin larvae on the dorsum. Picture taken by L. Saul‐Gershenz, courtesy of Proc. Natl. Acad. Sci. USA (Saul‐Gershenz and Millar, 2006).

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the return of the mother (Cocroft, 1999, 2002). Mothers will respond only to synchronized vibrations produced by the coordinated actions of the entire brood (Cocroft, 1999). However, only those offspring that are nearest to their mother’s body gain substantial protection from her presence. Once again, the benefits of signaling as a cooperative are available only to the fortunate few (Cocroft, 2002). G. WHO WINS? Information warfare perhaps allows more scope for individual offspring to skew resources in their favor than was previously thought possible. Individual offspring can gain a clear advantage over their rivals through success in the competition for tactical information (Section IV.C), or by strategic advertisement of kinship by one set of parental genes in the offspring at the expense of genes belonging to the other parent (Section IV.A). Similarly, the leaking of information to malevolent third parties (Section IV.D) can potentially confer power on dominant young and the spreading of disinformation can give individual offspring the upperhand too, providing it is a sufficiently rare strategy (Sections III.F and IV.B). In other contexts, the outcome of conflict is harder to predict. Blocking information (Section IV.E) sent by rivals presumably leads either to a stalemate, or to an unstable escalation of jamming and evasion, in which no party emerges the clear winner. Those offspring that do manage to advertise personal information will be under selection by parents to signal honestly (Section III.D). On average, this will theoretically result in parents winning intrabrood conflict (Section IV.A), although some brood members may be able to return investment levels toward their optimum by signaling dishonestly (Section III.D). V. INFORMATION FROM PARENTS TO OFFSPRING With so much recent interest in offspring–parent communication (e.g., Godfray, 1995a; Kilner and Johnstone, 1997; Mock and Parker, 1997; Royle et al., 2002; Wright and Leonard, 2002), it is little wonder that the information passing in the opposite direction has received relatively little attention. Nevertheless, there is good evidence that parents communicate with their young both before and after hatching. But is this information used to serve the interests of parents or offspring or both? A. INFORMATION BEFORE BIRTH: ADVERTISEMENT OR MANIPULATION? Before hatching, mothers know far more about prevailing conditions in the wider environment than their developing young possibly can, and information such as this can potentially guide the offspring’s development so that it is

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adapted for conditions after birth (Bateson et al., 2004; Groothuis et al., 2005; Hales and Barker, 1992; Mousseau and Fox, 1998). By advertising the prevailing environmental conditions to their unborn young, mothers can ensure that their offspring adjust their development adaptively. The invertebrates provide some evidence for this sort of cooperative ‘‘weather‐forecasting’’ by mothers (Bateson et al., 2004). For example, Daphnia mothers can convey information about the risk of predation to their developing young. Only mothers that have been exposed to the chemical signature of a predator produce offspring that develop a defensive helmet (Trollrian and Dodson, 1999). Similarly, the extent of crowding experienced by aphid mothers influences whether their offspring will bear wings. Mothers living in crowded conditions are much more likely to produce winged migratory offspring rather than sedentary ones (Dingle, 1996). Older mothers breeding later in the year are also more likely to produce dispersing offspring, presumably because environmental quality deteriorates as the season progresses (Mousseau and Fox, 1998). In these two particular examples, offspring are genetic clones of their mothers, which may explain why communication between them is highly cooperative in nature. But is all signaling between parents and offspring so harmonious? Among the birds, mothers can inform offspring about wider environmental conditions by dosing their eggs with small quantities of maternal androgens, antibodies, carotenoids, and other substances (reviewed by Gil, 2003; Groothuis et al., 2005; Mu¨ller et al., 2007). From clutch to clutch, doses of maternal testosterone added to each egg vary widely within species and this variation can potentially inform offspring about the social status of their mother (e.g., Leghorn chicken, Mu¨ller et al., 2002) and her developmental history (e.g., zebra finch Taeniopygia guttata, Gil et al., 2004; Naguib et al., 2006), local population density (e.g., American coot, Reed and Vleck, 2001; house sparrow Passer domesticus, Mazuc et al., 2003; starlings Sturnus vulgaris, Pilz and Smith, 2004), the density of ectoparasites (Tschirren et al., 2004), and the sexual attractiveness of their father (and hence his likely diligence in providing care) (e.g., zebra finch, Gil et al., 1999). Maternal ‘‘weather‐forecasting’’ is also common in mammals. For example, the photoperiod experienced by pregnant voles somehow informs offspring of the season so that young voles born in winter have thicker fur coats at birth and are presumably better able to conserve heat (Lee et al., 1987). Similarly, pigs exposed to a high fat diet in utero have a different tolerance to high fat diets after birth compared with those which experienced lower fat levels during fetal development (Gluckman and Hanson, 2004). But the form of maternal ‘‘weather‐forecasting’’ that has received most attention in the mammals is connected with the development of so‐called ‘‘thrifty phenotypes’’ and the ways in which information

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transmitted from the mother to her developing young can influence susceptibility to disease in later life (Bateson et al., 2004; Hales and Barker, 1992). The thrifty phenotype hypothesis was originally developed as an explanation for the etiology of type 2 (non‐insulin‐dependent) diabetes mellitus in humans (Hales and Barker, 1992), but has since been adapted to account for the incidence of coronary heart disease, obesity, high blood pressure, and other metabolic problems (reviewed by Wells, 2007). The worst outcome in many cases occurs when poor fetal nutrition is succeeded by a period of rapid growth in early life. One interpretation of this evidence is that offspring undergo fetal programming for the world they are likely to encounter after birth. Babies that are undernourished in the womb are born small, have lower metabolic rates, and so are uniquely adapted for life in an environment with few resources. They are poorly adapted, however, for a world where rich food is plentiful (indicated by faster growth rates after birth) and suffer accordingly (e.g., Bateson, 2001; Bateson et al., 2004; Gluckman and Hanson, 2004). The implicit assumption of the ‘‘thrifty phenotype’’ hypothesis is that such fetal programming is primarily for the benefit of offspring, but Wells (2007) has recently challenged this view, arguing instead that it serves the interest of the mother by enabling her to strike her optimal balance between offspring size and number. The conditions experienced in utero by offspring do not advertise features of the wider environment, he suggests, but instead primarily reflect maternal constraints imposed by her quality, which offspring are powerless to challenge (Wells, 2007). A similar argument has been made to account for the function of maternal yolk hormones in birds. These, it has been suggested, are not benign weather‐forecasters of the world to come after hatching but are instead agents of maternal control that enable mothers to manipulate offspring, and their behavior after birth, before they are even hatched (e.g., Schwabl et al., 1997; reviewed by Mu¨ller et al., 2007). The evidence in support of the latter contention comes principally from intraclutch variation in yolk androgen levels and seems rather compelling, at first sight. When yolks are experimentally manipulated by injection of androgens into freshly laid eggs, offspring respond by begging more intensely after hatching, growing more rapidly and behaving in a more alert and competitive fashion (see Table 1 in Groothuis et al., 2005). Within each clutch, eggs vary naturally in the dose of maternal androgens they receive and androgen levels can systematically either increase (e.g., canary, Schwabl, 1993; black‐headed gull Larus ridibundus, Eising et al., 2001) or decrease (e.g., cattle egret Bubulcus ibis Schwabl et al., 1997) with each successive egg laid. Maternal yolk androgens thus appear to give mothers even more power over their offspring than they already have by hatching

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their young synchronously. Mothers can either create favorites, first borns that are not only larger than their siblings but behaviorally dominant as well (e.g., Schwabl et al., 1997), or they can attempt to iron out the asymmetries imposed by hatching asynchrony by behaviorally handicapping larger offspring (e.g., Eising et al., 2001; Schwabl, 1993). Either way, offspring seem powerless to challenge their decision. Are mothers using prenatal cues to manipulate the development of their offspring to suit maternal interests, or are prenatal cues advertisements that serve the interests of the offspring? Mu¨ller et al. (2007) point out that there is an analogy between the prenatal cues sent from mother to offspring and the postnatal cues sent from offspring to mother (see also Haig, 1996a; Trivers, 1985). Just as advertisements of offspring quality have been selected by parents to convey honest information (see Section III.D), so prenatal cues of environmental quality should be selected by offspring for their honesty, provided that offspring have the power to resist attempts at maternal manipulation (Mu¨ller et al., 2007). In the case of the mammals, offspring could resist maternal attempts to supply resources at her optimum (Wells, 2007) by swamping appropriate maternal receptors with placental hormones (Haig, 1993, 1996a). Avian offspring could similarly resist maternal manipulation by changing their responsiveness to maternal androgens, perhaps by creating ‘‘sink’’ receptors (Haig and Graham, 1991) or by reducing the sensitivity of their functional receptors (Haig, 1993, Mu¨ller et al., 2007). According to this line of reasoning, prenatal cues might have evolved initially to enable mothers to manipulate their young, but they persist today because they serve the offspring’s interest by providing useful ‘‘weather‐forecasting’’ information. They allow young mammals to develop an appropriate metabolic phenotype for the resources available after birth and they enable young birds to adjust their behavioral development in relation to the costs and benefits of demanding care after hatching. Empirical work that tests this idea has yet to be done, however. B. PARTIALLY HONEST ADVERTISEMENT It is still possible for avian mothers to use prenatal cues to manipulate their young, even if their primary function is to serve the offspring’s interests. In George Orwell’s novel 1984, The Ministry of Truth imposes authority on the population of Oceania by exclusively providing selected, sometimes distorted, information (Orwell, 1949). In the same way, mothers represent the only source of information to their unhatched young about the wider world, and by advertising information with partial accuracy they can manipulate some members of the brood. We hypothesize that variation among families in maternal quality, and in the quality of the local environment, will

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maintain selection on offspring to attend to prenatal cues, such as maternal yolk androgens, that advertise these aspects of the posthatching world (e.g., Gil et al., 1999; Mazuc et al., 2003; Mu¨ller et al., 2002; Pilz and Smith, 2004; Reed and Vleck, 2001; Tschirren et al., 2004) and adjust their development accordingly. In theory, as long as these prenatal signals are honest on average at the clutch level, mothers can exploit offspring uncertainty about their birth order and the world into which they will hatch to her own advantage, and selectively handicap (e.g., Eising et al., 2001) or favor (e.g., Schwabl et al., 1997) individual members of the brood. For example, a black‐headed gull embryo developing within a first laid egg is exposed to a relatively low‐dose maternal testosterone compared with its sibling in the third laid egg (Eising et al., 2001; Groothuis and Schwabl, 2002), and this causes first hatched offspring from first laid eggs to develop less competitive behaviors than their later hatched siblings (Eising and Groothuis, 2003). Mothers appear to benefit from this arrangement because it equalizes the growth rate of each chick in the brood, which would otherwise be distorted by asymmetries in chick size (Eising et al., 2001). The first hatched chick, however, is presumably placed at a disadvantage by maternal manipulation in the egg. Nevertheless, it is powerless to resist such intervention because it has no knowledge of its birth order before hatching. Were it to hatch from a second or third laid egg, for example, the maternal yolk androgens would benefit the developing nestling. On average, therefore, it pays the embryonic gulls to treat the maternal prenatal cues as honest indicators of the wider environment and gamble that they are not the unlucky offspring developing within the first laid egg. This leaves an unfortunate minority vulnerable to manipulation by their mothers. C. INFORMATION AFTER BIRTH: NEGOTIATED SUPPLY AND DEMAND After hatching, parents continue to have more information about the wider world than their offspring, deprived of such knowledge through their confinement to a nest, or other refuge, and through incomplete development of their sensory systems (see Clemmons, 1995; Magrath et al., 2006). In particular, parents are better informed about the resources available to nourish offspring (e.g., Bateson, 1994), the number of adults that are provisioning young (e.g., Davies, 1992), and the likelihood that eavesdropping predators will be drawn to a nest fully of noisy begging offspring (e.g., Davies et al., 2004). In short, they have a better idea of the costs and benefits associated with nestling begging than their offspring do. In theory, the extent to which parents share this information with their young could influence how offspring solicitation is used to resolve parent–offspring conflict, although formal ‘‘negotiation’’ (McNamara

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et al., 1999) models are required to investigate this possibility in detail. Studies of parent–offspring communication in the birds in particular illustrate how parents might modulate the begging behavior of their young. To some extent, the benefits of begging are conveyed to young by parental cues during food delivery, through the likelihood of a nestling obtaining a reward after begging vigorously. Nestling house sparrows repeatedly exposed to less generous parents, as simulated with hand‐feeding trials, reduce their begging intensity accordingly (Kedar et al., 2000). Similarly, in great tits, parents differ in their generosity to offspring and each sex predictably feeds from a different location (Ko¨lliker and Richner, 2004; Ko¨lliker et al., 1998). Needier, smaller offspring learn to approach the maternal feeding location because she is more inclined to provide them with food (Ko¨lliker and Richner, 2004; Ko¨lliker et al., 1998). In some species, parents may more actively signal the benefits of begging by using a food call (e.g., Clemmons, 1995; Leonard et al., 1997; Madden et al., 2005; Platzen and Magrath, 2004). The signal may do little more than alert drowsy chicks to the presence of a parent bearing food, but it is possible that it also encodes information about the identity of the caregiver or the quality of food on offer. Parents can also influence the vigor with which offspring beg for food by using alarm calls to signal the threat of a nearby predator. The immediate effect of these calls, given the moment a potential predator comes within the vicinity of the nest, is to cause offspring to stop begging and crouch silently (e.g., Davies et al., 2004, Madden et al., 2005). In robins Erithacus rubecula, dunnocks, reed warblers, and white‐browed scrubwrens Sericornis frontalis, offspring will then resume begging in response to cues of parental arrival at the nest, but initially are reluctant to beg quite as vigorously as before the alarm (Davies et al., 2004; Platzen and Magrath, 2004). The alarm calls given by their parents thus influence the predation risk associated with begging, perhaps removing it completely (Platzen and Magrath, 2004) or perhaps ensuring that it is constant, whether a predator is near the nest or not. D. WHO WINS? The provision of information from parents to offspring has received relatively little empirical attention, and as yet lacks a supporting body of theoretical work. It is therefore too early to judge which party will gain the upperhand here. Importantly, however, it is not a foregone conclusion that mothers can use prenatal cues to win parent–offspring conflict, although they may be able to manipulate one or two members of their brood in this way.

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VI. CONCLUSIONS We suggest that information warfare can result in two possible outcomes to parent–offspring conflict. When rival factions are evenly matched in their ability to use and manage information, then perhaps an unstable coevolutionary arms race is the more likely outcome, with neither party emerging as a clear winner for long. An arms race like this could account for the exuberance of offspring begging displays, in which the capacity of offspring to manipulate their parents is equally matched by their parents’ ability to resist their offspring’s demands (Sections III.D and IV.A), or in which rival offspring jam and evade each other’s begging calls with equal fervor (Section IV.E). Although high costs of begging are hypothesized to have brought to this coevolutionary cycle to a stable conclusion (but see Haig, 1996a), empirical evidence to support this view is mixed (Section II.B.4.a). Perhaps the variation seen among species in begging intensity is due to variation in the extent of the arms race, facilitated by interspecific differences in relatedness or ecology. A similar arms race of manipulation and resistance might explain why the concentration of maternal androgens deposited in avian egg yolks varies so much among species (Section V.A; Groothuis et al., 2005; Mu¨ller et al., 2007). A stable resolution to parent–offspring conflict might be more likely when there is an inherent asymmetry in the information available to each party, just as a stable resolution seems to result when there is an inherent asymmetry in the extent of physical control between parents and their young (Section II.B.5). Whereas parents are placed at an advantage in the latter context by virtue of their larger size and physical dominance, offspring can gain the upperhand by using information warfare because they have access to private information, which their parents would dearly like to know. Whether it is personal information about paternity, maternity, or sex (Section III.G) or information about their true identity (Sections III. F and IV.B), offspring can potentially win parent–offspring conflict by concealing this knowledge from their caregivers or from rival young. When offspring compete among themselves in the absence of their parents, stable asymmetries in knowledge can result from differences in their competitive abilities, potentially enabling dominant individuals to win intrabrood conflict (Section IV.A). Larger offspring are better placed to win contests for access to tactical information (Section IV.C), while better‐ armed individuals can more easily leak information about their rivals to malevolent eavesdroppers (Section IV.D). Finally, advertisements of condition may be honest on average, but cryptic individual variation in the extent of honesty can consistently give some signalers the upperhand. The psychological manipulation of receiver

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uncertainty in this way bears some resemblance to Trivers’ (1974) suggestion for how offspring might compete effectively with their physically dominant parents. Some members of the brood may be better able than others to misleadingly advertise their condition and they can exploit parental uncertainty about precisely which offspring are being deceptive to gain an advantage (Section III.D). However, parents can also use information warfare to their advantage to exploit uncertainties in their offspring. Mothers, for example, can exploit offspring uncertainty about birth order to place misleading advertisements in the egg before hatching (Section V.B). In summary, we suggest that information warfare is likely to influence the evolutionary conflict between parents and their young, in ways that have previously been overlooked. Some forms of warfare may persist as a coevolutionary arms race, but in several cases, the conflict can reach a stable outcome where offspring can gain an advantage over their parents. By strategically managing and using information in parent–offspring conflict, offspring may be able to offset the disadvantages they experience by virtue of their relatively small size and physical inferiority. Acknowledgments RMK was funded by a Royal Society University Research Fellowship. CAH was funded by a NERC standard research grant (NER/A/S/2002/00776) and by a grant from the Isaac Newton Trust, Cambridge, each awarded to RMK. We are very grateful for the support of these funding bodies. We thank M. B. V. Bell, H. J. Brockmann, N. B. Davies, R. A. Johnstone, M. Naguib, C. N. Spottiswoode, and an anonymous referee for constructive comments on the manuscript and we are indebted to F. Trillmich, S. Sumner, K. Tanaka, and L. Saul‐Gerschenz for generously providing us with images to illustrate this chapter.

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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Hormones in Avian Eggs: Physiology, Ecology and Behavior Diego Gil departamento de ecologı´a evolutiva, museo nacional de ciencias naturales (csic), madrid, spain

I. INTRODUCTION Although the transfer of hormones in birds from mother to offspring via eggs had long been suspected (Riddle and Dunham, 1942), Schwabl’s study was the first conclusive evidence showing that eggs contained important and variable amounts of maternal androgens, and that these androgens were important for offspring development (Schwabl, 1993). This discovery quickly bridged the gap from endocrinology to behavioral ecology, and led to a highly productive line of research. A good measure of the evolution of the field can be obtained by counting the number of papers dealing with egg androgens that have been covered in the different reviews published to date: 16 in 1997 (Schwabl, 1997b), 32 in 2003 (Gil, 2003), 51 in 2005 (Groothuis et al., 2005b), and over 125 in the present review. This increase has widened the field considerably and new research has called into question initial assumptions, something that seems a good justification for the present review. Most research so far has centered on egg androgens, mainly testosterone (T), androstenedione (A4), and 5a‐dihydrotestosterone (DHT), and therefore this review will follow this bias. However, recent studies have found an interesting role for other hormones, such as corticosterone (CORT) (Love et al., 2005), and the door is not closed to further additions. Why are avian egg hormones that interesting? In my opinion, there are three main characteristics that motivate their appeal to behavioral ecologists: (1) egg hormones are hidden female contributions to offspring, a sort of cryptic decision that challenges previous ideas about the impossibility of females tampering with a sealed developing embryo; (2) benefits of androgens are often balanced by costs, such as those related to the immune response (Folstad and Karter, 1992), and this trade‐off has a strong appeal for evolutionary ecologists, used to the concepts of investment, costs, and fitness 337 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00007-7

Copyright 2008, Elsevier Inc. All rights reserved.

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returns; and finally; and (3) yolk hormones have been shown to affect phenotypes in the long‐term, and this makes them excellent candidates for the study of adaptive phenotypic plasticity in the context of evolutionary processes, a growing area of research in the last decade (West‐Eberhard, 2003). One particular problem for this area of research is that most studies are done by behavioral ecologists, eager to interpret patterns of variation in the light of evolutionary processes (Carere and Balthazart, 2007), but rather limited in understanding the physiological mechanisms. In contrast, the field has not enjoyed much attention from hard‐core endocrinologists, thus leading to a situation where there is a plethora of ecological experiments and measurements, against a vacuum of basic studies on endocrine metabolism and mechanisms. There are practically no detailed studies of metabolic pathways, enzymatic reactions, regulation of receptor number or activity, gene expression, or the like. Unfortunately, these are badly needed to allow a proper interpretation of the patterns found by behavioral ecologists. It is hoped that this review could attract the attention of endocrinologists and embryologists about the possibilities of study of the transmission of maternal hormones in birds. From an evolutionary perspective, hormones in eggs are a typical case of maternal effects. The concept of ‘‘maternal effect’’ refers to the particular source of environmental variance in studies of quantitative genetics that is explained by the fact of being raised by a particular mother (Falconer and Mackay, 1996). In other words, ‘‘maternal effects are any aspect of the mother’s phenotype that affect her offspring phenotype’’ (Ra¨sa¨nen and Kruuk, 2007). Maternal effects are not purely environmental sources of variance because maternal behavior is also influenced by genetics; a recent revision concludes that there is widespread evidence for a genetic basis for a wide range of maternal effects (Ra¨sa¨nen and Kruuk, 2007). The current consideration of maternal effects is that they can be used as mechanisms of phenotypic plasticity, facilitating the survival of individuals facing new environments, and thus the establishment of colonizing populations (Mousseau and Fox, 1998). Furthermore, theoretical models have found that maternal effects can have much higher levels of genetic variance at equilibrium than ordinary genes, allowing an accelerated pace of adaptation to novel environments (Wade, 1998). However, maternal effects can also constrain or reverse evolutionary change, depending on the sign of the genetic covariance between maternal and offspring traits (Kirkpatrick and Lande, 1989). II. PHYSIOLOGY Maternal hormones can get into the egg either by a direct incorporation from the steroidegenic cells that surround the follicle while the yolk is being formed or through diffusion from blood vessels or the female’s internal organs before the shell is formed.

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In the case of androgens, female birds have two main organs for producing these steroids: the ovary and the adrenal caps (Nelson, 2000). Evidence shows that most of yolk androgen that is found in an egg is created locally (i.e., at the follicle), whereas blood circulating androgens have been considered to have a minute impact (Hackl et al., 2003). However, since both systems likely influence each other (see Section II.B), and optimal androgen levels for females and eggs may not always coincide, this can lead to situations where egg hormone deposition may depart from an optimal allocation.

A. STEROIDOGENESIS For obvious reasons, most research in this field has been conducted in the domestic chicken. It is difficult to know whether reproductive patterns found in that species are universal or particular to the Galliformes. In fact, recent work on the house finch shows that things can be dramatically different between species (Badyaev et al., 2006a). Thus, the generality of the physiological patterns described here should be regarded with caution until comparative evidence is available. Early female chick embryos possess two ovaries but, as development proceeds, the right ovary regresses so that, in adult birds, only the left ovary is functional [with the exception of the Falconiformes in which both ovaries are functional (Romanoff and Romanoff, 1949)]. The number of oocytes in the chick embryo also changes through development, reaching around 480,000 at hatching time (Johnson, 1999). However, in the adult hen only a few hundred of these reach maturity. The functionally mature ovary of a domestic hen is arranged with a clear hierarchy of follicles of different sizes that correspond to precise developmental stages (Johnson, 1999). This hierarchy is, however, not a universal pattern, and studies in budgerigars (Melopsittacus undulatus) and in house finch (Carpodacus mexicanus) show synchronous development of many oocytes (Badyaev et al., 2005; Hutchison, 1977). Bird species can be divided into two different groups, depending on whether clutch size is determined or undetermined (Kennedy, 1991). This difference can be observed externally because, in indeterminate layers, females will either reduce or increase their clutch size in response to the experimental addition or removal of eggs before the end of the clutch. On the contrary, in determinate layers, clutch size is not affected by manipulation and a fixed number of eggs is laid. These two patterns of behavior correspond to very different physiological mechanisms in females (van Tienhoven, 1961).

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External factors such as day length, temperature, food, behavior, or social status provide the trigger for oogenesis and ovulation. Responsiveness to photoperiod is different between species and between populations of the same species (Lambrechts et al., 1996; Silverin et al., 1993). In passerine species of the temperate zone, ovarian growth can be induced by increasing photoperiod alone, but other cues such as presence of a nestbox (Hutchison, 1977) or male song (Morton et al., 1985) greatly contribute to this development. It has been proposed that cascade‐like effects of prolactin secretion may regulate most avian reproductive effort including clutch size, androgen deposition, and hatching asynchrony (Sockman et al., 2001, 2006). The central nervous system integrates the information from external factors and initiates the reproduction process by producing gonadotropin‐ releasing factor (GnRH) in the hypothalamus (Sockman et al., 2001). This factor regulates the synthesis and sequential release of two gonadotropins: luteinizing hormone (LH) and follicle‐stimulating hormone (FSH) in the hypophysis, which in turn stimulate gonadal growth and steroidogenesis. The final equilibrium between GnRH release and gonadal steroids is achieved through a series of positive and negative feedback loops (Harvey et al., 1986). Ovulation of a mature follicle is preceded by an increase of LH and progesterone (P) concentrations. A main process in the formation of the egg is the period known as rapid yolk deposition that allows a quick growth of the small follicles to the stage of large preovulatory follicles. The increase of yolk volume during this period follows a linear progression (Harvey et al., 1986). The length of this process can be studied by looking at the number of concentric rings of yolk (Badyaev et al., 2006a). The main source of hormones found in avian eggs is the maturing follicles of the female ovary that contain specialized cells that create different hormones during their development (Huang et al., 1979). However, the passage of hormones from the ovary to the egg is not well‐understood because hormones found in the egg are not an even sample of concentrations found in the female (Williams et al., 2004). Follicles consist of concentric layers of tissue that surround the oocyte and yolk. From the outside toward the inside, we first find the theca externa, followed by the theca interna, the granulosa cells, the perivitelline membrane, and the oocyte plasma membrane (Johnson, 1999). Several types of cells in the follicle walls are specialized in the production of steroids (Johnson, 1999). Granulosa cells produce progesterone, theca interna cells produce androgens, and theca externa cells produce estradiol (E) (Huang et al., 1979). Their activity is regulated by LH and FSH, but their response to these regulators depends on the degree of follicle development (Porter et al., 1989).

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The granulosa cells of the largest preovulatory follicles (F1) are the main source of circulating progesterone (Hernandez Vertiz et al., 1993). Theca cells produce both estrogen and androgens, and the production decreases with increasing follicular maturation (Etches et al., 1981). A comparison of T production in theca cells throughout the follicle hierarchy shows that production increases gradually during rapid yolk deposition, and then diminishes until becoming undetectable at the final preovulatory stage (Marrone and Hertelendy, 1983). The cells of the theca externa have an important steroid metabolizing activity, harboring enzymes that allow the conversion of A4 into T and E and of T into A4 (Hernandez Vertiz et al., 1993). Postovulatory follicles continue to produce steroids before final atresia, and their low androgen production during incubation is brought about by a decrease in LH stimulation rather than by a reduction of the steroidogenic capacity of the theca interna cells at that stage (Rodriguez Maldonado et al., 1996). Although these processes are shared by all oocytes, there is variation for any given clutch in how many oocytes develop at the same time (Fig. 1), thus influencing the degree of independence of steroid deposition per oocyte (Badyaev et al., 2005). B. FEMALE CONTROL Whether yolk androgen deposition is female‐controlled or not is a recurrent question in the yolk androgen literature. Although this is obviously a highly relevant problem, it often leads to arguments in which proximal and evolutionary explanations are confounded. What is actually meant by this question is whether modifications of androgen levels that serve a function in the female’s own physiology affect yolk androgen deposition in maladaptive ways. Imagine an ovulating female who is challenged by a rival over a nest site. Androgen levels may increase to allow the female to answer this challenge, and this could be reflected in her yolk androgen levels, thus leading these eggs to contain excessive levels of androgens. This is problematic because the consequences of an ‘‘uncontrolled’’ yolk deposition may not be advantageous for the female’s fitness. It would be surprising if yolk androgen deposition was not buffered against the daily variations of plasma androgens that females may encounter, particularly because yolk androgen deposition does have important offspring fitness consequences. Selection is thus expected to have promoted the evolution of mechanisms to control the system, particularly so because the time of egg production is also characterized by high levels of intra‐ and intersexual aggression for resources or mates. What is the evidence for this?

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A E1(F1)

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Fig. 1. Schematic illustration of the differences between the two possible patterns of rapid yolk deposition, ovulation, and egg laying: (A) traditional view in which there is a hierarchical order of oocyte sequestration (F1–F4) into the rapid yolk deposition phase that is maintained at ovulation and egg‐laying (E1–E4), and in which all oocytes have similar growths; (B) hypothetical view in which there is not a concordance between the growth during rapid yolk deposition growth rate, the order of sequestration (F1–F4) and the order of ovulation (this example: F2, F1, F4, F3). Growing oocytes would differ in the duration of overlap with RYD phase of other oocytes, and variation in growth patterns could expose growing oocytes to distinct maternal hormonal profiles (Badyaev et al., 2005). # 2007 The Royal Society.

We should begin by inquiring about the differential function of increased androgen for females and offspring. Unfortunately, almost the whole existing literature deals with effects in the offspring, and few studies consider the role of this high androgen production during oogenesis on female physiology (Staub and De Beer, 1997). In that respect, we do not know the function of this high androgen production for the female, apart from descriptive studies that relate ovarian androgen production to an internal signaling function within the reproductive cycle of the female (Staub and De Beer, 1997). However, no study so far that I know of has addressed the question of why there should be so much variation in yolk androgens levels, if the function of these is to provide an internal signal of ovulation.

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The proportion of different steroids in the plasma levels of the ovulating female (estradiol, corticosterone, androgens) is not mirrored by egg deposition levels, showing that there is not a simple passive mechanism from plasma to egg (Williams et al., 2004). That plasma levels do not have a major influence on follicle levels is further suggested by experiments involving direct bolus injections of T in laying females. These studies have detected only minute increases in yolk T levels with respect to the injected amount (Hackl et al., 2003; Rutkowska et al., 2005), although it is possible that these studies do not properly mirror the consequences of a continuously heightened plasma concentration. As things stand now, a positive correlation between plasma and yolk androgen levels as found in some studies (e.g., Schwabl, 1996b) could be due to either: (1) follicle steroidogenesis indirectly affecting plasma levels after local follicle production or (2) a higher‐order steroid production control (hypothalamic GnRH secretion) affecting in the same direction both follicle and adrenal steroid production. The latter possibility is, however, not supported by a recent study that shows that GnRH injections only increase T levels in females in the week preceding oviposition, and that these levels are positively correlated with yolk T levels (Jawor et al., 2007). This suggests that female androgenic responsiveness to external stimuli during oogenesis will be reflected in their yolk androgen concentration, but that androgen production outside this time is not controlled by GnRH. The possibility for independent control (or even a negative feedback loop) between the two androgen production centers of the incubating female (follicle and adrenals) is not unexpected. For instance, a decoupling of plasma and yolk androgen levels has been shown in the canary, in which increases in plasma androgens did not result in a parallel increase in yolk levels (Marshall et al., 2005). Similarly, a negative relationship between yolk T and plasma T levels after laying were found in the house sparrow (Mazuc et al., 2003a), and an experimental increase of aggression in female lesser black‐backed gulls failed to increase yolk androgen levels (Verboven et al., 2005). In contrast, studies using permanent T implants have led to parallel increases in plasma and yolk T levels (Clotfelter et al., 2004). If aggressive behavior is modulated by androgen levels in females and females cannot decouple follicle androgen production from plasma levels, eggs laid after females experience aggressive situations would have excessively high levels of yolk androgens. A study showing a positive correlation between aggressive encounters and yolk androgen levels in tree swallows (Tachycineta bicolor) (Whittingham and Schwabl, 2002) suggested that females cannot wholly control what goes into their eggs. The problem with these correlative data is that females that take part in frequent aggressive encounters may be different in many respects from less aggressive

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females, and thus the relation between aggression and yolk T need not be a causal one. An experiment in which eastern bluebirds (Sialia sialis) were exposed to same sex intruders during laying brought about the unexpected finding that experimental females had lower levels of plasma androgens a day after the intrusion, whereas the yolks of the eggs laid in the interim contained higher levels of androgens (Fig. 2; Navara et al., 2006c). The interpretation of this study is not straightforward. On the one hand, it could be said that it shows that females cannot control the levels of androgens that go inside their eggs. However, the authors suggest that females actively use yolk T deposition to regulate circulating levels, using the egg as a sink of excessive hormones. It is, however, strange to think of the generality of such a regulatory mechanism, because it could only be used for a very short period of the female’s life. There are many ways to deal with high T levels, including receptor regulation or production of enzymes, but if high levels are not needed, the easiest solution would be not to produce them at all (Wingfield et al., 1990).

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Fig. 2. Contrasting differences between levels of circulating androgens and yolk androgens in a study in bluebirds. Data in the first graph show yolk A4 (A) and T (B) values for eggs laid by stimulated and control females. Hatched bars, eggs in stimulated groups; solid bars, eggs in control groups. Data in the second graph show similar values for female hormone concentrations (Navara et al., 2006c). # 2006 Blackwell Publishing.

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Furthermore, the role of androgens in regulating female aggressive behavior is poorly understood (Ketterson et al., 2005; Staub and De Beer, 1997). Some studies suggest a link between female T levels and intrasexual aggression (Cristol and Johnsen, 1994; Langmore et al., 2002; Zysling et al., 2006), but the evidence is by no means unequivocal (see for instance: Elekonich and Wingfield, 2000). On the other hand, evidence shows a link between T and coloniality: there are higher T levels in colonial species (Møller et al., 2005) and a positive relationship between T levels and colony size in the cliff swallow has been found (Smith et al., 2005). More studies such as that by Navara et al. (2006c) are badly needed to ascertain whether and how ovulating females can canalize hormones to the two possible targets (female and egg) and, if not, how do they deal with the consequences of such a lack of control.

III. EFFECTS OF YOLK ANDROGENS The study of how offspring characteristics are affected by yolk hormones is typically undertaken by comparing the effect of injections of androgens dissolved in some lipophylic solvent in the yolk with injections of the solvent as controls (Groothuis and von Engelhardt, 2005). This is by far the best methodology since using hormone modifications in the female to modify the egg’s androgen content (Rutkowska and Cichon, 2006) may cause additional changes in egg composition that could confound the results. Because egg androgens covary with a series of other egg components (e.g., Groothuis et al., 2006), correlative studies relating nestling characteristics to natural variation in yolk androgen deposition are not reliable tests of the function of yolk androgens (Gil et al., 2006c; Schwabl, 1993). However, the methodology used in manipulative studies varies widely, particularly in the dose and the androgens injected. Although these studies do not use pharmacological doses, these can still vary widely. A recent study in house finches has detected dose‐dependent responses to injections (Navara et al., 2005), reinforcing the need to carefully control this aspect, since most likely there will be dose‐dependent responses in other species. Little is known of the metabolic route followed by maternal androgens in the embryo. Although there are androgen receptors in embryos very early in development (Godsave et al., 2002), we know very little of what happens with yolk androgens once incubation starts. Some authors have detected incredible fast reductions in androgen concentration after some hours of incubation (Elf and Fivizzani, 2002), suggesting strong metabolic changes in the yolk before the embryo was formed. However, a recent study in the

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zebra finch (Taenyopigia guttata) has found that this effect is likely due to dilution of yolk with albumin and that changes due to androgen metabolizing or production only start when embryos are 3–5 days old (the zebra finch has a 12‐day incubation period) (Gilbert et al., 2007). To my knowledge, no embryological study so far has followed radioactively labeled yolk androgen to identify target tissues or metabolic routes, and neither gene expression has been considered. These studies are badly needed if we wish to know the mechanisms of yolk androgen action. Furthermore, patterns of differential gene expression could direct future research into possible phenotypic long‐term effects. A. SHORT‐TERM EFFECTS IN NESTLINGS 1. Development A large number of studies in a variety of species has found that nestlings hatching from androgen‐injected eggs have shorter incubation periods, higher growth rates, or larger body masses than controls (Eising et al., 2001; Navara et al., 2006a; Pilz et al., 2004; Schwabl, 1996a; Tschirren et al., 2005). For instance, in the black‐headed gull (Larus ridibundus), yolk androgens led experimental chicks to hatch a day earlier than controls and to outcompete their fellow mates in body mass and tarsus length (Eising et al., 2001). Most of the above studies used an experimental design nested within clutches, so that differences in nestling growth between treatments could be due to competition between siblings and to a direct effect of androgen in growth. However, a recent study in the spotless starling (Sturnus unicolor) has confirmed a similar trend in a between‐clutches context, suggesting that growth benefits need not derive from competition between nestlings, but rather from direct growth effects or manipulation of parental feeding rates (Mu¨ller et al., 2007a). Development involves the coordinated growth of the whole body, but yolk androgens could affect this balance by prioritizing the growth of some structures over others. The development of the musculus complexus, the muscle that allows nestlings to break the shell at hatching, and later on to outcompete their siblings in stretching their necks while begging, is directly related to the levels of androgens to which the embryo has been exposed in the red‐winged blackbird (Agelaius phoniceus) (Fig. 3; Lipar and Ketterson, 2000). Similarly, in the spotless starling, it has been shown that chicks hatching from androgen‐injected eggs have wider gapes than controls (Mu¨ller et al., 2004). This mechanism can be used to attract a disproportional share of parental feedings. For instance, a similar differential development of the gape flange has been found in runts in situations of strong sibling competition (Gil et al., 2008a).

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Fig. 3. Data showing how the growth of the ‘‘begging muscle’’ in red‐winged blackbirds is influenced by yolk T. Graph shows the deviation of relative complexus mass from the mean of its clutch versus deviation of yolk T concentration from the mean of its clutch (Lipar and Ketterson, 2000). # 2000 The Royal Society.

