Movement Ecology of Afrotropical Forest Mammals 3031270290, 9783031270291

Table of contents :
Foreword
Acknowledgments
Contents
Contributors
Chapter 1: What Do We Know About Mammal Movements in African Tropical Forests?
1.1 Introduction to Tropical Forest Mammal Studies
1.2 African Tropical Forest Studies
1.3 Book Presentation
1.4 Future Directions
References
Chapter 2: Movement Patterns and Population Dynamics of Giant Forest Hog Groups in Kibale National Park, Uganda
2.1 Introduction
2.2 Materials and Methods
2.2.1 Study Site
2.2.2 Study Design
2.2.3 Data Analysis
2.3 Results
2.3.1 Home Range Estimation
2.3.2 Occupancy Rate (ψ), Detection Probability (P), and Relative Abundance Index (RAI)
2.3.3 Daily Patterns
2.3.4 Group Size and Behavior
2.4 Discussion
2.5 Conclusion
References
Chapter 3: Forest Elephant Movements in Central Africa: Megafauna Need Megaspaces
3.1 Introduction
3.2 Big Animals Need Big Spaces
3.2.1 How Big Is a Big Home Range?
3.2.2 The Devil Is in the Details
3.3 The Scaffolding of Forest Elephant Movement: Elephant Trails
3.4 What Drives Forest Elephant Movements?
3.4.1 Water Availability
3.4.2 Food Availability
3.4.3 Nutrient Availability
3.4.4 Social Organization
3.4.5 Personality and Sex
3.4.6 The Landscape of Fear
3.5 The Ecological Consequences of Forest Elephant Movement Patterns
3.6 The Implications of Forest Elephant Movement Patterns for Biodiversity Conservation
3.6.1 Forest Elephant Movement and Elephant Conservation
3.6.2 Forest Elephants and the Conservation of Ecosystem Processes
3.7 What to Do? The Same Old Wish List?
References
Chapter 4: Elephant Movements, Abundance, and Use of Water Sources in Kibale National Park, Uganda
4.1 Introduction
4.2 Methods
4.2.1 Study Site
4.2.2 Data Collection Methods
4.2.3 Elephant Abundance and Distribution Based on Census Data
4.2.4 Data Analysis
4.3 Results
4.3.1 Occupancy Models
4.3.2 Diurnal Patterns
4.3.3 Group Size
4.3.4 Arrival and Departures
4.3.5 Variables Explaining Movements
4.3.6 Elephant Abundance and Distribution Based on Census Data
4.4 Discussion
References
Chapter 5: Movement Ecology and Evolutionary History of Forest Buffalo
5.1 Introduction
5.1.1 African Buffalo Taxonomy, Distribution, and Population Estimates
5.1.2 Movement Ecology
5.1.3 Conservation Genetics
5.2 Forest Buffalo vs. Savanna Buffalo Home Ranges and Daily Movements
5.2.1 Forest vs. Savanna Buffalo Habitat Use
5.2.2 Forest vs. Savanna Buffalo Activity Patterns
5.2.3 Forest Buffalo Behavior
5.3 Conservation Genetics of the African Buffalo
5.4 Consequences of Movement for the Conservation and Future of Forest Buffalo
5.4.1 Wildlife Corridors
5.4.2 Translocation When Corridors Fail
5.5 Conclusion
References
Chapter 6: Site Fidelity and Home Range Shifts in a Leaf-Eating Primate
6.1 Introduction
6.2 Methods
6.2.1 Study Site and Data Collection
6.2.2 Data Analysis
6.3 Results
6.4 Discussion
References
Chapter 7: Primate Movements Across the Nutritional Landscapes of Africa
7.1 Primates and African Forests
7.2 Plant Compounds Important to Primates
7.3 Sensing Food and Toxins
7.4 Locomotor Behavior
7.4.1 Arboreal Locomotion
7.4.2 Terrestrial Locomotion
7.5 Primate Cognition and Movement
7.6 Lévy vs. Brownian Motion vs. Reuse
7.7 Future Directions
References
Chapter 8: Conditions Facilitating a “Landscape of Fear from Disease” in African Forest Mammals
8.1 Introduction
8.2 Pathogen Characteristics and Effects on Animal Host
8.3 Evidence That Animals Alter Behavior and Movement in Fear of Pathogens
8.4 Landscapes of Fear from Disease Framework
8.5 Learning Associations Between Landscapes and Disease Risk
8.5.1 Pathogen Characteristics
8.5.2 Movement Characteristics
8.6 African Forest Mammals
8.7 Applying the Landscape of Fear from Disease to African Conservation
8.8 Conclusion
References
Chapter 9: Do Seasonal Frugivory and Cognition Shape Foraging Movements in Wild Western Gorillas?
9.1 Introduction
9.2 Seasonality and Space Use
9.3 Seasonality and Spatiotemporal Memory
9.4 Straightness of Movement
9.5 Decision Rules and Recursions
9.6 Absence of Seasonal Differences in the Level of Cognition Used for Foraging
9.7 Conclusions
References
Chapter 10: Females Move in Tight Crowds, Males Roam: Socioecology and Movement Ecology of Mandrills
10.1 Introduction
10.1.1 Mandrill: A Fascinating Primate
10.1.2 Mysteries of Large Groups and Seasonal Male Influxes
10.1.3 Purpose of This Chapter
10.2 Movement Coordination and Adaptive Significance of Large Groups
10.2.1 How Do Group Members Coordinate Their Movement?
10.2.1.1 Group Crowdedness
10.2.1.2 Subgrouping and Long-Distance Calls
10.2.2 Why Do Mandrills Move in Extremely Large Groups?
10.2.2.1 Potential Disadvantages of Large Groups
10.2.2.2 Possible Adaptive Benefits
10.3 Seasonal Influxes of Solitary Males
10.3.1 How Do Solitary Males Find Groups?
10.3.2 Why Do Males Join and Leave Groups Seasonally?
10.4 Potential Keys to Unraveling the Puzzle
References
Chapter 11: Linking Movement Ecology to Conservation Biology
11.1 Movement Ecology and Why It Is Needed for Conservation
11.2 What Movement Ecology Research Can Provide
11.3 Next Steps
References
Index

Citation preview

Rafael Reyna-Hurtado Colin A. Chapman Mario Melletti   Editors

Movement Ecology of Afrotropical Forest Mammals

Movement Ecology of Afrotropical Forest Mammals

Rafael Reyna-Hurtado  •  Colin A. Chapman Mario Melletti Editors

Movement Ecology of Afrotropical Forest Mammals

Editors Rafael Reyna-Hurtado Department of Biodiversity Conservation El Colegio de la Frontera Sur Campeche, Campeche, Mexico

Colin A. Chapman Biology Department Vancouver Island University Nanaimo, BC, Canada

Mario Melletti Wild Pig Specialist Group, IUCN SSC African Buffalo Initiative Group IUCN, ASG Rome, Italy

ISBN 978-3-031-27029-1    ISBN 978-3-031-27030-7 (eBook) https://doi.org/10.1007/978-3-031-27030-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Rafael Reyna-Hurtado dedicates this book to Edith, Aranza and Emiliano for having enjoyed with him the beauty of Africa forests. Colin Chapman dedicates this book to all the friends who shared adventures with conservation efforts over the years, particularly Claire Hemingway and to the dedicated men and women of the Uganda Wildlife Authority. Mario Melletti dedicates this book to his friends Gianfranco Tortellini and Giuseppe Corte to the memory of the beautiful times spent together.

