Population Estimates of Large Carnivores and Their Conflict with Humans in East Africa and India Aleksander Ryszard Braczkowski Msc Zoology (Oxon)

Population estimates of large carnivores and their conflict with humans in East Africa and India Aleksander Ryszard Braczkowski MSc Zoology (Oxon)

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2020 School of Earth and Environmental Sciences

1 Abstract

Humanity is exerting unprecendented pressure on natural ecosystems and the species living in them. This pressure is particularly evident among the larger members of the order Carnivora. Their large body size (typically in the 25-600 kg range), life history traits, and reliance on large prey species places them at increased risk of extinction. The International Union for the Conservation of Nature (IUCN) Cat Specialist Group, and the Convention of Migratory Species (CMS) both recognize the deficiencies in robust data available on large carnivores across large tracts of Africa. Furthermore, the population estimates we do have are often drawn from less-reliable methods.

The overarching aim of this PhD thesis was to: 1) use a recently-developed population estimation technique (Elliot and Gopalaswamy 2017) to estimate the densities, population size, and population parameters of large carnivores in the Queen Elizabeth Conservation Area (QECA), Uganda, and use these data to inform their conservation status, 2) improve understanding of the conflict between large carnivores and human communities in Lake Mburo, Uganda, and Mumbai, India, and 3) explore alternative methods to fund conservation measures, including compensation and a wildlife imagery royalty.

In Chapter 1 as part of introducing my thesis, I examined the literature on historic and present methods being used to census African lions Panthera leo and together with a team of international collaborators I made a case for the adoption of spatially explicit capture recapture (SECR) methods for African lions. In Chapter 2 I built upon this and showed the utility of using population state variables (namely movement, sex-ratios, and density) in assessing the conservation status of African lions in a poorly known area of East Africa. I used a population of African lions in south-western Uganda’s Queen Elizabeth Conservation Area (QECA), as a model. I conducted a 93-day African lion census in 2017-2018 and compared the results to those from an intensive radio-collaring study from a decade ago. I hypothesized that if the population of African lions in the QECA was stable or increasing, lion movement distances and home ranges would be similar between the two study periods but if movement distances were larger and sex-ratios were male-biased, the lion population was likely declining. I found male lions expanded their ranges by > 400%, and females >100%, overall lion densities were low (2.70 lions/100 km2, posterior SD=0.47), and the sex ratio of lions in the system was skewed towards males (1 female lion: 2.33 males), suggesting a decline. I concluded this chapter with a discussion of the practical conservation 2 application of using this census technique in other parts of Africa, particularly where historic lion home-range data exist.

In Chapter 3, I used the same spatially explicit capture recapture models on data collected from 74 remote camera traps set across the QECA to assess the population densities of African leopards and spotted hyenas in this savannah park. We surveyed the northern, and southern sections of the QECA, and estimated leopard densities to be 5.03 (range = 2.80– 7.63), and 4.31 (range = 1.95–6.88) individuals/100 km2 respectively, while hyena densities were 13.43 and 14 individuals/100 km2. Estimates of hyena density were the highest recorded for the species anywhere within their range using SECR methods. I also suggested that the high hyena densities could be related to the evidence provided in Chapter 2 of African lion decline in the QECA. One hypothesis that could explain the inverse densities of hyenas and lions is that hyenas have experienced competitive release from African lions in the QECA. Similar findings have been reported in the Talek region of Kenya’s Maasai Mara, and Zambia’s Liuwa Plains. This chapter also provided the first SECR population estimates of leopards, and spotted hyenas anywhere in Uganda.

In Chapter 4 and Chapter 5 I addressed the most important threat to the existence of large carnivores: conflict with human communities, and their livestock. While conflict tends to dominate the narrative where large carnivores and humans co-exist, there can often be direct and indirect benefits to humans. In Chapter 4 I examined the ecosystem services provided to people by the Indian leopard Panthera pardus fusca, in Mumbai. The Sanjay Gandhi National Park (SGNP) is located in the city of Mumbai, India, and has some of the highest human population densities in the world. Large carnivores are known to control prey populations, suppress smaller carnivores, reduce parasite load in humans, and promote seed dispersal. However, this chapter is one of the first studies highlighting the ecosystem services provided by a large carnivore outside of a natural or protected system. I showed that leopard predation on stray dogs reduced the number of people bitten by dogs, reduced the risk of rabies transmission, and reduced dog sterilization and management costs. Our estimates showed that dog densities around SGNP (17.3/km2) were 40 times lower than four nearby urban informal settlements (688/km2) and were ten times lower than the citywide mean (160/km2). If it is, as we propose, leopards that are holding the dog population around the park at its current density, dog bites could increase from 3.6 bites/1000 people to 15.5 bites/1,000 people if leopards were to disappear. As over 78% of dog bites in Mumbai require treatment, and 2% require rabies post-exposure vaccination, the treatment costs 3 could reach as high as US$ 200,000 per year (compared to ~US$ 42,500 currently). As development pressures are threatening the region’s leopards, this work shows the potential costs of their local extirpation.

Chapter 5 assesses the landscape-level correlates of livestock attacks by two large carnivores, the spotted hyena, and African leopard in the cattle and sheep/goat farms bordering Lake Mburo National Park, south-western Uganda. I also make suggestions on how to improve the sustainability of a voluntary financial compensation scheme run by a local lodge (the Mihingo Conservation Fund) aimed at alleviating persecution of these species. I used ten years of depredation events to investigate the importance of seasonality and landscape features (ie. terrain ruggedness, proximity to roads, water, human settlements, and vegetation density) on livestock attack probability. I also examined the current costs of the compensation scheme of reported attacks. I showed that most livestock attacks in this region were caused by spotted hyenas, both predators killed at night, did not exhibit seasonal patterns in depredation, and attacks were owed to poorly fortified bomas (82% of leopard attacks and 64% of hyena attacks were made inside bomas). Attacks were also made near human settlements, close to the national park border, and in areas of rugged terrain. The compensation fund made more gross income from tourism activities than was paid in compensation in most years, but compensation costs had to be subsidised by the lodge because the funding was also used in other community development projects (eg. building of a school, and paying children’s school fees).

Chapter 6 of this thesis built upon the sub-theme of Chapter 5, funding of carnivore conservation measures and created a roadmap for a recently proposed idea of a threatened wildlife imagery royalty to stem the large budgetary shortfalls facing large carnivore conservation. The idea of a threatened species imagery royalty was proposed in two recent papers, Good et al. (2017) and Courchamp et al. (2018). I built upon these and discussed how such a royalty could be implemented, explored several legal avenues for its application, and also showed its potential scale in leveraging funding. The creation of a national law which charges a royalty from corporations using the imagery of their threatened wildlife, and a “Fairtrade” equivalent held the most promise for the development of a wildlife imagery royalty. Indeed, articles 3 and 6 of the Convention on Biological Diversity (CBD) encourage sovereign states to ensure activities within their jurisdiction and control do not damage the environment of other states. Similarly they are encouraged to develop national strategies, plans or programs for the conservation and sustainable use of biological diversity. The

4 funding that could potentially be leveraged from a wildlife imagery royalty is immense. I used large felids as a model group to show that the relevant 14 companies on the Forbes 2000 list alone could generate US$ 202 million–2.02 billion if they paid 0.1-1% of their profits in royalties.

My thesis addressed an important but often overlooked component of estimating large carnivore populations, the use of population state variables in informing conservation status. The use of animal movement, sex-ratio, and density information has wide application that transcends large carnivores. My assessment of leopard-dog interactions, and the potential implications for humans, was one of the first examples in the literature of the potential benefits a large carnivore may have to humans. The assessments of compensation and wildlife imagery royalties have important consequences on better managing and also leveraging funding for the conservation of large carnivores and other threatened, enigmatic species.

5 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, financial support 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 higher degree by research 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 and have sought permission from co- authors for any jointly authored works included in the thesis.

6 Publications included in this thesis

Braczkowski, A. O’Bryan, C.J. Stringer, M. Watson, J.E.M. Possingham, H. Beyer, H. (2018). Leopards provide public health benefits in Mumbai, India. Frontiers in Ecology and the Environment, 16 (3), 176-182. – incorporated as Chapter 4.

Contributor Statement of contribution A. Braczkowski Conception and design (50%) Analysis and interpretation (40%) Drafting and production (40%) C.J. O’Bryan (Candidate) Conception and design (50%) Analysis and interpretation (40%) Drafting and production (40%) M Stringer Analysis and interpretation (5%) J.E.M. Watson Drafting and production (5%) H. Possingham Analysis and interpretation (5%) Drafting and production (5%) H. Beyer Analysis and interpretation (10%) Drafting and production (10%)

Braczkowski, A.R. Gopalaswamy, A. Nsubuga, M. Allan, J. Biggs, D. & Maron, M. (2020). Detecting early warnings of pressure on an African lion population in the Queen Elizabeth Conservation Area, Uganda. Ecological Solutions and Evidence, 1 (1) – incorporated as Chapter 2.

Contributor Statement of contribution A.R Braczkowski (Candidate) Conception and design (60%) Analysis and interpretation (70%) Drafting and production (80%) A. Gopalaswamy Conception and design (20%) Analysis and interpretation (15%) Drafting and production (5%) M. Nsubuga Conception and design (5%) J. Allan Analysis and interpretation (10%) Drafting and production (5%) D. Biggs Drafting and production (5%) M. Maron Conception and design (15%) Analysis and interpretation (5%) Drafting and production (5%)

Submitted manuscripts included in this thesis

Braczkowski, A.R. Gopalaswamy, A. Bezzina, A. Isoke, S. & Maron, M. First SECR population estimates of leopards and hyenas in south-western Uganda. Submitted to PLOS ONE. – incorporated as Chapter 3.

Contributor Statement of contribution A.R Braczkowski (Candidate) Conception and design (60%) Analysis and interpretation (70%) Drafting and production (70%) A. Gopalaswamy Conception and design (20%) Analysis and interpretation (10%) Drafting and production (10%) A. Bezzina Conception and design (5%) Analysis and interpretation (10%) Drafting and production (5%) S. Isoke Analysis and interpretation (5%) Drafting and production (5%) M. Maron Conception and design (15%) Analysis and interpretation (5%) Drafting and production (10%)

Braczkowski, A.R. Fattebert, J. O’Bryan, C. Biggs, D. Schenk, R. & Maron, M. (in review). Human-carnivore conflict and compensation on the edge of Uganda’s Lake Mburo National Park: landscape drivers and financial sustainability. Conservation Science and Practice. – incorporated as Chapter 5.

Contributor Statement of contribution A. Braczkowski (Candidate) Conception and design (70%) Analysis and interpretation (70%) Drafting and production (75%) D. Biggs Conception and design (5%) Drafting and production (5%) M. Maron Conception and design (5%) Drafting and production (5%) R. Schenk Analysis and interpretation (5%) Drafting and production (5%) C. O’Bryan Analysis and interpretation (10%) Drafting and production (5%) J. Fattebert Conception and design (20%) Analysis and interpretation (15%) Drafting and production (5%) 8

Braczkowski, A.R. Biggs, D. Jones, K. Allan, J. & Maron, M. Marketing products with wildlife – how to make it benefit conservation (in review). PLOS BIOLOGY. – incorporated as Chapter 6.

Contributor Statement of contribution A.R Braczkowski (Candidate) Conception and design (60%) Analysis and interpretation (70%) Drafting and production (70%) D. Biggs Conception and design (5%) Analysis and interpretation (5%) Drafting and production (5%) K. Jones Conception and design (5%) Drafting and production (10%) J. Allan Conception and design (5%) Analysis and interpretation (5%) Drafting and production (5%) M. Maron Conception and design (20%) Analysis and interpretation (20%) Drafting and production (10%) M. Natali Conception and design (5%)

Other publications during candidature

Braczkowski, A.R. Ruzo, A. Sanchez, F. Castagnino, R. Brown, C. Guynup, S. Winter, S. Gandy, D. & O’Bryan, C.J. (2019). The Ayahuasca tourism boom – an undervalued demand driver for jaguar parts? Conservation Science and Practice, e126. https://doi.org/10.1111/csp2.126

O’Bryan, C.J. Braczkowski, A. Beyer, H. Carter, N. Watson, J.E.M. McDonald-Madden, E. (2018). The contribution of predators and scavengers to human well-being. Nature Ecology & Evolution 2, 229-236. – incorporated as Appendix 2.

Braczkowski, A. Holden, M. H. O'Bryan, C.J. Choi, C. Gan, X. Beesley, N. Gao, Y. Allan, J. Tyrrell, P. Stiles, D. Brehony, P. Meney, R. Brink, H. Takashina, N. Lin, M. Lin, H. Rust, N. Salmo, S. G. Watson, J. E. Kahumbu, P. Maron, M. , Possingham, H. P. and Biggs, D. (2018). Reach and messages of the world's largest ivory burn. Conservation Biology, 32: 765-773.

9 Biggs, D. Holden, M. H. Braczkowski, A. Cook, C. N. Milner-Gulland, E. J. Phelps, J. Scholes, R.J. Smith, R.J. Underwood, F.M. Adams, V.M. Allan, J. Brink, H. Cooney, R. Gao, Y. Hutton, J. Macdonald-Madden, E. Maron, M. Redford, K.H. Sutherland, W.J. & Possingham, H. (2017). Breaking the deadlock on ivory. Science, 358(6369), 1378-1381.

Negret, P. J. Allan, J. Braczkowski, A. Maron, M. & Watson, J. E. (2017). Need for conservation planning in post conflict Colombia. Conservation Biology, 31: 499-500. https://doi.org/10.1111/cobi.12935.

Biggs, D. Holden, M. Braczkowski, A. & Possingham, H. (2016). Track the impact of Kenya’s Ivory Burn. Nature: 534. 179.

Contributions by others to the thesis

Chapters 2-6 are based on manuscripts prepared for publication or published in collaboration with other authors. Chapter 2 is published in Ecological Solutions and Evidence, Chapter 3 will be submitted to PLOS ONE, Chapter 4 is published in Frontiers in Ecology and the Environment, Chapter 5 is in its first round of revisions at Conservation Science and Practice, and Chapter 6 is in its second round of revisions at PLOS BIOLOGY. I have retained the text in these chapters consistent with their published or manuscript forms, including the use of the plural first-person pronoun “we”. In Chapters 1 and 7, I use the first- person pronoun “I” to introduce and synthesise Chapters 2-6. I refer to my own published work as per the standard citation format (e.g. Braczkowski et al. 2018) and all other work by chapter number (e.g. Chapter 1).

Chapter 1: This chapter was written by the candidate with editorial input from Martine Maron, Duan Biggs and Arjun Gopalswamy.

Chapter 2: The candidate, Arjun Gopalswamy, and Martine Maron conceived the idea for the manuscript. The candidate collected and prepared all field data with field assistance from Mustafa Nsubuga. Arjun Gopalaswamy assisted with editing and modifying the R code from models detailed in Royle et al. (2009). The Candidate ran the statistical analysis in R. Interpreted the results, and received guidance from Arjun Gopalaswamy while writing these

10 up. The candidate wrote the manuscript with input from all authors (Arjun Gopalaswamy, Mustafa Nsubuga, James Allan, Duan Biggs, and Martine Maron).

Chapter 3: The candidate conceived the concept of the manuscript. The candidate collected all field data with Sam Isoke, and prepared it for analysis. The candidate identified individual leopards and hyenas with Alexander Bezzina. The candidate ran the models and received assistance in interpreting the results from Arjun Gopalaswamy. The candidate conducted the literature search, synthesis, and wrote the manuscript. All authors contributed to revising and improving the manuscript (Arjun Gopalaswamy, Julien Fattebert, Sam Isoke, Alexander Bezzina, and Martine Maron).

Chapter 4: The candidate and Christopher O’Bryan conceived the idea for the manuscript. The candidate identified methodologies to conduct the analyses, with input from Christopher O’Bryan, Martin Stringer, and Hawthorne Beyer. The candidate, Christopher O’Bryan, Martin Stringer, and Hawthorne Beyer conducted the analyses. The candidate and Christopher O’Bryan wrote the manuscript. All authors contributed to revising and improving the manuscript.

Chapter 5: The candidate conceived the concept of the manuscript with Martine Maron, and Duan Biggs. The candidate ran the logistic regressions models with guidance from Julien Fattebert. The candidate received assistance in interpreting the results from Julien Fattebert, and wrote up all results. The manuscript was written by the candidate. All authors contributed to revising and improving the manuscript.

Chapter 6: The candidate and Martine Maron conceived the idea for the manuscript. James Allan, Kendall Jones and Duan Biggs brainstormed examples of how royalties could impact large felid conservation, and assisted in collating company annual reports. The candidate wrote the manuscript, and received some input on legal pathways from Meganne Natali. All authors contributed to revising and improving the manuscript.

Chapter 7: This chapter was written by the candidate with editorial input from Martine Maron and Duan Biggs.

Appendix 1: This appendix contains a publication directly related to Chapters 1 and 2: Braczkowski, A.R. Gopalaswamy, A. Elliot, N. Possingham, H. Bezzina, A. Maron, M. 11 Biggs, D. & Allan, J. Restoring Africa’s Lions: Start with good counts. Published in Frontiers in Ecology and Evolution.

Appendix 2: This appendix contains a publication directly related to Chapter 4: O’Bryan, C.J. Braczkowski, A. Beyer, H. Carter, N. Watson, J.E.M. McDonald-Madden, E. (2018). The contribution of predators and scavengers to human well-being. Nature Ecology & Evolution 2(2), 229.

Appendix 3: This appendix contains supplementary material related to Chapter 2, Chapter 4 and Chapter 6.

Appendix 4: This appendix contains the animal ethics approval related to Chapter 2 and Chapter 3.

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

No works submitted towards another degree have been included in this thesis.

Research Involving Human or Animal Subjects

Research Chapter 2 of this thesis utilised a search encounter field approach where the author would drive a vehicle near the study animals and take their photograph with a digital camera. Similarly, Chapter 3 required the author to use remote infrared camera traps distributed across the study area to take photographs of leopards, lions, and spotted hyenas. Both of these activities required animal ethics clearance from the University of Queensland Animal Ethics Unit (approval number AEMAR40869) and this is attached as Appendix 3. This research was based in south-western Uganda and a field permit (permit number UWA/COD/96/05) was granted by the Ugandan Wildlife Authority to conduct all fieldwork. Chapter 5 utilised historic carnivore depredation data of medium-sized and large livestock in south-western Uganda. We addressed a query to the director of the office of research ethics (Mrs Nicole Makoviney) at the University of Queensland on whether these data would require human and animal ethics approval. As the data did not involve humans and no animal tissue was used no ethical clearance was necessary. A data transfer agreement between the University of Queensland and the owners of Mihingo Lodge was signed regarding the use of this data and is lodged with the School of Earth and Environmental Sciences (SEES).

13 Acknowledgements

I am indebted to Professor Martine Maron, my chief academic supervisor, who has guided and supported me in the completion of this thesis. I’ve been lucky to work with Martine since 2016, and I am grateful to her for not only taking me on as a student but also supporting me in my original application at the University of Queensland. Martine believed in my ability to successfully complete fieldwork in a challenging, and remote part of Africa, and always motivated me in developing strong reasoning around my key chapters. Martine is a perfectionist and an incredibly accomplished professor who pushed me to be more critical of my chapter conclusions, and the real inference I could draw from my results. My natural tendency during many of my earlier chapter drafts was to attempt to leap to conclusions about my results (particularly in my second chapter). Martine was incredibly helpful in drawing back a lot of these jumps, and I hope I can move forward with this skill in my future work on lions and carnivores. Martine worked tirelessly through a pile of manuscripts towards the end of my thesis, helping me realise my goal of submitting all of these for peer review before the submission of my thesis. I look forward to working with Martine in future as a collaborator as I return to Uganda to pursue research questions, and conservation activities that will aid the recovery of lions in Queen Elizabeth National Park, and Uganda at large. I have no doubt that our paths will cross soon on her travels through Africa, or in Uganda as part of her ongoing research projects.

I am also thankful to Dr Arjun Gopalswamy, who served as one of my two co-supervisors during my time at the Univeristy of Queensland. Arjun was a major inspiration for me pursuing a PhD. Ever since our days at Balliol college, Oxford, Arjun has always been there to field questions over Skype, phone and even Whattsapp, and I am grateful for the time he dedicated to spending with me in the field. I apologize profusely for the many conversations on issues of model convergence we had. The major lesson I learnt is no matter what, “maximize the number of your iterations. Even if it means your models running for two weeks!”. Arjun’s work on tigers is a sobering testament to the power of good science, even if that science doesn’t say what the public wants it to. I am proud that I could use the data collected on lions in Uganda as another case study to strengthen the argument for robust monitoring of African lions, we need it now more than ever.

Dr Duan Biggs is thanked for his role as the second co-supervisor of my thesis. Duan was incredibly helpful in motivating me in the last few months of my thesis and was always there 14 to field questions on the framing of my key thesis chapters. Duan’s experience in human- wildlife conflict and management issues in Africa were incredibly helpful in my last chapter, especially in blending the socio-economic aspects of compensation and the drivers of human-carnivore conflict. I am also thankful to Duan for his extensive support during my candidature with a few side projects on African elephants, which he helped fund. Duan also brought me into a couple of his own paper projects that I feel I greatly grew in terms of my knowledge about elephant management in Africa. I apologise to Duan for all the late night calls, where I pestered him with questions about conflict, journal outlets for chapters, and postdoctoral avenues after my PhD.

Dr Simon Nampindo and Dr Andy Plumptre from the WCS are thanked for their collaborative support in sourcing a permit for this research from the UWA and Ugandan Council for Science and Technology (UNCST), thanks to them both for helping kick-start my conservation science career in Uganda. Dr Margaret Driciru, and Dr Ludwig Siefert are thanked for their extensive help in the field, their commitment to the conservation of lions is inspirational and unwavering. Jimmy Kisembo, Nicholas Nuwaijuka and Daniel Tirwomwe are thanked for their support in my fieldwork while in the Queen Elizabeth National Park. These men kept me safe and their presence always made the work more meaningful and adventurous. Sam Isoke is thanked for his incredible support in the field as my research assistant. Sam helped me with the logistics, data collection and even helped me cook while in the field. We shared many adventures and stresses together. I look forward to us working together again. Dr Julien Fattebert, Dr Varun Goswami, Dr Matt Holden, and Dr Henry Brink are thanked for their useful inputs during my PhD, thank you to you both for all the chats and words of support in the final year of my thesis writing! Julien was particularly helpful in me getting the framing and interpretation of my results from Chapter 3 and Chapter 6 right. I am also grateful to many of my collaborators on both the Chapters in my thesis, and those submitted as side projects. Dr Hawthorne Beyer, Professor Hugh Possingham, Professor James Watson, and Dr Martin Stringer immediately come to mind.

I am grateful to my parents, Dr Aleksander Braczkowski and Dr Yvonne Brakovsky, who have continued to support and motivate me during my candidature. They have always supported my ambitions and career path, and I only hope that I can continue to make you proud. I am sorry I have been away from home for so many years and for so long! I will endeavour to spend more time together as a family! I am also grateful to my girlfriend Anna

15 Crysell for supporting me through the last six months of my thesis. Your love means the world to me.

I also thank my friend Nina Siemiatkowski who not only supported me financially during my time in Uganda, but continues to be an incredibly close supporter of my development as a scientist. Nina’s passion for lion conservation is unwavering and her continued financial commitment to the monitoring and conservation of Uganda’s lions is incredible. My friends and fellow PhD students Christopher O’Bryan, James Allan, Milton Andrade Jnr, Pablo Negret, and Sean Maxwell, I am thankful for all of our adventures in the surf and in academia. I hope the fun and collaborations continue to roll on! Jurgen Overhau and Erich Morgenbesser are thanked for their extensive administrative and technical support with servers, milestones and the last two months of my candidature in general! Matthew Myhill, you are the last person I thank in this section, but your contribution to my thesis and life is by no means the smallest. You saved my life in Tanzania, on my trip from Durban to Uganda. That more than 7000 km trip was one of the most memorable times of my life, and for that I will ever be grateful.

Why I pursued this PhD

“Africa is not Africa without lions”

Bernard Kissui – African Wildlife Foundation

Large carnivores have populated every major aspect of human culture since the dawn of our species some 200–300,000 years ago (Cann et al. 1987, Hublin et al. 2017, Richter et al. 2017). In the Chauvet cave of southern France, modern-day lions Panthera leo chase European bison Bison bonasus and rhinoceros’ across a > 30,000 year-old rock canvas (Packer et al. 2000). Christian, Hindu and Muslim texts describe lions and tigers Panthera tigris as both symbols of strength and evil (Rubenson 1965), while modern-day shamans in Peru and Brazil frequently report seeing, and even transforming into jaguars Panthera onca during their Ayahuasca-induced trances (Luna 2011). Even in our everyday lives, we are as likely to encounter a tiger on a cereal box as we are on the attire of our favourite sports team. Large carnivores, their symbology and presence are ubiquitous in our everyday lives.

How ironic is it then that most of the large carnivores that humanity has cherished for so long are now so threatened? Some might argue that they are an apt embodiment of the

16 earth’s sixth mass extinction. Indeed seminal papers by Ripple and Wolf (2017), and Ripple et al. (2014) show that through anthropogenic effects and reduced range extents, large carnivores may be in their 11th hour on planet earth.

Ultimately, there is no doubt that humanity will disallow the extinction of tigers, lions or their kin (and their genomes in captivity will be preserved, according to the genomic analysis of Luo et al. 2008). However, their functional extinction in the wild, or what it means to “be a carnivore” is the most harrowing thought. Indeed Tucker et al. (2018) show large carnivores exhibit some of the highest displacements in their movements when living near humans.

I cannot help but wonder how dramatically the roles of modern-day humans and carnivores have changed in the course of an eye blink in evolutionary time. To emphasize this point, I stumbled across an old video recording of a debate between the late political commentator and writer Christopher Hitchens and theologian Allistair McGrath. About halfway through this iteration of a classic Hitchonian rebuttal, one statement was particularly striking: “it is crazy to think that we were down to just 15 or 20,000, so vulnerable and dying from our teeth”. Hitchens was broadly referring to a period roughly 195,000 years ago, when the planet entered a glacial period known as Marine Isotope Stage 6 (MIS 6, see Haslam et al. 2011). For about 70,000 years conditions on earth were arduous, cold, arid and mostly uninhabitable (and indeed secondary infection from the eruption of our wisdom teeth was one of many causes of mortality). Humanity came precariously close to extinction, plummeting in number from an estimated 10,000 breeding individuals to just a few hundred (Marean 2010). These humans shared their landscape with not only modern-day lions Panthera leo but a member of the Homotherium genus, commonly known as a Scimitar- toothed cat (a relative of the Smilodon genus of sabre-toothed cats). I can only imagine the fear, and wariness, early groups of humans experienced towards these “beasts” when embarking on their foraging excursions. One only needs to turn to the musings of Patterson (1928), who documents how two African lions killed at least 28 Indian railway labourers in just a few months on a railway project in Tsavo, Kenya. Craig Packer and colleagues echo the seriousness of such events in their seminal Nature piece “Lion attacks on humans in Tanzania”, where they examine the patterns of human deaths resultant of lions between 1990–2004 (Packer et al. 2005). It is clear however that today, the pendulum of dominance has swung, and humans are now the masters of these same “beasts” fate.

Today we find ourselves in a situation where the overwhelming majority of carnivore populations are in a dangerous state of decline. Indeed, tigers in Asia may now number as 17 few as 3,200 individuals (Sanderson et al. 2019), cheetahs Acinonyx jubatus are estimated to number ~7,100 individuals while estimates of lesser charismatic (but even more imperilled) species like the dhole (or Asiatic wild dog Cuon alpinus) don’t even exist (Kamler et al. 2015). My motivation for pursuing this PhD candidature is captured by the gravity of the above studies and my goal for writing this thesis was simple: to add to a growing body of conservation research that will assist in the bettered management and conservation of large carnivores in Africa and India.

Financial support

A UQ Graduate School Scholarship supported this research and I am grateful to the University of Queensland for financing my university fees, stipend and living expenses and covering some of my initial fieldwork costs. In hindsight this financing of my degree allowed me to enjoy life in Australia over my candidature to the full and I am grateful to UQ and its benefactors who made this opportunity available to me.

I am grateful to the Siemiatkowski Foundation for their financial support while I was in the field. Their sponsorship of important research equipment and in fuelling my vehicle was critical to my work. The foundations commitment to lion research and supporting me has been unwavering and the funding for my home journey to South Africa was incredibly critical! I thank you from the bottom of my heart! The Scientific Exploration Society (SES) and the Sandford family is thanked for awarding me the Elodie Sandford Explorer award. This award financed my camera traps and critical field expenses while in Uganda. The Sandford family and the SES made the call to support me even at the last moment when I had to change my field locations. Your funding literally allowed me to go to Uganda. The National Geographic Society is thanked for the Early Career grant, which went towards my research equipment and camera traps. This seed funding was an important boost for my fieldwork. The Rufford Foundation is thanked for their small grant for conservation. This funding went towards my food, vehicle maintenance, camera traps and travel costs while in the southwest of Uganda. Additional funding from the UQ Graduate School allowed me to purchase field equipment and prepare my vehicle (and fuel it) for the 7500 km journey across five countries to get to Uganda.

18 Keywords

Population monitoring, human-carnivore conflict, large felids, conservation status, capture re-capture modelling, conflict mapping, conservation biology, East Africa, Uganda

19 Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 050202, Conservation and Biodiversity, 50% ANZSRC code: 050205, Environmental Management, 50%

Fields of Research (FoR) Classification

FoR code: 0502, Environmental Science and Management, 100%

20 Table of Contents

Abstract ...... 2 Declaration by author ...... 6 Publications included in this thesis ...... 7 Submitted manuscripts included in the thesis ...... 8 Other publications during candidature ...... 10 Contributions by others to the thesis ...... 11 Statement of parts of the thesis submitted to qualify for award of another degree ...... 13 Research involving human or animal subjects ...... 13 Acknowledgements ...... 14 Financial Support ...... 18 Keywords ...... 19 Australian and New Zealand Standard Research Classifications (ANZSRC) ...... 20 Table of Contents ...... 21 List of Figures and Tables ...... 24 List of Abbreviations used in the thesis ...... 30 Chapter 1: Introduction ...... 32 1.1 Large carnivores as a flagship of the biodiversity extinction crisis ...... 32 1.2 Threats to African carnivores in the Anthropocene ...... 36 1.3 The importance of estimating large carnivore populations robustly ...... 38 1.4 Obstacles to conserving large carnivores ...... 42 1.5 Thesis aims and structure ...... 43 1.6 Study sites and background to case studies used in this thesis ...... 48 1.6.1 The QECA and Lake Mburo National Park, south-western Uganda ...... 48 1.6.2 Leopards and human communities in Mumbai, India ...... 53 Chapter 2: Detecting early warnings of pressure on an African lion population in the Queen Elizabeth Conservation Area, Uganda ...... 56 2.1 Abstract ...... 56 2.2 Introduction ...... 57 2.3 Methods ...... 58 2.3.1 Study Area ...... 58 2.3.2 Field methods ...... 60 2.3.3 Data analyses ...... 61 2.4 Results ...... 66 2.4.1 Lion density, abundance, movement and home range size ...... 66 2.4.2 Model diagnostics ...... 68 2.5 Discussion ...... 68 2.5.1 African lion density and movements in the QECA ...... 68 2.5.2 Conservation implications ...... 71 2.5.3 Benefits and caveats of the method ...... 73

21 Chapter 3: First SECR population estimates of leopards and hyenas in south-western Uganda…………..…………………………………………………...... 77 3.1 Abstract ...... 77 3.2 Introduction ...... 78 3.3 Methods ...... 80 3.3.1 Study Area ...... 80 3.3.2 Field methods ...... 82 3.3.3 Analytical Methods ...... 83 3.3.4 State and observation process model ...... 83 3.4 Results ...... 88 3.4.1 Model diagnostics ...... 90 3.4.2 Density estimates ...... 90 3.5 Discussion ...... 90 3.5.1 Leopard densities ...... 95 3.5.2 Hyena densities: Are they reflective of competitive release? ...... 97 3.5.3 Implications for conservation ...... 99 Chapter 4: Leopards provide public health benefits in Mumbai, India ...... 102 4.1 Abstract ...... 102 4.2 Introduction ...... 103 4.3 Leopards living on an urban edge ...... 103 4.4 Leopard impacts on stray dog populations ...... 104 4.5 Dog impacts on human populations ...... 106 4.6 Leopards may benefit human health and dog management ...... 106 4.7 Human-wildlife conflict and the future of leopards in Mumbai ...... 109 4.8 Global impacts of large carnivores in urban environments ...... 110 Chapter 5: Human carnivore conflict and compensation on the edge of Uganda’s Lake Mburo National Park: landscape drivers and financial sustainability ...... 114 5.1 Abstract ...... 114 5.2 Introduction ...... 115 5.3 Methods ...... 116 5.3.1 Study Area ...... 116 5.3.2 The MCF and conflict monitoring ...... 119 5.3.3 Data analyses ...... 120 5.3.4 Temporal patterns of predation events ...... 120 5.3.5 Landscape predictors of livestock depredation by leopard and hyena ...... 120 5.3.6 Financial sustainability of the MCF ...... 123 5.4 Results ...... 123 5.4.1 Impacts and temporal trends of depredation by leopard and hyena ...... 123 5.4.2 Landscape predictors of hyena and leopard depredations ...... 124 5.4.3 Financial compensation fund sustainability ...... 124 5.5 Discussion ...... 125

22 5.5.1 Livestock depredation characteristics and spatial drivers of depredation ...... 127 5.5.2 Characteristics of the MCF compensation scheme ...... 129 5.5.3 Data limitations ...... 131 5.5.4 Management recommendations ...... 133 Chapter 6: Marketing products with wildlife to make it benefit conservation ...... 138 6.1 Abstract ...... 138 6.2 Introduction ...... 139 6.3 Royalties for the environment ...... 141 6.4 Non-human personhood laws won’t help a royalty in the short term ...... 141 6.5 The creation of national laws could work ...... 145 6.6 A “Fairtrade” equivalent certification scheme ...... 145 6.7 Potential scale of an imagery royalty: an example using large felids ...... 146 6.8 Caticorns and other unintended consequences ...... 148 Chapter 7: Conclusion ...... 151 7.1 Synthesis ...... 152 7.1.1 Novelty of using population parameters from SECR to inform carnivore population status ...... 152 7.1.2 Benefits of carnivores to humans and the need for similar assessments ...... 156 7.1.3 Human-carnivore conflicts and compensation: a clear need for wildlife monitoring ...... 159 7.1.4 Funding for large carnivore conservation: compensation and imagery royalties .. 161 7.1.5 Target setting for large carnivore conservation must be data-driven ...... 161 7.2 Concluding remarks ...... 163 References ...... 164 Appendix 1: Restoring Africa’s Lions: Start with good counts ...... 212 A1.1 References ...... 214 A1.2 Supplementary Information ...... 215 Appendix 2: The contribution of predators and scavengers to human well-being ...... 216 A2.1 Abstract ...... 216 A2.2 Introduction ...... 218 A2.3 Predators and scavengers regulate zoonotic diseases ...... 219 A2.4 Predators can indirectly increase agricultural output ...... 221 A2.5 Predators and scavengers provide benefits in urbanizing environments ...... 223 A2.6 Predator and scavenger conservation in the 21st century ...... 224 A2.7 References ...... 232 Appendix 3: Supplementary material for Thesis chapters ...... 243 A3.1 Correlation plots, Bayeisan p-values, and marginal likelihood values to accompany Chapter 2 ...... 243 A3.2 Web only materials to accompany Chapter 4 ...... 247 A3.3 Partnership examples and the scale of a royalty: accompaniments to Chapter 6 ...... 251 Appendix 4: Animal ethics approval for Chapters 2 and 3 ...... 261

23 List of Figures and Tables

Figure 1.1 A review of 169 peer-reviewed scientific articles (Web of Science and Google Scholar) which used data on lion numbers (abundance or density) to support an array of ecological and conservation questions. The review shows that the overwhelming majority of lion abundance and density data originate from direct observations of identified individuals, mixed methods, call-ups (ie. Audio lure) or track surveys. Most studies have also been implemented in Tanzania, South Africa, Kenya and Namibia (≥ 2 countries refers to studies that used lion data for meta-analysis, reviews or multiple country surveys). The protocol we utilised for this literature review is provided in Supplementary Information 1. The studies which used direct observations mainly used individual identification of animals and assumed that all individuals seen accounted for the total population (ie. a total count of known individuals).

Figure 1.2 A schematic diagram illustrating the two key thesis themes of my PhD (the monitoring of large carnivores, and human-carnivore conflict), the four key data chapters and their respective journal outlets, and the standalone solutions chapter exploring the threatened wildlife imagery royalty, submitted as an essay.

Figure 1.3 The Queen Elizabeth National Park and the adjacent Kyambura and Kigezi Wildlife Reserves of south-western Uganda. The three protected areas collectively comprise the Queen Elizabeth Conservation Area (QECA), an area of 2,519 km2. The QECA is also connected to the greater Virunga landscape, and comprises an area of over 10,000 km2, a globally significant wilderness block (Watson et al. 2016).

Figure 1.4 Two tree-climbing lions photographed during the lion census highlighted in Chapter 2 of this thesis. Almost all African lions have the ability, and will climb trees at some point in their lives. Sometimes they will do this habitually, and the behaviour may permeate through an entire pride. However, there are only three populations in Africa where the behaviour is found in all lions in a region. I term this “cultural tree-climbing” as every (if not, almost every) observable lion climbs trees (mainly Euphorbia candelabrum and Ficus sycomorus). Hypotheses on why the lions do this range from decreasing heat exposure to escaping biting flies. Photographs by Alex Braczkowski.

24 Figure 1.5: The 370 km2 Lake Mburo National Park in south-western Uganda. The park is located close to the Nakivali and Kachira lake systems and is located in the heart of Bahima pastoral land, a key component of East Africa’s cattle corridor.

Figure 1.6: The Sanjay Gandhi National Park (SGNP, 104 km2) in Mumbai, Maharashtra state. The park is flanked by the Aarey Milk Colony (16 km2) pasture and forest matrix and several large city suburbs including Mulund, Dahisar, Borivli and Thane. The buffer denotes the zone the leopards of Chapter 6 move in and are likely to predate upon stray dogs in.

Figure 2.1: The Queen Elizabeth National Park and adjacent Kyambura Wildlife Reserve and Kigezi Game Reserve, collectively representing the QECA. The dashed line represents the 25 km buffer around the QECA.

Figure 2.2: Search effort tracks, lion detections and pixel-specific lion densities generated from the search encounter lion survey 10 November 2017 – 10 February 2018 in the QECA.

Figure 2.3: Three separate photographs of a young 3-3.5 year old male lion (Jacob – M1) taken in Queen Elizabeth National Park during our 93 day lion survey. We used nose pigmentation and patterning (Whitman and Packer 2004), scars, whisker spots (Pennycuick and Rudnai 1970) and mane development (Miller et al. 2016) to assign individual identities to each of the lions photographed in our survey.

Figure 3.1: The QECA study area comprised of the Queen Elizabeth National Park and the Kyambura and Kigezi conservation areas. The dotted line represents the 25 km buffer placed around the QECA border.

Figure 3.2: Three separate spotted hyena individuals photographed in the northern section of the QECA. The top row shows individual 10 photographed on sampling occasion 4, 12 and 25. Middle row shows individual 1 photographed on occasions 22, 35 and 38 and the bottom row shows individual 8 photographed on occasions 13, 35 and 39.

Figure 3.3: A pixelated density map showing relative leopard densities across the north and south of the QECA respectively. This map reports estimated leopard densities per 0.158 km2 pixel.

25 Figure 3.4: A pixelated density map showing relative spotted hyena densities across the north and south of the QECA respectively. This map reports estimated hyena densities per 0.158 km2 pixel.

Figure 4.1: Leopards in Mumbai’s Sanjay Gandhi National Park regularly leave the confines of the park to hunt stray dogs. a) An adult female is photographed near a block of apartments bordering the parks eastern edge, b) a female photographed at a Muslim sacred site overlooking Mumbai city, c) a young leopard walking through a village in the Aarey colony (a 16km2 agricultural area joining the south-western extent of the park) and d) at a construction site in an informal settlement. Photographs: Steve Winter.

Figure 4.2: The 104 km2 Sanjay Gandhi National Park (SGNP) and 16 km2 Aarey Milk Colony joined to its southwest. The hatched buffer represents a 500m zone from the forest edge (buffer area = 43 km2) where leopards predate on stray dogs and an estimated 350,000 people live, mostly in informal settlements.