Significant effects of yolk androgens on growth have, however, not always been found in nestlings (Rubolini et al., 2006a; Tobler et al., 2007b), and indeed some studies have identified negative effects on growth. For instance, in American kestrels (Falco sparverius), growth was severely impaired when eggs in early laying positions were injected with androgen levels that made them resemble late‐laid eggs (Sockman and Schwabl, 2000). Similarly, posthatching body mass in yellow‐legged gulls (Larus chachinans) was decreased in nestlings hatching from T‐supplemented eggs (Rubolini et al., 2006b). 2. Begging Some studies have shown that begging (the probability of a nestling begging or begging intensity) is higher for nestlings hatching from androgen‐injected than control eggs (Eising and Groothuis, 2003; Schwabl, 1996a). For instance, in the black‐headed gull, chicks hatching from androgen‐injected eggs have higher activity levels, react earlier and

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for longer to parental visits, and managed to obtain more food than their control siblings (Eising and Groothuis, 2003). Thus, in a within‐nest situation, differences in androgen supplementation can affect nestling hierarchies caused by differential incubation patterns. However, Pilz et al. (2004) found that, in the European starling (Sturnus vulgaris), none of a comprehensive list of measures of begging effort and intensity were affected by yolk T manipulation. The young of some precocial birds produce embryonic vocalizations shortly before hatching, which are important for communicating the chick’s developmental process to its parents and thus synchronizing parental behavior accordingly (Brua, 1996). A study in the yellow‐legged gull has shown that embryos in T‐injected eggs produce louder vocalizations than controls (Boncoraglio et al., 2006), although no further differences in nestling begging were detected. Further studies are needed on the effects of yolk androgens on begging effort because this type of effect would imply that the costs of parental care could be higher for parents feeding chicks hatching from eggs with high levels of androgens (Moreno‐Rueda, 2007; Mu¨ller et al., 2007b), either because of higher necessities of these nestlings, or because their behavior breaks the ‘‘golden rule’’ of honest begging (e.g., Rodrı´guez Girone´s et al., 1996). 3. Metabolic Rate One possible source of costs of the increased growth promoted by yolk T could be higher energy expenditure in nestlings. An increased basal metabolic rates in nestlings hatching from T‐injected eggs has been found in zebra finches (Tobler et al., 2007b), but not in black‐headed gulls (Eising et al., 2003b). It is interesting to note that the detected basal metabolic rates increase in the zebra finch study was not linked to higher growth, suggesting that the costs of higher yolk T may depend on internal physiological processes not detected in external growth measurements. 4. Immunocompetence The immunosuppressive effect of androgens is a well‐known phenomenon (Folstad and Karter, 1992) that is applied in medicine to clinical situations in which it is necessary to either boost or depress the immune system (Bagatell and Bremmer, 2003). For instance, autoimmune diseases such as the rheumatoid arthritis can be controlled by androgen replacement therapies (Cutolo et al., 1991). Numerous studies in behavioral ecology have addressed the question of whether androgen‐mediated ornamentation is costly to bearers because of the immunosuppression caused by androgens

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(for a review see: Roberts et al., 2004). Following the same logic, one of the first possible constraints on androgen–yolk allocation that was proposed was that of immunosuppression (Gil et al., 1999). In the black‐headed gull, Mu¨ller et al. have shown immunosuppression of both the cell‐mediated (Fig. 4) and the humoral components of the immune response in nestlings hatching from androgen‐injected eggs (Groothuis et al., 2005a; Mu¨ller et al., 2005b). The presence of these negative effects together with possible growth benefits (see above) suggests that the final outcome of high yolk androgen exposure will depend on the posthatching environment of the developing offspring, including factors such as parasite exposure, degree of competition, or food availability. Differences in the strength of these factors might explain why immune costs could not be identified in a similar study in the great tit (Parus major) (Tschirren et al., 2004). In another example, injection of high T levels in eggs did not result in a decrease in immunocompetence in the Chinese painted quail (Coturnix chinensis) (Andersson et al., 2004). However, in this case, T treatment affected the positive covariance between cell immune response and growth, suggesting that T was affecting the normal developmental balance of the animals (Andersson et al., 2004). Finally, a study in house finches has provided the only evidence so far of positive effects of T egg treatment in nestling cellular immunocompetence (Navara et al., 2006a). To explain these results, Navara et al. propose

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that immunocompetence would only be a problem for chicks from T‐supplemented eggs when resources are scarce. This hypothesis assumes that immunodepressive effects of T are caused by competition for resources and not because of direct modulation of the immune response via specific receptors (Ahmadi and McCruden, 2006). 5. Survival Nestling survival has been shown to improve by egg androgen treatment in three studies, showing that T‐chicks are less likely to die or die later than control chicks (Eising and Groothuis, 2003; Mu¨ller et al., 2007a; Pilz et al., 2004). In contrast to these results, a study in the American kestrel showed that, when eggs laid in the first‐laying positions are injected with androgen doses designed to increase their levels to late‐laying positions, experimental nestlings are less likely to survive than controls (Sockman and Schwabl, 2000). 6. Hormone Levels Circulating androgens in nestlings have been linked to variation in sibling competition, begging, and development (Fargallo et al., 2007; Gil et al., 2008b; Goodship and Buchanan, 2006; Naguib et al., 2004). Sometimes, it is happily assumed that differences in nestling plasma T are caused by differences in yolk androgens (Sasvari et al., 1999). This link, however, has not been shown until recently in a study in the spotless starling (Mu¨ller et al., 2007a), in which male and female nestlings hatching from androgen‐injected eggs had higher T levels than control birds. This effect reinforces the notion that not only growth, but also nestling physiology is affected by yolk hormone variation. 7. Sex‐Specific Effects Three different studies have found sex‐specific benefits of higher T allocation in eggs. Remarkably, the effects differ depending on the species: in the zebra finch, males seem to suffer in terms of growth by this higher allocation (Rutkowska and Cichon, 2006; Rutkowska et al., 2007; von Engelhardt et al., 2006), whereas in the barn swallow, the effect is inverted, and females suffer while males benefit (Fig. 5; Saino et al., 2006). Interestingly, in the zebra finch study, the original sex differences in begging present in the control group were abolished in the experimental treatment (von Engelhardt et al., 2006). This implies that sex‐specific androgen effects can be used by females to balance their sex‐specific investment. However, sex‐specific effects are not always present (e.g., Eising et al., 2006; Mu¨ller et al., 2004). This variance

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Fig. 5. Sex‐specific yolk androgen effects in barn swallows: least square means of body mass at different ages (Saino et al., 2006). # 2006 Oxford University Press.

could be due to differences in development of sexual dimorphism pathways, but too little data are available yet to conduct a comparative analysis of this aspect. Few studies have examined the role of androgens in development through a reduction of the androgen effects rather than an enhancement. To do this, it is possible to block androgen receptors with an antiandrogen (for instance, flutamide), thus stopping receptor‐mediated androgen action. Mu¨ller et al. (2005a) have used this method, uncovering an interesting sex
treatment interaction. In a study conducted in the black‐headed gull, egg antiandrogen treatment resulted in a sexually distinct effect: an increase in male growth rate and a decrease in female growth and cell immune response (Mu¨ller et al., 2005a). Although not easy to interpret, this study lends support to the notion that developmental pathways of the two sexes including trade‐offs between immunity and growth are affected by androgens, thus setting the stage for a complex benefit/costs dynamics of androgen allocation. This type of sexually antagonistic effects of yolk androgens has likely led to mechanisms of sex‐specific androgen exposure, as shown in several studies in the house finch (e.g., Young and Badyaev, 2004). However, since within‐ clutch variation in yolk androgen levels is typically much lower than variation between‐females, females are restricted in the range of possible sex‐specific androgen quantity that they can deposit in their eggs (Saino et al., 2006). Such a constraint would be expected to lead to the evolution of highly biased or unisexual sex‐ratios, as found in some populations of the barn swallow Hirundo rustica (Saino et al., 2006).

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8. Appraisal of Short‐Term Effects There are several factors that could account for differences in the direction of effects (negative vs positive) between experiments. 1. Yolk androgen allocation is expected to respond to parent–offspring and parental conflicts (Mu¨ller et al., 2007b), and thus different species may find themselves at different evolutionary optima. Therefore, different effects would be gathered depending on whether, for instance, androgen deposition is already at a maximum or medium level. Since phylogeny can explain some patterns of androgen deposition (Gil et al., 2007), a way to control for this possibility would be to compare the effect of increased yolk androgens in similar species with low and high androgen levels for their taxonomic clade. 2. There are large differences between studies in the dose and type of androgens injected. Since dose‐dependent interactions may shift the balance from benefits to costs (Navara et al., 2005), experiments should in the future work with several doses (preferably moving away from the population mean in small steps: 1 SD, 2 SD, . . .). 3. It is likely that yolk androgens may require a particular balance with other egg components (e.g., carotenoids, immunoglobulins). Because these components often vary with the laying order (Royle et al., 2001), it has been proposed that T injections using either the first or the last egg of a clutch may bring about completely different effects on growth (Sockman et al., 2006). 4. Differences in nestling growth due to yolk androgens may only be detected in situations when food is severely reduced (Pilz et al., 2004), and some studies may thus be suffering from a ceiling effect, in which a faster growth cannot be further induced when food is plentiful. 5. Finally, the fact that females increase androgen allocation to eggs fathered by attractive males may be interpreted in the sense that benefits of yolk androgens could be limited to offspring with certain phenotypic or genotypic quality (Gil et al., 1999). Sex‐specific effects would be such a kind of genotype
environment interaction (Saino et al., 2006). These types of interactions are common in many maternal effects (Mitchell and Read, 2005), but to my knowledge no study so far has directly addressed this possibility as far as yolk androgens are concerned. B. LONG‐TERM EFFECTS ON THE OFFSPRING Most research on the effects of yolk androgens has focused on the early stages of development of the young bird. However, the results of a handful of studies that have followed birds until adulthood have revealed an

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impressive persistence of effects of yolk androgens in adulthood. The mechanisms by which these effects are expressed in adulthood have not yet been studied. Two main possibilities are: either a priming effect in adult hormone secretion or a modification of androgen receptiveness in target tissues (Strasser and Schwabl, 2004). However, it has been recently suggested that this type of organizational effects of hormones may actually be indirect effects of early social experience (Carere and Balthazart, 2007). This means that the different social experiences that nestlings experience in the nest as the result of differential androgen exposure would be the cause of adult behavioral differences. Although this is certainly possible, in the case of behavioral effects, it is more difficult to envisage how indirect effects of maternal androgens could play a role in ornament determination, suggesting that the most parsimonious explanation is one of organizational effects on androgen‐dependent physiology. It is in the long‐term context that the most exciting trade‐offs of yolk androgen deposition are most likely to be detected and variation in this maternal effect is expected to relate to variation in life‐history traits (Stearns, 1992). Long‐term studies are badly needed to understand the functional significance of variation in yolk hormone deposition. 1. Behavior The first effect of yolk androgens to be discovered was on adult dominance (Schwabl, 1993): canaries (Serinus canaria) hatching from eggs with high natural yolk levels had higher dominance ranks than birds hatching from low yolk T levels. Similarly, yolk T egg injections increased dominance in male and female house sparrows (Passer domesticus) defending a food source (Strasser and Schwabl, 2004). In the black‐headed gull, a similar experiment led to an enhancement of aggressive and behavioral displays that are used in competition for food or mates or space (Fig. 6; Eising et al., 2006). Personalities, or behavioral syndromes, are suites of behaviors that tend to appear together in individuals, within or between environmental or social contexts (Sih et al., 2004). Very often, the main axis of variation in personality is described by a bold‐shy continuum, the animal equivalent of the human variation in extroversion (Wilson et al., 1994). For instance, great tits can be classified while exploring a new environment into fast versus slow explorers (Verbeek et al., 1994). These individual differences are heritable, and their maintenance in populations are preserved through antagonistic selection regimes in different years and in different sexes (Dingemanse et al., 2004; Drent et al., 2003). Several studies have found significant effects of prenatal steroids in behavioral syndromes. For instance, high prenatal exposure to androgens

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Fig. 6. Long‐term effects of yolk androgen in behavior and ornamentation in the black‐ headed gull: (A) enhanced social behaviors (frequency) and dominance in birds injected with androgens (filled bars) compared with birds from eggs injected with oil (open bars). O, oblique display, F, forward display, C, Charge, P, aggressive peck, D, displacement; (B) stage of the nuptial brown head plumage and the juvenile characteristics of brown feathers on the wing in 10‐month‐old gulls (Eising et al., 2006). # 2006 The Royal Society.

induces bold and proactive behaviors in quail, irrespective of sex (Daisley et al., 2005). In this case, experimental birds took a shorter time while performing exploratory tasks, produced fewer distress calls, and were less likely to exhibit tonic immobility. Tobler and Sandell (2007) have shown that zebra finches that hatch from T‐injected eggs have shorter latencies to approach and to eat when confronted with a novel food situation at 9 months of age. However, in this study, the introduction of a novel object in the set‐up was found to elicit stronger neophobic responses in T‐males than in controls. This experiment thus suggests an organizing role of yolk androgens on adult behavior (Tobler and Sandell, 2007). Since the behavior

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of experimental birds is reminiscent of the effects of T treatment in the behavior of adult birds, these authors suggest that the effects may be caused by similar mechanisms, that is increased androgen activation in adulthood. The above evidence backs up suggestions for a physiological basis of individual differences in ways of coping with the environment (Koolhaas et al., 1999). The enormous plasticity of yolk androgen levels suggests that females could use these modifiers in order to fine‐tune their offspring phenotypes to match current selective regimes, to the extent that these can be predicted from environmental or social cues. 2. Ornaments Although sexual differentiation takes place in the early embryo by means of processes based on estradiol (Carere and Balthazart, 2007), the development of sexually selected characters in birds is tightly linked to hormonal processes in adulthood, of both androgenic and estrogenic nature (Kimball and Ligon, 1999). Two studies in two different species have reported that individuals exposed to high androgen levels in the egg develop larger ornaments in adulthood. Male house sparrow hatched from T‐injected eggs developed larger badges than those hatching from control eggs (Strasser and Schwabl, 2004). Similarly, in black‐headed gulls, the same treatment led to a greater development of nuptial plumage at 10 months of age in both males and females, suggesting an acceleration of sexual maturation (Fig. 6: Eising et al., 2006). However, there is also negative evidence in this aspect of yolk androgen effects. A study in pheasant found negative effects of increased yolk T in spur length in both males and females (Rubolini et al., 2006a), whereas no other male ornament was affected (ear tufts, wattle size, etc.). The remarkable results of this experiment are that, despite this lack of direct effect, the T treatment altered the covariance among these traits, suggesting an effect of prenatal hormones in the regulation of structural genes. 3. Dispersal In a Swiss great tit population, the presence of nest ectoparasites (hen fleas: Ceratophyllus gallinae) selects for short dispersal distances (Tschirren et al., 2007), possibly because of local adaptation of hosts to parasite pressure. In this population, it was found that experimentally parasitized females laid eggs with lower androgen concentrations than control females (Tschirren et al., 2004). One possible interpretation of this study was that parasitized females were reducing the cost of androgen‐induced immunosuppression in parasitized offspring, because their immune system would be more strongly compromised. However, a follow‐up study, conducted on nestlings of the same population, found little evidence for immediate

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immune costs of increased yolk androgens (Tschirren et al., 2005). The explanation to this paradox was provided in a subsequent study in which Tschirren et al. (2007) demonstrated that the dispersal distances of nestlings hatching from androgen‐injected eggs were larger than those of control birds. Furthermore, it was shown that the lifetime reproductive success of nestlings hatching from unparasitized nestlings was higher, if nestlings dispersed longer distances. This suggests that females are adaptively manipulating the degree of philopatry of their offspring as a response to levels of parasite infestation. 4. Other Traits No study so far has measured the long‐term fitness effects of variation in yolk androgen levels in the wild, and such a study would probably require insurmountable sample sizes. However, there are a number of hypothesis based on life‐history trade‐offs that such an approach could answer. For instance, it has been proposed that fast growth caused by maternal androgens can have negative consequences for survival (Birkhead et al., 2000). Studies in captivity have provided some clues toward this kind of trade‐off, suggesting that yolk T may have an influence on the development of sex‐ specific male and female reproductive traits. A study in the Chinese quail (C. chinensis) shows that birds exposed to relatively high levels of yolk T developed smaller testis in the case of males, and laid smaller eggs in the case of females (Uller et al., 2005). A transgenerational negative effect of yolk T in female breeding performance has also been detected in the form of reduced egg laying activity and egg fertility in female pheasants hatching from high‐T eggs (Rubolini et al., 2007). The suspicion that yolk androgens may interfere with developmental processes is also supported by data showing that digit ratios (i.e., the ratio of the length of the second and fourth fingers) in female pheasant (but not in males) are affected by yolk androgen treatments. Although our knowledge of the genetic and developmental basis of digit ratios in birds is rather limited, this effect suggests that the expression of genes of wide morphological and organizational effects, such as homeobox genes, may be modified by embryonic exposure to androgens (Romano et al., 2005). C. CONSEQUENCES FOR PARENTS 1. Physiology If we assume that increases in yolk androgen levels lead to increases in plasma androgen, it can be argued that females that deposit relatively higher amounts of yolk androgens in their eggs could be facing a series of

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costly consequences. Studies that have investigated the consequences of increased androgen levels in females have detected: lower immunocompetence (Duffy et al., 2000), decreased choosiness at selecting mates (McGlothlin et al., 2004), smaller likelihood of developing a brood patch (Clotfelter et al., 2004), reduced clutch size (Fig. 7: Rutkowska et al., 2005), etc. This body of evidence suggests that the female breeding optimum is likely to be one of relatively low T levels. Additionally, comparative analyses show that, although male and female T levels are correlated across species, female T levels are particularly reduced in species in which males have high T concentrations, suggesting antagonistic selection between sexes (Møller et al., 2005; Rutkowska et al., 2005). No study so far has attempted a manipulation of maternal yolk androgen deposition by means of a direct manipulation of follicle steroidogenesis, so all the evidence above is based on an assumed positive relationship between circulating and yolk androgen levels (see Section II.B, for a consideration of this topic). 2. Behavior However, since yolk androgen levels can change nestling begging behavior, size, or activity levels, it is to be expected that parents may pay a higher cost when raising offspring hatching from eggs with high levels of yolk T (Mu¨ller et al., 2007b; Winkler, 1993). In fact, it has been suggested that differential allocation of yolk androgens represents attempts at manipulating male parental care (Moreno‐Rueda, 2007; Mu¨ller et al., 2007b). Recent considerations about sexual conflict over parental care have proposed that females could manipulate paternal contributions by means of a self‐imposed

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handicap (Houston et al., 2005), such as a large clutch or high levels of yolk T (Mu¨ller et al., 2007b). It is to be expected that differences between sexes in provisioning rules would restrict the use of this manipulating mechanism, but it is also likely that these provisioning rules themselves are sex‐specific counter‐responses to this conflict (Mu¨ller et al., 2007b). A recent study in great tits has addressed the response of parents to yolk androgen manipulation (Tschirren and Richner, 2008). The authors blocked the effects of yolk androgens by means of injections of the androgen‐receptor blocker flutamide in experimental broods, and they further conducted a brood size manipulation. They found that both parents fed more enlarged broods, as expected from previous studies. However, paternal feeding rates were not affected by flutamide treatment, whereas females showed lower provisioning rates for enlarged flutamide‐injected broods than from enlarged control broods (Tschirren and Richner, 2008). This result suggests that female great tits cannot manipulate male parental care by allocating high concentrations of yolk androgens into their eggs. The authors suggest that female yolk androgen deposition has evolved through a process of coadaptation that matches maternal food provisioning and offspring demand. These results are in line with previous studies that have identified a positive covariance between female‐feeding rates and offspring‐begging levels (Ko¨lliker et al., 2000) and may provide an explanation for the common finding that males are less sensitive than females to modifications in nestling begging levels (e.g., Kilner, 2002). IV. VARIATION WITHIN CLUTCHES A. LAYING ORDER One of the first levels of variation that attracted attention was that within‐ clutches; in canaries, Schwabl (1993) found consistent increases of yolk T levels with laying order (Fig. 8). Since many bird species show asynchronous hatching, in which chicks from late‐hatching positions tend to hatch later than those from earlier laying positions, this distribution of androgens was thought to counterbalance the effects of asynchronous hatching (Schwabl, 1993). Indeed, later experiments in which within‐clutch differences in yolk T were experimentally manipulated confirmed this explanation (e.g., Eising et al., 2001). In contrast with this pattern, other species such as the cattle egret (Bubulcus ibis) show decreasing levels of androgens with increasing laying order (Schwabl et al., 1997). The function of this distribution would thus reinforce the effects of hatching asynchrony, possibly contributing to brood reduction and siblicide (Schwabl et al., 1997).

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Consistent differences between species in the distribution of yolk androgens with laying order have been found for many species. For instance, increasing patterns have been found for red‐winged blackbirds, canaries, or European starlings (Lipar et al., 1999; Pilz et al., 2003; Tanvez et al., 2007), while decreasing trends are typical in zebra finches (Fig. 9), American coots (Fulica americana), and all gull species that have been studied (Gasparini et al., 2007; Gil et al., 1999; Groothuis and Schwabl, 2002; Reed and Vleck, 2001; Royle et al., 2001). So far, a comparative study of how these differences in within‐clutch distribution relate to patterns of hatching asynchrony or brood reduction has not been conducted. Although within‐brood distributions of yolk androgens can be highly species‐specific, many differential patterns can be found within the same species. Given the effects of androgens in chick competitiveness, females could use within‐clutch distributions of androgens to balance or reinforce the effects of hatching asynchrony. A study in the house wren (Troglodytes aedon) failed to find differences between asynchronous and synchronous hatching broods in the way that androgens vary with laying order (Ellis et al., 2001). However, between‐female differences have been found in relationship to a number of variables. For instance, in the pied flycatcher (Ficedula hypoleuca), differences are found when comparing first and second broods (Tobler et al., 2007a) and in relationship to female ornamentation (Gil et al., 2006a). In the European starling, A4 increases with laying order for clutches laid by monogamous females, whereas the contrary is true in the case of polygynous females (Gwinner and Schwabl, 2005). However, it is in gulls where within‐brood differences in yolk androgens are the most extreme (Groothuis and Schwabl, 2002), and where the best

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evidence for a functional role of these differences can be found. In the black‐headed gull, clutches in which females induce a higher level of hatching asynchrony are precisely those in which a higher difference in yolk T between the first and last egg can be found (Mu¨ller et al., 2004), supporting the idea that gulls use this differential allocation to compensate the effects of hatching asynchrony. B. SEX A possible overall difference in peahen (Pavo cristatus) yolk androgen levels between eggs bearing male and female embryos (Petrie et al., 2001) was dismissed by further studies that showed that these differences were most likely due to differential use of androgens during incubation (Eising et al., 2003a; Pilz et al., 2005a; Loyau et al., 2007). However, female and male eggs have been shown to differ in yolk androgens under certain circumstances. For instance, in the zebra finch, male eggs have overall lower levels of T than female eggs, and this difference is largest in eggs laid in late‐laying positions (Gilbert et al., 2005). Sex differences are also affected by female fat condition and clutch size: in large clutches and in

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those laid by fat females, the female bias toward higher T disappears or is reversed in favor of males. An additional study from the same laboratory showed that sex differences in androgen levels were affected by female‐ feeding treatment (low vs high quality) (Rutstein et al., 2005). Similarly, in the domestic hen, the difference in hormones between eggs‐bearing male and female embryos depends on the rank order of the hens: high‐ranking birds allocate more androgens to male than female eggs, whereas the pattern is the opposite in the low‐ranking birds (Mu¨ller et al., 2002). The mechanism regulating these sex‐specific differences in yolk androgen levels has been investigated by Badyaev et al. in a series of studies (Badyaev et al., 2005, 2006a,b; Young and Badyaev, 2004). By using careful dissection of yolk rings, they have shown that oogenesis does not follow a strict follicle hierarchy as has been traditionally assumed (Johnson, 1999). In their study species, the house finch, several male and female oocytes develop in parallel (Fig. 1), and there are sex differences in the time that a given follicle overlaps with the rest of maturing oocytes (Young and Badyaev, 2004). By comparing two different populations, these authors showed that a temporal bias in the production of male and female oocytes allowed females to bestow distinct sex‐specific hormone allocations in their eggs (Badyaev et al., 2006b). The evolution of this mechanism makes sense in the light of the relatively strong sex‐specific effects of androgens that have been found in some species (Saino et al., 2006; von Engelhardt et al., 2006), since females would be expected to adapt yolk androgen levels as a function of embryo sex [or probably vice versa: produce different sexes as a function of yolk androgen contents (Badyaev et al., 2005)]. C. PATERNITY Birkhead et al. (2000) rightly suggest that, in the case of mixed‐paternity broods, females would not be able to deposit different amounts of hormones as a function of actual embryo paternity. This is because the effects of a given behavioral input over the physiology can only increase androgen levels in eggs that are laid 3–4 days after that moment. Thus, the time periods needed for an extra‐pair copulation to fertilize an egg and to affect yolk hormone levels would not match. The only exception would be if females could predict that an extra‐pair copulation is going to happen, but so far that possibility seems remote in birds (Birkhead et al., 2000). V. DIFFERENCES BETWEEN FEMALES Although differences within a clutch provides the basis for fine‐tuning mechanisms of female favoritism, one of the most impressive and suggestive levels of variation in yolk hormones is that between females.

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Particularly, in Passeriformes, differences between females in mean yolk androgen levels can be extremely high; consider for instance the range between 8 and 66 pg/mg T in a given population of pied flycatchers (Tobler et al., 2007a). Individual differences have been found to be repeatable between clutches laid in the same year by the same female in three different species (Gil et al., 2006b,c; Tobler et al., 2007a). This repeatability, together with those differences found between quail selection lines (Gil and Faure, 2007), is consistent with a significant heritability of this trait, although a formal quantitative study of this aspect still needs to be done. In this section, I shall deal with sources of individual differences in yolk androgens. Some of this variance has been shown to respond to plastic female deposition, showing that repeatability does not preclude important within‐female variance. A. FEMALE QUALITY Positive effects of androgens in nestling development (Schwabl, 1993) raised the question of why would such a deposition vary at all (Gil et al., 1999) because selection would be expected to lead to an invariant optimal allocation of yolk hormones. A tentative first explanation to this question was that androgens could be costly for either the female or the offspring (Gil et al., 1999). One way to answer this question is to look for patterns of condition‐ dependence, predicting that, if yolk androgens are costly, high‐quality females should deposit higher amounts than low‐quality females (Gil et al., 2004a). Several lines of evidence point toward a positive relationship between female quality and yolk androgen contents. In the great tit, experimentally parasitized females laid eggs with lower androgen levels than controls (Tschirren et al., 2004). Levels of yolk T have been shown to increase with increasing dominance in female canaries (Tanvez et al., 2007). In the European starling, higher yolk androgen levels were found for old females, and those laying early and large clutches (Pilz et al., 2003). An immune challenge in house martins resulted in a reduction of yolk A4 and a similar trend for T levels (Fig. 10; Gil et al., 2006b). Positive correlations between clutch size, arrival date, and yolk A4 levels in the barn swallow also suggest that high‐quality females can afford to invest higher levels of yolk androgens (Gil et al., 2006c). Furthermore, female zebra finches raised as nestlings in enlarged clutches lay eggs with lower T levels in adulthood (Gil et al., 2004a), suggesting that yolk T allocation is sensitive to early developmental stress. Additionally, in kittiwakes Rissa tridactyla, a positive relationship between yolk A4 levels and immunoglobulin concentration is also suggestive of a positive relationship with female quality (Gasparini et al., 2007).

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Fig. 10. Immune‐challenged house martin females decrease androgen yolk levels with respect to controls. Graph shows means of residual concentrations corrected for laying order (Gil et al., 2006b). # 2006 Springer Verlag.

However, although a positive relationship between female quality and yolk androgen levels is suggestive of costs of this deposition, it should be noted that this is not the unique explanation. If there is a relationship between female quality and alternative offspring phenotypes, differences in yolk levels may be due to females manipulating offspring and not to costs of androgen per se. For instance, in the case of the decrease in egg androgens observed in experimentally parasitized great tits, a follow‐up study has shown that this decrease is used by females to manipulate offspring dispersal distance (Tschirren et al., 2007). This is a remarkable example of a case where a straightforward inference about costs is shown to be incorrect. On top of these difficulties of interpretation, additional experimental studies suggest that female quality and yolk androgens levels may not always be positively related. Two independent studies in two different gull species found that food‐supplemented females transferred lower levels of androgens to their eggs (Gasparini et al., 2007; Verboven et al., 2003). Similarly, body condition and yolk androgen levels are negatively related in the pied flycatcher (Tobler et al., 2007a). The interpretation of these findings, which are completely opposed to the simplistic prediction of a cost assumption, is based on the idea that yolk androgens act as a short of compensation to resource deficits, and that therefore high‐quality females would not need to resort to this compensation. Given that yolk androgen deposition can be costly for the offspring (e.g., Mu¨ller et al., 2005b), selection would favor high‐quality females to reduce yolk androgen levels (Gasparini et al., 2007; Verboven et al., 2003).

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B. COLONIALITY Several studies in a range of different species have found that females breeding at a natural high density lay eggs with higher levels of yolk androgens than females breeding at low density (Fig. 11; Mazuc et al., 2003a; Pilz and Smith, 2004; Reed and Vleck, 2001; Schwabl, 1997a). A similar pattern was found when comparing black‐headed gulls living in the center and in the periphery of colonies (Groothuis and Schwabl, 2002). A possible interpretation of such a pattern is that increased female–female aggression in high‐ density colonies leads to these higher yolk T levels. However, Schwabl (1997a) proposed that such a mechanism could be used by females to provide their offspring with information about the conditions they would experience in adulthood, creating more competitive phenotypes. This interesting hypothesis is still waiting an experimental test, not just of the differences between colony sizes in yolk androgens (all based in correlations) but also of the adaptive consequences for the offspring. Studies that use intrusions to modify female behavior are not adequate tests of this hypothesis (e.g., Mazuc et al., 2003a) since what is required is large‐scale colony size differences. Schwabl’s transgenerational plasticity hypothesis requires colony size to be heritable and differences in offspring phenotype to be adapted to the A 100

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different breeding densities (Schwabl, 1997a). The first requisite is met because significant heritability of colony size has been found in nature, suggesting that offspring tend to inhabit colonies of similar sizes to those of their parents (Møller, 2002). However, nothing is known about whether offspring phenotypes created by differential levels of yolk androgens match the selective regimes of the different densities. Although the original hypothesis referred to creation of competitive phenotypes (Schwabl, 1997a), an additional possibility is that differences in yolk androgen may induce differences in dispersal distance (Tschirren et al., 2007). In this case, the basis of the adaptation may be linked to host‐parasite coevolution. This possibility could work in principle because parasite pressure is closely linked to coloniality (Tella et al., 2001). To the extent that increased yolk androgen leads to increased dispersal as shown for the great tit, differences in yolk androgen in relation to colony size would predict higher dispersal from large colonies. However, data for some species show dispersal to be reduced with increasing colony size (Serrano et al., 2005), exactly the opposite pattern to what it would be predicted if yolk T was a mechanism regulating dispersal distances. C. DIFFERENTIAL ALLOCATION Costly ornamentation can provide organisms with reliable information on the genetic and phenotypic qualities or condition of the bearer (Jennions et al., 2001). Current theory suggests that female preferences have partially evolved to take advantage of these correlates of quality because of indirect and direct benefits that they obtain from mating with males that have exaggerated ornamentation. Life‐history theory predicts that organisms should increase their parental investment when the value of a reproductive attempt is larger than average (Trivers, 1972). Thus, if male costly ornamentation indicates that offspring will be of higher phenotypic or genetic quality, it is expected that females should increase their investment in the offspring fathered by these males (Burley, 1988; Sheldon, 2000). This mechanism has been called differential allocation. Following this line of reasoning, and assuming that egg androgens are costly, several experiments have been conducted to investigate whether female birds show differential allocation of egg androgens in relation to male attractiveness. The first study to show this phenomenon used zebra finches, taking advantage of the fact that red‐banded males are perceived as high attractive, whereas green‐banded males are perceived as low attractive. In a cross‐over experiment, where females from two different experimental groups encountered the two treatments in different sequence, female zebra finches were found to deposit higher levels of T and DHT in

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Fig. 12. Differential allocation between females mated to attractive and unattractive males in the zebra finch. Graphs show mean levels of yolk TþDHT (A), DHT (B), and T (C). Open bars represent the condition when the females were mated to red‐banded males, and the shaded bars represent the condition when the females were mated to green‐banded males. Group 1 females were mated to a green‐banded male first and then to a red‐banded male. Group 2 females were mated to a red‐banded male first and then to a green‐banded one (Gil et al., 1999). # 1999 The American Association for the Advancement of Science.

their eggs when mated to red‐banded males than green‐banded males (Fig. 12). Exactly the same results were found in a similarly designed laboratory study, using canaries as experimental subjects and manipulating the attractiveness of song that was broadcasted to females (Gil et al., 2004b; Tanvez et al., 2004). Females in this case were found to increase their egg T levels by approximately 30%. A replication of this study by Marshall et al. (2005) resulted in a similar effect in plasma levels measured through fecal samples, but no differences in yolk androgen deposition. The first test of the hypothesis in the wild was conducted in the barn swallow (H. rustica), a species in which male attractiveness can be modified by manipulating tail length; again in this case, females mated to males with elongated tails deposited higher levels of A4 (the most abundant androgen in the eggs of this species) than those mated to males with shortened tails (Gil et al., 2006c). The first interpretation of these results was in the sense that females invested larger quantities of a costly resource in the eggs fathered by

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attractive males, as the hypothesis of differential allocation would predict. The fact that yolk androgens can be costly further suggests that either the benefits that females derive from mating with attractive males are large enough to compensate these costs or the offspring of attractive males are better at withstanding these costs (Gil et al., 1999), for instance, if male ornamentation affects nestling immunocompetence (Johnsen et al., 2000). However, alternative hypotheses have been proposed and these could also fit the data. A first possibility is that increased yolk androgen deposition was a mechanism that females could use to increase begging, and thus male parental care (Moreno‐Rueda, 2007). This would make sense because in some studies, highly ornamented males have been found to provide poorer parental care than males with poor ornamentation (Burley, 1988; De Lope and Møller, 1993). However, the predictions of this hypothesis have not been put to the test in any study so far. A second possibility is that the high androgen levels of females mated to attractive males are in fact an epiphenomenon of sex‐ratio adjustments. Evidence confirms theoretical expectations that females bias sex‐ratios toward males when mating with attractive males (West and Sheldon, 2002). Therefore, an increase in yolk androgen could be explained if either: (1) male‐biased sex‐ratios are caused by heightened androgen secretion by follicles or (2) male embryos require higher levels of androgens during incubation than females. Data so far are not conclusive enough to evaluate any of these possibilities (see Section VII). However, the predicted outcome of differential allocation has not been substantiated in all studies. For instance, in the house sparrow, Mazuc et al. (2003b) manipulated T levels in experimental males to increase their attractiveness and found no differences in yolk T levels between females mated to experimental and control males (Mazuc et al., 2003b). The opposite pattern to that predicted by the differential allocation hypothesis was found in two nonexperimental studies. In the first of these, collared flycatcher females (Ficedula albicollis) laid eggs with higher androgen concentrations when mated to young males (Michl et al., 2005). Similarly, in the Eastern Bluebird (S. sialis), females mated to highly attractive males laid eggs with lower androgen levels that those mated to lowly attractive males (Navara et al., 2006b). These patterns were interpreted as compensatory mechanisms used by females to make up for poor quality or young males (Michl et al., 2005; Navara et al., 2006b); in that sense, the stimulatory effects of androgen on growth or begging may mitigate the costs of pairing with low‐quality males. Although this mechanism could in principle work, the problem with these studies is that they are based on correlative data, and patterns of assortative mating can obscure the real effect of attractiveness. For instance, in the barn swallow study (Gil et al., 2006c), although the

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experiment showed that females laid eggs with higher androgen levels for males with elongated tails, there was no relationship between natural variation in tail length and yolk androgen. This discrepancy may stem from the fact that females paired to long‐tailed males are not a random sample of birds (Møller, 1991). Prelaying differential allocation has important implications for studies of good‐genes sexual selection because it shows that viability effects related to male ornamentation can be confounded by female differential investment (Gil and Graves, 2001; Gil et al., 1999; but see: Sheldon, 2000). The empirical measurement of good‐gene effects had got round the problem of postlaying differential effects (Burley, 1988) by means of cross‐fostering experiments (Johnsen et al., 2000), but now that prelaying effects have been found, experiments using artificial insemination are required (Welch et al., 1998). D. BROOD PARASITISM Conspecific brood parasitism is a strategy by which females lay eggs in the nests of other females of their own species (Rothstein and Robinson, 1998). Although the prevalence of this strategy is rather low, in some species, it can represent an important addition to female fitness (Ahlund and Andersson, 2001). It is to be expected that, if egg androgen deposition is used by females to manipulate male parental care (Moreno‐Rueda, 2007; Mu¨ller et al., 2007b), parasitic eggs should contain higher amounts of androgens that nests laid in the own nests. Two studies addressing this question in the European starling (Pilz et al., 2005b) and in the blue tit (Cyanistes caeruleus) (Vedder et al., 2007) have shown that parasite eggs do not contain higher levels of egg androgens than normal eggs. However, since in these two species the occurrence of conspecific brood parasitism is relatively low, it is perhaps not surprising that selection has not resulted in a fine‐tuning of hormone deposition. Furthermore, since at least in the case of starlings, conspecific parasitism is typically performed by low‐quality females (Sandell and Diemer, 1999), it might not be possible for these females to lay eggs with high androgen levels.

VI. COMPARATIVE STUDIES Differences between species in yolk androgen contents are huge and raise important questions about their physiological significance (Williams et al., 2004). As an example, consider two closely related sparrow‐like passerine species, such as the house sparrow (P. domesticus) and the African masked weaver (Ploceus velatus), in which average yolk T levels are,

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respectively, 40 and 4 pg/mg (Gil et al., 2007). It is difficult to find a key developmental difference between these species that would explain this tenfold difference in yolk androgen concentration. Allometry, for instance, is not an explanation: size differences between species do not explain variation in yolk T and only weakly so in the case of A4 (Gil et al., 2007; Schwabl et al., 2007). Data from the largest comparative study conducted so far (Gil et al., 2007) show that in the case of T, the taxonomic level at which most of the variation is explained is that of the family and the genus, whereas in the case of A4 is that of order (Fig. 13; Gil and Biard, unpublished data). However,

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Fig. 13. Estimates of variance due to different taxonomic levels for yolk T (A) and A4 (B). Unpublished analysis from data for 101 species presented in Gil et al. (2007) (Biard and Gil, unpublished data).

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despite this phylogenetic signal that could induce some evolutionary constraints, there was still significant variation explained at the level of the species, suggesting that ecological and social factors could play an important role in shaping yolk androgen levels. Comparative studies are necessary in evolutionary ecology to confirm that species‐specific traits are adaptations to the environment (Harvey and Pagel, 1991). Since adaptation is the result of historical processes, experiments in extant species cannot prove that a given character has evolved as an adaptation to a given environment. Comparative analyses are then used to examine whether evolutionary transitions of a character are associated with transitions in the proposed adaptation (e.g., Tella, 2002). Following this logic, most comparative studies so far have studied the suite of possible adaptations that studies conducted in individual species have previously identified, namely development, sexual selection, coloniality, and brood parasitism. Further studies have addressed possible less‐obvious across‐species correlates of higher yolk androgen, uncovering suggestive patterns that merit further study. Garamszegi et al. (2007b) have found a positive but curvilinear relationship between yolk T and brain size that the authors interpret as backing up the role of yolk T in the development of the brain during embryogenesis. The same authors have discovered a negative pattern between repertoire size and yolk T that could be explained as consequence of the accelerating effects that high levels T have for song crystallization during early development (Garamszegi et al., 2007a).

A. DEVELOPMENT The main axis of variation in bird development is described by the altricial–precocial continuum (Starck and Ricklefs, 1998). Whereas some birds hatch naked, unable to thermoregulate and depend completely on their parents to be fed, other species start to move and feed by themselves some hours after hatching. Because yolk hormones have been shown to affect development, a first question regards the link of these egg components with the mode of development (Schwabl, 1999). Data show that there is not a direct relationship between T or A4 yolk levels and developmental mode (Gil et al., 2007). When we correct for body mass, there is no relationship between mode of development (altricial–precocial spectrum) and yolk hormone levels. The positive relationship between body or egg mass and yolk A4 levels is consistent in two different studies (Gil et al., 2007, Schwabl et al., 2007), suggesting that embryos or nestlings require a concentration of A4

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proportional to their body size. This effect is reminiscent of other mass‐ related functional correlates of embryonic development, such as functional maturity of organs (Starck and Ricklefs, 1998). By gathering yolk hormone data from the literature, Gorman and Williams (2005) showed for the first time that there was a negative relationship between species‐specific yolk T levels and duration of the incubation period in Passeriformes (Fig. 14), thus suggesting that yolk hormones could have evolved as an indirect genetic effect (Wolf et al., 1998). However, they found no effect of yolk T levels on the duration of the nestling period. These patterns were consistent after correcting for the common phylogeny. Two additional studies that followed have brought conflicting evidence in this respect. The study with the largest sample so far (Gil et al., 2007) also found a similar negative relationship between developmental period and yolk T levels in the Passeriformes, but this relationship disappeared when similarity by common descent was controlled for (Fig. 15). In contrast, Schwabl et al. (2007) replicated the same results of Gorman and Williams, observing a strong negative relationship between embryonic incubation

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Fig. 14. Species correlations between log yolk T concentration and log (A) length of the prenatal and (B) postnatal developmental periods, and independent contrasts relationships for yolk T concentration and (C) length of the prenatal and (D) postnatal developmental periods (Gorman and Williams, 2005). # 2005 The Royal Society.

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period and yolk T in a sample of Passeriformes, also when a phylogenetic correction was applied (Schwabl et al., 2007). An even stronger relationship was found with DHT, which is the most powerful androgen in bird yolk, suggesting a strong role of this hormone in development. The same study identified a positive relationship between nestling predation rate and yolk T concentration, suggesting that predator selection for faster development in birds (Bosque and Bosque, 1995; Martin, 2002) may have acted through this maternal effect in Passerines.

