Handbook of ethological methods SECOND EDITION
PHILIP. N. LEHNER Depdrtmenl of Biology, Colorado Stote Universiry
h-eol
F,.
J,uu Psigtilw,tZ -(z
-;)
C.m,rnnrDGE UNIVERSITY PRESS
\
-__/,l'
Contents
pagexw
Prefoce 'l
lt
IT r,,'l
I
INTRODUCTION
l.l What
I
is ethology?
2
1.2
Why study animal behavior?
2
1.3
What to study?
3
l.j.l 1.j.2 l.j.j
4
l*vels
oJ behovior
Areasofstudy Categorics
of
4 questiore
E
1.4 Scientific method
l0
t.s
ll
Ethological approach
l3
1.6 Descriptive versus experimental research
I
GETTING STARTED
t7
2 A CONCEPTUAL MODEL OF ANIMAL BEHAYIOR
19
2.1 Continuous stream of behavior
l9
z.z
Predisposition to behave: dilfering contributions
20
2.2.1 2.2.2
m
Genotype, envircnmenl (experience) ond htemction
Anotomyond physiology
2.3 A model for
21
a behavioral act
24
2.3.1
The
2.3.2
Stimuli
24
2.3.3 2.3.1
Behavior
26
Ethologicol model ol innate behavior
26
2.i.5
Associotive learning paradigms
28
2.3.6 2.3.7
Feedbock
37
Feedlorword
37
mimol
24
2,4 Application of the model
38
2.1,1 Cancuptuallzlng the oreos ol study 2,1,2 OrynkW thc typcs of questioa
2,1,t 2,11
Focuhgrutorch Dowhpht an .xpanM ot mon locutd
,,1t Dlqfit st tnlr,lI, tfu
38 39 39
nobl
4t
ltchst h otlmb
4
I' CONTENTS
CONTENTS
4
2.5 Summary
3 CHOICE OF SUBJECTS
6.5.2 6.5,3
xi
Experimmtal designs
129
Rondom, haphazord and oppoilunistic samples
t4
47
6.6 Relative efficiency of experimental designs
147
3.1 Species-oriented research
47
6.7 Determination of
t48
3.2 Concept-oriented research
50
3-2.1
August Krogh principle
5l
3.2.2
Suitobility verus owilobility
53
sample size
7 EXPERIMENTAL RESEARCH z.l Thevaryingvariables 7.1.1 Noturol variation
4 RECONNAISSANCE OBSERVATION 4.1 How to observe 4.1.
I
4.1.2 4.1.3 4.1.4 4.2.1 4.2.2
Walching versus obreming
54 58
Fieldnotes
6t
Equipment
65
describe behavior
Empirical versuslunctionoldescriplions Cotalog, repertoire md elhogrom
4.3 Information 4.3.1 4.3.2 4.3.3
54
An exercbe in obserting
-- 4.2 How to '
54
resources
7.
z.r
8l
86-
Otherresearchers
97
Films ond videotapes
98
5 DELINEATION OF RESEARCH 5.1 Conceptualizingtheproblem
100
Whal are the questions?
100
5.L2
Statingobjectives
102
5.1.3
Research hypotheses
t02
t57 167
Field to laboratory: a continuum
177
COLLECTING THE DATA
Research desigrr and data collection Scales
of measurement
8.3 Sampling methods
-8.3.1 8.3.2 8,3.3
t00
5.1.1
5.2 Using the model for a behavioral act
t
8.2
155
167
8 DATA COLLECTION METHODS
93
l5l
7.2.1 In thefield 7.2.2 In the laborotory
II
8l
94
Artificial manipulation
7.2 Further examples of experimental manipulation
8.
Literature
1,2
150
Adlibit':um sampling Continuow recording sampling methods
E,5.2
Intro-obrner
183 183
€,
tc{ 197
206
2lo
8.5 Reliability
I
183
201
8.4 Observereffects E,5.
l8l
r89
Focal-animal (pair, group) versus all-animal samplhg
E,3.4 Time sampling 8.3.5 Summory
t03
174
212 or sef-reliability
Inter-observer reliability
219 214
6
DESIGN OF RESEARCH
105
ldentification and naming of individuals
221
6.1
Description versus experimentation
,105
E.6.1 Noturolmarks
221
6.2
Variables
107
8.6.2
6.3
Behavior units
109
E.6.3 Assigunent of
6.3.1 6.3.2 6.3.3 6.3.4
il0
6.3.5
Classifcation of behavior unils Spotial and lemporal aspecls
l14
Descriptions and definitions of behavior unils
il5
Number of behavioral units to meosure
t23 t24
Volidity
6.4 Rescarchcr-statistician
6.' Barlc oxpcrimcntd doltnl
6t,t l,fi',t r,rryt attffiitttltttr tfia*d
IN t26 _ 126
t,6
9 9,1
9,2
Copture and marking numbers and natnes
223 210
DATA.COLLECTION EQUIPMENT
233
Notobook and pcncil
235
Drb hnnr
236
9,2.1 Clnrclabtkt ol hhavlor unltt
236
11,1 Cohtmtai,ovt
217
tl *q )Al lrilrnlrlry*r,' '
238 238
CONTENTS
9.3 Clocks and counters 9.4 Calculators
CONTENTS
9.5 Strip-chart event recorders
250 253
254
9.8 Microcomputers
256
Datacollection Dala storage and manipulution
9.9 Audio-taperecorders
I 2 9.9 3 9.lo
I
266
267
Dato collection on uudio-tape recordcrs
267
9.9
Recording unimol sounds
269
Playback of sounds
2'13
347 348
Hypothesis testing
I 5 Selecting the alpha level ll 6 Poweroiatest I
282
I1
Computer analysis of recorded sounds
283
276
Still photogruphy
285 293
Filmanalysis
296
9.14 Metronomes
308
307
Determining geographic location
309
l5.l 9 l5 2
Global Positioning System and Argos Satellite System
3t0
Biotelemetry
313
IO SELECTED EXAMPLES OF DATA COLLECTION
AND DESCRIPTION
319
Individualbehavior
319
l0 I
1
Terreslriql tetrupod loc.omolion
10.2 Social behavior 10.2
l0 l0
I
Disploys
358
7.1 7.2
Sample disttibutions
358
Sumple mean, mode und median
358
7.3
Skevness
359
7.4
Location
359
7.5
7.6 I I 7.7 I I 7.8
284
299
9
I
275
Motion-picture photogruphy
352
Variubilily
361
Standard deviation
361
Sample mean confdence interval
362
CoeJfcient
of voriation
12 SELECTION OF A I
353
1.7 Sample statistics
276
l.l 2 3
351
I
Equipment
I
349
1.4 Type I and type II errors
I
9.12 Videotape recording and analysis 9.13 Stopwatches
lo.l
I
366
STATISTICAL TEST
I I Parametric versus nonparametric statistical 12 I I Paramelrictesls l2 I 2 Datu lranslbrmalions 12 I 3 Nonpurumelrictests
tests
369
377 379
lr I Completely randomized design l-1 I I Ttro indcpcndent sumples l -1 I 2 Thrta or ttrorc indepcnclent sumples I Randomized block, matched pairs or repeated l-l ) I Ttn rtlutcd or nul
369
369
I.I PARAMETRIC STATISTICAL TESTS
II
347
Statisticalhypotheses
Analysis of sound spectrograms
Photography
345
Statistical principles
9 l0
9.ll 9.ll
l5
I l.l ll 2 ll 3
INTRODUCTION TO STATISTICAL ANALYSES
9
Ultrasonic delectors
Analysis of animal sounds
9
9
II
I I II II II
9.4
I0.l 2 9. l0 j 9.1
257
9.9.
9
ANALYZING THE RESULTS
250
9.6 Stenograph 9.7 Computer-compatible data loggers
981 9.8 2
III
247
xlll
381 381
38t 384
measures design
386 386
319
32t
t2t
2.2
Dominant-suhordinate relutionships
326
2.3
So<.iol orguni:ation
138
14 NONPARAMETRIC STATISTICAL TESTS I (irnplctcly rantlomizcd design
l.l
ll I I 14 l) l\t,nriililtt (hx,vrriuhlr
l.l
,t Rlrttkrlizctl block, l1) I l)tr wrthlt
391
39t 391
4t2 nurtchcd pairs
lnd rcpcatcd mcasurcs design
421 421
;"
CONTENTS
CONTENTS
xlv
15 RATES OF BEHAVIOR
l5.l
SEQUENCES
440
Ratesofbehavior
q
15.2 Analysis of sequences 15.2.1 Intro-individual I
5.2.2
444
sequences
47
Inter-individual sequences
456
MULTIVARIATE ANALYSES
16
AND PRESENTATION OF
18 INTERPRETATION
AND ANALYSIS OF
RESULTS
l8.l
522 522
What do your results mean?
522
18.2 Visual representations
523
18.2.1 Graphs andfigures 18.2.2 Vector diagrams 18.2.3 Kinematic graphs 18.2.4 Conceptual models 18.2.5 Other illusuations
465
527
530 531
536
16.l Matric€s
465
18.3 Comparisons with previous results
539
16.2 Grouping behaviors
468
18,4 Re-evaluation
539
16.3 Grouping individuals
469
16.4 Describing differences among individuals
474
16.5 Discriminating among groups of individuals or behaviors
476
16.6 Analyzing sequences of behavior
479
16.6.1 Faclor analysis 16.6.2 Multidimensional
479 scaling
482
16.7 Summary 16.8 Software packages for statistical analyses
I
539
8.5 Revising and restating hypotheses
APPENDICES
A
539
Statistical figures and tables Table
AL Factorials.
Values
541
oft!
541
lor n* I to
Toble A2. Lagarilhms oflactorials
482
Table A3. Binomial coelficients
483
Table A4. Critical valuesfor the F-Max
542
199
543 test
lor
homogeneily of tariance
544 545
Table A5. Critical values of the t-stalisticlot o two'railed test
I7 SPATIAL ORIENTATION AND TIME: CIRCULAR STATISTICS AND SPATIAL PATTERNS 17.l
Tests for randomness of directions (or times) I
7. 1.
I
17.1.2 Rayleigh 17.1.3 Y test
485
487
test
489
49t
17.2 Difference between sample mean direction (or time period) and specified direction (or specified time period)
493
17.2.1 Smtple mean direction 17.2.2 Confidence interyal
494
17.3 Difference in sample mean directions between two, or morg groups animals
l7.j.l
17.3.2 llatsorlhlliams 1 7.
I
3.3
llatson-lAiilhms
I
I
coelficient
r,
551
Table A8,. Crilical values of
for of r for
Table A8r. Critical values Thbte A9.
Crilicol
values
r
the one-sample and two-sample runs test. lhe one-sample runs
of
the Mann-Whilney
Al I. Critical voluesfor the Kolmogorov-Smirnov two-samPle, one-toiled
nall samples
for
the Kolmogorov-Smimov twosample,
lw4ailed
bnot two-sample,,
wo- lo iled
rcsr
lor
es I
lor 5U
values values
Table
AlE. Criticul
volues
Tobte
Al9. Critical
volues
ol Kendoll's tau correlation Table A2!, Crlticul valuesoftllot Dunnctt's test Tabls A22, Valucsoflogrnond
coelficient
-plogrp tttt
567
569
cotlnc und tangcnl lor dhectlon and
lbbLA2l,Crltl(g,lwlwtofttlorthtY
555
small samples 568
test
of Tfor lhe l{ilcoxon malched-pairs signed-ronks of Spearman's Mo correlotion coefficient
Thble A20. Critical volues
A2t, Valutt ol ilna
variance
of Qfor Dunn's multiple comParison test ol Slor Kendoll's Coe!ficienl ol Concordancefor
Table At7, Probobilitieslor the two-tailedsign
Tabte
Mlt
t
samples
Table A14. Critical valuesfor the Kruskal-Wallis one'way analysis of
500
5t0
lot
560
mull isample lest
7.4.2 Animal
tesl
556
Toble A12- Critical values
496
502
554 555
small samples
two somple test
502
tests
test
Thble
values
552 553
Al0. Critical
large
495
tesl
of the chi-square distibutionfor one'tailed
ollJ for
Alpha=0.05
Table
Table A I 3. C r il ico I vo I ue s for t he Kolmogorov-Sm
Animal
7.4.
s49
Table A7. Criticol values
ofF for the onolysis of variance Alpho:0-05 o;[F for the analysis of variance. Alpho=0.01 for signifcance of the Pearson producl momenl correlalion
AI5. Crilical Table Al6, Critical
7.4 Spatial patterns: animal-animal and animal-
494
495
Two smtple runs test
56
A6, Criticol values
Tbble
485
Chi-square goodness-of-fit test
Table A6,. Critical values
test
570 571
57f 574 576
time
578 5E9
a'
t Introduction One does not meet oneself until one catches the reflection from an eye
other than human.
IEiseley, 1964:24]
ln choosing to study the behavior of animals you are setting off on a voyage across wuters that are often rough and, in some areag poorly charted. This book is basictlly a compilation of practical information that is intended to help smooth the wutcrs and assist you in charting your course. I have taken the liberty of infusing it with personal and borrowed philosophy. These philosophical interludes are meant lo $ment together concrete blocks of facts and invite you to stop and ponder what you have learned and what it means.
The most disheartening (and sometimes disarming) question that an uninitiated
clhologist can face is: 'So what?'You can be confronted with that question at technicrl meetings or cocktail partieq and answers vary from informative discourses to ohlcene threats. I hope that some of the discussions found in this book help you to wrcctle with that question.
The study of behavior encompasses all of the movements and sensations by which animals and men mediate their relationships with their external environments - physical, biotic and social. No scientific field is more complex, and none is more central to human problems and aspirations.
IAlexander, 1975:77]
Onc of the greatest challenges you face is to keep your own studies of animal hchrvior in perspective. Why did you choose that particular species or concept to rludy (lcctions 1.4, 2.1, 2.2)?What is the focus of your study? Three dimensions of
lhrr
qucstions are discussed in section 1.3, below. What is already known about the tubjcct (roction 4.3)? Are you replicating someone else's research, testing someone rll0l hypothcsig, or ar€ you answering your own questions? Will your results be
llmltod to tho individuals you are observing or will you consider them a representallw rrmplo of a lrrgor population?
hr
Errllcr I uld thrt tho rtudy of animal behavior is a fascinating voyage; rememto oontlotully mru whotu you arg wherc you havc bcen, and where you are
tolnl,
WHAT TO STUDY?
INTRODUCTION
ilACRO
I.I WHAT IS ETHOLOGY? Everyone seems to have their own definition of ethology, although a precise and widely accepted deflnition eludes ethologists. A verbal exchange among Konrad Lorenz, Niko Tinbergen, Theodore Schneirla and others on the definition of ethol-
ogy makes interesting history and reading (Schaffner 1955l'77-78). Eisner and
ltttolYloae I
Wilson ( 1975:1) deflne ethology as the: . . . study of whole patterns of animal behavior under natural conditions, in ways that emphasize[d] the functions and the evolutionary history of the patterns.
leBoReT0Rr
(highly controlled)
However, for our purposes, let us focus on Lorenz's ( 1960a) definition: 'Ethology can be briefly defined as the application of orthodox biological methods to the problems of behavior'. As a basis for this definition, Lorenz (op. cit.) described the
8p60
geneologyofethologyasfollows: '...biology...isitsmother.'.whereas...fora father, a very plain zoologist Charles Darwin'. Ethology's heritage should be kept firmly in mind. Ethology is a nearly limitless discipline which operates basically along the three dimensions illustrated in Figure l.l. These are continuous dimensions along which studies can be focused.
arc (cellular)
I.2 WHY STUDY ANIMAL BEHAVIOR?
l:ig. I
Reasons for studying animal behavior might emphasize either obtaining or applying knowledge. Drickamer and Vessey (1992) listed some of the reasons we study
animal behavior:
t Curiosity about the living world. 2 Learn about relationships between animals and their environments' I Establish general principles common to all behavior' 4 Better understand our own behavior. s Desire to preserve and maintain the environment. 6 Conserve and protect endangered species. 7 Control economically costly animal pests. When asked why she became an ethologist, Jane Goodall replicd'curiosity' (Anonymous, 1988). Curiosity probably rlrivcs most cthologists, bttt itt ortlcr to gct a brgader perspcctivc on thc qucstion I rccot'tttttctttl pcrtrsirtg l)cwshtrry's ( lt)ti51 conrpilatiott ol'tlrc ittttobiogrlphics ol' |() wcll ktlowtt clllologisls.
I
Three dimensions
of ethology (drawing by Brenda Knapp).
I3 WHAT TO STUDY? I trr some researchers this question is seemingly
rnost
ol their
unimportant slnce they have fixed of species, however, is only a
attention on a chosen species. Choice
tiul answer to the question: 'What to study?'Others want to emulate the media's l)()rtriryals of Dian Fossey, Jane Coodall, Konrad Lorenz, and Niko Tinbergen. I lrcy do not really care what they study as long as it is adventurous. However, what pru
rr prcscrrted in an hrlur-long television program about an ethologist's research are rlre lrighlights ol'thousands of hours of data collection. Powers (1994), while dis(
u\silrg ol' thc positivc impact ol' television on the environmental movement,
,,1Ir'rctl thc lirllowing. sotnewhat overstated assessment: ..
.
TV progllrtrs tcnrl to distort nature's rhythms and interactions, to
ol
its scopc urrtl tlivcrsity. Arlimals in the wild are mostly rncr t strrrrrirrg or groorrring tlrcrnsclvcs. Tclcvision abhors inertia. Thus, cvcn tlrc grrorl shows tllirsticirlly ctlit lrtttl crttrtlcttsc thc movctttcnt l)itllcnts ol irrrrrrrirls to tlrc poittl whctc, its rttrtlrol llill McKibhcn ptrts it, srry rrothirrg
WHAT TO STUDY?
INTRODUCTION
'nature films are like the highlight clips they show on the evening sportscasts, all rim-bending slam dunks and home runs and kneecrumpling knockout punches.'As for diversity, there are about 1.4
million known species on earth. Only
a minuscule
CANADA GEESE LEVELS Species
fraction of these pass
television's audience-appeal standards.
The serious student of ethology must be prepared to observe inactive animals for many uncomfortable hours under adverse conditions, when even the most avid
Population
ethologist would admit to dreariness and physical misery. Ethology has often been glamorized, but it is not always glamorous (See Lott's, 1975,'Protestations of a field
FamiryGrouprss-
person'). Even determining exactly what it is you want to study can sometimes be tedious, but this initial step is exceedingly important in the overall process. You can
Dyad
first define your research interests within the levels of behavior, areas of study and categories of questions, discussed below.
t.3.t
",,,,,,]f,f,*
tndividual
Levels of behavior
Behavior occurs at various organizational levels along the individual-species and macro-micro dimensions (Figure l.l), a perspective which the ethologist must obtain and maintain (Menzel 1969). Figure 1.2 gives an example of how these levels can be viewed for Canada geese (Branta canadensis). Another example of levels of
to'zoom in'and'zoom out'(Menzel
they are studying (Figure 6.2). We only
if
BehavioralType
€s)1 BehavioralAct "'-JJr'''Y'
Body
of behavior can approach a real understanding of behavior 1969) to and from the aspect
we maintain an accurate overview. Crews (1977) illustrated this
point in his
Parts
Muscles
study of the reproductive behavior of the American chameleon (Anolis carolinensis)
will concentrate on methods applicable to the study of whole organisms at the individual or group level under fleld conditions.
t.3.2 Areas of study Many ethologists (e.g. Shettleworth 1983) subscribe to Tinbergen's ( 1963) categorization of four areas of study in ethology:../unt'titn, cuusutiotl, ontog(n.1,, and cyrrlrtion.
'
ftutt'!itn
This ctn inclutlc thc sludy ol'proxirnrtc untl/or ultinrttc lirnc-
lion. rlr whut llintlc ( 1975) hnr cullctl lhc'wcnk rncunirrg'rrrrrl 'llrorrg
ffiff,l"," watktng to food Patch
leedlng
Bl1lg"n'*
L
bs
d
gastrocnemius
It
(Figure 1.3). Ethologists must decide at what level they will be conducting research and how their study will integrate with what is already known at the other levels. This book
_f #/
asontstc
encounter
,*)L,,
behavior, using the tvkey (Meleagris gallopavo), is provided in Lehner (1987). The concept of levels of organization is important to ethologists, and they should be able
W @ .J*N*n,..,,
Neurons Irg l2
#/-+
tlblal nerve
Levels of behavior in Canada geese. At the upper levels there can be interspecific and intraspecific interactions with other groups and individuals (drawing by Lori Miyasato).
sense'ol lunction, respectively. The proximate function refers to the immcdiate consequence of the behavior on that animal, other animals, or ihc cnvironmcnt. Through many observations correlations can be establishctl which lcatl trl conclusions ol cause and effect (i.e. proximate functiorr). Thc ultirnltc lirnction rcl'crs to adaptive significance of the bchuvior in lcrnrs ol'inrproving thc individual's litness. and how natural nclceliotr (!l',cnllc$ to rntintuin Ihc hchuvior.
WHAT TO STUDY?
INTRODUCTION
'
Levels of
Causation- What are the mechanisms that underly the behavior? What are the contexts in which it occurs, and what are the external (exogenous)
and internal (endogenous) stimuli that elicit the behavior? (See Figure
Anolysis
2.2)
'
Ontogeny - How does the behavior develop in the individual? What maturational and learning processes are important in the development of the
behavior?
' specles
Evolution
- How did the behavior
develop in the species? This includes a
phylogenetic comparative approach in which the behavior is studied in closely related species to reveal differences which may reflect evolutionary change (Lorenz, l95l ; Kessel, 1955). This often leads to the development of an 'ethocline'which presents the differences in behavior among several species along an evolutionary Pagel
( I 99 I )
continuum (Evans, 1957). Harvey and
provide an excellent overview of the new rigor which has
of behavior in an evolutionary context by the development and refinement of molecular techniques. They describe several comparative methods to test different models of the evolution of behavioral traits. been brought to comparative studies
'finbergen's four areas of study can be grouped into two sets which address ques-
of 'proximal'or'ultimate'causation (Wilson, 1975), or as Alcock (1989) o['how'or'why', respectively. The Table l.l below illustrates It'vcls ol study at which research on the two sets of questions is often directed, as Irorrs
rlirlcd, questions
orgonismol
s'el I as how the
two sets can be divided into the study of causes and origins.
ln addition, Marler and Hamilton (1966) suggested the following five broad ,rrcus ol investigation of animal behavior that overlap those of Tinbergen: motivalr()rr. ccology social communication, phylogeny and ontogeny.
I
peripherol slruclures
physiologicol
rg I
.l
Levels of research on Anolis carolinensis reproductive behaviour. At the species level, pure populations of A. brevirostni (polulation B) exhibit clinical variation
in dewlap color, whereas populations to the north and south, which are sympatic wilh A. litticltus. have unilorm dewlap colors. At the organismal level, aggresive posturing bctwcen male A. rurolinensr.r (on the top branch) involves erected nuchul und dorsal crcst, lateral compression of body black spot behind eye, cngorgctl throlt, lnd latcral orientation, whereas during the courtship display (bottom hrnnch), thc nralc has a rclaxed body posture and extended dewlap, and lhc crcst unrl cyc spot urc abscnt. Thc physiological lcvcl shows some of the prineipul intrinrie untl cxtrinsic lirctors rncdiutirrg rcproductivc cvcnts in vcrlchrulcr unrl thcir l'ccdbnck rclntionnhipn. (Atluptctl l'ronr ('rcws. 1977).
WHAT TO STUDY?
INTRODUCTION
l.
Table l.
The relationships
of
Tinbergen's
behavior occurs geographically or relative to other animals or environ-
four areas of study to questions of
mental parameters. Keep in mind that spatial characteristics are three
proximal' and' ultimate' causation
'
dimensional.
' Type of question: Research directed at:
Origins of the behavior: Causes of the behavior:
How -This includes the motor patterns used to accomplish a goal-oriented behavior (e.g. flying from one tree to another) as well as the under-
(Wilson, 1975)
(Wilson, 1975)
'How questions' (Alcock, 1989) Level of individual
'Why questions'
Studies of the relevant stimuli associated with behavior are included in
(Alcock, 1989) Level of individual or population
this category. The evolutionary and phylogenetic determinants of behav-
Ontogeny
Phylogeny (evolution)
Control (causation)
Function
lying physiological mechanisms (an aspect not covered in this book).
ior are also studied to answer ftow questions. For example: How did flight evolve in birds?
'
Why -Two basic concepts underlie the study of the why o[ behavior. These are motivation and ecological adaptation. These separate, but
related, concepts are generally treated in different disciplines: motivation in psychology and ecological adaptation in behavioral ecology. However,
t33
the latter is sometimes incorporated into ethological studies, and the
Categories of questions
former has figured prominently in conceptual behavior models (e.g. dis-
Behavior is what an animal does. However, ethologists do not restrict themselves to examining only what an animal does. Nielsen (1958) stated that ethology also
placement behavior). The focus of research designed to answer a w&y question depends on
includes the study of when, how and why, and I would add the study of where and
the emphasis the researcher puts on different parts
who.
of the question.
McFarland (1985) illlustrated this with the question: Why do birds sit on
' '
eoos?
-
Adescription of the behavior of the animal. Who - Behavior may differ within and between species, sexes, age groups, dominance ranks and individuals. It is often important to know who perWhat
Why do birds sit on eggs? Why do birds sil on eggs? Why do birds sit on eggs?
forms a behavior, who is present, to whom behavior is directed, etc. The refinement of DNA fingerprinting techniques has allowed researchers to determine the genetic relatedness of individuals, such as in the paternity of nestling house martins (R1ley et al., 1995), red-winged blackbirds (Westneat 1995) and dunnocks (Prunella modularrs) (Burke et al.,1989), and of black vultures roosting in aggregations (Parker et al.,1995). It is extremely important to know the genetic relatedness (kinship) of individuals involved in altruistic behavior (e.g. givers and receivers of care and'
alarm calls). An excellent overview of DNA fingerprinting molecular techniques and their application in determining kinship is provided by Avise (1994).
'
When-The temporal component of the behavior. This can include the occurrence of the behavior with respect to the animal's lifletimc, thc season, time o[ day, or positit.rn in a sc'qucncc. Thc duration ol'a hchavior and its contribution to an anirnal's tinrc buclgct arc also considcrcrl utttlcr
,
thc stucly ol' u'/rca.
Wht,t't' l'his
is thc
sputiuluspccl ol'u hcltuviot'. Slutlics ittelutlc wltctc
tt
lVhy do birds sit on eggs? The different emphases all relate to the adaptiveness of the behavior, but the focus
o[ the research
is
different (see McFarland, 1985:l-3 for further
discussion).
l'lrc
rry'ral
question is the starting point for all research; that is, we must determine
nlrirl thc animal does belore we can address any of the other questions. The who rlreslion is often addressed next, i[ we can determine sex, age, etc. The spatial and lr'rrf)orirl irspects (tvhcrc aru) uy'rcr questions) are usually relatively easy to measure ,ur(l rrrc lddrcsscd next. Thc ftr.,r, is more difficult to answer, and why questions are I lrt' rrrost dillicult. l ltc discussion above should give you a general idea
of the various
aspects
of
,rnrrrurl hchnviol that you might study. The lollowing sections provide an initial
',utlntL.ol'thc stcps iul cth()l()glist takcs in studying animal behavior.
ETHOLOGICAL APPROACH
INTRODUCTION
Perceive that a
l)rocesses,.,,hile studying animals in their natural habitat. I would especially recomnrcnd readr,lg Tinbergen ( 1958), Schaller ( 1973) and the compilation of autobiogra-
qu6tion exist3 I Y
plries by Dewsbury (1985). Dethier (1962) also gives an illustrative and entertaining tlcscription of the development of his laboratory research on blowflies. Ethologists .lrould be more than collectors and analyzers of data; they should seek to 'underrt rr nd' their animal subjects at a level higher than quantitative analysis can provide.
Formulate a hypothesis, ot tentative answer
lo the question
I
c
3
.9
I
Determine best means of test ng hypothes
When we watch animals at different levels on the evolutionary scale, as when Seitz watches fishes, when Dr. Tinbergen watches gulls, when Dr. Lorenz watches ducks, when Dr. Schneirla watches ants, or when I watch doves, the observer can get a feeling ol what is going to happen next, which is compounded in different degrees ol the intellectual experience of relationships that are involved on the one hand, and, on
s
o
tr B o
c o
.;o
o o
!o
o
4 Coll
o o o ! F
(al
the other hand, of building yourself into the
o
IE
(b)
Oata fail to supPort
Dara conf irm hypoth.sis
hypothesis
I)arling (1937) believed that in order to gain insight into behavior, the observer become'intimate'with the animals under observation. Lorenz (1960b) sug1,t'stod that an even higher level ol empathy with the animals under study is neces',,r r y lbr a true understanding of their behavior.
It takes a very long period of observing to become really familiar with an animal and to attain a deeper understanding of its behaviour; and without the love for the animal itself, no observer, however patient, could ever look at it long enough to make valuable observations on its behaviour. I Loren;, 1 960a: xii ]
I
I
Use conf irmed hypothesis as basis lor formulating theory lo coordinate these data into a cohesive
stalement of PrinciPle Fig. 1.4 Flow diagram
of scientific inquiry (from Jessop 1970). Serendipity (fortuitous
discovery) can operate at all stages
ol
the process.
Ethology is a science (McFarland, 1976), and as such, serious studies should adhere to established guidelines. The 'orthodox biological methods'mentioned in Lorenz's definition of ethology include the scientific method. The scientific method (Figure 1.4) is a logical, stepwise approach to research in all sciences including ethology
will foster additional insight into its I't'llrvior beyond mechanistic (machine or machine-like) data collection and analy',n llltirnately, valid research is conducted only by ethologists who have found a I'rol)cr balance between empathy and objectivity.
r
(Tinbergen, 1963; however,seeBaueq lgg2andHailman, 1975,1977 forotherviewpoints). This book is organized around the scientific method adapted for use in
5 Fl'IHOLOGICAL APPROACH Irthology . . . is characterized by an observable phenomenon (behavior, and by a type of approach, a method of study (the hiologicirl nrcthod). . . . The biological method is characterized by the g,cnclirl scicrrt ilic rrctlrocl. ITinbergen, 1963 41 I J
ethology (see section 1.5).
()r' nl()vcurcnt).
Now that I have focused in on the mechanistic approach that will serve as the foundation for this book, let me state that this does not exclude the pleasure. cxcitcment and artistry inherent in ethological studies. I also want to chanrpion thc researchers who get to 'know'and cnrpathizc with thcir anintitls. atrtl wlto rccogltizc and appreciate tlrc hurrnony thut hls cvrrlvc(l bctwccn tlrc itttitttitls bcirtg stutlictl irrttl t lrc cnvironnrctr I . Scvcttl rcscit rcltct's ltrtvc tlcsclihctl t lrcir st rrtc ol' rttitttl it tttl t ltorrgltt
l hc ethologists quoted above were not encouraging rampant anthropomor(see Chapter 4; at the expense of objectivity (Carthy, 1966). Rather, they were
'lrrsrn ,,r,rting that genuine interest in the animalper se I
I.4 SCIENTI}.'IC METHOD
ILehrman, ]9551
rnrrsl
I
5
situation.
rl
l hc ctlrokrgicirl irpploirch ( lrigurc 1.5) is thc rcsult of titting the scientific method r,111.;l,r*r. ll c()nsisls ol'ir cirlclirl stcpwisc procctlurc by which clata are gathered
,lr(l lulltly/c(l
rrrirrg tlcscriptivc strrtislics (tlcscriptivc rcscitrch) or tcst statistics in
INTRODUCTION
DESCRIPTIVE VERSUS EXPER IM ENTAL RESEARCH
Levels
PERCEIVE A OUESTION
A
Chaptors (1,2,51
that we had an ill-defined hypothesis about the animal's behavioral repertoire. Most people's approach to life is through constant hypothesis testing. For example, the very first time we go to work
(3)
ecssful trips our hypothesis that our office is located in Room 300 in the building at
I
rrre surprised by an unexpected behavior reveals
wc travel a speciflc route which we hypothesize
CHOICE OF SUBJECTS
will get
us there.
After
a series
of suc-
llrc corner of Oak and Center Streets gains considerable support. However, should ,,ur boss move our office to another building while we are on vacation we will have o reject our hypothesis and either revise it or formulate a new one.
I
(4,s)
More particularly, ethologists often perceive questions which arise from the rt'ycction of our long-standing theories about the way animals should behave. to study events that seem to contradict what we of our knowledge of non-living things. It is this discrepancy between what an animal 'ought to do'and . . . biologists are drawn
I
o
G
(2,6\
DESIGN RESEARCH
U
tn
have been taught to expect on the basis
UJ
lr uJ = z rU
F
lrJ
2
F ()
EXPERIMENTAL MANIPULATION
-
UJ
F
2
what it is actually seen to do that makes us wonder. Like a stone released in mid-air, a bird ought to fall; yet it flies away.
r\
at
[Tinbergen,1972:20J
NATURALISTIC OBSERVATION
I'hat is, we constantly seek the order which we believe exists in the universe
@
o
(t lrrros theorists notwithstanding), and when events occur which seem to upset
= z UJ
ul
4
(8.9,10)
DATA COLLECTION
lt
our
rr ;
econceived ideas of the ways things should be, we either ignore the situation or as
,,t
rcrrtists seek explanations through study.
llJ
o UJ
I 6 DESCRIPTIVE VERSUS EXPERIMENTAL RESEARCH
N A-.n
(11,12-17) I
lre
sc
,rt tlrrs
=o
H
INTERPRETATION
two types of research will be discussed in more detail in Chapter 6; however, point I want to stress the importance of both approaches (Figure 1.4). In the
sl ctl i t ion of this book they were designated as naturalistic observation and experrrnt'ntirl manipulation following a traditional distinction (e.g. Schneirla, 1950; \rt'vcr', 1968) which reflected a field versus laboratory dichotomy. Both descriptive I r r
(18)
Fig. 1.5 The ethological approach. The numbers in parentheses indicate the chapters in this book which discuss the respective steps in the ethological approach'
order to test hypotheses (exPerimental research). The ethological approach expands on the four basic operations described by Delgado and Delgado (1962): L the isola-
tion of objects or variables to be measured; 2. the establishment and definition of actual events and units; and 4. the
units of measurement; 3. the comparison of interpretatlon of the meaning ol the observed events.
Even during descriptive rescarch and thc citrlicst stagcs ol'cxPcrimcntal rcscarch
(i.e. rccrlnnaisslncc obscrvitiion). thc cthrllogicul lrpprottch gcttcrltlly inclutlcs illtlclincd hypothcsis tcsting. Whcn wc lirut hcgitt ohscrvirtB utt ltttittlltl. lhc lire t thul wc
cxpcrimental studies are conducted all along the continuum that connects the l,rl)olilt()ry and fielcl ( Figure l. I ; Chapter 7). 'ur(l
l)cscription and experimentation become connected out of curiosity and rrt'tessily. I)cscriptive research olten generates hypotheses which lead to experirrrr'rrlirl rcscarch (Bakcman and Gottman, 1986), and experimental research is pre-
r'rletl hy cornplctc dcscriptions and definitions of what an animal does lrlt'rt't'iplivc rcsclrch), Thc lirllowing excerpts from Tinbergen illustrate how his ,,lttrly ol'thc hcgging hchlvior ol'hcrring gull chicks evolved from descriptive to r
"
\
IcIittlcnllrl lcncitlch. Wlrcrr llrc clticks irt'c ir l'cw hours oltl thcy hcgin to crawl ahout uncler the l)nlcnl, crtttsing it lo shil'l rrttrl utl,irrsl cvcry so ollcn. Sonrctirncs thc
t4
DESCRIPTIVE VERSUS EXPERIMENTAL RESEARCH
INTRODUCTION
thought to be naturalistic studies in the field, and the psychologists'purview was hclieved to be experimentation in the laboratory. The physiological approach to animal behavior has become more prominent
parent stands up and looks down into the nest, and then we may see time in itre first begging behaviour of the young. They do not lose for the first see they head parent, whose contemplating or studying the quick' repeated' with away, right time, but begin to peck at its bill-tip remarkable Their bills' tiny and relatively well-aimed darts of their
ovcr the last few decades, especially with the merging
(Kolata, 1985; Konishi, 1989; Nottebohm, 1989), barn owls capturing mice
alone obviously released by very special stimuli which the parent bird billparent's the distinguish to chick .un p.orid., and which enable the world' its in tip from anything else it may encounter seemed . . . In the literature we had found some observations which few 'sign very only on dependent to show that here again was a reaction had chicks Gull Herring his (1928) that stimuli.' . . . Heinroth . ' . wrote kept were they when especially the habit of pecking at all red objects, to low, so that they could peck downwards' ' ' ' The special sensitivity elicit could Goethe that fact the red was further demonstrated by by red objects of various kinds, and of an appearance that
(Ktrnishi etal., 1988),batscapturingmice(Suga, 1990),andtoadscapturingflies {l'.wert, 1987). Neuroethology is an example of one fleld which provides ample r
'grportunity
(l igure
It
,\ r
not only that behavioural analysis can lead to analysis of neural fruitful in suggesting j0 I I Bateson, I 987 : -302
]
ll irreas, sub-disciplines and levels of study within ethology are important to a full
n rt
lcrstanding of animal behavior.
l,rrrtlcd solely by remotely recorded data
I'l,rt ctl in operant-conditioning chambers.
tendencyhadtobeexplained.Asregardsopportunityforexperimental work, the reactions to cherries and bathing shoes showed that it should Further' the be easy to design dummies capable of eliciting responses'
I can never help a shrewd suspicion that the worshipper of quantification and despiser of perception may occasionally be misled into thinking that two goats plus four oxen are equal to six horses. Counting pecks of pigeons in Skinner Boxes without observing what the birds inside really do, might occasionally add up to just this.
veryfactthatreactionstocrudedummieswerenotrare'showedthatthe
would chick's sensory world must be very different from ours, for we food' regurgitate never expect a bathing shoe to prevented Therefore, when in the summer of 1946 no war conditions students out zoology my I took field, in the us any longer from working Dutch the on colonies Gull Herring the of for a iortnight,s work in one Frisian Isles. we carried with us an odd collection of Herring Gull us dummies and thus started a study which was to occuPy and fascinate 1
is
',,',, l,orenz expressed his suspicion that experimental psychologists were being without observing the behavior of animals
asthebillwithoutreddidalsoelicitsomeresponse,theremustbemore in a parent bird's bill that stimulated the chick' Also, the downward
78'
study within ethology
rrrt'rrtation, using skilled observation in both, better to define and test his hypothe-
Thatthechickswererespondingtotheredpatchwasobvious;however,
1
of
l he astute researcher oscillates between refined descriptive research and experi-
Itseemedtousworthwhiletogointothisproblemalittledeeper.
1960h'
cross-fertilization between levels
directions for behavioural studies.
and was rather different from a Herring Gull,s bill: such as cherries, the red soles of bathing shoes!
ITinbergen,
for
1.2).
events, but also that neurobiology should become
responses
seasons.
of neurobiology and ethol-
r)gy into neuroethology (Camhi, 1984; Guthrie, 1987; Hoyle, 1984). Ncuroethology techniques are not discussed in this book, but an introductory ,,vcrview can be found in Camhi (1984) and Ewert (1980). Examples of neurocthology can be found in the synopses of the neural bases of bird song
'know-how,'notdependentonexperienceofanykindneverfailsto It impress one as an instance of the adaptedness of an inborn response' it watches one longer the but sight, seems so trivial and common at first themoreremarkableitappearstobe....Thereactionisinnate,anditis
during four consecutive
l5
84
186
ILorenz,1960a'72]
I ,,rr'n,,'s comment somewhat overstated the case, but is probably still valid in many two' (description and experimentation) is
r r',t,rrccs today. 1'he 'balance between the
]
tlrr' le y to good ethological research. However, my purpose here is not to campaign
tlircct obscrvirtion in the experimental psychologists'methods (Hutt and Ilrrtt, l()70.havccll'cctivclydonethis),butrathertoinsistthatethologistsrecognize tlrr' rnlx)rtiulcc ol'hotlr tlcscriptivc and experimental research, both of which rely l,,r
"r
r
11r,,''.
(
)lrscrvitl i()tt
ltttl t;ulntilicittion.
lrr l()6.1, whilc tliscrrssing thc history ol'cthology. Tinbcrgcn expressed concern rlr,rt
llrt lrulrrrrccwrrsshil'tingloolirrlowru'tlscxgrclintcntatiort.
l'l6
INTRODUCTION
not going to com€ to a premature ending. Already there are signs that we are moving into an analytical phase in which the ratio between experimental analysis and description is rapidly increasing. [Tinbergen, 1963:412] We must hope that the descriptive phase is
As we approach the twenty-first c€ntury a further caution is relevant. Theories and models should not greatly outdistance descriptive and experimental research. One theory and one predictive model each beg for a multitude of experimental studies which test their validity.
Ethology and its allied disciplines are now racing forward
in high gear.
Experimentation and quantification are no longer the tools limited to psychologists and observation the only tool of ethologists The disciplines have long since overlapped, merged, borrowed from other disciplines, and fractured into subdisciplines There is no longer a territorial battle claiming that methodologies belong solely to
one discipline or the other (Hutt and Hutt, 1970; Willems and Raush, 1969). Ethologists and psychologists no longer work in their private vacuum$ but rather they are united with strong subdisciplines in the quest for knowledge about behavior. See Burghardt (1973) for an extensive account of the development of ethological and psychological concepts and methods into a more holistic approach.
t Getting started
2
A conceptual model of animal behavior
I lrc
model for a behavioral act I develop and discuss below is an extension of the I presented in the first edition of this book. I have found the model to be
rrrodel
sirnple enough to be a useful research tool, yet complex enough to accomodate most
Irchaviors and environmental contexts.
It
has assisted students
of animal behavior
studying, understanding and explaining what an animal is doing now, as well as in prcdicting what it will do in the future. The model will not meet with universal approval amongst those who forage rrr
t
lr
rough this book. However, the model is not presented as a 'general law of behav-
r,rr'' nor is
it designed to quell or exacerbate controversy. The purpose of the model is
Itt;
t
Provide a framework on which to design and conduct ethological
z
Assist researchers in putting areas of study and types of question into an
research, and
overall perspective while at the same time focusing on their specific research objective. I lrose readers who do
not agree with the assumptions I make in building and apply-
to make their own modifications. To that end, I portion of the model in sulficient detail to explain how the model
rrrg the model are encouraged rlrscuss each
rvorks and to allow each researcher to rrrrrst
modify and expand those portions which are
important to their study (see section 2.4.4). Also, one should examine other with varying degrees of similarity that have been offered by several etholo-
rrrotlcls
I'rsts (e.g. Hinde, 1982; Lorenz,
l98l).
] I CONTINUOUS STREAM OF BEHAVIOR Anintals arc always behaving. They perform a continuous stream of behavior from llrc rrrorncnt whcn movcment can first be detected in the embryo until their death. It is inrpossihlc (at lcast irnpractical and inefficient) to study all of an animal's
lrelrirvirlr throughoul its lilbtinrc. Ilowcvcr, sincc an animal's behavior is not rrrttrlottt. thc rclutivc li'cqucncy und tlurulion ol' tlill'crcnt hchnviors can hc approxi-
PREDISPOSITION TO BEHAVE
A CONCEPTUAL MODEL OF BEHAVIOR
mated through sampling. Normally, our sample is a continuous, but restricted, segment (or segments) of the stream of behavior, or a series of samples at points in time (see section 8.3). The behavior being perlormed by an animal at any point in
tt)I11eflt, as well as their interaction (see below). There is no direct correspondence l't'lween a gene and an individual behavior pattern (Bateson, 1987), and the interacrr()rr of the genotype and environment is likely to be so complex that it is very diffi-
time can be considered a behavioral act (see Chapter 6). The modeldeveloped below
,
rr
rtlt to identify the genetic and environmental components of an individual l','ltltvior (Bateson, 1983), or the extent of their contribution. For example, Lyons 4r
for a point in time (behavioral act), but a series of models can be strung together to analyze a segment of the continuous stream of behavior. is
,/ ( 1988: 1323), in their study of the development of temperament in dairy goats, ,,;tclled only the general conclusion that An individual's genotype and its early 1',rsttlatitl environment both contributed to processes underlying the development
2.2 PREDISPOSITION TO BEHAVE: DIFFERIn-G CO NT R I BUT IONS
I stable individualdifferences in temperament [timidity] . . .,. Nevertheless, it is still heuristic to make some generalizations aboutthe relative ' "rttribution of the genotype and the environment as functions of both phylogeny
behave (i.e. probability of rcsponding to specific stimuli with specific behaviors) is the result of a combination ol' factors expressed
An animal's predisposition to
,rrrtl ttntogeny.
lior those species in which the environment (experience) plays a relatively large " 'lt' in the lif-e history of a species (e.g. birds and mammals), the relative contribu-
below.
Behavioral predisposition:(Gu* EB+
I
)+
(AB+PB)
rr()rr
Where: Gu:contribution of the genotype. primarily.
:
contribution of the environment (including experience), primarily. 1u:contribution from the interuction of the genotype and Environment.
Eo
lu:behavioral capacity provided by the animal's
of genotype and environment is believed to change ontogenetically (Figure
t)
uttcttomy.
Po:behavioral propensity and capacity provided by the animal's physiologic'ulmechanisms.
' r I
, r"
l:arly in life, in addition to the effect of it's prenatal experience, an animal's pre,l)()sition to respond to 'releasers' and 'unconditioned stimuli' (see section 2.3.5a) I'ritnarily genetically determined. Also, as the animal matures its predisposition l('itl'n more readily in response to certain stimuli and reinforcers may be geneti-
' r ll', controlled; this has been called 'preparedness' (Seligman, 1970). ,
Using a computer analogy, (GB+E'B+18) can be thought of as creating, modiflying, and containing the so.fix'are that consists of 'closed' and 'open' programs
).2.ta Genotype
(Mayr, 1974). Closed programs are primarily a result of the genotrpe and do not
\rr :tliom at the roots of ethology is that there is a genetic basis lor variation in
allow appreciable modification; open programs allow for modification as a result of environmenlal input that provides experienre. The (AB+P) is the hurclv'are which stores the programs (e.g. brain) and provides the machinery for their action (e.g.
r" lr,t'n'ior between individuals (e.g. Partridge. 1983). The entire field of behavioral "' rr\'{ics is based on this axiom. Behaviors that result from genetically fixed, closed l,r,'r'r'lrns are called innate (e.g.egg laying in a snail, Scheller and Axel, lgga);
sensory, nervous and muscular systems).
lr"rteVCl'. the environment provides r
z.z.t Genotype, environment (experience) and interaction The (G'+EB+18) portion of the predisposition to hehave modelis thc sarnc us thc
formula used by population geneticists (e.g. Futuyama, 1986) to exprcss thc total phenotypic variation in a population (V,) as the sum of thc vuriatiort tlrrc lo genotype ( 2,,), environment ( Zn), and the interaction ( [',) ol'gcnotype irntlcrrvirrrrrrrcnt:
Vr:V,+Vt+l/l Altttost ltllctltolopists(e.1'. l|;tlcsott. l()tl);uttl Psytltolol,isls(r'.,'. llttlrl', l()S.)llutvt' t'ottt'lttrlt'rl lllrl ;ur ;urnr;rl's lrr'lrrt r()r r', llrt' tr':ttll ol lrollr rl', y'r.'11,r1\ |t' ;rrrtl llrt't'rrvr
1,,
lor the proper development and expression of
,rs' i11n31e behaViorS.
\rtyone familiar with breeds of dogs selectively bred for various tasks, such as r' tr teviltg game and hercling livestock, recognizes that an animal's genotype contrrlrttlcs to its bcltavior. It is also evident that to realize its full potential, that pre'lr,lrositiotr to rctt'icvc or lterd must be shaped in the proper environment. The " ttt)/t'1,('itlso Provitlcs thc bltrcprint for the development of the onutomy and phys,i,,.ti1,.
( it'ttt's ('()tlttil)tltc to tlte obsct'vctl tlilll'r'cnccs bctween inclividuals.
lil,lll:l'
l,:;
"j)':,1
;';,i'1,:lll:::.i
lll'll, lllll,l:lili,::l:l',i':j'::llilll;',,,
Iltr' t trttlt tlrrtltr)t) III)nt Ilt(. (.nVil()ilnt('nl (.;tn
f
l),tt lrtrt. l(),\(t \ i I
PREDISPOSITION TO BEHAVE
A CONCEPTUAL MODEL OF BEHAVIOR
23
type and environment interact. Gould and Marler (1987) referred to'innately guided learning'. This interaction is reflected in: 1. Hailman's (1969) title, 'How an
instinct is learned', for the report on his study of food begging in laughing gull clricks; and 2. Ewert's (1987:331) conclusion that prey-catching in toads is 'mediltcd by innate releasing mechanisms (IRMs) with recognition properties partly
o
rrrodifiable by experience'.
"o Lr
Also, the genotype, along with the animal's anatomy and physiology place'bio-
tr
on learning' (e.g. Hinde and
o (J
Itrgical constraints
q)
Slrettleworth.1972). That is, '. . . learning, rather than occurring indiscriminately, is limitations and predispositions'(Roper, 'rrhject to species-specific and task-specific l()ll3:185). The classic example is Garcia and Koelling's (1966) demonstration that
.lJ cl 6J
&
Stevenson-Hinde,
19"73;
rrrts readily associate taste (internal cue) with illness and exteroceptive cues rlrsht/sound) with painful electric shock, but they don't readily associate taste with
Oo/o
fre-natal
,lrock or light/sound with illness.
,"6@8
In summary, behavior that is initially programmed in the genome can be devel,,1rcd and expressed only in the proper environment and can be modified by experi, .r
Individual's lifetime
ncc (learning) only within constaints provided by the genotype, environment, rrrtomy and physiology.
Fig.2.1 The relative contribution of the genotype and environment during an animal's
2.2.2 Anatomy and physiology
lifetime. Variation between species and individuals indicated by dashed lines (drawing bY Brenda KnaPP)'
\ rrrloffi! and Physiology both enable and constrain behaviors. Wings (along with '
2.2.1h Environment and experience
As described above, the environment (biotic and abiotic) provides lor the proper development and expression of innate behaviors and predispositions. This is also true lor behaviors that are primarily learned. That is, the environment also provides experience and the proper context for expression' For example, squirrels have to learn through experience how to efficiently crack open and eat a hazelnut (EiblEibesfeldt, 1963); they can develop and later express this behavior only in the presence of hazelnuts, or similar nuts. Experience results from the various associtttions
of stimuli, behavior and consequences which are the bases for learning and modif ying future behavior (Davey, 1989; also see section 2.3.5). The envirt])nmcnt itlstr aflects the anatomy and physiology through factors such as injury, cliscitsc. clittiittc. and nutrition.
2.2.1t' Intcrat'tion
o.l'
ganotypt' ttttil t'ttlifttttt"t'ttt ( t'tparienu')
'lt'lttttt'rl Althorrglt wc trttriltrrlr. 'irrrr;rlt" llclr;rVit)ts l)ilnlilltlV to lltt' l't'lttrlylx' iltlrl lltt'1't'ttrr lrt'lutviots l)illniiltlV lo llrt.r.rrYrr()liln('ltl (t'r1rt'ttt'tttt'). rt't';tl:.,r Ltl()\\'llt;tl
,
t
lrcr adaptations) allow a bat to fly, just as the lack
of wings contribute to a mouse's
rrr,rlrility to fly. From the earlier analogy, an animal's anatomy and physiology is the i,,rr,ltrut'c necessary to perform behavior and store feedback from the environment
r r('nr()r'y), allowing for modification of future behavior. Anatomy (including morI
,lr,,logyl and physiology develop according to
l,r('n l is rrll'ected by the
a
genetic blueprint, but that develop-
environment. For example, Zeki (1993218-219) summarized
rl,,' results ol'Hubel and Wiesel's extensive research in the 1960s and 1970s on the tl,'t l ol'scnsory deprivation on the development of the visual system of cats and rrr, rn kcyS hy stuting: 'at the cellular level, there is a critical period, during which ade-
,
,1rr,rlr' r, ,
vrsrnl stinrulation is mandatory if the animal is to be able to
see
at all, even
if
,rll ;rpl'rciu'iurces thc visual pathways and cortex appeared to be intact'.
Itt'st';rrclrcrs lrrrvc rtlcntilicd thc brain centers and neural pathways necessary for rlr,'tlt'r't'loprrrcnl. nrcn)()r'y lrttrl pcrlirt'rnunce of bird song (e.g. Nottebohm, 1989). I lr.' lrr;rin ('('nt('r's lirr birtl song tlcvclopmcnt arise in part from a genetic blueprint ,rr(l ur l);ul lrorn t'rrtltil'('n()us lrrrrl cxtlgctttlrrs stit-t-tttlatit.ln. The hormones (endoger, ,u'. 'rlrnrrrlr) n('(('\sirrV lirr slirttulrrtinp', tlcvclrll'llncttl itrc thcmselves triggered by , .('1,('n()lt\ \lrnrrrlr (('I l)lr()lo;lr'rrrltl rrtttl lt'ril1lt't':tlttt'e ). ltt s0tttc spccics thtlsc brain
,,trlr't., ,|t('lil(|rltltr'rl l,t t'r,]l'('n(]lt\ rltttrrtl;tlr()rl
111
lltt'lirt nt ()l sol)1ts ll'0ttt rllltct'
A MODEL FOR A BEHAVIORAL ACT
A CONCEPTUAL MODEL OF BEHAVIOR
males of the same species in order to develop the'innate template'into an'acquired
Exogenous
template'.
stimuli
Therefore, bird song occurs in response to stimulation from the appropriate neural pathways (physiology), and its form results lrom a genetic blueprint (genotype) and learned modifications (experience) stored in the brain centers (anatomy).
Positive feedback
t't
\
e--"--
'-?-->
For example, the genotype provides the 'innate template' in the young Swamp sparrow's brain that allows it to filter out all but swamp sparrow syllables from the
(G+E+D+(A+P)
irr
Behavior
Behavior
-+ ,,\, -a 't-a-
it hears in the environment (Marler and Peters,l9lT). The adult male swamp sparrow songs it hears provide the syllables to create the 'acquired template'which will be the basis for comparison when the young swamp sparrow begins singing its subsong, listening to itself, and improving it vocal output to create its crystallized songs
-s
-'
------
\\\r.
-)
Consequences
'-)
Feedforward
, (Contingencies)
,ttstl
Negative feedback
I ig.
2.2 The model for
a behavioral act.
primary song. 2.3.2a Endogenous stimuli
2.3
A MODEL FOR A BEHAVIORAL ACT
I rttlogenous stimuli arise from the animal's internal environment. An assumption of rlrc model is that all behavior is the result
of exogenous and/or endogenous stimula-
rron. Th&t is, we operationally define behavior as that which an animal does as the
2.3.t The animal The basis lor the animalin the model is the formula for it's predisposition to behave described above. That is, all the important components that predispose an animal to
perform specific behaviors under a given set of environmental conditions are provided by Gu, EB. IB, A,and P. Also incorporated into the animal portion of the model are endogenous stimuli (discussed below). Other parts of the model (Figure 2.2) are e-\ogenous stintuli, hehavior, ('onsequen('es, contingencies, feedbac'k and./bedforward, all of which are discussed below
rr'srrlt
of stimulation. We observe behavior being emitted, but we assume that all
1,,'lravior is elicited. Like Kuo, the Model assumes that'there is no such thing as ,lrorrtaneous behavior'(behavior thut is not stimulated) (quoted
in Marler
and
I;rntilton, 1966:605). However, there are behaviors that occur for which we cannot ,t('rcrmine the source of stimulation, and these are often called'spontaneous' I
1,,'lurviors (e.g. Hinde, 1966).
Although behavior does not occur spontaneously, it may be elicited by endogerr.rrs Stirnuli that arise spontaneously. . . . every cell in the nervous system is not just sitting there waiting to be told what to do. It's doing it the whole darn time. If there's input to the nervous system, fine. It will react to it. But the nervous system is
2.3.2 Stimuli
Stimuli are changes in the environment (internal or external) to which an animal normally responds. However, the environmental context in which the stimulus occurs will determine whether it is effective in eliciting a response. Therefore, an effective stimulus is a change in the environment which elicits a response at that point in the animal's stream of behavior when the environmental context (internal
L)r ('xiu.nple, Bekoff (1978a) found neural pattern generators in embryonic chicks rlr.rl slintulatc coordinateci limb movements and develop in the absence of any pat-
and external) is appropriate. Stimuli that are effective in one context may be filtered
I, rnr'(l sct'ri()ry inptrt.
(centrally or peripherally) in another context. For example, cat lirocl kibblcs will normally stimulate feeding only when the cat is hungry (interrral cttvirot.ttt'tcttt ) itrttl not involved in a higher priority activity (e.g. chasing a r.nousc extct'ttitl ctlvittrtl-
r)(,r
ment).
Stirrrrrli havc httll't trt'tit'tttirlrrrl ltttrl ttr,qtrtti:ttliorrrrl litttcliotts. only ittitittl,lrrrtl
olirll lrr'llrviot'rlit't't'lly,
'lltltl
is. tltev ttol
lrtrt lltt'Y ltlso lttr ililrtlt';tnr,l ttt,rittl,ttrr lt.'lt:n'
i0t lttttl ltrt'tlttltrttt';tll ;tllllllill l() l)ilt ltt ttl;tl lrt'll;tVtt,ts
prirnarily a device for generating action spontaneously IGraham Hoyle quoted in Allport, l986J
llrcrc rrrc llso bchirviors that occur in the absence of the exogenous stimuli that nrirlly clicit llrcnr (c.g. hirrls'nest building'without materials, cats'prey catching'
rtlrorrl grrt'v. tlogs 'btrryirrg'
'covering' food in a concrete floor). Lorenz ,.,1,1;rrrrt'tl llrt'ot't'rrrrerrr,'c ol'llrcsc'vircrrtrttr'behitviors its an excessive buildup of r( tr()n spt't'rlrt't'rrt'r;,y'1ullt'lt lirrcctl oPctt (ltc vltlvc itt lris Psychohytlraulic Model; l ,rrt'1t,,. l()\O) llorvt'r't'r.;tn()llrt'r Possilrilitv is lllrt v:rctrutu bclutvior is clicitctl irntl ,,r rt'nlr'rl 1,1 trrr,rl,nt;u \'('\ol'('n()lr\ slttttttlt't tt';tlt'rl lrv tlrt'ltttitttltl (ttlsrl sttl'llestetl by 'r
lrttl
A MODEL FOR A BEHAVIORAL ACT
A CONCEPTUAL MODEL OF BEHAVIOR.
Lorenz,l98l); lbr example, we refer to'hallucinogenic'play in cats, implying that
Releaser(s)
+
the'prey'is in the cat's'mind's eye'. Physiological needs are signaled through endogenous stimuli that elicit behaviors designed to meet those needs. For example, low blood sugar leads to the 'state
Key
Positive feedback
stimuli
v
of hunger'which stimulates the anirnal to seek, find and ingest food. There also appears to be a psychological need to perform a normal range of behaviors. Various terms have been applied to this psychological need, including 'ethological need' (Hughes and Duncan, 1988), 'drive' and 'instinct'. For example, Garcia et al. (1973:3) state that 'drive is the psychological concomitant of physiological need', and Dawkins (1986:64) states that 'instinct . . . refers to the inner drive
Animal lnnate releasing mechanism
--?-+
Behavior
+
Modal action pattern
F,...-
-) .r'
t-'-----
'... ,r,
Consequences (Contingencies)
-)
Feedforward
ootat'/-
Negative feedback
or motivational force that leads an animal or person to behave in a certain way'. The
model acknowledges that behaviors can be elicited by endogenous stimuli regardless
i\r
e--tt------isr\
I ig.
2.3 Where the ethologists' model of innate behavior fits into the model lor
of how you choose to perceive and label the basis for those stimuli.
a
behavioral act.
rrrtl physiology, and behavior. This hypothetical construct is diagrammed 2.3.2b Exogenous
stimuli
l,
as
rllttws'
Exogenous Stimuli come from the external environment (biotic and abiotic), take many forms (e.g. light, sound, odor) and arrive via the animal's various sensory receptors (e.g. eyes, ears, nose). The animal's anatomy and physiology are responsi-
Releaser(s)*key stimuli---innate releasing mechanism (IRM) J
modal action pattern (MAP)
ble lor receiving, processing and responding to exogenous stimuli, as well as gener-
ating behaviors which produce exogenous stimuli. Several terms have been applied to exogenous stimuli depending on the role they play in different behavioral para-
digms. Sign ,stirttuli, relea,sers, unconditioned ,stimuli, neutral stirnuli, conditioned stintuli, and disc'riminative stimuli are all discussed in the contexts of the ethological model of innate behavior and the learning paradigms below.
rilr'r)r irs .fi.rcd uction potterns. However. it is now recognized that experience also t,l;rvs un important role. For example, Gerard Baerends, who began his ethological
2.3.3 Behavior
Behavior results when an effective stimulus is received or generated by the animal. As shown in the model, when one behavior is elicited, an ongoing behavior may be
inhibited. This is required
A morphological structure and/or movement (releaser) emit one or more tey 'rrrttuli. The key stimuli operate on the innate releusing nrcchanism (at present, an ,rrritlcntified part of the animal's anatomy and physiology) which 'releases' a modal 'r, titttt pattern, a relatively stereotyped behavior pattern. Since these modal action l,.rllcrns are very similar between individuals of the same species, ethologists 1,,'licved that they are primarily under genetic control and originally referred to
if
the the two behaviors are mutually exclusive; that is,
they cannot occur at the same time (e.g. sitting and walking). A behavior may also
stop because it is no longer stimulated. For example. sleepingis inhibitcrl when an animal is ingesting food; ingestion will cease when the animal is satiated untl cirting is no longer stimulated by the sight and smell
of food.
r,.r'iu'ch six decades ago. recently concluded from his many years of studying 1,, ring gulls that'The infbrmation encoded in the lP.Mlinnate releasing mecha,,irrf iulrl the acquired inlormationfleurning]were found to work in combination.' r rt,rlrcs tninc, I9u5:37). llistoricirlly, cthologists and psychologists have been branded as having diametrr,;rlly o1-r1-rosctl vicwpoints on the relative contribution of the genotype and learnrrrj' lo bclurvior'. llowcvet. cthologists and psychologists are increasingly borrowing
lntl llrcorics. and overlap between ethology and learning tlrcotv ('rn nr()re relrtlily ltc lirrrrttl in thc litcr:rture. For example, adjunctive behavlrr )nr ('ir('l) olltcr''s rcsciu'ch
2.3.4 Ethological model of innatc bchavior
'l'lrcclltolog,tcltl tnotlel ol ittn;tlt'llt'lutviot llrt'lorv. ('.1'. l.ort'nz. l()liI)ist';rsrlv rrrtor l)()t;rl('(l tnl() lltt'tttorlr'l (l'llrut(' .' l)sttttt'rl rtrrolvt'r t'\,)1,('n()l\ slnnrrlr,;ur:rl()nlv
,,,r, r,l lt';ttttttrl, lltr'orrrl\'iu('r't't'y sitttilru'tlvltirtnicirlly irncl tirnctionally to what l,,rrt'lrt't'lt krrorvrr rrr llrt't'llrolol,it';rl litenrlrrrc lrs tlisPlirccntcnl irclivitios'(Davey, l't1.'t1 5t1,
A MODEL FOR A BEHAVIORAL ACT
A CONCEPTUAL MODEL OF BEHAVIOR
UCS/CS or SD
2.3.5 Associative learning paradigms
Positive feedback
\
Learning can be defined as the 'adaptive'modification of behavior as the result of experience (e.g. Lorenz, 1965). Natural selection has shaped various types of
k-"Animal
learning in animals (Staddon, 1983), but each type of learning incurs selective costs (in terms of fitness), as well as selective benefits (Johnston, 1981). Also, the
--?--> Behavior
+
(G+E+D+(A+P)
elicits
UCS
ta,
l'ig.
act.
The paradigm begins with a behavior being emitted and does not consider ,trrtruli that might elicit that behavior. However, the paradigm does include tliscrirurtrtrtit,c
stimuli. Figure 2.4 illustrates how both classical and instrumental condition-
lit into the model.
An unconditioned stimulus (UCS) elicits an unconditioned response (UCR). When a neutral stimulus (NS) is paired with the UCS, over time it becomes a conditioned stimulus (CS) capable of, by itself, eliciting a conditioned response (CR), a reasonable facsimile of the UCR. Note the similarity between classical conditioning and the ethologists'model of innate behavior (discussed above). The UCS is essentially the same as the etholois essentially the
ethologists'MAP(FAP).
) conditioning
The instrumental conditioning paradigm (below) states that a behuvirtr is emitted ancl
followed by consequences (So*, So ) that either maintain, increase or decrease the probability of that behavior occuring again (see Consequences scction bclow). is
Discriminative stimuli 15t), ttr SI) Ilcltlrvior' (r'rrriltt'tl)
i
v
e
st
imu
I
i
discriminative stimuli (S"*) predict that a specific behavior will be followed l,r l.rositive consequences, and Negative discriminative stimuli (So ) predict that a l'( )sitive
elicits
23.5b Instrumental ( operant
Feedforward
2.4 Where classical and instrumental conditioning fit into the model for a behavioral
D is c r iminat
UCR
UCR
--)
UCR
CS--.--..----.------*CR
gtst's releaser, and the
Consequences (Contingencies)
---------t'
elicits
NS+UCS \\ '..
,
Negative feedback
rrrs
.---.------------------.-*
+
-\trs
2.3.5a Classical conditioning
The classicul conditioning paradigm (below) describes how neutral stimuli become conditioned through association, thus gaining the ability to elicit specific behaviors.
IJCR
\/
benefits of a specific type of learning may be limited to the environmental contexts
in which it evolved (McNamara and Houston, 1980). Possible routes of evolution of the primary associative learning paradigms, clossic'al and instrumental c'onditioning, are discussed by Weisman and Dodd (1980) and Skinner (1988), respectively. Both of these learning paradigms are incorporated directly into the model.
Behavior
)
( tlnsetlrtcrtt't's
'Pr'cific behavior will be lollowed by aversive consequences (see discussion below rlrottt specific types of consequences). Since exogenous stimuli in the model include
l,,,th conditioned and discriminative stimuli as types of exogenous stimuli, rrrtcrcsting to note how Davey summarizes their similarity in function. . . . Pavlovian CSs or instrumental discriminative stimuli (SDs) elicit motivational state appropriate to the reinforcer and . . . this
it
is
a
motivational state in some way mediates the emission of the instrumental response. IDavey, l9B9:210J
llrt'rrbility to usc thc cnvirc>nmental context (including
SDs) to predict the consebcltrvior is a marjor benefit of learning. Tarpy presents a psychologist's t','rspcclivc without using the term discriminative stimuli.
,lu('nce s ol'
l,c:tt'ttittg is I'rrttrlunrcntally a process whereby the animal comes to crpcel rt lirlrrt'c cvcrtt birscd upon the patterns of stimuli in the ('n\'u()nntt. rrt or rr1'rtlll its own hcltitviol ITurpv, 1982:lB l9J
I tkr'ttlrt'. I r)l('tll l)l('s('ttls lttt r.'lltolollist's ltcrsPective rlrt prctlictirrg tlrc tltttcrllt.tc ol' rr rllrorrl rrrrrrll lltt'l('lln (lt\( tlnlnt;tltrr.slirnttlt.
l', lr,r\ lot. itl\o
A CONCEPTUAL MODEL OF BEHAVIOR
A MODEL FOR A BEHAVIORAL ACT
Reinforcing stimuli
Presented
Omitted
domestic chicks, during the process of socializatron, received 'self-reinforcement' from social experience during the early sensitive period. Positive reinforcing stimuli that meet these ethological needs are said to have an underlying 'hedonic value'
Positive (So*)
Positive reinforcement
Extinction
(Tarpy, 1982); that is, they possess a sub.jective quality of a positive affective state (Toates, 1988). Whether this is pleasure as we know it, or what Lorenz calls 'feeling
Aversive (So*)
Punishment
N egative
Table 2.1. During or immediately.follow,ing the behavior the rein/brcing stimulus is:
reinforcement
. . . a bird that 'wants'to carry out the beautiful motor pattern of nest building . . . learns to recognize the situation in which performing the
good'and'satisfaction'(Nisbett, 1977:138, 289), does not affect the operant conditioning paradigm or the model. Even reinforcers that meet basic physiological needs might be considered hedonistic.
I have see
nest-building movements gives the maximum satisfaction.
it.
ILorenz, 1981.291]
' Consequences
'
stimuli(So ) are stimuli which the animal perceives as 'bad'(e.g. pain). That is, under those conditions the animal will perform
Aversive reinforcing
s.
Positive reinforcemenr results when the consequence of performing a behavior is receipt of SR*. The probability of the behavior occurring again is maintained or increased.The SR*s which result in positive reinforcement are received under different contingenc'ies, discussed below.
behavior. Positive reinfrtrcing stimuli (So*) are often called rewards. They are reinforcing stimuli which the animal perceives as'good'. That is, in the appropriate contexts, the animal will perform the behaviors necessary to receive those SR*s. The SR*s are received by the animal under different contingencres of behavior called
'
Extinction ( omission ) can only occur after an animal has been positively reinforced for a behavior.
If that same behavior now
does not result in the
animal receiving the SR+, omission is the consequence and the probability
of the behavior occurring again deueases until it is extinguished. Extinction is not simply a dissipation of the response, but is an active
schedules of reinfbrcement (discussed below).
An SR* may be a single stimulus (e.g. food item) or the opportunity to perform a chain of behaviors (e.g. search, stalk, capture, kill, consume). Lorenz (1965) considered the consummatory act in a chain of behaviors as a reinforcer for antecedent
learning process (Davey, 1989).
'
Punishmen r results when the consequence
of performing
a behavior is
receipt of SR . The animal initially escapes the SR , and the probability the behavior occurring again decreases.
behaviors (appetitive behaviors). Also, behaviors may be organized in a 'preference hierarchy'(Premack, 1965) so that an SR* could be the opportunity to perform a
'
preferred behavior.
of
Negutive reinfbrcement can only occur after an animal has been punished a behavior. Ii under the same conditions in which the original behavior resulted in punishment, a different behavior results in avoiding the
lor
Although we often think of SR*s as meeting obvious basic physiological needs (e.g. food and water), ethological needs (discussed earlier) may stimulate an animal
Sr{ . then the probability of that behavior is maintained or increased.
to perform behaviors in order to obtain subjective rewards.
Anin-rals nt:ry pcrlrlrrn belrirviors llurl rrt'c pt'irttrtt'ily irttt;rtt' (r'1,, t'otttlsltip lttttl rrrirtirrg) ltccirrrsc tlrcy tc('('iv('rttlrt'tcttt. sttlrir't'tivr'tt'wttttls. lirr t'x;ttttplt'. irt Si1'rrrrrnrl's 1lt)t)1..)0i',i ) r'it.rr'. '1ll;rV rs tls ou'n t('\\'intl' (',rlllts (l()(r.)1 tlott'tl llt:tt
[Bolles, |9BS.450J
behaviors necessary to avoid those SR
Proximate consequences The fourproximate ('onsequences, in the matrix shown in Table 2.1. are the immediate result of a behavior. They are determined by the type of' reinforcing stimulus and whether the reinforcing stimulus is presented or omitted. Reinlorcing stimuli and consequences are defined by their effect on the animal's
. . . the opportunity to manipulate, to explore, or to merely observe is labeled a reward, reflecting the assumption that if learning hits ttccurrcd there must have been some reward, even though it citnttot bc crtrpirically specified. f (itrr<'itr ct ll . /97i i.i/
seen that rats drink more of stuff that tastes good. And I do not anything bad in thinking of this hedonistically; they like it, they do
llrcse lirur proxinrutc consequences probably rarely operate alone, but rather in ',r,nrr cr)r))birrirtion. ['or cxample, Balph ( 1968) found that individual Uinta ground ,(lun rcls (,\1x'rntttpltiltt.t ttrnrtttrr,r) diffbred in the probability their being retrapped ,)n('()r rrrorc lirrtcs. llirsctl ort ltis obscrvations. he concluded that each individual
,tlur rcl rlrlli'rcrrti;rllv we iglrctl tlrc 1'rositivc rcinlirrccrnent of the bait versus the punol lrt'rrr1'lrrrppr'tl l.rkr'rvisc. lrittirrg yorrr hlrking rlog with a newspaper
r',lrrrrt'rrl
(Sl'
) rs
rrol lrrrrrrslrrrrt'rrl rrrrlt'ss llrt'lr;rt'kin1, slops. ll'tlre llrrg tturitttirirts ot'ittcrcitscs
rl'. lr.tt lrlilf'. lltr'tt lltt' ttt'r'tttll ntl(',ttt ltt)u t\ Pr'lr'r'ttt'rl lrf' llrt'rl()l'. rts ll,lsitivt' rcirtlilt'ec-
A MODEL FOR A BEHAVIORAL ACT
A CONCEPTUAL MODEL OF BEHAVIOR
ment. That is, the dog's interaction with you (SR*) outweighs the aversive aspects of being hit with the newspaper (SR ).
behaviors such as the predisposition to show fear/avoidance response wanes and disappears (habituates)
if it
of novel stimuli. The
is neither immediately inhibited by
punishment nor maintained by positive reinlorcement. Satiation of a drive such as hunger can also be perceived in a similar manner.
Reinforcers and reinforc'ement as inJrtrmation Consequences in instrumental con-
ditioning provide not only immediate reinforcement, but also information
(e.g.
Cherfas, 1980). Garcia et al. (1913) argued that since natural selection'has favored' animals that seek and incorporate information, we should think in terms of animals
acquiring information rather than receiving reinforcement. I prefer to retain the terms positive and negative reinforcement while using the term 'information' in two contexts: l. as a reinforcing stimulus; and 2. as feedback about the relationship between stimuli (classical conditioning), and stimuli, behavior and consequences (instrumental conditioning). First, I agree with Garcia et ttl. (1973) that in/brmation can, by itself, serve as d reinfrtrcing stimulus. This concurs with Lorenz (1981) who stated that animals are motivated by curiosity to gather information. For example, an animal might seek more information about a novel object it sees in its environment by listening, smelling, tasting and touching it. What it hears, smells, tastes and leels are reinflorc-
Initially, the consequence of ingestion is positively reinlorcing stimulation (SR*); Itowever, the reinforcing value decreases until satiation is reached; at this point its rcinforcing value can be considered zero (neither positive nor aversive) and the behavior would normally cease. Should the animal continue to ingest, the food would begin to become aversive (S* ) and punishment would be the consequence of lirrther ingestion; hence the behavior would cease.
(lltimate Consequences Proximate consequences can be translated into ultimate ('onsequen('es within the context of natural selection and evolution. We can use the rrtodel to draw a parallel between instrumental conditioning and natural selection inner, I 98 1 ; Rosenberg, I 984) by envisioning positive reinforcement in the sense increasing individual fitness and punishment in the sense of decreasing fitness.
tSk
,,1'
. . . species-specific patterns are shaped by natural selection as operant behavior is shaped by reinlorcement IGart'iaeta1., 1973;5J
ing stimuli for the respective behaviors. The reinforcing stimuli all provide information, but they can also be perceived as positive (e.g.smells'good':positive
reinforcement), aversive (e.g. tastes t6rd':puflishment) or neutral (e.g. feels rough:no consequence (see below); provides only information about its texture). Secondly, the animal obtains in/brmation via /bedback about the relationship between the stimuli eliciting the behavior, the behavior, and the consequences (including information about the object). In all cases the animal is acquiring information (via feedback) about information (reinforcing stimuli). In the case where touching the object results only in learning that it feels rough (neither positive nor aversive), that information alone is a reinforcing stimulus, and the animal has obtained information about gathering information. That is, they have learned that when a novel object appears in their environment they can learn more about it by touching it. In this context, the use of the term information agrees with Plotkin (1988), who considers learning to be the acquisition of information, and most cognitive theorists who think of animals as acquiring information about their environment (Bolles, 1988). Likewise, classical conditioning is seen by many as the acquisition of information about stimulus relationships (Davey, 1989). The use of the term information in these contexts in no way negatcs the utility ol' applying the term reinforcement to conseqLlences in instrurrne ntalcontlitioning. ('ott,tt,tlttt,ttt'c,s
ttf tttt t,ttlttt Sontc llclurviors irl)l)elr l() r('sull irr t'orts,.'tlrt'nt'r's llt:rt
It:tvc tto t'cittlirt'cing vrrluc (i.c. tlrev rrrt'sr'r'nrirrl'ly pt'rt'r'ivt'tl ;rs rrt'illrt'r l)()srli\'('n()r rn'r'tsivt'). ( )ttt' t'rlrtnplt' ts llrc lttrltrtttttltt,tt o l unt ottrltltonr'.1 (llr nn;rttl\' ttttr;rlt')
natural selection will positively reinforce adaptive behaviors by allowing the to those behaviors to reproduce themselves in future generatrorrs (i.e. selfish gene hypothesis; Dawkins, 1976b). Conversely, genes that conI Ititt is,
lt. nes that contribute
tribute to disadvantageous behavior will be punished by decreasing their ability to rr'plicate. The most extreme punishment is death, which immediately reduces that rrrtlividual's future fitness to zero (unless a molecular biologist decides to clone its
t)Nn
).
'l'lris concept of 'Se/e ction by Con,sequences' (Skinner, 1981) has been developed l,r scvcral theorists, including Campbell (1956), Ghiselin (1973), Pringle (1951), Sl'irutcr (1953. l98l) and Simon (1966). Also, Lorenz (1965) discussed the relation'lrr1r bctween reinfcrrcement and natural selection, and Pulliam and Dunford (1980) ,lt'siuttctl it modcl to demonstrate how it would operate in a hypothetical predator. l'ullirrttt ancl l)untt>rd ( 1980) developed their mathematical model based on conse,
tu('n('cs. ll'ctlback. cxperience and evolution. Their hypothetical cybernetic lizard is
.rlrl1'1., clirssily ncurirl input I'rom eating red ants (toxic:punishment) and black
rnls (;rleirslrrrl lrrslc prositivc rcinforcement), store the information and recall it 'r lrt'n it rtcrtcttcotttttct's lttt lutt.
l',,,1 tll',,1(l
llll"
\\ lrt'rt' /',,, , llrolr ol t';tlin1' ;utl ()lt lti:rllr I rr lt',u nutl' lr;rttrtrrr'lcr (0 I )
I
A MODEL FOR A BEHAVIORAL ACT
A CONCEPTUAL MODEL OF BEHAVIOR
g:guided most by recent experience (good
if
ants are in discrete
patches)
1:guided most by earlier experience
P,,:probability of eating ant on trial n:residual innate tendency*accu-
Table 2.2. Contingencies e.ffective in protlucing the four basic consequences o/' behavior Consequences
Effective contingencres
Extinction
SR* does
Punishment
SR /bllov,.r every occurrence of the behavior
mulated reinforcement experiences
Z,:Reinforcement experienced on trial r (0:ant toxic;
l:ant
palatable)
Pulliam and Dunford assume that a lizard that has'a genetic program producing this neural architecture . . . is favored by natural selection, because it leads to the development of lizards that avoid poisoning themselves' (Pulliam and Dunford, 1980:
N
SR
egative reinforcement
not.fbllow any occurrence of the behavior
does not.follo-* any occurrence
of the behavior
follotvs after various time intervals or occurrences of the behavior: these include:
Positive reinforcement
SR*
l3)
Fired rutio
As Maynard Smith ( 1982) and Sigmund (1993) have pointed out, it is difficult to
Fixed interval
imagine how an animal can translate proximate consequences into fitness. In fact,
Vuriable ratio
natural selection makes that translation: hence, Maynard Smith operationally
Variable interval
defined the process, as follows:
An animal which performs the 'correct' action
- correct in fitnessmaximizing terms - when simultaneously experiencing hunger, thirst and sexual motivation, will, by definition, leave [the]most offspring. I Maynard Smith, l9B2
rrr
]
'reinforcing'in the definitional sense of 'strengthening' behavior. must also be things that in the history of the species have promoted inclusive litness; that is, if it leels good, it probably is good . . . things that are
I Staddon, I 980 : xt,iii J
There are potential pitfalls to avoid when using the rnodel to conceptualize selec-
tion by consequences, including:
t
Not all characteristics of
lllrnad,
all behavior acts of an indi-
Lewontin, 1 979; Johnston,
In other words, not all behaviors that are positively reinforced are necessarily adaptive (Skinner, l98l ). 1985 ).
which leads (at least in part) to the behavior which
is
selected. not the behavior which leads to the consequences (e.g. Dawkins, r
Contingencies (sc'hedules o/ rein/brcement )
r',rrrtingencies are the ways in which behavior and reinlorcing stimuli are paired
i\trrrltlorr, |
1980). Contingencies are also called schedules oJ reinfrtrcement (Ferster
,r lrrotlrrcing the consequen('es discussed above
in modilying behavior
(see Table 2.2).
l lrcsc lirr.rr basic contingencies for positive reinforcement produce different rates ,,1 lrt'lurvior'.
Ilutio contigencies are based on the number of occurrences of
a behav-
t,tr lrttt't't,u/contingenciesarebasedontheamountoftimethathaspassedsincethe l.r r
,l rentlirrcctl occurrcnce
rrrr('
o1-
a behavior. The number
ol
occurrences (ratio) or the
('lirl)scrl ( intcr.val) catt bc cither.fixecl or variable.
llrt'st' lirtrr corrturgcrrcics are combined (in the laboratory and, perhaps, in
e84).
: It then follows that'Consequences may be beneficial. yet not pnrvirlc material for the action of naturalselection . . .'(tlinrlc. l9tt2: lrig.l-1).
genes, and there is
lcction of a repertoire ol'operant behavior patterns, but the mechanisms are only ,rrpcrlicially similar and pressing the analogy this lar will confuse students'.
,rrrrl Skinncr. 1957). Only certain contingencies are effective a species (especially.
of
',e
vidual) are adaptive and have been selected fbr (Adaptionist F'allacy';
u It is the genotype
1984). Finally, I will pass along a caveat sent to me by Dale Lott after he
lrrrtl reviewed this chapter: 'There is selection for a population
Staddon expressed the same concept in slightly different terms.
(or was good, at least, for one's ancestors)
lbod) and punishment (predator risk) will leave more offspring. Of course, there of the concept of selec'tion by con.sequence.r (e.g. Catania and
lurvc been critics
rr,rtu rt') irr vrrr iorrs wirys to crcatc.
, lr,',lrrlr's
ol
lirr example, multiple, mixed, chained and tandem
rcirrlirrccrrre rrt ( liirrttirto and Logan
,
1979). When different contingen-
lo tlrllcrort hchitviors. or the same behavior in different llrt't lrrt'rt'lt'rrt'tl loirs('()rtt.ur.r'cttt scltctlttlcsol'r'cilt(ilrcement. Concurrent
, r,', ,rp1rly, ;rt llrt'strrrrc linrc
Sclection by conscclucnccfi (rrrrtl contirtgcrrcics see lrclow; is tlrt' ltirsit' llrt'rrrrsr.' bclrirrtl tlplittutl lirlrpirrp' lltt'ory. llrrrt is. lrrtitttlrls il'lrir'lr (';1rv I'r'p!'s l)r()1r()t11,' l)l()l)('l';lss(':islll('lll ()l ('ll('tl'\ ('\l)('il(ltlillr'\('t\il\ l)ostlt\t'tt'iltlott t'lttt'ltl (('il('ll'\ l';iln
, rr11l1'rl:.
,
lr,',lrrlcr;rrt'llrc
, rrI ',r ltt'rlrrlt",,rl
lr;rsis
ol
slrrrlir's ol'olrtirrrrrl lirrrrg,ur1,. itt plttcltcs ol'prcy with cliffler-
tt'tttlt)t( ('ltl('lll
A CONCEPTUAL MODEL OF BEHAVIOR
A MODEL FOR A BEHAVIORAL ACT
2.3.5c Leurning paradigms in the model
2.3.6 Feedback
I agree that 'the world of animals is not a gigantic "Skinner Box" in which they gradually learn, by trial and error, whai to do and what not to do' (Dawkins, 1986:60). Trial and error learning(operant conditioning\ and classical conditioning are only part of the animal's capacity to deal with its world. Other learning related phenomena, such as insight, latent learning, discrimination learning, habituation and sensitrzatron, also occur and can be encompassed by the model. Also, the Model is not subsumed within general process theory (e.g. Skinner), rather its flexibility concurs with Roper's ( 1983) statement that, . . . observations of natural learning tend to encourage the view that learning consists, not of a unitary general capacity, but of a collection of specialized abilities which have evolved independently in particular species
to do specific jobs
I Roper, 1983:205 J
The model does not assume that the types of learning involved can always be clearly distinguished. For example, Davey (1989) argued that in a typical classical
Feedback makes the model a closed-loop system (Pringle, 1951; Manning and Dawkins, 1992; McFarland, l97l;Toales" 1980) which is necessary for any comprehensive behavior model.
. . . all the phyla of animals which have evolved a centralized nervous system have hit on the 'invention'of feeding back to the mechanism initiating a behavior the consequences of its performance. ILorenz, 198I:70J The model provides./e edhack from the ('onsequen('es of behavior to the animal in
the lorm of infbrmution which allows it to update its experience (association between stimuli, behavior and consequences) and modify expectoncies through some'internal representation'(Hinde,1982, Ethology, Davey, 1989) which we call nremory. . . . Sx [a reinforcing stimulus] acts backward, like a feedback, I suppose,
to produce expectancies. . . . the quality of what the animal expects importantly influences its behavior. I Bolles, l9BB.450l
of salivation (e.g. Pavlov, 1927), it cannot be determined whether the dog salivates to the bell because the bell is associated with lood or because the food pellets reinforce salivation. Likewise, the relative role of classical and instrumental conditioning in filial imprinting remains unclear (e.g. Davey,
rrnimals to perform behaviors which result in receiving positive reinforcement and
1989;Rajecki, 1973).
rrvoiding punishment, respectively. Expectancies come into play when the animal is
Also, the model does not assume that classical and instrumental conditioning are mutually exclusive and work independently. In fact, the model encourages the researcher to consider both in behavioral analysis, as the lollowing illustrates:
rrrotivated to perform a particular behavior.
conditioning study
The sight of a stimulus associated with lood elicits, by a process of classical conditioning, an appropriate set of consummatory responses, but aspects of these responses may then be modified as the animal learns the correlation between variations in its behavior and variations in its success. Both processes are adaptive, for the opportunity lor successful instrumental conditioning would probably not arise withourt appropriate classical conditioning in the first place . . . IMuckittto.rh l9ll.] l6ti I Researchers should also realize that we cannot directly observe lcurning hut curr
only infer it from a change in behavior, called'performance'(Davcy. l9tt9) Wc also recognize that learning results in a change in thc aninral's irnatorrry lrnrl 1'rlrysiology. that is. thc ncrvttus systctn is usctl lirr thc rcccipt. st()r'irg,c (rrrcrrrory). ;rrrrl irt
[irrnurt iorr.
re
tlier,'lrl ol
Positive and negative feedback lorm two basic types of expectancy which direct
. . . both associative and motivational lactors may contribute to making some activities readily performed when they are reinlorced suppressed when they are punished.
or readily
I Shettlewortlt, I 983 :23 J
2.3.7 Feedforward I lrc nroclel recognizes that the next behavioral act in the continuous stream of lrclurvior citn be elicited by the consequence of the behavior that precedes it (i.e.
rr'irrlirrccr'-clicitccl behavior'. Davey, 1989:201). For example, aversive reinforcing ,lrnrrrli gcncrally clicit behaviors which allow the animal to escape the punishnlent, ',rrt'lr rrs spilting out tlistustcfirl lbod.
l"ectllirrwirrtl irlso occurs in a chain of behaviors (e.g. search, capture, consumpIrt,n ol'ptey)ls tlcsct'ibcrl by Ilinclinc (1988) in his commentary on Gardner and t irrrrlrrt'r' ( l()l"iS): As rlt'st trlrt'rl llry ( i;rttlttr't ltntl ( lltt'tlttct' i9tilll. 'll'etllirrwlrrtl'concerns Itt'llrt t,,t ('n\ lrotttttr'ttl tr'lltliotts itt tr lrit'lr ()nr' sittt;tliott cvrtkcs lut
APPLICATION OF THE MODEL
A CONCEPTUAL MODEL OF BEHAVIOR
38
'fable 2.3. Where Tinbergen's.fbur areas oJ-study are incorporated in tlrc modelfor a
Causation Exogenous
v
ltchqvioral act
Positive feedback
stimuli
l-----
Function
--?--) Behavior
(G+E+D+(A+P)
+ -
\a-
Evolution Fig.
Behavior OntogenY
-----___
Consequences
-)
Areas of study
Relevant portions of model
liunction
Proximate Consequences; Feedback
('ausation
Feedforward
(Contingenctes) (
)ntogeny
changes in environment, experience, anatomy, physiology,
-at'
----'
stimuli, and consequences.
Negative feedback
Irvolution
2.5 Where Tinbergen's areas of study fit into the Model for a behzrvioral act. organized pattern of action resulting in a new situation that evokes a different pattern of action resulting in yet another situation, and so on. This type of behavior-environment relation has long been described as applying to behavioral chains based on positive reinforcement.
Genotype; often studied using comparative studies of the behavioral phenotype (i.e. Behavior)
When/Where Exogenous
stimuli
Positive feedback
\
IHineline, l9BB:457]
k-'-'---
--i-, Behavior -" ..
of the term feedlorward is somewhat different. but not exclusive of its use by Toates (1980) and McFarland (1971:102) as a'phenomenon which enables an animal to anticipate the long-term consequences of behaviour and to take appropriate action to forestall such consequences'. That is, an animal that is not thirsty My
Exogenous and endogenous stimuli; feedforward Behavior changes during maturation; these result from
use
(G+E+D+(A+P)
+ _ fr'
'...
't--
might still drink in anticipation of a luture water deficit (McFarland,l97l).
Who/How 2.4
APPLICATION OF THE MODEL
I iu 2.(r Where the six types
Behavior
What
---------- -.a'
-) ,'
whv
Consequences'-)
.
Feedforward
(Contingencies)
Negativefeedback
of questions about behavior fit into the model fbr
a
behavioral act.
Below are examples of how the model can be used as a basis for: l. conceptualizing
the areas of study (Chapter 1);2. organizing the types of question (Chapter I ); 3. locusing research; 4. developing an expanded or more focused model; and 5. diagnosing and treating behavior problems. Chapter 5. on delineation of research, describes how the model can assist in defining questions, objectives and l-rypotheses.
2.4.2 Organizing the types
I
of question
2.6 illustrates where the types of questions discussed in Chapter 1 are l,rr'ttsctl in lhc nrodel. The first question to be answered in any research project is tr:,rtrt:
,rltvrtys 'What docs the animal do?'. The model demonstrates that all the other quesrr()rts iu'c
2.4.1 Conceptualizing the areas of study Tinbergen's four areas of study (Chapter l) can be locatecl itt portiorts ol'tltc tttrttle
I
(Figure 2.5, Table 2.3). This assists the rescarcher in sccing how ltis 1'ritt'licttlitt research intercst fits into lhc'big picturc': thal is. how il irttcgnttcs willt otltct :ttt'rts
2..1..1
tlircctctl at lactors that affbct what an animal does (see Table2.4)
lirrcusing rcscarclr
ol'stucly.
ll,ttttt1, tlt'lcttnnrctl tlrc irt'cit(s) lo bc strrcliccl ancl typc(s) of Questions to be .r,l.ltt'sst'tl. t('\(';rtt'lt tltr'rt lrr't'rlr))r's locrrsctl ()n ()nc. ()t't)t()rc. pitrts ol- the rnodel .
llxltnrplt's ol tt'st'lrtt'lt tlr;rt lirr'rrst's otr ltrltliolts ol lltt'lttorlt'l tt'lt'rltttl lo t':tt.lt :ttt'lt sl ol ltrlV itt(' l)tovlrlt'rl lrt'l, rrv
l(t'1'.il.llt':.s,rl ttllrl l)(rtltorr rrr.'lir(u\()lr.lr'st';rt't'lr l('sillts(lty rrcccssily)trlwirys l,,tr L lrr lltt',tlttttt,tl'., lrt'lrtvtot. tr'. tllttsllrlt'rl rrr llrt't'rlrttrPlt's llr'lorv
rCllrtC
APPLICATION OF THE MODEL
A CONCEPTUAL MODEL OF BEHAVIOR
Physiology: Landsman (1993) studied sex differences in electric organ Table 2.4. Where the sir types of questions are incorporated in the model.for u
disc'harge in a weakly electric fish (Gnathonemus petersii).
behavioral ac't
Exogenous stimuli:
Types of question What?
Nicoletto (1993) studied the effect of male ornamentation and display rate on the sexual response of female guppies.
Relevant portions of model and examples
Endogenous stimuli: Dethier (1966) describes how distention in the
Behavior
foregut of a blowfly sends a messoge via the recurrent nerve to the brain,
(e.g. courtship and
which inhibits extension of the proboscis, thus inhibiting further feeding.
territorial display)
Behavior: Fraser and Nelson ( 1984) described the frequencies and
Anatomy, physiology, environment, genotype
Who?
(e.g. large, healthy and motivated
Where?
When?
How?
sequencing
to breed, dominant,
behaviors in a Madagascan cockroach
male sage grouse) Exogenous stimuli
Consequences: Shettleworth (1978a,b) found that scrabbling behavior in
(in central territory, on breeding ground in North Park, Colorado with other males and females)
hamsters increased in rate when positively reinforcedwith seeds and decreased in rate when punishedwtth
Exogenous stimuli, endogenous stimuli
Contingencies: Krebs et al. (1918) determined the loraging strategies
(spring breeding season; Courtship behavior in presence
great tits (Parus maior) in two lood patches with different densities of
of females; Territorial displays in presence of males;
mealworms (i.e. variable ratio schedules of reinforcement).
reproductive 'drive') Anatomy and physiology
Feedback: Brown and Gass ( 1993) demonstrated that rufous hummingbirds (Se/a sphorus rufus) learn spatial associations between cues and
(endocrine and nervous system stimulate and integrate use of wings, tail, legs and gular sacs in 'strutting'and
rewarding feeders.
h
ina p or t ent o sa).
mild electric shock.
of
Feedfrtrw,ard: Lawhon and Hafner ( 198 I ) led seeds (millet) and seed mimics (glass beads) to kangaroo rats (Dipodomys) and pocket mice
'booming' displays)
(Perognathas). They found that glass beads were always rejected
Proximate consequences, ultimate consequences (Hedonistic value of displaying and mating to the
whv?
of l6 c'ourtship
(Gromp hador
(rather than pouched) after unsuccessful iltempts to husk them.
individual male; a male whose displays allows it to hold a central territory and attract and mate with females
2.4.4 Developing an expanded or more focused model
has a higher fitness) I hc
modelcan be expanded to include additional lactors or focused on portions of
nrorc importance to the researcher. Genotype: Ralph and Menaker ( 1988) found a mutation in hamsters in which v,ild-type (normal genotype) animals have a circadian locomotor rhythm of about 24 hours; heterozygoas animals have rhythms of about 22 I
h, and animals hontozygous for the mutation have rhythms close to 20 h. Environmen r: Holekamp and Smale
(
mother spotted hyaenas (Crot'uttt c'rocutu) during their oli.spring's ilgg,r'cs-
months of age. Artulonn,'. Chouclhury and Illack ( l99.ll sttrtlicrl ttutlc sclct'tion in ('ill)lt\'('
'l'ltcy lirtrntl llrrrt ltt'ttvit't lt'tttrtlt's ltntl birrnirclc gccsc (llrttttttt lrtrt'tt1t.ti,t'1. lltosc rvitlt r/,lll, t't'f(t( t'ltrtllt't n.t srttttPlt'tl sl1,1ti1ir.",ttllv lttr)t('lxrl('tllt;tl ttt;tlt's
lrntle ( l9ll2). in his discussion of behavioral development, proposed a model of the
rt'lrrlionship bctwr:e n ontogenetic and causal factors and consequences (Figure 2.7).
1993) found that the prc.st'nt't' ttf
sive interactions strongly influenced the outcomcs lirr.juve nilcs lcss lhirrr
)..t..tu Expanding the model
(r
I
lrrrrlc's rnorlcl incorporatcs a route of action for ontogenetic factors that I have not
,1rt't'ilicrl irr nry rrrorlcl. IIowever.
I
have identified ontogenetic factors
in
section
' I I . I lintlc lrrs rrlso inclrrrlerl (but not labeled) f-eedback (from functions to ontoger('lr('lir('tors):rrrtl li'ctllirrwirrtl(ll'orn gouls ttl cliciting factors).
A CONCEPTUAL MODEL OF BEHAVIOR
42
APPLICATION OF THE MODEL
Coneequences
SUB-SYSTEMS
BEHAVIOR SYSTEM
1a"n"r,"i,,
r____;
___
MODULES
OR
EXTERNAL RELEASERS
FI"ED ACTION
_____-_.ff:-r
l
ll
l
Predisposing factors
]ueutrat -|Harmful gITE-KILL
Fig.2.7 Relationships between ontogenetic and causal factors and consequences. The distinctions between ontogenetic and predisposing and between predisposing and eliciting factors are often somewhat arbitrary. Among the consequences, the categories of reinforcers, goals and functions only partially overlap. Although a goal is normally achieved as a consequence ol a behaviour, an internal representation (anticipation) may contribute to causation (dotted line). Consequences may be beneficial, yet not provide material for the action ol natural selection (functions, strong sense). Exceptionally, harmful consequences can be goals. The dashed line indicates evolutionary consequences on the next
generation (from Hinde, 1982).
ANOGENITAL.S}iIFF FEEOING BEHAVIOR SYSTEM
EREAX GR^SS STEMS
2.4.4h Focusing the model
l.
TFI^NSPORT IO FOOD SITE
The model can incorporate the 'behavior systems' approach (e.g. Timberlake,
1983; Timberlake and Lucas, 1989)
to modeling behavior by relating; i. innate INVESTI64TIVE
behaviors (modal action patterns); ii. 'motivational states'(behavioral systems); and
FOEAGING
iii. exogenous stimuli (Davey, 1989). For example, Davey's (1989:158) organization of leeding behavior in the rat (Figure 2.8) is incorporated by recognizing that the behavior system and subsytems are stored, modified. processed and activated (via
GNAW.BITE OFF
endogenous stimuli) within the animal's anatomy and physiology. The model can
then, lor instance, assist in visualizing the proximate and ultimate consequences of each
of the behaviors.
SWALLOW
f soL,o I
Davey's model is similar in structure to Baerends' (1976) classic model of interruptive behavior during incubation in herring gulls (see Davey, 1989:320).
2. Alcock
l--1""*J
of circadian rhythms that incorporates exogenous stimuli, anatomy, physiology, and rhythmic behavior- patterns (Figure 2.9).It focuses more explicitly on what I have labeled in a gcltcral way (1993) presented a rnodel for the control
i
,
as anatomy and physiology.
3. It
motivationul slirlcs rrntl tlce isiorrs by incorporating more explicit control thcory :rrttl incltrtling cornpiulrlors. t'olltrollcrs ittttl scrvottrcchltttisrtts:ts p:u'l ol'tlte ln:rlortty lrrrtl lllrysir)l()p,y (('.1' lo;rlt's. l9ll0; Mcliitt'llttttl, l()7 l). An exlttuplr' ol lr tttotlr'l u'lrit'lr us('s ;r t'onrp:ulrlor ls Ilrrttlittl'lirltl's (l()i-i.l) tnorltlir'ltllon ol ,Atr'ltt'l's (l()/(r)tnorlt'l ,,1 lt';l iur(l ;t,','t(",',t(rtl is possible to focus the moclel on internal
fr---r, I onel cues
-'l
L-)
liir. -).I'i 'llrr'lirtcliotutl orgltttizlrlion ol-a putativc ltc'cling behavior system for the rat (
ll orrr I )ltve
v
l
()li()
).
I
SUMMARY
A CONCEPTUAL MODEL OF BEHAVIOR
OBSERVED RHYTHMS
ENTRAINMENT PATHWAY
lenvironmental cues
(if discrepancy >> 0)
6D
I - | receptors f- (_/
ORIENTATION
RESPONSE
CLOCK MECHANISM
Fig.
ENVIRONMENTAL CONSEOUENCES OF BEHAVIOUR
2.9 A master clock may, in some species, act as a pacemaker to regulate the many circadian rhythms of an individual (from Alcock, 1993, after Johnson and
lnfluenced by
DECISION PROCESS 2
Hasting, 1986).
Escape or immobility?
location of stimulus etc.
Sensory input switched off
2.4.s Diagnosing and treating behavior problems in animals
it. For example, Figure 2.1 I illustrates how the model can be used to organize some of the factors to be considered in diagnosing and treating an aggression problem in dogs.
'hormonal state,
Sensory input no longer impinges on animal
in vertebrates (Figure 2.10). Huntinglord (1984) should be consulted lor a thorough discussion and examples of various types of models of motivation.
The model can be used by applied animal behaviorists as the basis for determining the etiology of a behavior problem, what is maintaining it, and what manipulations
lnfluenced by size of discrepency, hormone levels, past experience in fights etc.
trig.
can be made to mitigate or eliminate
2.10 Motivational models using control theory; a simplified version ol a control theory model of aggression and fear in vertebrates (from Huntingford, 1984).
EXTERNAL STIMULI (
isol ate,
e
liminate,
de
sensitize)
SURGERY
2,5 SUMMARY
kaslralbn)
CONSEQUENCES (maintain, modify)
The model presented above is the result of my attempting to provide a structure on
which ethological research could be designed and conducted. I have lound it uselirl in assisting graduate students in designing research by getting them to recognizc thc various factors, past and present, which contribute to behavirtr. For the behavior (what) of interest, you can'plr"rg into'the rnotlcl whirt yotr h:rvc learned from the literature about the variables tliat allcct that bchavior'. I,or llre
NEUTRALORSUBMISSTVE
-z)
AGGRESSION
t,/
lllltckltoltl'tl ;tlttl ltt:tiltslot ttt u'tllt t'ollr';11,11r's lo itlt'rrlill lrrlrltlt()lrill r;rr r;rlrlr.s 1t. 1' t'\o|1'11,rttsslttttttlt)ttlttrlrrltottlrllrt't,,trit,lr'rt'rl;rttrlPo11'gllr;rlrrrt'llr,,rl,,,,l trr,rrrrIrr l,rl ttr1,. nr('.t'.ulnl, or r'lllnt,tl ntl, l lro,,r' r,u r,rlrl...,
talts I
2t
REINIr)RCEMENT NEG RETNFORCEMENT
(type?) ./
\.
types of question (e.g. where'l whcn'/)y()r.r arc rttcr)rplirrg lo illlswcr. y()u cln bclin lrr identify the variablcs (c.g. gcnotypc. cntlogcnorrs slirrrrrli)tlurt;rrc lrkelv lo lr;rrt.rr l-llit.itlr cll'cct ()lt y()ul't'csttlls. Vrrr clrrr prrl (lre lllrrlitrlly r'onrplt'lt'tl rrtotlr.l orr llrr.
pos.
'l' --1)
"
rulvrsrtMENr EXTIN('T'ION
l{t( "t t()N
tnttt. . lr' Atrttttl)
I rt' ' I I \,1111 1'l llrr'l,tt lol .,ttt.tIIl1r'rl t'llt,rl,'l,r',1 ttttl,lrl t,n,,t(l(.t tn,lltl'tt,rstttl,lttttl llr.llllrl,,tlt.t),1'tr'..trrn lrtillrlr'ilr til,t rlill,lrlr,trrrrrl,l'1 lll, trrl,t Ktt.t;r1r)
46
A CONCEPT'UAL MODEL OF BEHAVIOR
In tirne, as most of the terms in the model become replaced with real variables, you begin to have more confidence in your grasp of the complexity of the research you are proposing. You can pose more clearly defined questions, lormulate rnore
3 Choice
of subjects
exacting hypotheses, and begin the task of detennining how you will answer those questions and test those hypotheses. That is what the rest of this book is about.
Ethologists corre to study particular species for various reasons, but all generally l. their special interest in a particular ,species; or 2. using a
travel via two routes:
species that is suitable for investigating a particular rcncept. These two routes inter-
twine so that a researcher may become interested in pursuing a concept after having studied various aspects of a species'behavior. Likewise, a researcher may initially study a species in pursuit of answers to a conceptual problem and become fascinated with other aspects of the species'behavior.
3.I SPECIES-ORIENTED RESEARCH Many ethologists who settle on a particular species to study are naturalists who discover a fascinating species while spending time in the field. The following excerpt l'ront C'uriou,y Nuturuli.vr.r describes how Tinbergen began his many years of research (
)n the digger wasp:
On a sunny day in the summer of 1929 I was walking rather aimlessly over the sands, brooding and a little worried. t had just done my finals, had got a half-time job, and was hoping to start on research lbr a doctor's thesis. I 'wanted very much to work on some problem of animal behaviour and had lor that reason rejected some suggestions of my wellmeaning supervisor. . . . While walking about, my eye was caught by a bright orange-yellow wasp the size of the ordinary jam-loving Vespa. It was busying itself in a strange way on the bare sand. With brisk, jerky movements it was ra,alking slowly backwards. kicking the sand behind it as it proceeded. 'l"hc sancl llcw away with every jerk. I was sure that this was a digger wasp. . . . I w:rtcherl thcse wasps at work all through that afternoon, and soon bccrrrrrc rrbsorbcrl in lincling out exactly what was happening in this busy irtscct tort'tt....
;\s I
thc wusps. I began to realize that here was a lirr rloing cxactly thc kind of field work I would lrkt'lo rlo llr'r'r'rvr'r't'ntluty lttttttlt'ctls ol'tliggcr w:lrips cxttclly wltich \l)r't tr's I tlrtl rrol lirros'rt'1. lrrrl llr;rl rrotrltl ttot llr'rlillictrll lrl lirrrl r)ul. . . l\lr rr()nr('\ \\'('r('('rr't. I Lnr'\\'\\lt;tl I tt'ltttlt'rl l,ttlo. lltis tl;tr'.;rs il rvrrs rv:rtching
rvontlcr lrrl ()l)l)()r'l trrrill,
.
CHOICE OF SUBJECTS
SPECI ES.OR I ENTED RESEARCH
turned out was a milestone in my life. For several years to come I was to spend my summers with these wasps . . . [Tinbergen, 1958.5 8J Dethier, who spent many years pursuing the behavioral biology of the blowfly, in the following
describes how he settled upon the blowfly as a research animal excerpt from his delightful book, To Know a Flv:
When choosing an experimental animal, therefore, why settle for anything so prosaic as the laboratory rat, so giddy as the guinea pig, so phlegmatic as the frog, so reptilian as the chicken, so cousinly as the chimpanzee? Why not choose an excitingly different creature like the aardvark or the dugong'? Why not choose the fly? With so many kinds of flies in nAture's burgeoning storehouse of life, how does one choose a proper species for study'l The answer is simple. Let the species choose you. This was how our laboratory came to work with the black blowfly flfteen years ago. I Detlier, 1962:7 I J The rest of Dethier's account of his early years of research on the blowfly makes heuristic and enjoyable reading. Von Frisch, a nobel laureate along with Lorenz and Tinbergen, began his many productive years on the study ol'bees through his attempt to'set the record straight' regarding the color vision of bees. Von Frisch describes the launching of his career of bee study in the preface to his fascinating book, The Dant'irtg Bees: . . . some forty-five years ago . . . a distinguished scientist, studying the colour sense of animals in his laboratory, arrived at the definite and apparently well-established conclusion that bees were colour-blind. It was this occasion which first caused me to embark on a close study of their way of life; for once one got to know, through work in the field, something about the reaction of bees to the brilliant colour ol flowers, it was easier to believe that a scientist had come to a false conclusion than that nature had made an absurd mistake. Since then I have been constantly drawn back to the world ol the bees and ever captivatecl anew. I have to thank them for hours of the purest joy ol discovery, parsimoniously granted, I admit, between days and weeks of clesplir and fruitless effort. I llnr I"ri.tt'h. 19.t.]:iii I Some researchers have begun their studies in animal behuvior otr u sirtglc s1'rccics
and then become interested in a limited type of behavior which lltcy hirvc ge ttct'itlized to other species. For example, Allee recoLlnts how lte bcgittt st trtlying ll'cslrivrttct isopods and then became interestcd in sociul bclritviot'itt gcrtct-rtl.
sltttlcrtt itt zoololll l u';ts cttl'.;tl't'tl irt slrrrlyirtp, tlre lrr'lurvior ol':i()n'r('('()nrnl()n :.inurll lrcslr rvrtlt't lttttnt;tls rl;tt';lllt't tl:tV l Prrt lols ol lirt'()l l('ll l\()l)o(1" tttltr t'lrllt'tl isollotls
Alntrlst lirrty yctrrs itg() lts
tr gnrtlrurtc
shallow water in a round pan. . . . when a current was stirred in the water the isopods from the streams usually headed against it; but those lrom ponds were more likely either to head down current or to
in their reaction. . . . Rather cockily I reported after a time to my instructor that I had gained control of the reaction of these animals to a water current. By the judicious use of oxygen in the water, I could send the indifferent pond isopods hauling themselves upstream, or I could induce the stream isopods to going with the current. I had not reckoned with another factor that presently caught up with me. After a winter in the laboratory it seemed wise as well as pleasant to take my pan out to a comlortable streamside one sunny April day and there check the behavior of freshly collected isopods in water dipped from the brook in which they had been living. To my surprise, the stream isopods, whose lellows all winter had gone against the current, now went steadily downstream or cut across it at any angle to reach another near-by isopod. When I used five or ten individuals at a time, as I had done in the laboratory, they piled together in small close clusters that rolled over and over in the gentle current. Only by testing them singly could I get away from this grolrp behavior and obtain a response to the curretrt; and even this reaction was disconcertingly erratic. It took another year of hard work to get this contradictory behavior even approximately untangled; to find under what conditions the attraction of the group is automatically more impelling than keeping looting in the stream; and that was only the beginning of the road that I have followed from that April day to this time, continuing to be increasingly absorbed in the problems of group behavior and other mass reactions, not only of isopods, but of all kinds of animals, man be indifferent
included.
As the years have gone on, aided by students and other collaborators and by the work of independent investigators, I have tried to explore cxperinrcntllly the implications of group actions of animals. IAllee, 1938.5
7J
Allcc pursuctl lris irttcrcst in the social behavior of animals and became an early ;rrtrl inllttcnti:rl ctlrologist. Thc unusualevents that led to E.O. Wilson's selection of
of study are recounted in his revealing autobiInsight into how severul other noted ethologists selected
rrrsccls. cspcciirlly itttls, lrs his lircus
,,1it'ltpltl'(Wils,rtt.
lltr'it tt'sr'rttt'lt ,,;,I;t;rlIlt's
199-+).
sP1'1'1q'5
t'lrn ltc lirrrrrtl in I)cwsbury's (198-5) cttnipilation of autobi-
l,llr,,l,r1,rsls ollt.n lrr.t.olrrt.rlt.r.Ply rrrlr.rr.slt.rl in Plrrlicrrllrr sllccics:rrrtl lrrrr.srrc rlil, 't\, lltt.\ :ru.,(. \\ ltrlt. ,,llt(lvltl' ollrt.t ;rs1lt.r.ls ol lltt. sltt.r.it.s'
It'lt'ttl ltttr". ,rl r;ttt'rll()n\
CONCEPT-OR
CHOICE OF SL]BJECTS
50
behavior. The cliche that 'research generally provides more questions than it
I
ENTED RESEARCH
5l
3.2.1 August Krogh principle
answers'keeps some ethologists studying one, or a few, species for their entire career.
This is olten efficient in that they can build on past experience with the species and are able to make ellective use
ol their time.
3.2 CONCEPT-ORIENTED RESEARCH
Although Lorenz came to study birds, particularly waterlowl, as an outgrowth of his apprenticeship with Heinrotli, he selected species with an eye to unraveling analogous and homologous relationships in patterns of behavior. Nisbett describes Lorenz's choice of subfects this way:
this statement'The August Krogh Principle'.
The indigo bunting was selected by Carey and Nolan (1975) to tesr the 'Verner-Willson C)rians hypothesis' that polygyny would evolve in avian species where critical resources are distributed ir-r widespread patches, if the advantages of one male mating with several lemales offset the disadvantages of reduced parental attention and possibly increased attraction of predators arnd depletion of food resources. Preliminary study of an indigo bunting population in Indiana had led
However his interest in an animal may have arisen in the first place - and this may in part have been by the interplay of change and curiosity - his chosen subjects did in lact lorm a coherent and rational array. The different species lell into several groups. First, there were those wliich were in their own right the central objects of his study; initially the jackdaws, then the herons, and now the geese. Second, were the closely related species: ravens lor comparison with jackdaws. or mallard ducks to watch out of the oorner of an eye wl-rile looking at geese. These showed not only what the ducks had in common with their geese cousins but also what they had developed diff.erently: he could ask himself 'why?' Heron society was markedly dilferent from that of jackdaws or geese; again 'wliy'l'Then there were the species unrelated to jackdaws or geese. but which liad similar elements of behaviour. This allowed him to look for patterns of behaviour to which evolution came indepenclently in
lirr human consumption.
Second, there is a wealth
available on these species.
It
different species.
was invoked when the rat was selected for the early psychology studies.
INi.sbett, 1977:44J
In some cases, researchers discover concepts that can be tested on their f avorite species.
Researchers who pursue answers to conceptual questions or concentrate their ellorts on a particular type of behavior attempt to study species that best represent the concept or type ol behavior under stucly. In 1929 August Kragh stated that 'For a large number of problems there will be some animal of choice. or a few such animals. on which it can be most conveniently studied'. Krebs (1915) has labeled
In other
cases,
previous research on
ar
species reveals aspects
of its behavior
which make it suitable lor testing a concept: this is illustrated by Alcock's 1lt)73) use ol red-winged blackbirds to test L. Tinbergen's (1960)'searcl-r imagc hypothcsis'. Ettrlier work with the species had suggested that thcy might selectivcly seitt'clt in patches where they had lound lood previously, but more importitntly lix' Alcock they were omnivorous ancl available. Therefore, he used this spccics in rttt cxpctimental tbod maze to elnswer the questions'do bircls lertrrr wltct'c llrcy rrrc likcll lo find lood and come to use locational cues to clirect thcir scarcltittg ruttl/ot'tlo bittls learn what lood they are likely to fincl itncl cor.t.tc to scurclt 1.rt'clL't'cttlirrllv lir llrrrt item on the basis ol'visual cucs irss()ciatctl witlr tlrc lirotl'l"l'ltc birtl irr tlris (irs('\\'ir\
thc rcrl-r.vingcrl blirckbirrl. ()llrcr lcse;uc'lre r':; llrr,'c lr'slr'tl 1.. I rllrt'llt'tt'r lt\'po1l11'11'' ()ll (':il litrn t'to$S (( .,, lt/\ ( (,t(,ilt'. ('l()./('. l()70). (l()lll('\ltt r'lttt k 1(,,tlltrt t',tlltrt. " l()/()) l)rrrrktn:. l()/l);rtt,l 1'tt',rl ltl', ll'rttu\tttttlt,r.l(o\;ttltlt
Carey and Nolan to predict that the poptrlation would be polygynous erncl therefore provide a good opportLrnity to test the hypothesis.
Experimental psychologists traditionally use rats, pigeons, or selected primates their subjects in studying learning theories that they believe will then be applicable to (or at least worthy of testing on) humans. Two reasons are generally given lor
as
pr-rrsuing concepts of learning on such f-ew species. First, many psychologists suggest that learning processes are basically the same in all species, differing in
itmount rather than kind. These species are being used as biological models in essentially the same way that the drug industry uses rats and mice to test drugs designed
of background data already is questionable whether the August Krogh principle
It was probrrbly cottvenience that initiated and maintained the momentum that established the nrt as the psychologist's choice subject (Beach, 1950). Several yeitrs ago while cooling olf in a local pub (after a hot afternoon observing yellow-hcttclecl blackbirds in a nrosqnito-infested marsh), Dr. Gordon Orians
crplttitled to.l. R. Watsort ancl n-ryself that he had become very interested in the slugs tlrirt lttragccl irt his hackyarcl garden in Seattle. His interest was more in testing ecologicirl lhcory thitn irr sitving his vegetables. Later Cates and Orians reported on tltcir lcsl ol'lltc ltypothcsis lhal 'early successionalplant species make a lesser comrrritrncrr( ol'rcsorrrccs to (lcl'cllrl agitinst herbivores, ancl should then proviie better lirotl resorrrt'es lor gcrrcnrlizctl lte rbivorcs than later successional and climarx plants,
((';tlt's:tttrl ( )ti:tlrs l()75:-ll0). Wh:rt worrltl It'sl l lrt'st'
pr
t'tl rt'l lol)s.) ('lrlr.'s
lr
l-rc ir goocl rcscurch anintal on rrtl ( )r'itr rrs er ;tllr irr:
( ir'lrt'r;rltzt'rl lrr'r lrrrr)t('s
whicli to
iil(' t('(lilrrr'rl lor lr'slrrrl' llrt.st. lltr'rlit.liot)St \I,C ll,t\,'lt',,',1 ttt ,,ttt ('\l)('ll1t('11', ltto,,;)('( t(.,, r,l ,,lrrl,,, lrp1l1 Irllp11 11 lrr 1,1.,r..'
Table 3.1. Credit-debit sheet of some characteristics to be considered when selecting a subject species Questions
Suirtthilitt'
Is the species suitable for the concept being studied (August Krogh principle)? Can you recognize individuals by natural marks or can they be easily marked? Does it engage in interesting behavior which you can observe repeatedly? Can you make the necessary manipulations on this species?
1;
tiluhilitv
Debit
Credit
Characteristic
Is the species found locally or will you have to travel to study it in the field? If it is found in a foreign country, what are the political ramifications?
If you want to observe the species in the wild, is it accessible in its habitat? Can observations be easily made without altering its behaviour? Is it nocturnal or diurnal? If you want to bring
the animal to you, can it be easily obtained? Is it on the rare and endangered species list? Can it be easily captured? Can subjects be replaced if they die? Can individuals be returned to the point of capture when the research is done? 1,i-;n
r
uhilitt
How will the animal adapt to life in captivity? Can you simulate its natural environment and provide for its special needs?
Are its habits compatible with yours? B _.-
,''
-.,tr, tttfitl riliLl'tit.)n
-\'.,i'r;_,.'i',
What is already known about the species? Is there a reasonable backlog of data on which to build? Has someone already done the research you are planning? Hou'does it help you answer the questions above and anticipate other problems? Total the credits, assess the financial commitment, and accept or reject the species
\our subject. \\-iil 1ou be able to lollow the guidelines for animal welfare and ethics in research? as
tsee Appendices C and
D)
-=,=-,--==7:/! ii ;i=__ri? . . =,? = - i_i lil: ;1 *=E A i 3 7i=ltr,;?Z! =Zn tEi=e={ tElI:; ''r ,Z-V==-;iE -e71=iEE[E*sigiIE [i '-[? =a r={ t€ #?cf st] =a"a g E:;z* iAt Id 'ix;: I q== 36 = ="8;x'serAEE =Ec7'=l--==
=;ei*==P+1I-iigiLiEiz i
=:q ?ie;u[1:+,gI1Ef; lEl ?==. =-..: i,.= ?.-; ,?i ?E:il?lgie:*f+s=a +a=a
E
a.Nee6:7i 9, 3En =i=!1,-i" 6{zA ie:=c7.iivs==3:=: !I u. cv L:-='o:s ;:AgaH jTfr !=EB€ 3-;
$ =
E3f3' i,1{;g?o 6P;
iIEgE i;E sqaqg=
r€i=2
]eEg !&iE =., a'fisB 3.:o-=
EE+ei+as$irtBxsE, ;Fe: j .-=(z- = c E-=-7 2.3++ =; f ='-iZ i:Yin ==E 3i*q f E! i"! Exi =f *s*} FaTa==3iiirEE5;Ei i:g +== : =1;=.iE,i=in&=2i I 43Zi 5$V -.'-*.
oo
(D
-l
o + (D
Fl
.D ja
v
Llr
'O
P ^:
(D pD
() +
CD
o'a r= U
J
o a
lrl V
4 a^)
-m t
:(,
A (D
o P
(J
A/r + )+
:1
5
Lrl
P () I
o-
=i* =irE=E3ili;EFg??i *F3E a? EadiBi=i?iFil=p=sr =€lr=6e=_xli1s \ ? BFp 332 tfr17 kfi[ q=?f;=itfrrEt,?$lFE[ ?zi; \ rxi =EEg=$:Ear;Zia,EE; 993= * +Y Ec 6;H
JA p2 1 -t) o-
5
r|
P
%
='
r_1
o
s
J.
+ Y ie
=
qi=
a.
3+;
=lPA2 ;: = x s7*= ??'1!:A= o-d) =s'!T Tg,gF3{r =;E*:;EE
\o
iiir
\
(A (D F0
o UJ
HOW TO OBSERVE
55
while watching they may develop interest in a particular species, behaviors and
4 Reconnaissance
questions; but obtaining answers to those questions requires careful observution,v.I recently watched a goshawk glide from it's perch in an aspen tree and snatch a red
observation
squirrel olf a log using the talons of its left foot. It was a chance occurrence which was exciting to watch and raised some questions, such as: Do goshawks usually
employ only one set of talons when capturing red squirrels'/ If so, are individual goshawks primarily left-footed or right-footed? Answers to initial questions, such as these, usually lead to additionalquestions.
Most visitors to zoos occasionally stop and watch animals. When they leave they can tell you about many of the animals they saw and some that they watched; lew
4.1 HOW TO OBSERVE
can relate any in-depth observations of particular behaviors. Many bird-watchers
An early step in the study of animal behavior involves intensive
reconnaissance
see
a bird, identify it, and rush on hoping to add more
species
to their
lists.
observation. This may occur befbre you have decided what aspect of behavior to study and probably befbre you have lormulated any hypothesis (Lorenz, 1935). This 'is the most early stage in which you become f,amiliar with the animal's behavior arduous ancl demanding aspect of behavioral study'(Marler, 1915:2).lt is extremely
Observers take the time to study the behavior of the birds, describing in their note-
important, for no successful research can be launched without this background knowledge. Methods have been suggested lor evaluating sampling plans for longitu-
of'animal behavior. Observation may be as much a state of mind and awareness as it is a clearly defined technique. This is reflected in the quotes in Chapter 3.
dinal stuciies of behavior basecl on 'pilot samples' taken during reconnaissance
Observers must be more than a visual recorder; they must also be aware of input
hooks the intricate details of individual and social behaviors and perhaps recording
the birds' vocalizations lor later relerence and enjoyment. Intense observation is generally more rewarding than superficialwatching. It is also necessary in the study
observations (e.g. Kraemer et ul.,1911).
to their other senses and must think. One must be disciplined enough to know when
helping you to clesign your research, these initial observations also provicle an important source of additional questions and hypotheses.
to bc a machine-like recorder of data and when to contemplate what is happening or lrls happened. The experienced and astute observer often develops and 'tests'
Besicles
lrypotheses mentally while keeping animals under observation. Obviously, attention
Having myself always spent long periods of exploratory watching of natural events, of pondering about what exactly it was in the observed behaviour that I wanted to understand belore developing an experimental attack, I find this tendency of prematurely plunging into quantilication and experimentation. which I observe in many younger workers, really disturbing, unless, as happens to come, they do, fronl time to time, return, more purposefully than before, to plain, though
to rapiclly occurring behavior is not consistent with theorizing, hence priorities must lrc established.
l{cscarchcr priorities while observing various behaviors and species in various lrrrbilats ure generally the same, as flollows:
I
t :
Tinbergen (above) talks about 'exploratory watching', 'more sophisticatctl wrttclting', ancl 'observed' behavior. The terms watching and observitrg ttrc ctltllttrottly interchanged (see quote from Huxley on p. 113), but I belicvc cthologists slrotrltl
r
more sophisticated watching.
ITinhcrgL'tr,
I 95 I : vi
draw a distinct dichotomY.
ILccorcl clata accurately and completely.
('hcck ccluipment to make sure it is working properly and repair it if neccssir ry.
'l'hirrk about what is happening. put it in perspective, and lormulate Irypollrcscs.
Think ubout the hypotheses, discard the easily disproven additionalthought and testing.
orrcs. rrnrl lvritc rlown those that call for
corrsitlcralion ol'hypotheses is a procedure to be used only n lrt'rr nrrkirrl irritilrl rccortrurissuncc obscrvations fbr the lormulation of rprt'slrorrs.olrjr't'tivt's;rrrtl lrylrolltcscs. llrlr,vcvcr.s()ntcsitntplingmethods I lris t'onsl:rrrt
.l.l.l
Watching versus observing
Baby-sittcr-s rvtttlt clriltl pcn: tlcvcloprrrcrrtrrl psyt'ltolop,ists rtlr.tr', t'r' lllt'ltt. ll rrlr ltrttt', t: ('lll()\' lrttllt tr,tlt lttttl' lr c:tstltl cp(l(.lt\,()t': ttlr.st,t yitt,t: ls ;t tir,,)t()1s l)t(rt't'ss. l'lltolof ir,t\ ;ilttl rrltsr.t Vitt1, ;,ttrnt;rls I lrr'l' t('( ('t\(' l)tll(' {'lll()\'lll('lll I l(rlll ll rtlr lttttt' ;lllllll,ll.' ,ttttl
((
l.i ;rrrrrl/or rrlrt'lir itr ,rl'tlrr'lrrrirrurl(s) tuttlct'obscrvlttitrn cittt rrlt'Pt'r rorl., rlrr nt1'tl;rl;t t ollt't'll()tl (ltll ltlt' rvlrit'lr v()tt ('tttt ltlltlrv V()ttt'l)trr\ ,,,'ll l,r llrrtrl. ,rl,,r111,r,ltlrlr,rtr,rl,1u(",1r()n',rrtt,l lt\Polltr'rt'r. lrttl ltt'rt'r :tllrlrv
lr:rptt'r
+
RECON NAI SSANCE OBSERVATION
HOW TO OBSERVE
your mind to wander to where it affects Priority I (above) and results in observer error (Chapter 8).
analysis of the distribution of deer droppings, the grumpiness of goose gatherings, or the ballistics of'bison bellowing as the intellectual link that completed the conceptual chain from molecule to mastodon. Your lips move a little as you take on a set of bored, cynical journalists who came to the press conference to play it for laughs and a chance to get in
Satisfy basic bodily needs. Plan your sampling periods so that your basic
bodily needs do not suddenly appear to be priority number one; however, should the urgency of urination and/or delecation prevail, carefully consider the valuable protocols provided by Meyer ( 1989). Meyer has provided carefully researched recommendations suitable for killer whale observers in sea kayaks, peregrine falcon observers suspended on a cliff face, and desert tortoise observers trudging the sands.
Once you have the above priorities firmly established in your research protocol, remember that periods spent in the field are likely to be some of the most treasured
times of your existence. They should be both productive and enjoyable, but they won't be constant fun. Lott (197 5) reflected the feelings of many ethologists when he wrote'Protestations of a field person': 'Welcome back! Have a good vacation?''I wasn't on vacation. I was in the field.'Well that's not what I mean. Being in the field isn't a vacation;
it's hard work, a hard life, and besides . . . But hold your tongue. People who spend months at a time noting the behavior of animals in odd corners of the world are usually greeted that way. We're happy with our work, of course, but for several reasons it doesn't qualify as a vacation. Sometimes just living there is a problem. If you can't find or afford a convenient house, camping out becomes living in a tent by the second week. And the kind of stick-to-your-ribs food that stores well in a burlap bag or metal box soon starts to stick in your throat. Some colleagues and I went to visit Patti Moehlman's burro study in Death Valley a couple of years ago, and brought along some steak. Patti had been without refrigeration for weeks. Her response was succinct and eloquent: 'GOLL-EE REDMEAT.' . . . So welcome was the steak that it hardly mattered that the water from a desert cloudburst streamed intcr our plates as we ate crouching under a picnic table. But food and housing are lar from the worst of it. Your can get used to eating almost anything and sleeping almost anywhere. The worst ol' it is that you get to be a little bit batty. To be more specific, you get to be sort of matnic-deprcssivc. Vrrr experience mood swings that increase as a direct iunction ol'thc nrrnrbcr' of seven-day weeks you've spent on the project. . . . Thc rnosl sirlicnt symptom is that yourevaluattion ol'thc stutly gcls l() bc wiltllv rrrrrcrrlistrt'. High noon rlay fincl your pulsc rircirtg irs y()ur rrrirrtl lot'rns llrc krrrtl of- modest. corttitincrl. bul 1'tcttclrirling rcrrrrrks tllrt 1rt'r'surrrlt' llrt' Nitlitlltitl Acittlctttv ol' St'ie rtt'r.'s l)l('niu v st'ssiort llrlrt llrt' Nolrt'l ('()nlniltt't'tltrl intlcctl knorv tvllrl tl rr';rs tlr)urlr 11 1,r.',, ll ( rl('(l \'oul
a dig at the granting agency that spent nearly $1.750
in support of your
research. A basic stock of fine ironic wit, a dash of captivating candor, an irresistibly lucid illumination of The Link in layman's terms and they
first sobered, then entranced. When you release them Irom your spell, they will sprint to their typewriters and set their two forefingers to banging out near poetry in praise of basic research and (blush) you. That evening you rnay be so sunk in shame that you want to change not only your study but your name. How could you have committed yourself to a study so barren and one for which you are so ill prepared? are
What will you say to that granting agency when they ask what became of more than $ I,750 intended to support significant basic research'l If you take the entire blame you'll never get another chance. Besides it wasn't all your fault; but how do you make them understand that fate has thwarted you at every turn, that your field glasses fogged up during nearly three goose gatherings, that the rnicrophone salesman was lying, lying when he told you how far away it would pick up bison bellows'l Yes, who will bear witness that your failure was not really your fault now that God has turned his lace lrom you? And so yoLl go on, ever more sublimely happy, ever nearer suicide. During your more lucid moments you realize, of course, that you're getting to be a little bit batty, and you come to crave some stabilizing influence to dampen your oscillations. Contact with an old friend becomes so welcome that you hold your tongue even if he says something stupid like, 'Welcome back! Have a good vacation?' ISee ulso Huilnnn's ( 1973
) discussion oJ.fieltli,sm]
The following quotation carries a hidden message: '. . . as the vtork [italics mine] by Cain and Sheppard has shown . . . '(Tinbergen. 1958). Whether intended or not,
the word work is indicative of what ethological studies can, at times, become. Even
though the overall experience is enjoyable and the results rewarding, it can become tircsomc. ancl you are often confronted with having to convince yourself that the cnrl.jrrstilics thc means. Chenev and Seylarth describe the 'syndrome'this way: Anyonc who has ever studied animals in the natural habitat recognizes tlurl cvcn tlrc nrost stirlLrlatirrg proiect includes moments of unrelieved lc'tlitutt. [ ('hcnc.r' und Sciurth. 1990;31 3 J
l'or crlrrrrplt'. l
slt's (l')(r7:,15; lrrlrrritlt'rl
llrlt't.lrl () )Ol'Nl .rrltrlt'lulrsottl
rrlr)n(',
lo nr';lr lrorctloru wltctt ltc wrotc.'A wcck llr('t,rllvtttl'r';tll ol ltvcttrtsltp'rtitttlislt':tctctl
R
58
ECON
NA
HOW TO OBSERVE,
ISSA NCF- OBS E,RVATION
me from the rather tediou.v .joh oJ recorcling gnu ut'tivit,r, putterns [italics mine].' Schaller's description of part of his study on the Serengeti lion is also illustrative:
My existence revolved around lions, I was wholly saturated with them. talked and wrote about them, and thought about them. . . . A few times. though, I saw too much of lions. Once Bill . . . and I decided to track a lion continuously by radio for several weeks. . . . The first lew days were
I
discovered some delightful insights and an eflective method in Frederick Franck's (1973,1979) books: Tlte Zen o/-Seeing and The Awukened,trl'e. First, I have
and
to admit that, like Hofitadter (1919:246) '['m not sure I know what Zen is'. But. lor our purposes, that doesn't matter; Franck's technique of teaching drawing through seeing is what is irnportant.
While I am SEEING/DRAWING, I take hold of the thing, until it fills my total capacity lor expcrience. Once I have thus taken possession of a hill, a body, a flace. I let go, let it go free again, as if I were releasing u butterfly. Yet it remains mine forever. After much SEEING/DRAWING my eye goes on drawing whether my hand draws or not.
rather pleasant. . . . As the days passed this delight vanished, and we went about our task with grim determination. . . . We stayed with the male for twenty-one consecutive days and suffice it to say that for once I had a surfeit of lions. ISclrullcr, 1973:90 9l ] The descriptions of behavior of wild animals that you read in the literature are olten the result of weeks. months and years of careful stalking, hiding and painstaking observations (e.g. Packer, 1994). Often hours are spent in a blind under
than ideal conditions, with inclement weather making you physically unconrlbrtable and your view of the animals poor, and the inactivity of the animals becomes frustrating. Your binoculars get beaterr about and rained and snowed Lrpon, and the pages of your field notes become limp and stuck together. Field research can be trying at times, but you can make the best of it by being physically and mentally prepared. Expect Murphy's Law. 'If anything can go wrong it will', to take elfect from time to time. Allow fbr some slack in your schedule to absorb days when you cannot collect data because of poor weather or equiprnent breakdowns. Often, you can release much frustration merely by recording the disasters in your field notes, realizing that in years to come you will remember even those days londly as you reflect on the data-rich days with pride. less
In conclusion. successfui data collection through observations necessitates your: l. having developecl the skills necessary for ellective and efficient observation (see exercise below); 2. having the proper equipment (e.g. binoculars and spotting
IFrant'k, 1973.125J
Although Franck developed lris techrrique for teaching drawing through seeing, I lound it equally effective in teaching seeing/observing through drawing. Based on Franck's description of his seeing/drawing procedure, I developed the following cxercise:
' .
Exercise in devektping observutional skill.s through drutring Objective: To develop observational skills, rlot to create an artistic rendi-
tion of the animal. However, it is important to attempt to recreate with the pencil what you see/observe with your eyes.
not 'making a picture,' yoLI are nol being creative. just conducting an experiment in SEEING, in undivided
Please realize you are We are
attention! Tlie experiment is successful if you sut't'eetl in f-eeling you have become that leaf or that daisy, regardless of what appears on the paper. I Frurtck, 1979:rviiJ
'
Materials: l. An easily observed animal, perhaps in captivity. It helps if tlie animal is not overly active. You might find it better to begin with
scopes)l 3. understanding the various ways to describe behavion 4. having a well-
stul-tecl aninrarl ancl go frorn Step
designed system lor recording your field notes; and 5. knowing when your data arc
procedure with a lrve animal.
sufficierrt. The observations we are discussing here are either utl lihitunt saruplcs (Chapter 8) or initial reconnaissance observations on which ar luture wcll-clcsignerl study will be based. Regardless. the skills used are the same: only thc rclative crnplt:rsis on the data collected is
likely to be dillerent.