Figure 5.1: The 370 km2 Lake Mburo National Park in south-western Uganda. The park is located close to the Nakivali and Kachira lake systems and is located in the heart of Bahima pastoral land, a key component of East Africa’s cattle corridor.

Figure 5.2: Spotted hyena, and African leopard attack locations included in our multivariate logistic regression examining spatial drivers of attacks on the eastern edge of LMNP, south- western Uganda.

Figure 5.3: Sample sizes and proportions of spotted hyena and African leopard livestock attacks on the edge of LMNP (collected between January 2009 – December 2018), and the proportion of attacks which took place at night (vs dawn, day and dusk). The contribution of cattle and sheep and goats in the diet of leopards and spotted hyenas was almost perfectly inversely proportional.

26 Figure 5.4: Spatial attack risk probability for spotted hyena depredations on livestock in the broader LMNP landscape.

Figure 5.5: Spatial attack risk probability for leopard depredations on livestock in the broader LMNP landscape.

Figure 5.6: Total MCF compensation scheme income, compensation costs, and other costs (defined as school fees, building of schools, and the administration of the annual Mihingo Marathon) recorded by Mihngo Lodge management between the 2012-2018 financial years.

Figure 6.2: A schematic flow diagram showing the fundamental differences in how a famous popstar can engage copyright laws in the USA, and how a high-profile member of a charismatic wildlife species cannot according to present day laws, we use the example of Machali the tigress.

27 Figure 7.1: A female leopard crosses a small bridge which straddles Thane city and the Sanjay Gandhi National Park in Mumbai, Maharashtra, India. Photographer Steve Winter took this image with a remote camera-trap in mid-2014. Reproduced with permission from Steve Winter.

Figure A2.1. IUCN global distribution of some species that are known to provide important services to humans over some portion of their range. Panel a) shows the ranges of some species known to contribution to agricultural production, Panel b) shows the ranges of some species that may reduce disease risk, Panel c) shows the ranges of some animals known to reduce species that cause human injury and death, Lastly, panel d) shows the ranges of some species known to remove dangerous organic waste.

Table 2.1: Model parameters, definitions and posterior summaries from our Bayesian SECR density model of African lion density in the QECA where was the full model, and it estimated the detection function (defined by θ) and was based on the assumption that detection probability is sex specific.

Table 2.2: Model parameters for our seven candidate models assessing African lion population state variables in the QECA

Table 2.3: Bayes p-values for our seven candidate models assessing lion density in the QECA. created to assess African lion density in the QECA.

Table 2.4: Shrink reduction factors for the key parameters from our seven candidate models created to assess African lion density in the QECA.

Table 2.5: A selection of studies implemented in the last ten years on the abundance and density of African lions using a combination of methods. These come from eight countries and two continents.

Table 3.1: Parameter definitions for the SECR models applied for our leopard and spotted hyena surveys in the QECA in 2018 (note we did not use sex in the hyena models).

Table 3.2: Iterations, Bayesian p-values and natural logarithm of marginal likelihood scores for our leopard and spotted hyena SECR density models.

28 Table 3.3: Flanks, individuals and recaptured individuals used in our two SECR density surveys in 2018 for the QECA.

Table 3.4: Parameter estimates with accompanying posterior SD from our leopard and hyena SECR density surveys in the QECA.

Table 3.5: Leopard density studies published over the last five years and which used SECR methods. We used the first ten pages of Google Scholar in our assessment.

Table 3.6: Spotted hyena density estimates using SECR and camera trapping in six locations across sub-Saharan Africa.

Table 5.1: Spatial covariates used in our multivariate logistic regression examining spotted hyena and leopard depredation probability in the LMNP, south-western Uganda.

Table 5.2: Distribution of age-classes of livestock depredated by spotted hyenas, and African leopards (collectively termed attacks), and the attack locations of livestock for hyenas and leopards (with missing data values).

Table 5.3: Distribution of age-classes of livestock injured and killed by spotted hyenas, and African leopards (collectively termed attacks). Other attacks are provided as footnotes underneath this table.

Table 5.4: Results of logistic regressions testing the effect of spatial covariates on leopard, spotted hyena, and combined depredation probability in LMNP, Uganda, 2009-2018.

Table 5.5: Results of logistic regressions testing the effect of spatial covariates on leopard and spotted hyena livestock depredation probability in LMNP, Uganda, 2009–2018.

Table 5.6: Results of logistic regression testing the effect of spatial covariates on boma occurrence probability in LMNP, Uganda.

Table 5.7: Results of linear regressions testing the effects of monthly rainfall on financial compensation for hyena, leopard and total depredation events in LMNP, Uganda, 2009– 2016.

29 Table A2.1: Featured case studies of predators and scavengers contributing to human well- being, their potential limitations, and suggestions for furthering the case human benefit.

Table A3.2.1: Dog bites recorded in various Mumbai newspapers from 2010-2015.

Table A3.1. Examples of historic and current collaborations between felid conservation NGOs and large corporate organizations. Main support includes cash injections and outcomes vary between projects. All figures are presented in USD$.

Box 6.1: Funding deficits for protected area management and threatened species conservation are vast. Even the most charismatic large felids such as African lions Panthera leo and tigers Panthera tigris face sever funding shortfalls. Data from Coad et al. (2019), Lindsey et al. (2018) and the Global Trends in Giving 2018 report.

List of Abbreviations used in the thesis

CMS – Convention of Migratory Species

IUCN – International Union for the Conservation of Nature

QECA – Queen Elizabeth Conservation Area

SECR – Spatially Explcit Capture Recapture

30 Chapter 1 statement of authorship: This chapter was written by the candidate with editorial input from Martine Maron, Duan Biggs, and Arjun Gopalswamy.

CHAPTER 1 Introduction

1.1 Large carnivores as a flagship for the biodiversity extinction crisis

Humanity is driving unprecedented rates of biodiversity loss across Earth’s terrestrial and marine surface (Pimm and Raven 2000). Current species extinction rates are estimated to be at least 1000 times background rates (Pimm and Raven 2000). Together with exponential rises in the burning of fossil fuels since 1800, the overwhelming majority of environmental studies examining defaunation agree that the human species has ushered in a new epoch in earth’s history: the Anthropocene (broadly defined as the current geological age characterized by human activities on the environment and climate, Stoppani 1873, Crutzen 2006). Even if human actions do not lead to our extinction, and extinction rates and broader biodiversity targets (eg. Aichi targets) are kept within habitable bounds (Rockström et al. 2009), the last 50 years alone will be etched in geological time as one of earths most tumultuous periods, and a likely precursor to the earths sixth mass extinction (Crutzen 2006, Steffen et al. 2007, Dirzo et al. 2014).

The Earth’s contemporary faunal extinction drivers remain largely the same as those mentioned in Diamond (1984) who described the “evil quartet” (overexploitation, habitat destruction, introduced species, and co-extinctions or cascades) to native biodiversity. Maxwell et al. (2016) in their review of 8 688 species show over-

32 exploitation, agricultural activity (through conversion of natural habitats), urbanization, and disease and pollution rank as the highest threats to species persistence globally.

The larger members of the order Carnivora represent one of the animal kingdom’s most apt flagships for earth’s recent extinction crisis. They typify many of the challenges facing the Earths biodiversity, and are victims of habitat loss (and transformation) and over exploitation (mainly from the medicinal trade and global snaring pandemic, Lindsey et al. 2013, Gray et al. 2018). Large carnivores have also suffered the biggest range contractions of all biodiversity (Ripple et al. 2014, Di Minin et al. 2016). Large carnivore guilds (defined as a group of species that fulfil a similar ecological role in a community, Dalerum et al. 2009) require large tracts of wilderness for their populations to persist. This is owed to the fact that they are sensitive to anthropogenic activities on the edges of national parks, such as cattle ranching (and associated retaliatory killings), trophy hunting, and poaching (see Balme et al. 2010, Planillo et al. 2018). However, Watson et al. (2016) show that at least 10% (or some 3.3 million km2) of the Earth’s wildernesses have disappeared in just two decades. Resultantly, most large carnivore populations are confined to small habitat patches bordered by seas of humanity (Pimm et al. 2006, Wibisono et al. 2018).

Gujarat’s Gir lions Panthera leo persica, the Javan leopard Panthera pardus melas, and the florida panther Puma concolor coryi are just three examples of populations threatened by habitat loss, a lack of habitat connectivity, and increased human pressures. For example the lions of Gir forest, Gujarat, India, are reported to have experienced a population recovery from 1970-2010, increasing from 180-411 individuals (Singh and Gibson 2011). However, they are limited to only one population, are susceptible to canine distemper virus, and are increasingly being affected by accidents with trains, and conflicts with cattle farmers (Jhala et al. 2019).

Some species are more resilient to habitat loss and conversion than others. Leopards in the state of Maharashtra, India, for example, live in heavily populated human settlements (> 30,000 individuals/km2, Odden et al. 2014). They regularly cross six- lane freeways, and even take temporary shelter in warehouses in urban metropoles (Vidya Athreya, pers. comm.). Similarly, Stolzenburg (2016) documented the 24- month dispersal journey of a three-year old mountain lion Puma concolor from the black hills of South Dakota to Conneticut, New England (> 2,500 km linear journey).

33 These species have smaller body sizes, and more diverse diets than larger species such as tigers or lions (Hayward et al. 2006). They will also actively use domestic animals such as domestic dogs in peri-urban environments (Odden et al. 2014), can temporarily switch their preferred prey size (especially when competing with humans, Braczkowski et al. 2012, Henschel et al. 2014) and may remain virtually undetectable in human landscapes (Riley et al. 2014). However, many large carnivores (typically those species exceeding 100 kg, Carbone et al. 1999), are more constrained in terms of the kinds of prey that can support them (Hayward and Kerley 2008). African lions, for example, prefer prey within the 190 – 550 kg weight range (Hayward and Kerley 2005), and tigers prefer prey weighing 60 – 250 kg, with the sambar deer Rusa unicolor (200 kg) and wild boar Sus scrofa (60 kg) being the most preferred species.

With increased pressures from human communities living on reserve edges, and poorly resourced national parks (which often cannot maintain their prey populations, Lindsey et al. 2018), many large carnivores are having to move further in order to find prey. This interplay between a species’ traits (eg. strong preference for larger prey, polygamy, and infanticide), and habitat loss is again seen in a multitude of the globes extant large carnivore populations. Reserve edges function as population sinks from human induced mortality (Andrén and Angelstam 1988, Murcia 1995, Woodroffe and Ginsberg 1998). These typically see immigration of sub-adults, or adults from adjacent protected areas, (especially when the protected areas are saturated, or group sizes become too large and split up, Packer et al. 2005b, Balme et al. 2009b). If mortality in these sinks is not balanced with reproduction and immigration of individuals, this edge effect can cause population decline (Balme et al. 2010). In the context of large carnivores, species most likely to disappear from areas are those that feature wide- ranging movement patterns (specifically defined by females), and therefore have high contact rates with threats on reserve boundaries (Woodroffe and Ginsberg 1998). Naturally, the area affected by the edge increases as area decreases (Levenson 1981, Woodroffe and Ginsberg 1998).

The lions of Kafue National Park, Zambia (Midlane 2014), and Waza National Park, Cameroon (Tumenta et al. 2010) are two examples of large felid populations that have experienced significant edge effects. In Waza, African lions on average expanded their home-range sizes by 59% in just a ten-year period in response to widespread and

34 intense bush meat poaching (Tumenta et al. 2013). As movements increased, lions were more exposed to human communities living on the edge of the park, increasingly depredated stock and suffered increased rates of retaliatory killings by villagers (Bauer and de longh 2005, Tumenta et al. 2013). This led to reduced lion populations in Kafue (mainly due to bush meat snaring, but also uncontrolled fires which decreased habitat productivity), and this is reflected by very low lion densities (1.83 individuals/100 km2), and large home-ranges (female ranges are as large as 665.8 km2). This shows that despite Kafue being 22,400 km2 in size (5th largest protected area in continental Africa, Midlane 2014) it is still affected by human activities even far from its edge.

The decline of African lions highlighted by the above studies is likely to have important knock-on effects, or what Leopold (1949) conceptualized as causing a trophic cascade (Pace et al. 1999) in their broader savannah environment. Trophic cascade theory predicts that the removal of a top-order predator (such as a lion or tiger), results in an increase in the abundance of primary consumers, and a decrease in primary producers (Terborgh and Estes 2013). Estes et al. (2011) warn that such “trophic downgrading” often drives increased spread of disease, wildfires, reduced carbon sequestration, invasive species expansions and the altering of biogeochemical cycles. Seminal examples range from sea otter Enhydra lutris collapse in Amchitka Island leading to kelp forest declines (a result of sea urchins Strongylocentrotus polycanthus grazing, Estes and Duggins 1995), to Paine’ (1966) seminal study in Mukkaw Bay on the Olympic peninsula. Paine showed the removal of purple starfish Pisaster ochraceus from the tidal zone caused explosive growth, and spatial expansion of barnacles and mussels and concomitant reductions in benthic algae and limpets (and nearly halved species diversity, Paine 1966). Examples of cascades caused by large carnivore removal are also diverse.. They range from gray wolves Canis lupus indirectly regulating stream flow through the effects of their predation on elk Cervus canadensis in Yellowstone National Park (although there is some debate regarding their influence on migratory ungulates, eg. Middleton et al. 2013), to multi-fold reductions in species diversity and density as a result of mountain lion Puma concolor extinction in Yosemite and Zion National parks in the western United states (Ripple and Beschta 2006, Ripple and Beschta 2008). Large carnivores also have significant economic value and are a key component of non-consumptive tourism activities including photographic safaris and trophy hunting (Lindsey et al. 2007, Balme et al.

35 2009b). For example in Uganda’s Queen Elizabeth Conservation Area, lions are estimated to each raise US$14 000 per annum (Plumptre and Roberts 2006). Similarly in the Brazilian Pantanal jaguar viewing from commercial safaris is estimated to generate US$ 6.8 million dollars annually (Tortato et al. 2017). Thus the loss of these species not only has ecological effects, but can impact local livelihoods.

1.2 Threats to African carnivores in the Anthropocene

Africa is home to the most diverse sympatric large carnivore assemblage on earth, both functionally, and phylogenetically (Dalerum et al. 2008, Di Minin et al. 2016). The guild includes lions Panthera leo, leopards Panthera pardus, spotted hyena Crocuta crocuta, striped hyena Hyaena hyaena, brown hyena Hyaena brunnea, cheetah Acinonyx jubatus and wild dog Lycaon pictus. They are significant for ecological, economic and cultural reasons (O’Bryan et al. 2018). This includes the regulation of prey, and plant communities (Ford et al. 2014), photographic and trophy hunting safaris (Mossaz et al. 2015, Begg et al. 2018), and traditional ceremonies across many human cultures (Goldman et al. 2013). However, populations of these species have suffered significant range contractions across their African range (mean range loss = 68.16%, range = 27.2 – 93.7%, Wolf and Ripple 2017). At present the densest populations of these species persist in the largest protected areas of Tanzania, Kenya, Zambia, Botswana, Zimbabwe, Mozambique, Namibia, and South Africa (Riggio et al. 2013, Jacobson et al. 2016). Contrastingly, the majority of populations, which live outside of these states, have suffered considerable declines. For example, Bauer et al. (2015) estimate that since 1990, except in four African countries (Botswana, Namibia, South Africa, and Zimbabwe), lions have declined in every country in Africa.

The extirpation of large African carnivores has been linked primarily to the interaction between instrinsic life-history traits (eg. strong parental dependence, infanticide and reliance on group size, Pianka 1970, Davidson et al. 2011), and three main anthropogenic processes (disease and the illegal trade also serve as additional drivers, Tricorache et al. 2018), 1) direct persecution, 2) removal of preferred prey, and 3) habitat modification. For example, in a 13-year study of African leopards in South Africa’s Sabi-Sand Game Reserve (a managed reserve whose population is

36 exposed to minimal human persecution), 49% of all cub mortality was attributed to male leopards killing cubs (Balme and Hunter 2013). Direct persecution of leopards, particularly males, may therefore cause rapid population declines, when harvest pressure from commercial trophy hunting, and problem animal control is not targeted (for example at specific cohorts). This is because male leopards cannot maintain tenure for long enough for females to raise cubs to independence, and rates of infanticide become artificially inflated by high adult male turnover (eg. Balme et al. 2009b, Williams et al. 2017). Similarly, another example of anthropogenic pressure and increased extinction risk increases is the case of African wild dogs, and when their pack sizes drop below a threshold (Vucetich and Creel 1999). Wild dogs rely on a high kill frequency, and prey size, and hunting success decline as pack size decreases (Fanshawe and Fitzgibbon 1993, Creel and Creel 1995). Snaring, direct killing in retaliation to livestock depredation, and vehicle collisions are the most important anthropogenic sources of mortality (and are additive) in wild dogs, and depending on their severity, all contribute to reduced or total elimination of wild dog packs (Woodroffe et al. 2007, Woodroffe and Sillero-Zubiri 2012). Diseases such as rabies can also drive significant declines of wild dog packs (eg. from rabies, Canning et al. 2019).

Some of the most significant anthropogenic pressures that cause large carnivore decline occur on reserve edges, or even within the protected areas themselves. Protected areas often suffer from porous edges (eg. caused by poor fence management or a lack of fences), lack an adequate number of staff, are underfunded and lack the expertise for effective management (Lindsey et al. 2018). Therefore, large carnivores are often impacted negatively by human activities in such areas (Packer et al. 2013). The chief threats arising from human communities include retaliatory killings by pastoralists when carnivores kill and consume livestock (eg. Trinkel et al. 2017), illegal bushmeat poaching (mainly through the use of indiscriminate wire snares) which depresses preferred prey of carnivores and sometimes maims or kills the carnivores themselves (Becker et al. 2013, Lindsey et al. 2013), destruction of suitable habitat (Watson et al. 2016) and displacement of prey populations due to grazing within suitable carnivore habitat (eg. Blackburn et al. 2016). Trophy hunting of species such as leopards and lions represents another form of additive mortality, and still occurs across the majority of the areas where these species occur (Balme et al. 2010).

37 The threat of lethal retaliation against large carnivores by humans for livestock depredations represents one of the two biggest threats to carnivore persistence globally (along with bushmeat snaring, Lindsey et al. 2013). This is a classical example of human wildlife conflict and it can drive carnivore populations into extinction. This is especially the case when large carnivore species occur at low densities and their home-ranges are large (Woodroffe and Frank 2005, Loveridge et al. 2007, Balme et al. 2010). For example, lion surveys implemented by Tumenta et al. (2010) from 2007- 2008 in Cameroon’s 1,700 km2 Waza National Park showed the population comprised 14-21 individuals and had declined on average by six individuals/year till this point from 2002. Most of this was ascribed to retaliatory killings by pastoralists for lion predation on stock (Tumenta et al. 2010). Similarly, Woodroffe and Frank (2005) reported a 4% decline of lions in the Laikipia ranch, Kenya over a period of 5 years due to retaliatory killings by pastoralists. Even though the ranch was only 180 km2 in size, it affected lions in an area of over 2000 km2, serving as a population sink (Woodroffe and Frank 2005).

1.3 The importance of estimating large carnivore populations robustly

The threats of human-carnivore conflict, poaching of preferred prey and habitat loss, call for the effective monitoring of carnivore populations, not only in setting baselines of population density where they are lacking, but also for future targets. In regions where significant declines have already occurred, robust monitoring can provide a current snapshot for recovery planning (Braczkowski et al. 2020). Indeed, the field of wildlife population monitoring represents one of the most central topics in conservation biology. The regular critique of wildlife population monitoring in the ecology literature (Gopalaswamy et al. 2019) and its feature in conservation discussions is owed to the fact that it is often used to delineate minimum viable populations (eg. Shaffer 1981, Moritz 1994), and is the most important measure of gauging conservation success (Stem et al. 2005).

The ongoing scientific debate regarding India’s tiger densities represents one of the best examples of why population monitoring is important for conservation and management. Asiatic tigers are one of the most charismatic species on earth, and they attract significant conservation funding and public attention (Gratwicke et al. 2007,

38 Courchamp et al. 2018). But the methods used to monitor their populations have come under scrutiny over previous years, and this peaked when tigers were revealed to have gone extinct in two national parks, which relied on a technique that predicted tiger numbers from their tracks (Sariska Tiger Reserve, and Panna Tiger Reserve, Karanth et al. 2003). Criticisms have continued by respected, and tenured scientists, in response to reported rises in tiger abundance of 16% in 2011 (Karanth et al. 2011), and again, a 33% rise in 2016 (Gopalaswamy et al. 2019). These papers criticized the results of the government reports, questioning the veracity of using tiger tracks and droppings in calibration experiments with camera traps (Jhala et al. 2011). Such debates often feature in the field of carnivore conservation, as scientists continuously search for census methods which will provide robust measures of density and abundance, while balancing budgetary constraints (eg. Du Preez et al. 2014, Braczkowski et al. 2016). This is largely owed to the fact that traditional methods of surveying large mammals (which are also cheaper) such as line transect-based distance sampling (Mathai et al. 2013), and aerial counts (eg. Chase et al. 2016) do not work for low-density carnivores with naturally elusive traits (Balme et al. 2009a).

Since the landmark work of Karanth (1995), and Karanth and Nichols (1998), who used automated photographic cameras and capture recapture statistics to enumerate tigers in Ngarahole India, several authors have attempted to increase the accuracy of carnivore population density estimates. Some of these have included increasing detection rates (usually by the placement of lures or baits, eg. Du Preez et al. 2014, Braczkowksi et al. 2016), increasing survey length while balancing considerations of demographic closure (eg. Weingarth et al. 2015), and combining the results of two survey techniques in one inferential model (eg. Gopalaswamy et al. 2012a). The development of robust carnivore census techniques which are repeatable on an annual basis, and which may be used to infer population status (particularly using key demographic parameters such as birth, death, immigration and emigration) over time, should form a core component of any large carnivore conservation initiative. This assists in assessing the efficacy of conservation interventions (eg. limiting harvests, reduction of human-carnivore conflict (eg. Balme et al. 2009b), and counter poaching initiatives (Balme et al. 2010).

39 Many recent population estimation methods use individual identity of animals in a capture recapture framework, and explicitly factor in the spatial information of detected individuals during a survey. These commonly termed “spatially explicit capture recapture” (SECR) methods have steadily increased across a wide array of carnivore species. Examples include African leopards (eg. Balme et al. 2009b , Balme et al. 2019), snow leopards (Jackson et al. 2006, Alexander et al. 2015), jaguars Panthera onca (eg. Silver et al. 2004, Maffei et al. 2011), cheetahs (eg. Marnewick et al. 2008, Broekhuis and Goplaswamy 2016), and even smaller species like African golden cats Caracal aurata (Bahaa-el-din et al. 2016), and servals Leptailurus serval (eg. Ramesh and Downs 2013).

The landmark practical works of Karanth (1995), Karanth and Nichols (1998) and theoretical pieces by Efford (2004), Royle et al. (2009) and Efford et al. (2009) are all important motivations for this work. These papers showed that by incorporating spatial information of individual animals into the capture recapture framework, user defined (and sometimes arbitrary) delineations of the “sampled area”, or “study area” were no longer required. For instance using the mean maximum distance moved (MMDM) by detected animals, or some modification of this (eg. 0.5 MMDM, see Soisalo and Cavalcanti 2006) . However, uptake of capture recapture statistics for the estimation of African lion densities has been slow when compared to species such as snow leopards, leopards, and tigers (where SECR has become almost ubiquitous). I implemented a comprehensive literature search of Google Scholar, Web of Science and the IUCN Cat Specialist Group’s Digital Cat Library, and found only four studies that had attempted to employ a capture recapture or mark resight approach to estimate lion density, and only one which incorporated spatial information into this framework (Figure 1.1, this figure forms part of a short communication submitted to the journal Frontiers in Ecology and Evolution and may be found in Appendix 1). It is plausible that the slow uptake of SECR methods for lions is owed to the fact that individual lions are not as easy to identify as jaguars Panthera onca, leopards, snow leopards or tigers who have conspicuous markings (Miththapala et al. 1989). Camera trap detection rates can also be low, requiring a large number of camera traps in order to obtain sufficient detections (Kane et al. 2015). Additionally under low capture and recapture rates, SECR models may also feature a lack of convergence under the Bayesian

40 hierarchical framework (Gopalaswamy et al. 2012). During these instances they may not be plausible or closed capture recapture methods may be preferable.

Figure 1.1: A review of 169 peer-reviewed scientific articles (Web of Science and Google Scholar) which used data on lion numbers (abundance or density) to support an array of ecological and conservation questions. The review shows that the overwhelming majority of lion abundance and density data originate from direct observations of identified individuals, mixed methods, call-ups (ie. Audio lure) or track surveys. Most studies have also been implemented in Tanzania, South Africa, Kenya and Namibia (≥ 2 countries refers to studies that used lion data for meta-analysis, reviews or multiple country surveys). The protocol we utilised for this literature review is provided in Supplementary Information 1. The studies which used direct observations mainly used individual identification of animals and assumed that all individuals seen accounted for the total population (ie. a total count of known individuals).

A recent search encounter approach incorporating capture-recapture under a Bayesian hierarchical framework (Royle et al. 2009) explicitly incorporates the individual identity, spatial location, and sex of detected lions into the density estimation framework, and mirrors the studies above. It is particularly useful to conservation managers because, 1) it estimates a movement parameter (σ) of lions that represents their home ranges in a linear form, 2) it uses sex-specific detection rates to inform sex- ratios, and 3) provides densities and population abundance, and shows how these two parameters vary across the sampled landscape (Royle et al. 2009, Elliot and Gopalaswamy 2017).

41 1.4 Obstacles to conserving large carnivores

When compared to sectors like defense, agriculture, health and religion, environmental conservation is dwarfed in terms of the funding it receives (Zentelis and Lindenmayer 2015), and this extends to funding streams originating from state and philanthropic sources (see: www.givingusa.org, Waldron et al. 2013). The cost of implementing actions, which will maintain and potentially increase large carnivore numbers in protected areas and reserve edges are significant (Packer et al. 2013), and require costly resources (eg. vehicles, anti-poaching teams, fortified fencing and skilled labour, Rachel McRobb personal communication). Consequently, the conservation of even the most charismatic species is obstructed by a lack of funding. For example, Walston et al. (2010) estimated that, to secure Asia’s remaining 42 source populations of tigers Panthera tigris, US$ 82 million would be required annually. NGOs, tiger range state governments and private funders already funded 57% of this figure, however an additional US$ 35 million was still required. Even smaller initiatives such as the Panthera Foundation’s Munyawana Leopard Project, a conservation and monitoring initiative for African leopards in South Africa’s Kwazulu- Natal province, cost approximately US$ 80,000 per annum to run between 2003-2012 (Luke Hunter pers. comm.).

More worryingly, the majority of African protected areas have severe funding shortfalls, particularly for on the ground protection initiatives such as the protection of park boundaries, removal of wire snares, prosecution of poachers and monitoring of key large mammal populations (Packer et al. 2013). For example, the current estimate to protect African lions and other large mammals in key lion conservation areas is somewhere in the region of US$ 1.25 billion per annum, with the current shortfall being US$ 1 billion (Lindsey et al. 2016). Much of this shortfall is attributed to the sheer size of African protected areas (eg. the 12th largest protected area on earth is a conglomeration of 17 parks in five countries spanning some 387,132 km2, see: www.kavangozambezi.org). Additional factors include a lack of tourism due to political instability, and remoteness (Lindsey et al. 2007), and importantly, the underperformance of protected reserves which are already experiencing large mammal declines due to poaching (eg. Zambia’s South Luangwa National Park, Lindsey et al. 2013, Watson et al. 2013).

42 NGOs and conservation charities involved in carnivore conservation share the same challenges as African governments. Interventions attempting to stem the decline of large carnivores, which arise from conflict with local communities, may be expensive, especially those attempting to influence human behaviour through monetary payments (Dickman et al. 2011). For example, over a five-year period between 2008-2013, a compensation scheme running in the Amboseli-Tsavo ecosystem of Kenya for Maasai cattle herders against carnivore depredation spent on average US$ 100,000 per annum (Bauer et al. 2017). Similarly, the Mbirikani Predator Compensation Fund spent on average US$ 55,000 per annum over a five-year period to reduce lion killings in the greater Amboseli region (Okello et al. 2014). Both these schemes reduced the number of lions killed (eg. the Mbirikani fund reduced killings by an estimated 87-91%, Hazzah et al. 2014), but cite sustainable funding streams as a crucial pre-requisite to their success (Bauer et al. 2017).

1.5 Thesis aims and structure

My thesis deals with two distinct research themes, 1) the importance of estimating large carnivore populations robustly, and 2) examining the conflict between large carnivores and people (the leading cause of their decline, Panthera 2017, Figure 1.2). It also examines the use of financial mechanisms for large carnivore conservation, through compensation for livestock losses, and a wildlife imagery royalty. Chapters 2 and 3 estimate the populations of threatened, and highly valuable carnivore populations in the East African state of Uganda, and highlight the utility of using population state variables as indicators of species status. The aim of Chapters 2 and 3 is to 1) assess the population densities of a large carnivore guild in south-western Uganda using recent robust SECR methods, and 2) use population state variables (namely movement, sex ratio, abundance, and density) to infer whether the populations are likely to be declining.

To my knowledge, the comparison of the lion movement parameter (σ) to historic home-range data in Chapter 2 is the first attempt to use this as an indicator of population stress (specifically that African lions are declining). I show using a recent population density method pioneered in Kenya’s Maasai Mara (Elliot and Gopalaswamy 2017), how African lion movements have increased over 400% for male

43 lions, and at least 100% for females in just ten years in Uganda’s Albertine Rift, and presently occur at low densities (2.70 lions/100 km2). These lions, which feature a rare culture of tree climbing, are an important part of Uganda’s tourism industry (Plumptre and Roberts 2006). I postulate that movement increases are a result of a decrease in the densities of preferred lion prey (due to poaching). I also show how sex-ratio estimates which account for space-use of African lions suggest direct removal of female lions from this system due to conflict in fishing villages where cattle are now being grazed. This chapter highlights the utility of the lion census technique of Elliot and Gopalaswamy (2017) in assessing the conservation status of African lions. Importantly, lion identification in this study relies on features such as whisker spots which are not readily visible in many camera-trap images.

Chapter 3 examines the population densities of African leopards and spotted hyenas in the Albertine Rift of Uganda. This chapter is meant to provide the first robust density information on leopards in Uganda, and a more robust measure (using spatially explicit capture recapture) of the spotted hyena densities provided by Omoya et al. (2014) using call-up surveys. Although the results achieve this objective, they also suggest a pattern consistent with competitive release theory (Ritchie and Johnson 2009). Spotted hyena densities were in the order of 5 – 6x higher than African lions, and the highest in the recorded literature (13.43-14 individuals/100 km2). This pattern is consistent with the Liuwa Plains National Park of Zambia (M’soka et al. 2016), and Talek region of the Maasai Mara National Park in Kenya (Green et al. 2018), with both regions showing inverse relationships between hyena and lion densities.

Although the effects of hyenas replacing lions in savannah ecosystems are poorly documented in the literature, this phenomenon could have important ramifications on the local tourism sector. The QECA has developed a global reputation for its famous tree-climbing lions and each animal is estimated to be worth US$ 14,000 per annum (Plumptre and Roberts 2006). African lions (along with leopards) are also the most sought after safari species in African savannas (Lindsey et al. 2007, Di Minin et al. 2013, Grünewald et al. 2016), and visitor numbers increase with predator sighting probability (Grünewald et al. 2016). The loss of lions, and replacement by hyenas in this system may therefore also have a negative impact on park tourism, especially if detection probability of lions decreases significantly, and if hyena detections are low.

44 Chapters 4 and 5 deal with the conflict between a peri-urban leopard population interacting with humans and their stray dogs in southern India, and conflict between Bahima pastoralists conflicting with leopards and spotted hyenas in south-western Uganda. Both chapters deal with human-carnivore conflict, however they take contrasting approaches of how the carnivores themselves are framed. Chapter 4 explores the potential benefit of a widespread but conflict-prone large carnivore, the leopard, to human communities in Mumbai, India. Chapter 5 explores the financial cost, landscape-level drivers, and financial compensation surrounding leopard and hyena attacks in western Uganda. My motivation for these latter chapters stems from one of the increasing challenges facing large carnivores, and conservation managers, their large body size, which places incredible metabolic demands on them (Carbone et al. 1999), and decreasing prey populations (Khorozyan et al. 2015, Lindsey et al. 2018).

Chapter 4 explores the provision of ecosystem services by a large terrestrial carnivore, the leopard Panthera pardus to human populations on the edge of Sanjay Gandhi National Park (SGNP). This is one of the first studies highlighting a large carnivores ecosystem service outside of a natural or protected system. For example the literature has an array of examples of ecosystem service provision by large carnivores, ranging from control of prey populations (Ripple et al. 2014), and suppression of smaller carnivores (Berger et al. 2008) to reducing parasite load in humans (Harris and Dunn 2010), and promoting seed dispersal (Sarasola et al. 2016). I show that leopard predation of stray dogs possibly reduces the number of people bitten by dogs, reduces the risk of rabies transmission and reduces dog sterilization, and management costs. Our estimates show that dog densities around SGNP (17.3/km2) are 40 times lower than four nearby urban informal settlements (688/km2), and are ten times lower than the city-wide mean (160/km2). Under the assumption that leopards are holding the dog population around the park at its current density, dog bites could increase from 3.6 bites/1000 people to 15.5 bites/1000 people if leopards were to disappear. As over 78% of dog bites in Mumbai require treatment, and 2% require rabies post-exposure vaccination, the treatment costs could also reach as high as US$ 200,000 per year. This model makes the explicit assumption that leopards are removed from the national park, and is motivated by recent developments inside it and on its border.

45 Chapter 5 assesses the most important landscape-level drivers of human-carnivore conflicts occurring on the small cattle and sheep/goat farms bordering the Lake Mburo National Park, Uganda. I also make suggestions on how to improve the sustainability of a voluntary financial compensation scheme run by a local lodge (the Mihingo Conservation Fund) aimed at alleviating persecution of leopards, and spotted hyenas. I use ten years of spotted hyena, and leopard stock depredation events to investigate the importance of, 1) seasonality of depredation events (particularly dry spells as recorded in 2016), 2) landscape features such as terrain ruggedness (in our case illustrated by the presence of large granite inselbergs near depredation areas), proximity to roads, water, human settlements and vegetation density, and 3) examine the current costs being spent by the compensation scheme on reported attacks.

Chapter 6 of this thesis aims to explore a recently proposed idea of creating a threatened wildlife imagery royalty, to stem some of the budgetary shortfalls discussed in Chapter 1 for conservation (see Braczkowski 2016, https://www.youtube.com/watch?v=RnPwe-CbqYU&t=22s). The idea of a threatened species imagery royalty is also discussed in Good et al. (2017), and Courchamp et al. (2018). These authors propose that corporations and businesses using the imagery of threatened taxa pay a royalty that goes back to supporting their conservation. Courchamp et al. (2018) motivate that corporations that use the imagery of threatened wildlife in their product marketing not only profit from them, but create a false sense of security amongst the public regarding their threat status by their sheer ubiquity on their favourite products (what they termed “paradoxical extinction”). I build upon Courchamp et al. (2018), and Good et al. (2017) by providing a pathway to how this royalty could be instituted, and explore legal avenues for its application. I also show the immense funding that could be leveraged for threatened species conservation using large felids as a model.

I conclude the thesis with Chapter 7, a synthesis which places the results of Chapters 2-6 in the broader context of the existing literature. It highlights key African lion management options that may assist lion recovery in Uganda, and draws upon several case studies of carnivore recovery globally. I refer to the rare but hopeful case studies of carnivore recoveries in Europe (Chapron et al. 2014), the United States (Smith and Bangs 2009) and parts of Africa (African Parks Foundation 2018).

Figure 1.2: A schematic diagram illustrating the two key thesis themes of my PhD (the monitoring of large carnivores, and human-carnivore conflict), the four key data chapters and their respective journal outlets, and the standalone solutions chapter exploring the threatened wildlife imagery royalty, submitted as an essay.

47 1.6 Study sites and background to case studies used in this thesis

1.6.1 The Queen Elizabeth Conservation Area and Lake Mburo National Park, south- western Uganda

The state of Uganda is located in East Africa, and is bordered by Rwanda and the Democratic Republic of Congo to its west, Kenya to its east, and Tanzania and South Sudan to the south and north respectively. It is 241,037 km2 in size. Despite its small size, it is diverse in terms of its human cultures (40 languages), and biodiversity (Jones et al. 2016). Due to its diverse elevation (up to 5,100 m), rich geology and rainfall, Uganda has five biomes. Key habitat types include tropical and montane forests, savannas, wetlands and grasslands which hold rare and key tourism species such as the endangered mountain gorilla Gorilla beringei beringei, African golden cat Caracal aurata, and shoebill stork Balaeniceps rex. Uganda is also one of Africa’s most important freshwater sites, holding three of the continents ten largest freshwater lakes (Victoria, Albert and Edward).

Uganda has 16.06 % of its terrestrial surface listed under some form of protection (mainly forest reserve, national park, wildlife reserve and wildlife sanctuary, IUCN UW 2019), however human pressures around its protected areas are intense (Venter et al. 2016), and its landscape is experiencing unprecedented rates of land cover change (Uganda lost 24.7% of its forests between 1990-2005, NEMA 2007). This land cover change is owed to several key factors, including a rapidly growing human population (the human population increased 40% between 1993-2009, Venter et al. 2016) that requires cropland and grazing. The need for grazing land is emphasized by Uganda’s massive cattle industry. Although it has the 7th largest cattle industry in Africa (Cook 2015), due to its small surface area, it has the highest cattle density per square kilometre on the continent at 54 cows/km2 (this is ~ 2-5x greater than the other 6 leading countries). Cattle production is particularly notable in the context of carnivore conservation as human-carnivore conflict was cited as the joint first threat to African lions in a recent threat assessment document compiled for the charity WILDAID by leading lion experts from Oxford’s Wildlife Conservation Research Unit and the large felid conservation charity Panthera (Panthera 2017). Human-carnivore conflict (owed largely to livestock-carnivore interactions) is also one of the most important threats listed on the IUCN assessments for African leopards and spotted hyenas range wide (Bohm & Höner 2015, Stein et al. 2016).

In the context of large carnivore conservation, Uganda is still largely data deficient (only three national parks have been surveyed for large carnivores (Kidepo Valley National Park,

48 Queen Elizabeth National Park, and Murchison Falls National Park), and information on species such as the leopard and golden cat are virtually non-existent across the majority of the areas they are thought to occur (UWA 2010). Importantly however, Uganda holds seven wild felids (one of only 16 countries globally with 7 species, and joint 6th highest, Dickman et al. 2015). The three largest national parks in Uganda are Murchison Falls National Park (3,893 km2), Queen Elizabeth National Park (2,300 km2), and Kidepo Valley National Park (1,430 km2), and they are recognized as having small but important populations of African lions (UWA 2010, Omoya et al. 2014), leopards and spotted hyenas.

Figure 1.3: The Queen Elizabeth National Park and the adjacent Kyambura and Kigezi Wildlife Reserves of south-western Uganda. The three protected areas collectively comprise the Queen Elizabeth Conservation Area (QECA), an area of 2,519 km2. The QECA is also connected to the greater Virunga landscape, and comprises an area of over 10,000 km2, a globally significant wilderness block (Watson et al. 2016).

Key threats listed in the UWA national carnivore action plan (UWA 2010) include 1) human wildlife conflict, 2) habitat modification, and 3) prey removal. Recent conservation planning exercises by Di Minin et al. (2016), and Dickman et al. (2015) suggested Uganda is mid- ranked as a carnivore conservation priority country, and Dickman et al. (2015) rank it as 94th/142 countries on the national conservation priority score (based upon IUCN status, body 49 mass, habitat, range within protected area, evolutionary distinctiveness, and conservation umbrella potential) and 31st on the national conservation likelihood score (based on governance, economics, welfare, human population pressures, and conservation policy).

Chapters 2 and 3 of my thesis were implemented in the Queen Elizabeth Conservation Area (QECA) in the southwest of Uganda, while Chapter 5 explores human-carnivore conflict in the Lake Mburo National Park. The QECA is a 2,475 km2 conservation area (Figure 1.3) and comprises the Queen Elizabeth National Park, Kyambura and Kigezi Wildlife Reserves. It is regionally important not only due to its size (it is connected to Virunga National Park, and with it, collectively forms a globally significant Wilderness block of more than 10,000 km2 in size, Watson et al. 2016), but because of its location in the Albertine Rift, an area with the richest array of vertebrates anywhere in Africa (Jones et al. 2016).