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These studies, while presenting some degree of conflicting evidence, suggest that selection for modifications of the length of the developmental periods is somehow linked to modifications of the embryo’s exposure to maternal steroids in the order Passeriformes. In contrast to the Passeriformes, a general analysis covering the whole Aves class has found little evidence for a direct effect of yolk androgens in reducing overall developmental periods (Gil et al., 2007). In a sample of 101 bird species, there was no relationship between developmental period (as measured by the joint covariance of incubation and nestling periods) and either yolk androgen (A4 or T). However, it was found that A4 was positively related to the relative duration of the incubation period over the nestling period, and negatively to the relative duration of the nestling period (Fig. 16). In other words, after controlling for the overall duration of development, species with developmental periods characterized by a relatively long incubation period and a relatively short nestling period had high A4 yolk concentrations (Gil et al., 2007). Previous comparative studies have found that relatively short incubation periods in birds are associated with higher parasitism‐driven mortality and higher prevalence of blood parasites (Møller, 2005; Ricklefs, 1992). It is difficult to understand how A4 may mediate this effect because we do not have enough data on the distinctive effects of A4 over other androgens (see Section VIII.C). However, two nonmutually exclusive mechanisms could be envisaged: a reduction in the nestling period through increased begging or an increase in the incubation period through a retarding effect on embryo development. Although specific experiments comparing the effects of A4 versus T have not been conducted, we could assume that those experiments in which a high A4/T ratio has been injected most likely provide us with information on the specific effects of A4. In that sense, a reduction of the nestling period through begging is consistent with data from the black‐headed gull in which chicks hatching from androgen‐ injected eggs begged more strongly than control chicks (Eising and Groothuis, 2003). The second possibility however is less clear since different studies provide conflicting evidence (Eising et al., 2001; Sockman and Schwabl, 2000). The large differences in longevity that exist between temperate and tropical species are considered to be a major cause of variation in life‐ history traits between bird species living in these areas (Martin, 2002). For instance, incubation periods are longer, and clutch sizes smaller in the tropics than in temperate zones, and experimental and comparative data suggest that this variation is explained by reduced parental investment and risk‐taking behavior in tropical species (Ghalambor and Martin, 2001; Martin, 2002). Thus, time‐dependent selection pressure in the tropics has

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Fig. 16. Relationship between yolk A4 concentration and residual incubation period (A) and residual nestling period (B) using species data (Gil et al., 2007). # 2007 University of Chicago Press.

led to low nest attentiveness, cooler average egg temperatures, and slower development. What could be expected in terms of yolk androgens? Assuming that yolk androgens decrease developmental periods, the logical prediction would be one of higher androgen levels in the tropics. However, recent data show exactly the opposite pattern: lower yolk T and DHT levels in the eggs of tropical birds (Martin and Schwabl, 2008). A possible explanation for this pattern is based on a negative trade‐off between growth rate and developmental quality (e.g., cellular maturity, immune function). (Arendt, 1997, 2000; Billerbeck et al., 2001; Case, 1978; Ricklefs, 1992). Long‐lived tropical species may require the developmental benefits of slow growth to build high‐quality offspring, and this could trade off with yolk

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T‐mediated rapid growth (Martin and Schwabl, 2008). Additionally, there might be some kind of physiological incompatibility between fast development caused by high androgen levels and incubation conditions characterized by low nest attentiveness and cooler average egg temperatures. Regardless of the reasons for the selective advantage of slow growth rates in tropical nestlings, the match of slow development with low levels of egg androgens in these species reinforces the evidence for a role of maternal hormones in shaping developmental periods across species.

B. COLONIALITY Although there can be considerable variation in colony size within a given species (Brown and Brown, 2000), different species can be classified by their degree of breeding colonial habit from strictly solitary to those always reproducing in large colonies. It is to be expected that, if high yolk androgen levels is an adaptive trait for individuals nesting in high densities (see Section V.B, e.g., Pilz and Smith, 2004), a positive relationship between yolk androgen levels and coloniality habit should also be found across species. Indeed, a comparative study of a wide range of avian species has shown a positive relationship between yolk A4 levels (but not T) and breeding coloniality (Gil et al., 2007). This result was confirmed by analysis of independent contrasts, which revealed that transitions to coloniality have coevolved with increases in yolk A4 concentration over evolutionary time (Fig. 17). Positive relationships between group size and yolk androgens have been hypothesized to be an adaptation because of the particular ‘‘competitive’’ phenotype that is assumed to be produced this way (Schwabl, 1997a). An alternative explanation to these adaptative views is that, increased androgen levels in the eggs of colonial species or individuals are an unselected consequence of increased androgen levels in female plasma (see Section II.B, for a lengthy consideration of the ‘‘female control’’ issue). In this respect, there is evidence linking T female levels and coloniality; for instance, colonial species have higher T levels than solitary species (Møller et al., 2005), and colony size and female T levels are positively correlated in the cliff swallow T. bicolor (Smith et al., 2005). However, if increased yolk androgen in colonial birds is a simple epiphenomenon of increased androgens in females, one would also expect a positive relationship between yolk T and coloniality. This lack of relationship with T levels, in the face of an increase in yolk A4 levels (Gil et al., 2007), suggests some kind of physiological filter in the female and that A4 may indeed be a mechanism used to modify the phenotype of colonial species.

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Fig. 17. Relationship between yolk androgen concentrations and coloniality, using species‐ specific data (A) and independent contrasts (B). The A4 concentration data is shown by solid circles and solid lines and T data by open circles and dashed lines (Gil et al., 2007). # 2007 University of Chicago Press.

C. SEXUAL SELECTION A link between sexual selection and yolk androgens is suggested by two different lines of research: (1) studies that have shown that females modify yolk androgen levels in response to male attractiveness (see Section V.B, or e.g., Gil et al., 1999) and (2) studies that have found accelerated development of ornaments in birds that have been exposed to high levels of androgens in ovo (see Section III.B.2 or e.g., Eising et al., 2006). This joint evidence leads to the prediction that species subject to higher levels of sexual selection should present higher levels of yolk androgens.

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A comparative study using two different correlates of intensity of sexual selection (sexual dichromatism and mating system) found, however, little support for this prediction (Gil et al., 2007). No differences were found between monogamous and polygynous species in either yolk T or A4. In the case of sexual dichromatism, although monochromatic species had lower T levels than dichromatic species, this difference was no longer significant after controlling for common descent. However, sexual dichromatism and mating system are only useful surrogates for the intensity of sexual selection, and it is desirable that future studies would address this prediction using more direct estimates of the intensity of sexual selection. D. BROOD PARASITISM

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Fig. 18. Yolk testosterone concentrations of brown‐headed cowbirds and three host species. Abbreviations: BHCO, brown‐headed cowbird; EAPH, eastern phoebe; RWBL, red‐winged blackbird; YEWA, yellow warbler (Hauber and Pilz, 2003). # 2003 University of Notre Dame.

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cuckoos (Clamator glandarius) have lower androgen levels (T and A4) that magpies (Pica pica) (Gil and Soler, unpublished data). Communally breeding guira cuckoos (Guira guira) have also relatively low concentrations of yolk hormones when compared to similarly sized species (Cariello et al., 2006). However, Hahn et al. (2005) found that egg T levels of brown‐headed cowbirds varied between different populations in a remarkable way; recently, expanding populations showed a trend for higher yolk‐T levels than ancestral populations. This difference, if confirmed by further analysis, suggests the possibility of adaptive phenotypic plasticity for establishment in novel areas and differential dispersal (Hahn et al., 2005; Tschirren et al., 2007). E. CORRELATED SELECTION Artificial selection is an excellent tool to explore the genetic architecture of traits. Some studies have taken advantage of artificial selection programs to analyze the correlated response in yolk hormones that has followed these selection programs. A comparison of Japanese quail lines selected for high and low social reinstatement responses (a measure of the strength of sociality) has showed that yolk T levels have diverged in a correlated way with this selected behavior (Gil and Faure, 2007). Female quails of the high line (high social motivation) laid eggs with higher T levels than quails from the low line (low social motivation), while the nonselected line had intermediate values between these two (Fig. 19). Since high yolk T levels induce a proactive phenotype in this species (Daisley et al., 2005), the match between yolk T levels and the behavior of the selected lines suggests that a causal link between yolk T and proactive behavioral phenotype, following a series of selection episodes, rather than pleiotropy or linkage disequilibrium (Price and Langen, 1992). Detailed genetic analyses would be needed to examine the genetic architecture of these traits, which suggest the presence of an indirect genetic effect (Wolf et al., 1998). VII. A MECHANISM FOR SEX‐RATIO ADJUSTMENT? In the last decade, many examples of adaptive sex‐ratio allocation in birds have been reported (e.g., Komdeur et al., 1997), suggesting that females can bias the overall sex ratio of their broods and even the position of different sexes along the hatching order (Badyaev et al., 2002). Since sex‐ determination (i.e., chromosome segregation) happens a few hours before ovulation, when the follicle is already mature, it is possible that differences between eggs in hormone concentration might affect sex determination (Krackow, 1995).

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Hormone concentration (pg/mg)

35 30 25 20 15 10 5 0 Low SR

Control Selection lines

High SR

Fig. 19. Yolk androgen in eggs coming from quails of different selection lines. Graph shows T concentration (black bars) and A4 concentration (shaded bars) in the yolks of eggs laid by females belonging to the low social reinstatement, control, and high social reinstatement lines (Gil and Faure, 2007). # 2007 John Wiley & Sons.

Different hormones have been proposed to act as sex‐ratio distorters, including P (Correa et al., 2005), CORT (Pike and Petrie, 2006), and androgens (Rutkowska and Cichon, 2006). However, evidence so far is only based on suggestive correlations, and a proper physiological model has not been proposed or tested. Suggestive evidence for a link between T and sex ratio comes from a study by Veiga et al. (2004) that showed that female spotless starlings that have been fitted with T‐implants‐produced male‐biased sex ratios. Remarkably, this effect was detected not only on the year of implantation, but also years later, when the implants should have been effectively empty (Veiga et al., 2004). Similarly, in the zebra finch, injecting T in laying females resulted in a modification of sex‐ratio variation along the laying order (Rutkowska and Cichon, 2006), with a tendency to overproduce sons over daughters. However, between‐female differences in mean yolk androgen levels were not related to brood sex ratio in the barn swallow (Gil et al., 2006c). The differences in growth of oocytes that become male and female in the house sparrow found by Badyaev et al. (2006a) also suggest a link between sex and hormone levels. However, the direction of the relationship is not easy to disentangle: is sex determined by hormone levels in the oocyte? or

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do females modify oocyte content as a function of the sex that is going to be produced? To sum up, although certain links between maternal hormones and sex ratio have been found, data so far do not show a clear endocrine mechanism by which sex ratios could be modified.

VIII. EGG COCKTAILS The vast majority of studies on egg hormones have concentrated on androgens, but there are several other hormones that are found in eggs and that have consequences for the offspring. A. ESTRADIOL Estradiol is a powerful estrogen involved in processes of sexual differentiation in birds (Adkins‐Regan et al., 1994; Balthazart and Ball, 1995), although its effects vary between species. Several studies have found that female birds injected with estradiol lay eggs with high estradiol content, producing strong modifications of the sexual differentiation of the embryo (Adkins‐Regan et al., 1995; Riddle and Dunham, 1942). For instance, female Japanase quail hatching from these eggs presented a atypically high incidence of right oviducts (Adkins‐Regan et al., 1995). Avian yolks contain small amounts of estradiol (Adkins‐Regan et al., 1995; Schwabl, 1993), but these are lower than those needed to modify embryonic sex differentiation (Carere and Balthazart, 2007). Studies so far have found surprisingly little variation between eggs in yolk estradiol levels despite huge plasma variation in females through oviposition (Williams et al., 2004), although differences along the laying order have also been described (Williams et al., 2005). However, and unlike in reptiles, where natural variation in egg estradiol can have a dramatic effect on phenotypes (Lancaster et al., 2007), no study so far has identified substantial relationship between estrogen variation and offspring morphology or behavior. B. CORTICOSTERONE CORT is the main stress hormone in birds, and is secreted by the adrenal glands after stimulation of the hypothalamo‐pituitary‐adrenocortical (HPA) axis (Sapolsky, 1992). The production of this hormone in adults is strongly linked to the stress response that involves a fast mobilization of energy, a shut down of reproductive and immunity activities and in general a preparation of the individuals to face the emergency situation (Wingfield and Kitaysky, 2002).

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Levels of this glucosteroid have been found in eggs, with large variation between species [from 2 pg/mg in quails to 30 pg/mg in peacocks (Love et al., 2008)] and is found in both yolk and albumen of avian eggs. In contrast to androgens, CORT does not arrive inside the egg by means of specific follicle cells. Rather, it is assumed that it gets there by passive diffusion from blood vessels, although evidence is equivocal. For instance, Rettenbacher et al. (2005) injected hens with CORT and found a rather reduced transfer of the hormone to the yolk. High amounts of CORT fed to hens did result in higher levels in yolk, but ACTH injections did not lead to significant increases in yolk CORT. The higher concentrations were found in the albumen and the outer layers of yolk, suggesting that this hormone reaches the egg through passive diffusion from the blood stream. There is some evidence that stressful events experienced by the laying female result in increases in egg CORT levels. For instance, Saino et al. exposed laying barn swallows to either a predator (cat) or a nonpredator (rabbit). They subsequently found higher CORT concentrations in the eggs of the females that had been exposed to the cat than in those exposed the rabbit (Saino et al., 2005). A comparison of quail selected for low‐ and high‐ stress responses has shown correlated responses in egg CORT levels, suggesting that either birds with high plasma CORT levels lay eggs with higher CORT concentrations, or else that artificial selection has acted on an partially correlated epigenetic mechanism, by which maternal CORT levels induce phenotypes characterized by an HPA activity upregulation (Hayward et al., 2005). Studies so far seem to coincide in that the consequences of higher CORT exposure are largely detrimental. Thus, in the barn swallow, CORT‐ injected eggs were less likely to hatch than control eggs, and those that did hatch were lighter and showed a poorer growth of tarsi and wings (Saino et al., 2005). CORT‐implanted Japanese quail laid eggs with higher CORT contents can control birds, and the chicks hatching from those eggs showed reduced growth and increased HPA activity in response to a stressful situation (Hayward and Wingfield, 2004). A direct injection of CORT into eggs of the same species resulted in similar negative effects in the case of male offspring, but not for females (Hayward et al., 2006). CORT has been shown to increase incubation time, reduce begging displays, and depress cell‐mediated immunity in yellow‐legged gulls (Rubolini et al., 2005). Since many of these effects on morphology and physiology can negatively affect survival probabilities, the most likely conclusion is that females cannot buffer eggs against detrimental increases in circulating CORT levels. Alternatively, other authors have suggested that this transfer may

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be adaptative (Hayward and Wingfield, 2004), in the sense that an upregulation of HPA responsiveness could be advantageous under certain circumstances. Another possibility is that the adaptative role of CORT may be based on its sex‐specific effects. For instance, a study in the European starling shows that CORT implants in females result in eggs containing increased levels and yolk CORT, leading to increasing male embryonic mortality and lower male nestling growth (Love et al., 2005). This evidence is backed up by an additional experiment linking CORT levels to female‐biased broods in peahens (Pike and Petrie, 2005). It has been suggested that such a mechanism could be advantageous since it would allow adaptive sex‐specific brood reductions, thus leading to a better match between female condition and reproductive investment (Fig. 20; Love et al., 2005, 2008). Further research is needed to examine this exciting possibility. C. THREE ANDROGENS, ONE EFFECT? Three main androgens have been found in avian yolks: testosterone (T), androstenedione (A4), and 5a‐dihydrotestosterone (5a‐DHT). No published study so far has examined the differential effects of each of these hormones in development.

Mean yolk corticosterone per clutch (ng/g)

24 P <0.01 B = −3.56 ± 1.23

22 20 18 16 14 12 10 8 −2.5

−1.5

−0.5 0.5 Maternal condition index

1.5

2.5

Fig. 20. Relationship between maternal condition and mean yolk CORT levels per clutch for individual female European starlings (Love et al., 2008). # 2008 Elsevier Inc.

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Schwabl et al. (2007) make the point that yolk androgens can be ranked in terms of their androgenic effect, stating that A4 is much weaker than T and T is much weaker than DHT as showed by in vitro studies that measure affinity for the androgen receptor (Sonneveld et al., 2006). However, DHT has only a stronger androgenic effect than T when measured in vitro, in the absence of enzymes that increase their clearance in vivo (e.g., hydroxylases, sulfatases, reductases) (Sonneveld et al., 2006). In fact, depending on the enzymatic milieu, DHT can be even less potent than T (Kumar et al., 1999). Therefore, it seems rather meaningless to judge the potential androgenic effect of the suite of possible yolk androgens without knowing the precise chemical matrix in which these androgens act in the embryo. Furthermore, since species‐specific concentrations of DHT and T are strongly correlated in Passeriformes (r ¼ 0.9) (Schwabl et al., 2007), it becomes rather difficult to ascertain the specific roles of each of these two hormones. However, the case of A4 is a different story. First, relationships between A4 and T are not very strong, although they are also positive (Gil et al., 2007; Schwabl et al., 2007). Second, some relationships between androgen concentration levels and ecological or behavioral variables are only found for either T or A4, suggesting that these two androgens are not identical (e.g., Gil and Faure, 2007; Gil et al., 2006b). In fact, evidence from other organisms shows that A4 could even inhibit grow (Dlugonski and Wilmanska, 1998; McGivern et al., 1996). Since the action of A4 depends on the conversion to T by the enzyme 17b‐hydroxysteroid dehydrogenase (17HSD) (Horton and Tate, 1966), its effects very likely will depend on the availability of this enzyme (Dlugonski and Wilmanska, 1998). 17HSD is present and functional in the developing avian embryo (Bruggeman et al., 2002), but it is not known whether species differ in 17HSD availability. Additionally, A4 can follow different conversions to both estrogen and DHT, if the required enzymes are present. As in the case of DHT, we are back to a situation in which we need hard data on metabolic conversions in vivo to know the precise roles of each hormone and be able to interpret pattern of variation in nature.

D. OTHER COMPONENTS Apart from basic nutritional components of eggs, such as lipids, protein, or water, a series of substances known to alter the immune system have been found in varying quantities in eggs. For instance, differences between eggs in lysozyme (Saino et al., 2002), carotenoids (Royle et al., 2001), or immunoglobulin content (Morales et al., 2006) can result in differences in nestling growth or fitness.

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Several studies have suggested that these substances may interact with T levels, and that adjustment of T levels within‐clutches may be specifically allocated in balance with these components (Royle et al., 2001). In gulls, where within‐clutch differences in egg size and composition are of paramount importance for nestling survival (Nager et al., 2000), it has been found that both carotenoid and immunoglobulin concentrations decrease with laying order whereas T increases (Groothuis et al., 2006; Royle et al., 2001). The interpretation of this pattern could be that egg production constraints eggs that are laid late in the sequence to contain less basic resources, but that females increase T in these eggs to increase the competitiveness of nestlings in situations in which abundant food would not be a limit to chick growth (Royle et al., 2001). It is worth mentioning that surprisingly very little research has been devoted to the implications of variation in thyroid hormone deposition, a hormone directly involved with differentiation and embryonic processes, and that is known to pass from mother to offspring via yolk (McNabb and Wilson, 1997; Wilson and McNabb, 1997). IX. CONCLUSIONS AND FUTURE DIRECTIONS Since the existence of androgens in eggs was discovered by avian ecologists (Schwabl, 1993), this field of research has known a tremendous surge. Research so far has been shaped by the assumption that yolk androgens are something of a magic component of eggs, an added extra that boosts nestling growth and increases reproductive success later on in life (e.g., Eising et al., 2001; Schwabl, 1996a). According to this premise, research has sought adaptive patterns of yolk deposition, such as differential allocation in relation to male quality (Gil et al., 1999, 2006c), breeding density (Pilz and Smith, 2004; Schwabl, 1997a), or brood parasitism (Hauber and Pilz, 2003; Pilz et al., 2005b). Consequently, large levels of variation in egg androgen levels between females have been explained by costs in either females (Gil et al., 2006b) or nestlings (Mu¨ller et al., 2005b). Although this scenario may still be true to some extent, some recent developments reviewed above have added several layers of complexity to it. Further research would greatly benefit by considering these implications to their full extent if we wish to avoid an erroneous interpretation of this maternal effect. Succinctly, these are the main points that in my view call for a revision of our initial views on egg androgens.  Growth benefits not so large: First reports on the effects of yolk andro-

gens in nestlings suggested that development could be substantially accelerated and asymptotic size increased by increasing the amount of

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yolk androgen (e.g., Schwabl, 1996a). Since then, several studies have reported negative effects for nestling growth and survival (Navara et al., 2005; Sockman and Schwabl, 2000), or benefits accrued only under some particular conditions (Pilz et al., 2004). All this evidence suggests that yolk androgens are not a universal mechanism that mothers can use to modify developmental speed and that this might not be their main selected effect.  Sex‐specific optima: Two different studies show that the sex of the

embryo is of paramount importance in determining whether high yolk androgen will be beneficial or not (Saino et al., 2006; von Engelhardt et al., 2006). This suggests patterns of ontogenetic sexual conflict that may constrain adaptive sex‐ratio allocation.  Neither beneficial nor detrimental: A recent study in the pheasant

(Phasianus colchicus) has shown that whereas birds hatching from androgen‐injected eggs are not bigger, heavier, or have larger ornaments, the covariance among different traits in these birds is modified with respect to controls (Rubolini et al., 2006a). This can be interpreted as androgens somehow interfering with main developmental processes or organizational gene expression, modifying the general development of the phenotype in a complex way, rather than boosting development quantitatively. Other authors have previously suggested that yolk androgens may not be costly resources in themselves, but rather modulators used to alter nestling phenotypes (Navara et al., 2006b).  Effects on behavioral phenotypes and adult development: The few

studies so far that have investigated the consequences of yolk androgen exposure in adulthood are finding long‐lasting effects in sensitivity to steroids, affecting sexual maturation patterns (Eising et al., 2006; Strasser and Schwabl, 2004). Specially interesting are modifications of behavioral phenotypes or dominance that underline the role of yolk androgens in engineering alternative behavioral strategies (Daisley et al., 2005; Schwabl, 1993).  Who benefits? The important final question of whether females or

offspring are benefited in the long run by differential androgen deposition (Mu¨ller et al., 2007b) cannot be answered unless we know in detail the short‐ and long‐term effects of yolk androgens in parents and young. These effects are largely unknown and therefore it is a rather frustrating exercise to try to examine the implications of yolk androgens for staging evolutionary conflicts.

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To summarize, recent research shows that the original view of increased yolk androgens as something intrinsically beneficial for the nestlings is misguided. Rather than boosting or improving development quantitatively, the effects of yolk androgens probably pertain more to the modification of phenotypes within a framework of alternative strategies in the development of morphology and behavior. These strategies are known to respond to density‐dependent cycles, and imply different payoffs depending on phenotype sex or individual quality (McNamara and Houston, 1996). In that respect, differential deposition of egg androgens by females in relationship to male attractiveness or breeding coloniality may be better interpreted as an active modification of the behavioral and phenotypic characteristics of these offspring. To sum up, as in some many other things in biology, the simple rule of more is better is likely not the case as far as yolk androgens are concerned. Acknowledgments At the time of writing this review, I was the recipient of a Ramo´n y Cajal fellowship from the Spanish Ministry of Education and Science, and my research was funded by a grant from the same organization to Pablo Veiga (CGL2005–05611‐C02–01). I would like to thank Marisa Puerta, two anonymous reviewers, and Jane Brockman as editor, for providing insightful comments on this chapter.

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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Neurobiology of Maternal Behavior in Sheep Fre´de´ric Le´vy*,{,{,} and Matthieu Keller*,{,{,} *inra, umr85 physiologie de la reproduction et des comportements, f‐37380 nouzilly, france { cnrs, umr6175, f‐37380 nouzilly, france { universite´ de tours, f‐37041 tours, france } haras nationaux, f‐37380 nouzilly, france

I. INTRODUCTION From an evolutionary point of view, to successfully transmit parental genes, mammalian (but not only mammalian) species require parental care. Parental care represents a large investment of parental resources especially for females. There are fewer opportunities for reproduction in females than in males so that for females investment in maternal care is crucial to increase the likelihood that the mother’s genes will survive into subsequent generations. Beyond the question of why has parental care evolved in mammals, maternal care is essential for the survival of the young and consequently of the species. The mother provides food, warmth, shelter, and protection from predators and conspecifics; increasing the probability that the young will survive to weaning. The mother also shapes the physiological, sensorial, emotional, and social development of the neonate. For instance, sensory preferences are developed through interactions of the mother and they will influence adult food selection, mate choice (Fillion and Blass, 1986), and maternal behavior (Shah et al., 2002). The mother is the first conspecifics that the young encounters and later social relationships are influenced by these early interactions. Mothers are also affected and changed by their interactions with their young. Sensory and endocrine changes associated with gestation, parturition, and lactation induce, in the new mother, changes that have long‐term effects on her own neural and behavioral development and in particular on her future maternal behavior. 399 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00008-9

Copyright 2008, Elsevier Inc. All rights reserved.

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Maternal behavior displayed by the mother directly or indirectly in response to the young assumes a wide variety of patterns, some of which are common to most mammals, and others of which depend primarily on the maturity of the young at birth. The species‐characteristic pattern of maternal behavior depends also on the social structure of the species and the habitat in which they live. It is crucial for the survival of the neonate that maternal behavior is fully functional at parturition and for most mammals this physiological period induces a very rapid interest in the newborn. Cleaning of the neonate and the consumption of amniotic fluid and placenta are a widespread behavior among mammalian orders, except in fully aquatic mammals (order Cetacae) and semi‐aquatic mammals (order Pinnipedia). Mothers of many mammals also emit characteristic vocalizations in response to their young and show retrieval, gathering, herding, or carrying behaviors that protect the young from predation and, often, keep the young in close proximity to the mother. Also, most new mothers protect their young from predators and conspecifics. However, nursing is the most important and common pattern of maternal behavior in mammals—in fact, is its defining characteristic—and occurs shortly after the birth. Two main categories of species can be distinguished according to their different styles of mothering. In ‘‘altricial’’ species (most rodents, canids, felids), the mother builds a nest in which she gives birth to a large litter of young that are not fully developed and have limited sensory and locomotor abilities. The mother is in the nest area, and crouches over the litter in order to warm it and to nurse it. If a pup gets away from the nest, the female will retrieve it by carrying it in her mouth to the nest. The female also licks the young, a behavior that functions to promote urination and elimination by the offspring. In most of these altricial species, the young stays together within the nest for the first days of life and hence there is little need for the mother to recognize individual members of her litter. Indeed, alien pups can be easily fostered to alternate rat ‘‘mothers.’’ In contrast to this behavioral pattern, ‘‘precocial’’ species (most ungulates and pinnipeds) tend to have a small litter of fully developed young. Nest building is generally uncommon and the young are usually capable of following the mother shortly after birth. Most precocial species live in large groups composed of many unrelated individuals and because own and alien young co‐occur in the same flock, nursing females potentially risk having their milk supply usurped by young that are not their offspring. Mothers of these species, therefore, develop a maternal care system involving a selective bond to one’s own young. The occurrence of a selective bond and the extreme rapidity of its establishment within the first few

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hours after parturition undoubtedly represent the essential characteristics of maternal behavior in precocial species. This behavioral trait is different from maternal responsiveness which represents the interest toward any newborn and occurs immediately at birth in both altricial and precocial species. Hence, these species offer a unique opportunity to understand mechanisms involved not only in the immediate receptivity and display of maternal care but also in the exclusive care for the young the mother has bonded with. Individual recognition of young has been demonstrated in sheep, goat, cattle, horse (Herscher et al., 1963; Hudson and Mullord, 1977; Klopfer et al., 1964; Maletinska et al., 2002), and also pinnipeds (Charrier et al., 2001). However, our understanding of the physiological, sensorial, and neural mechanisms by which maternal behavior emerges at parturition depend upon extensive studies in sheep over more than 30 years. Thus, this chapter reviews what we know at present about the mechanisms underlying these two characteristics of maternal behavior, that is, maternal responsiveness and selectivity in sheep. In Section I, expression of maternal behavior at parturition is comprehensively described together with their variations due to breed and parity differences. Section II deals with the neurobiology of maternal responsiveness in which we outline neural circuits mediating the hormonal and sensorial regulation that are responsible for the emergence of maternal responsiveness at parturition. Finally, in Section III, we examine how neural substrates process physiological and sensorial (mainly olfactory) factors that are involved in maternal selectivity. The possible relations between these two neural systems are discussed.

II. EXPRESSION OF MATERNAL BEHAVIOR IN SHEEP A. EXPRESSION OF MATERNAL BEHAVIOR AT PARTURITION In sheep, behavior of the females shows clear changes as parturition approaches (Le´vy et al., 1996b). While ewes usually exhibit clear gregarious behavior, they prefer to isolate themselves from the rest of the flock to give birth. In addition, while nonpregnant ewes demonstrate no maternal motivation and can even exhibit aggressive responses toward lambs, a complete shift is induced by the process of parturition. Indeed, ewes display immediate maternal care after the expulsion of the fetus and become strongly attracted to their young (Poindron and Le Neindre, 1980). This attraction is mediated through attractiveness of amniotic fluid and is restricted to the time of parturition as amniotic fluid is strongly aversive outside this brief interval

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(see Section II; Le´vy et al., 1996a, 2004; Poindron and Le Neindre, 1980). Attraction to amniotic fluid induces a vigorous licking activity that dries and stimulates the neonate. While grooming the lamb, the dam also emits numerous low‐pitched bleats performed with the mouth closed (Dwyer et al., 1998; Se`be, 2007). These vocalizations are specifically emitted during the immediate postpartum period and are thought to be involved in the vocal communication between the ewe and her offspring (Se`be, 2007). One hour after birth, the lamb is usually able to stand and the ewe spends more time licking and sniffing the anogenital region of her offspring (Poindron and Le Neindre, 1980). As the newborn is seeking to the udder, the ewe adopts a specific posture by arching her back, thus facilitating its access. Suckling is usually achieved during the second hour following birth and ingestion of colostrum and is of primary importance for lamb survival and future bonding with the mother (Nowak, 2007; Nowak et al., 2007).

B. ESTABLISHMENT OF MATERNAL RESPONSIVENESS The expression of maternal behavior in sheep is based on two complementary but distinctive components, maternal responsiveness and maternal selectivity (Le´vy et al., 2004). At parturition, the expression of maternal responsiveness is directed toward any neonate that is presented to the ewe and maternal fostering is easily performed (Le´vy et al., 1983; Poindron and Le Neindre, 1980; Smith et al., 1966). At a behavioral level, the criteria used to assess maternal responsiveness can vary, but usually licking, suckling, emission of low‐pitched bleats, and the absence of maternal aggression are considered as good indicators (Le´vy et al., 1996a) (Fig. 1). Maternal responsiveness is established during a sensitive period strictly related to parturition. Indeed, if the neonate is removed at birth, before the mother has the opportunity to interact with it, maternal responsiveness fades

A

B

C

Fig. 1. Behaviors indicative of maternal responsiveness at parturition. (A) Licking of amniotic fluid covering the neonate, (B) Maternal acceptance at suckling, (C) Maternal aggression: head butt.

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within a few hours. Ewes will be unable to display maternal care when reunited with their young after a separation of a few hours (Herscher et al., 1963; Poindron and Le Neindre, 1980; Poindron et al., 2007). The duration of the sensitive period during which mothers will spontaneously care for any young is usually considered as being shorter than 12 h (Poindron et al., 2007). Thus, maternal responsiveness is limited to a short period of time and parturition plays an important role for the onset of maternal responsiveness.

C. ESTABLISHMENT OF RECOGNITION OF THE YOUNG Within a few hours after birth and while ewes are grooming their lamb, they learn their sensory signature and this learning leads to the establishment of individual recognition. As a consequence, ewes subsequently restrict maternal care to their own offspring while vigorously rejecting suckling attempts by the alien young. This discriminative process is defined as maternal selectivity (Le´vy et al., 2004; Nowak et al., 2007; Poindron et al., 2007). Maternal selectivity is also characterized by a high level of maternal stress, exhibited through an increased locomotor activity and the emission of numerous distress bleats, if her own lamb is removed (Poindron et al., 1994). Therefore, maternal selectivity greatly reduces the success of fostering and adoption (Poindron et al., 2007). Thus, maternal selectivity in sheep has been regarded as a possible example of imprinting in adulthood (Gubernick, 1981), given that it shares some of the characteristics found in filial imprinting in birds (e.g., a rapid establishment of an individual preference to an attachment figure that takes place during a sensitive period (see for discussion, Poindron et al., 2007). Maternal selectivity, characterized by the exclusive acceptance of the familiar young at suckling, is a proximal recognition that is based on learning of the olfactory signature of the lamb. Indeed, olfactory bulbectomy, section of olfactory nerves, or irrigation of the olfactory mucosa with zinc sulfate solution prior to parturition results in the failure of mothers to develop discriminative nursing between familiar and alien lambs (Baldwin and Shillito, 1974; Le´vy et al., 1995b; Morgan et al., 1975). However, another type of maternal discrimination of offspring allows the localization of the young from a distance on the basis of visual and auditory cues (Keller et al., 2003; Terrazas et al., 1999). These two types of recognitions differ not only in their sensory basis but also in their dynamics of establishment. While maternal selectivity is established rapidly, within 2‐h postpartum (Keller et al., 2003; Poindron and Le Neindre, 1980), recognition from a distance is functional only after 6 h, at least in multiparous ewes (Keller et al., 2003). However, it must be noted that when ewes are provided only with visual or acoustic cues, distal

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recognition is achieved much later, suggesting that both sensory signatures act in a synergistic manner during the early postpartum period. The respective roles of hearing and vision have not been completely clarified yet. Recognition of the lamb on the basis of visual cues has been the matter of very few studies in comparison to the amount of work performed on visual recognition between adult sheep (Kendrick, 1994, 1998; Tate et al., 2006). Ewes can be trained to discriminate photographs of faces of unfamiliar lambs (Ferreira et al., 2004) but also of familiar versus unfamiliar lambs (Kendrick et al., 1996). This discrimination is only achieved on photographs of 3‐week‐ old lambs, suggesting that visual cues are not very salient before this age. Concerning the role of hearing, it has been demonstrated, for instance, that each lamb exhibits a unique acoustic feature (Searby and Jouventin, 2003; Se`be, 2007), supporting the behavioral data showing that ewes are able to recognize the bleats of their young (Poindron and Carrick, 1976; Searby and Jouventin, 2003; Se`be et al., 2007). A recent study shows that the development of this acoustic recognition is very rapid since it is fully functional after 24 h of mother–young contact (Se`be et al., 2007). Finally, both maternal selectivity at suckling, and visual/auditory recognition, appear to be independent processes. Anosmic ewes can discriminate between their own and an alien young from a distance even though they are not selective and are willing to nurse any lamb (Ferreira et al., 2000). This result demonstrates that the establishment of maternal selectivity is not a prerequisite for the development of a functional recognition from a distance and that there are no compensatory mechanisms between different sensory modalities in the control of maternal selectivity at suckling, at least during the early phase of mother–young interaction. D. LONG‐TERM MAINTENANCE OF MATERNAL RESPONSIVENESS AND MATERNAL SELECTIVITY Once maternal responsiveness and maternal recognition are established, they must remain efficient until weaning to ensure a successful maternal investment. In this context, the mechanisms underlying the maintenance of these two aspects of maternal behavior are not similar as maternal responsiveness and maternal selectivity is differently affected by a mother–lamb separation. Indeed, maternal responsiveness fades rapidly: A 36–72 h separation period, after a 4 h contact following birth, induces a clear rejection of the familiar lamb (Keller et al., 2005; Le´vy et al., 1991). This decline in maternal responsiveness cannot be compensated by an increase of previous mother–young contact as the same pattern of fading is observed when the separation is performed after a week of initial contact (Keller et al., 2005).

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Maternal selectivity can, however, be strengthened over time. Although selective mothers exposed to the lamb for 4 h just after birth are not able to retain selectivity after 24 or 36 h of separation, the lamb memory is maintained if ewes and their lambs have been in contact for a week (Keller et al., 2005; Le´vy et al., 1991). These results demonstrate that offspring recognition memory is rather labile and has a short duration during the initial postpartum period, whereas maternal selectivity strengthens over time, suggesting the involvement of consolidation processes. Once memory is consolidated, it can be constantly updated and modulated through reconsolidation mechanisms involving protein synthesis (Alberini, 2005; Dudai and Eisenberg, 2004). This view has been recently challenged in sheep showing for the first time that reconsolidation can occur in social memory. Indeed, recognition of the lamb during a subsequent test for long‐term selectivity is disrupted if, after 7 days of mother–young contact and an 8 h separation period, ewes are subcutaneously injected with a protein‐synthesis inhibitor (cycloheximide) during 10 min of reunion. By contrast, mothers injected with saline, or injected with cycloheximide but presented with an alien lamb for 10 min show no deficit in later recognition of their own lamb (Perrin et al., 2007a). Reconsolidation might provide a mechanism for strengthening the memory trace (Tronson et al., 2006). In sheep, natural period and frequency of separation from the young increases with time. During reunion, reconsolidation could strengthen lamb recognition memory and thereby increase its resistance to the passage of time.

E. MATERNAL EXPERIENCE INFLUENCES EXPRESSION OF MATERNAL BEHAVIOR Maternal experience is a prerequisite for the immediate expression of maternal behavior at parturition in sheep. Primiparous mothers (without previous maternal experience) usually delay the expression of maternal responsiveness (Alexander, 1960; Alexander and Peterson, 1961; Dwyer and Lawrence, 2000; Poindron and Le Neindre, 1980; Poindron et al., 1984) although the amount of grooming carried out over the first 2 h after parturition does not differ with maternal experience. Thus, the main effect of maternal experience is on negative maternal behaviors such as withdrawal and aggression (Dwyer and Lawrence, 2000). The greater maternal rejection of primiparous mothers may be due to greater fear of the novelty of interacting with a lamb and/or of reduced neuroendocrine mechanisms involved in maternal responsiveness (Dwyer and Lawrence, 2000; see Section II).

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In addition, a role of maternal experience in the recognition of the young has also been established; however, the influence of maternal experience depends on the senses involved. While it was initially thought that the development of maternal selectivity was influenced by maternal experience, so that primiparous mothers are slower to establish a selective bond with their lamb (Kendrick, 1994), recent results indicate that primiparous ewes are, in fact, as fast as experienced mothers to develop maternal selectivity (Keller et al., 2003). The initial thought that maternal experience influences maternal selectivity was probably due to poor mothering that delays bonding, rather than a deficiency in olfactory learning abilities. By contrast to maternal selectivity, recognition from a distance is established more rapidly in multiparous ewes than in naive mothers. Indeed, multiparous ewes are able to recognize their lambs within 6 h with the help of auditory and visual cues while this recognition takes 8 h in biparous and up to 24 h in primiparous mothers (Keller et al., 2003). The differential influence of maternal experience may be related to the fact that recognition from a distance requires an association between visual and auditory cues. This multisensory process could be more complex than learning in a single modality, and require maternal experience.

F. GENETIC VARIATION IN THE DISPLAY OF MATERNAL BEHAVIOR In sheep, there is some variability between breeds in the display of maternal behavior. Breed comparisons of grooming behavior have shown some variations in licking behavior, maternal bleating, and aggressive butts toward the newborn (Dwyer and Lawrence, 1998, 2000; Dwyer et al., 1998; Poindron et al., 1984). For example, among French breeds Romanov and Pre´alpes‐du‐Sud, ewes spend more time licking their lambs than Ile‐de‐ France or Lacaune ewes (Poindron et al., 1984). Also, the proportion of primiparous ewes failing to accept their lambs within 3 h after delivery is less in Romanov than in Ile‐de‐France breeds (Poindron et al., 1984). A similar picture is found with English breeds, with Scottish Blackface ewes being more responsive to their lambs and Suffolk showing an increased propensity to reject their young (Dwyer and Lawrence, 2005). This breed is slower to begin grooming the lamb, is more easily distracted and shows a reduced grooming duration. Distinct breed differences are reported in consistency of maternal behavior across parities, Suffolk ewes being fairly consistent in acceptance and rejection behavior, whereas Blackface ewes are less consistent in their rejection behavior (Dwyer et al., 2004).

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Finally, the use of shelters for lambing, cooperative behavior to enable lamb suckling attempts, lamb proximity to ewe, and incidence of lamb stealing are also influenced by breed (Alexander et al., 1983, 1990). Breed differences can also be observed in the display of maternal recognition at suckling with some breeds being less exclusive in the expression of maternal recognition, allowing other lambs to suckle to the detriment of their own young (Shillito and Alexander, 1975; Shillito‐Walser and Hague, 1981). Finally, variation in maternal recognition can also affect recognition from a distance with different breeds relying to various extents on visual and auditory cues to discriminate their lamb (Shillito‐Walser et al., 1982).

