Sacha Jellinek

Home , Acantholophus, Carlia tetradactyla, Cyclominae, Demetrida loweri, Morethia boulengeri, Sarticus

The value of revegetated linear strips and patches of habitat for faunal conservation: Reconciling ecological and landholder perspectives

Sacha Jellinek

A thesis submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy

February 2012

School of Botany The University of Melbourne

Abstract

A large-scale loss of biodiversity is currently occurring around the world, and it has been argued that extensive restoration of the landscape is necessary to reduce species extinctions. I assessed the effectiveness of revegetation for maintaining reptile (Reptilia) and beetle (Coleoptera) communities in two fragmented, agricultural landscapes of south-eastern Australia, and investigated how the management actions of landholders influenced the composition and structural attributes of revegetated and remnant areas. Reptiles and beetles were surveyed using pitfall traps from January to March in 2008 and 2009. I established the variation in reptile and beetle species richness and abundance in remnant, revegetated and cleared linear strips, in remnant patches of vegetation and in revegetated areas adjacent to the remnant patches. Habitat variables such as rock cover and the proportion of native plants were also recorded. In order to determine landholders‟ past and planned revegetation activities, the composition of plantings, and their attitudes towards and intentions to manage revegetated and remnant areas, I quantitatively surveyed 400 landholders using postal questionnaires. I then used Bayesian Networks to integrate the ecological and social data I collected and demonstrate the conceptual link between management actions and biodiversity outcomes.

Overall I found that reptile and beetle species richness, abundance and community composition did not substantially differ between revegetated, remnant, and cleared linear strips; revegetated and remnant patches; or between revegetated linear strips and revegetated patches. However, I found some reptile and beetle species showed a trend towards higher abundance in remnant linear strips than in revegetated and cleared linear strips. Interestingly, species richness and abundance of rare reptiles, overall reptile abundance, and abundance of Carlia tetradactyla increased in remnant linear strips but decreased in revegetated and cleared linear strips as distance from remnant patches increased. Reptile species richness and abundance were positively influenced by rock cover, and reptile and beetle community composition was substantially influenced by environmental variables such as rock, litter and herb

iii cover. Three-quarters of survey respondents had previously undertaken revegetation on their land, the majority of them having replanted with native trees and shrubs along linear strips. Respondents that had revegetated or planned to revegetate were usually Landcare members with an off-farm income. Landholder attitudes towards revegetated and remnant areas influenced their intention to manage these areas, with landholders who considered replanted and remnant areas to be detrimental to their property most likely to undertake management actions such as pest control.

Using Bayesian Networks, I determined that the management actions of landholders were more likely to increase reptile and beetle species richness in cleared linear strips than in other linear strips and patches. The most cost- effective management actions for increasing reptile and beetle species richness were weed control, planting trees and shrubs and adding leaf litter and fallen timber/coarse woody debris. The agricultural landscapes I studied are highly degraded, and the remaining reptile and beetle species are mostly a robust subset of previously present species, with species requiring good quality ground layers persisting along remnant linear strips. Bayesian Networks are an effective tool for integrating ecological and social data to inform management decisions, and increase the value of revegetation for wildlife.

Declaration

This is to certify that:

The thesis comprises only my original work towards the PhD except where indicated in the Preface,

Due acknowledgement has been made in the text to all other material used,

The thesis is fewer than 100,000 words in length, exclusive of tables, maps, bibliographies and appendices.

------(Signature) (Date)

Acknowledgements

This project was largely based in the field and would not have been possible without the assistance of numerous volunteers who helped me to set up, survey, and take down my field sites. Special thanks to my brother Nick, for his hard work and sceptical criticism of my project: it was a good laugh to be out digging holes with you. Anthony was also amazingly hard-working and put up with my bouts of disorganisation very well. Other volunteers include but are not limited to Rakhee, Lee, Laura, Chris, Achim, Abby and Squattie and friends.

Thank you to all the landholders who allowed me onto their properties and were interested in my project and results. Thank you also to Greening Australia, The Little Desert Lodge, the Regent Honeyeater Project and Trust for Nature for providing me with all of the information and contacts I needed to start this project. Thank you to Kate Daniels and Bruce Burnell for providing accommodation while I was out in the field.

Thank you to my supervisors, Dr Don Driscoll, Dr Kirsten Parris and Dr Brendan Wintle, for being so supportive throughout this project. Don, you were amazing to work with again and your constant guidance and critical reviewing, although sometimes painful, were fantastic. Kirsten, I really appreciated your guidance and advice, and your detailed editing – thank you for your support and advice and being around to bounce ideas off. Thank you Brendan for helping me get my head around Bayes nets in particular. Thanks also to the School of Botany and the AEDA/CEED lab at the University of Melbourne for supporting my project and providing me with guidance. I am also grateful to Libby Rumpff for reviewing a number of drafts of my Bayesian chapter and Mick McCarthy for helping me with my WinBUGS code. Thanks to Peter Dwyer and Monica Minnegal for their guidance, reviewing, and advice on the landholder questionnaires and the social chapter. Thanks also to Liz Gosling for helping me with my landholder questionnaire and finding landholders.

The CSIRO Entomology lab in Canberra assisted me with my beetle identification. I thank Tom Weir especially, who took the time to look at my collection. Thanks also to the CESAR lab at the University of Melbourne and Michael Nash for helping with beetle identification and giving me lab space. Thanks also to Fabian Douglas for helping with the initial identification of my beetles and Clive Crouch for his passion for all the critters in the Wimmera.

My scholarship was provided by the Albert Shimmins Postgraduate award. Funding for this project was provided by the Norman Wettenhall Foundation, Greening Australia, the Wimmera Catchment Management Authority, Vic Roads and AEDA and the School of Botany at the University of Melbourne. Travel grants to attend conferences were provided by the School of Botany, the Ecological Society of Australia and the David Ashton travel grant, supplementing funding from AEDA/CEED.

Finally, a huge hug and many thanks to my friends and family for putting up with me and keeping me somewhat sane. Much love to Anna for keeping life going as normal and for supporting me, you kept me going. Anna and my mum Judy played an instrumental role in editing my thesis so an extra big thanks for that. Prof. Geoff Cumming also assisted in editing and with statistics advice. Thanks also to Kel, Yoshi and the rest of the house and also to the Whallery gang, Kerryn and Bryn for your constant distractions and reality checks. Cheers to Briony, Sarah, Yacov, Marissa and many others from the various labs I was involved with for coffees and conversation.

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Table of Contents

Abstract ...... iii Declaration ...... v Acknowledgements ...... vi Table of Contents ...... viii List of Tables ...... xii List of Figures ...... xvi List of Appendices ...... xix Chapter 1. Introduction ...... 1 Background ...... 2 Ecological processes...... 2 Social processes ...... 5 Chapter outline ...... 6 Chapter 2. Biodiversity benefits of restoring vegetation: A review ...... 9 Introduction ...... 10 Methods ...... 12 Drivers of revegetation ...... 13 Variables influencing restoration effectiveness for biodiversity conservation 18 Connectivity ...... 18 Patch size ...... 22 Linear strip width and length ...... 22 Edge effects ...... 24 Patch quality ...... 24 Vegetation type and time-lags to vegetation maturation...... 25 Remnant vegetation in the landscape ...... 28 Climate change ...... 29 Species‟ interactions ...... 30 Strategic planning and funding restoration activities ...... 32 Priority research ...... 33 Conclusion ...... 36 Chapter 3. The value of revegetated areas and local habitat variables for reptile conservation ...... 39

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Introduction ...... 40 Methods ...... 43 Study region ...... 43 Site selection ...... 44 Data analysis ...... 47 Results ...... 52 Treatment relationships ...... 53 Relationships with habitat variables ...... 59 Influence of environmental and spatial variables on community composition ...... 66 Discussion ...... 69 Effectiveness of revegetation in fragmented agricultural landscapes ...... 70 Environmental and habitat variables influencing reptile species and communities ...... 71 Influence of distance along linear strips ...... 74 Conclusion ...... 77 Chapter 4. Beetles in agricultural landscapes: Is revegetation maintaining species in fragmented areas? ...... 79 Introduction ...... 80 Methods ...... 83 Data analysis ...... 84 Results ...... 86 Treatment effects ...... 86 Relationships with habitat variables ...... 92 Influence of environmental and treatment variables on community composition ...... 98 Discussion ...... 102 Overall species response ...... 103 Responses of functional groups to study treatments ...... 105 Individual species response to study treatments ...... 106 Influence of environmental and treatment variables on community composition ...... 107 Conclusion ...... 110

Chapter 5. Propensity of landholders to revegetate land and their attitudes towards vegetation ...... 111 Introduction ...... 112 Methods ...... 114 Revegetation and remnant vegetation in agricultural areas ...... 115 Attitudes towards revegetated and remnant vegetation, and intention to manage these areas ...... 118 Results ...... 120 Revegetation and remnant vegetation in agricultural areas ...... 120 Attitudes towards revegetated and remnant vegetation ...... 128 Intention to manage revegetated and remnant vegetation ...... 132 Discussion ...... 137 Revegetation and remnant vegetation in agricultural areas ...... 137 Attitudes towards revegetated and remnant vegetation, and intention to manage these areas ...... 141 Consequences of revegetation activities for native fauna ...... 144 Conclusion ...... 145 Chapter 6. Integrating ecological and social data using Bayesian Networks: Two case studies from south-eastern Australia ...... 147 Introduction ...... 148 Methods ...... 151 Data and process models ...... 151 Structuring and fitting models ...... 155 Sensitivity to findings analysis ...... 161 Comparing prior and posterior predictions of species richness ...... 163 Cost-effectiveness analysis ...... 163 Landholder scenarios ...... 164 Results ...... 166 Sensitivity to findings ...... 167 Comparing prior and posterior predictions of species richness ...... 169 Cost-effectiveness ...... 171 Landholder scenarios ...... 174 Discussion ...... 175

Integrating social and ecological data ...... 175 Expert opinion and field data ...... 177 Management actions and cost-effectiveness ...... 178 Future research ...... 180 Conclusion ...... 182 Chapter 7. Conclusion ...... 183 Reptile and beetle response to treatment type ...... 184 Habitat variables and management of revegetated areas ...... 186 Future research ...... 187 References ...... 194 Appendices ...... 222

List of Tables

TABLE 2.1. CURRENT GAPS IN OUR KNOWLEDGE OF THE VALUE OF REVEGETATED

AREAS FOR FAUNAL CONSERVATION...... 34

TABLE 3.1. DIC VALUES FOR THE 8 BEST-SUPPORTED MODELS OF SPECIES RICHNESS

AS A FUNCTION OF HABITAT AND LOCATION...... 60

TABLE 3.2. DIC VALUES FOR THE 7 BEST-SUPPORTED MODELS OF REPTILE ABUNDANCE

AS A FUNCTION OF HABITAT AND LOCATION...... 61

TABLE 3.3. DIC VALUES FOR THE 23 BEST-SUPPORTED MODELS OF MENETIA GREYII

ABUNDANCE AS A FUNCTION OF HABITAT AND LOCATION...... 62

TABLE 3.4. DIC VALUES FOR THE 7 BEST-SUPPORTED MODELS OF MORETHIA

BOULENGERI ABUNDANCE AS A FUNCTION OF HABITAT AND LOCATION...... 64

TABLE 3.5. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND TREATMENT VARIABLES

IN EXPLAINING THE VARIANCE OF REPTILE COMMUNITY COMPOSITION IN THE

BENALLA REGION ...... 66

TABLE 3.6. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE TREATMENT

MATRIX (TREAT) TO THE VARIATION IN THE REPTILE COMMUNITY COMPOSITION

MATRIX IN THE BENALLA REGION ...... 67

TABLE 3.7. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND TREATMENT VARIABLES

IN EXPLAINING THE VARIANCE OF REPTILE COMMUNITY COMPOSITION IN THE

WIMMERA REGION ...... 68

TABLE 3.8. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE TREATMENT

MATRIX (TREAT) TO THE VARIATION IN THE REPTILE COMMUNITY COMPOSITION

MATRIX IN THE WIMMERA REGION ...... 68

TABLE 4.1. DIC VALUES FOR THE 6 BEST-SUPPORTED MODELS OF OVERALL BEETLES

SPECIES RICHNESS AS A FUNCTION OF HABITAT AND LOCATION ...... 94

TABLE 4.2. DIC VALUES FOR THE 7 BEST-SUPPORTED MODELS OF OVERALL BEETLE

ABUNDANCE AS A FUNCTION OF HABITAT AND LOCATION ...... 95

TABLE 4.3. DIC VALUES FOR THE 9 BEST-SUPPORTED MODELS OF HERBIVOROUS

BEETLES AS A FUNCTION OF HABITAT AND LOCATION ...... 96

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TABLE 4.4. DIC VALUES FOR THE 12 BEST-SUPPORTED MODELS OF SCAVENGING

BEETLES AS A FUNCTION OF HABITAT AND LOCATION...... 97

TABLE 4.5. DIC VALUES FOR THE 7 BEST-SUPPORTED MODELS OF CARNIVOROUS

BEETLES AS A FUNCTION OF HABITAT AND LOCATION...... 98

TABLE 4.6. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND TREATMENT VARIABLES

IN EXPLAINING THE VARIANCE OF BEETLE COMMUNITY COMPOSITION IN THE

BENALLA REGION...... 100

TABLE 4.7. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE TREATMENT

MATRIX (TREAT) TO THE VARIATION IN THE BEETLE COMMUNITY COMPOSITION

MATRIX IN THE BENALLA REGION ...... 101

TABLE 4.8. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND TREATMENT VARIABLES

IN EXPLAINING THE VARIANCE OF BEETLE COMMUNITY COMPOSITION IN THE

WIMMERA REGION ...... 101

TABLE 4.9. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE TREATMENT

MATRIX (TREAT) TO THE VARIATION IN THE BEETLE COMMUNITY COMPOSITION

MATRIX IN THE WIMMERA REGION ...... 102

TABLE 5.1. VARIABLES USED IN LOGISTIC REGRESSION ANALYSIS SHOWING THEIR

CATEGORICAL OR CONTINUOUS STATES...... 117

TABLE 5.2. DEMOGRAPHY OF LANDHOLDERS WHO PARTICIPATED IN THIS STUDY FROM

THE WIMMERA AND BENALLA REGION...... 121

TABLE 5.3. PERCENTAGE OF LANDHOLDERS WHO HAD UNDERTAKEN REVEGETATION OR

WERE PLANNING TO REVEGETATE IN THE FUTURE, AND THE COMPOSITION OF

THESE PLANTINGS ...... 122

TABLE 5.4. PERCENTAGES OF LANDHOLDERS WHO HAD REPLANTINGS OF DIFFERENT

AGES, AND HAD RECEIVED FUNDING TO UNDERTAKE REVEGETATION; THE

PROPORTIONS OF OVERALL PROPERTY SIZE CONTAINING REVEGETATED AND

REMNANT HABITAT, AND THE SHAPE OF REMNANTS...... 123

TABLE 5.5. THE BEST-SUPPORTED MODELS OF THE PROBABILITY THAT LANDHOLDERS

HAD UNDERTAKEN REVEGETATION ON THEIR PROPERTIES...... 125

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TABLE 5.6. THE BEST-SUPPORTED MODELS OF THE PROBABILITY THAT LANDHOLDERS

WOULD UNDERTAKE REVEGETATION IN THE FUTURE ...... 126

TABLE 5.7. FACTOR LOADINGS FOR ATTITUDINAL VARIABLES TOWARDS REVEGETATION ...... 129

TABLE 5.8. ATTITUDES OF LANDHOLDERS TO REVEGETATION LOADED INTO THREE

FACTORS...... 129

TABLE 5.9. FACTOR LOADINGS FOR ATTITUDINAL VARIABLES TOWARDS REMNANT

VEGETATION ...... 131

TABLE 5.10. ATTITUDES LANDHOLDERS HELD TO REMNANT HABITATS LOADED INTO

TWO FACTORS ...... 131

TABLE 5.11. LANDHOLDER PERCEPTIONS OF RISKS TO THEIR PROPERTIES ...... 136

TABLE 6.1. LANDHOLDER DEMOGRAPHIC INFORMATION AND MANAGEMENT DECISIONS

AND THEIR RESPECTIVE STATES...... 161

TABLE 6.2. THE COSTS OF IMPLEMENTING REVEGETATION MANAGEMENT ACTIONS IN

AGRICULTURAL LANDSCAPES...... 164

TABLE 6.3. LANDHOLDER DEMOGRAPHIC SCENARIOS USED TO ILLUSTRATE THE

INFLUENCE OF DEMOGRAPHIC VARIABLES ON REPTILE AND BEETLE SPECIES

RICHNESS...... 165

TABLE 6.4. SENSITIVITY TO FINDINGS ANALYSIS SHOWING REPTILE SPECIES RICHNESS

SENSITIVITY TO HABITAT ATTRIBUTE NODES...... 167

TABLE 6.5. SENSITIVITY ANALYSIS SHOWING BEETLE SPECIES RICHNESS SENSITIVITY

TO HABITAT ATTRIBUTE NODES ...... 168

TABLE 6.6. SENSITIVITY TO FINDINGS ANALYSIS SHOWING REPTILE AND BEETLE

SPECIES RICHNESS VARIABLES SENSITIVITY TO LANDHOLDER DEMOGRAPHIC AND

LANDHOLDER MANAGEMENT DECISION NODES ...... 169

TABLE 6.7. REPTILE SPECIES RICHNESS CHANGE IN AN EXPERT-ONLY MODEL, FIELD

DATA-ONLY MODEL, AND A COMBINED EXPERT AND FIELD DATA MODEL IN LINEAR

STRIP HABITATS UNDER THREE MANAGEMENT SCENARIOS ...... 170

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TABLE 6.8. BEETLE SPECIES RICHNESS CHANGE IN AN EXPERT-ONLY MODEL, FIELD

DATA-ONLY MODEL, AND A COMBINED EXPERT AND FIELD DATA MODEL IN LINEAR

STRIP HABITATS UNDER THREE MANAGEMENT SCENARIOS...... 170

TABLE 6.9. REPTILE SPECIES RICHNESS INCREASE AS A RESULT OF DIFFERENT

MANAGEMENT ACTIONS (COMPARED TO A NO-MANAGEMENT SCENARIO) AND THE

COST-EFFECTIVENESS OF THOSE ACTIONS ...... 172

TABLE 6.10. BEETLE SPECIES RICHNESS INCREASE AS A RESULT OF DIFFERENT

MANAGEMENT ACTIONS (COMPARED TO A NO-MANAGEMENT SCENARIO) AND THE

COST-EFFECTIVENESS OF THOSE ACTIONS ...... 173

TABLE 7.1. SUMMARY OF AIMS FOR EACH DATA CHAPTER AND THE THREE MOST

IMPORTANT RESULTS FROM EACH CHAPTER...... 191

List of Figures

FIGURE 1.1. THE THEORY OF PLANNED BEHAVIOUR...... 5

FIGURE 3.1. MAPS OF THE (A) BENALLA AND (B) WIMMERA REGIONS IN VICTORIA,

SOUTH-EASTERN AUSTRALIA ...... 44

FIGURE 3.2. DIAGRAMS DISPLAYING THE SAMPLING DESIGN USED IN MY STUDY ...... 45

FIGURE 3.3. REPTILE SPECIES ACCUMULATION IN THE WIMMERA (SOLID CIRCLE) AND

BENALLA (OPEN CIRCLE) REGION OVER A 15 DAY TRAPPING PERIOD...... 53

FIGURE 3.4. SPECIES RICHNESS AND ABUNDANCE OF REPTILES IN LINEAR STRIP

TREATMENTS ...... 56

FIGURE 3.5. SPECIES RICHNESS AND ABUNDANCE OF REPTILES IN PATCH TREATMENTS ...... 57

FIGURE 3.6. SPECIES RICHNESS AND ABUNDANCE OF REPTILES IN REVEGETATED

AREAS ...... 57

FIGURE 3.7. REPTILE SPECIES RICHNESS AND ABUNDANCE IN LINEAR STRIP

TREATMENTS AS DISTANCE FROM THE REMNANT PATCHES INCREASED ...... 58

FIGURE 3.8. THE MULTIPLICATIVE EFFECT OF: ROCK COVER, MID-STRATUM DENSITY,

LITTER COVER, BARE GROUND, THE PROPORTION OF NATIVE PLANTS, AND

TUSSOCK COVER ON REPTILE SPECIES RICHNESS ...... 60

FIGURE 3.9. THE MULTIPLICATIVE EFFECT OF: ROCK COVER, BARE GROUND, MID-

STRATUM DENSITY, LITTER COVER, TUSSOCK COVER, AND THE PROPORTION OF

NATIVE PLANTS ON REPTILE SPECIES ABUNDANCE ...... 61

FIGURE 3.10. THE MULTIPLICATIVE EFFECT OF: ROCK COVER, BARE GROUND, MID-

STRATUM DENSITY LITTER COVER, TUSSOCK COVER, AND THE PROPORTION OF

NATIVE PLANTS ON M. GREYII ABUNDANCE ...... 63

FIGURE 3.11. THE MULTIPLICATIVE EFFECT OF: ROCK COVER, PROPORTION OF NATIVE

PLANTS, BARE GROUND, TUSSOCK COVER, LITTER COVER, AND MID-STRATUM

DENSITY ON MORETHIA BOULENGERI ABUNDANCE ...... 64

FIGURE 3.12. THE PERCENTAGE COVER OF VEGETATION VARIABLES MEASURED WITHIN

THE DIFFERENT STUDY TREATMENTS ...... 65 xvi

FIGURE 4.1. SPECIES RICHNESS AND ABUNDANCE OF BEETLES IN LINEAR STRIP

TREATMENTS ...... 88

FIGURE 4.2. SPECIES RICHNESS AND ABUNDANCE OF BEETLES IN PATCH TREATMENTS ...... 89

FIGURE 4.3. SPECIES RICHNESS AND ABUNDANCE OF BEETLES IN REVEGETATED AREAS ...... 89

FIGURE 4.4. SPECIES RICHNESS OF FUNCTIONAL GROUPS IN (A) LINEAR STRIP, (B)

PATCH AND (C) REVEGETATED TREATMENTS ...... 90

FIGURE 4.5. MEAN BEETLE FAMILY RICHNESS IN (A) LINEAR STRIP, (B) PATCH AND (C)

REVEGETATED TREATMENTS ...... 91

FIGURE 4.6. SPECIES RICHNESS OF FLYING AND FLIGHTLESS BEETLES IN (A) LINEAR

STRIP, (B) PATCH AND (C) REVEGETATED TREATMENTS ...... 92

FIGURE 4.7. THE MULTIPLICATIVE EFFECT ON BEETLE SPECIES RICHNESS OF MID-

STRATUM DENSITY, THE PROPORTION OF NATIVE PLANTS, LITTER COVER, TUSSOCK

COVER AND HERB COVER, AS SHOWN BY EACH VARIABLE‟S MEAN ...... 94

FIGURE 4.8. THE MULTIPLICATIVE EFFECT ON BEETLE ABUNDANCE OF MID-STRATUM

DENSITY, LITTER COVER, TUSSOCK COVER, HERB COVER AND THE PROPORTION OF

NATIVE PLANTS, AS SHOWN BY EACH VARIABLE‟S MEAN ...... 95

FIGURE 4.9. THE MULTIPLICATIVE EFFECT ON HERBIVOROUS BEETLES OF MID-STRATUM

DENSITY, THE PROPORTION OF NATIVE PLANTS, TUSSOCK COVER, HERB COVER

AND LITTER COVER, AS SHOWN BY EACH VARIABLE‟S MEAN ...... 96

FIGURE 4.10. THE MULTIPLICATIVE EFFECT ON SCAVENGING BEETLES OF LITTER

COVER, THE PROPORTION OF NATIVE PLANTS, MID-STRATUM DENSITY, TUSSOCK

COVER, AND HERB COVER, AS SHOWN BY EACH VARIABLE‟S MEAN ...... 97

FIGURE 4.11. THE MULTIPLICATIVE EFFECT ON CARNIVOROUS BEETLES OF MID-

STRATUM DENSITY, TUSSOCK COVER, THE PROPORTION OF NATIVE PLANTS AND

HERB COVER, AS SHOWN BY EACH VARIABLE‟S MEAN ...... 98

FIGURE 5.1. THE PROBABILITY THAT A LANDHOLDER HAD UNDERTAKEN REVEGETATION ...... 125

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FIGURE 5.2. THE PROBABILITY THAT A LANDHOLDER WOULD UNDERTAKE FUTURE

REVEGETATION ...... 126

FIGURE 5.3. THE RESPONSE OF LANDHOLDER TO: (A) IMPEDIMENTS THAT WOULD MAKE

LANDHOLDERS LESS LIKELY TO REVEGETATE AND (B) INCENTIVES TO ENABLE

LANDHOLDERS TO UNDERTAKE REVEGETATION ...... 127

FIGURE 5.4. NUMBER OF LANDHOLDERS WHO INDICATED THEY WOULD REVEGETATE IF

TIME AND MONEY WERE NOT AN IMPEDIMENT ...... 127

FIGURE 5.5. OVERALL LANDHOLDER INTENTION TO MANAGE REVEGETATED AREAS . 132

FIGURE 5.6. THE STRENGTH OF A LANDHOLDER‟S INTENTION TO MANAGE

REVEGETATED AREAS ...... 133

FIGURE 5.7. LANDHOLDER INTENTION TO MANAGE REMNANT AREAS ...... 134

FIGURE 5.8. THE STRENGTH OF A LANDHOLDER‟S INTENTION TO MANAGE REMNANT

AREAS ...... 135

FIGURE 6.1. STEPS IN AN ADAPTIVE MANAGEMENT CYCLE ...... 149

FIGURE 6.2. THE INFLUENCE OF TWO “PARENT” NODES, UNDERTAKE WEED CONTROL

(MANAGEMENT ACTION) AND HABITAT TYPE, ON THE “CHILD” NODE OF WEED

COVER (HABITAT NODE) ...... 158

FIGURE 6.3. A SUB-MODEL INCLUDING THE PATCH-LEVEL BAYES NET (ENCLOSED IN

THE DASHED-LINE BOX) AND THE LANDHOLDER BEHAVIOUR BAYES NET ...... 160

FIGURE 6.4. PATCH-LEVEL BAYES NET ...... 165

FIGURE 6.5. LANDHOLDER BEHAVIOUR BAYES NET ...... 166

FIGURE 6.6. THE EFFECTIVENESS OF NO MANAGEMENT COMPARED TO ALL

MANAGEMENT ACTIONS ON REPTILE AND BEETLE SPECIES RICHNESS ...... 174

FIGURE 6.7. INFLUENCE OF CHANGES IN LANDHOLDER DEMOGRAPHICS UNDER

DIFFERENT SCENARIOS ON REPTILE AND BEETLE SPECIES RICHNESS ...... 175

FIGURE 6.8. RESPONSE OF LANDHOLDERS TO DIFFERENT DEMOGRAPHIC SCENARIOS ...... 175

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List of Appendices

APPENDIX 1. LIST OF INTERNATIONAL STUDIES ON FAUNAL DIVERSITY IN REPLANTED

FOREST AREAS ...... 222

APPENDIX 2. WINBUGS CODE TO CALCULATE THE INFLUENCE OF LINEAR STRIP

TREATMENTS ON REPTILE AND BEETLE SPECIES ...... 226

APPENDIX 3. WINBUGS CODE TO CALCULATE REPTILE DISTANCE ALONG LINEAR

STRIPS AS DISTANCE FROM THE REMNANT PATCH INCREASES...... 227

APPENDIX 4. WINBUGS CODE TO CALCULATE THE INFLUENCE OF HABITAT VARIABLES

ON REPTILE AND BEETLE SPECIES ...... 228

APPENDIX 5. REPTILE CAPTURES IN THE WIMMERA AND BENALLA IN DIFFERENT

TREATMENTS ...... 229

APPENDIX 6. THE MEAN EFFECT OF REGION ON REPTILE SPECIES RICHNESS AND

ABUNDANCE IN LINEAR STRIPS, PATCHES AND REVEGETATED AREAS CALCULATED

USING A LOGISTIC REGRESSION WITH A POISSON DISTRIBUTION...... 232

APPENDIX 7. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND SPATIAL VARIABLES IN

EXPLAINING THE VARIANCE OF REPTILE COMMUNITY COMPOSITION IN THE BENALLA

REGION ...... 233

APPENDIX 8. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE SPATIAL

MATRIX (SPACE) TO THE VARIATION IN THE REPTILE COMMUNITY COMPOSITION

MATRIX IN THE BENALLA REGION ...... 234

APPENDIX 9. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND SPATIAL VARIABLES IN

EXPLAINING THE VARIANCE OF REPTILE COMMUNITY COMPOSITION IN THE

WIMMERA REGION ...... 235

APPENDIX 10. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE SPATIAL

MATRIX (SPACE) TO THE VARIATION IN THE REPTILE COMMUNITY COMPOSITION

MATRIX IN THE WIMMERA REGION ...... 236

APPENDIX 11. LIST OF BEETLE SPECIES RECORDED AND THEIR LIFE HISTORIES ...... 237

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APPENDIX 12. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND SPATIAL VARIABLES

IN EXPLAINING THE VARIANCE OF BEETLE COMMUNITY COMPOSITION IN THE

BENALLA REGION...... 242

APPENDIX 13. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE SPATIAL

MATRIX (SPACE) TO THE VARIATION IN THE BEETLE COMMUNITY COMPOSITION

MATRIX IN THE BENALLA REGION ...... 243

APPENDIX 14. PERCENTAGE INFLUENCE OF ENVIRONMENTAL AND SPATIAL VARIABLES

IN EXPLAINING THE VARIANCE OF BEETLE COMMUNITY COMPOSITION IN THE

WIMMERA REGION ...... 244

APPENDIX 15. THE CONTRIBUTION OF THE ENVIRONMENTAL (ENV) AND THE SPATIAL

MATRIX (SPACE) TO THE VARIATION IN THE BEETLE COMMUNITY COMPOSITION

MATRIX IN THE WIMMERA REGION ...... 245

APPENDIX 16. PLAIN LANGUAGE STATEMENT AND QUESTIONNAIRE SENT TO

LANDHOLDERS IN THE WIMMERA AND BENALLA REGIONS ...... 246

APPENDIX 17. WINBUGS CODE TO PREDICT LANDHOLDERS WHO HAD PREVIOUSLY

REVEGETATED OR WOULD REVEGETATE IN THE FUTURE...... 254

APPENDIX 18. WINBUGS CODE TO DETERMINE LANDHOLDERS INTENTION TO MANAGE

REVEGETATED/REMNANT AREAS AS A FUNCTION OF THEIR ATTITUDES TOWARDS

THESE AREAS...... 255

APPENDIX 19. COST CALCULATIONS FOR REVEGETATION MANAGEMENT ACTIONS

BASED ON PREVIOUS REVEGETATION AND RESTORATION PROJECTS...... 256

APPENDIX 20. THE EXPERT ONLY MODEL COMPARED WITH THE COMBINED EXPERT AND

FIELD DATA MODEL WITH CONFIDENCE LEVELS OF 1 - 50 ...... 257

APPENDIX 21. REPTILE SPECIES RICHNESS INCREASE AS A RESULT OF NON-OPTIMAL

MANAGEMENT ACTIONS (COMPARED TO THE NO-MANAGEMENT SCENARIO) AND

THE COST-EFFECTIVENESS OF THOSE ACTIONS...... 258

APPENDIX 22. BEETLE SPECIES RICHNESS INCREASE AS A RESULT OF NON-OPTIMAL

MANAGEMENT ACTIONS (COMPARED TO THE NO-MANAGEMENT SCENARIO) AND

THE COST-EFFECTIVENESS OF THOSE ACTIONS...... 259

Chapter 1. Introduction

Background

Ecological and social scientists have developed an array of theoretical approaches that help explain processes occurring in their field of interest. In ecology, many theories have focused on processes involved in habitat loss and habitat fragmentation, which have been blamed for causing much of the biodiversity loss we see throughout the world today (Harrison and Bruna, 1999; Donald and Evans, 2006; Collinge, 2009). In the social sciences, many theories have focused on how and why people make decisions, for example, what factors motivate people to undertake conservation actions (Ajzen, 1981; Stern and Dietz, 1994). My thesis used theories developed from ecological and social science to gain an understanding of the factors responsible for biodiversity loss occurring throughout the world, and how to reduce the rate of loss in agricultural landscapes (Abensperg-Traun et al., 2004; Lindenmayer, 2009). I chose to cover these two disciplines because natural resource problems, such as habitat loss and fragmentation, and the resulting loss of ecosystem function and biodiversity are underpinned by these interlinking factors of ecological and social systems (Allison and Hobbs, 2004; Olsson et al., 2006). I will briefly explain some of the theories that provide the framework for my thesis.

Ecological processes

The equilibrium theory of island biogeography proposed by MacArthur and Wilson (1967), predicted that, as the area of natural habitat decreased, there would be an ordered sequence of species that disappeared from assemblages, and that those species that were area-sensitive would disappear first. The theory also predicted that species richness would increase with island size and decrease with distance to the mainland as a consequence of the effects of these two variables on rates of colonisation and extinction (MacArthur and Wilson, 1967). Populations persisting on islands or isolated remnants are expected to be structured into local assemblages of breeding populations, or metapopulations (Burkey, 1995; Hanski and Simberloff, 1997). Metapopulations are thought to be prone to extinction, but maintained by recolonisation from other populations (Hanski, 1999). Driscoll (2007) points out that dispersal from 2 large populations that are unlikely to become extinct, to small populations prone to extinction, is equivalent to source-sink dynamics described by Pulliam (1988). While the theories of island biogeography and metapopulations do not account for species interactions (Harrison and Bruna, 1999), or account for species interactions via predation (Taylor, 1990), metacommunity theory considers communities located in isolated remnants that are linked by the dispersal of multiple species (Leibold et al., 2004). It is thought that metacommunity theory may be a more effective way of understanding species distribution in fragmented landscapes as it can better account for species‟ interactions (Driscoll, 2007).

The spatial structuring of animal populations may also be dependent on the degree of habitat loss and fragmentation in the landscape, and the abilities of animals to use fragmented areas (McIntyre and Barrett, 1992; Radford et al., 2005). McIntyre and Hobbs (1999) suggest that landscapes can be broken up into four categories: intact, containing more than 90% of the original native vegetation; variegated, containing 60 - 90% of the original native vegetation; fragmented, containing 10 - 60% of the original native vegetation; and relictual, with 10% or less of the original native vegetation remaining. In agricultural landscapes, natural habitats exist to varying degrees between the two extremes of remnant habitat and cleared mosaic, with many agricultural areas being variegated, fragmented or relictual (McIntyre and Barrett, 1992). Some faunal species can use these variegated habitats, depending on their level of tolerance to habitat modification and disturbance and their ability to move and disperse through the mosaic of agricultural land (McIntyre and Barrett, 1992). In agricultural areas this habitat disturbance can include livestock grazing, fertiliser application, cultivation, and weed spread (McIntyre and Hobbs, 1999).

In fragmented and variegated landscapes, wildlife corridors and stepping stones of habitat have been proposed as a way to link remnant areas together through the agricultural matrix (Soulé and Gilpin, 1991; Lindenmayer and Nix, 1993; Donald and Evans, 2006). However, there is much debate about the beneficial or detrimental effects of wildlife corridors such as linear strips on animal and

3 plant species‟ richness and abundance (Simberloff and Cox, 1987; Saunders and Hobbs, 1991; Simberloff et al., 1992). There is also debate about whether it is better to connect areas together with wildlife corridors, or to enlarge existing patches of remnant habitat (Simberloff and Cox, 1987; Tewksbury et al., 2002; Bailey, 2007). While the enlargement of remnant areas is underpinned by the theory of island biogeography, which suggests that larger habitats are more beneficial than smaller habitats because they support a higher diversity of organisms, there has been much dispute about which management actions are most effective in maintaining native animal communities (Simberloff et al., 1992; Bennett, 2003; Bennett et al., 2006a). In addition, there are few studies that show that for maintaining community composition in fragmented habitats it is better to enlarge remnant areas than to connect remnants (Simberloff et al., 1992; Beier and Noss, 1998; Donald and Evans, 2006).

Within these fragmented landscapes, animal and plant communities will respond differently to patch size, isolation, and environmental and habitat variables, with some communities persisting unaltered while others become altered as a result of species loss and gain (Bentley and Catterall, 1997; Fischer et al., 2004; Lassau et al., 2005). With regard to the development of vegetation communities, the landscape continuum concept considers that variations in vegetation community composition are a result of the dominant environmental gradients acting at a site (Austin, 1985) or across many sites (Fischer and Lindenmayer, 2006). Niche theory in animal communities is similar to that of the continuum concept for plants (Austin, 1985). It explains the relative location of animal and plant species as a result of their habitat and food requirements and competition for those resources (Austin, 1985). Thus, multiple factors such as habitat shape and size, degree of connectivity, and environmental and habitat variables play a major role in determining where animal and plant communities are found and how likely they are to persist.

Social processes

The theory of planned behaviour is one model that helps to identify factors important in motivating landholders to undertake conservation initiatives (Ajzen, 1981; Ajzen, 1985; Fielding et al., 2005). The theory tries to explain and measure a person‟s behaviour due to motivational factors and their intention to undertake certain actions; the stronger the intention, the more likely the behaviour (Ajzen, 1981). People behave in accordance with their beliefs, which are a result of experience, fact or possibly misconceptions (Beedell and Rehman, 1999). Ajzen (1985) stated that there were three types of beliefs; behavioural, normative and control, and that these predict a person‟s behavioural intentions (Figure 1.1). Behavioural beliefs involves an individual thinking that their actions will have a certain consequence or outcome. If the outcome is positive, they will be more likely to undertake that action in the future. A normative belief is a person‟s perception that they should undertake certain behaviours because of other people‟s beliefs or judgements. Control beliefs are a person‟s perception that certain factors will impede or facilitate their actions; for example, that providing funding will increase their ability to revegetate (Ajzen, 1985; Beedell and Rehman, 1999).

Behavioural Behavioural beliefs attitudes

Normative Subjective norm Intention Behaviour beliefs

Perceived behavioural Control beliefs control

Figure 1.1. The theory of planned behaviour, showing how behavioural, normative and control beliefs influence a person‟s behavioural intentions (Ajzen, 1981; Ajzen, 1985).

Another theory related to the likelihood of people undertaking conservation actions is their connectedness to nature, or the extent to which they feel a part of nature (Schultz, 2001). The degree to which someone feels connected to 5 nature is thought to shape their environmental attitudes and tendency to act in a environmentally responsible way (Schultz et al., 2004). This theory builds on the value-basis theory for environmental attitudes developed by Stern and Dietz (1994), which states that a person‟s attitude towards environmental issues is based upon how they value themselves, other people and the natural environment. These environmental attitudes are said to be aligned with three values: egoistic, in which the individual puts their wellbeing above others and nature; altruistic, in which a person judges an event according to how it will affect others; and biospheric, in which a person judges an event according to how it will affect non-human living things (Stern and Dietz, 1994; Stern et al., 1995). Ultimately, those people who have a connected/biospheric view of nature should be more likely to be environmentally responsible and undertake actions that benefit the natural environment (Mayer and Frantz, 2004). Using these theories it is possible to develop an understanding of why members of the community (in the case of this study, agricultural landholders) are more or less likely to undertake conservation actions. These theories can also help identify what incentives may make landholders more likely to participate in conservation activities.

Chapter outline

I investigated the benefits of revegetating native habitat for maintaining reptile and beetle species richness and abundance in agricultural landscapes of south- eastern Australia. I also surveyed landholders from the same regions to determine their degree of adoption of revegetation activities and their opinions regarding the benefits, or otherwise, of revegetated land and remnant vegetation. In order to better understand the interplay between uncertainty and decision making, I used Bayesian Networks to combine, describe, and quantify ecological and social data with expert opinion to highlight the importance of habitat structure and landholder decisions on reptile and beetle species richness. Below is an outline of the research I undertook for this study:

Chapter 2 focuses on our current knowledge of the effectiveness of revegetating areas of land for faunal conservation. I reviewed research undertaken throughout the world on a variety of faunal groups and vegetation types. I provide an outline of the factors that influence revegetation effectiveness for animal species, identify some of the gaps in our current knowledge about revegetating areas of land; and suggest future research priorities. This chapter also presents ways to increase animal species richness, abundance, and community composition in revegetated areas.

In Chapter 3 and 4, I describe research findings on animals located in agricultural landscapes of the Wimmera and Benalla regions of south-eastern Australia. Chapter 3 focuses on reptiles, and Chapter 4 focuses on beetles. In these two chapters I compare reptile and beetle species richness and abundance in revegetated linear strips with those in remnant and cleared linear strips, and remnant patches. I also compare reptile and beetle species‟ richness and abundance in revegetated patches of habitat with adjacent, remnant patches. In the reptile study, I show how distance along linear strips away from remnant patches influences reptile species richness and abundance. My research also describes how habitat and spatial variables influence reptile and beetle community composition, and I explore the results of other studies on reptiles and beetles and describe how these compare to my study findings.

In Chapter 5, I explore how landholder attitudes and management decisions - gathered using postal questionnaires sent to landholders in the Wimmera and Benalla regions - affect revegetation activities and remnant areas on private land. This information provides insights into what types of landholders are undertaking revegetation, what they are replanting and where. These revegetation activities have implications for the movement, dispersal, and survival of faunal communities in these revegetated habitats. The chapter also shows the extent to which revegetation activities can be enhanced on private land through the provision of incentives and extension activities.

Finally, in Chapter 6, I combine the ecological and social studies in a decision theory framework with the assistance of Bayesian Networks. This was done to highlight how natural resource management decisions made as a result of integrating ecological and social data can lead to effective management actions for revegetated and remnant areas. Bayesian Networks is an analysis tool that allows conceptual models of ecological and social processes to be parameterised with expert opinion and field data to show the costs and benefits of management actions. This chapter is important because it provides an example of how ecological and social data can be used to reduce ecological uncertainties and optimise species gains for the lowest financial cost.

Overall, my study: (i) has an experimental design that examines linear strips compared with patches of habitat, and replanted compared with remnant and cleared areas, and also focuses on distance effects along linear strips; and (ii) integrates ecological and social data to provide realistic scenarios of how landholder demographics, uncertainty about biophysical processes, and management decisions influence habitat processes and biodiversity outcomes. My study adds to the body of knowledge of the best management strategies for revegetated areas for maintaining and increasing reptile and beetle populations. It may be used to assist management agencies to better involve landholders in revegetation activities and to identify incentives for encouraging landholders to undertake conservation based management of revegetated and remnant areas.

Chapter 2. Biodiversity benefits of restoring vegetation: A review

Introduction

Land clearing, habitat fragmentation, and landscape degradation are major factors causing the loss of biodiversity throughout the world (Abensperg-Traun et al., 2004; Lindenmayer, 2009). Habitat loss and fragmentation are landscape- scale processes of habitat modification that leave behind small, isolated native vegetation pockets in a landscape of agricultural, urban, or otherwise disturbed land (Wilcox and Murphy, 1985; Burkey, 1995; Fahrig, 2003). Globally, 36% of the Earth‟s primary forests remain due to these processes (FAO, 2010) but over a third of this area is currently under threat (UNEP, 1999). Land conversion for agriculture and plantations is among the most significant reasons for forest loss in the world (Lindenmayer, 2009). Agriculture alone accounts for nearly 20% of the modification of the Earth‟s forested land, and is the single largest cause of tropical deforestation (UNEP, 1999). In order to maintain biodiversity in highly fragmented landscapes such as agricultural areas, researchers suggest that extensive revegetation of the landscape is necessary (Ryan, 2000, Freudenberger and Brooker, 2004, Vesk and Mac Nally, 2006). In the following review I will evaluate the effectiveness of revegetation for biodiversity conservation strategies throughout the world.

The Society of Ecological Restoration International (2004) defines ecological restoration as “…the process of assisting the recovery of an ecosystem that has been degraded, damaged, or destroyed” (Society for Ecological Restoration, 2004; Clewell et al., 2005). In this review I refer to revegetation as the replanting of native plants by manual or mechanical means. Revegetation can happen on a range of scales (from hectares to square kilometres) and involves the replanting of vegetation to make core areas larger and/or to provide linear strips or stepping stones of habitat with the goal of providing for animal movement and dispersal (Vesk and Mac Nally, 2006). Space, time and available funds usually constrain the extent and quality of revegetation in a given landscape (Merriam and Saunders, 1993).