Foreword

The extensive threats to the future of sub-Saharan Africa’s biodiversity cannot be overemphasized enough because the continent still has a lot of work in prioritizing these issues in urgent light. It is great to see how this book highlights the enormous challenges being faced by biodiversity against various factors across Africa and goes the extra mile in detailing the solutions that if considered can certainly alter the trajectory of the continent and the world’s future. This book compiles exemplary studies of the movement of some African forest mammals. Primatologists have produced a wealth of information across Africa that includes information on their ecology and movement. It was great to see the acknowledgment of there being room and need for supplementary information collection on critical species on the continent, hence providing the groundwork for even more research to enhance the study further. Researchers are now aware of how much altering extractive development patterns, foreign investment techniques, and rising human populations affect conservation outcomes. These elements are now incorporated into integrated management plans (like landscape approaches) to protect biodiversity and guarantee human welfare. Nonetheless, the complexity of integrated and adaptive conservation and management strategies, as well as the need to reconcile biodiversity conservation and ecosystem services with economic development and human health, is daunting, and likely out of reach for economically impoverished countries. All life requires movement. It influences evolutionary pathways, ecological processes, individual fitness, and how populations respond to anthropogenic perturbation.1,2 As a result, movement ecology has become essential to both ecology and conservation biology. Emerging technological advances such as telemetry systems, drones, and artificial intelligence have significantly positively altered the rate  Nathan, R. (2008). An emerging movement ecology paradigm. Proceedings of the National Academy of Sciences, 105:19050–19051. 2  Nathan, R., Monk, C. T., Arlinghaus, R., Adam, T., Alós, J., Assaf, M., . . . Bijleveld, A. I. (2022). Big-data approaches lead to an increased understanding of the ecology of animal movement. . Science 375:eabg 1780. 1

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and quality of movement data further defining a bright future for the role of movement ecology in conservation. Globally, 60 million ha of tropical primary forest were lost from 2002 to 20193 and 21% of this loss occurred in Africa.4 The forests of the Congo Basin cover 200 million ha, but it lost 16 million ha between 2000 and 2014, mostly to small-scale agriculture.5 As road infrastructure improves in the Democratic Republic of Congo and the Republic of Congo, forest loss in Africa is expected to increase dramatically. On the same note, the continent’s population is currently 1.4 billion and it is projected to quadruple by 2100 (UN 2015). This growing population will need energy and wood supplies 80% or more of domestic energy needs across Africa.6 In the Democratic Republic of Congo (DRC), fuelwood contributes 95% of energy needs, which amounts to an estimated 70 million m3 of wood each year.7 That said, Africa’s development is non-negotiable; hence why we emphasize that conservation is a foundational strategy for meeting the challenges of climate change, charting a pathway for green growth, and stabilizing society as Africa’s growing population faces massively increasing demands for food, fresh water, and energy. Mainstreaming biodiversity conservation considerations into the productive sectors of the economy is necessary to ensure these ecosystems provide everything they can to contribute to Africa’s growth and the empowerment of its people. This is what the book aims to delineate. The effect of climate change on Africa’s forests remains to be determined. However, climate change projections for Africa’s rainforest regions indicate a 3–4 °C increase in temperature by 2100,8,9 approximately double the estimated mean surface temperature increase for the earth in general.10

 Weisse, M., & Gladman, E. (2020). We lost a football pitch of primary rainforest every 6 seconds in 2019. Washington, D.C: World Resource Institute. 4  Estrada, A., Garber, P., & Chaudhary, A. (2020). Current and future trends in socio-economic, demographic and governance factors affecting global primate conservation. PeerJ, 8, e9816. doi:10.7717/peerj.9816. 5  Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.-E., Odongo-Braun, C., . . . Pickens, A. (2021). Forest disturbance alerts for the Congo Basin using Sentinel-1. . Environmental Research Letters 16:024005. 6  Chapman, C., Abernathy, K., Chapman, L., Downs, C., Effiom, E. O., Gogarten, J., . . . Sarkar, D. (2022). The future of sub-Saharan Africa’s biodiversity in the face of climate and societal change. Frontiers in Ecology and Evolution 744. 7  Mayaux, P., Pekel, J.-F., Desclée, B., Donnay, F., Lupi, A., Achard, F., . . . Nasi, R. (2013). State and evolution of the African rainforests between 1990 and 2010. Philosophical Transactions of the Royal Society B: Biological Sciences 368, 20120300. 8  Zelazowski, P., Malhi, Y., Huntingord, C., Sitch, S., & Fisher., J. B. (2011). Changes in the potential distribution of tropical forest on a warmer planet. Philosophical Transactions of the Royal Society of London 369, 137–160. 9  Malhi, et al. 2013; Malhi, Y., Adu-Bredu, S., Asare, R. A., Lewis, S. L., & Mayaux., P. (2013). The past, present, and future of Africa’s rainforests. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 368:20120312. 10  IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change 3

Foreword

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Understanding what shapes animal movements can provide crucial insights into the management of endangered species and populations. If animals repeatedly return to locations with specific resources, it suggests that these resources are critical to manage if conservationists want to help a population maintain or recover. It is refreshing to see the authors posit that if forest elephant movements in their present form are to be maintained, the planet’s rich nations must match and surpass the impressive legislation for protected areas made by forest elephant range states in their commitment to demand and create the economic conditions needed for the sustainable management of tropical forest resources, including elephants. The study demonstrated why understanding the movement of forest-living elephants is critical for reducing conflicts between elephants and farmers and understanding the variation between elephant herd movement and forest dynamics. The book further exhibits that sustainable management to maintain the species’ resilience to environmental changes and diseases, such as the establishment of protected corridors allowing gene flow between isolated populations, is critical to ensuring the species’ long-term survival. Animal movement data can reveal important food and mineral resources.11,12 For a variety of reasons, using this information in conservation efforts may be especially important in Africa. First, the United Nations has designated this as the Decade of Restoration, so funding for restoration efforts has increased significantly. Second, because 20% of Africa’s land surface (6.6 million km2) is degraded, an area twice the size of India, Africa will be a prime target for these restoration efforts.13 Finally, large sections of many of Africa’s national parks have been logged or converted to agriculture, often during periods of political unrest. These are prime areas for reforestation efforts because the areas are still legally national parks and the people who converted the forest to agriculture have been resettled in several cases. The book clearly demonstrates why better conservation strategies are clearly needed, particularly in Africa, which will face significant challenges in the coming decades. Movement ecology provides information that can be extremely beneficial to conservation. Exciting new avenues for gathering data on animal movement are being opened by new technologies. As a result, it emphasizes the critical need for

[Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen]. Cambridge University Press. In Press. 11  Reyna-Hurtado, R., Chapman, C., Melletti, M., Mukasa, M., & d’Huart, J. (2023). Movements patterns and population dynamics of giant forest hog groups in Kibale National Park, Uganda. . New York: Springer. 12  Thurau, E. B. (2023). Primate movements across the nutritional landscapes of Africa. R. Reyna-­ Hurtado, M. Melletti, and C.A. Chapman, editors. Movement ecology of afrotropical forest mammals. New York: Springer. 13  Archer, et al. 2018; Archer, E., Dziba, L., Mulongoy, K., Maoela, M., Walters, M., Biggs, R., . . . Dunham, A. (2018). Summary for policymakers of the regional assessment report on biodiversity and ecosystem services for Africa. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services . Bonn, Germany.

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increased research efforts that can lead to conservation to ensure that African forest mammals have a bright future. I am greatly honored to have had the opportunity to review this masterwork that will surely influence the strides being made in the biodiversity and conservation sphere. Congratulations to editors Rafael Reyna-Hurtado, Colin A. Chapman, and Mario Melletti for the great work and I look forward to referencing this book in my discussions with leaders going forward. Thank you. Kaddu Sebunya CEO, African Wildlife Foundation Contact information – [email protected]

Acknowledgments

Rafael Reyna-Hurtado wants to thank his wife, Edith Rojas, and his two children, Aranza and Emiliano, for being great partners in life, especially in all adventures and projects he undertakes. They are the reasons why he always wants to come back home. Great appreciation goes to Colin Chapman, Lauren Chapman, Martin Mukasa, Mario Melletti, Jean Pierre d’Huart, Alex Tumukunde, Patrick Kyaligonza, John Okwuilo, Mauro Sanvicente, Sophie Calme, Tony Goldberg, Dennis Tumugisha, and Patrick Omeja for invaluable help in Uganda. Rafael Reyna-­ Hurtado appreciated the help that National Geographic through the Committee of Research and Exploration gave to this project two grants (No. 9189-12 and 9839-16). RRH also thanks Fondation Segré for funding to investigate wildlife in Kibale National Park through the project “Conservation of Giant Forest Hog in a set of protected areas in Western Uganda” and El Colegio de la Frontera Sur for time to write this chapter and support Rafael research activities. Colin Chapman would like to thank all the friends and colleagues who he has worked with over the years, particularly Claire Hemingway. He would also like to express his gratitude to the hard working men and women of the Uganda Wildlife Authority who work hard and often risk their lives to make the parks of Uganda safe for wildlife. One ranger was killed by poachers just two days before this book was submitted. Colin Chapman was supported by the Wilson Center while writing this book. Mario Melletti would like to thank his mother for her continuous moral support to his work. A special thanks also goes to the rest of his family and to Giuseppe Corte. Mario Melletti appreciated the kind invitation of Rafael Reyna-Hurtado and Colin Chapman to be on board as part of the editorial team of this fascinating book.