III. NEUROBIOLOGY OF MATERNAL RESPONSIVENESS As for the majority of mammal species, in sheep, maternal responsiveness occurring at parturition is hormonally regulated by the endocrine events of late pregnancy and parturition. These hormonal factors regulate the ability of infant stimuli to induce maternal interest (Numan and Insel, 2003; Numan et al., 2006b). These include the steroid hormones, estradiol and progesterone which are synthesized by the ovaries and released into the circulatory system and the protein hormones, prolactin and oxytocin (OT) that are released within the brain and from cells or nerve terminals within the pituitary gland (Bridges, 1990; Insel, 1990; Numan, 1994; Rosenblatt, 1990; Rosenblatt and Snowdon, 1996). Associated with parturition, there also occur important changes of the neuropeptide; beta‐ endorphins, and neurotransmitters; dopamine, norepinephrine, glutamate, and gamma‐aminobutyric acid (GABA; Bridges, 1990; Caldwell et al., 1987; Keverne and Kendrick, 1990; Rosenberg and Herrenkohl, 1976; Young et al., 1997). A. HORMONAL CHANGES DURING PARTURITION As in most mammals, late pregnancy and the parturition period are characterized by a decrease in progesterone levels which have been high throughout pregnancy, followed by an increase in levels of estradiol. Two to four days before parturition, plasma levels of progesterone drop whereas estradiol levels rise 1 day before parturition, peaking at parturition and then decreasing to a basal level within the first 4 h postpartum (Challis et al., 1971; Shipka and Ford, 1991). The change of estradiol and progesterone steroid balance induces a rise in levels of the pituitary hormone, prolactin, which occurs 24–48 h before parturition; this high level is maintained during lactation as a result of suckling stimulation (Bridges and

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Goldman, 1975; Chamley et al., 1973). Contrary to the steroids, the rise in plasma OT, released from the posterior pituitary, is strictly contemporaneous with the actual phase of expulsion and is induced by the vaginocervical stimulation (VCS) occurring at this time. All of these hormonal changes reflect changes in the peripheral circulation. Because of the existence of a blood–brain barrier, one cannot infer the existence of similar changes in the brain, the actual site of hormonal effects on behavior. Parturition induces physiological changes within several brain areas. OT concentrations are greatly increased in the cerebrospinal fluid of ewes within the first few minutes after expulsion of the neonate (Kendrick et al., 1986) but not in animals that have an epidural anesthesia (Levy et al., 1992; Williams et al., 2001). OT synthesis increases at parturition in the paraventricular nucleus of the hypothalamus (PVN), and in the supraoptic nucleus (SON) of the hypothalamus, two main sources of OT projections in the brain (Broad et al., 1993b). Specific sites of OT release have also been found using in vivo microdialysis sampling techniques. OT release also occurs from OT terminals in numerous brain sites such as the main olfactory bulb (MOB), the bed nucleus of the stria terminalis (BNST) and the medial preoptic area (MPOA) that are involved in the expression of maternal behavior (Kendrick et al., 1988a,b; Le´vy et al., 1995a). This increase in OT release from PVN and SON terminals is also associated with an increase of levels of OT receptors in some of the same ‘‘maternal’’ sites such as BNST, MPOA, and also the medial nucleus of the amygdala (MeA; Broad et al., 1999). Thus, parturition is characterized by a synchronized synthesis and release of OT in various brain regions and this tremendous activation is amplified by an increase in the number of OT receptors (Kendrick, 2000). Estradiol appears to have a priming role in the activation of the OT system because it increases mRNA expression of OT (Broad et al., 1993a) and its receptor (Broad et al., 1999) in key brain structures (PVN, BNST, MPOA). Other neuropeptides and neurotransmitters that also show important variations within the brain at the time of parturition, and have both direct and interactive effects on maternal behavior, include the beta‐endorphins and the catecholamines. For instance, the mRNA expression of pre‐ proenkephalin, one of the two opiod precursor genes increases in the PVN (Broad et al., 1993b). Noradrenaline (NA) release increases in the same brain areas as found for OT, the MOB, the BNST, and the MPOA (Bridges and Ronsheim, 1987; Broad et al., 1993b; Hammer et al., 1992; Herbison, 1997; Kendrick et al., 1988a; Le´vy et al., 1993). This similarity in pattern of release suggests that these two systems can cooperate to induce maternal responsiveness. For instance, retrodialysis of OT in the MOB induces an increase in NA (Le´vy et al., 1995a). Overall, the fact that these

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hormonal and neurochemical simultaneous activations occur in different brain areas that are not directly connected, supports the notion that the physiological regulation of maternal motivation is multidetermined (Table I). Hormones and neurochemicals associated with gestation and parturition serve multiple functions. They prepare the mother physiologically, by acting on the uterus to initiate the expulsion of the fetus and by inducing analgesia during the birth process. They also stimulate mammary tissue in preparation for the initiation of lactation (Challis and Olsen, 1988; Hodgen and Itskovitz, 1988; Kristal et al., 1986). At a behavioral level, these physiological factors contribute to elevated maternal responsiveness shown by the newly parturient mother and they do so by activating on multiple brain sites (Numan et al., 2006a). B. HORMONAL REGULATION OF MATERNAL RESPONSIVENESS In precocial mammals, as in sheep, evidence for a facilitatory effect of estradiol on maternal responsiveness has been reported on several occasions, although results have not always been consistent. Acceptance of a lamb occurs only when estrogen levels are high, at estrus and at the very end of pregnancy (Poindron and Le Neindre, 1980). In addition, correlations have been found between plasma estradiol levels or estradiol:progesterone ratio in late pregnancy and maternal grooming behavior (Dwyer et al., 1999; Shipka and Ford, 1991). The period during which parturient ewes are positively responsive to lambs can be extended for 1 day by estradiol injection (Poindron et al., 1979). Moreover, a single high dose of estradiol injected into nonpregnant ewes induces maternal responses in 80% of the animals (Poindron and Le Neindre, 1980; Poindron et al., 1988). However, this treatment is probably pharmacologic, since it produces abnormal sexual behavior. Steroid treatments, using more physiologically appropriate doses and longer period of treatment, fail to induce maternal behavior (Kendrick et al., 1992a). Contrary to rodents and rabbits, in sheep, steroids are not sufficient to induce maternal responsiveness; instead, they seem to have priming effects, permitting the action of other physiological factors (Numan et al., 2006b). A role for glucocorticoids in the expression of maternal responsiveness has not been studied in sheep although it has been demonstrated in postpartum rats (Rees et al., 2004). However, there is some evidence showing that the HPA axis may be involved in the display of maternal behavior (Keverne and Kendrick, 1991). Intracerebroventricular (i.c.v.) infusions of corticotrophin‐ releasing hormone facilitate the expression of acceptance behaviors in nonpregnant females receiving concurrent VCS. The increase of CRF mRNA

TABLE I Plasma and Central Changes at Parturition of the Main Physiological Determinants of Maternal Responsiveness E2/P

CORT

PRL

OT

b‐End

NA

ACh

1, 2, 3

4

5 Undet

6, 7 BNST,

PVN

BNST,

MOB

8, 9, 10, 11

12

MBH, MPOA, PVN, MOB, S, SN 9, 10, 11

13

BNST,

Undet

Undet

Undet

Peripheral circulation References Brain changes

MBH, MPOA, PVN, MOB, S, SN 410

References Undet

Number of receptors

References

14

PVN, AON, MPOA, MBH, S, MeA, BDB 8, 15

ACh ¼ acetylcholine; CORT ¼ cortisol; P ¼ progesterone; E2 ¼ estradiol; PRL ¼ prolactin; b‐End ¼ b‐endorphin; NA ¼ noradrenaline; MeA ¼ medial amygdala; AON ¼ anterior olfactory nucleus; BDB ¼ Band diagonal of Broca; Undet ¼ undetermined. Correspondence between numerical and bibliographical references (see the list at the end of the chapter to get the full references): 1 Challis et al., 1971; 2 Shipka & Ford, 1991; 3 Dwyer et al., 1999; 4 Dwyer et al., 2004; 5 Chamley et al., 1973; 6 Kendrick, 2000; 7 Kendrick et al., 1986; 8 Kendrick et al., 1997a; 9 Kendrick et al., 1992b; 10 Le´vy et al., 1995a; 11 Kendrick et al., 1988a; 12 Broad et al., 1993b; 13 Le´vy et al., 1993; 14 Meurisse et al., 2005a; 15 Broad et al., 1999.

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expression at parturition in brain sites that have been implicated in the onset of maternal behavior (Broad et al., 1995) strengthens the possible role of cortisol although these data are interpreted in terms of CRF potentiation on the functioning of other neuropeptide systems, as OT system. Ovarian steroids are not able, by themselves, to stimulate maternal care in the first‐time ewe the way they can in rodents (Numan and Insel, 2003). Steroids have a priming effect allowing other factors to trigger the behavior. Among the influences primed by the steroids are other protein hormones such as prolactin and OT, as well as somatosensory stimulation normally associated with parturition, like VCS. The synchrony between parturition and the various physiological changes described above suggests that the actual process of expulsion of the fetus is of primary importance to the expression of maternal behavior. Full maternal responsiveness is induced in nonpregnant multiparous ewes primed with progesterone and estradiol, when they receive 5 min of VCS (Keverne et al., 1983). The dramatic effects of VCS have been further confirmed in two other breeds of sheep, with low doses of estradiol and even in ewes at estrus (Kendrick and Keverne, 1991; Kendrick et al., 1992a; Poindron et al., 1988). This stimulation not only increases the number of maternal ewes but induces licking, maternal bleats, acceptance at the udder, and reduction in aggressive behavior toward the young. VCS also induces an attraction toward amniotic fluid which is a characteristic trait of maternal ewes at parturition. On the other hand, VCS without a pre‐treatment of steroids is ineffective (Kendrick and Keverne, 1991; Poindron et al., 1988). This action of VCS has also been established in recently parturient ewes. Five minutes of VCS, 1 h after parturition, re‐induce maternal responses that normally occur only at birth, especially licking toward a new‐born lamb (Keverne et al., 1983). In contrast, if stimulation of the genital tract is prevented at the time of parturition by peridural anesthesia, maternal behavior is disrupted (Krehbiel et al., 1987). A series of experiments have demonstrated that central release of OT induced by VCS is a key factor for eliciting maternal responsiveness at the time of parturition. OT release in the cerebrospinal fluid rises at parturition and following artificial VCS (Kendrick et al., 1986; Levy et al., 1992). I.c.v. injection of OT in nonpregnant ewes, after having received a steroid pre‐treatment, induces maternal responses within a minute (Kendrick et al., 1987; Keverne and Kendrick, 1991). I.c.v. infusions of a receptor antagonist partially block the induction of maternal care by OT and the use of a pharmacological agonist is as efficient as OT itself (Kendrick, 2000). Interestingly, OT, like VCS, is ineffective when given without estradiol priming. In parturient females, inhibiting genital stimulation feedback with peridural anesthetic prevents both central OT release and

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maternal behavior (Levy et al., 1992). I.c.v. infusions of OT reverse the behavioral effects. Thus, OT is a physiological signal of primary importance to synchronize maternal responsiveness with parturition and the appearance of the young. However, this signal is further potentiated by other neuropeptide systems such as opiates. I.c.v. infusions of morphine alone do not affect cerebrospinal fluid levels of OT but potentiate it after VCS and induce maternal responses similar to that of the post‐parturient ewe (Kendrick et al., 1991a). C. SENSORY REGULATION OF MATERNAL RESPONSIVENESS Parturitional hormones also alter the reinforcing value of young and the mother’s responsiveness to infant cues. In sheep, olfactory cues, specifically odors from the amniotic fluid that coats the neonate, are very attractive for the new mother at parturition (Le´vy et al., 1983, 1995b). This olfactory attraction is necessary for the initial approach to the young. Washing the neonate greatly disrupts the onset of maternal care (Le´vy and Poindron, 1987; Poindron and Le´vy, 1990). Amniotic fluid is also sufficient, by itself, to induce maternal responsiveness in a context wherein females typically reject the young. Parturient, experienced, ewes accept 1‐day‐old lambs whose coats were treated with amniotic fluid (Basiouni and Gonyou, 1988; Le´vy and Poindron, 1984). The origin of amniotic fluid has no reliable effect on maternal acceptance which suggests that amniotic fluid would only contain cues responsible for general attractiveness but not for individual recognition (Poindron and Le´vy, 1990). This olfactory attraction for amniotic fluid is induced by the synergistic action of prepartum estrogen, VCS, and OT release associated with parturition. Peridural anesthesia in parturient ewes induces a loss of preference for amniotic fluid. Furthermore, attraction to amniotic fluid, once it has disappeared in postparturient ewes, can be restored by artificial VCS up to 4 h postpartum. The effect of VCS appears to be mediated in part by OT. I.c.v. infusion of OT in peridural anesthetized ewes restores the preference for amniotic fluid (Poindron and Le´vy, 1990). D. NEURAL SUBSTRATES OF MATERNAL RESPONSIVENESS As the preceding sections reveal, the emergence of maternal responsiveness is the consequence of endocrine changes associated with parturition that prepare the mother to be responsive to the infant’s cues. The following section will discuss the related primary neural circuitry in terms of where hormones act to stimulate the expression of maternal responsiveness.

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The use of immediate early genes (c‐fos) as markers of neuronal activation has allowed the investigation of the neuronal networks involved in the activation of maternal responsiveness in the ewe. In one study, brain activations resulting from a brief exposure to lambs after parturition were compared with those found in females receiving a treatment to induce maternal receptivity but without any exposure to lambs (Da Costa et al., 1997). In another study, these brain activations were measured in mothers rendered anosmic before parturition, which allows the display of maternal responsiveness but prevents the establishment of bonding (Keller et al., 2004a). Both studies reveal that the induction of maternal responsiveness involves extensive neural circuitry in the brain including various limbic and hypothalamic areas. Indeed, the pattern of gene expression observed between ewes displaying maternal behavior (i.e., maternally responsive and selective) and ewes receiving VCS or rendered anosmic before parturition (i.e., maternally responsive, but not selective), are remarkably similar in hypothalamic regions particularly the MPOA and the PVN (Fig. 2), and also in the limbic system particularly the BNST.

Medial preoptic area Paraventricular nucleus Main olfactory bulb

Piriform cortex

Intact ewes

3V

3V

Anosmic ewes

3V

3V

Fig. 2. Comparisons of fos activation between intact and anosmic mothers at parturition in hypothalamic and olfactory brain structures. Intact and anosmic ewes (maternal but not selective) show similar activation in hypothalamic structures whereas in anosmic ewes olfactory structures show an absence of fos activation.

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The functional involvement of some of these structures was explored using reversible inactivation. The effects of MPOA and BNST inactivation were investigated in primiparous ewes, by infusing an anesthetic before parturition and during the first 2‐h postpartum (Perrin et al., 2007b). MPOA inactivation greatly impairs the whole repertoire of maternal behavior at parturition (licking behavior, maternal vocalizations, and nursing), whereas inactivation of the BNST or of adjacent sites (septum or diagonal band of Broca) or infusion of cerebrospinal fluid does not. However, when interest for the lamb is challenged by a separation/reunion lamb test performed at 2‐h postpartum, the picture is quite different. Ewes with MPOA inactivation exhibit little reaction after separation of their lambs and do not show any motivation to reunite with them. Ewes with BNST inactivation are less motivated to join their lambs. These findings suggest that the role of the MPOA is not minor (Kendrick et al., 1997a). Instead, the MPOA is a key structure for the control of maternal responsiveness in the ewe and the rat (Numan and Insel, 2003). Furthermore, whereas the BNST does not seem to be involved in the different components of maternal responsiveness at parturition, this structure could regulate approach behavior toward the young, as was demonstrated in the rat. Neurochemical lesions that specifically destroy the BNST (the ventral part) without damaging the MPOA primarily disrupt retrieval behavior, although the other components of maternal behavior are not affected as it occurs after MPOA lesions (Numan and Numan, 1996). The possibility that the MPOA could also be involved in the maintenance of maternal responsiveness beyond the first 2‐h postpartum was evaluated by infusing maternal ewes with a local anesthetic, Lidocaine, for a 12‐h period (Perrin et al., 2007b). Deficits in maternal responses were obtained with inactivation of the MPOA. Ewes showed less reaction both at lamb removal and after the lamb was brought back in comparison to their behavior before Lidocaine infusion. The involvement of the MPOA in the maintenance of maternal behavior is in accordance with previous results showing that at 7‐h postpartum, fos expression is enhanced in the MPOA at reintroduction of the lamb after a short separation time (Keller et al., 2005). In rats, there is good evidence that the MPOA/BNST is involved in the maintenance of maternal responsiveness beyond the parturition period. Fos expression increases in the MPOA/BNST during maternal behavior in postpartum females (Fleming et al., 1994; Lonstein et al., 1998; Numan and Numan, 1994, 1995; Stack and Numan, 2000; Walsh et al., 1996). Also, MPOA and BNST damage or pharmacological manipulation disrupts maternal behavior in postpartum rats that have already initiated maternal

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behavior (Jacobson et al., 1980; Numan, 1974; Pedersen et al., 1994; Rubin and Bridges, 1984). Thus, it appears that the MPOA also plays a role in the regulation of ongoing maternal behavior in sheep. The functional involvement of the PVN in the onset of maternal responsiveness was considered through its OT release. Indeed, release of OT at birth and/or in response to VCS occurs primarily in the PVN, which is the main source of OT release in the brain (Da Costa et al., 1996). Furthermore, levels of OT immunoreactivity and mRNA expression are increased in the PVN at parturition, suggesting an enhancement of OT synthesis and storage (Broad et al., 1993a). Parturition also increases OT receptor mRNA expression in the cell bodies of the PVN (Broad et al., 1999). The extensive activation of the whole OT system of the PVN allows the induction of maternal responsiveness. As a matter of fact, when OT is infused into the PVN by retrodialysis in nonpregnant animals primed with a steroid treatment, maternal interest toward lambs is stimulated (Da Costa et al., 1996). Interestingly, this treatment induces the full maternal repertoire comparable to that reported following central infusion of OT and VCS (Kendrick et al., 1987; Keverne et al., 1983). Altogether, these findings indicate that VCS occurring at parturition induces OT release largely in the PVN and activation of the OT system is promoted by OT itself through autoreceptors (Da Costa et al., 1996; Kendrick et al., 1997a). However, OT release also occurs in the BNST, the MPOA and the MOB during parturition and/or following VCS (Da Costa et al., 1996; Kendrick et al., 1992b). Hence, OT may also stimulate components of maternal responsiveness within some of these structures For example, in steroid‐treated nonpregnant ewes, infusions of OT by retrodialysis in the MPOA or in the MOB reduce aggression toward lambs but no effects on acceptance behaviors are observed (Kendrick et al., 1997a). Part of the effects of OT in inducing maternal responsiveness could be mediated through its modulation of classical neurotransmitters (Kendrick et al., 1997a; Le´vy et al., 1995a). For instance, NA is released together with OT during parturition at a number of different brain regions including the MPOA, the BNST, the PVN, and the MOB (Kendrick et al., 1992b, 1997a; Le´vy et al., 1995a). Moreover, retrodialysis infusions of OT increase NA release in the MPOA (Kendrick et al., 1992b) and in the MOB (Le´vy et al., 1995a). Therefore, it is possible that OT acts on presynaptic noradrenergic terminals, and in this way a restricted pattern of potentiated NA release could occur at sites controlling maternal responsiveness. Along with OT, the opoid pre‐proenkephalin, and corticotrophin‐ releasing hormone, are also synthesized in the PVN and their mRNA expression is increased at birth (Broad et al., 1993b, 1995). These neuropeptides have a modulatory role in contributing to the induction of maternal

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behavior by OT. Central administration of the opioid agonist, Morphine, to steroid‐treated ewes facilitates the induction of maternal responsiveness by VCS and the concomitant release of OT in the cerebrospinal fluid, while its antagonist, Naltrexone, injected either peripherally (Caba et al., 1995), or centrally, has the opposite effects (Keverne and Kendrick, 1991). Similar results were also obtained after a central infusion of CRH (cortico‐releasing hormone; Keverne and Kendrick, 1991). E. MATERNAL EXPERIENCE AND BREED INFLUENCE HORMONAL, SENSORY, AND NEURAL CONTROL OF MATERNAL RESPONSIVENESS Many studies have shown differences in the expression of maternal responsiveness at parturition in different breeds and with different maternal experience (see Section I). Influence of these parameters has also been observed in the underlying neuroendocrine mechanisms of maternal responsiveness. For example, effects of maternal experience have been reported for the OT system. Release of the neuropeptide in the MOB at parturition is greater in multiparous than in primiparous ewes. Expression of OT receptor mRNA is enhanced in the PVN of experienced mothers (Broad et al., 1999; Le´vy et al., 1995a). In addition, inexperienced ewes have a lower density of estrogen receptor‐alpha, particularly in the PVN and the MPOA, that are crucial for maternal responsiveness at parturition (Meurisse et al., 2005a). These physiological variations could explain why it is not possible to induce maternal responsiveness in nulliparous ewes. Indeed, in inexperienced nonpregnant ewes, neither steroid priming nor VCS induce acceptance of the lamb (Kendrick and Keverne, 1991; Keverne and Kendrick, 1991; Le Neindre et al., 1979). Also, estrogen‐primed nulliparous ewes are unresponsive to other artificial means of inducing maternal responsiveness, either with OT, opiates or corticotrophin‐releasing factor (Keverne and Kendrick, 1991). Thus, one of the effects of maternal experience could be to increase the brain’s sensitivity to hormonal factors involved in maternal responsiveness. This brain maturation can occur very rapidly after a few hours of interactions with the young. For instance, more of NA, acetylcholine, GABA, glutamate, and OT release were measured in the MOB in experienced than in inexperienced mothers (Keverne et al., 1993; Le´vy et al., 1993, 1995a). However, the differences in transmitter release had disappeared 6 h after parturition because experimental VCS, an event that mimics parturition, was almost equipotent in multiparous and primiparous ewes in neurotransmitter release (Keverne et al., 1993; Le´vy et al., 1993). Maternal experience may alter the neural circuit that mediates maternal responsiveness. Recently, we found that inactivation of the MPOA, with an anesthetic, results in less impairment of maternal responsiveness in

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multiparous mothers than in their primiparous counterparts (Perrin et al., 2007b). This result indicates that maternal behavior relies less on MPOA influence in maternally experienced ewes. Thus, one could hypothesize that the neural network involved in maternal behavior is changed by maternal experience so that other brain structures could compensate for MPOA inactivation. The PVN and the BNST are good candidates since they are connected with the MPOA (Scott et al., 2003; Tillet et al., 1993), and they are involved in maternal behavior (Da Costa et al., 1996, 1999; Perrin et al., 2007b). Maternal experience can also modify the importance of sensory cues for the development of maternal responsiveness. Depriving parturient ewes of amniotic fluid by washing the neonate leads to a significant reduction of licking by 50% in experienced and by about 90% in inexperienced mothers. Furthermore, most primiparous ewes whose lambs have been washed failed to accept them, whereas multiparous mothers established a proper maternal behavior (Le´vy and Poindron, 1987). Therefore, amniotic fluid is absolutely necessary for primiparous dams to develop maternal care, whereas experienced mothers compensate without amniotic fluid exposure. Influence of breed in endocrine mechanisms underlying maternal responsiveness has been also reported. Blackface ewes, which show a higher maternal responsiveness than Suffolk ewes, have higher plasmatic estradiol and cortisol levels around the time of parturition compared to Suffolk ewes (Dwyer et al., 2004). Plasma OT levels at parturition is not affected by breed. However, OT levels tend to be higher in Blackface ewes compared to Suffolk during the first 30 min after parturition, and this period coincides with an intense maternal grooming (Dwyer et al., 2004). Because peripheral hormonal variations may not parallel those occurring in the brain, further investigation needs to be done to explore neuroendocrine mechanisms in relation with breed. IV. NEUROBIOLOGY OF MATERNAL SELECTIVITY Neurobiological mechanisms involved in the recognition of the young have only been explored in the context of maternal selectivity at suckling and no experiments have been conducted about the neural basis underlying recognition from a distance. A. PHYSIOLOGICAL REGULATION The neural signals resulting from the mechanical stimulation due to VCS induce not only maternal responsiveness but also maternal selectivity. Indeed, the VCS caused by the expulsion of the fetus is the starting point

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of a cascade of neurochemical events that facilitate the learning of the lamb olfactory signature. Central OT release subsequent to VCS is one factor facilitating the formation of maternal selectivity since i.c.v. infusions of OT in steroid primed ewes induce selective bonding, while infusions of an antagonist partially reverse this effect (Kendrick, 2000). In addition, VCS after the establishment of selectivity makes mothers establish a new bond with an alien lamb (Kendrick et al., 1991b; Keverne et al., 1983). However, it is not clear if the formation of this new bond is able to erase the olfactory memory of the initial young. As mentioned in the previous section, this effect of VCS is only achieved in ewes primed with estrogens.

B. SENSORY REGULATION Early establishment of maternal selectivity depends on intact olfaction as numerous observational and experimental studies consistently indicate that the sense of smell plays a primary role in selective acceptance of lambs for nursing. Indeed, while grooming the lamb during the first hours following birth, mothers learn the olfactory signature of their lamb and treatments disrupting this olfactory processing such as olfactory bulbectomy, section of the olfactory nerves, or destruction of the main olfactory epithelium through nasal infusion of zinc sulfate induce a lack of maternal selectivity (Baldwin and Shillito, 1974; Le´vy et al., 1995b; Morgan et al., 1975) as ewes accept alien young and their own young at suckling. Experiments in which the lambs’ sensory cues, rather than the mothers’ sensory capacity, are manipulated provide additional evidence that the main olfactory system allows olfactory recognition of the neonate and selective nursing to develop in the parturient mother. For example, ewes that were exposed for 12 h to their lambs that were confined in a 5‐cm‐thick double wire mesh cage (receiving olfactory stimuli, but allowing no physical contact or maternal care), nonetheless, developed a selective bond with that lamb (Poindron and Le Neindre, 1980); this is not the case if the young has been kept in a airtight box which eliminates the olfactory cues. The chemical nature or source of the olfactory cues responsible for the establishment of maternal selectivity has not yet been identified (Le´vy et al., 1996a) and these questions remain an open field of research. In this context, it is important to note that odors associated with amniotic fluid are not implicated in selective maternal acceptance. Indeed, ewes that have already become bonded to their familiar offspring reject alien young coated with amniotic fluid, regardless of whether it came from its own or an alien lamb (Porter et al., 1994).

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C. NEUROBIOLOGICAL BASIS OF MATERNAL SELECTIVITY 1. Mechanisms within the MOB Stimulation of the genital area induces changes in the olfactory system and leads to learning processes that ensure recognition and the exclusive nursing of the neonate. Using various methodological approaches such as electrophysiology and microdialysis, the activity of the MOB has been monitored during the peripartum period. Electrophysiological recordings from mitral cells in the MOB show a dramatic shift in their response patterns following parturition. Indeed, whereas during pregnancy the majority of these cells responded to food odors, they preferentially respond to lambs odors after birth and this shift is concomitant with the development of the attraction toward lambs. In addition, a significant proportion of these cells respond preferentially to the odor of the ewe’s own lamb, in contrast to responses occurring after stimulation with an unfamiliar lamb odor. This result supports the idea that a coding for odor familiarity takes place at the level of the MOB (Kendrick et al., 1992c). These electrophysiological changes are reflected by parallel changes revealed through microdialysis analysis of the release of the OT or neurotransmitters such as NA, GABA, glutamate, and acetylcholine (Kendrick et al., 1988a,b, 1992b, 1997a,b). The involvement of all of these peptides and neurotransmitters in the olfactory learning of the lamb’s signature was tested with pharmacological approaches. A lesion of noradrenergic projections to the MOB or direct infusions of a b‐adrenergic antagonist in the MOB significantly reduced the proportion of ewes developing olfactory recognition of their lamb without affecting odor perception (Le´vy et al., 1990; Pissonnier et al., 1985). Release of NA causes disinhibition of mitral cells either directly (Yuan et al., 2003) and/or through the intermediary granule cells (Trombley and Shepherd, 1992). This activation permits activation of NMDA (N‐methyl d‐aspartate) and/or AMPA (alpha‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid) receptors to stimulate nitric oxide release. Nitric oxide further potentiates glutamate release, resulting in long‐term changes in the granule to mitral cell connection. Blockade of nitric oxide release or of the neuronal enzyme nitric oxide synthase within the MOB also blocks the memory formation of lamb odor (Kendrick et al., 1997b). OT release within the MOB may also act to facilitate lamb odor memory because it modulates NA release. Infusion of OT within the MOB caused a significant increase in NA concentrations in the MOB (Le´vy et al., 1993). The fact that maternal behavior induced by i.c.v. OT infusions results in selective bonding supports this hypothesis (Kendrick et al., 1997a). Finally, infusion of a GABAa receptor antagonist, Bicuculline, in the MOB prevented lamb recognition after selective bonding (Kendrick, 1994).

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This large body of results showing the involvement of the MOB in maternal selectivity, in combination with the fact that severing the nerves of the accessory olfactory system did not prevent ewes from being selective (Le´vy et al., 1995b), have led to the conclusion that the vomeronasal organ (VNO) and the accessory olfactory system do not sustain lamb olfactory recognition. Two olfactory systems have evolved in terrestrial vertebrates, which differ in both their peripheral anatomy and central projections. The main olfactory system is usually conceived as a general analyzer that detects and differentiates among chemosignals from the environment (Firestein, 2001). Odors are detected by olfactory sensory neurons located in the main olfactory epithelium; these neurons then project to glomeruli in the MOB. The mitral and tufted neurons abutting these MOB glomeruli then transmit olfactory signals to various forebrain targets including the piriform cortex, the cortical nucleus of the amygdala (CoA) or the entorhinal cortex (Scalia and Winans, 1975). By contrast, the accessory olfactory system is thought to be involved in the detection of odors that influence a variety of reproductive behaviors (Brennan and Keverne, 2004; Brennan and Zufall, 2006; Keverne, 1999). Sensory neurons are located in the VNO and detect pheromones which gain access to the VNO by a pumping mechanism (Meredith and O’Connel, 1979). Indeed, sensory neurons in the VNO access mainly nonvolatile stimuli by active pumping of the organ during nasal contact with odorant sources (Wysocki, 1979), and activation of neurons from the AOB (accessory olfactory bulb) requires the nose of the subject to be in direct contact with the snout, face, or anogenital regions of the stimulus animal (Luo et al., 2003). VNO neurons send projections to the AOB. Mitral cells of the AOB project in turn to the MeA. Olfactory information is then dispatched to several hypothalamic regions such as the BNST, the MPOA, and the ventromedial hypothalamus (Scalia and Winans, 1975). In the context of maternal behavior in sheep, a possible contribution of the vomeronasal system has been challenged by Booth and Katz (2000) who claimed that the blockade of the VNO duct through cauterization impaired maternal selectivity, in contrast to destruction of the main olfactory epithelium with zinc sulfate. So far, we have no adequate explanation for the discrepancies of our results and those of Booth and Katz (2000) concerning the effects of the VNO lesions on maternal selectivity. Recently, a detailed histological study of the vomeronasal system of the sheep has shown that the strata of the accessory olfactory bulb are not as sharply defined as in other species, and the population of mitral cells is scarce, suggesting that the vomeronasal system is not fully functional (Salazar et al., 2007). However, it would be of interest to compare the effects of the section of the

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vomeronasal nerves (Le´vy et al., 1995b) with that of the cauterization of the nasoincisive duct (Booth and Katz, 2000). By contrast to the vomeronasal system, the discrepancy concerning the role of the main olfactory system is likely to be due to methodological differences. For example, the zinc sulfate solution administrated in the work of Booth and Katz may have been insufficient to induce complete lesions of the main olfactory epithelium in all animals (Poindron et al., 2003). In conclusion, it appears that in addition to the well established role of the main olfactory system in maternal selectivity, a potential role for the accessory system remains to be explored. 2. Beyond the Olfactory Bulb Changes occurring at the level of the MOB constitute only the first step of the processing of information that underlies lamb recognition memory. How the rest of the brain handles this information is, at present, not yet fully understood although progress has been made in recent years by coupling c‐fos immediate early gene mapping with central injections of anesthetic to inactivate target brain structures. These experiments reveal a clear differentiation between neural substrates controlling the olfactory memory process associated with selectivity and those involved in the expression of maternal responsiveness (see Section II). The neural network sustaining maternal selectivity was delineated by comparing brain activation in intact mothers developing a normal olfactory recognition of their young with anosmic mothers that are unable to display a selective recognition of their own lamb, or with non pregnant ewes receiving experimental VCS (Da Costa et al., 1997; Keller et al., 2004a). Intact ewes showed significantly increased levels of Zif268 or fos labeling relative to anosmic mothers or ewes receiving VCS, in olfactory structures belonging to the main olfactory system including the piriform, orbitofrontal, entorhinal, and medial frontal cortices and the amygdala (Fig. 2). The functional role of some of these structures was addressed through central injection of anesthetic to reversibly inactivate their function. Inactivation of the medial frontal cortex by infusing Tetracaine does not prevent the formation of an olfactory memory for the familiar lamb but inhibits aggressive rejection toward alien lambs (Broad et al., 2002a,b). In contrast, a role for both the MeA and the CoA, which receive olfactory input from the olfactory bulbs, has been recently demonstrated (Keller et al., 2004b). Inactivation of these structures with Lidocaine during the 8 h following parturition prevented mothers from learning the identity of their own lamb, and thereby blocked maternal selectivity. This effect was not due to Lidocaine effects on rejection behavior itself, since the rejection response was still not expressed 2 h after the end of the infusion, when the effect of the Lidocaine had worn off. Moreover, the fact that maternal care was not

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inhibited confirmed that the neural network involved in olfactory recognition of the lamb differs from the one involved in the control of maternal responsiveness. Interestingly, the cortical and medial regions of the amygdala receive direct input from the AOB and MOB and are connected with various regions involved in the display of social behaviors. It appears that, in sheep, the CoA receives inputs from both the MOB and AOB as well as from the MeA while the MeA is connected only to the AOB in addition to the CoA (Jansen et al., 1998; Meurisse et al., 2005b). Both nuclei project also to diencephalic structures involved in the control of maternal responsiveness such as the MPOA or the BNST (Perrin et al., 2007b). These results show that the medial and cortical amygdala form a hub in the networks governing social behavior in mammals (Brennan and Kendrick, 2006; Brennan and Zufall, 2006). For example, the MeA was found to be essential for individual recognition of conspecifics in mice. Infusion of an OT antagonist in this structure prevents the recognition of a familiar over an unfamiliar animal (Ferguson et al., 2001). In addition, OT infusion in the MeA of OT knock‐out mice restores normal olfactory recognition of a familiar conspecific (Ferguson et al., 2002). OT receptor activation in the MeA may also be involved in lamb olfactory memory since an increase of OT receptors in the MeA at the time of parturition was observed (Broad et al., 1999). The neural network involved in olfactory recognition of the lamb probably includes additional brain structures. Entorhinal and piriform cortex were also activated during lamb odor memory formation (Da Costa et al., 1997; Keller et al., 2004a) and these structure are known to be critical in olfactory recognition memory (Petrulis and Eichenbaum, 2003; Sanchez‐ Andrade et al., 2005). These data suggest that the perirhinal–entorhinal and piriform cortices may be important for the formation of olfactory memories and increased fos expression in the cortices of intact ewes, but not of anosmic ewes, might reflect such a contribution. Once established, maternal selectivity consolidates over time into a memory resistant to mother–young separation (see Section I). At the neurobiological level, consolidation processes induce time‐dependent reorganizations in the network engaged in lamb recognition. In this context, retrieval of memories across consolidation processes involves time‐ dependent participation of a set of cerebral regions and an enhanced engagement of the frontal regions compared with retrieval of labile memory (Frankland and Bontempi, 2005; Maviel et al., 2004). Although extensive immediately early genes activation is found throughout the olfactory processing network during lamb memory formation, only a few brain structures are engaged in retrieval of lamb memory once consolidated. During the early stages of memory consolidation, piriform and entorhinal cortices show significant expression of activation markers, while retrieval of

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more consolidated memory (7 days postpartum) leads to an enhanced activation of frontal and orbitofrontal cortices (Keller et al., 2004a,b, 2005; Sanchez‐Andrade et al., 2005). The functional significance of this enhanced activation remains to be explored. Finally, the role of the projections of the basal forebrain cholinergic system has also been investigated using a specific cholinergic neurotoxin, ME20.4 IgG‐saporin, injected into the brain in pregnant ewes (Ferreira et al., 2001a,b). Animals with severe immunotoxic lesions are impaired in olfactory lamb recognition without any evidence for sensorimotor, motivational, or olfactory deficits. Which of the cholinergic projection sites are responsible for this effect remains to be determined, but a potential candidate would be the CoA, since it receives cholinergic inputs, and i.c.v. injections of IgG‐saporin induced a loss of its cholinergic innervations (Heckers and Mesulam, 1994).

V. CONCLUSION Across mammalian species, sheep are unique in showing maternal care together with a specialized form of rapid olfactory learning producing recognition of individual young. These two behavioral processes are synchronized at parturition by VCS induced by the expulsion of the neonate. Olfactory cues from amniotic fluid are a prerequisite for the initial approach to the neonate and selectivity is strictly dependent on the learning of individual olfactory signature. At a physiological level, central OT, supported by estrogens, the opoid peptides and CRH, is the key factor for maternal responsiveness. At the neural level, the control of maternal responsiveness is mainly hypothalamic (PVN, MPOA, and BNST) and has little in common with the circuitry involved in selectivity which mainly concerns olfactory processing regions (MOB, CoA, MeA, and frontal cortices) but also cholinergic and noradrenergic systems (Fig. 3). Maternal experience influences the expression of maternal behavior and the underlying neuroendocrine mechanisms so that experienced mothers are more responsive to maternally relevant physiology and to young‐related sensory inputs. Maternal responsiveness, a common behavioral trait of altricial and precocial species, is mediated in sheep by endocrine factors that are similar to those found in altricial species, such as rats. In the majority of mammalian species studied so far, steroids, VCS, and OT play a role in the development of maternal response to the young although the relative importance of each may vary (Numan et al., 2006b). Contrary to sheep, in the rat, oestradiol is essential to stimulate maternal care and progesterone primes the female to respond to the rise of oestradiol occurring at parturition.

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Structures activated in the expression of maternal responsiveness and maternal selectivity at parturition

Lamb Odor

Frontal Cortex MPOA

Piriform Cortex

BNST PVN (Oxytocin)

Locus Coeruleus (Noradrenaline)

Amygdala Entorhinal Cortex

MOB

Vagino-cervical Stimulation Endocrine Stimulation Maternal Responsiveness

Maternal Selectivity

Fig. 3. Neural networks activated by vaginocervical stimulation (gray circle) or lamb odor (black circle) in parturient ewes.

