Ecological Studies, Vol. 169 Analysis and Synthesis
Edited by I.T. Baldwin, Jena, Germany M.M. Caldwell, Logan, USA G. Heldmaier, Marburg, Germany O.L. Lange, Würzburg, Germany H.A. Mooney, Stanford, USA E.-D. Schulze, Jena, Germany U. Sommer, Kiel, Germany
Ecological Studies Volumes published since 1996 are listed at the end of this book.
Springer New York Berlin Heidelberg Hong Kong London Milan Paris Tokyo
Alan N. Andersen Garry D. Cook Richard J. Williams Editors
Fire in Tropical Savannas The Kapalga Experiment
With 62 Illustrations
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Alan N. Andersen CSIRO Sustainable Ecosystems Tropical Ecosystems Research Centre Winnellie, NT 0822 Australia
[email protected]
Richard J. Williams CSIRO Sustainable Ecosystems Tropical Ecosystems Research Centre Winnellie, NT 0822 Australia
[email protected]
Garry D. Cook CSIRO Sustainable Ecosystems Tropical Ecosystems Research Centre Winnellie, NT 0822 Australia
[email protected] Cover and frontispiece illustrations: Experimental fires at Kapalga. (Photographs by Barbara McKaige.)
Library of Congress Cataloging-in-Publication Data Fire in tropical savannas: the Kapalga experiment / editors, Alan N. Andersen, Garry D. Cook, Richard J. Williams. p. cm.—(Ecological studies ; v. 169) Includes bibliographical references (p.). ISBN 0-387-00291-X (hc : alk. paper) 1. Fire ecology—Australia—Kakadu National Park (N.T.) 2. Savanna ecology—Australia—Kakadu National Park (N.T.) I. Andersen, Alan N. (Alan Neil), 1957– II. Cook, Garry D. III. Williams, Richard J., 1955– IV. Series. QH197.F5624 2003 577.2—dc21 2002044505 ISSN 0070-8356 ISBN 0-387-00291-X
Printed on acid-free paper.
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Contents
Preface Contributors
1. Burning Issues in Savanna Ecology and Management Alan N. Andersen
vii xi
1
2. Kapalga and the Fire Experiment Garry D. Cook and Laurie K. Corbett
15
3. Fire Behavior Richard J. Williams, A. Malcolm Gill, and Peter H.R. Moore
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4. Fuel Dynamics, Nutrients, and Atmospheric Chemistry Garry D. Cook
47
5. Streams Michael M. Douglas, Simon A. Townsend, and P. Sam Lake
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This book is a tribute to Michael Ridpath’s vision in establishing Kapalga as a dedicated research site and To Brian Walker’s scientific leadership and unstinting support throughout the fire experiment.
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Preface
Fire is a major agent of disturbance in many biomes of the world but is a particularly important feature of tropical savannas. Up to 50% of the extensive tropical savanna landscapes of northern Australia are burnt each year. This includes prestigious conservation reserves such as World Heritage— listed Kakadu National Park, in the Top End of the Northern Territory. As in other savanna regions of the world, the responses of biota to different fire regimes are poorly understood, such that fire management represents one of the greatest challenges to conservation managers and researchers alike. This is the context within which a landscape-scale fire experiment was established at Kapalga Research Station in Kakadu, which aimed to provide a sound scientific basis for conservation management in the region. The experiment was established by The Australian Commonwealth Scientific and Industrial Research Organization (CSIRO), but involved collaborators from a range of universities and government agencies, including the managers of Kakadu, the Australian Nature Conservation Agency (ANCA: now Parks Australia North). This book summarizes the findings from the Kapalga fire experiment and explores the implications for conservation management. We believe that Kapalga has provided important insights into the fire ecology of tropical savannas and has broad relevance for the conservation management of fireprone landscapes in general. This book should be of interest to researchers, graduate students, and land management agencies. vii
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Preface
We are extremely grateful to all our collaborators, both inside and outside CSIRO, for their involvement in the Kapalga experiment. We particularly acknowledge the primary roles of Pat Werner and Dick Braithwaite in the design and establishment of the experiment. We thank Parks Australia staff for their support, especially senior managers Peter Wellings and Tony Press, traditional Aboriginal owners Victor Cooper and the late Mick Alderson, and Rangers Grant Matson, Ollie Scheibe and Margie Rawlinson. Dave Bowman, Ross Bradstock, Malcolm Gill, Richard Marchant, and John Woinarski critically reviewed chapters of the book. Finally, this book is dedicated to all the CSIRO technical staff who made the Kapalga experiment happen, particularly Kapalga managers Peter Panquee, Darryl Murphy and Robert Eager, fire managers Peter Brady and Mick Gill, and technical assistants Jack Cusack, Mick Greatz, Tony Hertog, Lyn Lowe, Judy McCutcheon, Ivan McManus, and Gus Wanganeen. Special thanks also to Barbie McKaige, who was part of the fire crew and helped assemble photographs for the book, and to Lesley Dias, who helped track down elusive citation details. Alan N. Andersen Garry D. Cook Richard J. Williams
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Contents
6. Vegetation Richard J. Williams, Warren J. Müller, Carl-Henrik Wahren, Samantha A. Setterfield, and Jack Cusack
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7. Terrestrial Insects Alan N. Andersen, Jérôme Orgeas, Rosalind D. Blanche, and Lyn M. Lowe
107
8. Terrestrial Vertebrates Laurie K. Corbett, Alan N. Andersen, and Warren J. Müller
126
9. Synthesis: Fire Ecology and Adaptive Conservation Management Alan N. Andersen, Garry D. Cook, and Richard J. Williams
153
References
165
Index
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Contributors
Alan N. Andersen
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia.
[email protected]
Rosalind D. Blanche
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia. Current address: Wet Tropics CRC, CSIRO Tropical Forests Research Centre, QLD 4883, Australia.
[email protected]
Garry D. Cook
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia.
[email protected]
Laurie K. Corbett
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia. Current address: EWL Sciences Pty Ltd., NT 0821, Australia.
[email protected] xi
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Contributors
Jack Cusack
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia.
[email protected]
Michael M. Douglas
Faculty of Education, Health and Science, Northern Territory University, Darwin, NT 0909, Australia.
[email protected]
A. Malcolm Gill
CSIRO Plant Industry, Canberra, ACT 2601, Australia.
[email protected]
P. Sam Lake
Department of Biological Sciences, Monash University, Clayton, VIC 3800, Australia.
[email protected]
Lyn M. Lowe
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia.
[email protected]
Peter H.R. Moore
CSIRO Plant Industry, Canberra, ACT 2601, Australia.
[email protected]
Warren J. Müller
CSIRO Mathematics and Information Sciences, Canberra, ACT 2601, Australia.
[email protected]
Jérôme Orgeas
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia. Current address: Institut Méditerranéen d’Ecologie et de Paléoécologie, FST St Jéróme, case 461, France.
[email protected]
Samantha A. Setterfield
Faculty of Education, Health and Science, Northern Territory University, Darwin, NT 0909, Australia.
[email protected]
Contributors
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Simon A. Townsend
Northern Territory Department of Infrastructure, Planning and Environment, Palmerston, NT 0831, Australia.
[email protected]
Carl-Henrik Wahren
Department of Agricultural Science, LaTrobe University, Bundoora, VIC 3083, Australia.
[email protected]
Richard J. Williams
CSIRO Sustainable Ecosystems, Tropical Ecosystems Research Centre, Winnellie, NT 0822, Australia.
[email protected]
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1. Burning Issues in Savanna Ecology and Management Alan N. Andersen
1.1 Introduction 1.1.1 Fire in Savannas Fire is a major agent of ecological disturbance in many biomes throughout the world, from grassy deserts to boreal forests (Johnson 1992;Whelan 1995; Bond and van Wilgen 1996; De Bano et al. 1998; Bradstock et al. 2002). In these places, fire is an important tool for habitat management at the landscape scale (Gill 1977; Moore 1987; Morton and Andrew 1987). Nowhere is fire more a part of the ecological and cultural landscape than in tropical savannas (Dyer et al. 2001). Characterized by a continuous grass layer under a sparse canopy of trees, savannas are the dominant ecosystems throughout the tropics wherever rainfall is highly seasonal (Bourlière 1983). Grass fires are an inevitable part of the annual cycle of profuse herbaceous production during the wet season (Fig. 1.1), followed by seasonal drought lasting for up to 7 months. Savannas have experienced frequent fires throughout their evolutionary history, and this has been a major factor in the development of savannas from other vegetation types over geological time. Many savannas in less seasonal areas have been recently derived through repeated burning by people (Bourlière and Hadley 1983; Hopkins 1983; Cavelier et al. 1998). 1
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a
b Figure 1.1. Grass production in the monsoonal tallgrass ecosystems that are typical of subcoastal northwestern Australia. The summer wet season sees prolific growth of annual Sorghum species (a), resulting in abundant fuel for fire over the winter dry season (b). (Barbara McKaige.)
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Most savanna regions of the world have been highly modified by subsistence agriculture, and the majority of savanna fires are lit by people in the contexts of land clearing, livestock management, or protection of property. Seasonal burning is often an unquestioned tradition in these areas (D. Gillon 1983). By contrast, the extensive savannas of northern Australia (Gillison 1983; Mott et al. 1985) are sparsely populated and, despite widespread pastoralism, remain largely uncleared (Williams et al. 1997a). Most fires are still lit by people, but primarily within the broad context of conservation rather than pastoral management (Press 1988; Rose 1995). Aboriginal people own a large proportion of the savanna region of northern Australia (e.g., more than 40% of the entire Northern Territory), and fire management has been (Jones 1969; Nicholson 1981), and in many places still is (Stevenson 1985; Haynes 1991; Bradley 1995; Russell-Smith et al. 1997a), an integral part of traditional Aboriginal life. Archaeological evidence points to Aboriginal occupancy of northern Australia for more than 50,000 years (Roberts et al. 1990; Mulvaney and Kaminga 1999), indicating that frequent burning by people has been part of the Australian savanna landscape for millennia. From an international perspective, Australian savannas are therefore largely in good ecological health (Woinarski and Braithwaite 1990). However, there are clear signs of significant biodiversity decline (Franklin 1999; Woinarski, Milne, and Wanganeen 2001) in recent times, and there is widespread concern that inappropriate fire management is an important contributing factor (Garnett and Crowley 1994; Williams et al. 2002).
1.1.2 Fire Patterns Savanna fires vary enormously in intensity, depending on fuel load, fuel moisture, wind speed, and other factors (Chapter 3), but they fall in the low to moderate range for wild fires (D. Gillon 1983). Fires lit early during the northern Australian dry season (May–June), when the grass layer is still moist, tend to be low in intensity, patchy, and limited in extent (Haynes 1985; Braithwaite 1987; Fig. 1.2a,b). As the season progresses, and the grass layer dries out, fire intensity tends to increase (Chapter 3). Fires occurring late in the dry season (September–October) often completely incinerate grass layer vegetation, cause substantial leaf scorch in the canopy, and cover large areas (Braithwaite and Estbergs 1985; Day 1985; Haynes 1985; Fig. 1.2c,d). However, fuel conditions are such that the spectacular canopy conflagrations known from fire-prone forested biomes in temperate regions rarely, if ever, occur. Up to 50% or more of savanna landscapes in northern Australia are burnt each year (Russell-Smith et al. 1997b; Gill et al. 2000; Edwards et al. 2001), and the great majority of these fires are lit by people. Natural fires, caused predominantly by lightning during the onset of the wet season during October and November, are still common in some places but are limited in extent by burning earlier in the year. The dominant fire management para-
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digm in conservation areas of northern Australia is one of extensive prescribed burning early in the dry season, to limit the extent and severity of fires occurring later in the year. This is particularly the case for European land managers (Russell-Smith 1995), but the practice is also an important component of traditional fire management by Aboriginal people (Jones
a
b Figure 1.2. Contrast between early (a, b) and late (c, d) dry season fires and fireaffected landscapes. (Barbara McKaige.)
1. Burning Issues in Savanna Ecology and Management
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c
d Figure 1.2. Continued.
1969; Nicholson 1981; Stevenson 1985). Temporal and spatial details of traditional Aboriginal burning are available from only a handful of studies (Haynes 1985; Braithwaite 1991; Fensham 1997; Russell-Smith et al. 1997a; Crowley and Garnett 2000; Preece 2002), such that differences between traditional and contemporary fire regimes remain poorly documented (Braithwaite and Estbergs 1985; Press 1988). However, it seems
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clear that the frequency and extent of high intensity fires late in the dry season has increased since European settlement and the subsequent disruption of traditional burning practices (Russell-Smith et al. 1997b).
1.2 Fire and Ecosystem Management 1.2.1 Adaptive Fire Management Science is never more than part of the solution to land management problems, but it is an essential part (Policansky 1998). Science and management are often viewed as opposing paradigms, one theoretical and the other practical, such that the relationship between scientists and land managers is often ineffective. Scientists are frequently guilty of inadequate consultation during the planning and implementation of research projects, and are often perceived as being unable to produce useful research outcomes (Andersen and McKaige 1998). On the other hand, managers are often viewed by scientists as being insular, narrowly focused, and reluctant to seek or accept research advice (Mattson 1996).The end result is that science is often poorly applied to land management (Andersen 1999a; Botkin 2000; Davis et al. 2001). Adaptive management (Holling 1978), a framework for integrating science and management, is now widely accepted as best-practice ecosystem management throughout the world (Christensen et al. 1996; Mangel et al. 1996). Adaptive land management (Fig. 1.3a) is a highly structured, strategic approach to managing ecosystems that minimizes the risks associated with the inevitable incomplete understanding of the ecosystem(s) to be managed, while maximizing learning for the refinement of future management actions (McCarthy and Burgman 1995; Stanford and Poole 1996; Dovers and Mobbs 1997). A key feature of adaptive land management is ongoing monitoring of the consequences of management actions to improve ecological understanding and thereby the effectiveness of future management. Science and management are integrated through a shared need for ecological understanding. Science has two clear roles in adaptive land management. First, it delivers the ecological understanding that forms the basis of management decisions (Holling 1993). With fire management, science draws on experiential knowledge, descriptive studies, hypothesis testing, and experimentation to provide the necessary understanding of fire behavior and its ecological and other effects. Second, science drives the monitoring systems that assess the outcomes of management actions, to provide feedback for future management. If the principles of adaptive management are not applied, it is easy for science to be marginalized. This is especially true when management focuses on the implementation of particular prescriptions, rather than on
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Figure 1.3. (a) Schematic of adaptive management. Management begins with the establishment of clear and unambiguous management objectives (relating to desired outcomes) and the formulation of performance indicators as benchmarks against which the effectiveness of management can be assessed. Management prescriptions are continually refined according to feedback from previous management “experiments.” Careful consideration is given to the full range of management options, and specific plans are developed for monitoring management effectiveness. Management and science are tightly coupled, with effective research and monitoring being critical to these links. (b) Adaptive management breaks down when management focuses on tactical goals (management prescriptions) rather than strategic objectives. (Modified from Andersen 1999b.)
strategic objectives relating to management outcomes (Andersen 1999b). This is an easy trap to fall into with fire management, where most performance indicators listed in management plans are based on operational achievements (such as target reductions in fire fuels, the mobilization of suppression forces, and the implementation of particular burning patterns) rather than desired ecological outcomes. When this occurs, monitoring programs can easily become fixated on the implementation of management prescriptions, rather than on their effectiveness in meeting strategic objectives, thereby short-circuiting the entire adaptive management process (Fig. 1.3b). Ecological research and monitoring thereby become largely irrelevant. Thus, in conservation areas, assessments of the success of fire management need to focus on biodiversity outcomes (Braithwaite 1985) rather than on the management of fire itself (Johnson and Miyanishi 1995; Andersen 1999b). It might ultimately be possible to use burning patterns as surrogates of biodiversity (van Wilgen et al. 1994), but such surrogacy must be rigorously validated.
1.2.2 Traditional Aboriginal Burning Traditional Aboriginal burning is a particularly important issue in northern Australia, given the long history of Aboriginal occupation, and since much of the region is under Aboriginal ownership. Major conservation areas such
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as Kakadu National Park have joint management arrangements with their traditional Aboriginal custodians (Salmon 1992; Lawrence 2000), and in such cases the reimposition of traditional Aboriginal fire regimes is an explicit management goal, usually jointly with the conservation of biodiversity (Kakadu Board of Management 1991). The reimposition of Aboriginal fire regimes is often considered to be the best way of achieving nature conservation because the regional biota has experienced traditional Aboriginal burning practices for so long (Press 1987; Russell-Smith 1995). This is analogous to the proposition that much of Europe’s contemporary biodiversity is dependent on the maintenance of historical agricultural practices (Settele et al. 1996) rather than natural processes. The relevance of Aboriginal burning to contemporary nature conservation is a highly contentious issue (Horton 1982), particularly following Flannery’s (1994) much publicized claims that mass megafaunal extinctions were caused by Aboriginal hunting and habitat management. In truth, the impact of Aboriginal burning on the Australian environment is in large part a matter of speculation rather than fact, and will always be so (Bowman 1998). However, whatever the validity of Flannery’s claims, it is clear that Aboriginal burning was not motivated by a Western conservation ethic (Langton 1998; Keith et al. 2002), and some justifications for burning (such as the widespread clearing of the grass layer to facilitate walking and to reduce snake populations) could not be defended on conservation grounds. On the other hand, although details of traditional Aboriginal burning patterns remain elusive (Preece 2002), it can reasonably be argued that the outcome of these burning practices was a fine-scale fire mosaic resulting in considerable habitat heterogeneity, and this is likely to have a positive effect on biodiversity (J. Williams et al. 1994; Braithwaite 1995a,c; Yibarbuk et al. 2001). Ultimately, the questions raised by Aboriginal burning are probably more philosophical (e.g., To what extent should Aboriginal management be considered part of the “natural” environment?) than scientific. In the meantime, the potential conflicts between Aboriginal burning and nature conservation cause unfortunate tension between traditional Aboriginal owners and park rangers (Lewis 1989).
1.2.3 Ecological Understanding of Fire How robust is the ecological understanding that underpins fire management? Fire has attracted considerable research attention in tropical savannas outside Australia, particularly in Africa, and its ecological effects have been reviewed extensively (Coutinho 1982; Trollope 1982; D. Gillon 1983; Frost 1985; Andersen 1996). The widespread use of savannas for pastoralism is reflected in the concentration of research on the effects of fire on grass composition, biomass, and productivity (West 1965; D. Gillon 1983; Pandey 1988; à Tchie and Gakahu 1989; Silva et al. 1991) and on underly-
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ing ecosystem processes such as nutrient cycling (Ram and Ramakrishnan 1992). This pastoral focus is also evident in Australia, where the best-studied areas are the pastoral lands of Queensland, the Katherine region of the Northern Territory, and the East Kimberley region of Western Australia (Stocker and Mott 1981; Gillison 1983; Mott et al. 1985; Craig 1997). Although fire is widely recognized as an important management tool for savanna conservation in Australia, the ecological effects of different fire regimes are hotly debated (Duff and Braithwaite 1990; Andersen 1996). Patches of monsoon rainforest (Russell-Smith and Bowman 1991; Bowman 1992) and other fire-sensitive vegetation (Price and Bowman 1994; Bowman 1995; J. Russell-Smith et al. 1998) embedded in the savanna landscape have attracted much of the research attention, rather than the savannas themselves. In savannas, research has focused on the immediate recovery of vegetation following individual fires (Lacey et al. 1982) rather than on longer term, ecological responses to different fire regimes. Fire managers often have firmly held beliefs about fire, but these are derived more from perceptions of burnt vegetation than on results of scientific research (Andersen and Braithwaite 1992). Elsewhere, beliefs about fire in the absence of empirical evidence have proven unreliable, despite being intuitively reasonable (Johnson et al. 1998). The scientific uncertainty about the ecological consequences of fire provides an unwelcome backdrop to competing demands on savanna lands, ranging from the aspirations of Aboriginal landowners for traditional burning practices to the dismay expressed by tourists over landscapes extensively charred by prescribed fires.
1.3 Fire in Australian Savannas: Priority Research Issues Like its overseas counterparts, Australia’s savanna vegetation is visibly resilient to fire, with most woody plants having well-developed powers of vegetative recovery and the dominant grasses regenerating vigorously, either vegetatively or by seed (Lacey et al. 1982). Fire is generally regarded as a “secondary determinant” of savanna vegetation, acting to modify broad patterns determined primarily by rainfall and edaphic factors (Walker 1987). However, despite much speculation based on contemporary floristic and structural patterns, the extent of this modification remains unclear (Andersen 1996). It is widely recognized that long-term fire exclusion has a marked effect on the structure of Australian savanna vegetation (Stocker and Mott 1981; Bowman et al. 1988a) and associated faunal communities (Woinarski 1990; Andersen 1991). However, long-term fire exclusion on a landscape scale is not a viable management option, given the high likelihood of natural fire and unauthorized ignitions. In a management context, the important issues relate to the effects of fires of different timing, intensity, and frequency (Gill 1975). From an ecological perspective, it is impor-
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tant to understand the longer term effects of different fire regimes on underlying community and ecosystem processes. These effects are not necessarily indicated by the immediate impacts of particular fires. Elsewhere we have proposed a series of hypotheses that helped us frame the Kapalga experiment, many of which challenge popularly held beliefs or draw attention to neglected issues (Andersen et al. 1998). Rather than repeat these hypotheses here, we have highlighted below what we consider to be the priority research issues.
1.3.1 Geochemical Cycling The central tenet that ecosystems are fundamentally driven by the cycling of carbon, water, and nutrients is often ignored in fire management debates. To preserve species and communities in the longer term, clearly it is critical that the fundamental processes that support them be maintained. The soils of Australia’s tropical savannas are characteristically nutrient poor, and this is a major determinant of ecosystem structure and function (Chapter 2). Fire potentially plays an important role in the nutrient dynamics of savannas (D. Gillon 1983; Cass et al. 1984; Gill et al. 1990) and may result in nutrient loss from an already impoverished system (Cook 1992, 1994). The emission of trace gases from biomass burning is also an issue of concern in the context of global climate change (Hurst et al. 1994a,b; Cook et al. 1995).
1.3.2 Tree Demography In tropical savannas, frequent fire impedes the growth of seedlings into canopy trees. This is particularly relevant in the context of highly infertile soils and a long dry season, which place severe constraints on rates of seedling growth. At all but the most favorable sites, it is likely to take seedlings several fire-free years to grow above flame height and therefore escape complete defoliation or death (Fordyce et al. 1997). It is common for trees in Australian savanna forests and woodlands to have a bimodal size structure, comprising canopy trees on one hand and short “woody sprouts” on the other (Fig. 1.4), with the latter maintained in a suppressed state by frequent fire (Fensham and Bowman 1992). Given that canopy trees suffer an annual mortality rate of at least 1% and that this figure can be up to 15% after particularly intense fire (Lonsdale and Braithwaite 1991; Williams et al. 1999a), the continued suppression of these woody sprouts would have severe demographic consequences. If high fire frequencies are preventing the recruitment of juveniles into the canopy, then the open forests and woodlands might be experiencing long-term structural degradation (Hoare et al. 1980). Many African studies have shown that the height, cover, and biomass of trees is reduced with increasing fire frequency (Trapnell 1959; Kennan 1971; Trollope 1980).
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Figure 1.4. Bimodal size structure characteristic of tropical woodlands experiencing annual fire. The grass layer contains a high density of suppressed woody sprouts that are prevented from entering the midstory. (Barbara McKaige.)
1.3.3 Faunal Diversity and Composition There is much public concern about the effects of fire on fauna. The most important of these effects are usually difficult to predict because they are indirect, occurring through fire-induced changes to habitat, food supply, and rates of predation. Effects of fire on faunal diversity and composition can cascade through ecosystems, owing to the important functional roles played by animals that determine rates and directions of ecological processes. For example, major impacts on phytophagous insect communities are likely to have flow-on effects on rates of herbivory and energy transfer through food webs.
1.3.4 Fire Timing and Frequency The fire debate in northern Australia is dominated by discussion of fire intensity, with comparatively little attention paid to other important aspects of fire regimes (Andersen 1996). Timing and frequency of fire are considered primarily in terms of their impact on intensity, through their effects on fuel loads (frequency) and moisture content (timing). They ought to be considered to be important in their own rights (Woinarski et al. 1991a). Fire intensity tends to increase as the dry season proceeds, so that effects of fire timing and intensity are often confounded. The importance of vari-
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ation in intensity is obvious, but that of variation in timing is not. However, given the dramatic seasonal patterns of growth and reproduction displayed by the regional biota, timing per se is likely to be an important factor determining the ecological effects of fire, regardless of intensity. For example, it may be that savanna fires have the greatest impact on plants during periods of active growth because carbohydrate and nutrient reserves have been depleted, and new leaves are particularly susceptible to leaf damage (West 1965; Kennan 1971). Fire timing can have a marked effect on the timing and intensity of grass seed production (Trollope 1982), flowering phenology (Coutinho 1982), and more generally the availability of food resources to consumers (Crowley and Garnett 1999). Teasing out the relative importance of fire timing and intensity has important management implications given that under suitable wind and fuel conditions, the trend of low intensity fires early in the season and high intensity fires late in the season can be reversed, thereby providing managers with considerable flexibility (Chapter 3). The popular contrast between “managed” low intensity fires early in the season, and high intensity “wildfires” fires late in the season does not take into account the differences in frequencies of these fire types. The real comparison is between frequent (annual), low intensity fires on one hand, and less frequent, higher intensity fires on the other. The deleterious effects of repeated fire on seedling recruitment have already been discussed, and it has been suggested that three or four fire-free years are required before seedlings become resilient to fire (Hoare et al. 1980; Fordyce et al. 1997). Longer term studies suggest that frequent low intensity fires may lead to a decline in fauna (Woinarski and Recher 1997). There is evidence that relatively small changes in fire frequency can have important ecological consequences: for example, in a study of ant communities in experimental plots at Munmarlary in Kakadu National Park, the community in a biennially burned plot showed a greater resemblance to those in plots unburnt for 14 years than to those of annually burnt plots (Andersen 1991).
1.3.5 Individual Fires vs Fire Regimes The ecological effects of individual fires are often confused with those of particular fire regimes (sensu Gill 1981), with the unsubstantiated assumption that the effect of an individual fire is indicative of the longer term effects of a regime of fires of that type. This would certainly simplify fire research if it were true, but it is unlikely to be so. It is more likely that interactive or synergistic effects occur, such that the cumulative effects of any fire regime cannot be described as simply the sum of the effects of individual fires. There is also likely to be considerable ecological hysteresis as a result of past history of burning practices (Vlok and Yeaton 1999). It may take many years of a different fire regime to override the effects of the old one; indeed, past burning may have caused irreversible changes.
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1.3.6 Fire Compared with Other Factors Ecological systems in northern Australia are notoriously variable, and fire is only one of several factors potentially contributing to this variability. Indeed, as stated earlier, fire often serves only to modify patterns caused primarily by other factors, particularly those relating to climate. For example, the native rodent Rattus tunneyi was 400 times as common at Kapalga in 1986 as in 1992, owing primarily to variation in rainfall (Braithwaite and Griffiths 1996). There is enormous variability in the onset, intensity, and length of the wet season in the region (Ridpath 1985; Taylor and Tulloch 1985). These erratic rainfall patterns are known to drive population irruptions of native rodents (Friend et al. 1988) and to determine the breeding success of water birds (Frith and Davies 1961). In this context, it would be inefficient to direct resources to fire management when the effects of fire are minor in comparison to other factors. It is therefore necessary to identify which components of the biota are likely to be particularly affected by fire, and which are not. Particularly sensitive components of the flora, such as the conifer Callitris intratropica, have already been identified (Price and Bowman 1994), but such information is not known for the fauna.
1.4 The Kapalga Experiment Descriptive studies of postfire effects can provide useful information on ecological responses to particular fires, but a proper understanding of the effects of different fire regimes requires a more rigorous approach, such as the use of experiments (Whelan 1995). The maintenance of experimental plots subjected to various fire regimes has played an important role in understanding the effects of fires throughout the tropics (Moore 1960; Egunjobi 1971; San Jose and Medina 1975; Bowman et al. 1988a; van Wilgen et al. 1998). However, the size of experimental units in such studies is usually small (about 1 ha or less), which limits the applicability of the results. In Yellowstone National Park, for example, plant responses to hectare-sized fires were substantially different from those to fires burning at landscape scales (Turner et al. 1997). Small experimental plots preclude a valid assessment of the spatial patterning that is typical of low intensity fires (Gill et al. 1990), and may not allow for the development of high intensity fires (Gill 1977; Lonsdale and Braithwaite 1991; Cheney and Sullivan 1997). Most importantly, ecological processes that operate on a landscape scale, such as catchment hydrology, nutrient transfers, soil erosion, and faunal movements, cannot be effectively addressed (Andersen and Braithwaite 1992). There is increasing interest in landscape-scale experiments involving whole-ecosystem manipulation (Bormann and Likens 1991; Rasmussen et
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al. 1993; Beier and Rasmussen 1994). These experiments are beset by conflicts between appropriate spatial scales, funding and logistic constraints, and adequate replication (Carpenter 1990). Special statistical procedures are available to alleviate the problem of lack of replication (Jassby and Powell 1990; Reckhow 1990), and it has been suggested that the classical paradigm of treatment replication (Hairston 1989) is not even appropriate for landscape ecology (Hargrove and Pickering 1992). These authors argue that landscape ecologists should rely less on controlled experiments and more on “natural field experiments.” We agree that “natural experiments” can be very useful for understanding biotic responses to particular fires. However, we think that a proper understanding of the ecological effects of different fire regimes requires a rigorous experimental approach, incorporating the collection of extensive baseline (pretreatment) data, detailed measurements of the fires themselves, and adequate replication. This is the context in which we established the Kapalga fire experiment. A long-term, replicated fire experiment had already been established at nearby Munmarlary and was producing valuable results (Bowman et al. 1988a; Woinarski 1990; Andersen 1991; Cook 1991; Bowman and Panton 1995; Russell-Smith et al. 2003). However, it was limited by an absence of baseline information, limited data on the fires themselves, and, most importantly, by small plot size (1 ha). To deal effectively with landscape-scale issues such as nutrient dynamics, catchment hyrdrology, and wide-ranging fauna, we sought at Kapalga to overcome these limitations. We do not pretend that the Kapalga experiment addresses all the priority issues just outlined. We have largely adopted the “instrumental” paradigm favored by Peters (1991) for ecological research, with the primary aim of documenting a range of key ecological responses to fire, rather than incorporating fire in an ecosystem model that may not have the precision required for effective management.
2. Kapalga and the Fire Experiment Garry D. Cook and Laurie K. Corbett
2.1 Introduction Kapalga lies within the wetter and more fire-prone end of Australia’s seasonal tropics and forms part of World Heritage–listed Kakadu National Park. Its biota, landscapes, and climate are representative of much of subcoastal northern Australia. Its flora and fauna are structurally and compositionally similar to those occurring across extensive areas of Australia’s north (Taylor and Dunlop 1985; Burrows et al. 1988; Wilson et al. 1990). Kapalga’s subdued relief is also typical of much of northern Australia, which generally consists of extensive undulating plains with altitudes less than 500 m (Hays 1967). The native biota of the region is largely intact: tree clearing has been minimal, and no vertebrate species have been lost (Woinarski and Braithwaite 1990). There are only seven species of introduced vertebrates, all mammals. Of these, only water buffalo and pigs have had a substantial impact, and this has been largely restricted to the wetlands and wetland margins (Braithwaite et al. 1984; Cowie and Werner 1993). Introduced weeds account for only 3.7% of Kapalga’s flora (Taylor et al. 1991) and are also largely restricted to the wetland margins (Cowie and Werner 1993). Kapalga is free of large stands of wetland weeds such as Mimosa pigra and Salvinia molesta, and introduced savanna grasses such as Andropogon gayanus are largely absent. Kapalga provides an ideal site for a landscape-scale fire experiment, being bounded to the east and west by major rivers and to the south by a 15
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G.D. Cook and L.K. Corbett
highway. About 20 ephemeral streams drain the uplands, providing ample opportunity for replicate water catchments. A system of all-weather roads provides access to many of these catchments, and the road network and river boundaries help protect Kapalga from off-site wildfires.
2.2 Geomorphology Kapalga occupies approximately 670 km2 between the latitudes of about 12°20¢ and 12°45¢S. It is bounded to the east and west by the South Alligator and West Alligator Rivers respectively, and to the south by the Arnhem Highway. Kapalga consists of an upland plain (ca. 10–40 m above sea level) between seasonally inundated coastal riverine plains (<10 m above sea level). In the southern half of Kapalga, the upland plain is 10 to 20 km across, while in the north this distance decreases to approximately 5 km (Fig. 2.1).
Figure 2.1. Map of Kapalga showing experimental compartments and fire treatments.
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Figure 2.2. View of the extensive upland plain from a quartzite foothill in the south of Kapalga. The palms in the foreground are Livistona inermis. (David Curl.)
