Ecosystem-wide management of invasive species in the face of severe uncertainty

Yi Han B.Sc., M.Sc.

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2016 School of Biological Sciences

Abstract

The economic and ecological problems caused by biological invasions underline the importance of invasive species management. Evidence showed that invasive species management aiming to mitigate and reverse negative invasion impacts may lead to unexpected side effects to other ecosystem components, which call for integrating management into broader ecosystem goals rather than manipulating single invasive species in isolation. Decision-making is limited by a severe lack of knowledge yet confronted with urgent needs of conservation management. In this thesis, I focus on developing practical approaches to assist decision-making of invasive species managements in the face of limited information.

It can be challenging to resolve the conflicts between invasive species management and other conservation goals. In Chapter 2, I propose to incorporate potential management impacts into a formal decision analysis from a cost-benefit perspective. I conduct a systematic literature review of studies that reported perverse outcomes of invasive plant management, to provide insights for risk identification for future control activities. My results show that there are only 26 publications that identified negative outcomes from plant management. It is possible that such perverse outcomes are merely uncommon, yet we identify that research and publishing biases exist and suggest that the negative impacts of management may have been overlooked. Next, I discuss existing and potential approaches that can be used to identify management risks and reconcile management conflicts, and suggest ways to improve current approaches and facilitate better decision analyses for future management. In the following chapters, I illustrate a number of approaches to assist invasive species management decision-making and illustrate these approaches using two real world management problems.

Chapter 3 and Chapter 4 use network approaches to inform decision-making on the management of introduced cats (Felis catus) and rats (Rattus rattus) on Christmas Island. In chapter 3, I conduct a qualitative assessment based on available information to evaluate a number of potential management strategies, which vary in their intensity of both rat and cat control and also vary in cost of implementation. These actions were derived in consultation with on-ground managers from Christmas Island. The assessment suggests that given sufficient resources the optimal strategy is island wide cat eradication with rat control efforts. This suggestion is also supported by the network analysis. Island-wide cat eradication only may lead to secondary impacts through meso-predator release of rats, yet when the resource is limited this strategy still yields more benefits than other management actions such as island-wide cat control, or cat and rat control that are restricted in specific regions. i

In Chapter 4, I developed a novel approach by combining a qualitative modelling approach with Boolean analysis to examine the effects of different management strategies of cats and rats on Christmas Island. This approach qualitatively simulates species population dynamics under various management scenarios, which omits specific quantitative details yet is still able to provide robust predictions when little information is available. The results demonstrate that cat eradication without rat control incurs a higher risk to native species and additional rat control is likely to reduce this risk. Results of the assessment based on our current knowledge (Chapter 3) and results of the modelling approach (Chapter 4) complement each other, suggesting similar strategies for managing introduced cats and rats on Christmas Island: prioritizing rat control efforts in habitats of native animals of high conservation concern is likely to generate efficient benefits and minimize the risks of the cat eradication program.

In Chapter 5, I propose a structured framework to facilitate decision-making of non-native species management in Antarctica. The framework is based on a Structured Decision-Making process incorporating a set of decision tools, which is able to inform decision-making in a transparent and rigorous manner. I illustrate the utility of this framework using a case study of management of a non-native plant species, Poa annua in Antarctica Peninsular. In particular, through a Structured Decision Making process, management objectives and potential management options for P. annua eradication are identified. I then illustrate how a Bayesian network model can be useful for estimation of consequences of different management options, and how a number of tools and methods can be used to evaluate trade-offs and identify optimal management solution(s). This framework and its application to the P. annua case study bring forward a practical way to cope with future non-native species management issue for Antarctica.

The research in this thesis utilizes a range of approaches to predict management outcomes and evaluate trade-offs to inform decision-making on invasive species managements when they may lead to potential perverse outcomes. For real-world management problems, decision-making often hindered by a lack of knowledge and resources constraints. I show in this thesis that by using modelling approaches with limited information, we are able to provide robust suggestions that can be used by managers for future decision-making in invasive species management.

ii

Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

iii

Publications during candidature

Buckley, Y. M., and Y. Han. 2014. Managing the side effects of invasion control. Science 344:975- 976.

Publications included in this thesis

No publications included.

Contributions by others to the thesis

Chapter 1 was written entirely by the candidate, with editorial advice from Eve McDonald- Madden, Yvonne Buckley and Hugh Possingham.

In Chapter 2, the idea was conceived by the candidate with guidance from Yvonne Buckley. The literature search and systematic review were undertaken entirely by the candidate. The candidate wrote the paper with editorial input of Yvonne Buckley and Justine Shaw.

In Chapter 3, the idea was developed by the candidate, Eve McDonald-Madden and Yvonne Buckley. The candidate conducted the analyses entirely. The candidate wrote the paper with editorial advice of Yvonne Buckley and Eve McDonald-Madden.

In Chapter 4, the ideas for the paper were conceived by the candidate, Eve McDonald-Madden, Nadiah Kristensen and Yvonne Buckley. The model development and analyses were conducted by the candidate with advice from Eve McDonald-Madden, Nadiah Kristensen and Yvonne Buckley. Nadiah Kristensen developed the Boolean analysis algorithm. The chapter was written almost entirely by the candidate with editorial input of Nadiah Kristensen, Eve McDonald-Madden and Yvonne Buckley. Nadiah Kristensen provided detailed editing of the method section.

In Chapter 5, Justine Shaw and Yvonne Buckley developed the initial research question, but the research design were developed by the candidate, Justine Shaw, Yvonne Buckley and Eve McDonald-Madden. The candidate developed the Bayesian Network model and conducted the analyses. Justine Shaw provided parameter estimates. The candidate wrote the paper with editorial input of Yvonne Buckley, Justine Shaw and Eve McDonald-Madden.

iv

Chapter 6 was written entirely by the candidate, with editorial advice from Eve McDonald- Madden.

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

v

Acknowledgements

Firstly, I would like to thank my supervisors, Yvonne Buckley, Eve McDonald-Madden, Justine Shaw and Hugh Possingham for the continuous support and encouragement throughout my PhD research. I am always inspired by their wisdom, intelligence and creativity, and motivated by the joy and passion they have for research. Yvonne and Eve especially have consistently contributed time, ideas and great efforts for training me in the scientific field. I will always be thankful to Yvonne, who has led me to this exciting research field. She understood my passion and through her insight and encouragement, made it possible for me to undertake this PhD study. Thanks to Eve for always being available and providing speedy feedbacks and great assistance. I could not have done this without your generous support. Thanks to Justine for being a mentor and friend, who shows great patience and encouragement to help me overcome the hurdles in research.

I would like to thank my collaborator and unofficial supervisor Nadiah Kristensen for technical support and invaluable intellectual input in my research. Thanks to my thesis review readers Anne Goldizen and Simon Blomberg for the invaluable comments. Thanks to Michael Bode for generously sharing knowledge and discussing my many research questions. Thanks to Leonie Seabrook for editorial comments. I must also thank the members of Centre for Biodiversity and Conservation Science and the members of Buckley Lab, I have been lucky to work with these dedicated, intelligent and hardworking people.

Thanks to Dion Maple, Michael Misso and Judy West for providing invaluable information. Thanks to Dana Bergstrom, Peter Convey, Angelica Casanova-Katny, Kevin Hughes, Jennifer Lee, Macro Molina-Montenegro, Luis Pertierra, John van den Hoff and Jane Wasley for generously sharing their insightful knowledge.

Thanks to all my friends outside of academia for keeping me balanced in my life. Thank you for always being supportive, encouraging and inspiring, Shanshan Wang, Lixuan Yang, Silin Chen, Shuo Fang, Huiying Wu and Yina Liu.

And finally, thanks to my mum and my dad, for always believing in me and encouraging me to pursue a PhD. Your endless love and unconditional support are the greatest motivation in my life. I hope I have made you proud.

vi

Keywords

Invasive species, eradication, management side effects, ecological networks, uncertainty, decision- making, qualitative modelling, Boolean analysis, Structured Decision Making, Bayesian Networks

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 050103, Invasive Species Ecology, 50%

ANZSRC code: 050205, Environmental Management, 40%

ANZSRC code: 050202, Conservation and Biodiversity, 10%

Fields of Research (FoR) Classification

FoR code: 0501 Ecological Applications, 50%

FoR code: 0502 Environmental Science and Management, 50%

vii

Table of Contents

Chapter 1 Introduction ...... 1 Invasive species management – a topology of failure ...... 2

The overlooked perverse outcomes of invasive species management ...... 3

Incorporating perverse outcomes in decision-making of invasive species management ...... 4

Predicting and evaluating the outcomes of invasive species management ...... 4

Limited information, expert knowledge, and decision-making...... 6

Thesis Overview ...... 8

Chapter 2 Assessing the risks of side effects of invasion management ...... 10

Introduction ...... 10

The importance of assessing side effects of invasion management ...... 11

Review of the side effects of invasive plant management ...... 12

Potential research bias ...... 13

Synthesizing key findings: pathways of the side effects ...... 13

Moving from understanding to decision-making ...... 18

Dealing with the side effects of invasive plant management ...... 18

Pre-management: predicting management outcomes ...... 19

Detection of side effects for on-going practice ...... 19

Mitigating side effects for management in practice ...... 20

Conclusion ...... 21

Chapter 3 Qualitative assessment of the benefits and risks of invasive species management on Christmas Island ...... 22

Introduction ...... 22

Methods ...... 24

Ecological Networks ...... 24

Network analysis on topological properties ...... 28

Qualitative assessment ...... 29

Results ...... 32 viii

Network analysis on topological properties ...... 32

Qualitative assessment of species responses to different management strategies ...... 36

Discussion ...... 45

Topological analysis ...... 45

Comparing management strategies ...... 45

Dealing with uncertainties in the assessment ...... 46

Conclusion ...... 47

Chapter 4 Tools for predicting the ecosystem-wide impacts of cat eradication on Christmas Island, a complex environment with limited information ...... 49

Introduction ...... 49

Methods ...... 51

Modelling overview ...... 51

Ecological networks of Christmas Island ...... 52

The community matrix ...... 54

Model parameterization and validation criteria for the community matrix ...... 54

Probabilistic approach ...... 55

Identifying the rules of species’ responses ...... 55

Results ...... 57

Probabilistic approach ...... 57

Rules governing responses to cat and rat control ...... 58

Discussion ...... 66

A comparison between methods ...... 66

Implication rules ...... 67

Beyond the model: integrating model outcomes with ecological characteristics ...... 68

Conclusion ...... 70

Chapter 5 A structured framework to facilitate decision-making in management of non-native species in Antarctica ...... 71

Introduction ...... 71

ix

A structured framework – a case study of Poa annua ...... 73

Case study species ...... 73

Framework overview ...... 74

Objectives and evaluation criteria ...... 74

Management alternatives ...... 76

Estimation of consequences – a Bayesian Network approach ...... 76

Evaluation of trade-offs ...... 86

Learning and monitoring ...... 87

Discussion ...... 89

Improvement of the BN model ...... 89

Conclusion ...... 91

Chapter 6 General Discussion ...... 93

Synthesis ...... 93

Future research ...... 98

Reducing uncertainty and value of information ...... 98

Improvement of predictive powers ...... 99

Conclusion ...... 100

List of References ...... 101

Appendices ...... 125

Appendix 1.1 Supplementary article ...... 125

Appendix 2.1 Search terms used in Web of ScienceTM Core Collection ...... 127

Appendix 2.2 Criteria for study inclusion in the systematic review ...... 128

Appendix 2.3 Studies included in the systematic review ...... 129

Appendix 3.1 Ecological networks of the Forest and Town ecosystems under three management scenarios ...... 141

Appendix 3.2 Species responses to Strategies 3−7 for the qualitative assessment ...... 143

Appendix 3.3 Key evidence and supporting literature ...... 153

Appendix 4.1 The Boolean minimization method ...... 163

x

Appendix 4.2 Information of the nodes of the ecological networks ...... 166

Appendix 4.3 Outcomes of the probabilistic approach for the Town network ...... 168

Appendix 4.4 Implication rules for all web variations and for different scenarios ...... 169

Appendix 5.1 Probability tables of the nodes in the Bayesian Network model ...... 176

Appendix 6.1 The process for structural alterations of the Bayesian Network model...... 184

xi

List of Figures

Figure 1.1 Structural overview of thesis...... 9

Figure 2.1 Identification of costs and benefits arising from management of invasive species. The solid black line indicates a management effect on a target invasive plant. The solid grey lines indicate potential pathways of management side effects on native species. The double dashed lines indicate costs (orange) and benefits (blue) that occur during management...... 12

Figure 3.1 Ecological networks (a) Forest network (b) Town network. Each network comprises native species of conservation concern (blue), target invasive species (orange), functional species (tan), and food resource nodes (white). A line terminated with an arrow indicates a positive influence while a line terminated with a dot indicates a negative influence...... 25

Figure 3.2 Interaction matrices of the Forest ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal. A filled square in interaction matrices indicates an effect from a predator (top row) to a prey (left column)...... 34

Figure 3.3 Interaction matrices of the Town ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal. A filled square in interaction matrices indicates an effect from a predator (top row) to a prey (left column)...... 35

Figure 4.1 Ecological networks (a) Forest network (b) Town network. Each network comprises focal native species (blue), target invasive species of management (orange), the species affecting and connecting the target and concerned species (tan), and food resources nodes representing exploitative competition between species (white). A line terminated with an arrow indicates a positive influence while a line terminated with a dot indicates a negative influence. Dashed lines represent uncertain interactions...... 53

Figure 4.2 Aggregated responses of native species to cat control alone and combined cat and rat control for the Forest network. For cat control alone, the grey bar (solid and striped) represents positive response and the black bar (solid and striped) represents negative responses. For combined cat and rat control, species showed four categories of responses: positive responses to both cat and rat control (solid grey); negative responses to both cat and rat control (solid black); positive responses to cat control and negative responses to rat control (striped grey); and negative responses to cat control and positive responses to rat control (striped black)...... 57

Figure 4.3 Response rules for cat control alone of (a) the Forest network, and (b) the Town network. The bold font indicates the effects of cat/rat control on the population of the other target

xii species; and the grey shade of a node indicates a negative effect from cat/rat control while an unshaded node indicates a positive effect from cat/rat control...... 59

Figure 4.4 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for (a) the Forest network (rules up to length five); and (b) the Town network, all web variations (rules up to length nine). The ‘True’ node indicates the condition (i.e. the scenario ‘positive effect of cat control on rats, negative effect of rat control on cats’) on every implication. Specifically, the direct links from the ‘True’ node indicate that for these implication rules the ‘True’ node is the only condition to lead to the consequences. The grey shade of a node indicates a negative effect from cat/rat control while an unshaded node indicates a positive effect from cat/rat control...... 64

Figure 4.5 Response rules when cat control has a positive effect on rats and rat control has a positive effect on cats for (a) the Forest network (rules up to length seven); and (b) the Town network (rules up to length five). The scenario ‘positive effect of cat control on rats, positive effect of rat control on cats’ is the condition for every implication. The grey shade of a node indicates a negative effect from cat/rat control while an unshaded node indicates a positive effect from cat/rat control...... 65

Figure 5.1 Outline of the structured framework. The inner circle outlines the key steps of Structured Decision Making process. The outer circle outlines the tools and methods used for each step and indicates what has been achieved for each step in this study...... 75

Figure 5.2 Objectives, measurable attributes and evaluation criteria for eradication of P. annua in the Antarctic Peninsula. Dashed lines and dashed line boxes indicate evaluation criteria that were discussed but not used in this study because of the difficulties of assigning measurable values at the current time. These criteria can be applied when there is more information available which allows measurable values to be assigned...... 76

Figure 5.3 The Bayesian Network model for predicting management consequences including P. annua status after management (node ‘Poa management outcome’) and the responses of native taxa (‘loss of native plants’, ‘loss of microbes’ and ‘loss of invertebrates’) for the P. annua population around Arctowski station under five management alternatives. Shown here is the model set to calculate outcome probabilities for the ‘do nothing’ management alternative...... 79

Figure 5.4 Probabilities of (a) high, low and no presence of P. annua as a result of alternative management actions for the ‘Poa management outcome’, and (b) unacceptable and acceptable ‘loss of native plants’/ ‘loss of invertebrates’ for five management alternatives...... 85

xiii

Figure 3.A.1 Ecological networks of the Forest ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal...... 141

Figure 3.A.2 Ecological networks of the Town ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal...... 142

Figure 4.A.1 Aggregated responses of native species to cat control alone and combined cat and rat control for the Town network. For cat control alone, the grey bar (solid and striped) represents positive response and the black bar (solid and striped) represents negative responses. For combined cat and rat control, species showed four categories of responses: positive responses to both cat and rat control (solid grey); negative responses to both cat and rat control (solid black); positive responses to cat control and negative responses to rat control (striped grey); and negative responses to cat control and positive responses to rat control (striped black)...... 168

Figure 4.A.2 Response rules for cat control only: (a) all web variations (the full web, no interaction between rats and crabs, no interaction between rats and kestrels, and no interaction between rats and kestrels or rats and crabs) in the Forest network; and (b) all web variations (the full web, no interaction between rats and crabs, no interaction between rats and kestrels, and no interaction between rats and kestrels or rats and crabs) in the Town network...... 169

Figure 4.A.3 Response rules for rat control only responses: (a) three web variations (full Forest web, no interaction between rats and crabs in the Forest network, and no interaction between rats and kestrels or rats and crabs in the Forest network), (b) no interaction between rats and kestrels in forest network, and (c) all web variations in the Town network...... 170

Figure 4.A.4 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for Forest network variations: (a) no interaction between rats and kestrels, (b) no interaction between rats and crabs, and (c) no interaction between rats and kestrels or rats and crabs...... 171

Figure 4.A.5 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for the full Forest web (rules up to length 6). Only response rules involving a negative rat-control outcome are shown here...... 172

Figure 4.A.6 Response rules when cat control has a positive effect on rats and rat control has a positive effect on cats for all Town network variations (rules up to length 5): (a) no interaction between rats and kestrels, (b) no interaction between rats and crabs, and (c) no interaction between rats and kestrels or rats and crabs...... 173

xiv

Figure 4.A.7 Response rules when cat control has a negative effect on rats and rat control has a negative effect on cats for (a) the full Forest network, and (b) the full Town network...... 174

Figure 4.A.8 Response rules when cat control has a negative effect on rats and rat control has a positive effect on cats for (a) the full Forest network, and (b) the full Town network...... 175

Figure 6.A.1 Three key conceptual models in the process of constructing the Bayesian Network for predicting management consequences of P. annua...... 184

List of Tables

Table 3.1 A list of species of conservation concern (CC) and functional species presented in the ecological networks. Information includes the common and scientific name, category (native, endemic or introduced to Christmas Island), role in network (conservation concern or functional), and habitat (F indicates Forest and T indicates Town)...... 26

Table 3.2 A list of food resource nodes presented in the ecological networks. Information includes node name, description and the habitat where a node occurs (F indicates Forest and T indicates Town)...... 28

Table 3.3 Potential management strategies, management actions and costs specified for each strategy. The costs were measured by Australian dollars ($)...... 31

Table 3.4 Values of network properties for network variations of Forest network and Town network...... 33

Table 3.5 Qualitative predictions of the responses of species of conservation concern under seven different management strategies. The responses are specified by the likelihood of positive or negative responses (slightly likely, likely and highly likely) indicating the species population will show a positive/negative change, or neutral indicating that the species population is not affected by the management, or uncertain indicating the direction of population change is unknown. Three red colors from light to dark indicate slightly likely, likely and highly likely negative changes. Three blue colors from light to dark indicate slightly likely, likely and highly likely positive changes. Grey indicates neutral. Light yellow indicates uncertain...... 38

Table 5.1 Potential management alternatives for eradication of P. annua in the Antarctic Peninsula. ‘*’ indicates alternatives that were selected for estimation of consequences using the BN model.

...... 77

xv

Table 5.2 Definition of nodes and states used in the Bayesian Network model of P. annua management...... 82

Table 5.3 Sensitivity analysis of BN model. Entropy reduction indicated the degree to which the ‘Poa management outcomes’ or ‘loss of native plants’/ ‘loss of invertebrates’ was sensitive to each input nodes of the model, listed in decreasing order...... 86

Table 2.A.1 A summary of studies included in the systematic review...... 129

Table 3.A.1 A summary of evidence for cat abundance and diet on Christmas Island...... 153

Table 3.A.2 Supporting literature identifying the population status, habitat needs, threats and threat abatement strategies of species of conservation concern, or identifying the population status, ecology and potential impacts of the introduced or self-introduced non-native species on Christmas Island...... 154

Table 4.A.1 Focal species and functioning species presented in the ecological networks. Information includes common and scientific name, category (native, endemic or introduced) to Christmas Island, role in network (focal or functioning), and habitat (F indicates forest and T indicates town)...... 166

Table 4.A.2 Food resource nodes presented in the ecological networks. Information includes node name, description and the habitat where a node occurs (F indicates forest and T indicates town).

...... 167

Table 5.A.1 Estimates (i.e. the prior probability tables) of the input nodes ‘Poa coverage’, ‘Poa extent’ and ‘natural habitat to be managed’...... 176

Table 5.A.2 The conditional probability table for node ‘Poa management outcome’ in the BN model...... 177

Table 5.A.3 The conditional probability table for node ‘habitat recovery potential’ in the BN model...... 179

Table 5.A.4 The deterministic table for the deterministic node ‘loss of native plants’ in the BN model...... 181

Table 5.A.5 The deterministic table for the deterministic node ‘loss of invertebrates’ in the BN model...... 182

Table 5.A.6 The deterministic table for the deterministic nodes ‘loss of microbes’ in the BN model...... 183

xvi

List of Abbreviations used in the thesis

BN Bayesian Network

CEP Committee for Environmental Protection

EPBC Act Environment Protection and Biodiversity Conservation Act 1999

EVII Expected value of imperfect information

EVPI Expected value of perfect information

EVPPI Expected value of partial perfect information

MCDA Multi-criteria decision analysis

SDM Structured Decision Making

VOI Value-of-information

xvii

Chapter 1 Introduction

Biological invasions represent a pervasive and accelerated global change, which challenge the conservation of biodiversity and natural resources, and incur considerable economic losses (Strayer 2012, Simberloff et al. 2013). Introduced species have been shown to be the leading cause of extinction of birds and the second most prevalent cause of extinction of fish and mammals through predation, competition, disease and habitat modification (Clavero and García-Berthou 2005, Szabo et al. 2012). Invasive species can further alter ecological processes and ecosystem function by modifying physical environmental conditions, changing the frequency and/or intensity of disturbance regimes, and altering the trophic structure of ecosystems by changing species interactions (Vitousek 1996, Levine et al. 2003, Ehrenfeld 2010, Simberloff 2011).

The economic and ecological problems resulting from biological invasions underline the importance of management of invasive species (Simberloff et al. 2005). Invasive species management can lead to the spectacular recovery of native species and ecosystems (Cooper 1995, Kennedy et al. 2005, Ratcliffe et al. 2010, Le Corre et al. 2015, Sommerfeld et al. 2015). Yet a number of studies highlight the risks of management failures. Invasive species can be hard to manage given their inherent ‘invasive’ traits such as rapid growth, high fecundity, great dispersal ability, and superior ability to exploit resources (Mooney and Cleland 2001, Brown and Sax 2004, Pyšek and Richardson 2007). Even if the invaders are successfully suppressed, it can be difficult to restore an invaded ecosystem to a desired or historical state (Hobbs et al. 2006, Hobbs et al. 2009). Management interventions can also lead to unexpected perverse effects on native species and ecosystems, creating new management problems (Simberloff 2001, Zavaleta et al. 2001).

In this thesis I focus on addressing the potentially perverse effects of invasive species management and explore a suite of tools to assist decision-making for invasive species management which take both the ecosystem benefits and costs of management into account. The overall aim of this thesis is to emphasize the importance of considering and evaluating the potential risks of invasive species management actions (Chapter 2) and to develop predictive tools to evaluate the benefits and costs of different management strategies to assist decision-making and future practices for invasive species management (Chapter 3-5).

1

Invasive species management – a topology of failure

While invasive species management aims to reverse or mitigate the impacts of invasions, it may fail to achieve these goals and even worse, cause collateral damage to the systems we are trying to protect. In general, management failures from invasive species control fall into three categories: failure to reduce the target invasive species, failure of native ecosystem recovery and failure due to negative side effects of management on native ecosystems.

Management can fail to reduce populations of invasive species. Invasive species with traits such as high fecundity and dispersal ability are hard to eliminate (Mooney 2005, Van Kleunen et al. 2010). In addition, limited understanding of invasion ecology and inappropriate choices of management strategies can lead to ineffective management (Mooney 2005). For example, a tephritid fly released to control bitou bush Chrysanthemoides monilifera ssp. rotundata failed to reduce the population of this shrub (Thomas and Reid 2007). Approaches such as mechanical and chemical removal can effectively decrease invasive populations but re-invasion or an increase in abundance recurs after the control is ceased (Richardson and Kluge 2008).

The successful reduction of an invasive species may not guarantee recovery of native species and ecosystems, due to the legacies of invasion (Buckley 2008). For instance, native species are often subjected to seed limitations in highly invaded ecosystems, thus removing the weed alone is not sufficient to re-establish native communities (Simberloff et al. 2013). Native habitats that have been significantly modified by the invasive species might be no longer suitable for native species (Hobbs et al. 2009, Pyšek and Richardson 2010). Focusing narrowly on weed reduction merely creates disturbances for reinvasions and new invasions (Buckley et al. 2007); continuous management is necessary (e.g. active restoration) (Reid et al. 2009, Simberloff et al. 2013). In addition, invasive species may simply be a symptom of an underlying problem such as pollution, ecosystem degradation or other changes, rather than a regulatory factor of ecosystem disturbance and native species declines (Didham et al. 2005, MacDougall and Turkington 2005, Bauer 2012). In such cases, invasion management makes a minimal contribution to the restoration goals as the underlying threats need to be tackled.

Management actions implemented to control invasive species can have negative side effects on native species and ecosystems (Rinella et al. 2009, Kettenring and Adams 2011, Buckley and Han 2014). The management itself can directly cause collateral damage to native species, such as the poisonous effects of chemical control on non-target species or non-target feeding by biocontrol agents. Furthermore, management of an invasive species that is integrated into an invaded

2 ecosystem can cause indirect negative effects through trophic cascades (Zavaleta et al. 2001, Caut et al. 2009). For example, both theoretical and empirical studies have suggested that removal of top invasive predators can lead to the release of secondary invasive species and cause further ecosystem damage, known as the meso-predator release effect (Courchamp et al. 1999, 2000, Rayner et al. 2007, Bergstrom et al. 2009, Ruscoe et al. 2011). Some introduced species can also play dual roles by supporting some native species while consistently causing negative impacts on others, leading to a potential conflict for conservation managers (Rodriguez 2006, Sogge et al. 2008).

The overlooked perverse outcomes of invasive species management

In invasion ecology, issues associated with the first and second type of failures that are either ineffective in reducing target species or unsuccessful in reversing losses of native ecosystem have been widely discussed. A large body of literature has been devoted to understanding the ecological impacts of invaders and invasion mechanisms (Vilà et al. 2011, Ricciardi et al. 2013, Kumschick et al. 2015). Much work has been done in search of more effective management strategies and there is a growing number of studies on restoration involving invasion management (Hulme 2006, Kettenring and Adams 2011, Suding 2011). However, there has been relatively little focus on investigating the third type of failure – the perverse impacts on native ecosystems from invasion management.

Perverse outcomes of invasive species management have been overlooked for two reasons. First, it is expected that management efforts will have positive effects on species of concern, and that even if management fails to reduce the impacts of invasive species the interventions will be harmless to ecosystems. There are exceptions for specific interventions such as biological control, where considerable efforts have been made to risk assessments for introducing control agents (Culliney 2005, Messing and Wright 2006, Suckling and Sforza 2014). In a meta-analysis of invasive plant control (Kettenring and Adams 2011), more than half of the papers (49 out of 84) solely focused on target invaders and did not examine the responses of native species. Thus, the negative impacts of invasive species management were not examined and, indeed, could not be observed by these studies, leading to a research bias on this issue. Even if perverse outcomes were directly considered, ecosystems are complex and dynamic, making predictions of the impacts of management difficult. This leads to a second reason why perverse outcomes from management of invasive species have been overlooked – a lack of tools to provide reliable predictions to inform decision-making. Given these two reasons, to avoid substantial perverse outcomes and achieve better management outcomes, invasive species management should (1) incorporate potential perverse outcomes into the

3 decision-making process, and (2) develop tools to predict and evaluate perverse outcomes to assist decision-making.

Incorporating perverse outcomes in decision-making of invasive species management

Before undertaking a management action, three key factors need to be addressed: identification of risks of perverse effects, identification of probability of these perverse effects occurring and evaluation of the environmental consequences of perverse effects.

In Chapter 2, I first highlight the importance of incorporating potentially perverse management outcomes into management risk assessment which can inform management decision-making. Assessment of perverse outcomes of management requires estimates of the probability of side effects of management occurring as well as their likely consequences. Next, I present the first systematic review on the perverse effects of invasive plant management. The synthesis provides opportunities to learn from past experiences and provides insights to understand the mechanisms of how impacts occur. I also examine whether these studies integrated costs and benefits of management since identifying risks of management is the first step in decision-making, and further cost-benefit evaluation can inform wise management decisions. I also review existing and potential tools for understanding and predicting the outcomes of invasive species management, and tools for developing optimal management plans and mitigating strategies when perverse impacts are identified.

Predicting and evaluating the outcomes of invasive species management

Currently, few tools are available for understanding and predicting the outcomes of invasive species management, especially for real management problems (but see Raymond et al. (2011), Lampert et al. (2014)), yet potential tools can be developed using experience from similar research fields. Scientists have drawn analogies between studies on the negative impacts of invasion management and studies on the impacts of environmental change drivers (e.g. climate change, habitat degradation and fragmentation, biodiversity loss) (Tylianakis et al. 2010). Similar to the effects of invasive species management, the effects of environmental change drivers on individual species or species assemblages are not simply additive and can propagate and accumulate through ecological networks (McCann 2007, Montoya and Raffaelli 2010, Simberloff et al. 2013). For example, the impacts of invader removal are comparable to studies of the impacts of biological extinction, as both removal of invasive species or removal of native species (extinctions) can lead to cascading effects, affecting ecosystems at all trophic levels (Galiana et al. 2014, Lurgi et al. 2014). 4

Understanding and predicting ecosystem responses to anthropogenic environmental perturbations can be tackled using a range of approaches. Ecological network approaches, which graphically represent trophic interactions between species, play a central role in understanding the stability and resilience of ecosystems (Pascual and Dunne 2005). Using the topological properties of systems (how species connect to each other in a network), ecological modellers have examined, theoretically and empirically, network robustness against perturbations and used network metrics to understand what makes a system robust to perturbation (e.g. network connectance and nestedness) (Proulx et al. 2005, Tylianakis et al. 2010, Saint-Béat et al. 2015). In conservation biology, network analysis has been applied to species deletions to investigate the impact of extinctions on secondary extinction in a system (Proulx et al. 2005, Tylianakis et al. 2010). In Chapter 3, by drawing analogies between extinctions of native species and eradications of invasive species, I conduct a simple topological analysis to inspect the potential direct and indirect effects of the eradication of feral cats (Felis catus) on Christmas Island. The major advantage of this approach is that only knowledge of species composition and interactions is required. However, its predictive power has been questioned as the interpretation of particular metrics can be contradictory in different studies and systems (Ings et al. 2009, Heleno et al. 2012).

An alternative to the topological approach is one that parameterises not only the network structure but also the strength of interactions between species, allowing for the explicit modelling and prediction of the population dynamics of the systems (Yodzis 1988, Schmitz 1997). However, empirical studies using a fully dynamic approach are limited due to challenges of parameterization and problems of over-fitting (Eklöf et al. 2013). Therefore, in Chapter 4, I develop a modeling approach by coupling a qualitative model (similar to Raymond et al. (2011)), with a Boolean analysis method (Degenne and Lebeaux 1996) to examine the dynamic responses of systems to perturbations. The qualitative modeling approach requires only knowledge of the network structure yet allows for simulating dynamic responses by randomly assigning weights to species interactions. Given the simulation outcomes of qualitative modeling, the Boolean analysis can produce robust rules about species responses, which provides implications for eradication practice and subsequent monitoring. I illustrate this approach by examining the implications of feral cat (Felis catus) eradication on Christmas Island.

Networks articulating species composition and interactions are useful when research questions focus on the responses of interacting species or species groups (Ings et al. 2009, Saint-Béat et al. 2015), such as using trophic networks to predict the responses of native fauna to cat eradication on Christmas Island, or using pollination networks (mutualistic networks) to predict the responses of pollinators and native plants to alien plant removal (Carvalheiro et al. 2008a). However, these 5 approaches may have limited use when decision-making is concerned with broader, ecosystem-level responses. For instance, removal of an extensive patch of invasive plants may have substantial impacts on both biotic factors and abiotic factors such as habitat quality, nutrient availability and microclimate (Samways et al. 2011, Rojas-Sandoval et al. 2012). In such cases, depending on the research questions and management objectives, approaches allowing for the incorporation of both biotic and abiotic factors and their potential interactions can be more effective and informative than a detailed mechanistic understanding of complex species-specific interactions. In Chapter 5, I use a Bayesian Network approach to assess a subset of potential management options for a non-native plant species, Poa annua in the Antarctic Peninsula region. Bayesian Networks describe how a set of ecological components (biotic and abiotic) influence each other and how different management options affect these components. The outcomes of the Bayesian Network can be used for further evaluation of the trades-offs of different management options.

Limited information, expert knowledge, and decision-making

Since the ecosystems to be managed are often poorly understood, the utility of a proposed suite of decision-making tools to provide useful decision support can be affected by lack of information or poor parameter estimates. Managers and scientists are constantly confronted by large uncertainties when planning invasive species management, and decisions need to be made upon limited information without evidence of whether desirable or undesirable management outcomes will result (Regan et al. 2002, Shea et al. 2002). For an ecosystem with numerous unknowns yet requiring a rapid management response, the challenge is how to use the limited information wisely (Polasky et al.).

Synthesizing the best information available to inform decisions is widely used in conservation and environmental management (Cook et al. 2009, Williams et al. 2013). For example, Clapham et al. (1999) evaluated the population status of a number of endangered whale species based on previous empirical evidence. In Chapter 3, I conduct a qualitative assessment to compare and evaluate potential management strategies for cats and/or rats on Christmas Island.

Experts with extensive experience can provide valuable insights when severe uncertainties are present. As the demand for case-specific empirical information is likely to consistently exceed supply, there is an increase in the use of expert knowledge in ecological models in scientific research and related applications such as conservation decision-making (Martin et al. 2005, Murray et al. 2009, Kuhnert 2011, Johnson et al. 2012a, McBride et al. 2012). For example, expert knowledge can be used as prior information to incorporate into Bayesian statistical models with 6 scarce data, which has been suggested to increase the precision of the model (Kuhnert et al. 2005, Runge et al. 2011).

Although often questioned on its reliability, expert opinion is an important complement and supplement to empirical data and sometimes the only source of information (Martin et al. 2005, Johnson et al. 2012a, Martin et al. 2012a). Studies have shown that when elicited in a formal process, expert opinion can indeed be a reliable source of information, which can be collected in a cost-effective manner (Murray et al. 2009, Kuhnert et al. 2010, Speirs-Bridge et al. 2010, Kuhnert 2011, McBride and Burgman 2012). A Delphi process is the most commonly used elicitation method when involving multiple experts. This starts with eliciting individual responses from experts independently, then the individual responses are circulated amongst the group, and experts are encouraged to reconsider and adjust their estimates in a second round of elicitation (Kuhnert et al. 2010, Krueger et al. 2012, Martin et al. 2012a, McBride et al. 2012). It is believed during this ‘round to round’ process, the initially diverse opinions of the group converge towards consensus, which improves the accuracy of the estimates (Kuhnert et al. 2010, Martin et al. 2012a).

Besides serving as an information resource, expert opinion also plays an important role throughout the decision-making process. Experts and stakeholders are often consulted when defining management objectives, developing management options, and evaluating trade-offs between different management options (Lyons et al. 2008, Gregory et al. 2012c). However, decision-making on invasive species management can be challenging, since it is often confronted with conflicting objectives from different stakeholders, a pressing need to develop management options under time and resource constraints, and difficulties of evaluating trade-offs and selecting the ‘best’ option (Gregory and Long 2009, Liu et al. 2012). Structured Decision Making (SDM) provides an organized and transparent way to specify objectives that reflect stakeholders’ concerns and to evaluate the trade-offs of management alternatives when there are competing objectives, leading to more informed decisions (Ralls and Starfield 1995, Martin et al. 2009).

Expert knowledge has been used extensively in this thesis given that little information is available for our case studies. In Chapter 3 and Chapter 4, experts were consulted to populate ecological networks of Christmas Island for the topological network analysis and qualitative modelling. In Chapter 5, I propose a structured framework to assist decision-making on non-native species management in Antarctica using a case study of Poa annua. The framework is based on an SDM process, coupling a suite of analytical decision tools. This framework demonstrates how expert knowledge can be used to identify management objectives and management options, to estimate

7 parameters for a Bayesian Network model, and to further facilitate the evaluation of management options with potential decision-making tools.

Thesis Overview

In this thesis, I look at the potential perverse effects of invasive species management to assist conservation decision-making. I explore the utility of a diverse suite of tools to address this question. Figure 1.1 provides an overview of the chapters in this thesis. This introductory chapter provides a general background supplementing the specific introductions in Chapter 2-5, and also identifies the research questions and general aims of this thesis. Extending the ideas raised by the introductory chapter, the first section of Chapter 2 highlights the need for invasive species management to consider the risks of management side effects. Chapter 2 also systematically reviews studies reporting perverse outcomes of invasive plant management, and discusses whether these studies consider the perverse outcomes from a cost-benefit perspective. Chapters 3–5 use a suite of tools to assist decision-making for invasive species eradication projects in light of potential perverse outcomes. In particular, Chapter 3 uses a network analysis method and a qualitative assessment, Chapter 4 develops a qualitative modelling approach with Boolean analysis to inform cat eradication on Christmas Island and Chapter 5 demonstrates a structured framework incorporating a set of decision tools to assist decision-making for management of a non-native plant in Antarctica. Chapter 6 discusses the contribution of this PhD research to invasive species management, provides a synthesis of key findings and suggests future research directions.

8

Chapter 1: Introduction Problem identification

Chapter 2: Literature review Guiding principle: a cost-benefit perspective

Chapter 3: Chapter 4: Chapter 5: Structured Framework Network analysis and Qualitative modeling with Key decision tools: Structured Decision qualitative assessment Boolean analysis Making and Bayesian Network Case study: Cat eradication Case study: Cat eradication Case study: Eradication of a non-native on Christmas Island on Christmas Island plant in Antarctica Tools development: assisting decision-making

Chapter 6: Discussion

Figure 1.1 Structural overview of thesis.

9

Chapter 2 Assessing the risks of side effects of invasion management

*Components of this work are published in Science in 2014, see Appendix 1.1

Introduction

Invasive species present numerous challenges for the conservation of biodiversity and natural resources and in addition they cause considerable economic losses (Pimentel et al. 2005, Strayer 2012, Simberloff et al. 2013). While the goal of invasive species management is usually to reverse or mitigate the impacts of invasion, in many instances management actions may fail to achieve this goal (Bergstrom et al. 2009, Rinella et al. 2009). Invasive species integrate into ecosystems, and form new associations with native species and other invaders, across multiple trophic levels. Some invaders can play a dual role in the ecosystem by causing negative impacts while at the same time providing an ecological function previously carried out by native species (Rodriguez 2006). Therefore, the management of invasive species does not always guarantee recovery of native species, recapture of original species associations, and/or restoration of the ecosystem (Buckley 2008, Gaertner et al. 2014). Moreover, management actions themselves can cause side effects either directly or indirectly through various species interactions (Figure 2.1) (Bergstrom et al. 2009, Rinella et al. 2009, Zarnetske et al. 2010, Overton et al. 2014). A direct side effect is where management actions directly affect the fitness or survival of native species, such as herbicides killing non-target species (Weidenhamer and Callaway 2010), or biological control agents attacking native species (Louda et al. 2003). Alternatively, management effects can be indirect, where the removal of the invaders has side effects through the disruption of species interactions. For instance, removal of introduced predators may release populations of smaller invasive predators, leading to further decline of native prey species (Rayner et al. 2007). Invasive plants can also be released from regulation by consumers, e.g. an invasive shrub increased fivefold in density after the removal of pigs from a wet Hawaiian forest (Cole and Litton 2014).