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Contents

1

What Do We Know About Mammal Movements in African Tropical Forests?��������������������������������������������������������������������������������������    1 Rafael Reyna-Hurtado, Colin A. Chapman, and Mario Melletti

2

Movement Patterns and Population Dynamics of Giant Forest Hog Groups in Kibale National Park, Uganda�������������������������������������    9 Rafael Reyna-Hurtado, Colin A. Chapman, Mario Melletti, Martin Mukasa, and Jean Pierre d’Huart

3

Forest Elephant Movements in Central Africa: Megafauna Need Megaspaces ������������������������������������������������������������������������������������   27 Stephen Blake and Fiona Maisels

4

Elephant Movements, Abundance, and Use of Water Sources in Kibale National Park, Uganda ����������������������������������������������������������   59 Rafael Reyna-Hurtado, Mario Melletti, Martin Mukasa, Patrick A. Omeja, David Keeble, Alexander V. Georgiev, Graeme Shannon, and Colin A. Chapman

5

 Movement Ecology and Evolutionary History of Forest Buffalo��������   79 Lisa Korte, Mario Melletti, and Nathalie Smitz

6

 Site Fidelity and Home Range Shifts in a Leaf-Eating Primate����������   99 Urs Kalbitzer, Martin Golooba, and Colin A. Chapman

7

 Primate Movements Across the Nutritional Landscapes of Africa ����  115 Emma G. Thurau, Brynn E. Lowry, John Bosco Nkurunungi, and Jessica M. Rothman

8

Conditions Facilitating a “Landscape of Fear from Disease” in African Forest Mammals��������������������������������������������������������������������  133 Tyler R. Bonnell, James Robert Ochieng, and Colin A. Chapman

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9

Contents

Do Seasonal Frugivory and Cognition Shape Foraging Movements in Wild Western Gorillas?��������������������������������������������������  151 Benjamin Robira, Simon Benhamou, Terence Neba Fuh, and Shelly Masi

10 Females  Move in Tight Crowds, Males Roam: Socioecology and Movement Ecology of Mandrills ����������������������������������������������������  171 Shun Hongo 11 Linking  Movement Ecology to Conservation Biology��������������������������  187 Colin A. Chapman, Rafael Reyna-Hurtado, and Mario Melletti Index������������������������������������������������������������������������������������������������������������������  195

Contributors

Simon  Benhamou  Centre d’Ecologie Fonctionnelle et Evolutive, CNRS et Université de Montpellier, Montpellier, France Stephen  Blake  Department of Biology, Saint Louis University, Louis, MO, USA Max Planck Institute for Animal Behavior, Radolfzell, Germany Wildcare Institute, Saint Louis Zoo, Saint Louis, MO, USA

Saint

Tyler R. Bonnell  Department of Psychology, University of Lethbridge, Lethbridge, AB, Canada Department of Mathematics & Computer Science, University of Lethbridge, Lethbridge, AB, Canada Colin A. Chapman  Biology Department, Vancouver Island University, Nanaimo, BC, Canada Wilson Center, Washington, DC, USA School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China Jean Pierre d’Huart  Giant Forest Hog Project, Kibale National Park, Uganda Wild Pigs Specialist Group IUCN SSC, Hamme-­Mille, Belgium Terence  Neba  Fuh  Dzanga-Sangha Protected Areas, Central African Republic, Current affiliation for Terence Fuh: World Wide Fund for Nature – Germany, Berlin, Germany Alexander  V.  Georgiev  School of Natural Sciences, Bangor University, Bangor, UK Martin Golooba  Makerere University Biological Field Station, Fort Portal, Uganda xv

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Contributors

Shun Hongo  The Center for African Area Studies, Kyoto University, Kyoto, Japan Urs Kalbitzer  Department of Biology, University of Konstanz, Constance, Germany Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Constance, Germany David Keeble  School of Natural Sciences, Bangor University, Bangor, UK Lisa Korte  International Affairs, Africa Branch, US Fish & Wildlife Service, Falls Church, VA, USA Brynn  E.  Lowry  Department of Anthropology, Hunter College of the City University of New York, New York, NY, USA Anthropology Program, The Graduate Center, City University of New York, New York, NY, USA New York Consortium in Evolutionary Primatology, Bronx, NY, USA Fiona Maisels  Wildlife Conservation Society, Bronx, NY, USA Biological and Environmental Sciences, University of Stirling, Stirling, Scotland, UK Shelly  Masi  Eco-anthropologie, Muséum National d’Histoire Naturelle, CNRS, Université de Paris, Musée de l’Homme, Paris, France Mario Melletti  Giant Forest Hog Project, Kibale National Park, Uganda Wild Pig Specialist Group and African Buffalo Initiative Group IUCN SSC, Rome, Italy Martin Mukasa  Giant Forest Hog Project, Kibale National Park, Uganda John  Bosco  Nkurunungi  Biology Department, Mbarara University of Science and Technology, Mbarara, Uganda James  Robert  Ochieng  Department of Zoology, Entomology and Fisheries Sciences, College of Natural Sciences, Makerere University, Kampala, Uganda Patrick A. Omeja  Makerere University, Kampala, Uganda Rafael Reyna-Hurtado  Department of Biodiversity Conservation El Colegio de la Frontera Sur, Campeche, Mexico IUCN Peccary Specialist Group, Campeche, Mexico IUCN Wild Pigs Specialist Group, Campeche, Mexico Giant Forest Hog Project, Kibale National Park, Uganda Benjamin  Robira  Centre d’Ecologie Fonctionnelle et Evolutive, CNRS et Université de Montpellier, Montpellier, France Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Trentino-Alto Adige, Italy

Contributors

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Eco-anthropologie, Muséum National d’Histoire Naturelle, CNRS, Université de Paris, Musée de l’Homme, Paris, France Jessica  M.  Rothman  Department of Anthropology, Hunter College of the City University of New York, New York, NY, USA Anthropology Program, The Graduate Center, City University of New York, New York, NY, USA New York Consortium in Evolutionary Primatology, New York, NY, USA Graeme Shannon  School of Natural Sciences, Bangor University, Bangor, UK Nathalie  Smitz  Royal Museum for Central Africa (Biology Department), Tervuren, Belgium Emma  G.  Thurau  Department of Anthropology, Hunter College of the City University of New York, New York, NY, USA Anthropology Program, The Graduate Center, City University of New York, New York, NY, USA New York Consortium in Evolutionary Primatology, New York, NY, USA

Chapter 1

What Do We Know About Mammal Movements in African Tropical Forests? Rafael Reyna-Hurtado

, Colin A. Chapman

, and Mario Melletti

Abstract  Tropical forests have long fascinated people. These ecosystems are a source of food and water, medicine, clean air, materials to build houses, and inspiration and awe. For city people, tropical forests often represent the unknown and sources of threatening diseases and dangerous animals. They also represent one of the last frontiers for science as the interiors of some forests remain largely scientifically unexplored (e.g., Lomami Forest in the Democratic Republic of Congo, DRC; Nouabale-Ndoki in the Republic of Congo; Manu, Peru; Roraima Forest and Cordilleras in Venezuela and Guiana; Papua-New Guinea tropical forests). New species of animals and plants are being discovered in tropical forests every year. In a well-known study, Erwin (1988) demonstrated that we know only a small portion of the invertebrates of a Neotropical forest, especially those living in the canopy, and calculated that there is between 10 and 30 million species of plants and animals on Earth. This estimate was later reduced to 8 million species, but still the majority will be found in tropical forests (Mora et al., 2011). One research area that can provide information critically needed for conservation is movement ecology. However, studying movement ecology in tropical forests forests is often logistically very difficult. Therefore, it is not surprising that we know little about the movement of tropical forest species compared to those living in open areas. R. Reyna-Hurtado (*) Department of Biodiversity Conservation, El Colegio de la Frontera Sur, Campeche, Mexico IUCN Peccary Specialist Group, Campeche, Mexico IUCN Wild Pigs Specialist Group, Campeche, Mexico e-mail: [email protected] C. A. Chapman Biology Department, Vancouver Island University, Nanaimo, BC, Canada Wilson Center, Washington, DC, USA School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China M. Melletti Wild Pig Specialist Group and African Buffalo Initiative Group IUCN SSC, Rome, Italy © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_1

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R. Reyna-Hurtado et al.