Upland Kapalga is part of the widespread Koolpinyah Surface (Hays 1967; Isbell 1983), which is an extensive plain that formed due to extensive weathering and planation of Late Tertiary sandy sediments. However, unlike much of the Koolpinyah Surface elsewhere (Needham 1984), dissected foothills are a prominent feature of Kapalga, particularly in the southern half (Fig. 2.2). These hills provide local relief up to a maximum of 92 m above sea level, and developed where more resistant Early Proterozoic bedrocks protrude through the sandy plains originating from Late Tertiary sediments. Generally the hills are subparallel strike ridges of quartzite or gneiss separated by swales underlain by pelitic rocks. Despite the prominence of the hills, more than 95% of Kapalga is less than 40 m above mean sea level. About 20 temporary streams drain the upland plain (Fig. 2.3). They flow either eastward into the floodplain of the South Alligator River or westward into the floodplain of the West Alligator River. Most streams are 2.5 to 5.5 km long and consist of incised channels 1 to 2 m deep and 2 to 5 m across for much of their length. Such streams are largely absent from northern Kapalga, where few strike ridges occur, the maximum relief is just 26 m, and the catchments are smaller. Flow in Kapalga’s temporary streams accounts for between 10 and 30% of the annual rainfall (Townsend and Douglas 2000; Chapter 5). In their lower reaches, the streams are flanked by bands of sandy Quaternary alluvia, which can be several hundred meters wide (Cook et al. 2000).
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Figure 2.3. One of numerous seasonal streams that drain Kapalga’s upland plain. (Barbara McKaige.)
As well as the streams, about 15 closed depressions up to 500 m in diameter occur on the upland plain, forming seasonal or permanent water bodies. These may represent former spillways of a prior drainage network (M. Williams 1969).
2.3 Soils The Cahill and Hotham family soils (Hooper 1969) dominate much of Kapalga and are widespread on relatively intact parts of the Koolpinyah Surface. These are well-drained Kandosols (Isbell 1996) with a gradational texture profile ranging from 5 to 15% clay at the surface to 15 to 30% clay at depth. The surface soils are darkened with organic matter, and their
2. Kapalga and the Fire Experiment
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colors range from dark grayish brown to brown, while the subsoils are yellowish red to dark reddish brown. A surface veneer of ferruginous gravel with light red sand is common. Ferruginous nodules or fragments of ferruginized parent material are found throughout the soil profiles, but often increasing with depth. Typically these soils are 1 to 1.5 m deep, but on the edges of the old surface where dissection is active, the soils may be stripped to less than 0.3 m. The accumulation and cementation of coarser gravels has resulted in the formation of ironstone sheets in many places, forming a broken pavement. The soils of the uplands are generally low in soluble salts, organic carbon, nitrogen, phosphorus, sulfur, and exchangeable basic cations (Hubble et al. 1983). Small patches of higher fertility associated with clumps of perennial grasses and woody vegetation occur within the landscape (Ludwig et al. 1999). The soils of the strike ridges and rocky hills are skeletal, consisting of very shallow coarse sandy deposits between rock outcrops. The Quaternary alluvia along the streamlines dissecting the upland plain comprise soils of the Kapalga and Howard families (Hooper 1969). The Kapalga family soils are siliceous sands, while the Howard family soils are sands overlying clays and are typically found in lower slope positions. These soils suffer impeded drainage and temporary inundation during the wet season because of their position in the landscape. Because the soils are so young, profile development is limited to darkening of the A horizon through accumulation of organic matter. These soils are usually pale in color throughout most of the profile, partly as a result of the reduction of iron minerals and their subsequent removal through strong leaching. The soils of the coastal riverine plains have a very different history from that of the upland soils. Between 6000 and 8000 years before the present, at the end of the last glaciation, the river valleys were flooded. The resultant deposition of estuarine muds in the valleys of the West Alligator and South Alligator Rivers created the present coastal plains, which are up to 10 m above the pre-Holocene river valleys (Woodroffe et al. 1986). In contrast to the soils of the upland plains, these soils have a high (40–80%) clay content (White 1984). Being deposited in estuaries, these soils were originally saline, but are now extensively nonsaline owing to levee development, leaching, and the deposition of freshwater sediments (Woodroffe et al. 1986, 1989).
2.4 Climate The climate of Kapalga is classified as Aw (mean temperature of all months >18 °C, winter dry season) by Köppen and as V3 (tropical, 5–7.5 arid months) by Troll/Paffen (Müller 1982). Similar climates occur extensively across Africa, South America, and southern and southeastern Asia. Savanna ecosystems typically predominate under such climates (Cole 1986).
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In northern Australia, the winter dry season results from the belt of subtropical high pressure and divergence that covers most of the continent (Gentilli 1971). The counter clockwise descent of dry air in this high pressure system brings southeasterly trade winds and stable fine weather. The single wet season occurs in response to the shift of the high pressure system southward by 5° to 8° of latitude during summer (Gentilli 1971). This allows the intertropical convergence zone to reach northern Australia, bringing convective storms, monsoonal rains, and cyclones. Although dry southeasterly winds dominate the winter dry season, the proximity of the coast and the relatively high humidity compared with inland regions cause the atmosphere to be buffered against marked diurnal and intra-annual changes in temperature. Thus, because of the high levels of incident radiation, the climate is hot all year. At Jabiru, approximately 50 km east of Kapalga, all months have a mean daily maximum temperature between 31 and 38 °C (Fig. 2.4). The highest temperature recorded was 42 °C in October and December. Mean daily minimum temperatures at Jabiru range from 18 °C in July to 25 °C from October to March (Fig. 2.4). The lowest temperature recorded was 8.8 °C in July. The mean diurnal range is greatest from August to October at 14 °C, and least from January to March at 9 °C. In contrast to the temperature, rainfall is markedly seasonal. Of the median annual rainfall of 1287 mm at Jabiru, 84% falls from December to
Figure 2.4. Monthly patterns of mean rainfall (solid bars) and mean maximum (solid line) and minimum (dashed line) temperatures at Jabiru, Northern Territory (12.7 °S 132.9 °E).
2. Kapalga and the Fire Experiment
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March, with an additional 9% falling in November (Fig. 2.4). The potential annual pan evaporation is 2592 mm. Like most of northern Australia, Kapalga’s climate is relatively unaffected by topographic relief.
2.4.1 Fire Weather Fire weather in tropical savannas is determined by the annual arrival and departure of the monsoon (Tapper et al. 1993). McArthur’s Forest Fire Danger Index (FFDI) is often used to measure variation in fire weather (Luke and McArthur 1978; Noble et al. 1980). FFDI integrates the major determinants of the rate of spread of fire—air temperature, wind speed, humidity, soil moisture and fuel curing. Its values lie between 0 and 100, with values above 50 indicating extreme fire weather—the type that occurs during severe wildfires in southern Australia. Gill et al. (1996) examined the seasonal changes in FFDI for a 12-year period in Jabiru. During the peak monsoon period of January to early March, when the majority of the rain falls, the average daily FFDI is below 5 (Fig. 2.5). During this period the vegetation is essentially nonflammable. Average daily FFDI in the early dry season (May–June) remains below 20, increasing to around 20 during the late dry season (September–October). Average maximum FFDI during the latter months is about 40. The most extreme value was 60, well below peak levels of 100 that can occur on extreme days in southeastern Australia (Gill and Moore 1990; Williams and Bradstock 2000). Although FFDI declines
Figure 2.5. Average monthly values of daily 15.00 h Forest Fire Danger Indices for Jabiru over 11 years of records.The dashed line shows the absolute maximum values; the dotted line shows the average of the maximum values recorded each year; the solid line shows the average of all daily values for all years.
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again with the onset of the wet season, days when FFDI is in the range of 20 to 30 can occur in November–December, and fire is possible during this period as a consequence of lightning and prescribed burning (Stocker and Mott 1981; Williams and Lane 1999).
2.5 Vegetation 2.5.1 Structure and Floristics Kapalga has been mapped at the scale of 1 : 1,000,000 as Eucalyptus miniata, E. tetrodonta open forest with Sorghum grassland understory (Wilson et al. 1990). This unit covers nearly 52,000 km2 of subcoastal northern Australia, with structurally and floristically similar vegetation types covering another 240,000 km2. Open forest and woodlands dominate the uplands of Kapalga on welldrained sites (Fig. 2.6a–c). Mixed stands of Eucalyptus miniata and E. tetrodonta commonly comprise the tree layer, with Erythrophleum chlorostachys, Xanthostemon paradoxus, Eucalyptus porrecta, E. confertiflora, and E. latifolia being less frequent. A midlayer of broad-leafed species such as Terminalia ferdinandiana, Buchanania obovata, and Livistona humilis is often also present. The ground layer is dominated by grasses, including annual species of Sorghum, and perennial species such as Heteropogon triticeus, Chrysopogon fallax, and Allopteropsis semialata. A sparse to open shrub layer of lignotuberous resprouts of the major tree species is also an important component in frequently burnt areas (see Fig. 1.4). Along the stream lines and drainage depressions, vegetation varies considerably from grasslands dominated by short species such as Whiteochloa capillipes to woodlands and open forests dominated by a range of tree species including Melaleuca spp. and Pandanus spiralis (Fig. 2.6d). Monsoon rainforests occur in isolated stands of several hectares, usually near margins of the floodplains. Common species include Ficus virens, Bombax ceiba, Diospyros spp., and Livistona benthamii. The vascular flora of Kapalga includes 760 species from 398 genera and 133 families (Taylor et al. 1991). Exotic species are represented by only 28 (3.7%) species, and are generally uncommon. Graminoids, forbs and shrubs comprise two-thirds of the regional flora (Table 2.1), with most herbaceous species being annuals. Considering the flora of the broader region (Northern Territory 11°–16°S), over two-thirds of genera have a strictly tropical distribution (with 28% having pantropical distributions, 19% being centered on the Old World tropics, and 18% centered on the Indo-Malaysian region); only 15% of genera are restricted to Australasia (Bowman et al. 1988b). The struc-
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a
b Figure 2.6. Woodlands and open forests of upland Kapalga. (a) Eucalyptus miniata/E. tetrodonta open forest with grassy understory, the dominant vegetation type at Kapalga. (b) Eucalyptus miniata open forest with shrubby understory, typical of more productive or less frequently burnt sites. (c) Open woodland on the top of a crest dividing two catchments. (d) Open woodland dominated by Eucalyptus papuana and Pandanus spiralis adjacent to seasonal creekline. (a and b, Alan Andersen; c and d, Barbara McKaige.) Continued
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c
d Figure 2.6. Continued.
turally simple savannas host a much greater diversity of plant species than do the monsoon rainforests or the wetlands (Braithwaite 1990).
2.5.2 Phenology Deciduous trees are a feature of northern Australian savannas, increasing in their species richness with decreasing aridity north from Australia’s arid inte-
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Table 2.1. Percentage of species of terrestrial vascular plants of the Alligator Rivers Region with various life-forms and life histories.
Tree Palma Shrub Sub shrub Fern Vine Woody Herbaceous Graminoid Forb Total
Perennial
Annual
Aerial parts annual, roots perennial
Deciduous
Evergreen
Total species
0 0 2 2 0
0 0 2 2 2
4 0 1 0 0
12 1 13 2 1
16 1 18 5 3
0 1 11 21 36
0 4 1 8 19
1 0 0 0 5
2 1 8 1 40
3 5 20 29 100
a
Includes Cycadaceae and Pandanaceae, as well as Aracaceae. Source: Brennan (1996a).
rior (Williams et al. 1996). Trees in the Kapalga region fall into four main types based on their leaf phenology (Williams et al. 1997b): Evergreen species, which retain a full canopy throughout the year Brevideciduous species, which lose up to 50% of their canopy briefly during some dry seasons Semideciduous species, which lose more than 50% of their canopy each dry season Fully deciduous species, which lose all their leaves, and remain leafless for at least one month Of 49 tree species occurring at Kapalga, 24% are evergreen, 20% are brevideciduous, 20% are semideciduous, and 27% are fully deciduous (Williams et al. 1997b). For evergreen species, leaf flushing can occur throughout the dry season, but it peaks in the late dry season (Williams et al. 1997b). Although the timing of flushing shows considerable interannual and interspecific differences, for many deciduous species leaf flushing occurs in the late dry season before early rains. This can be as early as September, but is often from October to November (Williams et al. 1997b). Litterfall from trees during the dry season contributes substantially to the ground-layer fuel load at this time of the year. The reproductive phenology of the dominant trees Eucalyptus miniata and E. tetrodonta is completed within the dry season (Setterfield and Williams 1996). Floral buds appear in May/June, flowering occurs from May to August, and seeds fall from September to November. This typically occurs 2 to 8 weeks earlier in E. miniata than E. tetrodonta. Dry season flow-
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ering is also a feature of many other prominent woody species, including Cochlospermum fraseri, Calytrix exstipulata, Brachychiton megaphylla, and many other species of Eucalyptus (Fig. 2.7). Seeds of annual Sorghum species germinate in response to the first falls of about 15 mm or more of rain in the transition from dry to wet season.
a
b Figure 2.7. Dry season flowering in the Kapalga woody flora: (a) Cochlospermum fraseri (Bixaceae), (b) Calytrix exstipulata (Myrtaceae), and (c) Eucalyptus miniata (Myrtaceae). (a, Barbara McKaige; b, Alan Andersen; c, Samantha Setterfield.)
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c Figure 2.7. Continued.
They drop seeds and become senescent in early April (Andrew and Mott 1983), providing fuel for fires from the early dry season (Chapter 1). Tall perennial grasses such as Heteropogon triticeus cure 1 to 2 months later. Grasses may remain green for considerably longer along drainage depressions (Gill et al. 1996).
2.6 Fauna The uplands of Kapalga host a diverse native vertebrate fauna including 32 species of mammals, 224 birds, 73 reptiles, 20 frogs, and at least 10 freshwater fish. Nevertheless, social insects (ants and termites) dominate the fauna, having substantial impacts on all aspects of the functioning of the savannas (Andersen and Lonsdale 1990; Braithwaite 1990; Andersen and Braithwaite 1996). For example, more than 70% of Eucalyptus tetrodonta and E. miniata trees in Kakadu National Park are hollowed by termites, mostly Coptotermes acinaciformes (Andersen and Braithwaite 1996), and this hollowing can interact with fires and windthrow to contribute to tree death (Lonsdale and Braithwaite 1991). Kapalga supports some of the richest local ant communities ever recorded, with 100 or more species occurring within 0.1 ha (Andersen 1992). Ants are the major postdispersal seed predators in the region (Andersen and Lonsdale 1990; Andersen and Braithwaite 1996; Andersen et al. 2000) and are important seed dispersers (Andersen and Morrison 1998).
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The vertebrates of Australia’s savanna region represent a distinctive and very diverse eucalypt woodland fauna, rather than an attenuated rainforest fauna as in South America or a grassland fauna as in Africa (Braithwaite 1990). The marked seasonality of rainfall in northern Australia has probably acted as a significant barrier to the intermingling of the vertebrate fauna of humid tropical rainforest in New Guinea and northern Queensland, with the savanna biota of the remainder of northern Australia (Keast 1981). This is also largely true for invertebrates, where there is a major disjunction between the rainforest fauna of Queensland’s humid tropics, which is dominated by Indo-Malaysian elements, and the adjacent savanna fauna, which comprises mainly arid-adapted autochthonous elements (Taylor 1972).
2.7 Human History Aboriginal people have apparently occupied northern Australia for more than 50,000 years (Roberts et al. 1990). The Kurnbudj people occupied most of the Kapalga area until about a century ago. These people are believed to have been severely affected by the introduction of diseases and by aggressive acts of settlers, in common with Aboriginal populations in many parts of Australia (Kirk 1981; Braithwaite 1995b). European explorers first visited the area in the early 1800s and frequently commented on the extent of Aboriginal burning (Braithwaite 1995b). An Anglican mission operated at Kapalga from 1899 to 1903, and various buffalo hunting enterprises persisted until 1939. The area became depopulated after this period. Kapalga was established as a research station in 1976. It became part of Kakadu National Park in 1984 and ceased being a dedicated research area in 1995.
2.8 The Kapalga Fire Experiment 2.8.1 Experimental Design For the fire experiment, Kapalga was divided into 13 experimental units (compartments), each comprising a water catchment with an area of about 15 to 20 km2. Each compartment was burnt according to one of four treatments, selected to represent the range of fire types occurring in the region (Fig. 2.1): “Early”: fires lit early in the dry season (May–June), which is the predominant management regime in Kakadu National Park and other conservation reserves in the region (Chapter 1). “Late”: fires lit late in the dry season (September/October), as occur extensively in the region as unmanaged “wildfires.”
2. Kapalga and the Fire Experiment
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“Progressive”: fires lit progressively throughout the dry season, such that different parts of the landscape are burnt as they progressively dry out. We believe this to approximate traditional Aboriginal burning practices (Lewis 1989; Braithwaite 1991). Fires were lit at three times during the year: May/June, July/August, and September/October. “Unburnt”: no fires were lit, and wildfires were excluded. All fire treatments were applied annually for 5 years, from 1990 to 1994. Fire frequency other than exclusion was therefore not a factor in the experiment, owing to constraints in logistics, resources, and time. Therefore our approach was to apply the extremes of potential fire regimes, and to use simulation modeling to determine likely effects of variation in fire frequency.
2.8.2 Lighting the Fires An extensive series of roads and graded firebreaks secured experimental compartments from unplanned fires, and experimental fires were lit from them. The only exceptions were for the second and third Progressive fires, which were lit by dropping incendiary bombs from a helicopter along seasonal creeklines. These techniques are those used for fire management elsewhere in Kakadu National Park by park staff, who assisted in lighting our experimental fires. Winds tend to be from the southeast during the dry season, so back-burns were typically first set along the northern and western perimeters of each compartment. Back-burn fires quickly went out and served to eliminate fuel from these boundaries and therefore control the forward burns. Once adjacent compartments were fully secured by appropriate fuel-reduction burning, a single-line head fire, 2 to 5 km long, was lit on the windward side of the block. The forward-burning fires were allowed to burn until they went out within the compartment. The compartments were so large that the back-burning and forward-burning fires did not interact with each other. Most fires were lit between 11:30 a.m. and 3:00 p.m., corresponding to the time of peak daily wind speed (Gill et al. 1996), highest temperature and lowest humidity. Time of ignition was kept as constant as possible so that the diurnal changes in temperature, relative humidity, wind, and fuel moisture were not confounded with seasonal changes in the same fire weather variables. Temperature, relative humidity, and wind speed and direction were monitored at the time of each fire, either within several hundred meters of the fires or at Naramu camp, 2 to 15 km from the sites of ignition (Chapter 3). Trained and experienced teams, all in communication by radio, were responsible for lighting the fires. These teams comprised:
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A fire coordinator, with overall responsibility for burning operations One or two fire setters, either on foot (Fig. 2.8a) or on the back of fourwheel-drive vehicles (Fig. 2.8b) Several firefighters, each in a fire tender with a driver, who followed the fire-setters and extinguished escaped fires (Fig. 2.8c) Several “spotters” in four-wheel-drive vehicles, responsible for patrolling the perimeter of the compartment to detect any escaped fires Unfortunately, on two occasions compartments were burnt by unplanned fires: a Late compartment (F) in June 1990, and an Unburnt compartment (M) in September 1994.
a Figure 2.8. Lighting experimental fires (Barbara McKaige). Fires were lit by hand (a) or from the back of a vehicle (b). Teams of firefighters were always on hand to extinguish “escaped” fires (c).
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b
c Figure 2.8. Continued.
2.8.3 Data Collection Research was aimed at both describing fire behavior (Chapter 3) and determining ecological responses to the fire regimes. Biophysical and geochemical processes were assessed in terrestrial (Chapter 4) and aquatic (Chapter 5) ecosystems. Trace gas emissions from the fires were character-
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G.D. Cook and L.K. Corbett
ized by using light aircraft to collect smoke samples (Chapter 4). Botanical studies focused on the demography and reproductive phenology of trees, and on the floristic composition of the grass layer (Chapter 6). Aquatic macroinvertebrates (Chapter 5) and terrestrial insects (Chapter 7) were studied in detail. Finally, a range of vertebrate studies were conducted, encompassing broad surveys as well as detailed autecological studies (Chapter 8). Sampling within each compartment was focused on a permanent transect capturing the local variation in moisture availability. These “primary” transects ran from immediately adjacent to semipermanent water holes within the seasonal streams (Fig. 2.6d), upslope for 500 to 750 m to well-drained soils supporting the regionally dominant Eucalyptus miniata, E. tetrodonta open forest (Fig. 2.6a,b). The riparian habitats near the semipermanent water holes comprise a variety of open woodlands, low grasslands and sedgelands on seasonally waterlogged soils. They represent important foci for small mammals and granivorous birds (Andersen and Braithwaite 1996). Some samples were also taken along “secondary” transects of 500 m, located near the crests dividing the catchments (Fig. 2.6c). The compartments used for the different studies varied according to their particular requirements and logistic constraints (Andersen et al. 1998).
2.8.4 Statistical Analysis In designing this experiment we satisfied the assumptions made in formal parametric statistical modeling as far as possible within our logistical and security constraints (Andersen et al. 1998). In most projects, at least two and usually three replicates of each treatment were used. This might be insufficient for rigorous statistical modeling in some cases, but it does mean that outcomes that were broadly consistent between replicates could be attributed, by inference, to the experimental treatment. Although the assumption of independence of whole compartments could not be made for some projects, particularly those dealing with fauna with wide ranges, the relatively large separation between sampling sites allowed a reasonable assumption of independence. The heterogeneity within compartments meant that selected positions within the landscape could not be assumed to be representative of the whole compartment. However, our primary interest was in the changes induced by the treatments. Modeling of such changes required robust baseline (pretreatment) data, so that either the baseline values could be used as covariates or the existence of a treatment ¥ time interaction could be tested. Repeated-measures analysis (Crowder and Hand 1990; Kenward 1995) has been the principal statistical methodology applied. This allows testing of serial dependence across time, as well as the construction of analyses examining the spatial ¥ temporal interactions that were characteristic in many of the core projects.
3. Fire Behavior Richard J. Williams, A. Malcolm Gill, and Peter H.R. Moore
3.1 Introduction Fire behavior is the study of the physical properties of fires themselves. Knowledge of the manner in which fires release energy is important if the ecological impacts of fire, either as single fires or as fire regimes, are to be understood (DeBano et al. 1998; Johnson and Miyanishi 2001). Fire intensity, or the rate of energy release, is one important descriptor used in studies of fire behavior and the ecological impacts of fires. Fire intensity depends on the amount of fuel, the energy content of the fuel, and the rate of movement of the fire front. Fire intensity is also related to other aspects of fire behavior, such as flame height, flame depth, and the residence time of the flames at a point in the landscape. These physical properties of fire determine the temperatures experienced by living tissues subjected to fire, which in turn primarily determine the biological impacts such as the amount of the grass-layer that is consumed, the height to which vegetation in scorched, and the degree of mortality in populations of plants and animals. There have been few studies of interannual and interseasonal aspects of fire behavior in the savannas of the Top End. Gill et al. (1990) presented introductory information on fires and fire impacts in Australia’s wet–dry tropics. Cheney et al. (1993) and Cheney and Sullivan (1997) presented data on factors determining the rate of spread of fires in grassland at Annaburroo, near Darwin. The details of the fire behavior studies at Kapalga over the 33
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R.J. Williams et al.
a
b Figure 3.1. Postfire indices of intensity: scorch height (a) and char height (b). (Barbara McKaige.)
period 1990–1994 have been presented in Williams et al. (1998). In this chapter we summarize these data on fuels and fire intensity, as background to the ecological studies that follow. In addition to the seasonal patterns of fire behavior, we detail the relationships between intensity and immediate postfire indicators of fire intensity such as leaf-char height and leaf-scorch height (Gill and Moore 1994). Direct measurement of the intensity of fires is not always possible, and these postfire indicators may be all that is available to researchers and managers in fire studies. Thus, leaf-scorch height (the height above ground of dead, scorched leaves; Fig. 3.1a) may be used as a postfire index of fire characteristics such as flame height (Luke and McArthur 1978; Cheney et al. 1992; Burrows 1995) and intensity (van Wagner 1973; Rothermel and Deeming 1980; Burrows 1995; Engle and Stritzke 1995). Leaf-char height (the height above ground of blackened bark or leaves; Fig. 3.1b) may be used as a surrogate for flame height (Gill and Moore 1994) and is thus another potential postfire indicator of intensity. Fuel characteristics and fire behavior were measured in each of three replicate Early and Late compartments. The study was restricted to the vegetation of the better-drained soils: open forest dominated by E. miniata and E. tetrodonta, with the understory dominated by a mixture of annual
3. Fire Behavior
35
grasses such as Sorghum spp. and perennial grasses such as Heteropogon triticeus, a dominant vegetation type in the mesic savannas of northwestern Australia (Chapter 2).
3.2 Fine Fuels Fine fuels (leaves and twigs less than 6 mm in diameter) were harvested directly (Catchpole and Wheeler 1992) from each compartment, and their fresh weight was determined within one hour. Samples were returned to the laboratory, where oven dry weight (ODW) and percentage moisture content (as % ODW) were subsequently determined. There was substantial variation in fuel load, composition, and moisture content during the course of the experiment, both between fire regimes and between years. Average fuel loads varied from about 2 to 10 t ha-1 (Fig. 3.2a) and were significantly higher in the initial year of burning (1990) than in later years (1991–1994). Fuels were dominated by grass (72%) in June, but by September leaf and twig litter averaged more than half (57%) of fuel biomass and contributed up to 70% (Table 3.1). As a consequence, mean fuel loads of Late compartments (5.0 t ha-1) were substantially higher than those of Early (3.2 t ha-1; Fig. 3.2a). Fuel moisture content was significantly lower in Late compartments than in Early, with the fuels in 1993 significantly drier than those of other years (Table 3.1; see Chapter 4 for a more detailed consideration of fuel dynamics).
3.3 Direct Measurements of Fire Intensity Direct measurements of fire intensity—sometimes called “Byram fire-line intensity” (Byram 1959)—were taken for most of the experimental fires. Measurements were made within a single, relatively uniform area of approximately one hectare within each compartment, within 200 m of the windward (usually southern) margin, where safe access and egress could be made. All fire intensity measurements were made on main heading fires, within 1 or 2 minutes of ignition. Fire-line intensity I, a measure of the energy release along the fire front (Byram 1959), is defined as the product of the heat yield of the fuel (H), the weight of standing fuel consumed in the flaming zone (w), and the rate of forward spread of the fire line or perimeter (r). Measuring H can be done with bomb calorimitery (e.g., Bowman and Wilson 1988), but we assumed a value of 20 MJ kg-1 for H based on past studies (Williams et al. 1998). We measured fuel loads directly (Section 3.2), and obtained precise measurements of rates of spread by using a series of electronic temperature residence time meters (TRTMs) over a representative 0.5 ha area (Fig. 3.3), as described in detail by Moore et al. (1995) and Williams et al.
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Figure 3.2. Mean fuel loads (a) and fire intensity (b) for the Early (black bars) and Late (gray bars) fire treatments throughout the course of the Kapalga experiment. For each mean, n = 3, except for the Late compartments, 1990, when n = 2.
(1998). In all fires, the fine fuel was totally consumed at the sites of measurement of rate of spread, so consumption factors did not need to be applied in the calculation of intensity. Fire intensities ranged from 0.5 to 18 MW m-1 (i.e. 500 to 18,000 kW m-1; Fig. 3.2b). Average fire weather was more extreme at the time of Late fires than Early (Table 3.2), with stronger winds, higher temperatures, and lower humidities. These seasonal differences were less pronounced in 1991 and 1992 than in other years. The peak fire intensity was recorded on one of the
3. Fire Behavior
37
Table 3.1. Fuel composition by fire treatment and year.a Composition (%) Fire regime Early Early Early Early Early Late Late Late Late Late Average Early Average Late
Year
Moisture
Grass
Leaf
Twig
1990 1991 1992 1993 1994 1990 1991 1992 1993 1994 1990–1994 1990–1994
NA 21 ± 2.3 20 ± 1.1 17 ± 1.9 19 ± 1.5 NA 14 ± 0.7 13 ± 0.4 7 ± 2.7 11 ± 0.9 19.3 ± 0.8 11.1 ± 0.9
75 ± 5.0 69 ± 6.9 72 ± 7.9 71 ± 9.1 71 ± 9.2 30 ± 2.7 31 ± 4.7 51 ± 6.2 43 ± 6.4 51 ± 6.1 71.5 ± 3.5 41.1 ± 3.4
15 ± 4.0 14 ± 4.4 22 ± 8.0 21 ± 8.0 22 ± 8.2 60 ± 2.4 55 ± 5.5 35 ± 4.6 45 ± 5.6 38 ± 6.1 18.8 ± 3.2 46.7 ± 3.2
10 ± 3.0 17 ± 3.1 6 ± 1.2 7 ± 1.6 7 ± 1.6 10 ± 0.3 14 ± 1.1 14 ± 4.4 13 ± 1.8 11 ± 2.0 9.7 ± 1.4 12.2 ± 1.1
a Values are average percentage (±SE; by oven dry weight) for fuel moisture, grass content, tree leaf content, and twig content, for the Early and Late treatments for each year of the experiment. For each mean, n = 3, except for the Late compartments, 1990, when n = 2. Average figures (pooling years) for Early (June) and Late (September) fires are also given. NA—no data available.
Figure 3.3. Electronic temperature residence time meters were used to obtain precise measurements of rates of spread of experimental fires. (Barbara McKaige.)
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R.J. Williams et al.
Table 3.2. Summary weather characteristics for Early and Late fires, as measured at the time of the fires at Naramu Camp (2–15 km away), and long-term data (12 years) for 9:00 a.m. and 3:00 p.m. for the town of Jabiru (50 km from Kapalga) Wind direction
Wind speed (m s-1)
Temperature (°C)
Humidity (%)
Year
Early
Late
Early
Late
Early
Late
Early
Late
Naramu data 1990 1991 1992 1993 1994 Averages
SE SE SE ESE SE SE
SE ENE E SE SE ESE
0.3 1.5 3.4 2.5 2.3 2.0
1.1 1.4 3.4 2.4 3.3 2.3
23.7 28.0 29.6 28.7 32.7 28.5
34.0 34.7 33.6 33.1 35.3 34.1
45.3 44.3 36.3 45.7 31.3 40.6
28.0 38.7 34.3 34.0 13.7 30.6
Long-term Jabiru data 9:00 a.m. SE ESE 3:00 p.m. SE ESE
1.5 2.0
1.8 3.0
24.1 30.3
27.0 35.1
57.0 34.0
59.0 24.0
a
Averages are for the period of the experiment (1990–1994).
Late compartments in September 1990. The fires of 1990 were more intense than all other years, but there was no significant difference between years over the period 1991–1994. The mean intensity of Early fires over the whole study period (2.1 MW m-1) was less than one-third that of the Late fires (7.7 MW m-1; Table 3.3). There was, however, substantial variation in this pattern between years: in 1991 there was no significant difference in the average intensity of the Early and Late fires, whereas in all other years Late fires were at least three times more intense than the Early fires. Table 3.3. Mean Generalized Linear Modeling (GLM)-predicted values by year (pooling season of fire) and season of fire (pooling year) for fuel load, rate of forward spread, and fire intensity: for Year, values with different superscripts are significantly different ( p < 0.05); seasonal differences are all significant (p < 0.05). Mean GLM-predicted values Fuel (t ha-1)
Rate of spread (m s-1)
Year 1990 1991 1992 1993 1994
8.04a 3.08b 2.64b 3.36b 3.46b
0.51a 0.52a 0.61a 0.51a 0.73b
8.5a 3.5c 3.5c 3.6c 5.3b
Season Early Late
3.24 4.99
0.37 0.78
2.1 7.7
Year/Season
Fire intensity (MW m-1)
3. Fire Behavior
39
Rates of spread varied over an order of magnitude, from less than 0.1 m s-1 to 1.8 m s-1, but were generally between 0.4 and 0.8 m s-1 (Table 3.3). Between-year variation was significant, with rates being higher in 1994 than all other years. Seasonal variation was also significant, with spread rates lower in the Early than Late fires. Temperature residence times were generally of the order of 30 to 60 s. Crown fires did not occur during any of the Kapalga fires. However, “torching” (the ignition of foliage of individual trees) was observed in the tree-legume Erythrophleum chlorostachys and the palm/palmlike genera Livistona and Pandanus during several of the higher intensity fires (Fig. 3.4).
Figure 3.4. “Torching” effect of Pandanus spiralis due to persistent skirt of dead leaves. (Barbara McKaige.)