Side effects of management have been largely neglected by formal evaluations, such as risk assessment and cost-benefit analysis, though managers may already take side effects into account informally (Kerr et al. 2016). Side effects may also be ignored in practice due to a lack of research efforts, such as how side effects occur as well as their prevalence and severity. Predicting and evaluating side effects can be difficult given the inherent complexity and lack of detailed knowledge of ecosystems. To address these issues, first, we call for incorporation of the side effects

10 of invasive species management into formal decision analyses. Second, we present a systematic review on studies reporting side effects of invasive plant management, to provide a better understanding of the side effects and to learn from past experiences about what has been achieved and what needs to improve. Third, for future practice, we discuss existing and potential tools for understanding and predicting the outcomes of invasive species management, and for developing optimal management plans and mitigating strategies for when side effects are identified.

The importance of assessing side effects of invasion management

To minimize the likelihood of side effects of management, the ecological role of invaders should be carefully assessed, and ultimately invasive species management should be integrated with broader ecosystem goals (Zavaleta et al. 2001, Buckley 2008, Buckley and Han 2014, Dickie et al. 2014). When management of an invader is in conflict with other conservation goals, the benefit should be evaluated against the potential side effects. If the side effect is negligible when compared to the benefits of management, the management action may be acceptable. If the side effect rivals or outweighs the benefit, a more rational decision option may be to abandon the management effort and accept the invader’s impacts, or look for alternative management actions and compromise strategies that allow benefits to be maximized and costs to be minimized (Buckley and Han 2014, Lampert et al. 2014).

Cost-benefit analysis is commonly used to evaluate the overall desirability of a management action; however, such analysis rarely incorporates side effects of management. Benefits of invasive species management are generally expressed in terms of avoided ecological and economic losses caused by invasion (Panzacchi et al. 2007, Brown and Daigneault 2014); while cost is usually evaluated in terms of logistics costs of management, and ecological and economic cost due to the remaining invasion impacts (Panzacchi et al. 2007, Brown and Daigneault 2014), without considering potential costs of side effects (but see Lampert et al. 2014) (Figure 2.1). Although few studies evaluate ‘total cost’ in an integrated way, some studies, especially risk assessment of biocontrol agents, do pay attention to the costs of side effects (Messing and Wright 2006, Fowler et al. 2012).

Risk assessment requires estimates of the probability of occurrence and the intensity of the side effect; and intensity could be measured as environmental and/or economic cost, allowing for better integration of cost/benefit into assessment. Risk assessment is routinely conducted for introduction of new biocontrol agents, which assesses their side effects on non-target species through pre-release test on host specificity (Messing and Wright 2006, Fowler et al. 2012). There are also risk assessments for other management operations such assessing toxic effects of pesticide on non-target

11 species (Eason et al. 2002, Pitt et al. 2015); however, they are less put into practice comparing to risk assessment of biocontrol agents. Even fewer studies take a step further to examine the indirect side effects resulted from species interactions in formal decision analysis (Lampert et al. 2014); however, certain studies especially focusing on biological control and invasive mammal removal increasingly emphasize to examine ecological networks for indirect effects prior to management operations (Zavaleta et al. 2001, Fowler et al. 2012). We encourage that, despite management approaches, invasion management should be evaluated with a more comprehensive risk assessment, which explicitly examines both direct and indirect side effects and evaluates corresponding costs in an integrated way (Figure 2.1).

Review of the side effects of invasive plant management

Identifying the side effect of invasion management that have already taken place, and the pathways and extent of these effects, can provide valuable insights for future management decisions. The side effects of invasive animal species’ removal, particularly predatory mammals, have been widely discussed (Zavaleta et al. 2001, Bergstrom et al. 2009), yet syntheses of individual studies for the side effects of invasive plant management are lacking (apart from biological control) (Fowler et al. 2012, Suckling and Sforza 2014).

Cost of Management logistics

Direct side effects Benefits

Target Non-target invader species

Indirect side effects

Cost of remaining Cost of direct and indirect invasion impacts side effects of management

Figure 2.1 Identification of costs and benefits arising from management of invasive species. The solid black line indicates a management effect on a target invasive plant. The solid grey lines indicate potential pathways of management side effects on native species. The double dashed lines indicate costs (orange) and benefits (blue) that occur during management. 12

We used Web of ScienceTM Core Collection to generate a database of publications that reported side effects of invasive plant management (search terms are provided in Appendix 2.1). The search generated 8,299 unique studies which published during 1900 and 2014 (Web of ScienceTM Core Collection, April 2014), and then we examined these publications systematically for inclusion in the review (inclusion criteria are provided in Appendix 2.2). We identified 26 studies which reported side effects of invasive plant management on native species or communities. Seventeen papers described direct side effects while nine papers documented indirect effects. (see details Table 2.A.1 in Appendix 2.3). Among the 17 studies which reported direct effects, seven related to the non- target attack of biological control agents, and seven were caused by chemical controls, with one clipping, one burning and one synthesized approach (mechanical and chemical) (Table 2.A.1 in Appendix 2.3). Nine studies reported significant indirect effects of invasive plant management, of which four involved biological controls and three using burning, with one chemical control and one synthesized approach (mechanical, chemical and burning) (Table 2.A.1 in Appendix 2.3).

Potential research bias

Overall, the number of studies reporting side effects of invasive plant management (26 studies in total) was lower than expected, which suggests that side effects from management either rarely occur, are considered too minor to report or there is a lack of focus in perceiving and assessing management effects. Previous work has identified that side effects may indeed be overlooked due to the structure of most management studies (Reid et al. 2009). In a meta-analysis of invasive plant control, more than half of the papers (49 out of 84) solely focused on target invaders and did not examine the responses of native species (Kettenring and Adams 2011). There is also a taxonomic bias, with most studies focussed on a single species or a single trophic level, usually co-occurring plants; while fewer studies investigated species from other trophic levels (Heleno et al. 2010, Carlos et al. 2014). Further, predicting, detecting and evaluating side effects involving multiple trophic levels can be observationally, experimentally and analytically challenging, which limits the ability to monitor particular species or the ability to determine pathways of the indirect side effects. Thus, the potential side effects may not be examined and, indeed, could not be observed by these studies.

Synthesizing key findings: pathways of the side effects

In addition to direct and indirect side effects, we further classified the indirect effects into three types according to the ecological pathways through which they operated: (1) native-mediated; (2) invader-mediated; and (3) invader-native-mediated. We found that the majority of studies reported

13 on direct side effects (17 studies) while indirect side effects were less reported for invasive- mediated and native-mediated indirect side effects (9 studies) and no field-based studies documented the most complex interaction for the invasive-native-mediated indirect side effects. This is presumably due to the taxonomic bias and the increased challenge of analysing effects from complex ecological interactions.

Direct side effects. Invasive plant management can directly affect native species at an individual, population and community level. Managements can damage individuals such as unintended mortality, stunted growth and/or reduction of fitness and survivorship. For example, the caterpillar stage of a biocontrol agent, Cactoblastis cactorum, threatened individual survivorship of the very rare semaphore cactus (Opuntia spinosissima), an iconic species in the United States (Simberloff and Stiling 1996). Populations of problematic biological control agents are often sustained or facilitated by target weeds. Non-target feeding effects, by which viable agents can adapt or migrate to new native hosts, may not be immediately apparent due to a lag effect (Louda 1998, DePrenger- Levin et al. 2010). Management can also have negative effects on native plant seed production and development, which reduce recruitment potential and cause a decrease in native species populations over time (Louda 1998, Rinella et al. 2009, Takahashi et al. 2009, DePrenger-Levin et al. 2010, Havens et al. 2012). Further, within a plant community, native species can have differential responses (i.e. positive, negative or no change) to management, causing shifts in the prevalence of dominant native species and leading to further changes in community structure (Matarczyk et al. 2002, Ortega and Pearson 2010, Renteria et al. 2012).

Native-mediated side effects. The native-mediated indirect impact occurs when the direct influences of management on a particular native species lead to adverse effects on other natives. The decline of native species suffering direct side effects can affect other species that are reliant on them for habitat and resources. Two field experimental studies showed that after burning, native animal species declined, probably due to the damage of fire on native vegetation, which provided habitat for reptiles, and fruit resources for frugivorous birds (Valentine and Schwarzkopf 2009, Valentine et al. 2012). The direct non-target feeding of a biocontrol agent (weevil Rhinocyllus conicus) on a native thistle (Cirsium canescens) reduced the number and individual mass of the native herbivore (tephritid fly Paracantha culta) due to competition for flower heads (Louda 1998).

While management can directly cause non-harmful changes in native species (e.g. positive population shift), these changes may result in negative impacts on other species. Native-mediated side effects can occur when there is apparent competition induced by biological control agents. The

14 biocontrol agent Mesoclanis polana was used for the biological control of Bitou (Chrysanthemoides monilifera) in Australia. It is highly host-specific, however, it was ineffective in reducing the Bitou population. The population of this agent accumulated through time and consequently facilitated a population increase in native insect generalists, which led to a higher predation pressure on other native insect herbivores (Carvalheiro et al. 2008b). Another example is the host-specific fruit flies Urophora spp. on spotted knapweed (Centaurea maculosa). The fruit flies facilitated an increase of deer mice (Peromyscus maniculatus), resulting in greater seed predation pressure, and reduced emergence and establishment of a dominant native grass and a forb. However, the persistence or transience of these effects remains to be determined (Ortega et al. 2004, Pearson and Callaway 2008). As the primary reservoir for hantavirus, the highly abundant deer mice with a larger seropositive population also present a potential health risk (Pearson and Callaway 2006). Similar to the direct effect of non-target feeding, these examples show that a biocontrol agent’s population can be sustained or facilitated by a target weed, which can then expand the host’s range and result in attacks on native plants and increased competition with native herbivores. Even if control agents stay host-specific, the large population of the agents may lead to apparent competition.

Invader-mediated side effects. Invader-mediated indirect impacts arise from management that involves removal of the target invaders. Despite negative impacts, these invaders usually serve as habitat or food resource for some native species, or maintain the abiotic environment. The invasive grass Spartina spp. provides high quality habitat for the endangered California Clapper rail (Rallus longirostris obsoletus) (Lampert et al. 2014). After regional control of Spartina, the population of the rails declined and the highest densities of rails occurred in invaded areas compared with uninvaded and post-management sites (Overton et al. 2014). Additionally elimination of the invaders may also have indirect effects by disturbing and altering environmental conditions (Rhoades et al. 2002, Rojas-Sandoval et al. 2012). The removal of invasive riparian trees, Acacia spp., disrupted both riparian and aquatic habitats by reducing shade, increasing temperature, facilitating the growth of less palatable macrophytes, increasing bank erosion and changing water quality, which all affected or potentially affected invertebrate fauna (Samways et al. 2011). While tolerant and widespread taxa appeared to recover, the sensitive endemic taxa associated with tree canopies utilising shady, cool habitats with high oxygen levels showed a negative response (Samways et al. 2011).

15

Box 2.1 Pathways of side effects of management

The solid black line indicates a management effect on a target invasive plant. The solid grey lines indicate potential pathways of management impacts on native species. The dashed arrows specifically indicate that under a biological control management, the population of the biocontrol agent is sustained by the target weed.

(1) Direct side effects  Herbicide impacts  Non-target feeding by biocontrol agents.

Management

Direct side effects

Target Non-target invader species

* The agent population is sustained by the target weed in the beginning (dashed arrows), and the agent then attacks native plants.

(2) Native-mediated side effects  Damage to native vegetation providing habitat for native birds (Valentine and Schwarzkopf 2009, Valentine et al. 2012).  Biocontrol agents feeding on non-target native plants and out-competing the native herbivores (Louda 1998).  Biocontrol agents facilitate native generalists, which leads to increased predation on native herbivores (apparent competition) (Carvalheiro et al. 2008b, Pearson and Callaway 2008), or increases the health risk by elevating the virus carried by the generalists (Pearson and Callaway 2006).

Management

Target Non-target invader species

Native-mediated side effects

16

Box 2.1 Pathways of side effects of management (continued)

*The biological control agent’s population is sustained by the target weed (dashed line), which then attacks native plants leading to competition with native herbivores, or agent stays host-specific yet the large population of the agent lead to apparent competition.

(3) Invader-mediated side effects  Removal of invasive species which provides habitat for native species (Overton et al. 2014)

 Removal of invasive species which maintains beneficial ecological properties (Samways et al. 2011)

Management

Target Non-target invader species

Invader-mediated side effects

(4) Invader-native-mediated side effects  No empirical studies reporting this type of indirect side effect

Management

Target Non-target invader species

Invader-native-mediated side effects

17

Invader-native-mediated side effects. Invader-native-mediated side effects occur where interactions between invaders and native species change due to the elimination of the invader, causing negative impacts on other native species. We found no published field-based evidence for this effect. However, one study modelled field data identifying this potential pathway (Carvalheiro et al. 2008a). The study suggested that a possible scenario might involve removal of a weed, a key pollen source, which may disrupt the pollinator-plant interaction, causing a crash in native pollinators and affecting native pollinator-dependent plants (Carvalheiro et al. 2008a). However, two field studies from different ecosystems have found either a beneficial effect or a resilient pollinator-plant network, rather than a pollinator collapse, following invasive plant control (Baskett et al. 2011, Ferrero et al. 2013).

Moving from understanding to decision-making

Identifying the side effects of invasion management does not necessarily imply that a management action is undesirable, and it cannot simply be used as an excuse to suspend management actions (Dickie et al. 2014). Most studies that identified side effects of management have emphasized the importance of undertaking risk assessment (especially for biological control agents) prior to implementation, and have considered implicitly how to minimize management side effects by discussing alternative means or mitigation approaches; yet few have actually given an explicit cost- benefit consideration. Side effects of management are often unavoidable, but whether the extent and cost of the effects are acceptable compared with the benefit of management, is rarely discussed in these studies. One exception is Bower et al. (2014), which found that two native reptile species declined in abundance after weed management involving burning and grazing. However, they pointed out that as these two species were widespread and their abundant populations were likely to have been promoted by the invasive para grass (Urochloa mutica), the reduction of these lizard populations could be acceptable considering the greater benefits of weed control (Bower et al. 2014).

Dealing with the side effects of invasive plant management

In the synthesis of the side effects of invasive plant management, we identified the research biases which may lead to overlooking of potential risks, illustrated that side effects can be hard to predict and manage given the varied pathways and the potential long period for the side effects to accumulate and occur, and pointed out the gap between research and practice which is how to translate the research outcomes into effective decisions and actions. To cope with these challenges,

18 we discuss below a suite of tools that have been used or can be developed and applied at different management stages to inform future practice.

Pre-management: predicting management outcomes

Field-based experiments can be an effective and economically feasible approach to identify short-to moderate-term, direct or indirect side effects involving a few trophic levels. These experiments usually only involve a co-occurring native plant, or are conducted with a manageable-sized species assemblage (Table 2.A.1 in Appendix 2.3) (Zarnetske et al. 2010, Samways et al. 2011, Havens et al. 2012, Bower et al. 2014). Using field studies to investigate indirect effects across multiple trophic levels can be costly in time and resources, yet the urgency of conservation issues calls for quick responses and actions to avoid further losses. In addition, multi-trophic or community-based studies are often subject to sizeable uncertainties when applying the experimental outcomes at a large scale (e.g. landscape scale) practice (Ogden and Rejmanek 2005).

In such instances, a priori modelling can be used to identify the potential risks, especially for indirect side effects across multiple trophic levels. The impacts of species addition (e.g. species invasion) or deletion (e.g. species extinction) in an ecological network have been widely discussed in food web research (Tylianakis et al. 2010). Likewise, food web approaches have great potential to investigate effects of removing invaders or releasing biological control agents for invasion management (e.g. Carvalheiro et al. 2008a, Russo et al. 2014). Carvalheiro et al. (2008a) quantified a pollinator-plant network representing species abundance and interaction frequency using field experiment data and then simulated the possible consequences of invasive plant removal.

The mathematical modelling of small species assemblages can also provide insights, for instance, into risk assessment of biological agents (Rand and Louda 2004, Buckley et al. 2005). Some control agents are highly host-specific but they may form novel associations with native generalists and parasites, accumulating indirect effects through time (Carvalheiro et al. 2008b, Veldtman et al. 2011). Knowledge of the population dynamics of the agents can predict the time taken for the agent to reduce target weeds, and evaluate potential risks of indirect effects (Carvalheiro et al. 2008b, Shyu et al. 2013).

Detection of side effects for on-going practice

Side effects of management can sometimes be detected immediately after a management action or observed within a relatively short period of time (less than two years) (Table 2.A.1 in Appendix

19

2.3) (Baker et al. 2009, Whitcraft and Grewell 2012). However, sometimes the appearance and verification of side effects require a moderate or even a long period of time (Table 2.A.1 in Appendix 2.3). For example, several cases of biological control management show that it often takes decades for an agent to become problematic and subsequent changes can take even longer (Rand and Louda 2004, Takahashi et al. 2009). Without monitoring consequent changes, valuable opportunities will be missed for a fast response, leading to prolonged and exacerbated side effects of management. One study revealed adverse herbicide impacts after revisiting the managed site 16 years later, finding that the target invader became more abundant and two native forbs had become extremely rare, presumably due to residual chemical effects (Rinella et al. 2009). A case study on burning control demonstrated that consecutive burning resulted in the disappearance of a litter- dependent gecko species despite no significant effects being observed after the first burn (Valentine and Schwarzkopf 2009). Therefore, monitoring during and after management is important for the early detection of side effects. This allows for adaptive management to minimize side effects.

Different taxa can show diverse responses to the same management actions, and selection of the wrong indicator species can influence the perceived outcomes. For example, some research investigating the removal effects of exotic Tamarix spp. on native amphibians, reptiles, butterflies and fish assemblages, reported beneficial or at least non-damaging outcomes (Kennedy et al. 2005, Bateman et al. 2008, Nelson and Wydoski 2008), while other studies suggested some avian species using Tamarix habitats may have been negatively affected (Sogge et al. 2008, Dudley and Bean 2012). Thus, for large scale eradications involving numerous species, conclusions can be less informative and lead to ill-advised management decisions when these decisions are made upon the responses of a specific species group without consideration for others. When selecting indicator species, it is essential to explicitly consider management goals, which usually concern endangered species or keystone species performing important ecological functions. Native species which have benefited from invasion and increased in abundance should be avoided when choosing indicators, since reduction of these over-abundant native species can be acceptable (Bower et al. 2014).

Mitigating side effects for management in practice

Reduction of an invader is often the sole criterion for invasive species management success (Hulme 2006, Reid et al. 2009). However, benefits such as the recovery of native species may not happen naturally and management actions can cause side effects, leading to the failure of management both from broader ecosystem goals and from a cost-benefit perspective. Therefore, active restoration, via seed addition and seedling planting, has been widely used during or after invasion management to restore native communities. For species affected indirectly by weed removal, step-wise eradication 20 with restoration efforts are recommended (Carvalheiro et al. 2008b, Dudley and Bean 2012), which leads to a new challenge of making rational trade-offs between benefits and side effects in order to resolve conflicting ecosystem goals. Many studies emphasize the need to link experimental or modelling outputs to management needs, yet few achieve this (Snelgrove et al. 2014). Lampert et al. (2014) did it successfully by constructing a multi-objective optimal management model to constrain the side effects of Spartina removal on an endangered bird that uses Spartina for nesting.

Conclusion

New species continue to be introduced, which establish and integrate into the ecosystems and even assume ecological roles after the native species have been eliminated. Side effects from invasive species management are likely to become more common than is currently perceived. Instead of using the population reduction of invasive species as a sole criterion of management success, the risks of side effects of management should be evaluated in management decision-making, to fit into broader restoration and conservation goals (Hulme 2006, Reid et al. 2009). Researchers and practitioners have to address a number of scientific and technical challenges to achieve better management outcomes, such as the limited knowledge of ecosystems, complex pathways of the side effects and potential research biases constrained by time, resources and techniques. Future invasion management program should be developed with a comprehensive risk assessment incorporates costs and benefits, as well as with the assistance of novel approaches that can overcome the challenges to identify and cope with side effects.

21

Chapter 3 Qualitative assessment of the benefits and risks of invasive species management on Christmas Island

Introduction

Introduced predators are a major threat to global biodiversity, leading to severe declines and extinctions of a number of aquatic and terrestrial species (Courchamp et al. 2003, Clavero et al. 2009), particularly on insular ecosystems such as islands (Burbidge and Manly 2002), and lakes (Vitule et al. 2009). Island species may be particularly vulnerable due to a lack of shared co- evolutionary history and consequent anti-predator responses (Savidge 1987, Salo et al. 2007). For example, the introduced Nile perch (Lates niloticus) in Lake Victoria caused extinctions and sharp declines of hundreds of endemic cichlid species (Ogutu-Ohwayo 1990, Olowo and Chapman 1999). The invasive brown tree snake (Boiga irregularis) has extirpated twelve native bird species on the island of Guam (Savidge 1987). Cats (Felis catus) and rats (Rattus spp.) are the most damaging invasive predators, traveling with humans to most parts of the globe including remote islands. Cats are thought to be responsible for the extinction of 33 species of insular vertebrates (Medina et al. 2011) and rats for 58 (Towns et al. 2006).

Removal of introduced predators has proved to be an effective tool to ameliorate negative impacts on native species and to restore native ecosystems (Côté and Sutherland 1997, Nogales et al. 2004, Campbell et al. 2011). More than 300 successful eradications of Rattus spp. and 80 successful eradication events of feral cats have taken place worldwide, leading to spectacular recovery of native species (Keitt et al. 2011, Russell and Holmes 2015), especially seabird populations (Cooper 1995, Hughes et al. 2008, Ratcliffe et al. 2010). However, eradications may also cause unforeseen and unwanted effects (Zavaleta et al. 2001). For an ecosystem suffering multiple invasions, invasive species form new ecological interactions with each other and with native species across multiple trophic levels (Zavaleta et al. 2001). In such cases, eradication of top predators may cause the secondary release of meso-predators (Caut et al. 2009), or herbivores (Bergstrom et al. 2009) once regulated by the suppressed invader, resulting in higher pressure on native species (Courchamp et al. 1999, Ruscoe et al. 2011). Rayner et al. (2007) demonstrate that cat eradication on Little Barrier Island caused a reduction in the breeding success of Cook’s petrel (Pterodroma cookii) due to the secondary release of Pacific rats (Rattus exulans) but the petrel population increased after subsequent rat eradication. 22

Given the potential impacts of introduced species management, an increasing number of studies highlight the importance of understanding trophic interactions between multiple invasive species and emphasize the planned removal of predators using a whole of ecosystem perspective (Zavaleta et al. 2001, Bergstrom et al. 2009, Caut et al. 2009, Ringler et al. 2015). The indirect effects of invasive species removal have been explored through theoretical modelling (Courchamp et al. 1999, 2000, Fan et al. 2005, Tompkins and Veltman 2006), and empirical field experiments (Rayner et al. 2007, Ruscoe et al. 2011, Oppel et al. 2014). However, experimental trials can be limited by resources, practical difficulties (e.g. inaccessible areas such steep cliffs) (Howald et al. 2007), and time constraints if the urgency of conservation threats calls for rapid action to avoid further losses (Martin et al. 2012b). There is a need, therefore, for tools that all for the prediction of the success of eradication programs before they are undertaken, even under large uncertainty (Raymond et al. 2011).

A cat eradication project on Christmas Island, which is proposed to mitigate impacts of cat predation on native species, confronts all these challenges1 (Department of the Environment 2014c, Director of National Parks 2014a). The presence of the invasive black rat (Rattus rattus) complicates the cat eradication issue, since the rats are not only meso-predators preying on native species but are also a food resource for an endemic bird of prey (Hill 2004a, Beeton et al. 2010). Thus careful evaluation is required on whether controlling rat populations along with cat management will achieve better results than cat management alone. In this study, I explore the utility of a network analysis approach and a qualitative assessment to inform decision-making on cat and rat management on Christmas Island with limited information.

First, I conducted a network analysis on the ecological networks of the invaded ecosystem, which were constructed based on expert knowledge. Ecological networks, depicting species interactions in an ecosystem, can be used to describe the stability and resilience of an ecosystem through a number of topological properties (Saint-Béat et al. 2015). For instance, in conservation science, ecosystem resilience to secondary extinctions following species loss can be assessed by network properties such as connectance (the number of connections) and compartmentalisation (which split a network into relatively independent compartments) (Tylianakis et al. 2010). Similarly, the network response to invasive species removal can also be examined using network properties. The presence of invasive top predators is believed to increase connectance and reduce compartmentalisation of an ecosystem, resulting in highly connected networks that are less resistant to invasion impacts and

1 The work in this chapter was done in 2014. In 2015 a cat eradiation plan was developed and is now underway. 23 subsequent perturbations (Tylianakis et al. 2010, Saint-Béat et al. 2015). Thus, I considered that for invasive species removal, a reduction of connectance and an increase of compartmentalisation were favourable changes and so these two properties were evaluated for the network analysis.

The network analysis requires only knowledge of network structure (i.e. presence or absence of interactions between species) and focuses on system responses as a whole (Jordán and Scheuring 2004, Alcántara and Rey 2012). However, it lacks the power to give specific predictions on the responses of each component (i.e. species), which was required for this Christmas Island problem. Thus, I conducted a qualitative assessment to evaluate the responses of a number of individual species of conservation concern under a range of potential management strategies based on currently available information and knowledge of the ecological networks. The potential management strategies were then evaluated and compared by comprehensively considering the expected responses of species of concern and management costs. Although such qualitative assessment often reflects a subjective judgement, summarizing and synthesizing the best available information can still provide insights into conservation issues such as evaluating the population status of endangered species (Clapham et al. 1999), assessing the effectiveness of management practices (Briske et al. 2011), and providing support for future conservation interventions (Williams et al. 2013).

Methods

Ecological Networks

The basis of the network analysis and qualitative assessment was the ecological network of the invaded ecosystem of Christmas Island, which reflected trophic interactions between the invasive cats and rats and the species of conservation concern. First, species of concern that were under threat from cat and rat predation were identified during a workshop in 2013 by three stakeholders including an officer and a manager from Christmas Island National Park and a director from Parks Australia (Table 3.1, supporting evidence is given in Table 3.A.1 and Table 3.A.2 in Appendix 3.3). Then ecological networks were constructed to represent the trophic interactions between cats, rats and species of concern by adding additional functional nodes (i.e. species and food resources that are not of concern yet affect and connect the focal species) (Table 3.2). A link between two nodes indicated either a predator-prey relationship or interference competition. Exploitative competition between species was represented when species shared the same prey species or resource nodes.

24

I developed two ecological networks for two ecosystems according to the spatial separation of the two areas and the difference in the ecological communities represented. These included a Forest ecosystem (Figure 3.1a) and a network of the ecosystem in the inhabited area of the island (Town ecosystem, Figure 3.1b). The key difference in these networks was that several species were not present in Town ecosystem (the Christmas Island Goshawk, the Christmas Island Emerald Dove, the Christmas Island Imperial Pigeon, the Christmas Island Thrush, the Christmas Island White-eye, the Brown Booby, and the Giant Gecko) and one species was only present in Town ecosystem (the Red-tailed Tropicbird). Each ecosystem included invasive cats and rats, and native species of conservation concern (Table 3.1).

(a) Crabs

Cats Rats Goshawks Kestrels

Tropicbirds (W) Hawk-owls

Brown Boobies Flying-foxes Thrushes White-eyes

Geckos Pigeons Doves Chickens Insect resources (nocturnal)

Insect resources (diurnal) Fruit resources (canopy) Fruit resources (ground)

(b) Crabs

Goshawks Cats Rats Hawk-owls

Tropicbirds (W)

Tropicbirds (R) Chickens Kestrels

Flying-foxes Cultivated resources (ground)

Insect resources (diurnal)

Cultivated resources (above-ground) Insect resources (nocturnal)

Figure 3.1 Ecological networks (a) Forest network (b) Town network. Each network comprises native species of conservation concern (blue), target invasive species (orange), functional species (tan), and food resource nodes (white). A line terminated with an arrow indicates a positive influence while a line terminated with a dot indicates a negative influence.

25

Table 3.1 A list of species of conservation concern (CC) and functional species presented in the ecological networks. Information includes the common and scientific name, category (native, endemic or introduced to Christmas Island), role in network (conservation concern or functional), and habitat (F indicates Forest and T indicates Town).

Conservation Species Name Name in figures Category Role in network Status (EPBC Act) Habitat Priority

Red-tailed Tropicbird Tropicbirds (R) Native CC Listed marine Medium T (Phaethon rubricauda westralis)

White-tailed Tropicbird Tropicbirds (W) Endemic CC Listed marine High F, T (Phaethon lepturus) Listed as Endangered Golden Bosun (phaethon lepturus fulvus)

Brown Booby Brown Boobies Native CC Listed marine Medium F (Sula leucogaster)

Christmas Island Goshawk Goshawks Endemic CC Listed as endangered High F, T (Accipiter hiogaster natalis)

Christmas Island Hawk-owl Hawk-owls Endemic CC Listed as vulnerable High F, T (Ninox natalis)

Christmas Island Emerald Dove Doves Endemic CC Listed as endangered High F (Chalcophaps indica natalis)

26

Table 3.1 continued

Conservation Species Name Name in figures Category Role in network Status (EPBC Act) Habitat Priority

Christmas Island Imperial Pigeon Pigeons Endemic CC Not listed Medium F (Ducula whartoni)

Christmas Island Thrush Thrushes Endemic CC Listed as endangered High F (Turdus poliocephalus erythropleurus) Christmas Island white-eye White-eyes Endemic CC Not listed Medium F (Zosterops natalis)

Christmas Island flying-fox Flying-foxes Endemic CC Listed as Critically High F, T (Pteropus natalis) Endangered

Giant Gecko Geckos Endemic CC Listed as Endangered High F (Cyrtodactylus sadleiri)

Nankeen Kestrel Kestrels Self- Functional - - F, T (Falco cenchroides) introduced

Feral Chicken Chickens Introduced Functional - - F, T (Gallus gallus domesticus)

Red crabs Crabs Endemic Functional - - F, T (Gecarcoidea natalis)

27

Table 3.2 A list of food resource nodes presented in the ecological networks. Information includes node name, description and the habitat where a node occurs (F indicates Forest and T indicates Town).

Node name Description Habitat

Insect resources (diurnal) Insect resources consumed by diurnal species F, T

Insect resources (nocturnal) Insect resources consumed by nocturnal species F, T

Fruit resources (canopy) Fruit resources in canopy F

Fruit resources (ground) Fruit resources on ground F

Cultivated produce (ground) Cultivated produce on ground T

Cultivated produce (above-ground) Cultivated resource (i.e. fruits) above-ground T

Network analysis on topological properties

The network analysis was conducted on two network properties, connectance and compartmentalisation. The networks to be analysed were binary, with the presence or absence of nodes (i.e. species) and links (i.e. species interactions). Therefore, this network analysis on topological properties only allowed me to consider species removal scenarios, and was not able to incorporate the actual effectiveness of management actions to inform real-world practices. For instance, this approach can examine the network performance under a cat removal scenario by comparing network variations with and without the cat node and relating links, yet it lacks the ability to predict the effects of different control actions which reduce the cat population to different extents.

Three general management scenarios were considered including (1) no management, (2) cat removal alone, and (3) combined cat and rat removal. These scenarios were represented by three network variations for each ecosystem (Forest or Town), including (1) a network with cats and rats, (2) a network without cats, and (3) a network without cats and rats. I compared the network properties across the three network variations for each ecosystem to assess the effects of cat removal or combined cat and rat removal.

Connectance is defined as the proportion of possible species interactions that are realized (Pimm 1984), which is usually calculated as:

28

2퐿 퐶 = (1) 푁(푁 − 1) where N is the number of nodes and L is the number of links (Jordán and Scheuring 2004, Stevens 2009). Network metrics including the number of nodes and links and connectance were calculated using the function GenInd of the NetIndices package (Kones et al. 2009) implemented in R 3.2.0 (R Core Team 2015).

Compartmentalisation (or modularity) splits a network into network compartments, in which species interact frequently with each other yet interact little with species from other compartments (Stevens 2009). General algorithms for calculating compartmentalisation are commonly used to compare degrees of compartmentalisation in networks, for which the numbers of links and nodes are the same yet the distributions of the links are different (Jordán and Scheuring 2004, Stevens 2009). However, the networks of Christmas Island were very small, and removal of cats or removal of cats and rats had great influences on network structure (both numbers of nodes and distributions of links), which made the values calculated from these algorithms less comparable. Therefore, I made a qualitative comparison of compartmentalisation across network variations, which were visualised by matrices representing the interactions.

Qualitative assessment

I conducted a qualitative assessment to predicted the responses of species of concern to a range of potential management strategies of cats and/or rats on Christmas Island by consulting the best available information. Compared with the network analysis, this approach reflected a subjective judgement, but allowed assessment of the responses of individual species and consideration of the effectiveness of management strategies in real-world practices, as required for this Christmas Island problem.

Management objectives

The evaluation of responses for individual species of concern (Table 3.1) was required since these species were considered to reflect management objectives, which are to maintain the values of the Christmas Island National Park (Director of National Parks 2014a). Species that are endemic to Christmas Island and that are listed under the Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act), including the List of marine species and the List of Threatened Fauna, were prioritized higher than other native species in the networks.

29

Management strategies and costs

As a real-world management problem, an evaluation of practical management strategies was necessary for decision-making. Potential management actions and related costs were provided by the three stakeholders during the workshop. There were seven potential management strategies proposed including a ‘do nothing’ strategy and six strategies with actions, from six possible combinations of four basic management actions (island cat eradication, Town cat removal, Forest cat baiting and rat baiting) (Table 3.3). Costs in Australian dollars for the four management actions were provided by the stakeholders. The cost of island cat eradication ($5 million) was based on the information sources of the stakeholders. To date, there has been a $500,000 boost from the Australian Government and $1.35 million in funding from the island’s phosphate revenue, and additional funding will come from new partners (Department of the Environment 2014c). The costs of Town cat removal, Forest cat baiting and rat baiting were estimated by the stakeholders based on the costs of the cat and rat control activities that had been or were being undertaken on the island. Logically, the rat management action was not assessed alone as this action was regarded as supplementary to cat management. For instance, when the rat baiting was combined with Town cat removal, this rat baiting activity would have a focus on the Town regions. A consistent estimate was given for the cost of rat baiting for different strategies (Town focus, Forest focus or island focus), because the differences were minor as the costs of logistics and bait remained largely the same. Implementation of management strategies were discussed on the basis of a 10-year time scale, and outcomes of management were assessed over a 20-year time horizon (Director of National Parks 2014a).

Predicting species responses and comparing management strategies

I evaluated the responses of species of concern to the seven different management strategies. To do this, I consulted currently available information from published studies, accessible grey literature and government documents on cats, rats, species of concern and a few other species that were considered to be important for supporting and interpreting the predictions. Given that there is at present very little information available, I also consulted studies on similar island ecosystems, on comparable eradication projects such as cat and/or rat eradication in other ecosystems, and on the ecology of related species (e.g. same genus) to the species on Christmas Island.

30

Table 3.3 Potential management strategies, management actions and costs specified for each strategy. The costs were measured by Australian dollars ($).

Strategies Management actions Costs (1) Do Nothing No management interventions $0

(2) Island wide cat Cat eradication includes baiting, Cat eradication: $5,000, 000 eradication with trapping, and hunting in Town and (one-off investment) rat baiting Forest. Rat baiting: $15,000 p.a. Rat baiting includes setting up bait stations around located seabird nesting colonies and around houses to keep rats out of people’s houses.

(3) Island wide cat Cat eradication includes baiting, Cat eradication: $5,000,000 eradication without trapping, and hunting in Town and (one-off investment) rat baiting Forest.

(4) Town cat Town cat removal includes neutering Town cat removal: $75,000 p.a. removal with rat of pets, trapping of unregistered & Rat baiting: $15,000 p.a. baiting stray cats and baiting in a buffer zone Total: $90,000 p.a. around the Town. Rat baiting: same actions as stated in strategy (2), but with a focus in Town area.

(5) Town cat Town cat removal includes neutering Town cat removal: $75,000 p.a. removal without of pets, trapping of unregistered & rat baiting stray cats and baiting in buffer zone around Settlement.

(6) Forest cat Forest cat removal includes baiting on Forest cat removal: $150,000 p.a. baiting tracks around island.

(7) Town cat A combination of strategy (4) Town Town cat removal: $75,000 p.a. removal and rat cat removal with rat baiting and (6) Rat baiting: $15,000 p.a. baiting as well as Forest cat baiting Forest cat baiting: $150,000 p.a. Forest cat baiting Total: $240,000 p.a. 31

There were considerable uncertainties concerning the population status of species of concern. For certain species, the estimation varied from a couple of hundred to several thousand (Director of National Parks 2014a). Moreover, dietary studies of cats on Christmas Island showed great variation (Algar and Johnston 2010), and there were no quantitative dietary studies conducted for rats and native birds of prey (the goshawk and the hawk-owl). Given the lack of reliable information on species populations and interaction strengths between species, it was exceedingly difficult to predict the extent of population change caused by a certain management strategy. Therefore, in this qualitative assessment, I made predictions based on the likelihood of species responses. Specifically, species responses were first identified as ‘positive’, ‘negative’, ‘neutral’ or ‘uncertain’, where ‘neutral’ indicated that species were not affected by the management action and ‘uncertain’ indicated that species population changes were difficult to predict, and could either show a positive or a negative response. For ‘positive’ and ‘negative’ responses, each was further specified with estimated probabilities including ‘highly likely’, ‘likely’ and ‘slightly likely’. Given that few species of concern have been subjected to comprehensive study, I caution that most predictions should be regarded as tentative.

Species with similar ecological roles were grouped and discussed together, including: (1) seabirds (the White-tailed Tropicbirds, the Red-tailed Tropicbirds, and the Brown Booby), (2) birds of prey (the Christmas Island Goshawk and the Christmas Island Hawk-owl), (3) frugivorous land birds (the Christmas Island Emerald Dove and the Christmas Island Imperial Pigeon), (4) insectivorous land birds (the Christmas Island Thrush and the Christmas Island White-eye), (5) mammals (the Christmas Island Flying-fox), and (6) reptiles (the Christmas Island Giant Gecko). The responses of these species were predicted with supporting information, and are summarized in Table 3.5.

Results

Network analysis on topological properties

Connectance

As expected, for both the Forest and Town ecosystems the ‘no management’ scenario had the highest connectance value (0.28 for both), followed by networks simulating cat removal alone (0.24 - Forest and 0.25 - Town) (Table 3.4). Networks without both cats and rats had the lowest connectance (0.15 and 0.14) with an approximately 50% reduction in the number of links compared with networks for the other two scenarios (Table 3.4).

32

Table 3.4 Values of network properties for network variations of Forest network and Town network.

Network Number of nodes Number of links Connectance Forest network No management 19 95 0.28 Car removal alone 18 73 0.24 Combined cat and rat removal 17 42 0.15 Town network No management 14 51 0.28 Car removal alone 13 39 0.25 Combined cat and rat removal 12 18 0.14

Compartmentalisation

For the Forest networks, the network with presence of cats and rats was less compartmentalized as a number of generalists such as cats, rats and goshawks linked prey species together (Figure 3.2a, see Figure 3.A.1a in Appendix 3.1 for the structure of the ecological network). Under the cat removal scenario, although the flying-fox was less connected to other species, the other components of the network were still largely connected by rats and goshawks (Figure 3.2b, Figure 3.A.1b in Appendix 3.1). The network simulating cat and rat removal showed a greater compartmentalization, in which the brown booby was an independent node, and the flying-fox and the nocturnal group of species (i.e. hawk-owls, geckos and nocturnal insect resources) were less connected to the network (Figure 3.2c, Figure 3.A.1c in Appendix 3.1). The Town network under the cat and rat removal scenario also showed a greater compartmentalization, with four disconnected compartments (Figure 3.3c, Figure 3.A.2c in Appendix 3.1).

33

Figure 3.2 Interaction matrices of the Forest ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal. A filled square in interaction matrices indicates an effect from a predator (top row) to a prey (left column).