Revegetation has already been undertaken in many countries for a number of different reasons, such as erosion control, the production of plantation timber 10 and to maintain biodiversity (Hobbs, 2003; Walker et al., 2004; Cunningham et al., 2007). More recently, revegetation has been promoted as a way to allow native species to move between habitats to avoid biodiversity loss due to climate change (Opdam and Wascher, 2004; Bennett et al., 2006a; Hodgson et al., 2009). Seven percent of the worlds forest areas is now comprised of replanted habitat and most of this is in China, India, the United States of America (USA) and Vietnam (FAO, 2010). For example, Vietnam has recently started to revegetate forests that were destroyed by war and shifting cultivation in order to stop erosion, protect watersheds and gain an income from forest products (Byrne, 2008). In South Africa, the Working-for-Water programme has employed over 20,000 people to remove invasive plant species and undertake landscape restoration for poverty reduction purposes (Aronson et al., 2006; Neely, 2010). In south-eastern and Western Australia revegetation has been occurring since the 1980s, initially as a means to stop salinity and soil erosion on agricultural land, but more recently to maintain biodiversity in these degraded landscapes (Hobbs, 1993; Barrett et al., 2008; Munro et al., 2009). It may be useful to understand the different motivations for undertaking revegetation because these motivations may impact the biodiversity benefits that revegetated areas provide.

The effectiveness of revegetation as a management strategy to provide habitat for native animals globally has not previously been reviewed. Where regional reviews have been undertaken, they suggest that revegetation can increase faunal species richness and abundance (Munro et al., 2007). However, they also suggest that faunal community composition in revegetated areas is not representative of that found in remnant areas, at least in the first few decades of growth (Kimber et al., 1999; Ryan, 2000; Munro et al., 2007; Munro et al., 2009; Munro et al., 2011). There is also a lack of understanding of how revegetation influences different faunal groups, as previous research has focused primarily on the response of birds and mammals to revegetation. Only limited research has been undertaken on reptiles, amphibians and invertebrates such as beetles (Ruiz-Jaen and Aide, 2005; Grimbacher et al., 2007; Munro et al., 2007).

In order for management agencies to undertake effective revegetation they must have a good understanding of how to design revegetation activities. This understanding can be lacking due to limited data on the habitat requirements of many faunal groups (Duncan and Wintle, 2008; Rumpff et al., 2011). There is also a lack of knowledge on how much of the landscape needs to be forested in order to stop biodiversity loss in highly fragmented landscapes (Andren, 1994) and on the benefits of connectivity in sustaining biodiversity (Simberloff et al., 1992; Soulé et al., 2004; Lindenmayer et al., 2008a; Hodgson et al., 2009). This high level of uncertainty about ecological processes makes management decisions to strategically revegetate degraded areas difficult because benefits are difficult to estimate (Rumpff et al., 2011). However, without this information there may be a substantial expenditure of resources without the expected reduction in risk of species extinctions (Pollino et al., 2007; Duncan and Wintle, 2008; Rumpff et al., 2011).

In this review I ask how effective revegetation is in maintaining and increasing native animal communities that are persisting in fragmented areas. I review the global literature on revegetation of cleared land and focus on: (i) the drivers of revegetation, including the motivations, benefits and drawbacks of ecological revegetation, agroforestry and plantations, agricultural revegetation and mine site rehabilitation; (ii) the habitat and environmental variables that influence revegetation‟s effectiveness in maintaining and increasing faunal species richness; and (iii) the priority research gaps that need to be addressed. This information is vital in order to learn from current revegetation activities and reduce the uncertainty inherent in restoring ecological systems.

Methods

To undertake this review I used the ISI Web of Knowledge database (Thomson Reuters, 2011) to search for articles published prior to June 2010. The search topics included “revegetation”, “restoration”, “biodiversity”, “fauna”, “linkages”, and “connectivity”. I included only studies that focused on the effects of revegetation on native fauna. I found a total of 48 studies (Appendix 1) that

12 reported the effects of ecological plantings on native animals from Australia (17 listed in Appendix 1 and a further 10 reviewed by Munro et al., 2007), United States of America (USA) (11 studies), New Zealand (4 studies), Central America (2 studies), Asia (2 studies), Europe (1 study) and Africa (1 study) (Appendix 1). These papers were used as the basis for my review. I found that the most commonly studied faunal groups were birds (67%), with a smaller number investigating invertebrates (27%) or mammals (19%), with reptiles and amphibians being the least studied taxa (16%).

Drivers of revegetation

Ecological revegetation

Revegetation to maintain or increase biodiversity is usually undertaken in degraded landscapes and aims to accelerate the recovery of natural systems (Radford et al., 2007). While the overarching motivation for ecological restoration is to maintain or increase native plant and animal species richness, certain species or vegetation communities are usually chosen to provide evidence of revegetation success (Lindenmayer et al., 2002b). There are three basic theories on species protection and habitat restoration: (i) the framework- species approach; (ii) the structural-diversity approach; and (iii) the umbrella or focal-species approach.

The Framework-Species approach Revegetation to restore a structural framework of native vegetation aims to replant pioneer tree species that have an ability to propagate easily, grow quickly and be resilient to fire (Elliott and Kuaraksa, 2008). By restoring a structural framework, it is expected that native animals will be attracted, thereby bringing in other plant species and increasing plant diversity (Elliott and Kuaraksa, 2008). For example, a project in northern Thailand is using a framework tree species approach to restore areas previously used for shifting cultivation, to reduce erosion and increase biodiversity (Toktang and Elliott, 2005; Elliott and Kuaraksa, 2008). However, while native animals can increase the spread of native plants, they can also increase the spread of weeds, which

13 can invade and out-compete native species (Bartuszevige and Gorchov, 2006). Similarly, while fast growing tree species can provide initial habitat for some native animals such as birds, such rapidly developing forests may outcompete slower growing native plants and make the habitat unsuitable for animals that require these plants to persist (Twedt et al., 2002; Twedt et al., 2010).

The Structural-Diversity approach The second approach advises the replanting of a diverse array of native plant species that provide habitat and structural diversity to a variety of faunal species (Grimbacher and Catterall, 2007). This is distinct from the framework-species approach in that plantings do not rely upon fast-growing pioneer species, but rather the replanting of species that provide structural and floristic diversity (Grimbacher and Catterall, 2007). These habitats are thought to be colonised more rapidly by native animals than habitats with less diverse plantings, and are less likely to be invaded by weeds because native species already exist in the available niches (Grimbacher et al., 2007; Kavanagh et al., 2007; Saunders and Nicholls, 2008; Munro et al., 2009). However, structurally diverse plantings may be more difficult to establish, due to the diversity of plant species being replanted, and may also take a long time to mature, hampering the persistence of native animals requiring habitats such as tree hollows (Vesk and Mac Nally, 2006; Vesk et al., 2008).

The Umbrella and Focal-Species approach In order to conserve umbrella species, large tracts of habitat required by these species are protected or restored, and doing so protects other species that require similar habitats (Simberloff, 1998). Focal species on the other hand are sensitive to particular threats, and will only survive in areas where these threats are absent and where certain habitat attributes are present (Lambeck, 1997). Revegetation for the benefit of a focal species is distinct from the structural- diversity approach because it aims to restore habitat for specific species, not for a broad range of different species. Undertaking intensive management to conserve one species may not necessarily benefit other species. This is because management agencies often lack data on the habitat requirements of all the species present in a landscape, or on how the interactions of different 14 species influence species survival (Simberloff, 1998). Management of a single species is also expensive, so the expenditure of funds on one species can have a substantial opportunity cost if these funds are used inappropriately (Wilson et al., 2009). Lindenmayer et al. (2002b) also suggest that the focal species approach may be flawed because previous taxon-based surrogate schemes have not been successful in maintaining threatened species and because there may be a lack of data to guide the selection of focal species. However, other alternatives are seldom provided, and other approaches would also be subject to the same lack of data (Lambeck, 2002).

Overall, despite their limitations, these three types of ecological revegetation may be more successful in maintaining and increasing animal species richness and abundance than other types of revegetation mentioned below, because they result in habitat condition and plant species richness that is generally more similar to remnant areas (Grimbacher et al., 2007; Munro et al., 2009). This structural and floristic diversity can provide better quality habitat for a wider variety of animal species than simpler, more homogenous replantings (Kanowski et al., 2006; Grimbacher et al., 2007). However, ecological replantings may not attract identical animal communities to those in remnant areas due to differences in remnant vegetation structure (Kanowski et al., 2006; Munro et al., 2011).

Agroforestry and tree plantations

The motivation of revegetation for agroforestry or for tree plantations is generally to provide income to landowners, communities or corporations, while increasing forest area and reducing land degradation such as erosion (Paquette and Messier, 2010). Plantations can be composed of native and/or exotic species and can be undertaken on relatively small scales, such as revegetating a paddock, or over many hundreds or thousands of hectares (Fitzherbert et al., 2008). Plantations and agroforestry can restore areas previously used for grazing or shifting cultivation, providing local communities with an alternative source of income, while reducing erosion and increasing water quality (Bhatt et al., 2010; IUCN, 2010). Plantations also help to reduce reliance on wood 15 derived from remnant forests and can be important for carbon sequestration (Paquette and Messier, 2010).

Plantations can be beneficial in maintaining or increasing native animal species richness and abundance if they are established on areas that are already cleared, such as agricultural land (Kanowski et al., 2005; Brockerhoff et al., 2008). Studies also recognise that plantations will be more valuable to native animals if they are composed of a variety of native plant species and are structurally complex (Lindenmayer and Hobbs, 2004; Kanowski et al., 2005; Komar, 2006; Kavanagh et al., 2007; Najera and Simonetti, 2009; Proenca et al., 2010). Plantations also help to provide habitat to native animals if remnant areas are included within the plantation area, rather than cleared when plantations are established (Kanowski et al., 2006). Management actions that protect native animals in plantations, such as ensuring logging activities are appropriate, can also promote native animal persistence in these areas (Lindenmayer et al., 2009).

However, plantations do not support the same animal diversity as remnant areas or ecological plantings (Brockerhoff et al., 2008; Munro et al., 2009). Therefore, clearing native vegetation to establish plantations has well known detrimental effects on biodiversity (Kanowski et al., 2005; Brockerhoff et al., 2008; Felton et al., 2010). For example, in south-east Asia, introduced oil palm plantations are responsible for much of the current land clearing and biodiversity loss (Fitzherbert et al., 2008; Najera and Simonetti, 2009). While oil palms can be used as habitat by some native animals, they do not provide adequate habitat to maintain many of the native fauna that require diverse remnant areas (Rajaratnam et al., 2007; Najera and Simonetti, 2009).

Agricultural production

Agricultural areas are often revegetated with trees and shrubs to reduce water and wind erosion of bare soils, reduce salinity and provide shelter and windbreaks for livestock and crops, thereby increasing agricultural productivity (Bennett et al., 2000; Byrne, 2008; Smith, 2008). They can also help improve 16 water quality and quantity and provide forest products for communities (Byrne, 2008; Bhatt et al., 2010; IUCN, 2010). Agricultural revegetation can take a variety of forms, but in intensively farmed landscapes plantings are usually in the form of linear strips such as windrows, and sometimes as patches of habitat (Smith, 2008). Revegetated areas are usually composed of native trees and sometimes shrubs, but rarely contain specifically planted native grasses or other ground layers (Wilson et al., 1994; Smith, 2008). Revegetation is often undertaken on the poorest quality land so as not to reduce farm productivity (Bennett et al., 2000).

Agricultural revegetation can be beneficial for native animals if the plantings include native plants and are structurally diverse (Munro et al., 2007). Plantings that are in close proximity to remnant areas are also more likely to be valuable for native animal species than plantings that are isolated (Kanowski et al., 2006). However, the degree of fragmentation in agricultural landscapes may have detrimental effects on native animals by reducing movement and dispersal into replanted areas (Dorrough and Moxham, 2005; Duncan and Dorrough, 2009). Establishment of native plants can also be difficult, as agricultural landscapes are heavily modified and can have a greatly altered soil structure due to the impact from livestock and cultivation for cropping (Hobbs, 1993; Yates and Hobbs, 1997; Dorrough and Moxham, 2005). Weeds can also invade replanted areas, reducing habitat for native animals (Ries et al., 2001; Jellinek et al., 2004; Bateman et al., 2008).

Mine site rehabilitation

Mine site revegetation is undertaken primarily to stabilise the mined area or the mined solid waste, with the restoration of native flora and fauna habitat a secondary objective (Martinez-Ruiz and Fernandez-Santos, 2005). Mine site revegetation can be extremely variable; from the revegetation of mine tailings with seedlings and tube-stock, to the translocation of existing native vegetation to the mined area (Ross et al., 2000; Nichols and Grant, 2007). Depending on the type of mine or mining waste being restored, different revegetation methods are required to get the best restoration outcome (Bhatt and Soni, 1992; 17

Martinez-Ruiz and Fernandez-Santos, 2005; Zhang et al., 2005; Martinez-Ruiz et al., 2007). For example, the revegetation of a mined peat bog requires the replacement of islands of mined peat, which are seeded from the surrounding vegetation (Watts et al., 2008), whereas the replanting of a bauxite mine is a lot more labour intensive. It requires the recontouring of the mined pit, deep ripping to reduce compaction, replacement of overburden (rock and soil) and topsoil, and the direct seeding of over 70 native plant species (Nichols and Nichols, 2003; Nichols and Grant, 2007; Watts et al., 2008).

Mine site revegetation can also be quite different contextually to any other type of revegetation because usually the mine is surrounded by native vegetation, making restoration easier through natural regeneration and abundant nearby seed sources (Kimber et al., 1999; Passell, 2000; Martinez-Ruiz et al., 2007; Munro et al., 2007; Watts et al., 2008). Due to the surrounding vegetation, animals can recolonise mine sites quickly and easily (Munro et al., 2007). This can provide useful insights into how animals use revegetated areas when isolation and the fragmentation of the surrounding matrix are not confounding factors (Munro et al., 2007). Although a variety of native animals is able to recolonise revegetated mine sites (Munro et al., 2007; Nichols and Grant, 2007), animal species composition tends to take a longer time to recover, and may not be representative of remnant habitats until key habitat elements are restored (Nichols and Grant, 2007).

Variables influencing restoration effectiveness for biodiversity conservation

Connectivity

Connections link two or more natural areas together and are referred to as wildlife corridors, linear strips, habitat linkages, greenways, greenbelts and habitat networks (Hilty et al., 2006; Horskins et al., 2006). Broader areas of native vegetation linking larger natural areas together have been described as landscape linkages or continental-scale links (Soulé et al., 2004; Bennett et al.,

2006a). Connectivity can be divided into structural connectivity, which is the physical continuity of vegetation, and functional connectivity, which is a species response to structural connectivity and depends on the species mobility, habitat requirements and behaviour (Bennett et al., 2006b). Functional connectivity is important because it takes into account the structure of the surrounding matrix and its influence on the movement of the species of interest (FitzGibbon et al., 2007).

Functional connectivity

Functional connectivity allows animals to move and disperse from one area of habitat to another (Hobbs, 1993; Beier and Noss, 1998; Bailey, 2007). It is often provided by linear strips of vegetation that connect two or more patches of habitat together and can be beneficial to native animals in highly fragmented landscapes such as agricultural areas (Saunders and Hobbs, 1991, Merriam and Saunders, 1993, Hilty et al., 2006). Linear strips can allow animals and plants to emigrate to smaller populations, providing a rescue-effect in which species are rescued from going locally extinct (Brown and Kodric-Brown, 1977; Beier and Noss, 1998). Linear strips can also lessen fluctuations in population sizes caused by random demographic events, and stem inbreeding depression (Simberloff et al., 1992; Tewksbury et al., 2002).

Although there is some evidence demonstrating functional connectivity through remnant linear remnants, few studies have been able to show that revegetated linear strips benefit a wide variety of animals (Lindenmayer et al., 2007). There has been much debate over whether it is better to restore and enlarge existing patches of remnant vegetation or to use linear strips to connect remnants together in order to maintain and increase native animal species richness and abundance (Simberloff and Cox, 1987; Simberloff et al., 1992; Rosenberg et al., 1997; Tewksbury et al., 2002; Falcy and Estades, 2007; Hodgson et al., 2009). Some argue that there is not enough research available to justify the use of linkages over remnant enlargement (Simberloff and Cox, 1987, Simberloff et al., 1992, Lindenmayer and Nix, 1993, Beier and Noss, 1998, Bailey, 2007). For example, a review by Bailey (2007) undertaken in temperate European 19 countries found that there was a lack of firm empirical evidence that linear strips increased species diversity. Hodgson et al. (2009) argued that there is too much uncertainty inherent in linkages compared to more certain methods, such as protecting existing habitats.

Linkages are also seen as more expensive to create and maintain than enlargement of remnant patches (Simberloff and Cox, 1987; Tewksbury et al., 2002). Other criticisms of linkage are that they can increase the influence of humans on ecosystems through hunting and impacts from domesticated animals (Simberloff et al., 1992); increase death rates of animals in linkages adjacent to roads due to poisoning and road-kill (Simberloff and Cox, 1987, Hess, 1994, Downes et al., 1997b); and act as conduits for disease and habitat for introduced species (Downes et al., 1997b, Brown et al., 2006). Even so, linear strips may be the only suitable configuration for revegetation where space limitations apply, such as in urban or agricultural areas (Rosenberg et al., 1997; Mendham et al., 2007). Some species, such as the Asian elephant, also require much larger home ranges than can be provided by habitat protection and enlargement, so linear strips may be the only viable alternative for these species to link their remaining habitats together (Sukumar, 1989; Leimgruber et al., 2003).

Stepping stones and matrix permeability

Stepping stones may also facilitate movement of some animals between remnant areas (Haas, 1995; Baum et al., 2004; Drielsma et al., 2007). Studies have found that small, isolated paddock trees can allow the movement of some bird species (Fischer and Lindenmayer, 2002), as well as provide habitat for a number of mammal species (Law et al., 2000). Paddock trees are very important habitat elements for this reason, but over time these isolated trees can die off (Bennett et al., 1994), increasing the size of crossing gaps between remnant areas (Gibbons and Boak, 2002) and reducing necessary habitat for birds and mammals (Law et al., 2000). This requires that paddock trees be replanted to maintain these crossings, although no studies have been undertaken on the effectiveness of revegetated paddock trees as stepping 20 stones. Stepping stones are not useful for all species, such as shrub-foraging birds (Fischer and Lindenmayer, 2002) and animals with limited dispersal ability (Driscoll, 2004). The ability of animals to move along stepping stones may also be dependent on the permeability of the surrounding matrix (Baum et al., 2004).

Matrix permeability is the degree to which animals can move through areas of the landscape that do not provide appropriate habitat (Castellon and Sieving, 2006). It is dependent on the amount of remnant vegetation in a landscape, as these areas can reduce crossing distance (Fischer and Lindenmayer, 2002; Dixo and Metzger, 2009). Other habitat elements such as water bodies can also increase habitat permeability for fauna such as waterbirds and amphibians (Parris, 2004; Parris and Lindenmayer, 2004; Richter-Boix et al., 2007). A species mobility also helps determine its ability to move across the matrix (Golet et al., 2009). However, little is known about the dispersal ability of many native animal species through unsuitable matrix, or to what degree animal species are able to use the habitat matrix (Prugh et al., 2008). For example, in agricultural landscapes it is expected that paddocks would hinder the movement of animals with low dispersal ability such as reptiles and flightless beetles, although many species show an ability to survive in the grazed and cultivated areas (Driscoll, 2004; Driscoll and Weir, 2005). Some bird species are also able to move across disturbed areas, while others require some habitat cover to enable them to cross the agricultural matrix (Watson et al., 2005; Robertson and Radford, 2009).

Overall, we lack knowledge of whether it is better to create connections between remnant areas or to enlarge existing remnants in order to maintain and increase native animal species. To inform these management decisions, there is a need to know how permeable the matrix is and the ability of different animal species to use, move, and disperse through the matrix. There is also a need to know what animals are most likely to benefit from the inclusion of linear strips and stepping stones, or whether remnant enlargement would provide the greatest species benefit. To do this studies will need to show that linear strips

21 and stepping stones provide a benefit to species mobility, and to what extent these structures may be detrimental to species survival.

Patch size

It is important to determine how large remnant areas and linear strips must be to ensure animal species are able to persist in them, and whether revegetated areas can be as effective as remnant areas in maintaining species diversity. The theory of island biogeography (MacArthur and Wilson, 1967) suggests that the size of remnant and revegetated patches will influence animal persistence in fragmented landscapes, and that larger patches usually contain more animal species than smaller patches (Kavanagh et al., 2007; Morrison et al., 2010). For example, larger patches of revegetated and remnant forest 5 - 20ha in size will support a greater species diversity of birds, including more specialised bird species, than smaller areas that will support more generalist species (Kavanagh et al., 2007; Prugh et al., 2008; Fink et al., 2009; Morrison et al., 2010). However, different animal species respond to habitat size in different ways. Prugh et al. (2008) reported that while bird and mammal species appeared to be negatively influenced by decreasing habitat area, other faunal groups such as reptiles and amphibians did not. This may be related to these species‟ diet and habitat requirements, as birds and mammals would need substantially larger areas to survive, and may have more specific dietary requirements than either reptiles or amphibians (Prugh et al., 2008).

Linear strip width and length

The width of linear strips also influences the ability of faunal species to persist in linear elements of the landscape (Falcy and Estades, 2007). Narrower linear strips are thought to support fewer animal species than wider ones, because narrower linear strips are unable to support the same habitat amount, type, and structure (Merriam and Saunders, 1993; Lindenmayer, 1994; Bennett, 2003). Narrower linear strips are also thought to be subject to greater edge effects (Collinge, 1996), as discussed below. Sieving et al. (2000) found that linear strip

22 width was the main factor influencing the presence of understorey birds in temperate rainforest surrounded by agricultural land. They determined that birds were not found in linear strips narrower than 10m wide, but were more common in areas 23-50m wide (Sieving et al., 2000). However, few studies have investigated the movement patterns of other species such as reptiles and amphibians (Carthew et al., 2009) and greater research is needed to determine the most favourable linear strip width for different species.

The length of linear strips is also likely to influence species differently, as faunal groups have varying dispersal abilities (Haddad, 2000). The habitat quality along linear strips may also change as distance from the remnant patch increases, altering species ability to travel along the strip (Means and Simberloff, 1987). Still other species may use linear strips for habitat, possibly stopping other species from moving through these areas altogether (Lindenmayer et al., 1994b). Overall, in order for animals to use linear strips, they must to be designed to allow the movement and dispersal of the species of conservation interest (Tewksbury et al., 2002; Chetkiewicz et al., 2006). This requires knowing the habitat requirements of the species of interest, and also designing the linear strip to suit the geography and site-specific variables of the study area (Chetkiewicz et al., 2006).

While there are many studies that examine animal use of linear strips, few studies provide adequate data for comparing configuration of remnant and revegetated areas (Bolger et al., 2001; Freeman et al., 2009; Eggers et al., 2010). In one study that did compare landscape configuration, Lindenmayer et al. (2007) showed that bird species richness was substantially higher at the intersections of revegetated linear strips (20-40m wide) and in revegetated patches (>70m wide) compared to isolated linear strips and connected linear strips that were more than 100m from remnant areas. This shows that while the width of linear strips may be important, such as those greater than 25m (Sieving et al., 2000; Lindenmayer et al., 2007) as well as the length, the ability of linear strips to connect remnants together is also an important factor. The lack of studies that show how different configurations of revegetated and remnant

23 areas influence animal species diversity highlights the need for research focused on this aspect of linear strip design.

Edge effects

Edge habitats - areas on the perimeter of natural habitats, and thus subject to greater environmental variation - may influence animal species persisting in revegetated and remnant areas, depending on the size and shape of these habitats (Debinski and Holt, 2000; Laurance, 2000). Habitat edges are subject to greater wind and temperature variation (Debinski and Holt, 2000; Laurance, 2000), lower humidity (Collinge, 1996) and higher rates of windthrow (Collinge, 1996; Laurance, 2000) than interior habitats. As linear strips tend to be narrower than patches, they may be more influenced by edge effects, thus having a more detrimental effect on the faunal species living in these areas (Eggers et al., 2010). For example, Williams-Linera et al. (1998) reported that in tropical rainforest fragments in Mexico, linear strips and unfenced isolated trees were more influenced by edge effects than remnant patches of habitat. Their research and a study by Ewers and Didham (2008) in New Zealand showed that edge effects had a negative impact on some beetle species, which were confined to habitat interiors, while other beetle species could persist in edge habitats. Other studies also suggest that while some species are negatively influenced by edge habitats, other species can benefit from these environments (Weiermans and van Aarde, 2003; Twedt et al., 2010).

Patch quality

Remnant habitats have been shown to contain a greater diversity and abundance of animal species when compared to revegetated habitats of different ages and configurations (Bolger et al., 2001; Kavanagh et al., 2007; Munro et al., 2007). This is because remnant habitats usually have greater floristic and structural diversity than revegetated areas, providing a greater diversity of microhabitats for animal species (Munro et al., 2009; Munro et al., 2011). However, many animal species are still able to use revegetated areas if

24 these areas contain the habitat elements they require to survive (Bolger et al., 2001). In tropical north-eastern Australia, Freeman et al. (2009) and Jansen (2005) showed that specialist bird species reliant on rainforest remnants rarely used revegetated areas, whereas more generalist bird species were able to use revegetated areas as much as remnant areas. However, even in relatively old revegetated sites (20+ years), animal community composition still does not reflect remnant habitats because habitat structure is not complex enough (Nichols and Grant, 2007; Munro et al., 2011).

Larger remnant patches of native vegetation therefore tend to provide greater habitat for more animal species than revegetated areas. While animals that are habitat generalists are able to use revegetated areas, revegetation may not provide the same habitat elements as remnant areas, confining species reliant on specialised habitats to remnant vegetation (Munro et al., 2011). Linear strips are likely to provide better quality habitat if they are wider (>25m) and therefore less influenced by edge effects (Brudvig et al., 2009). However, some animal species can persist better in habitats influenced by edge environments (Twedt et al., 2010), so the width of linear strips and size of habitat patches may need to be designed to cater for those species most sensitive to habitat edges. This requires knowledge on all taxonomic groups, including reptiles, amphibians, and invertebrates, and how they respond to habitats of different shapes and sizes.

Vegetation type and time-lags to vegetation maturation

As mentioned above, habitat structure and floristic composition strongly influence an animal‟s ability to use revegetated areas (Lindenmayer, 1994; Nelson and Andersen, 1999; Barrett et al., 2008; Lomov et al., 2009; Lindenmayer et al., 2010). The habitat structure, and to a lesser degree vegetation type, is likely to change as the replanted vegetation ages (Vesk et al., 2008). Time-lags to the maturation of replanted areas are dependent on factors such as climate, rainfall and soil type, so plants will take longer to mature in temperate areas with infertile soils and intermittent rainfall than in tropical areas where rainfall is high (Morton et al., 1995; MacNally, 2006; Elliott

25 and Kuaraksa, 2008). As the vegetation structure of a replanted area changes over time, so will faunal diversity, meaning that species composition in revegetated areas will become more similar to remnant habitats as areas age (Reay and Norton, 1999; Cunningham et al., 2007; Grimbacher et al., 2007; Nichols and Grant, 2007; Barrett et al., 2008; Watts et al., 2008; Piper et al., 2009; Gibb and Cunningham, 2010). For example, a study by Gardali et al. (2006) in the western USA found that 11 out of 20 bird species increased in abundance in aging revegetated riparian areas. Other important habitat factors such as the cover of leaf litter and fallen timber are also likely to be greater in older sites (Barrett et al., 2008; Piper et al., 2009; Selwood et al., 2009; Lindenmayer et al., 2010). Similarly, Watts and Gibbs (2002) reported that in revegetated coastal sites on the north island of New Zealand, beetle species‟ richness and abundance increased as vegetation aged, due to changes in canopy height, tree density and habitat heterogeneity.

Proximity to remnant areas

Even though revegetated areas may take a long time to develop structural and floristic diversity, having remnant habitats in close proximity to revegetated areas may help to maintain native species. This is because remnants could be able to maintain faunal diversity until the habitats in replanted areas become more suitable (Huxel and Hastings, 1999; Kavanagh et al., 2007; Lindenmayer et al., 2010). However, some faunal species may not be able to persist in fragmented landscapes if the habitats they require, such as old trees, die and fall over before replanted trees mature (Vesk et al., 2008). For example, in a study of birds using revegetated riparian habitats in western USA, Anderson et al. (1989) found that although most birds returned within two years of replanting, those birds that were large and required hollows to nest in would only return when sufficient habitat was available. Structural elements such as hollows, may take as long as 100 years to develop in some landscapes (MacNally, 2006). Thus, it may be important for revegetated areas to be located near to remnant patches, as without these older structural elements, faunal species that require these habitats are unlikely to persist.

Ground layer attributes

Whilst the revegetation of trees and shrubs can be beneficial to some faunal species, it may not aid in the restoration of ground layers such as native herbs, grasses, fallen timber and logs, which are necessary habitat for ground dwelling faunal groups (Patten, 1997; Queheillalt and Morrison, 2006; Cunningham et al., 2008; Munro et al., 2009; Nichols et al., 2010). Previously, the assumption has been that if trees and shrubs were replaced, then other habitat attributes such as native grasses would follow, but in eastern Australia Nichols et al. (2010) reported that the opposite was true. They found that ground layers became colonised by weed species after revegetation, rather than by native grasses (Nichols et al., 2010). Weeds often thrive in disturbed soils where soil nutrients have been altered, and the removal or addition of nutrients is often required to get soils back to more natural levels and to allow the establishment of native grasses (Prober et al., 2002; Prober et al., 2005). Weeds can also alter soil nutrients, and even after weed removal soil nutrient levels may take several years to return to pre-invasion levels (Marchante et al., 2009). Thus, the replanting of native plants is often not enough to ensure the establishment of native ground layers, so weed control and the balancing of soil nutrients back to natural levels is often required before revegetation can occur. Weed cover can also negatively influence native animal species, firstly by reducing the native vegetation elements that native animals require (Jellinek et al., 2004; Bateman et al., 2008; Munro et al., 2011), and secondly by benefiting introduced animals, which can compete with or prey on native animals (Barrett et al., 2008; Arthur et al., 2010).

Microhabitat variables

The effectiveness of revegetated areas in increasing native animal species richness and abundance can be influenced by the location of the revegetated area, and the microhabitat variables present in the revegetated site (Patten, 1997; Munro et al., 2011). For example, revegetated riparian habitats can contain a greater diversity of native animals because they are more structurally complex than revegetated hillsides (Munro et al., 2011). Seasonal and temporal variation in plant resources, as well as animal use of revegetated areas over 27 different time periods, can also influence revegetation effectiveness (Brunton and Stamp, 2007; Golet et al., 2009). Brunton and Stamp (2007) showed that in New Zealand a native bird species had substantially lower abundance in revegetated habitats compared to remnant areas during the breeding season, possibly as a result of juvenile mortality, competition from other birds or a lack of nesting sites. To maximise native animal diversity, it may therefore be necessary to revegetate locations that contain a variety of microhabitats, and which can provide a variety of resources over different seasons.

Revegetated areas can benefit native animals, but it may take a long time for them to become structurally diverse enough to support some animals. The proximity of remnant areas and the inclusion of old trees within or near replanted areas may help to rectify these problems. Revegetated areas also need to include all habitat attributes, including native grasses and herbs to maintain faunal species reliant on these variables. This may require the alteration of soil nutrients to more natural levels to assist the recolonisation of native species and the removal of weed species that can alter the nutrient dynamics in the soil. While we recognise that animal and plant species richness and abundance vary over seasonal and temporal time scales, our knowledge of how revegetation alters over time, and how this influences animal species, is lacking.

Remnant vegetation in the landscape

There appears to be a critical threshold at which the amount of remnant vegetation remaining in a landscape affects the abundance and diversity of some faunal groups (Andren, 1994; Radford et al., 2005). A review by Andren (1994) on birds and mammals reported that the total size of a habitat would be of greater value than its spatial arrangement in landscapes where more than 30% of the habitat remained. In habitats where less than 30% of the original habitat remained, patch size and patch isolation would have the greatest negative impact on the remaining species. However, these threshold limits are likely to differ in different landscapes (Watson et al., 2005). For example, for

28 birds in agricultural landscapes in south-eastern Australia, Radford et al. (2005) suggested that the threshold limit was 10% native vegetation cover, beyond which he reported a sharp decline in bird species richness. Zuckerberg and Porter (2010) found the threshold limit for bird species decline in the eastern USA was 61% forest cover, but showed that these limits were species-specific, allowing some species to persist in highly cleared landscapes. However, the study by Zuckerberg and Porter (2010) reported that the threshold limit for birds to colonise a landscape was only 44% forest cover, suggesting that birds are able to colonise landscapes which have low forest cover, but may not be able to persist in these areas. The number of patches to be restored and the spatial configuration of the patches are important factors to take into account when revegetating fragmented landscapes, as is knowing the threshold limits of different faunal species (Castellon and Sieving, 2006; Cunningham et al., 2008).

Climate change

In the face of a changing climate many animal species will need to alter their distribution in order to adapt and survive. To allow species movement and dispersal, revegetation of habitat patches and functional connectivity between patches will prove vital in highly fragmented human-dominated landscapes (Opdam and Wascher, 2004; Bennett et al., 2006a; Hodgson et al., 2009). However, as mentioned above there are few studies that show definitively that linear strips and other landscape linkages are beneficial for species movement and dispersal. If species were able to move uninhibited across the landscape, it has been estimated that the average movement for a wide variety of plants and animals to keep up with their climate envelope will need to be six kilometres per decade (Parmesan and Yohe, 2003; Parmesan, 2007). Yet, we also lack the knowledge of how far individual animal species will need to move in order to find appropriate habitat, as this will vary for different groups of species and in different landscapes (Parmesan, 2007).

The interaction of climate change with other stressors such as habitat loss and habitat fragmentation will add an extra degree of complexity to the task of

29 managing biodiversity loss (Steffen et al., 2009). For example, bird species are expected to decline in habitat fragments under climate change due to a shortage of food resources, requiring patch quality to be enhanced and more fertile areas to be restored in order to maintain breeding birds (MacNally et al., 2009). This will require agencies and landholders responsible for restoring landscapes to rethink how they currently manage remnant areas, and how and where to revegetate in the future (Steffen et al., 2009). This will be made more difficult as the geographic ranges of plants that are currently used in revegetation projects change as climate varies, requiring agencies to identify the future climatic range of species they can use for revegetation (Vesk and Mac Nally, 2006). However, the relocation of plants as well as animals to different habitats is highly controversial as it can have unintended and detrimental consequences on the local flora and fauna of the new habitat (Ricciardi and Simberloff, 2009a). This is because conservation biologists often do not know enough about the impacts of the species they plan to move (Ricciardi and Simberloff, 2009a; Ricciardi and Simberloff, 2009b). Even though restoring natural areas and moving plants and animals to new areas are complex issues, organisms may not be able to adapt quickly enough to a changing climate so human intervention may eventually be necessary.

Species’ interactions

Species‟ interactions can influence restoration efforts, especially in linear strips where narrower habitats may increase competition and predation between species (Lindenmayer et al., 1993; Lindenmayer et al., 1994b). For example, individuals inhabiting linear strips may be displaced aged animals, socially excluded individuals or a sex-biased cohort that limit other individuals using these areas (Soulé and Gilpin, 1991). Although some studies suggest that linear strips do change species‟ interactions, such as increasing the abundance of male and decreasing that of female Antechinus stuartii in south-eastern Australia (Downes et al., 1997a), other studies suggest characteristics such as age, sex and weight of animals do not differ in linear strips (Bolger et al., 2001; Horskins et al., 2006). This lack of definitive research into whether revegetation

30 efforts increase animal interactions in already stressed environments is a key research gap that needs greater attention.

Revegetated areas such as linear strips may also increase the numbers of introduced animals. Downes et al. (1997b) reported that introduced rodents excluded native rodents from remnant linear strips through increased competition for food and potentially increased predation. Rodents have also been recognised as predators of birds‟ nests, especially in edge habitats (Lahti, 2009), and other authors suggest that predation rates may be greatest in edge environments (Burkey, 1993; Hoover et al., 2006; Weldon, 2006). However, some authors disagree that predation rates are greater as a result of habitat edges (Rodriguez et al., 2001; Lahti, 2009). It is unclear if predation rates are greater in habitats due to edge effects (Paton, 1994; Lahti, 2001), or if predation rates are higher due to the degree of habitat fragmentation in the landscape (Lahti, 2009). Other introduced predators such as foxes in Australia are also known to inhabit revegetated areas and possibly to benefit from revegetation as it enables them to access previously cleared habitat (Arthur et al., 2010). An analysis by Prugh et al. (2008) also suggested that carnivorous native animals are influenced by habitat area. This may suggest that while native carnivores decline in smaller habitats, introduced carnivores could fill these niches, causing further declines in native animal diversity, although further research is needed to confirm this.

Overall, species interaction may be greater in revegetated areas through competition and predation; although studies to show whether this is detrimental to native faunal species are lacking. Introduced animals may benefit from revegetation, causing declines in native animal species through predation and competition. However, even if introduced animals do benefit from revegetation, the benefit revegetation provides for native species may outweigh the negative impacts of these exotic species. Greater research is required into how edge environments and the degree of habitat fragmentation influences native animals.

Strategic planning and funding restoration activities

Historically, habitat revegetation has occurred on a small scale and largely haphazardly without a great deal of strategic planning or scientific justification (Giardina et al., 2007; MacNally et al., 2010; Twedt et al., 2010). Although restoration science is starting to evolve and restoration efforts have become larger in scale and better designed, there are still many limitations to effective restoration (Giardina et al., 2007). For example, in Australia, previous and current revegetation has led to many scattered small plantings and linear strips that do not link remnant areas together (Vesk and Mac Nally, 2006). Plants have also been replanted in much higher densities than were historically recorded (Vesk and Mac Nally, 2006) and native grasslands have been replanted with trees and shrubs (Morcom and Westbrooke, 1998).

In order to make restoration activities more strategic and therefore more effective for animal species, there is a need to understand the interlinking factors that influence revegetation success, such as ecological, social and political processes, and the multiple objectives of these different factors (Possingham, 2001). Such a decision-making framework involves clear decision-making tools and performance measures that demonstrate the success of management actions (Possingham, 2001; Keeney, 2002). Many organisations and governments do not have the time or resources available to undertake the long term ecological assessments necessary to measure restoration performance (Ruiz-Jaen and Aide, 2005). Thus, the integration of research bodies such as universities with agencies responsible for revegetation activities is required to ensure appropriate monitoring of revegetation sites. Management agencies also require the involvement and cooperation of local communities and private landholders (Bennett et al., 2006b; Berkes, 2007). Without the assistance of these groups, it is unlikely that conservation initiatives will ever be able to implement biodiversity programs on enough land to preserve all ecosystems (Simberloff and Cox, 1987). These integrated approaches involving research bodies, management agencies, local communities and landholders are likely to provide better conservation outcomes than decisions made by management agencies alone. 32

Priority research

This review strongly indicates that more research is needed into the effectiveness of revegetation for native animals, and that revegetation must be implemented in a strategic way in order to fully understand the benefits and drawbacks of revegetation for biodiversity. Below I outline the research gaps mentioned throughout the review and some of the studies needed to resolve these knowledge gaps, along with some of the literature that has contributed to our current knowledge (Table 2.1).

Table 2.1. Current gaps in our knowledge of the value of revegetated areas for faunal conservation. Column one lists our knowledge gaps and references that in part address the research questions raised. Column two sets out future research questions.

Research gaps Research questions Effectiveness of linear Do remnant and revegetated linear strips aid animal strips and plant movement and dispersal between remnants? (Merriam and Saunders, Does linear strip width and length influence species 1993; Sieving et al., 2000; movement, dispersal, and interactions? Tewksbury et al., 2002; Does structural connectivity influence metapopulations Driscoll, 2007) in fragmented landscapes? Effectiveness of remnant Are remnants containing revegetated areas more enlargement effective in maintaining animals than remnants alone? (Bolger et al., 2001; Is there an optimal size of remnant areas for Freeman et al., 2009) maintaining species in different landscapes? Value of revegetated Is the revegetation of individual paddock trees effective individual trees in reducing crossing gaps between remnant habitats? (Haas, 1995; Law et al., Do revegetated paddock trees provide habitat for 2000; Fischer and native birds and mammals? Lindenmayer, 2002) Influence of vegetation What is the best possible configuration of revegetated configuration on animals areas (number and type in a given landscape) for (Lindenmayer et al., 2007) maintaining native animals? Influence of edge Are edge effects more evident in linear strips than in environment on animals patches? (Weiermans and van How far do edge effects penetrate revegetated linear Aarde, 2003; Ewers and strips and patches? Didham, 2008; Twedt et What is the effect of edge habitat on native animals? al., 2010) Seasonal and temporal What effect does environmental stochasticity have on influence of research animal populations, and what does it tell us about the (Brunton and Stamp, resources provided by revegetation compared with 2007) remnants?

Vegetation type and Do animal communities differ in areas replanted with structure influence on trees and shrubs compared to those that also include native animals ground layers? (Driscoll, 2004; Jellinek et How do habitat attributes influence animals and how do al., 2004; Driscoll and these elements differ in replanted and remnant areas? Weir, 2005; Vesk and Mac Do introduced animals and plants have a greater Nally, 2006; Vesk et al., negative impact on native animals in revegetated areas 2008; Nichols et al., 2010) than in remnants? At what rate do native fauna colonise revegetation as these areas mature over time? Role of remnant How does distance to the nearest remnant influence vegetation and landscape native animals in revegetated habitats? context (Huxel and How does species‟ dispersal ability influence their use Hastings, 1999; of the habitat matrix and movement between Cunningham et al., 2008; remnants? Prugh et al., 2008; Golet What are the threshold limits of faunal groups with et al., 2009) respect to the amount of remnant habitat remaining in the landscape? Species‟ interactions, How do predator-prey dynamics and competition dispersal, and edge between native and exotic animal species influence effects native animal use of, or dispersal through, revegetated (Kotanen, 1997; Lahti, areas? 2001; Arthur et al., 2010; How do exotic plants and animals influence native Nichols et al., 2010) plants and animals in revegetated areas? Climatic influence on Do the connections between habitats reduce a species‟ animals and the habitats risk of decline or extinction because of climate change? they require How is climate change likely to influence the (Opdam and Wascher, establishment and persistence of native plant species? 2004; Hodgson et al., 2009) Social and political factors How can we integrate the multiple objectives of of revegetation ecological, social, and political factors to optimise (Curtis and Robertson, revegetation activities and native animal persistence? 2003; Pannell et al., 2006) What is the best way to include landholders and community groups in revegetation activities?

Conclusion

In this review I have shown that although there has been substantial research on habitat loss and habitat fragmentation, there is a relative paucity of information on revegetation of linear strips and enlargement of remnant patches of habitat to maintain and increase faunal communities, especially for reptiles, amphibians and some invertebrate groups (Bolger et al., 2001; Freeman et al., 2009; Thomson et al., 2009). While ecological replantings more closely resemble remnant habitat in their plant and animal species richness and abundance than does revegetation undertaken for other reasons such as plantations, revegetated areas generally do not support the same community composition as remnant areas (Munro et al., 2009; Munro et al., 2011). This is largely due to habitat structure and floristic diversity in ecological replantings not resembling remnant areas (Reay and Norton, 1999; Munro et al., 2007; Munro et al., 2011)

I found in this review that many of our revegetated areas are replanted with trees and shrubs, and that a lack of ground layer elements is probably contributing to our inability to provide similar habitat variables as remnant areas. To rectify this, areas previously revegetated with trees and shrubs and future revegetation sites would need to have their ground layers restored, through the control of weed species and the return of soil nutrients to levels that resemble those found in benchmark vegetation communities so native grass species could regenerate or be replanted (Marchante et al., 2009; Nichols et al., 2010). The addition of fallen timber, litter and rocks may also benefit native animal species reliant on understorey microhabitats, especially as these elements may take a long time to develop in young revegetated areas (Kanowski et al., 2006; Masterson et al., 2009). As revegetated areas age, it is expected that faunal diversity may increase as habitat structure develops (Vesk and Mac Nally, 2006; Vesk et al., 2008). Replanting near existing remnants would allow animal species to persist and colonise replanted areas as suitable habitat becomes available (Kavanagh et al., 2007).

