Fire in Tropical Savannas: The Kapalga Experiment (Ecological Studies) (v. 169)

Ecological Studies, Vol. 169 Analysis and Synthesis Edited by I.T. Baldwin, Jena, Germany M.M. Caldwell, Logan, USA G...

Author: Alan N. Andersen | Garry D. Cook | Richard J. Williams

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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.

© 2003 Springer-Verlag New York, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer-Verlag New York, Inc., 175 Fifth Avenue, New York, NY 10010, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use of general descriptive names, trade names, trademarks, etc., in this publication, even if the former are not especially identified, is not to be taken as a sign that such names, as understood by the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone. Printed in United States of America. 9 8 7 6 5 4 3 2 1

SPIN 10905654

www.springer-ny.com Springer-Verlag New York Berlin Heidelberg A member of BertelsmannSpringer Science+Business Media GmbH

Contents

Preface Contributors

1. Burning Issues in Savanna Ecology and Management Alan N. Andersen

vii xi

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2. Kapalga and the Fire Experiment Garry D. Cook and Laurie K. Corbett

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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

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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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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

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8. Terrestrial Vertebrates Laurie K. Corbett, Alan N. Andersen, and Warren J. Müller

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9. Synthesis: Fire Ecology and Adaptive Conservation Management Alan N. Andersen, Garry D. Cook, and Richard J. Williams

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References

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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


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


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


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


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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.)

1. Burning Issues in Savanna Ecology and Management

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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.)


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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

16

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 (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).

20


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

21

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.

22


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-


23

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

24


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-


25

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-

26


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.)


27

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).

28


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.”


29

“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:

30


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).


31

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-

32


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

34

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.

36


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.)

38


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.)

40


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).

42


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.)

3. Fire Behavior

43

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

44


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

46


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

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