The interactive effects of fire and

herbivory on understorey vegetation and

its dependent fauna

Claire Foster

A thesis submitted for the degree of Doctor of Philosophy of

The Australian National University

August 2015

© Copyright by Claire Foster 2015

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Declaration

This thesis is my own work, except where otherwise acknowledged

(see Preface and Acknowledgements)

Claire Foster

August 2015

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Preface

This thesis is structured as a series of connected papers that have been published in, or

were submitted to, peer reviewed journals at the time of thesis submission. These papers are

listed at the end of this preface and are referred to in the text by their roman numerals. All

papers were intended as stand-alone pieces of work. For this reason, there is some unavoidable

repetition between chapters, for example in the background material, and the description of the

study area and experimental design. Due to journal copy-editing there may be minor differences

between the papers presented here and their published versions.

The formatting and content of this thesis complies with The Australian National

University’s College of Medicine, Biology and Environment guidelines for ‘Thesis by

Compilation’. In line with these guidelines, an Extended Context Statement has been provided

at the beginning of the thesis. The Extended Context Statement is not intended to be a complete

literature review, but rather a framework for understanding the relationships between all aspects

of the research. Paper I provides a detailed review of literature concerning the effects of large

herbivores on other animals, and Papers II, III, IV and V each present a review of the literature

relevant to the focus of the paper.

I performed the majority of the work for the papers that form this thesis. This included

developing research questions and experimental designs, data collection and analysis, and

writing manuscripts. My supervisors (Philip Barton, Chloe Sato and David Lindenmayer) and

collaborators provided advice on conceptualisation, experimental design, data analysis, and

manuscript revisions. The addition of different co-authors to each paper reflects contributions

from collaborators, which are detailed below. The author contribution statements below have

been agreed to in writing by all authors. Other assistance is acknowledged in the

acknowledgments section at the end of each paper.

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Paper I: Foster, C.N., Barton, P.S. and D.B. Lindenmayer (2014) The effects of large native

herbivores on other animals. Journal of Applied Ecology. 51, 929-938.

Conceptualisation & design: CNF, DBL, PSB; Data extraction and analysis: CNF; Manuscript

drafting: CNF; Manuscript revision: CNF, PSB, DBL.

Paper II: Foster, C.N., Barton, P.S., Sato, C.F., Wood, J.T., Macgregor, C.I., and D.B.

Lindenmayer (2016) Herbivory and fire interact to affect forest understory habitat, but

not its use by small vertebrates. Animal Conservation. 19, 15-25.

Conceptualisation & design: CNF, DBL, CIM, PSB, JTW; Data collection: CNF, CIM; Data

analysis: CNF, JTW, CFS; Manuscript drafting: CNF; Manuscript revision: CNF, PSB,

DBL, CFS, CIM, and JTW.

Paper III: Foster, C.N, Barton, P.S, Sato, C.F, Macgregor, C.I, and D.B. Lindenmayer (2015)

Synergistic interactions between fire and browsing drive plant diversity in a forest

understory. Journal of Vegetation Science. 26, 1112-1123.

Conceptualisation & design: CNF, DBL, CIM, PSB; Data collection: CNF, CIM; Data analysis:

CNF, CFS; Manuscript drafting: CNF; Manuscript revision: CNF, PSB, DBL, CFS, CIM.

Paper IV: Foster, C.N, Barton, P.S., Wood, J.T. and D.B. Lindenmayer (2015) Interactive

effects of fire and large herbivores on web-building spiders. Oecologia. 179, 237-248.

Conceptualisation & design: CNF, DBL, PSB, JTW; Data collection: CNF, Data analysis: CNF,

PSB, JTW; Manuscript drafting CNF, Manuscript revision: CNF, PSB, DBL, JTW.

Paper V: Foster, C.N, Sato, C.F., Lindenmayer, D.B., and P.S. Barton (Accepted) Integrating

theory into disturbance interaction experiments to better inform ecosystem management.

Global Change Biology, doi:10.1111/gcb.13155.

Conceptualisation & design: CNF, PSB, CFS, DBL; Data extraction: CNF; Manuscript drafting:

CNF, Manuscript revision: CNF, PSB, CFS, DBL.

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Acknowledgements

Thank you to my supervisors David Lindenmayer, Chloe Sato, and Philip Barton for their

support and guidance during my candidature. David’s confidence in me was pivotal for

allowing me to get my experiments up and running within a few months of commencing. The

freedom David allowed me in my topics and approach enabled me to complete a PhD that I

have found both interesting and thoroughly enjoyable. Chloe’s advice on analyses, discussions

on applied management, and general assistance with the whole PhD process has been

invaluable. Philip’s advice, constructive feedback and patience with trawling through every first

draft have been fantastic. Our regular meetings have helped me stay focussed, and turn my

disjointed rambles into something worth writing about. My PhD would have been a very

different experience without your advice and encouragement. I would also like to thank Chris

MacGregor, Peter Lane and Jeff Wood for their assistance and advice at various times during

my candidature. In particular, Chris MacGregor’s cheerful company, willing assistance and

ongoing advice made my fieldwork, and especially the dreaded fence construction, so much

more enjoyable. Chris was always happy to keep an eye on my experiments, chainsaw a tree

from exclosure fences or help identify a difficult specimen. John Evans also provided invaluable

assistance with sorting and identifying invertebrate collections. All of my supervisors and

collaborators have been outstanding in providing constructive and timely feedback on my

written work, for which I am very grateful.

I would like to thank the Department of Environment and the landowners and co-

managers of Booderee National Park, the Wreck Bay Aboriginal Community, for allowing me

to conduct this study in Booderee National Park and providing in-kind support. I would

particularly like to thank Nick Dexter and Martin Fortescue for their ongoing support, advice

and interest in this project, and making the burning of my experimental plots a reality. Thank

you also to all the staff of Booderee National Park who were involved in conducting the

prescribed burns, especially Stuart James, who gave me training so I could participate myself.

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Much of my field work would not have been possible without the assistance of a band

of willing volunteers. Thank you to Angela, Stuart, Darren, Dylan, Helen, Emily, Xiying, Laura,

Kevin, Daniela, Anna and Luke for cheerfully putting up with the early starts, leeches and ticks

and wrestling with all the gates.

Thank you to the Long Term Ecological Research Network, the Australian Academy of

Science (Margaret Middleton Fund) and the Normal Wettenhall Foundation for granting

financial support to this project. Thank you also to the ANU College of Medicine and Biology

for providing me with a supplementary scholarship.

My fellow students and the staff at the Fenner School created an environment of

encouragement, active discussion and constructive feedback that made my candidature so much

more enjoyable. In particular, thank you to Ben Scheele for great discussions, good company,

and reading drafts, and Karen Ikin for your friendship, encouragement and the morning coffee

run. I would also like to thank Nélida Villaseñor, Darren Le Roux, Will Batson, Laurence

Berry, Nick Engerer, John Evans, Geoff Kay, Miles Kieghley, Megan Evans, Stephanie

Pulsford, Laura Rayner, Chloe Sato, Philip Barton, Ingrid Stirnemann, Martin Westgate, Alessio

Mortelliti, Jenny Pierson and Crid Fraser for numerous instances of assistance and

encouragement, as well as many great lunchtime discussions. Thank you also to Claire

Shepherd and Tabitha Boyer for your assistance with navigating the university administration

jungle.

Thank you to my family for their love and support throughout this experience. In

particular to my partner Stuart, who has provided love, good humour and positivity over the last

3.5 years. Thank you for continuing to visit during long periods of field work, even when I

made you get up at five, or count poo for your trouble, and for putting things in perspective

when I needed it. Finally, thank you to my Mum, for making the effort to understand what I do,

correcting my grammar, and encouraging my curiosity from the very beginning.

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Abstract

Interactions between multiple disturbances have been shown to have unexpected, and often

undesirable effects on ecosystems and biodiversity. Improving our ability to predict and manage

the outcomes of multiple disturbances is therefore an important research priority. In this thesis, I

focussed on the interaction between fire and grazing (or browsing) by large herbivores.

Evidence of the individual effects of fire and large herbivores is substantial, but there

has been little quantitative synthesis of the effects of native herbivores on biodiversity. Using a

systematic review and meta-analysis, I found that high densities of large herbivores usually

have negative effects on other animals. However, I found that interactions between large

herbivores and episodic disturbances, such as fire, remain poorly understood.

I therefore designed a field experiment to test the interactive effects of prescribed fire

and large herbivores on forest flora and fauna. I used full and partial exclosure fences to create a

gradient of herbivore pressure across both burnt and unburnt sites, and measured the responses

of vegetation, spiders and small vertebrates.

I found that fire and herbivory interacted strongly to affect vegetation, with herbivory

limiting the recovery of vegetation from fire. In contrast to the vegetation response, small

vertebrates responded to the individual, but not interactive effects of disturbance. I then

focussed on the mechanisms driving interactive effects on vegetation, and found that the

interaction occurred through both numerically mediated (concentration of herbivores in burnt

sites) and functionally moderated (stronger effect of herbivores post-fire) pathways.

The differing responses of plants and animals to fire and herbivory was at odds with

existing literature, where the effects of large herbivores on fauna are usually attributed to

vegetation changes. I therefore tested to what extent vegetation mediated the effects of fire and

herbivory on web-building spiders – a group sensitive to changes in habitat structure.

Vegetation structure partially mediated the negative effect of fire on spider density, while

negative effects of large herbivores on spiders were mostly independent of vegetation. Different

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types of web builders differed in their responses, resulting in important changes to the spider

community following disturbance.

The results of my experiments highlight the importance of focussing on mechanistic

pathways for understanding and managing disturbance interactions. However, in reviewing

recently published fire-grazing studies, I found that most reported only net effects of

interactions. I demonstrate how by failing to identify mechanistic pathways, or non-linear

effects, such studies are limited in their management applications. I describe adjustments to

disturbance interaction studies that would improve their ability to inform effective management

and advance theory.

Collectively, my research shows that fire and large herbivores can have strong

interactive effects on forested ecosystems and their associated biota, and highlights the

importance of considering large herbivores in forest fire planning. It also demonstrates the value

of a mechanistic understanding of interactions for the management of disturbance regimes. Such

considerations are of broad relevance to the management of multiple disturbances, particularly

in the context of increasing global change.

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Table of Contents

Declaration ................................................................................................................................... iii

Preface .......................................................................................................................................... v

Acknowledgements ..................................................................................................................... vii

Abstract ........................................................................................................................................ ix

Context Statement ....................................................................................................................... 1

Introduction ............................................................................................................................... 1

Overview of Aims ..................................................................................................................... 4

Study Area ................................................................................................................................ 7

Summary of Outcomes ............................................................................................................. 9

Synthesis and Management Implications ................................................................................ 11

Conclusion .............................................................................................................................. 17

References ............................................................................................................................... 18

Paper I: Effects of large native herbivores on other animals ................................................ 25

Summary ................................................................................................................................. 26

Keywords ................................................................................................................................ 27

Introduction ............................................................................................................................. 27

Materials and Methods ............................................................................................................ 29

Results ..................................................................................................................................... 33

Discussion ............................................................................................................................... 39

Acknowledgements ................................................................................................................. 46

References ............................................................................................................................... 46

Supporting Information ........................................................................................................... 51

Paper II: Herbivory and fire interact to affect forest understory habitat, but not its use by

small vertebrates ....................................................................................................................... 81

Abstract ................................................................................................................................... 82

Keywords ................................................................................................................................ 82

Introduction ............................................................................................................................. 82

Materials and methods ............................................................................................................ 85

Results ..................................................................................................................................... 88

Discussion ............................................................................................................................... 95

Acknowledgments ................................................................................................................. 100

References ............................................................................................................................. 100

Appendices ............................................................................................................................ 105

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Paper III: Synergistic interactions between fire and browsing drive plant diversity in a

forest understory ..................................................................................................................... 115

Abstract ................................................................................................................................. 116

Keywords ............................................................................................................................... 116

Introduction ........................................................................................................................... 117

Methods ................................................................................................................................. 119

Results ................................................................................................................................... 124

Discussion.............................................................................................................................. 129

Acknowledgements ............................................................................................................... 134

Literature Cited ...................................................................................................................... 134

Supporting Information ......................................................................................................... 138

Paper IV: Interactive effects of fire and large herbivores on web-building spiders ......... 145

Abstract ................................................................................................................................. 146

Keywords ............................................................................................................................... 146

Introduction ........................................................................................................................... 147

Materials and methods ........................................................................................................... 149

Results ................................................................................................................................... 156

Discussion.............................................................................................................................. 161

Acknowledgements ............................................................................................................... 165

References ............................................................................................................................. 165

Supplementary Material ........................................................................................................ 171

Paper V: Integrating theory into disturbance interaction experiments to better inform

ecosystem management ........................................................................................................... 183

Abstract ................................................................................................................................. 184

Keywords ............................................................................................................................... 184

Introduction ........................................................................................................................... 185

Recent advances in disturbance interaction theory ................................................................ 186

Where are the gaps between theory and experimental studies?............................................. 190

Bridging the gap to improve management applications ........................................................ 199

Conclusions ........................................................................................................................... 203

Acknowledgements ............................................................................................................... 204

Literature Cited ...................................................................................................................... 204

Supporting Information ......................................................................................................... 209

1

Context Statement

Introduction

Ecological disturbance is a key process driving patterns of temporal and spatial heterogeneity in

ecosystems worldwide. Disturbance regimes are strongly linked with drivers of global change,

and hence many disturbance regimes have been dramatically altered in recent decades, and

continue to undergo rapid change (Turner 2010). While there has been increasing interest in

interactions between global change drivers (e.g. Tylianakis et al. 2008, Oliver and Morecroft

2014, Mantyka-Pringle et al. 2015), and also global change and disturbance (e.g. Didham et al.

2007, Gibb et al. 2015), the interactions between disturbance processes themselves remain

poorly understood (Turner 2010, Buma 2015). This knowledge gap is of critical importance for

ecological management as disturbances are becoming increasingly managed in both natural and

modified ecosystems, rarely occur in isolation from other disturbance processes, and are

predicted to continue to change in frequency and intensity as a result of global change (Turner

2010, Buma 2015). Improving our understanding of both the individual and combined effects of

disturbance is therefore imperative for the successful conservation of ecosystems and

biodiversity.

Disturbance can be defined as any “relatively discrete event that disrupts the structure

of an ecosystem, community or population, and changes resource availability or the physical

environment” (White and Pickett 1985). A disturbance regime refers to the spatio-temporal

distribution of disturbance, including aspects such as size, intensity, severity and frequency

(Turner et al. 1998). Two of the most widespread and commonly studied disturbance processes

in terrestrial ecosystems are fire and herbivory (grazing and browsing by large herbivores).

Fire occurs over a wide range of temperate and tropical landscapes, and fire regimes

drive the distribution of vegetation types and dominant species in many ecosystems (Bond and

Keeley 2005). Fire regimes have undergone substantial changes in recent decades, from

widespread fire suppression in some ecosystems, to an increase in fire intensity, severity and

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frequency in others (Bowman et al. 2009, Syphard et al. 2009). Attempts to re-introduce

historical fire regimes to contemporary ecosystems can be contentious, particularly in human-

dominated landscapes, where wildfire is often perceived as a “devastating” destructive event

(Stephens and Ruth 2005, Stephens et al. 2013). Managing fire for the (often competing)

priorities of both life and property protection and biodiversity conservation is therefore a key

challenge for managers of many ecosystems (Driscoll et al. 2010, Gibbons et al. 2012).

Browsing and grazing by large mammalian herbivores is also a widespread disturbance

process that occurs in most of the world’s ecosystems (Danell et al. 2006). Rangelands, due to

their suitability for livestock grazing, have been particularly affected by changes to grazing

regimes (Gordon et al. 2004). However, wild herbivore assemblages have also undergone

substantial changes in recent decades, with dramatic declines from over-hunting in some areas,

and increases to historically high levels in others, due to relaxation of hunting pressure and loss

of native predators (Gordon et al. 2004). Changes in herbivore densities can have dramatic

effects on ecosystems, from altering dominance and diversity patterns in plant and animal

communities (Hester et al. 2006, Suominen and Danell 2006), to changing landscape-scale

processes such as nutrient cycling, erosion and hydrological cycles (Asner et al. 2004,

Tanentzap and Coomes 2012). Due to their strong effects on ecosystems, and widespread

changes in abundance, large herbivore populations are actively managed for conservation

outcomes throughout much of their range (Danell et al. 2006).

As common and widespread disturbances, fire and herbivory co-occur in a wide range

of habitats. Understanding how these processes interact in the different ecosystems in which

they co-occur is therefore essential if the management of either is to be effective and efficient

(Wisdom et al. 2006, Fuhlendorf et al. 2009). Existing studies of fire-grazing interactions

generally focus on one of two interaction types. The first interaction (referred to in this thesis as

an interaction chain) involves feedbacks between fire and grazing through space and time: fire

leads to a concentration of grazing pressure in burnt areas, which then reduces the chance of

future fire in those areas, but increases the risk in unburnt areas where reduced grazing pressure

allows greater accumulation of biomass (Fuhlendorf et al. 2009, Kimuyu et al. 2014). Some

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researchers argue that this coupling of fire and grazing in space and time should be thought of as

a single disturbance process, termed “pyric herbivory” (Fuhlendorf et al. 2009). The second type

of interaction (referred to in this thesis as an interaction modification) is generally studied at the

site-level and occurs when the effect of grazing on plants or animals is modified by fire (or vice

versa) (Eby et al. 2014, Koerner and Collins 2014). These interactions often occur via changes

to the traits or behaviours of plants or animals. For example, fire can remove the chemical and

physical defences of plants, making generally unpalatable plants palatable (Augustine and

McNaughton 1998, Augustine and Derner 2015). After fire, the selectivity of grazers, and

therefore their effects on patterns of plant dominance and diversity can be significantly altered

(e.g. Augustine and Derner 2015). While often studied separately, both of these interaction

types are likely to be acting when fire and herbivory co-occur in natural systems.

The majority of fire-grazing interaction studies to date have been of domestic

herbivores in rangeland ecosystems. This bias is understandable due to the large global extent of

livestock grazing in rangelands, as well as the comparative ease of manipulating domestic

livestock densities compared with manipulating populations of wild herbivores. However, fire

and large herbivores also co-occur, and are actively managed, in forested systems, but here their

interactions remain poorly understood (Wisdom et al. 2006, Royo et al. 2010a). Browsing can

affect the successional trajectory of forests, resulting in shifts in the abundance and composition

of both canopy, and understory species (Olofsson et al. 2005, Royo and Carson 2006, Mathisen

et al. 2010, Tanentzap et al. 2012, Hidding et al. 2013, Holm et al. 2013). As fire re-starts

succession, and can alter vegetation species composition, plant traits, and herbivore behaviour,

there is strong potential for fire to interact with browsing, with possible long-term consequences

for the state of forest ecosystems (Royo et al. 2010a, Pekin et al. 2014). Indeed, the few existing

studies to date indicate that fire-browsing interactions are prevalent in forests (Royo et al.

2010a, Kerns et al. 2011). The effect of fire-browsing interactions on biodiversity therefore

warrants urgent attention from both managers and researchers of forested ecosystems.

In this thesis I investigated how fire and browsing interact to affect understory flora and

fauna in a eucalypt forest ecosystem of south-eastern Australia. I focussed on this system as the

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role of native herbivores in Australian ecosystems remains poorly understood, but existing

studies show that the effects of macropod herbivores on both plants and animals can be

substantial (Bulinski and McArthur 1999, Meers and Adams 2003, Tuft et al. 2012, Howland et

al. 2014). Further, although both fire and macropods are managed in many Australian

ecosystems (Clarke 2008, Gowans et al. 2010, Lunney 2010, Wiggins and Bowman 2011,

Enright and Fontaine 2014), they are generally considered as separate issues in contemporary

management decisions (Meers and Adams 2003). Fire is usually managed with the aim of

reducing the risk of large, high intensity wildfires to prevent life and property loss, although

some burning is also done for ecological purposes (Morrison et al. 1996, Driscoll et al. 2010).

Macropod populations are also managed in many Australian ecosystems, either directly through

culling (or other control) of overabundant populations (Coulson et al. 2008, Gowans et al. 2010,

Wiggins and Bowman 2011, Howland et al. 2014), or indirectly via ongoing control of both

native (dingo, Canis familliaris) and introduced (European red foxes, Vulpes vulpes) predators

(Banks et al. 2000, Coates 2008, Nimmo et al. 2015). The few existing studies of the effects of

fire and macropod herbivores on biodiversity indicate that interactive effects are likely to be

prevalent (Catling et al. 2001, Meers and Adams 2003, Tuft et al. 2012, Dexter et al. 2013,

Pedersen et al. 2014). The forests of south-eastern Australia therefore provide an opportunity to

study general questions about the interactive effects of fire and browsing on patterns of plant

and animal diversity, and also to generate insights into how to account for fire-browsing

interactions in the management of these forests.

Overview of Aims

This thesis includes three related components (Fig. 1), each addressing different questions, but

all of which contribute to the central aim of improving understanding and management of fire-

herbivore interactions. These components are: a global review of the effects of large herbivores

on other animals (Paper I); a series of field studies to test the interactive effects of fire and

herbivory on forest flora and fauna (Papers II, III and IV); and a synthesis (Paper V) which

builds on my field studies and the broader literature to critically evaluate the way that empirical

studies inform the management of interacting disturbances.

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Numerous studies have highlighted the important role played by large herbivores in

shaping ecosystem structure and function (Danell et al. 2006). However, there has been limited

quantitative synthesis of the effect of native herbivores on biodiversity, despite wild herbivore

populations being managed for biodiversity conservation in many ecosystems worldwide. In

Paper I, I conducted a quantitative review of the effects of large native herbivores on other

animals. This paper aimed to (i) summarise generalities and trends in the effects of large

herbivores on different animal groups, (ii) identify gaps and limitations in the literature, and the

reasons for these gaps, and (iii) highlight priorities for future research.

Figure 1. Relationships between the key questions explored in this thesis.

Paper III: By which mechanisms do fire and large herbivores interact to affect forest plant diversity?

Paper II: How do fire-herbivore interactions affect forest vegetation and small vertebrates?

Paper IV: To what extent are effects on animals mediated by vegetation structure?

Field experiment: Fire-browsing interactions

Paper V:

Could a focus on interaction pathways improve the ability of fire-grazing studies to inform ecological management?

Paper I: How do large herbivores affect other animals?

Interactions with other processes poorly understood

Vegetation change cited as a key mechanism, but rarely quantitatively tested

Multi-taxa studies rare, but needed to identify key pathways

Different groups of biota have different responses to disturbance interactions

Fauna responses not predictable from vegetation responses

Herbivores, and the fire x herbivore interaction can have important effects on fauna that are not vegetation mediated

Mechanistic understanding of interaction types useful for targeted, efficient management, and identifying novel solutions

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Understanding of how the effects of large herbivores plants and animals are modified

by management practices and disturbance regimes remains limited, particularly for forested

ecosystems (Paper I, Wisdom et al. 2006). However, understanding these interactions is

essential for informing effective and efficient management of the effects of large herbivore on

biodiversity (Wisdom et al. 2006). I therefore conducted a field experiment to test whether large

herbivores and fire interact to affect the forest ecosystem. I used this experiment to: (i) test the

interactive effects of fire and large herbivores on forest understory plants and animals (Paper

II), (ii) identify the mechanisms by which fire and large herbivores interacted (Paper III), and

(iii) test the extent to which the effects of fire and large herbivores on other animals were

mediated by changes in vegetation (Paper IV). Collectively, these papers (II-IV) aimed to

develop an improved, mechanistic understanding of the interaction between fire and large

herbivores, and how this affects biodiversity in forested ecosystems.

Several recent studies have highlighted the value of understanding interaction pathways

for both predicting and managing the effects of co-occurring stressors or threats (Didham et al.

2007, Boyd and Brown 2015, Doherty et al. 2015). I also found that the multi-taxa, mechanistic

approach I took in Papers II, III and IV allowed me to critically evaluate different management

options. However, in exploring the fire-grazing literature, I encountered few examples of studies

which took a similar mechanistic approach. In Paper V, I examined whether a focus on

interaction pathways could improve the ability of fire-grazing studies to inform ecological

management. This final paper aimed to: (i) quantify the extent to which fire-grazing studies

do/do not take a mechanistic approach, (ii) investigate whether failing to take a mechanistic

approach limits the management relevance of studies, and (iii) identify adjustments to

disturbance interaction studies that could improve their ability to inform effective ecological

management. Overall, Paper V aimed to identify approaches to studying disturbance

interactions that provide valuable insights for management, and should therefore form the basis

of future studies investigate fire-grazing or fire-browsing interactions.

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Study Area

The empirical papers in this thesis (Papers II, III, and IV) are based on a field experiment that I

conducted in the Eucalypt forests of Booderee National Park (BNP). A description of the

experimental design can be found in Paper II (Paper II, Methods, and Appendix 1), and a map

of the study area is included in Paper III (Paper III, Fig. 1). Below, I provide a more detailed

description of the study area and its management, as well as some aspects of the methods, which

could not be included in individual papers due to journal space limitations.

Booderee National Park is located on a 6 500 ha peninsula of coastal South-Eastern

Australia (35°10′S, 150°40′E). The area has a temperate maritime climate with rainfall (average

1225 mm per annum) occurring throughout the year (Lindenmayer et al. 2008). The vegetation

of BNP comprises a spatially heterogeneous mosaic of vegetation types, the most widespread of

which are eucalypt forest, heathland, eucalyptus/banksia woodland, and shrubland (Taws 1998,

Lindenmayer et al. 2008). Fire occurs naturally in the ecosystems of south-eastern Australia,

and many native species are well adapted to intermittent fire, but not to frequent fire (fire return

intervals of < 8-10 years cannot be survived by many species, Morrison et al. 1996, Enright and

Fontaine 2014). Booderee National Park has a complex mosaic of fire histories, which includes

a large wildfire in 2003 which burnt over 50% of the park’s area (Lindenmayer et al. 2008).

While large wildfire events occur occasionally in BNP, because of the proximity of urban areas

and the presence of important populations of threatened species, small-scale prescribed fires are

used to reduce fuel loads and minimise the risk of large wildfires.

Management of BNP also includes an intensive pest control program, targeting the

European red fox, which has been in place since 1999 (Dexter et al. 2007). This program has

been successful in increasing the abundances of some native small mammals in the park (Dexter

et al. 2007). However, the loss of large predators (dingoes), and the absence of hunting, means

that without foxes, predation pressure on larger mammals is low (Lindenmayer et al. 2014).

This has resulted in increases in the abundance of native macropod herbivores, with counts

increasing ten-fold between 2003 and 2009 (Dexter et al. 2012, Lindenmayer et al. 2014). The

most abundant of these macropods is the swamp wallaby (Wallabia bicolor, a mixed feeder,

8

Davis et al. 2008, Di Stefano and Newell 2008), but eastern grey kangaroos (Macropus

giganteus, grazers, Davis et al. 2008), and red-necked wallabies (M. rufogriseus, grazers, Sprent

and McArthur 2002) also occur (Dexter et al. 2012, Dexter et al. 2013). Non-native European

rabbits (Oryctolagus cuniculus) also occur within BNP, however they are at low abundance and

do not appear to be increasing (Dexter et al. 2013, Stutz et al. 2015). The high abundances of

macropods currently present in BNP are compromising efforts to restore ex-plantation areas

(Stutz et al. 2015), and also may be limiting the recovery of some plant species following weed

control burns (Dexter et al. 2013). Park managers are therefore concerned about what broader

impacts these abundant macropods may be having on the flora and fauna of the park, and

whether these impacts interact with the prescribed burning program.

In the empirical chapters of this thesis (Papers II, III and IV) I report results from an

experiment where I used a factorial combination of prescribed fire (burnt, unburnt) and

herbivore exclosure (open, partial exclosure and full exclosure) treatments to test the responses

of a range of plant and animal groups to the interactive effects of fire and herbivory (see Paper

II, Methods, and Appendix 1). In this experiment, I monitored the use of sites by macropod

herbivores using scat counts, which allowed me to compare quantitatively the activity of

herbivores in each treatment, and between sites over time. In these counts, I did not attempt to

distinguish between the different macropod species. Most of the scats would have been from

swamp wallabies (browsers) as they are the most common macropod in Booderee National

Park. However, some scats would have been from grey kangaroos (grazers), which I

occasionally observed at the sites. For this reason, my conclusions and recommendations

throughout the thesis relate to macropod herbivores in general and further detail regarding the

identity of herbivore species using the sites would need to be obtained before implementing

management actions that are targeted toward individual species.

9

Summary of Outcomes

Paper I: The effects of large native herbivores on other animals

In Paper I, I conducted a systematic review and meta-analysis of the effects of large native

herbivores on other animals. I found that, on average, large herbivores had negative effects on

the abundance and diversity of other animal groups. However, most studies compared areas of

very high herbivore density (overabundance) with areas of very low density (exclusions), which

likely biased studies towards detecting negative effects. Most of the effects of large herbivores

on other animals were attributed to indirect effects, predominantly through changes in the

quantity and/or structure of vegetation, although mediating mechanisms were rarely

quantitatively tested.

I identified key knowledge gaps on the effects of large herbivores on other animals

including: (i) the prevalence of nonlinear responses to herbivore pressure, (ii) how responses

differ between different herbivores, (iii) the spatial and (iv) the temporal variation of responses,

(v) how the effects of herbivores interact with disturbance and land management activities and

(vi) the mechanisms driving cascading effects through ecosystems (including synthesis of multi-

taxa responses). This paper was the first quantitative synthesis of the effects of large native

herbivores on other animals, and provides important context for the growing literature on the

ecological effects of large herbivores.

Paper II: Herbivory and fire interact to affect forest understory habitat, but not its

use by small vertebrates.

In Paper II, I tested whether herbivory and fire interacted to affect understory vegetation and

small vertebrates. Fire and herbivore exclusion interacted to affect understorey habitat, with

vegetation cover, particularly of palatable vegetation, showing poor post-fire recovery on

browsed sites. However, despite these interactive effects on vegetation, small vertebrates

responded to the individual, not interactive effects of fire and herbivory. Small vertebrate

species had contrasting responses to disturbance. For example, the small mammal, brown

10

antechinus (Antechinus stuartii) responded positively to herbivore exclusion, while the delicate

skink (Lampropholis delicata) showed the opposite trend.

These results suggest that it may be necessary to manage the macropod herbivore

population after fire to prevent the decline of palatable plants, and maintain the dense habitat

required by some small mammals. However, as the invasive European rabbit was most active in

macropod-free sites after fire, any management must consider both types of herbivores. As

different combinations of management benefited different species, and fauna responses were not

predictable from vegetation responses, a mix of management practices may be needed to ensure

suitable habitat is maintained for all species.

Paper III: Synergistic interactions between fire and browsing drive plant diversity

in a forest understory.

In Paper III, I tested the interactive effects of browsing and fire on understorey plant diversity. I

found browsing increased plant community dominance, and reduced evenness and diversity in

burnt, but not in unburnt sites. This fire–browsing interaction was driven by both numerically

mediated and functionally moderated pathways: Fire both increased local browsing intensity,

and amplified the per-unit effect of herbivores on the plant community. The altered competitive

environment after fire, combined with heavy post-fire browsing created a depauperate

understorey, dominated by an unpalatable, fire-resistant fern species (austral bracken, Pteridium

esculentum). Due to the ability of bracken to suppress the establishment of other plants, this

fern-dominated state may be difficult to reverse. Integrated management of fire and large

herbivores may therefore be necessary to prevent the development of a depauperate understorey

in areas where herbivores are abundant.

Paper IV: Interactive effects of fire and large herbivores on web-building

spiders.

In Paper IV, I tested for the interactive effects of fire and large herbivores on web-building

spiders, and the mechanisms by which these effects occurred. I found that fire had a strong

negative effect on the density of web-building spiders, which was partly mediated by effects on

11

vegetation structure, while the negative effect of large herbivores on web density was not

related to changes in vegetation. Fire amplified the effects of large herbivores on spiders, both

by modifying herbivore effects on vegetation, and by increasing herbivore activity. The relative

importance of the different pathways driving the effects of disturbance differed between types

of web-building spiders, and led to potentially important changes in the composition of this

predator guild. This study is one of the first to quantitatively assess the role of vegetation in

mediating the effects of fire-browsing interactions on fauna, and highlights the importance of

considering the driving mechanisms both when investigating and managing disturbance

interactions.

Paper V: Integrating theory into disturbance interaction experiments to better

inform ecosystem management.

Theoretical studies have shown that developing efficient, novel solutions to managing multiple

stressors relies on a detailed, mechanistic understanding of their interactions. In Paper V, I used

a mini-review of the fire-grazing literature to demonstrate that few empirical studies of

disturbance interactions were designed to identify the mechanistic pathways driving interactive

effects, or to detect non-linear effects (key relationships highlighted in theoretical models as

being important for management). I then showed how these widespread limitations among

disturbance interaction studies can lead to management recommendations that are ineffective,

inefficient, or likely to have negative outcomes. To address this issue, I presented a series of

adjustments to the design and interpretation of empirical disturbance interaction studies that

would require minimal additional research effort, but would improve the contribution of such

investigations to both the theoretical understanding, and applied management of multiple

stressors.

Synthesis and Management Implications

My research tested the interactive effects of fire and large herbivores on forest flora and fauna. I

found that for vegetation, the effects of fire and browsing were strongly interactive: in the

absence of fire, the effects of browsing on vegetation structure and floristics were minimal, but

12

following fire, heavy browsing led to a depauperate understorey dominated by ferns (Papers II,

III). Surprisingly, these interactive effects only partially mediated fauna responses, and both

vertebrate and invertebrate fauna were most strongly influenced by the individual, rather than

the interactive effects of disturbance (Papers II, IV). These results indicate that at their current

densities, the native macropod herbivores may have minimal effects on an undisturbed forest

ecosystem, and that management of the herbivore population should be targeted towards post-

fire, or early successional vegetation.

While I observed strong interactive effects of fire and herbivory on vegetation, the

result that browsing had minimal effects on vegetation in the absence of fire will require further

investigation. This is because the experiments in this thesis were of short duration (two years)

compared with the lifespans of many understory plants in these forests (time to maturity > 5-8

years for many shrub species, Morrison et al. 1996, Morrison 2002). Herbivory can affect plant

communities through mechanisms that can take a number of years to become evident, such as

limiting reproductive output and preventing establishment of seedlings (Hester et al. 2006,

Mathisen et al. 2010, Tanentzap et al. 2012), and forest vegetation can be slow to recover after

release from chronic herbivory (Royo et al. 2010b, Tanentzap et al. 2011). Therefore, a longer-

term study may be needed to detect the full range of herbivory effects. Further, while the plant

community exhibited only limited responses to large herbivores in the absence of fire, some

fauna (brown antechinus, skinks, spiders) responded to herbivore exclusion regardless of

burning treatment. This suggests that some important effects of large herbivores on other fauna

were not vegetation-mediated, or, that subtle (undetected) changes in the vegetation in herbivore

exclusion sites played an important role in determining fauna occupancy.

Fire and herbivory had strong interactive effects on vegetation. However, it was not

possible to conclude whether post-fire herbivory had led the system to an alternate state that

would persist long-term, or had simply slowed the recovery of vegetation (Paper III). If post-

fire vegetation recovery was simply slowed by browsing, then in the long-term, the individual

effects of herbivory may prove to be more important than interactive effects in defining the

community structure and floristics. It will therefore be important that monitoring of the

13

experimental plots continues, so that remaining questions regarding the longevity of interactive

effects, and herbivore effects on vegetation succession and recruitment can be answered.