Overall, the literature shows that large patches of habitat seem to contain a greater diversity of native animals than smaller patches or linear strips, as predicted by the theory of island biogeography (MacArthur and Wilson, 1967). Revegetated linear strips and stepping stones may be important in allowing species movement and dispersal between remnant habitats, to stop species extinctions as a result of climate change or environmental catastrophes (Fischer and Lindenmayer, 2002; Baum et al., 2004). However, few studies have compared revegetated linear strips to other habitat configurations, and a one- size-fits-all approach to linear strip width and length design is not feasible due to the inherent differences between landscapes and the species found within them (Chetkiewicz et al., 2006). Having said this, linear strips 25m in width or wider can allow many animal species to use these areas (Bolger et al., 2001; Falcy and Estades, 2007; Lindenmayer et al., 2007; Carthew et al., 2009). Optimal linear strip and patch configurations will be dependent on the requirements of the species of conservation interest (Chetkiewicz et al., 2006). The ability of animals to persist in these areas will also depend on their response to edge effects, ability to move and disperse through the matrix, and species‟ interactions such as competition and predation between both native and introduced animals (Burkey, 1993; Lahti, 2001; Arthur et al., 2010). More research is necessary to understand these processes fully. Independent of the original motivation to revegetate natural habitat is the need to ensure that replanted areas provide adequate habitat structure and floristic diversity to maintain and increase native animal species richness, abundance, and community composition.

Chapter 3. The value of revegetated areas and local habitat variables for reptile conservation

Introduction

Habitat loss and fragmentation are major threats to biodiversity conservation around the world, and much of this loss and fragmentation is caused by land clearing for agriculture and the establishment of plantations (Harrison and Bruna, 1999; Lindenmayer, 2009). Many researchers argue that broad-scale restoration of habitat through revegetation will be necessary to maintain or increase biodiversity in modified agricultural landscapes (Huxel and Hastings, 1999; Ryan, 2000; Ruiz-Jaen and Aide, 2005). Revegetation could help to enlarge and reconnect isolated remnant habitats, allowing animals and plants to move between them (Hobbs, 1993; Beier and Noss, 1998), enabling recolonisation and reducing the risk of extinction (Simberloff et al., 1992; Hobbs, 1993; Beier and Noss, 1998; Tewksbury et al., 2002). However, there is debate about the effectiveness of revegetating for maintaining faunal species, as revegetated areas generally do not contain the same faunal communities as remnant areas, mainly because remnant areas are floristically and structurally more diverse (Munro et al., 2009; Munro et al., 2011).

There is also debate about whether it is better to revegetate linear strips or to revegetate patches of native vegetation that could enlarge existing remnant patches (Simberloff and Cox, 1987; Tewksbury et al., 2002; Bailey, 2007). Enlarged patches provide a larger habitat area and therefore contain a greater diversity of faunal species, as predicted by the theory of island biogeography (MacArthur and Wilson, 1967). Similarly, enlarged patches generally have larger interior habitats, while revegetated linear strips generally have a greater edge to area ratio, making edges of linear strips more prone to environmental variation, negatively impacting some fauna (Debinski and Holt, 2000; Laurance, 2000). Revegetation of linear strips is also more expensive than the enlargement of remnant patches of vegetation (Dorrough and Moxham, 2005), and such strips may be detrimental to floral and faunal communities by acting as conduits of disease and invasive species (Simberloff et al., 1992; Downes et al., 1997b; Brown et al., 2006). Yet, there are drawbacks to focussing solely on the enlargement of isolated remnant patches, as these patches remain separated from other similar habitats, making species movement difficult 40

(Bennett et al., 2006a; Hodgson et al., 2009).

The responses of faunal groups such as birds, invertebrates and mammals to habitat fragmentation (and to a lesser degree, habitat restoration) are reasonably well studied (Ruiz-Jaen and Aide, 2005; Munro et al., 2007). However, other groups such as reptiles have received little attention; few studies have reported on the effectiveness of habitat restoration for reptile species (Kanowski et al., 2006; Cunningham et al., 2007; Munro et al., 2007; Bateman et al., 2008). Reptiles are an important group to study because they may exhibit distinct responses to habitat loss, fragmentation and habitat restoration not seen in other faunal groups (Bell and Donnelly, 2006; Munro et al., 2007; Nichols and Grant, 2007; Marquez-Ferrando et al., 2009). Lower mobility than either birds or mammals (Andren, 1994; Bentley and Catterall, 1997; Fox and Fox, 2000; Bolger et al., 2001; Lindenmayer et al., 2002a), lower energy requirements and smaller home ranges than warm-blooded animals (Bell and Donnelly, 2006; Dixo and Metzger, 2009) may allow reptiles to persist in smaller areas of habitat, making them less likely to be negatively influenced by habitat loss and fragmentation (Dickman, 1987; McGarigal and Cushman, 2002).

While many reptiles seem to have persisted in fragmented landscapes (Kitchener et al., 1980; Smith et al., 1996), some reptile species and communities have declined in highly fragmented areas (MacNally and Brown, 2001; Driscoll, 2004; Driscoll and Hardy, 2005; Bell and Donnelly, 2006; Dixo and Metzger, 2009). Reptiles are strongly influenced by vegetation type and structure, and these habitat variables may have a greater influence than remnant shape or size in determining how reptiles respond to habitat loss and fragmentation (Dickman, 1987; MacNally and Brown, 2001; Jellinek et al., 2004; Kuhnz et al., 2005; Schutz and Driscoll, 2008; Michael et al., 2010). In agricultural landscapes that are heavily cleared and fragmented, vegetation tends to be degraded, impacting on reptiles in these areas (Hadden and Westbrooke, 1996; Smith et al., 1996; Brown, 2001; Leynaud and Bucher, 2005; Read and Cunningham, 2010). This suggests that to stop reptiles

41 sensitive to habitat loss and degradation from declining further, it is essential that studies determine how effective revegetation is for maintaining and increasing reptile communities in enlarged and connected habitats. However, the existing scientific literature does not provide clear guidance on where and how investments in vegetation restoration should be made in order to maximise the probability of reptile persistence. More specifically, it is unclear whether it is best to invest in increasing patch area, connecting habitat patches, or improving the site-level quality of habitats.

In this study I sought to establish the effectiveness of revegetated areas for maintaining and increasing reptile species richness, abundance, and community composition in two agricultural landscapes in south-eastern Australia. I wanted to know if reptile species richness and abundance, and abundance of individual species was influenced by: (i) the shape of the sampled area (linear strip or patch), the type of the sampled area (remnant, revegetated or cleared) and the interactions of shape and type; (ii) increasing distance from patches along linear remnants, replantings and cleared roadsides; (iii) environmental variables and how these account for variation in reptile community composition compared with shape and site-type.

I predicted that: (i) reptile species richness and abundance would be higher in remnant compared with revegetated areas, and higher in revegetated areas compared with cleared linear strips. This is because remnant areas should provide the best quality habitat (Munro et al., 2009; Munro et al., 2011); revegetated areas the next best quality habitat due to the presence of trees and shrubs, and cleared linear strips the lowest quality habitat. (ii) Revegetated patches would have higher reptile species richness and abundance than revegetated linear strips because patches would be larger (MacArthur and Wilson, 1967; Hager, 1998) and have a smaller edge-to-area ratio (Paton, 1994; Lahti, 2001; Twedt et al., 2010) than linear strips. (iii) Reptile species richness and abundance would decline in linear strips as distance from remnant patches increased, because reptiles would not move far from remnant patches as these areas would provide better quality habitat than linear strips (Williams-Linera et

42 al., 1998). (iv) Environmental variables would be more important in structuring reptile community composition than variables related to treatment shape and type, because environmental variables strongly influence reptile communities (Dickman, 1987; MacNally and Brown, 2001; Driscoll, 2004). This information could assist management agencies and community groups to plan revegetation activities that will best maintain reptile species in modified agricultural landscapes.

Methods

Study region

I undertook this study in two regions of Victoria, south-eastern Australia: the Wimmera and Benalla (Figure 3.1). The Wimmera region is located in the Wimmera Catchment in western Victoria and receives an average annual rainfall of 350 - 500mm with mean daily temperatures varying from 14 - 40°C (Bureau of Meteorology, 2010). Europeans first colonised in 1841 and the area has been heavily cleared for intensive agriculture since the 1850s (Morcom and Westbrooke, 1998). Prior to European settlement, this area supported grassy woodlands dominated by buloke (Allocasuarina luehmannii) and black-box (Eucalyptus largiflorens) on rises and flats, and grasslands on clay pans and shallow depressions. These vegetation types were associated with varying soil types and burning regimes in woodland areas (Morcom and Westbrooke, 1998). Around 5% of the native vegetation remains, resulting in small isolated patches of remnant vegetation impacted by livestock grazing (Duncan et al., 2007).

The Benalla region is located in the Goulburn-Broken Catchment in northeastern Victoria. It has an annual average rainfall of 400 - 670mm and mean annual temperatures vary from 15 - 31°C (Bureau of Meteorology, 2010). Clearing for intensive agriculture started in the 1840s (Radford et al., 2005) and the native vegetation that remains varies from box-ironbark forests containing red-ironbark (Eucalyptus tricarpa) or yellow gum (E. leucoxylon) and grey-box (E. macrocarpa) in the poorer soils of lowland hill areas to grey-box, white-box

(E. albens), yellow-box (E. melliodora) and river red gum (E. camaldulensis) grassy woodlands in the more fertile soils (Radford et al., 2005). Between 3% and 30% of the native vegetation now remains (Goulburn Broken CMA, 2010).

a) b)

Figure 3.1. Maps of the (a) Benalla and (b) Wimmera regions in Victoria, south-eastern Australia. Shaded areas correspond to reserves; black circles represent linear strip treatments; black triangles represent patch treatments surveyed as part of my study.

Site selection

Within each of the two regions (Wimmera and Benalla), I surveyed two treatment shapes: (1) linear strips and (2) enlarged patches of remnant habitat. Linear strips were defined as areas of habitat along roadsides or fence lines that were approximately 20 - 40m wide and at least 500m long. Linear strips always originated from a remnant patch. Patches were approximately square or oval-shaped and at least 4 hectares in size. Within linear strips there were four site-types (remnant, revegetated and cleared linear strips and remnant patch from which the strips originated), and within enlarged patches there were two site-types (remnant, revegetated). Each treatment was replicated in five locations (Figure 3.2). At each of the five locations in the linear strip treatments the four site-types were: a revegetated linear strip, containing native trees and shrubs 8 - 14 years old; a remnant linear strip, containing remnant native vegetation; a cleared linear strip, containing few trees or shrubs; and a patch of remnant native vegetation, from which the linear strips originated (Figure 3.2).

Although it would have been optimal to have a single remnant patch from which the three linear strips originated, in some cases this was not possible and situations arose where two of the three linear strips originated from one remnant patch while the third linear strip originated from a nearby remnant patch (<1 km away). In replicates where only one remnant patch was used, the patch was surveyed at two sites to ensure consistent trapping effort. Within the patch treatments, there were two site-types at each of the five locations: a revegetated patch, containing native trees and shrubs 8 - 14 years old; and an adjacent remnant patch, containing remnant native vegetation (Figure 3.2).

Region Benalla/Wimmera

Linear Treatment strip Patch

Location 1 2 3 4 5 6 7 8 9 10

Site i ii iii iv v vi

b) c)

Figure 3.2. Diagrams displaying the sampling design used in my study: (a) a flow diagram describing two regions, two treatment shapes per region and five replicate locations for each treatment. Within each of the (b) linear strip locations there were four site-types: (i) revegetated linear strip, (ii) cleared linear strip, (iii) remnant linear strip, and (iv) remnant patch, which was sampled twice. Within each of the (c) patch treatments there were two site-types: (v) revegetated patch and (vi) remnant patch. Circles represent pitfall trap lines.

Pitfall trapping

I established pitfall traps in the Wimmera region in November and December 2007 and operated them from January until March 2008. In the Benalla region, I established the traps in November and December 2008 and operated them from January to March 2009. I used pitfall traps because they are effective in capturing a broad range of reptile species (Friend et al., 1989; Enge, 2001; Moseby and Read, 2001; Craig et al., 2009), and because vegetation structure does not have an important influence on capture probabilities (Craig et al., 2009). I established five pitfall lines with a total of ten pitfall traps at each site- type. This gave me 50 pitfall traps at each of the linear strip treatments and 20 pitfall traps at each of the enlarged patches (Figure 3.2). Each treatment was surveyed twice daily for 5 consecutive days (4 nights) every month for three months. I identified captured reptiles, marked them with nail polish on the underside of their hind leg, and released them near the site of capture.

Each pitfall line was comprised of two 20 litre buckets joined by a 16m long, 30cm high drift fence (made of 200um black polyurethane plastic). Buckets were dug into the ground such that the rim of the bucket was level with the top of the soil. The drift fence bisected the top of each bucket and was kept perpendicular to the ground with two wooden stakes at either end and smaller metal stakes along its length. In each bucket, I placed 3 - 4cm of soil/sand and leaf litter, a 10x10x2cm block of untreated pine, and a 10cm long piece of halved 90mm PVC pipe. The soil and leaf litter were for the protection of burrowing animals and the PVC pipe and pine block were intended to protect captured animals from heat stress and possible flooding. To avoid mortalities caused by ants, the rims of the buckets were coated with Coopex Insect Powder™, which is a permethrin-based insecticide.

Habitat surveys

I conducted habitat surveys over an area of approximately 2 hectares in the middle of the trapping site-types. Each trap array (16x16m) was individually surveyed using visual estimates and the results then compiled and averaged for each site-type. Density measures were coded using the following system: 1 = 0 46

- 5%, 2 = 5 - 25%, 3 = 25 - 50%, 4 = 50 - 75%, 5 = 75 - 95% and 6 = 95 - 100% (O‟Shea and Kirkpatrick, 2000). The variables I surveyed were selected because they were thought to have an influence on habitat structure. Variables recorded were: density of tallest stratum of vegetation, density of second tallest stratum (mid-stratum density), ground cover, rock cover, cover of fallen timber, litter cover, tussock grass cover, cover of other grasses, herb cover, shrub cover, percentage of bare ground and degree of disturbance. Tree basal area was measured using the Bitterlich variable radius method (Mueller-Dombois and Ellenberg, 1974). I also carried out a vegetation survey within each site- type, noting all plant species that grew within the trapped area, from which I calculated the proportion of all species present that were native.

Data analysis

The first step in my data analysis was to validate the predictions as stated in the introduction. In phase two of my analysis (post-hoc comparisons) I selected a larger set of possible comparisons, but used extra caution when interpreting these results.

Treatment relationships

I analysed the response of reptile species richness and abundance to treatment type using linear mixed models with a Poisson distribution in a Bayesian framework (Lunn et al., 2000; Spiegelhalter et al., 2002; McCarthy, 2007). The graphed data had a Poisson distribution, like many faunal count data (Hilborn and Mangel, 1997). The Bayesian Poisson regression had uninformative priors for the intercept term (a ~ dnorm[0, 1.0E-6]) and the regression coefficients (beta[j] ~ dnorm[0, 1.0E-6]), where j was the explanatory variable in WinBUGS (Lunn et al., 2000; Spiegelhalter et al., 2002). I used reptile species richness and abundance as the response variables in the analysis, with region and site- type as fixed effects and location as a random effect. Region and site-type were fitted as fixed effects to take into account habitat differences between the Wimmera and Benalla regions and differences between the survey site-types. Location was fitted as a random effect because the areas sampled were 47 randomly positioned within each region (Appendix 2). Overall species richness and abundance was calculated as a total at each site.

I undertook three analyses with the following categorical explanatory variables (site-type): (1) revegetated linear strip, remnant linear strip, cleared linear strip and remnant patch; (2) revegetated patch and remnant patch; and (3) revegetated linear strip and revegetated patch. I also used the above analysis to determine whether there was an effect of distance from remnant patches on reptile species in linear strips. Reptile species richness and abundance was again fitted as the response variable and site-type and distance travelled (100, 200, 300, 400 and 500m) were fitted as fixed effects (Appendix 3). The abundance of individual species of reptiles that were captured in more than half of the site-types were analysed in the same way.

I used WinBUGS to generate 150,000 samples from a posterior distribution of each mixed effects model for each species, after discarding the first „burn-in‟ of 10,000 samples. All explanatory variables were centred by subtracting the mean from each variable to minimise autocorrelation between successive samples and improve efficiency of the Monte Carlo Markov Chain sampling. For each model, I ran three Markov chains with a suitable number of iterations so that convergence was reached for all variables on the basis of the Gelman- Rubin statistic (i.e., r < 1.05). WinBUGS calculated the mean and standard deviation of the model coefficients along with the 2.5th and 97.5th percentiles of the distribution, which represent 95% credible intervals (CIs). Credible intervals in Bayesian analysis are analogous to frequentist confidence intervals. It was therefore justifiable to use the overlap rule developed for pairs of independent confidence intervals (Cumming and Finch, 2005; Cumming, 2009). Independent variables were considered to display „some evidence‟ of a difference if there was a small overlap between credible intervals, that is, the proportion of overlap was no more than half the average of the two overlapping arms, and the two overlapping arms did not differ in length by more than a factor of two. Independent variables were considered to display „quite strong evidence‟ of a difference if there was no overlap, or a gap between credible intervals

(Cumming and Finch, 2005; Cumming, 2009).

To quantify the difference between treatments that showed „some evidence‟ of being different, I undertook a Bayesian analysis that calculated the mean expected difference between treatments, and the uncertainty (95% credible intervals) around that mean difference estimate. If the 95% of the posterior distribution of the mean expected difference between treatments did not encompass zero, I interpreted this as providing „some evidence‟ of a difference. These posterior distributions were included for results that showed „some evidence‟ of a difference between treatments.

Relationships with habitat variables

I analysed relationships between reptile species richness and abundance and the habitat variables measured at a site using a linear mixed model with a Poisson distribution in WinBUGS (Appendix 4). I used Deviance Information Criteria (DIC) values (Spiegelhalter et al., 2002) as an alternative to the Akaike Information Criterion (AIC) to compare the fit and complexity of the linear mixed models. DIC values, like AIC values, allow the user to rank the various models, where the best model is the one with the lowest value (Spiegelhalter et al., 2002). Models with DIC values within 2 points of the best model are considered to be very similar to the best model, so were kept. Models with a DIC value between 2 and 7 points were considered as being potentially relevant (Spiegelhalter et al., 2002).

To account for habitat differences between the Wimmera and Benalla, region was fitted as a fixed effect, while location was fitted as a random effect. The results for the null model (region only) were included in the results for the best models. Reptile species richness and abundance were again calculated as a total for each site-type. Habitat variables were first converted from their respective density measure (1 - 6) into a percentage and then analysed using a Pearson‟s correlation analysis in R version 2.9.1 (R Development Core Team, 2009) to establish the degree of association between pairs of variables in a sample (Sokal and Rohlf, 1995). Correlated variables (r > 0.4) were removed 49 from future analysis. While there are limitations to including multiple variables in a single correlation analysis, the problem of multi-collinearity was overcome by first discarding correlated variables based on biological knowledge (Elith and Leathwick, 2009). Uncorrelated variables were mid-stratum density, percentage of bare ground, rock cover, tussock grass cover, litter cover and the proportion of native plants. Menetia greyii and Morethia boulengeri was analysed to determine whether vegetation variables had an influence on their abundance. Other species were not analysed because their abundance and/or incidence of occurrence at the survey site-types was too low.

I assessed the importance of variables by calculating the multiplicative effect (with 95% credible intervals) of each of the six habitat variables (mid-stratum density, % bare ground, rock cover, tussock grass cover, litter cover and the proportion of native plants) on reptile species richness and abundance (Spiegelhalter et al., 2002). This allowed me to estimate the effect size of each variable through the comparison of credible intervals (McCarthy, 2007). The multiplicative effect was calculated as the exponent of the standardised coefficient in Poisson regression models:

Ei = exp(bi × rangei)

where Ei is the multiplicative effect of variable i, bi is the regression coefficient of variable i, and rangei is the range of values for variable i. The multiplicative effect size of 1 means „no clear evidence‟ of a change in species richness or abundance. If Ei is different from 1 (where the credible intervals of Ei do not overlap 1) it is likely to show „some evidence‟ of a change in species richness and abundance and have an important biological effect on species richness or abundance. Multiplicative effect sizes greater than 1 indicate a positive effect of the explanatory variable on species richness or abundance, while effect sizes less than 1 indicate a negative effect. The 95% credible intervals show a range of possible values for the effect of the variable.

Influence of environmental and spatial variables on community composition

I used a redundancy analysis (RDA) to investigate whether the composition of reptile assemblages was a function of environmental or treatment variables. This was done to show how much of the variation in reptile community composition could be explained by environmental factors and/or the site-type and treatment shape. In a separate RDA I examined the effect of environmental and spatial variables on reptiles to determine how much of the variation in reptile community composition was accounted for by geographic distance between sites. Redundancy analysis is a constrained linear ordination, analogous to a multivariate regression, in which the explanatory variables are constrained to be a linear combination of the measured environmental variables. The RDA was conducted in CANOCO version 4.0 (ter Braak and Sˇmilauer, 1998). The species matrix was constructed using the total abundance of each species observed transformed to produce the Hellinger distance between sites. Hellinger distance is recommended for the ordination of species abundance data (Rao, 1995) and outperforms other similar methods (Legendre and Legendre, 1998; Gagné and Proulx, 2009).

I analysed six explanatory environmental variables. Five of these were analysed according to their respective rank densities (Braun-Blanquet scale): mid-stratum density; rock cover; litter cover; herb cover; and tussock grass cover. The last environmental variable; native plants, was analysed as a proportion to exotic plants. I divided the treatment matrix into two separate variables: treatment shape (linear strips and patches); and site-type (revegetated areas, remnant areas, and cleared areas). In the analysis of the treatment matrix I included an interaction between the treatment shape variable and the site-type area variable because I thought areas such as remnant linear strips would probably respond differently from remnant patches to factors such as edge effects and other environmental processes. I also included a spatial matrix in a separate RDA analysis that contained normalised geographic co-ordinates centred by region in decimal degrees to four decimal places. The spatial matrix replaced the treatment matrix in the steps below.

I analysed the data in four steps: (1) RDA of the species matrix, constrained by the environmental matrix; (2) RDA of the species matrix, constrained by the treatment matrix; (3) partial RDA of the species matrix, constrained by the environmental matrix with the variation explained by the treatment matrix accounted for; and (4) partial RDA of the species matrix, constrained by the treatment matrix with the variation explained by the environmental matrix accounted for (Makarenkov and Legendre, 2002; Lepš and Šmilauer, 2003; Borcard et al., 2011). The purpose of the first and second steps was to determine how much of the variation in the reptile community composition was a function of the environmental and treatment matrix respectively. Step three determined how much of the variation in the community composition was explained by the pure environmental matrix (without the influence of the treatment matrix). Step four determined how much of the variation in the community composition was explained by the pure treatment matrix (without the influence of the environmental matrix). During the analysis I used a forward selection of explanatory variables, which is the same as a forward stepwise regression (Lepš and Šmilauer, 2003).

Results

I caught a total of 409 individuals from 14 different reptile species in the Benalla region, and a total of 368 individuals from 17 different species in the Wimmera (Appendix 5). These reptiles were caught over 10,050 trap nights, with an average of 13 individuals caught per 100 trap nights. More than 90% of the reptile species were caught during the first 7 to 8 days of trapping at each location, with only one species caught after that time (Figure 3.3).

richness 5 Reptile speciesReptile 0 1 3 5 7 9 11 13 15 Trap days Figure 3.3. Reptile species accumulation in the Wimmera (solid circle) and Benalla (open circle) region over a 15 day trapping period.

Treatment relationships

Reptile species richness and abundance showed no clear evidence of an effect of treatment across the treatment shapes or site-types (Figure 3.4; Figure 3.5 & Figure 3.6 a, b). The abundance of Boulenger‟s skink (Morethia boulengeri) (Figure 3.4; Figure 3.5 & Figure 3.6 c), Grey‟s skink (Menetia greyii) (Figure 3.4 d; Figure 3.5 & Figure 3.6 d) and the eastern striped skink (Ctenotus robustus) (Figure 3.4 f) also showed no clear evidence of an effect of treatment. While the southern rainbow skink (Carlia tetradactyla) showed no clear statistical evidence of being more abundant in any of the treatment types, it showed a trend towards having higher abundance in remnant linear strips compared to other treatments (Figure 3.4 e). No effect of treatment was evident when more abundant reptiles were excluded from analysis of reptile species abundance (Figure 3.4 g). There was no evidence that region influenced reptile species richness or abundance in the different treatments (Appendix 6).

I found a trend towards lower reptile species richness with increasing distance from remnant patches in revegetated and cleared linear strips, but not in remnant linear strips (Figure 3.7 a). However, I found some evidence of higher reptile abundance in remnant linear strips 400 - 500m away from remnant patches, compared to revegetated (400m - mean expected difference = 0.95, CIs = 0.17 - 1.73; 500m - mean expected difference = 1.32, CIs = 0.27 - 2.42) and cleared linear strips (400m - mean expected difference = 0.98, CIs = 0.22 - 53

1.76; 500m - mean expected difference = 1.32, CIs = 0.27 - 2.43) (Figure 3.7 b). Reptile abundance showed a trend towards decreasing in cleared and revegetated linear strips and increasing in remnant linear strips as distance from remnant patches increased (Figure 3.7 b).

After the most abundant species were removed from the analysis, reptile species richness showed some evidence of being higher in remnant linear strips compared to revegetated linear strips 300 - 400m away from remnant patches (300m - mean expected difference = 0.25, CIs = 0.03 - 0.48; 400m - mean expected difference = 0.35, CIs = 0.1 - 0.63), and quite strong evidence of being higher at 500m. The abundance of rarer reptile species similarly showed some evidence of being higher in remnant linear strips compared to revegetated linear strips 300m away from remnant patches (mean expected difference = 0.35, CIs = 0.11 - 0.61) and quite strong evidence of being higher in remnant linear strips compared to revegetated linear strips at 400m and 500m away from remnant patches. Overall, I found that rarer reptile species richness and abundance showed a trend towards increasing in remnant linear strips and decreasing in cleared linear strips as distance from remnant patches increased. I also found some evidence of decreasing reptile species richness and abundance in revegetated linear strips (richness - mean expected difference = 0.46, CIs = 0.07 - 0.97; abundance - mean expected difference = 0.45, CIs = 0.06 - 0.97) as distance from remnant patches increased (Figure 3.7 c, d).

Carlia tetradactyla displayed the most remarkable pattern as there was quite strong evidence of higher abundance in remnant linear strips compared to cleared and revegetated linear strips over 300m from remnant patches, and some evidence of higher abundance in remnant linear strips 200m away from remnant patches compared to revegetated linear strips at similar distances (mean expected difference = 1.75, CIs = 0.84 - 2.91). Overall, C. tetradactyla showed a trend of increasing abundance in remnant linear strips and decreasing abundance in revegetated linear strips as distance from remnant patches increased, and some evidence of decreasing abundance in cleared linear strips (mean expected difference = 1.0, CIs = 0.17 - 2.03) as distance

54 from remnant patches increased (Figure 3.7 g).

Morethia boulengeri showed no evidence of a difference between different linear strip treatments as distance from the remnant patches increased, although the species showed a trend towards decreasing abundance in all treatments (Figure 3.7 e). Menetia greyii showed some evidence of having lower abundance in cleared linear strips 300 - 400m away from remnant patches compared to remnant linear strips (300m - mean expected difference = 0.28, CIs = 0.04 - 0.53; 400m - mean expected differences = 0.31, CIs = 0.02 - 0.63) and revegetated linear strips (300m - mean expected difference = 0.34, CIs = 0.1 - 0.61; 400m - mean expected difference = 0.35, CIs = 0.1 - 0.67) (Figure 3.7 f). Ctenotus robustus showed quite strong evidence of being less abundant in remnant linear strips compared to cleared linear strips 100 - 400m away from remnant patches, and quite strong evidence of being less abundant in remnant linear strips compared to revegetated linear strips 200 - 400m away from remnant patches. Ctenotus robustus also showed some evidence of having lower abundance in remnant linear strips compared to revegetated linear strips 100m (mean expected difference = 0.58, CIs = 0.13 - 1.24) and 500m (mean expected difference = 0.71, CIs = 0.08 - 1.53) away from remnant patches (Figure 3.7 h).

a) Overall species richness b) Overall species abundance 6 30 5 25 4 20 3 15 2 10

Mean RichnessMean 1 5 Mean AbundanceMean 0 0 CLS RemLS RemP RevLS CLS RemLSRemP RevLS

c) Morethia boulengeri d) Menetia greyii 14 6 12 5 10 4 8 3 6 4 2

2 1

Mean AbundanceMean Mean AbundanceMean 0 0 CLS RemLSRemP RevLS CLS RemLS RemP RevLS

e) Carlia tetradactyla f) Ctenotus robustus 20 14 12 15 10 10 8 6

5 4 Mean AbundanceMean Mean AbundanceMean 2 0 0 CLS RemLS RemP RevLS CLS RemLS RemP RevLS

g) Overall reptile abundance

4.0 3.5 3.0 2.5 2.0 1.5 1.0

Mean AbundanceMean 0.5 0.0 CLS RemLSRemP RevLS

Figure 3.4. Species richness and abundance of reptiles in linear strip treatments: (a) overall species richness and (b) abundance; and abundance of (c) M. boulengeri; (d) M. greyii; (e) C. tetradactyla; (f) C. robustus; and (g) reptile abundance excluding M. boulengeri, M. greyii, C. tetradactyla and C. robustus. CLS = cleared linear strip, RemLS = Remnant linear strip, RemP = Remnant patch, RevLS = Revegetated linear strip. Bars represent 95% credible intervals. 56

a) Overall species richness b) Overall species abundance 6 15 5 12 4 9 3 6 2

1 3

Mean RichnessMean Mean AbundanceMean 0 0 RemP RevP RemP RevP c) Morethia boulengeri d) Menetia greyii 15 4

3 10 2 5

1 Mean AbundanceMean 0 AbundanceMean 0 RemP RevP RemP RevP Figure 3.5. Species richness and abundance of reptiles in patch treatments: (a) overall species richness and (b) abundance; (c) M. boulengeri abundance; and (d) M. greyii abundance. Bars represent 95% credible intervals. RemP = Remnant patch, RevP = Revegetated patch.

a) Overall species richness b) Overall species abundance 21 6 18 5 15 4 12 3 9 2 6

Mean RichnessMean 1 3 0 AbundanceMean 0 RevLS RevP RevLS RevP c) Morethia boulengeri d) Menetia greyii 10 10 8 8 6 6 4 4

2 2 Mean AbundanceMean 0 AbundanceMean 0 RevLS RevP RevLS RevP Figure 3.6. Species richness and abundance of reptiles in revegetated areas: (a) overall species richness and (b) abundance; (c) M. boulengeri abundance; and (d) M. greyii abundance. Bars represent 95% credible intervals. RevLS = Revegetated linear strip, RevP = Revegetated patch. 57

a) Overall species richness b) Overall species abundance 2.5 5.0 2.0 4.0 1.5 3.0 1.0 2.0

Mean RichnessMean 0.5 1.0 Mean AbundanceMean - - 100 200 300 400 500 100 200 300 400 500 Distance travelled (m) Distance travelled (m) c) Overall richness of rare species d) Overall abundance of rare species 1.2 1.4 1.0 1.2 0.8 1.0 0.8 0.6 0.6 0.4 0.4

Mean RichnessMean 0.2 Mean AbundanceMean 0.2 - - 100 200 300 400 500 100 200 300 400 500 Distance travelled (m) Distance travelled (m) e) Morethia boulengeri f) Menetia greyii 2.5 1.2 2.0 1.0 0.8 1.5 0.6 1.0 0.4

0.5 0.2 Mean AbundanceMean Mean AbundanceMean - - 100 200 300 400 500 100 200 300 400 500 Distance travelled (m) Distance travelled (m) g) Carlia tetradactyla h) Ctenotus robustus 3.5 2.0 3.0 2.5 1.5 2.0 1.0 1.5

1.0 0.5 Mean AbundanceMean

0.5 AbundanceMean - - 100 200 300 400 500 100 200 300 400 500 Distance travelled (m) Distance travelled (m) Figure 3.7. Reptile species richness and abundance in linear strip treatments as distance from the remnant patches increased: (a) overall species richness; (b) overall abundance; (c) species richness and (d) abundance excluding the most abundant species; and abundance of (e) M. boulengeri; (f) M. greyii; (g) C. tetradactyla; and (h) C. robustus. White columns = remnant linear strips, light grey columns = revegetated linear strips, dark grey columns = cleared linear strips. Bars represent 95% CIs. 58

Relationships with habitat variables

Reptile species richness at a site was most influenced by location, rock cover and mid-stratum density (Table 3.1). Species richness showed some evidence of increasing with rock cover, with an average effect size of 2.7 (i.e., sites with the greatest rock cover had 2.7 times as many species as sites with the least rock cover; Figure 3.8). Species richness also varied with location. The credible intervals of all of the other variables encompassed 1, so showed no clear evidence of changing reptile species richness. These were litter cover (mean effect size = 1.4), mid-stratum density (mean effect size = 1.3), tussock cover (mean effect size = 1.2), the proportion of native plants (mean effect size = 1.1) and the percentage of bare ground (mean effect size = 0.8) (Figure 3.8).

For reptile species abundance the most important variables were rock cover, which showed some evidence of increasing reptile abundance (mean effect size = 5.1), and the percentage of bare ground, which showed some evidence of decreasing abundance (mean effect size = 0.3). Location was also an important variable influencing reptile abundance (Table 3.2, Figure 3.9). Litter cover (mean effect size = 1.4) showed some evidence of increasing reptile species abundance, while the proportion of native plants showed no clear evidence of changing reptile abundance, although may have had a slight positive influence (mean effect size = 1.3). Mid-stratum density (mean effect size = 1.0) and tussock grass cover (mean effect size = 0.9) showed no clear evidence of changing reptile abundance (Figure 3.9).

Menetia greyii was influenced by a large number of habitat variable combinations that generally had wide credible intervals, suggesting low precision and high variation in the variables measured. Menetia greyii was most commonly influenced by mid-stratum density, rock cover, litter cover, and location (Table 3.3), where rock cover (mean effect size = 3.3) and litter cover (mean effect size = 2.9) showed some evidence of increasing M. greyii abundance. Tussock grass cover (mean effect size = 3.0) and mid-stratum density (mean effect size = 1.6) showed no clear evidence of changing M. greyii abundance, although may have had a slight positive influence. The percentage 59 of bare ground (mean effect size = 0.4) and the proportion of native plants (mean effect size = 0.4) also showed no clear evidence of changing M. greyii abundance, although may have had a slight negative influence (Figure 3.10).

Table 3.1. DIC values for the 8 best-supported models of species richness as a function of habitat and location. Location = geographic location where sampling took place, mid-stratum = mid-stratum density, rock = rock cover, tussock = tussock grass cover, litter = litter cover, bare = % bare ground, natives = the proportion of native plants.

Model Variable ΔDIC ΔDIC Difference Null model 320.8 1 Rock, location 244.9 0 2 Location 246.1 1.2 3 Rock, litter, location 246.1 1.2 4 Mid-stratum, rock, location 246.2 1.3 5 Rock, bare, location 246.3 1.4 6 Rock, tussock, location 246.6 1.7 7 Rock, natives, location 246.7 1.8 8 Mid-stratum, location 246.9 2

100

Mid- 10 stratum Tussock (4) Bare (5) (6) 1 Rock (3) Litter (3) Natives Effect SizeEffect 0.1 (7)

0.01 Habitat Variable

Figure 3.8. The multiplicative effect of: rock cover, mid-stratum density, litter cover, bare ground, the proportion of native plants, and tussock cover on reptile species richness. Numbers below the habitat variable label represent the best-supported model with that variable included. Bars represent 95% credible intervals.

Table 3.2. DIC values for the 7 best-supported models of reptile abundance as a function of habitat and location. Location = geographic location where sampling took place, mid-stratum = mid-stratum density, rock = rock cover, tussock = tussock grass cover, litter = litter cover, bare = % bare ground, natives = the proportion of native plants.

Model Variable ΔDIC ΔDIC Difference Null model 1791 1 Rock, bare, location 420.4 2.3 2 Rock, litter, bare, location 418.1 0 3 Rock, natives, bare, location 420.2 2.1 4 Mid-stratum, rock, litter, bare, location 419.9 1.8 5 Rock, tussock, natives, bare, location 420.3 2.2 6 Rock, litter, natives, bare, location 419.9 1.8 7 Rock, litter, tussock, bare, location 420 1.9

100 Rock (2) 10 Mid- stratum Tussock (2) (4) 1 Bare (2) Litter (2) Natives (6) Effect SizeEffect 0.1

0.01 Habitat Variable

Figure 3.9. The multiplicative effect of: rock cover, bare ground, mid-stratum density, litter cover, tussock cover, and the proportion of native plants on reptile species abundance. Numbers below the habitat variable label represent the best-supported model with that variable included. Bars represent 95% credible intervals.

Table 3.3. DIC values for the 23 best-supported models of Menetia greyii abundance as a function of habitat and location. Location = geographic location where sampling took place, mid-stratum = mid-stratum density, rock = rock cover, tussock = tussock grass cover, litter = litter cover, bare = % bare ground, natives = the proportion of native plants.

ΔDIC Model Variable ΔDIC Difference Null model 391 1 Mid-stratum, rock, litter, tussock, natives, bare, location 266 0.5 2 Mid-stratum, litter, tussock, bare, natives, location 266.8 1.3 3 Rock, litter, tussock, bare, natives, location 266.9 1.4 4 Mid-stratum, rock, litter, bare, natives, location 265.5 0 5 Mid-stratum, rock, litter, tussock, natives, location 265.6 0.1 6 Rock, litter, natives, bare, location 265.5 0 7 Rock, litter, tussock, natives, location 267.5 2 8 Mid-stratum, litter, natives, bare, location 266.3 0.8 9 Mid-stratum, litter, tussock, natives, location 265.8 0.3 10 Mid-stratum, rock, litter, bare, location 265.9 0.4 11 Mid-stratum, rock, litter, natives, location 266.2 0.7 12 Litter, natives, bare, location 267.1 1.6 13 Rock, litter, bare, location 267.3 1.8 14 Rock, litter, natives , location 267.1 1.6 15 Mid-stratum, litter, bare, location 266.3 0.8 16 Mid-stratum, litter, natives, location 266 0.5 17 Mid-stratum, rock, bare, location 266.3 0.8 18 Mid-stratum, rock, litter, location 267.1 1.6 19 Rock, bare, location 266.9 1.4 20 Mid-stratum, bare, location 266.1 0.6 21 Mid-stratum, litter, location 266.5 1 22 Mid-stratum, rock, location 267.2 1.7 23 Mid-stratum, location 266.3 0.8

100 Tussock Rock (4) (5) 10 Natives (4) 1 Mid- Litter (4)

Effect SizeEffect stratum 0.1 (4) Bare (4)

0.01 Habitat Variable

Figure 3.10. The multiplicative effect of: rock cover, bare ground, mid-stratum density litter cover, tussock cover, and the proportion of native plants on M. greyii abundance. Numbers below the habitat variable label represent the best-supported model with that variable included. Bars represent 95% credible intervals.

Morethia boulengeri was influenced by habitat variables in a similar way to overall reptile abundance: the strongest was the combination of bare ground, rock cover and location (Table 3.4), where rock cover (mean effect size = 4.8) showed some evidence of increasing M. boulengeri abundance and bare ground (mean effect size = 0.42) showed some evidence of decreasing abundance. The proportion of native plants (mean effect size = 1.6), litter cover (mean effect size = 1.3), tussock cover (mean effect size = 0.8) and mid-stratum density (mean effect size = 1.0) showed no clear evidence of changing M. boulengeri abundance (Figure 3.11).

Table 3.4. DIC values for the 7 best-supported models of Morethia boulengeri abundance as a function of habitat and location. Location = geographic location where sampling took place, mid-stratum = mid-stratum density, rock = rock cover, tussock = tussock grass cover, litter = litter cover, bare = % bare ground, natives = the proportion of native plants.

Model Variable ΔDIC ΔDIC Difference Null model 734.6 1 Rock, tussock, natives, bare, location 364.1 0.8 2 Rock, litter, natives, bare, location 365.3 2 3 Mid-stratum, litter, natives, bare, location 365.4 2.1 4 Mid-stratum, rock, natives, bare, location 365.4 2.1 5 Rock, natives, bare, location 363.3 0 6 Rock, litter, bare, location 364.7 1.4 7 Rock, bare, location 364.5 1.2

100 Rock (3) 10 Litter (6) 1 Bare (3) Natives Mid- Effect sizeEffect (3) Tussock 0.1 (1) stratum (4) 0.01 Habitat variable

Figure 3.11. The multiplicative effect of: rock cover, proportion of native plants, bare ground, tussock cover, litter cover, and mid-stratum density on Morethia boulengeri abundance. Numbers below the habitat variable label represent the best-supported model with that variable included. Bars represent 95% credible intervals.

I also graphed percentage cover of the seven most important habitat variables to determine those variables substantially dominant within each treatment. I found that litter cover and the proportion of native plants showed quite strong evidence of being higher in remnant habitats than in cleared linear strips. Litter cover showed some evidence of being higher in remnant areas compared to revegetated linear strips and quite strong evidence of being higher in remnant 64 areas compared to revegetated patches. The proportion of native plants showed quite strong evidence of being higher in remnant areas compared to revegetated linear strips. Herb cover showed some evidence of being higher in remnant patches than in revegetated and cleared linear strips. In revegetated areas, mid-stratum density showed some evidence of being higher compared to remnant patches and quite strong evidence of being higher compared to remnant linear strips. Mid-stratum density also showed quite strong evidence of being lower in cleared linear strips compared to other treatments. Rock cover, the percentage of bare ground and tussock cover were similar over the different treatments, although rock cover tended to be lower in cleared linear strips compared to revegetated patches (Figure 3.12).

100 Cleared Linear Strip 90 Remnant Patch

80 Remnant Linear Strip 70 Revegetated Linear strip Revegetated Patch 60 50 40 30 Percentage cover Percentage 20 10 0

Figure 3.12. The percentage cover of vegetation variables measured within the different study treatments. Bars represent 95% confidence intervals.

Influence of environmental and spatial variables on community composition

The environmental and treatment (shape, site-type) variables used in this analysis explained 29% of the variation in reptile community composition in the Benalla region (Table 3.5). Environmental variables accounted for most of this variation (21.5%) and this did not differ in the pure environmental matrix. This meant that treatment variables were not strongly correlated with environmental variables. The most influential environmental variables were rock cover and herb cover (Table 3.6). The treatment variables explained around 8% of the total variation in reptile community composition (Table 3.5). This was largely due to the interaction between treatment shape and site-type. Treatment shape also had a substantial influence in the treatment matrix but not in the pure treatment matrix (Table 3.6).

Table 3.5. Percentage influence of environmental and treatment variables in explaining the variance of reptile community composition in the Benalla region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Treat = treatment matrix; Env-Treat = environmental matrix with treatment matrix removed; Treat-Env = treatment matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 10 21.5 Treat 4 8 Env - Treat 11.5 21.4 Treat - Env 5.3 7.8 Total 29.4

Table 3.6. The contribution of the environmental (Env) and the treatment matrix (Treat) to the variation in the reptile community composition matrix in the Benalla region, explained by RDA and partial RDA. Env-Treat = environmental matrix with treatment matrix removed (pure environmental matrix); Treat-Env = treatment matrix with environmental matrix removed (pure treatment matrix).

Variables Matrix Variation % Matrix Variation % Rock Env 26.7 Env-Treat 24.1 Herb Env 16.7 Env-Treat 20.7 Mid-stratum Env 10.0 Env-Treat 6.9 Tussock Env 6.7 Env-Treat 10.3 Litter Env 6.7 Env-Treat 3.4 Natives Env 6.7 Env-Treat 6.9 Treat interaction Treat 13.8 Treat-Env 13.3 Shape Treat 10.3 Treat-Env 6.7 Site-type Treat 3.4 Treat-Env 6.7

In the Wimmera region the overall environmental and treatment matrix accounted for 35% of the variation in reptile community composition. Again, this was largely a result of the environmental matrix accounting for 27% of the total variation (Table 3.7). The environmental variables most strongly influencing reptile community composition were litter cover, herb cover and mid-stratum density; although mid-stratum density was substantially more influential in the pure environmental matrix. Rock cover was also marginally influential in the environmental matrix (Table 3.8). The treatment variables in the Wimmera accounted for around the same amount of variation as those in the Benalla area (7.5%) (Table 3.7), but were most influenced by site-type (revegetated, remnant, and cleared areas) and to a lesser degree treatment shape (Table 3.8).