34

Figure 3.3 Interaction matrices of the Town ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal. A filled square in interaction matrices indicates an effect from a predator (top row) to a prey (left column).

35

Qualitative assessment of species responses to different management strategies

The expected responses of species of conservation concern to the seven management strategies are summarized in Table 3.5. Species responses with supporting evidence to the first two strategies, the ‘do nothing’ and the ‘island wide cat eradication with rat baiting’ strategy, are provided to illustrate the assessment process. Species responses to Strategies 3−7 can be found in Appendix 3.2. The supporting evidence used for the evaluation of a particular species to different strategies was largely based on the same selection of literature for each species. Therefore, the supporting evidence is not presented repeatedly for each species under each strategy. However, some evidence is emphasized for predicting responses to particular strategies. A detailed summary of the key information with supporting evidence used for predicting responses of species of conservation concern is listed in Table 3.A.1 and 3.A.2 in Appendix 3.3.

Strategy 1 Do nothing

The ‘do nothing’ scenario indicates no cat and rat management actions would be implemented, thus there is no cost incurred.

Strategy 1 Cats’ and rats’ responses

Expected responses

Highly likely to maintain the current high level of abundance.

Supporting evidence:

The population of feral cats is estimated to number from a few hundred to around 1,000 on Christmas Island (Department of the Environment 2014c, Threatened Species Scientific Committee 2014c). The most recent survey in 2008 suggested an abundance index of 1.34 cats per km (Algar and Johnston 2010), which indicates that the cat population is relatively abundant on the island. Tidemann et al. (1994) reported that the diet of cats on Christmas Island was dominated by three vertebrate species including the Emerald Dove, the flying-fox and the introduced rat, which together constituted 80% of the food intake. Another survey conducted in 1997 found there was a significant proportion (30–40%) of the native reptile species in the diet of cats (Van der Lee and Jarman (1996) cited in Algar and Johnston (2010)). Two later studies showed that invertebrates, black rats and a number of bird species were the predominant dietary species, and found little evidence of cat predation on flying-foxes (Corbett et al. (2003), Algar and Brazall (2005) cited in Algar and Johnston (2010)). Besides rats, other introduced species such as waterhens (Amaurornis phoenicurus) and abundant domestic chickens are also food resources that facilitate the growth of the cat population (Beeton et al. 2010). This evidence suggests that there could be a dietary shift for 36 cats, which may indicate the strong adaptability and flexibility of cats in coping with the change of food availability. A detailed summary of the literature on cat abundance and diet on Christmas Island is provided in Table 3.A.1 in Appendix 3.3.

The black rat is a contributing factor to declines of endemic island species globally (Howald et al. 2007). On Christmas Island, it potentially affects a number of bird species including the Red-tailed Tropicbird, the White-tailed Tropicbird, the dove, the thrush, and the white-eye (Beeton et al. 2010). The rat population may be limited by land crabs through competition for food resources and/or predation on young rats (Algar and Johnston 2010). A recent study indicated the rat population was more abundant than previous studies and historical records suggested (Low et al. 2013). The home range of rats on Christmas Island was reported to be much smaller compared with similar ecosystems, suggesting that rat density on the island was relatively high (Low et al. 2013).

Strategy 1 Seabirds’ responses

Expected responses

Red-tailed Tropicbirds: highly likely to show a negative response

White-tailed Tropicbirds: highly likely to show a negative response

Brown Boobies: highly likely to show a negative response

Supporting evidence

Studies found very high chick mortality (100% and 96% of two surveys) of Red-tailed Tropicbirds as a result of cat predation (Ishii (2006) cited in Garnett et al. (2010)). A survey in 2011 found a dramatic increase in the nesting success (65.8%) of Red-tailed Tropicbird chicks following cat control along the shoreline of the Town area (Algar et al. 2012). The chicks and adults of Brown Boobies and White-tailed Tropicbirds are also considered under threat (Beeton et al. 2010), although empirical studies are lacking for these two species on Christmas Island.

Given our current knowledge of the detrimental effects on seabird species of Rattus spp. in other ecosystems (Ringler et al. 2015, Russell and Holmes 2015), these seabirds are likely to be threatened by the abundant rat population that was found around seabird colonies (Algar and Johnston 2010, Low et al. 2013).

These seabird species forage for fish and have little competition with land species. Although the native Christmas Island Goshawk feeds on seabirds, this is unlikely to present threats to the seabird populations since the goshawk has a low populations and is a generalist feeding on a variety of species (Hill 2004a).

37

Table 3.5 Qualitative predictions of the responses of species of conservation concern under seven different management strategies. The responses are specified by the likelihood of positive or negative responses (slightly likely, likely and highly likely) indicating the species population will show a positive/negative change, or neutral indicating that the species population is not affected by the management, or uncertain indicating the direction of population change is unknown. Three red colors from light to dark indicate slightly likely, likely and highly likely negative changes. Three blue colors from light to dark indicate slightly likely, likely and highly likely positive changes. Grey indicates neutral. Light yellow indicates uncertain. Management strategies & costs (1) Do (2) Island (3) Island (4) Town cat (5) Town cat (6) Forest cat (7) Town cat Nothing wide cat wide cat removal with removal baiting removal and eradication eradication town rat without town town rat with rat without rat baiting rat baiting baiting as baiting well as forest cat baiting $0 $5,000,000 $5,000,000 $90,000 p.a. $75,000 p.a. $150,000 p.a. $240,000 p.a. Species of concern (one-off) & (one-off) $15,000 p.a. Red-tailed Tropicbird White-tailed Tropicbird Brown Booby Christmas Island Goshawk Christmas Island Hawk-owl Christmas Island Emerald Dove Christmas Island Imperial Pigeon Christmas Island Thrush Christmas Island white-eye Christmas Island flying-fox Giant Gecko 38

Strategy 1 Birds of prey’s responses

Expected responses

Christmas Island Goshawks: slightly likely to show a negative response

Christmas Island Hawk-owls: neutral, unlikely to be affected

Supporting evidence

The population of the Christmas Island Goshawk is estimated to be fewer than 250 individuals (Beeton et al. 2010). This species is particularly vulnerable due to the low population, threatened mainly by habitat loss, impacts of the Yellow Crazy Ants, pressure of inbreeding depression and risks of natural catastrophe (Hill 2004a).

The goshawk preys on a variety of species including white-eyes, thrushes, doves, domestic chickens, tropicbirds, rats, reptiles and invertebrates (Hill 2004a). Although there is no direct connection between cats and the goshawk, the large dietary overlap may negatively affect goshawks through exploitative competition. No quantitative dietary study of the Christmas Island Goshawk is available, but it is suspected that introduced rats may have replaced two extinct rodent prey species and form part of the goshawks’ diet (Hill 2004a).

The goshawks are also likely to be influenced by a self-introduced raptor, the Nankeen Kestrel (Schulz and Lumsden 2009). Since the kestrel’s colonization, its population is believed to be increasing and widespread across the island, although the exact population size remains unclear. The impacts of this species on native fauna have not been documented. The dietary study showed that this species feeds mainly on Giant Grasshoppers (Valanga irregularis) (97% of total items) with a small number of other insects (Schulz and Lumsden 2009). The kestrel was also observed to prey on black rats (Dion Maple, personal communication). Its diet partially overlaps with that of the Christmas Island Goshawk. Since the juvenile goshawks may tend to prey on invertebrates, and given the increasing population of kestrels, there is a potentially negative effect from kestrels on goshawks through exploitative competition.

Given the potential negative influence of cats and kestrels, without any control intervention on cat population, I predict that the goshawk will be slightly likely to be negatively affected.

A recent study showed the population of the Christmas Island Hawk-owl was significantly overestimated by previous studies (e.g. Hill and Lill (1998)), although it is not known whether the population is stable or in decline (Low and Hamilton 2013). This nocturnal species is primarily insectivorous, feeding on a variety of invertebrates such as tree crickets (Gryllacris rufovaria) and beetles (Coleoptera) (Hill 2004b), and is also recorded to prey on vertebrates including rats and

39 geckos (Hill and Lill 1998). The Town area and its surroundings on Christmas Island support a relatively high density of hawk-owls, since these areas provide a high density of introduced prey species such as Asian House Geckos (Hemidactylus frenatus), cockroaches (Periplaneta Americana) and rats (Low and Hamilton 2013). The hawk-owl may rest close to the ground and may be exposed to the risk of cat predation, but no evidence at present indicates that cats have a significant impact on the hawk-owls (Hill 2004b). Therefore, I predict that the hawk-owl population will not be affected by the presence of cats.

Strategy 1 Frugivorous land birds’ responses

Expected responses

Christmas Island Emerald Doves: likely to show a negative response

Christmas Island Imperial Pigeons: neutral, unlikely to be affected

Supporting evidence

There are no reliable estimates of the dove population, One study using distance sampling suggested estimates of population sizes that varied from 900 to 3,500 individuals (Corbett et al. (2003) cited in James and McAllan (2014)). Yellow Crazy Ants, which affect a range of land bird species on Christmas Island, are thought to be the primary threat to the Emerald Dove, by reducing the availability of suitable breeding habitat and disturbing foraging behavior (Davis et al. 2008, Davis et al. 2010, James and McAllan 2014). Predation by cats and possibly rats may have impacts as the dove usually forages on the ground (Department of the Environment 2014b), but the extent of the impact remains unclear (Threatened Species Scientific Committee 2014a). If there are no control efforts, it is likely that the dove will be negatively affected given the possible increasing trends of cat and rat populations.

The Christmas Island Imperial Pigeon is currently considered abundant, with estimates of 35,000– 66,000 individuals (Corbett et al. (2003) cited in James and McAllan (2014)). The pigeon feeds on fruit in the canopy and occasionally descends to the ground for fruit and water (James and McAllan 2014). Although a dietary study found that the pigeon occurred in 9% of the guts of cats examined (Tidemann et al. 1994), no further evidence suggests that cats and/or rats are causing significant impacts on this species. Thus I predict that the presence of cats and rats is unlikely to affect the pigeon population (Threatened Species Scientific Committee 2006a).

40

Strategy 1 Insectivorous land birds’ responses

Expected responses

Christmas Island Thrushes: likely to show a negative response

Christmas Island White-eyes: likely to show a negative response

Supporting evidence

Populations of the Christmas Island Thrush and the Christmas Island White-eye were estimated at 15,000 individuals and 20,000 breeding birds, respectively, according to the only survey (Threatened Species Scientific Committee 2006b, Department of the Environment 2014f). The thrush forages primarily on the forest floor and in understory vegetation (James and McAllan 2014), and the white-eye forages from forest floor to canopy (Threatened Species Scientific Committee 2006b). Both species were recorded in the dietary study of cats (Tidemann et al. 1994). Black rats were assumed to be responsible for the extinction of a number of Passeriformes on other islands (Howald et al. 2007, Hutton et al. 2007). The presence of rats may reduce the nesting success of both species. Therefore, I predict that the thrush and the white-eye are likely to be affected without any management efforts for cats and rats.

Strategy 1 Mammals’ responses

Expected responses

Christmas Island Flying-foxes: likely to show a negative response

Supporting evidence

Christmas Island Flying-foxes are diurnal foragers for a wide variety of fruits and blossoms and are thought to be an important component of the island rainforest ecosystem as a seed disperser and pollinator (Threatened Species Scientific Committee 2014c). The population of the flying-fox was estimated at 331-1,469 individuals (Director of National Parks 2014b), and is considered to have undergone rapid decline (Threatened Species Scientific Committee 2014c, Woinarski et al. 2014). Tidemann et al. (1994) reported that the flying-fox formed a significant proportion of food intake of cats, yet such a high proportion was not reported by subsequent diet studies (Van der Lee and Jarman (1996), Corbett et al. (2003), Algar and Brazall (2005) cited in Algar and Johnston (2010)). There is no conclusive evidence to indicate to what extent this species is affected by cat predation. Given the abundant cat population (around 1,000 individuals) (Threatened Species Scientific Committee 2014c), the presence of cats is likely to have a negative effect on the flying-foxes.

41

Strategy 1 Reptiles’ responses

Expected responses

Christmas Island Giant Geckos: likely to show a negative response

Supporting evidence

There are no island-wide population estimates of the Giant Gecko (Threatened Species Scientific Committee 2014b). Although this species is still relatively abundant on the island, there is sampling evidence suggesting its population has declined dramatically (Beeton et al. 2010, Smith et al. 2012). Threats to this species includes habitat loss, disease, and predation and competition from introduced species (Threatened Species Scientific Committee 2014b). Predation is believed to be the primary cause for population declines of the all reptile fauna on Christmas Island (Smith et al. 2012). Specifically, the giant gecko has been recorded in dietary studies of feral cats (Van der Lee and Jarman (1996) cited in Algar and Johnston (2010)) and cats are suggested to be a long-term threat (Smith et al. 2012). Predation by rats, the introduced Asian wolf snakes (Lycodonaulicus capucinus) and the introduced giant centipedes (Scolopendra subspinipes), competition from introduced geckos and disruption by Yellow Crazy Ants may also impact the gecko (Smith et al. 2012), but there is no single factor that has been singled out as causing the decline (Threatened Species Scientific Committee 2014b). Therefore, I predict that the gecko is likely to be negatively affected by the presence of cats and rats.

Strategy 2 Island wide cat eradication with rat baiting

Managing cats and rats simultaneously can avoid the potential risk of meso-predator release of the rat population. The cost of this strategy is the highest with an estimated one-off cost ($5 million) for cat eradication and also at least $ 15,000 per annum ongoing cost for rat baiting.

Strategy 2 Seabirds’ responses

Expected responses

Red-tailed Tropicbirds: highly likely to show a positive response

White-tailed Tropicbirds: highly likely to show a positive response

Brown Boobies: highly likely to show a positive response

Supporting evidence

42

The cat control effort around seabird colonies has been effective, and has led to an increase in the nesting success of the chicks along the Town shoreline (Algar et al. 2012). Further removal of cats from the island is highly likely to benefit the seabird species. The abundant populations of rats around seabird colonies may reduce the benefits of cat control (Low et al. 2013). A combination of cat eradication and rat control around bird colonies will largely reduce the risks of meso-predator release and the increase in rat impacts.

Strategy 2 Birds of prey’s responses

Expected responses

Christmas Island Goshawks: uncertain

Christmas Island Hawk-owls: neutral, unlikely to be affected

Supporting evidence

Cat removal may indirectly benefit the Christmas Island Goshawk through an increase in native prey (see Strategy 1 Birds of prey’s responses). However, there is a concern that rat suppression will reduce food resources for this endemic raptor. No quantitative dietary study on Christmas Island Goshawks is available. Removal of rats may have some negative effects on the foraging and feeding of goshawks. Thus, the benefits and risks of this management strategy to the goshawks are highly uncertain.

There is no direct link between cats and the Christmas Island Hawk-owl, and their primary food resources differ. Due to the hawk-owl’s preference for invertebrates and the abundant Asian House Geckos and cockroaches, a reduction of the rat population is unlikely to cause a direct food shortage for this species (see Strategy 1 Birds of prey’s responses). Thus, cat eradication with rat control may have little influence on the hawk-owl population.

Strategy 2 Frugivorous land birds’ responses

Expected responses

Christmas Island Emerald Doves: likely to show a positive response

Christmas Island Imperial Pigeons: neutral, unlikely to be affected

Supporting evidence

This strategy is likely to lead to an increase of the Christmas Island Emerald Dove population (see Strategy 1 Frugivorous land birds’ responses). However, with the presence of super-colonies of

43

Yellow Crazy Ants, uncertainties exist on how much benefit the dove population will get from cat and rat management.

Management of cats and rats may have few benefits to the pigeon given its high population and canopy feeding behavior (see Strategy 1 Frugivorous land birds’ responses).

Strategy 2 Insectivorous land birds’ responses

Expected responses

Christmas Island Thrushes: likely to show a positive response

Christmas Island White-eyes: likely to show a positive response

Supporting evidence

According to the threats and population information (see Strategy 1 Insectivorous land birds’ responses), this management strategy is likely to benefit the thrush and the white-eye populations.

Strategy 2 Mammals’ response

Expected responses

Christmas Island Flying-foxes: likely to show a positive response

Supporting evidence

Given the predation pressure of cat, the flying-fox is likely to positively respond to this strategy, but it remains unclear to what extent this strategy will benefit population recovery (see Strategy 1 Mammals’ responses).

Strategy 2 Reptiles’ response

Expected responses

Christmas Island Giant Geckos: slightly likely to show a positive response

Supporting evidence

To what extent the giant gecko could benefit from this management strategy with presence of other threats remains unclear given the presence of other introduced species such as the wolf snake, the crazy ant and other non-native geckos (see Strategy 1 Reptiles’ responses). However, I predict that the gecko is slightly likely to benefit from the management of cats and rats.

44

Discussion

Topological analysis

Structural analysis of ecological networks can be used to guide the evaluation of management efforts when information on species dynamics is absent. In the networks simulating ‘no management’ scenario, cats and rats as super-generalists largely increased the overall network connectance and reduced compartmentalization. The effects of removing one species (both positive and negative) is more likely to propagate throughout the network; while removing both species is likely to avoid such secondary effects. However, this point only holds when the original ecological network has low connectance level and the generalists are able to connect with a large proportion of the species, such as the Christmas Island system shown here. Cat removal did not reduce connectance or increase compartmentalization due to the remaining presence of rats, thus the spread of perturbations (i.e. management effects) and potential trophic cascades may be facilitated by rats. An example of this is the potential of meso-predator release of rats after cat eradication which may lead to secondary effects on a number of native species including the seabird species, some land birds and the gecko. Removal of both cats and rats reduced network connectance and increased compartmentalization. Thus, managing cats and rats simultaneously is more likely to benefit native species through increasing network stability and to slow the spread of disturbance due to a change in network characteristics.

Comparing management strategies

The do nothing scenario is predicted to be the least favorable with the continuation of current negative trajectories for a number of species. Island-wide cat eradication with rat baiting (Strategy 2) leads favorable outcomes with three species showing ‘highly likely’ positive responses. If the rat baiting can be done through a step-wise manner, the probability of negative impacts on the goshawk due to food reduction will be largely reduced, which renders this combination of cat eradication with control of rats the most preferable strategy. This strategy (Strategy 2) reduces the potential risks of rat population release comparing to Strategy 3, and if applied with a proper manner it has the potential to address the negative effects on the goshawk. When considering costs, however, Strategy 3 may rival Strategy 2 as Strategy 2 requires ongoing rat control per annum.

The outcomes of the two Town strategies, Strategy 4 (Town cat removal with Town rat baiting) and Strategy 5 (Town cat removal without Town rat baiting), are less preferable because of the lower number of focal species covered and the ongoing costs required. These two strategies require the

45 lowest cost per annum, but forest species benefit little. Strategies 4 and 5 are likely to fail to meet the management goal of maintaining biodiversity and conserving a number of high priority species.

Under the scenario of Strategy 6 (Forest cat baiting), there is a high risk of population decline of Red-tailed Tropicbirds since this species is only present in Town areas; while Strategy 7 (with Town cat removal and Town rat baiting as well as Forest cat baiting) will largely reduce this risk. For both Strategy 6 and 7, the control action in Forest areas is cat baiting, which is less effective than cat eradication in reducing cat population. Thus, the positive responses of Forest birds and the flying-fox to these two strategies are less certain and less favorable compared with Strategies 2 and 3 which conduct cat eradication. Medium ongoing costs are required for both Strategies 6 and 7. If the resource input is ceased, the populations of cats and rats are likely to bounce back. Strategy 7 yields no advantages on Strategies 2 and 3 involving island cat eradication if the management is viewed over a 10-year time scale. In addition, the final outcomes are more difficult to predict over a relatively long time span (20 years) with the various uncertainties that exist in the ecosystem. However, over a short period if sufficient resources and funding are not available to carry out eradication, Strategies 6 and 7 could be considered and are more beneficial than the two Town-only strategies.

Dealing with uncertainties in the assessment

Predictions of the benefits and risks of management strategies were conducted by comprehensively reviewing available information and analyzing the structures of ecological networks, however, uncertainties exist with these predictions. The first type is the uncertainty of the magnitude of the impact of cats and/or rats on individual species. For instance, a strategy of controlling rat populations may reduce food sources and negatively affect the endemic raptor, the Christmas Island Goshawk. The relatively high level of connectance (the number of links to prey species) suggests goshawks may be relatively robust to fluctuations in prey species abundance (Tylianakis et al. 2010). Yet this robustness depends on the numbers of weak and strong links (distributions and patterns of interaction strength). Strong links indicate that species are more affected by population changes of the species they are linked to (McCann et al. 1998). For instance, if the interaction between goshawks and rats is strong, it indicates that goshawks may be more susceptible to rat control; while if the interaction is weak, it means that goshawks are more resilient. Monitoring and dietary analysis that provides information on the interaction strengths between the goshawk and its prey can reduce uncertainty in predicting the response of goshawks to cat/rat management.

46

Acquiring knowledge of species interactions can also help to identify the key threats to a particular species. For instance, predation by cats on flying-foxes and giant geckos has been recorded, but there is no reliable quantitative evidence to determine the extent of the impacts that cats have on these species (Algar and Johnston 2010, Beeton et al. 2010, Smith et al. 2012). There are many other rival explanations for the decline of these species such as habitat degradation, pollution, disease and negative effects from other invasive species (O'Dowd et al. 2003, Martin et al. 2012b, Director of National Parks 2014a). It remains unclear which threats are the most important.

The second type of uncertainty is the uncertainty about the population status of high priority species. The paucity of empirical information on populations hinders evaluation of species population trends. The Christmas Island Thrush and the Christmas Island White-eye are considered still to be abundant on Christmas Island yet no population trends are available. It is possible that these species are able to persist in the current situation. It is also possible that their populations have been affected by cat/rat predation without this being noticed. In such cases, the benefits of cat and rat management can be underestimated. The study of breeding success of Red-tailed Tropicbirds before/after cat removal provides a good example illustrating that reducing this type of uncertainty can lead to more confident predictions.

The assessment may also encounter the third type of uncertainty beyond identifying species status and defining interactions: the structural uncertainty of the ecological networks, since there could be species and interactions that can potentially be included and excluded from the model. Gaining additional knowledge may identify new interactions which have never been expected, or may increase the confidence to exclude links considered in the network.

Conclusion

With presence of multiple invasive species in an ecosystem, corresponding invasive species management often requires careful evaluation, given the risks of perverse indirect effects. In this study, I conducted a simple network analysis based on our current knowledge of trophic interactions and a qualitative assessment based on the best available information, to inform the cat and rat management issue on Christmas Island. Both the network analysis and qualitative assessment supported a similar outcome that manipulating cats and rats together is likely to yield greater benefits and minimize the risks. In particular, the qualitative assessment indicated that an island- wide eradication with prioritised rat baiting was the most favourable strategy given sufficient resources and funding. This study showed that articulating ecological networks and using the best available information to study species interactions networks can provide insights into decision- 47 making, especially given the paucity of information and urgent need for conservation management. However, these two approaches have a number of limitations in their predictive power. The network analysis has limited ability to specifically predict the responses of individual species, while the qualitative assessment reflects a subjective judgement and is unable to cope with indirect effects across complex pathways. Therefore, future research efforts need to focus on developing tools that can cope with great uncertainties yet are able to give objective predictions of species responses to invasive species management.

48

Chapter 4 Tools for predicting the ecosystem-wide impacts of cat eradication on Christmas Island, a complex environment with limited information

Introduction

Conservation-motivated eradications of invasive species have proved effective in ameliorating conservation crises, facilitating the recovery of native species, and restoring ecosystems (Nogales et al. 2004, Ratcliffe et al. 2010). Instead of manipulating a single invasive species, in recent decades, it has become more common to emphasize the importance of assessing the ecological roles of the invader, considering species interactions and managing the ecosystem as a whole to avoid unexpected outcomes from species eradication (Zavaleta et al. 2001, Glen et al. 2013, Simberloff et al. 2013). Invasive species can integrate into an invaded ecosystem, forming new associations not only with native species but also with other invaders at multiple trophic levels (Gaertner et al. 2012, Lurgi et al. 2014). Consequently some invaders can play a dual role in an ecosystem, causing detrimental impacts on native species and ecosystems while assuming some ecological functions by filling empty niches (Rodriguez 2006, Sogge et al. 2008). Elimination of invasive species, therefore, does not guarantee the recapture of the original species associations and the restoration of ecosystems (Buckley 2008, Rinella et al. 2009), and can instead cause adverse indirect effects on native species (e.g. trophic cascades) (Bergstrom et al. 2009, Ritchie et al. 2012). To avoid undesirable consequences, a priori research is required to predict potential management outcomes, identify the potential risks of invasive species removal, and inform better management decisions.

The impacts of removing species from an ecological network have been widely discussed in food web research (Dunne et al. 2002, Montoya et al. 2009). Broadly the analysis of species-removal impacts can be split into two groups: a qualitative approach, usually known as a ‘topological approach’, where network properties are assessed using structural attributes such as the distribution of connections (Proulx et al. 2005, Tylianakis et al. 2010); and a quantitative approach, where the population dynamics are explicitly modelled (Yodzis 1988, Schmitz 1997). Both suffer from drawbacks: the topological approach does not take into account species interaction strengths, whereas the quantitative approaches are often challenging because they require interaction strength to be known and are computational intensive for large ecosystems (Eklöf et al. 2013). Mathematical

49 modeling of small species assemblages has provided insights into the impacts of a single species focus on eradication, in particular, how higher order predator removals can cause release of lower order predators and thus result in negative ecosystem outcomes (e.g. meso-predator release) (Courchamp et al. 1999, Tompkins and Veltman 2006). While these results have important theoretical implications, they are of limited practical use in real, specific applications because this approach can only model a small system usually involving three or four species, whereas real-world management often needs to consider more species.

Although the approaches discussed above provide useful tools for identifying the potential risks of invasive species removal, various constraints limit their application to the existing invasion and conservation management issues. A key constraint for modelling ecosystems is a lack of information about species and the mechanisms of species interaction, and thus sizeable uncertainties around how species and ecosystems will respond to management intervention (Regan et al. 2002, McDonald-Madden et al. 2010b). The collection of additional information aimed at reducing this uncertainty is time consuming and resource demanding, while the urgency of conservation issues calls for quick responses and action to avoid further losses (Martin et al. 2012b). For an ecosystem the sheer magnitude of unknowns coupled with a need to make timely decisions, leaves us with a challenge in informing management decisions with limited information. Qualitative approaches have been developed to explore community dynamics in response to invasion management, which require only the knowledge of which species interact (network structure) (Dambacher et al. 2003, Raymond et al. 2011). While such approaches offer promise for informing conservation decisions without requiring onerous system knowledge, they do not provide robust solutions as current probabilistic interpretation of the outcomes of qualitative modelling yields large uncertainties (Raymond et al. 2011, Melbourne-Thomas et al. 2012), nor do they have the ability to uncover patterns of associations between non-control species (Dambacher et al. 2003). We use an alternative approach, the Boolean analysis, to interpret the outcomes of qualitative modeling, which provides deterministic interpretations and reveals patterns of species responses (Degenne and Lebeaux 1996). Despite the high level of uncertainty, these rules allow for a better understanding of network structure and more importantly, provide informative implications for practice of species eradication and follow-up monitoring.

We illustrate our qualitative approach by investigating the implications of cat eradication on an isolated Australian territorial island, Christmas Island, in order to inform future island-wide management decisions. Biological invasion has led to dramatic ecosystem change on Christmas Island and resulted in a number of recent extinctions and a call for proactive conservation decisions. One of the major threats to native fauna on the island is predation by introduced cats (Felis catus) 50

(Beeton et al. 2010, Algar et al. 2011, Martin et al. 2012b). In 2015 a Christmas Island Feral Cat Eradication project was launched (Department of the Environment 2014c). However, the presence and trophic role of the invasive black rat (Rattus rattus) complicate the management issue. Given our knowledge of the fatal impacts of black rats on other island seabird populations (Howald et al. 2007, Banks and Hughes 2012), there is concern about the potential accentuation of the impacts of rats on other species within the Christmas Island ecosystem when their key predator is removed. Simultaneous control, usually suggested as a solution for multi-invaded ecosystems (Glen et al. 2013, Orchan et al. 2013, Ringler et al. 2015), may result in perverse effects as well, since on Christmas Island rats are known to substantially subsidize the diet of an endangered endemic predator, the Christmas Island Goshawk (Accipiter fasciatus natalis) (Hill 2004a). It is thus necessary to investigate how cat management and additional rat control might affect native species. We used a qualitative modeling approach to identify the possible effects of different management scenarios (control of cats alone versus control both cats and rats) upon key species of concern, aiming to inform future management and monitoring for the cat eradication project on Christmas Island.

Methods

Modelling overview

We used a dynamic system approach to model the ecological networks of the Christmas Island ecosystems. Each network model was represented by a signed directed graph which contains species and the sign of the interactions between them (Section Ecological networks of Christmas Island). A community matrix, whose sign-structure reflected the signed graph description of the system, summarized the behaviour of the system near steady state once populated with numerical values (Section The community matrix).

Modelling and analysis consisted of two major steps: (1) a certain number of randomly parameterized community matrices were generated for each network model, and the responses (positive or negative) of all species to particular perturbations (control of cats and control of rats in this study) were recorded as predicted by each community matrix (Section Random parameterization and validation criteria for the community matrix) and (2) an analysis of the species’ responses recorded, which identified the rules governing what combinations of species’ responses to a particular management strategy were possible (Section Identifying the rules of species' responses). Potential management strategies were developed by specifying various combinations of management activities including cat control alone, or a combined control of cats 51 and rats. In addition, a probabilistic approach that was similar to Raymond et al. (2011) was used to analyze the outcomes of species’ responses in Section Probabilistic approach.

Ecological networks of Christmas Island

We used a dynamic system approach to model the ecological networks of the Christmas Island ecosystems. An ecological network is a representation of the trophic interactions between species. A link between two nodes indicated either a predator-prey relationship or interference competition, while the exploitative competition between species was facilitated when species shared the same prey species or resource nodes. All interactions (links) between species were categorized as certain or uncertain: certain links indicated that the relationship was considered influential enough to be included in the network while uncertain links were those that may or may not be important for the network (e.g. opportunistic predation). The inclusion or otherwise of the uncertain links led to multiple representations of the network structure (network structure variations).

We constructed two ecological network for two distinct Christmas Island ecosystems, the Forest ecosystem and the Town ecosystem, according to the spatial separation of the two areas and the difference in the ecological communities represented (Figure 4.1a and b). Both the Forest and the Town networks had a number of structure variations, given the presence of uncertain links (dashed lines). The key difference between Town and Forest ecosystems were: (1) several species were not present in the Town ecosystem including the Christmas Island Goshawk, the Christmas Island Emerald Dove, the Christmas Island Imperial Pigeon, the Christmas Island Thrush, the Christmas Island White-eye, the Brown Booby, the Giant Gecko, and (2) the Red-tailed Tropicbird was only present in the Town ecosystem (see details of species in Table 4.A.1 and 4.A.2 in Appendix 4.2).

Each network and its variations contained cats and rats which were under management consideration, focal native species which were of conservation concern and under threat of cat and rat predation, non-focal species that are not under concern but which affect and connect the target and concerned species, and food resource nodes which captured exploitative competition interactions between species (Figure 4.1a and b). Where species shared the exact same set of links they were lumped together into one node. The dove and the pigeon were represented by a ‘Frugivorous Birds’ node in the Forest network and White-tailed Tropicbirds and Red-tailed Tropicbirds as ‘Tropicbirds’ node in the Town network (Figure 4.1 and Table 4.A.1 in Appendix 4.2). We explored all possible network variations for the forest ecosystem (i.e. the full forest network, the forest network with no interaction between rats and kestrels, the forest network with no interaction between rats and crabs, the forest network with no interaction between rats and 52 kestrels nor rats and crabs the full town network) and for the town ecosystem (i.e. the full town network, the town network with no interaction between rats and kestrels, the town network with no interaction between rats and crabs, and the town network with no interaction between rats and kestrels nor rats and crabs).

(a)

(b)

Figure 4.1 Ecological networks (a) Forest network (b) Town network. Each network comprises focal native species (blue), target invasive species of management (orange), the species affecting and connecting the target and concerned species (tan), and food resources nodes representing exploitative competition between species (white). A line terminated with an arrow indicates a positive influence while a line terminated with a dot indicates a negative influence. Dashed lines represent uncertain interactions.

53

The community matrix

The analysis technique used for the population dynamics and responses to invasive-species control was identical to that in Raymond et al. (2011). Briefly, the population dynamics were described by a system of Lotka-Volterra equations, which allowed the population steady state to be summarised by the community matrix (Levins 1968). The steady state here referred to local stability — whether the system will return to equilibrium after a small perturbation. The sign-structure of the community matrix corresponded to the sign-structure of the ecological network, with zero entries between pairs of species with no direct interaction. If the non-zero entries in the community matrix were specified, both the local stability properties of the system and its response to press perturbations can be quantified (Yodzis 1988). The population was stable near the steady state if all eigenvalues of the community matrix had negative real parts. The response of each species’ population steady state to management (cat or rat control) could be determined from the sensitivity matrix, which was the negative inverse of the community matrix (Nakajima 1992). If the entry of the sensitivity matrix corresponding to the control species (column) and response species (row) was positive, the response of species’ population steady state to management was negative, and vice versa.

An eradication practice usually involves a sudden population decrease to very low abundance. However, a small perturbation simulates a depression on a target population, and it requires a long time for the target population to reach near-zero or zero abundance. This simulation approach cannot precisely reflect the effects of a real-world eradication practice; therefore, we used the term ‘control’ instead of ‘eradication’ throughout this study.

Model parameterization and validation criteria for the community matrix

Knowing the strengths of interactions between species (and therefore the numerical values of the entries in the community matrix) is rare in ecological systems and Christmas Island is no exception. We generated randomly parameterized community matrices for each possible network variation, with the magnitudes of non-zero entries of the community matrix chosen from a random uniform distribution, arbitrarily constrained to (0, 1). 108 valid matrices were generated, which was considered to be sufficient to produce short rules that were robust to network structural uncertainty (see details in Section Identifying the rules of species’ responses).

A community matrix was considered valid if it described a system that met the following criteria: (1) local stability, (2) a negative response of cats to cat control, or (3) a negative response of rats to rat control. The local stability criterion assumed the system would return to equilibrium after small

54 perturbations. The control-species response criteria assumed that each control action had led to a population reduction of the species targeted.

Probabilistic approach

According to Raymond et al. (2011), after the parameter sweep, we aggregated the species’ responses to a particular perturbation across a set of network structures, and then presented and explained the outcomes probabilistically. This approach assumed that the effect of press perturbations applied to multiple species on a single species was simply the sum of the responses to press perturbations. For perturbations for two or more species, the total effect upon a focal species may be approximated by the sum of the individual effects (Eqn 15, Nakajima (1992)), however, each individual effect must be weighted by relative magnitude of the size of the perturbation for that species. In this instance, the relative size of the cat control versus the rat control effort was not known a priori, and hence these weightings were not known. Therefore, when considering the effect of press perturbations upon two or more species, if the signs of responses for all perturbed species was the same, then the sign of the effect for combined perturbations can be determined (i.e. all positive response give a combined positive response and all negative response give a combined negative response); however, if the signs of two or more responses differed, then the sign of the response to combined perturbations was not known.

To emphasize the utility of our approach we also used a similar method to Raymond et al. (2011) to aggregate species’ responses under cat control and cat/rat control scenarios. In this analysis, 106 stable, valid matrices were generated. For cat control, species respond positively or negatively to the perturbation of cats; while for cat and rat control, species response were positive (i.e. positive to both cat and rat control), negative (i.e. negative to both cat and rat control), or uncertain (positive to cat control and negative rat control or vice versa). We contrasted the analysis and interpretation of the outcomes from these two analysis approaches and showed that using Boolean analysis provides more informative results with less uncertainty.

Identifying the rules of species’ responses

To analyze the species’ responses to cat and rat control, a Boolean analysis involving a Boolean minimization was performed (Theuns 1994). Boolean analysis has its background in the sociology literature, to find logical statements describing the contingency relationships between binary- response data, for example, in the analysis of questionnaire data (Degenne and Lebeaux 1996). Boolean minimization is a method of finding the shortest statement describing the relationships, and 55 it is widely used to design digital circuits that are small and cost efficient (e.g. Mano and Kime (1997)). In this application, they were used to find simple implication rules describing relationships between the species’ responses to cat and rat control, for example, ‘if species A responds positively to cat control, then species B will respond negatively to rat control’. In contrast to the probabilistic Monte Carlo simulation approach above, where results are dependent upon the parameter-value sampling distribution, the results of Boolean analysis are deterministic. Furthermore, Boolean analysis can uncover contingency-relationships between species’ responses that cannot be obtained from considering their independent probabilities alone.

For n species, each with two possible population-size responses to a given pest control (‘positive’ or ‘negative’), there is a theoretical maximum of 2n combinations of responses possible. However, due to the structure of the network imposed and the dynamic and validation constraints, not all of these 2n response patterns can be produced by the model. This implies that certain rules exist constraining the outcomes of the model. Finding these rules is equivalent to performing a Boolean minimization upon the impossible response patterns (see Appendix 4.1 for detailed method). The minimization produces the ultimate canonical projections (UCPs), which are the minimal descriptions of these impossible responses, which can then be converted into implication rules describing the relationships between species' responses as above.

To implement the above, a parameter sweep was performed, with the magnitudes of non-zero entries of the community matrix chosen from the same distribution as in Raymond et al. (2011) — a random uniform distribution U (0, 1) — until 108 valid matrices were generated. It should be noted that this did not obtain all possible response patterns in the model (and indeed that can never be guaranteed using a parameter-sweep method). However, it was found that, 108 was sufficient to produce short rules that were robust to network structural uncertainty (see Section Parameter sweep and validation criteria for the community matrix). The Boolean minimization was performed using the Espresso algorithm in the PyEDA Python module (example code available at https://github.com/nadiahpk/qualitative-modelling/tree/master/scripts/townweb). It should be noted that this algorithm is heuristic and therefore the result is not guaranteed to be minimal, only close to minimal. Although this heuristic approach speeds up the process, it was not able to handle the full minimisation problem of the size that this study has.

First, the Boolean minimization was performed on the unobserved responses to cat control, and response rules were determined. This described the species’ responses to cat control alone. Next, we investigated the effects of cat and rat control together. As stated in Section Probabilistic approach, the effect of cat and rat control on a single species cannot be simply the sum of the responses to cat

56 control and rat control. Thus, when exploring the effects of both controls together, we performed Boolean minimization on the unobserved responses to cat control alone and rat control alone respectively and then explored response rules for combined cat and rat control based on the unobserved responses of the cat control alone and rat control alone together.

Results

Probabilistic approach

For the scenario of cat control alone, species showed two types of responses: positive (grey bar including solid and striped grey) and negative (black bar including solid and striped black) (Figure 4.2). Cat control alone led to more than 75% positive responses for the Brown Booby, the Christmas Island Flying-foxes and the Christmas Island Goshawk from the Forest Network. However, positive responses for other native species were less supported, leading to sizeable uncertainty in predicting native species responses.

Figure 4.2 Aggregated responses of native species to cat control alone and combined cat and rat control for the Forest network. For cat control alone, the grey bar (solid and striped) represents positive response and the black bar (solid and striped) represents negative responses. For combined cat and rat control, species showed four categories of responses: positive responses to both cat and rat control (solid grey); negative responses to both cat and rat control (solid black); positive responses to cat control and negative responses to rat control (striped grey); and negative responses to cat control and positive responses to rat control (striped black).

57

For the scenario of combined cat and rat control, species showed four categories of responses: positive responses to both cat control and rat control (solid grey); negative responses to both cat control and rat control (solid black); positive response to cat control and negative response to rat control (striped grey); and negative response to cat control and positive response to rat control (striped black) (Figure 4.2). The Brown Booby, the Christmas Island Flying-fox and the Christmas Island Goshawk had the highest proportion of positive responses to both cat control and rat control, around 40%-50% (solid grey); while the proportion of positive responses to combined cat and rat control for other species were less than 25%. Mixed responses to the combined treatment were common across all species, representing up to 75% (positive response to cat control and negative response to rat control (striped grey), and negative response to cat control and positive response to rat control (striped black)). For these mixed responses, the net response direction of the combined cat and rat control could not be determined using this approach. The high proportion of the mixed responses (striped bars, Figure 4.2) indicated that sizeable uncertainty existed, leading to difficulties in drawing informative inferences. Predicting species response in the Town network was subject to the same problem of common mixed responses and consequent large uncertainty in effects (see Figure 4.A.1 in Appendix 4.3).