My research has shown that small-scale prescribed fires can lead to focal browsing

(localised, high intensity browsing, Fuhlendorf and Engle 2001) by macropod herbivores in

burnt forest patches, which simplifies site-level vegetation structure and reduces floristic

diversity. Focal grazing (particularly of domestic herbivores) has been found to have net

benefits for biodiversity in many grasslands, despite reducing local vegetation structure

(Fuhlendorf et al., 2006; Fuhlendorf et al., 2009; Fuhlendorf et al., 2010; Winter et al., 2012). In

grasslands, focal grazing after patch burning can increase the diversity of flora and fauna,

because the concentration of herbivores in burnt patches releases vegetation elsewhere from

grazing pressure, the contrasting grazing pressures creating a heterogeneous mosaic of

vegetation structure, supporting a diversity of species (Fuhlendorf et al. 2010). However, focal

browsing following prescribed burning in forests is likely to have markedly different effects on

biodiversity.

The effects of focal browsing are likely to differ between grasslands and forests due to

general differences between herbivore populations, fire extent and frequency, and plant traits in

these ecosystems. Most herbivore populations in forested systems are wild, and free to move

across large areas of habitat. When the area of habitat that is burnt is small compared with the

total available habitat (which is often the case for prescribed fires in forests), focal browsing in

burnt patches is likely to do little to reduce browsing pressure in unburnt areas (Fuhlendorf et

al., 2009). Further, even if browsing pressure is reduced temporarily in unburnt areas, the slow-

growing nature of many forest plant species and the lack of an evolutionary history of heavy

grazing (Milchunas and Lauenroth 1993, Díaz et al. 2007), mean that forest vegetation is likely

to be slow to recover following release from browsing pressure (e.g. Royo et al. 2010b,

Tanentzap et al. 2011, Tanentzap et al. 2012). Supporting this reasoning, I found that in

Booderee National Park, the concentration of herbivores in burnt areas lasted only months, but

interactive effects on vegetation persisted throughout the study (Papers III, IV). Therefore,

small burns which initiate focal browsing of herbivores in forests may lead to the simplification

14

of vegetation of burnt patches, but not allow vegetation complexity or density to increase

elsewhere. Thus, focal browsing in forests could potentially lead to the overall decline of

vegetation complexity, rather than creating the heterogeneous vegetation structure that promotes

biodiversity in grassland systems.

The potential for focal browsing after prescribed fire to lead to simplification of

vegetation is of concern for the management of forests in south-eastern Australia for two

reasons. The first is that ongoing control of both native (dingoes) and introduced (European red

foxes) predators for livestock protection and threatened species conservation mean that some

species of macropod herbivores are highly abundant in many forests of south-eastern Australia

(Banks et al. 2000, Coates 2008, Coulson et al. 2008, Gowans et al. 2010, Nimmo et al. 2015).

Where predator control is ongoing, high densities of these herbivores, combined with limited

predation risk (predation risk can discourage some herbivores from browsing in open areas,

While and McArthur 2006, Cromsigt et al. 2013, McArthur et al. 2014), means that there is

strong potential for fire-browsing interactions such as those observed in this thesis. The second

reason for concern is that current fire management policies in south-eastern Australia aim to

increase the proportion of fire that occurs through prescribed burning (rather than wildfire)

(Attiwill and Adams 2013, NSW Office of Environment and Heritage 2013). Prescribed fires

are usually of lower intensity and severity than wildfire (and so less likely to directly reduce

macropod populations), and of smaller spatial extent (and so more likely than wildfire to lead to

focal browsing in burnt areas). Further, many plant species in south-eastern Australia are

already at risk from increasing fire frequencies, which do not allow sufficient time between

return fires for plant recruitment (Morrison et al. 1996). As I found in this thesis that macropod

browsing slowed the recovery of vegetation from fire (Paper III), focal browsing after fire may

slow plant maturity, and therefore increase the risk of plants being locally extirpated by return

fires. This combination of release of macropod herbivores from predation and changing fire

regimes is therefore of concern for the conservation of flora and fauna in south-eastern

Australian forests, and warrants further consideration from both researchers and managers.

15

In this thesis, I have recommended that the management of macropod herbivores be

integrated with fire management decisions in forests. Where fire-browsing interactions are

considered to be negatively affecting conservation values, forest managers may need to reduce

the occurrence or strength of these interactions. Two potential ways managers could reduce the

strength of fire-browsing interactions would be to: (i) manage fire to reduce the concentration of

herbivores in recently burnt areas (manage the chain interaction), or (ii) target management of

the herbivore population to post-fire environments (manage the interaction modification) (Paper

V). The choice between these two strategies will depend on whether the fire-herbivore

interaction is being driven by chain or modification effects. In cases such as Booderee National

Park, where fire and herbivory are interacting via both chain effects (fire-driven browsing) and

modification effects (herbivore effects being stronger after fire), effective management of the

interaction may require a combination of both strategies (see Paper V).

Managing the chain interaction may be an effective way to manage many fire-herbivore

interactions, particularly in reserves where high visitor use makes implementing broad-scale

macropod control programs difficult. To manage the chain interaction, it may be possible to

alter the size, shape or timing of prescribed burns to reduce the concentration of herbivores in

burnt patches. However, before this could be implemented effectively, further work is needed to

determine how fire influences macropod habitat selection in forests. Previous studies have

shown that some small macropods, such as the swamp wallaby, forage less in open areas, even

if they contain high quality forage (While and McArthur 2006, Di Stefano et al. 2009, Stutz et

al. 2015), and it has also been found that many small savannah herbivores select more strongly

for small, rather than large burns (Sensenig et al. 2010). It may therefore be possible to reduce

the extent to which small macropods concentrate in burnt areas by increasing the size of

individual fires (e.g. burning one larger area, rather than a number of small areas), or reducing

the edge:area ratio of burnt areas (so that more of the burnt area is at large distance from the

edge).

Another strategy that may be effective in reducing the concentration of herbivores in

burnt areas would be to alter the timing of burning, either ensuring that burns are conducted

16

when there is ample fresh pick available elsewhere in the system, or if a number of burns are

planned in the same season, ensuring burns are completed close in time, so that herbivores must

choose between patches, rather than moving from one freshly burnt patch to another as burns

are completed. However, the success of such strategies may be limited in areas such as

Booderee National Park, where terrestrial predators are largely absent, as it is unclear whether

swamp wallabies will avoid open areas without the ongoing threat of predation. Changes to

prescribed burning must also be considered in the context of broader fire history, as any

strategies that result in high fire frequency or short inter-fire intervals could have negative

effects on many species (Morrison et al. 1996, Enright and Fontaine 2014). Further study is

therefore needed to determine how the size, shape, intensity and/or timing of prescribed burns

affect the selection of different macropod species for burnt areas, and whether this varies with

the presence or absence of terrestrial predators.

When interaction modification effects are strong, managing for an interaction chain may

do little to mitigate interactive effects, and if the strategy includes increasing the size of burns,

such management may actually increase interactive effects (see Paper V). Therefore, fire-

browsing interactions that are driven by modification effects may be more effectively managed

by controlling the herbivore population in recently burnt areas. Targeted population control (e.g.

culling) to protect vegetation from browsing in the period immediately following fire may be

more efficient than broad scale population control as it may also encourage herbivores to avoid

burnt areas (i.e. reduce chain effects) (Cromsigt et al. 2013). Targeted control of macropods in

burnt areas may also more closely resemble historic processes in this region, as indigenous

Australians traditionally used fire to attract macropods to areas for easier hunting (Benson et al.

1997, Yibarbuk et al. 2001). Prior to implementing any population control, managers would

need to confirm the identity and relative abundance of the macropod species foraging in the

burnt habitat, and ensure that control was targeted towards the species contributing to vegetation

decline.

In Booderee National Park, management intervention may be required to prevent the

simplification of vegetation following prescribed burning. As Booderee National Park attracts

17

high numbers of visitors, managing the fire to reduce focal browsing in burnt areas (managing

the interaction chain) may be an effective and achievable alternative to broad scale lethal control

of macropods. In the long-term, effective management of the interaction chain requires a better

understanding of how the size, shape or intensity of prescribed fires affects site selection by

different herbivore species. Until such information is available, managers could consider a

number of measures to reduce the likelihood of vegetation simplification after burning:

- Avoid very small sized prescribed burns, particularly in areas of sensitive or high-value

vegetation.

- Consider the timing of burns, and where possible conduct burns when forage is readily

available and widespread within the park.

- Monitor the recovery of vegetation following prescribed burns. If, despite measures to

reduce chain effects, recovery of vegetation is limited (i.e. bracken dominance exceeds

60%, see Paper III), consider short-term measures to deter herbivores from feeding in

burnt areas.

Conclusion

My research has shown that fire and macropod herbivores can interact via both numerically

mediated (chain) and functionally moderated (modification) effects to simplify the structure and

diversity of forest vegetation, and alter the density and composition of some fauna groups.

Under current management and policy conditions, it is probable that such interactions occur in

many of the forests of south-eastern Australia. Large herbivore populations, particularly those

that are highly abundant, should therefore be an important consideration for prescribed burn

planning in these forests. Future research that determines how long interactive effects persist,

and how fire properties and the presence of predators affect macropod site selection should be

prioritised to support these management decisions. Overall, this thesis has highlighted the value

of a mechanistic understanding of how co-occurring disturbances interact for determining when,

where and how disturbance interactions can most effectively and efficiently be managed.

18

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25

Paper I: Effects of large native herbivores on other animals

Numerous studies have highlighted the strong role of large herbivores in shaping ecosystem

structure and function. However, there has been little quantitative synthesis of the effect of

native herbivores on other animals, despite wild herbivore populations being managed for

biodiversity conservation in many ecosystems. In this paper, I consolidated existing knowledge

of the effects of large herbivores on other animal groups, analysed the reasons behind current

gaps and limitations in the literature, and identified approaches for further research to better

inform the management of large herbivores for conservation objectives.

Foster, C.N., Barton, P.S. and D.B. Lindenmayer (2014) The effects of large native herbivores

on other animals. Journal of Applied Ecology. 51, 929-938.

26

Summary

1. Large mammalian herbivores are major drivers of the structure and function of

terrestrial ecosystems worldwide, and changes in their abundance have resulted in many

populations being actively managed. Many empirical studies have identified that

abundant mammalian herbivores can have negative impacts on biodiversity, but there

has been no specific review of the impacts of native mammalian herbivores.

2. We assessed the peer-reviewed literature on the effects of large native herbivores on

other animals. We aimed to quantitatively synthesise current knowledge, identify gaps

and limitations in the literature, and highlight priorities for future research.

3. Most empirical studies of herbivory effects compared only two levels of herbivory

(76%), and meta-analysis showed that very high densities of herbivores, when

compared with very low densities, had mostly negative effects on other animal species.

These negative effects were usually attributed to changes in the quantity and/or

structure of vegetation. Only 24% of papers studied animal responses across a gradient

of herbivore densities; and non-linear responses to herbivory, as well as responses to

low and moderate herbivore densities, remain poorly understood.

4. The literature also was dominated by short-term studies (76% sampled animal responses

for 2 years or less) and there was a high incidence of confounding factors among studies

(38% of studies). In addition, many studies used only coarse metrics to assess effects

(e.g. only 33% of studies assessed species composition) and few included community-

level synthesis (only 31% of studies reported results from more than one animal class).

5. Synthesis and applications. Critical questions remain for both basic ecology and the

management of large native herbivores for biodiversity. Key knowledge gaps include

(1) non-linear responses to herbivore pressure, (2) how responses differ between

different herbivores, (3) the spatial and (4) the temporal variation of responses, (5) how

the effects of herbivores interact with other land management activities, and (6) the

27

mechanisms driving cascading effects through ecosystems. We identify ways to address

these gaps and emphasise the need for studies which employ contrasts over a gradient of

ecologically relevant herbivore densities and biologically meaningful timeframes.

Keywords

Browsing, biodiversity, ecological cascades, deer, grazing, herbivory, indirect effects, over-

abundance, species interactions, ungulates

Introduction

Large mammalian herbivores play a central role in the structure and functioning of terrestrial

ecosystems (Côté et al. 2004; Danell et al. 2006). Loss of native predators, and changes in land

use have led to large herbivores becoming over-abundant in many ecosystems worldwide (Estes

et al. 2011; Ripple et al. 2014). In other locations, large herbivore populations are threatened by

over-hunting, habitat loss, and habitat fragmentation (Mallon & Zhigang 2009; Festa-Bianchet

et al. 2011). Consequently, many species of large herbivores are now actively managed

throughout much of their range (Gordon, Hester & Festa-Bianchet 2004). This has resulted in a

demand for empirical studies to quantify the effects of large herbivores on ecosystems and

biota, and for data to both inform and justify management decisions (e.g. Owen-Smith et al.

2006).

Knowledge of the effects of large herbivores is substantial. Effects include changes to

primary production (Frank & McNaughton 1993), nutrient cycling (Molvar, Bowyer &

Ballenberghe 1993), soil properties (Abdel-Magid, Schuman & Hart 1987), fire regimes (Hobbs

1996), plant and animal communities (Goheen et al. 2010), and possibly even climate (Doughty,

Wolf & Field 2010). In this study we focus on the effects of large native herbivores on other

animals. The effects of large herbivores on other animals can occur via a range of mechanisms,

many of which are indirect. These include consumption of vegetation, modification of the

physical environment, provision and re-distribution of resources (e.g. dung, carrion), and direct

interference (Schmitz 2008; Marquis 2010). These effects can vary in both temporal and spatial

28

scale, from short-term impacts on individuals, to landscape-scale shifts in ecosystem state that

can be difficult to reverse (Côté et al. 2004; Tanentzap, Kirby & Goldberg 2012). The effects of

herbivory on other animals cannot be easily predicted from effects on vegetation as animal

responses have been found to differ from plant responses both in shape and magnitude

(Milchunas, Lauenroth & Burke 1998; Wardle et al. 2001; Grandin, Lenoir & Glimskär 2013).

Previous reviews on the effects of large herbivores on other animals have attempted to

synthesise these complex effects but have been specific in taxonomic or geographic focus. They

include effects on birds (Gill & Fuller 2007), small mammals (Flowerdew & Ellwood 2001),

butterflies (Feber et al. 2001) and invertebrates (Stewart 2001), and effects on specific

ecosystems such as European heathlands (Rosa García et al. 2013) and the North American arid

zone (Jones 2000). Work by Suominen and Danell (2006) took a more holistic approach,

providing a qualitative summary of the effects of large herbivores on both invertebrate and

vertebrate groups and in a range of ecosystems. However, the volume of literature available on

this topic has more than doubled since 2006, which both warrants renewed attention to this

topic, and allows the application of quantitative approaches not previously possible.

Furthermore, previous reviews have not distinguished between the effects of native and

introduced herbivore species. This is important because the effects of native herbivores may

differ from those of introduced species due to their shared evolutionary history with their

environment (Milchunas, Sala & Lauenroth 1988; Hobbs 1996). The management goals for

native herbivores are also likely to differ, being focussed on establishing an ‘optimum’

herbivore density, rather than on damage limitation or eradication (Côté et al. 2004; Gordon,

Hester & Festa-Bianchet 2004).

We conducted a systematic review and meta-analysis of the published literature on the

effects of large native herbivores on other animals. Our review had four principal aims: (1) to

synthesise current knowledge on the effects of large native herbivores on other animals, (2) to

identify key knowledge gaps or inconsistencies within the literature, (3) to identify the problems

which have led to these gaps, and (4) to suggest priorities for future research. While our review

focusses on effects on animal assemblages, many of the knowledge gaps and barriers we

29

identify also will be applicable to the study of other effects of large herbivores. While the

definition of a ‘large herbivore’ varies between studies, to be consistent with the previous

review, we use the definition of Danell et al. (2006), where the term covers all terrestrial

mammalian herbivores with an average body mass exceeding 2 kg.

Our quantitative and systematic approach to this review reveals key gaps in our

understanding of the effects of large native herbivores, and identifies specific limitations in

study design that have led to these gaps. We identify approaches for future research to overcome

these limitations and address critical questions for basic ecology and management.

Materials and Methods

We used a systematic approach to obtain a representative sample of the literature on the effects

of large herbivores on other animals. Briefly, we used key search terms to identify potentially

relevant articles, and selected articles which quantified the effects of large native herbivores on

other animals (full details are outlined in Appendix S1 in Supporting Information). This process

yielded 96 different studies from 69 different study areas, published between 1983 and 2013.

For details of these papers see Appendix S2 in Supporting Information.

Systematic Review

We used data from these 96 papers to address six specific research questions. Below we briefly

outline the methods used to address each of these, for full details see Appendix S1.

1. Which ecosystems and animal response groups are most commonly studied?

To assess geographical and taxonomic differences in study effort, we sorted all papers into

categories by two location variables (continent and ecosystem), two focus herbivore variables

(taxonomic group and abundance (as described by authors)), and two animal response group

variables (number of groups studied, taxonomic group studied). For each variable, we then

tested whether there were differences in the frequency of papers in each category using a Chi-

squared analysis. We also sorted papers from the original search to assess whether studies of

native herbivores were more or less common than studies of domestic or wild exotic herbivores.

30

2. Are there overall trends in the direction of effects of large herbivores on other

animals?

From each experimental study which was not strongly confounded (i.e. quality of evidence

score of iii or better as defined by Felton et al. 2010, see Appendix S1), we recorded the

abundance, species diversity and species composition responses of animal assemblages to

different levels of herbivory. We calculated the mean effect of herbivory for taxonomic groups

for which we could extract data from at least five studies. We selected species richness as the

measure of species diversity as it was the most commonly used metric. For each taxonomic

group in each study, we calculated a log response ratio (highest versus lowest herbivory

treatments in study) and its variance (Borenstein et al. 2009, p. 30). We then performed a

random effects meta-analysis to calculate mean log response ratios and 95% confidence

intervals for each taxonomic group (Borenstein et al. 2009, p. 69). Taxonomic groups were used

instead of functional groups as most studies used taxonomic divisions and the infrequent and

inconsistent use of functional groups between studies meant sample sizes were small. In

addition, where sample sizes were adequate, we found that taxonomic groups were able to

provide more detail than functional groups (e.g. Orthoptera and Lepidoptera versus leaf chewing

herbivores). We analysed Arthropoda overall, as well as arthropod orders individually, as

Arthropoda were often used as a measure of prey abundance for other taxa.

As species composition is not measurable on a continuous scale, we could not calculate

a mean response ratio for species composition. Instead, we assessed whether overall, results for

species composition differed from those for species diversity. We examined studies that

reported both species diversity (any measure of within-plot species diversity) and species

composition responses to herbivory, and compared the numbers of studies reporting a

significant effect or no effect for each of these measures. As some studies analysed data from

different animal groups separately, we included data from each analysis as a separate data point.

We also assessed whether the effects of large herbivores on other animals could be

attributed to any particular mechanism. To do this, for each study, and also for each animal

response group in each study, we recorded the mechanisms authors attributed to the observed

31

animal responses. For each response, we also recorded whether the importance of these

mechanisms was determined through an explicit test, or through author inference, and the

direction of the animal response (positive, negative or ‘other’).

Environmental factors can modify the way organisms respond to herbivory (Wisdom et

al. 2006), and the interactions can have stronger effects than the factors alone (Joern & Laws

2013). We therefore recorded the results of all studies investigating the interaction between

herbivory and another factor to identify patterns across studies.

3. What is the shape of the animal response to herbivory?

We defined the shape of the herbivory response as the relationship between the animal group

response variable (abundance, species diversity etc.) and increasing herbivore density. This

could be increasing or decreasing, linear or non-linear etc. We first quantified how many studies

were designed to test the shape of the herbivory response. To be counted, a study had to

measure the abundance or species diversity of an animal assemblage at a minimum of three

different levels of herbivory, and analyse these measurements against a continuous scale of

herbivory. Second, we quantified the frequency of non-linear responses to herbivory among the

reviewed studies. For each study which measured responses at three or more levels of

herbivory, we allocated the response of an animal group to one of the following four categories

for both abundance and diversity (any measure of within-plot diversity): (1) linear response, (2)

non-linear response, (3) no response/unable to tell from data or (4) no data. For studies which

used qualitative categories of herbivore pressure (e.g. low, medium and high) and reported a

monotonic response, it was not possible to differentiate between linear and non-linear

responses. Therefore, all monotonic responses from categorical studies were assigned to the

category ‘unable to tell from data’.

4. Has the design of herbivory studies changed over time?

We assessed the change in experimental approaches over time by examining the design, spatial

scale and ‘quality’ of experiments. To assess changes in design, we sorted papers by

experimental approach (herbivore exclusion, herbivore density manipulation, simulation of

32

herbivory or natural experiment) and herbivory treatments (binary – two levels of herbivory or

gradient – three or more levels of herbivory). We also sorted herbivore exclusion studies

according to whether they excluded all large herbivores or selectively excluded particular

groups of herbivores. We grouped studies by publication year into five year blocks, and used a

Chi-squared analysis to test whether the frequency of studies across experimental designs

differed among time periods.

To assess changes in scale over time, we used the experimental exclosure studies and

examined whether, for the same level of replication, the size of exclosures had changed over

time. To do this we calculated the mean exclosure size and the number of sampling plots (the

number of exclosures per treatment times the number of treatment combinations) in each paper.

We log-transformed both variables to improve linearity. To test for differences over time, we

performed a linear regression with groups (with five-year intervals as the groups).

To assess the ‘quality’ of studies over time, we used the quality of evidence categories

described by Felton et al. (2010) to classify studies as either not confounded (evidence quality

score of i or ii) or confounded (evidence quality score iii or iv). We also grouped studies

according to whether they used an experimental or non-experimental approach. We tested

whether the frequency of studies with confounding variables had changed over time using a

logistic regression in GenStat 15th Edition (VSN International 2012). We allowed interactions

between publication year and study approach to allow different slopes to be fitted to

experimental and non-experimental studies.

5. Do short-term studies provide a reliable representation of the effect of large

herbivores on other fauna?

We assessed whether the duration of sampling of animal responses affected the likelihood of the

study detecting a significant effect of herbivory. For each of three variables (abundance, within-

plot diversity and composition), we classified the response of each animal group in each study

as either ‘significant response to herbivory’ or ‘no response to herbivory’. We conducted a Chi-

squared analysis to test whether the frequency of studies detecting a significant result differed

33

between short-term studies (sampling for three years or less, = 1.56, n = 84) and long-term

studies (sampling over four or more years, = 6.25, n = 12). We also assessed the prevalence of

significant time × herbivory treatment interactions in long-term studies by recording whether or

not such an interaction was detected in each study.

6. What do authors recommend as priorities for future research?

One of the principal aims of our review was to identify ways to guide the direction of future

research. We therefore recorded research recommendations from each paper.

Results

Most commonly studied ecosystems and response groups

We found significant biases in the geographic location, ecosystem, types of herbivores and

animal response groups studied (all P < 0.001, Fig. S1 in Supporting Information). Studies in

forested systems of North America and Europe were the most common; with Arthropoda and

birds the most frequently studied animal response groups. Our search did not return any studies

of the effects of large herbivores on their predators. Only 31% of studies measured the

responses of more than one class of animal to herbivory. In 58% of studies, at least one native

herbivore was described as over-abundant or at higher than historical levels. In the initial search

of the literature, studies on the effects of introduced herbivores (n = 193, domestic = 167, wild =

27) on fauna were twice as common as studies on the effects of native herbivores (n = 99, P <

0.001, Fig. S2 in Supporting Information).

Effects of herbivory on other animals

Meta-analysis revealed significant overall responses to large herbivores for many animal taxa.

The abundance of small mammals, Arthropoda, Araneae, Hemiptera and Lepidoptera were all

negatively affected by high levels of herbivory (P < 0.05 for all groups, Fig. 1). The abundance

of birds, Coleoptera and Hymenoptera showed no significant overall response to herbivory.

Orthopteran abundance was the only variable showing a positive overall response to herbivory,

34

however this was not significant (P = 0.21). The effect of herbivory on species richness could

be calculated only for birds and Arthropoda and both groups were negatively affected by

herbivory (P < 0.001, Fig. 1). Arthropoda richness showed the strongest negative response of all

variables, with richness being 66% lower under high than low levels of herbivory.

Figure 1. Abundance (a) and richness (b) responses of animal groups to large native herbivores

(ln response ratio and 95% confidence limits). Arthropoda includes only data from

studies examining arthropods overall (i.e. not restricted to 2 or 3 focal groups). Data

from some studies are included in both Arthropoda and order-level analyses. Response

ratio equals abundance (or richness) at high herbivore density / abundance (or richness)

at low herbivore density. Values less than zero indicate that the animal group was more

abundant (or species rich) at low levels of herbivory. Values with error bars not

overlapping zero indicate a significant effect of herbivory (P < 0.05).

The effects of herbivory on the species composition of animal assemblages were

assessed in 32 papers (33 %) for 41 animal groups, and effects on both composition and species

diversity were analysed for 37 animal groups. Herbivory significantly affected the composition

but not the diversity of eight animal groups, while only two animal groups had the reverse

result.

35

Ninety of the 96 reviewed papers discussed mechanisms driving the effects of large

herbivores on other animals. Although 84 papers provided conclusions about which

mechanisms were likely to have caused the effects they observed, only 48 performed any test of

these mechanisms. Of these, 46 confirmed one or more mechanism as important, while only

eight reported evidence against a mechanism. Animal responses were most commonly attributed

to changes to vegetation biomass and structure (Fig. 2). Most mechanisms were associated with

negative effects of large herbivores on other animals, however changes to plant chemistry also

commonly associated with positive effects (Fig. 2). Vegetation structure and biomass were the

most commonly implicated mechanisms for effects on birds, small mammals, Arthropoda,

coleopterans and orthopterans (Table S1 in Supporting Information). Effect on coleopterans

were also associated with changes to the physical environment.

Figure 2. The mechanisms that authors considered to be driving the effects of large native

herbivores on the (a) abundance and (b) diversity of other animals, and the numbers of

animal responses associated with each mechanism. The animal response groups are

divided according to whether the mechanism was associated with a negative (black),

positive (white) or ‘other’ (grey) response to herbivory.

36

Nineteen studies (20%) considered how environmental or land management factors

interacted with herbivory effects. All three studies that investigated herbivory × productivity

interactions reported that herbivory effects were most strongly negative at low productivity

(Suominen et al. 2003; Pringle et al. 2007; Suominen et al. 2008). Recent fire (three papers),

addition of logs (two papers), vegetation age (two papers), vegetation density, tree thinning, soil

disturbance, nutrient input, rainfall and invasive plants were also found to modify the response

to herbivory of at least some animal groups. Studies of fire frequency (two papers) and predator

control identified no significant interaction with herbivory.

Shape of the response to herbivory

We found that 23 studies (24%) measured animal response variables at three or more levels of

herbivory, but only six of these performed their analyses using herbivore pressure as a

quantitative variable. We were able to assess the shape of the response to herbivory for only two

of these, and both linear and non-linear responses were reported. Among the 23 studies which

measured animal responses to a gradient of herbivore pressures, linear, exponential decay,

threshold, intermediate maximum and intermediate minimum response shapes were all reported

for at least one animal group. There was no significant difference in the number of animal

groups which showed linear and non-linear responses to herbivory for either abundance (P =

0.21) or diversity (P = 0.26).

Experimental approaches

The most common experimental approach was herbivore exclusion, followed by natural

experiments (Fig. S3a in Supporting Information). Chi-square comparisons indicated that there

was no difference in the distribution of papers across experiment types between time periods (P

= 0.29, Fig. S3b). In contrast, the number of papers using three or more levels of herbivory in

their comparisons tended to increase over time; from 10 % of pre-2003 papers to 30% of papers

published between 2004 and 2013 (P = 0.03, Fig. S3b). The number of papers including

selective exclusion of different herbivores in their design also increased over time (P = 0.017,

37

Fig. S3c). However, only seven of the 17 papers which had study designs including selective

exclusion actually included the selective treatments in their analysis of effects on animals.

The size of exclosures in herbivore exclusion studies varied greatly, from 0.1 m2 cages

surrounding individual shrubs to 2100 ha fences encompassing entire watersheds (median =

0.92 ha, IQR = 3.9 ha). There was a significant negative relationship between the log of mean

exclosure size and the log of the number of sample plots in a study, with studies employing

larger exclosures using lower levels of replication (Fig. 3). This relationship did not differ

between time periods, indicating that the scale of exclosure studies has not changed over time.

Figure 3. Relationship between mean exclosure size in a study and the number of study plots

(number of replicate exclosures per treatment combination times the number of

treatment combinations) in different time periods. The log of the number of sample

plots was negatively related to the log of exclosure size (r2 = -0.6, P < 0.001). This

relationship did not differ between time periods (P > 0.2).

We identified potential confounding effects in 38% of papers, with 13% of papers

confounded to the extent that their data were unusable for meta-analysis (data quality score iv,

see Felton et al. 2010). Logistic regression showed that there has been no change in the

incidence of confounded studies over time for either experimental or non-experimental studies

38

(P > 0.05). Experimental studies were far less likely to have problems with confounding factors

(20% of studies) than non-experimental studies (85% of studies, P < 0.001).

Effects of time

Most (76%) papers measured animal responses for two years or less, while only 12% measured

animal responses for four years or more. Chi-squared tests showed that there was no difference

in the proportion of animal groups showing significant responses to herbivory between short-

term (1–3 year) and longer-running studies (P > 0.5 for all variables). However, this analysis

had limited power due to studies being heavily skewed across both categories (only 12% ran for

more than three years and only 5% found no significant response to herbivory).

Among the 12 studies which measured animal responses for four or more years, five

reported a significant year × treatment interaction (Keesing 2000; McShea & Rappole 2000;

Gómez & González-Megías 2007; Mathisen et al. 2012; Parsons, Maron & Martin 2013), and

two found significant treatment effects in only some years (Neff, Fettig & VanOverbeke 2007;

Martin & Maron 2012). The remaining five studies did not contain any information on year–

treatment interactions.

Priorities for future research

Fifty-one of the 96 papers made explicit recommendations for future research.

Recommendations could be divided into two main categories; those relating to research topic

and those relating to study design (Table S2 in Supporting Information). The most frequently

suggested topic for future research was the possible cascade of observed herbivory effects to

other species or ecosystem processes. Other commonly identified research priorities included

studies of the mechanisms of effects, novel habitats or locations, and possible interacting

factors. Recommendations regarding the design of future studies were less common but

included: studies which measure responses at larger spatial scales and/or examine spatial

variation in responses; studies which can detect both direct and indirect relationships; studies

which can detect the shape of herbivory responses; and longer-term studies.

39

Discussion

Our review of the empirical literature has demonstrated that very high herbivore densities

usually have negative impacts on other animals. However, there remains only a limited

understanding of whether these effects are consistent across taxa (of both herbivores and

response animals) or ecosystems, and what factors modify these effects. We also have identified

key knowledge gaps around the shape of animal responses to herbivory, the consistency of

responses between herbivores, how responses vary with temporal and spatial scale and the

mechanisms driving the flow of effects through ecosystems. These gaps are, in part, due to

limitations in the design of studies. Below we discuss key findings and research gaps and

identify ways to advance future research in this area.

Effects of herbivory

Most animal groups showed a negative overall response to herbivory (Fig. 1). Small mammal

abundance and bird species richness were both negatively affected by high levels of herbivory,

mostly due to reduced biomass and structural complexity of vegetation and reduction in food

resources (Fig. 1, Table S1). This result is consistent with previous reviews (Flowerdew &

Ellwood 2001; Fuller 2001; Suominen & Danell 2006). Overall, bird abundance was not

affected by herbivory, which may be due to the small sample size in our analysis. However, the

effects of livestock grazing on birds are also mixed (e.g. Milchunas, Lauenroth & Burke 1998)

and the difference we found between abundance and diversity effects may reflect a

compensatory increase in generalist species under high herbivory (McKinney & Lockwood

1999). Arthropoda, Araneae, Hemiptera and Lepidoptera, also showed negative responses to

high herbivory, which was consistent with the summary of Suominen & Danell (2006), and was

again attributed to simplified vegetation structure and reduced plant biomass under high

herbivory.

Orthopterans (leaf-chewing herbivores), were the only group to show a positive overall

response to herbivory, although this effect was not significant. This result differed from the

other herbivorous analysed (Hemiptera - sap suckers and Lepidoptera - leaf chewers as larvae),

40

which responded negatively to the reduced vegetation biomass at high levels of herbivory. The

different responses may have occurred because studies of Orthoptera were predominantly from

grassland and savanna habitats. In these systems, although grazing reduces grass biomass, it

also can increase nutrient turnover and improve the nutritional quality of food available to

grasshoppers, while also increasing the heterogeneity of the grass canopy, which assists with

predator evasion and thermoregulation (Joern 2004).

We found that Arthropoda richness displayed the strongest negative response to

herbivory (Fig. 1), which differs from Suominen and Danell’s (2006) summary that richness is

often higher in grazed sites. As only three of the five studies in our analysis corrected richness

for abundance this strong negative response may be a slight over-estimate (Gotelli & Colwell

2001). However, it does indicate that effects on richness, at least at this coarse taxonomic level,

tended to be negative rather than positive. It is somewhat surprising that such an ecologically

diverse group as Arthropoda showed such a strong negative response, but this may partly be

explained by the nature of the comparisons in the studies we analysed. Most studies we

analysed were exclosure studies, where very high levels of herbivory were compared with very

low or no herbivory (Figs. S1, S3). As herbivore density increases, it will almost invariably

reach a point where effects on other species become negative, even if effects were positive or

negligible at lower densities (Milchunas, Sala & Lauenroth 1988; Suominen & Danell 2006).

By comparing two extreme (and unnatural) herbivore densities, exclosure studies in areas of

high herbivore density are therefore pre-positioned to detect negative effects of large herbivores

on other animals. At lower levels of herbivory, animal responses may be more varied, both

between sites and between animal groups.

Shape of animal responses to herbivory

A wide range of ‘shapes’ of animal responses to herbivory were reported, which reflects the

wide range of ecosystems, animal groups and herbivores included in this review. Both grazing

theory, and papers we reviewed, indicate that site-specific variables such as productivity

(Pringle et al. 2007; Vesterlund et al. 2012), history of herbivory (Milchunas, Sala & Lauenroth

41

1988), and the presence of other disturbances (Wisdom et al. 2006), can change the shape,

direction and magnitude of animal responses to herbivory. The variation caused by these factors

is in addition to the variation in responses between animal response groups and between

different herbivores (Pringle et al. 2011).

These variable results highlight the importance of not assuming a linear response to

herbivory and caution against extrapolation (or interpolation) from studies which include only

binary comparisons. If the relationship between herbivore pressure and an animal assemblage

were non-linear, a binary study could detect a positive, negative or no response to herbivory

simply depending on which two herbivore pressures were selected for comparison (Rooney &

Waller 2003)(Fig. 4). The use of binary studies to inform the management of large herbivore

populations could therefore lead to unexpected and undesirable outcomes. For example,

reduction of a highly abundant herbivore population intended to benefit other animals could

have no effect, or even a deleterious effect on the target animals, depending on the underlying

shape of the herbivory response (see Fig. 4).

42

Figure 4. The effect of non-linear responses to herbivory on the outcome of experiments

including only binary comparisons (74% of reviewed studies). If the ‘true’ response of

an animal group to increasing herbivore pressure was unimodal (bold line), a study

including only binary comparisons would fail to detect this, and depending on the two

herbivore pressures compared, could conclude that an animal was positively (e.g. a vs

b), negatively (e.g. b vs c) or not affected (e.g. a vs b) by increasing herbivore pressure.

A binary study would also fail to detect a threshold-shaped response (dotted line) and

might instead report no effect (e.g. i vs ii) or a negative effect (ii vs iii).