1.1 Introduction to Tropical Forest Mammal Studies Tropical forests have long fascinated people. These ecosystems are a source of food and water, medicine, clean air, materials to build houses, and inspiration and awe. For city people, tropical forests often represent the unknown and sources of threatening diseases and dangerous animals. They also represent one of the last frontiers for science as the interiors of some forests remain largely scientifically unexplored (e.g., Lomami Forest in the Democratic Republic of Congo, DRC; Nouabale-Ndoki in the Republic of Congo; Manu, Peru; Roraima Forest and Cordilleras in Venezuela and Guiana; Papua-New Guinea tropical forests). New species of animals and plants are being discovered in tropical forests every year. In a well-known study, Erwin (1988) demonstrated that we know only a small portion of the invertebrates of a Neotropical forest, especially those living in the canopy, and calculated that there is between 10 and 30 million species of plants and animals on Earth. This estimate was later reduced to 8 million species, but still the majority will be found in tropical forests (Mora et al., 2011). Despite their ecological, economic, and scientific importance, tropical forests are being negatively impacted faster than any other biome on Earth. Globally, ~60 million ha of tropical primary forest were lost from 2002 to 2019 (Weisse & Gladman, 2020), and 21% of this loss occurred in Africa (Estrada et al., 2020; Chapman & Peres, 2021). Thus, ecological research that can be used to protect these ecosystems are critically needed. One research area that can provide information critically needed for conservation is movement ecology. This field was recently launched as a new paradigm (Nathan, 2008). It was framed as a discipline of ecology that investigates the causes and consequences of animal movement. Traditionally animal movement was studied in narrow ways from several separate scientific disciplines; however, Nathan et al. (2008) argued that what was needed was a paradigm that integrated several disciplines, including locomotion abilities, neurocognitive abilities, internal states, external conditions, and the animal’s ecology. However, studying movement ecology in tropical forests is often logistically very difficult. In savanna systems, researchers can often observe animals from far distances, often from the comfort and safety of a vehicle; this is simply not possible in a dense tropical forest. Furthermore, many of tropical forest species are shy and avoid approaching observers, and they can be rare and difficult to encounter. Using technological advances in drones, telemetry, and camera trapping (Kays et al., 2015; Chapman et  al., this volume, Chap. 11) has allowed advancement in movement ecology of tropical forest mammals. However, capturing animals to deploy collars is difficult and dangerous (see Chap. 2 this book, Reyna-Hurtado et al., this volume­a, Chap. 2), and the humid environment of the forest means the longevity of devices is often much reduced (Reyna-Hurtado et al., 2016). Therefore, it is not surprising that we know little about the movement of tropical forest species compared to those living in open areas.

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1.2 African Tropical Forest Studies The study of African tropical animals has occurred in relatively few sites. Some have become long-term study sites. For example, at Nouabale-Ndoki, the study of forest elephants (Loxodonta cyclotis), lowland gorillas (Gorilla gorilla), buffalos (Syncerus caffer), and bongos (Tragelaphus eurycerus) has been possible since observations can be made at a bai where animals come to feed on mineral-rich soils (Korte et al., this volume, Chap. 5; Blake & Maisels, this volume, Chap. 3). Several of sites were established by primatologist including the Gombe National Park in Tanzania (Goodall, 1986), Virunga National Park in DRC (Fossey & Harcourt, 1977), Parc of Volcanoes in Rwanda (Fossey & Harcourt, 1977), Kibale National Park in Uganda (Struhsaker, 1975; Wrangham et al., 1991; Chapman et al., 1995), Tai Forest in Ivory Coast (Boesch & Boesch-Achermann, 2000), and Wamba in DRC (Kano, 1982). These long-term efforts facilitated a great deal of primates and non-primate research as they provided much needed logistical support. However, despite these efforts, there are still many species that we know very little about. For example, despite considerable effort, we were unable to find authors who could contribute a chapter to our book on forest carnivores or on some common, range-wide species such as red river hogs, duikers, or other antelopes. In 2019, we (Reyna-Hurtado & Chapman, 2019) brought together a series of studies on animal movement in Neotropical forests, and now in 2023, we are replicating this effort but with studies from Afrotropical forests. We hope these volumes stimulate more research on animal movement inside the fascinating, but fragile, tropical forests of the world and point to gaps in knowledge. In the next sections, we summarize the chapters of this book, and we suggest possible new directions for the study of animal movement in tropical environments.

1.3 Book Presentation This book presents 11 chapters on the movement ecology of several mammal species found in the tropical forests of Africa. In Chap. 1, Reyna-Hurtado, Chapman, and Melletti introduce the chapters and summarize a state of the knowledge of movement ecology for Afrotropical forest mammals and suggest new directions to design studies on movement ecology for mammals living inside Africa tropical forest. In Chap. 2, Reyna-Hurtado and collaborators present the first documented home range estimates for a group of giant forest hog (Hylochoerus meinertzhageni). This is one of the largest suids in the world and is an endangered species that, despite a wide distribution, is surviving in just a few protected areas. The study was done in Kibale National Park in Uganda over 5 years. The fact that giant forest hogs live in dense tropical forest, is shy, and is targeted by poachers made the study of this population extremely difficult. The authors present home range estimates (four

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methods), occupancy rate, detection probability, and daily movement patterns. They also provide ecological and behavioral information on group sizes, group structure, and feeding habits. In Chap. 3, Blake and Maisels analyze the movement of forest elephants (Loxodonta cyclotis) and consider driver movements. They found that movement was strongly influenced by a human-induced landscape of fear. They argue that large animals modify ecosystems at a scale larger than many protected areas. As a result, conserving forest elephants will require multicountry efforts to manage tropical forests across country borders. In Chap. 4, Reyna-Hurtado and colleagues present a multiyear analysis of elephant family groups in Kibale National Park, Uganda. Using camera traps set at water sources and mineral-rich sites, they provide information on visitation rate, occupancy rate, detection probability, index of relative abundances, and movement patterns. They also describe the time elephant’s family groups visit water sources and if these visits are related with crop raiding activities outside the park. Some behavioral data were obtained, such as family group size and visitation patterns (i.e., who arrives at water sources first and who leaves last). Understanding forestliving elephant movement is crucial to minimize conflicts between elephants and farmers and to understand the dynamics between elephant herd movements and forest dynamics. In Chap. 5, Korte et al. present an analysis of forest buffalos (Syncerus caffer nanus) for a Congo Basin tropical forest. They highlight that compared to the well-­ studied savanna buffalo (S.c. caffer, S.c. brachyceros, S.c. aequinoctialis), there is little information on forest buffalo. Their research reveals that compared to savanna buffalo, forest buffalo have smaller home ranges, shorter daily movement, no seasonal movement, and smaller group sizes. Korte et  al. also suggest that genetic health is an important parameter to take into consideration when developing management practices and suggest that establishing wildlife corridors that allow gene flow between isolated populations may be critical for the future of forest buffalo populations. In Chap. 6, Kalbitzer and collaborators examined 10 years of data on the leaf-­ eating (and endangered) red colobus (Piliocolobus tephrosceles) in Kibale National Park, Uganda. Using the most updated method to estimate home range (autocorrelated kernel density estimator), they evaluated if home ranges changed over the years. They found that red colobus shows high fidelity over the 10 years. The authors explore the relationship between site fidelity and type of food consumption. In Chap. 7, Thurau and collaborators reviewed the movement patterns in primates across Africa tropical forest and food acquisition. They argue that to find suitable food, animals must navigate through a matrix of resources that vary in their concentrations of nutrients, toxins, and digestion inhibitors while also avoiding multiple hazards, such as food competitors and predators. Thurau et al. reviewed the movement ecologies of primates in African forests with a focus on nutrient acquisition. They discuss how primates find different nutrients using a variety of sensory adaptations and adapt their movements to meet specific nutritional needs.