Rules governing responses to cat and rat control

Independent effects of cat control

For both the Forest network and the Town network, the effects of cat control were contingent upon the effect that cat control had on the rat population. If cat control had a positive effect on the rat population (e.g. through predator release), then all combinations of species’ responses to cat control were possible in the model, making it difficult to predict the outcome. However if cat control had a negative effect on the rat population (e.g. if cats facilitate rats), then certain positive outcomes were guaranteed (Figure 4.3a and b). This contingency upon the effect of cat control on rats was robust to structural variation (Figure 4.A.2 in Appendix 4.4.1). The interpretation of the implication rules can be found in Box 4.1.

The implication rules showed that the Brown Booby could benefit directly from the control of cats if cat control had a negative effect upon the rat population (Figure 4.3a). The frugivorous birds and the flying-foxes showed the following pattern: ‘if the cat control causes a negative response in the rat population, then either frugivorous birds, or the flying-foxes, or both species, will benefit from cat control’ (Figure 4.3a). Two other pairs — the tropicbird and the goshawk, and the gecko and the

58 hawk-owl — showed the same type of pattern (Figure 4.3a). The longer rule involving goshawks, white-eyes, thrushes and feral chickens together also showed a similar pattern.

To investigate the combined effects of cat and rat control, we also performed Boolean minimization on the unobserved responses to rat control and response rules were determined, which can be found in Figure 4.A.3 in Appendix 4.4.2.

(a) Cat_-Rats

Cat_+Brown Boobies or or or or Cat_+Frugivorous birds Cat_+Hawk- Cat_+Tropicbirds owls Cat_+Flying Cat_+Geckos (W) -foxes

Cat_+Goshawks Cat_+White- Cat_+Chickens eyes Cat_+Thrushes

(b) Cat_-Rats

or or

Cat_+Flying- Cat_+Chickens foxes

Cat_+Tropicbirds Cat_+Goshawks (W)

Figure 4.3 Response rules for cat control alone of (a) the Forest network, and (b) the Town network. The bold font indicates the effects of cat/rat control on the population of the other target species; and the grey shade of a node indicates a negative effect from cat/rat control while an unshaded node indicates a positive effect from cat/rat control.

59

Box 4.1 Interpreting the implications diagrams

Cat_-Rats

Cat_+Brown Boobies

The first two figure of response rules in this box are parts from Figure 4.3a, which represents the response rules for rat control, are read as logical implications. For example, the directed edge from Cat_-Rats to Cat_+Brown Boobies can be read as saying:

Cat_-Rats → Cat_+Brown Boobies

‘If cat control has a negative effect on rats THEN cat control will have a positive effect on Brown Boobies.’

The bold font indicates the effects of cat/rat control on the rat/cat population; and the grey shade of a node indicates a negative effect from cat/rat control while an unshaded node indicates a positive effect from cat/rat control.

Cat_-Rats

or

Cat_+Goshawks

Cat_+Chickens Cat_+White-eyes Cat_+Thrushes

The ‘or’ node represents logical OR. For example, the edges flowing from Cat_-Rats through ‘or’ to Cat_+White-eyes, Cat_+Thrushes, Cat_+Goshawks, and Cat_+Chickens can be read as saying:

Cat_-Rats → Cat_+White-eyes ∨ Cat_+Thrushes ∨ Cat_+Goshawks ∨ Cat_+Chickens

60

Box 4.1 Interpreting the implications diagrams (continued)

‘If cat control has a negative effect on rats THEN cat control will have a positive effect on white-eyes OR cat control will have a positive effect on thrushes OR cat control will have a positive effect on goshawks OR cat control will have a positive effect on feral chickens.’

The logical OR is not an exclusive OR; in the above example, it is possible that cat control will have a positive effect on any combinations of two (e.g. white-eyes and thrushes, or thrushes and goshawks etc.), three or all of the four species.

Cat_+Brown Cat_-Flying- Cat_+Frugivorous Boobies foxes birds

&

or

Rat_+Frugivorous Rat_+Goshawks birds

The ‘&’ node represents logical AND . This figure of response rules are parts from Figure 4.4a, which can be read as saying:

Cat_+Brown Boobies ∧ Cat_-Flying-foxes ∧ Cat_+Frugivorous birds→ Rat_+Frugivorous birds ∨ Rat_+Goshawks

‘If cat control has a positive effect on Brown Boobies AND cat control has a positive effect on frugivorous birds AND cat control has a negative effect on flying-foxes, THEN rat control will have a positive effect on frugivorous birds, or goshawks, or both.’

61

Combined effects of cat control and rat control

Cat control has already been planned for Christmas Island, so a key question is whether or not supplementing cat control with rat control will lead to better outcomes. Of particular concern is the scenario in which cat control has a positive effect upon the rat population (e.g. through release of rats from cat predation), and so we focused upon that scenario here. We considered two consequences of controlling rats in the combined rat and cat control scenario where rats respond positively: (1) rat control has a negative effect on cats, so that rat control has both direct benefits through a reduction in rats and indirect benefits through consequent reductions in cats, and (2) rat control has a positive effect on cats, so that while rat control may have a direct benefit, it may also have an indirect cost if cat numbers increase (e.g. if cats and rats were competing for similar resources and cats experienced competitive release when rats are controlled). The results of alternative scenarios where cat control does not have a positive effect on rats can be found in Appendix 4.4.6 (Figure 4.A.7, Figure 4.A.8).

Scenario 1: Positive effect of cat control on rats, negative effect of rat control on cats

For both the Forest and the Town network, if rat control had a negative effect upon cats, all rules predicted a positive effect of rat control upon at least one species of concern, and this result was robust to structural variation (Figure 4.4a and b, Figure 4.A.4 – 4.A.6 in Appendix 4.4.3 – 4.4.5). The one exception were the rules involving feral chickens: one of the possible predictions of these rules was that only chickens showed a positive response while other species do not.

In the Forest network (Figure 4.4a), all rules up to length five predicted a positive effect of rat control upon at least one species of concern, and this result is robust to structural variation (Figure 4.A.4 in Appendix 4.4.3), with the exception of the feral chicken rule mentioned above. The Brown Booby would benefit directly from the control of rats if rat control had a negative effect upon the rat population (Figure 4.4a). The frugivorous birds and the flying-foxes showed another pattern: if rat control caused a negative response in the cat population, then either frugivorous birds, or flying- foxes, or both species, would benefit from rat control (Figure 4.4a). Two other pairs — the tropicbird and the goshawk, the gecko and the hawk-owl — showed the same type of pattern (Figure 4.4a). The rule involving the goshawk, white eye, island thrush and the feral chicken together also showed a similar pattern (Figure 4.4a). Longer rules can also be derived for the Forest web, which revealed more contingencies implying a negative effect of rat control. However, these rules were less robust to structural variation (Figure 4.A.5 in Appendix 4.4.4).

62

In the Town network, complementary rules existed for the effect of rat control upon the hawk owl contingent upon the effect of cat control upon the species (Figure 4.4b). This result was robust to both structural variation and the sign of the effect of rat control upon cats.

Scenario 2: Positive effect of cat control on rats, positive effect of rat control on cats

For both the Forest and the Town network, if rat control had a positive effect upon the cats, we found symmetric rules where the positive effects of cat control on certain species implied negative effects of rat control on the same species, and negative effects of cat control on those same species implied positive effects of rat control (Figure 4.5a and b). In the situation where rat control had a positive effect on cats, for the Forest network, the only rule of up to length six (and also seven) involved the response of Brown Boobies to cat control as a central contingency (Figure 4.5a). For example, there is a rule Cat_-Goshawks ∧ Cat_-Tropicbirds (W) ∧ Cat_+Brown Boobies → Rat_ +Goshawks ∨ Rat_+Tropicbirds (W) ∨ Rat_-Brown Boobies. These opposite responses of species to cat control and rat control indicated that under this scenario sizeable uncertainty exists. Depending on the species’ response to cat control (positive or negative), rat control had the potential to reduce the positive effects or ameliorate the negative effects of cat control, respectively. In the Town network, as discussed above, robust complementary rules existed for the effect of rat control upon the hawk-owl contingent upon the effect of cat control upon the same species (Figure 4.5b, Figure 4.A.6 in Appendix 4.4.5).

63

Cat_+Brown Cat_-Flying- Cat_+Frugivorous Cat_+Hawk- Cat_+Goshawks Cat_+Geckos (a) Boobies foxes birds owls

True & & & &

Rat_+Brown or or or or Boobies or or or

or Rat_+Tropicbirds Rat_+Frugivorous Rat_+Hawk- (W) Rat_+Goshawks Rat_+Chickens Rat_+Flying birds owls -foxes Rat_+White- Rat_-Hawk- Rat_+Thrushes owls eyes Rat_+Geckos

(b) True Cat_-Hawk- Cat_+Hawk- owls owls or or

Rat_+Flying- Rat_-Hawk- Rat_+Hawk- Rat_+Chickens owls foxes owls

Rat_+Goshawks Rat_+Tropicbird (B)

Figure 4.4 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for (a) the Forest network (rules up to length five); and (b) the Town network, all web variations (rules up to length nine). The ‘True’ node indicates the condition (i.e. the scenario ‘positive effect of cat control on rats, negative effect of rat control on cats’) on every implication. Specifically, the direct links from the ‘True’ node indicate that for these implication rules the ‘True’ node is the only condition to lead to the consequences. The grey shade of a node indicates a negative effect from cat/rat control while an unshaded node indicates a positive effect from cat/rat control. 64

(a)

Cat_-Flying- Cat_- Cat_- Cat_+Brown Cat_- Cat_-Hawk- Cat_-Geckos foxes Frugivorous Goshawks Boobies Tropicbirds (W) owls birds

& & & &

or or or or

Rat_+Flying Rat_+Frugivorous Rat_-Brown Rat_+Tropicbirds Rat_+Hawk- Rat_+Goshawks Rat_+Geckos -foxes birds Boobies (W) owls

Cat_+Flying- Cat_+Frugivorous Cat_-Brown Cat_+Tropicbirds Cat_+Hawk- Cat_+Goshawks Cat_+Geckos foxes birds Boobies (W) owls

& & & &

or or or or

Rat_- Rat_-Flying- Rat_- Rat_+Brown Rat_- Rat_-Hawk- Frugivorous Rat_-Geckos foxes Goshawks Boobies Tropicbirds (W) owls birds

(b) Cat_+Hawk-owls Cat_-Hawk-owls

Rat_-Hawk-owls Rat_+Hawk-owls

Figure 4.5 Response rules when cat control has a positive effect on rats and rat control has a positive effect on cats for (a) the Forest network (rules up to length seven); and (b) the Town network (rules up to length five). The scenario ‘positive effect of cat control on rats, positive effect of rat control on cats’ is the condition for every implication. The grey shade of a node indicates a negative effect from cat/rat control while an unshaded node indicates a positive effect from cat/rat control.

65

Discussion

We have developed a novel approach to qualitative modelling, which uses Boolean analysis to summarise the predicted outcomes of invasive species control in ecosystem models. In contrast to previous methods (i.e. Raymond et al. (2011), Melbourne-Thomas et al. (2012)), Boolean analysis produces deterministic and qualitative rather than probabilistic predictions. We have demonstrated this approach by applying it to the proposed programme of eradication of the feral cat on Christmas Island, and to the question of whether or not the programme should be supplemented by rat control. A Boolean analysis revealed that cat eradication without rat control is a risk prone strategy, but that the addition of rat control is expected to ameliorate at least some of the potential negative impacts of cat control alone.

A comparison between methods

Existing qualitative modelling methods (i.e. Raymond et al. (2011), Melbourne-Thomas et al. (2012)) interpret the probabilities of outcomes occurring in a model ensemble as predictive of their probability of occurring in the real system. However the outcome probabilities are dependent upon the distribution of interaction-strengths used to generate the ensemble (Kirk et al. 2015). Interaction strengths are typically taken as uniform distributions upon the non-zero elements of the community matrix, representing a non-informative prior (Melbourne-Thomas et al. 2012). In contrast, the Boolean analysis method uncovers species-response rules that always occur in the models, regardless of the strengths of interactions between species. Therefore these predictions are independent of any assumptions about the interaction-strength distribution, about the appropriate choice of a prior, and avoids the philosophical difficulties that choosing an objective prior presents (Norton 2008).

Using Boolean analysis to identify the interrelationships between species' responses has three advantages over probabilistic analysis. First, it can reveal meaningful associations between species responses that cannot be discerned from probabilistic methods alone. For example, in the Christmas Island model only four species out of the total nine had a positive/negative response to cat control in more than 75% of the models generated, suggesting highly uncertain outcomes. However the Boolean analysis nevertheless revealed that deterministic rules governed the species responses. Second, species' response relationships could be useful for monitoring to inform adaptive management. For example, if cat control is implemented alone, an obvious choice would be to monitor for an increase in rat population size. Third, species response rules may add to our understanding of the system by comparing the species that tend to respond together with the 66 structure of the web. For example, the Boolean analysis revealed species that occurred in the same rule often shared a resource. However which species displayed this tendency could not be predicted from examining the original network structure alone, and this is typical of dynamical systems, where complex feedback relationships make it difficult intuit how the system will respond.

Implication rules

The rule involving goshawks, white-eyes, thrushes and feral chickens that were connected by the logical disjunction ‘or’ showed a special case. It was noticeable that one of the implications of this rule was that only feral chickens had a positive response while other native species (goshawks, white-eyes, thrushes) responded negatively. In this case, there was a potential risk that none of the native species in this rule would benefit from rat control. However, the positive responses of the goshawk have been suggested by another short rule involving the White-tailed Tropicbirds. This rule reduced the uncertainty of the positive response of the goshawk. These findings suggested that there were larger uncertainties about the positive response of the white-eye and the thrush. In comparison, the simple rules involving brown boobies responding to rat control showed little uncertainty.

When making inferences and interpreting the rules, there were several important points to consider. First, there were multiple possible interpretations of one rule involving several species connected by the logical disjunction. Second, most inference from the rules cannot distinguish between true ecological interactions and other non-random processes due to the rule length restrictions. Most importantly, since we used a qualitative approach, species responses were predicted as ‘positive’ or ‘negative’ without specifying the magnitude of the change and, therefore, the rules and the rule- based inference were also qualitative. While this approach limits our ability to distinguish the magnitude of changes in species populations, in reality, well-defined ecological understanding for parameterizing networks is rare and in most cases a description of the connections between species maybe the best we can achieve. Christmas Island is no exception with a significant knowledge gap existing in our understanding of the ecosystem. We have shown that this approach can still provide relatively robust implications for such a complex ecosystem, even with limited understanding.

The implication rules can provide insights into monitoring after control and, in turn, the information gained from monitoring can help to deduce other likely outcomes from implication rules. The idea of monitoring can be useful if there is a plan to implement cat control alone. The outcomes of this action can then be monitored before deciding whether or not to supplement it with rat control. For instance, the rules involving hawk-owls in the Town network suggest that if we observe that cat 67 control leads to an increase of the hawk-owl population, then rat control effort should be carefully applied as it is likely to have negative impacts. Vice versa, if the hawk-owl population is seen to decrease, then additional rat control is likely to lead to a positive effect to ameliorate the negative impacts of cat control.

However, when applying the rules to assist monitoring there are several limitations. First, most rules are scenario-dependent, which makes it difficult to provide robust, universal suggestions from a practical perspective. Second, the implication rules are qualitative without specifying the magnitude of species’ responses, which means that the population changes indicated by the rules may or may not be detected in reality. Further, the sensitivity of detection techniques can also hinder monitoring outcomes. In addition, when the strategy is controlling cats and rats together, in a real-world situation the detected changes would be the combined effects of cat and rat control. In such a case, which controls (cat control, or rat control, or both controls) contribute to the observable positive/negative effect would be unknown. Thus, the implication rules have limited power for informing monitoring practices.

Beyond the model: integrating model outcomes with ecological characteristics

It is considered likely that rat control would have a negative effect on cats. In this scenario rat control can have a mitigating effect reducing the potential negative impacts of cat control and subsequent positive changes in rats. Simultaneous management is often suggested for ecosystems where both invasive top predators and meso-predators are present, considering the potential risk of meso-predator release effects. However, the meso-predator release effects are identified mostly by theoretical studies and there are only a few field-based studies testing and providing empirical evidence for the theory (Rayner et al. 2007, Bergstrom et al. 2009). Field research has found that the behavioral ecology, life span, and age structure of a species will largely affect its response to cat and rat predation (Caut et al. 2009, Ringler et al. 2015). Some seabird species have long life spans and low productive rates, which are sensitive to cat predation on adults but less sensitive to rat predation on eggs and chicks. Thus, the negative effects of the increased meso-predator population are likely to be compensated for by the positive outcomes of top-predator control. In particular, when rat control efforts are prioritized, species with small populations and whose populations are more sensitive to rat predation (e.g. in contrast to some seabird species that are less sensitive to rat predation on eggs and chicks) should be prioritized with rat control efforts. On the other hand, as stated above all interpretations are qualitative, and these predictions do not necessarily imply a strong or detectable population shift. Under the most likely scenario, the extent of the ameliorating

68 effects of rat control is unknown, which may either largely compensate for the potential perverse outcomes of cat control or have small contributions that are not sufficient to lead to a population shift.

Prediction of the magnitude of population shifts following control is difficult since the strength of the interactions for most of the native species on the island with cats is largely unknown. A few studies have been done to look at potential interactions between Red-tailed Tropicbirds and cats. These studies showed almost complete chick mortality due to cat predation but a dramatic increase in nestling success after cat control (Algar et al. 2011, Algar et al. 2014, Sommerfeld et al. 2015). This information provided support for our implication rules which suggested that tropicbirds were very likely to benefit from cat control. However, such studies are still rare for most of the species on Christmas Island and there is little empirical evidence to provide support for the inferences of our implication rules. Furthermore, interactions strengths are not static. The ability of species to adapt their diet when faced with changes in food availability will also strongly affect the species performance during management. For example, Christmas Island Goshawks prey on a variety of species including white-eyes, frugivorous birds, tropicbirds, reptiles, rats, feral chickens and invertebrates (Algar et al. 2011). There is a concern that rat suppression may have negative effect on goshawks, however, to what extent is uncertain given rats may only make up a small component of their total prey consumption, and/or the goshawk may possess good adaptability and flexibility in cope with the change of food availability.

When informing decision-making, the implications of the rules need to be incorporated with the population status of species of conservation concern. For example, given the small population of the goshawk, the tolerance level of a negative response of the goshawk may be lower than for species with large populations. We found that under the most likely scenario where there was a positive effect of cat control on rats, and a negative effect of rat control on cats, the implication rule about white-eyes or thrushes showing a positive response to rat control were less robust than the rules involving other native species. On the other hand, the Christmas Island Thrush and the Christmas Island White-eye are considered still to be abundant in the field, with the populations estimated to be up to 15,000 and 20,000 breeding birds, respectively (Beeton et al. 2010). Therefore, when combining both field information and our model predictions, it is possible to conclude that these species are able to persist in the current situation or that their populations change could be minor to be monitored.

In addition, it needs to be kept in mind that given the presence of other threats, the species may not show the preferred population shift even with cat and rat control. The Christmas Island Flying-fox

69 and the Christmas Island Giant Gecko are the typical examples. Predation by cats on flying-foxes and giant geckos has been recorded (Beeton et al. 2010, Algar et al. 2011). However, besides cat predation, there are many other explanations for the decline of these species such as habitat degradation, pollution, and disease (Beeton et al. 2010, Martin et al. 2012b). The geckos may also suffer from predation by other invasive species such as the Asian wolf snake and the giant centipede (Threatened Species Scientific Committee 2014b).It remains unclear which threats are the leading ones, therefore, other management actions may be need to protect these species.

Conclusion

Island ecosystems are often poorly understood, and there are large uncertainties in predicting the responses of target invasive species and the systems we aim to protect to management intervention. Our study has shown that relatively robust conclusions can be derived to inform management actions by combining qualitative analyses and Boolean minimization, without having detailed information on species and their interactions. The implication rules suggest that cat eradication without rat control is a risk prone management strategy and that the addition of rat control may reduce this risk. Given limited resources for rat control activities it may be prudent to implement an island-wide prioritization of rat control efforts in habitats of native species of high concern and value on the island to minimize the potential negative impacts of the cat eradication program.

70

Chapter 5 A structured framework to facilitate decision-making in management of non-native species in Antarctica

Introduction

Antarctica, regarded as the Earth's last pristine continent, is facing substantial environmental threats due to climate change and anthropogenic influences (Tin et al. 2009, Chown et al. 2012b, Bennett et al. 2015). Non-native invasive species are recognised as one of the major threats to Antarctica, as they may cause declines in native species populations and change native ecosystem structure and functioning (Ehrenfeld 2003, Gurevitch and Padilla 2004, Didham et al. 2005, Thatje and Aronson 2009). The Antarctic Treaty System attempts to prevent the introduction of non-native species to ensure the protection of Antarctic ecosystems and biodiversity, however, several non-native species are already established in Antarctica (Olech 1996, Frenot et al. 2005, Hughes and Worland 2010). Given the rapid increases in human activities and accelerating climate change, non-native species incursions are likely to increase and those which are already established could potentially expand in range (Chown et al. 2012a). The invasion risk is particularly high in the Western Antarctic Peninsula, where the rate of warming is more than four times faster than the global average and the rate of human visitation is highest (Casper 2010, Chown et al. 2012a). Given the substantial damage to native ecosystems associated with invasive species of the sub-Antarctic islands (Frenot et al. 2005), the Antarctic Treaty acknowledges the importance of the threats from non-native species and calls for management actions on existing non-native species introduced to Antarctica to prevent further impacts.

The Protocol on Environmental Protection to the Antarctic Treaty provides legislation relating to non-native species issues (Antarctic Treaty Consultative Parties (ATCP) 1991). Annex II (Conservation of fauna and flora), Article 4 [4] of the Protocol states that “any other plant or animal introduced into the Antarctic Treaty area not native to that area, including any progeny, shall be removed or disposed of, by incineration or by equally effective means, so as to be rendered sterile, unless it is determined that they pose no risk to native flora or fauna”. The Committee for Environmental Protection (CEP), responsible for advising Antarctic Treaty Parties on conservation measures and implementation, has produced non-mandatory guidelines to address the issue of non- native species (Committee for Environmental Protection 2011). The CEP Non-native Species Manual (Committee for Environmental Protection 2011) emphasizes the need to prevent new 71 introductions of non-native species to Antarctica and highlights the importance of quick responses to introductions and the necessity of assessing the “feasibility and desirability of eradicating non- native species”. Rapid response is important given that in the early stages of a colonisation event, eradication of small populations is more achievable and the costs are much lower (Committee for Environmental Protection 2011, Hughes and Convey 2014). When eradication is not considered feasible or desirable, management actions should focus on control or containment to reduce the potential risks (Committee for Environmental Protection 2011). The Environmental Protocol does not provide a clear policy, nor does the CEP provide practical guidelines, regarding how to assess the feasibility and desirability of eradication and how to implement follow-up management. However, CEP has identified the development of a rapid response guideline with possible guidance on “practical eradication tools/means” as a further task (Committee for Environmental Protection 2011).

Although lacking formal guidance on eradicating non-native species, to date there have been several plant eradications undertaken in the Antarctic Peninsula region (McGeoch et al. 2015). All of these were in situations where there were only one or two individuals or a very small population, or a small patch, with no evidence of viable seedbanks (Hughes et al. 2015), thus the footprint of management was likely to have been minimal. However, eradication of non-native species with a relatively high distributional extent may lead to disturbance of native ecosystems. Due to the fragility, structure and biology of Antarctic ecosystems, typical strategies for invasive species management used elsewhere may cause substantial collateral damage to native Antarctic biota and habitats (Tin et al. 2009, Convey 2011). For instance, slow-growing native plant species with small populations can be very susceptible to standard management techniques such as mechanical removal (e.g. digging); while chemical control can potentially contaminate the entire habitat and stunt the growth of native species (Rinella et al. 2009). Physical disturbance such as vehicle wheel tracks, footpaths and ground equipment can all affect Antarctic lichen and moss and have long lasting effects (Campbell et al. 1998, O'Neill et al. 2012). Where management itself is likely to have negative direct and/or indirect effects on the ecosystem, decisions to manage non-native species are complicated by the Protocol which legislates for the protection of native species and habitats.

Besides the conflicts between non-native species management and conservation, decision-making challenges also arise from predicting management consequences. Outcomes of management can be hard to estimate due to particularly limited understanding of Antarctic ecosystems, which hinders our ability to evaluate and select appropriate management actions from the options available. For Antarctica, not only do we have limited knowledge about how native systems will respond to

72 management, but in many instances we do not even know which species will be affected and what the broader distributions and population sizes of these species are.

In developing guidelines as proposed by CEP to assess the feasibility and desirability of eradication and to devise practical tools for management, these challenges must be addressed. In this study, we demonstrated a structured framework to facilitate decision-making, bringing a step closer CEP’s vision to develop guidelines to deal with non-native species in Antarctica. The framework is based on a Structured Decision Making (SDM) process, which allows for systematic identification and evaluation of the management options available and enables better decisions to be made in complex situations (Gregory and Long 2009). By incorporating a set of decision-making tools into the SDM process, this framework can provide a defensible and transparent method to inform decision- making. We used a case study of potential eradication of the non-native plant, Poa annua, on the Antarctic Peninsula to illustrate the utility and application of this decision framework in Antarctica.

A structured framework – a case study of Poa annua

Case study species

Poa annua, was first found at Arctowski Station on King George Island in 1985/1986. This is the largest known patch of P. annua in Antarctica, although other small patches or individual plants have been recorded elsewhere. It is now spreading from the Arctowski station area and growing together with native plant species in the surrounding landscape and has also colonized rapidly deglaciating areas nearby (Olech 1996, Chwedorzewska 2008, Olech and Chwedorzewska 2011).

There are potential risks associated with eradicating P. annua such as disturbing local ecosystems and precipitating local extirpation of native species with a low recovery potential. These risks are highly undesirable and contrary to the Protocol on Environmental Protection to the Antarctic Treaty (Hughes and Convey 2014). The areas where P. annua currently occurs within the Antarctic Peninsula coincide with those predicted to have the highest invasion risk (Chown et al. 2012a). It remains unclear, however, whether the non-native P. annua has become invasive according to the definition provided in the CEP Non-native Species Manual. Experimental research showed that P. annua could outcompete the only two flowering native plants in Antarctica, Deschampsia antarctica and Colobanthus quitensis. Therefore, it is quite likely that P. annua will have negative impacts on native species and ecosystems as its distribution expands (Molina-Montenegro et al. 2012). Despite being present since the mid-1980s, and despite considerable research being undertaken (Olech 1996, Chwedorzewska 2008, Rodionov et al. 2010, Olech and Chwedorzewska

73

2011, Chwedorzewska and Bednarek 2012, Molina-Montenegro et al. 2012, Wodkiewicz et al. 2013, Molina-Montenegro et al. 2014, Wódkiewicz et al. 2014), there have been no reports of any attempt to eradicate the species at the site.

Framework overview

As the essence of the framework, the SDM process breaks down a decision process into five key steps: (1) articulating management objectives and selection criteria, (2) identifying management alternatives, (3) estimating consequences, (4) evaluating trade-offs and selecting preferred alternatives, and (5) implementing, monitoring and learning (Gregory and Long 2009, Gregory et al. 2012a). The first two steps including identifying management objectives and alternatives were implemented through two elicitation workshops. We then developed a Bayesian Network (BN) model to estimate consequences of a subset of management alternatives that were identified in step two. This subset of management alternatives was assessed by evaluating trade-offs. The steps of estimating consequences and evaluating trade-offs in this study were illustrated rather than fully implemented given time and funding constraints. To inform decision-making through a formal SDM process, further efforts were required such as estimating the consequences of all management alternatives. The final step focused on learning. We carried out a sensitivity analysis of the Bayesian Networks, and identified and discussed future research and available methods/tools. Figure 5.1 shows the structured framework and summarises what we have achieved for each step.

Objectives and evaluation criteria

In 2014, we initiated and held two workshops (‘eradication’ workshops) with eight stakeholders and experts in non-native species management and terrestrial ecology in Antarctica to identify objectives and management alternatives. The eight stakeholders and experts were: four university research academics, two senior scientists employed by government-funded research facilities, an Antarctic environmental policy government employee and a government-employed environmental officer. The eight were familiar with working in the Australian, Chilean, United Kingdom and South African national Antarctic programs and their associated environmental management. Six had previously been involved with eradication of non-native species in the terrestrial Antarctic region.

An explicit statement of objectives is important for SDM. Based on the Antarctic Treaty System, which governs environmental protection of Antarctica, experts and stakeholders identified three objectives for eradicating non-native species (including P. annua) in Antarctica: (1) to protect the intrinsic natural values of Antarctica, (2) to protect the scientific values of Antarctica, and (3) to

74 protect the wildness and aesthetic values of Antarctica. Evaluation criteria, also called performance measures, are used to evaluate the success of different alternatives in meeting each of the objectives. Experts identified a range of potential criteria through measurable attributes. For instance, one measurable attribute for the objective ‘protect intrinsic natural values’ was ‘species diversity’, and the evaluation criteria was ‘accepted maximum extent of species loss’ (Figure 5.2).

Value-of-information analysis 5. Learn and 1. Define monitor objectives Workshops with stakeholders and experts Multi-criteria Structured decision analysis Decision 4. Evaluate Making 2. Develop trade-offs alternatives

3. Estimate consequences

Illustrated Bayesian Network on a selection of alternatives for management of P. annua

Figure 5.1 Outline of the structured framework. The inner circle outlines the key steps of Structured Decision Making process. The outer circle outlines the tools and methods used for each step and indicates what has been achieved for each step in this study.

Due to a severe lack of information (see Chown et al. (2012b), Terauds et al. (2012a)), the experts found it challenging to assign measurable values for certain criteria such as nutrient fluxes and decomposition rates for ecological processes (dashed line boxes in Figure 5.2). Therefore, we only used two criteria for further evaluation in our BN model: accepted maximum extent of species loss and extent and density of P. annua (Figure 5.2). Units of measurement for each criterion were developed with the BN model (see section Estimation of consequences – a Bayesian Network approach).

75

Objectives Protect intrinsic Protect scientific Protect wildness and natural values values aesthetic values

Measurable attributes Species Habitat Ecological Presence of non-native diversity quality process species (P. annua)

Physical Biological Nutrient Decomposition properties activities fluxes rates

Evaluation criteria Accepted maximum Extent and density extent of species loss of P. annua

Figure 5.2 Objectives, measurable attributes and evaluation criteria for eradication of P. annua in the Antarctic Peninsula. Dashed lines and dashed line boxes indicate evaluation criteria that were discussed but not used in this study because of the difficulties of assigning measurable values at the current time. These criteria can be applied when there is more information available which allows measurable values to be assigned.

Management alternatives

Experts identified a range of management alternatives (Table 5.1), from which we selected five for illustration in the BN model evaluation including (1) do nothing, (2) herbicide, (3) herbicide with digging, (4) bulldozing, and (5) steaming. For formal decision-making, all alternatives need to be evaluated with the BN model once parameterized, as required by the SDM process.

Estimation of consequences – a Bayesian Network approach

Estimating the consequences of management requires a holistic view to assess the responses of both target non-native species and native species and ecosystems to management actions, for which the BN is a useful predictive tool. BNs are used increasingly in ecology and conservation to support decision making, such as natural resource management, projecting species distribution and status, and selecting suitable sites for translocation (Amstrup et al. 2008, Laws and Kesler 2012, Martin et al. 2015). BNs are probabilistic graphical models, representing cause and effect relationships between a set of variables (Marcot et al. 2001, McCann et al. 2006). In this case, BNs are used to describe how we think non-native species affect native biota and habitats, and how management alternatives influence the target non-native species, as well as the native biota. The major advantage

76 of BNs is their ability to explicitly represent uncertainties, and encapsulate expert knowledge and empirical data in one model (Cain 2001, Marcot et al. 2001, Marcot et al. 2006b).

Table 5.1 Potential management alternatives for eradication of P. annua in the Antarctic Peninsula. ‘*’ indicates alternatives that were selected for estimation of consequences using the BN model.

Management alternatives Description

* Do nothing No management action

* Herbicide Applying herbicide directly to above ground plants

Digging Digging up the plants

* Herbicide with digging Digging up the plants and applying herbicide

* Bulldozing Using bulldozers to remove the aboveground plants and shallow surrounding soil, thereby removing seedbank

* Steaming Using steam machine or hot water to scald aboveground plants

Shading/solar blanket Installation of solar blanket to block light, limiting photosynthesis and consequently kill plants

Burning Using fire to suppress target plants

Rehabilitation A series of rehabilitation methods such as manipulating nutrients, restoration plantings etc.

Biological control Releasing biological control agents

BN model overview

The BN model was constructed based on the information elicited from experts – two evaluation criteria (accepted maximum extent of species loss and extent and density of P. annua) and five management alternatives (do nothing, herbicide, herbicide with digging, bulldozing, and steaming). The reason that we selected and assessed only a subset of management alternatives is that we had to control the number of questions for the expert to answer in the process of model parameterisation, as both time and resources were limited for this work.

77

An initial BN was developed based on the information obtained from the eradication workshops, followed by an iterative process of revising the initial BN including adjusting network structure (i.e. nodes and links) and defining the states of each node during a workshop (the ‘BN’ workshop) with four experts specialised in ecology and non-native species biology and management. After reaching a consensus of BN structure and the states for nodes, the BN was parameterized by assigning conditional probabilities (which define probabilistic dependencies between nodes) and values for input nodes by one expert through a series of email elicitations. We developed the BN model using the Netica 5.15 (Norsys Software Corp. 2014).

To date P. annua has been recorded at several sites in the Antarctic Peninsula (Molina-Montenegro et al. 2014). The density and extent of P. annua populations varies across these sites. In this study we used eradication of the P. annua population around Arctowski Station (the largest population) as an example, to assess the effects of management alternatives. Thus, the values of input nodes were estimated for the P. annua population around Arctowski Station. This BN model is applicable to all other P. annua populations if the values of input nodes for each specific population are assigned.

Model construction and parameterisation

In a BN model, if node A has links going to node B (i.e. node A has an influence on node B), node A is called a parent node of node B and node B is called a child node of node A. Each child node has a conditional probability table (CPT) (e.g. Table 5.A.2 – 6 in Appendix 5.1), which describes the probability that a node will be in a certain state given the states of the child node’s parent nodes.

In general, our BN model consisted of three types of nodes: input nodes, intermediate nodes, and output nodes. Input nodes were parentless nodes, and intermediate nodes and output nodes were conditional upon their parent nodes. Input nodes consisted of the initial state of P. annua (density and extent), the natural habitat occupied by P. annua and therefore the area to be managed, and a selection of management alternatives (Table 5.2, Figure 5.3). The only intermediate node in this model summarised the habitat recovery potential after P. annua colonisation and P. annua management (Table 5.2, Figure 5.3). The output nodes represented management consequences, which were expressed as the status of P. annua after management, and the combined effects of P. annua colonisation and P. annua management on three native groups within the ecosystems (plants, invertebrates and microbes), corresponding to the evaluation criteria (Table 5.2, Figure 5.3).

78

Figure 5.3 The Bayesian Network model for predicting management consequences including P. annua status after management (node ‘Poa management outcome’) and the responses of native taxa (‘loss of native plants’, ‘loss of microbes’ and ‘loss of invertebrates’) for the P. annua population around Arctowski station under five management alternatives. Shown here is the model set to calculate outcome probabilities for the ‘do nothing’ management alternative.

The input node ‘management’ contained the five management alternatives. Input nodes including ‘Poa coverage’, ‘Poa extent’, and ‘natural habitat to be managed’ were parameterized for the P. annua population around Arctowski Station (Table 5.A.1 in Appendix 5.1). ‘Poa coverage’ and ‘Poa extent’ together described the initial state of the P. annua population before taking any management actions. At Arctowski station P. annua initially colonised human disturbed areas, where management was likely to have little impact on native species and natural habitat within those areas (Olech 1996, Chwedorzewska and Bednarek 2012). However, this population has already expanded into adjacent natural habitat as mentioned during the eradication workshop. Thus, management of P. annua may potentially negatively affect native species and habitat. In such situations, the area of natural habitat with P. annua colonisation that required management was expressed as ‘natural habitat to be managed’.

The intermediate node ‘habitat recovery potential’ described the potential for habitat to recover towards good habitat quality following management; i.e. the potential for native plant re- colonisation, supporting healthy microbial populations, higher nutrient turnover rates, and the return 79 of ecosystem processes (Table 5.2). The ‘Poa coverage’ and a particular ‘management alternative’ determined management intensity, which consequently determined the degree of disturbance on the habitat. On the other hand, the ‘Poa management outcome’ (colonisation status of P. annua after management) reflected the habitat suitability for other native taxa to re-colonise. Thus, ‘Poa coverage’ ‘management alternatives’ and ‘Poa management outcome’ together determined the ‘habitat recovery potential’ (Table 5.2, Figure 5.3).

The output nodes reflected management consequences on the target species and native taxa, corresponding to the evaluation criteria. ‘Poa management outcome’ indicated future status of P. annua, which was determined by ‘Poa coverage’, ‘Poa extent’ and ‘management alternatives’ (Table 5.2, Figure 5.3). This node took both existing population and potential viable seedbank into account. The future P. annua state after management was defined as when its population attained a new stability point rather than an immediate/transient status. For instance, after herbicide application, the visible population may have been extirpated, yet there could still be a large viable seedbank, leading to recolonization of P. annua and subsequently a large stable population.

Output nodes including ‘loss of native plant’, ‘loss of invertebrates’ and ‘loss of microbes’ indicated the extent to what the native taxa were affected, determined by three parent nodes including ‘natural habitat to be managed’, ‘habitat recovery potential’ and ‘management’ (Table 5.2, Figure 5.3). ‘Natural habitat to be managed’ indicated the extent to which native taxa were affected. ‘Habitat recovery potential’ determined the recolonization potential of native taxa. ‘Management alternatives’ indicated the direct effects of a particular management alternative on native taxa (e.g. direct mortality of native species caused by herbicide). We used the same criteria to determine whether the loss of native plants/ invertebrates was acceptable (Table 5.2), thus the outcomes of these two nodes were exactly the same. Experts found it was difficult to define to what extent the loss of microbes was ‘acceptable’ given the lack of information for these taxa in Antarctica (see Cowan et al. (2011)). Therefore, in the current BN model, we determined that any loss of microbes would be acceptable (Table 5.2). Although we kept this ‘loss of microbes’ in model construction, the analysis of outcomes did not consider this node since for all management alternatives the outcomes of this node would be always ‘acceptable’.

Conditional probability tables of intermediate and output nodes were developed through ‘elicited probability tables’ (EPTs) elicited from an expert (Cain 2001). EPTs are designed to reduce the number of probabilities elicited from experts, and full CPTs are calculated from corresponding EPTs. We followed the instructions as described by Cain (2001) to construct EPTs and to complete CPTs. In the process of eliciting information for input nodes and EPTs, we used a 3-point question

80 format to ask the expert to give lowest guess, highest guess and best guess for each probability (Speirs-Bridge et al. 2010). For each probability, the values of the best guesses were entered in the corresponding EPTs. The estimates of input nodes and the CPTs are provided in Table 5.A.1 – 6 in Appendix 5.1.

Model validation

Informal tests of model behaviour were performed during the model construction process (Amstrup et al. 2008). By applying different scenarios (i.e. different combinations of inputs) for parts or the whole model, we examined whether the probabilities of the intermediate nodes or output nodes were realistic. If unrealistic, one should consider to adjust CPTs or refine the nodes and states (Marcot 2001).

Our model was also evaluated using sensitivity analysis to identify the degree and rank of influence of input variables on each output variable in the model (Marcot et al. 2006b, Marcot 2012). Sensitivity analysis is a useful tool for verifying model structure and parameterisation in the model construction process (Marcot et al. 2006a, Kjærulff and Madsen 2013b); and those influential variables also indicate priority risks or identify knowledge gaps, providing guidance for further learning process such as expert elicitation and data collection (Pollino et al. 2007).