Table 3.7. Percentage influence of environmental and treatment variables in explaining the variance of reptile community composition in the Wimmera region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Treat = treatment matrix; Env-Treat = environmental matrix with treatment matrix removed; Treat-Env = treatment matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 16.3 27.3 Treat 4.2 7.5 Env - Treat 16.6 27 Treat - Env 5.7 7.2 Total 34.8

Table 3.8. The contribution of the environmental (Env) and the treatment matrix (Treat) to the variation in the reptile community composition matrix in the Wimmera region, explained by RDA and partial RDA. Env-Treat = environmental matrix with treatment matrix removed (pure environmental matrix); Treat-Env = treatment matrix with environmental matrix removed (pure treatment matrix).

Variables Matrix Variation % Matrix Variation % Litter Env 23.5 Env-Treat 17.1 Herb Env 14.7 Env-Treat 14.3 Mid-stratum Env 14.7 Env-Treat 20.0 Rock Env 11.8 Env-Treat 11.4 Natives Env 8.8 Env-Treat 8.6 Tussock Env 5.9 Env-Treat 5.7 Site-type Treat 11.4 Treat-Env 11.8 Shape Treat 5.7 Treat-Env 5.9 Treat interaction Treat 5.7 Treat-Env 2.9

In the environmental and spatial matrix I found that environmental and spatial variables accounted for around the same amount of variation (22% and 27%

68 respectively), explaining a total of 44% of the variation in reptile community composition in the Benalla region (Appendix 7). In this analysis rock cover (18%), herb cover (11%) and mid-stratum density (7%) were again the most important variables in the environmental matrix, but rock cover (5%) became less important in the pure environmental matrix and tussock grass cover (7%) and herb cover (12%) became more important (Appendix 8). In the spatial matrix latitude (21%) was the most important variable while the interaction term latitude * longitude (14%) was the second most important spatial variable. In the pure spatial matrix latitude (16%) still explained most of the variation, while the interaction term longitude * longitude (11%) became more important (Appendix 8).

In the Wimmera region the environmental and spatial matrix accounted for 60% of the variation in reptile community composition, with most of this being explained by the spatial matrix (40%) (Appendix 9). In the spatial matrix latitude was the most important variable and the interaction term latitude * longitude was also important. The environmental matrix accounted for 27% in the shared environmental and spatial matrix and 20% in the pure environmental matrix. In the environmental matrix, litter cover (13%), herb cover (8%), and mid-stratum density (8%) were all important variables, while rock cover (7%) became more important in the pure environmental matrix. Latitude was again the most important variable in the spatial matrix (25%) and the pure spatial matrix (27%) (Appendix 10).

Discussion

This study is one of very few that examine the relative importance of revegetation in linear strips and revegetation adjacent to remnant patches. Unexpectedly, I found that revegetation showed little evidence of having an effect on overall reptile species richness and abundance, regardless of whether it was a revegetated patch or linear strip. I also found that there was little evidence of an effect of site-type (remnant, revegetated or cleared) on overall reptile species richness or abundance, and only a minor effect of treatment

69 shape and site-type on reptile community composition. However, by examining distance from patches I was able to provide an insight into the use of linear strips by reptiles. The most important trend I found was that rare reptile species richness and abundance was likely to be higher in remnant linear strips compared to cleared and revegetated linear strips as distance from remnant patches increased, and that overall reptile abundance showed some evidence of being higher in remnant linear strips than in revegetated or cleared linear strips. Similarly, there was quite strong evidence that Carlia tetradactyla abundance was higher in remnant linear strips and lower in revegetated and cleared linear strips as distance from remnant patches increased. These results are important as many of these findings, especially the effect of distance from remnant patches along revegetated, remnant and cleared linear strips, did not appear in the literature.

Effectiveness of revegetation in fragmented agricultural landscapes

I found that overall reptile species richness and abundance was not influenced by revegetated, remnant or cleared habitats, or by linear strips and patches of habitat. This is further supported by the minimal influence of treatments on reptile community composition. This is an unexpected result, but probably occurred because many of the reptiles I studied were generalist species, and able to persist in a variety of different habitats (Harrison and Bruna, 1999; Driscoll, 2004; Jellinek et al., 2004; Brown et al., 2008). These results have implications for the value of revegetation in these highly fragmented agricultural landscapes, as they suggest that even if revegetation is undertaken, reptile species may not recover because many of the more specialist species that require restored habitats have already been lost (Driscoll, 2004; Brown et al., 2008). For example, while I recorded many of the reptile species known to exist in these agricultural regions, historic records show the presence of other reptile species that I failed to record, despite substantial sampling effort (Rawlinson, 1966). This suggests that either I missed these species or that they have already gone locally extinct from these landscapes, possibly due to habitat loss and fragmentation (Rawlinson, 1966; Hokit et al., 1999; Bell and Donnelly,

2006; Dixo and Metzger, 2009). These localised extinctions will be difficult to confirm and would require ongoing extensive sampling.

I found that many of the abundant generalist species were dominant in particular treatments, probably due to habitat variables within those treatments or other factors not recorded, such as competition, predation, or edge effects (Taylor and Fox, 2001; Carthew et al., 2009). For example, Morethia boulengeri was abundant in all treatments, probably because they are a widespread generalist species that prefers rocky habitats with low levels of bare ground (Cogger, 2000; Wilson and Swan, 2003) and can tolerate substantial levels of disturbance (Fischer et al., 2004; Cunningham et al., 2007; Michael et al., 2010). Ctenotus robustus was rarely present in remnant linear strips, but was abundant in cleared and revegetated linear strips, and is probably able to use the agricultural matrix (Cunningham et al., 2007; Michael et al., 2010). Menetia greyii is also an abundant generalist able to use the agricultural matrix (Wilson and Swan, 2003; Schutz and Driscoll, 2008). Menetia greyii was probably able to use all available habitats as long as ground layers such as rocks and litter were present (Smith et al., 1996). These results suggest that while generalist species are able to persist in more degraded landscapes, we know little about what other variables influence the persistence of these reptile species. For example, competition for habitat niches and food resources may be occurring within and between these species, restricting certain species to certain habitats (Nichols and Bamford, 1985; Williams et al., In press). Greater research is required into how competition, predation, and edge effects influence generalist reptile species in agricultural areas, and whether these factors are related to habitat shape or site-type.

Environmental and habitat variables influencing reptile species and communities

I found that environmental and habitat variables were highly influential in structuring reptile species and communities. Reptile species richness, abundance, and community composition responded strongly to environmental

71 variables and in particular to ground layer attributes. Environmental variables accounted for a substantial amount of the variation in reptile community composition, explaining 22% of the variation in the Benalla region and 27% of the variation in the Wimmera region out of a total of 29% and 35% respectively when treatment variables were included. Rock cover had a strong positive effect on reptile species richness, abundance and community composition, as rocky areas contained a disproportionate number of the key microhabitat elements that reptiles require (MacNally and Brown, 2001; Masterson et al., 2009; Croak et al., 2010; Michael et al., 2010). Other ground layer attributes such as litter cover and the proportion of native plants, as well as the proportion of bare ground were also important in influencing reptile species abundance. Litter, tussock and herb cover and the proportion of native plants were important in structuring reptile community composition (Bateman et al., 2008; Lindenmayer et al., 2008b; Valentine and Schwarzkopf, 2009). The density of mid-stratum was also important in structuring community composition, probably because mid-stratum contributes litter to the ground layer and reduces bare ground (Smith et al., 1996; Leynaud and Bucher, 2005). However, there were limitations to the habitat data I gathered as they were collected using visual estimates; studies suggest that while this method allows for quicker surveys over larger areas, it is less reliable than other methods such as line and point estimation. These methods may produce more accurate data for future restoration research (Floyd and Anderson, 1987; Vittoz and Guisan, 2007).

My results also suggest that spatial variables are very influential in structuring reptile communities. This is probably because environmental variables, such as vegetation type, are strongly influenced by temperature, precipitation and soil type and these variables are strongly related to spatial position (O‟Shea and Kirkpatrick, 2000; Prober and Thiele, 2004). Geographically similar sites also tend to support more similar faunal assemblages than sites that are geographically further apart (Legendre and Fortin, 1989; Parris, 2004; Bell and Donnelly, 2006). However, spatial, environmental and treatment variables only explain a proportion of the variation in reptile community composition, and there are other elements and possibly factors related to dispersal not studied here

72 that are likely to be important for structuring reptile communities. Determining what these elements are will further enhance our ability to revegetate areas so that they resemble remnant habitats.

Revegetation and ground layers

As mentioned above, some reptile species need ground layer attributes in good condition to survive (Brown, 2001; Fischer et al., 2003; Brown et al., 2008). A lack of these ground layers in revegetated areas (notably litter cover and native plant species) may have been the reason why some species could not readily use replanted habitats (Michael et al., 2004; Cunningham et al., 2007; Munro et al., 2007; Marquez-Ferrando et al., 2009; Masterson et al., 2009; Croak et al., 2010). Many of the above vegetation attributes can take years to develop, especially microhabitats created by leaf litter and logs (Vesk et al., 2008). Even so, rocks and other ground layer structures can be replaced during revegetation projects, enhancing reptile species in these areas (Michael et al., 2004; Marquez-Ferrando et al., 2009; Croak et al., 2010). Similarly, weed control and the replanting of native grasses can be undertaken (Gibson-Roy et al., 2010b), increasing reptile species that have declined as a result of weed invasion (Valentine and Schwarzkopf, 2009). However, to revegetate natural habitats more effectively we need to understand the specific needs of rarer reptile species, and how to restore ground layers without negatively influencing species that require more open habitats.

I suggest that the restoration of ground layers in revegetated habitats would increase reptile species richness and abundance, as described above, and thus influence reptile community composition. However, other studies have suggested that thinning revegetated and regrowth areas (Michael et al., 2011) and also burning these areas may help reptile community composition resemble that in undisturbed habitat (Craig et al., 2010). Yet other studies suggest that for reptile community composition to resemble remnant areas, revegetated areas require a variety of differently managed areas such as burnt and unburnt sites, and open grassy woodlands as well as denser forest (Perry et al., 2009; Santos and Poquet, 2010). Similarly, as revegetated areas age they will become more 73 structurally diverse and better suit a variety of reptile species (Woinarski, 1989; Masterson et al., 2008; Santos and Poquet, 2010). While the thinning and burning of habitats may benefit some species, these actions should take place in habitats where they have historically been used for management, and as suggested above, a number of differently managed areas should be developed to provide a mosaic of different habitat elements. For the management of more open grassy woodland habitats, my results show that restoration actions should focus on restoring the mid-stratum and ground layer attributes such as rocks, litter, and herbs.

Influence of distance along linear strips

Interestingly, I found that abundance of individual generalist reptile species was not influenced by distance away from remnant patches along linear strips, but that overall reptile abundance, abundance of C. tetradactyla and species richness and abundance of rare reptiles were influenced by distance effects. Importantly, there was a trend for rare species richness and abundance and C. tetradactyla abundance to increase in remnant linear strips, and decrease in revegetated and cleared linear strips as distance from remnant patches increased. While a number of studies have shown that linear strips can increase faunal movement and dispersal (Lindenmayer et al., 1994a; Hill, 1995; Estrada et al., 2000; Haddad, 2000; Tewksbury et al., 2002; Driscoll, 2004), few studies report on the distance species are located along linear strips (Haddad et al., 2003). My results provide insights into how some reptile species use linear strips.

One mechanism that could explain lower species richness and abundance of rare species in remnant patches compared to remnant linear strips is the patch- dynamics model (metacommunity theory), where species that are the weakest competitors are the best dispersers (Tilman et al., 1994; Huxel and Hastings, 1999; Driscoll, 2008). Under this model, species that are strong competitors, such as generalists, would be more likely to be found in remnant patches because these areas would generally contain a larger area of habitat (Tilman et

74 al., 1994; Driscoll, 2004). In remnant patches, rare reptiles may be outcompeted by the stronger generalist competitors, which would have a limited tendency to move into linear strips, resulting in higher species richness and abundance of rare species in remnant linear strips as distance away from competition increased (Driscoll, 2008). As I found no generalist reptiles were substantially more abundant in remnant patches, other faunal groups not studied here may be reducing rare reptiles in remnant patches via competition or possibly predation, suggesting that more research is needed to test this mechanism.

The decline with increasing distance of C. tetradactyla, overall reptile abundance and rare reptile species in cleared and revegetated linear strips is similar to those patterns described as a “peninsula effect” (Simpson, 1964), where species diversity declines as distance from the peninsula base (a remnant patch in this case) to the peninsula tip increases (Lawlor, 1983; Wiggins, 1999; Peck et al., 2005; Tubelis et al., 2007). Means and Simberloff (1987) reported this effect in a study of reptiles and amphibians in the south- eastern USA, and attributed the decline of species at the peninsula tip to a reduction in appropriate habitat. This implies that if the revegetated linear strips I studied had appropriate habitat attributes, reptiles may be able to use these areas. As rare reptiles could use remnant linear strips, this suggests that linear strip width (20m) and length (500m) should not be a limiting factor in the distance reptiles are located along revegetated linear strips of the same dimensions or larger. More research is needed to determine how far linear strips can extend from remnant patches before reptiles decline, and if linear strips less than 20m wide are useful for reptile species.

Carlia tetradactyla and the rare reptiles I recorded were probably more common in remnant linear strips also because they required either specialised habitats or food resources to persist in these areas (Michael et al., 2004; Driscoll and Hardy, 2005; Nichols and Grant, 2007; Brown et al., 2008; Schutz and Driscoll, 2008; Wilson and Swan, 2010). This result may be related to the coincidence of two factors. First, the vegetation in remnant linear strips was in good condition compared to other linear strips, so C. tetradactyla and rare reptiles were more

75 able to use these areas. For example, studies have shown that older roadsides, especially those that were designated prior to the 1900s, usually contain more established remnant eucalypt trees and a higher diversity of native shrubs than younger roadsides (Spooner and Lunt, 2004; Spooner and Smallbone, 2009). Second, remnant patches may have had lower habitat quality than remnant linear strips, but higher habitat quality than cleared or revegetated linear strips, increasing C. tetradactyla abundance and rare reptile species richness and abundance close to remnant patches in these more degraded linear strips. For example, C. tetradactyla are known to be abundant in habitats with tree and shrub cover, dense litter cover, fallen timber and tussock grasses (Fischer et al., 2003; Cunningham et al., 2007), so are probably more abundant in remnant linear strips due to these variables (Spooner and Smallbone, 2009).

There may be a number of factors altering the habitat quality in linear strips and remnant patches that could have resulted in better quality remnant linear strip habitat. The remnant patches I studied were grazed intermittently by livestock, and that grazing may have decreased vegetation quality, simultaneously decreasing reptile abundance (James, 2003; Driscoll, 2004; Fischer et al., 2004; Leynaud and Bucher, 2005; Brown et al., 2008). Remnant linear strips are also subject to greater water and nutrient runoff from paddocks and roads than remnant patches, increasing plant growth and potentially providing better quality habitat for rarer reptiles (Driscoll, 2004). Similarly, the connection of remnant linear strips to other remnant roadside habitat may have provided a rescue effect between remnant habitats (Brown and Kodric-Brown, 1977; Hanski and Gyllenberg, 1993). These results imply that it is important to protect remnant linear strips, in order to maintain rare reptile species and reptiles sensitive to poorer quality habitats. However, little is known about how reptiles use linear strips and what habitats rare reptiles require, or about how to appropriately manage remnant patches so that rare reptiles can persist in these areas.

Conclusion

With regard to my initial predictions, I found that (i) reptile species richness and abundance were not higher in any of the site-types (remnant, revegetated and cleared), and that (ii) there was no difference in species richness and abundance between linear strips and patches. This is probably because many of the reptiles I sampled were abundant generalists and able to use a variety of different habitats. I also found that (iii) overall reptile species richness and the abundance of individual generalist reptiles did not substantially decline in linear strips. As distance from remnant patches increased, overall abundance did decline in revegetated and cleared linear strips but increased in remnant linear strips. The same trend reported for overall abundance also occurred in rare reptile species richness and abundance and Carlia tetradactyla abundance. Carlia tetradactyla also showed a trend towards higher abundance in remnant linear strips. These results show that linear strips are an important element in fragmented landscapes and that habitat quality in remnant linear strips may be responsible for the persistence of some reptiles in these areas. Finally, I found that (iv) environmental variables did play a very important role in structuring reptile communities, as well as influencing reptile species richness and abundance. These variables included ground layer attributes such as rock, litter and herb cover as well as the proportion of native plants and the density of the mid-stratum.

My results have implications for the success of revegetation strategies in fragmented agricultural areas, as they suggest that revegetation may not be useful, as many reptile species are resilient to habitat loss and fragmentation and some of the more specialised species may already have been lost. However, revegetation of linear strips may enhance habitat for rare reptiles and individual species, and possibly provide movement and dispersal if native plants and high-quality ground attributes such as rocks, litter and herbs are present. Before this can happen, we need to have a better understanding of how far reptiles can move along linear strips, and if linear strips less than 20m wide can provide appropriate habitat for rare reptile species. We also need to determine what the habitat requirements of rarer reptile species are, and what specialised 77 species have been lost in agricultural areas, by surveying nearby continuous natural areas. There is also a need to know how competition, predation, and edge effects influence reptile species and how these factors influence where generalist and rarer reptiles are located. Overall, this study shows that linear strips are beneficial to reptile species, and that revegetated areas can provide habitat and potentially increase movement and dispersal if management agencies and communities undertaking revegetation activities include appropriate habitat elements such as ground layers.

Chapter 4. Beetles in agricultural landscapes: Is revegetation maintaining species in fragmented areas?

Introduction

Clearing natural habitats for agricultural production has caused widespread habitat loss and fragmentation, resulting in biodiversity declines throughout the world (Saunders and Hobbs, 1991; Saunders et al., 1991; Collinge, 1996; Lindenmayer, 2009). As a result of this clearing, the protection of state-owned remnant habitat is no longer enough to maintain biodiversity, because much of the remaining habitat is located on private land (Carr and Hazell, 2006; Mackey et al., 2007). Therefore, in order to maintain or increase animal and plant biodiversity in these modified agricultural landscapes, restoration through revegetation is necessary to form a network of nature reserves through both private and public land (Reay and Norton, 1999; Ryan, 2000; Watts and Gibbs, 2002; Freudenberger and Brooker, 2004; Vesk and Mac Nally, 2006). Revegetation is usually undertaken to connect remnant areas together to enable species movement and dispersal, and to enlarge remnant areas to increase habitat size (Saunders et al., 1991; Beier and Noss, 1998; Fischer and Lindenmayer, 2007).

Although there are many examples around the world of revegetation starting to occur (UNEP, 1999; Hobbs, 2003; Munro et al., 2007; FAO, 2010), ecologists and land managers have little knowledge of how effective revegetation is for conserving native flora and fauna (Kimber et al., 1999; Ryan, 2000; Munro et al., 2007). There is also a lack of knowledge of the best shape and size for restored reserves (Bolger et al., 2001; Freeman et al., 2009; Thomson et al., 2009) and the role of linkages such as linear strips in maximising biodiversity gains (Simberloff et al., 1992; Soulé et al., 2004; Lindenmayer et al., 2008a). While linear strips can provide connectivity, these areas may also increase disease spread and the dispersal of pest species (Simberloff and Cox, 1987; Simberloff et al., 1992). Similarly, enlarging remnant patches is likely to provide a larger amount of habitat and increase faunal diversity (MacArthur and Wilson, 1967; Prugh et al., 2008), but would not assist species to move if these patches of habitat became unsuitable. This uncertainty about the effectiveness of restoration in reducing native species declines and the trade-offs related to the benefits of connectivity versus patch enlargement suggests that a more 80 strategic approach to conservation management is required (Rumpff et al., 2008; Rumpff et al., 2010). This includes improved field data about how faunal groups use restored areas, to facilitate management decisions and effective allocation of funds (Ruiz-Jaen and Aide, 2005; Munro et al., 2009).

Beetles (Coleoptera) are an important group to study in revegetated areas as they provide functional roles as herbivores, carnivores, omnivores and scavengers (Davies and Margules, 2000; Davies et al., 2000; Lassau et al., 2005; Schaffers et al., 2008; Gibb and Cunningham, 2010; Vandewalle et al., 2010), as well as undertaking pollination, seed dispersal, decomposition of dung and litter, and soil turnover (Grimbacher et al., 2007; Nichols et al., 2008; Gibb and Cunningham, 2010; Vandewalle et al., 2010). Beetle functional groups, a higher taxonomic resolution than species richness, abundance or family groupings, can tell us which ecological functions have changed as a result of habitat changes, and which species may be at most risk of decline (Davies et al., 2000; Lassau et al., 2005). Analysis of functional groups can be particularly informative, as it can relate a group‟s general ecological traits to its responses to habitat disturbance and management actions such as revegetation and remnant protection (Ribera et al., 2001; Michaels, 2007). Beetle functional groups as well as beetle community composition can therefore be a better measure of habitat quality than species richness and abundance due to their reliance on environmental variables and ecosystem processes (Driscoll and Weir, 2005; Vanbergen et al., 2010).

While beetles are an important group to research for these reasons, few studies have examined the response of beetle species, families or functional groups to revegetation projects or human modified landscapes generally (Majer et al., 2007; Gibb and Cunningham, 2010). Previous research suggests that some beetle groups may recolonise revegetated areas if these areas are in close proximity to remnants and if suitable habitat components are present (Grimbacher and Catterall, 2007; Grimbacher et al., 2007), such as ground layer attributes (Nakamura et al., 2003; Schaffers et al., 2008). Beetles that can fly are also thought to colonise revegetated areas more rapidly than flightless

81 species, if revegetated areas are in close proximity, because they are more able to cross unsuitable areas to take advantage of restored habitats (Driscoll and Weir, 2005; Lassau et al., 2005). Research on habitat loss and fragmentation implies that the spatial arrangement of revegetation should be important, although little work has been done on this (Didham et al., 1998; Driscoll and Weir, 2005; Nichols et al., 2008). Evidence suggests that habitat condition may be more important in determining beetle community composition between revegetated and restored areas, as remnant areas contain more structurally and floristically diverse habitat than revegetated areas (Harrison and Bruna, 1999; Driscoll et al., 2010a; Hopp et al., 2010). Overall, we lack information on how beetles respond to revegetation, and which habitat attributes are important to restore beetle species richness, abundance, community composition, and functional groups.

In this study I assessed the effectiveness of revegetated areas in maintaining or increasing beetle species richness and abundance compared to remnant and cleared habitats in two fragmented agricultural landscapes of south-eastern Australia. The reason for studying these three distinct treatments was to determine whether beetle communities in revegetated areas were on a trajectory towards the beetle composition of remnant habitat, or were closer to the least desired state found in cleared linear strips. Specifically, I wanted to know whether: (i) beetle species richness, abundance, functional and family species richness, and ability to fly differed between revegetated, remnant and cleared linear strips and remnant patches; revegetated and remnant patches; and revegetated linear strips and revegetated patches, (ii) vegetation type and structure influenced beetle species richness, abundance and functional species richness; and (iii) variation in beetle community composition could be explained by environmental and treatment variables, or covariance between these two variables.

I predicted that: (i) Beetle species richness, abundance, and functional and family species richness would be higher in: remnant linear strips and remnant patches compared to revegetated linear strips and patches and cleared linear

82 strips, because remnant areas should provide better quality habitat (Harrison and Bruna, 1999; Munro et al., 2011); revegetated areas compared to cleared linear strips because revegetated areas would have a more diverse vegetation structure (Kavanagh et al., 2007); and patches compared to linear strips because patches would provide a larger habitat area (MacArthur and Wilson, 1967; Prugh et al., 2008). Beetles that could fly would be most common in nearby revegetated areas as they should be more able to recolonise these areas (Driscoll and Weir, 2005; Lassau et al., 2005). (ii) Vegetation type and structure would have a strong influence on beetle species richness, abundance, and species richness of functional groups because beetles respond strongly to these habitat variables (Grimbacher and Catterall, 2007; Grimbacher et al., 2007). (iii) Environmental and treatment variables would have a strong influence on beetle community composition, with beetle communities being influenced most strongly by environmental variables (Watts et al., 2008; Gibb and Cunningham, 2010). Documenting and understanding the processes that influence beetle species in revegetated areas compared to other habitats will help natural resource managers better design habitat connections and habitat enlargements in highly fragmented agricultural areas.

Methods

I used the same field survey described in Chapter 3 to sample beetles in areas of remnant, revegetated and cleared vegetation in two agricultural landscapes of Victoria, Australia. I established pitfall traps in the Wimmera region in November and December 2007 and in the Benalla region in November and December 2008. In the Wimmera region trapping took place from February to March 2008 and in the Benalla region from February to March 2009. I surveyed each location twice daily for 5 consecutive days (4 nights) every month for two months. Beetles were collected live and preserved in 70% ethanol for later identification to species level. Beetle identification and ecology was determined from Matthews (1997), Matthews and Bouchard (2008), Zimmerman (1994) and the Australian Faunal Directory (Australian Biological Resources Study, 2010). Identified beetles were checked with reference collections in CSIRO

Entomology, Canberra, Australia (Appendix 11). Functional groups were classified based on work by Driscoll and Weir (2005). As the pitfall traps did not contain any preservative it is acknowledged that specimens are likely to be biased towards terrestrial, flightless beetle species larger than 5mm.

Data analysis

Treatment effects

Beetle species richness and abundance were analysed as a response to treatment type using linear mixed models with a Poisson distribution in a Bayesian framework (Lunn et al., 2000; Spiegelhalter et al., 2002; McCarthy, 2007). The Bayesian Poisson regression had uninformative priors for the intercept term and the regression coefficients in WinBUGS (Lunn et al., 2000; Spiegelhalter et al., 2002). For more details on Bayesian analysis, site selection and general survey methodology, please refer to Chapter 3 (Appendix 2).

I undertook three separate analyses, comparing: (1) revegetated linear strip, remnant linear strip and cleared linear strip connected to a remnant patch; (2) revegetated patch and remnant patch; and (3) revegetated linear strip and revegetated patch. The response variables were overall beetle species richness and abundance; abundance of individual beetle species that were captured in more than half of the sites; species richness of beetles that could fly; species richness of beetle functional groups (carnivore, herbivore, omnivore and scavenger); and species richness in different beetle families (10 families). The explanatory variables were region and site-type as fixed effects and location as a random effect. Region was a fixed effect to take into account variation between the Wimmera and Benalla. Location was fitted as a random effect because the areas sampled were randomly positioned within each region. Location referred to the five trapping areas for each of the linear strip and patch treatments within the Wimmera and Benalla region. Within each location there were four site-types (revegetated, cleared and two remnant areas) for the linear strip treatments and two site-types (revegetated and remnant) for the patch

84 treatments. Overall species richness and abundance was calculated as a total at each site.

Relationships with habitat variables

I quantified relationships between beetle functional group, species richness and abundance, and habitat variables measured at a site using a generalised linear mixed model with a Poisson distribution in WinBUGS (Lunn et al., 2000). I used overall beetle species richness and abundance as the response variables in the analysis, with region as a fixed effect and location as a random effect. I used Deviance Information Criteria (DIC) values (Spiegelhalter et al., 2002) as an alternative to the Akaike Information Criterion (AIC) to compare the fit and complexity of the linear mixed models. Models with DIC values within 2 points of the best model are considered to be very similar to the best model, so were kept. Models with a DIC value between 2 and 7 points were considered as being potentially relevant (Spiegelhalter et al., 2002).

I assessed the importance of variables by calculating the multiplicative effect (with 95% credible intervals) of each of the six habitat variables on beetle species richness, abundance, and species richness of functional groups. To establish the degree of association between pairs of variables (Sokal and Rohlf, 1995), habitat variables were analysed with a correlation analysis in R version 2.9.1 (R Development Core Team, 2009). Correlated variables (r > 0.4) were removed from future analysis. Variables included in the analysis were mid- stratum density, percentage of bare ground, rock cover, tussock grass cover, litter cover, and the proportion of native plants.

Influence of environmental and spatial variables on community composition

I used a redundancy analysis (RDA) to investigate whether the composition of the beetle community was a function of environmental and treatment variables using CANOCO version 4.0 (ter Braak and Sˇmilauer, 1998). Redundancy analysis explains the variation in community composition through a succession

85 of linear combinations of explanatory variables (Borcard et al., 2011). The species matrix was constructed using the total abundance of each beetle species observed in each site transformed to produce the Hellinger distance between sites. There were seven explanatory environmental variables used in this analysis. The variables of mid-stratum density, rock cover, litter cover, herb cover and tussock grass cover were analysed as their respective rank densities (Braun-Blanquet scale). The proportion of native plants was analysed as a percentage, while soil type was analysed as its respective number.

I divided the treatment matrix into two separate variables: treatment shape (linear strips and patches) and site-type (revegetated areas, remnant areas, and cleared areas). In the analysis of the treatment matrix I included an interaction between the treatment shape variable and the site-type variable. The interaction was defined because treatment shape (linear strips and patches) may depend on site-type (revegetated, remnant and cleared), and vice versa. I also undertook a separate RDA to see whether the composition of beetle assemblages was a function of environmental and spatial variables, as geographically similar sites tend to contain similar faunal communities compared to sites that are further apart (Parris, 2004). The spatial matrix contained normalised geographic co-ordinates centred by region in decimal degrees to four decimal places. More details about the RDA analysis can be found in Chapter 3.

Results

Treatment effects

I caught 3,096 beetles from 96 different species, representing 10 different families. I found no clear evidence of a difference in overall beetle species richness or abundance in the three different treatments of linear strips (Figure 4.1 a, b), patches (Figure 4.2 a, b) or revegetated areas (Figure 4.3 a, b), as shown by the overlapping credible intervals. I also observed that there was little evidence of an effect of treatment on the abundance of the individual beetle

86 species analysed. The bronzed field beetle (Adelium brevicorne) was the most common species recorded and did not differ in linear strip treatments (Figure 4.1 d), revegetated treatments (Figure 4.3 c) or remnant patches (Figure 4.2 d). Seirotrana parallela, Sarticus discopunctatus and Promecoderus species also did not differ in the different treatments (Figure 4.1 f, g, h & Figure 4.2 e). While Adelium similatum and Chauliognathus nobilitatus showed no clear evidence of differing between the treatments, there was a trend for these species to be more abundant in remnant linear strips compared to revegetated and cleared linear strips and remnant patches (Figure 4.1 c, e).

a) Overall species richness b) Overall species abundance 15 3.0 2.5 10 2.0 1.5 5 1.0

Mean RichnessMean 0.5 Log AbundanceLog 0 0.0 CLS RevLSRemLSRemP CLS RevLSRemLSRemP

c) Adelium similatum d) Adelium brevicorne a.12 b. 3.0 10 2.5 8 2.0 6 1.5 4 1.0

2 0.5 Log AbundanceLog Mean AbundanceMean 0 0.0 CLS RevLSRemLSRemP CLS RevLSRemLSRemP

e) Chauliognathus nobilitatus f) Seirotrana parallela

4.0 c.2.0

3.0 1.5

2.0 1.0

1.0 0.5 Mean AbundanceMean

0.0 AbundanceMean 0.0 CLS RevLSRemLSRemP CLS RevLSRemLSRemP

g) Sarticus discopunctatus h) Promecoderus species 2.0 8

1.5 6

1.0 4

0.5 2 Mean AbundanceMean Mean AbundanceMean 0.0 0 CLS RevLS RemLS RemP CLS RevLSRemLSRemP Figure 4.1. Species richness and abundance of beetles in linear strip treatments: (a) overall species richness and (b) abundance; abundance of (c) A. similatum, (d) A. brevicorne, (e) C. nobilitatus, (f) S. parallela, (g) S. discopunctatus, and (h) Promecoderus species. CLS = cleared linear strip, RevLS = Revegetated linear strip, RemLS = Remnant linear strip, RemP = Remnant patch. Bars represent 95% credible intervals.

b) Overall species richness a) Overall species abundance 15 3 2.5 10 2 1.5 5 1

0.5 Mean RichnessMean 0 AbundanceLog 0 RemP RevP RemP RevP

c) Adelium brevicorne d) Sarticus discopunctatus 12.0 2.5 10.0 2.0 8.0 1.5 6.0

Abundance 1.0 4.0

2.0 0.5

Mean Mean AbundanceMean 0.0 0.0 RemP RevP RemP RevP Figure 4.2. Species richness and abundance of beetles in patch treatments: (a) overall species richness and (b) abundance; abundance of (c) A. brevicorne, and (d) S. discopunctatus. RemP = Remnant patch, RevP = Revegetated patch. Bars represent 95% credible intervals.

a) Overall species richness b) Overall species abundance

20 3.0 2.5 15 2.0 10 1.5 1.0 5

0.5

Log AbundanceLog Mean RichnessMean 0 0.0 RevLS RevP RevLS RevP

c) Adelium brevicorne

Mean AbundanceMean 0 RevLS RevP

Figure 4.3. Species richness and abundance of beetles in revegetated areas: (a) species richness and (b) abundance; and abundance of (c) A. brevicorne. RevLS = Revegetated linear strip, RevP = Revegetated patch. Bars represent 95% credible intervals. 89

Scavengers were the most species-rich and Omnivores were the least species- rich beetle groups I surveyed but neither group showed any clear evidence of being different between treatments (Figure 4.4 a, b, c). However, Omnivores tended to have higher species richness in cleared linear strips (Figure 4.4 a). Carnivores had high species richness in most treatments and also showed no clear evidence of differing between treatments (Figure 4.4 a, b, c). Herbivorous beetles showed some evidence of having higher species richness in cleared linear strips compared to remnant patches (mean expected difference = 1.34, CIs = 0.17 - 2.71) (Figure 4.4 a).

a) Functional species richness - Linear strips 5 CLS RevLS RemLS RemP 4 3 2

1 Species RichnessSpecies 0 Carnivore Herbivore Omnivore Scavenger

b) Functional species richness - c) Functional species richness - Patches Revegetated 5 5 4 RevP RemP 4 RevLS RevP 3 3 2 2 1 1

0 0

Species RichnessSpecies Species RichnessSpecies

Figure 4.4. Species richness of functional groups in (a) linear strip, (b) patch and (c) revegetated treatments. Light columns = cleared linear strip (CLS); light grey columns = revegetated linear strip (RevLS); dark grey columns = remnant linear strip (RemLS); mid-grey columns = remnant patch (RemP); beige columns = revegetated patch (RevP). Bars represent 95% credible intervals. 90

At the family level, Curculinidae (weevil) showed some evidence of having higher species richness in cleared linear strips compared to remnant patches (mean expected difference = 1.24, CIs = 0.34 - 2.34) (Figure 4.5 a). Carabidae (ground beetle) and Tenebrionidae (pie-dish or darkling beetle) were the most species-rich families but their species richness did not vary between treatments (Figure 4.5 a, b, c). Scarabaeidae and other beetle families also showed no clear evidence of being different between treatments (Figure 4.5 a, b, c).

a) Family species richness - Linear strips 6 CLS RevLS RemLS RemP 5 4 3 2 1 Species RichnessSpecies 0

b) Family species richness - c) Family species richness -

Patches Revegetation 5 RevLS RevP 5 RevP RemP 4 4 3 3 2 2

1 1 FamilyRichness

FamilyRichness 0 0

Figure 4.5. Mean beetle family richness in (a) linear strip, (b) patch and (c) revegetated treatments. Light columns = cleared linear strip (CLS); light grey columns = revegetated linear strip (RevLS); dark grey columns = remnant linear strip (RemLS); mid-grey columns = remnant patch (RemP); beige columns = revegetated patch (RevP). Bars represent 95% credible intervals.

There was no clear evidence of species richness changing in different treatments of beetles that could fly compared with those that could not (Figure 4.6 a, b, c).

a) Species Richness - Linear strips 10 Fly Flightless 8

2 Species RichnessSpecies 0 CLS RemLS RevLS RemP

b) Species Richness - Patches c) Species Richness - Revegetation

8 Fly Flightless 10 Flight Flightless 6 8 6 4 4 2

Species RichnessSpecies Species RIchnessSpecies 0 0 RemP RevP RevLS RevP

Figure 4.6. Species richness of flying and flightless beetles in (a) linear strip, (b) patch and (c) revegetated treatments. Grey columns = flight, white columns = flightless. CLS = cleared linear strip, RemLS = Remnant linear strip, RemP = Remnant patch, RevLS = Revegetated linear strip, RevP = Revegetated patch. Bars represent 95% credible intervals.

Relationships with habitat variables

Beetle species richness was influenced by a combination of the six habitat variables, especially mid-stratum density and the proportion of native plants, in all of the six best-supported models (Table 4.1, Figure 4.7). The credible intervals were relatively small, signifying that precision was high. Mid-stratum

92 density (mean effect size = 0.7) and the proportion of native plants (mean effect size = 0.6) showed some evidence of decreasing beetle species richness (i.e., sites with the greatest mid-stratum density had 0.7 times as many beetle species as sites with the least mid-stratum density). The other variables of litter cover (mean effect size = 1.3), herb cover (mean effect size = 0.9) and tussock cover (mean effect size = 0.9) showed no clear evidence of changing beetle species richness (Figure 4.7). Beetle species richness was also substantially influenced by the trapping location, within either the Wimmera or the Benalla region. Region (null model) and location were the most important variables influencing overall beetle abundance (Table 4.2), meaning that beetle abundance differed between the Wimmera and Benalla regions and between trapping locations. Predictions for these models were less precise, as shown by large credible intervals. None of the variables of litter cover (mean effect size = 0.7), herb cover (mean effect size = 0.5), the proportion of native plants (mean effect size = 0.8), mid-stratum density (mean effect size = 1.1) and tussock cover (mean effect size = 1.1) showed clear evidence of changing beetle abundance (Figure 4.8).

Species richness of herbivorous beetles showed some evidence of decreasing in response to the proportion of native plants (mean effect size = 0.4), while other variables showed no clear evidence of changing herbivore species richness as their credible intervals encompassed 1. These were mid-stratum density (mean effect size = 0.7), herb cover (mean effect size = 0.5), tussock grass cover (mean effect size = 2.1) and litter cover (mean effect size = 1.1) (Figure 4.9). Tussock grass cover may have had a positive influence on herbivorous beetle species, and mid-stratum density and herb cover a negative influence. Scavenging beetles were strongly influenced by location and the combination of location and litter. The variables of litter cover (mean effect size = 1.7), the proportion of native plants (mean effect size = 0.6), tussock grass cover (mean effect size = 0.8), herb cover (mean effect size = 0.9) and mid- stratum density (mean effect size = 1.0) all showed no clear evidence of changing the species richness of scavenging beetles. Litter cover may have had a positive influence on scavenging beetle species richness and the proportion of

93 native plants a negative influence (Figure 4.10). Carnivorous beetles were substantially influenced by location and mid-stratum density (Table 4.5). None of these variables showed any clear evidence of changing carnivorous beetle species richness. These were mid-stratum density (mean effect size = 0.6), herb cover (mean effect size = 0.8), tussock grass cover (mean effect size = 1.5) and the proportion of native plants (mean effect size = 1.0). Mid stratum density may have had a negative influence on carnivorous beetle species richness (Figure 4.11).

Table 4.1. DIC values for the 6 best-supported models of overall beetles species richness as a function of habitat and location. Location = geographic location where sampling took place; mid-stratum = density of second tallest stratum; natives = proportion of native plants; litter = litter cover; herb = herb cover; tussock = tussock grass cover.

Model Variable ΔDIC ΔDIC Difference Null model 1126 1 Mid-stratum, location 336 2 2 Natives, location 335.5 1.5 3 Mid-stratum, natives, location 334 0 4 Mid-stratum, litter, natives, location 334.9 0.9 5 Mid-stratum, tussock, natives, location 335.9 1.9 6 Mid-stratum, herb, natives, location 335.8 1.8

100

10 Natives Tussock (3) (5) 1 Litter (4) Mid- Herb (6) Effect SizeEffect 0.1 stratum (3) 0.01 Habitat Variable

Figure 4.7. The multiplicative effect on beetle species richness of mid-stratum density, the proportion of native plants, litter cover, tussock cover and herb cover, as shown by each variable‟s mean. Numbers below the habitat variable label represent the best- supported model. Bars represent 95% credible intervals.

Table 4.2. DIC values for the 7 best-supported models of overall beetle abundance as a function of habitat and location. Location = geographic location where sampling took place; herb = herb cover; litter = litter cover; natives = the proportion of native plants; tussock = tussock grass cover; mid-stratum = density of second tallest stratum.

Model Variable ΔDIC ΔDIC Difference Null model 138.9 0 1 Location 140.6 1.7 2 Herb, location 141.7 2.8 3 Litter, location 142 3.1 4 Natives, location 142.3 3.4 5 Tussock, location 142.4 3.5 6 Mid-stratum, location 142.6 3.7

100

10 Litter (4) Herbs (3) 1 Mid- Effect SizeEffect Tussock Natives 0.1 stratum (6) (5) (7) 0.01 Habitat Variable

Figure 4.8. The multiplicative effect on beetle abundance of mid-stratum density, litter cover, tussock cover, herb cover and the proportion of native plants, as shown by each variable‟s mean. Numbers below the habitat variable label represent the best- supported model. Bars represent 95% credible intervals.

Table 4.3. DIC values for the 9 best-supported models of herbivorous beetles as a function of habitat and location. Location = geographic location where sampling took place; herb = herb cover; litter = litter cover; natives = the proportion of native plants; tussock = tussock grass cover; mid-stratum = density of second tallest stratum.

Model Variable ΔDIC ΔDIC Difference Null model 263 1 Herbs, location 224.8 1.8 2 Natives, location 223 0 3 Mid-stratum, natives, location 223.1 0.1 4 Tussocks, natives, location 224.1 1.1 5 Herb, natives, location 223.9 0.9 6 Mid-stratum, litter, natives, location 224.9 1.9 7 Mid-stratum, tussocks, natives, location 224.5 1.5 8 Mid-stratum, herbs, natives, location 224.2 1.2 9 Tussocks, herbs, natives, location 224.9 1.9

100 Tussock 10 (4) Natives 1 (2) Litter (6) Effect SizeEffect Mid- 0.1 Herb (5) stratum (3) 0.01 Habitat Variable

Figure 4.9. The multiplicative effect on herbivorous beetles of mid-stratum density, the proportion of native plants, tussock cover, herb cover and litter cover, as shown by each variable‟s mean. Numbers below the habitat variable label represent the best- supported model. Bars represent 95% credible intervals.

Table 4.4. DIC values for the 12 best-supported models of scavenging beetles as a function of habitat and location. Location = geographic location where sampling took place; herb = herb cover; litter = litter cover; natives = the proportion of native plants; tussock = tussock grass cover; mid-stratum = density of second tallest stratum.

Model Variable ΔDIC ΔDIC Difference Null model 360.5 1 Location 263.4 0 2 Mid-stratum, location 265.2 1.8 3 Litter, location 263.4 0 4 Tussock, location 264.2 0.8 5 Herb, location 265.3 1.9 6 Natives, location 265.1 1.7 7 Mid-stratum, litter, location 265.2 1.8 8 Litter, tussock, location 264.7 1.3 9 Litter, herbs, location 265.2 1.8 10 Litter, natives. location 263.5 0.1 11 Mid-stratum, litter, natives, location 265.2 1.8 12 Litter, herbs, natives, location 265.3 1.9

100

10 Tussock (4) Herb (9) 1 Litter (3)

Mid- Effect SizeEffect 0.1 stratum Natives (2) (10) 0.01 Habitat Variable

Figure 4.10. The multiplicative effect on scavenging beetles of litter cover, the proportion of native plants, mid-stratum density, tussock cover, and herb cover, as shown by each variable‟s mean. Numbers below the habitat variable label represent the best-supported model. Bars represent 95% credible intervals.