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In Chap. 8, Bonnell and collaborators provide a theoretical contribution where they introduce the “landscape of fear from diseases” as a driver of animal movement. This original chapter argues that similar to when predation attempts create learned associations between landscape features and predation risk, disease could have similar effects and influence movement. In this chapter, Bonnell et al. examine evidence of a “landscape of fear from disease,” which is when individuals show avoidance or increased vigilance of disease threats in specific locations. The authors present a framework that identifies elements responsible for the development of a landscape of fear from disease. They then use this framework to pinpoint combinations of pathogen characteristics and host movement behaviors that are likely to facilitate learned associations between landscapes and disease threats. Some of these combinations are likely to occur in the context of African forest mammals and thus influence their conservation. In Chap. 9, Robira et al. present a study of what drives group movement in western gorillas (Gorilla gorilla) of the Central African Republic. They compile published and new evidence to investigating how the seasonal frugivorous western gorillas decide where to feed (movement heuristic and spatial knowledge), how to go (e.g., movement speed and straightness), and when to go (temporal knowledge) and come back to feeding sites (recursion pattern) to shed light on the foraging strategies and the underpinning cognition in response to their diet seasonal changes (high and low fruit seasons). Robira et al. found that western gorillas rely on spatiotemporal knowledge to decide where to go and when in both seasons. They argue that understanding mechanisms affecting animal foraging efficiency is crucial in the current context of global climate changes and its unpredictable consequences on food availability. In Chap. 10, Hongo Shun examined movements of mandrills (Mandrillus sphinx), an endangered species of primate that has received little scientific attention. This species has an unusual social system for a primate forming huge groups of hundreds of individuals, with males moving in and out of the group seasonally. Shun summarizes what is known about how groups remain in contact and how solitary adults rejoin groups. Group crowdedness and frequent exchange of long-­ distance calls are key to the collective movement of large groups that engage in regular subgrouping. The adaptive benefits of the large group size possibly lie in female tactics relating to infanticide avoidance and polyandrous mating. While very little is known about how solitary males find groups at the onset of the mating season, their seasonal influxes can be relatively well explained as foraging and mating tactics. Since the major questions of mandrill social organization are strongly related to their movement ecology, the intensive research of movement and positioning behavior using GPS telemetries and remote sensing is crucially needed to disentangle the social system of this intriguing monkey. Finally, in Chap. 11, Chapman, Reyna-Hurtado, and Melletti link movement ecology with conservation biology, and with clear examples, they make the case that conservation actions will benefit from information obtained within the framework of movement ecology. Chapman et al. summarizes the goal of this book – the need for scientific information that enriches conservation decision-making.

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1.4 Future Directions This book compiles exemplary studies of the movement of some African forest mammals. Primatologists have produced a wealth of information across Africa that includes information on their ecology and movement. However, it is worrisome that despite our searching intensively, little information is available about carnivore movement patterns inside African tropical forests. Nor was there adequate data on small more elusive mammals, such as pangolins, aardvarks, and porcupines. Even for some large and more common mammals, such as red river hogs (Potamochoerus porcus), several species of duikers, and other forest antelopes, there is little information available on their movement patterns. Therefore, we know almost nothing about group home range, dispersal, group size, and the area needed to conserve populations of these species. This means that it becomes impossible to construct informed conservation plans, as we have to rely on poor information at best or, in many cases, likely just guesses. This book represents an initial effort to study animal movement in African tropical forests with the goal of acquiring information to inform conservation. We hope this book inspires young African researchers to study the amazing species that live inside the few remaining tropical forests of Africa. We are optimistic that these future efforts will serve to protect these sites for future generations. As a result, in the future, it will still be possible to spot a group of forest elephants enter to a bai in Congo or more than 100 mandrills cross a forest gap in Cameroon or to glance a dark shadow inside a forest in Uganda that indicates that a group of giant forest hog is on the move.

References Blake, S., & Maisels, F. (this volume). Forest elephant movements in Central Africa – Megafauna need megaspaces. In R. Reyna-Hurtado, M. Melletti, & C. A. Chapman (Eds.), Movement ecology of Afrotropical forest mammals. Springer. Boesch, C., & Boesch-Achermann, H. (2000). The chimpanzees of the Taï Forest: Behavioural ecology and evolution. Oxford University Press. Chapman, C. A., & Peres, C. A. (2021). Primate conservation: Lessons learned in the last 20 years can guide future efforts. Evolutionary Anthropology, 30, 345–361. Chapman, C. A., Wrangham, R. W., & Chapman, L. J. (1995). Ecological constraints on group size: An analysis of spider monkey and chimpanzee subgroups. Behavioural Ecology and Sociobiology, 36, 59–70. Chapman, C.  A., Reyna-Hurtado, R., & Melletti, M. (this volume). Linking movement ecology with conservation biology. In R. Reyna-Hurtado, M. Melletti, & C. A. Chapman (Eds.), Movement ecology of afrotropical forest mammals. Springer. Erwin, T.  L. (1988). The tropical forest canopy. In Biodiversity (pp.  123–129). National Academies Press. Estrada, A., Garber, P. A., & Chaudhary, A. (2020). Current and future trends in socio-economic, demographic and governance factors affecting global primate conservation. PeerJ, 8, e9816. https://doi.org/10.7717/peerj.9816

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Fossey, D., & Harcourt, A.  H. (1977). Feeding ecology of free-ranging mountain gorillas. In T. H. Clutton-Brock (Ed.), Primate ecology. Academic Press. Goodall 1962. Goodall, J. (1986). The chimpanzees of Gombe: Patterns of behaviour. Harvard University Press. Kano, T. (1982). The social group of pygmy chimpanzees (Pan paniscus) of Wamba. Primates, 23, 171–188. Kays, R., Crofoot, M. C., Jetz, W., & Wikelski, M. (2015). Terrestrial animal tracking as an eye on life and planet. Science, 348(6240), aaa2478. Korte, L., Melletti, M., & Smitz, N. (this volume). Movement ecology and evolutionary history of forest buffalo. In R. Reyna-Hurtado, M. Melletti, & C. A. Chapman (Eds.), Movement ecology of afrotropical forest mammals. Springer. Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G., & Worm, B. (2011). How many species are there on earth and in the ocean? PLoS Biology, 9(8), e1001127. Nathan, R. (2008). An emerging movement ecology paradigm. Proceedings of the National Academy of Sciences, 105, 19050–19051. Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052–19059. Reyna-Hurtado, R., & Chapman, C. A. (2019). Movement ecology of neotropical forest mammals. Springer Nature. isbn: 978-3-030-03463-4. Reyna-Hurtado, R., Sanvicente-López, M., Pérez-Flores, J., Carrillo-Reyna, N., & Calmé, S. (2016). Insights into the multiannual home range of a Baird’s tapir (Tapirus bairdii) in the Maya Forest. Therya, 7(2), 271–276. Reyna-Hurtado, R., Chapman, C. A., Melletti, M., Mukasa, M., & d’Huart, J. P. (this volume-a). Movements patterns and population dynamics of giant forest hog groups in Kibale National Park, Uganda. In R. Reyna-Hurtado, M. Melletti, & C. A. Chapman (Eds.), Primate movements across the nutritional landscapes of Africa. Springer. Reyna-Hurtado, R., Melletti, M., Mukasa, M., Omeja, P. A., Keeble, D., Georgiev, A. V., Shannon, G., & Chapman, C.  A. (this volume-b). Elephant movements, abundance, and use of water sources in Kibale National Park, Uganda. In R. Reyna-Hurtado, M. Melletti, & C. A. Chapman (Eds.), Movement ecology of afrotropical forest mammals. Springer. Struhsaker, T. T. (1975). The red colobus monkey. University of Chicago Press. Weisse, M., & Gladman, E. D. (2020). We lost a football pitch of primary rainforest every 6 seconds in 2019. World Resource Institute. Wrangham, R. W., Conklin, N. L., Chapman, C. A., & Hunt, K. (1991). The significance of fibrous foods for Kibale Forest chimpanzees. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 334, 171–178.