Figure 1.4: Two tree-climbing lions photographed during the lion census highlighted in Chapter 2 of this thesis. Almost all African lions have the ability, and will climb trees at some point in their lives. Sometimes they will do this habitually, and the behaviour may permeate through an entire pride. However, there are only three populations in Africa where the behaviour is found in all lions in a region. I term this “cultural tree-climbing” as every (if not, almost every) observable lion climbs trees (mainly Euphorbia candelabrum and Ficus sycomorus). Hypotheses on why the lions do this range from decreasing heat exposure to escaping biting flies. Photographs by Alex Braczkowski. 50 The QECA is thought to hold important populations of African lions, leopards and spotted hyenas, however data on population trends are lacking for these species. The lion population in the region is important as a tourist attraction, mainly as it constitutes one of only three populations in Africa with a culture of tree-climbing (ie. almost every lion in the population climbs Ficus sycomorus and Euphorbia candelabrum trees every day when it does not rain, Figure 1.4). Each lion is estimated to be worth US$ 14,000 per annum yet there is general sentiment among the conservation community in the region (UWA, WCS and UCP) that their population is under threat and in decline. Robust lion monitoring in the QECA has been lacking in methodological and temporal consistency, and the last lion survey implemented in 2008 using call-up surveys reported abundance only estimates with high margins of error at the regional level (north = 72±49 lions, central = 41±30, and south = 27±25, Omoya et al. 2014). Moreover historical lion surveys from the QENCA in 1977- 1981 (n=400, Van Orsdol 1981, Din 1978), 1997-1999 (n=160-210, Driciru 1999), 2000- 2002 (n=90-190, Driciru et al. 2005) and 2004 (n=200, Bauer and Van Der Merwe 2004) used a mixture of audio lure surveys, individual id total counts which were divided by region, and expert solicitation to inform densities and abundance estimates over time (Figure 1.1).

My objectives for working in the QECA were to provide the most robust assessment of African lions implemented to date in this system, by incorporating spatial capture re-capture statistics and supercomputing. I opted to use a newly pioneered method on determining lion abundance pioneered in the Maasai Mara of Kenya (Elliot and Gopalaswamy 2017), as not only does this method incorporate spatial information of individual lions into the estimation framework but it also estimates population parameters which may be of consequence to managers. These include a movement parameter of African lions (sigma), and also an estimate of their sex ratios, which accounts for sex-specific movements.

One of my primary motivations for working in the Lake Mburo National Park (Figure 1.5) was that it represents a key model for many African protected areas experiencing human- carnivore conflict. Firstly, it is a small national park embedded in one of Africa’s oldest and most important cattle corridors (Kansiime et al. 2014), and is increasingly under threat from pastoralist encroachment. Secondly, it is unique in that it has a private compensation scheme run by a tourism lodge (Mihingo Lodge) to try and stem conflict between leopard, hyenas and livestock on the park edge. The region has historically been incredibly important culturally for Bahima pastoralists and was the site of an intense conservation conflict 30 years ago when hundreds of families were evicted from their land in the creation of the park

51 (originally 650 km2 in size, Kingdon 1985, Infield 2001). This was later reversed when > 50% of the park was degazzeted, mostly returning to pastureland.

Figure 1.5: The 370 km2 Lake Mburo National Park in south-western Uganda. The park is located close to the Nakivali and Kachira lake systems and is located in the heart of Bahima pastoral land, a key component of East Africa’s cattle corridor.

The region shows strong climate vulnerability and represents an apt model of how human- carnivore conflict adds to an already sensitive livestock production system. African lions were extirpated from the system due to intense conflict two decades ago and the park now relies on sightings of zebra, giraffe and leopards (UWA 2010). For example, the opportunity to view leopards in Lake Mburo are a key reason people purchase a night game drive in the park, and at least 1,585 people purchased a night game drive permit in 2018, equating to US$ 47,550 in revenue for the Ugandan Wildlife Authority (A. Kule pers. comm, chief park warden Lake Mburo).

Unlike the majority of regions where carnivores and stock farmers interact, Lake Mburo falls into a small number of sites in Africa that presently institutes a financial compensation scheme for livestock farmers when they lose livestock to leopards and spotted hyenas. The motivation behind Mihingo’s compensation scheme stems from the perceived value of 52 African leopards and hyenas. Their philosophy is simple: “large carnivores bring in tourist revenue and keep our business afloat, we therefore believe compensating farmers for losses is a small price to pay, especially if it can lead to their persistence”.

Interventions attempting to stem the decline of large carnivores, which arise from conflict with local communities, may be expensive, especially those attempting to influence human behaviour through monetary payments (Dickman et al. 2011). For example, over a five-year period between 2008-2013, a compensation scheme running in the Amboseli-Tsavo ecosystem of Kenya for Maasai cattle herders against carnivore depredation spent on average US$ 100,000 per annum (Bauer et al. 2017). Similarly, the Mbirikani Predator Compensation Fund spent on average US$ 55,000 per annum over a five-year period to reduce lion killings in the greater Amboseli region (Okello et al. 2014). Both these schemes had significant effect on reducing the number of lions killed (eg. the Mbirikani fund reduced killings by an estimated 87-91%, Hazzah et al. 2014), but cite sustainable funding streams as a crucial pre-requisite to their success (Bauer et al. 2017).

1.6.2 Leopards and human communities in Sanjay Gandhi National Park, Mumbai, India

India holds one of earths most important, yet fragmented guilds of large carnivores (Di Minin et al. 2016), and is home to roughly 60% of the world’s remaining tigers (Dutta et al. 2016). India’s protected areas cover only 5% of its terrestrial land surface (Dutta et al. 2013, Athreya et al. 2016), and it has lost an estimated 26 million hectares of forested land in the period between 1880–2010 (Tian et al. 2014).

India is also experiencing rapid economic growth and development (FAO 2019), and has some of the world’s highest human densities (with a population of 1.32 billion individuals, UN 2019). However, India is a fascinating outlier in the realm of carnivore conservation, in that significant populations of large carnivores continue to occur outside of protected areas, and are resident there (eg. Athreya et al. 2016). Moreover, interactions between large carnivores and human communities are often intense. This goes against Woodroffe (2000) who showed human densities strongly correlate with extirpation risk. Cultural tolerance and religiosity have been cited as two key contributors to population persistence of species such as Indian leopards Panthera pardus fusca, and striped hyena Hyaena hyaena outside of protected areas (Bhatia et al. 2017).

53 I used the 104 km2 Sanjay Gandhi National Park (SGNP, Figure 1.6) in Mumbai, the 13th largest city in the world as a model for Chapter 5 of this thesis. I chose this study site, a priori, after a short field season in 2015, when I met several researchers, and local villagers living on the park edge, and who revealed leopards to be significant predators of stray dogs. The leopards of Mumbai are a dramatic example of humans and large carnivores living in close proximity to one another. SGNP is a formally protected area characterized by moist deciduous forest while the Aarey cattle milk colony is a 16 km2 matrix of pastures, forest patches and human settlements connected to the southwest of the park. The park is encroached on by some of the largest informal settlements in Mumbai and 250,000 people are estimated to live within just 500 m of its border (Krishna Tiwari pers.comm, Prasad and Tiwari 2009) while another 100,000 live in Aarey (Purva Variyar pers.comm). Approximately 35 mature leopards live in SGNP and the Aarey colony (Surve et al. 2015). The city is also home to an estimated 96,000 stray dogs (Hiby 2014) - and these roam freely across both urban and rural informal settlements (Hiby 2014).

54 Chapter 2 statement of authorship: The candidate, Arjun Gopalswamy, and Martine Maron conceived the idea for the manuscript. The candidate collected and prepared all field data with field assistance from Mustafa Nsubuga. Arjun Gopalaswamy assisted with editing and modifying the R code from models detailed in Royle et al. (2009). The Candidate ran the statistical analysis in R. Interpreted the results, and received guidance from Arjun Gopalaswamy while writing these up. The candidate wrote the manuscript with input from all authors (Arjun Gopalaswamy, Mustafa Nsubuga, James Allan, Duan Biggs, and Martine Maron).

CHAPTER 2 Detecting early warnings of pressure on an African lion population in the Queen Elizabeth Conservation Area, Uganda

Published in Ecological Solutions and Evidence

2.1 Abstract

African lions are declining across much of their range, yet robust measures of population densities remain rare. The Queen Elizabeth Conservation Area (QECA, 2400 km2) in East Africa’s Albertine Rift has potential to support a significant lion population. However, QECA lions are threatened, and information on the status of lions in the region is lacking. Here, we use a spatially explicit search encounter approach to estimate key population parameters of lions in the QECA. We then compare home range sizes estimated from our models to those from a radio-collaring study implemented a decade earlier. We recorded 8243.5 km of search effort over 93 days, detecting 30 individual lions (16 female and 14 male) on 165 occasions at a rate of 2 lion detections/100 km2. Lion density in the QECA was 2.70 adult lions/100 km2 (SD=0.47), while mean abundance was 71 individuals (SD=11.05). Worryingly, the movement parameter for male lions was 3.27 km and 2.22 km for females, suggesting >400%, and >100% increases in home range size, respectively, compared to a decade earlier. Sex ratio of lions in the QECA was lower (1 male: 0.75 females), when compared to a previously published review (mean=1:2.33). Low lion density and large movements are likely a result of human driven prey depletion. Our results suggest lions in the QECA are in a precarious state. As lions are under pressure throughout much of Africa, our study presents the utility of a census technique that could be used elsewhere as an early warning of lion declines.

56 2.2 Introduction

The African lion (Panthera leo) is listed as an Appendix II species under both the Convention on the International Trade of Endangered Species (Bauer et al. 2016), and Convention on the Conservation of Migratory Species of Wild Animals (CMS). Estimates place the continental population at ~25,852 individuals (range 20,000-30,000) in 102 populations residing in approximately 2.5 million km2 (along with 628 in fenced reserves across South Africa, IUCN SSC 2018). The key threats to African lion populations include 1) ineffective protection in national parks and reserves (mainly through a lack of management resources), 2) habitat degradation and reduction of lion prey through “bushmeat” poaching, and 3) human-lion conflict mainly between lions and cattle farmers who kill lions in retaliation for stock depredation (IUCN SSC 2018). These threats are amplified through weak governance, and in many places a lack of resources, which may be both human and financial (Lindsey et al. 2016, Lindsey et al. 2018).

The East African state of Uganda, and its lions, serve as an important case study of the challenges facing the species in many parts of the continent. Lions are listed as critically endangered in Uganda (WCS 2016), and are known to occur in three of its largest national parks (Murchison Falls, Queen Elizabeth and the Kidepo Valley, UWA 2010, Omoya et al. 2014). The Queen Elizabeth Conservation Area (comprising the National Park and the Kyambura and Kigezi Game Reserves) has been viewed as one of Uganda’s lion strongholds (Omoya et al. 2014). The park was gazetted in 1952, and large mammal surveys in the 1960s - 1970s revealed it had the densest herbivore biomass on earth (18,800-19,928 kg/km2), being ranked 1st out of 24 African sites (Bourliere 1965, Coe et al. 1976). However, after Uganda gained independence, state-level resource management declined rapidly, and the Idi Amin and Milton Obote regimes (1971-1985) are thought to have brought on widespread political instability and poaching, which decimated much of the regions’ wildlife (Edroma 1986, Lamprey and Michelmore 1996). Political stability returned to Uganda in 1986 but years of poaching and unrest seemed to have contributed to a large mammal collapse in the park (Chritz et al. 2016). A radio-collaring study implemented from 2006- 2011 showed that the home range sizes of lions had expanded, and pride size had decreased in the QECA since the 1970s, indicating that lions were moving further in search of food (Mudumba et al. 2015). This suggested lions too were impacted by the large-scale decimation of prey species during the period of political instability. Recent aerial census data suggests that prey populations have been slowly recovering since 2011 (Lamprey 2018, Plumptre et al. 2014, Plumptre et al. 2010). 57 Despite being appreciated as a flagship species that generates significant revenue for the tourism sector (eg. lions in the QECA each generate at least US$ 14,000 annually from ecotourism revenue, Plumptre and Roberts 2006), lion populations in Uganda have not been monitored consistently over time. The history of monitoring lion populations in the park has ranged from total counts of lion sightings from intensive field surveys, counts by individually identifying lions (eg. Van Orsdol et al. 1985, Driciru 1999), expert elicitation (Bauer and Van Der Merwe 2004) to audio lure counts (Omoya et al. 2014). Moreover, the unusually high detection rates of lions in the QECA, due to their culture of climbing Euphorbia candelabrum and sycamore fig Ficus sycomorus trees, may have led field workers to be satisfied in applying less-robust lion monitoring methods in the QECA. The use of these different enumeration methods, which often have underestimated and wide confidence intervals, makes robust inference about population trends difficult (Elliot and Gopalaswamy 2017).

In this study, we sought to assess the status of African lions in the QECA using rigorous population estimation methods. We directly estimate lion density and other relevant state variables of interest using a newly developed Bayesian spatially explicit capture-recapture (SECR) approach (Russell et al. 2012, Royle et al. 2013) which involves a search-encounter technique to locate and identify individual lions in the landscape, to estimate lion abundance (see Elliot and Gopalaswamy 2017). This hierarchical model (see Royle and Dorazio 2008) helps us jointly estimate key state variables of interest (namely lion density, abundance, sex ratio and sex-specific movement), all in the face of imperfect detection. We then compare the findings from our study to the intensive radio-collaring study by Mudumba et al. (2015). We hypothesized that 1) if lion populations were stable or increasing in the QECA since the study, we would observe similar or decreased range sizes in our study. However, 2) if lions were declining due to anthropogenic pressures such as prey-depletion, range sizes would increase, and sex ratios of lions would be skewed towards males (when compared to a review of sex-ratios by Périquet et al. 2015). Our study highlights the utility of SECR-based search-encounter techniques for assessing the status of African lions, especially where historical information on lion home ranges and movements exist. We suggest this could be used in other African protected areas as an early warning to estimate threats to lions and other individually recognisable species.

2.3 Methods

2.3.1 Study Area

58 The Queen Elizabeth National Park is in south-western Uganda (1978 km2, Figure 2.1). It falls within the Albertine Rift Valley, a global biodiversity hotspot with the highest diversity of vertebrate species in continental Africa (Plumptre et al. 2007). Queen Elizabeth is connected to two nearby protected systems, the Kyambura wildlife reserve in the north (154 km2), and the Kigezi Wildlife Reserve in the south (269 km2), collectively making up the Queen Elizabeth Conservation Area (QECA). The southwest of the QECA is also contiguous with Virunga National Park (7,800 km2) in the DRC, and collectively these reserves comprise the greater Virunga landscape (Jones et al. 2016), and a globally significant wilderness block (10,000 km2, Allan et al. 2017). The region has an elevation ranging from 900-1,300 metres above sea-level (Salerno et al. 2017), and two rainy seasons occur in March-May and September-November, totalling 600-1,400 mm per year (Chritz et al. 2016).

The QECA is bisected by the small Kazinga channel, which connects two lake systems (Edward in the West, and George to the East). The area north of the Kazinga channel is dominated by grasslands and wooded grasslands, (Wronski et al. 2006), with dense thickets extending towards the western most park edge in Mweya and Katwe. The area south of the

59 Kazinga channel is characterized by wooded grasslands and acacia woodlands (Mudumba et al. 2015), and a large patch of tropical high forest (termed the Maramagambo forest). The main lion prey in the park include Cape buffalo Syncerus caffer, Uganda kob Kobus kob thomasi, topi Damaliscus lunatus, warthog Phacochoerus africanus and waterbuck Kobus ellipsiprymnus (Mudumba et al. 2015).

Figure 2.2: Search effort tracks, lion detections and pixel-specific lion densities generated from the search encounter lion survey 10 November 2017 – 10 February 2018 in the QECA.

2.3.2 Field methods Two observers intensively searched the QECA study area for African lions over a 93-day period (10 November 2017–10 February 2018). Searches were done daily in a Suzuki 4x4. Driving was done on roads and also off-road where access was permitted (Figure 2.2). The three-month survey period has previously been used to balance the requirements of population closure and obtaining enough detections for density estimation using spatial capture-recapture (Karanth and Nichols 1998, du Preez et al. 2014, Elliot and Gopalaswamy 2017). To measure search effort while driving, we used iPhones loaded with the open source application MapMyDrive (Ring 2017), which created GPX files of the total kilometres driven per day (Supplementary Information 1). Lions in the study area exhibit a rare culture of

60 regular tree-climbing, and rest in the canopy of Acacia spp, Candelabra Euphorbia candelabra and Sycamore fig Ficus sycamorus trees from the early morning till dusk (Mudumba et al. 2015). We therefore searched the study area, scanning both the ground and the upper canopy of the trees with binoculars. When a lion was sighted we collected its GPS location, the time and date it was sighted and the habitat type it was found in. Every lion was photographed with a REDTM EPIC-W or CanonTM 5D Mark III high-resolution camera equipped with a 70-300 mm CanonTM telephoto lens. We took photos of the left and right side of each individual for whisker spot id (Pennycuik and Rudnai 1970, Figure 2.3), and also noted ear tears, large scars and for males, their mane development (Whitman et al 2004, Miller et al. 2016). We excluded all lions that were evidently <1 year of age (based on facial scarring, body size and mane development) due to the high mortality in this age class (Packer et al. 1988).

2.3.3 Data analyses We estimated African lion densities and abundance in QECA using our individually identified lion sighting data in a Bayesian spatially explicit capture-recapture modelling framework

61 (Royle et al. 2013). To estimate lion densities over specific sub-regions, we used the posterior distribution of lion activity centres across our study area defined by equally spaced pixels (centroids being 0.3975 km apart), resulting in a fine-resolution discrete state space. Thus, a lion’s activity centre is defined by a multinomial prior distribution and allows for more than one lion to have its activity centre located at a pixel (see Royle et al. 2013). We used the spatial analyst tool of Arc GIS 9.3 to create a 25 km buffer around the borders of the QECA. This was done to ensure inclusion of all individual lion home ranges within our sampled grid. These were described by 8,5028 equally-spaced pixels, each representing an area of 0.158 km2 within a total state space area of 13434 km2. Areas judged as non-suitable lion habitat (large human settlements, crop-lands and water bodies) were excluded from the buffer (Mudumba et al. 2015, Braczkowski et al. 2016,). The remaining, potentially suitable, habitat was hence represented by 37,603 pixels (5,941 km2). The resultant state space was over larger than that used by Elliot and Gopalaswamy (2017) in the Maasai Mara, as we hypothesized lion densities would be lower than the Maasai Mara, and lions would move greater distances in the habitats of the QECA. We used an unstructured spatial capture- recapture sampling design (Russell et al. 2012), and created a standard capture-recapture matrix (trap locations, individual lions and sampling occasions). This allowed us to model the way in which individuals were detected in our lion survey. The “traps” in our study area were represented by 0.158 km2 grids of the study area available for searching. We included a covariate to account for search effort per trap per day (natural logarithm of kms driven), as some traps with intensive search effort could increase lion detection rates (Elliot and Gopalaswamy 2017).

Large terrestrial carnivores, including lions, regularly feature individual or group differences in their behaviours. This is most pronounced between the sexes and can affect home range size and capture probability (Karanth and Nichols 1998), and can affect inference from SECR (Sollman et al. 2011). To factor this into our modelling, we included a sex-specific covariate in the observation process and accounted for different encounter probability for males and females. Because detection probability of an individual animal declines with increasing distance between its activity centre and searched pixel, largely due to animal movement, the rate of decline in detection probability σ could be used as a surrogate for movement range. The ϑ (theta parameter) which defines the functional form of the detection function can be used to infer resource selection for African lions. In our analysis, we estimated ϑ and also fixed it at certain fixed values in our candidate model set. The detection function takes on a negative exponential form when ϑ=0.5 and a Gaussian form when ϑ=1. 62 Therefore, in our models, the probability of detecting a lion i in pixel j on sampling occasion k is defined by a complementary log-log function of covariates.

To estimate African lion density and abundance we created seven a priori models and assessed their results (parameter definitions are presented in Table 2.1). Model 1 was the full model, and it estimated the detection function (defined by θ) and was based on the assumption that detection probability is sex specific:

cloglog (πijk) = logλ0 + βeff[log(EFFORTjk)] + βsex (SEX9) − f[dist(i, j|ϑ, σsex)]

where f[dist(i,j|ϑ,σsex)] describes how detection rate is a function of distance between the activity centre of individual i and pixel j, which are conditional on θ and σ sex. The specific form of this detection function is:

!"#$%(#,()!" D[E9FG(9, H)|ϑ, σsex] = exp J ! K *+#$%

Model 2 was based on the assumption that basal encounter rate is independent of sex, thus, βsex was fixed at 0. Rate of decline in detection probability (σ) remained sex specific (i.e. dependent on sex) because this parameter is also related to animal movement.

Model 3 was identical to Model 2, but the detection function parameter θ was fixed at 0.75.

Model 4 was based on the assumption that basal encounter rate is dependent on sex, thus, βsex was fixed at 1. Rate of decline in detection probability (σ) also remained sex specific. The detection function parameter θ was fixed at 0.75.

Model 5 assumed basal encounter rate is dependent on sex but rate of decline in detection probability was independent of sex. The detection function parameter was fixed at θ = 0.75.

Model 6 was based upon the assumption that detection probability is independent of sex and that the rate of decline in detection probability was also independent of sex. The detection function parameter was fixed at θ = 0.75.

63 Model 7 was the same as model 1 but the detection function parameter was fixed at 1. These models are based upon previous search encounter techniques for African lions and cheetahs (Elliot and Gopalaswamy 2017, Broekhuis and Gopalaswamy 2016).

To run these models we used the package SCRbayes (https://github.com/jaroyle/SCRbayes) in the programming environment R (R Development Core Team 2015). We used Bayesian Markov chain Monte Carlo (MCMC) simulation and the Metropolis-Hastings algorithm (Tierney 1994) to run our models. We set each model to run for 11,000 iterations with a burn-in of 1,000 iterations (this is exclude a sample of iterations at the beginning of an MCMC run) . Each model was set to run for 4 chains (Elliot and Gopalaswamy 2017). If we did not arrive at a standing distribution, we increased the burn-in period further. MCMC convergence was assessed using the Gelman–Rubin diagnostic (Gelman & Rubin 1992).

Model adequacy was determined by examining the Bayesian p-value on individual encounters (Royle et al. 2009), assessing correlations between posterior parameters from the MCMC draws and by examining the HM estimator of the marginal likelihood of each model (Dey et al. in press). As such we did not entirely rely on the marginal likelihood estimate to perform model selection but used this in addition to the model adequacy test and inspection of correlation plots to make decisions on model choice. The five input files necessary to run these analyses are provided in Supplementary Information 1, while the accompanying R scripts are provided in Supporting Information 2. Finally, we utilised the information on lion movements from our models to determine a crude measure of sex- specific lion home range size. We then compared these to the estimates from Mudumba et al. (2015). To do this, we converted the movement parameters (σ) for males and females from the model that gained most support from our model selection process. However, as we wanted to compare range size with that of Mudumba et al. (2015), which represented a 95% kernel distribution, we reran the highest ranked candidate model, setting the θ parameter to 1. This enabled us to make comparisons of home-range estimates with the 95% kernel distribution estimated in Mudumba et al. (2015). To convert sex-specific σ to a measure of home-range size, we used the formula from Calhoun and Casby (1958):

π(σ√5.99)2

Posterior Posterior Parameter Definition mean SD n 30* - total number of lions detected during the survey period nz 400$ - number of lions augmented to n, so M = n+nz represents the maximum number of lions in the large state space S

σF 0.88 0.12 rate of decline in detection probability with increasing distance between the activity center of a lioness and the location at which she was found

σM 1.08 0.14 rate of decline in detection probability with increasing distance between the activity center of a lion and the location at which he was found

βsex 0.56 0.32 difference of the complementary log-log value of detection probability between a male and female lion

βeff 0.99 0.22 rate of change in the complementary log-log value of detection probability as the (log) effort changes by 1 unit (1 km of drive effort)

λ0 0.02 0.005 basal encounter rate of a lion whose activity center is located exactly at the centroid of a grid cell ψ 0.37 0.07 ratio of the true number of individuals in the population compared with the data-augmented population M Nsuper 160.98 28.05 total number of lions in the larger state space S ψsex 0.57 0.11 proportion of lions that are female

θ 0.55 0.04 determines the shape of the estimated detection function, value θ ranges from 0.5 (exponential form) to 1 (Gaussian) D 2.70 0.47 estimated density of lions per 100 km2

*16 females and 14 males $The data augmentation value was reduced to 400 after an initial run of 1500 showed no improvement in estimation of parameters

65 We used this formula as felid home-ranges (and animal species more broadly) approximately assume a circular or semi-circular shape (Calhoun and Casby 1958, Bunnefeld et al. 2011, Fattebert et al. 2015). We could reasonably assume that this could serve as a minimum home-range estimate. 5.99 is the value from the chi-square table corresponding to an alpha (α) level of 0.05 and 2 degrees of freedom. Two degrees of freedom are considered because movements are represented now by a "bi-variate" normal distribution, implying movement of lions along both X and Y axes. Although we were principally interested in estimating density, we also computed posterior mean abundance (i.e. total number of lions) across the QECA alone.

2.4 Results

We recorded 70 lion sightings during the 93-day survey period (34 sightings of lion groups, and 36 sightings of single lions) totalling 165 lion detections (Figure 2.2) of 30 individuals (16 female and 14 male). These detections were made after 8,243 km of search effort resulting in 2 lion detections/100 km2. All lions judged to be <1 year of age detected were excluded when found with their natal prides (n = 9 cubs in 2 prides). One lion was detected 13 times, five lions were detected nine times, four lions were detected eight times, three lions were detected seven times, one lion was detected six times, one lion was detected five times, two lions were detected four times, ten lions were detected 3 times, two lions were detected two times and one lion was detected once (all data are presented in Supplementary Information 1). Based on these lion detections we found mean group size was 3.2 individuals (n = 5 prides) and mean total pride size was 6.2 individuals (n = 5 prides).

2.4.1 Lion density, abundance, movement and home range size Lion density for the QECA was estimated at 2.70 individuals /100 km2 (posterior SD 0.47, range 1.83-3.62). The range of posterior density estimates per pixel (0.158 km2) was 0.0002- 1.89 lions per km2 showing a wide regional range in lion densities. We note, however, that since multiple individuals in lion prides will have the same activity centre during such discrete state space implementations, this range may be exaggerated, The lion movement parameter (σ) estimated from our highest ranked model (model 1, with θ set to 1), for males was 3.27 km, and 2.22 km for females (Table 2.2). When this was converted into an approximate estimate of minimum home range size (ie. π(σ√5.99)2), the average home range size for male African lions was 203.66 km2 (range 130.57-

Table 2.2: Model parameters for our seven candidate models assessing African lion population state variables in the QECA

bsigma sigma bsigma2 sigma2 lam0 beta effort beta sex psi psi sex Nsuper Theta Density Model Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD 1 0.45 0.1 1.08 0.14 0.67 0.13 0.88 0.10 0.02 0.005 0.99 0.22 0.56 0.32 0.37 0.07 0.57 0.11 160.98 28.05 0.55 0.04 2.70 0.47 2 0.50 0.11 1.02 0.15 0.60 0.13 0.93 0.12 0.02 0.005 0.99 0.22 0 0 0.38 0.07 0.57 0.11 161.66 28.31 0.55 0.05 2.71 0.48 3 0.16 0.03 1.79 0.12 0.28 0.04 1.34 0.1 0.01 0.002 1 0.22 0.58 0 0.37 0.07 0.57 0.11 156.96 28.14 0.75 0 2.63 0.5 4 0.19 0.03 1.65 0.11 0.25 0.03 1.42 0.1 0.01 0.002 0.99 0.22 0.00 0.00 0.37 0.07 0.57 0.11 157.07 27.28 0.75 0 2.64 0.44 5 0.21 0.02 1.55 0.09 0.21 0.02 1.55 0.09 0.01 0.002 1 0.22 0.12 0.21 0.35 0.07 0.53 0.11 150.64 27.24 0.75 0 2.52 0.46 6 0.21 0.02 1.55 0.08 0.21 0.02 1.55 0.08 0.01 0.002 1 0.21 0 0 0.35 0.06 0.0007 0.002 148.48 25.48 0.75 0 2.49 0.43 7 0.48 0.01 3.27 0.33 0.1 0.18 2.22 0.20 0.07 0.001 1.02 0.22 0.57 0.26 0.37 0.07 0.57 0.11 157.74 27.19 1 0 2.65 0.46

67 285.07), and 93.79 km2 (range 64.20-128.80) for females. The male to female sex ratio in our study estimated by ψsex was 1:0.75. The posterior mean abundance for the QECA was 71 lions (posterior SD 11.05).

2.4.2 Model diagnostics Bayes p-value was estimated at 0.70 - 0.78 (Table 2.3) which were within the bounds of the extremities to gauge model adequacy (0.15-0.85). All shrink reduction factors generated using the Gelman-Rubin diagnostic statistic were <1.10 (Table 2.4) indicating satisfactory convergence. The lowest marginal likelihood recorded for our seven models (Table 2.3) was our first model, which assumed detection probability, and the rate of decline in detection probability was dependent on sex (Table 2.1).

Table 2.3: Bayes p-values for our seven candidate models assessing lion density in the QECA. created to assess African lion density in the QECA.

Natural logarithm of marginal Model number Bayes p-value likelihood 1 0.74 -158020.56 2 0.75 -161676.60 3 0.74 -163470.45 4 0.78 -165528.85 5 0.73 -161916.87 6 0.70 -162227.85 7 0.75 -172241.9

2.5 Discussion

2.5.1 African lion density and movement in the QECA Our results represent the first attempt at estimating lion densities through a SECR approach in the QECA, and the first estimate of lion abundance there in a decade since the call-up surveys of Omoya et al. (2014) done in November-December 2008. We found lion density to be 2.70 individuals/100 km2, and approximate minimum home ranges were 203.66 km2 and 93.79 km2 for male and female lions respectively – much larger than those estimated by Mudumba et al. (2015). This implies that home ranges have increased significantly over just a decade. Our results also show a lower than expected number of female lions in the QECA system. Collectively the apparent increase in range sizes, low density and skewed sex-ratio lends support to our second hypothesis, that African lions in the QECA are under threat and in a state of decline, and require urgent conservation attention.

Table 2.4: Shrink reduction factors for the key parameters from our seven candidate models created to assess African lion density in the QECA.

bsigma sigma bsigma2 sigma2 lam0 beta effort beta sex psi psi sex Nsuper Theta Density Model number Est C.I Est C.I Est C.I Est C.I Est C.I Est C.I Est C.I Est C.I Est C.I Est C.I Est C.I Est C.I 1 1.02 1.01 1.03 1.03 1.01 1 1.01 1.01 1.03 1.02 1 1 1.01 1.01 1.01 1.01 1 1 1.01 1.01 1.02 1.02 1.01 1.01

2 1.01 1.01 1.02 1.02 1 1 1.01 1.01 1 1 1 1 NA NA 1.01 1.01 1 1 1.01 1.01 1.02 1.02 1.01 1.01 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.01 1.01 1 1 1.02 1.01 NA NA 1.02 1.01 4 1 1 1 1 1 1 1 1 1 1 1 1 NA NA 1.03 1.02 1 1 1.03 1.03 NA NA 1.03 1.03

5 1 1 1 1 1 1 1 1 1.01 1.01 1 1 1.01 1.01 1.02 1.02 1 1 1.03 1.02 NA NA 1.03 1.02 6 1 1 1 1 1 1 1 1 1 1 1 1 NA NA 1.03 1.02 1 1 1.04 1.03 NA NA 1.04 1.03 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.01 1.01 1 1 1.01 1.01 NA NA 1.01 1.01

69 Apart from the low lion density in apparently suitable habitat, the most striking result of our survey was the average home range sizes generated from the sex-specific movement parameters (σ) for males and females in this study. When these are compared to those recorded in an intensive radio-collaring exercise implemented by Mudumba et al. (2015) in QECA from 2006-2011, they reveal that range size estimates have increased by over 414% for males (n = 5 individuals averaged 39.64 km2 in Mudumba et al. 2015) and, by ~106% for females (n = 4 individuals averaged 45.58 km2 in Mudumba et al. 2015). The estimates of home ranges generated from our movement parameters are an under-representation of their true size, due to limited detections over a three-month sampling period. This means they are all the more striking. Recent satellite collar data from ten individual lions across two prides (including one male coalition) from June-September 2018, averaged 292.43 km2, and showed even higher increases in range size (Mustafa Nsubuga, WCS unpublished data). The range size study of Mudumba et al. (2015) is unlikely to have suffered from significant range size underestimation as only lions with 102 - 634 GPS locations were used in the analysis of home-ranges, well above the recommended 30-100 location point threshold for accurate seasonal home-range estimation (Girard et al. 2002).

There are a number of key factors that are important in shaping the density and movements of large carnivores in African savannas, including the availability of prey and the density of competitors. Loveridge et al. (2009) showed that the home-range size of lions in Hwange National Park, Zimbabwe, decreased with increasing prey biomass. Commercial and subsistence poaching of lion prey species is widespread across the QECA, and parts of the Ishasha sector, the park’s lake edges and rivers showed the highest poaching pressure from 1999-2012 (Critchlow et al. 2015). Furthermore, the commercial, and non-commercial harvest of animals increased during this time. We suggest the increased movements of lions we detected are likely a response to systematic prey depletion in the broader QECA. We can also not rule out the possibility that the increases in range size observed in our study are owed to direct anthropogenic pressures on lions. This could manifest itself in two main ways, snaring of lions (non-targeted) and retaliatory killings of lions through human-lion conflict over cattle. Indeed Fattebert et al. (2016) and Balme et al. (2009b) show that female leopard home range sizes were larger during times of anthropogenic pressure compared to no hunting. This was not observed in male lions. Lion conflict with cattle farmers is a regular occurrence in the QECA landscape and at least 47 adult and sub-adult lions were killed in the northern section of the park between 2006-2012 alone (Uganda Carnivore Program, unpublished data 2006-2012). 70 The high male to female sex ratio of one adult male to 0.75 adult females in the region is highly unusual. A review of 40 scientific papers reporting adult African lion male to female ratios reported a mean ratio of 1 adult male lion: 2.33 adult females (Périquet et al. 2015). Even the least-productive arid and semi-arid environments (eg. the Kgalagadi of Botswana and Namibia’s Etosha National Park) where lion home ranges are large and densities low feature higher adult sex ratios of 1:1.2 and 1:1.4 respectively (Stander 1991, Funston 2011). We also found a low mean number of females per pride (mean = 3.2 individuals, n = 5 prides), considerably lower than Kgalagadi (female pride size mean = 4.2±0.4 SE individuals, Funston 2011), and Etosha (female pride size mean = 4.8±0.5 individuals, Stander 1991). The mean number of females per pride was also lower than recorded for the QECA in 1998 (female pride size mean = 4.8 individuals, Driciru 1999), and the total number of prides detected was also significantly different (10 prides detected in 1999 vs 5 detected in 2017/18).

It is problematic to compare our lion density and abundance estimates to previous estimates in the QECA, and more broadly in the lion literature, as the methods used differ widely. For example a series of recent studies from the last ten years implemented in a combination of both open (eg. Tanzania’s Serengeti savannas), and those that feature mixed woodlands and bushveld (eg. The Moremi Game Reserve in Botswana) recorded anywhere from ~2.5- 11.87 African lions/100 km2 (Table 2.5). Unlike non-spatial techniques, SECR methods integrate the individual identity and landscape use of African lions in the density estimation process while explicitly accounting for search effort (Elliot and Gopalaswamy 2017). We could, therefore, only make direct comparisons to four other African protected area systems, in which a SECR approach has been used to estimate lion densities. Of these, our estimates of African lion density were similar to those found in the Okavango of Botswana (2.5/100 km2, Rafiq et al. 2019) and the Nikolo-Koba National Park, Senegal (3.02/100 km2, Kane et al. 2015. They were approximately ~ 6x lower than the Maasai Mara in Kenya, while movement parameters (sigma, which represents the average movement of male and female lions during the survey period) were 4x and 3x higher for male and female lions respectively. The Maasai Mara harbours plentiful prey, potentially resulting in the shorter movements of lions there (Elliot and Gopalaswamy 2017).

2.5.2 Conservation implications Taken together, the reduced home range sizes, low density, and unusually male-biased sex ratios suggest that the population of lions in QECA is under threat. The low densities of 71 African lions and the larger movement estimates observed in our study may be due to poaching of preferred lion prey in the region. The QECA was historically considered to have some of the highest mammalian biomass in Africa, but it declined from 19,928-24,764 kg/km2 in the mid 1970’s to 8,050 kg/km2 by 2009 (Treves et al. 2009). The SECR parameters generated from our study, suggest this downward trend has continued since the study of Mudumba et al. (2015), and recent aerial surveys (Edroma, 1986, Lamprey & Michelmore, 1996, Lamprey 2018) of preferred lion prey do not appear to accurately capture this.

Table 2.5: A selection of studies implemented in the last ten years on the abundance and density of African lions using a combination of methods. These come from eight countries and two continents.

Study name Location Survey type Density estimate (lions/100 km2) Belant et al. 2016 Serengeti, Tanzania Open savanna 14.40 Blackburn et al. (2016) Maasai Mara, Kenya Open savanna 11.87 Cozzi et al. (2015) Moremi Game Reserve, Botswana Mixed woodland savanna 5.80 Loveridge et al. (2017) Tsholotsho, Zimbabwe Mixed woodland and arid savanna 2.5-4.5 Midlane et al. (2015) Kafue, Zambia Mixed woodland savanna 1.83 Rosenblatt et al. (2014) South Luangwa, Zambia Mixed woodland savanna 10.40 Kane et al. (2015) Niokolo-Koba National Park, Senegal Mixed woodland savanna 3.02 Banerjee et al. (2010) Gir National Park, India Dry deciduous forest 5.6 Bauer et al. (2014) Khutse Game Reserve, Botswana Arid savanna 1.72 Begg et al. (2018) Niassa, Mozambique Call up surveys Abundance of 693-1071

The skewed sex ratios may be due to direct killing of lions by people living in villages located within and on the boundaries of the QECA. Indeed Woodroffe and Ginsberg (1998) showed that female home range size and increases over time were a key determinant of large carnivore population persistence. Our data shows that female home ranges have increased in the QECA over just ten years. This could mean that female lions are having increased contacts with villages due to these range increases. Direct killing of African lions in the system occurs regularly and in the northern section of the park alone, at least 47 adult and sub-adult lions were killed between 2006-2012 (Uganda Carnivore Program unpublished data, 2006-2012 - https://uganda-carnivores.org/field-updates/), equating to at least seven lions being killed annually. The incidence of male to female lions in this sample of 47 lions was a near 50:50 split, not reflecting that females are killed with a higher incidence than male lions. However it should be noted that these mortality data were collected opportunistically through animals that were radio collared or reports by villagers of lion mortalities. This high mortality is directly comparable to the Waza National Park in Cameroon, a similar-sized National Park (1700 km2) where the estimated annual removal

72 of six lions led to a population collapse (Tumenta et al. 2010). During this collapse, the lions in Waza also increased their home range size (59% increase in range size over a ten-year period), which the authors attributed to poaching of lion prey (Tumenta et al. 2013).

Treves et al. (2009) used predator carrying capacity models to show that if preferred lion prey in the QECA recovered to pre-conflict levels, the region could support approximately 500 lions. However, we recommend the immediate priority for managers in the QECA is to secure and stabilize the lion population. Key areas with the highest lion use include the southernmost extent of the Ishasha region, the eastern Kigezi and the savannah plains directly to the west and southwest of the Hamkungu fishing community (a human settlement with over 2000 cattle) (Figure 2.2). Stemming female mortality is particularly important as prides with ≥ 3 individuals are significantly more successful in raising cubs (Packer et al. 1988). Our abundance estimate of just 71 lions suggests this population is very low for an area that is ~2500 km2 in size. Additionally we recommend that robust investigations of prey availability for African lions be made in the key regions of the QECA where they still persist. Using tigers as a model, Karanth et al. (2004) show that large carnivore density is strongly correlated with prey availability (across 11 diverse ecological sites in India). Karanth and Stith (1999) and Karanth et al. (2006) also show that as long as prey availability is high, this can prevent carnivore declines (they show that in Ngarahole, despite 23% mortality in the tiger population, the population was stable due to a high density of ~56 prey animals/100 km2).