Experimental Approaches

The recent increase in the number of studies using a gradient of herbivore densities indicates

that researchers are recognising the limitations of simple binary designs. However, non-binary

studies are still in the minority, and animal responses are rarely analysed against a quantitative

gradient of herbivory. Studies of functional differences between herbivores are also increasing,

but many papers present data only from the two extreme treatments and therefore do not use

these studies to their full potential. Further progress in isolating the effects of herbivory from

43

confounding factors is also essential if studies are to detect the ‘true’ response of animals to

herbivory.

Due to logistical constraints, experimental studies were usually limited in either spatial

scale or replication (Fig. 3). A trade-off is made between gaining detailed information at small

spatial scales, or coarser information at large spatial scales. As a result, most studies did not

integrate considerations of spatial scale into their design, although a few notable studies found

important scale-dependent results (Jay-Robert et al. 2008; Barton et al. 2011). As the effects of

herbivory vary with spatial scale (Chaneton & Facelli 1991; Tahmasebi Kohyani et al. 2008),

contradictory results can simply be an artefact of the scale of measurements; small-scale

responses do not necessary multiply up to similar effects at larger spatial scales (Hester et al.

2006). While understandable, the bias of experimental studies towards low replication at large

scales and high replication at small scales means that they are unlikely to detect landscape-scale

changes in diversity such as biotic homogenisation (Olden 2006).

The need for long-term studies

Most studies measured animal responses to herbivory over short timeframes of two years or

less. However, such studies may give a poor estimate of the true response of an animal

assemblage to herbivory, even if herbivory treatments have been in place for a number of years.

Animal populations can fluctuate greatly within short time periods, particularly in short-lived

organisms or boom–bust ecosystems (Joern & Pruess 1986; Ernest, Brown & Parmenter 2000;

Ostfeld & Keesing 2000). Short-term studies, especially those with few sampling events, can

therefore not be reliably extrapolated to the multi-year times scales of interest to land managers

(Lindenmayer et al. 2012). This is demonstrated by the high incidence of significant time ×

herbivory interactions in long-running studies, where the effect of herbivory was significant

only in some years, and some studies even returned opposite results between years. Although

long-term studies are more costly and difficult to conduct, they are necessary if we are to detect

and distinguish between short-term populations fluctuations, which may require little

44

management intervention, and major shifts in ecosystem state which justify significant

investment of conservation resources (Andersen et al. 2009; Lindenmayer et al. 2012).

Implications for future work

We have identified key limitations in the design of existing studies that have led to knowledge

gaps in the effects of large herbivores on other animals. These gaps include; (1) the shape of

animal responses to increasing herbivore pressure, (2) how animal responses differ between

different herbivores and the (3) spatial and (4) temporal variation in animal responses (Table 1).

How these effects interact with other land management activities (5), and the mechanisms by

which effects cascade to other biota and ecosystem processes (6) also remain key areas for

future research. To address these gaps, study designs need to move beyond the traditional “lots

or none” herbivore exclusion approach, and employ collaborative and creative study designs

(see Table 1).

Table 1. Knowledge gaps surrounding the effects of large native herbivores on other animals and

suggested approaches to address these gaps.

To understand the shape of animal responses to herbivory, studies need to include a

gradient of ecologically relevant herbivore pressures and analyse animal responses against

herbivory as a continuous variable (Hester et al. 2000). In systems where more than one species

of large herbivore is active, studies which measure animal responses to different herbivores are

45

also critical for understanding the functioning of these systems (Wisdom et al. 2006; Pringle et

al. 2011). Combining experimental approaches with natural experiments (Hester et al. 2000),

and using experimental treatments to their full potential will go a long way to addressing these

gaps (Table 1). Considerable potential also lies in collaborations with land managers to measure

responses across different areas of active management and establish adaptive management

studies (Wisdom et al. 2006; Westgate, Likens & Lindenmayer 2013).

An understanding of the spatial and temporal variation in the effects of large herbivores

is essential for the efficient, targeted and timely management of large herbivore populations

(Gordon, Hester & Festa-Bianchet 2004; Wisdom et al. 2006). To detect this variation,

comparisons need to extend further than simple between-plot comparisons and employ sampling

regimes over biologically meaningful timeframes. Again, combining experimental approaches

with natural experiments and replicating experiments over different land management practices

or environmental gradients (e.g. Goheen et al. 2013) will be useful approaches (Table 1).

Metrics which examine changes in species composition and species turnover across landscapes

can also provide larger-scale insights (e.g. Newman, Mitchell & Kelly 2013), without great

increases in sampling effort above what is required for between-plot diversity comparisons. As

identified by previous authors (Table S2), multi-taxa studies able to test the mechanisms which

drive effects, and how effects cascade through ecosystems, are essential if we are to thoroughly

understand the processes underpinning the effects of large herbivores on other animals.

Conclusion

Studies of the effects of large native herbivores on other animals were biased towards short-

term, binary comparisons at limited spatial scales. These studies revealed mostly negative

effects of high densities of large native herbivores on bird, small mammal and arthropod

assemblages. To advance this field, future studies must address key knowledge gaps

surrounding non-linear effects, differences in effects between herbivores, spatial and temporal

variation, interacting factors and the mechanisms driving effects. These studies must give

46

careful consideration to study design if they are to give new insights for both basic ecology and

management.

Acknowledgements

We thank Peter Lane for statistical advice, Chloe Sato for assistance with figures and the

associate editor, Otso Suominen, Benjamin Scheele and an anonymous reviewer for comments

on earlier versions of this manuscript.

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Supporting Information

Additional Supporting Information may be found in the online version of this article:

Appendix S1. Detailed methods for literature search and meta-analysis.

Appendix S2. Details of the 96 papers included in the systematic review.

Table S1. Mechanisms driving the effects of large herbivores on other animals.

Table S2. Recommendations for future research given by authors.

Figure S1. Distribution of reviewed papers by study system and animal response groups.

Figure S2. Numbers of studies of effects of herbivores on other animals where herbivores are

native, wild exotic or domestic livestock.

Figure S3. The distribution of experimental approaches, contrasts and exclusion method used

by studies.

52

Appendix S1. Detailed methods for literature search and meta-analysis.

Literature Search and Paper Selection

We used a four stage systematic approach to obtain a representative sample of the literature on

the effects of large herbivores on other animals.

1. We searched the ISI Web of Knowledge Database with the following search string:

Topic=((Herbivor* OR graz* OR brows*) AND (Mammal* OR vertebrate* OR

overabundan* OR density OR deer OR ungulate*) AND (Fauna* OR reptil* OR herp*

OR frog* OR amphibian* OR bird* OR avifauna* OR “small mammal*” OR rodent*

OR invertebrate* OR insect*)). This search returned approximately 9500 results.

2. We reviewed the first 100 of these articles to identify additional key words that were

common to the titles of relevant papers. This enabled us to add an additional criteria

based on the article title: Title=(brows* OR herbivor* OR graz* OR deer OR cascade*

OR trophic). We performed the final search using both of these search strings on the

15th August 2013 and obtained 1453 unique matches.

3. We assessed each article for relevance using according to seven criteria. The criteria

were applied hierarchically, with articles only being assessed if they met all of the

preceding criteria.

4. Criteria:

i. Studied the effect of a native mammalian herbivore on fauna (268 articles)

ii. Empirical study published in English in peer reviewed journal (260 articles)

iii. At least one of the herbivores was native to the study system (91 articles)

iv. At least one of the native herbivores was large (> 2 kg) (85 articles)

v. Study was from a natural (or minimally modified) terrestrial ecosystem (76

articles).

vi. Study measured the impact of herbivory on native terrestrial fauna (70 articles)

vii. Study included contrasts (i.e. compared at least two levels of herbivory of

native herbivores) (67 articles).

53

5. Finally, we searched the reference list of the 67 articles identified above and located a

further 29 papers which met the seven inclusion criteria.

Classification of Studies

To assess biases in the literature we classified studies according to their study location and

herbivores and animal response groups. We classified studies by six variables, each with a

number of categories:

i. Continent: North America / South America / Asia / Europe / Africa / Oceania.

ii. Ecosystem: Forest or Woodland / Heath or Shrubland / Savanna or Grassland

iii. Taxonomic group of herbivores: at order level

iv. Abundance of herbivores (as described by author): over-abundant (or at higher than

historical levels) / not over-abundant

v. Number of animal response groups studied: single species / single taxonomic group

(class level) / multiple taxonomic groups

vi. Taxonomic group of animal response groups: class for vertebrates, order for

invertebrates.

We also returned to data collected in stage three of the paper selection process to assess whether

studies of the effects of native herbivores were less common than studies of introduced

herbivores. We sorted the 268 articles which studied the effects of herbivores on fauna

according to whether the herbivores studied were native, wild exotic or livestock (step 3iii). In

both of the above processes, if a paper fit into more than one category (e.g. studied both heath

and forest habitats) then it was counted in both categories. Multiple papers from the same study

site were included separately in this analysis as this was thought to summarise research effort

more accurately than if we pooled papers by study site.

54

Data Extraction and Conversion

For meta-analysis we extracted data from tables, figures and supplementary material on the

abundance and species richness of animal groups under different levels of herbivory. Animal

groups were taxonomic and grouped by class for vertebrates, order for invertebrates. As many

studies did not separate arthropods by order, we also analysed arthropods overall which

included any analysis of arthropods where data from three or more orders were combined. As

we were interested in community level effects, we excluded data which reported effects on only

one or two species (except for the effect of herbivory calculation for small mammals, where

studies of one or two species were the most common and so included in the analysis). Data in

figures were extracted using the web-based application Web Plot Digitiser V2.5 (Rohatgi 2012).

Papers reported data in a variety of formats and we converted all data to means and standard

deviations according to the methods in Table 1. If a study did not include all the information

needed to calculate a mean and standard deviation for each level of herbivory then the study

could not be included in the analysis. All data conversions were conducted before data were

combined. To reduce unexplained variation within the analysis we assessed studies according to

the quality of evidence criteria of Felton et al. (2010) and included only experimental studies

with a data quality score of iii or better (Table 2).

Studies with > 2 levels of herbivore pressure

In the calculation of mean response ratios we used data from the lowest and highest levels of

herbivory compared. This was thought to give the most consistent data across studies as most

studies with only two levels of herbivory compared absence (or very low) with very high

densities.

55

Combining Data

A number of studies had split data across years, sites, factorial treatments or taxonomic or

functional groups, however for our analysis we needed to generate a single data point per study.

For the purpose of analysis, a study was defined as “a project employing the same treatments at

the same geographical location measuring the same animal response group”. By this definition

some papers produced multiple data points while in other instances data from multiple papers

were combined to give a single data point. We used the following methods to combine data:

Data split by taxonomic or functional group:

Before calculating the response ratio and variation we summed the means to obtain an overall

mean. To calculate the summed standard deviation we used the rule: The variance of the sum =

the sum of the variances.

Data split by sites:

If the sites were split according to habitat type (e.g. both grassland and forest sites were studied

and each habitat was analysed separately), then each habitat type was included as its own data

point (as if they were separate studies). If sites were split by criteria other than broad habitat

types (e.g. landscape position) we combined data from the different categories using the

methods described by Borenstein et al. (2009) for ‘multiple outcomes or time points within a

study’, treating the categories as ‘outcomes’. As we did not know the level of correlation

between among categories, we took the conservative approach and assumed a correlation

coefficient of 1 (which overestimates the variance). As a trial of the same calculations with a

correlation coefficient of 0 and 0.5 had little effect on the variation estimates, we believe this

assumption had little effect on the final outcome of the analysis.

Data split by a factorial treatment (e.g. in a fire x herbivory factorial design):

We attempted to produce data which was as consistent as possible to studies without factorial

treatments. If the factorial treatments were with/without a second factor we used only data from

sites without the factor. If this was not possible we then assessed whether the factor affected

56

herbivore pressure. If it did, we treated the study as if it had > 2 levels of herbivore pressure (as

described above). Failing either of these methods we combined data from across factors using

the same approach outlines above for data split by sites.

Data split by year or sampling events:

We combined data across time periods using the methods described by Borenstein et al. (2009)

for ‘multiple outcomes or time points within a study’. We again assumed a correlation

coefficient between time points of 1.

Weighting of Studies within Meta-Analysis

Standard practice when conducting a random-effects meta-analysis is to weight each score by

the inverse of its variance, so that more precise scores are given more weight in the analysis

(Borenstein et al. 2009). In addition to this, we also weighted all data points by the square root

of the number of years of sampling so that long-running studies were given more weight.

References

Borenstein, M., Hedges, L.V., Higgins, J.P.T. & Rothstein, H.R. (2009) Introduction to Meta-

Analysis. John Wiley & Sons Ltd, West Sussex.

Felton, A., Knight, E., Wood, J., Zammit, C. & Lindenmayer, D. (2010) A meta-analysis of

fauna and flora species richness and abundance in plantations and pasture lands. Biological

Conservation, 143, 545-554.

Rohatgi, A. (2012) Web Plot Digitiser v2.5. Available from

[http://arohatgi.info/WebPlotDigitizer/app/].

57

Table 1. Methods used to convert different types of data to means and standard errors. Data formats not included below were unable to be converted to means and standard errors so

were not included in the analysis.

Original Format of Abundance or Richness Measure

Conversion to Mean Original Format of Variation Measure

Conversion to SD

Population estimate Taken as mean Standard Error (SE) SD = SE * sqrt (n)

95% Confidence Interval (CI) SE = CI (1.96*2)

Geometric mean Taken as mean Geometric 95% CI SE = CI (1.96*2)

Median Taken as mean Interquartile Range (IQR) SD = (IQR/1.34)

Median (log scale - ln(m)) Mean = exp((ln(m)+(s2/2)) IQR (ln scale) s(ln scale) = (IQR/1.34) SD = mean*sqrt(exp(s2)-1)

P value of t-test SE = (diff in means / t @ (p/2, df)) * (sqrt(n)/sqrt(2))

Raw data Calculate mean Raw data Calculate SD

Table 2. Categories used to assess quality of evidence in reviewed papers. From Felton et al. 2010.

Category Quality of evidence presented

i Randomized controlled trial with matched pairs of treatments and controls. Study conducted at an adequate scale for subject taxa

ii Controlled trial of adequate scale for study organism. Unpaired treatments and controls

iii Unpaired treatments and controls. Scale of study raises potential of confounding effects for the subject taxa considered

iv Evidence deemed inadequate due to inherent problems with experimental design

58

Appendix S2. Details of the 96 papers included in the systematic review.

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Bailey & Whitham

2002 North America

Forest / Woodland

Cervus canadensis

Herbivore Exclusion, Simulation of Herbivory

gradient i Fire 1 Single class Arthropods - Overall

Bailey & Whitham

2003 North America

Forest / Woodland

Cervus canadensis

Herbivore Exclusion, Simulation of Herbivory

binary i none 2 Multiple classes

Birds Arthropods - Overall, Phytophagous Insects

Bailey & Whitham

2006 North America

Forest / Woodland

Castor canadensis

Natural Experiment

binary iv none 2 Single class Phytophagous Insects

Baines 1996 Europe Shrubland / Heath

Cervus elaphus, Sheep

Natural Experiment

binary ii Predator Control

1 Multiple classes

Birds Araneae, Coleoptera, Diptera, Hymenoptera, Hemiptera, Lepidoptera

Baines, Sage & Baines

1994 Europe Mixed Habitats

Cervus elaphus

Herbivore Exclusion

binary i none 2 Multiple classes

Araneae, Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera, Plecoptera

Barrett & Stiling

2007 North America

Forest / Woodland

Odocoileus virginianus

Natural Experiment

gradient iii Nutrients 1 Single class Phytophagous Insects

Barton et al.

2011 Oceania Grassland / Savanna

Macropus giganetus

Herbivore Exclusion

binary i Log Addition

2 Single class Coleoptera

Berger et al.

2001 North America

Forest / Woodland

Alces alces Natural Experiment

binary iii none 1 Single class Birds

Bergstrom, Skarpe & Danell

2000 Africa Grassland / Savanna

Mixed ungulate assemblage

Simulation of Herbivory

gradient i none 1 Single class Phytophagous Insects

Bonal & Munoz

2007 Europe Forest / Woodland

Cervus elaphus

Herbivore Exclusion

binary iii none 2 single species

Beetles

59

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Bressette, Beck & Beauchamp

2012 North America

Forest / Woodland

Odocoileus virginianus

Herbivore Exclusion

binary i none 1 Multiple classes

Birds Arthropods - Overall, Araneae, Chilopoda, Coleoptera, Diplopoda, Hymenoptera

Brooks 1999 North America

Forest / Woodland

Odocoileus virginianus

Manipulation of herbivore density

binary iii Disturbance

3 single species

Amphibians

Buesching et al.

2011 Europe Forest / Woodland

Capreolus capreolus, Dama dama, Muntiacus reevesi

Herbivore Exclusion

binary i none 1 Single class Small mammals

Bush et al. 2012 Europe Forest / Woodland

Capreolus capreolus, Dama dama, Muntiacus reevesi

Herbivore Exclusion

binary i none 1 Single class Small mammals

Casey & Hein

1983 North America

Forest / Woodland

Cervus elaphus, Odocoileus virginianus, Ovus musimon

Herbivore Exclusion

binary iii none 1 Single class Birds

Christopher & Cameron

2012 North America

Forest / Woodland

Odocoileus virginianus

Herbivore Exclusion

binary i Weeds 2 Multiple classes

Arthropods - Overall, Acari, Araneae, Coleoptera, Collembola, Hymenoptera

Cumming et al.

1997 Africa Forest / Woodland

Loxodonta africana

Herbivore Exclusion

binary iii none 1 Multiple classes

Bats, Birds Hymenoptera, Orthoptera

Danell & Huss-Danell

1985 Europe Forest / Woodland

Alces alces Simulation of herbivory

binary i none 1 Multiple classes

Hymenoptera, Phytophagous Insects

60

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Decalesta 1994 North America

Forest / Woodland

Odocoileus virginianus

Manipulation of herbivore density

gradient i none 1 Single class Birds

Degraaf, Healy & Brooks

1991 North America

Forest / Woodland

Odocoileus virginianus

Natural Experiment

binary iii Tree Thinning

3 Single class Birds

Den Herder, Virtanen & Roininen

2004 Europe Forest / Woodland

Rangifer tarandus

Herbivore Exclusion

binary i Vegetation Age

2 Single class Coleoptera, Phytophagous Insects

Deveny & Fox

2006 North America

Shrubland / Heath

Odocoileus hemionus columbianus

Herbivore Exclusion

binary i none 1 Single class Small mammals

Feeley & Terborgh

2006 South America

Forest / Woodland

Alouatta seniculus

Natural Experiment

gradient iii none 1 Single class Birds

Gebeyehu, S. & Samways, M.

2006 Africa Grassland / Savanna

Mixed native ungulate assemblage, cattle, sheep

Natural Experiment

gradient iv none 3 Single class Grasshoppers

Gill & Fuller 2007 Europe Forest / Woodland

Capreolus capreolus, Dama dama, Muntiacus reevesi

Herbivore Exclusion

binary i Disturbance

5 Single class Birds

Goheen et al.

2004 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i none 1 Multiple classes

Small mammals Coleoptera, Hemiptera, Orthoptera

Goheen et al.

2010 Africa Grassland / Savanna

Mixed ungulate assemblage, Cattle

Herbivore Exclusion

gradient i none 3 single species

Small mammals

Goheen et al.

2013 Africa Grassland / Savanna

Mixed native ungulate assemblage

Herbivore Exclusion

binary i Rainfall 3 Multiple classes

Small mammals

61

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Gomez & Gonzalez Megias

2002 Europe Shrubland / Heath

Capra pyrenaica,

Sheep

Herbivore Exclusion

binary i none 3 Single class Coleoptera

Gomez & Gonzalez-Megias

2007 Europe Shrubland / Heath

Capra pyrenaica

Herbivore Exclusion

binary i none 6 Single class Phytophagous Insects

González-Megías, Gómez &Sánchez-PiÑero

2004 Europe Shrubland / Heath

Capra pyrenaica, Sheep, Goats

Herbivore Exclusion

binary iii none 2 Multiple classes

Arthropods - Overall, Coleoptera, Hymenoptera

Gough et al.

2012 North America

Mixed Habitats

Rangifer tarandus, mixed rodents

Herbivore Exclusion

binary i Nutrient addition

1 Multiple classes

Soil Invertebrates

Greenwald, Petit & Waite

2008 North America

Mixed Habitats

Odocoileus virginianus

Herbivore Exclusion

binary iv none 2 Multiple classes

Amphibians, Reptiles Arthropods - Overall, Gastropoda, Isopoda, Myriapoda, Oligochaeta

Hagenah, Prins & Olff

2009 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

gradient i none 2 Single class Small mammals

Herremans 1995 Africa Forest / Woodland

Loxodonta africana

Natural Experiment

binary iv none 2 Single class Birds

Hino 2000 Asia Forest / Woodland

Cervus nippon

Natural Experiment

binary iv none 4 Single class Birds

Hino 2006 Asia Forest / Woodland

Cervus nippon

Natural Experiment

gradient iv none 1 Single class Birds

Holt, Fuller & Dolman

2010 Europe Forest / Woodland

Capreolus capreolus, Dama dama, Muntiacus reevesi

Herbivore Exclusion

binary i Disturbance

10 single species

Birds

62

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Holt, Fuller & Dolman

2011 Europe Forest / Woodland

Capreolus capreolus, Dama dama, Muntiacus reevesi

Herbivore Exclusion

binary i Disturbance

3 Multiple classes

Birds Flying Insects, Phytophagous Insects

Holt, Fuller & Dolman

2013 Europe Forest / Woodland

Dama dama, Capreolus capreolus, Muntiacus reevesi

Herbivore Exclusion

binary i none 3 single species

Birds

Huffman et al.

2009 North America

Forest / Woodland

Cervus elaphus, Odocoileus hemionus

Herbivore Exclusion

binary ii none 3 Single class Arthropods - Overall

Huntzinger, Karban & Cushman

2008 North America

Mixed Habitats

Lepus californicus, Odocoileus hemionus

Herbivore Exclusion

binary ii none 2 Multiple classes

Gastropoda, Lepidoptera, Orthoptera

Ims et al. 2007 Europe Grassland / Savanna

Rangifer tarandus

Manipulation of herbivore density

binary i none 1 Multiple classes

Birds, Small Mammals

Jay-Robert et al.

2008 Europe Mixed Habitats

Cervus elaphus, sheep

Natural Experiment

binary iv none 2 Single class Coleoptera

Joern 2004 North America

Grassland / Savanna

Bos bison Manipulation of herbivore density

binary iii Fire 1 Single class Orthoptera

Joern 2005 North America

Grassland / Savanna

Bos bison Manipulation of herbivore density

binary iii Fire 1 Single class Orthoptera

Jonas & Joern

2007 North America

Grassland / Savanna

Bos bison Manipulation of herbivore density

binary iii Fire 1 Single class Orthoptera

63

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Jonsson et al.

2010 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

gradient iii none 1 Single class Araneae, Coleoptera, Hymenoptera, Orthoptera

Kanda et al. 2005 Asia Forest / Woodland

Cervus nippon

Natural Experiment

gradient iv none 1 Single class Coleoptera

Keesing 1998 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i none 2 Single class Small mammals

Keesing 2000 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i none 4 Single class Small mammals

Kuria et al. 2010 Africa Grassland / Savanna

Mixed ungulate assemblage, cattle

Herbivore Exclusion

gradient i none 1 Single class Coleoptera

Lessard et al.

2012 North America

Forest / Woodland

Odocoileus virginianus

Herbivore Exclusion

binary i none 1 Multiple classes

Arthropods - Overall, Acari, Araneae, Coleoptera, Collembola, Hymenoptera

Lind et al. 2012 North America

Forest / Woodland

Odocoileus virginianus

Herbivore Exclusion

binary i none 1 Single class Phytophagous Insects

Little, Hockey & Jansen

2013 Africa Grassland / Savanna

Mixed native ungulate assemblage

Herbivore Exclusion

binary iii Fire 2 Multiple classes

Birds Arthropods – Overall, Orthoptera

Manning, Cunningham & Lindenmayer

2013 Oceania Mixed Habitats

Macropus giganetus

Herbivore Exclusion

binary i Log Addition, Vegetation Density

4 Single class Reptiles

Martin & Maron

2012 North America

Forest / Woodland

Cervus canadensis

Herbivore Exclusion

binary i none 7 Single class Birds

Martin, Arcese & Scheerder

2011 North America

Forest / Woodland

Odocoileus hemionus

Natural Experiment

gradient ii none 1 Single class Birds

64

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Martinsen, Driebe & Whitham

1998 North America

Forest / Woodland

Castor canadensis

Natural Experiment

binary iv none 1 single species

Coleoptera

Mathisen & Skarpe

2011 Europe Forest / Woodland

Alces alces Manipulation of herbivore density

gradient iii Nutrient addition

1 Single class Birds

Mathisen et al.

2012 Europe Forest / Woodland

Alces alces Manipulation of herbivore density

gradient iii none 5 Single class Birds

Matlack, Kaufman & Kaufman

2001 North America

Grassland / Savanna

Bos bison, Cattle

Manipulation of herbivore density

binary iii Fire 2 single species

Small mammals

McCauley et al.

2006 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i none 3 Multiple classes

Reptiles, Small Mammals

McCauley et al.

2008 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i none 2 Multiple classes

Small mammals Parasitic Invertebrates

McShea & Rappole

2000 North America

Forest / Woodland

Odocoileus virginianus

Herbivore Exclusion

binary i none 9 Single class Birds

Melis et al. 2006 Europe Forest / Woodland

Cervus elaphus

Natural Experiment

gradient iv none 1 Single class Coleoptera

Melis et al. 2007 Europe Forest / Woodland

Alces alces Natural Experiment

gradient iv none 1 Single class Coleoptera

Mohr, Cohnstaedt & Topp

2005 Europe Forest / Woodland

Cervus elaphus

Herbivore Exclusion

binary iii Disturbance

2 Multiple classes

Arthropods - General, Araneae, Isopoda

Moser & Witmer

2000 North America

Grassland / Savanna

Cervus elaphus, Cattle

Herbivore Exclusion

binary i none 1 Multiple classes

Birds, Small Mammals

Neff, Fettig & VanOverbeke

2007 North America

Mixed Habitats

Cervus elaphus

Herbivore Exclusion

binary i none 4 Single class Lepidoptera

65

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Newson et al.

2012 Europe Forest / Woodland

Capreolus capreolus, Dama dama, Muntiacus reevesi

Natural Experiment

gradient i none 11 Single class Birds

Nkwabi et al.

2011 Africa Grassland / Savanna

Connochaetes sp.

Natural Experiment

binary iii Fire 1 Multiple classes

Birds Arthropods - Overall

Ogada et al.

2008 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

gradient i none 2 Multiple classes

Birds Arthropods - Overall

Olofsson & Strengbom

2000 Europe Grassland / Savanna

Rangifer tarandus

Herbivore Exclusion

binary ii none 2 Multiple classes

Phytophagous Insects

Palmer et al.

2008 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i none 1 Multiple classes

Arthropods Overall, Coleoptera, Hymenoptera

Parsons, Maron & Martin

2013 North America

Forest / Woodland

Odocoileus hemionus

Herbivore Exclusion

binary i none 6 Single class Small mammals

Pedersen, Nilsen & Andreassen

2007 Europe Forest / Woodland

Alces alces Manipulation of herbivore density

binary ii none 2 Multiple classes

Birds Arthropods - Overall

Pringle 2008 Africa Grassland / Savanna

Loxodonta africana

Simulation of herbivory

binary i none 3 single species

Reptiles

Pringle et al.

2007 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i Nutrients 2 Multiple classes

Reptiles Arthropods - Overall, Coleoptera

Rambo & Faeth

1999 North America

Grassland / Savanna

Cervus elaphus, Odocoileus hemionus, Cattle

Herbivore Exclusion

binary i none 1 Multiple classes

Arthropods - Overall

Roininen, Price & Bryant

1997 North America

Mixed Habitats

Alces alces, Lepus americanus

Herbivore Exclusion

binary i none 1 Single class Phytophagous Insects

66

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Saetnan & Skarpe

2006 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

binary i none 1 Single class Small mammals

Schwenk & Strong

2011 North America

Forest / Woodland

Alces alces Natural Experiment

gradient iii none 2 Single class Lepidoptera

Seki and Koganezawa

2013 Asia Forest / Woodland

Cervus nippon

Herbivore Exclusion

binary iv none 1 Multiple classes

Small mammals Coleoptera, earthworms

Shibata, Saito & Tanaka

2008 Asia Forest / Woodland

Cervus nippon

Herbivore Exclusion

binary iii none 1 Single class Small mammals

Shimazaki & Miyashita

2002 Asia Forest / Woodland

Cervus nippon

Herbivore Exclusion

binary iii none 2 Single class Phytophagous Insects

Smit et al. 2001 Europe Mixed Habitats

Capreolus capreolus, Cervus elaphus, Ovis ammon musimon

Herbivore Exclusion

binary i none 1 Single class Small mammals

Spalinger et al.

2012 Europe Grassland / Savanna

Cervus elaphus, Rupicapra rupicapra

Natural Experiment

binary iv none 1 Multiple classes

Orthoptera

Suominen 1999 Europe Forest / Woodland

Alces alces, Rangifer tarandus

Herbivore Exclusion

binary i none 1 Single class Gastropoda

Suominen et al.

2003 Europe Forest / Woodland

Rangifer tarandus

Herbivore Exclusion

binary i Nutrients 1 Single class Coleoptera

Suominen et al.

2008 Europe Forest / Woodland

Alces alces Simulation of herbivory

gradient i Nutrients 1 Multiple classes

Araneae, Hemiptera, Flying Insects

Suominen, Danell & Bryant

1999 North America

Mixed Habitats

Alces alces Herbivore Exclusion

binary i none 1 Multiple classes

Coleoptera, Orthoptera

67

Authors Year Pub.

Study Location (continent)

Study ecosystem

Herbivore(s) Studied*

Study Design Contrasts Data Quality Score

Interacting Factors

Study Duration (years)

# taxonomic groups studied

Animal Taxa Studied

Suominen, Danell & Bergström

1999 Europe Forest / Woodland

Alces alces Herbivore Exclusion

binary i none 1 Multiple classes

Arthropods - Overall, Acari, Araneae, Blattodea, Chilipoda, Coleoptera, Diplopoda, Diptera, Hemiptera, Hymenoptera, Isopoda, Gastropoda, Nemastomatidae, Psocoptera, Phalangiidae

Tabuchi, Ueda & Ozaki

2010 Asia Forest / Woodland

Cervus nippon

Herbivore Exclusion

binary ii none 1 single species

Phytophagous Insects

Takada et al.

2008 Asia Forest / Woodland

Cervus nippon

Natural Experiment

gradient iii none 1 Single class Araneae

Warui et al. 2005 Africa Grassland / Savanna

Mixed ungulate assemblage

Herbivore Exclusion

gradient i none 2 Single class Araneae

* Wild herbivores (native or exotic) denoted with scientific name. Domestic stock uses common name.

68

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Supplementary Tables

Table S1. Mechanisms driving the effects of large herbivores on other animals. Counts indicate the number of papers where authors conclude that mechanism to be important for

driving the effect of large herbivores on a particular animal group. The most common mechanism(s) for each taxonomic group appears in bold.

Mechanism

Number of papers concluding mechanism to be important

For any taxa

Birds Small mammals

Arthropoda Araneae Coleoptera Hemiptera Hymenoptera Lepidoptera Orthoptera

Vegetation biomass 48 14 12 6 1 6 1 1 1 4

Vegetation structure 47 21 9 5 3 5

2 4

Via changes in other animals (e.g. prey) 17 7 2 2 2 1

Vegetation chemistry / nutrient quality 13

1

2

1

Vegetation species composition 13 4

1 1 2 1 1 3

Physical environment 10

1 3 2 5 1 2

Direct interference 5

1

2

Nutrient input/cycling (e.g. dung) 2

2

Total number of papers giving any conclusions

84 26 14 11 5 17 2 4 1 6

76

Table S2. Recommendations for future research given by authors. We sorted recommendations according to whether they were related to research topic or to the design of future

studies. Fifty one of the 96 papers contained specific recommendations for future research.

Recommendation Number of papers

Research Topic

Cascading effects on other species or ecosystem processes 19

Mechanisms of effects 11

Novel habitats or locations 7

Interacting factors 7

Study Design

Larger spatial scales / spatial variation in responses 6

Studies able to detect both direct and indirect relationships 5

Studies able to detect the shape of herbivory responses 3

Long-term studies to distinguish between state shifts and short-term fluctuations

3

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Supplementary Figures

Figure S1. Distribution of the 96 reviewed papers by (a) continent and study system, (b) focal

herbivores, (c) number of animal response groups studied, (d) animal response groups:

class of vertebrates and (e) animal response groups: most common invertebrate orders.

Studies which belonged to more than one category were counted in both.

a.

b. c.

d. e.

0

5

10

15

20

25

30

35

NorthAmerica

Europe Africa Asia Oceania SouthAmerica

Num

ber

of

papers

Study location

Multiple habitats

Heath / Shrubland

Grassland / Savanna

Forest / Woodland

0

10

20

30

40

50

60

Single sp. Single class Multipleclasses

Num

ber

of

papers

Number of taxa studied

Both

Vertebrates

Invertebrates

0

10

20

30

40

50

60

70

80

Num

ber

of

papers

Order of focal herbivores

Not over-abundant

Over-abundant

0

5

10

15

20

25

Num

ber

of

papers

Animal response groups: Invertebrate orders

0

5

10

15

20

25

Num

ber

of

papers

Animal response groups: Vertebrate classes

78

Figure S2. Studies returned by our initial search which studied the effects of terrestrial

mammalian herbivores on other animals, sorted by the status of the focal herbivores

(native, exotic or livestock). Not all studies included under ‘native’ were included in the

final review as a number did not meet all criteria due to factors such as size of

herbivores, absence of contrasts etc. (see Appendix S1 for full details).

Native

LivestockExotic

75

12

154114

12

0

79

Figure S3. The distribution of experimental approaches (a.), contrasts (b.) and exclusion

method (c.) used by studies published in each five year period. The frequency of studies

using each of the experimental approaches did not vary among time periods (P = 0.29).

The frequency of studies employing more than two levels of herbivory, and including

selective exclusion fences was higher among studies published since 2004 (P = 0.03

and P = 0.017).

a.

b.

c.

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Paper II: Herbivory and fire interact to affect forest understory habitat, but not its use by small vertebrates

In Paper I, I found that large herbivores can have substantial effects on the abundance and

diversity of other animal groups. However, I also found that few studies have investigated the

impacts of Australian native herbivores, and that knowledge of how large herbivores interact

with episodic disturbances remains limited. In this paper, I experimentally tested for the

interactive effects of prescribed fire and macropod herbivores (kangaroos and wallabies) on

forest understorey vegetation and small vertebrate fauna.

Foster, C.N., Barton, P.S., Sato, C.F., Wood, J.T., Macgregor, C.I., and D.B. Lindenmayer

(2016) Herbivory and fire interact to affect forest understory habitat, but not its use by small

vertebrates. Animal Conservation. 19, 15-25.

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Abstract

Herbivory and fire are two disturbances which often co-occur, but studies of their interactive

effects are rare outside of grassland ecosystems. We experimentally tested the interactive effects

of prescribed fire and macropod herbivory on forest understory vegetation and its vertebrate

fauna. Fire and herbivory interacted synergistically to affect forest understory vegetation, with

palatable plants showing poor post-fire recovery in un-fenced sites compared with herbivore

exclusion sites. Despite this strong interactive effect on vegetation, small vertebrates responded

to the individual, and not the interactive effects of disturbance. The native insectivorous

mammal Antechinus stuartii was more frequently encountered on large herbivore exclusion

sites, as was the introduced European rabbit. In contrast, the small skink Lampropholis delicata

was more common on sites with high densities of large herbivores. Skinks, snakes and

European rabbits were also more active on burnt than unburnt sites. Our results suggest that it

may be necessary to manage the macropod herbivore population after fire to prevent the decline

of palatable plants, and maintain the dense habitat required by some small mammals. However,

as the invasive rabbit was most active in macropod-free sites after fire, any management must

include control of both types of herbivores. A mix of understory densities may also need to be

maintained to ensure the persistence of species preferring more open habitats. Our study

demonstrates that interactive effects of disturbance on vegetation communities may not lead to

predictable effects on animals, and highlights the importance of considering both multiple

stressors, and multiple species, in the management of disturbance regimes.

