The Impact of Climate Change on a Tropical Carnivore: from Individual to Species
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The Impact of Climate Change on a Tropical Carnivore: From Individual to Species Daniella Dakin Rabaiotti A dissertation submitted for the degree of Doctor of Philosophy UCL 2 Declaration I, Daniella Rabaiotti, confirm the work presented in this thesis is my own. The research was supported by NERC through the London NERC DTP. All data analysis data visualisation and modelling was done by Daniella Rabaiotti. Tim Coulson provided training in individual based modelling. Mike Croucher assisted in code optimisation for the individual based model. All chapters of this thesis were written by Daniella Rabaiotti, with guidance and comments from Rosie Woodroffe and Richard Pearson. Tim Coulson provided comments on Chapter 4, and Rosemary Groom, J.W. McNutt and Jessica Watermeyer provided comments on Chapter 3. Data from Laikipia, Kenya, on wild dog survival and movements were collected by Rosie Woodroffe and the Kenya Rangelands Wild Dog and Cheetah Project. Data on wild dog mortality in Savé Valley, Zimbabwe were collected by Rosemary Groom and the Savé Valley team at the African Wildlife Conservation Fund. Data on wild dog mortality in the Okavango, Botswana, were collected by J.W. McNutt and the Botswana Predator Conservation Trust. Data on wild dog distributions were provided by the Rangewide Conservation Programme for Cheetah and African Wild Dogs. Cover art was designed by Daniella Rabaiotti and created by Selina Betts. Graphical abstracts were designed by Daniella Rabaiotti and Gaius J. Augustus and created by Gaius J. Augustus. 3 4 Abstract Climate change is impacting species globally. Predicting which species will be impacted, where, when, and by how much, is vital to conserve biodiversity in a warming world. In this thesis, I evaluate the likely impacts of climate change on an endangered species, the African wild dog, Lycaon pictus, for which direct impacts of high ambient temperature on behaviour and recruitment have previously been identified. Wild dogs hunt mainly in daylight, and I show they are unlikely to be able to adapt to a warming climate by hunting at night. I found nocturnal hunting was constrained by the availability of moonlight, and by the need to guard pups in the den, restricting the use of cooler night-time hours. I also show high ambient temperatures are associated with increased adult mortality, appearing to increase mortality due to human causes and disease, which is linked to human pressures through transmission from domestic dogs. Having quantified the impacts of ambient temperature on key vital rates, I develop an Individual-Based Model to project the likely effects of climate change on population growth. I show that population projections for this species are sensitive to the emissions scenario and population size, with population collapse predicted for smaller populations under the worst-case scenario. Finally, I use my Individual-Based Model to make spatially explicit predictions of population changes throughout the species’ remaining range. My model predicts that populations in cooler coastal regions will suffer the smallest population declines, along with populations located in East Africa. Predicted threat status of the species was dependant on the emissions scenario. My study shows how behavioural and demographic data can be used to inform conservation planning in a changing climate. My findings also inform efforts to incorporate climate change impacts into assessments of species’ threat status by the IUCN Red List. 5 Impact statement Climate change is one of the biggest threats to species globally. In contrast with most other threats to species, protected areas provide little protection against climate change, which is slower and more difficult to reverse than threats such as habitat loss. Identifying how species will respond as temperatures rise, and where they are most likely to persist, will be vital to implementing conservation actions that ensure the survival of species under future climatic conditions. Whilst current methods of assessing species vulnerability work well with limited data, and can be used across high numbers of species, for those species where extensive data exist, focused, data driven models of the species’ responses to climate change may shed new light on climate change threats. The need for detailed, species specific models is especially acute for endangered species, which are often already range-limited due to other threats such as habitat loss or invasive species. This thesis presents novel evidence of climate change threats to the endangered African wild dog at individual, population, and species-wide level. Through identifying the drivers of temperature impacts on adult mortality it indicates that conservation interventions designed to mitigate other risks such as disease can also buffer the species from the effects of high