2.5.3 Benefits and caveats of the method Robust lion monitoring in the QECA has been lacking. The last lion survey implemented in 2008 using call-up surveys reported abundance estimates with high margins of error at the regional level (north = 72±49 SE, central = 41±30 and south = 27±25, Omoya et al. 2014). Moreover, historical lion surveys from the QECA between 1977-2008 used a mixture of audio lure surveys, individual id total counts which were divided by region (ie. n lions/total km2 of study area), and expert solicitation to inform densities and abundance estimates over time. This is dangerous, as one can never accurately draw on these estimates to understand lion population trends. Our lion density estimates performed with acceptable levels of precision (2.70±0.47 SD), and it is likely that this would have improved with search effort. Elliot and Gopalaswamy (2017) in the Maasai Mara ecosystem achieved narrow confidence bounds on their density estimates (17.08±1.31 SD) due to a higher number of total detections and individual lions (438 and 203 respectively). The advantage of using individual 73 animal identity and SECR is that the data from annual (or other time series) surveys can be incorporated into an open population model to estimate vital rates of a population (eg. Karanth et al. 2004, Sharma et al. 2014). This multi-season capture recapture method can estimate birth, death, immigration and emigration. Data from call up surveys and track counts cannot be used to estimate these parameters because information on individual lion identities cannot be garnered from these methods. Rafiq et al. (2019) also show that this SECR technique for African lions can be scaled up using safari tourist vehicles. This has important ramifications on costs, and in their study of lions in Botswana’s Okavango Delta showed that SECR for lions was as expensive as a track survey and cheaper than call up (audio lure) and camera trap surveys.

Some parts of the QECA could not be surveyed due to a lack of road and off-road access (namely Pelican Point, Maramagambo forest and the savannah plains southeast of Kasese). Although we did not survey these regions, our modelling framework incorporates this into the overall density and abundance estimation process through the estimation of search effort. We do not feel that our survey would have generated meaningful results in the Maramagambo forest and the region east of Kasese town as (1) lions in the QECA avoid lowland forest and have clear preferences for grassland and wooded grassland habitats (Mudumba et al. 2015). This is reemphasised by a 2014 camera trap survey in Maramagambo (3,552 trap nights across 36 stations), which yielded only two lion detections at the same station (Mills, 2018), and (2) the plains southeast of Kasese have been identified as having some of the most intense commercial and non-commercial poaching which has increased over time (Critchlow et al. 2015). This being said, there is likely some error that may be associated with extrapolating beyond the extent of our search encounter region (Efford and Fewster 2012). Efford and Fewster (2012) did however show that extrapolation to unsamled regions using SECR was relatively unbiased (<3% relative bias) as long as the broader region was under occupancy by animals. We feel that we achieved this by excluding non-lion habitat from our samling mask (mainly high density human village lands and agricultural areas, Gopalaswamy et al. 2012a). Under an optimal scenario we would have endeavoured to samle the eastern section of the park with a high utility 4x4 vehicle and performed call up surveys at night where we could have noted individual lion identity (Braczkowski et al. 2020a). It should also be noted that the method of Mudumba et al. (2015) to estimate home range size used VHF and satellite collar data collected over an extensive period (between 2005-2010) and at least 100 GPS points were used for each animal home range (range= 105-634, average=265). Our estimates of home range size 74 differ from those of Mudumba as we had much lower sample sizes (ie. 163 lion GPS detections total). Consequently our estimates of home range had higher error associated with them and these only represented a minimum range size. Based on a recent report by Nsubuga et al. (2018) our estimates of home range size are ~150 and 60% smaller than a recent GPS assessment of home range size. Our estimates of home range size are only meant to serve as an absolute minimum home range estimate. It should also be noted that the 95% kernel distribution used in the Mudumba et al. (2015) will provide much greater detail in information on range use and habitat selection. With this underrepresentation of home range size compared to these two studies our results suggest the reality of lion decline could be even more severe than we report here.

Finally, we did not include any habitat level covariates into our analysis due to similar detection rates between the northern euphorbia savanna of the park, and the southern fig tree savanna of the Ishasha sector. We recommend that future authors using the search encounter technique we use here under a SECR modelling framework could benefit from the inclusion of not only habitat as a covariate but other meaningful environmental covariates such as proximity of lion to prominent drainage line, proximity to road and grass height. These could assist in tightening the precision of estimates and also in identifying spatial hotspots of animals.

The method of Elliot and Gopalaswamy (2017) for censusing African lions considers the entire population when generating estimates of lion movement and their activity centres. Furthermore, it can be used under a citizen-science approach where safari guides and their tourists are used to collect photographs of lions on safari. This could make the costs of such surveys considerably lower than traditional methods (Rafiq et al. 2019).

75 Chapter 3 statement of authorship: The candidate conceived the concept of the manuscript. The candidate collected all field data with Sam Isoke, and prepared it for analysis. The candidate identified individual leopards and hyenas with Alexander Bezzina. The candidate ran the models and received assistance in interpreting the results from Arjun Gopalaswamy. The candidate conducted the literature search, synthesis, and wrote the manuscript. All authors contributed to revising and improving the manuscript (Arjun Gopalaswamy, Julien Fattebert, Sam Isoke, Alexander Bezzina, and Martine Maron).

CHAPTER 3 First spatially explicit population density estimates of African leopard and spotted hyena in the Albertine Rift, south-western Uganda

Submitted to PLOS ONE

3.1 Abstract

African leopards Panthera pardus pardus and spotted hyenas Crocuta crocuta are data deficient across much of their African range, and there are no reliable or recent population estimates for these species in Uganda. This has conservation planning and management ramifications, as both species are important for wildlife tourism, and African leopards are hunted for sport in several regions adjacent to national parks as part of a government-led revenue-sharing scheme to foster increased tolerance of wildlife. We ran a single-season camera trap survey in each of the northern and southern sections of the Queen Elizabeth Conservation Area (2,475 km2), Uganda’s second largest protected area system. We developed spatially explicit capture recapture (SECR) models to estimate the population density and abundance of leopards and hyenas in this area. We surveyed the north and southern sections of the conservation area and estimated leopard densities to be 5.03 (range = 2.80–7.63) and 4.31 (range = 1.95–6.88) individuals/100 km2 for the north and south of the conservation area respectively, while hyena densities were 13.43 and 14 individuals/100 km2. Estimates of hyena density were the highest recorded for the species anywhere within their range using SECR methods. We suggest that one explanation for the high hyena densities could be owed to the low density of African lions Panthera leo in the QECA, and this lends some support for competitive release from African lions. Our work provides the first robust population estimate of leopards and hyenas anywhere in Uganda.

77 3.2 Introduction Precise estimates of animal densities are a fundamental precursor for effective wildlife management, and understanding community dynamics (Karanth, 1995, White & Burnham 1999, Rayan & Linkie 2015, Duangchantrasiri et al. 2016). Density estimates assist with species threat assessments (e.g. Jacobson et al. 2016), the setting of harvest quotas (e.g. Balme et al. 2009b), targeting of management actions (Balme et al. 2010) and gauging the viability of individual animal populations (e.g. Sollmann et al. 2011). Measures of animal abundance and density are especially valuable for species that are exposed to significant anthropogenic pressures such as legal and illegal hunting (Balme et al. 2009b, Fattebert et al. 2015, Fattebert et al. 2016), are constrained to small habitat patches (Wibisono et al. 2018), and are important to the economies of developing nations through tourism or the ecosystem services they provide (Braczkowski et al. 2018, O’Bryan et al. 2018).

In Uganda, African leopards Panthera pardus and spotted hyenas Crocuta crocuta are examples of species whose population status is poorly known. They are listed as Vulnerable and Least Concern by the IUCN, respectively (https://iucnredlist.org). However, nationally, leopards are considered vulnerable and widespread country-wide (Ugandan National Red List), and hyenas are classified as critically endangered, and are thought to occur only in national parks (WCS 2016). Except one call-up survey study on hyenas in the QECA from 2008 (Omoya et al. 2014), there are no studies on species distribution, population densities, and population status of these two species in any of the places they occur across Uganda (Bohm & Höner 2015, Jacobson et al. 2016). Most of the information on potential leopard numbers and status in Uganda is based upon expert estimates and anecdotes (Jacobson et al. 2016).

African leopards and spotted hyenas are key tourism species (Lindsey et al. 2007). In South Africa, leopards are tourists’ favourite species to see on safari (Di Minin et al. 2013). In Uganda’s Lake Mburo National Park alone, 1,585 people purchased a night game drive permit for leopard viewing in 2018, resulting in US$ 47,550 in revenue for the Ugandan Wildlife Authority (A. Kule pers. comm.). Lindsey et al. (2007) also show that hyena species are highly sought after on safari and a sample of 627 tourists to South African national parks showed that both international and local tourists expressed a high level (65 and 70% respectively) of desire to see hyenas on a safari. However, both hyenas and leopards cause damage to human livelihoods across Uganda (Ochieng et al. 2015), particularly those of livestock farmers. They are the main predators responsible for redation on small and 78 medium livestock on farms located inside and on the edge of the QECA (Sheppard, 2014), Lake Mburo (Braczkowski et al. in review), and Murchison Falls National Park (Mudumba, 2011). Consequently, they are often killed in retaliation for stock raiding through poisoning, trapping and shooting. For example, at least 19 leopards were killed on the boundary of Lake Mburo National Park in a 4-year period from 2003-2006 due to poisoning, trapping and poaching (CITES COP14 doc3). No such mortality data are available for the species (or for spotted hyenas) in other Ugandan parks. African leopards in Uganda are also hunted for sport (country-wide annual CITES quota: 28 leopards, Braczkowski et al. 2015). Although harvests of leopards since 2007 have been low (17 skins, skulls and trophies exported from 2009-2017), the Ugandan Wildlife Authority (UWA) attempted to have the species downgraded from CITES Appendix 1 to Appendix 2 and proposed a quota of 50 leopards annually (despite the lack of population data, CITES CoP 14 Proposal 3).

Uganda’s protected areas are small in size. Only the QECA forms part of a globally significant 10,000 km2 wilderness block (Watson et al. 2016). The protected areas are also largely isolated, and have high human pressures on their edges (Venter et al. 2016, Plumptre et al. 2007). It is likely that the extinction risk of large carnivores in Uganda is high because of the above factors (UWA 2010). Lions (Panthera leo), for example, have already been extirpated from 50% of Uganda’s national parks and reserves (UWA 2010) and occur at depressed densities, even in its largest, transfrontier parks (Braczkowski et al. 2020).

It is also likely that a reduction in the density of African lions in some of these Ugandan national parks could provide a more favourable environment for sympatric species, like spotted hyenas. Indeed, there has been considerable theoretical and empirical interest over the past few decades to understand how ecological communities and guilds partition resources to coexist in spite of interspecific competition (Schoener 1974, Chesson 2000, Amarasekare, 2003, Finke & Snyder, 2008). African lions and spotted hyenas have been shown to exert considerable pressure on each other mainly through interference competition, and kleptoparastism. Much of this is owed to their strong degree of dietary overlap, and direct competition over the same food resources (Kruuk and Turner 1967, Hayward et al. 2006). The outcomes of this interference are however dependent on group size, the presence of male lions, and population densities (Cooper 1991, Höner et al. 2002, Watts & Holekamp, 2008, Lehmann et al. 2017).

The reliable estimation of abundance and densities of entire guilds of large carnivores has 79 been difficult over previous years, owing to logistical (Long et al. 2008), and analytical constraints (Williams et al. 2002). However, recent studies (Rich et al. 2019, Karanth and Nichols 2017), have shown promise at addressing this knowledge gap. This is particularly important in an applied conservation context because often many such large carnivore populations are iconic, often 'political'. This is particularly important in an applied conservation context because often, many large carnivore populations are iconic, 'political', and are an umbrella for species conservation initiatives (Darimont et al. 2018). And species recovery program plans of such species rarely account for the specific role of interspecific competitors, and specifically on the impact of anthropogenic pressures on large carnivore communities.

To begin to address the data gaps on population status of African leopards and spotted hyenas in Uganda, we sought to assess their abundance and densities across the QECA. The QECA is one of Uganda’s most-visited eco-tourism reserves, but also suffers high rates of conflict between large carnivores and livestock inside the fishing villages inside the national park and on the parks boundaries (Sheppard 2014). We had also obtained information on the abundance and densities of African lions in the QECA from another paper, a priori using a search encounter technique in 2017-2018 (Braczkowski et al. 2020b). We found lion densities to be low (ie. 2.7 lions/100 km2), and hypothesized that the densities of their closest competitors, spotted hyenas, would be higher than other similar savannah systems. This study represents the first assessment of African leopards undertaken in a protected area system in Uganda and provides an important reference on estimating spotted hyena densities using SECR (see Rich et al. 2019).

3.3 Methods

3.3.1 Study Area We camera-trapped leopards and hyenas in the Queen Elizabeth National Park, south- western Uganda (0.1641o S, 30.02030 E, 2,056 km2, Figure 3.1). The Park falls within the Albertine Rift Valley, a global biodiversity hotspot, and the most species rich area on continental Africa (Plumptre et al. 2007). Queen Elizabeth is connected to two nearby protected systems, the Kyambura wildlife reserve in the north (154 km2) and the Kigezi Wildlife Reserve in the south (265 km2), collectively making up the Queen Elizabeth Conservation Area (2,475 km2, hereafter QECA). The QECA is contiguous with Virunga National Park (7,900 km2) in the DRC, and collectively these reserves comprise the greater

80 Virunga landscape (Jones et al. 2016). The region has an elevation ranging from 900-1,300 m asl (Salerno et al. 2017) with two short rainy seasons, in March-May and September- November, totalling 600-1,400 mm per year (Chritz et al. 2016).

The QECA is essentially split into two by two lake systems (Edward in the West, and George to the East) with the small Kazinga channel between these. The most detailed habitat map created for the QECA using ENSO MOSAIC identified 21 habitat types (including 7 anthropogenic, Plumptre et al. 2010). The area north of the Kazinga channel is dominated by grasslands and wooded grasslands, characterized by Sporobolus pyramidalis grassland and Capparis tomentosa Euphorbia candelabra thicket clumps (Wronski et al. 2006), with dense thickets of Dichrostachys cinerea and Euphorbia candelabra extending towards the western park edge in Mweya and Katwe. The area south of the Kazinga channel is characterized by wooded grasslands comprised of open Themeda and Hyparrhenia grasslands and Acacia and Ficus woodlands (Mudumba et al. 2015), and a large patch of tropical high forest (called the Maramagambo forest) characterized by woody plants such as Sapium ellipticum, Cynometra alexandri, Carapa grandflora, Parinari excelsa and 81 Newtonia buchananii (Tumwesigye et al. 2000). The main prey species likely to be predated by African leopards and spotted hyenas include African buffalo Syncerus caffer, Uganda kob Kobus kob, topi Damaliscus lunatus, warthog Phacochoerus africanus and waterbuck Kobus ellipsiprymnus (Mudumba et al. 2015).

3.3.2 Field methods We conducted two camera-trap surveys in the QECA from 8 March 2018–24 June 2018 using CuddebackTM 20 megapixel Long Range IR camera traps set in a paired format in order to capture both flanks of an animal. The first survey ran from 8 March–25 April 2018 in the northern section of the park and encompassed 44 camera trap sites distributed across the Mweya, Kasenyi plains and crater lakes regions. The second survey was done approximately 70 km south, across 30 locations in the Ishasha sector from 27 April 2018 – 24 June 2018. Each camera trap site consisted of two camera traps, each mounted to a 1 m steel pole 40 cm from the ground. We positioned camera trap stations on vehicle tracks and roads as these are favoured travelling and hunting routes of leopards and hyenas (Balme et al. 2009a, O’Brien and Kinnaird 2011), and are known to increase detection probability for these species (Karanth and Nichols 1998). We positioned each camera station perpendicular to a vehicle track or game trail at a 60-75 degree angle. We checked each site every 7-10 days to repair animal damage, replace memory cards and to check battery functionality (Braczkowski et al. 2016). We set cameras to burst mode (5 images taken every time the infrared sensor was triggered).

To ensure that every animal in the camera trapping area has a non-null probability of being detected (Karanth 1995, Karanth & Nichols 1998), we set camera stations 2 km apart, a distance shorter than the smallest leopard home-range radius recorded in the literature (30 km2, Bailey 1993, and 23 km2 Fattebert et al. 2016). This spacing of camera traps also allows for the sampling of spotted hyena populations, as typical clan home ranges are between 30– 56 km2 in the productive savannah environments of the Maasai Mara of Kenya and Serengeti, Tanzania (Hofer and East 1993a, Hofer & East 1993b, Boydston et al. 2003).

The unique identity of leopards and spotted hyenas was determined from images of their unique rosette and spot patterns, respectively (Miththapala et al. 1989, O’Brien and Kinnaird 2011, Figure 3.2). In leopards, we were able to classify the sex of individuals by using distinctive morphological cues such as the presence of testes and the enlarged dewlap and sagittal crest in males (Balme et al. 2012). Aleksander Braczkowski and Alexander Bezzina 82 assigned individual identity to unique photographs from image bursts (i.e. one individual identified from 5 image burst). Only images of individuals for which there was consensus were included into the final density estimation process (Bahaa-el-din et al. 2016). Images were excluded if they were blurred, were too far away from the camera trap and those where observers could not agree on identity. For the purpose of building capture histories with known unique individual identities, we used both flanks of leopards in our analysis. For spotted hyenas, however, we chose the flank of the animal with the highest number of photographs recorded during our survey (Henschel et al. 2014, Table 3.3). We did this, as often times spotted hyenas walked around cameras and did not present a clear flank on both sides of a single animal, and several individuals moved around a single camera at the same time. We did not want to risk mismatching flanks and mistakenly overcount individuals.

3.3.3 Analytical methods

3.3.4 State and observation process model

83 We estimated African leopard and spotted hyena densities, abundance and sigma (σ, a linear representation of an animals home range, Royle et al. 2009) in the QECA using Bayesian SECR modelling (Royle et al. 2009). The modelling approach uses a state (leopard and hyena population size and locations in landscape), and observation process (Royle et al. 2009, Gopalaswamy et al 2012b). To accurately estimate leopard and spotted hyena densities and their home-range centres, we generated their potential activity centres across our study area in the form of 0.158 km2 equally spaced pixels. This state space assumes the number of leopards and hyenas found in these pixels are defined by a binomial process (Gopalaswamy et al. 2012a). The state space encompassed the QECA and a buffer of 25 km around it (including the eastern section of Virunga National Park and the southwest of Kibale National Park, Figure 3.1).

We masked out all agricultural areas within the 25 km buffer area as leopards and hyenas are unlikely to use intensive agricultural lands in their home-ranges (Gopalaswamy et al. 2012b, Fattebert et al. 2015b). This buffer size (total size=13,434 km2, of which 5,960 km2 deemed suitable) was larger than several previous studies in similar habitats (e.g. Strampelli et al. 2018 used a 10 km buffer and Braczkowski et al. 2016 used a 15 km buffer). We did this to account for the possibility that leopards and hyenas would move greater distances in the QECA due to poaching pressure recorded in Critchlow et al. (2017). We used an unstructured SECR sampling design (Russell et al. 2012) for each species analysis and created a standard capture-recapture matrix (trap locations, individual animals and sampling occasions). This allowed us to model the way in which individuals were detected in our surveys.

Large terrestrial carnivores typically feature differences in their home-range sizes, movement patterns and capture probability (Palomares et al. 2012, Srbek-Araujo 2018). This can affect the observation process in capture-recapture exercises (Sollman et al. 2011). SECR is considered a robust method for calculating animal densities because it accounts for heterogeneous capture probability (owed to sex, “trap happiness or shyness” or habitat differences). To account for heterogeneous capture probability in leopards, we included a sex-specific covariate in the observation process, which would account for different capture probabilities amongst males and females. We did not do this for hyenas as the visibility of testes was often obscured by the large tail of individuals, and because female hyenas feature a pseudo scrotum which makes sexing from camera trapping images unreliable (Muller and Wrangham 2002). 84

Because the detection probability of an individual animal declines with increasing distance between its activity centre and camera trap location, we could account for this uncertainty (Gopalaswamy et al. 2012a). We estimated a continuous ϑ parameter, which exhibits the resource use of leopards and hyenas, and two fixed detection function which were 0.75 and 1 (Royle et al. 2009, Gopalaswamy et al. 2012a). The detection function takes on a negative exponential form (i.e. ϑ = 0.5) and a Gaussian form (ϑ = 1). Therefore, in our models, the probability of detecting a leopard or hyena i in pixel j on sampling occasion k is defined by a complementary log-log function of covariates.

We assessed seven a priori models for African leopards and two for spotted hyenas (parameter definitions are presented in Table 3.1). Model 1 estimated the detection function (defined by θ) and assumed that detection probability is sex specific:

cloglog (πijk) = logλ0 + βsex (SEX) − f[dist(i, j|ϑ, σsex)] where f[dist(i, j|ϑ, σsex)] describes how detection rate is a function of distance between the activity center of individual i and pixel j, which are conditional on θ and σsex. The specific form of this detection function is −BCDE(C, F)!" A[BCDE(C, F)|ϑ, σsex] = exp H ! J 2σ#$%

Model 2 was based on the assumption that detection probability is independent of sex, (i.e. βsex was fixed at 0). The rate of decline in detection probability (σ) however, remained sex specific.

Model 3, as with model 2, had βsex fixed at 0 while the detection function fixed at θ = 0.75

Model 4 assumed detection probability was sex dependent, and rate of decline in detection probability also remained sex specific. The detection function was fixed at θ = 0.75.

Model 5 assumed detection probability is dependent on sex but rate of decline in detection probability was independent of sex. The detection function was fixed at θ = 0.75.

85 Model 6 was based upon the assumption that detection probability and rate of decline in detection probability was independent of sex. The detection function was fixed at θ = 0.75.

Model 7 was the same as model 1 but the detection function was fixed at 1.

As we could not accurately sex spotted hyenas, we only ran the first and seventh models for this species, allowing the model to estimate the detection function and to estimate it at 1.

Table 3.1: Parameter definitions for the SECR models applied for our leopard and spotted hyena surveys in the QECA in 2018 (note we did not use sex in the hyena models).

Parameter Definition

n total number of leopards detected during the survey period nz number of leopards augmented to n, so M = n+nz represents the maximum number of leopards in the large state space S σF rate of decline in detection probability with increasing distance between the activity center of a leopard and the location at which she was found σF rate of decline in detection probability with increasing distance between the activity center of a leopard and the location at which he was found βsex difference of the complementary log-log value of detection probability between a male and female leopard βeff rate of change in the complementary log-log value of detection probability as the (log) effort changes by 1 unit (1 km of drive effort) λ0 basal encounter rate of a leopard whose activity center is located exactly at the centroid of a grid cell ψ ratio of the true number of individuals in the population compared with the data-augmented population M

Nsuper total number of leopards in the larger state space S

ψsex proportion of leopards that are female

θ determines the shape of the estimated detection function, value θ ranges from 0.5 (exponential form) to 1 (Gaussian)

D estimated density of leopards per 100 km2

87 We used the package SCRbayes (https://github.com/jaroyle/SCRbayes) in the R environment (R Development Core Team 2018). We used Bayesian Markov chain Monte Carlo (MCMC) simulation and the Metropolis-Hastings algorithm (Tierney 1994) to run our models. We set each model to initially run for 50,000 iterations with a burn-in of 2,000 iterations. However, if we did not arrive at a standing distribution, we refined the number of iterations and burn-in period further. The total number of posterior samples for each chain are noted in (Table 3.2), and each model featured 4 chains (Elliot and Gopalaswamy 2017).

Model adequacy was determined by examining the Bayesian p-value on individual encounters (Royle et al. 2009), assessing correlations between posterior parameters and by examining the natural logarithm of marginal likelihood of each model (Dey et al. 2019). MCMC convergence was assessed using the Gelman–Rubin diagnostic (Gelman and Rubin 1992).

We also computed posterior mean abundance across the northern and southern study areas. For all iterations of the MCMC output, we took the sum of all pixels within each area of interest and computed posterior mean and posterior standard deviation of abundance (Broekhuis and Gopalaswamy 2016, Elliot and Gopalaswamy 2017).

3.4 Results After accounting for camera trap malfunctions (mainly due to animal disturbance), the northern survey included 2065 trap nights of survey effort while the southern survey included 1661 nights. We identified 42 individual hyenas (22 individuals recaptured at least once) in the northern survey and 38 (21 recaptures) in the south, while 19 (14 recaptures) and 12 (11 recaptures) individual leopards were recorded respectively (Table 3.3). Of the leopards identified in our study in the northern survey, eight were males and 11 were females, while in the southern population surveys, we identified three males, and nine females. Total percentage of useable images for African leopards was 97% for both surveys, while for spotted hyenas this was 47% across both camera trap surveys (Table 3.3).

Table 3.2: Iterations, Bayesian p-values and natural logarithm of marginal likelihood scores for our leopard and spotted hyena SECR density models.

Natural logarithm Species and location of Model Bayes p- Total Burn in required to reach of marginal survey number value iterations convergence likelihood 1 0.68 -56574.18 48,000 2,000 2 0.66 -54444.49 48,000 2,000

3 0.68 -58564.83 48,000 2,000 Leopards - northern survey 4 0.69 -58672.64 48,000 2,000 5 0.71 -59593.86 48,000 2,000 6 0.65 -59596.09 48,000 2,000 7 0.69 -61506.91 48,000 2,000 1 0.68 -42457.08 100,000 2,000 2 0.56 -42447.54 48,000 2,000

3 0.58 -42309.97 48,000 2,000 Leopards - southern survey 4 0.57 -42361.41 48,000 2,000 5 0.58 -42372.92 48,000 2,000 6 0.51 -42470.70 48,000 2,000 7 0.59 -42464.77 48,000 2,000 Spotted hyenas - northern 1 0.62 -90072.07 48,000 2,000 survey 2 0.0.61 -88146.85 48,000 2,000 Spotted hyenas - southern 1 0.59 -79958.28 48,000 2,000 survey 2 0.57 -77860.06 48,000 2,000

89 3.4.1 Model diagnostics Bayesian p-values for all of our leopard density models ranged from 0.51-0.71, indicating an adequate model fit (extremities 0.15-0.85). Convergence of models was indicated by a mean potential shrink reduction factor of <1.2 for each parameter for each model (Gelman and Rubin 1992, Supporting Information 2). Models estimating spotted hyena density also converged and featured adequate model fit (Bayesian p range = 0.57 - 0.69 and shrink reduction factor for all parameters <1.2, Supporting Information 3). Model selection using the natural logarithm of marginal likelihood of data within each model, Bayesian p-values, and examination of parameter covariance plots indicated that our second model gained most support for the northern (log likelihood = -54444.49, Table 3.3) survey, while model 3 gained most support for the southern survey (log likelihood = -42309.97). For spotted hyenas, a model which allowed the estimation of theta garnered most support for both the northern and southern surveys.

3.4.2 Density estimates Leopard density for the northern section of the QECA was estimated at 5.03 leopards/100 km2 (posterior SD 1.27, range = 2.80–7.63, Table 3.4), while for the southern section density was estimated at 4.31 leopards/100 km2 (posterior SD 1.40, range=1.95–6.88). The range of posterior density estimates per pixel for the two survey regions and greater state space (0.158 km2) was 0.004–1.30 leopards (Figure 3.3). The leopard movement parameter (σ) for males and females was 1.00 km and 0.94 km in the northern survey. For the southern survey we estimated a movement parameter of 2.29 km for males and 1.63 km for females. The male to female sex ratio for the north was 1:0.67, while for the south this was 1:0.26. Spotted hyena density for the northern survey (as estimated by the second model) was estimated at 13.43 individuals/100 km2 (posterior SD 2.51, range = 9.01–18.81), while for the southern survey it was 14 individuals/100 km2 (posterior SD 2.79, range = 8.52–18.54). The range of posterior density estimates per pixel across the QECA ranged from 0.04–0.61 hyenas (Figure 3.4). The hyena movement parameter σ for both sexes combined was 1.39 km in the northern survey, and 1.27 km in the southern survey.

3.5 Discussion Our results fill an important gap for two regionally important higher order predators with significant ecological and tourism value in Uganda. We estimated leopard densities to be 5.03 and 4.31 individuals/100 km2 for the northern and southern sections of the

Table 3.3: Flanks, individuals and recaptured individuals used in our two SECR density surveys in 2018 for the QECA.

Species Survey locality Left flanks Right flanks Useable flanks in final analysis Unique individuals Individuals recaptured Mweya 178 156 90 42 22 Spotted hyena Ishasha 185 132 80 38 21 Mweya 64 62 61 19 14 Leopard Ishasha 43 44 43 12 11

Figure 3.3: A pixelated density map showing relative leopard densities across the north and south of the QECA respectively. This map reports estimated leopard densities per 0.158 km2 pixel.

QECA, respectively. These density estimates fall approximately in the mid-range of recently published leopard densities from SECR methods (mean density from 17 studies in 20 locations was 4.96 individuals/100 km2, range=0.62–11.80 individuals/100 km2, Table 3.5). SECR estimates of spotted hyena populations have to date been rare in the peer-reviewed literature, and direct comparisons to estimates from spoor counts and call up surveys are not appropriate, mainly because of these methods assumptions regarding detection probability (Gopalaswamy et al. 2015a, Gopalaswamy et al. 2015b, Elliot and Gopalaswamy 2017). Compared to the five known studies that employed closed SECR models and remote camera-trapping to estimate spotted hyena densities, our estimates of 13.43–14 hyenas/100 km2 were the highest in the recorded literature (Table 3.6), for both the northern and southern sections of the QECA. It should also be noted that we took a conservative approach on the way that we identified candidate individuals, not only in using two image reviewers, but also excluding any animals that were not clearly identified. This being said, there are many non-spatial methods which have produced higher estimates of spotted hyena

92 densities (eg. Yirga et al. 2013 in Wukro district, Northern Ethiopia was 52 individuals/100 km2, Cozi et al. 2013 in northern Botswana 14.4/100 km2, M’soka et al. 2016 in Zambia’s Liuwa Plain 13-52/100 km2). Although we mainly focus on comparisons between SECR studies for reasons of consistency, it should also be noted that the study of Durant et al. (2011) also made efforts to model detection robability in density estimation of spotted hyenas in the Serengeti National Park. This study used distance sampling to compare population trends of the species and estimated ~20 individuals per 100 km2 over ~30 years.

Table 3.4: Parameter estimates with accompanying posterior SD from our leopard and hyena SECR density surveys in the QECA.

Species bsigma sigma bsigma2 sigma2 lam0 beta effort beta sex psi psi sex Nsuper Theta Density and Model location number Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD Est SD of survey 0.1 1 0.58 0.95 0.15 0.49 0.15 1.06 0.23 0.06 0.02 0 0 -0.94 0.52 0.21 0.06 0.61 0.14 311.12 83.42 0.54 0.04 5.22 1.4 5 0.1 2 0.52 1 0.13 0.6 0.15 0.94 0.14 0.04 0.01 0 0 0 0 0.2 0.05 0.6 0.14 300 75.85 0.53 0.03 5.03 1.27 2 0.0 3 0.22 1.55 0.19 0.13 0.39 2.03 0.33 0.03 0.01 0 0 -1.12 0.49 0.18 0.05 0.58 0.17 270.48 71.37 0.75 0 4.54 1.2 5 Leopards - 0.0 northern 4 0.17 1.73 0.21 0.16 0.04 1.81 0.3 0.02 0.005 0 0 0 0 0.17 0.04 0.56 0.14 261.62 64.57 0.75 0 4.39 1.08 4 survey 0.0 5 0.18 1.7 0.16 0.18 0.03 1.7 0.16 0.03 0.01 0 0 -0.76 0.41 0.19 0.05 0.63 0.13 295.05 78.86 0.75 0 4.95 1.32 3 0.0 6 0.17 1.75 0.19 0.17 0.03 1.75 0.19 0.02 0.005 0 0 0 0 0.17 0.04 0.0004 0.001 258.02 65.05 0.75 0 4.33 1.09 3 0.0 7 0.07 2.74 0.02 0.03 0.41 4.2 0.8 0.02 0.006 0 0 -1.21 0.45 0.17 0.04 0.58 0.14 252.94 64.7 1 0 4.24 1.08 2 0.0 1 0.09 3.16 1.87 0.17 0.11 1.98 0.56 0.02 0.02 0 0 0.27 0.84 0.17 0.05 0.80 0.14 253.95 81.33 0.84 0.12 4.26 1.36 8 0.0 2 0.11 2.7 1.13 0.16 0.1 2 0.55 0.02 0.006 0 0 0 0 0.16 0.05 0.79 0.13 247.03 76.75 0.84 0.12 4.14 1.29 8 0.0 3 0.12 2.29 0.84 0.2 0.06 1.63 0.25 0.02 0.03 0 0 0.22 0.86 0.17 0.06 0.79 0.14 256.6 83.29 0.75 0 4.31 1.4 6 Leopards - 0.0 southern 4 0.14 2.06 0.49 0.2 0.05 1.63 0.22 0.02 0.005 0 0 0 0 0.16 0.05 0.79 0.13 246.8 75.61 0.75 0 4.14 1.27 6 survey 0.0 5 0.18 1.73 0.23 0.18 0.04 1.73 0.22 0.03 0.03 0 0 -0.29 0.71 0.16 0.05 0.75 0.14 242.75 76.33 0.75 0 4.07 1.28 4 0.0 6 0.18 1.72 0.22 0.18 0.04 1.72 0.22 0.02 0.005 0 0 0 0 0.15 0.05 0.0004 0.002 232.49 70.01 0.75 0 3.9 1.17 4 0.0 7 0.04 4.86 3.16 0.08 0.03 2.64 0.45 0.02 0.001 0 0 0.32 0.78 0.17 0.05 0.8 0.14 251 80.22 1 0 4.21 1.35 3 0.0 1 0.08 2.62 0.27 0.08 0.01 2.62 0.27 0.008 0.002 0 0 0 0.52 0.72 0.09 0.0001 0.0004 797.99 143.21 1 0 13.39 2.40 Spotted 1 hyenas - northern 0.1 2 0.33 1.39 0.43 0.33 0.16 1.39 0.43 0.01 0.005 0 0 0 0 0.52 0.1 0.0001 0.0004 800.87 149.53 0.68 0.13 13.44 2.51 survey 6

Spotted 0.0 1 0.04 3.63 0.45 0.04 0.009 3.63 0.45 0.006 0.001 0 0 0 0 0.47 0.1 0.0001 0.0005 720.20 145.49 1 0 12.08 2.45 hyenas - 09 southern 0.1 2 survey 0.35 2 1.27 0.34 0.35 0.12 1.27 0.34 0.01 0.004 0 0 0 0 0.55 0.11 0.0001 0.0004 838.59 166.55 0.6 0.08 14.07 2.79

Figure 3.4: A pixelated density map showing relative spotted hyena densities across the north and south of the QECA respectively. This map reports estimated hyena densities per 0.158 km2 pixel.

3.5.1 Leopard densities Our study provides a baseline leopard density for the QECA, one of Uganda’s largest national parks, which (with Virunga in the neighbouring Democratic Republic of Congo) forms part of a globally significant wilderness block (Watson et al. 2016). The only previous population estimate known for leopards in Uganda was based upon a calculation, which correlated leopard density to rainfall and habitat productivity (Martin and de Meulenaer 1988). Although Uganda is estimated to hold only 1% of the African leopard sub-species’ range, it is important regionally, in that it encompasses nearly 10% of the leopard’s potential range in East Africa (Jacobson et al. 2016). This baseline could be of use to managers and conservation stakeholders in any future recovery efforts, however because we had only a

95 single snapshot in time, it was impossible for us to assess whether the leopard population in the QECA is stable, decreasing or increasing.

Table 3.5: Leopard density studies published over the last five years and which used SECR methods. We used the first ten pages of Google Scholar in our assessment.

Model used to Density estimate SD Study name Location Habitat type estimate density (leopards/100 km2) (SE) Balme et al. Sabi-Sands Game Reserve, Borchers and 2019 South Africa Semi-wooded savanna Efford 2008 11.80 2.60 Borah et al. Tropical forest and Borchers and 2014 Manas National Park, India mountains Efford 2008 3.40 0.82 Braczkowski Phinda Private Game et al. 2016 Reserve, South Africa Savanna Royle et al. 2009a 3.55 1.04 Braczkowski Phinda Private Game Borchers and et al. 2016 Reserve, South Africa Savanna Efford 2008 3.40 1.20 Devens et al. Baviaanskloof mountains, Mountain fynbos and thicket 2018 South Africa (forest in gorges) Royle et al. 2009a 0.24 0.10 Devens et al. Langeberg mountains, Mountain fynbos and thicket 2018 South Africa (forest in gorges) Royle et al. 2009a 1.89 0.30 Du Preez et Bubye Valley Conservancy, Borchers and al. 2014 Zimbabwe Mopane woodland (savanna) Efford 2008 5.28 0.89 Du Preez et Bubye Valley Conservancy, al. 2014 Zimbabwe Mopane woodland (savanna) Royle et al. 2009a 5.46 1.14 Hedges et al. Kenyir Wildlife Corridor, Borchers and 2015 Malaysia Dipterocarp forest Efford 2008 3.30 1.28 Hedges et al. Kenyir Wildlife Corridor, 2015 Malaysia Dipterocarp forest Royle 2011 3.06 0.91 Kittle and Borchers and Watson 2017 Horton Plains, Sri-Lanka Montane forest Efford 2008 13.40 6.3 Ngoprasert et Ban Krang, Kaeng Krachan Borchers and al. 2017 National Park, Thailand Evergreen forest Efford 2008 2.50 1.20 Qi et al. 2015 Laoye mountains, China Deciduous forest Royle et al. 2009a 0.62 0.15 Rahman et Ujong Kulon National Park, Borchers and al. 2018 Java, Indonesia Tropical forest Efford 2008 12.80 1.99 Rahman et Ujong Kulon National Park, al. 2018 Java, Indonesia Tropical forest Royle et al. 2009a 11.54 1.22 Ramesh et Ndumo Game Reserve, al. 2017 South Africa Woodland savanna Royle et al. 2009a 1.60 - Ramesh et Western Shores, South al. 2017 Africa Coastal savanna Royle et al. 2009a 8.40 - Rostro Garcia et al. Srepok wildlife sanctuary, 2018 Cambodia Dry deciduous forest Royle et al. 2009a 1.00 0.40 Selvan et al. Borchers and 2014 Pakke Tiger Reserve, India Tropical forest Efford 2008 2.82 1.20 Strampelli et Xonghile Game Reserve, Woodlands and thickets Borchers and al. 2018 Mozambique (savanna) Efford 2008 2.59 0.96 Swanepoel et Farming matrix, Waterberg, Borchers and al. 2015 South Africa Livestock and game farms Efford 2008 6.59 5.20 Swanepoel et Lapalala Game Reserve, Mountain bushveld Borchers and al. 2015 South Africa (dystrophic savanna) Efford 2008 5.35 2.93 Swanepoel et Welgevonden Game Mountain bushveld Borchers and al. 2015 Reserve, South Africa (dystrophic savanna) Efford 2008 4.56 1.35 Thapa et al. Parsa Wildlife Reserve, 2014 Nepal Dry deciduous forest Efford et al. 2004 3.78 0.85 Thapa et al. Parsa Wildlife Reserve, 2014 Nepal Dry deciduous forest Royle et al. 2009a 3.48 0.83 Williams et Soutpansberg mountains, Matrix of livestock farms, al. 2017 South Africa nature reserves, mountains Royle et al. 2009a 5.34 0.02

96 Braczkowski et al. (2020) found African lion movements in the QECA had increased dramatically in the region over a ten-year period, and the proportion of females to males was low. Similarly, our leopard surveys detected a low ratio of adult female leopards in both regions of the QECA, particularly the south. Balme et al. (2019) estimated a ratio of 1 adult male: 1.8 adult females in the Sabi-Sands Game Reserve of South Africa, arguably one of the best-protected and prey-rich protected areas in Africa. Our estimate of 1 male: 0.26 females in the south of QECA was nearly six times lower than this, while the northern ratio (although higher at 1:0.67) was nearly 3x lower. The reasons for this sex-ratio imbalance require further investigation.

3.5.2 Hyena densities: are they reflective of competitive release? Spotted hyenas are the most successful of Africa’s large carnivores, in that they are widespread, and highly adaptable (Hayward 2006, Mills & Harvey 2001). This is due to their catholic diet and ability to access a variety of prey sizes, mainly resultant of both group and solitary foraging (optimal prey range of 56–182 kg). They also have the ability to scavenge (Hayward 2006), and their exceptionally large premolars allow them to access nutrients from bone material in ways inaccessible to sympatric carnivore species (Werdelin, 1989, Binder & Valkenburgh 2000, Tanner et al. 2008).