59
'
2. I'apcr', clipboard and pencil.
' '
Ste
I
a
to Step 4: then go through the entire
I)nrccrlu rc:
p: l. l'ind a conrlirrtable place to sit and conduct yourself as an indivitlturl rrlonc u'ith thc i.rnimal. Pay no attention to anything else around vorr. hcsitlcs tlrc uttinritl.
'
4.1.2 An exercise in observing
Lcarrt to scc wlrrrt vtltr lrrc Iorlkinu
l'ir seveltl
l,r'rrts
Ilrlrrl strrrlllt'tl lo
;rl
1111.1
;ur t'llr'r'lrrr
ltott'lo oltst'l\(' ;tlllntill lrr'lutr tot I lrt'rr ..,)lu(' l('n
.1. SPr'1111
l'('lr('r '.ltttlr'ltl', ,t lt,ttt,l
ir
I
lr li'rv rninrrtcs l'r'ccly obse rvirtg the animal to
get'a feel'for its
llt'lutvirll' llttl tct'lts.
' \ Atsonrt'Poirrt irrtinrc'lrecze'rr rncrttrtlirrrrrgcol'tltcitnintitl:utcl r'lo,rc tt,ut t tt'r Kt't'gr \'()ur ('\('\r'lost'tl;tntl ltoltl lltt'tttclttrtl iltutgc rrrttil it llrtlcs. Itt'pt',rlllrr.,rrrrlrlYrrrrr'iurtlr;rrrllr('()ulltttt'.rl
lltt':tttitttrtlrrillr\'()tlt('\'('s
HOW TO OBSERVE
RECONNAISSANCE OBSERVATION
60
closed; that is, trace the outline of the mental image. It's not important whether your drawing looks like the animal. Forcing yourself to draw
form a more stable mental image. Repeat this process until the mental image is fairly stable and more detailed. 4. Again'freeze'the animal in time and space, but keep your eyes open. Stare at the space where you'froze'the image and hold it regardless of what the animal does and where it goes. Repeat this process until you can forces you to
hold the image strongly and long enough to draw it while focusing on the image in space. Treat your mental image of the animal as the object that Franck is refer-
ring to below:
Allow the image on your retina to set off the reflex arc that goes directly from your eye, through your body, to the fingers that hold the pencil. . . . There is no thinking, judging, labeling in this reflex arc: it goes from the eye to the hand and skips the thinking, judging, discriminating brain: just allow the reflex to work, to take over! . . . Don't be surprised: in the beginning your concentration span will be short. When your attention flags, stop for a moment.
'
5.
IFrunck, 1979:35,38J
After you have become reasonably good at Step 4, begin to observe the
part of your research, you should use standards, such as Smithe's ( 1972) 86 standard colors which are based on Ridgway (1912) and Palmer (1962). After learning to observe through seeing postures, movements and colors, you can learn to 'observe' sounds through hearing in a similar manner; sounds should then be incorporated
into your descriptions. Each step in the exercise outlined above takes time, and Steps 3.4 and 5 will need to be repeated several times. That time and effort will serve as a good measure of your disposition and aptitude lor ethological research. If you aren't comfortable with the concentrated effort required in this exercise, you shouldn't try to conduct re search that requires extensive observations of an animal's behavior. For additional exercises in observing you can consult Roth (1982); he offers scven exercises involving increasingly more difficult activities in a chapter entitled
' I'he art of seeing'.
.{.r.3 Field notes
I iclrl notes are often the best, and sometimes the only, record you have of your ,rt'tivities and observations in the field. The method of taking field notes described lrt'low is primarily applicable to inlormal field trips and reconnaissance observa-
It
for some studies, data can be collected in a field note format which
I r(
continuous motion. Locomotion (e.g. different gaits of horses) provides
r
r(
l,
rr lq';srning those skills is a knowledge
opportunity to develop this skill. At this stage you should be able to close your eyes and 'replay' a segment with mental images. 6. Write a description of a segment of the animal's behavior. Repeat the process until you see and retain more. an excellent
'
)r)s. However,
animal in motion, first as a series of disjunct'fieeze frames'and then as
)r'l)ol'rrtes previously designed and prepared data lorms (Chapter 9). ( ioorl lield notes are the end result of developing a skill into an art, and the basis
(
Note taking . . . is an art requiring sensitivity and skill. Perlection is to be sought and pursued through selective training lnd persistent practice just as beauty or reality are sought by the sculptor, the musician or the poet. IEmlen, 1958.l7Bl rrnattarinable, a goal
is most important that you strive to draw in Steps 3 and 4. Just as recopying
class notes helps you retain the information, drawing the animal compels you to focus your energy into observing and retaining the mental image. This is reflectccl irr
Walther's ( 1984:xi) account of why he draws animals.
I draw animals not to compete with Leonardo da Vinci but to make surc that I have seen them correctly. As one of my anatomy teachcrs plrnrsccl it: you have only seen something when you have made a skctch ol'it. Drawing the animal locuses your attention on its nrorphology. Movcrrrcnls :rntl
posturing result in changes in the relative positions ol'parts ol'thc rrrrirrurl's
rrror
phology. Observing the details ol'thc posturcs. rrrrrl thcn llre rrrovcrrrerrts. g.rrtlrr:rllv becomeseasier. At lirst, yorr will pnrllrrbly ol'rscrvc prinurrrly irr lrlrrt'k lrrrtl w'lrrlt', lrrrt
littcr ytltt will obsct'vc luttl renl('nrl)er t'olor. ('olors ;rntl llrt'rr t'lr;url,('\ ('rn lrt' ;111 itttlloltltttt ttl('irllsol totttnttnu(lrlr()n nr ilntttltls ll tr'tt,trlrtl'tololr
r\iln rnl)orl,llrl
of fundamentals.
I lr(' svslcnr ol' taking notes that you decide upon will determine their value to ,,u ,rrrrl olltcr rcscarchers in the future. Most ethologists with whom I have spoken ,r ,{ ',()nr(' vrrt'ilrlion ol'thc system developed by Dr Joseph Grinnell of the Museum
,,1 \r'rlt'lrr:rlc /.oology. IJnivcrsity of California, Berkeley. The usual lormat is to llrt' Itolclrook ittto tlrree sections: l. journal; 2. species accounts; and 3. ' rt.rl,r1' llrr'specics-lrccotrnts scction is used to keep records of your observations ,lrr r,lt'
I
, .pr'r t('\, nr t'orttr.;rst ttt thc.iorrrnal. where yilur observations are recorded by date rrr,lrrrrrr. Ilrt.r'rrtlrlorr,sccliorrisusctl torccordspecimenscollectedinthefield.The Io lollorv is lr:rsetl rr1'rorr (irinncll's systcm, but will be concerned only Irrrt'rl prrpr'r slrorrltl lrt'rrsetl.
lt
slrorrlrl bc rr borrtl, hirving
a
Iltt'rtzt'slt,,ttlrl lrc lrltllto\nltill('lV (r' ,irtt'ltes lly li r', irrchcs
62
(
HOW TO OBSERVE
RECONNAISSANCE OBSERVATION
l6 centimeters by 2l
%
centimeters) preferably looseleaf ancl kept in a six- or three-
ring binder. An irnportant reason for a ring binder notebook is that many fielclworkers preler to use two notebooks. One is taken into the field fbr the day's records, and the other contains past records lor the year and is kept safely in camp or at home. Some field ethologists prefer
outdoor
to Llse bound notebooks wliich are designed lor
use, such as durable,45,/t" x
72" notebooks with 80 lined pages and polyethx7") and relatively 'water-
ylene covers, or polyethylene covered ring binders (4t/{'
proof' loose leaf paper. Black waterproof ink is usr,rally recommended; Forestry Suppliers sell a ballpoint pen which will write underwetter, upside down and in temperatures to -50"F (-45.5"C). A hard pencil is the second best choice. You should always carry a pencil, however. for both a backup and fbr writing in heavy mist or rain: ink is not waterproof until it is dry. If you r-rse a Rapidograph pen, try to use a 'noncloggir-rg' drawing ink. The lollowing three inks are recommended: l. Pelikan Drawing Ink; 2. Higgins Eternal Black Ink; and 3. Koh-l-Noor Rapidograph lnk. A sarnple field note (journal) page is shown in Box 4.1.
Box
4.1
63
(cont.)
Lot'ulity'. Specific locality, clirection and estimatecl distance frot.t.t known point (e.9. E shore of Cobb Lake, 6 km NE of Ft. Collins, Colorado); section, township and range are also useful inlornration. Dutt': Date of observations. travel, meetings, etc.
Miltuge (beginning, stop points, untl entlinS4):This provides a record to confirm route of travel and may later provide useful additional inlbrmation when giving directions to others (or conferring with the rRS).
Othcr observcrs'. Provides for later verilication and elaboration (Remsen, 1971). Ll'euther: Precipitatiorl, percent cloud cover, wind speed anci direction, temperature, etc. Insert cltunges in
yp71v
ttoles us tlrcy o('('ur.
Temperature can be measured by carrying a small. metal-shielded
thermometer with you into the lield. For most str.rdies it will be sufficient, anc'l worthwhile (e.g. Mrosovsky and Shettleworth. 1915), to estimate measLlres of the other weather variables. tn 1805. Commander Francis Beaufbrt of the British Navy devised a scale of
Box
4.1
nine categories ltrr classifying wincl lorce at sezi. More recently a The field note journal
A sample field note (journal)
on Beaufbrt's scale ) tbr use on land. The scale's l3 categories are convenient for cstirnutiri,r; wind speed while scale was developed (based
page is shown below: Page no.
Name
in tlie field: Butu.fbrt',t ,stult'.f or vind ,speed
Locality Date Beginning mileage Mileage at stop points
Scale Wind velocitv Environmental indicators
0
Movement of the air is less than I mile
(.ultrt
(1.6 km) per hour. Smoke rises vertically;
Other observers Weather
Habitat type Time into field
bodies of water are mirror-srnooth.
I
l,ichr
uir
I
)
l.i,qltt
ltn,c:t,
4 7 n-riles (6.4
(
1.6-4.8 kn"r) per hour. The drift of
smoke indicates the direction of the breeze.
Time: Observations and remarks Time: Observations and remarks
3 miles
11.3 km) per hour. Leaves
begin to rustle.
\
(it'tttlt'
ltn,t:t,
8
l2 niiles (12.9 19.3 kni) per hour. Leaves
lrnrl twigs in urotion: crests on waves begin
lo hrclrk. T'itnc otrl ol'licltl I
:tttlirty' ntt
lt'1r1,1'
I
l
lrtrlt't'tttt'l,tt't'.,'
I
I
ll"i
(-ltl.',t l() 0 ktn)1tct'Itottr. SntitIl
lrr :rrrr'lrr's
nl()\'(': tlrrst r ises: nlllnV rvltitcc:r1-ls
ott l;ttI't' lr,rtltr"' rrl rtltlt't
R ECON NA I SSANCE
Box
4.1
HOW TO OBSERVE
OBSERVATION
Route of travel
(cont.')
5
Hours of
19-24 miles (30.6-38.6 km) per hour. Small
Fresh hreeze
in leaf begin to sway. 25 3l miles (40.249.9 km) per hour. Large
Strong breeze
observation
Weather
Species observed
trees
6
65
General impressions about the day's observations
branches begin moving. Moderate gale
32-38 miles (51.5-61.1 km) per hour. Whole trees in
motion.
Strong gale
impressions are the most accurate and that in recopying you are prone to edit them, therebymakingthemlessaccurate.
47-54 miles (75.6 86.9 km) per hour. Foam
extremelyvaluable, and loss ordestruction in thefield, home orofficemust be avoided.
Pastjournalsshould bekeptbyyear. Field notesare
Regardless of how you choose to take and store your field notes, remember that
uprooted; huge waves build up with
they are a record of your fieldwork - be c'omplete. They are a source of data and hypotheses and can be the basis for future research be accurate. They may be used
overhanging crests.
by other researchers for similar purposes
55-63 miles (88.5-101.4 km) per hour. Trees
Whole gale
day's observations (e.g., Schaller,
on trees break off. blows in dense streaks across water at sea.
l0
a
1973; Remsen, 1977; P. Johnsgard, pers. commun.). Others believe that your first
3946 miles (62.8-14.0 km) per hour. Twigs
Fresh gale
Some fieldworkers recopy their field notes after
l1
Storm
64-75 miles (103.0-120.7 km) per hour.
- be c'lear and concise. Last, but not least, your field notes will be a diary and a source of memories; insert thoughts you will
12
Hurricane
Wind velocities above 75 miles (120.7 km)
cnjoy having again,2O or40 years later.
per hour.
If you need accurate
(+3"1') measures of wind velocity, small hand-held, digital anemometers which read in three scales (fpm, m/s, mph, and knots) are available for example from Cole-Palmer Instrument Co., 7425 North Oak Park Ave., Chicago, Illinois 60648.
Habitat type: General topography and vegetative cover; note prominent physiographic features. Time into .field: Time at which you begin your fleld-related activities (e.g. 1345-1510). Midnight:0000; Noon: 1200; 6:00 pm: 1800.
4.t.4 Equipment 'l-his
section will deal with the three basic tools used in field ethology. More sophisti-
cirted, high-tech data collection and analysis equipment will be discussed in ('lrapters 9 and 13-17, but lor reconnaissance observations and some descriptive studies a blind, binoculars and a field notebook will often suffice. Huxley's (1968:15.76) account of his classic study in 1914 could easily be used to recount a tlcscriptive study of today.
A good glass, lbinocularsl
Ohservations and remarks'.
1
Record the species, number, age, and sexes
2
Describe behavior as accurately, clearly, concisely, and completely as
if
possible.
unknown lacts about the crested grebe, but also had one of the pleasantest of holidays. . . . Some of the watching was done concealed in the boat-houses, and some from a screened punt, but the major part ll'orn the bank. This in many ways the most useful . . . every action can bc casily krllowed, the birds are not scared, the field of view is rrrrirrte rruptccl, and it is far easier to follow the actions of the same pair ol'bircls lirr':r long pcriod of time.
possible (see discussion below).
3
Include remarks such as unusual occurrences (Short, 1970), thoughts. and ideas about the behavior. Lindauer (1985:7) reports that Karl vorr
Frisch'had a weakness for little things. for unexpected rcsults. lirr called singularities' Record observations at
so
.
on('e
tkt nol lrusl lo nt(nt()r.t'.
Time oul o/'liekl: It is clcsirablc to rccorrl tirnc rrt wlrrclr yorr lr';rvt'llrr'
.t.
t..tu lllinls
llrt'prnposcol
sturly sitc. lrs wcll irs whcrr yrrtr rcirclr Ir0rrre ()r'r'iuill).
(itttt'r:tl ('t)tntnt'nl.\". At tlrt't'ntl ol t';tt'lt rl;tv's ltt'lrl olrst'tr;rlrotrs rl rrllt'tt rlt'sit;tlllt'lo ltr'rtrl:r tt('\\'rlt1't'( ir'ttt'trtl ('o1111111'111 r;tttrl lt'.1
a notebook, some patience, and a spare
fortnight in the spring - with these I not only managed to discover many
ts
;r lrlrrrtl 1or
lrrtlc)islo:rllorvobscrvlrlionol'itttitnitlswithaslittledis-
(' ;t\ Possrlrlt' (t' ,' Sr)r('nsott. l()().1) I lr:rl is. y,ltt ltrlpc yrltt :tt'c obsct'viltg Irt'lt.trlot \\ll( lr l:. ult.rllt't lr'tl l11 yottt l)r('\('n(('. t'\r'tl tl V()tlt l)t('s('tt('r'ts Pett't'ivctl lry lrrr lrlut(
66
R
ECON NAISSANC E OBSERVATION
HOW TO OBSERVE
67
the animals. In situations where animals are accustomed to hur-nan presence. or where you can habituate them to yourself, no blind is necessary. At the other extreme are species with whom great caution must be employed. You can think
of
too much activity in the field as having the lollowing effect:
A walker displaces the territory
as a swimmer does water, but a quiet sitter is a dropped stone and his ripples subside and water laps back in:
submergence. Illinds are of two general types natural and
[Haut-Moon, l99l;-]67J
artificial
and may be in. on or
beside water; or they n-ray be below, at or above ground level. Knowledge
of
the
animal's reactions to novel objects at various places in their environrnent is necessary lor selecting the proper blincJ. For example. sitting in a tree will sulficiently hide yc.ru
from many species that are not prone to be look vigilantly tzrr above ground
level(e.g. deer).
Artificial blincls are generally designed to blend in with the habitat as closely as An unobtrusive blind is less likely to disturb the animals and attract
possible.
curious humans. However, rarther than selecting a natural blind (or constructing an artificial blind) with as little disturbance as possible, you might be able to use an obviours structure (e.g. vehicle, tent) and allow the animals time to habituate to it.
For example. since coyotes in the National Eik Refuge were accustomed to seeing dirt roads. Ryden (1975) rented a bright yellow van to use as a
vehicles along the
blind lor her observations. The same ploy was used by Kucera ( 1978) in his study of mule deer in Big Bend National Park. by Walther ( 1978) in his observations of oryx in Serengeti National Park. by Renouf (1989) in his str.rdy of harbor seals. ancl by Laurenson and Caro (1994) in their study of cheetahs. Even with domestic animals, similar procedures are often necessary to ensure that the animals under observatiorr are not clisturbecl by the researcher's presence. For exermple. Baldock ct ul. (1988) described their use of the same technique in their stucly of domestic sheep:
All
observations of behaviour were made from a parked vehicle overlooking the fielcl containing the ewes. Other vehicles were rcgularly parkecl nearby: We obtained access discreetly ancl quietly. nrukirrg cvcrv ef-fort not to disturb the sheep. As far as we could tell. thc hchaviorrr ol'
'fhc lirllowing charactcristics
1970).
shor-rlcl be considered when selecting
a uatural
blinrl or conslructing irn artificialblind:
the sheep was not affbcted by the observer's presencc. f I)ultlttt A, ct
lrig.,1.l lrxumples ol observation blinds (fiom Pettingill
ill /(/,\,\ i/)/
Nylon or canvas tents clfien make goocl urtificial blinds. Snrirll. liglrtw'ciplrt tt'rrts can easily be movecl between obscrvatiott sitcs. irntl lirrgcr tcrrls clrrr lre stlrketl intrr the grottncl to prtlviclc ir ntorc pcnniurcnl blirrrl. Il'yort rlct'rtlc to lrrrrltl rr lrlirrtl. llrcrt' itrc scvct'ltl soutccs ol'irtsttur'liolts lir lrrriltlinl r"rriorrs l\ l)('\. ir\ rrt'll .rs rrlt';rs lor t rl rtlilt! votil ()\\'rl rlt'sit'n. l ot r'rlrrrt;llt'. \\oorlur (l()S \) rlt",t rllrt'r t,rtr'.lrut lron ,rl ,l Potl;rlrlr'rrtttlrtr'll;r lrlrtrrl ;rtttl l{,,.1r'nlrou',('iut(l llt'.,l 1lt)li \)rlt',,t llrt'r on',lut( lrrrrl ol ,r l)()tl,rlrlt'lr'\\r'r lrl||r(l l tt,ttt, l l rllrr',lt,rl, , lltr'rlr".r1'rr rrl lrr| 11|r",,,1 lrlrtrrl
t
llclrlrvior-ol'irttintitl
ir.
l{crrctiorr to str'rrngc ob.jccts. itpproach or avoidJ
lr
SP;llirrl rlistribtrlion ol'bchltvior pitltcrns: ure you at the right spot to ,
'
rlrsr't vr'
( )lr.,r'r
,r
II
ri' lrt'lt;tr,' irlt''.)
\;tl toll;tl t lrlr;rllrlrll
Nrrrrrlrr'r tut(l \r./('()l ()l)('nttt1's lot olrst'lvllliott lttttl lilrrrrrrp
Ir ( ,r1r.tr tl\ l,)l lttltttlrt'l t lrr'llll,llt(
lt(
r'
oI ,rlr',t'l t('l', illlltt tlrltlt'tI
R
HOW TO OBSERVE
ECON NA I SSANCE OBSERVATION
a. Can
d. Splrcric'al oberrotion Results in the inability to foous the binoculars
it be permanent fbr several weeks or years'l Will the animals be
sharply.
there during your observation periods?
t
Should the blind be temporary and portable (e.g. Winkler, 1994)? How
+
+ Alignment.The
of the binocular should
s Eye relieJ'. When the binoculars are held comfortably should
see a
to the eyes, you
full field of view. Eyeglass wearers should check the effect of
the retractable cups found on many binoculars.
to reduce your olfactory and auditory stimuli from reaching the animals.
There are two numbers engraved on all binoculars. These are commonly used to designate the'type'of binoculars, such as'7 by 35':
that you can function effectively as an observer/recorder only if you are rea sonably conr lortable.
7x35 magnification diameter of objective lens (mm)
Further descriptions and discussions of blinds and their use can be found in Hanenkrat (1977), Roth (1982) and outdoor photography literature, such as Baufle and Varin (1972). Ettlinger (1914), and Marchington and Clay (1914).
images seen through each barrel
align and merge perlectly into a single image.
a. Severity; must it be built to withstand severe wind and precipitation? b. Prevailing winds may be important for locating the blind downwind
and portability are often conflicting objectives. Rernember
of distance
over which they are in focus.
olten and how rapid will the moves be? A camouflage suit can be considered the most easily and rapidly moved artificial blind. Climatic conditions
c. Comlort
Range of resolution. Good optics should have a wide range
Mugnific'atiorr tells you the number
of
times greater than normal that an object
being viewed will appear. Although increased magnification would appear to be tlesirable, it often carries with it some problems. Generally, the following occurs with increased magnification:
4.t.4h Binoculars and spotting scopes
t The field of view becomes smaller (see below). z Clarity is lost, since the more powerful the lens the more the imperfec-
Binoc'ulars One of the most important pieces of equipment to the ethologist is a good pair of
tions in the lens are also magnified.
binoculars.Infact, asTinbergen( 1953:132)has said, they'. . . arealmost indispensable' Humans are a visually oriented species; therefore, ethologists tend to rely very
I Light transmission is decreased. + Increased blurring results from movement
heavily on what they see. A very large percentage (probably over 95'Yu) of what we record as observations are what we see. Equipment that will provide us with better vision or in other ways make the animals more visible, such as a strategically located blind, will pay off immensely. Always consider the species to be studied and the dis-
-Ihe
of the binoculars.
first three problems can, of course, be overcome by the manufacturer, but
tlris rvill result in higher-priced binoculars. For most field studies,6 to 8x magnifica-
will be observed when selecting binoculars or spotting
tron will probably be sufficient. When greater magnification is necessary, spotting \('()l)c:i rrre commonly used (discussed later). However, Huxley (1968) used l2x
scopes (see below).
lrrrroctrlars in his classic stucly
Bergman (1981) has listed several criteria for evaluating the optical qLrality ol' binoculars, including the fbllowing:
l,rt'lrcs ll\ttlicrlt,s rri,stulus\, even though the optics at that time were not nearly as lootl irs thcy arc toclay. Flegg (1972) can be consulted for recommendations on the
tances over which they
t Brightnes,r of the image (see below for a measure of relartivc brightncss). 2 Resolution of the image which can be affected by the firllowing tlcll'cts. a. Edge-of-field de/bcLr. The margirts
of the ficlcl ol'vicw shorrlrl
rr1'rprirri-
mate the sharpness of the center.
Pintushitn tli,slorliol. Pitt'ltllcl littcs ctrrssirrlt tlrc licltl ol vicw rrrrv ilppcill't() cul'vc lowlrrtl lltc ccrrte r'. b.
c.('ttrrttlttt't'ttf lltt'ittttt.r,r'. Asllti,'ltl surllrt'r'.srrtlr ;rr;r rr:rllln;t\;tl)l)(.iu ('()ll('ll\'(' ()l
('(
)ll\ ('\
I
in
1914
of the courtship behaviors of
great crested
r'pt' ol. binoculars (mag. x clbjective lens diameter) to be used for observing birds in
tlrllt're rrt light lcvcls ancl lrabitat types, and over different distances.
()ltjt't tir'<'
\\
lt'rr.s'
tlitutrt,tcl irll'ects the amount
lr;rl is irrrporlrrrrl. lrowcvcr. is thc amor-rnt
.t ulirr
lt'rrs (lillrl
of light that enters the binocular.
ol light which finally
passes
through the
lnrrrsrnissiorr or rclativc brightncss). Everything else being equal, tlrr'l;ul,t'r llrr'olrjt't'livc lerrs lltc gt'clrlcr tltc rcllrlivc hrightncss: tlrcrctitrc. binoculars rrrllrl;rr,'t'olrlcr'lrvt'lt'ns;ut'llt'llt'r ttntlt't lorvlilltl t'otttlitirltts(c.g.tlttwtt.tlrrsk).Ttl t onrli,ur'1t1,111 lr:ur\nr\\rorr lot trrt o,rlt'tl lrtttor'ttl:tt\ u\('lltt'lirllolvtttlt, lilt tttttllt:
.15:Ei:gir Iu ii?r3i: r-i_-==Ea;: t=ir+,; fr , ;-ia;"lE,-+! =+ =?lt+i*ei;s -=;! =-=Z'iliniSrs+ig1 iii; Eaiie: i 122;EIir#;= € ':i ?=iZ z=V":
v
a -a .7ao J! "q1
g
Bli;A
'a
2..?ae =z
-
-.r
.i
=,aa:rr3r- F +Ei+EF-* 6 E ,zztr grEiE; Aitizii*Ei a ,Ziti F-"i?u !-'==i;1if:Ea -z777ii*i'Fi -: aEi ;i;gl cr :eA[aE * tAci;s- [''q E* ii:
s=ae.E: x= s,gE s iE i e;E:r-sqQE,E"
*
EE i,Zi?i; E e aE i oq!.7 -.: ;E;EfE =Zl=, =?if -=22 :i:;"ad E+ i=i egrE=A:a =d; Aia ==' E3 g1 ia t=.1t= Ii?gt1;1=i aiE -== gg 4=7.;l 7Zr;sz-=" sr-i. s3 :;1=7 =: aiZl'tE = ;; o i 1*g{; ''- . : d ?. * a..E a =;g+gg = = =.= = i7=z=,=" ;i='-!z i- ia;El =7i=_ ==:1. =-12.= Aig+i
===a
ii ?'=iii,
i".i7;Zi :-2'.;i;
Ea geaEE. i:
i=i -=
xi
== EZi ?', EEa ;i \E; fng EE -=H
A
lEA ;S: 331
i=? 4 + oe
E^ V
-,'
A
T.,:.: J.I. 5rrnt, spetiftt'utiorts for representative binoc'ulars Relative light elflciency (RLE)
Diameter
\l . ;el ..r1,/i
^,
1-{
_.U
-
'
Relative
(mtn
brightness
)
Percentage of coating )0"/,,
80"1,
6.8
8.7
1001,
Field (at
Field (angular
Relative
1000 Yds)
degrees)
fleld (lt)
.tttttttlunl ltc'll 2.5 5
6.2 25
21
35
31.5
370
l
450
8.5
2700
325
6.2
2275
7.3
2660
2220
ls
2.6
-:5
5
25
26.5
35
37.5
380
7.1
50
55
70
75
380
7.3
2660
3.15
14
15.5
20
330
6.3
2640
- jrt . lr) 3
r
eut pupil
,,
6.8
1.5
9.5
ulur.\ .yemi-tt'ide .field und wide field
- ' -1,i : . -1tt i .:30 S'rl0
4t6 4.2 525 3.15 3.15 525
9
3.9
l5
5
25
-,1+ n , l-i
< 15
I rt ..r _50
\,,111'1'r':
636
t2
3816
26.2
511
1l
3462
t0
3675
22 11.5
21.5
35
31.5
525
t4
15.5
20
1.4
3 120
15,5
20
2l 2t
390
t4
450
8.5
3600
27.5
From Reichert and Reichert (1961).
21.5
35
31.5
315
7.2
3000
2t
23
390
1.4
3510
35
37.5
310
7
3700
=
!
an
a z z a a
z
a an
a rn
Iz
R
HOW TO OBSERVE
ECON NAISSANCE OBSERVATION
yards (900 meters); that is, the width of the scene you can see at 1000 yards (900 meters). The field of view is controlled primarily by the field lens. The normal field of
Table 4.2. Sumntary
d aclvantages uncl disadvantoges that uccrue from /batures in
birutcular,s
view can be increased (semi-wide or wide field) by the manufacturer by using a differ-
ent ocular system, especially a larger field lens. This. of course, also increases the price of binoculars. The following formula converts field of view in degrees (Table 4. I ) to an
approximation of field of view in leet at I 000 yards (Robinson, 1 989).
Features
Advantages
Disadvantages
1. Greater
Viewing animals more
Smaller field of view
magnification
Lower light transmission
closely
Field of view:Field of view (degrees)x53 (ft at 1000 yds)
Poorer image clarity Increased movement
Generally, the greater the magnification the smaller the field of view. Reichert and Reichert (1961) provide a formula to calculate the relative field for diflerent
Greater weight
types of binoculars.
2. Larger objective
Relative field:magnificationXfield at 1000 yards
More light transmissron
Binocular locusing systems are of two types. With individual eyepiece focus the
If
you
focus them while viewing a distant object, your binoculars will be in focus at all dis-
tances beyond about 30 feet (9.1 meters). You must refocus for closer objects. Binoculars constructed with individual eyepiece focus are generally better sealed against moisture and dirt than are center focusing binoculars. When adjusting center focus binoculars the observer uses the center focus wheel
to focus the left eyepiece while the right eye is closed, and then the right eyepiece is focused while the lelt eye is closed. Now the observer can focus the binoculars fbr varying distances by using the center focus alone. This is an advantage over individ-
3. Larger field of view 4. Coating 5. Individual
lncreased size of scene
lncreased price
Increased light transmission
Increased price
Better sealing against moisture and dirt
Individual focus of
6. Center focus
Use of center locus alone
Less well sealed against
7. Rubber'armored'
Better withstands'hard' use
eyepiece focus
from this overview of binoculars that the choice of tlie proper binocular is always going to be a trade-off of advantageous and disadvantageous
for varying distances covering
Less reflection
An advance in prism binoculars has been the development of roof-prisrns. Lcitz was the first to introduce roof-prism binoculars, and they still produce high-rluality optical instruments. The optical characteristics of roof-prism binoculars, altlrorrglr essentially the same as those lor porro-prism binoculars, are in s()rne irrstirnccs
moisture and dirt Inceased price
of light
frtble 4.3. Some representative optical specific'ution.s.f or roof -prism hinoculars
be clear
characteristics. These are summarized in Table 4.2.
eyepieces at close
distance
ual eyepiece focus binoculars.
It should
Heavier
lens
The larger the relative field, the closer the binocular approaches the ideal (i.e. high magnification and large field of view). observer locuses each eyepiece separately while keeping the other eye closed.
of
binoculars
Relative
Field (at
Field
1000 yds)
(Angular Relative degrees field (ft(mm))
Diameter exit pupil (mm)
brightness (ft(m))
6x l8
-1
9
420 (128)
8
2s20 (768)
7x2l
3
9
372 (tt3)
7.6
2604 (7e4)
9
366
7
2e28 (892)
Model
lix24
(l I l)
better. Representative figures are given in Table 4.3.
The greatest advantages of roof-prism binoculars arc thcir clrse ol'luurrllirrl, and light weight, which result from their slim shapc anrl snurll sizc conrplrr.t'tl lo porro-prism binoculars. IJltra-crlnrpitcl nrol'-1'l'isrn birrot'rrlrrr s lr;rvt' lrt't ornt' popular with tniuty licltl cthologisls ttow llr:rl tlrerr oplit';rlt'lr;rrrr'lcrislics;u('('()nr pitt'ltblc lo l:u'gct'. lreltvit't' tttotlr'ls (llrllle .l .l) llrt' u't'i1'111 ,rl rrllrir ('onlp:r( l rool pt'isttt hittrlr'ttl:us ('iur lrt' otrl\' |\ .)\",, lllrl ol r'orrrP;rr;rlrlr' ',l,rrrrl,rrrl ',r.zt'rl lrttt,tr'ttlttt'. l(rtol grt t',ttt l,tn,,t ltl,u',,1t('nlr)t('('\lrt'tr',ttt'llr.ur Ir)lu lrr',rrr lrnr,,, ll
lrtt's. brrl \ ('r'sus
only tlrc inrliviclual consumer can properly weigh differences in leatures
tlrc rlilli'r'crrccs ilr cost.
llittot'ttlrt t's tlurt clcct r ort icirlly zoonr (c.g. Copitrtr z-ooll frr>rn 7 X to l 5 X ) are avail,tlrlt' ltottt rt lt'tt' rlistrtbrrlot's, lrowcvcr', tlrcsc birrocrrllrrs prrlbirbly hirve pottrcr optical
tllttrttlt'ttsltcsllt;ttttttosl
r'lltolo;'j51suill litrtllrt't't';lt:rlllt'lilrlotrt-lcrnrrlltsct'vilti()lls.
Itr,,1,.rorl tlrrt rrsst()n\ ()l lltr';'1'1,.',,r1 t lt;rtltt'lt.urltt's ol lttnrlt'ttl;tts lt|trl lltr.it
.lilt 11,';,1ttilrl't,rrrlr,'1,)utr(lttlllr't1,nl,ilt(l'))il),trrrll(trllilt.,(ril(l()fi())
rtsr.
T.rble
1.1.
Sontc representutive spec'it'icution,s Jrtr
ultra-light rorf-prisnt binot'ulurs (/t(nt) )
Weight
Field
Field (at 1000
(oz.(g))
(dee.)
Yds)
Eye
Near
Exit
relief (mm)
focus
pupil
brightness
(ft(m))
(mm)
index
(t3e)
Relative
.'.1-i Celestron WA Monocular .r:t t Pentor Mono/Micro -r.lr t Pentax MonoiMicro ^rluB Zerss Monocular
4 (113.4)
8.7
4s6
9.8
6.2
32s (99)
l0
l5 (4.s) 6 (l.8)
3.1
6(170.1)
3.1
14.0
2.5 (70.8)
1.5
393 (120)
ll
l 1 (3.3)
2.8
1.7 (48.2)
6.9
362
6(170.1) 9 (2ss.l )
7.0
368
l5 l0
6.6 (2.0) 13 (3.9)
J.J
rl I Oritrn Super Compact . rl5 Onon Super Compact
(l l0) (l l2)
2.6
5.5
289 (88)
9
l s (4.s)
2.5
6.3
l0 (280.3)
8.7
451 (t3e)
5
l5 (4.s)
3.1
9.8
.
.'.15 Ceiestron WA Mini
-r-,, Pentitr Compact
*il', P:nt;.rr Conlpact .r.' r \ikt'rn Sprtrtstar ..'.1
.
-.1-
6.9
.4 (209.8)
1.5
394 (120)
t2
8 (2.4)
2.9
8.2
1
.4 (209.8)
6.2
326 (99)
8 (2.4)
2.2
4.9
7.5 (212.6\
6.3
331
e (2.1)
2.5
6.3
6.0
315 (e6)
t2 l0 l5 l3 t4
l8 (5.s)
2.6
6.9
(
l0l
)
(l0s)
(2ts.s)
6.6
346
1.5 (212.6)
5.4
284 (86)
1.6
\lini
8.2
ll.l
1
t4 (396.9)
- C'elestrr)n \\'aterproof .,.1 ,B 5qi.i1'lrrski Habicht
5
13 (3.9)
2.5
6.3
l8 (5.s)
2.5
6.3
<J
1E . = _,-t;=i?. I.?E ! -18 7 _.; =; r i 3E==e g; -; F 1_?'r-i=>Eti3f =;? 47;=+=5 =-a -=-1 ;2'ivi*Ai=i-?iii L =; i_
U E
=ia i;7 5 =7;_7i=X2j;3 =E 1 e'=l 3,a:;=qaEE =2.===??:1 g 4 e i I E 7 r lE i : 1 Va n =_:zZit = == :i JEZ iiVE 4r :f :]E ?a F ii '.=?;Ftniff:iE;i=-ii.E= rif ; 1=-i;q?5! ?'ii t=l
,
(D
=
a
: ?ii=
! 0a V) .D
^.-JJiia='O!
1
=3Li',Iii=7i 7i qr r=7 F
=:ilao: =?.=5I=d --27t2=
tqeiq
A;.Ev..aJ!
T g2 a7 qi +; ==7:u '=21{ef3
=6 74 '-=E??=Z t'iiE;;+i j'7L=gV i? '+= i i5 ;! i ?;? i == =, :1;alil ??
={E 3=;
,Ja:
;s 7= 2=
=ciiie; ;EsItEi EE =:1=-;?i iLiGZ,1: e= =-=1*aEi i::S1=1 =i =;1?=t;t Ev=:EIu P+
€I
7Z *r,
€B
7?; 31ift?i3=?i+=.E? :=;L* == 1; aa;:? a =t +?
i Xia: a; r{
=EiiG
t,
\+
n? =E
?i E-
an
t
=__!
tl
a-1 .D=
op +X cD3 IC
o= CDFj vtX .D3 OE o
-a'
5 +o:
o7.
(-(D
rn
o-
ou)
c,h O d(:D
e{ (D5
o? oa5 .) )c.
rDx qe
tJ
Or>= NJ) ooa
o oa (DD U)
()
ia \)
!
SurN) XXX
7 oq
o d
Ss
Rii'
-
an
?a9 S.D
l,
z z
tn
a (n rt; 4
<\
z
o .J
Fl
c
^
a .is -r N) t.J NJO@
(D
a
a:
3
\:. a:
ras oc
%%
-\ (\ a-
.D
:1
T-,^.: J.6. .\rttnt, spet'ific'uti\ns for represenative spotting scopes Relative light efficiency (RLE)
Diameter \\l {
t -' -:,
_
,-1,
_-
,-
r
_ ' j', .-t'fr - br :
.
:
'ftt
h(
Relative
(ntml
brightness
2
4
3.1
9.6
2.5
6.2
r
1
l6
t
J
9
I
t
- ,. 6{)
\
erit pupil
a
Percentage
of coating
20,,/"
80u/',
4.5
5.6
100"/, 6 14.5
Field
1000 yds)
(angular
Relative
ltttm))
degrees)
field (ft(m))
tjt
(32.6)
2.1
2140 (6s2.2)
200 (60.9)
3.8
3200 (97s.3)
l 18 (3s.9)
2.3
2360 (719.3)
n.5
22.5
24
150 (4s.1)
J
22s0 (685.8)
l0
12.5
13.5
n2
(34.1)
2.1
2240 (682.1)
13.5
170 (s1.8)
J. -'
3400 (1036.3)
80 (24.3)
1.5
2400
6l
1.2
2440 (743.7)
1
8.8
9.4
-1
9
2
4
4.5
5.6
6
1.5
2.2
2.4
3.1
11 J.
,1;.r'. From Reichert and Reichert (1961).
Field (at
_1
(18.5)
(73t.s)
-l
z
R
78
ECON N AISSAN
C
HOW TO OBSERVE
E OBSERVATION
19
ITT Night Vision 7635 Plantation Rd.
Roanoke. YA24091. USA i
Night vision monoculars and binoculars are sold commercially by numerous outdoors sporting equipment dealers.
Night vision scopes were developed along with binoculars. They photomultiply by 20 000-50 000 x and have proved uselul for nocturnal observations (e.g. Clapperton, 1989; Waser, 1975a). Bausch and Lomb's Night Ranger 150 (developed by ITT; can be fitted with a 3-in- I rnagnification lens which extends the normal 2.2x magnification to 6 x . Additional specifications and ordering the environmentalillumination
infbrmation can be obtained from the sources listed above. Also. Noctron Electronics and Javelin Electronics (6357 Arizona Circle, Los Angeles, Calilornia 90045)manulacture starlight scopes which are relatively small and practical lor ethological observations (e.g. Randall, 1994). They are available in dilferent models which vary ilr physicaldimensions. light arnplification, and magnification and have options for use with stilland motion-picture cameras. Fig.
4.2 Andy Sancloval observing bighorn
sheep through a spotting scope mouuted on a
as
wellas video recorders. An excellent review
of instruments for nocturnal observations is provided by Hill and Clayton ( 1985). Infrared viewing devices can also be used lor nocturnal observations. They flood I
gunstock.
iln area with infrared light, and an infrared sensitive scope is used to view the area itnd observe the animals'behavior (Figure 4.3)
L/ i e
lr
i
n
g
i
ns t r ul
nu
tt
s
.fil
r
n o t'
t ttr ttu
l
tl
b st, rl, u
t
i ( )11
S
Defense and tile Night vision binoculars were developed by the US Department of low light extremely of conditions Soviet Union in the lg60s fbr military use uncler infrared au required The technology has irnproved from Generation 0' which
Night vision equiprnent can olten be obtained on loan from the US Army and Navy. Inquiries should be directed to:
Army Technology Transler Program
levels.
N
and decreasecl light source. to Generation II, with improverJ photo-intensifiercapacity I I I. which uses a sLlpersize. Generation II is available tbr civilian use. but Generation
10221 Burbeck Ste 430
Ft. Belvoir. VA 22060 5806, USA Naval Air Warfare Center Weapons Division Measurements and Support Systems Branch (Code P2391)
MN such as Damark (7101 winnetka Ave. N., PO Box 299(X). Mittttcitl'rolis" Nielrt l'l"l"s arc 55429 0900). The prirnary American technology binocttlars rvctt' Mari,er ancl Bausch ancl Lomb's Night Ranger 250: both thesc hittoctttrt|s
Iicltl ttl' vicr" ('10 developed by ITT. The American technology provicles bettcr lrlrtl hlil'lrl \()tll( (' versLlS 10"). waterprtltll constt'ttctiotl' httoyittrcy. lighlwcighl oh(rrtttt'tl itttttt Irc cllll (Arrer.rvrnotrs. l()()5). Atlrlitiorrll ittlirt'ttttttioll llttlst'lt & I ,rtttlr
I
I i.t t c I I t t t t t'
I I
rtt
t,:
tt
I t,t' t' t'
r' t t
Ii
ttt ttt
I
t
Ic
yi
t L'.t
Sr'rcr lrl tyPcs ol'sPccilrl rttiscclllrncous obscl'vittiortal tlcvices ltave been developed by
rrrrliritlrrrl rt'st'lrlt'lrt'rs to rnr't't tlrr'ir pirrtierrllrr rrcctls. l'ir crlttnl'tlc. I)itrkcr'(1972) ,rnrl Sttrtllr rrrtrl Sllt'rrt't't (l()'/6) rlt'rt'loPr'tl ntirol rurtl Poli'tlt'r,'ir'cs ulriclt lrllowcrl lltt'ttt tt, lr't'1. tttlo 1111'lr lrttrlr' trt'sl: l\ltrl;rrtl\ ;rrrrl Nlr('orttlr (l()S.))(l('\t'lrlllq'1 1 ;q '.r',tllt l,,l llrrotll'lt('ttl
(l r(l(l( orl\ h'rlr(,'l
Point Mugu, C A93042-5001. USA
Itl,,'t ,r;rltr',',\',lr'tttlt,t
Spotl', ( )pltt r I)tr t'-totl ()rrrl.rrr,ll',ttl'
ight Vision Directorate
ATTN AMSEL RD NV TSD PET (Miller)
agencies' selsitive photocathode, is only available to military and government 30(xx)x' approximately These binoculars photomultiply the available light by tccl.tnology but they magnify the image only approximately 2.5x. The Russian antl clistrihrt(ol's' binoculars are available commercially from a lew manufacturers
prtttcetirtl
lL
lrt lr, lt, t
,1r,
,rlr',t't\illl,illltt'r'(,t\tlt(",
ttl ,t1'ltlf tttl' \{rlll
l)t",till)ltrrtt',trl
t,ilttr11'..1.'tl(('\iil('
l lo\\{ \r't lttttr'r,llt rrll('tl Io1,rrl,rt ltl('t,lllrr, rr\\ ll lll)'r'llllll\ 1,, ,lr \i l'rl}tll}' ,l rlr'\ lr r' lil ltlr'r'l \{rlll
lltr'lr'r ltttt,.tl.tttrl
1
80
R
HOW TO DESCRIBE BEHAVIOR
ECON N AISSANCE OBSERVATION
needs than in searching for something suitable in the literature. Also, you can prob-
ably save yourself a great deal of money.
4.2 HOW TO DESCRIBE BEHAVIOR
At the heart of the modern approach to the analysis of behavior in animals is the problem of description. IMarler 1975.2 J When developing a catalog names
to the behaviors you
of behaviors, you will
be describing and applying
observe. For descriptive studies, your catalog (dis-
cussed below) can contain narnes for behaviors which carry implicit descriptions.
well as dest'riptions which, by themselves, serve as terms lor those specific behaviors. As you shape your catalog of behaviors into an ethogram (discussed below) as
you will probably apply ternts to the behaviors you have named and described, priease of data collection. For further ease of data collection. you may replace the terms with code letters and nurrbers (Chapter 8). For experimental
marily lor
studies. especially, you will further sharpen the descriptions into operational defin-
itions (Chapter 6). The discussion below applies primarily to the early phases of a study in which you are making reoonnaissance observations, taking ad lihitum field notes, or begin-
ning to compile a catalog of behaviors. Therefore, 'term','name' and'description' as synonyms.
*mxru
I
have interspersed the words
4.2.t Empirical versus functional descriptions As you first observe the behavior of an atnimal you will likely be conlused by the complexity of what the anirnal does; but in tirne some order will appear in the types
of behavior
engaged in, the contexts in which they appear, and the movements and
postures that are involved (Marler, 1975). Familiarity with an animal's behavior and
insight into its function are continuing processes that generally lead to revision of both hypotheses and terminology.
Fig.4.3
ttl tlbscrvc bttt'rowittpr t'ul Linda Pezzolesi and the infrarerJ night scope slte usetl behavior (Photo bY R. Scott Lutz)'
Nevertheless, in time, it will be necessary to describe what you have observed in terms which are clear yet unassuming. The problem of desoription is resolved thror"rgh cxperience in observing the animal's behavior and your ability to select ternrinrllrlgy that will assist, not hindel future analysis.
Thcrc irrc two birsic typcs o(' behuvioral description (see Tables 4.7 and 4.8):
Illilill'i,l;,:lllll
:::ll J 'ill:,1,i'i:'::.]ill'rr
i ': '1e.':rs
'lrr
bodv pa*s
' l:tttt, lrttttttl rlr'\r t tIlirtil. ltl( ()t l)()l;tlt()t) ()l tt'lt'tt'tlt't'lo lltt'llt'lt:tViot's littlt'Itiltr I1tr\ilil,tllr rrt trllrtrr,rlt'l\ (t'l' lr,rtr'tl lr'r'llt llttt';tl)
HOW TO DESCRIBE BEHAVIOR
ECON NA I SSANCE OBS ERVATION
R
82
83
responding to a stimulus from which it was motivated to escape. We probably do not Table 4.7 . Erumple,s obsarvtttion o/
a
o./'
empiric'al und.fint'tittnul dest'riptions
(
ternts ) ./br
really know
flying ntourning tlove (see tert )
if
that was the true function of the flight or if, for example, it had fin-
ished feeding and was merely flying to the tree where
it could rest with relatively
greater safety. Behavior description
Type of descriPtion
This example illustrates that the same behavior may be used in severalcontexts.
Mounting may occur in sexual or dominant-subordinate contexts in dogs, just as urination may be marking or merely elimination (Bekoff, 1919b). Functional descriptions should be avoided, except when the function is intuitively obvious (see below) or supported by data, since they can be confusing and misleading (Marler,
a. Behavior X Emp
i
r icu
I
de s c' r iP t i o
ns
b. Rapid alternate contraction and relaxation
of
the pectoralis muscle c. Wing flapping Fun
t' t i o
no
I
de
s c
r iP t i o
tt,;
1915) and lead to changes in terminology as the study progresses (Tinbergen, 1959).
The type of behavior, as well as the type of data being collected, often force the
d. Flying e. Escape flight
use
(1970): These types are nearly synonymous with the two types used by Hinde
l'
patclescription by spatio-temporal patterns of muscular contraction. including respectively' consequence" terns of limb ancl body movement;and 2. 'tlescription by Wallace ( 1973) calls Hinde's first type 'description by operation'. of the The type of clescription selected will depend in part on your knowledge
describes the context of a behavior. For example, W.
useful to examine the list and identify those terms that are borderline, as well as tl-rose
tions is not always clear-cut, so that the problern is generally resolved in terms of the observer's intent. For example, does 'sniffing' imply searching lor olfactory stimuli
Smith ( 1968) studied the use of
or merely wiggling the nose and vibrissae. This type of confusion over the observer's
intent is clarified through the definition
from the 'kit-ter' call by the eastern kingbircl (T)'rannus t)'runnu's) and concluded indecision data that it provides information relative to the caller's
lield'
t.
llvittl'\\('r'('l
Your descriptions should inlorm others of your observations in an objective way r','ithout bias to your own experiences or personal beliefs. Antltroportutrphisnt, the
lllolt\;tltorr
llt tlt..,t ttlrrrrt, tl ;t., (,.,(itI,' llrt,lil rrt' lll(' ll\\lllllllll'
I
tttttlr'tl\ tttt' lll;rl lllt' tl.,t(' 1'\'1"
;tsstttllitll';ltttllttttt':tllottl ltit.trrre ,l'tltr'llt.lurvior lrrrtlslrll;rtr'ttol
section
rnust be clearly described and/or defined for each species.
tto ittlirl'Describing (i.e. naming) the behavior as'behavior X'provicles trs witlt l(ilPitl ttsctl. bcing mation unless we have access to a definition of the ethogratn coclc trs sotttclltitllr alternate contraction and relaxation of the pectoralis rttusclc tclls r', rtlt tttttt'lt cthologist thc provicle not does but about the mechanics of the behavior ctltrilrtf isl. ol'tlrc tttitttl thc in imagc useful information. Wingflappingcreates an (Pt'tlt;t1rs:ttt rviltps its but we d. n.t kn.w il-thc devc wus stunrling unrl lllrpl-rirrg tlrc lrt'ltrtViol lts
in
the motivation and goal of the behavior appears obvious. Fbr example, the terms 'nest building'and'egg retrieval'are accepted in ethological parlance, but they still
ancl
As another example, let us say we are walking through itr a trcc -50lll m ahead of us a mourning clove flies up out of the stubble ancl lands 50 lcast' livc to our right. We can clescribe the behavior of the dove in flight r-rsing. at (Table 4.7)' dilferent levels of description
i.tcrtitln ur()vcurcnl)orlrctrrlrllv llvirrg. llv rlcscrihirrg
(discussed
Some descriptive terms are clearly f unctional, but they are readily accepted since
flying versLls about flying versus staying put, flying towards versus flying away' or vocalization'' hesitance landing. Hence, he labelecl the call the'locomotory a wheat stubble
of behavior units
(r.3.3 ).
observational
elct,.e
that are clearly empirical or functional.
As Table 4.8 illustrates, the distinction between empiricaland functionaldescrip-
a term which more clearly
use J.
suggests that since
Eisenberg (1961) provided a list of behaviors lor rodents (Table 4.8) that included both empirical and functionarl terms lor convenience of presentation. It is
be thought animal's behavior an<] type of study you wish to pursue' Descriptions can addiconveying stage, At some to lie along a continuum of inlormation conveyed. function' tional information generally entails
After careful study the researcher may be able to
of both empirical and functional descriptions. Hinde (1970)
threat and courtship behavior in birds involves both relatively stereotyped motor patterns and an orientation with respect to the environment, both description by operation (empirical) and consequence (functional) are necessary.
rrttribution ol human characteristics to nonhuman animals. is often considered one ol' thc gruvcst sins that an ethologist cern commit (Carthy. 1966); recently, the use of ;rnthrol-ronrorphisnr hy cthologists has become a more controversial topic. ,Arrllu'o1-rorrror'phisnr is lr lirrnr ol' 'l'unctional description' (described below), but
llow t'rrrr ue t'rrleg,orizc ils virrious lirr"nrs ol'usagc'l Is it a fatal flaw when used in rt'st';rrclr'.' ('rrrr il. in lrrct. lrc rrsclirl to ctlrologists'l Antltroprorlorphism. as I'vc tlt'lint'rl rl .rlrott'. ts otrl\ ont'ol tlrrct'lirtttts tlcscl'iltr'tl ltv'lir1toll'(l9ll7)rtntl is in tltc ( :rl('t'()r\ 'nrl('ll)r('lr\(' iurllr()l)()nr()rl)ln\nr ir('('(rtrltttl' lo I'isltr'r''s ( l()()0) scltctttc (l t',1t,'t',lt,rttl,ll,,',,,tt',ttll,',ll,,1,1l,lttl,,',,,1,1t,'t',lt('t',1)('(lt\('()tlrtttllttr)l)()lllotlllttsttl).
R
HOW TO DESC]RIBE BEHAVIOR
ECON NAISSANCE OBSERVATION
utilizingboth entpir"it'ul and Table 4.8. List 0.f rodett gerteralmrtinterrunce behaviors
Table 4.8. (cont.)
.functional terms Sleeping ancl resting
Care
d
the bodY sur.fat'e and
t'omJbrt movement,\
Curled Stretched
On ventrum On back Sitting Locottttttitttt On plane surface
Swimming Care
ol
the body su(ac'e and c'omf rtrt
m)vemenls Washing
Urination Marking
Jerking
Rigid upright Freeze (on all fours)
Holding
Perineal drag
Pushing and patting
Escape leap
Ventral rub
Combing
Sniffing the substrate
Side rub
Molding
Whiskering
Depositing
we
Dragging, carrYing Pickittg uP
tlirected by our vocabulary (symbolic behavior) in describing observed behavior.
Forepaws
I will readily admit that observation has one great drawback; it is hard to convey to others. Experimental conditions oan be reproduced, pure observation unfortunately cannot. Therefore it does not have the same objective character. The observer who studies and records behavior patterns of higher animals is up against a great difficulty. He is himself a subject. so like the object he is observing that he cannot be truly objective. The most 'objective'observer cannot escape drawing analogies with his own psychological processes. Language itsell forces us to use terms borrowed lrom our own experience. I Loren:, 1935:92 J
Digging Placing
Scratching
Pushing with forePaws
Sneezing
Pushing with nose
Cough
Covering
I tl'cnZ (1914) has also sr-rggested that in some instances the use of terms like'falling rrr krve,' 'l-r'icndship'or''.jcak>usy'is not anthropomorphic, but rather refers to iunc-
Push
Sandbathing Ventrttm rub
Pat
Diggirtg ForePaw m()vemcllts
Ytwtr
Kick hirck Tttrtl lttttl lltrslr (lill'cllltw's ;ttttl lrtt'lrsl 'l'ttrtt lttttl lltrslr (ttosc)
Slr:r kc
M
Stretch
if not impossible, to do so (Crocker, l98l). It can be argued that
directly or indirectly; therefore, researchers sometirnes unconsciously slip into its trse (Kennedy, 1992). As Rioch (1967) has remarked, we are both limited and
Chopping with incisors
Rolling over the back Writhing
of how strongly one might attempt to avoid anthropomorphisrn, it is
cannot have knowledge of anything which we have not ourselves experienced either
Sifting
Licking
Side rub
very diflficult,
and c'ac'hing
Nibble
Wiping with the fbrePaws Nibbling the toenails
l,
Regardless
Holding with the forePaws Gat he'r ing t'bodstulf.s
lr
Sourc'e: From Eisenberg (1967).
Swallowing
Mouth Hauling in
Mouthing the fur
Upright Testing the air
QuadruPedal saltation BiPedal walk
Diagonal coordination Fore and hind limb alteration
Elongate, investigatory
Biting
Manipulatin with forePaws Drinking (laPPing) Gnawing (with incisors) Chewing (with molars)
Climbing
Gathering
Defecation
Diagonal
JumPing
Isolated animol exp loring
Stripping
Ingestiort
Bipedal saltation
Nest building
oltlittl'
)
lrolurll! rlctcrrrtinerl conccpts. In tliis regard. anthropomorphism might be useful as r rrtctlrphot' lot'tlcscrihing wlrir( urt irnintirl does (Kennedy. 1992; Ristau, 1986) and rrlrrrl ils'cnl()liorr;rl'rurtl ttroliv:rtiorrirl slirtcs ul)pcar ttr bc (c.g. I'car). witliout irlplyrttl llt;tl \()nrr' l('\r'l ol t'orrsciorrs tlro111,,l11 is irrvolvctl. l'or exarrtple, tlrc 1'lht'itsc'tltc ,ltt1l 7r/1r1s',' lltc sr';t l)r()\ trlt's lts rr itlr ;r r tstt;tl itnrr;,,.' ;ur;rlo1'ous lrl ;1 1;g,',t.t"t''s 1'llrlw ;,rtslttltl';t\l(lt'lltr",,,rl \\"rlr't (l')S I l('/)ttolt". llt.tl '11llrtttktttt':tl16ttt lpltpttttttili,.'t
I
R
86
HOW TO DESCRIBE BEHAVIOR
ECON NAI SSAN CE OBSERVATION
drawn from human interaclor manipulation in animal "ornpgnir:ation, analogies i,clude 'cJeceit" 'selfishness" and tions tencl to dominate'; commonly used terms 'these familiar words tnake visu,spite'. ( 1983: 167) concludes that the use of
I
mates the complete repertoire. The size of the repertoire will,
we should provide technical definialization of technical discussions easier', but misleading inf-erences' tions of the terms in order to 'guard against lir-re'l The saf-est approach is to avoid Where and how cloes the beginner draw the ttse only empirical descriptions' You using terms that could be misir-rterpreted an
species
to species
as
One decision that you must make during reconnaissance observations is when to precise hypotheses, and design a sound research project'l At what point do you have
lor the animal(s)'l If we were to observe an individual animal continuously lor an extended period of time and record the behaviors that it showed, we could then plot the cumulative number of observed behaviors by the time (Figure 4.4a). An asymptote is reached after many hours of observation (arrow, Figure 4.1a). beyond which few additional behaviors are seen for each unit of time spent observing. This asymptote may take tens, hundreds or thousands of hours to reach. depending on the species studied. Nolan (1978), lbr example. spent 5524 hours observing the behavior of prairie warblers (Dctrdroit'tt clist'olor), yet he saw only nine copulations. If only one type of behavior (e.B.agonistic) is under study, then the a reasonably complete ethogram
Beach(1978,197g)haswarnedbothofthedangeroftakingwordsfrom Commonusageandapplyingspecializec]meaningtothemwithout (taking a worcl from detrnition. ancl of resorting to Hurr-rpty-Durnptyism
commonusageandreclefiningittclmeanonlywhatyouwantitto mean).Bothoftheseproblemsexistinthecurrentapplicationofthe
termrapetotron-hutnanbehavior.(E'stcpundBrttt,<',19s1..127)) species that we know as rape in What should we call behavior in nonhuman humans?E,stepandBruce(1981)suggesttheterm'resisteclmating'asapurely
copulation'(e.g. Sorenson' 1994)' descriptive term. or we could use the term'lbrced perceived as a gradient: In summary. anthropomorphism can be No anthroPomorPhism' Human terms technically defined' }{uman terms used as a nietaPhor'
time to the asymptote will generally be shortened. Fagen and Goldman (1977:268) concluded that 'familiarity with an animal's behavior will tend to require years of experience
if
the animal is a mammal or bird with a complex repertory. But
if
the
animal's behavior is simple and relatively stereotyped such familiarity may be gained in a lew months'. The objective ol'descriptive studies of a species (and to a certain degree of developing an ethogram) is to determirre the true frequency of rare or unusual behaviors. short-term stuclies record too many unusual behaviors. The result is that we ofien overestimate the importance of some unusual behaviors since
+Humantermsfreelyusedwithalltheunderlyingimplications. at what point along the graclient You shoul
rl
we lack the perspective provided by long-term studies (Weatherhead, 1986).
Hailman and Sustare (1973) described an interesting laboratory exercise in'the analytical power of biological observations'. The objective was to deduce the 'behavioral organization'of a talking, stuffed toy elephant Horton. The first step consisted of listening to Horton's total vocal repertoire by pulling the string and listing the vocalizations ernittecl. These data rvere transferreci to a cumulative graph
(lrigurc 4.,5) itncl cramitrccl hrr an asymptote to determine
4.2.2a Catalog and rcPcrtoirc
course, vary from
stop. When do you have sulficient inlormation to ask incisive questions, lormulate
lollows:
4.2.2 Catalog, repertoire and ethogram
ol
well as between individuals, depending on sex, age and experi-
ence.
the issue of redefining terms' stated as Wiley's (1983) call for technical {efinitigns t6
Descriptiol]Sareoftenquantifiedtoclelineatemoreaccuratelyarrclctlrtlplctcly the berravior. Surne cxa^plcs deli'eate what the animal does when it performs thesequatltitativedescriptiorlsareillustratedinChapterl0.
courtship), sex or age group we are interested in studying. The catalog is only a portion of an anirnal's rapertoire - all the behaviors that tlie animal is capable of perfbrming. We call the catalog tn ethogruri when we believe that it closely approxi-
wiley
t : I
81
if the entire
repertoire
r l(X) succcssive vocalizutions. Attotltcr u':rv lo look irt thc hchaviorll rc;rertoire ol'an anirnal is through the tirrrr'tk'rrrtetl to plrr'(icrrlrrr l'rchlrr,'iors (i.c. tirrrc htrrlgct) or by f'r'ctlucncy ol occrirr('n('('(llrtll;rrrtl llrrll.l()70).Sirrt'e llrt'l't'r't;u('lr('\'ol'occur.l-crrccvirt'icslirrthcbclurvIr:rrl hccn rccorrlccl ulie
t()ts ttt:tl);ttltttt;tl': t('l)r't lr,it,'.;t lrl,rl ol t'tttrtttlrtlt\('l)('l('('ttl:ll,r's ol'lol;11 litttc sPr'ttl itt
lltt't.rttott,lrt'lt;ttt,rt'.iil,,lttt',1
lltr'tt t.rtrL,'t,1,'t l,\ lt,'rllt('n(\ rrl otr'llt('ll('('tttllslt,,tt
R
88
HOW TO DESCRIBE BEHAVIOR
ECON NAI SSANCE OBSERVATION
a
E-=10 oo=
:e !
5
2"31
o '5
20
10
0
G
40
30
o
50
60
70
80
90
100
Number of vocalizations recorded
-o
c
o o
of different vocalizations as a lunction of the total number ol vocalizations recorded (from Hailman and Sustare,l9l3). The cumulative number
Fig.4.5
= o o
.o E
l c
o
,2 a f E
)
C)
c o o
acts observed Hours of observation or number of behavior
;
1oo
&eo
ioo
o j40
3so o o
i20 o b
;s o
€o ) z
E
'a
--t--t:? --r-1'c. -nftT{-.a
10
5
o
.z
o f E
=--c''-'-
f
(J C)
lOO 2OO
5OO 1OOO
m'00
5OOO 10000120000
Number of acts (tokens) seen
Fig.4.6 Plot of cat behavior data with fitted regression line Y:4.\lyr':c 1solid line) and MoSt frequenl
of occurrence Rank order of behaviors by frequency
951Xr
confidence bounds for regression line (dashed lines) (from Fagen and
Goldman, 1977).
plottcd Fig.4.4 A hypothetical example of cumulative number of different behaviors b asymptotc' approximate the denotes against hours of observation. The arrow plotted ats a cumulativc repertoire animal's an of Conceptual representation from Hutt lttrcl percentage of the time spent in the various behaviors (adapted Huu, 1970).
Since the regression line has no finite asymptote, it does not allow the observer to predict repertoire size. However, this procedure did encourage Fagen and Goldman (1971:263) to recommend the following rule: A ten fold increase in the total number
ol
will, on the average, double the number of behaviour types in the catalog'. probability that the (Fagen rrcxt bchuvioral act will be a new type and Goldman, lgll). If 0 approaches l. thc pnrblrbilily ol'obscrvirlg u new behavioral act is low. acts
We can estimate t>ur .sumplc ('overage (0) bV calculating the
bcltrtviots a curve which reaches an asymptote at the less frequently occurritlg
(Figure 4.4b). llcltltviotrtl t'rtlrt Fagen and Golclman (1977) rescarchccl mcthotls ol' ttrtitlyzittg ol rtt'ts ttct/ittttttlrt't (ty1-rcs ol'bclrltviot'itl I.gs anclc..clurlcrl tSirt nros( rlistribtrtiorrs tll-rscr.vctl
)c.rrlrl l-lctlescriSctl hvlr loplrritltnricrelt,tr'ssiottslopt'ol ill)l)l()\illl:llt'lr'o
(t'1' liil'ttlt"l
(')
.t/ 0I'
I
A', ts llrt'ttttntlrt't
\
ol
lrt
ol lrr'lt;tviot
l\',('('n Wlrctr ,\',
rr snr;rll
l\'1lt's s('('n ()nlV ()n('('. lrtttl /ct1tt:rls llrc
tt'llrll\r'lo / llrt'rr orrrll
totirl nrrnrhcr
;rPProlrt'lr I. llrt't'loser tlrtrt
[)
R
90
HOW TO DESCRIBE BEHAVIOR
ECON NAI SSANCE OBSERVATION
Altmann approaches 1, the more complete the sample coverage. For example, S' (1965) observed 5507 acts in rhesus monkeys and saw 32 behavioraltypes only once'
^3? t- '-
tt--
:0.9942
Table 4.9. Court.ship behuvior,s perfornted by ntula and./bmale Maclagas('an his.sing c'oc'kroucltes
Behavioral
s507
Fagen and This indicates that Altmann's sample coverage was essentially complete' behavrare of significance the emphasizes method Goldman (1917)caution that this fracrepertory the lor estimating ioral acts. They provicle a more complex procedure
Approach Antennate
types' tion which properly weighs the frequency of occurrence of different behavioral Of primary importance to any ethological study and particularly to a considerasize of tion of catalogs and repertoires is a determination of a behavioral unit. The been have units behavioral the catalog will vary according to the way in which
such as dcfined. The more inclusive (i.e. lumping several dilferent behavioral acts, yet mututhreat and submission, into a single behavioral unit - agonistic behavior),
Hiss
will
catalog ally exclusive (e.g., agonistic versus ingestive; see below), the smaller the the objecto according vary will be. The selection and definition of behavior units tives an
behavior ethogram is a set of comprehensive descriptions of the characteristic behavpatterns of a species ( Brown, lg7 5).lt is the result of refining your catalog of descripand recording) audio (in cases some iors after many hours of observation tion. and it should be the starting point lor any ethological research' especially species-oriented research. Schleidt
et
ul.
Mount and palpate Posture
(1984) provide a brief history of the use of
ethograms by ethologists.
When concept-oriented research is conducted, researchers may compile an in which ethogram of only those behaviors within, or closely related to. the category ethggrttn1 they are interested. For example, Fraser and Nelson (1984) compiled an of the courtship behaviors of male ancl female Madagascan hissing c6ckroachcs (Gro m p h atlrt rh inu p o r t e n t o s u) (Table 4'9)' itt Limiting our knowledge, as well as our research. to only one typc tll'bchitvior thrtt (1953)itrgtrctl a species does pose potential hazarcls. For example. Tinbergen tltc gt'crtlcl'tltc the more we restrict our view of the animal's totalbehavior pattcrtts.
Cross-over
Copulation attempt
Description One animal (either male or female) moves lorward and makes contact with the other animal Two behaviors can be distinguished: mutual antennation by both male and female where contact is antennae*antennae; and solo male or lemale antennation of the other animal's dorsal surface. The latter form of antennation can take the
lorm of 'tapping' or'horizontal rubbing' movements. These movements are very different lrom the rapid vertical 'fencing' movements of aggressive behavior (Nelson and Fraser 1980) This audible sound results from the lorcef ul expulsion of air through a specialized abdominal spiracle. Male courtship hissing can be separated by its acoustic characteristics and behavioral context into type-l and type-2 hissing. Type-l hisses are isolated, soft hisses, whereas type-2 hisses are the acoustics of sound production, see Nelson and Fraser, 1 980) One animal puts one or more legs on the other and taps the other animal's body surface, usually the dorsum. with the labial or the mandibular palps Male stands high olf the substrate with the abdomen curved upward and extended. This is sometimes accompanied by extrusion of the phallomeres and/or type-l hissing. During posturing a distinct odour is noticeable to the (human) observer Female crawls over the posterior tip of the male's abdomen, dragging her abdomen over his Male attempts to copulate by rapidly thrusting the tip of the abdomen towards the flemale's abdomen. Usually the male starts thrusting towards the lateral ventral surface of the f'emale abdomen, moving to an opposed position for
copulation Stanci
probability of misinterpreting results. The need lbr a broacl. ttbservatitlnitl apprtllch crtrttl()t bc stt'cssctl loo yottrtl' much. Thc ttatttritl tcrltlctrcy ol' tttittty llcoplc. pltt'ticttlltt'iv lrl lo ltV lo lttltl ()ll illl isollttctl lltolllt'ttt hCgipsCt's. is ltl Ctlttt'ctllt'ltlc l' t'l t'l"t' t ltt't irr lrt'l't'Pl llltlsl lx'1(.ltitl(.i1to il I ltrs l;rrrrllrlrlr'rrrr'liturti()ll lo t'""ttll'oltttt't lt'tl tll',t 'l ' il lt.lrtlr l() ;ttt ;l( ( tllttttl;tltott ,,l lr:tt lt;tl
unit
shorter and occur in trains (for a more detailed description of
4.2.2h Ethogram
A,
9t
Movc irwiry
Fernale stands with legs braced laterally, body close to the gror,rnd. the abdomen flexed downward at the tip. This
normally rrccompanies copulation attempts by the male Onc or both unimals walk or rLln away from each other
Nrtlt'.'
lrrrrrrr l'r:rst'r rurrl Nr'lsorr (l()l'i,l) ,\'rllr r' '{ ',,;,1 ,rl,ltlt'rl lrt' ll;rrllrt'rc trrrrllrll
R
ECON NAI SSANCE O BSERVATION
HOW TO DESCRIBE BEHAVIOR
collection of sociological oddities. A broad, descriptive reconnarissance of the whole system of phenomena is necessary in order to see each individual problern in its perspective; it is the only saleguard for a balanced approach in which analytical and synthetical thinking can cooperate. This, of course, is true not only of sociology, it is true of each sc-ience, but in ethology and sociology it is perhaps forgotten more often than in other sciences. ITinbergcn, 1953. I 30 J Descriptions of the behaviors in the ethogram should be clear and concise. yet complete. A useful adjunct to a written description is a photograph or line drawing.
Figure 4.7 shows how Enqr,rtst et al. (1985) used line drawings to supplement the
written descriptions
in tlieir ethogram of
behaviors performed
by
lulmars
(Fulmurus glacialis) when competing for fish.
Schleidt et a\.11984) pointed out the large variation in description, format and completeness of published ethograms. They designed a 'standard ethogram'which
Bill-pointing' The bird points with the bill against the opponent. This behaviour varies in inten_ sity from a turn of the hearJ to an unsuccessful attempt to peck the opponent. The mouth is often open and a sharp sound is utterred. The owner also directs this behavior to flying birds. Bill-pointing is always cornbined with Wing_r.aising.
they hoped would serve as a prototype for future ethograms of birds and, perhaps,
other taxa. The ethogram, which consists of the 60 most commonly observed visual behavior patterns, was tested and refined in a study of the bluebreasted quail (C
o
tur nix
c
h ine n s is).
The discussion above dealt with ethograms for descriptive studies or experimen-
tal studies of normal behaviors in a naturalenvironment. An ethogram of behaviors to be measured is also compiled when conducting a manipulation experiment (e.g. Godwin, 1994); in this case, the behaviors are operationally defined, instead of being described (Chapter 6), and they are normally mutually exclusive.
4.2.2c
Breast-to-braasr' The two birds meet, orientating the body somewhat upright
with their breasts touching' Breast-to-breast is nearly always preceded with rushing behavior performed by one, or both, bir<Js. Fig'
Mutually exclusive behaviors
4'7
Tindall.
Mutr-rally exclusive behaviors are those that cannot occur simultaneously, either because the anirnal cannot perform them simultaneously or we have defined them so
as to eliminate two behaviors being recorded simultaneously. The behaviors described in your ethogram must be mutually exclusive if you are determining tirle
of time devoted to each behavior). Time budget studies also require that your ethogram be erhuu^stive (i.e. the animal musl always be engaged in one of the behaviors in your catalog). Also, the bchaviors nreubudgets (i.e. the amount and percentage
sured in an experimental study are almost always defined scl that thcy:rrc nrrrtuirlly exclusive, so that the animal is recorded as responding with only onc o('scvcrrl possible behaviors (see Chapter 6). The behaviors in your ethogrtv:n .slttttrld hc nrutually cxclrrsivc il' yorr lr rc rrrrlrlllc
lr
r
rccorcl ltccttrittcly Inttrc tltittt tlttc l'lelurv'iol'lrt orrcc. eillrcr l'reclrrrsc ril'.\,orrr l;rt'k ol cx1'rct'icttcC rlt'hcclttlst. y()tl :ll('ttrirtl'tt tlltt;t lrl1.,1't't (()l s()ll1'lttt')llr;rl trrll ttttl ttll,* lrtr tlrt'tr't'otrlittl',rl sintttlt;tlt('(rus ltt'ltrrr tols (( 'lurPlt'r ()l
Line drawings a,d descriptions of two behavior patterns performed by fulmars when competing lor rood (rrorn Enquist at nr., r9g5). Copyright by Baiiliere
Using only mutually exclusive behaviors
may provide a high enough level of resolution to answer some research questions, but it is unrealistic to believe that it will accurately reflect the animal's true behavior. Probably all animals are capable
and do perform, simultaneous behaviors.
of,
.I.] INI;ORMAT-IoN RESoURCES '['ltc trsrrirlll'ltcccl-tlctl tlogtna states that befbre embarking on your research you sltottltl lr"lttll:ts ttlrtcll ils v()r-r citn irboul yorrr sub.iect aninral, especially about its bclr:rv'r.1. lr.\\'r'r'r.'r. trris rrrigrrr lllt.11;1;11i,tt' ttt:rrlrlili'rtl
t'.llt't'l
(llt'\()lll(('\
rr.t rrrwlrys hc rr.rrc (scc sccti., 4.3.r). v.,, srro,ld l, \',trt irrili;rlrr'r'.rrrrrrissr,rr.c.bscl.vlrtirlas. {l..l, itll
ll \()lll rltrp,",;11. trr,'lrr.lrrrl';rr;ul:rlllt'lilt.t;rlr1t..
rl;tllr ll.tlltt tllltet.
HOW TO DESCRIBE BEHAVIOR
RECONN AISSANCE OBSERVATION
94
95
When you reach the level of experience and stature clf a Nobel laureate, your research will be the 'cutting edge'; perhaps then your mind will generate sufficient
and films and researchers'efforts (personal interviews, logs. diaries, field notes, etc'), videotapes.
ideas and direction to keep your research on a productive course.
I assume that the
reader of this book has not reached tliat level. 4.3.1 Literature
The How much time shoul
There are lour classes of literature which can be read and reviewed (Fenner and Armstrong, 1981):
Pre-primary literature includes'in-group' memos and technical reports.
the behavior' ature might provide you witli biases and a restricted, myopic view of spend too can researchers beginning and prejudice your observations. Secondly, a Nobel Medawar' P'B' fielcl' much time acquiring a breaclth of knowleclge in their
These are produced
Beginning ethologists, especially those in academia, will have limited access
prize winnilg experintental pathologist, offers the following in his Advice to a Ytung
or . . . considerations apply to a novice's inclination to spend weeks crab may learning book months'mastering the literature.'Too much of and confine the imagination, and endless poring over the research . . .The strbstitute. others is sometimes psychologically a research Few beginner must read, but intently and choosily and not too much. seen to be always worker slghts are sadder than that of a young research to become way best the hunched over journals in the library; by far I hlt'tlavur, 1979: 16,l7
enon.
major proportion of the 'literature.' st> The ultilrate expensively and llerve-wrackingly produced, is rarely read or cited' ' ' ' irony is that a
cietailed information about research on species or concepts of interest. Secondary literature inclr"rdes indexes and abstracts of the primary literature that are compiled by various services, such as Anintal Behuviour Ab.stratl.s.
+ Tertiary
if
the only because of the simple fact that so much is published' even in still and it all read both cannot one that .post narrowly defined fielcls. 1984:18-t ] have time to add one's own peerless contribtrtions. ITlutnr';ort, on the btltly However, for experimental studies it is generally advisable to be current will hclp This yoLl strrclyilg' are species or cgucept of knowledge available about the clsc's you to generate new hypotheses and avoid unknowingly duplicating sotl-tcottc prclcl' wotrltl yo1t research. Even this approach may consume much of the time on your own research. For example, S.E. Luria (1984). it Nrtbcl l)rizc
spenclilg 'iln cttgcr scckcr ol' iltlirl'lttltwinning microbiologist, acknowledges that he is not tion'. I like to operate with rtnly a ll'irctiorr tll'tltc cll()t'lll()tls ltccttlltttlltliott ol kl6wlctlgc. Wltcrr I rrl)cn il t)cw issrrt'ol'lt st'ir.'ttlilit'iorrrtrlrl I tlo t)ol st'ltlt tlle t:rllle ol't'otltr.'llts lookittl' lot t'rt'ilitll' tlo'"t'll1' ott lltt't'oltlt;ttt' I r I tttr't' lt)'\'l I l' Ittrlrt.ll.;rl tltr'tt't',tll lrt'ttt,llttttl'ttt tl tllltl I tttrtsl tt';ttl
literature includes syntheses of the literatr-rre such as textbooks,
encyclopedias and handbooks. Tertiary literature is often where a begin-
ning ethologist's interest in particular specics and concepts begins.
]
inforResearchers'inability to read and absorb the enormous body of published phenomwidespread a is mation available. while stillconducting their owlt research, said: Thomson, in his informative discourse on the state of scientific literature
to the pre-primary literature.
Priniary literatr,rre includes all tlie prof-essionaljournals in animal behavior or related disciplines. Journal articles will be the best written source ol
St'ienti,st'.
proficient in research is to get on with it
primarily in state and lederal agencies and industry.
Access
First,
to prirnary literature fiournal articles) can be sought in three ways.
to a recent article on the topic. You can find one by looking in a recent secondetry source (see below),in Current ('ontents La/b S<'ient'e.t. by searching one
of
ref-er
the journal online catalogs such as Unc'over, Articlel,st and C'ontentslst, or by
asking a researcher in that area. Look at the articles relerenced by the author. of interest to yor-r. By reading these articles and using
These articles will often be
their ref-erence lists. you can go back many years, sometimes to the original paper trn the subject. Second. you can r-rse the secondary literature (indexing and abstracting tools) which provide references to journal articles by subject. Since most of these tools are now prodr.rced by computer. they can be searched in three ways: through the printecl issues. online through commercial vendors such as
ItOM in libraries
Dialog and STN, and on CD-
()r on your own conrputer using Internet. Bitnet or other zrccesses
to thc inlirt'nrittion 'sLrperltighway'. Some are now available as part of online cataIogs ol' librirries. Onlinc ancl CD-ROM versions generally do not cover the entire nurgc ol'vcru's llrrl lrirvc l-rccn pLrblishcd. For example. the online version of llirtlrt,tlit trl .llt,:ttttt lr (lliosis) c()vcts l9(r9 lo thc prcscnt: the CD-ROM versiorr ('()\'('ts l()fi : lo lltr' Prr's('nl. llrt' pr irtl re rsiorr c()vcrs l()l(r trl thc prcscnt. Dittes ot' t'lt't'ltrllll( \('l\lr)ll\. ('\l)('( titllt lrt llrr'( l ) l{( )N,l lir1 111',1. ;rrt' likelV lp CltiltgC irt thC Ittltttr',1'.rr()tt'()l111'1 1rt trlt'tl t',\lt(",,l|('lr,ilr',trtlrr'tl l();l (()llll)il1('l tt'tttlltllle l0tttItt.
R
HOW TO DESCRIBE BEHAVIOR
ECON N AISSANCE, OBSERVATION
using printed Several steps are involved in conducting a literature search, either sources or electronic formats:
Divide your search topic into concepts and label them.
+Undereachconceptlabel,writeappropriateterms(keywords).
s
to Decide on appropriate abstracting and indexing services or databases search.
The United States government is the world's largest publisher. Many important
publications are produced by various agencies within the Departments of Agriculture and Interior. These can be identified in online catalogs and in the publication from the Government Printing Office entitled Monthly Cutalog of United States Government Puhlication^y. Federal and state documents may be available from local or state offices of governmental agencies. They should also be available in state
(title Review instructions on access points in each service or database words, assigned subject headings, subject codes, etc')' (and, or, For electronic formats, select appropriate Boolean operators
government depository libraries.
not). For example, 'dogs and wolves, not prairie dogs''
laborious and time consuming 'manual' searching. Today, the same indexes and abstracts can be searched (with the results printed) in a few minutes. Tomorrow, they will be readily and widely available through the electronic networks or online library catalogs using your office and home computer. A reference librarian at a university or public library will be able to tell you the current status of availability of
Do the search. or Look at your results and go through steps 1-8 again with new words terms suggested in articles identified' Your success will There are usually several ways of conducting any literature search' the researchers that are often depend on how well you know the area of research and publishing. journals where you Third, you can scan the yearly indexes in the professional published' These, if they exist' suspect articles on your specific topic may have been volume' Also' search the can usually be found at the back of the last issue in each behavior' animal on (textbooks) subject indexes in the tertiary literature you can find out who subject, Fourth, if you know a classic or original paper on a St'ienc'e Citotion Index is currently citing it by using the citation index portions of same (SCI) and Soc'ial Sc'ience Citation Inclex (SSCI). SCI and SSCI work on the SSCI and SCI what' is citing you who principle as the first method above, by telling indexes' also have author (name) indexes and key-word-in-title subject published Other sources of information about current research are programs' Addresses of presenters abstracts, and published proceedings of scientific meetings' more inlornratit'rn t-rr for directly author the are usually provided, an
est. Online catalogs allow you to access single words in titles, plus subject headings and authors.
tWriteashortSentenceorparagraphstatingyourneed' z Decide if you want a lew relevant articles or'everything'.
I
phrase Animals, habits and behavior of'lollowing the name of the species of inter-
The world of information is changing almost daily. Indexes and abstract journals were once available only through the printed format which required tedious,
these services.
Once you have a growing list of citations of references you should consider using
Appendix B) and a reference manager software package for of references. Most software packages will allow you to retrieve references by various key words in the title, journal and author. Some will print your selected list of citations in the format required by the professional journal where you will submit your publication. Four of the more popular reference manrrgers are ProCite (Personal Bibliographic Software, Inc. P.O. Box 4250, Ann Arboq M I 48106, USA; or Woodside, Hinksey Hill, Oxford 0X I 5AU, England), Refbrence Munoger (Research Information Systems, Inc. 2355 Camino Vida Roble, Carlsbad, ('A 92009-1572, USA, EndNote (Niles and Associates, Inc. 2000 Hearst St., a microcomputer (see
easy storage and retrieval
llcrkeley, CA 94109, USA), and Papyrzrs (Research Software Design, 2718 S. W. Kclly St., Portland, OR 97201, USA).
1.3.2 Other researchers
o[ the manuscriPt.
otr itrlitllitl Monographs and books are aiso important sources of informatit)tl in this ftr,r,t. .lir.c behavior. It is not unusual for long-term studies to be published irt lllc Goodall, for example, first published her observations on thc chitnpltttzccs scl'ies. Mttttogt'lt1'rlt Bt'hut'iortr Gombe Stream Reserve as one volume in the Aninurl Lltttr. Ptrblislrctl irt l()7 I ' but her most well-kn.wn work is thc book Itr rltt, ,\lttrrltnr of B.oks cul hc lilulrl hy searclrirrg strbiccts itt ottlittc cltlitlogs ot tltt'ttlttliliottrrl ltt cltttl t'lttltlol''s' t'llit'it'rtt il('t'ess is lrt'sl olrl;tittt'tl lrv lirsl cirt-tl cltlltlttp.s
ol'lillrlrrit's'
rt.lt.rri,1,l. tlrt'I tlrt;tty ol ( ()lll'l('\\ Srtlrlt't't Ilt';ttlitll's
(
I\llr r'tlrr l()()')) tttrrlt't lltt'
'orrtlct othcr rcscarchers who are working (or have worked) on concepts or species rrr wlriclr you ru'c intcrcstccl. Discr-rss your proposed research relative to your own itlerrs. l)o rrol prrrirsilizc irntl irsk clucstiotrs that imply'Tellme everything you know rlrorrt . . .'. Mosl rt'seru't'lrcrs;rrc gllrtl lo tliscuss ongrling proiects, but are unwilling Ioprorirlcrrrirri lr't'lrrrt'slor pt'o1llr'wltolt;tvr'rtrll rtlt'citrlyittlcntptetl tolcarnasmuch ,rr lltt'\ r';ttt lltt()ul'll olltt'l sr)tlt(('s
(
I
R
98
ECON NA I SSANCE OBS ERVATION
f
How
ro
DESCRIBE BEHAVIoR
99
Audio VisualServices 4.3.2a Pro.fessional meetings
The Penn State University l7 Willard Building
professional meetings, such as the Annual Meeting of the Animal Behavior Society. are excellent opportunities not only to expand your knowledge about the various behaviors of a wide variety of species, but also to meet and talk with other
University Park, PA 16802, USA
lollow the technical paper presentations, 'buzz'with more infbrmal discussions. Always, always lobbies ancl and hallways
Boxl2
Rockefeller University Film Service
researchers. Question and answer sessions
1230
York Avenue
realize and respect the fact that most prolessionals are cautious about divulging the crucial details of their research until it is nearing completion, has been presented at
NewYork. NY 10021, USA
a meeting, or is in published form. Young ethologists are strongly encouraged to
UCLA Media Center
attend, present papers, and join in the discussions at every prof'essionalmeeting pos-
Instructional Media Library
sible.
405 Hilgard Avenue Royce
4.3.2h Telecommunications
to quickly contact many researchers through electronic mail (Email). Some researchers are willing to carry on extended discussions about research questions, designs. proceclures, results, and numerous other topics over E-mail'
It
is now possible
Hall No.
8
Los Angeles, CA 90024, USA Companies that provide hlm and videotape lootage to production companies for
incorporation into programs are another potential source fbr ethologists. Three of these companies are:
Keep in mincl the caveat given above that most prolessionals are cautiotts about discussing crucial aspects of their own research during its early stages.
Dreamlight Images 932
There are also several electronic bulletin boards and newsletters devoted to animal behavior. For example, you can subscribe to ABSnet by contacting -lim Ha via E-mail:jcha(a)milton.u.washington.edu. See Appendix B for further discussion
N. LaBrea Ave., Suite C
of telecommunications.
2690 Beachwood Dr.
Hollywood, CA 90038, USA Energy Production Los Angeles, CA 90068, USA
4.3.3 Films and videotaPes
Films and videotapes are additional sollrces of preliminary information about an animal's behavior. Viewing films before embarking on a project is a strategy perlected by successtul fbotball coaches. In most cases, the more familiar you are witl'l the animal, the greater the probability ol success in your project. Other researchers are oflen willing to discuss not only their rcsearch atlcl yottr proposed project with you, but will often loan motion-picture totltagc atrtl viclco-
Fabulous Footage. Inc.
l9 Mercer St. Toronto. Ontario M5V 1H2 Canada
A rel-erence fbr approximately 140000 commercial videotape programs and ncarly 2000 sources is Klisz (1995). The Video Source Book.2 vols. Gale Research
Another source of films and videotapes is film librarics. whe rc trrtcilitctl lirotitgc or polished pro
Bldg., Detroit. MI. 3988pp. Additional sources of films and vidcotllrcs citn be obtained by contacting the chair of the Animal Behavior Socicty's Iiilnr ('onrniittcc and educational media departments at colleges and uni-
listed below:
vcrsi(rcs.
tapes to responsible investigators.
Encyclopaecl ia Ci rlenlrtt ogntpll icrt A I'cll i vc
I lrc l'ctut St:rlc I Iltivctsitv ll I Mitclrell Iltriltlirtl l lirirt'rsrlr l'lrtl. l'.\ l('l'io ) llS,'\
Inc..
83-5 Penobscot
CONCEPTUALIZI NG TH E PROBLEM
s
observation of the goings-on in a gullery faces one with a great number of problems - more problems, as a matter of fact, than one could hope to solve in a lifetime.
Delineation of research
The real problem arises when we attempt to isolate and define a question, or a group of related questions. A clear statement of the question (problem) in scientific research is perhaps one of the most dilficult steps in a continuing process (see Bronowski, 1973).
5I
It
CONCEPTUALTZTNG THE PROBLEM
below because of separate chapter has been devoted to the concepts discussed is the cornerstone research of their relatively great importance. Proper clelineation is their researchers of any successful study. A commotr f ault among many beginning objecthe clearly the questions they are attempting to answer and
A
inability to state Before the actual tives they are striving to meet (see also Barnard et al., 1993)' As menaccomplish' to trying are research can begin you must decide what yott with concerned be will tioned in Chapter l, most of our activities in ethology defined' (discussed later)' Broadly hypotheses-testing in a broad or specific manner hypotheses arise from a process
of
observation-question-hypothesis' Questions
and hypotheses are a natural product
of our thought
processes and cannot be
listening
animals. or turned off and on. Therefore, while we are reading, observing to other researchers, we are constantly generating questions and formulating method' stated his hypotheses. Medawar ( 1960:73), in a discussion of the scientific
states that in studies
ent concepts:
l.
of learning there are two types of question which reflect differof animals, such as 'Can
Can questions address learning capabilities
species X learn by higher-order classical conditioning?'; 2. Does questions are more
ecologically oriented. such as 'Does species Xlearn behavior I'by higher-order classical conditioning?'Examples of clear and concise ethological research questions are quoted in section 5.2 from Crump's ( 1988) study
of aggression in harlequin frogs (Atelopus vurius). " A duplicative type of research can result because you have an interest in a question that has already been addressed by another researcher. Perhaps, you are not
convinced, based on your own observations, that you would get the same results if you replit'atecl their study. There are two types of replication studies (Martin and Bateson, 1986):
l. Literal
raplic'ation is an attempt to duplicate the study exactly
using the same methods and species under the same conditions;this would provide a test of the internal validity of the other researcher's results; 2. Con,\tltc'tive replic'a-
belief that hypotheses arise as follows: my far as I can tell from my own experience and lrom discussion with simply One colleagues, hypotheses are thought up and not thought out' of ,has an idea'and has it whole and suddenly, without a period So
iit.t lrypothesis is akin
gestation in the conscious rnind' The creation of mind' to, and just as obscure in origin as. any other creative act of
are born should be The thought processes from which questions and hypotheses but they must be allowed to run f ree cluring our initial reconnaissance observations, creep in (Chaptcr tl)' held in check durilg actual data collection when bias may
s.l.l
is imperative that your research question clearly addresses the knowledge you
are attempting to obtain (see also Barnard et a|.,1993). For example, Miller (1985)
What are the questions?
tion involves the use of other measures, similar conditions, or other species to determine whether the same (or similar) results provide external validity for the other
in Chapter 6). Although replicating someone else's research may seem mundane to an established researcher, it can be a valuable experience lor a beginning scientist. as described by researcher's results (internal and external validity are discussed
Medawar (1979:17):
It is psychologically rnost importzrnt to g?/ reslllts, even if they are not original. Getting results, even by repeating another's work, brings with it grcat accession to self-confidence; the young scientist feels himself one ol'thc club at last, can chip in at seminars and at scientific meetings with 'My own cxperience wirs. . .'or'I got exactly the same results'. . .
ctlnstrttttly gcncrlrlirlg Ethologists are never really at a loss for questions. We ttre otll'owtt t'cscrtt'cll' questions as we observe animals (Lorenz, l99l) ancl prtrsttc (l(xrolr:rrr) Reflecting on his many years ol'rescarch on hcrring gtrlls.-l'irtbcrgert statecl:
.ttt' Srl.tt ltl'lr..r'stlttlittl,stt('ll ll sltttlV.l ll s.t'i:rl lltttl's t'.tttttlttlttlt' ltlt" (;ll('llll ltYt'tt ;ltt lt,,ttl", 11r.1,11'r lo tt.;rlrzt.lt,rtt lttllt.,rtrt'Inil\\\
Wlrrlcvcr y()ur'(lucstion. you must rrrrsrvcrirrl.r tlr:rt tlrrcstrorr:
be
committed to it and genuinely interested in
othcrwisc. you nright be prone to making observer errors,
('ll()l\ ol lt't'ottlittl' ;ttttl/op e()llll)tllillipltitl ct'trl;s (('[11ltcr 8). Thlrt
is.
if
yOu are
('()nl('lrl lrr 1111'1r'lf lirrrl (///;ttrs\\'('r to lltc lt'st'ltt'clt t;trcstiott. t-lttltcr thltn //tc iutswer. \'()u ntit\ ,tllrrrr \otlst'll lo t ontlttr'l tttr;tltrl tt'st';ttt'lt
USING THE MODEL F'OR A BEHAVIORAL ACT
DILENEATION OF RESEARCH
102
alternative hypothesis is proposed here, that this flock structure is a lorm of orientation communication, enabling the individuals of these flocks to take maximal advantage of the collective orientation experience of
s.l.2 Stating obiectives one or more questions' The The objective of research, as statecl above, is to answer method' a not research objective should be stated as a goal'
behavior in domestic chickens' For example, an ethologist may be studying social to vary with flock It is noticed that the frequency of agonistic encounters appears research question is: What is the size. But cloes it vary systematically'? A suitable encounters'l The research agonistic of relationship between flock size ancl frequency objective would be stated as:
.
flock size and freTo tletet.nrine whether there is a relationship between quency of agonistic acts in domestic chickens;
' tl)l us'. . To rtrca,;ure
103
the group.
IHuntilron, 1966:64J
Lissaman and Shollenberger (1970), provided evidence in support of the hypothesis
of reduced flight energy, but Hamilton's alternative hypothesis remains untested
(as
I am aware). Unlike the thinking that went into Hamilton's hypothesis (above), the transition ll'om a question to a research hypothesis can sometimes be rather sudden, with the researcher not fully aware that they are fbrmulating in their mind a probable answer (research hypothesis) to a question they have been pondering. In fact, Medawar ( 1960) has suggested that this sudden leap of insight is the normal mechanism lor f'ar as
generating hypotheses. sizes in thefrequency of agonistic acts in flocks of diff-erent
domestic chickens'
The overall objective of
5.2 USING
is to experimental stu{ies. such as that just mentioned'
answer a question through tests of an hypothesis'
THE MODEL FOR A BEHAVIORAL ACT
A f urther look at Crump's (1988) study of aggression in harlequin frogs (Atebpus ruriu,t) (mentioned above) provides an example of how the Model lor a behavioral act (Chapter 2) can be used to help delineate research. During preliminary studies in
s.1.3 Research hYPotheses
undefined nature or speAs discussed in Chapter 1, hypotheses may be of a broad tested' Specific cific and defined in such a way that they can be experimentally hypotheses are
of two types: reseurch ltv'potheses and ^r1rr1i^l'ticul ltypttthc';es'
hypotheses and erre the basis fbr Statistical hypotheses are the outgrowth of research to l7' statistical tests; they will be discussed in Chapters I I behavior and other Research hypotheses are conjectural statements about the 'true situation" From the related variables. They are what you perceive to be hypothesis might be that agonistic example in the previous section, your research is the relationship that yor'r hclicvc encounters increase as flock size increases. This and an indepenclcnt variitblc exists between the behavior (agonistic encounters) (questitlrrs). to prutlttrltilitit''t (flock size). ln science we progress from pos,;ibilitic^i (research hypotheses, basecl on limitecl observatiilns). to ut't't'ltltrhlc ltntltttltilitit"t' tcsts)' (based on experiments. statistical hypotheses, and statistical
hy1-rothcsis is [Itsctl olt Generally, the transition from question to rcscitrch itt sotttc cltscs' Ptt'tiotts thought and careful consicleration of tlhservatitltrs itttcl.
experiments. F()r cxample.
in l9(r6. Hirmilton introrlrrccrl tlrc lirllortitt!'
test'rttt lt
hypothcsis: 'I llt't'ttt tt'ttt t'r1ll;tttltlitttl ol V lirt ttl;tlioits irt llirtl llot l'r ts lll;tl lltt'r ,'sl:rlrlt:lt l;trt't;tlrlt'illl ( lll lt'ttl" lt'rlttt trrt' lltt'lrt ('ll('ll'\ l('(lllll('lll('lll"
'\ll
('osta Rica, Crump: l. lbund that individual fiogs were abundant along a stream throughout the year; 2. observed site fidelity and aggression in both males and lcn'rales, 3. observed that after intrasexualaggression the loser left the area resulting
in more even spacing among individuals; and 4. observed unusual
intersexual
aggression. Therelore, this population of l-rarlequin frogs provided Crump with the o1-rportunity to address several questions about the function and causation of their
lggression that cor"rld be compared to other anuran species and other animal s11)Lrps; tlrat is. this population met both the .suitubilit-v and avuilubility criteria (('hapter3). The numbers ol the statement and questions below correspond to the numbers in Iiigurc 5.1 showing where they Iit into the model. The only type of question not rrtlrlrcsscd in this reseurch was a 'How'question.
I
('r'rrnr1-r I
f
irst hacl to cle(ifie operartionally l0 behavioral patterns involved in
Irc ll'ous' irr:glcssivc cnc()r.lntcrs.
' 'Arc tlrcrc scrrsonaltlillcrcnccs in behavioralinteractions among fiogs, :rnrl il so. tlo tlrcv rcl:rlc lo tinring ol'hreecling'l' r 'Wlr:rl rrrr'llrc lrPPlrrcrrI liutcliorts tll'tlrc vlritlrrs lirt'ms ol'aggression'l' t '\\lrrt lr rrrtlrr rrlrr;rl rlrtts;slr,r1'\\.\'- e r)r'ornllcrs: I'csitlcttt tlt'itttrtttlcr']' ' l','.ttt t t",'. t;tlt'rt'lltlr'rl lo ltotl\ r'r,'''' r' I ),rt', lltt'1.'r,'1,'l .tl'l'l(""'l()ll \,ll \ \\ tllr t lt:rlt1','t,ll l)()l)lllilll()ll (l('llsil\".)'
DILENEATION OF RESEARCH
6 Design
(2) (4) When/Where Exogenous strmull
Positive feedback
(6,8)
_-r-\i\ l ---t--
v
(3)
'-i--> (G+E+D+(A+P) Endogenous
+
whv
Behavior "'. Consequences'-) Behavior What
-)
:I I
i
t
The dichotomy of description versus experimentation was introduced in Chapter I
and will be expanded on in this chapter. Also, reconnaissance observations were previously discussed (Chapter 4) as a means of gathering background inlormation
Negative feedback
about an animal (group
(4) (7) 5.
6.I DESCRIPTION VERSUS EXPERIMENTATION
Feedforward
(Contingencies)
t5 -_-_!,]_ - __-.,,/'
Who/How
Fig.
of research
or
species) before designing
a
research project.
Reconnaisance observations help not only in formulating questions and defining
( l98B) study of The application of the model for a behavioral act to Crump's to questions reler parentheses in numbers The aggression in harlequin liogs. (see explanation)' for text Crump addressecl by
objectives, but also in determining what aspects of behavior can be measured, what
manipulations are leasible, and the degree of variability that is to be expected. These
initial observations occur at Level C in the Ethological Approach (Fig. 1.6); they nlay occur before, or simultaneously with, delineation of research (Chapter 5). Once
than I year) males and adult males aggressive towards each other'l If so does success rate vary with age'?' '[s aggression related to the availability of prey in the habitat'/'
z Are subaclult 8
(less
Her
research
questions' Crump's research objectives were to answer these research questions) were based the to answers the be woulcl hypotheses (i.e. what she believed (Chapter 4)' Her literature the on her reconnaissance observations and review of hypotheses (chapter 1l), research questions and hypotheses then lecl to statistical which factors to measure the research clesign (Chapter 6), and a consideration of sampling arthropod and manipulate (see Chapters 6 and 7). These factors inclucled 'intruder' frogs near resiplacing populations, observing natural aggression, ancl activity)' previc'rt-ts their dent frogs (called 'originals'because she couldn't document
9). data analysis Crump then proceeded with data collection (Chapters 8 and (Chapters l lto l7) an
the research has been delineated, it must be designed (Level D), and an approach (description or experimentation) selected (Level E). Descriptive studies usually involve observations under natural conditions since we want to describe normal behavior; therefore, in the first edition
of this book I
referred to descriptive studies as naturalistic observation (see Chapter I ), and I contrasted those with experimental manipulation; this is the same dichotomy used by
Martin and Bateson (1986). [n contrast, I am now convinced that the clearest and rrrost uselul primary dichotomy should be between describing behavior (de,sc'riptive rc,seurt'h) and testing one or more hypotheses (experintental reseort'lt). Experiments rrre
f
urther divided into Mensurutive and Manipulutive, which are the observational
rrnd experimental approaches, respectively,
of Martin and Bateson
(
1986).
My defi-
nition ol an experiment, a test of a hypothesis, is the same as that given by Medawar 1960). The division of experiments into two types, mensurative and manipulative, lirllows Hurlbert ( 1984).
(
Iloth rlcscriptivc and experimental research should include quantification, but tlrurntification antl statistical analysis must not become the overlords of goodtlrrlrlity obscrvlrtion. Sclcctcd examples quantifying descriptions of behavior are prcscnlctl in ( 'lrrrptcr 10. Sonrctinres initial observations are difficult to quantify, but IIrr'ir r,';rIrrt' is not rrcccssirrily gr-ca1ly climinishccl. Norr;rtl;r1,s, rt is rcglrt'tlctl lrs tnotlcrtt to sct cxpcritncntation above
lll'l
l,l;:i::,,"',1,'1.'il:l:ll':1,,";lll :iill"',l"llll,ll,ii:;i,l ;'il:",':lJ:.:
iil;:l:HIill,,
DESIGN OF RESEARCH
106
VARIABLES 101
to lorget that description is the foundation of all science. I do not rnean to question the value of experiments, but observation must come first, in order to generate qr-restions fbr experirnentation to answer. The emphasis on blind, quantitative experimentation without prior observation is based on the erroneous assumption that scientists already know the questions to ask about the natural world. A theoretical interest and patience are not the sole qualificatiorls for arriving at the principles that govern the patterns of social behavior in higher animals. That aim can be achieved only by those of us whose attention is riveted on the behavior of our animal subjects because of the great pleasure we, amateurs and dilettantes. take in our workl I Loretr:, l99l :7 J Descriptive research involves observing and describing the behavior of anirnals
:
Description using naturalistic observation was the approach used by Nice (1937,
1943) in her stLrdy of song sparrows, Evans (1957) in his stuclies ol wasps, Geist (1971) in his study of mountain sheep, Schaller (1972, lg73)in his studies of-the mountain gorilra and African rion, and Darri, go93r)in his study of red deer. Altliough experintentation (discussed in Chapter. 7) and clescription represent
the
role of the two approaches in primate stuclies (additional cliscussi,n is lbund in Mason 1968); note that he usecl the term 'naturalistic'instead of descriptive.
I am, iti fact' convincecl that nzrturalistic and experimental studies can be compatible with each other; that their respective methods can be applie
it occurs naturally with as little human intrusion as possible. Naturalistic observation does not have to be conducted in the wild. Often an environment can be created in the laboratory (e.g. for insects or fish) or captivity which closely approxiuse
naturalliabitat of the animal. Gibbons et ul. (1992) discuss the design and
of naturalistic environments in captivity for animal behavior
research; they
include a timely and relevant discussion about concerns for animal wellare and regulations (also see Appendices C and D;. Description was the approach used by Tinbergen in his initial scientific study of herring gulls (Zaru$ urgcntotrr^i), building on informal observations made throughout the earlier years of his life.
Throughout the years of my boyhood watching the life in the large gullery rvas complete happiness . . . watching the snow-white birds soaring high up in the blue sky . . . It was this sentiment that sent me back to the gulls in later yezlrs, when I returnecl with a matured scientific interest. intent on exploririg the secrets of their community lif-e. I
T itrltt,rga
n,
I 960b :.r
iii
[ )ltcn:cl t969;78 J
The further splitting of both clescriptive and experimental research into field anci laboratory studies is cliscussed in the next cliapter; rvhichever approach is selected, a prescribed plan lor collection of clata is necessary. If experirnental research is selected in order to test a hypothesis, then a carelul consideration experimentaI desigris is necessary.
social beliavior and social organization of herring gulls living in a colony. Later, his
long-term studies evolved into experimental and comparative stuclies corrtluctecl by himself and his students (Tinbergen. 1973). Descriptive research involving naturalistic observation was thc basic irppnrirclr usecl by Kruuk (1972 5) in his study of the spottcd hyena (('r'ttt'ttttt crrt<'ttttrl in tlrc Serengeti National Park. T'he approach is reflectccl in the behuvionrl rlrrcstiorrs lre
ol
variables and
(I.] VARIABI-ES
J
Tinbergen's initial objective could be stated as: To describe the indivirlual behavior.
A
vuriuhlc is a property that takes on difl'erent values (e.g. cluration botrts)' Trvo types of variable are involvecl in experinrental studies (also sion in scction 6.-5):
of fbecling see cliscus-
Itrtlcp<'rrtl<'trt wriuhle'. the property that changes (or is manipulatecl: e.g. Iiglrt lcvcl) .,cl is bericve
Arr irtlcpcrrtlcrrt v:rri.brc is the presr.rmeci cause or-the clependent
asked:
rrrri;rlrlr'. rlrc prcstrrrrcrr
lttttl ltrlrt' tloes I lris ('()nrl)irtt' tr r( lr tlo lltr".r' ( ()l)lltiltt' tr tllt lltr' lt't'rltttl
a
dichotorny, they also complement each other. and most Iong-term research programs oscillate between the two approaches (Tinbergen l9-51 ). Menzel (1969)6iscussed
as
rnates the
How are the cluestions in t ancl 2 related,l
cfrcct.
IKt,rringer 1g64.-]9J
l)r'lttutlt'nl ytrtrtrltlr,-. tlrr..proltr.llyllrtrl isltclicvctl trtbCtrfl.CCtCcl bya t lt;ttlt't'ttt lltt'ttttlt'Pt'lttlt'rrl r;rri;rlllr, ll rssr)t)tr.lit))r.st.:rltcrl
lltcttrcitsrtrccl
r ,rt
t,tlll(' ( )t ,t.,.,t,,ttr., I t;t t t.tl,lr.
108
BEHAVIOR UNITS
DESIGN OF RESEARCH
109
that in turn alfect behavior; Kerlinger (1967:434) czrlls intervening variables 'in-the-head'variables. All potential sources ol undesired variation are nuisance variables (e.g. previous experience, perceptual ability). Nuisance variables can be controlled in lbur ways:
However, it can be an Behavior will most often be treaterj as the dependent variable' on another's)' behavior animal's one of independent variable, as well (e.g. the effect varidependent the and X variable is generally designated as
processes
The independent the two such that changes able as Y. Some relationship is presumed to exist between more complex in Ycan be predicted from changes in x. Correlations are often much is beyond the correlations those of than these linear correlations, but discussion
t : r
scope of this book.
and clualitative variVariables can be further divided into quuntitatit'e variables qualitative variables vary rn kinds' ubles. Quantitative variables vary in amounts; part. determine the scale of meain will, dependent variable measured
Eliminate the nuisance variable, if possible.
Hold the nuisance variable constnnt lor all subjects. Irrclude the nuisance variable as one of the variables in the experimental design.
+ Treat the effects of the nuisance variable
The type of require a nominal scale' surement you can use. For example, qualitative variables can be used with quantiwhereas ordinal, interval, and ratio scales of measurement is important in selecting tative variables (Chapter 8). The scale of ffIeasurenlent
statistically through the use of
covariance analysis (not considered in this book). Besides nuisance variables there is also rutndentonit'intru,sion which occurs in all
experimental studies. Hurlbert (1984:192) defines nonclemonic intrtrsion as 'the impingment of chance events on an experiment', such as weather changes. If these
appropriate statistical tests (Chapters 12-11 )' one dependent Many experimental designs used by ethologists measure only dependent variable variable at a time: therefore, selection of the most appropriate of behavior units (e.g. 'sitting'versus 'inactive') is very important (see cliscussion variables that can many the among that below). For example, Schleidt (1982) argues
intrusions do not aflect all samples equally, they will increase the experimental error. Examples of independent, clependent, and nuisance variables to be considered in
ethological research will be discr.rssed in the next chapter.
givel behavior pattern, those that are the most stereotyped are the of dependent varimost useful in characterizing that behavior pattern. Selection
be measured in a
6.3 BEHAVIOR UNITS
able(s) to measure should be based on five factors:
Measurements in ethology are made on carefully selected, described, and defined (in experiments) beliavior units. Whether you are conducting a descriptive study or
.
indepenValidity- the variable should accurately reflect an effect oi the
'
(see discussion dent variable and not some other extraneous variable(s) later in this chaPter)' of showing an Sensitivil.t'- the variable should have a high probability
.
eflectduetoachangeintheindependentvariable(s). Retiabilily , the variable chosen should provide the most consistent
(lr.rcncy reproductive behaviors
.
results;those results should be repeatable' distribDistributirrt _it is valuable if the variable will produce normetlly applied be only uted measurements; parametric statistics can normally
r)rcirsure (e.g. courtship. mating. copulation,
rrn
rrrt'itt species X. or tcst tlte hvpotltcsis that increasing temperature decreases the fre-
in
species
X. In either case. you
must clearly
,lcscribe. and in experinrents define. the units of reproductive behavior that you will
birth).
'[-hc
choice of appropriate behavior units to be measured is at once one of the rrrtrst inrportitnt antl clitllcult decisions to be made (Barlow 1977). As we shall see
when this condition is met'
l;rlcr in tlris chaptcr. this can be crucial to the validity of your research. The choice of
'Practic'alit1'_ ilseveraldependentvariablesemergeascancliclatcsrtfier
.u) irl)propriirtc bchavior unit is generally based on experience. tradition. logistics
mtlrc thltt itrc consideration of the four factors above, choose one Or ol'ctltrillltrcttt' easily measured;how much cost can you allbrd ilt tcrtl.ts
,rrrtl irtlrritiott.
time and wages'/
'l'ltcsc
ltte ttrttlt'sitt'tl All experirnents itre alst'r atltctetl by tttti's'tttt<'<' t'ttt'ittltl<"r'' tt't'rr1'ttizt'tl tltt'tt sorlrccs al'vitri.tion which cirn bilrs llre rcsrrlts. I'sycltolopists trttrrtltlr' ittlt'tt'.'ttitt.ti tr't ttt tltt' t'oirrctl irrirl.lility ltl c.rrlnrl rrll llrt. r,lrrirrlrlcs lrrrtl ('lirlttuttt. l()\X) ttt ltt't ottlll lot llll('lllitl
experiment, one or more categories of behavior (e.9. reproductive behavior) will
lrc selected firr measurement. For example. you coulcl de.;t'rihe reproductive behav-
ttol rlttt't lll' olrrt'lr:rlrlt' I'-\'t ll'rlrt1'tt
tl
l:r'crr rvlrerc tlrcr-c is irgrccment about what general kind of description is rrpproprirrtc. tlrcrc rrriry bc rlisugrecntcnt between 'lumpers'and 'splitters' ;rlrtrrr( rrlurl slrorrltl he corrrrtctl lrs lr ttttil ttf lttlttn'ittr lirr cltrantitative l)ntl)(,sr's \\'lrt'r('\()lt)(' tlrllf lilltls. sonl:s trrrtl jorrrrrcvs. tltltct's tlrlly I
lltrt.
1977 I .\,\
I
BEHAVIOR UN ITS
DESIGN OF RESEARCH
Table 6.1. Scott's (1950
6.3.1 Classification of behavior units (e.g' Bekoff, l9l9a; Hinde' 1966' Behavioral units can be classifled in several ways communication in rhesus monkeys' 1970). In the report of his field stucly of social the units of social behavior involves S.A. Altmann ( 1965) stated that 'categorizing lump'. He goes on to point out that two major problems: when to split and when to lumping that one does is, ideally, a there are natural units, 'Thus, the splitting and do'' The aim in categorizing reflection of the splitting and lumping that the animals as Fentress (1990) points however' behavior units is to be as objective as possible; and methods of categorization all out, ethologists'personal perceptions, constructs units from a stream of behavalfect their separation and recombination of behavior
ior.
A reasonable approach to classilying behavior units
is to
work from the general
the !evel of organization along to the specific, using a scheme which closely follows I (Figure l'l)' Throughout the species-individual dimension discussed in Chapter
)
c'la,ssification o/' behavior
General types of adaptive
behavior
Deflnition
Ingestive
Eating and drinking
Investigative*
Exploring social, biological, and physical environment Seeking out and coming to rest in the most favorable part
Shelter-seeking*
of the environment Sexual
Behavior associated with urination and defecation Courtship and mating behavior
Epimeleticx
Giving care and attention
Et-epimeletic*
Soliciting care and attention
Allelomimetic*
Doing the same thing, with some degree of mutual
Agonistic
Any behavior associated with conflict, including fighting,
Eliminative
stimulation
thisbooklwillbeusingthelollowinghierarchyofcategories:
escaping, and freezing
.Generalcategory:Thisisthebroadestlevelofclassification;Scottcalls Note:
this category a'Type'(below; e'g' ugonistit')
.Behaviortype:AtypeofbehaviorwithinageneralCategory(e.g.uggression)
.
and Delgado's scheme Social Interaction:This follows part of Delgado (below; e.g. t'ha'sing)
.
behavioral acts Behavior pattern: This refers to the linking of several pattern; it cointogether into a reasonably predictable ancl stereotyped
cideswithpartofDelgacloandDelgado'sscheme(belowle.g. threat chase)
.Behavioralact.Thisiseitheranelementofabehaviorpattern(e.g.tltcttt)
.
Note that these are functional terms which must be supported by data before they are applied in any particular study.
or a single act that normally occurs alone act (e'g' burtitt'g Component part: This is one portion of a behavioral
Behavior types can be lurther classified according to complexity and soc'ial inter-
ttt'tion by following a scheme prepared by Delgado and Delgado (1962). this scheme was an outgrowth of their studies of modifications in the social behavior of rnonkeys. Their classification of behavior units'evolved from a system of definition' rtnd was used within the framework of an explanatory model of behavior which they also developed. Briefly, their classification scheme is as follows:
A t
Simple behavior units
Individual
(i) Stutit' or po,tturul units can be identifled
teeth duting threat) J'P' Scott 1950: Tahlc 6' I )' one general classilication scheme was developed by ol' 'The list provides a convenient guide lbr the clcscriptiort
He suggested that, case certain typcs [gcrtcrlrl clttcbehavior in a new species, but in any particular goriesl may be absent'(Scott, 1963:23)' The generulc,ute96ricsancl /-)pt^r
of
behuviorsclcctctl lirr sttrtly will trc tlit'trttr'tl' irr
it1-rlttrrltclt sclcctt'tl' Itt tlt'st'ttPlirt' large part. by the qucstions being askerl irntl thc cltlctr'otit's ol l'rt'lt;tVtrtl' \\'llll(' \\(' stutlics wc usrrirlly gilthct' tlitt:r tltt IllllllY llcttctltl lrt'lr;rrl()r tll ('\l)('ttlll('tllitl .l.tcrt ttteltsttr.t.r.rr,l'r,u,ir,r rrrrirs *rtrrrrr rlrrl\ ,).('l\r)('()l
tt'st':ttt'lt
by static relations (e.g. sleep-
ing alone)
(
(ii)
D.rtrtuttic or gc.tturul"unilscan be clefined and identified only by a sc(lucncc
ol'spatial relations (e.g. climbing
a
wall)
lrl ltx'uli:ul involving only part of the system (e.g. moving legs) tb) .qrttrrulitl involvirrg a change of position of the whole system in rcltrtiorr lo its cnvinrrtntcnt (c.g. walking on ground)
'Sot'ilrl (tl '\'lrrtrr (,'l' ttt,rltkt'Vs slt't'llittt'. t'tttllt;tt'ittl't'ltt'lt rllltCt') ( rr) /)t uruntr (r'l' ;t nto11l11'1 t lt;r',ttry, :rrrollrr'r )
DESIGN OF RESEARCH
112
BEHAVIOR UNITS
Static or
Individual
L Dynamic
Sirnple
postural or
gcstural
bchavioral units
sociar
_t
lt3
5 Localized
-l
L Gcncralizcd
il,ll:.,. 9OC tAL TNTERACTION
Simultaneous Sequential
Complcx behavioral units Fig.
Syntactic
Active
Roles
Passivc
6.1 Delgado and Delgado's
(1962) scheme
BEHAVIORAL ACT
of classification of behavioral units.
B Complex behavior units I Simultanetlus 2 Sequential 3 Syntactic - the significance of the behavioral unit may vary with context 4 Roles (e.g. groomer-groomed, threatener-threatened)
COMPONENT PART
(i) At'tive (ii) Pc.s.rruc
1
rl
Delgado and Delgado's (1962) scheme of classification of behavior units is illustrated diagrammatically in Figure 6.1.
At the next level in the hierarchy, specific bcltuvior puttern,\ can be isolated. For example, a duck can move (locornotion) from one place to another by using one of lour behavior patterns: walking. swimming. diving, or flying. These patterns could be broken down further according to social, spatial and temporal variables such as flocking and height and speed ol flight.
The next level specifies hehuviorul ut't,t within a given behavior pattern. Firr example, flying can be broken dowrr into the acts of taking ol1. flight ancl lancling. Acts can be f urther classified rnto utrrtpt)nent purts. Taking olf can bc clividetl
into rnovements of various parts of the body (e.g. head. wings. lcgs), anatortricirl structures (e.g. muscles and bones). and neurological activity. Str-rtly ol'thc intcr.nrrl.
physiological, component parts
of behavior are beyond the scopc ol'tltis lrook.
however. several excellent ref'erences are available (c.g. ('anrhi. l9li-l: l:rvcrt. l()l'i0.
Guthrie. lc)80, l9tl7. Kantlcl.
1977).
[.,thologists slrtlrrlrl rto( corrccnltlrtc otrll'ott ltclurr,'iot rrrtils;rl llte p;rrlit trl;rt lt'rt'l
sclcclr'tl
lirt irtlr'ltsivt'slrtrl\'; lltr'\'r'rrrt
I
Fig
1':rilr rrrlrliliott;tl rtnrlt'tsllrtrrltttl llrtottr'lt lltt'
'l{}t rl',tlll' tll ;lll(l 'l,,, rl'\lll,' (rttl ltr)llt \(,. lttl tlll('lil( l)l{}r'r'sr 0l
ll()ll', l(r
(
()llll)()ll('lll
6
2
An observer fbcusin,e in and out between the sociar interactions, behavioral act and conrponent parts ol a herring gulls,oblique Iong call (drawing by Dan
Thompson; based on Tinbergen, 1959, 1960b).
Pitrts of behavior (Figure 6.2). This is irnportarlt not only during reconnaissance .bservations when you are deci
' ' ' t|rttt ltitt.q ttl t ltttt' tlrrrrrlt'r.r ntust be adopted in order to work out the t'xrrt'( nrerrrrirrl ol'caclt sc1-111r.atc bit of behaviour. Only when the general ('()tll'\('.l cl'eltls ltlts llcctt ltltrgllly trirccd arrcl srlrtte hypothesis. h,r*ever \:lJ'll(" lt;ttttt'tl t'rtttt't'l'ltitt! it irr tlrc w'rrlclrcr''s rrrintl. ciur thc ftnc shacles of lrt'lt;ttlrrttl l,',t',';lll\ lll(';rttinl lil lrirrr ll is irrrpossihlc trl nrlricc rlr rccrlrcl
('\('lVllttttt' ;tlttl.111i'ttltt'tl rr)rr('l'('n('r;tl
r,tltt,',rl ,rrr\ l,rr I lrr.lrl,lrr,rlt
rrlt.rt l1,ts lrr.r.rt
,rl,l)t(.{ t;tl{.{l
ll r: ()tl lln\
t,ltirtetlclttt lltc it(.(.(}ttnl
llflfl
I
l4
DESIGN OF RESEARCH
BEHAVIOR UNITS
say. always begin by distunt watching; otherwise you will not be able to see the wood for the trees.
would
In Figure 6.2, the observer
is focusing in on the alerrmed
Temporal questions:
r At what ages do different behaviors appear i. the animar? z When is the individual observed (year, month, day)l, : How does its occupation of a particular location correlate
herring gull described by
Tinbergen:
(e.g.
Some of these movements and postures are not dilficult even for the human observer to appreciate, though the detection of most of them requires careful study. There are a multitude of very slight movements, most. if not all, of them characteristic of a special state of the bird. The student of behavior is to a high degree dependent on his ability to see and interpret such movements. [r'r the beginning, he will notice them unconsciously. For instance. he will know very well on a particular' occasion that a certain gull is alarmed. without realizing exactly that he knows it. Upon more conscious analysis of his owll perception (an important element in beliaviour study), he will notice that the alarmed gull has a long neck. Still later, he will see another sign, the flattening of the whole plurnage. which makes the bird look thinner. Upon stillcloser study, he will see tliat tl-re eye of an alarmed bird has avery special expression. due to the fact that it opens its eyes extremely wicle. ITinbergen, 1960h.7
ll5
+
s o
with season
migration)/
How does its behavior vary on a daily cycle? How is its daily activity broken up into clifferent generalcategories of behavior or behavior types (i.e. time budget)? What is the duration of occurrences of specific behavior acts (or
bouts
behavior)?
z
s
]
of
What is the relative timing of behavioral acts in a behavior pattern (e.g. synchronization of leg movements during locomotion)?
Wrat is the relative timing of different component parts during a behavioral act (e.g. rnovements of parts of the body during threat),/
It can be seen that the spatial and temporal questions nrerged together in Questions 6 and 7. These two dimensions are interrelated ancl separated by the ethologist either because of the emphasis of the research question(s), or lor convenience and efficiency. Spatial aspects are an integral part of Drummond,s Domains of Regularity (described below).
6.3.2 Spatial and temporal aspects
In Chapters I and 2 we discussed the what, wlten, where, who, how and why questiorrs of beliavior. In this section. we are dealing specifically with the when and x'lrcre questions. These two dimensions are inherent in all behaviors (Chapter 2: Figure 2.6); howeveq they may either be an important aspect of our research question. or they may be ignored relative to other aspects (e.g. who, how). Exarnples of the study of spatial and temporal patterns will be provided in Chapter 10. Spatial and temporal parameters of behavior are usually relutit't' tlitncti.:'iort.:'. This will become clearer as we consider the questions below, which focus l'rom the general to specific spatial and ternporal dimensions. Spatial questions: I
2 3
4 5
In what geograpliical location is the animalfound'l In what habitat is it located'l At what position (vertically and horizontally)'l What is its location relative to other membcrs tll' its group or' poprrlirtiort'l Howdoitsl-t"tovemcntsvitryt'clittiv'cttlwltct'ci((;trttl olltct'tttt'tttl'rr-'tsol tls groLlp tlr ptlprrIation
(r
) lr
rc loclrIcrl'.)
llrlr.vtlollte nto'n't'tttcrrlsol ()n('l)irtlol lltr'lrotlvt'ottr'l:tlr'trilltttt,r\t'tttt'nls ol olltt't lrotl\ Prrtls(r'1' sP:tlt;tlltllrr.r.'tttr.'ttlol lt'r'l tltlttttl'lot'ontols11s1;'t
6.-3.3 Descriptions and definitions
of behavior units
Once behavior units are chosen lbr study they rnust be clearly described (Chapter 4), ittrd especially fbr experimental studies they must be cleerrly cteftnect.Clear and comPlete clesct'iptions are rrot only necessary lor;uar research, but they also provide a, Itccurate picture of the behavior fbr other researchers; this sometimes results in the Published descriptions of behaviors being more complete than the actual criteria trsecl by the researcher to determine when a behavior unit is
occurring. Behavior units rtsed in experimental sludies are generally operatittrrutt,r; deftncd (c'g' Giles itnd Hr-rtttinglord. 1984; see below) in order to increasc reliability (both ilttrit- ancl itrtcr-obscrver). For example, we can say that when behavioral acts x 'rrttl l'occLlr togctltct. lvc will rec6rd an occurrence of behavior pattern
I
Z;thatis,
l'cltitrior Prttlct'tl Z is operationally clelined as the simultaneous occurrence of lrelt:tviot'itl ltcls .\'ltrltl r. we may have chosen to operationally define behavior lr:tltct'tt
'/ ittl lris lv:ty lirt'5;cvgt',1
r'cAS()ns:
l.
we consicler them
the.most important,
lrt'ltrtrirrtrtl rlt'ls itt lreltltr'iot'P:rt(cl'rr /..2. .\'itntl l'arc thc most easily clbserved l,r'lutr rt,1 ;,,'1s ilr lrt'lt;rviol 11;sllcrn ,/. l. .\ :rrrtl ). lrr.c lhc lclrsl trrbilrirrilV clcfinccl ;tr'ls. :rrrrl .l llrt. :lrlrrlliul(.()lt\ ()(.(.utt(.n(.(. ol \ ;rrrrl ) lr;rs lree rr trsctl lts lltc ct.ilcl.iort I r\ r rl ltr't I r.',r',r I t lrt.r .,
ill
BEHAVIOR UNITS r
117
DESIGN OF RESEARCH
l6
i
apparent that the identity of each one resides in certain regularities, in those proper-
during tha pike Table 6.2. S0nte o/ the behtrviors ret'ortlad
tests ttntl
heron tests of'
ties which are common to all instances, and that the regularities lie
unti-pretltttorbeltttt,iorinstic,klebttcks(Gasterosteusaculeatus)
t z
Definition
Behavior Position in tunk
* At surface * At bottom * In weed
Plry.sic'ul topogruphy s
of the animal
(see
section 10.2.
1,
on displays)
Intrin.sic properties of the animal (e.g. changes in color, temperature, and
electrical and chemical properties)
s
Plt.vsit'ul
e./l'act.s
induced in the environment by the anirnal (see section 7. I )
6..1.3a States ancl events
Locontotittn 4'Pa * Sneaky swimming
using caudal Smooth swimming usually along the bottom lowered spines ventral anil pectoral fins with dorsttl anrJ by typified hns swin-rming using pectoral
Determining the exact duration of a behavior is often very difficult, not because the instrumentation is not available, but rather we olten do not have the necessary skill and observational experience; tliat is, it is often difficult to detenline when a behav-
Fast agitated
Jerky swimming
abrupt stoPPing and starting pauses and * Nornral swimming Slow bouts ot'pectoral swimming with frequent
ior begins and ends. Nevertheless, alter observing animals lor only a short period of time it becomes obvious that most behaviors can be divided into two categories based on their relative duration (Altmann, 1914):
stopPages
experimental tank Remaining stationary in any part of the lor more than 0.5 s with little or no fin Slow vertical ascent to water surface expansion bladder movements, facilitated by swim
* still Barrage
Lotution of the animal in relation to its environment (see below) Orientution of the animal to the environment (see section 10.2.1" on displays)
than 0'5 s Remaining at tl-re water surface tilr more fore more than 0'5 Laying upon the substrate, usually still' provided in Remaining within artificial weecl clump experinlental tank, tbr more than 0'5 s water surlace' Remaining at least I cm away from the 0'5 s substrate or weed clump for more than
* Open water
within a limited
number of domains'. as lollows:
balloon
fin causing A rapid bocly flexure and stroke of the caudal 'leap'through the water usually to a place of cover
* Jump awaY
' Stute the behavior
an individual, or group, is engaged in; an ongoing
behavior (e.9. a robin flying); a behavior you can time with a stopwatch;
a
tluration nrcuningf u/ behavior (Sackett, 1978).
' Event
a
a change of states (e.9. a robin taking offl; it approaches an instantaneous occurrence that happerrs so fast that you just count its
fast
occurrence: a tnonlentart' behavior (Sackett, I 978). Note:
* Behaviors
inclr"rded
For example, in Table 6.2 'sneaky swimming'would be a state, but Jump away' could be considered an event. Likewise. 'normzrl swimming' (state) can be interrupted with fiequent'pauses' (events). Throughout an animal's lile it is constantly cycling through states and events. In
l6)' in principal component analyses (see chapter Tindall' Bailliere ( 1984)' Copyrighted by
Sourca;From Giles and Hr,rntingford
It
what we cannot define' is obvious that we cannot measure
It
is eqtrally
truethattlrei,vaywe
them.
tlutt'
stuclying etrtirnal behavior we are merely sampling selected states and events. either lrs they 'l'j
I
rrrcnts arc listctl
Asanexanrple.Table6.2listssonreofthetlperatitlritrltlc(irrititltrsttsctlllv(iilcsks hcr*vi-r'.r'srickrcb:re Huntinglbrd (1gg4) i. their str.rdy.f tlie a,ti-prcclut.r
ancl
in response to Pike ancl herons'
sttttlics' otte sllottltl t'ottttrlt't whcn tlclinirrg llcltitvitlr tttti(s lirI cr1'rct itttcrttlrl 'll ttr rir,.tlttrtr.itt'. r)rrrrrrrrr.rr.r ( r()l"i r.\.(r) sllrtt's rlr;rl
r)r.rrrrrrrrr.tr.s ( r()ri \\'('('\ltlllttlt'lllt'rt't\
occlu-'nuturally'ol'are induced by the experimenter.
Evetrts itntl slitlcs can be measured in various ways. The most frequent measure-
r\
r)tt,trtitt.:,
lrtt';ttl l;tlt1"' t'' Iltt'ttt)lll('llil
tt't':tttlt'tl;ts
lrt'ltltrllrl
|illlt'ttt"
tl t"
in'lhblc
(r.3.
h.t.th Iiltrtt.t
Iltr'lr't
lo l lr rt'Pt'lrlivt'ot't'uncncc rll'tltc s:rtttc bclr:rv1,1 |r..'r. kltt1') ot t ir rr'l;rltrt'l\ ',lr'rr'oltllt'rlst't;il('n('('ol llr'lt;tvirlts
nt l,t,ults 1'r.',,..',',llr :r1l;llit'rl
l()t;tl;t( l(r'1,
;t lro1ll
118
BEHAVIOR UNITS
DESIGN OF RESEARCH
I
t9
Table 6.3. Meusttre's.f ttr stolc'\ uncl events I
Type of measure
Definition
Usual apPlication
Total frequencY
Number of occurrences Per samPle unit Unknown Percentage of total occurrences Per samPle unit Number of occurrences per unit time Amount of time Per behavior unit
Events, states
Partial frequencY Rate
Duration
Events, states Fig.
Events
6.3 Hypothetical record from marked every l0 seconds.
States
an event recorder for behavioral events A and B. Time See
text tbr explanation.
bouts: l. a change in the level (intensity) of motor output; and2. change in orienta-
bout). Analysis of the that occur i. a behavior pattern (e.g. a courtship-display 15' latter type of bout will be considered in Chapter lor defining bouts of behavThere are two criteria (qualitative and quantitative)
tion. A bout criterion interval should only be designated alter the observer
ior:
could elapse between behavioral acts of the same rat in order to consider them part of the same sequence (bout). Dane and Van der Kloot (1964) studied inter-individ-
t
After becoming familiar with the social behaviors of laboratory rats. Grant and Mackintosh (1963) selected three seconds as the maximum amount of time that
a more than one behaviour is being observed' then type of behaviour bout of'one behaviour is said to end when a different exatnple: For begins' (Machlis 191 1
Clrunge in heltavior'.
'If
"9)'
tsout: a consecutive series
of
songs which may vary in
qulu). From their observations, they set a maximurn time
could elapse between displays
minor ways but
A more objective method of defining the BCI is to examine the fiequency hislogram of intervals between behaviors, or the log survivorship curve (Figure 6.4). I hc log survivorship curve describes the probability of an behavioral act occurring rclative to the time elapsed since the last act. When behaviors occur in bouts, the slopre ol the curve is steep initially and then becomes gradual as the intervals
intervals'(Machlis l9l'7 '9)' For example: in intervals of rnouth activites of the same kind fbllowed each other
lesstliiinl6seconcls.tlreyareconsideredtobea.bor.rt'.
I Hciligcnbcrg' 1965' 161
of
stimulus response sequence (bout).
X, is chosen to sep2lrate Itttert,uls bettt,een ()(cLtrrence.y. A criterion interval than x ,are classigreater to or one bout from anotl-rer. Al1 intervals equal than x, as within btlut fied as between bout intervals aucl all those less
If
of male goldeneye ducks (Buc'eplrulu clanof five seconds which two ducks in order to consider them a
tral courtship display sequences in groups
neverthelessconformstoaparticularsongtype.IMulligun'1963..2761
2
has
become acquainted with the species'behavior.
lerrgthen. The curve is generally considered to break into two portions:
I
allects the arralysis ol' Figure 6.3 illustrates how the usc of clilferent criteria obvitltts ll-our tltc seem bouts that bouts. Behavior A appears to occur in discrete equally well to tlclining thcsc record. Both bout criteria (above) could be applied itittl b.uts dcpcritlirrg.tt tltt' obvious bouts. Behavior B. however, will fall differently are twtl boltts' Il'wc trsc cIilccriterion chosen. If we choose criterion I then therc tltcrt tltct'c ltt'c lltl'cu bottls' rion 2 with a bout criterion interval (Bcl) of 30 scconcls. if we use a BCI of l0 secotrcls thcn thcrc arc fivc bottts' rvllltl ur'('(rllsl(l('l Scptrrati...l-ir str.crrrrr rll'trclr:rvior irrlo borrls sltottltl tellt't't ( lt) /i"i ) (ll\ l{t)st'lll)llllll rltttrPlt'. e l'ot Irl.lltrrll.rr.ilrlc lirr tlre sPeCies \t'(' ill't. sttttlvittl' llt. li,tttttl ttst.l ttl ttt tlt'ltttt.:ttlttl' t.ttsst.tl lrr'rr tlttttt.ttsir}ll\ ()l ltttttllttt lrr'lt;tr,tt,r tr,ltit'lt
l. a steep
'.t'ction of'short within-bourt intervals and 2. a gradual section of longer betweenlrorrt intcrvals. Thc break point between the steep and gradual sections of the curve ,;rrr trsrrally bc approxin-rated by visually inspecting the curve (Slater 1974) (Figure {r 5).
Ilrc grirrlual portion of' the'curve can usually be fitted with a straight rrrrrkirrg,
it
rrrr
line,
cxponcrrtirrl l'unction. Machlis (19'71'.14) devised a procedure'to deter-
rrrrrrr'llrc rrrlrrirnrrrrr nurrrbcr rll'long intervatls which cnn be incorporated into the t;rrl"
ol llrt'('ur\e
brrt strll hirvc this tail
lit
reasonably well to an exponential func-
rr,n' r.rl
l lris lrror irlr's ir \e rv objcctivc pnrccrlur-c lirr dclrning the bout criterion inter\\/lrr'rr \()ur l)lur:u v irtlr'tt'sl is irr tlrc ltcltrtvirlt'which ()ccurs itt thc stitrt ttl bt.lttts.
',l,rlr't ;llrrl I t':lt'r (1')i-i 'l r('('()nril('n(l st'lt't'l rrr1' lr linrr' inltt'r.:rl sliglrlly lrtngct'tltitn tlr,rl ,rl rrlrr, lr llr,',,1,)|t'r lr;url'(", nr()\l r;rlrr(ll\ W'lrr'rr \()ut l)nnllrY int('r('\l rs irt llte
oS
100 80
r
Ea
t2l
BEHAVIOR UNITS
DESIGN OF RESEARCH
120
l.uro
60
=C CG) L-
5r; a
3 o)
10o
o ^oro.\.