Table 4.5. DIC values for the 7 best-supported models of carnivorous beetles as a function of habitat and location. Location = geographic location where sampling took place; herb = herb cover; natives = the proportion of native plants; tussock = tussock grass cover; mid-stratum = density of second tallest stratum.

Model Variable ΔDIC ΔDIC Difference Null model 282.5 1 Location 232.3 0 2 Mid-stratum, location 232.6 0.3 3 Tussocks, location 233.8 1.5 4 Herbs, location 234.1 1.8 5 Natives, location 234.3 2 6 Mid-stratum, Natives, location 234.3 2 7 Mid-stratum, Herbs, location 234.3 2

100

10 Natives (5) Herb (4) 1 Tussock Effect SizeEffect 0.1 Mid- (3) stratum (2) 0.01 Habitat Variable

Figure 4.11. The multiplicative effect on carnivorous beetles of mid-stratum density, tussock cover, the proportion of native plants and herb cover, as shown by each variable‟s mean. Numbers below the habitat variable label represent the best- supported model. Bars represent 95% credible intervals.

Influence of environmental and treatment variables on community composition

Together the environmental variables in the Benalla region - soil type, rock cover, herb cover, the proportion of native plants, mid-stratum density, litter cover, and tussock cover - explained 37% of the variation in beetle community

98 composition. Overall the environmental and treatment matrix accounted for 43% of the variation in beetle community composition. The variance explained in the pure environmental matrix was 31%, when the effect of treatment was accounted for. The treatment matrix accounted for 12% of variance explained in beetle community composition, and 5.9% when the environmental matrix was accounted for (Table 4.6).

I found that soil type was the most important environmental variable structuring beetle community composition in the Benalla region, accounting for almost half (46%) of the total variation explained in the environmental ordination axes, but this dropped to 33% in the pure environmental matrix (Table 4.7). The habitat attributes of native plant species (9%), herb cover (7%) and rock cover (7%) were also important in structuring beetle community composition in the pure environmental matrix (Table 4.7). In the treatment matrix the interaction between treatment shape (linear strip and patch) and site-type (revegetated, remnant and cleared) was the most important variable, explaining 17% of the variation in beetle community composition. This reduced to 7% in the pure treatment matrix. Treatment shape also accounted for a moderate percentage of the variation in the beetle community composition matrix, accounting for 10% and 5% in the treatment and pure treatment matrix respectively (Table 4.7).

In the Wimmera region, I found the environmental matrix accounted for 30% of the variation in beetle community composition. The treatment matrix accounted for 9% of the variation. Overall, the environmental and treatment matrices explained 40% of the variation in beetle community composition (Table 4.8). The higher variance explained by the constrained environmental and treatment matrices indicates that there are interactions between the two sets of variables (Lepš and Šmilauer, 2003; R Development Core Team, 2009; Borcard et al., 2011).

In the environmental matrix, tussock grass cover, soil type and litter cover were the most important environmental variables structuring beetle community composition in the Wimmera region (Table 4.9). When the treatment matrix was

99 accounted for, these environmental variables either did not change, or slightly increased. This indicates that these environmental variables are either highly variable or that they are negatively correlated to one another (Borcard et al., 2011). In the unconstrained model, the interaction between these two treatment variables explained the greatest amount of variation in beetle community composition (Table 4.9).

In the analysis of environmental and spatial variables, I found that the environmental variables in the Benalla and Wimmera regions are consistent with the analysis of the environmental and treatment variables above. In the Benalla region the environmental and spatial matrices explained 62% of the variation in beetle community composition (Appendix 12). The spatial matrix explained 45% of this variation, with longitude explaining 28% in the unconstrained spatial matrix and 14% in the constrained spatial matrix (Appendix 13). In the Wimmera region 54% of the variation in beetle community composition was explained by the environmental and spatial matrices (Appendix 14). The spatial matrix again explained a high proportion of this variation (28%), where longitude was the most important spatial variable. In the unconstrained spatial matrix longitude accounted for 27% of the variation while in the constrained spatial matrix this dropped to 20% (Appendix 15).

Table 4.6. Percentage influence of environmental and treatment variables in explaining the variance of beetle community composition in the Benalla region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Treat = treatment matrix; Env-Treat = environmental matrix with treatment matrix removed; Treat-Env = treatment matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 21.4 37 Treat 8.4 12.4 Env - Treat 15.1 30.5 Treat - Env 2.9 5.9 Total 42.9

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Table 4.7. The contribution of the environmental (Env) and the treatment matrix (Treat) to the variation in the beetle community composition matrix in the Benalla region, explained by RDA and partial RDA, showing the percentage of total variation explained in beetle community composition (43%). Env-Treat = environmental matrix with treatment matrix removed (pure environmental matrix); Treat-Env = treatment matrix with environmental matrix removed (pure treatment matrix).

Variables Matrix Variation % Matrix Variation % Soil Env 45.5 Env-Treat 33.3 Natives Env 11.4 Env-Treat 9.5 Herb Env 9.1 Env-Treat 7.1 Rock Env 6.8 Env-Treat 7.1 Mid stratum Env 4.5 Env-Treat 2.4 Tussock Env 4.5 Env-Treat 4.8 Litter Env 4.5 Env-Treat 4.8 Treat interaction Treat 16.7 Treat-Env 6.8 Shape Treat 9.5 Treat-Env 4.5 Site-type Treat 4.8 Treat-Env 2.3

Table 4.8. Percentage influence of environmental and treatment variables in explaining the variance of beetle community composition in the Wimmera region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Treat = treatment matrix; Env-Treat = environmental matrix with treatment matrix removed; Treat-Env = treatment matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 10.3 30 Treat 4.6 8.6 Env - Treat 11.1 31.4 Treat - Env 5.5 10 Total 40

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Table 4.9. The contribution of the environmental (Env) and the treatment matrix (Treat) to the variation in the beetle community composition matrix in the Wimmera region, explained by RDA and partial RDA, showing the percentage of total variation explained in beetle community composition (40%). Env-Treat = environmental matrix with treatment matrix removed (pure environmental matrix); Treat-Env = treatment matrix with environmental matrix removed (pure treatment matrix).

Variables Matrix Variation % Matrix Variation % Tussock Env 17.1 Env-Treat 17.9 Soil Env 14.6 Env-Treat 17.9 Litter Env 14.6 Env-Treat 12.8 Rock Env 9.8 Env-Treat 12.8 Herb Env 9.8 Env-Treat 7.7 Mid stratum Env 4.9 Env-Treat 5.1 Natives Env 4.9 Env-Treat 5.1 Treat interaction Treat 10.3 Treat-Env 4.9 Site-type Treat 5.1 Treat-Env 12.2 Shape Treat 5.1 Treat-Env 7.3

Discussion

I found that overall beetle species richness, abundance, and community composition did not differ substantially between revegetated, cleared and remnant areas or between linear strips and patches. This is contrary to many other studies that suggest beetle species and communities do differ substantially between remnant and cleared areas (Harris and Burns, 2000; Grimbacher and Catterall, 2007; Barton et al., 2009) and between remnant and revegetated areas (Grimbacher and Catterall, 2007; Hopp et al., 2010). Interestingly, overall beetle species richness and abundance were neutrally or negatively influenced by the proportion of native plants and cover of tussock grasses and herbs: vegetation and habitat variables that are usually associated with increasing beetle species richness (Samways et al., 1996; Crisp et al., 1998). However, environmental variables explained more than a quarter of the variation in beetle community composition, showing that soil type, rock cover and native vegetation variables were still very important in structuring beetle

102 communities. Two beetle species showed a trend towards higher abundance in remnant linear strips, and may prefer these areas because of greater habitat quality. Herbivorous beetle species and beetle species from the family Curculinidae were recorded more often in cleared linear strips compared to remnant patches, suggesting that different beetle species and beetle functional groups are responding to treatments and habitat variables in different ways. These results have implications for future revegetation activities in highly fragmented agricultural areas.

Overall species response

The extensively cleared nature of the landscape I studied may help explain why I did not find any major species richness, abundance or compositional differences in beetles as a result of my different treatments, where other studies have found major beetle species richness and compositional differences (Harris and Burns, 2000; Grimbacher et al., 2007; Barton et al., 2009). Native beetles are known to survive well in agricultural pastures as well as surrounding natural habitat (Harris and Burns, 2000; Grimbacher et al., 2007; Horgan, 2007; Bridle et al., 2009; Gibb and Cunningham, 2010), which may help explain why a clear division between beetle functionality and composition was not found (Grimbacher et al., 2007; Gibb and Cunningham, 2010). Similarly, the beetle species I recorded may not have been representative of those present prior to clearing, because the landscapes I studied have been fragmented for more than 100 years and substantially degraded. A review undertaken in England of beetle fossil records dating back to the mid-Holocene (9500 - 2000 BCE) by Whitehouse and Smith (2010) strongly supports my assertion that beetle communities are altered as a result of agriculture, as they found beetle diversity significantly fluctuated with the onset of agriculture, favouring beetles that preferred open grassy habitats. Driscoll and Weir (2005) also suggest that beetles were likely to decline in agricultural landscapes if they were flightless and lived underground. If the beetles I studied are a resilient and robust subset of beetle species previously present, then the beetles persisting today may be

103 relatively resilient to habitat loss and fragmentation and able to survive in any treatment type.

As few historical studies exist and because little is known about Australian beetles (Majer et al., 2007; Gibb and Cunningham, 2010), it is difficult to confirm that the species I recorded are a resilient subset. However, my results suggest that beetles were neutrally or negatively influenced by the proportion of native plants, herb and tussock cover, and mid-stratum density - habitat variables usually recognised as having a positive influence on beetles (Crisp et al., 1998; Lindsay and Cunningham, 2009; Mysterud et al., 2010); a finding that may support this interpretation. Remnant vegetation in the landscapes I studied may have been sufficiently degraded by habitat clearing, livestock grazing, cultivation, and introduced plants (Prober et al., 2002; Prober and Smith, 2009) that many beetle species are representative of these degraded habitats (Harris and Burns, 2000). For example, the high abundance and broad distribution of scavenging species across different treatment types may illustrate this shift towards species that have more generalist habitat requirements (Buse, 1988). Similarly, ability to fly may not matter if beetle species are able to use a variety of habitats and move within and across the agricultural matrix (Driscoll and Weir, 2005).

If the beetle communities I studied are not representative of communities prior to clearing, this has ramifications for future revegetation and restoration actions, as these actions may be ineffective if the species that rely on high quality habitat have already gone locally extinct (Selwood et al., 2009). In order to restore these more vulnerable species, either translocation of beetles and their habitats and/or the connection of existing remnants to large continuous blocks of habitat may be necessary (Keesing and Wratten, 1998; Laurance et al., 2002; Grimbacher and Catterall, 2007). However, translocation of beetle species or the habitats they require is very difficult and the success of such strategies unknown (Grimbacher and Catterall, 2007), or possibly detrimental to the areas to which they are relocated (Ricciardi and Simberloff, 2009b; Ricciardi and Simberloff, 2009a). In addition, a lack of historic records makes it almost

104 impossible to know what beetle species were in these regions prior to clearing. Similarly, good quality continuous woodland that contains vegetation representative of that present prior to clearing is rare (Selwood et al., 2009), making the usefulness of connectivity to these habitats difficult to assess. A better understanding of the beetle communities in these landscapes and surrounding continuous landscapes may provide more insight into restoration options. Other trapping methods that capture flying species may also increase our understanding of beetle species and the vegetation they require.

Responses of functional groups to study treatments

The most important trend I found was that one functional group was influenced by treatment effects, with herbivorous beetles being more species rich in cleared linear strips than in remnant patches. This functional group was most likely found in these landscapes due to their habitat preferences and food requirements, as will be discussed below (Lassau et al., 2005; Watts et al., 2008; Barton et al., 2009; Hopp et al., 2010). However, other factors may have caused me to capture more species in these areas. Firstly, linear strips may benefit herbivores because these areas have greater edge environments, enabling species to move between linear strips and surrounding paddocks and attain higher densities in linear strip areas (Driscoll and Weir, 2005). Secondly, my use of pitfall traps to survey beetles meant that I may mainly have captured ground dwelling and flightless species. I may also have had greater sampling success in more open habitats such as cleared linear strips and revegetated patches because species may have been more active due to a lack of dense vegetation (Baars, 1979; Thomas et al., 2001; Thomas et al., 2006). However, it seems unlikely that edge effects, ability to fly or higher activity rates caused greater captures in these areas, because other functional groups did not show equally high capture rates in these habitats.

Herbivorous beetles

Herbivores preferred cleared linear strips, and this was correlated with higher levels of tussock grass cover but lower proportions of native plants such as 105 herbs, possibly as a result of an invasion by pasture grasses (Davies et al., 2000; Dorrough et al., 2004). Herbivorous beetles generally forage on plant material such as roots and leaves, and especially on grasses and decaying matter (Matthews and Bouchard, 2008; Gibb and Cunningham, 2010), making them able to use degraded areas such as cleared linear strips and paddocks (Gibb and Cunningham, 2010; Vanbergen et al., 2010). Herbivorous beetles were dominated by Scarabaeidae (dung beetles), Tenebrionidae (darkling beetles), and Curculionidae (weevils): families known to be more common in open habitats (Matthews and Bouchard, 2008; Gibb and Cunningham, 2010). For example, Curculionidae were more species rich in cleared linear strips than in remnant patches, probably because these species are mostly specialist herbivores feeding on grasses and forbs (Zimmerman, 1994; Jonsen and Fahrig, 1997; Matthews, 1997). Cleared linear strips may also have been more productive, as a result of higher amounts of sunlight reaching the ground layer and higher nutrient and water runoff from roads and paddocks compared to remnant patches (Driscoll and Weir, 2005). This would have resulted in productivity-species richness relationships (Abrams, 1988; Rahbek, 1997; Wright and Jones, 2004), whereby a more productive ground layer increased herbivorous beetle species richness after beetles that were native plant specialists were eliminated due to clearing and invasion of pasture grasses.

Individual species response to study treatments

Individual beetle species showed a stronger trend towards a treatment effect, although not a clearly evident one, than overall beetle species richness and abundance. For example, I found two beetle species showed a trend towards higher abundance in remnant linear strips compared to other treatments: Adelium similatum and Chauliognathus nobilitatus. Adelium similatum, a flightless scavenging beetle from the family Tenebrionidae, prefers remnant treed habitats with logs and leaf litter (Matthews, 1997; Matthews and Bouchard, 2008), while the facultative herbivore C. nobilitatus uses flowering plants during its adult stage and leaf litter during its larval stage (Matthews, 1997). The presence of leaf litter and other habitat attributes may have allowed

106 these species to become more abundant in remnant linear strips (Barton et al., 2009; Barton et al., 2010). However, greater research is needed to adequately define the habitat requirements of individual beetle species and the importance of remnant linear strips in providing habitat elements that may not be present in other treatment types.

Slightly higher abundances of these two species in remnant linear strips compared to remnant patches may be because remnant linear strips are less disturbed by factors such as livestock grazing. Livestock periodically grazed the remnant patches I surveyed, and are known to reduce ground layer structural diversity through trampling and grazing (Vohland et al., 2005; Brown et al., 2008), negatively influencing beetles that require these habitat elements (Lindsay and Cunningham, 2009; Mysterud et al., 2010). Remnant linear strips are also subject to greater nutrient loads, increasing plant growth, which can favour some beetle species (Driscoll and Weir, 2005). This suggests that these two species may be benefiting from a more diverse and less disturbed understorey in remnant linear strips. It also highlights the need to protect remnant areas - particularly remnant linear strips - to enable beetles to persist in these areas, as they may be in better condition than remnant patches.

Influence of environmental and treatment variables on community composition

Environmental variables, and to a lesser degree treatment variables, explained a large amount of the variation in beetle community composition in both the Wimmera and Benalla regions. Environmental variables alone accounted for three quarters of the explained variation in beetle community composition, with the most important variable being soil type in both regions, along with tussock grass cover in the Wimmera region. Soil type is thought to be an important factor, as for many beetle species larval development takes place in the soil (Sanderson et al., 1995; Vohland et al., 2005). Soil type is also recognised as influencing vegetation type and potentially food quality of individual plants, as well as soil moisture levels related to habitat structure, all of which have an

107 influence on beetle community composition (Sanderson et al., 1995; Lassau et al., 2005; Hopp et al., 2010). For example, Driscoll (2005) found that soil moisture levels had the greatest influence on beetle communities in Tasmania, Australia, reporting that 60% of beetle species were restricted to drier forest habitats, while the remainder were found in the wetter buttongrass matrix. It is unclear however how soil type influences revegetation success, a research area outside the scope of this project, and in turn how this structures beetle communities.

I found that tussock grass cover and litter cover in the Wimmera region, and the proportion of native plants and herb cover in the Benalla region, as well as rock cover in both regions were also very important variables structuring beetle communities, probably because ground layer elements provide microhabitat variables such as shelter and food resources (Lassau et al., 2005; Grimbacher and Catterall, 2007; Barton et al., 2010). While these habitat elements generally showed no clear evidence of changing beetle species richness and abundance, beetle communities still require these elements to persist (Lindsay and Cunningham, 2009). Replanted areas lacked elements such as litter, herbs, and a high proportion of native plants, and the lack of these elements may be stopping some beetle species from colonising these areas. These elements are likely to take a long time to restore due to disturbances such as livestock grazing (Lindsay and Cunningham, 2009). More research needs to be undertaken to determine if restoring these ground elements would increase beetle community composition.

Treatment variables also accounted for about one third of the total explained variation in beetle community composition, most of which was explained by the interaction between treatment shape and site-type. The interaction between the treatment variables indicates that there may be factors not measured in this study that influence beetle composition, such as edge effects. Edge effects are likely to be greater in revegetated areas and cleared linear strips than in remnant areas because revegetated and cleared areas lack dense vegetation (Williams-Linera et al., 1998; Yu et al., 2006; Baker et al., 2007). This requires a

108 greater understanding of the effects of edge on beetle communities, and the effects of edge in revegetated linear strips and patches.

The strong influence of spatial variables on beetle community composition and in particular the influence of longitude is also an important factor rarely covered in the literature (Didham et al., 1998). Interestingly, Prober and Thiele (2004) reported a floristic gradient from east to west in woodlands of eastern Australia, linking these trends to changes in soil type, as well as climatic factors. Climatic factors were unlikely to have major impacts within my two study regions, as most sites were relatively close together (within a radius of around 60km). However, changes in soil type on an east-west gradient concur with my results, because when spatial variables were taken into account in the environmental matrix, the influence of soil type decreased dramatically in both regions. Soil type probably also accounts for beetles in geographically more similar locations resembling each other more than beetle communities in locations that were further apart.

My findings clearly show that soil type, soil changes on a longitudinal scale, and ground layer elements are very important for structuring beetle community composition. The influence of the treatment matrix on beetle community composition indicates that these different treatments did have an effect on beetles and that other factors not studied here may also be influential in structuring beetle communities. This requires more studies to fill knowledge gaps on the effects of edge on beetle species, and other variables that may influence composition. In addition, more needs to be understood about how effective the addition of ground layer attributes in revegetated areas would be for beetle communities. A better understanding of spatial variables‟ influence on beetles and especially their link to soil type also needs greater research attention.

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Conclusion

My predictions were largely not consistent with the results of this study: (i) revegetated areas were not transitional habitats, and beetle species richness and abundance showed no overall trend between revegetated, remnant and cleared areas. I suggest that this is because the beetle fauna I studied is only a subset of previously present beetle communities, and that only robust species have been able to persist in these highly altered agricultural landscapes. This is supported by (ii) vegetation type and structure having a neutral or negative effect on overall species richness and abundance, contrary to other studies. These results also suggest that the vegetation communities have altered towards a more degraded state, leaving beetle species and functional groups that are better adapted to agricultural vegetation variables. However, (iii) environmental variables did have a strong influence on beetle community composition, showing that at a community level beetles are still responding to native vegetation variables and to variables such as soil type. Two species, Adelium similatum and Chauliognathus nobilitatus, had slightly higher abundances in remnant linear strips. This suggests that remnant linear strips may be important for the persistence of more specialised beetle species, and so should be protected from disturbances.

My results have consequences for future revegetation activities, as they show that revegetation may not increase beetle species richness or abundance in these highly degraded landscapes because the species present are largely adapted to more disturbed habitats. Translocation of more specialised beetles and the habitats they require may be one way to increase species richness and restore beetle community composition to a state that resembles remnant habitats, but little is known about the effectiveness of translocations. This requires greater research on continuous habitats near agricultural areas, and the beetle communities that utilise these habitats. However, beetle community composition may still benefit from revegetation if habitat elements such as ground layer attributes are included in future revegetation projects. A better understanding of beetle communities in revegetated areas with these ground layer attributes is necessary to determine the benefits of such actions. 110

Chapter 5. Propensity of landholders to revegetate land and their attitudes towards vegetation

111

Introduction

Much of the Earth‟s land is privately owned and used for agricultural production (McDonald et al., 2007; Cox and Underwood, 2011). Many agricultural landscapes are largely cleared and contain only fragments of the vegetation that was once there (Lindenmayer, 2009). This has been detrimental to many native plant and animal species that rely on natural habitats (Fahrig, 1997; Fahrig, 2003; Lindenmayer, 2009). Remnant vegetation, in an agricultural setting, may not be representative of previous habitats, as they are often on the least productive land and are isolated from continuous native vegetation (Fischer and Lindenmayer, 2007; Wilson et al., 2007). Although valuable natural habitats do remain on private land in agricultural landscapes, it is argued that to maintain biodiversity within remnant vegetation, revegetation and restoration needs to occur to reconnect and enlarge habitats and thereby enhance animal and plant movement and dispersal (Soulé et al., 2004; Carr and Hazell, 2006; Mackey et al., 2007).

The revegetation and restoration of natural habitats on privately owned land must be initiated and driven by the landholders, and their decisions to take this step are influenced by personal, social, cultural, and economic drivers (Pannell et al., 2006), as well as by natural resource management agencies. These drivers include personal experiences and experiences of their neighbours in undertaking conservation projects, and the ability of their farm to remain productive and profitable (Pannell et al., 2006). Landholder decisions to undertake conservation initiatives are also influenced by values, beliefs, and personal and social norms (Ajzen, 1985; Stern and Dietz, 1994; Stern et al., 1995; Barr and Cary, 2000; Whittaker et al., 2006). Social theories such as the theory of planned behaviour (Ajzen, 1981) and the value-basis theory (Stern and Dietz, 1994; Stern et al., 1995) have helped describe how people‟s behaviour is influenced by their beliefs. Even if landholders understand the benefits of conservation activities such as revegetation for biodiversity, their attitudes may not translate into altered behaviour unless it benefits productivity (Cary and Wilkinson, 1997; Bennett et al., 2000; Pannell et al., 2006).

112

There is a need to understand the attitudes of landholders towards vegetation because of the link between landholder attitudes, conservation incentives and on-ground action (Crosthwaite et al., 2008; Morse et al., 2009; Polasky et al., 2011). However, there has been little research into the adoption of revegetation practices by landholders and their attitudes towards remnant or restored land (Smith, 2008; Gosling and Williams, 2010; Morton et al., 2010). This information is important for organisations providing guidelines and financial incentives for revegetation so they can better involve landholders who have not previously been interested in implementing conservation practices (Carr and Wilkinson, 2005; Carr and Hazell, 2006). It can also help revegetation activities become more strategic through the involvement of groups of landholders (Giardina et al., 2007; MacNally et al., 2010; Twedt et al., 2010). The resulting information will help natural resource managers to engage landholders more effectively and integrate their attitudes and knowledge into revegetation and extension programs. Knowledge of the typical shape and size of revegetated areas and the diversity and composition of plant species used in revegetation is also important, because these variables impact the value of revegetated areas as habitat for native flora and fauna (Simberloff et al., 1992; Lindenmayer and Luck, 2005; Munro et al., 2009). Although studies have investigated the impacts of replanting of linear strips and enlargement of remnant patches on native animals (Ruiz-Jaen and Aide, 2005; Munro et al., 2007), few provide definitive prescriptions for revegetation and fewer still have established how landholder revegetation actions influence biodiversity (Pannell et al., 2006).

This study aims to determine the degree to which landholders adopt revegetation activities, and to ascertain the opinions of landholders regarding the benefits, or otherwise, of remnant vegetation and revegetated land. My research was undertaken in two regions of south-eastern Australia, the Wimmera region and the Benalla region and investigated: (i) whether landholders have undertaken or are planning to undertake revegetation on their farms and what demographic factors help predict past and future revegetation actions; (ii) the shape, size and vegetation composition of past and future

113 revegetated areas and the shapes and sizes of landholders‟ existing remnant vegetation; (iii) the impediments and incentives to undertaking future revegetation; and (iv) the attitudes landholders hold towards remnant and revegetated areas, and how their attitudes influence their intention to manage these areas for conservation.

Methods

Landholders in the Wimmera and Benalla regions of south-eastern Australia (see Chapter 3 for study area description) were surveyed in October 2009. I used a quantitative survey method in the form of postal questionnaires to establish the adoption of revegetation works on private land, and landholders‟ attitudes to revegetated land and remnant vegetation. Postal questionnaires were used because they create no “interviewer effect” – i.e. no bias in the respondents‟ answers due to the presence of an interviewer (Bryman, 2004). However, they have drawbacks as response rates can be low, and participants may misinterpret questions if they are not well framed (Bryman, 2004; Moser and Kalton, 2004). The respondents to postal questionnaires are also self- selecting, so people with positive attitudes to revegetation may be more likely to complete the survey. Finally, there is a risk of researcher bias in the ways in which questions are phrased and how responses are interpreted.

I gathered landholder names and addresses from publicly available documents although, due to Victorian privacy laws (Victorian Information Privacy Act 2000), it was difficult to get up-to-date landholder lists. To compensate for this I used versions of the Country Fire Authority map-book published prior to privacy legislation taking effect. Two hundred landholders were selected at random from each of the regions using the Country Fire Authority map-books (CFA, 1993; CFA, 1994; CFA, 1997a; CFA, 1997b). Names and addresses of landholders taken from the map-books were cross-checked in the White Pages 2008 telephone directory (Whitepages Directory, 2008). Potential participants were included only if they lived on a property outside a major town. Reply-paid

114 envelopes were enclosed to encourage landholders to return the questionnaires.

I divided the postal questionnaire into three sections: (1) general demographics; (2) attitudes, practices and preferences with regard to revegetation; and (3) remnant vegetation management and attitudes towards remnant vegetation. The demographic information collected provided information on the respondent‟s age, property size, primary source of income, enterprise type, participation in Landcare groups and their status as a landholder or manager (Appendix 16). Questions were based on those included in similar studies of agricultural landholders (Dettmann et al., 2000; Schultz, 2000; Fielding et al., 2005). Questions were trialled with ten landholders and Landcare coordinators prior to questionnaires being mailed out. These trials helped to minimise the potential biases mentioned above and confirmed that the questions asked were both suitable and easily understood.

Revegetation and remnant vegetation in agricultural areas

Survey questions

I asked respondents whether they had previously undertaken revegetation on their properties and whether they were planning to undertake revegetation in the future. To establish landholder preferences for different revegetation activities the questionnaire sought responses about the shape (linear strips, patches or individual trees), size, vegetation type (trees, shrubs and grasses) and use of native and/or exotic plants in previous (Appendix 16, question 7) and future (Appendix 16, question 8) revegetation. The questionnaire also asked whether landholders had remnant vegetation on their properties and the shape and size of these areas (Appendix 16, question 14). They were also asked about possible impediments and incentives for future revegetation and what area of land they would revegetate if there were no impediments of time or money (Appendix 16, questions 9 to 11). In order to determine landholders‟ opinion about public revegetation, I asked respondents whether they thought

115 public land, such as roadsides, should be revegetated (Appendix 16, question 13).

Data analysis

To identify characteristics of landholders who had previously undertaken revegetation or who were planning to undertake revegetation in the future, I undertook two analysis using a logistic regression with a Bernoulli distribution in WinBUGS (Lunn et al., 2000) using a Bayesian framework (McCarthy, 2007). The Bernoulli distribution is a special case of a binomial distribution and was used to analyse those variables that were discrete. The Bayesian Bernoulli regression had uninformative priors for the intercept term (a ~ dnorm(0, 1.0E-6)) and the regression coefficients (Replant[i] ~ dbern(p[i])), where Replant[i] was the explanatory variable in WinBUGS (Appendix 17) (Lunn et al., 2000; Spiegelhalter et al., 2002).

In the first analysis, I used previous revegetation activities as a binary response variable; that is, the landholder had previously revegetated part of their property, or they had not. Explanatory variables were first analysed using a Polychoric correlation analysis in R version 2.9.1 (R Development Core Team, 2009) to determine the degree of association between categorical variables (Sokal and Rohlf, 1995). Those correlated variables (r > 0.4) were removed from future analysis. The uncorrelated explanatory variables were landholder age, region, enterprise type, primary source of income, property size, and Landcare membership (Table 5.1). A full model selection with all possible combinations of variables plus the null model was undertaken, giving a total of 64 models. To identify landholders who would most likely revegetate in the future, I used planned revegetation activity as the response variable and the explanatory variables described above plus a binary variable for previous revegetation activity (Table 5.1). A full model selection was undertaken (128 models).

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Table 5.1. Variables used in logistic regression analysis showing their categorical or continuous states.

Variable State Possible answers Previous revegetation Categorical Yes/no Planned revegetation Categorical Yes/no Landholder age Categorical <26, 26-35, 36-45, 46-55, 56-65, >66 Region Categorical Wimmera, Benalla, Unknown Enterprise type Categorical Livestock, cropping, mixed, other Primary source of income Categorical On-farm, off-farm, both Landcare member Categorical Yes/no Property size Continuous Continuous variable (in hectares)

I used WinBUGS to generate 150,000 samples from a posterior distribution of the above models after discarding the first „burn-in‟ of 10,000 samples. To minimise autocorrelation between successive samples, the explanatory variables were centred by subtracting the mean from each variable. This improved the efficiency of the Monte Carlo Markov Chain sampling. Three Markov chains were run for each model, with a suitable number of iterations so that convergence was reached for all variables on the basis of the Gelman- Rubin statistic (i.e., r < 1.05). After completion of modelling runs, I recorded the mean of the model coefficients and the 2.5th and 97.5th percentiles of the distribution, which represented 95% credible intervals on the coefficient estimate. Credible intervals are equivalent to confidence intervals in frequentist analysis (McCarthy, 2007).

I selected the best-supported models using Deviance Information Criteria (DIC) values (Spiegelhalter et al., 2002). The values were used as an alternative to the Akaike Information Criterion (AIC). These values allowed me to rank the best (lowest DIC value) and the worst (highest DIC value) models (Spiegelhalter et al., 2002). I kept all models with DIC values within 2 points of the best model, as these were considered to have a similar level of support. Models with DIC values within 2 - 7 points of the best-supported models were also considered to 117 be potentially relevant (Spiegelhalter et al., 2002). To distinguish between the best supported models, I considered their relative probabilities, expressed as a percentage, that landholders had undertaken revegetation in the past, or planned to revegetate in the future.

Attitudes towards revegetated and remnant vegetation, and intention to manage these areas

Attitudes towards revegetated and remnant vegetation

I undertook two analyses to determine; (i) the attitudes of landholders to revegetation on their property, and (ii) the attitudes of landholders to remnant vegetation on their property. Eleven questions were asked to assess landholder attitudes to revegetated areas; these included statements such as “Increases the beauty of your farm or the surrounding land” and “Increases weeds on your property” (Appendix 16, question 12). I received 102 responses to these eleven questions. Similarly, ten questions were asked to assess landholder attitudes to remnant vegetation (Appendix 16, question 14), of which I received 154 responses. Responses to attitudinal questions were requested on a Likert scale that offered five possible answers (Bryman, 2004).

To do this I used principal components analysis (PCA) with a varimax rotation in SPSS 18 (SPSS, 2009). Variable loading scores were used to categorise the attitudes landholders held to revegetated areas and remnant areas into different factors (Tabachnick and Fidell, 2007). The factors were groupings of uncorrelated variables, and to determine the number of factors maintained for further analysis I used the Kaiser criterion (Tabachnick and Fidell, 2007). This criterion only maintains those factors that have eigenvalues greater than one. I used Schultz‟s (2000) general value scale (egoistic, altruistic and biospheric) to classify the factors with eigenvalues >1 onto different motivational scales, which reflect different conservation motivations. The Kaiser-Meyer-Olkin measure was used to assess sampling adequacy (Tabachnick and Fidell, 2007), and Cronbach‟s alpha (α) was used to test the internal reliability of each of the retained factors (Bryman, 2004). Internal reliability measures the extent to which

118 all the items in the test measure the factor of interest. The measure ranges from zero to one, with values of 0.70 or higher being more reliable (Bryman, 2004)

Intention to manage revegetated and remnant areas

I also undertook two analyses to determine; (i) the probability that a landholder intended to manage revegetated areas on their property as a function of their attitudes to these areas, and (ii) the probability that a landholder intended to manage remnant areas on their property as a function of their attitudes to these areas. I asked five questions to determine how landholders intended to manage revegetated areas. For example, these included intentions to: “Leave or replace old logs, branches and leaf litter” and “Control pest animals” (Appendix 16, question 8d). With regard to remnant areas, I asked seven questions to determine how landholders intended to manage these areas (Appendix 16, question 8d and 14a). Answers to these questions were requested on a Likert scale that also offered five possible answers (Bryman, 2004). Intention to manage scores were calculated by averaging the response to each management question to gain an intentions index, and then split at the mid- point (3) to give a positive or negative score (Fielding et al., 2005).

To undertake these analyses I used a logistic regression with a Bernoulli distribution in a WinBUGS (Appendix 18), as described above. The binary responses for the intention to manage scores were response variables in this analysis. The explanatory variables were the attitudinal factor scores, derived from PCA analysis, which explained the greatest variation in the attitudinal matrix. For each of the revegetated and remnant area data-sets the attitudinal factor scores as well as region (1 = Benalla, 2 = Wimmera) were added sequentially into WinBUGS. To determine if any of the PCA factors for the revegetated and remnant areas were correlated with any of the intention to manage scores I ran a correlation analysis in R version 2.9.1 (R Development Core Team, 2009), as described above.

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Results

I received 179 completed questionnaires (45% of the number posted), 51% from Benalla, 42% from the Wimmera and 7% that failed to identify region. Most respondents were between 56 and 65 years of age and had an on-farm income, although landholders in the Benalla region received most of their income from off-farm activities (Table 5.2). Most farms had either mixed cropping and livestock or just livestock enterprises. Of the 18% of Benalla landholders who indicated “other” for enterprise type, the majority (15%) said they had a lifestyle property. Most landholders were Landcare members. Ninety-eight percent of respondents owned the property they managed. Properties in the Wimmera were substantially larger than properties in the Benalla region (Table 5.2).

Revegetation and remnant vegetation in agricultural areas

Extent of revegetation activities

I found that most landholders had previously undertaken revegetation (76%), and many planned to revegetate in the future (64%) (Table 5.3). Most landholders also thought that revegetation should take place on public land, such as along roadsides (60%). A high proportion of landholders either revegetated (31%) or planned to revegetate (36%) along linear strips, although most landholders undertook (53%) or planned to undertake (43%) a mixture of replanting shapes. Few landholders replanted individual trees (2%-5%). The majority of plants used in revegetation were trees and shrubs (60%), with few people planting trees, shrubs and grasses (13%). The majority of plants used in revegetation projects were native (78%). There were no major regional differences in the composition of revegetated areas or future revegetation composition (Table 5.3).

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Table 5.2. Demography of landholders who participated in this study from the Wimmera and Benalla region. Sample sizes (n) indicate the number of landholders who responded to each question. CI = 95% confidence intervals. Bold indicates highest percentage in each case.

Wimmera Benalla Overall % Age n=75 n=91 n=179 26-35 7% 0% 3% 36-45 16% 7% 11% 46-55 29% 32% 30% 56-65 28% 36% 32% 66+ 20% 35% 24% Income n=75 n=90 n=178 On-farm 79% 43% 59% Off-farm 20% 46% 35% Both 1% 10% 10% Enterprise n=75 n=90 n=178 Livestock 3% 41% 24% Cropping 21% 3% 11% Mixed 71% 37% 53% Other 5% 18% 12% Landcare member n=75 n=91 n=179 Yes 68% 50% 57% No 32% 51% 43% Property size n=74 n=90 n=177 Average 1,233ha 302ha 706ha (1000-1466 CI) (221-383 CI) (581-831 CI)

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Table 5.3. Percentage of landholders who had undertaken revegetation or were planning to revegetate in the future, and the composition of these plantings. Bold indicates highest percentage in each case.

Wimmera Benalla Overall Previous Future Previous Future Previous Future Revegetated n=75 n=73 n=90 n=89 n=178 n=175 Yes 71% 59% 81% 71% 76% 64% No 29% 41% 19% 29% 24% 36% Planting shape n=49 n=41 n=71 n=60 n=128 n=107 Linear strips 35% 37% 30% 35% 31% 36% Patches 22% 17% 10% 17% 14% 17% Individual trees 0% 2% 1% 7% 2% 5% Mixture 43% 44% 59% 42% 53% 43% Plants used n=53 n=43 n=72 n=63 n=133 n=112 Trees 25% 23% 15% 13% 20% 17% Shrubs 6% 2% 3% 11% 4% 7% Grasses 2% 0% 0% 0% 1% 0% Trees shrubs 55% 51% 64% 57% 60% 55% Trees shrubs grasses 9% 16% 17% 16% 13% 17% Other mix 4% 7% 1% 3% 2% 5% Species n=53 n=43 n=72 n=63 n=133 n=122 Native 72% 67% 83% 78% 78% 74% Native & introduced 28% 33% 17% 22% 22% 26%

The majority of replantings were fairly young, being 1-5 years in age (40%), and over 80% of revegetation in both regions had occurred in the last 10 years. Around half of landholders had received grant money to undertake revegetation activities, and had usually revegetated around 6% of their land, although landholders in the Benalla regions revegetated a higher proportion of their land (8%) than landholders in the Wimmera (3%). Similarly, landholders in the Benalla region had a higher proportion of remnant vegetation (12%) on their properties than landholders in the Wimmera (7%). Ninety-eight percent of landholders had some remnant vegetation on their properties, usually in the form of scattered individual trees (58%) (Table 5.4). 122

Table 5.4. Percentages of landholders who had replantings of different ages, and had received funding to undertake revegetation; the proportions of overall property size containing revegetated and remnant habitat, and the shape of remnants.

Wimmera Benalla Overall % Planting age n=53 n=73 n=134 <1 year 9% 24% 19% 1-5 years 51% 33% 40% 6-10 years 23% 24% 23% 11-15 years 9% 8% 8% >15 years 6% 3% 5% Ongoing 2% 8% 5% Grant money provided n=53 n=72 n=134 Yes 49% 56% 52% No 51% 41% 46% Proportion of land area Revegetated 3% (n=45) 8% (n=64) 6% (n=116) Remnant 7% (n=59) 12% (n=55) 10% (n=122) Remnant shape n=74 n=90 n=177 Patches 49% 33% 40% Individual trees 51% 66% 58%

Predicting revegetation actions

The demographic variables of Landcare membership, primary source of income, region and enterprise type were included in the five best-supported models of the likelihood that landholders had revegetated in the past (Table 5.5). Being a Landcare member showed some evidence of increasing the likelihood of having undertaken revegetation (Figure 5.1). For example, Landcare members who were from the Wimmera region with a mixed enterprise type and both an on- and off-farm income were 24% more likely to have undertaken revegetation compared to those who were not Landcare members. Other factors that slightly increased the likelihood that a landholder had revegetated were: being from the Benalla region compared to the Wimmera region (10%), having an off-farm income compared to an on-farm income (6%) or both an on-farm and off-farm income (2%), and operating a livestock or

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„other‟ enterprise compared to a cropping (5%) or mixed cropping and livestock (10%) enterprise (Figure 5.1).

Similarly, the demographic variables that increased the likelihood that landholders would revegetate in the future were previous revegetation activity, Landcare membership, and primary source of income (Table 5.6). Landholders who had revegetated previously were 60% more likely to revegetate in the future than landholders who had not revegetated in the past, were from the Wimmera, were not Landcare members and had a mixed enterprise and both an on- and off-farm income. Landholders who were Landcare members were also more likely to revegetate in the future than landholders who were not (16%). Landholders were also slightly more likely to revegetate in the future if they had an off-farm income compared to an on-farm (15%) or both an on-farm and off-farm income (13%) (Figure 5.2).

Landholders considered that lack of money (77%) and time (74%) were major impediments to future revegetation activities (Figure 5.3 a). Less than half of the landholders thought that losing productive land, having to manage revegetated areas or altering management practices were impediments to revegetation. Landholders stated that they would revegetate if they were paid for the loss of productive land (77%) or for the amount of carbon any replanted trees stored (81%). Less than half of the landholders considered that permission to harvest and sell the trees in the future or permission to clear another piece of land to offset areas revegetated would be an incentive to revegetate (Figure 5.3 b). Even without time and financial constraints, 55% of landholders across both regions said they would revegetate less than 10% of their land. In the Benalla region, 52% of landholders asserted that they would revegetate 10 - 20% or more of their land if there were no time and money impediments (Figure 5.4).

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Table 5.5. The best-supported models of the probability that landholders had undertaken revegetation on their properties. Landcare = whether landholder is a Landcare member; income = where landholder gains their primary source of income; region = landholder from either the Wimmera or Benalla region; enterprise = major farming enterprise operated on the property.

Model Variable ΔDIC ΔDIC Difference 1 Landcare 189.4 0.5 2 Landcare, income 189.5 0.6 3 Landcare, region 188.9 0 4 Landcare, enterprise 190.8 1.9 5 Landcare, region, income 190.7 1.8

a) Landcare member b) Primary source of income 1 1 0.8 0.8 0.6 0.6

0.4 0.4 Probability 0.2 Probability 0.2 0 0 Yes No On-farm Off-farm Both

c) Region d) Enterprise type

1 1 0.8 0.8 0.6 0.4 0.6 0.2 0.4 Probability 0

Probability 0.2 0 Benalla Wimmera

Figure 5.1. The probability that a landholder had undertaken revegetation as a result of: (a) being a Landcare member; (b) having on-farm or off-farm income; (c) being located in the Benalla or Wimmera region and (d) having a particular enterprise type. Bars represent 95% credible intervals.

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Table 5.6. The best-supported models of the probability that landholders would undertake revegetation in the future. Revegetation = landholder had previously undertaken revegetation; Landcare = whether landholder is a Landcare member; income = where landholder gains their primary source of income.

Model Variable ΔDIC ΔDIC Difference 1 Revegetation, income 175.1 1.2 2 Revegetation, income, Landcare 173.9 0

a) Previous revegetation activity b) Landcare member 1 1 0.8 0.8 0.6 0.6

0.4 0.4 Probability Probability 0.2 0.2 0 0 Yes No Yes No

c) Primary source of income 1.2 1 0.8 0.6 0.4 Probability 0.2 0 On-farm Off-farm Both

Figure 5.2. The probability that a landholder would undertake future revegetation as a result of: (a) previous revegetation activities; (b) being in a Landcare group and (c) having an on-farm or off-farm income. Bars represent 95% credible intervals.

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a) Impediments to revegetate 100 90 n=150 80 n=151 70 60 50 n=151 n=149 40 30 n=139 20

Landholder(%) response 10 0 Lack of Lack of time Lose Alter Manage money productive management revegetated land practices areas

b) Incentives to revegetate 100 n=158 80 n=158

60 n=150 40

20 n=147

0 Landholder(%) response Paid for Paid for Harvest or Clear loss carbon sell trees other land

Figure 5.3. The response of landholder to: (a) impediments that would make landholders less likely to revegetate and (b) incentives to enable landholders to undertake revegetation. n=number of respondents.

100 Wimmera 80 Benalla 60

40 Landholder

response (%) response 20 0 0 <10 10-20 >20 Unrestricted replanting (%)

Figure 5.4. Number of landholders who indicated they would revegetate if time and money were not an impediment (n=170). 127

Attitudes towards revegetated and remnant vegetation

Revegetated areas

In response to questions asked about landholder‟s attitudes to revegetated areas, the majority of people thought that revegetated areas reduced wind damage to crops (72%) and livestock (97%) and increased native animals (99%) and the aesthetic value of their properties (87%). They also thought that revegetation increased habitat connectivity on their farm (80%). However, landholders also thought that revegetation increased pest animals on their property (70%), increased fire risk (74%), and reduced farm productivity (48%, with 27% undecided). A little under half of landholders thought that revegetation would not increase weeds on their property (47%), although 21% were unsure. Many landholders thought that they would have to spend time managing the revegetated areas (75%).