Moreover, the fall in progesterone secretion at the end of pregnancy synchronizes the onset of maternal behavior with parturition. Estrogen and progesterone have been also implicated in non pup‐directed nest‐ building behaviors, in rabbits (Gonzalez‐Mariscal et al., 2007), in mice (Lisk, 1971), and in hamsters (Swanson and Campbell, 1979). In nonhuman primates, there is evidence that steroids may account for changes in maternal responsiveness. In common marmosets, the rate of bar‐press responses for visual presentation of an infant was stimulated by the exogenous administration of late‐pregnancy blood levels of estradiol and progesterone (Pryce et al., 1993). On the other hand, some hormones, like prolactin, are essential for the development of maternal responsiveness in rats, rabbits, and mice but it do not seem to be important in sheep. Suppression of prolactin by the antagonist, bromocryptine, delayed the expression of maternal retrieving and crouching and the addition of prolactin reversed these effects (Bridges et al., 1990). Prolactin‐receptor knock‐out mice exhibited a dramatic deficit in retrieval and crouching behaviors (Lucas et al., 1998). In does, preventing prolactin in late pregnancy by injecting bromocryptine antagonized nest building (Gonzalez‐Mariscal and Poindron, 2002; Gonzalez‐Mariscal et al., 2000). In addition, the importance of some hormones seems to be unique to one species. Only in the sow, a major role of prostaglandin (PGF2a) has been reported in nest building (Burne et al.,

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2000). Cholecystokinin, has been found to facilitate the rapid onset of maternal behavior when it is given to estrogen‐primed rats (Linde´n et al., 1989) but this effect has not been reported in other species. As cholecystokinin stimulates the release of OT, one could consider a possible synergic action between these two peptides in ovine maternal behavior. Clearly, more studies on the endocrine regulation of maternal responsiveness need to be done in sheep. A role of olfaction is clearly established for the onset of both maternal responsiveness and selectivity at suckling. Amniotic fluid is necessary for the initial approach to the young and mothers learn the individual olfactory signature of their own neonate to discriminate them from alien lambs. Olfactory signature is partly genetically influenced but transmission of odors from the mother to the neonate, known as maternal labeling, can also contribute to the development of the olfactory individuality (Romeyer et al., 1993). The exact chemical nature of these individual olfactory cues is not yet identified yet although such signals would have practical applications to facilitate fostering of lambs. Despite the major role of olfaction, sheep mothers are able to discriminate their young on the basis of auditory and visual cues. The exact nature of the visual and auditory cues that are learned by the mother and the associated neurobiological determinism are also important topics to study. Recent findings of neurogenesis in the olfactory bulb provide an additional mechanism through which maternal olfactory experience may be encoded. Pregnancy and parturition induce a high state of plasticity of the olfactory system that may facilitate maternal olfactory learning (Shingo et al., 2003). It is likely that this neurogenesis facilitates olfactory memory formation. For instance, in mice exposure to an enriched olfactory environment increased the number of new neurons and consequently enhanced specifically olfactory memory (Rochefort et al., 2002). Furthermore, neurogenesis was induced by central administration of prolactin, a hormone which is naturally elevated during parturition (Shingo et al., 2003). Therefore, the possibility exists that the physiological factors of pregnancy and parturition enhance neurogenesis so that the ability of the ‘‘maternal brain’’ to learn olfactory information is maximized. Interaction with pups can also influence neurogenesis. Rat mothers that have experienced a 2‐h period of contact with pups show an increase in the survival of new cells in parts of the maternal neural network (Akbari et al., 2007). In sheep, owing to the primary importance of learning the lamb olfactory signature, it could be that this high demand for rapid learning is facilitated by an increase of the number of neurons within the olfactory bulb. Experience gained by interacting with young can lead to other forms of plasticity in the brain, and long‐ lasting structural changes. For instance, experienced rat mothers show

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enhanced astroglial glial fibrillary acidic protein in the MPOA (Featherstone et al., 2000). In sheep, experienced mothers show more increase in neurotransmitter release within the MOB than inexperienced females and it is possible that some kind of structural changes that reorganize the neural substrate can also occur. This chapter describes the neural networks involved in maternal responsiveness and selectivity. How these two neural networks are functionally interconnected is currently unknown. Using anterograde and retrograde tracers, we observed an interconnection between the MOB and the CoA, but also between the BNST and the CoA and between the MPOA/BNST and the MeA (Le´vy et al., 1999; Meurisse et al., 2005b). Both olfactory nuclei were found to be interconnected with a more intense projection from the CoA to the MeA. Based on these anatomical studies and the neurobiological data reported in this chapter, a hypothetical neural model is presented to explain how the two sets of brain regions come into play around parturition to control maternal responsiveness and selectivity (Figs. 3 and 4). At parturition, VCS induces activation of OT neurons in the PVN via neurotransmitter pathways including, noradrenergic, serotoninergic, and dopaminergic systems (Fig. 4). This activation coordinates release of OT in a number of brain regions and, more specifically in the MPOA/BNST. Downstream from these two structures, the brain regions activated to promote maternal acceptance behaviors (licking, bleating, nursing) are not known. If we refer to rat studies, the ventral tegmental area, the nucleus

Maternal Responsiveness MPOA/ BNST

PVN/OT/CRH/Opioids

NA, 5HT, DA VTA/NAcc/VP? Vagino-cervical Stimulation

Maternal Acceptance Fig. 4. A neural model of the regulation of maternal responsiveness established at parturition in sheep. OT ¼ oxytocin; CRH ¼ corticotrophin‐releasing hormone; MPOA ¼ medial preoptic area; BNST ¼ bed nucleus of the stria terminalis; PVN ¼ paraventricular nucleus; NA ¼ noradrenaline; 5‐HT ¼ serotonine; DA ¼ dopamine; VTA ¼ ventral tegmental area; NAcc ¼ nucleus accumbens; VP ¼ ventral pallidum.

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accumbens and its major efferent projection, and the ventral pallidum, could be good candidates (Numan and Insel, 2003). Also, at parturition VCS induces activation of the cholinergic system of the basal forebrain and the locus coeruleus which project to the MOB (Fig. 5). This noradrenergic activation induces neural changes within the MOB which result in an enhancement of pattern activity in response to the familiar lamb odor, favoring its discrimination. The changes of mitral cell activity induced by the familiar lamb odor would activate the CoA and the MeA. Thus, these nuclei would respond more strongly to odor from the familiar lamb than the alien lamb. This is consistent with the findings showing a discriminative response of the MeA to urine from familiar and unfamiliar males (Binns and Brennan, 2005). In turn, this olfactory network, when stimulated by the familiar odor, would activate the MPOA/BNST/PVN network to promote maternal acceptance. Consecutively, nursing would activate the MPOA/ BNST through oxytocinergic system of the PVN. This results in a tighter coupling between the MOB/CoA/MeA and the MPOA/BNST/PVN networks such that after a few hours postpartum, learned odors systematically evoke maternal acceptance. On the other hand, stimulation of the MOB by alien lamb odor would result in an inhibition, or a lack of activation, of the MPOA/BNST/PVN network. Concomitantly, unfamiliar lamb odor would activate brain regions that regulate rejection behavior, like the medial

Maternal Selectivity Familiar Lamb Odor PP Ctx

MOB

NA

LC

ACh ACh

VCS

CBF

CoA ACh

Ent Ctx MeA

+ MPOA/ BNST

Maternal Acceptance

PVN Ocytocine

Nursing

Fig. 5. A neural model of the regulation of maternal responsiveness and selectivity established at parturition in sheep. VCS ¼ vaginocervical stimulation; MPOA ¼ medial preoptic area; BNST ¼ bed nucleus of the stria terminalis; PVN ¼ paraventricular nucleus; MOB ¼ main olfactory bulb; CoA ¼ cortical amygdala; MeA ¼ medial amygdala; LC ¼ locus coeruleus; CBF ¼ cholinergic basal forebrain; NA ¼ noradrenaline; ACh ¼ acetylcholine; PP Ctx ¼ posterior piriform cortex; Ent Ctx ¼ entorhinal cortex. Striped arrow ¼ hypothetical relation.

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frontal cortex (Broad et al., 2002a). Additional studies are clearly needed to reveal which structures control acceptance behavior and learning of lamb olfactory cues. LIST OF ABBREVIATIONS AMPA AOB BNST CoA CRH GABA I.c.v. MeA MOB MPOA NA NMDA OT PVN SON VCS VNO

Alpha‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid Accessory olfactory bulb Bed nucleus of the stria terminalis Cortical nucleus of the amygdala Cortico‐releasing hormone Gamma‐aminobutyric acid Intracerebroventricular Medial nucleus of the amygdala Main olfactory bulb Medial preoptic area Noradrenaline N‐methyl D‐aspartate Oxytocin Paraventricular nucleus of the hypothalamus Supraoptic nucleus of the hypothalamus Vaginocervical stimulation Vomeronasal organ

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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38

Individual Odors and Social Communication: Individual Recognition, Kin Recognition, and Scent Over‐Marking Robert E. Johnston DEPARTMENT OF PSYCHOLOGY, CORNELL UNIVERSITY, ITHACA, NEW YORK 14853-7601, USA

I. INTRODUCTION Individuals are the basic units of social behavior, social networks in local areas, and social organization within a species. In addition, natural and sexual selection work through the relative reproductive success of individuals. In order to understand how all of these processes occur, it is essential to understand how individuals recognize one another and what they know and remember about other individuals. In order to understand the evolution of social behavior, we must understand the mechanisms underlying the recognition and memory of individuals and kin. In many species, ranging from invertebrates to mammals, odors provide exquisitely nuanced information about individuals, relatedness of individuals to one another, social groups of individuals and, among invertebrates, nests or colonies of individuals. In this chapter, I summarize research from my laboratory that provides insight into communication by odor signals and how such signals are used for recognition and memory, competitive advertising by individuals through scent marking and scent over‐marking, and how these processes influence social behavior. A. HISTORICAL CONTEXT During the first half of the 1900s, evolutionary biologists were primarily concerned about the characteristics of populations and species (Dobzhansky, 1951; Mawr, 1963; Simpson, 1953). The major thrust of research was to document the evolution of species‐typical characteristics and differences between populations and species. Ecologists and biologists in the ethological 439 0065-3454/08 $35.00 DOI: 10.1016/S0065-3454(08)00009-0

Copyright 2008, Elsevier Inc. All rights reserved.

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tradition, for example, focused much of their attention on the similarity and differences of displays and other communication signals across related species as an indication of adaptation to the environment (Manning, 1967; Tavolga, 1969; Tinbergen, 1964). The primary interest was in the survival of populations and species. Although founded on Darwin’s ideas, such thinking was rather non‐Darwinian in that it ignored the fundamental underlying mechanism of evolution, the relative reproductive success of individuals. Stimulated by the ground‐breaking theoretical work of R.A. Fisher and George C. Williams (Fisher, 1958; Williams, 1975), animal behaviorists, behavioral ecologists, and evolutionary biologists in the 1960s began to return to Darwin’s original ideas and to stress the mechanisms of natural selection, specifically differential reproductive success of individuals due to differences in individual characteristics (Alcock, 1979; Dawkins, 1976; Wilson, 1980). One effect of this focus on the characteristics of individuals was an increased interest in the relationships between individuals and the ability of individuals to discriminate between and recognize one another. Among the earliest experimental studies demonstrating that animals could indeed discriminate between the cues from different individuals were the studies in which mice and one mongoose were trained to discriminate between the odors of two individuals (Bowers and Alexander, 1967a; Rasa, 1973). Very quickly afterward, the ability to discriminate between individuals and kin was demonstrated in a wide range of species (Falls, 1982; Gorman, 1976; Halpin, 1974, 1980). The interest in relationships between individuals was especially apparent in research on highly social animals such as primates (Cheney and Seyfarth, 1990a,b; De Waal, 1982, 1989). In many species, individuals not only recognize other familiar individuals but also have knowledge about the relationships between these individuals. In vervet monkeys, for example, Cheyney and Seyfarth showed that when an infant gave a distress call, other monkeys in the group not only looked at the infant but also selectively looked at the infant’s mother, indicating that they knew something about the relationship between these two individuals (Cheney and Seyfarth, 1990a,b). In the past 30 years, the basic ability to distinguish between individuals has been demonstrated in a wide range of species, from tiny shrimp and insects to elephants and whales (Barrows, 1975; Barrows et al., 1975; McComb et al., 2000; Payne, 2003; Whitehead, 2003). In contrast, there has been relatively little attention paid to characterizing different levels of sophistication in these abilities or to developing and testing specific hypotheses about the extent of knowledge that one individual has about others of the same species. For example, we should at least distinguish between (a) the sensory ability to discriminate between features of different individuals

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(odors, faces, vocalizations, etc.), (b) the recognition of categories of individuals, such as familiar versus unfamiliar individuals or own group versus other group, (c) ‘‘true individual recognition’’—recognition of specific individuals, each with its unique personality and significance to the perceiver, and (d) knowledge about special relationships between other individuals, such as mates, friends, enemies, coalitions, and relative dominance status. In order to understand these different levels of social knowledge, we need to know more about the underlying perceptual and learning mechanisms that support these sophisticated social abilities. It is also important to learn more about the social context within which individuals operate—the type of social system and the nature and extent of the social network in each species—that is, the number of familiar individuals that regularly interact and know one another in local communities of animals (McGregeor, 2005). There are numerous ways of approaching the level of sophistication that animals are capable of with regard to their social knowledge. At a relatively simple level of analysis, do animals have complex, multicomponent representations of other individuals? That is, can they recognize others equally well by cues in more than one sensory modality (e.g., odors, vocalizations, or visual cues) and can they recognize others by several different cues within a modality (e.g., different odor sources or types of vocalizations)? Another fundamental question is, just how complex are the memories of other individuals? Do animals remember specific cues in isolation, or do they have integrated memories that include several, different cues? Do they remember specific incidents or interactions, or do they have simpler emotional valences attached to the memories of others, such as negative reactions (fear) or tendencies to affiliate? A reasonable hypothesis is that the degree of social knowledge in different species should be related to the degree of sociality and, perhaps more specifically, to the degree to which the social groups have permanent or long‐term memberships. In some highly gregarious species, it is clear that individuals have highly differentiated relationships with others in their group, as demonstrated by friendships, coalitions, reconciliations, reciprocal altruism, and so on (De Waal, 1982, 1989, 1996). Furthermore, individuals in a group may have information about the relationships between other individuals in their social group (Cheney and Seyfarth, 1990a, 1990b). Relatively solitary, asocial species also appear to have surprisingly sophisticated abilities to discriminate between individuals and degrees of relatedness between themselves and others and may form complex, long‐lasting, multicomponent memories of other individuals (Johnston and Bullock, 2001).

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B. FOCUS OF THIS REVIEW In this chapter, I review our research on how hamsters and meadow voles discriminate between and remember individuals, recognize kin, engage in competitive advertising by scent marking and over‐marking, and analyze scent over‐marks to learn about the relative quality of other individuals. Mammalian scents are always mixtures of chemical compounds, and usually these mixtures contain a large number of chemical compounds. Individuals’ odors differ in the relative proportions of these compounds, resulting in differences in the perceived odor quality (Gorman, 1976; Singer et al., 1997; Smith et al., 2001). Although some insects may have odor signals that have only a small number of chemical components (e.g., sexual attractant signals), social recognition processes among insects usually involve the perception of the odor quality of complex mixtures (Downs and Ratnieks, 1998; Fishwild and Gamboa, 1992; Gamboa et al., 1996; Hernandez et al., 2002; Lucas et al., 2005). Individual recognition is an important, highly conserved ability and it is likely that there are similar mechanisms for recognition across a wide range of species. The perception of the odor quality of mixtures is probably the most sophisticated function of the sense of smell, yet we know little about just how this pattern‐recognition process works. Other distinctions that depend on analysis of the quality of complex mixtures include the identification of foods and the quality of foods, identification of plants useful for other purposes, identification of predators, and obtaining other types of information about individuals of their own species [sex, status, reproductive state, stress, disease, genetic relatedness, similarity of major histocompatibility complex (MHC) type, etc.]. For many species, these olfactory abilities are the primary means of understanding the world. Our primary research model is the golden (or Syrian) hamster, Mesocricetus auratus. This species, as well as other species in the genus Mesocricetus, live solitarily, with one adult per burrow (Gattermann et al., 2001; Murphy, 1985; Johnston, unpublished observations). There has been relatively little research on the behavioral ecology of hamsters (Wynne‐ Edwards et al., 1992), but in 2004 we began a field project on golden hamsters in southern Turkey. This work confirms that adult individuals live one to a burrow (except when a mother has pups) and adults have relatively infrequent interactions with other adults. They necessarily interact when mating and males have brief aggressive interactions in the context of competition for an estrous female. Golden hamsters spend most of their time in the burrow, even during the reproductive season. Several papers from our field work on foraging, spacing of occupied burrows, social interactions, and general natural history are in preparation. Perhaps the biggest

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surprise of this work was that, although hamsters in captivity are strictly nocturnal, in nature they were predominantly diurnal (Gattermann et al., 2008). Our hypothesis is that hamsters display a diurnal pattern in nature because their primary predators are nocturnal (fox, owls, feral dogs, and possibly feral cats). Another important model species we have used is the meadow vole, Microtus pennsylvanicus. Much more is known about the behavioral ecology of this species and related Microtus species, but these species are also very difficult to study in nature. Consequently, not much is known about the details of social behavior in the wild. This species is also primarily solitary, with males having much larger home ranges than females (Getz et al., 1981; Madison, 1980; Madison and McShea, 1987; Tamarin, 1985).

II. INDIVIDUAL DISCRIMINATION AND RECOGNITION A. METHODS FOR ASSESSING DISCRIMINATION BETWEEN ODORS OF INDIVIDUALS By 1990, it was clear that individuals in many mammalian species could discriminate between specific odor sources (urine, scent glands) of different individuals. In most early studies on the discrimination between odors of individuals, the method used was to train animals to distinguish between odors from different individuals. It was shown, for example, that mice could be trained to discriminate between the odors of urine from two individuals (Bowers and Alexander, 1967b) and Mongolian gerbils (Meriones unguiculatus) could discriminate between the odors of individuals on the basis of urine and ventral gland secretions but not feces (Dagg and Windsor, 1971; Halpin, 1974). Similarly, one dwarf mongoose was trained to discriminate between individuals on the basis of anal gland secretions but this mongoose could not discriminate between individuals on the basis of cheek gland secretions (Rasa, 1973). These results suggested that not all odors are individually distinctive and that an important first step in studying individual recognition should be to determine the sources of odors that can be discriminated. Training methods provide an excellent assessment of the capabilities of the sensory system—that is, can the sensory system distinguish between cues (odors, sounds, visual cues) from different individuals? Indeed, training methods have been shown to demonstrate discrimination when other, more naturalistic methods (e.g., habituation–dishabituation methods) have not shown the ability to discriminate between two odors (Brown et al., 1987).

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In order to understand the natural social behavior of animals, however, it is more relevant to determine what discriminations animals make spontaneously, without specific training on the odors involved. The habituation–dishabituation technique is excellent for this purpose (Brown et al., 1987; Johnston, 1993; Johnston et al., 1993). In this method, the subject is repeatedly presented with a cue from one individual and its responses are measured (e.g., sniffing, approach, visual display, vocalizations, etc.). Such responses generally decline over repeated presentations of the same stimulus, but when the same type of stimulus from a different individual is presented, responses increase. In our lab, an odor sample from one particular source (e.g., flank gland) is presented to the subject on a glass plate in the animal’s home cage. The amount of time spent sniffing the odor is measured across 4–5 habituation trials (see Fig. 1); it can be seen that investigation time decreases with each trial (i.e., habituation occurs). Then, on the test trial, we present the same type of odor (e.g., flank gland) from a different individual. A significant increase on this test trial indicates that the subject noticed the difference (i.e., dishabituation occurred; Johnston et al., 1993). Lack of an increase in investigation indicates no discrimination. This is a robust technique and it does not depend on specific parameters such as the length of time between trials. In hamsters, for example, we varied the intertrial interval between 1 s and 2 days and got very similar results (Johnston, 1993). A slight variation of this technique is to present two stimuli on the test trial: (1) the same stimulus used in the habituation trials and (2) the same odor from another individual. This method has the advantage of being more sensitive because of the simultaneous presentation of stimuli (Johnston, 1993). Other researchers have likewise found habituation–dishabituation methods to be extremely sensitive and robust (Brown et al., 1987). In our investigation of the sources of individually distinctive odors in golden hamsters, we initially hypothesized that odors from everywhere on the body surface would be individually distinctive, in part because we assumed that odors from specific sources (e.g., flank gland, ear glands, vaginal secretion) would be distributed over the body during grooming. We found that five odors were individually distinctive (flank gland, vaginal secretion, ear glands, urine and feces; Fig. 1) but that five other sources were not discriminated (saliva, feet, behind the ear, fur from the back, and fur from the chest; Johnston et al., 1993). It was particularly surprising that odors from the fur and feet were not individually distinctive, since we suspected that odors from the flank glands and saliva would be distributed onto much of the body by grooming. The results indicate, however, that individually distinctive information is concentrated in a limited number of sites on the body. Similar results were found for a species of dwarf hamster,

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Phodopus campbelli (Lai and Johnston, 1994). This suggests that there may be specializations of specific odor sources for individual variability (Johnston et al., 1993). Indeed, it turns out that each source of odor has several types of information (e.g., sex, reproductive state) and that different sources have different combinations of information (Johnston, 2003). Do individuals of one species spontaneously discriminate between odors from individuals of another species? One could imagine that individuals would not discriminate between individuals of a different species because of selection for intraspecific communication and recognition. On the other hand, the olfactory system of mammals is extremely good at discriminations between complex mixtures of chemicals that differ in subtle ways. In tests of cross‐species discrimination of individual odors by golden hamsters,

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Turkish hamsters (Mesocricetus brandti), and a species of dwarf hamster (Phodopus campbelli), the results showed that subjects in all three species did discriminate between odors of individuals from another species (Heth et al., 1999; Johnston and Robinson, 1993). These initial studies thus indicate that individuals readily notice the differences in the odors of individuals from both their own species and other species and that this type of information is located at specific locations on the body.

B. USE AND VALUE OF HABITUATION–DISHABITUATION METHODS FOR ASSESSING DISCRIMINATION BETWEEN ODORS OF INDIVIDUALS Habituation–dishabituation methods are easy, the results are consistent across experiments, and discrimination can be demonstrated using a wide range of intervals between trials (Johnston, 1993). Sometimes there are differences in the results when a two‐stimulus test trial is used compared to a one‐stimulus test trial, with discrimination demonstrated using a two‐ stimulus test trial (familiar odor and new odor presented simultaneously) but not with a single‐stimulus test trial (Brown et al., 1987). This difference is not surprising because it is clearly easier to compare two odors simultaneously than to compare them on sequential trials. Simultaneous presentation of odors from two individuals may not be as relevant to the situations in which animals make discriminations in nature. Especially with solitary species, nearly simultaneous comparisons of the odors of two individuals are relatively uncommon, except during investigation of scent counter‐ marks. Another situation in which simultaneous comparison of odors may be likely is when two or more males are attracted to an estrous female. Habituation methods are also quite robust; using simultaneous presentation of the familiar and novel odor on the test trial after five, 5‐min habituation trials we found that male hamsters showed a significantly higher level of investigation toward a novel individual’s odor 10 days after the habituation trials (P ¼ 0.0005); after 3 weeks, investigation of the novel individual’s odor was elevated compared to the familiar odor, but the difference was not statistically significant (Johnston, 1993). Despite the consistency of the results with rodents in habituation— dishabituation tests, the details of this method may need to be modified for some species. For example, in initial tests with the urine of dogs and wolves and using 15 or 30 min intervals between trials, subjects did not even approach the stimulus on the second trial, and thus it was impossible to measure habituation. However, when we shifted to a 24‐h interval between trials, both dogs and wolves showed clear habituation and discrimination

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between urine odors from different individuals (Brown and Johnston, 1983). The failure to show discrimination with short intervals between trials was not because the dogs and wolves could not discriminate the odors of two individuals. Rather, it appeared that the dogs and wolves were so sensitive to these odors that, when presented with an odor from the same individual on the second trial, the subjects knew from a distance that it was the same and they did not bother to approach and sniff it carefully. Thus, we could not measure the duration of investigation nor determine that they had habituated to the odor. When we used an intertrial interval of one or more days, however, both dogs and wolves approached and sniffed the stimulus on successive trials, showed habituation to odor from the same individual, and showed clear dishabituation to the odor of a new individual on the test trial (Brown and Johnston, 1983). Habituation methods may not be well suited for all species, however. Some species typically sniff odors very briefly, making it difficult to observe decreases in investigation across habituation trials and/or increases toward the novel odor. House mice, for example, spend much less time sniffing odors than hamsters do. Some species are so wary in the laboratory environment that their behavior is extremely erratic and thus they do not produce consistent investigative responses (e.g., wild‐type Peromyscus species; Johnston, unpublished observations). For most species, habituation–dishabituation experiments are a reliable and efficient means to determine which sources of odor are spontaneously discriminated and contain individually specific information. Similar techniques have also been used to demonstrate discrimination between familiar versus unfamiliar individuals, such as the ‘‘juvenile recognition test’’ using laboratory rats or house mice (Gheusi et al., 1994; Kogan et al., 2000; Thor and Holloway, 1982). In this method, investigators generally use just one familiarization trial, consisting of an interaction between the subject and a stimulus animal. After an interval, subjects are then exposed to the same, familiar individual, in which case a decrease in investigation indicates memory for the stimulus animal. Curiously, laboratory rats housed individually and tested in this manner show memory for the familiar rat lasting only 1–2 h at most. When longer delays between the exposure and test trials were used, no decrease in investigation to the same stimulus animal was observed (Burman and Mendl, 1999; Thor and Holloway, 1982). This apparent lack of memory is surprising. Using habituation methods much like those described above in our experiments with hamsters, rats have been shown to discriminate between closely related laboratory rats that differ genetically only in the MHC region of the genome, indicating that rats are highly attuned to differences between odors of genetically similar rats (Brown et al., 1987, 1989, 1990). The lack of response after an interval

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longer than 1–2 h in the juvenile recognition test may not be due to a lack of memory for the odors of the stimulus animal but could be due to other factors, such as motivational factors. Alternatively, adult male rats may not have much interest in juveniles and/or may tend to treat juveniles as a class rather than treating them as individuals. The context in which the juvenile odors are presented can reduce investigation, suggesting that a novel context distracts rats from investigating the juveniles (Burman and Mendl, 1999). The juvenile recognition method has also been used to study memory of individuals in house mice. In this species, subjects show evidence of memory for familiar juveniles up to at least 7 days (Kogan et al., 2000). A 24‐h period of social isolation disrupts such long‐term memory as measured in this task. This could be due to stress caused by isolation in a species that usually lives in social groups. Similar habituation methods have been widely employed by researchers studying a variety of other species, although the method used is not always identified as a habituation or a habituation–dishabituation test. Probably the most common examples are studies of neighbor recognition among songbirds (Emlen, 1971; Falls, 1982). In these experiments, the habituation phase is the normal singing of one bird and its neighbors; for the test phase, the subject is exposed to a playback of either a familiar neighbor’s song or a novel individual’s song. Birds respond with more singing toward the stranger’s song than to the song of a familiar neighbor. Interestingly, a change in the direction from which a song is played back is often enough to elicit an increased response, indicating that songbirds remember both the individually distinctive characteristics of songs and its usual location with respect to their own territory (Emlen, 1971; Falls, 1982; Stoddard, 1996). To summarize, habituation methods are extremely useful to determine whether individuals spontaneously discriminate cues from different individuals, but this basic technique does not provide much information about whether a particular individual is recognized as an individual or if the individual has a specific significance to the subject.

C. INDIVIDUAL RECOGNITION 1. Integrated, Multi‐feature Memories of Individuals The experiments described above show that hamsters, dogs, wolves, rats, mice, and a variety of other species readily discriminate between odors of different individuals and that birds discriminate between songs of other individuals. These experiments do not, however, indicate that the subjects recognize an individual as such, nor do they fully characterize the content of the memories of familiar individuals beyond the distinctiveness of the cues

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used (odors, vocalizations, visual patterns, etc.). True individual recognition involves at least two additional attributes beyond mere discrimination: (1) an integrated memory of multiple sources of distinctive information, indicating that what is being recognized is a specific individual rather than just one feature of the individual and (2) an association between the sensory information that identifies the individual and the experiences that the subject has had with that stimulus animal (e.g., the emotional significance or meaning of the stimulus individual). Humans, for example, recognize familiar individuals on the basis of many different types of sensory information (e.g., speaking voice, singing voice, the face, body odor, gait, and even the feel of the skin) (Bruce, 1991; Cutting and Kozlowski, 1977; Kaitz, 1992; Nolan, 1983; Porter and Moore, 1981; Porter et al., 1986). Memory of another person includes associated emotions and details of specific interactions with that individual; even second‐hand accounts of that individual’s behavior are incorporated into memories of individuals. Observations of animals in the wild and in naturalistic enclosures suggest that some mammals and birds do have memories of other individuals that are rich in detail and emotional and motivational content (Boesch, 2003; Cheney and Seyfarth, 1990a,b; McComb and Reby, 2005; McComb et al., 2000, 2001; Naguib and Todt, 1997; Payne, 2003). Experimental characterization of the content of such memories is, however, quite rare. Humans automatically categorize other humans as specific, unique individuals and our behavior is guided by our representations of known individuals. To what extent do animals understand the social world as one made up of individuals, each with unique significance? How complex are the representations of other individuals for nonhuman animals? In different species of songbirds, males vary considerably in the knowledge they have of their neighbors’ songs, as indicated by the nature of the exchanges that take place. In some species, a male sings his own song type regardless of what the neighbors sing. In other species, a male may reply to a neighbor by repeating a song type that the neighbor produced (song‐type matching) (Beecher et al., 1994; Searcy et al., 1995; Stoddard, 1996). In yet other species, however, it has been shown that males display a more thorough knowledge of their neighbors. When a male hears his neighbor sing, he may reply with a song that is in the repertoire of the neighbor but that the neighbor did not recently produce, thus indicating that he knows the characteristics of the neighbor (or he at least knows the neighbor’s repertoire). This repertoire‐ matching tactic suggests targeting song at the neighbor (‘‘I know you’’) and not merely repeating the song type that the neighbor just produced (Beecher et al., 1994; Stoddard, 1996). Such responses also demonstrate that the male knows most or all of a neighbor’s repertoire of song types and he uses this knowledge to target or to direct his song at a particular

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neighboring individual (Beecher et al., 1996; Stoddard, 1996). Most male songbirds have more than one neighbor, so they have this type of detailed knowledge of several neighbors. Thus, male songbirds exist in a network of competitors, all of whom have multifactorial knowledge of each other. It is also clear that females have knowledge of nearby males and their characteristics, and they base mate choice decisions on the relative quality of the song and the results of singing competitions between males in the immediate area (Beecher et al., 1996; McGregor and Dablesteen, 1996; McGregor et al., 1992; Otter and Ratcliffe, 2005; Peake, 2005; Searcy and Yasukawa, 1996). In species that migrate south in the winter, there is even evidence that when males return north in the spring they may remember their neighbors of the previous year (Godard, 1991). There is much less in‐depth experimental information about memory for individuals in other groups of animals, but there is substantial observational evidence and some experimental results that indicate that many animals have extensive, complex memories of individuals in their own social group, their local social network, and even of individuals that are only rarely encountered (e.g., elephants, primates, dolphins, whales, lions, deer, etc.) (Boesch, 2003; Cheney and Seyfarth, 1990a; De Waal, 1982, 1989; McComb and Reby, 2005; McComb et al., 2000, 2001; McGregeor, 2005; Payne, 2003; Tyack, 2003). In the following section, I describe experimental work that we did to examine the nature of the memories that animals have of one another and to determine if these memories are organized into integrated, multicomponent representations (i.e., a unitary concept of an individual). 2. Evidence for Multi‐Cue Memories of Individuals One way to determine if animals have multicomponent representations of other individuals is to examine whether one cue can substitute for another cue as a representation of a particular individual. For example, do the face and the voice of a friend mean the same thing, for example, your friend ‘‘David.’’ We tested this idea by using the habituation— dishabituation method in a unique way. In these experiments, males first interacted with two females, one at a time, for 3 min in the male’s home cage on four successive days. On the fifth day, the male was tested using an across‐odor habituation technique (Johnston and Jernigan, 1994). First the subject was exposed to the vaginal secretion of one of the familiar females (e.g., female A) on four, 3‐min trials; then, on the test trial, he was exposed to a different odor (flank gland) from either the same female (A) or a different female (B; see Fig. 2). Males investigated the new odor (flank gland) from both females longer than they investigated the vaginal secretion stimulus on the last habituation trial, because the flank gland odor is novel—it has a very different odor quality compared to that of the vaginal

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secretion. However, the duration of investigation was significantly greater toward the flank gland odor from the new female (female B, bottom graph hatched bar) compared to the flank gland odor from the female that provided the vaginal secretions (female A, top graph hatched bar), suggesting that during the habituation trials the male subject had habituated both to the type of odor (vaginal secretion) and to the specific individual

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(female A) that had provided the habituation scent (see Fig. 2) (Johnston and Jernigan, 1994). That is, during the habituation phase of the experiment, males habituated both to the specific odor presented and to the memory of female A, which included several distinctive odors. On the test trial, males investigated the flank gland scent of female B more than the flank gland scent of female A because they had habituated to the representation or idea of female A but not to the representation or idea of female B. An alternative interpretation of this experiment might be that the results we obtained were merely due to chemical and perceptual similarities in the two odors (flank gland and vaginal secretion) from the same female. That is, the same chemical components might occur in the two different secretions of female A that were not present in the secretions of female B. This could result in perceptual similarities across the two odors. This explanation is unlikely for two reasons. First, if the results were due to chemical and/or perceptual similarity across odors from an individual, one would expect the subjects to show the same across‐odor habituation effect even when the male subjects were not familiar with the odor donors—that is, males should notice the similarities across odors from the two, unfamiliar females. In fact, if males have not interacted with the female odor donors, this across‐odor habituation effect does not occur (Johnston and Bullock, 2001). Second, flank and vaginal secretions are very different chemically. Flank glands are specialized sebaceous glands whereas vaginal secretions are entirely different in their tissue of origin, chemical composition, and odor (Adams, 1980; Albone, 1984; O’Connell et al., 1978, 1979; Thody and Shuster, 1989). The vaginal secretion has a strong, pungent odor to humans and contains many sulfur compounds, whereas the flank gland secretion is a sebaceous secretion with lipids and waxes and other long‐chain carbon compounds and it is either not detected by humans or only barely perceived. Thus, we conclude that hamsters learn multi‐odor representations of other individuals during social interactions and they represent the social world in terms of individuals, each with several unique odors. Recently, Jill Mateo, a former post‐doc in my laboratory, carried out an heroic replication of these experiments with captive Belding’s ground squirrels living in large outdoor enclosures. She showed that dorsal, oral, and anal gland odors were individually distinctive and that the across‐odor habituation effect was obtained using these odors. This is the only other species that I am aware of in which this effect has been demonstrated (Mateo, 2006). A particularly interesting new finding is that contact between the male subjects and the female stimulus animals is necessary in order for these multi‐cue memories to be formed. These integrated representations of individuals are formed when males have exposure to females across a

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wire‐mesh barrier that allows minimal contact to occur or they investigate anesthetized females, but such memories did not occur in similar experiments with several types of barriers that did allow close approach but did not allow the two animals to touch on another (Johnston and Peng, 2008). These experiments provide convincing evidence that the memories that a hamster has of other, familiar individuals is an integrated representation of several different features (odors). This suggests that hamsters, like humans, represent the social world in terms of individuals with a set of unique characteristics and, presumably, emotional significance (see the following section). It would be particularly valuable to show similar cross‐habituation effects using signals in different modalities, since this would indicate that several different types of information could be integrated into a representation of an individual. Such results would also indicate that the representations of individuals were truly higher‐order representations because they integrated information from different sensory inputs. It would also be extremely interesting to find species differences in the type of representations that animals developed. Hamster memories of others may be limited to the olfactory realm because an individual’s ultrasonic vocalizations are extremely variable from one time to another and are apparently not individually distinctive (Floody and Pfaff, 1977b,c). It is not known if hamsters can discriminate between individuals using visual cues, although it seems unlikely. In contrast, many species have communication signals in several different modalities that are likely to be individually distinctive and capable of being integrated into a representation of an individual. It is clear that humans do this and observational studies suggest that many other species do recognize each other using multiple cues (McComb and Reby, 2005; McComb et al., 2000, 2001; Payne, 2003). An obvious prediction is that the complexity of the representations of individuals in different species should be related to the degree to which individuals live in permanent social groups, the size of such groups, and the degree of close social relationships in those groups. What is the selective advantage of developing complex, multicomponent memories of other individuals? One advantage is that representations composed of multiple cues are much more likely to provide unambiguous information about an individual than a memory that involves only one type of information. One source of information may change more than another type of information over time, or with changes in an individual’s reproductive state, diet, level of stress, or other environmental influences. Thus, the most reliable memories of others should incorporate several, independent types of information. I am not aware of any systematic studies on the consistency of different characteristics of humans across time, but in my personal experience, people’s faces appear to change much more over the years than the quality of their voice.

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3. True Individual Recognition—Individuals with Unique Significance and Some Underlying Functional Neuroanatomy The cross‐habituation experiments described above provide evidence that individuals have integrated, multicomponent memories of familiar individuals but they do not provide evidence for a further characteristic of true individual recognition, namely that known individuals have unique, emotional, and motivational significance to the subject. In many experiments with animals, differential responses to individuals are based on simple categorical aspects of experience, such as being familiar or unfamiliar with a member of group A that lives to the east versus a member of group B that lives to the west. In order to demonstrate true individual recognition, experiments need to demonstrate different responses to individuals with different significance to the subject. One early experiment with rodents did suggest the differing significance of two males to a subject, but there was a possible confound with dominance status and cues that may have indicated differential status (Martin and Beauchamp, 1982). In order to determine if hamsters actually recognized individuals as having unique significance and to investigate the neural mechanisms related to such memories, we carried out a series of experiments in the context of aggressive interactions and learned fear of familiar winners (Lai et al., 2004, 2005; Lai and Johnston, 2002; Petrulis et al., 2004). In nature, golden hamsters live solitarily and are well known to be highly aggressive to other individuals (Floody and Pfaff, 1977a; Gattermann et al., 2001; Huhman et al., 1992, 2003). In our experiments, subjects fought briefly in a small arena in three separate encounters. We allowed the loser to escape at any time. At various intervals after the third encounter, losers were tested in a Y‐maze for their reactions to the odors and sounds of the familiar winner and other stimulus animals. We investigated the areas of the brain that were active during the recognition phase of the experiment, as measured by the activity of two immediate early genes. We carried out three related sets of experiments, all of which used similar methods (Lai and Johnston, 2002; Lai et al., 2004, 2005). The behavioral part of these experiments consisted of three phases. First was a phase in which the subjects were habituated to the Y‐maze that was used later in the test phase. In the second phase, male subjects either fought briefly on three trials with another male or had other experiences, such as investigating a clean arena or having other types of interactions (see later experiments). The aggressive interactions took place in a clean, empty cage with no lid; this allowed animals to escape whenever they wanted and it resulted in relatively brief aggressive interactions (average duration of interactions in the first encounter was 40–75 s, depending on previous experiences of the

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subjects) and less than 10 s in the second and third trials (Lai and Johnston, 2002; Lai et al., 2004, 2005). The third phase was a test phase; it occurred 30 min–7 days after the aggressive interactions to determine the reaction of subjects to cues from stimulus animals. Subjects were observed in two conditions in the Y‐maze, one in which the maze was clean and did not contain a stimulus hamster and the other in which a stimulus animal was confined in a compartment at the end of one of the arms of the Y. This compartment was separated from the rest of the maze by a clear Plexiglas divider with holes drilled in it to allow air to pass through. Note that the test phase was not a preference test in which stimuli from two males were available; rather, there was a stimulus animal in just one arm of the Y. We compared the behavior of subjects in this Y‐maze when there were no stimulus animals or odors present (clean Y‐maze) and when odors of another male were present in one arm. We measured the duration of time spent in different parts of the Y, the latency to go into different parts of the Y, and the number of times the animals crossed from one area of the Y to another (a measure of overall activity). This method allowed us to test the reactions of the subject to the cues from one stimulus male at a time. In the first set of experiments we found that males that had lost a fight spent less time in the arm of the Y containing the familiar winner than males that did not fight (Lai and Johnston, 2002). Subjects that lost fights also spent more time in the base of the Y (starting area) than males who had not lost fights (see Fig. 3). The no‐fight control males approached a stimulus male much more quickly than the subjects that had lost to the stimulus male. Subjects that had lost fights also went to the end of the clean arm much more quickly than they approached the end of the Y with the stimulus male (Lai and Johnston, 2002). In this experiment, different groups of subjects were tested at four different intervals after the fights (30 min, 1 day, 3 days, and 7 days) and similar results were found in all four of these tests. Thus, the results showed that males that lost fights avoided the cues from a familiar winner and this avoidance persisted for at least 1 week. These results also suggest that males recognized the male that beat them in a series of fights but the results could also be explained by losers becoming more cautious of cues from any other male (Lai and Johnston, 2002). The overall level of activity in the Y‐maze, however, was the same for both types of subjects (the males that lost and the males that just explored a clean cage) indicating that the losers did not appear to be generally more cautious or inhibited in this novel environment. In further experiments, we tested males that lost fights with cues from either the familiar winner or an unfamiliar winner (i.e., a male that had won a series of three fights against another subject male). Male subjects showed a longer latency to approach the familiar winner than the unfamiliar winner

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FIG. 3. The mean (
SE) number of seconds that males (no fight experience—left side; losers of fights—right side) spent near the stimulus male (winner of fight—black bars), in the clean arm of the Y (open bars) and in the base of the Y (criss‐cross bars). Data is shown for four test trials that occurred 30 min, 1 day, 3 days, and 7 days after the encounters or control experience.