We calculated the sensitivity of each management outcome to the four input nodes using entropy reduction in the modelling shell Netica® (see Marcot (2012) for method and equation). Entropy reduction calculated the degree to which the input conditions (e.g. ‘management alternatives’, ‘Poa coverage’ and ‘Poa extent’) influenced the ‘Poa management outcome’. A higher value indicated a higher sensitivity of ‘Poa management outcome’ to the corresponding input condition. In the sensitivity analysis of this BN model, the prior probabilities of all input nodes were set to a uniform distribution (Marcot et al. 2001), which reflected the inherent conditional probability structure of the model rather than measuring sensitivity of output nodes to particular input conditions (Jay et al. 2011).

81

Table 5.2 Definition of nodes and states used in the Bayesian Network model of P. annua management.

Node Description States

Management Potential management Do nothing options. Herbicide Herbicide and digging Bulldozing Steaming

Poa coverage The percentage of the Low – low coverage (< 20%) area occupied by P. Medium – moderate coverage (20% – 50%) annua per square meter, High – high coverage (>50%) reflecting the density of P. annua

Poa extent The extent of P. annua Low – extent of P. annua at low level (< 10 m2) Medium – extent of P. annua at moderate level (10 m2 – 100 m2) High – extent of P. annua at high level (> 100 m2)

Natural habitat to be Total area of natural Low – low level of total area of natural managed habitat that is occupied habitat being managed (< 50 m2) by P. annua and is Medium – moderate level of total area of subjected to management natural habitat being managed (50 m2 – 100 m2) High – high level of total area of natural habitat being managed (> 100 m2)

Habitat recovery The potential of habitat Poor – low potential to recover from potential to recover towards good management disturbance habitat quality High – high potential to recover from management disturbance

82

Table 5.2 continued

Node Description States

Poa management Future status of P. annua No – No presence of P. annua outcome after management Low – presences of P. annua with low coverage (< 20%) within low extent (< 10 m2) High – presence of P. annua with higher coverage (>20%) within any extent, or any coverage level higher than 20% within 10 m2

Loss of native plants The extent of loss of Acceptable – permanent loss within an area native plant species less than 50 m2, or transitory loss within any extent where the plants are likely to recover in the near future Unacceptable: permanent loss within an area over 50 m2

Loss of invertebrates The extent of loss of Acceptable –– permanent loss within an area native invertebrates less than 50 m2, or transitory loss within any extent where the invertebrates are likely to recover in the near future Unacceptable – permanent loss within an area over 50 m2

Loss of microbes The extent of loss of Acceptable – all acceptable native microbes Unacceptable

83

BN model outcomes

The utility of the BN model was illustrated by running the BN to estimate the consequences of the subset of management alternatives. Estimation of consequences for all alternatives is required for the formal decision-making.

If there was no management intervention for P. annua, the probability of P. annua being present (‘Poa management outcome’) was high (81%) and the probability of P. annua presence was the highest for the ‘do nothing’ management out of the five alternatives (Figure 5.4a). The option of no management also yielded the second highest probabilities of unacceptable ‘loss of native plants’ and ‘loss of invertebrates’ (39%) (Figure 5.4b).

Although ‘bulldozing’ had the lowest probability of there being a high presence of P.annua and the highest probability of no presence of P. annua (Figure 5.4a), this management alternative resulted in the highest probabilities of unacceptable loss of native plants and invertebrates (77%) (Figure 5.4b), a significant trade-off.

‘Herbicide with digging’ had the second lowest probability of high P. annua presence (19%) and the second highest probability of no presence of P. annua (47%) (Figure 5.4a), and also led to the lowest probabilities of unacceptable loss of native plants and invertebrates (31%) (Figure 5.4b).

Management alternatives ‘herbicide’, and ‘steaming’ led to similar low levels of probability for no presence of P. annua (18% for herbicide and 20% for steaming) (Figure 5.4a), and similar high levels of unacceptable loss of native plants and invertebrates (40% for herbicide and 38% for steaming) (Figure 5.4b).

Model sensitivity

The outcomes of sensitivity analysis suggested that ‘Poa management outcome’ was most sensitive to the management alternatives, followed by the extent of P. annua and the percentage of coverage of P. annua (Table 5.3). This result coincided with our expectation that effectiveness of management alternatives strongly influenced the status of P. annua after management. The extent of P. annua (‘Poa extent’) was the second most important factor as, given a greater extent of P. annua, there was a lower chance for successfully reducing its population.

84

(a) 100% High 80% Low No 60%

40% Probability 20%

0%

(b) 100% Unacceptable 80% Acceptable 60%

40% Probability 20%

0%

Figure 5.4 Probabilities of (a) high, low and no presence of P. annua as a result of alternative management actions for the ‘Poa management outcome’, and (b) unacceptable and acceptable ‘loss of native plants’/ ‘loss of invertebrates’ for five management alternatives.

The native taxa outcomes (‘loss of native plants’/ ‘loss of invertebrates’) were most sensitive to the area of natural habitat, followed by management alternatives, while ‘Poa coverage’ and ‘Poa extent’ had very limited influences (Table 5.3). This is understandable as the native species were only present in natural habitats. ‘Management alternatives’ was the second most important variable influencing the state of native taxa following management, given different management alternatives had different intensities and impacts on native species. ‘Poa coverage’ and ‘Poa extent’ were two general measurements for P. annua status for both natural and artificial habitat (e.g. stations), and were therefore less influential. These findings verified the structure and parameterisation of our BN model. 85

Table 5.3 Sensitivity analysis of BN model. Entropy reduction indicated the degree to which the ‘Poa management outcomes’ or ‘loss of native plants’/ ‘loss of invertebrates’ was sensitive to each input nodes of the model, listed in decreasing order.

Poa management outcome Loss of native plants/ Loss of invertebrates

Node name Entropy Node name Entropy reduction reduction

Management alternatives 0.17300 Natural habitat to be managed 0.29500

Poa extent (m2) 0.03290 Management alternatives 0.03200

Poa coverage (%) 0.00569 Poa coverage (%) 0.00047

Natural habitat to be managed 0 Poa extent (m2) 0.00045

Evaluation of trade-offs

Evaluating BN outcomes

The BN provided a useful tool for estimation of consequences, and these outcomes can be further evaluated to determine the feasibility and desirability of potential management options. Here, we examined the subset management options for P. annua.

Currently there is no formal management project for P. annua around Arctowski station on the Antarctic Peninsula. Our models showed that the ‘do nothing’ approach resulted in the overall worst outcome in comparison with all other management options we considered, with a high probability of greater population level of P. annua and a relatively high risk of species loss. Given the Antarctic Treaty System calls for removing any non-native species introduced to Antarctica to protect native biota and habitat, if control and/or eradication attempts of P. annua are absent, P. annua is likely to pose higher impacts on native ecosystems in the future, which highlights the importance of undertaking P. annua management.

‘Herbicide with digging’ yield the best overall outcome among the five alternatives, as it had the least impact and was the second most effective following ‘bulldozing’. ‘Bulldozing’ yielded the best outcome for P. annua management. However, this management action was destructive to native habitats, causing high disturbances to biotic and abiotic processes, which led to an extremely high probability of unacceptable species loss. Therefore, if the area of natural habitat to be managed

86 is high, bulldozing is not a desirable option for P. annua management given the high level of collateral damage on native species. ‘Herbicide’ application alone was less favourable than the combined approach (‘herbicide with digging’) as localised digging can be effective to extirpate root systems and seed bank. We originally considered ‘steaming’ was likely to be a superior management option as it would cause the least collateral damage to native habitat and species. However, when the extent of P. annua is relatively large, this management option can be very time consuming and resource-heavy, and less effective at eradicating all above ground individuals and the seed banks in the area. Therefore, the higher potential of presence of P. annua might still present higher negative impacts on native species.

As emphasized in the Framework overview, both the estimation of consequences and evaluation of trade-offs were illustrated by assessing the subset of potential management alternatives to show the utility of BNs in this structured framework. A formal decision-making process for providing management suggestions requires further refinement of the BN model and a thorough evaluation of all management alternatives.

Further research - multi-criteria decision analysis

In our BN model, there were only three evaluation criteria in use (of which two criteria have measurements and outcomes) and the consequences of the management alternatives varied significantly. Therefore, evaluating trade-offs and making management choices in this study is relatively simple. However, when more criteria come into use, evaluating trade-offs with multiple conflicting criteria cannot be done easily, in which case MCDA analysis can be performed to facilitate decision analysis (Kiker et al. 2005, Sheppard and Meitner 2005). The core concept of MCDA is to assign weights to evaluation criteria, reflecting expectations of the relative importance of the criteria (Mateo 2012). Predicted outcomes of the evaluation criteria with weights allowing for scoring and ranking management alternatives and the alternatives with the highest ranks will be suggested as solutions (Mateo 2012).

Learning and monitoring

Decision-making for non-native species management can be challenging in Antarctica given the limited knowledge of the ecosystems and great uncertainties about the outcomes. Gaining extra information can potentially reduce uncertainty and consequently improve the ability to provide informed decisions (Gregory et al. 2012c). However, the benefits of gaining information may not outweigh the costs (McDonald-Madden et al. 2010a). For Antarctica, it is often difficult and

87 requires substantial investment to undertake elaborate surveys for both the target non-native species and native biota due to logistical costs and accessibility (e.g. snow cover). Thus, it is necessary to determine which uncertainties are the most worth reducing in further learning and monitoring process to improve decision-making performance.

Sensitivity analysis

In this study, by identifying influential variables, a sensitivity analysis of the Bayesian Network can help to determine further focus of elicitation efforts during the model improvement process (Pollino et al. 2007, Gregory et al. 2012b). For example, we have identified that ‘management alternatives’ was the most important variable influencing management outcomes of P. annua and the second most important variable influencing the loss of native plants and invertebrates. This outcome implied that improving knowledge of control intensity and effectiveness of different management options was relatively important to decision-making.

Further research — Value-of-information analysis

Value-of-information analysis is a standard approach to determine the worth of gaining additional information (Yokota and Thompson 2004, Yokomizo et al. 2014). Value of information analysis usually calculates the expected value of perfect information (EVPI), the expected value of partial perfect information (EVPPI), or the expected value of imperfect information (EVII) (also known as the expected value of sample information) (Yokomizo et al. 2014). As the most basic and central concept, EVPI measures the improvements in management performance if all uncertainties are resolved (Runge et al. 2011, Havlik and Jurickova 2015). EVPI can be used to indicate whether extra information should be gained, given that acquiring perfect information is worthless when it would not change a management decision (Yokomizo et al. 2014). The EVPPI enables analysis of uncertainties that are worth reducing and determines their relative importance (Yokota and Thompson 2004). In most cases, perfect information cannot be obtained, thus EVII is usually used to measure the increase in management performance derived from imperfect (sample) information (Yokomizo et al. 2014). Given time and resource constrains, value-of-information analysis enables decision-makers to prioritize learning and research activities for the improvement of future decisions (Moore and Runge 2012, Sanchirico et al. 2014).

88

Discussion

With non-native species already established in Antarctica (Hughes et al. 2015) and the increasing risks of more introductions (Chown et al. 2012a) there is a pressing need for the development of guidelines to address non-native species management (Committee for Environmental Protection 2011). We used P. annua management as an example to demonstrate that a structured framework, coupling a Structured Decision Making process with a suite of analytical decision tools, was able to provide support for decision-making, through a transparent and adaptive process.

Within this structured framework, we have fully implemented the first two steps of the SDM process including defining objectives and developing alternatives for the P. annua case study. It is the first time that objectives and management alternatives have been explicitly proposed for the management of non-native species in Antarctica. Considering the resources constraints for management, cost is an important factor that can be incorporated as an objective for further evaluation of the cost-efficacy of management options (Kerr et al. 2016). Cost estimates can be hard to determine due to uncertainty, but differences of magnitude of costs between management options can be used to address this challenge.

In the next step of estimating consequences, the utility of a Bayesian Network approach was illustrated by assessing outcomes of five selected management alternatives, although assessment of all potential management alternatives is required for a formal SDM process. The following two steps, evaluation of trade-offs and learning and monitoring, were partially illustrated. The analytical tools that can be used for these two steps were discussed, including multi-criteria decision analysis and value of information analysis. Besides implementing these new tools/methods, further development of our current approach such as improving predictive power of the BN model are also required.

Improvement of the BN model

Estimating consequences is an important step in SDM since it reflects how different management alternatives achieve the objectives, and provides support for evaluating trade-offs and management implications. Estimating consequences, however, is the most challenging step in our study since little empirical information is available and we have limited understanding of the Antarctic ecosystem. We also found that experts were less confident in providing judgements on the responses of certain species groups in particular locations (e.g. responses of microbes to management around Arctowski station), as required by this case study. Given the incomplete

89 information and potential biases of the information feeding into the BN, further expert elicitation to update the BN and additional model validation methods should be performed to increase the reliability of the outcomes predicted by BN models (Kuhnert and Hayes 2009).

One of the advantages of BN is that it can explicitly incorporate and represent uncertainty, propagate the uncertainty through the network, and express it in the outputs (Cain 2001, Marcot et al. 2006a). It is widely acknowledged that there are significant information gaps for Antarctica, even though Antarctica has a relatively simple ecosystem structure (Chown et al. 2012b, Terauds et al. 2012a). For instance, there is very little known about micro-organisms, yet experts identified that they were a major group to be considered in management. For situations where there are knowledge gaps, BNs are very useful as they can be updated and uncertainties will be reduced when new information is available.

When there is a paucity of empirical data, expert knowledge is a useful information source (Johnson et al. 2012b, McBride and Burgman 2012) and can be obtained quickly in a cost-effective way (Murray et al. 2009, Kuhnert et al. 2010, Kuhnert 2011). Given current limitations, we were only able to organise workshops for the elicitation process with a small number of experts (one to four individuals) for BN model construction and parameterisation. Additional experts from more diverse backgrounds can reduce the uncertainties. For instance, involving expert Antarctic microbiologists would enable the conditional probabilities of the ‘loss of microbes’ node to be updated. This would allow the micro-organism evaluation criteria to be incorporated in assessment of management alternatives. Information synthesized from additional experts is likely to be more robust compared with the information provided by fewer experts (Martin et al. 2005, McBride and Burgman 2012).

It is essential to rigorously use expert knowledge in a transparent and credible manner. When involving multiple experts, a Delphi process is the most commonly used elicitation method (Kuhnert et al. 2010, Martin et al. 2012a). The Delphi process begins with eliciting individual information independently, and the responses are then presented amongst the experts, allowing the experts to reconsider and adjust their estimates. Given the difficulties to coordinate a suitable time and location for all experts from different countries and locations, most communication with experts in our future study need to carry out via email or telephone (McBride et al. 2012). However, better elicitation outcomes can be achieved when experts are allowed to discuss in person since the parameterization of the BN model requires extensive efforts of data entry and adjustment. A face- to-face expert workshop should be facilitated when funding and resources are available.

90

There are potential extensions for using BN. In this study, we assessed management options and their impact for the current P. annua population around Arctowski Station. The predicted future distributions of P. annua (e.g. in 10/20 years) could also be used as priors for the BN model, to compare outcomes under current and future scenarios. This is likely to highlight that delaying management may lead to greater cost and difficulties for eradication and higher impacts on native ecosystems. In addition, the best solution for the current scenario may not hold for the future scenario, since the changes of outputs (loss of species) are not linear to the changes of inputs (P. annua status) in a BN. Instead of using expert knowledge, empirical information may be available in the near future to inform such prior parameterisation. For instance, a Species Distribution Model can be developed for P. annua to indicate its potential distribution under different climate scenarios (Martin et al. 2015).

In addition to incorporating new information, model validation should also be performed to assess the model predictions. Besides the sensitivity analysis we have used, another validation method that is practical for future research is a peer review validation. Formal peer review from third-party expert/s to examine model structure and parameters, and suggest edits or confirm the model construction would be useful (Marcot et al. 2001, Marcot et al. 2006b).

Empirical data can also be used to test the accuracy of the model (Cain 2001, Marcot et al. 2006b). A simple way to do this is to use a confusion matrix, comparing values of predicted probabilities of the model with actual outcomes (Marcot et al. 2006b). Similarly, evidence-driven conflict analysis can be conducted based on empirical data, which will detect potential conflicts between the model and evidence (Kjærulff and Madsen 2013a). If a conflict is detected, the model may be unreliable and misleading (Kjærulff and Madsen 2013a). Kjærulff and Madsen (2013a) describes a detailed procedure for conflict analysis including conducting conflict analysis, determining the origin of the conflict, solving the problem, and discussion to explain potential conflict. However, like many remote regions, the lack of empirical data for Antarctica terrestrial ecosystems means that it is unlikely that data-driven validation will be available for our study in the short-term.

Conclusion

The Antarctic Treaty calls for removal of any non-native species introduced to Antarctica and the CEP responsible for advising Antarctic Treaty Parties has indicated that it is necessary to develop guidelines to better inform non-native species management in Antarctica (Committee for Environmental Protection 2011). Information availability for assessing management actions for other non-native species on Antarctica will probably be as low or lower as for P. annua and yet the 91 consequences of management actions are likely to be complex (Hughes and Worland 2010). The structured framework presented in this study enables the available information to be used effectively to inform the decision-making and further learning processes. The process demonstrated by the framework is iterative and adaptive, and is able to provide more informed management suggestions as new data become available. Based on our research we believe that the structured framework we proposed provides a promising approach to address CEP’s requirements to assist decision-making of non-native species management in Antarctica.

92

Chapter 6 General Discussion

Synthesis

The main aims of the research presented in this thesis were to provide a better understanding of invasive species management problems with regard to potential perverse effects of management (Chapter 2), and to explore the utility of a suite of tools to inform decision-making for real-world invasive species management problems (Chapter 3–5). I proposed that invasive species management requires systems thinking, and the management should be evaluated from a broader ecosystem level (Chapter 2). Based on this holistic perspective, I conducted a network analysis and a qualitative assessment (Chapter 3), and developed a novel approach coupling qualitative modelling and Boolean analysis (Chapter 4) to provide suggestions for cat eradication on Christmas Island. I proposed a structured framework incorporating a set of tools to inform management of non-native species in Antarctica (Chapter 5). In this general discussion, I synthesise the key findings and implications from Chapters 2–5 and identify future research needs and extensions to my research.

Since biotic and abiotic components influence one another within an entire ecosystem, manipulating invasive species alone without considering the other components in an ecosystem can worsen the current problem or create new problems requiring further mitigation (Zavaleta et al. 2001, Buckley and Han 2014). As a consequence of these perverse effects of management, the final costs of management can be substantially higher than the conservation benefit (Bergstrom et al. 2009). To minimize unnecessary losses and achieve better outcomes, I proposed in Chapter 2, that invasive species management needs to consider the risks of side effects which includes the probability of occurrence as well as the resulting consequences.

However, the complex species interactions and limited in-depth knowledge of ecosystems often hinders our ability to predict the outcomes of management. In the second section of this chapter I systematically reviewed published studies that reported on perverse outcomes as a result of invasive plant management. I categorized these outcomes into four types, which represented four general pathways through which the outcomes occur, to provide insights into risk identification and evaluation for future management. I found only 26 studies that reported negative management impacts, and only one study discussed management from a cost-benefit perspective by contrasting the negative impacts with management benefits. Given this lower number of studies than expected, 93 it is possible that such perverse outcomes are merely uncommon cases, but I indicated that there could be a potential research bias (e.g. focusing on responses of single species or certain groups of species that can be easily monitored and measured) leading researchers to ignore this management problem for invasive plants. I called for research efforts which consider potential negative impacts during the development of management plans and can thus make more prudent choices of species to monitor.

In the last section of the chapter, I discussed existing tools and potential approaches that can be used to predict management outcomes, reconcile management conflicts and develop optimal management strategies. Chapter 3–5 are the extensions of this discussion on predicting management outcomes, in which I applied some of the approaches introduced in this section and developed new tools to assist decision-making in two real-world management cases.

Chapter 3 and Chapter 4 examined the potential impacts of alternative strategies for cat and rat management on Christmas Island to assist conservation decision-making.

Chapter 3 focused on a qualitative assessment that contrasted the risks and benefits of a number of potential management strategies for cats and rats on Christmas Island based on the best available information. The results showed that better management outcomes could be achieved for the ongoing cat eradication campaign if rat control was also prioritized in sites where native species of conservation concern occur. However, this qualitative risk assessment was subject to two main drawbacks. First, predictions of the likelihood of species responses in the assessment may have been subjective based on limited information from the literature (Ricciardi and Rasmussen 1998). Second, when multiple species interact across several trophic levels with a number of feedback loops, it was difficult to predict the behaviour of such complex systems due to the capacity of the human mind (Sterman 2006). The supplementary network analysis shows that the topological approach lacked the power to predict the response of each component within the system, limiting its utility for identifying impacts on individual species of concern, as is required on Christmas Island.

In Chapter 4, I developed a novel approach combining a qualitative modelling approach and Boolean analysis, which gave more robust and greater predictive power compared with the qualitative assessment and network analysis in Chapter 3. Instead of studying system behaviour, this approach qualitatively simulated the population dynamics of each species in networks and revealed patterns (rules) of species responses, allowing for the provision of robust implications for eradication and follow-up monitoring. Boolean analysis also adds to our understanding of the system. By identifying groups of species that repeatedly appeared in rules together, the Boolean

94 analysis may provide us with some insights into the ecological drivers of species-response associations. The Boolean analysis showed that species groups appeared to correspond to the connectance via resources within the network. While these species-response associations appear interpretable in terms of the structure of the original web, this could only be done retrospectively; the response associations could not have been predicted from examining the network structure alone.

However, this approach has a few limitations. First, the rules are for the species' qualitative response (positive/negative) regardless of its magnitude. The rules are only meaningful if the magnitude of the real-world response is large enough. Second, in general, the longer a species- response rule is the less useful and informative it is. This is because a single species' response in an n-species-length rule is only guaranteed contingent upon the responses of the other n-1species satisfying the rule. Third, the contingent nature of the rules obtained can reduce their usefulness for guiding decision making. In the Christmas Island application, the rules were highly contingent upon rat response to cat control and cat response to rat control. It means that, if we do not know a priori which of these scenarios is most likely, then it is difficult to give general advice on the best management strategy to pursue.

Qualitative modelling with Boolean minimization, providing a transparent, rapid and rigorous analysis, can be very useful when little information is available to model an ecological system (in the early stage of problem formulation), as we found on Christmas Island. Gaining these advantages by sacrificing precision, the qualitative models are not substitutes of quantitative analysis but rather a complement.

Considering multiple species and depicting their interactions during management planning can help to identify potential risks and avoid cascading problems. The ecological network approaches illustrated in Chapter 3 and 4 captured the full complexity of biotic interactions of an ecosystem, and can offer insights into how a system responds to changes caused by invasive species management, and to what extent a system is robust to cascading effects. However, in addition to the trophic cascading effects demonstrated in the Christmas Island case study, management impacts can occur through other pathways to affect a range of biotic and abiotic factors such as habitat quality, ecological processes, and microclimates, which require a broader application of network thinking beyond trophic interactions.

Non-native species pose a high risk to Antarctic ecosystem yet management may lead to perverse outcomes, such as further disturbances on native species and habitats (Hughes and Convey 2014).

95

Given the unique conservation status of Antarctica and its governance, non-native species management requires careful consideration of trade-offs between different objectives to achieve maximum environmental benefits (Committee for Environmental Protection 2011). To assist this goal, in Chapter 5, I proposed a structured framework to facilitate decision-making on non-native species management in Antarctica, using the management of an introduced plant, Poa annua, on the Antarctic Peninsula as a case study. The structured framework, based on Structured Decision Making, demonstrated an organized process and illustrated a set of decision tools within this process. Facilitated by the framework, further progress was achieved with the non-native species management issue in Antarctica. First, a clear statement of objectives and a list of potential management options were articulated in a systematic way for management of P. annua in Antarctica for the first time. I then illustrated how a Bayesian Network model could be developed and applied to estimate management outcomes, and addressed a number of issues with model construction and validation. I also discussed how the predicted outcomes of the BN model could be further evaluated to inform management decisions with potential decision tools. Based on the work that has been accomplished; I believe this structured framework highlighted a practical way to cope with non-native species management issues in Antarctica.

BNs are similar to the qualitative models in the way of graphically depicting a system. As acyclic graphs, BNs have limitations in capturing feedback loops compared to the qualitative model. Compared to the qualitative modelling approach which focuses on model structure and omits problem-specific quantitative details, nodes and links of a BN model have to be parameterized. BNs are able to provide semi-quantitative evaluations of the consequences, yet the most significant challenge of BNs is the time and expertise needed to realistically represent a system with limited understanding. The key question lies on how to construct a conceptual network of the BN which is representative of the system yet is relatively simple in structure for parameterization. For the case study in Chapter 5, this conundrum is even more prominent since that 1) negative effects of invader management have never been investigated, let alone getting information for the evaluation criteria, and 2) there is no empirical information to specifying the population or even the presence of certain species. Therefore, expert knowledge is not only required for parameterization but also required throughout the study including refining conceptual network, developing evaluation criteria, and specifying potential management actions. A major contribution of Chapter 5 is that, by addressing such a conundrum using expert knowledge, for the first time, this study makes an invasive species management for the Antarctic Peninsula ecosystem assessable.

It should be noted that the Antarctic ecosystem and the relating management problems are quite distinctive from ecosystems elsewhere. The sites where P. annua presents and is predicted to spread 96 locate in Antarctic Peninsula region. More specifically, these sites are within the western Antarctic Peninsula coast and the islands off the coast, which is classified as a distinct conservation biogeographic region with an approximate area of 5081 km2 by a recent work (Terauds et al. 2012b). For this region, most of the ground is covered by ice and heavily glaciated area are not suitable for establishment of vascular plants. The distribution of the two native vascular plants can be used to indicate the suitable habitat for P. annua given the likely future of spread. Offshore islands have the most ice-free surfaces, supporting the largest native vascular plant stand (about 10,000 m2), and extensive stands are very rare in the Peninsula, presented as zonal communities (Komárková et al. 1985). Given the large area of the biogeographic region and the small patches of plant stands, the management problem of P. annua is extensive yet localised, since preventing its further spread will lead to ecological benefits for the whole region, however management actions need to be planned and evaluated at local sites.

Loss of species within a relatively small scale can be viewed as significant for Antarctica. The native species in Antarctica are very susceptible to disturbance since they are often slow-growing with small populations. Given that the largest native vascular plant stand is estimated at only 10,000 m2, it is very likely that a management action can extirpate or have profound impacts on the entire native plant community of small scale. Such local extirpation can be ecologically significant, yet management actions are less likely to result in complete extinctions of native species. In contrast, as an insular system, Christmas Island has a greater risk of species extinctions due to ill-advised managements.

Although comparing the suite of tools presented in this thesis across similar ecosystem would be optimal, there are differences between Christmas Island and the Antarctic Peninsula, owing to their different natural environments and ecological interactions, and the different invasion stages of the target species. Cats and rats are long-established introduced predators, which have integrated into the invaded ecosystem, thus trophic interactions are the key issue for estimating management consequences. For the Antarctica ecosystem, the spread of P. annua is a relatively recent event (thus mutualisation and trophic interaction with native species are not formed), and management impacts are likely to be direct or indirect mediated by alteration of habitat condition. The size of Christmas Island ecological networks precludes using the Bayesian Network approach due to the parameterization difficulties; while the Antarctica problem lacks trophic interactions (e.g. predation, interference competition), making the qualitative modelling unsuitable. These two case studies illustrate that tools selected for a management problem should be able to test the hypothesis made upon that the way target invaders may interact with ecosystem and the way that a management action may impact the ecosystem. 97

Prediction models play an important role in decision-making since knowing management consequences are key to decisions. Even without full information, explicitly constructing models representing objective systems are still helpful since stakeholders’ mental models are just internalized and thus cannot be evaluated by anyone external to the process. Generally, management actions are determined by stakeholders’ beliefs, thus a model is less influential in management decision-making unless it changes or improves the stakeholders’ understanding of the situation. Engaging stakeholders in model studies ensures that the research objectives reflect stakeholder’s values, and increases the utility and significance of the models. Model output is unquestionably the most important product of modelling studies. However, stakeholders’ perception on the management issue can be illuminated through the process of unifying knowledge to formulate research questions and to justify model structures. Even though the model output at this time may seem to be intuitive, such conclusion may not be reached without the elicitation efforts. As shown in Chapter 3–5, research question formulation and model construction are realised through a communication process interacting with stakeholders. During this iterative elicitation process, people were encouraged to think and refine their responses. For example, through the alteration of conceptual network of the BN in Chapter 5 experts were able to think in-depth of the system, which in turn facilitate the following parameterization (see structural alterations in Figure 6.A.1 in Appendix 6.1). Such engagement is likely to increase stakeholders’ confidence and belief on the modelling approaches.

Future research

Reducing uncertainty and value of information

One major challenge throughout this thesis were the high levels of uncertainty resulting from our limited knowledge and lack of information on the species and ecosystems under investigation. Gaining additional information can potentially reduce uncertainty and improve model accuracy to inform decision-making. Additional information can be gained through further research efforts such as collecting ecological data and expert elicitation, or through monitoring processes as management interventions proceed. When multiple uncertainties exist but time and resources are limited, we face the problem of determining which uncertainties are worth reducing for better decision-making before these additional information sources are available (Yokota and Thompson 2004, Kjærulff and Madsen 2013c). Value-of-information (VOI) analysis is an approach that calculates the value of extra information and identifies which types of information have a probability of significantly affecting management performance (Yokota and Thompson 2004, Yokomizo et al. 2014). VOI

98 enables prioritization of learning and research activities, as well as informing future monitoring and information collection (Runge et al. 2011, Sanchirico et al. 2014). For example, VOI analysis can be applied to monitoring the implementation of on-going cat eradication on Christmas Island to identify priority species for monitoring, which will serve as indicators for predicting or inferring the responses of other species, and which monitoring approaches are more cost effective.

Improvement of predictive powers

Additional information can be used for two major purposes. First, new information can feed directly into a model to update model structure and improve the accuracy of parameter estimates. For instance, for the qualitative model in Chapter 4, new information on species interactions can inform model structure by adding or removing interaction links or refining interaction strengths within a given range. In Chapter 5, new information can be used to update model structure, and revise estimates of inputs and conditional probabilities for the Bayesian Network model.

Second, besides feeding into models directly, extra information can also be used to validate models. Model validation is crucial for simulation models, determining a model’s accuracy in representing the real system of study and predicting consequences (Thacker et al. 2004). In Chapter 4, only two essential validation criteria were used, namely: (1) local stability, e.g. assuming the system will return to equilibrium after perturbation, and (2) the target species (cats/rats) will response negatively to corresponding controls, e.g. assuming that the control management is successful. Increasing the number of criteria in validation such as species responses can potentially increase the predictive accuracy of the model. With cat eradication proceeding on the island, more information on species responses to management is expected from monitoring in the near future. Future research can use this information to validate and update models, and consequently provide increased accuracy for further monitoring and management.

Beyond the problems of model accuracy, there should be a thorough assessment of how well the model achieves the purposes and objectives of research (Kuhnert and Hayes 2009). For the Bayesian Network model developed in Chapter 5, model validation for this purpose is of great importance, since the outputs of the BN model were a direct reflection of the evaluation criteria used to assess management options. For instance, in the BN model for P. annua eradication, one output node represented the population status of P. annua after management. This node had three states including ‘no’ ‘low’ ‘high’ and each state was defined by experts. Therefore, different definitions of the states would lead to different interpretations of the outcomes. Further validation of the node states will lead to more credible and defensible outcomes. Formal peer review from third-

99 party experts could provide the best way to validate model structure and the states of nodes for such purposes; while validation approaches such as sensitivity analysis, influence analysis and data- driven validation can be used to improve model accuracy (Marcot et al. 2006b, Marcot 2012, Kjærulff and Madsen 2013b).

Conclusion

There is increasing recognition of the need to address the perverse effects of invasive species management to avoid new environmental problems. However, few studies have addressed the problem of whether, given a potential management risk, we should abandon current management or accept this risk and proceed with management. Answering these questions to inform decision- making requires knowledge of the possible consequences of management. Yet to date, there have been only a few attempts which predict the consequences for real-world management practices, and even fewer which consider management actions from an ecosystem perspective (see Raymond et al. (2011)), To fill in these knowledge gaps, I explored the utility of a suite of tools to inform decision- making for two real-world invasive species management problems. I showed that although decision-making on management practices is often hindered by paucity of information and time and resources constraints, we are still able to provide robust management suggestions through appropriate decision-making and modelling tools. Further research efforts need to improve the tools introduced in this thesis and develop new tools to inform invasive species management issues.

100

List of References

Alcántara, J. M., and P. J. Rey. 2012. Linking topological structure and dynamics in ecological networks. The American Naturalist 180:186-199. Algar, D., and R. I. Brazall. 2003. Results and recommendations, from a feasibility study, to control feral cats on Christmas Island. Unpublished report to Christmas Island Phosphate. Algar, D., and R. I. Brazall. 2005. Efficacy of a baiting technique to control feral cats on Christmas Island. Unpublished Report to Christmas Island Phosphates. Algar, D., N. Hamilton, M. Holdsworth, and S. Robinson. 2012. Eradicating cats and black rats from Christmas Island. Landscope 27:43–47. Algar, D., N. Hamilton, and C. Pink. 2014. Progress in eradicating cats (Felis catus) on Christmas Island to conserve biodiversity. Raffles Bulletin of Zoology 30:45-53. Algar, D., S. Hilmer, D. Nickels, and A. Nickels. 2011. Successful domestic cat neutering: first step towards eradicating cats on Christmas Island for wildlife protection. Ecological Management & Restoration 12:93-101. Algar, D., and M. Johnston. 2010. Proposed management plan for cats and black rats on Christmas Island. Government of Western Australia Department of Environment and Conservation. Amstrup, S. C., B. G. Marcot, and D. C. Douglas. 2008. A Bayesian network modeling approach to forecasting the 21st century worldwide status of polar bears. Pages 213-268 in E. T. DeWeaver, C. M. Bitz, and L.-B. Tremblay, editors. Arctic sea ice decline: observations, projections, mechanisms, and implications. American Geophysical Union, Washington, D.C. Antarctic Treaty Consultative Parties (ATCP). 1991. Protocol on Environmental Protection to the Antarctic Treaty. Baker, W. L., J. Garner, and P. Lyon. 2009. Effect of imazapic on cheatgrass and native plants in Wyoming big sagebrush restoration for gunnison sage-grouse. Natural Areas Journal 29:204-209. Banks, P. B., and N. K. Hughes. 2012. A review of the evidence for potential impacts of black rats (Rattus rattus) on wildlife and humans in Australia. Wildlife Research 39:78-88. Baskett, C. A., S. M. Emery, and J. A. Rudgers. 2011. Pollinator visits to threatened species are restored following invasive plant removal. International Journal of Plant Sciences 172:411- 422.

101

Bateman, H. L., A. Chung-MacCoubrey, and H. L. Snell. 2008. Impact of non-native plant removal on lizards in riparian habitats in the southwestern United States. Restoration Ecology 16:180-190. Bauer, J. 2012. Invasive species: “back-seat drivers” of ecosystem change? Biological Invasions 14:1295-1304. Beeton, B., A. Burbidge, G. Grigg, P. Harrison, R. How, B. Humphreys, N. McKenzie, and J. Woinarski. 2010. Final report of the Christmas Island expert working group to the Minister for Environment Protection, Heritage and the Arts. DEWHA, Canberra. https://www.environment.gov.au/system/files/resources/f8b7f521-0c69-4093-bf22- 22baa4de495a/files/final-report.pdf. Bennett, J. R., J. D. Shaw, A. Terauds, J. P. Smol, R. Aerts, D. M. Bergstrom, J. M. Blais, W. W. L. Cheung, S. L. Chown, M.-A. Lea, U. N. Nielsen, D. Pauly, K. J. Reimer, M. J. Riddle, I. Snape, J. S. Stark, V. J. Tulloch, and H. P. Possingham. 2015. Polar lessons learned: long- term management based on shared threats in Arctic and Antarctic environments. Frontiers in Ecology and the Environment 13:316-324. Bergstrom, D. M., A. Lucieer, K. Kiefer, J. Wasley, L. Belbin, T. K. Pedersen, and S. L. Chown. 2009. Indirect effects of invasive species removal devastate World Heritage Island. Journal of Applied Ecology 46:73-81. Bower, D. S., L. E. Valentine, A. C. Grice, L. Hodgson, and L. Schwarzkopf. 2014. A trade-off in conservation: weed management decreases the abundance of common reptile and frog species while restoring an invaded floodplain. Biological Conservation 179:123-128. Briske, D. D., J. D. Derner, D. G. Milchunas, and K. W. Tate. 2011. An evidence-based assessment of prescribed grazing practices. Pages 21–74 in D. D. Briske, editor. Conservation benefits of rangeland practices: assessment, recommendations, and knowledge gaps. United States Department of Agriculture, Natural Resources Conservation Service, Washington, DC Brown, J. H., and D. F. Sax. 2004. An essay on some topics concerning invasive species. Austral Ecology 29:530-536. Brown, P., and A. Daigneault. 2014. Cost–benefit analysis of managing the invasive African tulip tree (Spathodea campanulata) in the Pacific. Environmental Science & Policy 39:65-76. Buckley, Y. M. 2008. The role of research for integrated management of invasive species, invaded landscapes and communities. Journal of Applied Ecology 45:397-402. Buckley, Y. M., S. Anderson, C. P. Catterall, R. T. Corlett, T. Engel, C. R. Gosper, R. A. N. Nathan, D. M. Richardson, M. Setter, O. R. R. Spiegel, G. Vivian-Smith, F. A. Voigt, J. E. S. Weir, and D. A. Westcott. 2006. Management of plant invasions mediated by frugivore interactions. Journal of Applied Ecology 43:848-857. 102

Buckley, Y. M., B. M. Bolker, and M. Rees. 2007. Disturbance, invasion and re-invasion: managing the weed-shaped hole in disturbed ecosystems. Ecology Letters 10:809-817. Buckley, Y. M., and Y. Han. 2014. Managing the side effects of invasion control. Science 344:975- 976. Buckley, Y. M., M. Rees, A. W. Sheppard, and M. J. Smyth. 2005. Stable coexistence of an invasive plant and biocontrol agent: a parameterized coupled plant-herbivore model. Journal of Applied Ecology 42:70-79. Burbidge, A. A., and B. F. J. Manly. 2002. Mammal extinctions on Australian islands: causes and conservation implications. Journal of Biogeography 29:465-473. Cain, J. 2001. Planning improvements in natural resources management. Centre for Ecology and Hydrology, Wallingford, UK 124. Campbell, I., G. Claridge, and M. Balks. 1998. Short-and long-term impacts of human disturbances on snow-free surfaces in Antarctica. Polar Record 34:15-24. Campbell, K., G. Harper, D. Algar, C. Hanson, B. Keitt, and S. Robinson. 2011. Review of feral cat eradications on islands. Pages 37-46 in C. Veitch, M. Clout, and D. Towns, editors. Island invasives: eradication and management. IUCN, Gland, Switzerland Carlos, E. H., M. Gibson, and M. A. Weston. 2014. Weeds and wildlife: perceptions and practices of weed managers. Conservation & Society 12:54-64. Carvalheiro, L. G., E. R. M. Barbosa, and J. Memmott. 2008a. Pollinator networks, alien species and the conservation of rare plants: Trinia glauca as a case study. Journal of Applied Ecology 45:1419-1427. Carvalheiro, L. G., Y. M. Buckley, R. Ventim, S. V. Fowler, and J. Memmott. 2008b. Apparent competition can compromise the safety of highly specific biocontrol agents. Ecology Letters 11:690-700. Casper, J. K. 2010. Global warming cycles: ice ages and glacial retreat. Infobase Publishing, New York. Caut, S., E. Angulo, and F. Courchamp. 2009. Avoiding surprise effects on Surprise Island: alien species control in a multitrophic level perspective. Biological Invasions 11:1689-1703. Chown, S. L., A. H. L. Huiskes, N. J. M. Gremmen, J. E. Lee, A. Terauds, K. Crosbie, Y. Frenot, K. A. Hughes, S. Imura, K. Kiefer, M. Lebouvier, B. Raymond, M. Tsujimoto, C. Ware, B. Van de Vijver, and D. M. Bergstrom. 2012a. Continent-wide risk assessment for the establishment of nonindigenous species in Antarctica. Proceedings of the National Academy of Sciences 109:4938-4943.