temperatures. Similarly, through highlighting areas where the species is predicted to experience the highest and lowest level of climate-driven population declines the results of this thesis provide a valuable tool for conservationists to use when planning conservation actions for the species. By highlighting the ability of data-driven individual based model to assess future Red List Criteria for species this thesis also highlights ways in which predictions of climate change impacts on species can be used alongside the Red List Framework to predict species’ threat level. It also highlights some concerns with how these kinds of models are integrated into red listing. The methods used in this thesis are unique, but readily adaptable to other species, or even multi-species systems. They provide an example that can be followed for other species for which large datasets exist, which include many culturally and economically important species which are likely to be a priority for climate change mitigation actions in the future. 6 Acknowledgements There are many people I need to thank for their help and support throughout my PhD, without them this PhD would not have been possible, and a much less enjoyable process. A huge thankyou needs to go to my supervisors – Rosie Woodroffe and Richard Pearson. They conceived the project, and supported me throughout. Without Rosie I would have no data, and wouldn’t be half the writer that I am today. Thank you so much for welcoming me to team wild dog, encouraging me to pursue my interests throughout the course of this PhD, and helping me grow as a scientist and a person. Thanks to Richard, who kept me on track, and encouraged big-picture thinking, guiding my thesis narrative and progress throughout the course of this project. Huge thanks also go to my ‘unofficial supervisors’. Primarily Tim Coulson, who put up with me crashing his office in Oxford for weeks at a time and asking a constant stream of questions. His patience and support while teaching me IBMs was invaluable, with his assistance I went from weeping into R to constructing the IBM used in this work from scratch. Thanks also to Mike Croucher, who helped me optimise my code so that it didn’t take a ridiculously long time to run. Thanks to Rosemary Groom and Tico McNutt for the use of their long term datasets, and to Jess Watermeyer for always answering any questions I had about the data. I have had amazing support from both of my departments – CBER and IoZ. I am incredibly grateful for my lab group, team wild dog: Helen O’Neill, Tom Smallwood and Susie Gold. In particular Helen, who welcomed me both in the office and in the field as a first year who knew nothing about African wild dogs. Without my tea breaks with Tom, and the ability to bounce ideas and coding issues off him, I would probably still have infinite wild dogs in my model. Helen, Tom and Susie have not only been amazing work-mates but great friends (when Susie turns up). Thanks to Stef Strebel and Dedan Ngatia who managed the field project throughout the course of my PhD, sending updates, weather station data and pup photos to the rest of us back in the office. Huge thanks also has to go to my three Masters students – Aimee McIntosh, Sophie Morrill, and Ben Chapple, all of whom assisted with side projects linked to this work, and forced me to actually comment my code. 7 I am hugely grateful to the IoZ tea group, especially Henny Schulte to Bühne and Isla Watton, who made sure I actually took breaks, and even left my house, in the final few months. I could not wish to be surrounded by a better group of colleagues and friends. Nathalie Pettorelli has been an amazing mentor and inspiration to me through her support for early career researchers and women in science. A huge thanks to Cass Raby for being someone I could always panic with as I wrote up. Linda DaVolls is an absolute hero. I can’t thank Cheryl enough for her wizardry with IoZ finance. I could not have acknowledgements in this thesis without thanking the amazing science community on Twitter. The encouragement and support from people around the world has been invaluable, whether it has been sending papers, helping with R code or commiserating paper rejections. The early career support from other researchers on Twitter has been invaluable. I would especially like to thank Anthony Caravaggi and Steph Januchowski-Hartley, who have become good friends and collaborators. A massive thank you to Nick Caruso, who not only wrote three books with me throughout the course of this thesis but also helped me master the art of ggplot. I couldn’t hope for a better co-author. Hopefully we can actually meet each other soon! Without my publishers, Quercus and Hachette our books would not exist and I would not have the science communication skills, media experience or financial security that I do today. Thanks to my agent Chris Wellbelove who helped me navigate the stress of doing a PhD whilst also being a first time author.