The high density of hyenas, lend some support to our a priori hypothesis that they may have experienced a degree of competitive release, resultant of a decrease in lion density in the QECA (this decrease over a ten-year period is described in Braczkowski et al. 2020). Creel and Creel (1996) found spotted hyenas to outnumber lions in some of Africa’s largest and most pristine protected area systems (Selous, Kruger, Ngorongoro and Serengeti) on average by a ratio of 1 hyena: 0.33 lions (range=1:0.17–1:0.75). Hyenas also outnumbered lions in the QECA, however more prolifically. African lion densities in this system are estimated to be 2.70 individuals/100 km2 (posterior SD=0.47, Braczkowski et al. 2020), translating into a ratio of approximately 1 hyena: 0.15 lions in the north of the QECA, and 1:0.21 in the south.

African lions are an important source of interference competition for spotted hyenas, and pressure from lions (mainly through kleptoparasitism) was shown to outweigh prey availability as a limiting factor of hyena densities in Amboseli National Park, Kenya (Watts and Holekamp 2008, Watts and Holekamp 2009). Trinkel & Kastberger (2005) showed spotted hyenas in Etosha National Park, Namibia could not prevent lion stealing their kills, 97 nor could they themselves steal lion kills unless they outnumbered lions by a factor of >3. Watts and Holekamp (2008) showed that lower lion densities (0.079-0.135 lions/km2 in Amboseli vs 0.44 in the Maasai Mara, as estimated by Ogutu and Dublin (2002) using call- up surveys), led to a 24% greater lifetime reproductive success in spotted hyenas in Kenya’s Amboseli ecosystem compared to those in the Masaai Mara. They showed that lower lion densities allowed for increased food intake, more scavenging of lion kills and higher body condition scores, despite overall prey densities being lower in Amboseli. High hyena to lion ratios also mean they can successfully deter lions from killing cubs at den sites through aggressive mobbing behaviour (Kruuk 1972).

Table 3.6: Spotted hyena density estimates using SECR and camera trapping in six locations across sub-Saharan Africa.

Model used to Density estimate SD Study name Location Habitat type estimate (leopards/100 (SE density km2) ) Fouche et al. Tsauchab River Valley, Farmland – arid Royle et al. 0.1 0.85 2020 Namibia savanna 2009 4 Moremi Game Reserve Borchers and 2.6 Rich et al. 2019 and cattle matrix, Semi-wooded savanna 11.80 Efford 2008 0 Botswana Schenk 2018 - Lake Mburo National Royle et al. unpublished Semi-wooded savanna 9.20 2 Park, Uganda 2009 data Tropical dry Briers-Louw Majete Game Reserve, Royle et al. 0.4 woodland/miombo 2.69 2017 Malawi 2009 8 savanna woodland uMhkhuze Game Royle et al. De Blocq 2014 Reserve, South Africa Semi-wooded savanna 2009 10.59 2.1 O'Brien and Semi-wooded Borchers and Kinnaird 2011 Mpala Ranch, Kenya savanna/cattle ranch Efford 2008 4.93 1.7

The relatively high hyena densities in the QECA could also be explained by the species’ resilience to human persecution and disturbance (e.g. snaring and spearing of prey). Henschel and colleagues (2010), in their surveys of African lions in west African protected areas, showed spotted hyenas continued to persist in three of the 10 national parks where lions had been extirpated. Green et al. (2018) suggested spotted hyenas were not as affected by the edge effects (namely killing by cattle herders) that impacted lions in the Talek area of the Masaai Mara. They attributed this to the fact that hyenas were less likely to kill cattle during the day, (opposed to lions, which were more likely to kill cows during the day, and were targets of retaliatory killing). Similarly, M’soka et al. (2016) found an inverse relationship between spotted hyena and lion densities in the Liuwa Plains National Park of Zambia, and argued that hyenas can compensate for anthropogenic disturbances if prey density remains high and competition from lions is low. 98 3.5.3 Implications for conservation Our study is the first estimate of leopard population density anywhere in Uganda using robust and accepted SECR methods. We encourage UWA and future leopard researchers to adopt such a methodological approach, and extend it to other parks in Uganda. Repeat annual or bi-annual surveys could also assist in gauging the true status of this leopard and spotted hyena population. This has been implemented effectively in South Africa for leopards, and historically for tigers in India, to not only estimate population trends (e.g. Balme et al. 2009b , Williams et al. 2017), but more robustly in estimating birth, death, emigration and immigration rates (e.g. Karanth et al. 2006). There are a number of advantages to using individual animal identities and SECR opposed to non spatial methods (Gopalaswamy et al. 2012b). These include 1) data from annual (or other time series) surveys can be incorporated into an open population model to estimate vital rates of a population (eg. Karanth et al. 2004, Sharma et al. 2014). This multi-season capture recapture method can estimate birth, death, immigration and emigration, and 2) there is no arbitrarily defined spatial surface for which the density estimate applies to (eg. The maximum daily distance moved by animals or some measure thereof). Importantly, data from call up surveys and track counts cannot be used to estimate critical population parameters like birth, death, immigration and emigration because information on individual lion identities cannot be garnered from these methods.

Estimating the population status of species such as African leopards, and spotted hyenas robustly is important as these species are generally sensitive to human impacts. For example, in South Africa’s Kwa-Zulu Natal province, a conservation complex of 660 km2 (comprising the 440 km2 Mkhuze Game Reserve, and 220 km2 Munyawana Private Game Reserve) was exposed to significant decreases in leopard density (independent of prey availability and interspecific competition) closer to its edge (densities in core of Mkhuze were 11 leopards/100 km2, declining to 7.17 km2 on the smaller Munyawana, and 2.49 in the non- protected area), and non-protected areas to the east of the Munyawana Game Reserve acted as a predator trap. All of the existing population data on leopards in Uganda originate from Martin and de Meulenaer (1988). These authors estimated an abundance of 4292 leopards in Uganda based upon a hypothesized relationship between rainfall and habitat. We agree with the critiques of Norton (1990) and Strampelli et al. (2018), that a failure to consider anthropogenic pressure, status of prey base and a lack of field data on leopards is of little use for informing local governments on management decisions (e.g. increasing patrol effort or setting of trophy hunting quotas). There are a plethora of conservation NGO’s 99 involved in supporting the Ugandan Wildlife Authority in the conservation and management of large carnivores in Uganda. We suggest that this study serves as an important motivator for more meaningful and science-based research and conservation on both African leopards, and spotted hyenas across the state of Uganda. The window for the conservation of these species is likely shrinking, and the disappearance of African lions from three large conservation areas in the last 15 years (Toro Sem-Liki, Pain-Upe, and Lake Mburo, UWA 2010) should be an important warning that these species too require robust monitoring of their populations.

100 Chapter 4 statement of authorship: The candidate and Christopher O’Bryan conceived the idea for the manuscript. The candidate identified methodologies to conduct the analyses, with input from Christopher O’Bryan, Martin Stringer, and Hawthorne Beyer. The candidate, Christopher O’Bryan, Martin Stringer, and Hawthorne Beyer conducted the analyses. The candidate and Christopher O’Bryan wrote the manuscript. All authors contributed to revising and improving the manuscript (Christopher O’Bryan, Martin Stringer, James Watson, Hugh Possingham, and Hawthorne Beyer).

101

CHAPTER 4 Leopards provide public health benefits in Mumbai, India

Published in Frontiers in Ecology and the Environment

4.1 Abstract

Populations of large carnivores are often suppressed in human-dominated landscapes because they can kill or injure people and domestic animals. However, carnivores can also provide beneficial services to human societies, even in urban environments. We examined the services provided by leopards (Panthera pardus) to the residents of Mumbai, India, one of the world’s largest cities. We suggest that by preying on stray dogs, leopards reduce the number of people bitten by dogs, the risk of rabies transmission, and the costs associated with dog sterilization and management. Under one set of assumptions, the presence of leopards in this highly urbanized area could save up to 90 human lives per year. A further indirect benefit of leopard presence may be an increase in local abundance of other wildlife species that would otherwise be predated by dogs. The effective conservation of carnivores in human-dominated landscapes involves difficult trade-offs between human safety and conservation concerns. Quantitative assessments of how large carnivores negatively and positively affect urban ecosystems are critical, along with improved education of local communities about large carnivores and their impacts.

102 4.2 Introduction Management of the world’s large carnivores is a polarizing issue (Ripple et al. 2014). In many areas, carnivores are vilified for attacks on livestock, charismatic wildlife species, and humans (Packer et al. 2005b, Dickman 2015), which often leads to the retaliatory killing of carnivores (McManus et al. 2015). Yet large carnivores are often flagship species for many of the world’s ecosystems (Ripple et al. 2014), and play essential roles in regulating numerous ecosystem processes, from controlling prey populations (Ripple et al. 2014) and suppressing smaller carnivores (Berger et al. 2008), to reducing parasite loads in humans (Harris and Dunn 2010) and promoting seed dispersal (Sarasola et al. 2016). The contribution of large carnivores to human well-being in shared landscapes has received little attention. Along with the growth of human populations in many developing nations, there have been concomitant increases in the populations of “pests”, such as stray dogs, in both urban and agricultural landscapes (Hughes and Macdonald 2013). We estimated the ecosystem service value of a small population of ~35 leopards (Panthera pardus, Surve et al. 2015) that feed on stray dogs. This population of leopards lives in and around the 104 km2 Sanjay Gandhi National Park (SGNP), which borders the city of Mumbai, India, currently ranked as the sixth largest urban agglomeration in the world (UN 2015). Mumbai is home to an estimated 96,000 stray dogs (Hiby 2014), which regularly attack people (Harris 2012) and whose primary predator is the leopard (Hayward et al. 2006). The leopards of Mumbai are a striking example of humans and large carnivores living in close proximity, and of how a large carnivore may benefit humans through their regulation of stray dog populations.

4.3 Leopards living on an urban edge Approximately 35 mature leopards live in SGNP and the adjoining Aarey Milk Colony, a suburb of Mumbai (Figure 4.1, Surve et al. 2015). Sanjay Gandhi National Park is a nationally designated protected area characterized by moist deciduous forest, whereas Aarey, a former dairy colony connected to the southwest corner of SGNP, consists of a 16 km2 matrix of pastures, forest patches, and human settlements (Figure 4.2). The park is slowly being encroached upon by some of the largest informal settlements in Mumbai, and at present approximately 250,000 people are estimated to live within just 500 m of the park’s borders (K Tiwari pers comm, Prasad and Tiwari 2009), with an additional 100,000 people living in and around Aarey (P Variyar pers comm, WebPanel 1 on page 241).

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Mumbai is home to one of the largest populations of stray dogs in the world (an estimated 96,000 animals), which roam freely throughout both urban and rural informal settlements (Hiby 2014). This abundance of stray dogs arises as a result of human tolerance and the hundreds of tons of uncollected refuse and carrion that accumulate within informal settlements (Prasad and Tiwari 2009), exacerbated by the catastrophic decline in carrion- eating vulture populations over the past 20 years due to the widespread use of diclofenac, an anti-inflammatory drug, used to treat cattle (Markandya et al. 2008).

4.4 Leopard impacts on stray dog populations Dogs have been the primary prey source for leopards in SGNP over the past 15 years (Edgaonkar and Chellam 2002, Prasad and Tiwari 2009, Surve et al. 2015). Dog biomass represents approximately 42% (range 25–58%, Edgaonkar and Chellam 2002, Surve et al. 2015) of the diet of leopards inhabiting this area. Assuming a leopard daily food intake of 4.7 kg (Odden and Wegge 2009), and given that 17.1 kg of an average dog can be consumed (assuming 95% of the carcass is eaten, Stander et al. 1997, Athreya et al. 2016), a population of 35 leopards will kill about 1,500 dogs per year (range 878–2036, depending on diet range) in and around SGNP (see WebPanel1 for a detailed explanation). 104

Few dogs, if any, live within the interior of SGNP itself (Surve et al. 2015), which may suggest behaviorally mediated distribution effects by leopards or a paucity of resources for the dogs (Butler et al. 2004). We assumed that leopard activity in urban areas is concentrated within 500 m of the forest edge, based on expert opinion and 10 sightings of leopards outside the park (see WebPanel1 for details). This 500 m strip around the park covers an area of 43 km2 and, given a mean dog population density of 17.3 ± 0.3 dogs km2 (Surve et al. 2015), we infer that this region could contain 730–760 dogs, or about half the number of dogs the leopards consume. This difference between the number of dogs on the periphery of the park and the number of dogs consumed by leopards in the same area suggests that dogs disperse into this low-density area from surrounding neighborhoods and are subsequently preyed upon.

The value of leopard predation in combating the stray dog problem can be assessed in comparison with the local government’s ongoing dog sterilization program, which is conducted at a cost of US$ 11.90 per dog (www.wsdindia.org). If the total number of dogs that leopards consume in this system (ie ~1500 individuals) is multiplied by the cost of

105 sterilizing each dog (US$ 11.90), then predation by leopards is arguably worth about US$ 18,000 in saved sterilization costs, equivalent to ~8% of Mumbai’s existing annual sterilization budget (US$ 208,000, HT Correspondent 2015).

4.5 Dog impacts on human populations Although rabies transmitted by stray dogs are responsible for the deaths of over 20,000 people in India per year (Biswas 2016), it is illegal to kill stray dogs (Section 428 of the Indian Penal Code, 1860, and The Prevention of the Cruelty of Animals Act, 1960), so Mumbai citizens often carry rocks and bamboo rods to fend them off (Harris 2012). Stray dogs are the primary source of rabies transmission to humans (Knobel et al. 2005), and an average of 74,603 bite cases have been reported per year among a human population of 21 million people in Mumbai (2011–2015 5-year mean, Table A3.2.1, Appendix A3.2). For Mumbai’s 96,000 stray dogs, this corresponds to 0.78 bites per dog per year, or 3.6 bites per 1,000 people per year. This is likely a conservative estimate, however, as disease incidents are greatly underreported in developing areas, for example, Singh et al. (2006) estimated that leishmaniasis was underreported by a factor of 8.13 in Bihar, India, and even in the US state of Pennsylvania, dog bites were greatly underreported in 1980 (Beck and Jones 1985).

As stray dogs gravitate toward the resources available in and around informal settlements, their populations often overlap with those of humans, and thus dog bites and rabies transmission disproportionately affect the poorest members of society (Acosta-Jamett et al. 2010, Gogtay et al. 2014). Although rabies vaccinations and post-exposure treatments are subsidized by some hospitals (Gogtay et al. 2014), they cost on average US$ 33.75 (range US$ 26–42), which represents a substantial expenditure for the majority of bite victims, who typically live below the poverty line. On average, people living on the periphery of SGNP earn just US$ 0.75–1.50 per day (CPDR 2000).

4.6 Leopards may benefit human health and dog management The statistics noted above can be used to estimate the reduction in exposure to dog bites for the 350,000 people living within leopards range (ie the 500 m buffer), as compared to typical exposure for those living elsewhere in the city. The estimated dog density in the park periphery (17.3 km2) is 40 times lower than densities in four urban informal settlements located deeper within the city (a dog density of 688 dogs km2 is found 2.5–13.6 km [mean=7.4 km] from the park boundary, Hiby 2014) and nearly 10 times lower than the citywide average (96,000 dogs in 603 km2 = ~160 dogs km2). Assuming that bite rates will 106 be roughly proportional to both dog density and human population, we estimated that people living immediately adjacent to the park experience about 10 times fewer dog-bite incidents than is typical for the city. At an average bite rate of about 3.6 bites per 1,000 citizens for the city as a whole (approximately 74,603 bitten out of a total population of 21 million), a region of 350,000 people would expect to see around 1,200 bite incidents per year. However, because the dog density near the park is just 11% of the average density for the city, people living immediately alongside SGNP may experience just 11% of the number of bites, or perhaps fewer than 140 bites in total annually. In other words, leopard predation may prevent over 1,000 bites per year in this region. Importantly there are both wealthy and extremely poor human communities living in the areas immediately adjacent to SGNP (Engineer 2018). This is evidenced by regions like the Thane municipality where large informal settlements are flanked directly by large high rise buildings (Zerah 2007, Nair 2019). Importantly the Hiby (2014). Importantly our dog density estimates did not have the resolution to be able to examine fine scale socioeconomic in living standars of human communities in different areas. We only used the three regions provided by the Surve (2015) report for the density estimates of the park and compared them to the broader estimates provided by Hiby (2014).

If leopards were absent from the park, then it is possible that the surrounding dog population would be expected to increase not merely to the average value but to match the many hundreds of dogs per square kilometer that are found in other informal settlements, suggesting that the benefit derived from the presence of these large carnivores is even greater than it would initially appear. We used a simple Lotka-Volterra predator–prey model to explore the potential increase in stray dog numbers and subsequent attacks on humans per year around the park in the absence of leopards (Figure 4.3). We assumed that the stray dog carrying capacity is equivalent to the highest documented dog density in the region (688 dogs km2), and that if leopards were removed from the system, then dog densities would increase to carrying capacity. We estimated the dog population growth rate (r) – based on the assumption that leopards are holding the dog population around the park at its current density – using the equation r = (p/N0)/(1 – N0/K), where p is the number of dogs predated by leopards per year, N0 is the current number of dogs within the leopards’ range, and K is the carrying capacity.

Under this scenario, the increase in dog numbers resulting from the absence of leopards could lead to increases in dog bites of humans from under 140 to over 5,000 per year (Figure 107 4.3). Given that >78% of dog bites in Mumbai require medical treatment and 2.14% require treatment with immunoglobulin (ie bites that pose a risk of rabies exposure, Gogtay et al. 2014), it follows that nearly 4,000 medical treatments and 90 lives may be saved each year by leopards limiting the expansion of the dog population in this region. Under the worst-case scenario, medical treatment costs in this area could reach as high as ~US$ 200,000 per year (Figure 4.3). This estimate is based on an average treatment cost of US$ 33.75 per person, 350,000 people, and a bite rate of 3.6 bites per 1000 people that increases to 15.5 bites per 1,000 people as a result of the dog density rising from 160 to 688 dogs km2, and assuming that every bite victim requires post-exposure treatment.

With both human and dog populations likely to increase over the coming decades, the value of retaining the leopards in SGNP may become even greater than these estimates indicate. Mumbai’s human population is projected to double by 2050 (to 42.4 million people, Hoornweg and Pope 2014), and if accompanied by a doubling of the dog population, epidemiological theory would predict that the number of dog bites to humans, along with the associated costs to human health and livelihoods, would increase a further fourfold (Figure 4.3).

Figure 4.3: Projected dog bites per year and dog sterilisation costs (top) along with human treatment costs and potential additional human lives lost (bottom) resultant of a loss in leopards from Sanjay Gandhi National Park, Mumbai. 108 Although our estimates are based on known leopard diet and dog densities within and around SGNP, there remains substantial uncertainty about the valuation of this ecosystem service. For example, we assumed that dogs continue to predominate in the leopards’ diet, however, large carnivore diets are variable across space and time (Johnson et al. 1993). Moreover, only approximate estimates of dog bite rates on humans (Sharma et al. 2016) and the human population size for the area around the park are available. Recent research on bite rates from stray dogs in Delhi, India, revealed an annual per capita bite rate of 0.025, which is considerably higher than our estimate of 0.0034 bites per person per year (Sharma et al. 2016). We also assumed that dog bite rates were consistent across the region, reflecting the findings of Sharma et al. (2016), who determined that bite rates were similar across urban areas.

Regarding leopard spatial dynamics, local knowledge and newspaper reports led us to conclude that leopards frequently roam in and around a 500m buffer zone bordering SGNP, but there are no published data detailing leopard movements in this area. Overall, it is unclear whether our work over- or underestimates the value of services provided by leopards in this system. Additionally it is clear that our work cannot prove the causality between leopard predation on stray dogs and reduced bites on humans. There are clear correlations in the number of dog bites and dog densities, and this is argued to be caused by leopards. However future work needs to be dedicated to teasing apart the effects of dog carrying capacity (based on food availability) and the effects of leopard predation. Further research on the interactions among leopards, dogs, and people will improve the accuracy of these estimates and the areal extent over which they occur, but our analysis indicates the value of these services to be substantial. However, linking leopard predation of dogs to human well-being also requires careful assessment of the costs of leopards as well, including mental health effects (such as the stress and fear associated with living in such close proximity to an apex predator) that could offset any indirect benefits of leopard presence.

4.7 Human–wildlife conflict and the future of leopards in Mumbai The negative impacts of leopards on humans around SGNP have been managed and largely mitigated, with leopard attacks in Mumbai dropping substantially (to one or two cases per year) following the abandonment of leopard translocation programs in 2003, and the development and implementation of dedicated environmental awareness and “best practice” campaigns directed toward people entering the forest. As of 2015, there had not been a single human death from leopards in Mumbai since October 2013 (Surve et al. 2015). By 109 comparison, attacks on humans by leopards peaked at 25 incidents in 2002 (Athreya et al. 2011). Previous attacks were largely attributed to intraspecific conflict caused by translocations of foreign “problem leopards” to the park by local Forest Department personnel (Athreya et al. 2011, Bhatia et al. 2013). In March 2017, however, a leopard attacked a child in the Aarey Milk Colony near SGNP (Alok 2017). This attack, in combination with other reports of attacks on humans by neighboring leopard populations, will likely increase fear and stress levels among the local residents. The negative impacts of leopards also reach far beyond direct human injury and death, and include depredating both livestock and domestic pets in areas around the park, and reducing the abundance of bushmeat species that are valued by local people (Inskip and Zimmermann 2009).

Conservation of leopards in Mumbai will therefore be a challenge in the future. With urban Mumbai expected to grow 26% by 2020 (Moghadam and Helbich 2013), the informal settlements will likely further encroach upon forested areas and the leopard habitat they provide (Supporting Information 3). Furthermore, SGNP and the adjoining Aarey Milk Colony are under constant threat from development, and the recent approval of the Metro III train car shed project in the Aarey colony is likely to lead to the clearing of large swaths of leopard habitat.

4.8 Global impacts of large carnivores in urban environments Nineteen other studies across Africa and Asia have shown that leopards prey on stray dogs (Butler et al. 2013), suggesting that our results are not isolated and that leopards may benefit humans more broadly across their range. More generally, these benefits may be realized in shared landscapes where wildlife frequently prey upon stray dogs. This may be limited to areas where stray dogs and felids (eg. Jaguars Panthera onca) still occur. Dog attacks on humans have a wide range of consequences above and beyond direct injury, including time off work or even job loss, lost wages, medical expenses, and reduced ability to care for dependents (Knobel et al. 2005, Gogtay et al. 2014). In many countries, dogs are infected with rabies, which can be fatal to humans and livestock if post-exposure treatment is not administered quickly (Gogtay et al. 2014). Unfortunately, high densities of people and stray dogs often occur in the poorest communities, such as informal settlements, where dog attacks can have the most severe impacts (Gogtay et al. 2014). As populations of large felids are threatened and declining in many areas (Ripple et al. 2014, Jacobson et al. 2016, Ripple et al. 2017), there is a risk that the benefits of their regulatory effects on dog

110 populations will be reduced or lost, further exacerbating the impacts of stray dogs on local human populations (Treves and Bonacic 2016).

Large carnivores are valued for their ecological roles in regulating trophic levels and habitat structure in protected areas (Fortin et al. 2005, Ripple and Beschta 2012). However, less is understood about the role of carnivores as ecosystem service providers in shared landscapes. Previous research has established that European jackals (Canis aureus moreoticus), a subspecies of the golden jackal (Canis aureus), and spotted hyenas (Crocuta crocuta) reduce organic waste by scavenging in urban areas of Serbia and Ethiopia, respectively (Yirga et al. 2015, Ćirović et al. 2016). In addition, Gilbert et al. (2016) postulated that indirect benefits to humans (eg reduced loss of life and injury, lower rates of property damage) would result from the re-colonization of North America by cougars (Puma concolor) via reductions in vehicle collisions with prey species as a result of lower prey densities. Further research is needed to better quantify the full range of social, economic, and ecological impacts of carnivores in shared landscapes.

Wildlife attacks on humans, which are often featured in and sensationalized by the media (Bhatia et al. 2013), may result in risk-averse management strategies at local scales, for example, the Government of Western Australia initiated a shark-culling program as a direct result of media coverage of shark attacks (McCagh et al. 2015). It is critical that such attacks from carnivores, though tragic, do not prompt ill-considered and reactionary management responses, such as local eradication programs, because there is little or no evidence that such programs are effective and in fact they may even be counterproductive (McCagh et al. 2015). It is essential that the reduction in attacks on humans achieved through carnivore eradication be weighed against the potentially much greater number of lives saved, among other benefits, by the presence of these carnivores.

The long-term survival of carnivores in shared landscapes requires the effective management of human–carnivore conflict. Whereas the negative effects of carnivores have been well documented in the scientific literature (Inskip and Zimmerman 2009) and in the popular media (Bhatia et al. 2013, McCagh et al. 2015), the benefits provided by carnivores to human well-being and ecosystem services have not (eg Jacobson et al. 2012). Tolerance of large carnivores and their acceptance by humans (Bruskotter and Fulton 2012) are more likely to occur if the benefits of the species are understood (Bruskotter and Wilson 2014). Experimental studies have shown that the perceived benefit of the presence of large 111 predators and scavengers by local societies is a predictor of tolerance levels (Bruskotter and Fulton 2012, Bruskotter and Wilson 2014). For example, Egyptian vultures (Neophron percnopterus), populations of which are declining globally, thrive in the towns and villages of Socotra, Yemen, an archipelago of four islands, because of local recognition of the valuable livestock and human waste processing services they provide. These services are otherwise lacking in this area (Gangoso et al. 2013). Tolerance of large carnivores is also highly dependent on social factors, such as whether or not a neighbor tolerates the species (Treves and Bruskotter 2014).

Education and communication initiatives are important components of programs geared toward improving tolerance. For example, Slagle et al. (2013) found that people were more tolerant of black bears (Ursus americanus) when given information describing the benefits of the presence of bears. In this regard, the popular media may be an important avenue for communicating carnivore benefits. For instance, Bhatia et al. (2013) found that mass media focused on human–carnivore conflicts in India, were willing to correct erroneous perceptions, and in some areas even helped to facilitate proper management and mitigation. Research into the ecosystem services associated with wildlife must be actively communicated in order to establish a more balanced perspective on the value of wildlife to the general public. The continued persistence of carnivores in shared landscapes is contingent upon identifying ways to mitigate detrimental impacts while simultaneously recognizing and facilitating the benefits provided by these species.

112 Chapter 5 statement of authorship: The candidate conceived the concept of the manuscript with Martine Maron, and Duan Biggs. The candidate ran the logistic regressions models with guidance from Julien Fattebert. The candidate received assistance in interpreting the results from Julien Fattebert, and wrote up all results, and a first draft of the manuscript. The manuscript was written by the candidate. All authors contributed to revising and improving the manuscript (Ralph Schenk, Christopher O’Bryan, Arjun Gopalaswamy, Duan Biggs, Varun Goswami, Julien Fattebert, and Martine Maron).

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CHAPTER 5 Human-carnivore conflict and compensation on the edge of Uganda’s Lake Mburo National Park: landscape drivers and financial sustainability

Revision requested at Conservation Science Practice

5.1 Abstract Human–carnivore conflict is a widespread conservation challenge that continues to increase as human populations grow, and their livestock production expands. We assessed characteristics of human-carnivore conflict between Bahima pastoralist communities, and two large carnivores, the spotted hyena Crocuta crocuta and African leopard Panthera pardus, on the eastern edge of Lake Mburo National Park (370 km2), south-western Uganda. We found hyenas were responsible for the most livestock depredation (69%), and killed significantly more cattle than did leopards (70% vs 36% of cattle depredation events). Both predators preyed on livestock mostly at night (97% and 89% of all depredation reports, respectively). Depredation was more likely to occur in rugged areas, closer to human settlements, and the national park border, and further away from water. Livestock depredation primarily occurred inside livestock protection pens (bomas), indicating that these were likely inadequately constructed. Livestock depredation in the region was covered by a private financial compensation fund run by a safari lodge, the Mihingo Conservation Fund. We found that for five of the seven years between 2012–2018, the fund earned more from tourism activities than it spent on compensation costs. However, as the fund also covered other community development projects, compensation payments from the fund were subsidised by the lodge (annual range of subsidy=USD$ 1,623–20,546). We propose a trial of investing in strengthening bomas to reduce livestock depredation and associated compensation payments.

114 5.2 Introduction Human–carnivore conflict is a global conservation challenge (Treves and Karanth 2003), and ranges from human deaths due to African lions Panthera leo in Tanzania (Packer et al. 2005b), and livestock depredation by Asiatic tigers Panthera tigris in Bhutan (Wang and Macdonald 2006), to brown fur seals Arctocephalus pusillus eating Atlantic salmon Salmo salar in fish farms in New Zealand and Australia (Robinson et al. 2008). Such conflicts often result in retaliatory killings, or adverse policy against large carnivores, such as culling and translocations (Treves et al. 2007, McCagh et al. 2015).

In sub-Saharan Africa, human–carnivore conflict is largely associated with livestock production near national parks (Packer et al. 2013). The vast majority of these parks have porous borders, and are under-resourced (Lindsey et al. 2018). Carnivore depredations are a large source of livestock mortality in already-marginal farming habitats (Hemson et al. 2009), and they cause significant loss of income for families financially reliant on their livestock (Holmern et al. 2007). These carnivore depredations are likely to become more common as natural prey are depleted due to bushmeat snaring and other forms of poaching, suggesting a dire need for improved knowledge on the spatio-temporal dynamics of conflict, and how it can be abated (Woodroffe and Ginsberg 1998, Khorozyan et al. 2015).

One common tool for reducing human-carnivore conflict globally is financial compensation, and it is used by both governments (e.g. South African National Parks; Anthony and Swemmer 2015), and conservation NGOs (e.g. Landmark Foundation Leopard and Predator Project; McManus et al. 2015). Financial compensation schemes often reduce the number of large carnivores killed by people (e.g. Maclennan et al. 2009, Hazzah et al. 2014, McManus et al. 2015, Bauer et al. 2017), though some have been criticised as ineffective (Nyhus et al. 2003, Nyhus et al. 2005). However, the effectiveness of tools to reduce human- carnivore conflict is often context-dependent, and relies on understanding the local patterns of conflict, the species involved, and the local and landscape variables that explain livestock loss (Haswell et al. 2017).

Previous studies of the distribution of large carnivore depredations on livestock and humans in Africa and India have shown varying factors that explain depredation risk (Miller 2015). For example, Rudnai (1979) and Karani (1994) found increases in depredations during periods of drought in the farming systems of Kenya, while Patterson and colleagues (2004) and Kissui (2008) found more livestock depredations by these species during the wet 115 season. Wang and Macdonald (2006), Abade et al. (2014), and Rostro-Garcia et al. (2016) also found that depredations were more likely closer to water sources, rivers, and human settlements. As such, patterns of conflict are highly context dependent and require assessing not only the factors that drive conflict, but also the tools that reduce future conflict.

Drawing from previous knowledge of the above studies, we assessed patterns of livestock depredation by spotted hyenas and African leopards on the eastern edge of Lake Mburo National Park (LMNP), south-western Uganda. We explored the distribution of livestock depredations over time, evaluating whether these showed seasonal signatures associated with wet and dry months. We also investigated spatial patterns arising from landscape-level drivers of livestock depredation. Finally, we assessed whether the local Mihingo Conservation Fund (MCF) was able to adequately accrue funds from tourism activities to cover all financial compensation costs between 2012–2018.

We hypothesized (1) that livestock depredation by spotted hyenas and African leopards would follow a clear seasonal pattern, as there is a higher density of large ungulates outside the borders of LMNP in the wet season (Rannestadt et al. 2006). This would reduce reliance on domestic species (Khorozyan et al. 2015), leading to more depredations in drier months. LMNP and the surrounding livestock farming areas also have many granite inselbergs. We therefore also hypothesized (2) that depredation would be more likely due to the cover they provide for hyenas and leopards. We expected areas of thick bush, and in close proximity to water to be similarly prone to high probabilities of depredation in the landscape. Our results are the first assessment of landscape-level drivers of human–carnivore conflict in Uganda, and they have an important bearing on the design and implementation of other proposed financial compensation schemes.

5.3 Methods 5.3.1 Study Area We studied human–carnivore conflict in the Lake Mburo National Park (LMNP, 370 km2, 300 47’–310 04’E and 000 30’ – 00 30’ S), Kiruhura district, south-western Uganda (Figure 5.1). The park falls within the Akagera savannah ecosystem, which extends from Rwanda and north-western Tanzania down into south-western Uganda (Van de Weghe, 1990). Most of the region is dominated by deposits from the Pleistocene which give rise to fine sandy loams along ridges and slopes, as well as peat and alluvial clays at the bottom of the valleys (Rwaguma et al. 1997). The park experiences a bimodal annual rainfall pattern (October– 116 December, and February–June) averaging 800 mm, and daily temperature averages 280 C (Moe et al. 2016). The park’s woody vegetation is characterized by dry Acacia savannah dominated by Acacia hockii, woodlands, thickets and swamps, which occur on the edges of the lakes Kachera, and Mburo (Rannestad et al. 2006). The most common grasses include Loudetia kagerensis, Chloris gayana, and Sporobolus pyramidalis.

LMNP, and the surrounding pastoralist areas support the last remaining population of impala Aepyceros melampus in Uganda, the most common and preferred prey of the African leopard (Hayward et al. 2006). The park also has populations of Burchell’s zebra Equus burchelli, Cape buffalo Syncerus caffer, Defassa waterbuck Kobus ellipsyprymnus defassa, bushbuck Tragelpahus scriptus and warthog Phacochoerus africanus (Rannestad et al. 2006). Preferred prey of both these predators is available beyond the borders of the LMNP, and Rannestad et al. (2006) noted higher densities of bushbuck, impala, reedbuck Redunca redunca, waterbuck and zebra outside of LMNP during the wet season.

Several unpublished studies have suggested Uganda may be an important country for human-carnivore conflicts (UWA 2010, Mudumba 2011, Sheppard 2014), and LMNP

117 represents a classic conflict area. Many Ugandans have a very strong cultural and economic connection to cattle production, with an average of 50 cows/km2 (Cook 2015). Uganda has the highest density of cattle in Africa, and the 8th highest in the world (Robinson et al. 2014). Local Bahima pastoralists belonging to the Banyankole tribe around LMNP generally view large carnivores, namely spotted hyenas Crocuta crocuta and leopards Panthera pardus, as a threat to their livelihoods (Infield and Namara 2001).

By depredating cattle, sheep and goats, hyenas and leopards cause additional financial burden to pastoralists (Tweheyo et al. 2012), and these species were listed as the 3rd and 118 6th most important stock/crop raiding species (out of a list of both carnivores and ungulates) around LMNP (Tweheyo et al. 2012). Nevertheless, local tourism lodges and the Ugandan Wildlife Authority (hereafter UWA) view the carnivores as a valuable tourism resource that generates income for the safari industry. For example, in 2018, 1,585 people purchased a night game drive permit for leopard viewing in LMNP, equating to US$ 47,550 in revenue for the Ugandan Wildlife Authority (A. Kule pers. comm.).

LMNP is bordered by a matrix of small human settlements, small-scale subsistence crops, dairy ranches, and communal grazing lands (Ochieng et al. 2015). The pastures around LMNP are mainly degazetted national park land, an artefact of the fall of the Milton Obote political regime that initially evicted 300,000 Banyankole pastoralists when originally gazetted (Kingdon 1985, Infield and Namara 2001). The pastures around LMNP have significant numbers of preferred prey, particularly in the rainy season (Rannestadt et al. 2006). This makes livestock pastureland suitable for carnivores, and regular predation of livestock occurs.

5.3.2 The MCF and conflict monitoring The Mihingo Conservation Fund (MCF) was established in late 2007 due to the longstanding conflict between Bahima pastoralists, and large carnivores on the borders of LMNP. For example, at least 19 leopards were killed on the boundary of LMNP between 2003-2006, and two hyena clans that were regularly viewed by tourists were poisoned in 2007 (each >14 members in size, Ralph Schenk pers. comm). The MCF’ aim is to “encourage coexistence of wildlife and humans beyond the boundaries of LMNP”. We present data for the ten-year period on human-carnivore conflict extending from January 2009–December 2018.

Livestock depredation events were collected by Mihingo Lodge managers and two scouts trained in the identification of animal tracks, and predator depredation characteristics (i.e. bite marks and drags) whenever they were reported on the lodge’s telephone line. The scouts would visit farms reporting depredation (typically within a 24-hour period). A livestock depredation event is defined as an incident in which a predator killed or injured one or more livestock, meaning that several livestock could be depredated in a single event (Kissui 2008). At each conflict location, the identity of the animal killed, and the identity of the predator responsible for the depredation, were noted. This was determined through a combination of visual examination of animal tracks, feeding sign, or injury marks left on livestock, and through the eye-witness reports of the livestock herder or owner (Ogada et 119 al. 2003, Kissui 2008). The livestock herders in this region generally have extensive experience in identifying the main predators responsible for depredations due to regular contact with their livestock. For each event additional information on the time of the depredation, the location of the depredation (ie. inside boma or outside), site characteristics, and the type and age of the livestock killed were noted. When available a GPS location of the depredation site was also taken (Figure 5.2).

5.3.3 Data analyses 5.3.4 Temporal patterns of predation events We assessed the observed frequencies of predation on different livestock types, and the spatial context of depredations (i.e. inside boma, outside boma or chased/dragged outside by a predator that was outside the boma), by spotted hyenas and leopards, which were the overwhelmingly most common predators (1,102 of 1,125 livestock depredation reports, 98%). We used whether the number of depredation events on each livestock type by hyenas and leopards differed from random using a Chi-squared test. To assess the temporal distribution of depredations across seasons, and more specifically across wet and dry months, we used a negative binomial regression. We lumped depredations by month and year (e.g. January 2009, March 2011, and so forth) and used these monthly depredation occurrences as our response variable. We then used long term, monthly rainfall data from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (version 2018–1891–2016, see: https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html) for the region around LMNP at 0.5 degree latitude × 0.5 degree longitude resolution. We calculated the monthly mean of the 4 cells, which encompassed the LMNP between January–December for the years 2009- 2016. We assessed the influence of rainfall on the number of depredations for spotted hyena and leopard depredations per month separately, and in combination. We also examined the effects of season (wet and dry), year and month on hyena, leopard and combined depredation events through individual negative binomial regressions.

5.3.5 Landscape predictors of livestock depredation by African leopard and spotted hyena To investigate the effects of landscape variables on the probability of depredations by leopard and hyena, we built multivariable logistic regressions analogous to a resource selection function in a used vs. available design (Manly et al. 2002) at the first order of selection (Johnson 1980, Meyer and Thuiller 2006). We used spatially-explicit locations of 120 leopard and hyena depredations as presence data. To define the domain of availability to sample environmental conditions, we built a convex polygon encompassing all the depredation points (Fattebert 2015; Miller 2015). We generated random pseudo-absences at a 1:5 ratio to used locations (Fattebert et al. 2019). We extracted landscape variables at each used, and available point using the package raster (Hijmans 2013) in the R environment (R Development Core Team 2015). We chose a suite of candidate explanatory variables based on previous large carnivore occurrence or conflict studies (Balme et al. 2007, Abade et al. 2014, Fattebert et al. 2015, Rostro-Garcia et al. 2016), which have highlighted their importance as potential drivers of both occupancy and depredation probability (see Miller 2015 for a comprehensive review). These included a measure of tree cover, terrain ruggedness, and distance to rivers, and water bodies, to roads and vehicle tracks, to human settlements, and to the national park boundary (Table 5.1). We built two models, one for each species, and applied a manual backward-stepwise model selection procedure, removing all non-significant variables from the multivariate model, until the effects of all remaining variable were significant (p=<0.05; Hosmer et al. 2013). We then projected the predicted values spatially, as:

w(x) = exp(β1x1 + β2x2 + … + βnxn) where βi is the coefficient of variable xi (DeCesare et al. 2012). Because pseudo-absences could sample areas where depredation did occur but were not recorded, this RSF is proportional to a resource selection probability function (RSPF) in a Used-unused design, and is not bounded between 0 and 1 (DeCesare 2012). Therefore, we reclassified the projected values into 10 equal-area bins using percentile breaks at 10% intervals for mapping (Fattebert et al. 2018). Importantly, we wanted to test if boma location rather than the landscape variables would predict attack probability. We hypothesized that because so many attacks occurred inside bomas, boma location in the landscape could explain attack probability, mainly because bomas occur near human settlements. We therefore created a separate model for bomas, where we digitized all bomas in the landscape manually, and generated pseudoabsences in the broader landscape. If model coefficients were similar between the depredation probability models, and our boma model, we would not be able to tease apart broader landscape effects on attack probability, and attack probability could be modelled largely based upon the presence of boma location alone.