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and also spent much more time near the unfamiliar winner than near the familiar winner (Lai and Johnston, 2002). Subjects also showed a longer latency to approach the familiar winner than the end of either arm in a clean Y‐maze. Thus, male subjects specifically avoided cues from the male that had beaten them in a fight but they did not avoid an unfamiliar winner. This indicates that the subjects did recognize the male that beat them and that the subjects were not afraid of unfamiliar, dominant males that won fights and had odors characteristic of winners. It is noteworthy that there were again no differences in general exploration of the Y‐maze between the subjects that lost and the control subjects that did not fight during the experience phase of the experiment. Thus, the results cannot be explained by a general fear of exploring the Y‐maze (Lai et al., 2002). In a second paper, we replicated the basic behavioral results and also investigated areas of the brain that might be involved in the memory and behavioral/emotional responses of losers to familiar winners. Staining for the protein products of the immediate early genes c‐fos and egr‐1, we found that several areas showed increased staining correlated with the recognition tests in the Y‐maze; specifically, losers exposed to confined, familiar winners had a greater density of stained cells in the basolateral region of the amygdala, the CA1 region of the anterior, dorsal hippocampus and in the dorsal subiculum than males in control groups (Lai et al., 2004). These experiments confirmed that male hamsters that lost fights had emotionally specific memories of individuals that they lost to, but once again these results compare responses to the familiar winner versus an unfamiliar individual, so the results could be due to a difference between categories of individuals rather than reactions to a specific individual. In an experiment with a different design, a different testing arena and additional measures of behavior, we found that males that lost engaged in more stretch‐attend postures (a measure of risk assessment) and more escape behaviors in response to odors from a familiar winner than males that won showed to odors of losers (Petrulis et al., 2004). These results again suggest recognition of familiar winners and more caution in the presence of a familiar winner compared to an unfamiliar winner. Thus, using a different method and type of testing area we obtained similar results showing learned fear/anxiety specifically toward an individual that won fights against the subject but not toward a stimulus animal that won fights against another subject. Nonetheless, these results still do not prove true individual recognition because the results could be due to different responses to different categories of stimulus animal (familiar vs unfamiliar). To provide evidence for true individual recognition we investigated how male hamsters behaved after different types of experience with two stimulus males (Lai et al., 2005). In the exposure phase, subjects were first

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exposed to a stimulus male across a wire‐mesh barrier during three, 3‐min trials, a method that allows hamsters to learn the multiple characteristics of one another (Johnston and Peng, 2008). Two hours later, subjects had a series of three aggressive encounters with a second stimulus male that had previously won fights. Thus, subjects got to know one male across a wire‐ mesh barrier and a second male that beat them in a series of three aggressive encounters. In the test phase, subjects were tested in the Y‐maze with either the familiar winner or the familiar neutral male. Subjects displayed a much longer latency to approach the familiar winner than the familiar, neutral male (see Fig. 4) (Lai et al., 2005). Similarly, males spent much less time investigating the confined male that beat them than they spent investigating the familiar, neutral male. Subjects in all conditions spent a similar amount of time moving about the Y and entered a similar number of sections of the Y. This experiment demonstrates that male subjects react differently to two familiar stimulus males with different significance— evidence for true individual recognition (Lai et al., 2005). It is also important to note that the Y‐maze tests were carried out in a different room and run by a different experimenter than the exposure phase, so there is no confound between the type of social experience and contextual information. 180

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FIG. 4. The latency (seconds
SE) before males in group 1, 2, or 3 approached the stimulus compartment. Group 1 (left side)—when a familiar winner was in it and the subject had lost to that male (black bar) or the same stimulus compartment was clean (open bar). Group 2 (middle)—when the stimulus was a familiar, neutral male (black bar) or a clean compartment (open bar). Group 3 (right side)—arena control group—no contact with another male.

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In these experiments, we also investigated the neural correlates of memory for familiar individuals by using immunohistochemistry for the immediate early genes c‐fos and egr‐1. The results implicated a number of brain areas in memory for individuals, including the CA1 region of the hippocampus, basolateral amygdala, posterior dorsal subiculum, piriform cortex, and posterior dorsal hippocampus‐dentate gyrus (Lai et al., 2004, 2005). Activity in the CA1 region of the hippocampus was the most consistent across the two experiments in which we used these methods. Thus, we also carried out an experiment to determine if this area was essential for memory of a familiar winner. Male subjects had a cannula implanted into the brain with the tip in the CA1 region on one side of the brain. On the other side of the brain, a unilateral olfactory bulbectomy was performed so that olfactory information could not be obtained on that side. We used this procedure because we had difficulty maintaining an implanted cannula on both sides of the brain. Animals were infused with either lidocaine or saline just before the recognition task. Subjects with saline injections avoided the familiar winner, but subjects with lidocaine treatment of the CA1 did not avoid the familiar winner, indicating that this region of the hippocampus is essential for the memory of individuals in this context (Lai et al., 2005). It is worth noting that if male hamsters have repeated, prolonged aggressive interactions, a very different phenomenon is observed. In this case, males that lose are afraid of any other male, even a small, nonaggressive male that is introduced into the subject’s own home cage (Cooper et al., 2005; Huhman et al., 1992, 2003; Potegal et al., 1993). This ‘‘conditioned defeat’’ effect is a useful model for studies of social stress and extreme differences in status, but it is a totally different type of response compared to the more nuanced relationships that develop between individuals during the relatively brief interactions they had in our experiments. It is worth noting, however, that even these interactions were long compared to the scuffles that we observed between males in the wild. Actual contact between males ranged from a fraction of a second to 3 s at the most, at least for males on the surface and not in a burrow (Johnston, Song, Larimer, and Johnston, unpublished observations in Turkey 2005 and 2006). These experiments thus provide evidence for true individual recognition in a solitary and relatively asocial rodent species. Rodents that live in social groups with multiple adult males and females, such as many species of rats, or in single‐male polygamous groups, such as house mice, should have even more highly developed abilities to recognize and remember individuals and their significance. It would be especially valuable to have more information about the nature of memories for individuals in species with more gregarious social systems in order to compare the complexity of memories for individuals.

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III. DISCRIMINATION AND RECOGNITION OF KIN With the advent of sociobiology and renewed interest in the mechanisms of natural selection, another new focus of attention was on the importance of genetic relatedness as a variable in the types of interactions that occur between individuals (Dawkins, 1976; Wilson, 1980). In particular, the ground‐breaking theoretical work by Hamilton on inclusive‐fitness theory to explain social evolution, cooperation, and altruistic behavior (Hamilton, 1964) led to an explosion of new research on kin recognition and preferential treatment of kin. New concepts were developed that influenced all of behavioral biology, including theories of inclusive fitness, cooperation, and reciprocal altruism (Barnard et al., 1991; Blaustein et al., 1991; Dawkins, 1976; Grafen, 1990; Halpin, 1991; Hepper, 1991b; Holmes and Sherman, 1983; Porter et al., 1983; Sherman and Holmes, 1985; Sherman et al., 1997). Naturally, this caused a surge of research on the mechanisms underlying recognition of kin (Hepper, 1991a; Holmes, 1986, 1988; Sherman and Holmes, 1985; Sherman et al., 1997; Tang‐Martinez, 2001; Todrank and Heth, 2003b). Although many studies have shown that odors are used to discriminate between and recognize kin, few studies have involved a systematic approach to the chemistry of odors to determine just how odors or other cues provided this information and what the underlying principles were. In mice, it has been shown that the major urinary proteins (MUPs) are an important source of cues that are involved in discrimination and recognition. A male subject’s brother with the same MUP type is treated the same as the subject, whereas a brother with a different MUP type is treated differently (Hurst et al., 2001). MUPs are also crucial for individual recognition when the genetic background of the subjects is quite variable (Cheetham et al., 2007). With regard to kin recognition, it is also clear that odor similarities and differences between individuals, populations, subspecies, and so on are related to the degree of genetic similarity or difference (Heth and Todrank, 2000; Heth et al., 2001; Todrank and Heth, 2003). In this section, I review our work on the mechanisms underlying discrimination of odors from individuals of different degrees of relatedness and how such discrimination relates to kin recognition. Our research focused on two different aspects of the kin recognition process. First, what are the perceptual mechanisms underlying the discrimination, categorization, and recognition of odors from animals of differing degrees of relatedness? Second, what are the mechanisms underlying the classification of odors of individuals as kin versus not kin or classifying animals on a continuum from unrelated to closely related.

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A. DIFFERENCES IN ODOR QUALITY ARE CORRELATED WITH DEGREE OF GENETIC DIFFERENCE 1. Introduction and Rationale The sensory and perceptual mechanisms underlying discrimination between individuals of different degrees of relatedness must be quite similar to the mechanisms used to discriminate between individuals—namely, perception of the unique odor quality of odor sources from other individuals. As stated earlier, differences in odor quality of individuals are dependent on differences in the proportions of chemical compounds in a complex mixture (Gamboa et al., 1996; Smith and Breed, 1995; Smith et al., 2001). This is the same type of perceptual process that is involved in discrimination between similar but different varieties of various foods (e.g., red wines, coffees, apples, etc.), ripe fruits versus unripe fruits, and other discriminations between similar but different objects. The sources of odor on the body of an animal that provide information about genetic relatedness are likely to be the same as those used for individual discrimination and recognition. There is no experimental evidence supporting the contrary view that a specific odor source has the limited function of providing information just about kin recognition or individual recognition. The primary difference between the mechanisms of individual recognition and kin recognition is that the significance of other individuals is learned by direct experience, either by direct interaction with those individuals or, possibly in some species, by observation of individuals. In contrast, the significance of closely related versus distantly related individuals has an additional mechanism involving the evaluation of relatedness. Such mechanisms are generally thought to involve the comparison of a new sample of odor (or other cue) with a memory or neural representation of what kin should smell like. This representation can be developed in a number of different ways, such as exposure to characteristics of parents, siblings, other relatives, or the individual’s own features (self‐referent matching). Although several authors have suggested that there could be direct genetic mechanisms responsible for such recognition, for example, recognition alleles (Dawkins, 1976; Hamilton, 1964), no one has been able to develop a method to prove this hypothesis or to eliminate the possibility of learning taking place instead. Differential responses to kin versus nonkin have been demonstrated in a wide range of organisms (Hepper, 1991b; Holmes, 1988; Sherman and Holmes, 1985; Sherman et al., 1997; Tang‐Martinez, 2001). In most studies of kin recognition, the focus has been on differences in behavioral reactions to related versus unrelated individuals or to individuals of different degrees of relatedness. In field studies it is often not known how these discriminations are made—that is, what the cues are, how the cues vary with degree of

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genetic relatedness, and which sensory systems are involved. Nonetheless, different responses toward kin and nonkin or graded responses correlated with the degree of relatedness do indicate that cues must vary in direct relationship to the genetic similarity between individuals (Hepper, 1987; Heth and Todrank, 2000; Heth et al., 1999, 2001; Todrank and Heth, 2003; Todrank et al., 1998, 1999a). Odor cues are especially well suited for providing graded signals (differences in odor qualities) because most odorous secretions produced by animals are complex mixtures of a large number of chemical compounds and the composition of such mixtures can be influenced by many different biochemical pathways. Slight differences across individuals in enzyme activity, enzyme cofactors, or the amount or activity of other chemical compounds involved in metabolic processes can influence the relative abundance of specific chemical products, and these products will produce differences in the odor of a secretion by altering the ratios of chemical compounds. For example, it is well documented that hormone levels differ across individuals and that differences in hormone levels produce changes in an individual’s odors (Ferkin and Johnston, 1993; Ferkin et al., 1992, 1994, 1995). It is also likely that individuals have different types or proportions of microflora associated with scent glands due to differences in the environment or to genes or alleles of the MHC, and such differences may cause differences in odor quality (Beauchamp et al., 1985; Brown et al., 1989, 1990; Yamazaki et al., 1980, 1990, 1991, 1999). Finally, in mice, the MUPs have been demonstrated to provide genetically determined cues to individual identity (Cheetham et al., 2007). Differences in the composition of a secretion or excretion, and thus differences in odor quality, appear to be directly related to differences in the degree of genetic difference between individuals (Todrank and Heth, 2003). Although this idea has been in the literature for many years, there was relatively little research that demonstrated the nature of the specific cues that provide information about degrees of relatedness. Since we already knew quite a bit about the use of specific odor sources that were used to discriminate between individuals, we began a program of research to determine if these odors also provided information about kinship and the degree of relatedness between different individuals. 2. Discrimination of Odors from Animals of Different Degrees of Relatedness In our initial studies, we used habituation–dishabituation techniques (Todrank et al., 1998). Subjects were separated from their mother and siblings at 30 days of age and housed individually. A month or more later, experiments began. In our initial method, subjects (Golden hamsters,

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M. auratus) were habituated to the flank gland odor from a familiar, same‐ sex sibling over four exposure trials (3 min each with 15 min intervals between trials) and the time they spent sniffing this odor was measured. Fifteen minutes after the last habituation trial, we tested subjects on two test trials—first, exposure to the flank gland odor of another familiar sibling and, second, with the flank odor from an unfamiliar individual from another family. There was a significant increase in investigation on the first test trial, indicating discrimination between two of the subject’s same‐sex, familiar siblings, and a further increase in investigation on the second test trial, indicating that the odor from an individual from another family was more different than that of a full sibling (see Fig. 5) (Todrank et al., 1998). Similar results were obtained for both male and female subjects (op cit.). These results show that the odors of full siblings are perceived as relatively similar compared to the odor from an individual from another family but also that the odor quality of flank glands from full siblings are different. Furthermore, these results show that the odor of flank glands from individuals from two families differ more than flank odors of full siblings within a family (Todrank et al., 1998). Similar results were obtained for male and female subjects of Turkish hamsters, M. brandti (Heth et al., 1999). One potentially confounding aspect of this first set of experiments, however, was that odor donors from the same family as the subjects were familiar to the subjects but the scent donors (siblings) from another family were unfamiliar. The differences in investigation of the flank gland odor of the familiar sibling and the unfamiliar animals from another family could be due to differences in familiarity and not to differences in odor quality. Therefore, we repeated this experiment using odor donors that were all unfamiliar to the subjects. After habituation to flank gland odor of an individual from family A, there was no increase in investigation of a second, same‐sex sibling from this family but there was a significant increase to the flank gland odor from an individual from family B (Todrank et al., 1998). These results indicate that the odors of same‐sex siblings are so similar that a subject does not spontaneously discriminate between them if the subject has not interacted with these individuals. Hamsters do, however, readily discriminate between same‐sex donors from two different families without experience with these individuals (Todrank et al., 1998, 1999a,b). It could be argued that the lack of discrimination between siblings that were unfamiliar to the subject was due to the restricted genetic origin of all the stocks of golden hamsters (derived from one brother–sister pair in 1930) (Gattermann, 2000; Murphy, 1985), resulting in relatively low genetic variance between individuals. However, we found the same pattern of results with Turkish hamsters (M. brandti) (Heth et al., 1999). Although domestic stocks of this species are by now also inbred, the origin of these stocks was

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FIG. 5. Mean time (
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based on a much larger number of individuals, and thus genetic diversity and odor diversity should be much greater. These findings with Turkish hamsters reinforce the interpretation that flank gland odors within families are more similar than flank gland odors between families (Heth and Todrank, 2000; Heth et al., 2001). These experiments suggest that the degree of difference between odors of individuals is directly related to the degree of genetic relatedness of those individuals—that is, the results of habituation–dishabituation tests seem to mirror genetic differences quite accurately. One further example of this point comes from an experiment in which golden and Turkish hamsters were first habituated to flank gland secretions from conspecific males and were then tested with the flank gland odor from a male of the other species. In this case, there was a very large increase in investigation toward the heterospecific odor, which mirrors the large genetic differences between the two scent donors (Heth et al., 1999). It is interesting to note that the closest relative of the golden hamster (M. auratus) is not the Turkish hamster, which presently is the closest species geographically. Genetic analyses indicate that the closest relative of M. auratus is actually M. raddei, which presently inhabits the eastern side of the Caucus mountains (Neumann et al., 2006). 3. Kin Recognition as Assessed by Scent Marking A limitation of the habituation–dishabituation method is that it just provides evidence about whether subjects notice the difference between the quality of two odors and the extent of this difference. These tests do not demonstrate whether a stimulus animal is treated differently in some functional way. In order to demonstrate actual recognition of kin, subjects must react differently in a functionally significant manner to individuals based on the degree of relatedness between the subject and stimulus animal. We developed an assay for kin recognition in the laboratory based on scent marking behaviors. Hamsters in the genus Mesocricetus have two scent marking behaviors, each with different functions. Flank marking is a competitive, agonistic behavior; it is stimulated by the odors of, or interactions with, an animal that the subject is likely to fight with. The rate of flank marking is thus a measure of agonistic tendencies (Albers et al., 1992; Ferris et al., 1987; Johnston, 1975a, 1977b, 1975c, 1981). The flank glands of males are much larger than those of females and the size and rate of secretion of these glands is testosterone dependent (Hamilton and Montagna, 1950; Montagna and Hamilton, 1949; Vandenbergh, 1973). Flank marking behavior is also testosterone dependent (Johnston, 1981). This scent marking behavior appears to be a means of competition in both males and females (Johnston, 1975a,c, 1977b; Johnston and Brenner, 1982).

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Female hamsters also deposit a specialized vaginal secretion that is attractive to males and stimulates male sexual behavior (Johnston, 1974, 1975d, 1980; Johnston and Kwan, 1984; Kwan and Johnston, 1980; Macrides and Singer, 1991; Murphy, 1973; O’Connell and Meredith, 1984; O’Connell et al., 1978, 1979, 1981). The frequency of vaginal marking is dependent on a female’s reproductive state; very low rates of vaginal marking occur during pregnancy and early to mid lactation but the rate of vaginal marking begins to increase late in lactation and during the first two nonestrus days of the estrous cycle. The highest rate occurs during the 24 h immediately before sexual receptivity begins (Johnston, 1972, 1977b). This pattern suggests that vaginal marking is used by females to advertise for mates; indeed, females vaginal mark more in the region of a semi‐natural enclosure where the dominant male lives compared to where the subordinate male lives (Huck et al., 1985), and males are attracted to the odors from vaginal marks (Johnston and Kwan, 1984). Flank and vaginal marking behaviors are easily studied in the laboratory and, since they are clearly related to intraspecific agonistic motivation (flank marking) or sexual advertising (vaginal marking), predictions about the relative level of marking toward kin and nonkin are straightforward: males and females should flank mark more in response to odors of like‐ sexed nonkin than toward kin because they should be more aggressive toward nonkin. Similarly, females should sexually advertise (vaginal mark) more toward unrelated males than toward related males. This pattern is exactly what was found (Heth et al., 1998). Males flank marked more in response to the odors of unrelated males than toward the odors of their brothers and females flank marked more toward the odors of unrelated females than toward the odors of their sisters (Heth et al., 1998). Similarly, females vaginal marked more toward the odors of unrelated males compared to odors of their brothers. Indeed, these scent marking behaviors proved to be sensitive to a yet finer degree of genetic differences. For both males and females, marking rates showed a graded pattern in response to odors of full siblings, half siblings, and nonsiblings (see Fig. 6) (Heth et al., 1998). 4. Mechanisms of Kin Recognition—Evidence for Self‐Referent Phenotype Matching A major question concerning kin recognition is how animals develop the ability to recognize kin. One mechanism that has been demonstrated for a number of species is recognition by direct experience—that is, young animals have experience with their parents and siblings in the nest and they recognize these individuals later (Holmes and Sherman, 1982; Holmes, 1986; Porter et al., 1983, 1985, 1986; Sherman and Holmes, 1985).

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12.5

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FIG. 6. The mean number of flank marks in response to flank gland odors of different stimulus animals. Marks were counted in response to the subject’s own odors (Own), odors of a familiar sibling (SRT—sibling reared together with the subject), SRA—sibling reared apart from the subject, FSRT—foster (unrelated) sibling reared together with the subject, BFSRA—brother of a foster sibling reared apart from the subject, and NRSA—nonsibling, reared apart from the subject (Heth et al., 1998).

But, how might animals recognize kin that they do not know by direct experience? One possibility is that young animals form a generalized memory or template of the characteristics (e.g., the smell) of their kin while they are in the nest and, when confronted with an unfamiliar individual later in life, they compare the smell of that individual with the template of ‘‘kin odor’’ and decide if the match is close or not. Evidence for kin recognition by phenotype matching has been shown for a number of rodent species, including house mice, Belding’s ground squirrels, spiny mice, and golden‐ mantled ground squirrels (Hepper, 1991b; Porter et al., 1983, 1985; Sherman and Holmes, 1985). Our scent marking assays provided particularly clear evidence for a phenotype matching mechanism. We compared the frequency of flank marking by males toward odors of six types of stimulus animals: the subject’s own odor, a male sibling reared with the subject, a sibling reared apart from the subject (fostered into another litter), an unrelated male reared with the subject, an unfamiliar brother of an unrelated male reared with the subject, and an unrelated male reared apart from the subject. Males flank marked at a higher level toward all nonrelatives, whether they were familiar through direct experience or not, compared to the level of marking toward their own odors or their siblings’ odors, and it did not matter if these siblings were known through direct experience or not (see Fig. 6; Heth et al., 1998). These data clearly demonstrate that the crucial factor in eliciting flank

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marking is the degree of genetic relatedness. The relative amount of familiarity of the subject with the stimulus animal or with siblings of the stimulus animal had no influence. At the time, our interpretation of this experiment was that it provides strong evidence for kin recognition by matching the perceived odor to a stored memory of relatives (phenotype matching). However, this interpretation does not explain the results for foster siblings reared together (FSRT) with the subjects—these animals were treated like other nonrelatives, even though they were raised with the subjects. The most parsimonious explanation of this data is that animals are comparing their own odor to the odor of the stimulus animals (self‐referent matching). The rate of flank marking can be explained entirely on the basis of the genetic relatedness between the subjects and the stimulus animals. Although experience with littermates or parents can be an effective way to develop a template for the characteristics of kin, it is not ideal because the maximum degree of relatedness in a typically out‐bred population is not greater than 0.5. In species such as hamsters in which females may mate with several males, littermates may be related by considerably less than 0.5 and thus a template formed on the basis of odors from littermates will not be as closely matched to an individual’s own odors as they would be if there was only one father. Using the mother’s odors as a template is better because relatedness is at least 0.5. The template that best matches an individual’s phenotype, however, is the individual’s own odor. If a template of the self is not provided directly by genetic mechanisms, it must be provided by an animal’s experience with its own characteristics (e.g., its own odors). Proof of this hypothesis is not abundant, however, because it is extremely difficult to design an experiment in which individuals do not have experience with the odors of relatives, especially the mother. Jill Mateo and I carried out an experiment to minimize the exposure of pups to odors of family members and provide evidence for self‐referent matching. Between 1 and 12 h after birth, we culled litters and cross‐ fostered pups such that each mother raised one son, one daughter, and one foster daughter. These foster daughters were the subjects in our experiments. They grew up with nonrelatives except for the time in utero and 1–12 h after birth. It could be argued that both of these periods of exposure could provide relevant cues on which to develop a kin template from their mother or siblings. This is, however, extremely unlikely because neural connections from the olfactory bulb to projection areas in the brain are not formed until at least 2 days of age and the growth of these projections and connections to the central nervous system are not complete until 13 days of age (Leonard, 1975). Although hamster pups avoid some organic chemical odors as early as 3 days of age (Cornwell, 1975), these are probably protective responses mediated by the trigeminal nerve. Interest in odors

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of home‐cage bedding begins about 7–8 days of age and interest in specific sources of hamster odors and food‐related odors begins at 13–15 days of age (Crandall and Leonard, 1979; Gregory and Bishop, 1975; Johnston and Coplin, 1979). Furthermore, the neural mechanisms necessary for discrimination of individual odors is a higher‐order process that requires a full range of peripheral input. Indeed, even simple odor discriminations of single chemical compounds appear to be dependent on central nervous system structures (Wilson, 1998, 2002; Wilson and Stevenson, 2003). We tested female hamsters between 41 and 61 days of age; these sexually mature, cross‐fostered females were tested for their investigation of odors from stimulus animals and for scent marking responses to the odors of stimulus animals. During the active period 24–12 h before estrus, female hamsters responded differently to the odors from stimulus animals compared to their responses on the other days of the estrous cycle. Females approached the flank‐gland odors of unfamiliar, nonsibling males (nonsiblings reared apart) more quickly than the odors of either male siblings reared apart or nonsibling males reared together with the subject (Mateo and Johnston, 2000, 2001). Females also spent more time investigating odors of males that were nonsiblings reared apart compared to the odors of both nonsiblings reared together with the subject and siblings of the subject that were reared apart from the subject. These results indicate that females approaching sexual receptivity are most interested in unfamiliar, nonkin males. Flank marking showed a pattern consistent with the sniffing data. Female subjects showed the least flank marking (i.e., lowest aggressive tendencies) toward unfamiliar, nonkin males and most flank marking toward unfamiliar, kin males. That is, females showed the least aggressive tendencies toward males that were unfamiliar nonkin compared to familiar nonkin or unfamiliar kin, a pattern that fits the predicted pattern of preferential sexual interest in nonkin. These results clearly show that female hamsters make functionally consistent responses to stimulus animals that vary in their relatedness and familiarity when the only experiences relevant to making the distinction between kin and nonkin were their own odors deposited in their home cage or detected directly from their own body. One could argue that females got some information about relatives before birth or after birth during the few hours they were with their mother and siblings. However, as mentioned above, the main olfactory system, that is primarily responsible for discrimination of individual odors in hamsters (Johnston and Peng, 2000; Johnston and Petrulis, 1996; Leonard, 1975) is not mature at this time. Given that discrimination between the complex chemical mixtures that make up an individual odor requires higher‐order processing, it is unlikely that 1‐ to

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12‐h‐old pups can make discriminations between individual odors or remember these differences. It is also relevant that young hamsters spend almost no time investigating conspecific odors, potential foods, or water when they are less than 10 days of age (Crandall and Leonard, 1979; Gregory and Bishop, 1975; Johnston and Coplin, 1979), suggesting that such odors are either not perceived or at least that such odors have little relevance early in life. The only source of cues that the pups experienced that could provide information about genetically similar animals was their own odors. Thus, these data provide strong evidence for self‐referent recognition of kin (Mateo and Johnston, 2000, 2001). After Jo Todrank and Giora Heth worked in my laboratory, they explored the relationship between genetic similarity and odor similarity in a number of studies, including pairs of closely related species in the Mus species complex and the S. ehrenbergi superspecies of mole rats. They found additional evidence for the relationship between the similarities of odors and the degree of genetic relatedness (Heth and Todrank, 2000; Heth et al., 2001). They have also advanced a theory, which they call odor‐genes covariance, which takes the position that ‘‘there are no odor‐based mechanisms that have evolved specifically for recognition of kin versus nonkin.’’ Rather, there are two mechanisms underlying discriminative responses based on individual genotypes: ‘‘individual recognition through association’’ and ‘‘genetic relatedness assessment through individual odor similarities.’’ The habituation–dishabituation experiments described above with hamsters and the more recent work with mice and mole rats all fit this model and support the hypothesis that odors differ with degree of genetic relatedness and that individuals spontaneously perceive these differences. 5. Self‐Referent Phenotype Matching: Synthesis Kin recognition by self‐referent matching and kin recognition by odor‐ gene covariance appear to me to be essentially the same concept but with a slightly different emphasis. Both ideas predict the same results and they may both be based on the same mechanism for recognition—self‐referent matching. The idea of odor‐genes covariance is a straightforward prediction that the similarity of the odor of two individuals is directly related to the degree of genetic similarity. At present there is no proposed sensory or perceptual mechanism for how these similarities are assessed. The simplest and most straightforward mechanism would appear to be comparison of an odor with one’s own odor—for example, self‐referent matching—although at present the details of the underlying mechanisms for such matching are not known.

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It may be possible to test this theory experimentally by altering an individual’s odor such that it does not correspond to the genetically determined odor. The difficulty with this approach is in providing relevant and realistic changes over nearly the entire life‐course of an individual after birth (and possibly before birth). It seems unlikely that a simple manipulation such as adding one prominent component to an individual’s odor would be a sufficient or convincing manipulation, since odors of individuals are regularly changing with reproductive state, status, diet, health, and so on. Some classes of odorous compounds may be more important for individual discriminations than other classes of compounds, and without knowledge of which types of compounds are important, experimental manipulations of body odor might fail simply because compounds were manipulated that are not used as part of an individual’s signature. It has generally been assumed that pups develop their kin template during the time in the nest with their mother and siblings before they venture out of the nest and begin interacting with individuals from other families. In our studies, however, females did not treat foster siblings as kin, suggesting that they do not develop their kin template during this period or, if they do, then they reject odors of foster siblings as a template because they are not similar enough to their own odors (nonmatch to self). Since moving out of the natal burrow and into a new burrow and a new environment is a major shift in circumstances, this may be an ideal time for an animal to learn its own odors. Such animals are probably in a unique physiological state, and such a state could facilitate such learning. If animals use their own odors as a referent, what are the mechanisms underlying recognition by degrees of relatedness? Individuals could ‘‘reject’’ as a referent odor all odors perceived on the body of another individual and all odors that they find deposited in the environment (outside of their own nest or burrow). If the critical period for developing a kin template is after weaning and if young animals use odors in their own burrow to develop a kin template, it should be possible to manipulate odors so that the template is influenced by the odors of other individuals. On the other hand, if the mechanism for producing a kin template is that young animals use only the odors they perceive from their own body during grooming and sniffing themselves, odors of other animals in their burrow may not influence the kin template. But, if so, the mechanisms for development of a kin template would differ in species with different social organizations. Species in which each individual lives by itself after weaning, such as golden hamsters, could easily focus on their own odors as the cues to use for a kin template. More gregarious species, however, may be continuously exposed to odors of others in the group and some of these odors would be transferred to the subject of the experiment. Thus, it would be much more

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difficult for individuals in gregarious species to develop a self‐referent kin template. The differences between gregarious and solitary species in the mechanisms underlying kin recognition are likely to be a fertile area for future research. Another possibility is that there is no template in long‐term memory, but rather individuals use a kind of on‐line instantaneous comparison between a perceived odor and their own odor. The mechanism could be as simple as being continuously habituated to one’s own odor and noticing the degree of difference when an animal smells another individual’s odor. This would make discriminations based on the degree of difference between one’s own odor and the odors of another individual a linear function of the difference in odor quality—presumably the level of neural response to a new odor would correlate directly with the degree of difference between ‘‘self’’ and ‘‘other.’’ Such a mechanism would not depend on any specialized memory device, but rather would depend on elegantly simple and well‐known sensory response mechanisms: in this case, the degree of dishabituation shown in sensory responses would reflect the degree of genetic difference between the subject and the stimulus animal. Todrank and Heth reject this hypothesized mechanism because, in studies with mice, ‘‘subjects showed stronger responses (greater interest) to members of their own population than to another population and to their own species than to a different species, that is, more genetically similar individuals’’ (Todrank and Heth, 2003). This conclusion, however, rests on the assumption that sniffing investigation is always directly correlated with the degree of genetic similarity between self and other. Without actually knowing the detailed chemistry of the odors, one cannot assume that genetic similarity is always directly related to odor similarity. Furthermore, there may be other features of some chemicals, not tied to genetic similarity, which make animals with those chemicals particularly attractive. In order to fully understand the relationships between genetic similarity, odor similarity, and behavioral responses to odors, it is essential to know much more about the chemical composition of the odors being used by the animals in question. IV. INDIVIDUAL ADVERTISEMENT AND COMPETITION BY SCENT MARKING A. INTRODUCTION: SCENT MARKING BEHAVIOR Scent marking is similar to signaling in other sensory channels in many ways but there are also major differences in the way scent marking is used. The most obvious difference is that, once scent marks are deposited, they persist in the environment for a considerable period of time; scent marks

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can be effective over days, weeks, or even months (Johnston and Lee, 1976; Johnston and Schmidt, 1979). Even humans can detect the odor of hyena ‘‘pasting’’ scent marks 30 days after deposition in the wild (Mills et al., 1980). Also, unlike all other types of signals, it is not necessary for the sender to produce the signal in the presence of the receiver. Unlike vocal, visual or electrical signaling, the sender can deposit scent marks without being immediately challenged. In addition, the persistence of scent may allow a receiver to obtain information about the relative freshness of the signal and thus the likelihood of encountering the sender in the immediate future; this may provide information about the relative degree of threat from that individual. In locations where two or more individuals engage in counter‐marking, scent marking interactions have a history that can be perceived by third parties (eavesdropping), even at a remote period in time, because perceivers can determine the relative position (top or bottom—see below) and they may also be able to evaluate the relative success of individuals in scent marking competitions by determining which individuals marked most often or marked on top of the scent of other individuals most frequently (see below). Virtually all terrestrial mammals appear to engage in some type of scent marking behavior (Brown and Macdonald, 1985; Ewer, 1968; Gosling and Roberts, 2001; Johnson, 1973; Thiessen and Rice, 1976). Although some species, such as skunks, use scent as a defense against attack by predators, the vast majority of scent glands and scent marking behaviors appear to be related to competition for mates, status, or environmental resources. The identity of the animal depositing the scent is an essential aspect of the signal that is deposited. In some cases, the use of scent glands appears to be directly competitive in that the glands are exposed or the odorous product is emitted during aggressive interactions, as in the ‘‘stink fights’’ of male ring‐tailed lemurs (Jolly, 1966) and urination–defecation displays by many species of ungulates, such as gazelles and wildebeests, during male–male aggressive interactions (Estes, 1967, 1969). In hamsters, subordinate males living in a semi‐natural enclosure in captivity were obviously of lower status and ran from the dominant male, yet they often flank marked while defending the entrance to their nest and food hoard from a dominant male (Johnston, 1975c). In contrast, scent marking often occurs when no competitor is nearby and thus the act of marking is not obviously competitive; nonetheless, such marking is often stimulated by the scent of a rival and it may be clearly related to defense of a territory or some other resource. In the majority of species, males engage in more marking than females, but there are many exceptions.

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Scent marking by females is often used to advertise for mates. Although such marking is not usually associated with overt female–female aggression, it may nonetheless be competitive in the sense of being more or less successful in attracting males (Brown, 1979; Brown and Macdonald, 1985; Ewer, 1968). In species that live in close‐knit social groups, such as dwarf mongooses, meerkats, or European rabbits, dominant individuals usually mark more than any other individual, but most or all members of the group usually contribute some scent marks to communal marking sites (Ewer, 1968; Mykytowycz, 1968, 1970; Rasa, 1973). Such group marking posts often function in defense of a group territory or the core area of a home range but, since dominant individuals usually mark most frequently at such locations, this marking behavior may also be involved in competition between individuals within the group for dominant status (Mills et al., 1980; Mykytowycz, 1968; Rasa, 1973). When one individual or group marks in the immediate vicinity of the marks of another individual or group, this behavior is called counter‐ marking. I distinguish between two types of counter‐marking: (1) over‐ marking, in which one individual deposits its scent partially overlapping or completely covering the scent mark of another individual, and (2) adjacent marking, in which the scent marks of two individuals do not overlap but are in close proximity. Both adjacent marks and over‐marks may occur during a single bout of scent marking, but over‐marking has effects that adjacent marking does not. In this section I review our research on the functions of over‐marking, the use of scent marks and over‐marks as competitive advertising and the amazing abilities that have evolved to perceive and interpret scent over‐marks. B. FLANK MARKING BY GOLDEN HAMSTERS Golden hamsters and other Mesocricetus species have two different types of scent marking. Flank marking is performed by both males and females and involves a stereotyped, arched‐back posture and rubbing the entire side of the body on the substrate (Johnston, 1972, 1975a). The primary source of odor deposited is secretion from the flank glands, which are oval‐shaped regions of enlarged, pigmented sebaceous glands on the dorsal flank. These glands are about 9  4 mm in adult males and 2.5  1.5 mm in adult females. The histology of these glands has been described, as has the embryonic and post‐natal development (Algard et al., 1964, 1966; Hamilton and Montagna, 1950; Montagna and Hamilton, 1949). The larger size and greater rate of secretion of flank glands in males is testosterone dependent (Vandenbergh, 1973). For both sexes, the odors of other hamsters stimulate an increase in the rate of flank marking compared to marking in a clean area (Johnston,

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1975a,b, 1977b). The odors of individuals of the same sex have a greater stimulatory effect on flank marking than odors of the opposite sex (Johnston, 1975a,b, 1977b). In female hamsters, two specific odors from males stimulate flank marking (flank and mouth odors) whereas other male odors (urine, feces) decrease flank marking by females. These results suggest specific effects of odors on scent marking behavior (Petrulis and Johnston, 1997). Agonistic interactions also stimulate flank marking (Johnston, 1975a,c, 1977b). This pattern of results suggests that flank marking in both males and females is associated with agonistic motivation and competition, especially between like‐sexed individuals. Some of the neural, hormonal, and neurochemical mechanisms underlying flank marking have been characterized. In males, flank marking is testosterone dependent (Albers et al., 1992; Ferris et al., 1984, 1986, 1988; Johnston, 1981) and injection of arginine vasopressin (AVP) into the medial‐preoptic‐ anterior hypothalamus, lateral septal nucleus, bed nucleus of the stria terminalis, and periacquiductal gray causes a dramatic and immediate increase in flank marking by males. Lesions of the medial preoptic‐anterior hypothalamus area reduces or eliminates the effect of AVP injections (Albers et al., 1986, 1992; Bamshad and Albers, 1996; Ferris et al., 1985, 1990a,b). In females, the rate of flank marking is higher toward other females or their odors than toward males or their odors (Johnston, 1977b). In the presence of another female’s odors, the rate of flank marking is very high and this rate does not differ much across the days of the estrous cycle. In the presence of male odors, however, the frequency of flank marking is very low when females are in estrus and intermediate on other days of the cycle (about 1/3 the rate shown to female odors) (Johnston, 1977b, 1979). These data are consistent with the interpretation that flank marking by females is related to aggressive motivation and to competition between females; in contrast, sexual motivation in females reduces flank marking.

C. VAGINAL MARKING BY FEMALE HAMSTERS Females use another scent marking behavior to deposit vaginal secretions that are produced in specialized, lateral pouches of the distal vagina. Vaginal secretion scent marks are highly attractive and sexually arousing to males (Johnston, 1975d, 1977a; Kwan and Johnston, 1980; Macrides and Singer, 1991; Macrides et al., 1974; Murphy, 1973). The rate of vaginal marking is stimulated by exposure to males or male odors but females also mark more in response to female odors than they do to a clean testing environment (Johnston, 1977b, 1979), suggesting both sexual and competitive functions for vaginal marking.

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The rate of vaginal marking varies dramatically with the female’s reproductive state, with lowest levels during pregnancy, early lactation, and estrus. The rate of vaginal marking increases late in pregnancy and the highest rate occurs 24–12 h before sexual receptivity (Johnston, 1977b, 1979). This high rate of marking is at least partly due to elevated estrogen levels in the ventromedial hypothalamus (Takahashi and Gladstone, 1988; Takahashi and Lisk, 1985). The pattern of vaginal marking across reproductive states suggests that vaginal marking is used primarily as an advertisement of an immanent period of sexual receptivity. Advertising before actual receptivity may be particularly useful in solitary species that live at relatively low density (Johnston et al., in preparation—field work in southern Turkey). In semi‐natural living areas in captivity, females vaginal mark more in their own area near their nest compartment than they do in the male’s area, suggesting that one function of vaginal marking is to attract males to the female’s burrow area for mating (Huck et al., 1985). Consistent with this hypothesis, vaginal scent marks are attractive to males from a distance (Johnston, 1985; Johnston and Kwan, 1984). As described in the preceding section, females mark more with vaginal secretions in response to the odors of nonkin males than related males, indicating a preference for mates that are not closely related (Heth et al., 1998). Meadow voles primarily mark with urine and/or anogenital scent (Ferkin, 1999; Johnston et al., 1997b). Urine used for scent marking is generally deposited as relatively small spots, although larger areas covered by urine probably also have communicative functions. Anogenital scent is primarily deposited as small spots and occasional short streaks.