In the analysis of attitudes towards revegetated areas, PCA identified three factors with eigenvalues of 2.70, 1.49 and 1.15, explaining 67% of the total variation. The Kaiser-Meyer-Olkin measure for sampling adequacy was 0.70, indicating that there was adequate sampling (Tabachnick and Fidell, 2007). Questions 7, 9 and 11 were found to be a poorly correlated with the three factors so they were excluded from further analysis (Table 5.7). Factor loadings for each variable ranged from 0.62 to 0.85 (Table 5.7) and thus provided a good to excellent measure of the variable (Tabachnick and Fidell, 2007). The three factors to which the response items contributed most strongly were property detriment (factor 1), environmental benefit (factor 2) and property benefit (factor 3; Table 5.8).

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Table 5.7. Factor loadings for attitudinal variables towards revegetation, calculated from a PCA with a varimax rotation. Bold numbers represent the highest factor loading scores for the selected variables.

Factor

Question Attitudinal variables 1 2 3 1 Reduces wind exposure to your crops 0.06 0.13 0.85 2 Reduces wind exposure to your livestock -0.21 0.07 0.79 3 Increases pest animals on your property 0.82 0.00 -0.08 4 Increases weeds on your property 0.82 -0.16 -0.01 5 Increases birds and other native animals on your property 0.10 0.79 0.07 6 Increases fire risk to your property 0.81 -0.09 -0.10 7 Reduces the productivity of your farm 0.59 0.30 0.33 8 Increases the beauty of your farm or the surrounding land -0.44 0.62 0.00 9 Requires you to spend time/money to maintain the 0.48 0.06 0.08 replanted area 10 Helps to connect existing patches of remnant vegetation -0.15 0.80 0.17 11 Increases the productivity of your farm 0.48 0.38 0.48

Table 5.8. Attitudes of landholders to revegetation loaded into three factors based on the factor loadings shown above.

Property detriment Environmental benefit Property benefit (Factor 1) (Factor 2) (Factor 3) Increases weeds on Helps to connect Reduces exposure of your property existing patches of your crops to wind Increases pest animals vegetation Reduces exposure of on your property Increases birds and your livestock to wind Increases fire risk to other native animals your property Increases the beauty of your farm/surrounds Cronbach‟s α = 0.79 Cronbach‟s α = 0.66 Cronbach‟s α = 0.50

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Remnant vegetation

Landholders who responded to the attitudinal questions relating to remnant areas thought that remnant areas reduced crop (63%) and livestock damage (94%) and increased native animals (97%) and their farms aesthetics (91%). Many landholders also thought that remnant areas increased animal pests (71%) and increased fire risk (63%), while 40% thought that remnant areas increased weeds (17% undecided). Around half of landholders thought remnant areas increased the productivity of their farm (49%) while 84% thought remnant areas increased habitat connectivity.

Attitudes towards remnant vegetation identified two factors after PCA (Table 5.9). Questions 7 and 10 were found to be a poor measure of the two factors so they were excluded from further analysis. The eigenvalues for the two factors, which explained 59% of the total variance in the attitudinal matrix, were 2.83 and 1.91. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.71. Factor 1 included attitudes about environmental and property benefits while factor 2 included attitudes about property detriment (Table 5.10).

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Table 5.9. Factor loadings for attitudinal variables towards remnant vegetation, calculated from a PCA with a varimax rotation. Bold numbers represent the highest factor loading scores for the selected variables.

Factor

Question Attitudinal variables 1 2 1 Reduces wind damage to your crops 0.72 0.01 2 Reduces wind exposure to your livestock 0.76 0.05 3 Increases rabbits, foxes and other pest animals on your property -0.16 0.85 4 Increases weeds on your property -0.09 0.79 5 Increases birds and other native animals on your property 0.73 0.07 6 Increases fire risk to your property 0.08 0.81 7 Reduces the productivity of your farm 0.09 -0.59 8 Increases the beauty of your farm or the surrounding land 0.68 -0.31 9 Helps to connect other patches of remnant vegetation 0.67 -0.23 10 Increases the productivity of your farm 0.48 -0.37

Table 5.10. Attitudes landholders held to remnant habitats loaded into two factors based on the factor loadings shown above.

Property and environmental benefit Property detriment (Factor 1) (Factor 2) Reduces wind damage to your crops Increases rabbits, foxes and other pest Reduces wind exposure to your animals on your property livestock Increases fire risk to your property Increases birds and other native Increases weeds on your property animals on your property Increases the beauty of your farm or the surrounding land Helps to connect other patches of remnant vegetation Cronbach‟s α = 0.74 Cronbach‟s α = 0.77

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Intention to manage revegetated and remnant vegetation

Revegetated areas

I found that landholders would have a greater intention to manage revegetated areas if they thought revegetation would be beneficial to their property, by reducing wind damage to their crops and livestock; or if they thought animal and plant pests or the risk of fire would be detrimental to their property (Figure 5.5). In contrast, landholders who thought that revegetation benefited the environment; such as increasing connectivity, native animals, or the farm aesthetic; had a slightly weaker intention to manage revegetated areas (Figure 5.5).

If landholders had a positive attitude that revegetation was detrimental to their farm, then they had a stronger intention to manage revegetated areas than those who had a negative attitude that revegetation was detrimental to their farm (Figure 5.6, a). Similarly, landholders who had a positive attitude that revegetation was beneficial for the environment had a stronger intention to manage revegetated areas than those who held negative attitudes (Figure 5.6, b). However, landholders who considered that revegetation benefited their property were not more likely to manage these areas than landholders who did not consider revegetation would have these benefits (Figure 5.6, c).

1 0.8 0.6 0.4 0.2

Intention to manageIntention to 0 Property Environmental Property detriment benefit benefit Attitude

Figure 5.5. Overall landholder intention to manage revegetated areas, on a scale of weak intention (0) to strong intention (1) established from the three most explanatory attitudinal factors: property detriment (factor 1), environmental benefit (factor 2), and property benefit (factor 3). Bars represent 95% credible intervals. 132

a) Property detriment (factor 1) b) Environmental benefit (factor 2) 1 1 0.8 0.8 0.6 0.6

0.4 0.4

Intention Intention 0.2 0.2 0 0 -1 0 1 -1 0 1 Attitude Attitude

c) Property benefit (factor 3) 1 0.8 0.6 0.4 Intention 0.2 0 -1 0 1 Attitude

Figure 5.6. The strength of a landholder‟s intention to manage revegetated areas, from a scale of weak intention (0) to strong intention (1) in relation to their attitude towards revegetated areas on a scale of negative attitude (-1) to positive attitude (1), based on the three attitudinal factors: (a) property detriment (factor 1); (b) environmental benefit (factor 2) and (c) property benefit (factor 3). Dotted lines represent 95% credible intervals.

Remnant vegetation

I found that landholders‟ attitudes were linked to their intention to manage remnant areas for conservation and that this intention was strongest in landholders who considered remnant areas to be detrimental to their property in comparison to those who considered that remnant vegetation benefited the environment or their farm (Figure 5.7).

Landholders who had a positive attitude that remnant areas benefited their 133 property and the environment, also had a stronger intention to manage remnant areas than landholders who had negative attitudes that these benefits would occur (Figure 5.8 a). Landholders who had positive attitudes that remnant areas would be detrimental to their property had an equally high intention to manage these areas compared to those landholders who had a negative attitude towards factors having a detrimental effect (Figure 5.8 b). Landholders in the Wimmera region had a stronger intention to manage remnant areas than landholders in the Benalla region (Figure 5.8 c).

1 0.8 0.6 0.4 0.2

Intention to manageIntention to 0 Property/environmental Property detriment benefit Attitudes

Figure 5.7. Landholder intention to manage remnant areas on a scale of weak intention (0) to strong intention (1) established from the two most explanatory attitudinal factors: property/environmental benefit (factor 1), and property detriment (factor 2). Bars represent 95% CI.

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a) Property/environmental benefit b) Property detriment (factor 2) (factor 1) 1 1 0.8 0.8 0.6 0.6

0.4 0.4

Intention Intention 0.2 0.2 0 0 -1 -0.5 0 0.5 1 -1 0 1 Attitude Attitude

c) Region

1 0.8 0.6

0.4 Intention 0.2 0 Benalla Wimmera

Figure 5.8. The strength of a landholder‟s intention to manage remnant areas, from a scale of weak intention (0) to strong intention (1) in relation to their attitude towards remnant areas on a scale of negative attitude (-1) to positive attitude (1), based on two attitudinal factors: (a) property/environmental benefit (factor 1), and (b) property detriment (factor 2) (dotted lines represent 95% credible intervals); and the variable for (c) region (bars represent 95% credible intervals).

With respect to landholder‟s perceptions of weeds and pest animals I found support in the scientific literature for the perceptions that revegetation increased weeds and pest animals (Arthur et al., 2010; Nichols et al., 2010). However, I found no evidence in the literature to either support or counter the perception that revegetation increased fire risk (Table 5.11).

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Table 5.11. Landholder perceptions of risks to their properties as a result of revegetated and remnant areas and the evidence that supports or rejects these perceptions.

Landholder Evidence Overall perception Weeds are more abundant under trees and Supported, shrubs in revegetated areas than in areas outside although weed of a revegetated canopy (Nichols et al., 2010); control can help Increased weeds invade disturbed areas more readily than establish native weeds native plants (Kotanen, 1997), requiring weed plant species control to establish natives (O'Dell and Claassen, (O'Dell and 2006; Gibson-Roy et al., 2010a). Claassen, 2006) Exotic animals are more abundant in revegetated Supported, areas than in cleared areas (Arthur et al., 2010), although weed and predators are more abundant in agricultural control can landscapes that have a mosaic of vegetated reduce habitat for patches and corridors than those without these some exotic elements (Pita et al., 2009). However, in animals (White et Increased Australia, carnivores such as foxes are more al., 2006) animal pests common in habitats dominated by weeds such as gorse and blackberries, and not native vegetation (White et al., 2006). Also, large native herbivores and livestock are common in revegetated areas and are known to decrease planting success (Thomas, 2009; Davis and Coulson, 2010) No evidence to suggest that fire risk is greater as No evidence to a result of revegetation or the amount of remnant support or reject vegetation in agricultural landscapes, although perception. Increased fire substantial evidence that fire can be a useful risk management tool for restoring and maintaining native forests (Kauffman, 2004; Morton et al., 2010; Shinneman et al., 2010).

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Discussion

This study shows that many landholders had undertaken revegetation on their farm (76%), and that the majority of these revegetated areas included trees and shrubs (60%) along linear strips (31%) or in a mixture of linear strips, patches and individual trees (53%). Very few landholders had planted individual trees only (2%) or included native grasses (13%) in their restoration works. These practices have implications for the native fauna living in these revegetated areas, including the reptiles and beetles (Chapter 3 and 4), which I will discuss in greater detail below. Landcare membership and primary source of income were important factors in predicting which landholders had undertaken revegetation, or planned to revegetate in the future, as were previous revegetation activities. Landholders had three major attitudes to revegetated areas and two major attitudes to remnant areas. These attitudes focused on how revegetated and remnant areas would positively or negatively influence their farm, and also how revegetated and remnant areas would benefit the environment and farm aesthetics. Landholders who perceived that revegetated and remnant areas were detrimental to their farm considered this to be a result of pest animals, fire risk, and to a lesser extent weeds, and these perceptions made them most likely to manage revegetated and remnant areas. These results have implications for future revegetation projects on private land, and the role landholders and management agencies can play in planning revegetation activities and remnant management.

Revegetation and remnant vegetation in agricultural areas

Predicting past and future revegetation

My results suggest that past behaviour is a strong predictor of future intentions (Ouellette and Wood, 1998; Fielding et al., 2005), because landholders most likely to undertake future revegetation were those who had revegetated parts of their property in the past. Similarly, Fielding et al. (2005) found landholders who experienced the benefits of conservation actions were more likely to undertake riparian management actions in the future. This means that revegetation activities are likely to become more common in agricultural areas if the benefit 137 of restored areas becomes acknowledged (Fielding et al., 2005; Pannell et al., 2006).

Off-farm income and Landcare membership were important factors in increasing the likelihood that landholders had revegetated or would revegetate in the future. Off-farm income is probably important because these landholders are often better able to afford the often onerous cost of fencing and revegetation and are more likely to relinquish otherwise productive land (Schrader, 1995; Pannell et al., 2006). Landholders with the highest proportion of off-farm income were those with a lifestyle property located in the Benalla region and these landholders also had the highest proportion of revegetated and remnant vegetation on their properties. This may suggest that landholders with an off- farm income are more likely to have stronger environmental values than landholders that rely on their properties to be productive. For example, in the central United States of America, Schrader (1995) found that landholders who earned most of their income off-farm had stronger environmental values than those who relied upon on-farm income. Landcare membership was important because Landcare members are more likely to undertake conservation works than landholders who are not Landcare members (Curtis and Cook, 2006; Curtis et al., 2008a).

Impediments and incentives to revegetation

Even though I found that the provision of grant funding was not associated with the likelihood of landholders to revegetate, many landholders claimed that, for them, the biggest impediment to future revegetation was a lack of money and time. This may be because without financial incentives, landholders are unlikely to undertake conservation actions because these actions do not provide financial gains and because they take time to implement, time which could otherwise be spent on crop and livestock management. This is because the costs and time taken to revegetate are likely to exceed the short-term on-farm benefits (Cary and Wilkinson, 1997; Moore and Renton, 2002). For example, in my study one landholder commented that:

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“…more trees means less water for the crops, which means even less profit in tough times.”

While financial incentives exist for landholders to undertake more conservation- focused farming practices, there are greater incentives to continue traditional farming methods, even though over the long term these methods are likely to have large and negative environmental consequences (Vanclay, 1994; Crosthwaite et al., 2008). This suggests conservation techniques need to be profitable and practical for widespread adoption (Wilkinson and Cary, 1992), because otherwise they will be adopted only by landholders who have strong environmental goals and sufficient funds, or if they produce conservation outcomes when implemented on a small scale (Curtis and Robertson, 2003; Pannell et al., 2006). Although revegetation and remnant protection can increase farm productivity through natural pest control (Thomson and Hoffmann, 2010) and protection of crops and livestock from water and wind damage (Fischer et al., 2010), these activities are likely to be profitable only over the long-term. Thus, large-scale revegetation on agricultural properties will occur mainly where off-farm income is available and/or where the landholder has environmental goals.

On productive farms, revegetation will probably occur in smaller areas and on unproductive land (Curtis and Robertson, 2003), as reflected in my study, and most landholders indicated that they would not revegetate more than 10% of their land. Only landholders in the Benalla region who usually had off-farm income intended to revegetate more than 10% of their property. One landholder I surveyed said:

“Farmers‟ time and resources are stretched to the limit at present. Maybe "carbon credits" will give farmers a strong incentive to carry out replanting work on their farms”.

However, there seems to be little certainty in the carbon markets. For example, in the United States of America, Fletcher et al. (2009) found that although

139 landholders were interested in selling their carbon, they would do so only if the carbon price was high enough. This sentiment was reflected by landholders taking part in the Landcare program Carbonsmart:

“I receive carbon credit payments from Landcare Carbonsmart - a scheme which is neither lucrative financially or administratively reliable.”

However, carbon credits may offset carbon produced by farming (Petersen et al., 2003; Flugge and Schilizzi, 2005), allowing properties to remain profitable and more environmentally sustainable. Thus, while impediments to future revegetation activities seem largely financial, other forms of income or incentives may help to increase revegetation uptake. This includes the increase in farm productivity linked with the revegetation of land (Fischer et al., 2010; Thomson and Hoffmann, 2010), and confidence in a carbon market (Fletcher et al., 2009). A better understanding of the benefits of revegetation to farms, and the ability to show this in a financial and ecological sense will be an important knowledge gap to fill.

Overall, these findings suggest landholders who are in a Landcare groups and who have an off-farm income are most likely to have undertaken revegetation, or will revegetate in the future. Landholders who have undertaken past revegetation activities are also likely to revegetate in the future, which suggests that it is important to show that revegetation actions are beneficial in order to initially involve landholders in these activities. While incentives such as financial incentives and paying landholders for carbon credits may help to involve some landholders in revegetation activities, being able to integrate revegetation actions with landholder goals may be more effective in involving landholders over the long-term. This would require extension staff as well as social and ecological scientists to have a better understanding of landholder objectives, and to tailor revegetation actions to suit these objectives.

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Attitudes towards revegetated and remnant vegetation, and intention to manage these areas

An important finding in this study was that landholders who had a strong attitude that revegetated and remnant areas had environmental benefits (provided habitat for native animals, increased habitat connectivity, and farm beauty), had strong intentions to manage these areas for conservation purposes. This is possibly because landholders who consider that native vegetation has ecological benefits also consider that these areas increase farm productivity (Dettmann et al., 2000; Moore and Renton, 2002; Measham, 2007). Management in revegetated and remnant areas included the removal of livestock, controlling weeds and pest animals, and leaving fallen timber and other ground elements. This means that landholders who understand the benefits of remnant and revegetated areas are more likely to act in a way that benefits these natural areas, benefiting native fauna.

I found that overall landholders were most likely to manage revegetated areas to prevent an increase of weeds, pest animals and fire risk on their property (Moore and Renton, 2002). Landholders were equally likely to manage revegetated areas if they considered these areas protected their livestock and crops (Bennett et al., 2000; Pannell et al., 2006; Measham, 2007; Seabrook et al., 2008; Smith, 2008). Although pest management is a major financial cost to landholders (Curtis and Robertson, 2003; Curtis et al., 2008b), the control of weeds and pest animals were probably also undertaken to achieve landholder objectives such as increasing crop and livestock productivity. A landholder I surveyed responded by saying:

“The reforestation was a huge success and quickly resulted in an abundance of new birdlife and resident kangaroos. The down-side is the enormous, on-going weed control problem. Had I known that life would be like this then I most certainly would not have proceeded with large-scale planting.”

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In line with landholder perceptions, revegetation with trees and shrubs does increase weed species rather than native grasses in the ground layer (Nichols et al., 2010), and that animal pests such as foxes are found in greater densities in revegetated areas (Arthur et al., 2010) (Table 5.11). Weeds tend to dominate revegetated areas because they prefer disturbed soil (Kotanen, 1997), but if weed control is undertaken, the survival of native grass can be enhanced (O'Dell and Claassen, 2006). White et al. (2006) also demonstrated that for Australian environments, foxes were more common in habitats dominated by weeds rather than native species. These findings suggest that weed control can reduce weeds and exotic animals in revegetated and remnant areas, which may benefit the growth of native grasses and also reduce impacts of weeds and pest animals on private land. A reduction in weeds and pest animals is also likely to benefit native animals, as discussed below.

With respect to fire risk, landholders responded that:

“Retention of fallen dead timber for habitat is outweighed by the fire hazard and the need to provide shade for stock, in my opinion.”

“Planting of trees on roads is a fire hazard…In the future our land will be wiped-out by fire.”

I found no research suggesting that fire risk is greater due to remnant or revegetated areas on properties (Table 5.11). Instead, while it has been suggested that fire is an effective land management tool for reducing weed spread and aiding restoration (Kauffman, 2004; Morton et al., 2010; Shinneman et al., 2010), landholders do not perceive it as such, but identify livestock grazing as a legitimate management action (Morton et al., 2010). For example, one landholder commented that:

“We graze areas where the trees are old enough to cope with cattle and we do this for fire safety purposes”.

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Similarly, studies by Measham (2007) and Moore and Renton (2002) reported that landholders grazed revegetated and remnant areas with livestock because they thought it helped to manage threats of fire risk and weed and pest animals. Some landholders also consider that grazing has no adverse effects on remnant and revegetated areas (Curtis and Robertson, 2003), although ecological studies show that grazing is detrimental to native flora and fauna (Dorrough et al., 2004b; Lindsay and Cunningham, 2009; Mysterud et al., 2010). This suggests that an obstacle to management of revegetated and remnant areas for conservation purposes may be landholders‟ perceptions that fire, weeds, and animal pests are a problem and that grazing can reduce these risks. Important future research may be to determine how revegetated and remnant areas increase weed species and pest animals, and if fire risk is higher when revegetated and remnant areas are present.

In summary, the landholders I surveyed were more likely to manage revegetated and remnant areas if they perceived these areas could have detrimental impacts to their property or if they thought that their farm would benefit, and less likely to manage these areas for conservation or aesthetic purposes. Landholders with strong environmental attitudes are still likely to undertake management, such as reducing livestock grazing, which benefits native plants and animals in remnant and revegetated areas. Landholder perceptions that fire risk, weeds and animal pests are greater in revegetated and remnant areas, even though some of these links are not proven, suggests that further research is necessary to determine the influence of revegetated and remnant areas on these risks. Management actions to reduce threats such as weeds and animal pests in revegetated and remnant areas are likely to benefit properties adjacent to these areas, and may also benefit floral and faunal communities in revegetated and remnant habitats. A better understanding of these benefits for native plants and animals is another area which requires more detailed research.

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Consequences of revegetation activities for native fauna

The restoration of trees and shrubs by landholders without the inclusion of native grasses in revegetation activities, as reported in this study and by other researchers (Wilkinson and Cary, 1992; Bennett et al., 2000; Smith, 2008), may be detrimental to some native fauna that require ground layers to persist in fragmented agricultural areas. This is because a lack of ground layer attributes, such as tussock grasses as well as leaf litter, rocks and herbs, are likely to exclude some species, such as some species of reptiles and beetles (Chapters 3 and 4), from revegetated areas. While diverse native plantings can provide a variety of habitats that benefit native fauna (Lindenmayer and Hobbs, 2004; Brockerhoff et al., 2008; Munro et al., 2009), without these ground elements, ground dwelling animals may be less likely to use or persist in these areas (Jellinek et al., 2004; Munro et al., 2009). Native grasses as well as litter and rocks have probably not been included in revegetation activities as it previously has not been seen as a priority by landholders and management agencies, and also because many of these elements are difficult and expensive to restore (Gibson-Roy et al., 2010a). Many agencies have also focused revegetation activities on providing habitat for birds and mammals (Pouliot, 2007; Thomas, 2009), and probably have not seen reptile and beetle species as being as important, or possibly as iconic, as these other species in the landscape. However, because reptiles and beetles do play significant functional roles in the landscape, and respond differently to habitat loss and fragmentation than birds and mammals, so a greater focus on these faunal groups is warranted in future revegetation activities (Lassau et al., 2005; Kanowski et al., 2006; Nichols and Grant, 2007; Schaffers et al., 2008; Gibb and Cunningham, 2010).

The revegetation of linear strips by landholders may be important to enable the movement and dispersal of animal species between remnant areas. My previous results suggest that linear strips 25m wide and 500m in length are able to maintain reptile and beetle communities, as long as these areas have good quality remnant habitat and low disturbance (Chapter 3 and 4). Remnant linear strips of these dimensions may also maintain other faunal species (Sieving et al., 2000; Bolger et al., 2001; Driscoll, 2004; Lindenmayer et al., 2007; Brudvig 144 et al., 2009). As suggested above, if revegetated areas contained more elements of the ground layer then rarer reptiles and beetles may be able to use these areas. The maintenance of remnant and revegetated areas of land on private property can greatly increase connectivity through the landscape and maintain faunal species, but these areas must need to be restored and managed appropriately so ground layer elements are maintained.

In summary, the current revegetation activities using native trees and shrubs along linear strips are likely to benefit some faunal species, but without the inclusion of ground layer attributes these revegetated areas are unlikely to be used by rarer ground-dwelling fauna such as reptiles and beetles. Ground layer elements are possibly not included because landholders and natural resource management agencies do not see beetles and reptiles or habitat elements such as ground layers as a priority. Revegetation with the inclusion of ground layers needs to be shown as important and compatible with current replanting activities. This may require more research that shows the benefit of ground layers for a range of faunal species, and the best size and shape of revegetated areas to increase rare reptiles and beetles while maintaining farm productivity.

Conclusion

Within the Benalla and Wimmera regions, most landholders who have undertaken revegetation in the past have only used native trees and shrubs, usually along linear strips, and are likely to revegetate in a similar way in the future. These landholders are usually in a Landcare group and are less reliant on their properties being profitable as they usually have an off-farm income. However, these revegetation practices are unlikely to provide suitable habitat for native animals such as reptiles and beetles that require certain ground-layer elements, such as native grasses, rocks, fallen timber and litter and native herbs to persist. Similarly, using revegetation of linear strips to link areas of remnant vegetation may also increase their value as habitat for native fauna (see Chapters 3 and 4). Incentives such as paying landholders for the loss of productive land, and for the carbon their remnant and revegetated areas

145 sequester, may increase revegetation activities by reducing the impediments of time and money.

Landholders usually undertake management of revegetated and remnant habitats in order to reduce risks to their properties and enhance farm productivity, as well as to enhance environmental outcomes such as habitat for wildlife. The perceived or real risks of remnant and replanted vegetation are often managed by landholders through weed control or by livestock grazing. The provision of funding for weed spraying may encourage landholders to more actively manage weeds in revegetated and remnant areas, thereby minimising the need for livestock grazing and ultimately lowering disturbance and benefiting native plants and animals in these areas. Replanting of native grasses along with trees and shrubs would further reduce weed invasion and habitat for exotic animals, and provide a greater variety of habitat for native species. These actions are more difficult than the replanting of trees and shrubs, and may require natural resource managers to help implement these actions. More needs to be known about the best size, shape, and vegetation composition of replanted areas to enhance biodiversity whilst maximising farm benefits. More detailed studies of landholder attitudes towards revegetated and remnant areas and the accuracy of their perceived risks to property are also needed to better inform revegetation actions and remnant protection.

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Chapter 6. Integrating ecological and social data using Bayesian Networks: Two case studies from south-eastern Australia

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Introduction

Natural resource management decisions in agricultural landscapes are influenced by social, ecological, economic and political factors (Allison and Hobbs, 2004; Olsson et al., 2006). The outcomes of these management decisions influence biodiversity gains and losses and impact agricultural productivity (Duncan and Wintle, 2008). Increasingly, management decisions to maintain or increase biodiversity via habitat protection and revegetation are being undertaken on privately-owned land rather than public land, because private land covers a higher proportion of many continents and habitat loss is increasingly occurring in these areas (Soulé and Sanjayan, 1998; Soulé et al., 2004). This is especially true in intensively farmed agricultural areas where much of the landscape modification is taking place (UNEP, 2005).

There is little certainty about what environmental benefits and ecosystem services may result from revegetation, or what types of actions bring the greatest biodiversity benefits per dollar spent (Hobbs, 1993; ANAO, 1997; Vesk and Mac Nally, 2006; ANAO, 2008; Duncan and Wintle, 2008; Rumpff et al., 2011). This is partly because evaluation and monitoring of management actions are rarely undertaken, resulting in uncertainties about management benefits (Duncan and Wintle, 2008; Rumpff et al., 2011). As a result of this uncertainty, natural resource managers have difficulty predicting the effectiveness of on- ground works (Walters, 1986; Duncan and Wintle, 2008). Due to limited funding, few extension staff and short timelines to undertake monitoring and resolve these uncertainties (Marris, 2007), management decisions continue to be based on intuition and experience (Theobald et al., 2005; Editorial, 2007).

Given that environmental management budgets are small relative to the scale of the challenge to reduce biodiversity loss, it is important to learn about management actions that are effective in maintaining animal species (Polasky et al., 2008; Polasky et al., 2011). Accordingly, there is a need to evaluate the biodiversity benefits and cost-effectiveness of restoration to justify the investments made (Measham, 2007; Rumpff et al., 2008; Rumpff et al., 2011; Sebastián-González et al., In press). A major impediment to undertaking cost- 148 effectiveness analysis on restoration is the uncertainty surrounding the biodiversity benefits expected to arise from any given investment. This uncertainty can be addressed and in some cases reduced by incorporating social and ecological knowledge into the decision making process (Possingham, 2001; Allison and Hobbs, 2004; Olsson et al., 2006). By integrating social and ecological data we can better understand the impacts of landholder behaviour on biodiversity (Ticehurst et al., 2011). To reduce uncertainty, adaptive management can also be used to facilitate “learning by doing” through the monitoring and evaluation of management outcomes and then updating knowledge about the system being managed (Figure 6.1) (Walters, 2007; Duncan and Wintle, 2008; Howes et al., 2010).

Measurable management goals (e.g., Define candidate management increase reptile and beetle species options (e.g., revegetate with richness) trees)

Learning (e.g., better to replant fewer trees, do more weed control) Competing conservation models (e.g., control weeds) Management and monitoring (e.g., revegetate with trees and monitor reptile and beetle species richness)

Figure 6.1. Steps in an adaptive management cycle. The activities in the dashed-line box (setting management goals and defining management options) are social, while the remaining steps in the process (defining competing models, implementing management and monitoring and updating knowledge through learning) are predominantly technical activities, generally handled by experts and managers (Duncan and Wintle, 2008).

A structured approach to making complex decisions that includes an adaptive management aspect requires a process model to represent the conceptual links between management actions and biodiversity outcomes (Rumpff et al., 2011). Such models enable investigation and evaluation of competing management investment options and underpin good management decisions (Duncan and

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Wintle, 2008; Rumpff et al., 2011). Bayesian Networks (Bayes nets) are a good basis for building process models as they help to structure reasoning and to identify causal relationships between multiple variables (Korb and Nicholson, 2004; Burgman et al., 2010) while incorporating uncertainty (Rumpff et al., 2011). Bayes nets are statistical tools that quantify the influence of environmental variables on ecological response variables, and help to predict the effect of altering management actions on the ecosystem or species of interest (Marcot et al., 2006). These interlinking factors constitute a “causal web” of predictions and responses (Marcot et al., 2006). Bayes nets are especially useful in a management context as they provide a conceptual framework that is easily interpreted and intuitive (Rumpff et al., 2011).

Bayes nets also allow the integration of field data and expert opinion to model the impacts of implementing management decisions or technologies on conservation outcomes (Pollino et al., 2007; Burgman et al., 2010; Ticehurst et al., 2011). This makes them useful in adaptive management, because prior knowledge can be updated with new data to reflect a better understanding of the natural system (Bashari et al., 2008; Rumpff et al., 2011). Although the combination of expert elicitation and ecological and social data in Bayes nets has been acknowledged to be important, there are few examples of these models in the literature (Pollino et al., 2007; Ticehurst et al., 2011). Nonetheless, it is beneficial to integrate multiple information sources in decision processes to help make more robust management decisions, especially in inherently uncertain ecological systems (Rumpff et al., 2011; Ticehurst et al., 2011).

In this chapter, I will use Bayes nets to integrate expert opinion with ecological and social data taken from a revegetation study undertaken in two regions in south-eastern Australia. I will; (i) model ecological processes using expert opinion and ecological data to determine those management actions most effective in increasing reptile and beetle species richness; (ii) calculate the most cost-effective options that provide the greatest species gains; and (iii) illustrate how landholder demographics and land management decisions may be

150 integrated with ecological models to predict the influence of those social processes on reptile and beetle species richness. This information will be beneficial to management agencies attempting to achieve multiple ecological and social objectives, and will identify cost-effective management actions for maintaining reptile and beetle species in agricultural landscapes.

Methods

Data and process models

The ecological “field data” are from a two-year study investigating the response of reptiles (Reptilia) and beetles (Coleoptera) to habitat type and environmental variables (study described in Chapters 3 and 4). For these models reptile and beetle species richness data were used as this was found to be the most appropriate measure of species response to habitat variables. The benefits and limitations of species richness measures will be covered in more detail in the Discussion. The landholder “behaviour data” were gathered using postal questionnaires sent to landholders in the same regions in which the field data were collected (see Chapter 5).

The study took place in two agricultural areas in south-eastern Australia: the Wimmera region and the Benalla region. The habitat types studied were remnant, revegetated and cleared linear strips, and remnant and revegetated patches. “Revegetated areas” referred to those areas that had been replanted in the last 10 - 15 years with native species, usually trees and shrubs. “Remnant areas” had older established native trees, shrubs, and grasses that had not been planted. “Cleared areas” referred to those areas of land previously cleared of trees and shrubs. “Linear strips” were areas approximately 20m wide along roadsides or fence lines that were either revegetated, remnant or cleared. “Patches” were blocks that were either revegetated or contained remnant habitat. For more details on field data collection refer to Chapters 3 and 4.

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Two process models were developed in this study. The first was a patch-level Bayes net that described how management actions at the patch-level, such as introducing rocks or controlling weeds, influenced reptile and beetle species richness. The impact of these management actions depended on the combination of habitat variables, such as tree density and rock cover, within revegetated, remnant and cleared linear strips, and within revegetated and remnant patches. The patch-level is defined as the local scale of habitat and species variables located within a landscape. This differs to landscape scale processes that incorporate multiple patch-level processes (Graham and Blake, 2001). Detailed exploration of landscape level processes and interactions were beyond the scope of this project. The second process model expanded on the patch-level net to include the influence of landholder demographic variables and the sorts of management decisions these landholders were most likely to implement. The landholder behaviour Bayes net showed how the propensity of particular demographic groups to make particular land management decisions influenced species at the patch-level.

Patch-level management actions

The five management actions described below influence habitat structure and floristic attributes, and ultimately reptile and beetle species richness. The management actions could take either of two discrete states: yes or no. For example, if the management action was to plant trees and shrubs, the decision would be to plant trees and shrubs (yes) or not to plant trees and shrubs (no).

Weed control Weeds compete with native plants for resources such as space, water, nutrients and sunlight and can take over areas of native vegetation (O‟Shea and Kirkpatrick, 2000). Habitats with a high percentage of weeds have fewer native animal species than areas dominated by native plants, possibly because weeds do not provide the habitat conditions these animals require (Hadden and Westbrooke, 1996; Jellinek et al., 2004; Munro et al., 2009). Other studies have found that weeds can dominate the understory of revegetated areas (Nichols et al., 2010) and areas that have been disturbed by livestock grazing, nutrient

152 enrichment or land tillage (Kotanen, 1997; Yates and Hobbs, 1997). Weeds also compete in agricultural systems and reduce farm productivity, so weed control measures are used to kill weed species and allow other plants to become established (Yates and Hobbs, 1997).

Plant tussock grasses Native grasses are necessary in grassland and grassy woodland revegetation projects as they provide valuable habitat for animals such as reptiles and beetles (Fischer et al., 2004; Michael et al., 2004; Leynaud and Bucher, 2005; Hannah et al., 2007; Masterson et al., 2009). Grasses benefit reptiles and beetles because they provide microhabitats not otherwise available in disturbed agricultural areas (Fischer et al., 2004; Barton et al., 2010).

Add litter and timber Leaf litter and fallen timber are vital habitat elements as they provide shelter and food resources, especially for beetles (Day and Majer, 1999; Barton et al., 2010; Hopp et al., 2010) and reptiles (Taylor and Fox, 2001; Kanowski et al., 2006). However, some landholders are likely to “clean up” leaf litter and fallen timber (Hamilton et al., 2004). Litter and timber are also seldom used in revegetation projects, but the replacement of these elements can enhance faunal colonisation (Michael et al., 2004; Marquez-Ferrando et al., 2009).

Plant trees and shrubs

Revegetation using trees and shrubs is a common management action taken to restore degraded areas (Bennett et al., 2000; Smith, 2008) and provide habitat for native animals (Kavanagh et al., 2007; Munro et al., 2009; Selwood et al., 2009). The replanting of diverse tree and shrub species is more beneficial than replanting monocultures such as plantations, as diverse plant species contain a variety of structural attributes required by a greater range of fauna (Lindenmayer and Hobbs, 2004; Munro et al., 2009; Barton et al., 2010). Over time trees and shrubs also provide shade, leaf litter and fallen timber (Barton et al., 2010; Gibb and Cunningham, 2010), as well as nesting hollows and roost sites (Bennett et al., 1994; Vesk et al., 2008).

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Add rocks

Like leaf litter and fallen timber, rocks can create a more diverse ground layer and are beneficial to ground fauna as they generate a range of microhabitats (Masterson et al., 2009; Michael et al., 2010). Rocks are often removed from agricultural properties prior to cultivation, resulting in habitat loss and declines in species dependent on rocky habitats (Masterson et al., 2009). The addition of rocks or artificial structures during revegetation can be beneficial to faunal species such as reptiles (Croak et al., 2010). However, the addition of rocks may also alter faunal communities by increasing rock specialists and reducing habitat for other species.

Landholder behaviour data

I gathered information on landholder demographics and landholder management of remnant and revegetated habitats from 400 quantitative questionnaires sent to farmers in the Wimmera and Benalla regions in 2009 (Chapter 5). The survey received a 45% response rate. There were five landholder demographic nodes used in the landholder behaviour net, which were:

Region The region a landholder comes from is defined by the geographic location of their property. Location often determines what type of farming enterprise can be undertaken due to factors such as soil type, rainfall, topography, and mean annual temperatures (Schrader, 1995; Pannell et al., 2006).

Enterprise type A property‟s enterprise type has impacts on the remnant areas within and outside the property boundary (Smith, 2008). For instance, cropping activities can impact remnant areas through spray and fertiliser runoff (Driscoll, 2004). If allowed to graze in remnant areas, livestock can also degrade remnants by disturbing ground layers such as tussock grasses and leaf litter cover (Smith et al., 1996). Farming enterprises benefit from remnant and revegetated areas via wind protection and natural pest controls (Thomson and Hoffmann, 2010). 154

Primary source of income A landholder‟s primary source of income can come either from farming the property, or from external activities. Earning an off-farm income can increase a landholder‟s ability and propensity to undertake conservation actions as they are less likely to be concerned about losing productive land to revegetation (Pannell et al., 2006).

Landcare membership Landcare is a community-based natural resource management group that relies on voluntary participation (Yatich et al., 2007) and in Victoria is responsible for many of the environmental works on private land (Curtis and De Lacy, 1998). Membership in a Landcare group may indicate that a landholder is more likely to undertake conservation actions (Curtis and De Lacy, 1996).

Structuring and fitting models

Influence diagram

To develop a Bayes net for the patch-level model I first developed a network diagram to represent the influence of management actions on habitat variables that in turn influenced reptile and beetle species richness. These diagrams show pathways of cause and effect known as “influence diagrams” (Marcot et al., 2006). In the patch-level influence diagram (Figure 6.3), the management actions (shown in blue) influenced aspects of the biotic (e.g., weed cover and tussock grass cover) and abiotic (e.g., rock cover) environment. These biotic and abiotic factors (habitat attribute nodes - shown in green) were located in one of five habitat types (e.g., cleared linear strip and remnant patch), and influenced the species richness of reptile and beetle species (shown in purple). For example, a cleared linear strip would normally contain no trees or shrubs, but may contain native grasses. If a management action were to plant trees and shrubs in this habitat type, this would increase tree and mid-stratum density, and over time increase the amount of coarse woody debris. This in turn would increase ground habitat for reptile and beetle species. I developed the influence diagram after reviewing the literature on reptile and beetle species‟ response to

155 revegetation and habitat variables (Watts and Gibbs, 2002; Driscoll, 2004; Leynaud and Bucher, 2005; Kanowski et al., 2006; Grimbacher and Catterall, 2007; Grimbacher et al., 2007; Munro et al., 2007; Nichols and Grant, 2007; Watts et al., 2008; Marquez-Ferrando et al., 2009; Gibb and Cunningham, 2010), and after consulting ecological experts (D. Driscoll pers. com.).

Developing a Bayes net

The patch-level influence diagram (Figure 6.3) provided the causal network structure to underpin the development of a Bayes net in Netica (Netica 4.09, 2009) (Figure 6.4). A Bayes net is a graphical tool that assists decision making under uncertainty. Bayes nets use Bayes‟ theorem - which states that the probability of an event occurring is conditional on the prior probability of the event taking place - to make predictions about what might happen in the future (Korb and Nicholson, 2004). Bayes nets are directed acyclic graphs that depict the cause and effect relationship among variables within a system. These cause and effect relationships are quantified using probabilities (Pearl, 1988; Korb and Nicholson, 2004). Variables are represented by nodes that are joined by arrows that represent direct causal links and a directional influence (Marcot et al., 2006). In a Bayes net, each node has a conditional probability table (CPT). Netica uses these CPTs to determine how a node changes in relation to the nodes that link into it. Conditional probabilities (the probability of event A occurring given event B) are calculated using Bayes‟ theorem. The CPTs initially contain initial information, “priors”, obtained from expert opinion or previous data. In Netica CPTs can be updated with case-files that contain new information in order to give a future prediction or posterior probability (Korb and Nicholson, 2004). In this study, prior CPTs were obtained using expert opinion and were updated with field data.

To expand the influence diagram into a Bayes net, management actions were broken into five separate nodes. Nodes represented distinct variables, where “parent” nodes fed into “child” nodes (if there is a link leading from node A to node B, then node A is called the “parent” node) (Figure 6.3) (Marcot et al., 2006). The model includes four general classes of node (Figure 6.3): (i) the 156 management action nodes were at the top of the model and so had no “parent” nodes; the (ii) habitat attribute nodes and (iii) habitat type node were intermediate in the model, thus were influenced by management action nodes; (iv) species richness nodes (reptiles and beetles) were the dependent variables in the model, and thus influenced by all parent nodes via the intermediate habitat nodes. Each node took a range of states that were either discrete (e.g., yes or no), or continuous (e.g., % cover of tussock grasses) (Netica 4.09, 2009).

Field data and conditional probability tables The patch-level net had seven habitat variables (habitat attribute nodes): herb cover, tussock grass cover, cover of coarse woody debris, rock cover, mid- stratum density, tree density and weed cover. In order to incorporate the ecological data into the model and reduce the complexity of the Bayes net I converted the cover variables, initially categorised using a Braun-Blanquet scale (ranging from 1 - 6), into three discrete states: low (0 - 25%), medium (26 - 50%) and high (>51%). The proportion of weed cover was also converted into the above three states. Reptile and beetle species richness data were converted to three states: low, medium or high. In the case of reptiles, the presence of 1 - 2 species was considered low, >2 - 4 species was considered medium, and >4 - 6 species was considered high. In the case of beetles, 1 - 6 species was considered low, >6 - 12 species was considered medium, and >12 - 18 species was considered high. The definition of categories, while somewhat arbitrary, was based on the consensus judgment of three biologists with relevant expertise.

For each “child” node in this model a conditional probability table (CPT) was filled-out. The CPT described the influence the “parent” nodes had on the various states of the “child” node (Marcot et al., 2006; Netica 4.09, 2009). For example, weed cover was a “child” node for the management action of weed control and for habitat type (Figure 6.2). Weed cover had three states (low, medium and high) that represented the percentage of weeds in a sampling area. If weed control, a binary variable (yes or no), was turned to “yes”, then the weed cover variable would change from high to medium or medium to low, depending on the habitat. For instance, when weed control was undertaken in 157 bare linear strips, the cover of weeds was likely to be 49% (Figure 6.2). Once the CPT for each “child” node was specified, the Bayes net could be used to test the influence of combinations of management actions on species richness (Marcot et al., 2006).

Weed control Yes 100 Habitat type No 0 Revegetated Patch 0 Remnant Patch 0 Remnant Linear Strip 0 Bare Linear Strip 100 Revegetated Linear Strip 0

Weed cover Low 5.00 Medium 60.0 High 35.0 49.4 ± 22

Figure 6.2. The influence of two “parent” nodes, undertake weed control (management action) and habitat type, on the “child” node of weed cover (habitat node). Taken from the patch-level Bayes net (Figure 6.4).

Field data-only model

To set-up the field data-only model I first parameterised the CPTs of weed cover, tree density, mid-stratum density, herb cover and tussock grass cover with uninformative prior probabilities, which set each of the categories of high, medium and low cover equally at 33.33%. This was done to test the influence of the field-data assuming no prior information. The CPTs for rock cover and cover of coarse woody debris were parameterised to reflect the influence of adding rocks and adding litter and fallen timber because these actions had deterministic outcomes. For example, adding rocks to any habitat would increase the rock cover to high, whereas not adding rocks would result in low cover in revegetated and cleared areas and moderate cover in remnant areas. This is because remnant areas were expected to have moderate rock cover in the Wimmera and Benalla regions, while revegetated and cleared areas were expected to have low rock cover prior to restoration due to the likely removal of rocks during cultivation.