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3.4 Post Fire Indices of Fire Intensity Three variables were investigated as potential post fire indices of fire intensity: leaf-scorch height, leaf-char height, and percentage area of grass layer burnt. Following each fire, average char and scorch heights were measured on 10 to 20 saplings (char) and adult trees (scorch) (Williams et al. 1998). Scorch heights were measured to within 1 m, within 2 days after each fire. Both char and scorch heights were taken to be the maximum height of char or scorch on a given tree. To determine the area of grass layer burnt by each fire in the wider compartment, a line intercept method (Mueller-Dombois and Ellenberg 1974) was used to assess the relative area of burnt and unburnt patches of understory along the primary transects (Chapter 2). Measurements were taken the day after the fire. The relationships between fire intensity and leaf-char height, leaf-scorch height, and percentage grass-layer consumption were variously curvilinear. Maximum char heights were of the order of 5 m; fires in excess of 10 MW m-1 had average char heights of 3 to 4 m (Fig. 3.5a). In fires of about
Figure 3.5. Effects of fire intensity on various postfire indices. (a) Leaf-char height. The solid line is described by the equation: y = 3.7(1 - e-0.19x);
r 2 = 0.91.
(b) Leaf-scorch height. The solid line is described by the equation: y = 20.1(1 - e-0.41x);
r 2 = 0.83.
(c) Percentage area of the grass-layer burnt.The solid line is described by the equation: y = 95.6(1 - e-1.55x);
r 2 = 0.51.
3. Fire Behavior
41
Figure 3.5. Continued.
3 MW m-1, leaf scorch occurred to about 10 to 12 m, or roughly the base of the canopies of the taller trees. At intensities of 10 MW m-1 or more, leaf scorch occurred to around 20 m, or the tops of the tallest trees (Fig. 3.5b). Grass-layer consumption was greater than 90% at intensities greater than 2 MW m-1 (Fig. 3.5c), with all Late fires completely incinerating the grass layer (Fig. 3.6).
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3.5 Discussion 3.5.1 Annual and Seasonal Patterns of Fuels and Fires The total fuel loads (2–10 t ha-1; usually 2–5 t ha-1) were comparable to those reported in other savanna areas in Australia, both mesic and semi arid. For example, Cheney et al. (1993) reported 2 to 6 t ha-1 for a grassland savanna in July/August at Annaburroo, 100 km west of Kapalga. Bowman and Wilson (1988) measured 6.3 t ha-1 in September at a eucalypt savanna site at Gunn Point, near Darwin. For a savanna near Katherine (annual rainfall 950 mm) dominated by perennial grass, Mott and Andrew (1985) reported grass fuel levels of 2 to 4 t ha-1 in biennially burnt systems, and 6 t ha-1 in savannas protected from fire for 4 years. In Rockhampton (annual rainfall 890 mm), Walker (1981) indicated levels of 3 t ha-1 in annually burnt savannas and 6 to 7 t ha-1 after 3 years following fire. The fuel loads at Kapalga are also broadly comparable to the range of 2 to 10 t ha-1 reported in savannas elsewhere in the world, including in Africa (Sabiiti and Wein 1988; Shea et al. 1996) and South America (Kauffman et al. 1994).
a
b Figure 3.6. Before and after a Late fire in September 1994, showing complete incineration of the grass layer. (Barbara McKaige.)
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Fuel load varies with time since fire, generally increasing up to a maximum, equilibrium value, rather than increasing continually (Walker 1981). In mesic savannas such as at Kapalga, the equilibrium fuel loads are about 10 t ha-1, but such levels can be achieved relatively quickly (in 2–3 years) in the absence of fire (Walker 1981; Mott and Andrew 1985; Cook et al. 1995; Chapter 4). This has important implications for the Kapalga fires because all compartments had remained unburnt for at least 2 years prior to the initial fires of 1990. Thus, fuel loads in that year (generally 8–10 t ha-1), were close to their potential maximum. Post-1990 fuel loads under the annual fires, whether Early or Late, were most commonly between 2 and 5 t ha-1, which clearly indicates that annual burning maintains fuel loads below potential maxima. Fuel loads in excess of 10 t ha-1 may occur in eucalypt savannas that are unburnt for a number of years (e.g., 13 t ha-1 on one Late fire compartment in September 1990 at Kapalga; 10–15 t ha-1 for some savannas in the Alligator Rivers region; Gill et al. 1990), but reports of such levels are rare. The seasonal changes in fuel loads and fuel composition (grass/leaf litter ratios) were a consequence of leaf phenology. Whereas deciduous trees commence leaf fall early in the dry season, semideciduous and evergreen species have peaks of leaf fall later in the dry season (Wilson et al. 1996; Williams et al. 1997b, 1998). Hence, by late in the dry season, fuels loads are higher, with a greater proportion of leaf litter, than early in the dry. The average ratio of grass to leaf litter measured in Late compartments (0.79) was similar to that reported by Bowman and Wilson (1988) for similar forest in September (0.81). Soil moisture and relative humidity decreases as the dry season progresses, as does fuel moisture due to senescing annual grasses (Gill et al. 1990, 1996; Cheney et al. 1993). Fire intensity varied significantly with season. Although the average intensity of Early fires in our 1 ha plots (2.1 MW m-1) was only about 30% of that of Late fires (7.7 MW m-1), compartment-wide average intensity of Early fires was likely to have been even lower, given the incomplete combustion of fuels as measured along the primary transects. The compartmentwide value for the Late fires is likely to be close to that measured at the 1 ha sites, since fuels were completely consumed over the whole compartment in all but one case. Higher and drier fuel loads were undoubtedly key factors contributing to seasonal patterns of fire intensity. However, temperature and humidity also play a role, with midday temperatures being 3 to 5 °C higher, and relative humidity 10 to 20% lower, during Late than Early fires. This local variation in fire weather variables is consistent with the regional, longterm Forest- and Grassland Fire Danger Indices as determined by Gill et al. (1996) from weather records at nearby Jabiru. The FFDIs increase from June to September, because of seasonal changes in temperature, relative humidity, and wind speed. Afternoon relative humidity decreases progressively throughout the dry season, thus increasing the potential
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rates of spread (Gill et al. 1990; Gill and Knight 1991; Cheney et al. 1993). Moreover, winds, especially during the afternoon and evening, are, on average 1 m s-1 faster in September than in June (Gill et al. 1996). Fire intensity at Kapalga ranged from less than 0.5 to 18 MW m-1. There have been few other studies in northern Australia or other tropical savannas with which to compare these data. At nearby Munmarlary, where experimental plots were 1 ha, the comparative figures were about 1 and 4 MW m-1 for Early and Late fires, respectively (Russell-Smith et al. 2003). From the Annaburroo data (Cheney et al. 1993, p. 38), we can estimate intensity based on the reported fuel loads and rates of spread. Assuming a value of H = 20 MJ kg-1, the experimental fires lit in July/ August 1986 in that study ranged from about 1.1 MW m-1 to 23 MW m-1. Elsewhere in northern Australia, figures of 0.5 to 2 MW m-1 have been reported for dry season fires in the north Kimberley of Western Australia (White 1998) and in the Atherton region of Queensland (Unwin et al. 1985). The intensities of the Kapalga fires are broadly similar to those reported from savannas elsewhere in the world. Fires of about 3 MW m-1 have been reported for acacia/grass savanna in Uganda’s Queen Elizabeth National Park (Sabiiti and Wein 1988) and in Brazilian cerrado (Kauffman et al. 1994). Intensities ranged from about 3 to 6 MW M-1 for a series of experimental savanna fires lit in August/September in southern Africa as part of the SAFARI campaign in 1992 (Shea et al. 1996). Although our Early fires were, on average, about 30% of the intensity of the Late fires, some Early fires of 1990 were of relatively high intensity, estimated to be 5 to 10 MW m-1 on the basis of leaf-char and leaf-scorch heights, presumed fuel loads, and fortuitous observations of rates of spread. The occurrence of such fires indicates that relatively intense fires may occur early in the dry season, especially where fuels have accumulated for several years. Gill et al. (1996) indicate that extreme FFDIs in June can be as high as those in September. Thus, as argued by Bowman (1988), time of year alone is not necessarily a precise predictor of fire intensity. This point has substantial management implications (Chapter 1), inasmuch as in some years relatively intense fires occur in the early dry season; in others, Late dry season fires may be of relatively low intensity, especially if traveling at night under conditions of low wind.
3.5.2 Post Fire Indices of Fire Intensity Leaf-char height and leaf-scorch height were both closely related to fire intensity over the range of 0.1 to 10 MW m-1. Both measures therefore have high potential as postfire indicators of fire intensity for fires within this range, which covered 90% of Kapalga fires. The relatively low, outlying value of char height (2.5 m) for the most intense fire (18 MW m-1) may have been due to stronger winds than usual, increasing flame tilt, and consequently decreasing flame height. Flame heights in excess of 4 m were
3. Fire Behavior
45
observed directly for the 13 and 18 MW m-1 fires, but high flame angles were also observed. Hence, given variation in local winds, as well as variation in the size and moisture content of leaves, leaf char height may not always be closely correlated with flame height. Percentage area burnt was the least efficient indicator of intensity, as evidenced by the lowest r 2 value and the tendency toward maximum values (100%) at relatively low intensities (1.5–2.0 MW m-1). Fires in excess of about 2.0 MW m-1 will consume virtually the entire grass layer. These relationships provide useful ground-based rules of thumb for land managers wishing to monitor the intensity of prescribed fires. For example, fires that produce char or flame heights less than 1 m are unlikely to scorch the tallest trees higher than the base of the canopy (ca. 8 m) or are likely to be patchy with respect to grass-layer consumption. Fires with char/flame heights of 2 m are likely to scorch most trees, although not completely, but will consume all of the grass layer. Fires with char/flame heights of 3 to 4 m will consume the entire grass layer and scorch almost all the tallest trees to the top of the canopy.
3.5.3 How Do Savanna Fires Compare with Those of Southern Australia? The overall mean intensity of the 29 fires studied at Kapalga (4.9 MW m-1, pooling season and years), and indeed the peak fire intensity (18 MW m-1), was low relative to potential peak intensities exceeding 100 MW m-1 that may occur during wildfire in the forests of southeastern Australia (Gill and Moore 1990; Gill and Knight 1991). There are several likely reasons for this. First, maximum fuel loads in northern Australian eucalypt savannas appear to be in the order of 10 t ha-1, and do not reach the levels greater than 20 t ha-1 that often occur in temperate eucalypt forests (Walker 1981; Attiwill and Leeper 1987; Ashton and Attiwill 1994). Second, relative humidity in northern Australian savannas, even during the peak fire period of September, remains relatively high (>20% during the day; >60% overnight) and almost never reaches the low levels that may occur on “blowup” days in southeastern Australia, when relative humidity may be under 10%, and winds may gust at more than 50 km h-1 (Anon. 1984). Although the overall intensity of the Kapalga fires was lower than that of bushfires in southern Australia, the average intensity (ca. 2 MW m-1) of the Early dry season fires, which are commonly used for fuel reduction, is high compared with fires used for the same purpose in southern Australia, which are generally 0.5 to 7.5 MW m-1 (Luke and McArthur 1978). In addition to maximum fire intensity, there are other differences in fire behavior between eucalypt forests and woodlands in northern and southern Australia. First, the heights of leaf char and leaf scorch generated by
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fires of a given intensity in the tropical savannas at Kapalga appear to be substantially lower than those measured or predicted for the eucalypt forests of southern Australia, and those predicted by the response models of Rothermel and Deeming (1980) and van Wagner (1973) for temperate forests in North America. In temperate Australian forests, char/flame heights of 1 to 2 m are associated with fires of about 0.2 to 0.8 MW m-1 (Luke and McArthur 1978), whereas fires of 2–5 MW m-1 were needed to generate such flame heights at Kapalga (as indicated by the char heights). Scorch heights showed a similar story, with Kapalga fires producing lower scorch for given fire intensities than is the case in southern Australian eucalypt forests. For example, Cheney (1981) indicated complete scorch of most open eucalypt forests in fires of moderate intensity (0.5–3.0 MW m-1). Burrows (1995) indicated 20 to 25 m scorch for 1.5 MW m-1 fires in eucalypt forests in southwestern Australia; similar values were predicted by Cheney et al. (1992) for fires of 1.5 MW m-1 in eucalypt forests in southeastern Australia. These differences are most likely due to fuel type and architecture. Open grassy fuels, as opposed to more compact litter fuels, may mean that colder air is drawn into the combustion zone, thereby reducing the temperature of the convective column. This, coupled with rapid spread, may result in less heat damage to the leaves of trees. No crown fires were observed at Kapalga, despite the occurrence of several fires in the order of 10 MW m-1. Fires of such intensity would almost certainly crown in temperate eucalypt forests of southeastern Australia (Cheney 1990). Reasons for this apparent lack of crowning may include the interactive effects of generally light fuel loads (generally <5 t ha-1), the low canopy density of the dominant trees (Leaf Area Index of ca. 1–1.5; Gill et al. 1996; O’Grady et al. 1999), lower oil contents and calorific content of the leaves (Webb 1968; Dickinson and Kirkpatrick 1985; Bowman and Wilson 1988), the general absence of a dense midstory of shrubs (Wilson et al. 1996), resulting in a lack of “ladder fuels” (Cheney 1990), and the generally flat terrain in northern Australia (Williams 1991). The rates of spread of Kapalga fires varied between roughly 0.3 and 5 km h-1. The upper range, for fires of 7 to 10 MW m-1, appears to be far higher than those reported for southern Australian eucalypt forest fires of similar intensity, where spread rates may be 1 km h-1 (e.g., Cheney 1990). This discrepancy is likely to be due to the higher proportion of fine grassy fuels in the northern Australian savannas. Thus, on the basis of the fire characteristics, and the immediate postfire impacts on the trees, the eucalypt savanna fires of northern Australian are more akin to grass fires than to the eucalypt forest/litter fires of southern Australia.
4. Fuel Dynamics, Nutrients, and Atmospheric Chemistry Garry D. Cook
4.1 Introduction The amount of fine fuel or litter directly affects fire intensity and thus the survival of trees and other plants (Chapter 3). Combustion of this litter changes pathways of carbon and nutrient cycling through soils, vegetation, and the atmosphere (DeBano et al. 1998). Therefore understanding the dynamics of fuel and the consequences of its combustion is central to understanding the behavior and effects of fires in northern Australia. This chapter examines the interactions of fire regimes with production and combustion of both fine and coarse fuels, and the flow-on effects on the atmosphere, nutrient cycling, and vegetation.
4.2 Fuel Dynamics 4.2.1 Background Fuel loads depend on the balance between the production of litter and its removal by various agents such as microorganisms, termites and other invertebrates, grazing mammals, and fires. A simple approach to modeling this balance (Olson 1963) shows how the steady-state fuel load (Lss) can be estimated over time (t) from the mean annual litter production (La) and the decomposition rate of litter (k) as follows: 47
48
G.D. Cook Lss =
La (1 - e -kt ) k
(4.1)
Quantifying these factors allows extrapolation of field data to different fire management scenarios including variations in fire frequency.
4.2.2 Litter Production Senescent grass is a dominant component of the combustible fuel in tropical savannas (Chapter 3). Grass production at Kapalga varied from about 1.7 to 3.0 t ha-1 y-1, with production generally increasing with decreasing tree density, consistent with other studies (Scanlan and Burrows 1990). The lower values were typical of the open forests and woodlands of well-drained soils, with higher values occurring in open woodlands fringing streams. Senescence of these grasses during the dry season provides the continuous cover of dry fuel required to support a regime of frequent fires. The common annual grasses, Sorghum spp., typically senesce in late March to early April (Andrew and Mott 1983), while the common perennial grasses, Heteropogon triticeus and Allopteropsis semialata, senesce about this time or slightly later. Grasses in wetter parts of the landscape may not senesce until June or July. Although fires in tropical savanna woodlands and open forests are usually called grass fires (Cheney and Sullivan 1997), litter fall from trees at Kapalga is substantial (Chapter 3; Table 4.1), and tree litter can represent up to two-thirds of total fine fuel. Clearly the dynamics of tree litter must be considered in understanding fuel dynamics and fire behavior in these savannas. The amount of annual litter fall increases with increasing total basal area of tree stands (McIvor 2001), and because tree basal area is more easily measured than litter fall, it can provide a good indicator of the contribution of trees to fuel loads. The relationship between tree basal area and mean annual litter fall at Kapalga before the experimental fires was consistent with data from other tree stands in Queensland, New South Table 4.1. Characteristics of fuel loads in three vegetation types at Kapalga.a Fuel load (t ha-1) Vegetation type
Tree basal area (m2 ha-1)
Forest Woodland Open woodland
13.1 (1.8) 5.9 (3.5) 4.3 (2.7)
a b
Annual litter production (t ha-1) Grass
Tree
Annually burnt (early)
1.7 (0.8) 2.0 (0.5) 3.0 (0.9)
4.4 (0.9) 2.0b 1.4b
5.7 (0.5) 3.7 (0.5) 3.3 (0.6)
Data are means, with standard deviations in parentheses. Estimated from basal area.
Long unburnt 8.3 (2.7) 5.8 (2.3) 5.0 (1.2)
4. Fuel Dynamics, Nutrients, and Atmospheric Chemistry
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Wales, and Western Australia (Fig. 4.1). The highly predictive regression (r2 = 0.73, p < 0.01) shows that across these data, litter fall from trees generally increases by about 0.24 t ha-1 y-1 for each 1 m2 ha-1 increase in total basal area of trees. The first experimental fires in 1990 caused litter fall to decline by about 35% in 1991 in both Early and Late compartments (Fig. 4.2). These initial fires were more intense than the subsequent fires because the fuel loads were greater following the absence of fires for at least 2 years (Chapter 3). In Early plots, rates of litter fall recovered to preburn levels within 3 years, indicating a recovery of trees that had been damaged but not killed by the fires. In contrast, litter fall remained low in Late plots as a consequence of the substantial death of trees and damage to surviving trees (Chapter 6). In Unburnt plots, litter fall fluctuated from year to year, but remained high. Fires also affected the timing of litter fall if they were of sufficient intensity to scorch the canopy. If intensities were below about 2.0 MW m-1, fires caused a negligible increase in litter fall from trees. In contrast, fires with intensities greater than about 7.5 MW m-1 generally scorched most of the
Figure 4.1. Relationship between mean annual litter fall from trees and total tree basal area for various Eucalyptus forests and woodlands throughout Australia: , Queensland data (Burrows and Burrows 1992; McIvor 2001; Grigg and Mulligan 1999); , New South Wales data (Fox et al. 1979); , Western Australian data (O’Connell and Menage 1982), and , Kapalga data. Data from Queensland are scaled to convert total basal area measured at 0.3 m stem height to that at 1.3 m stem height.
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G.D. Cook
Figure 4.2. Variation in litter fall from trees over time for three burning regimes at Kapalga. The five bars for each treatment represent values in each successive year from 1990 to 1994. Error bars are standard deviations.
canopy and caused a substantial proportion of leaves to fall within several weeks (Chapter 3). In the absence of fire, the timing of litter fall from individual trees varies according to a range of factors, including species characteristics, weather patterns, and position in the landscape (Chapter 6). Nevertheless, at the scale of the whole plant community, litter fall from trees shows marked and predictable seasonal patterns. Litter fall on Kapalga reaches a peak in August during the dry season and is at its lowest during February (Fig. 4.3). This pattern results in fuel loads increasing during the dry season and contributes to the generally greater intensity of Late fires (Chapter 3). Fitting a sine curve to seasonal patterns of litter fall allows the temporal dynamics of fuel loads to be modeled more precisely: Lt = L0 [1 + sin(2 pt + Dtmax )]
(4.2)
where Lt is the daily litter fall (t ha-1), at time t(y), L0 is the mean daily litter fall (t ha-1), and Dtmax adjusts for the lag between the start of the calendar year and the date of maximum litter fall. This curve explains about 65% of the variation in litter fall on Unburnt compartments over a 5-year period. Studies from eastern Australia indicate that such a sinusoidal pattern could be applied widely. However, in eastern (both tropical and temperate) Australia, peak litter fall typically occurs during summer (McIvor 2001), in marked contrast to the winter-dominated pattern at Kapalga. In regions with peak litter fall during summer, a Dtmax value closer to zero would be
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51
Figure 4.3. Seasonal variation in daily litter fall (Lt) from trees in an unburnt E. miniata/E. tetrodonta savanna on Kapalga. Modeled daily litter fall (solid line) is derived from the equation Lt = 0.01 (1 + sin(2pt + 3.75)), where t = time in years.
more appropriate. The seasonal patterns in vapor pressure deficit (VPD) may be a major cue to leaf fall (Myers et al. 1998), helping to explain these regional differences. Peak litter fall occurs 1 or 2 months after the date of peak mean daily VPD, which occurs in June at Darwin, but in November to December in eastern Australia.
4.2.3 Fuel Decomposition and Accumulation The rate of litter decomposition on Kapalga was estimated by using litter bags containing samples of tree litter and grass (Anderson and Ingram 1989). The bags were left in the field and subsampled every 2 months for a year. Mean annual decomposition rate (k) was 0.8. With an annual production of about 6 t ha-1 and a decomposition rate of 0.8, the steady-state maximum fuel loads should be about 7.5 t ha-1 [Eq. (4.1)]. Further, 90% of this final fuel load should have accumulated in about 2.3/k years (Olson 1963), or about 3 years at Kapalga. These estimates based on annual litter production and observed decomposition rates are consistent with measured fuel loads in plots at Kapalga that had remained unburnt for 3 or more years (Table 4.1). The simple approach to modeling fuel dynamics presented in Eq. (4.1) assumes constant rates of litter fall throughout the year. However, the substantial seasonal fluctuations in litter fall suggest that modeling fuel dynamics monthly is more appropriate. By combining Eq. (4.1) with the sinusoidal pattern of tree litter fall and patterns of grass production, seasonal patterns
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Figure 4.4. Simulated accumulation of fuel at Kapalga with increasing time since fire: Solid curve represents total fuel; dotted curve, cured fuel.
of fuel accumulation can be predicted. This pattern shows that for the first 2 years after a fire, total fuel loads increase by about 0.2 t ha-1 every month (Fig. 4.4). By the end of the first fire season after a previous fire, fuel loads can reach about 5.3 t ha-1. In the following 3 years the rate of accumulation declines and fuel loads reach about 6.6, 7.2, and 7.6 t ha-1, respectively. Rates of fuel accumulation decline in these subsequent years because of decomposition during the wet season, such that seasonal fluctuations in total fuel load become apparent. Because fuel loads rapidly reach steady state in this environment, there is little to be gained through fuel reduction burning per se at fire frequencies less than annual. However, this does not take into account the strategic use of fire to create burnt firebreaks at landscape scales.
4.3 Fuel Combustion 4.3.1 Production and Composition of Charred Fragments Fires in the early dry season are often patchy, leaving lower and wetter parts of the landscape unburnt, and even within higher parts of the landscape, small areas may remain unburnt (Chapter 1). Within the areas that actually have fires pass over them, about 7% of the fine fuel consumed by fires at Kapalga remains in situ as charred fragments (Cook 1994). In the dominant
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Eucalyptus miniata/E. tetrodonta savanna communities, these fragments contained 40 to 70% of the phosphorus, potassium, calcium, and magnesium originally occurring in the fuel, but only 8 to 20% of the more volatile elements nitrogen and sulfur. Some proportion of the charred fragments produced by the fires does not remain in situ but is entrained within the smoke plume. Most of these fragments are likely to settle within several kilometers of the fire (Hingston and Galbraith 1989), so that this process causes local redistribution of nutrients rather than a loss from the terrestrial system. The estimated mass of entrained fragments from the fires on Kapalga varied from 6 to 18% of the total transfer to the atmosphere.These fragments were estimated to account for more than 80% of the transfers to the atmosphere of phosphorus, potassium, and magnesium. In contrast, nitrogen and sulfur were mainly transferred to the atmosphere in the form of pyrogenic gases, with less than 25% of the transfers occurring through charred fragments (Cook 1994).
4.3.2 Nutrient Dynamics and Fire A preliminary nutrient budget was derived by considering the amounts of nutrients removed in the smoke plume, and the amounts being deposited with rainfall by deposition of the charred fragments. Nitrogen was the only nutrient for which the estimated gaseous losses due to annual fires were likely to exceed the gains from dry deposition and rainfall (Cook 1994). For the ecosystem to remain in a steady state, this nitrogen loss of about 1.4 to 2 kg ha-1 y-1 would need to be replaced by biological fixation. If this is not occurring, vegetative growth would have to rely on mineralization of humified soil organic matter, and the system would gradually reduce its nitrogen stocks. Unfortunately, reliable estimates of nitrogen fixation in Australian savannas are unavailable. In West African savannas, nonsymbiotic fixation associated with grasses contributes about 1.2 kg ha-1 y-1 (Balandreau 1976), but such fixation has not been investigated in Australia. Fixation by termites is another possible source of nitrogen in these savannas. Combustion leads to an enrichment of the stable nitrogen isotope 15N in the charred fragments (Cook 2001). Although this may lead in part to the enrichment of 15N in soil nitrogen pools, evidence from the region suggest that frequent fires lead to the depletion of 15N in plant tissues and generally lower concentrations of total nitrogen in foliage. This is probably because fires alter the pathways of nitrogen cycling. The declines in nitrogen concentrations are likely to reduce both plant growth rates and the palatability of foliage for herbivores.
4.3.3 Savanna Fires and the Atmosphere Worldwide, biomass burning plays a major role in atmospheric chemistry and climate through the production of trace gases and aerosol particles (Crutzen and Andreae 1990). More than 80% of the world’s biomass
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burning occurs in the tropics, much of it because of savanna fires (Lobert et al. 1990). Some of the important emissions from savanna burning include the following (Levine 1996): Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), which are greenhouse gases that directly contribute to heat retention within the atmosphere. Methane and nitrous oxide have, respectively, 21 and 290 times the global warming potential of carbon dioxide. Taking these warming potentials into account allows the use of CO2 equivalent emissions to compare greenhouse gas emissions. Chemically active gases that lead to photochemical production of ozone in the troposphere, including methane, nonmethane hydrocarbons, nitric oxide (NO), and carbon monoxide (CO). Methyl chloride (CH3Cl) and methyl bromide (CH3Br), which lead to chemical destruction of ozone in the stratosphere. Particulate matter, which can reduce visibility and cause health problems including increased respiratory symptoms and increased mortality (Beer and Meyer 2000). This section focuses on greenhouse gases released by fires, with a brief discussion of particulate emissions. During the fires in 1991 and 1992, over 100 samples of smoke were collected from Kapalga (Fig. 4.5). These samples, which came both from the ground and from a light aircraft flying at 50 to 700 m above the fires, were
Figure 4.5. Extensive smoke produced by fire at Kapalga. (Garry Cook.)
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Figure 4.6. Emission ratios for DCO/DCO2 measured in ground-based smoke samples from different fire types. (Data from Hurst et al. 1994b.)
analyzed for a range of carbon- and nitrogen-based trace gases. Groundbased samples showed that the relative proportions of highly oxidized CO2 in the emissions decreased with decreasing efficiency of combustion from flaming through mixed to smoldering fires. The proportions of more reduced gases such as methane increased (Fig. 4.6). Measurements from aircraft provide integrated emissions from all types of combustion and showed emission ratios more similar to those of flaming than of smoldering combustion, indicating the dominance of flaming combustion in these fires during the sampling period. The proportions of elemental carbon and nitrogen emitted as various gas species from the fires at Kapalga were similar to those emitted from fires in other savanna areas of the world (Hurst et al. 1994a). This reflects similarities in fuel composition and fire behavior throughout the savanna biome. In contrast, Kapalga fires produced lower proportions of CH4, CO, and nonmethane hydrocarbons, and a slightly greater proportion of CO2, compared with fires in southern Australian forests (Table 4.2). This is due to the better aeration and therefore greater oxidation of the grassy fuels in savannas. Reduced carbon-based gases are emitted in greater proportions during smoldering combustion of woody fuels, which are relatively more important in forest fires. The trends for nitrogen-based gases were less clear, partly because they were measured with lower precision (Hurst et al. 1996). Carbon dioxide dominates the greenhouse gases released from the combustion of fine fuels at Kapalga (Table 4.2). Indeed the annual release of carbon dioxide from all Australian savanna fires is of similar magnitude to
56
G.D. Cook Table 4.2. Mean values for trace gas emission factors for Australian tropical savannas and temperate forests. Emission factorsa Trace gas CO2 CO CH4 Nonmethane hydrocarbons NOX NH3 N2O
Savannas
Forests
0.87 0.078 0.0035 0.0091 0.21 0.23 0.0076
0.85 0.091 0.0054 0.022 0.15 0.17 0.0077
a
Emission factors represent the fraction of the burned fuel C or N released to the atmosphere as each trace gas. Source: Hurst et al. (1996).
that from Australian fossil fuel consumption (Hurst et al. 1994b). However, net emissions of greenhouse gases from savanna fires over an annual cycle are usually assumed to include only the release of methane and nitrous oxide from the combustion of fine fuels, since vegetative regrowth takes up amounts of carbon dioxide roughly equivalent to those released during fires. Thus the net annual release of greenhouse gases from the combustion of fine fuel Kapalga ranged from 0.13 to 0.21 t ha-1 of CO2 equivalent emissions. The emission factors derived from aircraft sampling at Kapalga and nearby locations (Hurst et al. 1994b), along with estimated areas of savanna fires and estimated fine fuel loads, have been used to estimate emissions for the Northern Territory (Beringer et al. 1995) and for the Australian National Greenhouse Gas Inventory. In 1996, for example, 32 million hectares of Australian savannas was burnt, releasing an estimated 0.27 Mt of methane and 0.013 Mt of N2O (National Greenhouse Gas Inventory Committee 1998). These emissions had a global warming potential equivalent to 10 Mt of carbon dioxide, or 2.4% of the national inventory emissions. The regional importance of savanna burning is relatively greater in northern Australia, with savanna fires accounting for about 29% of anthropogenic CO2 equivalent emissions within the Northern Territory. Savanna fires have a range of other effects on greenhouse gas budgets that currently are not accounted for in the ANGGI. These effects include changes in the store of carbon in soils and trees. About 90 t ha-1 of carbon is held in relatively deep soils of the Kapalga region (Calder and Day 1982). Much of this store is likely to be relatively recalcitrant black carbon formed during combustion of litter (Kuhlbusch and Crutzen 1996). However, evidence from Munmarlary, near Kapalga, shows that even in the most sensitive top 5 cm of soil, 25 years of fire exclusion has had little impact on carbon contents (Andersen et al. 1998). Woody vegetation in Kapalga’s savannas
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can contain up 70 t ha-1, based on typical tree densities and published allometry data (Werner and Murphy 2001). Unlike the carbon stored in soils, that stored in trees is subject to substantial variation over years and decades in response to fire-induced changes in tree cover (Williams et al. 1999a). When the impacts of tree death caused by fires on Kapalga (Chapter 6) were taken into account, the net emissions per fire are increased by 15 to 44 times (Table 4.3) over that when fine fuels alone are accounted, using calculations based on those for land clearing (National Greenhouse Gas Inventory Committee 1994). The release of greenhouse gases (expressed as CO2 equivalents) from individual fires was substantially higher in Late than Early fires (Table 4.3). Highest values were recorded from an unplanned fire that burnt one of the Unburnt compartments (M) late in the 1994 dry season (Chapter 2). These differences reflected the differences in fuel loads and tree mortality (Table 4.3; Chapter 6). Therefore, changed fire regimes can greatly impact greenhouse gas emissions and carbon storage. The emissions of fine particulates per unit area from fires in the Darwin region are comparable to those from industrial and other sources in other Australian cities (Denlay et al. 2000). Further, the levels in Darwin city occasionally exceed the national air quality standards. Data from Jabiru, near Kapalga, indicate that the ambient air quality for particulates remains within acceptable limits (Vanderzalm et al. 2000). Nevertheless, particulate emissions will continue to have significant policy implications, given their
Table 4.3. Estimated mean emissions of greenhouse gases from Early and Late fires, and from the unplanned fire on comparment M (which had not been burnt for at least 7 years). CO2 equivalent emissions per fire (t ha-1) Early
Late
Flaming combustion of fine fuels CO2 4.597 7.08 CH4 0.074 0.114 N2O 0.061 0.094 Subtotal 4.732 7.288
Compartment M 11.35 0.18 0.15 11.68
Smoldering combustion of dead tree stems 1.887 7.063 13.309 CO2 CH4 0.146 0.545 1.027 N2O 0.025 0.093 0.176 Subtotal 2.058 7.701 14.512 Grand total
6.79
14.989
26.192
Source: Data based on results presented by Williams et al. (1999a).
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potential impacts on human health (Beer and Meyer 2000; Johnston et al. 2002). Continuing work will be required to determine the magnitude of any potential problems and to develop fire management regimes that satisfy both human and environmental health values.