Keywords

Disturbance interaction, browsing, grazing, indirect effects, kangaroo, synergistic effects

Introduction

Disturbance regimes play a central role in ecosystem dynamics (Willig and Walker, 1999).

However, in many parts of the world, natural disturbance regimes have been disrupted, with

unwanted outcomes for biodiversity (Hobbs and Huenneke, 1992, Sinclair and Byrom, 2006).

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Thus, many biodiversity conservation programs aim to reinstate natural disturbance regimes

(Fuhlendorf et al., 2010, Noss et al., 2006), but achieving this in systems where multiple

disturbances co-occur may be difficult. Co-occurring disturbances can interact to produce

distinctly different outcomes from what would be expected based on individual effects (Didham

et al., 2007, Tylianakis et al., 2008) and a poor understanding of these interactions can lead to

unexpected and undesirable management outcomes (Lindenmayer et al., 2010, Tylianakis et al.,

2008).

The importance of disturbance interactions for structuring grasslands and heathlands has

been widely recognised, and re-establishing fire-grazing interactions is identified as a priority

for maintaining biodiversity in these habitats (Fuhlendorf et al., 2010, Van Langevelde et al.,

2003). However, understanding of how fire and herbivory interact to affect species in forested

habitats remains limited (Foster, Barton and Lindenmayer, 2014, Royo et al., 2010, Wisdom et

al., 2006). As the interactive effects of fire and herbivory depend on the scale, intensity and

timing of these disturbances, the outcome of interactions can be highly variable (Fuhlendorf et

al., 2010, Wisdom et al., 2006). For example, at a local-scale, deer browsing after fire supressed

dominant shrub species, increasing herbaceous plant richness in a forest understory (Royo et al.,

2010). Conversely, heavy macropod herbivory following fire limited grass and forb recovery

(Tuft, Crowther and McArthur, 2012). At a larger scale, Bailey and Whitham (2002) found that

elk (Cervus canadensis) browsed more heavily in areas of aspen (Populus tremuloides) that

burned at high intensity, compared with moderate intensity. This heavy browsing reversed the

positive effect of browsing on arthropod richness that occurred after moderate intensity fire.

While such studies indicate that fire-herbivory interactions are likely to be prevalent in forested

ecosystems (Royo and Carson, 2006), investigations of animal responses to the combined

effects of these disturbances remain rare (Foster et al., 2014, Wisdom et al., 2006).

We combined prescribed fire and large herbivore exclusion treatments to test the

interactive effects of fire and herbivory on understory vegetation and small vertebrates in a

temperate forest ecosystem. As the management of disturbances is often targeted at plants, with

the assumption that this will also cater for the needs of animals (Clarke, 2008), it is important to

84

understand whether such assumptions are valid, and whether fauna respond in a predicable way

to disturbances. Our study addressed the following questions: (1) How do fire, herbivory and

their interaction affect understory habitat structure at the site level? (2) How do these

disturbances affect site occupancy by small vertebrate fauna? We expected that vertebrate

species would respond differently to the experimental treatments due to differences in their

habitat and dietary preferences, and that these responses would be mediated by changes in

vegetation structure. For example, we expected that both fire and herbivory would reduce

understory cover, and lead to negative effects on site occupancy by vertebrates preferring dense

understory habitats (Table 1). We provide recommendations for biodiversity conservation based

on our findings.

Table 1. Predicted effects of fire and large herbivores on habitat and food resources for vertebrates and

the corresponding predicted responses of two small mammal and two reptile species, based on

their diet and habitat preferences. Habitat preferences are attributes which have been associated

with higher abundance in forest habitat for that species.

Response group Habitat preference Diet Predicted response

Fire Herbivores

Habitat and food resources

Understory cover - -

Understory height - -

Leaf litter depth - None

Fresh plant growth + -

Invertebrate prey - -

Vertebrates

European rabbit (Oryctolagus cuniculus)

Open understory a Forbs and grasses b

+ -

Brown antechinus (Antechinus stuartii)

Dense, complex understory a,c

Tall understory d

Abundant logs d

Invertebrates c - -

Delicate skink (Lampropholis delicata)

High canopy cover e Deep litter f,g Tall understory g

Invertebrates e - ?

Eastern small-eyed snake (Cryptophis nigrescens)

Warm diurnal refuge h Skinks h + ?

aCatling and Burt (1995), b Davis, Coulson and Forsyth (2008), c Bennett (1993), d Knight and Fox (2000), e Bragg, Taylor and Fox (2005), f Taylor and Fox (2001), g Howard, Williamson and Mather (2003), h Webb et al. (2004)

85

Materials and methods

Study site

We conducted our study in Booderee National Park (BNP); a ~ 65 km2 peninsula in south-

eastern Australia (35°10′S, 150°40′E). We established sites within Eucalyptus pilularis forest,

which is the most widespread vegetation type in BNP (Taws, 1998). An intensive fox (Vulpes

vulpes) baiting program has been conducted in BNP since 1999 to protect native species from

predation (Dexter et al., 2012). Over the last decade, there has been a tenfold increase in native

herbivores in BNP (predominantly swamp wallaby, Wallabia bicolor, and eastern grey

kangaroo, Macropus giganteus), which is attributed to reduced predation by foxes

(Lindenmayer et al., 2014). Small-scale exclosure trials indicate that this high abundance of

herbivores could be driving changes in vegetation composition (Dexter et al., 2013), and there is

concern about flow-on effects for smaller vertebrates, which include a number of threatened

species (Dexter et al., 2012). As fire is a naturally occurring disturbance within BNP, occurring

both as wildfire and low-intensity prescribed burning (Lindenmayer et al., 2008), it is important

to understand how native herbivores interact with fire regimes.

Study design

We quantified the interactive effects of fire and herbivory on vegetation and small vertebrates

using a randomised blocked experiment. We combined three levels of large herbivore exclosure

and two levels of burning treatment in a factorial design (Appendix 1). We replicated each

treatment combination across four blocks to give 24 sites. For the exclosure treatments, we

excluded macropod herbivores from 25 × 25 m sites, using 1.1 m tall wire fencing, in June

2012. We created three levels of herbivore exclosure treatment: high activity (open treatment –

no fence), intermediate activity (partial treatment – sites were fenced but gates opened and

closed at two month intervals to simulate lower herbivore pressure), and no large herbivores

(exclosure treatment). For the burnt treatments, we conducted 50 × 50 m burns in August 2012,

with the 25 x 25 m site in the centre of the burnt area. Fire was low-intensity, removing

86

approximately 95% of understory vegetation and did not reach the canopy (scorch height 1.5 - 4

m) or burn large logs.

Data collection

We surveyed understorey vegetation prior to treatment in April/May 2012 and repeated surveys

biannually until May 2014. We sampled four 3 × 3 m quadrats in each site, with each quadrat at

least 1.5 m from the fence. We recorded four vegetation variables, representing important

attributes of fauna habitat; total understorey projective cover (%), projective cover of bracken

(Pteridium escelentum) (%), understorey height (averaged across 10 locations per quadrat using

the stick-and-disc method of Smit et al. (2001); disc 100 mm diameter, 4.7 g weight) and litter

depth (averaged across 10 locations per quadrat).

We surveyed macropod herbivore activity by counting scats along two 25 × 2 m

transects (100 m-2) per site, summing counts to give one count per site. As macropods defecate

more while feeding than resting (Johnson, Jarman and Southwell, 1987), pellet counts can give

a comparative measure of macropod feeding pressure between sites (Howland et al., 2014). We

surveyed transects every two months from August 2012 to May 2014, removing scats after each

survey to avoid re-counting. We also monitored European rabbit (Oryctolagus cuniculus)

activity using these transects, counting the number of rabbit diggings every two months from

June 2013 to May 2014.

We surveyed site use by small mammals through live trapping in April/May 2012 and

then every six months until May 2014. For each survey, we set eight Elliott traps per site for

four consecutive nights. We ear marked animals with a permanent marker to identify recaptures

within a survey.

We monitored reptiles using iron sheeting as artificial substrates. We set out four 1 × 1

m sheets per site in July 2012, and checked them on two consecutive mornings approximately

every two months from October 2012 to May 2014. To minimise biases due to time of day, we

rotated the order of site checking so that each site had one early morning and one late morning

check per sampling period. To address non-independence of counts within a survey, we used the

87

maximum value of the two consecutive counts for each species. Weather conditions meant that

some surveys returned few individuals. Therefore, for data analysis, we excluded surveys with

fewer than three detections for that species.

To measure arthropod prey availability, we sampled ground-dwelling beetles and

spiders using pitfall traps, counting the total captures per trap. We deployed four 250ml (100

mm diameter) traps per site (2/3 filled with non-toxic polyethylene glycol solution) for two

weeks in November 2012 and 2013.

Data analysis

To assess how fire-herbivory interactions affected habitat structure (question 1), and fauna

occupancy (question 2) we tested treatment effects on dependent variables using linear mixed

models (LMMs) for vegetation variables and generalised linear mixed models (GLMMs) with

Poisson errors for animal counts. Vegetation variables were understory cover (%), understory

cover excluding bracken (%, total understory cover minus cover of bracken), understory height

(m), and leaf litter depth (mm). We analysed understory cover excluding bracken because

bracken is a dominant, unpalatable species which could mask responses of other plants. Bracken

also provides little of the ground-level structure important for small vertebrates (Bennett, 1993).

Animal count variables were macropod scats, rabbit diggings, antechinus captures, delicate

skink (Lampropholis delicata) and eastern small-eyed snake sightings (Cryptophis nigrescens),

and spider and beetle captures.

We fitted each dependent variable with the full fixed effects model of herbivores ×

burning × time, and random effects of block/site/quadrat for vegetation and block/site for

animal variables to account for the repeated measures. A first-order auto-regressive covariance

structure on the random effects was trialled for the vegetation LMMs but was not used as it did

not improve model fit (ΔAIC < 2, Pinheiro and Bates, 2000). We did not define a covariance

structure for animal responses as inspection of residuals indicated little evidence of temporal

autocorrelation, and methods for fitting such structures with GLMMs are not well developed

(Zuur et al., 2009). We used Akaike information criterion, corrected for small sample size

88

(AICc), to select the most parsimonious model from all possible subsets of the full model (19

models) (Burnham and Anderson, 2002). If models within two AICc of the top ranked model

included predictors not included in the top model, we also discussed these alternate models. We

excluded pre-treatment surveys from analyses to avoid spurious time × treatment interactions.

The properties of some animal variables meant that adjustments to the full model were

necessary. Specifically, to adjust for over-dispersion of macropod scat data (φ = 6.9), rabbit

digging data (φ = 2.7) and arthropod data (φ = 3.1), we added an observation-level random

effect to the models for these variables (Harrison, 2014). Further, for the macropod model, we

divided the partial herbivore treatment into two categories: partial – open months, and partial –

closed months, to better describe this treatment. As there were low numbers of macropod scats

in exclosure and partial – closed month treatments, they were excluded from this analysis. For

the GLMM of antechinus captures, we ran model selection on all subsets of the full model of

herbivores × burning × time, plus an alternate model with ‘season’ substituted for ‘time’ (giving

33 different models for comparison). This accounted for the strong seasonal variation in

antechinus abundance (Lazenby-Cohen and Cockburn, 1991). Finally, as low mean counts for

the two reptile species meant some models including the burn × time fixed effect did not

converge, we excluded five models for the delicate skink and three models for the small-eyed

snake from model comparisons. LMMs were performed using the lme function in the package

MASS, GLMMs using the glmer function in the package lme4 and AICc model ranking using

the package AICcmodavg, within R version 3.0.1 (R Core Team, 2013).

Results

Vegetation structure

Vegetation structure responded both to the burning and herbivore exclosure treatments, as well

as their interaction (Table 2). Understory vegetation cover at unburnt sites declined in open and

partial treatments over time, but remained stable in exclosure sites (Fig. 1a). After an initial

reduction after fire, a similar decline was observed for burnt, open sites. However, burnt partial

89

and exclosure sites remained stable. (Fig. 1a, Appendix 2). When bracken was excluded from

understory cover, there was a strong exclosure × time interaction, as cover on open and partial

exclosure sites declined or remained low, while on full exclosure sites cover increased through

time (Fig. 1b, Appendix 2). The burning × time interaction was also important, as non-bracken

vegetation increased over time on all burnt sites. Compared with other treatments, burnt, open

sites had a high proportion of bracken, with very little non-bracken vegetation present across all

time periods (Fig. 1b). Both understory height and leaf litter depth responded to the burning ×

time interaction, but not to any other interaction terms (Table 2). Understory height was reduced

by fire but had recovered after 21 months (Fig. 2a). Litter depth recovered more slowly,

remaining lower in burnt than unburnt sites across all time periods (Fig. 2b). The second ranked

model for understory height also included an exclosure main effect, where vegetation was

slightly taller on exclosure than on open sites (Appendix 2).

90

Table 2. Model rankings for linear mixed models (LMMs, vegetation) and generalised linear mixed

models (GLMMs, animals), testing the fixed effects of burning (B - burnt or unburnt), exclosure

(E - open, partial or full exclosure), time (T - sampling event - categorical), and their interactions

on vegetation structure and animal occurrence. For brown antechinus, we also ran model ranking

on all subsets of a model with season (S - autumn or spring) substituted for time (i.e. B*E*S), to

account for the strong seasonal variation in the abundance of this species. K is the number of

parameters estimated in the model, Δ AICc is the change in Aikaike’s Information Criterion

(corrected for small sample size) from the best-ranked model. AICcWt is the Aikine Weight of

the model, LL is the Log-likelihood.

Data Model terms K Δ AICc AICcWt LL

LMMs

Understory cover B + E + T + B:T + E:T + B:E + B:E:T 28 0 0.97 -1454.7

Cover excluding bracken B + E + T + B:T + E:T 20 0 0.74 -1389.6

Vegetation height B + T + B:T 12 0 0.61 266.5

B + E + T + B:T 14 1.59 0.28 267.8

Litter depth B + T + B:T 12 0 0.82 -429.5

GLMMs

Macropod scats B + Ea + T 15 0 0.6 -500.6

Rabbit diggings T 9 0 0.37 -441.0

E + T 11 1.09 0.21 -439.2

B + T 10 1.64 0.16 -440.7

Brown antechinus E + season 6 0 0.36 -172.0

B + E + season 7 1.4 0.18 -171.5

Delicate skink B + E + T 13 1.01 0.46 -162.8

B + T 11 1.35 0.39 -165.3

Eastern small-eyed snake B 4 0 0.67 -60.4

Beetles and spiders T 5 0 0.61 -187.4

B + T 6 1.85 0.24 -187.0

a Compares only partial-open months with open treatment (see methods)

91

Figure 1. Vegetation structural responses to exclosure and burning treatments across sampling

periods, (a) total understory percent cover, and (b) understory percent cover, excluding

bracken (Pteridium esculentum). Values post-treatment are predicted means and

estimated SE from the top-ranked models. Pre-treatment data (May 2012) were not

included in the LMM, but are presented here (mean and SE) to allow comparison with

post-treatment data. Arrows indicate timing of prescribed burning.

Co

ver

exc

l. b

racke

n (%

)U

nd

ers

tory

co

ver

(%)

Aut'12 Spr'12 Aut'13 Spr'13 Aut'14

0

10

20

30

40

50

60(a)

(b)

Time period

Open Partial Exclosure

Aut'12 Spr'12 Aut'13 Spr'13 Aut'14

Burnt

Unburnt

0

10

20

30

40

50

60

Aut'12 Spr'12 Aut'13 Spr'13 Aut'14

92

Figure 2. Response of understory height (a) and leaf litter depth (b) to burning treatment across

time periods. Values post-treatment are predicted means and SE from the top-ranked

models. Pre-treatment data (May 2012) were not included in the LMM, but are

presented here (mean and SE) to allow comparison with post-treatment data. Arrows

indicate timing of prescribed burning.

Vertebrate responses

Exclosure treatments successfully excluded macropod herbivores, with very low scat counts in

exclosure treatments (�̅� = 0.71 ± 0.52). When partial treatment gates were open, scat counts

were 56% lower in partial than in open treatments (Fig. 3a). Macropods also responded to

burning, showing higher activity in burnt than unburnt sites (Table 2, Fig 3). There were three

competing models for rabbit diggings, with models including time, time plus exclosure and time

plus burning all explaining similar levels of variation in the data (Table 2). Rabbit activity

peaked in summer and tended to be higher in sites without macropods (partial and full exclosure

treatments) and in burnt sites (Fig. 4, Appendix 2).

0

0.1

0.2

0.3

0.4

0.5

0.6

BurntUnburnt

0

10

20

30

40

50

60

(a)

(b)

Time period

Un

de

rsto

ry h

eig

ht (

m)

Litte

r d

ep

th (m

m)

May '12 Nov '12 May '13 Nov '13 May '14

93

Figure 3. Number of macropod scats in burnt and unburnt sites through time in open (a) and

partial exclosure sites (b). Note that closed months of the partial exclosures (Nov 2012,

Apr 2013 etc.), and full exclosure sites were not included in the analysis as means for

this group were too low to allow model fit. Values for open sites and partial-open

months are predicted means and estimated SE from the top-ranked model. Arrows

indicate timing of prescribed burning (note Aug ’12 counts were after implementation

of herbivory treatments but before burning treatment).

Brown antechinus captures were highest in herbivore exclusion sites, and in Autumn

(May) surveys (Table 2, Fig. 5). The second ranked model also included burning as a fixed

effect (Table 2), where antechinus captures were slightly lower in burnt than unburnt sites

(Appendix 2). Both the delicate skink and the eastern small-eyed snake were encountered more

frequently in burnt sites than unburnt sites (Table 2, Fig. 6, Appendix 2). Delicate skink

numbers also tended to be higher in open and partial than in full exclosure sites and were higher

in the first survey (three months post-fire), than at any other time (Table 3, Fig, 6). Beetles and

spiders captures were 65% higher in the first year (2012, �̅� = 31.3 ± 3.1) than the second (2013,

�̅� = 18.9 ± 1.96), and the second ranked model indicated that captures were also slightly higher

in burnt than in unburnt sites (Table 2, Appendix 2).

94

Figure 4. Number of rabbit (Oryctolagus cuniculus) diggings per site in the different exclosure

treatments (a) and burning treatments (b) over time. Values are predicted means and

estimated SE from the 2nd (a) and 3rd (b) ranked models respectively. The effect of time

alone (first-ranked model) can be clearly seen in both plots.

Figure 5. Number of individual brown antechinus (Antechinus stuartii) captured per site in

different seasons and herbivory treatments. Values post-treatment are predicted means

and SE from the top-ranked model based on two years of data (November 2012 to May

2014). Pre-treatment data (May 2012) were not included in the GLMM, but are

presented here (mean and SE) to allow comparison with post-treatment data.

0

5

10

15

20

25

Jun '13 Aug '13 Oct '13 Dec '13 Feb '14 Apr '14Nu

mb

er o

f ra

bb

it d

igg

ing

s BurntUnburnt

0

5

10

15

20

25

Nu

mb

er o

f ra

bb

it d

igg

ing

s OpenPartialExclosure

(a)

(b)

Time period

0

1

2

3

4

5

May May November

Nu

mb

er o

f in

div

idu

als Open

PartialExclosure

Pre-treatment Post-treatment

Season

95

Figure 6. Number of delicate skinks (Lampropholis delicata) detected under artificial substrates

in the different burning and exclosure treatments across time. Values are predicted

means and SE from the top-ranked model.

Discussion

Fire and herbivory can interact strongly in space and time to shape the structure of vegetation

communities (Koerner and Collins, 2014, Royo and Carson, 2006, Van Langevelde et al.,

2003). However, animal responses to the fire × herbivory interaction are rarely studied (but see

Fuhlendorf et al., 2010, Kimuyu et al., 2014, Kutt and Woinarski, 2007). In our experimental

test of the interactive effects of fire and large herbivores, we found that forest understory

structure responded to the fire × herbivore exclosure interaction, but vertebrate site occupancy

was affected only by the individual effects of disturbance. This suggests that local changes in

vegetation structure may not be an adequate predictor of animal responses to disturbance and

that animals warrant individual consideration for the management of ecosystems that are subject

to both recurring fire and herbivory.

Question 1: Changes to habitat structure

As expected, fire and herbivory both affected habitat structure, and effects differed with

disturbance type. The limited recovery of non-bracken vegetation on burnt sites with high

densities of large herbivores (Fig. 1), was consistent with previous studies (Meers and Adams,

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Nov'12

Feb'13

Apr'13

Jun'13

Aug'13

Oct'13

Dec'13

Apr'14

Nov'12

Feb'13

Apr'13

Jun'13

Aug'13

Oct'13

Dec'13

Apr'14

Nov'12

Feb'13

Apr'13

Jun'13

Aug'13

Oct'13

Dec'13

Apr'14

Burnt

Unburnt

Nu

mb

er o

f skin

ks

Time period

Open Partial Exclosure

96

2003, Tuft et al., 2012). Both of these previous studies attributed the stronger effect of

herbivory on burnt sites to greater herbivore pressure, driven by the attraction of herbivores to

the fresh plant growth following fire. This is a commonly reported mechanism explaining fire ×

herbivore interactions (Klop, van Goethem and de Iongh, 2007), for which we also found

evidence, as indicated by macropod activity being greatest on recently burnt sites (Fig. 2b).

In contrast to the non-bracken vegetation, bracken recovered well in burnt, open sites,

and made up a large proportion of the vegetation in these sites (Fig. 1). Bracken can regenerate

rapidly following fire and suppress other plants. However, after an initial post-fire pulse,

bracken cover usually declines over time, as other plants become dominant (Tolhurst and

Turvey, 1992). Our results indicate that abundant macropod herbivores may be disrupting this

successional process by selectively feeding on more palatable vegetation, maintaining the

bracken-dominated understory. As prescribed fire is commonly used in this system to reduce

forest fuel loads and promote vegetation heterogeneity and floristic diversity (Morrison et al.,

1996), our results suggest that herbivore management following fire may be important to

maintain a heterogeneous forest flora.

Question 2: Habitat use by vertebrates

Despite the strong effect of the fire × herbivory interaction on vegetation, habitat use by

vertebrates was affected only by the individual effects of disturbances and not their interaction.

The increased rabbit activity we observed on herbivore exclusion and recently burnt sites was

consistent with previous studies of small herbivore responses to large herbivore removal

(Keesing, 1998) and fire (Leigh et al., 1987, Moreno and Villafuerte, 1995). Competitive

release of small herbivores following a reduction in large herbivore densities can result in

increased herbivory by small herbivores, with subsequent impacts on vegetation communities

(Lagendijk, Page and Slotow, 2012). Our results suggest that control of native macropod

herbivores may favour introduced rabbits, particularly after fire when fresh plant growth is

abundant. Therefore, management of the native herbivore population should carefully consider

97

the potential for competitive release of the European rabbit, which is a destructive pest species

in Australia (Davey et al., 2006).

Antechinus responded positively to large herbivore exclusion, with more individuals

captured in sites without macropod herbivores, regardless of burning treatment. Despite the

small size of our exclosures, this response likely indicates a preference for herbivore exclusion

areas, as antechinus have small foraging ranges (approximately 0.4 ha for females and 0.9 ha for

males, Lazenby-Cohen and Cockburn, 1991). Further, our result is consistent with Pedersen et

al. (2014), who found that antechinus captures were negatively correlated with wallaby density.

The preference of antechinus for sites without large herbivores may have been due to the dense

understory cover in exclosure sites (Bennett, 1993, Knight and Fox, 2000), providing increased

foraging habitat (antechinus are scansorial) and/or greater protection from predators (e.g. Stokes

et al., 2004). Other studies have found that some small mammal species prefer habitats with

lower densities of large herbivores (Bush et al., 2012, Keesing, 1998, Kutt and Gordon, 2012),

but our study is the first to experimentally demonstrate this response to macropod herbivores.

Contrary to other studies from south-eastern Australia (Fox, 1982, Lindenmayer et al.,

2008), we found only weak support for a negative response of antechinus to fire. This was likely

due to the low intensity, small-scale fires used in our study. The availability of arthropod prey

(Table 2, Appendix 2), combined with the persistence of logs and the proximity of unburnt

vegetation to burnt areas in our study, may have sufficiently maintained habitat quality for

antechinus. It is likely that antechinus may have responded more strongly to a larger-scale, or

higher intensity burn (Lindenmayer et al., 2008, Penn et al., 2003).

Delicate skinks were more common in open than in herbivore exclusion sites, and both

delicate skinks and small-eyed snakes were more common in burnt than unburnt sites. The

negative response of delicate skinks to herbivore exclusion was likely due to increased shading

from recovering vegetation, which could have reduced the thermal suitability of the

environment for this heliothermic species (Howard et al., 2003). The positive response of the

delicate skink to burning was contrary to our expectation for this species, which generally

98

recovers slowly from fire, in response to the slow accumulation of leaf litter (Taylor and Fox,

2001). Our result may therefore reflect a change in micro-habitat use following burning, rather

than a change in site occupancy. Burning reduced litter depth, and in the absence of suitable

leaf-litter habitat, the delicate skink may have increased its use of the artificial survey substrates

for shelter, basking and foraging, as found by Croft, Reid and Hunter (2010).

A lack of alternative refuges also may explain the higher numbers of small-eyed snakes

under the artificial substrates in burnt sites. However, there are two other possible explanations

for this response: First is that the small-eyed snake was more common after burning as a result

of reduced shading of substrates, which provided warmer, more desirable diurnal refuges (Webb

et al., 2004). Second, the density of skinks, a key prey item for small-eyed snakes (Shine, 1984),

may have attracted snakes to the substrates in burnt sites. Previous studies of small-eyed snakes

show that reduced shading after fire can improve the thermal properties of diurnal refuges

(Webb et al., 2005), but large wildfire can lead to population declines, possibly due to increased

predation (Webb and Shine, 2008). While the possible biases in our reptile sampling technique

mean results should be interpreted with caution, the greater numbers of reptiles in burnt sites

and skinks in the open treatments, indicate that maintaining areas of open understory may be

important for the persistence of reptiles in these forests (Webb et al., 2005).

Conservation implications

Our results have four key implications relevant to the conservation of ecosystems subject to

both recurrent fire and herbivory. First, the dominance of bracken and limited recovery of other

vegetation in burnt, open sites indicates that short-term management of abundant macropod

herbivores following prescribed fire may be useful for the conservation of structurally and

floristically complex vegetation. Second, the preference of antechinus for herbivore exclusion

sites suggests that management of native herbivores to promote dense understory habitat is also

likely to benefit small mammals dependent on such habitats. Third, the increased rabbit activity

we observed on recently burnt sites, and sites without macropods, suggests that any plan to

improve the post-fire recovery of vegetation by controlling native herbivores also should

99

include management of rabbits. Fourth, the positive response of reptiles to the open understory

of burnt sites and sites with more macropods differed from the antechinus response, and

suggests that a mixed management scenario might be more appropriate.

While our experiments were small-scale, the contrasting responses of different species

to herbivory and fire indicate that a mixed management strategy promoting a heterogeneous

understory may be important for the persistence of all species in our study. Heterogeneous

landscapes have commonly been suggested as a desired goal of land management, as such

landscapes are more likely to allow the co-existence of species with different niches, as well as

species that require a mix of habitats (Law and Dickman, 1998, Stein, Gerstner and Kreft,

2014). In Australian landscapes, fire patch-mosaics have been recommended to promote fauna

diversity, although key questions around the appropriate spatial and temporal scales of such

mosaics remain unanswered (Allouche et al., 2012, Clarke, 2008, Driscoll et al., 2010). While

not designed to address questions of spatial scale, our study suggests that maintaining a mix of

habitat types and conditions may be important for fauna in forested systems.

Although both fire and herbivory are often actively managed in forested systems

(Gordon, Hester and Festa-Bianchet, 2004, Morrison et al., 1996), these processes are usually

considered independently (Royo and Carson, 2006, Wisdom et al., 2006). However, the

interactive effects of fire and herbivory observed in our study indicate that integrating large

herbivore management with fire management practices is likely to be important for achieving

vegetation heterogeneity in forests. This could be through the fire-dependent management of

herbivores (e.g. controlling large herbivores across only part of a burn or after only some

prescribed burns), or through planning fires to consider large herbivore behaviour (e.g. reducing

the edge-area ratio of prescribed burns, as macropods can be reluctant to enter open areas and so

feed more heavily at the edge (While and McArthur, 2006)). Our study shows the value of

experimental studies that quantify disturbance responses both individually and collectively, and

highlights the importance of considering both multiple stressors, and multiple species, in the

management of disturbance regimes.

100

Acknowledgments

Staff of BNP conducted prescribed burns. We thank the Department of Environment and the

landowners and co-managers of BNP - the Wreck Bay Aboriginal Community. The Margaret

Middleton Fund, The Norman Wettenhall Foundation and the Long Term Ecological Research

Network provided financial support.

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Appendices

Appendix 1. Spatial scale and construction of experimental treatments.

Figure 1. Schematic diagram of blocked design showing types and site dimensions of the six

treatment combinations (not to scale). Exact spatial arrangement varies between blocks

Figure 2. Example of a recently burnt herbivore exclusion site (3 months post-fire), showing

fence design and forest type.

106

Appendix 2. Full model rankings for LMMs and GLMMs, and parameter

estimates for fixed-effect terms in top-ranked models.

Table 1. Full model rankings for linear mixed models testing the fixed effects of burning (B - burnt or

unburnt), exclosure (E - open, partial or full exclosure), time (T - sampling event - categorical),

and their interactions on vegetation structure. K is the number of parameters estimated in the

model, Δ AICc is the change in Aikaike’s Information Criterion (corrected for small sample

size) from the best-ranked model. AICcWt is the Aikine Weight of the model, LL is the Log-

likelihood.

Data Model terms K AICc ΔAICc AICcWt Cum.Wt LL

Understory cover

Full model (BxExT) 28 2970.0 0 0.97 0.97 -1454.7

B + E + T + B:T + H:T 20 2977.2 7.22 0.03 1 -1467.4

B + E + T + B:E: + B:T + E:T 22 2980.9 10.92 0 1 -1467.0

B + T + B:T 12 2985.8 15.81 0 1 -1480.5

B + E + T + B:T 14 2987.1 17.14 0 1 -1479.0

B + E + T + B:E: + B:T 16 2990.7 20.69 0 1 -1478.6

B + E + T + E:T 17 3088.1 118.14 0 1 -1526.2

B + T 9 3089.4 119.48 0 1 -1535.5

B + E + T 11 3090.7 120.74 0 1 -1534.0

B + E + T + B:E: + E:T 19 3091.7 121.77 0 1 -1525.8

B + E + T + B:E 13 3094.2 124.22 0 1 -1533.6

E + T + E:T 16 3095.8 125.83 0 1 -1531.2

E + T 8 3096.2 126.2 0 1 -1539.9

E + T 10 3098.5 128.5 0 1 -1538.9

B 6 3206.4 236.39 0 1 -1597.1

B + E 8 3207.6 237.58 0 1 -1595.6

B + E + B:E 10 3211.0 241 0 1 -1595.2

intercept 5 3213.1 243.14 0 1 -1601.5

E 7 3215.4 245.38 0 1 -1600.5

Cover excluding bracken

B + E + T + B:T + E:T 20 2821.4 0 0.74 0.74 -1389.6

Full model (BxExT) 28 2824.5 3.02 0.16 0.9 -1381.9

B + E + T + B:E: + B:T + E:T 22 2825.4 3.94 0.1 1 -1389.3

B + E + T + B:T 14 2854.7 33.23 0 1 -1412.8

B + T + B:T 12 2855.9 34.47 0 1 -1415.5

B + E + T + B:E + B:T 16 2858.5 37.02 0 1 -1412.5

B + E + T + E:T 17 2902.1 80.7 0 1 -1433.2

B + E + T + B:E: + E:T 19 2906.0 84.56 0 1 -1433.0

E + T + E:T 16 2917.6 96.13 0 1 -1442.0

B + E + T 11 2924.2 102.71 0 1 -1450.7

B + T 9 2925.5 104.02 0 1 -1453.5

B + E + T + B:E 13 2927.9 106.43 0 1 -1450.4

B + E 8 2933.3 111.85 0 1 -1458.5

107

Data Model terms K AICc ΔAICc AICcWt Cum.Wt LL

B 6 2934.7 113.23 0 1 -1461.2

B + E + B:E 10 2936.9 115.5 0 1 -1458.2

T 8 2937.9 116.49 0 1 -1460.8

E + T 10 2939.7 118.21 0 1 -1459.5

intercept 5 2947.2 125.73 0 1 -1468.5

E 7 2948.8 127.38 0 1 -1467.3

Vegetation height

B + T + B:T 12 -508.1 0 0.61 0.61 266.5

B + E + T + B:T 14 -506.5 1.59 0.28 0.89 267.8

B + E + T + B:E + B:T 16 -504.4 3.71 0.1 0.98 268.9

B + E + T + B:T + H:T 20 -500.0 8.03 0.01 1 271.2

B + E + T + B:E: + B:T + E:T 22 -497.8 10.29 0 1 272.3

Full model (BxExT) 28 -494.3 13.77 0 1 277.4

B + T 9 -448.4 59.67 0 1 233.4

B + E + T 11 -446.9 61.2 0 1 234.8

B + E + T + B:E 13 -444.8 63.24 0 1 235.9

B + E + T + E:T 17 -439.3 68.81 0 1 237.5

T 8 -438.7 69.37 0 1 227.5

B + E + T + B:E: + E:T 19 -437.1 71 0 1 238.6

E + T 10 -435.9 72.12 0 1 228.3

E + T + E:T 16 -428.4 79.66 0 1 230.9

B 6 -331.4 176.68 0 1 171.8

B + E 8 -329.9 178.14 0 1 173.2

B + E + B:E 10 -328.0 180.11 0 1 174.3

intercept 5 -321.7 186.41 0 1 165.9

E 7 -319.0 189.09 0 1 166.6

Litter depth

B + T + B:T 12 883.9 0 0.82 0.82 -429.51

B + E + T + B:T 14 887.6 3.7 0.13 0.95 -429.21

B + E + T + B:E + B:T 16 890.4 6.55 0.03 0.99 -428.46

B + E + T + B:T + H:T 20 892.4 8.53 0.01 1 -425.03

B + E + T + B:E: + B:T + E:T 22 895.4 11.52 0 1 -424.29

Full model (BxExT) 28 904.4 20.49 0 1 -421.89

B + T 9 942.8 58.94 0 1 -462.16

B + E + T 11 946.4 62.57 0 1 -461.86

B + E + T + B:E 13 949.2 65.35 0 1 -461.11

B + E + T + E:T 17 952.8 68.9 0 1 -458.54

B + E + T + B:E: + E:T 19 955.7 71.82 0 1 -457.8

T 8 989.9 106.06 0 1 -486.77

E + T 10 994.1 110.21 0 1 -486.74

E + T + E:T 16 1000.3 116.47 0 1 -483.42

B 6 1023.8 139.9 0 1 -505.77

B + E 8 1027.3 143.46 0 1 -505.47

B + E + B:E 10 1030.0 146.18 0 1 -504.72

intercept 5 1070.9 187.05 0 1 -530.38

E 7 1075 191.14 0 1 -530.35

108

Table 2. Parameter estimates (best), standard errors (SE), degrees of freedom (df) and marginal

probabilities (P) of fixed-effect terms for linear mixed models within two AICc of the top-ranked

model for the vegetation variables.