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Field data were added as “case-files” representing the values for habitat attributes and habitat type in relation to reptile and beetle species richness. “Case-files” consisted of a data table that included all of the collected data for the habitat and species richness variables. Netica allows the user to “learn” the conditional probabilities in a model by automatically updating CPT values with case data (Marcot et al., 2006), in this case the habitat and species richness data. Netica requires the user to indicate the level of confidence one has in the prior data before the model is updated. Low confidence can be specified (a hypothetical sample size of 5 to 10) if the expert data are unreliable, or higher confidence can be specified (a hypothetical sample size of 10 to 20+) if expert data are highly reliable (Pollino et al., 2007). In the field data-only model, for which only uninformative data were available, I assigned the prior a sample size equivalence of one.

Expert and field data model

In the expert-only model, conditional probabilities were estimated using expert opinion from an ecological scientist with extensive experience studying reptile and beetle populations (D. Driscoll pers. com.). The CPTs parameterised with this expert data were the nodes for habitat attributes and reptile and beetle species richness. After the expert-only model was compiled and initial testing undertaken, this prior model was updated with field data. These field data were updated into the expert only model as a “case-file”, as described above, to make predictions based on both sources. In the process of updating the expert Bayes net with field data, it was necessary to specify how confident I was in the existing expert model. To do this I tested a number of different confidence levels (1, 5, 10 and 50) to gauge how confidence level influenced the results from the prior (expert) model to the posterior (combined expert and field data) model.

Incorporating landholder behaviour in the patch-level Bayes net

The patch-level net, containing the combined expert and field data model described above, was expanded to integrate landholder behaviour in the process model, enabling exploration of the implications of landholder

159 demographics and preferences for biodiversity. The landholder behaviour model tried to characterise the influence of landholder demographic attributes such as Landcare membership on management decisions made by landholders. These management decisions influenced habitat variables, and ultimately reptile and beetles species richness (Figure 6.3).

Landholder demographic nodes

Landholder management decision nodes

Management action nodes

Habitat attribute nodes Habitat type node

Species richness nodes

Figure 6.3. A sub-model including the patch-level Bayes net (enclosed in the dashed- line box) and the landholder behaviour Bayes net showing the causal link between landholder demographics (red) and management decisions (grey) on management action nodes (blue), habitat attribute nodes (green), and species richness nodes (purple). Landholder management decisions have a direct effect on the management action nodes and a habitat attribute node. Habitat attribute nodes are also influenced by habitat type nodes. Landholder demographic nodes = region, enterprise type, primary source of income, and Landcare membership. Landholder management decision nodes = weed management, revegetate with tussock grasses, remove litter and timber, revegetate with trees and shrubs, and revegetation type. Management action nodes = weed control, plant tussocks, plant trees and shrubs, add litter and timber, and add rocks. Habitat attribute nodes = rock cover, weed cover, tussock cover, cover of coarse woody debris, tree density, mid-stratum density, and herb cover. Habitat type node = remnant, revegetated and cleared linear strip, remnant patch, and revegetated patch. Species richness nodes = reptile and beetle species richness.

The landholder demographic information and management decisions (revegetate with tussock grasses, revegetate with trees and shrubs, and revegetation type) were answered on a binary scale in the questionnaire (Table 160

6.1). Landholder management decisions for weed management and removing litter and timber were gathered using a Likert scale that offered five possible answers ranging from „definitely‟ through to „definitely not‟ (Bryman, 2004). A binary “intention to manage” score was then calculated by averaging the response to these two management questions and using the lower limit as a positive response and the upper limit as a negative response. The mid-point in the management questions, „unsure‟, was not included in this calculation. The landholder demographic and management decision data were added to the posterior (combined expert and field data) patch-level Bayes net as a case-file (Figure 6.5).

Table 6.1. Landholder demographic information and management decisions and their respective states.

Landholder State Management State demographics decisions Region Wimmera Weed management Yes/no Benalla Revegetate with Yes/no Unknown tussock grasses Enterprise type Livestock Revegetate with trees Yes/no Cropping & shrubs Mixed Remove litter & timber Yes/no Other Revegetation type Linear strips Primary source of On-farm Patches income Off-farm Individual trees Both Mixture Landcare membership Yes/no None

Sensitivity to findings analysis

In the patch-level model, sensitivity to findings analysis was used to identify the habitat attributes that had the greatest influence on the query nodes in my networks: reptile and beetle species richness. For reptile species richness these habitat attributes were rock, weed, litter and tussock cover, and mid-stratum 161 density. For beetle species richness these were herb, weed, litter and tussock cover, and mid-stratum density (Figure 6.4). As my habitat attribute nodes were categorical variables, sensitivity to findings analysis in Netica used entropy reduction (or mutual information) to identify variables that had the greatest influence on reptile and beetle species richness (Korb and Nicholson, 2004; Pollino et al., 2007). Sensitivity to findings analysis was also undertaken for the landholder behaviour model to determine how sensitive reptile and beetle species richness was to landholder demographics and landholder management decisions.

Entropy, H(Q), measures the average amount of information (the inverse of the amount of uncertainty contained in a random variable) that a query variable (Q) (e.g., species richness) contains. Mutual information, the amount of information shared by two variables, I(Q|F), measures the effect of one variable on another, where F is the finding variable (i.e., habitat attribute). To calculate the total potential of F to reduce uncertainty (known as entropy reduction in Netica), H(Q|F) is subtracted from the original uncertainty in Q before F is considered; H(Q). The difference between H(Q) and H(Q|F) describes the expected reduction in mutual information (I) of species richness (Q) to habitat attributes (F). The habitat attribute responsible for the highest entropy reduction in species richness is the one that has the greatest influence on that node. The entropy reduction value can be calculated as follows:

I = ∑q ∑f Pr(q, f) log[(Pr(q, f)/((P(q) * Pr(f)))] where q is the state of the query variable (Q) and f is the state of finding variable (F) (Pearl, 1988; Chee, 2005). Entropy reduction provides a ranking of habitat variables‟ importance described as their ability to change the posterior probability of a given state of species richness (high, medium, or low). For example, in the combined expert and field data model, the entropy reduction value for weed cover is 0.076 out of a total entropy of 1.31 of reptile species richness (Q), explaining a total of 5.8% of the variation in the entropy of reptile species richness (Table 6.4).

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Comparing prior and posterior predictions of species richness

In order to determine how model predictions influenced reptile and beetle species richness in the patch-level Bayes net, I compared the field data-only model, expert only model and the combined expert and field data model. Model predictions were gathered under four different management scenarios; no management in remnant, revegetated and cleared linear strips, all management actions in remnant, revegetated and cleared linear strips, planting of trees and shrubs in remnant and cleared linear strips, and weed control in revegetated linear strips.

Cost-effectiveness analysis

Economic costs and the ecological benefits of each management action were combined using a cost-effectiveness analysis to determine the most cost- effective options for increasing reptile and beetle species richness (Table 6.2). I have provided a description and rationale for the estimation of costs based on restoration expenditure included in previous research (Appendix 19). Expert only data and the combined expert and field data models were used in this analysis. Field data-only models were not included in this analysis because previous analysis had shown this information to be uninformative. Ecological benefits were measured as the change in the expected beetle and reptile species richness arising from a unit change in management effort. To estimate cost-effectiveness, expected reptile and beetle species richness (predicted by the Bayes net) was recorded under a no-management scenario. The change in species richness under eight management scenarios for reptiles and six management scenarios for beetles provides an index of the ecological benefit of undertaking any given management strategy. To determine the cost- effectiveness of the management action or actions I divided the degree of change in species richness by the economic cost of the management action or actions (Korb and Nicholson, 2004; Joseph et al., 2009; Stewart-Koster et al., 2010).

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Table 6.2. The costs of implementing revegetation management actions in agricultural landscapes.

Management action Revegetation unit Unit cost Cost per hectare (ha) Weed control Herbicide spray and $190 labour a Add rocks Artificial rocks b $15 per rock $3,750 (250 rocks) Quarried rock c $50 per m3 $1,500 (30 m3) Add litter and timber Litter $28 per m3 $280 (10 m3) Logs $2,700 Plant tussock Scarification a $60 grasses Tussock grass seed $300 per kg $4,500 (15 kg) (Poa species) Plant trees and Deep ripping and $67 shrubs mounding a Tubestock a $0.50 each $500 (1,000 plants) Guards and stakes a $250 (1,000 plants) a (Schirmer and Field, 2000), b Croak pers. com., (Croak et al., 2010), c (Rushton, 2006)

Landholder scenarios

To demonstrate how landholder demographics influenced reptile and beetle species richness, I analysed how species richness changed under five demographic scenarios (Table 6.3). In these scenarios the management nodes for adding rocks and adding litter and timber were switched to “unknown”, because landholder demographics did not influence these variables.

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Table 6.3. Landholder demographic scenarios used to illustrate the influence of demographic variables on reptile and beetle species richness.

Region Landcare member Income source Enterprise type Scenario 1 Benalla No Off-farm Cropping Scenario 2 Benalla Yes Off-farm Livestock Scenario 3 Wimmera No On-farm Cropping Scenario 4 Wimmera No Off-farm Other Scenario 5 Benalla Yes On-farm Mixed

Add rocks Weed control Plant tussock grasses Add litter and timber Plant trees and shrubs Yes 50.0 Yes 50.0 Yes 50.0 Yes 50.0 Yes 50.0 No 50.0 No 50.0 No 50.0 No 50.0 No 50.0

Tree density Mid-stratum density Tussock grass cover Low 21.0 Low 21.0 Low 35.0 Medium 43.8 Medium 43.8 Medium 55.6 High 35.2 High 35.2 High 9.45

Cover of coarse woody debris Low 18.1 Medium 56.1 Habitat type High 25.7 Revegetated Patch 20.0 Remnant Patch 20.0 Weed cover Remnant Linear Strip 20.0 Low 15.0 Bare Linear Strip 20.0 Medium 48.2 Revegetated Linear Strip 20.0 High 36.7

Rock cover Low 32.5 Herb cover Medium 62.5 Low 55.0 High 5.00 Medium 26.0 High 19.0

Reptile species richness Beetle species richness Low 38.7 Low 26.2 Moderate 50.3 Moderate 55.5 High 11.0 High 18.3

Figure 6.4. Patch-level Bayes net showing interaction of management action nodes (blue), habitat attribute nodes (green), habitat type nodes (orange), and species richness nodes (purple). In each node the name of the node is in the upper box, below which are the states of the node. The management action nodes directly influence the habitat attribute nodes, as do the five different habitat types. The habitat attribute nodes have a direct influence on the species richness nodes (reptiles and beetles).

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Landcare membership Primary source of income Region Enterprise type Yes 50.0 OnFarm 33.3 Wimmera 33.3 Livestock 25.0 No 50.0 OffFarm 33.3 Unknown 33.3 Crop 25.0 Mixture 25.0 1.5 ± 0.5 Both 33.3 Benalla 33.3 2 ± 0.82 2 ± 0.82 Other 25.0 2.5 ± 1.1

Revegetation type Revegetated linear strip 20.0 No plantings 20.0 Weed management Revegetate with tussock grasses Remove litter and timber Revegetate with trees and shrubs Revegetated patch 20.0 Yes 50.0 Yes 50.0 Yes 50.0 Yes 50.0 Mix 20.0 No 50.0 No 50.0 No 50.0 No 50.0 Individual trees 20.0 0.5 ± 0.5 0.5 ± 0.5 0.5 ± 0.5 0.5 ± 0.5 2 ± 1.4

Add rocks Weed control Plant tussock grasses Add litter and timber Plant trees and shrubs Yes 50.0 Yes 50.0 Yes 50.0 Yes 50.0 Yes 50.0 No 50.0 No 50.0 No 50.0 No 50.0 No 50.0 0.5 ± 0.5 0.5 ± 0.5 0.5 ± 0.5 Tree density Low 21.0 Cover of coarse woody debris Medium 43.8 Mid-stratum density Low 27.8 High 35.2 Medium 56.1 45.4 ± 26 Low 21.0 Tussock grass cover High 16.1 Medium 43.8 Low 35.0 High 35.2 36.6 ± 22 Medium 55.5 45.4 ± 26 High 9.45 32.3 ± 20 Habitat type Weed cover Revegetated Patch 20.0 Low 15.0 Remnant Patch 20.0 Medium 48.2 Remnant Linear Strip 20.0 High 36.8 Bare Linear Strip 20.0 47.5 ± 25 Revegetated Linear Strip 20.0

Rock cover Herb cover Low 32.5 Reptile species richness Beetle species richness Low 55.0 Medium 62.5 Medium 26.0 High 5.00 Low 41.3 Low 27.5 High 19.0 31.2 ± 17 Moderate 49.1 Moderate 55.8 High 9.62 High 16.6 30.9 ± 25 2.57 ± 1.2 8.35 ± 4.3

Figure 6.5. Landholder behaviour Bayes net showing the influence of landholder demographic nodes (red) and management decision nodes (grey) on the patch-level net. The landholder demographic nodes each have a different number of states that influence the binary states of the landholder management decision nodes. These influence patch-level management action nodes and a habitat attribute node.

Results

I determined that a confidence level of one was the most appropriate level to use when incorporating field data into the expert-only model as other levels gave too much weight to the expert-only model (Appendix 20).

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Sensitivity to findings

Patch-level Bayes net

The field data-only model predicted that reptile species richness would be most sensitive to rock cover, mid-stratum density and weed cover, although the total entropy reduction achieved by the field data model was only 0.92% (Figure 6.4). Both the expert-only model and the combined expert and field data models predicted that reptile species richness would be most sensitive to weed cover, although differed in their predictions of sensitivity to mid-stratum density or litter cover. All models predicted that reptile species richness would be least sensitive to tussock grass cover (Table 6.4). The field data-only model for beetle species richness predicted that beetles would be most sensitive to litter cover and tussock grass cover. The expert-only model and combined model showed that mid-stratum density and weed cover explained a high proportion of the total entropy. Both of these models also predicted that beetle species richness would be sensitive to litter cover (Table 6.5).

Table 6.4. Sensitivity to findings analysis showing reptile species richness sensitivity to habitat attribute nodes using field data-only models, expert-only models, and combined expert and field data models. Entropy displays the degree of uncertainty in the model, where the calculated entropy of the habitat variable is a proportion, given as a percentage, of the total entropy (Q).

Habitat Calculated Habitat Calculated Habitat Calculated (field data entropy (expert only) entropy (expert + entropy only) (Q = 1.89) (Q = 1.18) field) (Q = 1.31) Rock 0.005 Weed 0.077 Weed 0.076 (0.3%) (6.5%) (5.8%) Mid-stratum 0.005 Litter 0.065 Mid-stratum 0.053 (0.3%) (5.5%) (4%) Weed 0.004 Mid-stratum 0.054 Litter 0.048 (0.2%) (4.5%) (3.6%) Litter 0.002 Rock 0.035 Rock 0.018 (0.1%) (3%) (1.3%) Tussock 0.000 Tussock 0.020 Tussock 0.016 (0.02%) (1.7%) (1.2%)

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Table 6.5. Sensitivity analysis showing beetle species richness sensitivity to habitat attribute nodes using field data-only models, expert-only models, and combined expert and field data models. Entropy displays the degree of uncertainty in the model, where the calculated entropy of the habitat variable is a proportion, given as a percentage, of the total entropy (Q).

Habitat Calculated Habitat Calculated Habitat Calculated (field data entropy (expert only) entropy (expert + entropy only) (Q = 21.2) (Q = 15.8) field) (Q = 15.65) Litter 0.281 Mid-stratum 2.832 Weed 1.434 (1.3%) (17.9%) (9.2%) Tussock 0.213 Weed 2.612 Mid-stratum 1.409 (1%) (16.5%) (9%) Weed 0.019 Litter 0.794 Litter 0.692 (0.1%) (5%) (4.5%) Herb 0.006 Herb 0.451 Tussock 0.158 (0.03%) (2.9%) (1%) Mid-stratum 0.001 Tussock 0.220 Herb 0.115 (0.03%) (1.4%) (0.7%)

Landholder behaviour Bayes net

All models showed that reptile and beetle species richness was not substantially influenced by any of the landholder behaviour variables, as shown by the proportion of total entropy explained by each of the demographic and management decision variables. While the proportion of total entropy explained by landholder demographics was similar, landholder management decisions to remove litter and timber and revegetate of trees and shrubs had a slightly stronger influence on reptile and beetle species richness (Table 6.6).

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Table 6.6. Sensitivity to findings analysis showing reptile and beetle species richness variables sensitivity to landholder demographic and landholder management decision nodes, updated with expert opinion and ecological data. Entropy displays the degree of uncertainty in the model, where the calculated entropy of the habitat variable is a proportion, given as a percentage, of the total entropy (Q).

Reptiles (Q = 1.31) Beetles (Q = 15.46) Landholder Calculated Landholder Calculated demographics entropy demographics entropy Enterprise type 0.0003 (0.03%) Region 0.0019 (0.01%) Region 0.0002 (0.02%) Landcare membership 0.0016 (0.01%) Income 0.0001 (0.01%) Enterprise type 0.0014 (0.01%) Landcare 0.0000 (0.01%) Income 0.0006 (0.00%) membership Remove litter & 0.0053 (0.41%) Remove litter & timber 0.0375 (0.24%) timber Revegetate trees & 0.0033 (0.17%) Revegetate trees & 0.0329 (0.21%) shrubs shrubs Revegetate tussocks 0.0007 (0.05%) Weed management 0.0053 (0.04%) Weed management 0.0006 (0.05%) Revegetation type 0.0021 (0.01%) Revegetation type 0.0003 (0.02%) Revegetate tussocks 0.0002 (0.00%)

Comparing prior and posterior predictions of species richness

Predictions of how reptile and beetle species richness varies under a set of management scenarios were strongly influenced by the expert (prior) model and the combined expert and field data model (Table 6.7 & Table 6.8). In the field data-only model, predictions for reptiles and beetles were generally uninformative. The combined expert and field data model was moderately influenced by field data as reptile species richness was greater under no- management scenarios and also when planting trees and shrubs and weed control actions were undertaken, than predicted by the expert-only model (Table 6.7). In the combined expert and field data model species richness was greater under no-management scenarios in remnant and cleared linear strips, than in the expert-only model. Beetle species richness was predicted to be lower in the combined model than in the expert-only model when weed control was undertaken in revegetated areas (Table 6.8). 169

Table 6.7. Reptile species richness change in an expert-only model, field data-only model, and a combined expert and field data model in linear strip habitats under three management scenarios. Pie charts represent the probability of species richness being low (0 - 2 species = white), medium (>2 - 4 species = grey), or high (>4 - 6 species = black). Management Linear strip Expert Field Model Expert + Field Scenario type Model Model

No Management Remnant

All Management Remnant

Plant Trees & Shrubs Remnant

No Management Cleared

All Management Cleared

Plant Trees & Shrubs Cleared

No Management Revegetated

All Management Revegetated

Weed Control Revegetated

Table 6.8. Beetle species richness change in an expert-only model, field data-only model, and a combined expert and field data model in linear strip habitats under three management scenarios. Pie charts represent the probability of species richness being low (0 - 6 species = white), medium (>6 - 12 species = grey), or high (>12 - 18 species = black). Management Scenario Habitat Type Expert Field Model Expert + Model Field Model

No Management Remnant

All Management Remnant

Plant Trees & Shrubs Remnant

No Management Cleared

All Management Cleared

Plant Trees & Shrubs Cleared

No Management Revegetated

All Management Revegetated

Weed Control Revegetated

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Cost-effectiveness

Reptile species richness

Reptile species richness moderately increased under all management scenarios and also when rocks, litter, and timber were added to revegetated patches. Other scenarios that substantially increased reptile species richness were a combination of adding rocks, litter and timber, and adding rocks and planting trees and shrubs (Appendix 21). Undertaking all management actions compared to no-management showed some evidence of increasing reptile species richness in remnant areas and cleared linear strips (average increase of 0.4 species) (Figure 6.6). Under both models I found that the most cost- effective actions were weed control in revegetated patches, planting trees and shrubs in remnant and cleared linear strips, and undertaking weed control in remnant patches (Table 6.9). In remnant patches the expert-only model predicted that adding rocks would also be the most cost-effective management action. Interestingly, in revegetated linear strips the expert-only model predicted that weed control would be the most cost-effective management action, while in the combined expert and field data model planting trees and shrubs was predicted to be more cost-effective (Table 6.9). The expert-only model and the combined expert and field data model predictions differed most in revegetated and cleared linear strips (Appendix 21).

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Table 6.9. Reptile species richness increase as a result of different management actions (compared to a no-management scenario) and the cost-effectiveness of those actions per $1,000 spent.

Expert model Combined expert + field Habitat Management Species Cost- Species Cost- type actions gain efficiency gain efficiency RevP Weed control 0.08 0.42 0.06 0.32 RevP Add rocks & litter 0.41 0.09 0.39 0.09 RevP All management 0.47 0.06 0.22 0.03 RevLS Trees & shrubs -0.06 -0.07 0.29 0.36 RevLS All management 0.42 0.05 0.52 0.06 RevLS Weed control 0.08 0.42 -0.12 -0.63 RemP Weed control 0.03 0.16 0.02 0.11 RemP Add rocks 0.24 0.16 0.16 0.05 RemP All management 0.61 0.07 0.47 0.06 RemLS Trees & shrubs 0.16 0.20 0.13 0.16 RemLS All management 0.61 0.07 0.45 0.05 CLS Trees & shrubs 0.30 0.37 0.29 0.36 CLS All management 0.86 0.10 0.52 0.06

Beetle species richness

Beetle species richness was predicted to be greatest by the expert-only model and the combined expert and field data model when all management actions were undertaken (average increase of 1.2 species) (Table 6.10, Figure 6.6). The best single actions predicted by the expert-only model and combined expert and field data model were the addition of litter and timber in revegetated areas and the planting of trees and shrubs and addition of litter and timber in remnant areas and cleared linear strips (Appendix 22). The combined expert and field data model also predicted that the addition of litter and timber would be the most cost-effective management action in revegetated patches, revegetated linear strips, and cleared linear strips. Interestingly, the expert-only model predicted that weed control would be the most cost-effective action in revegetated areas and the planting of trees and shrubs would be most cost- 172

effective in cleared linear strips. Both models predicted that weed control would be the most cost-effective management action in remnant patches, and the planting of trees and shrubs would be the most cost-effective management action in remnant linear strips (Table 6.10). The expert-only model and the combined expert and field data model predictions differed most in revegetated areas and cleared linear strips (Appendix 22).

Table 6.10. Beetle species richness increase as a result of different management actions (compared to a no-management scenario) and the cost-effectiveness of those actions per $1,000 spent.

Expert model Combined expert + field Habitat Management Species Cost- Species Cost- type actions gain efficiency gain efficiency RevP Weed control 0.24 1.26 0.03 0.16 RevP Litter 0.47 0.16 0.51 0.17 RevP All management 0.57 0.07 1.06 0.13 RevLS Weed control 0.25 1.32 -0.06 -0.32 RevLS Litter 0.47 0.16 0.68 0.23 RevLS All management 0.61 0.07 1.30 0.15 RemP Weed control 0.10 0.53 0.10 0.53 RemP All management 1.34 0.16 0.92 0.11 RemLS Trees & shrubs 1.30 1.59 0.80 0.98 RemLS All management 2.15 0.25 1.51 0.18 CLS Trees & shrubs 2.87 3.52 0.28 0.34 CLS All management 3.93 0.46 1.61 0.19 CLS Litter 0.20 0.07 1.30 0.44

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4 12 10 3 8 2 6 4 1 2

0 0

Beetle speciesrichness Beetle Reptile species richnessspeciesReptile

Figure 6.6. The effectiveness of no management compared to all management actions on reptile and beetle species richness. Predictions were calculated from the combined expert and field data model. CLS = cleared linear strip, RemLS = Remnant linear strip, RemP = Remnant patch, RevLS = Revegetated linear strip. Clear columns = no management, grey columns = all management actions. Bars represent 95% credible intervals.

Landholder scenarios

I found that scenarios 2 (Benalla, Landcare member, off-farm income, livestock enterprise) and 3 (Wimmera, not in Landcare, on-farm income, cropping enterprise) showed some evidence of increasing beetle species richness (0.44 to 1.77 species) in cleared linear strips compared to scenario 4 (Wimmera, not in Landcare, off-farm income, other enterprise). Reptile species richness (0.09 to 0.31 species) was also higher in cleared linear strips as a result of scenarios 2, 3 and 5 (Benalla, Landcare member, on-farm income, mixed enterprise), although there was no clear evidence of a substantial difference. In revegetated and remnant treatments there was no clear evidence of a difference in reptile and beetle species richness resulting from the different demographic scenarios (Figure 6.7). Landholders in scenarios 1, 2 and 4 were least likely to undertake revegetation with trees or shrubs, but landholders in scenarios 1 and 4 were more likely to revegetate with tussock grasses (Figure 6.8). Landholders in scenarios 2 and 5 were most likely to undertake weed management and least likely to remove litter and timber (Figure 6.8).

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11 3 9 2 7 5 1 3

0 1

Beetle speciesrichness Beetle Reptile species richnessspeciesReptile

Figure 6.7. Influence of changes in landholder demographics under different scenarios on reptile and beetle species richness in different habitat types. White columns = scenario 1; light grey columns = scenario 2; mid-grey columns = scenario 3; black columns = scenario 4; and dark-grey columns = scenario 5. Bars represent 95% credible intervals.

100 Scenario 1 Scenario 2 90 Scenario 3 Scenario 4 80 Scenario 5 70 60 50 40 30 20 10

Landholder(%) response 0 Weed Revegetate Remove Revegetate management tussocks litter/timber trees/shrubs Landholder management decision

Figure 6.8. Response of landholders to different demographic scenarios expressed as a percentage, and how these influenced their management decisions.

Discussion

Integrating social and ecological data

The integration of landholder information such as demographic data and propensity to make management decisions is beneficial in determining how

175 landholder behaviours influence habitat variables and faunal species (Ticehurst et al., 2011). This study synthesises landholder behaviour data, expert opinion, and field data into a single model of how human demography and ecology interact to influence biodiversity. I found that reptiles and beetles were only slightly sensitive to landholder demographics and management decisions.

Importantly, even under the best-case landholder management scenarios, most species increases occur when cleared areas are restored, and species gains are minimal in other habitats. In cleared areas, beetle species richness can be increased up to 23%, and reptile species richness up to 12%, but in all other habitats species gains are relatively small (3 - 5%). These findings suggest that under current management scenarios, reptile and beetle species richness is unlikely to increase, in accordance with results in Chapters 3 and 4. However, there may be management actions not in this current model that would result in greater species increases. These actions may include the reduction of livestock grazing (Smith et al., 1996; Dorrough et al., 2004b; Lindsay and Cunningham, 2009), pest animal control (Arthur et al., 2010), creating connections between remnant areas (Moilanen and Nieminen, 2002; Moilanen et al., 2005; Chetkiewicz et al., 2006; Bailey, 2007) and broad-scale revegetation (Munro et al., 2007). However, we lack quantification of the effectiveness of these actions. An ongoing research priority should be to expand the current model to explore and quantify the relative biodiversity benefits of these actions.

While many researchers have discussed the importance of integrating social and ecological knowledge and data as well as economic and political aspects into biodiversity management, very few studies have been successful in doing so in a transparent and quantitative way (Carr and Hazell, 2006; Olsson et al., 2006; Chan et al., 2007; Raymond et al., 2010). Some impediments to this integration include the subjective nature of most social and scientific information and studies; the differing theories underpinning social and ecological science; and the difficulty in applying integrated knowledge due to political and social factors (Chan et al., 2007; Raymond et al., 2010). Yet, as biodiversity continues to decline in human-dominated landscapes the need to integrate social

176 knowledge and data into conservation management questions becomes increasingly urgent (Liu, 2001; Pannell et al., 2006). The Bayes net I developed provides a way to integrate these different data sources, facilitating a transparent representation and analysis of the many uncertainties.

Expert opinion and field data

I found that the field data-only model was generally uninformative compared to the expert-only model and the combined expert and field data model. This is probably because the field data-only model was based upon uninformative data, and because the field data collected were highly variable. The expert-only model and the combined expert and field data model predicted similar species responses under all management actions, but differed under no-management scenarios. This suggests that there was a high degree of similarity between the predictions of the expert-elicited information and the field data. This is not always the case, and expert opinion can differ substantially from field data, possibly because one of the data sources is wrong. This highlights the need for robustness in both field data and expert opinion (Kuhnert et al., 2005; Martin et al., 2005).

Gaining robust expert opinion is important because the use of this type of information is becoming increasingly common in the evaluation of management actions and their impact on ecosystem condition and biodiversity (Cook and Hockings, 2011). While quantitative indicators are more appropriate for evaluating management decisions, these data are seldom available because ecological interactions are difficult, time consuming and costly to measure (Johnson and Gillingham, 2004; Kuhnert et al., 2010; Cook and Hockings, 2011). As a result, expert opinion is often used to help make conservation decisions when ecological data are lacking (Marcot et al., 2006; Kuhnert et al., 2010; Cook and Hockings, 2011). Expert opinion can also be useful to better inform existing ecological data to make more accurate management decisions (Czembor et al., 2011).

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While expert opinion can be useful, there are many limitations and risks in using it, as it can provide misleading results and lead to ineffective management decisions due to cognitive biases and overconfidence (Burgman et al., 2011; Cook and Hockings, 2011; Czembor et al., 2011). For instance, expert overconfidence can impact heavily on an analysis because false predictions can give weight to incorrect opinions, as evidenced by the magnitude of effects in some of the expert models compared to the combined expert and field-data models (Speirs-Bridge et al., 2010). Different experts can also have differing opinions on ecological processes and management actions, making elicited responses variable and potentially biased if consensus is reached (Czembor et al., 2011). People may also lack confidence in expert opinion as the method of collecting it is often not rigorous (Cook and Hockings, 2011).

Approaches have been proposed to increase the accuracy of expert-elicited data (Burgman et al., 2011). These include training experts in how to provide better judgment, and training researchers how to measure expert opinion objectively and how to structure expert elicitation procedures to limit bias (Burgman et al., 2011). Similarly, measuring the accuracy of expert opinion can help to develop less biased and more confident future expert assessments (Cook and Hockings, 2011). A systematic literature review on the subject of interest can also provide very accurate information, as can a meta-analysis of a number of studies (Cumming, 2008; Speirs-Bridge et al., 2010). Finally, Bayes nets can be used in conjunction with conventional statistics and expert assessment to better interpret results of management actions (Ticehurst et al., 2011). These results suggest that, while the expert opinion used in my study provided similar results to the field data, this is not always the case; in future studies robust expert opinion from multiple experts should be used combined with other data sources.

Management actions and cost-effectiveness

This study quantifies the benefits of undertaking a particular set of management actions to increase reptile and beetle species richness in revegetated, remnant

178 and cleared habitats in agricultural landscapes. I found that the combination of undertaking all management actions could increase reptile species richness by 6 - 18%, and beetle species richness by 9 - 19%, depending on the habitat type being restored. The addition of rocks in revegetated patches was also a very beneficial action for increasing reptile species richness. The greatest gains in species richness for reptiles were achieved in cleared linear strips and remnant areas, and for beetles in revegetated, remnant and cleared linear strips. These findings provide some guidance about where and how the greatest biodiversity gains might be made in agricultural landscapes.

Weed control, the planting of trees and shrubs, and the addition of litter and timber were the most cost-effective management actions. Weed control was important in revegetated areas because these areas are usually replanted with trees and shrubs, and weeds tend to invade the understory of these areas (Munro et al., 2009; Nichols et al., 2010) having a negative impact on native animals such as reptiles (Chapter 3) (Hadden and Westbrooke, 1996; Jellinek et al., 2004; Munro et al., 2009). The addition of trees and shrubs benefits reptiles and beetles because they contribute to the ground layer by dropping litter and timber (Fischer et al., 2004; Munro et al., 2009) and increase shading and soil moisture (Barton et al., 2010). The addition of litter and timber provides fine-scale habitat heterogeneity for ground dwelling fauna in forested landscapes (Kanowski et al., 2006; Whitfield et al., 2007; Dixo and Metzger, 2009; Marquez-Ferrando et al., 2009; Barton et al., 2010). Adding rocks increased reptile species richness in most management scenarios because rocky areas provide valuable microhabitats (Driscoll, 2004; Masterson et al., 2009; Hopp et al., 2010; Michael et al., 2011). These results highlight the need for maintaining some original habitat in fragmented landscapes if possible, in order to provide a reference for the replacement of microhabitats that resemble those in natural areas.

Cost-effectiveness was an important factor to include as it provides a more realistic scenario for managing degraded agricultural areas. This is because management agencies have limited budgets and must determine how best to

179 allocate limited funding (Possingham, 2001; Holzkamper and Seppelt, 2007; Segan et al., 2011). Cost-effectiveness also provides a way of comparing competing management strategies to achieve the best ecological result (Holzkamper and Seppelt, 2007). However, cost-effectiveness is highly sensitive to cost-estimates, and may fail to take into account other measures such as opportunity costs (Drechsler and Hartig, 2011), so it is important to properly estimate management costs. The alternative to considering cost- effectiveness is an ad-hoc approach to restoration that may be ineffective in maintaining biodiversity and may not make the best use of conservation funding (Lunney et al., 1997).

Species richness as a measure of ecological condition

I chose to use species richness as a measure of conservation value as it responded to habitat variables in a similar way to other species measures (Chapters 3 & 4). However, although richness is assumed to be a good indicator of the conservation value (Fleishman et al., 2006), species richness alone is not a good measure on which to base conservation goals, because it provides no information on species functional roles or on the processes of extinction and colonisation that resulted in current species distributions (Didham et al., 1996; Lassau et al., 2005; Fleishman et al., 2006; Lomov et al., 2009; Mayfield et al., 2010). Similarly, species of conservation concern may decline while species richness remains relatively constant, because more common species replace those rare species (Munro et al., 2011). In order to make these models more robust, species richness could be used in conjunction with other measures such as species composition, functional importance or species persistence probabilities (Fleishman et al., 2006; Sebastián-González et al., In press). Species richness can be a valuable indicator of habitat condition, but should be used with caution and in conjunction with other measures.

Future research

The Bayes nets I developed highlight the benefits of this method in supporting management decisions. For example, cost-effectiveness analysis can contribute 180 to the process of optimising revegetation investments, and this process would benefit natural resource managers by allowing them to show, in a transparent way, how to get the best “bang” for their limited management budgets (Duncan and Wintle, 2008). Given the high degree of uncertainty in the models developed here, refinement of CPTs should be made with ongoing data collection. Detailed information is also needed on other potential management actions, such as the effects of reducing livestock grazing and increasing connectivity. This new information could then iteratively update the existing patch-level and landholder behaviour models, helping managers to adapt revegetation actions to reflect the best scientific knowledge in the spirit of adaptive management (Duncan and Wintle, 2008). Indeed, a major contribution of this work is to provide a coherent framework for doing just that.

While there are drawbacks to this method, such as subjectivity in structuring and parameterising the Bayes net, these drawbacks can be addressed. This can be done by increasing the amount of field data and the reliability of expert knowledge; increasing the number of experts used (Kuhnert et al., 2005; Burgman et al., 2011; Czembor et al., 2011; Rumpff et al., 2011); and by undertaking an in-depth review of the literature along with a meta-analysis of different studies (Speirs-Bridge et al., 2010). This will serve both to improve the estimated effectiveness of management actions and to better characterise epistemic uncertainty reflected in the differences of opinion between experts (Czembor et al., 2011).

Although natural resource management questions are often thought of as single-objective problems, they usually leave-out real objectives such as human aesthetic, economic, and community benefits derived from land management, which if properly considered would make them multiple-objective decision problems (Keeney, 2002; Hajkowicz, 2008). This highlights the need for more data on competing objectives to inform decisions on natural resource management where there is substantial uncertainty (Duncan and Wintle, 2008; Driscoll et al., 2010b). The work described here takes a small step towards achieving a multi-objective approach to decision analysis in agricultural

181 landscapes by illustrating how landholders, driven by multiple objectives of productivity, aesthetics, and social norms, influence biodiversity through their management choices. However, I do not explicitly model and trade off the competing objectives of economic prosperity and biodiversity conservation, or the often synergistic objectives of increasing biodiversity and aesthetic value of land. This is an important area of future research that can be partly informed by what has been achieved here.

Conclusion

My study is rare in that few researchers have shown how a combination of expert opinion, ecological and social data can inform management decisions to maximise biodiversity gains given limited budgets. This integrated information can be used to investigate realistic scenarios describing how landholder demographics, uncertainty about biophysical processes, and management decisions influence patch scale processes and biodiversity outcomes. The two resulting Bayes nets, with the input of more robust social and ecological data and expert opinion, can be used to help evaluate management options and efficiently allocate funding to maximise biodiversity gains. However, there are limitations to my implementation of this method, such as the use of species richness as a measure of conservation effectiveness; the drawbacks associated with using a single expert opinion and a single model structure; and the problems surrounding the measures of cost-effectiveness. The high degree of variability inherent in ecological and to a lesser degree social data is a universal problem that can be only partially overcome with more sampling. Future improvements to the models would aim to address these shortcomings. The alternative to the investment prioritisation approaches demonstrated here is the ad-hoc decision making that plagues much of our current natural resource management decisions. The Bayes net described above allows the integration of expert knowledge along with ecological and social data that can be iteratively accrued and synthesised. This information will provide management agencies with a valuable tool when dealing with multiple objectives to manage revegetated areas cost-effectively.

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Chapter 7. Conclusion

My thesis contributes new and important information on revegetation activities and their effectiveness for maintaining faunal species richness, abundance, and community composition. It does this through the integration of ecological data and social information gathered over a two-year period in two intensively farmed agricultural regions of south-eastern Australia: the Wimmera and Benalla regions. The integration of these two fields of study - ecological and social science - is rarely undertaken in the science literature, but doing so enables a richer understanding of the impacts of revegetation and farm management on faunal species than either field alone could provide. The ability to analyse these two sources of information together with expert opinion using Bayesian Networks allows research results to better inform natural resource management decisions.

The objectives of this study were to investigate the benefits of revegetating native habitat for maintaining reptile and beetle species richness, abundance, and community composition: and to survey local landholders to determine their degree of adoption of revegetation activities and how their intentions to manage revegetated and remnant areas were influenced by their attitudes towards these areas. To do this I first undertook a review of the global literature on how effective revegetation is for native faunal conservation. This review showed that while there has been extensive research on habitat loss and fragmentation and its effect on native faunal species (Fahrig, 1997; Harrison and Bruna, 1999; Fahrig, 2002; Donald and Evans, 2006), relatively few studies have explored the effectiveness of revegetation or whether revegetated linear strips are more effective for maintaining faunal species compared to enlarged patches (Simberloff and Cox, 1987; Tewksbury et al., 2002; Bailey, 2007). Faunal groups least studied in the restoration literature included reptiles and amphibians (Kanowski et al., 2006; Cunningham et al., 2007; Munro et al., 2007). Overall, finding show that revegetated areas are not as effective in maintaining faunal communities as remnant areas, and studies proposed that

183 this is because revegetated areas lack the same structural and floristic attributes as remnant habitats (Munro et al., 2007; Munro et al., 2011).

My review found that there are many gaps in our current knowledge and understanding of revegetated areas. It outlined a number of priority research areas for better understanding the effectiveness of revegetated linear strips and patches, and what habitat attributes are necessary to enable faunal communities to colonise restored areas. My two ecological chapters focusing on reptiles (Chapter 3) and beetles (Chapter 4) and their response to revegetated areas, looked specifically at these questions of revegetated areas and how they compared to remnant and cleared habitats, revegetated areas of different shapes, and the role environmental variables such as vegetation type and habitat elements play in structuring reptile and beetle species and communities (Table 7.1).

Reptile and beetle response to treatment type

My results showed that overall reptile and beetle species richness and abundance did not differ substantially between revegetated, remnant and cleared areas (site-type) or between linear strips and patches (treatment shape) (Chapter 3 and 4). Similarly, reptile and beetle community composition was not substantially influenced by site-type or treatment shape. These results are contrary to the results of other studies, which suggest that faunal communities do differ substantially between cleared, revegetated and remnant areas (Driscoll, 2004; Driscoll and Weir, 2005; Grimbacher and Catterall, 2007; Brown et al., 2008; Munro et al., 2011). I suggest that in both faunal groups, declines and possibly local extinctions of more specialised reptiles and beetles have occurred as a result of large-scale clearing and intensive agriculture. The remaining reptile and beetle species are more robust, generalist species that are able to persist in these degraded landscapes and able to use a variety of different areas including the treatments I studied, and potentially the agricultural matrix as well (Driscoll, 2004; Schutz and Driscoll, 2008; Bridle et al., 2009; Gibb and Cunningham, 2010) (Table 7.1). These results have implications for

184 the value of revegetation in agricultural landscapes, as they suggest that even extensive restoration may not recover those species that have already been lost.

Importantly, I recorded a reptile species, Carlia tetradactyla, and two beetle species, Adelium similatum and Chauliognathus nobilitatus, that showed a trend towards higher abundance in remnant linear strips than in other linear strip areas. Few studies have recorded individual reptile and beetle species being more abundant in remnant linear strip habitats (Driscoll, 2004; Driscoll and Weir, 2005). My findings suggest that these species may require better quality habitat in order to persist in agricultural landscapes, and remnant linear strips provide these high quality habitat attributes (Spooner and Smallbone, 2009). Remnant patches may not provide similar quality habitat either because they are intermittently grazed (Vohland et al., 2005; Brown et al., 2008) or because they do not exhibit the greater plant growth as a result of nutrient and water runoff from roads and adjoining paddocks that can be found in remnant linear strips (Driscoll and Weir, 2005).

Interestingly, species richness and abundance of rare reptiles, Carlia tetradactyla and overall abundance of reptiles increased in remnant linear strips as distance from the remnant patch increased. These species showed the opposite trend in revegetated and cleared linear strips, declining as distance from the remnant patch increased. The impact of distance on faunal species in linear strips is seldom reported in the scientific literature (Haddad et al., 2003). Competition and predation in remnant patches by dominant faunal species may explain why rare reptiles and Carlia tetradactyla, that were better able to disperse, were mainly found in remnant linear strips (Tilman et al., 1994; Huxel and Hastings, 1999; Driscoll, 2008). Revegetated and cleared linear strips probably do not provide appropriate habitat to maintain these rarer species, as discussed in more detail below.

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Habitat variables and management of revegetated areas

While beetle species richness and abundance was not substantially influenced by environmental variables such as vegetation type and structure, reptile species richness and abundance and both reptile and beetle community composition were substantially influenced by environmental variables. Important environmental variables for both faunal groups included ground layer attributes such as rock, litter, tussock and herb cover, as well as soil type for beetles (Table 7.1). Thus, reptiles and beetles that require good quality habitat offering these ground layers may be more likely to use revegetated areas if these attributes are included in revegetation activities. The revegetation of linear strips with ground layer attributes similar to remnant linear strips may provide better quality habitat for reptiles and beetles and possibly also allow greater movement and dispersal between remnant areas.

My results suggest that landholders have not included ground layer elements such as tussock grasses in past or planned revegetation works (Chapter 5). The revegetation of trees and shrubs only is unlikely to benefit the rarer reptile and beetle species I studied, at least over the short term (8 -10 years), due to their requirement for good quality ground elements as discussed above. Landholders I surveyed were also less likely to manage revegetated and remnant areas for environmental benefits such as the increase of native faunal species than for actions that benefited their property, such as reducing weeds, pest animals and fire risk (Table 7.1, Chapter 5). Similar findings have been reported by other studies (Bennett et al., 2000; Pannell et al., 2006; Smith, 2008; Gosling and Williams, 2010). Therefore, ground layers and reptiles and beetles that rely on these habitat elements are unlikely to be a priority for landholders unless these reptile and beetle species can be shown to benefit farm productivity (Pannell et al., 2006). While some studies do show that some native animals can benefit agricultural production (McAlpine and Wotton, 2009; Thomson and Hoffmann, 2010), more research is required to determine the benefits of different faunal groups for farm productivity, and to provide these findings to land managers.

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Even if ground layers are included in revegetation actions, analysis of landholder demographic scenarios using Bayesian Networks suggests that the management decisions landholders make are likely to increase beetle species richness only in cleared linear strips (Chapter 6). Management decisions to restore revegetated and remnant areas are unlikely to substantially increase reptile or beetle species richness (Table 7.1). This means that other management actions not studied here will need to be incorporated in order to determine more effective actions for increasing reptile and beetle species richness. Such actions may include translocating beetle species that were present in the landscape prior to clearing, back into remnant and restored habitat. However, little is known about the effectiveness of translocation, and greater research would be needed to determine the influence translocated species would have on local plants and animals (Ricciardi and Simberloff, 2009a; Ricciardi and Simberloff, 2009b).