4.4 Conclusion Much of the Top End of the Northern Territory experiences fire every year, or in two years out of three (Chapter 1). This chapter has presented a range of reasons for concern over such high fire frequency, including: The potential for unsustainable losses of nitrogen Emissions of greenhouse gases from fine fuel combustion Reduced carbon sequestration in woody biomass Possible effects on human health of particulates in smoke The effects of fire regime on carbon sequestration in woody biomass is a particularly important issue requiring further research. Modeled fuel accumulation curves show that frequent burning has only a marginal impact on longer-term fuel loads, unless it is annual, owing to rapid decomposition. It is therefore not appropriate to use highly frequent fires to manage fuel accumulation, such as might be the case in temperate forests. However, management can influence fire intensity through the seasonal timing of fire. To the extent that prescribed fires lit early during the dry season reduce the extent of higher intensity fire later in the year, fire management can have a significant effect on tree biomass and thereby carbon sequestration. Further research is required into the long-term dynamics of tree populations and their associated carbon storage under various fire regimes.
5. Streams Michael M. Douglas, Simon A. Townsend, and P. Sam Lake
5.1 Introduction 5.1.1 Tropical Savanna Streams The highly seasonal rainfall characteristic of tropical savannas is reflected in the distinctive patterns of seasonal discharge from savanna streams (Haines et al. 1988). Savanna streams experience high flows during the wet season, when many of the larger rivers inundate extensive floodplains. Rainfall is negligible in the dry season; consequently dry season discharge originates from groundwater sources, with many of the rivers and streams ceasing to flow. Because the dry phase in these streams is predictable on an annual cycle, they are termed intermittent or seasonal streams as distinct from ephemeral streams that fill and dry far less predictably (Comin and Williams 1994; Boulton and Brock 1999). Intermittent streams drain much of the world’s tropical savannas (Dodds 1997), but, despite their widespread occurrence, they are probably the most frequently overlooked and most poorly understood components of savanna ecosystems. None of the standard texts on the ecology and management of tropical savannas include a single chapter on streams (see Huntley and Walker 1982; Bourlière 1983; Cole 1986; Werner 1991). There have been some investigations of the fish communities of a few of the larger tropical savanna rivers in Africa and South America (Welcomme 1979; Lowe59
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McConnell 1987), and studies of large savanna rivers have been fundamental to the development of the flood pulse concept of stream dynamics (Junk et al. 1989). In general, tropical savanna streams have received far less attention than streams in more temperate regions (Dodds 1997). There have been few studies of savanna streams in northern Australia, and these have generally focused on the water chemistry (Hart and Ottway 1986; Hart et al. 1987) or aquatic fauna (Marchant 1982; Hardwick et al. 1995; Paltridge et al. 1997) of larger streams and their permanent billabongs, with a view to examining the impacts of developments such as mining (Jeffree and Williams 1980; Faith et al. 1995). The Kapalga experiment provided a unique opportunity to gain a greater understanding of the small seasonal streams so abundant throughout northern Australia’s tropical savannas. This chapter begins by describing the seasonal dynamics of Kapalga streams to provide a context for assessing the effects of catchment disturbance. It then reviews the effects of catchment burning on streams elsewhere and describes the effects of fire management on streams at Kapalga.
5.1.2 Seasonality in Kapalga Streams A detailed description of the seasonal patterns for two Kapalga streams (both in Unburnt catchments) is given by Douglas (1999). The streams remain dry for 4.5 to 6 months (Fig. 5.1a) but generally form pools with the onset of wet season rains during November and December (Fig. 5.1b). The preflow pools last for about a month before flow begins, usually in late December or January (Fig. 5.1c). Flow continues for 4 to 5 months with two distinct periods. During the early flow period, flooding is quite common owing to intense wet season rainfall. When the rains stop, the streams enter a phase of diminishing or recessional flow as the dry season begins. Once flow has stopped, postflow pools form (Fig. 5.1d) and can persist for another 1 to 2 months before the streams finally dry out. Changes in stream water quality (Table 5.1) reflect these dramatic changes in hydrology. Preflow pools are characterized by high temperatures, conductivities, and nutrient contents, and by shallow depths and low dissolved oxygen concentrations. With the start of flow, dissolved oxygen concentrations and depths increase, and conductivities and nutrient concentrations decline. Over the January–March period, when most rain falls, the water quality of the streams differs between storm runoff, when the streams are flooded, and base flow, the period between runoff events. Concentrations of all water quality variables were higher during storm runoff compared with base flow periods, except for conductivity, which showed the opposite trend. The diminishing flow period is characterized by declining depths and temperatures. There is also evidence that in May/June, conductivity rises as a result of the relatively long residence time of groundwater (enabling it to take up soluble ions), and nutrient
5. Streams
61
a
b Figure 5.1. Major phases of Kapalga streams (Michael Douglas). (a) Empty stream channel late in the dry season. (b) Small preflow pools following first rains of the wet season. (c) Stream flow at the end of the wet season. (d) Large postflow pool during the early dry season. Continued
c
d
Figure 5.1. Continued.
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Table 5.1. Typical values of physical and chemical characteristics for each of the four major flow phases of Kapalga streams.
Flow phase Preflow pools Main flowa Diminishing flowa Postflow pools
Depth Temperature (cm) (°C) 18 40 37 26
35 30 29 25
Dissolved oxygen (mg L-1)
Electrical conductivity (ms cm-1)
Nitrate/ Nitrite (mg L-1)
Total phosphorus (mg L-1)
2 7 7 6
177 32 44 67
0.02 <0.001 0.005 0.007
0.05 0.004 0.018 0.03
a
Values for main and diminishing flow indicate base flow rather than storm runoff conditions. Source: Data from Douglas (1999).
concentrations increase, probably because of decomposition of macrophytes and attached algae as water levels fall. Once flow ceases, nutrient concentrations and conductivities continue to increase (although at faster rates) in the postflow pools, while temperatures, dissolved oxygen, and depth steadily decline. This broad seasonal pattern of identifiable flow phases is quite predictable, regardless of rainfall variability between catchments and years. As with physicochemical data, the macroinvertebrate communities show a distinct seasonal pattern, with four assemblages discernible throughout the year: pioneer, transitional, midflow, and diminishing/postflow (Douglas 1999). The pioneer assemblage is dominated by crustaceans (copepods and cladocerans) and tadpoles. With the onset of flow there is a transitional phase, lasting for approximately a month, which is also dominated by noninsect taxa such as crustaceans (copepods, cladocerans, and ostracods), oligochaetes, and nematodes. The midflow assemblages are similar in composition to the transitional group but include a greater contribution from insects, such as chironomid midges and the leptophlebiiid mayfly Bibulmena sp. As flow diminishes and pools form, the fauna is dominated by insects, such as chironomid and ceratopogonid midges and mayflies, ostracods and to a lesser extent, cladocerans. Highly mobile predatory beetles such as dytiscids, and hemipterans, are also abundant in the drying pools. Taxonomic richness is low at the beginning of the wet season and increases over time, particularly as flow begins to diminish (Fig. 5.2). Abundance is highest in preflow pools but declines sharply when flow starts, then increases steadily as flow recedes. A total of 134 macroinvertebrate taxa were collected from the two Kapalga streams, which is similar to other intermittent streams in the region (Tripodi 1996), but much lower than intermittent streams from southern Australia (Boulton and Lake 1992a,b). The lower richness is probably a consequence of the relatively short period of flow, the long dry period and the relative lack of dry season refugia (cf. Boulton 1989). The streams dried completely each year, so there were no free-water habitats in the stream channel. The sandy sediments overlay an impervious clay or lateritic base, so it was highly unlikely that the hyporheos provided any refuge for benthic fauna. The streams were initially recolonized by the few taxa able to persist in the sediments as dormant stages
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Figure 5.2. Typical seasonal patterns of taxon richness and abundance per sample for aquatic macroinvertebrates in the Kapalga streams. The hydrological history for the stream is shown below the data: gray bar, preflow pools; black bar, main flow; striped gray bar, diminishing flow). Note that abundance is shown on a log 10 scale.
(e.g., crustaceans and oligochaetes), then by a more diverse group of insects, which must have flown in from permanent water bodies elsewhere (both streams were <5 km from permanent billabongs). Despite the obvious potential for highly variable assemblages to arise owing to stochastic colonization, the seasonal periodicity of the macroinvertebrate fauna was remarkably consistent between years. No seasonal data were collected for macrophytes (e.g., Oryza rufipogon, Caldesia oligococca, Isoetes coromandelina, Cyperus aquatalis) in the Kapalga streams, but these usually appeared after flow commenced and were most abundant during recessional flow and in postflow pools.
5.1.3 Fire and Stream Ecosystems Streams cannot be separated from the catchments they flow through (Hynes 1975). Consequently, catchment management practices may have substantial effects on stream ecosystems. This is particularly true of disturbances, such as catchment burning, that alter features of hydrological significance such as canopy cover, ground cover, and riparian vegetation (DeBano et al. 1998). The potential effects of fire on stream ecosystems can be divided into immediate effects (also referred to as direct or primary effects) and delayed effects (also called indirect or secondary effects) (Minshall et al. 1997). The direct effects include increased water temperature caused by flame heat or
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increased nutrient concentrations due to the diffusion of chemicals (such as ammonia) from smoke (e.g., Hall and Lantz 1969; Spencer and Hauer 1991; Minshall et al. 1997). Fire invariably causes some loss of catchment vegetation, and this may have important delayed consequences for stream hydrology and water quality (DeBano et al. 1998). Owing to reduced evapotranspiration (Bosch and Hewlett 1982), burnt catchments typically export more water than do unburnt catchments (e.g., Wright 1976; Bayley and Schindler 1991; Belillas and Roda 1993; Scott 1993). The loss or reduction of vegetation through fire also disrupts nutrient cycling, as nutrient uptake by plants is reduced while mineralization and leaching from the catchment increases, usually leading to greater export of nutrients (Nakame et al. 1983; Chessman 1986; Bayley and Schindler 1991; Spencer and Hauer 1991; Bayley et al. 1992). Fire can also result in greater sediment yields from the catchment due to erosion. Burning removes vegetative and litter cover, leaving bare soils exposed, and the reduction in canopy cover further increases the erosive potential of rainfall (Waters 1995). Wood and organic matter inputs to the stream may initially increase following fire, as dead trees fall into the stream channel and ash and litter are washed into the stream from upslope (Britton 1990; Davies and Nelson 1993; Ewing 1996; McIntyre and Minshall 1996). Organic matter inputs may then decline as the forest regenerates, before slowly increasing as the forest matures and trees senesce (Minshall et al. 1989). Changes in the abiotic features of a stream following catchment burning can have a range of effects on stream biota. For example, increases in light (through the loss of canopy cover) and nutrients should promote autotrophic production; but increased sediment levels, turbidity, and channel scouring may lead to the filling of interstitial spaces, reducing habitat area and heterogeneity for benthic fauna (Minshall et al. 1989). Riparian vegetation is an important moderator of effects of catchment disturbance on stream ecosystems (Cummins 1986; Malanson 1993; Naiman and Décamps 1997). Therefore, disturbances that affect riparian vegetation and impair its function as the stream–catchment interface will have a great influence on the stream. Despite the extensive potential impacts, there is surprisingly little information on the actual effects of catchment burning on streams (Bayley and Schindler 1991; Minshall et al. 1997). Effects are likely to vary regionally owing to differences in vegetation and climate (Minshall et al. 1997). For example, sediment loads following fire range from undetected impacts (Richter et al. 1982; Britton et al. 1993) to hundred-fold increases (Brown 1972; Wells 1985). This reflects the range of factors that affect the hydrological response of catchments to fire, such as the frequency, intensity, and spatial extent of burning, climate (notably rainfall pattern), catchment characteristics (e.g., slope, soil, ground cover, land use, the proportion of vegetation burnt and its regrowth), and the time interval between burning and subsequent runoff.
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5.2 The Stream Study The Kapalga experiment, using entire catchments as the units of experimental replication, offered a unique opportunity to examine the effects of catchment burning on streams. No other investigation of the impacts of fire on any aquatic ecosystem has used a replicated experimental manipulation of entire catchments. The stream study focused on four broad areas: (1) catchment features of hydrological significance, (2) riparian vegetation, (3) stream water quality, catchment water yield, and export coefficients, and (4) aquatic biota (macroinvertebrates and macrophytes). Riparian vegetation and aquatic biota were compared in streams from three replicate compartments of the Unburnt and Late treatments (Douglas 1999). Riparian vegetation was sampled once, at the end of the dry season, in the upper, middle, and lower reaches of each stream. Aquatic macrophytes and macroinvertebrates were sampled twice a year (during Early flow and in postflow pools) for two wet seasons from two pools in the lower reaches of each stream (Fig. 5.3). Water quality, catchment water yield (volume of water transported by stream flow from a catchment, expressed as a depth over the catchment), and export coefficients (mass of a water quality variable transported by stream flow, corrected for catchment area) were compared from a single compartment for each treatment (Unburnt, Early, and Late). Water level was monitored continuously, and water samples were collected in response to stream discharge (Townsend and Douglas 2000).
Figure 5.3. Suction sampling of benthic macroinvertebrates. (Barbara McKaige.)
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5.2.1 Catchment Features Catchment canopy cover was similar in the Unburnt and Early treatments (76 and 79%, respectively), but both were significantly higher than in the Late treatment (51%) (Townsend and Douglas 2000). This difference was attributable to high tree mortality in the Late treatment due to fire (Chapter 6). Dry season sampling showed that bare ground cover was higher in the Late than in Early and Unburnt treatments. The exposure of the soil surface caused by the burning of the litter layer was compensated to some extent by heavy leaf fall, resulting from canopy scorching, a few days after the fire (Chapter 3).
5.2.2 Riparian Vegetation A total of 76 species of woody plants from 26 families were sampled in the riparian zones. The Myrtaceae contained 25 species, and other important families were Mimosaceae (7 species), Combretaceae (6), and Euphorbiaceae (5). Sixteen species each accounted for at least 2% of total stems, and eight species—Pandanus spiralis, Planchonia careya, Breynia cernua, Melaleuca nervosa, Lophostemon lactifluus, Terminalia platyphylla, Xanthostemon eucalyptoides, and Erythrophleum chlorostachys— accounted for over half of all trees sampled (Table 5.2).
Table 5.2. Abundance (total stems recorded per treatment) of the 16 most common woody riparian species (those that contributed at least 2% to overall abundance) from Unburnt and Late catchments, summed over the three replicate catchments. Species
Unburnta
Latea
Pandanus spiralis Planchonia careya Melaleuca nervosa Breynia cernua Lophostemon lactifluus Terminalia platyphylla Xanthostemon eucalyptoides Erythrophleum chlorostachys Eucalyptus alba Terminalia pterocarya Antidesma ghaesembilla Xanthostemon paradoxus Eucalyptus polycarpa Flueggea virosa Melaleuca viridiflora Cochlospermum fraseri Other species Total
390 (7) 478 (8) 212 (4) 475 (8) 371 (6) 378 (7) 299 (5) 306 (5) 211 (4) 1 (<1) 253 (4) 208 (4) 174 (3) 189 (3) 163 (3) 48 (1) 1596 (28) 5752 (100)
388 (23) 86 (5) 263 (15) 0 (0) 45 (3) 25 (1) 84 (5) 27 (2) 78 (5) 257 (15) 0 (0) 9 (1) 28 (2) 0 (0) 15 (1) 116 (7) 280 (16) 1701 (100)
a
Percentage abundance of each species is given in parentheses.
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M.M. Douglas et al.
a
b Figure 5.4. Riparian vegetation in Unburnt (a) and Late (b) catchments. (a, Michael Douglas; b, Ian Morris.)
Late fires had dramatic effects on the composition and structure of riparian vegetation (Fig. 5.4). Nearly half (35) of all species recorded occurred only in the Unburnt treatment. Of the 33 species that occurred in both Late and Unburnt compartments, 29 were more abundance in Unburnt. Three species—Breynia cernua, Antidesma ghaesembilla, and Flueggea virosa— were abundant in the Unburnt treatment but absent under Late fires. In contrast, Terminalia pterocarya was abundant in the Late treatment but represented by a single plant in Unburnt. Many of the species in Unburnt riparian vegetation are characteristic of monsoon rainforest rather than savanna habitats. Species richness, density and canopy cover of woody vegetation, and the species richness of vines were all significantly lower in Late than in Unburnt catchments (Table 5.3). The clear separation of Late and Unburnt riparian communities is illustrated by multivariate ordination (Fig. 5.5). Surprisingly, burning did not seem to increase susceptibility to invasion by exotic species; rather, the two most common weed species encountered in the study (Passiflora foetida and Hyptis suaveolens) were almost completely absent from Late catchments. As well as differences in the structure and floristic composition of riparian vegetation, burning reduced sexual reproduction in Eucalyptus alba,
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Table 5.3. Summary data for woody riparian vegetation and vines in Unburnt and Late catchments.
Richness (species/quadrat) Density (stems/quadrat) Canopy cover (species/quadrat) Vine richness (species/quadrat) a
Unburnta
Latea
15.6 (4.5) 159.8 (59.8) 62.6 (23.1)
7.3 (3.1) 50.0 (28.3) 30.3 (20.4)
7.1 (1.5)
2.2 (1.4)
Means and, in parentheses, standard deviations.
the dominant riparian eucalypt in the lower and middle reaches of most streams. Only 10% of E. alba in Late catchments bore fruit, compared with 87% in Unburnt catchments. Therefore, burning not only reduced the abundance of riparian vegetation by increasing mortality of established trees, but it also reduced the capacity for vegetation to regenerate by seed after fire. Fire can reduce fruiting, seed output, and seedling survival in other savanna trees (Chapter 6). In addition to the study of the Late and Unburnt treatments, a survey of the woody riparian vegetation in Early catchments was undertaken in 1997 (Douglas, unpublished data). Although not directly comparable to the
Figure 5.5. Multivariate ordination showing the effects of fire treatment (open squares, Unburnt; solid circles, Late) on composition of woody riparian vegetation. Each symbol represents a replicate vegetation sample (600 m2 quadrat).
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M.M. Douglas et al.
surveys conducted in 1993, results indicate that riparian tree densities were similar to the Unburnt treatment. Changes in the riparian vegetation are reflected in the inputs of allochthonous (externally derived) material to the stream. In the dry stream bed, the standing crop of benthic organic matter (BOM) was three times higher in streams of Unburnt compared with Late catchments (means of 0.12 and 0.04 kg m-2 respectively). Soon after flow began, however, BOM was significantly higher in streams of Late compared with Unburnt catchments (1.36 vs 0.89 kg m-2). BOM was similar in both treatments by the time streams stopped flowing.
5.2.3 Stream Water Quality In the years 1992 to 1995, there was no rainfall between May and September, with the first wet season storms occurring in October (Fig. 5.6). Con-
Figure 5.6. Total daily rainfall (a), and hydrographs for a stream from Late (b), Unburnt (c), and Early (d) and catchments. Arrows indicate episodic runoff events in the Late stream; these did not occur in other streams. (Redrawn from Townsend and Douglas 2000, with permission from Elsevier Science.)
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tinuous wet season flow in the three streams commenced between late December and early January and ended between mid-May and late June. In the Late stream, however, episodic runoff events in November and December each wet season preceded continuous wet season discharge (Fig. 5.6). These episodic events did not occur in the Early or Unburnt treatments. Water yields from the three catchments varied within and between wet seasons, but no consistent differences were discernible between the fire treatments (Fig. 5.7). Episodic runoff events from the Late catchment carried ash and high concentrations of all the water quality variables tested (Table 5.4). The volume-weighted mean concentrations of these were as much as 10 times higher than those measured later in the wet season in a Late stream (Townsend and Douglas 2000). The volume of these episodic runoff events was relatively small, averaging 0.23% of total annual discharge. Because of their relatively high concentrations of all variables, however, they contributed a disproportionately large amount to the total annual mass of exports from the Late catchment. On average, episodic runoff events
Figure 5.7. Total wet season yields (a) and storm runoff (b) for Late, Unburnt, and Early catchments for the 1993, 1994, and 1995 wet seasons. Early catchment not sampled in 1993. (Redrawn from Townsend and Douglas 2000, with permission from Elsevier Science.)
72 Table 5.4. Mean concentrations of (a) episodic runoff events from the Late catchment, preceding continuous wet season flow (these events did not occur in the Unburnt and Early catchments), (b) storm runoff during continuous wet season flow, and (c) base flow periods for the three fire treatments.
Fire treatment
Volatile suspended sediment (mg L-1)
Phosphorus (mg L-1)
Nitrogen (mg L-1)
Iron (mg L-1)
Manganese (mg L-1)
390
5400
200
(a) Episodic runoff
Late catchment
79
110
2400
(b) Storm runoff
Late catchment Early catchment Unburnt catchment
58* 9 14
10* 5 4
13 9 15
330 360 300
950* 470 530
46* 11 14
(c) Base flow
Late catchment Early catchment Unburnt catchment
4 4 4
3 3 2
5 7 4
230 240 190
440 500 380
18 10 10
* Statistically significantly different (p < 0.05) from other fire treatments for the same flow category (see Townsend and Douglas 2000).
M.M. Douglas et al.
Flow category
Suspended sediment (mg L-1)
5. Streams
73
exported 5% of total suspended sediments (TSS), 3% of volatile suspended sediments (VSS) and P, and 2% of annual total load of N, Fe, and Mn. Volume-weighted mean concentrations of storm flows were significantly higher in the Late catchment than in the either Early or Unburnt catchments for all variables except P and N (Table 5.4; Townsend and Douglas 2000). In contrast to the results from storm flow, the same analyses for base flow water quality revealed no statistically significant differences between the three catchments or between years (Table 5.4). Annual export coefficients varied by a factor of at least 2 between wet seasons (Fig. 5.8). This is ascribed to variation in stream discharge volume rather than to the concentration of water quality variables (Townsend and Douglas 2000). The predominance of volume, rather than concentration, in determining catchment export coefficients concurs with that reported elsewhere (e.g., Grobler and Silberbauer 1985; Crosser 1989). Most (about 70%) of the stream load was transported during storm runoff rather than base flow. Despite the high proportion of load transported by storm runoff and the higher storm runoff concentrations in the Late stream for most of the water quality variables, only TSS export coefficients were significantly higher (average 2.4 times) than in the Unburnt stream (Fig. 5.8; Townsend and Douglas 2000). Export coefficients for VSS, P, N, Fe, and Mn did not differ significantly between catchments. The water quality patterns seen in the three hydrographic stations were reflected in spot measurements taken from three Unburnt catchments and three Late catchments (Douglas 1999). The base flow water quality in these six streams showed no consistent differences between the two fire treatments.
5.2.4 Aquatic Biota A total of 28 macrophyte species from 18 families were collected over the two wet seasons. The four most abundant species—Oryza rufipogon, Caldesia oligococca, Isoetes coromandelina, and Cyperus aquatalis—made up over half of the total biomass. Eighteen species occurred only in the Late treatment, whereas no species were restricted to the Unburnt treatment. Macrophyte richness per quadrat was significantly higher in Late than in Unburnt streams, which contained over four times as many species per quadrat (6.9 vs 1.5; Fig. 5.9). Macrophyte biomass was an order of magnitude higher in Late streams (mean of 15.1 vs 1.7 g per quadrat). A total of 140 macroinvertebrate taxa from 17 families were collected from the six streams. About two-thirds of all invertebrates were noninsects, mostly crustaceans. Diptera (mainly chironomids) made up a quarter of the total fauna; Hydracarina, Ephemeroptera, Annelida (mainly Oligochaeta), and Nematoda were also abundant. The macroinvertebrate fauna showed clear responses to catchment burning, with taxon richness being consistently higher in Late than in Unburnt treatments (Fig. 5.10). Abundance
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Figure 5.8. Annual suspended sediment, volatile suspended sediment, P, N, Fe, and Mn export coefficients from the Late, Unburnt, and Early fire regimes for the 1993, 1994, and 1995 wet seasons. Early catchment not sampled in 1993. (Redrawn from Townsend and Douglas 2000, with permission from Elsevier Science.)
5. Streams
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Figure 5.9. Aquatic macrophytes in a Late catchment. (Michael Douglas.)
was also consistently higher in the Late treatment, but this difference was significant only during the early flow period of the second year of sampling. Macroinvertebrate community composition differed between the two treatments, with communities in Late streams being more similar to each other than those in the Unburnt streams (Fig. 5.11). However, none of the common taxa showed statistically significant differences in abundance between the two fire regimes.
5.3 Conclusions The Kapalga study provides the first experimental evidence of the strong linkages between tropical savanna streams and their catchments. It shows that fire management can result in marked changes in both catchment and riparian vegetation, and, even though fires occur when the streams are completely dry, burning still has significant effects on the aquatic biota. In most stream systems, increased runoff and storm flow, coupled with increased transport of sediments, have been linked to detrimental effects on in-stream biota following fire (Minshall et al. 1997). At Kapalga, however, there was no evidence of increased water yield or storm flow, and increases in sediment export were relatively modest. The reduced canopy
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a
b Figure 5.10. Macroinvertebrate (a) richness and (b) abundance per sample during early flow (20 days after flow began) and postflow (20 days after flow stopped) sampling in the 1991–1992 wet season (Y1) and the 1992–1993 wet season (Y2). Note that abundance is shown on a log10 scale.
5. Streams
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Figure 5.11. Multivariate ordination showing the effects of fire treatment (open symbols, Unburnt; solid symbols, Late) on composition of aquatic macroinvertebrates during the postflow period (20 days after flow stopped) of the 1991–1992 wet season (circles) and the 1992–1993 wet season (squares). Each symbol represents the combined replicate samples from each of the two pools on the six streams.
cover in Late catchments, which would result in decreased rainfall interception and reduced evapotranspiration from tree foliage, may have been countered by high evapotranspiration losses by grasses (Townsend and Douglas 2000). Native grasses such as the regionally dominant annual Sorghum spp. have higher rates of evapotranspiration than do savanna trees (P.G. Cook et al. 1998). Townsend and Douglas (2000) attribute the relatively minor effects of fire on water quality to the very low catchment slopes (average 0.5%), low soil fertility in the region, the maintenance of a protective surface gravel layer, regrowth of catchment vegetation over the wet season, and the lengthy delay between burning and runoff. Changes in the aquatic biota of the Kapalga streams following Late fires can be linked to changes in the riparian zone. First, riparian vegetation is known to reduce sediment and nutrient inputs to streams by stabilizing stream banks and sediment within the riparian zone itself, or by trapping sediment and nutrients that have been transported from areas beyond the riparian zone (Peterjohn and Correll 1984; Naiman and Décamps 1997). The much lower density of riparian vegetation in the Late versus Unburnt catchments probably contributed to the greater sediment export and to the higher nutrient concentrations in the short-lived episodic flows. Second, riparian vegetation is an important determinant of organic matter inputs to streams (Gregory et al. 1991; Wallace et al. 1997). Late fires killed vines and woody species, and pruned the canopies of surviving trees; they also caused a shedding of leaves several days after the fire had passed. This increased the amount of leaf litter in the riparian zone itself, as well
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as in the streambed, but standing crops of leaf litter were still not as high as in Unburnt streams. However, after burning, the lack of vines and other retentive structures meant that litter was readily transported into the stream, and, soon after flow began, standing crops of organic matter in burnt streams exceeded those in unburnt streams. By the end of flow, standing crops of benthic organic matter were similar in the two fire regimes, indicating greater transport of organic matter from the burnt stream, possibly because the burning of debris dams had resulted in poorer retentive capacity. The lower canopy cover and thus higher light availability in the Late versus Unburnt catchments also favor the growth of aquatic macrophytes. Although no other studies appear to have examined the effect of fire on macrophyte communities, the negative correlation between macrophytes and canopy cover has long been appreciated, and shading by riparian vegetation has been used as a means of controlling macrophytes (Wade 1994). Macrophytes may also have derived nutrients from the breakdown of the greater amounts of organic matter in Late streams. Finally, the increased sediment loads in Late streams may have been accompanied by increased bedload and deposition of bedload material to provide a favorable rooting medium for rooted macrophytes. The greater richness and abundance of macroinvertebrates in the burnt streams at Kapalga was most likely due to the greater diversity of habitats and possibly food resources, resulting from the higher standing crops of benthic organic matter at the start of the wet season, and aquatic macrophytes at the end of the wet season. The use of litter and macrophytes as a substrate, rather than food resource, may be particularly important to the macroinvertebrates in the relatively unstable sandy substrates of the Kapalga streams, especially when the streams are flowing. It is likely that macrophytes provide a substrate for epiphytic algae, which in turn may provide a valuable nutritional resource for macroinvertebrates in these streams. We did not measure fauna in higher trophic levels, but the greater richness and abundance of macroinvertebrates represent an increase in food resources for fish, as well as for terrestrial fauna, such as insectivorous birds, that feed on emerging macroinvertebrates. Thus the Kapalga study provides the first experimental evidence of the strong linkages that exist between tropical savanna catchments and their streams. It shows that fire management can result in significant changes in the riparian vegetation and intermittent stream biota, even though fires occur when the streams are completely dry, many months before the appearance of water. Therefore, fire management in tropical savannas can no longer be considered simply a land management issue: it has clear consequences for the management of riparian and aquatic resources, as well.
6. Vegetation Richard J. Williams, Warren J. Müller, Carl-Henrik Wahren, Samantha A. Setterfield, and Jack Cusack
6.1 Introduction This chapter explores the effects of three fire regimes—Unburnt, Early, and Late—on aspects of the vegetation of the savannas at Kapalga. We look in detail at variation in basal area and survival of adult trees and juvenile sprouts, the vegetative and reproductive phenology of the trees, and the diversity and composition of the grass layer. A single, unplanned, late dry season fire occurred on one of the Unburnt compartments in September 1994 (Chapter 2), 5 years after the start of the experiment, and the impact of this fire on tree structure is also discussed. Just as the fire regimes of northern Australia are a product of the distinct seasonality of rainfall (Chapter 2), so too is the structure and phenology— the seasonal rhythms—of the savanna.The wet season produces lush growth in the grass layer, but with the onset of the dry season this begins to dry out and senesce (Egan and Williams 1996). Thus the grass layer may be largely dormant, dead, or present only as the seed bank by the time dry season fires commence. The woody vegetation in the shrub and tree layers, in comparison, may remain phenologically active during the dry season (Brennan 1996b; Williams et al. 1997b, 1999b; Eamus et al. 1999; O’Grady et al. 1999). Woody sprouts and small saplings of the dominant trees may also continue to grow during the dry season (Prior et al. 1997a,b). The mid- and late dry seasons are peak periods for leaf flushing, the development of floral buds, 79
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and flowering (Williams et al. 1997b; 1999b). Thus, an understanding of the interplay between the seasonality of fire and the seasonality of the vegetation is necessary if we are to understand the impacts of fire on savannas.
6.2 Fire and Vegetation: Hypotheses and Approaches A number of hypotheses concerning fire–vegetation interactions can be tested by the Kapalga experiment. Given the fundamental association between savannas and fire (Huntley and Walker 1982; Bourlière 1983;Tothill and Mott 1985), the strong seasonality of the vegetative and reproductive phenology of trees and grasses in savannas (Sarmiento and Monasterio 1983), and the seasonality of fire behavior (Chapter 3), interactions between plant phenological state and variation in fire intensity through the dry season may cause fire to have different effects on the grass and tree layers. We may hypothesize that the more intense and extensive Late fires will have a greater impact on tree survival than less intense Early fires. Alternatively, overall mortality may be higher in slower moving, less intense fires owing to longer residence time of flames at the tree base (Scholes and Walker 1993). We may also suggest that there will be interspecific variation in the level of susceptibility to fires of a given intensity, as well as intraspecific variation, because smaller trees will be more vulnerable than bigger trees, and also species differ in bark thickness. Late fires may have a greater impact than Early fires on tree phenology, given the peak of leaf flush and flowering late in the dry season (Williams et al. 1999b). In the absence of fire, the tree layer can be expected to thicken, as occurs in neotropical and African savannas (Sarmiento 1984; Scholes and Walker 1993; Menaut et al. 1995; Benshahar 1996), which, in the extreme form may lead to replacement of savannas by rainforest (Menaut and Cesar 1979). We may hypothesize that fire will have relatively little impact on diversity or composition of the grass layer, since the plants have desiccated and are largely dormant by the time dry season fires commence. Alternatively, fire may modify diversity by affecting the competitive influence of dominant herbaceous species. With respect to seedlings and other small juvenile trees—sprouts and small saplings that are still within the grass layer— Werner (1986) hypothesized that Early fires will be more detrimental to the seedling and small sapling bank (the “sprout bank”) than Late fires because the juveniles are still actively growing during the early dry season. Sprouts in Unburnt sites may suffer higher mortality than those subject to fire as a result of density-dependent mortality and competition with adult trees (Wilson and Bowman 1987; Fensham and Bowman 1992). On the other hand, sprouts in unburnt conditions may have a higher probability of recruitment into higher size classes than those subject to frequent fire. We may also hypothesize, as for adult trees, that postfire mortality in the sprout bank will depend on both species and size.
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We tested these hypotheses in open-forest habitats dominated by Eucalyptus tetrodonta and E. miniata (Chapter 2). There were three replicated compartments for each of the Early, Late, and Unburnt treatments, with sampling occurring on permanent plots and transects (Chapter 2).
