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Modeling the Distribution of a Widely Distributed but Vulnerable Marsupial: Where and How to Fit Useful Models?

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Modeling the Distribution of a Widely Distributed but Vulnerable Marsupial: Where and How to Fit Useful Models? Diego Brizuela Torres ORCID ID: 0000-0003-0227-7938 Submitted in total fulfilment of the requirements of the degree of Master of Philosophy November 2020 The University of Melbourne Faculty of Science School of BioSciences I Abstract The greater glider (Petauroides volans) is the largest of the Australian gliding marsupials. Once abundant, it is now nationally listed as vulnerable because evidence of population decline exists across its distributional range. This decline and its likely relation with wildfire and logging, has prompted focus on conservation of this species. Species Distribution Models (SDMs) relate species occurrences to environmental variables at observation sites to predict distributions or make inferences about their key drivers. Conservation planning and land management use SDMs to deliver predictions of species distributions across landscapes. When modelling a broadly distributed species like the greater glider, data are often gathered from sources that vary in quality. In such cases, accounting for sampling biases and selection of geographic extent for model fitting are two key methodological steps that can largely influence models results. In this thesis I tested the effect of taking alternative decisions regarding occurrence data processing, modelling method and geographic extent on models’ predictive performance and how different decisions might (or not) provide different information for conservation and land management actions in a region subject to commercial logging. In the first research chapter, I tested different methods for dealing with sampling biases when modelling the distribution of the greater glider across its entire range. I compiled a dataset of occurrence data of the greater glider and other arboreal marsupials and tested alternative ways to use this large but biased dataset. I used modelling methods that utilize different types of occurrence data, namely, presence-background and presence-absence methods. I found that using presence-absence models fitted to an expanded presence-absence dataset in which some data were inferred provided the best performing models. In the second research chapter, I compared range-wide and local SDMs to predict the distribution of the greater glider in East Gippsland, Victoria. I found that two models: a range- wide one, and a local model fitted with higher quality variables, were the best performing. Models delivered somewhat different spatial predictions but broadly agreed on the largest patches of high predicted probability and gave similar estimates of the proportion of habitat across different land uses in the East Gippsland Regional Forest Agreement. I also completed a preliminary assessment of the extent of greater glider habitat burnt during the 2019-2020 wildfires that affected eastern Australia. I found that a large proportion of habitat was affected, including recently established protected areas. Throughout this thesis I show that decisions regarding data processing, selection of modelling method and geographic extent can lead to substantially different distribution predictions. In a context of local conservation planning such as the East Gippsland Regional Forest Agreement, different models, nevertheless, provided similar information on the implications that forest management and logging restrictions may have on the conservation of greater glider habitat in this region. Although the solutions we implemented relied on the broad availability of biodiversity data in Australia, we advocate for modellers and users to undertake thorough assessments of the data available in their regions and think carefully on how to make the best use of it. II Declaration This is to certify that: (i) the thesis comprises only my original work towards the Master of Philosophy, except where indicated in the preface; (ii) due acknowledgement has been made in the text to all other material used; and (iii) the thesis is fewer than the maximum word limit in length, exclusive of tables, maps, bibliographies and appendices or that the thesis is 50,000 as approved by the Research Higher Degrees Committee. Diego Brizuela Torres 01/07/2020 III Preface This thesis is written as a thesis with publications and is comprised of five chapters. The first chapter is a general introduction and the second one a literature review. Chapters three and four constitute the research chapters of this thesis and will be revised to comply with journal requirements before being submitted as research papers in peer-reviewed journals. The fifth chapter is a general discussion and conclusion that draws together the results of both research chapters. I am the primary author of all manuscripts, which are co-authored by my supervisors: Natalie Briscoe, Gurutzeta Guillera-Arroita and Jane Elith. I am responsible for designing and conducting the research, collecting and analysing the data, and writing the manuscripts. The co-authors provided advice on study design, assisted in improving the methodology, data handling and polishing the manuscripts. I was supported by a Melbourne Research Scholarship – 2018 granted by the University of Melbourne and by a CONACYT-Regional Occidente 2019 scholarship, number 739843, granted by The Consejo Nacional de Ciencia y Tecnología – CONACYT (the Mexican National Council for Science and Technology). IV Acknowledgements To my supervisors, Natalie Briscoe, Gurutzeta Guillera-Arroita and Jane Elith, to whom I am deeply grateful for their constant support, patience and encouragement. To Brendan Wintle and Lindy Lumsden for their ideas and feedback throughout the development of this project. To all the beautiful people in the Quantitative and Applied Ecology group (Qaeco) and the Centre of Excellence for Biosecurity Risk Analysis (CEBRA), for their friendship and support. To my familia and amigos back home, gracias siempre. V Table of Contents 1 Introduction .................................................................................................................1 1.1. The greater glider ........................................................................................................................ 1 1.2. Species distribution models and data related challenges ........................................................... 2 1.3. Species distribution models to inform conservation actions at the local scale .......................... 3 1.4. Thesis aims and structure ........................................................................................................... 8 1.5. Bibliography ................................................................................................................................ 9 2 Literature Review ....................................................................................................... 14 2.1. Species distribution models and their applications .................................................................. 14 2.2. Species distribution models to assist conservation decisions .................................................. 15 2.3. Imperfect data: sampling bias and data related challenges in species distribution models .... 18 2.4. Selection of geographic extent and resolution for model fitting .............................................. 20 2.5. Species distribution models to aid greater glider conservation ............................................... 22 2.6. Concluding remarks .................................................................................................................. 25 2.7. Bibliography .............................................................................................................................. 26 3 Different Methods to Model Biased Occurrence Data Result in Divergent Predictions of a Widely Distributed Vulnerable Marsupial ........................................................................ 37 3.1 Introduction .............................................................................................................................. 38 3.2 Methods .................................................................................................................................... 40 3.2.1 Study area ........................................................................................................................ 40 3.2.2 Occurrence data ............................................................................................................... 40 3.2.3 Additional species ............................................................................................................ 41 3.2.4 Generating model fitting and evaluation datasets .......................................................... 41 3.2.5 Predictor variables ........................................................................................................... 47 3.2.6 Model fitting ..................................................................................................................... 50 3.2.7 Model evaluation ............................................................................................................. 54 3.2.8 Qualitative analyses of spatial predictions ....................................................................... 55 3.3 Results ....................................................................................................................................... 55 3.3.1 Presence-background models .........................................................................................
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