Bayesian Networks also showed that the greatest increase in reptile and beetle species richness would come from implementing all management actions. However, the most cost-effective single actions were found to be weed control, planting trees and shrubs, and adding litter and timber. While adding litter and timber is likely to benefit reptiles and beetles generally, other cost-effective management actions are unlikely to benefit the rare reptiles and beetles discussed above. This implies that the cost-effective management actions proposed by this modelling will not alter reptile and beetle community composition to resemble communities in remnant areas, and that a greater amount of information on the species of interest and management actions that would benefit these species is needed.

Future research

This thesis has helped to answer some of the questions raised in Chapter 2 with regard to the effectiveness of revegetation for faunal species such as reptiles and beetles and the role environmental variables play in structuring communities of these species. Yet many questions remain unanswered, and

187 more research is needed to more fully understand the processes that are influencing these reptile and beetle species, and how landholder management decisions can influence these faunal communities on private land. Some of these important research questions are given below, together with contributions from this study: What habitat variables are most important for reptile and beetle species that are located in remnant linear strips? - Ground layer attributes such as rock, herb, litter and tussock cover as well as mid-stratum density and proportion of native plants are important elements structuring reptile and beetle community composition (Chapters 3 and 4). How do reptile and beetle species‟ richness, abundance and community composition, as well as functional groupings, change over time when habitat variables such as rocks, native grasses, and herbs are replaced in revegetated areas? - Revegetated areas currently lack ground layers as they are replanted with trees and shrubs only, making revegetated areas unsuitable for reptile and beetle species that require these habitat elements (Chapter 3 and 4) Is the translocation of beetle and reptile species a viable option to increase species richness in these landscapes, and what positive and/or negative effects could these translocations have on other native or introduced species? How do competition and predation, as well as edge effects, influence reptiles in linear strip habitats compared to those in remnant patches, and as distance from remnant patches increases? - Individual reptile and beetle species that were able to use degraded habitats were most abundant in certain treatments, and it was unclear if this was a result of the above processes (Chapter 3, Figure 3.4 f, d; Chapter 4, Figure 4.1 g, h). - Rare reptile species richness and abundance and Carlia tetradactyla abundance increased in remnant linear strips as distance from remnant

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patches increased. This may be due to competition and predation in remnant patches (Chapter 3, Figure 3.7 c, d, g). What factors, such as incentives or better focused extension work, are likely to encourage and enable landholders to undertake restoration of ground layer attributes such as native grasses? - Landholders are more likely to undertake management actions if they will benefit their property. My study lacked sufficient information to determine what factors may help landholders to restore ground layer attributes (Chapter 5). Does revegetation on private land provide financial as well as ecological benefits to landholders and their properties? - Landholders who recognise the benefits of revegetated areas will manage these areas to increase biodiversity, but few studies document the financial benefits of native animals in increasing farm productivity (Chapter 5). In what ways do weeds, pest animals and fire risk impact on farm productivity and native faunal species in agricultural landscapes, and how do revegetated areas and remnant vegetation increase or decrease these impacts? - Landholder perceptions and scientific literature suggests that revegetated areas increase weeds and animal pests, although few studies suggest that fire risk increases due to revegetation. Few studies also document the effects of weeds and pest animals in revegetated areas on native animals and properties adjacent to replanted areas (Chapter 5, Table 5.11). Other than species richness, what other measures of conservation effectiveness are useful to integrate into Bayesian Networks? - While species richness was useful in determining management actions, other methods such as community composition in conjunction with species richness may be more effective for future studies (Chapter 6). To what extent would more accurate expert opinion and field data decrease the variability in the patch-level model I developed, and how would reptile and beetle species richness respond to this new information?

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- The expert-only model and the combined expert and field data model predicted similar species richness responses, although predictions under no management varied between the two models. More accurate expert opinion and field data may provide better information on species richness responses to management actions (Chapter 6, Table 6.7 and Table 6.8). How would more robust landholder demographic and management decision data, and the addition of data on other landholder management decisions, such as reducing livestock grazing in revegetated and remnant areas and increasing connectivity, influence reptile and beetle species richness in revegetated areas? - Reptile and beetle species richness increased mainly when the studied management actions were undertaken in cleared linear strips, which suggests other management actions may be more effective in increasing species richness in revegetated and remnant areas (Chapter 6, Figure 6.7). Would the modelling of economic factors as well as ecological and social factors substantially increase the accuracy of these models in predicting changes in faunal species richness or other measures of conservation effectiveness?

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Table 7.1. Summary of aims for each data chapter and the three most important results from each chapter.

Aims Results Conclusion Chapter 3: Overall reptile species richness Due to habitat loss Establish the and abundance did not differ and fragmentation, response of reptile between revegetated, remnant only robust reptile species richness and and cleared areas; or between species persist in abundance to (i) revegetated linear strips and these landscapes a treatment type , (ii) patches. increasing distance Rare reptile species richness and Remnant linear strips along linear strips abundance and Carlia provide better quality from remnant tetradactyla abundance habitat than patches, and (iii) increased in remnant linear strips revegetated or vegetation type and compared to revegetated and cleared linear strips or structure; and (iv) cleared linear strips as distance remnant patches, determine the amount away from remnant patches potentially due to less of variation in reptile increased. disturbance, composition explained competition and by environmental and predation. treatment variables Species richness, abundance, Habitat variables such and community composition were as ground layer strongly influenced by attributes are environmental variables such as important and need to rock cover. be included in revegetated areas. Chapter 4: Overall beetle species richness, Beetle species Determine the abundance, and community represent a more response of beetle composition was not influenced robust subset of species richness and by treatment shape or site-type. previously present abundance, and beetle fauna. functional and family Beetle species richness and Beetle communities species richness to (i) abundance were not strongly rely upon good quality a treatment type , and influenced by vegetation ground layers to (ii) vegetation type variables, although variation in persist and may and structure; and (iii) beetle community composition require elements such

191 determine the amount was substantially explained by as litter, herbs, and of variation in beetle environmental variables, native plants to composition explained especially soil type and ground colonise revegetated by environmental and layer attributes. areas. treatment variables Chapter 5: Many landholders have Revegetation of trees Investigate (i) undertaken revegetation on their and shrubs alone is landholders‟ previous properties (76%), usually unlikely to benefit and planned replanting linear strips of native fauna such as reptiles revegetation actions trees and shrubs. Few and beetles that and the demographic landholders include native require native grasses factors that predict grasses in their revegetation and other ground these actions; (ii) the activities. layer elements. shape, size and Most landholders who have Landholders are more vegetation previously revegetated, or plan to likely to revegetate if composition of their revegetate are Landcare they understand the revegetated and members and may have an off- benefits of these remnant areas; (iii) farm income. Those who plan to actions and can afford impediments and revegetate have usually to do so. incentives to undertaken revegetation activities revegetation; and (iv) in the past. attitudes of Landholders held attitudes that Weeds, animal pests, landholders to revegetated and remnant areas and fire risk are remnant and were either beneficial to their perceived to be revegetated areas, property and/or the environment, detrimental to and how intention to or were detrimental to their property. Determining manage these areas property. A landholder‟s intention the true influence of is influenced by their to manage revegetated and these factors on attitudes remnant areas were strongest if it private land and mitigated the perceived native fauna will help detrimental effects on their engage landholders property. in revegetation. Chapter 6: The ability to integrate ecological It is important to have Integrate expert and social data and expert robust expert opinion opinion with opinion in a Bayesian Network is and field data in order ecological and social a very effective way to inform to make accurate 192 data from Chapters 3, management decisions on predictions regarding 4 and 5 to model restoration actions and increase revegetation ecological processes biodiversity gains given management actions. to determine the budgetary constraints. management actions Cost-effective management Cost-effective that are (i) most actions for increasing reptile and measures are useful effective and (ii) most beetle species were found to be to inform cost-effective in weed control, planting trees and management increasing reptile and shrubs and adding litter and decisions but do not beetle species; and timber. Species increases were provide the greatest (iii) illustrate how greatest in cleared linear strips species increases. landholder behaviour when these management actions influences reptile and were undertaken. beetle species While there are limitations related More information on a richness to the accuracy of expert opinion, range of management subjectivity in network structuring actions is needed in and in the measures of cost- order to increase effectiveness and species reptile and beetle richness; by addressing these species richness in constraints, this method can agricultural better inform management landscapes. decisions. a Revegetated, remnant, and cleared linear strip and remnant patch; revegetated and remnant patch; and revegetated linear strip and revegetated patch.

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Appendices

Appendix 1. List of international studies on faunal diversity in replanted forest areas Author Study Location Taxa Replanting Comparison habitat Vegetation type Climatic zone age (Morrison et al., Southern Birds & 3-4 years Remnant Montane humid Temperate 2010) Costa Rica arthropods forest (Twedt et al., 2010) South-eastern Birds 2-5 years Forests Subtropical USA (Lindenmayer et al., South-eastern Birds 2-20 years Remnant & cleared Woodland Temperate 2010) Australia paddock (Gibb and North-eastern Beetles 5-8 & 12-17 Remnant & cleared Woodland Temperate Cunningham, 2010) Australia years paddock (MacNally et al., North-eastern Birds 2-26 years Woodland Temperate 2010) Australia (Fink et al., 2009) Southern Birds 2 years Remnant Montane humid Temperate Costa Rica forest (Selwood et al., South-eastern Birds 9-111 years Box Ironbark forest Temperate 2009) Australia (Piper et al., 2009) Northern & Ants 5-70 years Remnant, paddock, Rainforest Tropical & sub- eastern regrowth & plantation tropical Australia

(Freeman et al., Northern Birds 1-7 years Remnant Rainforest Tropical 2009) Australia (Lomov et al., 2009) Eastern Ants 8-10 years Remnant & cleared Woodland Temperate Australia paddock (Munro et al., 2009) South-eastern Mammals 2-26 years Remnant, paddock & Woodland Temperate Australia plantation (Golet et al., 2009) Western USA Birds, rodents 1-15 years Remnant Riparian Mediterranean & invertebrates (Bateman et al., South-western Reptiles 7 years Remnant Riparian forest Arid to semi 2008) USA arid (Smith et al., 2008) Eastern USA Birds 8 years Regrowth & paddock Riparian Subtropical

(Watts et al., 2008) North Island, Beetles 1 month-6 Remnant Peat bog vegetation Temperate New Zealand years (Le Viol et al., 2008) Northern Spiders 14 years Cleared roadside Hedgerows Temperate France (Saunders and Western Birds 4, 6 & 20 Remnant Woodland Semi-arid Nicholls, 2008) Australia years (Cunningham et al., North-eastern Birds 7-20 years Remnant, paddock & Woodland Temperate 2008) Australia regrowth (Barrett et al., 2008) North-eastern Birds 2-3 years Remnant & cleared Woodland Temperate Australia paddock (Brunton and Stamp, North Island, Birds 10-20 years Remnant Woodland Temperate 2007) New Zealand

(Grimbacher and Northern Beetles 2-4 and 6- Remnant & paddock Rainforest Tropical Catterall, 2007) Australia 17 (Kavanagh et al., North-eastern Birds 3-25 years Remnant & paddock Forest & woodland Temperate 2007) Australia (Lindenmayer et al., North-eastern Birds 15-20 years Woodland Temperate 2007) Australia (Majer et al., 2007) Western Invertebrates 30 years Remnant Forest Temperate Australia (Nichols and Grant, Western Mammals, 30 years Remnant Forest Temperate 2007) Australia birds & reptiles (Gardali et al., 2006) Western USA Birds 1-9 years Remnant & plantation Riparian Mediterranean

(Queheillalt and Western USA Birds, 1-3 years Remnant Riparian Temperate Morrison, 2006) mammals & reptiles (Jansen, 2005) North-eastern Birds 1-3 years Remnant & regrowth Riparian & rainforest Tropical Australia (Toktang and Elliott, Northern Birds 1-5 years Remnant Rainforest Tropical 2005) Thailand (Weiermans and van Eastern South Birds, 10, 14 & 18 Remnant Mine site Subtropical Aarde, 2003) Africa millipedes & years rodents

(Twedt et al., 2002) South-eastern Birds 2 & 10 Forests Subtropical USA years (Watts and Gibbs, North Island, Beetles 5, 17 & 100 Remnant Shrubland & forest Temperate 2002) New Zealand years (Bolger et al., 2001) Western USA Birds & Not Remnant Coastal scrub Temperate rodents mentioned (Passell, 2000) North central Birds 3 years Remnant & mine site Mine site Tropical Indonesia (Nelson and Western USA Butterflies 1-16 years Remnant Riparian restoration Mediterranean Andersen, 1999) (Reay and Norton, South Island, Beetles, 12, 30 & 35 Remnant & regrowth Wet forest Temperate 1999) New Zealand spiders & birds years (Patten, 1997) Western USA Mammals 2 years Remnant Desert scrub Temperate (rodents) (Anderson et al., Western USA Birds 1-7 years Riparian Mediterranean 1989)

Appendix 2. WinBUGS code to calculate the influence of linear strip treatments on reptile and beetle species

Model { for(i in 1:50) { Y[i] ~ dpois(p[i]) resid[i] <- p[i] - Y[i] p[i] <- exp(beta[Treat[i]] + re_site[site[i]] + loc[Loc[i]]) } loc[1] <- 0 # loc[i] loc[2] ~ dnorm(0, 1.0E-6) for (j in 1:4) { beta[j] ~ dnorm(0, 1.0E-6) ea[j] <- exp(beta[j]) } ea12 <- ea[1] - ea[2] ea13 <- ea[1] - ea[3] ea14 <- ea[1] - ea[4] ea23 <- ea[2] - ea[3] ea24 <- ea[2] - ea[4] ea34 <- ea[3] - ea[4] for(k in 1:10){re_site[k] ~ dnorm(0, p_site) } sd_site ~ dunif(0, 1000) p_site <- 1 / (sd_site * sd_site) } list(beta = c(0,0,0,0), re_site = c(0,0,0,0,0,0,0,0,0,0), sd_site = 10, loc=c(NA, 0)) Loc[] site[] Treat[] Y[] 1 1 1 7 END

Appendix 3. WinBUGS code to calculate reptile distance along linear strips as distance from the remnant patch increases.

Model { for(i in 1:150) { Y[i] ~ dpois(p[i]) resid[i] <- p[i] - Y[i] p[i] <- exp(beta[Treat[i]] + re_site[site[i]] + loc[Loc[i]] + b[Treat[i]] * Dist[i]) } loc[1] <- 0 loc[2] ~ dnorm(0, 1.0E-6) for (j in 1:3) { b[j] ~ dnorm(0, 0.00001) beta[j] ~ dnorm(0, 1.0E-6) } for(j in 1:5) { pred_bare[j] <- exp(beta[1] + b[1] * j) pred_nat[j] <- exp(beta[2] + b[2] * j) pred_rev[j] <- exp(beta[3] + b[3] * j) } for(k in 1:10){re_site[k] ~ dnorm(0, p_site) } sd_site ~ dunif(0, 1000) p_site <- 1 / (sd_site * sd_site) } list(beta = c(0,0,0), re_site = c(0,0,0,0,0,0,0,0,0,0), sd_site = 10, loc=c(NA, 0), b=c(0,0,0)) Loc[] site[] Treat[] Dist[] Y[] 1 1 1 1 4 END

Appendix 4. WinBUGS code to calculate the influence of habitat variables on reptile and beetle species

Model { mvar1 <- mean(Var1[]) mvar2 <- mean(Var2[]) mvar3 <- mean(Var3[]) for(i in 1:67) { Y[i] ~ dpois(p[i]) resid[i] <- p[i] - Y[i] p[i] <- exp(loc[Loc[i]] + re_site[site[i]] + beta[1] * (Var1[i] - mvar1) + beta[2] * (Var2[i] - mvar2) + beta[3] * (Var3[i] - mvar3)) } loc[1] <- 0 loc[2] ~ dnorm(0, 1.0E-6) for (j in 1:5) {beta[j] ~ dnorm(0, 1.0E-6) } for(k in 1:20){ re_site[k] ~ dnorm(0, p_site) } sd_site ~ dunif(0, 1000) p_site <- 1 / (sd_site * sd_site) for (i in 1:100) { a[i] <- (i - 1)var_1[i] <- beta[1] * (a[i] - mvar1) } list(re_site = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), sd_site = 20, beta = c(0,0,0,0,0), loc=c(NA, 0)) Loc[] site[] Y[] Var1[] Var2[] Var3[] Var4[] Var5[] 1 1 3 2.5 15 15 62.5 37 END

Appendix 5. Reptile captures in the Wimmera and Benalla in different treatments. RemP = Remnant Patch, CLS = Cleared Linear Strip, RemLS = Remnant Linear Strip, RevLS = Revegetated Linear Strip, RevP = Revegetated Patch.

Region Treatment Amphibolurus Carlia Christinus Cryptoblepharus Ctenotus Ctenotus Delma Diplodactylus nobbi tetradactyla marmoratus carnabyi orientalis robustus inornata tessellatus Wimmera RemP 5 0 1 3 0 9 0 2 Benalla RemP 0 22 2 0 0 1 0 0 Wimmera CLS 0 0 0 0 0 19 1 0 Benalla CLS 0 13 5 0 0 0 2 0 Wimmera RemLS 0 0 1 10 0 2 0 0 Benalla RemLS 0 32 5 0 0 0 0 0 Wimmera RevLS 0 0 1 0 0 18 1 0 Benalla RevLS 0 7 0 0 0 0 1 0 Wimmera RevP 0 0 0 0 4 4 0 0 Benalla RevP 0 2 0 0 0 0 1 0

Region Treatment Lampropholis Lerista Lerista Menetia Morethia Parasuta Pogona Pseudonaja guichenoti bougainvillii punctatovittata greyii boulengeri nigricepts barbata textilis Wimmera RemP 0 1 0 5 84 1 6 5 Benalla RemP 5 3 0 38 53 0 0 1 Wimmera CLS 0 6 0 1 33 0 0 4 Benalla CLS 0 0 0 12 24 0 0 0 Wimmera RemLS 0 3 0 0 35 0 5 2 Benalla RemLS 0 0 0 27 24 0 1 0 Wimmera RevLS 0 3 1 10 12 0 2 1 Benalla RevLS 1 0 0 20 38 0 0 0 Wimmera RevP 0 0 0 6 23 0 4 0 Benalla RevP 0 8 0 15 20 0 2 0

Region Treatment Ramphotyphlops Ramphotyphlops Ramphotyphlops Ramphotyphlops Tiliqua Varanus bicolor bituberculatus nigrescens proximus scincoides gouldii Wimmera RemP 1 0 0 0 0 2 Benalla RemP 0 0 4 3 0 0 Wimmera CLS 0 2 0 0 0 0 Benalla CLS 0 0 1 0 0 0 Wimmera RemLS 0 1 0 0 0 0 Benalla RemLS 0 0 0 1 0 0 Wimmera RevLS 0 1 0 0 0 0 Benalla RevLS 0 0 1 0 0 0 Wimmera RevP 0 0 0 0 0 0 Benalla RevP 0 0 0 1 1 2

Appendix 6. The mean effect of region on reptile species richness and abundance in linear strips, patches and revegetated areas calculated using a logistic regression with a Poisson distribution.

Mean effect of region Credible Intervals (95%) Linear strips Species richness 1.0 0.6 - 1.6 Species abundance 1.1 0.5 - 2.2 Patches Species richness 0.8 0.4 - 1.5 Species abundance 1.0 0.5 - 1.8 Revegetated areas Species richness 0.8 0.5 - 1.5 Species abundance 0.9 0.4 - 1.4

Appendix 7. Percentage influence of environmental and spatial variables in explaining the variance of reptile community composition in the Benalla region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Space = spatial matrix; Env-Space = environmental matrix with spatial matrix removed; Space-Env = spatial matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 10 21.5 Space 15.5 26.6 Env - Space 7.9 16.6 Space - Env 11.6 22.2 Total 43.6

Appendix 8. The contribution of the environmental (Env) and the spatial matrix (Space) to the variation in the reptile community composition matrix in the Benalla region, explained by RDA and partial RDA. Env-Space = environmental matrix with spatial matrix removed (pure environmental matrix); Space-Env = spatial matrix with environmental matrix removed (pure spatial matrix).

Variables Matrix Variation % Matrix Variation % Rock Env 18.2 Env-Space 4.8 Herb Env 11.4 Env-Space 11.9 Mid stratum Env 6.8 Env-Space 4.8 Tussock Env 4.5 Env-Space 7.1 Litter Env 4.5 Env-Space 4.8 Natives Env 4.5 Env-Space 4.8 Lat Space 21.4 Space-Env 15.9 Lat * long Space 14.3 Space-Env 9.1 Long * long Space 11.9 Space-Env 11.4 Lat * Lat Space 9.5 Space-Env 9.1 Long Space 4.8 Space-Env 4.5

Appendix 9. Percentage influence of environmental and spatial variables in explaining the variance of reptile community composition in the Wimmera region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Space = spatial matrix; Env-Space = environmental matrix with spatial matrix removed; Space-Env = spatial matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 16.3 27.3 Space 24.1 39.5 Env - Space 12.3 20.6 Space - Env 20.9 32.7 Total 60.1

Appendix 10. The contribution of the environmental (Env) and the spatial matrix (Space) to the variation in the reptile community composition matrix in the Wimmera region, explained by RDA and partial RDA. Env-Space = environmental matrix with spatial matrix removed (pure environmental matrix); Space-Env = spatial matrix with environmental matrix removed (pure spatial matrix).

Variables Matrix Variation % Matrix Variation % Litter Env 13.3 Env-Space 10.2 Herb Env 8.3 Env-Space 5.1 Mid stratum Env 8.3 Env-Space 6.8 Rock Env 6.7 Env-Space 6.8 Natives Env 5.0 Env-Space 3.4 Tussock Env 3.3 Env-Space 1.7 Lat Space 25.4 Space-Env 26.7 Lat * long Space 16.9 Space-Env 11.7 Long * long Space 8.5 Space-Env 6.7 Lat * Lat Space 8.5 Space-Env 5.0 Long Space 6.8 Space-Env 5.0

Appendix 11. List of beetle species recorded and their life histories. Scav = Scavenger, Abun = Abundance. Superfamily Family Subfamily Genus species Trophic Level Flightless Habit Abun Elateroidea Cantharidae Chauliognathinae Chauliognathus nobilitatus Predator/ herbivore No Terrestrial 151 Caraboidea Carabidae Broscinae Promecoderus species Carnivore/ Predator Yes Terrestrial 432 Caraboidea Carabidae Chlaeniinae Chlaenius australis Carnivore/ Predator No Terrestrial 1 Caraboidea Carabidae Harpalinae Gnathaphanus species Omnivore No Terrestrial 1 Caraboidea Carabidae Harpalinae Hypharpax species Omnivore No Terrestrial 3 Caraboidea Carabidae Harpalinae Notiobia species Omnivore No Terrestrial 9 Terrestrial/ Caraboidea Carabidae Helluoninae Gigadema species Carnivore/ Predator No 1 Arboreal Terrestrial/ Caraboidea Carabidae Lebiinae Demetrida species Carnivore/ Predator No 1 Arboreal Caraboidea Carabidae Lebiinae Philophloeus planus Carnivore/ Predator No Arboreal 20 Caraboidea Carabidae Lebiinae Philophloeus species Carnivore/ Predator No Arboreal 1 Caraboidea Carabidae Oodinae Oodes modestus Carnivore/ Predator No Terrestrial 1 Caraboidea Carabidae Paussinae Arthropterus species Carnivore/ Predator Yes Terrestrial 2 Caraboidea Carabidae Pseudomorphinae Sphallomorpha species Carnivore/ Predator No Arboreal 1 Caraboidea Carabidae Psydrinae Mecyclothorax punctipennis Carnivore/ Predator No Terrestrial 1 Caraboidea Carabidae Pterostichinae Catadromus lacordairei Carnivore/ Predator No Terrestrial 1 Caraboidea Carabidae Pterostichinae Platycoelus proxilus Carnivore/ Predator No Terrestrial 4 Caraboidea Carabidae Pterostichinae Pseudoceneus sollicitus Carnivore/ Predator No Terrestrial 1 Caraboidea Carabidae Pterostichinae Sarticus cyaneocinctus Carnivore/ Predator Yes Terrestrial 24

Caraboidea Carabidae Pterostichinae Sarticus discopunctatus Carnivore/ Predator Yes Terrestrial 50 Caraboidea Carabidae Pterostichinae Sarticus esmeraldipennis Carnivore/ Predator Yes Terrestrial 37 Caraboidea Carabidae Pterostichinae Sarticus species Carnivore/ Predator Yes Terrestrial 6 Caraboidea Carabidae Pterostichinae Simodontus species Carnivore/ Predator ? Terrestrial 12 Terrestrial/ Caraboidea Carabidae Scaritinae Carenum ordinatum Carnivore/ Predator Yes 1 Fossorial Terrestrial/ Caraboidea Carabidae Scaritinae Carenum scaritoides Carnivore/ Predator Yes 11 Fossorial Terrestrial/ Caraboidea Carabidae Scaritinae Carenum tinctillatum Carnivore/ Predator Yes 1 Fossorial Chrysomeloidea Cerambycidae Cerambycinae Phoracantha recurva Herbivore No ? 1 Chrysomeloidea Cerambycidae Cerambycinae Phoracantha semipunctata Herbivore No ? 3 Chrysomeloidea Cerambycidae Lamiinae Corrhenes picta Herbivore ? ? 1 Curculionoidea Curculionidae Cyclominae Acantholophus species Herbivore Yes Terrestrial 17 Curculionoidea Curculionidae Cyclominae Cubicorhynchus species 1 Herbivore Yes Terrestrial 39 Curculionoidea Curculionidae Cyclominae Cubicorhynchus species 2 Herbivore Yes Terrestrial 53 Curculionoidea Curculionidae Cyclominae Listroderes costirostris Herbivore Yes Terrestrial 3 Curculionoidea Curculionidae Cyclominae Rhinaria species 1 Herbivore No Arboreal 3 Curculionoidea Curculionidae Cyclominae Sclerorhinus species 1 Herbivore Yes Terrestrial 6 Curculionoidea Curculionidae Cyclominae Steriphus species 1 Herbivore Yes Terrestrial 4 Curculionoidea Curculionidae Entiminae Leptopius species 1 Herbivore Yes various 1 Curculionoidea Curculionidae Entiminae Naupactus leucoloma Herbivore Yes Terrestrial 943

Curculionoidea Curculionidae Entiminae Polyphrades species 1 Herbivore Yes Arboreal 5 Curculionoidea Curculionidae Entiminae Prosayleus species 1 Herbivore Yes Arboreal 134 Elateroidea Elateridae Agrypninae Agrypnus species Omnivore No Terrestrial 2 Elateroidea Elateridae Agrypninae Agrypnus species 1 Omnivore No Terrestrial 6 Elateroidea Elateridae Agrypninae Agrypnus species 2 Omnivore No Terrestrial 2 Elateroidea Elateridae Agrypninae Agrypnus species 3 Omnivore No Terrestrial 16 Elateroidea Elateridae Agrypninae Agrypnus species 4 Omnivore No Terrestrial 4 Elateroidea Elateridae Cardiophorinae Paracardiophorus species Omnivore No Terrestrial 1 Cucujoidea Erotylidae Cnecosa melancholica Carnivore ? ? 1 Hydrophiloidea Histeridae Saprininae Saprinus pseudocyaneus Carnivore No Terrestrial 1 Scarabaeoidea Scarabaeidae Aphodiinae Aphodius tasmaniae Herbivore No Terrestrial 2 Scarabaeoidea Scarabaeidae Dynastinae Anomalomorpha anthracina Herbivore No Terrestrial 5 Scarabaeoidea Scarabaeidae Dynastinae Dasygnathus tritubarevlatus Herbivore No Terrestrial 1 Scarabaeoidea Scarabaeidae Dynastinae Heteronychus arator Herbivore No Terrestrial 2 Scarabaeoidea Scarabaeidae Dynastinae Neodon pecuarius Herbivore No Terrestrial 1 Scarabaeoidea Scarabaeidae Dynastinae Novapus carnei Herbivore No Terrestrial 4 Scarabaeoidea Scarabaeidae Dynastinae Semanopterus rectangulus Herbivore No Terrestrial 4 Scarabaeoidea Scarabaeidae Melolonthinae Byrrhomorpha basicollis Herbivore No ? 1 Scarabaeoidea Scarabaeidae Melolonthinae Colpochila species 1 Herbivore No ? 4 Scarabaeoidea Scarabaeidae Melolonthinae Colpochila species 2 Herbivore No ? 1 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx australis Herbivore No Terrestrial 1 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx excisus Herbivore No Terrestrial 3

Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx praecox Herbivore No Terrestrial 7 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species Herbivore No Terrestrial 2 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species 1 Herbivore No Terrestrial 1 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species 2 Herbivore No Terrestrial 1 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species 3 Herbivore No Terrestrial 4 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species 4 Herbivore No Terrestrial 2 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species 5 Herbivore No Terrestrial 1 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species 6 Herbivore No Terrestrial 2 Scarabaeoidea Scarabaeidae Melolonthinae Heteronyx species 7 Herbivore No Terrestrial 2 Scarabaeoidea Scarabaeidae Melolonthinae Liparetrus ater Herbivore No ? 4 Tenebrionoidea Tenebrionidae Alleculinae Homotrysis species Herbivore No Arboreal 1 Tenebrionoidea Tenebrionidae Alleculinae Metistete species Herbivore No Arboreal 1 Tenebrionoidea Tenebrionidae Lagriinae Adelium angulicolle Herbivore (Scav) Yes Terrestrial 5 Tenebrionoidea Tenebrionidae Lagriinae Adelium brevicorne Herbivore (Scav) Yes Terrestrial 184 Tenebrionoidea Tenebrionidae Lagriinae Adelium similatum Herbivore (Scav) Yes Terrestrial 510 Tenebrionoidea Tenebrionidae Lagriinae Adelium species 1 Herbivore (Scav) Yes Terrestrial 12 Tenebrionoidea Tenebrionidae Lagriinae Adelium species 2 Herbivore (Scav) Yes Terrestrial 11 Tenebrionoidea Tenebrionidae Lagriinae Cardiothorax behri Herbivore (Scav) ? Terrestrial 3 Tenebrionoidea Tenebrionidae Lagriinae Isopteron aversum Herbivore (Scav) No Terrestrial 3 Tenebrionoidea Tenebrionidae Lagriinae Isopteron species Herbivore (Scav) No Terrestrial 2 Tenebrionoidea Tenebrionidae Lagriinae Seirotrana parallela Herbivore (Scav) No? Terrestrial 97 Tenebrionoidea Tenebrionidae Stenochiinae Zophophilus convexiuscolus Scavenger No Terrestrial 4

Tenebrionoidea Tenebrionidae Tenebrioninae Agasthenes species Scavenger Yes Terrestrial 1 Tenebrionoidea Tenebrionidae Tenebrioninae Amphianax subcoriaceus Scavenger Yes Terrestrial 1 Tenebrionoidea Tenebrionidae Tenebrioninae Celibe species Scavenger Yes Terrestrial 5 Terrestrial/ Tenebrionoidea Tenebrionidae Tenebrioninae Chalcopteroides species Scavenger No 26 Arboreal Tenebrionoidea Tenebrionidae Tenebrioninae Cillibus blackburni Scavenger Yes Terrestrial 1 Tenebrionoidea Tenebrionidae Tenebrioninae Gonocephalum species Herbivore (Scav) No ? 3 Tenebrionoidea Tenebrionidae Tenebrioninae Helea ovata Scavenger Yes Terrestrial 19 Tenebrionoidea Tenebrionidae Tenebrioninae Nyctozoilus species Scavenger Yes Terrestrial 34 Tenebrionoidea Tenebrionidae Tenebrioninae Pterohelaeus bullatus Herbivore (Scav) No Terrestrial 5 Tenebrionoidea Tenebrionidae Tenebrioninae Pterohelaeus species Herbivore (Scav) No Terrestrial 38 Tenebrionoidea Tenebrionidae Tenebrioninae Saragus costatus Scavenger Yes Terrestrial 1 Tenebrionoidea Tenebrionidae Tenebrioninae Saragus species Scavenger Yes Terrestrial 7 Tenebrionoidea Tenebrionidae Tenebrioninae Toxicum gracile Scavenger ? ? 6 Scarabaeoidea Trogidae Omorgus candidus Scavenger ? Terrestrial 3 Scarabaeoidea Trogidae Omorgus euclensis Scavenger ? Terrestrial 2

Appendix 12. Percentage influence of environmental and spatial variables in explaining the variance of beetle community composition in the Benalla region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Space = spatial matrix; Env-Space = environmental matrix with spatial matrix removed; Space- Env = spatial matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 21.4 37 Space 26 45.4 Env - Space 6 19 Space - Env 11.9 27.2 Total 62.4

Appendix 13. The contribution of the environmental (Env) and the spatial matrix (Space) to the variation in the beetle community composition matrix in the Benalla region, explained by RDA and partial RDA. Env-Space = environmental matrix with spatial matrix removed (pure environmental matrix); Space-Env = spatial matrix with environmental matrix removed (pure spatial matrix).

Variables Matrix Variation % Matrix Variation % Soil Env 30.3 Env-Space 4.4 Natives Env 7.6 Env-Space 5.5 Herb Env 6.1 Env-Space 2.2 Rock Env 4.5 Env-Space 2.2 Mid-stratum Env 3.0 Env-Space 2.2 Tussock Env 3.0 Env-Space 2.2 Litter Env 3.0 Env-Space 2.2 Long Space 27.5 Space-Env 13.6 Lat * Lat Space 15.4 Space-Env 3.0 Long * long Space 14.3 Space-Env 9.1 Lat * long Space 12.1 Space-Env 10.6 Lat Space 9.9 Space-Env 6.1

Appendix 14. Percentage influence of environmental and spatial variables in explaining the variance of beetle community composition in the Wimmera region with redundancy analysis (RDA) and partial RDA showing the percentage of total variance explained in the first axis and all ordination axes. Env = environmental matrix; Space = spatial matrix; Env-Space = environmental matrix with spatial matrix removed; Space- Env = spatial matrix with environmental matrix removed.

Variance explained Variance explained Matrix (%) First Axis (%) All Axes Env 10.3 30 Space 15.7 28.3 Env - Space 8.5 25.7 Space - Env 13.3 24 Total 54

Appendix 15. The contribution of the environmental (Env) and the spatial matrix (Space) to the variation in the beetle community composition matrix in the Wimmera region, explained by RDA and partial RDA. Env-Space = environmental matrix with spatial matrix removed (pure environmental matrix); Space-Env = spatial matrix with environmental matrix removed (pure spatial matrix).

Variables Matrix Variation % Matrix Variation % Tussock Env 12.7 Env-Space 11.5 Soil Env 10.9 Env-Space 5.8 Litter Env 10.9 Env-Space 9.6 Rock Env 7.3 Env-Space 5.8 Herb Env 7.3 Env-Space 5.8 Mid-stratum Env 3.6 Env-Space 3.8 Natives Env 3.6 Env-Space 3.8 Long Space 26.9 Space-Env 20.0 Lat * long Space 7.7 Space-Env 9.1 Lat Space 7.7 Space-Env 5.5 Long * long Space 5.8 Space-Env 5.5 Lat * Lat Space 5.8 Space-Env 3.6

Appendix 16. Plain language statement and questionnaire sent to landholders in the Wimmera and Benalla regions

Appendix 17. WinBUGS code to predict landholders who had previously revegetated or would revegetate in the future.

Model { a ~ dnorm(0, 1.0E-6) b[1] <-0 b[2] ~ dnorm(0, 1.0E-6) b[3] ~ dnorm(0, 1.0E-6) c[1] <-0 c[2] ~ dnorm(0, 1.0E-6) c[3] ~ dnorm(0, 1.0E-6) d[1] <-0 d[2] ~ dnorm(0, 1.0E-6) d[3] ~ dnorm(0, 1.0E-6) d[4] ~ dnorm(0, 1.0E-6) e[1] <-0 e[2] ~ dnorm(0, 1.0E-6)

for (i in 1:176) { logit(p[i]) <- a + b[Region[i]] + c[Income[i]] + d[Farmtype[i]] + e[Landcare[i]] Replant[i] ~ dbern(p[i]) } for (j in 1:3) { logit(predReg[j]) <- a + b[j] + c[3] + d[4] + e[2] } for (k in 1:3) { logit(predInc[k]) <- a + b[3] + c[k] + d[4] + e[2] } for (l in 1:4) { logit(predFarm[l]) <- a + b[3] + c[3] + d[l] + e[2] } for (m in 1:2) { logit(predLandc[m]) <- a + b[3] + c[3] + d[4] + e[m] } } list(a=0, b=c(NA,0,0), c=c(NA,0,0), d=c(NA,0,0,0), e=c(NA,0))

Region[] Income[] Farmtype[] Landcare[] Replant[] 1 2 1 1 0 END

Appendix 18. WinBUGS code to determine landholders intention to manage revegetated/remnant areas as a function of their attitudes towards these areas.

Model { a ~ dnorm(0, 1.0E-6) b ~ dnorm(0, 1.0E-6) c ~ dnorm(0, 1.0E-6)

for (i in 1:141) { logit(p[i]) <- a + b* Remfact1[i] + c * Remfact2[i] Remop[i] ~ dbern(p[i])

} for (j in 1:11) { logit(predp[j]) <- a + b* (j*0.5-2) + c *(2.70347E-17) } for (k in 1:10) { logit(predp2[k]) <- a + b* (1.3E-07) + c *(k*0.5-2) } logit(predb) <-b logit(predc) <-c } list(a=0, b=0, c=0)

Remop[] Remfact1[] Remfact2[] 1 0.01514 -0.26347 END

Appendix 19. Cost calculations for revegetation management actions based on previous revegetation and restoration projects.

Management Cost calculations action Weed control The cost of weed control, including herbicide and labour, can cost around $190 per ha for spot spraying, (Schirmer and Field, 2000). Add rocks To make artificial rocks out of fibre-reinforced cement (Croak et al., 2010) would cost approximately $15 per rock (Croak pers. com.). If one rock was added every 2 m2, 250 rocks would be needed to cover 1 ha, costing $3,750. To get rocks from a quarry would cost approximately $50 per m3 (Rushton, 2006). If there were 125 large rocks per m3, 30 m3 would be needed to cover 1 ha, costing $1,500. Add litter and Litter costs approximately $28 per m3, not including delivery. If timber 1 m3 of litter were used to cover a 10 m2 it would cost $280 to cover 1 ha. In a river restoration project the sourcing and placement of 80 logs for a 1 km stretch costs $27,000 (Rushton, 2006). To do 100 m of river would cost $2,700, roughly equivalent to restore enough logs to 1 ha of land. Plant tussock It costs $60 to scarify 1 ha of land (Schirmer and Field, 2000) grasses Tussock grass seed (Poa species) costs approximately $300 per kg and 15kg of seed would be needed to sow 1 ha, costing $4,500. Plant trees and Ground preparation costs $67 per ha for deep ripping and shrubs mounding. Tubestock costs approximately 50c each and usually 1,000 plants are needed to cover 1 ha (Schirmer and Field, 2000), costing $500 to replant 1 ha of land. Guards and stakes would cost $250 for 1,000 trees.

Appendix 20. The expert only model compared with the combined expert and field data model with confidence levels of 1 - 50. Comparisons were made under no management actions (no mngt) and all management actions (all mngt) in remnant linear strips (Rem LS), cleared linear strips (CLS) and revegetation linear strips (Rev LS) for (a) reptile species richness and (b) beetle species richness.

a) Reptile species richness Expert only Confidence = 1 Confidence = 5 Confidence = 10 5 Confidence = 50 4.5 4 3.5 3 2.5 2 1.5 1

Reptile species richnessspeciesReptile 0.5 0 Rem LS CLS Rev LS Rem LS CLS Rev LS

No mngt All mngt No mngt All mngt No mngt All mngt

b) Beetle species richness Expert only Confidence = 1 16 Confidence = 5 Confidence = 10 Confidence = 50 14 12 10 8 6 4

Beetle speciesrichness Beetle 2 0 Rem LS CLS Rev LS Rem LS CLS Rev LS

No mngt All mngt No mngt All mngt No mngt All mngt

Appendix 21. Reptile species richness increase as a result of non-optimal management actions (compared to the no-management scenario) and the cost-effectiveness of those actions per $1,000 spent.

Expert elicitation Combined expert + data Habitat Management Cost- Cost- type actions Species gain Species gain efficiency efficiency RevP Add rocks 0.19 0.13 0.12 0.08 RevP Litter 0.19 0.06 0.18 0.06 RevP Add rocks & trees 0.11 0.05 0.03 0.01 RevP Tussocks 0.04 0.01 -0.06 -0.01 RevP Trees & shrubs -0.07 -0.09 -0.06 -0.07 RevLS Add rocks 0.15 0.10 0.20 0.07 RevLS Add rocks & litter 0.35 0.08 -0.14 -0.03 RevLS Litter 0.17 0.06 0.34 0.23 RevLS Add rocks & trees 0.08 0.03 0.26 0.11 RevLS Tussocks 0.04 0.01 -0.25 -0.05 RemP Add rocks & trees 0.28 0.12 0.17 0.07 RemP Add rocks & litter 0.48 0.11 0.35 0.08 RemP Litter 0.18 0.06 0.12 0.08 RemP Trees & shrubs 0.03 0.04 0.02 0.02 RemP Tussocks 0.01 0.00 0.01 0.00 RemLS Weed control 0.03 0.16 0.01 0.05 RemLS Add rocks & trees 0.33 0.14 0.21 0.09 RemLS Add rocks 0.14 0.09 0.11 0.04 RemLS Add rocks & litter 0.30 0.07 0.15 0.03 RemLS Litter 0.13 0.04 0.01 0.01 RemLS Tussocks 0.02 0.00 0 0.00 CLS Add rocks & trees 0.45 0.19 0.26 0.11 CLS Weed control 0.03 0.16 -0.12 -0.63 CLS Add rocks & litter 0.19 0.04 -0.14 -0.03 CLS Litter 0.10 0.03 0.34 0.23 CLS Add rocks 0.05 0.03 0.20 0.07 CLS Tussocks 0.02 0.00 -0.25 -0.054

Appendix 22. Beetle species richness increase as a result of non-optimal management actions (compared to the no-management scenario) and the cost-effectiveness of those actions per $1,000 spent.

Expert elicitation Combined expert + data Habitat Management Species Cost- Species Cost- type actions gain efficiency gain efficiency RevP Tussocks 0.24 0.05 0.04 0.01 RevP Trees & litter 0.04 0.01 0.28 0.07 RevP Trees & shrubs -0.44 -0.54 -0.21 -0.26 RevLS Tussocks 0.24 0.05 0.00 0.00 RevLS Trees & litter 0.07 0.02 0.54 0.14 RevLS Trees & shrubs -0.41 -0.50 -0.12 -0.15 RemP Trees & shrubs 0.41 0.50 0.38 0.47 RemP Trees & litter 1.14 0.30 0.67 0.18 RemP Litter 0.63 0.21 0.27 0.09 RemP Tussocks 0.07 0.02 0.08 0.02 RemLS Weed control 0.09 0.47 0.07 0.37 RemLS Trees & litter 1.77 0.47 0.94 0.25 RemLS Tussocks 0.10 0.02 0.13 0.03 RemLS Litter 0.31 0.10 0.02 0.01 CLS Trees & litter 3.31 0.87 1.53 0.40 CLS Weed control 0.05 0.26 -0.78 -4.11 CLS Tussocks 0.06 0.01 -1.73 -0.37

Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: JELLINEK, SACHA

Title: The value of revegetated linear strips and patches of habitat for faunal conservation: reconciling ecological and landholder perspectives

Date: 2012

Citation: Jellinek, S. (2012). The value of revegetated linear strips and patches of habitat for faunal conservation: reconciling ecological and landholder perspectives. PhD thesis, School of Botany, The University of Melbourne.

Persistent Link: http://hdl.handle.net/11343/37004

File Description: The value of revegetated linear strips and patches of habitat for faunal conservation: reconciling ecological and landholder perspectives

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