6.3 Tree Basal Area and Survival Tree basal area was calculated from tagged plants in three 0.1 ha plots within each regime (Williams et al. 1999a). Within each plot, the height and diameter of every tree with a diameter at breast height (dbh) greater than 3 cm and height greater than 3 m was measured. This population of trees— about 2100—was resurveyed every June from 1990 to 1995. For each tree, stem survival and whole-plant survival were determined separately. Over this period, additional recruits to the >3 m size class were also identified, tagged, and measured. For each year, total live tree basal area (m2 ha-1) was calculated from the sum of the individual tree trunk areas in each 0.1 ha plot. For some analyses, species were grouped into six functional types according to life-form and leaf phenology: (1) evergreen eucalypts, (2) deciduous eucalypts, (3) evergreen (noneucalypt) trees, (4) deciduous (noneucalypt) trees, (5) palms (all evergreen), and (6) acacias (all evergreen). Linear and logistic regression were used to examine survival of individuals over time (Williams and Douglas 1995; Fordyce et al. 1997; Williams et al. 1999a).
6.3.1
Tree Basal Area
Total live stem basal area remained more or less unchanged over the 1990–1995 period under both Unburnt and Early regimes, but declined by about 20% under Late fires (Fig. 6.1). Most of this reduction occurred in the 2 years following the fires of 1990—the most intense fires of the experimental period. Basal area was reduced by about 40% (from 11.4 m2 ha-1 to 6.6 m2 ha-1; Williams et al. 1999a) following the unplanned fire in 1994 on one of the Unburnt compartments (M). Both under the Late regime and following the unplanned fire, the reduction in live basal area was driven by substantial stem mortality in the largest trees (Williams et al. 1999a).
6.3.2 Tree Survival: The Importance of Stem Size and Species The survival of tree stems varied with fire treatment, tree size, and tree functional type. For individual compartments (pooled species and size classes) there was a highly significant, linear decline in the survival of both stems and whole plants with increasing fire intensity (Fig. 6.2). Whole plant survival over time was highest in the Unburnt regime, with little difference between Early and Late (Fig. 6.3a). Stem survival was significantly lower under Late fires, compared with Early and Unburnt (Fig. 6.3b). Most stem
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Figure 6.1. Relative change between 1990–1995 in live basal area of trees (with basal area in 1990 equal to 100%) in relation to fire regime; Unburnt, solid line; Early, long dashes; and Late, short dashes.
Figure 6.2. Relationship between tree survival and the maximum fire intensity to which a plot was subject over the period 1990–1995. The dotted line and open circles refer to whole plant survival; the solid line and solid circles refer to stem survival.
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deaths occurred in the 1990–1991 period, following the relatively intense fires of the initial year of the experiment (Chapter 3). The survival of the stems of five of the most common trees—E. miniata, E. tetrodonta, E. porrecta, Erythrophleum chlorostachys, and Terminalia ferdinandiana—was modeled as a function of fire regime and tree size. For each species, survival was significantly higher in the Unburnt and Early regimes than in Late. For Unburnt, average tree survival in all size classes exceeded 95%, with no effect of tree size on survival. Under Early and Late fires, in contrast, the effects of both tree size (both the linear and
Figure 6.3. Cumulative survival of the 1990 cohort of (a) whole plants and (b) stems over the 1990–1995 period, in relation to fire regime; Unburnt, solid line; Early, long dashes; Late, short dashes.
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quadratic components), and fire regime were highly significant. In all species, stem survival under Early fires was significantly higher than that under Late. For each of the individual species (Fig. 6.4) stem survival was close to 100% in the Unburnt regime, and significantly higher in Early than either Late or following the unplanned fire. Survival across all species and most size classes was substantially lower in Late compartments than following the single high intensity fire. Stem survival was markedly humped in E. miniata—survival of large stems (>40 cm dbh) was relatively low, and similar to that of saplings (Fig. 6.4a). Peak survival was in the range of 15 to 30 cm dbh. Eucalyptus. tetrodonta (Fig. 6.4b) showed higher survival in the larger size classes than did E. miniata. In both Early and Late compartments, and also following the unplanned fire, survival was lower in E. porrecta (Fig. 6.4c) than in either of the dominant eucalypts, in all size classes below 30 cm dbh. The survival of stems of Erythrophleum chlorostachys and Terminalia ferdinandiana was less than 20% across virtually all size classes under Late fires (Fig. 6.4d,e). Net recruitment of stems into the >3 m stratum—as measured by changes in stem density—was significantly higher in Unburnt than in Early compartments, which in turn was significantly higher than under Late fires (Table 6.1). There was a similar pattern with respect to changes in stem basal area of these recruits. However, the contribution of this component to changes in total basal area was small (<0.3 m2 ha-1 in stands where basal area was 7–14 m2 ha-1). Importantly, most of the change in stem density and basal area was accounted for by seven species: Terminalia ferdinandiana, Xanthostemon paradoxus, Acacia aulacocarpa, Erythrophleum chlorostachys, and the three main eucalypts—E. porrecta, E. miniata, and E. tetrodonta. The last two species were the dominant trees in all stands, and the others are all common trees within Kapalga savannas. The remaining species that recruited into the shrub and tree layers over the course of the experiment were also common savanna species—very few were species more typically associated with rainforest, even in Unburnt compartments. Thus, there was no evidence of recruitment of rainforest species in the absence of fire over the 5-year experimental period.
6.4 Juvenile Woody Sprouts Woody sprouts (Fig. 1.4) were defined as individuals with a maximum stem size under 3 cm dbh, and less than 3 m tall. The vast majority of these individuals were less than 1.5 m tall, and therefore within the grass layer and zone of flaming combustion of most dry season fires. The term “woody sprout” is used here in a generic manner to include both genets (i.e., true seedlings) and ramet/clones that have arisen vegetatively, because these
6. Vegetation
Figure 6.4. Continued. 85
85
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Figure 6.4. Continued. Net survival of the 1990 cohort of tree stems over a 5-year period (i.e., survival in 1995) as a function of fire regime and stem size: (a) E. miniata (evergreen); (b) E. tetrodonta (evergreen); (c) E. porrecta (semideciduous); (d) Erythrophleum chlorostachys (semideciduous), (e) Terminalia ferdinandiana (deciduous). Fire regimes: Unburnt, Early, Late, and a single unplanned, high intensity, late dry season fire in September 1994 (Unplanned).
cannot be distinguished in the field without excavation. Density and size of all woody sprouts were measured in the central 4 m ¥ 50 m strip of the 0.1 ha plots within which tree survival was measured. Additional size and density measurements were taken of all eucalypt sprouts in another 4 m ¥ 50 m strip in each of the 0.1 ha plots. Sprouts had been tagged in 1989/1990 in three Unburnt and one Early compartment; the remainder were tagged,
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Table 6.1. Changes in stem density (ha-1; columns 3–5) and basal area (BA: m2 ha-1; columns 6–8) of saplings of woody species at Kapalga.a Stems
% BA
Unburnt 1992–1995
Early 1992–1995
Late 1992–1995
Unburnt 1990–1995
Early 1990–1995
Late 1990–1995
Species
Life-formb
Terminalia ferdinandiana Xanthostemon paradoxus Acacia aulacocarpa Erythrophleum chlorostachys Eucalyptus porrecta Eucalyptus miniata Eucalyptus tetrodonta Acacia mimula Eucalyptus clavigera Planchonia careya Brachychiton diversifolius Breynia cernua Syzigium bleeseri Persoonia falcata Coclospermum fraseri Buchanania obovata Flueggea virens Eucalyptus bleeseri Brachychiton megaphylla Planchonella pohlmaniana Alphitonia excelsus Gardenia megasperma Grevillea spp. Pandanus spiralis Wrightia saligna Terminalia latipes Average stem number, (ha-1) Basal area change, (m2 ha-1)
Dectree
57.8
5.6
1.1
7.9
0.0
2.6
Dectree
55.6
1.1
0.0
1.3
0.0
0.0
Acacia Dectree
38.9 36.7
4.4 14.4
0.0 0.0
5.3 17.1
0.0 0.0
0.0 0.0
Deceuc Evgreuc Evgreuc Acacia Deceuc Dectree Dectree
24.4 23.3 20.0 3.3 5.6 7.8 7.8
6.7 17.8 22.2 0.0 0.0 2.2 0.0
11.1 27.8 1.1 0.0 0.0 0.0 0.0
7.9 23.7 26.3 0.0 0.0 2.6 0.0
0.0 50.0 0.0 0.0 0.0 0.0 0.0
26.3 68.4 2.6 0.0 0.0 0.0 0.0
Dectree Evgrtree Evgrtree Dectree Dectree Dectree Evgreuc Dectree
0.0 5.6 4.4 3.3 3.3 3.3 0.0 1.1
0.0 0.0 0.0 1.1 0.0 0.0 2.2 1.1
0.0 0.0 0.0 0.0 0.0 0.0 -1.1 0.0
0.0 0.0 0.0 1.3 0.0 0.0 2.6 1.3
0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Dectree
1.1
1.1
0.0
1.3
0.0
0.0
1.1 0.0 1.1 1.1 1.1 0.0 307.8
0.0 0.0 0.0 0.0 0.0 1.1 81.1
0.0 0.0 0.0 0.0 0.0 0.0 40.0
0.0 0.0 0.0 0.0 0.0 1.3
0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
0.27
0.08
0.05
Dectree Dectree Evgrtree Palm Evgrtree Dectree
Other tree species recorded in plot Species
Life-form
Acacia dimidiata Acacia lutescens Acacia oncinocarpa Clerodendrum floribundum Denhamia obscura Eucalyptus tectifica Ficus spp.
Acacia Acacia Acacia Evgrtree Evgrtree Evgreuc Dectree
a
Species Livistona humilis Livistona inermes Petalostigma quadriliculare Stenocarpus cunninghamii Terminalia carpentaria Terminalia grandiflora Vitex glabrata
Life-form Palm Palm Evgrtree Evgrtree Dectree Dectree Dectree
Changes in stem density have been measured as the net change in those stems that that have recruited from sproutto-sapling stages (i.e., from <3 m to >3 m height classes) over the period 1992–1995. Changes in basal area are percentage contributions of each species to total change (indicated at the base of columns 6–8) due to recruitment and growth of the stems of the saplings. The species in the bottom part of the table (“Other tree species recorded in plot”) did not recruit from sprout-to-sapling stage but either were present on the plots over the course of the experiment or were recorded as seedlings or sprouts that remained less than 3 m tall. b Life-form/functional groups: Dectree, deciduous trees; Deceuc, deciduous eucalypt; Evgrtree, evergreen tree; Evgreuc, evergreen eucalypt; acacia, and palm.
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R.J. Williams et al. Table 6.2. Average survival of woody sprouts less than 3 m tall as a function of species (pooling fire regimes). Predicted values ±SEa
Species Deciduous Planchonia careya Buchanania obovata Terminalia ferdinandiana Semideciduous Erythrophleum chlorostachys Eucalyptus porrecta Evergreen Eucalyptus miniata Eucalyptus tetrodonta
89.8 ± 2.2 83.7 ± 2.3 76.5 ± 3.8 86.6 ± 2.6 89.4 ± 1.9 85.6 ± 3.3 83.8 ± 2.2
a
Derived from logistic regression models that tested differences between species and fire regime in survival over the 1991–1995 period.
as for the adult trees, in March 1991, one year after the initial experimental fires. Sprout size/survival was assessed each year until 1995. All new recruits to the woody sprout bank after 1991 were also tagged and their size/survival followed in subsequent years. Survival of the 1990/1991 cohorts of sprouts over the 1991–1995 period was remarkably high: 77 to 90%, pooling species, regimes, and years. Survival was highest (90.5 ± 1.3%) in Early compartments, compared with 85.4 ± 1.7 and 79.7 ± 1.9% in Late and Unburnt, respectively. There were some significant but relatively small interspecific differences in survival, which was lowest in Terminalia ferdinandiana (77%; Table 6.2). Logistic regression showed no significant difference between phenological groups (evergreen, semideciduous, and fully deciduous) in the patterns of survival (Table 6.2). Inclusion of both prefire density and prefire height in 1991 as covariates in the foregoing analyses indicated no significant effect for either variable in any species across all regimes and years. In all analyses there was no species ¥ regime interaction, indicating that the differences between species were consistent across fire treatments.
6.5 Tree Phenology Observational and analytical techniques described in Williams (1997) and Williams et al. (1997b, 1999b) were used to study leaf and reproductive phenology in five common tree species that span the range of leaf phenological types. The species studied were the evergreens Eucalyptus miniata and E. tetrodonta, two semideciduous species, E. porrecta and Erythrophleum chlorostachys, and the fully deciduous Terminalia ferdinandiana.
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Within each replicate compartment, 10 individual trees of each of the five species were sampled. Each was a reproductively mature tree, at least 10 m tall for the eucalypts and Erythrophleum chlorostachys and 6 m tall for T. ferdinandiana. Over the 1991–1995 period, each tree was scanned at monthly intervals for the various leaf and floral phenological characteristics and scored for the presence of reproductive structures (buds, flowers, and/or fruit) as described by Williams et al. (1999b). For each year, a stand-based index (from 0 to 10) of the abundance of buds, flowers, and fruits was calculated (Williams 1997). A rank-order technique was used to determine a quantitative index (from 0 to 100) of within-tree canopy stature or fullness and the abundance of new foliage for each tree (Williams et al. 1997b). Quantification of the effect of fire regime on ovule production, fruit development, and seedfall in E. miniata and E. tetrodonta was conducted in 1994 by collecting all reproductive parts that fell into mesh baskets placed under tree canopies. Baskets were placed under 10 trees of each species in each of three Unburnt, Early, and Late compartments. In addition, 1350 trees of both species were surveyed to quantify the proportion of trees per stand that initiated floral buds, by the foregoing methods, but on a different population of trees (Setterfield and Williams 1996; Setterfield 1997a,b).
6.5.1 Leaf Phenology Fire caused relatively rapid reductions in the canopy index, as a consequence of leaf scorch and subsequent leaf fall, in the major evergreen and semideciduous trees (Fig. 6.5). Terminalia ferdinandiana had completed leaf fall by the time of all Early fires. Canopy scorch was generally partial (<50%) following Early fires and complete to heights of 20 m or more following Late fires. Following both Early and Late fires, most individuals of Eucalyptus miniata resprouted new leaves from most branches within a month, over all years of the experiment (Fig. 6.5a,b). The patterns were similar for the other evergreen eucalypt, E. tetrodonta, and for the two semideciduous taxa, E. porrecta (Fig. 6.5c) and Erythrophleum chlorostachys. Thus, Early fires caused the evergreen and semideciduous trees to flush earlier in the dry season (July) than they normally would (September–October). In the evergreen eucalypts (E. miniata and E. tetrodonta), leaves that survived individual fires did not persist beyond the end of the dry season and were shed at the usual time of leaf exchange during the late dry season/buildup period. Leaf exchange in these species also occurred at this time in the Unburnt regime. There was a marked decline in canopy index following Late fires (e.g., in E. miniata; Fig. 6.5a) but this was followed by rapid flushing of leaves, within weeks of fire (e.g., in E. miniata; Fig. 6.5b). Peak
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leaf flushing occurred in E. miniata, E. tetrodonta, E. porrecta, and Erythrophleum chlorostachys at the same time of the year in all regimes—the late dry season, prior to the arrival of the first rains. There was no effect of fire on canopy index of T. ferdinandiana (Fig. 6.5d), where leaf flush did not occur until after the first rains. The canopy indices of all species had
Figure 6.5. Variation in leaf phenology as a function of fire regime (Unburnt, solid line; Early, dash-dot line; Late, dotted line) and species: 1991–1995. Within-tree canopy index (a) and within-tree growth index (b) in E. miniata (evergreen); (c) canopy index in Eucalyptus porrecta (semideciduous); (d) canopy index in Terminalia ferdinandiana (fully deciduous).
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Figure 6.5. Continued.
returned to maximal levels by the early wet season (December) under all fire regimes, although the absolute level of canopy cover was less under Late (owing to the reduction in live trunk basal area) than under Early and Unburnt.
6.5.2 Reproductive Phenology There was considerable interspecific variation in the timing of, and interannual variation in, the magnitude of budding, flowering, and fruiting. Euca-
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lyptus miniata and E. tetrodonta commenced flowering in the early–mid dry season (May–July); Erythrophleum chlorostachys flowered in the late dry season (September), and E. porrecta and T. ferdinandiana commenced flowering in the early wet season (October–December). Flowering in T. ferdinandiana, like leaf flushing, did not occur until about 2 weeks after the initial rains of the wet season. Budding, flowering, and fruiting occurred in all species in all years. The indices of flowering and fruiting were low (<1) in 1991 for E. miniata (Fig. 6.6a,b) and E. tetrodonta. The study of the development of flowers and capsules, and seedfall, in E. miniata and E. tetrodonta showed that the majority of ovule losses occurred at the bud and flower stages,
Figure 6.6. The relationship between fire regime (legend as for Fig. 6.3) and flowering (a) and fruiting (b) of E. miniata and flowering of E. porrecta (c), Terminalia ferdinandiana (d) and E. tetrodonta (e): 1991–1995.
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Figure 6.6. Continued.
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with approximately 80 to 90% of floral buds initiated in the canopy not developing to mature seed-bearing capsules. The proportion of ovules that survived did not vary significantly between years (Setterfield and Williams 1996). All species were nonserotinous; that is, there was no supra-annual carryover of fruit and seed in the canopies. Indices of flowering and fruiting were low (<0.5) in all treatments in each year of the study for Erythrophleum chlorostachys. There were highly significant effects of fire regime on reproductive activity. The indices of flowering were significantly lower over all years under Late compared with Early and Unburnt regimes for E. miniata, E. porrecta, and T. ferdinandiana (Fig. 6.6a,c,d). For these three species, there was no significant difference between Early and Unburnt in the index of buds, flowers, or fruits. There was no significant effect of fire regime on the index of flowers or fruit in E. tetrodonta in 1991–1993 (Fig. 6.6e), although both flowering and fruiting were significantly lower in the Late than other two regimes in 1994–1995. No statistical assessment of fire impact was undertaken for Erythrophleum chlorostachys because the frequency of flowering was low.
6.5.3 Fire, Seed Production, and Seedling Recruitment In E. miniata and E. tetrodonta, both the proportion of trees that initiated floral buds and the density of floral buds within the canopy were lowest under Late fires. Under the Early regime, although the proportion of trees that flowered was not significantly reduced compared with Unburnt, the proportion of floral buds that developed to seed-bearing capsules was reduced significantly. Thus, for E. miniata, seedfall was highest in Unburnt (18.1 seeds m-1), and an order of magnitude lower in both Early and Late (1.5 and 1.8 seeds m-1 respectively; Table 6.3). This suggests that both timing and intensity of fire are important determinants of fecundity, and both Early and Late fires can significantly reduce seed supply in the dominant eucalypts.A similar result was shown for the common shrub Acacia oncinocarpa, where seed production was much higher in the Unburnt than in the other treatments (Setterfield 1997b). The effects of fire on seedling establishment of E. miniata and A. oncinocarpa were assessed experimentally by sowing 200 seeds into six 0.5 m2 quadrats in two sites within each of three Unburnt, Early, and Late compartments. Seeds were sown in November, after all experimental fires that year. Seedling establishment—the proportion of these seeds that had germinated and taken root by the end of the first wet season—varied significantly with fire regime. For E. miniata 8% established as seedlings in Unburnt compartments, compared with 6.5% and 3.5% in Early and Late, respectively (Table 6.3). For E. tetrodonta, the comparative figures were 10, 6, and 3%, respectively. In both species, this variation between regimes was statistically significant (Setter field 2002).
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Table 6.3. Seed and seedling survival budgets calculated for E. miniata, and A. oncinocarpa under Unburnt, Early and Late regimes. Budget for E. miniata
A. oncinocarpa
Unburnt
Early
Late
Seedfall (seeds m ) Seedling establishment (proportion of 200 sown seeds) Density of established seedlings after 6 months (seedlings m-2) Survival of established seedlings after 1 year (seedlings m-2)
18.1 0.08
1.5 0.065
1.8 0.035
1.4
0.1
0.06
0.2
0
0
Seedfall (seeds m-2) Seedling establishment (proportion of 200 sown seeds) Density of established seedlings after 6 months (seedlings m-2) Survival of established seedlings after 1 year (seedlings m-2)
60 0.065
5 0.02
3.9
0.1
1.2
0.03
-2
a
The lines in these budgets represent the density of natural seedfall under the canopy of a seeding individual in 1994 (Seedfall), the percentage survival of 200 experimentally sown seeds on 0.5 m2 plots, after an entire wet season (Seedling establishment), the density of established seeds after an entire wet season (Density) (=Seedfall ¥ Seedling establishment), and the survival of established seeds after one year (one wet season and the subsequent dry season; Survival). The fate of A. oncinocarpa seed was not calculated for the Late regime because mature stands of this species did not occur on these compartments. Source: Data from Setterfield (1997a,b, 2002).
The effects of fire regime on the density of surviving seedlings of E. miniata and A. oncinocarpa, both 6 months and 1 year after sowing, are also summarized in Table 6.3. These seed/seedling budgets have been calculated from the density of natural seed-fall and the survival of the experimentally sown seed on the 0.5 m2 plots. In the absence of fire, seedling establishment after one wet season was 1.4 m-2 which was an order of magnitude higher than under both the Early and Late regimes (0.1 and 0.06 m-2, respectively). One year after sowing (i.e., after one full wet season and the subsequent dry season), less than 1% of sown seeds had survived, and then only under the Unburnt regime (at 0.2 m-2); seedling survival under both the Early and Late regimes was virtually zero.Thus, fire—whether Early or Late—reduces the availability of seed and substantially reduces the chances of both establishment and survival of seedlings during the wet season of establishment and in the subsequent dry season.
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6.6 Grass-Layer Composition Variation in the composition of the grass layer was assessed each year, from 1990 to 1995, in a series of 5 m ¥ 1 m permanent quadrats established along the primary transects in each compartment (Chapter 2). Within each quadrat, the percentage cover of each species was estimated to the nearest 10%. We used ordination (nonmetric multidimensional scaling; Minchin 1989) to examine spatial and temporal patterns of variability in the floristic composition of the compartments. Vector fitting was used to test the significance with respect to patterns of species composition of continuous environmental variables such as fire intensity, annual rainfall, and the cover of selected species. The effect of fire regime on floristic composition was tested via ANOSIM (Clarke 1993). The ordination of the three replicate compartments of each fire regime showed some systematic variation in the initial composition prior to the imposition of the fires (Fig. 6.7a). Unburnt compartments tended to have higher scores along the first ordination axis, corresponding to relatively low scores along the vector representing the cover of annual Sorghum, compared with Early and Late compartments. However, ANOSIM showed that this preexisting “treatment” variation was not statistically significant. Over the subsequent 1991–1995 period there was substantial variation in floristic composition, with most of the variation occurring parallel to the second ordination axis (Fig. 6.7b). However, none of this variation was related to fire regime or fire intensity. ANOSIM showed that there were no significant differences between fire regimes in grass-layer composition, either in 1990 prior to the imposition of the fires or in the 1991–1995 period, subsequent to the fires. Vector fitting showed that the variation in grasslayer composition was strongly correlated with Sorghum cover, year of sample, and the amount of wet season rainfall, but not fire intensity. Repeated-measures analysis of variance showed that for both species richness and Shannon diversity, there was no significant effect of fire regime or
Figure 6.7. Variation in floristic composition of the grassy-layer of E. miniata/ tetrodonta woodland by fire regime, 1990–95. (a) GNMDS ordination (stress = 0.17) of initial floristic composition (in 1990) of individual compartments prior to the imposition of the fire treatments (compartments C,M,S; Unburnt; compartments E,K,P; Early; compartments F,G,L, Late). (b) GNMDS ordination (stress = 0.13) of the variation in floristic composition over 1990–1995, for individual compartments; compartment letters as per (a); E0 indicates compartment E, 1990, L4 indicates compartment L, 1994 etc. Vectors that have significant correlations with the ordination score (sorghum cover and preceding wet season rainfall) are indicated below.
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fire intensity; however, both measures declined significantly with increasing wet season rainfall and increasing Sorghum cover. Yet some individual species did show responses to fire regime. The cover of Alloteropsis semialata increased significantly in Unburnt compartments, whereas it remained relatively constant in Early and Late. The cover of Sorghum, however, changed little in relation to fire, remaining low in Unburnt and relatively high in Early and Late.
6.7 Discussion 6.7.1 Fire Regimes and Tree Density Annual Late fires caused a 20% reduction in stand basal area. However, this regime was not sufficient to kill the tree layer completely or to result in clumps of trees (interspersed with treeless areas), as may occur in the higher rainfall areas of West Africa (Hochberg et al. 1994). There was relatively little change in tree basal area under either the Early or Unburnt regime. This is somewhat surprising, given that tree thickening as a consequence of reduced fire frequency or intensity is common in savannas and other seasonally dry forests elsewhere in the world (Trollope 1982; Blasco 1983; D. Gillon 1983; Sarmiento 1984; Scholes and Walker 1993; Bullock 1995) and in Australia (Bowman et al. 1988a; Fensham 1990; Bowman and Panton 1995; Dyer et al. 1997). At Kapalga, at least over the 5 years of the experiment, increases in tree basal area as a consequence of girth increment of larger trees, and recruitment of juvenile stems to the tree layer, were offset by mortality in a few larger trees. However, given the size of the sapling bank in Unburnt plots, and the tree thickening reported from nearby Munmarlary over two decades of fire exclusion (Bowman and Panton 1995), some increases in tree basal area in the longer term absence of fire could be expected at Kapalga. Despite considerable differences in fire intensity between regimes, whole-tree survival was relatively high (85–98%), and there was relatively little difference between regimes. This indicates a high level of fire resistance, via resprouting, at the level of individual tree, across species and major functional types (Gill et al. 1990). The capacity for vegetative regeneration following fire is well developed in virtually all woody species in Australian savannas (Lacey 1974), as it is in South American and African savannas (Coutinho 1982; D. Gillon 1983; Sarmiento 1984; Sarmiento et al. 1985; Menaut et al. 1995). In contrast, fire regime had dramatic effects on trees at at the level of individual stem. There was little difference between Early and Unburnt regimes, with stem survival in both treatments about 90%, even though all plots subject to Early fires were burnt to some degree. This was in stark contrast to the effects of the Late regime, where stem survival was less than
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36%. The impact of the single unplanned fire on forest structure and composition was substantial—live basal area was reduced by 40%, and only 26% of stems survived the fire. Stem survival in Late compartments, and following the intense, unplanned fire, was related to stem size, but in a curvilinear manner. Stem survival is usually positively correlated with stem area or height (owing to greater bark thickness) or because canopies were above the lethal flame zone (D. Gillon 1983). At Kapalga this was true over the low to midrange of stem sizes (2–30 cm dbh). However, survival was generally lower in the largest stems (40–50 cm dbh) for the dominant eucalypts. We could find no reports of this phenomenon in savannas elsewhere in the world, but it has been reported by Bowman (1991) in the largest individuals of the rainforest tree Allosyncarpia ternata following fire elsewhere in Kakadu National Park. The high mortality of the largest stems in the present study may be related to the high incidence of termite “piping” of trunks and larger branches in such individuals (Fox and Clark 1972; Bowman 1991; Lonsdale and Braithwaite 1991; Gill 1995; Williams and Douglas 1995). The low survival of the smaller stems is likely to be compensated for by high rates of lignotuberous resprouting in these individuals, and the cohort of basal sprouts and woody sprouts having a diameter at breast height of 2 to 3 cm should persist in the medium term (decades) in all regimes. Growth of woody sprouts may even be stimulated by a reduction in competition, owing to the death of adult trees, as argued for similar mesic savannas in the Northern Territory by Wilson and Bowman (1987) and Fensham and Bowman (1992). However, both under the Late regime and following the intense unplanned fire, the low survival in the intermediate-sized stems (20–60% of those with a diameter at breast height between 5 and 10 cm), along with the low survival in the taller trees, suggests that intense fire can have a long-term impact on vegetation structure. Further research and monitoring is needed to determine over what period of time, and indeed whether, growth in the remaining midsized trees following intense fire can compensate for high mortality in both the intermediate-sized trees and the very large trees. Because of the greater impact of fires on large trees, their relative density within a stand will strongly influence the effect of fire on total live basal area. Compartment M had an atypically high density of large trees (Williams et al. 1999a), which, given their particular sensitivity to fire, explains why the reduction in total basal area was so pronounced. The major taxa and functional types were differentially affected by fire, with the deciduous noneucalypt species showing higher mortality than the dominant evergreen eucalypts. The variation in sensitivities among taxa and functional types following the unplanned fire was similar to that described at Kapalga by Lonsdale and Braithwaite (1991) for an unplanned fire of unknown intensity, and by Williams (1995) for the relatively intense fires of 1990 at Kapalga. Bowman and Panton (1995) also indicated interspecific
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sensitivity to fire at Munmarlary. These studies show that deciduous noneucalypt taxa, such as Terminalia ferdinandiana and Erythrophleum chlorostachys, are more fire sensitive (sensu Gill 1981) than are the dominant eucalypts such as E. miniata.
6.7.2 Fire Regimes and Tree Phenology Reproductive Phenology Four of the tree species—the three eucalypts and T. ferdianandiana— displayed a regular and annual pattern of flowering (sensu Newstrom et al. 1994). At a stand level, these species flowered each year, at about the same time, albeit with varying degrees of interannual variation in magnitude. In contrast, Erythrophleum chlorostachys flowered infrequently and irregularly. The dry season was the peak period for floral and fruit phenology in four of these common species, as it is for the majority of woody species in Australian savannas (Williams et al. 1999b), and in other areas in the seasonal tropics (Daubenmire 1972; Sarmiento and Monasterio 1983; Sarmiento 1984; Borchert 1996; Patel 1997). All species were nonserotinous; that is, there was no supra-annual carryover of seed-bearing fruit in the canopy, as was demonstrated for other populations of eucalypts at Kapalga (Setterfield and Williams 1996) and for a site near Darwin over the 1991–1995 period (Williams et al. 1999b). Nonserotiny is a general phenomenon of trees from Australia’s mesic savannas (Bowman et al. 1991; Dunlop and Webb 1991; Fensham 1992; Brennan 1996b; Williams et al. 1999b), and seedfall is not triggered by fire. This is in contrast to many canopy and understory trees and shrubs in temperate eucalypt forests and woodlands, where seed-bearing fruit may be held within the canopy for years, and seed release is triggered by fire (Lamont et al. 1991; Gill 1997; House 1997). Significant year-to-year variation in flowering and fruiting was observed in the two dominant eucalypts, with both 1991 and 1993 being years of low floral abundance. In other stands of these species at Kapalga, flowering and seed set also were relatively low in 1993 (Setterfield and Williams 1996). Years of low floral activity do not appear to be associated with low rainfall, however, inasmuch as in both 1991 and 1993 rainfall during the preceding wet seasons had been average or above average.A biennial pattern of heavy flowering commonly occurs in temperate eucalypts of southern Australia (Ashton 1975; Andersen 1989). Fire had a marked effect on patterns of floral phenology in five of the six species studied: Acacia oncinocarpa, E. miniata, E. tetrodonta, E. porrecta, and T. ferdinandiana. In each species, the Late regime caused a significant reduction in the abundance of either flowers, fruit, or seeds, relative to Unburnt. For most species, there was no significant difference in the indices of floral activity between Early and Unburnt. However, compared with Unburnt, Early fires did reduce the incidence of seeding in both E.