103

Chown, S. L., J. Lee, K. A. Hughes, J. Barnes, P. Barrett, D. M. Bergstrom, P. Convey, D. Cowan, K. Crosbie, and G. Dyer. 2012b. Challenges to the future conservation of the Antarctic. Science 337:158-159. Chwedorzewska, K., and P. Bednarek. 2012. Genetic and epigenetic variation in a cosmopolitan grass Poa annua from Antarctic and Polish populations. Polish Polar Research 33: 63–80. Chwedorzewska, K. J. 2008. Poa annua L. in Antarctic: searching for the source of introduction. Polar Biology 31:263-268. Clapham, P. J., S. B. Young, and R. L. Brownell. 1999. Baleen whales: conservation issues and the status of the most endangered populations. Mammal Review 29:37-62. Clark, D. L., and M. V. Wilson. 2001. Fire, mowing, and hand-removal of woody species in restoring a native wetland prairie in the Willamette Valley of Oregon. Wetlands 21:135-144. Clark, L., R. E. Ricklefs, and R. Schreiber. 1983. Nest-site selection by the Red-tailed Tropicbird. The Auk 100:953-959. Clavero, M., L. Brotons, P. Pons, and D. Sol. 2009. Prominent role of invasive species in avian biodiversity loss. Biological Conservation 142:2043-2049. Clavero, M., and E. García-Berthou. 2005. Invasive species are a leading cause of animal extinctions. Trends in Ecology & Evolution 20:110. Cole, R. J., and C. M. Litton. 2014. Vegetation response to removal of non-native feral pigs from Hawaiian tropical montane wet forest. Biological Invasions 16:125-140. Committee for Environmental Protection. 2011. Non-native species manual. Secretariat of the Antarctic Treaty. Convey, P. 2011. Antarctic terrestrial biodiversity in a changing world. Polar Biology 34:1629- 1641. Cook, C. N., M. Hockings, and R. W. Carter. 2009. Conservation in the dark? The information used to support management decisions. Frontiers in Ecology and the Environment 8:181-186. Cooper. 1995. A success story: breeding of burrowing petrels (Procellariidae) before and after the eradication of feral cats Felis catus at subantarctic Marion Island. Marine ornithology 23:33. Corbett, L., F. Crome, and G. Richards. 2003. Fauna survey of mine lease applications and national park reference areas, Christmas Island, August 2002. Appendix G. Pages 24–26 in Phosphate Resources Limited, editor. Christmas Island Phosphates Draft Environmental Impact Statement for the Proposed Christmas Island Phosphate Mines. Phosphate Resources Limited, Perth. Côté, I. M., and W. J. Sutherland. 1997. The effectiveness of removing predators to protect bird populations. Conservation Biology 11:395-405.

104

Courchamp, F., J.-L. Chapuis, and M. Pascal. 2003. Mammal invaders on islands: impact, control and control impact. Biological Reviews 78:347-383. Courchamp, F., M. Langlais, and G. Sugihara. 1999. Cats protecting birds: modelling the mesopredator release effect. Journal of Animal Ecology 68:282-292. Courchamp, F., M. Langlais, and G. Sugihara. 2000. Rabbits killing birds: modelling the hyperpredation process. Journal of Animal Ecology 69:154-164. Cowan, D. A., S. L. Chown, P. Convey, M. Tuffin, K. Hughes, S. Pointing, and W. F. Vincent. 2011. Non-indigenous microorganisms in the Antarctic: assessing the risks. Trends in microbiology 19:540-548. Culliney, T. W. 2005. Benefits of classical biological control for managing invasive plants. Critical Reviews in Plant Sciences 24:131-150. Dambacher, J. M., H. W. Li, and P. A. Rossignol. 2003. Qualitative predictions in model ecosystems. Ecological Modelling 161:79-93. Davis, N., D. J. O'Dowd, P. T. Greem, and R. Nally. 2008. Effects of an alien ant invasion on abundance, behavior, and reproductive success of endemic island birds. Conservation Biology 22:1165-1176. Davis, N. E., D. J. O'Dowd, R. Mac Nally, and P. T. Green. 2010. Invasive ants disrupt frugivory by endemic island birds. Biology letters 6:85-88. Degenne, A., and M.-O. Lebeaux. 1996. Boolean analysis of questionnaire data. Social Networks 18:231-245. Department of the Environment. 2014a. Accipiter hiogaster natalis in Species Profile and Threats Database. Department of the Environment, Canberra. http://www.environment.gov.au/cgi- bin/sprat/public/publicspecies.pl?taxon_id=82408. Department of the Environment. 2014b. Chalcophaps indica natalis in Species Profile and Threats Database. Department of the Environment, Canberra. http://www.environment.gov.au/cgi- bin/sprat/public/publicspecies.pl?taxon_id=67030. Department of the Environment. 2014c. Christmas Island feral cat eradication. Department of the Environment, Canberra. https://www.environment.gov.au/biodiveristy/threatened/publications/factsheet-christmas- island-feral-cat-eradication. Department of the Environment. 2014d. Consultation document on listing eligibility and conservation actions Phaethon lepturus fulvus (white-tailed tropicbird (Christmas Island)). Department of the Environment, Canberra. http://www.environment.gov.au/system/files/pages/e918cb04-9c23-4cde-a3d6- cb75e3e91891/files/white-tailed-tropicbird-christmas-island-consultation.pdf. 105

Department of the Environment. 2014e. Phaethon lepturus lepturus in Species Profile and Threats Database. Department of the Environment, Canberra. http://www.environment.gov.au/cgi- bin/sprat/public/publicspecies.pl?taxon_id=26021. Department of the Environment. 2014f. Turdus poliocephalus erythropleurus in Species Profile and Threats Database. Department of the Environment, Canberra. http://www.environment.gov.au/cgi-bin/sprat/public/publicspecies.pl?taxon_id=67122. DePrenger-Levin, M. E., T. A. Grant, and C. Dawson. 2010. Impacts of the introduced biocontrol agent, Rhinocyllus conicus (Coleoptera: Curculionidae), on the seed production and population dynamics of Cirsium ownbeyi (Asteraceae), a rare, native thistle. Biological Control 55:79-84. Dickie, I., B. Bennett, L. Burrows, M. Nuñez, D. Peltzer, A. Porté, D. Richardson, M. Rejmánek, P. Rundel, and B. van Wilgen. 2014. Conflicting values: ecosystem services and invasive tree management. Biological Invasions 16:705-719. Didham, R. K., J. M. Tylianakis, M. A. Hutchison, R. M. Ewers, and N. J. Gemmell. 2005. Are invasive species the drivers of ecological change? Trends in Ecology & Evolution 20:470- 474. Director of National Parks. 2014a. Christmas island national park management plan 2014-2024. Commonwealth of Australia, Canberra. Director of National Parks. 2014b. Monitoring, research and management of the Christmas Island Flying-Fox (Pteropus melanotus natalis). Commonwealth of Australia, Canberra. Dudley, T. L., and D. W. Bean. 2012. Tamarisk biocontrol, endangered species risk and resolution of conflict through riparian restoration. Biocontrol 57:331-347. Dunlop, J. 1988. The status and biology of the Golden Bosunbird Phaethon Leturus Fulvus. Australian National Parks and Wildlife Service, Canberra. Dunne, J. A., R. J. Williams, and N. D. Martinez. 2002. Food-web structure and network theory: the role of connectance and size. Proceedings of the National Academy of Sciences 99:12917- 12922. Eason, C. T., E. C. Murphy, G. R. G. Wright, and E. B. Spurr. 2002. Assessment of risks of brodifacoum to non-target birds and mammals in New Zealand. Ecotoxicology 11:35-48. Ehrenfeld, J. G. 2003. Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6:503-523. Ehrenfeld, J. G. 2010. Ecosystem consequences of biological invasions. Annual Review of Ecology, Evolution, and Systematics 41:59-80. Eklöf, A., S. Tang, and S. Allesina. 2013. Secondary extinctions in food webs: a Bayesian network approach. Methods in Ecology and Evolution 4:760-770. 106

Fan, M., Y. Kuang, and Z. Feng. 2005. Cats protecting birds revisited. Bulletin of Mathematical Biology 67:1081-1106. Ferrero, V., S. Castro, J. Costa, P. Acuna, L. Navarro, and J. Loureiro. 2013. Effect of invader removal: pollinators stay but some native plants miss their new friend. Biological Invasions 15:2347-2358. Firn, J., A. P. N. House, and Y. M. Buckley. 2010. Alternative states models provide an effective framework for invasive species control and restoration of native communities. Journal of Applied Ecology 47:96-105. Fleet, R. R. 1972. Nesting success of the Red-tailed Tropicbird on Kure Atoll. The Auk 48:651-659. Fowler, S. V., Q. Paynter, S. Dodd, and R. Groenteman. 2012. How can ecologists help practitioners minimize non-target effects in weed biocontrol? Journal of Applied Ecology 49:307-310. Frenot, Y., S. L. Chown, J. Whinam, P. M. Selkirk, P. Convey, M. Skotnicki, and D. M. Bergstrom. 2005. Biological invasions in the Antarctic: extent, impacts and implications. Biological Reviews 80:45-72. Gaertner, M., R. Biggs, M. Te Beest, C. Hui, J. Molofsky, and D. M. Richardson. 2014. Invasive plants as drivers of regime shifts: identifying high-priority invaders that alter feedback relationships. Diversity and Distributions 20:733-744. Gaertner, M., J. Fisher, G. Sharma, and K. Esler. 2012. Insights into invasion and restoration ecology: time to collaborate towards a holistic approach to tackle biological invasions. NeoBiota 12:57-76. Galiana, N., M. Lurgi, J. M. Montoya, and B. C. López. 2014. Invasions cause biodiversity loss and community simplification in vertebrate food webs. Oikos 123:721-728. Garnett, S., J. Szabo, and G. Dutson. 2010. The action plan for australian birds 2010. CSIRO Publishing, Collingwood, VIC. Glen, A., R. Atkinson, K. Campbell, E. Hagen, N. Holmes, B. Keitt, J. Parkes, A. Saunders, J. Sawyer, and H. Torres. 2013. Eradicating multiple invasive species on inhabited islands: the next big step in island restoration? Biological Invasions 15:2589-2603. Grechi, I., I. Chadès, Y. M. Buckley, M. H. Friedel, A. C. Grice, H. P. Possingham, R. D. van Klinken, and T. G. Martin. 2014. A decision framework for management of conflicting production and biodiversity goals for a commercially valuable invasive species. Agricultural Systems 125:1-11. Gregory, R., L. Failing, M. Harstone, G. Long, T. McDaniels, and D. Ohlson. 2012a. Foundations of structured decision making. Pages 21-46 in Structured decision making: a practical guide

107

to environmental management choices. Wiley-Blackwell, Hoboken, N.J, Chichester, West Sussex. Gregory, R., L. Failing, M. Harstone, G. Long, T. McDaniels, and D. Ohlson. 2012b. Learning. Pages 239-261 in Structured decision making: a practical guide to environmental management choices. Wiley-Blackwell, Hoboken, N.J, Chichester, West Sussex. Gregory, R., L. Failing, M. Harstone, G. Long, T. McDaniels, and D. Ohlson. 2012c. Structuring environmental management choices. Pages 1-20 in Structured decision making: a practical guide to environmental management choices. Wiley-Blackwell, Hoboken, N.J, Chichester, West Sussex. Gregory, R., and G. Long. 2009. Using Structured Decision Making to help implement a precautionary approach to endangered species management. Risk Analysis 29:518-532. Gurevitch, J., and D. K. Padilla. 2004. Are invasive species a major cause of extinctions? Trends in Ecology & Evolution 19:470-474. Havens, K., C. L. Jolls, J. E. Marik, P. Vitt, A. K. McEachern, and D. Kind. 2012. Effects of a non- native biocontrol weevil, Larinus planus, and other emerging threats on populations of the federally threatened Pitcher's thistle, Cirsium pitcheri. Biological Conservation 155:202- 211. Havlik, J., and I. Jurickova. 2015. Value of information analysis for use in health technology assessment. Pages 1371-1374 in D. A. Jaffray, editor. World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada. Springer International Publishing. Heleno, R., M. Devoto, and M. Pocock. 2012. Connectance of species interaction networks and conservation value: is it any good to be well connected? Ecological Indicators 14:7-10. Heleno, R., I. Lacerda, J. A. Ramos, and J. Memmott. 2010. Evaluation of restoration effectiveness: community response to the removal of alien plants. Ecological Applications 20:1191-1203. Hill, F. A. R., and A. Lill. 1998. Density and total population estimates for the threatened Christmas Island Hawk-owl Ninox natalis. Emu 98:209-220. Hill, R. 2004a. National recovery plan for the Christmas Island Goshawk Accipiter fasciatus natalis. Commonwealth of Australia,, Canberra. Hill, R. 2004b. National recovery plan for the Christmas Island Hawk-owl (Ninox natalis). Commonwealth of Australia, Canberra. Hobbs, R. J., S. Arico, J. Aronson, J. S. Baron, P. Bridgewater, V. A. Cramer, P. R. Epstein, J. J. Ewel, C. A. Klink, A. E. Lugo, D. Norton, D. Ojima, D. M. Richardson, E. W. Sanderson, F. Valladares, M. Vilà, R. Zamora, and M. Zobel. 2006. Novel ecosystems: theoretical and

108

management aspects of the new ecological world order. Global Ecology and Biogeography 15:1-7. Hobbs, R. J., E. Higgs, and J. A. Harris. 2009. Novel ecosystems: implications for conservation and restoration. Trends in Ecology & Evolution 24:599-605. Howald, G., C. J. Donlan, J. P. GalvÁN, J. C. Russell, J. Parkes, A. Samaniego, Y. Wang, D. Veitch, P. Genovesi, M. Pascal, A. Saunders, and B. Tershy. 2007. Invasive rodent eradication on islands. Conservation Biology 21:1258-1268. Hughes, B. J., G. R. Martin, and S. J. Reynolds. 2008. Cats and seabirds: effects of feral domestic cat Felis silvestris catus eradication on the population of Sooty Terns Onychoprion fuscata on Ascension Island, South Atlantic. Ibis 150:122-131. Hughes, K. A., and P. Convey. 2014. Alien invasions in Antarctica – is anyone liable? Polar Research. Hughes, K. A., L. Pertierra, M. Molina-Montenegro, and P. Convey. 2015. Biological invasions in terrestrial Antarctica: what is the current status and can we respond? Biodiversity and Conservation 24:1031-1055. Hughes, K. A., and M. R. Worland. 2010. Spatial distribution, habitat preference and colonization status of two alien terrestrial invertebrate species in Antarctica. Antarctic Science 22:221- 231. Hulme, P. E. 2006. Beyond control: wider implications for the management of biological invasions. Journal of Applied Ecology 43:835-847. Hutton, I., J. P. Parkes, and A. R. E. Sinclair. 2007. Reassembling island ecosystems: the case of Lord Howe Island. Animal Conservation 10:22-29. Ings, T. C., J. M. Montoya, J. Bascompte, N. Blüthgen, L. Brown, C. F. Dormann, F. Edwards, D. Figueroa, U. Jacob, J. I. Jones, R. B. Lauridsen, M. E. Ledger, H. M. Lewis, J. M. Olesen, F. J. F. Van Veen, P. H. Warren, and G. Woodward. 2009. Review: ecological networks – beyond food webs. Journal of Animal Ecology 78:253-269. Ishii, N. 2006. A survey of Red-tailed Tropicbird Phaethon rubricauda at the Sitting Room and Rumah Tinggi, Christmas Island, April–July, 2006. Unpublished Report to Parks Australia, Christmas Island Biodiversity Monitoring Program. James, D. J., and I. A. McAllan. 2014. The birds of Christmas Island, Indian ocean: a review. Australian Field Ornithology 31:1-176. Jay, C. V., B. G. Marcot, and D. C. Douglas. 2011. Projected status of the Pacific walrus (Odobenus rosmarus divergens) in the twenty-first century. Polar Biology 34:1065-1084. Johnson, C. J., C. A. Drew, and A. H. Perera. 2012a. Elicitation and use of expert knowledge in landscape ecological applications: a synthesis. Pages 279-299 in A. H. Perera, C. A. Drew, 109

and C. J. Johnson, editors. Expert knowledge and its application in landscape ecology. Springer. Johnson, D. M., and P. D. Stiling. 1998. Distribution and dispersal of Cactoblastis cactorum (Lepidoptera : Pyralidae), an exotic Opuntia-feeding moth, in Florida. Florida Entomologist 81:12-22. Johnson, S., S. Low-Choy, and K. Mengersen. 2012b. Integrating Bayesian networks and geographic information systems: good practice examples. Integrated environmental assessment and management 8:473-479. Jordán, F., and I. Scheuring. 2004. Network ecology: topological constraints on ecosystem dynamics. Physics of Life Reviews 1:139-172. Keitt, B., K. Campbell, A. Saunders, M. Clout, Y. Wang, R. Heinz, K. Newton, and B. Tershy. 2011. The global islands invasive vertebrate eradication database: A tool to improve and facilitate restoration of island ecosystems. Pages 74-77 in C. Veitch, M. Clout, and D. Towns, editors. Island Invasives: Eradication and Management. IUCN, Gland, Switzerland Kennedy, T. A., J. C. Finlay, and S. E. Hobbie. 2005. Eradication of invasive Tamarix ramosissima along a desert stream increases native fish density. Ecological Applications 15:2072-2083. Kerr, N. Z., P. W. J. Baxter, R. Salguero-Gómez, G. M. Wardle, and Y. M. Buckley. 2016. Prioritizing management actions for invasive populations using cost, efficacy, demography and expert opinion for 14 plant species world-wide. Journal of Applied Ecology 53:305-316. Kettenring, K. M., and C. R. Adams. 2011. Lessons learned from invasive plant control experiments: a systematic review and meta-analysis. Journal of Applied Ecology 48:970- 979. Kiker, G. A., T. S. Bridges, A. Varghese, T. P. Seager, and I. Linkov. 2005. Application of multicriteria decision analysis in environmental decision making. Integrated environmental assessment and management 1:95-108. Kirk, P., M. Y. R. Delphine, L. M. Adam, and P. H. S. Michael. 2015. Conditional random matrix ensembles and the stability of dynamical systems. New Journal of Physics 17:083025. Kjærulff, U., and A. Madsen. 2013a. Conflict analysis. Pages 291-301 in Bayesian Networks and Influence Diagrams: a Guide to Construction and Analysis. Springer New York. Kjærulff, U., and A. Madsen. 2013b. Sensitivity analysis. Pages 303-325 in Bayesian Networks and Influence Diagrams: a Guide to Construction and Analysis. Springer New York. Kjærulff, U., and A. Madsen. 2013c. Value of Information analysis. Pages 327-339 in Bayesian Networks and Influence Diagrams: a Guide to Construction and Analysis. Springer New York.

110

Komárková, V., S. Poncet, and J. Poncet. 1985. Two native antarctic vascular plants, Deschampsia antarctica and Colobanthus quitensis: a new southernmost locality and other localities in the Antarctic Peninsula area. Arctic and Alpine Research 17:401-416. Kones, J. K., K. Soetaert, D. van Oevelen, and J. O. Owino. 2009. Are network indices robust indicators of food web functioning? A Monte Carlo approach. Ecological Modelling 220:370-382. Krueger, T., T. Page, K. Hubacek, L. Smith, and K. Hiscock. 2012. The role of expert opinion in environmental modelling. Environmental Modelling & Software 36:4-18. Kuhnert, P., and K. Hayes. 2009. How believable is your BBN. Pages 13-17 in 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, Cairns. Kuhnert, P. M. 2011. Four case studies in using expert opinion to inform priors. Environmetrics 22:662-674. Kuhnert, P. M., T. G. Martin, and S. P. Griffiths. 2010. A guide to eliciting and using expert knowledge in Bayesian ecological models. Ecology Letters 13:900-914. Kuhnert, P. M., T. G. Martin, K. Mengersen, and H. P. Possingham. 2005. Assessing the impacts of grazing levels on bird density in woodland habitat: a Bayesian approach using expert opinion. Environmetrics 16:717-747. Kumschick, S., M. Gaertner, M. Vilà, F. Essl, J. M. Jeschke, P. Pyšek, A. Ricciardi, S. Bacher, T. M. Blackburn, J. T. A. Dick, T. Evans, P. E. Hulme, I. Kühn, A. Mrugała, J. Pergl, W. Rabitsch, D. M. Richardson, A. Sendek, and M. Winter. 2015. Ecological impacts of alien species: quantification, scope, caveats, and recommendations. BioScience 65:55-63. Lampert, A., A. Hastings, E. D. Grosholz, S. L. Jardine, and J. N. Sanchirico. 2014. Optimal approaches for balancing invasive species eradication and endangered species management. Science 344:1028-1031. Laws, R. J., and D. C. Kesler. 2012. A Bayesian network approach for selecting translocation sites for endangered island birds. Biological Conservation 155:178-185. Le Corre, M., D. K. Danckwerts, D. Ringler, M. Bastien, S. Orlowski, C. Morey Rubio, D. Pinaud, and T. Micol. 2015. Seabird recovery and vegetation dynamics after Norway rat eradication at Tromelin Island, western Indian Ocean. Biological Conservation 185:85-94. Levine, J. M., M. Vilà, C. M. D. Antonio, J. S. Dukes, K. Grigulis, and S. Lavorel. 2003. Mechanisms underlying the impacts of exotic plant invasions. Proceedings of the Royal Society of London B: Biological Sciences 270:775-781.

111

Levins, R. 1968. Evolution in changing environments: some theoretical explorations. Princeton University Press, Princeton, NJ. Liu, S., T. Walshe, G. Long, and D. Cook. 2012. Evaluation of potential responses to invasive non- native species with Structured Decision Making. Conservation Biology 26:539-546. Louda, S. M. 1998. Population growth of Rhinocyllus conicus (Coleoptera: Curculionidae) on two species of native thistles in prairie. Environmental Entomology 27:834-841. Louda, S. M., A. E. Arnett, T. A. Rand, and F. L. Russell. 2003. Invasiveness of some biological control insects and adequacy of their ecological risk assessment and regulation. Conservation Biology 17:73-82. Louda, S. M., and C. W. O'Brien. 2002. Unexpected ecological effects of distributing the exotic weevil, Larinus planus (F.), for the biological control of Canada thistle. Conservation Biology 16:717-727. Louda, S. M., T. A. Rand, A. A. R. Kula, A. E. Arnett, N. M. West, and B. Tenhumberg. 2011. Priority resource access mediates competitive intensity between an invasive weevil and native floral herbivores. Biological Invasions 13:2233-2248. Louhaichi, M., M. F. Carpinelli, L. M. Richman, and D. E. Johnson. 2012. Native forb response to sulfometuron methyl on medusahead-invaded rangeland in Eastern Oregon. Rangeland Journal 34:47-53. Low, B., and N. Hamilton. 2013. The current status of the threatened Christmas Island Hawk-owl 'Ninox natalis'. Australian Field Ornithology 30:47-52. Low, B. W., H. Mills, D. Algar, and N. Hamilton. 2013. Home ranges of introduced rats on Christmas Island: a pilot study. Ecological Management & Restoration 14:41-46. Lurgi, M., N. Galiana, B. C. López, L. Joppa, and J. M. Montoya. 2014. Network complexity and species traits mediate the effects of biological invasions on dynamic food webs. Frontiers in Ecology and Evolution 2. Lyons, J. E., M. C. Runge, H. P. Laskowski, and W. L. Kendall. 2008. Monitoring in the context of Structured Decision-Making and adaptive management. The Journal of Wildlife Management 72:1683-1692. MacDougall, A. S., and R. Turkington. 2005. Are invasive species the drivers or passengers of change in degraded ecosystems? Ecology 86:42-55. Mano, M. M., and C. R. Kime. 1997. Combinational logic design. Pages 604-605 in Logic and Computer Design Fundamentals. Prentice Hall, Upper Saddle River, NJ. Marcot, B. G. 2012. Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling 230:50-62.

112

Marcot, B. G., P. A. Hohenlohe, S. Morey, R. Holmes, R. Molina, M. C. Turley, M. H. Huff, and J. A. Laurence. 2006a. Characterizing species at risk II: Using Bayesian belief networks as decision support tools to determine species conservation categories under the Northwest Forest Plan. Ecology and Society 11:12. Marcot, B. G., R. S. Holthausen, M. G. Raphael, M. M. Rowland, and M. J. Wisdom. 2001. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest ecology and management 153:29-42. Marcot, B. G., J. D. Steventon, G. D. Sutherland, and R. K. McCann. 2006b. Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36:3063-3074. Martin, J., M. C. Runge, J. D. Nichols, B. C. Lubow, and W. L. Kendall. 2009. Structured decision making as a conceptual framework to identify thresholds for conservation and management. Ecological Applications 19:1079-1090. Martin, T., H. Murphy, A. Liedloff, C. Thomas, I. Chadès, G. Cook, R. Fensham, J. McIvor, and R. van Klinken. 2015. Buffel grass and climate change: a framework for projecting invasive species distributions when data are scarce. Biological Invasions 17:3197-3210. Martin, T. G., M. A. Burgman, F. Fidler, P. M. Kuhnert, S. Low-Choy, M. McBride, and K. Mengersen. 2012a. Eliciting expert knowledge in conservation science. Conservation Biology 26:29-38. Martin, T. G., P. M. Kuhnert, K. Mengersen, and H. P. Possingham. 2005. The power of expert opinion in ecological models using Bayesian methods: impact of grazing on birds. Ecological Applications 15:266-280. Martin, T. G., S. Nally, A. A. Burbidge, S. Arnall, S. T. Garnett, M. W. Hayward, L. F. Lumsden, P. Menkhorst, E. McDonald-Madden, and H. P. Possingham. 2012b. Acting fast helps avoid extinction. Conservation Letters 5:274-280. Matarczyk, J. A., A. J. Willis, J. A. Vranjic, and J. E. Ash. 2002. Herbicides, weeds and endangered species: management of bitou bush (Chrysanthemoides monilifera ssp rotundata) with glyphosate and impacts on the endangered shrub, Pimelea spicata. Biological Conservation 108:133-141. Mateo, J. R. S. C. 2012. Multi-criteria analysis. Pages 7-10 in Multi criteria analysis in the renewable energy industry. Springer London. McBride, M. F., and M. A. Burgman. 2012. What is expert knowledge, how is such knowledge gathered, and how do we use it to address questions in landscape ecology? Pages 11-38 in

113

A. H. Perera, C. A. Drew, and C. J. Johnson, editors. Expert knowledge and its application in landscape ecology. Springer New York. McBride, M. F., S. T. Garnett, J. K. Szabo, A. H. Burbidge, S. H. Butchart, L. Christidis, G. Dutson, H. A. Ford, R. H. Loyn, and D. M. Watson. 2012. Structured elicitation of expert judgments for threatened species assessment: a case study on a continental scale using email. Methods in Ecology and Evolution 3:906-920. McCann, K. 2007. Protecting biostructure. Nature 446:29-29. McCann, K., A. Hastings, and G. R. Huxel. 1998. Weak trophic interactions and the balance of nature. Nature 395:794-798. McCann, R. K., B. G. Marcot, and R. Ellis. 2006. Bayesian belief networks: applications in ecology and natural resource management. Canadian Journal of Forest Research 36:3053-3062. McDonald-Madden, E., P. W. J. Baxter, R. A. Fuller, T. G. Martin, E. T. Game, J. Montambault, and H. P. Possingham. 2010a. Monitoring does not always count. Trends in Ecology & Evolution 25:547-550. McDonald-Madden, E., W. J. M. Probert, C. E. Hauser, M. C. Runge, H. P. Possingham, M. E. Jones, J. L. Moore, T. M. Rout, P. A. Vesk, and B. A. Wintle. 2010b. Active adaptive conservation of threatened species in the face of uncertainty. Ecological Applications 20:1476-1489. McGeoch, M. A., J. D. Shaw, A. Terauds, J. E. Lee, and S. L. Chown. 2015. Monitoring biological invasion across the broader Antarctic: a baseline and indicator framework. Global Environmental Change 32:108-125. Medina, F. M., E. Bonnaud, E. Vidal, B. R. Tershy, E. S. Zavaleta, C. Josh Donlan, B. S. Keitt, M. Le Corre, S. V. Horwath, and M. Nogales. 2011. A global review of the impacts of invasive cats on island endangered vertebrates. Global Change Biology 17:3503-3510. Melbourne-Thomas, J., S. Wotherspoon, B. Raymond, and A. Constable. 2012. Comprehensive evaluation of model uncertainty in qualitative network analyses. Ecological Monographs 82:505-519. Messing, R. H., and M. G. Wright. 2006. Biological control of invasive species: solution or pollution? Frontiers in Ecology and the Environment 4:132-140. Molina-Montenegro, M. A., F. Carrasco-Urra, I. Acuña-Rodriguez, R. Oses, C. Torres-Diaz, and K. J. Chwedorzewska. 2014. Assessing the importance of human activities for the establishment of the invasive Poa annua in Antarctica. Polar Research 33. Molina-Montenegro, M. A., F. Carrasco-Urra, C. Rodrigo, P. Convey, F. Valladares, and E. Gianoli. 2012. Occurrence of the non-native annual bluegrass on the Antarctic mainland and its negative effects on native plants. Conservation Biology 26:717-723. 114

Montoya, J., G. Woodward, M. C. Emmerson, and R. V. Solé. 2009. Press perturbations and indirect effects in real food webs. Ecology 90:2426-2433. Montoya, J. M., and D. Raffaelli. 2010. Climate change, biotic interactions and ecosystem services. Philosophical Transactions of the Royal Society of London B: Biological Sciences 365:2013-2018. Mooney, H. A. 2005. Invasive alien species: a new synthesis. Island Press, Washington, DC. Mooney, H. A., and E. E. Cleland. 2001. The evolutionary impact of invasive species. Proceedings of the National Academy of Sciences 98:5446-5451. Moore, J. L., and M. C. Runge. 2012. Combining Structured Decision Making and Value-of- Information analyses to identify robust management strategies. Conservation Biology 26:810-820. Murray, J. V., A. W. Goldizen, R. A. O’Leary, C. A. McAlpine, H. P. Possingham, and S. L. Choy. 2009. How useful is expert opinion for predicting the distribution of a species within and beyond the region of expertise? A case study using brush-tailed rock-wallabies Petrogale penicillata. Journal of Applied Ecology 46:842-851. Nakajima, H. 1992. Sensitivity and stability of flow networks. Ecological Modelling 62:123-133. Nelson, S. M., and R. Wydoski. 2008. Riparian butterfly (papilionoidea and hesperioidea) assemblages associated with Tamarix-dominated, native vegetation-dominated, and Tamarix removal sites along the Arkansas River, Colorado USA. Restoration Ecology 16:168-179. Nogales, M., A. MartÍN, B. R. Tershy, C. J. Donlan, D. Veitch, N. Puerta, B. Wood, and J. Alonso. 2004. A review of feral cat eradication on islands. Conservation Biology 18:310-319. Norsys Software Corp. 2014. Netica application v.5.15. Norsys Software Corporation, Vancouver. Norton, J. 2008. Ignorance and Indifference*. Philosophy of Science 75:45-68. O'Dowd, D. J., P. T. Green, and P. S. Lake. 2003. Invasional ‘meltdown’on an oceanic island. Ecology Letters 6:812-817. O'Neill, T. A., M. R. Balks, J. López-Martínez, and J. L. McWhirter. 2012. A method for assessing the physical recovery of Antarctic desert pavements following human-induced disturbances: a case study in the Ross Sea region of Antarctica. Journal of Environmental Management 112:415-428. Ogden, J. A. E., and M. Rejmanek. 2005. Recovery of native plant communities after the control of a dominant invasive plant species, Foeniculum vulgare: implications for management. Biological Conservation 125:427-439. Ogutu-Ohwayo, R. 1990. The decline of the native fishes of lakes Victoria and Kyoga (East Africa) and the impact of introduced species, especially the Nile perch, Lates niloticus, and the Nile tilapia, Oreochromis niloticus. Environmental Biology of Fishes 27:81-96. 115

Olech, M. 1996. Human impact on terrestrial ecosystems in west Antarctica. Pages 299-306 in Proceedings of the NIPR Symposium on Polar Biology. National Institute of Polar Research. Olech, M., and K. J. Chwedorzewska. 2011. Short Note: The first appearance and establishment of an alien vascular plant in natural habitats on the forefield of a retreating glacier in Antarctica. Antarctic Science 23:153-154. Olowo, J. P., and L. J. Chapman. 1999. Trophic shifts in predatory catfishes following the introduction of Nile perch into Lake Victoria. African Journal of Ecology 37:457-470. Oppel, S., F. Burns, J. Vickery, K. George, G. Ellick, D. Leo, and J. C. Hillman. 2014. Habitat- specific effectiveness of feral cat control for the conservation of an endemic ground-nesting bird species. Journal of Applied Ecology 51:1246-1254. Orchan, Y., F. Chiron, A. Shwartz, and S. Kark. 2013. The complex interaction network among multiple invasive bird species in a cavity-nesting community. Biological Invasions 15:429- 445. Ortega, Y. K., and D. E. Pearson. 2010. Effects of Picloram application on community dominants vary with initial levels of Spotted Knapweed (Centaurea stoebe) invasion. Invasive Plant Science and Management 3:70-80. Ortega, Y. K., D. E. Pearson, and K. S. McKelvey. 2004. Effects of biological control agents and exotic plant invasion on deer mouse populations. Ecological Applications 14:241-253. Overton, C. T., M. L. Casazza, J. Y. Takekawa, D. R. Strong, and M. Holyoak. 2014. Tidal and seasonal effects on survival rates of the endangered California clapper rail: does invasive Spartina facilitate greater survival in a dynamic environment? Biological Invasions 16:1897-1914. Panzacchi, M., R. Cocchi, P. Genovesi, and S. Bertolino. 2007. Population control of coypu Myocastor coypus in Italy compared to eradication in UK: a cost-benefit analysis. Wildlife Biology 13:159-171. Pascual, M., and J. A. Dunne. 2005. Ecological networks: linking structure to dynamics in food webs. Oxford University Press, New York. Pearson, D. E., and R. M. Callaway. 2006. Biological control agents elevate hantavirus by subsidizing deer mouse populations. Ecology Letters 9:443-450. Pearson, D. E., and R. M. Callaway. 2008. Weed-biocontrol insects reduce native-plant recruitment through second-order apparent competition. Ecological Applications 18:1489-1500. Pimentel, D., R. Zuniga, and D. Morrison. 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics 52:273- 288. 116

Pimm, S. L. 1984. The complexity and stability of ecosystems. Nature 307:321-326. Pitt, W. C., A. R. Berentsen, A. B. Shiels, S. F. Volker, J. D. Eisemann, A. S. Wegmann, and G. R. Howald. 2015. Non-target species mortality and the measurement of brodifacoum rodenticide residues after a rat (Rattus rattus) eradication on Palmyra Atoll, tropical Pacific. Biological Conservation 185:36-46. Polasky, S., S. R. Carpenter, C. Folke, and B. Keeler. Decision-making under great uncertainty: environmental management in an era of global change. Trends in Ecology & Evolution 26:398-404. Pollino, C. A., O. Woodberry, A. Nicholson, K. Korb, and B. T. Hart. 2007. Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software 22:1140-1152. Proulx, S. R., D. E. L. Promislow, and P. C. Phillips. 2005. Network thinking in ecology and evolution. Trends in Ecology & Evolution 20:345-353. Pyšek, P., and D. Richardson. 2007. Traits associated with invasiveness in alien plants: where do we stand? Pages 97-125 in W. Nentwig, editor. Biological Invasions. Springer Berlin Heidelberg. Pyšek, P., and D. M. Richardson. 2010. Invasive species, environmental change and management, and health. Annual Review of Environment and Resources 35:25-55. R Core Team. 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Ralls, K., and A. M. Starfield. 1995. Choosing a management strategy: two Structured Decision- Making methods for evaluating the predictions of stochastic simulation models. Conservation Biology 9:175-181. Rand, T. A., and S. M. Louda. 2004. Exotic weed invasion increases the susceptibility of native plants attack by a biocontrol herbivore. Ecology 85:1548-1554. Ratcliffe, N., M. Bell, T. Pelembe, D. Boyle, R. Benjamin, R. White, B. Godley, J. Stevenson, and S. Sanders. 2010. The eradication of feral cats from Ascension Island and its subsequent recolonization by seabirds. Oryx 44:20-29. Raymond, B., J. McInnes, J. M. Dambacher, S. Way, and D. M. Bergstrom. 2011. Qualitative modelling of invasive species eradication on subantarctic Macquarie Island. Journal of Applied Ecology 48:181-191. Rayner, M. J., M. E. Hauber, M. J. Imber, R. K. Stamp, and M. N. Clout. 2007. Spatial heterogeneity of mesopredator release within an oceanic island system. Proceedings of the National Academy of Sciences 104:20862-20865.

117

Regan, H. M., M. Colyvan, and M. A. Burgman. 2002. A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological Applications 12:618-628. Reid, A. M., L. Morin, P. O. Downey, K. French, and J. G. Virtue. 2009. Does invasive plant management aid the restoration of natural ecosystems? Biological Conservation 142:2342- 2349. Renteria, J. L., M. R. Gardener, F. D. Panetta, and M. J. Crawley. 2012. Management of the invasive Hill Raspberry (Rubus niveus) on Santiago Island, Galapagos: eradication or indefinite control? Invasive Plant Science and Management 5:37-46. Rhoades, C., T. Barnes, and B. Washburn. 2002. Prescribed fire and herbicide effects on soil processes during barrens restoration. Restoration Ecology 10:656-664. Ricciardi, A., M. F. Hoopes, M. P. Marchetti, and J. L. Lockwood. 2013. Progress toward understanding the ecological impacts of nonnative species. Ecological Monographs 83:263- 282. Ricciardi, A., and J. B. Rasmussen. 1998. Predicting the identity and impact of future biological invaders: a priority for aquatic resource management. Canadian Journal of Fisheries and Aquatic Sciences 55:1759-1765. Richardson, D. M., and R. L. Kluge. 2008. Seed banks of invasive Australian Acacia species in South Africa: role in invasiveness and options for management. Perspectives in Plant Ecology, Evolution and Systematics 10:161-177. Rinella, M. J., and B. J. Hileman. 2009. Efficacy of prescribed grazing depends on timing intensity and frequency. Journal of Applied Ecology 46:796-803. Rinella, M. J., B. D. Maxwell, P. K. Fay, T. Weaver, and R. L. Sheley. 2009. Control effort exacerbates invasive-species problem. Ecological Applications 19:155-162. Ringler, D., J. C. Russell, and M. Le Corre. 2015. Trophic roles of black rats and seabird impacts on tropical islands: mesopredator release or hyperpredation? Biological Conservation 185:75- 84. Ritchie, E. G., B. Elmhagen, A. S. Glen, M. Letnic, G. Ludwig, and R. A. McDonald. 2012. Ecosystem restoration with teeth: what role for predators? Trends in Ecology & Evolution 27:265-271. Rodionov, A. V., N. N. Nosov, E. S. Kim, E. M. Machs, E. O. Punina, and N. S. Probatova. 2010. The origin of polyploid genomes of bluegrasses Poa L. and Gene flow between northern pacific and sub-Antarctic Islands. Russian Journal of Genetics 46:1407-1416. Rodriguez, L. 2006. Can invasive species facilitate native species? Evidence of how, when, and why these impacts occur. Biological Invasions 8:927-939.