Chapter 2

Movement Patterns and Population Dynamics of Giant Forest Hog Groups in Kibale National Park, Uganda Rafael Reyna-Hurtado , Colin A. Chapman Martin Mukasa, and Jean Pierre d’Huart

, Mario Melletti

,

Abstract  African wild suids living in forests have been poorly studied, and some species such as the giant forest hog (Hylochoerus meinertzhageni) – one of the largest species of wild pigs in the world – are disappearing at alarming rates. Of particular concern are the eastern Africa populations that are endangered by habitat encroachment and illegal hunting. Here, we present results of the first ecological study on this species in the Kibale National Park, a mid-elevation, tropical forest in western Uganda. The goal of our research was to determine group’s movement patterns, group size and structure, habitat use, and foraging patterns. At intermittent R. Reyna-Hurtado (*) Department of Biodiversity Conservation, El Colegio de la Frontera Sur, Campeche, Mexico IUCN Peccary Specialist Group, Campeche, Mexico IUCN Wild Pigs Specialist Group, Campeche, Mexico Giant Forest Hog Project, Kibale National Park, Uganda e-mail: [email protected] C. A. Chapman Biology Department, Vancouver Island University, Nanaimo, BC, Canada Wilson Center, Washington, USA School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China M. Melletti Giant Forest Hog Project, Kibale National Park, Uganda Wild Pig Specialist Group and African Buffalo Initiative Group IUCN SSC, Rome, Italy M. Mukasa Giant Forest Hog Project, Kibale National Park, Uganda J. P. d’Huart Giant Forest Hog Project, Kibale National Park, Uganda Wild Pigs Specialist Group IUCN SSC, Hamme-Mille, Belgium © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_2

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periods over 5 years, we tracked a group of this species using handheld GPS, and for 2 years we deployed automated camera traps in salt licks and bathing places. Home range of the group of giant forest hogs was estimated at 11.2 km2 (kernel density estimator at 99%) with a core area of 2.4 km2 (kernel density estimator at 50%). Areas with dense bushes and sparse trees surrounded by mature forests were their favorite habitat where they feed on the herbaceous plants and rest in shady areas of approximately 5 × 5 m located under the densest thickets called “sleeping sites.” Giant forest hogs were captured on camera traps 7893 times within 141 independent events that showed that group size is highly variable and ranges from 3 to 11 individuals with occasional sightings of solitary individuals, usually subadult males. Occupancy rate value was medium (ψ  =  0.778; SE  =  0.01), and the species presented a low detection probability (P = 0.172; SE = 0.01). Large groups are composed of a dominant male, one or two additional males, several adult females, and up to four juveniles. Conservation of this species in eastern Africa requires the protection of forest ecosystems and associated mosaic of habitats with dense bushes and open gaps surrounded by mature forest. Keywords  Hylochoerus meinertzhageni · Occupancy models · Kernel density estimator · Minimum convex polygon · Group size

2.1 Introduction Wildlife, especially large mammals, are suffering range and population reductions across the globe mainly due to human activities (Ripple et al., 2015). We are destroying forests and transforming ecosystems, hunting or capturing wildlife at alarming rates, and introducing exotic species everywhere (Silva-Rodríguez et  al., 2010). Animals that survive in fragmented or perturbed forest tend to move less (Tucker et al., 2018) and become nocturnal (Gaynor et al., 2018). This is especially true for large mammals and is more acute for large herbivores of which most of the species considered are at risk of being endangered or are already endangered according to the IUCN (Ripple et al., 2015). The study of animal movement has raised human attention since Aristoteles (Nathan, 2008) and in the last few years has received considerable scientific attention due to the development of better tracking technologies (Kays et al., 2015). An ecological paradigm has been launched (Movement Ecology by Nathan et al., 2008) to integrate all aspects of animal movement into an ecological paradigm to understand causes and consequences of animal movement. In addition to scientific value, animal movement studies have conservation interest as they can identify sensitive areas where animals migrate, or important wildlife corridors, or the amount of area needed to sustain a viable population, as well as natural behavioral patterns or behavioral changes when conditions are not optimal (Nathan et al., 2008; Reyna-­ Hurtado et al., 2009; Kays et al., 2015).

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The giant forest hog (hereafter GFH; Hylochoerus meinertzhageni) is one of the largest suids of the world with males reaching more than 275 kg and more than 1 meter in height (Wilson & Mittermeier, 2011). The massive body is covered by black hair and has naked prominent cheeks and tusks that protrude from the mouth horizontally (Fig. 2.1). This species lives in tropical Africa from Ethiopia to Western Africa in scattered populations inhabiting diverse vegetation types, ranging from bamboo forest and subalpine forest to lowland swamps and secondary growth thickets (d’Huart, 1978; Kingdon, 1997; Reyna-Hurtado et al., 2017). A highly herbivore species, the GFH feeds in habitats that range from dense bushes or thickets to grasslands, but they always occur close to forest that they likely use for refuge. The GFH lives in family groups with a dominant male, and several females with piglets, typically forming groups from 8 to 12 individuals, but sometimes aggregations of 40 animals have been seen (Kingdon, 1997). The GFH is listed as Least Concern on the IUCN Red List (http://www.iucnredlist.org/), but there is evidence that at least the eastern African populations have been decreasing at alarming rates in the last 30 years (Tumukunde et al., 2014). Kibale National Park (Kibale hereafter) is a 795 km2 park at the foothill of the Rwenzori Mountain in western Uganda. The park is famous because it is one of the few remnants stretch of mid-elevation tropical forest in the region and the largest, holding populations of 12 primate species, elephants (Loxodonta africana, L. cyclotis, and hybrids; Mondol et  al., 2015), golden cat (Profelis auratus), and several mammal’s species, among others (Chapman & Lambert, 2000). Kibale and the adjacent Queen Elizabeth National Park (Queen Elizabeth hereafter) hold the largest population of GFH in the country and are one of the strongholds of the populations for eastern Africa (d’Huart & Kingdon, 2013; Reyna-Hurtado et al., 2017).

Fig. 2.1  Male of giant forest hog (Hylochoerus meinertzhageni) in a water point in Kibale National Park, Uganda

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In the1970s, a major study was conducted on the ecology of the GFH by J.P. d’Huart in Virunga National Park, DRC (Virunga hereafter), a park that is the ecosystem continuation of Queen Elizabeth in Uganda. The seminal work of d’Huart (1978), Kingdon (1997), and Klingel et al. (1999) in Queen Elizabeth reported GFH as a highly herbivore species that lives in groups that often split in subgroups (especially subadult males or pregnant females) and live in high densities (10.5 individuals per km2 in Virunga National Park, DRC; d’Huart, 1978) and in sometimes larger groups (up to 24 individuals in Queen Elizabeth (Klingel & Klingel, 2004) and 13.2  in Virunga (d’Huart, 1978)). It was determined at that time that groups need an area of 5.03 km2 in Virunga to fulfill its basic requirements (d’Huart, 1978). However, in a closed forest ecosystem such as Kibale, no study has focused on the ecological aspects of GFH, neither in the area required by groups in this type of habitat, and how often they change the area was unknown. Also, it was unknown the occupancy rate and relative abundance of the species in forest ecosystems. Therefore, since 2012, we have investigated ecological and population aspects of the GFH of Kibale. We aimed to investigate GFH ranging patterns as home range and socioecological aspects, such as group size and social behavior. We also estimated occupancy rate, detection probability, and an index of relative abundance obtained from camera traps set in water sources.

2.2 Materials and Methods 2.2.1 Study Site Kibale is a 795 km2 park at the foothill of the Rwenzori Mountain in western Uganda (Fig. 2.2 0°13′–0°41′N and 30°19′–30°32′E) at 1500 m a.s.l (Chapman et al., 1997). The main vegetation type is tall-closed canopy rainforest (57%) with a mosaic of swamp (4%), grasslands (15%), regenerating forest following the harvest of pine plantations (1%), and recolonizing forest (19%) (Chapman & Lambert, 2000). Rainfall is bimodal with two wet and two dry seasons each year and an annual average rainfall of 1646 mm (Chapman et al., 2021). Daily maxima and minima temperature averaged 23.7 and 15.5 °C, respectively, from 1990 to 1998 (Gillespie & Chapman, 2001).