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miniata and E. tetrodonta, and flowering in the understory shrub Acacia oncinocarpa. Like many midstory species, A. oncinocarpa initiates floral buds and flowers during the early dry season. Its complete canopy, and the lower part of the canopy of the eucalypts, is usually within the scorch zone of early dry season fires (up to about 10 m). Thus, fire during this time, even of relatively low intensity will result in substantial reduction of either floral or seed resources. The greater impact on abundance of flowers of the Late compared with Early fires is related to differences in fire intensity. Thus, in species such as E. porrecta and Terminalia ferdinandiana, which begin to flower during the early wet season, Late fires occur at about the time that floral primordia are in the early stages of development. Since these subdominant species are invariably shorter than E. miniata and E. tetrodonta, the impact of such fires may be magnified. The reduction in fruit abundance as a consequence of Late fires, or the reduction of seed outputs by Early, may affect patterns of seedling recruitment. All species studied are capable of vegetative regeneration following fire (Lacey 1974). However, a reduction in flowering and fruiting of 40 to 90%, as occurred in burnt compartments at Kapalga, may reduce the chances of seedling recruitment by limiting seed supply (Setterfield 1997a). The impact of Late fires on fruit abundance (and thus seed supply) of E. porrecta and T. ferdinandiana is of particular concern, because mortality in both species is significantly higher than that of either E. miniata or E. tetrodonta following fires of medium to high intensity (Lonsdale and Braithwaite 1991; Williams 1995; Williams et al. 1999a). The reduction in abundance of floral reserves as a consequence of Late fire in species such as E. miniata, E. porrecta, and T. ferdinandiana may also have an impact on consumers (Woinarski et al. 1991b; Chapter 8). Numerous mobile nectarivorous bird species, such as honeyeaters, depend on E. miniata for dry season resources (Franklin 1997). Eucalyptus porrecta, on the other hand, provides floral resources at a time of the year—the transition between the wet and dry seasons—when few other tree species are flowering (Brennan 1996b). Terminalia ferdinandiana is fleshy fruited, and fire has been shown to reduce the abundance of fruit of such species in other savannas (e.g., in Brazil) (Sanaiotti and Mangusson 1995; Hoffmann 1998, 1999). Frugivorous mammals, such as tree rats (Friend 1987), may be adversely affected by the impact of Late fires on the abundance of T. ferdinandiana fruit. Although Early fires did not affect flower production in the dominant eucalypt species, it did substantially reduce seed production by limiting ovule development.As a result, seed rain beneath the canopies of E. miniata and E. tetrodonta was similar in the Early and Late regimes, and only about one-tenth that in Unburnt compartments (Table 6.3). Seedling establishment was also reduced by fire—both Early and Late—compared with the Unburnt regime. This result is partly attributable to seed losses to harvesting ant species, which have a higher abundance in frequently burnt habitats
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(Andersen 1991), and the losses from experimental seed depots are approximately double those from Unburnt sites (Setterfield 1997b). The difference in successful seedling establishment was also due to the effect of fire on the abundance and quality of ground cover, hence of suitable microsites for establishment, under the Unburnt compared with Early and Late regimes (Setterfield 1997a, 2002). In the absence of fire at Kapalga, peak seedling establishment following dense flowering in the dominant eucalypts was of the order of 1 seedling m-2 of fecund tree canopy after a wet season, and 0.2 m-2 after a year. This is high compared with the density of adult trees. It is also much higher than could be expected for resprouting eucalypts in most open forests and woodlands in southern Australia in the absence of fire. Although data on recruitment and growth of eucalypt seedlings in southern eucalypt forests in the absence of fire are scant, the establishment of eucalypt seedlings beyond the cotyledonary stage, and stem growth to 10 to 50 cm often are very rare or nonexistent without fire (Gill 1981, 1997; Ashton and Attiwill 1994). Even when significant seedling recruitment can occur in the absence of fire, such as in snowgum (E. pauciflora) woodlands (Ashton and Williams 1989) and mallee shrublands (Wellington and Noble 1985), it is greatly stimulated by fire. In the northern savannas, in contrast, seedling recruitment in the eucalypts is not dependent on fire and is inhibited by annual fire, whether Early or Late. The Kapalga data suggest that where populations of woody plants are subjected to annual fire, there will be limited, if any, sexual reproduction occurring. For species reliant on sexual regeneration, a regime of annual fire will be disadvantageous and, in time, a change in the vegetation composition may become evident. Given the potential impacts of fire on various processes that determine either seed set or seedling abundance, establishment and survival, a fire-free interval of 3 or more years may be necessary for successful recruitment of seedlings of the dominant eucalypts in the mesic savannas such as occur at Kapalga (Chapter 1). Such fire-free intervals have presumably been a feature of the fire regimes experienced by northern Australian savannas over historical time. Further research is needed to determine the time required for eucalypt seedlings to grow beyond flame height, and the age at which they develop the capacity to resprout after fire. Leaf Phenology The major periods for leaf fall were the early dry season (April–May) for deciduous trees and the mid to late dry season (July–September) for semideciduous and evergreen species. The main period for leaf flush was the mid to late dry season in all species except Terminalia ferdinandiana, which flushed only after the first rains of the wet season. The same seasonal patterns were described for these species at a similar site near Darwin (Williams et al. 1997b). Fire had relatively little impact on these patterns, other than
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Early fires promoting earlier leaf fall and subsequent leaf flush in the two evergreen eucalypts (E. miniata, E. tetrodonta) and the two semideciduous species (E. porrecta, Erythrophleum chlorostachys).The major period of leaf exchange was in the late dry season in all these species, and, in all species in all fire regimes, canopy cover was completely restored by the early wet season. This is in contrast to eucalypts of southern Australian sclerophyll forests, where restoration of the canopy to prefire levels may not occur for several years following complete canopy scorch (Gill 1997).
6.7.3 Fire and the Grass Layer Woody Sprouts Survival of woody sprouts in all fire regimes was greater than 75%. Although there was significant variation between fire regime and plant species, it was only of the order of 5 to 10%. In contrast to stem survival in adult trees, there was no effect of size on sprout survival, and no difference in sprout survival between deciduous and evergreen functional groups. We conclude that annual fire, whether Early or Late, is not a threat to the size and persistence of the sprout bank. Fire does not appear to have adverse effects on the sprout bank owing to burning at a time when sprouts are still phenologically active. On the basis of stand population structure, Fensham and Bowman (1992) also concluded that frequent (biennial) fire in the humid savannas of Melville Island was not depleting the size of the sprout bank. Hoffmann (1996) indicated that seedling establishment in Brazilian savannas was reduced in the first year following fire, but not in the second. Thus, in E. miniata/E. tetrodonta savannas, an interfire interval of 2 years, and maybe more, appears to be necessary to maintain recruitment of seedlings into the dense bank of sprouts in the long term. Species Composition and Diversity The composition and diversity of the grass layer at Kapalga showed considerable year-to-year variation, but this was little affected by fire regime and appeared to be driven primarily by variation in annual rainfall. Spatial patterns in composition and diversity were also affected by the cover of annual Sorghum. The lack of grass-layer response to fire in the vegetation plots described here mirrors that of the grass layer monitored independently on the arthropod plots (Chapter 7). Bowman et al. (1988a) also concluded that there was little impact of fire regime on the overall floristic composition of the savannas at nearby Munmarlary. Despite the lack of responses during the Kapalga experiment, there are potential longer term effects of fire on the grass layer in the region. Fensham (1990) in E. miniata/E. tetrodonta vegetation near Darwin, indicated that plant species richness was highest under a moderately burnt regime and lowest under long-term (20-year) fire protection. Bowman
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et al. (1988a) also noted that there were significant individual species responses to fire regimes at Munmarlary. Over longer periods of fire exclusion, increased shading due to enhanced midstory development is likely to have a substantial effect on the grass layer (Andersen 1996). The impact of fire on annual sorghum is of particular interest because this is the major fuel for fire in the Top End. Recent data from Munmarlary (Russell-Smith et al. 2003) indicates that the cover of annual sorghum has declined over 25 years on unburnt plots. The cover of annual sorghum is also low (<5%) at the Solar Village site, near Darwin, which has been unburnt for decades (Fensham 1990; Williams et al. 1997b). Thus, although the cover of sorghum did not vary significantly with fire regime at Kapalga, evidence from other similar, mesic savanna sites indicates that sorghum cover will decline in the long-term (decadal) absence of fire, with midstory shading presumably an important factor (G.D. Cook et al. 1998). Annual sorghum is also known to decline, at least in the short term, in response to wet season burning. Wet season fire was not studied at Kapalga, but it is widely promoted as a management tool for reducing fire fuels. Individual wet season fires, if timed appropriately in the early part of the wet season, can virtually eliminate annual sorghum from the grass layer, because of a lack of a persistent soil seed bank. Such fires can also cause short-term shifts in grass-layer composition, such as declines in the abundance of perennial grasses, and can favor annual over perennial forbs (Stocker and Sturtz 1966; Lonsdale et al. 1998; Williams and Lane 1999). In the semiarid savannas further south in the Northern Territory, such as in the Katherine region, annual burning may decrease the yield of some perennial grasses, but burning every second year has little impact (Mott and Andrew 1985). In the Victoria River District, southwest of Katherine, Dyer et al. (1997) have noted the importance of soil type and land condition as determinants of fire response in the grass layer. Annual and biennial fire had little impact on the composition and productivity of tussock grasslands of the black soil plains. This was also the case for woodlands on red calcareous loam soils with a high initial cover of perennial grasses. However, overgrazed woodlands with a low cover of perennial grasses, and a high cover of annuals and bare ground, suffered substantial declines in diversity and biomass after fire. Elsewhere in the world, changes in grass-layer diversity as a function of fire regime have been reported. In South Africa, diversity increased in the medium-term absence of fire at Natal (Everson and Tainton 1984). At Nylsvley there was some evidence for decline in species richness with increasing fire interval (Yeaton et al. 1986), but whether these changes were due to fire or to variation in annual rainfall was difficult to determine (Scholes and Walker 1993). In montane grasslands of South Africa, there is evidence that the relationship between species richness and fire frequency varies between sites. Richness was highest at intermediate fire frequencies
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at a mesic site in KwaZulu, whereas it was highest in fire-exclusion treatments at a semiarid site in the Eastern Cape (Uys 2000). Although overall there may be little discernible effect of fire in the grass layer, fire regime can differentially affect the cover, and therefore other resources such as seed reserves, of key component species. For example, the perennial grass Alloteropsis semialata increased significantly in Unburnt relative to Early and Late compartments. The seed of A. semialata is an important component of the diet of granivorous birds in the Top End of the Northern Territory (Woinarski 1993; Fraser 2000). Other grasses known to be important to granivores, such as species of Schizachyrium, occur at Kapalga. Although there was no detectable impact of fire on Schizachyrium cover at Kapalga, Crowley and Garnett (1999) indicated that burning in the early dry season on Cape York Peninsula could reduce its seed production.
6.7.4 Fire Exclusion, Tree Thickening, and Succession to Rainforest At Kapalga, there was very little change in tree basal area, and very little recruitment of rainforest elements, at either the seedling or sapling stages, in the absence of fire over the 5-year experimental period. Similar results were obtained over a 20- to 25-year fire-free period in similar forest eucalypt savannas at nearby Munmarlary (Bowman et al. 1988a; Bowman and Panton 1995) and at Solar Village (Fensham 1990; Williams et al. 1997b). What does this indicate about the relationships between fire, savannas, and rainforest in northern Australia? Unlike the situation at Kapalga, cessation of burning in some high rainfall savannas elsewhere in the world can lead to rapid succession to rainforest. Examples include parts of the higher rainfall zones of West Africa (Hopkins 1965; D. Gillon 1983; Menaut et al. 1995; Cook and Mordelet 1997; King et al. 1997) and India (Misra 1983). This may occur through recruitment of rainforest species into the savannas or by expansion of gallery forest (Puyravaud et al. 1994; Kellman and Meave 1997; King et al. 1997; Biddulph and Kellman 1998). Succession to rainforest may take as little as 20 years in the Ivory Coast of West Africa (Menaut and Cesar 1979; Menaut et al. 1995). The rapid response of the savannas to cessation of burning in many of these areas reflects their geologically recent origin— given their derivation from rainforest by recent (Holocene) anthropogenic burning. There is no evidence for such rapid and widespread rainforest invasion under contemporary conditions in the savannas of northern Australia. Localized shifts in the mix of rainforest and eucalypt forest have undoubtedly occurred over the late Pleistocene and Holocene periods, in response to changing patterns of climate and fire (Bowman 2000). There have been both contractions (e.g., Kershaw 1985, 1992; Panton 1993) and expansions
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(e.g., Russell-Smith 1985; Hopkins et al. 1996; Bowman et al. 2001) of rainforest. However, the Kapalga and Munmarlary evidence, combined with more direct experimental results such as the failure of transplanted seedlings of rainforest species to survive in the savanna (Bowman and Panton 1993), indicate that at present there appears to be a high level of resistance in Australian savannas to invasion by rainforest elements in the absence of fire. The Australian mesic savannas, unlike some of their African counterparts, are unlikely to have been derived from rainforest by recent (late Pleistocene/Holocene) landscape-scale burning (Bowman 2000; RussellSmith and Stanton 2002). Paleobiogeographic evidence indicates that the savannas are ancient, not recent, biomes. The wet–dry tropical climate of northern Australia has existed since at least the end of the Tertiary (Williams 1991; Pole and Bowman 1996). Bowman et al. (1993) argued that, because of their high biological diversity, the savannas of the Kakadu region are highly evolved and ancient ecosystems. Savannas are therefore likely to have been a dominant feature of the landscape, as a consequence of severe seasonality in rainfall, for millions of years. The high level of fire resilience of many elements of the savanna vegetation at Kapalga is likely to be a function of this association over evolutionary time between the vegetation, a tropical wet–dry climate, and fire.
7. Terrestrial Insects Alan N. Andersen, Jérôme Orgeas, Rosalind D. Blanche, and Lyn M. Lowe
7.1 Introduction Insects and other arthropods represent the major portion of what E.O. Wilson has called “the little things that run the world” (Wilson 1987), regulating many processes fundamental to ecosystem structure and function, and contributing the bulk of terrestrial species diversity. Of the approximately 1.4 million described species of all living organisms, more than half are insects (Wilson 1986). Given that most insect species are undescribed— one estimate is that there are actually 30 million species (Erwin 1983)—this is a very conservative measure of the contribution of insects to global biodiversity. Despite the great diversity and functional importance of savanna insects, they have received little attention by savanna ecologists, particularly in Australia (Andersen and Lonsdale 1990). Insects play three broad ecological roles in tropical savannas, as they do in most terrestrial ecosystems. Insects are (1) ecosystem engineers, particularly through their effects on soil properties and processes, (2) regulators of plant populations through herbivory, seed predation, and other direct interactions with plants, and (3) drivers of consumer food webs, as competitors, predators, parasites, and prey. These roles provide a basis for the use of insects in savanna management, as bioindicators of ecosystem health (Disney 1986; Rosenberg et al. 1986; Kremen 1992; Milton and Dean 1992; Andersen 1999c), and as weed biocontrol agents (Julien 1987; 107
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Lonsdale and Abrecht 1989; Moran and Hoffman 1989; Cullen and Delfosse 1990).
7.1.1 Insects as Ecosystem Engineers Insects and other arthropods are integral components of the soil fauna, helping to regulate soil structure and fertility and to control soil processes such as organic matter decomposition, carbon and nutrient mineralization, water infiltration, nutrient cycling, and nutrient uptake by plants (Seastedt 1984; Lal 1987; Anderson et al. 1991; Lee 1991). Ants and termites are the primary soil engineers in tropical savannas, with their foraging and nestbuilding activities having profound effects on soil texture, structure, bulk density, and porosity (de Bruyn and Conacher 1990), and consequently on surface hydrology (Elkins et al. 1986). Termites feed primarily on dead plant material and therefore make a direct contribution to the decomposition of litter and soil organic matter. Although in most ecosystems soil and litter microorganisms play the overwhelmingly dominant role in carbon and nutrient mineralization (Peterson and Luxton 1982; Seastedt 1984), termites can make a significant contribution in tropical savannas (Crawford 1981; Holt and Coventry 1990). Termites differ from other decomposer organisms in that carbon and other nutrients are fundamentally redistributed in the recycling process, creating enriched and depleted patches (de Bruyn and Conacher 1990; Jones 1990). Such nutrient redistribution can cause marked vegetational patterning in Africa (Glover et al. 1964; Harris 1966), but its effects in Australia are more subtle (Spain and McIvor 1988).
7.1.2 Insects as Herbivores Many orders of insects, including Orthoptera (grasshoppers and crickets), Hemiptera (true bugs), Coleoptera (beetles), Lepidoptera (butterfly and moth caterpillars), Hymenoptera (wasps), and Diptera (flies) (Duffey et al. 1974; Andrzejewska and Gyllenberg 1980; Risser et al. 1981; Y. Gillon 1983), are important herbivores in grassy ecosystems. Insects are often the dominant group of herbivores in grasslands and savannas, especially on infertile soils supporting a low biomass of herbivorous mammals (Andersen and Lonsdale 1990). Insects can be highly significant herbivores even in grasslands supporting large populations of herbivorous mammals, such as on the Serengeti plains (Sinclair 1975) and elsewhere in Africa (Gandar 1982). The consumption of biomass and energy by phytophagous insects can affect growth rates, rates of recruitment, and competitive interactions among plants, all of which have important implications for plant population dynamics (Weis and Berenbaum 1989). Phytophagous insects can also induce plants to expend energy on chemical defences (Feeny 1975), disrupt physiological processes (Poskuta et al. 1977), intensify transpiration (Poskuta et al. 1977), and introduce toxic substances (Fewkes 1967).
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Damage by insects can facilitate entry of pathogenic bacteria and fungi, and some insects serve as vectors for plant viruses (Stevenson 1970). The role of insects as folivores is virtually unstudied in Australian savannas (Andersen and Lonsdale 1990), but both seed predation (Andersen and Braithwaite 1996; Setterfield 1997b; Andersen et al. 2000) and dispersal (Andersen and Morrison 1998) by ants appear to be particularly important.
7.1.3 Insects in Food Webs As competitors, predators, parasites, and prey, insects regulate populations of other faunal groups. On the soil surface and on vegetation, insects, particularly ants, are dominant predators, and competitors of other arthropods, such as arachnids, myriapods, and crustaceans. As prey, insects form the food base for a diverse array of insectivorous reptiles, birds, and mammals. Some of these insectivores, such as the bizarre myrmecophagous (antand termite-eating) mammals of Africa, Australia, and Central America (Redford 1987), are highly specialized to feed on their insect prey. Variation in insect diversity and biomass can have a marked impact on the structure and dynamics of assemblages of higher order consumers. For example, trophic partitioning of an unusually high diversity and biomass of termites appears to be responsible for the extraordinary diversity of lizards in spinifex grasslands of the Australian arid zone (Morton and James 1988; James 1991). In the tropical grasslands and savannas of northern Australia there is a positive relationship between termite diversity and the diversity of insectivorous reptiles, birds, and mammals (Braithwaite et al. 1988).
7.1.4 Insects and Fire The effects of fire on insects and other arthropods operate through a variety of mechanisms, expressed at different temporal scales. Fires can have an immediate effect through direct mortality or through forced emigration (Gillon 1970). In the shorter term, fire-induced modifications to habitat can have important effects on foraging sites (Andersen 1988), food supplies (Benzie 1986), microclimate (Samways 1990; Andersen 1991), and rates of predation (Knutson and Campbell 1976). In the longer term, arthropods respond to the effects of fire on fundamental ecosystem processes such as nutrient cycling and primary production (Miller et al. 1955). Given the wide range of possible fire types, and the varied ecological requirements of different arthropod groups, it is hardly surprising that a great variety of responses of arthropods to fire have been reported in savannas and other grasslands (Warren et al. 1987; Andersen and Müller 2000). Somewhat surprisingly, however, in forests and woodlands of southern Australia fire often appears to have little long-term effect on either the composition or the abundance of arthropod assemblages at the ordinal level (Abbott et al. 1984; Majer 1985; Neumann and Tolhurst 1991; Friend and Williams 1996; Greenslade 1997). In other words, even if there are sub-
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stantial changes at the species level, the ordinal composition of arthropod assemblages often appears to be resilient in these ecosystems (Friend 1996). Such resilience in relation to fire can also be shown at the family level for large arthropod groups such as beetles (Collett 2000). Some of the most extensive studies of the effects of fire on savanna insects have been conducted at Lamto, Ivory Coast (D. Gillon 1983). In these studies, fire had a marked direct effect (through mortality and emigration) on insects of the grass layer, with only 32 to 60% of original arthropod biomass present the day after burning (Gillon 1970). The vulnerability of different taxa was related to their flying ability—it was estimated, for example, that 88% of acridid grasshoppers flew away from the fire front, with only 4% perishing. The biomass of grass-layer arthropods began to build back up soon afterward, but with changed community structure due to changes in the structure of the grass layer. Regular burning caused marked reductions in the biomasses of detritivorous and predaceous soil arthropods, although groups such as ants and fungus-growing termites were relatively unaffected (D. Gillon 1983). In the longer term, regular burning reduced total arthropod biomass by about 30%, with cockroaches, lygaeid and pentatomid bugs, and carabid beetles being the most severely affected. Against this general trend, acridid and tettigoniid grasshoppers were consistently favored by frequent burning (D. Gillon 1983).
7.2 Sampling Insect studies at Kapalga addressed the longer term effects of different fire regimes on ordinal-level composition of total assemblages and on species richness and composition of selected groups. Detailed studies of immediate effects of fire and postfire recovery were not attempted, nor were studies of insect-mediated processes such as herbivory. Insects and other arthropods were sampled at woodland and open forest sites in three replicate compartments for each of the Unburnt, Early, and Late treatments (Chapter 2; Andersen and Müller 2000). Ground-foraging arthropods were sampled by means of pitfall traps (Fig. 7.1a) during the middle of each dry season and during each dry–wet transition. Grass-layer arthropods were sampled with sweep nets (Fig. 7.1b) during the middle of each wet season and during each wet–dry transition.
7.3 Arthropod Responses 7.3.1 Ordinal Level Eleven ground-foraging and 10 grass-layer ordinal taxa were recognized from pitfall and sweep catches, respectively (Fig. 7.2). Pitfall catches were completely dominated by ants (80% of all specimens), whereas several taxa
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a
b Figure 7.1. Arthropod sampling was conducted using pitfall traps (a) and sweep nets (b). (a, Barbara McKaige; b, Liz Poon.)
were predominant in the grass-layer fauna. With one exception (grass-layer spiders), ordinal taxa were similarly abundant in woodland and forest habitats. The total abundance of only 4 of the 11 ground-foraging taxa was significantly affected by fire treatment, with ant abundance being markedly reduced in the absence of fire (Fig. 7.3a), and spiders (Fig. 7.3b),
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Figure 7.2. Composition of pitfall and sweep catches, based on totals of approximately 120,000 and 20,000 specimens, respectively, collected throughout the experiment. In pitfall catches, the ordinal taxa Crickets, Silverfish, Homoptera, Wasps, and Heteroptera are included in “others.” (Data are from Andersen and Müller 2000.)
Homoptera, and silverfish all declining in abundance under Late fires. Similarly, only 4 of the 10 grass-layer taxa were significantly affected, with beetles (Fig. 7.3c) and crickets (Fig. 7.3d) declining in the absence of fire, caterpillars declining under Late fires, and Homoptera having an inconsistent response. In none of these cases was there a significant interaction between fire and habitat.
7.3.2 Beetles A total of 200 beetle species from 39 families were recorded in pitfall traps, and 233 species from 26 families in sweep nets (Table 7.1). Remarkably, there was almost no overlap between the ground- and grass-layer faunas, with only four species being recorded in both pitfalls and sweeps. The dominant families in the ground layer were Staphylinidae (rove beetles; 15% total species, 26% total beetles), Carabidae (ground beetles; 15%, 6%), Scarabaeidae (scarab beetles; 13%, 14%), and Curculionidae (weevils; 6%, 17%), and in the grass layer Chrysomelidae (leaf beetles; 39%, 60%) and Curculionidae (18%, 31%). Interestingly, the six richest beetle families overall at Kapalga were the same as those in Mkomazi savannas in Tanzania (Table 7.2). Differences in sampling methodologies notwithstanding, Kapalga appears to be unusually rich in chrysomelids, and poor in scarabs and carabids, compared with Mkomazi. Beetle abundance, richness, and composition on the ground were similar for woodland and forest habitats at Kapalga (Blanche et al. 2001); in the grass layer, however, abundance was lower, and composition more variable, in woodland than in forest (Orgeas and Andersen 2001).
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Figure 7.3. Examples of ordinal taxa whose abundances were significantly affected by fire treatment (Unburnt, solid squares; Early, open squares; Late, open triangles). Time has been divided into three periods: Pre, before the imposition of fire treatments; Post 1, first half of treatment period; and Post 2, second half of treatment period. (Data are from Andersen and Müller 2000.)
Continued
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Figure 7.3. Continued.
The ground- and grass-layer beetle faunas showed different responses to fire. On the ground, beetle abundance and richness were reduced during the dry–wet transition under Late fires, compared with Unburnt and Early treatments. However this response was contingent upon substantial rainfall occurring prior to sampling. It therefore appears that repeated Late fires prevented the increase in beetle abundance and richness that would otherwise follow the first rains of the wet season. The extent to which this effect persists through the wet season is unknown. In contrast, Late and Early
Table 7.1. Overview of the beetle fauna collected in pitfall traps and sweep nets during the Kapalga experiment; there was virtually no species overlap between the two collecting methods. Numbers of species per family Family Acanthocnemidae Anobiidae Anthicidae Anthribidae Apionidae Archeocrypticidae Attelabidae Brentidae Bostrichidae Buprestidae Byrrhidae Carabidae Cerambycidae Chrysomelidae Cleridae Coccinellidae Colydiidae Corylophidae Curculionidae Dermestidae Dytiscidae Elateridae Geotrupidae Histeridae Hybosoridae Hydraenidae Hydrophilidae Laemophloeidae Languridae Lanthridiidae Leiodidae Lycidae Melyridae Mordellidae Nitidulidae Phalacridae Pselaphidae Ptiliidae Rhipiphoridae Scarabaeidae Scirtidae Scraptidae Scydmaenidae Staphylinidae Silvanidae Tenebrionidae Trogidae Total
Pitfalls
Sweeps
1 6 4 — — 1 — 2 2 1 1 29 1 14 2 — 1 3 12 — 1 7 3 1 2 2 2 1 3 1 3 — 4 1 3 — 6 1 — 26 1 1 2 30 2 16 1 200
— 1 — 3 7 — 1 — 1 1 — 2 4 91 1 10 — — 41 1 — 16 — 1 — — — — — — — 3 7 2 — 3 1 — 12 9 4 1 — 3 — 7 — 233 115
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A.N. Andersen et al. Table 7.2. Contribution of major families to total beetle richness at Kapalga (pooled catches from pitfall traps and sweep nets) compared with the savannas of Mkomazi in Tanzania (Davies 1998). Percentage of total beetle species Familiesa Chrysomelidae Curculionidae Scarabaeidae Staphylinidae Carabidae Tenebrionidae
Kapalga
Mkomazi
24.2 12.2 8.1 7.6 7.2 5.3
13.9 9.6 13.9 6.5 13.2 5.9
a
The six richest families were the same at both Kapalga and Mkomazi.
fires had the same effect on grass-layer beetles, leading to increases in abundance and richness, and changes in composition, compared with Unburnt plots (Fig. 7.4).
7.3.3 Grasshoppers A total of 95 species from 66 genera and 6 familes of grasshoppers (using the term in its broadest sense to include the superfamilies Acridoidea, Eumastacoidea, and Tettigonioidea) are known from Kapalga (Table 7.3; Fig. 7.5), out of 161 species from 90 genera from the whole of Kakadu National Park (Andersen et al. 2000). A total of 70 species from 64 genera were recorded in sweep nets during the Kapalga experiment, with the fauna being dominated by acridids (56% of total species, 44% of individuals), and to a lesser extent tettigoniids (28%, 34%) and eumastacids (18%, 22%) (Table 7.4). Many more grasshoppers were collected from woodland than forest habitats, but species richness was similar between habitat types, as were relative abundances of different families. The relative abundances of the most common species, however, varied markedly between habitat types (Table 7.4). Fire treatment had no significant effect on either total grasshopper abundance or the abundance of the three most common species. Total grasshopper richness showed an apparent increase under Early and Late fires compared with the Unburnt treatment toward the end of the experiment (Fig. 7.6), but this was not statistically significant. When tettigoniids were considered separately, however, species richness during the middle of the wet season increased significantly ( p < 0.001) in the two burnt treatments compared with Unburnt compartments during the second half of the experimental period. Multivariate analysis indicated that fire had a significant effect on overall grasshopper composition, with plots from the two burning
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Figure 7.4. Ordination showing effects of fire treatment (Unburnt, solid squares; Early, open squares; Late, open triangles) on composition of grass-layer beetles. Prior to the imposition of fire treatments (a), there were no systematic differences in ordination space between plots representing different treatments. However, plots representing the two burning treatments had diverged from Unburnt plots by the end of the experiment (Post 2, period; b). (Redrawn from Orgeas and Andersen 2001.)
Table 7.3. List of grasshopper species known from Kapalga: Nomenclature follows Andersen et al. (2000). Acrididae Acridinae Acrida conica Aiolopus thalassinus Caledia captiva Calephorops viridis Gastrimargus musicus Heteropternis obscurella Locusta migratoria Pseudaiolopus keyi Pycnostictus seriatus Catantopinae Adlappa erythroptera Aretza sp. 1 (ANIC) Aretza sp. 4 (ANIC) Asoramea erythroptera Caloptilla australis Coryphistes sp. 4 (ANIC) Curpilladia flavocarinata Erythropomala amaena Euomopalon sp. 1 (ANIC) Goniaea angustipennis G. furcifera G. vocans Gen. Nov. 59, sp. 1 (ANIC) Gen. Nov. 61, sp. 2 (ANIC) Gen. Nov. 70, sp. 1 (ANIC) Gen. Nov. 95, sp. 15 (ANIC) Gen. Nov. 105, sp. 1 (ANIC) Gen. Nov. 105, sp. 2 (ANIC) Goniaeoidea bicolor Goniaeoidea sp. 3 (ANIC) Kakaduacris minuta Macrazelota sp. 3 (ANIC) Macrocara conglobata Macrocara sp. 1 (ANIC) Macrolopholia sp. A. (Kak) Macrotona sp. 17 (ANIC) Micreola sp .17 (ANIC) Pardillana ampla Perbellia picta Rectitropis australis Stenocatantops angustifrons S. vitripennis Xanterriaria mediocris Xypectia sp. 1 (ANIC) Cyrtacanthacridinae Austracris basalis A. guttulosa Valanga irregularis V. meleager Oxyinae Bermiella acuta B. curvicercus Daperria accola Gesonula mundata Tolgadia bivittata
T. infirma Tolgadia sp. 1 (ANIC) Eumastacidae Morabinae Geckomima handschini Hastella koongara H. longirostris H. spinipinnis Hastella sp. 1 (ANIC) Spectriforma bifurcata Pyrgomorphidae Pyrgomorphinae Atractomorpha similis Tridactylidae Tridactylinae Tridactylus australicus Tetrigidae Tetriginae Austrohyboella sp. 1. (ANIC) Coptotettix sp. 1 (ANIC) Loxilobus sp. 1 (ANIC) Loxilobus sp. 2. (ANIC) Tettigoniidae Conocephalinae Agraeciini Gen. Nov. 10, sp. 6 (ANIC) Conocephalus upoluensis Conocephalus sp. 2 (Kak) Conocephalus sp. 3 (Kak) Conocephalus sp. 4 (Kak) Conocephalus sp. 12 (Kak) Conocephalus sp. 13 (Kak) Conocephalus sp. A. (Kak) Conocephalus sp. B.(Kak) Nicsara sp. 4 (ANIC) Pseudorhynchus lessonii Pseudorhynchus sp. 1 (Kak) Secsiva sp. 10 (ANIC) Listroscelidinae Hexacentrus sp. (ANIC) Paraphisis sp. 1. (ANIC) Yullandria sp. (ANIC) Meconematinae Phlugidine sp. A (ME1, SP. 1.) (ANIC) Phlugidine sp. B. (Kak) Phlugidine sp. C. (Kak) Phlugidine sp. D. (Kak) Phaneropterinae Caedicia sp. 5 (Kak) Caedicia sp. 7 (Kak) Caedicia sp. 9 (Kak) Caedicia sp. 10 (Kak) Ducetia japonica Gen. Nov. 16, sp. 2 (ANIC) Polichne parvicauda Torbia sp. A. (Kak) Tettigoniinae Antipodectes gigantea
a
c
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Figure 7.5. Some Kapalga grasshoppers. (a) Short-tailed Polichne, Polichne parricauda (Tettigoniidae); (b) Giant green slant-face, Acrida conica (Acrididae); (c) Mating pair of the Matchstick grasshopper, Bundinja sp. (Eumastacidae). (David Rentz.)
Table 7.4. Summary data for grasshoppers collected in sweep nets during the Kapalga fire experiment. Total abundance (%) in parentheses Families
Woodland
Forest
Total
Acrididae Eumastacidae Pyrgomorphidae Tettigoniidae Tetrigidae Tridactylidae Total
676 (42) 389 (24) 1 (<1) 550 (34) 8 (<1) 3 (<1) 1627 (100)
487 (47) 197 (19) 0 349 (34) 0 0 1033 (100)
1163 (44) 586 (22) 1 (<1) 899 (34) 8 (<1) 3 (<1) 2660 (100)
Number of species
Acrididae Eumastacidae Pyrgomorphidae Tettigoniidae Tetrigidae Tridactylidae Total
Woodland
Forest
Total
34 5 1 18 2 1 61
34 4 0 20 0 0 58
40 6 1 20 2 1 70
Abundance of most common species
Conocephalus sp. 2 Gen. Nov. 70 sp. 1 Conocephalus sp. 3 Tolgadia infirma Goniaea augustipennis Nicsara sp. 4 Xanterriaria mediocris Kakaduacris minuta Rectitropis ?australis Macrotona sp. 17 Xypectia sp. 1
Woodland
Forest
Total
239 148 122 57 90 56 20 28 40 57 43
112 29 29 67 11 36 55 46 28 10 10
351 177 151 124 101 92 75 74 68 67 53
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Figure 7.6. Effects of fire treatment (Unburnt, solid ellipses; Early, open squares; Late, open triangles) on mean species richness of grasshoppers. The vertical dotted line indicates when fire treatments were first imposed. F = February; M = May. (Data are from Andersen et al., unpublished.)
treatments diverging from Unburnt plots over time (Andersen et al. unpublished data), as in grass-layer beetles (Fig. 7.4).