Model Model fixed effects Factor levels best SE df P

a. Understory cover

Top ranked model: full model (BxHxT)

Burn burnt -31.71 6.78 15 <0.001

Exclosure partial 3.94 6.78 15 0.570

full exclosure -6.25 6.78 15 0.371

Time May'13 2.69 3.14 270 0.392

Nov'13 -10.44 3.14 270 0.001

May'14 -8.75 3.14 270 0.006

Burn x Exclosure burnt.partial -1.66 9.59 15 0.865

burnt.full 4.68 9.59 15 0.632

Burn x Time burnt.May'13 21.65 4.43 270 <0.001

burnt.Nov'13 31.03 4.43 270 <0.001

burnt.May'14 25.03 4.43 270 <0.001

Exclosure x Time partial.May'13 4.63 4.43 270 0.298

full.May'13 19.88 4.43 270 <0.001

partial.Nov'13 7.75 4.43 270 0.082

full.Nov'13 18.44 4.43 270 <0.001

partial.May'14 -3.94 4.43 270 0.375

full.May'14 14.63 4.43 270 0.001

Burn x Exclosure x Time burnt.partial.May'13 7.35 6.27 270 0.242

burnt.full.May'13 -16.68 6.27 270 0.008

burnt.partial.Nov'13 -2.65 6.27 270 0.673

burnt.full.Nov'13 -14.81 6.27 270 0.019

burnt.partial.May'14 14.98 6.27 270 0.018

burnt.full.May'14 -4.62 6.27 270 0.462

b. Understory cover excluding bracken Top ranked model: B + E + T + B:T + E:T

Burn burnt -26.60 3.05 17 <0.001

Exclosure partial 3.67 3.73 17 0.340

full exclosure -3.14 3.73 17 0.412

Time May'13 -9.21 2.13 276 <0.001

Nov'13 -13.00 2.13 276 <0.001

May'14 -12.99 2.13 276 <0.001

Burn x Time burnt.May'13 12.83 2.13 276 <0.001

burnt.Nov'13 16.98 2.13 276 <0.001

burnt.May'14 19.24 2.13 276 <0.001

Exclosure x Time partial.May'13 9.93 2.61 276 <0.001

full.May'13 12.61 2.61 276 <0.001

partial.Nov'13 3.71 2.61 276 0.156

full.Nov'13 11.83 2.61 276 <0.001

partial.May'14 3.06 2.61 276 0.241

full.May'14 13.68 2.61 276 <0.001

109

Model Model fixed effects Factor levels best SE df P

c. Vegetation height Top ranked model: B + T + B:T

Burn burnt -0.301 0.046 19 <0.001

Time May'13 0.047 0.020 282 0.021

Nov'13 0.030 0.020 282 0.132

May'14 0.084 0.020 282 <0.001

Burn x Time burnt.May'13 0.114 0.028 282 <0.001

burnt.Nov'13 0.200 0.028 282 <0.001

burnt.May'14 0.216 0.028 282 <0.001

2nd ranked model: B + E + T + B:T

Burn burnt -0.301 0.043 17 <0.001

Exclosure partial 0.036 0.049 17 0.476

full exclosure 0.082 0.049 17 0.112

Time May'13 0.047 0.020 282 0.021

Nov'13 0.030 0.020 282 0.133

May'14 0.084 0.020 282 <0.001

Burn x Time burnt.May'13 0.114 0.029 282 <0.001

burnt.Nov'13 0.200 0.029 282 <0.001

burnt.May'14 0.216 0.029 282 <0.001

d. Litter Depth Top ranked model: B + T + B:T

Burn burnt -2.837 0.197 19 <0.001

Time May'13 0.496 0.126 282 <0.001

Nov'13 0.498 0.126 282 <0.001

May'14 0.240 0.126 282 0.059

Burn x Time burnt.May'13 -0.008 0.179 282 0.964

burnt.Nov'13 0.692 0.179 282 <0.001

burnt.May'14 1.275 0.179 282 <0.001

110

Table 3. Full model rankings for generalised linear mixed models testing the fixed effects of burning (B -

burnt or unburnt), exclosure (E - open, partial or full exclosure), time (T - sampling event -

categorical), and their interactions on animal occurrence. For brown antechinus, we also ran

model ranking on all subsets of a model with season (S - autumn or spring) substituted for time

(i.e. B*E*S), to account for the strong seasonal variation in the abundance of this species. K is

the number of parameters estimated in the model, Δ AICc is the change in Aikaike’s Information

Criterion (corrected for small sample size) from the best-ranked model. AICcWt is the Aikine

Weight of the model, LL is the Log-likelihood.

Data Model terms K AICc Δ AICc AICcWt Cum.Wt LL

Macropod scats

B + E + T 15 1035.9 0 0.6 0.6 -500.6

B + E + T 15 1035.9 0 0.6 0.6 -500.6

B + E + T + B:E 16 1038.4 2.52 0.17 0.77 -500.6

E + T 14 1038.7 2.8 0.15 0.92 -503.3

B + E + T + E :T 19 1041.1 5.21 0.04 0.96 -497.7

E + T + E :T 18 1043.8 7.9 0.01 0.98 -500.5

B + E + T + B:E : + E :T 20 1043.8 7.93 0.01 0.99 -497.7

B + T 14 1045.7 9.81 0 0.99 -506.8

T 13 1046.0 10.16 0 1 -508.3

B + E + T + B:T 24 1046.1 10.23 0 1 -492.7

B + E + T + B:E + B:T 25 1049.0 13.17 0 1 -492.6

B + E + T + B:T + H:T 28 1052.6 16.74 0 1 -489.4

B + E + T + B:E : + B:T + E :T 29 1055.8 19.91 0 1 -489.2

B + T + B:T 23 1056.0 20.16 0 1 -499.3

B + E 6 1056.4 20.49 0 1 -521.8

B + E + B:E 7 1058.4 22.57 0 1 -521.7

E 5 1059.3 23.41 0 1 -524.4

Full model (BxE xT) 33 1062.2 26.36 0 1 -485.1

B 5 1066.3 30.43 0 1 -527.9

intercept 4 1066.9 31.02 0 1 -529.3

Rabbit diggings

T 9 901.4 0 0.37 0.37 -441.0

E + T 11 902.4 1.09 0.21 0.58 -439.2

B + T 10 903.0 1.64 0.16 0.74 -440.7

B + E + T 12 903.9 2.59 0.1 0.84 -438.8

B + T + B:T 15 904.0 2.69 0.1 0.94 -435.1

B + E + T + B:T 17 905.4 4.03 0.05 0.98 -433.3

B + E + T + B:E 14 908.3 6.96 0.01 1 -438.5

B + E + T + B:E + B:T 19 910.1 8.73 0 1 -433.0

E + T + E:T 21 920.8 19.48 0 1 -435.6

B + E + T + E:T 22 922.7 21.38 0 1 -435.2

B + E + T + B:T + H:T 27 925.0 23.62 0 1 -429.0

B + E + T + B:E: + E:T 24 927.9 26.56 0 1 -434.9

B + E + T + B:E: + B:T + E:T 29 930.5 29.16 0 1 -428.6

intercept 4 939.6 38.29 0 1 -465.7

E 6 940.3 38.98 0 1 -463.9

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Data Model terms K AICc Δ AICc AICcWt Cum.Wt LL

B 5 941.2 39.82 0 1 -465.4

B + E 7 941.7 40.37 0 1 -463.5

B + E + B:E 9 945.7 44.37 0 1 -463.2

Full model (BxExT) 39 958.8 57.4 0 1 -425.4

Brown antechinus

E + S 6 356.9 0 0.36 0.36 -172.0

B + E + S 7 358.3 1.4 0.18 0.54 -171.5

S 4 359.8 2.92 0.08 0.62 -175.7

E + T 8 360.4 3.47 0.06 0.68 -171.4

E + S+ E:S 8 360.4 3.49 0.06 0.74 -171.4

B + E + S + B:S 8 360.5 3.65 0.06 0.8 -171.4

B + S 5 361.4 4.52 0.04 0.84 -175.4

B + E + S + B:E 9 361.5 4.6 0.04 0.87 -170.7

B + E + T 9 361.9 4.99 0.03 0.9 -170.9

B + E + S + E:S 9 361.9 5 0.03 0.93 -170.9

T 6 363.1 6.18 0.02 0.95 -175.1

B + S + B:S 6 363.5 6.66 0.01 0.96 -175.3

B + E + S + B:E + B:S 10 363.8 6.96 0.01 0.97 -170.6

B + E + S + B:S + E:S 10 364.2 7.36 0.01 0.98 -170.8

B + T 7 364.8 7.89 0.01 0.99 -174.7

B + E + T + B:E 11 365.3 8.41 0.01 0.99 -170.1

B + E + S + B:E + E:S 11 365.3 8.43 0.01 1 -170.1

B + E + S + B:E + B:S + E:S 12 367.8 10.97 0 1 -170.0

B + E + T + B:T 12 369.3 12.4 0 1 -170.8

Full model (BxExS) 14 371.1 14.21 0 1 -169.0

B + T + B:T 10 371.8 14.95 0 1 -174.6

B + E + T + B:E + B:T 14 373.1 16.2 0 1 -169.9

E + T + E:T 14 373.9 17 0 1 -170.3

B + E + T + E:T 15 375.8 18.89 0 1 -169.9

B + E + T + B:E + E:T 17 380.0 23.11 0 1 -169.1

E 5 382.5 25.58 0 1 -185.9

B + E 6 383.8 26.94 0 1 -185.4

B + E + T + B:T + E:T 18 384.4 27.52 0 1 -169.8

intercept 3 385.5 28.6 0 1 -189.6

B + E + B:E 8 386.9 30.02 0 1 -184.6

B 4 387.0 30.15 0 1 -189.3

B + E + T + B:E + B:T + E:T 20 389.1 32.22 0 1 -168.9

Full model (BxExT) 26 402.4 45.57 0 1 -165.1

Delicate skink

B + E + T 13 353.7 0 0.46 0.46 -162.8

B + T 11 354.1 0.34 0.39 0.85 -165.3

B + E + T + B:E 15 356.0 2.24 0.15 1 -161.6

T 10 364.5 10.75 0 1 -171.6

E + T 12 366.7 12.96 0 1 -170.5

B + E 6 376.5 22.82 0 1 -182.0

B 4 377.2 23.5 0 1 -184.5

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Data Model terms K AICc Δ AICc AICcWt Cum.Wt LL

B + E + B:E 8 378.4 24.71 0 1 -180.8

B + E + T + E:T 27 380.7 26.97 0 1 -158.7

B + E + T + B:T + H:T 34 382.7 28.96 0 1 -149.8

B + E + T + B:E: + E:T 29 383.8 30.05 0 1 -157.5

intercept 3 387.8 34.07 0 1 -190.8

E 5 389.7 35.95 0 1 -189.7

E + T + E:T 26 393.2 39.52 0 1 -166.4

Eastern small-eyed snake

B 4 129.2 0 0.67 0.67 -60.4

B + E 6 132.8 3.59 0.11 0.78 -59.9

intercept 3 133.5 4.32 0.08 0.86 -63.6

B + T 7 133.7 4.55 0.07 0.93 -59.2

B + T + B:T 10 136.3 7.19 0.02 0.95 -56.9

B + E + B:E 8 136.4 7.28 0.02 0.97 -59.4

E 5 137.3 8.12 0.01 0.98 -63.3

B + E + T 9 137.6 8.46 0.01 0.99 -58.8

T 6 137.9 8.72 0.01 1 -62.5

B + E + T + B:T 12 140.6 11.44 0 1 -56.4

B + E + T + B:E 11 141.7 12.49 0 1 -58.3

E + T 8 142.0 12.83 0 1 -62.2

B + E + T + E:T 15 146.5 17.29 0 1 -55.2

E + T + E:T 14 150.4 21.29 0 1 -58.6

B + E + T + B:T + H:T 18 150.6 21.4 0 1 -52.8

B + E + T + B:E: + E:T 17 151.3 22.12 0 1 -54.7

Beetles and spiders

T 5 386.2 0 0.61 0.61 -187.4

B + T 6 388.1 1.85 0.24 0.85 -187.0

B + T + B:T 7 390.8 4.53 0.06 0.92 -187.0

E + T 7 391.5 5.29 0.04 0.96 -187.4

B + E + T 8 393.6 7.4 0.02 0.98 -187.0

intercept 4 395.2 8.97 0.01 0.98 -193.1

E + T + E:T 9 396.0 9.72 0 0.99 -186.6

B + E + T + B:T 9 396.6 10.38 0 0.99 -186.9

B 5 397.0 10.8 0 0.99 -192.8

B + E + T + B:E 10 397.4 11.21 0 1 -185.7

B + E + T + E:T 10 398.4 12.15 0 1 -186.2

E 6 400.2 14.01 0 1 -193.1

B + E + T + B:E + B:T 11 400.8 14.52 0 1 -185.7

B + E + T + B:T + H:T 11 401.7 15.45 0 1 -186.2

B + E 7 402.3 16.08 0 1 -192.8

B + E + T + B:E: + E:T 12 402.9 16.7 0 1 -185.0

B + E + B:E 9 406.1 19.83 0 1 -191.7

B + E + T + B:E: + B:T + E:T 13 406.6 20.39 0 1 -185.0

Full model (BxExT) 15 414.5 28.26 0 1 -184.7

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Table 4. Parameter estimates (best), standard errors (SE) and marginal probabilities (P) of fixed-effect

terms in the generalised linear mixed models within two AICc of the top-ranked model for

animal occurrence variables.

Model Model fixed effects Factor levels best SE P

a. Macropod scats

Top ranked model: B + Ea + T

Burn burnt 0.481 0.192 0.012

Exclosure partial (open months) -0.858 0.209 <0.001

Time Nov '12 0.558 0.294 0.058

Feb '13 -0.544 0.246 0.027

Apr '13 -0.016 0.298 0.956

Jun '13 0.443 0.238 0.062

Aug '13 -0.084 0.298 0.777

Oct '13 -0.153 0.242 0.527

Dec '13 -0.819 0.307 0.008

Feb '14 -0.675 0.245 0.006

Apr '14 -0.840 0.308 0.006

b. Rabbit diggings Top ranked model: T

Time Aug '13 -0.372 0.195 0.057

Oct '13 -0.340 0.194 0.080

Dec '13 0.316 0.185 0.087

Feb '14 0.717 0.181 <0.001

Apr '14 -0.420 0.196 0.032

2nd ranked model: E + T

Exclosure partial 0.5116 0.314 0.104

full exclosure 0.5716 0.315 0.069

Time Aug '13 -0.3712 0.195 0.057

Oct '13 -0.3393 0.194 0.080

Dec '13 0.3155 0.185 0.088

Feb '14 0.7175 0.181 <0.001

Apr '14 -0.4188 0.196 0.033

3rd ranked model: B + T

Burn burnt 0.230 0.278 0.408

Time Aug '13 -0.372 0.195 0.056

Oct '13 -0.341 0.194 0.079

Dec '13 0.316 0.185 0.087

Feb '14 0.718 0.181 <0.001

Apr '14 -0.420 0.196 0.032

c. Brown antechinus Top ranked model: E + season

Exclosure partial -0.116 0.182 0.524

full exclosure 0.352 0.163 0.031

Season spring -0.750 0.147 <0.001

2nd ranked model: B + E + season

Burn burnt -0.132 0.137 0.336

Exclosure partial -0.116 0.182 0.524

full exclosure 0.352 0.163 0.031

Season spring -0.750 0.147 <0.001

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Model Model fixed effects Factor levels best SE P

d. Delicate skink Top ranked model: B + E + T

Burn burnt 1.085 0.228 <0.001

Exclosure partial 0.002 0.246 0.995

full exclosure -0.553 0.265 0.037

Time Feb '13 -1.068 0.348 0.002

Apr '13 -2.080 0.528 <0.001

Jun '13 -1.163 0.360 0.001

Aug '13 -1.386 0.393 <0.001

Oct '13 -1.520 0.415 <0.001

Dec '13 -0.470 0.284 0.097

Apr '14 -1.068 0.348 0.002

2nd ranked model: B + T

Burn burnt 1.077 0.260 <0.001

Time Feb '13 -1.068 0.345 0.002

Apr '13 -2.080 0.524 <0.001

Jun '13 -1.163 0.358 0.001

Aug '13 -1.386 0.390 <0.001

Oct '13 -1.520 0.412 <0.001

Dec '13 -0.470 0.281 0.095

Apr '14 -1.068 0.345 0.002

e. Eastern Small-eyed snake Top ranked model: B

Burn burnt 1.551 0.600 0.010

f. Beetles and Spiders Top ranked model: T Time Nov'13 -0.505 0.131 0.000

2nd ranked model: B + T Burn burnt 0.135 0.152 0.376

Time Nov'13 -0.505 0.131 0.000 a Compares only partial-open months with open treatment (see methods)

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Paper III: Synergistic interactions between fire and browsing drive plant diversity in a forest understory

In Paper II, I found that fire and herbivory interacted strongly to affect vegetation, with herbivory

limiting the recovery of understorey vegetation from fire. In this paper, I focussed more closely

on the mechanisms driving the interactive effects of fire and herbivory on plant diversity, and

investigated whether these effects occurred via numerically mediated (interaction chain) or

functionally moderated (interaction modification) pathways.

Foster, C.N, Barton, P.S, Sato, C.F, Macgregor, C.I, and D.B. Lindenmayer (2015) Synergistic

interactions between fire and browsing drive plant diversity in a forest understory. Journal of

Vegetation Science. 26, 1112-1123.

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Abstract

Questions: Does browsing by large herbivores affect forest understory diversity by modifying

assemblage dominance? Does fire interact with browsing to affect forest understory diversity?

Does this interaction occur via a numerically-mediated or functionally-moderated pathway?

Location: Booderee National Park, Jervis Bay Territory, south-eastern Australia

Methods: We tested the interactive effects of fire and browsing by native herbivores on

understory plant diversity using a randomised blocked experiment in an open eucalypt forest.

We monitored the percentage cover of every vascular plant species in 24 sites over four

experimental blocks. We applied a different treatment to each of the six sites in the four blocks.

Treatments were a factorial combination of three levels of herbivory treatment (open, partial

exclosure, full exclosure) and two levels of prescribed fire treatment (burnt, unburnt).

Results: Browsing increased plant community dominance and reduced evenness and diversity,

but only in burnt sites. Heavy browsing following fire created an understory dominated by an

unpalatable, fire resistant fern species (bracken, Pteridium esculentum). This fire-browsing

interaction was driven by both numerically-mediated and functionally-moderated pathways:

Fire both increased local browsing intensity, and amplified the per-unit effect of herbivores on

the plant community.

Conclusions: The altered competitive environment after fire, combined with heavy post-fire

browsing created a depauperate understory, dominated by bracken fern. The ability of bracken

to supress the establishment of other plants means that, once established, this fern-dominated

understory may be difficult to reverse. Our results demonstrate the key role of fire-browsing

interactions in forest vegetation dynamics and highlight the importance of integrating large

herbivore management with fire planning in forest ecosystems.

Keywords

Alternate stable states; Browsing; Community structure; Dominance; Disturbance interaction;

Eucalyptus forest; Herbivory; Macropod; Plant diversity; Pteridium esculentum; Understory

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Nomenclature

Robinson (1991)

Introduction

Herbivory is a core process driving the structure and diversity of plant communities in many

ecosystems worldwide (Milchunas et al. 1988; Hester et al. 2006; Borer et al. 2014). In forested

systems, selective browsing by large herbivores can have transformative effects, altering canopy

tree species dominance and driving cascading effects through the rest of the ecosystem (Royo &

Carson 2006; Holm et al. 2013; Côté et al. 2014). Browsing has also been shown to exert strong

effects on forest understory vegetation. However, there is little agreement among studies about

whether browsing enhances or reduces the diversity of forest understory plants. Positive effects

(Royo et al. 2010a), negative effects (Rooney & Waller 2003; Jenkins et al. 2014), and no effect

(Kerns et al. 2011) of browsing on understory plant diversity have all been reported, with

discrepancies between studies attributed to differences in herbivore densities, ecosystem

productivity, disturbance history and other site-dependent variables (Hester et al. 2006; Royo et

al. 2010a).

While the net effects of herbivory on plant diversity are variable, it has been suggested

that most responses can be explained by effects on plant species dominance (Hester et al. 2006).

In systems where herbivores selectively feed on dominant plant species, they will reduce

assemblage dominance, and increase resource availability for competitively inferior plants,

thereby increasing diversity (Côté et al. 2004; Hester et al. 2006). However, when the dominant

species are unpalatable, herbivores will have the opposite effect. A number of recent studies

from grassland systems have supported this hypothesis, finding that the effects of herbivory on

ground-level light availability (which is driven by the abundance of dominant species),

consistently explained herbivory effects on plant diversity, despite the sites having a wide range

of abiotic conditions and disturbance histories (Borer et al. 2014; Eby et al. 2014; Koerner et al.

2014).

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Understanding the effects of grazing and browsing can be useful for predicting the

outcomes of management interventions (Hester et al. 2000). Such predictions, however, are

complicated by the fact that herbivory can interact strongly with other episodic disturbance

events (e.g. fire, timber harvest, drought; Royo & Carson 2006; Wisdom et al. 2006). These

interactions can produce vastly different outcomes than would be predicted from studies of

individual effects, and can lead to unintended management outcomes (Tylianakis et al. 2008).

Ecological disturbances can interact via two main pathways: an interaction chain (a

numerically-mediated process), and an interaction modification (a functionally-moderated

process) (Didham et al. 2007). In the context of disturbance-grazing interactions, a numerically-

mediated interaction could occur through disturbance changing the local abundance of

herbivores, in turn affecting vegetation. A functionally-moderated interaction would occur if

disturbance modified the per unit effect of herbivores on vegetation.

Both these interaction pathways are commonly described in studies of fire-grazing

interactions, although they are not often explicitly identified as such. For example, patch

burning of grasslands can initiate a numerically-mediated fire-grazing interaction; fire

stimulates fresh growth which attracts large herbivores (Allred et al. 2011). The resulting

concentration of herbivores increases local grazing intensity, while reducing it elsewhere, which

increases vegetation heterogeneity (Fuhlendorf et al. 2006). By modifying the competitive

interactions among plant species, or selectively acting on some plant traits, fire can also have a

functionally-moderated effect on plant responses to herbivory. For example, in a North

American mesic grassland, annual burning amplified the negative effect of grazing exclusion on

plant diversity (Eby et al. 2014). This was due to the dominant grass species in the system being

both a post-fire increaser and highly palatable. These two functional traits gave this grass

species a competitive advantage following fire and allowed it to dominate the plant community

in the absence of large herbivores (Eby et al. 2014).

As disturbances can interact with the life history traits of organisms, as well as with

each other, predicting the outcomes of disturbance interactions is problematic, even if plant

traits are well understood (Royo & Carson 2006). Therefore, understanding such interactions

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requires manipulative experiments that are able to test the non-additive effects of these

processes (Didham et al. 2007; Tylianakis et al. 2008). Manipulative tests of the interactive

effects of herbivory and disturbance have become reasonably common in grassland systems

(e.g. Fuhlendorf et al. 2006; Collins & Calabrese 2012; Koerner & Collins 2014), but remain

rare in forested systems (Wisdom et al. 2006; Royo et al. 2010a). The high densities of large

herbivores in many forests worldwide (Côté et al. 2004), and the prevalence of active fire

management in forest ecosystems (Stephens & Ruth 2005; Boer et al. 2009), mean that

improving our understanding of the effects of fire-browsing interactions on vegetation dynamics

is of high importance for the management of forest ecosystems (Wisdom et al. 2006).

We studied the effects of fire × browsing interactions on plant diversity in a temperate

forest understory, testing the hypothesis that effects on plant diversity would be similar to those

described for grassland ecosystems. Our study had three main predictions: (1) browsing by large

herbivores would affect plant diversity by modifying assemblage dominance. Specifically, as

the dominant understory species in our study system was of low palatability (Pteridium

esculentum, Di Stefano & Newell 2008; Fletcher et al. 2011), we predicted that browsing would

increase assemblage dominance and reduce diversity. We also predicted that: (2) fire would

amplify the effects of browsing on plant diversity, and that (3) this interaction would occur via

both numerically-mediated and functionally-moderated pathways. The numerically-mediated

effect would be driven by more herbivores browsing in burnt sites, while the functionally-

moderated effect would be driven by fire amplifying the effects of herbivory on P. esculentum

dominance.

Methods

Study site

We conducted this study in Booderee National Park (BNP); a ~6 500 ha peninsula in south-

eastern Australia (35°10′S, 150°40′E, Fig. 1). The most widespread vegetation type in the park

is open eucalypt forest (Taws 1998). This forest type is dominated by Eucalyptus pilularis,

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Corymbia gummifera and E. botryoides in the overstory (> 10 m), Banksia serrata, Acacia

longifolia and Monotoca elliptica in the midstory (2 – 10 m) and Lomandra longifolia and P.

esculentum in the understory (< 2 m) (Taws 1998; Lindenmayer et al. 2008).

An intensive baiting program targeting the introduced red fox (Vulpes vulpes) has been

in place within the park since 1999 to protect native small mammal species from predation

(Dexter et al. 2012). As hunting has been long discontinued and dingoes (Canis lupus dingo)

now occur only rarely in the park, the removal of foxes means that predation pressure on

herbivores is low (Lindenmayer et al. 2014). Since the removal of foxes, there has been a

tenfold increase in the numbers of native macropod herbivores within the park (Dexter et al.

2012; Lindenmayer et al. 2014). The most common of these macropods are the swamp wallaby,

Wallabia bicolor, a generalist browser, and the eastern grey kangaroo, Macropus giganteus, a

grazer (Davis et al. 2008; Dexter et al. 2013). Small-scale exclosure trials have indicated that

the current high abundance of these herbivores could be driving a decline in some plant species

(Dexter et al. 2013).

Study design

We quantified the interactive effects of fire and large herbivores on understory plant diversity

using a randomised, blocked experiment. We examined three levels of herbivory treatment and

two levels of burning treatment in a factorial design (Fig. 1). We replicated each of these six

treatment combinations across four experimental blocks to give a total of 24 sites. For the

herbivory treatments, we manipulated the density of macropods within 25 × 25 m plots using

exclosure fences to produce three levels of herbivory: full herbivory (open treatment),

intermediate herbivory (partial treatment – plots were fenced but gates opened and closed at two

month intervals to simulate lower browsing pressure), and no herbivory (exclosure treatment).

We constructed exclosure fences in June 2012 using 1.1 m tall feral-proof stock fencing, which

we found to be effective at excluding macropods (see results). For the burning treatments, we

conducted 50 × 50 m, low severity burns across half of the herbivore treatment sites in August

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2012. Burns removed approximately 95% of understory vegetation from the site and did not

reach canopy foliage (tree scorch height 1.5 - 4 m).

Figure 1. Study location and design showing (a) location of Booderee National Park in south-

eastern Australia, (b) location of the four experimental blocks (A-D) within Booderee

National Park, and (c) a schematic diagram of the factorial design showing the

experimental treatments and their arrangement within the four experimental blocks (not

to scale, exact spatial arrangement of sites varies between blocks). Dark grey shaded

area in (b) shows the extent of the Eucalyptus pilularis forest type within BNP, black

outline indicates park boundary, light grey shading depicts ocean.

Data collection

We surveyed understorey vegetation prior to treatment implementation in April/May 2012

(austral autumn) and repeated surveys three months after burning, and then every six months

until May 2014. To capture variation within each site, we sampled vegetation using four small

quadrats, rather than one large quadrat in each site. We placed four 3 x 3 m quadrats at fixed

locations in each site, with one quadrat in each of the four quarters of the site, ensuring each

was at least 1.5 m from the fence. For each quadrat, we visually estimated the projective cover

of each vascular plant species, using the same observer for all estimates. Prior to any data

processing, we averaged the cover values of each plant species across the four quadrats to give

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one value for the 36 m2 area sampled in each site. We used these site-level data to calculate four

standard metrics of community diversity: species richness (species/ 36 m2), species diversity

(Simpson’s reciprocal index - 1/D), evenness (Simpson’s evenness – E1/D), and assemblage

dominance (Berger-Parker index – d) (Magurran 2004).

We surveyed site use by macropod herbivores by counting their scats along two 25 × 2

m transects (100 m2) in each site, summing counts to give one overall count per site. We

surveyed transects every two months from August 2012 to May 2014. We removed scats from

transects after each survey to avoid double counting. For analysis, we summed the two scat

counts preceding each vegetation survey to give an estimate of the average level of herbivore

activity in a site leading up to each sampling event.

Data analysis

We tested the effects of herbivory, fire and their interaction on plant species richness, diversity,

evenness and dominance using linear mixed-models (LMMs) in R (R Core Team 2013). The

full model for these analyses included the fixed effects of herbivory × fire × time, plus the pre-

treatment value (to account for any pre-existing differences between sites), and random effects

of block/site. Instead of the categorical herbivory treatments, we used the scat counts at each

site as our measure of herbivory. This allowed us to account for variation in herbivore pressure

both between and within levels of the herbivore exclosure treatments, and also to test whether

the relationship between herbivore pressure and plant diversity metrics differed between burnt

and unburnt sites (i.e. whether there was a functionally moderated interaction). Prior to

inclusion in the model as a fixed effect, scat count data were natural log (ln) transformed to

reduce skew, and standardised by centring, and dividing by two standard deviations to allow

comparison of the main effects of fire and herbivory and aid in interpretation of interaction

terms (Gelman & Hill 2007). After checking the fit of the full models by inspecting residual

plots (Zuur et al. 2009), we used the “dredge” function in the package “MuMIn” (Barton 2014)

to rank all possible subsets of the full model, based on minimising Akaike’s Information

Criterion, corrected for small sample sized (AICc) (Burnham & Anderson 2002).

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As our study included repeated measures at sites, we tested for temporal autocorrelation

between repeated measures at sites using the “acf” function in R (R Core Team 2013).

Autocorrelation for all variables was low, and including a first-order autoregressive correlation

structure on the random effects did not improve the fit of the LMMs. Therefore, we did not

include correlation structures in our final models (Pinheiro & Bates 2000). We also considered

fitting the species richness data with a generalised linear mixed-model (GLMM), with Poisson

errors, which is often used for count data. However, this was not used in the final analysis as the

characteristics of a Poisson distribution (skewed distribution and heteroscedasticity) were not

evident in our data (Zuur et al. 2009), and a trial GLMM showed poor fit at the extremities

when compared with the LMM.

To assess whether fire also interacted with herbivory via a numerically-mediated

pathway, we tested whether fire increased herbivore activity (as measured from scat counts)

within the different exclosure treatments. We used GLMM with a Poisson error distribution and

a log-link function for this analysis, with exclosure treatment × fire × time as fixed effects and

block/site as random effects. As very low scat counts in exclosure treatments ( = 1.78 ± 0.53,

59% of counts = 0) led to complete separation of factor combinations (Gelman & Hill 2007),

the exclosure treatment was excluded from this analysis. Scat count data were found to be over-

dispersed, which we accounted for by including an observation-level random effect in our

model (Harrison 2014).

We tested for differences in community composition among the six treatment

combinations both pre- (May 2012) and post-treatment (May 2014), using nonparametric

blocked multi-response permutation procedures (MRBP, McCune & Grace 2002) in PC-ORD

(McCune & Mefford 2006). We excluded rare species (those occurring at 2 or fewer sites) from

the site × species matrix and relativized abundances within sites, prior to analysis (McCune &

Grace 2002). If MRBP indicated significant differences in species composition among

treatments, we then used indicator species analysis (Dufrene & Legendre 1997) to identify

individual species associated with the different treatments. We used the function “multipatt” in

the R package “indicspecies” (De Cáceres & Legendre 2009), which allows the identification of

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species associations with combinations of groups (treatments) rather than just a single group

(De Cáceres et al. 2010). We set the maximum number of groups (treatment combinations) to

be combined to three as this allowed us to identify associations with both individual treatment

combinations, as well as with complete factors in the factorial design. We accounted for the

blocked structure of sites by constraining permutations within, rather than among blocks.

Significance was determined from 9999 permutations of the data.

Results

Effect of browsing and fire on plant diversity metrics

We detected 111 understory plant species over five surveys in the two-year study period

(Appendix S1). In the top-ranked LMMs, fire modified the effect of herbivory on plant

community diversity, evenness, and dominance and this interaction was consistent across time

(Table 1, Fig. 2, Appendix S2). Plant species diversity, evenness and dominance were not

related to herbivore activity on unburnt sites, but burnt sites had lower community diversity and

evenness and higher assemblage dominance as herbivore activity increased (Table 1, Fig. 2).

This indicates a functionally moderated interaction. The difference in community diversity and

evenness between burnt and unburnt sites varied through time and was highest 15 months post-

fire. In contrast, the highest-ranked model for assemblage dominance indicated that the effect of

fire was consistent across time; at high levels of herbivory, the relative abundance of the

dominant species was 30 % higher in burnt than unburnt sites (Fig. 2). This increase in

assemblage dominance was driven by P. esculentum, which comprised approximately 70 % of

the vegetation cover in burnt-open sites, but less than 40 % in burnt and unburnt exclosure sites

(Fig. 3). In contrast to the other diversity metrics, none of the models for species richness with

ΔAICc < 2 included the herbivory × fire interaction (Appendix S2), and the effects of fire were

short-term, with species richness of burnt sites returning to the level of unburnt sites within 15

months (Table 1).

125

Table 1. Estimates of coefficients (Est.), their standard errors (SE) and probabilities for the top-ranked

linear mixed model testing the effects of fire (burnt or unburnt), large herbivore activity (scats

per 100m2, ln transformed), time since fire (3, 9, 15 or 21 months) on plant species richness

(species / 36 m2), diversity (Simpson’s reciprocal index, 1/D), evenness (Simpson’s evenness,

E1/D) and dominance (Berger-Parker, d). Pre-treatment values were included as a fixed effect to

account for pre-existing differences between sites. Reference states for comparisons were

unburnt sites three months post-fire. Random terms for each model were block/site.

Species richness Diversity (1/D) Evenness (E1/D) Dominance (d)

Fixed effects Est. SE df P

Est. SE df P

Est. SE df P

Est. SE df P

Intercept 6.15 5.33 66 0.253 2.25 0.72 64 0.003 0.075 0.024 64 0.002 0.266 0.078 67 0.001

Pre-treatment value 0.85 0.19 18 <0.001 0.39 0.20 18 0.060 0.373 0.168 18 0.040 0.296 0.126 18 0.030

Fire -9.40 1.45 18 <0.001 1.04 0.53 18 0.066 0.095 0.020 18 <0.001 0.107 0.031 18 0.003

Herbivores (ln trans.) 0.28 0.48 64 0.558 0.012 0.016 64 0.455 -0.022 0.046 67 0.636

Time since fire 9 mo 0.50 0.78 66 0.523 -0.43 0.48 64 0.378 -0.016 0.020 64 0.410 0.115 0.038 67 0.003

15 mo -0.25 0.78 66 0.749 -0.02 0.48 64 0.973 0.001 0.020 64 0.975 0.063 0.038 67 0.100

21 mo 1.17 0.78 66 0.139 0.13 0.49 64 0.785 0.001 0.020 64 0.973 0.053 0.039 67 0.177

Fire:Herbivores(ln) -1.68 0.64 64 0.011 -0.059 0.021 64 0.006 0.209 0.061 67 0.001

Fire:Time B:9mo 6.67 1.10 66 <0.001 -1.69 0.67 64 0.014 -0.110 0.028 64 <0.001

B:15mo 10.33 1.10 66 <0.001 -1.88 0.67 64 0.007 -0.126 0.028 64 <0.001

B:21mo 10.92 1.10 66 <0.001 -1.67 0.69 64 0.018 -0.122 0.028 64 <0.001

126

Figure 2. Response of plant community diversity (Simpson’s reciprocal index, 1/D, a-d),

evenness (Simpson’s evenness, E1/D, e-h) and dominance (Berger-Parker, d, i-l) to fire

(burnt or unburnt) and herbivore activity through time. Values are predicted means and

95 % confidence intervals from the top-ranked model for each metric. Pre-treatment

values were fixed at the mean for all predictions (3.15 for diversity, 0.106 for evenness

and 0.53 for dominance).