0)
s
_o
\
E f
z
o
c
\
50
=
o LL
30
200 400
600
Gap length (sec) Fig. o: )R
.Z-1
lnterval length (5 x 100)
E-
6.5 Log survivor lunctions fbr intervals
between pecks at lbocl for three birds. Each point gives the number of intervals longer than gap length shown on the abscissa. Arrows indicate the point chosen lor bout definition for each bird (from Slater, 1974).
=C tro) LJ
a* (J6
time allocated to dilferent behaviors (i.e. time budget), Slater and Lester (1982) rec()mmend selecting the time interval at which the slope changes most rapidly in order
to recluce the number of within and between bout intervals being assigned to the \\'r()ng category.
When long-terrn records of behavior are examined, bouts may be for"rnd clustcrccl into 'super-bouts' (Machlis. l97l). The log survivorship curve may then be a
of three types of bout intervals: L the initial steep portion of withinlrout intervals; 2. the intermediate slope reflecting between-bollt intervals; and 3. tlrc very gradr-ral slope representing between-cluster intervals (Figure 6.6). The strper-bouts' may reflect diurnal rhythms, such as morning and evening feeding pt'r'iotls. Wc tltcn have a hierarchy of behavior units as in Figure 6.7. Sibly cl ul. (1990) rccommencl using log frequency rather than log survivorship ( ur\cs lirr splitting bchavioral ifcts into bor"rts. The data points in log frequency
, orr.rposite
s (J
C
q)
f
U o)
LL
t
irrtlcltcrrtlcn t o l' cach other so that non-linear curve-fitting procedures can
r'l (tl ll(y)ll) .lrorrltl l)e c()nsullctl lir'1hc rrpplicablc nrcthocls. as well zts how to ,r;rplr lr';rsl \(lu;rrt'st'slinlrlr'slollte lortnul:r ptrrvttlcrl bySlltcrttrtd Lcslct'(1982).
"rlrlv 1
r.rpr'ri1rt'trl I rrslt1';r:itry,l,'rrlrilr'pttr (rr \()). W l\' ('\l)('lllll('lll'l rr.,ttr1"t "ttt1'lt' ( \l)('unt('nl lt.llll'lrtt, rrlltlt'1rttt. (rt l')) (lr('lll i\l't( lrll *lttlr.grrrr {rr '(t) \\'\\' '
l,)// lll,ltr./)
\ cS lr rc
1,,';rpPlrcrl rrrrtl ;rrrrrlysis ol'variance tests can be used to determine whetlier the I ,,'lr;n ior ;rl ;rt'ls ir e l)r()l)crly split into bouts. Since tliis is a more complex procedure,
lnterval length (5 x 100) I lt't1ttt'ttt Fig. (r.4 Bsuts 6l- pccks by ('obb's Ilighllrrrtl rrrrrlc clricks ltt ltttlics'llrrl Pirls. W lll l,,r'\tlt\l\(,tsltiIttttrt's Itistogftttrtsol'intelvtrls l)el$('crr llt'tks.,tllrl
Ur
llroottt (l()/())r';rrr lri't'ortsttllt'tl l()r nr('lltorls l() iulirlVzt'sct'ics ol':t ltltltvirlr ttt rrlrrr lr l)r'rrorltr rlt r',',rr',lrt'r'lt',1 (t,' rlttllrrrr',) Ilrt",r'tnt'lltorls tnt'ltttlr'rtttlo-t'onel:t-
BEHAVIOR UN ITS
DESIGN OF RESEARCH
Evening feeding period
A
()
.
o)
Fanother
c
Moves to mea<
f
O"rr-ffi
o)
o
-nt ntm
.z
o l
bites
E
()f
Frg.
6.7 Hypotlretical record ol
a portic'rn
of an elk's spring feeding pattern broken into a bout:time in meadow spent
f'eeding period. super bouts, bouts and bites. Super
primarily in feeding; bout-time with head bent to ground biting vegetation or
lnterval length
seeking other clumps, separatecl by head-raised posture.
tion (e.g. Roberts, 1994) and Fourier-type spectral analysis. The analysis of bouts of group of different behaviors is discussed in detail in Haccou and Meelis (1992).
B
a
o 6.3.4 Number of behavioral units to measure
o)
o o)
Selection
o)
of the number of behavioral units to measure will
be alfected by several
llrctors. The ntitrintarr number will be dictated by a determination of what units are
.z
(o
) ) (J
inrportant in answering the research question; that is, what is necessary for a valid
E
tcst of the hypothesis. The ntaxintunt number will be determined by the experimentrrl design, sampling method. data collection procedure (i.e. equipment), the
lnterval length
rrnd experience
.rn observer is unable to cope reliably with more than 15 separate behaviors. llowever, many examples can be lound in the literature where more than 15 behavror'5 ]1sys been recorclecl; nevertheless, reliability (Chapter 8) rnay decrease as the
C Between-cluster intervals
Within-bout intervals (State l)
r-1
llll ilil
il
i;::":i;
(State
n
r-l
llll llll llll il ffi
lll
rrrrrnber
lll)
ilillllll
of behavioral units measured
increases.
Logistics may restrict the number of behaviors that a single observer can rncirslrre. Behaviors of'interest may be distributed throughout a24h period such
ililll
Bout cluster Fig.6.6
ability
of the observer, and logistics. Hutt and Hutt (1970) concluded that
Model for the log survivorship curve based on the assumptitlrt tltitt tltc ittlct'vrrls
((')lntl llrll tlrcse between pecks represent three clifferent states withirl thc chick intervals are Poisson generatetl. Such intcrvals will hc cx1'rottcttlrlrllv tlistttlrttletl (lt).'l ltc c,rttlP,'stlt''rl lltt'st' ancl will havc log survivrtrship lirnctiorts its sltowtt ilr l()77)' (A)(lirttt Mttclrlis. in showtr distributions is
tlnrt a single observer wcluld have to make observations continuously;
ar-r
efficient,
if
rrot impossible proposition if extended over several days. In these situations, several
,,bscrvcrs irrc rcsponsiblc lirr recording data during different periods
of the 24h
lrr'riorl. l.ikeu,isc. thc bchaviors ol interest might be spread out spatiallysucli that no ',rrrglc obscr''u'cr crn rcc()r'(l all the data. For example, Krebs (1974) used three r'\'crs in lris sltrrly ol'colottiitl nesting and social feeding in great blue herons in ,,rrlr'r lolrrllrcr t'orrrplclclrrrtl irccurirtctlataot-tl'eedingflightscluringthepeakof the 1t'rl11, lrt'l iorl
{)l)\,t.
)nt'olrst'rr('r \\;rs slrrtiont'tl irr tlre ohsr.'r.r,';rtitlrr lritlc in thc colotty rtntl \\,r', rr t:rrll,, r',rttl;tr'l tt tllt rt \('('()tt(l ollrt't \'('t ()tt lltr'sltol'r' littr'r'ltlsr'l
(
i+Ei !&i.ii,=
N)
=Eii€[tgu-i?=i?
i{1il
:
, ail?iii
s
?lillEi -ei=a::i li3i iEgBE ur:
an
2
z 'i,1
zril1i, ?i;;ei
i|Z*'*ii ;ii* ue;ii*Ei-gi +i;;aI iiE:ai lrg =ziz; t=7_, ilglgtf tfiai ziia
;5
i i;e::ra?3
7zi1 ig? :i'i € lErElEiEi il;la
,=1;
e lie:E Eig?Eig ?
ii
ii3Eii
Threats to external validity
\nr \ni
il ..:.':",
erent (other than tlie intervention) change over time that may result from
processes
l. Generality
across
subjects
li'ithin the subject
included in the investigation 2. Generality across
::, i, i,,,,
e
n t ar i o
n i:*,l:i:.:::1ffi','L:1,iil1l';he
measuring
instrument or assessment procedure over timp
:i {rissir)n
another that might be due to a reversion of
Selection
Any diff-erences between groups that are due
scores toward the mean
to the assignment of subjects to groups
ll.1sL's
- \rtrition :.
DrtTusion
treatment
settings 3. Generality across response measures
Any change from one assessment occasion to
St.it istical
of
extended to subjects whose characteristics may dilfer fiom those
\n1,'change that may be attributed to the r
The extent to which the results can be
tcr
other situations The extent to which the results extend to behaviors not included in the experiment
4. Generality across times
The extent to which the results extend beyond the times that the invervention is in eflect
5. Generality across
Any change in overall scores
behavior change
Occurs when the intervention is inadvertently
agents
provided to part or all of the control group
The extent to which the results extend
6. Pretest sensitization
The extent to which the intervention etfects can be extended to other researchers
The possibility that assessing the subjects before treatment in some way
or at the times when teatment should not be in eflect.
sensitizes them to the intervention that
lollows 7.
Multiple-treatment
interference
The results may only apply to other subjects who experience both of the treatments in the same way or in the same order
Sourcc: Adapted and abridged from Kazdin (1982). Coyrighted by 1982 Oxford University Press. Inc. Reprinted by permission.
rn
a
rn
f
BASIC EXPERIMENTAL DESIGN
DESIGN OF RESEARCH
126
out to
before setting designs (below) and statistical proceclures (Chapters ll-17) choices of experimental collect data. However, il-you are uncomfbrtable with ygur
Independent variable(s) (A,8. . .)
An
with a statistician' clesign and statistical tests, you shoulcl review therr-r
The independent variable in Table 6.5 is the natural time o.f day, and two values of time of day were selected for determining their ellects on house rnice activity: noon (l I ) and midnight (A2).Table 6.5 illustrates a mensurative experiment in which the
both in planning technical advice and assistance on quantitative aspects good designs only produce and in interpretation . . . the statistician can
two values of the independent variable were measured.
In manipulative experiments where we artificially change the independent vari-
ifheunderstandssomethingoftheparticrrlarfieldofresearch,andthe general principles experimenter will receive better help if he knows the can be combined roles two of design and statistical analysis. Indeed. the
ables, the manipulations are called trcotment,e. For example, il' the experiment in
Table 6.5 had been conducted in the laboratory and the photoperiod had been manipulated to create artificial noon and midnight, those two values of time of day
is prepared when an experimenter with a little mathematical knowledge
experiments.
I FinneY' 1960'
-]
intlepenclent utriable is an environmental factor that l-ras diflerent discrete or
continuous values; we study the effect that different values of one, or more, independent variables (1,8...)have on one, or more, dependent vuriables (behavior units).
as though they . . . to write of the 'experimenter'and the 'statistician' with is concerned are separate persons is often convenient; the one yet with accurately and undertaking a piece of research comprehensively provide is to other the reasonable economy of tirne and materials,
tolearnenoughtheoryofdesigntobeabletodesignhisown
l
would be considered treatments.
the As Finney has stated above, just as the researcher relies on the statistician' data. statistician relies on the researcher to provide the proper in this book are The discussions of experimental designs and sttttistical analysis to be the minimum introductory and cLlrsory. The material represents what I believe with insight and research their cou
Sample(s) (Sr,, Srr. . .i
A
^sumple
is a set of measurements
. . .)
of the dependent
vuriable (behuvior unit) on a
studying, is indicated by a nun-rber ( 1,2 . . . tl).For example. in Table 6.5, one sample (,t,,) is taken at Noon (Al), and one sample (Sr2) is taken at midnight (A2). Each ple contains eight
ln experimental
65 BASIC EXPERIMENTAL DESIGNS
S,, Sr,
number of individuals. Each value of the independent variable, whose effects we are
Srr rrr
nte u sur
e
nle n t,\
.
designs where we take two, or more, samples from the same
of the independent variable. those multiple samples are called replications.ln the majority of ethological studies we take only one sample tl'om each value of the lrrlLre
manipulating indepenExperimental designs are protocols for measuring and/or eflects on the behavior dent variables in such a way that their singular or combined of the proper experibeing studied (
127
rrrtlcpendent variable; therefore, we commonly refer to the number
of
rrllter than the number of values of the independent variable, when
samples,
discussing
('\l)crir.nental designs and statistical tests.
ol' to be tested; 3. feasibility of gathering various types of data; 4' types
of power and efficiency' experimental designs available and their relative attributes 11) is based' in large The selection of an appropriate statistical test (Chapters 12 parrt, on the exPerimental design.
Block(s)
(Br...
B,)
llltrcks irt'c lrscd in a rondotni:cd hlock design to control lor the eflects of a nuisance r;rrirrhlc; tlte l'at'iouslevclsorczttegoriesof thatvariablearedenotedbyB,., .,,. In l:rlrlc (r..5. thc inrlivirlual nrice are blocked by sex: lemales (8,) and males Br).
mcasurcmcnts 6.5.1 variables, samples, treatments, blocks, individuals and 1o lltriltl lll)l)r()l)liirte There are six terms that are necessalry to understancl in orclcr tle lirritiotts I ttotrttiott:ttttl Tlrc tcsts. experimental clesigns ancl apply valicl statistical lt'rts Iltt' titItt't ll.ttt tlil'li'r cirsy t6 updcrstuntl unrl rcnrcnrbcr'. btrt tlrcy rrrlry Llse
^re
tlclincrl hclor.l,irr tlrt, lricrlrrc'lrr;rl llrurr('t irr r'rltit'll lltt'v;ttt'ltPPlit'tl ('\lx'lilll('lllltltlt'si1'tts(tt'lt't lt' l;tltlt'() \):ltttlstltltslit';tllt'slr
Ic'.rs
irr.e
to
lnliriluult
(l | . . .
1,,\
\\ lrt'rr llrr' r'llt't l ol t';rt'lr rlrlrtr' (or lrerrtttte nt) ol' lltc intlel-rcrtclcnt vitriablc is rrl(',t\ltlt'tl rtll lltt' :;tttt(' lll(ll\l(lttttls ( tt'lt('(tl.'tl tttt'(t\lttt'.\ rl<'.:i,qtt'. tliSCttSsCtl llftCt' itl tlrr', , lr,rIlt't ) llt,' rtr,ltr t,lrt,rl', ,rt,' ttttltt,tlr'tl lrl /, ,, lrr llrt' tt'1lr':ttr'tl ttt('itsut.cs
BASIC EXPERIMENTAL DESIGN
DESIGN OF RESEARCH
Table 6.5. A ltypothetical exuntple o/'a rt'peated meu.vure)^ experimentul cle.sign u,secl
Measurernent(s) (x,,)
to detarntine the e/fect o./'time o/-day on activity o.f'ltouse mic'e. Fotu'mule antl.firur /bmole house mic'e n'cre rantlomly .selet'ted urul placed outtloors in t'uges vith running wheels in order to measure the ntmtber o.f ntiruttes eac'h individual ntouse ,\pent using
A measurement is the property (category or quantity) of the dependent variable (behavior unit) assigned to an observation. In all experimental clesigns, except the
tlrcir running wheel tlurirtg one hour ut ruton (1200 -1300) and one ltour at ntidnight
repeated measures design, each measurement is made on a different inclividual. [n the repeated measures design illustrated in Table 6.5, the measurement of minutes of wheel running is made on each individual (1) under each value (noon ancl rnid-
(24004100). (
The octivity data in tlte tuhle are.t'ront the.fir.st cluy's nteusurentents.
See text ./br./urther e-rplanotion)
night) of the independent variable (time of day). When the elfect of only one Independent Variable is being measured. the subscripts of ,r denote the sample (i) and the number of the measurement (7), which is also the number of the individual
Time of day (,4)r
Noon (A1)2
Midnight (A\2
individuals
Individuals
S,,,.
Sr,-'
Females (8,)
Il
I I .3 (.r, ,)
I)
8.6 (-r,r)
Blocks by sex of
Males (8,)
52.2
in a repeated measures design (Table 6.5). Another example of the use of sonle of these terms is provided by Topoff an6 Zimmerli's ( 1993) manipulation experiment on the takeover of colonies of slave ant species (Rtrntit'cr sp.) by queens of a parasitic ant species (Polyergus brevigeps).They introduced each of ten Polvergu.r queens who had attacked a dead F. gnavuqueen into five colonies each of F. gnuva and F.oc'c'ulutu. They determined whether
(r.,)
49.4 (.r",.)
I3
10.5
50.5
I4
9.3
51.7
I1
9.6
50.0
IZ
10.3
5t.1
I3
r0.9
52.6
I4
9.1
s0.3
l\t1.1;gt'gv5 queens wor-rld be accepted by colonies of the same species as the dead (lueen (F. gnuvrt) and colonies of another Forntic'a species (F. occulata) (Table 6.6).
. '
Notes:
I
'
Independent variable.
2 Two values of the independent variable.
design illustrated in Table 6.5, there are four female mice (1, mice (1,
,)
Santples: One sample lrom each treatment
(f, gnrv, and F. ot,culuta). Each sample consisted of measurements from five different colonies. Depentlent yuriuble. Takeover of colony.
. '
3 One sample of measurements from each Value of the Independent Variable.
Independent variuhle: Slave ant species (Fornticusp.) Treotments:They introduced the parasitic queens into colonies o[ two species: F. gnut,a and fl occ,ulota
Meusurement'. Two measurements
of the dependent variable were made
for each of five colonies: category data: Takeover? (yes or No) euantity data: Takeover time (min.)
and four male
o).
6.5.2 F)xperimental designs Dependent variable
(
s
) (Y)
The dependent variable is the behavior unit on which nteusLtratn('nl,r urc nratlc. l'htrt
behavior unit will have one or more properties in which observations will dil'lcr irr 'l'lrc rlcpcttsome measureable way (e.g. occurrence, fieqr.rency. duration. Iirtcncy). dent variable in Table 6.5 is total tinie sperlt whccl rurrrrtirtg rlrtritrg caclt ortc-ltotn
t )rrlv livc cxperimental designs
will be discussed in this book. For a more extensive 'lrst'ttssioll ol' cxpcrintcntal clesigns applicable to animal behavior research you 'lrottltl cotlsttlt orlc or tnore of'the texts devoted to that topic (e.g. Kirk, l96g;
l'ltr';trrls'
,
lgti-5).
l('\rln\. eorrsrrll
sample period.
l'irt'gootl
cliscussions ol'time-series, single-case (single subject)
K r.;rtoclrwill (
l97tt) irncl Kazdin ( l9g2).
I lrt'lrrt't'\lr1'1i111.'11trrl tlcsigrts tlisctrsscrl be Iow can be cliviciecl
tr
t.ltlltl,'
l','tt(l('nl
l() trltt'lltt't lllt' tt'st';trr'ltr'r rvrll bc slutlying lhc elll.cts
t rrI
t,rlrlt's
into two categories
tll'.ne or two
inde-
BASIC EXPERIMENTAL DESIGN
DESIGN OF RESEARCH
130
Table 6.6. Polyergus queen tukeot'er o/'unreluterl F. gnava c'olonie'; uncl o./'
l3r
that Vives conducted; however, most of the data, and some of the experiments, are hypothetical.
F. occulata colonies
Successful
Takeover time
Successful
Takeover titne
Colony
takeover?
(min.)
takeover'?
(min.)
I
+
l5
2
+
70
-1
+
I
+ +
60
4 5
6.5.2a Completell' randonized design
F. ot'culata colony
Foreign F. gnava colonY
20
NA NA +
2 days
NA +
3 days
Notes:
NA: Not applicable. t : Successlul takeover; -: utlsuccessful takeover. Source; From ToPoff & Zimmerli ( 1993). Copyrighted by Academic Press.
This is one of the simplest designs, but it can be used to compare any nurnber of samples (or treatments) of a single independent variable. The samples are the qualitative or quantitative diflerences in the independent variable which you have hypothesized have a measlrrable effect on the behavior you are studying. [f the experiment is ntensurutive, the measurements taken in each sample are from individr-rals rartdomly selected lrom tlte population; the experiment may consist of a single sample.
One indePendent variable Completely randomized design
984). One suntplc
Tabulur.fbrnt One sample (or treatrnent) of tlie independent variable
Randomized block (repeated rneasures; matched pairs) design Incomplete block design
S,,
rtt
Latin square
'
-r
Two indePendent variables
completely randomized two-factor designs. Most of the examples provided for the experimental designs dcscribctl hclou' are based on Vives'(1988) laboratory stucly of parental chtlicc by llrvill c()l)vit'l cichlids (Cichlusonta nigrulfasciutunt). He released the lurvitl cichlitls itllo tlte t'e tllt't' aquarium where they could choosc to stity itt closc 1'rt'oxitttitv lo itttlt
of al6.litre
ol'lltr'rttltrttittltl (i t' trtlltttt 'llre llrrvlrl t'tt'ltrls \\'('l(' l;ttst'tl tttltlt'l cm 6l' cpcl6strrc c6ltirrtrrlsly lilr l5 nrirr.).
viclual ciclicls held in scrccnccl cnclosurcs:rl cithcr cnrl
j
) ,ttc .l' lu,() (.()l)(liliorrs: L irr llrt' l)rr'\('n( (' ol prt'rlrrl()l\ ()l l11. ttt llol ltt llrt' 1rlt"' (.ll(.(. (rl lltt.tl;tlrlts trl ll \ S()||l(. trl lllt. (.\;lllll)l('.. lrltr\ ltl...l .rtt. :lt llt.rl ..r1)(.1 llll(.1|1..
t:
,rt:
Completely randomized two-factor design Almost all the more cpmplex designs, not discussed here, can be built by combin(Kirk' l96tt)' ing two or more completely randomized or randomized block designs Statistical tests (Chapters 12-11) will be provided lor analysis o1'data fiom cotlpletely randomized, randomized block, repeated measllres. nlatchccl-pairs. ittttl
experiment, there
'zero' treatment in which everything is the same except that the experimental variable has not been imposed. However, a control can also be: l. a'procedural' treatment (e.g. mice injectecl only with saline versus saline plus drug treatment); or 2. any treatment against which other treatments are compared (Hurlbert, l
'
If the samples are treatments in a ntanipulation
must be a minimum of two treatments (or treatment plus control), and individual animals are randomly assigned to each treatment. A t'ontolis often considered a
.ri,,
Sirmple Mean:i,
\ ,, :
tlie onc sunrplc (or one treatment) of the independent variable r,.-Mcasurcnrer)t ol'the dependent variable on the second individual in the sitrnplc.
ir
rr birr'()vcr ir
total inrlicutes the mean lor that total. The dot in the subscript
tlcnoles lltc vrrrirrblc ovcr-which the surnmation occurred; in this case. this is tlrt'nrr'rrrr lirr llre srrrrr ol'thc ulcasLrrenrcnts in this one sample.
lt,rrttltl,' \/rr('\(l')ss)l)llt ,r l);u('nlr'rt'lrlitl inclrt'lrol'lhcscrccnctl cnclostrresatthe ('n(l\ (rl llrr'.rrprilnunl rrrrtl rlr'lt'nnnr('(l rrlrt'llri't tlrt'ir l;tttltt't'llosc lo stity itt l-ltoxirntl\ lo rltt('ot lltr',,llt,'t ,,1 llt,'l),ltt'ttl',, ot t lt,'',r'ttr'tlltt't
ti
I32
DESICN OT.RESEARCH
BASIC EXPER IM ENTAL DESIGN
S,rr (One parent at each end of the aquarium) + (r,,; the first individual in the sample is indicated a having chosen a parent) (-r,,; the second individual in the sample is indicated as having not chosen a Parent) :
+
(-r,ur; rhe 69th individual in the sarnple is indicated as having chosen a
parent)
Of the 69 individuals tested.47 chose to stay in proximity to one of their parents, and22 did not choose either parent.
Table 6.1. An exumple o/'dutu in a c'ontpletel'; rantlotni:etl clesign tt,ith ttro sumples (see tert frtr e.rplunution)
Groups from broods raised with predators
Groups from broods raised without predators
S,,,
S,,
123 (.r, ,)
305 (,r,,)
207 (.r,,)
215 (,r.,,)
186 (.r,.,)
3l I (-r,.,)
:
:
42 1 (,r,
Ttro sttntple,v Tabulcrr Fornr
356 (.r,,*)
,,)
Sourt'e: Based on Vives (1988).
S,,
S,,,
-rtt
.Y:
.Y
t:
_r
t:
Three or nlore sumple.s
t
Tubulur.fbrnt
,Y:z l-1
sr,
S,'
:
Sample means:
l3l
l" :-Yl.
"'
s
Y:r
Y::
T.
Y:r
Y.
.Y:.
Grand mean:.r
:
where: S,,:Sample 2 (or Trealment 2) of, the independent variable .y,.:MeAsurement of the depenclent variable on the second individual in
-r
Sample nreans:-r,
,yr:a bar over a total indicates the mean for that total. The dot in the subscript denotes the variable over which the summation occurred; in this this is the mean for the sum of the measurements in Sample
2.
found that the length of time until a choice was ntatclc ( rcttction time:latency to choice) tended to be shorter for young lrom brtltlcls rcirrccl itt the presence of predators of yollng versus those raised with no pretlittors ol' yottttg
E.turnple
Vives
( 19S8 )
present (see Table 6.7).
Therewere33inclividualsinSample Alan<1 l4individrtitlsitrsrttnplc.ll.'l'lrc rJata show that the first individual(-r,,) trom a broocl ritisetl 'uvilh 1-rt'ctllrlot's (SrttttPlc S.,,) took 123 s to make a choice. Thc last intlivitluirl (t,,,) ll'ottt:t bt'ootl rrtist'tl wilhttut precl;rtors prcscnt (Santplc
"Y" ... -r i.,. ... .\,
Grand mearn:,r
samPle 2.
case,
.Y,
,S
,.) lotlk 156 s. to tttltke tt ,-'ltoit't'.
'
.S.,,
:
-rr,: .I'r
:a
Sample I (or Treatment I ) of the independent variable MeAsurement of the depender-rt variable on the second individual in Sample
I
.
bar over a total indicates the mean lor that total. The clot in the subscript
dentltes the variable over which the summation occurred; in this case, this is the mean ftrr the surn ol the measurements in Sample l.
liturtrplt A thirtl
sumple coulcl be addecl to the example above by addilg a sitttlplc ol' yotttlg rltiscil in the presence of predators of adult cichlids. Reaction titttcs wotlltl tltcrt hc titkcrt ll'om rernclomly selected individuals from each group (scc lrrlrle (r.X).
Itt lltts ltl'Potllt'lrt'rrl erlttttplc rvc lutvc trscrl 2l ipcliviclguls 1[rm brgods raisecl i. lllr' pt('\('n( (' ol p11'11;1[1ls ol ;rtlrrlt gigltlirls (S;rrrrltlc A1;. 'f[c lirst i,rliviclt*rl tlc,_ rtttt'tl lr llrt.,,,tlltl) ( r ,, ) lr:trl:r tt';tr.li,rtt ltttrt.ol l.) I s(,(.()lt(ls.
DESIGN OF RESEARCH
t34
Table 6.8. An exumplc
rf'a
BASIC EXPER IMENTAL DESIGN
c'otnpletely rurulomized de,sign v'ith three suntples
135
6.s.2b Randomized hrock, repeated meesures and matched pairs designs
illustratecl with hl,pothetit'al data./rom lurval t'ic'hlids (see text.fir explturution)
Ra ndom i: e d ht o c.k d e.t ig rt
Raised with
Raised without
Raised with
predators of young
predators of young
predators of adults
S,,
s.,,
sr,
123 ("r,,)
305 (-r,,)
216 (.v,,)
215
215 (.r.,)
186 ("r,,)
3l I (.r,.,)
195
356 ("v.,,*)
323 (.r.,,,)
421 (-r,,.,)
subjects that are similar in one or more characteristics (e.g. same sex, age, is, subjects within each block should be more homogeneous than subjects between blocks. Blocks can also be treatecl as a second independent variable (e'g. two-way analysis of variance,
litter; to blocks' That
207 (x,r)
(.r.,.,,)
This design controls lor additional variability (expressecl in the error effect) by assigning
not discussed in this book).
(.r,,) Tuhulurjbrm Siintples of indepentlent variable
Blocks Linettr ntodel A simple equation can be used to show all the sources of variation
Bl
that affect the indiviclual measurements in the three completely randomized block
B,
S, Sr, Sr,... s, 'Ytt -Y:r -Y3r .'. -\,r
8,,
Sample
The rnodel (equation) states that each individual measurement (.v,,) is equal to the population mean (p) plus the sample (or treatment) elfect (a,) plus an error
are used toestimate those parameters. In theexamples of experiments above,lt. ai and
e,,
are unknown: but they are estimated by the following:
&:,t
(estimates p)
ir,:(.r-', -.\- )(estimates rr,)
e,,:(x,,- I ,) (estirnates
e,r)
The error elfect is the summed elfect of all the uncontrolled rnrisurtt't, turiultl<,.s. that is. all of the elfects not attributable to a particular sarmple (or trcatnrent). Wc can rearrange the linear model to show that the error el-fect (e,,) is whll r'crnrrins ol' ari individual measurement (.r,,) alter the sample (or treatnrent)cll'cct atrrl 1'lo1'rulirtion mean are substracted from it.
?,r:'v,r-(&,*P)
-r
l
r
rt
means:
Grand mean:i
effect (e,r) which is unique for each individual subject.
As in most experiments, population paretmeters are not known. but the samples
Block means
-r.l
designs described above.
.r,,: lt,* o,* ,,j
I
-r,, is a measurement from a difrerent individual or a total fbr several indi. -Each viduals in that brock and sample. hidividuars are brockecr (e.g.B,) according
some characteristic such as sex. age, genotype, place, or time.
Example vives (1988) tested the larval cichlids at a. age of two days free-swimming, because by that time they f'eed exogenousry and show good mobirity, and they had previously been shown to responcl n-rost readily to models when less than six days free-swirnnling' If it were not possible to lrave large numbers of young available at tlie age tll'two days f ree-swimming, an
raiscd wiIhouI ;-rr-cdutors of young. -I'lrc ltyPothclicrrlrlittit in Tublc 6.9 show that the total time to make a choice for (lrc lir,e itrtlrvitlrr;rls r.rrisctl wirh ltrcclirtors ol -young, ancl testecl at age I clay free_
se..ttrls. 'l'lrc irc irrtlivitluirl linres nraking \\'oltlrl lrr. tlt'st1,n;tlt,rl lrs r, \ rr,. \ I I,. \ ilnrl r,,,. r r. r r r. srt
ittttttittr'
(t
,,) ttlts
(t'50
f
I
to
rl
I
ri I
,p that tot.l
I
',1
BASIC EXPERIMENTAL DESIGN
DESIGN OF RESE,ARCH
136
Table 6.10. An e.runtple of'u repeutatl nteu,sure,s des'ign n'itlt twu ,santple,s illustratetl with hypothetical cluta.front larval t'ichlicls (.see te-rt fbr explanation)
Table 6.9. A runclomi:ecl block design illustruted with ltypctthetit'ul duta.fbr tha tinrc it took larval cic'hlitls o/ dif/brent ages to c'hoose to stu1, near one d'their parents
Age
Individuals of age two days
Raised with
Raised without
predators of young
predators of young
flree-swimming raised with
No Odor of predator
Odor of predator
predators of young
So,
Sn,
(days free-swimming)
S.,,
S,,,
I
650 (-r,,)
1235 (-r,,)
Il
203 (-r, ,)
2
587 (,r,,)
1307 (-rrr)
I2
185
1 J
673 (-r,.,)
1214
4
706 (,r,,,)
1292 (x.^)
(xr,)
te
d nrc us ure s
175 (.r.,)
(r,,)
168
(r,,)
'I,u
226 (.r,,,,)
choice when no predettor odor was present,and Re pe u
r37
des i gn
213 (.r,,,,)
it took
175 s. when the
odor was
present.
The repeuted measures cle,sign is the same as the randonizetl block design except that:
r
fu[ u t clte d pu i r.s cl e.t i gn
Individuals (e.g. 1,) are the biocks (Table 8.4)or subsets of blocks (Table
This design is a subset of the randomized block and repeated measures designs.
6.s)
u A measurement
in each sample (or treatment) is made on each individual.
For example. the measlrrements,yrr,-r:r . . . ,r,rr are all rnade on individual I t.
Either design is usually relerred to
'fubulur
Tuhulur fbrnt
sr
Il
S.r, sr., "'
I2
r -Y::
;
-rr,,
Sample means: .tr
-Y:
-Y-r
t
Bl
_\'
-\-n:.
-rr,,
,Y
B
If
we decided that the greatest ell.ect
S.r,
!:1
Srrntple ntcllns:
.Y, .tr ... .t,
S.r,
'Ytt -Y:l -Yt: .r::
B.
.\=,
tr
Grand mean: ,r
Grand mean:-t
E.rumple
fitrntut
individuals
s.r,
-r::
puirs when only two samples (or treat-
Samples of independent variable Blocks or
Samples of independent variable
Individuals
as mcttchecl
ments)are used.
ol being
ruiscrl r,vith pretllttot's ol
young was shown by young at age 2 days free-swinrtling. wc ntigltt Iltctt cltoosc lo use ten individuals from that age groLlp to test lirr thc clll'ct ol'1'rt'ctlrttor rttlrtl itt tlrr' test aquariLlnt on their time to nrakc it chrlicc. Wc rvottltl I'ltntlotnlv sr'lr'r'l lirt'itttlt viduals to be testccl in (hc pl'cscncc rll'tltc otlor lit'sl. tlte otlrr'r'lir('\\('\\'oltltl lt'sl lttrl turtlcr 1lrc crltttlilirltts ttl' tto 1'rt'ctlltlot otlttl l lrt'lrrlltrlltr'lrt';rl rl;rl;r rrr l;rlrlr'(r lO rlrorr', llr;tl ltl(lt\ t(lrlrl /, lool' t0 l', lr, ttt,tl\(',t
l"'\(tt)tl)l( Sirlcc both ol'the extp-rples lor the randomized block design and the r('l)crllc(l lllcilstrl'cs tlcsign (above) have only two samples, they are also considered tt
r;rlt'lretl plr r rs tlcsigrrs.
I tttt',tt tnrtrl<'l Iltt' t'r;ttltlt.11 lot 11,..' tlttttl,rttttzt'rl lrlot'k. lr'l)t'lrlr'rl ltrclrsrn'cs lrntl ntirlcltctl ,1,",t1'1s', ul( lu(l(.,. /i tlrr't.llr.t I trlIr rl,ul;rlrlt.lrr Ilrt.Tllr lrlrrr.[ (or nttlrr itltlrl):
pitirs
DESIGN OF RESEARCH
138
-Y,,:
BASIC EXPERIMENTAL DESIGN
P*u * F,*r,,
Therefore, by assigning similar subjects to blocks (or taking a measurement lrom the same individual in each sample) we have partitioned an additional source of
variability out of the error effect. We can show this by rearranging the litrear model:
i,,: x,,-
Table 6.11. The densities o/'mare untr/bnrare wrocrruts sttrdy of ,social behu'r,ior in AIleghetry wtxtclrat.s Sexes
in groups
(&,+ F,+ tt)
r
F,:tB ,-
t
and
(i, --\'
), respectively.
) (estimatet B/
u.yecr
in Kinsey's (r976)
Density (individuals per 65 m2) 2
Estimates lor g, and rr, remain
139
All Male
x
AII Female
X
Mixed
X
912
6
t4
X X
x
X
x
X
therelore, the error effect is estimated by:
€r:r,r-[(.r,-x )+(r- -t )+-t If
As an example, Kinsey (1976:181) tested his research liypothesis that..
]
the block effect (F,) Ir appreciable then we will have beeu successful in reducing
the error eflect (e,,) by blocking. The relative power of the statistical analysis we use
will be increased by reducing the error effect
as much as possible.
6.5.2c Incomplete block design
This design is applicable when the number of subjects available lor study is not large enough to measure each sample (or treatment) effect lor each block; that is, you cannot complete a normal randomized block design.
Samples of independent variable
Block
Bt
s,r, s.r, sr, Jrr
B2 B1
density (5-14). Table the groups used.
6.ll shows the densities
low density
(24)
and high
ancl sexes that were represented in
At this point the qtrestion arises: If I have a limitecl number of subjects should I go to an incornplete block design or lall back on the cornpletely randomizecl
Tahulur /itrm
Blocks
Allegheny wooclrats (Neotomo.floriclurut mttgi.ster) would exhibit territorial behavior when confined in relatively low-density populations in a large observation cage and at populations of higher density would e.xhibit increased agonistic interactions and a dominance hierarcliy type of social organization'. He placed wilct-trapperJ male and female woodrats together in a 65 mr enclosure at densities varying from 2 to l4' Howevet in this partially balanced design not all densities (treatments) were represented nor did each group (block) contain both males and females. For analysis, the cJensities were combi,ed into two groups:
-Y::
J
t
Sarnple means: .[',
..,
clesign,?
u.s
trt reduce rrrc
means
\lt
J.r
r-r2
t,
Lineur tnodel The equation lbr the incomplete block clesign is the same as that fbr the randomized block clesign:
-Y.
'Y::
tr.
Ytu .should ulvul'.t ,;rrit'e ro trcsigrt rha dcttu coilection irt .tut,rt a trut)) crntr ef/bct as ntuch us possihle.
j'r
.t',r:it-t,* tt,1- p,
I
e
Grand mean:.t This design is balanced. which means that each block contuins thc sittnc ttuttthct. of subjects. each sample (or treatment) occurs thc satnc rrutnbct'ol'titttcs. lttitl sttbjects are assigned to the samples (or trcatt-r-rcrtts)so tlutt cltclt possiblc;t;til ol lte;tl
nrent lcvcls occurs within circlr block lur c(ltutl nrttttbcr ol titttr's. Itt p;trtrlrlll hltlittlcctl tlcsip,tts s()lllt'l)llils ol'ltt';tllttt'ttl lt'rt'l: t't't'tll ltrl'1'1ltt'l ntlltttt lltt'lllt't'ks nl(rt(' (rllt'tt Ilt;ttt rl.I ()Iltt't 1r;tttI
d.\.)/ l,utin .\qttilrc tlcsign I lrrs tlcstgtt ttscs
.
hlockillll t() I'c(lLlcc the error
ef
fbct from two nuisance variables. The
It'tt'ls,l'lltt'luortttis;tttce tltt'iltblcslrrclrssigrrcdtotherowsandcolunrnsof aLatin '-(lll;llt' \irtt tttttsl lt;tVt'lltr.'s;ttttr'nttnrber ol's:unplcs rll'circh pf the three variables
(lltr'rrrrl('l)(.lt(l(.nl rlrrlrlrlt.rvlr,,st.t.l]t.t.l \.()u ilrt.strrrlVirtt,ltttrl tltc lwtl ttttisiutcc vitri,rltlt'ttlt,t',,','1lt't
1.,
\ilU;il(.
1);il ltltilt11J,,,, 1rUl 11t llrt, lrl.1t.hs)
BASIC EXPERIM ENTAL DESIGN t40
DESIGN OF RESEARCH
Lineur ntodel The equation for this design inclr-rdes the zl variable effect (a,). the B variable
Tabulu".fbrnt
variable of interest ('4) The exanrple below uses three samples of the independent (8.O. The C variable and three samples of each of the two nuisance variables square in a balanced fashionl samples are assigned to each of the cells in the Latin in both the rows and represented that is. each level of the c variable is equally number of rows' same the columns. This necessitates the Latin square having columns. ancl levels lor each variable'
Independent variable
A,
A 1
Bl
B,
A1
cl
cr
"rttt
'Yt
(-,
Cl
'r
t
C] 'rl:-, 2:
cr cr
B.
.r 1..,-, -Y:r
t
-Yl:
t
c, 'Y3-,:
inclependent
assigned to the combinations
cichlids we could Irleasure Using our example of the experiment of choice by larval young on their time to make a the effect of three different regimes of raising the and age when tested' The morph choice, while at the same time blocking for color ttbove: fbllowirrg variables can be applied to the tabr-rlar format ,4
(raising regime):
raised without
the presence
ol predators
': in the presence of predators of young lr:raisecl 1.,: raised in the presence of preclators of adults Nuisance variable B (color morPh):
B,:gold
N uisance
a4x4x4
ancl a
1x4x4
6X6X6
I 123456
l2 3,1 1234 2 3 4 | 3 4t2 4t23
of
levels (1,2.3, etc.)
of
nuisance
6x6x6 Latin square clesign.
Variable A
B l 2 3 4
Variable
B l 2 3
4 5
6
123456 231-561 3 4 s 612 4 5 6 | 2 561234 612 3 4
I
3
5
6.5.2a Completell' ranlomized two-factor design
This design is usecl to measure the effects of two independent variables. It can be extended to three or more v'ariables by continued blocking of the variables so that blocks are split into addititlnal bkrcks of another vzrriable. Each measurement (.v,,)is
taken on it clill'ercnt incli','idr"ral. The indivit'luals are randomly assigned to the combinatiorts ol' vu riablcs. (
)rtc ,strntplt
ut
Thltrrlur f ot ^\
.Br: green
B-,:wilcl
- ( a + B +ir, + 0)
of variables'
E.rumPlc
variable I
:,l,rr,
tt
lor B. ancl level
Inclependent
By partitioning out the two nuisarnce variables, we have reduced the error ellect. This carn make the Latin square a more powerf ul design than either the completely randomized or randomized block designs, although it is rarelv used in ethological
variable C to the cells of
of each
randotlly
u,-t 9,* I'l t€,lr
The lollowing matrices show the assignr"nent
The subscripts tbr each measurement (-r,,^) denote the level 2lorvariablel'level 3 variableintheorder A,B.C.Forexample,.t,,, denoteslevel a different individual' on ('r,,^) is taken I for C. Each measurement The individuals are
.rilr:F*
e,,r-
B
lect
research.
I
Nuisance variable
ef
(F,). and the C'variable eff-ect ( r'^).
,,
\.,1
Onc santple each of indepenclent variables
I
ancl B
^\,,,
\l,t
tYPe
varitrhlc ('(lgc wllctt lcstctl
'1'
): :
('t
I tlltr ltt't'-:tritttttlittl'
('
.)tllt\s ll('(' \\\'lllllllllll'
(
I,l,rt"IIr'r''\\lllllllllll'
i
I
BASIC EXPERIMENTAL DESIGN
DESIGN OF RESEARCH
Table 6.12. Exttntple o.f u t'ompleteb' rundomized twovuriahle fitt'tor design v,ith rtne suntple.fbr euch
(see illustrtecl with hypotheticul tlata./br lorval c'ic:hlids t
t'.t t .f i t r t'.t' P I u t tu
ti
tt
n
)
Table 6.13. Example of a completely randomized two-factor design with two samples illustrated with hypothetical data for whether larval cichlids of two color
morphs and raised under two conditions chose to stay in close proximity to one of their parents Independent variable
Raised in presence of:
predators
Raised without predators
Predator odor
Predator Visual stimuli
Raised with
sil
S,,
(N:40)
(N:40)
S,,
S,,
2l 3 (-r,,,)
187 (.r,,,)
162 (-r,,r)
219 (-v,,,)
Color morph Totals
Gold
:
174 ("rr,,,)
235 (-r,,,,,)
I
S,
l6 (,r,,)
4 (.Y.r)
20
l7 (.r,,)
l0 (xrr)
2l
JJ
t1
47
(N:40) Wild type Su,
(N:40) to test the effect of Example In the study of choice by larvalcichlids, we might want versus being raised where the being raised in the presence of the odor of a predator cichlids (time to mztke a choice)' pre
days free-swimming tbr randomly select 10 individuals from each group at age two nor visual stimuli present' testing. They woulcl be testecl with neither predator oclor individual from the groLlp second In this hypothetical example (Table 6.12). the to choose one of the raised in the presence of predator odor (-r,,,) took 162 seconds
Totals
being raisecJ with predators of young on whetlier age two day free-swimrning larval
cichlids choose to stay in close proximity of a parent, or not.
In this hypothetical example (Table 6.13), 47 of the 80 cichlids tested chose to stay in close proximity of one of the parents; 33 of those were raised in the presencre
of predators, and 27 were ol the wild type. Seventeen had both features; that type raised in the presence of predators.
parents. '
Tn' o s arnp I c,s o.f ttt'o
in
tl a p e ntle n t w r i u b I e s
Tcthulur lbrm
of indePendent variable Samples of
Samples
variable B
S,,
Variable means:
Samples
S,,
s.r,
means
t rt:
'r:t
-\-
J::
,tl
'Yr
S,,
Samples
I
Variable B
independent
I
Thrac, or tnor(, ,suntples o.f' two intlelterrclent variublcs Tubular fbrnt
|
of independent variable
I
of Variable B
inclependent
Sr, "'
variable B
s,,
S,,,
,t,,
,r:t .Y-rt 'Yj. 'Y:: -Y:: 'Yr
.r.2
,\',,,
rn \ r: Iri
,\,,,,
t,,,
t,,,
r
,sr-,
Sr,,
means Y.r
:
\'l
,
.\'.
\'
( it':tlttl lll(':111.
i
rtlttr lt \\(' ll;l\(' lx'('ll 1,.'t,ttttltlr, lrr tlrr. (,\l)('t nn('lll u tllt ( ll()l( (' Itr l:rt t ltl t tt ltlrtl'' ill r r)lilt tll(,t;,11 ,1;1,1 t'llt'r | ll,,lill' :t.,,tlt ('\,tttrIlr' tt,'.1.'. l.lr' lil l.lt,l' 'tl lltt t t'tltlrtttt'tl
Vrrrilrlrlt' .l
',,,
is,
wild
I
BASIC EXPERIMENTAL DESIGN
DE,SIGN OF'RESEARCH
Table 6.14. Exuntple q/'u t'onrpletell' runclonti:ctl twt-firt'tor tlasign vith tltree strntples illustrutacl y,ith h!,potheticul clutu.lbr v'hether lurvul cichlicls of'three c'olor morph,s ttncl raisetl uncler thrae t'onditions cho,se
to
sttt.v
in
t'lo,se
prorimity
trt one o.f'
lheir purents
baboons and found that the researchers tended to select the largest group available to them as their study group; they point out that this will likely introduce significant biases in analyses whenever group size is an important independent variable. When selecting individuals for a randomized design or selecting random samples
of behavior from an individual, it Independent variable
I
145
r a n do
nt, lrup h u : u rd and
'
is
op p o r t u n is t
i
important to understand the dillerence between t' samples.
Raised with
Raised without
Raised with
predators of
predators
predators of
ment taken from an individual, in such a way that all possible samples
adults
have the same probability
(N:60)
in a population could be assigned numbers and then the sample of indi-
larvae (
1/:60)
1/:60) S,,
(
Color Morph
sfl
Gold S,
l6 (.t,,)
4 (,r,,)
l2 (,r,,)
l7 (.r,,)
l0 (,r,,)
l4 (,r.,)
Runclont suntp[e'.
An individual selected from
of being
a
population, or a measure-
selected. For example, all individuals
viduals to be observed could be randomly drawn from a hat.
s.r,
'
Hupha:urd suntple: A sample taken on some arbitrary basis, generally convenience. For example, we might take measurements on the individuals in a
(r/:60)
I
type S, (l/:60) Green So, ( I/:60)
Wild
I
3 (,r,,)
7 (-r,,)
group that are closest to us, or we might take measurements belore lunch and after dinner, or when we think the animals are likely to be most active.
'
l0 (,r,.)
Opportuni.vtit'suntple'. A sample of behavior taken when it occurs in any individual in the population being observed. The individualselected, then. is based on the behavior whish it is performing. For example, we
might be studying an unusual behavior in a species that forms large leeding aggregations; we watch all the individuals untilwe see the behav-
As an example of this design we will add to the example given above (see Table 6.14).We will add an adclitional color morph (green) and atn additional condition under which the young are raised (with predators of adults).
Exuntplc
Linettr
ntsdcl With the completely randomized design with two indeperrdent vari-
of independent variable A (4,), independent vari(7 B (F,) and their interaction ).
ables we have includecl the effect able
ior occur, then we record our observations on that individual perfbrming that behavior (all-animalall occlrrrences sampling, Chapter 8). Some researchers use the term rundorl to reler to the way in which their samples wcre collected. when really what they collected were huplta:urd samples; for cxample:
This study is based on facts gleaned for the most part from random
observations.
t,,:F*u+B+l,ilr,i In
clesigns clescribecl above.
it is assumecl thirt irttlivitlttirls
1935;90J
by early ethologists sullered from a lack of knowledge
;rl'rout cxpcrinrcntal clesigns. For example. Tinbergen admitted that in his earlier
6.5.3 Random, haphazard and opportunistic samples
In the experimental
son.rc cirses, research
[Loran;,
(ol'
groups) selecte<J to receive treatments, or selected tbr observittiott lrtttl bcltrtviot'rtl measurements, are ranclomly selected from the poptrlation. Irl lltct. it is ttsrtrtllv Vcl'v
difficult(ifnotimpossible)toaccomplishthiswlrcrlctltttlttctitlgcllttlltlqic:tlt...s..lttt'lt in the field. Instead. we atternpt to suntplc a lul'gc ntrrnbcr ol'intlir,itlttltls ttttrl st'lt't l indivicltrals in a wity tllitt wc llclicvc t'csttlts ill:l ,r'rr'rt,,11111t11'111t1t1tt\ittrttlrrtrr ttl il t.itttrltltt.t sitptltlc.'l'ltc resclrt'r'lte t rnrrst lrltr'ntpt ltl rtvotrl ;t lltltst'tl r:ttltPlt' ll()lll ;l l)()l)tl .,ltttltt"' )) 'rl llttitltl. l'ol t'r;ttttPlt" '\'ltltt tttltll illl(l I )ttttlrlrl ( lt)fi t'\;lttltttt'tl 'l ltt'ltl
strrtlics with grayling butterflies. they had presented their models in a haphazard r
:rlltcr tlrlrrt t'irttrlr)nr scclLlcnce. Wc v:rrierl thc sccluence ol the models irregularly (not being soplristicirlcrl cnough to vary them in a random way). ITinbargan, I958:I82J
,\lllr,rrrl,lr lrrrt'riu)(l()ln srrrrtplinu is ol'lt'rt tlillicrrlt in ctlrologicul lield stuclies. it is .ur ,r',',unrPlrr)lr ()l nr(rrl sl;rltrltt';tl lt'sls 'llrt'rt'li)r('. t('s(':rtt'ltet's sltottltl ctltttbitl thc l('iltIl,tlt(1il l,'l,rl't'llt('(',t',\ \\,t\ rrrrl ,ilr(l ',,rtrrIlt'lt;rIlt;tz;tttllf . tltt'f sltottltl tttltke
146
RELATIVE EFFICIENCY OF EXPERIMENTAL DESIGNS
DESIGN OF RESEARCH
behaviour was sampled during the morning, midday, and evening. This was not always possible. I Yoerg, 1 994. 579 J
every attempt to achieve or approximate random sampling. S. Altmann(1965a:494) '. . . tried to sample at ranclom from among the monkeys. However, no systematic
randomizing technique was used'. Ethologists often sample opportunisticulb'from a large nnn'rber of animals that cannot be recognizetl individually, especially if their interest is in a particular behavbehaviors. For example, Cresswell (1993) studied escape responses by redshanks (Tringa totanu,s) in response to avitrn predators by making opportunistic observations of attacks by sparrowhawks, peregrines and merlins on
ior, or
sequence
of
individuals in a large population of redshanks. Opportunistic sarnples are neither random, nor completely haphazard' We should randomly sample individuals, but instead we sample based on behavior, and We can we do not know how many dilferent individuals are included in the sample. we will the behavior of ol occurrences sample enough large a only hope that with
if you could not collect data on the randomly individual, you should select another and continue that process until equal samples had been collected lor each individual. Collins (1984) used instantaneous samples on every baboon in order to estin-rate the amount of time each animal spent in each part of the troop. However, he had difficulty randomly selecting the local animal, so he settled on the following procedure which combined opportunistic For a particular sample period,
selectecl
sampling
attacked during a one-hour observation period, if the attacks were randomly distributed throughout the redshank population. A respectable attempt at random sampling diflerent individuals can sometitrtes be nrade by stratifying your sample by sex or body size and attempting to sample ranclomly within those groups. If you are observing a large group of individuals' such as geese feeding in a field, you can divide the group spatially using an imaginary grid or marker stakes placed in the field before the geese arrive. You can then sample from different and distant portions of the grid assuming that an individLral goose is unlikely to move that distance during your samplipg period. When individuals can be recognized, it may still be dilficult to follow a ratldottt
equalizing samples:
Sample subjects [focal animals] were selected in sequence from a predetermined random-order list, but some choice had to be introduced to speed up sarnpling. To do this. the list was divided into triplets, and the subject chosen was the first one seen of the next triplet provided that it 1a) was more than 25 m from the site of the previous sample. and (b) had not been sampled during the previous hour nor more than once that day. Some animals were however given priority over others in their triplet (before the start of the day's sampling) il their sample total to date had lagged behind . . ICollins, 1984:5391
also have sarnpled a reasonably large number of individuals. Even if we could recognize individuals and randomly select a sample of individuals for observatit-rn, the an chances of observing the behavior of interest would be remote without spending
inelficiently long period of time making observations. For example, in Creswell's (1993) study of recishanks. he observed 696 raptor attacks on a population of 200-500 ildividual redshanks. during 2551 h of observation. [f Cresswell haci randomly selected an individual for observation from a popr-rlation of 200 redshanks, that reclshank woulcl have had a probability of approximately 0.0014 of being
ar-rd
6,6 RELATIVE EFFICIENCY OF' EXPERIMENTAL DESIGNS A measure of the efficiency of an experimentaldesign should include the cost (time and money) of collecting the data balancecl against the accuracy and validity of
of two designs is often assessed by cornparing thcir respective experimental errors (error effects). Experirnental error is the extrarlcous variation in the measurements due to all the nuisance variables. Its ultimate those c1ata. The relative efficiency
cll'ect is to mask the ellect due to the independent variable(s). Federer ( 1955: I 3) proposed the lollowing lormula to meersure efficiency:
/"'',\
sampling protocol because of the availability or observabilty of specilic inclivitltrlrls. To make efficient use of your time, you might then attem pt tt't ctluuli:t' thc rtttttrllct. ten"rporal clistributiol of observations among individuals. Firr cxlttlll.rlc. yott
(-) t++)
and
might obtain l0 one-hour samples of t-eeding behavior lhrnr citclt intlivitlrral tlttt'rltg each of three sample periods (early morning, noon. lltc ul'tct'noott) ovct' llte Pctiotl of a month;however, this type of sampling protocol is ol'lcrr tlillicrrlt lo rtt't'otrtPlislt.
I :rttcrrrltlctl tp tlislrilttrle olrst'rr';tliolts t'r't'ttlv tlttottt'llottl lltt'rllrl'lr1'lrl lot.t1'ttt1' lt()uts srl llt;tl lirt r'ltt'lt llirtl ,lttl tttl't';tt lt ;ll'(' l)('l l()(l
f!.rr\
\-= / \ar',+:/ cllicicncy:-
l rtrrtnbcr rll'srrl-l.iccts r t'ost ol'tllrtlr collcctiott pcr sub-iect rll ('\l)('nln('ntrrl e r rrrt'tlcgrccs ol'll'cctlotn
rr lrt'r'r'
l ()r ( orttlrlt'lr'lt rttttlontizr'rl rlt'si1'tt: lol,rl.ll r.rtttPlt"-,ll
r'ttot tll
I48
DETERMINATION OF SAMPLE
DESIGN OF RESEARCH
where:
totaldf:n- I df: ntunber of sart"rples- I
finc1
that we have measured the durations of six individual nocturnal howls as
ri:estimate ol' experimental error pef observation Tl]e experimental error
5.2 s 6.1 4.3
meacan be estimatecl by calculating the rnean of the deviations of each Inean: sLtrement frorll its samPle
:rr,,tt - .,t. )'l
tt
I
3.1
7.2
mean:5.4
s
6.7
We calculate the standa rd cleviation (^s): I .5.
1
I
greater thau The subscripts clesigpate the two experimental designs. If the ratio is the liniited ol Because secolid. the one. then the lirst design is more efficient than necessary' usually controlethologists generally have i1field stuclies. compromises are
The tabular r value for live degrees of freedom level:2.51 I
6.] DETERMINATION OF SAMPLE, SIZE experiThe suntplt,si:c is the number of nreasurements in a sample' Since in most prental clesigls each nrezrsurement is made on a diflerent inclividual. sample size generally ref-ers to the nul. ber of individuals (sLrbjects) in the sample' Experimental
psychology and psychophysical stuclies are sometimes based on single-subject (time-series) clesigns (Kazclin, 1982; Kratochwill. 1978)in which a very few subjects ln coutrast. are exposed to a series of treatments over an extended period of tiltle. most studies in ethology require the use of several subjects.
ttl be aclequate to provide sufficient power lor ytlur statistical the sample test (Chapter ll), br.rt it shor.rlcl not be excessive. In orcler to determilie of the variabilsize required for your particular study. yor-r shor-rld have an estimate San"rple size needs
or using ity of the data. This ca1be cletermined by gathering some preliminary data' then and researchers. other by or clata gathered during recclnnaissance observatious I l (see sectitln '7'6)' The the stanclarcl deviation of the measurements calculating
following lbrmula lor cletermining required sample size lSnedecor' 1946) tbr a test of two means can then be used. ,rl /l
samPles
r.r'here.r--standard deviation:
{
required:-; tl-,-rt /r,,, ttt_ |
(Table A5) at the selectecl ctlrlliclcrtcc lcvcl (sccti11ll I l'5)' ancl lor the degrees of frceclom 1p. t)t)) itr yottr sillllplc r/: tla rgin ol errtlr ( tnea tt X dcsi gtrtrtctl ltcctt l'itc,v )
1:tabular'l'value
tlttt;tltptts For cxlttt't1'rlc. lcl trs sitv \\,C lvlrrrt tp rlctcr rliltr' tltt'tlrllt'tt'ttt't' itt tllt';ttt ()lll ltt'ltl rl()l(""lll(l rll' crlvrltr.' ltow ls !'irt'tt ttot'ltttllltllt ;rrrtl ,litrrrrlrllt Wt'tt'lt't l(|
(6-l)
at the 0.95 confidence
.
We decide to accept a 0.05 level ( 1.5)2
of accuracy, then:
(2.51t)2 (2.2s)(6.6t)'_1tl
"- (5y'x01)5): -
ri:number of
149
follows:
samples
fI
SIZE
I ((x)?) -- -
Thereflore, we must obtain 212 samples ol nocturnal coyote howls in order to have a reasonable estimate
of the true
mean duration. We would have to make the same
calculations based on a sample of cliurnal howls. If you are unable to obtain an estimate of the variation to be expected in the data, then you should err on the side of a sample size which may be larger than necessary. Stuti.stit'.t estintute poptrlution purumetcr.s.fiurtt suntple nlcdsurcnterit.r'; there-
lbre, the larger the san-rple size, the better the probability that the sarnple statistics will closely approximate the population values. You can also determine the necessary sample size by solving lor nrulas
lbr
l/ in power
fbr-
specific statistical tests. Power lormulas lor two t-tests are described in
Cliapter I I ; otl-rers can be lbund in various texts (e.g. Cohen, 1988, Zar, 1984). You should not attempt to increase the sample size by increasing the number of observations on individuals already in the sample and then pooling those 'k'observations on'rr'individuals to create a larger sample size consisting of 'ni'observations; this is 'pseudoreplication'(Hurlbert, 1984) or the 'pooling fallacy'(Machlis er rrl.. 1985) which increases the probability of committing a Type I error. Increasing sample size is not the only way to increase the power of your experinrcnt (sec cliscr"rssion of power in Chapter I l). For example, Still (1982) has proposcrl scvcral ultcrnatives to consider including: I. selecting a better experimental
,lcsign. 2. rrsing u lirrgcr alpha level (section I L5): and
-3.
secluential rnetliods.
THE VARYING VARIABLES
7
Research questions may require that measLlrements and manipulations be ma6e
Experimental rese arch
in the field in order to be valid. This sometimes becomes evident when the same experiments are conducted in both the field and the laboratory. As an example, McPherson (1988) tested fruit prelerences of cedar waxwings (Bomhyc,ilta cedrorum) (categorized by species, color and size) in both the field and laboratory; the diflerences
were explained as follows: 'The lack of complete agreement between prelerences for fruits in the field and in the laboratory suggests that factors important in the field but controlled in the laboratory (e.g. abundance, location) override
In the previous chapter, description and experimentation were discussed relative to designing a research project. Description is the approach which uses naturalistic observation in order to construct an ethogram for a species as it behaves normally. In experimental studies, we test hypotheses about the relationships between independent variables (individual or environmental variables) and dependent variables
As a general rule, all ethological studies should be conducted in the field when feasible; this is especially true lor descriptive studies and mensurative experiments. Changing the emphasis of your research from description in the field to experimen-
(behavior units). We can either allow the independent variables to change naturally.
tation in the field is a natural progression.
preferences lor certain fruits' (Mcpherson,
or we can manipulate them.
manipulative laboratory experiments. The key difference between field and laboratory studies is that, if everything else is equal, descriptive studies are more valid when conducted under natural condi-
tions (i.e. usually the field), and experimental manipulations can best be controlled under laboratory conditions. Nevertheless, it is sometimes difficult with certain
to make naturalistic observations under field conditions. The
researcher
should then consider whether natural behavior of the species can be expected, and unobtrusive observations can be made, in a laboratory environment. Even though we might want to shift our field studies into the more controllecl captive or laboratory setting, some species cannot be easily and properly maintained in captivity. For example, Tinbergen, after reflecting on some of the shortcomings of his field studies on gulls, concluded:
ggg:961).
The observational work has to be lollowed up by experimental study. This can often be done in the field. The change from observation to experiment has to be a gradual one. The investigation of causal relationships has to begin with the utilization ol'natural experiments.' The conditions under which things occur in nature vary to such a degree that comparison of the circumstances in which a certain thing happens often has the value of an experiment, which has only to be refined in the crucial tests. rgen, r 953; r 36
In addition, if we make a distinction between field and laboratory studies, we can categorize ethological research along a continuum from descriptive field studies to
species
l
[Tinbe
J
Descriptive studies and mensurative experiments in captivity, or the laboratory, will likely be more efficient, but less valid than field studies. Likewise, manipulative experiments should be carried out in the species'normal habitat when they can be conducted in a valid manner. Neverthelcss, the controlled laboratory setting olten plays an important role in ethological studies; several examples are given in sections later in this chapter. Peeke and Petrinovich ( 1984) can be consulted for another discussion
It would
seem to be more efficient to try to improve the lield methocls than to try to keep a large colony of gulls under laboratory conclitiorrs.
f
'l'inlrcrgctt, l95il ).5I
I
Not only is it sometimes difficult to move fleld stuclics inlo thc lirborirtory. btrl conditions in the field often make manipr"rlations vcry dillicrrlt. Al'ter rcviewirrl, ethological and behavioral ecology stuclics ol'All'icun trngtrlirtcs. l.ctrllroltl
(lt)l'/l
concluded that.
Ilxperirncnts htrvc rlrrcly bccrt t'rrt't'ictl orrt so llrt. Plrtllv lrt't rrusr' tnttt'lr tlcscl'iPtivc wot'k wirs t('(lttitctl rtl lirst. rrntl P;ttllv lrt't'ltust'ol lltt'Plttrtt;rl rlillit'rrllit's ol rn:uriprrllrlinl, srlrl urt1111 ,1,'r nl ('\l)('run('nl;rl .'tlu.rlt()n'f
l,rrrlt,'l,l l't'i lil
of the relIttive advantages and disadvantages of field and laboratory studies in animal behavior' Also. Mertz and McCauley (1980) present a similar discussion for ecological stLrclics which can alsc-r be applicd to ethorogicalresearch.
7.t'l'lttr VAltytN(; VARIABLES Ilre ohict'livc ol'ittt cx1'rct'irncttt is to test one, or lnore, hypotheses about the relalx'l\\'('('ll t';tl'ittltlt's. 'l'ltcsc nury bc cirusc-ancl-cll'ect relationships between ttlrlt'Pt'tttlr'lll illl(l tlt'Pt'tttlt'ttt rtttitrlrlt's. ol'r'orrcllrlirlrr:rl rcltrti6ps5ipls hetwcc, i.del)('ll(l('ttl t;ttlltlrlr's I lrt. rt'l;rlrorrslul)\ 11t\ lrt. l,,lr1, l(.11 (1.\,()ltrtirlrt:1.y ;lttl tlltlttgc_ ll('ll( )(rt :,lttrtl l('tilt(1tttr111rr,rl ('lr,rIlr.r )) Itot15l1i11'
THE VARYING VARIABLES
EXPERIMENTAL RESEARCH
152
r53
temporal activity patterns, such as circadian rhythn-rs and seasonal cycles. That is, temperature, humidity, wind and light levels may all be important in determining the activity patterns shown by an animal or group of animals.
As discussed in Chapter 2, the organism's physical and behavioral phenotype of natural selection on the genotype over many previous genera-
bears the mark
tions. The phenotype includes the action and interaction of the genotype and environment (including experience) and the animal's anatomy and physiology. The organism can exert forces on both the biotic environment (e.g. intra- and interspecific social behavior) and the abiotic environment (e.g. a badger burrowing into a hillside). Since all the f'actors in Figure
J.l
are variables
that have a potential effect on all
behavior, the researcher must be judicious when selecting the one (or f'ew) variable to study at any one time. The complexity of the interactions that can result from two, or more, variables must be recognized and dealt with as skillfully as possible within the limits available to the researcher (e.g. experimental designs in Chapter 6). For example, Prinz and Wiltschko ( 1992) studied the eflf'ect of the interaction of stellar and magnetic inlormation during ontogeny on the migratory orientation of pied flycatchers (FicecluIu hypoIeut'u).
The first step is to list all the variables that are known to affect the behavior in question or are suspected of having some effect. Some examples of the dillerent types of variable are listed below:
I
Environmental Variables
A
Biotic
I
Members of social group
2 Predator prey relationships Fig.
3 Vegetative characteristics of habitat B Abiotic
between an organism and its biotic and abiotic extcrnal environment. Arrows indicate factors from the environment acting on the organism and vice versa, (see text lor further explanation; drawirlg by Lori Miyasato).
7.1 Interactions
To understand which variables may affect an organism's behavior. wc must flrst recognize that an incliviciual exists in time and space in a dynamic statc. contittttitlly under the influence of its environment, continually imposing its tlwrl cllccts Llpoll the environment, and behaving as a result, in part. of its evtlltttitlrlitry ittltl otttogc-
netic history. Figure 7.1 is a simplified diagram of'the relittiottship bctwcctl rttt organism and the environment (also see Moclel. Chaptcr 2). T'hc cttvit'olttttcttl ltrts both biotic (biological)and abiotic (physical) lclturcs. s()nrc ol'rvlriclr u'ill rrl'li't't tlrc clrganism. Ascxlrlplcs. biotic lL.irtrrrcs rrlry inclrrtlc vcg,r'llrtion lyPe itt lr;rlrrl;tl st'lt't' titll. iltclspccilic pretllrtor r.lli't'ts ()n l)rev lr,.'lltviot irtl(l ttlttltslrt't llit ('()tlll\llll) sit,tlltls ott nurlt.st.lr,r'lion Alrroltt lt';rlttt(':r lllil\ lrr'ttttI,rt l;tttl ttt tt'1'rtl:tlllll' :l "l)('( l('\'
I
Temperature
2 Wind 3 Humidity 4 Cloud cover -5 Topography
(r Tinrc circadian and seasonal
ll
(
)r-grrnisntirl variubles A ( icrrolyl-rc
I
Scr
.) l'lrtr'rtl slrlck ll l'ltt'ltol1'pt' I llt'lr;rr tot:tl t lt tt;tt lt't t',ltt',
THE VARYING VARIABLES
EXPERIMENTAL RESEARCH
a Description of behavior
BENNDORF. The physicist advised him to check lor polarized light. Next summer, in 1948, VON FRISCH did the crucial experiment: He
(general categories, types, patterns and
behavioral acts)
b
placed a polarizer above a bee which performed its recruitment dances on a horizontal comb, and as he rotated the polarizet the direction of
Frequency
c Rate d Duration
the bee's dances changed correspondingly (VON FRISCH 1949). This was the first demonstration that an animal used skylight polarization for adjusting the direction of its course. I Wehner ancl Ro.vsel, 1985. I 3 J
e Temporal patterning-circadian and circannual
f 2
Spatial characteristics
Physical attributes
a Morphological characteristics;
b
e.g. shapes,
color patterns
Physiological characteristics
Alter identifying the variable of interest, the usual procedure is either to manipulate that variable systematically, as von Frisch did with light polarization, or to lollow it through natural changes, measuring both the variable and the behavior of interest. The other variables you have identified as potentially having an effect must
This procedure of attempting to list all the important variables is useful not only when you are thinking about the potential causation of a particular behavior in anticipation of designing a study, but also when you have already decided on the variable(s) you want to measure or manipulate and want to account for other poten-
tial sources of variation (e.g. eliminate or measure them). The number of variables that could potentially affect a behavior is extremely large; therefore, the researcher should be willing to spend time compiling the list. Important variables that you overlook can olten be identified by other ethologists, therelore, you should enlist the help of your colleagues in identifying other variables, as well as concurring on the variables of most concern for measurement, manipulation or control. The impor-
remain constant or vary randomly, so that they can be considered to have no systematic elfect on the behavior being studied. The variable being manipulated is the independent variable (e.g. light polarization), and the behavior being measured is the dependent variable (e.g. orientation of the bee's waggle dance; see Chapter 6 lor a
further discussion of manipulating variables). Several variables may be manipulated and/or measured simultaneously in order
to determine both individual effects and interactions. Selected analyses of this type are discussed under multivariate analyses in Chapter 16.
When planning your experiments, always keep in mind how the various results will (could be) interpreted.
tance of consulting colleagues is illustrated by Wehner and Rossel's (1985) account of how Karl von Frisch came to make his dramatic discovery that bees use polarized
light from the sky
as a celestial compass.
IV. DeGlrett pers. commun.J
. . . two decades earlier . . . FELIX SANTSCHI had already observed that ants could find their way even when they could see nothing but a small patch of blue sky. In an interesting but unfortunately neglected work . . .SANTSCHI ( 1923) literally asked the question 'What is it in this small patch of sky that guides the ants back home?'SANTSCHI . . . could not tell. In one experiment he had even used a ground glass disk (which depolarized the light from the sky) and put it above a homing ant. The ant instantly stopped and searched around at random, bttt SANTSCHI did not draw the right inlerences fiom this importattt observation. . . . After a quarter of a century had passed, in 1947. V()N FRISCH did an experiment with bees almost identical to tltc cxl.rct'itllcttl SANTSCHI had perlormed with ants. He got the suntc rcsttlt. itskctl tlrc same question, and horriblc clictLr coulrl ttot tcll citlrcr'. Ilttlvcvct'. VON FRISCH. thctr IIcarl ol'thc [)cparlrttcttl ol'Zoolou.y rrl lltt' tlnivcrsity rll'(inrz. wlrs irr lr hctte r'position lo;ul\\\'('l sttt'lt t;ltt'sllotts tlurrr
Bourbon on the rocks, scotch on the rocks, vodka on the rocks, gin on the rocks all can make you drunk must be the ice cubes.
SAN'lS('lll llrtl t'\,t'r lrt't'rr. Al orrt'ol lltt'ttt'\l l it('ttllv
Itr'lolrl lltr'slotY lo ltt'.tt,llt';tt'll('()l
llrt'l'1tr.'11
"
l)t'|;ltltllt'ttl
N1
t't'lttt1"'
ll'\NS
1.t.1 Natural variation The first step in experimentation is to obtain clear descriptions and definitions of thc behaviors to be measured (Chapter 4). This requires obtaining those descriptions from secondary sources (Chapter 4) and/or making your own observations of tlre behaviors under conditions of naturally occurring changes in the biotic and rrl'riotic environment. Even if you use descriptions and definitions from other rcscarchcrs, you should still gain experience in observing the behavior before contl trct i ng
cxpcrinrcnts.
of natural changes in the environment to \ru(ly thcir clll'cls on tlrc bchavior of selected species. For example, Pengelley and ,\snrrnrtlsorr (l()71) rlcrnonstnrtctl that thc yearly activities of golden-mantled 1'rrrrrrrtl stlrrrrt'ls (,\,1tt't tttttltltilltr,s lrttt't'rrli.r) lltrclttitlctl itt syttchrony with climatologi, ;rl vlrrr;rlrlt's nt tlrt' r'rrr,'irt)nnr('nl l'irrtf ittl' rrt'livrtV rll' thc tttlctttrItttl bcc l,\'1t11,',,,,1,,ti,t:lttt lr'\ttntt) rurs sltorrtt lr1 trr'tlor'1 1l()(r/) to llt' llttsctl olt tltc lttttltt' Mcnsru'irlive cxperiments make use
IlXPER I M ENTA L
156
R
ESEARCH
THE VARYING VARIABLES
cycle. Sunrise and sunset apparently trigger the onset and cessation
of activity in
cottontail rabbits (Srlvilugu.; .floridunu.r) and snowshoe hares (Lepus
unrarit'urtu.s)
(Mech et u1.,1966).
,?i::{kr
1
Some enviroumental lactors fluctuate within seasonal ranges but vary somewhat irregularly from day to day. For example, decreasing light levels near sunset apparently trigger the initial departure towards the roost of loraging starlings (Sturnu.s'
20.6
2
vulgaris; Davis und Lussenhop 1970). Nisbet and Drury ( 1968) compared nleasure-
\h
8.3.
ments of the density of songbird and waterbird migration to l9 weather variables in the area
0.5
I
4
5
--21
t-11
The age and experience of the animals under investigation can be allowed to
3.5
Percentage of total number (2589) of seconds of observation
several domestic dog
breeds from birth to maturity and were able to divide their development into fbur Fig'
7'2
2
5.0
\\\
-1.2
t",'*)
trl\
/.",'
6
advance naturally and their behavior observed at various stages. Scott and Fuller
(1943). Drori and Folntan(1967) showed a marked elfect of experience on tlre coplr-
3.8
B
in each zone. He lbund that the five species distributed themselves on the trees such that utilized dilferent microhabitat variables (FigLrre 7.2).
periods: neonatal, transition. socialization and juvenile. Development of behavior in the song span'ctw (11[clospi:tr mclotlitL) was divided into six sin-rilar stages by Nice
/.il li/ '/ /
3
with high and rising temperature. low and f alling pressLlre, lclw but rising hurmidity, and the onshore component of wind velocity. The response of animals to simultaneous variations in the environrnent can also be studied. For example, Heinrich (l9l I ) examined the leeding pattern of the caterpillar (Munclu<'tt.sr,,r/rr) ancl fbund that it was consistent for given leaf shapes and sizes. Simultaneous variation has also been the basis for many field studies of habitat selectiort. MacArthur (1958), for example, studied the distribution of five congeneric species of warblers while they fed on individual white-sprllce trees. He clivided the trees into l6 zones ancl measured the percentage of the total number of seconds of observation and the percentage of the total observations for each species
in behavior ol
3.8
4
of takeoff. They lbund that migration densities were significantly correlated
(1965), fbr exatnple, observed the changes
151
3.7
Percentage of total
number (80) of observation
Cape May warbler lecding positions. At least 50'2, ol'the actrvity is in the stipplerJ zones. Each branch was divided into threc zones: B bare of lichen-covered base. M. olcl ttcedles; and T. Itew (less than L5 years old) needles and bucls. (fro1t
MacArthur. l95tl).
latory behavior of male rats, and Carlier anci Noirot ( 1965) demonstruted that experience improvecl pup retrieval in female rats. Stefanski ( 1967) slir>wed that the average
territory size of black-cappec'l chickadees (Prrru.r utricupillu.r)
r,'ariecl cluring
six stages of the breeding season: prenesting, nest building, egg ltrying. incubation, nestling. and fledgling.
ttt-t1lt>rtittlt thing seents to t-ne is not to miss the natural experiments anci yct to know when it becomes necessary to continue by planned tests.
ITinbargcn, 1955;259J
The use of natural variation has limitations which are both clualitativc arrrl cprirrr-
titative. Waiting lor the proper conditions to arise' ancl atternpting to gltlre r ir sulii-
cient number
of
observations st'rt'netinrcs clrivcs
thc cthologist to
rrrtilicirrl
manipulation:
''\ttolltt't t'tPt'tilttt'ltlltl lt1'rlttrxtclt trl thc sttrrlv tll'cirrrse
Systcntittic erltlrlitrrtirln ol'srrt'lr rtrrtrulrl t'xlx'riln('llls llr:rl r\. \\\l('ntitlt( r'()t)tl)irtisort ol tltt'stltt;rli()n\ \\'l)it'lt rlo trrrrl lltost'r\lrr, lr,l,) n()l tt'lt';rrt';t t'tt't'tt t('Sl)()ll\('
i.t.z Artilicial ntanipulation
t;ttt lrt'ltltllrtsl
ll\ l'()(,rl tlr ;rl,ttltt,',1 r'\lrt'l ttttt'ttl'.
lltt'
arrcl etlect
o{'behavior is to
l:tkt't'ottllol ol lllt'v'ltt'iltllles:tttrl tnlrrriprrllrtr'llrcnr irr thc Iiclrl rlr the labrt*tt.ry.
.\lllt()ttl'lt llrr'rrrlilill)ltlitltr)lt t\;illtlt(.t;tl. t.rcrY lrllt.nrgrl slrorrlrl lte rrrlrtlc t() lll)l)t.()xittt;tlr'lltt'rr,tr ttrtrl.,lrtrttrlt;tttrl llrt,n,tlut,tl, 1r,rrr1,,..,,r., t 1o,.1.11 :rr Por:iltlt,.
THE VARYING VARIABLES
EXPERIMENTAL RESEARCH
t58
7.t.2t Elimination, disruption and manipulation When manipulating the animal or exogenous stimuli to answer questions about 'how'an animalperlorms a behavior there are dilferent levels of intervention which lead to differing degrees of validity of results. For example. in determining the exogenous stimuli and corresponding sensory systems (or endogenous stimuli and corresponding hormonal/neuronal systems) involved in a behavior you can eliminilte, di.>'rupt or nrunipulate variables. These interventions (all of which are usually referred to as 'manipulations') can be made on the stimuli or the animal's anatomy
(e.g. sensory system). Elimination, disruption and manipulation represent decreased levels of perturbation, respectively; in general, manipulation provides more rigorous and valid results than does elimination or disruption (see exan-rples of bird orientation/rnigration studies later in this chapter). When stimuli are manipulated (e.g. von Frisch changing the plane of polarized light received by a dancing p. 155), you can make predictions about the resultant behavior (e.g. orientation of the bee's dance will track with the changing plane of polarization); that is. you can invoke research and statistical hypotheses with higher resolution and greater bee,
159
Since the pigeons homed successfully, you might harve concluded that they don't use the sun as a compass and therelore hypothesized that they use the earth's magnetic field. If you then attached bar magnets to their backs (to tli.srupt the magnetic field
around them), but tested them on a sunny day, they would still have homed, but this tirne they would have been using the sun as a compass. Further, unless you recognized that you should have been controlling more than one of the variables at a time, you might conclude that the pigeons use neither the sun nor geomagnetic field as cornpass cues. Even if you recognized your design error and proceeded to disrupt
their orientation by applying magnets and testing the pigeons on overcast days. you would not have as conclusive results as you could have obtained by monipulatirzg the variables and predicting the changes in orientation (see experiments clescribed in section 7.3).
When you eliminate or disrupt an animal's sensory system you also run the risk systems which could be important
of affecting other anatomical and physiological
for the behavior(s) you are measuring. The lollowing story illustrates how attempts
to eliminate a sensory system can have additional elfects on the animal's behavior and the researcher's ability to interpret results:
statistical power (Chapter ll). With elimination ancl disruption you are only attempting to eliminate or disrupt the behavior being studied (often in an unpredictable manner). For example, in attempting to locate the circaclian pacemaker it was known that surgical ablation (elimination) of the suprachiasmatic nucleus (SCN) in the brains of mammals eliminated overt behavioral rhythmicity; those
A zoology student had succeeded in training cockroaches, ernd he proudly displayed the results of his long efforts to his professor. He had his cockroaches fall in, and he gave them the command: 'Forward, march!'the cockroaches marched lorward. 'Column left!' the student commanded, and all the cockroaches turned left. The professor was about to congratulate the student on this remarkable accomplishment, but the student interrupted him. 'Wait!' lre said. 'l still have to show you the most important thing.' The student picked up a cockroach from the last row, pulled off its legs, and put it back in its place. Once again he commanded: 'Forward, march!'
experiments provided sorne evidence lor the SCN being the pacemaker. However, conclusive evidence was provided when Ralph et al. (1990) conducted transplanta-
tion experiments with normal hamsters and a mutant strain with a short circadian period. They demonstrated that srnall neural grafts fl'om the SCN of donor hamsters restored circadian rhythms to arhythmic hamsters whose own SCN had been ablated; the restored rhythms always exhibited the period of the donor genotype, normal or mutant (short). If you eliminate or disrupt stimuli in order to determine their role in a eliciting or
The cockroaches marched as before. except, of course. lor the one without legs. 'Column left.'Again, all the cockroaches turned on command. except lor the one that lay where it had been placed. The prof-essor looked inquiringly at the student.
orienting a behavior, the behavior may come under the control of other stintuli anrl sensory systems. Thus, when the behavior does not disappear, or is not clisruptccl.
Thc studcnt said proudly, 'This experiment proves conclusively that cockroaches hear with their legs.' I Eigen and Winkler, l98 I :298 -299
you could draw incorrect conclusions. Indeed. this is what occurrecl in c:rrly cxpcriments designed to determine the environmental cues used for oricrrlulion by liu'rrg-
]
important questions: l. Was the manipulation appropri-
ing bees and migrating birds (Gould, 1982). For exarnple, if you wcrc an ctlrologrst
I Itis lrrlc grvcs risc to three
in the early part of the century and were interestecl in thc cnvironn.rcrrlirl errcs llt;rt homing pigeons use to orient back to the hornc lol't. you rrrighl lr:rvc rlcsiltn('(l('\lx'l iments to elimirtatc rlr rlisrrrgrt polcrrlilrl crrcs. ll'yott ltypolltr'sizr'rl tlrtl pi1't'ons usr' thc strn its:l c()n)l)ltss rilttl tesletl llrt'nr ott rl!'r'rt'lrsl tl:rVs (ltt t'littttttrtlr'lltt'srilr ;rs rr r'ttc). tltt'lli,'t'otrs rvrlrrlrl slrll lrlrvt'll()nr('(l ltstttl'lltt't'lttllr': ttt;r1'trr'ltr ltr'lrl:tr llrr't ttt'
rrlt'lo obllrin vllitl rcsults'/ If so,2. Was this severe a manipulation necessary to iurs\\'('r llre tescrrtclr tlrrcslirln'l Il'so.3. Wts the answer to the research question rrorllr tturkinl llris s('verc lt tnlutipttllttiort'l Sincc thc rttrswcr tt> questions I and 2 is 'No', lrllr
(':ur t ottt lrrrlt' tlr;rl llrt'slrttlt'ttl u'lrs t'illtcr rr rtlrivc or sittlistic rescarcher. If \\r';'11t' llrr'rlrrrlr'nl llrt'ltt'ttt'ltl ,rl ,r',.,unutr1' lltt'1 \\('r('{rnlV tt:rivt'tttttl ittscttsillvc. wc
THE VARYING VARIABLES
EXI'ER I M ENTAL RESEARCH
160
l6l
should recommend that they answer those questions befbre making any manipula-
tion in their next experinrent. Manipulation of variables (versus elimination or disruption) is the method being employed when the researcher uses rnoclels and dummies. or conditioning (all are discussed below).
7.t.2h Models and dummies
ll[ot{cl,t constructeci to rnimic animals. or parts of animals. and tlumnzre.s (stutled of animals) have a long history ot' use in ethology. Dun-rmies were used by Allen (1934) in his study of the courtship of rufled grouse (Bortu,su wnbellu:; L.), in Chapman's (1935) study of courtship in Gould's manakin (Murtucus vetellinus
skins
t,ircllinu,t), and by Lack ( 1943) in his study of aggression in robins (Eritltucu.s rubeczrla: also see Table 8.3). Models and dummies have the advanta-ee
of allowing the
(e.g. visr-ral. auditory, chemical. tactile) in a systematic
experimenter to vary stimuli way in order to measure the effect
of qualitative and quantitative
ditferences.
Tinbergen was an early and exemplary proponent of the use of models (Dawkins cl u1.,1992).
As a typical example. Tinbergen and Perdeck (1950) presented models of an aclult herring gull's head to herring gr"rll chicks. They fbund that the color of the spot on the bill (qualitative property)of the nrodel had an eff-ect on the number of pecks given by the chicks (Figure 7.3A). Tinbergen and Kuenen (1939) used simple moclels to demonstrate that the gaping response of nestling blackbirds (Turtlu.s nterulu nterula) and thruslrcs(Turclu.s ericetonnn ericelrtruni) is oriented by the relative size of the parent's head to their bocly (Figure 7.38).
Moller (1987) stuclied the role of badge size (extent of dark coloratiou on the throat and breast) on status signaling in house sparrows (Pusscr tlonrcstit'u,s) by placing stulfed male house sparrows (dummies) near nests. Stout ancl Brass (1969) placed pairs of dummies, or wooden-block models witli tiltable boclies and adjustable stuffed heads (Figure 7.4).
in glaucous-wingecl gull territories: tlicy
demonstrated that the head and neck are the parts of the bocly that relcusc territol'ial aggression displays in this species. Some researchers have incorporated movement ztnd/ot' ttclors lttttl stlttrttl irtto their models and dummies. For example. Esch (1967) used et wootlcn. tt.totot'-tlt'ivctt model in his research on communication ol'firod source locittion in ltortcybces. I lte rnodelwas the approximate size ot'the honcybccs bcing sturlicrl. btrt it rlitln't t lost'll resemble them physically: this probablv ltirtl littlc cl'll'ct sirtcc lltc cxlrerittt('lrls \\'('r(' carrieclttut in a tlark lrivc.-l-lrc rttorlcltlirl Ilrr,c tltc itle lrtit'rtl.,tlot rtl lltt'lttrr"s tltlrtlr ititnls itnrl ltcrlirnut'(l ;t 'n()nltitl'n'lr1'1'1r.' rl;rrrt',.'. lrttl no lrt't's lt'll llrc lttrt'l,r:t';lr'lt lol
litotl itr llrc tltrr'( ll()n ptrrl l;lttttt'rl lrt lltt'ntotlr'l r tlrtttt t' l ',,1t ,,,tt, ltl,l,',1 llr,rl ',r)lll('
lir
71
A. A cultlbtxrrrl n.roclelol a herring gull head being presented to a chick (adapted l'r'onr'l'inbcrgcn l9(r0b by Lori Miyasato). B. Presentation of models of the l)iucnl\'lrcrrtl. both'rrnrl tail to study the rc-lationships that orient nestlings' lrrpirrl' r'esl)()nsc (ltlirp(ctl ll'ont Tirthcrgcn 1972 by Lclri Miyasato).
EXPERIMENTAL RESEARCH
THE VARYING VARIABLES
(Chocton aurign)
validity of his
use
to a cleaner (Labroides pltthrirophugus), he demonstrated of
the
a cleaner model through three indicators: pose duration, pose-
to-inspect ratio, and approach behavior of the host fish to both live cleaners and his models.
Not only must the
Lrse
of rnodels and dummies be carefully planned. but
the
results of such experiments must be carefully interpreted. As an example, in another
of Tinbergen and Perdeck's (1950) experiments on the begging response in neonatal herring-gull chicks, they changed the position of the red spot from the
aspect
model's bill to its forehead. The ohicks delivered significantly n"rore pecks to the model with the spot on the bill than they did to the model with the spot on the forehead. They concluded that it was the position
of the red spot on the head that
caused the decrease in the chicks'responses. Hailman (1969) re-investigated this
phenomenon by placing the rnodels at different distances from the pivot point of the rod holding the model. Further, he adjusted the height of the chick so that it was always at eye levelwith the red spot. He had created three models: a 'normal model'
with the spot on the bill, a model with the spot on the forehead and the pivot point the same ('slow model'), and a model with the spot as on the bill-spot model ('fast model'). The fast forehead-spot model received more pecks than the slow lorehead Fig.
7.4 Models and a durnrny (2d;
used by Stout and Brass ( 1969)
in their study ol
glaucous-winged gulls. la, upright, threat-postured body 1b, trumpetingpostured body; lc, choking-postured body; 2a, basic wooden model;2b, upright threat posture; 2c, model without wings: 2d, dummy showing upright threat posture with wings.
thing more than the dance was necessary to elicit foraging. More recent research used a motor-driven model bee which not only danced, but also vibrated artificial wings and exLlded sugar-water samples; this dummy bee was much more successful
in recruiting foragers (Mollett .1990). Hunsaker (1962) used a head-bobbing machine to move the model heacls ol' lizards (Sc'eloperu^r sp.) in dilferent species-typical patterns. He firuncl thiit l'emalcs selected those rnodels which head-bobbeci in the pattent typical ol' thcir own spccics. Jarvi and Bakken (1984) used three dummy great tits to str-rdy thc f'unction ol'tltc
variation in the breast stripe. Their dummies could be turned 360'. by radio cont rol. to keep them always oriented in the direction from which tlre livc hirrls irpprolclrctl.
Models shor-rld contain the important f-eatures ol-thc livc irnirrurl. irntl tlrev should be used in a normal context (see Cr,rrio 197.5 lirr an cxccllcrrt cxlrrrple ol extensive and proper use ol'modcls). Irt olltcr wortls.';rrr rrrrtlerlying rrssrrrrrptiorr ol the ntcthrttl is thlrt rcsll()ltsc to
1l19
tttotlcl tlcPcttrls ()ll r))u('lr lltt's;rtttt't';ruslrl slslt'nr
ilst'csll()llsclolltctt;ttttltlsttrrrtrltts'(l.ost'\'. l()77.).).1; llrrlor lrrn;rlt'lr titlttistlttt'lVr:tlirl;rlr'tl
llrrs;rssrrrrrP
llttttr'\('r lnl,r:t'\''st'\[t'1g1111'1ll:.onllrt't,",1)on'.('ol lro',l lr',lr
model, although f-ewer than the 'normal model,'revealing the elfect of speed of the red spot on the chicks'responses. Therefore, Tinbergen and Perdeck (1950) were correct in concluding that position of the spot is important; but I{ailman demonstrated that speed of the spot is also a contributing factor.
Models and dummies shor.rld be used with appropriate caution. They may be either too simple with the inrportant stirnuli absent, or too complex with extrarleous stirnuli confounding the experiment. As with any tool, however, in the hands of a skilled researcher. models and dummies can be an important means of manipula-
tion in the field.
7.t.2c Instrumental and classical conditioning
An iniportant technique fbr manipulating variables in the laboratory is through the ruse ol' instrurnental and classical conditioning. Conditioning is a powerful method lirr studyrng 'cirus;ution' ancl answering 'how' questions; the basic paradigms for irrstrtrrncntuI irntl classicaIconclitioning were discussed in Chapter 2.
('orrtlitronrng is thc busis lirr many psychophysicalstudies designed to determine
'lrrrw' rr spccics tliscrirninirtcs bctween varic'rus stimuli. For example, May et al. ( f ()lili ) strrtlit'rl lrorv .lrrprrrrcsc lnirca(l ucs ( lllttcuut.f ir.st'ulu) discriminate between diflr.'tt'rr( ( ()() \ot;rliz;tliotts ltv ttsutp, itt.ttt tttttt'ttlttl t'otttliliottitt,q lct trairr inclividr"ral lnir( ir(lu('s lo tlrst rrrnrr;rlt' \nroollt t';tr lr'' l;;1'11' ;trttl 'slt)orltlr llrtc ltiglt'ctlo sttttnds. Ilrt'rrrrrt rr,lu('\ \\'('rt'lrrrrrt'rl lo nr;rk,'lt,rtt,lt
o111:tr'l
tt'tllt;t tnt'lrtlt'Vlnttlct itt I'csPottsc
164
F.X
t'hlL l M
E
NTA L
R
THE, VARYING VARIABLES
ESE,ARCH
r65
to one type of vocalization and release contact in response to the other vocalization.
First, generalization tests showed that the macaqLles responded appropriately to both natural and computer-synthesized coo soutrds. Then acoustic f'eatures were systematically removed from the computer-synthesized sounds to determine the minirnal elements necessary lbr the macaques to recognize them as distinct coo sounds. Pietrewicz and Kamil (1977\ studied the ability of blue jays (Ct'rzrnc'ittcr t'risttrttt) to detect cryptic moths by instruntcntully t'onditiorting them to respond diff'erentially to the presence and absence of moths in projected images (slides). If the projected slide contained a nroth. l0 pecks on the stimulus key resulted in the blue jay being positively reinfbrced with half a mealworm. The jays were able to detect
the moths, but their ability was allected by the background upon which the motl-t was placed and the moth's body orientation. In a later study, Pietrewicz and Kamil (1919) used the same in.slrruncntul cotrtlitioning procedure to stucly search irnage formation in blue jays.
Often questions about 'how' an animal uses environmental
cr-res
begins with
studies of what a species'can'perceive (Miller 1985); that is, what stimuli they are capable of perceiving and responding to. For example. Lehner ancl Dennis (1971) hypothesized that waterfowlrnight use atmospheric pressure changes as a cue lor orientation during migration. They used instrumcntul t'onditiortirtg to train mallarcl
Fig.
ducks. in a barometric pressure chamber, to peck one microswitch when the pressure increased and another microswitch when the pressure decrensed. They then exposed
7.5 Coyote in tcst chambcr uscd by Horn and Lehner (1975) to dctermine the coyote's scotopic (clirrk aclaptcd) light scnsitivity. A stinrulus patch is at the coyote's eye level at the ccnter ol the right wall; it is not illuminated in this photo. Two loot treadles Are on the floor, separated by a plexiglas partition.
the ducks to sequentially smaller changes in pressure and demonstrated that the ducks could perceive atmospheric pressure changes as small as 0.4 psi. Kreithen and
at the coyote's eye level. They were then instrumentally conditioned to step on a foot
Keeton (l9l4a) used r'lrr.r'.ricul t'ontlitioninglt-t test the capabilities ol- homing pigeons to detect atmospheric pressure changes. The procedure was to place the pigeons indi-
treadle to their right when the light was on. and a treadle to their leli wlien it was ofl. Once they consistently perlbrn-red this discriminzrtion task, then intensity of tl"re
vidually in an airtight cl-ramber. change the pressure over a 5 second irrterval(neutral stimulus), hold the pressure steady lbr the llext 5 seconds, and then deliver electric
light stirnulus rvas pr"rt under the control of'tl-re coyotes. When they stepped on the right lbot treadle (indicating they could perceive the light stimulus), the light auto-
shock (r,rnconclitionecl stimulurs) to the pigeon. causing the heart rate to incrcusc (unconditioned response). After a f-ew presentations, the pressure change becullc it
ruatically decreased in intensity: conversely. stepping on the left treadle (indicating they cor-rld not perceive the light) ar.rtomatically increased the light intensity. The
conditioned stirnulus that caused the heart rzlte to increase (conditionctl t'cspottsc) without the electrical shock being adrninistered. Then, the pigcons'pcrccption ol'rlil-
irtterrsity ol-the light stin-rr.rlus was continuously recclrded resulting in a graph of the coyotcs'psychophysicaI threshold lbr vision at nigl"rt.
ferent amounts of pressure change was determinecl by observitrg chungcs irt tltcir' heart rate. They determined that the homing pigeon is ahle to tlclcct ltttttospltct'it' pressure changes of l0 mm of H,O. or lower. Kreithcn iurcl Kccton ( 197-lh) ttsctl lltt'
rrg
Ittstt'untcrttal contlitioning has sl
bc-en an irnportant technique in studies of foragnrtcgics. As ittt cxanrple. Har ct a/. ( 1990) irt.strLunentully t'ontlitioneclcaged gray
i;rvs ( /'r'r'i lr tt't'tr.:' r'trnttrlt'l,rn') to
As part of thcir rcscarch oll coyotc 1'rrctlrrliort. llont lrrtl l t'lrtt't (lt)i.l)rr;tttlr'tl t1r clctcnlinc tltc lrlu'csl ctt'u ilontnt'rtl,rl lilltt lt'rt'ls llt;tl t'ovolt's. tt lrtt lt lttttrl ;)ttnt:ll
altcrnate hclps on two perches in order to receive fbod l'lrc lirritgc corrltl in two'lirod patches', each of which had two perches irrvs ;rellcts. ;ttttl :t pt'llr'l tlispt'nscr (liigtrrc 7.(r). Thc tirod pellets were delivered on variable ratio ',t'ltr'tlttlr's (\11(. sr'r'('1t;tptct l) irr botlr 1'rirlcltcs. l}trth VR scherlules hac'l the salne ttt(';rtt(r'l' ntt';tll ()l ,l01lr'tt'lt ltops Vl{.10). l)ul ()nt' plrlclt lrirtll high vitriirnccabclut
ilyirl rrip.lrt.erlttltl Pt'tt'rrvr' ('rr\'oli's\\('rr'n)(lr\rrlrt,rlll ll;rtttr'rllo',l,ttttl ttt rt,l.ttL 1,"'l t'l11rtnlrr'r (l'i1'rrrt' / r)iilr(l lltr't';r rlrrrrrtlrt', lr1'lrl Irrllt't lt'rl ()tr .tn ill).t(ltrt' 1rl.r',1t, ,lt'1.
lltt'tttr',ttt:tttrl lltt'o(lrt't ;t lr)\\ \ittl;lltr'r' Ilrr't'r;1 rtt lltt' lrry'lr r,u r,ln( (' lor rtl P;rlt lr
same r'1a.ssit'ul t'ontlitiottittg procedure to detcrnritrc thc
ability ol. ltotttiltlt pigcotts lo
detect polarized light. a cue usecl by bces itt ot'ictttlttiott.
1;tf
: t'l)psr'ttl lilt'ltt]c
1t;ClcpClfliif
lly
ill EXAMPLES OF EXPERIMENTAL MN NII'IIT-ATION
EXPERIMENTAL RESEARCH
166
161
7.2 F'URTHER EXAMPLES Otr EXPERIMENTAL
MANIPULATION 7.2.1 In the field
Many experiments arise from descriptive studies in the field and progress through mensurative experiments to artificial manipulation of the animal and/or its environment.
7.2.ta Manipulation of the animal
Manipulation of the animal involves altering the anatomy and/or physiology of the animal (A and P in the model in Chapter 2). For example, the role of sensory receptors and physiological state can be studied by manipulation of the animal per se. Layne (1961) studied the role of vision in diurnal orientation of bats (Myotis' uus'troriparius) by releasing normal, earplugged, and blinded bats (two types of sensory elimination) at various distances from the home cave. None of the eye-covered bats homed, suggesting that vision is an important in homing behavior. Ehrenfeld and Carr ( 1967) measured the role of vision in the sea-finding behavior of lemale green turtles (Chelonia m1,das) by blindfolding them or fitting them with spectacles containing dillerent filters (elimination and disruption of the visual sense). Blindfolded
Perch InPut lines Fig.
jay the instrumental conditioning apparatus used to study gray to perches attached two of consisted each patches loraging strategies. The two and an were reached' pellets clispensed which through hole a microswitches, operated automatic pellet dispenser. A microcomputer recorcled perch hops and
7.6 Diagram of
turtles and those wearing red, blue, and 0.4 neutral density filters had significantly reduced orientation scores.
ai'' the feeclers. as well as controlling lights ancl backgound noise (from Ha et Press' 1990). Copyrighted by Academic
Morphological changes are occasionally made on animals in the field, and the effect on the animal's ability to obtain and/or retain a mate, social status or a terri-
tory is then measured. In these studies, it is the change in behavior of other individLrals that engage in interactions with the altered individual which is usually being
opporLaboratory research using classical or operant conditioning provides the resultatnt animal's the measLlre and tunity to manipulate variables very precisely how tcl tritnsbehavior very accurately. The drawback is that we don't always know its normal crlvilate these laboratory results into what the animal actually does in whethcr tlrirt ronment. We only determine what the animal'can'do; we are not sllre
rneasured; but an ellect can also often be found by observing the altered individual.
As an example, Bouissou (1912) showed that dehorning and reduced weight tlecreased the ability of domestic cattle to obtain and maintain high social rank in
the herd. Harris sparrows (Zonotrichia quereula) signal their dominance status by variations in the amount of black leathering on their crowns and throat. Rohwer (l9l1l rankecl individuals into 14 'studliness' categories (Figure 7.7) and then
is'how'they normallY do it. If you are interested in more detailed inlormation ttn spccilic cotltlitiottittpr otl t'esertt't'lt methods, you should consult the primary literature tbr papcrs rcpot'tittg otl lcrtrtlitll' similar to what you are planning. Also, there atre scveralgoocl tcxt books l()S I (c'8 1)ilvcv' Itlctltotlology basic the and experimental psychology that present
:
Iverson and Lattal, l99l ).
rrltcrctl thc unrount of black feathering on selected individuals to determine the elll'ct orr thcir status. Subordinates dyed to mimic the highest ranking birds were still pcrsccrrlctl l'ry lcgitimate'studlies,'and bleached birds eventually exerted their rrornurllv lrrglr-rrrrking tlorninrtnce. The data suggested that 'cheating' (i.e. lowerrrrrkinllrrtlslreirruelcv:rlcrl instatrrssinrplybyhavingaclarkercrownandthroat)is '.,,r'rrll\ t onlr,rllctl Molle r'(l()li7) rrsctl sirrrilru' ttltrtipulittions itnd demonstrated a ',1.rltrr rtltr;rltttr'' lttttt'liott lirt lrlttl,'t'stzt'(tllttk r'olot;tlirlrt olt llttrxtl irtttl brcitst)in ft,,tt',,'',1);tt t(ltr'. ( /ilr \t r rlrtrttr'\//{ r/\)
I
hXAMPLES OF EXPERIMENTAL Mn Nll'trt-ATION
169
The role of the red epaulets of male red-winged blackbirds (Agcluius phoeniceus) was studied by D. G. Smith (1912) by dying the epaulets black on selected
rr-
males. He lbund that the epaulets were important in maintenance
o.
against rival males, but they had little eff-ect on the males'ability to obtain mates. N.
L
0.)
f-
$
territorial
of territories
G. Smith (1967) changed the eye-ring color of one member of mated pairs of sympatric glaucous gulls(Lurus hyperborea^r), Kumlien's gulls (L. gluuc'oirlc.r) and herring
o E
o
gulls. In all cases where the female's eye-ring color had been changed the pair broke
&
up. but alterir-rg the male's eye-ring appeared to have rro ef-fect on the pair's behavior. o
It
!
o.
(o
(f)
L L
()
important in all research where animals are manipulated and the elfects are studied in interactions with other individuals to observe the ellects on the manipulated animal, as well as on others responding to it. This is true in both intra- and interspecific studies, such as the effects of altered rnales on selection by females and is
altered prey on selection by preclators.
1o
7.2.th Manipulation of the envintnment
s !
Altering the biotic or abiotic environment (see section 2.3.2b) in order to study its resultant eftect on behavior ranges from gross-perturbation experin-rents to subtle
O
changes in one clr a lew stimuli.
o
-o O
$
() !
o
ro
o
L
()
'a c L
aJ)
E A)
U
a)
N
O)
a r-l) a)
,|)
t:
'-)
.,
@
'i
l-.
Stewart and Aldrich ( l9-51 ) were able to get an indication of the extent of the surplus'floating'population of unmated rnale birds the spruce fir tbrests by drastically reducing (by shooting) a large number of territorial holders on a 4O-acre tract. During nine days in June. they removed 148 territorial males, reducing the population to 19"/,, ctl the original. They continued to shoot birds as they moved into the area. and by July 8 they had collected a total of 455 individuals. This is a rather drastic perturbation experinrent, and as they adrnit 'the breedin-u territories were completely disrupted durring the period when the original occupants were being rernoved and at the serme time new adult males were constantly invading the area'. On a smaller scale, Krebs ( 197 I ) shot six pairs of great tits occupying territories and observed thitt residents expandecl their territories ancl fbur new pairs took up occupancy. In contrasl to these major manipulations. Tinbergen was prone to concen-
lrate on srrbtlc environrncntal changes in order to study ef-fects without greatly tlisturbing llrc nornralactivities of the animals. 'l lrc trick is. to insert experiments now ancl then in the normal lif-e of the :rrrirrurl so tlrat this normal lif.e is in no way interrupted; howeverexciting tlrr'rrstrlt rrl'l lcst nriry bc lirr us. it must be a nratter of daily routine to llrr';rrrirrrrrl. A rrurrt u'lro lrrcks thc lccliltg lirr this kind of work will nt('\ llltlrlV t'onttttit ollt'ttst's ittst ;ts s()ltlc l)c()lllc citttttttt help kicking and rl;rrn,rr'urt'tlt'lrt;rlt'llrtntlur('urir t()r)nl \\illtottl e\en ll()ticirrg it. f l'tttl't t..t"'tr. /(i-5-i /-i'\/
I
I
I
EXAMPLES OF EXPERIMENTAL MAN II'III-ATION
EXPERIMENTAL RESEARCH
66606@ r:1
.3
b
r:1
.5
c
r:2.3
r:1
d
t
.3
importance of the various configurations in releasing egg retrieval was l. largerlsmaller;2. speckled>not speckled; 3. greenlblue, red>grey; and 4. shape, other than roundness, was relatively unimportant. The titation method used by Baerends and Kruijt is worthy of careful consideration lor other studies using models. This method allowed them to rank the models on a relative basis between and within the four categories of features. Our experiments with the size series showed position preference to be a quantitative phenomenon. A first choice for the smaller egg in the preferred position can always be overcome by increasing the size of the model in the non-preferred position. With our series of models gradually increasing in size it was possible to identify stepwise, in successive tests with the same bird. the minimum size of a model required to overcome position preference, when in competition with a dummy of a smaller size in the preferred position. Thus, through this 'titration,'a model was lound the value of which. in combination with that of the non-preferred site, could just outweigh the combined values of the smaller model and the preferred site. Empirically it turned out that the birds were acting in accordance with the ratios between the surlaces of the maximal projections (maximal shadows when turned around in a beam of parallel light) of the models. Different pairs of models, matching each other with respect to other parameters tries (e.g., volume), or equal with regard to the dillerence instead of the ratio in the parameters used, proved to be unequal in counteracting position preference. The ratio between sites olten remained constant for a couple of hours, and within that period the relative value of dummies with any kind of stimulus combination could be measured and expressed with reference to the standard size series. IBaerand.s ancl Kruijt, I973:30J
@666@@ r:1
F-ig.
X>:8
.3
r:1.3
X: <1O
7.8 The'tritration'methocl lor determining the value of an
r:1.7
r:1.3
egg model' The circle
two models on the rim' The represents the nest with one egg in the nest bowl and R' x is the model to be series size the of models the to refer 12 to 7 code numbers
of the model on the measured: r is the ratio between maximal projection surfaces of the value ol I, determination one. nest rim. The black model is the preferred remains preference This pref-erred. is side right Ia the In the position preference. be overcome by (lb), can but 7 model smaller the by replaced is 8 when model of the position preference replacing 8 by 9 (lc); this sequence shows that the value another pair of when holds lies between r =1.3 and r = 1.5: this conclusion that the size le shows test (ll). control is used ratio dummies with the same of model x' this gull exceeds size I l. II, determination of the value optimum for
unchanged'
IIa. IIc. and IIf show that the position pref-erence has remained and succeeding tests' Test IIb and IId indicate, in combination with the preceding reference size l0 0f the 8 and models of those that the value of x is between
Tests
series
(from Baerends and Kruijt' 1973)'
Baerends and Kruijt's results (Figure 7.9) show how the releasing value
into fbur types: Manipulation of the environment can be conveniently divided mitnipLtenvironmcntal intraspecific, interspecific, other biotic factors, and abiotic plut.t'rht'nt'lttt's\ (Atru's lations. The intraspec'ific facihtating effect of a female mallard ancl Darlcy ( 197 I ) by on male courtship clisplays was demonstrated by weidmann
clf threc nlttlcs in thc sp|irtg introducing a strange female or male to resident groups itt horlcybcc ltivcs lttttl and autumn. Free (1g61)manipulated the bioti( cnt'irttnnturl with tlrc lllll()r'llll ol' ht'orttl showed that the amount of pollen collected increasccl olt to isollttc s()lllc ()l tll(' present and decreased in the absencc tll- it cltlccll. Ilc wcrtt slirilttl;tlirrp, llolle rl t'ollt't lt.tt stimuli proclucecl hy thc brotltl which irrc inrPorllrrl irr tltt't if tt' stitllttlt lllll)()ll;ttrl ttt Illrcrcrrtls irrttl Krtriit (l()71) tlef el'tttittetl lltt trtlr'rr r.clelrsirrl,(.1,1,.
tttrttlt'l t'1'1"'
t('ltit'r';tl itr llt'ltittl'1'ttlls lrl ptt'st'ttltttl'lllt'ttt rt'tllr llttt't'tlllll('tl\l()llill lrtt',rl 't ltltl('r)ll llrt't'tll't'dl 't tlt"'l (l t1'rll' / )'11 llrt'rt'l'rltrt'
l)lttt'tl
of
a
rnodelegg with respect to size is affected by the other manipulated variables, such as
changing the egg shapes into a round-edged block, omitting the speckling on brown rnodels. ancl adding speckling to green models. Baerends and Kruijt caution that the
It was limited by the step in tlrc'titrittion'series, and there was considerable variability in the results of inrlivirlual 1csts. However, it is clear that their 'titration' procedure provided irrcrcirscrl turrlcrst:rnrling of the role of the various stimuli in the egg retrieval behav-
cxactitucle ol' the method should not be overestimated. sizcs
ior'. Also n()lc lhrrt thcy conclucted over 10000 tests in theirexperiment.
Mruriptrlrrtiorr ol' cggs. irltlrough consiclcrecl here as an intraspecific manipulaIron. rnrl'lrl lrr'lrrl'11gi1 lohcrttlrrtilrrrllrtiortol'tlrcbioticcnvironment.Theanswerlies tn
lltt' ('\('\ ol llrc lrt'ltolrlt'r'. lltt'1'ttlls. ,,\n rrtlr'r tltr'r rltr tnttntl,ulttttr,r/
lrt'ttt
t'trr"ll ptoh:tbly
ttcvcr bc sttre.
\\,r" ttt,t,l,'l,t I rlllt';olttt;tlttl M:ttlirr (l()(r9) in thcir
I
I
trX
172
l'trlt
I M EN
TAL
R
EXAMPLES OF EXPERIMENTAL MANII'TJLATION
ESEARCH
2.O log. cm!
t73
R series
A ,,fi
ffi Model
ffi
value
-
C
-*
B
bto*n
l:l
El grccn
Fig.7.l{) Digger wasps memorize the landmarks around their burrows in order to find them when they return from hunting. To test this, Tinbergen (1972) arranged a circle of pine cones around a burrow (A), and the wasp memorized it. When the circle of pine cones was rnoved a foot or two, the wasp looked for its burrow within the pine cone ring (B). When the pine cones were arranged in a triangle. and rocks were arranged in a circle, the wasp looked for its burrow in the circle of rocks, demonstrating that it was the geometric configuration (circle) it was remenrbering, not the objects (pine cones) (drawing by Brenda Knapp, based on Tinbergen, 1972).
tcl tlte relcreltce size Fig.7.9 The averagc valucs tound l'crr various models rvith respect ol dillerent types ol' position The models). scries R (standarcl brown. speckled cgg-shapecl (b): (a)l urtspecklecl' brown' block-shapcd (brow1. speckled. mgclel
green.unspecklecl,egg-shapecl(c):green'speckled'egg-shaped(d)'eachin Figurc dilllrenl sizes. was tleterminecl with tlre rnethocl clescribed irl the legend ol' linear thc of l618 to 4/B respectively' fbr 7.8. The cocle numbers 4 to l6 stand prolectiorl sltrfaccs of dimensions of the lrormal egg size 1$ = 8/8). Thc Irarintal (egg centcrs) alclng the thc. eggs of the rel'crencc series have bec'n plottcd poittts ou this logarit|mic scale (cmr)ol the abscissa' Equal distances bctweetl
scaleimplycqttalratiovalttes(lrclmBaererrdsandKruiit'1973t.
Tinbergen and Kruyt ( 1938) investigated the role of landmarks in the ability of
tlf
['sttrdttltltrvtrt study of acoustic interactiot-t between tw'o syrnpatric species rllatitlg cttll ol' ,scrrtirturrntrtrutu and C'riniu Iit'lorittnrt. They played a tape-recortlctl 1'r'og.
l'. .st'rttirtt(tt'tttor(tl(t' C. t,it,trrittrru ancTsynthetic signals to individual calling rnalcs ttl' sigrtitls rt'itlt;t pulsctl synthetic ancl t1B. The call ot'Cl yir'10 riuttu.if played above 80
/i carrier t'requency of 1500 to 2500 Hz. were all efl'ective in inhibiting
,t't'tttitttrrt'
t-emale cligger wasps (Philunthus triungulunr)
to locate their burrows. They manipu-
lated the type and geometric arrangement of objects around or near the burrow and recorclecl thc response of the returning wasp (Figure 7.10). This is typical of the simple, yet cogent. type of experimentation lor which Tinbergen is famous. Iternarking on Tinbergen's methods, Lorenz (1960b:xii) stated, 'He knows exactly
nlorutu males from caliing. Munipulution 0f'tltt,ern,it0ntn('nt clttl.take matry lilrtlls. Tw'o bitslc Pttrcctltttt's;tlt'
how to irsk cprcstions of natlrre in such a way that she is bound to give clear answers.'
' oftenusecl: l.changetheenvinrr-rrnentinwhichthcartirtrtl is1'rt'cscrttlvlot':ttt'tl'.t l" tt:t'tl s ltlt' l't'ttt't;tllV t9 1161lrcr clvirtlrrrrrcrrt. l'lrcsc 1'lt'ttcctlttte
nrcirsurc tlrcir rrbility to nuvigate to their normal winter areas. He captured adult and
a,i'tirl clll'ct tll' tlrc trl'liotic lttttl/ot lliotit' (t'.1'. r('l'('l;lll()ll) ('ll\ ll()lllll('lll thc clctcr,ti,c tll( llll('l:l( ll()ll" ill( ()ll('ll Ittlrvcvt,l.. tltr't.ottlotttttltttl t.llt'tls ol ittlt;t inr(l rrll('tr;rt't relt.,catc the
tlittit'trlt lo i'ltttttlt:tlr'
Pe
r
tlcck ( I 9.5[J ) rclocated migrating starlings (Sturnus vulgoris) geographically to
irrvcttik'slrrrlirtgs irr'l'hc Ncthcrlunrls tluring their south\,ry'estward falln-rigration and r :utsPor lctl llrr'rtt sorrl lt lo S'uvitzcr lirntl wlrcrc thcy wcre rclcasecl. Aclults were recov-
I
1 n,rtlltrvt'sl ol lltr'rt'lr'rtst'sitt's itr lltt'tt notttt;rl u'ittlcl'lll'clts itlong thc crlrtst ot' Wt'rlt'ttt I ttt,,1rt' I lrt' lrrrt'tttlr'., lt(r\\('\('t \\('l(' r('( ()\('tt'tl u't'st ol' lltt' t'e le;tse sttCs.
t'tr',
EXAMPLES OF EXPERIMENTAL MANII'T] LATION
EXPERIMENTAL RESEARCH
orientation, not navigating indicating that they had continued to follow a westward the adults' northwest to adjust for their southerly displacement as did
7.2.2 In the laboratorY
in the field' and I No one would argue that ethologists are found studying behavior research when it is both have suggested that the fleld is the best place for ethological conducted in captivis also feasible and valid. However, much ethological research judged by where they ity or the laboratory. Scientists (including ethologists) are not work but bY what theY do.
and diencephalon resulted in the transfer of certain aspects of species-typical crowing behavior' (Balaban et al., 1988: I 339). By studying age- or genotype-dependent behavior in the laboratory, one is essenof natural variation. Fuller (1967:470) focused on genotype elfects and demonstrated that'albino [house]mice otherwise cogenic with strain C57BLl6J escaped more slowly from water, were less active in an open field and made more errors on a black-white discrimination task than their pigmented congeners'. Van tially making
manipulated and observed in Some species can olten be more easily and accurately contribute to valid results' the laboratory than in the fiel
decreased significantly' sion rate increased. but their ability to locate the platform the platform as located When the ear plugs were replaced with hollow tubes, they
well as when the ears were not plugged'
studies' hormones on behavior have been investigated in numerous
between strains DBA|ZJ, C57BL|6J, their
of testosterone restored it' /us), castration reduced the behavior, and injections (cutting strips of paper) in peachEstrogen can stimulate nest-material preparation before it would normally faced lovebirds (Agapornis roseicollis) at least two weeks 1967). Lindzey ct ul' (orcutt, occur, but only alter the female is at least 98 days old in male mongolian gerbils ( Merittttt"s ( 1968) measured territorial-marking behavior operated (contrtll) at 30 clays ol' unguic,ulutas) which were either castrated or sham when injected with tcstostcrorlc tltcy age. Marking did not develop in castrates, but than did thc cotttrols' began to mark earlier and reached higher frequencies gcncritl bcllitviot'Plttlcttts The efl-ects of stimulation of various brain sites on wct'c sttltlictl by votr chickctts in don-rostic crowing) (e.g. sitting, standing, eating, (l()'51-i) wctc rthle l() Holst and von Saint Paul (1961). f)cthicr itttcl llotlctrstcitt ltt tlte lrtltilt si1'ttltls lot'cgtrt tltc demonstrate that thc rccurrcnt ncrvc rrrnning l.Rrrtt lt't'tltltl' tll't6c bl.wlly wlrcrr tlrc rircprrt is tlislerrrlr'tl lrtttl irrltilrrts ltttllrt'r br.i,
llycrrttirrgllre
rt.(.ur.r.(.llt n(.r\'(.
llrt't,rvcrt'lrlrlt'losltou llr:rl lllt'lrl"$ll\ tttllt.ttltttttr'
llr,'r"lt''rl Itr ir1,t.sI rrrrtrl rl lrrrrrlr lllrl;rlr;r t r'l rtl ( l()l-iS)',lttrltt'tl
lrt'tllt tlt'rt'l"Illl('lll
Fl
hybrids, and homozygous and het-
erozygous short-ear animals.
Dilger (1962) studied the behavior of hybrids between peach-faced lovebirds (Agapornis roseicollis), which carry nest material under feathers on their backs, and
Fischer's lovebirds (A. personata.fischeri), which carry nest material in their bills. The hybrids initially tried tucking nest material in their plumage as well as carrying
it in their bills.
Even though feather tucking was unsuccessful for these hybrids,
it
took two years belore feather tucking diminished to any great extent and carrying in
the bill was almost exclusive (Dilger 1962). This demonstrated the interaction between genotype and experience.
that injections of proFor example, R.J.F. Smith and Hoar (1961) demonstrated (Gasterosteus uculeulactin failed to induce fanning behavior in male sticklebacks
tlie
use
Abeelen (1966), also interested in genetic effects, used 30 behavioral components performed by individuals and pairs of male house mice to measure differences
7.2.2t ManiPulation of the animal
The effects of
on species-typical behavior patterns. They created domestic chick-quail chrmeras by transplanting part of the neural epithelium from a quail embryo into the developing brain of a chick embryo from which the corresponding brain region had been removed. They found that 'transplants containing the entire quail mesencephalon
7.2.2b Manipulation
of
the environment
Examples of environmental manipulation in the laboratory are widespread in the cthological literature. As in the field, manipulation of the environment in the labo-
ratory can consist of altering intraspecific factors, interspecific factors, other biotic lactors and physical-environment variables. For example, providing domestic hens with expe ricnce in an intraspecific'flock can change the dominant-subordinate relationships sccn in later paired encounters (King, 1965). Marsden (1968) artificially irrduccrl changcs in rank in young rhesus monkeys by introducing a 'strange'adult rrrirlc al ir tirnc whcn the second-ranking female was in estrus or by removing and re
irrIrotlrrcing tlrc currcntly top-ranking female.
N rrrrrcrrrrrs rrntl vru'iotrs irrtcr.spt,t'if ic manipulations have been made in laboratory ('\lx'r'inr('nts. As irn ('xiul)l)lc. Klrlirroski ( 1975) ohscrvccl agonistic behavior between
lr()us(' lirrt lrt's (('ril
1ttt11111
',rrrrPll' l,\' trt;rtnl;rttrnl'
Irollr'rl tlr.' ',I('(
r("-
il.\' tttt'\i( ttilu.\) ;ttttl lt0ttsc sl)ill'|1)ws (l\t,tscr dontc,tlit'us) 1,1()ul)\ rr l:rllot;tl()r v ('iu'('\. Ile systcttllrlicirlly cttn-
nr\t'rl
,rtt,l '-.'\ ( onrlo',tlto1; ,,', lollotr', ( itrtttp I
lirttt Itt:tlt'
lttlttsc
EXPERIMENTAL RESEARCH
finches and four male house sparrows; Group II sparrows; Group
III - four
FIELD TO LABORATORy: A CONI-t
- lour male finches and four
female finches and lour male sparrows; Group IV
female
that wild caught adults and hand-reared isolated adults prelerred the pine, but that handreared individuals, which had been previously exposed to oak, pref-erred the oak. Emlen et al' (1976) tested the orienting capabilities of indigo buntings (pu,s.serina cyaneu) in cages with minimalexposure to visual cues but with an artificial geomagnetic field provided by Hemlholtz coils surrounding the cage. when the horizontal component of the magnetic field was deflected clockwise by 120", the orientation of the buntings shifted accordingly (clockwise to geographic east_southeast). The effect of different types of feedback (see model, chapter 2) provided by diflerently treated seeds was testecl in black-capped chickadees
experiment). Turner (1964) investigated social leeding in house sparrows and chaffinches (Fringilla coelebs) by allowing a caged 'reactor' (either species) to observe simultaneously two individually caged 'actors'(both of the same species, either chalfinch or
sparrow), one which was leeding and the other not l-eeding. He found that individuals of each species were attracted to feeding and nonfeeding conspecifics. Also, chalfinches were attracted more by leeding than nonl'eeding sparrows, but this was
not true for sparrows observing chaffinches.
(puru.s tttric,trpillu.s)by
The responses of a caged chalfinch to a stufled owl located at various distances were measured by Hinde ( 1954). He fbund that at distances closer than l7 f'eet the chaffinch moved away while at greater distances it moved predominantly towards
Alcock (1970)' He presented the birds with striped seeds which were empty, filled with mealworm to which salt had been added, or contained mealworms treated with
quinine sulphate (an emetic). There was a rapid and stable avoidance of the empty and ernetic seeds, but they continued to attack the salted mealworms, perhaps because the food reward outweighed the punishment (salty taste).
the stuffed owl.
As another exarnple of manipulation of interspecific stimuli. Wells and Lehner 1978) were able to differentially
af
tbct the ability of coyotes (Cunis lurruns) to find a
rabbit by manipulating the sensory stimuli available to them. Visual, auditory and olfactory stimuli were eliminated, respectively, by testing the coyotes in the dark,
7.3
with dead rabbits and with an intense masking odor of rabbit feces and urine. Also. Metzgar (1967) exposed pairs of mice to a screech owl (Ola.s asio) in a laboratory test area), and the other was a 'transient mouse' (had no prior experience in the
1969)' Beck ( 1977)emphasized the value of captive studies in conjunction with field research' For example, for the six yeiirs following their l3 year field study on vervet
area). The owl captured'transient mice' significantly more frequently. efl-ects
ol
l in his study of
the
environmental noise on separation crowing by Japanese quail ((bturni.r
coturnixjuportica). He lound that ambient noise increased the frequency of separation crowing and the number of crows per bout, both of which should increase the
detectibility
of the signal and the localizability of the sender.
(
monkeys' Cheney and Seyfarth (1990:ix) 'supplemented [their] research on the Anrboseli vervets with stu
of Hanradryas baboo ns ( pupio lrunrudryus). At the two extremes we have the mensurative (non-manipulative)
system
Bradbury and
of light in a flight chamber ancl measured thc ability of auditorily impaired and untreated little brown bats (Mwtic luci/irgrt.rl l
1969) varied the amount
irlstr rtrrt trrttlcr rcl)'ige r':rIirrrr.
Klolrli'r (l()(rl) ttsr.'tl ttutttillttl;tliott ol llrc lttrtltt t'utttt,nttt('nl tt lltt' l;tlr,rt;rlot \ lo strrrlt' (lrt' rolt' ol t';rtlt r'rl)('rr('n('(' r,rr lr;tlrtl;rl ''r'lr'r ltol1 11, llr,' r lrtllrrltl' ''P,rttotr
FIELD To LABORAToRY: A CONTIN UUM
Field and laboratory studies Iepresent the extremes along one of the conceptual dimensions of ethological research (chapter l). However, in practice they complement each other in a cyclical continuum called a 'research cycle, by Kelly (1967,
test area for 2-30 minutes. One was a 'resident moLlse' (had spent several days in the
Potash (1912) measured changes in the abiotic' environnrcn
rrr jM
(Spi:ella posserinrt). He released individuals into a room in which he had placed pine boughs on one side and oak branches and leaves on the other. He found
- four
female finches and three f-emale sparrows (one female sparrow died prior to the
(
N
experiments in the field and the highly manipulative studies in the laborarory. The middle of the ctlntinuum is illustratecl by studies conducted in enclosures in the field. For cxanrple' wecker (1961) set up an instrumented enclosure that was half in an oitk hickor-y wootllot ancl half in a field, in order to investigate habitat selection in
lrritiric tlcct'ttticc (Pcntrrr.t'scus'rnurticulutus). Wells (1977) used a large outdoor ltclosttt'c to itlvcstigitte the relative priority of the coyote's distance senses in preda-
e
li.tl olt l':tbbits. .\t
whrr( ptlint cloes the Ileld become the laboratory and vice versa? stucly ol rhcsits monkey behavior, l('il('ll il (ollll)tirtttist'irr irrr cnt'losurc willr'rr rn.clcratelycomplexenvi-
llintle irn(l Sl)cnccl.-lloolh (19(r7:l(r9), in thcir 'tllt'tttPlt'tl
l.
l()lllll('lll tttttlt't t'ottrltli,tts ttltit lt lx'rnul ,t ttr
I trior lr't
trlr.lt
pt r.r'r,,t' I r.t r rt 1 1111;,
:r
rrrorlr.rlrlt,rlt.p.r.ec.l'cx,ct.il,c,1ltlcantrol iil l
,,1
178
F
EXPERIMENTAL RESEARCH
comHoffman and Ratne r (1973:541) suggested 'that laboratory investigations
a natural setting'' For plement and explain the frequently puzzling data obtained in Seyfarth (1990) conducted a l3-year field study of social
example, Cheney and
In order to test the interactions, including comlnllnication, in vervet monkeys' used playbacks of they groups, hypothesis that vervets recognize members of other used to study been had vocalizations (chapter 9) employing the same technique that when recognition in songbirds (Brooks an<] Falls, 1975)' Nevertheless' neighbor
and Seyfarth (1990: interpreting some of their results on vervet'concepts'Cheney of solving social capable are monkeys g4_!5)concluded that'Definitive proof that can only come prerequisite, analogies, and that language training is not a necessary
lrom laboratorY tests'. cycle several Ideally, however, research should undergo the fielcl-laboratory (1969) Menzel each. of attributes tinres, utilizing to best advantage the important a lens' with 'zooming out' considers this process analogous to 'zooming in' and (1951) Matthews Avian orientation/migration studies provide a good example' clear skies were under territory unfamiliar in lound that homing pigeons released became disorithey overcast was sky able to fly off directly toward home, but if the support by further given ented. That the sun was a clle used in orientatic-rn was
Kramer(|952)whoplaceclstarlingsinacirculalrcagewithsixwindowsgivinga
(Zugunruhe)' flutview of the sky only. The starlings showed migratory restlessness in random direcbut clear was sky the tering in the proper migratory direction when of the sun with position tions when it was overcast. Kramer altered the apparent
manner' Schmidtmirrors and was able to reorient the starlings in a predictable six hours out of Koenig (1961) kept pigeons under artificial day-night conditions 90" from oriented they released were pigeons phase with the normal day. when the clock atrd the sun's the current direction. showing that they were using a biological trays in a circuposition as a cue. Kramer trained starlings to find lood in particular was covered and the lar cage using only the sun as a cue to direction. when the cage as if it were the light the used they light, starlings were presented with a stationary ( 1964) usetl Meyer 150/hour. moving sun ancl changed their direction at the rate of
to show that instrumental conditioning, discrimination tests in tl-re laboratt>ry '/hour' movement of l5 pigeons could indeed detect wcrc tcstctl Night-migrating warblers (Sytvia atricupillu, s. futrin and s. r'urrrrtttl Ittltliotl' ot'ie itt crtcs stcllar use irr a planetarium by Sauer (1g57)and were shown to
irltligo bttttlitlgs otttEmlen (1967) measuretl the nocturnal orientittion of'cagccl tltc 1'rllrrtclltl'ittttt. rvlte tt' doors under the natural night sky ancl then took thcnr into skv rvrts st'l lot they continued to orient thcrnsclvcs ctlrrcclly wltctt tlrc Pllrttctlrt.ittttt ol lltt'Plltttt' :trts sottllt llrr'rrotllt Iaculc..6ititlns. Tlrcy rcvcrscrl llrcrrsclves w'lre rr $;r'' llrt'1rl;rll('lillllltll tlrt.v rrr'rt'tlrs,,rit'tttr'tl $lrr'tt "Lr lttrittttt sky wtts t.('\,(.t.s(.(1.;rrtrl ()l ( lt,/(l) ,t1",, tt,,',1 ltt'ttttIrtl'tll,ll rrrlr'rr I rl,rrl,r'rrt'tl or "l'lt rlillrrst.l' rllrrrrrrrrrtt.tl
IELD TO LABORATORY: A CONTIN t jtlM
pattern movement in a planetarium to demonstrate that the axis of celestial rotzrtion
was important in the development
of migratory orientation by young indigo
buntings.
Both field and laboratory studies have provided convincing evidence that geomagnetic fields are an orientation cue sometimes used by birds. Moore (l9ll) showed that nocturnal free-flying passerine migrants responded to natural fluctuations in the earth's magnetic field. Electromagnetic fields produced by large antennas were shown to alter the path of free-flying migrants (Larkin and Sutherland. 1977) and gulls held
in an orientation
cage (Southern, 1975). Homing pigeons
become disoriented when released under an overcast sky with a bar magnet attached
to their backs (Keeton, l9l4) or with Helmholz coils on their heads (Walcott and Green, 1914). In carefully controlled laboratory investigations with a cage surrounded by Hehnholz coils. use ol the inclination of the axial direction of the magnetic field (increased downward dip as the magnetic north pole is approached) fbr orientation was demonstrated in the European robin (Erithacus rubet'ulu) (Wiltschko and Wiltschko 1912) and indigo bunting (Emlen er ul.1976). The research described above is a very lew examples of the multitude of studies that have been conducted in the field and laboratory using botlr mensurative experiments and various degrees of manipulation. Literature reviews of species, concepts and behavior types will generally provide studies representing all approaches from
description in the field to manipuliition in the laboratory. Examples can be lound in D.E. Davis's (1964) review of the relative contribution of field and laboratory research to our understanding
of aggression and the role of hormones in aggressive
behavior. Above all, astute researchers recognize the value of both description and
experimentation (mensurative and manipulative) and how the various approaches can be applied in both the field and laboratory best to answer their research ques-
tions (e.g. Holldobler and Wilson 1990).
I see neither halos nor horns on either a real experiment or on accurate observations. Any method is a special case of human experience, and it cunnot surpass the limitations of its human interpreters. IMen:el, 1969:80J
d d -c {-)
C)
*{r-1 f)
o0 ?1
F{
ofi
{-) () C)
F( F{
o
U lrl
l-t
B Data
collection methods
We are now at the point where: l. the research question has been asked; 2. the subjects chosen; 3. reconnaissance observations made; 4. the objectives formulated; 5. a
descriptive
or experimental approach determined, and, if experimental; 6.
the
research hypotheses stated; I . the behavioral units to be measured determined; and 8. the experimental design established. Now it is necessary to decide on the procedures to be used to collect the data. You should also select the statistical tests to be used in the analyses (see Chapters 12-17) belore beginning to collect data.
8.I RESEARCH DESIGN AND DATA COLLECTION Research design and data collection are mutual dictators. The research design chosen will dictate the data to be collected; likewise, a knowledge
of the type
and
amount of data that can be collected will partially dictate the research design to be used. Research design and data collection are in harness together, and the pushing and pulling that each does to the other will depend on the individual study and the experience
of the researcher. Experienced observers will
use a knowledge
of
the
animal's behavior and types of data that they can expect to collect to push for the best research design. On the other hand, neophyte researchers may allow a research design, selected for statistical attributes, to pull them around in the field, attempting
to collect nearly impossible (and sometimes behaviorally meaningless) data. Research design and data collection must complement each other lor the study
to be efficient and the results valid. Even seemingly well-planned research can sometimes benefit from redesign and additional (or modified) data collection. Do not be afraid to evaluate carefully your research design and data collection methods while you are conducting your study. However, do not redesign your reseorch until you have curc.filll.t'
IJ
]
r/.r.r'r,^r,!(,r/.1,(tur
Si(.A I,E,S
I)ltir collcclion
prc.sent antl.future /osses in time, money and clata.
OF MEASUREMENT
involves the assignment
of
numbers to observations and observa-
lirrrrs to t'rrteg,orics.'l'hrs proccss is rcf'crrecl to as meusuremenl. ,\'r'rllr'r ttf tnt'tt.vttt't)t('ttl al'c lcvcls rll'r'csolttlittn (or itccttracy)
of measurement.
Ilrc lr)ur \(;rlr's (Slt'rr'rrs. l()-l(r). r('l)r'('s('nl lloirtls lrlottg ir cotttintttttl ol'rcsoltttion. I lt;rt t:,. \()nr('lvlx'\ ol rl;rl;r rt'rll rrIIr';u lr' l;rll lrt'l\\'('('n l\\'():ir'itlcs lttttl rtrtry hc rlilli( llll ll) t,tlt','ilIl/t'
ii EIrAsie=r iii[ l= J:83 i=i7-i=sEESEiEiAggTil *i =i;gEn=v.;;:14=:AtAifi
==;?3slulaiEsl_?i
=7. iI i a+ qfi ?= .lzdg5'X
i[l
' * ll
,+a
2ll i., 5'll -tllE:.=
ll
il
ll
=; '- : i.G
==#,* Z zeZ G= q =i;;ai c"'=2''t'i il5= Ei =??Eg?n;Zi;e o 'J 6 5 5 =Ii+E .3, ? *a a 5 f,oo o 6I i'5 E tr p aH ; \ E 4
z==
{E;
=;=-
-
=_
= ig=;:=
= = =a ia a
n?, E
E"== irEE;: arrizi E* ; c., a;=qoac e A!:!' TaZ r ;2 c ii(i'_;i =-; ;;5Ej =' el =Ip lEi 2i7;?# ;Aar= ;1
7=.