118

Rojas-Sandoval, J., E. Melendez-Ackerman, and D. S. Fernandez. 2012. Plant community dynamics of a tropical semi-arid system following experimental removals of an exotic grass. Applied Vegetation Science 15:513-524. Runge, M. C., S. J. Converse, and J. E. Lyons. 2011. Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program. Biological Conservation 144:1214-1223. Ruscoe, W. A., D. S. L. Ramsey, R. P. Pech, P. J. Sweetapple, I. Yockney, M. C. Barron, M. Perry, G. Nugent, R. Carran, R. Warne, C. Brausch, and R. P. Duncan. 2011. Unexpected consequences of control: competitive vs. predator release in a four-species assemblage of invasive mammals. Ecology Letters 14:1035-1042. Russell, J. C., and N. D. Holmes. 2015. Tropical island conservation: rat eradication for species recovery. Biological Conservation 185:1-7. Saint-Béat, B., D. Baird, H. Asmus, R. Asmus, C. Bacher, S. R. Pacella, G. A. Johnson, V. David, A. F. Vézina, and N. Niquil. 2015. Trophic networks: how do theories link ecosystem structure and functioning to stability properties? A review. Ecological Indicators 52:458- 471. Salo, P., E. Korpimäki, P. B. Banks, M. Nordström, and C. R. Dickman. 2007. Alien predators are more dangerous than native predators to prey populations. Proceedings of the Royal Society of London B: Biological Sciences 274:1237-1243. Samways, M. J., N. J. Sharratt, and J. P. Simaika. 2011. Effect of alien riparian vegetation and its removal on a highly endemic river macroinvertebrate community. Biological Invasions 13:1305-1324. Sanchirico, J. N., M. R. Springborn, M. W. Schwartz, and A. N. Doerr. 2014. Investment and the policy process in conservation monitoring. Conservation Biology 28:361-371. Savidge, J. A. 1987. Extinction of an island forest avifauna by an introduced snake. Ecology 68:660-668. Schmitz, O. J. 1997. Press perturbations and the predictability ofecological interactions in a food web. Ecology 78:55-69. Schulz, M., and L. Lumsden. 2009. Diet of the Nankeen Kestrel Falco cenchroides on Christmas Island. Australian Field Ornithology 26:28-32. Shea, K., H. P. Possingham, W. W. Murdoch, and R. Roush. 2002. Active adaptive management in insect pest and weed control: intervention with a plan for learning. Ecological Applications 12:927-936.

119

Sheley, R. L., and M. K. Denny. 2006. Community response of nontarget species to herbicide application and removal of the nonindigenous invader Potentilla recta L. Western North American Naturalist 66:55-63. Sheppard, S. R. J., and M. Meitner. 2005. Using multi-criteria analysis and visualisation for sustainable forest management planning with stakeholder groups. Forest ecology and management 207:171-187. Shyu, E., E. A. Pardini, T. M. Knight, and H. Caswell. 2013. A seasonal, density-dependent model for the management of an invasive weed. Ecological Applications 23:1893-1905. Simberloff, D. 2001. Eradication of island invasives: practical actions and results achieved. Trends in Ecology & Evolution 16:273-274. Simberloff, D. 2011. How common are invasion-induced ecosystem impacts? Biological Invasions 13:1255-1268. Simberloff, D., J.-L. Martin, P. Genovesi, V. Maris, D. A. Wardle, J. Aronson, F. Courchamp, B. Galil, E. García-Berthou, M. Pascal, P. Pyšek, R. Sousa, E. Tabacchi, and M. Vilà. 2013. Impacts of biological invasions: what's what and the way forward. Trends in Ecology & Evolution 28:58-66. Simberloff, D., I. M. Parker, and P. N. Windle. 2005. Introduced species policy, management, and future research needs. Frontiers in Ecology and the Environment 3:12-20. Simberloff, D., and P. Stiling. 1996. Risks of species introduced for biological control. Biological Conservation 78:185-192. Smith, M. J., H. Cogger, B. Tiernan, D. Maple, C. Boland, F. Napier, T. Detto, and P. Smith. 2012. An oceanic island reptile community under threat: the decline of reptiles on Christmas Island, Indian Ocean. Herpetological Conservation and Biology 7:206-218. Snelgrove, P. V. R., S. F. Thrush, D. H. Wall, and A. Norkko. 2014. Real world biodiversity– ecosystem functioning: a seafloor perspective. Trends in Ecology & Evolution 29:398-405. Sogge, M. K., S. J. Sferra, and E. H. Paxton. 2008. Tamarix as habitat for birds: implications for riparian restoration in the southwestern United States. Restoration Ecology 16:146-154. Sommerfeld, J., T. Stokes, and G. B. Baker. 2015. Breeding success, mate-fidelity and nest-site fidelity in Red-tailed Tropicbirds (Phaethon rubricauda) on Christmas Island, Indian Ocean. Emu 115:214-222. Speirs-Bridge, A., F. Fidler, M. McBride, L. Flander, G. Cumming, and M. Burgman. 2010. Reducing overconfidence in the interval judgments of experts. Risk Analysis 30:512-523. Sterman, J. D. 2006. Learning from evidence in a complex world. American Journal of Public Health 96:505-514.

120

Stevens, M. H. 2009. An introduction to food webs, and lessons from Lotka–Volterra models. Pages 211-226 in M. H. Stevens, editor. A Primer of Ecology with R. Springer New York. Stokes, T. 1988. A review of the birds of Christmas Island, Indian Ocean. Australian National Parks and Wildlife Service, Canberra. Strayer, D. L. 2012. Eight questions about invasions and ecosystem functioning. Ecology Letters 15:1199-1210. Suckling, D. M., and R. F. H. Sforza. 2014. What magnitude are observed non-target impacts from weed biocontrol? Plos One 9:e84847. Suding, K. N. 2011. Toward an era of restoration in ecology: successes, failures, and opportunities ahead. Annual Review of Ecology, Evolution, and Systematics 42:465-487. Szabo, J. K., N. Khwaja, S. T. Garnett, and S. H. M. Butchart. 2012. Global patterns and drivers of avian extinctions at the species and subspecies level. Plos One 7:e47080. Takahashi, M., S. M. Louda, T. E. X. Miller, and C. W. O'Brien. 2009. Occurrence of Trichosirocalus horridus (Coleoptera: Curculionidae) on native Cirsium altissimum versus exotic C. vulgare in North American tallgrass prairie. Environmental Entomology 38:731- 740. Terauds, A., S. L. Chown, F. Morgan, H. J Peat, D. J. Watts, H. Keys, P. Convey, and D. M. Bergstrom. 2012a. Conservation biogeography of the Antarctic. Diversity and Distributions 18:726-741. Terauds, A., S. L. Chown, F. Morgan, H. J. Peat, D. J. Watts, H. Keys, P. Convey, and D. M. Bergstrom. 2012b. Conservation biogeography of the Antarctic. Diversity and Distributions 18:726-741. Thacker, B. H., S. W. Doebling, F. M. Hemez, M. C. Anderson, J. E. Pepin, and E. A. Rodriguez. 2004. Concepts of model verification and validation. Los Alamos National Laboratory, Los Alamos, NM. Thatje, S., and R. B. Aronson. 2009. No future for the Antarctic Treaty? Frontiers in Ecology and the Environment 7:175-175. Theuns, P. 1994. A dichotomization method for Boolean analysis of quantifiable co-occurrence data. Pages 389-402 in G. Fischer and D. Laming, editors. Contributions to Mathematical Psychology, Psychometrics, and Methodology. Springer New York. Thomas, M. B., and A. M. Reid. 2007. Are exotic natural enemies an effective way of controlling invasive plants? Trends in Ecology & Evolution 22:447-453. Threatened Species Scientific Committee. 2006a. Advice to the Minister from the TSSC for the unsuccessful nomination of the Ducula whartoni (Christmas Island Imperial-Pigeon). Department of the Environment, Canberra. 121

http://www.environment.gov.au/system/files/pages/a6dd37a5-080c-4751-ae71- 8109ff9adaba/files/ducula-whartoni.pdf. Threatened Species Scientific Committee. 2006b. Commonwealth listing advice on Zosterops natalis (Christmas Island White-eye). Department of the Environment. http://www.environment.gov.au/system/files/pages/5c9ac91c-ecee-443c-9cbf- 646efcd4054a/files/zosterops-natalis.pdf. Threatened Species Scientific Committee. 2014a. Approved conservation advice for Chalcophaps indica natalis (Christmas Island emerald dove). Department of the Environment, Canberra. http://www.environment.gov.au/biodiversity/threatened/species/pubs/67030-conservation- advice.pdf. Threatened Species Scientific Committee. 2014b. Commonwealth conservation advice for Cyrtodactylus sadleiri (Christmas Island Giant Gecko). Department of the Environment, Canberra. http://www.environment.gov.au/biodiversity/threatened/species/pubs/86865- conservation-advice.pdf. Threatened Species Scientific Committee. 2014c. Commonwealth conservation advice for Pteropus natalis (Christmas Island Flying-fox). Department of the Environment, Canberra. http://www.environment.gov.au/biodiversity/threatened/species/pubs/87611-conservation- advice-31102015.pdf. Tidemann, C., H. Yorkston, and A. Russack. 1994. The diet of cats, Felis catus, on Christmas Island, Indian Ocean. Wildlife Research 21:279-285. Tidemann, C. R. 1989. Survey of the terrestrial mammals on Christmas Island (Indian Ocean). Unpublished report to Australian National Parks and Wildlife Service, Canberra. Australian National University, Canberra. Tin, T., Z. L. Fleming, K. A. Hughes, D. G. Ainley, P. Convey, C. A. Moreno, S. Pfeiffer, J. Scott, and I. Snape. 2009. Impacts of local human activities on the Antarctic environment. Antarctic Science 21:3-33. Tompkins, D. M., and C. J. Veltman. 2006. Unexpected consequences of vertebrate pest control: predictions from a four-species community model. Ecological Applications 16:1050-1061. Towns, D., I. E. Atkinson, and C. Daugherty. 2006. Have the harmful effects of introduced rats on islands been exaggerated? Biological Invasions 8:863-891. Tylianakis, J. M., E. Laliberté, A. Nielsen, and J. Bascompte. 2010. Conservation of species interaction networks. Biological Conservation 143:2270-2279. Valentine, L. E., and L. Schwarzkopf. 2009. Effects of weed-management burning on reptile assemblages in Australian tropical savannas. Conservation Biology 23:103-113.

122

Valentine, L. E., L. Schwarzkopf, and C. N. Johnson. 2012. Effects of a short fire-return interval on resources and assemblage structure of birds in a tropical savanna. Austral Ecology 37:23-34. Van der Lee, G., and P. Jarman. 1996. The status of cats Felis catus and prospects for their control on Christmas Island. Unpublished report to Parks Australia North. Van Kleunen, M., W. Dawson, D. Schlaepfer, J. M. Jeschke, and M. Fischer. 2010. Are invaders different? A conceptual framework of comparative approaches for assessing determinants of invasiveness. Ecology Letters 13:947-958. Veldtman, R., T. F. Lado, A. Botes, Ş. Procheş, A. E. Timm, H. Geertsema, and S. L. Chown. 2011. Creating novel food webs on introduced Australian acacias: indirect effects of galling biological control agents. Diversity and Distributions 17:958-967. Vilà, M., J. L. Espinar, M. Hejda, P. E. Hulme, V. Jarošík, J. L. Maron, J. Pergl, U. Schaffner, Y. Sun, and P. Pyšek. 2011. Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecology Letters 14:702-708. Vitousek, P. 1996. Biological invasions and ecosystem processes: towards an integration of population biology and ecosystem studies. Pages 183-191 in Ecosystem Management. Springer New York. Vitule, J. R. S., C. A. Freire, and D. Simberloff. 2009. Introduction of non-native freshwater fish can certainly be bad. Fish and Fisheries 10:98-108. Weidenhamer, J. D., and R. M. Callaway. 2010. Direct and indirect effects of invasive plants on soil chemistry and ecosystem function. Journal of Chemical Ecology 36:59-69. Whitcraft, C. R., and B. J. Grewell. 2012. Evaluation of perennial pepperweed (Lepidium latifolium) management in a seasonal wetland in the San Francisco Estuary prior to restoration of tidal hydrology. Wetlands Ecology and Management 20:35-45. Williams, D. R., R. G. Pople, D. A. Showler, L. V. Dicks, M. F. Child, E. K. Zu Ermgassen, and W. J. Sutherland. 2013. Bird conservation: global evidence for the effects of interventions. Pelagic Publishing, Exeter. Wodkiewicz, M., H. Galera, K. J. Chwedorzewska, I. Gielwanowska, and M. Olech. 2013. Diaspores of the introduced species Poa annua L. in soil samples from King George Island (South Shetlands, Antarctica). Arctic, Antarctic, and Alpine Research 45:415-419. Wódkiewicz, M., M. Ziemiański, K. Kwiecień, K. Chwedorzewska, and H. Galera. 2014. Spatial structure of the soil seed bank of Poa annua L.–alien species in the Antarctica. Biodiversity and Conservation 23:1339-1346. Woinarski, J. C. Z., S. Flakus, D. J. James, B. Tiernan, G. J. Dale, and T. Detto. 2014. An island- wide monitoring program demonstrates decline in reporting rate for the Christmas Island Flying-Fox Pteropus melanotus natalis. Acta Chiropterologica 16:117-127. 123

Yodzis, P. 1988. The indeterminacy of ecological interactions as perceived through perturbation experiments. Ecology 69:508-515. Yokomizo, H., S. R. Coutts, and H. P. Possingham. 2014. Decision science for effective management of populations subject to stochasticity and imperfect knowledge. Population Ecology 56:41-53. Yokomizo, H., H. P. Possingham, M. B. Thomas, and Y. M. Buckley. 2009. Managing the impact of invasive species: the value of knowing the density–impact curve. Ecological Applications 19:376-386. Yokota, F., and K. M. Thompson. 2004. Value of information analysis in environmental health risk management decisions: past, present, and future. Risk Analysis 24:635-650. Yorkston, H. 1981. Cat survey – Christmas Island. Unpublished report to Australian National Park and Wildlife Service. Zarnetske, P. L., E. W. Seabloom, and S. D. Hacker. 2010. Non-target effects of invasive species management: beachgrass, birds, and bulldozers in coastal dunes. Ecosphere 1:1-20. Zavaleta, E. S., R. J. Hobbs, and H. A. Mooney. 2001. Viewing invasive species removal in a whole-ecosystem context. Trends in Ecology & Evolution 16:454-459.

124

Appendices

Appendix 1.1 Supplementary article

Managing the side effects of invasion control

(Published in Science)

Invasive species can threaten the conservation of biodiversity and natural resources and incur considerable economic losses. Invasive species management programs therefore aim to reverse or mitigate the impacts of invasion, but these programs can have severe negative impacts on native species and ecosystems (Bergstrom et al. 2009, Rinella et al. 2009), because invasive species integrate into their new ecosystems and can assume ecological functions previously carried out by native species. Indirect effects of management are likely to become more common as existing invaders form new interactions and new species continue to be introduced. Lampert et al. report an optimal management model that shows how invasive species control can be combined with other ecosystem goals (Lampert et al. 2014).

Rapid removal of an invasive plant is important for reducing its population, preventing further spread, and helping to ultimately attain eradication. However, rapid removal can lead to problems if native species use the invader as a resource. The program aiming to eradicate an invasive cordgrass species (a hybrid between the invasive Spartina alterniflora and the native S. foliosa) in San Francisco Bay was proceeding quickly and effectively (see the figure) when scientists noticed declines in an endangered bird, the California clapper rail (Rallus longirostris obsoletus), which nests in invasive hybrid Spartina. The control program for invasive hybrid Spartina was temporarily halted, leaving 8% of the originally infested area still containing the invader. The bird also nests in native Spartina, but the native plant was slow to recover after removal of the invader (Lampert et al. 2014). How can the clapper rail population be protected while preventing the remaining invasive hybrid Spartina population from recolonizing the bay?

To determine the best management strategy for such a situation, Lampert et al. (2014) constructed a model that constrains the side effects of hybrid Spartina management on the clapper rail. The model combines invader removal with the restoration of the native habitat to replace the beneficial function of the invader. The time scale of the management program is therefore controlled by the rate of recovery of the native and its ability to replace the habitat lost through invader removal.

Removal of invaders can have various negative impacts on non-target species and ecosystems (Zavaleta et al. 2001). Native fruit-eating birds, bats, and mammals can incorporate invasive species 125 in their diets (Buckley et al. 2006), presenting conservation conflicts when removal of the resource has an impact on the native consumers. Removal of an invasive predator can lead to overabundance of prey (Bergstrom et al. 2009) or increased populations of other damaging predators (Rayner et al. 2007). Such non-target effects of invader management are commonly recognized (Firn et al. 2010, Fowler et al. 2012) but are rarely incorporated into optimal management models and are even more rarely assessed in terms of costs (Lampert et al. 2014).

When management of an invader is in conflict with other conservation goals, there are three main options: to continue to manage the impacts of the invader and accept the collateral damage; abandon the management effort and accept the invader impacts; or seek a compromise management strategy that allows both goals to be attained. Several studies have highlighted the importance of assessing or predicting negative effects of invader removal and have recommended the integration of invasive species management with broader ecosystem goals (Zavaleta et al. 2001), but few management plans have successfully resolved these conflicts.

This gap between theory and practice is difficult to bridge for several reasons. The complexity and uncertainty of species interactions makes it hard to predict the potential negative impacts of invader removal (Raymond et al. 2011). Population reduction of the invader is often used as the sole criterion for judging the success of invasive species management, and time and resource constraints often limit follow-up monitoring and restoration efforts (Reid et al. 2009). Another important barrier to an ecosystem based management approach is the lack of common valuation systems (currencies) that sum and compare the management costs, damages, and benefits (Grechi et al. 2014).

Lampert et al. were able to combine the monetary costs of eradication, restoration, and the damages incurred by invasive hybrid Spartina with a constraint on the total amount of clapper rail habitat required to find an optimal management strategy. In other systems, however, it can be difficult to determine the costs of impact, and how reversible those costs are with invader removal (Yokomizo et al. 2009). The costs of the side effects of management actions can also be difficult to assess and quantify. In these cases, it may be appropriate to use evaluation approaches that do not require conversion of different units of measurement into a single currency (Grechi et al. 2014).

There are often large uncertainties in predicting the responses of managed systems. These uncertainties hinder the accurate identification of conservation conflicts and thus the optimization of solutions. We will never be able to predict all risks of environmental management, but the risks of not acting at all may be more serious. Tools like those constructed by Lampert et al. are crucial for reconciling the increasing number of conflicts likely to occur.

126

Appendix 2.1 Search terms used in Web of ScienceTM Core Collection

We used the following search terms in Web of ScienceTM Core Collection: (TS = (invasive species OR invader* OR nonnative species OR non-native species OR non-indigenous species OR non indigenous species OR exotic species OR exotic* OR alien species OR alien* OR weed* OR introduced species OR biological invasion) AND TS = (restor* OR control OR remov* OR eradicat* OR weeding OR management) AND TS = (plant* OR seed* OR herb* OR flora* OR veget* OR botan* OR tree* OR shrub* OR grass*) AND (TS =(((non-target OR nontarget OR off- target OR off target OR indirect OR negative OR adverse OR unexpected OR unwanted OR undesirable OR inadvertent OR unforeseen) AND (impact* OR effect* OR outcome* OR result*)) OR side-effect OR side effect* OR control impact* OR control-impact) OR TS= ((damage* OR impact* OR effect* OR response*) AND (non-target OR nontarget OR native OR community)))) AND LANGUAGE: (English)

Refined by: Web of Science Categories: (Ecology OR Plant Sciences OR Environmental Sciences OR Entomology OR Biodiversity Conservation)

Timespan: 1900-2015.

Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, CCR-EXPANDED, IC.

The search period finished on 4 March 2015 and the search produced 8,301 publications.

127

Appendix 2.2 Criteria for study inclusion in the systematic review

1. Studies should address negative responses of native species to invasive plant management.

2. Studies were not included if the negative effects of management were documented as short term effects or spill-over effects. If the original study identifies a side effect as short term and there is evidence to support the conclusion (e.g. species recovered during the research), this side effect will not be included. A non-target feeding is identified as a spill-over effect if the study found there was no evolution of the host range of the biocontrol agent and the spill-over effect was identified as temporary and minor with following monitoring during the research.

3. Studies should be based on a field context. Studies that are included should not be a laboratory, greenhouse or agricultural setting, and should not be a common garden experiment with monoculture or polyculture.

4. Studies were not included when the invasive plant control was only a part of a restoration program involving confounding factors such as active seedling, nutrient addition, and etc.

5. When a single study reported various effects (positive, neutral or negative) on species/community we counted the negative responses.

6. When multiple publications presented as sequential studies addressing the same side effect (i.e. the same target invasive species, the same management operation, and the same non- target species affected by the same side effect), we only included the latest or the most comprehensive study.

7. When a particular negative effect from a single research experiment was used/investigated in more than one publication, we only included the latest and most comprehensive study.

128

Appendix 2.3 Studies included in the systematic review

Table 2.A.1 A summary of studies included in the systematic review.

Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Baker et al. Direct Bromus Herbicide NA A suite of native October 2006 June 2007 0.67 E (2009) tectorum L. (imazapic) forbs (e.g. (one time) years Packera multilobata and Sphaeralcea coccinea)

Louhaichi Direct Taeniatheru Herbicide NA Eleven native Autumn 2001 Repeated 2.3 years E et al. (2012) m caput- (sulfometuron forbs (e.g. (one time) sampling in medusae (L.) methyl) Lupinus saxosus May of Nevski T.J. Howell, and 2002, 2003, Agoseris glauca and 2004 (Pursh) Raf)

Ortega and Direct Centaurea Herbicide NA Balsamorhiza October 2002 Repeated 2.5 years E Pearson stoebe L. (picloram) sagittata (Pursh) (one time) sampling in (2010) Nutt. 2003, 2004 and 2005

129

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Renteria et Direct Rubus niveus Herbicide NA Native woody 2001 to 2005 Repeated 4 years M al. (2012) (glyphosate, species sampling in picloram) 2006 to 2010

Rinella et Direct Euphorbia Herbicide NA Native forbs and June 1982 (one Repeated 16 years M al. (2009) esula (picloram) grasses (e.g. time) sampling in Solidago 1, 2, 4, 16 missouriensis years after and Achillea treatment millefolium)

Sheley and Direct Potentilla Herbicide NA Native plant June 1998 (one Repeated 2 years E Denny recta L. (picloram, community (e.g. time) sampling in (2006) metsulfuron, Festuca 1999 and clopyralid) idahoensis, and 2000 Agropyron smithii)

130

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Whitcraft Direct Lepidium Herbicide NA Native grasses Repeated Repeated 2 years E and Grewell latifolium (imazapyr) and forbs (e.g. treatments in sampling in (2012) Frankenia salina, 2007 and 2008 2008 and and Distichlis 2009 salina)

Clark and Direct Woody Burning NA Deschampsia Repeated June 1995 3 years E Wilson species (e.g. cespitosa treatments in and June (2001) Rubus October 1994 1997 discolor, and and September Rubus 1996 laciniatus)

Rinella and Direct Euphorbia Simulate NA Native grasses Repeated Repeated 2 years E Hileman esula grazing and forbs (e.g. treatments sampling in (2009) (clipping) Pascopyrum during 2005 to 2005 to smithii, and 2007 2007 Bromus inermis)

131

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Zarnetske et Direct Ammophila Synthesized NA Native plant 1990 2007 17 years M al. (2010) arenaria and approach community Ammophila (bulldozing, breviligulata herbicide and etc.)

DePrenger- Direct Carduus Biocontrol Rhinocyllus Cirsium ownbeyi Released in Repeated ~ 40 M Levin et al. nutans L. conicus S.L. Welsh 1960s sampling years (2010) Frölich between199 8 and 2005

Havens et Direct Cirsium Biocontrol Larinus Cirsium pitcher Accidentally Repeated ~ 40 M al. (2012) arvense L. planus Torr. and Gray introduced in sampling in years Frabicius the 1960s, then 2010 and actively 2011 redistributed for biocontrol

132

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Johnson and Direct Opuntia spp. Biocontrol Cactoblastis Opuntia stricta Released in Repeated ~30 M Stiling cactorum 1960s onto sampling years (1998) Berg Caribbean between islands and October recorded in 1991 and 1989 at Florida January 1994

Louda Direct Carduus spp. Biocontrol Rhinocyllus Cirsium Released in Repeated ~ 30 M (1998) conicus canescens 1960s sampling years Frölich from 1990 to 1996

Louda and Direct Cirsium Biocontrol Larinus Cirsium Released in Repeated ~ 8 years M O'Brien arvense L. planus undulatum var. 1992−1993 sampling in (2002) tracyi 1999 and 2000

133

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Rand and Direct Carduus Biocontrol Rhinocyllus Cirsium Released in Repeated ~ 30 M Louda nutans L. conicus undulatum 1969−1972 sampling in years (2004) Frölich 2001 and (Nutt.) Spreng. 2002

Takahashi Direct Carduus spp. Biocontrol Trichosiroca Cirsium Released since Repeated ~ 30 M et al. (2009) and Cirsium lus horridus altissimum L 1974 sampling in years spp. Panzer 2004 and 2005

Bower et al. Indirect: Urochloa Burning and NA A native skink Repeated Repeated 3 years E (2014) native- mutica grazing (Lampropholis treatments in sampling mediated delicate) and a 2004, 2005 between frog and 2006 2005 and (Limnodynastes 2007 convexiusculus)

134

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Carvalheiro Indirect: Chrysanthem Biocontrol Mesoclanis Dipteran seed Released in Repeated ~ 20 M et al. native- oides polana herbivores and 1996 sampling years (2008b) mediated monilifera parasitoids between ssp. April and rotundata June 2006 (L.) T. Nord

Louda et al. Indirect: Carduus spp. Biocontrol Rhinocyllus Paracantha culta Released in Sampling in ~ 40 M (2011) native- conicus 1960s May 2000 years mediated

Pearson and Indirect: Centaurea Biocontrol Urophora Peromyscus Released in Repeated ~30 M Callaway native- maculosa affinis, and Maniculatus 1970s sampling in years (2006) mediated Urophora (deer mice), and 2001, 2002 quadrifascia hantavirus and 2003 ta

135

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Pearson and Indirect: Centaurea Biocontrol Urophora Peromyscus Released in Repeated ~30 M Callaway native- maculosa affinis, and Maniculatus 1970s sampling years (2008) mediated Urophora (deer mice), and between quadrifascia Balsamorhiza 2001 to ta sagittata (a 2004 native forb)

Valentine Indirect: Cryptostegia Burning NA Reptile December Repeated 3 years E and native- grandiflora assemblages (e.g. 1999 and sampling in Schwarzkop mediated Heteronotia August 2000 January and f (2009) binoei and Carlia March 2001 pectoralis) and 2003

Valentine et Indirect: Cryptostegia Burning NA Bird assemblages Repeated Repeated 5 years E al. (2012) native- grandiflora (e.g. Dicaeum treatments in sampling in mediated hirundinaceum, 1999 and 2001 2001, 2003 and Aprosmictus and 2004. erythropterus)

136

Table 2.A.1 continued Reference Pathway Target Management Biocontrol Non-target Management Sampling Time Study invasive operation agent (if species time time span* setting† species applicable)

Overton et Indirect: Spartina spp. Herbicide NA An endangered Treatment Sampling 3 years M al. (2014) invasive- bird (Rallus from 2007 to from (imazapyr) mediated longirostris 2010 January obsoletus) 2007 to March 2010

Samways et Indirect: Acacia spp. Synthesized NA Sensitive The target Repeated > 2 years M al. (2011) invasive- approach endemic invader had sampling in mediated (felling, macroinvertebrat been removed January and burning and es (e.g. Petroplax for more than March 2004 herbicide) sp. and 2 years. Athripsodes sp. C)

* The time span indicates the time span between management operation and sampling for side effects. † The classification of “study setting” depend on whether the management operate as an experiment (“E”) or a real management program (“M”).

137

References to Appendix 2.3

Direct side effects

Baker, W. L., J. Garner, and P. Lyon. 2009. Effect of imazapic on cheatgrass and native plants in Wyoming big sagebrush restoration for gunnison sage-grouse. Natural Areas Journal 29:204-209. Clark, D. L., and M. V. Wilson. 2001. Fire, mowing, and hand-removal of woody species in restoring a native wetland prairie in the Willamette Valley of Oregon. Wetlands 21:135-144. DePrenger-Levin, M. E., T. A. Grant, and C. Dawson. 2010. Impacts of the introduced biocontrol agent, Rhinocyllus conicus (Coleoptera: Curculionidae), on the seed production and population dynamics of Cirsium ownbeyi (Asteraceae), a rare, native thistle. Biological Control 55:79-84. Havens, K., C. L. Jolls, J. E. Marik, P. Vitt, A. K. McEachern, and D. Kind. 2012. Effects of a non- native biocontrol weevil, Larinus planus, and other emerging threats on populations of the federally threatened Pitcher's thistle, Cirsium pitcheri. Biological Conservation 155:202- 211. Johnson, D. M., and P. D. Stiling. 1998. Distribution and dispersal of Cactoblastis cactorum (Lepidoptera : Pyralidae), an exotic Opuntia-feeding moth, in Florida. Florida Entomologist 81:12-22. Louda, S. M. 1998. Population growth of Rhinocyllus conicus (Coleoptera: Curculionidae) on two species of native thistles in prairie. Environmental Entomology 27:834-841. Louda, S. M., and C. W. O'Brien. 2002. Unexpected ecological effects of distributing the exotic weevil, Larinus planus (F.), for the biological control of Canada thistle. Conservation Biology 16:717-727. Louhaichi, M., M. F. Carpinelli, L. M. Richman, and D. E. Johnson. 2012. Native forb response to sulfometuron methyl on medusahead-invaded rangeland in Eastern Oregon. Rangeland Journal 34:47-53. Ortega, Y. K., and D. E. Pearson. 2010. Effects of picloram application on community dominants vary with initial levels of Spotted Knapweed (Centaurea stoebe) invasion. Invasive Plant Science and Management 3:70-80. Rand, T. A., and S. M. Louda. 2004. Exotic weed invasion increases the susceptibility of native plants attack by a biocontrol herbivore. Ecology 85:1548-1554. Renteria, J. L., M. R. Gardener, F. D. Panetta, and M. J. Crawley. 2012. Management of the invasive Hill Raspberry (Rubus niveus) on Santiago Island, Galapagos: eradication or indefinite control? Invasive Plant Science and Management 5:37-46. 138

Rinella, M. J., and B. J. Hileman. 2009. Efficacy of prescribed grazing depends on timing intensity and frequency. Journal of Applied Ecology 46:796-803. Rinella, M. J., B. D. Maxwell, P. K. Fay, T. Weaver, and R. L. Sheley. 2009. Control effort exacerbates invasive-species problem. Ecological Applications 19:155-162. Sheley, R. L., and M. K. Denny. 2006. Community response of nontarget species to herbicide application and removal of the nonindigenous invader Potentilla recta L. Western North American Naturalist 66:55-63. Takahashi, M., S. M. Louda, T. E. X. Miller, and C. W. O'Brien. 2009. Occurrence of Trichosirocalus horridus (Coleoptera: Curculionidae) on native Cirsium altissimum versus exotic C. vulgare in North American tallgrass prairie. Environmental Entomology 38:731- 740. Whitcraft, C. R., and B. J. Grewell. 2012. Evaluation of perennial pepperweed (Lepidium latifolium) management in a seasonal wetland in the San Francisco Estuary prior to restoration of tidal hydrology. Wetlands Ecology and Management 20:35-45. Zarnetske, P. L., E. W. Seabloom, and S. D. Hacker. 2010. Non-target effects of invasive species management: beachgrass, birds, and bulldozers in coastal dunes. Ecosphere 1:1-20.

Indirect side effects

Bower, D. S., L. E. Valentine, A. C. Grice, L. Hodgson, and L. Schwarzkopf. 2014. A trade-off in conservation: weed management decreases the abundance of common reptile and frog species while restoring an invaded floodplain. Biological Conservation 179:123-128. Carvalheiro, L. G., Y. M. Buckley, R. Ventim, S. V. Fowler, and J. Memmott. 2008. Apparent competition can compromise the safety of highly specific biocontrol agents. Ecology Letters 11:690-700. Louda, S. M., T. A. Rand, A. A. R. Kula, A. E. Arnett, N. M. West, and B. Tenhumberg. 2011. Priority resource access mediates competitive intensity between an invasive weevil and native floral herbivores. Biological Invasions 13:2233-2248. Overton, C. T., M. L. Casazza, J. Y. Takekawa, D. R. Strong, and M. Holyoak. 2014. Tidal and seasonal effects on survival rates of the endangered California clapper rail: does invasive Spartina facilitate greater survival in a dynamic environment? Biological Invasions 16:1897-1914. Pearson, D. E., and R. M. Callaway. 2006. Biological control agents elevate hantavirus by subsidizing deer mouse populations. Ecology Letters 9:443-450.

139

Pearson, D. E., and R. M. Callaway. 2008. Weed-biocontrol insects reduce native-plant recruitment through second-order apparent competition. Ecological Applications 18:1489-1500. Samways, M. J., N. J. Sharratt, and J. P. Simaika. 2011. Effect of alien riparian vegetation and its removal on a highly endemic river macroinvertebrate community. Biological Invasions 13:1305-1324. Valentine, L. E., and L. Schwarzkopf. 2009. Effects of weed-management burning on reptile assemblages in Australian tropical savannas. Conservation Biology 23:103-113. Valentine, L. E., L. Schwarzkopf, and C. N. Johnson. 2012. Effects of a short fire-return interval on resources and assemblage structure of birds in a tropical savanna. Austral Ecology 37:23-34.

140

Appendix 3.1 Ecological networks of the Forest and Town ecosystems under three management scenarios

(a)

Crabs

Cats Rats Goshawks Kestrels

Tropicbirds (W) Hawk-owls

Brown Boobies Flying-foxes Thrushes White-eyes

Geckos Pigeons Doves Chickens Insect resources (nocturnal)

Insect resources (diurnal) Fruit resources (canopy) Fruit resources (ground)

(b)

Crabs

Rats Goshawks Kestrels

Tropicbirds (W) Hawk-owls

Brown Boobies Flying-foxes

Thrushes White-eyes

Doves Pigeon Geckos

Insect resources (nocturnal) Chickens

Insect resources (diurnal) Fruit resources (canopy) Fruit resources (ground)

(c)

Tropicbirds (W) Goshawks

Brown Boobies

Hawk-owls

Thrushes White-eyes Geckos Insect resources (nocturnal) Kestrels

Crabs Pigeon Doves Chickens Flying-foxes

Insect resources (diurnal)

Fruit resources (ground) Fruit resources (canopy)

Figure 3.A.1 Ecological networks of the Forest ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal.

141

(a)

Crabs

Goshawks Cats Rats Hawk-owls

Tropicbirds (W)

Tropicbirds (R) Chickens Kestrels

Flying-foxes Cultivated resources (ground)

Insect resources (diurnal)

Cultivated resources (above-ground) Insect resources (nocturnal)

(b)

Crabs

Goshawks Rats Hawk-owls

Tropicbirds (W)

Tropicbirds (R) Chickens Kestrels

Flying-foxes Cultivated resources (ground)

Insect resources (diurnal)

Cultivated resources (above-ground) Insect resources (nocturnal)

(c)

Goshawks

Tropicbirds (W)

Tropicbirds (R)

Hawk-owls

Chickens Crabs

Flying-foxes Kestrels

Cultivated resources (ground) Insect resources (diurnal)

Cultivated resources (above-ground) Insect resources (nocturnal)

Figure 3.A.2 Ecological networks of the Town ecosystem under three management scenarios: (a) no management, (b) cat removal alone, and (c) combined cat and rat removal.

142

Appendix 3.2 Species responses to Strategies 3−7 for the qualitative assessment

Strategy 3 Island wide cat eradication without rat baiting

Eradication of cats may result in release of rats, which can be a potential threat to many species. The goshawk and the hawk-owl may have control effects on rats; however, given the low density of goshawks and that the two species also prey on a number of other species (Strategy 1), this control effect is less likely to compensate the meso-predator release effect of cat eradication. The cost of this strategy is estimated as a one-off cost ($5 million) for cat eradication.

Strategy 3 Seabirds’ responses

Expected responses

Red-tailed Tropicbirds: likely to show a positive response

White-tailed Tropicbirds: likely to show a positive response

Brown Boobies: likely to show positive a response

Supporting evidence

The abundant population of rats around seabird colonies may reduce the benefits of cat control (Fleet 1972, Ratcliffe et al. 2010, Low et al. 2013). The potential of rat population growth without cat regulation may be a future threat to these seabirds.

Strategy 3 Birds of prey’s responses

Expected responses

Christmas Island Goshawks: slightly likely to show a positive response

Christmas Island Hawk-owls: neutral, unlikely to be affected

Supporting evidence

Rats will still be available as a food resource for this raptor under this management strategy.

As discussed in Strategy 1 and 2, cat eradication may have little influence on the hawk-owl population.

Strategy 3 Frugivorous land birds’ responses

Expected responses

Christmas Island Emerald Doves: likely to show a positive response

143

Christmas Island Imperial Pigeons: neutral, unlikely to be affected

Supporting evidence

Predation pressure from cats will be removed. The extent of the impact of rats remains unclear.

This management strategy may have little benefit to the pigeon due to its canopy feeding behavior (see Strategy 1 Frugivorous land birds’ responses).

Strategy 3 Insectivorous land birds’ responses

Expected responses

Christmas Island Thrushes: likely to show a positive response

Christmas Island White-eyes: likely to show a positive response

Supporting evidence

Cat eradication is likely to lead to a positive response for both species. The threat of rats still exists but the extent is unknown (see Strategy 1 and 2 Insectivorous land birds’ responses).

Strategy 3 Mammals’ responses

Expected responses

Christmas Island Flying-foxes: likely to show a positive response

Supporting evidence

Cat eradication is expected to reduce the predation pressure on the flying-fox (see Strategy 1 and 2 Mammals’ responses).

Strategy 3 Reptiles’ responses

Expected responses

Christmas Island Giant Geckos: slightly likely to show a positive response

Supporting evidence

See Strategy 1 and 2 Reptiles’ responses.

Strategy 4 Town cat removal with Town rat baiting

This strategy focuses on the Town area and will have no effects on Forest populations in the Forest network, thus the Forest species were predicted to have the same responses under this strategy as under Strategy1 Do nothing. Cats are likely to move from the Forest to the Town (spillover), which

144 will require constant control efforts. This strategy had relatively high ongoing costs ($75,000 p.a. for Town cat removal and $15,000 p.a. for Town rat baiting).

Strategy 4 Seabirds’ responses

Expected responses

Red-tailed Tropicbirds: highly likely to show a positive response

White-tailed Tropicbirds: uncertain

Brown Boobies: highly likely to show a negative response

Supporting evidence

Removal of cats with rat baiting in Town was highly like to benefit the Red-tailed Tropicbird since this species mainly inhabits along the shoreline in the settlement area (Algar et al. 2012). While present in both Town and Forest regions, the White-tailed Tropicbirds in Town will benefit from this strategy but in the Forest this species is still at risk. Without spatial ecology information, it is not possible to predict the trend for the whole White-tailed Tropicbird population. This strategy has no benefit for the Brown Booby as this species inhabits the Forest area.

Strategy 4 Birds of prey’s responses

Expected responses

Christmas Island Goshawks: neutral, unlikely to be affected

Christmas Island Hawk-owls: neutral, unlikely to be affected

Supporting evidence

Cat removal in Town may indirectly benefit the goshawk through an increase in native prey yet rat baiting may reduce its food supply (see Strategy 1 and 2 Birds of prey’s responses). This species is also present in the Forest where cats and rats still exist. This strategy was expected to be less likely to affect (either positively or negatively) the goshawk.

As discussed in Strategy 1 and 2 Birds of prey’s responses, cat and rat managements have little influence on the hawk-owl population.

Strategy 4 Frugivorous land birds’ responses

Expected responses

Christmas Island Emerald Doves: likely to show a negative response

145

Christmas Island Imperial Pigeons: neutral, unlikely to be affected

Supporting evidence

As Forest birds, the dove and pigeon will benefit little from this strategy focusing on the Town area. The expected responses are the same as Strategy 1 Do nothing.

Strategy 4 Insectivorous land birds’ responses

Expected responses

Christmas Island Thrushes: likely to show a negative response

Christmas Island White-eyes: likely to show a negative response

Supporting evidence

As Forest birds, the expected responses of these two species are the same as Strategy 1 Do nothing.

Strategy 4 Mammals’ responses

Expected responses

Christmas Island Flying-foxes: slightly likely to show a negative response

Supporting evidence

Flying-foxes are present in both Town and Forest region. In the Town region where the flying-fox forages, it is likely that this species would benefit from cat removal (see Strategy 2 Mammals’ responses). In Forest areas where the flying-fox forages and lives, it is expected that the population response will be similar to Strategy 1 do nothing. Thus, I predict that this management strategy is still slightly likely to negatively affect the flying-fox population.