121

Table 5.1: Spatial covariates used in our multivariate logistic regression examining spotted hyena and leopard depredation probability in the LMNP, south-western Uganda.

Layer Layer spatial Covariate Metric calculated year resolution Source URL http://download.geofabrik.de/africa/ugan

Road density 2019 250 m Euclidean distance to roads OpenStreetMap da.html 250 m http://ubos.geo- 2019 solutions.it/layers/geonode:water_points Waterholes and Euclidean distance to rivers

Uganda Bureau of Statistics _1 rivers and waterbodies 250 m http://download.geofabrik.de/africa/ugan 2014

OpenStreetMap da.html Human settlement 30 m resampled Euclidean distance to https://www.ciesin.columbia.edu/data/hr

density 2015 at 250 m human settlements Columbia University sl/#data Vegetation continuous field https://lpdaac.usgs.gov/products/mod44

(VCF) 2018 250 m Focal mean United States Geological Survey bv006/ Terrain Ruggedness 30 m resampled Regional Centre for Mapping http://opendata.rcmrd.org/datasets/ugan

Index (TRI) 2018 at 250 m Focal mean Resource for Development da-srtm-dem-30-meters Protected area Euclidean distance to World Database on Protected https://www.protectedplanet.net/lake- (LMNP) 2019 250 m protected area Areas (WDPA) mburo-national-park

122 5.3.6 Financial sustainability of the MCF

We calculated the total yearly budget spent on financial compensation, and compared the difference to funds accrued from tourist activities from fundraising for the 2012–2018 financial years. We compared yearly and mean monthly compensation payments between total income, and compensation costs, and between species (i.e., compensation costs of hyenas vs leopards) using a two-sample t-test.

5.4 Results 5.4.1 Impacts and temporal trends of depredation by leopard and hyena 1125 livestock depredation events were recorded between January 2009, and December 2018. Spotted hyenas, and leopards were deemed responsible for the overwhelming majority of events (n=1102; 98%), and spotted hyenas caused more than twice the total depredation losses compared to leopards (69 vs 31% of all events; Figure 5.3). This difference was significant across the livestock species depredated (X=110.38, p=<0.05). Depredation events by other species comprised male lion (1), olive baboons Papio anubis, African rock pythons Python sebae, and unidentified species (n=23 or 2% of all depredation events). The majority of spotted hyena depredation reports were of cattle (64%). In contrast, leopard depredation reports comprised 30% cattle, and 70% goats and sheep (Table 5.2). Multiple killings (ie. where ³ 2 animals were killed in the same depredation event), were recorded in 52 events for leopards (15% of all leopard depredation reports), and 99 events for spotted hyenas (13% of all hyena depredation reports). Adult livestock were preyed upon most often by both spotted hyenas and African leopards (Table 5.3), followed by juveniles, and sub-adults. African leopard depredation reports were primarily (82%) inside bomas or involved chasing livestock outside of them. Similarly, 64% of hyena depredation events were reported from inside bomas (or involved chasing livestock out of them; Table 5.3). Our GLMs indicated no significant effect of monthly rainfall on depredation of livestock by hyenas, leopards or the combination of the two species (Table 5.4). This pattern was also consistent by season (Table 5.5 and Table 5.6), but we did find an effect of year but not month.

123

Figure 5.3: Sample sizes and proportions of spotted hyena and African leopard livestock attacks on the edge of LMNP (collected between January 2009 – December 2018), and the proportion of attacks which took place at night (vs dawn, day and dusk). We had no data on depredation period for 137 hyena and 48 leopard depredations.

124 5.4.2 Landscape predictors of spotted hyena and African leopard depredations Depredation probability by spotted hyenas (Figure 5.4) increased with distance away from water sources, and also in areas closer to the National Park boundary, and human settlements (Table 5.5). Hyena depredations also happened in more rugged areas. The highest probability of depredation by leopards (Figure 5.5) was also closest to human settlements, in areas with rugged terrain, close to the national park boundary, and away from water. Distance to roads, and proximity to dense vegetation had no significant association with depredation probability for either species of predator. Our boma occurrence model contrasted with the results of our predator attack risk models (ie. excepting ruggedness and proximity to settlements, all other coefficient effects were the opposite to our hyena and leopard depredation models (Table 5.6). This suggested that the other landscape-level variables were important in shaping attack probability of leopards and hyenas on livestock, and not simply the presence of bomas in the landscape.

5.4.3 Financial compensation fund sustainability The MCF compensation scheme paid mean total annual compensation of US$ 6,188, and, raised mean US$ 7,439 in total revenue from tourism activities (Figure 5.6). Mean total monthly compensation paid for spotted hyena, and leopard depredation events combined was US$ 335. Mean monthly compensation payments were significantly higher for spotted hyenas than for African leopards (t=6.33; df=111; p=<0.05), with spotted hyena compensation averaging US$ 266 per month, opposed to leopard compensation payments, which averaged US$ 69 per month. Annual compensation costs were not significantly different from the annual total MCF income (t=0.64; df=12; p=0.53), and only the 2014 and 2017 years recorded higher compensation costs than MCF income. However, excepting for the 2015 year, the MCF fund spent more funds than it collected (mean annual expenditure was US$ 14,531), mainly on other community development projects. We found no relationship between the monthly costs of financial compensation, and monthly rainfall (Table 5.7).

5.5 Discussion We provide the first scientific assessment of livestock depredation characteristics by wild carnivores in Uganda. We found spotted hyenas were the most significant source of cattle depredation. In contrast, leopards mainly preyed upon goats and sheep. The risk of depredation was highest close to human settlements, the national park boundary, in more rugged areas, and away from water, and roads for both predators.

125

Table 5.2: Distribution of cattle, sheep and goats injured and killed by spotted hyenas, and African leopards (collectively termed depredation) between 2009-2018. Other depredation events are provided as footnotes underneath this table.

Number of livestock killed by leopards Number of livestock killed by hyenas Number of livestock injured by leopards Number of livestock injured by hyenas Year Cattle Sheep and goats Cattle Sheep and goats Cattle Sheep and goats Cattle Sheep and goats 2009 1 6 12 9 0 0 2 0 2010 3 12 13 15 1 1 2 3 2011 5 8 19 19 1 1 2 1 2012 7 13 11 15 2 1 2 0 2013 13 10 33 8 0 2 5 0 2014 8 58 44 25 2 2 7 2 2015 9 20 91 11 10 2 9 0 2016 10 27 71 60 6 1 3 2 2017 10 27 86 50 3 8 3 2 2018 7 38 64 53 4 1 7 1 Total 73 219 444 265 29 19 42 11

126 Most livestock depredation occurred either inside livestock bomas, or livestock were dragged or chased outside of them. Despite this, our boma model contrasted with our predator depredation models (bomas were more likely to be located close to water, areas of thick vegetation, and away from the national park). The MCF compensation fund raised enough funds from tourism activities to cover livestock compensation costs in five of seven years. However these funds were also used for other fund activities (eg. building schools, and paying school fees), and revenue from bed nights was used to sustain the compensation payments.

Figure 5.4: Spatial attack risk probability for spotted hyena depredations on livestock in the broader LMNP landscape.

5.5.1 Livestock depredation characteristics and spatial drivers of depredation locations The proportions of different livestock species depredated by leopards and hyenas reflected their prey size preference. Leopards typically prefer species weighing 15–40 kg (Hayward et al. 2006), while spotted hyenas prefer prey weighing 56–182 kg (mode=102 kg, Hayward 2006). However, the predominance of cattle in the hyena depredation records was considerably higher than previous studies implemented in both Kenya, and Tanzania (e.g., Kissui 2008, Mitchell et al 2019). These studies found stronger overlap between leopard and hyena diets. A possible explanation for this is the higher cattle to goat and sheep ratio in the Bahima pastoralist lands on the edge of the LMNP (Ocaido et al. 2009). Indeed Ocaido et 127 al. (2009) found that the average cattle ranch in the Lake Mburo region had 288 cattle and 9.3 goats (n=39 ranches interviewed).

We expected to see more depredation events during dry months. Wet season migrations of large carnivore prey from protected areas to communal lands have been noted widely in Tanzania and Kenya (e.g., Lamprey 1964, Kahurananga 1981, Kahurananga and Sikiluwasha 1997, TMCP 2000, Kissui 2008), and evidence for this was found in our study area during 1993–1995 by Baranga et al. (1996), and in 2003 by Rannestad et al. (2006). However, we found no evidence of an effect of seasonality on depredation frequency. This contrasted with studies in other East African sites. For example, Rudnai (1979) and Karani (1994) found more livestock depredation during periods of drought in the farming systems of Kenya. In contrast, Patterson et al. (2004) and Kissui (2006) found more livestock depredation by these species during the wet season. These authors postulated that the explanation for the wet season peak in these sites was due to carnivores following the migratory prey onto pastoral land, and increasing their contact rates with livestock. This is an example of the resource supplementation hypothesis (Vanak and Gompper 2009, Ritchie et al. 2014). This predicts that an abundant prey species attracts a large carnivore, and the less abundant prey species faces a higher than normal predation pressure because of co- occurrence with common prey.

Our results supported our second hypothesis that granite inselbergs would explain depredations, and we found that rugged terrain coupled with proximity to human settlements, the national park edge and distance away from water were the most important variables that increased the likelihood of both leopard and hyena livestock depredations. These spatial characteristics make ecological sense as explanatotry variables for livestock depredations, as 1) human settlements are typically associated with livestock bomas, and higher livestock densities (Wang and Macdonald 2006, Abade et al. 2014, Rostro-Garcia et al. 2016), and 2) high predation risk close to LMNP’s edge also suggests resident hyena and leopards may be temporarily using pastoralist lands to make their kills, before returning to the national park (Kissui 2008). Our pattern of increased depredations close to the LMNP edge follows several other studies which have shown reserve edges are often the site of some of the most intense human-carnivore conflicts (Nyhus and Tilson 2004, Goodrich and Miquelle 2005).

128 5.5.2 Characteristics of the MCF compensation scheme The MCF financial compensation scheme is unique amongst a suite of East African, and Southern African compensation schemes in that it attempts to only use money raised by tourism activities to support itself. We found that revenue from tourism outpaced compensation costs, however, the MCF compensation scheme struggled to meet its annual operational costs as it contributed to activities beyond livestock compensation. Another reason that the scheme struggled to cover its annual expenses was due to the fairly flexible rules around compensation of livestock depredations. Typically compensation payments by the MCF were still made in part, even if reports of livestock loss were made late, or livestock were left to wander. This was sometimes due to threats of poisoning by livestock owners. This is evidenced by the fact that 18 and 36% of leopard and hyena depredations happened outside of bomas. Many other compensation schemes penalise claimants of compensation for any losses that occur outside protection bomas. If rules around depredations made outside of bomas were tightened, compensation costs could be reduced. For example, in the Amboseli-Tsavo region of Kenya, compensation schemes withheld a portion of the compensation where husbandry practices were inadequate (i.e., animals left outside of protective bomas), and this withholding was shown to decrease the percentage of claims over time (although the authors contend they remained high, Bauer et al. 2017).

Figure 5.5: Spatial attack risk probability for leopard depredations on livestock in the broader LMNP landscape.

129

Table 5.3: Distribution of age-classes of livestock preyed upon by spotted hyenas, and African leopards (collectively termed depredation), and the depredation locations of livestock for hyenas and leopards (with missing data values).

Age class Depredation location Species Adult Sub-adult Juvenile No data Inside boma Outside boma Chased/dragged No data Spotted hyena 514 43 73 132 136 148 129 349

African leopard 170 37 73 60 90 33 58 159

Total 684 80 146 192 226 181 187 508

*Lion kills across all years amounted to 1 (1 goat) ±Baboon kills across all years amounted to 1 (1 goat) ^Python across all years amounted to 1 (1 goat) "Unidentified kills/injuries across all years amounted to 20 (6 cow kills, 10 goat kills, 2 injured cows and 2 injured goats) Total number of records for entire dataset is 1125 depredations Total number of leopard and hyena records is 1102 depredations

130 However, withholding compensation can result in more killings of carnivores (Hazzah et al. 2014), suggesting a need for balancing penalties with other forms of interventions, such as carnivore protection programs in areas where views towards conservation are negative (e.g. Lion Guardians in Kenya; Hazzah et al. 2014).

Importantly, our data show that the number of claims due to depredations has on a yearly average tripled over time. In the first five years of the scheme only 258 claims of depredation were made which contrasts strongly to the last five years which recorded 844. These data likely point to one of three processes; 1) conflict is intensifying due to increased availability of prey for these two carnivore species, 2) conflict is intensifying because predators are experiencing a decline in their natural prey base (Khorozyan et al. 2015), or 3) herding and livestock husbandry have become relaxed due to the “safety net” of compensation. These three hypotheses require ground truthing and we cannot rule out that an environment of moral hazard has been created through this scheme (Nyhus et al. 2005).

5.5.3 Data limitations We acknowledge uncertainty around the relative importance of landscape variables on depredation probability in the Lake Mburo landscape. First, we randomly sampled the background conditions in our used vs. available design. Doing so enabled us only to predict a risk proportional to the true probability of risk that a use-unused design (DeCesare et al. 2012, Yackulic et al. 2013), or an occupancy-modelling framework would generate (Goswami et al. 2015). This is typical of resource selection function studies reporting habitat selection probabilities (e.g. from tracking data), as GPS collars only record use locations (DeCesare et al. 2012, Fattebert et al. 2015, Fattebert et al. 2018, Fattebert et al. 2019).

131

While it is unlikely that we missed conflict events in the area as reporting depredations was motivated by the perspective of being compensated, we do acknowledge that our spatially- explicit dataset is a subset of c. a third the 1102 conflict events we recorded in total. In future we recommend that authors implementing similar studies make use of either an occupancy approach (Goswami et al. 2015), or the case-control/random approaches recommended in Keating and Cherry (2004) where conflict monitors are primed in monitoring the landscape (or cells thereof) in non-depredation locations, on a monthly or even weekly basis. However this would need to be agreed upon between community members and the fund manager as it would be logistically challenging. The spatial variation in depredation events observed in our models may also be reflective of the observational process (i.e., lower reporting in some locations vs others due to some factor). Again an occupancy approach would have dealt with this better, as occupancy explicitly incorporates detection probability.

132 Table 5.4: Results of negative-binomial regressions testing the effect of monthly rainfall on hyena, leopard and total monthly depredation occurrences in LMNP, Uganda, 2009–2016.

Model Coefficient β SE z-value p-value Spotted hyena Intercept 1.620384 0.172932 9.37 <0.00001 depredation Rainfall (adjusted for month) 0.007308 0.047591 0.154 0.878 Intercept -0.86358 0.199 4.33 <0.00001 Leopard depredation Rainfall (adjusted for month) 0.2024 0.05451 0.371 0.71 Intercept 2.00485 0.15209 13.182 <0.00001 Total depredation Rainfall (adjusted for month) 0.01159 0.04183 0.277 0.782

The assessors of the causes of killed livestock used a combination of carcass characteristics, the presence of predator tracks, and verbal confirmation from claimants. These are widely accepted forms of livestock depredation assessment (Ogada et al. 2003, Kissui 2008, Abade et al. 2014). The overwhelming majority of livestock depredation events in our ten-year sample were only paid compensation when physical evidence of a carcass was produced. However, this does not rule out the chance that some of the animals that were reported as killed died from poor body condition, disease, and were left to be eaten by the predators. This may have been particularly the case when assessments were made late (i.e., 2 or more days after the day of the depredation). Finally, our models and interpretation of this conflict modelling approach could have been strengthened by data on the location and density of the carnivore species themselves (ideally through an SECR model which can indicate regional levels in density). This would have allowed us to incorporate density and occupancy patterns into our modelling and better predict riority areas for management action (ie. The deployment of herders, erection of bomas).

5.5.4 Management recommendations Human–carnivore conflict is an important process that affects both large carnivores, and human livelihoods in the LMNP region. This is evidenced by the functional extinction of African lions in the region (there is one male lion in the park, likely a vagrant, UWA, 2010). The single most important result from our assessment of conflict at the edge of the LMNP is that most depredation events occurred while livestock were inside livestock holding facilities (bomas; 82% for leopards, and 64% for hyenas). Hyenas are known to break through poorly fortified boma walls, while leopards simply jump or dig underneath them (Kissui 2008). Our results mirror those of Kissui (2006), who found that a high percentage of bomas in northern Tanzania were poorly built and contributed to increased depredations. 133 Our management recommendations for the MCF scheme are:

1) Apply a treatment period where there is a transfer of funds from direct compensation of livestock losses, to the strengthening of livestock protection bomas, and/or the provision of human or non-human herders. Ogada et al. (2003) found that a higher herder to livestock ratio reduced rates of depredation in Kenya. Similarly, Abade et al. (2014) and Lichtenfeld et al. (2015) found the fortification of livestock protection bomas in Tanzania led to significant decline in predation rates. At least a slow transition away from direct compensation and towards boma fortification is recommended. Additionally, because the majority of attacks occurred outside bomas, a trial with livestock protection dogs could also be applied to the broader Lake Mburo region as the predators involved are medium-sized (ie. Hyenas and leopards, McManus et al. 2014). Livestock guarding dogs have been shown to have significant success in reducing livestock losses with an extensive Namibian trial showing that they could even help reduce carnivore killings (Potgieter et al. 2015). Innovative herder and guardian schemes such as that proposed by Hazzah et al. (2014) could also be trialled instead of the current compensation scheme.

Table 5.5: Results of logistic regressions testing the effect of spatial covariates on leopard and spotted hyena livestock depredation probability in LMNP, Uganda, 2009–2018.

Model Coefficient β SE z-value p-value

Intercept -131 0.36 -3.62 <0.01 Distance to water -0.0007 0.0001 5.60 <0.01 Spotted hyena depredation Distance to settlements -0.0007 0.0001 -5.70 <0.01 Distance to national park -0.0002 0.00004 -4.17 <0.01 Terrain ruggedness -0.02 0.01 2.59 <0.01

Intercept -1.75 0.60 -2.91 <0.01 Distance to water 0.0007 0.0002 3.84 <0.01 Distance to settlements -0.0009 0.0002 -3.86 <0.01 Leopard depredation Distance to roads 0.0005 0.0002 2.13 <0.05 Distance to national park -0.0002 0.00007 -2.40 0.02 Terrain ruggedness 0.03 0.01 2.31 0.02

134 2) The “living walls” sustainable bomas in Tanzania by Lichtenfeld et al. (2015) cost US$ 125 each. These bomas are made using a combination of native tree species, and wire. The average total compensation paid annually through the MCF compensation fund was US$ 6,187, which translates to at least 49 of these bomas. The number of bomas built could increase even further if the MCF channelled most of the net income from their program to the erection of bomas (even for one or two years). However the species used in this program should be sustainably sourced, fast growing and thorny such as Commiphora sp. suggested by Lichtenfeld et al. (2015). One can limb such species of tree and prevent the cutting down of adult trees, therefore preventing deforestation

3) Tighten compensation scheme rules. If claimants did not place livestock in protective bomas at night, or if there is evidence of gross negligence on the part of livestock owners/herders, either withold or warn compensation payees. Nyhus et al. (2003) and Nyhus et al. (2005) warned of the dangers of moral hazard in compensation programs, and these have been echoed in several other studies (Mmopelwa and Mpolokeng 2008). One potential remedy for this moral hazard is to create a fund which incorporates a small fee from activities on community land. This has been implemented in several Kenyan conservancies, where conservancy members contribute one third of all funds generated from lion tourism on their land into a compensation fund (Okello et al. 2014). The remainder of funds is raised through external donors and NGOs.

Table 5.6: Results of logistic regression testing the effect of spatial covariates on boma occurrence probability in LMNP, Uganda.

Model Coefficient β SE z-value P-value

Intercept -0.38 0.32 -1.19 0.23 VCF -0.07 0.02 -3.45 <0.01 Distance to Water -0.0002 0.0001 -2.05 <0.05 Boma occurrence model Distance to Settlement -0.00006 0.0001 -5.19 <0.01 Terrain ruggedness 0.03 0.01 3.40 <0.01

135 Our results suggest many bomas on the edge of LMNP are inadequate, and improved husbandry is urgently required in the region (Lichtenfeld et al. 2015, Miller et al. 2015). Although it is not yet proven whether the compensation scheme has reduced the killings of large carnivores on non-protected land surrounding LMNP, our study makes important recommendations on how to better manage the MCF to ensure financial sustainability for the fund and similar proposed schemes in the future.

Table 5.7: Results of linear regressions testing the effects of monthly rainfall on financial compensation for hyena, leopard and total depredation events in LMNP, Uganda, 2009–2016.

Model Coefficient β SE z-value p-value Intercept 230.25 54.49 4.39 <0.0001 Hyena compensation Rainfall 0.4 0.48 0.833 0.407 Intercept 64.38 15.41 4.18 <0.0001 Leopard compensation Rainfall 0.05 0.14 0.35 0.72 Intercept 294.63 60.24 4.89 <0.00001 Total compensation Rainfall 0.45 0.55 0.82 0.42

136 Chapter 6 statement of authorship: The candidate and Martine Maron conceived the idea for the manuscript. James Allan, Kendall Jones and Duan Biggs brainstormed examples of how royalties could impact large felid conservation, and assisted in collating company annual reports. The candidate wrote the manuscript, and received some input on legal pathways from Meganne Natali. All authors contributed to revising and improving the manuscript (James Allan, Kendall Jones, Meganne Natali, Duan Biggs, and Martine Maron).

137

CHAPTER 6 Marketing products with wildlife – how to make it benefit conservation

Third round of revisions at PLOS Biology

6.1 Abstract A key obstacle to wildlife species conservation is a scarcity of funding. A recent piece by Courchamp et al. (PLOS Biology, April 12 2018) illustrates how corporations’ widespread use of threatened wildlife imagery can create complacency in the public about the conservation status of those species. A “wildlife imagery royalty” whereby corporations, which use threatened wildlife in their marketing, pay a small percentage of their sales to their conservation could be revolutionary for wildlife conservation funding. But corporations are not currently compelled to support the protection of the species espoused in their products. We build upon the arguments presented by Courchamp et al. (2018) for why corporations should support the species they utilize, and propose two pathways that may help bring a wildlife imagery royalty to fruition.

138 6.2 Introduction A key obstacle hampering biodiversity conservation is a lack of funding (Waldron et al. 2013, McCarthy et al. 2012, Box 1). Conservation funding shortfalls are pushing scientists and conservation donors into searching for innovative funding mechanisms. Good et al. (2017) and Courchamp et al. (2018) present an example of the latter. They propose that corporations using the imagery of threatened wildlife in their marketing should contribute towards the conservation of those species. Good et al. (2017) suggested that a wildlife imagery royalty would herald a revolution in conservation funding. This idea is not unlike the one proposed by Jepson et al. (2011), who called on the film and media industry to contribute financially to the conservation of the species they film.

At a glance, the rationale for wildlife imagery royalties is akin to payment of license fees for merchandise, for example, a t-shirt manufacturer paying the singer Madonna for the rights to use her photograph or likeness on t-shirts (Courchamp et al. 2018). However, at closer inspection, the idea of a corporation paying a fee for using images of wildlife opens a Pandora’s box of questions. Firstly, corporations generally do not exist for altruistic reasons, but for profit (Drucker 1954, Friedman 1970). Therefore, would companies or their shareholders actually care about the wildlife species their products espouse enough to sign up to such an initiative? Ought there to be legal rights to the imagery of a Bengal tiger Panthera tigris? And how far should this idea go - if Tiger Beer pays towards tiger conservation, should Apple Inc. pay proceeds to the conservation of threatened Malus species?

Although their study has some underlying assumptions, in our opinion, Courchamp et al. (2018) capture a potentially important motivation for why such a royalty should exist. They suggest that through their marketing, corporations are creating a perception in members of the public that populations of wildlife are common and therefore not of conservation concern. The mechanism for this is the public’s exposure to an abundance of iconic wildlife imagery in products and advertising. Courchamp et al. (2018) and others cite this as a key reason why corporations should pay a fee to display these species on their products. In addition, many companies actually negatively impact the species whose images they utilize. This can be done directly, for example through habitat destruction (eg. the Kellogg’s Company purchases palm-oil from the Wilmar Group. The palm-oil is sourced from Riau, Sumatra (Indonesia) – locally important for both its forests and tiger populations. Wilmar has been linked to deforestation and unsustainable palm-oil production. Deforestation reduces 139 suitable habitat for tigers, causes intraspecific strife and may have numerous cascading effects on tiger prey, conflict with people and their livestock. Kellogg’s uses tigers in their marketing campaigns and “Tony the tiger” has been the face of their “Frosties” cereal for over 65 years, https://issuu.com/greenpeaceinternational/docs/licencetokill_eng), or indirectly through unintended anthropogenic effects (eg. increased exploitation because of popularization in a film or book, Figure 6.1). Importantly the evidence for this hypothesis is mixed (eg. Verissimo et al. (2020) suggest the movie “Finding Nemo” had little impact on the imports of blue tang fish Paracanthurus hepatus into the USA, while Nijman and Nekaris (2017) suggest the “Harry Potter” movie franchise led to increased trade in pet owls in Indonesia).

If conservationists and policy makers can agree that these motivations hold merit, we can progress to ask if a legal mechanism for the implementation of such a royalty exists. Many companies voluntarily support conservation projects (see Table A3.1 in Appendix 3), but a compelled royalty would likely be more stable than a reliance on corporate social responsibility (CSR) and philanthropy, which can be affected by market trends (Reich and Wimer 2012). Some models already exist: in India corporations generating >US$ 105 million per year are compelled by law to pay 2% of their profits to charities (Singh and Verma 2004). A compelled royalty for the use of wildlife imagery could help maintain funding continuity to conservation projects, a key determinant of successful recovery programs (Crees et al. 2016).

In this paper we investigate if a wildlife imagery royalty is a thing of fantasy, or has some chance of realization in the future via a legal or policy mechanism. We refer to recent legal personhood case successes for river, glacier and broader nature rights globally (Charpleix 2018, O’Donell and Talbot-Jones 2018, Pecharroman 2018), and failures of individual animal personhood cases for captive animals (eg. chimpanzees Pan troglodytes and Asian elephant Elaphus maximus, Staker 2017, Boyle 2016) in the United States. We argue that although nature-based laws are making progress, their evolution and adoption is slow, and would require significant changes to support a generalized wildlife imagery royalty.

We recommend two pathways that could allow for faster implementation of such a royalty. These include, 1) a nationally instituted royalty on products bearing the images of threatened wildlife (and corporations recognized as threatening them), and, 2) a certification mark (akin to FairTrade or 1% for the Planet) which allows the bearers to display it on their products, in 140 exchange for a small royalty payment (and which itself is protected by copyright). To conclude, we use charismatic and threatened larger members of the Felidae as a model, to illustrate the immense potential for such schemes to augment funding for conservation, and discuss potential unintended consequences of a wildlife imagery royalty.

6.3 Royalties for the environment The fundamental premise of a royalty payment is that if an intellectual asset is sold/used by a party, a royalty payment is made to the creator or representative thereof. Royalties also permeate the natural world, and the fields of environmental conservation, and even human development. For example, the government of Western Australia mandates a mining/onshore petroleum royalty from companies extracting crude oil, and this is returned to regional projects engaging in infrastructure, and community development (see https://www.agric.wa.gov.au/royalties-regions). Similarly, the pharmacy company Merck paid advances and royalties to a Costa Rican NGO INBio for bioprospecting biological samples in Costa Rica until its collapse in 2015 (Zebich-Knos 1997). Similarly, although not directly a royalty, the “Dingle-Johnson Act” (https://www.fws.gov/laws/lawsdigest/fasport.html), employs an excise tax on fishing equipment in the USA, and this funds state fish management. Similarly, the “Pittman- Robertson Act”, places a 10-11% tax on sporting arms and ammunition, and directs it towards the state-level management and restoration of wildlife. These Acts have leveraged billions of dollars for the conservation of natural resources in the USA since their inception. This is similar to a newly proposed levy on the sale of pet dogs and dog food in Australia which could fund efficacious non-lethal dingo Canis lupus dingo management (Brink et al. 2019).

6.4 Non-human personhood laws won’t help development of a royalty in the short term Nature and environmental laws have been going through a paradigm shift internationally over the past decade (Charpleix 2018). For example, “legal personhood” that corporations, trade unions and states may all be ascribed, has now been granted to rivers in New Zealand, glaciers and rivers in India, and more broadly to nature and its services in Bolivia (Pecharroman 2018). The recent granting of personhood to New Zealand’s Whanganui River in 2017 (Charpleix 2018) is particularly revolutionary, and after a 170 year legal battle, the crown signed a treaty with the Iwi people (an indigenous Maori tribe), recognizing them as legal guardians of the river. The river’s interests can be protected and New Zealand 141 courts can award damages to a trust on behalf of the river, for instance in response to pollution (O’Donnell and Talbot-Jones 2018). Similarly, in 2017, the Uttarakhand High Court in India recognized the River Ganges, its glaciers and its tributaries as a legal entity, and the high court granted the river three human representatives to protect, conserve and preserve the river (Pecharroman 2018).

142 Attempts at gaining legal personhood for individuals of a wildlife species have not yet been successful. To date, there is no state or country that recognizes an animal species or an individual animal to have legal standing. Several US courts have heard the pioneering non- human personhood cases of Steven Wise and colleagues for individual (not for an entire species) chimpanzees (Grimm 2015), and a lone Asiatic elephant (Morris 2018). They have however, been unsuccessful due to a lack of subject matter jurisdiction – i.e. the power of a court to adjudicate a particular type of matter and provide the remedy demanded (Staker 2017, Boyle 2016). These cases have importance because legal standing is the foundation of law that allows an entity to engage copyright and intellectual property laws (e.g. USA https://www.copyright.gov/ and Australia https://www.ipaustralia.gov.aul, accessed 10 January 2019). For example, animals are unable to possess statutory standing under the US copyright act (United States Copyright Act of 1976, 17 U.S.C. §§ 101-1332, consolidated version of June 2009). US copyright laws also presently bar copyright protection for “works of nature” (ie. any products/creations created by a living organism) and require “human authorship” before protecting any works (United States Copyright Office 2017, Guadamuz 2016). Even if a member of a wildlife species manages to take “selfies” using complex photographic equipment like remote camera-traps, they do not own the rights to those images, and cannot accrue profits from them.

The law can ascribe rights to all kinds of entities if it finds adequate reason to do so (Chapron et al. 2019). However, given the slow progress of rights-based laws for nature, they are likely to be of little immediate use in the creation of a wildlife imagery royalty. Even if standing for an individual, or an entire animal species is granted, there is at present no legal instrument with which their human representatives can make claims under copyright or right of publicity laws on their behalf (Figure 6.2).

143

Figure 6.1: Three examples of the mismatch between direct benefits accrued by companies in the use of threatened wildlife imagery, and potentially negative effects exuded on those species by the companies. 1. shows how the Kellogg’s Company purchases palm-oil from the Wilmar Group. The palm-oil is sourced from Riau, Sumatra (Indonesia) – locally important for both its forests and tiger populations. Wilmar has been linked to deforestation and unsustainable palm-oil production. Deforestation reduces suitable habitat for tigers, causes intraspecific strife and may have numerous cascading effects on tiger prey, conflict with people and their livestock. Kellogg’s uses tigers in their marketing campaigns and “Tony the tiger” has been the face of their “Frosties” cereal for over 65 years. 2. shows an unintended effect caused by the dramatic rise in popularity of the “Harry Potter” franchise. Harry’s owl, and others seen in the film is believed to have increased the demand for pet owls by children in Malaysia. The franchise, characterized by the movies, books and merchandise benefited from the use of owls, however increased exploitation is likely to have placed pressure on these top-order avian predators. 3. Illustrates the competition of a virtual population of elephants (used on beer bottles, in the recent “Dumbo” film and plush toys) for public attention. Courchamp et al. (2018) show some evidence that the knowledge of in situ wildlife population species status in the public is being dumbed down by the ubiquity of these “virtual” populations. 144 6.5 The creation of national laws could work Under articles 3 and 6 of the Convention on Biological Diversity (CBD, see: https://www.cbd.int/convention/articles/default.shtml?a=cbd-06, accessed 13 June 2019) sovereign states are encouraged to ensure that activities within their jurisdiction and control do not cause damage to the environment of other states. They are also encouraged to develop national strategies, plans or programs for the conservation and sustainable use of biological diversity. Therefore, these articles could be a basis to support the development of a national law, which would institute “royalties” on corporations bearing the images of threatened wildlife species. This royalty could then lead to a financial participation in the conservation of the species that these brands threaten, or even merely espouse in their marketing. Such a law could require royalty payments for the use of wildlife imagery. For example, in the USA, the creation of a bill (and subsequent act) proved to be a successful venture for the People for the Ethical Treatment of Animals (PETA), and stakeholders interested in the welfare of the orcas Orcinus orca at SeaWorld, a large animal aquarium and theme park in San Diego, California. In 2013 the documentary film “Blackfish”, released globally in theatres, showed the challenging captive conditions several orcas faced at SeaWorld, and how a spate of attacks on aquarium workers was related to mistreatment of the animals. This led to a public outcry that eventually assisted in the creation and passing of the “Orca Welfare and Safety Act (AB1453)” in the state of California in 2016. This led to the shutdown of planned expansions of Orca enclosures to the value of US$ 100 million dollars.

6.6 A “Fairtrade” equivalent certification scheme A second, albeit voluntary pathway for realizing such a royalty would be through the creation of a certification mark similar to that of 1% for the Planet or FairTrade (see Hamilton and Zilberman 2006, Blackman and Naranjo 2012, Blackman et al. 2014). This is a more structured approach to voluntary donations by corporations, unifying them under one collective umbrella for increased impact. 1% For The Planet, developed by American clothing manufacturer Patagonia, encourages companies to donate 1% of their total sales to environmental causes (see: http://onepercentfortheplanet.org/). To date, more than 1,800 member companies in 42 countries have donated US$ 200 million to approved environmental conservation non-profits. These companies then advertise the 1% logo on their products, giving consumers the power to discern and make sustainable choices through their purchases. Similarly, FairTrade provides certification to businesses engaging in more transparent, equitable trade with famers globally. In exchange for the FairTrade 145 label, companies ensure that farmers receive a minimum price for their produce, and that production practices adhere to certain environmental standards (see: http://www.takepart.com/flashcards/what-is-fair-trade/index.html, accessed 7 February 2019, Jaffee 2014). Research shows that customers recognize and remember eco-labels (Sammer and Wüstenhagen 2006), and price premiums of labeled products may increase over time (Barham and Weber 2012). Moreover, this type of labeling may positively influence a customer’s willingness to pay for a certain product, as has been shown in the appliance (Sammer and Wüstenhagen 2006), fisheries (Teisl et al. 2002) and furniture (Veisten 2007) industries. Because of this, the number of products awarded these types of “eco-labels” has grown exponentially across many industries over the past two decades (Grolleau et al. 2016).

6.7 The potential scale of a wildlife imagery royalty: an example using large felids To assess the potential scale of a wildlife image use royalty on threatened species conservation, we examined the extent of marketing and corporate name use for just one family of charismatic species, the larger members of the Felidae (lions Panthera leo, leopards Panthera pardus, tigers Panthera tigris, jaguars Panthera onca, mountain lions Puma concolor, snow leopards Panthera uncia and cheetahs Acinonyx jubatus). These species are threatened across much of their range (Hunter and Barrett 2018), and have significant top-down ecosystem, and economic value to humans (Braczkowski et al. 2018, O’Bryan et al. 2018).

We found that the use of large felids in marketing is widespread, and even modest royalty payments from companies using large felids in their marketing could leverage substantial funding for conservation. For example, if the 14 companies using large felid branding on the Forbes 2000 list were to pay a royalty of 0.1-1% of profits, they would collectively leverage US$ 202 million – US$ 2.02 billion annually. Even individual companies, such as Tiger Beer could make sizeable contributions to the conservation of individual species (Table A3.3 of Appendix 3). The royalty margins we suggest err on the conservative side of several philanthropic initiatives. For example the “1% for the planet” initiative donates 1% of all sales (1% for the planet 2014), Wells Fargo & Company donates on average 1.2 – 1.5% annually, while Goldman Sachs donated 3% of sales in 2015 (Preston 2016).

146

Machali, the world’s most famous tigress A daily telegraph article estimated India’s most famous tigress, Machali, generated ~USD$ 10 million annually for the Indian tourism industry until her death in 2016. Despite being the subject of a plethora of television shows (including National Geographic and Discovery Channel), numerous magazine articles, there was, and remains no legal mechanism for her or any tigress in India to quarantine profits for tiger conservation. Image ©Bhavik Thaker.

147 A system to allocate funds generated from a royalty scheme could work via two mechanisms. First, corporations could send royalties to projects that demonstrate their conservation outcomes using evidence-based methods. There is an emerging suite of evidence-based conservation projects that address the development needs of local people and also stem the decline of the species they aim to conserve. Examples of evidence-based large felid projects include the Lion Guardians project in Kenya (Hazzah et al. 2014) and Tanzania, Munyawana leopard program in South Africa (Balme et al. 2009b) and the FFI’s and Indonesian government’s tiger project in Sumatra (Linkie et al. 2015). Second, the funds could complement the existing, voluntary “Lions Share Fund” (see: https://thelionssharefund.com/) which partners with companies who donate a portion of their media budget to could be linked to an evidence-based list of successful, big cat conservation programs such as these, for which ongoing funding would otherwise be uncertain. Such projects could be a win-win for corporates as population recoveries are possible in some cases over just a few years (Balme et al. 2009b). Such projects could be a win-win for corporates as big cats breed quickly, and population recoveries can take place over short temporal scales ranging from 3-5 years (Aryal 2015, Balme et al. 209b) meaning that returns for their advertising and social media campaigns could also be relatively rapid and could aid their brand image and financial performance.

6.8 Caticorns and other unintended consequences It is foreseeable that a wildlife imagery royalty could lead to a series of unintended outcomes and behaviors (from both members of the public and corporations). Courchamp et al. (2018) suggested that some corporations could make small modifications to the wildlife imagery being used in their products and advertising, in order to avoid paying a compelled wildlife imagery royalty. If this strategy was employed by corporations at large, it could further alienate the public from wildlife and the awareness around its conservation, not just through competition for attention (Courchamp et al. 2018), but by replacing real species with fictional ones. A more perverse consequence could be a selection of corporations choosing to abandon the use of any wildlife imagery (real or synthetic) in their marketing and products, taking the above example to an extreme.

In conclusion, in this paper we have presented possible pathways and the necessary actions required for corporations that use wildlife imagery to fund species conservation. We have shown that the funding generated from such a program could indeed be vast, and important to conservation projects of threatened species. To operationalize this will require action by 148 national governments, international NGO’s, and other stakeholders. In its development, care should be taken to minimize perverse outcomes. Finally, we hope that our paper will advance the necessary thinking and legal exploration of the funding opportunity described by Good et al. (2017) and Courchamp et al. (2018).

149 Chapter 7 statement of authorship: The candidate wrote the chapter, and received editorial input from Martine Maron. The chapter was also reviewed by Duan Biggs and Arjun Gopalaswamy before submission.