V. SCENT OVER‐MARKING Both hamsters and meadow voles engage in scent over‐marking. After one individual has marked, a second animal tends to deposit scent on top of and partially overlapping the scent that was deposited first. It is important to distinguish between targeted over‐marking and over‐marking by chance. This is especially important in small laboratory testing chambers in which scent marks deposited randomly have a good chance of overlapping previously deposited scent marks. It is often the case that over‐marking by chance is not adequately controlled for in laboratory experiments. This can be done by comparing the frequency of marks that are deposited over real scent marks of another individual and imaginary marks (e.g., pencil outlines on the substrate) of the same size, shape, and comparable locations as the real marks (Johnston and Song, in preparation).

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Using both golden hamsters and meadow voles, we carried out experiments to determine what information animals obtain from scent over‐marks and how this information influences subsequent behavior. These experiments indicate that there are special mechanisms that have evolved for the perception and interpretation of scent over‐marks, specifically for the determination of which individual’s scent is on top.

A. PERCEPTION OF OVER‐MARKS AND MEMORY FOR INDIVIDUALS Our research on individual discrimination and recognition generated new questions about the causes and functions of scent marking, particularly scent over‐marking. We had observed that hamsters tended to over‐mark previously deposited flank marks and, similarly, that voles over‐marked anogenital and urine marks of other voles. These observations raised questions about what information an individual obtained from scent over‐marks (Ferkin, 1999; Johnston et al., 1997a,b). We initially suggested three mutually exclusive hypotheses (Johnston et al., 1994). First, over‐marking sites might be a kind of bulletin board—after two or more individuals marked in one location, other individuals could determine which individuals had been at this site. If so, individuals might also be able to determine the relative ages of the marks of different individuals and thus judge the likelihood of meeting these individuals. Second, one scent mark could completely cover a previously deposited mark and mask the information in the earlier scent mark. Third, over‐marking might result in a mixture of the scents of the individuals that marked in the same location and thus the individually distinctive information might be lost. We hypothesized that such mixtures of scent from several individuals might also be a means of creating a unique ‘‘group’’ odor. Of these three hypotheses we initially favored the masking hypothesis because this type of effect would be useful in promoting one’s own claim to a particular area. A masking function was also suggested by the typical sequences of behavior of hamsters when flank marking: after marking a particular spot once or several times, individuals often sniff the marked location and mark again in the same place, suggesting that the individual is attempting to cover the odor of other individuals (Gorman and Mills, 1984; Johnston, 1975a,b,c; Mills et al., 1980). Although few studies have investigated the precise topography of marking and over‐marking in nature, some species do appear to carefully place scent marks on top of previously deposited scent deposits, for example, American beaver, pronghorn antelope, and brown hyenas (Butler and Butler, 1979; Gorman and Mills, 1984; Mills et al., 1980; Mu¨ller‐Schwarze, 1980; Mu¨ller‐Schwarze et al., 1973). House mice

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also do this at one particular type of urine marking location—scent posts; most urine marks deposited by male mice, however, are distributed all over a male’s territory and not just at these scent posts (Bronson, 1976; Hurst, 1987). We evaluated these three hypotheses about the function of over‐marking by investigating what information an individual obtained from an over‐ mark. In order to be sure we knew the exact location of these scent deposits, we created the marks, either by depositing hamster vaginal secretions with a spatula on glass plates or by picking up the hamster and rubbing its flank region against a glass plate in a precise location. Similar experiments were carried out in meadow voles using anogenital scent and urine (Ferkin, 1999; Ferkin et al., 1999; Johnston et al., 1997a,b). We first investigated the case in which the scent mark of one individual was completely covered by the mark of a second individual. To assess the information that a third party (an eavesdropper) obtained from over‐marks, we used the habituation–dishabituation technique in a novel way (Johnston et al., 1994). First, we created scent over‐marks by depositing one hamster’s scent and, after a delay of about 1 min, we deposited a second animal’s scent such that it was on top of the first scent mark and covered it completely. For flank marks, we rubbed the individual’s flank region on a glass plate using a card‐stock template that allowed scent only to get onto the plate in one place that mimicked the approximate size and shape of a naturally deposited flank mark. For vaginal secretions, we deposited this secretion with a small spatula in a streak about 1.5 cm long and 0.3 cm wide (the average dimensions of a vaginal scent mark); vaginal secretion scent marks leave a translucent film on the glass so the experimenter can see where the first scent was and could over‐mark it with the vaginal secretion of a second female. Subjects were exposed to over‐marks (individual B over those of A) in a series of five trials; we measured the time they spent investigating these over‐marks. On the test trial, we presented the odor of a novel individual and either the odor of the individual that contributed the bottom scent (A) or the odor of the top‐scent individual (B). Again we measured the time that subjects investigated these odors. If the top scent masked the bottom scent, subjects should treat the top scent as familiar and the bottom scent as novel (Johnston et al., 1994). If the two individuals’ scents were both perceived, both individuals’ odors should be treated as familiar. If, however, the two scents mixed together to form a new odor quality, both scents should be treated as novel. The results supported the hypothesis that the top scent masked the bottom scent. After habituation to over‐marks, the top scent was treated as familiar (less investigation than a novel individual’s scent) and the bottom scent was treated the same as a novel individual—a higher level of investigation (Johnston et al., 1994). We obtained the same results with

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male flank gland scent and female vaginal scent (Johnston et al., 1994). Using the same basic method with meadow voles, we obtained very similar results using the anogenital scent of males. This scent was obtained by rubbing a cotton swab on the anogenital area and transferring scent to a microscope slide (Ferkin et al., 1999). The results of both experiments suggested that one individual can mask the scent mark of second individual by thoroughly covering another individual’s scent with its own odor and that scent over‐ marking may be a form of competition in which an individual attempts to mask the scent marks of rivals. The individual doing the over‐marking could nullify or reduce the apparent degree of advertising done by a rival. A weakness of this hypothesis is that it may be difficult or impossible for one animal to completely cover all of the scent marks of another individual, especially if there are many scent marks or if scent is sprayed and ends up as many small, discrete spots of scent (e.g., urine marking in dogs, wolves, cats, etc.) or if scent marks consist of many small spots deposited one at a time (as voles and house mice do). If some of these small spots are not covered, or if even a small portion of a larger scent mark is not covered, this could provide information about the identity of the bottom‐scent individual. To investigate what information hamsters and voles obtained from over‐ marks in which there was only partial overlap of the first scent (the bottom scent) by the odor of a second individual (the top scent), we carried out a series of experiments similar to those described above. We again simulated over‐marking by rubbing the flank area or depositing vaginal secretions on glass plates, only this time we placed the two scents in the pattern of a cross (þ) so that there was an area of overlap in the middle but most of the scent of the two individuals was not covered. The obvious prediction was that the scent of both donors should be treated as familiar because both donors contributed odors that were not covered by another individual’s scent. The results did not follow the predicted pattern, however, and were a complete surprise. After habituation to the crossed scent marks of two donors (in hamsters, male flank gland or female vaginal secretion; in voles, anogenital scent), male subjects showed selective or preferential memory for the top‐scent individual (Ferkin et al., 1999; Johnston and Bhorade, 1998; Wilcox and Johnston, 1995). That is, after habituation to the over‐mark, subjects treated the odor of the top‐scent donor as familiar (less investigation than a novel individual’s odor) but they treated the odor of the bottom‐scent donor as unfamiliar (the same level of investigation as a novel individual; see Fig. 7) (Ferkin et al., 1999; Johnston et al., 1995; Wilcox and Johnston, 1995). The same results were obtained whether we used vaginal secretions or flank‐gland secretions. These results indicate that hamsters had a preferential memory for, or a preferential valuing of, the odor of the top‐scent individual and that they had a selective forgetting or

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Scent donors Female #1-bottom scent Female #2-top scent Female #3-novel scent

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FIG. 7. (Top) Schematic representation of the configuration of stimuli during habituation trials (left side) and test trials (right side). (Bottom) The mean (
SE) number of seconds males spent investigating vaginal secretions of females during habituation trials 1–3 and on the test trials. During the test trials in the top scent versus novel scent condition, males investigated the top scent much less than the novel scent, indicating that they were familiar with the top scent. In contrast, in the bottom graph, there was no difference in the duration of investigation of the novel scent and the previous bottom scent, indicating that the bottom scent was unfamiliar.

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devaluing of the odor of the bottom‐scent individual. These results also indicate that our interpretation of the ‘‘masking’’ experiment given above is not correct, since both hamsters and voles have preferential memory for the top scent whether or not the bottom scent is completely covered by another individual’s scent (Ferkin et al., 1999; Johnston and Bhorade, 1998; Johnston et al., 1995). Rather, these animals appear to preferentially value or remember the top scent regardless of how much of the bottom scent is covered. It seems most likely that the animals actually remember the odors of both individuals but show preferential treatment of the top scent individual. Indeed, in an unpublished experiment, Marisa Schiller and I carried out an experiment in which male hamsters were habituated to crossed, flank‐ gland over‐marks; in the test trial, males were exposed to one flank gland stimulus, either the ‘‘bottom’’ scent (10 males), the ‘‘top’’ scent (10 males), or a novel scent (10 males). The results clearly showed that in the test trial the highest level of response was to the novel scent, the next highest was the bottom scent, and the least investigation was shown toward the top scent, thus indicating that the subjects remembered the top scent best, the bottom scent at an intermediate level, and the novel scent least (e.g., not at all). There were significant differences between all comparisons. Thus, hamsters and voles use scent over‐marks in ways that no one had previously imagined. Rather than just determining which individuals had scent marked in a location, both hamsters and voles focused on which individual’s scent was on top in a scent over‐mark. Our assumptions about the physical layering of scent marks were far too simplistic and ignored the possibility that animals may have evolved special strategies to evaluate scent over‐marks (Ferkin et al., 1999; Johnston, 1999, 2003, 2005). In order to simplify presentation of further experiments with this general design, I will refer to this pattern of results as the ‘‘preferential memory effect’’—that is, individuals preferentially remember (or value) the scent of the individual whose scent was on top in an over‐mark. B. HOW DO ANIMALS DETERMINE WHICH INDIVIDUAL’S SCENT IS ON TOP IN OVER‐MARKS? The preceding results led us to re‐evaluate how we thought about scent over‐marking, the information animals were getting from such over‐marks, and the functions of over‐marking. Rather than thinking about investigation of scent over‐marks from the perspective of sensory function and the information that animals obtained about which individuals had marked in a particular location, we began to think about scent over‐marking as an evolved strategy used in competition between individuals. The goal in these competitions might be to keep one’s scent on top of rivals in order to demonstrate one’s vigor and quality, with the ultimate function being

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related to reproductive success. Similarly, the animals’ perceptual strategies seemed to be aimed at one particular type of information from over‐ marks—which individual’s scent was on top. This information could be important because it provides reliable information about which individual or individuals were the most vigorous and consistent in advertising themselves, presumably an honest reflection of the overall vigor and quality of those individuals. This information would be valuable in both mate choice and aggressive contexts. The purpose of the next set of experiments was to determine what type of information is used to determine who was on top. In addition to relative position (being ‘‘on top’’), other features of scent marks could reflect the quality of individuals, such as the relative amount of scent deposited (the quantity of scent deposited, the surface area covered by scent marks, or the number of scent marks in an area). Since we had been using the preferential memory effect in hamsters as our assay, we continued using this measure in the next set of experiments; later we also used preference studies to determine if mate preferences were influenced by the analysis of whose scent marks were on top. One strategy that an animal might use to advertise itself and provide an honest signal of quality would simply be to deposit more scent than rivals do. This could be accomplished either by depositing a larger number of marks than other individuals or by covering more surface area with scent marks. To examine whether these hypotheses influenced the preferential memory effect, we first habituated subjects (male hamsters, male and female voles) to a glass plate with a cross‐shaped over‐mark on one half of the plate (individual B over individual A) and an additional scent mark of A on the other half. During the habituation trials, there was no difference in the time spent investigating the over‐mark versus the ‘‘scent of individual A’’ by itself. During the test trial, male hamsters, male voles, and female voles behaved as if they remembered the top scent (individual B) better than the bottom scent (individual A) as shown by less time investigating the top versus the bottom scent. Subjects investigated the bottom scent from the habituation trials the same amount as a novel individual’s scent, indicating that they forgot the odor quality of the bottom scent, even though there was nearly twice as much of the bottom scent present during the habituation trials (see Fig. 7) (Ferkin et al., 1999; Wilcox and Johnston, 1995). These results indicate that being on top in an over‐mark leads to selective memory for that individual’s scent and to discounting, forgetting, or lowering the priority of the scent of the individual whose scent was on the bottom in scent over‐marks. These results suggest that neither the area

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covered by scent marks nor the number of scent marks deposited were important for hamsters (Wilcox and Johnston, 1995) or meadow voles (Ferkin et al., 1999). Another strategy for demonstrating vigor and quality could be for an individual to attempt to have the freshest scent marks (most recently deposited). We examined the hypothesis that the relative freshness of scent marks could be the basis for the preferential memory effect (Johnston and Bhorade, 1998). Animals might remember fresh scents better, either because the fresh scents are stronger smelling or because a fresh scent suggests more regular and recent scent marking. We deposited the scent of one female and then the scent of another female about 30 s later on the other half of the glass plate. This timing was the same as that for producing over‐ marks. If hamsters were using relative age of scent marks to selectively remember the freshest scent, they should show this in the test trial. The responses to the fresh and old scents in the test trial were, however, the same relative to a novel scent. One could argue that 30 s difference in age might not be enough of a difference for hamsters to discriminate. In subsequent experiments with female vaginal secretions and male flank gland odors, we used differences in age ranging from 30 s to 24 h and found no effect of the relative age of scent marks on memory for them (Cohen et al., 2001; Johnston and Bhorade, 1998). Similar results were obtained with meadow voles, using both male and female subjects and anogenital scents from opposite‐sexed donors as the stimuli (Ferkin et al., 1999). If neither the relative number of marks, the area covered by marks, nor the relative age of marks is crucial for the preferential memory effect, what features of over‐marks are important for this effect? Additional experiments were carried out to determine if relative position (top or bottom in an over‐mark) or spatial configuration might be relevant cues (Johnston and Bhorade, 1998). In all of the experiments described so far, the spatial layout and position were the same—that is, we always used a crossed pattern of scent marks in which one individual’s scent was perpendicular to that of another individual. Can animals determine top versus bottom position if the scent marks have a different geometry, or is the geometry crucial? The specific geometry used in the preceding experiments (the bottom and top scents forming a cross) was not necessary for the observed effects. In hamsters, scents from two individuals in the pattern of an ‘‘L,’’ with overlap only at the intersection of the two marks, was effective in producing the selective memory effect for the top scent. This was true for males tested with vaginal secretions from females and flank scents from males (Cohen et al., 2001; Johnston and Bhorade, 1998).

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In voles we also investigated the cues used for this preferential memory effect and found similar results. Males and females were used as subjects in a series of experiments in which they were first exposed to over‐marks in a variety of patterns; we also used scents of different ages and degrees of overlap, as in the experiments with hamsters. These results were consistent with those for hamsters in that the crucial feature for the preferential memory effect was the overlap of one individual’s scent over that of a second individual’s scent. If there was no overlap, the preferential memory effect did not occur (Ferkin et al., 1999; Johnston et al., 1997a). These results all indicate that animals have the ability to determine which of two individuals’ scents is ‘‘on top.’’ I am not aware of any information that gives us insight into how they can do this. I speculate that the odor quality in an area of scent overlap is different than the odor quality in an adjacent region where only one animal has marked. The odor quality of the mixture of the scent of two individuals in the region of overlap may smell more like the top‐scent individual than the bottom‐scent individual and thus provide information about which one is on top. With respect to the geometrical pattern of scent deposition, is it possible that animals can also analyze the spatial layout of scent marks and determine which one has been deposited over the other? A visual analogy is helpful. When looking at a natural scene or a simple pattern created by two sticks that form a crossed pattern, we determine that one is in front of the other because the object in front has an uninterrupted outline whereas the outline of the object in back is interrupted by the object in front. Theoretically animals might be able to determine which of two scents was deposited most recently by analyzing contours of two scent marks, as long as the marks were deposited in a linear streak. To test this hypothesis, we presented scent marks in one of two patterns, either a crossed pattern or in a pattern that was an apparent cross (Johnston and Bhorade, 1998). The apparent cross was created by depositing one scent as a continuous streak and the other scent was two streaks at right angles to the first; these streaks approached but did not touch or cross the continuous streak; that is, the second scent mark was actually two separate streaks on either side of the first scent with a very small gap (1–2 mm) between the long streak and the two shorter streaks at right angles to it (see Fig. 8, top). In one experiment we used female vaginal secretions and in another experiment we used male flank glands; both yielded the same results. Male hamsters showed preferential memory for the continuous scent but did not remember the interrupted scent (Cohen et al., 2001; Johnston, 1998). These experiments thus indicate that hamsters can analyze the spatial arrangement of two scent marks and determine from the layout which scent is ‘‘on top’’! These amazing results suggest again that hamsters have evolved special

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Interrupted scent Continuous scent Novel scent A Continuous scent group

20

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B

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0

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1

2

3

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2 Habituation trials

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FIG. 8. (Top) Configuration of scents forming an apparent cross, used during habituation trials. (Bottom) Time spent sniffing scents during habituation trials showing no difference in investigation of stimulus scents. During the test trial, males showed familiarity for the continuous scent compared to a novel scent. In contrast, the interrupted scent is treated the same as a novel scent, indicating preferential memory for the apparent top scent but lack of familiarity for the apparent bottom scent.

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mechanisms to determine ‘‘who is on top’’ in scent over‐marks and they thus indicate that information about scent over‐marks is valuable for hamsters in social interactions, presumably because it helps in the evaluation of potential mates or rivals. One difference between voles and hamsters is that voles did not treat ‘‘apparent over‐marks’’ as a real over‐mark—that is they did not show preferential memory for the individual with a continuous scent mark versus an interrupted one. This is probably because vole scent marks are not deposited as linear streaks but are deposited as small spots of scent. Since they would never be confronted with the type of pattern of marks used in this experiment, it is not relevant to the evolution of such perceptual mechanisms. C. THE PREFERENTIAL MEMORY EFFECT AND THE FUNCTIONS OF SCENT OVER‐MARKING Why should individuals have a preferential memory for the top scent in an over‐mark? I suggest that hamsters, voles, and other animals evolved the relevant perceptual and learning abilities because of strong selective pressures to evaluate the relative quality of potential mates and rivals. The ability of an individual to keep its scent on top of the scent marks of rivals must be energetically expensive due to the time and energy involved, especially because neighboring individuals are also trying to keep their marks on top. A preference for the most successful individual in over‐ marking contests should result in offspring with similar high levels of energy and resources. We carried out further experiments on both voles and hamsters to test the prediction that females use information from over‐ marks in mate‐choice decisions. In these experiments we produced the over‐marks so that the actual vigor, physiological state, and competitiveness of the male scent donors were not relevant. Female preferences could only be due to the pattern of over‐marks. Since mate preferences are much easier to test in female voles than in female hamsters, we started with voles. 1. Mate Preferences in Female Voles Based on Male Over‐Marks Like hamsters, meadow voles are a solitary species in which males and females live separately; females raise their litters alone. Female meadow voles have a post‐partum estrous and are often pregnant and lactating simultaneously. Males have much larger home ranges than females (Madison, 1980; Madison and McShea, 1987) and dominant males mate with several females. Subordinate males are often excluded from mating by the dominant male. Male meadow voles scent mark with urine and anogenital scent and over‐mark scent deposits from other voles. These marks

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fluoresce when exposed to long‐wave UV light, which makes counting of scent marks and their positions relatively easy (Johnston et al., 1997a,b). The shape and size of vole scent marks differs considerably from those of hamsters. Vole scent marks tend to be either small spots or larger, roughly circular or oval shapes from urine deposits. Patterns of marks by voles are thus quite different than the linear streaks produced by hamsters. Nonetheless, most of the perceptual, memory, and preference effects described for hamsters also occur in meadow voles. Voles readily demonstrate preferences for classes of individuals based on a variety of characteristics of the stimulus animals. For example, female meadow voles show reliable preferences for males with higher testosterone levels and for males maintained on a diet high protein diet verses males on diets with lower protein content (Ferkin and Johnston, 1994, 1995; Ferkin et al., 1994, 1995, 1997a,b). In the first experiment we examined the effects of natural marking patterns by males on preferences by female voles. Males were allowed to explore and mark a testing cage in which the floor was covered by brown paper; we drew outlines of the marks produced by exposing the paper to UV light. After a second male marked the paper, we carried out the same procedure to confirm that the second male did produce over‐marks. A female subject then spent 15 min in a cage with this paper on the floor and was tested for her preference between the top‐scent male and the bottom‐scent male in a Y‐maze. Females significantly preferred the male that had provided the over‐marks (Johnston et al., 1997b). In a second experiment, one male (bottom‐scent male) lived in an initially clean cage for 2–3 days. A second male (top‐scent male) was then allowed to explore and mark this cage for 30 min; 15 min later a female explored this cage for 15 min and she was then tested in a Y‐maze for her preference of the two males. Once again, females preferred the top‐scent male, despite the fact that there was much more odor present in the cage from the first, bottom‐ scent male (see Fig. 9) (Johnston et al., 1997b). In both of these experiments, the scent donors did their own marking so other characteristics of the males might have been evident in their odors. In another series of experiments with meadow voles, we chose male odor donors randomly from the colony as scent donors and systematically examined different patterns of scent marks and over‐marks to determine what characteristics of over‐marks female voles were using to evaluate males. In these experiments, we deposited male anogenital scents so that we could control the precise locations of the scent marks. We deposited scent marks in a variety of different patterns and found that if there was no overlap of scent marks from two males, female voles did not show a preference for either male (Johnston et al., 1997a). In contrast, if there were over‐marks,

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Preference test 60

Exposure trial

50

*

Seconds

40 30 20 10 0

Visitor Home cage male male

FIG. 9. (Left) Schematic representation of the home cage of a male vole and the scent of this male throughout the bedding material (gray). The visitor male deposits a few scent marks in this cage (black). (Right) The mean (
SE) number of seconds that females investigated the odors from nesting material of the home cage male and the visitor male (Johnston et al., 1997b).

females preferred the top‐scent male. Perhaps most strikingly, when we deposited a relatively large area of scent from one male in the arena and then placed a much smaller spot of scent on top of it, females preferred the top‐scent male (Johnston et al., 1997a) (see Fig. 10). If, however, we covered a large area with scent of the first male but left a small, clean area in the center and then put the scent of a second male in that clean area (with no overlap of the two males’ scents), the female did not show a preference for either male (Johnston et al., 1997) (see Fig. 11). This experiment dramatically demonstrates that the overlap of scent marks is an important feature for the evaluation of males by female voles (Ferkin et al., 1999; Johnston et al., 1997a). These results demonstrate that female voles can determine the relative position (top or bottom) of the scent marks of two males and that these features result in a preference for the top scent male, regardless of whether the bottom‐scent males had a greater number of scent marks or covered more area with scent marks (Ferkin et al., 1999; Johnston et al., 1997a,b). In contrast, the freshness of male scent marks in exposure trials (fresh vs 60‐min old) did not influence female preferences, that is, if freshness was the only difference between the marks of two males, females showed no preference for the male that had the fresher scent marks (Ferkin et al.,

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FIG. 10. (Left) Schematic representation of scents during the exposure phase with a small area of top scent (black) on a large field of bottom scent. (Right) Mean
SE time that females spent investigating whole‐body odors from bedding material of the two males that provided the top and bottom anogenital scents during the exposure phase (***P ¼ 0.005) (Johnston et al., 1997a).

1999). These results again indicate that scent position (top scent vs bottom scent) is the critical feature used by female voles use to evaluate the scent marks of males (Ferkin et al., 1999). Female voles did not demonstrate preferences based on the spatial configuration of scent marks of two males. That is, using the same method as described above for continuous versus interrupted streaks of scent with hamsters, female voles did not show a preference based on the apparent (but not real) overlap of two streaks of scent from different individuals. This difference between species is probably because, unlike hamsters, voles do not deposit their marks as linear streaks. Thus, it is unlikely that the spatial pattern (interrupted vs continuous) is a reliable cue for voles as to which individual’s scent is on top in an over‐mark. Recently, it has been shown that meadow voles do pay attention to the relative number of scent marks and that relative numerousness does affect female preferences. Specifically, females discriminated between and preferred to investigate males that deposited the larger number of marks. In each of the following ratios of scent marks, females preferred the male with the greater number of scent marks: 7:0, 5:2, and 4:3 (Ferkin et al., 2005).

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B

Test 30

A

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Time (s)

20

10 Centre scent Surround scent 0

Centre

Surround Male

FIG. 11. (Left) Schematic representation of the scent marks of two males during exposure phase; there was no overlap of the scents from the two males. (Right) Females show the same level of interest in the whole body odors of males that provided the two scents during the exposure phase (Johnston et al., 1997a).

2. Mate Preferences in Female Hamsters Based on Male Over‐Marks Recently Sabiha Barot and I carried out an experiment in a semi‐natural enclosure (184  92  30 cm, interior dimensions) with hamsters to examine in a more naturalistic way if female golden hamsters would show a preference for the top‐scent male after exploring a part of the arena flank marked and over‐marked by two males. Each animal (one female and two males) lived most of the time in their own area (30  90 cm) with a nest box (13  12  13 cm) below the level of the floor of the main enclosure. Once per day for 6 days during their usual period of activity (the dark phase), each animal was allowed to explore a common‐use area twice the size of their home area. The order of such exploration was always the same: male number 1, followed by male number 2, and then the female. All three animals scent marked in this common‐use area and the males over‐marked one another. Since male 2 always came after male 1, the scent marks of males that the female investigated always had a predominance of over‐marks by male 2. On the sixth and seventh days, we tested females for their reactions to these two males by confining the males in small Plexiglas boxes (9.5  10.5  13.5 cm) with many small (3 mm) holes drilled in the wall at 0.5 cm intervals so that

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females could smell the odors of the males. Females were tested during both estrus and the day before estrus, when females advertise for males by vaginal marking. We measured the time spent investigating these two males (nose within 0.5 cm of the box) time in proximity to the box (within 8 cm), lordosis, flank marking, and vaginal marking. The results are shown in Fig. 12. Combining the data for the two test days, females spent more time sniffing the top‐ scent male than the bottom‐scent male and on their pro‐estrus day they vaginal marked more often within 8 cm of the top‐scent male than they did toward the bottom‐scent male. Female pygmy loris also prefer a male whose marks are predominantly on top of a second male (Fisher et al., 2003). The experiments with meadow voles and hamsters are quite similar, despite differences in the scent sources used, the shapes of the scent marks of the two species, and the methods used to determine how scent marks were perceived and interpreted. Both species apparently use the strategy of determining ‘‘who is on top?’’ as a means to evaluate the relative vigor and quality of males. Both meadow voles and golden hamsters have evolved perceptual mechanisms for determining which of two individuals has scent marks on top of another individual. Assuming that individuals of other species also use ‘‘who is on top’’ as a means of evaluating potential mates, what factors have led to the evolution of this characteristic versus other means of evaluating potential mates and rivals? It seems likely that this rule of thumb would be particularly useful

Top male 300

p < .05

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FIG. 12. Responses of females to the male that scent marked first versus second in the semi‐ natural arena. (Left graph) Time females spent sniffing the two confined males. (Right graph) The number of flank and vaginal marks deposited near the two males.

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for solitary species in which most sexual advertising is indirect, with relatively few actual interactions. Individuals that live in permanent social groups should have much more knowledge of the characteristics of individuals from direct interactions. Nonetheless, in many species that live in permanent social groups, there does appear to be frequent group marking of particular locations as well as marking of other individuals (e.g., meerkats, dwarf mongoose, ring‐tailed lemurs, species of Rattus) (Brown, 1979; Brown and Macdonald, 1985; Ewer, 1968; Jolly, 1966; Rasa, 1973). In these species, observation of scent marking may be another means of evaluation of males. How do the cues used to evaluate potential mates or rivals differ across species? Given the ‘‘who is on top’’ rule of thumb shown by both hamsters and voles, what other species are likely to use the same mechanism? Depositing a scent mark as a linear streak may not be the most prevalent pattern of marking. Anogenital marking can easily occur as linear streaks or spots whereas urine can be deposited in a variety of ways—in one large pool, small spots, or sprayed over a relatively broad area. Animals with glands on the flank, ventral surface, or head may rub just the scent gland or much of the surface of their body against the substrate. One pattern of scent marking that is not conducive to analysis by the ‘‘who is on top’’ strategy is marking by urine spraying. In such marks, there is little in the way of any regular topography; marks are spread over an area containing many spots of urine. Arrays of small spots would be extremely difficult to cover. Perhaps such a marking strategy has evolved specifically because it is difficult to cover. Such marking patterns would be easier to analyze by scent freshness, relative amount or relative number as criteria for evaluating potential mates or rivals. D. SUMMARY: FUNCTIONS OF SCENT MARKING AND OVER‐MARKING— COMPARISONS WITH OTHER SIGNALING SYSTEMS Although communication by scent marking differs dramatically from most other modes of communication due to the differences between the properties of odors and other types of signals, there are phenomena similar to scent over‐marking in other communication channels. Most striking is the parallel between scent marking and counter‐singing in birds and chorusing among frogs and insects. Because bird song has been so thoroughly studied and the literature is so rich in both theory and data, a brief discussion of some of the parallels between bird song and scent marking is instructive. Since the 1970s, it has been known that male songbirds recognize the songs of their neighbors and engage in counter‐singing interactions with these neighbors (Beecher, 1989; Beecher et al., 1996; Beer, 1970; Brooks

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and Falls, 1975; Falls, 1982). One way that males demonstrate selective responsiveness to neighbors is that a male, after hearing a song of its neighbor, responds with the same song type from his own repertoire, a response pattern known as song‐type matching (Beecher, 1989; Beecher et al., 1996; Beer, 1970; Brooks and Falls, 1975; Falls, 1982, 1985). Since males of species that do this have a number of different song types, matching of song types with a neighbor suggests a targeted response to that neighbor. On the other hand, this type of response does not necessarily involve individual recognition because the responder may just be repeating a song type that he just heard. However, more recent research indicates that white‐crowned sparrows and other species have a much more sophisticated type of response that does suggest memory of the characteristics of individuals. When a male hears a neighbor sing, he will often reply with a different song type, but one that is in the repertoire of that particular neighbor (Beecher et al., 1996). Repertoire matching clearly indicates targeted response to a particular individual. There is also an enormous amount of data showing that females pay attention to the singing interactions between males. The knowledge obtained from eavesdropping on male–male interactions has dramatic effects on a female’s choice of a primary mate and it can also influence her choices for extra‐pair copulations. Female chickadees, for example, choose a mate based on the singing interactions of nearby males but they also actively solicit extra‐pair copulations after their own mate ‘‘loses’’ a singing competition with a simulated intruder (interactive song playbacks) (Mennill et al., 2002). Even more similar to scent over‐marking competitions are the singing competitions between male nightingales. When a song of one male overlaps songs of a rival, it is a sign of dominance. Using playbacks, Naguib and colleagues staged simulated interactions between two males and recorded the responses of a target male. After listening to these interactions, target males responded more strongly to the songs of the overlapping male than the other male, suggesting that the target males were selectively competing with the perceived ‘‘winner’’ or dominant male in the staged interaction (Naguib, 1999; Naguib and Todt, 1997). Research on scent over‐marking in hamsters, voles, and other species suggests the existence of amazing sensory processing to determine which individual’s scent is on top versus on the bottom in an over‐mark. Odors also provide information relating to hormonal state, dominant status, quality of the diet, health, MHC type, and other aspects of physical condition. These types of information may influence preferences for mating partners by females or influence competitions between individuals for resources. Much more needs to be known about the chemical nature of this information and how the chemistry is altered when scent over‐marking occurs. The

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chemical basis of kin and individual recognition can provide an initial entry into this difficult task, but we need new tools to describe the complex odors that represent odor quality. Similarly, we need to know much more about the neural basis of the sensory and evaluative processes underlying the perception of complex mixtures and the even more complex spatial patterns that are created by scent over‐marking. The ultimate puzzle is how all of the small units of information (single molecules) get integrated into subtle differences in sensory activation that results in a unified perception of odor quality that then influences behavior.

References Adams, M. G. (1980). Odour‐producing organs of mammals. Symp. Zool. Soc. Lond. 45, 57–86. Albers, H. E., Pollock, J., Simmons, W. H., and Ferris, C. F. (1986). A V1‐like receptor mediates vasopressin‐induced flank marking behavior in hamster hypothalamus. J. Neurosci. 6, 2058–2089. Albers, H. E., Hennessey, A. C., and Whitman, D. C. (1992). Vasopressin and the regulation of hamster social behavior. Ann. N.Y. Acad. Sci. 652, 227–242. Albone, E. S. (1984). ‘‘Mammalian Semiochemistry.’’ Wiley, New York. Alcock, J. (1979). ‘‘Animal Behavior.’’ Sinauer Associates, Inc, Sunderland, MA. Algard, F. T., Dodge, A. H., and Kirkman, H. (1964). Development of the flank organ (scent gland) of the Syrian hamster. I. Embryology. Am. J. Anat. 114, 435–455. Algard, F. T., Dodge, A. H., and Kirkman, H. (1966). Development of the flank organ (scent gland) of the Syrian hamster. II. Postnatal development. Am. J. Anat. 118, 317–325. Bamshad, M., and Albers, H. E. (1996). Neural circuitry controlling vasopressin‐stimulated scent marking in Syrian hamsters (Mesocricetus auratus). J. Comp. Neurol. 369, 252–263. Barnard, C. J., Hurst, J. L., and Aldhous, P. (1991). Of mice and kin: The functional significance of kin bias in social behavior. Biol. Rev. 66, 379–430. Barrows, E. M. (1975). Individually distinctive odors in an invertebrate. Behav. Biol. 15, 57–64. Barrows, E. M., Bell, W. J., and Michner, C. D. (1975). Individual odor differences and their social functions in insects. Proc. Natl. Acad. Sci. 72, 2824–2828. Beauchamp, G. K., Yamazaki, K., Wysocki, C. J., Slotnick, B. M., Thomas, L., and Boyse, E. A. (1985). Chemosensory recognition of mouse major histocompatibility types by another species. Proc. Natl. Acad. Sci. USA 82, 4186–4188. Beecher, M. D. (1989). Signaling systems for individual recognition: An information theory approach. Anim. Behav. 38, 248–261. Beecher, M. D., Campbell, S. E., and Burt, J. M. (1994). Song perception in the song sparrow: Birds classify by song type but not by singer. Anim. Behav. 47, 1343–1351. Beecher, M. D., Stoddard, P. K., Campbell, S. E., and Horning, C. L. (1996). Repertoire matching between neighboring song sparrows. Anim. Behav. 51, 917–923. Beer, C. G. (1970). Individual recognition of voice in the social behavior of birds. In ‘‘Advances in the Study of Behavior’’ (D. S. Lehrman, R. A. Hinde, and E. Shaw, Eds.), pp. 27–74. Academic Press, New York. Blaustein, A. R., Bekoff, M., Byers, J. A., and Daniels, T. J. (1991). Kin recognition in vertebrates: What do we really know about adaptive value? Anim. Behav. 41, 1079–1083.