127

Figure 3. Species rank-abundance curves for burnt and unburnt sites under each of the

herbivory treatments in the final survey period (May 2014). Species with relative

abundances > 0.1 are identified on each plot.

Effect of fire on herbivore activity

Fire affected herbivore activity levels, with both the first and second ranked models (ΔAICc =

0.46) showing that scat counts were higher on burnt than unburnt sites (Table 2), indicating a

numerically-mediated interaction between fire and herbivory. In the first-ranked model, this

effect was consistent across time, but in the second-ranked model, this difference was

significant only in the period from three to nine months post-fire, where counts in burnt sites

were more than double those in unburnt sites (Table 2, Fig. 4).

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Table 2. Results of generalised linear mixed models testing whether fire affected the level of herbivore

activity (scats per 100m2) in the different exclosure treatments (open or partial exclosure)

through time (3, 9, 15 or 21 months post-fire). Full exclosure treatments were not included in

this analysis due to very low values ( = 1.78 ± 0.53). Reference states for comparisons were

unburnt, open sites three months post-fire.

Top ranked model Second ranked model

Fixed effects Est. SE Z P

Est. SE Z P

Intercept 4.26 0.23 18.8 <0.001

4.36 0.25 17.2 <0.001

Fire 0.46 0.20 2.2 0.025

0.27 0.33 0.8 0.408

Exclosure (partial) -1.63 0.20 -8.0 <0.001

-1.63 0.20 -8.1 <0.001

Time since fire 9 months -0.51 0.24 -2.1 0.038

-1.04 0.32 -3.2 0.001

15 months -0.25 0.24 -1.0 0.306

-0.20 0.31 -0.7 0.515

21 months -0.89 0.24 -3.6 <0.001

-0.81 0.31 -2.6 0.010

Fire:Time B:9m

1.01 0.44 2.3 0.022

B:15m

-0.09 0.43 -0.2 0.831

B:21m -0.18 0.44 -0.4 0.690

Figure 4. Herbivore activity in each of the experimental treatment combinations over time.

Values are the predicted mean scat count and 95 % confidence intervals (a-d) based on

the second-ranked generalised linear model. Full exclosure treatments were not

included in this analysis due to very low values ( = 1.78 ± 0.53).

Effect of browsing and fire on plant species composition

Species composition did not differ among experimental treatments prior to treatment

implementation in May 2012 (MRBP, P = 0.15). However, by May 2014, species composition

differed significantly among treatments (MRBP, P = 0.004). Pairwise comparisons revealed that

burnt-open sites had significantly different species composition to all other treatment

129

combinations (all P < 0.05). Burnt partial and burnt exclosure sites also differed significantly in

composition (P = 0.03).

Indicator species analysis identified six individual species that were associated with the

experimental treatments, each of which was positively associated with three of the treatment

combinations (all P < 0.05, Appendix S1). The fern P. esculentum was positively associated

with both burnt and unburnt open sites, as well as burnt partial sites. Conversely, the grass

Themeda australis and the herb Galium propinquum were associated with both burnt and

unburnt exclosure treatments, as well as unburnt partial treatments. A further three species were

associated with burnt exclosure treatments but differed in their other associations; the small tree

Synoum glandulosum was also associated with burnt partial and unburnt exclosure sites, the

sub-shrub Marsdenia suavolens was also associated with unburnt exclosure sites and burnt open

sites, and another small tree Persoonia linearis was associated with all burnt treatments.

Discussion

We tested plant diversity responses to browsing and the fire-browsing interaction using a

manipulative experiment in a temperate forest understory. As predicted, where browsing

increased plant community dominance, it reduced plant diversity and evenness. Prescribed fire

interacted strongly with browsing, with herbivore activity affecting these measures of plant

diversity only in burnt sites. This fire-browsing interaction was generated through a

combination of numerically-mediated and functionally-moderated pathways; fire both increased

herbivore activity levels, and increased the per-unit effect of herbivores on the plant

community.

Effect of browsing and fire

Browsing had little effect on the plant community in the absence of fire, but browsing in burnt

environments increased community dominance, reduced plant community evenness and

diversity, and altered species composition. Specifically, browsing in burnt environments

increased the dominance of the fern P. esculentum and reduced the abundance of a number of

130

palatable grasses and herbs. This pattern was consistent with previous studies where plant

diversity responses to grazing depended on the effect of grazing on the dominant plant species

(Mathisen et al. 2010; Royo et al. 2010a; Borer et al. 2014). As suggested by Royo et al.

(2010a), the variable effect of browsing on the dominant plant species can potentially explain

the wide range of reported effects of large herbivores on forest plant diversity. In systems where

large herbivores have been at high abundances for extended periods, understories are largely

dominated by a few unpalatable species; this is the case in many forested systems of North-

America (Tremblay et al. 2006; Rooney 2009) and Europe (Kirby 2001) where deer are highly

abundant. In such systems, continued browsing maintains the dominance of unpalatable species,

while herbivore exclusion can allow palatable species to recover, thereby increasing plant

species richness and evenness. In systems where large herbivores are not highly abundant,

browsing may help to maintain understory diversity by reducing the dominance of fast-growing

palatable species, particularly following disturbance (Royo et al. 2010a).

Despite having strong effects on plant community diversity and evenness after fire, we

found that browsing did not affect plant species richness, either with or without burning.

Mathisen et al. (2010) reported a similar result for moose browsing in Swedish pine forest. This

may be because much of the vegetation in both the study by Mathisen et al. (2010) and in our

study was comprised of shrubs (Appendix S1). Browsing is often not lethal for established

individuals of woody plant species (Hester et al. 2006). Thus, in forest systems with woody

understories, browsing will affect the relative cover of different species more strongly than the

relative number of individuals. In addition, as shrubs are slow-growing and long-lived

compared with many herbs and grasses (McFarland 1998), the two-year time frame of our study

may have been insufficient for differences in colonisation and extinction rates to become

apparent.

Interaction pathways

The stronger effect of herbivory on vegetation after burning occurred both via numerically-

mediated and functionally-moderated pathways. Burning both increased the herbivore activity

131

at a site (Fig. 4), and increased the per unit effect of herbivore activity on the vegetation (Fig.

2). The attraction of large herbivores to recently burnt patches has been well documented in

grassland ecosystems (Allred et al. 2011), and has also been shown for some forest-dwelling

ungulates (Fisher & Wilkinson 2005). However, previous studies of macropod herbivores

following fire have found little effect of large wildfires on macropod densities (Garvey et al.

2010; Arthur et al. 2012). These contrasting results indicate that fire-browsing interactions

driven by the attraction of herbivores to burnt areas may be more likely to occur following

small, patchy fires (as occurs with prescribed burns), than following large-scale wildfires. The

effects of prescribed fire on browsing intensity should therefore be considered in fire planning

in these systems, especially where herbivores are particularly abundant.

The greater per-unit effect of herbivores on vegetation in burnt than in unburnt sites was

likely due to the competitive release of P. esculentum following fire. After fire (and other

disturbances), Pteridium species can rapidly regenerate from below-ground rhizomes and

dominate recently disturbed environments (Skre et al. 1998; Spencer & Baxter 2006). However,

in the absence of further disturbance, the dominance of P. esculentum usually declines with time

since fire, as other plants become more established (Spencer & Baxter 2006). In our study,

browsing maintained assemblage dominance two years after fire, with P. esculentum

comprising almost 70 % of understory vegetation cover in burnt, browsed (open) sites,

compared with less than 40 % in burnt, un-browsed sites (Fig. 3). The dominance of P.

esculentum could have important consequences for the ability of the vegetation to recover to its

pre-fire state, as P. esculentum can form a shade canopy which supresses the establishment of

other plants (Tolhurst & Turvey 1992). Therefore, our results suggest that heavy browsing after

prescribed fire may lead to an understory dominated by a few fire- and browsing-resistant

species, which could be difficult to reverse.

An understory dominated by browse- and fire-resistant plants was suggested by Wisdom

et al. (2006) as a likely outcome of low severity forest fires under moderate or high herbivory.

Previous studies of the individual effects of chronic herbivory (Rooney 2001) and reoccurring

fire (Spencer & Baxter 2006) have documented transitions towards depauperate understories

132

dominated by ferns (Dennestaedtia and Pteridium respectively). Our study, and an earlier pilot

study (Dexter et al. 2013), show that, in concert, fire and herbivory can have synergistic effects,

creating a depauperate, fern-dominated understory over a relatively short time period. Our

results also show that at low levels of herbivory, differences in vegetation diversity between

burnt and unburnt sites are small. Therefore, in forests where predation by foxes and dingoes

keeps macropod herbivores at low densities, fire-browsing interactions may have limited effects

on vegetation diversity.

Although the timeframe of our study means we cannot predict whether the effects of

these fire-browsing interactions will persist in the long-term, alternate stable states driven by

chronic herbivory have been documented from a range of forest ecosystems worldwide (Royo

& Carson 2006; Raffaele et al. 2011; Tanentzap et al. 2011; Hidding et al. 2013). These systems

are characterised by understories with low floristic diversity which are dominated by a few,

unpalatable species (de la Cretaz & Kelty 1999; Horsley et al. 2003). Once such states have

developed, legacy effects can prevent the recovery of plant diversity, even if herbivore densities

are reduced (Royo et al. 2010g). Preventing the development of such states through the

integrated management of disturbances such as herbivory and fire is therefore of key

importance for the conservation of diverse forest understory flora (Royo & Carson 2006;

Wisdom et al. 2006).

Can effects on dominance always be used to predict the outcome of fire-

browsing interactions?

We were able to predict the outcome of the fire-browsing interaction in our study. However,

this was most likely due to the particularities of our study system rather than a predictability of

fire-browsing interactions in general. In our study system, the most dominant and widespread

understory species is P. esculentum, which is of low palatability (Di Stefano & Newell 2008)

and responds positively to fire (Spencer & Baxter 2006). Therefore, both fire and browsing

acted to increase the dominance of P. esculentum, and their functionally-moderated effects were

synergistic. In addition, fire increased browsing activity, meaning that the numerically-mediated

interaction between fire and browsing also had positive synergistic effects.

133

By contrast, in many other systems, the outcomes of fire-browsing interactions will be

far less predictable. For example, in a system where fire and browsing act antagonistically (e.g.

fire increases dominance, but browsing has the opposite effect, as in Eby et al. 2014), where

more than one species is dominant (and fire or browsing responses differ between these species)

and/or where functionally-moderated effects are in opposition to numerically-mediated effects,

the outcome will depend on the balance of these opposing effects. Further, fire and browsing

may interact in more subtle ways, such as modifying the strength of each other’s effects on

dominant species. For example, the tissues of plants regenerating after fire often lack the

physical and chemical defences of mature plants, meaning generally unpalatable plants can be

highly palatable after burning (Augustine & McNaughton 1998). If differences in palatability

between species are reduced, herbivores feed less selectively, so the strength of browsing

effects on assemblage dominance can be reduced or even eliminated (Augustine & McNaughton

1998). In addition, low severity fires, such as prescribed burns can have different effects on the

plant community than high severity fires that often occur as wildfire (Morrison 2002). Different

fire severities may therefore have differing effects on dominant species, and so the outcomes of

fire-browsing interactions are likely to vary with fire severity. Therefore, predicting the

outcome of fire-browsing interactions will only be possible in some ecosystems, and where

there is detailed knowledge of the palatability and functional traits of plant species. Outside of

these systems, manipulative experiments will continue to be critical to improving understanding

of the dynamics of fire-browsing interactions.

Conclusion

In forested systems that are dominated by unpalatable, fire-resistant plant species, prescribed

fire and browsing are likely to interact synergistically to increase assemblage dominance and

reduce plant community diversity. In such systems, integrated management of fire and large

herbivores will be necessary to prevent the development of an understory dominated by a

narrow set of fire tolerant, unpalatable species. In forested ecosystems where unpalatable, fire

resistant species are not dominant, the outcome of fire-browsing interactions will not be so easy

to predict. Therefore manipulative studies which can examine both the individual and

134

interactive effects of these disturbances, and their interaction pathways, will continue to play a

central role in developing our understanding of fire-browsing interactions in forest ecosystems.

Acknowledgements

Staff of BNP conducted prescribed burns. We thank the Department of Environment and the

landowners and co-managers of BNP - the Wreck Bay Aboriginal Community for supporting

this project. The Margaret Middleton Fund, The Norman Wettenhall Foundation and the Long

Term Ecological Research Network provided financial support.

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Supporting Information

Additional supporting information may be found in the online version of this article:

Appendix S1. Plant species list

Appendix S2. Tables of model rankings

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Appendix S1. Plant species list

Appendix S1. List of species sampled in experimental plots and treatment associations of individual

species that were identified by indicator species analysis as having a significant association with

one or more treatments. Nomenclature follows Robinson (1991). Letter codes for treatment

associations are as follows: BO – burnt, open; BP – burnt, partial exclosure; BE – burnt,

exclosure; UO – unburnt open; UP – unburnt partial exclosure; UE – unburnt exclosure.

Life-form Species Treatment associations P

Ferns

Calochlaena dubia

Pteridium esculentum BO, BP, UO 0.019

Palms

Livistona australis

Grasses

Austrostipa pubescens

Entolasia marginata

Entolasia stricta

Imperata cylindrica

Microlaena stipoides

Oplismenus aemulus

Oplismenus imbecillis

Poa labillardierei

Themeda australis BE, UE, UP 0.024

Sedges / Rhizomatous Perennials

Ficinia nodosa

Hypolaena fastigiata

Lepidosperma concavum

Lomandra cylindrica

Lomandra filiformis

Lomandra glauca

Lomandra gracilis

Lomandra longifolia

Lomandra micrantha

Lomandra multiflora

Restio fastigiatus

Xanthorrhoea resinifera

Herbs and Forbs

Acianthus sp.

Actinotus helianthi

Caladenia catenata

Commelina cyanea

Corybas sp.

Dianella caerulea

Dichondra repens

Galium gaudichaudii

Galium propinquum BE, UE, UP 0.042

Gastrodia sesamoides

Lagenifera stipitata

Patersonia sericea

Poranthera microphylla

Pratia purpurascens

Pterostylis sp.

Scaevola ramosissima

Schelhammera undulata

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Life-form Species Treatment associations P

Selaginella uliginosa

Viola hederacea

Climbing / Scrambling Herbs

Cassytha pubescens

Comesperma volubile

Desmodium brachypodum

Desmodium rhytidophyllum

Desmodium varians

Glycine clandestina

Glycine microphylla

Glycine tabacina

Sub-shrubs

Billardiera scandens

Coronidium elatum

Dampiera purpurea

Dampiera stricta

Gonocarpus teucriodes

Hardenbergia violacea

Opercularia aspera

Marsdenia suaveolens BE, BP, UE 0.017

Shrubs

Acacia suaveolens

Acacia terminalis

Acacia ulicifolia

Bossiaea ensata

Bossiaea heterophylla

Breynia oblongifolia

Correa reflexa

Dillwynia sp.

Epacris pulchella

Glochidion ferdinandi

Hibbertia aspera

Hibbertia empetrifolia

Hibbertia linearis

Hibbertia obtusifolia

Hovea linearis

Leptomeria acida

Leucopogon lanceolatus

Melichrus sp.

Monotoca elliptica

Monotoca scoparia

Petrophile pulchella

Phyllanthus hirtellus

Pimelea linifolia

Platylobium formosum

Platysace lanceolata

Pultenaea paleacea

Pultenaea rosmarinifolia

Ricinocarpos pinifolius

Styphelia triflora

Xanthosia pilosa

Zieria smithii

Vines

Cissus hypoglauca

Clematis aristata

Eustrephus latifolius

Hibbertia dentata

Hibbertia scandens

Marsdenia rostrata

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Life-form Species Treatment associations P

Parsonsia straminea

Sarcopetalum harveyanum

Smilax glyciphylla

Trees / Tree Seedlings

Acacia longifolia

Allocasuarina seedling

Banksia serrata

Ceratopetalum gummiferum

Corymbia gummifera

Elaeocarpus reticulatus

Eucalyptus seedling

Notelaea longifolia

Persoonia linearis BE, BO, BP 0.012

Pittosporum undulatum

Syncarpia glomulifera

Synoum glandulosum

BE, BP, UE

0.047

References

Robinson, L. 1991. Field guide to the native plants of Sydney. Kangaroo Press, Kenthurst,

NSW, Australia.

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Appendix S2. Model rankings for linear mixed models (LMMs) and generalised

linear mixed models (GLMMs).

Table S1. Model rankings for linear mixed models (LMMs) testing the fixed effects of fire (B, burnt or

unburnt), herbivory (H, number of scats per 100m2) and time since fire (T, 3, 9, 15 or 21

months) and their interactions, plus the starting condition (Pre), on plant community diversity

metrics (species richness, Simpson‘s reciprocal diversity index, Simpson’s evenness, and

Berger-Parker dominance). All models included the random effects of block/site. Details are

provided for all models with ΔAICc < 4.

Data Model Terms K LL AICc ΔAICc AICcWt

Species richness

Pre + B + T + B:T 12 -222.09 471.9 0 0.498

Pre + B + H + T + B:T 13 -221.23 472.9 0.96 0.309

Pre + B + H + T + B:H + B:T 14 -221.15 475.5 3.54 0.085

Simpson’s reciprocal diversity (1/D)

Pre + B + H + T + B:H + B:T 14 -150.94 335.1 0 0.158

Pre + T 8 -159.14 335.9 0.87 0.103

H + T 8 -159.19 336 0.96 0.098

Pre + H + T 9 -157.99 336.1 1.01 0.095

B + H + T + B:H + B:T 13 -153.05 336.5 1.47 0.076

T 7 -161.00 337.3 2.21 0.052

Pre + B + H + T + B:H 11 -156.19 337.5 2.46 0.046

B + H + T 9 -158.80 337.7 2.63 0.043

Pre + B + T 9 -158.84 337.8 2.71 0.041

Pre + B + H + T 10 -157.70 338 2.92 0.037

Pre + B + T + B:T 12 -155.14 338 2.97 0.036

B + H + T + B:T 12 -155.32 338.4 3.33 0.03

B + H + T + B:H 10 -157.91 338.4 3.35 0.03

Pre + B + H + T + B:T 13 -154.16 338.8 3.69 0.025

B + T 8 -160.59 338.8 3.78 0.024

B + T + B:T 11 -156.89 338.9 3.85 0.023

Evenness (E 1/D)

Pre + B + H + T + B:H + B:T 14 161.53 -289.9 0 0.534

B + H + T + B:H + B:T 13 158.98 -287.5 2.35 0.165

Pre + B + H + T + B:H + B:T + H: T 17 163.92 -286 3.89 0.076

Dominance (d)

Pre + B + H + T + B:H 11 58.75 -92.4 0 0.302

Pre + B + H + T + B:H + B:T 14 62.52 -91.9 0.51 0.234

Pre + B + H + B:H 8 53.94 -90.2 2.13 0.104

B + H + T + B:H 10 56.09 -89.6 2.77 0.076

B + H + T + B:H + B:T 13 59.58 -88.7 3.65 0.049

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Table S2. Model rankings for generalised linear mixed models (GLMMs) testing the fixed effects of fire

(B, burnt or unburnt), exclosure treatment (E, open or partial), and time since fire (T, 3, 9, 15, or

21 months) on site-level herbivore activity (number of scats per 100m2). All models included

the random effects of block/site. Details are provided for all models with ΔAICc < 4.

Model terms K LL AICc ΔAICc AICcWt

B + Ep + T 9 -281.81 584.9 0 0.330

B + Ep+ T + B:T 12 -277.65 585.4 0.46 0.262

Ep + T 8 -284.02 586.7 1.71 0.141

B + E + T + B:Ep 10 -281.69 587.5 2.58 0.091

B + Ep + T + B:Ep+ B:T 13 -277.50 588.3 3.33 0.062

145

Paper IV: Interactive effects of fire and large herbivores on web-building spiders

Understanding of how the effects of large herbivores cascade through ecosystems is limited

(see Paper I), but is important for both detecting and managing impacts. The effects of large

herbivores on other animals are often attributed to changes in vegetation (Paper I). However, in

Papers II and III, I found that plants and animals differed in their responses to the fire-herbivory

interaction. In this paper, I examined the extent to which vegetation mediated the effects of fire

and herbivory on web-building spiders – a group that is usually sensitive to changes in habitat

structure.

Foster, C.N, Barton, P.S., Wood, J.T. and D.B. Lindenmayer (2015) Interactive effects of fire

and large herbivores on web-building spiders. Oecologia. 179, 237-248.

146

Abstract

Altered disturbance regimes are a major driver of biodiversity loss worldwide. Maintaining or

recreating natural disturbance regimes is therefore the focus of many conservation programs. A

key challenge, however, is to understand how co-occurring disturbances interact to affect

biodiversity. We experimentally tested for the interactive effects of prescribed fire and large

macropod herbivores on the web-building spider assemblage of a eucalypt forest understorey,

and investigated the role of vegetation in mediating these effects using path analysis. Fire had

strong negative effects on the density of web-building spiders, which were partly mediated by

effects on vegetation structure, while negative effects of large herbivores on web density were

not related to changes in vegetation. Fire amplified the effects of large herbivores on spiders,

both via vegetation-mediated pathways, and by increasing herbivore activity. The importance of

vegetation-mediated pathways and fire-herbivore interactions differed for web density and

richness, and also differed between web types. Our results demonstrate that for some groups of

web building spiders, the effects of co-occurring disturbance drivers may be mostly additive,

whereas for other groups, interactions between drivers can amplify disturbance effects. In our

study system, the use of prescribed fire in the presence of high densities of herbivores could

lead to reduced densities and altered composition of web-building spiders, with potential

cascading effects through the arthropod food web. Our study highlights the importance of

considering both the independent and interactive effects of disturbances, as well as the

mechanisms driving their effects, in the management of disturbance regimes.

Keywords

Araneae, browsing, disturbance interaction, grazing, synergistic effects

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Introduction

Disturbance regimes drive the structure and function of ecosystems worldwide, and altered

disturbance regimes are an important cause of biodiversity loss (Sinclair and Byrom 2006).

Maintaining or recreating appropriate disturbance regimes is therefore the focus of many

conservation and restoration programs (Hobbs and Huenneke 1992; Halme et al. 2013).

Two of the most common and widely studied disturbance drivers in terrestrial systems

are herbivory and fire (Bond and Keeley 2005; Danell et al. 2006). Browsing or grazing by

large mammalian herbivores has been shown to shape the structure and function of ecosystems,

from plant and animal communities, through to nutrient cycles and even climate (Côté et al.

2004; Danell et al. 2006; Foster et al. 2014). Fire is an episodic disturbance, and the frequency,

intensity and spatial extent of fires also drives the structure and function of ecosystems

(Thonicke et al. 2001; Bond and Keeley 2005). Both of these disturbances can affect biota

either directly (e.g. through direct mortality), or indirectly (e.g. by modifying habitat), or both

(Thonicke et al. 2001; Côté et al. 2004). A key mechanism linking both large herbivores and

fire to effects on biological communities is altered vegetation structure and complexity (Bond

and Keeley 2005; Foster et al. 2014).

As disturbances rarely occur in isolation, understanding how disturbance drivers

interact to affect biota is critical for effective conservation management (Wisdom et al. 2006;

Didham et al. 2007; Mantyka-pringle et al. 2012). Although the ecological effects of

disturbance have been widely studied, investigations of the interactive effects of disturbance

drivers are much less common (Wisdom et al. 2006; Didham et al. 2007; Foster et al. 2014).

Interactions between fire and herbivory have been reported in a range of ecosystems, and can

occur in a number of ways. For example, patterns and intensity of herbivory can affect fuel

loads and hence modify the spatial extent or intensity of fire (Wisdom et al. 2006; Kimuyu et al.

2014). Similarly, as many herbivores are attracted to the new growth available in recently burnt

areas, fire can affect the spatial distribution and intensity of herbivory (Allred et al. 2011). Fire

and herbivory also can interact via what is termed an interaction modification, where fire

changes the mode of action or per-unit effect of herbivory on organisms (sensu Didham et al.

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2007). For example, Royo et al. (2010) found that moderate levels of deer browsing increased

understorey plant richness in burnt deciduous forest, but not in unburnt forest. Interactions

between disturbance drivers can be synergistic (i.e. effects magnified, e.g. Barton et al. 2011) or

antagonistic (i.e. effects diminished or reversed, e.g. Matlack et al. 2001), and by definition,

differ from what would be predicted from the additive effects of each driver occurring in

isolation (Didham et al. 2007; Crain et al. 2008).

Most studies of fire × large herbivore interactions have investigated effects on

vegetation (e.g. Royo et al. 2010; Kerns et al. 2011), and to date only a small number of studies

have investigated how these interactions affect animal assemblages. Among these studies, there

appears to be a consistency between vegetation and animal responses; most studies which report

interactive effects on animals also report interactive effects on vegetation (e.g. Matlack et al.

2001, deer mice; Bailey and Whitham 2002, arthropods), while studies which find no interactive

effects on animals also find no interactive effects on vegetation (e.g. Jonas and Joern 2007,

grasshoppers; Underwood and Christian 2009, ants).

Web-building spiders are a group of animals that have been found to respond to both

fire (Buddle et al. 2000) and large herbivores (Miyashita et al. 2004; Warui et al. 2005), but the

responses of these spiders to fire-herbivore interactions have not previously been studied. Web-

building spiders respond strongly to changes in vegetation structure (Langellotto and Denno

2004), and are the dominant invertebrate predators in terrestrial food webs (Riechert and

Lockley 1984; Carter and Rypstra 1995). Therefore, disturbance-induced changes in vegetation

structure may modify spider densities, which could have important consequences for trophic

dynamics (Schmitz 2008). In addition, different types of web-builders may differ in their

response to habitat complexity, for example, Halaj et al. (2000) found that sheet weaving

spiders, which build complex three-dimensional webs, responded more negatively to habitat

simplification than orb-weaving spiders. As different types of web-builders target different prey

items (Nyffeler 1999), changes in vegetation structure also may affect food web structure by

altering the composition of the predator guild. As disturbance effects can cascade through

ecosystems via both trophic and non-trophic pathways (Ohgushi 2005), it is important to

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understand the extent to which disturbance effects are mediated by changes in vegetation, and

whether managing disturbances to maintain vegetation condition will also cater for the

requirements of fauna (Clarke 2008).

In this study, we tested for the effects of fire, large macropod herbivores and their

interaction on the web-building spider assemblage of a eucalypt forest understory. We used a

randomised, blocked experiment, combining prescribed fire and herbivore exclusion treatments,

to address three questions: (1) Do large herbivores and fire interact to affect the density or

richness of web-building spiders? (2) To what extent are the effects of these disturbances

mediated by changes in vegetation? And (3) Do these effects differ between different types of

web-builders? We predicted that both fire and large herbivores would reduce web-building

spider density and richness by reducing the structural complexity of vegetation, with sheet web-

builders responding most strongly to vegetation simplification.

Materials and methods

Study site

We conducted our study in Booderee National Park (BNP); a ~6 500 ha peninsula in south-

eastern Australia (35°10′S, 150°40′E, see Online Resource 1). We established sites within the

Eucalyptus pilularis forest of BNP, which is the most widespread vegetation type in the park

(Barton et al. 2014). An intensive baiting program targeting the introduced red fox (Vulpes

vulpes) has been in place in BNP since 1999 to protect native small and medium-sized mammal

species from predation (Dexter et al. 2012). Loss of native predators and a lack of human

hunting mean that without foxes, predation pressure on native macropod herbivores is low

(Lindenmayer et al. 2014). Over the last decade, there has been a tenfold increase in the

numbers of these herbivores in BNP (predominantly Wallabia bicolor, a generalist browser and

Macropus giganteus, a grazer, Family Macropodidae) (Dexter et al. 2012; Lindenmayer et al.

2014). A short-term exclosure trial has indicated that this high abundance of native herbivores

could be driving a shift in vegetation composition (Dexter et al. 2013). As prescribed fire is

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commonly used in eucalypt forests to reduce the risk of high intensity wildfire, promote

regeneration of senescing vegetation, and/or increase habitat heterogeneity (Williams et al.

1994), it is important to understand how fire interacts with high abundances of herbivores to

affect biodiversity.

Study design

We tested the interactive effects of prescribed fire and large herbivores on understorey

vegetation and web-building spiders using a randomised blocked experiment. We combined

three levels of herbivore treatment (open, partial and exclosure) and two levels of burning

treatment (burnt and unburnt) in a factorial design. We replicated each of these six treatment

combinations across four experimental blocks to give a total of 24 sites (Online Resource 1).

We created the herbivore treatments by using exclosure fences to reduce the density of

macropods within 0.125 ha (25 m × 25 m) plots, to produce three treatments: full herbivory

(open treatment), intermediate herbivory (partial treatment – plots were fenced but gates opened

and closed at two month intervals to create a lower browsing pressure) and no herbivory

(exclosure treatment). Exclosure fences were constructed in June 2012 using 1.1 m tall wire

fencing, which we found to be effective at excluding macropods (see Results). Smaller animals

were observed to move freely through the fence, and other large animals are rare in the park, so

effects of exclosure fences were assumed to occur primarily via their effect on macropods. For

the burning treatments, small (50 × 50 m), low-intensity burns were conducted across half of the

herbivory treatment sites in August 2012.

Data collection

We sampled spider webs and vegetation within four 3 × 3 m plots within each site (one in each

of the four quarters of the site). Data were collected three months post-fire (November 2012)

and 15 months post-fire (November 2013). Plots were established at least 1.5 m from the edge

of the site.

We counted spider webs as a surrogate measure for the web-building spider

assemblage. This method was described and tested by Gollan et al. (2010), who found that the

151

diversity of web-types (based on web architecture) was strongly correlated with the diversity of

spider genera in a site. As the richness of spider genera can be a viable surrogate for the species

richness of spiders (Foord et al. 2013), this method allowed us to assess compositional and

diversity responses of spiders, in addition to density responses. We used vaporised water,

applied with a pressurised spray mister, to assist in locating webs, and counted and identified all

spider webs within each of the four plots in each site. Each web was assigned to one of 32 web

types based on their architecture, according to the key of Smith (2008) (see Fig. 1 for

examples). From this, we generated measures of web density (number of webs per 3 x 3 m plot),

web richness (the number of different web types per plot) and web composition (the assemblage

composition of web types) for each plot. To minimise variation due to weather conditions,

surveys were delayed for 48 hours following strong wind or rain to allow spiders to rebuild

damaged webs.

We recorded the following vegetation variables from each plot; total understorey

foliage projective cover (the proportion of ground area covered by foliage held vertically above

it, Specht and Morgan 1981), foliage projective cover of vascular plants by life-form,

understorey height (measured at 10 evenly spaced locations per plot using the stick and foam

disc method of Smit et al. (2001), disc 100 mm diameter, 4.7 g weight), litter depth (measured

at 10 evenly spaced locations per plot) and stem density (number of live woody stems at ground

level within a 1 × 1 m sub-plot). Life-forms were grouped into the broad structural categories of

ferns, grasses (Poaceae), herbs (including forbs and climbers), sedges (which included grass-

like perennial herbs) and shrubs (including sub-shrubs and tree seedlings).

We measured the effects of burning and the herbivory treatments on herbivore activity

using scat (pellet) counts. Scat counts are a commonly used method to assess the comparative

density of macropod herbivores between sites (e.g. Howland et al. 2014; Pedersen et al. 2014).

We counted the number of macropod scats along two 25 × 2 m transects (100 m-2) in each site

approximately every two months from August 2012 to December 2013. Counts from the two

transects were summed to give one count per site. We removed scats from transect lines on each

sampling event to avoid double counting. For each site, we summed the count preceding and

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that following the spider web counts to give an approximate index of herbivore activity in each

site at the time of the web count.

Figure 1. Examples of web types encountered in surveys. One example of each of the four

groups of web types is shown: (a) web 9, an orb web, (b) web 28, a sheet web, (c) web

23, a lace web and (d) web 32, a tangle web.

Data analysis

Effects of fire and large herbivores on web density and richness

We used generalised multi-level path models (Shipley 2009) to test the effects of fire and large

herbivores on spider web density and richness, and the extent to which these effects were

153

mediated by vegetation changes. Generalised multi-level path analysis uses directional-

separation (d-sep) tests to assess the goodness of fit of a hypothesised causal diagram to the

patterns of dependence and independence within a dataset (Shipley 2002). This analysis is based

on a structural equation modelling (SEM) framework, but has been generalised to accommodate

hierarchical designs and non-normal response variables (Shipley 2009).

We hypothesised that the effects of fire, herbivores and their interaction on spiders

would be largely vegetation-mediated, but would also occur via other mechanisms that operate

independently of vegetation structure (Fig. 2). Our rationale for the construction of this causal

model was as follows: Fire can affect spiders through changes in vegetation structure (Brennan

et al. 2006), or via other mechanisms that operate independently of vegetation (the direct fire-

spider pathway in Fig. 2), such as fire-induced mortality (Bell et al. 2001), or changes in prey

availability (York 1999). Similarly, large herbivores have been found to affect spiders by

modifying vegetation structure (Miyashita et al. 2004), but could also affect spiders through

other mechanisms (the direct herbivore-spider pathway in Fig. 2) such as physical disturbance

of webs (Chmiel et al. 2000), or changes to key resources such as arthropod prey (Foster et al.

2014). We also identified three possible pathways by which fire could interact with large

herbivores to affect spiders; (1) a chain effect, where fire attracts herbivores to burnt areas,

increasing the level of herbivore activity in burnt sites (Allred et al. 2011), (2) an interaction

modification, where fire alters plant traits, modifying the effect of herbivores on vegetation

(Augustine and McNaughton 1998), or (3) an interaction modification where fire increases the

vulnerability of web spiders to other impacts of large herbivores (e.g. by reducing prey

availability, increasing the likelihood of spiders abandoning webs after physical disturbance,

Chmiel et al. 2000).

Confirmatory path analysis does not allow for reciprocal effects among variables

(Shipley 2009), but a number of vegetation variables we measured were likely to be reciprocally

related (for example, high grass cover would lead to low average understory height and low

stem density in a site). Therefore, we selected three vegetation variables that were not correlated

with each other, but made up a large component of the vegetation and were correlated with

154

other vegetation variables, to include in the path analysis (cover of shrubs, sedges and ferns).

While this approach avoided reciprocity between variables, in excluding some vegetation

variables, we potentially omitted variation in vegetation structure that could explain spider

responses to disturbance. Therefore, we also conducted a sensitivity analysis (sensu Ruffell et

al. 2014) to test whether including additional vegetation variables in our path model would

increase the extent to which disturbance effects were mediated by vegetation (Online Resource

2). To do this, we used principal components analysis to reduce our nine vegetation variables to

seven orthogonal components, and used these as the measures of vegetation structure in our

path analysis. We did not use principal components in the main analysis as principal

components are less correlated than would be expected by chance, and including them in the

path analysis would reduce our chance of rejecting an incorrectly specified path model (Ruffell

et al. 2014).

Figure 2. Hypothesised causal diagram of the effects of fire and large herbivores on web-

building spiders. We predicted that the effects of fire and herbivores on spiders would

be largely vegetation-mediated, but may also occur via other mechanisms that operate

independently of vegetation structure. We also predicted that fire may potentially

interact with large herbivores by increasing herbivore activity (an interaction chain), or

by modifying the effects of large herbivores (an interaction modification).