150

CHAPTER 7 Conclusions

7.1 Synthesis

The status of the worlds large carnivores has become increasingly dire over the past several decades (Ripple et al. 2014). My thesis helps to fill two priority knowledge gaps as identified by Balme et al. (2014) in their review of large carnivore research priorities: 1) the robust estimation of large carnivore populations and 2) the conflict between large carnivores and humans, which along with bushmeat snaring is one of the two main drivers of their decline (Lindsey et al. 2013). The most novel component of the thesis relates to the use of population parameters reported in SECR models, particularly movement and the ratio of male to female animals. My review (Braczkowski et al. 2020a in Chapter 1) shows clearly how these parameters are often missed in more crude methods currently being used to survey large carnivores (I show this with lions being the model species). Chapters 2 and 3 show the importance of robust population monitoring and Chapter 5 clearly illustrates how a lack of population monitoring can serve as a missing link in strong conservation decision making. In Chapter 5 I highlight the characteristics of livestock depredation in a small national park in Uganda’s west, the LMNP. Although I had 10 years of data on livestock attacks by spotted hyenas and leopards and was able to identify some of the landscape features which might increase depredation probability. I could only make some management and financial recommendations in order to make the scheme of paying farmers for livestock losses could more sustainable (ie. tightening some of the rules around claims). Importantly, the lack of carnivore population data in the system meant that I could not ask an important question; is the compensation scheme achieving the goal it was deisnged to (ie. reduce the killings of large carnivores)? My thesis also explored the utility of using legal mechanisms to harness the marketing capital of charismatic large carnivores for conservation funding, an aspect of conservation which is largely lacking across huge tracts of carnivore range (Lindsey et al. 2018). In this Conclusions chapter I examine some of my core chapter results and place them in the context of the international literature, highlighting the key knowledge advances and future work that can be done in these research streams. 151 7.1.1 Novelty of using population parameters from SECR to inform carnivore population status

There have been a series of recent studies to use SECR to estimate African lion population densities and these include Kane et al. (2015), Elliot and Gopalaswamy (2017), Rafiq et al. (2019), and Rich et al. (2019). These all use essentially the same field sampling and modelling framework as data Chapter 2 of my thesis: camera trap detections or search encounters of individual African lions, processed under a Bayesian SECR modelling framework (see Royle et al. 2009; Russell et al. 2012). In this way this chapter is only moderately novel in a Ugandan context, however, where this chapter brings novelty to the scientific and conservation literature is its use of two SECR parameters (animal movement and sex ratio) to examine the status of a lion population. Through the comparison of these parameters we could compare these data to the historic study of Mudumba et al. (2015) to assess the status of lions in the QECA. The comparison of minimum home range size of lions in the QECA in Chapter 2 (emanating from a recently developed SECR population estimator applied for lions in Kenya, Elliot and Gopalaswamy 2017) from 2017-2018, to range estimates from a decade ago (Mudumba et al. 2015) has a much wider application to assessing the status of not only lions in other African sites but more broadly to other carnivores. Specifically I suggest this because animal movements are strongly linked to resource use and also to anthropogenic pressures/barriers and increases in an animal’s home range over a short temporal period often reflect stress and human perturbations such as the depletion of prey, modifications to habitat or direct persecution (eg. Tumenta et al. 2010, Tumenta et al. 2013, Fattebert et al. 2015). Additionally, the relationship between prey availability, movement and density in large felids generally follows a concave-up, decreasing relationship. For example, Efford et al. (2016 showed that as tiger densities increase, there is a concomitant decrease in their average movement distance per day. The relationship between prey density and tiger density is described further in the long-term field studies of Karanth and Nichols (1998) and Karanth et al. (2004) for Asiatic tigers Panthera tigris. These studies found a positive relationship between tiger abundance and that of their prey under a variety of ecological conditions. This pattern also holds true for African lions (Bauer et al. 215). Typically, areas with more prey and better protection (defined by strong law enforcement and anti-poaching patrols, functional electric fences, and private financial resources) harbour higher lion densities with smaller core home ranges (Packer et al. 2013). The application of the method presented in Chapter 2 could assist the African lion conservation community to better understand the status of African lion populations,

152 particularly where historical movement data exist. Spatial information on lions (and large felids more broadly) is often one of the first research activities implemented at a field site (see for example the long-term radio collar studies of Loveridge et al. 2009; Davidson et al. 2011 in Zimbabwe; and in South Africa by Funston et al. 2003 and Funston 2011). This is all the more pertinent with recent African lion recovery efforts, for example by the Lion Recovery Fund and its recent support of 52 African lion conservation projects (see: https://www.lionrecoveryfund.org/projects/) through new funding sources (for example the Disney Conservation Fund). Extending the application of SECR-based methods for African lions could therefore help underpin any planned recovery efforts.

Apart from the moderate novelty of bringing new insights of densities of spotted hyenas and leopards to western Uganda Chapter 3 builds upon Chapter 2 by using a robust SECR approach to estimate leopard densities which were previously estimated by a primitive rainfall model of Martin and de Meulenaer (1988). This model correlated leopard density to rainfall and habitat productivity (Martin and de Meulenaer 1988) but it was criticized because it did not use capture recapture data on leopards, did not incorporate anthropogenic threats to leopards, and did not include information on prey or leopard population dynamics (see Norton 1990). I recommend that the surveys I implemented in the QECA should be extended across Uganda, both in the largest national parks and reserves (ie. Murchison Falls, Kidepo, Sem-Liki and Pian Upe), and in the places they are presently hunted for sport to enable estimates of sustainable take.

The hyena densities reported in this chapter are notably also the highest reported in the peer-reviewed literature and in unpublished studies using SECR. This relationship between the spotted hyena densities reported in Chapter 3 and the lion densities in Chapter 2 also align with those reported in the studies of Green et al. (2018) and M’soka et al. (2016), in Kenya and Zambia respectively which suggest that competitive release from African lions may be the key contributor to the densities observed at these sites. In this context, competitive release is defined as a hyena populations ability to increase due to reduced competition with the larger, more dominant predator species. For example, Trinkel and Katsberger (2005), and Watts and Holekamp (2008) found that lions kleptoparasitize kills from hyenas, and can dominate them during contacts unless outnumbered by a factor of 3:1 (specifically 3 adult hyenas: one adult lioness). Importantly, although I cannot say with certainty that this competitive release is happening in the QECA, my estimates of 13.43-14 hyenas/100 km2 compared to 2.70/100 km2 for lions does lend some support for this train of

153 explanation. Some further support for this is provided by the results from Chapter 2 which point to a lion decline in the QECA over the past decade (this is reflected by the male- skewed sex ratios and large African lion movement distances). More data are required on individual spotted hyena cohort survivorship and levels of hyena off take from the system (for instance through snaring and poisoning) to better understand the differences between anthropogenic and competitive pressures on spotted hyena populations.

The results from Chapter 2 and Chapter 3, although based on short 93 and 45-day snapshots in time, provide important clues about the status of African lion, spotted hyena, and leopard populations in the QECA. These clues are increased movement distances, high densities of hyenas and low densities of lions, and male-skewed sex ratios (for lions and leopards). The simple illustration of lions increasing their movement distances and females being depleted from a system imply lions are declining and depleted well below carrying capacity. These data are richer than a mere comparison of counts from two points in time, which do not provide any explanations on why lions are in decline. My data on the expansion of ranges likely points to a depletion in lion prey densities, and the low proportion of female lions could reflect higher susceptibility of this cohort to anthropogenic killings. Both pieces of data can lead to the development of a priori hypotheses and tests to determine drivers of lion decline. In the case of the QECA this would be surveys of preferred lion prey, and an assessment of lion mortality in fishing villages (for example using the randomized response technique of St John et al. 2011 which accounts for untruthful responses on illegal activities such as carnivore killings). The most critical management recommendations I make from these two chapters are as follows:

1. Intensive monitoring and protection of adult lionesses, which are the group most exposed to poisoning and direct killing by humans – this is already partly happening with efforts by the Wildlife Conservation Society, and Uganda Carnivore Program (each with a small field team in the QECA). This needs to be boosted and a permanently deployed team in the northern Kasenyi section of the park, and southern Ishasha-Kigezi region would be of great benefit. This would enable rapid responses to any lions that stray into one of the 11 fishing enclaves inside the QECA, or the cattle communities on the edge of the protected area.

2. Robust assessment of prey and snaring – the enlarged lion movements recorded in this study are a splitting image of Tumenta et al. (2013) who showed a similar

154 expansion of home ranges in WAZA, Cameroon before the subsequent population collapse of African lions there. Lions have already become functionally extinct in Pian Upe (an area as large as the QECA at 2304 km2), Toro Sem-Liki (542 km2), and the Lake Mburo (370 km2) protected area systems. The law enforcement and snare removal efforts in the QECA, and more broadly across Ugandan protected areas are incredibly under-resourced (Critchlow et al. 2016). Snaring also represents one of the most important threats to wildlife in sub-Saharan Africa (eg. Murchison Falls; Lindsey et al. 2013).

Additional deployment of snare removal teams could be an important step to stabilizing lion densities in the QECA. Indeed Karanth et al. (2006) found that the maintenance of high prey densities (~56 ungulates/km2; Karanth et al. 2004) in the Ngarahole National Park offset high turnover of the population (permanent emigration outside of Ngarahole, and mortality was 23% annually), and allowed for overall high tiger densities to be maintained over time (7.3-21.7 tigers/100 km2). A recovery of prey in the QECA would also facilitate the decreased movement of female lions and this is the single biggest predictor of extinction in large carnivores (especially in regions with communities on park edges and where park sizes are small; Woodroffe and Ginsberg 1998).

3. Establishment of lion conflict monitors in fishing villages – a key threat to African lions in the QECA is conflict with local communities living inside the park, and on its edge (mainly 11 fishing villages that all contain cattle). Meetings with the leadership of the 11 fishing enclaves (which also farm with cattle), and communities bordering the conservation area should be a key priority for both NGOs (WCS, and UCP), and the local wildlife authority (UWA). Furthermore, the establishment of a central communication network (either through a radio network or social media group) that can be reported to by salaried conflict monitors would be incredibly beneficial. This could allow staff rom the WCS, Uganda Carnivore Program and Uganda Wildlife Authority to respond rapidly to any conflict events and stem retaliatory killings before they occur. It is clear that with another poisoning event of 11 African lions in April of 2018 (3 adults, and eight cubs, which formed part of the sample to derive lion densities in Chapter 2), such an effort should be a key priority in the QECA.

155 7.1.2 Benefits of carnivores to humans and the need for similar assessments

One of the most important drivers of large carnivore decline globally is human-carnivore conflict (Panthera 2017). The literature is rapidly expanding with studies that investigate three key themes, 1) where human-carnivore conflicts occur, and how this conflict impacts populations of large carnivores (eg. Hazzah et al. 2014), 2) characteristics of landscapes or human communities that underpin these conflicts (eg. Abade et al. 2014), and 3) examinations of conservation interventions that may help stem this conflict (eg. Naughton- Treves et al. 2003; Bauer et al. 2017). Carnivore populations often impact the livelihoods (and lives) of human communities (Packer et al. 2016). Consequently, these communities often retaliate against carnivores, killing them and other biodiversity (for example, through poisoning).

The key results and conclusions in Chapter 4 along with studies by Prowse et al. (2015), the review of O’Bryan et al. (2018; Appendix 2), and a study on jackals in the Czech republic by Ćirović et al. (2016), represent some of the first economic valuations of the benefits a large carnivore may have on human communities, other than through tourism. The Prowse et al. (2015) showed that the dingo Canis dingo can increase pasture biomass by 53 kg/ha (or USD$ 0.83/ha) on cattle farms in Australia’s New South Wales through predation on kangaroo species. Similarly, O’Bryan et al. (2018) showed in a literature review of 23 species showed that species such as the mountain lion Puma concolor and red fox Vulpes vulpes may save human lives, and reduce associated medical and insurance costs arising from accidents and diseases. The study by Ćirovićet al. (2016) on golden jackals Canis aureus showed that this species may save more than EUR€ 0.5 million in waste removal costs per year in Serbia, due to their removal of discarded animal waste and removal of crop pests. The literature to date examining the benefits of large terrestrial carnivores in natural ecosystems such as national parks and protected areas (i.e. in locations largely devoid of human presence) is rich. Seminal studies on wolves (Fortin et al. 2005) and mountain lions Puma concolor (Ripple and Beschta 2006; Ripple and Beschta 2008), showed that these species not only exude top-down pressures on ungulates through direct offtake, they also structure prey movements (Willems and Hill 2009), vigilance levels (Switalski 2003), and their predatory impacts (Ripple and Beschta 2012). These pressures have far-reaching impacts across wide spatial scales. For example, Ripple and Beschta (2006) found that the presence of mountain lions in North Creek Drainage, Utah, USA, led to increased species diversity across multiple trophic levels when compared to Zion National Park (an area immediately east), which lost its mountain lions due to increased tourism pressure, 156 approximately 80 years earlier (the extirpation is estimated to have occurred sometime in the mid 1930’s). Similarly Ford et al. (2014) showed that the presence of African leopards and wild dogs in Kenya, influenced the movement patterns of impala Aepyceros melampus. Where predation risk from wild dogs and leopards was low, thorny trees such as Acacia etbaica dominated the landscape, while in areas of higher risk, there were fewer trees.

The studies of Prowse et al. (2015), Ćirović et al. (2016) and data presented in Chapter 4 are different to the studies implemented in national parks, and protected habitats, in that they directly examine the effects of predators living inside or near human communities. The above studies of Fortin et al. (2005), Ripple and Beschta (2008 and 2006), and others, only examined natural systems, mainly well protected national parks. This is very important, as a central challenge in conservation biology is the mismatch between the entities that benefit from large, charismatic mega-fauna, and those that suffer from its presence (eg. Bauer et al. 2017). For example, human communities living alongside the Sanjay Gandhi National Park (SGNP) in Mumbai suffered an average of four human attacks between 2003-2011 (27% of all attacks were fatal; Athreya et al. 2011) and also regularly lost livestock to leopards in this period. The SGNP did not pay proceeds of park entry or commercial filming fees to these communities during this period, despite individual families suffering immense financial and emotional strain due to leopard attacks.

A better understanding of how large carnivores benefit human communities may contribute to better informing the communities impacted by their negative effects and society at large (Bhatia et al. 2013). Specifically, it is plausible that information on the positive effects of large carnivores to human communities and human health could discourage outright retaliatory killings for livestock losses, or even lead to opposition to development that might impact such carnivores (eg. Tortato et al. 2017). This may especially be the case if such information revealed benefits to human health or control of pest populations attributed to leopards. The results from Chapter 4 show a key indirect benefit of the Indian leopard Panthera pardus fusca to human communities: improved human health (mediated through a reduction in stray dog bites which may have costly consequences for humans). Our compilation of data from peer-reviewed publications (Prasad and Tiwari 2009; Edgaonkar and Chellam 2002), unpublished reports (Surve et al. 2015), and newspapers (Harris 2012, Biswas 2016), showed that the predatory impacts of leopards in SGNP over the last decade were significant. Our review and analysis showed that dog densities around SGNP are likely to

157 be strongly mediated by leopards, and the leopards living on the edge of Sanjay Gandhi National Park could prevent around 1000 bites per year.

Figure 7.1: A female leopard crosses a small bridge which straddles Thane city and the Sanjay Gandhi National Park in Mumbai, Maharashtra, India. Photographer Steve Winter took this image with a remote camera-trap in mid-2014. Reproduced with permission from Steve Winter.

Chapter 4 contrasts with the majority of studies in the ecosystem benefits literature in that it examines potential positive impacts of a large carnivore to human communities. These results have particular consequence for the media industry, and field researchers in Mumbai engaged in leopard conservation. Local media entities regularly portray leopards in the SGNP as having negative impacts upon human communities, and this is largely a result of their direct attacks on people (Bhatia et al. 2013). Intense public pressure often causes “knee-jerk” reactions from local governments and managers attempting to solve conflicts in the field. For example, it is thought that media coverage of shark attacks was a contributing reason for the government of Western Australia’s shark-culling program (McCagh et al. 2015).

Our assessment of positive impacts of leopards on human communities via predation on stray dogs could help decision makers have a better and more complete picture of the positive and negative effects large carnivores may have on human communities. Chapter 4 was also the inspiration for a collaborative study led by O’Bryan et al. (2018) which can be found in Appendix 2, which identified and reviewed the most important studies on the benefits of predators and scavengers to human wellbeing. In a review of 23 wildlife species, this study showed that the effects of predators and scavengers are widespread and worth millions of US dollars. Examples include the expansion of mountain lions into the western

158 United States, and estimates that bat communities controlling moth and beetle species could be worth as much as US$2.13 and 1 billion dollars in pest reduction benefits annually.

Attributing causality to a particular component in a complex system is challenging. The leopard-dog-disease system explored in Chapter 4 is certainly a complex system, and as we were limited to using newspaper articles, unpublished reports, and three peer-reviewed studies on leopard diets in Mumbai our conclusions must be caveated. For example, 1) we cannot rule out the possibility that lowered dog densities around SGNP are mediated by other effects, not just leopards. Lowered food availability and habitat type (national park vs urban or peri-urban informal settlement) could be causing this low density. A field-based approach examining other landscape-level drivers of dog density could help to disentangle environmental effects from leopard predation. Our study also did not attempt to estimate the effects of leopard presence on dog densities through the landscape of fear hypothesis (Willems and Hill 2009, Laundré et al. 2010, Lone et al. 2014), likely underrepresenting the relative value of leopards in the Mumbai system. The landscape of fear denotes relative levels of predation risk across a prey species’ area of occupancy. This reflects the level of fear prey species experience in this area (Laundre et al. 2010). Thus dog density around SGNP could also have been lowered not only through direct predation of stray dogs, but through the movements and presence of leopards in the buffer area we denoted in our study (eg. Madin et al. 2011). Indeed, the presence and occupancy of predators in areas where their prey occur often influences their movements, feeding patterns and densities indirectly. This is exemplified by studies of Madin et al. (2011) and Shrader et al. (2008).

7.1.3 Human-carnivore conflicts and compensation: a clear need for wildlife monitoring

Chapter 5 contrasted with Chapter 4 in that it examined the livestock depredation risk, and associated costs, related to two large carnivores in Uganda. This chapter’s framing considered human livelihoods, and impacts thereupon by large carnivores. The main conclusions from Chapter 5 reiterate the view of some of the most seminal studies in the human-carnivore literature. For example, Lichtenfeld et al. (2015), Mkonyi et al. (2017), Weise et al. (2018) all show that boma fortification is key to reducing human-carnivore conflicts by species such as African lions, leopards and hyenas. One of the key findings from Chapter 5 is that the majority of livestock deprtedations on the edge of LMNP occur inside livestock rotection bomas, or animals are attacked inside them and dragged outside

159 (82% and 64% for African leopards and spotted hyenas respectively). This reflects the fact that livestock protection bomas in the greater LMNP system require reinforcement, and a better understanding of the materials used in their building so that such reinforcement can take place. However a key aspect missing from the MCF scheme, and this is missing from many similar conservation interventions which aim to stem human-carnivore conflict is monitoring carnivore populations. It is clear that the monitoring of hyena and leopard densities, specifically in terms of changes in the population densities of these predators over time would be of benefit in the LMNP system. At this stage there is no information available on whether the densities and population dynamics of spotted hyenas and leopards is actually positively impacted by the MCF scheme (and this was the reason the MCF was established by the Mihingo lodge in the first place). Despite the scheme being operational for more than 10 years, no assessment of the status of the species it attempts to protect has been undertaken (ie. specifically in the non-protected area where both carnivores and their prey still survive). This is problematic as the data from this chapter show that compensations and depredations have increased linearly over time. This could make the compensation fund unsustainable over time and increase conflict if it breaks down (Nyhus et al. 2014).

I suggest that an experiment where leopard and hyena densities are monitored over time while the compensation scheme is being applied (ie. a conservation treatment) would be of value in this system. Specifically, these densities could be monitored using the camera trap design used in Chapter 3 (at least 3-4 years) while controlling for the effects of preferred hyena and leopard prey densities and drought. If densities decline an alternative treatment could be applied. This could include a diversion of funds from compensation to boma fortification, a payment for presence scheme (whereby payments are made for the presence of predators on private land, for instance as recorded on camera traps) or some other alternative funding mechanism (Dickman et al. 2011). For example, Balme et al. (2009b) and Balme et al. (2010) examined how, following an adaptive management decision (a trophy hunting policy reform), leopard densities responded over time. They found that over a five-year period leopard mortality rate decreased from 40% to 13%, and densities increased from 7.17 to 11.21 leopards/100 km2. These authors also controlled for leopard prey abundance and mortality from lions to ascertain that these population changes resulted from the conservation treatment and not other ecological factors. Our Chapter 5 results also mirror those of Abade et al. (2014) in that that hyenas and leopards are using the cover of darkness to attack livestock, attacks occur closer to human settlements, and the national

160 park boundary. Attacks in the LMNP system also occurred in areas of rugged terrain, and away from water.

7.1.4 Funding for large carnivore conservation: compensation and imagery royalties

Chapters 5 and 6 of my thesis discuss one of the biggest limits to biodiversity conservation; a lack of funding. Chapter 5 shows that although tourism activities can help raise the funding necessary for conservation actions (in this case financial compensation for livestock damage caused by leopards and hyenas), they often still require subsidies. Chapter 6 builds on the proposal of Good et al. (2017), and Courchamp et al. (2018) that corporations, and businesses using the imagery of threatened taxa pay a royalty that goes back to supporting their conservation. I provided a roadmap for how a threatened wildlife imagery royalty could be implemented, through the development of national laws. I also found that such a royalty could be immensely powerful if instituted, leveraging hundreds of millions of US$ dollars for threatened species. A “Fairtrade” equivalent certification scheme (albeit voluntary) could also provide a tool for conservation NGOs to engage with large corporations bearing the images of threatened species.

7.1.5 Target setting for large carnivore conservation must be data-driven

In a Ugandan context which much of this thesis focuses on, the UWA are the overarching government authority tasked with managing large carnivores in the country. The UWA (2010) “Strategic Action Plan for Large Carnivore Conservation in Uganda” (a joint target setting and state of knowledge report by several NGOs and the Ugandan Wildlife Authority) set out a number of key population recovery goals for African lion, leopard and spotted hyena conservation in the country in 2010, namely 1) Maintain and manage at least 100% of suitable habitats for large carnivores inside protected areas and 60% outside protected areas - (Threats: Habitat loss and fragmentation), 2) Reduce poaching of large carnivores and their prey inside and outside protected areas to a point that populations are stable or increasing (killing for body parts, lack of prey), 3) Reduce conflict between livestock owners and large carnivores to levels that do not impact population viability adversely - (Threats: poisoning, killing over livestock loss, grazing in protected areas and poor livestock

161 husbandry), 4) Increase national awareness and support for large carnivore conservation among political leaders, law enforcement agencies (including judiciary), communities living with large carnivores and school children (future leaders) - (Threats:political interference, negative attitudes by people, road kills), 5) Establish a health monitoring and management program for carnivores in Uganda. (Threats: disease), 6) Map distribution and abundance of large carnivores in Uganda, monitor their populations, and quantify the major threats to their long-term survival (lack of knowledge). The results from data chapters 2 and 3 suggest some of these targets need to be re-assessed. Our data suggest that objectives/targets 1 and 2 in this plan have not been realised since 2010. Any carnivore recovery program should be underpinned by a good account of population numbers (Appendix 1 – submitted from Chapter 1). This is lacking for many carnivore conservation interventions across Uganda, and more broadly across the African continent. Taking stock of key populations of African lions (and other sympatric carnivores) in Uganda’s other national parks (ie. Murchison and Kidepo) needs to form the backbone of any conservation planning exercise (and future target setting exercise).

Lion conservation in Uganda could benefit from a prioritization protocol, where population size, threats, current conservation actions and funding is assessed in a scientific framework, to aid stakeholders in carnivore conservation to make sound management decisions (Joseph et al. 2009). At this stage stakeholders simply do not know where their resources are likely to facilitate the recoveries of lions (see Lindsey et al. 2016; Lindsey et al. 2018). For example, USD$ 141 000 was recently committed by the Lion Recovery Fund to the QECA (see https://www.lionrecoveryfund.org/project/tackling-human-lion-conflict-in-and- around-queen-elizabeth-national-park/). With no prioritisation of funds, we do not know whether they could have achieved greater conservation benefits in Kidepo or Murchison Falls National Parks, for example. In Murchison and Kidepo there are no communities living inside the park, and there are no cattle inside the park. Instead, snaring of lions appears to be the most important threat there. It is possible that investment in desnaring those parks could yield a better return on investment compared to attempting to mitigate conflict in the QECA, as well as desnaring the park. Spatially and politically the QECA is the most difficult national park to protect in Uganda (and to facilitate a lion recovery). Not only does it have 11 fishing villages which now all feature cattle populations but it has a “narrow” spatial design acting as a sink to the conflict-prone Virunga National Park to its west, and it has dense human settlements to its east. Murchison Falls and Kidepo Valley National Park do not have human communities residing inside their borders. The main poaching threats in Murchison

162 originate from across the river Nile, while in Kidepo, extensive, highly elevated guard posts make anti-poaching patrols more accessible when compared to the QECA (pers. obs.).

7.2 Concluding remarks

The main results from both the monitoring and conflict chapters of this thesis add to a growing body of work that show large carnivores are in a perilous state across much of their range (Di Minin et al. 2016). However, they also provide clear ways forward, key solutions emanating from data Chapters 2 and 5; suggest boosting the prey populations of large carnivores in the QECA would reduce the movements of lions and fortifying bomas would likely lead to a reduction in attacks on livestock by leopards and spotted hyenas in the Lake Mburo system. Furthermore, success stories are becoming more and more common with one of the most inspiring being that of the Gorongosa National Park in Mozambique; once war-torn region, it now holds important populations of African lions, elephants, and just a few weeks ago researchers detected its first leopard in more than 20 years. When the philanthropist Greg Carr began his love affair with Gorongosa (he has invested over US$ 40 million already, and commited another US$ 100 million through 2043), he stated “When I came along, nobody talked about it, nobody remembered it. And people said, don’t bother, there’s nothing there anymore”. Such optimism cannot be ruled out for the countries and broader study systems my PhD discusses. For example, there has been a marked decline in the number of human attacks (and fatalities) in the SGNP after translocations of livestock- killing leopards (caught outside of SGNP) to the park were halted (Athreya et al. 2011). Similarly, the Murchison Falls National Park (Uganda’s second largest protected area) appears to have experienced an increase in its elephant numbers from 2007-2013 (Robson et al. 2016), similarly elephants in the QECA and Virunga nearly tripled between 1995-2014 (Chase et al. 2016). However, the window for conserving Africa’s large carnivores is diminishing and many states with lions will face significant human population growth in the next 70 years (for example Uganda’s human population alone will double in the next 20 years, before doubling again by the turn of 2100; UNFPA 2019, see: https://tinyurl.com/reomsnc).

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210 Appendix 1 statement of authorship: The candidate, James Allan, and Martine Maron conceived the idea for the manuscript. The candidate wrote the manuscript, and received some help in conducting the literature review of African lion research papers from Alexander Bezzina. All authors contributed to revising and improving the manuscript (Alexander Braczkowski, Arjung Gopalaswamy, Nicholas Elliot, Hugh Possingham, Alexander Bezzina, Martine Maron, Duan Biggs, and James Allan).

211 APPENDIX 1 Restoring Africa’s Lions: Start with good counts

Published in Frontiers in Ecology and Evolution

Disney’s new Lion King movie (released July 19th 2019) has put lion conservation in the global spotlight, with millions of dollars being committed to recovering Africa’s lion populations through the “Protect the Pride” campaign (a partnership between the Lion Recovery Fund and Disney Conservation Fund). This money must be targeted strategically to achieve this goal, which depends on robust measures of lion density and abundance. However, good population estimates of African lions are almost completely lacking (Elliot and Gopalaswamy 2017).

Lion conservation strategies, policy documents and funding priorities are currently guided by lion population estimates, which frequently draw on expert opinion or unpublished surveys of variable reliability (Elliot and Gopalaswamy 2017), leading to inestimable uncertainties or wide confidence intervals (e.g. 90 ± 42 lions, Kirsten et al. 2016). And often, due to underlying data overdispersion, the true confidence intervals are wider than reported (Gopalaswamy et al. 2015). This could have catastrophic consequences since underestimating lion numbers might cause us to triage an otherwise viable population, while overestimating density might cause under-investment in a population that requires active management. Furthermore, non-robust methods can often produce spurious trends in lion population dynamics, which can further mislead conservation investments (Elliot and Gopalaswamy 2017).

Advances in spatial capture re-capture statistics (SECR methods) now allow for highly robust animal density estimates, and are used routinely for all big cats with the exception of lions, and there are only two published case studies to date (Elliot and Gopalaswamy 2017, Kane et al. 2015) out of 169 papers we identified in the literature, Figure 1.1). This is a missed opportunity because SECR methods capitalise on individual lion identification to allow estimation of sex-specific movements, territory sizes, and sex-ratios. These parameters provide important insights into population health that the more popular methods (e.g. audio lure surveys or track counts, Omoya et al. 2014) cannot. For example, enlarged movements or home ranges, and skewed sex-ratios, can signal prey depletion and imminent population collapse (Tumenta et al. 2010). Furthermore, the precision of SECR density

212 estimates will allow future surveys to examine population change, and since these methods track individuals over time, repeat surveys allow estimation of vital rates, such as mean survival (Karanth et al. 2006). Therefore, SECR methods have the potential to both guide conservation interventions and provide crucial insights about lion population dynamics. SECR-derived population estimates are typically lower compared to non-spatial CR methods (Noss et al. 2012), and other methods generate estimates with large uncertainty. So we fear lion populations across Africa are not as large or stable as currently thought. Therefore, we argue that rigorous, on-ground, SECR monitoring methods should be urgently implemented across all African lion populations, and the last remaining population of lions in Asia (Gir National Park, India). A project of a similar ambition to Kenya’s national lion survey, which incorporates the latest SECR methods is envisaged. Accurate continent-wide counts would then underpin lion recovery efforts, ensuring adequate allocation of resources from funding efforts like the “Protect the Pride” campaign, and others like it. These could be directed to where they will have the greatest impact.

*Mixed methods typically include a combination of track counts, call-ups and/or another method ^Other methods range from expert guesstimates to a ratio of lions relative to hyenas +An examination of the literature, usually extensive in nature

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Noss, A. J. Gardner, B. Maffei, L. Cuéllar, E. Montaño, R. Romero-Muñoz, A. ... & O'Connell, A. F. (2012). Comparison of density estimation methods for mammal populations with camera traps in the K aa-I ya del G ran C haco landscape. Animal Conservation, 15(5), 527-535.

214 A1.2 Supplementary Information

Supplementary Figure 1: An adapted PRISMA flow diagram summarizing the identification, screening and inclusion of scientific articles found on Web of Science (WOS) and Google Scholar. 40 records were excluded as they did not explicitly report numbers, simulated lion numbers or their results were not intuitive to the assessors (AB and AB). The search period for articles incorporated all articles on Web of Science and Google Scholar from 1900-2018 (16 October). We limited the search to the first 47 pages on Web of Science and first 10 pages in Google Scholar.

Literature Review protocol We searched peer-reviewed articles in two comprehensive scientific article databases (Web of Science and Google Scholar). We limited our review to only include journal article’s with the keywords “lion” AND “population size”, “density” or “numbers”, and the same with “Panthera leo”. These keywords were selected after reviewing a subset of articles on the topics of lion abundance and densities. We used all the results pages from WOS, and limited our results to 100 (ie. the first ten pages) of Google Scholar. We only included English language and peer-reviewed articles. Articles were screened by Alex Braczkowski (lead author), and Alex Bezzina (5th author).

215 Appendix 2 statement of authorship: The candidate, served as second author on this manuscript. The candidate assited Christopher O’Bryan (lead author) with the compilation of case studies for the review, assisted in the writing and revision of the manuscript. All authors contributed to revising and improving the manuscript (Christopher O’Bryan, Alexander Braczkowski, Hawthorne Beyer, Neil Carter, James Watson, and Eve McDonald- Madden).

216 APPENDIX 2 The contribution of predators and scavengers to human well-being

Published in Nature Ecology and Evolution

A 2.1 Abstract

Predators and scavengers are frequently persecuted for their negative effects on property, livestock, and human life. Research has shown that these species play important regulatory roles in intact ecosystems including regulating herbivore and mesopredator populations that in turn affect floral, soil, and hydrological systems. Yet predators and scavengers receive surprisingly little recognition for their benefits to humans in the landscapes they share. We review these benefits, highlighting the most recent studies that have documented their positive effects across a range of environments. Indeed, the benefits of predators and scavengers can be far reaching, affecting human health and well being through disease mitigation, agricultural production, and waste-disposal services. As many predators and scavengers are in a state of rapid decline, we argue that researchers must work in concert with the media, managers, and policy makers to highlight benefits of these species and the need to ensure their long-term conservation. Furthermore, instead of only assessing the costs of predators and scavengers in economic terms, it is critical to recognize their beneficial contributions to human health and well-being. Given the ever-expanding human footprint, it is essential that we construct conservation solutions that allow a wide variety of species to persist in shared landscapes. Identifying, evaluating, and communicating the benefits provided by species that are often considered problem animals is an important step for establishing tolerance in these shared spaces.

217 A 2.2 Introduction

Coadaptation, the ability of humans and predators and scavengers to modify their behavior based on benefit trade-offs, is recognized as key for their coexistence in the 21st century (Carter and Linell 2016, Chapron and Lopez-Bao 2016). However, coadaptation relies on human tolerance and the recognition of the wide range of benefits that predators and scavengers provide humanity (Carter et al. 2012, Treves and Bruskotter 2014). It is well established in the ecological literature that predators play regulatory roles in intact ecosystems as they exert top-down pressures on prey communities, thereby reducing herbivory of plant species important to humans (Ripple and Beschta 2012) and scavengers consume large amounts of carcasses and organic waste (Du Pont et al. 2012, Cirovic et al. 2016). It is accepted that the disappearance of predators and scavengers from ecosystems can cause a suite of deleterious effects including the loss of plant species diversity, biomass, and productivity that in turn affect disease dynamics, carbon sequestration, and wildfire risk (Ripple et al. 2014). As a result, predators and scavengers are considered flagship and keystone species (Macdonald et al. 2015) and are sometimes treated as surrogates for the health of entire ecosystems (Thornton et al. 2016).

Despite their ecological value, predators and scavengers often have a poor public reputation because of their real and perceived negative impacts on humans (Ogada et al. 2012, Bhatia et al. 2013, Penteriani et al. 2016). These negative impacts include livestock depredations (Suryawanshi et al. 2013), killing of pets (Vickers et al. 2015), attacks on humans (Penteriani et al. 2016), and harboring of diseases and parasites (Han et al. 2016). The human culture of fear associated with predators hinders many local and regional species recovery efforts (Barua et al. 2013). Populations of many predator and scavenger species are already declining (Ripple et al. 2014, Jacobson et al. 2016) and are projected to continue to dramatically decline over the next 25 years in response to increasing human populations, political uncertainty, and climate change (Ripple et al. 2014, Chaprone et al. 2014, Di Minin et al. 2016).

An understanding of the benefits of predators and scavengers on human well-being is important in strengthening conservation efforts in shared landscapes (Soulsbury and White 2015, Blackburn et al. 2016, Chapron and Lopez-Bao 2016). For example, Egyptian vultures Neophron percnopterus, which are declining globally, thrive in the towns and villages of Socotra, Yemen where they are valued for their service of removing livestock and human

218 waste (Gangoso et al. 2013) that would otherwise cause water contamination and are expensive to remove (Markandya et al. 2008, Yirga et al. 2015, Cirovic et al. 2016). Similarly, the Tigray region of northern Ethiopia harbours high populations of spotted hyenas Crocuta crocuta that are tolerated by human societies, as they consume cattle and donkey carcasses as well as human corpses in urban settlements, reducing disease risk (Yirga et al. 2015). Yet, these examples of human communities cohabitating and actively conserving scavengers and predators are few and far between.

Here, we highlight several key, yet often overlooked, benefits provided by native predators and scavengers in shared landscapes with humans (Figure A2.1). These potential benefits include disease regulation through host density reduction and competitive exclusion, increasing agricultural output through competition reduction and consumption of problem species that destroy crops, waste disposal services, and regulating populations of species that threaten humans. Although there are a growing number of examples of benefits provided by predators and scavengers, it is often unclear how widespread these benefits may be. While some benefits, such as carcass disposal, may be common and general, other benefits, such as protection from zoonotic disease, may be highly context-dependent effects that are localized in both space and time. Management of predators and scavengers must also, therefore, be context-dependent and try to appropriately balance detrimental and beneficial effects. We focus primarily on economic and health aspects of human well-being, but we recognize that well-being can encompass other material, social, and subjective components of the human experience that are not covered in this paper (Milner-Gulland et al. 2014).

A 2.3 Predators and scavengers regulate zoonotic diseases

Zoonoses, diseases that are maintained in animal populations but can be transmitted to humans, pose direct threats to human health as exemplified by recent outbreaks of the Zika virus (Rodriguez-Morales et al. 2016), Ebola virus (Olivero et al. 2016), and H5N1 avian influenza (Chen et al. 2005). Accounting for over 60% of known human diseases (Taylor et al. 2001), zoonotic disease outbreaks can decimate human societies and economies. For example, not only did the Ebola virus cause loss of life (>12,000 lives, Narasimhan 2016), but it virtually halted all tourism to West Africa leading to dramatic economic suffering due to both local perception of disease risk and continent-wide economic concerns (Mizrachi

219 and Fuchs 2016). Because of these human health and economic impacts, control of zoonoses and their vectors is important and while they may be hosts themselves in some cases (e.g. carnivores sustaining rabies cycles in some African ecosystems, Lembo et al. 2008), predators and scavengers may play a role in disease regulation (Harris and Dunn 2013). Indeed, some case studies have shown that they can control diseases by reducing host and vector densities (Moore et al. 2009), through local competitive exclusion, or directly through feeding on infected hosts (Khalil et al. 2016).

Reduction of host species densities by predators can reduce the risk of disease transmission to humans by limiting the prevalence of disease in host populations when within-host transmission is density-dependent (McCallum 2001). Predators can also reduce absolute 220 host numbers, thereby limiting the opportunity of spillover to humans when within-host transmission is either density- or frequency-dependent (McCallum 2001). For example, reduction in dog densities by leopards (Panthera pardus) greatly reduces the frequency of dog bites and hence human exposure to rabies near the Sanjay Gandhi National Park in Mumbai, India (Braczkowski et al. 2018). Similarly, generalist predators such as foxes may reduce Lyme disease risk in humans by controlling mice populations (Peromyscus sp.), the main reservoir for infected nymphal tick vectors Ixodes scapularis (Ostfeld and Holt 2004, Brisson et al. 2008, Levi et al. 2012), and frog tadpoles may play a global role in reducing dengue fever by feeding on mosquito eggs (Bowatte et al. 2013, see Figure A2.1 for global distribution of these species).

Predators and scavengers can also reduce disease risk in humans through competitive exclusion, the action of outcompeting disease hosts for resources or space. For example, vultures have been shown to outcompete stray dogs in finding and consuming carrion (Markandya et al. 2008). Markandya et al. (2008) linked the severe decline in vulture populations in India (92% loss from 1990-2000) to the widespread use of diclofenac and the striking increase in stray dog populations. They suggest in the absence of vultures consuming carrion, stray dog populations will continue to rise, resulting in an increase in human dog bites and exposure to rabies. Furthermore, other facultative scavengers can replace vultures, including gulls, rats, and invasive foxes (Buechley and Sekercioglu 2016), all of which can pose risks to humans and can themselves be disease hosts.

A 2.4 Predators can indirectly increase agricultural output Species that consume crops account for 10-20% of agricultural financial losses globally and current control measures are estimated to be only 40% effective on average (Oerke and Dehne 2004). Conventional pest-control methods, particularly chemical control, can be detrimental to human health (Alavanja et al. 2013) and costly. Biological control provides an alternative to unhealthy chemical control methods (Barzman et al. 2015), and some case studies have shown that natural predators can reduce financial burden and crop loss by consuming problem species.

Airborne predators can play an important role in agricultural management (Labuschagne et al. 2016), a reason why some bat and bird species are often considered the most economically important non-domesticated group of animals (Kunz et al. 2011). For example, field experiments show that some bat communities in the USA suppress pest larval densities 221 of the detrimental corn earworm moth Helicoverpa zea and cucumber beetle Diabrotica undecimipunctata howardi by nearly 60% and significantly reduce associated pest fungal growth in large-scale corn productions (Maine and Boyles 2015). Based on these experiments, the authors estimate that bat control of crop pests may save farmers more than US$1 billion globally per year, thereby providing a substantial service to farmer livelihoods (Maine and Boyles 2015). Similarly, birds and bats in the tropical cacao plantations of Indonesia’s central Sulawesi have been shown to save over 30% of crop output (~US$730 ha-1) by hunting pest populations of Lepidoptera and Heteroptera species (Maas et al. 2013).

Large avian predators can also have marked impacts on problem species that cause agricultural damage (Table A2.1). For example, the barn owl (Tyto alba) has a diet made up of ~99% agricultural pest species and reduces rodent density by over 33% in the alfalfa Medicago sativa fields of California, USA (Kross et al. 2016a, Kross et al. 2016b). Similarly, barn owls reduce man-hours worked and baiting costs for rat Rattus sp. control in oil palm plantations of Malaysia (Wood and Fee 2003). Likewise, New Zealand falcons Falco novaeseelandiae have increased winery output in six New Zealand wineries by preying on four crop-raiding bird species (Kross et al. 2012).