=i=.=='3rDi.;
ll
ll
il::
ll
il
ll ..
ll
"-\ocotrr
i x cc=- ell a lt i= *ilxT
Oq (I0 llll
3ilil: =i -'ll
ll
!-== F;; E =l11\q,,=* ;td.tE -'_== ;== 7r=>1 z=7 ==?*';1 a-':9 eP .- = - c. + :i a T = =F = =.= a3 5 ii== ieri; ,?-= SiiEEB ::? =i== E?i?;3 i.-feid ;? ll-ooc i.iEi$; e;ii= =1,= =1= v,i=-3F 3"iqa+ ;=-? === ll
il-
ll ll ll
rl
L''!
ll
Score Activitv levei2
tt
I
Sleeping
tS u-19.9 Itt -i- 1 7.,
2
Lying alert Sitting Standing Walking (slow pace) Walking
>
-1r t.
6
t
10..1
_r.7-6.
1
-)
4
1
5
2.2 3.6
6
1.-3-2.1 o. ,1-l .2
0..1-0.6
0.2-0.3 <0.2
ill Ell iil
Vocal response
Dlstance rt-ttrdell
o
llilT s il=
+ll
ll
ir..nr - ::
-
il\
ll
il
=. :"
ilr il:
ll
S-
-: .-i. =.=
a ll i-,. 3ll
ll
Score
Score
level3
0
None
Intruder antennated
Few. intermittent Few, close
mobile. is not followed. if intruder is stationary, resident ant does not stop Intruder antennated for less than 2 s: if mobile, intruder is lollowed slowly for severalcm; if intruder is stationary, resident stops Rapid antennation of intruder, antennae extended lor greater than 2 s Mandible gaping, rapid antennation;
1
2 -)
4 5
(steady pace) Gentle play (mouthing. etc.) Excited pacing Chase-play (rough-
and-tumble)
Many, intermittent Many, close Full or continuous
Aggressionlevela (as
in 2), but
if
'sidling' (maintaining a lateral orientation to and slowly circling intruder) Alarm (running, abdomen elevation and vibration) and recruitment Intruder'held'. but released; biting; no abdomen-curling Intruder'held'(as in 8), but released, abdomen-curling ( stinging posture) by residents, but no stinging;biting
Intruder surrounded and 'held'in mandibles by petiole and appendages; appendages pulled bitten off, eventual
stinging Lnmediate lunge. grab and stinging .\-olss.' 1 '' From Studd and Robertson ( 1985). I From Bekoff and Corcoran(1975). I Examples lor ants, from Obin and Vander Meer (1988). Sourc'e: Copyrighted by Bailliere Tindall.
@
5
90
ll =5 ll !s llll sx ll -= ='. :I llilsa il {.f ll llili::= llll '+ :k
!ii-3
ii .-= 3_f rIa+*z'ieEArUZz Ir 1=='= ?*v.ii37i;v?'rZzirz i: i ll ?=; -E-Fi*r ?i 3 s, i + Z=7 7+i'1i*:€itl:i3*
=7=-do ,*2rD:
Ell 8g
-..t
--
an
o
:
z ? r,1
-l
'Tl
a
SCALES OF MEASUREMENT
DATA COLLECTION METHODS
(Perisoreus inJhusscored level of aggression during feeding bouts in Siberian lays of 0 to 5 to score a scale (1985) used /as) on a scale of 0 to 3; Studd and Robertson
Table 8.3. Interval scalefor measuring the behavior
encounters in movement of yellow warblers; Riechert (1984) scored agonistic the examples in spiders on a scale of I to 35. Note that the number of intervals in Table 8.2vary from 6 to 9. variThese have all been examples of using an ordinal scale on the dependent indethe of values rank to able (behavror units), but ordinal scales can also be used I to 5 to pendent variable. For example, Moller (1987) used an ordinal scale of and the variation in badge size (amount of black coloration on the breast
./bmale in a copulatory position plac'ed in their territory for 5 minutes Score I
rank size throat) in house sparrows; Rohwer (1977) used 14 intervals to rank badge (1990)' in ul' et (Figure Alatalo 1'1)' ('studliness'categories) in Harris'sparrows grey and/or their study of female preference, ranked the percentage of brown using an feathers on the backs of male pied flycatchers (Ficedula hypoleuca) 24 fish in dichromatism sexual (1983) measured ordinal scale of I to 7. Ward and head. the areas: three on species using a four-point ordinal scale of skin color as the used was the dorsal and ventral surfaces; the sum of the three ranks scale of meameasure of sexual dichromatism. Another example of an ordinal velocity meawind of Scale Beaufort is the surement for an independent variable surement described in ChaPter 4. to Measurements (scores) based on an ordinal scale are sometimes weighted
occasionally reflect relative differences in the intensity of behaviors, and they are of both of Example score' a compttsite create to combined with other measurements choice lemale of (1987) study these are provided by Lightbody and Weatherhead's xantfio(Xunthocephalus versus male competition in yellow-headed blackbirds to l0 and l3' In cephulu,s)(Table 8.3); note that the ranks jump lrom 6 to 8, then and then addition, the ranks were multiplied by the durations of the male's behavior summed lor a comPosite score' behaviors Also, all the behaviors do not have to have different scores. [f several
As an example' reflect the same intensity, they can be assigned the same rank' by recordbimugulatus Octopus in use den and Cigliano (1993) studied dominance were behaviors These behaviors. ing the occurrence of eight attack and withdrawl behavattack the weighted on an 'intensity of response scale'of 0 to 4, but four of the ell-cct iors were all given the score of 2. Likewise, Barki et ul. (1992) deterrrlinecl by of size and morphotype on dominance in prawns (Mat'rttbrttt'hittttt ftt'st'nlt(rciil ttr ol' J scitlc ordinttl an on scored were recording l8 agonistic behavior acts which + 3. with some behavioral acts receiving the samc score' Inlervul scale..This scale is the silnrc irs thc ortlittitl scalc cxccPl
tltill
tlre ;llll()tlllt
)wll. tlt of the clifl-erenccs bctwccu rcspcct ivc clr(cgorics is I lre slr rrrc lr rttl is k l)( t (r; lltt' zt'trr (st't' l'lrlrlc tttlrltltvliv pettttils sitirtcs ir trrril rll'rrrclrsrrrcrrrcnl wlriclr ltt(';t\tll('lll('lll'.trllilllllll('lr;tlrr'ltlt' ,,i.t isll()l kn()wn.()l tslrrlrrlt;rtilvrlt'littt'rl.lot ,\ r()llll||r,tt,'t,ttrrlrl,.l., lr.illl]('t,tliltt'trrt'.t',ltt('ttt('llt lltt' r,'tt] lr('llll r",rtlrttt'tl\ lt' ts ttt't't's
of
I
male ltellow-headed btackbirds (Xanthocephalus xanthocephalus) towards a taxidermic' mount o/' a I
2
Behavior
Distance (>5 m) and non-attentive Close (<5 m) and non-attentive
3
Distant, silent observation
4
Close, silent observation
5
6
Distant, agitation Distant, agitation and vocalization
8
Close, agitation
l0
Close, agitation and vocalization
l3
Direct attack/mount
Source: From Lightbody and Weatherhead (1987).
wit the different zero points on the centrigrade and fahrenheit scale is the zero point the absence
of temperature; likewise,
80
scales. In neither "F is not twice as
warm as 40'F. Compass directions and time divisions (e.g. time of day, weeks, years) are also divisions on an interval scale (Zar,l984). The length of time it takes individual birds to fly alter an alarm call is given (latencies) would generally be considered to be on an interval scale of measurement. These time-to-fly latencies can be compared to each otheq but the zero point for flight is not really known, although we would probably use olrr hearing of the alarm call as an arbitrary zero point. Interval scales of measurement are uncommon in ethological studies except when measuring spatial or temporal characteristics. However, Maxim (1976) constructed an interval scale of l7 behavior categories, including'attack', 'stare', 'lipsmack' and 'grimace'. fbr use in studies of social relations in pairs of rhesus monkcys. The scale was based on observations of 120 pairs of monkeys; a scale verlue lirr each behavior category was then established by the relative frequency distribution ol' responses between categories. The theory behind Maxim's interval scalc wirs 'J'hc Law of Categorical Judgement', that 'is a set of equations which, rrsing l'r'ct;rrcrrcy tlistributions
of
responses
to a set of stimuli, establishes the para-
r s ;rrttl rtrc:rrr sclrlc virlucs ol'thosc stinruli'(Maxim, 1976 125). lirttitt,sr'ttlt". 'l'lris st'lrlc is llre siull('irs tlre rnlcrvlrl scirlc cxcept thc zero point is
rncle
kttoutr lltts st'ltlc rs t'onuttottly
ttscrl tvtllt tr)nlinu()us v:tt'irtblcs, sttclt its rlttt':ttitttt
Irrrrl rlr.,l,trrr't' l ,,r r'1 tttr,rlt'. Nlt",r r' (l()()
l) '.lrr,lr,'rl ',lrt'll sr'k't'lion llr'lr;r','rot itt
lrvo
SAMPLING METHODS
DATA COLLECTION METHODS
l2). Remember to keep the original data, for you may want to come back to it later, and at that time you may need the higher-resolution scale of measurement. The scale of measurement chosen for collecting data will, in part, determine the experimental design (Chapter 6), as well as the statistical tests that can be used (Chapter l3 and 14). Only nonparametric statistical tests should be used with tests (see below and Chapter
Table 8.4. Time requiredjbr .five incliviclual hermit crabs (Pagurus samuelis) to locate a shell in the light and in the dark
Time (s)
Dark
nominal and ordinal data, whereas either parametric or nonparametric tests can be used on interval and ratio data (Chapter l2). These restrictions are based on the
J
323
8
24r
operations which are permissible on data from the different scales of measurement
2 J
37
216
4
J
118
5
4
57
Individual 1
Light
(Table 8.6). For additional information on scales of measurement consult one of the
many, enlightening discussions available (e.g. Drew and Hardman, 1985; Ghent 1979;Walker, 1985).
Srturc'e; Abriclged from Mesce (1993)' Copyrighted by Academic Press'
8.3
The discussion of sampling methods which follows is based almost entirely on J. Altmann's (1974) excellent review. The sampling method you select for your research will be based on: l. your research question(s); 2. your experimental design; 3. the number and types of behavioral units you have selected to measure (states
Table 8.5. Examples of'clota v'ith di//brent scales of'meusurentent
Data rec'orded
of
Behavior code
Scale
Behavior
for measurement
measurement
,4 occurred
A
I
A2
Nominal Ordinal
A2l1300
Interval
occurred at intensitY level 2
.4 occurred at intensitY level 2,
and/or events); 4. the scale of measurement; and 5. a multitude of practical considerations, such as observability, experience, and availability of equipment.
Experimental designs (Chapter 6) and statistical tests (Chapter l3 and l4) will be used on randomly selected individuals and/or on behaviors randomly selected from individuals. You assume that the sampling methods described below
might want to review section 6.5, on random, haphazard and opportunistic samples.
at 1:00 pm ,4 occurred at intensitY level 2, at l:00 pm, at a distance of 8 m
SAMPLING METHODS
A2l1300-8
Ratio
In the discussion to follow, an example of how each method is used will be based
on Figure 8.1, a hypothetical behavior record for six mule deer
(Odocoileus
hemionus) showing only two events (standing-up and lying-down) and one state
from the stimulus model
(feeding). In order to be sure that you understand the diagram, confirm the following statements: reqLrired hermit crab species; one measure of their use of vision was the time (Table 8'4)' locate a shell in light and in dark thc clatt rr'r rl To determine the scale of measurement used in a study, examine tcr
v,as recorde(t (i.e. the 'raw data'), as shown in Table 8'5' citn hc cxtrrtctetl Note that data with a scale of measurement of less resolutitln
A2 ll'otrr from those with higher resolution (e.g. ordinal trom intcrvitl or rittio.
with ll()lll)rlt'rtrtte lA2l1300 or A2l1300 g). This is sometimcs ckrnc lirr rlata unalysis lrig.lr:t tr.'solttliott lrs ric tests (Chapter l3). IIowcvcr. it is best to collcct tlirtir witlr s:tt'tilit't'lltt'tt'solttti.tt l;tlt't sculc tll-alclrsur-cn.lcnl lrs is ll.lrsihlc rrrrrl vrrlitl.lrrrtl llrctt (i.t. t..ttvt.tl ;rlto lo ottlirlrl tl;rtlr) tl v()u llr(' n()l ('()tlVtllt t'rl ll u'lts t ollt't'lt'tl :tt t tt .'l'tlt"ltt'tl I,lt,llll('lttt llrlt.lvlrrrrl/.t \()1 11t(lllt,rt llrt.rl;rt;trlorlol tttr't'l lltt'tlllt'tt.tl,'t
t :
All individuals fed during the 90-minute period. All individuals fed during at least two of the three 30-minute periods.
3
None of the individuals fed between the events of lying down and standing up.
+ t..t.I
lntlivirlual IV was the only one that did not feed every time it was standing. liocal-:urirnal (pair, group) versus all-animal sampling
rrrcllrotl. llre lirsl t;rrcsliott yotr tt'tust ask is which of the lx'l()\\' rlr'st'rrlrt's \'()ur rr'st';nt'lr I llrl t'lroit'c will lrclp yrltt tlccitlc
;r srnrplirrl,,
x$iiLiPSS
fErilifrneil : I e ? x.t 2': Ilaa
ll
it is a i i
ll
7'?"
ll
iry.i3 ? iai ? n?:o=13 *
5 ro PD ^o-a
I)E
dw. l)
w: 5e=* OPd-rP-iroaii
i s
3
si
Es=eR :aas 6g=^ a€ 3; =fi ?;' i;i.
i
g
t
*
#
rD;O-I^X=i.S
* ll';;r
ao +
ll
,Ea --'ll
.'
e*
:-C)
=6
ll
? ='
8ll Bll
v l. oiY
I
*
lls ll 3.
t
ll s
a-
d
llll *] ilR ll s llll iF
r ll i I.llt
ll il
Fl
G
S l"rll lo ll s
lE ll t
ll ll ll ll
X
X
o Fl
\EI$ X
"
o
il
llls
f-
I
oro -o
?Lr
a (D
ll
BB c'
d,
U
(D
zo
B= xh
?I
(t
=
ll ll
ll
tsoN) a. :-dll
C'
S
ll
{all
9P
iiE
(,
dc
s lq E€ llr=rT*=-ix ;ai;,=6 :2'a?; ? = E3 B *G X:ag I 3a ilf?;, e?iB T6n '-) = i* ie i atseD i S
o-
I
!.
olro
a-o
cf ,, a-o
?v ?VI
Time scale
-o
(min.)
Sampling methods and sample time units: (see text for explanation) Ad libitum
Focal-animals/All-occurrences All-animals/All-occurrences Sequences
IITTTTTTTTI
One Zero Focal-animals/lnstantaneous All-animals/Scan
Fig.
1rilililil iltiltilil
III'NT'IT]-I'I
I-I'I'ITTTTTTTI
ililililil
1ilil1ilil
il1ililil1
llllltltil
Iilltrtlil
lltlttlltl
8.1 Hypothetical record of occurrence of three behaviors in six penned adult mule deer: O:standing up (event);C:lying down (event); l:feeding (state). The time period recorded by each sampling technique is shown in the lower part of the figure.
o o
-o : rn
z ?
rn
-t (,
SAMPLING METHODS
DATA COLLECTION METHODS
Primary Interest in FEI'l BEHAVIoRS
If samPle intervals are short relative to behavior or bout
and/or
SEVERAL INDIVIDUALS
duration
(e.g. synchrony of feeding in grouP)
lf number o individuals
bly have been feasible to also record those behaviors for other individuals (e.g. the other males).
Although a specific individual receives highest priority when local-animal sampling, this method does not necessarily restrict us to only that individual. For example, when social behavior is recorded, a focal-animal sample on an individual
provides a record of all acts in which that animal is either the actor or receiver
Ethogram oeveloPment
Altmann, 1974). Some research requires that observations be made on two individuals simultaneously (e.g. mating. aggression/submission). For example, Huxley (1968) made his observations on onefoc'ul-pair at a time in his study of courtship in
SEQUENC E SOC IOMETR
(J.
IC
MATR 1 X
great crested grebes. Accurately recording the behavior of two individuals simultaneously can often be difficult so Smuts used two observers in her study of the formation of relationships in olive baboons (Papio t't'urtot'ephalus); one observer fbcused
Primary lnterest in SEVERAL BEHAVIORS
FOCAL-AN
and/or FElrl INDIVIDUALS
(Pair,
IML
on the female and the other on the male (AckermAn, 1988).
Group)
(e.q. daily activities of alpha male)
If
ln order to answer some
research questions, observation of ./ocal-groups may be required. As an example, Fujioka ( 1985) observed nine focal-families, one at a time,
sample
i nterval s
are short
relative to
behavior or
t
Fig.
in his study of sibling competition in cattle egrets (Buhulus ibis).
duration
The dashed 8.2 Flow chart for selecting animal behavior sampling methods'
lines
denote alternatives (see text for explanation)'
a..t.t
pairetl with one of the other samto be paired with only some pling methods discussed below, and each is best sLlited of those other methods (Figure 8'2) Focal-animal and all-animal sampling
are (tlwu)'s
b Out-of-sight time
Focal-animals in the field and captivity oflen disappear from view of the observer. Therefore, it is necessary to record the time intervals in each sample period that the
individual being observed is out ol.sight; the result is 'missing data'for those time intervals. The sampling protocol/data problem created by an animal under observa-
tion temporarily disappearing lrom view (an error of apprehending. 8.-1. t
a
is the focus of observa-
with focat-animal sampling, one indiviclual (pair. group) yoll can accurately tions cluring a particular sample period' With this method only one (focalobserving by intlividtutls several beltaviors in selet'tec{ measure
( 1984) observed lbcal animal) at a time. For example, Goss-Custard ancl Sutherlancl of three dift-erent feeding methods animals for periods of five minutes in their study all your in oystercatchers (Huematopus ostrctlegr.rs). Since you are concentrating a few, behavigrs' lbcal-anipral effort on a single in
cluratitltrs tll'bchavior whctt sampling can provide accurate data on frequencies and 8'2;' combined with All Occurrences sampling (see below; Figure (Figure 8' I )' wc lilctts ot't ttlitlc I v rrrttl For our example of focal-animal sampling lyirrg dowtt. At thc l'rcgitrrlirtg ol' record all occurrences of standing up. l-eecling. uncl
pltrt ol'tltc tliitgt'lrrll w('ll()t.. the 30 minute observutitln pcriotl showtl irl thc lowcr tllittttlt" Ircgitts lt't'tltlt'"rllt' that hc is lying tlowu. Ilc slittttls ttp tlttt'ittg llrc l5llt tttttttttt" ;lll(l ls sttll lvrrr,' .ri.rrlc l.lcr., lr.ctls lirr I I rrrirrrrlt.s. lir.s rlou,rr rlrrr rrr1, tlrt'ttt'rl sltlttlllt'l)t't lorl t'ttrl'' ltt lltt" \('l \'\lllll)lllit'rl t'r;tlttPlt' tl rvottlrl lllrrlr;1 tlt)\r'' t'ltt'tt .ttt
see
section 8.4)
has not been successfully resolved. That is, there is no truly valid procedure for
Focal'aninrul samPling
determining what behavior(s) occurred while that animal was out-of-sight. However, there is a general relationship between the predictability of what behavior
occurred, the duration of behaviors most commonly observed, and the time the animal is out of sight. Four rnethods for dealing with this problem are discussed below relative to the duration of behaviors and time out of sight. Although none of' tlte,;c tncthods i,v trul.y valid, the hazards in using them can be reduced by following these guidelines:
Fur out-ol'-sight periods of long duration and when the durations of common behtviors are short (relative to the out-of-sight periods) do one of the following: I)clctc thc limc or"rt-of'-sight from the sample;duration of the sample pct'iotl is rctltrcctl irccortliltgly. I )r'lt'te lltc tirrrc orrt-ol-sigltl lhrttt llrc sitrttplc, but ittcreuse observation lirrrt'turlil tlre tirrrr'tlrc;rnrrrr:rl rvrrs:rr'lultlly obsct'vctlcrltrals thc time l('(IilIt'rl l.,t Iltr' 1ttt'tlt'lr't ilrilr('(I s;rtrrllt' Pt't iotl
DATA-COLLECTION
M ETHODS
SAMPLING METHODS
These two methods are valid only when the observer believes that the probability of perlorming any of the behaviors while the animal is in view is the same as when it is out of sight. For out-ofl-sight periods of short duration and when the durations of common behaviors are long (relative to the out-of-sight periods) do one of the following:
t
Assign the behavior seen when the animal goes out-of-sight to the out-of-
sight period.
z Assign
the behavior seen when the animal comes back into view to the
out-of-sight period.
If the behavior is the same lor I and 2, then
the behavior recorded is more likely
to be valid. Behaviors occupying the largest percentage of the animal's time budget are those
that are most likely to be interrupted. Two factors which should, perhaps. override or dictate use ol the above methods are experience and common sense. Probably no one knows the animal better than you do; therefore, lollow the course of action which you consider to be the most appropriate. Also, it is often wise to deal with data using two or more methods fbr comparison.
Losito et al. (1988) developed a 'fcrcal-switch' sampling method to determine time budgets of mourning doves in view-restricted habitats. They used a'standard wait period'to decide when to end sampling or purslle flocks lost from sight. This method reduced the out-of-sight data. increased the observers' efficiency by
you could score fbr each individual pronghorn whether it was feeding at selected points in time to determine the synchrony of the hercl's loraging behavior (see example of ull-animals sc:crn sampling below). All-anirnals sampling also allows you to collect data opportunistically on a specific behavior as it is sporadically perlbrmed by inclividuals in a large population. For example, you could observe the same herd of pronghorn antelope and opportunistically collect data on courtship sequences as they occur (see section g.3.3a).
As in focal-animal sampling, individuals clisappearing from view can be a problem for all-animal and acl libitum sarnpling (cliscussed below). Since it is rare that all individuals are equally visible, some researchers have attempted to measure individual observahilfty (Chalmers, 1968; SEG, 1966). At some regularly scheduled time period (e.g. halfl-hour intervals) censuses are taken of the individuals which are visible; these are called observahility sarnple* J. Altman n (1974:239) suggests that these adjustments are of limited compensatory value: 'Observability samples provide an accurate correction only to the extent that the probability of a behavior being recorded if any individual performs that behavior is directly proportional to the percent of time that the individual is visible.'Altmann goes on to point out that there are, at least, the following three potential sources of failure to obtain consistently proportional samples:
I
Individual or class-specific diflerences in observability may vary with different behaviors.
12"/u,
z A specific behavior may affect observability. : Observers'preferetrces (decisions) in sampling
and saved 24\'1, of the samples from premature termination.
Focal animals may be selected because of a research objective (e.g. mothers: duration of maternal behavior) or an experimental design restraint. For example, the design might call lor a random sampling of individuals within a population, age
specific behaviors introduce biases. as well as their attempts to compensate for these biases (see observer bias in section 8.4).
class or treatment group.
Focal-animal sampling may strain the observer's ability to record data accurately. especially in dense aggregations or highly social species (e.g. monkeys). However, focal-animal all-occurrences sampling does provide lor a rigorous exami-
nation of the behavior of individuals, and it is this type of sampling which will provide the most accurate and valid data to test hypotheses. For this reirsort. .1. Altmann (1974) concluded tliat with the proper choice of behavior units. sarnplc periods and local individuals this rnethod will generally be the bcst to usc.
8.3.1t' All-animals santpling
With this mcthotl yrlrr sirtrtple
rllr.
lill' cxlttuPlt'. yort r'ortltl
;t ft'w lrrlttn'irtt',t itt tt tt'ltrtirt'lt'lttt.t!t'tttrutl,t't ttl rrttlit'trlrr srrtnlllt' All ( )r't'rrtr('n( ('s (st't' lrr'lott ) ol ulnirlr()n nr
l
Itr'ttl ol l)t()n,'lr()ln lullt'lo[t' lo tlr'lt'tnutl('ll', ltt't1ttt'ttt \' :rl tltllt'tt'ttl lttttt"' ol rl,r\ ( )t
8.3.2 Ad lihitum sampling
As ud libituttt implies, no restraints are employe
What is generally recorded are the behaviors of those individuals (or groups) that are most easily observed.
Ad liltitum sarnpling is used during early reconnaissance observations and when you urcr clcvcloping an ethogram (Chapter 4). This type of sampling is also com_ nrtrrrly ttsccl itl tlescriptive research. J. Altmann (1974:235) stated that, .In field sltrtlics ol'bcllrtvitt [utl libituttr sampling is] perhaps the most common form ot' bcll:lviot l'ccol'tl'. rttt ttrtlirrtunate and regretable fact. That is not to say that.typical
licltl rrolt's':ttt'tlrtl itllPorl;tttt. brr( r'lrllrcr'(lrirl thc relative abundance of observaliotts llt:trlt'ttt lltis \\':ty. ('r'('n lirr rlcst'rrplivt'slrrtlies. corrltl hirve l;een inrproved by
llsllll'
\(
rlttt' lonn ol
l ll
llrlottt ( )t ..\.\l(.1t;tl tr
s;t
t1plttt1,,
DATA COLLECTION METHODS
SAMPLING METHODS
During periods in the field when you are not involved in collecting data lor your experimental research, ad libitum field notes can often yield insights. The value of these unplanned, ad libitum field notes was heralded by Tinbergen: Scientific examination naturally requires concentration, a narrowing of interest, and the knowledge we gained through this has meant a great deal to us. But it has become increasingly clear to me how equally valuable have been the long periods of relaxed, unspecified, uncommitted interest . . . an extremely valuable store of lactual knowledge is picked up by a young naturalist during his seemingly aimless wanderings in the fields. Nor are the preliminary, unplanned observations one does while relaxed and uncommitted without value
to the strict experimental analysis.
ITinbergen, I95B:2B7J
Since ad libitum sampling is most often used when ethologists are recording as much as they can during an unplanned encounter with a species, during reconnaissance observations. or when they are developing an ethogram, the value of those field notes as data for hypothesis testing is very limited. When the observations are
8.3.3 Continuous recording sampling methods
With continuous recording sampling methods, the researcher records a complete account of all behavior units of interest; that is, we would obtain data on: occurof both states and events. These sampling methods provide the most complete and accurate data. rence, duration and sequences
8. J.
I o "A
ll-o c carre nce s s amp ling
It rnay be desirable to concentrate on one, or a limited number of. behaviors and record all occurrences (called 'event-sampling'by Hutt and Hutt, (lg74,and .complete record'by Slater, 1978). All-occurrence sampling is often combined with focalanimal sampling since it is dilficult to record all occurrences accurately on several individuals simultaneously. All-occurrence sampling of selected behaviors is possible
if
the lollowing conditions exisr:
treated as data and quantitative comparisons made, the lollowing assumption generally must also be made: the true probability of observing the dilferent sexes, age
I z
Comparisons cannot be made across time since the number of samples is probably small and the samples were not taken
The animals and the behaviors are easily observed. The behaviors have been carefully defined so that they are easily recognized.
:
The behaviors do not occur more often (or more rapidly) than the observer can record them.
classes and behaviors is reflected in your notes.
randomly.
For example, we might encounter the six deer represented in Figure 8.1 and observe them for the 32 minutes indicated in the lower part of the figure. During that period we would record that: l. all but f'emale I fed; 2. all individuals laid
of the six
individuals stood up. That is, we could have recorded those behaviors or we could have been temporarily fucusing on other behaviors and missed portions of the complete record. Let us say that when female VI laid down at minute 25, she laid close to female III, and they began head-butting intentions and making threats to each other. We might have focused our attention on them tbr the next 15 minutes and missed seeing male II stancl up, feed, and lie down again. That is, unless we had decided previor,rsly to recorcl all occurrences of the behaviors diagrammed in Figure 8.1, it is unlikely that wc could have duplicated that portion of the diagram in our 32 minute stttlplc lnrttt
down: and 3. three
our field notes. Besides providing information about the f'easibility of- a plartnctl sttttly (r'ccottnaissance observations) and development of itn ethogntttt, tttl liltilttttt sitlttplittg pt'ovides questions, ideas ancl hypotltcscs lrlr lirtttrc rcsc:tt'clt ltrttl ol'lctt t'tvcltls t';tte. lrttl signilica nt. bchuviot'ul cvctt
t
s.
This method of sampling can provide accurate data on the following: Frequency and rate of occurrence (and temporal changes in rate) of the selected behavior(s). Restricted sequencing (see example below). Behavioral synchrony (see example below).
For example. assume we are interested in the seqllence of the initiation of leeding (event) and the synchrony of leeding (state) in the six mule deer in Figure 8.1. We selected the 60-minute sample period shown in the lower part of diagram and recordecl the initiation and termination of feeding for each individual (events inclicatcd in the diagram). We can then examine the data lor the sequence of, initiatiorl ol' lccding lirr the six individuals, as well as how many a1d which individuals lctl at lltc sitttlc tirtrc (behavioral synchrony). In this case, we can gain information
itbottt lt stltlc (ll'ctlirtg) by recording all occurrences of two events (initiation and tertttiltrtl iorr ol' lcctlirrg).
SAMPLING METHODS
DATA COLLECTION METHODS
198
MALE BEHAYIOR
FEMALE EEHAVIOR oPPeors
'-3
2 -3-.
f
lies
olights on herboge
6)
lolds wings
I
hoirpencils while hovering
olights loterolly ocguresces Fig.
duck, involves the 8.3 courtship of mallard duck, a species of surface-feeding (2) stretch-shake' (3) tail-shake' (3) tail-shake, behaviors: of following sequence
(3) tail-shake, initial postrrre. (5) initial postLrre, (l) head-flick. (4) grunt-whistle, head-up-tail-up,(6)lookirigtowardsthefemale'(7)nod.swirnming'anc.l(8) (9) and bridling (10)' back of the head. Also shown are clown-up
w_. tFI'
showing the
copuloles
Posl-nupliol
Kacher itr Bridling is a postcopulatory display' (Drawing by Hermann Kacher') Hermann by granted collaboration with Konrad Lorenz;permission
llight
8.3.3h Sequence samqling
pcrlirrtnctl
These rnay bc In sequence sampling the focus is on a chain of behaviors' ducks; Figurc 8'3) or thcy rtrity by a single individual (e.g. courtship displays in male individuals (c.g' cottt'tshiP in tlrc be behaviors alternating between two (or more)
queen butterflY; Figure 8.4). by thc bcgittttirtg ol'll The initiation of a sample period is r,rsr-rally clctcrtt.tittctl lrnticiPrrlc tlrc irritiltli.tt rtl lt st't1ttt'ttt't' sequence. An experiencecl obscrvcr cirn ol'tcn
t\\'() ()l lll()lt'tlttltr'ttltt;tls in an i.6ivirlt,tl irrttl ittt itltl'rctttlitlg. itttct'ltt'liott llt'l\\'t't'll llllllill("' -fltC s:ttttPlC st'r1tl('ll('('l('l Vt'tl ltet'irltl etltls lv'ltt'lt lllt'ollst't 't "t'tlll('ll(("r"rrt'll lll.'|t'ttl\lll;r1;,,'lrt'1'tttllllll';ltttlt'tt'l"l lltt.tt.rrr;rVlrt.tlrllitrrlt\
Iiig
s
-l
('ourtship bchavior of the queen butterfly. Note that the female permits coptrllrliorr to occllr only alier the male completes a series of courtship actions, rrrrlrrtlirrg crtrurling thc hairpencils and releasing pheromones (from Brower c/ rrl . l()(r5).
SAMPLING METHODS
DATA COLLECTION METHODS
200
at random' J' Altmann as in selecting individual sequences or social interactions (1g74) discussed the sampling bias caused by differing lengths
of
individual
sequences (or social interactions):
If...theobserveralwaysbeginssamplingattheonsetofasequence
among sequence and chooses the next sequence to sample at random each length in of onsets or in any other way that samples sequences data will be resulting proportion to their /i'e quen('y rt.f' oc'currence, the with spent time total unbiased with respect to sequence length: the where /, d,times.f,,. to Sequences of, say' duration d,, will be proportional
isthefrequencyofsequencesoflengthd,.Thenthetimespentwith ..q,.n.., of different lengths, not the probability of choosing such taken up by sequences sequences, will be in proportion to the total time
ofthatlength.[J.Altntann,1974..250] Sequence sampling
of social interactions has some other potential
833c Sociometric matrix
A sociometric' matrir is really an experimental design or a way of tabulating data. Collection of data for a sociometric matrix can be considered a special type of allo('currences sampling in which the observer records interactions between pairs of individuals (e.9. transmitter receiver, groomer groomee) or records social interactions of an individual (fbcal unima[) during a specified sampling period. For example, we might want to measure the synchrony between individuals in standing up in the group of six mule deer in Figure 8.1. We suspect that the behavior
of certain individuals, when standing up, stimulates others to stand up, so we construct the sociometric matrix below. We record the initiator and follower. if the follower stands up in less than 60 seconds alter the initiator has stood up. The data in the matrix below are from the 60 minute sample period in Figure 8.1.
problems'
Follower
group may break up Interactions may branch or converge; that is, an interacting group which is join interacting an (branch) into subgroups or other individuals may
III
under observation (converge)' and conIt is up to the observer to record as clearly as possible the branching that are often important parts verging of interactions. These are recognizable events observer to develop of social interactions among large groups. They may lorce the
IV
V
VI
II
but they should be additional observational skills, including peripheral vision; eliminated from the experiincluded in the observational record or they shoulcl be (1965) restricted their observaBossert For example, Hazlett and
Initiator
III IV
I
mental design.
tionstointeractionsbetweentwoindividualcrabs(dyads).
of behaviors that the mule As an example, we might be interested in the sequence from the time they stand up to leed until they lie
deer in Figure 8.1 go through sampling show our obserdown. The flrst two bars (sample perio
N.tc t5itt ip t5c y-lr-cviorrs
trritre
llre sc(lue il(.(.ol irritilttiotl ttl lt't'rlttlf
irr
Ilrt'rl\ tlt't'l
VI The above data sugge.sl that individual IV is more of a leader than a follower; but we would,
ol course, need to record
a large number
of
these interactions in order to
demonstrate significant correlations.
In most instances the researcher
uses a
ness in dyadic interactions. Therefore, an
sociometric matrix to test for one-sided-
attempt is made to record as many interac-
tions as possiblc without regard to random or systematic sampling. Hence, the data cannot be comparecl between cells, and the matrix cannot be treated as a true contingcncy tlrblc. but nrore as a lorm lor tabulating data.
n.
l.{
'l'irtrc s:utr;lliltg
Wltt'tt u\nrl,:r lintt's;rrrrPlrrtl'nrcllrotl. llrc olrsetvct rccot'rls cithcr: l. the behavior sl;rlr' llt;rl llrt' ;rrnrirl(s) rr Pt'tlounlnl' ,ll l)()rrl\ nl lrn(' (rrrst:tttl:tttcrltts/scillt silln-
SAMPLING METHODS
DATA COLLECTION METHODS
state or event pling; point sampling, Dunbar 1976); or 2' whether a behavior points in time (one-zero sampling)' occurred during a sample interval delineated by conditions: These methods are often used under the following
simultaneously sampling We want to gather data on a few behaviors while (e'g. studies of behavioral syna relatively large group of individuals
r
of l0 sample
'l' scores, the third set three
'l' scores, and the fourth set two 'l' scores. Although, by definition, one zero sampling requires that a behavior be scored only once per sample period regardless of the number of times it occurs, some researchers have recorded all occurrences but analyzed the data as one-zero scores (e.g. Kummer, 1968). Slater (1978) suggested that one-zero data may be useful as a
sPecific interest).
first-approximation look at associations between behaviors, by determining how of behaviors on
a few
individ-
uals(e.g.juvenilefemales),thanwecanwithcontinuoussampling mutually exclusive methods (e.g. time budgets for an exhaustive list of
frequently they occur together in the sample intervals. J. Altmann (1974:253) pointed out a common fallacy in interpreting one-zero data: 'lt is too easy lor both author and reader to forget that a one-zero score is not
behaviors).
the frequency of behaviorbut is the frequency of intervals that included any amount
(see below) when we want to maintain high inter-observer reliabilty and experience are by several observers with varying levels of ability necessity involved gathering data'
of time spent in that behavior'. However, the data from one-zero samples are sometimes presented as Hansen.fiequent'ies (the number of sample periods in which the behavior occurred/total number of sample periods, based on Hansen,1966). when the sample periods are short in duration but large in number. S. Altmann and
:
8.3.4a One-zeYo samPling
occurs (one)' or With one-zero sampling, the observer scores whether a behavior It is suitable for recordnot (zero), during a short interval of time (sample period)'
'time-sampling'(Hutt
to as ing states and/or events. This method has been referred This method has the lo1lg12)' (Fienberg, and Hutt, lg74)or the 'Hansen system' lowing features:
t z : +
(not frequency In each sample period the occurrence or non-occurrence of occurrence) is scored' in each sample Behaviors of one or more individuals can be recorded
Wagner ( 1970) described a method for using Hansen frequencies to estimate the mean rate of occurrence of events when the events approximate a Poisson distribution (Chapter I 1); that is, ' that the behavior occurs randomly at a constant rate, that the chance of two or more simultaneous occurrences of the behavior is negligible, and that the chance that a particular behavior will occur during an interval is independent of the time that has elapsed since the last occurrence of that behavior'
(Altmann and Wagner, l9J0:182); see Chapter 15 lor a discussion of calculating rates
of behavior.
Caution should also be used when converting one-zero scores to percentage of
period.
at some point occurrence refers to either an event or a state (ongoing
time spent in a behavior (Simpson and Simpson 1977). This would be accurate only if the behavior lasted for the complete sample periods in which it was scored. If researchers desire to use one-zero data for 'time-spent'estimates, they must deter-
during the samPle Period)'
mine how closely their data approximate the above condition.
and several (e'g' The sample periods are generally short (e.g. l5 seconds), 50) are used in succession'
:
of the to record data using this method; it is not as demanding tlbservirlonger use often observer,s total concentration, hence you can
It
is easy
tion Periods.
witlr 1 lcvcl O lnexperienced observers can quickly learn to use this n-retftocl rcliilbility (scc of accuracy that results in high measures of intcr-obscrvcr below).
As
periods would all contain '0' scores, the second set two
chrony;dailyactivitypatterns;percentageoftimespentinbehaviorsof We want to gather data on a larger number
z
event of standing up (for any individual in the group), then the first set
silllll)lt's ol l0 lr, cx:r.rplc. ip liigtu'c fi.l.lltcrc;rrc lirrrt'!t()tll)s ol ollt' /('l()
lltt'r' sitttt,le ,et.i.tlselrt.lt. lltr.slrntlllt'pt.ri,rtlsilr('()l .nt'nrlrrtllt'tlttt:tll()ll.;lllllt'ttl'lt .|( ()t l,l lltt' (l||1. |||,' /t.|() tIl rr'.,ttltl ll()| lllillIl lrt. tlttt.'lt ..||rrt lt'I ll trt. ltl(' llll(.It...1t.,1
Another potentialproblem arises when recording a state (ongoing behavior) that continues through several sample periods and is scored for each one. In this case there is no close relationship between the number of scores (i.e. intervals in which the bchavior occurred) and actual frequency ol occurrence (Dunbar, 1976). Howcvcr. il'thc sample pericld is sufficiently short relative to the behavior's duration itntl tltc inlct'virl bctwccn successive occurrences, then the observer can obtain (with t'crtsoturhlc itcctu'ircy) both tiecluency and duration through careful data analysis. l'irr tlris lo bc lrrirly rrcctrratc. thc probability of both a termination and an onset ()('('utlttf in ()n('sirtttplc rtttrst bc rrcrligiblc. Mtrrtin anrl Butcson (1986, 1993) illusttltlt';t tttr'lltorl ol rlt'lt'ltttlttinl' tltt'lt'tt1,llt ol s;tntlllr'ittlct-vltls whcn usitig any times;ttttPltnl, tttr'llrorl ll ts l,lrsr'tl on olrl,nnltl' ll ll(' ltt't;rrt'nt'it's tuttl tlttrltlirttts lhrtlttgh
DATA COLLECTION
SAMPLING METHODS
M ETHODS
the amount of error that would have continuous sampling and then determining had been time sampled at different intervals' been introduced if the same behavior .behaviors' computer.generated In ad
r
that combines the perTo provide a single measure of social relatedness
centageoftimeindividualsspendtogetherandtheiractualfrequencyof interaction.
: :
To provide high inter-observer reliribility' previously collected using To obtain data to be comparecl with data
one-zero samPling'
+Toavoidarbitrarydefinitionsofabehavior'sstartandendtimes'
s
for less time' effort and To obtain information about social relateclness expense than other methods'
of her analysis of the use of In contrast. Kraemer (]91g)summarized the results as lollows: olle-zero sampling of primate behavior ...inordertomaximizethepossibilityofcomparingresultsofstudies of changes which research *ilie.,., to render the type
done in different
8.3.4b Instantaneous and scan sampling
I n.s t ant aneo u s scunp I i ng Instanturreous sampling is a special type
of time sampling in which the observer
scores an animal's behavior at predetermined
'points'in time. This method has been
called'time-sampling'by Hutt and Hutt(1974), 'point sampling'by Dunbar (1976). and'on-the-dot sampling'by Slater (1978). The major benefit of instantaneous sampling is the relative ease of recording data versus all-occurrences sampling. This rnethod works wellwith behavioral states, but it is not recommended for use with events. Behavioral events and the sampling points are both instantaneous; hence the
probability of them occuring together is remote.
Instantaneous sampling is often used to obtain data on the time distribution of behavioral states in an individual; that is, to determine time budgets. This method provides reliable estimates of true time use if the sampling interval is short relative to the ntean duration of the states being measured, and if average time use is calculated frorn data collected from several individuals. It is olten used to sample states, since the
probability of scoring events with this method is remote. This method can
be used to obtain data on the time distribution of behavioral states lor an individ-
ual. For example, Dunbar (1916) fbund that sampling intervals of 5, 10, 15,30 and 60 seconds all gave reliable estimates of time use for states that varied in mean dura-
tion from
14.0
to
124.6 s. Likewise, Tyler (1979) found that the same sampling inter-
vals, used by Dunbar, gave reliable estimates
of time
budgets for behavioral states
that ranged in mean duration fiom 40 to 50 s. Poysa (1991) cautions that, although
from several individuals may provide reliable estimates of time use. data from individual instantaneous samples may differ greatly fiom true time use; this can be a serious problem if you try to relate this individualvariation in time use data averages
to an independent variable. As an example, suppose we wanted to record the time distribution of feeding in III in Figure 8.1 . We set or-rr sampling points in the middle of the periods we
areseentooccurclearlyinterpretableintermsoftlrecharacteristicsof
f-emale
the behavior, and to eliminate the possibility
r"rsed lbr one zero sampling. Occurrences of leeding would be recorded at sample points 2 thnrugh 9 in the first set,4 through l0 in the second set, I through 4 in the thircl set. itncl at no sample points in the fourth set. The frequency of occurrence
of obscuring a real change
inbehaviorbyanunfortunatechoiceofsamplinginterval,metlrtlds other than One-zero sampling are
preferred
IKruurrt'r' 1979"24'] I
Themajordisadvantageofthissanrplingmethodisthataccttratcitllilrttrittitltl
may be lost. espccially il' tltc obscrvct' about actual frequency and actual duration doesnotuseappropriatesampleintervals.Thcresetlrclrcrhitsttrwciglrtlristlislrtl_ lrutl tlrc high itttct'-..'rse t'r''ct te liltvantage against the ease of rec.rcliug .hscrvirti..s
(l()7'1251-i)cotteltrtlt'tl tltrrt 'ltt sltotl' bility which this mcthotl provitlcs..l. Altrlrirrrn ;rrlt't1tt;tlr' ;ttsttlt( illl()ll ttcitltcr cltsc.r. .sc r.r()r.rlrlscr.vcr.ilIre(..1(.rt r)t't 't' 1lr.r'rrlr':;rrr
lirt Iltt' tlst' ol tltts tt't'lrrtirprt"
rccorulcd clccrcasccl as oLrr sample periods progressed; you can see how closely this lr1-rproxinrlrtcs thc rcal situation in the diagram.
,\'
r' t t t
t,s'
ttttt
p
Iit
t,q
lirrttt ol'ittstirtttilncous sanrpling in which several individ1rt'etlclet'rttinetl poirrls irr lirrrc lrnd thcir bchavioral states are \('()r('(1. tlr;rl rs. rrrsl;rnllult'olrs r:rrnPlcs;rrr'ltrl\('ll on scvcr';rl irrrlivitlrurls itt tltc srrtnc ,\'(trn.\(unplur,q is sirrrply lr
tt;tls;u("\('irttttt'tl'lrl
SAMPLING METHODS
DATA COLLECTION METHODS
accurate data on a few behaviors time. This allows the observer to record relatively we could use the same sample example, For for a relatively large number of animals. were used in the previous that points in Figure 8.1 for scan sampling all six deer
Table 8.7. Sampling methods and recommended uses State or event
example to instantaneous sample lemale III' locusts' in individual conFor example, in a study of locust feeding behavior, five
and one of four behaviors was tainers, were scan sampled at 10 second intervals, pairs of harp seals were scan sampled scored (Simpson et a|.,1988). Six mother-pup scored in a study of maternal at 30 second intervals, and one of l8 behaviors were grey seals were scan sampled at l0 behavior (Kovacs, 1987). As many as 80 female (Anderson and Harwood' min. intervals to obtain time budgets for l l behaviors is determined by the interval From these examples, it can be seen that sampling
Sampling method
sampling
Recommended uses
l.
Either
Primarily of heuristic value;
Ad lihitum
suggestive; records of rare but
significant events 2.
Sociometric matrix
Event
Asymmetry within dyads
Either
Sequential constraints, percentage
completion -)^
Focal-animal
1985).
time; rates; durations; nearest
of behaviors being recorded' number of individuals being scanned and the number of behaviors cannot be so The number of individuals scanned and the number so short, that all individuals cannot all be easily
neighbor relationships
4. All
large, nor the sampling interval to take the next scan sample' An sampled, with time to spare, before it becomes time as possible' for the longer they observer should attempt to be as instantaneous a series of short focallinger on one individual, the more the sample approximates time spent scanning individuanimal samples of unknown durations. Estimates of scanning the entire group, spent als which are diflicult to observe, as well the time should be made.
One important use centage
of instantaneous and
of trme that individuals
spend
in
occurrences
of
Usually event
Synchrony; rates
Either
Sequential constraints
selected behaviors
5. Sequence 6. One zero 7. Instantaneous
and
Usually state
None
State
Percentage of time; synchrony;
scan
subgroups
Source: From J. Altmann (1914)
scan sampling is to estimate the pervarious activities (i'e' time budgets)'
in different behaviors only
one-zero sampling is effective in determining time spent Simpson,1977), hence it is not under limited conditions (see above; Simpson and
rates and relative frequenrecommended. caution should be used when estimating tell us nothing about the frecies for instantaneous and scan samples, since they and ended' The caution quencies of the behaviors or when each state actually began 'In special case where the the is the same as that expressed for one-zero samples' that no more than one traninterval between instantaneous samples is short enough consecutive samples, the resulting data are essentially
sition can occur between ancl relative frequency cstiequivalent to that of focal-animal sampling for rate estimates' (J' Altmlrnrt' mates, but have a greater margin of error for duration 1974:261).
trigure 8.2 provides a flow chart that will assist in deciding which sampling method is appropriate for your study. J. Altmann (1914) has also provided a table to assist in the selection of the proper sampling method (Table 8.7). Dilferent sampling methods produce diflerent types of data, including different of measurement. Hence, the validity of the research and the statistical tests that can be used will be affected by the sampling method employed. Dunbar (1976) presented an excellent demonstration that the use of various scales
behavioral parameters and sampling methods to answer the same question can lead to different conclusions. His demonstration challenged the internal validity of several methodologies. He estimated'social relationships' among individual gelada bttboons (Tlrcrutpitltecus gelada) divided into I I age-sex classes, using three different be Iritvirrrttl trnits that showed the extent to which individuals l. interactedwitheach otltcr'; 1. ,qrrtotnL'tlcach other; and 3. were in a particular spatialrelationship. I)ittlt wct'c collcclccl in the following seven combinations of behavioral units and
8.3.5 SummarY
s:r
ttscs. Ily l)l'()lx'llv ('()lll Each sampling rnctliocl rlcscribctl irbovc lrirs rccottttttctttlctl
bining lilcill-irninrirl or itll-itttittt:tl sltlttPlitll' witlt ollt'
of
ol tltt' otllt't
tttt'lltotls'
;t
t'llit tt'ttt l'ol tlltl;t t ollt't ltott t'cscltt'cltct'is ltllle ltl tlt;txitttizt'ilt'('tllil('\'' rt'lilrlrrlttY;tttrl lt lr1 lrtrlltt"'t" l("'('illt 11"' ltn(l ('nslttt'lltltt rlllttl tllrlil ill('( ('llt'r'lt'tl lot lt"'lllr|
rtrpl i rrg rrrcl Irorls:
t
I lre tttttttlre
t'ol'sociltl cottlircts l'rclwccrt intlivicluals (i.e. the number of
Ittnt'slltt'l'ittlt'ltt'tt'tl.int'spt't'livt'ol llrt'rrrrnrl'rcrol'sociirlirctscxchangccl ttl ('it( lr tttlr't;rt'llott) (lor';rl ;rrrrrn;rl;rrt,l:tll
,,1'1'111 11'11t'r's
slttttltliDp,).
-z
ii?AFi
tJ
e
co
iE aE;{iE3#
-1
-t
iai
'i:!LeEli=gggaEE :=ii sE9 ig 'gi?q?aaiE
ieEe a?i+H
.i?=ti+78
ii= E*eui*i'I
E
zt,:Zlt* i[
=
Bia E:i[iiiEg :i aE=IE qi $a; :EE;aiegg Ei : g E i i ?lz,ilr" ,i z;l="*t ia,, f€i ;E-tieral i EE : Eg + l F t i *i*lix E: * Ei 11=i'11'=7 i * ii 1?7 3iiesi gE + 7 i i:i,;t ; ii i s =; + s+ = =ii1='r+?, i :-;-= EX ;lc?iI
i
7
T::.: :.:. F/i,1ildrck ' rirh vltidt subjects iteraLted' vith the yurious agt-sex classes os determined in different wal,s ( all subjects ":' ti.,! dt1d. bused ott these data rclative strength of relationship with each qge-sex class as detemified by the dffirent estimators giren Age-sex class of interactee
1,,.;tttnt.r' oI ittteructitttt . \unrber of contacts
I : : : -
\umber of One-zero
interacting
interacting Gr..trming bouts P.rint grooming Spatial measure Pornt
.?. ...iIir r, s trengt
. l.
acts
3y
male
female
2y male
2y
lernale Yearling
Inlant
Total
t47 | I
305
J
I
2
8
ll
J
J
2
l0
l9
l0
J
7
47
34
8
l5
8
t7
34
-1J
J
t7
99
49
ll
39
9
l0
15
15
I
7
44
19
1 J
I
9
I
5
38
l8
7
l6 l5
5
16
2
J
l5
l5
I
7
44
t7
J
16
4
45
1n )t
4
l9
lll
51
l7
40
2t
67
3
0.021
0.043 0.170 0.041 0.278 0.0s6 0.32s 0.056 0.349 0.044 0.333 0.057 0.361 0.046 0.267
0.234 0.210 0.161 0.151
0.064
0.064 0.089
0.043
0.213
0.021
0.047
0.041
0.101
0.006
0.036
0.
128
0.030
0.033
0.003
0.024
0.127 0.132
0.040
0.008
0.000
0.061
0.018
0.026
0.000
0.025
0.13
0.041
0.096
1
One-zero interacting 1. Point interacting -<. Grooming bouts 6. Pornt grooming Spatial measure
0.11I
0.064 0.059 0.
108
l9
0.018 0.01 0
0.008
0.1 19
0.1
0.140
0.079
0.009
0.t23
0.r23
0.008
0.108
0.089
0.010
0.
158
0.139 0.123
r
t14 122
0.033
0.000
0.000
0.051
0.161
0.007
These date werc pooled and treated as though they came from the same individual or sex-age class for the purpose
merhodologies. No conclusions about gelada-baboon behavior should be drawn from the data. Soroce. From Dunbar (1976).
169 126
a
h of relationship
-1.
-.
4y Adult male female
3y
male
3
\umber of contacts 0.064 \umber of acts 0.n2
rrl
o
,_?
5y
--
z z
EE' eEH EEia11gEi
Adult 6y male male
o
of comparing
415
rn -1
a
DATA COLLECTION METHODS
OBSERVER EFFECTS
Sackett (1978). Also. an overview of behavior sampling methods and exarnples
of
211
Error of Apprehending
them being used in a zoo is provided by the videotape program created by Steven Hage and Jill Mellen: Researc'h Methods .for Studying Animal Behavior in o Zoo Setting (available from the Minnesota Zoological Garden, Apple Valley, MN
]
Observer Error Oberver Bias
ssr24).
84 OBSERVER EFFECTS As methods have biases, so do observers, but bias is only one factor that contributes
to observer error (Rosenthal,l976). Observer errors can contribute to a decrease in both reliability and validity; therefore the results are only as good as the observer
Error of Recording
(Kazdin, 1982). Good data mean that tl-rey are an uc'c'urate measurement of the true situation. Observers may be precise; that is, their data will not vary greatly, but because of biases they might not be accurate. For example, consider two riflemen firing at separate targets at a rifle range. Rifleman A groups his five shots closely (good precision)
Computational Error
and in the bull's-eye (good accuracy). Riflernan B groups his five shots closely (good
precision) but to the right of the bull's-eye (poor accuracy). Rifleman B's accuracy is biased by his habit of pulling his rifle slightly to the right while squeezing the trigger. To carry the analogy to completion, we may say that each man's shots were mea-
J
(Anatysis)
Fig'
8'5
Types
->
Results
of observer effects encountered in ethological research (drawing by Dan
Thonrpson).
Error oJ apprehending is due to the physicar arrangement of the animar and/or the it difficurt to observe the behavior. For example, in a study of mockingbird behaviot Breitwisch (l9gg) stated his avoidance observer making
of error of
hending as follows:
appre-
surements of the location of the bull's-eye. Rifleman lt's measurements (shot holes
I did not record wing-flashing, a dispray sometimes given in the of predators and other contexts . . . because
in target) were both reliable (precise) and valid (accurate). Rifleman B's measurements were reliable. but inaccurate. Several potential observer erro[s, which may have severe effects on the results
ethological studies, have been discussed by Rosenthal (1976) and are depicted in Figure 8.5. Observer ef/bct is due to the visual presence of the observer, or other
stimuli (e.g. odor) lrom the observeq and results in a change in the animal's behavior. In psychology and sociology, this change in the subject's behavior is known as the How,thorn e.f.fect (Martin and Bateson, 1986). An indirect observer eflect was measured by Gotmark and Ahlund (1984). They determined wlrether observers attracted predators (crows and gulls) to the nests of common eic'lers (a result that would affect both the reproduction and behavior of the eiders); they tbund that human disturbance did increase loraging ef-fort and suscess lor gr-rlls, but n<.lt firr crows. Examples of direct observer eflects on several species. in both ficltl lrnrl lirhoratory studies, are described in Davis and Ballour
Of course. it
(
1992).
llrirl wc cvcr rlo achievea str-icllir tlrltrrirlistic 1'licttrrc rll'bclrrvtor'.'lo tlo so rvotrltl rr'tlrrirr' is shcer nretaphysical conccit to clirirrr
ilccuis lo pltcrtontcl)ir 'irs lltey;uc.''l'lte lrt'st \\,'('(':rrr
birds wing-flashing in the crowns of nest
of
presence
I courd not reliably observe
trees.
IBreirttist,h, rgss.63J
err.r is caused by many factors, incruding
observer
inexperience and poorry defined behavioral units. observer error also includes observer .drift,and ,decay, defined by Hollenbeck ( 1978:96) as follows: 'Drift refers to the ntrventent o./ urt observer in time from some base point either in a positive or negative direction. Deoay' on the other hand, implies that rlte instrument, which includes the observer and the scoring categories' is drifting beyond the bounds of acceptable measurement error.' ob's'arvcr Btrt'r is principally related to expectancies ofl the observer. Biases may be cottsistent or inconsistent and either high or low. Rosenthal and Rubin (197g)
provitlc it thorottgh review of this bias, and Arber (19g5) addresses it from the .l'lltc philosophy of science. Following his study of mountain sheep,
stittttlp.ittt ( icist ( 197
I)
srrrtctilris co,cern about this bias as rorows:
ll.rv c.,rrrtr I. lhc si,ric pcrs... scparerte the process of data gathering l)ottt :ttt.y'lltotrgilll I rrriglrl lrlrvc lrhotrt th.sc cl.t,,/As the lclne lll\('sli;';tlot I lrlrtl t() t('('ottl olrst'r \';tliotts rtrtrt sitttttll;ptc
;tlro111
tlrr..rlr..t.r\(.(l(.\t.nl., rrr orrh.r l() t(.(.(,1,rrizt.tt.l:rtiglslti;ts..l.ltis is tt
R
DATA COLLECTION METHODS
Veryimportantproblem,forifdatagatlrerirrgisnotclearlyseparated 1971"'rii] out, it
can be biased by some subconscious
preference'
IGei'st'
prophesies' and to strengthen consee them come true' In
personal ln truth. research hypotheses are really of us would like to most fldence in our ethological insight
observer bias to
experimentsbyusingablinclprocedure.Themanipulationsaremadebyone to
the meaSurements 'blind'to the treatment researcher, and a diflerent person takes Martin and Bateson (1986, 1993) provide a which each animal has been expose
experiments' good overview of the effectiveness of blind
few observers are likely to have the same biases. An accuracy criterion can be established by using an 'expert' observer (e.g. the principal investigator). or a 'calibrating'
observer (Kratochwill and Wetzel, 1977), or the consensus
of several
observers.
8.s.1 Intra-observer or self-reliability Observers do not see and hear all they would like to during each observation. Many times in the field, after having observed an unusual behavior, I have muttered 'I wish I could see that again'. We rarely have that opportunity. Only
if
we have captured
it
on videotape or film are we afforded the privilege of seeing it again (then only in two
Errort|.recorrlinlimaybeduetopoortechniquesandequipment,mentallapses have chosen to use a separate
in the observer, and inexperience. Some researchers and et ttl.' 1988): depending on the abilities observer and recorder (e'g. Simpson of error the decrease or of both, this proceclure could either increase experience
recording. manipulation and analysis of data' It is Contputational errttris due to incorrect
theerrorthatresultsifaninappropriatestatisticaltestisuse
dimensions), and few observers are able
to
videotape
all their
observations.
Therefore. we must be as efficient and accurate as possible in recording behavior as
it occurs. Experience is the most inrportant factor in increasing efficiency and accuracy and is necessary with the following aspects of allethological studies:
t Observing animals, in general z Observing individuals of the species to be studied : The behavioral units to be measured + The sampling methods to be employed s The data recording methods and equipment
to be used
Intra-observer reliability can be measured by videotaping or filming a segment of the animal's behavior which is under investigation and then observing the same
RELIABILITY
and scale of measurement' states or events' Selection of the proper behavior unit, 6)' study's reliability and validity (chapter sampling method will all affect your much how is' That of the measurements' Retiabitityrefers to the reproducibility relia' (inffu-observer obtain very similar data again can we rely on our own ability to replito studies' shoulcl also be able bitity)lOther observers, in this sturly or future reliuhilitl'' we should have good inter-observer cate our measureflents; that means
Thisis,ofcourse,oftendifficult,sinceskillinobservationdevelopsthroughprac-
is not only a general skill (gotld ethologist)' tice. Accurate and precise observation ethologist or good acottsticitl-ctrntnrtttribut also a specific skill (e.g. good butterfly
cation ethologist) Hollenbeck(1978)cor-rcludedthatreliabilityctlnsts
accuracy will almost certainly aflect inter-ob,sat't,er rcliuhility (Kazdin, 1982), since
Measurements of reliability are discussed below.
addition.Bell(|g1g)hasarguedthathumansareirrherentlymore.subjective,than reduce .objective'. we must guard against these basic human traits if we are to manipulation in controlled an acceptable level. This bias can be
8.5
ELIABI LITY
;ts
tlf both st^bilitv
rttttl ltcctt-
racy'However,thisistrueonlyofinter-tlbsct.vcrrclilrhility.Aswltstliscttssctlcltt.licl lle tt,littlllt,bll (p.210).aslong,,tsllrcci,titttr(stirhilily)istttltitttltittctl'ltlltrl.rsct.,''et'lr,ill tt'lItlriltl\ ts so[t'lv il llt('il\lll(' ()l rnight still hc inttt.t.rrrttlt,.'l'lrc|clir|c. irrl,,r-olrst'rVt'r llllt't l: r;tlttltll (('lr'rItt't (r) Il.\\t'\t'l st:rhility (.r. 1,r.r.rsi.n). rrltt'tt'lts il('('lllil( \'
segment several dillerent tirnes at varying intervals. Your perception of the behav-
ior, and the data you record, should not differ whether you see the segment three times in succession or at two-day intervals. We all question the accuracy of our own observations and are prone to attempt improvement. However, strive lbr objectivity and stability; do not redefine behavior units as you proceed, unless you are willing to
start the rcscarch over again. Be careful not to overlay interpretations on your observatiorls as you record data. This is often very difficult; but every observer must
check thc tenrlency to see the behavior
of animals in light of the observer's own of your data and the
cxpericrtccs (sce ('hupter 3). Continually assess the validity
lrchirvitrntl urtits you hitve selected. However, do not redesign your reseorch in mid\lr((tt)t willrrttrl t'ttt'rf trl t'otr.vidcruliort ttf' tlte consequen('es. Search for and acknowlctlgc wcrrkr)csscs irr yorrr stutly. such us irn inability to idcntif y inciividuals.
Mrtttv (y1rcs ol'sltttly. pltrlit'rtlrrt'lv ol'sot'ilrl bclrin'ior. rrcccssitatc lhc rct'tt,qttilirtn rtl ittrliyitlttttl rtrtirtrrtl:. Attl'krttl,ittrtlttt:rlslrrrll'ol ;1 1's,1111r ol'lrrrirrr:rls is strcrrglltcrtctl
llV lt',,11'tttltott (rl ln(ltttrltt,tl',
I lrr',
,,rn lr,' ,t',',t'.lr'tl l,\' trt:rtktrrl' trrrlirrrlrlrls (tlis
R
ELIABI LITY
DATA COLLECTION METHODS same. This can also assist you
(e.g.
individual variation in morphology cussed later in this chapter) or by relying on walks with a limp)' D'K' Scott's reliabilantler configuration) and/or behavior (e.g. of the social behavior of Bewick's swans
in detecting observer efl-ects as a source of aberrant
data.
ity in identifying individuals in her study
(1971)' More than 100 (Cygnus coluntbianus bewickii) was measured by Bateson were photographed in color by individual swans were identified by Scott as they and shown to Scott l4 days lateq at Bateson. The 30 clearest slides were selected She then correctly identified 23 which time she correctly identified 29 of the 30' test demonstraterJ that Scott could reliswans from 30 inf-erior photographs. This and provided increased credibility ably identify a large number of individual swans
individual swans' to her claim that she could identify some 450
For example, suppose that four observers are collecting instantaneous samples (every 20 seconds) of behavior of zebra stallions living in small family groups in central Africa. They are interested in developing a time budget for the following behaviors: feeding (F), resting (R), grooming (G), walking (W), agonistic (intragroup: A,intergroup: Ar).In order to measure inter-observer reliability they decide to all collect data for the same l0-minute period (30 samples) on the same zebra stal-
lion (focalanimal). The hypothetical results are presented in Table 8.9. The four observers recorded the same behaviors only 60"1' (18/30) of the time. However, Observers l. 2 and 4 recorded the same behaviors 93"1' (28130) of the time, and Observers 1 and 4 had 100'2, reliability. Therefore, the team as a whole was not very reliable (60"1'), and Observer 3 was the most unreliable. Observer 3 should not
8.s.2 Inter-observer reliability
be allowed to collect data. and efforts should be made to increase his or her reliabil-
...thegreatestSourceoferrorsinusingobservationsmadebyothersis see the same thing' that no two people who look at the same thing
ILorenz,
1935:93
]
perceptions of the same Lorenzwas referring to basic differences in observers' learnecltraits' For example' poor eyebehavior that are a result of both inheritecl and
ity to )>95'Yu. The reliability ol 93'th of Observers l, 2 and 4 is probably acceptable, but attempts should be made to improve it. However, reliability )901'l' may be the most we can hope for in field studies, due primarily to error of apprehending. Haihnan (1911) tested the elfect of stimulus preference on the begging response hatched laughing-gull (Larus utit'illa) chicks. Most data were collected
of newly
sightandhearingmaybeinherited'buttrainirrgwillprovicleanclbserverwithtlre
simultaneously by two observers, and inter-observer reliability was checked using
ability to use his senses with increased effectiveness' to observer effects also affect Several factors mentioned above as contributing
'percentage agreement'. In another experiment, Hailman tested all the chicks, and
inter-observer reliabilitY
:
.ErroroJ,apprehettding:Forexample.caneachobserverSeeandhearthe
.
animal(s) equallY well? units clearly defined tbr all observer error'.For example, are the behavior poor intra-observer reliability will be reflected in poor interobservers?
.
observer reliabilitY' haive preconceptlons observer hias: For example, do one. or all, observers diff'erent situtain perform about what behavior the animal is likely to tions?
.Errortt,rec,ording:Forexample,iseachobserverrecordingdatainthe same manner?
Poorinter-observerreliabilityisafrequentprobleminfieldstuclics.wltcrccl.ll)rS observer ell'ects citrt rtsttitlly bc of apprehending are difficult to overcorne. The other experience' reduced to a minimum through training and pcriotlicirlly iit ot'tlcl' to ittstrtc nrcasurcrl bc shoulrl Inter-observer reliahility llleilstlle irl tlrc sttrtly its lt wltolc. A sirrlPlc' litsl :tpptrtxilttlttiott
irnrlvalitlity tltist'lttlttlslltkittl';t slttttplt'ol trl'itrtcr.-rllrsclvcl'r'clilrlrrlityis;rr'lt'r'lll(t!lt'ttt',t't'('ttlr'lll ollst,tt.;tlt()ttstt|lttlt.lrv;r!l,,lrrr.tvr.t\illl(lrlr'lt'ttlltllllll'lltr'P('l(t'lll;ll'('\\ltttlr'rtt'lltt'
accrlr-trcy
then another observer tested the same chicks while Hailman was not present. The results showed perfect rank-order agreement with regard to stimulus orientation; but the repeated testing had suppressed the response rates.
of inter-observer reliability can be used to measure bias in interexperimenter reliability, as was Hailman's objective (above). However, to measure Successive tests
inter-observer reliability best the observers must
see the same
taneously in real-time or by videotape or film.
If
behavior, either simul-
videotapes or films are used,
observers viewing the behavior in these media should not be tested against those
viewing the live behavior. Simply put. inter-observer reliability can be accurately rneasured only
if everything
is the same except the observer.
Sevcral eruthors have cautioned that 'percentage agreement'does not provide an
rrccuratc rneAsure of inter-reliability since it includes agreement that could have rrcctrrrctl by cltitttcc alone (e.g. Sackett et al..1978; Hollenbeck, 1978; Kratochwill
l()ll. Aclanrs and MacDonald, 1987). In addition, Hollenbeck listed lirrrr lirrllrcr wcrrkncsscs ol' 'percentage agreement' (provided by D.P. Hartmann, l')7.))rrntlcorrclrrrletl lhirt il slroukl not bc uscd at all. M,,11'lrt't rrrrlt'n)rilsures ol'inle r'-oltscr'\'cr reliirbility irrc correlrttion coefficients, rrf rrt lr rt'llt't l lltt' tlt'1'11'1' ol ;t,'lct'tttt'ttl lrt'ltt't'r'll oltscl'vcts rvlrich is ultttt't' tltuncc
:rrrtl Wclzcl.
l
l
DATA COLLECTION METHODS
RELIABI LITY
Table 8.9. Hypothetical re.sult,; of ./bur ohservers collec'ting in,stantaneous santples of .feeding (F ), resting f Ri, grooming (G ), wulking (W ) and agonistic behavior
(intragroup A,, intergroup Ar) Jiom u single zebru stullion (/ocul uninrul) Same observations
Observer
Instantaneous
observers
sample
1,2,3,4,
1.2,4
r
ir
.V
"'(
I
F
F
F
F
2
F
F
-1
F F
F
W
F F
4
G
G
G
G
,Y
5
G
G
G
G
r
6
G
G
R
G
x
7
F F
F
F
-x
W
9
F F F
W
W
F F
10
F
F
W
F
ll
G
G
G
G
"Y
-Y
8
.Y
r
t2
G
G
G
G
r
-Y
W
W
W
W
x
r
t4 l5
Al Al
A2
A2
A.l
A2
16
A1
Al
A.
Al Al Al
l7
G
G
G
G
-Y
l8 t9
Al
Al
Al
Al
,Y
F
F
F
F
.Y
20
W
W
W
2t
F
F
F F
F
r
22
G
G
G
G
-Y
W
F
F
F
F
F F F
R
F
26
R
R
R
R
27
R
R
F
R
28
R
R
R
R
\
29
F
F li
F'
t;
\'
24
F F
25
30
Ttrtltls
t;
J;
t:
The phi coefficient can be used to measure correlations between two sets of two nominal variables, which are cast into a 2x2 matrix in the format below. It varies
from 0 when the observers'scores are statistically independent to complete agreement.
1
when thev are in
t.4
A
B
A+B
C
D
C+D
A+C
B+D
,Y
13
23
t.S.2a Phi cofficient
phi:
v[(A+B)--LD-BL (C+D) (A+C) (B+D)]
lf
there is complete agreement, the B and
Phi:
l.
AD
For example, from the crata in Table g.9, we rnight suspect that the major probrem
observer 3 is having is in determining whether the zebrais feeding (F) or walking (w)' We use observer I as our calibration observer and compile their data fiom each instantaneous sample into the matrix below. It shows the number of times they both scored the behavior as F or both scored it as w, and the number of times observer 3 scored w when observer l scored F and vice versa.
Observer
3
w F
8
I
w
l'lri
5
A
B
C
D
*C: *C:9 B*D:6
s5 \
A *B: l3
C+ D:2
I
A
l:{
cells are zero so phi becomes
VrnxDxAaDJ -l
Observer
\
c
l( I r)(2)1t1;
3 161;
r7. s
-o
ot{
I ltt:l't'rV lr*' l'lrrt'.t'rlit it'rr (r('rr()rsrl;rrt.s rrr;rr ( )rrst,r't.r I lr;rtl lnnun,' rrlrr'tlrr'r llrt.zr,lrr;r ..1;rllrr)n \\it,, rr,rlkrn1,o1 11.1.1111;;,
rr
rlillictrlt tirnc tlclcr-
|, i
ELIABILITY
R
DATA COLLECTION METHODS
p.:(0.43 x0.47) + (0. 10x0.10)+(0.23 x0.23)+ (0. 10x0.07) +(0. l0x 0. l 3) + (0.02 x0.00)
Table8,|O.Anagreementmatrix.forobservers]and2inTable8.9
=(0.20)+ (0.01) +(0.0s) + (0.01) +(0.01)+ (0.00) Observer G
W
Al
A2
Proportion of total for observer 2 (P.,)
0
0
0
0
l3/30 = 0.43
0
0
0
3/30 = 0.10
0
0
0
7130
2
0
0
0
-)
0
3/30 = 0.10 3/30 = 0.10
0
I
0
1/30 = 0.03
1
Behavior
F
codesl
13 0 030 007 100 000 000
F R G
Observer
2
W
Al A2
Proportion of total lor
= 0.28
(0.93-0.28) : rcapRa:
0.65
11_g:g) on:o.go
The kappa value of 0.90 is very close to the simple'percentage agreement'which
= 0.23
is 0.93 (see above); however, note that by taking chance agreement into account kappa is lower. What is an acceptable kappa coefficient? Fleiss (1981) considers a kappa of 0.60 0.75 as good and >0.75 as excellent, but Bakeman and Gottman (1986) are inclined to be concerned with a kappa less than 0.7. Bakeman and Gottman ( 1986) illustrate how to calculate whether a kappa coefficient differs sig-
nificantly from zero (based on Fleiss et ul..1969).
o.4l 0.10 0.23 0.07 0.13 0.00
observer I (Pl)
8.5.2c Pearson product moment correlation
Notes: I See Table 8.9 for details of codes'
8.s.2b KaPPa
which is the percentage agreement Kappa is a statistic developed by Cohen (1960) 1987). MacDonald, corrected for chance agreement (Adams and 1P,,
-
P.)
Another measure of inter-observer reliability that has been used is the Pearson product moment correlation coefficient. This will rneasure the correlation between two sets (e.g. two observers) of interval or ratio data (described in Chapter 8). However, Hoffman (1987) used this correlation coefficient to measure the interobserver reliability of two observers lor recording number of lunges (nominal data) and duration of encounters (ratio data) in fruit flies (Drosophila melanogaster) (Table 8.1 l). B.s.2d Kendall's coefficient
napPa:11 _ 41
cofficient
of
concordance
Kendall's Coelficient of Concordance (W) was developed by Kendall (1948), and is
where: P,,:observed proportion of agreements P.:chance of proportion of agreements
used to measure the correlation between ranked variables (ordinal data). cussed in C--hapter
l4
as a
It
is dis-
statistical test for data analysis, but it is also described here
shows agreement on the diagoKappa is derived from an agreement matrix which
fbr use in measuring inter-observer reliability. It can be used when several observers
nal.Asanexample,wewillmeasuretheinter-observerreliabilitybetweenObservers
have ranked behaviors with regard to some parameteq such as frequency, duration
I
or intensity.
in Table 8' 10' The diagonal in and 2 in Table 8.9; their agreement matrix is shown
between the two gbservers tbr the table is the number of occurrences of agreement
H',-
each behavior.
whcr-c:
/,, l,
(P, X
/")
)
R tr
/l\
\[/
tvltt'tt'. /i
( ltlr clrclr l'rcllltviot (e .f . lr) tt't'ottletl lrv )llst'rr''t'r I' lllt' l)t()l)()ltl()ll tttt l' l.l;rl rrrrrrrlrt.r pl lrt'lr;rr rrrt;tl lrt ls t('( (,lrlt'tl llt:rl rrt'lt'lrt'ltltr ( )lr',('l\('l llrt..,,rrrr,.;r,,;t1,,rrt. lrill lot
_
I .'^ lrt tNr-lvy
entries:4:0.93 ,, _sum of diagonal n-totul ' number of entries 30 P.:
;1R,
()l tll('
/i
tttr'rur srtnt
ltttk
:/('
ol tlrr.'lrrrks lot t';tt'lt llt'lt;t'v'iot
tttttttlrt't ol ltt'lt;tVtot t';tlt'l,ot A
tttttttlr,'t oI oIr',r't t r'l',
tt':.
t'lrlr'grl'y:rct'():is tlhscrvcrs
IDE,NTIFICATION AND NAMIN(; OI.' INI)IVIDUALS
DATA COLLECTION METHODS
Table 8.11. Reliabilitie.; /or tluratiort o.f'encounter und nuntb e r rf' lun ge s : t' r t r r e la t i on c' o e.ffi t' e n I's b e t we e n en('ounters in a D. melanogaster
triul
Table 8.12. Data./ront Table 8.9 organized in an ordinul.frtrmat./or the calc'ulation
of Kendall's W,vtat
ist ic'
,scored twic'e by
Behavior code
ttt,o obserl,ers
Al
Observer
A)
Second time scored
Observer
First time scored Durat ion
I
Observer 2
o.f' sequenc' e
1
4
2
5
-1
6
2
1
4*
2
4
4
6
-)
I
4
2.5
2.5
6
5
1
4
2
5
-)
6
16
23
I
1.000
0.997
4
Observer 2
0.991
0.999
fi-
I
0.992
0.992
Note:
Observer 2
0.993
0.994
Observer
a
I
t6
4
8.5
16.5
Number o.f'lwrye,t
Observer
x The value 4 is given
to behaviors R, W and Al lor observer 2, since he observed
each three times. This is the average lor ranks 3, 4 and 5.
Notes:
Coefficients are based on scores for 1 l6 encounters. Sourt'e: From Hoffman (1987). Copyrighted by
the researcli questions as validly as possible and transmit that information to others
Bailliere Tindall.
as accureltely as possible. Tl-rese objectives can be met
As an example, the data from all four observers in Table 8.7 have been treated in ordinal format by ranking the behaviors in order of frequency of occurrence as recorded by each observer (Table 8. l2)
1
:225.7
(l/12)$)1(216-6)
sance observation stage, is that the observer be able to recognize individuals. In par-
ior of animals, it
: - I 0)2 + (2)r + ( - 5. 5)2 + (2. 5)r + (2)r + (9)2 : ( 100) +(4) + (30.2) + (6.2s)+ (4) + (8 I )
2251
A necessary prerequisite for many ethological studies, beyond the initial reconnais-
of time) make individualrecognition necessary. As more
R- N6 Rlz
IDENTIFICATION AND NAMING OF INDIVIDUALS
ticular. longitudinal studies (i.e. studies of individuals or groups over long periods
N:6 _ >R,': g4 :14 (R,-
8.6
only if your data are reliable.
is learned about the behav-
becornes increasingly clear that generalizations are difficult to
rnake. Early naturalists talked about the behavior
of
species. We
now know that
there are ollen major differences between populations, social units and individuals.
Ilence. ethological research is building from knowledge gained from studies of various individuals and groups and attempting to produce limited generalizations.
22s.7 280
tt.6.r Naturalmarks
The calculated coefficient ( W:0.80) indicates relatively low inter-obscrvcr rcliablity between the lbur observers when their data are ratrk ordcl'ccl itccortlittg to ll'c-
lhc bcst silrurtion itn ethologist can
quency of occurrence.
Io bc sttrtlying
Reliability (intra- and inter-obscrver)can bc ntcitsttrctl lirt'rtgl'cctttcttl ol'heltrtviclr recorded lor each institntitncrltts stuttltlc. l-r'ct;ttettt'ics. tlttl;tliotts. l;llr'ttt'it's lttttl
pr
sequollccs.ttsittg()llc()l'llltll'col'lltt'tttt'ltsrttt'stl'.'st'lilrt'tl;tlrovt"llvr)tlll('\('ill(ll iltvrllvCs tt|rtt'r'llItn 0ilt'ollst'tlt't. lt'lttr'lttllr't llt;tl \r)lll lllill()t ()l)l('( ll\('l\ l() illl\\\('l
l
s1'rccics
have
with regard to individual identification
is
in lvhich morphological diff-erences are sufficiently great to
ovirle e;rsy itlcrtt ilic:rl iolt. lrr sornt'r'lrsr's tlil'li'r'('n('('s rrre ohv'iorrs l() cvcn tlte citstutl tlbscrvcr. Thcse can be
llrt'rt'srrll ol rt;rlrrr;rl rrlrlil;rlrorrs ()r nrulirliorl5 11'.1' lrrtlkt'tr lrrlrrrs. rrrclitrtislic grottrttl ',(ltlllt('1") llottr'rt't. lltr".t'l\Pt", ol ttt,ttl.ttt1,', ittr'|1'11l''',tll\'ltol srtllir'it'tttlV wirle-
,O -(
,/ 2
_/\
Nick
IDENTIFICATION AND NAMIN(; ()I; INDIVIDUALS
M ETHODS
DATA COLLECTION
,/7
RN
I
CF
,f< 4)\
/L /r New
DN
RN
Nip
Moon
-/(
2
\ NC
N.N
\ )\ Smooth
BF
__I\ cFc I
t\ )\
Sm
LSN
Sm Flag
Nip
8.6 A sample of 24 fine variations in dorsal fins lound within
^
the pattern of vibrissa spots, which lie in four to five parallel rows between the upper
lip and nose (Figure 8.7A). The individual vibrissa-spot patterns were recorded in the field on schematic proflle sheets. Figure 8.7B shows the schematic profile of an individual with its pattern of vibrissa spots and nick in the left ear. Note also that the vibrissa-spot patterns are not the same on both sides. Photos of each side of the lion's head were 8.78) drawn in the field.
,/(
A
/{ Fig.
ows
,a7
\ \
,/ '-4t* -\-
a
population of
bottlenose porpoises. Lines within the fin boundaries represent light pigment spots or scar tissue (from Wursig and Wursig, 1971)'
The basis for individual identification was the number and position of the vibrissa spots in Rows A and B (Figure 8.7A). Decisions were made whether the spots
in Row A were above or between spots in Row B. This pattern was then transferred to a schematic version (Figure 8.7C). The spots in Row B were numbered consecutively from anterior to posterior. All the possible positions for spots in Row A (above and between spots in Row B) were provided in the diagram. With continued practice Rudnai was able to use this method of individual identification with ease and confidence. This same method has been used by Patty Moehlman (pers. commun.) as an aid in identifying individual black-backed jackals (Canis rllesontelas).
Pennycuick and Rudnai (1970) also developed a method to test the reliability of identifying individual lions in this manner. Their method was based on an estimate of the probability that two individuals would have markings that could not be distinguished. They began by determining the probability of occurrence of the different patterns in the population studied. Spots in Row A have never been observed in positions other than I through 13. Regardless of the physical characteristics selected for use in identifying individuals, photographs of each individual are valuable as a continuing'fleld guide'(Figure 8.8). Pennycuiok
spread in a population to allow large numbers of individuals to be recognized. Many species provide sufficient individual variation for easy recognition, e.g.. lacial patterns in oryx (Saiz , l9l5);stripe patterns in zebras (Eguus grunti) ( Klingel. 1965);
pelage patterns and scars in grey seals (Halichocrus 54r1'us) (Anclcrstltl :ttlcl Harwood, 1985); wing-tip patterns of kittiwakes (Tinbergen, 1974); spots ort crtclt (it'ttl'rcr'. side of the body of bonnethearJ sharks (Sphyruu tihuro\ (Myrbcrg ittltl 1914). Variations in the dorsal fins rll'bottlcllttsc pol'pttiscs ('l'trt"tirtlt't lrrrttttrlrttl (Figure 8.6) allowc{ Wursig tntl Wtn'sip-(l977) lo itlentil'y 51 irrtlivitltrrrls rltrtitrll 1166llr slrrtly rll'p,rrrrrlt t'prlltosiliott. lltc srttttr'irt,lrvi,lrr:rl t'll;tt;tt'lt'ttrltts wet'r'ttsr'tl ltv ('ottttot ;ttttl Stttolkt'r (l()S\)
thcir
2l
(Punthera leo) based on variations in
blown up and the vibrissa-spot patterns were checked against the schematic (Figure
TS
WR
n Fuzzy
2
_/'\
Fuzzy's calf
of individual identification for African lions
\
K- n
\o.t
is often necessary for the ethologist to spend considerable time in order to
solve the problem of individual identification. Rudnai (1973) developed a method
)\ LF
_-_/\
New
It
\
(
1978) provides an overview
of identiflcation
using natural mark-
ings.
t.6.2 Capturc and marking Wlrcn rrirtrrrirl nrarkings ure not available or when individuals are observed only r';rrcly (c.g. Irorrrirrg.lrntl rnigration studies), the researcher must mark the animals in
s()r))r'w;lv. Ilrrs gcrrcnrlly rtcccssilirtcs capturc of tlre itnimal. although some technl(lu('\ llrvc lrr't'n tlr'r'elopr'tl lirt'tturrking;rt lr tlisllrncc (c.g. tlye clarts) ancl for selfttt:r r k ttrl' lr1' I lrr' ;tttunlrl.
IDENTIFICATION AND NAMING
DATA COLLECTION METHODS
224
O}T
INDIVIDUALS
225
A
Row Row
B
C
Row D
B
\a c LEFT Row A Row
B
ffi RIGHT Row A Bow
Fig.
8.7 A Profile ol
B
a lion's face, showing the vibrissa pattern. Row B is usctl as a scalc
for recording the positions of the spots in Row A. B. Schcnralic profilcs. on lhc form used for recording in the field. The lirll-lircc outliuc is rrsctl to rcconl strclr imperfections as nicks in thc cars. Thc lion's scr, irl.lprrrrinrirlc lrgc. lrrrrl prirlt'(il any) arc rccordcd on tltc sirtttc shcct. ('. Sclrcrrrirtic lcl)r'c\cr)lirtiorr ol llrt's1rol pirtlcnr sltowrt irr A.'l'lrc Posiliorrs irr llow ll rrrt'lrv rlclirrrlr()n (()n\('( ulnr'lt lrllt',1 strrltirrl'. r.villr No I, rrlllrorrl,lr llrr'lol:rl rrrrrrrlrt'r rr r;rrr;rlrlt'(,rtl,r|lr'rl lrorrr l't'ttlttt rrtr'k lttttl l(tttln,tt l() /())
lrig. li
li
('itt'los Mc.jia with photos of individual giraffes for identification in the field (ll'orrr Moss. 1975).
i
IDENTIFICATION AND NAMIN(i OI, INDIVIDUALS
DATA COLLEC]TION METHODS
The overiding factors when considering various capture techniques is humaneness and efficiency; efficiency reflects elfectiveness relative to expenditure in time and
Marking individual, nocturnal animals lor observations after dark may pose special problems. Some animals will habituate to visible lights, natural marks or marks designed tbr daylight observation can be used (Hill and Clayton, 1985). Often, however, observations of nocturnal animals require that night-vison devices
money.
be used (Chapter 4). or special markers be attached to the animals.
The various capture techniques available for the numerous species studied by ethologists are too numerous to discuss here. but excellent reviews are available. young (1g75\ provides a general guide to capturing wild animals. The Canadian Wildlife Service and US Fish and Wildlif'e Service published a manual which
A marker that allows the observer to follow visually the nocturnal movements of individual small rodents is the betalight. These are sealed glass capsules coated
8.6.2a Capture
includes capture methods for birds (Anonymous, 1977). Techniques for capturing birds and bats are reviewed by Bub et al. (1991), and techniques for birds and mammals are reviewed by Day et a/. (1980). The use of drugs in the capture and restraint of animals was reviewed by Harthoorn (1976). Some marking techniques require that animals be recaptured lor identification or remarking. Recapturing using the same technique can be difficult for some species, especially so for 'trap shy'individuals. For example, our experience trapping gray jays (Perisorius c,turuclensis) has demonstrated that individuals are diflicult to capture a second time in Potter traps, but they are relatively easily recaptured using mist nets. Mech et al. (1984) developed a radio-transmitter collar for wolves that contained a radio-triggered anesthetic dart which allowed the animal to be easily recaptured for measurements of the animal or repair to the collar. Capture. recapture, han
as
practical implications, such as changes in behavior (see below and Chapter 9).
8.6.2h
Morking
internally with phosphor and filled with tritium gas, which emits low-energy beta particles causing the phosphor to glow. They can be obtained in disc or tube shapes ranging in length from approximately 6.5 to 76 mm (Figure 8.9A). Available in a wide variety of colors, they are being used to a limited extent on small mammals, especially in conjunction with binoculars or starlight scopes (O.J. Reichman, pers.
commun.). Davey et al. (1980) used betalights for individual identification of rabbits. Betalights are available through Saunders-Roe, Ltd., Middlesex, United Kingdom, but can only be used under a permit from the Nuclear Regulatory Commission in Washington, DC. Also, Wolcott (1911) describes a miniaturized optical telemetry transmitter that he field tested on nocturnal crabs; it produces accurately timed flashes that can be seen for several hundred meters with a night vision scope. Batchelor and McMillan ( 1980) developed a collar with light-emitting diodes (LEDs) for their study of wallabies (Petrogale penicillara penic'illata), the LEDs could be programmed to give individually identiflable flashes, and the flasher switched olf during the day to conserve battery power. Lemen and Freeman (1985)
of using low toxicity fluorescent pigments (red, orange and green) on small mammals. They found that when illuminated with ultraviolet light the animals could be easily located and their movements traced up to 900 m. A wide variety ot' markers designed lor nocturnal observations are reviewed by Hill and tested the efficacy
Clayton (1985).
Various types of markers lor animals have been developed for dilferent species and purposes (e.g. dyes, leg bands, ear tags, collars, and radio transmitters, see p. 313).
Coulson and Wooller (1984) individually marked kittiwakes with different amounts of 60cobalt (radioactive) sealed into aluminum leg bands; they used Geiger
The following are a few examples of the diversity of techniques employed: Milinski
tubes connected to a ratemeter to measure the amount
(1984) marked individual stickleback fish by tying white ancl/or black conical' plastic cylinders to their first or second dorsal spine. Kovacs (1987) individually marke
nramntals are discussed by Linn (1978).
of radiation, which then identifiecl the individual kittiwake. Radioactive marking techniques for small Thc type of marking to be used should be determined after considering the fol-
flipper, and 16l of these pups were also dye-rnarked with inclivicltrally tlistirrct color patterns. Howard (1988) gluecl numbered tags to thc hcads ol' Attrcl'icittt loittls.
lowiltg l)tctors:
Fagerstone and Johns ( 1987) describe a microcltip tritttspotttlcr which plor,'itlctl lt l0 digit cocle lbr identifying t'crrcts (lllttslcltt lttrlot'itt,: firrrt. rt1. tti.qt iltt',tl.lrollt itt-ltrttttl
I '
()l llttirttltls rrs srtt:tll run6 rcntrt(cly. Microchip inrpllrrrts lor irrrlil'itlulrl itletrtilit';rtir)tl SPttttl'Vrrllt'l '\r't" Wt'sl .'\\ Stslt'llls. :rs rtricc itt'c rtr':til;thlr'l't rrttt llio Mt'rlit'l):ttlt Mltvu.rorl. N l
N rrrrrhcr
ol' inrlivicluals to be iclentified.
l)is(;ur('('()ve i'wlriclr irlcntilicrrlion is rtcccssitry;marking should beconsl)r('u()us t'rrorrglr to
111
,kc irrrlir,'itltrrrl itlcnliliclttiott casy litr the
t('\('iut'lrt'rsllrrl rrol sot'\'t'r'ltltlrrrl'.;tntl ;'lrtisltltslobct'cscttlctl bythc pttlrltt (t'1' l'r'lLrr Sr'tt', r'l,r/ l()S''l
i
IDENTIFICATION AND NAMIN(i OI.' INI)IVIDUALS
DATA COLLECTION METHODS
Length of time identification is necessary.
.,
4
Elfects the marker might have on resultant behavior, even though some studies have found no effects; for example, water voles (Leuze, 1980),
lions (Orford et a|.,1989), cheetahs (Laurenson and Caro, 1994). and polar bears (Ramsay and Stirling, 1986).
of capture.
5
Ease
6
Effect capture might have on the animal or group.
A volume on animal marking, edited by Stonehouse ( 1978), contains chapters by
different authors on marking a variety of animal groups including: zoo animals (Ashton), laboratory animals (Lane-Petter), mammals (Twigg), birds (Patterson), reptiles (Swingland), snakes (Spellerberg and Prestt), seals (Summers and Witthames), whales (Brown), bats (Stebbings), fish (Laird), and invertebrates (Southwood). Marking techniques for mammals, birds, and reptiles are reviewed by Day et al. (1980). Bird banding techniques are provided by Anonymous (1991) and Bub er al. (1991), and an annotated bibliography of bird marking techniques has been prepared by Marion and Shamis (1977). Emery and Wydoski (1987) provide
an indexed bibliography of marking techniques for aquatic animals. Walker and Wineriter ( 198 I ) present an excellent summary of marking techniques for insects. In addition, technical reports and papers on particular species often contain methods of marking developed lor that species. The number of unique combinations of individual marks available with various schemes can be calculated using the following formula (also see Walker and Wineriter, 1981): a/
lt
nl'
'i'^ k! g-
k1t'
where: N:number of unique combinations available; that is, the number of individuals that can be uniquely marked.
n:number of different positions on the animal where a mark will
k:
be placed
number of positions that will be marked on each individual
For cx:rr.ttple. we select six positions where we can put a black mark on a mouse (thrcc positions along each side), and we decide to mark two positions each time. 'l'hc ntrnrbcr ol'nricc we can individually mark is: Fig.
(Pcronrt"s;u,s lcut'ttpu,t) (photo hy M1r'k Stromberg). B. Numbered-tag l0 attachctl to thc bitck ol'rl wilsp (l'ltiltrtrthrtt hit'inetus) (photo by Darryl Ciwynnc).
8.9 A. Betalight attached to mouse
,ry (';
6!
)! 1rr lt!
15
( l()(rl() tlt'r,'ist'tl lr t'onrprrtct' l)l()g.l'ilt)t
lirr
ge
ncrating indi-
DATA COLLECTION METHODS
IDENTIFICATION AND NAMING ()I; INI)IVIDUALS
Table 8.13. Relative advantages and disadvantages
d natural marks
and artific'ial
ntarkers Type of
mark
Natural
Artificial
Advantages
Disadvantages
Unnecessary to capture
Possible ambiguity (lack of
or handle the animal
reliability) in the markings: L Change of markings over time 2. Often less inter-observer reliability
Positive identifi cation
Markers beings lost (Royall et al..
Table 8.14. Potential biases associatecl n'ith assigrting iclentifier,r to aninruls Type of identifier
Example
A.
No.
B.
Number or letter
Named for obiect or physical
17;
Capturing, handling, or marking
C. Named
for behavioral
'White face'
Only those biases we superstitiously associate with numbers like 13, our birth date, higher numbers being 'better' or'worse', or our initials Only those characteristics reminding us of other individuals towards whom we are biased
'Limpy'
characteristic
affecting behavior or survival (Herzog, 1979; Southern and
RG
'Gibraltar'
characteristic
1974\
Researcher unconsciously carrying over behavior attributes which
actually change
D. Named for person
Southern, 1983) M arkers themselves affecting
behavior (Boag, 1972 Perry,
1
Potential biases
I 98 1)
Public attitudes towards marked
'Leakey'
Naming in honor of a person or because animal possesses particular characteristics similar to that person's; the researcher might not want to see those characteristics change or that
animals (Petko-Seus et a|..1985)
honor tarnished (e.g. alpha individual becoming most subordinate)
natural marks or artificial markers before launching a marking program. Some factors to consider are provided in Table 8. 13. Two of the most important potential effects of artificial markers is on the behavior of the marked individual and other individuals interacting with the marked individual. For example, Ramakka (1972) lound that radio-transmitters resulted in atypical breeding behavior in male woodcock. Burley (1981. 1988) reported that zebra finches selected mates on the basis of preferences for certain leg band colors, and Swaddle and Cuthill (1994) found that female z.ebra finches selectively chose symmetrically leg-banded rnales over asym-
metrically banded ones.
8.6.3 Assignment of numbers and names
Ethologists have devised many rettionales and clever scherncs firr assigning nunrbcrs
or names to animals. For example. Cheney and Seyfttrth's 1lt)t)0:313) systcnr'lirr' naming vervets was originally devised to enliven thc nlrny hours wc lrntl tirrr colleagues spent watching slccping ot'solilirry rrtonkcys. . . . At llre bulinrrirrl',r1 ,,,,, wc lrssigttetl irll lL'rnlrles. tturlcs. rrttrl jrrvertrles in r'lrclr ,'r()ul) lo ;s 1,,rlit'trl;rr tltctttc. srtt'lt lrs st':rttrl;tls. Ptisorts. t'nt1'nr;rs. ('()unlrrt'r llr;rl ;rrt'n'l r'ounlrt's' iurtl str olt.'Sittt't'llr('rnl;rnl\ \\t'tt'tltllir rrll t() \('\. llrt'\ :rllt'nrIlr'rl l,,,rrurrl lrr;t', lr\ ;r',',rl'trrrrl,
str,rcly
names that were not specific to either sex. It is this potential bias in making objective observations which can become a problem when numbers or names which are par-
ticularly meaningful to the observers are assigne
'Fifi', 'Leakey" and 'Graybeard'and the Gardners' 'Washoe'. Certainly, the indivi6ual aninlal given a name will be remembered longer than the anirnal which is assigncd it tlttnlber. However. names carry certain connotations or overtones lor citch ol' Us' This is obvicrus in the naming of our children and our hearing a name ittttl lot'tttittg rt lttcttlitl ilrtage of the person. Tinbergen has been acutely aware of (llcsc Potcntiirl birtscs. br,rt aPpreciates the intinlacy one gains through assigning llrllllcs lrt itttlivitlrrrrls tttttlct'slurly. Hc rcflects on a female kittiwake that was in the r'ololtv slrrtlicrl lrl I )r. l.sllrr.r' ('rrlk'n.
\
;ut()lll('l ltrrtl.;r l(.ln:tl(.. rr,;rs loo slrV lo;11.11a. ttlllrotrglt shc kCpt l'.llllll' ttt;tlr". lltt,rtl'lr',r',r.'()n ,rll,'r ',,'rr,.,rn. rltt.\\1it\ ittrv;tys l()() t).,t.v()1s
i
DATA COLLECTION METHODS
s Data-collection
to stay with any of them. (This bird was inadvertently named Cleopatra before her character was
known.)
[Tinbergen, l95B;210]
equipment
Although less likely than names, numbers can also create potential biases. We may have a favorite, or lucky, number which when assigned to an animal will instill a desire in us to see that animal do well in competition for food or agonistic encounters over a territory or dominance.
am not suggesting that ethologists must necessarily randomly assign only numbers to their research animals and remove all romanticism, empathy and levity lrom their observations and discussions with colleagues. Rather, we must be aware
Data-collection equipment varies lrom a notebook and pencil to computers and automated data-collection devices. Most of us are mesmerized by technologically advanced electronic equipment; therefore, we commonly strive to use the most
of the potential biases which arise when naming and numbering animals and guard against letting them influence our observations, data recording and interpretation of results. The biases inherent in the types of identifiers applied to animals are listed in
sophisticated electronic methods available lbr collecting data, but there can be inherent problems. For the increased speed and ability to handle quantities of data
I
increasing order in Table 8.14.
provided by a data logger or computer. there might be a decrease in reliability, although Whiten and Barton (1988:146)'are confident that these worries are unfounded'. Nevertheless. you can see what you have written in a notebook or on a data tbrm, but you cannot be sure that what you have spoken into a microphone is being recorded on tape, or what you have typed on a keyboard (even though it appears on the screen) is stored properly in memory. When data loggers and computers are working properly they are extremely powerful tools fbr data collection and storage. Although the reliability of audio-tape recorders and computer hardI
ware is now very high, problems can still arise. The data-collection equipment necessary for your study will be dictated by your
i
experimental design (Chapter 6) and sampling methods (Chapter 8). A good rule-
of-thumb
I
is: use only equipment w,ith a level of' technology necessary to collec't o conl1
plete und a('curote ret'ord ry'' the behavior
(s
)
o/' interest in
analysis. For example, collecting limited samples
a
./brmut .fbr
fficient
of a lew behaviors fiom
data
a small
number of animals, such as Sordahl's (1986) test of whether avocet (Rec'urvirostra umerit'anct) and sttlt (Hinrctntopus mexic'anus) distraction displays were directional (see
Chapter l7). requires nothing more than pencil and notebook; the chi-square
test (Chapter l4) used by Sordahl cor.rld also be calculated on a piece of paper using
the data from his field notebook. Likewise, in their field study on the function of copulatior.t calls in f'emale baboons (Papio t:ynoc'ephalus), O'Connell and Cowlishaw (
1994) collccted their data using a dictaphone and checksheets. However, collection
ol'litrgc cltnrntities o1'data on several, rapidly occurring behaviors from several indivitluirls cirn rccluirc a data logger or computer; that data should be collected and sttrt'ctl irr rr lirltturl that cart be analyzed by computer. Several otherfactors to consitlet'u'ltr'tt selcclirtg tlltu-collcction cquipmcnt are illustrated in Table 9.1; the t('it(l('t rt'ill rtttrlotrlrlerlly bc rthlc to lttltl scvct'itl lttctors ol' thcir own (see also Table 3 tn llolttt. l()/li ,:rtrrl Mlrtlirt lrrrtl ll;rlt's.rtt l()()l) llltot ttlrrtr;tttrlslollrttl,\( ()l'r('.. \\('t('(lr',t rrrst'tl rtt ('lt:t1rtt't -1.
I
l
li I
fl I
NOTEBOOK AND PENCIL
DATA-COLLECTION EQU IPM ENT
problems can be overcome with different equipment and others can be overcome with practice. If you fall asleep during this'thought data collection'you might con-
Table 9.1. Charac'teristics of two data-collec'tion methods
sider a different research project, or perhaps a different profession.
Characteristic
Computer keyboard
Notebook
9.I NOTEBOOK AND PENCIL
Reliability .?
Equipment
High
Inter-observer
Less
Intra-observer
High Low Medium
Speed
Quantity of data Data feedback
Power
training
Can be reviewed in the field
Easy, which is
At the first
not alwaYs
level of data collection equipment is the notebook in which ad libitum
notes are recorded. Ad libitum notes are the written descriptions found in typical field notes (Chapter 4) and are recommended only for reconnaissance-type observations (Chapter 4). However, some sampling methods lend themselves to data collec-
computer access
tion Jbrmal.r that can easily be accomodated with a notebook and pencil. For example. you might take typical field notes in a descriptive study of social organization of species X but have a format for recording dominant-subordinant interac-
possible
tions between individuals and groups. That format might be,
crunched; remote
Battery restricted when in field
Limits of the observer
Altering data-collection data
More training High Hieh High Must be comPuter-
4----7 WO---YR
More dilficult
(Group 4 dominates Group 7)
(IndividualWhite/Orangedominateslndividual Yellow/Red)
a good characteristic
and these notations would be embedded in your notes where you would more fully describe the interactions. Data collection formats are a step below data fitrms (dis-
of Several other types of data-collection equipment are described below' Some in part the older, simpler equipment used to streamline data collection are presented Also, for their historic value and as a demonstration of the ingenuity of ethologists' researchers for those a description of that equipment might serve as encouragement who have more time to locate or construct (or modify) simple, suitable equipment and comthan they have money to spend on technologically advanced data loggers relatively of advent the point out. (1993) Bateson puters. However, as Martin and custom-built for need the inexpensive. portable computers has all but eliminated equipment. Nevertheless, it is not dilficult (nor is it wise) to use technological overkill when collecting data. you need, a1d have the opportunity to use, a data logger or computer fbr data as a collection, it is always wise to have a simpler method (e.g. check sheets)available
If
(Whiten lrncl backup. Although portable computers have become very reliablc Barton, 1988) remember Murphy's Law: 'lf anything catl g1l wrotlg' it rvill'' Irvcrt sottlctiltlcs before you conduct reconnaissance observations (Chaptcr 4)' you cittl ytrttt'cttttclt: tllis anticipate problerns by conducting 'imaginary clata collcction' I'trrttt l.rtv lr:tt'k. is similar to tlre 'thor.rght expcrinrcnts'tlcso'ibcrl by Sot'cttsctt 1lt)()l). t'ollt't'ltolt clsse yptrr cycs. cnvisiorr thc:rninurl. lrrrtl crrvision lltr'l)l()("('ss ol tlrll:t
lirCttsrl, l.rt.,ltlt.ttrssrrt.lrlrslrt'l;tyiprs,,t't'rrrittl, l()()l;t;lt,llr lol('((,1.1:tttlll;tlt'lVollll (rll \()lll ( t'llll)lll('t S(rlll('()l lltt"'t' t.ltt,t.k sltt.t.l or rlrllit rtllV liilrlilr1, llrr'r'orrr't l kt'\'r
cussed below) which also can be included in your field notebook.
Trotter (1959) added a time base to his note-taking by constructing a motorized device in which a strip of paper was slowly unwound from a roll and passed behind a slit
in small steps. The steps were of equal size and occurred at a set time interval.
This method can be viewed as either utl libitum, sampling with an automatic time base, or a type of event recorder with flexibility in the type of behavioral observa-
tion to be recorded at each interval. The size of the slit must be designed for the amount of data to be recorded, the speed at which the paper steps must be in concert
with the data being collected. Too small a slit and steps ocourring too rapidly could be very fiustrating. Handwritten notes can also be made directly into a portable computer for storage and latcr retrieval. Input occurs when the observer writes with a special stylus directly onto the computer screen. With some systems, such as Apple's Ncwton irntl Write-T,rp (Linus Technologies. [nc. 1889 Preston White Dr. Reston, Vn 21091). sol'lwarc cnables the computer to learn the user's handwriting. I l:rnth.vr ittcn inprrl is thcn cligitized ancl a character-recognition algorithm converts llrt'u,rillt'rr svrnlrols irrto n S('ll. ()tlrcr systcms (e.g. Casio's ScreenWriter Digital l)rrr1')rt't'orrls;rtttl slot'r's tltr'rvntinl', 1,, :r gntphic lirrrrtut and clisplays it just as it \\';r:,\\'rlll('n Ilst'rrl llrt'st'torrrPrrlt'r svslt'rrrslltollrrblytlocsnrllollcritttysignificant rtrlr,tttl,rl,r' or('r p,lp('r ;ur(l 1rt'trr tl
DATA FORMS
DATA-CO L LECTION EQLI IPM ENT
Recording your verbal descriptions of behavior into an audio-tape recorder (or other device) can be substituted for written notes as well as some of the data lorms discussed below (see section on audio-tape recorders below). Also, brief notes can be recorded on small, tapeless recorders which store the verbal descriptions on a computer chip. For example,'Speak!'(Voice Recognition Technologies) is a battery-
Behavior
0800
\--
powered. hand-held voice data entry device with built in voice recognition capabili-
08 12
also has a 50-key keyboard and bar code reader. However. the capacity of these pocket-sized recorders varies. 'Voice It' has only a two minute capacity size (Voice It Technologies, Inc. Fort Collins, CO 80525); 'Voice Organizer' uses 512 KB
ties.
It
of memory to store 99 timed notes (Voice Powered Technology International, Inc" 15260 Ventura Blvd., Suite 2200, Sherman Oaks. CA 91403); but 'Flashback' (Norris)
uses interchangeable 30
a
minute or I hour clips.
92 DATA FORMS Data forms. or check sheets. are the next level of organization above ud lihitum data collection and Trotter's method. They are the minimum data-collection method for any well-designed study. Hinde (1973:393) cautions that'every check sheet [data florm]must be designe
of the observer.' Kleiman (1914) clescribed two types of check sheets (data forms) used at the National Zoological Park. One is a 'time-sample check sheet'which is used for focal-animal samples of 4-5 minutes taken every hour. The second lbrm is used for all-occurrence sampling, in which the animal(s) is observed for 30-60 minutes at a prescribed time each day and the occurrence and duration of selected behaviors
crasies
0900 Fig.
9.1
E,xanrple ol a two-dimensional data lorm representing a three-dimensional format. Animal A engaged in Behavior 3 at Time 081 2 during a sample periocl which ran lrom 0800 to 0900.
quantitative treatment, it is as important to know that an item of behavior did not occur as to know that it did, so items selected for recording must not be missed. IHinele, 1973;396] Be aware
of whether your behavioral units
are
mutuully exc'lu,sive, purtially over-
lapping or t'onrpletely ret{unclunt.The more exclusion there is between behavioral units, the more easily the data can be recorded.
9.2.2 Columns and rows
recorded.
The steps cliscussed below for designing data forms are the same as those that should be used before employing one of the more complex data-collection methods. Those methods are merely more sophisticated ways of collecting the same data you would get from a data form. A computer does not do anything (indeed it cannot) that cannot be done by hand.
Most data lorms are two-dimensional representations of three (or four-) dimensional (or variable) data. Figure 9. l, for example, illustrates three variables (individual, behavior and time) ol the four commonly recorded in behavior studies. The tourth, spatial relationships, can be treated separately or incorporated on a standarcl data tirrnr. It could be incorporated on the form in Figure 9.1 by merely includ-
ing the cotlc N W with the 08 l2 to indicate that it occurred in the northwest quadrat
s.2.t Characteristics of behavior units
An essential part of the design of research is defining the trtlits ol'heltitviot'to hc measured (Chapter 6). The units selectecl will not only allcct thc vrrlitlity ol'llre study but willalso determine the eliicicncy with which tltc tlrtt:r ltt'c crtllet'tetl
Thc clcgrcc pt'sclcclipl lpl'[clrlrviourl trrritsltlr'pt'tt.ls itt gr:ttl ott lltt' lll\'('lsc tt'l;tllrtll lrt'ltvt't'lt 1-tt'ohlcrrr. l'rtrl rrlsrl on llrt'llrcl llt;tl tltt'tt'is tttl It1rg'ptrrt'lr ts t('(orrlr'rl ;tttrl lltt'ptr't l'.loll \\llll ttllt. lt tl t.' l,tl.t'tt l"t
rll'thc cnclosurc. ('olrrrrrrrs shoulcl be gntup<,t1and clearly delineated to reduce the tirne it takes the obscrvcr to lintl tlrc appropriate column. Often behaviors can be grouped according Io lrrr'l.rcr crrlcgorics ()r'anir.nirls can be grouped according to social units. The crrrplursis. lrowcvcr. slrotrltl bc orr clrsc ol'r'ccorrling with itn eye to organization for
rl:rl:r llrnslt'r lrtlcr. l'irt r'rrrrnplt'. il tlrt'tl;rlrr rrtr'lo l'rc cttlct'crl into ir conrptrter sptr';trlsltr't'l or (litl;tlr;tst'. tl tr ltr'lPlrrl 1,, lt;rtt'lltr'rl;tl;t lot ttr ()t,,iuti/('(l so llltl V()u cllll
DATA-COLLECTION EQU IPM ENT
238
DATA FORMS
can read across rows entering data without having to skip over columns. This, however, should be of lower priority than ease of getting data accurately onto the
Table
9
'2 Examples
d data codes Jbr behaviors,
animal.s, time and spatial locations
forms. Leave both blank column.s and rows and a large column on the right for c'om-
Behavior
ntents. Notes written in the comments column are sometimes the key to later inter-
pretation of enigmatic results. Blank columns and rows provide flexibility for adding important behavioral units or animals to your data collection. These columns are probably used more often than not.
Animal
e.2.3 Coding
Coding data increases speed of recording. The shorter the code the more rapidly it can be recorded and the smaller the space that is necessary for entering the data.
Preening
P
Flying
F
Preening
Pr
Pecking
Pe
Edward
E
Mary
M
Flash
F
Red
R
Green
G
Blue
B
should be made to devise a code that is sirnple and easy to recall. Some examples are
Left blue Right blue
LB (colored leg bands,
given inTable9.2:
RB ear tags, or marks)
Left green
Specific coding schemes are often designed for particular studies. For example, Sackett et al. (1973) designed a three-digit three-dimensional coding system for
LG
Right green
RG
However, accuracy and reliability should not be sacrificed for speed; so every effort
recording the behavior of a single monkey (Table 9.3) and a four-digit four-dimen-
Time
sional coding system for recording the social interactions of a focal monkey tested (5 min. trials) in a group
of fbur monkeys (Table 9.4).
Spatial
Schleidt et al. (1984) designed a generalized letter/symbol lormat lor coding visual behavior patterns and tested and refined it in a study of bluebreasted quail
(Coturnix chinensis) behavior. They suggested that the format had the potential to serve 'as a prototype for a generally usable standard for behavioral coding systems
i
First sample period/first minute
Ut
Third sample period/fifth minute
315
Quadrat
l
7
Southeast quadrat
SE
In pond
P
On hillside
H
Next to Edward
IE
in birds, or for the members of other taxa' (Schleidt et a1.,1984:193). I recommend examining their coding system for possible adoption or revision before designing
your own, although Bakeman and Gottman (1986) argue against adopting someone else's coding scheme. I also recommend perusing Bakernan and Gottman's
(1986) chapter on developing a coding scheme.
Whatever coding system you devise or adopt, easier,
it should make data collection
not more difficult. Also, it should be obvious. not cryptic (e.g. Yotsumato
t976).
s.2.4 Data-form examples
The lollowing clata-fbrm exunrplcs:rrc sinrplilietl (o servc:rs rr l'xrsis lirrrr wlriclr individual rescitrchcrs cilll crtttslt'ttcl lltcit'owrr lirt'tns l() lll('cl lltcit itttitvtrlrr;rl ttcctls.'l'ltc lorlrrs ru'c rlesililtctl lo t'orrt'sportrl to lltt's;rtttplinl' rrtt'lltorls rlrsctrsst'tl rn
('lrrptt't
li
9.2.4a Sociometric
matrix
Social-relationship data are generally collected as all-occurrences(e.g., agonistic) or in in';tunlunt'()Lt.t .santple.t (e.g. nearest neighbor at a particular time). However, other rncthods can also be used (Dunbar, 1976). Tho lirrnts shown in Figure 9.2canbe used in the sampling of all o(,cttrren(.es. TItc lirt'rlls in Figr-rre 9.3 may be used when collecting sociometric data involving itt'slttttlttttt'tttt's' ,s'ttttrplc,r. We can add zones
of concentric
circles around individuals
rrrrtl rccortl tltc intlividuals in each zone (Figure 9.4).
Atltlitiortrtltlrttrt cortccrttirtg thc cxact locution of individuals can be gained by ttsittll lt rl;tlrt sltt't't lvitlt lltc'slxrli;rl li'lrtul'cs rcl)r'cscnlctl directly. For example,
tttil'ltf lrt' ttsll)ll ;l t'otttllt;tltott ot lirt;rl ;rrrirrrrl;rrrrl irrslrrnliu)corrs ttttttt'llrt",,,t t;tl
tr.l:r1t.r1,.1,,,r,, lrr.lur.r.n
we
.t.tttttltlittg trl tlcter-
,ulnr.rl .\ lrrtrl llrt.,lltt.t. llttr,t,tttr.tttltct.s ol. lltc
?i* FZE fg;u-illill .i; r :E?9=€ r?EE: -o:" s ?Z
=3i -. F +c'L-'r^;
1;=? ;X;t
? .t.-997.\E =?
i=gE
(u
?=1S\
:I
#=e ;i1= ll
R' V=8,r q:se:EF* t H=' .'=r:=F,e.>==:7.i.T?ll
?qi ?Z Ii*=3
=:1
=7=
:?=
.l=€=r. :7.-
'2=7 '-E
?2= =;-? =1
i: -' i
e..1.
-
l
tdttnligit - lbu,-dintettsional coding
lbr
-l
i>
*x '-.i ^,X
(,
',4
a
E
I
ll-NJ =i
1tr'(})l.Jo v\J:=1 Jn)7^aq)-=
z, p
oo PP IH
^l
5 If
3
:h-![
recording social interactions of
,V 4
(\
a
\\:
r\
oa
6,c 36' .Do x+ ()
.i OC
aai ='tl aD !.
a
'r
a
focal individual tested in a four-nonkey group
Digit position
r-
,-;:
1
I
2
-1
4
Role of focal individual
Focal individual behavior
Interactor behavior
S
\
rrnsocial
Initiate with physical contact Inttiate - no physical contact Reciprocate - with physical contact Reciprocate - no physical contact Ignore - with physical contact Ignore - no contact
ID-direction
Passive
Passive
Nonsocial
Explore
Explore
Monkey
Withdraw
Withdraw
Monkey 2
Disturbance-fear
Disturbance-fear
Ro
ck-hu ddle
-
sel
f-c
la sp
Ro
c
k-huddl
e - se
I
I
Monkey 3 f-cl a sp
Monkey 4
Stereotypy
Stereotypy
Self
Play
Play
Toy
Sex
Sex
Ladder-shelf
Threat-aggression
Threat-aggression
Window
No response2
The four monkeys in the group are arbitmrily labeled from I to 4 as subject ID (SID) codes. : The no response category for the third-digit interactor behavior Code 9 rcfers to social interactions initiated by the focal subject that produce no change in behavior of the potential interactor from that occurring befor€ the initiation. S,x?,ce. Frcm Sacketr et al.(1973).
C 16
z -l
oa
o ,1
ETggIc'€Esi po-br=-r :.D; :cV, U
e
A)
\Ooo-IO\Un5'+lt9O
-J
? w
(D
+ v
-+
q
(D
H)
F
I
);-
i.
a
rllJ
=
o
z
X!D ()(D
(9
--rn
rL)
H/-
ll
3,
system
a
G
e? illl a3 h-'; €e Il EE ? 3 llll pi a
: EL
v.
qIt.,
\
;:En illl -o
i ii
Bql ?.<
ll
tsT r a llll
do::'lldr-
(D
ll
aH
23ii =,= -"a-i* +: ?=*"_ 7 gi .: 7=_ =: ;s 3i ==
aD ='R*-
Ft
a
:,F.1 a ?;-6.H 5 a e-1
a
-40
#fi8'i rr €'= '-s ^ ET ll R; g ll
€5!?. i*-=s P a'r
4(D
5
;*,
5
s('
:.t
:I ii
E;[E 4;;E
N)
t'J
i
Dr =
=
:6=x =i7 e;+p- llll S a:=X; a';. E ag3 ag dql-fl G i'u!7ilv
u+ 3 a.,
a
-v
;I7il
(,
ffi eae*-u odY AE 6.?P
F
ll ll
G';5f
O\ (^ 5
DATA FORMS
DATA-COLLECTION EQUIPM ENT
Sample
Supplantee
BC
a
Individuals and zones
period
D
A q)
243
I 2 3
B
A
| 2
3
C
B
BC | 23 A
B
A
B/C
C/A
D
| 2
3
t 2
D D
A
3
C C
A/B
Note that here reciprocirl relationships are obligirtory, so that we can collect data on onlv half the animals.
C A check is made in the aPProPriate box every 6me one individual supplants another.
D
Fig.
9.4 Example of an instantaneous sampling data collection form with a higher resolution of spatial inlormation than in Figure 9.3.
Supplarrtee
Strpplarrtcr
Sample
Sample 2
1
Sample 3
in- the The cocle for the inrliyiduat srrpplanted is entered ser;ue-ntially glrtained' data are boxes to the ,igit .rf iL" .r,,pplnri"r; nole that scr;ucrrlial
@
Fig.9.2 Examples of sociometric matrix data collection lorms. Fig.
9.5 An example of a'bull's-eye'data lorm which combines focal-animal and instantaneous sampling.
Nearest neighbor
Sample
ABCD
period I
B
A
D
2 3
D
C
B
B
4
B
C A
D D
C C C C
Time of occurrence
Behaviour
The code for the nearest neighbor is entered under each individual at
Grooltring
Ilcsting
0915 0918 0935 0937, o9l7 09372 0956 0856 0900 0951
each instantaneous samPle; note
Agolristic
1002
that reciprocal relationships (e.g', sample period Fig.9.3
Animal:
Examplc
1r.t
l)
are not obligatory'
ilts(utttttttcrltts sltlltlllittg
Fct'rling
0943
Notc that we also obtain frequency of occurrence and gross sc(lucnccs; Iiner sequences can be obtained by showing the ortlcr ol'occtrrrcltcc ol-cvcnts indicatcd at simultaneous times .'l 0917, . /l 0() 1(r, )
(e .1r..
'\tt ,'r,rtttPlt' r,l .t lot,tl ,rrrrrrr,rl
,,trrr111111y,
rl,rl,r r ollt.r lrorr lirr ttt.
244
DATA-COLLECTTON EQU IPM ENT
DATA FORMS
Behavior:
9.2.4c All-occurrences santpling
Anintal
We are often interested in the who, when, and where
of a particular behavior (or
0830/sE
behaviors). A typical data form would be as follows in Figure 9.7.
If
we are using quadrats
use a data
(NW NE,
When and where the behavior occurred 0900/sE
A
SE, SW) as indicated in Figure 9.7, we can
form that resembles a quadrat layout (see Figure 9.8).
0840/N\V
9.2.4d Instantaneous and scan sampling
B hr contrast to all-occurrences sampling. here we are interested in the who, what, and where of behavior at particular points in time (see Figure 9.9).
9.2.4e One-zero sampling
0702/\\1/
071s/sw
070?N\1
0717/s\1/
C
In this type of sampling we might ask: During sequential one-minute samples, what individuals are engaged in a particular behavior? (see Figure 9.10). We could reverse the'Behavior'and Animal'labels and ask: During sequential
one-minute samples, what behaviors did the focal animal engage in?
D 9.2.4f Sequence sampling
With this type of sampling we are interested in either intra- or inter-individual of behavior. For example, we may be interested in all grooming sequences of male mallard ducks that are initiated by a tail-shake. We decide to discount the individual performing the behavior but begin sampling whenever a tail-shake (TS) occurs, then recording head-shakes (HS) and wing-flaps (WF) as they occur (Table 9.5). We could also list the possible movements and number them according to the order of occurrence, as in Table 9.6. We might also be interested in the sequence of occurrences of events in a group of individuals engaged in some type of behavior (e.g. encountering predator or prey or leaving a sleeping site). Here we can record the times of occurrence of significant events (Table 9.7). This is essentially a scan sample with the sample period heing designated by the occurrence of significant changes in events. Hinde (1913) also sr.rggests the use of a data sheet with a prccalihratctl tinrc sculc (Figure 9.ll). This lorm is essentially the same as thut provitlcrl hy iur cvcnt sequences
Fig.9.7 An example or an all-occurrences sampring data coilection rorm.
Anirnirl
A
B
0840 0830 0900 C
D
0702
0702
07r5
0717
recorder, which is highly recomrnended il'availablc.
Tlrr'tinrcs
;r;r;rro;>ri:rtt, r1' r)
r;
rr;rrl
)i \rr 'rll t)( ( lll l('ll( t""',lllll)llttl' ttt I t1'111,.,) / r1 ,r rlrllr.rr.rrl
are then recorded in the
r;ttr. rl,tl,r r,,llt't lr,n lr)nlr l()r
1,,;1,,.,,
rt't..111111,1,
llrr. srttttc rl;tl;t
rts
CLOCKS AND COUNTERS
DATA.COLLECTION EQUI PMENT
Animals, behavior and location
Table 9.6 A hypothetic'al example oJ'sequence sampling based on the data in Table 9.5
Movement
Fig. 9.9 An example of a scan sampling data collection form'
Behavior:
0800 0801 0802 0803 0804
HS
WF
I
214
J
2
2
J
TS
a
J
214
J
4
2
-1
5
2
Table 9.7 Individuals and behavior
Animals
Sample
times
Sample
A
Time
D
C
A
B
.\
x
0520
St
x
0521
w w
,t
x
0526
lve *e.ely mirke
D L
0528
x a check mark
053 I
i[
C
D
Y Y
St
S
Y
S
w
W
St
D L
w
w
L
L
an individull animal is Performing the behavior during Lach s,rmPle Period' Fig.
9.3 CLOCKS AND COUNTERS
9.10 An example of a one-zero sampling data collection form.
For many studies in the field, counting and tirning behaviors can be accomplished using a hand-held mechanical counter and stopwatch (e.g. Carpenter and Grubitz
Table 9.5. A hypotheticalexample o.f sequence sampling the grooming behovior of-rnale nrullurd clut'ks Sequence
Sample
initiated
bY TS
TS
HS
WF
IIS
2
TS
HS
3
TS
IIS
wlr wl,
ils
wll
1S
4 5
IS
lls lts
IS
counter cluring l0 minute scan samples. When several different behaviors are being measurerl, other types
of multi-channel
event recorders can also be used (see
bclow).
I
't's
1961;also see below). For example, Anderson and Harwood (1985) recorded the of each bull and cow grey seal (Halichoerus grypus) on a multi-key
behavior
Pushbulton switchcs connected to electromechanical clocks and counters have lirl nnny ycars in laboratory studies (e.g. Mitchell and Clark, 1968). rr swilclt l'r'cssirrl.r lrtlvlrrtccs l courttcr:rncl uctivates the respective clock which runs urrtrl llrt'srvitt'lr is rclelrsrtl. llre resrrll is srrrrrrlrlry rlata in thc li>nn of number of
bccn trsctl
lol;rltlttlrtton lirt t';rt lt lrt'lt;rriot lirr lltc strtttlllc l-rcriorl . In arlclition Io lltt't lrtt Ls rttt.l r'.rttttlt'tr lltt', t'rltttl)nr('ttl n('( ('\\rl:tlt's;t )l'i Voll l)('1r0wct'srrltply
()('( un('n('('s lrrrtl
1
BABY
Whiskv DATE
6l5nl
NO.
I
TEllP.
ll'C lvlND
15-20 CLOUD ZUS9_
On Mother
On Mother
Missed Total on & offRI (mothermoves)R2 (mother - rejects passively) ,i13 (mother pushes, etc,) F.i
9.1
I
Total )60 cms only
Total <60 crns only
-
AI A2 rf,
Total
off-
(motlrer puts lnn rorrnd) (rnother accepts passively) (mother's initiative) lVhoos (on) Wlroos - (off)
-
Check sheet for mother-infant relations in captive rhesus monkeys Each row rcpresents one half minute. Ticks are placed in the columns if the activity in question occurred during the half minute except that leaving' and 'approaching' (that is, distance between mother and infant increases Ircm less than 60 cm to more, or vice versa) are recorded each time they happen. Rr.R,. and R3 are categories of rejection of the infant by the mother, Ar.A,. and M are categories of acceptance. C, R, and T mean that animal C initiated rough-and-tumble play with
Whisky.'<60 cm only'refers to number of half mins in which infant was off mother and not more than 60 cm from her (from Hinde, 1973, after Hinde and Spencer-Booth, 1967).
DATA-COLLECTION EQUI
PM
STRIP-CHART EVENT RECORDERS
ENT
251
l
and pulse formers, all of which were a mainstay in most experimental-psychology laboratories but were later replaced by integrated circuitry (e.g. McPartland et al., lg16)and more recently by computers (Flowers and Leger 1982). Nevertheless, the
Baseline information
!12.f081 !1. 160r.. t52.
old electromechanical equipment is still available in many laboratories and is suitable for many observational studies. However, this equipment is not readily adaptpower able to field work since it is bulky and uses 1|Q-volt AC or a 2$-volt DC I l0-volt AC' from supply which is normally inverted and transformed
r18. t L?t 1. r1454r
9.4 CALCULATORS
Flock geometry Time Weather
Fig.9.12 Portion of a calculator printout recording the activity of a white-fronted goose. Activities were recorded every 5 s (10 different 5 s intervals/line). Activities: 0=loser of 'social', I =feed, 2=alert,3=extreme alert, 4=walk/swim. 5=social, 6=sleep, 7=rest, 8=preen, 9=fly (from Ely 1987).
computer in the laboratorY.
The output from a typical event recorder (e.g. Esterline-Angus) consists of
Strip-chart event recorders can be used to record the occurrence and duration of events and states. With the time-honored Esterline-Angus 20 chantiel event move a pen
Location Habitat
8112i111111. !1:1ll(1111. E11i24LLl1lr. ,t?11111116. :11111r1111. t112I221111. :11112t, 1188. B?L222L7111.
different behaviors was assigned a number (0-9) on the keyboard. He first printed out baseline inlormation for each sampling session, then entered a behavior for each 5 second scan sample (Chapter 8). A metronome (see below) signaled when to record a behavior and when to press the return key. which resulted in the row of numbers being printed onto a paper tape (Figu te 9 .12). Each row of numbers could
9.5 STRTP-CHART EVENT RECORDERS
Flock number
81112511121. t?'ri1111111. r1:,:i111727. s11.21121211'
and Giles ( 1980) review several activity-recording instruments for wildlife studies' Ely (1987) used an inexpensive, hand-held, portable, printing calculator (Casio HRl2) to record the behavior of white-fronted geese (Anser albdrons). Each of 10
a
Date
Actlvity
An inexpensive hand-held calculator can be used as a counter in the field. but without behavior. Where a printout capability (see below), one calculator is required for each automatic counting of a single type of event (e.g. hops on a perch) is required' an inexpensive calculator can be modified to accept inputs from photoelectric cells, solenoids, microswitches an<] sound-activated switches (Knight et al., 1985). Schemnitz
represent a time, individual or group, for either Scan or instantaneous sampling (Chapter 8). The printouts were sorted and stored in the field and later entered into
-
to one sicle as it is track-
recorder, switch closures activate solenoids that ing along a piece of moving chart paper. The microswitches can be assemtrlcd inttl a keyboard (Figure 9. 13) so that the observer can record the occurrencc tl[' it bchitvior
by depressing the assigned microswitch and holding it down lirr its lottg its tltc behavior continues (Staddon, 1972). Eisenberg (1963) ttsctl switchcs tltitl lockctl rtt cottltl lrc closed when lifted up, cor-rlcl be dcprcssccl into thc ncutrirlo1-rcrt 1'rosiliott. pushcclclewl-t 11cl rcuraincrlclosctl irs long rrs tlrcy wcrc tlcl'rt'cssctl. Slvilt'llr.'s t'rtlt ltlsrt
hclr.iggcr.ctlhylltt:ttlitttltl(e.g.Week1'1'|()(r.l)'rrlllVtitttt.ts.t.tt..'ittlltt.t.r;lt'tt Itlt'tllt't's :lltst'ttt't'
series
of tracings which travel at a speed
a
set by the observer. The tracings are dis-
placed when a switch is closed, indicating the occurrence of a behavior (Figure 9.l3). Thcse traces then provide a continuous record of the fiequency, duration and temporal patterning of behaviors (Mason 1960).
Hutt antl Ilutt (1974) describe the 60-channel
Peissler event recorder with a
brrilt-in kcybolrcl (scc Fig. 8.4 in Lehner, 1979). [t was originally designed for use in sttrtlics ol'llrc sociirl bchirvior ol- squirrel monkeys (at the Max Planck Institute fur l)syt'lri:rtric irr Mrrrriclr) :rrrrl hrrs sonrc Iimitations for widespread adoption. llowt'vt'r. rl slrll ;rvrriltrlrlt'. il rrurv lrr' srrilrrblc ruttl srrllicicrtt lirr data collection in s()ilr('slttrltt's rrr I t'lrrrr'r l()/()) ;rrt'rr spccitrl typc rll-
STENOGRAPH
DATA.COLLECTION EQU I PM ENT
Animal
Animal 2
1
3 s Category A 6 s Category A 3 s Category
B
1 s Category
T T
?
E
5 s Category B
7
8 s Category A
T 3 s Category A 3 s Category
3 s Category D
D
2 s Category A
3 s Category A
T
Fig.9.l3 Recording behavior using
a
20 key microswitch keyboard and a 20 channel
Fig.
Esterline-Angus event recorder.
electro-mechanical event recorder which have been replaced by electronic printers. These recorders have been a standard method for recording animal responses (e.g. bar presses) per unit time in operant conditioning studies. They can be useful in any
2 s Category C
T
1 s Category
F
T
4 s Category
B
T
5 s Category B
T 3 s Category C
9.14 Sample printout lrom a stenograph machine used to record behavior from two rats simultaneously. The behaviour has been separated into nine categories, A I (from Heimstra and Davis, 1962\.
9.6 STENOGRAPH
study where the researcher wants to illustrate the rate of some behavior since the recorder's output is essentially a graph of cumulative animal reponses across time.
Heimstra and Davis (1962) described their use of a stenograph machine lor recording behaviors simultaneously from two animals. However, the stenograph is adapt-
Wolach et al. (197 5) modified a cassette tape recorder to record responses, converted
able to several additional formats of recording data. It prints several letters as well as numbers liom 0 to 9. One or more numbers and/or letters can be depressed simul-
the output into a relay closure, and then played the tape back to operate a cumulative recorder. The event recorders described above are inconvenient to use since the researcher
usually must transcribe the tracings of the chart output into numerical clata. ancl then enter the data into a calculator or computer for analysis. Data loggcrs itrttl computers (discussed below) eliminate these steps, making clatit collcctiott ttot ottly
taneously. On older stenographs, the record is printed on a 2.5-inch-wide paper tape which steps one line afier a key (or keys) is depressed (Figure 9.14). Newer machines
stttrc thc tritnscription in a computer-compatible format, and some can be intea cornputer to provide an instant translation of shorthand and display it
grittctl with
on ir vitlco nronitor. The stenograph is relatively compact, light, and very quiet.
more efficient but often more accLrrate; every aclclitional stcp which lltc t'csertt'cltct' must perform by hand, fton-r thc raw dirta ttl tlrc uulrlysis. ittlt'otlttccs ltrlrliliotutl
t
probability lirr hunrittt crrors.
(n
I lcrnrs( r'rr rrtttl
l)avis
( I 962)
studied the elfect of various drugs on the behavior of
rtts p;tiretl irr rt sntir ll woorlcn box. Thcy scparated the behavior
ol ltt'ltrrr"iolrl strrlt's hy tlcl'rrcssing tlrc sanrc key at oneon(l rnlr'rrlrls. t rrt.tl lr\';trt t.lr'r'lrr)nt( nt(.lr()n()nt(.(tlist.rtssr'tl lltlct'itt tlrischirltlcr').
llrr't'tt't'o;111'11 (lur:rli()ns ..('(
into nine categories
l)rrntl tr't'orrlcrl llrc occrrrrcncc ol'lhc bclraviors ol-thc twu rats simultaneously.
COM PUTER-COMPATIBLE DATA t-o(;(; ER S
DATA-COLLECTION EQU I PM ENT
Data recorded on a stenograph are limited to the number of different keys available, but conversely are expanded by the user's capacity to depress any number of keys simultaneously. The stenograph could probably be modified to step one line automatically at set intervals through a motor drive rather than through the depression of the keys. Data recorded on a stenograph can provide inlormation on: 1. the
OBS€RVEO EVENT
Event occurs at rccatron 5.6
SWITCHBOARO
Tot.rh wand to rnaD
DODE MATRIX
Matru cotjrnn 5. .5V
The Microwriter (Microwriter,25l East 61 St. New York 10021;) is a hand-held, portable, electronic device, similar to the stenograph in that letters and numerals are encoded using combinations of only five keys. A small screen displays the input as it
Iffiffi
occurs. However, like stenography, 'microwriting'is a skill that must be learned.
-)
OV
SHIFT REGISTER [.oGC
Start
Row I
Column 5
.5V SHIFT REGISTER CUTPIJT
fNCOD ER
-
5,E
l-lalnr row 8'.5V All Olh"r LnaS t
frequency and rate of occurrences, 2. duration; and 3. sequences.
bcardt
o I rmC
a
9.7 COMPUTER-COMPATIBLE DATA LOGGERS CODEO SIGNAL
Data loggers are used to collect (encode) and temporarily store data which is then transferred to a microcomputer (or mainframe) for reduction, long-term storage, organization and analysis (e.g. Morgan and Cordiner, 1994). These processes, illus-
RECORDED gGNAL
frilm
ffiil
ffiilflH
flti
"
fir#-iltTtt-t*1tffit-ffi :;
trated for the Digitorg system in Figure 9.15, are essentially the same for all data .5V
logger systems, whether the data is recorded on magnetic tape or on computer chips. DECOOED SIGNAL
In 1913, Sackett
e/ a/. summarized the use and availability of behavioral data acquistion systems for recording data in the laboratory or field. Data logger hardware and software systems available prior to 1978 were reviewed by Lehner (1979).
tions such as factory production monitoring and quality control, but which may
-
S}{FT RESSTER LOGC
Some of those systems are still worth investigating as possible low cost. yet ellective alterantives to newer data logger systems discussed below. Many of those earlier data loggers are no longer being manufactured, but they are not extinct. For example, the Datamyte 900 (Conger and Mcl-eod, 1971, Scott and Masi, 1977; Torgerson,lgTT: Smith and Begeman, 1980), 801 and 1000 (Gerth et u\.,1982) are
still used by some ethologists. They have been replaced by the DataMyte 3055 (Allen-Bradley Co., Minnetonka, MN), which was designed lor business applica-
o
trm?