2.2.2 Study Design We attempted to capture and attach radiotelemetry collars to individuals for several months using several techniques. We tried tracking them and shooting a dart filled with anesthetics, we waited for the group in salt licks or bathing points, and we attempted to drive them to specific designed nets, but no technique produced results. Therefore, since mid-2012, we relied on the tracking skills of three dedicated field

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Uganda

All boundaries are approximate

Legend 0

5

10

20 km

N

Papyrus, elephant grass Shrub Short grass Forest

Notes: Land cover classification derived from Landsat ETM+, acquired January 31, 2003 Map by: Joel Harter, University of New Hampshire

Fig. 2.2  Map of Kibale National Park, Uganda, with images showing the area with forest cover, the forest, and the forest understory

assistants. Three field assistants tracked one group of GFH living in areas close to Makerere University Biological Station in Kibale and, with the help of a handheld GPS (GPSMAP62s GARMIN, Inc., Olathe Kansas, US), collected daily data on group location, feeding habits, group composition, and habitat use. Groups were tracked 5 days a week, 3 weeks per month, from August 2012 to June 2017 with some months of intermittency due to funding limitations. Following Klingel (1997) and Klingel et al. (1999), we identified the group due to the facial characteristics of the dominant male (with the help of camera traps), by the group composition, and by tracking the group continuously. In addition, one of the field assistants spent all these years in the area visiting the site at least once per week from 2012 to 2017. Usually, they record a single point per day when they located the group early in the morning. More GPS points were acquired if the group did not run away. When the group ran away, the field assistants stopped to track them that day but returned the next day. If the group allowed them to remain close (usually 50 m or less), a GPS point every half an hour was recorded. Additionally, habitat type, group structure, feces, and other interesting facts, such as sleeping site location, were recorded with a GPS point. Starting in July 2013, we deployed eight automated camera traps (Reconyx HyperFire C800 Professional IR and six CuddeBack and two Bushnell) in salt licks or bathing places inside the preliminary home range already estimated for the group. The cameras were visited every month to download data and check battery status. Camera settings were set up in high frequency with no delay between photos. They

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Fig. 2.3  Kibale National Park, Uganda, with the locations where camera traps were deployed

were set between 30 and 50 cm high in selected trees to record animals visiting the water sources or the salt lick. Finally, from 2019 to 2021, we replaced the old cameras and set nine new camera traps (Reconyx Inc. M800) in nine sites during February 2019 to December 2020 and in only three sites from January to October 2021. These cameras were programmed to take photos continuously in the rapid-fire mode and for 24 h without delay between photos. The cameras were attached to the base of the nearest tree of selected water sources where wildlife came to drink, feed, or lick soil around the water source. The cameras were attended by the same team of dedicated field assistants who changed batteries, replaced memory cards, and assured the cameras were still in place. Some cameras malfunctioned, sometimes due to humidity and due to animals that moved them, and in a couple of cases, some were stolen by poachers (Fig. 2.3).

2.2.3 Data Analysis GPS’ locations were used to construct home ranges using kernel density estimators (KDE) with 99% and 95% of the observations and the core used areas with the 50% of observations. We used three methods to select the smoothing factor (h), the least squares cross validation (h-LSCV), the reference value (href), and the manual smoothing factor (h-manual), and then we tested a relative goodness of fit by

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contrasting them with the area under the curve (AUC) value and using the Wilcoxon analyzes to select the best KDE given the location data of GFH. Minimum convex polygons (MCP) of home ranges were also constructed using 100%, 95%, and 50% of the observations. This method is very common and allows comparison with other studies, and more importantly, MCP also shows the area where animals or groups can reach or visit independently of preferences (that are estimated with kernel density method; Kernohan et al., 2001). We used R-studio (adehabitatHR package) to estimate both the KDE and the MCP values. Additionally, we estimated a relative abundance index (RAI) from the cameras deployed between 2019 and 2021 with the following equation:

RAI  N / SE *1000 camera  nights

with N = number of independent records and SE = sampling effort measured as the number of days multiplied by the number of cameras. Photos obtained were analyzed in terms of social and sex structure of groups, and group size was estimated when possible. We estimated occupancy rate (ψ), detection probability (P), as well as the daily activity patterns for the GFH in all the sites from the camera trapping period of 2019 to 2021. We obtained 7893 photos of GFH during these 3 years (n = 5635 camera days). These photos were reduced to 141 independent events. Independence time was set at 60 min to be comparable with other studies of ungulates (Reyna-Hurtado et al., 2019). The occupancy rate (ψ) and detection probability (P) were estimated for the 3 years and separated by year using R-studio (CamtrapR package) and with data separated by 7-day periods as repeated visits within 3 months period. We estimated the time of day that GFH visited the water sources along a 24-h continuum by constructing a circular plot using CamtrapR (R-studio package; Niedballa et al., 2016).

2.3 Results 2.3.1 Home Range Estimation Kernel density estimator (KDE) indicated that the GFH’s home range is between 4 km2 and 13 km2 using 99% of observations. According to the AUC selection criteria, the best estimator was the KDE with the smoothing factor selected manually (h-manual). This estimator indicated that the GFH group is moving in an area of 11.2 km2 (99% of observations) with a core area of 2.4 km2 (50%). The MCP indicated 8.7 km2 (100% of observations) and a core area of 2.7 km2 (50% of observations). Both methods are similar with respect to the size of the core area (Table 2.1; Figs. 2.4, 2.5, 2.6 and 2.7).

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Table 2.1  Home range estimation using kernel density estimator and minimum convex polygon methods using 50%, 95%, 99%, and 100% of observations of a group of giant forest hog (Hylochoerus meinertzhageni) in Kibale National Park, Uganda Method Minimum convex polygon

50% 95% 99% 2.69 km2 7.14 km2 8.68 km2 (100%) 2.43 km2 8.51 km2 11.23 km2

Kernel density estimator (h-manual) Kernel density estimator (h-ref) 2.76 km2 9.34 km2 12.64 km2 Kernel density estimator 0.61 km2 2.73 km2 3.78 km2 (h-LSCV)

AUCdf (with Wilcoxon test) NA 0.992 0.9914 NA

N = 547, from August 13, 2012, to June 6, 2017

Fig. 2.4  Home range estimated with the minimum convex polygon method of a giant forest hog (Hylochoerus meinertzhageni) group in Kibale National Park, Uganda. The areas represent 100%, 95%, and 50% of the observations

2.3.2 Occupancy Rate (ψ), Detection Probability (P), and Relative Abundance Index (RAI) GFH had a medium occupancy rate in all years at all the water sources with an occupancy rate (ψ) of 0.778 (SE = 0.0139) and a low probability of detection (P) of 0.172 (SE = 0.0192). 2021 was the year with the lowest occupancy rate, and GFH groups occurred in more sites in 2019 (Table 2.2).

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Fig. 2.5  Home range estimated with kernel density estimator of a giant forest hog (Hylochoerus meinertzhageni) group in Kibale National Park, Uganda. The areas represent 99%, 95%, and 50% of the observations. The smoothing factor was selected manually (h-manual)

Fig. 2.6  Home range estimated with kernel density estimator of a giant forest hog (Hylochoerus meinertzhageni) group in Kibale National Park, Uganda. The areas represent 99%, 95%, and 50% of the observations. The smoothing factor was selected with the reference value (h-ref)

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Fig. 2.7  Home range estimated with kernel density estimator of a giant forest hog (Hylochoerus meinertzhageni) group in Kibale National Park, Uganda. The areas represent 99%, 95%, and 50% of the observations. The smoothing factor was selected with the least squares cross validation value (h-LSCV) Table 2.2  The occupancy rate and detection probability of giant forest hog (Hylochoerus meinertzhageni) visiting water sources in Kibale National Park, Uganda Year 2019 2020 2021

Occupancy rate (ψ) 0.805 0.493 0.292

SE 0.14 0.18 0.15

Detection probability (P) 0.232 0.150 0.156

SE 0.03 0.03 0.05

From 2019 to 2021, we estimated an index of relative abundance (RAI) of 25.02 (given 141 independent records over 5635 camera days times 1000) for groups of GFH visiting the water points of Kibale.

2.3.3 Daily Patterns Groups of GFH were mainly diurnal from 8:00 to 20:00 with a peak of activity early in the afternoon around 16:00; there were few photos of GFH at night after 20:00.

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2.3.4 Group Size and Behavior GFH average group size was six members (excluding solitary animals), with a maximum of 11 individuals. Age structure was typically composed by a dominant male, two or three adult females, and up to five juveniles. Solitary individuals were mostly subadult males or females with piglets. It is worth noting that despite some documented infanticide in this species in Ethiopia (Siege, 2011), in our research we have recorded, thanks to photographic evidence, several instances where the dominant male is interacting positively with newborns of the group. The preferred areas are forest gaps full of herbaceous species in the middle of mature forest and swampy areas. Preferred food species observed were Mimulopsis solmsii, Ipomea spp., and Piper umbrellatea. These plants were very abundant in the dense bushes of the gaps surrounded by forest. Besides finding food species, GFH used these dense bushes as refuge by constructing large tunnels inside that used to escape from danger in a very secretive way through the dense thickets. GFH uses bushes as sleeping sites as well. We have found more than 50 “sleeping sites” of approximately 5 × 5 m under dense coverage where the soils have been cleaned of debris and where the groups have rested several times. Often, there are also latrines associated with these sites separated 1 or 2  m from the “sleeping sites.” Additionally, in several instances, the dominant male has priority to wallow, while other members wait their turn outside the muddy areas.