Livestock depredation by carnivores can be costly for pastoralists (Suryawanshi et al. 2013), resulting in retaliatory killings of predators (Treves and Bruskotter 2014). However, in pasture environments where livestock and wild herbivores are present, predators may increase livestock productivity by reducing competition with other herbivores (Sundararaj et al. 2012). For instance, the dingo Canis lupus dingo has been shown to increase agricultural output by controlling populations of red kangaroo (Macropus rufus), Australia’s largest native herbivore and a major competitor with livestock on commercial grazing land (Prowse et al. 2015). Cattle farmers often kill dingoes due to their reputation for killing valuable livestock but dingoes are estimated to increase pasture biomass by 53 kg ha-1 and improve profit margins by US$0.83 ha-1 (Prowse et al. 2015).

The value of other predatory species as pest regulators requires further investigation. For example, pest insects form over 50% of the diet of a suite of frog species in the Nepalese rice plantations of Chitwan (Khatiwada et al. 2016) and in southeast China, frog species depredate rice leaf rollers (Cnaphalocrocis medinalis), a problematic species that causes blight. By consuming leaf rollers, frogs increase the number of seedlings and stem width of 222 rice plants (Teng et al. 2016) that may ultimately increase health and crop size for rice farmers. Similarly, skunks Miphitis spp. in North America have been shown to reduce pests in family gardens, potentially reducing the need for pest management (Rosatte et al. 2010).

A 2.5 Predators and scavengers provide benefits in urbanizing environments Negative human-wildlife interactions are a longstanding and growing problem (Barua et al. 2013) that is often exacerbated in areas with high human density and an abrupt ‘wilderness’ interface (Soulsbury and White 2015). Many species are attracted to the high calorie food items, shelter, and breeding resources common to urban areas, and they may form permanent populations in shared areas irrespective of wilderness proximity (Samia et al. 2015). For instance, bobcat and puma densities in Colorado, USA, are the same across semi-urban areas and wildland habitats provided that prey densities are similar (Lewis et al. 2015). As a result, predators and scavengers will utilize urban areas, and some case studies have shown that they may provide benefits to humans above and beyond the disease benefits discussed above, including waste regulation and reduction of species abundances that cause direct human injury and death (Cirovic et al. 2016, Gilbert et al. 2016, Braczkowski et al. 2018).

Scavengers provide organic waste regulatory services by feeding on carcasses or decaying food matter (Figure A2.1). For example, golden jackals (Canis aureus) reduce >3,700 tons of domestic animal waste in Serbia per year, including road-killed animals and waste dumps (Cirovic et al. 2016). One estimate indicates that jackals remove >13,000 tons of organic waste across urban landscapes in Europe amounting to >US$0.5 million in saved waste- control (Cirovic et al. 2016) that would otherwise cause groundwater contamination and other health risks (Markandya et al. 2008). Vultures can also provide long-term carcass removal services for the livestock industry, leading to savings in man-hours and reduced disease risk in valuable herds6. This service has been observed in many developing regions, particularly in Africa and Asia where waste-disposal infrastructure is lacking (Gangoso et al. 2003, Markandya et al. 2008, Olea and Mateo-Tomas 2009).

Large terrestrial predators can provide services in urban landscapes by reducing abundances of species that cause human death and injury (Figure A2.1). For example, leopards reduce the density of stray dogs in Mumbai, India, thereby reducing bites and injury accrued on residents and save the municipality nearly 10% of their annual dog management

223 budget (Braczkowski et al. 2018). Stray dogs are responsible for thousands of bites on Mumbai’s citizens annually that result in hundreds of work days lost and subsequent financial burden (Gogtay et al. 2014). As stray dog populations currently exceed well over 1 billion globally and are expected to continue to grow as the human population increases (Treves and Bonacic 2016), large wild predators in these urban landscapes should be considered a valuable asset in reducing the ongoing and potential damage accrued from urban stray dogs on human health and well-being. Predators can also reduce the abundance of species that are responsible for costly wildlife-vehicle collisions (Table A2.1). Where large carnivores have declined or been extirpated, herbivore populations have often increased (Ripple and Beschta 2012). This trophic response not only impacts ecological structure, but can directly influence human well-being. Gilbert et al. (2016) found that the potential recolonization of cougars over a 30-year period in the eastern United States would reduce deer populations and thereby curtail deer-vehicle collisions by 22% (Gilbert et al. 2016). They estimated that this reduction in collisions would result in 155 less human deaths, 21,400 less human injuries, and US$2.13 billion saved in costs. This study illustrates how the ecological effects of large predators can potentially save human lives and decrease government spending.

A 2.6 Predator and scavenger conservation in the 21st century

Only 12.5 percent of the earth’s terrestrial surface is protected for conservation (Watson et al. 2014), and as the human population grows, and our global footprint expands, ‘shared’ landscapes will prevail across Earth’s terrestrial surface (DiMinin et al. 2016, Venter et al. 2016). Currently, predators and scavengers receive relatively high attention in protected landscapes (Verissimo et al. 2011), but receive relatively little conservation attention in shared landscapes (Dobrovolski et al. 2013, Di Minin et al. 2016) considering large portions of many species ranges occur in these areas (Di Minin et al. 2016). For example, leopards have disappeared across 78% of their historic range (Jacobson et al. 2016), African lions Panthera leo are predicted to continue to decline by half outside of protected areas (Bauer et al. 2015), and 17 out of the 22 vulture species are declining due to human activities (Buechley and Sekercioglu 2016). Shared landscapes must be managed to achieve effective conservation for all species and improving our understanding of the services provided by predators and scavengers may facilitate their conservation (Frank and Schlenker 2016).

224

Table A2.1: Featured case studies of predators and scavengers contributing to human well-being, their potential limitations, and suggestions for furthering the case human benefit.

225

226

One obstacle to effective conservation of predators and scavengers in shared landscapes is bias in media, government, and public perception. Skewed viewpoints can sensationalize the negative effects of predators and scavengers (Bhatia et al. 2013, McCagh et al. 2015) that can have long-lasting repercussions on human perception, behavior, and policy (Kissui 2008, McCagh et al. 2015). For example, much of the media framed leopards as the

227 perpetrators when attacks occurred in the city of Mumbai, India (Bhatia et al. 2013), and the main local newspaper in Bangladesh pointed to the tiger Panthera tigris as being the cause of conflict with a 2x higher frequency when compared to the international “The Guardian” newspaper Sadath et al. 2013). In Florida, USA, instead of taking a neutral stance, local newspapers asserted risks that Florida panthers (Puma concolor coryi) might harm people and domestic animals (Jacobson et al. 2012). Likewise, most media coverage in the USA and Australia emphasized the risks sharks pose to people despite the threatened status of many shark species (Muter et al. 2013). An emphasis on wildlife-related risks from the media can lead to risk-averse policy such as when the Western Australia Government deployed drum lines to catch and kill sharks thought to be a threat to the public (Sadath et al. 2013). These “signals” the public receives from governments can influence human behavior directed toward wildlife. For example, Chapron and Treves (2016) suggest that the repeated policy signal to allow state culling of wolves in Wisconsin and Michigan, USA, may have sent a negative message about the value of wolves or acceptability of poaching to the public. The authors contend that these policy signals contributed to poaching of wolves and slowed their population growth (Chapron and Treves 2016).

Another issue is the asymmetry between stakeholders that incur the costs from wildlife, such as the local communities living near them (Howe et al. 2014), and those that benefit from wildlife, such as specific industries (e.g. tourism) or society as a whole. For example, the international community values orangutans for their conservation and intrinsic value in Indonesia, yet local people incur the cost of crop raiding and personal injuries from orangutan attacks (Davis et al. 2013). Consequently, local people kill orangutans to reduce those costs (Davis et al. 2013, Davis et al. 2014). Likewise, although ecotourism companies benefit from predator-viewing activities in Bhutan’s Jigme Singye Wangchuk National Park, low-income agropastoralists suffer from depredated livestock by tigers and leopards. These losses amount to more than two-thirds of average annual household income (Wang and Macdonald 2006).

Initiatives that have directly provided local stakeholders with benefits from large predators and scavengers have achieved substantial and sustained reductions in conflict. Two seminal examples include profit sharing and compensation schemes in Kenya’s Kuku group ranch and Mbirikani ranch, which provide local stakeholders with a proportion of tourist industry revenue. This has led to reductions in the incidence of lion deaths resulting from poisoning (Hazzah et al. 2014, Bauer et al. 2017). Such schemes may help balance the economic 228 benefits between private stakeholders and the local public who accrue most of the costs of predators and scavengers. Similar incentive schemes have been used successfully by conservation NGO’s and governments to promote changes in human behavior, such as reducing carnivore killings (Nyhus et al. 2003). However, the success of these schemes can be jeopardised if they lack sufficient logistic and financial support, they do not award adequate compensation to offset losses, or if compensation is awarded inequitably (Dickman et al. 2011). Such schemes may also have limited effectiveness in reducing killings motivated by cultural, political or historical reasons (Goldman et al. 2013). Hence, profit-sharing and compensation schemes must be implemented in conjunction with broader management programs that attempt to identify and address the wide range of factors that contribute to killing of wildlife, and that encourage the participation of all stakeholders in an inclusive decision-making process that recognizes multiple systems of knowledge and values (Pascual et al. 2014).

In addition to improving equity in various forms associated with predators and scavengers, there is also an urgent need to promote human tolerance to these species through education about benefits (Steinmetz et al. 2014, Reid et al. 2016, Skupien et al. 2016, Marley et al. 2017). Dedicating outreach teams to communicate the benefits of endemic predators and scavengers to local communities could be an effective conservation strategy. Demonstrations of the effectiveness of education programs include: an improvement in the belief in potential for co-existence with alligators Alligator mississippiensis following education (Skupien et al. 2016), greater tolerance of black bears Ursus americanus following education of benefits provided by bears (Slagle et al. 2013), and greater tolerance of bats among Costa Rican men following education regarding ecosystem service provision (Reid et al. 2016). Although more research is required to understand how long the benefits of education programs may last and how best to deliver them to people from a variety of cultural, educational and religious backgrounds, education can be an effective tool for conservation of predators and scavengers in shared landscapes.

In addition to the benefits predators and scavengers provide to the public as a whole, they may also benefit a wide range of business, agricultural, and tourism interests. Much can be done to bolster the services of predators and scavengers in these sectors through local government and individual action. For example, Italian city councils are encouraging residents to purchase bat nesting boxes in response to increasing mosquitos that cause chikungunya fever (Day 2010), although it is unclear the extent of impact that bats have on 229 disease-carrying mosquitoes in this region. Similarly, the city of Dubai in the United Arab Emirates invests in consultancies that work with peregrine falcons to reduce feral pigeon populations that cause severe damage to infrastructure (Choksi 2015). Ecotourism revenue can be substantial, though it is often difficult to estimate how much particular species contribute to overall economic value (O’Mahony et al. 2017). The presence of jaguars Panthera onca in Brazil, for example, may contribute greatly to Pantanal ecolodges. One study estimates that the large felids bring nearly US$7 million in annual land-use revenue, which is 52 times higher than other industries in the region (Tortato et al. 2017).

Predators may also benefit vehicle drivers by reducing insurance premiums in areas where predators have been effective in reducing the abundance of large prey like deer, which can be a leading source of vehicle collision damage (Gilbert et al. 2016). Similarly, obligate scavengers have been shown to save ca. $50 million in insurance payments by farmers and national administrations in Spain by supplanting transportation of livestock carcasses to processing facilities (Morales-Reyes et al. 2015). Scavengers may also provide savings by reducing costs associated with meat contamination (Whelan et al. 2015). More work is needed to document the financial benefits of predators and scavengers to different sectors of society.

Managing the trade-offs between the costs and benefits of accommodating predators and scavengers in shared landscapes is a difficult and unresolved problem due to the complexity of human and ecological systems. Risk-averse management may tend to place undue importance on eliminating the detrimental impacts of predators and scavengers over maintaining the benefits, particularly if the impacts include direct hazard to human life. In some cases, however, this may be a short-sighted and poorly justified perspective that could lead to a net increase in risk to humans if these animals also provide benefits that reduce exposure of risk to humans. Important unanswered questions include: how do the benefits from predators and scavengers change as the density of those species varies over time (Courchamp et al. 2006)? How does the composition of the predator guild alter human perception of the costs associated with those predators (Dickman et al. 2014)? Integrating the natural and social sciences can help answer these questions by evaluating the full range of both costs and benefits. Doing so will enable conservationists to determine if and when there is a net-benefit in shared landscapes and develop strategies to encourage net benefits (Carter et al. 2014). Moreover, as the extent of shared landscapes increases globally, it is

230 imperative that we identify new approaches to management that allow wildlife and humans to coexist. Failing to do so is likely to result in the extinction of many species.

Human societies depend greatly on the living components of the natural world (Pecl et al. 2017), and these natural services are being altered by human dominance of landscapes (Worm and Paine 2016) and climate change (Scheffer et al. 2015). While, predators and scavengers currently face great threats in shared landscapes (Buechley and Sekercioglu 2016, Ripple et al. 2017), they can coexist in areas where local communities accept and tolerate these species (Gangoso et al. 2013, Treves and Bruskotter 2014, Skupien et al. 2016). Traditional conservation approaches such as safeguarding land may not lead to comprehensive protection of species in human-dominated areas (Di Minin et al. 2016), leading to a requirement for alternative approaches for saving species in these shared landscapes. An important alternative is using services that predators and scavengers provide for human well being to enhance protection (Frank and Schlenker 2016). By adopting an approach that communicates and educates these benefits to communities that live with predators and scavengers while accounting for cultural values and equitable conservation decision-making, we may be able to stem the decline of these persecuted guilds and make progress toward more expansive protection and increased instances of a net-gain in shared landscapes.

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242 APPENDIX 3 Supplementary materials for Chapter 3

A 3.1 Correlation plots, Bayesian p-values, and marginal likelihood values to accompany Chapter 2

We used three lines of evidence (marginal likelihood using the approach and code from (Dey et al. 2019, code in Supplementary Information 2), correlation plots of state variables and Bayesian p-values to contribute to the selection of the model we used to 1) estimate African lion densities and abundance, and 2) estimates of movement and home-range size. Importantly, our estimates of both lion density and abundance were similar across all candidate models.

Global Sex Marginal likelihood=-158020.56 Marginal likelihood=-161676.6 Bayes p=0.74 Bayes p=0.75

Theta Sextheta

243 Theta Sextheta Marginal likelihood=-163470.45 Marginal likelihood=-165528.85 Bayes p=0.74 Bayes p=0.78

244 X Y Marginal likelihood=161916.87 Marginal likelihood=162227.85 Bayes p=0.73 Bayes p=0.70

245

Movement model Marginal likelihood=-172241.9 Bayes p=0.75

246 A 3.2 Web only materials to accompany Chapter 4

WebPanel 1. Explanation of leopard dog consumption and occurrence around SGNP Evidence of leopard consumption of dogs Dogs were estimated to constitute 25% (Surve et al. 2015) and 58% (Edgaonkar and Chellam 2002) of the total leopard diet biomass in Sanjay Gandhi National Park (SGNP). Acknowledging that large carnivore diets can exhibit pronounced seasonality (eg Johnson et al. 1993), we adopted the mean of these two studies (ie 42% dog biomass in leopard diet), which was consistent with the estimate calculated by Prasad and Tiwari (2009) that dogs comprised 47% of leopard diets.

Number of dogs consumed by leopards around SGNP annually Assuming dog biomass constituted ~42% of leopard diet, we applied the following calculation: leopard daily food consumption ([4.7 kg, Odden and Wegge 2009] × dog biomass of diet [42%]) × (365 days)/(average consumed dog weight assuming leopards eat 95% of carcass [17.1 kg, Stander et al. 1997, Athreya et al. 2016]) × (35 leopards) ≈ 1475 dogs consumed per year by the 35 leopards residing in and around SGNP. We rounded this figure up to 1500 dogs in the manuscript to avoid a false sense of accuracy. The range for this estimate was 878–2036 dogs, depending on biomass estimates of 25% (Surve et al. 2015) or 58% (Edgaonkar and Chellam 2002), respectively.

Assumption that leopard impacts are concentrated within 500 m of SGNP Local scientist K Tiwari (Mumbaikers for SGNP, a local NGO working on leopard-human conflict and education in communities surrounding SGNP) reported that leopards rarely stray farther than 500 m from the SGNP boundaries (K Tiwara pers comm). We believe this estimate is prudent in light of several reports and photographs of leopards outside of or near to residences that straddle the park (mean distance = 282 m, range = 0–1.04 km):

(1) Leopard sighted in Hirandani, near Supreme Business Park (1.04 km from forest edge): www.mid-day.com/articles/leopard-spotted-paw-mumbai-powai-building-supreme- business-park-hiranandani/17632181.

(2) Leopard sighted and photographed in Kujapada (157 m from forest edge): www.dnaindia.com/mumbai/report-need-anymore-proof-of-leopard-in-kajupada-2245528.

247 (3) Leopard cub captured in Mumbai Indian Institute of Technology (2003, within forested area): www.rediff.com/news/2003/aug/05leo.htm.

(4) Leopard found in Mumbai Indian Institute of Technology (2016, within forested area): www.ndtv.com/mumbai-news/leopard-enters-iit-bombay-campus-hides-behind-a- generator-593120.

(5) Leopard crossed Mumbai’s 42 State Highway, which hugs the western edge of SGNP (1 km from forest edge): http://colabradio.mit.edu/when-a-leopard-crosses-state-highway-42- in-mumbai-to-get-into-the-forest/.

(6) Leopard killed a Rottweiler in a residence near Ghodbunder Road, Thane (206 m from forest edge): www.hindustantimes.com/mumbai/mumbai-locals-live-in-fear-as-leopard-cub- kill-dog-in-thane/story-cn2CFHdidyKcttLX4AIe4L.html.

(7) Leopard photographed being chased by a stray dog near the Hill View building in Mumbai (104 m from forest edge): www.ndtv.com/mumbai-news/mumbais-chase-of-the-year-stray- dog-chases-leopard-away-552594.

(8) Leopard attempted to attack a dog at a house in Borivali (within forested area): www.ndtv.com/mumbai-news/caught-on-camera-dog-chases-away-leopard-from-house-in- mumbai-775451.

(9) Leopard repeatedly sighted on the Khatau Mill/Ekta Meadows boundary (within forested area): http://mumbaimirror.indiatimes.com/mumbai/other/Panic-as-Leopards-venture-into- Borivali-society/articleshow/19942529.cms?

(10) Leopard sighted in Poonam Nagar (314 m from forest edge): http://timesofindia.indiatimes.com/city/mumbai/Leopard-caught-napping-in-staircase-of- Andheri-building/articleshow/17472551.cms.

Evidence that leopards do not occur in districts located farther from SGNP We found no online or newspaper reports of leopards occurring in suburbs located >2 km from our designated 500-m buffer beyond the forest edge. Leopards require the safety of forest and vegetation cover (at least intermediate levels of cover, Balme et al. 2007) in order 248 to hunt successfully. This has been shown in a diversity of habitats, ranging from woodland savannas (Balme et al. 2007) to semi-deserts, where leopards hunt in dry riverbeds in which trees are present (eg Bothma and Le Riche 1984). The only reports of the occurrence of leopards in suburbs included in the city-wide dog census were (1) Bhandup West (a part of this suburb directly borders the SGNP), (2) Borivali (borders the park), (3) Mulund (borders the park), (4) Kandivali (borders the park), and (5) Goregaon (borders the park).

Estimate of human population within or directly adjacent to SGNP P Variyar (Sanctuary Asia, pers comm) and K Tiwari (Mumbaikers for SGNP, www.mumbaikarsforsgnp.com, pers comm) suggested that ~350,000 people live within or directly adjacent to SGNP (this is a more recent estimate than the last human population census, which was conducted in 2011).

Table A3.2.1: Dog bites recorded in various Mumbai newspapers from 2010-2015.

Year # bites Source

2010 77,484 http://timesofindia.indiatimes.com/city/mumbai/Dog-bite-cases-in-2010-2nd- highest-in-decade/articleshow/7323928.cms 2011 67,463 http://timesofindia.indiatimes.com/city/mumbai/2011-dog-bite-cases-67463- ampamp-counting/articleshow/11214730.cms 2012 82,247 www.hindustantimes.com/mumbai/82-247-cases-of-dog-bites-reported-in- 2012/story-MNlG9XGA4JErNmF0Gd3aYP.html 2013 81,716 http://timesofindia.indiatimes.com/city/mumbai/Stray-population-at-66K-yet-dog- bites-increase-to-82K/articleshow/40310447.cms 2014 83,273 www.freepressjournal.in/mumbai/dog-bite-cases-on-the-rise-in-city/566698 2015 58,317 www.hindustantimes.com/mumbai/58-317-dog-bite-cases-in-mumbai-this- year/story-wR4lI5YiK5OWGyI8qhPBFO.html Average 74,603

249 Web References

Athreya V, Odden M, Linnell JD, et al. 2016. A cat among the dogs: leopard Panthera pardus diet in a human-dominated landscape in western Maharashtra, India. Oryx 50: 156–62.

Balme G, Hunter L, and Slotow R. 2007. Feeding habitat selection by hunting leopards Panthera pardus in a woodland savanna: prey catchability versus abundance. Anim Behav 74: 589–98.

Bothma JDP and Le Riche EAN. 1984. Aspects of the ecology and the behaviour of the leopard Panthera pardus in the Kalahari Desert. Koedoe 27: 259–79.

Edgaonkar A and Chellam R. 2002. Food habit of the leopard, Panthera pardus, in the Sanjay Gandhi National Park, Maharashtra, India. Mammalia 66: 353–60.

Johnson KG, Wei W, Reid DG, and Jinchu H. 1993. Food habits of Asiatic leopards (Panthera pardus fusea) in Wolong Reserve, Sichuan, China. J Mammal 74: 646–50.

Odden M and Wegge P. 2009. Kill rates and food consumption of leopards in Bardia National Park, Nepal. Acta Theriol 54: 23–30. Prasad K and Tiwari K. 2009. Wild leopards of Mumbai prefer domestic prey. Oryx 43: 327– 28.

Stander PE, Haden PJ, Kaqece II, and Ghau II. 1997. The ecology of asociality in Namibian leopards. J Zool 242: 343–64.

Surve N. 2015. Ecology of the leopard in Sanjay Gandhi National Park, Maharashtra, with special reference to its abundance, prey selection and food habits. Mumbai, India: Maharashtra Forest Department.

250 A 3.3 Partnership examples and scale of a royalty: accompaniements to Chapter 6

Wildlife imagery royalties and partnerships

A “wildlife imagery royalty” has been discussed by academics and conservation non- government organisations (NGOs) for over 20 years. Baltz and Ratnaswamy (2000) examined the number of tertiary education institutions in the USA contributing financially to the conservation of their tiger mascots under a program called Tigers for Tigers (Tigers for Tigers 2018).

There are many recent examples of corporations and private companies collaborating with conservation NGOs to support the wildlife species used in their advertising, branding and trademarks. Examples include Lacoste’s TM partnership with the IUCN and the “Save Your Logos” initiative to fund crocodilian conservation (Lacoste 2018) and the 2018 Commonwealth Games organizers raising US$ 73,000 through the sale of the “Borobi” mascot for injured koalas Phascolarctos cinereus at the Currumbin Wildlife Hospital Foundation in Australia (Jackson 2018). Such initiatives span the fashion, food, beverage, sports and automotive industries (see Table A3.1 for the most recent examples of large felid NGOs partnering with corporations), and often have win-win outcomes for both corporations and conservation NGOs. A further example is the “Put a tiger in your tank” marketing campaign for Esso fuel, a brand name of the United States oil and gas company ExxonMobil. Esso launched the sale of fake material “tiger tails” and bumper stickers with the slogan “I’ve got a tiger in my tank” across the USA. They sold ~2.5 million “tails”, which remain in demand to this day. After recognizing the contribution of the tiger in its marketing schemes, ExxonMobil launched the “Save the tiger Fund” in 1995 in collaboration with the US National Fish and Wildlife Foundation to help preserve the tiger. Since its inception, it has become an important funding body for tiger conservation projects globally and from 1995-2009 donated US$ 17.3 million dollars in 336 grants (Gratwicke et al. 2007).

CSR activities like this may lead to increased financial performance (Campbell 2007, Orlitzky et al. 2003, Brammer and Millington 2005) and improved social image of the corporation (Brammer and Millington 2005). However, corporations fundamentally exist to engage in activities designed to increase their profits (1970), and CSR is a secondary objective. Therefore, CSR contracts between a corporation and NGO may be terminable at any time, usually only last for a specific period and are sensitive to market forces and recessions

251 (Reich and Wimer 2012). For example, ExxonMobil terminated its “Save the Tiger Fund” after 15 years after cancelling its relationship with the National Fish and Wildlife Foundation. Similarly, the clothing manufacturer PUMA, and Tiger Beer made one off financial commitments to Game Rangers International (a Zambian NGO) in the Kafue National Park and WWF respectively to bolster lion and tiger population recovery efforts (Wilderness Safaris 2013). These types of donations, although important in short-term project maintenance, do not allow for long-term conservation planning and funding continuity, key to successful species recovery programs (Crees et al. 2016). Furthermore, the objectives of potential CSR donor recipients (e.g. a conservation NGO) and corporations themselves are not always likely to align, and conservation NGOs, national governments and the species whose images are being used in marketing and media currently hold no legal right to compel a corporation to pay wildlife image royalties. The question is: is there a pathway for a wildlife imagery royalty scheme to be legally mandated?

The United Nations Development Program-backed “Lions share fund” and “Save your logos”, (a program of the Endowment fund for Biodiversity), are two recent examples of voluntary wildlife imagery royalty programs attempting to scale-up and consolidate previous examples of individual corporations donating to wildlife species conservation (United Nations 2018). They propose that corporations using animals in media campaigns or logos donate a percentage of their profits to wildlife conservation programs and aim to generate USD$ 100 million per year.

252 Table A3.2.2 Examples of historic and current collaborations between felid conservation NGOs and large corporate organizations. Main support includes cash

injections and outcomes vary between projects. All figures are presented in USD$.

Felid Corporate/organisation Corporate Funding/support offered by conservation Industry Outcomes Source supporting NGO mascot corporate NGO

Conservation science activities, Save the Tiger US$1 million donated to the Save local empowerment for people http://www.exxonmobil.com.au/UK- Exxon Mobile (Esso) Oil and Gas Tiger Fund the Tiger Fund living in tiger landscapes, welfare English/about_history_esso_tiger.aspx of local people

Funds injected into Funds paid for production of 18 Panthera Cartier Jewellery Leopard Panthera/Peace Parks 000 fake furs for Shembe church http://www.peaceparks.org/story.php?pid=1021&mid=1500 Foundation fake fur program in 2017

Fashion and 100% of funding dedicated to http://www.forbes.com/sites/ashleaebeling/2016/01/14/hermes- Panthera Hermes lifestyle Leopard Portion of from a line of limited field conservation activities goes-cat-crazy/#41f2af85619f products edition scarves will be donated to Panthera

Portion of proceeds go to Security and law enforcement, Kitchen and Panthera Tiger Towel (Royale) Tiger Panthera' Tiger conservation tiger number stability, population http://www.royale.ca/Community/ utility programs censuses

Proceeds from the "Play for Life" Fashion and clothing line (Africa Unity kit, First reliable estimates of lion Mountain UNEP Puma clothing lifestyle Unity-tees & Kehinde Wiley densities and occupancy for http://www.unep.org/sport_env/Puma_PlayforLife.aspx lion products lacelets) supported lion surveys in major protected areas in Zambia Zambia's Kafue National Park

Farmer-leopard conflict Cape Leopard Food and Cape reduction, fundraising support for http://capeleopard.org.za/news-and-media/news/story/169/leopards- Leopards Leap Wines Portion of wine profits donated to Trust (CLT) beverage leopard CLT, operational savings for CLT leap-wines-support-the-clt-boland-project Cape leopard trust field research, (accommodation) accommodation for researchers.

253 Funds assist in developing a WWF- functional tiger corridor between National tigers for tigers Education and Raised US$ 6,000 for both India/WWF- Tiger Pench-Kanha and community http://www.tigersfortigers.org/#!tiger-conservation/f7w7d coalition sport projects in 2015. Russia support and empowerment for tiger conservation in Russia

Conservation activities 21st Century Food and Unspecified support but http://www.21stcenturytiger.org/about-21st-century-tiger/corporate- Tiger Malabar Pepper Tiger represented by 73 projects in Tiger beverage presumably a cash donation sponsors/ seven tiger range states

Conservation activities 21st Century Tiger Time Ice Cream (Tip Food and Tiger tail 30 cent donation for every 16L represented by 73 projects in https://www.tiptop.co.nz/tigers/ Tiger Top Ice Cream) beverage and print carton of Tiger Time ice cream seven tiger range states and 10 cents for every 2L tub sold

Increase in environmental education and knowledge on Proceeds of profits donated to Cheetah Whiskas Pet food Cat cheetahs in the wild, farmer- http://www.cheetah.co.za/sp_trust.html cheetah outreach. Outeach South cheetah conflict workshops and Africa manual production

Cheetah Farmer-cheetah conflict Conservation Embassy of Finland in Government Lion Funding support for CCF mitigation, cheetah research, http://cheetah.org/about-us/partners/ Fund (CCF) Namibia Namibia. cheetah re-introductions

254 References

Baltz, M. E. & Ratnaswamy, M. J. (2000). Mascot conservation programs: using college animal mascots to support species conservation efforts. Wildlife Society Bulletin, 159-163.

Crees, J. J. Collins, A. C. Stephenson, P. J. Meredith, H. M. Young, R. P. Howe, C. ... & Turvey, S. T. (2016). A comparative approach to assess drivers of success in mammalian conservation recovery programs. Conservation Biology, 30(4), 694- 705.

Gratwicke, B. Seidensticker, J. Shrestha, M. Vermilye, K. & Birnbaum, M. (2007). Evaluating the performance of a decade of Save The Tiger Fund's investments to save the world's last wild tigers. Environmental Conservation, 34(3), 255-265.

Jackson, E. (2018). Games mascot raises $100k for wild koalas. Brisbane Times, 24 May.

Lacoste. (2018). http://www.saveyourlogo.org/en/partners/lacoste/

Reich, R. & Wimer, C. (2012). Charitable giving and the Great Recession. Palo Alto, California: The Russell Sage Foundation and the Stanford Center on Poverty and Inquality.

Tigers for Tigers. (2018). https://www.tigersfortigers.org/

United Nations. (2018). UNDP announces The Lion's Share fund with founder FINCH and founding partner Mars to tackle crisis in wildlife conservation and animal welfare. https://www.undp.org/content/undp/en/home/news- centre/news/2018/undp-announces-the-lion-s-share-fund-with-founder- finch-and-foun1.html

255 Potential scale of a wildlife imagery royalty Using one of the world’s largest and most reputable company search engines (Hoovers), we found 23 897 companies which use large felids in their names. We found another 355 on the Securities and Exchange Commission website, 17 on the Forbes 2000 list of largest companies and 214 companies using an ad hoc search of the fashion, food and beverage, automotive and sports industries (Table 1, Supporting Information 2). Across all four search mechanisms, the most commonly used species was the lion (32-86% of results) followed by the tiger. Of the 17 corporations from the Forbes 2000 list which had a big cat on their emblem (defined as a distinctive badge of an organization), 14 recorded a profit in the 2014 fiscal year.

If we consider a scenario where these 14 profit-making companies were charged a wildlife imagery royalty based upon 0.1-1% of their sales, they would collectively generate between US$385 million - US$3.85 billion in donations per year (Table 2). These royalty margins err on the conservative side of several philanthropic initiatives. For example the “1% for the planet” initiative donates 1% of all sales (1% for the planet 2014), Wells Fargo & Company donates on average between 1.2 – 1.5% annually, while Goldman Sachs donated 3% of sales in 2015 (Preston 2016). The median philanthropic contribution of Fortune 500 companies is 1% of pre-tax profits (Preston 2016). These philanthropic contributions contrast strongly with royalty payment margins, and are generally negotiated 1) based on the profitability of patented technologies incorporated into products and 2) the so termed “25% Rule” (Epstein 2012, Goldscheider et al. 2002). Based upon an analysis of 3887 companies across 14 industries Kemmerer & Lu (2008) showed that royalty payments generally fall between 25% of gross margins and operating margins, and suggest a company’s EBITDA (Earnings before interest, taxes, depreciation, and amoritization) is a reasonable framework for applying the 25% rule. Even if we consider the very conservative margins of the 14 companies on our list (as defined by 0.1-1% of profits), then this figure would still amount to US$202 million - US2.02 billion. Similarly, the application of these donation figures to the sales and profits of the specific products of Tiger Beer (Heineken Group), Kellogg’s Frosties (Kellogg’s), vehicles (Jaguar and Peugeot) and ticket sales to sporting games (Chelsea Football Club and Carolina Panthers American football team) could leverage between $US14-181 million annually

256 (Table 3). To place these figures in context, $ 2.02 billion could make up the shortfall for conserving and stabilizing Africa’s largest and most critical protected areas which feature African lion populations, for two years (Lindsey et al. 2018) and would have a plethora of other broader benefits for other species living in lion range (Lindsey et al. 2018). Similarly, from the estimated 5.1 million hectoliters of Tiger Beer (a Singaporean brand famous across Asia) produced annually by the Heineken Group, 1% of sales (assuming 80% of this figure is sold) sold at $1.3 per 330 ml can (pre markup price), would equal $ 16.07 million annually. This figure represents 46% of the current tiger conservation shortfall as calculated by Walston et al. (2010).

Global enforcement and effective spending We have described several pathways for the creation for a wildlife imagery royalty, and with the evolution of nature-based laws, these pathways could assist the creation of such a scheme in the future. However, further discussions need to take place on how the laws and mechanisms mentioned above would apply to entities from two sovereign states. For example, large corporations can protect their intellectual property through trademark and intellectual property laws nationally and globally through international treaties (eg. The Patent Cooperation Treaty (PCT), see: https://www.wipo.int/pct/en/). Corporations often use the services of companies which search and sue entities for patent and intellectual property infringements. For example, the United States International Trade Commission can direct action against countries and companies engaged in unfair trade, copyright and patent infringements (Hahn et al. 2007). A seminal example of this is the Apple (headquarters in California, USA) vs Samsung (headquarters in Seoul, Korea) smartphone patent infringement case, which saw a California jury award US$ 1 billion in damages for Samsung’s infringement of six of its patents (Gobble 2012). We envisage that the laws regarding such a royalty would only be enforced within the state applying them (and not beyond its borders). For example if the state of India, which is home to ~60% of the world’s tigers (Verma et al. 2017) instituted a wildlife imagery royalty to be paid by local corporations, it would not be able to claim this royalty from a company producing a beer with a tiger logo in Malaysia, unless a similar treaty to the PCT was signed by all the states instituting this royalty program.

257

Table A3.2. The number of companies found through our respective search mechanisms for each of the seven large felid species in our assessment.

Search Number of Mechanism Species Source Criteria Companies Tiger 5945 Lion 9685 Mountain lion* 5622 www.hoovers.com Company name Hoovers Jaguar 1624 Leopard 642 Cheetah 355

Snow leopard 24

Total: 23897 Tiger 89 Lion 176 Mountain lion* 58 www.sec.gov Company name SEC Jaguar 15 Leopard 6 Cheetah 11

Snow leopard 0

Total: 355 Tiger 0 Lion 14 Mountain lion www.forbes.com/global200 0 Mascot

0/ Forbes 2000 Jaguar 0 Leopard 0 Cheetah 0

Snow leopard 0

Total: 14 Tiger 56 Lion 68 Mountain lion* Mascot, product, 17 www.google.com Ad-Hoc name Jaguar 13 Leopard 57 Cheetah 2

Snow leopard 1 Total: 214

*We used panther, cougar, mountain lion and puma in our search term for this species.

258 Table A3.3. The 17 companies found from Forbes’ 2000 Largest Companies List for the 2015 year. The Forbes ranking of the world’s largest and most powerful public companies is measured by a composite score made up of a particular company’s revenue, profit, assets and market value. These four metrics are considered equally. Companies with NA suffered profit losses in the 2014 financial year. All figures are presented in USD$.

Forbes 1% of 0.5% of Company name Country ranking Industry Emblem Sales Profits 1% of sales profits profits 0.1% of profits

Royal Bank of Canada Canada 53 Financial management Lion 38.9 billion 8.3 billion 389000000.00 830000000 415000000 83000000

ING Group Netherlands 92 Financial management Lion 65.7 billion 2.6 billion 657000000.00 260000000 130000000 26000000

Generali Group Italy 129 Insurance Lion 111.7 billion 2.2 billion 1117000000.00 220000000 110000000 22000000

Baidu China 546 Web services Cat footprint 7.9 billion 2.1 billion 79000000.00 210000000 105000000 21000000 Advertising and Public Publicis Groupe France 617 Relations Lion 9.6 billion 955 million 96000000.00 95500000 47750000 9550000

Peugeot France 635 Automotive Lion 71.1 billion -936 million 711000000.00 NA NA NA

UPM - Kymmene Finland 792 Wood production Lion 13.1 billion 679 million 131000000.00 67900000 33950000 6790000

Schroders UK 934 Asset management Lion on coat of arms 3.2 billion 680 million 32000000.00 68000000 34000000 6800000 St James's Place Wealth Management UK 1014 Financial management Lion 7.5 billion 310 million 75000000.00 31000000 15500000 3100000

Delhaize Group Belgium 1027 Food retail Lion 28.7 billion 118 million 287000000.00 11800000 5900000 1180000 Hospitality and MGM Resorts US 1029 entertainment Lion 10.1 billion -149 million 101000000.00 NA NA NA

London Stock Exchange UK 1031 Financial management Lion on coat of arms 2.1 billion 270 million 21000000.00 27000000 13500000 2700000 Black mother cat Yamato Holdings Japan 1176 Transportation carrying kitten 13.2 billion 384 million 132000000.00 38400000 19200000 3840000 Annaly Capital Management, Inc. US 1235 Financial management Lion on coat of arms -115 million -842 million NA NA NA NA Bank of the Philippine Islands Philippines 1243 Financial management Lion on coat of arms 1.5 billion 406 million 15000000.00 40600000 20300000 4060000

Chimera Investments US 1533 Financial management Lion 697 million 589 million 6970000.00 58900000 29450000 5890000

RMB Holdings South Africa 1671 Financial management Lion 2 million 620 million 20000.00 62000000 31000000 6200000 Total contribution scenarios 3.85 billion 2.02 billion 1.01 billion 202 million

259 Table A3.4. Sales and profit margins of specific products sold by Kellogg’s, Heineken Group, Jaguar, Peugeot, Chelsea Football Club and Carolina Panthers. We applied company profit margins to specific product profit margins and assumed them to be equal. All figures are presented in USD$.

Masc Units Total Company profit 0.5% 0.01% 0.5% 0.01% Company Product Product price 1% sales 1% profit ot sold sales margin sales sales profit profit

Frosted 8880212 2760196 2760196.1 1380098. 276019.6 248417.6 124208. 24841.7 Kellogg's Flakes Tiger 3.108254803 0 16 0.09 6 1 16 5 83 7 Heineken (Asia Pacific 1236363 1607272 16072727. 8036363. 1607272. 1446545. 723272. 144654. Breweries) Tiger beer Tiger 1.3 636 727 0.09 27 6 73 46 73 55 Motor Jagu 9784353 97843539. 4892177 9784353. 1174122 5870612 1174122 Jaguar vehicles ar 71948.3076 87174 956 0.12 56 0 96 4.75 .37 .48 Motor 6351530 63515306. 3175765 6351530. 1270306. 635153. 127030. Peugeot vehicles Lion 41300.0237 153790 645 0.02 45 3 64 13 06 61 Football 26-120 (tickets), 137- 1048113 1048113.4 524056.7 104811.3 30152.1 Chelsea Football Club tickets Lion 311 (suites) 90531 43.7 0.06 37 2 44 60304.21 1 6030.42 Football Pant 5703021. 28515.10 5703.021 Carolina Panthers (NFL) tickets her 78.82 72355 1 0.24 57030.211 6 1 13687.25 6843.63 1368.73 Total contribution 181.29 90.65 18.13 14.78 7.39 1.48 scenarios million million million million million million

260 APPENDIX 4 Animal ethics clearance for Chapter 2 and Chapter 3

261

262