Cotumn 5
p661{916
Ta,PE
Row 8
Two words wrtttcn on rnagnctr tapc tor each evant 1 lreotltrcatton c, evcnt 2 Trne o, occurrence ot
Fig.9.l5 Information
processing by the Digitorg system. Stages are shown from top to bottom (from Gass, 1977\.
evcnt
of information processing
have application in behavioral data collection. Likewise, Microprocessor Operated
Recording Equipment (MORE) Co.. built a data logger which was clesignecl for collection of behavioral data (e.g. Kodric-Brown, 1988). It was replaced by the OS-3 data logger (Observational Systems, Seattle, WA) which is still being usccl to collect
('upability: What are your objectives?
behavioral data but is no longer being manufactured. Other data krggct's wltich ttrity still be available are the SSR System 7 (Stephenson and Robcrts, 1977; Sctttiotic
Will it do what you want? Will it do more than you need? I low cliicient is it in logging the data you need?
Systems Corp., Madison, WI), The Assistant (Hutnitn Tcclttttllogics. lrrc.. St. Petersburg, FL), and the Polycorrlcr-(Onrnirlltu Itrtcrnrttiorurl lrtc. l.ttg.rtrr. [ ]trrlr)
Wlrrrt rs ils ori-tlrc-.job rccortl'l
evaluated firr liclclwork hy Mlc('r'irckcrr
<'t
ttl. I l(),\.1).
Wltcn sclcctirrg lr tllrllr-lopplcr svsl('llr lltr'lt'sr.'lttt'ltt'l slt,,ttltl rut\\!'('t \('\'('ltlr;ttt's lo1'1'1'1's rltllt'tt'ttl 'ltlrtltltcs'
lirltts tr'l;rlt'tl lo lltt'rl:rt;r
lillitrltilitr: l),r
1,,,,, ()r
Is it it pr
y()llr otl'lrnizrrlion llrve llrc cirptrbilitics lilr rcpair'? lltt'rYslt'ttt ( ()nll)lrlrlrle lvitlt yottt'ltrcscttt tllttit collcction
('ttrr11tt111lt1ltlt' ls
MICROCOMPUTERS
DATA-COLLECTION EQU IPM ENT
. ' ' ' ' .
How difficult will it be to integrate it? Portability: Can it be easily carried into the field? What is the battery life?
e.8.1 Data Collection
ln ethology, microcomputer-based data collection can occur in at least the following four ways: 1. recording of visual and/or acoustical observation data via
How much trade-off has there been in capability for portability? Accountubility: Is it really worth the cost? Can it be quickly utilized or will large amounts of time be lost in adapting
of spatial data, usually from fllm or videotapes
to the system?
phy and videotape analysis later in this chapter); and 4. recording of radiotelemetry
Remember that data loggers are only a t'aster and more efficient way of collecting and storing data. They will not substitute for a poorly designed study by magically
changing useless data into useful data. They can be no more accurate than the researchers who use them.
observer input; 2. tbe animal automatically recording its own behavior; 3. recording (see discussions
of digital photogra-
data (see section 9.15.2).
9.8.ta Data collection via observey input
A microcomputer (or laptop, notebook or hand-held computer) and appropriate software constitute a'system'that can be used by an observer to record each occur-
98 MICROCOMPUTERS
of a behavior in either live animals or from a video recording. For example, Mendl (1988) used the Madingley Interactive Computer for Recording Observations ('MICRO'; Styles, 1980) to collect data on play behavior in the rence
Microcomputers can be used to collect (observer or animal input), organize, store
and analyze data. Microcomputers, such as the IBM PC (and compatibles or clones) and the Apple Macintosh, are powered by I l0 VAC and are designed for desktop use. Over the last 15 years microcomputers have become increasingly
domestic cat. The system allowed him to record when a behavior was performed, who performed it, and to whom or what it was directed. Godwin (1994) used the
popular for collecting data in enclosure and laboratory settings (Flowers and Leger. 1982). A discussion of the types and specifications of microcomputers, including an
Behavior Events Acquisition and Analysis System (BEAST; WindWard Technology, 45415 Akimala St. Kaneohe, Hawaii 96744) to record the behavior
overview of their use in ethology is found in Appendix B' Computers have been shrinking in size and weight from desk top microcomput-
patterns of anemon efish
ers to portable laptop, notebook and hand-held computers. One laptop computer that is designed to be used under harsh conditions in the field is the Bison Itxplorer
fioraging behavior of several color-marked European starlings (Sturnus vulguris). They wrote a program which allowed the observer to record the bird identifier. the location and the bird's activity (arriving or departing the nest box). To lacilitate data
(Bison lnstruments, Inc. 5708 W. 36th St. Minneapolis, Mn 55416;). Notebook and hand-held computers have become increasingly popular for the collection and short-term storage of behavioral data since they are very compact. lightweight and have a relatively long battery life and large data storage capacity (Noldus et al.,
(
A mp h ip r
io
n m e lano pu s).
Hensler et ol. (1986) used a portable computer (TRS-80 Model 100) to record the
entry, they relabeled the microcomputer's keys with adhesive stickers. Giraldeau er ul. (1994) also used a TRS-80 programmed as an event recorder in their study of foraging behavior in chipmunks. Unwin and Martin , (1987) designed a behavioral data
1989). This large data capacity has enabled many to also be used for limited data analysis. Competition has caused prices to fall and capabilities to increase. Some
collection system also based on a portable computer (Epson PX-S) and specially
are: Acer, Apple. AST. Canon.
ria listed below (Llnwin and Martin 1987:88 89), which are appropriate when the researcher ir-rtends to assemble a portable microcomputer based data collection system by purchasing the computer and writing the software, in contrast to using cortttttct'ciirlly availuble software - also see Martin and Bateson's (1993: 110 ll2)
manufacturers
of popular notebook computers
Compaq, IBM. NEC, Tandy and Toshiba. Hand-held computers that have been used for behavioral data collection include: Hewlett-Packard HP4l.7l :rncl 95LX, Husky Hunter, Psion Organiser and Series 3, Sharp Wizard, atrcl Tantly 102. Ytrtr should select a computer that can be easily programmed or usecl with lvirillrblc tltrtir collection soltware and interfircecl with y
designed software. Their system ('computer event recorder') was based on the crite-
lislings ol'tlcsirahlc irnclcssential features for an event recorder:
lr)'llrc evrnl
l'cc()l'rlcr shorrkl hc birsctl on a standard, commercially prorlrrcr'tl nr('r()('()nlprrlr'r lllrt is r'rrrrerrlly lvirilirblc. In contparison with ( nsl()nt lltttll lrttrltr:rtt'. tltts slrottltl ttutkt'it lt'ss cx1'xj11:iiyc. lrvailablc lirr
ttttlttt'tlt.tlt'ust'. tt'lt.tlrh'.tn.l ttt.lr'pt'ttrlt'ttl ol spr't'ilrlisl
sttpglot'1
.
DATA-COLLECTION EQU I PM ENT
M
b) The hardware should be small, light, portable and suitable for
recording under field conditions. c) Memory should be protected by battery back-up. to prevent the loss of data and software in the event of a momentary power failure. d) The event recording software should be relatively simple and written in a way that can be understood and modified by non-expert users. This means that it must be written in a high level language such as BASIC. e) The software should run on other computers without extensive modiflcation, so that the user is not committed to one particular machine. This means that the programs must be written using commands or functions that are available in most dialects of BASIC. I The event recorder should have similar capabilities to check sheets. including the ability to record social interactions involving two or more identified individuals and comments written in specialized notation or
plain English.
It should be possible to obtain a 'hard copy'print-out of the data immediately [after] the recording ends, both in the form of a literal record of each key-press and in summary form. h) It should be possible to transfer data to another computer for permanent storage and further analysis. g)
As another example, Whiten and Barton (1988) used lightweight, hand-held computers (HP4l, HPTl) to record the behavior of baboons, which they followed from dawn-to-dusk over difficult terrain in the climatic extremes of Africa. The computers were programmed in BASIC to provide accurate real times, durations
ICROCOMPUTERS
259
lished in Behavior Research Methods and Instrumentution. For example, somewhat specialized programs were published by Bernstein and Livingston (1982) and Hargrove and Martin (1982), and two more generalized programs were published by Flowers (1982).
Data-collection software is commercially available through several sources; it differs widely in capability and price. Features to look for when contemplating the purchase and/or use of data collection software (other than compatibility with available hardware, availability of technical support, and price) include: l. clock resolution (a slow program will 'miss'rapid key presses), 2. the maximum number of dift'erent behaviors (subjects, locations, etc.) that can be recorded, 3. simultaneous
of 2 or more behaviors; 4. appropriate summarization of data; 5. the ability to calculate descriptive statistics; and 6. the storage of data in data flles compatible with your spreadsheets, databases and statistical packages. Box 9.1 gives brief overviews of four software packages available at the time this book was written. These overviews are not exhaustive and will be inaccurate for updates. Contact the supplier of each software package for current capabilities, compatabilities and costs. All four programs described below use on-screen menus to assist the researcher in configuring the data collection format and selecting recording
options for data summarizing, display, storage, analysis and transfer. Whether you write, borrow or purchase data-collection software, it need only have the capabilities necessary for your research. However, especially when purchas-
ing software, consider that your research may expand into more complex experirnental designs and sampling rnethods in the future.
and latencies of multiple behaviors. Tones of various pitches and durations could be
Besides using the keyboard or mouse, behavioral data can also be input to a
programmed to signal that the correct key had been pressed or signal the time for a
computer using a bar-code reader. For example, Line et al. (1987) developed a computer/bar-code system lor recording behavioral observations in their studies of
scan sample. So.ftware
for data collection can either be: 1. written by the
researcher; 2.
obtained from other researchers who wrote the programs; or 3. purchased commercially. The basic information recorded by data collection software is the occurrence and
rhesus macaques. They recorded the frequency and
duration of 51 behaviors which
were printed with a unique bar code on a plasticized sheet. Behaviors were entered
into the computer by scanning the bar code for the behavior with a light pen; it took approximately I second to obtain a correct reading.
time of a behavior lor each input by the observer. From this data, the program can then derive frequencies, durations, latencies and sequences of dilferent behaviors. As an example, a flow chart of Whiten and Barton's ( 1988) fircal-aninral all-occurrences and scan sample programs is shown in Figure 9.16.
Many programs also allow the observer to record additional ittput lor citclr behavior, such as who performed it, where it was pcrlirrnrccl rrnrl (o wlrttrtt ot'wltitt i( was directed (e.g.Mendl, 1988; also scc ovcrvicw tll'corrrrttcrci:rl sol'twltrc lrclorv). Researchers whrl hitve rlcvclol'rctl tlrcir 0wtt tlltlrt-r'ollt't'tl()il l)r()1, lilrils;ttt'ollr'tt willirrg loslrirrc tltcttr rvillt r.'ollr';rgrrt's. ltt;rrlrlili()lr. s('v('r;tltllrl;r tollcr'lt()n l)t(,1'tiun\" wltielr t'lrrt lrt'rrsr'tl tlitr't'llv ot tttorltlit'rl lot ',1r.'r.'',,'r("i(';rt.lt rt,','.1'., ltlttt'lrt't'tt ltttlr
s.t.th Datu collcction via animal input Anotlrcr lypc ol'data collection involves having the animal record its own behavior. I'irr cx:rnr1'rlc, in irrr cnclosure or laboratory operant arena, a microcomputer can be l)11)l-lriunnrctl lo rccorrl l"rrr rlr key prssses (basically any type of switch closure) on a l-l lr lr;rsrs (t',' ll;t t't ttl..l990). In lrrrn. lhc nricrocomputercan trigger rewards on v'itltt;rllr':rtrtllPt'ol rt'irrlirtr.'r'tttcnl st'ltr'tlrtle irtutgirrirblc(ll,:tyfielcl, 1982,Gordonc/ rrl . l()li l. h;rlltrr;nt. l()l((r. M;tlllrr'tr',;rtttl I rrrlcrvil'. l()().1, ( )'l)clliutrl.lltckson. 1986). I lrrs l\'lrt' ol \\'\l('nt t('ln(r\t", lltr' r('(llrl('nr('trl ol ;ttt olrsr'tvt't t'ottlitttltlly ltcirig
260
M ICROCOM PLITERS
DATA-COLLECTION EQU I PMENT
261
DISPLAY: series of 'prompts'e.9. for social grooming, prompts for lD of partner, etc.
3. A key press denotes an event which requires a
START DISPLAY'prompts' for initial data,
string of data{ypes
e.g.'lD?'
STORAGE of data with date and time
CONTINUOUS SAMPLING PROCEDURE USER presses keYs to record behaviour in various waYs e.g.
DISPLAY: when nothing else is happening, shows focal time elapsed, lD, and number of nexl point samPle
DISPLAY shows these as a check
USER enters data requested, either by pressing pre-assigned keys or typing in alphanumeric codes
STORAGE with or without times
ALARM TONE SOUNDS AFTER 90 SECONDS
I
t 1. Key press records start of a bout: same or different key records identity of behaviour assigned to it, and bout finish time
POINT.SAMPLE PROCEDURE
TONE/DISPLAY: oPtions include unique tones to signal correct key presses, and/or disPlaY of flagnumeral when a behaviour state is active
STORAGE: of, for example, (a) behaviour code, start time and finish time, (b) behaviour code, start time and duration
DISPLAY: shows flag-numeral0, and prompts as in option 3 above
USER enters data as requested, as in above
DISPLAY: shows these as a check
STORAGE: of string of data entries
2. Each press of a sPecific key denotes a specific event, e.g. each bite of a particular food
TONE/DISPLAY: signalling of correcl entries, as above
STORAGE: of, for examPle, (a) behaviour code and real time of event, (b) food code, number of items, consumPtion rate and bout duration
F'ig
9.16 Generalized outline of a routine used to record a J0-rllirttttc lircitl ollscr.vlltl()ll ()ll a loraging baboon illustrates sevcral clptions likcly to bc rclevltttl ltt tllost ttscrs. An alarm tone signals the epci ol'thc lircal itt .10 tnirttrtcs. ltrttl tltc tlispllrt l)li)tlll)l\ the user to conlirnt thc cntl (irt whiclt 1'toittt rttt ctttl-ol-lirr.'ltl totlc is slot't'tl)ot poslp()ltc il, ltcrrrritlirrgcrlrrrltlclion ol :r litrrrlrlltlrt t'ttlt\'(ltotll Wltrlt'tt lttrrl Ilttt'totr. I()lili)
Whichever occurs last, is followed by return to CONTINUOUS SAMPLING PROCEDURE
DATA-COLLECTION EQUI
PM
ENT
MICROCOMPUTERS
Box 9.1 Behavioral data collection software
Box 9.1 (cont.)
BEHAVIOR CHRONICLES
EVENTLOG
H arcln'a re
II ardw are Re q uireme
Re
t1
u
ir e nte n
t.s :
IBM-PC or compatible; at least 286 processor with 2 megabytes RAM, VGA monitor and at least a 40 megabyte hard drive; this program runs in a Windows J.1 environment.
htput: Keyboard
Input
User determines which keys to activate and assigns labels to those
Mouse; clicked on icons or labels.
keys.
The Setup menu is used to configure the subject and behavior files and record the name of the observer. Observations can be divided into
Keys are pressed and held down while a behavior is occurring; several keys can be depressed simultaneously.
initiator, behavior and recipient, and they can be recorded as a group or single entry (date, tirne, initiator, behavior, recipient). Behaviors are classified as events or states which dictates whether
User can sets timers with auditory or visual signals to cue intervals for one-zero and instantaneous/scan sampling. Data recording can be interrupted to add typed notes to the data file.
the compr-rter records the behavior immediately in the data file (event) or waits lor you to click on the finished icon (state). select live observations, observations
The Mode menu is used to videotape (the program controls the VCR)or analysis'
Output:
from
To screen, printer or disk. Analyses:
Clicking on the Edit icon allows the observer to add comments or delete observations in a file.
Files are compatible with most major statistical packages. Summary:
Output:
Easy to learn and use.
To screen, printer or disk; data format is compatible with most Source'.
statistical packages.
Conduit
Analyses'.
The University of Iowa, Oakdale Campus lowa City, Iowa 52242 Henderson, R.W (i988).
Summary and descriptive statistics; time-line arrays for autocorrelation and other analyses using ABOUT TIME utility program. Sumntary'.
EVENT-PC 3.0
Somewhat more difficult to learn because of its increased capabilities. hut the manual is very helpful. Researchers may find this program ditiicult
to use for recording rapidly occuring interztctions since you ntust ttsc the mouse to first click on the initiator's name, then click ort thc behavior, and then click on the recipient's name. A kcy-drivctt vct'siott is being written expressly for data collection. Pricc ritngc is tttetlittttt lirt
utility progriults. 'l'ltc pt'ogntttt free to members ol'thc Antcricirrt /,orl irtttl Arltrtritttn Assot'ilttiott.
the main program and its associaterl
,Sttttr<'('.
('rispe n (
n t s'.
IBM-PC (or compatible)
l{. Wilson
'ltt'slt'tlrclrl l\l(
)
fI urdtt,u r t, Re q u i r c m e n t.s : IBM-PC (or compatible), Apple Macintosh, Commodore 64 Ittptrt: Kcyboa rcl
'lrvtt scls ol'20 kcys (selectable through the shift key) can be assigned
is
to bclurviors. scxcs. inclivirluals, ctc. and given labels. I
rvo
iltllrrl lorrrr:rls:
l l'tr'ss ;tttrl ltolrl kt'r' l()t ()n(' lrt.lr;rr ior trl lr lirltc. ' l'tt'rs l\('\'()n(r',rl ..1,uI,lt(l.r1111.;rl t'Itl ol t'ltt.lt ltt.llitvirtt Io l('( r)t(l
trrrtlltl,l,,ttttilll,rtt,',,u', lrr.lt,l\ tot.,
,11
MICROCOMPUTERS
DATA-COLLECTION EQUIPM ENT
Box 9.1 (cont.)
Box 9.1 (cont.)
Input:
Output:
Keyboard
To screen, printer or disk; disk storage lormat compatible with most spreadsheets and statistical packages.
Can be configured to record up to 100 subjects and 1000 behaviors using the following sampling methods: od lihitum, local animal,
Analyses'.
instantaneous/scan, all occurrences, and one-zero.
Graphic 'strip-chart recorder'output for visual inspection of interval relationships (mimics an Esterline-Angus event recorder output).
The researcher designates behaviors as events or states and which are mutually exclusive. Behavioral elements can be grouped in up to l6
Summary statistics (N, mean, standard deviation) lor each behavior
classes. Each class contains up to 99 elements.
categorY.
Two input formats:
Sequential analysis (SEQ) program available which uses EVENT-PC files to calculate: l. monad, dyad and triad frequencies, 2.
l.
uncertainty and stereotypy indices; and 3. chi-square expected frequencies, goodness-of-fit values, and degrees of freedom for dyad
Key press lor start/end. A single key press signals the start of a behavior and the end of the previous behavior if it has been designated mutually exclusive.
2.
frequencies.
Press and hold key
for the lull duration of the behavioral state.
Behaviors can overlap, but the number of keys that can be Summary'.
depressed simultaneously depends on the type
Easy to learn and use; will meet the needs of many researchers conducting rather'simple', straightforward studies, especially when
of keyboard used.
You can select to record from single (focal animal) or multiple actors. Modifiers can be used to code the receiver, object, intensity, or
the behaviors are defined as mutually exclusive (i'e' only one behavior can occur at a time); inexpensive.
direction of the behavior. You can interrupt data collection to edit the data file. When interfacing The Observer with a video cassette recorder (see 'Hardware
Source'.
Dr James C. Ha 9402224th5w
requirements'above), you can change the playback speed of VCR (slow-motion. pause. reverse, etc.) while the software keeps the
Edmonds. WA 98020 email:
[email protected]
behavioral data stream in synchrony with the video tape. Output:
To screen, printer or disk. Data format is compatible with most
THE OBSERVER 3.0
spreadsheets, databases and statistical packages. H ardw are
Re
quir e me nt
s'.
Base package: PC (versions available
lor DoS and windows) or Apple
Macintosh Support package lor hand-held computers: several ntoclcls ol' ltittttlheld computers manufacured by Psion, I-lewlctt-Pttckrtrtl, lltrsky Support packagc litr vitlcolirl'rc trnirlysis: virleo cltsscllc tccottlt't, r'itlt'.r titrrc-cotlc gcncl'ill()l'ttrrtl relrrle t ( )lrlton;tl: lltllt't'ttttltollt't, t ll;rl;l( l('t ;lt()t.
Will calculate statistics across a complete sampling period, for single ohse rvations. or for event- or time-based windows within an rlhsc rvltt itl rt.
Anrrlysis o1'rtions incluclc: intra- and inter-observer reliability; time-
and others.
,'.('rt('l
A nu l.t',rt'.t''.
lt
tttttt' l't ltlrllt't
e
ve
rrt lrrl'rlcs trrrtl plols (ntirnics Irstcrline-Angus event recorder),
tlt'st't iptivc slrtlistit's ott l'tt't;ttr'ttcy ol' occrrrrcncc trncl clr-rration of
t'\'r'nls ot rlllt's. nt'rlt'rl ;rrr;rl\'rt'r (r'l' lrt'lt;rr,'iots ttntl ltlcltliolts);
I
jl
AUDIO-TAPE RECORDERS
DATA-COLLECTION EQUIPM ENT
is not recorded in a computer-compatible form, then these storage and manipulation programs provide a convenient data entry option for later analysis. A short dis-
Box 9.1 (cont.) correlations; lag sequential analysis; integrated analysis of continuous signals (e.g. heart rate) and observed behavior.
cussion of software packages for statistical analyses can be found in Chapter 16.
9.9 AUDIO-TAPE RECORDERS
Summary:
This is the most comprehensive data-collection software available. It is relatively easy to learn, considering the choices you must make between its various configurations for recording data. Its support packages for numerous hand-held computers make it very useful for
Ethologists use audio-tape recorders for three different purposes: l. to record observations verbally described by the researcher 2. to record sounds produced by animals under study;and 3. to store data in a format compatible for later transfer to a
computer for storage and analysis (also
see
section 9.7 on data loggers).
field studies. Since it is the most comprehensive package, it is also the most expensive.
9.9.1 Data collection on audio-tape recorders
Source'.
'Ihis method of data collection has several advantages and disadvantages. The most
Earlier version of The Observer (v. 2.0): Noldus (1991). Software review of The Observer 2.0: Boccia , (1992) (in Multiple
Authors 1992) Noldus Information Technology bv Costerweg
noteworthy advantages are: l. being able to observe continuously while recording data;and 2. flexibility of input; additional observations and comments can be easily recorded. The greatest disadvantages are: l. recorders stopping and tape running
out;2. real time being difficult to measure accurately if the recorder changes speeds, slightly, at different temperatures; 3. transfer of data from the tape often being difflcult since most observers do not adhere to a strict format when recording the data;
5
6702 AA Wageningen
The Netherlands
and4. speaking into the microphone may disturb the animals being observed. At the beginning of each data-collection period you should record the same preliminary data that you record in written field notes (Chapter 4). The lormat for
e-mail: info @ noldus.nl
puter-compatible form) than the electro-mechanical devices which have been used. Wildhaber et al. (1994) used a microcomputer to control and monitor continuously their experiment on loraging behavior in bluegills. A passive type of data logging is
recording observations can be the same as written ad libitumfield notes or any of the other sampling methods (Chapter 8). As with check sheets, if the behavior is complex or occurs rapidly, some type of coding should be used (discussed earlier in this chapter). The code must be clearly defined and the sounds of the code words
possible through the use of treadles or light beams, which are recorded by the micro-
must be easily discriminable for future transcription.
present, and is generally more accurate and provides more information (in a com-
on-or-off
Data transfer from audio-tapes can take many forms. You can transfer the data
switch closures, are quite inexpensive to design and simple to program (Symonds
to a check sheet (e.g. Tacha, 1988; also see section 9.2) or, for some studies, a complete sequential transcript may be advisable, such as that used by Hutt and Hutt (1974) in their study of 'free field' behavior in children (Box 9.2). Data can also be
computer. Systems such as these, in which the microcomputer must detect and Unwin, 1982).
transf'crecl directly into computer data files using standard data collection software
s.8.2 Data storage and manipulation
E.rccl) ordatabuscnrilnagers(Aslttorr-'lhtc's
lirr clirect computer input (see above). Also, some programs are designed spccilicirlly lirr rccording data from audio-tapes. For example, Noldus Information 'l-ccltrtologics ollcrs an Audio-Tape Analysis Kit lor use with The Observer 3.0 softwrtt'c wlriclr pnrvitlcs lccurirtc cocling ancl timing of behavioral data from audioclcsignccl
The storage, editing and manipulation of ethological data is no clillcl'e ttl tltart tltitt of any other kind of data. Most commonly, sprcaclshcct progrittrts (sttclt its l.oltts
t 2 3 orMicrosoft
l
l)lttt,s't'. Mic'lrrt'irtt's
Rhu,yc.6r Microsolt's /,i/r,) irre trsctl. ll'tlirtir-collcclion sol'lwrttt'is ttsr'tl. il is ttttp.rt titttl trl rctttctrrl'lcr tltlrl tltc t'rlllr't'tion sol'l wlu('lnlrsl lltotlttt't'rl:tlrt lilt's ott rltsk tvlttt lt
iu'(.('()lltl)ittilrlt'rvitlrlltt'rlrsklik'srrst'rllrVllrr.'slot;t1,1'/11v;ttttPttl.rltott',olltt;ttt'llrl.tllr
l
I
lr1tc.
Arrrlio l:tpt'tt't'ottlt'ts:tvrttl:rlrlt'lor nolt'llrkirrg vlrry in sizc l}om small pocketsl/('(l ttttr'tot;tssr'llt' tr't otrlr't', (lrr'ltl ',ttttltt".) lo l;rr';'r.' r't'e l-to-t'ccl rlccks (cttcltlsttrc ll
DATA-COLLECTION EQU
268
Box9.2
I PM
ENT
AUDIO-TAPE RECORDERS
Transcript of the tape-recorded commentary made during a three-
minute session of observation on a child's'free field' behavior. Numbers designate location in the room; strokes designate termination of an activity; numbers above the strokes are the duration of the activity (from Hutt and Hutt 1974\
9t/.
2t/.
Standing 4 looking bricks, holding wire / / walks 7/8 twirling / / looking bricks ^tt / L /:
3'/:
/ / walks to screen l5 / / turns to 0's call, walks
l0lll l/
twirling, looking bricks
7224 twirling I I // walks l0/l I to 2 I I bangswall
//
looking bricks, twirling
516
II
9.9.2 Recording animal sounds
2
walks 7 picks brick / / runs screen 15, puts brick in mouth and bangs on screen
63Z',/ // rubbing
screen
walks l3 to 9 // puts brick window I I standing 5 bangs brick
24 4
6
3'/.
at 0 I I walks 8, climbs on chair / / sitting chair, looks screen to 0 to window / /
21/.
5'/:
4'h
date, time and climatological conditions. This may be given as commentary on the tape immediately before or alter the recorcling, or simultaneously
2 3 0 l/ throws brick at / / turns, runs 8, climbs 3'/, 8
4'/: chair I I
'
2
524 back of chair, hand in mouth / / looks door / / gets olf chair looking l2 / / turns. walks l l lo 9 lo,5.
3
l0 2'/,
li,,,
to l5l / picks hrick tlrnrws it to I / / w';rlks lo
5
H
r'lirnlrs ,,rr lo t'lr;rrr / /
tdB).
Signal-to-noise ratio: the ratio of background noise from the recorder to the signal put on tape. A good ratio should be about 55-60 dB. Tape .speecl: represents a
trade-off between quality of recording (high
Motrttphonic rccortlcrs come in models to record onthe.full track or half track.
38 bitingjumperwhilewalkingto6to 7//throws hrick to l(r// twirls//u,rrlks
Frequenc)'response: range from highest to lowest in hertz (Hz5cycles/s). The number of decibels (dB) lrom a flat curve is also usually indicated
speed) and economy (low speed). Good recorders should have the capacity lor 38 cm/sec.
chair looking over side at floor / / looks window / / looks ceiling. leaning over
//
' '
window holding on to chair / / turns to 0's signal, reaches lbr 0's brick / / sits
walks l3 / / stands l3 bitingjumper looking corner
Recorders used for animal sounds, in contrast to recorders used for note-taking, should uc, of the highest quality and fidelity. The following characteristics should
(e.g.
3'/.
13
be
kept in your field notebook.
checked when selecting an audio-tape recorder for this purpose:
stands arm of chair holding door frame / / jumps on seat. turns / / bangs
9
of the tape (see below). Additional written recorcls of the recording should also
be
looks at brick / / gets off chair walks 7 I I picks up brick throws at screen / /
4t/.
When recording animal sounds on audio-tape you will find it important to identify the recordist(s), animal(s), habitat, and behavioral context, geographic location, on another track
on window / / turns throws brick I 5 and goes alter it / / picks up brick l5 throws
banging screen walks l3l14
and laboratory studies). Since there is no need for high fidelity in this type of use, microcassette recorders can be run at half speed (1.2 cm/s) to get 90 minutes of recording time per side of tape. Voice activation of the recorder or an on-ofF switch on the microphone is almost a necessity, and a rechargeable unit is especially useful to the researcher who makes long, daily forays into the field. Keep the recorders clean and in good working order; check from time to time while you are recording (vU meter or indicator light) to be sure your voice is being recorded. Examples of the use of audio-tape recorders for data collection are provided by Brockway (1964), Burley et ul. (1994), Eisenberg(1967),Kaufman and Rosenblum (1966), Polak (1994), Poysa (1994), Rosenblum et at. (1964), and Sorenson (lgg4).
The firll-trttck recordings are probably of the highest quality; but the half-track nltlclels illlow yot-t to turn the tape over and record on the second side. There is some Itrss trl't;trirlity sirtce only one half of the '/,,-.inchtape is beilg recorded on each side.
l(r
,\lt't't'ttltlrtttrit' t't't'ortlrrr'(reel-to-reel or cassette) split the tracks and allow you to l'ccot'tl llottt lw() s()tll'ccs sinrttltirncotrsly. Thirt is. you can record animal sound on ottt'ltltt'k lttltl ;t Vcthltl tlcst'riPliott ol'llre ollrcr lr':rck sintultune6,sly (tw.-track).
Alsrt. l\\'o 1rl'r11.1r't.rs. t.;rt'll t('\l)()n\tltlr. lirr (r1(. itrtittt;tl. t.lttt silttlllillC()lsly fCCrlftl lltt'tt .lrrt'rr;rlrr,n'.. ()n('()n t.,rr lr rr,r, l, (( ir.rrrr ,rrrrl l\l;rr krrrl.slr. l()(rl). l,ilt11.-11.,..;, It't ol1lt't',,rllott lltr'l('( (rtrlttr1,,,l l\\rr,.utrrtll,ur,.,rtr,. lt,tt l\,, (rtt (.,t( lt.,trlr.
ll
270
AUDIO-TAPE RECORDERS
DATA-COLLECTTON EQUTPMENT
271
For many years ethologists used reel-to-reel recorders exclusively since they generally made higher-quality recordings than cassette recorders, although suitable recordings could be made with the best cassette models (Bradley, l97l). Now, cassette recorders are gaining increasing use because their capability for making high-quality recordings is coupled with their compact size. The two portable reel-to-reel recorders most often used by ethologists for recording animal sounds (including lootdrumming by kangaroo rats; Randall, 1994) are the Uher (Figure 9.11) and Nagra (Figure 9.18), although other high-quality recorders are also used. Sony, Marantz and Uher all make high-quality cassette recorders. Some of the reel-to-reel and cassette recorders being used for recording animal sounds are:
'
Reel-to-reel:
Nagra IV-S (Brown and Waser, 1984) Uher 4000 (Eales, 1985; Randall, 1994) Uher 4200 (Randall, 1994)
'
Cassette:
Marantz CP430 (Adhikerana and Slater, 1993) Marantz PMD 221 (Elowson and Snowdon, 1994) MarantzPMD 360 (Given, 1993) Sony TCD-5M (Brenowttz and Rose, 1994) Sony
WM-D6C (Rothstein
er
a/., 1988)
The above list is by no means exhaustive as to the makes or models of recorders that
are suitable for recording animal sounds. You should check the literature and consult other ethologists who have made recordings of the same, or similar, sounds and species you will be recording. Also consult with reliable suppliers of animal sound recording equipment, such as Saul Minneroff Electronics, [nc. (574 Meacham Ave. Elmont, NY 11003). Audio tapeshave three important characteristics: l. thit'kness;2. hut'king; and s
ignal- t o-no
is e
3.
rat io.
Tapes are generally available in three thicknesses: 0.5, 1.0. and 1.5 mm. Traclc-
olfs are involved when you choose the thickness of tape lor your rccorrlings. Thc thinner the tape, the more stretching that may occur, the nrore tapc you will gct pcr' reel, and the more chance for print-through, i.e., the tendcncy lirr a rccortlctl sigrrirl to magnetize the adjacent wound tape (Braclloy 1977). n 1.0 rnrn tu1'rc is rrsrrirlly rr reasonable com promise. Backings are gcrtcritlly citlrcr llrc rrcwcr polycslcr plrrslic (Mylrrr )ot tlrr'oltlt't t'r'l
Ittlosc ltcclltlc. Illlycstcr pllrstic is ptcli'r rt'tl, stttt'r' r't'llrtl.)\(' ;l(('l;tlt' lt'ttrls wt'lrkr'r rrnrl isnrorl'g'v11rnt'losltt'lt'ltttrl'.\\';rl)ull'rrtttl rrttnklttrl,
lo
lrt'
l'iq t; ;7 Arrtlio plrryback cquipment:a. Uher4000 Report_L recorder; b. Realistic MpA_20 rrrrrPlilicr'; c. llcrrlistic PA-12 trul.npct spcakcr. B. Equipment i1 A assembled into
t':rrryirrg
r.lrse.
DATA-COLLECTION EQU
I PM
ENT
AUDIO-TAPE RECORDERS
must match the recorder and the output should be approximately 57 to -53 dB (Bradley, 1977).
Differential directionalcharacteristics are provided by omni-directional, c'ardoid, and supert'ardoid mikes. Omnidirectional mikes are essentially sensitive to sounds in all directions, while cardoid mikes are most sensitive to sounds in front of them. Supercardoid mikes (shotgun mikes) are highly directional and increase the relative intensity of the sound at which they are directed (within a small angle in front of the microphone) by remaining insensitive to unwanted sounds (noise) outside of that angle. In essence, they increase the signal-to-noise ratio. Walbott (1982) provides
additional information on microphones. Cardoid microphones may be used with a parabolic' reflector that focuses sound received over the width of the reflector onto the microphone, which is set at the focal point of the parabola (Figure 9.18). Parabolic reflectors should be wider than the wavelength of sound that you are recording. For example. many songbird vocaliza-
tions are in the frequency range of 2-8 kHz, with wavelengths of 0.03 m to 0.15 m. Therefore, a parabolic reflector with a width of 0.46 m is sufficient. However, coyote
vocalizations are often around 500 Hz, with a wavelength of 0.61 m; therefore the
parabolic reflector should be at least 0.61 m in diameter in order to make highquality recordings. Parabolic reflectors are more effective than shotgun microphones when recording over distances that exceed l0-25 m. Several parabolic microphones are available, such as PBR-330 (Saul
Minneroff Electronics, Inc. -
see address
above)
and Dan Gibson E.P.M. (R.D.Systems,290 Larkin St. Beffalo, NY 14210).
Many of the microphones used by ethologists are models manufactured by Sennheiser, although other excellent microphones are also available (e.g. Saul
Minnerof Electronics. Shure). Once again check the literature (e.g. the
relerences
listed with the recorders, above), other ethologists, and suppliers.
Acoustic biotelemetry has been used to transmit animal sounds lrom animals equipped with microphone/transmitters to receivers and recorders a short distance away(e.g.Gaultier,1980;MontgomeryandSunquist,l9T4).
AlkonetaL (1989)devel-
oped and tested an acoustic biotelemetry system which transmitted usable sounds
from Indian crested porcupines (Husrix indit'u) for a distance up to I km. They tested Fig.
9.18 Parabolic reflector covered with camouflage netting attcl wincl-shiclclctl microphone wired to a Nagra IV audiotape recorder.
Low-noise tapes are superior to normal tapes in their ability to itttpt'ovc lltc signal-to-noise ratio. Some recorders have a separate scttirtg lirr low-ttoisc (rtpcs. Mic.ntplrunc.r (mikes) come in two basic clcsigtts: rlylrttttic (tttovittg-coil) or t'ott denser. Thc coldenscr niikc nuty lrc sttl-rct'irlt'. htrt it is Ilt()lc ('()lllplit'ltlt'tl ;ttttl ,,llt'tt
lilr lit'ltl l.r.t.rlt'tlilt1,. tl\,tt;tttlit.ttltkt.s;ll(.|)lrrlr;tIlIt lllt'llt'sl tlrr' itttltt'rlrtttt r';ttltl t)ulltul I ltt'ttttt to1r1111111"'. lllll)('(l.lll((' clttrit.c. l.rlttk t'luelirllV;tl rctlttircs ittltlititlrrltl
I.c1.xtit.'
the ability
of seven briefly trained observers to identify correctly behaviors from
recordings ol'seven difl-erent behaviors (feeding, drinking, snilfing, walking, digging,
l2"l'(sr)of thebehaviors llrrnr l lrc rccortlirtgs and were 93(Yocorre"ct lor recordings of leeding and walking.
rnovirtg.thrcathufli).Overall,theycorrectlyidentified82t
().e.
I l'!:t\
ll:rck ol' soultrls
Sottttrls lrtt'pl;rvr'tl lo lrttittt;rls ttt .ur;rll('lnl)l to rlt'lr't'tttitrc lltcirclll'ctivcncss in stimttl;rlttr1,
,'r rrrlrrlrrltttl'
lrr'lt.rt tot lrr llrr',
rr,tt lltt' lunt li()n ;ur(l/()r t'lli't'l rll' biolic
or'
AUDIO-TAPE RECORDERS
DATA-COLLECTION EQU IPM ENT
abiotic environmental sounds can sometimes be deterrnined. For an overview of the use of playback in ethological research see McGregor (1992)' Normal (unmodified) animal sounds are often played to conspecifics and their resultant effect observed. For example, Waser (1975b) demonstrated that playback of the gray-cheeked mangab ey's (Cercocebus albigena)'whoopgobble' vocalization mediated intergroup avoidance, and Lehner (1982) demonstrated that coyotes differentiated between'group howl' and'group yip-howl' vocalizations by their different vocal responses to playback of the two vocalizations' Animal sounds are sometimes modified to determine the functions of their dif--
lerent components. For example, Emlen (1912) modified the recorded songs of indigo bultings (Passerinu cyanea) and through playback dernonstrated that: l. species recognition is coded in the note structure, inter-note interval, and note length; 2. individual recognition is coded in the details of note structure; and 3. motivation cues are reflected in song length and singing rate' I--lsing a unique
and stored in a computer where it can be stored, modified and manipulated and then converted back to an analog signal (normal sound) for playback. All of the newer sound analysis software programs have these capabilities (see section 9.10.3).
For playback of either natural, modified or synthesized sounds, the recorder, amplifier and speaker should be of sufficient quality and fidelity to broadcast a good reproduction of the sound. Lehner (1976) used the playback equipment in Figure 9.ll in his study of coyote vocalizations; the study required the broadcast of relatively low frequency, olten 'noisy'(few pure tones) vocalizations at high intensity (volume).
The audio-tape recorders discussed above (section 9.2.2) are also suitable for playback of most animal vocalizations and mechanical sounds. Some of the reel-toreel and cassette recorders being used by ethologists to playback animal sounds are:
'
Nagra IV-S (Dyson et a|.,1994) Revox A700 (Eiriksson, 1994)
approach, Simmons (1971) picked up bat cries in two condenser microphones and played them back to the bat with diflerent delay times, simulating'phantom targets'
Tandberg Series 14 (Chaiken et a|., 1993)
at different distances.
playback can also be used to reveal the significance of interspecific sounds. For example, Cade (lg7 5) showed that female parasitoid flies (Euphcrsiopteryr ochracea) containilg living larvae were attracted to dead crickets attached to speakers, through which cricket songs were played. The ellects of natural (or synthesized) abiotic environmental sounds also can also be studied by playilg them to animals. For example, Larkin (1917) showed that tape recordings of thunder" bird calls, and artificial sounds played to migrating birds through a loudspeaker system slaved to a tracking radar often caused the birds to turn away from the sound. Heppner (1965) found that high-intensity noise had no effect on the ability of captive robins (Turdus migratorius) to find earthworms,
llrther supporting the hypothesis that the robins were primarily using visual cues' Several types of equipment and method can be used in playback studies. Several older techniques can now be replaced by computer technology.An example of older technology (that rnay still be suitable for some research) is the puttarn pluybuck I'htrl was desigled to synthesize human speech for research on the recognitiorl tll'cottsttnants (Denes and Pinson.lgl3).It is essentially the opposite o1'a sottncl spcctl'ograph. A souncl spectrogram pattern is drawn on a piece of paper that is tltcrt t'tttt
through the Pattern Playback, which converts the images drawtr ott tltc l'lltPcl 111111 sound played through a loudspeaker. The Petttern Playback ltits bcctt ttsctl lo scFl'cgate the relative impgrtance ol- cclr-t-tp()ncnls ol'srltttttl itt lt'ltttstltiltirrg rrtlolrrlrtliotl ('()ll\tllll Likewise, Etllcn (1972)' in his sttrtly ol'irttligo btttttittp s()lll,. tlst'tl lltt'litttt'
tccSrritltrc rll'crrlliryr irrrtl slllicirrl, :rrrtli,, lltpt' itt rrltlr.'t lr) l(':ll lilll|1' 1ltt' ,'ltlt'l ol lltc itttlil,,p lrttttlitrl,'s rt6lt.s li,r pl:rv'lrlrt'k Sortttrl t,ll;ltltll,r llt;lt'(;lll llott lrt'tltl',1,,t't'
i.g
Reel-to-reel:
Uher 4000 (Brown and Waser, 1984)
'
Cassette:
Marantz CP430 (Adhikerana and Slater, 1993) Marantz PMD 200 (Marzluff, 1988) Marantz PMD 430 (Given,1993: Allan and Simmons, 1994) Marantz PMD 3340 (Rothstein er a/., 1988) Sony TCD-5M (Brenowitz and Rose, 1994)
Like the list of recorders given earlier, this list is not exhaustive as to the makes or models of recorders that are suitable for playback of animal sounds. You should check the literature and consult other ethologists who have conducted playback
of
the same, or similar, sounds to those you will be broadcasting. Once again you should consult with reliable suppliers of playback equipment, such as Saul Minneroff Electronics, Inc.
9.9.4 Ultrasonic detectors
There are twr> types
ol commercially available detectors which have been used to of animals (Fenton et al., 1973). These devices have
cletcct thc ultrasonic sounds
bccn uscrl in rcscarch on bats (Fenton, 1970; Kunzand Brock, 1975) and insects Scc Sirlcs antl Pye (19741ftir a review
(Klcirr. l()55).
( )ttc rtllr':rsor)ie tlctcclor is rrurrrtrlhe ttrrctl by [{olgates of Totton, Southampton, I lrrilr'tl Kinl'1l1v1,t ll trst's lr t'lr1l;rt'tlln('('nli('r'o;lltonc cirpirblc rll'rcsponcling to frc-
(lu('n(r('\lrclrvt't'rr l0;rnrl lXOLllz:r',rrt'll;rst'lt't'ltorut'lrrrrirrl', lolrnril lltcirrprrrl ll:utrl
i
ANALYSIS OF ANIMAL SOUNDS
DATA-COLLECTION EQU IPM ENT
width. Another that uses a crystal microphone adjusted for maximum sensitivity at 40kHzis manufactured by Alton Electronics Co., Gainesville, Florida. Paige et al' (1985) provide a schematic diagram for constructing an inexpensive, hand-held
(Figure 9.208). This provides only a relative measurements and says nothing about
ultrasonic detector.
versus frequency is the section display (Figure 9.20C). This display samples the
the actual intensity of the sound.
Another feature of the Sona-Graph that provides a relative measure of intensity recording at six or fewer predetermined points and presents relative amplitude as a
9.IO ANALYSIS OF
ANIMAL SOUNDS
available Several types of sound spectrographs and computer/software systems are below. described briefly are tools for the analysis of animal sounds. Some of these
down to 2.5 ms.
e.lo.l EquiPment e.to.ta Kay Sona-GraPh Model7029A for conThe Kay Sona-Graph Model 1029|(Figure 9.19A) is an electronic device an from input sound records It display. visual a to verting tape recorded sound reproduced is then sound recorded audio-tape recorder onto a metallic drum. The burns a by a stylus which scans the various frequencies across time and electrically on the vertical axis sheet of paper to produce a 'picture'of the sound with frequency
of representing frequency on a on a linear scale, although reproduced is usually
axis. You have the option
and time on the horizontal linear or logarithmic scale;
it
Marshall (1917) argued for use of the log scale. The frequency range and duration spectrogram produced depends on the speed at which you set the
of the sound
a sound metallic recording drum to spin while recording from the tape' For example' of duration have a will axis vertical on the spectrogram which displays 20-2000 Hz several produce It will 9.6 s, and an 80-8000 Hz display will have a duration of 2.4 s. mechanitypes of display, all of which are useful in the analysis of vocalizations or
cal sounds (e.g. grasshopper calls)' 9'204' The norntal clispla-v of the Sona-Graph is the sonagram shown in Figure (pitch) reprcis is represented on the horizontal axis (l s/mark), frequency
Time
sented on the vertical axis
horizontal mark at each frequency, inverted from the normal sonagraph (frequency increasing from the top to the bottom of the paper). Note that the section through the bark shows a much wider range of frequencies than that through the howl. Marler and Isaac (1960) describe a device for modifying the sound spectrograph to make frequency-versus-amplitude sections serially through a syllable at intervals
(1
kHzlmark), and amplitude (intensity)
is represetrtetl hy
l4'5 crtt x the blackness of the mark. Sound spectrograms are produced on paper 9'20' Figure in 32.4 cm.only a portion of which is shown From the sonagrams above we can see that the coyote's howl bcgittl with thrcc bcirtg clcirt'ly bursts of energy over several frequencies, with none ol'the fi'eqtrencics llcgrttt 111 11 1'r-'l:ldefined;these are essentially introductory'barks'. The howl portiotl t'cttt;rirtctl lirt tively low frequeng:y and then rose to approximzrtcly l.(r kllz. wltcl'c it o11'sllrrrPly. tltrrppctl approximately two secgncls, at which point thc l'r'ctltrcrtcy lrlltt'kttt'ss)' tt't' Sincc thc dillcrcnce bctwccn intcrrsitics is one ol'rle1',11'1'(rt'l;rtivc
ci,l .sc lltc r,plrlrrrr t'rli:ylttt'lrl rlt'littt'lrlr';tlt';ts ()l ('(lllill llll('ll\llV lll \('\('ll l'l:l(lillt(tll\
Sections are useful for determining the relative amplitude of frequencies in a particular syllable or sonagram. However, they cannot be used to make absolute measurements (e.g. number of decibels) without considerable difficulty, and they should not be used for comparisons between sonagrams since they are affected by the investigator's choice of settings on the sound spectrograph. Vocalizatktn terntinologv has been rather inconsistently applied, with few authors using similar terms. Kroodsma (1977) used the terms in Figure 9.2lAto
detail song development in the song sparrow. These terms are similar to those used by Rice and Thompson (1968) for indigo bunting vocalizations. Although not
totally satisfactory (Kroodsma, pers. commun.), these terms are applicable to vocalizations of numerous other species and are uselul in sonagram analysis. Temporal patterns are extremely important in insect sounds. Bentley and Hoy (1972) developed the terminology in Figure 9.218 for their study of the genetic control of cricket
(Te
leogryllus gryl lus) song patterns.
The Kay Sona-Graph Model 1029Ahas been used for more than two decades in ethology, but it is no longer being manufactured (although limited parts are available; Kay Elemetrics Corp.
-
address below). Although more sophisticated equip-
ment is now being marketed (see below), used 7029A machines may still be available, and they are satislactory for analyzing sounds in many studies (e.g. Miller l994,Payne and Payne 1993).
e.tl.th Kay
DSP Sona-Graph Model550A
The Kay DSP Sona-Graph (Figure 9.198) is a workstation that combines a real-time souncl spectrograph, a computer-based data-acquisition system and a dual channel
I;li'l'rrnlrlyzcr'. Sounrl input is stored digitally for display and analysis, and it can be tlowrrlo:rtlctl lo lrnollrcr corrrl'rtr(cr lirr storlgc tlr analysis by other computer prof,,liuns (st't'lrt'lorv). l( is l ln('nrr tlrivr'rr svst('nl llrlr( tlisplrrys rlscilltlgrtnrs (wave lirttttr). ('(rnt(!ut l)o\\'('t sPt'r'lttun',,ur(1 .'1x'r'lt{r|riun\ rrrr lltc vitletl lll()l)il()t'llLtt citn
ANALYSIS OF ANIMAL SOUNDS
DATA-COLLECTION EQU IPM ENT
Fig.9.19B Kay Elemetrics DSP Sona-Graph Model 5500 interface with a microcomputer.
then be printed. It has a history of use in ethological studies (e.g. Brenowitz and Rose, 1994) and is available with several hardware and software options. The newer
CFL 4300
Model
is a completely computer-based system
that may replace the DSP 5500 for animal sound analysis (Kay Elemetrics Corp., l2 Maple Ave. Pine Brook, NJ 07058).
9.I0.tc Uniscan II and Ubiquitous Spectrum Analyzer Two sound-analysis machines that are no longer being manufactured, but may still be available for use, are the Uniscan II and the Ubiquitous Spectrum Analyzer. Both can be used for analyzing animal sounds. The Uniscan II (Multigon Industries Inc.) system includes a keyboard, processor, monitor and printer. It produces a real-time display of a sound spectrogram to
the monitor or printer in several selectable frequency ranges. Any 1.6 4(XX) Fig.9.19A Kay Elemetrics 70294 Sona-Graph and LJher
lutlio-trtpc rccoltlcl
second
scglttcttt can be liozen on the display for measurements of frequency and duration. I Ihitltritous is the trade narre for the Federal Scientific Spectrum Analyzer.It is a
t'cltl-lirnc. titnc-corttprcssion scirnrrirrg unalyzer which can be used for analysis of rttrtttutl \'()ellli/iltirtns willr llre ;rtltlrtiorr ol'rr tlispllry syslcll (tlrtpkins rl u1..1974). A tltl'tl;rlsvstt'rtt is rrst'tl lo sPt't'tl rrp tlrc rrl'rrrl. iul(l itrr;rrr;rlop,s\slct)l swccl)s tltc tirttc-
11
DATA-COLT-ECTION EQU IPM ENT
ANALYSIS OF ANIMAL SOUNDS
A
Note comPlex
Trill
Phrase
Phrase
'r_ I x53-
-r+.
t I
IJ
I *{
281
Trill
Note complex
Phrase
Phrase
'+
{ l'-Syllable
'n'*,fu Ini
tr!!'fufi'
I
l'* Syllable 1.0 s
B
kJc ffi
Et-
F .;.
E iFr
F * r
k
Fig.
-
Pulse
9.21 Terminology used in a study of song development in the song sparrow (from Kroodsma, l9T7): B. Diagram of structural components and terminology of Telegryllu,s songs: upper line: T. ot'eonit'u.y; lower line: t'ommodus.lnterchirp interval : interval between onset of A-pulses. Intrachirp interval : interval between onset of B-pulses. Intertrill interval-interval between onset of last Bpulse in one trill and the first pulse of the next trill (from Bentley and Hoy,1972).
F
I
D
frr-*
l
compressed signal through a filter. Spectrograms can be displayed on a storage
s
oscilloscope or photographed for permanent copy. Narrow and wide bandwidth analyses are possible, and section displays can be made at intervals as short as 3.125 ms.
Fig.
'l display of the same howl; C. Section display abovc thc norttutl tlrspliry. itrte i' marked on the horizontal axis in or-rc-sccoutl itttcrvltls. lrrctlttcttcy is tttlttkctl ol' the vertical irxis ip onc kllZ irrtcrvirls. (Scc tcrl lirt'irrt erlllirlt:tlttttt ol llrt'l!pt's,,1
One advantage of this system is the speed at which spectrograms can be produced. Hopkins et al. (1974) report that a 2.4-second-long spectrogram take approxittrrrtcly 1.3 minutes to analyze on the Kay SonagraphT02gA and only 9.6 sccotttls ott tho I Ibiquitous. Another advantage is the relative ease with which scctiott tlisplays c:rn bc proclucecl. Spectrograms produced by the Ubiquitous are gt;rittir't tluttt lltosc ptrrtltrcctl hy tlre Sonirgnrph: however, this apparently does not
sottttrl s;lccl rrlgt'rtltts.
;rlli't'l utlcr
9.20 A. Normal display sound spectrogram (sonagram) o[ a coyotc howl:
)
lJ.
('otttottt
grtr'l;rl iort ( I loPk ins,'1 ,r/
.
l
()7.1 )
ANALYSIS OF ANIMAL SOUNDS
DATA-COLLECTION EQUIPM ENT
283
redraw the trace and compare lor its accuracy in representing the original spectro-
g.t0.td Desktop computers
gram.
Desktop computers, commonly the IBM-PC (and compatibles) and the Apple Macintosh, are frequently used with specially designed software to store and
Duration measurements are generally made from wide band pass spectrograms. However, the mark intensity can affect the measurements if they are either under- or
analyze animal sounds digitally.
over-burned.
Digitizing tape recordings of animal sounds is accomplished with an analog-todigital (A/D) converter, often a circuit board inserted into the computer where the
All the measurements described above (and many more) can be made more quickly and accurately with a desktop computer and special software.
sound will be stored. For example, Drosophila courtship songs were digitized using
a Campbridge Electronics Design 1401 (Ritchie and Kyriacou,
1994) and a
Canopus Sound Master (Tomaru and Oguma, 1994), and Gerhardt et al. (1994) used a Soundfx interface board to digitize tree frog calls.
9.10.3 Computer analysis
of recorded sounds
A specialized field of data analysis
has developed around the use
ers in bioacoustics. Software currently exists to permit 9.10.2 Analysis
vals between them; and 3. relative intensities
of portions of the sound. They
can
also be used to compare components of the sounds between and within individual animals. Hall-Craggs (1979) provides several useful suggestions for basic sound spectrogram analysis, and Thompson (1979) offers suggestions for preparing sound spectrograms for publication. The techniques described below involve using hand-
operated mechanical devices (e.g. rulers, calipers, computer stylus) and human judgement. Although time consuming and generally less accurate than computer analysis, they may be suitable for some studies. Frequencies are measured from either a narrow band.filter display on a normal spectrogram or from a section display. Transparent overlay grids are useful in the
making of these measurements. Frequency measurement is more accurate when lower-frequency spreads are used for display (e.9.20-2000 versus. 160-16000 Hz). Contour displays are often useful to determine more accurately the dominant frequencies when large areas are burned. Horii (1914) described a method for producing digital sound spectrograms with simultaneous plotting of intensity and
fundamental frequency. A digitizer system (X. Y cursor, Teletype and computer) was used by Field (1976) to analyze sound spectrograms of wolf vocalizations. Thc cursor is moved along a selected frequency band (e.g., dominant frequency). Thc X. axes
of the cursor's plane of movement
input of animal sounds from it is digitized and stored,
a tape recorder or microphone to a microcomputer where
of sound spectrograms
Sound spectrograms (hardcopy) are generally used to measure: 1. frequencies (Hz), both dominant frequencies and harmonics; 2. durations of sounds and time inter-
Y
of microcomput-
represent time and treclucncy. rcspcc-
tively. The operator depresses a button at predetermined points along tltc tt'ltcc, ittttl the X, Y coordinates are transmitted to the compute r lilr storugc ittttl pt'itllctl rtttl otl
using hardware such as the Unisonic (for IBM-PC and compatibiles), MacRecorder digitizer, or various analog-to-digital interface boards (see section 9.10.1d); the maximum length of sound that can be digitized and stored at one time is limited to
the computer's available random access memory (RAM). Once the sound is digitally stored, the soltware can quickly produce spectrographs of frequency, time and intensity, and oscillographs of time, amplitude and frequency. These spectrographs and oscillographs can then be printed out or analyzed further. Some software can make matches between sound segments (e.g. MATCH; Payne and Payne, 1993) and produce three-dimensional visualizations of sound measurements.
Another important aspect of this bioacoustical software is the ability to manipulate sounds which have been digitized and stored in the microcomputer. Portions of the sound can be deleted, duplicated, moved, reversed or frequency-altered at the touch of a key. The modified sound can then be played directly to an animal or fed back into a tape recorder with a built-in (or peripheral) digital-to-analog converter (e.g.
Allan and Simmons, 1994; Randall, 1994). Sounds can be created from scratch
using these programs, or even more easily and inexpensively, using any of the large number of music programs on the market. Davis ( 1986) describes the Personal Acoustics Lab (PAL), which is a microcomputer based system lor digital signal acquisition, analysis and synthesis. Some of the commercially available sound analysis soltware packages are listed below:
'
('unur1, (Apple Macintosh)
Ilioacoustics Research Program
('orncll Labr>ratory of Ornithology
the teletype. Field (1976) usctl an ovcrlayirrg gritl to locitlc coot'tlitutlc s:tttlplt' poitlls
l5() Sirpsrrckcr Woocls I{rl.
evcry 0.0-5 sccrlntl. Thc grcirlcr tlrc vlrt'ilrliott itt l'tetlttcttt'y ol lltt'sotttltl, lltt'tttolt' ol'lcrt llrc r'orlrrlirurlt's slrorrltl llt'slrrrrlllt'rl I ltt't'.r.rttltnltlt'st'lr t'ltll lltt'tt lrt'ttst'rl l,r
Itlurcrr. NY l-1ti50
' i'lttt
,\1tt'r't
lt I
rtlt
(A;rplt' M;lt tttlpslt
)
DATA-COLLECTION EQUI
PM
PHOTOGRAPHY
ENT
GW Instruments P.O. Box 2145 264 Msgr
record environmentalconditions, lens used, film type, shutter speed, lens opening
(/
stop), filters, and any exposure compensations made. This log may be kept as part
of
your field notebook.
O'Brien HwY #8
The techniques of good photography are beyond the scope of this book; com-
Cambridge, MA 02141
plete and useful discussions can be found in Blaker (1976) and Anonymous (1970).
STGNAL (IBM-PC)
Engineering Design
e.ll.l
43 Newton St.
Belmont. MA 02178 SountlEdit v.2.0.3 (Apple Macintosh; can edit frequencies only from 0-11
KHz; not designed for computer analyses) Farallon ComPuting
The most useful still camera for the ethologist is the 35-mm single lens reflex (SLR)
camera. A distinct advantage of the SLR camera is that the image you see in the viewfinder is the same (93-100'2, accuracy) as the image that is recorded on the film. The SLR camera is also compact, lightweight and versatile. It accepts
2150 Kittredge St.
film types
Berkeley, CA94704 SoundEdit Pro (APPle Macintosh)
(see
a
large variety
of
following sections), although the most commonly used is color-slide
film. Ideally, an ethologist entering the field in an unfamiliar area should be prepared with two camerasloadedwithdifferent types of filmdependingupon the proposed use
MacroMind ParacomP, Inc.
of the photos or slides. Typically, a black-and-white negative film for black-and-white prints is kept in one camera and a color slide film for presentations is loaded in the
600 Townsend St. San Francisco,
Still photography
CA 94103
Although most sound-analysis programs will perform the functions described in greater previous paragraphs, some are easier to use, have faster sampling rates and dynamic ranges, and have additional graphic and analysis capabilities;for example, more Weary and Weisman (1993) state that MacSpeech Lab and SIGNAL are ,sophisticated'software packages than SoundEdit v.2.0.3. You should obtain additional information from researchers who have used the software, as well as the dis-
tributors of the software. before choosing a software package to use or purchase, available sometimes demonstration programs (shortened. simplified versions) are
for you to trY.
other. Color prints can also be made from the slides, if necessary. It is helpful if both
cameraswill accept the same lenses
so
that theycan be easilychanged orinterchanged.
There are a large number of makes and models of 35-mm SLR on the market today, most of which have their own group of ardent supporters. Nikon and Leica
quality and versatility; however, Leica Minolta, Canon, Pentax and Olympus are other camera manufacturers to consider seriously, each having within their systems the necessary equipment lor simple to complex photography. These camera brands will have a complete line of accessories to cover your photographic needs, including a wide variety of lenses, motor drives (automatic film advance), and flashes. The following features are excellent camera systems known for their
is very expensive.
should be considered necessities in any camera you use or purchase: 9.I
I
PHOTOGRAPHY research. Pictures shotrlcl
Ethologists should make a photographic record of their are ncccsbe sharp, well composed and suitable for reproduction if needed. Prints orttl prclirr useful (slides) very are sary for publication, while color transparencies sentations.
photos should depict the: l. study site; 2. animals studied; 3. cqtriprttctrt ittttl methodolo gyl4.results of data analysis (tables and figures). ancl 5. yottr itttct'Pt'ctrthe tion of the results (e.g. models; Chapter l8). As photcls arc takctt it log sltottltl lrtkt'tt wrts thc wlry Plloto kept of photo number, {ate. time. location. suhicct nr:tttcr. (i.e. what yoll were trying trl tlcpict)irntl whirl itt pitt'licttlltl yott sllottlrl ttttlt'u'ltt'tl lllilV you sec t5c tprnsl-lltrcllcv tlr'pl'irrl. ln;rtltlitiott. lo itttPtovt'l'ttltttt'Pltolos, \'()ll
t Maximum z Automatic
shutter speed of at least l/l000th second. and manualexposure settings with a maximum lens opening
of
at least./ 1.9; that is, the/'stops to go as low asfl.9.
r Through-the-lens light meter. .t Black camera body to reduce reflections
and glare directed to the animal.
Tlrc lirllowing are optional features to be considered: Irlcctnrnic cable release - enables remote firing of the camera (connects to I
ltc rttrtl ot' tl rivc).
l)epllr-ol-licltl prcvicw Ptr.'r it'rv
grcrrrrits y()u
lo stop thc lcns down rnanually to
tlt'Ptlr-rrl lit'ltl rvtllt lt I'tvt'tt / slolt.
286
PHOTOGRAPHY
DATA-COLLECTION EQUIPM ENT
Interchangeable finder screens - allows replacement of a split- image focusing Screen with a clear matt Screen for easier focusing. is, Data back - provides on frame information when photo is taken; that provided (information settings exposure number, date, time, and
frame
depends upon capability of different backs)' rubber Water resistence or waterproof - some cameras are sealed with
for a gaskets that resist leakage to moderate depths underwater. A rating camera if the and rain heavy in depth of only 3 meters will be worthwhile is dropped in a stream.
l5 years ago' Computer Today, cameras have advanced far beyond the cameras of functions includchips, instead of manual mechanisms, control most of the camera's
OM-3, Canon Fing fbcus, exposure and flash photography. However, the Olympus therefore l. and the pentax K- 1000, have mechanically controlled shutter speeds and does however K-1000, Pentax The rely on batteries only to run the exposure meter. design years of not accept a motor drive or data back. Electronic cameras, through Their ability to and testing, have reached a high level of reliability and performance. auto-focus, and self-adjust the exposure in difhcult and contrasty lighting conditions, However, since research. in tool imprint data on a photo make them a very valuable that recommended is it electronic (automatic) cameras rely on batteries to function, batteries be replaced yearly and spare batteries be kept on hand.
All of these exposure metering systems will, under normal lighting conditions,
give
you the correct exposure. However, when selecting a camera for use or purchase determine whether that camera has the system that best meets your needs. Lenses play an
important role in the quality of your photographs. The quality of
a lens varies optically and in durability. A very expensive camera will take poorquality photos if a poor-quality lens is used. The 'standard' Iens that is most often supplied with a 35 mm camera is a 50 mm focal length lens. It is considered to have a normal perspective (angle-of-view). Any lens that has a focal length longer than 50 mm is a'telephoto'lens, while a lens with a shorter focal length is a'wide angle'lens.
Wide angle lenses are used where a wider perspective is desired (e.g. habitat photos, photos in tight quarters), and telephoto lens are used to magnify subjects that are
far away. The magnification of an object is directionally proportional to the focal length of the lens; that is, a lens with twice the focal length will double the magnification (e.9. a 100 mm lens produces twice the magnification of a 50 mm lens). Zoom lenses have variable focal lengths built into them, such as a 28 80mm zoom lens. Their advantage is that you can carry one or two zoom lenses rather than several fixed focal length lenses. The disadvantage of zoom lenses is that their quality varies greatly and is often not as good as a fixed focal length lens. Commonly used zoom lenses are 28-80 mm and 80-200 mm.
An accessory which many ethologists find useful is a motor-drive unit which automatically advances the film after each shot is taken. A motor drive comes builtin to some cameras. The speed of film advancement ranges from 1.5 to 5 frames-per-
in The majority of auto-focus cameras have the ability to self-focus accurately available focusing manual standard near dark conditions; they will usually have the choices liom fgll also. Exposure metering systems in cameras give you a variety of electronmanual exposures to fully automatic exposures controlled by the camera's and systems metering exposure of types several ics. The following list describes
to maintain continual observation of the animal(s) through the viewfinder without moving it to advance the fllm manually. You can then concentrate on the animal's behavior and photograph carefully selected behavior units, especially sequences, for
defines their [unction:
later analysis or presentations.
Standard program - the camera sets both shutter speed and lens aperture with a bias of hand-held shutter speed of l/125 s or above. Z Wide program - the camera sets both shutter speed and lens aperture with a bias of smaller aperture over shutter speed lor greater depth-ol'-
I
:
field. Tele program
-
the camera sets both shutter speed and lens apcrturc
witlt
bias towards liigher shutter speeds to freeze action' tllc Shutter-priority auto - you set the shutter speed and the calllcril scts lens aperture.
Aperature-priority
auto
you set thc lcns rlpcrttll'c (/ slpp) lirl. tlcpllt-ol'
second. Besides allowing you to take photos very rapidly, a motor drive allows you
Automatic film advances are necessary when cameras are left set up in the field and are triggered by an animal's activity. For example, Savidge and Seibert (1988) used an infrared device to trigger a camera that photographed predators when they visited artifical nests. Electronic.flashes are helpful when additional illumination is necessary and the subject is within range of the flash output. For nocturnal animals, this may be the
only means to photograph them in their natural habitat; however, flashes are likely to alter their behavior. Photographing small animals (e.g. field mice) by natural light otiert procluccs unsatisfactory photos. The combination of a slower film for quality rrnrl rt snritll apcrturc lor clepth-of-field lorces you to use a slow shutter speed and a
field ancl the cuptcrit sclccts tltc c6r't'cct slttttlct'spcctl. Mtrlrrirlcxp()sr1'c y6rr scl lrollt llte sltttllt't slleetl ;ttttl lltt'l('lls;llx'lllll('
tripotl lirt'strppot'l. A llirsh will allow thc usc ol'a small aperture lorgreaterdepthol-licltl. lrctlt't tlclrril on low lilllrt srrlrjet'ts. rrtrtl tlrc rrbility to ll'cczc nrovcn-rcnt with a
witlr tlte p'ttttl;tllt'e ol tlrt'lrrrrll ttt lt1'ltl lll('l('l
Ittl'11r.',
slttttlct sPt't'tl.'l
tV
lr, lr\('il ll;r'-lr llt;rl
ts
lltt's;rntt'lllrrttl
lrs
llrt'(',lln)cl:t so lllrl il
i.J
oo oo
-.1
?1?#g
o
1
g
i EEi Ei EE1BE1E EE1FEi ?11z1it?i11*i g
iI i;
g
E
EE
i
E EEEEE
g
i I E+e Er I E
zt;iica+ +il
--
g
sI
q
EEE
rn
o .l
i
EE
a
i
i
'
Table 9.8. Selccred,(o d1k black-and+,hitu rtlm for use in 35 mm still camerus Definition Speed
ISO
r-\t
\x 100
T:.-\ Pan
T-\IAX
400
of
Sharpness
enlargement
Very high
Very high
Very high
High
For prints. A good all-around film that combines reasonable speed with high definition qualites
Extremely
Extremely high
125
Very fine
High Very
32
Degree
power
fine
P.::t.tt trnr iC-X
P.-:-\ Pan
Resolving
Graininess
allowed Suggested uses
Very high
High
Sharper than PLUS-X
Medium
Very high
Moderate
For prints. Its major quality is high speed which can be pushed to ASA 800 in some cameras. It can be used in very low light (e.g. forest) or to stop
Fine
Medium
Very high
Moderate
Sharper than TRI-X Can be pushed up ro 1600 ISO
Very fine
400
motion (e.g. running antelope)
i-l15 Recording 1000 tEstar-Ah Base)
Contrast
Coarse
Low
Very high
Low
For prints. This is a poor-quality film which has only its speed to recommend it. It should be used only when very low light or high speed call lor it
Fine
High
Very high
Very high
For copying printed materials (e.g. photos, charts, tables, drawings, etc.). Useful in preparing visual aids for presentations and field trips
3200
Medium
Medium
High
Low to Moderate
multi-
coarse
Has an ISO Range of 1000 6400. Allows photography in very low light at high speeds with good results
64
Copy Film 5069
T-MAX
P3200
it will
fine Medium
100
to high
Hi_eh
For prints. With a special reversing process produce slides. It shoudl be used when rhe emphasis is on very-high-quality prints for publication or enlargement
400
speed
rn ^.)
=,=liTEE,txlEi'gE66EEEIliil1tllatzitz
F:.:l
z = z rn
z -t
Table 9.9. Selected color-reversalfilmfor use in 35 mm still cameras Definition
Daylight ISO
Graininess
power
Sharpness
of enlargementallowed Suggesteduses
25
Extremely
High
High
Slides2
Resolving
speed
Film Kt-rdachrome
25
Type of picture and degree
Has high color quality and a wide exposure
latitude. It should be used under most daylight
fine
conditions when sufficient light is available and fast
High
motion does not need to be stopped Kodiichrome
64
Extremely
64
High
High
Slides2
Combines good definition with relatively high speed.
It
does not have the color
High
so
it should be
fine
F..,:-ichrome
100
Extremely
100
Medium
Medium
Slides
Should be used as a substitute for Kodachrome-64
Moderate
when you expect to do your own processing
fine
! r::.-hrtrme 200
Extremely
200r
Medium
Medium
For use in dim light, shade, or to stop rapid
Slides
movement; also with telephotos lacking large lens
fine
I - :chr..nre -i0
Extremely
50
High
High
fine
Extremely
High
High
fine
l-..
;;:
ISO
be pushed to -100
1 _-:-:-=,.:.. ::-r:.;:n
openings, in order to increase depth
Slides2
Same as Kodachrome 25.
High
Excellent color rendition
Slides2
Good definition with higher speeds. Excellent blues
High
and greens. Can be pushed to ISO 200 with good results
uith special processing.
11s.. be t-rbtained throu-sh an
tili g
additional process.
{Ei q 3q= E€ g;rE x; a1"; ?
5
+
. Ft (D
=
€
*'= E Z3Ei i c-g g
?
f 3{
= 1q3
=
?'
a:
3
c
E ='4 e';as.*1 rESg
(J
e,J
X
+
a
(D
o-
E
::r-lidId
3 si
+fi:io A; :i q5;
=E
x=xYa;:l=. =rD=x(v):fro 3?ii6=)6'=^
rDQ-O-O_-i^a:
E- &
aD!,i.aXA:-!
NJ
UJ
x € 3P+E L-H
-IJ()^;^L
{z9ItsE = gE-=) = xx=ocb_]J
3 # ,v 3
:-+(,
qiFi* 1;= t.a i? EsZlirsErir ii i;it8-ii'ei=q iii7 5a [=E ;iig ei 11 iliEi Ell ig i6EEg ;tg sg *iei:+Es ia;x }f ;iii: ie {;Ef tsi=35:; is ril;i Egf Ei
Y
E
o(D
H
="
=i
of field
Moderate
E; [=sg liEgE lisF i =t s 7 tiz,Z|:i : i 1 ! aii 9 i I r =: r i. I i Ei l I 11 i e?iii5g ii*iig sEcr: x is #: + iia sE r J ''. i.;;I ). a a = j o = '==-z jE a=''' 7 i t':; oe, i V {= af -#iE fi: i:fII = .iE i-!r6I;a;i=1 riE+ii!3;i i: i 13 2i5 i 6t rE ZlfrE 1(??ti+: :;? I 7i:= EE = i = r sElEi+ryE i1fli; e1 iaE =:? +;FE;F,aqsEB? =fi?iE5ie= E* gEg5 ,..?iE:Ii=B ;i'gET;i itr i_._E[;,*g: iEZ i-1ZLE??=€ +;
?=i
quality of Kodachrome 25,
used only when extra speed is necessary
lr
V-L-J
cv--;\:f:.O-O aD--=
il
a
\3
o F,t (D
@
x a F-t CD
NJ il
? a
vAAA
6-
6 x_M
) 5 0 a 3 5)-{ xFSd; .LLlXcT 58 r.-B:H x==? Z +
i-P56S.
3i.g
d, x f B'
=-rdJ='.D oj 3aI H o :E +2. *' ? = ?
=3 -
B-$
il
9.5-
,, + :7
i
PHOTOGRAPHY
DATA-COLLECTION EQUIPMENT
Static electricity caused by rewinding film too rapidly in cold weather will cause Table 9.lO Reciprocity ancl rec'ontmenclecl f stop correc'tirttts.for 35 rum color Jilms
streaks or dots on the film. Also, X-ray inspection units in airports, despite their claims, may damage film whether it is new or exposed. X-ray damage is cumulative
Film type
1s
l0s
Kodachrome 25
*% stop
*2
Kodachrome 64
* % stop * 1 stop * % stop
N/A
Ektachrome 400
*% stop
* lrl
Fujichrome 50
No change No change No chage
*% stop *% stop
viewing. A cataloging system will organize your photos and may be based on: l. separate research projects; 2. behavior types; 3. species; or 4. field seasons.
* 1 stop
Computer programs are available that will catalog by numbers and captions, search fbr specific slides, and print out labels lor slides. Ethologists must develop a system
Ektachrome 100 Ektachrome 200
Fujichrome
100
Fujichrome 400
*
and may not show up until after additional exposure. Check film through by hand
or protect it in special, lead-lined bags available at camera stores.
stops
Prints, negatives and slides should be kept in a cool, dry area where they will be sale from damage, but where they can be easily retrieved. Store negatives and slides
1rl stops
in archival. plastic pages which can be put into three-ring binders or stored in a file cabinet. This will protect your original photos and provide for easy access and
N/A stops
Note: One stop:doubling the exposure
Reciprocityfailure is the loss of a film's light sensitivity during long exposures, normally longer than one second. This can be corrected by doubling the exposure (differing time for shots of one second. or more. Color films may show a color-shift sensitivity to different wavelengths of light) during exposures over two seconds. Table 9.10 lists the reciprocity and recommended J' stop corrections for several common films: Most negative films require an increase of one stop with exposures longer than one second. In additio n, inJiarefl films are available for special uses. Kodak High Speed Infrared film is available in 36-exposure rolls for 35-mm cameras. It is fine-grained with moderately high contrast, and medium resolving power and sharpness' It can be used to photograph through haze or to record behavior of nocturnal animals lighted by infrared bulbs. The speed of the film is highly variable, depending on the ratio of visible to infrared light available. Storageis an important consideration lor all types of film. All films are damagcd packby high temperature and high humidity. Films can be obtained in vapor-tight extctttls filnr Refrigerating aging if you anticipate working in areas of high humidity. its useful life well beyond the expiration date printed on the box. Betol'e using lilrn that has been refrigerated, allow 2-3 hours for the film to reach anrbicnt tcrllpcrrtKotlir k ture in it's plastic container (condensation may form on the film il' retllovccl). lilnr: and whitc makes the following storage recommendations for black
For storage periods of uP Keep lilm
below:
ttl:
2
7'5
l;
6
I?
60 Ir
50 l"
tttottllts
Kccp lilrrr lrwlry l'nlnt irrtlrrstri;rl llrtst's. nt()l()t t'rlt;tttsl.;tttrl tltl,rlr ol lllotlrlr;rllr lirr nt;rlrlt.lrl,rlt', solrt'ttlr, t lt'ltll('l\. ilttrl ttttltlt'tt ()l llllll'll" l)l('\('lll'tlt\t'''
which they find most useful. In addition, attempt to reduce possible losses or damage in the mail by sending photos properly packaged in separate packages: if possible. send duplicates instead of original slides and prints instead of negatives. Another storage medium is provided by digital photograph'!-. As examples, Kodak's models DCS 420 and DCS 460 (high resolution) combine digital imaging with a Nikon N90 SLR camera body. They are available in monochrome, color and infrared models which store the images on removable 170 MB RAM cards. One card will store from 30 high-resolution images (6 million pixels; DCS 460) to 100 images (DCS 420): by changing storage cards, 300 images can be captured on a single I hour battery charge. Using appropriate interfaces, the images can be downloaded to Apple Macintosh il, Powerbook. Quadra and IBM-PC (and compatibles) computers. They can then be used in computer displays, made into prints or slides, or stored in portfolio CDs. Additional information can be obtained frorn: Digital & Applied Imaging, L&MS. MC 00532, Eastman Kodak Co., PO Box 92894, Rochester,
NY
14692-9939.
e.tr.2 Motion-picture photography The obvious advantage of both motion pictures and videotape is that they allow you to record a two-dimensional visual representation of entire behavior patterns. The two-dimensional restriction can be overcome, in part. by the use of two or more
stratcgically located cameras. In addition, it provides the capacity synchronously to rccortl souncl (produced by the animals or the environs, or dictated by the observer).
llult
rrntl
llult
(1974) list fivc situatit'rr.rs in which motion pictures and videotape
;rrc especilrll-y rrsel'rrl: l. swil.l :tcliorr: 2. conrplcx actiorr; 3. strbtle bchavioral t'll;ttt1'1'r''.1. t'otultlt'r lrr'lrrrviot;tl s('(lu('n('('\. rtnrl 5. lltc rtcctl lilt'grt'ccisc nrcitsurcntt'trls rll P:rr ittil('l('t\ I lft'lfr',1 r ltort ('\(ril lt,t\t'lrr nr,rLr'r', llr, lrltrr ',r.', l,rr trr,tl \rllt \\,lltl trl tt\(' l ltr'trr.r
PHOTOGRAPHY
DATA-COLLECTION EQUI PMENT
Table
9.ll
Relative advantage of Super 8 mm and I6 mmfilming
Super 8 mm
l6 mm
1. Cheaper cameras, film and processing
l.
Pictures with greater sharpness,
2. Lighter equiPment
2.
resolution, and definition Pictures brighter when projected
to same
3.
29s
Convenience of cartridge film
size
3. Cameras often more durable 4. Film often easier to handle for editing and analYsis
5. Larger film caPacitY 6. Better for sync. sound
film has essentially disapbasic choices are 16 mm and Super 8 mm; standard 8-mm 9' 11; however, it is basiTable in listed are each of peared. The relative advantages
(16 mm)' If you cally a choice between lower cost (Super 8 mm) and higher quality If you intend don't intend to do much filming, borrow or rent a Super 8 mm camera' 16 mm (Figure to make filming an integral part of your studies and can afford it, use
e.22). and In selecting a camera you will be confronted with a trade-off between cost etc')' durability' certain features (e.g. lenses, built-in exposure meter, filming speeds,
what you need, these features are discussed below Remember to purchase but not more than you need. Also, if possible, try before you buy. for your equipLensesshould be selected with an eye toward the uses you intend such as 10 lenses, the camera has a lens turret, then you might select three Some
of
ment. If necessary to mm (wide-angle), 26mm(standard) ,andJ5 mm (telephoto). It may be Kloot 1964) or 1000 mm' use a telephoto lens as large as 600 mm (Dan and van der (26 mm to 75 or 100 mm) is zoom a If the camera will handle only one lens, then tubes are very useful. If you are working with insects, a close-up lens and extension g.fl'l ' often desirable. Select high-quality lenses with large apertures approachin thc confuse as to great so is movie-making for available of
The diversity
neophyte. Selection
filnts
of the proper film
is generally a trade-otT between film speetl
(amount of light necessary for proper exposure) and picture cluality' Illirck-antlwhile color lilltt white film is cheaper to purchase but more expensive ttl proccss. bttl ol'tctt provides an additional dimension which is not only csthctictlly Plcirsittg lilrrrs tlrrrl Kotl;tk ol' rr lisl provitlcs 9.12 necessary in some ethological studics. Tahlc t'rrtt lrt' lilttts slrr't'irtlizctl nr()t't. are uselirl lirr lilnting llinrll bclrirvior'. Atltlrtiorrrrl. (ittttlr" trtl: lllttlt't lirtrrrrl irr Ii.irstrrr,rr K,tlitk's lttr[lit'lrtigtt /( i I , hrtrhtl, 1t1,,'1,11',l.rtltlttt r''ttl lrt'tl"t'tl ltl "'lt Its u't'llits llolll olltt't ttt;tttttl;tr'ltll('l\ l(rl ('\:ltttl,11" tttlt;ttt'rlltltrr
Fig.9.22 Bolex H-16 l6 rnm movie camera with 75 mm telephoto lens.
junction with infrared lighting (e.g photofloods and infrared filters) to obtain motion pictures under nocturnal conditions. (Delgado and Delgado 1964). The.filnring speetlyou choose will depend on the purpose of the filming. Normal projection speeds for Super 8 mm and l6 mm are l8 and 24 frames/second, respectively. The ellect of accelerated motion is produced by filming a slower speeds (e.g. 2 l0 frames/second), and slow motion is produced with greater filming speeds (e.g. 32-64 frames/second). If you are interested in frame-by-frame analysis (see below) then the faster you film. the smaller the change in the animal's position from frame to frame. Faster filming speeds also allow for unsteadiness by the cameraman; but it means changing film more olten and increased costs in purchasing and processing the larger amount of film.
Various filming speeds and the authors'rationale for their use can be found in
of frames per second. 2-7 or 48 (EiblEibesfeldt, 1972), l6 (Clayton, 1976. Havkin and Fentress, 1985), l8 (Fleishman, 1988), 22 (Diakow, 1975), 24 (Kruijt, 1964; Dane and Van der Kloot" 196{), 32 (Duncan ancl Wood-Gush, 1gl2),64 (Bekoff, 1917a),128 (Hildebrand, 1965), and
the literature. For example, in terms
800 ancl 1000 (Grobecker and Pietsch . 1979) have all been used. Time-lapse photog-
nrphy can be used to clbtain instantaneous samples of behavior over extended pcriorls ol' linrc (ulso sce scction 9.11.3. on lilr-r-r analysis). For example, Capen (l()7ti) ttserl rrtt i"i ttrlrr rrrovic cluncl'irs scl lo llrkc lr ll'lrnc lrl citlrcr rlrtc-. l.-5 or twtltttittrrtt'nrl('rvill', trr ltrs slrrrlv ol rrt'stllrl' l',r''1r,tr',.rr irr rrltilt'rllisr.'s. Wlrerr llrr't';rntcr';rs \\('1r".('l,tllt\l tttttlttlt'tttlt'tt.rl', lrr,',1,t\'.,t1 l,lt,rlr,'.tr,rtlrllrr'olrl;11111'llllotttottr'ltlttt
PHOTOGRAPHY
DATA-COLLECTION EQU IPM ENT
Table 9.12. Selected Kodak rever,vul motion-picture.films Daylight
Speed (lSO)
Film
16 mm/
Super
8
Characteristics
Suggested uses
High degree of sharp-
General outdoor
Black-and-rt:hite
Plus-X
I
6/8
ness. good contrast.
2.5.
Fig.
of the bower to a reflector. When the beam is interrupted, a super-8 motion-picture camera exposes one lranre every two seconds. Birds were also observed from blinds. The system enabled the researcher to monitor the behavior and identity of bower owners and visitors at 33 bowers for the -50 day mating season. The researchers are now using a more sophisticated system based on videocameras. From Borgia ( 1986). Copyright O 1986 by Scientific American, Inc. All rights reserved.
gradation 200
l618
Excellent tonal gradation
9.23 The monitoring system used in a study of the satin bowerbird. An inlrared beam, invisible to the bowerbird, is projected through the avenue
photography
and excellent tonal
Tri-X
3 METERS
Under adverse lighting conditions
Color Kodachrome
Good color rendition
40
Ceneral outdoor photography
40
[]ktachrome
Higher speed
Adverse lighting
Good color rendition
General outdoor
160
l6
E,ktachrome
and sharp images
1239
Ektachrome
400
High speed
photography Adverse lighting
high speed
and high-speed
daylight
photography
cartridge. Borgia ( 1986) used an infrared system to trigger a super 8 motion picturc camera (Figure 9.23) in his study of bowerbird behavior' Both Super 8 mm and l6 mm films and cameras are available tbr simultitneotts recording on a sound truc'k. The sound reproduction is generally not of high quality.
but can be useful for recording the observer's commentary durillg lilnling. (iootlquality sound recordings are best made with l6 mm cameras (e.9.. Bolcx Il-16' Figure 9.22) that will synchronize with a high-quality tape recordcr. sttclt rts tltc Nagra IV-L.
(Milinski,
individual movements (Hailnnn,l96J', Havkin and Fentress, (Hildebrand, 1965; see also Chapter l0) and social displays (Barlow, 1977: Bekoff, l9l7 a,b); c) intra-individual sequences (Tinbergen, 1960a); Balgooyen,l9l6); (d) inter-individual sequences (Diakow 1975): and (e) 1984); (b)
1985), including locomotion
spatial relationships (Dane and Van der Kloot, 1964). Analysis of film is conducted either frame-by-frame or by sampling frames at regular intervals, e.g. every 24th frame (Golani, 1973).If frames are to be selected at intervals for analysis, an intervalometer can be coupled with the camera to expose frames at set intervals (Figure 9.24). This provides a more efficient use of film. Steele and Partridge (1988) projected Super-8
film of courting male Drosophila
onto the underside of a glass table and copied the males'movements onto tracing paper;from these tracings they measured each r.nale's maximum angular lag and top speed during their courtship dance. Analyses are generally conducted with either film editors that have a built-in projection screen (Hutt and Hutt, 1914). an optical data analyzer (e.g. LW International; Milinski, 1984;Havkin and Fentress, 1985), or an analyzer-projector (e.g. Lafayette Analyzer; Lafayette Instrument Co., Lafayette, Indiana). The latter projects the film onto a large screen (Dane and Van cler
Kloot.
1964). Whichever system is used,
cor,rrtter. Thc fiame counter, coupled with the
it should
have a reverse and a frame
filming speed, provides
a time base
lor
nreasuring the Iatencies, durations and inter-act periods of behaviors.
A rligitizing tablet can be used to record directly into a computer, data on the e.t
sprttirtl positiort ol'un irninral
1.3 Film analysis
Ethologists take motittn pictLrrcs lor bitsicrrllV two l)ttl'l)()ses: l. lo ltltvt'rt vtstt;tl recrtrcl o1'thc hclrirvior lirr illrrslt'trlivc l)lll'l)()ses (ptt'sr'ttltlti()lls;tttrl pttlrlit';tllolls, t'1' .1.M. l)lryis lr)75): lrrrtl/pr
.) lirr lulrlv:ts ol (lrl spr't tltr' ttt,ltvtrltt;tl
lrt'ltrtt'tot'.
r>r part of its body. For example, Fleishman ( 1988) digitizerl llre ltc;rtl rrrrtl tlcwlrrl'r 1'rositiort ol-tlispllying /troli,t' lizards lrom Super-8 movie llttttt's. ( l ltt'sr'siun('l('('llrritlrrt's rrrr'rlt'st'tilretl lirr vitlcotll-lcs in ir lirtcr section.)
It;ttttt'l11'lt;ttttt'lrtt;tlYstslt;tslrr','rtil\('(ll()nt('itsUt('lltt'tttovetttcrtlsrll'lltcl0ngttc
VIDEOTAPE RECORDING AND ANALYSIS
DATA.COLLECTION EQUIPM ENT
299
actively interacting. If this were the case, correlations which actually exist might be overlooked. (4) Finally, though unlikely, a movement which was too subtle to be detected on the film, might be a stimulus for another individual. IDune and Van der Kloot, 1964.285J
A computer system lor frame-by-frame analysis of film, FIDAC (Ledley, 1965), has been described by Watt (1966). The system consists
of
a cathode-ray-tube gen-
erator which projects an ordered array of rows and columns of spots of light through the film frame, where the intensity of the light transmitted is measured by a photocell as one of seven different levels of gray. This information is then transmitted to a digital computer. The computer can be programmed to control the location of the array of spots of light, their density in the array, and the area covered. The system has both high speed and high resolution. This system, or a similar one, may
find useful application in ethological studies of movement where the animal
is
filmed against a light background.
Fig.9.Z4 An 8 mm sequence camera and intervalonteter inside a weatherproof housing.
In summary, I have not mentioned the vast array of additional equipment (e.g. light meters, fllters, tripods) that may be necessary for proper filming. These items should be discussed with your local camera dealer. Likewise, the various techniques which will improve your motion pictures and their analysis can best be gathered through discussions, experience and literature (Dewsbury, 1975; Matzkin, 1975: Wildi,
of boas (Csnstrictt)r constrictor) (Ulinski,1972) and the loot of a mollusc (Cardium echinatum) (Ansell, 1967).Illustrations of the results of their analyses are shown in Figure 9.25. Head movements relative to particular behaviors have been analyzed frame by frame lor the Burmese red jungle fowl (Galltt,; gallus spadic'eous) (Krut1t, 1964),laughing full chick (Laru,s utricillu) (Hailman, 1967). and domestic duck
(Clayton. 1 97 6 ; Figu r e 9 .26). Spatial relationships between courting goldeneyes were Ineasured by Dane ancl Van der Kloot (1964)by projecting film frame by frame onto a screen that they hacl divided with 16 equally spaced vertical lines. Distances perpendicular to the
(
A nas p la r y r hy nc' h o s)
camera's line of sight are relatively easy to lneasllre; but the perspective tlf depth is lost in measurements parallel to the line of sight. Dane and Van cler Kloot list tlthcr' complications and restrictions which are common to sin'rilar types ttl' {rlni atlalvsis:
(l) Birds are olten passing in and out of the field of'vicw ol'thc cttrltcril. When the final analysis is undertaken, there is always thc cltittlcc lltrtl lttt action given by a bir<1 outside of' the field is aflecting thosc t'ccol'tlctl ott film. This problem was minimized by analyzing tliscrctc gnrtrps. ( 2 ) Computing the distance betwecn hiruls. itntl llttts lltc I'cl:rtivc positiott rrl each incliviclgal llock. is diliicLrlt wltctt ttsittg lt lclcpltoto letls. 1\lWlrt'rr tcsting lirr u rclirtiorrslrip hctwcctt lltc ttcliotts o['lu'o lrittls. lltt'tt'ts llr;rt orrr'rs nol rlistittl'stlsllttl1'lltt'1r;ttt tt'ltt, lt ts :rlwlrVs llrc
llpssilrilrlv
1973). Both 8 mm and 16 mm film can be transfered to videotape for analysis provide additionalcopies for yourself and colleagues. or to
9,12 VIDEOTAPE RECORDING AND ANALYSIS Behavior is often thought of as an animal doing something. Only movie and video cameras will accurately capture that activity, although computer cameras and highspeed motor-driven slide cameras are sometimes acceptable alteratives. Videotapes
(or films) can be used to simply gain experience with an animal's behavior, even before reconnaissance observations are made on live animals. By viewing the same footage several times you learn to anticipate behaviors; you see subtleties in behav-
ior which you often miss in a single observation. Videotape has several advantages and disadvantages relative
(Walbott.
1982; Table 9.
to movie film
l3). Movie film is often used to document behavior for long-
tcrn.r stonrgc, but films can also deteriorate. When you want to record behavior to be
rcvicwctl soon afier Irtcttrlctl
it
occurs, and frequently in the future, videotape is recom-
.
Vitlcotlrpittg syslcrrts virry l)rlnt scprtrltlc cilrnera and recorder (reel-to-reel or clrssr'ltt') lo lltc cotttbin:rliott rrl'L',lln)cl:l :urrl t'ccot'tlcr inttl ir crtrncrlrcler. Camcorders,
tvlut'ltlttt'lltt'tnosl ;tPlttoPti;tlt'\\'\l('nt:.ilirr liclrl u'otk.ttsccltssctlctirpcswhichvary nr
\r/(' lr()nr lltt' l.ttl,r' Vl lS t() llr(' \nr;rll
S
ntnr
VIDEOTAPE RECORDING AND ANAT,YSIS
DATA-COLLECTION EQUIPM ENT
A I
CLUSTER
START
3
rP'l /7
/
/-:ey
/7
/P.
/-a9) END
START FLICK CLUSTER
/r=>
//;:---:* 'r'
/-,3 /'';,-V
/'::^ /=,2)\ r
,'P\
/':=\
t
''l
/'7 P\
\
"l /
/-->
/'.9\
/---;=-->* ,-J
Ia a
i
/ r-'TP
R
FLICK CLUSTER
3
a
t
I
I
7
/''+Pl
/P\ / ---->/ / ,''l_--
rpa
/'-:a>J l' '
/
J:-
/'',-'"\
aV "l ,
'--,,1
7.5 cm.
END FLlCK CLUSTER
7
Fig.9.254 Pattern of boa tongue movements in lateral view. Tracings of each frame in motion pictures of two complete flick clusters are illustrated. Successive pictures are about 42 ms apart in time. The ends of the protrusion phase (P), the oscillation phase (O), and the retraction phase (R) are indicated by vertical lines. The figures should be studied from left to right in each line. (from Ulinski, 1972).
The lightweight, compact, battery-powered VHS-C and 8 mm cassette palnrcorders (Figure 9.27) make videotaping in the field relatively easy. Motor drivcrr
I:ig 9.25B Thc ntovements ol the foot of the bivalve mollusc, Cartliunt e(hinutum,during a singlc lcap. shown with relerence to the shell as a fixed object. The positions were titkctt ll'om motion picture of the movcment, the numbers indicating the number ol' t hc ll'itme corresponding to each position ( I 6 frames/second). Active (frames l2 lo 2.j)rttttl rccovery (lrames 23 to 50) are shown separately (from Ansell,196l\.
zoom lenses allow the researcher to obtain a broad or locused vicw ol' bclurvior'. Built-in microphones record environmental sounds (those from thc uninrirls lrc
svslcttt spccilically clcsigned lbr field use. It is an integrated, all weather, compact, tertl-litttc v'itlco tttortitot', r'cnrotc camera (infrared and visible light sensitive) and
generally not of sufficient quality for analysis) and also allow thc rcsclrrcltct' lo rtt:rke
rr't'ortlirr;:systerrr. llirttcricswillpowertheexternalcamerasystemlorupto20hours
verbal notations while recording. Although these populur canrcortlcrs
;ttltl lltt'r'lttt)t'riirlcl'ltttrl Ittoniltlr lilr up to l2 h; the camcorder will recorcl up to 120 lirlx' S('\'('rrrl lf iclrlclrrtt syslcrrrs irrc lrv;rilablc ll-or-r-r Fuhrman Diversifiecl.
ir
rc rclrrt ive lv
resistant to moisture and light impact. tlicy urc rtot rlcsignctl lirr llrc lt:rtslt t'orttli tionstowhich manyfielcl cthologists nriglrI cxposc lltcrn. As witlt still('iun('r'irs. v()u shoulcl chcck thc clr;l:rhililics lirr l)11)l)e r rrst' ltttl tr'sisllur('(' lo ;rlrrrst' lirr ;rttY t:rtrt cot'tlct' V()u ittt' t'ottsitlt'titt1' ttslt1, l ltt' liit'ltlt lun rs irrr ti rrrrrr t'l,rr..'rl t rrt rrrl vrtlt'rr
llrler tllrl:r rccrlrtling
virlt'ot:tpt'rl ll ll-i lr
VIDEOTAPE RECORDING AND ANALYSIS
DATA-COLLECTION EQU IPM ENT
27
I 18
Table 9.13. Relative advantages and disadvantages of videotape and ntovie .film ethological studies
for
Movie Film
Videotape
17 16
I
Advantages
l.
Immediate playback
1. Better quality
2.
Reusable
2. Easily analyzable frame by frame providing an accurate time base for studies of movements 3. With wind-up cameras, time in the field limited only by the amount of film 4. Equipment generally light
6
5
3. Tape relatively inexpensive
a
3
4. Easily duplicated Disadvantages
l.
I 1
This composite Fis.9.26 The duckling's drinking response illustrating the bill-lift element. line drawing is based on frames liom a motion picture lilm (16 frames/second). The sequence ol numbers corresponds to the frame numbers beginning as the bill leaves the water (from Clayton, 1976)'
videotrials in their study of mating behavior in water striders; they point out that taping 'allows the detection and accurate quantification of short-lived behaviour patterns and continuous monitoring of behaviour of long durations'(p.895)' Data from videotapes can be recorded on check sheets or input directly into a computer
using standard data-collection programs (see section 9'10'1d)' For example'
comRoberts (lgg4), in his research on vigilance sequences in sanderlings, used a puter-based event recording System to record the times of behavior events from videotape. Also. several specialized systems and programs have been designed the specifically for recording data from videotapes. Krauss et ul. (1988) describe
hardware and software
of a
computerized multichannel event rectlrder'
lor analyzingvideotapes. It records a starting and stoppitrg sigtlal on audio track of the videotape to mark the beginrritlg atrtl clltl ol' tltc
Videologger,
the second
its itrtcrrrlrl segment being analyzed. The microcomputer uses thc sigtritls to t'csct store in rnem6ry tlrc tlnscl tirnc ittttl tlttl'ltliott ol'kcyllt'csscs lot ltttv clock ancl
I( wirs tlcsigncrl to ltrrr ()n lttl Apgrte llt'otttllttlr.'t lrttl rVlttt lt c0nvctlerl lql lttl ltlM lirt tlr;tt Ilrt'svslt'tll t'ollslsls ol liVt'solitt;tl('l)l(),'l;tttts ruunrbcr o('hcltlrvi0r.s.
t'ltrt lrt'
1. Time delay lbr developing
Poorer-quality picture with less expensive video recorders
l.
2. Film
Most now analyzable frame by
usable only once
and stop action
l.
3. Film relatively
Equipment run off batteries with
expensive
limited chargeable life
4. Duplicating more
-1. Equipment sometimes heavy
expensive
;rrc available gratis from the authors. The Behavior Chronicles software (see section (). 10.
lcl) includes a videotape analysis mode in which the computer screen clock is
svrrchronized with the VCR and an icon on the computer screen allows the r('scarcher to control the VCR with the computer's mouse. Scvcral programs designed for recording data from videotapes are available comi:rlly. CAME,RA is a system which includes software and a keyboard which the r('scrrchcr intcrlaces with an IBM-PC; each button on the keyboard generates a ',r,untl with rr unicluc pitch providing the researcher with immediate auditory feedrrrr're
lr;rt'k.
('n MlrltA wts
reviewed by van der Vlugt et al. (1992) and is available from
l'ro(iAMMn. l'.(). Ilox 841, 9700 AV Groningen, The Netherlands. PRO( ()l{l)l:lt is:rnollrcrptl)gratrlirrrecorclingbehavioraldatafromvideotape;itwas r,'r'rr'\r't'tl lrv lrrpp rrrrtl Wrlrlcn ( lt)t).1)irrtcl is:rvailable lrom Jon Tapp and Associates ,
/,r hrrr
I;r1rp. l0(r l.ibclty l.trne.
l.rrve
t'grtc.
'l'N
370tJ6. Nolclus Inlormation
l,'. lrtr,r1111'1' lrllcr s lr Vitlt',r'l;ryrt' Atutlvsis Svsl('nt lirt' ttsc lvitlr'l'lrc Ohscrvt:r 3.0 stlli\\,r!(' rl t',;tr;ttl:tltlt'ttt Ilttr'r'rltllt'tt'ttl rrl)ll()ll l,lt, kltl't's
VIDEOTAPE RECORDING AND ANAI-YSIS
DATA-COLLECTION EQU I PM ENT
Angle of attack
palmcorder, model PV-S62' to Fig.9.21 Stephanie Bestelmeyer using a Panasonic VHS-C behavior. record waterfowl researcher's Videotape can be reviewed at slower or faster Speeds to enhance the her study ( in ability to observe and measure behavior. For example, Grandin 1989)' of pigs, found that high-speed reviewing of videotape recorded at 0'9 frames/s
I
rr ().lll Schematic representation ol a spoonbill's sweeping, showing the various geometric parameters. Also shown are the simulated prey items placed on the bottom to test displacement (undisturbed pattern on the left) and the bill tip vortcx streamlines, indicating shell motion on the right. U: Sweeping velocity; L: lili; A A: cross section of the bill; D: distance of the tip of the bill to bottom; VTX: induced vortex; SH: empty snail shells ('prey') (from Weihs and Katzir, l99zl). Copyrighted by Academic Press.
of the revealed subtle nosing and rooting movements as easily seen vibrations (1994) to Katzir and Weihs snout. Frame-by-frame analysis of videotape allowed (Figure demonstrate the hydrodynamic function of bill sweeping in the spoonbill 9.28).
samples of Time-lapse vicleotaping is often useful to obtain instantaneous/scan time-lapse (1987) used Grant over long periods of time. For example,
behavior
a beagle bitch video-recording to provide a 'continuous' record of the behavior of has also been used and her pups over a three-week period. Time-lapse vicleotaping red jr-rnglein studies of the behavior of calves (Dellmeier et crl..l985), and Burmese
fbwl (Hogan and Boxel, 1993). trsitrg Movements ancl spatial relationships of animals are frequetrtly tlleasttrccl lttl 9tt itttitltitl videotapes. Earlier, researchers often trace<] the movement ol' thc (e.g' ('rawlirrtl. l()1i"1)' llrc acetate transparency layed over the vicleo monittlr to lt cotttptttct ttsillg bc tftrnslcrrctl thcn animal,s position on the acetate sheet coulcl -l'crnrinirl I)isplily Systcrrrs l,t(1.. littittrt|t rttltl wt'lrh. a cligitizing tablet (e.g. L('-12. tltllt'tlttt't'llt' lgtttt). Mctlrotls lrr-c irvtril:rblc lor tligitizrrrp tltr'rrnirrrrl's l)()sili()ll il( loss .'Pt't t;tl solltt;tl(' illl(l ll.otn tltr. r,itlt.rl rr,rrlrrilor tlrrs tt.t lrrtttlrtt' t('([lltt's lltt' tl'r(' il1
t)('ul)lrcntl cclLtiptnent (a screen digitizer) which records (or allows the user to r,'t ortl) llrc positiort ol an animal on the video monitor screen and translers that ,l11'rlrzr'tl posilion to a computer. For example, Richardson (1994) recorded the ()l' rnir)nows ll'tltn a videotape and then translerred that data to a computer rrlrrt lr u;rs rrsctl to cirlculatc all inter-fish distances. Watt and Young (1994) videolr rt ,111,)n
t.rpt'rl tlrrirlrrrirr (wirlcrllcas) in a water tank illuminated with polarized light using a
l'.rrr,rir)nr('In(xlcl n(;6720 tinrc lapse video-recorder; they then recorded data by ,lrlrlrzrnl'llrt'ntovcrrrcrrllnrcksol'(ivcrandomlyselecteddaphnia.YoungandGetty
{l')li/) lr;rtkt'tl llrt'rrrol'errrcrrls ol'tllrphniir in thrcc , .ilnr'r,r',1
ilt'til.
tlr'sr't
I ltr' r t,lr',r
tllt'tl llr'lrlrr
climensions using a novel two-
:
s\rlt'nr lr;rrl ltt() \('l):rt:rlt' lll;rt'k lrrrrl-rvlritr'vitlco clullcl-:ts
(lrrrk ltlt'lo()lrllllrtrlr Pr,,tlutctl vrt'rrrol llrt'llrrrli lronrrlilli'rt'rrl
STOPWATC H ES
DATA-COLLECTION EQU IPMENT
into the computer; these include the Fotoman Digital Camera with IBM-PC and
directions. One camera needed its horizontal and vertical scan directions reversed for both cameras to produce pictures the same way round and the same way up. A single video-recorder (National NV8030) was used to store both images, which were electronically multiplexed on recording and re-separated on playback. The two images were displayed as red and green pictures on an RGB monitor, and created an anaglyphic stereo display when viewed through red/green glasses. When viewed directly, each animal was represented by a pair of dots, one red and one green. whose distance apart (disparity) increased steadily if the animal swam
Apple Macintosh models (Logitech [nc. 6505 Kaiser Dr. Fremont, CA 94555-9911), Apple Computer's QuickTake 100, and the IBM-PC compatible EDC-1000 (Electrim Corp. P.O. Box 2074, Princeton, NJ 08543). Video cameras can be made sensitive to infrared by replacing the normal vidicon tube with an infrared-sensitive tube. For example, Davis and Hopkins (1988) videotaped the behavior of electric fish (Gymnotus c'arapo) in a near-infrared illuminated tank using an infrared-sensitive video camera (GTE 4 Te E-44 with a Newvicon tube). Wells and Lehner (1978) flooded a large room with infrared light and used a
upwards in the tank. Digitizing the position of both red and green dots enabled us to compute x, y and z coordinates for the animal. 543 J
infrared-adapted video camera to study the predatory behavior of coyotes in the clark. Conner and Masters (1978) described a video system for viewing in the near
you Software used in conjunction with the digitizer cursor or stylus often allows polar coordinates; or to: l. calibrate your own coordinate system or select Cartesian
infrared (700 to 1000 mm), which was used to observe the nocturnalcourtship of an irrctiid moth and nocturnal predatory behavior of the Florida mouse (Peromyscus flrtridunus). Grant (1987) used a low light/infrared sensitive camera (Sanyo) and
IYoung and Getty, 1987.542
of the behavior of a beagle bitch and her pups over a three-week period. Video cameras can also be used to view rtltruviolet light (Eisner et aI..1988), such as the ultraviolet reflectance by gorgets of
and2. choose whether to digitize single points, continuous data stream, or user-
tirne-lapse video-recorder for his'continuous'record
defined increments (e.g. SigmaScan Measurement System. Jandel Scientific, 65 Koch Road, Corte Madera, CA94925). Some commercial systems include all the equipment necessary, including the animal enclosure. Coughlin et al. (lgg2) used a commercial system (Critte;Spy filming apparatus) to measure the swimming and search behavior of clownfish
srrnangel hummingbirds (Bleiweiss, 1994). Videotapes can also be made underwa-
al. (1994) used an RCA model CMR 300 with wide ,rrrgle lens in a Jaymar housing to record the parental behavior of catfish. rcr'. For example. McKaye et
larvae in three dimensions. Other video-computer systems designed to measure animal activity in small enclosures are commercially available from Columbus Instruments International Corp. P.O. Box 44049, Columbus, OH 43204, MED Inc' Associates, Inc. P.O. Box 319, St. Albans, VT 05478, and Omnitech Electronics,
9.I] STOPWATCHES Slrrl.rwtrtches (Figure 9.29) are a time-honored piece
of equipment in ethological
lor
srritlies. which are still useful today (e.g. Randall. 1994; Yoerg. 1994\. They are used
gait analysis (see Chapter 10), but one specifically designed for that purpose is avail-
l,rrrrrirrily to measure durations and latencies. Electronic digital stopwatches have r;rgrrtlly rcplaced the old mecltanical stopwatches (Carpenter and Grubitz, 1961) in
5090 Trabue Rd. Columbus . OH
43228. Some
of
these systems can also be used
S. Revere Parkway, Suite 601,
from Peak Performance Technologies, Inc.7388 Englewood, CO 80112. Software for tracking animals from frame-to-frame on videotape which runs on Apple Macintosh computers is available from James B.
able
Hoy, USDA-ARS-MAVERL, P.O.Box 14565,Gainesville. FL 32604.
Digitizing systems can be automatic, scanning the screen at a given frequency and recording position(s) of animals by the contrast in color or gray scale between the animal and the background. These automatic systems are not stlphisticated enough at this time to record positions of rapidly moving objects with a high dcgree of accuracy, and they are quite expensive (Scienc'e 1985, vol'227'1567)' Mrttlttrtl systems, in which the observer touches the position
of the animaltltr tltc scrccrt rvitlt
lll'c rl)()l'c a wand or light pen to record automatically this position in thc cotD;'rttlct'. (i.c. lhc cttlit'c ll'iunc Picltttc) t':ttt accurate and much less expensive. An cntirc singlc
hccligitizeclbyircontputcrcithcrl.l.(rntvitlcollrpcor lrvt'rrsirtgilr'illllclll ill)(lirllrtttlt' gr.lrllltcr. brllrr.tl (c.p..
l,('VlSl()NPlrrs
l;r'lrttt,.' (
itltlrlrt't. lttt;t1'ttt1' lt't'ltttolol'y. lttt
)
('()1tl)ltl(.t (.i1ll(.tit\;1(';1.,r, lrrrrrl.rlrlt'trltt, lt plrttlttt t',t,lt1'tl,tl tttt,ll't'llr,tl t" lltt'tr lt'tl
I
rrir
ny cthologists' pockets.
Most clcctronic digital stopwatches are comparable to good mechanical stoprr
rrlt'lrcs in size. but they are generallylighter,less expensive andeasierto use. However,
',r
)n)r' r'cscirt'cltcrs irrc prone to question the
t
lro r rg,lr
I
dependability of electronic devices (even
hcir lrccuracy is greater) and do not like to tie themselves to batteries.
l'lt'r'trrrrtic tligital stopwatches are easier to read, and many have several funcI r( )ns Ilr;rt lu'c rrscltrl to the ethologist. For example, the Heathkit stopwatch (Figure
') rr
)())l)rovitlcs livc lirnctions. listecl in Table 9.14, plus two programmable functions lrtr'lt nri1rll1 t)l()vc trsclirl lirr labrlratttry work. lr,rls lot rlif il;pl stol',rv:rlchcs clul bc prurchirscrl
lirrn
several rnanulacturers (e.g.
('lrrlos. (':rlilirrrri:r). Wol:rclt rl ul. (197,5) descr-ibed an econ( )nl( itl nrr'lltorl lor t'onvt'rlntl' lul t'lt't'lrorrit' lr;rrrrllrr'lrl ctrlt'rrllrtor ittltl it tl igilirl stoprr,rlt lr rr rllt tttr't('nr('nlr rrr O l(lrr'r'onrl'. f,rrrrt'',
l'lt't lt()nr('s.
Slrn
DETERM IN ING GEOGRAPH
DATA.COLLECTION EQU IPM ENT
I(' I-O('A]'ION
S
Table 9. 14. Functions provided by Heathkit Model GB- l20l E digital stopwatch
Function
Illustration
Description
A
Duration of separate
B
C
#
behaviors plus total
*totalduration 0throughC
0
duration of session Time from one event to another; latencies; plus total
-->
A
--->
B
---> C
0nn
from 0 to C
time
At
Accumulated time
lor several occurrences
of
a partic-
ffi
A1
nn AiA. AJAiA. *total
0
ular behavior; plus total time of
A2
0
time
thorugh 1.,
-*totaltime
session
Latencies lor
Fig.9.29 Left to right: electronic digital stopwatch; Heathkit programmable digital
events
stopwatch; mechanical analog stopwatch.
from
a
*total
single starting
point; plus total
g.I4 METRONOMES
latencies
occurrences of a behavior; plus total
A',
A.
# o
*totalduration At+ A.+ Ar
cluration of all occLrrrences
'{)
I lr('
r\ l)li'l'llltMINING GEOGRAPHIC LOCATION
r'('()[t"lltltic Iocatirlr-t of'etnimals is necessary to determine in order to plot home lct't-itor-ics (FigLrre 17.8). and in some cases to locate them lor observa-
r,rrl'('\ rttttl
at the 0.5 to 20 seconds and includes a light-emitting diode providing a visual sigrlal tnctrotlonrc an electrotric (1970) designed set intervals (Figure 9.30). Wiens et ul. which emits tone pulses through a small earphone at intervals which catt bc vrtrictl
lr')n
from I to 20 seconds. Their metronome was usecl by Dwyer (1975) in his sttrtly ol'
I
( time budgets in gadwall ducks (Anus streperut). Reynierse ancl Ttrctts 197-l ) tlcscr ibc lo olte Pel 10 pcr scc()ll(l livc {hrnt ol a metronome which produces pulse rates
lrr,11''1 1n
seconds and can be built gsing thc circuit rliitgt'ltttt tltcy Ptor,ttle. M;rllitt rttttl Batestln(1993)itlstrprtlvitlcltscltclttlrticlilr'illll('ll.()lt()llle.l..i1'111.,..()'}ll'tr,t.slrr,rr scltctttltl it's litr t'ollsl l ttt't itl1' yrt11; orr tl t'h"t'l l ()lll(' lll('l l ( )ll()lll('
At
Duration of separate
Metronomes provirJe a time base for field observations. They allow observers to enter a time point in their notes (e.g. every 10 seconds put a slash). time instantaneous/scan samples (Chapter 8), and provide an electronic signal (audio or visual) duraat intervals (e.g. one seconcl) which can then be counted in order to determine tions of behaviors. An electronic metronome designecl and constructed by Jim Starkey and used in our studies can be set to beep at I or l0 second intervals through a small earphone. It is both small and lightweight, which makes it suitable for fieldwork. Lockard (1g76)describe<1 a metronome which has a pulse rate continuously adjustable f rom
latencies
A+B+C
lltr'tcscru'eltct'crrrt nrakc accLlrate locations based on grids marked out in the (r'.1'. lrigrrt'c 17.7) ancl reasonably accurate determinations based on a
lrtrl\ ;ut'rt rrr
lltc ittttttctliitlc study arca (e.g. Figure 11.6). Less accurate determinalrt'nrttle ()\'crlilrscgcogrirphicar0irsif theresearcheriscapableof locating
r11li'111'1'ol
tlr, tt 1)()\tlton on l()l)(),'rirlllric rnrrps l-lrsctl rln lltc lcrririn rtnttrncl them. The Global l',',111,)ttlnl' S\slt'nt lt;ts ilttlltor't'tl llrt'lrt't'rrrrt'v ol'tlctcrrrrining ll rcscill'chcr's gett-
r'r.tl)llt lrrt ttlton trrrtl lrtolt,lr.rrrr.lr\ lrlr,, rrr;rrlr, llrt. lot.lrlirllt ol' tntohscl.vctl lutitttltls lltt tr'trl .tttrl tt',t',,rtt,rlrlt ;t( ( ul,tl('
3t0
DETERMINING GEOGRAPH IC L0('ATIONS
DATA-COLLECTION EQUIPM ENT
3ll
250K
10K
Low lmpedance Earphone
ffi
HIGH IMPEDANCE EARPHONE (Crystal)
ffi
NE 555 timer 2 C or D cells total 3 volts lOOO MFD 25 volt capacitor 100 MF D 25 volt capacitor 100.000 ohm resistor 20,OOO ohm potentiometer adjust for desired period 470 ohm
1C B Cl Cz Fr RZ r93
-
Note I
.
Period between ticks is
2. (earphonc) and Fig.9.30 An electronic metronome which provides an auditory signal 1976)' (from Lockard' visual signal (light emitting diode)
A,rernate switchable rirrree !eleclion
srrlrrlrlute lor R1 + R2
e.ls.l Global Positioning
IM
System and Argos Satellite system
Irt.t.rrr.:rlt.
,rlsiliprr lly lr itrrrl,rrl;rtion
I lrr.'
glr
ltt'tplt'ol lltt'ottt'
l('('('l\'('1. l\\'()
(()l lll()l(')
that is, 8000 ohm resistor and 5000 ohm pot lor a period of 1 second
M
(lrigrrl'c ().ll) trsc llre able for civilian use. Hancl-hclcl clcctrollic rcccivittg ttnils sig..ls 11...-r tw., ()r'nr()rc. ol'llrc lrl orbitirrg slrtcllilcs irr tllc svstettt to cltlcttlrtlt';ttt
lr'
', \l
ltr,,
lr
lrr
ll lr,'111 1,,, l,.rt,l
I
rl ( ilil'.lt
Most capacitors have a tolerance of + 20 to -50% usually requ iring higher values of 81 + 82 than calculated
Select resistor and potentiometer for desired range,
@7_.
LIS l)cpitrttttcttt ol' The Global Positioning system (GPS) was developed fbr the bcctt tltittlc ltvrtilltits ltrltl systenl Def'ense as an acclrrate targeting and navigational
approximalely
T -- 1.1 R1C2
R2
ltr lilt,'
ttt r'lt't ltot111 ,,',',totl()ttl('(A
l')
'(,
)
ltottt I Sl:rrkr'V
312
DETERM INI NG GEOGRAPH
DATA-COLLECTI ON EQU I PM ENT
I(' I,()('N'I'IONS
They can also keep you frorn getting lost by clirecting you back to the point where you started. The receivers vary in capabilities and displays, but most will track several satellites, update position every second and operate for 4 to 20 hor"rrs on batteries. They can be obtained from sporting goods/outdoor equipment stores. or yoLr can contact manufacturers clirectly, such as Magellan (960 Overland Court, San
Dimas, CA9l773).
An overview of the more technical aspects of the GPS is provided by Logsdon (1992), and a popular (but inlonnative) account was written by Brogdon ( 1993). Animals can be automatically tracked worldwide using the Argos Datit Collection and Location System (DCLS), a joint venture between governmental agencies of the tJnited States (NASA and NOAA) and France (CNES) .Harris et al. (1990) review the use of the DCLS to nionitor movements and activities of l0 species of large mammals in Alaska and the Rocky Mountain region; mean error of locations of captive animals was estimated to be 954 m. An overview of animal tracking by satellite is provided by Taillade \1992), and Priede and Swift ( 1992) conttins a series of chapters on tracking various species by satellite. Animals are more commonly locerted and tracked using earth-based biotelemetry systems (described l-relow).
s.ts.z Biotelemetry lliotelemetry has been used to record remotely information from a wide variety of lor several clecades. Many additional species have been radio-tracked since llrlnder and Cochran compiled their list in 1969, and many technological advancerrrcnts harve been made sincc the overviews of Slater (1965), Fryer et al. (1976),and l.orrg (1917). and the bibliographies by Schladweiler and Ball (1968) were pub-
spccies
lrshcrl; ht>wever. useful basic inlcrrmation on biotelemetry can still be found in these \( ) tll'ccs.
Iliotclemetry has tbund many unique and valuable applications in animal behavr()r'r'csearch(MucdonaldandAmlaner, Fig.9..-\2Magellanhand-heldglobtlptlsitioningSystenlreceiVcr(photoctlttt.tcsytll. Magellan SYstems CorP')'
of two receiver' tlnc tratlstnittct' transmitting satellites GPS system, is the opposite
t
'lo locirtc an unobserved animal for plotting its movements and calculat-
,
'lir
ing its honre nlnge (see Chapter l7).
9'34' biotelernetry system illustrated in Figure
(|
AresearchercanstandonthespotthatwasoccLlpieclbytltcirtlittlirllrtttltrsctltc .n the curth by l.titurlc l..gitrrtrc :r.tl [ 1'l'M
receiver to determine their location
hlrvc. s.rirll
sc'ce r) rvlrir'lt
5() ft.'fhc'cccivc.s ooor.inirtes with il. ilcc.rilcy t. witlri. tirrrr'.;rrrtr l,r;rrrrrit'rlispllrvs l.;rrsrsl r.revlrri.rr,lrrtr c..r.trir'rrcs. will tris,lrry l.clrri.rr (t'1' ll('\l \lt(' rlr'tt' rltsPl;tV I'tt',tttttl) itr n:rvi1,lttill1'lo lt l)l(.l)l()l'l'tllllll('(l lotltlt()tt
l98l:PriedeandSwift, 1992),butithaspri-
rrr;rlily bccrr usctl firr the following purposes:
t
rrssist in locirting an animal rrtrttrc ()..1.1).
lir rt't'ortl rrclivity
rrrrtl plrysiologiclrldittit such as EEG. EKC. respiratory ot srrtllrt't' lrotlv lctttpct'ltlttt'c. l.,lr;rrsrnrl iuttnrirlrorrntls lor 11'l,,,rrlitt1, ltnrltlt'le t'tttirtltliott ol'lrcltitvitlt'
r:rlr'. lul(l inl('rlrirl
I
lor direct observation of its behavior
('.t't' ',t't Ilol1 () ()
r1
314
DETERMINING GEOGRAPH I(' LO('ATIONS
DATA.COLLECTION EQU IPM ENT
I ig 9.34 Diagram
315
of location of rabbit at A. determined by using directional inlormation
received from two receiving stations. R, and Rr.
Basically, the location of an unobserved animal is determined by triangulating llrrm the simultaneous directional information provided by two or more antennae
lrigure 9.34) or receiving stations. llcsides animal location, diverse additional information can be trar-rsn-ritted rrrclucling physiological data (Mackay, 1993), and behavior, such as animal sounds
t
(r'rrcrrlizations or mechanical sounds which indicate specific behaviors; Alkon et al. l()li(). irlso see section 9.9). Animal activity can be determined using various techrrrtltrcs such as motion-sensitive transmitters (e.g.
Gillingham and Bunnell, 1985) or
lrtrttirrg thc rccciving antennae close to a specific location (e.g. nest; Benedix 1994). ,\lso. rr clrangc in the characteristics of the signal received can often be correlated rr
rtlr spccilic bcltaviors, such as walking and wing-flapping. (
radiotelemetry receiver and yagi alrtcrlr)il lo Fig. 9.33 Andy Sandoval using a hand-held locate bighorn sheep for behavioral observettions'
iivcrr
lll (lrc inlirrrnation and data that biotelemetry can provide
rt'st'rrrcl)cr'. it is not without its problems
zrs
the
is to be expected. As examples, trian-
lrrl:rlrorr clr'()r's ciln ()ccr.n'duc ttl characteristics of the signal and the environment
(t'1' I ;rrrrrtlre t't ttl.. l9li7).
transn.rittcrs can bc lost (e.g. Schulzand Ludwig 1985) ()r ( iru\(' lrrllrolol,it'rrl t'orrtliliorts (( ittyrttt t'l trl., l9tt7). itncl repeated captures to r('l)iur lr;rrrsrrrillt'rs tlur ('rrrs('nr()rl;rlilv (Kirr1'lrtttl l)ttvitll, 1990). Likcwise, the
(.rl)lnrt' ,rlrl lr;rrr.llrrrl' n('r'r'\\;rr\' lrr ;rllrrt lr lr;rnsnrillt'r's.
rrs
wt'll lrs lltc lt'lrttstnitlcrs
DETERMIN I NG GEOGRAPH I(' I,o('N'I'I0NS
DATA_COLLECTION EQU I PM ENT
316
311
Benedix, 1994; see Anderka and Angehrn. 1992. lor u review of transtnitter attachtnetit methods). Receiving systerlls can be as simple as a portable antenna and receiveq tuneable to the frequencies being broadcast by each animal's transmitter. The researcher obtains a directional fix on the animal by rotating the antenna until tl-re signal strength is tlre greatest, indicated by a VU meter and/or an audio signal in earphones. Decades ago, utttomatic radio-tracking systems were devised which transferecl time and directional infbrmation into a mainframe computer where it was stored and plotted by an X I plotter to show an animal's movements and home range (Cochran et ul.. 196-5). Today, automated biotelemetry systems vary from large,
ltermanent radio-tracking systems to small portable systems. As zrn example of ir perrnanent, large (yet sophisticated) system, the Starkey Project in Oregon was cstablished in a 25 000 acre study area to monitor contiuously the rnovements of
a l0-year period. This federal government's Loran-C navigational system as
Iu0 radio-collared atiimals (elk, deer and cattle) over systetn makes use lirllows:
of the
A
Fig.
base station computer sends out a location request to a different collar every l5 seconds. Once a collar receives the computer's signal, a pager inside tlie collar turns on a transmitter and receiver. During the next l0 secouds, the receiver collects Loran-C signals from three out-ol'state Loran towers located in George, Washington; Fallon. Nevada; and Middleton, California. The transmitter inside the animal's collar sends the signals over a microwave link to one of Starkey's field station towers, which retransurits the signals to the base station's Loran-C hardware. Positioning sotlware translates the Lorarn signals into Northing and
(photo by A' Olsen)' 9.35 Raclio-collared coyote, receiver, ancl hand-held yagi arltenna
(see chapter 8; Cuthill' I991; themselves, cAn have elfects on the animal's behavior not rlecessarily to be Laurenson and Caro, 1994). These potential ellects are be guarclecl against througli proper procedttres and
expected, bgt they should the effect of radiomonitoring. For example, research has been conducted on dilterent duck transmitters (external ancl implants) on the behavior of several 1973: Korschgen at ul" species, including mallards (Greenwoocl and Sargent, 1984). blue-winged teal 1984), canvasbacks (Perry. l98l; Korschgen et ul., (Siegfried ct ul',1977). redducks (Greenwood and Sargent. 1973). African black (Korscltgen cr pintails Butler, 1975; Korschgen et trl., 1984),
and (woakes and Butler' 1975); various 1984). and tuftecl ducks and pochards
hea
ol.,
behavioral el-lects were founcl' is a valtritt'rle rcscitrch Potential problems aside. when used properly, biotelemetry ctlttipltlcttt' sophisticittccl tool that has promoted the development of an array of battcry livcs lttt''l signirl Various types and sizes of radio transmitters with varioLts ol'sgrccics.'ll'rttlsttlitlct's crtrt strengths have becn developecl fbr r.rse on a wicle variety (c.8 lt:tt'ltcsscs' coll;tts' stlttttlltl'tl v'iu'iotts be attachccl extcrnally ttl thc uniuurl using .rl 1le tl1'ttitt: (llt';rtll' tltt'lrltt'k ott I;igrrr.c 9..15)lr.rl rrnitlrrc lccluritltrcs. strt'lr lrs r,'clcto ( itt't'tt ('/ r'l ' l()S') l()fi7): llrrrsnrrll('t:i ('ittt ltls,r llt' ittlPllrltlt'tl ttt lllt' :tttttttltl {t'1'
Easting cctordinates, then computes locertions using dilferential statistics. IArutnt,ntous, 1989J
At thc otlrcr extreme, portable microcomputers can be used in field-operated lrtrrlclct't.tclry systents automatically to record data. such as animal location t \rrrcrlr.iot'tt itrttl Bccker, 1992) and activity (Cooper and Charles-Dominique. |
()t"i
5 ;.
Ilrolclcnrctrv cc1ui1-rncnt can be developed and/or built by researchers (or technilnrrtt lltc nlrny l-rublished schematic diagrams, or the researcher can purchase r'tlull)ntr'lll llirttt crtnil-rirnics specializing in the development and manufacture of r,ttltolr'lt'n)r.'try crlttiprucnt. lirr adclitional information on equipment, applications r r.ur\)
.rnrl tn;tttttl:tt'lttrcls/srr1'rplicrs. you sltoLrkl consult the ret-erences below as well as l),rPr'tr Pttlrltsltt'tl on llrt'rrsc ol'biolclcrnclr-y with yorrr species ol interest. orclosely t
r'l,tlt'rI \l)('( t('\
trn\()t\ o\('t\t('\\'ol llrt.polerttilrl lirr ttsiltg ltiotclcntctt'y ttt r'lltolol'lr,tl tr".r',rt,lt I ot ,rrlrltlt,'tr,rl tnlotnrirlron. ( ()n\illt tlrt' lirllorvintr. r'rlrrrI lt,rrr'l)l()\l(lt'rl ,,rrl\
;r
DATA-COLLECTION EQU IPM ENT
with various specles' ln pendiums on the use of biotelemetry for various purposes, and Macdonald (1980)' Asa both the field and captivity: Amlaner (1989), Amlaner Kenward (1987), Mackay (1980), (1991), Cheesman and Mitson (1982), Cochran Garrott (1990)' (1993), Mech (1983), Priede and Swilt (lgg2) and white and
10
Selected examples
of data
collection and description
As stated in previous chapters, all ethological research begins with a description of v'hat the animal does. In some cases a quantitative description of behavior is the objective of the study; in other cases it is the foundation for experimental research (Chapter 6). Sometimes your previous research will provide the descriptions necessary for experimentation, or the literature and other researchers can provide that
inlormation (Chapter 4). Often, however, researchers find themselves collecting and rluantilying data in order to provide complete and accurate descriptions of behavior. This chapter provides a few examples of this process.
IO.I
INDIVIDUAL BEHAVIOR
It is generally preferable to study individual behavior before examining interactions lrctwcen two or more individuals. Knowledge of individual behavior, experience in *l;rlu collection, and discipline in concentration on particular units of behavior can tlrcrr be applied to the study
of social behavior.
r0.r.r Terrestrial tetrapod locomotion Sttrtlics ol'terrestrial tetrapod locomotion begin with descriptions of the spatial and It'nrl)t)r'ill rclationships of limbs. The position of the limbs is sampled at high rates of ',grt'r'rl. gcncrally I
,r
,
with the use of motion pictures. Videotape generally does not
,r'irlc I lte l'runte-by-frame analysis possible with movie film.
lltrllock (1974) studied locomotion in the pronghorn antelope (Antilocapra
Ilc took movies with a Pathe Prolessional Reflex l6 mm movie camera ()n il nrodilicd rifle stock, using black-and-white Kodak Plus-X Reversal
,uu('t i( (tn(!). rrrt,111vlg1l
Irlrn r'rlrosctl at l'i0 ll'urncs per second. He studied the film frame by frame with a
/r'rss lkorr Moviscop Viewcr littccl with a 2x magnifying lens. To facilitate more in,lt'1rllr slrrtlv. llrrllock rrlso Pro.jcctctl thc Iilnr onto a solid screen with a 35 mm film,ln1r pto;t't lot. ltc llrcrr tnrc'ctl tlre sct;rrcrrccs irr silhotrcttc lilrru on the screen. These .rtt.tl\',t". I't'nt't;tlt'rl loolllrll lirt tttttllrs;rntl 1,,rril rlitrl'.t':rrtrs ltlr tltc lttrlttgltortr's vitritttts 1',ttl.' l lrr",t'rlt",t t tpll()llr, (l('l ttt' lt,rttt tlr'lt'l llllllllll'. tyltt'rt t':tt'lt lirtll is tltl :tltrl tlll'tlrC
EXAMPLES OF DATA COLLECTION ANI) DESCRIPTION
320
SOCIAL BEHAVIOR
10.2,
32t
SOCIAL BEHAVIOR
lo.2.l Displays L R
A display is 'a behavior pattern that has been rnodified in the course of evolution to convey information' (Wilson, 1975:528; Beer, 1977). Displays are often dramatic,
oo + ao
eye-catching behaviors and have attracted the attention of ethologists since the inception of the discipline. The classical studies of the comparative behavior of the Anatidae (Heinroth, l9l I ; Lore nz. 1941) were based primarily on displays. to.2.ta Description of displays
Loa RO
l'he first step in the study
of behavior pattern is description. The components of the tlisplay must be described clearly and completely, without bias as to interpretation (('hapter 4). Many hours of observation are generally necessary before you
o
O
will feel
8C DE ri+ rJ
t----l
I I I I
I I
Fell + I
t'onrlbrtable with your description. tJ
I
only by watching, writing down, drawing, rearizing how much you
I
I
I
F---+-l
a
D--l
are
not certain about, watching again, and thus completing your description step by step. can you attain a reasonable accuracy and completeness.
t-----]={ t-{ t-l
I Tinbergen,
I 9-i3 : I 3 I J
I
of the comparative behavior of gulls (Luridae)has long served as ,r rrtotlcl of careful description of displays. As an example the lollowing is his I rrtlrcrgen's study
oroz0toao!oco Motion Picture frames
Fig.
10.
t
Footfall lormula (above) and gait diagram of a pronghorn antelope employing lateral (rotary) gallop (from Bullock,l974).
,k'sr'r'i1"rtion
of the choking display.
cHoKING [Figure 10.2]. In this posture. the bird squats an
a
lirrward. The tongue bone is usually lowered. the neck is held in an Sbend. and the bill is pointing down. In this position the head makes rirpid ckrwnward movements, usually however without touching the
ground. Bullock was able to do this at l/80th-second intervals. For example, Figure l0.l illustrates the footfall formula and gait diagram for the lateral gallop. Since the movie was taken at 80 frames per second, only 0.75 s is clepictecl in the gait diagram. From the inlonnation in Figure 10. l, it can be shown that this gait is
gnrund. Thc carpaljoints are often raised, and the wings may even be r':riscrl ancl spread. and kept stationary lor seconds. A mufflecl. rlrythrrricul sor.rnd is given which may or may not be in tinie with the pccking nlovcnrents. The breast is 'heaving'strongly, particularly in large llrrlls. ()licn thc latcral ventral f-eathers are raised. The bird may be lrrcirrg rrnolhcr hirri. or l)rce away from it, or take up an intermediate
both rapid and asymmetrical (see Hildebrand, 1977; Muller-Schwttrze. l96tt). Bullock's (1974) analysis also included: l. support intervals o('lust gaits: 2. Icatls: 3. turning; 4. change of gait; 5. synchronization ol'gait uncl lcittl; tttttl (r. s1-rcctl.
(
Cocatre-Zilgien ancl Dclcomyn (1993) have pnrposctl tltc ttsc ol"stttlc tliltg.t'rtttts'ltt reveal rrorc suitirbly gait trcnrls:rnrl rlilll'r'cnccs ltclrvcett gltits ltt lt glltttt't'. Merrtlcl
)ilt.
n t,;ll
t()n.