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Index

A Acrocephalus arundinaceus, see Great reed warbler Acrocephalus schoenobaenus, see Sedge warbler Acrocephalus scirpaceus, see Will reed warblers Across-odor habituation technique, 450 Action-based learning, 143 Action-level imitation, 119 Active sensing, 18 Active vision, 19 Actuators for specialized tasks, 7 Adolescence, 233 Adults, voracity in, 232 African masked weaver, 368 Agelaius phoniceus, see Red-winged blackbirds Aggregation economy, 63–64 Aggressive behavior ecological circumstances for, 86 modulated by androgen levels, 341, 345 toward mates, 259 toward young, 411 Aggressiveness, 227–228, 238, 248 among proactive individuals, 249–250 in animals, 235 and boldness, 239 effect, 246 of individuals, 255 in male behavior, 259 males in water striders, 250 towards females, 229 variation in, 245–246 in Western bluebirds, study, 265

Agonistic interactions, 475 Alarm calls given by parents, 324 mammals, by, 144 mimicked, 144 parents for predation risk, by, 324 to signal threat, 324 Aleochara bilineata, 313 Alpha-amino-3-hydroxy-5-methyl4-isoxazolepropionic acid, 419 Altricial species, 400 Ambystoma trigrinum nebulosum, see Arizona tiger salamander American coots, 97, 303, 359 American kestrels, 347, 350 Amniotic fluid and maternal behavior, 412 AMPA, see Alpha-amino-3-hydroxy5-methyl-4-isoxazolepropionic acid Androgen, in egg, 337, 382, 384–385, see also Yolk androgen level androgenic effect, 382–383 immunosuppressive effect of, 348–349 sex-specific effects of, 361 Angiogenin gene, 300 Animal aggregate, species of, 125 Animal behavior aggression, implications for use of, 85–86 aggressive interactions, 84 breeding success, 85 concept of mechanism ‘electric dog’ tropisms in animals, 3 electronic transduction mechanisms, 2 507

508

INDEX

Animal behavior (continued ) function of parts, at different levels, 1 mechanistic explanation, for optomotor reflex, 2 phenomena of interest, 1 replication mechanism of DNA, 1 robot approach and psychic machines, 3 visual processing algorithms, 2 daily routines, evidence for, 82 food-finding technique, 88–89 food storing and retrieving, 82 foraging, 82 knowledge limitation and learning ability, 88–89 mate choice, 84 opponent assessment, for, 84 parental care, 96–97 quality of environmental parameters influencing, 84 scrounging location as constraint for, 89–90 low-energy reserves, 82 social dominance, effect of, 87–88 Animal cognition, 60 Animal learning and convenient reward, 60 Animal personality, 227–228, 250 study in Europe, 230 Animal’s foraging ecology, 60 Animal social learning, 105 Anogenital scents, 483 Antennae, 15 inspired artificial sensors, 18 testing on robot, 16 Anthropogenic sounds, 151 Antipredatory vigilance, 91 Apanteles sensu lato, 297, 301 Apis mellifera, see Honey bee Arctocephalus galapagoensis, see Gala´pagos fur seal Arginine vasopressin, 475 Arizona tiger salamander, 313–314 Artificial model bees, 127 Artificial selection, 378

Ascending neurons (AN1 and AN2), 28 Asocial conditional discrimination training, 142 Asocial instrumental learning, in rats, 129 Asocial learning mechanisms, 116 theory, 113 Asocial species abilities to discriminate, 441 learning mechanisms in, 116 multicomponent memories of, 441 Asset protection principle, 241, 243 Asynchronous hatching, 358–360 Avian egg hormones androgens, 337, 382–383 (see also Yolk androgen level; Yolk androgens effects) corticosterone (CORT), 380–382 estradiol, 380 other components, 383–384 source of, 340 study of, 337–338 AVP, see Arginine vasopressin B Badge hypothesis for indirect benefits, 204 of songbirds, 205 Banded mongooses, 305 Barn owl, 312 Barn swallow, 350–351, 362, 366, 381 Bar-press action, rats, 148 Bed nucleus of the stria terminalis, 408 Begging, 347–348 costs for, 298 Behavior, see also Animal behavior interactions in closed loop, 6 in nonhuman animals, 106 and physical interface, 6–7 principles of learning, 3 Behavioral carryover, 229, 236

INDEX

consistency, 228, 247 (see also Behavioral syndrome concept) competitive contests, 247 emphasis, 246 proximate mechanism, 236 correlation, 230 detection of, 236 proximate mechanism governance, 236–237 ecologist, 229 population variation in behavioral type, 235 ecology, 229 cooperation among individuals, 262–263 focus of, 229 mating behavior, 257–260 study, 248 mechanisms, 60 plasticity, of individual, 234, 240 (see also Behavioral syndrome concept) syndromes, 353 prenatal steroids effects in, 353–354 tendency in cooperative scenario, 263 types, 228, 243 maintenance of, 246 morphological mechanism, 238–239 negative and positive feedback, interplay between, 242 physiological mechanism involvement, 239 regulation by social status, 233 Behaviorally stable strategies, 67 Behavioral syndrome concept, 227, 234, 251 association with, 248 behavioral consistency exhibition of, 233 involvement, 232 behavioral context, variation in, 231 behavioral plasticity, 233–234, 267 boldness association, 248

509

choosiness, among individuals, 256–257 clustering of behavior, 228 criticism, 228 definition, 231–232 dispersal behavior (see Dispersal behavior) focus on individuals, 247 long-term stability of, 242 multiple analysis, 268 pathways for, 269 rank-order difference among individual, 234 sealed bid model (see Sealed bid model) social benefits, of predictability, 247 stability of, 232–233 study, 230–231 variation adaptive hypotheses, 239–240 asset protection principle, 241 behavioral correlation, changes in, 237 behavior of individual, 246 high state benefits, 243 importance, 236 in individual sensitivity, 252 individual tendency, 241 in metabolic rates, 239 morphological mechanism, 238–239 patterns in, 235–236 in trustworthiness, 247 Bethylid parasitoid wasps, 297 Bimodal behavior, types of, 229 Biodiversity, 61 Birds demonstrators responding, 143 development and yolk hormone level, 368–375 hopping, 71 nightingales, 151 posture, for foraging, 70 preening, 134–135 vocal imitation, 145

510

INDEX

Blackbirds demonstrator mobbing, 131, 136 predator recognition in, 130 Black-headed gull, 321, 346–349, 351, 353, 355, 360 Bluebirds, 344 Blue tit, 368 Blue-winged warblers, songs, 143 BN, see Brain neurons BNST, see Bed nucleus of the stria terminalis Boldness, 227–228, 238 and aggressiveness, 239 among proactive individuals, 249–250 association with individual exploratory tendency, 248 in male behavior, 259 measurement, 263 towards predators, 229 variation in, 245–246 Bombus terrestris, see Bumblebees Brain centers involvement, in song learning, 202 Brain neurons, 29 Bronze-cuckoo, 309 Brood parasitism, 96, 368 Brown-headed cowbird, 377–378 BSSs, see Behaviorally stable strategies Bubulcus ibis, see Cattle egret Budgerigars, 126, 339 Bumblebees, 290, 303 Burrower bug, 299 Burying beetle, 299, 303 C Cadherin gene, 300, 314 California, male song sparrow survival, 188 Campaniform sensilla, 15 Camponotus floridanus, see Carpenter ant Canaries, 353, 358 Capuchin monkeys, 70, 137 Carib grackle, 74–75

Carpenter ant, 311 Carpodacus mexicanus, see House finch Cattle egret, 321, 358 Cebus apella, see Capuchin monkeys Ceratophyllus gallinae, see Hen fleas Cercal hair sensors, 30 Cerci, 16 Cervus elaphus, see Red deer Chalcites basalis, see Bronze-cuckoo Chalcites lucidus, 309 Chemical sensing, 36 Chick embryo, and oocytes, 339 Chimpanzees artificial fruit studies on, 147 poking action unit, 146 with rake-like object, food, 124 sequential imitation, 145 Chinese painted quail, 349 Chiroxiphia linearis, see Long-tailed manakin Choosiness syndrome, among individuals, 256 behavioral syndrome hypothesis, 257 dependence, 256 Chordontonal organs, 15 Chronic fatigue syndrome, 233 Cistothorus platensis, see Sedge wren Clamator glandarius, see Great-spotted cuckoos Cliff swallow, 375 Clutches, variation within laying order, 358–360 paternity, 361 sex, 360–361 Clutch size, 339 CoA, see Cortical nucleus of the amygdala Cockroaches flagellum, 15 mechanoreceptor system in, 16 three-legged wheels (Whegs) robot design for, 20 Cognitive ability for improving individual performance, 254 to process concurrent information, 72

INDEX

Collared flycatcher, 367 Collective interactions, 6 Colony size and female T levels, 375 heritability of, 365 variation in, 375 in yolk androgens, 364 Columba livia, see Feral pigeons Common cuckoo, 309, 377 Comparative studies of differences between species, 368–370 in evolutionary ecology, 370 of four species of warblers, 201 of incubation periods in birds, 373 of songbirds, 168, 171 Compass direction, 14 Competitive contest, in behavioral consistency evolution, 247 Competitive interactions, among foragers, 61 Computer-simulated song tutor, 198, see also Song learning, of song birds Conditioned stimulus, 112, 131 Cone of confusion, 19 Conflict resolutions, 298–299 Conspecific brood parasitism, 368 Context-specific imitative effect, 142 Contextual escape, 36 Contextual imitation, 116–117, 124 action-based learning, 143 copied by blind, 153 emulation, 141 manner analogous, 146 on quail and pigeons, 141 Cooperation among individuals, 262–264 Coordinated walking, 6 Coot, 294 Copidosoma floridanum, see Parasitoid wasp Coping style, 227, 229, 237 Corollary discharge, 37 Corporate foraging efficiency, 90 CORT, see Corticosterone

511

Cortical nucleus of the amygdala, 420 Corticosterone, 337, 379–382 Corvus caurinus, see Crows Costs of care, 284 Coturnix chinensis, see Chinese painted quail Counter marking, see also Scent marking scent marking interactions, 473 types of, 474 Cowbirds, 172, 378 as avian brood parasites, 308 Cricket behavior, 4, 6 dependence on nontrivial neural processing, 24 directional hearing, 8 calling song, wavelength of, 8 tracheal system and auditory directionality, 9 tracks of robot, calling songs and carrier frequency, 10–11 escape response, 30–31 filiform hairs on cerci, for escape behaviors, 18 mechanoreceptor system in, 16 neural network, supporting behavior, 28 dynamics of turning response, 29–30 instantaneous spike rates, measures of, 29 thoracic AN1 neurons and motor control turning, 30 phonotaxis, 24–26 brain neuron (BN) responses, 25–26 mate-finding behavior, 25 and response to visual stimuli, interaction between, 37–38 simple circuit for controlling, 27 tympanal vibration amplitude, 24–25 robot interfacing analog VLSI sensor, for optomotor response, 39

512

INDEX

Cricket behavior (continued ) operating over uneven surfaces, 20 platforms to test models of, 5 using polarized skylight for path integration, 14 visual homing studies, 31–34 recorded tracks of cricket in heated arena, 34 visual stabilization of trajectories, 13 Cross fostering, 262 Crows, 88 Cryptic coloration benefits of, 91 evolution of, 91 CS, see Conditioned stimulus Cuckoo butterfly, 309 Cuculus canorus, see Common cuckoo Cuculus fugax, see Hawk-cuckoos Cultural transmission, efficiency of, 89 Cyanistes caeruleus, see Blue tit Cyprinodon pecosensis, see Pupfish D Daphnia, information from mother to offspring in, 320 Decision making best possible sequence of, 82 for mating, 85 Dendritic terminals, 30 Depth information from vision, 7 Desert ants, 14 DHT, 337, 365–366, 372, 374, 382–383 Diet choice, among individuals, 256 policy, 62 preference, 132 Differential allocation, 365–368 5-Dihydrotestosterone, see DHT Discrete behavioral variation, types, 229 Disembodied movement task, 155

Dispersal behavior, 264–265 Do-as-I-do experiment, 136, 148 Dolphins vocalizations, 145 Xitco’s experiment, 149–150 Domain-specific, sensorimotor loops, 40 Domesticated Canary, 304 Doomed vs. dominant offspring, 296 Dunking, 74 Dunnock, 311 Duplicate cage method, 124 Dwarf hamster, 444, 446 E Eastern Bluebird, 367 Eastern population, of song sparrows, 209 Emulation controls, 156 copying of sounds, 152 effect, 155 ghost control, 138 goal, 120 object movement reenactment, 121 R–S learning, 121 two-action test, 138, 154 Endoparasitoid larvae, 316 Environmental responsiveness, 248–249 Environmental sensitivity, 248 Erithacus rubecula, see Robins Escape response, 30 Estradiol, 380 Estrildid finches, 69 Eudyptes schlegeli, see Royal Penguins European starling, 348, 359, 362, 364, 368, 382 e-vector information, 14 Evolutionary game model, 86 Evolutionary squabbles, 287–288 Exploratory behavior, 115

513

INDEX

F Fairy-wren, 303 Falco sparverius, see American kestrels Female breeding, 357 Female–female aggression among animals, 474 patterns of, 364 Females, and variation in yolk androgens, 361–368 brood parasitism, 365–368 coloniality, 364–365 differential allocation, 365–368 quality, 362–363 Females behavior, towards males, 258–259 aggressive males, preference over, 260 Female spotless starlings, 379 Feral pigeons, 89 Ficedula albicollis, see Collared flycatcher Ficedula hypoleuca, see Pied flycatcher Field sparrow male, returning in breeding area, 203 Finder’s share, 69 Fixed vs. plastic prey, 240 Flank marking competitive, 465 frequency of, 467 by Golden hamsters, 474–475 pattern of, 469 rate of, 465, 468 and sexual advertising, 466 Flightless blister beetle, 316 Follicles, structure and function of, 340–341 Follicle-stimulating hormone, 340 Food extraction techniques in rats, 60 handling techniques, in chimpanzees, 60 neophobia, 60 preferences, 113–114, 132–133 sharing, 64 webs, 61

Foraging antipredatory vigilance, 91 competitors, 92 efficiency of group, 92 food rewards as means of studying, 61 models, types of, 68 payoffs, 62 and population phenomena, 61 research, 59 use of social information, 60 Formica truncorum, see Wood ant Fos gene, 414 Foster siblings reared together, 468 Frequency-dependent game, 92 learning, 88 FSH, see Follicle-stimulating hormone FSRT, see Foster siblings reared together Fulica americana, see American coots Fulica atra, see Coot G GABA, see Gamma-aminobutyric acid Galapagos finches, 61 Gala´pagos fur seal, 287–288 Game-theoretic approach, 62 Game theory, 229 behavioral type fitness, 265–266 and effects of social group composition, 265–268 evolutionary, 62, 65 fundamental tenet of, 266 interaction between individuals, 229 Gamma-aminobutyric acid, 407 Gasterosteus aculeatus, see Three-spined stickleback Genetic adaptation hypothesis, 203–204 Genetic basis, of individual, 232–233 Genetic correlation, in parents and offspring interaction, 299 Genomic imprinting, in mammals, 290–291 Gestational drive, 314

514

INDEX

Ghost movements, 155 Giraldeau’s model, 86 Glucocorticoids, in maternal behavior, 409 Gnamotogenys striatula, see Ponerine ant GnRH, see Gonadotropinreleasing factor Golden digger wasp, 96 Golden (or Syrian) hamster, 442 aggressive interactions, 454 distinctive odors in, 444 flank markings by, 474–475 mate preferences in, 490 vaginal marking by, 475 Gonadotropinreleasing factor, 340, 343 Gorilla hand gesture, 136 imitated, 148 Goslings, 127 Greater racket-tailed drongo, 143–144 Great reed warbler, 201, 377 Great-spotted cuckoos, 308, 377–378 Great tit, 299, 303–304, 324, 349, 353, 362–363 Greylag geese, 126 Grey parrots, 151 Gryllus bimaculatus, 10 Gryllus campestris, 25 Guira cuckoos, 378 Guira guira, see Guira cuckoos Guppies, 94 Gyroscopic sensors, 15 H Habitat assessment, 84 selection, 61 Habituation–dishabituation method for assessing discrimination between odors of individuals, 446 limitations of, 465 Habropoda pallida, see Solitary bee Hair sensor, 31

Halteres, 15 Hamsters ability to discriminate, 442 behavioral ecology of, 442 scent over-marks, 442 Hand-raised birds, see Virtual tutor method, study Hawk-cuckoos, 309 Hawk–dove games, 86 Hen fleas, 355 Heterospecific alarm, 131 Hirundo rustica, see Barn swallow Honey bee, 315 Host–parasite coevolution, 365 House finch, 339, 345, 349 House martins, 362 House sparrows, 320, 324, 353, 367–368, 379 House wren, 359 hPL, see Human placental lactogen 17HSD, 383 Human placental lactogen, 300 17 -Hydroxysteroid dehydrogenase, see 17HSD Hymenopteran queen and workers, sex ratio wars of, 289–291, 294, 297, 301, 310 Hypothetical probability distributions, 126 I ICV, see Intracerebroventricular Igf 2 and Igf 2r, genomic imprinting of, 290–291 Imitation chimpanzees, 124 contextual, 116–117 process of, 107 production, 117–120 two-action test, 138–140 types of, 116 Immunohistochemistry, 459 Imprinted genes, 290 Individual interaction, types of, 460

515

INDEX

Individual recognition capabilities of species, 442 integrated, multifeature memories of species for, 448–450 multi-cue memories for, 450 neural mechanisms for, 454 by songbirds, 449–450 in species, 441 Individual variation behavioral ecologists attention towards, 229 in behavioral type, 228 study of, 229 in cooperativeness, 263 in dispersal behavior, 264 in innovation, 255 males, 250 in sensitivity, 250 stages, 249 Information sharing, 64 Information warfare, and parent– offspring conflict, 311 charming resources from parents, 302 disinformation, by offspring, 308–310 information use and outcome of conflict, 308 offspring begging displays, to blackmail parents, 305–306 selection for honest communication, 306–307 solicitation displays chemical communication, and social interactions in insects, 302–303 visual and auditory displays, in birds, 303–304 vocalization, in mammals, 304–305 withholding information, from parents, 310–311 Information warfare, impact on interactions among siblings, 319 advertisement of disinformation, 315 information, 312–315 blocking information, 316–317 collective bargaining, 317–319

leaking information, 316 tactical information, competition for, 315–316 Information warfare, parent communication to offspring, 324 information after birth, 323–324 before birth, 319–322 partially honest advertisement, 322–323 Insect behavior, 40, 43 STDP, as learning mechanism, 41 structures for multimodal coordination association and learning, 41 Insensitive cooperators, 247 Insensitive individuals, 247 Interbrood conflict, 285, 289, 299 International Society for Behavioral Ecology, 228 Interspecific diversity, of songbirds, 200 Intrabrood conflict, 285 Intracerebroventricular, 409 IS, see Information sharing J Japanese quail lines, 378–381 Johnston’s organ, 15 Joystick pushing task, 152–153 Junco hyemalis, see Juncos Juncos, 87 Juveniles recognition test, 447 voracity in, 232 K Kin recognition process as assessed by scent marking, 465 mechanism of, 466–470 odor-gene covariance, by, 470 phenotype matching, by, 467 self-referent matching, by, 470

516

INDEX

Kinship, advertisement of, 313–314 Kittiwakes, 362 Kleptoparasites, 74 Kleptoparasitism, 64 Kramer treadmill, 28 L Larus chachinans, see Yellow-legged gulls Larus ridibundus, see Black-headed gull Lasius niger, 311 Lawes parotia, 303 Laying order, 358–360 Learning future behavior alteration, 249 individual differences in, 253 social learning system, 255 mechanisms of, 254 proactive vs. reactive individuals, 254 Lekking species, song sharing in, 203 LGI and MGI neuron pairs, 31 LGMD, see Lobular giant motion detector LH, see Luteinizing hormone Life history syndrome (see Behavioral syndrome concept) theory, 230 Live tutor, rapprochement of, 198 Lobular giant motion detector, 12 Local enhancement bumblebees, 127 chicken, 125–126 interference effect, 126 nonlearning processes, 111 in rats, 128 Locust visual system, 13 Lonchura punctulata, see Nutmeg mannikins Long-tailed manakin, 303 Long-term stability of behavioral syndrome, 242 vs. short term stability, 232 Low inertia antennae, 15

Low state individuals, 243 Luteinizing hormone, 340 M Macacca mulatta, see Rhesus macaques Maculinea rebeli, see Cuckoo butterfly Magpies, 378 Main olfactory bulb, 408, 428 mechanisms of, 419–421 Major histocompatibility complex, 442 Major urinary proteins, 460 Male house sparrow, 355 Male–male aggressive interactions, 473 Male ornamentation, 365 Malurus cyaneus, see Fairy-wren Mammals alarm calls, 144 aquatic and semi-aquatic, 400 competitive behaviors in, 294 food preferences in, 114 genomic imprinting in, 290–291, 294 learning capability in, 145 marine, 151 maternal behavior in, 400 maternal–fetal interactions in, 314 maternal weather-forecasting in, 318 olfactory system of, 445 parental care, 399 parent–offspring conflict in, 287, 290 pattern of maternal behavior in, 400 physiological manipulation in, 301 propensity for speciation in, 301 scents in, 442 social behavior in, 422 solicitation displays in, 302–305 variation in placental morphology in, 300 vocal imitation, 145 weaning tantrums in, 288, 301 Manipulandum, food, 121 Marsh wrens, 211 Masked booby, 295 Mate-choice, 59 copying, 124–125

517

INDEX

Maternal behavior, of sheep establishment of individual recognition, 403–404 genetic variation, 406–407 maternal experience, 405–406 maternal responsiveness, 402–403 maternal responsiveness and selectivity, 404–405 neurobiology of maternal responsiveness in hormonal changes, 407–409 hormonal regulation, 409–412 maternal experience, 416–417 neural substrates, 412–416 neurobiology of maternal selectivity, 417 basis of, 419–423 physiological regulation, 417–418 sensory regulation, 418 at parturition, 401–402 Maternal effects, 338 Maternal hormones, entry in egg, 338–339 Maternal testosterone, for informing offspring, 320 Mating behavior, 231 behavioral tendency, 257 and parental care, 261 search for partners, 249 in social monogamous system, 258 Mating system, 377 Meadow voles ability to discriminate, 442 scent over-marks, 442 Mechanoreceptive hairs, 15 Mechanoreceptors, 15–18 Medial preoptic area, 408 Meerkats, 305 Meloe franciscanus, see Flightless blister beetle Melopsittacus undulatus, see Budgerigars Melospiza melodia, see Song sparrow Memory, individual differences in, 253 MEMS, see Microelectromechanical system

Mesocricetus auratus, see Golden (or Syrian) hamster MHC, see Major histocompatibility complex Mice, 299 Microelectromechanical system, 18 Microtus pennsylvanicus, see Meadow voles Migratory population, memorization of songs, 202–203 Migratory syndrome, see Behavioral syndrome concept Mimic gesture dolphin, 149 in great apes, 150 transfer test, 149 MOB, see Main olfactory bulb Molothrus ater, see Brown-headed cowbird Molothrus bonariensis, see Nestling shiny cowbirds Molothrus rufoaxillaris, see Cowbirds Monogamous species, 85 repertoire size in, 201 Movement, disambiguating sensory signal, 19 MPOA, see Medial preoptic area Multicomponent representations, in animals, 441 Multimodal mechanism for escape, 37 Mungos mungo, see Banded mongooses MUPs, see Major urinary proteins Musculus complexus, 346 Mus musculus, see Mice Mynah birds, 151 Myrmica schencki, 309–310 N NA, see Noradrenaline Naive chickadees, 123 Nash equilibrium, 67 Natural selection, 67 alternative alleles coding for, 65 evolutionarily stable strategy, 65

518

INDEX

Natural selection (continued ) mechanics of, 440, 460 parental strategies for, 285 and sexual selection, 258 Negotiation model, 261 and parent–offspring conflict, 298 Nelson–Marler theory, 174, 176 Nestling begging behavior, and yolk androgen levels, 357–358 Nestling shiny cowbirds, 308 Neural circuit, for multimodal escape, 38 Neural encoding of sound, for direction to turn, 26 Neuromorphic visual motion sensor circuits, 38 Nicrophorus vespilloides, see Burying beetle Nine-spined sticklebacks, 129 NMDA, see N-methyl d-aspartate N-methyl d-aspartate, 419 Nonaggressive scramble foraging tactics, 86 Nonhuman primates, 64 Nonimitative processes, 107 Nonmimic trials, 150 Nonproprioceptive function, 15 Noradrenaline, 409 Nutmeg mannikins, 69–72, 77–78, 90–91 O Observational conditioning excitatory-aversive, 154 female–male proximity, 124 rhesus monkey and snake, 112 snake fear in monkeys, 130 social learning consistent, 129 stimuli, relationship, 113 stimulus enhancement, 124 stimulus-stimulus learning, 112 Observational R–S learning, 120, 152–153 Ocelli, 14–15 Odor quality

difference in, 442, 461 of individuals, 461 Odors complex mixtures, quality of, 442 signals, 442 Olfactory search in moths, 36 Omega neurons (ON1 and ON2), 28 Ommatidia, 13 Optimal diet, 61 Optimal foraging theory, 61 Optimal prey models, 63 Optomotor response, 37–39 genetic algorithm for synaptic parameters, use of, 40 Oscine passerines, 167 OT, see Oxytocin Ovulation, factors affecting, 340 Oxytocin, 407 P Paper wasp, 303 Parasitoid wasp, 297, 313 Paraventricular nucleus of the hypothalamus, 408 Parental behavior, 231 aggression, and, 261 mate attraction, 261 ornament size, 261 towards their offspring, 260 Parental care by aggressive males, 265 brood parasitism and, 368 conspecific, 96 cooperation in, 260 duration of, 288 level of, 261 manipulating mechanism, 357–358 in monogamous species, 85 ornamented males, provided by, 261 provision of, 308 quality of, 261 sexual conflict over, 357 styles of, 260–262

INDEX

for successful rearing of offspring, 96–97 Parental investment amount of, 286 benefits of, 285, 302 levels of, 285 life-history theory and, 365 parent–offspring conflict and, 282–286 provision of, 283, 285 reduction in, 292–293 supply of, 284 Parent–offspring conflict evidence of, 289–291 genomic imprinting of Igf 2 and Igf 2r, 290–291 sex ratio wars, 289–290 evolutionary squabbles vs. evolutionary conflicts, 286–289 few incidences, reasons for conflict resolution, 296–297 powerful parents, 294–296 small zone of conflict, 292–294 testing difficulty, 291–292 genomic analyses and, 300 negotiation models and, 298 and nestling begging behavior, 297–298 placental morphology role, 300 as selective force in nature, 284–286, 292, 294, 296–297, 301 theory of, 284–286 Parotia lawesii, see Lawes parotia Parrot, 152 Parus atricapillus, see Naive chickadees Parus major, see Great tit Passer domesticus, see House sparrows Passeriformes, 362, 371–373, 383 Patch exploitation time, 63 Pattern-recognition process in species, 442 Pavlovian conditioning, 112 Pavo cristatus, see Peacock; Peahen Payoff functions, 65, see also Producer– scrounger game Peacock, 303

519

Peahen, 360 Person-situation debate, on human personality, 233 Pheasant, 355 Phenotype-limited model, 88 Phenotype matching mechanism, 467 Phenotypic constraints spatial position, in, 90 on use of producer and scrounger strategies, 86–87 Phenotypic diversity descriptive models of, 63–64 extent of, 63 Phenotypic resolution models, 297–299 Pheromone, 7 Phodopus campbelli, see Dwarf hamster Phonotaxis, 5, 19, 27–30, 37–40 Physalaemus pustulosus, see Tungara frog Pica pica, see Magpies Pied flycatcher, 359 Pigeons blindly imitate, 153 contextual imitation, 141 demonstrator peck, 128 Placental hormones maternal receptors with, 322 in nonpregnant females, 300 role in mother and offspring conflict, 300 Plasticity advantage, in song sharing of birds, 202 Ploceus velatus, see African masked weaver Poecilia reticulata, see Guppies Polarization pattern and sensors, 14 POL1 interneurons, 13–14 Polistes canadensis, see Tropical paper wasp Pollination syndrome, see Behavioral syndrome concept Polygynous species, repertoire size in, 201 Ponerine ant, 303 Population dynamics ecological selection pressures in, 265

520

INDEX

Population dynamics (continued ) implications for, 95 regulation of, 91 Population effects, implications for, 90 behavioral plasticity, evolution of, 92–94 cryptic coloration, evolution of, 91 population dynamics, regulation of, 91–92 Population-level phenomena, 61–62 Post-dispersal learning, in songbirds, 188 Praying mantis, 19 Precocial species, 401 Predation danger of, 82 risk of, 85, 95, 227, 235 Predator–prey interactions, 61 Predator–prey population dynamics, 91 Predators, 92 air movement in, 16 boldness towards, 229 Prelaying differential allocation, 368 Prey detection errors, 91 selection, 62 species exploited by predators, 91 Proactive individuals dominance of, 249–250 vs. reactive individuals, 249, 254 Producer–scrounger game empirical studies of, 87 expected solutions to behaviorally stable strategy, 66–67 ESS and BSS, 67–68 evolutionarily stable strategy, 65–66 game characteristics, 65 for non-food resources mating opportunities, 94–96 parental care, 96–97 state-dependent, dynamic, 82–83 stochastic dynamic state-dependent model of, 82 on use of producer and scrounger strategies, 86–87

Producer–scrounger (PS) decisions, 62, 64–65 Production imitation, 147 learning by observation, 117 novel action, 118–119 Production learning, 151 Program-level imitation, 119 Proprioceptive signals, 15 Pro rata models, 306, 308 Protein-rich food pellet, 129 Prunella modularis, see Dunnock PS game, see Producer–scrounger game Psiharpax project, 6 PS information games, 84–85 PS model, rate-maximizing, 68–69 altered costs, of producing and scrounging, 73–74 experiment with carib grackles, 75 finder’s share to alter BSS, 72 evidence for existence, 69–70 group size to alter BSS, 73 incompatible search modes, 70–72 predicted rate-maximizing BSS, evidence for, 74–76 birds foraging in apparatus, 78 Lonchura punctulata, foraging alternatives, 77 SEF calculation, 69 Public vs. private information, 255 Public vs. private learning, 255 Publilia concava, see Treehopper insect Pungitius pungitius, 129 Pupfish, 95 PVN, see Paraventricular nucleus of the hypothalamus Q Quail, 354 mate-choice copying, 108 Quantitative genetic resolution models, 299 Quiscalus lugubris, see Carib grackle

521

INDEX

R Rana computatrix, 6 Rank-order difference, among individual, 234, see also Behavioral syndrome concept Rats, 60 bar-press action in, 148 local enhancement, 128 Reactive individuals vs. proactive individuals, 249, 254 Real antennae, 15 Red deer, 288–289 Red-flanked Bluetail, 309 Red-winged blackbirds, 346–347 R–S learning, 130 Reflex loops, 40 Reliable cooperators, 247 Repertoire hypothesis, of songbirds sexual selection, 201 of song learning program, 200 Repertoire matching, 182, see also Song sparrow Residual neural activity, 114 Resolution models, in parent–offspring conflict other coevolutionary hallmarks of conflict, 299–300 phenotypic resolution models, 297–299 quantitative genetic resolution models, 299 Response facilitation, 114–115, 142 birds, color stopper, 142 bouts of preening, 134 external cues, 134 on feeding and drinking, 135–136 species, 134 production imitation, 147 sequential structure, imitation of, 147 three-bowl test, 135 Rhesus macaques, 289 Rhesus monkeys, 124 observational R–S learning, 130 snakes fearfully, 136

Rheumatoid arthritis, 348 Rissa tridactyla, see Kittiwakes Robins, 324 Robot cricket, 4–5 Robotic helicopter, 13 Robotic implementations, in behavior study, 6 active vision, approach in, 19 for application of learning mechanisms, 40–42 of associative learning, 40 consideration of physical, 7 electronic auditory system, 28 fly-vision inspired in, 19 homeostatic tuning of receptors, mechanism for, 18 Machina speculatrix, 24 for optomotor response, 37–40 phonotaxis, 19 and model for crickets, 26–28 to produce right motor output, 24 robot RHex and Whegs, for leg movement, 20–21 turning motions, description, 21–22 tracking zig-zag shaped wall, 15 visual homing study, 31–33 for behavior of animals in ‘place memory,’ 33 root mean square (RMS) and natural images, 32–33 snapshot model, 32 Robotic lobster, 36 Robotic mechanisms, 6 Rotational visual motion, 19 Royal Penguins, 136 S Saccadic flight motion, 19 Sahabot robot, 14 Scalar property of time estimation mechanisms, 60 use of, 60

522

INDEX

Scent marking, see also Scent over-marking causes and functions of, 477 communication by, 492 by female animals, 474 functions of, 492–494 individual advertisement and competition by, 472 kin recognition, for, 465 as means of competition, 465 type of, 473 Scent over-marking, 476–477, 492–494 Scrounger alleles, 66 Scrounger strategy, 82 Scrounging, 64 Sealed bid model, 261 Sedge warbler, 200, 303 Sedge wren northern population of, 207, 209 song learning of, 207 tropical population of, 207 SEFs, see Stable equilibrium frequencies Sehirus cinctus, see Burrower bug Self-defence techniques urination–defecation displays, 473 Sensitive vs. insensitive individuals, 252 Sensorimotor behaviors, 36 Sensors bionic sensor, use of, 18 function, 7 hair sensors, for air movement, 16 leg-like sensor, 18 and mechanical properties, of cockroach antennae, 15–16 MEMS version of hair sensors, 18 VLSI sensor, for optomotor response, 39 Sensory ecology, 60 Sensory modalities, in animals, 441 Sensory neurons, 16, 30 Sericornis frontalis, see White-browed scrubwrens Serinus canaria, see Canaries Sex ratio distorters, 379

and maternal hormones, 378–380 wars, in social Hymenoptera, 289–290 Sexual advertising, 466, see also Vaginal marking behaviors Sexual behavior, 59 Sexual cannibalism in females, 258 in fishing spiders, 259 Sexual dichromatism, 377 Sexual selection, 59 Sharing hypothesis, of song learning program, 200 Sheep, expression of maternal behavior in establishment of individual recognition, 403–404 genetic variation, 406–407 maternal experience, 405–406 maternal responsiveness, 402–403 maternal responsiveness and selectivity, 404–405 at parturition, 401–402 Sheep, neurobiology of maternal responsiveness in hormonal changes, 407–409 hormonal regulation, 409–412 maternal experience, 416–417 neural substrates, 412–416 sensory regulation, 412 Sheep, neurobiology of maternal selectivity, 417 basis of MOB, mechanisms of, 419–421 olfactory bulb, 421–423 physiological regulation, 417–418 sensory regulation, 418 Short-term behavioral consistency, 232 Sialia sialis, see Bluebirds Siblicide, 295–296, 301 Signaling model, of offspring need, 306–308 Single-male polygamous groups of rodents, 459 Single stimulus learning, 111, 123 Six-legged walking, 19–22 Sky polarization, 14

INDEX

Social behavior of discriminations, 444 habituation–dishabituation technique, 444 Social dominance, effects of, 87–88 Social eavesdropping, 174–175 Social enhancement of food preferences, 113–114, 132–133 Social facilitation, 114–115 on behavior, 136 penguins, breeding cycle, 136 on task learning, 137 Social foraging theory, 61 categories of, 62 decisions within patches, 63 descriptive models of phenotypic diversity, 63–64 group membership decisions, 62–63 PS decisions, 64–65 frequency-dependence characterization, 61–62 Social information, 60 Social interactions, 60 Social learning correlation with personality, 255–256 sociable individuals exposure, 255 Social learning processes, 107 bumblebees, 127 in chicks, 113 classifications of criteria, 107 emulation, 120–122 food preferences, social enhancement of, 113–114 imitation, 107, 115–120 local enhancement, 111–112 observational conditioning, 112–113 observational R–S learning, 120 psychological processes, 106, 108 response facilitation, 114–115 social facilitation, 115 stimulus enhancement, 108–111 definition of, 105, 109–110

523

demonstrator, direct observation of, 123 empirical evidence for action-specific effect, 140 adaptive specialization, 133 anecdotal evidence, 122 contextual imitation, chimpanzees, 124 contextual imitation, on quail and pigeons, 141 emulation effect, 155 greater racket-tailed drongo, 143–144 odor cues, in rat, 128 response facilitation effect on feeding, 134 response facilitation of drinking, 135–136 sequence learning, 148 single-stimulus learning, 123 spontaneous movements of objects, 138 in guppy, 125 of matching behavior, 106, 137 in nonhuman animals, 106 novelty, 118 Social learning scheme, 107 Socially monogamous system, 258 Social modeling theory, 175 Social network in each species, 441 in local areas, 439 nature and extent of, 441 Social recognition processes, among insects, 442 Social selection theory, 266 emphasis of, 268 Social sensitivity among individuals, 247–253 elements, 263 in mating, 259 Social status, of individual, 233 Social systems, types of, 441 Social transmission in blackbirds, 130 of information, 107

524

INDEX

Social transmission (continued ) matching behavior, 131 predator recognition, 131 Solitary animal, 64 Solitary bee, 317–318 SON, see Supraoptic nucleus Song learning, of song birds developing theories of phases, 174 tape tutor paradigm, 174 dimensions of, 171 functional hypotheses of focus on adult song repertoire, 200 programs, 200 repertoire hypothesis, 201–202 functions in, 168 hypotheses of, 173 information extraction for, 176 maximization of songs, 206 methods and approach post-dispersal learning, 188 song sparrow males study, 189 origin, 168 from overhead tutor, 196 phylogenetic approaches, 212–213 programs, 168, 172, 206–207 rules of, 189–192 sedge wren, 207 seminatural lab study, 192–195 social factors, approaches to analyze, 172–173 social interaction for, 199 in songbirds, 168 tape tutor experiment, 169 types of, 197 virtual tutor method repertoire of, 198 subject identification, 198 virtual tutor software (see Virtual tutor software, programming of) virtual tutor study, 197–200 Song performance, 206 Song repertoire, in polygynous species, 201 Song sharing, in songbirds, 200 closed-ended learner, 207

distribution, 212 hypothesis badge hypothesis, 205 genetic adaptation hypothesis, 203–204 migratory population, of birds, 202–203 mimicry hypothesis, 204–205 plasticity advantage, 202 song sparrow population, 209 in western song sparrow, 188 Song sparrow eastern and western, comparison between, 211 radio-tracking young, 193 song function and learning background, 177–181 research program, 176–177 usage of songs by, 182–188 song-learning program, strategy for, 208–210 in summer season, 169 tape tutor method, 172 trill rate and frequency bandwidth of, 206 Song-type matching, 449 Song usage by song sparrows intersexual context, 186–187 singing rule in intrasexual context, 184–186 song matching background, 182–184 song sharing and repertoires, function, 187–188 Spadefoot toads, 315 Spatial contiguity, 113 Spatial relationship, 113 Spea spp., see Spadefoot toads Species ability for discrimination and recognition between individuals, 440 of kins, 460 adaptation to environment, 439 characteristics and differences, 439 communication signals in, 440

525

INDEX

discrimination between odors of individuals habituation–dishabituation methods for assessing, 446–448 methods for accessing, 443–446 evidence for multi-cue memories of, 450 functional neuroanatomy, with, 454 individual recognition capability in, 442 learning mechanisms in, 441 multicomponent memories of, 441 pattern-recognition process in, 442 population characteristics, 439 recognition of specific individuals, 441 sensory abilities in, 440 social behavior, evolution of, 439 social system, types of, 441 survival, 440 Specific visual reflexes, 6 Sphex ichneumoneus, see Golden digger wasp Spice finches, 69 Spike timing dependent plasticity, 41 Spotless starling, 346, 350 S. sialis, see Eastern Bluebird Stable equilibrium frequencies, 67 STDP, see Spike timing dependent plasticity Steroidogenesis, 339–341 Stimulus enhancement animals learn, 123 definition of, 109 demonstrator’s actions, 123 feeding demonstrator, 128 of food preferences, 132 observational R–S learning, 124, 137 observer’s response, 111 with food, 124 generalization, 111, 123 Stochastic dynamic state-dependent model of PS foraging game, 82

results of PS game, 83 Stochastic, risk-sensitive models, 76, 79 empirical evidence, 80–82 in European starlings, 81 static stochastic model, 79–80 Sturnus unicolor, see Spotless starling Sturnus vulgaris, see European starling Suboptimal behavior, 232 behavioral syndrome association with, 233 Suckling duration, and parent-offspring conflict, 288–289 Sula dactylatra, see Masked booby Superb lyrebird, 151 Supraoptic nucleus, 408 Suricata suricatta, see Meerkats Swamp sparrow, tap tutor experiment in, 170 Sweet potato washing, in Japanese macaques, 60 T Tachycineta bicolor, see Tree swallows; Young tree swallows Tactics use, for foraging, 88 Taeniopygia guttata, see Zebra finch Tape tutor, rapprochement of, 198 Tarsiger cyanurus, see Red-flanked Bluetail T. bicolor, see Cliff swallow Temporal relationship, 113 Testosterone (T), in avian yolks, 337, 343–344, 347–349, 353, 355–360, 362–375, 377–378, 382 TFT, see Tit-for-tat model Theca externa, 340–341 Thoracic ganglia, 30 Thornbug treehopper, 303 Three-bowl test, 135 Three-spined stickleback, 129 Thrifty phenotypes hypothesis, 320–321 Thyroid hormone deposition, 384 Tit-for-tat model, 263 Tolerated theft, 64

526

INDEX

Transgenerational plasticity hypothesis, 364–365 Treehopper insect, 97 Tree swallows, 343 Tripod gait, 19–20 Troglodytes aedon, see House wren Tropical paper wasp, 295 Trustworthiness, among individuals, 247 Tungara frog, 94 Two-action task, 153 Two-action test, 149 Tyto alba, see Barn owl U Ultrasonic vocalization species, in, 453 Umbonia crassicornis, see Thornbug treehopper Unambiguous evidence, 122 Unconditioned response (UR), 112, 131 Unconditioned stimulus (US), 112, 131 Underwater robot, to study lobster’s behavior, 6 UV/green spectral opponency, 14 V Vaginal marking behaviors, 466 by female hamsters, 475–476 Vaginocervical stimulation, 408 Vampire bats, regurgitating blood meals, 61 VCS, see Vaginocervical stimulation Ventral optic flow, 13 Vespa orientalis, see Paper wasp Vespilloides orbicollis, see Burying beetle Vespula germanica, see Wasp Virtual tutor method experiments, 199–200 identification of subject, 198 repertoire of, 198

study of hand-raised birds, 199 hybrid design use for young birds, 199 singing, 199 Virtual tutor software, programming of, 198 Visual homing, 31–34 Visual local enhancement, 127 Visual motion, to control heading, 12 Visual sensors, 12, 14 Visual stabilization, 12 of trajectories, in insects, 13 VNO, see Vomeronasal organ Vocal imitation, 145, 151 emulation, 154 Vocalizations types of, 441 ultrasonic, 453 Vocal learning, 122 in primates, 167 sharing of vocalization, 203 Vocal mimics, 151 VOF, see Ventral optic flow Vomeronasal organ, 420, 428 Voracity, in juveniles and adults, 232 Vultures, carrion-feeding, 125 W Wasp, 127 Water striders, hyperaggressiveness in, 250 Weaning tantrums, in mammals, 287–288, 301 Weather-forecasting, by mothers, 320–322 Western population, of song sparrows, 209 Western song sparrow, 169 data interpretation on, 188, 200 Wheeled robot, for model of leg dynamics, 16 White-browed scrubwrens, 324

527

INDEX

White-crowned sparrows song learning pattern of, 171 songs, 143 study on, 204 Whiten’s experiment, 145 Will reed warblers, 303 Wind sensing and vision, 36 Wood ant, 290 Worker policing, in hymenoptera, 315 Y Yellow-legged gulls, 347–348 Yolk androgen level aggressive encounters and, 343–345 coloniality, 375–376 and dispersal, 365 and female control, 343–344 high production effect, 341–342 and plasma levels, 343 and relative duration of the nestling period, 373–374 residual incubation period and, 373–374 sex determination and, 378–80 sexual selection and, 376–377 Yolk androgens effects, 345–346 long-term effects, on offspring, 352–353 behavior, 353–355 digit ratios in birds, 356 dispersal, 355–356 female breeding performance, 356 ornaments, 355 other traits, 356

sex-specific reproductive traits, 356 on parents behavior, 357–358 physiology, 356–357 short-term effects, in nestlings, 352 begging, 347–348 development, 346–347 on hormone levels, 350 immunocompetence, 348–350 metabolic rate, 348 sex-specific effects, 350–351 survival, 350 Yolk hormones, and comparative studies, 368–370 brood parasitism, 377–378 coloniality, 375–376 correlated selection, 378 development, 370–375 sexual selection, 376–377 Yolk T, see also Testosterone (T), in avian yolks and brain size, 370 Young tree swallows, 304 Z Zebra finch, 346, 354, 360, 362, 365–366, 379 breeding performance in, 357 hatching from T-injected eggs, 348 local population density, 320 post-hatching, 173 sex-specific effects in, 350 social status of, 70 tactical choices, 87