For each of web density and web richness, we constructed two causal diagrams, one for

each year of the study, with the cover of shrubs, sedges and ferns as measures of vegetation

structure (each with their own node). For each of these diagrams, we tested goodness of fit

using d-sep tests (Shipley 2009). Once we had tested the goodness of fit of the full path model,

Fire Herbivores

Vegetation

Structure

Spiders

Vegetation-mediated

effects

Other effects

Interaction modification

Interaction chain

155

we simplified the model to a more parsimonious one using a backward selection approach

(sensu Ruffell et al. 2014). Backward selection based on minimising AIC was used to simplify

each sub-model within the full model (Zuur et al. 2009). Each sub-model was a mixed-effects

model fitted with maximum likelihood estimation, and included an endogenous variable as the

response and its direct causal parents as predictors. Once all sub-models had been simplified,

they were grouped back into a single path model and the fit of this model was tested using

generalised multilevel path analysis and d-sep tests as described above.

All analyses were completed using R (R Core Team 2013). For GLMMs we used the

glmer function in the package lme4, using a Poisson distribution and log-link function (Bates et

al. 2014), while for LMMs we used the lme function in the package nlme (Pinheiro et al. 2014).

Random effects for all GLMMs and LMMs were sites within blocks (block/site). When used as

predictors, count variables were ln-transformed and continuous variables were centred on their

means. Response variables were checked for over-dispersion and model residuals were

inspected to verify that the data met model assumptions (Zuur et al. 2009). After deciding on the

final model for both web abundance and richness, we calculated path coefficients as the

estimated slopes of the variables within each of the sub-models (Shipley 2009). Unstandardized

path coefficients can be interpreted as the change in the response variable for a one unit change

in the predictor variable (Aiken and West 1991). However, in the presence of an interaction,

these path coefficients are conditional effects. Because all predictors were centred, the

conditional effects can be interpreted as the effect of the predictor on the response variable

when the interacting predictor is held at its mean value, or in the case of categorical predictors

(fire and herbivore exclosure treatments) at the control value (unburnt and open treatments

respectively). The interaction term indicates the amount of change in the slope of the regression

of the response on the predictor with a one-unit change in the value of the interacting predictor.

Effects of fire and large herbivores on different web types

To test whether different types of webs responded differently to large herbivores and fire, we

estimated unstandardized regression coefficients when the density of that web type was

156

substituted for overall web density in the full path diagram. Each of the individual web types

was assigned to one of four groups based on similarity of web characteristics - orb webs, sheet

webs, lace webs and tangle webs (Fig. 1, Online Resource 3). Such groupings are commonly

used in studies of web-building spiders, and while there is some overlap, these categories

broadly sort spiders into groups of families (e.g. Halaj et al. 2000). Orb weavers are generally in

the families Araneidae, Tetragnathidae, and Uloboridae, sheet weavers in the families

Linyphiidae, Theridiidae and Stiphidiidae, lace weavers belong to Desidae, and tangle web

spinners are generally in the family Theridiidae (Online Resource 3). We used a separate

GLMM for each web type grouping in each year with a Poisson distribution and log-link

function and random effects of block/site. We used backward selection as described above to

simplify the model for each web type in each year.

We also analysed the effect of our treatments on the composition of individual web types

using partial (or conditioned) canonical correspondence analysis in R (R Core Team 2013),

using the “cca” function in the package “vegan” (Oksanen et al. 2013), and using the Bray-

Curtis dissimilarity measure. This analysis allowed us to partial out the spatial variation

associated with the experimental blocks, before analysing the variation in web type composition

that was associated with our experimental treatments (herbivory × fire × year) (Borcard et al.

1992). We then ran permutation tests using the function “anova.cca” to test the significance of

our constraints (treatment combinations) using 10 000 permutations of the data. To reduce the

incidence of zero values in the data caused by rare web types, we pooled web counts to the site

level for this analysis and excluded web types occurring at two or fewer sites.

Results

Density and richness of spider webs

We counted 3687 spider webs, representing 28 different web types, across the two sampling

periods (Online Resource 3). Generalised multilevel path analysis indicated that herbivores and

157

fire affected spider web density via both vegetation-mediated and other pathways, but effects on

web type richness were of short duration and not mediated by changes in vegetation.

For web density, the simplified path models (2012: χ2 = 34.7, df = 30, P = 0.25, 2013: χ2

= 29.0, df = 28, P = 0.41) gave as good fit as the full models (2012: χ2 = 25.9, df = 22, P = 0.26,

2013: (χ2 = 28.5, df = 22, P = 0.16) in both years. The simplified model for web density in 2012

indicated that fire had a strong negative effect on web density, which was partly mediated by

negative effects of fire on vegetation cover (Fig. 3a). In contrast, the small, but significant,

negative effect of large herbivores on web density was not mediated by changes in vegetation

structure. By 2013, the effects of fire on vegetation variables were weaker, which was

associated with a recovery in vegetation (Fig. 3b). Both the vegetation-mediated effects, and the

other effects, of fire on web-density were reduced in 2013 compared with 2012. While the

effect of fire on shrub cover was reduced in 2013, shrub cover remained a strong determinant of

web density in 2013. In 2013 there also was a marginally significant interaction between fire

and herbivores, where herbivore activity was positively associated with fern cover in burnt, but

not in unburnt sites (Fig. 3b, Online Resource 2 - Figs. OR3a,b). High fern cover was associated

with higher web density in the path model (Fig. 3a). As in 2012, in 2013 there was a small

negative effect of herbivores on web density which was not explained by vegetation structure.

Sensitivity analysis indicated that including all vegetation variables in the path model did not

increase the amount of variation in web density that was explained by vegetation-mediated

pathways (Online Resource 2).

Model fits for web type richness were similar to those for web density, where our

simplified model (2012: χ2 = 35.6, df = 34, P = 0.39, 2013: χ2 = 30.2, df = 34, P = 0.65) gave as

good a fit as the full model (2012: χ2 = 23.8, df = 22, P = 0.35, 2013: χ2 = 25.0, df = 22, P =

0.30) in both years of the study. The simplified path models indicated that the effects of

disturbance on web-type richness were not mediated by changes in vegetation structure (Fig.

3c,d). Fire had a short-term negative effect on web-type richness which was no longer evident

in 2013 (15 months after fire). Large herbivores had a small negative effect on web-type

richness in both years, and this effect was strongest in 2012, when herbivore activity was

158

elevated in burnt sites (Fig. 3c,d). As with web density, including all vegetation variables in the

path analysis did not increase the strength of vegetation-mediated effects on web type richness

(Online Resource 2).

Figure 3. The effects of fire, herbivores and their interaction on web-building spider density

(a,b) and richness (c,d), and the extent to which these effects were mediated by

vegetation. Arrows represent causal paths between the experimental treatments (square

boxes) and plant and animal responses (rounded boxes). Path coefficients are

unstandardized partial regression coefficients. Dashed arrows indicate non-significant

relationships, significance levels of path coefficients: ^ P ≤ 0.1, * P ≤ 0.05, ** P ≤ 0.01,

*** P ≤ 0.001. Herbivore activity was ln transformed when used as a predictor.

Fire Herbivores

Shrub Fern

Web density

Partial Exclosure

-1.7 *** -3.4***

0.62***

Sedge

-0.61*** -0.13*

0.96**1.13***

-0.08*** -0.15*** -0.04*

Fire Herbivores

Shrub Fern

Web density

Partial Exclosure

-1.0 *** -4.0***

-0.14^

Sedge

-0.46*** -0.09*

1.8*** 0.43^

-0.03^ -0.06* 0.03

0.03^

-0.004

Fire Herbivores

Shrub Fern

Web type richness

Partial Exclosure

-1.7 *** -3.4***

0.62***

Sedge

-0.08*** -0.15*** -0.04*

-0.46*** -0.06*

Fire Herbivores

Shrub Fern

Web type richness

Partial Exclosure

-1.0 *** -4.0***

-0.14^

Sedge

-0.03^ -0.06* 0.03

0.03^

-0.004

-0.03

(a) Web density

2012

(c) Web type richness

2012

(b) Web density

2013

(d) Web type richness

2013

Positive effect Negative effectDriver makes effect

more negative

Driver makes effect

more positive

159

Responses of different web types

Substituting different web type groupings into the full model in place of web density revealed

key differences in the factors affecting different types of webs. Orb web density was higher on

sites with higher shrub cover, a relationship which mediated the negative effects of fire on orb

webs in both years (Table 1). In 2013, orb webs also were positively associated with higher fern

cover. In contrast, while some of the effects of fire on sheet webs were vegetation-mediated,

there was a strong negative effect of fire on sheet webs in both 2012 and 2013, which was not

vegetation mediated (Table 1). Sheet webs also responded negatively to herbivore activity on

both years, a response which was also not explained by changes in vegetation. Tangle webs and

lace webs showed only weak or inconsistent responses to the experimental treatments, with

tangle webs showing a shrub-mediated negative response to fire and a negative response to

herbivore activity in 2012, but no responses to disturbance in 2013 (Table 1).

Table 1. Estimated coefficients (and SE) of parameters for the four different types of webs, when

substituted for overall web density in the path diagrams for 2012 and 2013. All models included

the blocking structure of block/site as a random effect. Significance levels: ^ P ≤ 0.1, * P ≤ 0.05,

** P ≤ 0.01, *** P ≤ 0.001

Model term Estimate (se)

Orb webs Sheet webs Tangle webs Lace webs

2012 2013 2012 2013 2012 2013 2012 2013

Shrub cover

1.23 (0.64)^

2.87 (0.63)***

- - 2.18 (0.77)**

- - -11.1 (4.0)**

Sedge cover

- - 2.11 (0.37)***

-1.34 (0.49)**

- - - -2.9 (1.9)

Fern Cover -1.42 (0.90)

1.55 (0.41)***

- -1.25 (0.45)**

- - - -

Fire -0.76 (0.24)**

- -1.6 (0.23)***

-1.84 (0.29)***

- -0.07 (0.12)

- -

Herbivores -0.13 (0.08)^

- -0.19 (0.07)**

-0.19 (0.08)*

-0.17 (0.05)***

0.02 (0.05)

- -

Fire * herbivores

- - - - - -0.12 (0.07)

- -

160

Compositional differences in web-types reflected the strongest effects in the analysis of

web types. The first two axes of the partial CCA analysis accounted for 14.8 % and 12.4 % of

the variation in web type composition, respectively. There was a significant interaction between

year and burning treatment (P = 0.03, Fig. 4). There were also significant differences in web

type composition between burning treatments (P < 0.01), and years (P < 0. 01), but not between

herbivory treatments or any of its interaction terms (all P > 0.05). The composition of burnt

sites appeared to be a sub-set of unburnt sites, with the two most common sheet webs (web 25

and 27, belonging to the families Theridiidae and Linyphiidae respectively), strongly associated

with unburnt sites (Fig. 4).

Figure 4. Site scores (linear combinations of variable scores) for axis 1 and 2 of the partial

canonical correspondence analysis on the distribution of individual web types with

respect to the experimental treatments (burning × herbivory × year), after blocking

effects had been partialed out. Ellipses indicate one standard deviation from the

centroid of each burning × year treatment combination. Numbers identify individual

web types: 1-20 – orb webs, 21-24 lace webs, 25-29 sheet webs, 30-32 tangle webs (see

Online Resource 3 for individual descriptions). Overlapping web numbers are replaced

with points (+).

161

Discussion

We used a combination of burning and herbivore exclusion treatments to test the combined

effects of fire and large herbivores on web-building spiders in a forest understory. Fire and large

herbivores interacted via a chain effect, where fire increased herbivore activity, which in turn

affected the web-building spider assemblage. Fire and large herbivores also interacted via

vegetation-mediated pathways to affect spiders, although this interaction was only marginally

significant. The extent to which vegetation structure mediated disturbance effects and the

importance of interactive effects differed for web density and richness, and also between web

types. Strong effects of disturbance on web-building spiders that were not mediated by

vegetation indicate that managing disturbances to maintain vegetation structure is unlikely to

adequately address the needs of fauna in this system.

Fire, but not herbivore effects on web density were vegetation-mediated

Web density was most strongly affected by fire, and much of this effect was mediated by

changes in vegetation structure. The loss and subsequent recovery of vegetation after fire (Fig.

3) was mirrored by a loss and partial recovery of spider web density, with spiders likely

responding to changes in web-site availability and litter accumulation that occurred with

changes in vegetation cover (Brennan et al. 2006; Podgaiski et al. 2013). However, fire also had

strong effects on web density that were not explained by vegetation responses, and these effects

persisted in the second year of the study (Fig. 3a,b). As our experimental burns were small (50 x

50 m), and many spiders are able to rapidly disperse via aerial ballooning (Bell et al. 2005;

Langlands et al. 2011), this strong residual effect of fire is unlikely to be limited by

recolonization ability. We suggest that other changes that can occur after fire, but were not

measured this study, such as reduced soil/litter moisture, or reduced abundances of arthropod

prey, may have limited the re-establishment of high densities of spiders following fire

(Neumann and Tolhurst 1991; York 1999).

Fire and large herbivores interacted to increase the cover of ferns, which had a weak

positive effect on web density. This interaction likely occurred due to selective browsing by

162

herbivores allowing the less palatable bracken to dominate in burnt, browsed environments

(Augustine and McNaughton 1998). This increased cover of ferns was positively associated

with web density, with fern cover likely providing suitable web sites in an environment where

cover of other lifeforms remained low (Online Resource 4). Overall, very little of the effect of

large herbivores on spiders was mediated by vegetation, which contrasts with a number of

previous studies, which have attributed negative effects of large herbivores on spiders to

changes in vegetation structure (e.g. Miyashita et al. 2004; Warui et al. 2005).

Path analysis revealed that large herbivores negatively affected web density (and also

web type richness) via non-vegetation-mediated pathways, likely through the physical

disturbance of webs. Web damage is one of the main triggers for a spider to abandon a web site

(Chmiel et al. 2000). Repeated web disturbance may therefore have reduced web density by

causing spiders to move out of sites with high herbivore activity, or by reducing spider fitness,

as spiders which move web sites must expend considerable energy in web reconstruction

(Rypstra 1983; Chmiel et al. 2000), and are more vulnerable to predation when moving between

sites (Lubin et al. 1993). The effect of herbivores on web density was greatest in 2012 (Fig. 3a)

and this was likely due to the chain interaction between fire and herbivores, where herbivore

activity was higher in burnt sites. This type of interaction has been reported by many previous

studies, where burning focusses herbivore activity in burnt patches (Klop et al. 2007; Allred et

al. 2011). This greater level of herbivore activity in recently burnt sites would have led to

greater rates of web disturbance, and hence the stronger negative effects on spiders.

Disturbance effects on web type richness were not vegetation-mediated

In contrast to web density, the effects of disturbance on web type richness were of short

duration and were not mediated by effects on vegetation structure (Fig. 3c,d). The short-lived

effect of fire suggests that spiders were able to rapidly re-colonise sites as the vegetation

recovered from fire, a result that is not surprising given the small scale of the burns in our study

(50 x 50 m) and the high capacity of spiders to disperse via both ground movement and aerial

ballooning (Bell et al. 2005; Langlands et al. 2011). Such short-term effects of fire on spider

163

richness are consistent with previous studies from fire-prone environments, where post-fire

recovery of spiders can be rapid (e.g. Brennan et al. 2006; Podgaiski et al. 2013). Spider

richness also was directly affected by herbivore activity, where sites with higher herbivore

activity had slightly (but significantly) lower web richness (Fig. 3a). This is likely due to a few

web types being particularly vulnerable to physical disturbance, causing them to occur only

rarely on sites with high herbivore activity.

Responses differed between web types

Different web types showed clear differences in their response to fire, large herbivores, and

their interaction. Orb web weavers showed a strong post-fire recovery which was largely

mediated by vegetation, while sheet web weavers showed a strong negative response to fire,

with little recovery after 15 months. These differences are attributable to key differences in the

ecology of these different types of web spinners. Firstly, orb weavers tend to build their webs in

higher strata of the vegetation than sheet weavers (Janetos 1982), and so are more likely to be

able to escape being killed by a low intensity fire. Second, orb weavers tend to have high

dispersal capabilities, allowing them to re-colonise rapidly following disturbance (Bell et al.

2005). Third, sheet weavers often have a high proportion of litter arthropods in their diets,

compared with orb weavers which target aerial prey (Harwood et al. 2003). Therefore, the dry

litter conditions which usually occur after fire may have supported low abundances of the

decomposers which are key prey items for sheet weavers (Neumann and Tolhurst 1991; York

1999). Fourth, orb webs are more efficient at prey capture than sheet webs (Zschokke et al.

2006), and so orb-web weavers may be able to persist with low post-fire prey densities than

sheet web builders. Finally, orb web builders may be better able to use the post-fire vegetation

than sheet web builders. Orb weavers responded positively to the recovery of fern cover in burnt

sites in 2013, which likely allowed their rapid recovery after fire. In contrast sheet web density

was negatively related to the cover of ferns. An abundance of ferns may provide suitable

structure for orb weavers to build webs spanning open spaces (Rypstra 1983; Halaj et al. 2000),

but may not provide sufficient ground-level structure for sheet webs (Janetos 1982, pers. obs.).

164

While orb weavers, sheet weavers and tangle weavers all responded negatively to the

high level of herbivore activity in burnt sites in 2012, sheet weavers were the only group to

show a consistent negative response in 2013, when herbivore activity was lower. Many sheet

webs are larger than tangle and lace webs, are constructed between, rather than within, plants,

and are built close to the ground (e.g. web 28, Fig. 1b), all of which would make them

vulnerable to trampling and physical disturbance by herbivores. Further, sheet webs have lower

rates of prey capture than orb webs, as well as greater costs of initial web construction

(Zschokke et al. 2006), which may cause sheet weavers to be more likely to abandon a site after

web disturbance (Chmiel et al. 2000). As sheet web building spiders were more strongly

affected by disturbance and were slower to recover than other types of web builders,

disturbance caused a shift in the composition of this important predator guild. As different types

of webs target different types of arthropod prey (Nyffeler 1999; Harwood et al. 2003), these

compositional changes to the spider assemblage could have important cascading effects through

the arthropod food web. Understanding how the effects we observed for spiders affect the rest

of the arthropod community therefore remains a key area for future research.

Conclusions

Our study has shown that fire and large herbivores can interact both via vegetation-mediated

pathways, and via chain effects, to affect web-building spiders, an important component of the

forest understory fauna. In our study system, the use of prescribed fire in areas with high

densities of native herbivores could lead to reduced densities and altered composition of web-

building spiders, with potential cascading effects through arthropod food webs (Riechert and

Lockley 1984; Carter and Rypstra 1995). Managing the ecosystem to reduce the effects of

herbivores on vegetation post-fire (i.e. managing the interaction modification) may be

ineffective in mitigating the combined effects of disturbance, as the chain effect of fire on

herbivore activity also had important short-term effects on spiders. Our results reinforce the

importance of considering both independent and interactive effects, as well as the different

types of interactions, when managing disturbance regimes (Wisdom et al. 2006; Didham et al.

2007; Crain et al. 2008).

165

The differing strength of vegetation-mediated pathways between web types observed in our

study revealed the importance of understanding the mechanisms driving effects for both

predicting interactions and managing disturbance effects (Didham et al. 2007; Crain et al.

2008). In this system, monitoring and managing the effects of disturbance on vegetation (a

common approach to management of disturbances such as fire (Clarke 2008)), is unlikely to

detect or prevent important changes in the spider assemblage. Understanding of mechanistic

pathways is also essential if studies are to be used to inform management in un-studied

locations (Ruffell et al. 2014). To identify the mechanisms driving disturbance interactions,

long-term, multi-taxon studies, which simultaneously address multiple stressors, will be needed

(Tylianakis et al. 2008; Foster et al. 2014). When applied to such studies, analytical approaches

such as path analysis will give valuable insights into the importance of different interaction

pathways. This mechanistic understanding will be useful, not only in predicting the outcomes of

interacting disturbance drivers, but also in identifying appropriate actions to manage their

effects on biodiversity (Didham et al. 2007; Crain et al. 2008).

Acknowledgements

Staff at Booderee National Park conducted prescribed burns. Chris Macgregor assisted with

fence construction and plant identification. Many volunteers assisted with data collection. Chloe

Sato provided valuable feedback on the manuscript. The Margaret Middleton Fund, The

Norman Wettenhall Foundation and the Long Term Ecological Research Network provided

financial support. The experiments comply with the current laws of Australia where the research

was performed (ANU Animal Ethics protocol A2012/24 and Booderee National Park Research

Permit BDR12/00005).

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Supplementary Material

Online Resource 1 – Study location and experimental design

Site Description

This study was conducted within the Eucalyptus pilularis forest of BNP (Fig. OR1). We

selected this vegetation type as it is the most widespread vegetation type in BNP and it occurs

widely through coastal areas of Eastern Australia. In BNP this vegetation is dominated by an

overstorey of Eucalyptus pilularis, Corymbia gummifera and E. botryoides (Taws 1998;

Lindenmayer et al. 2008). The midstorey is comprised predominantly of Banksia serrata,

Acacia longifolia and Monotoca elliptica, with Synoum glandulosum and Ziera arborescens

becoming more prevalent in moist areas. The understorey is dominated by Lomandra longifolia

and Pterideum esculentum.

Figure OR1. Location of study site within the Eucalyptus pilularis forest type (grey) within

Booderee National Park (bold line). Map shows the arrangement of the 24 study sites

into four blocks with one replicate of each of the six treatment combinations in each

block. Abbreviations for levels of herbivory and burning treatments: E = exclosure, P =

partial, O = open, B = burnt, U = unburnt.

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Site selection

Sites were selected using a three step process. (1) We selected the four experimental blocks by

using vegetation maps of BNP to identify areas of continuous Eucalyptus pilularis forest which

had not been recently burnt (>8 years since fire) and which were large enough to contain six

experimental plots (approx. 12ha), and then maximising the distance between selected blocks

(minimum of 2 km between blocks, see Fig OR1). (2) We selected six sites within each block

which both minimised the total distance between plots (to reduce variation between plots) and

maintained a minimum distance of 150 m between plots (to reduce the chances of interference

between treatments, See Fig. OR2). (3) We then randomly allocated the six treatments to the six

sites within the following logistical constraints: burnt sites could not be > 50 m from an access

trail (due to operational requirements) and fenced sites could not be adjacent to an existing

long-term monitoring sites (to avoid interfering with other studies). Pre-treatment vegetation

surveys indicated there were no significant differences in vegetation structure between

treatments prior to commencement of the study.

Figure OR2. Schematic diagram of blocked design showing types and site dimensions of the

six treatment combinations (not to scale). Exact spatial arrangement varies between blocks (see

Fig. OR1).

173

References

Lindenmayer DB et al. (2008) Contrasting mammal responses to vegetation type and fire.

Wildl. Res. 35:395-408

Taws N (1998) Fire and vegetation management in Jervis Bay Territory, Report. Environment

Australia, Canberra

174

Online Resource 2 – Reanalysis of path model including all vegetation

variables

In our main path analysis, we included only three of our nine vegetation variables in the

hypothesised causal diagram (cover of shrubs, sedges and ferns). This was because many of the

nine vegetation variables were highly correlated and potentially reciprocally related. However,

Generalised Multilevel Path Analysis is unable to handle reciprocal relationships between

variables (Shipley 2009). By including only three vegetation variables in the path analysis, we

were omitting a large amount of the measured variation in vegetation structure. Therefore, any

significant effects of vegetation structure on spiders that were not captured by the three selected

variables, would be attributed to “direct” rather than vegetation-mediated effects of disturbance

on spiders. To test whether the significant main effects of disturbance that were not explained

by vegetation in our main analysis were in fact due to additional variation in vegetation

structure, we conducted a ‘sensitivity analysis’ including all of the nine vegetation variables in

the analysis.

This sensitivity analysis involved re-running the path analysis to include all of the

vegetation variables we measured. However, to avoid the issue of reciprocity between variables,

we first had to generate a set of uncorrelated vegetation variables (which we did using principal

components analysis), and then use these as the vegetation structural variables in the path

analysis. We did not use this approach in the main analysis, as principal components are less

correlated than would be expected by chance. Including principal components in the path

analysis, could therefore compensate for unacceptably high levels of correlation among other

independence claims, and reduce our chances of rejecting an incorrectly specified causal model.

As traditional PCA does not allow for hierarchically structured data, we used the

“phyl.pca” function in the “phytools” package in R to conduct a hierarchically structured

phylogenetic PCA of the vegetation data, where plots were nested within sites, within blocks

(see Ruffell et al. 2014 for a more detailed description of this method). We conducted a separate

PCA for each year of data, and based the PCA on the correlation (rather than the covariance)

matrix as vegetation variables were measured on different scales. We used the first seven of the

175

principal components in the path analysis, which accounted for 96% of the variation in

vegetation variables in each year (Table OR1).

Table OR1. Details of the seven principal components of the vegetation variables used in the path

analysis.

PC1 PC2 PC3 PC4 PC5 PC6 PC7

2012 Variation explained (cum. %) 33.9% 49.5% 62.3% 74.0% 83.3% 91.2% 96.0%

Variable Loadings

Total understory cover -0.95 -0.06 -0.05 0.12 -0.06 -0.03 -0.18

Stem density -0.56 0.15 0.14 -0.35 -0.03 0.70 0.14

Understory height -0.77 -0.31 0.28 0.08 0.13 -0.16 -0.05

Litter depth -0.59 0.50 -0.19 0.09 0.31 -0.24 0.45

Fern cover -0.51 -0.58 0.44 -0.29 0.01 -0.19 0.11

Grass cover 0.10 -0.53 -0.18 0.55 0.53 0.30 0.03

Sedge cover -0.54 0.41 0.20 0.61 -0.29 0.07 -0.14

Shrub cover -0.55 0.06 -0.65 -0.35 0.22 -0.06 -0.29

Herb cover -0.21 -0.50 -0.57 0.13 -0.55 0.03 0.24

2013 Variation explained (cum. %) 28.2% 44.8% 58.0% 70.3% 80.7% 89.3% 96.0%

Variable Loadings

Total understory cover 0.91 -0.05 0.21 -0.09 -0.14 -0.22 -0.03

Stem density 0.56 -0.07 0.05 0.15 -0.09 0.80 0.11

Understory height 0.83 0.10 -0.19 -0.11 0.00 -0.08 -0.12

Litter depth 0.24 0.68 -0.16 0.30 0.23 -0.13 0.54

Fern cover 0.58 -0.58 0.42 -0.11 0.21 -0.18 0.24

Grass cover 0.01 -0.35 -0.23 0.51 -0.71 -0.17 0.16

Sedge cover 0.25 0.73 0.45 0.06 -0.33 -0.04 -0.27

Shrub cover 0.42 0.06 -0.79 -0.36 -0.05 -0.03 -0.09

Herb cover 0.27 -0.12 -0.15 0.76 0.43 -0.07 -0.35

The full path model including all seven principal components had a high level of support for

web density in 2012 (χ2 = 54.9, df = 74, P = 0.95), and in 2013 (χ2 = 50.88, df = 74, P = 0.98),

and also had a high level of support when claims among principal components were excluded

(2012: χ2 = 29.6, df = 32, P = 0.59; 2013: χ2 = 30.8, df = 32, P = 0.53). Similarly, models for

web-type richness were well supported in both years (2012: χ2 = 54.9, df = 74, P = 0.95; 2013:

χ2 = 46.5, df = 74, P = 0.99), and when independence claims among principal components were

excluded (2012: χ2 = 29.6, df = 32, P = 0.58; 2013: χ2 = 26.4, df = 32, P = 0.75).

176

The models including the seven principal components gave qualitatively similar results

to our main path analysis. The negative effects of fire on web density were largely mediated by

vegetation, but also occurred via the “direct” pathway, while negative effects of herbivores on

web density occurred mostly via the “direct” pathway (Fig. OR3a,b). The negative effects of

fire and herbivores on web type richness were not mediated by changes in vegetation structure

(Fig. OR3c,d). The strength of “direct” pathways in the analysis including all vegetation

variables was not reduced compared with our main analysis. This indicates that our main

analysis captured sufficient variation in vegetation structure to provide a reliable estimate of the

strength of vegetation-mediated versus other effects of disturbance.

References

Ruffell J et al. (2014) Discriminating the drivers of edge effects on nest predation: Forest edges

reduce capture rates of ship rats Rattus rattus, a globally invasive nest predator, by altering

vegetation structure. PLoS ONE 9:e113098

Shipley B (2009) Confirmatory path analysis in a generalized multilevel context. Ecology

90:363-368

177

Figure OR3. Effects of fire, herbivores and their interaction on web-building spider density

(a,b) and richness (c,d), and the extent that effects were mediated by vegetation. Arrows

represent causal paths between the experimental treatments (square boxes) and

plant/animal responses (rounded boxes). Path coefficients are unstandardized partial

regression coefficients. Grey arrows are negative effects, black arrows positive effects,

and blue connectors are interactions. Dashed arrows indicate non-significant

relationships, significance levels of path coefficients: ^ P ≤ 0.1, * P ≤ 0.05, ** P ≤ 0.01,

*** P ≤ 0.001. Herbivore activity was ln transformed when used as a predictor.

178

Figure OR3 cont.

179

Online Resource 3 – Functional group and associated taxa of the recorded

web types

Table OR2. Functional group of web types recorded in surveys, and the taxa constructing each type in

the region of the study (adapted from Smith 2008).

Functional group

Web number

Brief description Taxa commonly constructing web

Sites present

Individuals recorded

2012 2013 2012 2013

Orb webs

W1§ Decorated - silk X

Araneidae: Argiope keyserlingi & some other Argiope spp.

0 2 0 2

W2

Decorated - silk vertical line

Araneidae: Araneus eburnus and Araneus bradleyi

4 1 4 1

W3§ Decorated, sloping Uloboridae: Zosis & other genera 0 1 0 2

W4§

Decorated - line of prey and eggs

Araneidae: Cyclosa 0 1 0 1

W5 Decorated with doily

Araneidae: Argiope keyserlingi (juv.) & some other Argiope spp.

3 0 5 0

W9 Leaf at web centre

Araneidae: Phonognatha graeffei & other Phonognatha

15 23 43 314

W10

Leaf away from web centre

Araneidae: Araneus dimidiatus 8 8 15 11

W12§ Sloping, missing sector Araneidae: Arachnura higginsi 1 2 1 2

W13

Pie slice, vertical, retreat at hub

Araneidae: Phonognatha graeffei (juv.) & other Phonognatha

22 24 180 303

W14

Pie slice, signal line to retreat

Araneidae: Metepeira 22 17 221 155

W15 Offset hub, horizontal Uloboridae: Philoponella & other genera 5 1 9 1

W16§ Plain, many radial lines

Nephilidae: Nephila plumipes and Nephila edulis (juveniles)

1 0 1 0

W17 Plain, web hub filled Araneidae: juveniles of many genera 10 18 25 47

W18 Plain, hole at hub

Araneidae: Eriophora & other genera Tetragnathidae: Tetragnatha & Leucauge

4 10 6 13

W19 Horizontal, hole at hub Tetragnathidae: Tetragnatha & Leucauge 10 7 23 12

W20§

Horizontal, v. small, cone

Theridiosomatidae 1 0 1 0

Lace webs

W21

On bark, funnel-like entrance

Desidae: Badumna longinqua, Badumna insignis

1 3 1 3

W22

Radiates from hole in twig

Desidae: Paramatachia 11 17 19 51

W23

Fans out from silk retreat

Desidae: Badumna 23 4 169 6

W24 Web around nest Desidae: Phryganoporus candidus 7 7 10 8

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Functional group

Web number

Brief description Taxa commonly constructing web

Sites present

Individuals recorded

2012 2013 2012 2013

Sheet webs

W25

"Witch's hat", irregular weave

Theridiidae: Achaearanea mundula & related spp.

20 22 492 475

W26

"Witch's hat", regular weave

Araneidae: Cyrtophora 3 0 7 0

W27

Small sheet in low vegetation

Linyphiidae 20 21 127 119

W28

Platform, burrow top of sheet

Stiphidiidae: Corasoides australis 14 7 41 27

W29

Sloping sheet, retreat below sheet

Stiphidiidae: Therlinya, Pillara, Couranga, Stiphidion & Jamberoo Theridiidae: Steatoda

7 13 13 17

Tangle webs

W30

Silk retreat, web between leaves

Theridiidae: Achaearanea, Theridion & others

20 24 83 169

W31§

Silk retreat, lines to firm surface

Theridiidae: Latrodectus, Achaearanea & Steatoda

1 0 1

W32 No silk retreat

Theridiidae: Achaearanea, Theridion & other genera

24 24 220 231

§ These rare web types were excluded from the CCA analysis.

References

Smith H (2008) BugWise Web2Spider supplement, Available at

http://publications.australianmuseum.net.au/pdf/1561_complete.pdf. Accessed 15 October

2012

181

Online Resource 4 – Response of vegetation to disturbance treatments

Figure OR4. Vegetation response to burning and herbivory treatments (selection of ‘typical’

sites).

183

Paper V: Integrating theory into disturbance interaction experiments to better inform ecosystem management

Theoretical studies of ecological disturbance have highlighted the value of understanding

interaction pathways for both predicting and managing the effects of co-occurring disturbances.

In this paper, I synthesised the findings from my experiments (Papers II, III and IV), and critically

evaluated related literature on fire-grazing interactions, to assess the ability of studies to test the

key relationships and mechanisms that are important for ecological management. Based on this

evaluation, I identified several ways that future studies can be improved to better inform the

management of disturbance interactions.

Foster, C.N, Sato, C.F., Lindenmayer, D.B., and P.S. Barton (Accepted) Integrating theory into

disturbance interaction experiments to better inform ecosystem management. Global Change

Biology, doi:10.1111/gcb.13155.

184

Abstract

Managing multiple, interacting disturbances is a key challenge to biodiversity conservation, and

one that will only increase as global change drivers continue to alter disturbance regimes.

Theoretical studies have highlighted the importance of a mechanistic understanding of stressor

interactions for improving the prediction and management of interactive effects. However,

many conservation studies are not designed or interpreted in the context of theory, and instead

focus on case-specific management questions. This is a problem as it means that few studies test

the relationships highlighted in theoretical models as being important for ecological

management. We explore the extent of this problem among studies of interacting disturbances

by reviewing recent experimental studies of the interaction between fire and grazing in

terrestrial ecosystems. Interactions between fire and grazing can occur via a number of

pathways; one disturbance can modify the other’s likelihood, intensity or spatial distribution, or

one disturbance can alter the other’s impacts on individual organisms. The strength of such

interactions will vary depending on disturbance attributes (e.g. size or intensity), and this

variation is likely to be non-linear. We show that few experiments testing fire-grazing

interactions are able to identify the mechanistic pathway driving an observed interaction, and

most are unable to detect non-linear effects. We demonstrate how these limitations compromise

the ability of experimental studies to effectively inform ecological management. We propose a

series of adjustments to the design of disturbance interaction experiments that would enable

tests of key theoretical pathways, and provide the deeper ecological understanding necessary for

effective management. Such considerations are relevant to studies of a broad range of ecological

interactions, and are critical to informing the management of disturbance regimes in the context

of accelerating global change.