ITinbergen, I
959: I 6- I 7 J
nt ' th (htuntif.t,irt.g ilitpluy.t
llrc usc ol' ll';rrrrr'-hv-l'nrntt' ;ttltlvsis ol lltt' 1';til Prttlt'llls illl(l sl('l) lerrgtltsol (lrt'llut'r.tot'tl slollr'slr;rrrrls;rtrtl lr'r'l rlttnltl'(lnttl)tttl';ttttl lt'tlt'sltt:tll,rt'rt
(l9ll-S1 tleseribcs
ll,tItt'tttl',r'l ttl (l()"''r)sltttltr'rl llrt't,rrrrlrlrrlrrlrspllrvsol llrt. l1ll)l)v (l.t,lti,stt,.t.t.t, lit.ttlttl l'\ ,'lr',t'l \ tttl' lltt'ttt ttt l,tt,'{' ,rrllr.n unll'. tlt ..rrlr'r rlr'st t rlrtnli ;tttrl t;tr;rrrlil'yirtg
Ittrrtttltt
rrr' i
322
SOCIAL BEHAVIOR
EXAMPLES OF DATA COLLECTION AND DESCRIPTION
323
//2 56
456 Fig. 10.2 choking display in black-headed gull (from Tinbcrgen, 1959).
45
various postures and movements, they also studied the occurrence of the black markings on the males. For ease in description and recording data, they assigned a number to each of the markings (Figure 10.3.A) and then measured the lrequency of occurrence of the various patterns in different behavioral contexts (Fig. 10.3B). The relative variability of displays (e.g. duration) can be determined using the coefficient
245 24 6
4
of variation (ChaPter I l). Bekoff (1971a) used movement along a single coordinate to measure the variability in the duration and forn-r of the play bow in three canid species and one hybrid. Movies (Super 8 and l6 mm) were made of individuals at 64 frames/second and then analyzed frame by frame. Duration was determined according to the
0510152025303540 B I
rl l0.l A. Black markings
that may develop during courtship in male guppies. Number (not shown) denotes the overall darkness of the entire body. B. Relative lrcqucncy ol' occurrence of marking patterns associated with copulatory attcnrpts (O) sigmoid postures (l), and sigmoid intention movements (A). The nrcirn I'rcquency ol each pattern is shown by the curve (from Baerends el a/.,
number of frarnes during which the bow was maintained. Form was meastlred as a declination of the shoulders relative to standing height on a grid system (Figure 10.4A). Similar techniques were usecl by Hausfater (1977) to stLldy tail carriage in
I
baboons (Papio cynoc'ephalzzs) (Figure I 0.4B).
coordinated limb movements
in
( iolrrrri (19701 usccl scveral
limb and body axes (Figure 10.6) in applying the | ,lrl..,l W:rc'lrrrrrurn (l: W) r-r-rovement notation to a description of the motor t rlu('n('('s irr tlrc irrtcnrctions
t\,trr'rt1tl11ltt: ltrtrri.tii\.'l'hc
ol'golden jackals (Canis aureus) andTasmanian devils system uses a coordinate system (Figure
Ir W notittion
ltl /) lrorrr rrlrit'lr to tlescribc
ir ntovcnrcn( tll'erny part of the body. The coordinate ,r ,lt'rrr :ur l)('lrpplit'tl rr'llrtivc lo: l. (lrc inrlivirltrirl's htlcly;2. the environment;or 3. a
clomcstic clricks (llckoll.
(
,',rr,lll);rrlrrr'r l'll'ult l0l'(slrow'slltr't'oolrliturlcsvslcr)lccttlct'ctl ()niutowl'sheadin
1916).
Havkin untl Iicrrtrcss (l9lt-5)rlclirrcirtcrl ttittc eottlltt't zrtl)t.s ott lltc lrotlit's ol woll ptr;lswlriclt tlrev rrscrl lir;st'9rirr1,.s1oirl ('()nl;l('ls,ltttittl'ttllt't;tt litttts Ilrt'v lllt'tt ltttlt lvzetl l(r tryrr liltrr l.) 1t('it\urr.llrt.r'llt'r'l ,rl lrorlr, lttltlt ',tt,rttl tolll;ttl llllttt'tttt'ttl,
of
tlr,' prrps.
Tets ( 1965). He divided the vertical component into nine 30o sectors and nlcasttred (l-igtrrc the frequency of occurrence of the tail elevations during different displays 10.5). Similar techniques have been used to measure joint angles in thc sttrtly ol'
of
1
95-51.
rnrrlrr;rl oricnlution ancl combative strategies on the unbalancing (falling) of one
In Bekoff's (l9l1a)study, each individual's shoulder height was divided into ten equal segments to normalize individual differences. Each of the ten segments was then divided into fourths to increase the resolution of measurement' Tail position as a component of pelecaniform displays was measurecl by Van
the development
Relative frequencY (%)
I I I
rrrlr't lo tlr':,r't tlrt'ltr';trl nt()\'('tlt('nls. Lr, lr tilil\('ilr('il1 ti llolt'tl ()lt ;r \( (tl(' l),rl'(' rll l('r ttts tll: l lloirrt rtl llcgitlttiff g; 2. l,,,ilrl ,rl r'rrrltill' ,lr(l I ',I,rlt,rl ,rrr,l I l( ttrl(,t,rl tttttt:, rrl iltr]\'('ilt('il1 l'irt t'rlrtttll[',
,
324
SOCIAL BEHAVIOR
F,XAMPLESOFDATACOLLECTIONNNI)DE,SCRIPTION
I
at
t
t
PROXIMAL
ANGLE 2
,
t
l-ig. 10.5 Diagram showing the nine 30 degree sectors that were used for frequency
distributions of tail elevations.
JUNCTURE
thc owl's head movements illustrated in Figure 10.8 would be noted as in Figure 10 9:
In this case we are using the beak position as an index of head position. The beak stirrted at point 210 and, stopped at point 2/6. This represents two
DISTAL
(3) units of mea-
srrrcment since the coordinates are set at 45" angles. Each block represents 0.2 sccond: the movement thus took L2 seconds.
What has been presented is a simplified version of Golani's movement notation rrrcthorl, and his paper should be consulted for more detailed descriptions of addi-
lionirl notittions and more sophisticated uses. However, be cautioned that many :trrtlics will not require this intense an analysis in order to answer the research questr()ns. Wcigh the incrcased resolution obtained r
rrr
with the method against the fact that
rity will nrakc it initially tedious and perhaps introduce errors of recording. ll or'lrirrr ( 1976) rlcscribecl a three-dimensinal method for quantifying body posi-
lrurr ilirr
Irorr lleil'hl. witltlr. irntl rlcJrth ol'vari<'rus body points are taken from videotape. plily bow (lirrn llckoll' I()77rr) Fig. 10.4 A. Coordinates lbr measuring form ol'thc canicl (cottt'tcsy ol ( i. lllttrsl:tlct ltltltrlorls irt currilgc tuil B. Coordil.tcs lor r.ncasuring
I )
lrt'st't'oortlinrrles e:rrr lltcn bc gr:tplrctl hy
l
contputer (Figure 10.10) and analyzed
I.r
\:n r()lrs prrr':rrrrr'lt'rs. rrrt'lutlirrp, rrttcr'-rrtrlivirltrlrl tlistitncc ancl body activity. Ir,,r'lrrnrrlt'st'ttlrt'sllrr'tollt't'lion;tttrl lttt;tlysisol tlltl;t,rtswcll itsthccornpttterpro-
I'r,un', rrlrrr lr lrt'tlr'r't'l,rPt'tl n
rr'il
1'.
( ()nun('rr'rrrlr,rlln'ltt('rs n()\\'ltvltilltltlc lrl i111i,1rra
llt()vc-
EXAMPLES OF DATA COLLECTION AND DESCRIPTION
326
SOCIAL BEHAVIOR
327
6)
Fig.
10.6 A pair of golden jackals during precopulatory behavior. The superimposed bars indicate the body parts that were considered as separate limb segments during study of displays (from Golani,1916).
a
t0.2.2 Dominant-subordinate relationships
Another example of data collection, organization and description is illustrated here
for dominant-subordinate relationships. Studies of dominant-subordinate
rela-
tionships have been made on individuals in the same group (e.g. sooty mangabeys; Gust and Gordon, 1994), different groups of the same species (e.g. vervet monkeys; Cheney and Seyfarth, 1990), and groups of different species (e.g. horses, deer,
bighorn and pronghorn; Berger, 1985). The concept of one. or more, individuals (groups) dominating one, or more, other individuals (groups) has been studied lor many years by ethologists using several procedures.
I 0.2.2
a
M eas uring
do
minant-s uho rdinate
re
latio
ns hip s
Typically. determination of dominant-subordinant relationships involvcs ( hc lirllowing (Boyd and Silk. 1983):
t .'
Itlcntilyirtg:t hcltitviot. ot'hclr:tviot's. ;rssocirrlerl witlt tlotritniu)ct.. l:stlrblislrirt|lrctilt'tiotrlly'nvlrit'lrrr'rrrrrt'rs;rrrl lrls('ls()l int('rilcliorrst';rrr lrc tltsl tltl' tttsltt'rl llllil lll lrtl' t1,,,,''1t
0 I
r11
lo
7
Thc l"skol Wachmann coordinate system for measuring displays. For each pair ol' tltttllcritls. the lower indicates the horizontal, and the upper the vertical. coortlittitlc. ln Colani's (1976) study, one unit of displacement equals 45". (By pc'rrissi.. .r'thc Movement Not:rtion Society, Israel.)
(',llccrirg rlirt. .n interactions
between individuars.
Asscssirrg rlrc tcrnp.r-,1 consistency
of the outcomes of interactions (e.g. c.rsisrcrrlly tl.rri,.nt .ver indiviclual B). ( 'ollsll tl('lillll lt tlylttlic iltte t';rcliorr rnlrtrix ilr which indivicluals are ordered ll ;t('('ot tl;ltt.t. tvt l lt sr )nt(. ('ot tt,t.tt l iot t ( st,t, ltclrlw ). I)t;tl'tilllllllltllt lllt'rlottlttt:utt t'lrrr'r:rrt'lrv u,lrit'lt rt'strlls lhrrtt tltc r.ltnk ortler ttt llrr'iltl('t,l( ttrrn ilt,tltt\ (.,(.(. lrt.lrr11 ;
irrlil'itlrrlrl A
is
SOCIAL BEHAVIOR
EXAMPLES OF DATA COLLECTION AND DESCRIPTION
328
329
(>
q N
co
o o .f
c{
o q (o
!,7
il l, ti
o -Q
jI,il
So '6 I
ilt/
C)
o.
fl
i(r
t
(\,
)'"
o lrl/
ol ol
lul
T1
lt
EI (ol
,,J
!\*
eJ
sl Fig.
10.8
I
The Eskol Wachmann coordiuate system superimposed on a owl's head in order to denote the movement. In this example the head has moved 90" to the lelt from point; to point i. (S"e text for explanation.)
t0
24.00
l()
-16.00
-8.00 0.00
8.00
16.00
24.00 32.00
width
Front-vicw ol' Cal-comp graph of body position for two human subjects (fiom
Trochim. 1976).
o
2
0
--t
t0.9 Eshkol-Wachmann notation lor
o
6 the owl's head movement in Figure 10.8.
Measurements of dominant subordinant relationships are gerierally conducted an using one, or both, of the following methods (contexts): l. the resettrchcr stages is. clrch equal number of dyadic interactions between all individuals in a grotrp: that
individual is matched with every other individual in the group att ctluitl trttlttbct'ol' times (e.g. Smith and Hale, 1969); or 2. the researcher rectlrds trittttrltlly occttrittg interactions in wild or captive groLlps (c.g. ('hcncy irnrl Scyllrrlh. l()()0). soltrt'litttcs manipulating thc cpvironnrcn( lo cncoul'lp,c crtttllict (e.g. irllt'otlttt'ilt1', rr lirrrilt'tl 'llris scr',rrtl rrrt'llrorl l'('n('rilllv tt'srrlts itt tltllt'tt'tll rrr.rtlrr,t rll' prcle'r.rctl lilotl). Itrt,tltr,ts ()l i.l(,1.(.li.rrs llt.lryt,t'rr rltllr.tt.ll ,lt;r,lrt r ontlrnlttltottr ol ttttltVttltt;tl', {t'1'
llr,rnrcks ancl Hunte. 1983). The two methods can provide conflicting data. For in two of six llocks of chickens studied by Guhl ( 1953), he found correlaI rr,rrs bctwccn numbcr of contests won (method I ) and number of individuals domrrr,rlcrl rn tlrc l)ock (n-rethod 2) were less than 0.50. Data collection in the second rrirturirl. gnrrrp) contcrt is nrore tinre consuming. but it will generally allow you to
, r.rrrrplc.
,
(,n.,lrucl lr nr()l'c virlrtl hicrarchy. llrc helurvior':rl trnits sclected fi>r measurement (e.g. Hausfater, 1975), the
r .1111'ql(s) rrr
u'lriclr irrlcrircliorrs urc obsel'ved (described above), and the criteria of
,l,,lllllf :rrrt'r' lrrvr' vrrrrctl rvitlcly (llckoll. lt)llb, Kaufhrann, 1983). Ivan Chase (pers. r r,nunun ) lr;rs srrl'1'1'slerl tlurl lltcrc ltt'c lrt lclst tltt'cc tnethttcls of deciding when one
.rrur,rl
lr.r'. tlorrrrrr;tlt'rl iur()lll('r'rlrrtittp,;ul irpprcssivc inlct':tctirltt. Irirst. yoLl cein use
l)rlr,u \ \('l urlrrrlrrr'. t rrlt'r lotr lr;rsr'rl rr;lorr ollsr'tt;tliotts ol'tltc itttitttitls'behitvIot ('\,unIl1'. ('lr;rst'(l')S ))rrst'rl llrt'l,rll,r\\nll'( lrl('ulr in ltts sttttly ol'lricnuclty
,rrr .rr
I',t l,
r1 111,11
lr
)ll ltl ltt'tt r lttt l.t'lt',
SOCIAL BEHAVIOR
EXAMPLESoFDATACoLLECTIONANDDESCRIPTION
One animal was considered to dominate another if she: (l) delivered any jump ons, combination of three strong aggressive contact actions (pecks, and claws) to the other and (2) there was a 30 minute period following
Table 10.1. An example of a dyadic interac'tion ntutrix (see text.for explanation) Loser
the third action during which the receiver of the actions did not attack IChase, l9B2:220] the initiator. an Secondly, you can use a binomial approach; that is, in each aggressive interaction e.g' fleeing; or submission (based on individual is scored as either a winner or loser it wins 1963). An animal is considered dominant over another individual if
Brown,
significantly more (e.g. binomial test; see chapter l4) than it loses in encounters with the other individual. Thirdly. Chase has suggested that you could use a combination of the first two methods. priAnother common criterion for the expression of a dominance relationship is ority of access to a limited resource (e.g. lbod, shelter, space, estrus female, etc')' demonstrated through the supplanting of one individual by another without overt aggression being displayed (see Richards' l0 measures and below). Dominance does not always provide priority of access to all valued resources; hence, there may be dilferent dominant-subordinant relation-
Priority of
access can be
D D Winner
10. I ).
C 24
E
0
A
2t
11
C
t2
B
5t
t6 3l
a
J
0
0
13
0
0
0
t4
The dyadic interaction matrix that results provides the basis for generating a
tIominance hierarchy.
Brown (197 5) provided the following list of steps to lollow in the construction of rr clyadic interaction matrix (dominance matrix):
limited
Observation.r:
BlD. C>A, B>A, C>B, B>D, etc.: BID
(Huntinglord and Turneq 1987). When individuals are ranked by different criteria, the rankings are often not comparable (Bekoff, 1977b; Bernstein, 1970; Syme, 1974). However, Richards of (1974) used the ten factors listed below to assess dominance rank in six groups
an encounter
with D. In most
captive rhesus monkeys and lound that they produced comparable results'
matrix. as illustrated in Table 10.1.
ships for different resources
t
Starting matrix'. Enter the number of wins and loses observed in the Treutment of'reversals: A win by one individual over another that has won the majority of encounters with the first is termed a reversal. Rearrange the order so that only reversals fall below the diagonal, so far as possible;
that is, change the above order to CBDAE or CBEDA or CBAED. Trt'utntent of intransivity: An order in which an individual dominates another (wins the majority of encounters) that dominates the first is tcnncd intransitive or circular. Rearrange to minimize the inevitable
encounters
rrnrbiguity. F'rom the circular relationship shown in Figure
+ Gestures for fear-submission
l0.l I there are
llrrcc nurin irlte rnatives, as shown. In the three alternatives not shown. the
a Yielding ground/avoidances b Cautious aPProaches c Nonsexual presentations/mountings d Fear-grins lo.2.2b Dominance hierarchies tlctcttrtirttrtp, The data collectecl on aggrcssive intcrirclions. blsctl ort lltc ct'itcrilr lirr (lrr'[',1()tll) is listt'tl ilr itrtlivitlrr;rl l'lrt'lt nr:rlrix. lr irrlo crrlcrctl winncr-s rrntl loscr.s. lrc
Irl.tt',t.:rt'ltltxisol'tlrt.nt:trti\.()n(.;t\lslsllrllr'lr'rl
forms of
Starting order: Choose an arbitrary order, e.g. DEACB.
Order to approach experimenter during food offers
z Agonistic : DisPlaYs
means B won
cases, these encounters take the
supplanting rather than fighting.
PrioritY to food incentives a Order to dailY food ration b Order to milk bottle c Interactions at milk bottle
d
0
t7 4t
\\nllr('r'.. lltt'otltt't lost'ts(st't'l;tlrlt'
I
) ---=---=. [i 24
lr,'lttll
I )t.tl,t,tttt,,1 llr,' rttlt,tn',tlttr' lroll llrr' trr,rlrrr ttr l,rlrlr' lll
rrrtlivirlrrlrls
A. I) lrrrtl l:
SOCIAL BEHAVIOR
ITXAMPLE,S OF DATA COLLECTION AND DESCRIPTION
332
o
t33
one' Place departure from linearity involves two individuals rather than pro(lowest relationships ambiguous the individuals that are in tlie least the minimize to tends procedure portion of reversals) in linear orcler. This
inclividuals except the alpha, the third-ranking individual (gamma) dominates all inclividuals except alpha and beta. and so on down the hierarchy. In nonlinear lricrarchies. there are one or more intransitive (circular) relationships, such as indi-
total number of encounters entered below the diagonal. Final mati.r'.The one order that best reflects the order of dorninance within the gror.rp is then CBADE. A matrix may then be constructed'
vidr-ral
A dominating B, and B dominating C, but C dominating A. Rankings in a lricrarchy and type of hierarchy can change. For example, Murchison (1935) found tlrat the ranking in a group of six domestic fowl roosters changed from a nonlinear Io a linear hierarchy as they matured (Figure 10. l2). Perfectly linear hierarchies are relatively rare, making most hierarchies techni-
Best
r'llly nonlinear. Perfectly linear hierarchies are unidirectional. They can contain
DE
rcvcrsals (i.e., a subordinate wins an occasional encounter with a dominant individ-
D
A D
A
E
E
D
A
rutl), but they cannot contain any individuals of equal status or have any circularity ''trclt as: A---B---,C. The nonlinearity is, however, of varying degrees, and some so r
losel! approximate perfectly linear hierarchies that they should be considered
Irrrcitt'.
r
BADEWinsLosses
I-utrclau's index oJ' linearity has been discussed by Bekoff (1977b) and Chase l')71\. The index (ft) is calculated according to the following formula:
,r:(fr;)
59
C
109
t4
32
14
D
2l
70
l',
E
l3
t25
f
B
)[,
,-@-2lttlL
rr lrcl'C:
A
an ordinal scale The dominance hierarchies described above rank individuals on Boyd and Silk ( 1983) (see chapter 8);that is. C ranks above B, and B ranks above A' cardinal index of domdescribe a more complex, statisticalmethod lbr generating a
paired comparisons' It inance rank (versus the ordinal heirarchy above) based upon information on interactions that result in wins. losses ar-rd ties' They
incorporates
.then
Io.2.2c AnalYsis o.f lincaritY rrttttlirtt'ttt" D.nrinrtncc 5icrurc5ics llrc gcncnrlly tlivitlctl irrlo lw() lvl)cs: lirtt'trt'|nd (lrlPlr;r)rlotlti lilttktrrl'trttlivrtlttltl rtttt'lol) is. lltlrl Linclrr lricrtrrcltics illc tl'ltllsiliVe: (lrt'l;l) (l(!llllll:ll('\;lll rt;rtr.s lrll.lltt.r l,r()11) 1r('nrl)t.rs. llrt'.,r'r'r,rt.l t;tttktttl'tttrltrttlrt;tl
l-
number of animals in the group number of animals that individual'd dominates
'lrc portion of the tbrmula
r'r( )ul).
V,-l(n-
l)/21 is calculatecl lor each individual in the
and these are then summed.
ternr l2l(n3 - n) normalizes the index so that it ranges from 0 (nonlinear) to I 1,,'rli'ctly linezrr). Bekoff (1971b) agreed with Chase (1974) that h > 0.9 would be a I lrc
r
,,.rsorurblc lirlthough arbitrary) cutoff criterion for'strong', nearly linear hierarI r tt's.
\s rrrt cxutnplc. wc willcalculate the Lanclau index of linearity for the dominance l r' r iu ('lly ol' l(r-wcck-olcl clomestic-fowl roosters in Figure l0.l2,{.
tt6 \'\' rloutintrlcrl lllrrc tlottt irltlcrl (; tlorrrirurlcrl It rlotnirrrlctl (l()ttllttitl('(l W \
(l()lllllt;ll('(l
Bluc, G. R. W Y
WR,Y llluc. I{. Y
w.Y
It.(; ll()ll('
V',
:5
" Vo,u":3
V,, :3 vR -2 Vrn 2 V\0
334
SOCIAL BEHAVIOR
DESCRIPTION EXAMPLES OF DATA COLLECTION AND
B
Vrru":4
c
ft
:
335
(0.057x 6.25 + 2.25 + 0.25 + 0 _25 + 2.25 + 6.25)
:3 v^ (r VR
:2
Vw
:l
h:(0.0s7)(17.s;:
1
.s
vY -o The ft value of I is just as we would expect for this perfectly linear hierarchy. koff ( 1978b) demonstrated an h value of I in litters of coyote pups (Canis latrans) rrt vzrrious ages. Calculating the index of linearity for the hierarchy in Brown's ('\iunple (above) will generate an h of less than 1. When individuals are close in rank, that is they supplant each other approxirrrrrlely equally, then assigning clearcut dominance status to one may indicate more lrrrcarity to the hierarchy than truly exists. However, Landau's index can still be used rr lrcn individuals are of equal rank by applying the following rule: l'or individuals of equal rank: lle
Vo:l
for each individual dominated plus 0.5 for each individual of
equal rank.
Fig.l0.l2A.Nonlineardominancehierarchyinagroupofl6-week-olddomesticfowl roosters'B.Linearhierarchyinthesamegroupofroostersat32weeksofage.(A of linear hierarchy in B; all B after Murchison, 1935). C. Shorthand diagram
lrrr cxample: lndividuals D and E of equal rank
and all those below' individuals above are assumed to dominate
A I B I C \
^:lz,)i[,,-?]' 12
h: nr-n . \6.25+0.25+0'25+0'25+0'25+6'25) 12 t,:i1(
t")
13.5):
i*
,13's;
:
0'0s7(
old roosters. index of linearity lirr the domAs another example, we will calculate the Landau roosters in Figure l0' I 2t]' By cominance hierarchy of 32-week-old domestic-fowl you would expect thc ltcirarclty o1' paring the two diagrams (A,B) in Figure 10.12 of linctrrity' the 32-week-old roosters to have a higher intlcx
-6
I
',,
,
vr:4
Vr:3 vo:1.5 Vu:1.5
h:(0.057)(6.25+2.25+0.25 + 1+ I +6.25)
:(0.057X17):0.97
Vu:o II
\
l3'5):0'77
Thelow/rvalueof0.lTreflectsthehighdegreeofnonlinearityinthel6-week-
il
l)
vo:5
,/ I,'
\
rr
r'vrrrrl'rlc wlrcrc irrtlivicluals B, C, D and E are of equal rank:
n |'
'" , ,,
Vn:5 l'rr:2'5
.
l:
I(
2.5
1.,, .l.r
lr ')\ I r l)
/,
(0.0-57)(6.25+ 6.25)
(o 057x I 2..s0)-0.71
EXAMPLES OF DATA COLLECTI()N AND DESCRIPTION
SOCIAL BEHAVIOR
irrteractions initiated in his ratio since he assumecl that the horses with the highest of successful bouts and the lowest number of bouts initiated by them *'cre dominant within the band. He then calculated the intra-band dominance as lirllows:
to.2.2d Dominunce indiccs
l'r'ccluency
Dominance indices provide a measure of how dominant an individual is in a group, rather than their rank relative to other individuals, as in the hierarchies discussed above.
Dominance Coelficie
Barki et al. (1992) measured the dominance of individual freshwater prawns involved in agonistic interactions. They assigned each of l8 agonistic behavioral acts a weight on an
ordinal scale of aggressiveness of from -
3
to
t
3. For each
where:
i
t
I(act's frequency)
The winner of an interaction was assigned to the individual with the higher dominance index.
Most dominance indices are some lorm of the ratio of an individual animal's wins (or other indications of dominance) to the individual's total number of interactions, as shown below.
dominant-subordinant interactions
with
rrrrl initiates one.
Arr index of relative dominance between two inclividuals was used by Beilharz rrrtl ('trx (19671in their study of swine. It is based
inclividualpig in interactions between them.
rr
lrcrc:
other
l. It does not take into account the individual's
with different opponents, only the total for all opponents. Eden (1987) used a dominance index (below) in his study of rnagpies which incorporates the WIT ratio lor encounters with each opponent. success
,v,lT
p:no. of wins by pig ,i, Ir:totalno. interactions
I lrrs irrtlcx ranges f,rom 0
l rrr.rl llrc
-+-
between pig ,i,and pig 7,.
l.
or+I I yri+ I
o^tr)+ )'^lt)
no. of opponents
rr lr( r r.':
(,),\',,
)
index is essentially the same as that usecl by C'rook and []uttcrlicltl ( lt)70) cxccpt thut
oI
ewes
or trre same
age
or older dominated by the subject ewe.
llo'ol'olclcr
'\'r)
llo' ol' cwcs ol' tltc sattte age or younger that dominated the subject
)
(N- l) in thc dcrrornirtatot'.
Ilcrgcr' (1971\ rlcvisctl lr 'tlortrittlrrtce eocllit'it'nl' l() nr('irsure tltc rt'lrrtivc tlonri-
Ilt'rtr'otPolrlt'rl lltt'ntrrttlrcr
rro.
(),,
ewes
with whom the subject ewe interacted with no
clclrr rlutconte.
This index varies from 0 to l. If the individual has the samc numhcr ol' intcractions with each opponent, this yields the same index as the sirlplc ratio ( llll'l'). I:tlcrr's
r)iulcc rtl'li'rrrl lr()l.s('s rritlrirr llrt'tr r('slx'( ltvr'lr;rtrtlr
to
p _n-p 7,:dr: nn
lollowilrg ratio:
t
W,:rto. of wins in interactions with opponent'i'. Ii:total no. interactions with opponent 'i'.
they used
on the proportions of wins by
I t'sl;r-lliitnchet (1991) used a dominance index (based on Clutton -Brock et al. | 'tli(r ) itt hcr study of bighorn sheep which removes the effects of age. She calculated t lr' tl,tltirtitttcc index fbr each of the
N
N:total
from 0 to 100. Anyone considering the use of Berger,s dominance
of interactions initiated (i) in the denominator. For example, an 'rrtirtlitl that wins only 5 out ol l0 interactions (50'2,) and initiates none has the same r l.;11i11i1nss coefficient (50) as an animal that wins l0 out of l0 interactions ( 100,2,)
individuals
where:
no. bouts
no. interactions initiated
Relative dominance between individuals ,i'and
where: W:no. of wins
Pl: -t-
no. successful bouts
rrstttg the number
T
This simple index varies from 0 to
:
fi:g?I loo) (i+l)
.cllicisnl for the species they are studying should carefully consider the effect of
.r, lr
(Dl\:Y
T:total no. of
:
I ltis index varies
frequency X act's weight)
Dominance Index
u
b:total
indi-
vidual a Dominance Index (DI) was calculated using the following formula:
DI: I(act's
331
ol'
I' t'r
| ',"
i,li',,.,,rlrc.
cwcs
( |('iIl ()III('()|||('
wilh wlr.rrr thc s.l-r.icct cwe interacted with no
llt,rrr( lr('l ilr( lil,lt'tlllrr'un( l(.;lr ulr(.r,rr rr,r,,.,lrrr.r.rlrt.,lrlt.r (.!v(.w()r)()],,1,ol. lhc ,trlr r,tr lroll',.lltr'tt.lotr..,,ltr',t,,.,ilt1t(.(lllr,rl tr,,rtrr,rll\ tlrt. lltlr.t t.tt.t.sltplltl lt;5,t.rvrltt;t1
EXAMPLE,S OF DATA COLLECTION AN D DESCRIPTION
SOCIAL BEHAVIOR
interaction with a younger ewe. The ratio (above) was used to rank the ewes in each
/-: /\AGE/SEX
cohort, then the ranks were divided by the number of ewes in the cohort. This provided dominance indices from 0.1 I to 1.00. Changes in dominance between age cohorts of cock red grouse hatched from age
, . soctAL
CLASSES
STATUS, I
With this index: when there is no change, C:0; when N:0.5N,,
C:*17;
when
17. Moss and Watson added the Cs together from year to year to
7\
,f-1,
RELATIONSHIP
INSTTTL'TtON'S
"ur'
u- -' STRUCTURE
\
t.. -t' ,) _r'
/
/.rrEcls" or \-1
provide a cumulative index.
,NrenecrtoNs/ rON'
rNTER^crto*s
t0.2.3 Social organization Society: a group of individuals belonging to the same species and organized in a cooperative manner. IWil,son,
of the society's members. Analysis of social organizatron
is one
"
-;il'
\
:;""^^'
\ )
RELATIONSHIPS
cenieiX
rsycxotocrieu
1975.7 J
IANDI
nrysrouocrcel
r,rntrauEsJ
Social organization is the behavioral organization (type, temporal, and spatial)
of the most complex
endeavors an ethologist can undertake, because it necessitates the integrative analy-
of social behavior both within and between group
/\
\
---\)\
N:no. of wins by cocks hatched in year r. N,:no. of wins by cocks hatched in year r* l.
sis
,-\ /\BLOOD
,5
FORCES
where: C:change in dominance between years.
N:2N, C:-
I
't. . *I \L)
year to year was measured by Moss and Watson (1980) using the following index:
N' x loo-50 c: N+Nr
/
5J
members. This includes the
oF- rl- ,',F rNTER^cTton\l
TYPES
development of social behavior in the individual (socialization) and the interaction
of group members over time (social phases). Interactions between individuals serve as the basis for social relationships, which
I
l(t
rr'
rr'r'l
I
are then integrated into the society's social organization. Hinde and StevensonHinde (1976) have presented these relationships in diagrammatic, but rather complex, form (Figure 10.13).
In order to understand fully the social-behavior matrix which is the structural basis for social organization, the researcher should begin the analysis
of social org'anization at the level of individual behavior, then dyadic interactions. Horvever, a superficial knowledge of social organization can be gained by looking at relationships and perhaps even structure.
S.A. Altmann (1968) determined the relationships among and within sex age
of monkeys by observing interactions among indivicluals. This irllowctl hirn to describe the socialorganization of rhcsus r-norrkcys:rt thc rclatiorrslrip lcvcl irr lhc
classes
diagrammatic lirrm ol' it sociogratrt ( liigrrt'c 0. l.l 1
)
Ittlcl'itcli()ns. r'cllrliottsltiPs. rrtttl slrrrt'ltttt':rrt'spt't'i(it'lo irrtlivitlrr;rl 1,lru1l sot'itrl ()ll,illti/'tliolls itttrl sltottlrl ttol lrt'l'('ll('t:tll/('(l lo olltt't '.1rt't'tt's ()l ('\'('lt ollrt't PoPtrl;r
rlr'rtr
\
l)iltgritmntatic representation ol the relations between interactions, relationships, Itlttl stlciitl structure, shown as rectangles on three levels, with successive stages of Itbstritcti.rt lhrm lel't to right. The discontinuous circles represent ina.p.n,i.n, o. irtlcrvctting vitriables operating at each level. Institutions, having a dual role, are slrown in hoth a rcctangle and a circle.
llte sirttte sPccicswithoutconfirmingevidence.Differencesingroupcomposi7c.
irntl ltitbititt czrn all affect social organization. Forexample,
at the l' ' I ')l''r()tll) rrll('r'il('tiolts. liltt't'itntl IIerrnkind ( lg]4)measured the frequency of occurI rrr r' I rl t'ttttllslliP tlisPllt.vs irr llrc guppy (Paot,iliu rcti(ulata)at different densities of l' rrr ' lltt't',ttt'l;ttiott bctwcclt 1'rrir rlclrsity ancl courtship interactions is shown in I rlrrrr' lO l\ Ilrt ttpt': ol \lltl('ltttt'lirtttttl itt lrrrirn;rl sot'it'lit.s lr;rvc bccrr cl:rssificrl in cliverse '' l'\ .'('\('l;ll lrttllt.ts ()ttr',1 lltt'rrr,sl rrst'llrl ()\'(.r:rll t'l;rssrlir.;rliolts.l's.ciitl 'rr',rtilz,rlt.tr,, r,. llrtrl ltt.,P.,st.rl lrt llrr)\\il [l()/,r) trr l;rlrlt. lO ) ( )llrt.r t.l;rssilicttliott ' lt' ttlt ' lt'tt. lrr't'tl lrl,ltrr'.t'rl l,,t ',;rr'r rlrr p,lrlrtl),, (,1 ,tttttrr,rl.,. ,,ttr.lt;ts
l;tilniilr
s ( l()7.1)
EXAM PLES OF DATA COLLECTIoN AN D DESCRIPTION
SOCIAL BEHAVIOR
::i:
l
pair
;il
II m
bs
o o o C
2 pairs
J
O C)
o o of rhesus monkeys. Probabilities ol various intractions, shown by the figures and the thickness of the lines, are calculated lrom a hypothetical population with equal representation of
Fig. 10.14 A
modelof socialinteractions between and within
age-sex classes (from
S.
classes
O
I
30F
I
o
) g
Altmann, 1968).
ur,,,r,
o)
lJ-
(Emlen and Oring, l97l: Wittenberger. 1979, 1981), mammals (Clutton-Brock, 1989), birds and mammals (Davies, l99l), insects (Thornhill and Alcock, 1983). and males and females (Alcock, 1993).
tof ,or
categorization of African antelope social organization into five classes. Likewise. mating systems have been classified for several animal groups including vertebrates
I
3O
l( t'tttlttllt,'ttttttt
\tnllltn ) t', ,t t ott( t',(' t'\,ttttPlt'
ol ,t 'l\lrtt,tl'
'.ot t;ll
o pairs
t-
_t
pa irs
to 20 N umber of displays
should focus on certain aspects of social interactions at eerch lcvcl irr liigurc I0.13.
rr'lrtlt' I1ilil,r..'t,t',
1
'ifi,,,,,,,,,,,,,,,,,i,
t I
Within the conceptual framework discussed above, how do you go about studying social organization'/ Using the methods and equipment discussed previously, you should begin at the level of individual behavior and interactions (such as those discussed in the section 10.2.2 on dominant-subordinate relationships) and build through relationships to the level of structure. This can be accomplished only through intensive and extensive observation spread over severitl scusons. One This can be accomplished by following the cluestionnuire cotnpilctl by Mcllriclc (1976\ following a committee's discussion rrt'thc inlirrntatiort rtcccssiu'y lo tlcscribc adequately thc social orgunizatiotr ol' l spccics. Nturtcrorts cstrrblisltctl ctlrologists cottt rihrtlctl l o ( ltc (l uc:il i()n nir i r c l)r'cl)ir t't'tl by Mt' llr irlc. Irr ;rrltlrlion. ( )rvt'rr Srrritlt's (l()7.1) rlt'st'nPtrorr ol llrr' sot rlrl ot;,rrtttz:tlr()n ()l lll('
5 pairs
!o
,'\tttttIrill,it\(.tir,'('.l lit.tlrrt.nt.!,rl ,lti
ulr'.1's
\;llton lx'u()(l
ll
rolrr l
.r.lrr.r.r,r)t.r.,1.
111;11!,
llul)l)yt.,urlslrrPrlrr,l:11..,
;rr r ;rrrtl l lt.r t lkirrrl. l,) /.1)
111
SOCIAL BEHAVIOR
AND DESCRIPTION EXAMPLES OF DATA COLLECTION
nrble 10.2. (cont.)
groups Table 10.2. Types of intraspecific animal
fypes of groups
Examples
Types of grouPs
Examples
Aggregations (coincidental groups) iroups formed by physical factors
5.
l.
Kin grouPs
Clones
-
groups formed bY asexual
(
Colonial coelenterates
acting on migrating or moving
reproduction of sessile colonial invertebrates, typically in permanent physical contact Families - groups formed by one or two parents and their most recent olfspring
Extended families - groups lormed from families by failure of many offspring to leave Parents
Harems
-
groups in which a male
mountain pass Whelks on a sheltered ocean rock
Goose and swan families Coyote (canis latrans)
Stream-surface insects on a calm eddy
Prairiedogs (CYnomYs) Some primate groups
( iroups formed by attraction rvlrter
Lar Gibbon (HYlobates lar) Red deer (Cervus elePhas)
Lek birds and mammals ies
Hawaiian DrosLtPhila Many fishes and amPhibians
no provisioning of Young 3. Colonial grouPs Groups formed by colonial nesting of pairs or one-male harems; Young
lt
t
(
A
gcluius
tricolor) sea
birds
Some bats and scals
4. Survival grou7S Groups formecl by aggrcgtttion tll' ratltltlttlly rclatctl. trstta Ily ttottbt'cctl i ttp
irttlivitl trlls rvlto lll't';tt lt trlltt't
Tricolored blackbird
Many
provisioned at nest
t'
tt ttt t ttlt
llv
lrt I t ltt'tr'tl
t I
,)rgrrnization study. Although it is lacking in some respects, it can serve as a model l,r1 s[sdls5 of this type. Also, consult Kummer's excellent study (1968) of the social
,,rl:urization of hamadryas baboons, Crook et al.'s excellent conceptual model of tlrr' structure and lunction of mammalian social systems (1976), and Eisenberg's ,lrse ussion of social organization in mammals (1966).
communal mating ground; eggs or young Produced elsewhere groups of both sexes formed at localized spawning grounds;
Bears at a garbage dump
\or.rrcei Adapted from Brown (1975).
HilltopPing butterfl
-
a
Scrub jay (Aphelocomo crterulescens)
males (and subsequently females) to a
Spawning groups
to
c()mmon resource. such as food or
Mexican jay ( Apheto coma ultramar ina)
attempts to keep females together and away from other males, with or without cooperation of lemales Leks - groups formed by attraction of
ridge
Land birds migrating through a
animals
Gray wolf (Canis luPus)
2. Mating grouqs Pairs - monogamous groups of two
Hawks migrating along a mountain
t'oritgirtg llocks Ni1'lrt l()()sls ol'Ncw Worltl hl;rt'kbirtls ks ;tlltl l't't'st' rlttrl' ttt;ttttttt;tl" I t',lt'., lt,"'1" ll,t, lr,'l,rl l'|lilll[.' rtl \\'llrlll
| )rrt I lt't
V)
:) t-{ t -) U) C) ? F(
()
(1 t+ {J
a0 ?1
l-(
ofi
N
h
F-{
d c4 F{
|rl Fi lrl
11 Introduction
to statistical
analyses Alpha-coefficient: Equivalent of an Italian sports car. Type I error: Making one misteak. Type II error: Making two misteakz. INctrman and Streiner
1956J
Irrttl.t',sis is the ordering, breaking down, and manipulation of data in order to ,,blain answers to research questions. What we will be primarily concerned with in tlris clrapter and Chapters 12-17 are: L initial ways to look at data;2. first approxi-
rrr;rtion sample statistics;and 3. parametric and nonparametric statistical tests.
II I STATISTICAL PRINCIPLES \
rtt
t i.t I
it'.s
are measures computed from observations in a sample. Statist ical tests are
I,r()cc(lLrres whereby hypotheses are tested.
Kerlinger has defined statistics in the
'r:ry lltc term is most commonly used: Statistics is the theory, discipline, and method of studying quantitative from samples of observations in order to study and cornpare sources of variance of phenomena, to help make decisions to clata gathered
acccpt ur reject hypothesized relations between the phenomena so sttrrlicd. and to aid in making reliable inferences from observations. I Kerlinge r, 1964.
148
J
Irr onlct'lo rttirkc statements concerning the results of their experimental rr.,',utlr.cllrologistsrtrustsupporttheirconclusionswithstatisticaltests.Likeother I'r,rlo,'rt'irl scicttlists. thc cthologist assumes that there is some order to animal l''
lr.rr ror ;rntl
it
is.
tlrcrclirrc. lrr-ncrruble to statistical testing.
!rr lriology n)()sl phcn()nrcnil arc all'ccted by many casual factors, urr('onlroll:rlrlu itr llrcir vlt'illion irntl olicn uniclcnti{iable. Statistics is nt't'rlt'rl lo nrt"lsrrt'srrclt vlrtirrlrlt' Plte rronrcrtlr willr rr prcrlictirble errclr lrtt,l l,) ir\(('rl;rrrr llrt'rt'lrlrlv ol nrrnrrlt'lrrrl itttllrlrlrrrrl tlilll'r'crtccs. \\'ltr'llr,'t lrlol111'1,,,1 Iltt'tt,rlll('ll,l ,ll('lll l:tt l lttttrllrtttt'tt(ltll.y tlclcrrrrilristic ,rttrl ottl\ lltr' r,tt tt'l\ r)l (,rll'.,r1 r,rt t,rlrlr",,tttrl ,rut tn;tlrtltlV lo 1'lrttltol
INTI{ODUCTION TO STATISTICAL
N
HYPOTHESIS TESTING
NALYSIS
biological these nrake these phenomena appear probabilistic. or whether mechanics quantum in postulated as probabilistic, truly are processes
for elementary particles, is a deep philosophical question. ISokul and Rolil/, 1969-5 J
Hr: p"*100 rurd is a nondirectional alternative hypothesis. The alternative hypothesis for the sccond I/,, is
Hr: p.,<100
intriguing as it is, our purpose here is not to deal with the employ philosophical question, but rather to justify the assertion that ethologists
;rnrl is a directional alternative hypothesis. Hypothesis testing
statistics.
rcscarch usually involves an exact I1,, and a nondirectional
Needless to say, and as
//,.
in
ethological
Whether the null
lrypothesis is exact or directional determines whether a one-tailed or two-tailed statrslical test is applicable (see below).
II.2 STATISTICAL HYPOTHESES When
I
discussed the scientific method in Chapter
l, I stated that the scientific
I
we examined the metht>d is basically a matter of hypothesis testing' Also, when is our best guess as design of ethological research I stated that a reseurch hypothesis the phenomena to the answer to our resecrrch question. Research hypotheses refer to ontogeny, or lunction, the causation, that is. tentative predictions about
Griftin,
l9l7:Davis, 1984; Krebs, 1917)' population parameters that are about statements Statisticul lrypotheses are hypothesis is either a rtull statistical A amenable to evaluation by statistical tests. in order to hypotltesis(H,) or an alternutive hypothe,rls (l/1)' Both need to be stated
but not falsifiable (e.g. Campbell and Blake,
a researcher conduct a statistical test. The statistical test is a procedure whereby and exhaustive chooses which one of the {ichotomous set of mutually exclusive This is accepted' be is to one which and hypotheses (11,, and 11,) is to be rejected (Type Type I and decision 6one at some predetermined risk of making an iucorrect
II
errors, to be discussed later).
nondirectional' Statistical hypotheses are either exact, directional, or inexact, (i ) given by a (pc) vocailizations of number For example. the hypothesis that the rlean by population of bobwhite quail each clay is 100 is expressed
H": P':l{)o and is an exact null hypothesis. greater than, 100 I
1,,: 1t,'-
Slatistical tests are designed to determine whether you can reject the null hypoth-
,
the hypothesis that tlie rltttlbcr is ctlttitl t6' ttt
tlrt'lttsl //,,
t.'
thus accept the alternative hypothesis. Therefbre, the alternative hypothesis
r t rurt iorl to be). That is, if you are attempting to demonstrate statistically what you l', lrr'vc to be the case from observatious, your F1, should state what you have
if
you believe that a population of quail rarely if ever call ,iri )rc lhan 100 times a clary, your statistical hypotheses should read as follows:
,
,lr'-t'r'VCd.
For example,
lI,,: p">100 11, : l
, rr,
,
l
( ( )r
p<
100
orrr statistical test is significant and we reject the
.Fl,,,
we then inler that the
ll,
rec(. ln this particular example we are interested in testing whether the popula-
'n nrcirrr lor thc ttrtlber of quailcalls per day is equal to or greater than 100. That rl rt rs sigrrilicantly lcss than 100, we will reject the H,,and accept the Il,. Since we
,, rrl('rc\lctl
in only onc side of the distribution, in this case the left-hand side of
rlr, ,lrslrihuliort (scc Iiigurc
,, t ,,rrt'rrssoei;rlcrl
ll.l).
our statistical test will be one-tailed. One-tailed
rvith cxact (tlirectional) null hypotheses. At the 0.05level of sig-
,,rlrr,urt('u"e rvorrltl hrrvc that probability ({).05) of committing a Type I error (see r, .. I ,,t't l lt )ll
).
ll rrt'rvt'rt'sintplv
les(irrg rvltcllrct'tltc 1'rtlptrlir(iott tncatt lirr nunrbet'of quailcalls rr;rsstltttlit':tttllytlil'lr'rcrtt lloru 100(//,,:p l(X). //, : p*l(X)).thenhalf of rlr, (l{)'rProlr;rlrrlrlvol ;r ltPt'l('n()r \\.rrrlrl lrt';rssoci;rlr'tl rvitlrcltclttlril rll'llte cttrve ' ,, I rl,rrr,' I I ') I lrt'rclr)r('\\('rr,,ul,l l,r',,)rr(llr( lurl,;r lrvo l;rilt'tl lesl w'lttr'lt isitssrl' r.rl,,l rt tllr lrt'r,tt I (trorr .lttt','lro11,ql, rtrrll lrt 1rrrllrt",r", 1', r ,1.r1
l(lll
istrtllr't'tiott;tl ttttll ltVllolllr'sis Iltt';tltt'ttl;tltrt'lt\1t'rlltt'rtrl'rt
.rs rrnd
lr,rtrlcl closely approximate the research hypothesis (i.e. what you believe the true
r
an<1
3 HYPOTH ESIS TESTING The main plrrpose of int-erential statistics is to test research hypotheses by testing statistical hypotheses. tKerlinger, 1964 l73l
of nature;
philosopher of evolution of some aspect of behavior. Karl Popper, a contempory (l assume he would include science, has said that in order to be scientific, a theory 1984)' This research hypotheses) must be testable and falsifiable (Maynard Smith' (e'g' zrwareness animal concerning problem has plagued many of the hypotheses intriguing very are lgl6, lg84a, b); to many ethologists, these hypotheses
I
TYPE I AND TYPE II ERRORS
INTRODUCTION TO STATISTICAL ANALYSIS
pling distribution of anticipated values lor a sample statistic (e.g. mean). If the sample statistic, generated by the data collected, falls within the sampling distribution of anticipated values, then we fail to reject the null hypothesis.
. . Reject
Sample statistic: statistic generated from data that are used to estimate population parameters (e.g. mean, standard deviation) Test statistic: statistic computed from data; used to test a statistical
hypothesis (e.g.f , t, F)
Ho
The following is the stepwise procedure used in hypothesis testing:
Fig.
I 1.1
failure to reject, the null Regions under the normal curve for rejection, and
hypothesisthatthenumberofquailcallsperdayisequaltoorgreaterthan (one-tailed test;
see
t 100
text).
From your research question and research hypothesis (Chapter 5) formulate a null hypothesis
z :
(H)
and the alternative hypothesis (F1,).
Select an appropriate sample statistic and test statistic. Select a level
of significance (alpha level;
see
below) and a sample size
(N) 0.025
0.025
t,
\
+ Collect the data (Chapters
:
8,9).
Compute the sample statistic and the test statistic. If the test statistic's value falls in the region of rejection, the IIn is rejected and the fI, is accepted. Failure to reject the 1/o is not the same as accepting it, although
Reject H,, Acccpt H Fig.
I
Accept Ho
Reject Hn
this is often done.
Accept H,
1
to reject, the null 1.2 Regions under the normal curve for rejection, and failure is significantly different from per day quail calls of number the that hypothesis 100 (two-tailed test; see text)'
I ;rilrrre to reject the Ho,can result from two conditions:
t z
The FIn is true. The experiment was not a valid test of the hypothesis; that is, the experiment could have been improperly designed or conducted.
you are It is important to know which type of statistical test (one- or two-tailed)
statistical tables in conducting in order to obtain the correct values from the
are the same as for oneAppendix A. values in a statistical table for two-tailed tests the alpha value); that is' tailed tests with twice the level of significance (i.e. one-half the tabular values are then is 0.05 of significance lor the two-tailed test
if
the level
vice versa' fbr a one-tailed test with a 0.025level of significance' and in designing' anaHypothesis-testing procedures are important to the ethologist not be allowed ttl blind the lyzing,and interpreting research. However, they should careful observer or overshadow common sense'
the same as
tltitt ititl lttt Hypothesis-testing procedures should be viewed as totlls experimenterininterpretingtheoutctlmetll.rcscarclt.sttcltl-tt.tlcctlttt.cs ol'logic by ittt :tlct'l should not be permittecl to rcplacc thc.iudicial ttsc analytic exPcritllctttcr. tttltkt' A sttrtisticlrl lcsl is rrsetl l() ('()llll)11r.. lltt'trrrll ;rnrl ;tltt'ttt:tlivt'llyPolltt'st's;ttttl \illll .l il 'l ll.ll ltt'lrrrll lt\'prrlltt"'l', ( //,,) t" t'rst'llltlrllt' :r Ptt'rltt Ir t.lr.rit.t.lrt.lr'r.r.rr lltt'ttt
of the two conditions (or lrt,th) produced the lailure to reject the/llu. Generally, we will flrst scrutinize our \\/lrcn our test fails to reject the
F1n,
we don't know which
r('\earch dcsign (e.g. behavior units measured, sample size) and our data collection
(, 1,. l)()tcntial observer errors). [f we are convinced that our experiment was valid, rlrr'n wc itrc mors willing to accept the
110
is true and take the chance
of committing
r I l,pc II crror (scc bclow). a jury lails rlclcntlcnt 'gLrilty'they are considered'not guilty', but they are not neces-
l lris conccpt is vcry sirnilar to the judicial concept of 'guilt'. That is, if r, r li111l :r
.rrrlv ctinsitlcrctl 'innoccnt'. There are several possible reasons for failure to flnd ,'rrrll. orrly ()llc t'c:rs()ll is that thc def'endent is innocent.
l I I \'l'lr I nNl) lYl'lr ll llltlt()ltS lir",r',rrclrt'rs llrkc lr t lr;rrrtc rrr lr1'pollrr'it:. lr'sltn1' llrcv (':lt) e()llllltit birsiclrlly two l\l)(",()1 r'iltlt\lnnlitLtttl';t
rlr'. l',l,rll lrr,lrrt'Il
1r1
tr'1t'r'l ;t IrrrllltVP()tltesiS.
ffi1'd'
I
352
NTRODUCTION TO STATISTICAL
POWER OF A TEST
A NA LYSIS
True State of The World
Ho False
Ho True
Decision:
Correct decision p, : power
Typ. I error
p:a
p:L-
Fail to
Correct decision
Type
reject Ho
P:L-a
Reject Ho
P:
II
error
B
results Fig. I 1.3 Possible outcomes from making decisions about the
ol statistical
tests
lor others to accept that researcher's conclusion (e.g. song duration of individuals in population Xis dift-erent fiom song duration of individuals in population )/). That is, if the research procedures and statistical test were valid, and the statistical test was significant at the criterion alpha level we accept as a truth that the song duration of individuals in population X is significantly different from the song duration of individuals in population I. The criterion alpha level for most researchers is 0.05 (significant), but for others it is 0.01 (highly signiflcant). These alpha levels are used for no other reason than that it is generally accepted that they represent a reasonable risk of making a Type I crror. Generally, values that are greater than 0.05 are not considered to be statistically significant (Sokal and Rohll 1969:161).
(from[lowell.l992).CopyrightedbyWadsworthPublishingCompany. Distribution assuming H1 is true
Distriburion assuming Hg is true
The alpha level selected is for a single, independent, statistical test of a hypothesis. When
multiple non-independent tests are conducted, such as when the effect of
probability of a type one error increases. When ru tests are performed, the probability becomes:
a variable is measured on several subgroups, then the
Probability of a Type I error: I -0.95'
100 Fig.
It
I1.4 Example of regions of sampling clistribution II (B) errors.
represented by Type I (a) and Type
Il'a
researcher is conducting multiple tests. then Bonferroni's Correc'tioru should be
trscd
to determine the alpha level to be applied to each test (Bakeman and Gottman,
re86).
Alpha level for each test:
. .
cr is the Type I error: Rejection of the I1,, when it is true. The probability risk of making ATYPe I error' making a Type II error: Failure to reject the t1,, when it is false' The risk of
TyPe
II error
I (
()r example,
if the researcher
Alpha level lor overall study Number of tests performed
wants to maintain an alpha level
of 0.05 for
the
)vcrall study, but he or she is conducting ten tests, then:
is designated as B'
These errors are a reflection
Alpha level fbr each test:o,uJ:n.oot
of our decisions to accept or reject hypotheses relative
Type I and to the true state of the world (Figure 11.3). The probability of rnaking Type
II
errors is illustrated in Figure I
II.6 POWER OF A TEST
l'4'
I lrc 1'rowcr
II 5 SELECTING THE ALPHA LEVEL is gcnerirlly clone by Selecting the probability level for a Type I error (alpha levell particulilr sttrtistictrl convention. This is also called the level of significance for a test. papers presented at professional rneetings ancl publishcd itt.iout'ttltls ittc I'c1.rot'ts ll'ottt lllosc litcls' on acquired facts (data) and thc rcscarchcr's concltrsions tlt'ltwtt ('l'ittit'ttt' wlrit'lt ittc n9t lecess itrily trtttltr'. Sccotttlly. t ttttt ltttirrlt,\' llt't' First.
fttcl,t
lltt'ls SPt't'rlit';tllllrlr othct's lllllY ()l'tttlty Itol sltltre. l'vell lllottl'lt oPittiotts lttt';tlso ttrlll lt\'Polll('sl5 illl(l l ;t l('l('( ltr ltt't rr'\('illt ;r li)r k.vt,ls 11tt.1,1.11,.,.1111,;11.1.r.Plt.rl t.trlt.il;t
ol-a test is the probability of rejecting the null hypothesis when it is false
.rrrtl lhc irltcrnativc hypothesis is true; that is, the probability that you will make a
r.rcct
rlccision in your firvor while avoiding a Type
t)( )\\'cr I
,,rtlt'r to
II
error (B). Therefore,
[3. llcrncnrhcr that you should state your hypotheses in such a way that in strPlrorI y()ur rcscarch hypothesis you must reject the null hypothesis.
In( r('ils('(l l)()\\'e r ol' rr lcst irtcrcuscs thc ;rnrhlbility of your rejecting the null hypothis cot'r'ccl. Iirr cxut-nplc. if yor,r believe that male
,',rr rl \'()ur I('s('irrclr Irypotltcsis
r',,l.llrslr sPr'rlrl tlrllr'rr'rrt ;rnrourrts ol lrnrc irr slr:rtlctl lrrrtl strrtlit itrcits (rcscarch lrt Pollrr'sts). \orr llrt'rr sllrlt' \'r,ttt lt\1tolltr",r". ttt sttclt ir \r'irV llutl yrltt cxltct to I't'ject t
lrr rrrrll lr\ pt,llrt",r:,
NTRODUCTION TO STATISTICA L
I
POWER OF A TEST
A NALYSIS
where: F1:rrreofl of population
Ho : &.nua":&sunlight Ht
"
p2:rfiadfi of population
a:
furnua"*Psunlight
You then collect data by making numerous sample observations on several rangoldfish is domly selected male golfish by recording the length of time that the each you compute can in the shaded and in the sunlit area (see Chapters 6,7,8). From this
p5: u'- uu Qr1 ..2
where: a... Y2 'rl - --.-:o:/211_
p1
where: p:correlation between the two variables; p equals zero
in your statistical test will determine whether you will reject the null hypothesis' if in time of amounts fact it is flalse; that is, these male goldfish actually spent different
Table
Howell, 1 992):
ll.l
Conyentional value.s o.f eJfect sizeJbr use in
determining power of a statistical test
The alpha level you have chosen (i.e. the probability of a Type I error).
2
The sample size (A).
Size
The size of the difference between the two populations you want to detect relative to the amount of the variability in the populations. This is called
to be detected
Effect size (ES)
Small
0.20
'effect size'(ES).
Medium
0.50
Large
0.80
Below is a description of the general procedure for determining power and et examples for two l-tests (see Chapter 13) based on Howell (1992) and Welkowrtz of the calculation of al. (1916\. Besides these references, ad
r z Calculate, or set, the eJJbct size (ES). Effect
Tlie power of a statistical test, for any given level ol significance, can be increased rrr
General procedure for determining power: Specily the alPha level.
size can be calculated
basically flve ways:
I
necessary for reasonable power.
+
Use Table 11.2to determine the power
and alPha.
Calculations of E.S. and delta for: Student's /-test for independent mcans
IIS
Pt-
lLo
of the test
as a
fitnction ol' cle lta
In order to reduce the amount of animal
sr,rfl'ering, Still recommends that researchers carefully consider other
11.1) which Welkowitz et al. (1976) describe as arbitrary but reasonCalculate delta (see below).
Increase tlte sample size. Still (1982) cautions that increasing the sample size may be done too hastily resulting in more animals being used than is
using the appropriate formulas (see below). When the data are not readily available, ES can be set using the conventional vaiues (Table able.
of the dilference
Sourc'e: From Cohen (1988).
power for various statistical tests can be found in numerous texts, including Cohen (1988), Glantz (lgg2). Kraemer and Thiemann (1987) andZar (1984)'
:
they are inde-
Delta: ESVN
I
3
if
pendent (Howell. 1992)
the shaded and sunlit areas. The power of a specific statistical test is a function of three interacting factors 19921,
population standard deviation
Matched-sample r-test
(l-r.had", &",,,,) and mean times (sample statistics) for each male goldfish in each area The power of hypothesis' your null you can test which generate a test statistic with
(Glantz,
I
2
nrcthods ol- increasing power (below).
:
(if valid) rallrcr than a nonparametric test (see power-efficiency in Chapter l2).
r
Sclcct irn cxpe rimortal design that more precisely measures treatment clll'cls irntl hus u snraller error elfect (Chapter 6). Still (1982) suggests
[ ]sc a morc powerful statistical test. such as a paralnetric test
rrsirrp rc1'rcrrlctl nrcirsurcs nrlhcr than randomized groups but cautions
lrttl [rorcrltlm af'lecting the measured lrt'lr;rt tots. Also. Mt'( '()n\\';r\' ( lt)t).'r ) ttolr's llt:tt cx1-rosing inclividual
;rlrorrl (':lr'r'\()vcr clli'cls. lclrr rrrrrg
irrnlirls lo rrrrrlltllc lrt';rlrrtt'rrl', nrir\ rt'srrll irt rtrotr'strllL'r'irrg llt:rtt exposnr,' nr( )r (' rtr(ltt t,ltt,tl', lo',ttr1,lr'
I t (',ll
tn('nl"
I
NTRODUCTION TO STATISTICA L
POWER OF A TEST
A NA LYSIS
Table 11.2. (ront.)
Table 11.2. Pov'er as a.function oJ'clelta and significunt'c c'riterion (a) One-tailed test (a) 0.05
0.25
One-tailed test (a)
0.01
0.05
0.005
Two-tailed test (a) Delta
0.10
0.05
0.0
0.10'
0.05
0.25
0.0
r
0.005
Two-tailed test (a)
0.02
0.01
Delta
0.10
r
0.02
0.01
3.2
r
0.02
0.01
-1.-1
0.0s
0.02
0.01
0.94
0.89
0.81
0.73
0.96
0.91
0.83
0.77
0.1
0.10'
0.05
0.2
0.1I'
0.05
0.02
0.01
3.4
0.96
0.93
0.86
0.80
0.3
0.121
0.06
0.03
0.01
1.5
0.97
0.94
0.88
0.82
0.4
0.1
0.07
0.03
0.01
.1.6
0.97
0.95
0.90
0.85
0.02
3.1
0.98
0.96
0.92
0.87
31
0.5
0.14
0.08
0.03
0.6
0.16
0.09
0.04
0.02
3.8
0.98
0.91
0.93
0.89
0.1
0.18
0.1
r
0.05
0.03
.1.9
0.99
0.97
0.94
0.91
0.8
0.2r
0.13
0.06
0.04
-1.0
0.99
0.98
0.95
0.92
0.05
.1. I
0.99
0.98
0.96
0.94
0.99
0.99
0.97
0.95
0.9
0.23
0.15
0.08
1.0
0.26
0.
l7
0.09
0.06
+.2
l.l
0.30
0.20
0.11
0.07
-i.3
0.99
0.98
0.96
0.99
0.98
0.97
0.99 l
0.99
0.97
0.99
0.98
0.99
0.98
0.99
0.99
0.99
0.99
1.2
0.33
0.22
0.13
0.08
1.4
t.3
0.37
0.26
0.15
0.
l0
t.5
l6
t.4
0.40
0.29
0.l8
0.12
1.5
0.44
0.32
0.20
0.14
1.6
0.48
0.36
0.23
0.16 0.19
1.1
0.52
0.40
0.21
1.8
0.56
0.44
0.30
0.22
1.9
0.60
0.48
0.33
0.25
2.0
0.64
0.52
0.31
0.28
2.1
0.68
0.56
0.41
0.32
2.2
0.71
0.s9
0.45
0.35
2.3
0.74
0.63
0.49
0.39
2.4
0.71
0.61
0.53
0.43
2.5
0.80
0.7 |
0.57
0.41
0.74
0.6I
0.5I
2.6
0.83
2.7
0.85
0.17
0.65
0.5
2.8
0.88
0
tiO
0.6t{
0
2.9
0.90
0.ti.1
o1)
0 (rl
1.0
0 9l
0 /\
0
tt
0()\
0 ss (i ti /
{)
o/0
2
[1 l8
l9 i0 rl
2
'1 )
'
0.99 0.99 2
V;rltrcs irraccurute for onc-ttriled test by more than 0.01.
' l lrc
1'rowcr ut und below this point is greater than 0.995. \.tut'(t': Atlirptctl lhrnt Wclkowitz ct ul. (1976).
'l
5
of nreilsurements during data collection. This rrrir'lrt crtltril ittct'citsiltg thc rcsolution of measurements (Chapter 8) from lttct'c:tsc thc ltrccision
It
5()
(r(r
,
ttotttittltlol ol'tlilltl scltlc trl;rrr inlu'vll tll'r-lrtio
scale so that a nonpara-
lll('lll('sl;rlislit'trl lesl t'ottlrl lrt'tr'pl;rt't'tl witlt ;r nr()r'c 1'rtlwcrl-rrl ptrrirrnetric sl;rlrslrt;rl lr'sr (rl rlrr',rlrt'r r'rr('u;r ;u(.nl(.1. ('lr:rPtt.r l-l). Sr'lt'r I ;r ltrrl'r.r ,rlIl1,1 lt.rr.l ,,rr, lr,r.,(l0,, 1,rlltr.r llrlrrr0 0l
I
3s8
SAMPLE STATISTICS
NTRODUCTION TO STATISTICA L ANA LYSIS
data on the male fighting fish, there is no clear mode since 3.7, 4.4 and 4.5 all occurred twice (see listing below). But if we had made 100 measurements on each
II.1 SAMPLE STATISTICS are used to define the nature Sample statistics (often called 'descriptive statistics') They should be calculated immediately to give the
and distribution of the data. Sample statistics will often researcher a first approximation look at his or her results. or not' Many provide insight into whether statistical tests will show significance, statistics and' in some pocket calculators have provisions for calculating sample tests. statistical cases, are pre-programmed to conduct selected
fish, a mode probably would be evident. The sample median is the measurement with an equal number of measurements
on either side of it. It can be determined by arranging the measurements in order.
For example, the measurements for the 25 male fighting fish above would arranged as follows:
r
reflect both the populaSample data from a population show characteristics that sampling methods tion,s properties and the sampling methods used. Proper
The choice of appropriate statistical tests
2.5 3.3
) J.
/
z 3.8 s 4.1 s 4.2
a large extent on the
distribution of the samPle data.
t q
6 J./
populations can (Chapters 6 and 8) must be selected so that a valid measure of the
will be based to
4.4 rr 4.4 tz 4.5 *tt 4.5 Median ru 4.7 ts 5.6 16 5.8 n 5.9 to
1.6
z 2.4
11.7.1 SamPle distributions
be made.
be
18 6.6
te 6.9 20 7.6 2t 7.7 22 8.6 23 9.5 z+
10.8
zs
ll.l
With 25 measurements, the median value will be the 13th measurement; in thrs case
in 25 male siamese EXAMPLE: We measure the duration of fighting a mirror image fighting fish (Betta sPlendens).
rl is 4.5.
In order to plot the frequency distribution, the measurements are placed into cryual intervals. For example, we can place the 25 measurements into one-second
Duration in seconds:
3.7 4.2 3.8 9.5 3.7 8.6 10.8 4.5 5.9 2.4 6.9 4.4 5.8 I .6 1r.l
intcrvals (Table 1 1.3). 1.7
5.6
3.3
2.5
4.1
4.1
4.4
4.5
6.6
7.6
These measurements are then plotted in a histogram (see Figure I 1.5).
The difference between the sample median and sample mean in the above fre-
distribution (see Figure I 1.5) demonstrates that the sample data are not norrrrally distributed. That is, sample data are normally distributed when their lrcrluency distribution is the same on either side of the mean (see below).
(
lue ncy
we can now calculate sample statistics from the above data.
n.7.3 Skewness 11.7.2 SamPle mean, mode and median
We compute
()) the sample mean (,f,) for the data above by surnming
the sample
(Ar)' measurements (x,) and dividing by the sample size
- Ir, x: r
142.5
N
:t''
tltc lllcrlll nliglrt The sample mean should not be considerecl Ihe nttrm sitrce tlttt'ittion ol'liglrtirrg:r rarely, if ever, occur. For example. wc nlight lllcilsl'll'c tltc rttttl lritvt'ttotlt'ol mirror image in cach ol'the 25 nralc sirrnrcsc liglrring lislr l(x)litttes lltt' ttot tll is lltt' thc cltrrirti.,trs ctl trirl 5.7 sccontls. A riloIr'r'('l)r('s('nlirlttt'tttt'rtstltt'()l :';ttttPlt'ol slll;lll otlt lt't ()ll('ll lll lltt'tl;tllt lllrl otr'rtts tttosl ttttttlt,.t ltt..rr.trstrt(.nt(.nt
Wlrcn sarnple data are not normally distributed, they are skewed, either positively (llrc curvc tailing olf to the right toward higher values) or negatively (the curve t:rrlirtg oll'towar,-l krwer values) (see Figure 11.6). See Chapter 12for a further dis( ussi()n trrrrl u
II.1.t
rlcscription ol a test for skewness.
Loc:tliorr
I),rl;r s;rnrplc tlrslrilruliorrs rnlrv lrt' ;rlrkc rrr lirrrrr brrl nriry rlil)cr in location. For r'\.unplr'. lltt'ltvr)(ut\('\ut l't1,q;rr'll/.ut'lr,rlltskctvt'tl llrlsitivcly:ttttl lutvctltcsante r,rtt,tlrtltlt lrttl rltllt't ttt lltt'tt l,r( ,rlton utt lltr",, ,rl,',,1 ntr';rsut('nt('nls.
SAMPLE STATISTICS
INTRODUCTION TO STATISTICAL ANALYSIS
TableIl.3The25tlurcttionsofmirror.fightingb.|'the to the fighting.{ish organized according secontl one w'ithin of'tlurations number of occ'urrenc'es intervals.from0 to l2 s
mctle siamese
Interval (s)
No. occurrences
0-l t-2
0
2-3
2
31
4
4-5
1
5-6
J
6-l
2
7-8
2
8-9
I
9
Fig. I1.6 Illustration of normal and skewed distributions ol data.
I
1
-10 I
I
l1-12
I
l0-l
l:ig. I 1.7 Illustration of two data distributions that have the same form (i.e. same positive skewed distribution and same variability) but differ in location (i.e. their means differ).
r
t.7.s Variability
1
4
occurTences
3
):rtu samples
lurvc the same location, but may differ in variability. That is, the frequency
5
Number of
fiom two. or more, populations may both be normally distributed and distribulr()r) ol the data on either side of the mean may be the same within each population, lrrrl he diflerent between the populations. For example, curves A and B (see Figure I l.li) are two data samples which are both normally distributed. However, curve A r('l)r'cscnts much more variable data spread over a larger range ( l0-50). Skcwncss artd variability are characteristics which combine to determine the Iru tttol ir santplc distribution. |
6
2
I 0
5lU Duration (s)
image tbr 25 rrralc siitmcsc Fig. I1.5 Histogram plot oi durations of fighting a mirror
t
fightingfishbrokenintoonesecondintervals(datalromT.rblcll.]).
t.'t.6 Stantlard deviation
\\'lrr'rr nrciu)s rrc conrparccl. it is also important to know how much variability there
tltrirlltitics strch its tltc lllcllll The location of a sample distribution is speciliecl by and the median. These arC rclcrrccl to its /ocrlli()tt l)(tt'(ttttt'l(r'\" lt visttltl itttlt,'r.' ol' tlistrih.ti6rrs iu.c ol'tcn wrlrllr lrloltirrg rrr ot'rlr.'t'lo olllltitt Sa,rplc
llrcir.skcwrrcss I vlrr rtrhrlrtv
(
lirt ttt)lttttl lot'ltliolls
r', rrr
llrt'rrrip,irr;rl nlclrsurcr)rcnts (.r,) ll'onr which thr>se means were derived.The stun-
'l,tt,l rlt'vittlittrt (tl is rr rrre:rsrrrc ol'lltlrl vluilrhility lrbtltrt thc nrean and is represented I't llrt' lor tttrrl r'
SAM PLE STATISTICS
INTRODUCTION TO STATISTICAT' ANALYSIS
c .9 (! q,
l0
20
30
-o
50
40
o o
that have the same mean but differ Fig. IL8 Illustration of two normal data distributions in variabilitY.
o)
-o E f
z
t_
l2(x,-xl' o' s:{-j-t
[-=]
Procedure:
t ComPute the samPle mean, (X) z calculate the deviation from the mean for each measurement' : Square each of the deviations' (x,- f )2 + Sum all the squared deviations ' 2(x,-
(-r,- x
-ls
-2s
)
l:ig.
ll.9
X
+2s
Frequencydistribution of anorntullydistributedsampleolmeasurements (observations) and the percentage mean a ls and 2.r.
*12
*1s
ol
those measurements encompassed by the
sDividethesumofthesquareddeviationsbythesamplesizeminusone, The sample ntean confidence interval (O is computed by dividing the standard ,leviation of the sample mean (s) by the square root of the number of measurements
X.t,- x )' N-1 o Take the square root of the number
t,V) and multiplying by a factor (r) based on the confldence level (probability level)
computed in Step 5'
,lr'sired ancl the number of measurements.
ls\ (': \ - +' rl'\vlu/
Thestandarddeviationcanbeusedtoreflectthedistributionofthedata'Ifthe
(Figure 1 1.9), the range included in the mean sample data are normally distributed the mean -r2 stan+ 1 standard deviation includes approximately 68"/uof the data' | data, and the mean 3 standard deviadard deviations includes about 96',Yu of the 1'9)' tions inclu des99J'Yuof the data (Figure I
'l'hc valr.rc ol'.s/V,N is also referred to as the standard error of the mean (sr"): Slr. :
'
.f
\N
Ilrcrelirrc thc conlidence interval (C) can also be calculated by multiplying the ,l.rrrtllrrrl crlol' ol' tltc rtrcirn by r:
11.7.1 Sample mean confidence
interval sirrcc
it
(' is
(hc t.rc 1'r.1'rrrrirli.rr'r'rcrrrr. The sampre mean usually only appr.xinrirtcs cltlctttil'c tlrc lloPttllttiott' wrllclttt' ltowevct" generally basccl tlnly tltr it srttltlllc ltnrtrr t'.rrlitlt'ttt tltt'1l,,ttlttti.tt r,crr. irr wlrrt'lr 1r rll.rlc irrrrrrrrrl tlre slr.rPle crrrlrtc
tttr"lttt lit's
'vt'li't'l
'11st,r)
l irr Appcltrlix A. Thc confidence level is t'orrlitlt'rrtt' lcrt'l tt ltt irr 'l;rltlc A.5) lrrrtl llrc tlcgrccs of
l lrt' r,rrlrrt' lirr / is olrlrrrrretl l'nrrn'lrrlrlt' A
,lt'lr'rnrrrt'rl lirsl (t'l'. Itt't'tlorrr
(tll) ,\'
I
()(
)",,
364
I
SAM PLE STATISTICS
NTRODUCTION TO STATISTIC'A L ANALYSIS
I
4.6
2
5.3
'
1
J
3.1
5
6.4
6
5.3
1
4.7
8
4.8
9
5.0
l0
4.4
: 7:
Total
l0
ll.5
l:ig.
t J
--ilffi
5 observations
t'7
.10 Ordering of the data from Table I I .5 to obtain an initial indication of normality (i.e. nunrber ernd range of observations olt either side of the mean).
II
l)XAMPLE: I
We measure the duration
of l0 singing bouts in a male bird
I 4. We calculate the standard deviation
48.0
!r--{l
1/-l
4.8
as in Table
1
as
in Table
1.5.
:6.36 _0.71 9
root of this number provides the standard deviation.
t:t/0.11
Culc'ulation o./' tlrc stundurd clevitiort of' tlrc
singing bouts fronr Tuble I l -4
Bout No.
x
3.1
I hc square
Table
1.6
irbore.f,
4.7 4.6 4.4 4.4
4.4
4
4 observations
4.8 = .[
(s)
Duration
Bout No.
Range
6.4 5.3 5.3 5.0
ll.4 Hypothetic'al tneosurements o.f' the tlurtrtiorts ol t0 singing bout.s by u rttule bird
Table
365
:0.84
(n,-R
(r,-T)'
\Ve cun
ll
look for normality in the data by ranking the observations as in Figure
10.
I
-0.2
0.04
2
0.5
0.25
-1
-0.4
0. 16
-
t.7
2.89
1.6
2.56
n()unrlly distributed without also knowing the actual frequency distribution on , ,rt lt sitlc
4 5
6
0.5
0.25
l
-0.1
0.01
8
0.0
0.(x)
9
0.2
l0
-0.4 Total:)'(.r
0.04
l'ltc nteasurentents appear to be normally clistributed with lour observations .rlrove tltc mean and five below, the ranges above and below the mean are almost t'r
ol'the r.ncan. Nevertheless, we can observe how our data are being distribrrtt'tI w,illr rcgurl to tlte standard deviation as in Figure I1.1l. lo tlclirtc tltc conf iclcncc limits for the mean we begin by calculating the standard
r I tl)t
0. 16
-Tf
lur l, bcing I .6 and I .7, respectively. However, we do not know if the data are really
r)l lltc tttctilt: sr
:6.36
.
r
0li4
' \,ry l.l('
O.)7
lltc t'ottlirlr'rtt't'inl('rvlrlis llrcrr c:tlcttllrtctl by urultiplying thc st,r- by r. We set our r,nll(lt'ttr't' lt'rt'l ;rl ():",, (0 05). ;rntl x ()ut tlt'11'ccs ol' ll'ccrlorrr lrrc N- l-i). The t,rlrrrl,rr rttlrrc lor / rs.' .'(,.'( llrlrlt',\r)
(
'/("t r)
"(,)({l
'/l
to{rl
I
366
NTRODUCTION TO STATISTIC'A
SAMPLE STATISTICS
NNALYSIS
L
*ls
-ls
Table 11.6 Hypotheticol tneusurcttt{,nt.t of' tlta durations o/
I
l-
oseo
all
I
Use
I
of
Then we are confident at the 95'Z,level that the population mean lies between:
4)g J-.6!
4.g
+0'61,5.41
I1.7.8 Coefficient of variation We may wish to compare the amount
Duration (s)
I
5.7
2
3.2
1
Table I 1.4.
5.41 i.e.
II.5)
Bout No.
of the standard deviation to illustrate the variability in the data lrom the
4.19 and
singing bouts by u rnule bird'B' /or
I
all observations Fig. I I .l
l0
c'omparison with the tneasurement.s /br ntale bird'A'
(Table
or
obser'ations 96Vo
6.48
5.64
4.8
3.96
3.12
-l
7.5
4
6.9
5
3.4
6
4.9
7
7.7
8
6.9
9
3.8
l0
4.5
Total 54.5
of variation about the mean for two or more
7 = 5.4
sampies ol clata. For example, is the variation in song-bout duration lor male B different from that for male A, measured previously? We now must calculate the stan-
dard deviation for Male B (see Table 1 1.6)' Male
A:
X:4.8
361
s:
| .72
I:ven after adjusting lor the difl-erences in means, the CV's demonstrate that male It's song duration is much more variable than male As song duration.
s:0.84
By comparing the ten bouts for each individual we can see that the durations fbr male B are more variable, an
an{ may contribute to the larger variation. That is, it is possible to have greater variation aroulcl larger means than arrouttd smaller means. This is called the floor e./l'ett: since zero is the bottom limit on durations, a smaller mean is closer to this limit. The
Significant difl'erences between CVs can be determined using the test statistic C r I );1vyIips and Daw,kins, 1973):
t-
(cvr-cv:) v(Scv,r+Sr",r.l) CV
V2N
converse is the geilinS4 e.//bt't,where there is an upper limit to the data. We therelore generate the sample statistic coeffit'ient of'vuriatiorz (CV) that expresses thc standarcl cleviation as a percentage of the mean. The greater the CV the greater the variability
I lrr' Ptrrbrtbility itssociated with the test statistic C is obtained from the table for the ,lr',ltibtrliott lirt'I ('flblc A,5). LJsing this method we can test lor a siglificant differ-
in the data.
, ,( (' l)('tu'ccrr llrc
cv:iX xlo0 0.84
o. l 7s'
l(x) l 7.s'
Male A:
CV * 1, x t00
M;rle ll.
('v \' .10 " . ,,,,, o tlt{. t(x) ll i'i",
,
('vs lirr
song duration between Male
(,
(o 175 0 l llt) \. (St'v,'lSt'v,')
\
to
Sr\,'
0li '' 0l/',' ,N ,O
(l){}lr)y (}lX}l
A and Male B, above.
I
NTRODUCTION TO STATISTICAL ANALYSIS
Scv'l:
,-L
-
0.3
I 82
- *
: 0.ry, :(0.07 20
(0. I 75-0.3 I 8)-0' 143
0.006
o.oll
:
I
t2 Selection test
)2:0.005
1.86
of 2.26(9 Since 1.86 is less than the tabular value
dt
of a statistical
0'05 level)' we conclude that
in the duration there is no signiflcant difference in variability
of
songs between
Kolmogorov-Smirnov test: Assay for the purity of vodka. Mann-Whitney test: Determination whether a cotton gin were transported across state lines lor immoral purposes. Rank correlation: Stinkingly low I Nonnan anel Streiner
Males A and B.
used to determine the extent to which a The coefficient of variation has also been wiley, 1973)' Some behavior patterns show behavior is'fixed'or'stereotyped'(e.g. to obtain the (12'.Yu).often at the limit of the equipment used
I 986 J
very little variation
measurements (Slater, 198 I )' to the coeffisient Barlow (1911) has proposed a measure relatecl which he calls stereorYPY (ST):
ST:
X '-
of variation
*0.01.r-
s
in order to reler to a behavior The maximum values of ST that are allowable relatively arbitrary. Since the commupattern as 'stereotyped' are undecided and context' guiclelines for the use of ST nicative value of many displays varies with
measuresaredilficulttoformulate(Bekoff.|97]b).
Statistical tests are used to test hypotheses about one or more samples of data. The rcsults of these tests will also add to our current knowledge about the scientiflc (lr.rcstions you are investigating, whether they result in rejection sis
of the null hypothe('significant results'), or not (Rosenthal and Rubin, 1985). The statistical test you select for analysis of your data will be dictated primarily
l,v your experimental design and scale of measurement, therefore, the type of statis-
trcul analysis should be considered when designing the data-collection format. Ncither the statistical test nor the experimental design should entirely dictate the ,
)thcr, but they should be coordinated.
'luble
l2.l will assist in selecting appropriate statistical tests for: l. completely rrrrtlomized designs, and 2. randomized block, matched pair or repeated measures ,lr'signs. When using this table.
it might help to reler back to the experimental
,lt'sigrts section in Cliapter 6. Many statistical analysis computer programs lead the r('scirrchcr through a step-by-step decision-making process
of
selecting the appro-
1r i;rlc tcst. Also, several statistics textbooks provide tables and charts that assist in ,r'lr'r'lrrrg statistical tests, including Glantz (1992). Krauth (1988), Meyer (1916),
liolrson (1971). Sicgcl (1956), Siegel and Castellan (1988). and Sokal and Rohlf questionswhichwill lr, lP rlirccl yort lo tltc ltcrtincnt tcsts.
rI()l(Ih). Mirr.tittund llatcson(I986,1993)provideaseriesof
r't
Iltl(' Vl:ltStlS
I'n l{AMlr I t.s ts
tl I i l':u':uttclrir'
NONI'}n RAMETRIC STATISTICIAL
!r'sls
I lr,'tr'.rlt'|,'",',,rllf
l,rlll ,t','.ltllllrllillt.
tlr,rl r,tnnol
lr, ttt, l ttt , ll1,'1,'1'tr,tl tr .'.ltr ll lr, tt,,' ut'r t",'.tl,tliltl,
.tlrt,r\',
lltlr, tr'il1 ilr llr,'1r.tl,lnl('ltlr'slltltsltr';tl
ttt,rrlr'l lltt' tt',r'ol
D.l.
T.tble
Experimentctlclesigns, st'ttles of'nteu'urentertt
uncl
correspondirtg 'statistic'al tests Scales
of measurement Nonparametric
Parametric
i r:erimental
designs
Ratio or
interval
, f .,rnpletel.u- randomized design (each measurement \ One r ariable
Nomimal
Ordinal 'Yr/
is from a different individual)
'. (-),:, rtttlU)lt' .l
One sample runs test
One sample Chi-square test
Kolmogorov-smirnov one sample test
Binomial test
Mann WhitneY
Chi-square goodness-of-fit test Test of two Percentages
.\.- .
- I
',t,lt'Pe ntlent sutttPle,s
I
Student's r-test
.-1"
,.
-,\:
{,r-test
Kolmogorov Smirnov two sample test Wald-Wolfowitz runs test
r
.\..
Chi-square goodness-of-fi t test
One-wayanalysis Kruskal-Wallisone-wayANOVA of
variancs
Dunn's multiple comparison test
(ANOVA)
Scales
Nonparametric
Parametric
Ratio or -, :1
T
ll
interval
of measurement
Ordinal
Nomimal
Mann-Whitney Li-test Kolmogorov-Smirnov two sample test Wald-Wol low itz run s test
Chi-square goodness-of--fi t test
Chi-square test of independence 2x2
Chi-square test of independence 2x2
Test of two percentages
Fisher's exact test
Kendall's coelficient of concordance
Chi-square test of indepe' ;,cnce (rxk)
..rnlbles { independent and/or dependent)
...
,;trtlrlr'
{
r
ersus .8,
Student's r-test
Two-way ANOVA
Test of two percentages
Con t in gency coelfi cien t
Table 12.1. (cont.) Scales
Nonparametric
Parametric Erperimental designs
lI
Ratio or
interval
of measurement
Nomimal
Ordinal
design (each measwement x,,may Randonti:ed block, matche(l pqirs ot rcpeated ,keasures
be
from di/ferent indivfuluals'
ot
from the
'.;tltL' ittdivicltrul./br each santple )
t-)':. t ttt'iuble
.
T',ro ntcttclted somPles
;.1 3 ,\. 3. \:
McNemar's test
A.
Paired r-test
Sign test
.\:r
Walsh test
Wilcoxon matched-Pairs signed-
----=
ranks test
r:: Correlation
a
a^,,,,,
measures: Pearson's
Product
SPearman's rho
moment correla-
tion
coefficient
) T).,'.t rtt' tltot'€ tttcttchetl sattltles 1 .{. --1. . .. A,, One-waY repeated .\:. .\'.I .\,,/ meas ;UTC ANOVA '. 5 3. i - .\':: .r,,: -\,,1 ::::i .-r
=-===i==?=i;
i..ii?1?i=
Kendall's tau
Friedman's two-way
ANOVA
Cochran's Q test
Dunnett's multiple comparison test
/.'t o' 5
i
a or93!
- r:i
$ AgigiSAiEieE*rf$3 eAii$
-Ea
* ;i'iii +r;rs=ii:SairEei;BEgii El i ii1-;-E; riE *Airi 5:e:iii 1 t i = ? &zi e '-',
ai;iE*iEF=,
liii? 33E lEaaiiisi-+iE+?$*E3Hs ii 1 1=+iE sil ;E?:i ?*ei i:i#iPlfi;i; ;i ; iaEi ,Fis ca;lE itti Ei.€F+r*i3lt ri I iiii Et? A[iAi Ecsi iiEE *;r;Ens lz a?ii !i,;! I; i?.i FiE iEeEe.. ia g;; =+; 5 *aa i+:iA?Ei[;+i ;+E+ sE
? Lll
-J
v o ln
a
C U)
z z
a a -l tri a -l (A
EE
\)
PARAMETRIC VERSUS NONl,n l{n
SELECTION OF A STATISTICAL Tlrsl'
1 Sample
rI
Ordinal
-
315
It,v-.t,)i ful: ,N
- 1, 2
Nominal
MI:'t-RI('TESTS
3. 4
therefore: i,l
I(.r-.r.)r
-1/
Lyl ---
r- -t;r
I{ L[.: '1/
I(.r -.r.;l M.:.. UN :
Ratio, lnterval
3,ormore, f lndependent Samples
Ordinal-
I I
-
25
10
Nominal-
Skewness (SK) is calculated as:
8
SK:
Completely Randomized Design
Batio, lnterval
OrdinalNominal
-
-
1---+Samples
L
Ordinal
-
ordinai-
Variance of
-
VSK
-
Nominal
-
21
,27 ,28
16, 17 ,22,23
- 18
Ratio, lnterual- 29 3, or more,
Matched Samples
Fig. l2.l
l , Onc
Ordinal
-
Nominal
19
-
20
( sittnplc rtrls tcst, 2. Kolrnogorov Srttit ttov ( )ttt' slrlttlllc lt'st; l. )rte srttllPlt'
(r. I clri-srltllrr.c tcst;-1. l]irrorttitrllcst:5. M:ttttt Wlttllrt'r I li'51.
+ 1.96 then skewness is significantly different from zero at the 0.05 level. test f or significant kurtosis (i.e. KUR ditfers significantly f,rom zero) we calcu-
ll Z>
'lir
Ordinal
6N(N-l) (N-2)(1i + l Xl/+3)
7SK
13
Ratio, lnterval
SK:VSK:
26
- 14, 15
Nominal
test for significant skewness (i.e. SK differs significantly lrom zero) we calcuof SK, then calculate the Z value.
11,12
Ratio, lnterval
more,------|lndependent I Samples I
TLr
26
lrrle the variance
J--
3, or
M
M1
8, 9
-
Nominal
M2
KUR:i4
- 11
I
,'
Kurtosis (KUR) is calculated as:
5, 6, 7
Ratio' lnterval 2 lndependent
24
M,
M)V
( ltt st;tllttt' K,l.t.l,.trrt Sttttttt,rr, lurts;ttttplt'lt",l / \\"rlrl \\'oll,tttlz ttlttrlt'rl'li oll(' \\'l\ \V'rllr., rrr"l l" ll) ol lt\o lr"'l 'rl lt"'l.r) ' l)lolr,llt"tt )',,,,,111,".'. r'l ltl
lrr(c tlrc variance
of'KUR. then calculate the Z value.
Vuriance ol'
,/,
l)vsK KUR:VKUR-4(r/2(t/- 3xt/+5)
KUR
\/VKTIR
A N( )vA: I I . ('hi-stluirrc tcst ol inclependence (2X2); 12, Fisher's exact test; r3, Kt'rtrl;tll's cocllicictt( ol'cottcorrlancc; 14. Chi-square test of independence (rXk); I \. ( orrlirrlt'ntv t'ocllicicrrt. 16. SiIrr tcst: 17. Wilcrtxon n-ratched-pairs signed_
r,rrrIrlr'rl.lX.N,ltNr.rrrrrr's(r..sl.l(). lirir'rlrninl'slwr)-uttf,AN()VA;2(),Cochrirn'se_ lr"'l 'I l\',tt',ott"ItotltrtItttotttr'trl(r)ttr'lirlrorr1,,,.'11;.r.'rrl:]2,Spc:tt'nlut'srhtt: 23, kt rrrl,rll., l,rrr '.1 Strrrlr.rrt ,, r lt.,.l ', ( )rrr. rr.r\ ,,\N( )\i\..)(r. lu,o-w,irV AN()VA: ',/ l',rtt,,l/
l, ,l 'li \\.rl,,lr 1,..,1 'il
(
lplr \\.t\ tr.l'r..rlr.tlrrrt..trrrrr.s AN()Vz\
PARAMETRIC VERSUS NONI'ARn Ml: ll{l('TESTS
SELECTION OF A STATISTICAL TIIS.T
Table
Hypotlretical clata usecl to illustrate tlte measures
12.2.
oJ' skew-ness
4(,V:- I )VSK ' : 4(99)0.4720) VKIIR: _]
and
(N-3XN+5)
186.912
7(15)
105
:
1.7801
kurtosis Song duration
(s)
(x-r)3
(.r-x)'
x
(.r-.r)'
z: Vvrun-Vt.zsor 5UR _ -1.1888_-1.1888:_0.891 - r.3342 v'w/
not >-r1.96, the kurtosis is not significantly rliflerent from zero at the 0.05 level; that is, this distribution of song durations is neither significantly leptokurtic nor platykurtic. Since the calculated
Z of -0.891
is
4.7000
0.6400
0.s 120
0.4096
5.1000
1.4400
1.7280
2.0736
3.2000
0.4900
-0.3430
0.2401
4.2000
0.0900
0.0270
0.0081
3.7000
0.0400
-0.0080
0.0016
0.0400
0.0080
0.0016
I-his test determines whether the variances
4.1000
0.3600
0.2160
rrre
4.5000
0.t296
3.6000
0.0900
-0.0270
0.0081
2.9000
1.0000
- 1.0000
1.0000
3.0000
0.8100
-0.1290
0.6561
39.0000
5.0000
0.3840
4.5284
If Z>t
t2.t.tb F-max
variance of Pop.
z
0'0384 :o.lo86 M1 on"o: o.50(o.7o7l) MrlM, __ 3)
:
:
.7
421:9.55
largest variance smallest
_0.68_ l '23 variance 0.55 ,
Obtain the tabular value of Flrom Table A4. Two diflerent degrees of
the smaller variance. In our case both degrees of freedom are 9
(cll'-
,_ SK - 0.1086 -0.1086-0.158 --Vvst<{o.qzo 0.6870
N
I ).
The tabular F'value lor the 95(Zr confidence level is 4.03.
of 1,23 is not larger than the tabular value (4 03), wc conclude that there is no statistically significant difference in Sirtcc our cetlculated value
>'- '+ I .96, the skewness is not signilicantly Since the calcula ted Z of 0. I 58 is not of song dttrtttiorts is trot different from zero at the 0.05 level; that is, this distribution
fr(, o.45ll( Ktll{ it,: I os'
5r2:(0
f reedotn are needed. Those across the top of the table refer to the sample which had the larger variance; those on the side are for the sample with
540:(\.4120
8(11X13) tr44
significantly skewed. Test for significant kurtosis:
tr:5 oz:(0.822):9.63
3:
Calculate F':
t:.._
Test for significant skewness:
2)(N+ l XN+
Determine the variance for each sample population by squaring the standard deviation:
variance of Pop.
(p. 382) and test whether uted (Table 12.2).
VSK: (N-
in the measurements from two samples
Procedure:
in two populations of birds As an example we will use the data on song duration distribthe measurements in Population B are normally
6(10x9)
of variance
l\rpulation A and Population B. The F-max test assumes that the data are normally rlistributed, but it is robust and valid even when this assumption is violated slightly.
t
1)
test for homogeneity
significantly different. From the example in Chapter 13 on p. 382 we can deterrnine whether there is a significant difference in song-duration variability between
zero at the 0'05 level' 1.96 then kurtosis is signiflcantly different from
6N(N-
t
--n
thc vlrriirnccs of'the samples; that is, the song durations do not vary sig-
rrilic:rrrlly ntorc in onc popr"rlation than the other.
It.t.: l):rl:r lr:rrrslirrrnaliorrs l
I
I l.i:'i
li
;u(' s()nr('lttttt's lr:rtrslottrrt'rl rrr ,,trlr'r lo 111,'l,'1 llrt' ;tssrrrrtPtitllts rll' plitt'iunctric ,l,tlt'-ltt,tl tt".l'' (rlt'.t tt'.'.t'tl ,tlrort'; I lt,'t,' ,rtt' lltt('(' ttl;tl()t t(';tsotts lilt ttsittl, tlltlit It.ttt',lrrIttt.tltr)il',ilrIlr('.ilr.rlr',t ,,,1 \.ilr.lrrrr {l'.il1,. l'tt,}it I ).rl:r
PARAM ETRIC VERSUS NON PA I{ A M I]'I-II. IC TESTS
SE,LECTION OF A STATISTICAL TI]ST
378
t To achieve holnogeneity of error variance' z To achieve normality of measurement distributions 3 To obtain additivity of treatment effects'
12.1.3 Nonparametric tests
within samples'
Nonparametric statistical tests are distribution-free tests which do not demand thirt
one of the goals above (1968) also states that a transformation that achieves will usually accomplish the other two' on nominal or ordinal data in order Some researchers have used transformations to use parametric statistics to achieve a normal distribution and then proceederJ data should not be used in para(e.g. Mendl, 1988). However, nominal and ordinal receive' No transfrtrntotion c'un metric tests, regardless of the translormations they data' Some of the more orclinal or (.reute intervul rtr ratig data otrt o.f' nominul common transformations are given below'
the assumptions of parametric tests be met.
Kirk
I
2.1.2a Square root transformation
more homogeneity o'f'variun6e' The The square root transformation is used to create lollowing: transformed measurement 'r,,' is calculated by the
If a// measurements > 10 use: '',,':V-*,, If an1'measurement (10 use: *,,':f {t,,+0'5) normal distribution (e'g' Mendl' This transformation has also been used to create a 1988 ).
A nonparametric statistical test is a test whose modeldoes not specily conditions about the parameters of the population from which the sample was
drawn.
tsiegal, t9-t6..1l
I
Since there are lewer constraints on nonparametric tests, they are usually lcss powerful when used to analyze data where parametric tests are applicable (howcvcr
Blair and Higgins, 1985). Therefore, researchers often proceed with paranrctrie without having necessarily satisfied the four criteria listed above (p. 373). This is supportable, in part. by the fact that some parametric tests are robust; that is, they can be used with reasonable validity even when some ol the parametric model assumptions are violated. For example, Student's /-test can be used even when there is considerable deviation from normality and/or homogeneity of varianoe, except in an independent-samples design with unequal numbers of scores; however, analysis of variance is highly sensitive to the kurtosis of a population ( Govindarajulu, 197 6). Overall, there are several factors which should be considered when selecting tretween parametric and nonparametric tests. Gibbons (1993) has compiled a list which serves as the basis for a safe (yet sometimes conservative) guideline. According to Gibbons (1993) (Jse a nonparafiTetric statistic'al test then uny o./'tha see
tests
Iollow'ing zre true:
t2.l.2h Logarithmic ffanqformation
t
create a norntal distribution in The logarithmic transformation is generally used to used when the measurethe measurements (e.g. Lawrence 1985): it is commonly
categories: nominal scale of measurement).
z r
is given by: ments are skewed to the right. The transformed measurement
some
of
the measurements are zero. or very small" use:
rtnk rttlwrt.
n)()l'c rcprcscntutive than tlre mean.
irrrtl pl'()P()l'lirrtts (o ctclttc lt The arcsine transformatior-r is used with percentagcs
tlistrr' norrrrnldislrihuti,, (e.g.Shcrryt'ltrl..l98l:Mc.tll. lgltl{)'cs1'rcciirllVtvltctrtlre
\,r
.r;tlt'slttt'\
shapes of'the distributions from which the samples are drawn are
: TItc sarnple size is small. r' 'f ltc tncasurentents are imprecise. u l'hcrc ltrc outliers and/or extreme values in the data, making the median
2.1.2c At'csine transformation
blrti.n al-,-rcirsrrr.cnrurts
The assumptions required for the validity of the corresponding paramet-
+ The
,r,,':log,,,(x,rf l)
t
The data are measured on an ordinal scale.
ric procedure are not met or cannot be verified.
r -':log,u('t,r) If
The data are counts or frequencies of dilferent types of outcomes (i.e.
ts is birrorrriirl.'l'lrc lnrnslirt'tttctl tttt'ltsttte tttt'ttt
r,,
I'irt'tt lt\
ll
tlrt'tllrlrt rnccl llrc rrsstrnrpliorrs lirr prrrrrurctric tcsts then parametric
tests
will
lrr'tttr)r('l)()\\'('rlrtl. lr,ru't'r,r't. llrt'nrotr.'lltc rlltl:t viol:rlr'(ltc lrssrrrttl'rtions litr paramet-
ttt'lr'sls.lltt'tttt)t('l)()\\'('tlttllr,,tr;)iu;rttl('lttt (/;rr. l')S.l)
lt'rl',ltt'r'otttt'tt'l;tlirt'lrlllltt':ttttctt'ictcsts
SELEC.TION OF A STATISTICAL TT]ST
Powar-e/ficiency is a measure of the amount of increase in sample size necessary
to make test B as powerful as test tests which can be
I
t3 Parametric statistical tests
(Siegel, 1956). It can be used to compare any two
validly applied to the data, such as comparable parametric and
nonparametric tests (Welkowitz et al., 1916). Given that the data meet the criteria fbr use of parametric test, then fbr a given difference between population rleans, a given alpha level and a specified power, power-eflficiency is a ratio expressed as a percent as lollows: Power-efficiency of nonparametric Where:
{:sample P
t.rt:9x
size for the parametric test.
t/ilp :sample size necessary for the nonparametric test to make it as the
Since much of the data gathered in ethology do not meet the assumptions necessary to use parametric statistical tests, only a few of the more commonly applied para-
100"1,
N,,t,
as
metric tests are describecl below. Also, nonparametric statisticaltests can be applied to data which meet the assumptions for parametric tests; however, in those cases the parametric tests will be more powerful.
powerful
parametric test.
For example. if the parametric test requires a sample size of 80 and the nonparametric test requires a sample size of 100 to make it as powerful as the parametric test, then the power-efficiency is: Power-efficiency of nonparametric
In this
test:
case, the nonparametric test is 80'2, as
Jqx 100
I3.I CoMPLETELY RANDOM TZED DESIGN 100'Zr:80'Zr
13.r.l Two independent samples
powerlul as the parametric test.
The power-efficiency is provided for several of the nonparametric tests discussed in
tJ.l.te Standard ewor of
the
the foll owing ch apters.
dffirence betneen means
we can compare means from two samples ancl determine if they are significantly tlifrerent, that is. whether they came frorn significantly different populations or
rvhether there was a significant treatment eflect. The standard error of the difference means is computed according to the foilowing rormura:
rl'the
sEo-rri:-:
l, 1 //'t'-*t:-\ -
{ \1,r,
1\
l,r,
I
/
The symbols.r,z, s,2 and Np N, represent the variances and sample sizes of s:tttrplcs I and 2" respectively. If the difl-erence between the two means is larger than tNo titres the standard error of the difference. they are significantly different. Iirr exitmple. assume we want to test the research hypothesis
that the mean dura-
lr.tl ol'sottg bottts in Population A of a bird species is significantly different than it
rs
itt l)opttlittiort
IJ.
we randomly sample l0 males lrom each population and recorcl
llrt'tlttt':ttiorl ol'ottc ranclomly selected song bout from each male rr.trkl rr.r'rr:rlly rirkc, rnuch larger sample than this).
t
(see below; we
( 'rtlt'trlrrtc lllc lotitl rtttrl tttclrr song bout duration (in seconds) lor each poprrl;rtiorr
382
COM PLETELY RAN DOM
PARA M ETRIC STATISTICAL TESTS
Sample Ponulation
b
SamPle
A
PoPulatiort B
0.8
4.8
5.1
1.2
1.44
6.4
-1./
-0.1
0.49
5.3
4.2
0.3
0.09
3'l
3.1
-0.2
0.04
5.0
4.1
0.2
0.04
4.4
4.5
0.6
0.36
5.2
3.6
-0.3
0.09
4.9
2.9
-
4.7
3.0
-0.9
Total:49.0
39.0 3.9
sr:
(-t,-X)' 0.09
0.81
Total:ffi: f(.r,- X )r 9
J
We can then calculate the standard
0.01 2.25
sE--: /tn'*tu': 'A'B NB
0.16 3.24
error of the diflerence of the means:
floos.oi'ru): Vo. 124:0. r24:0.35
Vt
V/Vo
tO
The diflerence between the means (4.9-3.9:
0.01 0.25
sr-\A rR - :0.35x2:0.10;
0.09
significant.
1.0)
is larger than twice the
therefbre, the difference between the means is statistically
0.00 0.04
Total= 6.14-X
\,-.\'r)
!1"I {)':94:o.u, 9 N-l
,^:,/[
1.00
1.0
N-t
Sample: PoPulation A
(,.,-X) 0.3 -0.1 1.5 0.4 - 1.8 0.1 -0.5 0.3 0.0 -0.2
0.64
Ir.r,-xrr ' !_ : 5.00 :0.55
Calculate the stanclctrtl tleviution for both samples'
a
$,-*)
(x,- x )'
4.1
4.9
3ul
Sample: Population B:
5.2
Mean:X:
lZEl) I)l:S l( ; N
!''i
l'l:vo
t.1.t.lh Student's t-test Student's /-test is also used to test for significant difl-erences between two sets of data
comparison of means. We will use the same data on song duration ll'orn the two populations that we used in the previous examples. In those examples wc testecl lirr the assumptions of most parametric tests and found: 1. there is homo-
rund is based on a
oa:0 8]
gcrrcity ol' variirnr.:c bctween Populations A and B, and 2. the data from Population
Ii irrc rrcithcr siunilicantly skewed nor leptokurtic or platykurtic.
We should check
tlrc norrrr:rlitv ol'llrc riatit in Population A belore we proceed, but we also krtow that tlre l-test is srrllicicrrlly nrbrrst so llrat lhcsc r.rssunrptions can be violated to a reason;rlrlr.'t'xlr'rrl u'illrorrt ;rllt'c'tirrp'llrc vrrlitlity ol'lhc tcst. Also. all the lirctors in the l( )r
lulrl;r lt:rr t' lr l t t';trlV l)('('n
(';r
lt ul;rlt'rl ( rtlro1
1'
1.
(x^-x,)l( /t
COMPLETELY RAN DOM IZED I)trs t( ; N
ETRIC STATISTICAL TESTS
PA RA M
,ffi)
luo- I xsA:)+( Nu-
Table 13.1. Song bout tluration
| x,sB2)
(s)
Population samples
D
// ro, rot 4.9-3.9 | ! \ l0+ l0/
Row totals
(r)
I
/r
lo-
I
V
xo.7l )+( ro+
lo-
ro, -
4.7
3.9
5.1
18.9
5.1
4.2
5.9
20.0
6.4
3.2
3.9
4.8
18.3
5.3
4.2
3.1
'oV(^ l
5.2
17.8
3.1
_t.
I
3.6
4.9
15.3
5.0
4.1
4.1
5.3
18.5
4.4
4.5
3.2
5.4
t7.5 16.6
VL r8
|
l
(1.0\(2.24t 2.24i-:2-84 2.24 i-- ; v0.62 0.]e l(6.12+s.4 \
V\ r8 )
difference between means (above).
13.1.2 Three or more independent samples
3.6
3.0
4.8
2.9
2.7
5.2
15.7
4.7
3.0
2.9
5.5
l6.l
39.0
34.6
52.1
Table 13.2 Sum Source of variation
df
Mean square
lletween samples (columns)
BSSS
BSMS
Within samples
WSSS
WSMS
lirtal
TSS
tests fbr significant diflerences between
applied to a wide variety of experimental designs; see Meddis (1973) for a clear and
calculate the correction Term:
concise overview. The one-way ANOVA described in the example below is fbr thc
completely randomized design. We will once again use the hypothetical clata orr song duration that we used in the examples above; however, wc will expantl our hypothesis and samples from Populations A and B to include two nlorc popr.rlirtions C and D (Table 13.1).
z
of
squares
three or more independent samples of measurements. Variations of this test can be
t
114.7:GT
(rows)
l3.I.2a One-way analysis of variance
of variance (ANOVA)
5.2 4.9
Column totals(r):{9.9
We then obtain the tabular value for I from Table A,5 for l8 degrees of freedom (dl) and a significance level of P:0.05. Since our calculated I of 2.84 is larger than the tabular r value of 2.101, we conclude that the data are from two distinctly different populations. That is, song duration in Population A is statistically greater than it is in Population B; this agrees with our comparison using the standard error of the
One-way analysis
5.2 4.8
/r roo i /f rqrto.os r +r2y1o.s6)
l:
xo.s6)
I
Cirlctrlitlc lhc lrlt:rls lirr c:rclr slrrrrlllt'(t'olrrnrll.
tr
I
t
//:total
5.lr t4.lJr1(r.4r...
-5.5r
ll.0-l
.
17 0-l I
'/()li
.)
1
.10.()(r
.
10.25
I
( 'ttlt ttl;tlt'lltr'lol.rl,,urrt ol ,.rltr.rrt.., ( ISS)
,,,,
I iI I
number of measurements:40
('alculutc thc sum of squares of the measurements (),r,r2); that is, square circh ol'thc inclividual measurements and sum them.
)'\,,
Complete the analysis ol variance (Tahlc 13.2) hclow by nrlkirrg llrc r':rlculations in Steps 2 13. I{csults irrc lorrtttl irr 'ljrble I l.l
t ('ltlt'ttlrttt'lltt'tol;tls lirt t'rtt'lt r()\\' , | I ('rrlr rrlrrlt'llrt'1,1.ur(llol,rl(( i1 I t iI
whcre:
174'12 :763 CT:GT': N 40
ISS )r
( I
1,)li'l
'(,
I
r'',1
PARA M ETRIC STATISTICAL TESTS
RANDOMIZED BLOCK. MAT('ttt:t) t,n tRs
Calculate the between sample sum of squares (BSSS): L2+ t.2+ r,2+ r *^2
BSSS:''
'
n(,
both conditions (treatments) or they could be meersurements paired by some characteristic (e.g. litter, time, location).
-CT
t
where: n,,:number of scores in each column (sample) 240 t
+ t s2r + t te7l9
a)!4
_ t 63
Calculate:
: 20.36
It:
- l-o* 7115'', ! V N-I
Calculate the within samples sum of squares (WSSS):
N
WSSS:TSS-BSSS
:35.21-20.36:14.85
Where:
Caiculate the degrees of freedom (d0: Between-samples
df:Number of
samples (columns)-
1:3
l
Within-samples df:(Number of rows - 1)(Number of columns) : ( 1 0- I )(4): 36 Total df:Number of measurements (4,1- l:39
ll Calculate each mean square (MS) by dividing the sum of squares by the
il
D:diflerence between each pair of measurements D:mean of the diflerence between each pair ol measurements ly':number of pairs of measurements
z
Compare the calculated t to the tabular varue (Table A5), where df:N - 1. If the calculated value of r>tabular value. then the null hypothesis of no signiflcant difference between the samples is rejected.
Table 13.3
corresponding df Between samples mean square
Within
20.36
Sum
(BSMS):
-:6.78 y!:O.orrt samples mean square (WSMS): '36
Source of variation
l3
Fof
16.445 is larger than the tabular value
(2.81), we reject the null hypothesis of no significant clifl-crer.rce between the samples.
13.2
RANDOMIZED BLOCK. MATCHED PAIRS OIt I{l:l'>lrA'l'lrl) MEASURES DESIGN
l3.z.t Two rclated or matched samplcs 1.1.2.1t Paircil t-tast
l'lrt.1l:rirt.,l I tr'sl rs rrst'tl lo tt'sl lirr sil,tttltt:rnl rltllt'r('lt(("' lrr'lttt't'tt ltto tr'l;tlt'tl ot ttt;tlt llt'tl ";tttr1,l,"' llr."'t'
r
ilttltl lrt' ttlr',t',tll('lll('lll ' Ill lll'",tlll('
Within-samples
20.36
6.78
36
14.85
0.4t
39
35.21
16.44
(rows)
Total
16.445
Compare the calculated Fvalue to the tabular value (Table A6) using the between-samples df (3), the within-samples df (36)and the appropriate alpha level. The tabular value for P:0.05 is 2.87 (extrapolated lrom 2.92 and 2.84). Since our calculated
Mean square
(columns)
duration between the populations calculate the between-samples Fvalue:
t?TPlgnl4! : r: q:l:ttn Within-samples MS
of
squares
Between-samples
To test the hypothesis that there is a significant dillerence in song bout
Between-samples
dr
lll(ltt t'ltt'tl" rttttlt't
As an example, we will provide hypothetical data on songbird species
r
(Table
l3'4)' similar to that recorded by Reid (1987) lor Ipswich sparrows (pas.serculus princcp.r). our research question is whether time spent singing is greater than time spent fbraging in a habitat with an abundant food supply. We t'itttcloltlly selccted l0 individual males from a population of l8 and took focal,rundwiL'hcn,si,s
lrtlitllitl/itll-occurrences samples, measuring the time spent singing and foraging tlttriitg lltc ltours 06(X) to 0900. Total observation time for each male was l0 hours.
PA RA M
388
Table
RANDOMIZED BLOCK. MAT( llI:l) I)AlRS
ETRIC STATISTICAL TESTS
Table 13.5. Copulutions hy males w,ith eli//ert'nt mutirtg histories to virginJbmulc
hy ten male 13.4. H1-pothetical dota on time ,spent singing ancl.fctroging
monarch butterflies
songbirds
Singing
Individual
Foraging
pz
D
A
105
152
47
2209
B
97
202
r05
11025
C
ll5
117
2
D
95
233
E
120
105
l5
F
87
215
188
G H
103
176
89
260
I
t12
131
l9
J
109
139
30
| 032
| 190
Totals:
I
Time since
Total time 1min.)
138
t-)
t7l
788(rD)
985
1986
last mating
Number
0/
Number
/0
(days)
tested
Mated
tested
Matctl
0{<
/
\')
396
JJ
489
30
4
I
49
57
36
3r
19044
2
88
65
36
56
3
52
56
27
30
4
40
63
42
40
5329
5
29
48
32
44
2924r 36r
6
l9
31
38
29
7
2l
62
27
30
24
42
225
35344
103
900
8
682(;D2)
Note: + Virgins.
38
8
Source: Copyrighted by Bailliere Tindall.
6:IoNl0 -788:7g.g
t-
I3.2.lh Measuye of association
I
Pear.yon produc't moment correlution c'oeffic'ient The Pearson correlation coefficient (r) is used to determine if there is a relationship
l
between two sets of paired data. The data must be either interval or ratio.
78.8
roqlL -ro-r
/fro: osz-
Vl l0 :il1rr] 78.8
ry7{Y__U)W)_ r:____ ' V[4,2x'-(>x)'] [Atrr
j]Y)21
Where: 3.162
_ 78.8 _78.8:3.66 67 .97 21.5 3.162
2'262' Silcc ttttr citlcttThe tabular / value (Table A5) for df:9 and P:0'05 is //,,' irtrtl cottcltrtlc tltit( the reject we lated f (3.66) is larger than the tabular value, singirrg. trrttl there is a significant difference between time spcnt lirraging itt ttl:tlc As another example, oberhauser (1988) sttrtlictl tttittittg slt'ltlcp,it's rtl'tttltlcs tttltlctl itl l')s(t tlt:rtt monarch butterflies ancl lilLrnrl thirt ir lowcr'1'rcrccrrl:rgc () ()ll)' ;l tt.sttll sltr' ;tlttilltttt.tl lrr lttl in 1985 (Trrhlc 13..5; |llrir.ctl l l.l' (ll. l.i. /, lrr, lltt'ttttltllrt't .l ttttttstrtlly t.rl.l srrnunt.r. Nolt. llutl llrr'nr('ir\lnr'rttt'ttl\ itl('l);lttt'rl
tllrVs stltt t' t lrt' llr'.t ttt:tl tttl'
l/: Xf: X: l: .\'r )"'
Number of pairs of scores
of the products of the paired scores of the scores of one varil,, -'(X) sr.rrtr of the scores of the other v. , iable ( Y) surn ol'the squared scores of the Xvariable sr.rnr ()l'thc squared scores of the I'variable sum
sunr
l'lrc nrn1,,t'ol'
r'is
l.(X) to + l.(X). antl thc sign of r denotes whether the correla-
Ironisposilivt'orrrcp,lrlive. l'lrcllrrgcr llrcr vrrlrrc.tltcnrorchighlycorrelatedarethe trro st'ls ol rl;rllr Ilrt'srllnilir'rurt'r'ol r (Irottt rl to llrt'r.rlrrr','l r. ur l;rl,lt','\
/
I lrr., r',.r
l\\,'
r
O)r'rrtt l'rr'tlclct'lrtittctl hy crtrtrpltt'ing
l,nlr'rl tt'sl li,t ;t stl,ttilit';tttl t'ott'cllt(iort.
rt';';rr,llt".', rtl lltr",tf,n ol r ll r t', l,rt1',', llr.rtr r llr,'rr \r)u r('l('( I llrr.'//, ;rtttl tottt'ltttlt' llr,rl llrr'l\\l t,tt t,tlrl,'', ( \ ) l,rt, ,t1'tttlt, ,rrrll\ ,,'t t''l,tl, rl
RAM ETRIC STATISTICAL TESI'S
PA
390
on a songbird species that As an example, we will use the same hypotheticaldata since this is only a hypothetical was used for the paired /-test (above). However, by l0 in order to keep the calculaexample, we will divide each of the measurements to calculate Pearson's coelficient tions more manageable. All the variables necessary assign silging as the X variable and are provicled in Table 13.6. We will arbitrarily
t4 Non par ametric statistical
tests
foraging as the Yvariable' t
:
tI
:Vtt :
-!t!1? [L?2!I - I 0rz'l t t ot :s 3s'94) -
I 0( I 7 87. 60)
V{
0(
r
oio-os)
l7 876.00- l8 472.80 ro zoo'so- ro oioi+L(-rs 3se'40-
va, ,r.*!"9' *o,,:
1
79'021
}
Nonparametric statistical tests are the most commonly used statistical tests in ethological research. They are simple tests that can easily be calculated by hand or with a
-32 041'00)l
hand-held calculator. This means they can be used reliably while you are in the field access to a computer.
without
;t'2#: -o'e8
Since nonparametric tests are so easily conducted, it is tenrpting to apply them to alldata. However, parametric statistical tests should be r-rsed when the data meet the
criteria (see section
Table 13.6 Total time (min.;
Male
Singing
Foragtng
(n
(v)
Y
XY
,n 231.04
r0.5
15.2
159.6t)
9.7
195.94
94.09
408.04
B
20.2
C
I 1.5
tr.1
134.55
t32.2s
136.89
90.25
542.89
l)
9.5
23.3
221.35
E
12.0
10.5
126.00
144.00
21.5
239.2s
G
10.3
11.6
181.28
106.09
309.16
8.9
26.0
231.40
19.21
676.00
H
I
11.2
13. I
146.12
125.44
171 .61
13.9
I 51.51
193.21
J
10.9
I 18.81
03.2
179.0
I 076.08
I535.94
Sums:
()x')
(;n
1
787.60
()xr)
GXN
One variable
l1.l.lq
One sample
7
(f l'l
S,
S,.,
,rl
:
-r,:
Sample of variable rl (or Treatment) Measurement on Inclividual No. I
_Y1
s6.25
8.7
r
l.t.l.l
I 10.25
15.69
F
l. I ), since they are more powerful.
I4,I COMPLETELY RANDOMIZED DESIGN
110.25
A
12.
:
.Y,,
()ttc .sutttpIL' run,t I(,tI 'l'his tcst tlctcrntines whether a sequence of two dillerent items (in time or space) is n()n-ril D(l()ln.
Slrrrplc sc(lr.rctrcc: A A B B B A B B B B A A B A The calculated coefficient
of -0.98
Since the calculated r.or- --0.gg is rargcr
inclicates a vcry strotlg ttcg:ttivc cttt't'clrttiott' tlic tubrrl.r'r', .r'0 (r-rr. u'e rr'rr'('t tlrr' //,,
trrir.
slrcrrr ar- na c.rrcl.titlrr irrtr c.rtclrrrrc thrrt lirrrc clt tlt lv. ttel'ltt ively t'ot.t t'l;ttt'tl'
lir*r,irr,
rrrrtl sirrl'rrrr';rr. sr,'rrili
As cxlrrrrplr.'s. !ltc sc(lucncc irbove cotrkl lrc the:
I '
St'r
1
t
t('tl('(' t tl' or'r'u
Sr't1ttt'nlt;tl,rs1lr,'I It'lt'Pltr)n('\\
t't
r'n('('
(
)l l tlo lrr'lrlrvirlrs
(
A. l|
).
ol ttr;rlt't,\)iut(ll('nlitlt'(ll)st;rrlingsg'lcrclrctl irlrtng:r
il('
1 St'rIl('nlr.rlurrlt'r
,'l ',1r,', 1,1 \ l.urrlr rrrr , tllt lrr,'tl rrl,rl rr lt'r'tl lrtttth
NON PARAM
COMPLETELY RAN DOM lZF.l) I) llS I ( ; N
ETRIC STATISTICA L T.I:STS
or separated in want to determine whether the two items are clustered chance' The two non-random the sequence more than would be expected by extremes,for the example above, would be:
ln any
case we
Table 14.1. Numbers of ./bmale grasshopper,\ thut hucl rutt given response chirps to males that subsequently mated or did not mute Trials which ended with:
BBBB Non-randomly clusten:r/: A A A A A A B B B B
q Non-randomly separared:B A A EA Example: Ho: The sheep
1
q4E4E4B
B
Mating
No mating
ll
23
in2h
Female did not give
bunk' (A) and cows (B) are randomly distributed along the feed
response stridulation Sourc'e: From
Determine the number of runs (r)'
Butlin et al. (1985).
AABBBABBBBAABA,,-,
T 2 3 4 Ti'7'-'
Table 14.2. Calc'ulation of chi-square .from data in Table
14.
I
Determine:
Q-D2
No:number of A items:6 Nu:number of B items:8
Response
o
A82) for No and N'' The Compare r with the tabular values (Tables A8t' (r) is significant at the 0.05 level, if: r is less than or equal
number of runs
Mating No mating
23
t7 t7
totheisvalueinTableA8,orrisgreaterthanorequaltothevaluein
'fotals
34
34
Table A8,.
than
Q-q2
-6
36
2.1
+6
36
2.1 18
l8
12
In our example, 7 is larger than 3 (Table 18,) and smaller (Table,48,);therefore,wecannotrejectthef/othatthesheepandcowsare
Expected
randomlY distributed' One samPle c'hi-stluare te'\t
can make one of only two This test is used to analyze clata in which individuals or right, fly or don,t fly). It should be used responses (e'g. accept or reject, turn left less than 5, use the binontial test when the expected values are >5. If they are only
the chi-squared statistic in (described below). Additional precautions when using (1992)' ethology are given by Kramer and Schmidhammer
(E):total
number of responses equally distributed between
each category
of response:
ll +23:34
3412:17
We would expect 17 to mate and 17 not to mate
if they had
an equal
(50:50) chance of reponding either way.
f :2:4.236 .t ('orrpare the calculated I to the the value in Table ,{9 with a degree of ll'ccclonr ol' I (df:no. categories- I ). If the calculated f is greater than or
:
. 2 \x.,-: -[(observed-exPetttq]-0'5]1 exPected
Calculate chi-square,
ctltral to thc tabular value, then the
F10
that there is no significant differ-
crrcc in thc lcnralcs' responses is rejected.
'l'ltc ('llculatcd
f'enlalc gritsshoppcrs For example, Butlin et al. (1935) measured whether ttl tl]alcs strhsctltrctrtly (Chorthippus brunnneus) that did not give respc-rnse chirps mated (Table 14.1).
lo
H,,: Females that clid not givc rcspottsc cllit'Ps nratc. as ttot tl-tittc.
I ('otttplt'tt'tltt'( l:rlrlt' I I )) ( )lr.'r'l tr',1 ( t )) t'' tltt'tl,tl'l ll(llll l 'rlrlt' I I
ll
(o-D
f
ot-
4.236 is larger than the tabular value of 3.841
10.05 lcvcl ol'sisrrilicirncc); thcrcfirre, we reject the I1,,and conclude that
tlrt' li'turrlcs rttlrlctl lcss ol'lcn lltirrt woultl bc cxpccted by chance.
tttrtlcs wct'c etlttitlly likcly ltr lJtttrtttttrtl lt'.tl I rl.t'lltt't
ltt ',(ltlitt('lt'rl;tlrort'
rtr rr ltt. lr llrr't('t',,r I
lltt'l)lll()lttt,rl
lrrr'rltr lr'rl tlrlr rlrnlr,lr
1,".1 t'.;t ()tt('slttttPlt','rttltltless-rll-lit
l,r'lr\r'r'r
l\\o rlt',t rt'lt'r ,tlr'1,ot tt's ll is ll
lcst l'1r1y1l
394
N
ON PA RAM ETR
IC STATISTICA L I. I,STS
COMPLETELY RANDOM \ZF.D I)t:S l(
replacement fbr the chi-square test when any o1'the expected tiequencies are (5. The binomial test determines the probability of obtaining ,r events (the smallest observed value), or fewer, in one category and l/-"r events in the other category, out
of
a
Table 11.3. Alurnt reac,liotr of lttutl luryut, to <,hcrrtitul
No. of Iarvae spending rnost
Procedure lor determining the probability of obtaining exactly -r events in one
category and
l/-.r
events in the other category out
of
a
of time (>600 s) towards
total of Iy'events.
Experimental half
Calculate the probability,p(x), using the lollowing fbrmula: N!
/(v): ,t t,ny- rttPt 2A
395
(ue,\
total of l/ events. The researcher must specify the expected probabilities.
l.
;N
Control half
N
l1
20
t
Sourt,e: From Hews (I9gg).
where: N! means the factorial of
l/
2' A more appropriate pr-obability to calculate for most ethological experiments probability of obtaining .y, or fewer, events in one category and N-r events in the other category out of a total of Nevents.
P:expected proportion of r
Q:l-
is the
P
__ N!_ : : o,'omial .r! (I/-r)! (T)
coerficient
This part of the formula can be calculated using lactorials (Table
Al) or by
determining the binomial coefficient using Table A3.
As an example, Hews (1988) tested the alarm reaction of toad (Bu.fo boreas) larvae to chemical cues released from predation by a waterbug on conspecific and heterospecific larvae. The data in Table 14.3 below are only fbr the tests with preda-
tion on a conspecific larva in the experimental half of the test tank. The 20 larvae could choose the half of the tank where the predation was occurring (experimental half) or the other half (control hal0. We will use an expected probability of 0.50 in eacl-t half of the tank. That is,
P:0.5
and
To calculate this probability, sum the probability of the observed events r and all the more extreme distribr-rtions. This is accomplishecl by successively reducing,r by I and calculating the probability lor each value including zero. This total probability is the probability of obtaining the observed distribution of events, or more extreme values. For our example, the probability of having 3. 2, I or 0 larvae
choose the experimental lialf of the tank is the sum of each of the individual prob-
abilities.
/ A/\
1(-t):\,
p(3):0.00 I (calculated above)
N:20 -r:smallest number of larvae choosing half of tank:3
,('):(T)Pxe'r-' :(
/,(' I
i()ll
).
+
):(/20\,ir0.s,Xo 5,',):20(0.5X l.9l,,): 1.90-s /
Thereft>re.0.001 is tltc problrbility ol'lrirvirrg exrtcllv lltt'cc lrtt'r'rtc t'lloosc tlte t'rpt'tintcntirl lurll'ol'llrc llrrrk. il'tlte c\[rr'('t!'11 rtrrrnlrt't is l0 (lr;tst'tl ()ll iln t't1tt;tltltsltilrtt I
r,il
l0\
(
1t(0)
140x0.5r)(0.5r7)
I 140X0. 125\(l .63 n):0.(X)
'
t0:(20)(0.5r)(o 5IB): I90(0.2511.1.8t n):l.8l
Q: I -P:0.5
:(l
)r'Q'
.l)
t ,,i"''s",10'5r{);: l(l)(9.53'):9.53
0.(x)l+l.gl
11 1.90 5+9.53 7:0.0012
I lrt'rt'lirrt'. llrt- pr6[lr[ilily 6l' lrrrvirrg lh.cc. .r' rcw'cr, Iarvae choose the experimental lr,rll ol tlrt.t;rrrk rs0 ()()l-)
ir i
I
NONPARAM ETRIC STATISTICA L TI:STS
396
t4.l.tb
COMPLETELY RANDOM IZED I)trs t(
Table l4'4' Hypothetic'al dattt on song tlurution,t fiLtnt ttto populations oJ'bircls ancl rankings used in the Mann-Whitney L) te.sr (,rce te.ut )
Two independent ssmples
Ar
Az
xt
r -trz
xzt
,\,I -1
-Y^. t7
xzz
l,:
Sample or Treatment No. I
Song duration (s)
1,, :Measurement on Individual No. I in Sample No.
I
i: .r^
_Y.
lil
Mann-Whitne1, U test The Mann-Whitney [/ test is the nonparametric counterpart of Student's l-test lor independent samples. Whereas tl-re r-test determines significant differences between means, the Mann-Whitney U test uses the medians to test for a significant difference in the location
;N
of the sample data. It
rs 95"1, as powerf
ul as the Student's /-test
(Mood, 1954). This test can be used when the data are, at least, ordinal. For large samples, the Mann-Whitney U Test is more powerful than the Kolmogorov-Smirnov Test; for
Ranks
Population A
Population B
sample
Population A
Population B
sample
sample
sample
4.7
8.t
l0
5.3
t5
4.2
t2
3.6
7
6.7
J
5.1
9.5
4.0
ll
t3
l6
2.7
5
4.1
2
r.8
6
3.8
I
7.8
4
t4
1.3
8
1.4
9
Sum
of the ranks
: ?": 68
very small samples, the Kolmogorov-Smirnov Test is more powertul (Siegel and
Castellan, 1988). If the samples are correlated (paired or matched), use the Wilcoxon matched-pairs signed-rank test. The use of both the Mann-Whitney U test and the Wilcoxon matched-pairs signed-rank test on independent and paired
where:
N":number of measures in the smaller sample ly'.:number of measures in the larger sample
data, respectively, is illustrated by Breitwisch's (1988) study of parental defense in
(J
mockingbirds. As an example, we will use samples of song durations from two populations in which the variances are obviously ditferent; that is, an F-max test would be expected
to show a significant difference in the variation of bird-song duration from
these
two populations. Therefore. we will use a nonparametric statisticaltest.
r:
(7
x9 ) +1(7
%:trrN. 4
-_
P
Ur:
-,68
:
63 + 28
1l1p1- 23
:
-
68
:
23
40
Obtain the tabular value lor N,.:7, tabular value: l2
N.:9
in Table
Al0.
Llr:23 Procedure:
t
Ur:40
Rank the data (Table 14.4) using both samples. The smallest measurement gets rank no.1.
Determine the surn of the ranks in the smaller sample
it is Population B).If both samples are the same lirst sample. ?":68 Calculatc tlre {/, irnrl t/r s(:rtistics:
{',
,NrN,
' ,' I ,ry.(,ry.
ll)
I
(f;
in our cxanrplc
size. sLrnr thc ranks
ol'thc
'[.lrcrc is a sigrlilicant difference if either of the observed values (u, or ur) is r'tlttttl ltt ttr lr't't' lltutr lhc tabular vetlue. Hence, in our example the song durations are rr,t 5;ig11ilit'lrrrtly tlillcrent between the two populations. This you would suspect, 'tttt't' lllct'c is cl).t1gl1 v'ariubility in Sample B to overlap the values in Sample A; we ( iur scc tlrrs bcltcr.itr gr.irphic litrm (Figure l4.l). It, t f 1 1
1
1
1.1,,
1
1.,,
t,,\'
t t t i t. t t t t
t. I lt.r t
.t, t t t
t
t
I t I t,
I t,,s,
I
llrts lt'sl ts;t sttlrsltltllt'lirt lltt'M;rrrrr wlrrlrrt'v li lt.sl. csPcci:rlly whcn the sample ''ll('l\ rttt;tll ll rlt'lt.unnt(',, rr lr,.llr..r l\\o,,,trrrplr.r tlrllt.r siprrilit.trrrtly irr cif ltcr. litrrtr rtr Iot;lltoil I lr,rl r', rl 1t..,1,. llr,' //, llr,rl lltr.l\\,r,,.trrr;r11.,,,p(.p1t si,,rrilit.lrrrtlf,tlilli,r.t.rr(.
NON PA RAM ETRIC STATISTI('A t.'l' I,STS
398
COMPLETELY RANDOM IZED I)I:S I(;
10
N
Table 14.5. Hvpotheticuldata on tht, lurtrtiort
399
o.f
/eecling bouts in tottt herds o.f'tlecr
Feeding bout duration (rnin.)
Herd A
Herd B
43
24
62
3l
Song Duration
t
47
IJ
35
ll
Population 14.
l9
69 1-
AB Fig.
8l
29
64 Graph of hypothetical data on song duration from two populations of songbirds.
The scale of measurement must be at least ordinal. Even
if
l8
l8
the data are interval or
21
89
43
67
tl
59
ratio, they are analyzed as an ordinal scale, which means that resolution is lost. Therefore, a parametric statistical test (Student's l-test) wor-rld normally be used if
65
t2
28
the criteria (section l2.l.l) are met; however, when the sample size is small the Kolmogorov-Smirnov test is96"l' as powerful as Student's l-test (Dixon, 1954).
I Small suntples The procedure below is used when the number of measurements is
':25 in both
samples. As a hypothetical erample, we
will determine whether
the
duration of leeding bouts are significantly dilferent between two sn-rall herds of deer lrorn dilferent habitats but with the same sex and age composition (Table 14.5).
lormula would be:
Procedure:
I
1J:rnaximum [q,-^t,,]
Convert ratio or interval data to an ordinal scale. Since we are working
with ratio data. the scale of measurements must be divided into intervals. An interval should be selected such that no single interval contains more than two to three ffreasurements. Each sarriple is then arrangcd in orcler ol'
Tcst Statistic: DXmXn
llrc litrgest
dill'erence in the pretlit'ted direction is at the intervals of 45*-49 and ,{) 5-l rnirrulcs whcre .1,:0.916 and S,,ltt:0.333.
rnagnitude, and the cumulative frequencics of observutions lirr each
l)
sample up to each intervalare determined (Trrble 14.6).
:
Calculate the ratios of the cumulative freqr-rencies til thc lolrrl rrurnbcr ol' measurernents in each sample.
where:/??:no. measLlremcnts in Slnrplc I
/,:
n(). nrcirstn'cnrurts irr Srrnrplc
I
(llcxl A) (I
lctrl lf t
,S,,, t'ltlio tll' r'tttttttllrtivt' lrt'r1rtr'ttt'\, lo lr
,\
Find D, the largest clilference between {,, and S,. For a one-tailed test, D is calculated as the maximurn difference in the pretlic'ted direction. For example, if our research hypothesis was that the feeding durations in Herd A were significantly longer than in Her6 B, the
t;llto ol ( untrrl;rltrt'ltr'tIrt'rrt \ lo
//
I
0 9l
(r
0.333:0.583
cst st:rlisric-0.-5t{3X I 2X
l
2:
g3.95
( onrl)itrc tlrc t':rlt'rrllrlctl lcsl strrlislic lo thc lirbular.vulue (Table Al l) for the appro_ l)rr,rlt'r;tlttt's ttl tn.n lrntl k.r,r.l ol sil,ltilit.;rrrt.c.'l'lrc lirbtrlis.vltltrc fbrlll: 12,n:12at l' O O\ tr /.) Srrrr't.()ut (.;tlcrrl;rlt.rl r,;rlrrc rrl 51 t11 rs llrrllr,r. lllrrt lltc tlrbrtlltr
''
rrr
vitlue of.
\\r'rr';t't l llrr'//,,rlrrr rlrt'lt't'rlrrr1,,lrrr,rrr.r,, r, Ilr.rtl,A lrrt.r.l l1r;lggt rl,ttt llrcvrtt.c
ll,'r,l ll
COMPLETELY RANDOMIZED I)lrsl(i
L TI:STS NON PARAMETRIC STATISTICA
Smirttov fir'o samPle test./br small Table 14.6. Calcttlations .fbr the Kolmogorlt' 14.5 samples using the data.from Table
Interval (no. minutes)
Rearranged
Cumulative
measurements
frequencies
Herd A
Herd B
Herd A
Herd B
N
Table 14.7. The number o.f duys it look rccd v,arhlers to re.ject model cuckoo eggs by either ejection or desertion
Ratios
(s,,,) Herd
A
(s,,)
Number of nests with rejection
Herd B
Rejected during
By ejection
By desertion
0
0
0.000
0.000
0-4
Day I
l0
0
0.000
0.000
5-9
0
Day 2
4
7
2
0
0.166
0.000
Day
3
2t
t4
J
J
0.250
0.250
Day 4
2t
18
0.411
Day
l0-14
I 1,12
15-19
18
20-24 25-29 30-34
17, I 8,
l9
21,24
3
5
0.250
5
25
20
28,29
J
1
0.250
0.583
Day 6
29
23
3l
J
8
0.250
0.667
Day
3l
24
0.250
0.750
_1
9
31
24
t0
0.333
0.833
0.333
0.916
4
il ll
0.333
0.916
5
1l
0.411
0.916
7
ll
0.583
0.916
9
t2
0.750
1.000
l0 l0
l2 l2
0.833
1.000
('ompare the calculated test statistic to the tabular value (Table Al2) for the appro-
0.833
1.000 1.000
lrriate values of m, n and level of significance. The tabular value for nt:12, n:12 at /':0.05 is 72. Since our calculated value of 83.95 is larger than the tabular value of
1.000
72we reject our
35-39
35
40_/.4
43
4
47
4
43
4549 50-54 s5-59
59
60-64
62,64 67,69
65-69 7
65
t-\
70-74
4
5-19
80-84
8l
ll
t2
0.916
85-89
89
l2
l2
1.000
m:12
Totals:
n--12
7
Total rejected:
Source: From Davies and Brooke ( 1988).
D:0.916-0.333:0.583 Test statistic
F1o
llcrd A and Herd l.(tr,q(
:
0.583
x l2x l2:83.95
of no significant difference in duration of feeding bouts between
B.
wntltlas If the number of measurements in either sample is >25, a different
trrblc and procedures are necessary depending on whether you are conducting a one-
the nlaximlm ub,;olutt,differencc For a two-tailed test, D is calculated as between
S,,,
and
trrilcd or two-tailed test.
As an cxample, Davies and Brooke (1988) studied nest parasitism by cuckoos
S,,.
D:maximum 1S,,,-S,, Test statistic:
I
Dxmxn
rr';ts
lirt ottt ottt' l;ttlt'rl lr'sl
( 'trt'tt ltt.s t'rt trur
tt,;)
I
lrcv nrclrstrrcrl
t hc nunrber
on reed warblers (Acroc'ephalus scirpaceus). As part of the research,
of days it took reed warblers to reject model cuckoo eggs by
I \\'( ) r rrclhorls, c' jcct ion of'the egg
from the nest and desertion of the nest (Table 14.1).