7.3.4 Ants A total of 160 ant species from 36 genera are known from Kapalga (Table 7.5; Fig. 7.7), out of more than 300 species from 48 genera from all of Table 7.5. The known Kapalga ant (Hymenoptera: Formicidae) fauna, comprising 160 species from 36 genera: data in parentheses are numbers of species per taxon, considering native species only. Pseudomyrmecinae (1) Tetraponera (1) Cerapachyinae (8) Cerapachys (7) Sphinctomyrmex (1) Aenictinae (1) Aenictus (1) Ponerinae (31) Anochetus (3) Bothroponera (6) Hypoponera (2) Leptogenys (6) Odontomachus (2) Platythyrea (1) Rhytidoponera (10) Trachymesopus (1) Dolichoderinae (16) Iridomyrmex (11) Ochetellus (1) Papyrius (1) Tapinoma (2) Technomyrmex (1)
Formicinae (43) Acropyga (2) Calomyrmex (1) Camponotus (10) Melophorus (7) Oecophylla (1) Opisthopsis (4) Paratrechina (5) Polyrhachis (13) Myrmicinae (60) Anillomyrma (1) Cardiocondyla (2) Crematogaster (4) Meranoplus (11) Monomorium (19) Oligomyrmex (1) Pheidole (13) Podomyrma (1) Strumigenys (2) Solenopsis (3) Tetramorium (3)
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a
b Figure 7.7. Two of the dominant ant species at Kapalga. The green tree ant, Oecophylla smaragdina (a), is an arboreal ant with Southeast Asian affinities that prefers shaded habitats and is therefore favored by infrequent fire. In contrast, the northern meat ant, Iridomyrmex sanguineus (b), prefers open habitats and is therefore favored by frequent fire. (a, Tony Hertog; b, Greg Miles.)
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Figure 7.8. Relative abundances of ant functional groups in plots burnt annually (a) and unburnt (b) after 14 years at Munmarlary. Functional groups are DD, Dominant Dolichoderinae; HCS, Hot Climate Specialists; CS, Cryptic Species; OPP, Opportunists; and GM, Generalized Myrmicinae. Data are means from two replicate plots. (Andersen 1991.)
Kakadu National Park (Press et al. 1995). A large proportion of Kapalga’s species co-occur at small spatial scales, with up to 100 or more recorded within less than 0.1 ha (Andersen 1992). As previously mentioned, ants totally dominated the ground-foraging arthropod fauna (Fig. 7.1a), and their abundance was considerably promoted by fire (Fig. 7.2a). The sorting and analysis of ant species has not been completed at the time of writing. However, species-level results appear to be consistent with those from the Munmarlary experiment elsewhere in Kakadu National Park (Chapter 1; Andersen 1991). At Munmarlary, fire exclusion resulted in marked reductions in ant abundance and richness and caused major shifts in community composition (Fig. 7.8). In the absence of fire, arid-adapted groups such as behaviorally dominant species of Iridomyrmex (Dominant Dolichoderinae) and thermophilic species of Melophorus (Hot Climate Specialists) gave way to more broadly adapted, shade-tolerant species of Monomorium (Generalized Myrmicinae), as well as litter-dwelling taxa such as Solenopsis (Cryptic Species).
7.4 Conclusions 7.4.1 Response Syndromes Not unexpectedly, arthropods showed a variety of responses to fire. However, distinct ground- and grass-layer response syndromes are apparent. On one hand, a range of ground-foraging taxa was affected by the Late
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treatment only, indicating a sensitivity to fires of high intensity. For example, the overall abundances of spiders, silverfish, and Homoptera were reduced under Late fires, and Late fires prevented the rapid increase in ground-foraging beetle abundance otherwise associated with early wet season rains. Responses to Early and Unburnt treatments were the same in all these cases. Ant abundance and richness were higher in the Early than in the Unburnt treatment, and even higher under Late fires. For grass-layer taxa, on the other hand, the primary contrast was between fire and no fire, with the two fire treatments causing similar responses. Early and Late fires similarly affected the overall abundance, richness, and composition of both grasshoppers and grass-layer beetles. These apparent response syndromes can be explained by differences in a combination of direct mortality during fires and microhabitat changes following fire. Grass-layer taxa tend to be stronger fliers than their groundforaging counterparts, and therefore probably suffer less direct mortality (Gillon 1970). This is likely to make them less sensitive to the immediate impacts of high intensity fire. High intensity fire is also likely to have persistent impacts on microhabitat structure on the ground, particularly through the elimination of fallen wood and decomposed litter. In contrast, high intensity fire had little discernible effect on either the structure or the composition of grass-layer vegetation (Chapter 6; Andersen and Müller 2000).
7.4.2 Taxonomic Sufficiency Insects are relatively easy to sample, but their vast diversity and the difficulty of establishing their taxonomic identity are serious impediments to their widespread use in conservation planning and management. There has been considerable interest in simplifying insect surveys by restricting analysis to the highest taxonomic level that is sufficient to reveal the response of interest (Ellis 1985; Gaston and Williams 1993). An important aspect of our insect work at Kapalga has been an examination of the extent to which analyses at the family level are sufficient to reveal responses evident at species level. For ground-foraging beetles, site dissimilarity matrices based on abundances at family and species levels were highly correlated, and family-level analysis revealed the effects of Late fires on beetle richness following rainfall early during the wet season (Blanche et al. 2001). Similarly, family richness in grass-layer beetles was highly correlated with species richness, and mulivariate analysis at the family level revealed the compositional changes in relation to fire that were evident in analyses at the species level (Orgeas and Andersen 2001). In summary, analysis at family level was sufficient to reveal the responses of assemblage structure and richness to fire, at least for beetles.
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7.4.3 Fire and Arthropod Dynamics From a broader perspective, what is the role of fire in the dynamics of arthropod assemblages at Kapalga? Fire is certainly a significant factor, but it must be concluded that insect assemblages are generally resilient in relation to fire. The treatments at Kapalga represented the most extreme fire regimes possible in the region (i.e., from total fire exclusion to annual fires of the highest intensity), but the overall abundances of the majority of ordinal-level taxa remained unaffected. Most of the common beetle and grasshopper species were also not affected. This is consistent with the general resilience of arthropod assemblages in woodlands and forests of southern Australia (Friend 1996). Even when fire had a significant effect on assemblage structure, as was the case for beetles and grasshoppers, this effect was relatively small compared with the effects of year-to-year variation in the onset, duration, and intensity of rainfall during the wet season (Orgeas and Andersen 2001). Fire can therefore be considered to be a “secondary determinant” of arthropod assemblages at Kapalga, as it is for savanna ecosystems in general (Walker 1987). Ants are possibly an exception to this generalization, with overall abundance, richness, and composition varying fundamentally in relation to fire. Although ants are largely protected from direct mortality during fire by virtue of their colonial structure and subterranean nests, they are highly sensitive to fire-induced changes to habitat structure and microclimate (Andersen 1991). Such sensitivity makes them potentially useful “focal taxa” (Lambeck 1997) for assessing ecological responses to fire management (see Vanderwoude et al. 1997). The overall resilience of insect assemblages at Kapalga reflects a long history of association of savanna insects in northern Australia with frequent fire. In forests and woodlands of southern Australia, where fire is important but historically less frequent, arthropod assemblages are generally resilient to individual fires (Friend 1996) but not to frequent fire (York 1999a,b). The limited effects of fire on arthropods at Kapalga are also consistent with the situation in the Mkomazi savannas of Tanzania, where fire has relatively little effect on arthropod diversity (T. Russell-Smith et al. 1998). Both Kapalga and Mkomazi stand in contrast to Lamto in West Africa, where arthropod assemblages are far more sensitive to fire (Y. Gillon 1983). This possibly reflects the relatively recent derivation of Lamto savannas from forest due to extensive clearing and burning by people, such that Lamto arthropods have not experienced the long evolutionary history of frequent fire that is typical of other savanna regions.
8. Terrestrial Vertebrates Laurie K. Corbett, Alan N. Andersen, and Warren J. Müller
8.1 Introduction Despite 150 years of European colonization and the extensive use of Australian savannas for pastoralism, vertebrate assemblages remain largely intact. Indeed, Australian savannas act as refugia for significant elements of the Australian fauna that have suffered marked declines elsewhere in the country (Woinarski and Braithwaite 1990). However, there are clear signs of major range contractions and population declines for some species in recent times (Braithwaite and Müller 1997; Franklin 1999; Woinarski et al. 2001), and there is widespread concern that inappropriate fire management is an important contributing factor. Much public concern over fire is directed at its immediate effects on fauna. Several studies have recorded large numbers of animals being killed by wildfire, either directly by the flames or by smoke inhalation (Recher et al. 1975; Christensen 1977; Fox 1978; Wright and Bailey 1982; Bowland and Perrin 1988). For example, the corpses of 42 vertebrate species were recorded after a wildfire in southern New South Wales (Christensen and Recher 1981). However, the vast majority of animals survive wildfire, either by sheltering in refugia (such as burrows, hollow logs, or termite mounds; Fig. 8.1) or the tops of trees (Lawrence 1966; Main 1981; Griffiths and Christian 1996b), or by moving to safe ground (Christensen 1977). 126
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Figure 8.1. Termite mounds can be important refuges for vertebrates during savanna fires. (Dick Braithwaite.)
As in the case of invertebrates (Chapter 7), the most important effects of fire on vertebrate populations are indirect, mediated by changes to shelter, food supplies, and predation (Newsome and Catling 1983; Bowland and Perrin 1988; Lunney and O’Connell 1988). For example, bird abundance can decline dramatically after fire as a result of a reduction in their
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prey (Wooller and Calver 1988). Fire often stimulates the growth of protein-rich grasses, and this characteristically attracts large mammalian herbivores (Moe et al. 1990), including macropods in Australia (Southwell and Jarman 1987). Increased predation is often a particularly important factor following fire (Newsome et al. 1983). For example, Christensen (1977, 1980) concluded that only one of 74 bettong (Macropodidae) deaths in recently burnt areas was attributable to starvation, with most being due to increased predation. In temperate regions of Australia, vertebrate assemblages show clear successional patterns of species composition and abundance following fire (Fox 1982; Catling et al. 2001). In forest and heathland at Myall Lakes in New South Wales, for example, the structure of small mammal communities shifted from one initially dominated by Pseudomys spp. through a period of dominance by dasyurid marsupials, then into the longest period dominated by species of Rattus (Fox 1990). These shifts were associated with successional changes in habitat structure. More generally, postfire successional changes in habitat complexity drive successional changes in vertebrate assemblages (Catling et al. 2001). Such successional patterns are disrupted by the frequent prescribed burns that are commonly used to minimize fuel accumulation in temperate forests, leading to long-term reductions in the richness and abundance of bird and mammal assemblages (Catling 1991, 1994; Woinarski and Recher 1997). The effects of different fire regimes on Australian savanna vertebrates have been poorly documented, especially for mammals (Chapter 1). However, the postfire successional paradigm of infrequently burnt temperate regions is clearly inappropriate for frequently burnt tropical savannas. Rather, fire acts more as a shorter term modifier of habitat selection than a trigger for longer term successional change (Andersen and Braithwaite 1996). This was illustrated earlier at Kapalga by a study of fire–habitat relationships of lizards (Braithwaite 1987), where different species were characteristic of unburnt habitat, open habitats produced by high intensity fire, and patchy habitats resulting from very low intensity fires, and still others appeared to be relatively unaffected by fire. A preliminary assessment of lizards early during the Kapalga experiment (Trainor and Woinarski 1994) suggested that the gecko Heteronotia binoei and the agamid Diporiphora bilineata were most abundant in Early compartments, and the skink Carlia amax was most abundant in Unburnt.
8.2 Terrestrial Vertebrates at Kapalga A total of 349 native terrestrial vertebrate species are known from Kapalga, comprising 20 nonvolant mammals, 12 bats, 224 birds, 45 lizards, 23 snakes, three turtles, two crocodiles and 20 frogs (Table 8.1). Six species of feral animals also occur, comprising cats (Felis catus), horses (Equus caballus),
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Table 8.1. Summary of Kapalga’s terrestrial vertebrate fauna: data are numbers of native species. Amphibians Myobatrachidae Hylidae Reptiles Crocodylidae Chelidae Gekkonidae Pygopodidae Agamidae Varanidae Scincidae Typhlopidae Boidae Acrochordidae Colubridae Elapidae Birds Casuariidae Megapodiidae Phasianidae Anseranatidae Anatidae Podicipedidae Anhingidae Phalacrocoracidae Pelecanidae Ardeidae Threskiornithidae Ciconiidae Accipitridae Falcodidae Gruidae Rallidae Otididae Turnicidae Scolopacidae Jacanidae Burhinidae Recurvirostridae Charadriidae Glareolidae Laridae Columbidae Cacatuidae Psittacidae Cuculidae Centropodidae Strigidae Tytonidae Podargidae Caprimulgidae
20 7 13 73 2 3 8 3 6 7 21 2 5 1 6 9 224 1 1 2 1 9 2 1 4 1 11 5 1 18 6 1 4 1 2 13 1 1 2 7 2 4 8 5 6 8 1 3 2 1 2 Continued
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L.K. Corbett et al. Table 8.1. Continued. Aegothelidae Apodidae Alcedinidae Meropidae Coraciidae Pittidae Climacteridae Maluridae Pardalotidae Meliphagidae Petroicidae Pomatostomidae Neosittidae Pachycephalidae Dicruridae Campephagidae Oriolidae Artamidae Corvidae Ptilonorhynchidae Alaudidae Motacillidae Passeridae Dicaeidae Hirundinidae Sylviidae Zosteropidae Mammals Tachyglossidae Dasyuridae Peramelidae Petauridae Phalangeridae Macropodidae Pteropodidae Emballonuridae Molossidae Vespertilionidae Muridae Canidae
1 2 7 1 1 1 1 1 6 15 3 1 1 4 9 5 3 6 1 1 1 2 6 1 1 5 1 32 1 5 1 1 1 2 3 3 1 5 8 1
donkeys (Equus asinus), pigs (Sus scrofa), cattle (Bos taurus), and swamp buffalo (Bubalus bubalis). A variety of approaches was taken to assess vertebrate responses to different fire regimes. First, a broad overview was provided by general surveys that were conducted in two replicate compartments of each of the four fire treatments. Sampling occurred along both primary and secondary transects (Chapter 2) during the dry–wet (November/December) and wet–dry (April/May) transitional periods, from 1988 to 1995. All mammals, reptiles,
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and amphibians detected during sampling were recorded, as were predatory and common insectivorous birds. We did not include honeyeaters, parrots, finches, and some other common birds because of their unpredictable within-compartment movements that are tied to fluctuating food supplies according to local flowering, fruiting, and seeding. To make generalizations about responses to fire, species were classified into functional groups according to higher level taxonomy, use of space (arboreal, terrestrial, litter), major activity period (diurnal, nocturnal), and general feeding ecology (generalist, specialist, opportunist). Second, a detailed study was conducted on small mammals, an assemblage that has experienced considerable population declines in recent years (Woinarski et al. 2001). Small mammals were trapped (Fig. 8.2) inside permanent plots located at the top and bottom of the primary transects of two replicate compartments of each of the four fire treatments (Chapter 2). This was done at 2-monthly intervals from July 1989 to May 1995, giving 6 assessments prior to the imposition of fire treatments and 30 after. Finally, a detailed study was conducted on populations of one of the faunal icons of northern Australia, the frilled lizard Chlamydosaurus kingii (Fig. 8.3). This is a large (up to 80 cm total length) bipedal agamid; its enormous frill lies in folds around the neck and shoulders when at rest but is spectacularly raised when the lizard is alarmed (Fig. 8.3). The frilled lizard has highly seasonal activity patterns, with greatly reduced energy expenditure during the dry season (Shine and Lambeck 1989; Griffiths and Christian 1996a). At Kapalga, lizard populations were monitored in
Figure 8.2. Trapping a northern brown bandicoot, Isoodon macrourus. (Barbara McKaige.)
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Figure 8.3. The frilled lizard, Chlamydosaurus kingii. (David Curl.)
Unburnt, Early, and Late compartments using mark–recapture techniques and radio-telemetry (Griffiths and Christian 1996b).
8.3 Functional Groups The general surveys amassed nearly 20,000 records of terrestrial vertebrates, comprising 139 species classified into 13 functional groups (Table 8.2). Fire treatment had no significant effect on species richness within any functional group; it had a significant effect on total abundance in only 3 of the 13 groups (Table 8.2). The abundance of arboreal frogs was reduced in Unburnt compartments compared with all burnt compartments, with this response being driven by the northern dwarf tree frog Litoria bicolor (common names follow Stanger et al. 1998, throughout). The abundance of
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Table 8.2. Effects of different fire regimes on the abundance of vertebrate functional groups and common species, as measured during general fauna surveys. Effectsa Functional group and species Forest passerines (9 spp.) Black-tailed treecreeper, Climacteris melaneura Gray shrike-thrush, Colluricincla harmonica Gray-crowned babbler, Pomatostomus temporalis Lemon-bellied flycatcher, Microeca flavigaster Northern fantail, Rhipidura rufiventris Red-backed fairy-wren, Malurus nelanocephalus Rufous whistler, Pachycephala rufiventris White-bellied cuckoo-shrike, Coracina papuensis Willie wagtail, Rhipidura leucophyrs Diurnal raptors (15 spp.) Brown falcon, Falco berigora Brown goshawk, Accipiter fasciatus Whistling kite, Haliastur sphenurus Other diurnal predatory birds (7 spp.) Pied butcherbird, Cracticus nigrogularis Torresian crow, Corvus orru Blue-winged kookaburra, Dacelo leachii Forest kingfisher, Todiramphus macleayii Red-backed kingfisher, Todiramphus pyrrhopygia* Sacred kingfisher, Todiramphus sanctus* Nocturnal predatory birds (9 spp.) Australian owlet nightjar, Aegotheles cristatus Tawny frogmouth, Podargus strigoides Barking owl, Ninox connivens Barn owl, Tyto alba Southern boobook owl, Ninox novaeseelandiae Ground birds (4 spp.) Brown quail, Coturnix ypsilophora* Red-backed button-quail, Turnix maculosa Arboreal frogs* (4 spp.) Northern dwarf tree frog, Litoria bicolor* Green tree frog, Litorai caerulea* Roth’s tree frog, Litoria rothii* Terrestrial frogs (15 spp.) Remote froglet, Crinia remota* Long-footed frog, Cyclorana longipes Striped rocket frog, Litoria nasuta* Pale frog, Litoria pallida Tornier’s frog, Litoria tornieri* Marbled frog, Limnodynastes convexiusculus* Ornate burrowing frog, Limnodynastes ornatus* Toadlets, Uperoleia inundata and/or lithomoda* Arboreal lizards (12 spp.) Northern dtella, Gehyra australis* Two-lined dragon, Diporiphora bilineata Yellow-sided two-lined dragon, Diporiphora magna* Swamplands lashtail, Lophognathus temporalis*
N
p
5172 634 436 164 358 314 295 986 611 362 426 65 60 50 2419 177 127 517 558 64 196 725 308 77 70 133 83 425 220 42 735 288 214 77 2547 281 52 151 46 491 392 199 621 1253 180 304 54 73
0.391 0.620 0.442 0.487 0.014 0.055 0.521 0.534 0.094 0.109 0.213 0.779 0.032 0.354 0.357 0.519 0.572 0.537 0.145 0.047 0.794 0.363 0.006 0.932 0.055 0.243 0.232 0.093 0.661 0.020 0.016 0.007 0.609 0.422 0.143 0.081 0.183 0.874 0.770 0.571 0.347 0.828 0.252 0.379 0.592 0.233 0.363 0.041 Continued
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Table 8.2. Continued. Effectsa Functional group and species
N
p
Spotted tree monitor, Varanus timoriensis Callose-palmed shinning-skink, Cryptoblepharus plagiocephalus* Terrestrial lizards* (22 spp.) Bynoe’s gecko, Heteronotia binoei* Bauxite rainbow-skink, Carlia amax* Shaded-litter rainbow-skink, Carlia munda* Desert rainbow-skink, Carlia triacantha Lowlands plain-backed Ctenotus, Ctenotus essingtonii Robust ctenotus, Ctenotus robustus Top End firetail skink, Morethia storri* Litter lizards and snakes* (8 spp.) Northern mulch-skink, Glaphyromorphus darwiniensis* Glaphyromorphus isolepis* Other snakes (16 spp.) Arboreal mammals (6 spp.) Northern quoll, Dasyurus hallucatus Sugar glider, Petaurus breviceps Northern brushtail possum, Trichosurus vulpecula arnhemensis Terrestrial mammals (12 spp.) Delicate mouse, Pseudomys delicatulus Dingo, Canis lupus dingo* Northern brown bandicoot, Isoodon macrourus Agile wallaby, Macropus agilis Macropod signs (primarily M. agilis)
92 325
0.019 0.428
4669 355 2404 788 49 243 46 90 265 76 170 45 579 306 106 131
<0.001 0.072 <0.001 0.991 0.692 0.100 <0.001 0.395 0.029 0.198 0.582 0.152 0.153 0.212 0.024 0.279
645 102 67 279 41 106
0.573 0.026 0.152 0.750 0.633 0.669
a
N = total number of individuals recorded; p = p-value from analysis of variance for interaction between fire treatment and time period (before vs after treatment), with significant (p < 0.05) effects shown in bold, and values approaching significance (0.05 < p < 0.1) in italics. * Log-transformation required. Source: Unpublished data of Corbett and Müller.
terrestrial lizards was reduced by Late fires, with this response being driven by Carlia amax. Finally, the abundance of litter lizards and snakes was reduced by the Late and Progressive treatments. Functional groups based on higher level taxonomy and trophic position were therefore relatively insensitive to fire treatment. When there was a significant effect, it tended to be driven by a single common species, rather than being broadly representative. Moreover, analysis at the functional group level masked changes within constituent species—in several cases the abundances of individual species showed a significant response to fire treatment when the overall abundance of their functional group did not (Table 8.2). The one functional group that did appear to show a general response was litter reptiles, which are known to be particularly sensitive to high intensity fire (Fyfe 1980; Mushinsky 1992; Woinarski et al. 1999a).
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8.4 Frogs There have been few studies of the effects of wildfire on frogs, presumably because of their association with wetter, hence less fire-prone habitats (Friend 1993; Wilson 1996). At Kapalga, frog records were closely associated with local rainfall events and were therefore highly patchy in time, with records being extremely variable from one week to the next at the same site. Such sampling variability potentially confounds results of statistical analysis. With this caveat in mind, we report that of the 11 frog species that were recorded commonly enough for statistical analysis, only one was significantly affected by fire treatment—the tree frog Litoria bicolor (Fig. 8.4a) declined in abundance in Unburnt compartments (Fig. 8.5i). This decline in the absence of fire is surprising on two counts. First, one might have expected frogs to be strongly associated with unburnt habitat, as has been shown for Limnodynastes dorsalis, Myobatrachus gouldii, and Geocrinea lutea in eucalypt forests of southern Australia (Bamford 1992; Driscoll and Roberts 1997). Second, one might have expected tree frogs to be particularly sensitive to fire (Friend 1993), rather than to be favored by burning. The response of the remote froglet Crinia remota (Fig. 8.4b) was marginally significant at Kapalga—its abundance declined across all treatments, but least so under Progressive (Fig. 8.5j).
8.5 Reptiles 8.5.1 General Fifteen lizard species were recorded commonly enough during the general surveys for statistical analysis, and only four of them showed a significant response to fire treatment (Table 8.2). The arboreal monitor Varanus timoriensis (Figs. 8.5l and 8.6a) and the terrestrial skink Carlia amax (Fig. 8.5n) both showed population declines in Late compartments, the arboreal dragon Lophognathus temporalis (Figs. 8.5k and 8.6b) increased in abundance in Unburnt relative to burnt compartments, and the terrestrial skink Ctenotus robustus (Fig. 8.5o) showed population declines under all treatments, but particularly when burnt. Varanus timoriensis and Carlia amax are therefore sensitive to frequent high intensity fire, whereas Lophognathus temporalis and Ctenotus robustus are sensitive to fire of any intensity. These results are broadly consistent with the earlier assessment of lizard responses at Kapalga, 2 years after the commencement of experimental burning (Trainor and Woinarski 1994).
8.5.2 Frilled Lizards Frilled lizards sheltered from fires by climbing to the tops of trees or by taking refuge in termite mounds. None of the 17 individuals monitored
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a
b Figure 8.4. The abundances of only 2 of the 11 frog species studied at Kapalga appeared to be affected by fire: Litoria bicolor (a) and Crinia remota (b). (John Wombey.)
during Early fires were killed by fire, but mortality was about 30% (n = 24) during Late fires. There was a common tendency for lizards to move into recently burnt areas from adjacent unburnt areas, apparently because of increased prey accessibility. Overall lizard densities were considerably higher in Late (0.78 -1) and Early (0.65 ha-1) compartments than in Unburnt
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(0.13 ha-1), and body masses were significantly higher in burnt than in unburnt habitat (Griffiths and Christian 1996b). These results indicate that environmental conditions are better for frilled lizards in burnt than in unburnt habitat, especially under Late fires, where lizard densities were high even though the populations suffered considerable direct mortality during fire. However, the extent to which superior body condition in burnt habitats leads to increased reproductive output, or whether the high populations in burnt habitats depend entirely on migration from nearby unburnt habitats, remains unclear. Lizard densities in burnt compartments were measured near compartment edges, so the figures might not have been representative owing to inflation by migration. The proportion of adult females that were gravid was highest in Early
a
b Figure 8.5. Effects of fire treatments (E, Early; L, Late; P, Progressive; U, Unburnt) on abundances of species from the general survey showing a significant ( p < 0.05) or near-significant (0.05 < p < 0.1) response. (L. Corbett and W. Müller, unpublished data.) Mean values before fire treatments commenced (open bars) are compared with those after (solid bars); p values are provided for the interaction between fire treatment and sampling period. Continued
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c
d
e Figure 8.5. Continued.
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f
g
h Figure 8.5. Continued.
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i
j
k Figure 8.5. Continued.
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l
m
n Figure 8.5. Continued.
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o
p
q Figure 8.5. Continued.
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a
b Figure 8.6. The arboreal monitor Varanus timoriensis (a) declined in abundance under Late fires but was not affected by Early fires, whereas the arboreal agamid Lophognathus temporalis (b) was negatively influenced by any fire. (John Wombey.)
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compartments (52%), followed by Late (44%) and Unburnt (33%). However, no data are available on variation in clutch size, hatching success, and juvenile survival across different fire treatments. If high populations in burnt compartments were being maintained more by migration than by recruitment, then the significant mortality experienced during Late fires is unsustainable (Brook and Griffiths 2003).
8.6 Birds Whistling kites (Haliastur sphenurus) and black kites (Milvus migrans) were frequently observed feeding on insects and probably also devoured other small prey at the fire front (Fig. 8.7), and brown falcons (Falco berigora) often were seen feeding on the ground in the immediate wake of fire (see Braithwaite and Estbergs 1987). Similarly, straw-necked ibis (Threskiornis spinicollis) and Australian white ibis (T. molucca) were sometimes seen feeding in recently burnt areas. Fire treatment had a significant or near-significant ( p < 0.1) effect on the abundance of only 8 of the 25 common bird species sampled (Table 8.2). Three of these species (lemon-bellied flycatcher, northern fantail, whitebellied cuckoo-shrike) were forest passerines that increased in abundance in Unburnt compartments but declined in Late (Fig. 8.5a–c). This is consistent with results from experimental fire plots at nearby Munmarlary, where
Figure 8.7. Large numbers of kites are often attracted to fire fronts. (Dick Braithwaite.)
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highest abundances of forest passerines occurred in unburnt plots (Woinarski 1990). Such high abundance is presumably related to increased complexity of midstory vegetation and is a common response of forest passerines to low fire frequency in eucalypt woodlands and open forests throughout Australia (Woinarski and Recher 1997). A complex midstory provides nesting sites, increases food supply, and reduces rates of predation. The barking owl, which also increased in abundance in Unburnt compartments (Fig. 8.5g), is also known to favor denser and more structurally complex habitats, such as riparian vegetation. Two diurnal predators (brown goshawk and red-backed kingfisher) showed increased abundances in Late and Progressive compartments (Fig. 8.5d,e). This is consistent with a range of studies throughout the world showing increased predation by birds in burnt areas, including unburnt patches within burnt areas (Andren and Angelstam 1988). Raptors have been shown to be attracted to recently burnt areas elsewhere in the Northern Territory (Woinarski et al. 1999a). Similarly, kestrels and buzzards move to recently burnt areas to exploit the increased availability of small mammals and large insects in South African grasslands (Barnard 1987).
8.7 Mammals Relatively few effects of fire treatment on mammal populations were detected during general vertebrate surveys (Table 8.2). The exceptions were the arboreal sugar glider (Fig. 8.8), which increased slightly in abundance in Unburnt compartments but declined under fire (especially Late; Fig. 8.5p), and the delicate mouse, which increased markedly in abundance in Late and Progressive compartments, while remaining unchanged in Early and Unburnt (Fig. 8.5q). The latter is an r-selected generalist species that is known to be favored by disturbance (Braithwaite and Brady 1993). Its overall abundance in northern Australia has increased in recent years, in contrast to the population declines shown by many other small-mammal species (Woinarki et al. 2001). Agile wallabies were frequently observed fleeing in the wake of fires. Dingoes were occasionally observed feeding on wallabies that presumably had been killed or disabled by fire. However, fire treatment had no significant effect on wallaby abundance. The general survey used sampling techniques that provided insufficient records for most small- to medium-sized mammalian species. The detailed mammal study, on the other hand, revealed such species to be highly sensitive to fire treatment. Total small-mammal abundance, species richness, and the abundance of six of the seven most common species were all significantly affected (Fig. 8.9). A common theme (shown by the northern quoll Dasyurus hallucatus, fawn Antechinus Antechinus bellis, northern brown bandicoot Isoodon macrourus, and grassland melomys Melomys burtoni) was for abundances to remain relatively constant in Early com-
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Figure 8.8. The arboreal sugar glider, Petaurus breviceps, prefers unburnt habitat. (Ian Morris.)
partments, increase substantially in Unburnt, and decline in Late and usually also Progressive, and this is reflected in total small-mammal abundance (Fig. 8.9h). The abundance of the northern brushtail possum, Trichosurus vulpecula (Fig. 8.9d) increased substantially in Late as well as Unburnt compartments. We speculate that this might be related to increased nutritional quality of the leaf flush in trees that may occur following high intensity fire. The pale field rat, R. tunneyi (Fig. 8.9g), was unusual in that its abundance declined dramatically in all compartments, although especially in Late and Progressive. Braithwaite and Griffiths (1996) have reported that the abundance of this species declined dramatically at Kapalga from the mid-1980s to the start of the fire experiment. This decline continued during the experiment but is clearly not driven by fire. Populations have remained extremely low up to the last census in 1999 (Woinarski et al. 2001). The dusky rat, Rattus colletti, occurs primarily in floodplain rather than savanna habitats and was not significantly affected by fire treatment (Fig. 8.9f).
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The abundances of most small-mammal species varied markedly along the primary transects (R.W. Braithwaite and W.J. Müller, unpublished data). Grassland melomys and the dusky rat were both virtually absent from the (well-drained) tops of transects, whereas possums and quolls occurred primarily there. In two cases, fire treatment interacted significantly with transect position. The increases in abundance of bandicoots in Unburnt compartments (Fig. 8.9c) occurred only at the bottoms of transects, whereas the increases in abundance of possums in Unburnt occurred primarily at the tops of transects (increases were uniform in Late).
a
b Figure 8.9. Effects of fire treatments (E, Early; L, Late; P, Progressive; U, Unburnt) on abundances of small mammal species. (R.W. Braithwaite, W. Müller, and A.D. Griffiths, unpublished data.) Mean values before experimental fire regimes commenced (solid bars) are compared with those after (solid bars); p values are provided for the interaction between fire treatment and sampling period. Continued
c
d
e Figure 8.9. Continued. 148
f
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h Figure 8.9. Continued. 149
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Fire had complex effects on demographic variables such as transience, survival, and reproduction (A.D. Griffiths and R.W. Braithwaite, unpublished data). In bandicoots, for example, transience was higher and survival lower in Progressive and Late compartments compared with Early and Unburnt, and rates of reproduction were highest in Unburnt. Annual survival was highest in Unburnt compartments for three of the four species with sufficient data (northern brown bandicoot, northern quoll, and grassland melomys), and reproductive rates were also highest in Unburnt compartments for grassland melomys.