Strategy 4 Reptiles’ responses

Expected responses

Christmas Island Giant Geckos: likely to show a negative response

Supporting evidence

As a Forest species, the gecko will benefit little from this strategy which only focuses on the Town area.

Strategy 5 Town cat removal without Town rat baiting

146

This strategy focuses on the Town area and will have no effects on Forest species in the Forest network, thus the Forest species are predicted to have the same responses under this strategy as under Strategy1 do nothing. Cats are likely to move from the Forest to the Town (spillover), which may require constant control efforts. This strategy is estimated to have high ongoing costs of $75,000 p.a. for Town cat removal. Without rat control, the rats will still be a threat in the Town area and may also lead to negative perceptions of cat control among the Town’s human residents.

Strategy 5 Seabirds’ responses

Expected responses

Red-tailed Tropicbirds: likely to show a positive response

White-tailed Tropicbirds: uncertain

Brown Boobies: highly likely to show a negative response

Supporting evidence

Removal of cats is likely to benefit the Red-tailed Tropicbirds; however, there is potential risk of rat population increase (see Strategy 1 and 3 Seabirds’ responses).

See Strategy 4 Seabirds’ responses for the responses of the White-tailed Tropicbird and the Brown Booby.

Strategy 5 Birds of prey’s responses

Expected responses

Christmas Island Goshawks: neutral, unlikely to be affected

Christmas Island Hawk-owls: neutral, unlikely to be affected

Supporting evidence

See Strategy 4 Birds of prey’s responses for the responses of the goshawk and hawk-owl.

Strategy 5 Frugivorous land birds’ responses

Expected responses

Christmas Island Emerald Doves: likely to show a negative response

Christmas Island Imperial Pigeons: neutral, unlikely to be affected

Supporting evidence

147

See Strategy 4 Frugivorous land birds’ responses for the responses of the dove and the pigeon.

Strategy 5 Insectivorous land birds’ responses

Expected responses

Christmas Island Thrushes: likely to show a negative response

Christmas Island White-eyes: likely to show a negative response

Supporting evidence

See Strategy 4 Insectivorous land birds’ responses for the responses of the goshawks and hawk- owls.

Strategy 5 Mammals’ responses

Expected responses

Christmas Island Flying-foxes: slightly likely to show a negative response

Supporting evidence

See Strategy 4 Mammals’ responses for the flying-fox.

Strategy 5 Reptiles’ responses

Expected responses

Christmas Island Giant Geckos: likely to show a negative response

Supporting evidence

See Strategy 4 Reptiles’ responses for the gecko.

Strategy 6 Forest cat baiting

This strategy will have no effects on Town species in the Town network. This strategy is estimated to cost $150,000 p.a. but needs ongoing control efforts (baiting is unlikely to completely remove cats). Since baiting is less effective than eradication, the species threatened by cats will gain less benefit from baiting compared with eradication. The rats will still be present in Forest area as a threat.

Strategy 6 Seabirds’ responses

Expected responses

148

Red-tailed Tropicbirds: highly likely to show a negative response

White-tailed Tropicbirds: uncertain

Brown Boobies: likely to show a positive response

Supporting evidence

As they are only present in the Town area, Red-tailed Tropicbirds benefit little from this strategy and will be negatively affected by cats and rats in Town region (see Strategy 1 Seabirds’ responses). It is not possible to predict the trend of the whole White-tailed Tropicbird population as it is present in both Town and Forest regions but the spatial distribution is unknown (see Strategy 4 Seabirds’ responses). The Brown Booby will benefit from this strategy but there are potential risks of rat release and persistent impact from cats remained in the Forest (see Strategy 1 and 3 Seabirds’ responses).

Strategy 6 Birds of prey’s responses

Expected responses

Christmas Island Goshawks: likely to show a positive response

Christmas Island Hawk-owls: neutral, unlikely to be affected

Supporting evidence

Cat baiting may indirectly benefit the goshawk through an increase in native prey; and rats as a food resource will be still available (Strategy 1 and 3 Birds of prey’s responses). Overall, it is likely the goshawk will benefit from this management strategy.

As discussed in Strategy 1 and 2 Birds of prey’s responses, cat and rat managements have little influence on the hawk-owl population.

Strategy 6 Frugivorous land birds’ responses

Expected responses

Christmas Island Emerald Doves: slightly likely to show a positive response

Christmas Island Imperial Pigeons: neutral, unlikely to be affected

Supporting evidence

Given the risks of rat release and cat persistence, this strategy may only slightly likely to benefit the dove population (see Strategy 1 Frugivorous land birds’ responses).

149

This management strategy may have little benefit to the pigeon due to its canopy feeding behavior (see Strategy 1 Frugivorous land birds’ responses).

Strategy 6 Insectivorous land birds’ responses

Expected responses

Christmas Island Thrushes: slightly likely to show a positive response

Christmas Island White-eyes: slightly likely to show a positive response

Supporting evidence

Given the risks of rat release and cat persistence, uncertainties exist how much benefit these species will get from cat baiting. I predict that this strategy may be only slightly likely to benefit the populations of both species (see Strategy 1 and 2 Insectivorous land birds’ responses).

Strategy 6 Mammals’ responses

Expected responses

Christmas Island Flying-foxes: slightly likely to show a positive response

Supporting evidence

Cats may still persist to a certain extent and the benefits may not be significant with other threats still present (see Strategy 1 Mammals’ responses).

Strategy 6 Reptiles’ responses

Expected responses

Christmas Island Giant Geckos: slight likely to show a negative response

Supporting evidence

Cats may still persist to a certain extent and the benefits may not be significant with other threats still present (see Strategy 1 Reptiles’ responses).

Strategy 7 A combination of (4) and (6) with Town cat removal and Town rat baiting as well as Forest cat baiting

This strategy is aimed at both the Town and Forest areas. It requires a higher ongoing cost ($240,000 p.a.) since baiting is unlikely to completely remove cats and rats.

150

Strategy 7 Seabirds’ responses

Expected responses

Red-tailed Tropicbirds: highly likely to show a positive response

White-tailed Tropicbirds: likely to show a positive response

Brown Boobies: likely to show a positive response

Supporting evidence

Removal of cats with rat baiting in Town area is highly likely to benefit the Red-tailed Tropicbird (see Strategy 4 Seabirds’ responses). The White-tailed Tropicbird is also likely to respond positively since both Town and Forest populations are managed yet there is a potential impact from remaining cats and rats in Forest region (see Strategy 4 and 6 Seabirds’ responses). The Brown Booby will benefit from this strategy but there are potential risks from remaining cats and rats (see Strategy 6 Seabirds’ responses).

Strategy 7 Birds of prey’s responses

Expected responses

Christmas Island Goshawks: likely to show a positive response

Christmas Island Hawk-owls: neutral, unlikely to be affected

Supporting evidence

Combining both Forest and Town managements, it is likely the goshawk will benefit from this management strategy (see Strategy 4 and 6 Birds of prey’s responses).

As discussed in Strategy 1 and 2 Birds of prey’s responses, cat and rat management has little influence on the hawk-owl population.

Strategy 7 Frugivorous land birds’ responses

Expected responses

Christmas Island Emerald Doves: slightly likely to show a positive response

Christmas Island Imperial Pigeons: neutral, unlikely to be affected

Supporting evidence

This strategy is slightly likely to benefit the dove population (see Strategy 6 Frugivorous land birds’ responses).

151

This management strategy may have little benefit to the pigeon due to its canopy feeding behavior (see Strategy 1 Frugivorous land birds’ responses).

Strategy 7 Insectivorous land birds’ responses

Expected responses

Christmas Island Thrushes: slightly likely to show a positive response

Christmas Island White-eyes: slightly likely to show a positive response

Supporting evidence

This strategy is slightly likely to benefit the populations of both species (see Strategy 6 Insectivorous land birds’ responses).

Strategy 7 Mammals’ responses

Expected responses

Christmas Island Flying-foxes: slightly likely to show a positive response

Supporting evidence

Cats may still persist to a certain extent and the benefits may not be significant with other threats still present (see Strategy 1 Mammals’ responses).

Strategy 7 Reptiles’ responses

Expected responses

Christmas Island Giant Geckos: slightly likely to show a negative response

Supporting evidence

Cats may still persist to a certain extent and the benefits may not be significant with other threats still present (see Strategy 1 Reptiles’ responses).

152

Appendix 3.3 Key evidence and supporting literature

Table 3.A.1 A summary of evidence for cat abundance and diet on Christmas Island.

Sample Size Dietary study Resource Abundance (dietary study only) (Proportion by weight where available) Yorkston 0.13/km - - (1981)*

Tidemann 0.3/km - - (1989)*

Tidemann et - 93 gut/scat Black rats: 45% al. (1994) Christmas Island imperial pigeon: 28% Christmas Island flying-fox: 21%

Van der Lee 0.19/km 19 stomachs Native reptiles (including the Giant and Jarman Gecko): 30–40 % (1996) * Invertebrates: ~30–40% Majority of the stomachs contained mammal hairs, presumably black rats Algar and 0.15/km - - Brazall (2003) Corbett et al. - 92 scats Black rats (2003)* Grasshoppers Various bird species. Algar and - 17stomachs Predominantly grasshoppers Brazall A number of terrestrial bird species. (2005)*

* These works are cited in Algar and Johnston (2010).

153

Table 3.A.2 Supporting literature identifying the population status, habitat needs, threats and threat abatement strategies of species of conservation concern, or identifying the population status, ecology and potential impacts of the introduced or self-introduced non-native species on Christmas Island.

Species Key messages Supporting Evidence Red-tailed Population Estimated population: 1400–2000 Stokes (1988) cited in Tropicbird status* breeding pairs Beeton et al. (2010), (Phaethon James and McAllan rubricauda (2014) westralis) Habitat This species breeds in tree hollows and Clark et al. (1983) and rock crevices on terraces, making the ecology eggs and chicks vulnerable to predation by cats and rats. Major Severe predation by cats on chicks on Ishii (2006) cited in threats Christmas Island. Two surveys found Garnett et al. (2010) high chick mortality (100% and 96% respectively) from cat predation. Chicks and eggs could be taken by rats. Algar and Johnston (2010), Low et al. (2013) Evidence from other ecosystems: Fleet (1972) approximately half of the total number of chicks were killed by Pacific Rats (Rattus exulans), on Kure Atoll, Hawaii. Threat Nesting success increased by 65.8% after Algar et al. (2012) abatement cat baiting along the shoreline of the town area. White-tailed Population Estimated population: 12,000–24,000 Dunlop (1988) cited Tropicbird status breeding birds in 1998 in Garnett et al. (Phaethon (2010) lepturus) Golden Bosun (phaethon lepturus fulvus)

154

Table 3.A.2 continued Species Key messages Supporting Evidence White-tailed Population The degree of decline remains unknown, Department of the Tropicbird status but the population seems to be lower than Environment (2014d) (Phaethon observations in 1980s. The current lepturus) population is estimated to be fewer than Golden Bosun 10, 000, yet requires new population (phaethon assessment to validate. lepturus fulvus) Habitat This species breeds in tree hollows in Department of the rainforest, and appears more common on Environment (2014e) the upper terraces than on the shore terrace. Major Predation of nestlings by cats. Eggs could Department of the threats be taken by rats. Environment (2014d), Stokes (1988) cited in Beeton et al. (2010) Threat Evidence from other ecosystems: this Ratcliffe et al. (2010) abatement species recolonised on the mainland of Ascension Island after cat eradication and increased through time (from one individual in 2002 to 25 individuals in 2007). Brown Booby Population Estimated population: about 5000 Stokes (1988) cited in (Sula status breeding pairs, yet supporting data is Beeton et al. (2010), leucogaster) required. This species is not considered James and McAllan as threatened globally. (2014)

Habitat: The brown booby nests on the ground at James and McAllan the edge of the shore terrace and inland (2014) cliffs. Threats Chicks at risk from cat predation, as cat Stokes (1988) cited in scats are frequently found in the colonies. James and McAllan (2014)

155

Table 3.A.2 continued Species Key messages Supporting Evidence Brown Booby Threat Evidence from other ecosystems: this Ratcliffe et al. (2010) (Sula abatement species increased from six individuals to leucogaster) 29 individuals on the mainland of Ascension Island after cat eradication. Christmas Population The population size of this species is very Beeton et al. (2010) Island status small, estimated to be fewer than 250 Goshawk individuals (Accipiter There are no detailed population Hill (2004a) hiogaster estimates. The total population size may natalis) be as few as 100 adults.

Habitat This species mainly inhabits suitable tall Department of the and trees in native rainforest. Environment (2014a) ecology This species feeds on a variety of species Hill (2004a) on Christmas Island including white- eyes, thrushes, doves, domestic chickens, tropicbirds, rats, reptiles and invertebrates. Threats The goshawk is particularly vulnerable Hill (2004a) given the low population. Threats include by habitat loss, impacts of Yellow Crazy Ants, inbreeding depression and risks of natural catastrophe. Christmas Population Population estimated in 1994–1998: Hill and Lill (1998) Island Hawk- status about 820–1200 birds. Hill (2004b) owl A recent study showed the population of Low and Hamilton (Ninox natalis) this species was significantly (2013) overestimated by previous studies (e.g. Hill and Lill (1998)). Further research is needed to estimate the population size and trends.

156

Table 3.A.2 continued Species Key messages Supporting Evidence Christmas Habitat The habitat of this species ranges from James and McAllan Island Hawk- and forests on the coastal and inland terraces, (2014) owl ecology to secondary vegetation. (Ninox natalis) This nocturnal species is primarily Hill and Lill (1998) insectivorous, feeding on invertebrates Hill (2004b) such as tree crickets (Gryllacris rufovaria) and beetles (Coleoptera). It was also recorded eating vertebrates including rats and geckos. This species forages in the Town for a Low and Hamilton number of introduced species, including (2013) Asian House Geckos (Hemidactylus frenatus), cockroaches (Periplaneta Americana) and rats. Threats The species is vulnerable given the small Hill (2004b) population size. Habitat loss and Yellow Crazy Ants are considered as major threats. The hawk-owl may roost close to the ground, increasing exposure to the risk of cat predation. Christmas Population Estimated population: 900–3,500 Corbett et al. (2003) Island status individuals (by distance sampling) cited in James and Emerald Dove McAllan (2014) (Chalcophaps This species is regarded as a common James and McAllan indica natalis) endemic subspecies. (2014) Habitat This species is a habitat generalist, which Department of the and nests in most forest habitats on Christmas Environment (2014b) ecology Island. This species usually forages on the ground for fallen fruits and seeds.

157

Table 3.A.2 continued Species Key messages Supporting Evidence Christmas Threats The Yellow Crazy Ants are thought to be James and McAllan Island the primary threat to the Emerald Dove. (2014), Emerald Dove They reduce the availability of suitable Davis et al. (2008), (Chalcophaps breeding habitat and disturb foraging, yet Davis et al. (2010) indica natalis) it remains arguable whether this introduced species has already led / will lead to a substantial population reduction in Emerald Doves. Predation by cats and possibly rats may James and McAllan have impacts on this species, yet the (2014) extent of the impact remains unclear. Threatened Species Scientific Committee (2014a) Christmas Population Estimated population: 35,000–66,000 Corbett et al. (2003) Island status individuals (by distance sampling) cited in James and Imperial McAllan (2014) Pigeon (Ducula whartoni) This species is regarded as an abundant James and McAllan endemic species. (2014) Habitat This species occurs throughout the island, Threatened Species and and mainly inhabits in the forest on the Scientific Committee ecology island plateau. (2006a) The pigeon feeds on fruit in canopy and James and McAllan occasionally descends to the ground to (2014) feed and drink. Threats A dietary study found that the pigeon Tidemann et al. occurred in 9% of the cats examined. (1994)

158

Table 3.A.2 continued Species Key messages Supporting Evidence Christmas Population There are no reliable estimates. The most Corbett et al. (2003) Island Thrush status recent estimate of population is 20,000– cited in James and (Turdus 50,000 individuals (by distance McAllan (2014), poliocephalus sampling), yet this estimate is suggested James and McAllan erythropleurus) to be unreliable. An estimate of 15,000 (2014), individuals is also commonly used. Department of the Environment (2014f)

Habitat This species inhabits most habitats on the Department of the and island. Environment (2014f) ecology The thrush forages primarily on the forest James and McAllan floor and in understory vegetation. (2014), Threats The Yellow Crazy Ant is thought to be James and McAllan the major threat to this species, by (2014), reducing habitat availability, disturbing Davis et al. (2008), foraging and directly attacking injured Davis et al. (2010) individuals, but there is no evidence of detectable impact of the ant on the thrush. This species is under threat of predation Stokes (1988) cited in by cats and rats. James and McAllan (2014) Evidence from other ecosystems: Rattus Hutton et al. (2007) rattus led to extinctions of five terrestrial birds on Lord Howe Island including a thrush species (Turdus poliocephalus vinitinctus) and a species from the Zosterops genus (Zosterops strenua).

159

Table 3.A.2 continued Species Key messages Supporting Evidence Christmas Population Estimated population: 20,000 individuals Threatened Species Island White- status Scientific Committee eye (2006b) (Zosterops Habitat This species is common all forest habitats Threatened Species natalis) and and forages from forest floor to canopy Scientific Committee ecology (2006b) Threats The Yellow Crazy Ant is suspected to be Threatened Species a threat through habitat transformation, Scientific Committee but no direct impacts have been (2006b) measured. Predation by cats and possibly rats poses Stokes (1988) cited in a risk. James and McAllan (2014), Beeton et al. (2010) Evidence from other ecosystems: Rattus Hutton et al. (2007) rattus led to extinctions of five terrestrial birds on Lord Howe Island including a thrush species (Turdus poliocephalus vinitinctus) and a species from the Zosterops genus (Zosterops strenua). Christmas Population Estimated population: 331–1,469 Threatened Species Island flying- status individuals Scientific Committee fox This species has undergone a dramatic (2014c), (Pteropus decline. One study reported the Director of National natalis) population had declined 39% between Parks (2014b), sampling in 2006 and 2012. Woinarski et al. (2014)

Habitat This species forages mainly in the forest, Threatened Species and but also occurs in gardens and mines Scientific Committee ecology rehabilitated with native vegetation (2014c) communities.

160

Table 3.A.2 continued Species Key messages Supporting Evidence Christmas Threats Threats to this species includes habitat Threatened Species Island flying- loss, pollution due to phosphate mining, Scientific Committee fox predation by feral cats and indirect (2014c) (Pteropus impacts from Yellow Crazy Ants. The natalis) major cause of the population decline is not precisely known. The decline is possibly the result of a combination of a number of threats. A dietary study reported that the flying- Tidemann et al. fox occurred in 10% of cats examined. (1994) Giant Gecko Population No estimates of the total population size Threatened Species (Cyrtodactylus status are available. Scientific Committee sadleiri) (2014b) Although this species is still readily Beeton et al. (2010) found on the island, sampling suggests its Threatened Species population has declined dramatically. Scientific Committee (2014b) Habitat The Giant Gecko mainly occurs in the Threatened Species and forest on the plateau of the island and Scientific Committee ecology feeds on small invertebrates among the (2014b) forest understory. Threats There are multiple threats to this species Threatened Species including habitat loss, disease, and Scientific Committee predation and competition of introduced (2014b) species. Potential predators include cats, rats, the introduced Asian wolf snakes (Lycodonaulicus capucinus) and the introduced giant centipedes (Scolopendra subspinipes). Disturbance by introduced geckos and Yellow Crazy Ants may also affect this species.

161

Table 3.A.2 continued Species Key messages Supporting Evidence Black rat Population The rat may be more abundant than Low et al. (2013); (Rattus rattus) previous studies suggested. Christmas Island rats have a much smaller home range compared with those in a similar ecosystem (Hawaii), suggesting the rat density is relatively high. Ecology The unique large population of the land Algar and Johnston crabs on Christmas Island may play a (2010) role to limit the rat numbers, through either competition for food resources and/or predation on rat nestlings. Impacts Rats pose a significantly threat to the Beeton et al. (2010) native seabird populations, especially the two tropicbird species, and also affect a number of land birds such as the Emerald Dove, the thrush, and the white-eye. Nankeen Ecology A self-introduced species, which is Schulz and Lumsden Kestrel considered to be widespread on the (2009) (Falco island. This species feeds on Giant cenchroides) Grasshoppers (Valanga irregularis) (97% of total items) with a small number of other insects. This species has been observed to prey Dion Maple, personal on rats, yet it remains unclear whether communication this predation is opportunistic.

162

Appendix 4.1 The Boolean Minimization method

Step 1: Identifying unobserved patterns

Consider a model in which the direction of change in the population sizes of three species — A, B, and C — are recorded in response to control of cats. Imagine that an exhaustive search of the parameter space is performed, and three combinations of responses are never observed:

1. A, B, and C simultaneously have a positive response to cat control,

2. A has a positive response to cat control, B has a negative response to cat control, and C has a negative response to cat control, and

3. A has a positive response to cat control, B has a positive response to cat control, and C has a negative response to cat control.

The complete truth table describing the species responses to cat control is as follows. For an individual species X, the positive response is denoted as x and negative response is denoted as x’ (X indicates species A, B or C). Combinations which have been observed are encoded as ‘1’ or ‘true’ and combinations which have never been observed are encoded as ‘0’ or ‘false’.

A B C Observed a’ b’ c’ 1 a’ b’ c 1 a’ b c’ 1 a’ b c 1 a b’ c’ 0 a b’ c 1 a b c’ 0 a b c 0

Step2: Enumerating UCPs from unobserved patterns

A Boolean minimization on the table (with ‘-’ indicating irrelevant responses) for combinations that have never been observed results in:

163

A B C Observed a - c’ 0 a b c 0

The elimination of the response of B in the first row is the result of the Boolean algebra:

(a ˄ b’ ˄ c’) ˅ (a ˄ b ˄ c’) = a ˄ c’ ˄ (b’ ˅ b) = a ˄ c’

The minimized table gives us a minimal description of the combinations of responses that are never observed. There are two:

1. A, B, and C simultaneously have a positive response to cat control (combination abc), and 2. A has a positive response to cat control and C has a negative response to cat control (combination ac’).

Each of these minimal combinations of never-observed responses, known as ultimate canonical projections (UCPs). Several algorithms allow for enumerating all UCP, and here we use Espresso algorithm with the Python module PyEDA.

Step 3: Constructing implications based on UCPs

Each UCP can be turned into 2k- 2 equivalent implication rules, where k is the number of responses in the UCP.

For example, given the UCP ac’, we have

a ˄ c’ = 0, which implies a → ¬ (c’) ⇒ a → c or c’ → ¬ a ⇒ c’ → a’

Therefore, for the UCP ac’, the implication rules are:

1. a → c, 2. c’ → a’, which can be interpreted as:

1. if A has a positive response to cat control, then C will have a positive response to cat control

164

2. if C has a negative response to cat control, then A will have a negative response to cat control

For the UCP abc the implication rules are:

1. b ˄ c → a’ 2. a → b’ ˅ c’ 3. a ˄ c → b’ 4. b → a’ ˅ c’ 5. a ˄ b → c’ 6. c → a’ ˅ b’ which can be interpreted as:

1. if B has a positive response to cat control and C has a positive response to cat control then A will have a negative response to cat control 2. if A has a positive response to cat control then B will have a negative response to cat control or C will have a negative response to cat control 3. etc.

Thus the entire response behavior of the species in the model can be completely and succinctly described by the set of UCP or, equivalently, by a set of logical implications where each implication rule corresponds to one UCP. Implications that have ease-of-interpretation and intuitive appeal can be selected to inform real ecological problems (Degenne and Lebeaux 1996).

165

Appendix 4.2 Information of the nodes of the ecological networks

Table 4.A.1 Focal species and functioning species presented in the ecological networks. Information includes common and scientific name, category (native, endemic or introduced) to Christmas Island, role in network (focal or functioning), and habitat (F indicates forest and T indicates town).

Name in Role in Species Name Category Habitat figures network Red-tailed Tropicbird Tropicbirds (R) Native Focal T (Phaethon rubricauda westralis) * White-tailed Tropicbird Tropicbirds (W) Endemic Focal F, T (Phaethon lepturus) (Phaethon lepturus fulvus) Brown Booby Brown Boobies Native Focal F (Sula leucogaster) Christmas Island Goshawk Goshawks Endemic Focal F, T (Accipiter hiogaster natalis) Christmas Island Hawk-owl Hawk-owls Endemic Focal F, T (Ninox natalis) Christmas Island Emerald Dove Endemic Focal F (Chalcophaps indica natalis) Frugivorous Christmas Island Imperial Pigeon birds ** Endemic Focal F (Ducula whartoni) Christmas Island Thrush Thrushes Endemic Focal F (Turdus poliocephalus erythropleurus) Christmas Island white-eye White-eyes Endemic Focal F (Zosterops natalis) Christmas Island flying-fox Flying-foxes Endemic Focal F, T (Pteropus melanotus natalis) Giant Gecko Geckos Endemic Focal F (Cyrtodactylus sadleiri) Nankeen Kestrel Kestrels Self- Functioning F, T (Falco cenchroides) introduced

166

Table 4.A.1 continued Name in Role in Species Name Category Habitat figures network Feral Chicken Feral chickens Introduced Functioning F, T (Gallus gallus domesticus) Red crabs Crabs Endemic Functioning F, T (Gecarcoidea natalis)

* In the Town Network, the nodes of Red-tailed Tropicbirds (Tropicbirds (R)) and White-tailed Tropicbirds (Tropicbirds (W)) are lumped into one node as Tropicbirds (B).

** In the Forest Network, the nodes of the Christmas Island Emerald Dove and the Christmas Island Imperial Pigeon are lumped into one node as ‘Frugivorous birds’.

Table 4.A.2 Food resource nodes presented in the ecological networks. Information includes node name, description and the habitat where a node occurs (F indicates forest and T indicates town).

Node name Description Habitat

Insect resources (diurnal) Insect resources consumed by diurnal species F, T

Insect resources (nocturnal) Insect resources consumed by nocturnal species F, T

Fruit resources (canopy) Fruit resources in canopy F

Fruit resources (ground) Fruit resources on ground F

Cultivated produce (ground) Cultivated produce on ground T

Cultivated produce (above- Cultivated resource (i.e. fruits) above-ground T ground)

167

Appendix 4.3 Outcomes of the probabilistic approach for the Town network

Species response in the Town network was subject to the same problem of sizeable uncertainty. For the scenario of cat control only, the support for positive responses (grey bar including solid and striped grey) of goshawks and flying-foxes were more than 75%, while the positive responses of the two tropicbirds and hawk-owls were less supported. For the scenario of cat and rat control, the support for positive responses to both controls (solid grey) for all species were less than 50% (less than 20% for hawk-owls); and around 50% of the responses involved opposite signs (striped grey and striped black), indicating sizeable uncertainties in predicting species’ responses.

Figure 4.A.1 Aggregated responses of native species to cat control alone and combined cat and rat control for the Town network. For cat control alone, the grey bar (solid and striped) represents positive response and the black bar (solid and striped) represents negative responses. For combined cat and rat control, species showed four categories of responses: positive responses to both cat and rat control (solid grey); negative responses to both cat and rat control (solid black); positive responses to cat control and negative responses to rat control (striped grey); and negative responses to cat control and positive responses to rat control (striped black).

168

Appendix 4.4 Implication rules for all web variations and for different scenarios

Appendix 4.4.1 Response rules for cat control only for all web variations

(a)

(b)

Figure 4.A.2 Response rules for cat control only: (a) all web variations (the full web, no interaction between rats and crabs, no interaction between rats and kestrels, and no interaction between rats and kestrels or rats and crabs) in the Forest network; and (b) all web variations (the full web, no interaction between rats and crabs, no interaction between rats and kestrels, and no interaction between rats and kestrels or rats and crabs) in the Town network.

169

Appendix 4.4.2 Response rules for rat control only for all web variations

(a)

(b)

(c)

Figure 4.A.3 Response rules for rat control only responses: (a) three web variations (full Forest web, no interaction between rats and crabs in the Forest network, and no interaction between rats and kestrels or rats and crabs in the Forest network), (b) no interaction between rats and kestrels in forest network, and (c) all web variations in the Town network.

170

Appendix 4.4.3 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for all Forest network variations (a)

(b)

(c)

Figure 4.A.4 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for Forest network variations: (a) no interaction between rats and kestrels, (b) no interaction between rats and crabs, and (c) no interaction between rats and kestrels or rats and crabs.

171

Appendix 4.4.4 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for the full Forest web

We find that, for all but the potential negative effect on white-eyes, the negative effects of rat control also guarantee a positive effect for at least one species of concern (Figure 4.A.5). For example, for a longer rule Cat_-White-eyes ∧ Cat_+Frugivorous birds → Rat_+Thrushes ∨ Rat_+ Frugivorous birds ∨ Rat_-Gecko, one possible implication is that if cat control has a negative effect on white-eyes and a positive effect on frugivorous birds, then rat control will have a negative effect on geckos. However, by the rule TRUE → Rat_+Geckos ∨ Rat_+Hawk-owls (‘TRUE’ indicated the scenario ‘Positive effect of cat control on rats, negative effect of rat control on cats’) (Figure 4.4a in Chapter 4), which can also be expressed as TRUE ∧ Rat_- Geckos → ∨ Rat_+Hawk-owls according to Boolean algebra, the negative effect of rat control on geckos implies a positive effect on hawk-owls.

Figure 4.A.5 Response rules when cat control has a positive effect on rats and rat control has a negative effect on cats for the full Forest web (rules up to length 6). Only response rules involving a negative rat-control outcome are shown here.

172

Appendix 4.4.5 Response rules when cat control has a positive effect on rats and rat control has a positive effect on cats for all Town network variations

(a)

(b)

(c)

Figure 4.A.6 Response rules when cat control has a positive effect on rats and rat control has a positive effect on cats for all Town network variations (rules up to length 5): (a) no interaction between rats and kestrels, (b) no interaction between rats and crabs, and (c) no interaction between rats and kestrels or rats and crabs.

173

Appendix 4.4.6 Response rules for the scenario when cat control has a negative effect on the rat population

For completeness, we also considered the combined cat and rat control implication rules for the scenario in which cat control decreases the rat population. When cat control has a negative effect on rats and rat control has a negative effect on cats, for both Town and Forest networks the rules predict a positive effect of rat control upon at least one species of concern with two exceptions (Figure 4.A.7). First, for the rules involving feral chicken, one of the possible predictions is that only chicken show positive response while other species do not. The other exception rule exists for the negative effect of rat control upon the hawk-owl contingent upon the negative effect of cat control upon the same species (Figure 4.A.7b and Figure 4.A.8b). When cat control has a negative effect on rats and rat control has a positive effect on cats, only one rule involving the frugivorous birds and the goshawk is found for the Forest network, indicating all combinations of the other species are possible in the model (Figure 4.A.8a).

(a)

(b)

Figure 4.A.7 Response rules when cat control has a negative effect on rats and rat control has a negative effect on cats for (a) the full Forest network, and (b) the full Town network.

174

(a)

(b)

Figure 4.A.8 Response rules when cat control has a negative effect on rats and rat control has a positive effect on cats for (a) the full Forest network, and (b) the full Town network.

175

Appendix 5.1 Probability tables of the nodes in the Bayesian Network model

Table 5.A.1 Estimates (i.e. the prior probability tables) of the input nodes ‘Poa coverage’, ‘Poa extent’ and ‘natural habitat to be managed’.

Node: Poa coverage

Low Medium High

85.00 14.00 1.00

Node: Poa extent

Low Medium High

5.00 30.00 65.00

Node: Natural habitat to be managed

Low Medium High

8.00 20.00 72.00

176

Table 5.A.2 The conditional probability table for node ‘Poa management outcome’ in the BN model.

Level of Poa management

outcome Node: Poa Node: Poa Node: management No Low High extent coverage alternatives Low Medium Do nothing 12.54 19.40 68.06 Low Medium Herbicide 25.12 16.96 57.92 Low Medium Herbicide digging 50.45 17.60 31.95 Low Medium Bulldozer 67.34 18.40 14.26 Low Medium Steaming 25.12 16.40 58.48 Low High Do nothing 10.79 19.90 69.31 Low High Herbicide 23.70 14.41 61.89 Low High Herbicide digging 49.68 15.85 34.47 Low High Bulldozer 67.01 17.65 15.34 Low High Steaming 23.70 13.15 63.15 Low Low Do nothing 10.00 20.00 70.00 Low Low Herbicide 23.05 13.90 63.05 Low Low Herbicide digging 49.34 15.50 35.16 Low Low Bulldozer 66.86 17.50 15.64 Low Low Steaming 23.05 12.50 64.45 Medium Medium Do nothing 3.87 15.40 80.73 Medium Medium Herbicide 18.06 37.36 44.58 Medium Medium Herbicide digging 46.65 31.60 21.75 Medium Medium Bulldozer 65.71 24.40 9.89 Medium Medium Steaming 18.06 42.40 39.54 Medium High Do nothing 1.89 10.90 87.21 Medium High Herbicide 16.46 60.31 23.23 Medium High Herbicide digging 45.78 47.35 6.87 Medium High Bulldozer 65.34 31.15 3.51 Medium High Steaming 16.46 71.65 11.89 Medium Low Do nothing 1.00 10.00 89.00 Medium Low Herbicide 15.73 64.90 19.37 Medium Low Herbicide digging 45.39 50.50 4.11

177

Table 5.A.2 continued Level of Poa management

outcome Node: Poa Node: Poa Node: management No Low High extent coverage alternatives Medium Low Bulldozer 65.17 32.50 2.33 Medium Low Steaming 15.73 77.50 6.77 High Medium Do nothing 3.00 15.00 82.00 High Medium Herbicide 17.36 39.40 43.24 High Medium Herbicide digging 46.27 33.00 20.73 High Medium Bulldozer 65.54 25.00 9.46 High Medium Steaming 17.36 45.00 37.64 High High Do nothing 1.00 10.00 89.00 High High Herbicide 15.73 64.90 19.37 High High Herbicide digging 45.39 50.50 4.11 High High Bulldozer 65.17 32.50 2.33 High High Steaming 15.73 77.50 6.77 High Low Do nothing 0.10 9.00 90.90 High Low Herbicide 15.00 70.00 15.00 High Low Herbicide digging 45.00 54.00 1.00 High Low Bulldozer 65.00 34.00 1.00 High Low Steaming 15.00 84.00 1.00

178

Table 5.A.3 The conditional probability table for node ‘habitat recovery potential’ in the BN model.

Level of Habitat recovery

potential Node: Poa Node: Poa management Node: management Poor High coverage outcome alternatives Low No Do nothing 13.78 86.22 Low No Herbicide 13.07 86.93 Low No Herbicide digging 13.31 86.69 Low No Bulldozer 13.78 86.22 Low No Steaming 13.07 86.93 Low Low Do nothing 47.50 52.50 Low Low Herbicide 40.47 59.53 Low Low Herbicide digging 42.81 57.19 Low Low Bulldozer 47.50 52.50 Low Low Steaming 40.47 59.53 Low High Do nothing 70.00 30.00 Low High Herbicide 58.75 41.25 Low High Herbicide digging 62.50 37.50 Low High Bulldozer 70.00 30.00 Low High Steaming 58.75 41.25 Medium No Do nothing 14.41 85.59 Medium No Herbicide 13.58 86.42 Medium No Herbicide digging 13.86 86.14 Medium No Bulldozer 14.41 85.59 Medium No Steaming 13.58 86.42 Medium Low Do nothing 53.75 46.25 Medium Low Herbicide 45.55 54.45 Medium Low Herbicide digging 48.28 51.72 Medium Low Bulldozer 53.75 46.25 Medium Low Steaming 45.55 54.45 Medium High Do nothing 80.00 20.00 Medium High Herbicide 66.88 33.13 Medium High Herbicide digging 71.25 28.75

179

Table 5.A.3 continued Level of Habitat recovery

potential Node: Poa Node: Poa management Node: management Poor High coverage outcome alternatives Medium High Bulldozer 80.00 20.00 Medium High Steaming 66.88 33.13 High No Do nothing 15.00 85.00 High No Herbicide 14.06 85.94 High No Herbicide digging 14.38 85.63 High No Bulldozer 15.00 85.00 High No Steaming 14.06 85.94 High Low Do nothing 60.00 40.00 High Low Herbicide 35.39 64.61 High Low Herbicide digging 36.41 63.59 High Low Bulldozer 42.50 57.50 High Low Steaming 36.41 63.59 High High Do nothing 90.00 10.00 High High Herbicide 75.00 25.00 High High Herbicide digging 80.00 20.00 High High Bulldozer 90.00 10.00 High High Steaming 75.00 25.00

180

Table 5.A.4 The deterministic table for the deterministic node ‘loss of native plants’ in the BN model.

Node: Natural habitat to be Node: Habitat recovery Node: management Loss of native managed potential alternatives plants Low Poor Do nothing acceptable Low Poor Herbicide acceptable Low Poor Herbicide digging acceptable Low Poor Bulldozer acceptable Low Poor Steaming acceptable Low High Do nothing acceptable Low High Herbicide acceptable Low High Herbicide digging acceptable Low High Bulldozer acceptable Low High Steaming acceptable Medium Poor Do nothing unacceptable Medium Poor Herbicide unacceptable Medium Poor Herbicide digging unacceptable Medium Poor Bulldozer unacceptable Medium Poor Steaming unacceptable Medium High Do nothing acceptable Medium High Herbicide acceptable Medium High Herbicide digging acceptable Medium High Bulldozer acceptable Medium High Steaming acceptable High Poor Do nothing unacceptable High Poor Herbicide unacceptable High Poor Herbicide digging unacceptable High Poor Bulldozer unacceptable High Poor Steaming unacceptable High High Do nothing acceptable High High Herbicide acceptable High High Herbicide digging acceptable High High Bulldozer unacceptable High High Steaming acceptable

181

Table 5.A.5 The deterministic table for the deterministic node ‘loss of invertebrates’ in the BN model.

Node: Natural habitat to be Node: Habitat recovery Node: management Loss of managed potential alternatives invertebrates Low Poor Do nothing acceptable Low Poor Herbicide acceptable Low Poor Herbicide digging acceptable Low Poor Bulldozer acceptable Low Poor Steaming acceptable Low High Do nothing acceptable Low High Herbicide acceptable Low High Herbicide digging acceptable Low High Bulldozer acceptable Low High Steaming acceptable Medium Poor Do nothing unacceptable Medium Poor Herbicide unacceptable Medium Poor Herbicide digging unacceptable Medium Poor Bulldozer unacceptable Medium Poor Steaming unacceptable Medium High Do nothing acceptable Medium High Herbicide acceptable Medium High Herbicide digging acceptable Medium High Bulldozer acceptable Medium High Steaming acceptable High Poor Do nothing unacceptable High Poor Herbicide unacceptable High Poor Herbicide digging unacceptable High Poor Bulldozer unacceptable High Poor Steaming unacceptable High High Do nothing acceptable High High Herbicide acceptable High High Herbicide digging acceptable High High Bulldozer unacceptable High High Steaming acceptable

182

Table 5.A.6 The deterministic table for the deterministic nodes ‘loss of microbes’ in the BN model.

Node: Natural habitat to be Node: Habitat recovery Node: management Loss of managed potential alternatives invertebrates Low Poor Do nothing acceptable Low Poor Herbicide acceptable Low Poor Herbicide digging acceptable Low Poor Bulldozer acceptable Low Poor Steaming acceptable Low High Do nothing acceptable Low High Herbicide acceptable Low High Herbicide digging acceptable Low High Bulldozer acceptable Low High Steaming acceptable Medium Poor Do nothing acceptable Medium Poor Herbicide acceptable Medium Poor Herbicide digging acceptable Medium Poor Bulldozer acceptable Medium Poor Steaming acceptable Medium High Do nothing acceptable Medium High Herbicide acceptable Medium High Herbicide digging acceptable Medium High Bulldozer acceptable Medium High Steaming acceptable High Poor Do nothing acceptable High Poor Herbicide acceptable High Poor Herbicide digging acceptable High Poor Bulldozer acceptable High Poor Steaming acceptable High High Do nothing acceptable High High Herbicide acceptable High High Herbicide digging acceptable High High Bulldozer acceptable High High Steaming acceptable

183

Appendix 6.1 The process for structural alterations of the Bayesian Network model.

(a)

(b)

184

(c)

Figure 6.A.1 Three key conceptual models in the process of constructing the Bayesian Network for predicting management consequences of P. annua.

185