Keywords

Antagonisms, compound effects, disturbance ecology, fire, grazing, higher order effects,

multiple stressors, terrestrial ecosystems, synergisms

185

Introduction

Disturbance is a major driver of change in ecosystems, and is central to both fundamental and

applied ecology. As climate change and anthropogenic pressures are driving substantial changes

to disturbance regimes such as fire, severe weather and biological invasions, understanding the

factors affecting disturbances and their ecological impacts will be essential to the effective

future management of terrestrial ecosystems (Dale et al., 2001; Turner, 2010). Recent advances

in disturbance theory include improved understanding of the spatial variability of disturbance

effects (Turner, 2010), and the potential for cross-scale effects (Peters et al., 2007), and legacy

effects (Essl et al., 2015) to produce unanticipated changes in ecosystems. Another key

advancement, and one with strong management applications, is the acknowledgement of the

prevalence of disturbance interactions (Wisdom et al., 2006; Turner, 2010), their ability to

produce ecological surprises (Lindenmayer et al., 2010; Doherty et al., 2015), and the range of

mechanistic pathways by which these interactions can occur (Didham et al., 2007; Buma,

2015). An important question, however, is whether empirical studies have kept pace with

theoretical advances, and the implications this has for informing effective ecological

management.

As theoretical models are widely used in conservation decision making, advances in

disturbance theory have the potential to guide substantial improvements in the management of

disturbance regimes (Driscoll & Lindenmayer, 2012). For example, several recent papers have

demonstrated how a disaggregated, mechanistic understanding of the effects of disturbance and

land use change on ecosystems could lead to more effective, and potentially novel, solutions for

ecological management (Didham et al., 2007; Fuhlendorf et al., 2009; Peters et al., 2011). For

theory to effectively inform management, however, it needs to be paired with empirical studies

that test its applicability to the local context (Driscoll & Lindenmayer, 2012). Yet, while there

are many excellent examples of such studies (e.g. Mandle & Ticktin, 2012; Kimuyu et al.,

2014), the majority of empirical conservation studies are not well integrated with theory, and

this potentially limits both theory development and management applications (Belovsky et al.,

2004; Fazey et al., 2005; Barot et al., 2015).

186

Here, we examine how well the experimental literature has integrated recent theoretical

developments on disturbance interactions, and how this affects management implications. We

focus on examples from studies of fire-grazing interactions as they are among the most

commonly studied disturbance interactions in terrestrial ecosystems, and as fire and grazing are

both commonly managed in terrestrial ecosystems, much research has focussed on how

prescribed fire (e.g. burn frequency) and grazing regimes (e.g. stocking density) can best be

managed for biodiversity conservation. We use a mini-review of recent fire-grazing interaction

experiments to highlight the gaps between theoretical and experimental studies, and the effect

this has on the ability of experimental studies to inform management decisions. We then discuss

how relatively simple changes to the way interaction experiments are designed and interpreted

will strengthen their links with the theoretical literature, and in doing so, will improve the

ability of experimental studies to provide useful information for the management of multiple

disturbances.

Recent advances in disturbance interaction theory

The study of disturbance interactions has evolved largely from work on multiple stressor effects

in marine systems, which is based on the ‘additive’ model of interactions (Folt et al., 1999;

Crain et al., 2008). Under this model, interactive effects occur when the combined effects of

two environmental stressors is greater than (synergism) or less than (antagonism) the sum of

their individual effects (Folt et al., 1999). Although the additive model has formed the basis of

countless experimental studies of the effects of multiple stressors (reviewed by Brook et al.,

2008; Crain et al., 2008; Darling & Côté, 2008; Jackson et al., 2015; Przeslawski et al., 2015),

current theory recognises disturbance interactions as complex, spatially and temporally variable

processes (Peters et al., 2004; Wisdom et al., 2006; Turner, 2010; Buma, 2015). This theory

includes two recent advances which have the potential to dramatically improve our ability to

predict and manage the outcomes of disturbance interactions; (1) characterisation of the

different mechanistic pathways driving interactions, and (2) increasing acknowledgement of the

likelihood of non-linear interactive effects.

187

Several recent syntheses have focussed on the mechanistic pathways by which multiple

disturbances (or stressors) interact, and the value of identifying interaction pathways for

designing effective and novel management interventions (Didham et al., 2007; Tylianakis et al.,

2008; Oliver & Morecroft, 2014; Boyd & Brown, 2015; Buma, 2015; Doherty et al., 2015). A

variety of terms have been used to describe these different interaction pathways, but despite

differences in the stressors discussed, most studies separate interactions into two broadly similar

“types” (Table 1). We adopt the terminology of Didham et al. (2007), who separate interactions

into (1) interaction chain effects, where one disturbance affects the occurrence or magnitude of

a second disturbance, and both disturbances have a direct effect on the response variable, and

(2) interaction modification effects, where the per-unit effect of one disturbance on the response

variable depends on the environmental context of a second disturbance (Fig. 1). We use the

definitions of Didham et al. (2007), as they are among the first presented in the literature, and

while originally developed in the context of habitat modification and invasive species

interactions, have been successfully adapted to characterise interactions between other stressors,

both biotic and abiotic (e.g. Mandle & Ticktin, 2012; Oliver & Morecroft, 2014). Regardless of

the terminology and definitions used, a focus on identifying the mechanistic pathways driving

disturbance interactions has the potential to provide valuable insights for ecological

management, and potentially lead to novel solutions for processes that may be difficult to

manage directly (Didham et al., 2007; Peters et al., 2011; Boyd & Brown, 2015; Doherty et al.,

2015). For example, feral predators have substantial impacts on many native species, but broad-

scale control is often unfeasible. However, as predation success is modified by habitat

complexity, it may be possible to conserve native species by improving fire management to

maintain complex habitats (i.e. managing the interaction modification) (Doherty et al., 2015).

The theoretical literature also acknowledges the likelihood that many interactions will

have non-linear effects on ecosystems, potentially leading to alternate states (Peters et al., 2004;

Wisdom et al., 2006; Oliver & Morecroft, 2014; Kayler et al., 2015). These non-linear effects

can occur both in chain (Didham et al., 2007) and modification interactions (Dunne, 2010).

Further, when chain and modification interactions both occur, their net effects may become

188

non-linear through cross-scale interactions (Peters et al., 2007). When the effects of

disturbances and their interactions are non-linear, knowledge of key points, such as when a

small change in disturbance intensity has a large effect on biodiversity (or vice versa), are

potentially invaluable for informing targeted and effective management (Peters et al., 2004;

Huggett, 2005; Groffman et al., 2006; Didham et al., 2007). For example, the effect of fire on

the susceptibility of forests to pine-beetle attacks is non-linear: low severity fire can increase the

susceptibility of forests to pine beetle outbreaks (Kulakowski & Jarvis, 2013), while high-

severity, stand-replacing fires can reduce forest susceptibility to the same pests (Kulakowski et

al., 2003). Knowledge of these non-linear effects, and estimates of the threshold at which

effects switch from negative to positive would allow managers to adjust fire management

practices, and/or conduct target management of local beetle outbreaks, to minimise the risk of

widespread pine-beetle infestations.

189

Table 1. Summary of the terms and definitions used to distinguish interaction “types” in recent reviews

of ecological interactions. Types are sorted according to how they align with the definitions of

interaction chains and interaction modifications provided by Didham et al. (2007).

Paper Context Interaction "types" and their definitions

Ecosystem

Disturbances/ stressors

Interaction chain Interaction modification

Didham et al. (2007)

Broad range of both terrestrial and marine ecosystems

Invasive species and habitat modification

Interaction chain (numerically mediated): Total impact changes because of a numerically scaled response (of the first driver) to a second driver variable

Interaction modification (functionally moderated): the per capita impact of one driver is dependent on the level of effect of a second driver

Oliver and Morecroft (2014)

Terrestrial ecosystems

Climate change and land use change

Interaction chain: one driver changes the magnitude of another driver and both drivers have a direct effect on the response variable.

Interaction modification: the per capita effect of one driver changes depending on the level of another driver

Ban et al. (2014)

Coral reefs Multiple stressors, both biotic and abiotic

Stressor interactions: where one stressor directly influences another

Synergistic effects: where two stressors interact to produce an effect on a response variable that is greater than purely additive

Doherty et al. (2015)

Terrestrial ecosystems

Invasive predators and disturbance

Numerical impact: where a disturbance increases the abundance of the invasive species

Functional impact: where a disturbance changes the per-capita impact of an invasive species

Compounding impacts: where the effect of disturbance on a population changes its ability to cope with invasion

Boyd and Brown (2015)

Marine ecosystems

Environmental drivers, focussing on warming and ocean acidification

Physiochemical interactions: interactions that occur directly between drivers, outside of organisms

Physiological interactions: where an organism’s physiological response to one driver changes with the level of another driver

Food web interactions: where interactive effects on organisms occur indirectly, via changes in food webs or ecosystem functioning

Buma (2015)

Broad range of both terrestrial and marine ecosystems

Wide range of disturbances

Linked interactions: one disturbance increases or decreases the likelihood, spatial extent, intensity or severity of a second disturbance

Compound disturbance: Recovery from a second disturbance is more or less likely, or at altered speed, due to the initial disturbance

Jackson (2015)

Marine, terrestrial and freshwater ecosystems

Multiple invasive animals

Interactions among invaders: where the presence of one invader influences the performance of a second invader

Combined impacts: where the presence of one invader influences another's impact on the ecosystem

190

Figure 1. The two major pathways by which fire and grazing can interact to affect biota

(adapted from Didham et al. 2007). (a) An interaction chain effect, where fire affects

the spatial distribution or intensity of grazing, which in turn has a direct effect on the

response variable (e.g. plant or animal abundance), but the per-unit effect of grazing on

the response variable remains unchanged. A chain interaction also can occur in the

opposite direction, with grazing affecting the spatial location, spread, and intensity of

fire. (b) An interaction modification, where the per-unit effect of grazing on the

response variable is modified by the occurrence of fire. This interaction can also occur

where grazing modifies the effect of fire on the response variable. The response curves

shown are only one example of many possible relationships. The slope and direction of

responses, and the direction and strength of interactions will vary depending on the

ecosystem, disturbances and organisms (response variables) studied.

Where are the gaps between theory and experimental studies?

Many experimental studies of disturbance interactions aim to inform management decisions,

citing conservation challenges as motivation for the study, or providing management

recommendations based on their findings. However, few studies focussing on conservation

problems are well integrated with theory (Fazey et al., 2005; Barot et al., 2015), despite the

acknowledged value of using a strong conceptual framework to guide empirical research

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(Driscoll & Lindenmayer, 2012; Barot et al., 2015), and the popularity of conceptual models for

guiding management decisions (Williams, 2011). To assess the extent of this problem among

studies of disturbance interactions, we focussed on the two recent conceptual advancements

highlighted previously: (1) the mechanisms driving interactions and (2) non-linear interactive

effects on ecosystems or biota, and quantified the extent to which these concepts have been

addressed in recent studies of fire-grazing interactions. To do this, we reviewed 50 recent papers

that focussed on fire-grazing interactions and were published between 2011 and 2015

(Supporting Information, Appendix S1). This mini-review was conducted to obtain a

representative sample of recent studies of fire-grazing interactions (most of which were

experimental) which we could use to answer specific questions about the current literature, and

is not intended as a comprehensive literature review. The 2011 cut-off was selected as a number

of key conceptual studies were published in the years 2007-2008 (e.g. Didham et al., 2007;

Peters et al., 2007; Brook et al., 2008; Crain et al., 2008; Darling & Côté, 2008), and the 2011

cut-off allowed some lag time for these ideas to become integrated into experimental studies.

Mechanisms driving interactions

Do experimental studies identify interaction pathways?

Fire and grazing can interact via a number of different pathways. For example, fire can affect

the intensity and spatial distribution of grazing as many herbivores preferentially graze in burnt

areas (a chain interaction often referred to as fire-driven grazing or pyric herbivory) (e.g. Allred

et al., 2011). Similarly, grazing can affect the location and intensity of fires by modifying the

distribution of fine fuels (also a chain interaction) (e.g. Kimuyu et al., 2014). At a more local

scale, fire and grazing, by altering the composition, traits or condition of biological

communities or individual organisms, can alter each other’s effects on those organisms (an

interaction modification) (e.g. Eby et al., 2014). However, few of the studies we reviewed

identified the mechanistic pathways driving the fire-grazing interaction, despite 80% of these

papers citing management applications as a motivation for the study. Of the 50 studies

reviewed, only one quantified the relative contributions of both chain and modification effects

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in the interaction (Mandle & Ticktin, 2012), and 30 did not quantify any pathway, reporting

only net effects of the interaction (Fig. 2). Of these 30 studies, five included description of more

than one potential pathway driving the observed effects, 15 described only one potential

pathway, and 10 did not provide any ecological definition of how the “interaction” might be

occurring (Fig. 2).

Overall, 30% of the fire-grazing studies we reviewed discussed only one interaction

type, but did not provide evidence that this interaction type was driving the observed effects.

For example, 13 of the 22 studies that defined only chain interactions (mostly fire-driven

grazing) actually tested for net interactive effects, and did not quantify the extent to which fire

affected herbivory (Fig. 2). As it has been demonstrated that burning does not always affect

herbivore site selection (McGranahan et al., 2012), the interaction detected by such studies may

be caused by a chain, or a modification, or both. By defining only one interaction type, but

testing for net effects, these studies make the implicit assumption that the defined interaction is

driving the fire-grazing interaction, which may or may not be true.

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Figure 2. A comparison of the interaction pathways described or defined by studies of fire-

grazing interactions, versus the pathways actually tested in the study (chain =

interaction chain, mod. = interaction modification, net = tests for interactions that do

not isolate interaction pathways, and hence may result from chain, or modification

effects or a combination of the two). Numbers are based on a review of 50 fire-grazing

interaction studies published between 2011 and 2015. * Indicates combinations where

the definition used was appropriate for the interactions tested (n = 13).

How does not identifying the interaction pathway affect management applications?

Studies which make untested assumptions about interaction pathways could potentially be

harmful, rather than useful for land management. We illustrate this with an example, based on a

recent study by Foster et al. (2015), who found that fire and herbivory by native macropods

interacted via both an interaction chain, and an interaction modification, to reduce the diversity

of forest understory vegetation. If the authors had measured the net effect of the interaction, and

based their management recommendations on an assumption about the interaction pathway,

then these recommendations would be likely to lead to little improvement, or even deterioration

of biodiversity (Fig. 3). For example, if the authors had assumed that the interaction occurred

through an interaction chain, they may have recommended changing fire management to

prevent herbivores concentrating in burnt areas (e.g. by increasing the area burnt). However,

due to the presence of an interaction modification, reducing grazing pressure in burnt sites by

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increasing burn size is unlikely to mitigate all interactive effects, and may actually increase

negative effects on plants by increasing the proportion of the population that is exposed to the

interactive effects (Fig. 3).

Similarly, the converse assumption (that the interaction occurred solely through an

interaction modification) also could lead to ineffective management recommendations. If an

interaction modification was assumed, the authors may have recommended control measures to

reduce the abundance of macropod herbivores. However, due to the presence of an interaction

chain, where herbivores were attracted to burnt areas, general population control may do little to

reduce grazing pressure in burnt areas, and therefore not mitigate negative interactive effects

(Fig. 3). As population control of native herbivores is both resource-intensive and socially

unpopular (Nugent et al., 2011), a control program that does not achieve the desired benefits for

biodiversity is not only a waste of resources, but could compromise the ability of managers to

implement similar programs in the future. This example demonstrates how failure to integrate

theoretical concepts on interaction pathways into the design and interpretation of experimental

studies could limit their ability to effectively inform the management of multiple disturbances.

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Figure 3. Schematic diagram illustrating how incorrect assumptions about interaction pathways

can lead to ineffective, or even damaging, management interventions. In the presence of

an interaction chain herbivores congregate in burnt areas (grey shading), while in the

presence of an interaction modification the per-herbivore impact is greater in burnt than

unburnt areas (larger size of herbivore indicates larger per-herbivore impact). Managing

for one interaction (e.g. an interaction chain), when the observed effects are

predominantly driven by a different interaction pathway (e.g. an interaction

modification), can result in unexpected outcomes. Note that the ‘outcomes’ in this

example are based on the study of Foster et al. (2015), where fire and grazing interacted

to negatively affect plant diversity, and so the aim of management interventions would

be to maintain plant diversity by reducing interactive effects.

Non-linear interactive effects on ecosystems and organisms

How well do experimental studies test for nonlinear effects?

Among the 50 papers we reviewed, there were two common study designs used to investigate

fire-grazing interactions; patch burning experiments (15 papers) and factorial experiments (27

papers). Few studies (12%) of either type were able to detect nonlinear effects. Most patch-

burning studies compared “homogenous” grazed areas with “heterogeneous” patch burnt and

grazed areas, under constant grazing intensity and burn size. Only two of the 15 studies using a

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patch-burning design varied any characteristics of the burns (e.g. size of the burnt area), and

none tested the how interaction strength was affected by grazer density or species. While not

testing for non-linear interactions, a further three studies did test the consistency of interactive

effects across sites with different management histories.

All of the studies that identified an interaction modification (nine studies), and many

studies which tested net effects (18 studies), used a factorial study design, combining different

levels of fire (most often burnt/unburnt) and grazing (most often grazed/un-grazed) at the plot

level. To detect non-linear interactive effects, factorial studies must use more than two

treatment levels for at least one disturbance (i.e. include treatment levels in addition to

grazed/un-grazed and burnt/un-burnt, or measure natural variation within these levels), and

ideally include that factor as a continuous variable in analyses. However, of the 27 factorial

studies we reviewed, only nine used more than two levels for either fire or grazing, and only

three of these used either factor as a continuous variable in analyses. Thus, only three of 27

factorial studies and two of 15 patch-burning studies were of a design that could detect non-

linear interactive effects.

How does not identifying non-linear effects affect management applications?

The management implications of failing to detect non-linear effects differ slightly between

chain and modification interactions, but most relate to the ability to extrapolate (or interpolate)

results to untested situations or locations. Studies using patch-burn designs are testing for the

ecological effects of a chain interaction (“pyric herbivory”), where herbivores are attracted to

the fresh growth after fire, resulting in high grazing intensity in burnt patches (Fuhlendorf et al.,

2009). However, although it is acknowledged that the extent to which fire drives grazing

patterns may vary with the intensity, duration and timing of grazing, as well as with the size,

shape, intensity and timing of fire (Fuhlendorf et al., 2009; Sensenig et al., 2010; Allred et al.,

2011) (Fig. 4), most patch-burning studies did not test whether any of these factors affected the

strength of the fire-grazing interaction (but see McGranahan et al., 2013; Hovick et al., 2014),

and the consistency of such relationships remains poorly understood. Most patch-burning

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studies are therefore able to detect the presence of an interaction at the levels of burning and

grazing selected for that experiment, but cannot reliably be used for inference beyond this

specific combination of conditions. A manager who is faced with different conditions from

those tested in a study is therefore provided with little useful information about whether

changing stocking rates, harvest quotas or prescribed burns will improve biodiversity outcomes.

By comparison, studies that tested effects over a gradient of fire sizes, or a range of stocking

densities, would be able to give insights into the probable outcomes of changing management

practices, as well as improve our fundamental understanding of the fire-grazing interaction.

Figure 4. Generalised relationships between burn size, and the extent to which herbivores select

for the burnt area, demonstrating how chain interactions may vary with disturbance

properties such as spatial scale, and between ecosystems (Fuhlendorf et al., 2009).

Selection for burnt areas also varies between herbivore species (Sensenig et al., 2010),

and under certain conditions, herbivores may select against burnt areas (e.g. if dense

post-fire regrowth hinders herbivore movements, Wan et al., 2014).

Studies of interaction modifications are also often limited in their ability to inform

changes to management practices. This is because most studies are based on factorial

experiments using binary treatments (i.e. 2 × 2 factorial designs). When the effects of

disturbances, and their interactions, are non-linear, the key points of interest from a

management perspective are the points at which changes in one disturbance will have very large

(e.g. points of inflection), or very small (e.g. beyond thresholds), effects on the variable of

interest (Groffman et al., 2006)(Fig. 5a). Knowledge of the existence of these points, and their

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approximate values, would allow managers to identify: when management is most likely, or

least likely to be successful; what level of management is required to achieve an outcome; or

how management should differ in the presence of other disturbances (Groffman et al., 2006).

However, factorial studies employing binary contrasts cannot detect these points (Fig. 5), and

cannot be generalised beyond the levels chosen for study (Inouye, 2001), and hence are of

limited use for predicting the outcome of changes to management.

Factorial experiments employing binary comparisons are also problematic because

whether they detect synergisms or antagonisms can be entirely dependent on the levels chosen

for study (Fig. 5)(Dunne, 2010), but the management implications of synergisms and

antagonisms are very different. Synergistic interactions can accelerate biodiversity decline, but

synergisms may be more likely to respond to management, as managing one disturbance can

mitigate the effects of both (Brown et al., 2013; Piggott et al., 2015). By contrast, mitigating the

effects of antagonistic interactions usually requires both disturbances to be managed

concurrently (Didham et al., 2007; Brown et al., 2013). Therefore, if the results of factorial

studies are used to inform management, but the underlying relationships are nonlinear,

unintended outcomes are likely to result.

As an example, Figure 5a presents a hypothetical non-linear interaction between fire

and herbivory. In this example, a factorial study would detect; (1) a synergism if the herbivore

densities compared were low and moderate; (2) no interaction if herbivore densities were low

and high; and (3) an antagonism if herbivore densities were moderate and high (Fig. 5). If a

factorial study compared low and high herbivore densities, the authors would likely conclude

that fire and grazing were not interacting, and that reducing grazing intensity is likely to be the

most effective action to increase the response variable. As can be seen in Fig. 5a, even if

herbivore density were halved in this situation, there would be little change in the response

variable in burnt environments, resulting in a waste of limited conservation resources. As field

experiments tend to employ extreme treatments, with large effect sizes (Foster et al., 2014), and

synergisms are more likely to be detected when individual effect sizes are small (Folt et al.,

1999; Piggott et al., 2015), factorial experiments may also under-estimate the occurrence of

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synergisms in natural ecosystems. Clearly, single-level factorial studies can miss a large (and

potentially important) part of the picture if used to study non-linear interactions. Therefore,

researchers who aim to inform more efficient and effective management of terrestrial

ecosystems through disturbance interaction studies should consider whether non-linear effects

are likely, and if so, explore alternatives to factorial designs which include a gradient of

biologically and management-relevant treatment levels.

Figure 5. When interactive effects are non-linear, differences in interpretation can occur when

examining disturbances as continuous or factorial treatments. (a) An hypothetical, non-

linear, interaction modification between fire and herbivory, showing the key points of

management interest (turning points, shifts in marginal rates, and limits; open circles)

and how these vary across different levels of fire and herbivore density (low density =

L, moderate density = M, high density = H). (b) The likely results of factorial studies in

the same study system, showing how the experimental results will vary depending on

the treatment levels chosen for comparison (L v M => synergism, L v H => additive

effects, M v H => antagonism).

Bridging the gap to improve management applications

There is a lack of integration of theory into the design and interpretation of disturbance

interaction experiments. We have demonstrated this by reviewing recent studies of fire-grazing

interactions. We have also shown that this lack of integration means that interaction

experiments, despite often being motivated by management problems, fall short of their

potential in effectively informing the management of multiple disturbances. Land managers,

faced with complex systems, competing demands, and a mandate for transparent decision

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making, often rely on system and management models to inform management practices

(Underwood, 1995; Williams, 2011). A core role of experimental studies should therefore be to

improve our understanding of the relationships on which such models are based (Underwood,

1995). As we have argued in this paper, to do this requires experimental studies of disturbance

interactions to more effectively, and broadly, engage with the theoretical literature. Closing the

gap between the theoretical and experimental literature on disturbance interactions is not

straightforward – interactions are complex, multiscale processes which can be difficult to

manipulate experimentally. However, there are some adjustments that could be made to the

design and interpretation of disturbance interaction studies which would both improve links

with theory, and generate more useful information for the management of multiple disturbances.

While we have focussed on studies of terrestrial disturbance interactions in this paper, similar

experimental designs are used to study many other types of interactions, in a range of

ecosystems. The problems and solutions presented below will therefore be relevant to the study

of many kinds of ecological interactions.

Study design

1. Focus on testing the key relationships and interaction pathways identified in theoretical

models as important for management. Most studies report the net effects of an

experimentally manipulated interaction (Fig. 2), but it is difficult to generalise the

results of such studies, and they often do not test the key relationships that make up

management models. It is not always feasible, or desirable, to test all interaction

pathways in a single study. However, future studies of disturbance interactions should

explicitly identify the interaction pathway(s) being tested, and ensure that the study is

conducted at an appropriate spatial and temporal scale. Such experimental tests of

theoretically important relationships and pathways would allow theoretical models to be

verified, which could then be used to inform decisions at management-relevant scales

(Inouye, 2001; Belovsky et al., 2004; Cottingham et al., 2005; Denny & Benedetti-

Cecchi, 2012).

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2. Quantify variation in disturbances/stressors, as well as their ecological effects. Few

experimental studies measure or analyse disturbances as continuous variables.

However, environmental variation, and/or chain interactions mean that even if

disturbance treatments are experimentally imposed, disturbance intensity/extent is

likely to vary within treatments (i.e. between replicates). If measured, this variation can

be used to a researchers’ advantage, as by pairing this quantitative measure of

disturbance directly with responses, researchers will be able to separately quantify chain

and modification effects, and test for non-linear effects (Oliver & Morecroft, 2014).

3. Spread replicates across gradients, rather than pooling within treatments. Many studies

are based on the additive model of interactions, and employ factorial designs, where

single levels are replicated multiple times. However, as demonstrated in Fig. 5, factorial

designs are inappropriate if interactions are non-linear, as the results may be subjective

(dependent on the treatment levels selected by researchers), and therefore misleading.

Alternative regression-based study designs, where replicates are instead spread across a

gradient of levels of a factor (e.g. stocking densities / fire sizes), would be better able to

detect and describe nonlinear effects (Cottingham et al., 2005). It would not be feasible,

or even desirable, to test all combinations of two disturbance gradients in a single study.

However, a gradient could be achieved for a single disturbance without a substantial

increase in research effort compared with a factorial design. For example, a study with

24 sites in a factorial design would have 6 replicates for each of four treatment

combinations (i.e. a 2 × 2 design). The same number of sites, if employed a regression-

based design, could be spread across 12 different levels of one disturbance, and the two

levels of the second disturbance (i.e. a 12 × 2 design).

4. Extend gradients outside their current “natural” range. Most of the reviewed studies

used binary comparisons of treatments in their experiments (e.g. burnt/unburnt or patch

burnt/uniformly burnt), which are usually at “moderate” or average levels of

disturbance size or intensity. However, chain interactions with other disturbances, or

with external global change drivers such as climate, have the potential to generate

conditions, and levels of variation, that are outside the current natural range (Thornton

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et al., 2014; Kayler et al., 2015). Studies which test responses to conditions both within,

and beyond current natural limits are therefore necessary to highlight potential

thresholds or state-changes that are currently unlikely, but that may occur with these

novel combinations of conditions (Belovsky et al., 2004; Kayler et al., 2015). For such

studies, simulation and modelling tools will be useful for informing how far outside

current ranges treatment gradients should extend (Kayler et al., 2015).

Interpretation

1. Clearly define the interaction being tested. Many studies do not clearly define which

aspect of an interaction they are testing (see Fig. 2). This makes it difficult for readers

to interpret results. Clearly defining which pathway/aspect of an interaction is being

tested, and ensuring that the design and analyses used actually test this component of

the interaction, will allow the study to be interpreted within a broader context.

2. Frame and interpret studies in a broad theoretical context. Many studies of disturbance

interactions focus their discussion and interpretation solely on the specific interaction

being studied (e.g. most of the reviewed papers focussed exclusively on the fire-grazing

literature in framing their studies). However, theoretical studies of interactions are often

more generalised, or are focussed on other interactions or ecosystems, and so go

unacknowledged in studies that are very specific in approach. Exploring and testing

ideas developed from studies of different types of interactions and different systems is

key to bridging the gap between theory and experimental studies (Peters et al., 2011;

Buma, 2015).

3. Discuss results in the context of existing theoretical models. System models and

management models are frequently used to inform management decisions. Yet many

relationships in such models have not been experimentally verified (Knapp et al.,

2011). Interpreting results in the context of existing theoretical models, and discussing

whether results support theoretical pathways is key to improving these models, and

hence management decisions.

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Conclusions

Managing ecological interactions is a key challenge to biodiversity conservation, and one that is

becoming increasingly important as global change drives rapid shifts in threats, abiotic

conditions, and disturbance regimes (Brook et al., 2008; Turner, 2010). This challenge also

presents an opportunity, through which a detailed, mechanistic understanding of ecological

interactions can be used to develop novel solutions for biodiversity conservation. For example,

failure to account for interactions between habitat modification and invasive species can cause

invasive control programs to be ineffective, or even harmful for native species (e.g. Norbury et

al., 2013). However, a mechanistic understanding of habitat-predator interactions is now being

used to develop management interventions that may be both more efficient and effective than

lethal control for protecting wildlife from introduced predators (Didham et al., 2007; Doherty et

al., 2015).

Carefully executed empirical studies, which are well integrated with theory, are

essential for developing the detailed understanding that these novel solutions require. For

example, novel strategies to conserve frogs that are threatened by the disease Chytridiomycosis

have been identified, not from solution-focussed studies, but from well-designed empirical

studies, grounded in theory, that investigated the multiple interacting processes affecting disease

prevalence and impacts (Scheele et al., 2014; Heard et al., 2015). The suggestions we have

presented for the design and interpretation of disturbance interaction studies aim to guide

research that will provide a similar understanding for managing multiple stressors. By gaining a

deeper, mechanistic understanding of interactions, such studies will be able to provide context-

specific information to guide management while also testing and refining theoretical models,

which can then be translated to other processes and ecosystems.

Many studies of disturbance interactions aim to improve the management of

ecosystems. However, studies which focus on specific management problems and fail to

integrate relevant theory are often limited in their broader management applications. There are

many studies of ecological interactions that are well integrated with the theoretical literature

(e.g. Mandle & Ticktin, 2012; Kimuyu et al., 2014), but such studies remain the exception,

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rather than the rule. Until this trend is reversed, disturbance interaction experiments will

continue to fall short of their potential in informing effective ecological management.

Acknowledgements

Ben Scheele provided valuable feedback on the manuscript. This research was financially

supported by The Margaret Middleton Fund, The Norman Wettenhall Foundation and the Long

Term Ecological Research Network. P.S.B. was supported by an ARC DECRA Fellowship,

D.B.L. was supported by an ARC Laureate Fellowship.

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Supporting Information

Appendix S1. List of the 50 fire-grazing studies reviewed.

Studies were identified by searching the ISI Web of Knowledge Database for articles including

the terms fire AND (grazing or browsing), and which were published between 2011 and the date

of the search. This search was performed on the 17th April 2015 and returned 268 results, 50 of

which were empirical studies that focussed at least in part on the interaction between fire and

large herbivores.

Allred BW, Fuhlendorf SD, Engle DM, Elmore RD (2011) Ungulate preference for burned

patches reveals strength of fire-grazing interaction. Ecology and Evolution, 1, 132-144.

Allred BW, Scasta JD, Hovick TJ, Fuhlendorf SD, Hamilton RG (2014) Spatial heterogeneity

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Augustine DJ, Derner JD (2012) Disturbance regimes and mountain plover habitat in shortgrass

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of Wildlife Management, 76, 721-728.

Augustine DJ, Derner JD (2014) Controls over the strength and timing of fire-grazer

interactions in a semi-arid rangeland. Journal of Applied Ecology, 51, 242-250.

Blackhall M, Veblen TT, Raffaele E (2015) Recent fire and cattle herbivory enhance plant-level

fuel flammability in shrublands. Journal of Vegetation Science, 26, 123-133.

Bock CE, Jones ZF, Kennedy LJ, Bock JH (2011) Response of rodents to wildfire and livestock

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Dalldorf KN, Swanson SR, Kozlowski DE, Schmidt KM, Shane RS, Fernandez G (2013)

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Doxon ED, Davis CA, Fuhlendorf SD, Winter SL (2011) Aboveground macroinvertebrate

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210

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Hierro JL, Clark KL, Branch LC, Villarreal D (2011) Native herbivore exerts contrasting effects

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Lunt ID, Zimmer HC, Cheal DC (2011) The tortoise and the hare? Post-fire regeneration in

mixed Eucalyptus-Callitris forest. Australian Journal of Botany, 59, 575-581.

Mandle L, Ticktin T (2012) Interactions among fire, grazing, harvest and abiotic conditions

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Masunga GS, Moe SR, Pelekekae B (2013) Fire and grazing change herbaceous species

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211

McGranahan DA, Engle DM, Fuhlendorf SD, Winter SJ, Miller JR, Debinski DM (2012)

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Moranz RA, Fuhlendorf SD, Engle DM (2014) Making sense of a prairie butterfly paradox: The

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Biological Conservation, 173, 32-41.

Nayak RR, Vaidyanathan S, Krishnaswamy J (2014) Fire and grazing modify grass community

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Nield AP, Monaco S, Birnbaum C, Enright NJ (2015) Regeneration failure threatens persistence

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Osem Y, Lavi A, Rosenfeld A (2011) Colonization of Pinus halepensis in Mediterranean

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Pekin BK, Wisdom MJ, Endress BA, Naylor BJ, Parks CG (2014) Ungulate browsing maintains

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vegetation dynamics in the Cape renosterveld shrublands of South Africa: insights from a

six-yr field experiment. Applied Vegetation Science, 17, 456-469.

212

Ramirez AR, Pratt RB, Jacobsen AL, Davis SD (2012) Exotic deer diminish post-fire resilience

of native shrub communities on Santa Catalina Island, southern California. Plant Ecology,

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Richardson AN, Koper N, White KA (2014) Interactions between ecological disturbances:

burning and grazing and their effects on songbird communities in northern mixed-grass

prairies. Avian Conservation and Ecology, 9, 5.

Scasta JD, Engle DM, Talley JL, Weir JR, Stansberry JC, Fuhlendorf SD, Harr RN (2012)

Pyric-herbivory to manage horn flies (Diptera: Muscidae) on cattle. Southwestern

Entomologist, 37, 325-334.

Schutz AEN, Bond WJ, Cramer MD (2011) Defoliation depletes the carbohydrate reserves of

resprouting Acacia saplings in an African savanna. Plant Ecology, 212, 2047-2055.

te Beest M, Mpandza NJ, Olff H (2015) Fire and simulated herbivory have antagonistic effects

on resistance of savanna grasslands to alien shrub invasion. Journal of Vegetation Science,

26, 114-122.

Thomas-Van Gundy M, Rentch J, Adams MB, Carson W (2014) Reversing legacy effects in the

understory of an oak-dominated forest. Canadian Journal of Forest Research, 44, 350-364.

Traore S, Tigabu M, Jouquet P, Ouedraogo SJ, Guinko S, Lepage M (2015) Long-term effects

of Macroternies termites, herbivores and annual early fire on woody undergrowth

community in Sudanian woodland, Burkina Faso. Flora, 211, 40-50.

Tuft KD, Crowther MS, McArthur C (2012) Fire and grazing influence food resources of an

endangered rock-wallaby. Wildlife Research, 39, 436-445.

Wan HY, Rhodes AC, St Clair SB (2014) Fire severity alters plant regeneration patterns and

defense against herbivores in mixed aspen forests. Oikos, 123, 1479-1488.

Winter SL, Fuhlendorf SD, Goad CL, Davis CA, Hickman KR (2011) Topoedaphic variability

and patch burning in sand sagebrush shrubland. Rangeland Ecology & Management, 64,

633-640.

Winter SL, Fuhlendorf SD, Goad CL, Davis CA, Hickman KR, Leslie DM, Jr. (2012)

Restoration of the fire-grazing interaction in Artemisia filifolia shrubland. Journal of

Applied Ecology, 49, 242-250.

Winter SL, Hickman KR, Goad CL, Fuhlendorf SD, Gregory MS (2013) Seasonal fires, bison

grazing, and the tallgrass prairie forb Arnoglossum plantagineum raf. Natural Areas

Journal, 33, 327-338.

213