'l lrr rrscrrlch tlucstitln wirs whcther the distribution of times to reject the model
AswestatedatthebeginningofthisexampleoLlr(]uCSti()llwllswlrctltcr.tllct.cislr .r'the rlcc'i. thcsc tw. rrc.trs' rlre'clir'e' significant duration in the t-eeding b.uts tlrr' r-rcrwccrr rrrc ri'ctrirrg trrrrrri.rrs irr .ur H,, is that thcrc is .. sig.iricurt triflL.r.c,cc \'l \O lttttl 'lr) 't'.c llr'gcs I ttltsttlttlt' tlil'li'rcrlt'e is ;rt llrt' ittlt'trltls .l 'l\ twtr lrerrls. tttittttlt's (lltt's;ttltt';ts il
I
)
,'l'l's lry
rlr'se
rli()n wlts sigrtilicirrtlly rlill'crcnt than by eiection.
l'tot't'rltltt':
I
I
)t'lt'r trrrrrr'llrr't rrrnrrl,rlrrr' lrr'r1ttt'rrr'r('\ iul(l t;rlios lilt'clrcIt 1'rct'itltl, tts irt
l;rlrlt' l,l
li
NON PARAM ETRIC STATISTICA L
402
.I.I]S
COM PLETELY RANDOM IZED I)
I.S
1,S
I(
403
;N
than the tabular value (5.99), we lail to rcject our hypothesis that the nests Sntirrutv tt:o santple test.fbr large Table 14.8. Calcttlations .fbr the Kolmogttrov 14.7 ,samples using thc data.fiont Table
are rejected by desertion sooner than by ejection. F-or a
two-tailed test, D is calculated as the maximum absolute differ-
ence betweefl S,,,and S,,. Since we are testing whether there is a significant
Ratios of cumulative frequencies to total
Cumulative no. nests with rejection bY: Rejected Desertion
Ejection
within
Ejection
(S,,,)
ence.
Desertion (S,,)
4
0.322
2 days
l0 l4
0.166
ll
0.451
0.458
3 days
2l
l4
0.677
0.583
4 days
ZI
18
0.611
0.750
5 days
25
20
0.806
0.833
6 days
29
23
0.935
0.958
7 days
3l
24
1.000
r.000
I day
difference in either direction. we will use the maximum absolute differ-
D:maximum 1S,,,-S,,
I
The largest absolute dillerence is at Day I where S,,,:0.322 and S,,:0.166.
D:0.322-
0.
166:0.
166
This calculated D is compared to the D value obtained by entering the observed values of lzl and
ru
in the expression given in Table A l3 at the
appropriate P value. For our example, we I
Total
z
critical value of
24:n
3l:tn
rejected:
will
use the expression in Table
A I 3 lor P:0.05 as follows:
D:
I
.-16 /f '"
*t
)
V \mxn/
:trrlt:-,;)
For a one-tailed test' D Find D, the largest difference between S,,, and 'S,,' rnthe pretlit'ted dtrection, just as is calculatecl as the maximum difference if our hypothesis is that the in the small sample case above. For example, by ejection, the formula for D nests are rejected by desertion Sooner than
:(1.36)v0.0739 :0.370
would be:
:
D:maxirnum (S,,-S,,,) is at 4 days where The largest dill'erence in the predicted direction : 0'677' S,, : 0.750 and S,,,
to reject the mocleleggs by clesertion and by ejection.
Wull Wollowit:
D:0.150-0.671:0'073 rForalargesample,one-tailecltest.achi-squarevalueisnowcalculatedas follows: - tttXn
/\i:4D: tltl
tt
:4(0.07_t r.
:,1(o.oo5)
3l x24
,, (I
ttt'tt sttmple rltns test
l.ike the Marnn Whitney U test and the Kolmogorov-Smirnov test this test is used to tcsl thc 11,, that that there is no significant difference between two indepenclent srrrnplcs. It wrll re'ject F1,, if the two samples difler significantly in either lorm or locatirrrt. lt is appttxiniately J5'Y' as powerful as Student's l-test for sample sizes of rr1'rpnrxinrirtcly 20 (Srnith. 1953). Since it is less powerful than the Mann Whitney L/ It'st lrrrtl llrc Kolrnog()rov Smirnov test, the primary advantage of this test is its simlrlrcity.
n2O
3.53) -0.21
of 0.166 is smaller than 0.370 we fail to reject the H,,that there is no significant difference between the distribution of times Since our calculated D
As rrn ('\iu))l)lc. r.rc I
tll ') lltr' .l ('rlnr1'llr|cllte t'ltlt'rtllrtctl 1'totlle lltlrttllrt r';tlttt'(llrlrlt'A())lirr l) t)() Sttlt t'trlll t 'llt rrl'rlt'rl 1'rrl '/ I r'' rttt;tllt't l:rlrrrllrt r;rlrrt.;rl l' O gr ts:
will tlclcrrnirtc rvlrcthcr the lrypothetical frequency of agonis-
ol sorrllrirtls tlilli'rs si1'nilit';rnllv lrelrvccrr birrls trt fcec'lerType A and at It'r'rlt't ltpt' ll Wt'rrtll rrsrttttt,'llr,tl ttt'r oll1'1 | lltt'lryPollrclictrl tlitllr rl rr ring rrinc Irc lrt'lr:rrior
11'1
rrnr'lltt111 ',;1111lrltttl, l)r'ttrrrl'.,rt
t,trltlr'r'rl,t II1rr'II,rlrlt'
1,1
tr,
COMPLETELY RAN DOM IZED I) l:S
NON PARAMETRIC STATISTICA L T IISTS
Feeder Type B
sample
sample
(;N
dent measurements when using chi-square has bcen emphasized by many authors, including Kramer and Schmidhammer (1992). Whether the chi-square is a good-
Table 14.9. Hypothetical data on the.lrequertcy oJ' agonistic hehavior oJ' songbirds at tv'tt types o'f'feeders Feeder TyPe A
I
ness-of-fit test or a test of independence, the measurements that are summed to provide the observed cell frequencies must be independent in order to have a valid test. The assumption of independence may be violated if an individual contributes more than once to a data set.
16
ll
The application of chi-square with two samples is described below; its use with three, or more, samples will be discussed later in this chapter. It is used with nominal
9
6
data and compares observed frequencies with frequencies that would be expected in
l8
J
6
15
t2
t1
8
13
7
10 15
5
t4
5
Procedure:
and cast them into Rank all the measurements in order of increasing size from which each score comes a single order. Then identify the population and rJetermine the number of runs accordingly' populations: Measurements listed in order and their corresponding
355 667 8 910 tll2 131415161718 BBBBBBB AA B B AAAAAA 1234
Runs:
Number of runs
less
rnore often:
' ' ' '
Parental choice:
Maternal, 35 Paternal,12
Total47 Procedure:
Determine the expected by either assuming: l. a random expected distrib-
('):4
2 obtain the tabular value from Table A8 r. If the observed value is equal or
uniform or random distribution. As an example, Vives (1988) studied parent choice by larval cichlids (Cichlasoma nigro.fasciatum) that were reared under two treatments: l. in the presence of predators of fry; and 2. not in the the presence of predators of fry. Later, the free-swimming lry were placed in an aquarium where they could choose to stay in close proximity to their mother, lather or neither (see the analysis of Vives'2x2 tnatrix of data in the discussion of Fischer's exact test later in this chapter). Vives combined the data from the two treatments and tested whether the larval cichlids chose to stay in proximity to either their maternal or paternal parent significantly a
to
0'05 level' thanthetabular value, then the //o is rejected at the
tl,:number of measurements in the first sample:9 nr:number of measurements in the second sample:9 r(4) is stnttlle r In our example the tabular value:5. Since our calculated sigrtilictrrrt tlilno is there that than the tabular value of 5, we reject the 11,, the trequency of agonistic behaviors betwce tt sottghirtls
ution and randomly assigning each of the 47 measurements to one of the two categories (maternal or paternal); or 2. a uniform expected distributiorr and assigning 50'X, of lhe 47 measurements to each category, as has been clone in Table 14. 10. Calcr"rlate the
I
lor each category (as shown in the Table l4.l l):
(o-Ef ti
ference between
ll
at the two feeder tYPes'
t'lri-stluru'c lcsls whct'c lltc rlcgrcc ol'll'ecrlonr is I (e.9. Parker, 1979). This
is ol'tcrr rccor)mlcnclcd
that Yutas' torrct'tion.frtr t'ontinuitybe used in
t'onsislsol'tr'tlrtt'in1, lltt'nulnt't;tlor lry0 5bclirrcitisscluared,asfollows: C
lt
i'.s
t1
tut
rt'
gt tt t tl t t t"s't - r t l - I i
t
t
t"t
t'
I tt' t )'\(
tt)
t
I
)I
t'
\
rlt'tt't rtttrtt'tVltt'lltt'r lrr"o. ()l lll()l(" Thc chi-st1'llt-c g()(xlrtcss-trl-[il tcst clrn bc rrsctl to I lr.'trrrlt(,l l,tllt t'r'l ltltr llll' lll(l('l)('ll irrtlelrt.ntlr.rrt slrrrrPk.s;rrt.si1,rrilit.:rrrllv tlillt'rr'rrt
l{( )lrst'rvr'tl l'rlrt't
tt'tl)
l r pr't lr't l
{l
',1'
406
COMPLETELY RANDOMIZE D l)lrsI(i
NON PARAM ETR lC STATISTICA L'l'l:S'l-S
Table
gootlnessTable 14.10. Caluilation o/'the Ohi-squurc t'ic'hlids lurval clrcice purental b' ol-fit test on clatu.fitr
(o-
(O)
Maternal
35
23.5
5.63
Paternal
t2
23.5
5.63
Total
47
47
Srturce ;
n.26
'n:
i,
N,- Ntlttlbcl-rll'
re
\rl
\'
N,P2 - -: N.
pl1
'ry'
r I
I
il
vt 0.4-s
0.
l-i.s
-0.63s)+0.635(r -0.6.rs)l
I
22
r
l
3' 103
Sinct' \ l0l litllsorrlsitlctlrclirrrilsol. I l.()(rlo -l.96.weconcludethatthetwo sp()tlsc itr Stttuplc No'
i1
22(.41)+22(.86\':0.635 22+22
0.41-0.86
N,
Il'tcitsrtrclllcllls irt Slrrrtlllc
,ryr/'r Lry,/',
l'
+
/l o.o.rs1r
Pr P: nt*rr t -rrrl
Percetltage of
/\y'r
the percent-
rtt -
where:
:
N tPt+
2l- ,, Pt-l): Lll t tl pl p(l ll Nr -t
Calculate:
P,
44
Nr:22 Pr:0.86
Vt
of l\t'| Percentlges
{L
22
(fed intact. live honeybees) and control toads (t-ed dead honeybees, with stinging apparatus removed) ate droneflies (honeybee mimic) in different proportions (Table
I 1.26 is larger
1/, '
22
l6
N,:22 P-0.41
significa.t dirlerence This test is used to determine whether-there is a samples (or treatments)' ages (proportions) of a response in two
fl
(14'Y,,)
For example, Brower and Brower (1962) determined whether experimental toads
parent, and we conclude by chance' significantly more often than would be expected
-
3
(59"1,)
0.05 level.
of than the tabular value (3.84) we reject the H,, paternal and the maternal a unifbrm distribution of choices between parent that the larval cichlids chose the maternal
7,
28
or <:- 1.96, then the proportions are significantly different at the
If Z:-+1.96
(Table 49)forp:0'05 and compare the calculated I to the tabular value Since our calcudf:no' categories- |:2-1: l ' The tabular value is 3.84.
./,
19
(86"1,)
4.tt).
:5.63 + 5.63 : | | .26 s\9-4 LE
Test
Total
Sourt'e: From Brower and Brower (1962\.
(as shown in the table):
of
toads
l3
Totals
From Vives ( 1988)'
lated x2
Control
9
Rejected dronefly
l4' l '2b)' square 2x2 testof indepenclence in section figures calculated in Step 2 the summing by value obtain the chi-square
+
Experimental toads
(41"/,')
be overly conservative and The use of Yates,correction is considered to discussion on the chiunnecessary by niany statisticians (see additional
3
of experimentul und ('ontrol toutls to drone.flies (see text)
Ate dronefly
E
ExPected (E)
Observed
Response
Response
E\2
Choice
l4.ll.
401
N
( l)r ll)( )r I r(
I
No'
I
)n\
;r t t'
sir'nllir"t ttt lv tlil lt'r t'ttl
V I)t'( ilrt'll (l)('t\ (()nunun ) t,llr'r', llrc loll,'rvin1, ,",rtlirln wltctt ttsittg this test: l;rLr'llrt',,rrr;rllt'r ,,1 llrt' lrr,, r,rltt,",7r o1 | /,,nr(l trrrtlltllr, rl ltr tltr'stttttllct' ,N. il'thc Itorlrrt I r', ', llt,'tr llr,' t,tltu,,rtt lr, tnlr tlrl lr'rl ,t, ,r / r:tlttt' tl ttol. lltt'sl;rtistit'
NON PARAMETRIC STATISTICA L T I;STS
COM PLETELY RANDOM l7.ED D l:S
Table 14.12. Hypothetical data on birdsong dttration (in seconds) .from three habitats
Table 14.13. Runks of data ./rom Tuhlc I 4. I 2 /br calculation oJ'the Kruskal Wulli:; ona-\'u),unulysis of
I
(;N
variance
Habitat A
Habitat B
Habitat C
sample
sample
sample
Habitat A
4.4
6.9
9.2
5
9
3.4
7.1
8.1
1
l0
6.1
5.2
8.3
8
7
t7 t2 t4
3.8
4.3
7.2
2
4
ll
4.1
8.2
9.1
3
13
8.9
6
t6 l5
Ru:43
Rc:85
5.0
RA :25
Habitat B
Habitat C
When 0'365 cannot be interpreted. In the example above, p:0.635 and 1 -p:0-365' is is multiplied by the smaller N (22) the product is 8.03' Since 8'03
erly interpret
-
3.103 as a
>5 we can prop-
Z value.
z
Divide the square of each of the Rrs by the number of measurements in that sample.
I4.l.tc
Thtee ov more independent samples
Kru,skal-Wallis one-wa)' ana$:5i5 of variance whether The Kruskal-Wallis one-way analysis of variance is a test for determining nonparathe is It different' three. of more, independent samples are significantly (discussed in metric counterpart of the parametric one-way analysis of variance in locaChapter 13) and ts95u/nas powerful (Andrews ,1954).It tests for differences
tion and requires at least ordinalmeasurement' populaAs an example, we will use hypothetical song durations from different hypothetical The tions, as we dicl with the parametric one-way analysis of variance' of the same data in Table 14.12 are mean song durations from one-hour samples are small we will species of songbird from three different habitats. Since our samples whether we determine to variance of not calculate the lormality or homogeneity ntlnparametric the with can Llse a parametric statistical test; instead we will proceed we recluce this ratio test, even though we will be losing resolution in the data when data to an ordinal scale of measurement. Procedure:
t
ry Nj
(43)r:369.80 (81)t:
Q5\2
56
6
Do4.r7
Sum the figures just calculated.
)--Ni : 104.17 + 369.80+ 1204.17 :1618.14 .
R,2
Calculate the test statistic
11.
I n x R,2\ , H--t \N(N+ I ) Ni,,l-3(N+l) whe re:
N:total
number of measnrements
12 x(1678.14)l-ltr7+l;:11.79 u II .^ .'-lTrl) ll7( l I
Rank all the measurements from the three populations us ollc gr()tlp beginning with rank I for the smallest measuret.llent. Il'tics occttt' lltc tllc:tlt between two or more meilsurcntcnts. lrssigrt cltclt tttcttstll'clllcllt
Wltcn lltctc irt'c tlu'cc srrnr;-tlcs antl thc nurlber of measurements in each
of'thc ranks lirr which it is tictl.'l'hcn c:tlcttlltlt'lllr.'stttlt ol'llte t'rtttks lirt cltclt coltttttrr (/(,)('l'rrble l'1.I 1)'
s;ttttlrlr'(;tr ttt olu ('\irtttplt') ( ()ntl);rtr'llrc crrlt'rrl;tlt'tl r:tlttt'ttt l;rlrlt',,\t) rll trrr .| ',,rtrt1,1,", I
ol tltcs;rrnlrlcsis'5.('()nll):rt't'llrt'r-'rrlcrrl:rlcrl //tollrcvalueinTableAll. Wlti'tt lltt'tt':rtt'lltrt't'()r nl(|tr,'s;rtttltlt's rrrrtl '5 nlelstu'ctttcttls itt citCh
ll lttl
lte t'lri-st1rr:tt'c
NON PARAM ETRIC STATISTICA l. 'l'l:STS
410
df:3 - I :2
COMPLETELY RANDOM IZED I)I:S I(
;N
4n
Fbr example, we will use the example fiom the Kruskal-Wallis test (above) and test for a significant difference in song duration between the samples from
Tabular i0115..:5.99
Habitat A
and Habitat B.
Since our calculated H ( I L78) is larger than the tabular chi-square value
(5.99), we reject our
110
un:fr:r
and conclude that there is a statistically significant
5t6:4.2
difference in song duration between the three samples (habitats). In order
to determine which pairs of habitats (A versus B, A versus C, B versus C) are significantly different use Dunn's test below.
ur:ff:0315:8.6 N: l7
Dunn's multiple c'ontparison test
No:6 N*:5
t | '' ,:u#,':tr:#,
k:3
n,tu r\ to: It on s):3'oo5 V
This test can be used to determine which pairs of samples differ significantly when the null hypothesis is rejected by the Kruskal Wallis one-way analysis of variance test.
:t.44
Procedure:
t
Calculate the test statistic Q:
The calculated Q of I .44 is smaller than the tabular e (2.3940 0r..,). Thererore, we cannot reject the /1u of no significant difference between the samples
from Habitats
A
.',: Ru-Fn
and B' This irnplies that the difference that contributed to the significant Kruskal-wallis test probably came from Habitat A versus Habitat
SE
lrom Habitat B versus Habitat C. you can again
Where:
use Dunn,s test to
C and perhaps find out.
.Rr:mean rank for the -/th sample Chi-,squara goodness-o.f-fit test, rhree rtr rnore sutttpres
-R^ R^:j
This test is used to determine whether three, or more, independent samples are significantly dill-erent. As a hypothetical exarnple, we observe male songbirds of species x singing from three dilferent species of trees. This test is merely an extension of the two-sample test rjescribed earlier in section 14.1.1b. It is used with nominal data and compares observed frequencies with frequencies
^No
n.-:& nN*
that would
where:
No:number of measurements in Sample A Nr:number of measurements in Sample B I
sF:
/f
V
rutru+ t11
L tz
t
I \l
t,ro*our/-l
:
measurements in all the santples
Compare the calculated Q to the vuluc in Tahle A 1.5 lirr thc tlcsirctl lcvcl of significancc ancl wherc /i is thc totirl rtrrrrrbcr ol'sirrrrplcs. ll'tlrt't';rlctt-
latctl
distribute the total number of observations between the three tree species, ranclornly assign each observation to a tree species, or generate random expected
values via calculatitlti, such as the negative exponential distribution use6 by Krebs
(1974).
we hitvc tletcrrtlined that the three species of trees are equally distributed
tltroughottt citclt rlrale's territory, so that
where:
N:total number ol
be
expected in a r"rniform or random distribution. That is, we can unilormly (equally)
p-llbtrlirrQ.tlrcrrtlre //,,o1'rrosip'rrilit'rrttt tlilli'rcnt'clrt'lrtt't'rtlltr'
Irvo slrrttltlcs is tt'jct'lt'tl.
s;-rccics
if there were no prelerence for any tree citclt lttrtlc rvottltl sing cqually often liom each species of tree. we collect the
tlitllt sltowtr irr 'lirblc 14. l't lirrn (r0 nrrlcs lntl thcn ASSLrffle a uniform expecled distrihttlion h.v;rssillltirrg ct;rr;rl lrrrrulrt.r.s (.)0) lo r..trt.lr tr.cc sl-rccics.
lllt'ttutttlrcr ol
rlt.l,l(.t"s
ol lrt.t.tl,ln
ut ;t ,roorllr.ss-rll.-lil lcst ct;rrirls lltc pttlrbcr91,
tlrlt'1'1)r('\r,rlrs.rr'lr(lrr r';r.,('\\('rr,r\t.rlrrt't'rrr't's;rr.1.11,\(n,r];rttrl (.).rltct.c[lt.c tll I | ' I r,,'krrr1, ,s1 l,rlrl,. ,,1,1 rr rllr , ,ll \\r. .,t.t. llt,rl ,,rrr r.;rlt.rrl:rlr.rl 1,,,1 .-r.1.1
NON PARAM ETR IC STATISTICA L
412
Table 14.14. Hypotheticul tlata on the.fi'equen('y species
of
.I.IlSTS
d
COM PLETELY RANDOM IZED l)lrS
Table 14.15. Contparison
birtls ,:inging.fi'om three dif/brent
(;N
two c.rpcrintt,ntul designs:
one sumple oJ'tn,o independent vuriuhlas, untl ty,o
trees
samples
No. males observed singing
Tree
oJ'
I
Expected lrequencY (-B) based
on equal
Q_D) E
Species
observed frequencY (O)
distribution
A
38
20
16.2
B
10
20
5.0
C
t2
20
3-2
Tables
60
60
f
of one independent voriuble
One sample of two independent variables
independent variable
Al
Al
Two samples of one
Bl
A2
xzt
-r 2l
xtz
xzz
x 22
,,,
;,,
r2n
-rr
r
:24.4
that the exceeds the tabular values even at alpha level 0.001. Therefore, we conclude male songbirds do not sing equally from all three tree species.
14.1.2
Table 14.16. The priority rcttios.fbr obtaining./bod relative to the hatching order v'ithin sibling cuttle egret dyuds
TWO VARIABLES
Priority ratio Elder sib
l4.t.2a One samPle in design to One sample of each of two independent variables (l r, ,8,) corresPonds chapter)' in this earlier (Ap A2, discussed two samples of one independent variable
Hutching order
These two designs are compared Table l4' I 5 ' tests Since both designs are testing two inclependent samples, the same statistical
are used. Therefore, for one sample of two indepenclent variables, the appropriate
nonparametrtc tests are:
'
' All of
Younger sib
525
362
t2
(53e)
(348)
99
40
3-4
(8 5)
(s4)
-fotals
624
402
887
t39 1026
lYttlt,:
For ordinal data: Mann-Whitney Utest Kolmogorov-smirnov two sample test
I'he observed (o) measures are the upper figure in each cell. The Expected values are provided lor each cell (in parentheses)
Wald-Wolfowitz runs test
,\tttrrt'c: From Fr"rjioka (I985)
For nominal data: chi-square goodness-of-fit test test of two Percentages
h:tsccl on the dcgree
these tests were described earlier in this chapter'
t4.t.2b Two indePendent samPles Chi-squttre te,st of indcpcnd<'ttk (2x2
Totals
(t)
of difl-erence between the observed measures and what would hc cx1-rcctcd by chance. For our example we will use research which involved two srrnrplcs cach o1'two variables (2X2).
lrtr,\t' ,t'tttttltlt'.s' As ittt cxanrple, Fujioka (1985) studied sibling competition in the rtlllr' r'1lrt't ( lilrlrttlt'rr,s'ilrii). 1'hc priority nrlios tirr obtaining food relative to the Irtlr'lttttl', ortlt'r uitlrirr srlrlirrl, tlvlrtls \\,('t(.ntelsrrrcrl (-l'itblc 14.16). The priclrity ratio rr,ts lltt'pt'tt'r'nl;t1't'ol lttttr'()n('\rl) olrllrrncrl lirrxl 111,r111 llre plrrcnl pl'ior ttl the other
t )
tlttlc This test is uscrl ttl rlctcrprirlc lhc rlcgrcc ol'ttssocilttiott lttttolll: nl('irstllr'.s itt tw'o ll ts rltt tltltlt' ol ;rrrulltr't ()l lll()lt'slttttplt':' t\\'() llll(l vlrtiltlllc pCtttlcttt slttttgtles ol'tlttc
.'tlr tt ltt'tt lroI lt stlrlttt;",
lr1'1,1'1'11
',ttrrtrll,t,r'r
rt1',11
I
ON PARAM ETR
N
COMPLETELY RANDOMIZEI)
IC STATISTICA L I.I:S-IS
values, and
Row total X Column total
Continuity when calculating the chi-square value lor each cell. The correction
t"tuf
order l-2): For example, for the upper left cell (elder sibxhatching
x62!:
887
Expected:- W26
lQ!ryrw9spglrq qil' expected
Once again, there is controversy over the necessitity of this correction, including
9!..rved- exPected )2 x.,-. -1 expected .
involves reducing the numerator by 0.5 belore squaring, as follows:
fig
(f):
Calculate chi-square
N
Another common recommendation lor small sample sizes, small expected all 2X2 malrices (Denenberg 1976) is to use Yates' Correc'tion Jbr
Expected values for each cell are calculated as follows:
Expected:--C*rA
l)llsl(;
(
recommendations that
it not
be used when the sample size is larger than 20 (e.g.
Sokal and Rohlf, 1981) or not used at all (e.g. Howell, 1992).If the researcher wishes
(O- Dz
to be conservative, use Yates'correction or Fisher's exact test.
Cell
Fisher's exact test
of
Elderx 1-2
0.363
Fisher's exact test, like the chi-square test
Elderx3-4
2.305
analyze contingency tables for significant associations. However, in contrast to the
l-2 YoungerX3 4
0.563
chi-square test it can be used when one, or more, expected values are less than 5. We
3.629
use the same
YoungerX
f 3 Compare
format as above:
:6.86
the calculated
f
value to the tabular value (Table A9)'
Degrees of free
I
-
I
)(No' columns
- I ):
1
of 6.86 is larger than the tabular value (3'84,,t,r) the
independence (above), is used to
Al
A.
Row totals
Bl
A
B
RT,
B2
C
D
RT,
CT
CT
Grand total (GT)
Column totals
Hu of no association is rejected'
above are large; however' it is Sntall sutrples The expected values in the example are smaller than 5' Fisher's commonly recommended that when expected values in place of chi-square' Several authors exact test (described below) should be used
One method of calculating the probability (P) of a set of observed values (A.B.C,D) is by using the factorials (!) of the observed values and the marginal totals as follows:
1971) and sug-
(see Everitt' have investigated the validity of this recommendation (van Hoof' 1982)' The acceptable be may 0.5 as gested that expected values as small 8,.,,) is: format tbr two samples of two variables (Ar.rand
Al
A2
Row totals
P-
'ilrc probability can be calculated using a hand-held calculator or the factorial table ('l'irhle Al). The probability should be <0.05 to reject the null hypothesis of a r
Bl B.
Colum
otals
A
B
C
D
A+C
B+D
A+B C+D
willt srrt:rll srttttplc sizcs: The lollowing is a simplc lirrnrttlit lirr chi-stltritt'c
(.ll) /l(')'N , x t t I /l)(('l /))( I I ('x/lr /))
(RT,!) (RT,!) (CT,!) (CT,D (At) (Bt) (c!) (D!) (Gr!)
:r
rtrlortt tlistribution.
A scconrl nrcthocl
is based on the logs
of the factorials (Table A2) and an antilog
(Sokrrl :rrrtl I{ohll, l9t{l). In this method:
l' rrrrtilog(/'(') rrltr'tr'
/ ('
lol'( 'l ,' I l.11'('l | l,1'l tt 1t i lt rl'/ll I l.l'(
(1,,1'll I ,! t lo1'll l, ,1'
l
,r I
,!) llt(
i'l'!
NON PARAM
Table
l4.ll.
COMPLETELY RANDOM \ZED I)trst(;
ETRIC STATISTICA L TI]S-I-S
q The e.ffect
oJ"
rearing condition
d'
parentul choic'e by' lartal cichlids
Determine if this probability is suriicie.rry smalr (e.g. <0.05 or <0.01) to reject the nulr hypothesis of a random distribution.
A second, and perhaps simpler; method ror obtaining the more extreme proba_ bilities (Feldman and Kluge r, 1963; Zar, l9g4)is as follows:
Parental choice Rearing condition
Maternal Paternal
Totals
Predators Present
23
l0
JJ
Predators Absent
t2
2
l4
Totals
35
l2
47
t
Designate the smallest observed value as a andthe observed value in the
diagonal cell as d.
z
Designate the observed value in the remaining cell in row I as b and the observed value in the remaining cell in row 2 as c. P is the probability of a given tabre of observed values, and p. is the prob_ ability of the next more extreme table. In the next more extreme
:
Source: From Vives (I988).
becomes
As an example, Vives (1988) studied parent choice by larval cichlids that were reared under two treatments: f . in the presence of predators of fry, and 2. in the absence of predators of fry. Later, the free-swimming fry were placed in an aquarium where they could choose to stay in close proximity to their mother, father or neither. The data in Table 14.17 are for the young that chose one of the parents.
if we attempted to use the chi-square test, the expected frequency in cell D (predators absentXpaternal choice) would be less than 5; (l2xl4)147:3.57 . Therefore, we will use Fisher's exact test. We can see that
T
:
C
:22.412+6.559 + 8.680 + 0.3 0l
(36.938 + I 0.9 40 + 40.0
P:antilog
(31 .1
+
8.
680)
:
-
59.4 I
3
:
3l .l 59
37 .9 52
59-37 .952): vntilog -0.193:0. I 6l
Since the calculated probability
hypothesis of
I4
of 0.161 is larger than 0.05
we cannot reject the chance distribution. That is, we conclude, as did Vives (1988), that
being reared with, or without, predators of fry had no effect on which parent the larval cichlids chose. For a test of significance of a one-tailed test, we must obtain the total probability for the observed values plus the probability for all the more extreme values (as in the
binomial test). This can be done as follows:
t
Keeping the marginal totals fixed, reduce the smallest value by I. adjusting the other values in the cells accordingly, and calculate thc ncw proba-
bility. Note that the numerator stays the same.
z Repeat step 1 reducing
the value by one
probability when the smallest cell
I
N
until you havc calculalctl thc
is 0.
Acld thesc probahilitcs togcthcr to ohtrrin lhc tollrl prrrblrbility lirr tlrt' onc-lirilctl lcst. Mtrltiplv tlris probrrhility lry.) to olrl:rirr llrt';rrolr:rlrililv lirr Ir lu,,o l:rilt'tl t(.st (Si(.,'(.1. l()\(r; Sok;rl;rrrrl l(olrll l,tHl)
+
b' and,c becomes
table, b
c,.
calculate P' for all the more extreme tables and add them to the p for the observed values.
p,:
,o.d ,(p)
DC
s
Three or more independent samples
At
Bt Jrr 82 xn B..Jr:
;,,
.,,,,
Ke ndul l's
t, o
qffic ient
of
c
onc, o
rdance
Kendall's coefficient of concordance is a measure of association. It determines the cxtent of correlation among several sets of rankings (ordinal scale). For example. we might be interested in how well three measures of dominance compare in ranki.g individuar worves in a pack's dominance heirarchy.
As anrther exampre. whitfierd (19g6) ranked the quarity ('lrrnuriu ittttrltrt"t) territories using five
of
19 turnstone
measures; he then determined the correlalir. .r,r..g thc rrrks ror the difrerent measures (Table l4. rg). Tir illtrslt'itlc tltc ttse tll' Kenclall's coelficient of concordance we,ll reconstruct the lltblc rts il'.rtly :'icvcll lcrrilorics ( rl 7) hlrtl been ranked using only three measures: tttttttlrt't lttttl tlt'ttsi(y ,1'cltit.tt.rrritls. ;rrrtl l.ttrtt.t'tlcnsity (T.ble 14.l9).
A
rrrtntllt.t st,ls
ol
r;rrrk
rrrl,s
I
Ar ttttnllrr't ()l tl(.ltt., (lr.trrlrrl(...1 r,rttl.r,tl /
l)l:SI(iN
COMPLETELY RANDOMIZED
NON PARAMETRIC STATISTICAT. I'l:S'l'S
418
419
Calculate Kendall's coefficient o1'concorclance ( tr41: o.f'territory qualitt' Table l4.lg. Ranking o/'territories using severul nlcusurcs
Larid
s w-_ MP
Chironomid
Chironomid
Lurus
Sternu
no.
density
density
density
density
Y4
YI
Y4
where:
S:sum of
squares
of observed deviations from mean of
R-
N1
Y2
Y5
Y3
Y2
Y4
Y1
Y1
Y1
Y3
Y3
Y3
Y4
Y5
Y5
Y6
Y2
Y3
Y6
Y5
Y5
Y6
Y4 Y6
o1
Y6
MP:maximum possible sum of the squared deviations; that
Y2
o2
o6 o3
T2
o1
rankings were in perf-ect agreement.
o2
N1
o4
T1
T2
o5
o5
o1
Y2
o4
T1
N2
YI
o4
TI
N2
N1
NI
o6
o6 TI
o1
N3
o6 o3
o3
Y7
N3
NI
T2
o7
o4
O3
o2 o5
o2 o5 N2
N3
N3
ol
TI
o6
o7
T2
N2
o1
o4
T2
o3 o1
o2 o5
Y7
N3
Y1
Y7
:I(Ri- R,lN' :
MP:
IR,lN:8417:l2l (
II
-
tD2 + O
-
12)2
+ ... (21
-
tZlt -- rrO is,
if
the
lt l2K-(N'-,Y): I 29(313-ll:252
s : 134 :0.53 I4'-MP 252 The value of 0.53 reflects the degree of agreement between the three mea-
o1
sures
of territory quality.
For small samples (1/=7) the significance
of
W can be determined by
comparing the value of S to the tabular value (Table A l6) at the appropriate level of significance. Since the calculated S of 134 is smaller than
N2
the tabular value
Y7
of
157.
n'
we f ail to reject the
,l11,,
that the rankings by
the three measures of territory quality are independent. That is, in order
to show a significant association the l/,, must be rejected (i.e. the calcuNote:
lated S must be larger than the tabular S).
of concordance: The rankings are essentially the same by Kendall's coefhcient W:0.698, Source:
f
When N>7, significance can be determined by calculating the chi-
:62.82. df:18, P<0'001'
sqLrare villue, as
Tindall' From whitfield (1986). Copyrighted by Bailliere
Table 14.lg.
U,se
of
territories three ntensures to rctnk the quulity o.f seven turnstonc
r'-,s1 I
tll'--
Territort' Y1
Y2
Y3
Y4
Chironomid number Chironomid densitY
2
6
4
-1
I
2
Luru,r density
6
2
3157 5461 1451
ll
()
()
()
/r I
I
rrr
Wlrrllrt'ltl
( l()S(r)
Y5
Y6
Y7
:K(N-l)w
,)K(//)(l/+
l/-
follows:
I)
I
'l'hc cirlctrlatccl chi-square value is then compared to the tabular value ( lrrhlc A()1.
('lri ()
l(,
ll
ttltrrrrt' lt'tl ttf itttlt'1tt'ttrlt'n t't' / r'X k /
I lrrr lt'rl r\ lr\('(l lo tlr'lt'r trrirtt'ulrt'llrcr lltt'tc is;r sil'nilir';rttl rrssocittlitln betwccn mea',ur('\tnlt\o1;ltrtlrlt's.trtlltr'.rrtrr1,l,",ol ()n('r;ttt:tlrlt'lrttrl/'slttttltlcsrll'lltcrtthCr. Itis r,tlr ttl,rlr'rl ttt lltt'',,lnr(' \\.1\ ,r', \\,r',,1,".r trl',',1 pr('\ l()rt',1\ lot lltt' .) ' .) ('lti-st1tIil'c lc:;l ,'l tn,l,'lr('n(lr'n( r' r'\r ('lrl llt,tl llt, t, t.,r l,rty,, t trtttrrl,i t ,'l r r'll',
RANDOMIZED BLOCK. MAT('ll lrl)
NON PA RAMETRIC STATISTICA L T IISl.S
Table 14.20. The Number
males and song types shoretl between replacen'tettt
(i
14.2
the preceding year; and previous owners; replacements and other mciles in
l'>A I
l{S
RANDOMIZE,D BLOCK. MATCHED PAIRS AND REPEATED MEASURES DESIGNS
replacements ancl the previous nrule's neighbors 14.2.1 One variable
Number of songs shared l4.2.Ia Two matched samples Samples
Previous owner
Other males
5
5
276
117
62
ll
37
9
0
54
Neighbors
1
15
B2
At trr t Xtz
B
Xlu
Block,s
Bl
A2
J:r xzz
Notes: uo,':6'57, P:0'4' comparisons between replacement Figures given in the table are the numbers of (N:26), other males breeding in males (N:26),previous owners of the territory varies from year to year' the same yeaf as the previous owner (the number
f
range:|7-28),andneighborsofthepreviousmales(variesftom2tol.
,r-ltI
In the randomi:ed block de.sign:
I z
Each
-r
t.l
is a measurement from a different
individual.
Individuals are blocked across (e.g. block B,) according to some characteristic such as sex, age, litter, place or time.
**ss--4.1+-0.31
Copyrighted by Bailliere Tindall' sourt.e: From McGregor and Krebs (1984). song learning and deceptive For example, McGregor and Krebs (1984) studied research they measured the their of part mimicry in great trts (Parus ntaior). As with: l' previous owners of the number of songs shared by new territory owners previous owner's breeding year; and 3' all same territ ory;2.all other males of the a 3x4 matrix of nominal data neighbors of the previous owner. Their data form
In the repeated measures design:
I z
Each block (e.g. 81) is an individual. Each -r, is a measurement made on individual B,in each sample (such as
different treatments),4,.
The matclted-pairs design is a special case
of either
the randomized block or
rcpeuted meusures designs in which there are two samples; hence, the matched pairs.
(Table 14.20). Sign test
r z
Procedure review: (CT) and grand total (GT)' Calculate the row totals (RT), column totals Calculate the expected values for each cell:
RTXCT
Expected: GT
:
observed (O) arrd Calculate the f value for each cell from the cell's expected (E) values:
Cell
p:
(o-
-
E\:
r ()hririrr llrc loltrl .yr l'ry srrrttnrirrl,tlrc ilttlir,'itlrt;tlt'cll 1's. \ ('()ttll)iltt'tllt'lol;11 t'rritlr tltt't;tlrttllrt Yltltt'' ttt l'rlrlt''\()
'fhe sign test is used when the measurements in the two samples are matched (i.e. blocked or paired). It tests for significant differences in form or location between lhc two sanrples'measurements. The sign test can be considered a first-approximation tcst whiclr is less powerful than the Wilcoxon matched-pairs signed-rank test sincc it tlocs not take into account the magnitude of the difference in the paired nrclsrrrcnrcrrts. It is95"/,' as powerful as the paired /-test when the sample size is
to an asymptote of 63"1, as sample size (Sicgcl. nr('r'cases 1956). An illrrs(r'ir(ion ol'thc use of the sign test is found in l(rt't'lrctl rrrrrl lletlriek's ( lt)()l)slttrly ol'litrrcss-linkctl bchavior traits in the spider,
srx. lrrrt porvcr'-cllicicncy rlccreases
I.t't'It ttt,lt\l.\ tIl,t't Itl.
.\s;rnollrr'l r'rlrnrplt'.\\('\\rlltt'.r',l,rl.r',tnttl;rt lollrost'tt't't'otttpttt'ctl willttltcstitn,l;rtrlr'tror ol llrr'tltllt'tr'ttr r'ltr'ltrr'r'tt lltr'nrr',rn', ,',rt ltt't ttt lltts t'lt;tPlt't ( l'rrllle l-1.]l).
422
NON PARAM ETRIC STATISTICA
L'I
ntutclred at't'ording to oge und thc ranks u,s.rignul.fbr
Bout No.
Age (v) Sign
sample
I
4.6
4.1
2
5.3
5.1
3
4.4
3.2
4
3.1
4.2
5
6.4
3.1
6
5.3
4.1
l
4.7
4.5
8
4.8
3.6
9
5.0
2.9
+ + + + +
10
4.4
3.0
+
+ +
I c' o
ro n mat
ch
e
tl-pai rs
Sound-enriched Sound-impoverished
Differences
Rank
8.2
6.2
-2.0
4
l.t
4.3
-2.8
6
1.5
5.4
-2.1
5
6.8
1.3
-
8
7.8
7.9
8.1
l.l
-
6.9
2.0
-4.9
1.4
8.0
0.6
5.5 0.1
1
1.0
3
2
the standard error of the difference between the means that we used on similar data.
tr4/ i I c o
^r,,^
/r\;f f
tscoreeachpairofmeasurementsasaplus(+)ifthenreasuretnenttn
the
columll; score a minus letl-hand column exceeds that in the right-hancl is no difl-erence (see ( if the reverse is true, or score a zero (0) if there
-
)
Table 14.21).
L:no. of times least frequent sign occurs:2 7:total no. of pltlses and minuses:10 Determine from Table A l7 the probability 7 signs' less frequent sign out of a total of
of obtaining L or f.ewer
s().p tcsl rir'sigririclrrrt tri|ri'r'e rrt't's be rrvce , tlrt' :r tr'st r.r srl,rrrrrr';rrrt rrrrti'tt'rrt.lr.rr't't'rr Prprrlrrri.rrs is l. rrlirize (
r.
trr
t c lt e
d- p u i r s
s ig
nrore powerf ul test. For an example
ne tl- r u n k
s
tes
t
of its
use
in the ethology literature
see
Slotow cr
1993).
lltt'tl'tl't
)ttt'"ttt ll ttlt';lstllt't''
were
nrrtchcrl uccording to age, lour to each age group, and randomly assigned to one of lwo Ir'catnrcnts: I. maintained in a sound-enriched environment: and 2. maintained sountl-inr1'roverisltccl environment. After they had been in tlie treatment conditions lirr tu,o wccks wc nrclsr.u'crl thcir song durations when placed individually in ,ur olrst. rvlrlion trrorrr ('l.rrblc l-l.ll) rrr rr
VisualinspectiontlftlreclatitsttggcststhirtstltrgtlttrittitlrllilrllrlllttllttittrrAis (lrtt;lli()ll\ lltt'r;ttt;tlrtltll
nt u
As a hypothetical example. we will use l6 adult male songbirds which
between the two populations is not significant'
.l(.l,ts ll'rl;tlsrt l;tkr'r llll.;ltt.ttttl
n
counterpart of the paired samples /-test (above). Mood (1954) concluded that this tcst is 95"/uas powerful as the paired /-test, but Blair and Higgins (1985) demon-
,11. (
(e.g.95,,/u:0.05),weconcludethatthedilferenceitrstlttgtlttrlttitltt
rw.
o
small even fbr normal distributions. and overall the Wilcoxon test is more of,ten the
of the
the signilicancc lcvcltlccidccl ttpotl Since this probability is greater than
realry grciltcr. A,.tlrcr.wlry
x
This test. like the sign test (above). is used when the measurements in the two samples are matched (i.e. blocked or paired). It tests for significant dilferences in Iocations (medians) of the two samples'measurements. lt is the nonparametric
strated that the power advantages of the paired / test over the Wilcoxon test are
P:0.110 for L:2 and 7.:10
rrrrrrt tlrc
Wi
carlier in this chapter.
Procedure:
:
e
Song duration (s) maintained in:
PoPulation B
sample
th
signed-ranks test see text )
Song duration (s)
A
423
(
see text )
Population
l'A I ItS
Table 14.22. Hvpotlrcticul datu on song durution.fitr l6 adult nrule songbirds
Table 14.21. Ilypothetical clatu on '\ong tlurutions 'fi"ont usecl in the sign test ttto popttltttions rf'birds untl signs (
I:l)
RANDOM IZED BLOCK. MAT('ll
I:STS
l'to1'1'11111r'
t
rntttr'llrr'rltll('r('n( (". lrr'l\\t'r'tr,'.rr lr P:ur ol st'rttt's .llrr\\ ll ltr l.rl,l,' I l t') ',( ol(' lr'll r,'ltllttll ,i,'l, I )t'lr'r
(r iplrt
t'tllttrltlt
RANDOM IZED BLOCK, MAT(.il
NON PARAMETRIC STATISTICA L T IISTS
Rank the scores (ignoring the signs). The lowest score gets rank no. I
On Perch
r:
7':sum of ranks for differences with
less
t,A I t{S
After l5 min.
(shown in Table 14.22).
Calculate
l:t)
Off
Perch
+
frequent sign.
T:l*2:3
On perch +
A: 19
B:
Off perch
-
Cl:5
D:l
Calculate the
f:
a () ()I !
Compare the calculated Zto the tabular value (Table Al8).
0.
17
where: N:number of paired scores:8
Tabular T,.,ur:4
If
O- ll2 r:' [(B-Bi-
than or equal to the tabular value, then there is a significant difference between the two samples. Since our calculated f (3) is smaller than the tabular T (4), we reject the FIn of no significant differthe calculated
i"is
less
ence between the two samples and conclude that the type
:(L_- j)-_1ll
of sound envi-
t7
ronment had a significant effect on the birds'song duration.
t2t ___5 22
McNemar's test The McNemar test is a variation of the chi-square test used to determine the direction and extent of change in pairs of repeated measures (e.g. the same individuals
3
+5 {
compare the calcurated rows
f to thevarue in Tabre A9, where df:(no. of - I ) x (no. corumns- l ) : r. Since our calcura ted, f(5. 5) is rarger than
are measured before and after treatment). For example, Tokarz (1985) used McNemar's test to analyze data on whether male brown anoles (Anolis sagrei)
the tabular value at P:0.05 (3.84) we reject the flu of no significant difference and concur with Tokarz's (1985:749)
perched higher or lower in their cage, before and after encounters with larger and
change occurred in the perching location of the smaller males from belore the test to l5 min. after removal of the partition . . .,
conclusion that A significant
smaller males.
McNemar's test is 95"1' as powerful as the paired /-test when the sum of the A and D cells is six, but the power-efflciency decreases to an asymptote of 63,,1,as the sum of A andD increases (Siegel, 1956).
Procedure:
t
Cast the data into a 2x2matrix as shown below.
This creates two cells (B and C) which reflect change: Cell B (+ to
-)
and Cell C
(-
to +)
Alter + 0) 1r
€()
+
A
C
B D
M e a,yure.y o/' tt s so c, ia t iott These analyses are used to test for the association between paired measurements in
two samples' The measurements might be made on individuals of the same age or at the samc places and/or times (rantlomi:ed block: each measurement on a difl-erent individual) or one measurement in each sample can be made on the same (
rc p c tt I ed
tttc
individual
tt,t ttt.t,,t).
wc citlt itlstl examine the correlation between paired measurements in two I is the independent variable and sarnple 2 is the
srtrttplcs irr which Strmple
As an example. we will generate hypotheticul clit(u lirr Tokrrrz's ( l()l{.5) experiment on Iizards(sccTuble Iin Tirkurz).'l'lrc tnitlt'ix below is lirr tltc
snritllcr rttitlcs ol'tlrc lltit's. lt sltows tlte tttttttllt't ol'tltttlt's ott ( i )ot oll' ( )tlre perclr.lrr'lorr'tlrc lt'st ;rrrtl I5 nun ;rll('r t('nlovttlol lltr'P;ttlittott lrt'l rvt't'tt I ltr' I rt ( | lll;l l('\
6epentlcttl vrt'irtblc"I'hcsc a,c trrtrlt'lrt'rl pttir,s irr which a measure i, Sample 2 (dependent r":tt'irtl'rle) is tthtrtiltctl l,l t'rtt'lt n)('irsru'c in.Sirrnple I(inclepen6e.t variable). llr'st';rtt'ltt.rs slroultl lirsl pt(.1)ilt(.;l .,r.;rllt.r;,;.11,, ol. lftcir.rlirtir lil-vistrtrl inspectiol. st'ltllt'tJ'lilllls Ilrr\lrlr';t ;"rr11 l 1111lrr,rlr,rr.l I tt llt'l ltr'r rl t.. 1ril,,tl t\ r. I )t tt{.l,,tl t\
rrlrr.llrr.r'lr r..r.r.cl:tli0rt cxisls; iurtl
(.
2.
N
ON PA RAM ETR
I
o
o
f,
c.)
aaa
a c-a 'i9o .'Xcs
RANDOMIZED BLOCK, MAT(,til,t) l,n il{s
a 4
3
a
a
a o
o
aaa 0.)
IC STATISTICA L'l'lrS'l'S
a o
ao a
oo aa
aaa
a
o
Table 14.23. Hypotherical daru ort ut,tittit), leyel und song./requenty in male.y o/'ct tcrritoriul .rottgbircl
a o
spec'ies
o a
a a
a a
o
o
a
o
Independcnt variable X-axis Fig. 14.2 Scattergrams
Activity units (flights/5 min.)
Individual A
4
B
7
8
C
l2
14
D of hypothetical data illustrating: (1) no correlation; (2) low positive
correlation; (3) perfect positive correlation; and (4) perfect negative correlation.
Song frequency
(songs/min.)
-1
6
5
E
r8
F
l6
t5
G
ll
9
l3
Figure 14.2 illustrates how the data are plotted with the dependent variable on the Y-axis and the independent variable on the X-axis. The examples illustrate: l. no correlation; 2. low positive correlation: 3. perlect positive correlation: and 4. perfect negative correlation.
The two correlations described below Kendall's tau and Spearman's rho, are both used with ordinal, interval or ratio data. Both correlations generally have the same power to detect an association (Siegel, 1956). However, if there are no tied ranks Spearman's rho is the prelerred statistic sir-rce it uses both the direction and the difference in magnitude of the ranks. If there are severaltied ranks use Kendall's tau (Nonnan and Str-einer, 1986). Both Kendall's tau and Spearman's rho are approximately 9l(Zr as powerful as Pearson's product moment correlation coefficient (Hotelling and Pabst, 1936).
th
E
Speurntun's rho Speannan's rho (p.) is a rlonparametric correlation coefhcient which measures the covariation between two rank-orderecl variables. Only an
i' ro (J
ordinal scale of measurernent is necessary. as it is with Kendall's tau. As an example, we will use the hypothetical data on activity levcl and f'requency of song in males of a species of territorial songbird in the Ttrblc 14.23. This rlata sct consists of rnatched pairs of measurements of two dependent vuriublcs: l. activity units lflights/min.); and 2. song fl'equency (songs/ niin.). We will assunlc tlrrrt tlrc data are medians liom several one-hour samples tirr eirch bird. l-hc tllIrr irre sirrrilrrr
to the exarnple we used
lix
Pcitrsort's ptrrtltrcl nrorrrcnl corrcll,lion (seetiorr
al0
ll.l.l)
exccpl thitl tltttit wct'c in tltc t'rtlio sclrlc ol'nlcir:iurcnt('nl. Wc ltclrer,'e llt:tl lltr'se lrvo rlr'Pctttle rtt 'nltti;rlrlt's;u('( or rcl;rlt'rl \() \\(' lrt'1,i,' 1,t'r.'.,,'
sltrrt.llill' 't .,
',,1t'ty,1
ltrr (l t1,11rr' l.l l) I trlttr llrr' '.,,rllr'r1,r,ilrr ,rlilnt' llrt'tt' trtlrrltl
20 So11;'
I rl I I
I
1
I
('(lll('tt( \'
Sr ,rllt rj,t,ul ll l;l pollr;lrr .rl rl.rl.r ,, lr'tl lot r.\lrl,1t,tlt., )
itllrl
s1r11l
lictltrcrrt.,y.
(SCC
428
NON PARAMETR
RANDOMIZED BLOCK. MAT('III I) I'N II<S
IC STATISTIC A L I. I:STS
in Trble 14.23 for calcttlution Table 14.24. Ranks oJ'measurements.from the datu Spearmun's rho (see text )
I
X variable
Activity units
Individual
Table 14.25. DiJ/brent'es betn,een lha runks ol tlrc tncu.\urenents in Tuble 14.24./br culculution o.f' Speurntan's rho (see te,rt ) X variable
), variable
lndividual
rank
rank
A
2
2
0
0
B
J
-1
0
0
variable
Song frequencY
Rank
oJ
429
d?
Rank z
A
4
2
6
7
J
8
J
B
C
5
6
I
I
C
t2
5
l4
6
D
I
I
0
0
D
3
1
5
I
E
1
7
0
0
E
l8
1
t6
1
F'
6
4
2
4
F G
15
6
ll
4
G
4
5
I
9
4
13
5
activity increases, Song frequency appear to be a positive correlation; that is, as of Spearman's rho and calculation increases. Howeveq we can proceed with the
Since our calculated rho
of 0.89 is larger than the tabular value (0.7140 ()s),
Alternatives when N is >20:
assigned X and Y Rank the measurements for each variable, arbitrarily
Convert rho to the't'statistic:
(Table 14.24).
I
ll N-2 \ I:rho ll /v - I
either measurements within a variable are equal, then been have would that ranks assign to each of them the average of the tau (below)' especially assigned had they not been equal, or use Kendall's
If two or more
V\l-rhc,f
Compare the calsulated 'r'with the tabular value (Table .{5), where: df: N -2.|f the calculated r is larger than the tabular /, at the appropriate
if
there are several ties. pair of measureDetermine the difference between the ranks for each (Table 14'25)' (r/r) clifferenoe ments (r/) and calculate the square of that Determine the total for the d2s (as in Table 14'25)'
P value. then the Huof no correlation is rejected. Although some statisticians have recommended always converting rlio to 'r', Siegel and
Castellan
Calculate SPearman's rho:
(
1988) recommend using Table A
l9 whenever
ly' is less
than 50.
('onvcrt rho to the ':'statistic:
6(>d2l
-1r_1U
where:
,d2
significant correlation between activity and song frequency.
Procedure:
t
: 6:
we reject the 1/,,of no correlation and conclude that there is a statistically
determinewhetheritisastatisticallysignificantcorrelation.
,.:
I
Total
z-r'ho V'N-
lirr
N:no. of paired measurements:7
I
ir two-t:rilecl test. rho is significant at the 0.05 level
rrrrc-lrrilctl lcst, rho is signilicant at the 0.05 level if
6(6) :,_19:l_o.l07:0.89 _ _,_ t- l+t-l-'P,: 336
,\' llrr rrl l)lllt('(l lll('il\lllt'tttt'ttls
il :
is > I .96. For a
is >1.64.
lrrrr I rkc SPr'ru lnlul s rlto. Kcrttltrll's tlrrr rlctcrrnines the tendenc:y of two r.utI r,trlt'ts ol rlrrl;t lo llt':rtntl.rt hr'trtllrll's llru rs l)r('lr'rrctl wltctt thcrc ttrc several hr'tttltrll':
When N<50: ('()llrl)ltl.cllrcclrlt'rrltrtr.'tl tllo(0s())r"itlrtltt'lltlrttl:tt
;
r;llttt'(llrlrlt'Il())lol
/ Nolt'llr'rl llrt.| r.''l ()ll(' l;rtlt'tl lt'sl
Irr'rI tltttks
.'\.'.rtr.'r.ttttIl,' \\('\\tll rr',,'llt, ',,tttt' lr\lrrrll11'ltr',rl tl:tllr llt;tl w,r'ttst'tl rvillt
430
RANDOMIZED BLOCK. MAT('lll:l) I'n IRS
IC STATISTI('A L'l' l:S'tS
ON PARAM ETR
N
Tuble Table 14.26. Arrongement o.f'the runk'; ffutm (st'c te'tt ) t4.24 for c'alculutiort of' Kenclall's tau
Column I Activity units (flights/5
Individual
iorts
fi'ont tlta scattergrunt
(
Number of individuals with higher
Song lrequencY
(songshnin')
I
I
A
2
2
B
3
3
G
4
5
C
5
F
6
4
E
7
7
D
6
two measures Spearman's rho in order to compare the
.fitr Kendull',; lutt tt,;ing cluta Figurc 14.3: .saa tr.rt )
Table 14.27 . Calc'ulat
Column 2
min.)
of association' Although
Individual
ranks in both variables
A
5
B
4
C
I
D
6
E
0
F'
I
G
2
S:2(
l9)-
ltl - lt' :.18 42 - ): )
Kendall's tag will yield a lower coeffithey are consideletl to be equally powerful, dztta, because they have difl-erent cient valge than Spearman's rho, on the same
are tied ranks go to Step 5.A
underlying scales.
Calculate tau:
Procedure:
(i'e' put them in Arrange the ranks in order along one of the variables we will rank order (Table 14.26) rank order in Column l). In our example could be used' tl-rem by the activity units, but either the number of individuals count G) (A z For each indiviclual through unit ranks and s.ng freactivity below it in the table with both larger
r
quel]cyranks(Table|4.21).Donotcountties.Forexample,belowindi. vidual A there are five individuals (B' G' C' F
ancl E) that have both
ranks' larger activity unit ranks aucl song frequency (Figure 14.3)arlcl lirr each scattergram Another method is to use the largcr x ancl Y have which inclividual count the uumber of individr'rals right antl rtbttvc in thc variable measurements (i.e. those incliviclr-rals to thc
:
Calcnlate the total (S* ): tiom Table 14'21
+ Calculate ,s:2,s
-
If
:
19
17
there are no tied ranks in either column continue with Step 5;
tAU:
stltl N(N
:
- r)/2 t0 - tll2
:0.8 2t
if
there
I
As predicted the tau of 0.81 is smaller than tlre rho of 0.89 calculated itbove on the sarne clata. Compare the calcr-rlated tau to the tabr-rlar value (Table A20). If the calcu-
4, of no significant correlation is rejected. Since our calculated tau (0.81) is larger than the tabular lated value is more than the tabular value then the
tau (0.63), we reject tlie I1,, of no significant correlation and conclude that thcrc is a significant positive correlatior-r between activity and f requency of s()ng. The size
of the calculated tau
ol'thc corrclation, with trr
scattergram) (Table 14.27 )'
431
rr is 1'rosit
,Arr irrvcrsc
a
is,
ol course,
a measLrre
of the strength
tau of I .00 indicating a perlect correlation. When
ivc t hc relationship is direct (i.e. the variables increase together).
rclirtionshil-r is inrlicutcd by a negative tau.
(';rlcrrlrrliorrs rvlr,.'rr llrt'rt'lrrt'lit'rl rlrrtks irr citlrcrcolltntn:
S:
,ry(N
I
l or ( olrrrrtt Itlt'lt'trrrrnt'llrt'tttttttlrt't ol ntr.'ltsut'ctttcltlstluttttt'cticcl.aticlcat('1,()l/('('ir(ltltt',t'. lrt'ttr1'.r',t lol ltro llut't'r'lr' l()l ('it('llsetrll'ticscltlcttltttc:
)
-)
I
I
rvltt'tt' A' llo ol l)illls ol "t olt's
I
\r\
lr
l)
li
432
RANDOMIZED BLOCK, MAT('lll:l) l'n
NONPARAMETRICSTATISTICAI-'ll:S'l'S
where:
64 Add
X= thenumber of measurements
lx(x-l
the
tiecl (i.e. set
IY:
Dice's:
2
2u : 2(4) :o.go U 2(4)+2
f,:
2a*
N(N- ll-
)
Yule's:
-l(,o
8A Repeat r",.p, sa and 6A for column
T':
V
W:X/ l0A Obtain I
lA
ud-bt: 4(3)-l(l): ll :0.85 ad+ht' 4(3)+ l(l) l3
2, and calculate 7,.
N(N-l)_p 2 -K._
eA Calculate
Jttrt'tt rd'.;'.
u4:
a* U 4+2
trZ:
where:
the value fbr
V:TrxT,
M7 : N9
s 5
above.
other indices of association between Other intlices o.f ttssociation There are several of coetficients of association paired measurements in two samples. Four examples coefficient will be calcuEach are given below (from v. DeGhett, pers. commun.)' latecl from the lollowing hypothetical data:
A
:0.78
The diflerent values these coefficients yield from the same data reflect
,
tau (Table A20)' as in Step r2A Compare the calculated tau with the tabular
Frog
:067
Sokal und M ic'hener's'.
S from Step 4 above'
Calculate tau:
tau:
M* U:J 1-2:9
C oeJ ftc' ie nt s o.f' as s o c ict t i on'.
- _>[x( x-ll1 nl_
Ta-
433
M:a*d:4*3:7 U: b* t: I -11:2
of two, three, etc')
and divide by 2 to obtain )l lor each set of ties together
R,:
1A Calculate
lltS
Frog B
vocalizes'l
vocalizes?
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No No
No
Yes
Ntr
Ntr
Ntr
Yt's
Yt's
Ncr
researcher's concept
each
of how an association should be measured.
Irrtcrpretution o./'an ttssot'iution A significantly large tau or rho indicates a high rlcgree of association whether positive or negative. There are basically three interprctations lor a high degree of association (i.e. high correlation):
t : r
Frog B
Change in variable
I
causes a change in variable B.
Change in variable B causes a change in variable
Neitlier interpretations I or
2 are
l.
correct, but rather a change in some
other variable (C)causes a change in both A and B.
'l'lrc corrclation coefficients will not tell us which of these interpretations is r orrccl. Wc can only judge (with varying degrees of validityl which is correct ,rt
Frog A
t'orrlirrg to our prrior knowledge of the variables and their relationships.
434
NON PARAM ETRIC STATTSTI('A L'l'l:S'l
RANDOMIZED BLOCK" MA-I('ll I:l)
S
"'
435
blocked
intct three time periods
Sumple,s
A1
ltS
tt britl,v./rutm.fbur habitat,y
Table 14.28. Hvpothetic'al duta on song durutiort
t4.2.lb Three or more matched samples
I'>A I
Mean song duration (s)
Au
Habitat A
r:r -Y::
\.
Habitat B
Habitat C
Habitat D
10.2
6.5
Sunrise
6.7
8.6
Noon
3.4
4.3
6.5
2.5
Sunset
6.3
8.3
9.9
7.1
_1t
Frieclnutis two-w'ul' trnulysis of' turiunce is a nonparametric test used to deterTlie Friedman two-way analysis of variance blockecl measurements or repeated mine if several (three or more) samples with significantly diflerent. It requires ordinal data measures on the same inclivi6gals are compared to the paratnetric ANOVA' (at least) and tests fbr differences in location. (van as powerful for ten samples iI is 12,,/uas powerful fbr three samples and 87"/u Elteren and Noether, 1959)' durations. tiom four habitats; but this As an examPle, we will again sample sc'rng time periocJs: sunrise' noon' and sunset time we will block our samPles into three
Table 14.29. Ruks of'eot'h rov'o.f nl(osltcments in Tuble 14.28 /br t'alculution Friedman's lw'o-\ruy unulltsis of variunc'e (,see tert )
o.f'
Habitat A
Habitat B
Habitat C
Habitat D
Sunrise
2
3
4
I
Noon
2
J
4
I
Sunset
I
-)
4
2
-5
R*:9
t2
Rr:4
R^
R.:
(Table 14.28).
r
\ y::( / . ^ll-- x266 I-gtst:53.20-45:8.10 "t \12(5) I
Procedure: (blocks) separately (Table 14'29)' Rank the measurefilents for each row and provides a more powerThis minimizes the between-row differences
6
fultestofthebetween-column(sample;habitat)differences. Calculateeachoftlreranksums(i.e.columntotals;R.,,Table14.29). Calculate each of the R.r}s'
Rn':25 Rot:81
Rr't:144
df:N- l:2 tabular 1, ,,.1:5.99
R,rr: l6
Sirrce our calculate
+ Sum the Rr2s: 2R
:
l:25+
81
+ 144+ 16--266
the different
tion in birds fiom the diff-erent habitats.
Calculate x,l according to the ormula:
t2
*,'-- r,rytru+ l )rR I
'lrrhlc l;1..10 illustrates Frieclman's twcl-way analysis
i
"
|-3At
tv+ l)
l'rrble
l-l I I is tlrc rcsult ol' r'anking l()(r I (r7(r I l ).)5
.)/i,'
(ltt'tltttl" ()l([('t.sVslt'ttt*lts li:nutnbCt'ol t'tlws" tlt'lltc ltttttlllct'ol titttt's ttsctl
ttrttttlrt't ,rl t oltttll" ()l llllllllrt'l t'l t'tttl'
of variance with
lt()t)s.
where:
\
of no dif(erence in song durations in birds fron"r
habitats and conclude that there is a significant difl'erence in song durer-
I
,l
Compare the calculated X,2 to the tabular value (Table ,{9) lor
'
\,
l^
r,',',,,t",
1
thc measurements from Table 14.30.
.'r.'r5 ) ll-l
I t/'( \ I lr
l) )\))
itrl '' l
,/(
5)
replica-
RANDOMIZED BLOCK. MAT('ll1,l) I'n IltS
N()N PARAMETRIC STATISTI('A l.'l'l:S'IS
436
431
:154.88- 135.00:19.88 (in saL'ttncls) o/ birtls /rom.fitttr Table 14.30. IIl,potltetit,al clata on song, duratiort unctlysis of periocrs iilustrutirtg Friedtncrn's ru'o-lr'(/I' hcrbitcrts bktcketr i,to trtree tinte var iant' e
w' i t
h
re P
Ii
tabular 1,,,r2:15.51 Since ourcalculated X,r (19.88) is larger than the tabular varlue (15.51), we reject
t'trt i ons
the H,, that there is no significant dif'ference in song duration between birds in the
Mean song duration (s)
Sunrise
Noon
Sunset
four habitats.
Habitat D
Habitat A
Habitat B
Habitat C
6.1
8.6
10.2
6.5
6.4
6.9
8.4
5.8
5.5
1.0
9.8
5.9
3.4
4.8
4.6
2..5
2.7
4.1
1.5
0.5
4.r
3.9
7.4
2.9
6.3
8.3
9.9
1.1
5.8
1.0
l.l
6.1
4.9
6.6
8.1
6.7
I
Dunnett's ntultiple c'ontparison
of
te,st This
test can be used to determine which pairs
samples differ significantly when the null hypothesis is rejected
in
Friedman's
of
variance test (Glantz. 1992). When the two samples have unequal sample sizes use Dunn's Test (section 14.I.I ).
two-way analysis
Procedure:
t
Calculate 4: lR, - Rrl q:1,;,wt)l
JL 6
l
Where:
lR,-Rrl:tl-re absolute difference between the rank
surns
fitr the two
samples being compared.
p:the number of samples
spanned by the comparison after ranking the rank sums in order (ascending or descending)
Tablel4.3l.Rankso/-eac'hrott'tt.f'ttte(tsttren'tentsinTablel4'30lbrculculationof' x.ith replic'tttiotts (see te.rt Frieclnlun,,s l||'()-|l,uy antth,,si,y o.f,vttriutrce
Sunrise
Noon
n:the
number of blocks in each sample, or the number of indi-
Habitat A
Habitat B
Habitat C
Habitat D
2
-l
4
I
2
J
4
I
I
4
2
-1
2
4
.,
I
I
3
4
2
Rank of rank sums:
2
4
I
a J
4
2
(l ) R, :
I
J
4
2
I
2
4
Ro: l4
Ro:26 R,rl:676
R,':35 R( ):1225
a
-)
Sunset
)
I
Rn':196
/1,, 1l'
,'
,15 125
viduals in a repeated measures design Using the same example we used fbr the Friedman's two-way analysis we rvillcompare the sar-nples fronl Habitats A and C.
of variance (above), I
R,-R, l:R,.-Rn: l2-5:J (Frorn Table
p-3
l2 (2) RB:9 clf':r
(3)
u.3
"
11 , I v6 (' /1 I '.1(-l)
I I
1.86
R,r:5
14.29)
(4) Rr:4
RAM ETRIC STATISTICA L
N ON PA
438
.f
I:SI-S
RANDOMIZED BLOCK, MAT(.U t:t)
Table 14.32. Hltpothetic:'al data on the number ol'trial.; in n'hiclt eac'h tf'.five.juvenile (oyotes lruw'led in response to eat'lt ofthree trcutments (see terl./or explunation)
Al
A,
I
4
3
2
A-
:I1CT,z+CTrr.
2
I
7
5
2
0
7
4
5
3
0
8
5
4
2
I
7
. . . RT,,2)
For our example:
df:2 SS,.r:22:* 122+22 :494+144+4:632
:
SS*.,.
22
. . CT,,2)
l1 K--)
0
4
Column totals (CT)
439
SS..r:sum of squares of column totals
:11RT,:+RTr.
Row Totals (RT)
-)
-
S
SSnr:sum of squares of row totals
Treatments (samples)
lndividuals
I,A I It
36:Grand total (GT)
12
7: 1
72
+
7
)
+
82
+72
:49+49+49+64+49:260
(k- r)(kxss(.r)-c11
Q::
Compare the calculated 4 to the tabular value (Table A.2l ) fbr p comparisons. Since our calculatecl 17 (2.86) is larger than the tabular q at P:0.05 (2.21). we reject the I1,,of no significant diff-erence between the two
(txGT)-ssRr i
_t[3(632)- l2e6]: (ix36) _ 260'
t52:7'894
samples and conclucle that there is a si-snificant clif-ference in mean song
compare the calcurated,
durations between songbircls in Habitats A and C.
Since our calculated
Cochran's Q-rest
and conclude that the juvenile coyotes'howling responses to the dilferent playbacks were significantly dillerent.
(5'99) we.eject the
of
McNemar's test, described earlier in this of repeated rneasures differ among tbrm nominal data in the of frequencies or proportious in themselves. It is used with
Cochran's Q-test is an extension chapter.
lll
r200
It
eto
i1l
the tabular crri-square varue (Tabre 49).
e e .gg4)is rarger than the taburar varue at p:0.05 ^Ilu of no significant dirference between the treatments
tests whether three or more samples
a randomized block or repeated measures design.
As a hypothetical example, we will assume we have measured whether juvenile coyotes (Canis latrans) (8, ,) howled in response to playbacks of howls ot': litter-
(1,), adults of their pack (1,), and adults of a distant pack (1.,). We gave eacl.r juvenile coyote 5 trials of each treatment (Ar r) when it was etlone attcl tneasured wlretlrer it howled in response, or not (Table 14.32). mates
l
ti
Procedure:
r
Calculate Q:
GT]l
l[lL r)^_lk- (Axcr) v-SS,., |
xss(
lii ir
"
wlrcrc:
/, ltrtrttlt,.'r'ol t'olttttttts ( i.t' l)t';tlntt'ttts l, rll k I
l,,l
RATES OF BEHAVIOR
1s
More complex problems associated with the analysis of rutes o.f behavior were addressed by Altmann and Altmann (l9ll). They identified the lollowing questions that commonly arise when dealing with rates o1'behavior:
Rates of behavior and analysis of sequences
t
Can the frequency distribution of the observations be accounted fbr by the population composition (age and sex class distribution)/
:
What are the expected values of these frequencies if the mean rate of behavior is independent of class?
I
How can reasonable estimates of class-specific behavior rates be obtained from a set of data and tested in a new sample?
I5.I RATES OI-- BEHAVIOR bouts are generally described in terms of Temporal patterns of behavioral events speciper (period), or number of events (Chapter 6), time between successive events inversely related to period (Gaioni and fied interval of time (rate), with rate being Evans, 1984).
from clata collected by all-occurrences Rates of behavior are usually calculated
sampling(Chapter8);however,AltmannandWagner(1970)describedamethod, when fbr estimating rates from one-zero samples based on the Poisson distribution, (also see Chapter 8)' the sample period duration is small
+ What
of behavior per class when the rates are unknown but are assumed to be constant or independent of class. and the population composition is a. The expected frequency
stable is calculated according to the following forn-rula: Nur
,)UM [r-
provides a good example' Analysis ot' repetition rate of avian vocalizations Gaioni and Evans (1984) well' as acts, which will be applicable to other behavioral repetition rate from period have been tradinoted that two metho6s of calculating from individual First, localized estimates of rate are calculatecl
N:total number of
In order to illustrate the use of the formula we will use the hypothetical data on the number of threats in a herd of 50 deer in Table 15. L See Altmann and Altmann (1977) for an additional example. We can determine the expected frequency
of threat behavior in adult males
as
lirllows:
N:100
etitionrate.Second,anaverageperiodlengthiscalculatedandthisisusedtc'rcalcuthat was recommencled by Scoville and overall rate. It is this second method
M:50
late
lll,:
lollows: I
|
-5
l00x l5_ L!0!:30 L :N,,,,_
Repetition rate (R; notes per second): Ll+g
"M5050
where: d:average note duration
'I lrcrclirrc. tlrc cx1'rcctcrl I'r-crlLrcncv ol'thrcats in adult males is 30 compared to the
.q: average inter-note interval (c.g.
sti.rtrri rrsctl krr'
p.ttc..ctl ucts Gaioni and Evans ( lgg4) arguecl that temporally lilte : lct'ttts ol' 1'rr\ltir'tl' tltllte t lltlttt t''\lPt'lili()ll ctlmlnunicatitln) shotrlrl hc tlcsclihctl itt tltst'tr'lt' (l()SJ)ltt'Pttt'tl trttt llt;tl lot rtt ts \('l'l('l'"ll('(l ittlo Itorvcvct'. Millcl lrrrtl llllriell lrottls. l('lx'titi(rll lill(' is tltt' lrt'tlt'l lltt';lslllt'
of all
m,:number of individuals in class.r M:number of individuals in the sampled population
to provide an overall estimate of repperiods, and these localized rates are averaged
as
r
occurrences of behavior n for all individuals
classes in the sample
tionally used.
Gottlieb ( 1978).
\
where: E,,:expected frequency of behavior a for members of class
continuous
measures are needed'
lor dyadic interactions, with pairwise
independence?
rate of a behavior from The intuitive method for determining the of the behavior by the length of the samples is to divide the number of occurrences
at a measure of rate for behaviors that occur sample period. This provicles a valid events are the same or very similar)' steady rate (i.e. the periods between successive will not occur at a steady rate and better For most behaviors. however, the events
are the expected frequencies,
olrst'r.r,'t'tl l'r'et;tre rrcy
ol'li0.
l lris c;rrr lrc rlortc lirr lrll :rgc clitsscs. ancl then a clti-square
It'st r';rrr lrc tontlttt'lt'rl lo rlt'lt't nulr(' \\ltr'lltt't tlrt'rlil'li'rertccs ltclwectt tltc ohscrvcd ,rnrl
r'tpt'r'lt'tl ltt'r;ttr'nt tt':';ttt'''lrtlt',ltr.rllt ',t1'trtlt( itttl
l1
RATES OF BEHAVIOR & ANALYSIS
Table
15.1.
oI
RATES OF BEHAVIOR
SIIQUENCES
+[(sx 13)-(1 I x 15)+(4x
o/-50 deer Hypothetic'uldata on the nuntbey ttf'threut's in a herd
Immatures
Total
males
females
15
20
15
50
80
l5
5
100
Number of individuals Observed threats
r2)]
:263+314+278:915
Adult
Adult
443
:r
Eu
2,t,t't7.,
ii>:i ;,
: 26s1263 te t s) :
Itl
1
s
Therefore, the expected frequency of threats in adult males in this changing pop-
ulation is 75 compared with the observed number of 210. Once again we could make
the same calculations for the adult females and immatures, and then use a chisquare test to determine
when the the conditions are the b. The expected frequency of behavior per class composition changes' is calculated same as in (a) (above), except that the population
if
the differences are significant.
Uniform, class-specific rates of behavior are expressed as a mean number of occurrences per individual per unit time, according to the lormula below:
according to the following formula:
s.
ur:*t*'L: u L;21t1t11;1
Eu
:2,2f fl
E
:the hypothetical
ri
expected frequency of behavior a for
members of class x
where:
E,,:expectedfrequencyofbehaviorrllormembersofclassr
N 2/
fr,,
total number of occurrences of behavior
:
all classes in the samPle total sample time for all individuals of class -t in the entire
a
Lambda,:hypothetical mean participation rate per member of class x
for all individuals of
:
study
2Ff ,, :total
(lambda) for adult males by referring to Table 15.1.
Lambda,:
x)
2,2,tyt,,:totalsampletimeforallindividualsofallclassesfortheentire study (time in sample period oneXnumber of individuals in all
classes)*(timeinsampleperiocltwoXnumberofindividualsin + . . (time in final sample period x number of indiall classes)
' viduals in all classes)
Asanillustration,wewillusethehypotheticaldatairtTablel5.2(seeAltmann determine the expected freand Altmann. lgJJ, for another example).we can of changing cort]position as quency of threats in adult males in this population lollows:
N 2l
:260 r,,, :[(5x l0)+( ''- -50 +
in the entire
study
individuals in .y)+ . . . (time in final sample periodXnumber of
:
r
As an illustration we can obtain the hypothetical mean participation rate
=ltimeinsampleperiodoneXnumberofindividualsinclass 't)+(timeinsampleperiodtwoXnumberofindividualsinclass class
sample time for all individuals of class
l(r5 I
IIx
l5)+14x l2)l
4l{ 261
)',)',/,/il,, .)(rl ll(5' l:'i)l(ll
.)o)
l('l " lt'11
2/ lrr,
Eu
:0.80x263:210
Therelore, the hypothetical expected frequency of threats for adult males during the entire sample period (20 hours, Table 15.2) is 210. The total number of males observed was 37, so that the mean rate per individual was 21 0137
:5.7
threats for the
l0-hour sample. This hypothetical mean rate can then be compared to the observed nrcirn rate in this sample or from observations gathered later from the same or a diflcrcnt population.
Altnrirnn and Altmann (1977) proceed to the consideration of interactions bctwccn intlividuals. They provide procedures lor calculating expected rates of lrclurvior lirr- syrnruetric and asymmetric interactions at constant rates and interaclions wrth lrypotlrcticrrl clrrss-spccific ratcs ol- bchavior. Michener (1980) noted that
llrt'
;rssrrrrrptiorr
pr'rrotl
'
80
100:0.80 :(5x l0)+(1 1 x l5)+(4x12):263
ol
in Allnlrrur lrnrl Allrrr:rnn's (l()77) lirrnrulae that lor any given
lrnrt'rrrrv rrrtlrvrrlrurl lrrs llrr'polt'rrlr;rl lo irrlt'nrct wilh trny olhcr intlividual.
rvlttlt'ltttt'lrlt l't;tl,ittlott',',r('(t(", t',ttol lttlr'lot ln,rn\'\Pt't'tr'su,ltett'ccl'lltittifttlivirl
fr-
l
444
RATES OF BEHAVIOR & ANALYSIS
()l
SI:QLIENCES
ANALYSIS OF SEQUENCES
Tlie following discr-rssion of behavior sequence anarysis is cursory and meant only as an overview' More detailed discussions of the general topic of sequence analysis' including in-clepth descriptions of specific methocls not discussed i, this book' can be lound in: Bakernan and Gottnran
Table 15.2. HypothetiL'al cluta on the nutnber of'tltrcut,s itt u lterd of mule tleer
Adult
Adult
males
females
Immatures l3
4l
60
l8 l3
2
t5
Number of individuals
l5
20
l5
50
Observed number of threats
80
l5
5
100
Sumplapariod2
Suntple periocl
Totals
( l 9g6), castellan (lg7g).Fagen and Young (1978), Gottman and Roy (1990). Haccou an
I
(5 lt ) Number of individuals Observed number of tlireats Sarrtplc period
(
t0
Golani (1976)developed
individuals' This terminology has not received wide zrcceptance an
3 (4 h) 12
l6
12
40
Observed number of threats
70
t4
I
85
1e82).
a vocabulary to
(llh)
Number of individuals
t
Total number of observed threats (Sample periods
1,2 and 3)
210
42
8
:
260
the rater part of the occurrence orp, q or partof it arso occurs, i.e. p starts before q and,ends after the start of 4 but before or together with the end of q,therelationship will be designated by the suflx '-vade'.
of social acts observed per animal per
unit tir-ne are appropriate when infonnation is required on the proportion of time that individuals spend behaving socially. . . . When information is required on interaction rates. numbers of interactions must be expressed relative to the number of pairs of animals available to The procedures for determining interaction rates (Altmann and Altmann.
t
rf p starts after or together witli the start of ends after the end of c1, the rerationship wilr
be designated by the
sr-rffix '-case'.
1977;
variants within the five groups are distinguished by the prefixes pre_ (befbre) and super- (arter); in- (going in) and ex- (going out); pri- (prior), ttnd post- (later); con- (together);end- (within);and
I5] ANALYSIS OF SEQUENCES
en_
((iotani, tgl6')
The temporal relationships between two, or more, behaviors (from thc srrnrc or tlill ferent individuals) are often complex and dilficult to analyzc. Firr cxirntplc. in s()nre bird species the malc ancl Icnralc ol'a Puir sirrg scpiu'lrt('l)()rlions ol'lr tlrrcl. citlrcr sinrultancously (poly1'rlrorticlrlly)or srtcccssivelv (rrttliplr,'rr;rlll ) lloueve t. llrt' lt'rrt
r',r;tttlt'tort l;rtrl
but beflore the end of- 17, and be
-s If q occurs only during the middle part of p, i.e. p starts before the start of. q and ends alter the end of q, the relationship will
382 J
Michener, 1980) are somewhat complex extensions of the fbrmulae lbr individual rates of behavior, discussed above, and will not be described here.
pot'ltlplt((t't'nitt1,ol r.'ltr'lt rntlirrtltr:rl'sr',,ltlrrlrrrlronlollr,',lrrr'l
relationship will be designated by the suffix .-
r If only d,ring
viduals, or number of dyads).
I Miclrcnt,r, l9B0:
tl"re
dure'.
tance of choosing the correct denomiuator for the lormulae (i.e. the number of indi-
lnteract.
If p and q are temporally contiguous, i.e. lollow each other immediately, the rerationships wilr be designatecr by the suffix .-vene,. If during every 'instant' of the occurrence of p. uoccurs, i.e. p either starts together with or rater than the start of t1, and pends together with or
earlier than the en
als never occupy the same space. Michener ( 1980) goes on to point out the impor-
Rates expressed as the number
445
(around).
'fltcsc p.clixcs itrtcl sttllixes cun then be combinecl to descr-ibe the various temporal t'clrrliorrslril)s sll()wn in lrrblc
1.5..1.
lrt'ttltt'ss;ttttl Slillrrt'll (l()7l) ,st'tl
z;tli.tl I
.l
sr'tltl('ll('('s
.l
lrt'lt;tr t,t;11
ltt't r'rl,l,rrrr,., l tlr.. 1rrn( rIl,.
,r.,
;r
,l;rrrlr.l.
,,,\ ,\('(l
1, r11,,11 .,
irr
rlcscl-rbc rlrc
l;rt't.t,l,.trilrg
hicntrchi.l .rgunis(,(l,cr)ces l.ly rrticc.
Table
15.3. vocabulary
,f
terms used to clescribe the temporal patterning
p ends just belore q starts (contiguous) ..',
starts before
(i'
two behavioral units
p ends after q started but before
17
ended
ll prevene
with
q
p ends after 17
ended
ll
L_l
17
started
p ends together
invade
convade
encase
pridure
condure
concede
entdure
postdure
excede
-- slilrts together
rrith
17
.:lrted but belore :: ertded -- >:.1rts irnmediatelY rtier 11 ended
ll
supervene
rctrntinguous) 5,,:ri'..'. Adapted from Golani (1976)'
i >
=1
t =-;Ei]a lie;E,' : =9 P<
? a 66-:3
H 9E
X*?^1:;f1?3a
5i;'sire r;-5=;- i.'uiiiA+?1,;ur{![#1: 1 :7 i+ rlE ilE-iEiiESEt Ciii:;+Eir: riii+
ii=--'=
z
2 a ,rl
a
rn
i[Ei':IgE,* Eir Efi;i;$r iEiE ea* ii uss s=E iiiaiEa*iraE :iiE =Ei iE
,eg'E ;=;'
EiiiE iF ii=: i 16iiiEiAei; ii =1 t. iqigsi;Ei#i-E;5= iaiii ;r iili ,= €:-iii z3 iia; s: "-ii ?ia;ii4l; ii i+q;:
1E
s
;3#ie ia :1 ilittirfa.slelirSl ==1 Ei?uF.* it *a ei *1Ee[5;1i iiStlili il +i;-$BEE;E; iE;.! E; i=
=t
=ii"|',"l
C an
z
o an
a
ANALYSIS OF'SEQUENCES
oI"SITQUENCES RATES OF BEHAVIOR & ANALYSIS
sequence' This is rare in behavior' lowed by B is 100'2,, then it is a deterministic This is level of probability less than 100'z'' More often A is followed by B at some (or stochastic sequence)' called a probabilistic sequence 100'2,
(
rence in the repertoire.
Ashby (1963:165-166) has provided the following illustrative example of the application of Markov chains.
Deterministic sequence (rare) Probabilistic sequence
be stationary (see below). Markov chain analysis should take into account the of occurrence of sequences based on the frequency of occur-
expected fiequency
100'2,
(stochasticsequence;common) A Kinematicgraphs(Chapterl8)canbegeneratedwhichshowtheconditional This is a useful procedure when sequenprobabilities of several different behaviors' tial eflects are strong' Thereareseveralexplanationslorwhyonebehaviorisoftenfoundtofollow particular
is the relative frequency with which another (see above). Another factor the The more frequent the two behaviors' behaviors occur in the animal's repertoire.
Suppose an insect lives in and about a shallow pool sometimes in the water (W), sometimes under pebbles (P), and sometimes on the bank (B). Suppose that, over each unit interval of time, there is a constant probability that, being under a pebble, it will go up on the bank; and similarly for the other possible transitions. (We can assume if we please, that its actual behaviour at any instant is determined by minor details ancl events in its environment). Thr.rs a protocol of its pc-rsitions might read:
WBWBWPWBWBWBW PWBBWBW PWBW PW BWBWB BWBWBWBW P PWPWBW BB BW
morelikelytheyaretooccurtogether.Anextremeexampleofthisisahypothetical four sequences behaviors, A and B' only the animal which is capable of only two
Suppose, for definiteness, that the transition probabilities are as
shown below.
given below are Possible'
w
1 A-A 2 4-------+B 3 B-=-B 4 B-----'A
B U4 314 w 314 0 P0v4
by the sequences is determined not only The trequency of occurrence of these observer's criteimpossible combinations ancl the size of the repertoire, but also by difficult to and encl of a sequence' It is also otlen rion for determining the beginning
determinewhetherabehaviorwasrepeatedlA--_.-A)orwhetheritwassimplya single occurrence (A)'
l/8 314
l/8
These probabilities would be lound . . . by observing the animal's behavior over long stretches of time, by finding the frequency of, say B-----------W and then finding the relative fiequencies, which are the probabilities. SLrch a table would be, in essence, a summary of actual past behaviour. extracted from the protocol.
Note that Markov chains are referred to in 'orders' rather than dyads. triads, etc. (Table 15.4).
I5.2.ln Markov chains
only single transitions from one behavior The sequences discussed above considered Higher-level sequences are shtlwn below: to another. They are referred to as dyads'
DYad 6--->S Triad A------'B---'C Tetrad A--'-+B-----)g---+P
What constitutes a sufficient sumple size lor analyzing Markov chains? Fagen ancl Yor"rng ( I978)provided the following'rule-of-thumb'(based on simulations run by I'agcn) Ibr analyses of first-order Markov chains (Table 15.4): I'or': /l:number of individual behaviors for which sequences will be ntclsurecl
Ilrrrr: l/(r:
i. which it c.. bc sh.w, t'irt Markov chains are sequences or behavi.rs (). ().('lrrr.lltct'ltl s,l'tlt' rlI trrc hchirvit)rs:r'c trcPe rrtrr.'rrr sitions betwccn twa ar 'r.rc
lcvcltll.prtrlllrlrilitvptcltlct.tltlttlelt:tttec.ltlsrr.tlrt'lr'rt.|()l|)l(,lr:tlrtlttVisltsstttttt.tlt.'
insLrllicicnt srrnrplc sizc
5/t': lrortlcr lirrc
tlrc t.rrrrI
0/i'
slrrrrPlc sizr'
strllit'tr'nl \iuttPlr'
s171'
ol
RATES OF BEHAVIOR & ANALYSIS
450
Table 15.4. Orders of Markov
Table 15.5. Transition.frequent'ies urnong bchuviot' puttern categories in c'onlests involving .supplemented and unsupplementetl ol'ner.\ o.f poor-quality \grrit,,r'.t
c'hains
(N:25
Definition
Order of Markov chain 'zeroth-ord er'
ANALYSIS OF SEQUENCES
SIQtJENCE'S
The behavioral events are independent
(A.8,()
The probabilitY of occureuce of
first-order (B'--C)
a
particular
behavior is dePendent on onlY the immediatelY preceding behavior
second-ord er (A-'-
B-
The probabilitY ol occurence of a particular behavior is dePendent on the two immediatelY
C)
preceding behaviors
5.2.1
h
Locate
130
80
0
0
0
73
435
l5
l0
30
0
8
30
-1
0
0
-:r
0
l0 l0
3
R
0
0
5
Signal Threat Contact Retreat
25
J
1
0
5
Signal
13
85
20
0
28
Threat
0
8
l8
0
20
Contact
0
0
0
0
0
Retreat
-l
8
0
0
5
included are not indepenclent
ow' ne r s
Source: From Riechert ( 1984). Copyrighted by Bailliere Tindall.
9
l5
that appear to difll-er greatly between the two samples. For example. Reichert (1984) rrieasured transition frequencies of agonistic behaviors in spiders tliat owned poor-
4
t7
30
5
8
32
Observed occrlrrences
Following behavior B
C
I
6
20
B
9 19
behavior
29
Have we collectccl en.ugh tlata'l Accorcling
d
Row totals
Transition matrix
34
te
should be sufficient. In our example, R:3 and 10 Rr:90; therefbre, since our sample size of 97 is larger than I0R2, it should be sufficient. The matrix of observed frequencies can initially be inspected lor cells which show large frequencies. For example, Lemon and Chatfield (1971) contructed a transition matrix of preceding and lollowing song types for cardinals (Richntondena t'urdinuli,s); their initial inspection of the matrix showed that switches between certain song types occurred very frequently. Two matrices, each from a dillerent value of the independent variable, or treatment, can be initially examined for cells
of each other. Below is a hypothetical transition
A
LI n s Lrp p I e me n
of
matrix lor three behaviors'
Column t otals
Retreat
25
based on observe
C
Contact
preceding behaviors
occurrences' It should be contillgency table since the events note{ that the trausition matrix is 1ot a trtte
Preceding
Threat
Locate
the use of a transition matrix zrnd Markov chains are generally analyzecl through rnodel is one that generates expected comparison to a random nrodel. The random
of occurrence
Signal
Supplementetl ov,ners
a
Transition matrices
frequencies
Locate
hetv:een-,vumple group tlil/brences in bold-face
particular 'r'immediately behavior is depentlent on the
Ttre probability of occurence of
nth-order(...X-Y-Z)
t
frtr each; mujor
t'ontests
34
tluality tcrritories which were either food supplemented, or not supplemented. 'l'rrblc l-5.5 shows the trunsition matrix for each treatment, a chi-square test showed thrrt lhe bokllircctl cclls wcrc significantly dilf-erent between the treatments.
()l (' I t i -.tt
to lrugcrr rttttl Yottttg's ( l()7ll) tttlt'-.1(st't1rrt'rrt'es't't'.trIr'tl)
thtra-rb (prcvi.rrs scctitlrt). ir'tlrc,rrtrrbcr-tll'l,rr'lrsrrr('r)r(',ts .r rritt.rt,rrr rrt'rr,rr rrrl") rrr,'rr trrt' s;rrrr,[t' sr/t' cr1.rrls. ()l. e\r..'(.(rs. r0/i, (/i rrrrrrlrr.r
1 ttt
t
t't'
I t'.t I
,,\ltt'r r'rlrrnrrnll' llrt' olrscrrcrl lr('(lur'n( \ lt;rttsili()n nlirtrir (1t. -150) lirr llrrgc dill'er('n( ('\ lrr'tst't'rr r't'll:. lllr't'\lrt't lt'rl lrr'rlll('lr( \ ttlrlt tr rs r'onslt ttr'lr'tl lrv r';tlt'ttlltlirrg lhc ('\lx'( tr'rl ltt'r1rlt'ttr \ lrrl r',lr lr,,'ll,r, r illrlttll'1il lllt'lr)l llllll;l
RATES OF BEHAVIOR & ANALYSIS
expected frequency:
olr Sl:QUENCES
ANALYSIS OF SEQUENCES
row totalXcolumn total grand total
Table 15.6. pttttern o.f'.tut,t,t,.r.s:fill searc'lt in
g ancl
u,re
ol
n,a i t i ng tu(, I i (,\
Transition matrix
Initial tactic
Subsequent tactic usecl
used
Searching
Expected occurrences
Following behavior A
B
C
Searching
ow totarls
53
(s3.5) Preceding behavior
A
12.3
10.4
12.3
35
B
r
0.5
9.0
r0.5
30
C
tt.2
9.6
t1.2
32
34
9l
Column t tals
29
34
Waiting
36
(35.s)
Waiting 39
(38.4) 25
(2s.5)
Expected values are in parentheses. There was no significant effect of the subsequent tactic used successfully (12
:0.03,
Sourc.e:
tlf: l, Ns).
From lrincke (19g5). 1
much larger than the expected fiequency 10.4). (
I I
This provides a first approximation look at the data. The researcher can then
t,:*#oos;: toa
proceed with a chi-square test fbr the entire matrix (e.g. Lemon and Chatfield l97l),
of the matrix
(e.g. Dawkins and Dawkins, 1976).
or reduce it to the most
important cells (e.g. 2X2 matrrx) and conduct a chi-square test (e.9. Stokes, I962). Some transition matrices will contain sequences
of only two behaviors to begin with
(e.g. Fincke, 1985;Table 15.6).
When conducting a chi-square test, are very small
(<5), the sample
if
any of the calculated expected frequencies
size may be
too small fbr a valid test. In our example
since our number of sequences observe d (g7)is close to the number suggested by our calculations ( r 0g), and our sample is sufficient according to Fagen young,s and 'rule-of-thumb" we wiil concrude tliat we probabry have a rarge enough sample. Therefbre' we will proceed with the chi-square analysis (see the discussion of the chi-square rXfr test of inrJepencrence described in section r4.r.2).
above, we decided that the sample size of 80 approached a sufficient sample, but we also notice that the expected frequency lor the BB sequence is only 9.0. Bakeman and Gottman (1986) developed a fbrmula fbr calculating sample size based on expected frequencies; their formula was derived from a calculation suggested by Siegel (1956). The minimum number of sequences that need to recorcled (l/,i) is
AA
3.22
AB
8.86
determined by:
AC
0.88
I}A
0.21
ar rv'$-P(
9
l- pl
(Observed Cell
calculatec'l expected probabi
li
ty ol lhc
lc
us t / r( ( l t t ( n
t sc(l tlr)ncc
( '(
'
r)
o (l() J
)
t
.1
02
s
-lt
.)
.)(
o ')I
()0
l'
expected)2
2.77
ll(' ('n ('B
Filr our cxanrplc:
-
Expected
BI]
where:
P:
I
initial tactic used successfully on the
The individual cells in both tables cAn now be searched lor those where the observed frequently is much larger or smaller than the expected frequency. For example, the observed frequency of the A-----B transition in our example (20), is
segments
l
r
(/,
/
.'
I
)
ri
,0
)
454
RATES OF BEHAVIOR & ANALYSIS
oI
ANALYSIS OF SEQUENCES
SITQtJENCES
frequencies of the sequences 2g.50>g.49, we infer that the (iom
ln fact, you can see difl.erent trom random chance. value at P:0'001' lated I is larger than the tabular
Table A9. that our calcu-
of occurrence of dilferent
song types between the hrst and second halves of their sample periods. To overcome the stationarity problem, Oden (1917) developed a rnethod for analyzing behavior seqLrences based on fitting ascending-order nonstationary Markov pl'ocesses to tlre data.
ties
ThetabularValueforffor4dfatP=0.05isg.4g.Sinceourcalculatedvalueof observed are significantly
For a more complete discussion of the analysis of sequences using Markov chains and transition matrices see Gottman and Notarius
Cruner"sPhi .:-^r..-. a matrix of any slze ls association between cells in of measure convenient Another 0 to I and is calcuto a coefficient that varies from cramer,s phi. lt converts the I latecl as follows (2ar,1984)"
/. '(: lY
<0.
v
1978), Gottman and Roy
l5.2.lc
Lag sequerttial analysis
selves and each other at various lag steps. Lags are the number ol event, or timeuuit, steps between sequential behaviors. An overview of the method was given by
where: N=sample size For the examPle above: N:91
Bakernan ( 1978) as follows:
i:28.50 /zs.so:0.54
Q:./ \, ql
Although the chi-square test'
above' was
not signilicant' the phi value does
also be calculated as association' Cramer's phi can reflect a very high clegree of 1979): follows (Howell, 1992; Castellan'
.
/r 4. l @,:ilntr-r,l where:k:thesmallerofthenunrberorrowsorthenumberofcolumns
/t
(
(1990), and Haccou and Meelis (1992).
Sackett (1914. 1978, 1979) developed a method called lug sequenriul onulysrs for measuring the frequency with which selected behaviors precede or fbllow then-r-
N
For our examPle,
45-5
/r:3'
therefore:
l:n * @,:JIrrtr-r)]-"" zs
The analysis begins by designating one behavior the 'criterion behavior' (this procedure can be repeated as many times as there are behaviors, so that each behavior can serve as the criterion). Therl a set of "probability profiles'is constructed, one for each ol the other behaviors. Each prolile graphs the conditional probabilities lor that particular behavior immediately following the criterion (lag 1), lollowing an intervening behavior(lag 2), fbllowing two intervening behaviors (lag 3), and so lorth . . . Peaks in tlie profile indicate sequential positions lollowing the criterion at which a given behavior is more likely to occur, whereas valleys indicate positions at which it is less likely to occur.
so
is necessary to indismaller phi coeflicieut' so it This method produces an even was used' cate which method of calculation tlot chitnging one behavior following zrnother of probability Stationarity (the wc kuow that ht)wevcr' analysis of transition matrices' over time) is assumecl in the cltrily cxittlrplc' I'or of an animal over time' stationarity rarrely exists in the behavior ['r'cclttctrcy trends in the clata; that is' thc (circadian rhythms) are likely to cause
IBukamun, 1978:71J
For example, in the fbllowing seqLlence of four behaviors (A,B,C,D) we have designated behavior A as the t'rilcrion hehuv'ior and exarnine the behaviors that occur at
Lag l, Lag 2 and so on:
Lag:123 A B A C B D A B D
Ilchitviorseqlrence:
A
ltt
Positions of criterion behavior A
cycles
tlrc trirynot likely t. be stublc thr.ugrr.ut or occurrence of most behaviors is irr .is Mark.v criai, a'irrysis w.:.i .ot ir''ricrr'lc Staddon (rg7,)concludecl that sl;ttitrttlttilr' .l llrt'k ia ('rtrtttttrtrt /i'irr heelrrrsr'.rr'ir strrcly trf behavi.ral sccltrc.ccs I,cttrtlttlttttl(.hlrrlicltllIt)71)tcslctltlteittl;tt;rIirtsl;rll()llillil\,lrt.ltltt.lttr,t't.t.tlittt' t"' ttr lltt'Pt'rlr;rlrtlt lt'sf r'tl l()l \ll'lllll( 'rtrl tltllt'lt'llt uitlr Mlrtkov t'ltltttt;ttt;tlvsts Ilrt'v
.ltttoltr.rl rrrrrlysis irrr,olr,cs tlclcrrninirrs tlrc conclitional probability of a behavior
lirllott ittl ilst'll ;rl t';rclr
l;rl
lit'lrrrr ior ,\ r,t't'urs lirl lorrr orrt ol'1ltc tcn bchavioralacts
,,lt:t'l\r'tl ttt llt('('\ittrr1,l,':rlrrr\r' llr,'r,'l,rrr' llrr'un('()ll(liliotlrl ll'ohlrhilrtV lirr
ltClutv-
r,rr .,\ r', l/lO l) ll) \\i ,,lt lt',( llrr,,'1)(,',tlt()n,,,,1 .\ lr llrt'st't1u(.1)('('(intlit'lrtetl lty
RATES OF BEHAVIOR & ANALYSIS
ol:
ANALYSIS OF SEQUENCES
STTQUENCES
itself at arrows) to determine the conditionalprobabilities Ibr behavior A following position' first in the A lags I .2 antl3; the three lags in the example are for Using all three positions of A in the example, it can be seen that A never followed itself at lag l, so the conditional probability of A following itself at lag I is zero. condiBehavior A followed itself at lag 2 one out of the three possible times. so the at itself lollowed A Behavior tional probability for A fbllowing A at lag 2rs'/,:0.33. From is %:0'33' lag 3 one out the 3 possible times, so that conditional probability
of folthis very simple example, we would suggest that A has a greater probability how ( illustrate 1978) Notarius and Gottman l. lag at lowing itself at lags 2 or 3 than
Table l5'7 ' An exantple o.f'o .sot'iontatrit' tnutri.r .shrnrittg thc rc,ytrlts o.f'cn.ttuttters be
twcen mule dark-el,ecl.iuncos irt an
Loser
AI AI
lags.
Lag sequential apalysis can be used with intra-individual (above) or inter-individual sequences. For exarnple, Sackett (1914) applied lag-sequential analysis to sequential data from a crab-eating macaque mother and her infant' More complete discussions of Lag sequential analysis can be found in Bakeman an
l-34 l2 066
A2 Winner
A3
can be the standard deviation of the expected probability (based on a large sample) signot lag is each used to test rhe null hypothesis thar the conditional probability at
nificantly larger than the unconditional probability' The same procedure used for the autolag analysis above, can be used lbr cross various /ag analysis of each behavior tbllowing each of the other behaviors at the
lt,iurv
A4 A5
tz4
A6
011
A2
A3
A4
A5
A6
43
40
61
158
58
l0 l6
t4
9
l0
B
2t
7
8
2t
0
4
Sourt'e: Abridged from Yasukawa and Bick (r993). copyrighted by Baiiliere
Tindall.
loosely describe as communication. Under those conditions we cast the data into contingency table called a sociometric matrix.
15.2.2a
a
Sociometric nmtrices
A sociometric rnatrix
is a special type
of transition matrix which reflects interac-
tions (Table 15.7). including sequential behavior between two individuals. A
sociometric matrix
is olten the lormat in which
inter-individual
sequences are collected during sociometric matrix sampling (see section 9.2.4).
t
5.2.t
d
MultivaYiate analYses
Another method of analyzing sequences is the use of multivariate analysis. The applications of factor analysis and multidimensional scaling to sequences of behavior are discussed in ChaPter 16.
An example shown below.
of a hypothetical
sociometric matrix implying communication is
Sociometric matrix Observed frequencies Receiver's behavior
1s.2.2
Inter-individual sequences
irltlivitlIn the analysis of 'l'hirt is. hcltitviol. ual's behavior is its primary source of stimulation tbr subsequcnt 'l hat tlccithe behavior of other inclividuals is disregardecl as an irnportant variablc. ol sion has traditionally been a sLrbjectivc one lcf't to thc cxPct'icrtcc rttttl tliscrcliolt tltrtt tltt' t'stts1'reels llrt'tt'sr'rttclte wlrcre is coirr o['thc thc rcsc.rchcr. Tlrc othcr sitlc .r.sl irrtll.r.ltrrrt s(irrtrrli lrre 3ri1,ip:tlinll lrorrr ollrt't ln(ll\ t(ltt;tls ;t sittt;ttiott \\'(' ('illl sequences discussed above we have been assuming tlrat irrl
'll':rrrsrrrittcr''s
llcllrvior'
('olttttttt
)
C
Row totals
A
-)
l8
21
48
I}
24
1
35
66
('
l()
l()
6
,54
t(r
t.l
lol ,rl',
(r
li
I
(rli
RATES OF BEHAVIOR & ANALYSIS
()I
SLQUENCES
ANALYSIS OF SEQUENCES 459
Once again we can use a chi-square test to analyze lbr significantly large correla-
o'*'':'
tions between the transmitter's behavior and the receiver's behavior. In inter-individual matrices there is no problern involved with measurements lound along the diagonal; however, stationarity can again be a problem, as it is in all continger-rcy tables. Bekolf (1977b) listed the following five conditions in which social-behavior data are often collected and which" generally, do not satisfy the assumption of stationarity:
t z Lumping data lor ditferent individuals (see Chatfield, l9l3) : Developmental studies; individuals forming social relations and percep-
,r:a
behavioral act :monad
_v___-,:dyad
)1+,f=-=+1,:tfiad p,:probability of p,..,
each behavior (,r;monad)
:probability of
each dyad (_r___,11
P,,,.,..,,:probability ol' each triad (y_rr-_.--.----t,)
a. For ecluiprobable
tualmotor skills being acquired.
monads, dyads, or triads. etc:
L/: uncertainty : Iog.
+ Motivational studies (see Slater, 1973). 5 Studies of signals having a cumulative'tonic'ef-fect (Sclileidt, 1973).
where:
As with the intra-individual analyses discussed earlier, associations found
lV
1/:the number of different
Log, r/can be rouna in Tabre A22. eq uiprobable behaviors, below.
between a transmitter's behavior and a receiver's behavior are only that; associa-
tions. No causal relationship is explicitly demonstrzited, although it is implied wlien
behavior sequence:
::
Two or more individuals are interacting (sociometric matrix).
we use the terms transmitter and receiver.
o,'
b'
For non-equiprobubremonads,
mona
rf,>
100 use the formura for non_
dyads, or triads. etc:
r' Uncertainty, when the antecedent behavio r- is rt.t kno*n..
(i) tror monads:
t
s.2.2b Information analysis
Another way to analyze sequences, both intra- and inter-individual sequences, is through the application of information theory. Information analysis has been used with a wide diversity of sequential data. As examples, inlormation analysis has been applied to aggressive communication in mantis shrimps (Gonodactt,lus hredini) (Dingle. 1969. 1912). grooming behavior in flies (Dawkins and Dawkins, 1976), odor trail laying by fire ants (Wilson, 1962), leadership-rank in lallow deer (Gilbert and Hailman, 1966), to movements of mice (Fentress and Stillwell. l9l3) and rat letuses (Robinson
& Srnotherman,
1987).
This approach consists of calculating Ihe unt'crtainty (U) in preclicting what behavior will occur next; it is measured in 6irs o.f infitrntutiorr recluired to rnake the prediction. Like Markov chain analysis, it assumes stationarity. (Jncertaintl,
Uncertainty can be measured for the lollowing conditions:
I : r
When the behaviors are equiprobable or non-ecluiprohahlc. When the anteccclent (prccccling)bchavior is trot krtow'n. Whcn thc irntccctlcrrl bchirvior is kn()\\'n.
t Wltcttlltt'llvrl.()l
tll()t('. ltttlt't'r'tlr'ttl llt'lt;ttl(rt";rtr'knrr\\tl
,.,::1u.rrge uncertainty in preclicting
fP,
the ne.rt niona
log, p,
Determine the p, rog, p. firr each of the individ,ar behaviors (_r) with their respective prob,biritics (p,) and then sum them to carcurate u,,,. The p, log, p, can be founcl for each p, in Table A22. As an exampre. r0r 'vocarizations'were ericited rrom a .Bugs Bunny,toy. Each dillerent'vtlcaliz,ti.n'type was
coded (A, B . . .) as it occurrea producing a continu_ ous seqtlcllce (A'tl'C'D,E,tr,B,G,C,H,B,G . . .; of l0l vocalizations of l0 differenr vocalizatit), types (A-J; Table l5's). For the info,riation anarysis, onry the first I(x) vocitlizitlit)l1s wcre used so that the frequency of occurrence (n) equaled the prohrrbility (/") lirr each vocalizationtype (-r). From Tirble 15.g. if can be seen thul rrrt. itnarvsis
ror non-eqriprouuure nronads produced an uncertainrv .r
ili;!T;ll, (
ii
1
1
';
','
tly.tls:
1,,,,
il\t'l;t1'1'lltr{('ll.r,rl\ -)/'
1,,1,
1'
,r ,f
1.1lrr
lrrtt, lll(.,(,\ltl,vlttl
(v_____.'.1,;
460
RATES OF BEHAVIOR & ANALYSIS ot'SITQUENCES
ANALYSIS OF SEQLIENCES 46t
Table |5.8. Inforrtlutiot1 unuIy"sis./br I0
Table 15.9. In/brmation
dilferent 'vot'uli:ation' type,s given h.y, u'Bugs Buttrtl" to1'. Since vot'uli:utiotls v'ere elicited 100 titrtcs, the nwnbcr ofoc't'urent'es
(n)
dvttd,s./ronl o sequen(,e by u 'Bug,r Bunn,y'
ul,so
thot
equuls the pnfiubility o/'o(('urran('e (P*) ./or eoch vocalizution
tlpe
(
see text ./br
tli//erent d.wrl (e.g. AB, AD) equal,t 1
Vocalization
(monads) n: P
-P,
log, P
A
2t
0.4128
B
1l
0.3s03
C
13
0.3826
D
il
0.3503
E
lt
0.3503
F
6
0.2435
G
4
0. r858
H
9
0.3r27
I
9
0.3t27
J
log, P,:3.1726
n:P
AA
I
0.0664
2
0.n29
-7
0.2686 7
AF AJ
{,,
r.
The Pr,..,.i logz Pr,.,,.i can be lbund in Table A22.
For example, the sequence of l0l 'vocalizations'elicited from'Br,rgs Bunny' (above) were divided into the 50 dyads of the l0 'vocalization' lypes. Tlie results are shown in Table 15.9. It can be seen that the inlormation analysis lirr rrott-ecluiprobable dyads produced an uncertainty of 4.8198 bits.
(iii) For triads: (,,..,.,.):average uncertainty of predicting the next Triacl (rr'---- + r--- t')
l)clct'tttine {,,,,,,,ttsitt1t lllr'slttttt'l)tott'rlrtt('(tt\iltl'llrlllt'A.).));ts rlr'st't tlrr'rl lot Iltt' tn,,lt:trlr ;tttrl rlt;trl',,tlr,",t'
0.2686
I
0.0664
1 -1
0.t5t8
BC
5
0.2161
BH
I
BI CA
0.0664
5
0.2161
9
a3n7
CC
I
0.0664
CE
I
0.0664
CF
I
0.0664
CJ
I
0.0664
DB
I
0.0664
5
0.2161
DC DG DH
2
0.1t29
.,
I]
0.Isl8
5
Irl:
0.2161
I
0.0664
t1
l:ll
5
IA
0.2t61
I
0.0664
tl
I
l,
ll)
0.0664 1,,
(;,4
(;l il\
I
I
0.l.s |8
t.t ,
-P.., Iog: P,
AB
AD
Determine the P.., logz P,., fbr each of the individual dyads (,r,,t')with their respective probabilities (P,., ) and then sum them to calculate (..,
: -)1',, , , lt)g. 1',, ,
euc,lt
probubilir.y
(,see te.ut./itr./urrher e.rplunution )
AE
bits:
.fitr
ir,s
Vocalization
r00
-IP.
P.r)
dyads
0.2161
5
Uncertainty:
to1.. Onl1, the 36 di//erent tlyarl, in the s,urnple o/.t00 tlvucls ure
included in the tctble. Since t00 ,lyr,rl,, ,rrrn .tumpled, the numher o.f.ot,turent,es (n)
./urther
explunation )
types
oc,c'urr.ecl
ctnul.y,.,-is /itr rltc cli//brarrt of I 01, yocttli:cttiort,;, given
I
(l (l(r(r.l
i
o I\lti
I
I
I
ll(l(,(r
I
l) I ii ,ti
I
1i
!
tl
ANALYSIS O}.SEQUENCES 462
463
OI-' SEQUENCES RATES OF BEHAVIOR & ANALYSIS
Table 15.9. (cont')
If
Reduc'tion in uncertainty (T ) a behavior (-),) is contingent, in part, upon the immediately preceding behavior (x)
then:
Vocalization
-
P,.,, logz P..,
dyads
fr:
HC
2
HI IA
-)
2
0.1 129
IE IF
J
0.1518
4
0.1 858
JB
2
0.1129
JG
2
0.1 129
JH
I
0.0664
P r.,
U,U)
0. I 129
a. Reduction from monads to dyads:
0.1518
{,,,,,):decrease in uncertainty about behavior y that is derived by observing behavior,r.
:
U,CY)
From the 'Bugs Bunny'exanlple above, the decrease in uncertainty about vocalization y that is derived by hearing vocalization -r is: I(.,,,,'): 3'17 26
100 U
U,t,-
ncertaintY: - fP,., log, P,.,.:4'8
198
bits: (.,.'r
b.
-
|'641
2: |'5254
Reduction from dyads to triads: I(,,.,,,,):decrease in uncertainty about behavior y that is derived by
z Uncertainty.
when the antecedent behavior
rs
observing behaviors w and x.
knov'n:
: U r(!)-
(i) For dYads:
c. Reduction from triads to tetrads:
U,(l):averageuncertaintyofpredictingbehavior(-r)whenthe known' antecedent behavior 0') is
: {'''''t- {'l
'
{"' ';',:dffii:iliff[ilJ],,1:::':'havior-u
calculated above' these two variables were
{,.,(-r')
Fromthe,BugsBunny,exampleabove,theaverageuncertaintyinpredicting
:4'8
198
-
3' 17
26: 1'6472 bits
the two of predicting a behavior (r') when U,,..(y):average uncertainty are known' antecedent behaviors (lu,x) {,,.r,,)-
The objective ol
U,.,,.,(J')
Slrannon's meAsLrre of redundancy to calculate an 'index of stereotypy'(see below)
(ii) For triads:
:
-
that is derived bv
As an example. S.A. Altmann (1965) terminated his analysis of sequences ol lrchavior in the rhesus monkey at tetrads (sequence of four). Altmann used
vocalization(x)whentlreantecedentvocalization(y)iskrrownis: U,Li')
U,,.r(-y)
t()r'circh order of approxirnation. This varied from zero fbr the zero-order of ,rpproximation (all beliaviors equiprobable) to over 0.9 lor his lourth order of :rpl.rroxinurtion (tctrad). Since a behavior of the rhesus monkey is almost completely ,lt'tcr.nrinctl by thc three preceding behzrvioral events, Altmann chose to go no Irrrllrcr'.
(.r.,')
the nttnrbcr ot' hcltavioritl information analysis is to rjetermine ,\'
unitswhichmustbeincludedinaSequence(e.g.dyads,triatls.ctc.)irrtlt.tlcrttl perhaps <0.10'-I-htrs' tlrc tlil'tl't'tow levcl.
acceptatty reduce the uncertainty to an tltotlcls rvill piVt'rr uncertitinty yicltlctl by sttcccssivc of metlsures the ence between lrrrttlttttt .l
ittlirtlttlt
lncilsLlrca't'crrccrcirscitrrrtrccrrlri.ty(.r-c().\'('r's('lvtlrr.'t'rtrr (.()lll(.\ \()()ll lltt'llrrr .,1 tlttttttttsltittl' tt.lttttts titlrr) yic[tlerl lly tltltl ttttltlel. Ilttu,cvt't.. .llrl'lrtl\ lilt "ttt ( (""'l\t'ttlt"lt'l'' ('l llltttr\ tlt't t(';l\('\ (lrrll itrl() Pl;tv lllt(l lll('tlll(
tt't't'rtl vpr itttlc
t
(S)
llrt'slt'rr'olvPV ulrlcx is rrsctl t() ('()nll)irt'c lltc contli(itlnrtl uncertainties for monads, ,lr;rtls;rnrl lrr;rtls(r'lrlr'rrlrrlt'rl lrlro\t')rrrllrllrr'ir tttlrrirnrrrttpossiblcvitlttcs.Thestcreo-
ItPt,
11111r'r
tttlltttr'tt';tst'ltottt nr()n,t(l', l,,,ltrt,l', 1,,ltl:trls
& ANALYSIS OI] SEQUENCES RATES OF BEHAVIOR
464
16
For monads:
s,: l-
Ut'l max
where: max
e,l {,r:equiprobable
U"':logz
1/
Forthe.BugsBunny,exampleabove,thestereotypyindexforthemonads(Table
Multivariate analyses treat several variables and compare two or more groups. They can be used for: l. initial data exploration and hypothesis development; 2. classification (grouping according to similarities); and 3. limited hypothesis testing. These techniques are useful in helping to clarify results through illustrative visual representations, such as dendrograms and three-dimensional diagrams.
15.8) is:
6ax U1,r:log,l0:3322
e:t-\
Multivariate analyses
3.1126 :r-0.955:0'045 3.322
However, they should be used to express results, not to impress readers. In addition,
For dYads:
S,(J'):,
-
the researcher should fit the method of analysis to the animal and the problern under investigation, not the animal to the method (Aspey and Blankenship, 1977;
U,.,(-t')
Bekoff. L977b:Tinbergen, I 95 I ). This section is a brief overview which explores selected multivariate analysis techniques by: l. explaining what they will do fbr you in terms of how they treat
n.,o* U,(,r.)
U,(l'):max (..'l-ffi4x Ut'l
where: max
index for
The
follows:
10
different vocalization types
combinations (e'g' AA'AB'AC '
max (..,
' .
(A
J) can be given
in
yr-rur data; 2.
100 possible dyadic
JJ). Therefore:
Srreath and Sokal (1973), and Sparling and Williarns (1978). Keep in mind that,
overall, 'multivariate analyses are powerful diagnostic tools: (l) for uncovering homogeneous subgroups from naturally-selected heterogeneous samples; and (2) lirr identifying relationships among multiple variables when the underlying source. or biological basis, or individual variation is unknown'(Aspey and Blankenship,
r:log' I00:6'644
max U'(Y)
:
6'644
- 3'322: 3'327
l 6!?:l S,Lt'):t- t.t , -o'496:o'504
It)l7:77\.
For triads:
I6I
s,,.r(),):, -;#;'"a{5 f/,,'.'.' )-max where: max U,,.,(-l'):max
('''t in cthology can
bc
analysis the use of intbrmation (1977)' Ad
rbund
Hoof
Wilson (le7s)'
Van 1':*11^..:n
illustrating how they have been used; and 3. describing or referencing
the methodologies for this use (Table 16. I ). For more extensive coverage see Aspey rrnd Blankenship (1977,1978), Manly (1986), Maxwell (1911), Morgan er ul. (1976),
MATRIC-ES
M:rny cthokrgical data are gathered, or can be organized, into matrices in which \i'vcl'rl inrlividtutls or behaviors are being studied (Figure l6.l). These data may be rr rtlcly virriablc. be scalecl in arbitrary units, and frequently include interacting vari.rlrlcs (Aspcy rrntl lllarikenship, l91l\.lnitial visual inspection of these matrices is ,,llr'n eonl'rrsirrg. llrirt is whe rc rnullivariatc analyses come into play. I lrt' rrsc ol'sir rnrrltiv:rrilrlc tcclrttitlrrcs (/l-llctor itrtulysis, p-tuctor analysis. prin, tlrill t'orrrPont'rrl lrrr;rllsis. t'lrrslr'r lilr;rlVsis. tlisCtitttirriutt litttctitltt itnitlysis. and tnulIrrlrrrrt'nsrorr;rlst:rllrry')rrr llrt';rrr.rlr''t'- ol rlrrlrr l,,rrrtrl lr ttt;rllict's likc tltosc in lrigtrre It' lrrrlllrt'.lr,,r'rr'.',r'.1 ll,'trr'\r'r llr,',r1,g,ltt,rltlrt,,l (ltt".r';ttt:tl\rt'sisttot t'esltit'(crl lo (ltt,.,t'rlt'.r rr',',,',t lrtt( ttt.tt 1,,' 1,,,1 l,',rtt,tl\ , .ur\ r lttr'l.tltolt ttt,tlttt Nr'rt'tlltt'lt'ss.
T,ible \1-:'.rl\
the use o.f 16.1. Selectecl reJerences on
multivariute analyses Procedure references
Multivariate analysis
Example references
Principal
Huntingford
components analYsis
Discriminant analysis
Cluster analYsis
Factor anall'sis
(I
reproductive behavior 976) three-spined-stickleback
Halliday (1975) newt sexual behavior (1976) Apll'siaburrowing behavior Aspey and Blankenship pengurn Bekoff (1978a) ontogeny of Ad6lie of avian vocalizations (1978) components Sparling and Williams behavior (1970)rabbit Dudzinski and Norris of gerbils DeGhett (1980) maternal behavior in wasps differentiation caste (1983) Gadagkar and Joshi song recognition Falls and Brooks (1975) avian spiders in dominance Aspey (19'71c) taxonomy in canids Bekoff et al. (1915)behavioral penguin behavior Adelie of ontogeny (1978a) Bekoff of avian vocalizations components (1978) Williams Sparling and avian vocalizations Sparling ancl Williams (1978) of gerbils behavior maternal (1980) DeGhett differentiation in wasps Gadagkar and Joshi (1983) caste (1973)marmot response to mirror image Svendsen and Armitage (1976) Apll''siu burrowing behavior Aspey and Blankenship
Seal ( I 964)
program Cooley and Lohnes (1971) Fortran program Overall and Klett (1912) computer (1978) Frey and Pimental Pimentel and FreY (1978)
program' Cooley and Lohnes (1971) Fortran
BMD Program Nie et al. (1915) SPSS Program Pimentel and FreY (1978)
DeGhett (1978)
Fruchter (1954) ComreY (1973)
Schmitt 0911) BASIC Program program Overall and Klett (1912) computer
\l.iltiraritte .:rt,tlr sis
Example references
Procedure relerences
\lultidinrensional
Aspey and Blankenship (1976) Aplysiu burrowing behavior
Overall and Free (1912)
scaling
Cluster
anall'sis
Morgan et ul. (1976) chimpanzee social associations
Morgan et al. (1976) SLCA
Ralston (1977) horse social associations
Jardine and Sibson
(
1
968)
Sibson (1973) Fortran program
\l ultidimensional
Morgan et al. (1976) chimpanzee social associations
scaling
Kruskal (1964) Shephard et al. (1972) computer program
Nie er al (1915) SPSS program Factor analysis
Baerends and Van der Cingel (1962) heron snap display
Nie er al (1915) SPSS program
Baerends et al. (1970) herring-gull incubation behavior ( 1 961 ) bitterling reproductive behavior Van Hoolf (1970) chimpanzee socialbehavior
Wiepkema
Cluster analysis
Dawkins and Dawkins ( 1976) grooming in flies
DeGhett (1978)
Maurus and Pruscha (1973) squirrel monkey communication M ultidimensional
scaling
Principal components analysis
S.-.' Figure
I
6.1
.
Golani
(1 973)
jackal precopulatory behavior
Lingoes (1966) computer program
Guttman et al. (1969) mouse behavioral sequences
Spence
Giles and Huntinglord (1984) anit-predator behavior in
Huntingford
sticklebacks
(
1
978) (197
6, 1982)
GROUPING INDIVIDUALS M L] LTI VAR IATE AN ALYSES
468
(1961) labeled his three major extracted lactors as'aggressive, flight, and sexual
A
tendencies'.
c
B
Following Behavior
lndividual
Behavior
1 2 3 "'
1 2 3"
23
.9 1iz
>J
;>J
E
o
Eo
t,) 3 .9
!o
o
:
Principal component analysis (also called principal-axis lactor analysis) utilizes an orthogonal rotation of the data (e.g. Huntingford, 1982). Factors analyzed using
rotation account for maximum possible variance among the observed behaviors. Cooley and Lohnes ( 1971 ) provide a FORTRAN program listing lor Varimax rotation. Giles and Huntinglord (1984) use principal components analysis to describe the anti-predator responses of sticklebacks to a model heron or a live pike.
'l
o E 2
p^
) io
n
:
o)
:
o- n
I6.3 GROUPING INDIVIDUALS
n
If it is suspected that groups of individuals
are behaving in a similar way, then Qf actor analysis can be applied to the data in Matrix A. The analysis extracts individ-
Fig.l6.lThreetypicalmatricesinwhichethologicaldatacirnbeorganized:A.Different B' Irrteractic'rns between individuals:
ual-related lactors based on their observed behaviors.
inclividutls: behaviors performecl by rJiflerent
sequences' C. Inter- or intra-individual behavior
In contrast to principal component analysis, powered-vector factor analysis It places maximum emphasis on biological rele-
cxtracts factors without rotation.
the objectives of analysis shoul<J be matched to the characteristics of the particular
vancy, whereas the principal-axis method emphasizes parsimony (i.e. maximum variance accounted for by a f-ew f actors) (Aspey and Blankenship, l91l).
the stuclY (see Gottrnan' 1978)' Since one of the advantages
rrrollusc Aplysia bru.silianu. They recorded the occurrence
they allow of these multivariate methorjs is that
selected graphical displays (Rohlf' 1968)' visualization of the variables through examPles will be Provided'
I(.1.2
GROU PIN G
B
E'HAVIOR
S
'factors'basecl on their into a smaller number of Figure factors A. this analysis extracts the behavior-related ties. when applied to Matrix varialnce' fbr a large percentage of the total 16.1)
which account
Forexarnple,Aspey(|971c)recorcledtheoccurrenceof20ditferentbehaviors
in40individualspiders.HeuseclR-factoranalysistoextracttburbehaviorlor 20 different behaviors) which accounted related factors (groups from the after then descriptively labeled the tttctors 14.3,,/uof the total variance. Aspey labelecl
them. consequently, Factor I was exarnining the behaviors comprising .rur.r/retrcat.' liitcttlr lV .approach/signal', Factor II .vigorous pursuit,, Factor III labelecl 'tl.tr-li.ki,g" uninterpretable' but was then since none
of the
linkcd witlt composite behavio.s was significantly
tcrs in 32 individuals (Matrix Type A). Q-factor analysis extracted three factors (groups of individuals) which accounted lor 80.2"/u of the burrowers. Burrowing characteristics, interpreted relative to ef-ficiency, were examined and the three r11)ups were labeled 'inefhcient burrowers'. 'efficient burrowers'and 'intermediate
(e'g' behaviors in Matrix A' large number of variables R-fnctor analysis organizes a underlying similari-
at first seemed biologically
Aspey and Blankenship (1976) studied burrowing behavior in the marine ol l0 burrowing parame-
..y
.tltct'
sl:tlctl
interactitlns. Altlrotrgh it is ctlttlttltlttlY behavior cluring inter.indivicltral t'e cxislcrr.c ,l' . c()rll'r(),',1()livltti.ttltl that trse .r- r.ctar irn.lysis irssrr.rctl [spe v ltlts lttt' l':tt'tot (Sllrtt'r' l() / \) ttolt' 11t"1 strttc' tttttlcrlvirlu cltclt cxtt'ltc(ctl ritlt.tlIlritlr,tlt'st'rrlrtir(..lillltt'tllt:tttltttlt'lttrtt.tll.rl,.'1,'ltltrrtt.'lt;tsl.Wtt'1rk('lllil
irrrrrowers'. The three-dimensionalrepresentation of these 32 individuals relative to
tlrc three extracted factors illustrates their location into three distinct groups t
lrigure 16.?).
Ilicrarchial cluster analysis groups variables (e.g. individuals or behaviors) on tlre birsis ol' similarities (or differences) among oommon characteristics. Simple ,lrstrrncc-l'unctiorl cluster analysis is sensitive to variability in the data and tends to .,1rlit'thc vuriablcs intc> more groups (Aspey, pers. commun.). Cluster analysis olten rrrrkcs ll'wcr assunrptions than other methods and therefore is easier to understand
gln tt ttl.. 1976; Sparling and Williams, 1978). llrcr':rrehrll cltrstcr anulyses begin with a matrix of similarities (or dissimilaritrr'r). rurtl r;rrirrblcs rlc sc(lucntially.itlincd on the basis of their relative similarities rrrlo tlr't)tlol'llll)ls (liigttrc 16.11. lvlticlt ltt'c silttplc. vistrllly iptcrpretable representaI rrlll\ (rl tlrt' rr'srtlls ol' lltt' ;'to1111i;11,1. l\ttrt1'lttr t't rtl (l()/(r) rrst'tl lrtrrrr,ltr,rlt ltt:lr't ;rtt;tlVsis ott rl;tllt l'l'rrttt lt ttutlt'ix tll' llrr'tttt'r'lt;rttir.'sol'r'otttlttr'titttl lrlr'll;ln(l l)torrtlt'rl;r',lr,rr1'lrllor\\,rrrlrlr",tttllrr)nr)l ,r'.rrr1'11' lrrrl' , lrr..lr'r ,rrr,rlr',r', {\l t \ I ,r', rrr'll ,r'.,1r',r rr',',rtr1'llrr'Porllnt':rltrI ttt'1';11i11'
t N'l or
M U LTI VAR IATE, ANALYSES
470
GROUPING INDIVIDUALS
4tt
+
o o
.$ |{
o
87
}\ 9 6
100
e
o
t.z Indlvidual Doe Deer
o
o
/C
u*, on, v"7
nuo'tt
16.2 Factor loadings for Aplt'siu burrowing behavior projected onto coordinate axes corresponding to the three factors extracted by Q-factor analysis. The origin falls in the center of the tliree-dimensional space. Factor I (large circles) represents 'inelficient burrowers'. Factor II (small stippled circles), 'eflicient burrowers'; and Factor III (triangles), 'intermediate burrowers'(lrom Aspey and Blankenship. 1976\. i
l
attributes
of the method. Their paper lormed the basis lor the discussion which
follows.
In conducting a single-level cluster analysis, let us assume that wc warrt lo tlclcr'mine the relative association anlong intlividr.rals in a hcrrl ol'cight utlLrlt rloc tlccr irr rr wildlif-e prcscrvc. Wc rlbscrvc lhcrrt lirt'ir lrlllrl ol'5(X) ltotrt's lrtrrl rccortl (ltc lrtttorut( o(
tirtrc tlrirt irrtlivitlrurls lrre closer tlurrr li)ur nr('l('ts llrrolrl,ll tr.",rr s;unplirty' ('\'r'rv tttittttlt' l';tt'lt,)((tltl('ll(('(rl :ttt;tssot'i;tltt)lt l\,r'.''tlllr('(l l(l lt'Itt'it'tll ()ll('lllllltll('{)l
B'vuEBGE A lrriti:tltlerrtllirl'r.rtttt lirt.;tss.t'i;rti.rs i, rr lrvp.llrcticirl hcrcl of eighr adult cloc tlt'r'r. ll. I irr;rl rlt.rrrlrol,r;rrrr l,rr llrt.lrssot.ilrlrolll i11 ,\.
I
MU
472
Table
16.2.
GROUPING INDIVIDTJALS
LTIVARIATE ANALYSES
Hypotltetic'nl ussocitttion
claru
deer' Dut? Jbr u hertl o/ aight adult doe
rtre the nLtmher of-hours observetl in association
doe der
in Tuble 16.2
A
H
G
F
t)
C
A
Table 16.3. Similarities.lbr the us.sot'iution,; irt tlta hyytthctit'ul herd of'eight udult
D
C
B
G
H
A
A B
2l
C
18
D
30
E
l6
F
38
22
l1
ll
ll
G H
3t
67
48
4l
47
31
21
17
151
348
Total hours
3l
2l l0
48 22
28
3l
30
25
23
34
191
202
203
208
168
B
53
C
45
D
15
5l
t17
E
44
27
59
74
F
109
6l
30
46
34
G
69
122
87
74
9l
61
H
1l
69
56
50
82
61
76
t29
247
observed
The similarity is then multiplied by 1000 for convenience, so that we can deal with whole numbers.
lor a discussion association (see section 8.3 on sampling assumption). The procedure is as follows:
r
z
of the hazards of this
shown in Table 16'2' This The data are flrst organized into the association vertically and horizontally in table. a matrix of Type B, can be read both
ordertodetermineassociations'However,itisstilldifficulttoseemuch Hierarchialcluster pattern of associations from the data in the table' of the associations' presentation visual analysis will provide a better (associations) similarities of table The next step is to generate a triangular of time' periods total each deer was seen for different
similarity
:
0.053 X I 000
:
53
We then complete the table
of similarities (Table 16.3).
Next. we search the table for the range of similarities. The highest is 129 for H*G, and the lowest is 27 for E+B. The vertical axis of our dendrogram should include this range, so for convenience we will set up a vertical scale of from 0 to 150; note that the scale increases from top to bottom
(Figure 16.3A).
of association observed, which they derived from Dice's coelficient coefficient of assoCole's same as ( 1945). Note that this is essentially the
linking individuals on the basis of similarities, starting with the largest similarity and working to the smallest. Individuals H+G provide our first association. The next similarity l22is between G and B. Tlris nreans we link B up with both G and H at the lZ2level. This is an cxanrple of 'chaining', which is an undesirable characteristic of the mcthod, since it links through intermediates (Jardine and Sibson, 1968)
ciation ( 1949) (section 17'4' lb)'
irncl is clifTicult to interpret visually. That is, the apparent association
among deer. Since
wemustfirstnormalizethedatatoadjustforthosedifferences.
Morganetul.(|g16)describeamethodfornormalizingdatafortime
SimilaritY:;:., where:
Xy:totaltime when individuals Xand X:total time Xwas observed )/:total time Ywas observecl
Ywere observctl togctlicr
/l irl'l'rrblc l(r'l Thcrclilrc. thc sirlril:rrity lirr intlivitltrirls "l rttltl Sirrrilrrritv
,l r ll r()l r .)0.) o 0s l
We then begin
hctwcur [] and H in Figure 16.3A is really due to B's association with G. M orurr n c t ul. ( I 976) d iscuss the problems of 'chaining' in more detail. 'l'lrc next irssociution is D*C at thc I l7 level of similarity. We then t'orrlirtrrc thc sirrrillritics ('lhblc 16..1) trntil all the inclividuals have been
l lris lr;rppt'rrs tvltr'rr rt'r'nurkc lltc lrss
is'
lo1'1'11',..',.
/r lrt'l\\'t'r'n l).nr(l ,^ ll tlrcrr'.rrr'lrr's
',,ttttt'lt'tr'l llr,'.1'.'.,r(l,tllr,ltl',lttr'r'ttl.ttt,l(,t.'tlrllrttrlttpl'tltplt,lX'('itttsC
M
LJ
DESCRIBING DIFFERENCES
LTIVARI ATE ANALYSES
E must be linked to the association between B and
G+H (Figure
N
M,N(i INDIVIDUALS
16.3A).
This type of association becomes clearer when the individuals are rearranged on the abscissa.
(Fighters)
s It can be seen that we did not use over half of the ordinate in our dendrogram and that the ordering of individuals on the abscissa makes interpre-
=
tation of the dendrogram difficult. Therefore, we correct these shortcomings by rearranging as in Figure 16.3B.
o
o The dendrogram can now
o)
(Siners)
o_
E
o o
of the associations. We can easily see the relative 'strength' of the various individual be used in a visual inspection
E .g o
o':p.
C
associations and the associations among three or more individuals.
L
Because of the chaining which occurs in this two-dimensional representation, distortion occurs and increases toward the lower levels of similarity. This distortion can be measured, but not tested statistically, by Sokaland Rohlf's (1962) cophenetic
correlation coet-ficient, and Jardine and Sibson's (1971) distortion measure. Maternal behavior of Mongolian gerbils was studied by DeGhett (1980) using
Principal component
hierarchial cluster analysis and principal components analysis. Both analyses revealed the existence of a multi-cluster or multi-factor set of systems in maternal
lt2
behavior. Gadagkar and Joshi (1983) used principal components analysis and hier-
archial cluster analysis to examine the behavior of 20 wasps (Ropalidia marginata) over time. They lound that three behavioral castes existed (Figure 16.4), although
I
S
ls
t
It
lt, e
there was no morphological caste differentiation.
t
r S
DeGhett ( 1978) presents an excellent discussion of the use of hierarchial cluster
B
il
i
analysis in ethology.
t7 ,
t0, 5,
164 DESCRIBING DIFFERENCES
F
AMONG INDIVIDUALS
Once the individuals in Matrix A have been grouped by Q-factor analysis. we might want to know more about the differences among individuals and about the parameters (components) of their behavior that are most important in distinguishing dif-
i
ilh
g
lr.
t
67-
r S
B. 2-
ferences.