8.8 Conclusion The general vertebrate survey showed three dominant responses among the species that were sensitive to fire treatment at Kapalga (Table 8.3): population increases in Unburnt compared with burnt treatments (6 species), declines under Late/Progressive fires (5 species), and increases under Late fires (3 species). However, the great majority of terrestrial vertebrate species remained unaffected by experimental fires (Table 8.4). This was despite the treatments encompassing the extremes of potential fire regimes in the region, ranging from annual high intensity fire for 5 years to no fire at all. Only 5 of 25 bird species, 5 of 16 lizards (including the frilled lizard), and 1 of 11 frogs were significantly affected by fire treatment. This demonstrates a remarkable degree of resilience in relation to fire and is consistent with results from a recent survey of Kakadu showing a generally poor relationship between vertebrate assemblages and recent fire history (Woinarski Table 8.3. Summary responses of vertebrate species to different fire regimes, based on general surveys. Response Increase in Unburnt relative to any burning Reduction under Late/Progressive Increase under Late/Progressive Reduction in Unburnt relative to any burning a
Speciesa (White-bellied cuckoo-shrike), (barking owl), Lophognathus temporalis, (Heteronotia binoei), Ctenotus robustus, sugar glider Lemon-bellied flycatcher, (northern fantail), Australian owlet nightjar, Varanus timoriensis, Carlia amax Brown goshawk, red-backed kingfisher, delicate mouse Litoria bicolor
Species listed are those whose abundances were significantly affected (those approaching significance in parentheses) by fire treatment (Table 8.2). The list does not include the remote froglet, whose abundance declined across all treatments, but least so under Progressive (Fig. 8.5i), or the red-backed button-quail (Fig. 8.5h), which increased under all treatments (especially Late) except for Progressive.
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Table 8.4. Summary responses of nonmammalian vertebrate species to experimental fire regimes at Kapalga: data are numbers of species showing a significant response (frilled lizards included). Response Unaffected Reduction under Late/Progressive Increase under Late/Progressive Increase in Unburnt relative to any burning Reduction in Unburnt relative to any burning
Frogs (n = 11)
Lizards (n = 16)
Birds (n = 25)a
10
11 2
20 2 2
1
2 1
a
The bird species not indicated is the red-backed button-quail (Fig. 8.5h), which increased under all treatments (especially Late) except for Progressive.
Total Captures
et al. 2002). The findings support previous assessments that Top End vertebrate assemblages are structured primarily by factors other than fire, such as variation in topography, soils, and moisture availability (Trainor and Woinarski 1994; Andersen and Braithwaite 1996). Small mammals, however, represent major exceptions to this pattern, with eight out of nine common species (sugar glider and delicate mouse from the general surveys, plus six of the seven most common species from the dedicated study) showing a significant response to fire treatment. There were some clear effects of fire intensity, with lowest small-mammal abundances generally occurring in Late and Progressive compartments. However, the major contrast was between burnt compartments in general, which tended to have relatively low numbers of small mammals, and Unburnt compartments, with high abundances (Fig. 8.10). These results are
Years Figure 8.10. Total numbers of small mammals captured under different burning treatments throughout the Kapalga fire experiment. Fire treatments were imposed after the 1989–1990 wet season.
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generally consistent with an earlier study at Kapalga, where the smallmammal assemblage of an annually burnt site was compared with that at a site that had remained unburnt for at least 5 years (Friend and Taylor 1985). The latter site had greater habitat complexity (especially in the mid story) owing to its lower fire frequency, and higher abundances of bandicoots, fawn Antechinus, and black-footed tree rats (Mesembriomys gouldii). The authors concluded that forest types with dense midlevel foliage and abundant hollow logs and leaf litter had the greatest mammal richness and abundance. Despite widespread concern over the ecological impact of high intensity wildfire late in the dry season (Chapter 1), only a small proportion of vertebrate species were negatively affected by such fires at Kapalga, even when applied annually over 5 years. A similar result was found for bird assemblages in semiarid savanna elsewhere in the Northern Territory, where most species had no association with particular fire regimes, and there were no significant differences in long-term impacts between fires early and late in the dry season (Woinarski et al. 1999a). On the other hand, a wide range of species showed population declines under all burning treatments. Key elements of Kapalga’s vertebrate assemblages are therefore favored by relatively low fire frequency. This has also been recognized for Australian savanna birds; for example, it has been suggested that ground-feeding birds such as the masked finch (Poephila personata), the partridge pigeon (Geophaps smithii), and the red-backed fairy-wren (Malurus melanocephalus) would benefit from increasing the interval between fires (Woinarski et al. 1999a). Results from Kapalga showed no population increase for the red-backed fairy-wren in Unburnt compartments. However, they show that a relatively low fire frequency (e.g., fire every 3–5 years) is especially important for the maintenance of high abundances of small mammals.
9. Synthesis: Fire Ecology and Adaptive Conservation Management Alan N. Andersen, Garry D. Cook, and Richard J. Williams
9.1 The Kapalga Experiment Fire represents one of the greatest challenges to savanna managers and researchers alike. Fire management has important social, cultural, and philosophical dimensions; however, a sound understanding of the ecological effects of fire is vital to the success management efforts. In northern Australia, there has been very limited understanding of the broader ecological effects of different fire regimes. Most of our knowledge relates to short-term responses to particular fires and is heavily influenced by the visual appearance of burnt vegetation. A true understanding of fire ecology requires a longer term perspective, considering fire regimes rather than individual fires, and focusing on elements and processes that are fundamental to ecosystem sustainability. Correlative studies of floristic patterns across the landscape can provide a useful indication of ecological responses to fire, but are insufficient for gaining the predictive understanding that is required for best-practice fire management. This is the context within which the Kapalga fire experiment was established. The strengths of the experiment are that it is multidisciplinary, treatments were applied at a landscape scale with replication, extensive measurements were taken of fire behavior, and measurements of ecological responses were supported by extensive baseline data. The experiment also had a number of limitations. First, the fire treatments were limited. We 153
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did not cover the complete range of fire types; in particular, we did not examine fires of very low intensity that occur during the transition between wet and dry seasons (April/May), or fires occurring early in the wet season (November/December) that are increasingly being used to control grassy fuels (Chapter 6). Moreover, since treatments were applied only over the medium term, we could not directly test long-term responses or directly examine different fire frequencies. A second limitation is that many of our studies, and particularly those relating to fauna, were phenomenological rather than mechanistic. In other words, we have extensive information on how various taxa respond, but little direct evidence of why. Such a lack of mechanistic understanding limits our ability to generalize and predict (Resetarits and Bernardo 1998). Despite these limitations, the Kapalga experiment has yielded important insights into the fire ecology of tropical savannas and can make a valuable contribution to their conservation management.
9.2 Ecological Responses to Fire 9.2.1 Fire and Catchment Processes One of the strengths of the Kapalga experiment is that it enabled us to look at the effects of fire on catchment-scale processes, including the link between catchment burning and stream dynamics. Like the soils throughout most of Australia, Kapalga’s soils are extremely infertile (Chapter 2). How, then, does fire affect soil fertility? The particulate matter in smoke is redistributed across the landscape, but nutrients removed as gases, primarily sulfur and nitrogen, are lost from the ecosystem. About 1.5 kg of sulfur per hectare is lost during fires in gaseous form, but twice as much as this is deposited by wet season rainfall (Chapter 4). Therefore sulfur losses from fires are not considered to be a problem. In contrast, nitrogen losses during fires are up to 20 kg ha-1 with only a fraction of this returning to the earth’s surface in wet season rainfall. Nitrogen fixation from legumes (primarily species of Acacia) is likely to be less than 12 kg ha-1, suggesting a nitrogen shortfall. However, contributions from other potential sources of nitrogen, such as free-living bacteria and those associated with termites, are unknown. Nevertheless, there is evidence that foliar nitrogen concentrations increase with decreasing fire frequency (Cook 2001), and thus frequent burning does reduce nitrogen availability. Soil organic matter levels in this region are slow to respond to changes in fire regimes because of removal of litter by termites and microorganisms, and the lack of qualitative shifts in plant communities. The development of a litter layer in the absence of fire provides an increased protection of the soil surface from the slow rates of water erosion. Savanna fires also release a range of greenhouse gases that potentially contribute to global warming. An estimated 80 million tons of carbon
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dioxide (CO2) is released to the atmosphere each year in northern Australia. Normally, a roughly equivalent amount of CO2 is taken back from the atmosphere by plant growth during each wet season, given that it is mostly dead herbage that is burnt. However, repeated high intensity fire can cause substantial tree death (Chapter 6), and any reduction in tree basal area (see Section 9.2.2) will inevitably result in a net release of CO2 into the atmosphere. Catchment burning has a major impact on stream dynamics. Riparian vegetation is severely degraded by repeated Late fires. In contrast, streams in Late catchments supported over 10 times the biomass of aquatic macrophytes compared with Unburnt catchments and contained 6 times as many macrophyte species (Chapter 5). Aquatic macroinvertebrates were also strongly favored by frequent Late fires, with streams in these catchments having up to twice as many species as those in Unburnt catchments. The higher productivity of stream biota in Late catchments can be attributed to the greater inputs of solar radiation (because of reduced cover of riparian vegetation) and sediment and nutrients (through increased soil erosion). Subsequent research at Kapalga indicates that, compared with Late and Unburnt regimes, Early burning results in intermediate riparian tree density, riparian canopy cover, and the biomass of aquatic plants (M. Douglas, unpublished data). Catchments burnt early in the dry season had similar water quality, similarly high richness of riparian vegetation, and similarly low richness of aquatic plants to that found in Unburnt catchments.
9.2.2 Fire and Vegetation Structure Annual Late fires at Kapalga reduced tree basal area by 20% over the 5year experimental period (Chapter 6). It is not clear if, or to what extent reductions in basal area would continue if such a regime were to be maintained in the longer term. Moreover, the experimental regime of annual Late fires is an unrealistically extreme one for conservation management and the more relevant management issue concerns the effects of Late fires at different frequencies. Extrapolation from the shorter term effects on tree basal area are confounded by complex responses of flowering, seed production, and seedling establishment to variation in the timing, intensity, and frequency of fire (Chapter 6). We have addressed these issues by developing the tree population simulation model FLAMES, which integrates the various threads of tree population dynamics in relation to fire at Kapalga (Cook and Liedloff 2001). The model allows the processes underpinning the dynamics of trees, fuels, and fires to be extrapolated so that the effects of continuing the fire experiment for the longer term, as well as a range of other fire management scenarios, can be simulated. Table 9.1 gives estimates for fire intensity under different combinations of fire timing and frequency. Preliminary scenarios tested with FLAMES indicate that the marked declines in tree basal area noted at
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A.N. Andersen et al. Table 9.1. Effects of variation in the frequency and timing of fires on mean fire intensity (MW m-1): results from the FLAMES computer simulation model. Frequencya Timing Early Late
Annual
Biennial
3.2 (0.9–6.2, 1.2) 4.9 (0.6–8.8, 2.1)
4.5 (0.8–9.3, 2.5) 6.3 (0.8–15.8, 3.4)
a
Numbers in parentheses are the range and the standard deviation, respectively.
Kapalga with the late fires will not continue, but a stable population of midsized fire-tolerant trees may persist for at least several decades even under annual Late fires (Fig. 9.1). Further, because of fuel accumulation, fire frequencies may need to be reduced to less than one in 2 years before the total basal area of trees shows any significant difference to that under annual burning.
Figure 9.1. Effects of varying the frequency of late dry season fronting fires on total basal area of trees at Kapalga: results of a simulation experiment using the FLAMES computer simulation model.
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9.2.3 Fire and Fauna The Kapalga experiment has provided information on the responses to fire of a wide range of faunal groups. A diversity of responses was inevitably revealed. On one hand, many taxa clearly favor unburnt habitat. These taxa range from litter invertebrates and reptiles through forest passerines and nocturnal raptors to small marsupials. In contrast, a range of other taxa such as ants, frilled lizards, and diurnal predatory birds favor frequently burnt habitat and were particularly abundant under Late fires. Despite such a diversity of responses, two recurring themes emerge. First, although experimental treatments encompassed the most extreme fire regimes possible, from annual fires of maximum intensity on one hand to no fire at all on the other, the responses of fauna were generally limited. The overall abundance of most faunal groups, both invertebrate and vertebrate, remained unaffected by fire. There were many clear responses at the species level, but these were often subdued in the context of the overall population dynamics of affected species. It is very likely that we would have found stronger and more widespread effects if experimental fires were applied over a longer period. However, our results clearly demonstrate that much of the savanna fauna is highly resilient to fire, such that population dynamics are driven primarily by other factors. Throughout northern Australia, rainfall appears to be the primary driver for many faunal groups (Frith and Davies 1961; Friend et al. 1988; Andersen and Braithwaite 1996; Braithwaite and Müller 1997; Woinarski et al. 1999c). The second theme to emerge is that many animal species were influenced more by whether their habitat was burnt than the time of year (and therefore its intensity) at which fire occurred. It is clear that high intensity Late fires do not have nearly the widespread “destructive” effect on fauna as the visual appearance of burnt vegetation might suggest. Some faunal groups, particularly those closely associated with litter (invertebrates and small reptiles) and trees (some arboreal mammals), are indeed sensitive to Late fires. However, much, if not most, of the fauna showed similar responses to Early and Late fires, such that the main contrast was often between burnt and unburnt habitat, rather than between high and low fire intensity. The conclusion is that fire intensity per se does not have as great an impact on the Top End savanna fauna as had been thought. On the other hand, fire frequency appears to be more important than previously recognized. More particularly, a range of small mammalian species—a group known to be prone to severe population declines—strongly prefer habitat that remains unburnt for several years. Time-since-fire is widely recognized as a key conservation management issue in temperate Australia (Gill and McCarthy 1998), and this also appears to be true for the tropical north.
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9.3 Fire and Conservation Management 9.3.1 Strategies for Biodiversity Conservation Two classes of strategies for conserving biodiversity can be recognized: “coarse-filter” strategies that deal with the management of habitat diversity, and “fine-filter” strategies that focus on the specific needs of particular species (Haufler 1999). Coarse-filter strategies relating to habitat diversity are usually the strategic basis for fire management in fire-prone ecosystems, following a philosophy that “pyrodiversity begets biodiversity.” For example, it has been suggested that the best way of conserving biodiversity in forests of the U.S. Pacific Northwest is to provide a balanced range of postfire successional stages (Oliver 1992). Similarly, it has been advocated that the goal of fire management in fire-prone ecosystems be to impose a variety of fire regimes as a surrogate of habitat diversity (van Wilgen et al. 1994). This is the basis for a highly sophisticated patch mosaic burning system that has been developed for Pilanesburg National Park in South Africa (Brockett et al. 2001). The management of fire for habitat diversity is an intuitively attractive coarse-filter strategy, particularly as a means for “covering all bases” when the effects of different fire regimes are poorly known (McCarthy and Burgman 1995). However, it runs the risk of unnecessarily expensive overkill if ecosystems are so resilient to fire that complex management regimes have little ecological significance. It also needs to be tempered by some concept of “naturalness” and an explicit recognition of relative conservation values. There is no sense in reducing the extent of habitat of high conservation value to produce a variety of low value habitats. As a contrived example, conservationists would not advocate the routine burning of areas of lowland tropical rainforest to promote habitat diversity, which it would undoubtedly do! Similarly, regular prescribed burning of eucalypt forests and woodlands in southern Australia reduces their conservation values (Gallus 1994; Woinarski and Recher 1997; York 1999a,b), rather than enhancing them through an increase in the range of forest types. The importance of considering habitat quality in the context of habitat diversity is illustrated by results from a study of the responses of birds to fire regimes at Kidman Springs in the Northern Territory’s semiarid tropics (Woinarski et al. 1999a). Considering a single habitat in isolation, a range of fire regimes is likely to maximize bird diversity through increased habitat variability. However, burning favors opportunist habitat generalists, such that with frequent fire the bird faunas of the different habitats become increasingly similar. The birds of conservation value, on the other hand, are habitat specialists that favor low fire frequency. The implication is that any “range” of fire regimes that results in large areas of the landscape being subject to frequent fire has the potential to reduce conservation values by homogenizing different habitats. In summary, it makes no sense to aim for habitat diversity without giving due consideration to habitat quality. Habitat homogenization can also be an inadvertent outcome of attempts to apply “compromise” management solutions across the landscape. For
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example, results from Kapalga showed a clear trade-off between riparian vegetation (which is severely degraded by Late fires) on one hand and instream biodiversity (which is strongly promoted by Late fires) on the other (Chapter 5). The question inevitably arises, Which do we want—healthy riparian vegetation or high aquatic biodiversity? The health of riparian vegetation and in-stream biodiversity is more evenly balanced by Early fires, so such a regime could be viewed as a useful management compromise. If universally applied, however, it would mean that neither riparian vegetation nor aquatic biodiversity would ever be fully expressed. It is therefore important to take a landscape perspective—it is actually possible to have both healthy riparian vegetation and a diverse aquatic biota, just not in the same place. In other words, a fire regime that might generally be “best” for biodiversity in any particular place might not be best at all from a regional perspective. A refinement of the “habitat diversity” strategy is to manage for the historical range of variability that habitats have naturally experienced, rather than for habitat diversity per se (Aplet and Keeton 1999, Swetnam et al. 1999). This approach might be more reasonable philosophically than is untrammeled “pyrodiversity begets biodiversity,” but it is severely challenged by problems associated with characterizing “historical range of variability.” In northern Australia this challenge is confounded by having to choose from a range of “histories”—prehuman, Aboriginal, and European (Chapter 1). If the aim is to reproduce variability that occurred during the evolutionary history of the northern Australian biota, and to minimize the influence of humans (Noss and Cooperrider 1994), this is prehuman history (Hunter 1996). Such a philosophy has recently been the basis for fire management in South Africa’s Kruger National Park (Biggs and Potgieter 1999) and has been proposed for Namibia’s Etosha National Park (Stander et al. 1993) and North American boreal forests (Hunter 1993). During prehuman history in the Top End, fires were presumably restricted to the transition between dry and wet seasons, which would have been the only time an ignition source (lightning) regularly coincided with an availability of dry fuel. Alternatively, if the aim is to reproduce the variability characteristic of recent millennia, then this is the Aboriginal history of burning throughout the year, but particularly during the early and mid-dry season. The major shifts in the northern Australian climate during the recent geological past (Kershaw 1985), along with anthropogenic climate change anticipated for the near future, complete the dilemma. A rather different coarse-filter strategy is the more pragmatic application of fire regimes that are deemed best to accommodate current needs and circumstances (Gill and McCarthy 1998). This strategy uses the argument that a system that “worked” in the past will not necessarily be effective today, because (1) the landscape has changed through fragmentation and invasion by weeds and feral animals, such that “old” practices no longer produce the desired outcomes; (2) patterns of human occupation have changed to the extent that the implementation of “old” practices is not feasible; or (3) social values and requirements, and therefore desired ecological outcomes, have changed (Andersen 1996; Keith et al. 2002).
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9.3.2 Adaptive Fire Management Management Goals The starting point of adaptive management is the identification of clear and unambiguous goals. As is the case elsewhere, conservation managers in northern Australia are usually confronted with a range of management issues relating to fire, such as protection of life and property (Bradstock and Scott 1995) and health concerns over smoke (Rye 1995; Johnston et al. 2002), that sometimes receive priority over conservation concerns. This is particularly so in Kakadu National Park, where the cultural requirements of traditional Aboriginal owners have priority over western conservation concerns (Kakadu Board of Management 1999). Leaving aside such inevitable compromises, the range of conservation strategies outlined in the preceding section shows that the setting of clear conservation goals is difficult in its own right (Sedjo 2000). Although science can help clarify the options, the setting of conservation strategy is more a matter of philosophical than scientific debate, and unambiguous definitions of ultimate conservation objectives may simply be unattainable. However, even if ultimate objectives remain somewhat woolly, it is still possible to set shorter term goals that represent significant steps in the right direction. In other words, a path forward can still be clear, even if a precise definition of the ultimate destination is not. A way of moving forward is to focus on elements of biodiversity that are most at risk under current management regimes (Samson and Knopf 1999). In the Top End, this approach has been taken at the broader regional scale by focusing conservation efforts on the more fire-sensitive habitat types such as rainforest (Russell-Smith and Bowman 1991) and sandstone heathland (Russell-Smith et al. 1998), which appear to be declining under current fire regimes. However, there has been no such guiding focus within savanna habitats. The Kapalga experiment has identified fire frequency as a key factor in fire–fauna dynamics. In particular, many elements of the fauna strongly favor less frequently burnt habitat.This includes a range of extinction-prone small mammals whose abundances have substantially declined across northern Australia in recent times (Woinarski and Braithwaite 1990; Woinarski et al. 2001). Frequent fire can also reduce seed production by key grass species (Chapter 6), which might have important implications for savanna granivores (Woinarski 1993; Franklin 1999). As for savannas more generally (Peterson and Reich 2001), it is likely that woody vegetation requires substantial fire-free intervals for effective seedling recruitment (Chapter 6). Given that only a fraction (<5%) of Top End savannas remain unburnt for more than 5 years (Gill et al. 2000; Edwards et al. 2001), a worthwhile management goal for biodiversity conservation is to increase the area of such infrequently burnt habitat. A reduction in fire frequency would also help alleviate concerns over nutrient loss and atmospheric emissions (Chapter 4).
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Burning Practices How might we achieve a reduction in fire frequency—or, more particularly, an increase in area remaining unburnt for several years? If it is assumed that fires occur randomly in space, then fire frequency is a function of the total area burnt each year (Fig. 9.2a). If half the total area is burnt each year, then at any given time 50% of the total area will have been burnt within the previous year, 25% within 2 years, and so on, with 6% having remained unburnt for 5 or more years. This appears to be a close fit to the burning profile of savannas in the Kakadu region (Gill et al. 2000). If the total area burnt each year is increased to 70%, then the area remaining unburnt for 5 or more years is negligible (<1%). At 30%, this figure increases to 24%. More generally, the area remaining unburnt for 5 or more years increases markedly as the total area burnt each year declines from 40% (Fig. 9.2a). The high incidence of unauthorized burning that commonly occurs in fire-prone ecosystems can make it extremely difficult for managers to implement desired burning regimes. This is a particularly acute problem in the Top End, where many fires, and particularly those occurring later in the dry season, are lit by unauthorized sources. Indeed, fire managers in the Top End appear to have little influence over the total area burnt. For example, a remarkably constant 50 to 60% of Kakadu’s savannas is burnt each year regardless of the extent of prescribed burning early in the dry season (Gill et al. 2000). A reduction in fire frequency will therefore not necessarily be achieved by a reduction in prescribed burning. If fire managers cannot control the total area burnt, then an increase in the area remaining unburnt for 5 or more years can be achieved only by locating fires more strategically. If the total area burnt is held constant, then the areas of different post-fire ages are determined by the extent to which fires are burning land burnt the year before (Fig. 9.2b). If it is assumed that half the total area is burnt each year, the area remaining unburnt for 5 or more years can range from 6% to 50%, depending on the extent to which areas are reburnt. This relationship is nonlinear; for example, half of the previously burnt area needs to be reburnt to maintain 15% of the total area free from fire for 5 or more years. Monitoring Monitoring is one of the most critical components of adaptive management (Chapter 1). Effective monitoring has two key elements (Hellawell 1991). The first is effective sampling of appropriately identified performance indicators that characterize desired ecological outcomes (Rogers and Biggs 1999; Fig. 1.2). Landscape monitoring in a conservation context is best served by combining both coarse- and fine-filter approaches, addressing habitat as well as particular target species (Hansen et al. 1999). For fire management, burning patterns are a readily available surrogate of habitat
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a
b Figure 9.2. Proportion of total area of different postfire ages according to different fire management scenarios. (a) Fires are assumed to occur randomly in space, and the total area burnt is varied. The vertical dotted line indicates areas of different fire ages, if half the total area is burnt randomly each year. (b) The total area burnt is held constant at 50%, and the proportion of previously burnt area that is reburnt is varied. (R.L. Eager, Unpublished data.)
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diversity (van Wilgen et al. 1994), especially through the application of remote sensing technology (Russell-Smith et al. 1997b; Gill et al. 2000). However, this needs to be complemented by a fine-filter approach to ensure that desired biodiversity outcomes are indeed being met (i.e., that fire patterns are reliable surrogates of biodiversity). Target species for monitoring should include representatives of taxa at most risk (Hansen et al. 1999). In the Top End, the northern quoll (Dasyurus hallucatus) and the partridge pigeon (Geophaps smithii) are ideal candidates because they have suffered marked range contractions elsewhere in northern Australia but are locally abundant and comparatively well studied (Oakwood 1997; Fraser 2000). However, target species should also include less glamorous taxa, such as key invertebrates (Andersen 1999c), that can act as indicators of broader changes in biological integrity. The second key element of monitoring is effective feedback, such that results are translated to useful information for future management. It is all too easy for monitoring programs to become bogged down in masses of accumulated data that remain unanalyzed or are difficult to interpret. For effective monitoring, ecologically meaningful changes in performance indicators need to be specified a priori, and their expression needs to trigger changes in future management. This distinguishes monitoring in the strict sense from more open-ended “surveillance” (Hellawell 1991). In this context, a range of “thresholds of potential concern” have been proposed for guiding fire management in Kruger National Park (van Wilgen et al. 1998).
9.4 Conclusion A sound understanding of the ecological effects of fire is fundamental to its successful management, and the Kapalga experiment has made an important contribution to this. However, any scientific understanding will be useful only if it is effectively integrated into the broader management process. One challenge is to achieve acceptance of research results by land managers and other stakeholders, given that popular perceptions about ecological processes can be more powerful than the results of scientific research (Davis et al. 2001).Another challenge is to recognize when an issue is primarily philosophical rather than scientific. Scientific understanding will be of limited use whenever land management disputes are driven more by differing values than by differing scientific interpretations (Policansky 1998). With this in mind, here are suggestions for the enhancement of fire management on conservation lands in the Top End of northern Australia. 1. Clearer articulation of ultimate conservation objectives. Even if a precise definition of ultimate conservation objectives cannot be achieved, it is important that the guiding strategy be clearly articulated. This issue is
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more philosophical than scientific, but it must be addressed to provide a framework for guiding where science can be most effectively applied to management. Moreover, unless ultimate goals are openly articulated and discussed, debates among fire managers, scientists, and members of the public will inevitably be less productive than is desirable, and more acrimonious than necessary (Policansky 1998). 2. Identification of specific management goals. It is important to set clear management goals that contribute to strategic objectives, even if the ultimate objectives themselves cannot be unambiguously defined. Current fire management in Top End savannas focuses primarily on fire intensity. Fire intensity can obviously be important, but fire frequency deserves more attention than it has received. We propose a conservation goal of increasing the area remaining unburnt for 5 or more years to at least 10% at any given time. 3. Greater control of unauthorized burning. Effective fire management in the Top End is severely compromised by extensive unauthorized burning. Yet we are unaware of any systematic analysis of unauthorized ignition sources, or the location and extent of such fires. Accidental fires are inevitable in fire-prone landscapes, but most unauthorized fires appear to be deliberately lit. Who is lighting these fires, and why? There needs to be an audit of unauthorized fires as a first step toward reducing their extent (Andersen 1996). There also needs to be an audit of the degree to which prescribed fires that are lit to limit the extent of unauthorized fires actually achieve this goal. Can the extent of unauthorized fires be reduced by better targeting prescribed burning? 4. Improved monitoring. The Top End has an excellent fire-mapping service coordinated by the Northern Territory Bushfires Council, which provides extensive information on fire patterns for monitoring purposes. However, ecological monitoring programs are less well developed. Monitoring plots have been established in the major national parks, but to a large degree these are designed to meet ranger training purposes, rather than specifically to provide timely feedback for fire management (J. RussellSmith, personal communication). In particular, target taxa that provide clear signals to management need to be better selected and monitored. Scientific experiments such as ours at Kapalga are crucial for providing insights into the behavior of ecological systems. Establishing and managing such landscape-scale experiments are enormously challenging tasks. However, a more important challenge lies in achieving the effective application of research results to management. This requires close partnerships among scientists, policy makers, and land managers, as part of an adaptive management process that strives for continual improvement.
References
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Index
A Aboriginal burning, 3, 4–6, 7–9, 29, 159, 160 Adaptive management, 6–7 Africa. See International comparisons Ants. See Insects Aquatic invertebrates, 63–64, 73, 75, 76, 78, 155 Aquatic marcrophytes, 62, 64, 73, 75, 78, 155 Area burnt, 3 B Beetles. See Insects Birds, 13, 127, 128–131, 133, 137–139, 144–145, 151, 152, 158, 163 C Carbon dioxide, 54–58, 154–155. See also Trace gases Climate, 19–21 Conservation management, 3, 4, 7, 8, 157–159
E Eucalyptus miniata, 22, 23, 27 leaf phenology, 25, 88–90 reproductive phenology, 25, 27, 89, 92, 93, 100–101 seed dynamics, 92, 95, 101–102 seedling establishment, 94–95, 101–102 survival, 84, 85, 87 Eucalyptus tetrodonta, 22, 23, 27, 88–90, 92, 93 leaf phenology, 25 reproductive phenology, 25, 89, 93, 100–101 seed dynamics, 95, 101–102 seedling establishment, 94–95, 101–102 survival, 84, 85, 87 Experimental design, 13–14, 28–29, 153–154 F Fire behavior, 33–46 intensity. See Fire regimes 193
194
Index
Fire behavior (cont.) rate of spread, 33, 35, 38, 39, 46 Fire experiments, 13–14, 153–154, 164. See also Experimental design Fire frequency. See Fire regimes Fire fuels, 34–38, 42–43, 46, 47–53 litter decomposition, 47–49 litter fall. See Fire fuels, litter production litter production, 47–51 Fire intensity. See Fire regimes Fire regimes, 12, 28–29 frequency, 1, 10, 11–12, 155–156, 160–162 intensity, 3 ecological effects, 11–12, 96, 98, 155–157 measurement, 33, 34, 35–38, 43–44. See also Fire regimes, intensity, post-fire indices post-fire indices, 34, 40–41, 44–45 seasonality. See Fire regimes, timing timing, 3, 11–12, 33 Fire weather, 21–22, 36, 38 FLAMES simulation model, 155–156 Frilled lizard. See Reptiles Frogs, 128–135, 140, 151 Fuel. See Fire fuels G Goals. See Management objectives Grass layer composition, 22, 80, 96–98, 103–105 insects, 110–112, 124 Grasshoppers. See Insects Greenhouse gases. See Carbon dioxide; Trace gases H Hydrology, 64–65, 70–73, 77 I Insects, 11, 27, 28, 63, 107–125 ants, 27, 101, 108, 110, 111, 113, 121–123, 125 assemblages, 109–110, 112, 125 beetles, 108, 110, 112, 114–116, 117, 124, 125 as bio-indicators, 107, 125, 163
ecological roles, 107–109 grasshoppers, 108, 110, 116, 118–121, 124, 125 taxonomic sufficiency, 124 termites, 27, 99, 108, 109, 110, 126, 127 See also Aquatic invertebrates International comparisons, 3, 28, 59–60, 105, 159 Africa conservation management, 159, 163 fauna, 28, 108, 110, 112, 116, 125 fire and grass-layer diversity, 104–105 fire and vegetation structure, 80 fuel loads, 40 streams, 59 South America fauna, 28 fire and vegetation structure, 80 fuel loads, 40 streams, 59 L Litter. See Fire fuels M Mammals, 13, 15, 128–131, 145–152, 163 Management goals. See Management objectives Management objectives, 7, 158–160, 163–164. See also Conservation management Modeling. See FLAMES simulation model Monitoring, 6, 7, 161, 163, 164 N Nitrogen, 19, 53–58, 72–74, 154. See also Nutrients Nutrients, 9, 10, 19, 53, 65, 72–74, 77, 154 O Objectives. See Management objectives P Phenology. See Trees
Index R Rainforest, 9, 22, 80, 105–106, 158 Reptiles, 128–137, 141–143, 151 frilled lizard, 131–132, 135–137, 144 Riparian vegetation, 64, 67–70, 77, 155, 158–159 S Seed dynamics, 9, 26–27, 92, 95, 101–102 Seedlings establishment, 94–95, 101–102 See also Woody sprouts Simulation modeling. See FLAMES simulation model Smoke, 54, 160. See also Trace gases Soils, 18–19. See also Nutrients Sorghum, 2, 22, 35, 104 Streams, 17, 18, 59–78, 155. biota. See Aquatic invertebrates; Aquatic macrophytes physicochemistry, 60, 63, 71–73 seasonality, 60–64
195
South America. See International comparisons T Termites. See Insects Trace gases, 10, 53–58, 154. See also Carbon dioxide Trees growth, 10 leaf phenology, 24–25, 79, 81, 88–91, 102 mortality. See Survival reproductive phenology, 25–27, 89, 91–93, 100–102 survival, 10, 80–88, 98–100, 155–156 W Water quality, 70–73. See also Streams, physicochemistry Weeds, 15, 68 Woody sprouts, 10, 11, 22 survival, 80, 84, 86, 88, 99, 103