Principal components analysis separates individuals in a sarnple in terms of a few composite components. The first principle component is the one whiclr accounts for the maximum individual difflerence, and the second principal component is that combination of variables (e.9. behaviors), uncorrelated witlr thc lirsl principal component, that accounts for the largest proportion ol' thc rcttritittittg individual differences. The analysis can be extended to additionul corrrl-rottcrrls. il necessary, to explain the greatest proportion of indiviclual clil)-:r'cl)ccs. Individuals may be reprcscntcrl on a rriultitlinrcrrsiorurl ligrrrc lo 1'rt'ov'irlc rr vistrrl irlirgc ol'tltc rcstrl(s o('lrrurlysis. Srthgtrrtrl)s ('iut rtlso lrt'rlt'littt';tlcrl lirt'lt t'lr':u('r l)l(' st'ttllr(iott ( l"il,tttr's l(r .l;r. lf, s;.
20.
F
o
t9-
r
t5-
illa g
t6-
e r
s
318-
t3-
o8
o.7
lirrrrrl,rr rly
llr'lr'rtt'rt'll r'r"rr"'rrr '111r,r.1r, .rr,\\rr r,1 ;,1 'rrrr).rrrr)lrr),ttr'ttrsrttt;trYsrs(A)lrttrr llt' l'tt' ltl'tl ' lrt"l' t ,ttt.tlt .r, l lll llr"rr r,.r,l.rl,l..rr ,rrrrl lr,.,lrr lr)l"i l) ( ()l)\,l,lrl(.(l l)\ ll,rtllr, r, ltrr,l,rll
415
416
M
I.J
DISCRIM INATING AMON(; (; IToI I I'S
LTI VA R I ATE AN ALYSES
417
I .9
2.O
lJ.
a
I
o
'Pl r ,-l
1.0
+J
c
o Prihcipal-ComPonent
I
sss
E
(59% Vorionce)
O .D
o
o
=
.9 -1
o C, o
! o o o
CL
E
o o6 ,68
s
t
D
o
c
-.i (s)'
s
o
CLi
'o> tr-o A,T Fig.l6.5PrincipalComponentsanalysisof32burrowingApll,sittbrasiliunushowirrgtwo extractedsubgroups.Tlrefirstprincrpalcomponent(shadedareaortright) generally corresponded to individuals accor.rnted for 59"/uof the variallce ancl
whichhaclhigherFactorl(approach/signal)andFactorll(vigorouspursuit)and for 12.2i, ol tlre variance Tlre second prirrcipal Component accounted values.
correspottrJedtoindiviclualswitlrlowFactorllvaluesandhigliFactorlll 1978)' (run/retreat) values (from Aspey and Blankenship'
Goo
of principal component analysis in ethology are
Pimentel (1978)' Dudzinski and Norris ( 1970) and Frey and
-40 -30 -2.0 -1.0 0o 1.0 2.O 3.0 First Discriminant Function
4.O
50
of l6 dominant (D), 8 intermediate (l). and 16 subordinate (S) adult male Schi:ot'osu t'russipes plotted in a geometric space of minimum dimensionality by multiple stepwise discriminant analysis on the basis of the frequency of 20 behaviors observed during agonistic encounters. The first discriminant lunction (abscissa) is plotted against the second discriminant function (orrlinate) and * denotes group means. The spiders were initially groupcd its dominant, intermediate, or subordinate by a donrinance irrdex (DI). Dottccl circlcs represeut spiders'misclassed'by tlie DI relative to tlie discriminant
Fig. 16.6 The locations
analysis (from Aspey, 1977c).
OF INDIVIDUALS I65 DISCRIMINATING AMONG GROUPS OR BE,HAVIORS groLlps
among several identified Discrirninant analysis: L determines relationships analysis or principai cc'rrnpotrent atlaly(e.g. predefined or resulting from Q-factor or the groups; ancl 3' places incliviclr-rals sis); 2. assesses the discriminability between 1978)' in the appropriate groups (Sparling and Williams' behaviors
analysis to tliscrintittrtlc Aspey (1911c) used multiple stepwise discriminant (schi:.t'.su .nt.s';i1tt',t) which Q-l'rrct.t further between three groups of 40 spiclers l,bclctl .s'n.r,i'ir,t'.'rutcrurctlirt(c" rrrrtl analysis rrad extractecr and Aspey h^d .Sub.rclir.r,tc.. rrr his irrrirlysis.ll scrl,clrcc tlI trisc'i.rir*rrrt.tgtrlrti().s \vlrs r'()llll)ttt('(l *';rs lrtlrlt'rl l. llrr'r'r[.illl.t irl t';tt lt slt'p' in lt stcPwisc lrtil*lret.s. tr*tr ().(.\'lr.ilrr)tr'
;rnrl a onc-way analysis of variance F-statistic was then used to determine which rrrriirhlc (i.c.. bchavior) should join the function next. The variable added is the one rnrrking llrc grcatcst rcduction in the error sum of squares'(Aspey and Blankenship
It)ll9(ll. 'l'lrc rcsults
were then plotted (Figure 16.6). The first discriminant funclr()n (rrbscissir)sc1'raratccl thc'tlominant'and'subordinate'spiders, but it took the \r'('()r)(l tlrseritttirtlrrtt ltutclion (ot'tlinittc) to scparatc out the 'intermediate' group
Itrrttt tlt,.'olltt't Iu'o L'tottlts. lltt'tlll('('pl()tll)s \\'cl'c crrcirclccl ip the figufe fOr added t llrttlr'. Irr r'orrlr;r',1 lo ,,\'.;rt'1 ', ( l',/ /r l rr'.t' ,,1 unrllrPlr' '.lt'1111 rsr' tlisr'r'itttiltlttrl ltttltlysis.
llr'lrrrll t t,tl (lt)i ',)tt',r'rl lttt,'.rt ,lt',t ttnun,rrl .ul,rlt',r', lo,r',\('\s lltr'llt\()ll()lilit'tt'ltt-
418
ANALYZING SEQU ENCES ol; ltl: I I nV lolL
MULTIVARIATE ANALYSES
agreement with the same type
ln
trprt-tatrors ,ris
-5O -4O '39
-60
'29 NctEnglond conlds
Yolrcs
(/upu)
Ne
w Englond conids
of analysis donc on various anatomical
measure-
ments. c
orolcs (l olrans
)
(Dz)
In contrast to the other multivariate analyses discussed in this section, discriminant analysis can be used to test hypotheses (Sparling and Williams, 1978).
16.6
l'o's7
ANALYZING SEQUENCES OF BEHAVIOR
Multivariate analyses are only one method for analyzing sequences of behavior Chapter l5).
Jroyorcs
(see
l6.l.t Factor analysis
,,,\
Whereas Markov chain analysis tends
\,,,, \t \l
to emphasize 'sequential effects'
(Slater,
1973), factor analysis assurnes that the measured variables (e.g. behaviors) do not depend causally on each other (Blalock, 1961), but rather that there are underlying
'motivational processes'cornmon to the behaviors grouped around the extracted lactors (Andrew, 1972: Hutt and Hutt. 1914). Slater (1973) points out that both motivational changes and sequence effects are probably present in all behaviors so
\t
)ou* Fig.l6.TTlreresultsofabehavioraltaxonomystudyonintantcoyotes(Canislatrans\ wolves((,.tupus)andNewEnglarrdcanids(easterncoyotes).Therelative frequenciesofoccurrenceofsocialplayandagonisticbeliaviorwereusedas Top: Linear discriminant values of behavioralcharacters (Bekott. et ul"-,1975).
knownC.lupusandC-.latranslitterscastonalupus--latr(lnrjdiscriminantaxis'
NewEnglandcanidsareprojectedontothistheytallbetweenlupusandlatrans, units based
that neither analysis is perfect. Factor analysis was used to analyze behavioral sequences (Matrix type C, Figure l6.l) by Wiepkema (1961) for bitterling (Rhodeus omarus Bloch) reproductive behavior, and Baerends and Van der Cingel (1962) for common-heron (Ardea cinerea L.) snap displays. Both started with matrices of type C, and then generated
(Dr; in discriminant function but closer to lu|rans.Bottom: Distances
transition frequencies on the basis of observed/expected frequencies. Expected fre-
onpairwiseanalysesoflupus'latran'sandNewEnglandcanids'Notetheclose relationshipbelweencoyotesandNcwEnglandcanidsarrdthatbothfall from wolves' This is an example of the approximately the same distances an area wlrere these of multivariate analysis of behavior to taxonot]ry,
qr"rencies are calculated the same as
trpplication
analysesarecommonlyusedonmorphologicaldata(fromJardineandSibson, l97l: Srreath and Sokal' 1973)'
expected freq uencie,
for a chi-square test:
: IgILtgt'1I99!*U9t'1 grand total
Calculating transition freqr"rencies on this basis provided a ratio which indicated relative frecluency with which each of the behaviors preceded or followed the other, but
it also inclr-rdecl transitions between a given behavior and itself which may be impossiblc or clilllcult to interpret. Slater and Ollasc'rn (1972) discuss these dilficulties and
tionsl-ripsamonginfantwolves,coyotes,and.NewEnglanclCanids.(Silverand (e'g'
con'lpares the means ol' variables Silver, 1969). Linear discriminant analysis produces (e.g.individuals from a canicl taxon) ancl behaviors) from two populations thc relative measure of the dilferencc bctwccrl a discriminant function which is a behaviors (social play antl itgonistic populations. Bekoff et al. (1975) used two among the three canicl'ty1'rcs'"1'ircir' behavior) in their analysis of differences |urrcti., irxis ( trig.rc l6'7 ). slr.rv I l,rl results, when plotted on a linear discrimi,ant tlrc'Ncw r:.gllrrrtl ('rrrritls' seplrr-ir(ctl. irrrtr the wolves and c.y.tcs wcrc crcurly
lr'rr
tt.sttlls lttc ttt lttll tltt.t'()Y()l(.S, ltttt.tt.sltttl,l\,. lltt.it wct.c itttcrtllctlilttc. hrrt li.ll Cl()scsl ttl
srrggcst another method provided by Goodman (1968). Wiepkema (1961) and llircre rrrls and Van
cle
r Cingel (1962) then ranked the transition frequencies and gen-
cnrtctl corrclirtion cocfficients using Spearman's rank correlation, in contrast to the l't'lrson pnrtltrcl-nron.rcnt correlations used by Aspey (1977c) and Van Hooff
(l()70) lilrclor
lrrurlvsis wlrs thcrr rrscrl to cxtract thc nrinimum nunrber
of common
('iilrsirl llrt'lors n('('('ssirIv lo t'rPlrrirr llrt'sctlrrcrtccs tlhscrvccl. Since three factors t'rPllrrnt'rl tlrc rrlrlonl\ ol llrt'r,rtt:rlrrlrlY nr lltr'tl;rtlr, lr llrrcc-tlirncttsitlnttl vector nr,,rlt'l \\ir\u\('(ll,rtlltt.'lt,rlr'llt,',ll',lctttt1,ol
(l t1'tttt' l{'
li1
lltclrt'lt;triotsluoullrl tlrctltt'ce lltctot's
ANALYZING SEQUENCES Ol lll:IInVlolt MULTIVARIATE ANALYSES
Fig. 16.9 Examples
of vector angles in factor analysis.
tive side of the sexual factor. The length of each vector represents the amount of the common variance of the behavior accounted for by the factors. The use of factor analysis in ethological studies in general has been questioned
by several investigators (Andrew, 1912: Morgan et al., 1976; and Maurus and Pruscha, 1973). Factor analysis often makes more assumptions than some other rnultivariate analyses (e.g.cluster analysis) (Morgan et ul., 1976; Sparling and Williams, 1978). Once causal lactors have been extracted, some researchers quesFig.l6.8Vectorcliagramofthebelraviorofthemalebitterling(Rhotleustlmarus). flickering; FL:fleeing; HB:head CHF:chafing; CHS:chasing; FF:fin : quivering; :
:jerking; LE leading; QU DP: head-down posture; ;11 beat (from Wiepkema' TU:turning SK:skimming; SN:snapping;
butting;
H
1961)'
For represents the extent of their correlation' The angle between any two vectofs accordis represented by an angle determined vectors of unit length, the correlation ing to the following formula:
Correlation Coelficient: COS 9oo angle whose cositre equals the correlation Therefore, the vector angle equals that will be is perfect (r: l '00) the two vectors coefficient (Table A23).If the correlation
thesame(lirrFigurel6.g).Ifthereisnocorrelation(r:0.00)thetwtlvecttlrswill the anglc will hc
If there is a positive correlation diverge by 90" (2 in Figure l6'9). 90'rttttl and if it is negative it will be betwccrt between 0 and 90" (3 in Figure 16.9), 180' (4 in Figure l6'9)'
Inthevectorcliagram(Figr-rrcl6'8)itcanhcscctltltltttlrcllchlrvi()l.SCltlSl(.1 ar.trn.thrccrrxcs.-flrcsct.rcclrxcs:r'ct'cclrrrsrrl 'lrt'rrrs Irp,.P,r.cssir,,t'l'lrr'lot:l is f lrc positivt'sitlt'ol'lltt'ttolll('l)lt'rlttt
lrstlrt',.sitivt'sitlt'.1'tltt' ttrt'l:tr tot \ is tltt'post
tion what they really mean. Slater (1973 145) concludes that '[t is doubtful whether the extraction of lactors which are themselves of complex causation advanced tutrderstanding'. However,
it
does allow us to visualize our data more clearly and
llcnerate hypotheses and experiments that truly will advance our understanding. lirr example, Aspey and Blankenship's (1976) experimental study of Aplysiu gls,N out of factor analysis of groups and advanced our understanding of the function of lrurrowing and its relationship to reproduction behavior. Sltort and Horn ( 1984) make the following lour points which will serve as cauti()ns to ethologists using factor analysis:
t : r
One should ensure that factored variables are reliable.
.t
( )rtc
slrottlrl bc wiu'y uborrl trsirtg product moment correlations in factor
tnlrtr
lrirr1.r.
Lisr"rally
it is unwise to
use
lactor analysis when the sample size is small.
I)crivcrl variables can hopelessly confound any basis lor interpretation of lirclor ir rrirlysis results.
M
LJ
SOF'TWARE PACKAGES F'OIt S'I'N I'IS'I'I('AL ANALYSES
LTIVAR I ATE A N ALYSES
16.6.2 Multidimensional scaling
or behaviors from a matrix and disMultidimensional scaling orders individuals along coordinate axes' Golani ( 1973) criminates between them in terms of distances of precopulatory behavioral used multidimensional scaling in his analysis aureus)' He utilized the (Cani's jackals in two pairs of golden sequences
Guttman-Lingoes multidimensional scalogram
analysis (L' Guttman'
1966)' of
(Lingoes, 1966)' Guttman et al' (1969) had which a computer program is available
previouslyusedtheanalysistonreasuresequentialbehaviorinmice. (1961) data' using multidimenMorgan et al. (1916) reanalyzerl Wiepkema,s
results. They suggested that wiepkema's sional scaling, and discussed their different may tend to oversimplify the sitgrouping of behaviors around three causal factors
uation.
to discuss in detail here; Multidimensional scaling is too complex a method
above' consult Shepard (1980) and the references
displacement symptom - the rnorc tinre spent in manipulating the computer, the more incisive thc psychological research. (3) The Lorenzian-territorial sympton-r the more expensive the computer, and the larger the laboratory in which it is housed, the greater the personal authority exerted either in the academic or in the institutional setting. I Notterman, 1973 J
In short,
to generate and to test hypotheses; however, avoid methodological overkill. As Fagen and Young (1978:114) suggested'The future may well belong to those who can use simple methods effectively to test a theoretical hypothesis, or who are prepared to construct original models of behavior should no simple technique be available'. use the best methods and equipment available both
16.8 SOFTWARE PACKAGES
FOR STATISTICAL ANALYSES
Data analysis is generally perlbrmed using commercial statistics packages, although there are some statistics used by ethologists that are generally not available, such as
circular statistics (see Chapter l7). Statistics software usually has several modules: database manager, graphics and
16.7
SUMMARY
methods to treat ethological data in Multivariate analyses provide a diversity of techniques that should be used primatrices. They are fundamentally descriptive in the data through grouping and marily: l. to aid in interpreting relationships for further testing' In order to visual representation; and 2. to generate hypotheses fesearchers must have their objective interpret the results of multivariate analyses, data' the analyses to the objectives and type of
clearly in mind and match that rlultivariate analyses are Finally, Aspey ancl Blankenship (l9ll:18)'caution behaviorist lor determining inhersimply one research tool available to the animal analyses in ethology could most cerent data structure . . . misusing multivariate sterile papers'' tainly lead to a bewildering array of extraordinarily be accurate' Moving up the scale of Regardless of the mechanics, analysis must to large computer will speed up the sophistication {rom paper and pencil to calculator it will not change the data' C1rmputers are analysis and may improve its accuracy, but of data' and their use should be encoltrtremendous tools for the storage and analysis
aged.Howeveqpoorlycollectedandinvaliddatacannotbelar-rrrderedinactlmpttter. it by wrapping it in complex attitlyscs' tyirtg although some researchers will try to sell itwithacomputerandpresentingitwithcomplicateddiagrams.
for'compute ritis cliscitsc'. whicir ltirs Notterrnan cautions us to be on the lookout
the lollowing symptoms: (
lltt' tt'st':tlt'llet's l) Thc tlirtrrhcrt syrtt;llottt tltis is ttlltttili'stt'tl ttt
tltt' ('\lt('tttll('tlt (')l I lrt' irssullll)l irrtts tltltl llte ltlott' tl;tt;t. tlrt' lx't(t't
statisticaI tests. The database manager (otten in the form of a spreadsheet) is usually
relatively rudimentary and is meant to handle data entered directly lrom the keyboard, and to provide a method of perlbrming manipulations on the data, such as transformations (e.g. iirc-sine, log). It is recommended that any reasonably large set
of data be handlecl through
a true database manager or spreadsheet, as described simply read into the statistical program. Most statistical packages come with some form of graphic presentation ability. ln the minimum form, simple low-resolution scatter plots and histograms can be
rubove, and these files be
generated lor a quick survey
of the data belore testing. Larger packages, particularly those custom written lor microcomputers, may have much better graphic abilitics. Generally, presentation-quality graphics must be prepared
in
specialized
gruphics sofiware (see Chapter 18).
lrinally. all of these statistical packages contain a set of statistical tests and rrranipulations. The important criterion is that the package perform the tests you rrcctl ('l ablc I 2. I ). A complete package, like those listed below, contain at least the lirllowing crrtcgorics of statistics: descriptive, nonparametric, simple regression, tIr'sts, ,rrrrrrlysis ol' var-iar-rce, and multivariate analyses. Multivariate analyses may
rnclrrtlc: rlist'r irtrirlrrtl lirrtctiott. lltctor itrtulysis. principle components analysis, l'('ll('rirl linelrr rnotlelirtl. linrr'sr'r'it's rrtt:rlvsis. Irrtrltilllc itncl stepwise regression. and rrrrrllrplt'l:rtlor ;11;;1l1rrs ol \';nr:urtt' Ilrr'llrtl't'sl ;lrtt'kltgcs (ttsttitlly olclcr packages
(on\'('rlt'rl lt()nr nrirtttlt,rtn(' ( ()nrlrttlt't',) stll rr)nl;rttt ;trltlilirrtltl st:r(islics. IlcsiclCs ro11lllrrrrrr1,llr,rl ,r ll,r,1'.r1,,'lrr'tl,,ttrr', llr. lr",l', \olt t('(1rttt,'. r'ltr'tk ,,n lltt'l'ltttEle ot'
MULTIVARIATE ANALYSES
handle missing data transformations that are available, the ability of the software to the ease of per(many packages clon't), the ease of importing'foreign'data files, and
orientation and time: circular statistics and
17 Spattal
analyses' torming last-rninute data manipulations or'quick-and-dirty'graphical packknown The best B)' (Appenclix Statistics software comes in several types versions' computer ages are those which have been modified from mainframe
spatral patterns
often uses comSoftware in this category is generally well-known and tested, and These promands which are familiar to users of the software in mainlrame versions' BMDP-PC (SPSS Inc')" grams include SIS-PC (SAS Institute, Inc.), SPSS/PC+ (BMDP Statistical Software, Inc.). Programs written expressly for microcomputers inclu
(Systat,Inc.)'
magazines and Reviews ol these programs are oflen published by conrputer perform a programs weeklies (e.g. PC Week). Generally, the mainframe-based memoryare but variety of statistical tests. are well tested and familiar,
Circular statistics are used to analyze data points that are distributed on a circle,
designed hungry and non-intuitive to use. PC-based progfams are more efficiently are often lor microcomputer hardware and for non-statistically-minded users' They are less and tests. statistical of but are less complete in their repertoire less expensive,
As examples, these data points may represent angular degrees from magnetic north taken by homing pigeons when they disappear from sight, or the periods of activity for gerbils during a 24-hour period. The calculations described below will
(but incomplete)packwell recognizecl by journal reviewers. A number of low-cost Systat Inc')' and free ages are available for microcomputer users (e.g' Mystat frorn (or'public- domain') software is available and surprisingly good, if limited (e'g'A-
they can also be easily applied to points in time using the same methods (Table 423). Readers who wish to pursue circular statistics beyond this cursory introduc-
locate a STAT). The user must decide what statistical tests are required, and price, compatibility, tests, package which provides the best combination of required
tion should consult Batschelet (1965, 1972,1981). The use of basic circular statistics proceeds in two steps:
larger
easy to use' and ease-of-use. Sy.stat (Systat, Inc.), while not inexpensive, is complete, well on microcomputers. check for academic site license versions of
and works many of these programs at your home institution'
such as direction and time (Figure 17.l).
only be applied to examples of directional data, but the reader will recognize how
I
Delerntine v'ltetlrcr llte ungulur diret'tions (or tintes) nteasured are ran-
if the sample directions randornly distributed. If the sample directions are in a unimodal
domly distrihutetL You can continue analyses only are not
distribution, then the tests will also determine whether the sample directions are significantly clustered about the mean direction.
: If the sample directions are not randomly distributed,
you can determine
whether:
u thc surnple meun diret'tiort (or tinte) cliJf'ers
signifit'antly./rom a spec'ified
diret'liort (or time ). a
ncl/ttr
b
thcrt i.t tr .sigttific'unt di//'erent'e in santple meun diret'tions (or lttlwt't,tt ll'o, or nlore, groltps of'unimals.
I Ircsc stcps irrc srrrnnriu'izctl
time,s
)
in thc (low chart (Figure l1.2).
l,'t I I SIS I ()lt l(r\Nl)()N'lNl
SS
()lr l)l It lr(' I'l()NS (OIl TIMES)
I ltr' t ltt ',(|lt;rIr' lt",l r'rttt lrr' tt.',',I lilt ttillilllr,rI tl,rl.r ',rrclr ;ts llrr' ttrnttlrr'r' tll' tlbsct'vltllt}ll', 111',('l'lltr'lll',,rl,l r llr lr' {r' 1' {) l'O' l'l' 'Il) ,rrrrl 'll" \(,1)" or lrt,lu't't'il
TESTS FOR RANDOMNESS
CIRCU LAR STATISTICS
Magnetic North or True Nofth 0oor 3600
Ol l)lltI:('l-IONS
0000 or 2400 h
OBSERVER
DIRECTION
N
Fig. 17.3 Directions can be divided into quadrants and equal divisions of quadrants for analysis with Chi-square. Orientation of the quadrants is dependent upon the research questiott, such as whether distraction displays are given primarily in the quadrant including the nest or in quadrants away Irom the nest (see text for explanation).
DIRECTION Fig. l7.l
TIME
time 1e'g' activity patterns' Circular statistics can be applied to directiorr or drawing bY Lori MiYasato)'
North and East, East and South, South and West, and West and North). If you have interval data, such as angular degrees (or hours and minutes) then the Rayleigh and Ztests are more powerf ul. When there is an expected direction such as would occur when homing pigeons are clock-shifted (i.e. photoperiod altered), then the
L/
test is
more powerful than the Rayleigh test.
t7.t.t the directions (angular ffiffi, ' degrees)nr"randomlY distributed?
This test (described in Chapter l4) can easily be applied to circulzrr data. It tests whether the sample of directions (angular degrees, times) diflers significantly from
METHOOS :
Less Powerful: Ch'i-square Goodness-of -Fi t Test More Powerfu l RaYl
randomness. It is not as powerful as the Rayleigh test or V test if you have interval dtrta (e.g. degrees fiom magnetic north), since that data is reduced to nominaldata
:
eiqh Test
(if there is an exPected
RandomlY
Can qo no
,/
in this test.
Not RandomlY Dlstributed
Dlstrjbuted
Chi-square goodness-of-fit test
Procedure:
Dividc tlie compass (or clock) into equal sectors (e.g. Figure 17.3). The rrrrnrhcr ol sectors and their orientation relative to direction is not
further.
oUfsJIOl, Are the sample mean directions siqni f icantl Y airierent fronr the sPecified (exPected) direcLlon?
HETHOD:
Calculate the Confidence lnterval around the --Hlp'l e nean d irecti0n nlrd rleternrlne if it incIrtrles the spccif iul rl irt:t:tirttr'
QUISTI0N: Are the two' or rnore' samole nean directiolls
siqni f icantlY
dif
fcretrt
?
rcstrictccl, except as stated in No. 2, below. Assigrr tltc cxprcctccl nuttrber of observations to each sector according to tlre lrypolltcsis ol' t'iutrlor)tt'tcss. That is, thc total number of your observa-
liotts sltottltl lrt' nttttlotttlv rtssir',nctl to llrc sccttlrs or cqually clivided l)('l\\'('('tl Iltt' st'r'l()t \ ( untl()l nt tlrslt ilrrrliort). lll,1l'1o
olr',1'; t ;tltottr
llrt rrl ,r't lrrl',
TESTS FOR RANDOMNESS
CIRCULAR STATISTICS
488
Table
17
date on the di,ytribution of'uv,t,ct urul .stilt distra.tion displays relati,e to the diret,tion tow.rcl.y their ne,sts (see
tlistribution divided into four or eight sectors (see Figure 17.3 ) Observed
Expected
A
25"1,
12.5"1,
B
25
12.5
C
25
text )
@-D2lE
Sector
Expected
Observed (O- D2lE
12.5
A
1.92
t2.5
20
0.00
E
t2.5
C
F
12.5
D
(25'/,,i) 20.25 (25,1,) 20.25 (25"1,) 20.25 (25'N,) 20.25
t4
B
G
12.5
H
12.5
D
25
f :2(O- D2lE:
or
Sourc'e: Data
( att'cty'
8l
t:5.94
from Sordahl ( 1986).
.fi'om tlrc nest ) /rom Tuble 17.2
Sector
the calculated value is
chi-square value to the tabular value (Table ,{9).
>
the tabular value then the sample directions
are not randomly distributed but are significantly clustered.
s If
0.24
Expected
Observed (O- E)llE
A
(50,'1,) 21.5
t4
2.61
C
(50"1,)
29
2.6t
your data are angular degrees from magnetic north (or time) then also
21.5
43
^):5.22
conduct a Ztest (below) fbr confirmation of significant clustering.
As an example, Sordahl (1986) tested whether the distraction displays
of
14
avocets and 5 stilts were directional, especially whether they would lead an observer
away from the nest. He stood
l0
20 m
Plays in the other two quadrants would be considered misleading,
nrost researchers.
from the nests and recclrded the position of
colored-banded, displaying birds in a circle with him at the center. F-or analysis, he divided the circle into lour equalquadrants with one of them subtended by an angle of 45o on either side of a line from him to the nest (Figure 17.3). He observed 8l clis-
rr
distribution) display distributions
IicirnI Iy
Il you use r.lnly the numbcr ol clisplays towru'tl
(tlrrirtlrirrrl A)irtttl rtwiry llont lltt'
('). tltcchi-srltrirrc lcsl sltorvs llrrl lltost'tlisplrrvs u.'r'tr'rlisltilrtttt'tl si1' trilierrrrllv rlillr'rt'ttl llt;ttt t:tnrloltt (st't' l';rlrlt' I / \) llo\\r'\r't ,lt"t,'.1';ltrlttt;' lltr tltr
rlilll'l'cnt.
I lris rr
lcsl ts trsctl lo 1lc1gl'111iltc whetlrer the sample of directions (or tirne) diflers sigilit'rrrr l lv ll ottt t'rttttlolt.t. -f hc tlatu nrust be in angular degrees from north (0.).
plays were in the quadrant away from the nest. the direction of'all dislnrciiorr tlisplays was not significantly different from random.
by
t"t.t.2 Raylcigh test
is given in Table 17.2.
The calculated clii-square value of 5.94 is smaller than the tabulitr vulLtc (3 dl. alpha level:0.05) of 7.81. This means that although most of the clistrlctiott dis-
if not invalid,
Tlre calculated chi-square ol 5.22is larger than the tabular value of 3.g4 (l di lPIla level :0'05 ); therefbre, the number of clisplays in these two quaclrants was sig-
rr i
traction displays from the l9 birds. The observed and expectecl (based ort a uniflorn.t
ncst (cluaclrant
l
Ey:
+ Compare the calculated If
3.78
18
Table 11.3. The c,hi-square gooclness-o/-fit tubte using only the duta.fiont Sec,tor,; A ( tot;-arcl the nest ) ancl C
Calculate the Chi-square value (see Table 17.1):
,.r:r(oALE
29
-
The expected number/sector must >4.
:
489
Table 17.2. The c,hi-scluare gorrlnt,.s,.s-o/-lit table /or
.1. Layout./br c'hi-squure goodness-o/-fit tesl of' data.fiom a c'ircular
Sector
ot, t)llrrr(,.t.tONS
Ittol'1'111111''
t
( 'trlt'rrl;rl('llt(.\lltn ,'\
\
'I t:ur )',rrrr.
1r1. 11.,,.,1
ol
llrr, \///r,\ iuttl r.r,r///r,r ttl.tltc slrtttltlc tlil.cctiOrts. T0ble
l,,t ,.tllrr.t ,rtr1,rtl,1
rlt.1,1t.r.r
pl
li1lr.,.
I
TESTS FOR RANDOMNESS
CIRCU LAR STATISTICS
490
northeast o/' c'ampus while blindfolcled
Alpha level N
0.0s
0.01
0.001
30
2.91
4.50
6.62
50
2.98
4.54
6.14
100
2.99
4.57
6.82
200
2.99
4.59
6.81
500 (or larger)
2.99
4.60
6.89
r
c2)
Calculate the test statistic Z'
Z:RzlN +
Directions pointed while blindfolded (angular degrees from magnetic north)
R'
R:V(sr+
where: N:number of sample directions
Clompare the calculated
Zto
the tabular Z"inTable
17
'4'
+0.2156
-0.9613
88
+0.9994
+0.0349
t44
+ t).5875
-0.8090
328
+0.8480
290
-0.5299 -0.9391
-0.4067
tt4
+0.9135
180
0.0000
128
+0.7880
t52
+0.4695
108
+0.9511
178
+0.0349
-0.469s Totals:
R:V(s2+ c\:t Z
Asanexample'Icollecteddataonthedirectionalorientationabilitiesofl3stu-
miles northeast of the
+0.0698
+0.9976
When the sample directiops are nonrancantly clustered about the mean directiorr' mean direction and cletermine whether dom, you can go on to calculate the sample direction (e'g'home)' it is significantly rJifferent from the predicted
10
Cosine
86
208
cues when they were deprived of normal visual dents in my ethological methods class to a van in a postion). They were blindfolded ancl transported
Sine
164
the directions are nonIf the calculated Z is greater than the tabular Zu,then directions appear to be unimodal' then random. If the distribution of the sample proves that the sample directions are signifisignificance in the Rayleigh test also
(landmarks and sun secludecl location opf .o*i,rutely
491
Table 17.5. Direc'tions l3 blind/bldecl stutlcnl,s poitttctl to intlit'ate the direction to the Colorudo State Univer,rity camplts a.f'ter huvittg bacn driyen approximately l0 niles
Table 17.4. Critical values o.f Zu
z Calculate
Ol l)llLl:("1'IONS
:
R2 I
N
:
S: +4.078
(16.630+3 1.047):\/ 4i .677
47 .67 7 I 13
:
:
+0.3420
-
L0000
-0.6157 -0.8829 -0.3090 -0.9994 -0.8829
C: -
5.512
6.904
3.661
Since the calculated Z of 3.661 is larger than 2,,,,(2.97) we reject the I/n that the
directions pointed by the students are random.
Colora{o State University
and to provicle data lor the students to analyze campus. The exercise was conducted use to ability their the methods' It was not a valid test of
to allow them to critique
Able' field (see Baker, 1980' 1987; Gould ancl other cues, such as the earth's magnetic stttclctlts were not coverecl' and some tll'tlic 1981); for example, the van windows rt cttc ttr its srlll afterngotr the the heat from sitting next to the windows reported using still w'ile v.rr trrc inclivicrually red fr..r westerly direction. The stude'ts were the
'l hcv
the canrpus fi-..r thcir r,r'sc.l blindtblded anci askecl the direction t. ''rrsiti,' (). i'r(l tlle. rir'st wrrrr rrrc hrirrtrirltl still were then askecr t. p.i.t t.war.trs c.,rrr:i. rlrr.strrrrt'rrrs p.irrt'rr lvrrirc lrrirrtil'rrrlt'rl. with trrc bli.tilirrtr r.e.r.vctr..r'rrc trir.ceti.rs
rlt'1't('("':ll('l'l\t'll lll l'rlrlt'l / \ Ittttllltt.siltt.:l:tntlt'ositlt'srrl llto\('illll'tll;lt
t7.1.3 V test Whctr tlte rc is art expected direction, the Ztest is prelerable to the Rayleigh test since
it
is rttorc powcr'f'ul. For example, when homing pigeons are clock-shifted 6 hours
lrrtc. llteir crpcctcrl tlisappcirrance direction upon release is 90" clockwise lrom the
'l'lrc I 'tcst lvill lcrrrl to sigtrilicance only if there is sufficient clustering lrl,,tttttl lltr' ptr'rlir'lr'tl tlitt't'ltott Ilr)\\'('vcr'. tlrc l'1cst shoulcl t>nly be ttsed to test Ior r;rtttl.rnrnt'ss. ll rl,'t's rrol lt",l rrlrt'llrcr llrt's;rrrrplt'rrrclrrrtlircctirlrt rlevitttcssignifi(:tttll\ ltotttlltr'Ptr',1t,lt',1rlttr'rltrrtt lltt'rlttttrlt'ttr't'tttlt'trltlsltottltlltettsctllirrlltlrt 1' )l l)llll)(r"t'(ll,rl"t ltt'lt'l lt)lil,('{",(r ltr,tt | Itorrrc tlirect rort.
492
SAMPLE MEAN AND SPE('ll
CIRCI]LAR STATISTICS
We determine the sinc arrd cosinc
Procedure:
I
R
cos(O-
r:
0,,)
of the mean angle:ffi/r': *0.3l4l5.3l l: +0.5912 Cosine of the mean angle:c-os lr: -0.429rc.531 l: -0.8017
R was calculated previously (see Rayleigh Test)
the test statistic
Using Table A23 we find that the angle whose sine approximates +0.5912 and whose cosine approximates -0.8077 is 143.5". This concurs with our
rz.
previous calculation.
Calculate test statistic lr.
':J(i)(')
@:sample mean angle: 143.5"
where: N:number of sample directions
0,,: predicted direction (campus): I 96'
Compare the calculated a to the tabular value lr,, (Table A24). tf the calcuand laled uzu,, then the sample directions are not randomly distributed
Vt:
R cos(@-
are clustered significantly about the expected direction.
143.5"- I 96o:
Irtrr t ": J(;)(
(
(see Tuble
r7-r
: *0'3 I 4 cos: C/l/: - 5.5721 13 : - 0.429 + 4.01 8l 13
slnlcos: -0.7319 UsingTableA23wefincJthat l4Sohasatitngcrttol'0.7.516rttttl l'1'1"Ititsrt
ol -(\.7265.
Sincc oltr citlcttllttctl tlttlgcttl
ol'
rnately pritlwiry [ctwccrr llte twrl lltttgcttts irr tlrt'l:tl'rlt'.
O'71 l() is
()lll tlrt'll
rtpptori-
rvt'ill)l)l()\tttlltlt'lltt'
sittttltlc ttlc:t ll :t ttglc lrl I'l 1 5''. .\'r,r.rttttl tn(,tlttt(l^lts lt r'ltt't k ()l)
.5"
'
lz
zo+ 0.3922 l:.,/ i.r+ ):
(4.204\:
I .648
llt,'ltt"l ttt,'llrt'tl
Lr
(
1.648) is larger than the tabular
Ilirylcigh test.
A23)
where:
tangent
307
with the tabular rr,, (Table A24). Since the calu,,of 1.647 we reject the l/,, that thc sample directions are random. This concurs with the results from the
(sinlcos)
sin: S/N:
:
We compare the calulated a
culated
:angle whose Tangent equals (;fi/cos)
52.5"
:6.904 (0.6090):4.204
Fir.st methotl:
lD:arcfan
-
:6.904 (cos 307.5')
reject the FI, of randomness.
First we calculate the sample mean direction
0,,)
:6.904 cos (143.5"- 196')
For example, since there was a predicted direction (Colorado State University above' we campus) for the data on the students used in the Rayleigh test example randomcould have usecl the v test as a more powerful test. Even though the ^f1,, of data to same the to test V |he apply we'll test, ness was rejected using the Rayleigh to direction predicted the around determine whether there is sulficient clustering
I
R was calculated in our exarnple using the Rayleigh test.
RIN
Sine
for calculzition of sample mean direction'
:
use
:6.9AU13:0.531I
where: 0,,:expected direction (angular degrees) @:observed sample mean direction (angular degrees); see below
z Calculate
ol'thc sirnrplc mean angle and then
491
Table A23 to find the sarnple moan anglc.
Calculate Zr.
Vt:
ll'l) l)ll(l:( l'lON
l)ll,'lrlll{l:N('ll BETWEEN SAMPLE MEAN DIRECTION (()l{ l'lMlr I'}IlRIOI)) AND SPECIFIED DIRECTION (OR SI'lr('l I'l lrl) l'l M Ir I'}tllt I()t))
Ilrt'torrlirlt'nt't'inlcrrlrl is
rrsetl lo tlr'lt'rItrirre w'ltctltcr tltc sittnplc tnean direction n()n r irrrrlonrl\ rlisl r rlrult'slrrrtPlt'(rlr'lt't ruinr'tl rrlrove) is significltntly dillcrent ll(,llr,r Irt'rltr'lr'rl or s1r1'1;li1'tl rlttt't lr()n \lr( lt:r', lt,)ln('\\irtrl rlttt'r'liott l'ltc lirsl slcl'l is I r o111 ;1
lil
111'11'l
ttttttr'
l
lt,",;ttttplr'
lll(',llr rlttr'r
l tll11
1,,rt1,rtl,rt rlt';'tr'r"')
DIFFERENCES IN SAMPLtI Mlrn N I)lltlr("1'lON
CIRCULAR STATISTICS
Procedure:
t
t7.2.1 SamPle mean direction
using the second method to determine the sample mean angle.
the arithmetic The mean of the sample directions cannot be determined by taking if you assign even three), than mean of the angles (especially if the sample is larger examples)' for 1981, degrees to angles between 180" and 360' (see Batschelet,
r:RlN z
minus
Use one of the methods described below'
z
:
and C were calculated in the Rayleigh test' above'
:
5, the
interval in degrees about the mean.
direction (e.g. home direction) lies outside the confidence interval then the sample mean direction is significantly different than the
ar ctan
f si
n-elco si ne
specified direction.
CIN
Calculate the sample mean direction (mean angle; @; (D
or A2 (for 95 and 99o/o confidence, respectively), use r
: If the predicted
sine:sine :S/N
Mean cosine:Eosine S
Al
Nto determine
Confidence interval (95 or 99"/,,7: qt-r 5
Calculate:
Mean
From Figures and
First method:
I
Calculate the length of the mean vcctor (r'). This was calculated above
As an example, we will test whether the Sample mean angle for the l3 blindfolded students (see example in Rayleigh and Ztests above) is significantly different from the predicted direction (Colorado State University campus).
I We already calculated r as 0.531 l, and N: 13. z Using r and Nwe extrapolate on Figure Al and find that 5 is approxi-
I
whose Simply stated, the sample mean direction is the angle in Table A23 cosine. mean tangent equals the mean sine divided by the
mately 44o for the95"l, confidence limit.
:
The 95(Zr confidence interval: @* 6
Second method:
:143.5o -r 44"
This method is a good check on the first method' t Calculate r (length of the mean vector)'
:99.5" to
r:
Since the predicted direction
degrees) are not significantly different. That is, the mean angle
z Calculate:
of the
directions pointed by the blindfolded students differs significantly from the direction to the campus.
ly':number of samPle directions'
173 DIFFERENCE
IN SAMPLE MEAN DIRECTIONS
sine of the mean angle:sine/r
I}ETWEEN TWO, OR MORE. GROUPS OF ANIMALS
cosine of the mean angle:c-osine/r
l'hcsc tcsts arc used to determine whether there is a significant diflerence between
thtlse cltlThe sample mean angle is the angle whose sine and cosine cqual culated in Step 2 above. Use Table A23'
llrc slttnprlc mciln directions (or times) of two, or more, groups of animals, such as a t'orrlrol gr'()up ancl one, or more, treatment group(s). r7.
17.2.2 Confidence
r.r
'l'wo s:unplc nurs (csl
interval
(ltttgttlltt'tlcgl'ccs)lttc ttsetl lt' Confidence Intcrvals abrlut thc suntplc nlciln tlrrt't'tiott ll()lll tltt'Prt'tlit'tt'tl tcst whct5cr l5c sirlr1'llc nrclrr tlir-cctiorr is si1'1,11,, ,rrtlv rltllt'lt'ttt tlit'ce t iotr
187.5"
196') lies outside the confidence interval for
the sample mean angle, we reject the H,,that the two directions (angular
RIN
R was calculated previously (see Rayleigh test)'
:
(
I ltts ts;t r;uit'k ltttrlsitttplt'l\\'o s;unl)l('l('.,1 l() ('()n(lu('1. lt is rrrcrcly an application of It\o srlnplt'ttttts lt'sl. tlt",r ulrr',lr',rt lt,'t ttr ( 'lrrlrlt'r l l l lris tr.'sl rctlrrccs intcrvitl clittit Io ottltn;rl .l;rl,l ;lrtl ',lrorrlrl rrol lrr' u,r'rl rrrllr l,rr1,,",,trrrPlr's rirtr't'its l)()wct'is low ( ll,rl',t lrt'lt't l')li I t
496
C' I
RC' L]
LA R STATISTICS
DIFFERENCES tN SAMpLt: Mtn N t)llilr(..t.tON
Procedure:
Plot all the sample directions, fbr both groups, on a circle or list thern all in order, indicating which group each direction belongs to.
Table 11.6. Diret.tion.y I3 ,;.tutlt,tt/,t. ltttitttal, .t,till blind/blded und after the blitrd/itltl,s, trL,rt, r<,rtrt^,r,tl, indic'ate tlte direuiort to the Cttlorudo Stutt,
Determine the total number of runs (i) in the two samples. If the same direction (angular degrees) occurs in both samples (ties), their positions
ofier hat,ing heen driyen (tppt.t).\.iilt(t/c11, l0 tttilc.y rutrthetrst of' c.ampus while blincl/itldad. Thc
in the order must be randomly allocatecl (e.g. coin toss). Find the probability for /i in Table A25, where rr and ru are the sample sizes of earch group; if there are diflerent sample sizes, rz is used lor the
nteosuret?rcnls ure orclered to tletarmirtc lha totul numher of' runs./br d Ttt,o Sontple Run,t kst
of
smaller sample. You may have to extrapolate the probability lor your cal-
'signifir'ant cli//brenc'e in tlirection,s pointecl under the ttyo c'ortditions (.;ee text )
culated fi. As an example, we will use the data fiom the 13 blindfolded students that we used
Blindfolded
in the Rayleigh and Ztests. We will test whether the directions the students pointed
with the blindfolds on differed significantly frorn the directions pointed after the
86
blindfolds had been removed. Table 17.6 shows the angular degrees from magnetic
88
north listed in order for each sarnple. The total number of runs (l) is 13. Using Table A25, we can see that when both sample sizes (rz and n) are 13, the number of runs would have to be 9, or less. in order to have a probability less than 0.05. Therefore, we cannot reject the I1,, that the
Not blindlolded
Runs
106
2
ll0
4
108
J
112
lt4
two samples are the same (i.e. drawn from the same circular population).
r28 17.3.2
132
Watson-Williams two sample test
140
This is also a test lor whether two samples of directions (angular degrees) are signil'-
142 144
degrees) resulting
icantly dillerent. However, this test uses the interval scale of measurement (angular' in a more powerful test than the two sample runs test described
152
above.
164
n0
Procedure:
t
178
Calculate fbr each sample:
t78
S,:sum ol the
sines
lor Sarnple I
S":sum of the
sines
fbr Sample
:
180
182 2
188
surn of the cosines for Sample
I
C.,:sum of the cosines lbr Sample
2
C,
R,:V{S,r+ ('rr)
/t, v'(,t,'t ('.')
t,
Lltrivcr^sit.v
r0
194 198
l0E )()(
II
)
ll) ' I
)l-i
tl It
491
LAR STATISTICS
CIRCI.J
498
z Calculate
DIFFERENCES IN SAMPLT:
Table 17.7. The ,s,ine ancl .r.s.irt<'.fitr th<, ttrrgrtlur degrees (directions) the l3.student:s prtitrtt,tl uf rar thcir blind/blds y)ere renloved (,;ee Tuhla t7.5 ). These tlata are used in the Wutson-Williunt.s, two ,sumple test (.see texl )
s:s,*S, C,
R.:V(E,+c.,) Obtain the value g.
a.
Calculate the mean vector length (r.).
,:(4rt4r) N. b. Calculate
the mean sample size.
-N N:; c. From Table ,{26 obtain
an estimatecl value
of
& by using the mean
vector length and mean sample size (calculated above) instead of r and n, respectively.
d.
Calculate g:
g:l+: "8k
1,t
Calculate the test statistic l-:
Angular degrees
Sine
Cosine
ll0
+0.9391
194
-0.2419
n2
+0.9272
140
+0.6428
-0.3420 -0.9703 -0.3746 -0.7660
302
-0.8480
+0.5299
t42
+0.6157
-0.7880
106
+0.9613
-0.27 s6
r98
-0.3093
-0.951
132
+0.7431
-0.6691
F:t!(N -2)'N.
s
178
+0.0349
-0.9994
-0.0349
-0.91)94
170
+0.17.16
-0.984ri
188
-0.1392 Sums: S.: *3.465
- 0.9903 (',: - 8..st3 I
(Rr+Rr)
Compare the calculated F with the tabular value (Table ,46) where: df: I (for the numerator) and N -2 (for the denominator). Note that all the tabular values lbr this test are in the first column of the table. If the calculated Fis larger than the tabular Fthen the two sample mean directions
As an example, we will again test whether the l3 students pointed in signilicantly
different directions when blindfolded and with the blindfolds removed.
t
We will designate the blinclfolded group as sample
S,
:
..
V( + 3.4(r-5r+
Wc trou' r'trlctrlirIc
,ry, /vr i/v,
and C,.
t4.078 Cr-
: V(r + 078r i- 5.572r) :V( 16.630+ 3t.047y:f ql.oll -(-t.904 tr)
Wcltttwcltlculirtctltcstttnol lltr'sinr's;ur(l(()\nl('\lot lltt's;rnrplr'u'rllrorrl
/l
9.2-s4
lirr Ilrc crlnrltilrctl sirrrrplcs:
llllt.)(,
\', ,\'r I ,\"
-5.572
blirrtllirltls (srrrrrplr'.')( lrrlrl..' I i
3.,58
Vt t :.olro r 73.(,3.1;: Vs-s o.tq -
l: thcrclirrc, rclcr-
ring back to the Rayleigh test example, we have alrcarly calcrrlirtcrl
Sr:
R,:V1S,2+('r2)
R,:V(.!,r+(',r)
are signifi cantly different.
I
182
R'+R2-R..
'
49e
for the combined samples:
{:N,+N, Cr: C r*
Mln N t)il{t:(,'IION
l,l (l/li t( t I lrr',f
'
',
lt
5OO
CIR('ULAR STATISTICS
:\-
5.572)+
R.:V{S.2+
(
-
8.581
+
): -
q')
(
V(so.896 + 200.307
R, + Rr) _
):t/ of
Lsl .203
-
I
R,,Rr'..Rk
z Calculate
26
R.:VE'+c'
:13
From Table A26. using r and N instead of r and n. respectively, we obtain an esti1.50.
s We then calculate a1
-')
c'
+ R.)
From Table 426 obtain tl^re as/irrrutcrlvaluc ol'/i by rrsirrg r culated above) as estimates ol r.ancl l in thc tirblc. d. Calculate g:
tr: l* "8[
0.r28 (24)^ ' '9 _ ^:(30) (0.013):0.39
We then compare the calculated
where the
df:
I and
N
).
where: k : number of groups (sanr ples)
742
z
(N
N:Nrlk
the test statistic F.
- ^ (6.904+9.254)- 16.037 l'25 ()6-2) 26-(6.90 4+g.254)
:t.25
+Rr)
b. Calculate the mean sample size
(R,+R")-R
:
Obtain the value of g. a. Calculate the mean vector length (r).
_ (R,+R,... li.
g.
'ff.-(Rr
r
r:l-
:l*0.25:1.25
o We then calculate
'
+Nk
{:S,+Sr...*So C,:Cr*Cr.. . +Ck
-:0.621
g:l+ -):l+ "8kt2
lor the combined groups (samples):
{:N,+l/r...
26
of
ck
.q.
: 16.158
mated value of k
Calculate S, Cand R lor each groul) (srrnrl.rlc). Llse the same calculations
C| Cr.
6.031
6.904+9.254
N.
_N26 ': ,\22
.501
S,,sr...sk
t4.l 53),]
We then obtain the value
-_
t
14. I 53
MtAN r)tt(I(,.t.tON
as were used in the Rayleigh test.
: V[( +7 .5412 + ( :
DIFF'ERENCES IN SAMpt_t:
A, (r.,rl
-l
Calculatc the test statistic F:
f (0.39) with the tabLrlar value (?rblc A6)
r-2:26-2:24.
rlrtl
Sirtce our calculatctl 1"tt('0.39 is
smaller than the tabular value (4.26) we fail to rejcct thc 1r',,o1't.to significant difference between the directions pointed r,vhcn blindlirldcrl :rrttl nol
blindfblded.
Ix:R,-/( l: '-,,(N, (( I tt //..- \ /(,
)
)
('onrr)irrc thccirlculutctl /,'witrr thc tirbular-virlrrc ('llrble n (r). \\lr(.r(. (ll I rtrttl ly', L ll'thcclrlcullrlctl /,'isl:rr.gcl.llrirn ltrcltrbrrl;rr /,.llrt.rr llrr.s;rrrrPh. rtt('rn rlirt'r'li()r)\'r'('lr()r ;rllt'r;rurl Ilris is rr rr.,st lirr lr.rrrt),,(,,(.rr\.:un()rr,,,l ;rll tllt's;tlttlllt'nl('intrlrrt.t.lrorrr(lllrlrt.ltt.lt.l.
17.3.3 Watson-Williams multisanrplc lcsl
Tlris tcst is rrsetl lrl tlr'lt'trrrirrt'u'ltclltt't llrt's:ttrrplc nr('inr rlttt't lr()lrs lro1v1 llnt'r'.,,t nt()r'('. I'l()ul)s ;il(' st1,1111tt ltttll\ rltllt'tt'ttl l'r,'t t't rl tlr, ',,rn(' \\ir\ ir', lt,1 llt,' W;tlsorr \\'rllt,ttrr', l\\r)',,tlnl)l('l(",1 rtlt,,\,' ('\( ('lrl llt /, ,,ttrt1,l,'.
tltst l tlttttt;tlt()n ,ulrr1;1 ltrltt lrlrt,rl
l()liI)lrrtrltlr)(.sn()l ;rll,rrr lor
.,,1tr;r1r,1111,;1tt
rlitr.r.lir>tts
: SPATIAL PATTERNS
CIRCULAR STATISTICS
ANIMAL ANIMAL AND 17.4 SPATIAL PATTERNS: ANIMAL ENVIRONMENT of anirnals relative to: 1' other animals Ethologists generally measure the location environment (animal environ(animal-animal; e.g. mother-infant); and 2. the spatial relationships are ment; e.g. home range). Although arrimal-environment measuredrelativetothephysicalenvironment'tlreyolienreflecttlreet-fectsofother
animals(intra-orinterspecific).Forexample,Lockie(1966)loundthatwhenthe
dramatically' they rarely encountered density of weasels (Mustela nivalis)decreased disintegrated: then' rather than each weasel's each other, and their territorial system they waldered throughout the study home range being restrictecl to its territory, area and all their home ranges overlapped' of the analysis of two types of The following discussion is a brief overview to the literature' An excellent synopsis of spatial patterns with selectecl references and Fingleton (1985)' Circular statistics spatial data analysis is providecl by Upton earlier in this chapter' and the for analyzing animal orientation data are discussed spatial analysis are discussed in videotape, digitizing and computers in use
of film,
Chapter 9.
(1984) was interested in the association ol-intlivitlual ycllow baboons with different groups of individuals (defined spatially) within thcr r.r.roving troop. He used instantaneous samples of focal animals to record tlrc unrr>unt of tirle they spent in each part
of the moving troop (front,
side, rear, middle or clusters; see Collins, 1984, for a
diagram). When associations between individuals are based on distances, the researcher must decide on a criterion distance between individuals in which the probability of their interacting greatly increases and beyond which they commonly approach one
another in order to re-establish the association; this decision is based on the researcher's experience. For example, Grant (1913) used as his 'measure of association'between individual grey kangaroos (Mocropu,s gigunteus) the number of times each animal occurred within 120 cm of another at set l5-minute intervals (instantaneous samples). With this procedure, the accuracy of observers in determining the distances between individual animals can be a problem, especially in field studies involving distant observations. For example, Morton (1993) measured the accuracy of observers in determining the locations of individual elk in small herds. The distance discrepancy between observed animal locations (from observer diagrams) and actual animal locations (from aerial photographs taken simultaneously with ground observations) averaged 5.6 body lengths. Sullivan and Morton
(
1994) measured the
ability of ground observers to judge inter-animal distances in groups of life-size 11.4.1
Animal-animal spatial relationships
Animal-animalspatialrelationshipscaninvolveintra-orinterspecificassociationsthe the research question can require between individuals or groups. Additionally,
or between known individuals over time' measurement of distanoes or associations simultanesampled being of the group spatial relationships among all members
artifical deer. They took photographs from different observer viewing angles of dif'f-erent herd sizes with the animals oriented in different directions (facing away or perpendicular to the line of sight); then observers diagrammed the animals' locations from the photographs. Larger herds, lower viewing angles, and perpendicular orientation of the animals produced greater discrepancies between observer perccived and actual animal locations.
ously.
your ability to accurately determine their The observability of the animals aud to each other will influence the type positions, either in the environment or relative
ofsamplingmethodemployed.Themethodsbasicallyinvolvetwodifl-erentproce-two recording when (frequency and/or duratiou)
Enclosures or small field study sites are often gridded to improve the observer's :rccuracy in determining animal locations. For example, Vastrade ( 1987) studied the spacing behavior 1.25 th
ol nine free-ranging domestic rabbits in a meadow gridded into
mxl.25 m squares; he scan sampled the animals'locations every l5 min. for
rcc 24 h pcriods and recorded their locations
irr rrrcitsttring thc spacing pattern recl.s.
within individual quadrats. To assist
of butterflyfish (Chaetodon trifust'iatus) at coral
Srrtlon (l9ll-5)surveyed an area of approximately 8000 square leet at each of
lrissltrtlysilcsintougricl systcr-r-rol'l0mXl0mquadrats; thegridlineintersections
generallyinvolvesall-occurrencesorfocal-animalsan-rpling.Thcscctltttll)r()cc(lIlI.c etrclosure stuclie s atrcl uscs sclttt sittttplittg' is more common in laboratory or
\\('r'(' nr:rrkt'tl wil lt cotlctl strllsrrrltrcc btroys.
irltlir,'itltrlrls ttsctl tlr tlelirle iIll ;|\\()(.liI Mitani t,l ttl.,l99l), tlr tltc tlistitttcc bctwcclt ('\lx'lI rrrt. r('\('rrr'rrr'r's.lr;r'r'ri't's lrrrrl
Polllott:tl lo lltt' stzt' ol lltt' t1u;r,lrtl.' tt'l:tlir.' l,r lltr' sizc rll' lltc itttintitls. since an ,urtnr:tl ts tct,rtrlt'rl r,ttlV rts lrctn;, ltt(",r'tll tlt ir l);rt ltt'ttl;tl 1,t itl. t('lt,ttt'(llcss ol'its itctttitl
(cl'' rlistuttcc i'rctrvcctt itttli\ititrrtls when the first pr.ceclure is usccl, thc critclion
ti.rt. tlcg.rcrttls,rr
(rrc sPecics trrrrre r sttrtrv lrrrrr
lllt rlt"l'tttr t"' lrt'l\\'('('ll lll(lt'ttlttltl., r'illr tlrlrt s1tt.t i,.'s. ltl s.tttt' sltl(ll('\. l,'::' "Pt't ( trllttr" lltt'l'lr)lllr" rrtll "rrlltt' l ']l ('\'llllll[t' irrr,l til,It(.[('il('t;rlrZ.',l lot,tltolt', trllltttl
cttt.r.
lltt'tt'soItlliott ol'ltssot'i;tliott rl;tllt l;tkt'lt
l't'()lll lttlilltttls itt rr gridded area is pro-
lr()',rlr(ril ilr llt.rl I'tttl l't1'rttr' I / I rllil',ll,llt , lriltr ,r l;rt1,,'l'il(l sYSl('nr nr,llV nol cl.'l1t';U rt'llr'r I tlt.',t, lrr,rl rlt',(,tll((', I'r'l\\', tr ,ltrtln,ll lrr,lrrr,lrr.rl',.\ ;ttr,l ll;ttr'ttt il(ll()nltnt'
504
SPATIAL PATTERNS
CIR('LJLAR STATISTICS
#A
B
h
#c
of eight individual pronghorns (A H) in two gridded Lori Miyasato)'
enclosures (see text for explanation; drawing by
qua
viduals E and F, and G and H, which are actually the sarne distance ttpart br,rt separated by one and two quadrats. respectively. lf the grid on the lelt wits tlivitlccl inttr an 8x8 format, then grid columns 2 and 3 would be halved, separatillg intlivitltrtrls C and D by two quadrats, but leaving A and B in adjoining quaclrtrts: this wottltl more accurately reflect their true clistances. However. smttller clttitclrltts tloti't ltlwltvs improve resolution uncl accuracy sincc it ttt:ty bc tlillictrlt ttt tlclct'lttiltc rt'lticlr
qttittlritl ittt ltltitttltl ts ilt. 'l'lrclrccrr;lrevel';rrril;rl-lot'lrlitlntllrtlt lltt.slrrtlv;rrr.:r.llrt.tlt'rrsrllol
lll l)illt.llvlltt'stz.'ol lhrtttl,tttlsttltttttlt'tl llt,',pt,t,lt,tl.'tt'l,rllrr'lolllt'\l/('
lrrrirrr;rlslrtttl lltt'\t/('ol
F
#
D
Fig. 17.4 Hypotheticzrl positions
h
rfE
\,
50.5
G
h H
trl- tlre animal. To control fbr this potential problem. Burgess (1979) compared the sllatial behavior of rhesus monkeys. neon tetras. communal and solitary spiders,
cockroaches and gnats by observing them in gridded open-field arenas, scaled to the Ittlittral's size. FIe scan sampled the animals' locations by photographing them at irr
tcrvals.
Accttrttcy is also aff-ected by the criteria for deciding when an animal is in a tlttittlt'ut urttl thc observer's ability to nrake that determination (affected by viewing :rttglc. tlislitttcc itnd oricntation of the animal). Researchers have often designed 'pecirtl ctlttiprttcltl itrtrl tcchniclucs to give them r-nore accurate animal location data. I o; g'.;111111le. l'ilcltcr'(l()71) rrscrl ir gritltlcrl llow tank. mirror and camera (Figure l/ 5) t() tll(';t\utt'ltttintltl-lrttirn:rl sp:rli;rl relrrtrorrslril-rs in his research on minnow ''t ltool1111'.
,'\ttttttltl" t'lltt lrt';tr't ttt;tlt'l\ 1,,,,11,',1 ttr llrr' l;rlrr,1;11()t\'. r'lt('l()sut'cs, ()t'snlitll sttrtly ,ll(';l',. Itt lrlrllllll' ot \ltlr',)l,tlllttl'ltull rlrrr'r ll\ ,llr,rrr'(,,r ;r lrrl'lr rit'u'itt1':tttp,lc)lttttl (',lr llt,'tt rllytlttllllr lt,tttttrt,rl', l,r ,tlt,,n lt,,ltt.t ,.r t, r'1 rrl 1 1,1,.,,t111r11g1,,, l,irt r'f;tlt1llt'.
SPATIAL PATTERNS
CIRC'ULAR STATISTICS
of
members
the group or aggregation sinrultirncously (c.g. nearest neighbor analy-
sis); or 2. the degree
of spatial association bctwccn individuals, groups or
(e.g. association indices or coefficients
species
of association).
reom flow
ish
l7.4.ta Neayest neighbor analysis
colibroiion grid on f ront of tonk in observotion zone
There are several formulas available for calculating indices of 'aggregation', 'cohe-
sion', 'crowding'and 'dispersion'which reflect the overall spatial relationships of animals in a group or population (Southwood, 1966). One method is nearest neighbor unalysr-r (Clark and Evans, I 954); lor example, Campbell ( 1990) used this analynstreom
conlrol gole
sis in
describing the spatial relationships of singing crickets.
There are two basic approaches to nearest neighbor analysis: L select an individ-
ual at random and measure the distance between it and its nearest ncighbor (true nearest neighbor technique): and 2. select a point and measure the distance to the
nearest or nth nearest individuals (closest individual techniques). The simplest formula is from Clark and Evans (1954). of the flow tank and apparatus used in experiments oll minnow schooling. The Xaxis was taken as running along the length of the tank;the Y axis across itl ancl the Z axis wirs tzrken as vertical. Therefore the photographs showed fish and their reflections in the XZ plane only (from Pitcher, 1973)'
density per
Fig. 17.5 Diagram
Mankovich and Banks ( 1982) Llsed time-lapse photography and computerized film digitization to analyze the position and social orientation of five female dornestic lowl in a flock. (See Chapter 9 for aclditional discussions of the use of films and videotapes lor spatial analyses.) Analysis of location data frorn animals in a gridded areii can be based on distances (quadrats) between known individuals or overall spatial paltterns between all
members of the group. Aspey (1977a.b) has written computer programs in BASI(' for computing inter-individual distances within a gridded rectangular areil
(RECDIS) and a gridded circular area (CIRDIS). Strickltn ct ul. (1971) describecl ir FORTRAN program which analyzes relative distances between individual anittlttls within a gridded enclosure (square, circular, or rectangular) ancl also cottsidcrs citclt animerl's angle of orientation on a 360" scale. This allows the calctrlatiort ol'itrtgttlitt' relationships and a determination of the angles any two indivicltrals wottltl ltitvc ltr turn in order to be facing each other. Ludwig and Reynolds (l9t{tt) tlcsct'ibc cotttputer programs in BASIC for scvcral typcs ol'spit(iitl prtttcl'll ttttitlysis. sttt'll rls pa ired-t; tuttl
nt( vlt t'ilt ttcc.
l{cgitltl lcss 9l'ltrlw rl is 11lttlrirrctl
1c.1'.
1'.r
itls, lrtolt'lrtttt'1t1. rltl'tltzt'tl ltottt tttlt',,
llrpe),ltttitttllllot';tliotttl;tlltt';tllllt'ttst'rllr';ttt;tl\zt'ltlr'"'1t'tlt'tltt'l;tltottsltt|s'rllrll
where:
i:mean
unit urro:*:
O!--
distance between nearest neighbors
When second, third, . . . nth nearest neighbors are measured, the fbllowing statistic ('lhompson, 1956) can be calculated, which is distributed as a I with 2lldegrees of ll'ceclom: ; trrnl
Sr l a'rt
//
Soutltwood (1966) can be consulted for more detail on basic nearest neighbor ,rrutlysis. Ripley (1979) described the complication of edge effects caused by rrclrcst neighbor analyses being applied in small areas. Donnelly (1978) pror itlctl firrmulae to correct for edge effects, and DeGhett (pers. commun.) ,lesct'ibccl methods lor dealing with both the effects of edges and small nrrrrrbu's. Stapunian et ul. (1982) suggested using sampling grids where edge ,'lli'els irrc a prroblem.
t -..t.
th
S('\'('tirl
..lssttt'iuliott indicts
tt.t,t'ot ittlitttt irttlitt'.t trsctl to ntcirsut'c't ltc li'cr1 trcnCy of aSSOCiatiOn betWeen rrr,lrrtrltrtlsn'r'tr'tr't'icttr'rl lrr'(':rirts;rrrrl Sr'lrrr'rr1rt'r (l()l'i7)irrrrl (]insbcrgandYoung (l')')tI l'()ur (()nlnlonlvu'.t'tlrtt.lt,("',u('Irt".r'trlt'rl lrt'l,,s.lirllrlrr'rrrgtltctcrntitttllogy ,rl ( .ult.,.ut(lSt lrrr,r;,t'r 1l()li/l
CI RCT]
508
H uff -w'e igh t
u,;
s
o t'
LAR STATISTICS
SPATIAL PATTERNS
ia t io rt incl e x:
509
D,:number of days individual I wils sccrr irr lcntalc groups Dr:number of days individualJ wirs seer) irr ll'nrale groups
r Qt.,*n)12
Square root as.yociation inclex:
where: -r:number of observation periods during which individuals A and B are observed together
+r,,n)j
no:total number of observation periods during which A is observed
rn:total
where:
number of observation periods during which B is observed
/,:number of observation periods during which only A is observed
This association index is also known as Cole's, Dice's, Sorenson's and the coherence association index. It is the association index most commonly used by etholo-
-r,'n:number of observation periods during which only B is observed J',n:number of observation periods during which A and B are both obscrvt.tl
gists (Cairns and Schwager, 1987). As an example, Penzhorn (1984) used this index
in separate groups
to measure individual associations in Cape Mountain zebras. Try
ic'
e-x:e i glt t as
s
o
c'
ia
Iio
Lott and Minta (1983) derived this index to measure associations betwccrr irrrlr vidualAmerican bison.
n i nde x'.
The Half-weight, simple ratio and square root indices are allthe samc whcrr lr.tlr individuals (A and B) are seen during every observation period; the twicc-werlrlrr and simple ratio indices are identical if A and B are never observetl in
-7, TiT,+Tb where:
{:x:number
seprrr;rrr.
of observation periods during which individuals A and B
groups (-t'uo:0;Cairns and Schwager, 1987). The biases inherent in thcsc intlir.t.s rrr,. discussed by cairns and Schwager ( l9g7) and Ginsberg a,d young ( 1992
are observed together
).
7",:number of observation periods during which A is observed in the absence
of
B
In:number of observation periods during which B is absence
Several researchers have calcr,rlated the probability of associations tluc le t.l;rrrt,t, and used those as expected values to compare to their observed values.
observed
in
Iirr.cxrrrrr,lt.. Festa-Bianchet (1991) measured the association of kin in her stucly ol'billlr.rrr sheep sociality. She recorded group composition (which could ilclrrrlc cw(.ri;rr(l
the
of A
As an example, Myers (1983) r-rsed this index to measure associations between individual sanderlings.
theirsonsanddaughters)dLrringcensusesof thewinterrangeancl scirr.clres.l tlrr. summer range. She calculated the probability that a ewe would bc in thc siun(. )11) as her
trr(
daughter if they were distributed at random (T) as:
Sintple Rat io A s,;oc'iat ion Index'.
7:(n_l)lN_l)
-Y
-r+-y
where:
N:
nunrber ol ewes two years of l:number-in the groLlp
where:y:number of observation periods during which A ancl/or B are observed
As an example, Clutton-Brock r,/ ul. (1982) used this index to n-rcitsurc thc asst'rci-
prtlbability that tl ewe wor-rlcl occur by chance in thc s.rnlc son or cluLrghtcr (7-) was calculated as:
ations between individual red deer. Poole ( 1989) used the firllowing nrodilication ol' the simple ratio index to measure associations between pairs of'scxuitlly itctivc nritlc
Association
ic
older in thc 1-roprrllrli61
-fltc
in separate groups
elephants ( I-o xodo n t a u/|
age and
unu).
T
inclex:(l)t+ t)t)
I'
rrl
g11)r.rp rrs
lrr.r.\,t.;,lrr;,
ll
IItccxllcctetl ttrtlttllct'ol'titttcs tltlrt rr rrroIlre rrrrrtl Ircrtlirrrghter.w.rrlrl lllrvr. llt.t., Iogclltet'(/i) il'tlrt.y lrr.lt;rrr.rl irrrlt.Pt.nrlt.rrllr \\,;ts(.itlcrrl:rtctl trs:
'I'
st.t.rr
I
t )r,,,
lr/r
\
t
)
whcrc:
I' tttrttllt't ol tllty's lltt' pltit
tt';rs st't'tt
l()l'('llr('r nr ,r l'tot;I ol lt'ttt;tlr's
I
t"'l'r llt'r,,t
1,,', ,,1',,',",1,t1,1t',lr,,l r
ttlr r,l l"r rl, r, 111111' t,rltrl
r.t1,1t11r1,,.,,1 ,r,,,,rr( r,l
SPATIAL PATTERNS
CI RCT-]LAR STATISTICS
seen together tions. The nun-rber of valid sightings when mother and offspring were signedmatched-pairs Wilcoxon (E") with was compared to the expected number
ranks tests.
Mitani et al. (1991) calculatecl expected rates, durations and proportions of total developed time of associations between individual orang-utans by adapting models chance was calby waser (1982). The average duration of associations expected by culated as:
T"*o:2'467 r(v 7 + v7) lt)
angle ol turns, lelt- and right-handedness, and the statistical significance ol-clrirrrgt's in these parameters. (Similar systems using films or videotapes, digitizing, antl corn puter storage and analysis are described in Chapter 9.)
of an individual is that urcil c()v('r('(l during normal daily activities (Blair, 1953); researchers have sometimes clloserr t. refer to these distributions of an animal's time relative to space as'activily liekls' (e.g. Smith and Dobson, 1994). An animal's home range may overlap thc lr,rnt' ranges and/or territorie,y. The home range
use
r:the distance criterion u,:the mean travel velocity of individual i vr: the velocity of all other conspecifics or age
sex class
of individuals
in associaAn estimate of the expected proportion of the time each individual spent tion with conspecifics was calculated as: P",o:4.93412P,
p :the density of groups of
The observed versus
species
7
to derive a maximum likelihoocl estimator lor
ranges of other individuals (or groups, Figure 17.6) and may, or may n()1. cont;urr
the individual associations'
estimators Ginsburg and Young (lgg2) suggest that several maximum likelihood choose if researchers that recommend they may be needed for each stu
of each ' For only the spatial relationships between inclividuals, but also the behavior but reltttiollships; example, copulation, fighting, and nursing all require close spatial their interpretation is quite different.
Home ranges and territories are calculated liom successive locutiorrs ol'rrrrlrr
spatial relationships
erttt The procedures 6escribed above lirr mcasuring arrinrirl irttittrtl sPittill P:t(lct'tts irr tlrt'lltlr0til Ir'lrrltottsltiPs sprrli;tl cnvinrrurrcrrl also be r-rsed in stuclics ol'aninrirl svslr'tll rrl lllt' lit'lrl tary. c,cltlsr.lr-cs ()r. licltl. l,ilr crrurrple. Wct'rlt'rr ( l()()\) ttst'rl ;t 1'llrl hl('('l('l\()l)('t ("t't' tl','rttit' I lrt'liltr ) I. ,rr.lrs1t.r.. sllltlr:rl lllrllt.uls tll lt('(' sl)lltt()\\\
r,l
or groups, obtained through: l. tliret't
ob,sarvutions (continuous or lrrrc (a) locatit'rn ol naturul sigrrs (c.g. tr rrt k: ) samples): or 2. indire(t tnetltods, inclucling
uals,
(b) capture-recapture, (c) radioactive material. (d)dyes for urirrc antl lcccs.
1r'1 1rlr,,
tographic devices, and (0 radiotelemetry. Some of these technitprcs rrrc tlisr.'rrssr'.1
rrr
Chapter 9, along with recommended references. Weeden (1965) used a gridded area to measLlre thc tcrritorics ol'tlr't's1ruro11', (Spi:ellu urborcu) (Figure 17.1). An observer spent un entirc lirrtt'-hotrr olrst'r \:rlrorr period with a single pair of sparrows (fbcal pair) plotting cvcry locrrliorr vr\rl('(l , )n ,l rnap. Weeclen then used the observation-itrea cLrrve urctltotl to rlt'tt'urnrrt'llrt' number ol' observrttions necessary to calcttlate ar reas()nirhly rrccru;rl(' l('rrrlorr.rl a
rea.
The ohservation-trrea curve method (Figurre 17.[3)wirs tlcvclopctl lrv ( )tlunr inr(l Kucnzlcr'(19-55) to assist in clctcrnrining thc nunrbcr tll'obscrvlrtiorrs (lot:rlrorr',) r)ccessilry lo tlelcrrttinc tlrc lcrrilory sizcs ol'scvcrirl birrl spccics.
ll
i:i lrrrst'rl on tlrr'
slrrilc pt incrplc lrs lltc ctttr,'t's ufi('(l l() t'orrstrrrcl lrtt cllttlgt'rttil:rtttl lrsscss llrr'tr'llt'r 11.4.2 Animal--environment
;r
territory, an area defended against members of the same species and occ:rsiorr;rllr other species. The designation of territories is olten the result of linding contil'rr.rrr'. and non-overlapping home ranges, suggesting that the entire honre nurg('\ iu(' mutually exclusive and are thus assumed to be territories (e.g. wcirscls" Lot l' rt' 1e66).
expected ciurations of associations and proportions of time in
associations were tested using the Wilcoxon test' the data Cairns and Schwager ( 1987) describe how assumptions about biases in then be can which values collection can be used to {evelop lormulae for expected used
system different than that in Figure 17.-5. IIc uscd photosensors in a 50x50 l' ) grid to detect the position of fish relative to ollirctory gradients in a large tank. An on-line computer then analyzed the Xancl Ycoordinate positions of the fislr. llrc time of occurrence of a change in position. velocity of movement, distance covcr'('(1,
Long-term animal-environment spatial patterns are generally reflectecl irs /,oru,'
where:
where:
(1969), in his laboratory study of fish cr)vir'()nr))crrt spirtial relationships, used a gritl
size (('ltrtqltt'r
.l).tlrlrt
is.
lilrrI
ltstltt'tttttttlrt't.rl olrrt'r\irtioltsittct'eltsr's.tltt'lrtcol rrtrt';r:,t'
(l'rl'rttt' itt lttr'lr ristlr.'tl lrl llrr.'lrtttl tlt't tt',1',t", ',,r llt,tl :rn :r\\nll)l()lit't'ttlrr'tt'srtllr ,t'lt't lr'rl lltt'()n('l)('lt't'ttl lr'tr'l otr llrr' I / li) ()rltttn rrttrl httt'ttzlt't ( l()'r',f,rllrtlt,tttlt ',r.'r'\\.r',,1r'lt'tttttn('(l I lrt' l',, lr't,^l t', llr,rl ( ut\(';t', llrt'p()rrl ;rl ttlrt. lt lltr'lt'tttlr,r\ lr()lltl r,tt lltt' ( lll\r' trlt,'tr' r',tr lr ,trl,ltlt,,tt,rl ,,1',, r\.rlloll It,,rltt.,", 1,",', llt,ttt ,t I
SPATIAL PATTERNS
CIR('T]LAR STATISTICS
A
El$enros usED mTENsTvELY O CftOS USED MODERATELY
O o
.
co
GRIOS USED SLIGHTLY
NEST SITE MALES
il "lo o-rr--*;p-rp :ol_O'E! tr E Et 0,o El o c El;o;o \
N
li.itric
i
o
;€l 9io io,
o
=t,oiojr.p ololol
/ ololcl
si?i o1 I
-
rnilo
ii ol o ie
el:
rloi.l. ol0 olol.,o!!
slccping cllffs
B
big. 17.7 Locatiort and utilization
of total activity spaces ol males, location of total activity spaces ol females, and location ol nest sites ol tree sparrows (from Weedcn. 1965).
---- Corc oroo
irtcrcitse in the area calculated as the territory. The number
o
Slccping eliffs -.-- l,lorlhcrn limit o{ ronling b}, S --- t{orthcm 6 southcrn llmit of ronging by C -.- Sosthorn llmit ol ronging bv N f] lreo oI orartop bctu/con group!
>>- Boboon trocK
It
lir| gRrttP S lrrltl Fig. 17.6 Baboon group home ranges and core areas. A. Tcn-clay ritngcs llrc itltlicittcs linc sltitrp Tltc Rcscrvc. group Cape C. 2l day 11lges for ('lrrrtl S llv occttllictl At'ctts I|. ntttgc. hotnc gr()up's 9lclch limit lpproximatc irr o'nt'tlltP ol grogps ttrtrl si.;ullrclp lirrrit ol'N grrrtrp's lirtll'('. rrrrlitlrltitl':ttttottttl llrlttlc
t'ltttg.cs
lttttl loclttiolt ol'tolt'lttt':ts
(ltotlt I)t'\'ott
of
observations neces-
to rcitch thc l'Z,level varied with the species and the stage of the nesting cycle. Srurtlcrson ( 19(r(r) suggested that live-trapping mammals would probably provide lnstrllicicnt tlitlit to apply the observation-area curve method, but that radiotelemesru'y
'rrlrl Il;rll
lt)('r)
t
Ptobirhlv worrltl.
'llrc'u'rrlitlitv ol'llrc cirlcrrllrlctl hornc riurgc. or tcrritory. will depend on the techttirlttr'r'lttltlovr'tl. Wltr'tt rlitr't'l olrsr'ttlrlions rrr-c rrrirrlc. lhc irnimal's location can be r'onsirlt'tr'rl ;tlo111';ur ('\:r('nlltllY conlnrrrorrs rlislrilrrrliolr,'l'ltlrt is. it rnity bc corttirtuotl:.lV oltst'tvr'tl llttottl'ltr)ttl. itn(l lounrl,rl ,rtt\ l)('lll \\'tllrrrr. ils lt'ttr. ltolttC r':trtgC.'l-ltis ',,tlttltltlt;, 1,,,',lt,rrlr,ur ltt't otr',trlt'tr'rl trr',l,rnl,ltrr)ll',',,nrrIlnt1'ol ;t lirt':tl;tttinlrl ttsittlt,
\('t\ ',ttt,tll',,rttt1rlr'tttlr'tt,tl', \\,',',1( r ( l')("
I tt'rrr,' I i /)',,rrrrPl1'11 rrstrrl,lot'lrl
Pltrr.
CI RCTILAR STATISTICS
514
Wood
SPATIAL PATTERNS
all-occurrences of visits to dilferent cluarlrirts. Son-rc indirect measures, ssclr ir\ tracking in snow, can also provide continlroLls clata, ancl biotelemetry can plrvirlr.
Pewee
nearly continuous data by rapidly scan sanrpling inc'lividual locations (sec ('lrrrptcr
- 1--o/ /o
/o
e).
Other methods, such as the commonly
Llsed capture-recapture mctlrorl l.r discontinuous spatial distribution of' l()cirlr()n:. restricted to trap sites. Thereflore, the validity of this method is affectctl h.y trrlr spacing (Stickel, 1954), as well as trapping interval (time of trap scl rp tr;r;, check), sample size (number of trapping intervals) and the responses ol'irrrlrrrrl Llal animals to the traps (Balph, 1968; see below). This sampling nrctlrprl t.i1r lrr. considered a one zero sample (Chapter 8) for each trap site. That is. lirr t.;rt.lr trap site each individual is either captured, or not captured,
o
small mammals, provides
o;o
ts*
a
interval.
Most small mammal trapping designs used to determine home rilngcs rrrrtl lt.rrr tories are grids similar to that in Figure 17.9, but with more traps. I lowcve r. I .r.k rt. (1966) placed his traps near leatures in the environment likely to bc l'r't't1rrt'rrlt'rl l,\ weasels (Mustela nivalis) and stoats (M. erminea). With capture recapture methods, the animal is indivi
fbllowingassumptit-rnsareinherentintheuseofcapture recaptur.ctlirtrr lor.;rlrrrl:rtr. home ranges:
t L.
Theanimalwill betrappedoverallof theecologicirllysigrrilit.rurt;rr',r
r,l
its home range;that is, it willbe trappcd whcrcvcr it gor.s givcn llrt. l.,ll,,rr ing conditions:
100 ft
(a) The grid being as large as (or lurgcr tharr ) ils h.rrrc r';rrrr,(.. (b) On cncountering a trap thc plnrbability ol'ca1-rtruc lrcirrp lrrl,lr t2
(c) Thcprtlbabilityol'capturconcncor.lr)tcringrr lnrplrt.irrl, llrt.s;r1rr.
fl
thrttughtlLrt its hrturc raugc. TItcl'r'cc;
Ee o {6
l:ltch itninritl hils rtn ct; trrtlclurncc 0l'lrcing crrPlrrrerl ul)()n (.n(.()uill(.rilrl, (
rr2030ro!oCo706090 Number of observations Fig.
Observed positir-lns o[' it ntalc wootl ltcwcc (( (utt(tl)u.\'r,ilr,a.r ) rrl lire-rrritrtrlt. intervitls (stlllrll circlcs; ltlttlvc). nitlt ttutrirruun ()l)\('r rt'tl lrrr'rr t'nt lost'rl rn solrtl lirlcs lrllcr srlct'cssirc lctts rtl'ttlrst'l'u:tliotts Ilrt'lrruIr'tr lnt(, lt llrt.rrPPr.r rlr.rlr.rnr Cttr'lost'slltct:rlttrllrlt'rl rrurrrrirrrrrrlt.rll,rt\.,t/(.(l(l).i .r,t,.,).lt llr,. l",, lr.rt.l (.,r.r. It'rl lirr t'r;ll;111;111()ll) il\ slt,rtttr ()ll llr('l}lr',r'tr,tlt,,11 ,1tr,t | iltrr'lrr'lr'\\ (lii]||t ( )rlrttrr ,ttt,l hrr, rr,, l,'r l')',',)
,l
t'rt 1t.
I Irrlirrlrrrr:rtclv. llrcse trssrrrrrl.rliorrs:rr-c Plrrh:rl-rly rrcvr.r.t.orn1llt.lr.l1, rrrt.l :rrrtl Ir.r1r,r1,,,
st'ltltlttt lrlllll oxilnlrlr'rl I
17.8
trettcyol'cit1'ttttrcrttitpitrticuliu'tr':rpsitcrcllet.lstlrt, lrt.r1rrr.rrt1.l
visits by tltc rrrrilrurl.
lrr'lirllou irrl, lrrt.(lrr'nt()t('(.(rlilnt()lt. srrrrPlt.rrrt.llrrrrls ol rlt.st.ttlrrrrl, ltrrntr. l,lrrl,l.,, lrlril l,l rrls (Slrt kt.l l,)', l)
lr;rsr'tl ()n (litl:r lrorrr
I Nlttttlttut)t.u(';t lttt.lltorl Illr.o11lr.lllrr,,l r.tlllutr..,tlr..,iu(.(.r)iltl(,( lt.rllrt ',lt,rt1'ltl ltttt", (,'l' l tl'lttr' I / ') \ ,11,,,, ,rll, ,l tlr( rlnnnl11( r)1\(.\
l),)l\1,r,1
Ittr'lltur l)
SPATIAL PATTERNS
CIR('I]LAR STATISTICS
516
Observed range length. Tltc tlistrrncc lrctwccn tlre two most widely sepa-
A
4
1
a
aa
6
rated capture sites is measurctl (c.g. liigurc 17.9A).
o
Adjusted range length. The lirrthcst tlistlncc Acr'oss the home range calculated by the boundary strip nrcthod is nreasr"rred (e.g. Figure 17.98).
Another method which does not incorporate unoccupied grid cells, or traps
bo
where the animal was not caught. has been recr.xnmended by Waser and Wiley (1979). Getty (1981)and Lair (1987). Stickel (1954) concluded that the boundary
ca
strip method and the adjusted range length provide closer estimates of the true
"O..
home range than the other methods. Stickel also lound that trap spacing altered the apparent size of the home range even when trap visitations are random and biologi-
d.
cal factors are excluded.
These two-dimensional methods can be extended to a third dimension. and home range volume can then be calculated for arboreal species (e.g. cricetid rodents, Meserve, 1977). Koeppl et ul. (1977) describe a three-dimensional home range
ea
model.
An individual will occasionally be caught at a great distance fron-r the cluster of other captures. These'outliers'n-ray reflect occasional excursions out of the animal's
B
true home range and are often disregarded in delineating the home range using the
O./ /.-4
,/
'o
clescriptive methods discussed above, or statistical procedures (Samuel and Garton 1985; examples listed below).
o a
An individual animal's behavioral response to a trap reflects its own unique predispositions and responses to experience (Chapter 2). Balph (1968) observed the belravioral responses of uinta ground squirrels (Citellus urnrutus) to live traps. He IoLrnd that the trap was initially an attractant which could be enhanced by baiting; Iroweveq because of the con{iguration of the trap there was an equal probability of cirpture on the first encounter whether the trap was baited or not. Capture appeared
a
o F'ig. 17.9
Hypothetical captures of an individual small mammal in a grid ol livc traps. Circled dots denote sites of captures. Home range are indicatcd as: A' tnillit-tlum area ancl observed range length; B. boundary strip atld atljustcd ritttgc lcngth'
to be punishing while the bait served as a reward
( ictz ( 1972) usecl multiple captures (more than one
tnclltotl).
model; Chapter 2); this pro-
individual in the same trap)
Irr irrlL'r-rrssociirtittns between sex ancl age groups in a population of Microtus penntvlvttrtit'rr.s'.llc cornl'xu'cd tlic number
Boundary strip method. Points lialfway between the clttterttlost citlltrll'c (c'g' sites and the next closest trap sites are connectecl by straight lirtcs Figure 17.98). The rationale behincl this nrcthorl is thirt ott lllc itvct'ltgc tltc animalwill liavc trlvelcrl hlllwiry lo lltc ttr'xt lt'ltp silt'tlttt'irlg its ttlorr.' prclts (scc Stickcl. lt).5.1. l'rrr tlrc ittt'ltrstvt'ttttrlt'rt lttsttt'r;ttiltliotts oll lltls
(see
tlucctl a conf'lict between tlie tendencies to approach and avoid the trap on subsetltrcnt cncounters. and recaptures were influenced by the relative strengths of these tcnrlcttcics in tlill'erent individuals (Figure 17.l0).
ol multiple captures of the different
sex and
;u,r.'t:rlt'gorics lo lltirt cxpcctcd tkrrn thcir rclative frequency in the population. FIe ust'rl lr clri-stlurrrc lcsl to rlclu'rnirrc wltclhcr lhcy wcrc liruncl together more fre-
t;rrt'rrllv or
lr.'ss l'r t't;rrt'rrll.v tlrrur exPcclerl lrrrtl irrli'l'r'ctl lllt'lrction ancl avclidance, ltit'lv Slltrlt'(l()i(r)r'lrrlrot;tlt'tl ltttlltt't ott ( it'l/'s l)r()('('(lut'e. Itt,ltr r,ltt;rl'.;rrr'ollr'rrt:rpltn('(l nrott'llrrn ()n( (' ttt llrr'rtnrt'llitl). l)('tl)itlts t'cllcctilt| ,l (1t.,It,,;rr rt I tr )n,llr' il ,r' il| | lr(}'.t' l)( )t I t()n', r )l tl', lt, '111,' t,rtr1,1' I lltt ttt'( I t) l()) r'ltlt'tt-
r('\l)('(
518
C]
I R.C I.J LA
SPATIAL PATTERNS
R STATI ST ICS
519
Columns
Atz
34
1. 2c
I
o
trv
€4 o
a
1X 0=O
O
2x 2:4
a
3X
.o o fi) @@ o. o
4.
aa \t xx (v) [il
illt o(o
N
a
o
r,,
f!
sr
o x
tJl
il
:T
Times Captured
x
3
6:
18
t 4X 4=16
@
o 5o o(Y) XX
2tr 9J
6
o
O
ci
z
5
o
:3.
.E
aa
o
5X O=0 38
: Total
(o il
o
Total = 4O Column total ' = Column center of activity Total no.. captures captures - 49 ,r,/= 3.3
1 2 34
B
3
I a
(E
.E
c {tt
2o
o
3 ._.
c,
=
4o
o
ol
o
s
6
o
.
_ al r _1_'
+ ol oao
o
38 Row total i-::---::: flgvv Center Total no. = -12 =:r,l =
Of aCtiVity
Gaptures
5o
57
trig.
l7.lI
Times Captured
Calculation <>f center o/ at'tiviry (triangle, grid B) based on hypothetical recaptures (circled numbers, grid A). The numbers in the circles denote the nunrber
lrrtcd the ('enter
!6
ol
captures at each trap site (see text lor explanation).
of'ut'tivity, the mathematical center of the distribution of total cap-
trrrcs within the grid, taking into account the number of captures at each trap site
.E
c
{
15
lrigure
17. I I ).
This can be obtained by weighting the rows and columns of the trap
o
rrricl urtcl mr.rltiplying the number
C;
trvc wcight. Thc totals
z
1357
13
Times Captured Fig.
17.10 Distribution
9l'lurnbcl ol'tintcs irttlivitlrrirl ttitttlt gtotttttl stlttit'tcls
'clpttrrctl'irr irrr Lrrrllrilcrl arrtl l'rrrtcliottltl lrltp (A).
rr
tvt'tt'
lrlrilt'tl. lrttl ttottltttttltolt:ll
tr-trlt(ll).trrrrllrlrlritt,rl;rrrrllirnr'li()nrlll;rl)(('l Sltotrslltt't'llt'tlol lt'ttltltl(lr;rrl) lrttrl llrrttisltllr'11 ((itl)l1tr.'1t it lurtt ltr,tt,tl lr.r1r) utt rltllr'tr'lll lll(ll\ttltt,rl "tlttttlt'1"' l,t'lt:tr t,rl" (llr)lll ll;rlIlr, l')('!i1
of captures in each row and column by its respec-
lbr all rows
is then divided by the total number of captures in tlrc griri to rlctcrminc the center of activity lor the rows; the same procedure is rcl'rctrlctl lirr thc columns. The point at which the centers of activity (means) for the rows rrrrtl colrrrnns inlcrscct is thc center of activity for the animal's home range (1"i1'111'g 17. t t ). l{lrtlrcr than using thc rnc:rns ol- cirptures along the coordinates of lirr' lrr itl. Molrt' ruttl Sttrtttpl' ( l()(,6) sttggcsletl rrsirtg tltc tnctliitns of captures in the -r ,rrrrl l t'oortlin:rlt's ol lltt'1'.r irl to tle te rtttirte lltt' rrretli;rn ccnle l tll'lrctivity. In crlntrast Io llri'lrlltt't rttttplt't;tlt ttl;tlr()tts tt('('('ss;tt\"lo rlt'lr't tttittr'llr('ltt('iul ()l tl)c(liiltt ccntCt' ,rl ;rr'lrt rll |)r\on ,rr(l ( lr,rInliur ( l()li(l) ProP()',('(l .r rrr,)r('( ()nrl)l('r rrrt'lltotl lill ctrl(ltl;tlntl, lltr'rr'ttlr't ,,1 ,rrlrrrlt lr,l',t'rl,rnllrt'lt,tttrr,)nl( nr(',ur l,rrr (l()li/)totttp;ttt'tl
520
SPATIAL PATTERNS
CI R('LILAR STATISTICS
the three methods using data from red squirrels and lbund that the harmonic mean was the only method that generated a center of activity which coincided with the behavioral lbcal center whenever the latter could be identified by direct observa-
tion. The center of activity does not necessarily reflect the location of anything specific (e.g. the animal's nest or burrow), but the distance between centers of activity Ibr residents of adjacent territories. or home ralnges, might be used to infer their relative avoidance throughout the year (see Clark and Evans' 1954). Koeppl et ul. (1975) suggested that the center of activity might better be called the 'center of lamiliarity', based on Ruff's ( 1969) correlation of uinta ground squirrels'(Spcnnophilus urntutus) heart rates with their locations in their home ranges. Koeppl and his colleagues also demonstrated how confidence ellipses can be calcr-rlated around the center of activity in elliptical home ranges and then used to cleterrnine the probability of finding the resident at any given location. Weeden (1965) r-rsecl the relative number of visitations to the different quadrats in the tree sparrow's
territory to determine'central cores ol more concentrated use'(Figure ll.1). Disproportionate use of the home range is also revealed by continual following, or tracking, of individual animals (or groups) lbr several days by direct observation or radiotelemetry (e.g. Sargent l9l2'. Chapter 9). Primatologists ofien observe groups of primates lbr ten-clay periods in order to constrttct ten-day ranges, within which are generally found areas of heavy usage designated 'core areas'(e.g. DeVore a11d Hall, 1965; Figure 17.6). White and Garrott (1990) provide statistical methods for comparing utilization distributiot.ts. Beyond the graphic methods discussed above (e.g. boundary strip method), several mathematical/stertisticalmodels and methods have been developed lor calculating home rrrnge and center of activity from large data sets of animal tnovements and locations. These methods include the use of: Fourier estimator (Anderson, lg82), harmonic mean (Dixon and Chapman, 1980), Jennrich Turner estimator (Jennrich and Turner, 1969), Dunn estimator (Dunn and Gipson. 1911). bivariate (Koeppl et al., 1975). weighted bivariate normal estitlator (Sarntrel and Garton, 1985), and minimLrm convex polygon (Southwood, 1966). White atrcl (iarrott ( 1990) clescribe the application
of several rnethods to radiotelemetry data.
Software packages are available lbr home range calcr.rlatit>ns ot't ttlicrocotttpttters. McPcrul is a menu-driven software package for analyzing atrinlitl locittiott tlittit on IBM-PCs and compatibles; it calculates horne rangcs r.rsing thc cottvcx 1'rolygolt, concave polygon. 95'2, ellipse. fcluricr and harnrottic tttcittt tttclltotls. Mcl'ltltl is;rvrril('e ttle t'. Nltliotlttl Zoololit'ltl able from Michael Stuwc. Conscrvirtion itntl llcscrtt't'lt Park, Frttnt Rtlyll. VA l2(r10. ll'iltltrttli is 1 prrt'kit1r' ,,, tt()lll)irtrttllt'ltit'llotttr' lilll,'('
itrlrlyscs lilr tlrc Altltlt' M;rt'inloslt. Atutlvst'r ttt, lrttlt' ,tttllll,lll()ll lltllot'r)ll('litll(tll. tlr rl t. tlVrr;rt1it' ilrlt'r it( l to1. 1,1(l r'r'll ;tll;tl\',t', lllu\ r'lllt'lll , 1,,,11 1', )ll ,lll;llt'"t.' ;ttl.l
521
static interaction. Wildtruk 2 will huvc irrltliliorrrl rrrr:rlyscs including habitat preflerence and harmonic mean.
Wildtrak is avirilablc ll'orrr I)r lan Todd, 6, Sollereshott
House, Linkside Ave.. Oxford, OX2 8JA. t lnitcd Kingrlonr.
of home ranges is sometimes determined (e.g. Geffen and Macdonald. 1992; Swihart. 1992). As an example. Lazo (1994) calculated tl-re home ranges firr lcral cattle using the minimlrm convex polygon method, then calculated the home range overlap for all possible dyads of Once home ranges have been calculatetl, ovcrlap
individuals using the following formula: Home range tlverlao: .
tr':
2P,
I
(P^+
PBI
where:
P,:area of the polygon delimited by the intersection of the home Po:area of the lrome range of individual A
ranges
Pu:area of the home range of individual B r"rsed to measure home range overlap, such as Lazo's (above) does not take into account the lrequency ol use of that area by each individ-
The methods commonly
ual. Smith and Dobson (1994) describe a method fbr calculating asymmetrical weighted overlap values between neighboring individuals. including a computer program written lor Statistical Analysis Soltware (SAS Institute, Cary, NC) which will calculate those values. The method(s) selected for sampling animal locations and describing or calculating the home range (or territory) should be based on a knowledge of the species' behavior. As examples: l. How easily can they be observed (nocturnal'J dense vege-
tation?), and what is the relative size of their suspected home range (e.g. m,. hectetres, kmr)'l Will these conditions require the use of racliotelemetry'?, 2. How rapidly and continuously do you sllspect they move throu-qhout their home range'/ I low does this affbct your choice of sampling intervals (Swihart and Slade, 1985)? San-rpling rtrethods will be limited by constraints on your time, equipment and rrbilities. artcl detern-rination of home range will be limited by the quality and quan-
tity ol'thc anintal location data. For example, based on simulations, Bekoff
and
Mcch ( l9tt4) suggest that fieldworkers should ascertain 100 to 200 animal locations irt ot'rlct'to cstitttittc rcliahly ltome range area (also see White and Garrott, 1990). l{cscrtt'cltct's sltoulcl cortstantly assess the validity of both their sampling and lr()nrc nu)ltc tlctcrttrinrrtiort rnctlrods. I-irr cxanrple, Jones ancl Sherman ( 1983) comprttetl tlre ltottte rilnp,es ol-rttclrtlow volcs rrsirrg: l. grirl trappinu (capture-recapture) ;rttrl
t;trlioli'll'rlt'ltv to tilrllrirr llte
lrrt';rlirlrr tl:rllr:;rrrtl
lltt' ltr)tll(' t;tttl'('\ )rrlt lltt'('()n\'('\ (
t".ltllt:llt'1,,1 rl.tl,r
tt r'r
,111
t' lr',r'rl
l.
scr,'cl'lrl
nrcllttlds ttt calculate
Poll'1'1rtr rrrr'lltorl 1,;11r.';rppt'oxitn:tlcly
lll(ll\ttltt,rl', lt,,tttt't.tltl'('ttltr'lltr't
tltc s:ulc ,'tt(l lt:tl)|rll1| ot t;rrlirllClCtttCtl'y
VIS
18
Interpretation a nd present atton
of results
I8.I WHAT DO YOUR RESULTS MEAN? The end point (temporary pause in the ethological approach cycle) in your research
of your data analyses, but their interpretation. Have you been able to reject your null hypothesis (Ho)? If so, you can accept the alternate hypothesis (I/,) and take temporary pleasure in your accomplishment. [f you were not able to reject your IIn, then you have negative results which are difficult to interpret (Kerlinger, 1964' Chapter ll). Most importantly, note that failure to reject the H,, does not mean that you automatically accept it. Rather, it means that several factors could have contributed to your results, only one of which lvas that I1,, was true. Also consider your techniques - were they reliable and valid? Did you overlook an important parameter in your original design? Are you now convincecl that the ^I1,, is true or should you design another experiment to test the H,, in what is not the results
you consider to be a more valid approach?
If you were merely making reconnaissance observations or using analyscs (e.g. cluster analysis) in hypothesis seeking, can you now generette a mcanitigl'ul ancl testable hypothesis'/ is
ofien useful to prepare a visual rcprcscntation lirr'
1
$x
ot, EO
bE }E oo
r-O
oo, s6 Etr J: zb
CL
loo
200
300
Number of worms per square meter
I8.l
Redshank (Tringu totanus) loraging behavior. The numbers of worns above 30 mg dry weight taken per meter searched in relation to their clensity in the mud (from Goss-Custetrd, lgjl ).
uselul in presenting your results to other people, but also help you interpret the results and see relationships that were not apparent in the tabular data (Wolff and Parsons, 1983). Increased insight into interpretation and new hypotheses are often
the result
of careful contemplation ol visual
representations; Cleveland (1993)
shows the relationship between visualization and classical methods
of data analysis. Diflerent visual presentations of the same data sometimes allow you to recognize subtleties in relationships which were previously hidden; Tufte (1983) illustrates numerous ways to display quantitative information visually. The dendrogram, described previously for cluster analysis (Chapter 17), is obvitool. others which are useful are described below.
or.rsly a valuable
18.2.1 Graphs and figures
There are several types o1'graphic lormats which have proven valuable in interpreting resr"rlts. Thc simple scatter diagram (scattergram) is generally used to graph cor-
rclittion data. The interpretation of the graph is dependent on the location and tlistribution ol'the points relative to the axes (Chapter 14).
lirr
cxitnrplc, Goss-Custard (1977) examined the hypothesis that the redshank tlte sizcs of polychaete worms (prey) that maximize the hi()lllilss irtgcslctl pct'ttttit tinrc. IIc plottctl thc nuntbcr 6{- large worms taken relative
lo lltelr tlcrrsrlv
VISUn L REI'}RFISIIN l',A'l'l()NS
Mtlst rcscltt'clrcrs clttt lcru'n nl()r'e rtlrottl lltt'tt':rrllr,,l
NS
('l'r'itr,gtt ttttturtr.s'l sclccts
further inspection.
18.2
EPRESENTATIo
o
Fig.
it
R
eO
gP
Discovery consists in seeing what everybody else has seen and thinking what nobody else has thought. [Albert Szent-Gyorgy] 'Science'interprets. That means that a number of minds agree that in a given phenomenon there is something that occurs with regularity, can be reproduced, and can be traced back to recognizable causes, something, indeed, that can be 'interpreted'. [Eigen and Winkler, 1981:21]
Once you have your results
UAL
rrr lltt.rrrrrtl
(liigurc lS.l).'l'lre l,lrrPlr
r.cvc:rlcrl irn apprrrent positive
t'ot tr'l;tliott llr;rl rvlrs llrt.rr \lt()\\'lt lo lrr.sl;rlrslit':rllf si;,r,11;...,,,,. rr
r(",(',r,lr
1rr,r;r't I lr1'slrrtlVrrrl'
l'istutl l)t('s('lrllrltons (ollrt't tlt;rtt l;rlllt's)ol tr",ttlt', I r1,utr",,rrrll,r,rltlt',.tt.'ttol ortl\
l"tt'r;ttt'ltt \ rltrltll)il1trril., ;ilt. Plollt.rl ilt l11rr (.(rnil11(,lt l()l ilt.tl\ I'(}tlr ;r trtl ltt,,lr )l't,illl.,
l't.CtltrCItCy 1t0ly-
INTERPRETATION AND PRESENTA'I'I0N OF RESULTS
524
VISUAL REPRESENTATIONS
52.s
100
\
80
\
s o 0) a o
,/
60
\
/
o a
E,
40
O-
,(
lrJ
€
2 o
o-
J
20
\ o
1-4
5-8
9-12
13-16
17-20
21-24
'.'
J
\ \
0)
,z .to o
c,
\
= z6
N-32
25-28
bJ
Age of the imprinting experience (h after hatching)
=4
trig. 18.2 Mean scores made in testing, 24 hours alter the imprinting experience, by ducklings which had been given the imprinting experience at cliflerent ages (from Hess, 1962).
Frequency polygons are usually prodLrced by connecting points along a continuI
fiequency distribution. Points should generally not be connected if the distribution is not continuous or if the sample points are distantly separated. Connection oLls
irnplies that the line between the points is a reasonable representation of the missing data. Hess (1962, 1972). in his studies
of irnprinting,
model (decoy) lor a limited period early in life and later tested then-r lbr the imprinting response (Figure 18.2). The frequency polygon shows that the highest percentage
of positive
I'rg l8'3 The mean number ol courtship displays per observation period for specific male guppics. Each line represents a single male (from Farr and Herrnkind, lg14\.
exposed clucklings to an adult
responses was given by dr-rcklings exposed to the rnodel
at l3-16
hours after hatching, the'critical period'or'sensitive period'tor this spccics.
Frequency polygons are sometimes used with discontinuous cluta to sliow rclative changes from one condition to another, while the identity ol'scvcral intlivicluals etre maintained. Figure lti.3 shows the nrcln nunrbcr ol' courtship displays lor I I rnale guppies (futet'iliu raticuluta) at dill'crcnl 1'roprrlrrliorr
or levels of another variable
t.irtes the change in rerative occurrence (
llurttr.tu
rurtrur.t') as
of five behaviors in female woodchucks their infants grew olcler (Barash, lg74). See Figure 18.7 for
.rnother approach tt> illustrating similar data. Sonte cthologists have founci it illustrative to present results in a three-dimensiottrtl lrct;ttcltcy polygon. Forexample, Figure 18.5
ol"t sl ''lchitck cittittg a food item is clue, in part. to a complex relationship lltt tlltlttlltlivc tttnnhcr ol'cats antl re.iects which prececle the encounter
lrt'lrvcctt
densities. As the population clensity increasecl bcyontl two pairs. thc nrcrrn rrurrrl'rcr'
rritlrlltt'lirotl itctrr('t'ltorrurs. lglll.llcirrrbcscclllhrrl.ingcncral.theprobabilityof
of displays for most malcs irtcreascd or rlccrcirsctl irr lrrt rrttl'rrctlicllrble
,ur ('irl o1'1'11;1j11f is I'rt';rlt.sl rvlrt.rr llrr.. nrrrnlrr.r ol. llr.ior lltt' tttttttlrt'r' ol P1 l()t (.;tl\
ancl
l{crrnkirrtl,
tttlrnrre r'(l"rrrr
I 974).
Scvct'ttl I't'cr;tte ttcy l)()lYr'()tts t'rttt lrr't'otttllittr'rl nrlo:r ',rrl'l(' 1,rlrIl; ltr slrott t'lr;n),'t'r itt sevt'ltltlt'pt'ntlt'nl r';tnlrlrlt':; tt'llrttvt'l() iur rrrlr'pt'1111r'nl \.lrr,rlrl,' I t1'rttr' li"i .l rllrrs
rr.
jct.ls is low irt.cspcctive
I l('(ltt('ttr'\ rlt',lttl11111,rrr,, r\tllttn t.1t1t.1,,,,,,.., ol rlr,.r olllrrrltrs (t t. rlist.tt.lt.) lr('ll(l('lll t'ttt'tlrlr'.tl'' "llr'tt tllrt.'lt,rlt'rl rrrllr lrt,,r,,1,r,rrrr.. \\'lrr.rr llrt.rl(.irl\.l :t
itfrlr,_
rrt,lr.
VISUAL REPRESENTATION
INTERPRETATION AND PRESENTATION OF RESULTS
526
S
521
o UJ cr
5 o
roo
o o &.
o
t80 U ao
2
i60 (, o
iao
B
rn =
z
o20 F
IE
D
6
co
o u
1.2 3.4 5.6 7-8
o
x
Fig.
9,,t0 .n.12 13-14 15,16 t7.r8 19-20 21-22 23-24 25-26 27-28 29-30 31-32 33-34 35-36 37-38 39-4 AGE OF INFANTS (DAYS AFTER BIRTH}
18.4
Behavior of mother woodchucks immediately lollowing onset of sclueaking by infants (cumulative data for three litters, based on a total of 387 incidents, with minimum
of l4 per two-day
a
interval) (from Barash,l9l4).
variable are illustrated, the standard error of the mean (Figure 18.6A;generally pre-
lerred to the standard deviation) is informative relative to the statistical significance of the difference between the means. For example, Nyby et al. (1977) showed that male mice made significantly more ultrasounds in response to facial chemicals from females than from either males or controls (i.e. clean surgical cotton swabs), and
their responses to male and control facial chemicals were not significantly different (Figure 18.64.).
It is often illustrative to incorporate two or more groups within an independent variable (e.g. dorninant or subordinate within sex) into the same histograrn (Figurc
I rr:
18.68).
The basics of graphing data are proviclecl by ('lc:vclatttl (l9t{5).'l'ltctc is it plethora of computer sot'tw:rrc pr(UIritnrs lrvlrilrrble lirt'gcttcnrling lwo-:rtttl llleedimensional colol grirplts rll'lrll lyltcs. consttll ollrt't t'lltolo1,ss1s lttttl t't)ntl)ill('t sl()t(' pcrstlttttcl lilt' srll'l wtrtr' 1l:rt'kitl'('\ llutl ltt(' ( r)nrP;tltltlt' tr tllt lottt tl;tl;tll;tst' ;tttrl l)r()(lu('(' lltr' r'rtt t('lV ol I'tltllts yott tt';tttl
cat rlccrrrlirrg (/'r )in;rrrr cllc()tltllcr cort'clrttctl itgitirtst thc lccunrulltivc tttrtrtbct's ol'lirlcgoirrl' ..'',,r ()n ()n('
plltttc lttrtl lirrcgoittg
A type of histogram can be employed to show the emergence and disappearance of behavior over time. For example, Figure 18.7 illustrates the timing o1' emergencc of postural. locomotor. and related skills in the laboratory ritt (Altttran itntl Sudarshan, 1975). Note that the presentation of results in this ligurc rrrc sintilur to those presented in the combined frequency polygon (F-igure lU.4).
ls.5 Sticklcbirck lcctlirrg bcltuvior. I)robirbilitics ol'arr rc'
jccls on thc othcr (l'nrrrr 'l'lrorrrirs, I()77).
lt.2.l Vccirlr rliagranrs lol tli;r1',uuns ;ltc rrsctl lo illrrstr:rlc tlrc tlislrilrrrtiorr ol' tllrllr re ltrtlr,'r' lo l\\,o or tttrttt' t'oottlittltlr' ;trt's. !lt't'ltll tlrlrt llttr't'-tlintr'ttsiott;rl lr't lor rlr;rl'r'lrnls \\('t(' l)l('\ ,rrt:,1\ rllst ltssr'rl ;ts lt ttst'lttl tttt'llttltl lot lltt'st'nlllt1' rrrrrl ilrlr'tl)t('lilll' lltt' tt':ttll:, rll l,tr lot ;ttt;tlyst:, (( ly;1plt't l(r) Vt't
I
Itr,, rlttll('tt',ton,tl tr'r lot rlt,tl't;tnl', iilt' rrllt'rr r',r'rl lo slllr',lr,rlt' llrt' tlrtt't lrorr,rl tr".ltotl'.("t ,tl ttt,ltr t,lrt,tl .tttttrr.rl', ttr ol('nl,llrr)n '.lu(lr( , l ,,l , r.rtrrllt', I r1'rrr,' lli li
INTERPRETATION AND PRESEN'INTION OF RESULTS
528
VISUAL REPRESENTATIONS
529
RIGHTING ON SURFACE
NEGATIVE GEOTAXIS (15' INCLINE' ! E
= o a (o
PIVOTING
=f,
::]:)':;;:^[JT
P VOT NG
O)
'-C
(o
c o
O U)
lz (J o -o
lo c(o
OUADRUPED BALANCING (HIND LIMB)
0)
female
A
male
-
HEAD POINTING (SMOOTH SURFACEI
control
H]NDLIMB SUPPORT (SUSPENDEDI
Type of facial stimulus
100
c(o
80
.= E
o
o
(little or
60
o)
f
O)
(!
c o o
ighting)
REARING (W]THOUT SUPPORT)
40
0)
.]I]IVPING (ACROSS
o-
CLIFF)
[.
20
9
18.6
I ll AGE IN
7
9=d
Relative weight of females to males
A. Mean number of 5 second blocks containing ultrasoutrd lhrrn I)llA/.12 irrbrctl male house mice in response to facial chemicals otl a ctlttott swttl.l (measurements:mean + srl) (from Nyby cl ul..l97l).1]. Mllc ll'trrrtlc
dominance record in hamsters irs a l-unctiort ol'rclittivc borly rvciglrt. A !L'ttrltlc was considcrcd lightcr tl'its wcight wrrs l0 g ()r'nr()r'c bclor.,'' tltltl ol lltc tltltlt'. (l-rotr Mitrrlttcs lttttl Virlctrstcirl. I ()77).
I rr' lli
T
13
15
t7
DAYS
Sulttrttirry diagrum of the emergence of different postural, locomotor and related skills in tltc laboratory rat. in the majority of instances performance level (vcrlicitl uris ol'cach grapht0. 25. 50. 75, and 100'2,) relers to the percentage of rrrtitturls srrcccsslirl in thc I'ull clisplay of the response. In a few instance the le li't'cttcc is to lcvcl ol' pcrlirrrrturrcc with rcspcct to asymptotic response lret;rrerrcy (llorrr
Altnlrrr lntl Srrtlrrrslr;rrr.
I97-5).
irr rr,'lrit'lr irrrlir,'irlrr;rl lrcrr rrrrllrrrtls vlrrrislrctl lnrrn sight
'rrs;tttrl ( ook. lt)I I )
I
lrt'r'llt'r'l ()l ()v('l('itst c'rttttlilirltts tltslltttt't's
530
VISUAL REPRESENTATIONS
INTERPRETATIONANDPRESE,NTATIONOFRESULTS
53r
DOWN-UP
GRUNT-WHISTLE
l-1l' J
l--
,/
A
Fig.
released under (a) Fig. 18.8 The superimposed vanishing bearings of borough fen mallards the mean indicates arrow centrifugal The sunny and (b) overcast conditions. (m) whose length (r) increases the tighter the bearings cluster about the
vector
mean. (from Matthews and Cook 1977)'
I
distetnces and positions of male green-winged teal in rclitl iotl lt I t lrt' lemale (center arrow) during performances ol gruntwhistle, down-r-rp. britllirrl'. and turn-back-of-head displays (see Figure 8.3). Note the precisc litlcrirl lrorlt orientation of males when performing the grunt-whistle, and shortcr tlis(rttlt t' from the lemale in the case of the down-up. Grunt-whistle can occttt'wltctt ,,ttl\ one male is present, but clclwn-up is performed only when a secontl tttltlc is
8.9 Orientations,
present. Thc cllstance between concentric circles is one foot; a swittttttittp'. lcrrl measures slightly less from
bill-tip to tail-tip (from McKinney'
1975).
during selected and positions of the male green-winged teal relative to the |-emale courtship displays (Figure 18.9).
ts.z.3 Kinematic graphs
Iti.2..l Conceptual models
transitions Kinemattic graphs (often called flow diagrams) are useful to illustrate the use of in detail' between behaviors (see Figure 8.3)' Sustare (1978) discussed' Figure various systems diagrams including information networks (e'g' sociogram and kinematic 10.14), association diagrams (Figure 18.12), state-space diagrams the sexual show to graph kinematic graphs. Halliday (1975) used two types of includes l8'10 [-igure behavior sequence in the smooth newt (i"r'irurus vulguri.r). reatlcr to'visualdrawings of the male and female, which increases the ability ot-the ize,the sequence through the orientation of the two
sexes.
provicles increased information on the probability ol' pltrticttllrt' clttt bc sttl-rtransitions occurring (width of arrows). Altering the size tll'the itrl'ows ol' plemented or replaced with the actual numbcr (Masscy. lgttt{) or Ptrrl'xtbililics
Figure
l8.ll
( t'olttPttlct'Ptotlttt'transitions in percentirgcs. Malalirnt arrtl lwcctlic l()t'il)tlcst't'ibc lll('tlttttllrt't ol t'itt'lt'illl(l lt lts tli'pir'lt'rl is tion rtl'kinctqgr.:rlrs in which clrclr stlrtc lrt'lrt't't'tt li.cs l.rctwcerr r.ircles irrrlit.lrtirrl tlrr.rrr;rl,rriltrrlr'rrl llrt'lt,ill'.tlt(tll lttrlrltlttltly
lltt'slttlt's.
('rtlccptual modcls
erre a
nteans
ol'maintaining perspectivc abottt tltc ctltite t'ottlt'rl
ip wlrich the beliavior(s) ol- interest occurs (see tlie t.noclcl cliscttssctl itt ('ll;r1rlt'r .'1 'l'[cy 1ll1lw y9u to lit togetl-rcr pieces of infi>rmtttiotr abottt it bcliitviol'lllsYsl('lrr (('I' rcprocluctivc bclurvior) in an attclrpt to undcrstettttl bcttct'tllcir ciltlscli lttttl lttttt liprrs lrrrtl illustratc thc irrtcrrclltionships bctwectt bcltitvitlrs. Motlcls ttt.e l't'ltt't;tll\ ltypollrcticll irnrl tcutporlry. bcitrg chitngetl as Ilcw t'csttlts c()lllc lirrtlr. l'itt t'rrttttPlt', rrrl('l li:rc;crrtls(l()76)1tnr1'rosctlllttotlcl(l;igtrrclti.l2)tocxplititltltcoccttt't't'll('t'()l tttotlt'l l'rtrrkt' lltt' rrrPti'u'r. bclurvior tlrrrirrg lltc irrcrrbirlion itt lrcrrirtg gtrlls. llitcrctttls rrrlp'svslr.rrrs'.'srrlrsyslclts'. ittttl 'ttcls'wlrrt'lr ltt'tt'l;ttt's to'l irlllctgctr's ( I()\0)t':ttltt't
r.prrt'r'l)lrr;rl tprrtlr.l rll' llrt. lrir.rlrrt'lrit'ltl ()t,'lltttzlrltott
ol ltclltvittl (st't' :tlso l)lttt'kttls.
l()l.i\) lrrrrtirrl'lr)t(l ( l()l.i,l) rllrr'.lt.tlt". :ttttl tlist ltss('s s('\('t:tl ;trltlt l()/(rtr i1t(l Iro11;11 torrtr'Plrlrl rrrorlr'l: r,l lttollt;tllr,tt l\1, l,rrl,rtr,l (l')/l). l\lr Ilrtllrrr.l illl(l llpttrl6rr(l()lil),rrr.l lr,,rlr'.,(l()li(l)tolll,lltt,trlrltll,rll,llr'\,lllr1tlr"',rl '.\'-lt'ttt''ltt,,,lt'l', ( ilrs:..
Sotttr'llltt,',,,,11t,
I
1rrrnr('lrl',.ltttl
ttt,', lt,ttrt.tll.
r,lll
lrt
tt',,'.1 'l',,lll,llo1'lt',
u1
VISUAL REPR ESENTATI()N
INTERPRETATION AND PRESENTNTION OF RESULTS
S
WAVE AND WHIP
STATI C
DISPLAY lrig. I ti.l
I
Kinematic graph of the spermatophore-transler phase of the smooth newt's of arrows is proportional to the lrequency of transition. Arrows pointing to lhe left are returning to retreat display; arrows pointing outwards erre leaving sexual behavior, fbr example, to breathe. Br:brake; C:creep, C.O. :,creep-on; P.B. : push-back, e :quiver; R. D. - retreat display; S - spermatophore depositi on ; T.T. : touch -tail ( from sexual behavior sequence (Figure 18.10). Width
Halliday, 1975).
RETREAT D IS
PLAY rttctaphors to help visualize, and often better understand, behavioral processes. For ('xample, Lorenz's original (1950), and revised (1981), psycho-hydraulic model ol' rrrotivation has appeared to some to be analogous to a flush toilet (e.g. Goodenough
t't u\.,1993); however, it served as the basis for much early theorizing about innate ;rrtitrral behavior. For example, discussing Lorenz's early models, Thorpe (1919)
te EE SPERMATOPHORE TRAN
SF
ER
+
+\rFF ie'6
CREEP & FOLLOW
s
Some of his models were obviously analogous only - but the very of 'analogy'is its imperfections which challenges rethinking. One did not suppose them to be'true'but they were valuable in being
OUIVER
essence TOUCH TAIL
highly suggestive. DEPOSITION
Fig.
18.10
Kinematic graph of the sexual behavior sccprcncc is in black (from Halliday. I97-5;.
It:t'u'c I
BRAKE &
TOUCH TAIL
r()nr iln cvoltrtionat'y perspcctive (e.g. Maynard Smith, 1982). I
PUSH BACK
ol'thc snrootlt trcwt. lhc
IThorpe, I979:I03J
Irkcwisc. giultc theory models, such as Prisoner's Dilernma (e.g. Axelrod, 1984), sct'vetl its a usclirl rnetaphors (Sigmund. 1993) for envisioning animal conflict
+\ETrTi3il,
fl q
tlrtecl:
trurlc
rt
lt't ltct' 1'rct's1-rccl
ivc is provirlccl by generalized conceptual models which help the
,'tltologisl vistutlizc lltc cottrplcx ol'vuriahlcs wlrich impinge on behavior (Chapter ') Sottlc tttotlcls rtitl rcsclrrclrcrs rrr rccogrrizing how thcir rcscarch fits within the'big Irtt ltttr"rttttlltssisls itr itlcrttil'yirrg, irtr1rorllrrrI vtrritrblcs Io invcsligtrtc in lirture str-rrlies. \ tt'tV l'.t'ttr't;tl tttotlt'l ol' tltis lyllt'rr';rs tlt'st'rrllt'tl ;urtl tlist'rrssctl rrr tlcllril by ('rook cl ,tl (lt)l(t) I lrc lrr,rlrrl irst. ;rllorvt.rl lr1, llrt.rr rrrorlt.l rs illrrslrlrlt'tl rrr I,igrrr.c Ili.lj.
( oll':trt(l()/li )l)l()\'t(l(':.;r 1'oorl()\'('rirllrlrrrrri:.rortol llrt'r,,lr'ol
rrrlrlt.llrr,,, ll(.tltrll69-
r{,tll('\(';il( lt ( rrg1t1'1rl tt,tlttt,,,l,'l',,,11,'nlr',t,11,,7,1 ,',lt, lrtt'nrttrlr'/r rrlttrlt,tt,.,.tlrt(.,.,1(.(ltttttt;tlltr.
VISUAL REPRESENTATIoN
INTERPRE,TATION AND PRESENTNTION OF RESULTS
S
I
External Environmental Variables
m
(EEV)
i I I
Fig. 18.13
i
A conceptual model showing how externalenvironmental variables (EEV) are expected to interact with species parameters (SP, for example, morphological and physiological characteristics) to determine social structure (measured as the
I I
principal social system variables (PSSV) and social dynamics (changes in PSSV over time)). The dotted arrow takes note of the lact that EEVs also affect SP. but
a
c t
on a slower (evolutionary) time scale than the effects on PSSVs, which may of an individual through learning (from Crook et al..
o
I
change within the lifespan 1916).
matical terms to enable tests of their validity; that is, they should result in lalsifiable lrypotheses (e.g. Drickamer and Vessey, 1982). Predictive models are built from data sets. Generally, the larger and more accurate the data set, the more accurate the
rnodel; however, Gauoh (1993) has argued that a model can be more accurate than the tlata used to build it since the model amplifles hiddern patterns and discards noise.
Predictive models can be rather general, such as Regelmann's (1984) model for Itow competing individuals should distribute themselves between food resource pittches, or they can be more specific such as Altmann's (1980) mathematical model cxpressing the relationship
of
a baboon mother's feeding time requirement to her inlitnt's age. Tliesc models are beyond the scope of this book, but good discussions crtrt be lirLrncl in Colgan (1978), Hazlett and Bach (1977) and Mangel and Clark
(letili). corollirry on lhc input lor incubation. This input is fed llrrottlllt rt tutil (/). rtcccssrrly to crplain tlrc inhibition o['settling and building rvlrt'rt li'erlllttk ttutlcltcs c\l)c('tiur('v. Ilrc cllt'ct ol'll'ctlhlck tliscrcpancy on Iy' (:rrrtl /). /:,:rrtrl /'. trtn lrt'tr';rrl llorrr llrc iur()\\'s.'l lrc rrlrin syslcnls rnutually \tllrPll'1''()ll(';lll()llrt't. /'rrlltrlttl'ltl lrr01q111:tstttlt'tIrrPliVr'llr'ltlt'"'i0ttt tlurlttglt rlt.'tttlrtlrtll()r(|l \';tttrl / /'trrtr lrr':rtlrr'rrlr'rlrlrrt'rllYlr\t'rlt'ltrlrlslirrrttll likcrltrsl l.llll ol lr,ll,t"llr"' / (.ttl.tl',,,1,1'',lttttttl,tlt'rl lrr rlr',ltttl',ur,t", ollrt'l tlr;rtt rlr.litit.ttl l,',',11,,r, l. lr,'ur llr,', lrrlr lr (lrntrr ll,t, r, n,l,, l,lit') lrrr clle'r'cncc copy or
Fig. 18.12 Model for the explanation
of the occurrence ol'intcrruptivc hclutviot'tltu'irtg tltc
incubation ol a herring gull. The {ixccl actiort pitttcrns ut'c itt tltc t'igltt coltrltttt and superimposed control systcnrs tll'first lrrrtl sccorttl otrlct'ltt'c t'elltesr'ttlerl icll ol thcnr (IV=inctrbtrtiort syslctu. /i cscrrllt'\y\l('nr. /' ptt't'ttttt1, s\sl('ltt I I ltt' l;rr1't' vcrticttl ilt't()ws trl)t'esct)l olit'nl;tlion t otnltottr'ttl', tr tllt tr'1':tttl lo lltr' ttr'sl Itrt'rrlrlrlirrl,is llrt't'orrsrlrrrrlrlorv rrr'l I t't'rll,,r, l. ',lrnrrtl,rltotl ltr,ttt llt,'t lttlt lt.,tllt't lrt'ttt1'ptor't'rsr'tl rrt //'. llorrs l() it uilll (( / I ult,'t,'tl t. r,rtlrl),ttt'rl trtllt ('\lx'( l,tlt( \.
tii
INTERPRETATION AND PRESENTATI0N OF RESULTS
VISUAL REPRESENTATION
S
RANK
18.2.s Other illustrations
1970 The type of visual representation employed and its value in interpreting results are limited only by the ingenuity of the researcher. Simplicity in illustrations is generally
1971
420
022
a virtue worth pursuing. For example, Bercovitch (1988) used a pie-chart to illus-
06o
trate the percentages of the different types of consort change-overs (e.g. feed, fight) in adult male baboons. Patterson (1917) used a simple diagram which clearly
o2o 530
demonstrates the rank-order changes of male shelducks (Tadornu tadorna) over a
two-year observation period (Figure 18.14). The positional and relative extent of the changes in rank order are obvious and conducive to further interpretation. Hutt and Hutt (1910) followed up on a suggestion by Altmann (1965) and
012
of a phase structure grammar model to the analysis of
013
described the application
behavioral sequences. The model was first developed by Chomsky (1957) for the study of psycholinguistics. The model consists of the sequential partitioning of a sentence into its constituent parts based on its explicit meaning. The result is a tree
-
diagram of sequentially smaller clusters of words that together carry the meaning of the sentence. This hierarchical model, discussed by R.Dawkins (1976a) and
of syntax in the repro-
023
ductive behavior of the pigeon (Figure 18.15). The 'Catch-22' ol this method lor the ethologist is that to apply the model to gain understanding of the message in communication, we must first understand the
432
message.
This difficulty is illustrated by applying the analysis to the sentence'We fed her dog bones', which can have two meanings; hence it can be diagrammed in two ways (Figure 18. 16).
530 383 013
/ /.. ,//
015
010
572 613 380 019
321 330 007
624 017
523 /
this level of resolution in analyzing sequences of animal behavior'/ Altmann ( 1965a)
_y
with sufficient experience it can be done.
If one's
'lit stttttttt:tt'ize, rrll ol' tltr'sr' lt't'ltttirlttr.'s ol \ lsurrl tt'1111",1'111,r1ro11 (lrrrrl ollrt'rs rrol tlist'ttsst'tl)t':ttt;ttrltttlltt'tttlt'tPtt'l;rll()n ()l r(",ull'. llr,'\ ',lr,,rrl,ll,, , \,unnl('(ltrol orrl\
-
017
As Dale (1976) states, the ambiguity does not arise from a difference in words or
goal is to draw up an exclusive and exhaustive classification of the animals' repertoire of socially significant behaviour patterns, tl-ren these units of behaviour are not arbitrarily chosen. On the contrary,thcy can be empirically determined. One divides up the continuum ol'actiorr wherever the animals do. If the resulting recombination units lu'c themselves communicative, that is, if they affect the behaviour ol'othcr' members of the social group, then they arc social messagcs. J'htrs. tlrc splitting and lumping that onc clocs is. itlcrrlly. ir rcllcctiorr ol'thc splitting und lumping tlrirt lhc rrnirrrrls tlo f lltttrrttttt. l()lt:,tt .lt).' f
440
015
in their ordet but rather from a dillerence in their constituent structure. Do we have suggests that
563
/
005 531
Westman (L917), was used by Marshall (1965) in his study
546
408
395 053
il1
518
023
425
399 018 057
I rr ll'i l.l
('lr;rrry'r'r rrr r;rrrk ()t(l('t
ol slrt.lrlrrr k., lrt.lrr(.(.n \(.:rrs I lrt.lil,rrlt.s:rr.e llrc scrill ttt,ttkcrl trr,rl,",,rrr,rrr1,r'tl rrr r.rrrl. ortlr.t llttrls rrltitlt wet.c t,tttk,rl ttt lrollr \(',u.,,1t(.lotn(.(l lr\,uto\\, Ilr,.lrrl,lrr,r r.rrrl.rrr1,lrrrrls ttr l()/0 lr'ttrlr'rl lo tr'1',,,,,, lrrl,lr rrr l,l/l (lr,,rrtr I .,rrltil.utr,\\ ,) 1,s11 llr,.trrrtlrllt.lrrtrl., trr l()/0 tttttttlrt'ts
ol
11111;1t,ltt,tl
(lltttttr, t',,'lt,l.ilrrr\\',1 lr'ttrlr,l l,r 1,,,, r,rtrl r, l.rlrr, 1,,11r,,,, ,lllil\\',)(lt"lll
l'.tllr
t ,iltt
l'r'r)
1,,11 1,,
lrr/{l(rl,r.,lrr.rl
REVISING AND RESTARTIN(; ll\ l'()llll:SlrS
INTERPRETATION AND PRESENTNTION OF RESULTS
model'l Does your interpretation help you to tlcvclopr nr:w models and generate new hypotheses? At this point it is again importunt to consider what other research has
SSSeq
,/l\ /t\ /\ t
\
Prep
lnt
w
Wa
ASS
/\
Dr
lnt
D
Bi
lnt
Bw
Aoo Bw
/
Pre
/
M
\
shown.
Co
\
Co
I8.3 COMPARTSONS WITH PREVIOUS RESULTS How do your results compare with those of other researchers? Discuss your results with the same researchers you consulted before beginning your study (Chapter 4). Even though you reviewed the literature belbre beginning your study, it is wise to again search for relevant material in the light of your results. You may want to know rnore about similar behavior in other species or different behavior in the same
Wa
,/\ gE_iw
I
A
species. You may discover that your results have a bearing on a general concept or current theoretical issues. The importance of results are often unforeseen when a
study begins, but become apparent as the study proceeds and finally come to light as
\
the results are carefully interpreted.
qi
P.
of generative grammar in its recursive form to reproductive behavior of the male pigeon. SBSeq:sexual behavior sequence; Prep : preparatory behavior, Con : consummatory behavior; Int : introduce; Wa:warm up; Agg:aggressive behavior; Bw: bowing; Dr:driving; A:attacking; D:displacement preening; Bi:billing; M:mounting: Co:copulation. The underlining represents the final behavior that results from the previous steps. The dots indicate where the pigeon can backtrack in the
Fig. 18.15 Tree diagram showing application
sequence
(from Hutt and Hutt, 1970).
We
fod hor dog
-.Aed
know where you began, and you think you know what your results mean. Even lhough your results were seemingly conclusive, your study could have been better. I{e-evaluate the economics, efficiency and validity of your methods. Did you
plctccl.
fed her dog bones fod
hor
dog boner
-4r
her dog
-,A't.
her
Yrru've now reached the point where you can re-evaluate the entire study. You
sclect the proper species, study area, behavioral units, data-collection method, rrnalytical tests, etc.'J Re-evaluate your study at each phase of the ethological lpproach (Chapter l). You should improve your methods with each study, but tltis can only come through a critical re-evaluation of each study as it is com-
We fed her dog bones
fed her dog bones
I8.4 RE-EVALUATION
dog
III
dog
Fig. 18.16 Two-phase-structure grammar models of a single sentence to illustratc thc different meanings (adapted from Dale, 1976).
to understand better the particular bchavior stutliccl. but itlso to pttt it irt pcrspcctive relative to the various lcvcls ot'bchuvior (('ht1'rte r' | ).
Art'lltt'
5 REVISING AND RESTATING HYPOTHESES
bones
t'csttlls sirrrillrl'lrt lltost'
secl l(lr tltltcr hchitviors. itrrrl spccics illr(l ttnrlt't olltt't t'ttt it()lllllr'lllill t'olltlilitrtts'.' Arc tlre l'csrtltst'otlsistt'lll witlr;r ('()ll('('l)lttltlttt"rlt'l .'t r'tlrt;ll'lt'l.t ttrt'lll il l)lt'rltt ltr''t"
\irrr rtt:ry wunt to rcvisc or restatc your hypotheses whether your results were posilivc or ncglrtivc. Tcsting rcvisccl hypotheses can help reinlorce positive results and rsolrrlc llrc sotrrcc ol'ncgutivc resulls. Ytru might choose to isolate additional vari,rlrles ol tcsl tlrc cxlcrrurl virlirlity ol'yotrr rcsults on othcr spccies. Wlrt'tlteryott terisr'.r'eslltlc.()t !('neI'lltcItcwltypotltc:.ics.y()r.rilrcIl()wbackatthe l,r'l'itutinl'ol lltt'r'lltolo1,i1';11:tllPto;rclt t'vt'lr'(('lr;tPlt't I). r't'rrtly lo bcgitt irgitin. This Ittnt'yt)lt ;tI('ilt()l('('\l)('tl('n(('(1.;rttrl. lr()Pt'lrrlly. trtsr'r Sltr';rklttr ,'' ,rn r'\olttttottltt\ lrtttl,rl,t:.1 . I ( ) Wtl'-,rtt ollr'rr'tl tlrt' lirlltrtvittB ttr',t1'lrl
INTERPRETATION AND PRESENI'ATION OF RE,SULTS
Love the animals for themselves first, thell strain for general explanations, and, with good fortune, discoveries will follow.
APPENDIX A
lf
don't, the love and the pleasure will have been enou gh. Jwilson,
they 1994.
191
Statistical figures and
J
For ethologists, having had the pleasure of observing animals and learnedwhat they do is generally exceeclingly rewarding without having yet fully understood vthv.
tables
Table Al. Factorials. Values ctf n! nl
t7
0l 1l 22 36 424 5 6 7 8 9 l0 Ir 2 rI 14 t.5 r6 ll I ti () l )0 I
120 720
5040
40320 362 880 3 628 800
39916800 479001 600
r
6?.27 020800
87 t78 29t200
I
307 614 368 0(X)
20922 789 tt88 (XX) l-5-5 (,tt7 6
l -1
42fl 096 (XX)
402 17.3 705 72tt 0(x) (xx)
l l 6.t5 t(x)"101{ til2 t
I tlttr
(x
)l{ I 76
6-10 (x x )