2.4 Discussion This is the first time that movement patterns of GFH have been estimated for a forested ecosystem. The group of GFH moved in large areas (up to 11 km2) but had a core area that was used intensively (2.5 km2). The group typically moved in a cohesive way but sometimes split temporarily in small subgroups, especially subadult males and females with piglets. Foot tracking technique was the best choice given the expertise of field assistants; however, GPS collars would be a further advantage as a more detailed list of locations would be collected in a much less time. In Kibale, there is still poaching, and GFH is a preferred species. This fact made groups to fear humans to the extent that habituating them is very difficult and darting them is almost impossible. In a previous study in Virunga National Park in Democratic Republic of Congo, d’Huart (1978) estimated 5.03 km2 for groups living in a mosaic of open and closed habitat. Home range differences are likely driven by characteristics of the habitat. Groups living in Kibale are moving in large areas due to visiting the few gaps inside a continuous forest, while the groups on Virunga National Park have access to the mosaic of vegetation types all the time. GFH is a highly herbivore species and needs open areas to feed but sleeps and rests inside closed forest (Reyna-Hurtado et al., 2014).

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Klingel et  al. (1999) studied GFH in Queen Elizabeth, which has abundant savanna and bushes, and GFH moved in small areas (up to 2 km2). Queen Elizabeth is connected to Virunga, so probably both estimates respond to ecological conditions there. The abundance of grasses and bushes is higher than in the forest ecosystem of Kibale. More evidence is needed, but contrary to what we expected, groups of GFH move in larger areas within forest ecosystems than in bushy savanna areas. This finding is consistent with another ungulate species. White-lipped peccary (Tayassu pecari) in forested areas of Mexico have larger home range areas (Reyna-­ Hurtado et al., 2009) than those in the Cerrado ecosystem of Brazil where there is mix of forest and agricultural lands (Jorge et al., 2019). Forest elephants also move in large areas inside forest ecosystems when they need to feed or when they are in a landscape of fear as stated by Blake and Maisels (this volume). Direct observations in areas where GFH groups range allowed us to determine that forest gaps containing herbaceous species in the middle of mature forest and swampy areas are preferred habitats. Commonly fed upon species include Mimulopsis solmsii, Ipomoea spp., and Piper umbrellatea. These plants were abundant in the dense bushes of the gaps surrounded by forest. GFH used these dense bushes as refuge and constructed large tunnels to escape from danger. Giant forest hog uses bushes as sleeping sites as well. GFH often visited salt licks and bathing/wallowing sites and preferred to come to these areas between 16:00 to 20:00 (Fig. 2.8). Camera trap data indicated that the dominant male often had priority to wallow, while other members waited their turn outside the muddy areas. From the calculated home range, we did an exercise of estimating how many groups may exist in the park assuming our study area is similar to other areas of the Fig. 2.8  Daily patterns of water source visitation by GFH in Kibale National Park, Uganda

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park. We estimated that at least 50 groups could exist in Kibale. However, given the high rate of poaching in some areas (especially in the south and eastern parts of the park), this is likely an overestimate (Hance, 2015). For example, in 4 days of patrolling, the snare removal team of Kibale removed 59 snares (J.  T. Okwilo Snare removal team chief, pers. comm.). Given this, a conservative estimate is that approximately between 20 and 30 groups occur in Kibale. Despite this conservative low estimate, these potential number of groups that Kibale could support make the park one of the strongholds of the population in eastern Africa eco-region. In 2016, we visited three parks adjacent to Kibale: Queen Elizabeth National Park, Semuliki National Park, and Toro-Semliki Wildlife Reserve. We conducted approximately 20 semi-structured interviews with personnel of Uganda Wildlife Authority and experienced researchers. We designed a booklet with the photo of the three suids species potentially present there (common warthog (Phacochoerus africanus), bushpigs (Potamochoerus larvatus), and GFH) and showed it to the people interviewed. This indicated that GFH was present in Queen Elizabeth and Toro-­ Semliki Wildlife Reserve in apparently large numbers. The GFH has not been seen in the last 10 years in Semuliki National Park, but a fourth species was common here, the red river hog (Potamochoerus porcus). This is interesting as Semuliki National Park is the eastern tip of the Congo Basin lowland forest where the red river hog is common and is probably the only area in Uganda where this species can be still found. A result of the interviews was that a possibly large population of GFH occurs in Toro-Semliki Wildlife Reserve, a park composed mostly of savannas with forest stretches and abundant herds of Uganda kob and buffalos. However, the park contains some patches of forest with a chimpanzee population that has been studied since 1998 and an apparently significant population of GFH that need to be studied. We deployed some cameras there and found a large group of giant forest hogs composed between 15 and 25 individuals (Fig. 2.9). This is larger than the groups of Kibale (six individuals in average), so it may mean larger groups exist here and probably a larger population. Additional research demonstrated that group size is slightly larger in Toro-Semliki Wildlife Reserve and Queen Elizabeth than Kibale (R.  Reyna-Hurtado, J.P. d’Huart, M.  Melletti, and M.  Mukasa unpublished information). Salerno et al. (2017) suggest that connectivity for wildlife species movement is poor and deteriorating in the Albertine rift arch. The finding of Toro-Semliki Wildlife Reserve is important for some forest species because these three areas can form a belt of suitable habitat that might connect parks far away such as Bwindi Impenetrable Forest in the south with Budongo Forest or Murchison Falls National Park in the north. However, the high human density living in the matrix surrounding these parks probably cannot allow any large mammal to move across, isolating these protected areas. This is not the best scenario for the conservation of GFH or any other large mammals; efforts must be made for restoring connectivity across these protected areas along the Albertine rift arch, or to protect the stretches of forest that still connect Queen Elizabeth with Kibale, or, if this is not possible, to consider translocations in the future to ensure genetic diversity.

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Fig. 2.9  A large group of giant forest hog (Hylochoerus meinertzhageni) in Toro-Semliki Wildlife Reserve Uganda. (Photo credits: Rafael Reyna)

GFH occupancy rate was in general high, especially in 2019; however, detection probability is consistently low. Thus, GFHs were recorded in most of the water points but not frequently. This is in accordance with previous experience of some of us that have seen the species only once in over 30 years of research (C. A. Chapman pers. obs.). Despite that occupancy rate was in general high, it was lower than elephants living in Kibale (Reyna-Hurtado et al., this volume). GFH may have some preferred water points that visit regularly but in general remains a rare species within Kibale and not so frequently recorded in our study (R.  Reyna-Hurtado; C. A. Chapman, pers. obs.; unpublished information). GFH is a diurnal species that rarely is seen at night; GFH visits water sources frequently and in particular during early in the afternoon. This is consistent with research conducted in Virunga (d’Huart, 1978), Queen Elizabeth (Klingel et  al., 1999; Klingel & Klingel, 2004), and Mt. Aberdare in Kenya (Kingdon, 1997).

2.5 Conclusion This is the first ecological study on forest-dwelling GFH. Five years of tracking data indicate that GFH living in forest ecosystems have larger home ranges than those living in mixed forest and open areas (d’Huart, 1978; Klingel, 1997). GFH of Kibale depends on the dense bushes that grow in the gaps. These areas are kept open by

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elephants that feed on herbaceous species (Struhsaker et  al., 1996; Lawes & Chapman, 2006), and GFHs visit these areas frequently to feed on herbaceous vegetation. Therefore, by maintaining these large areas of herbaceous vegetation, elephants may promote GFH populations. This possible association needs further research. Group size in Kibale is smaller than reported elsewhere (Virunga and Queen Elizabeth; Klingel, 1997; d’Huart & Kingdon, 2013), but social structure is similar to groups living in open areas. We estimate that no more than 30 groups of GFH may live in Kibale. We documented the presence of the GFH in three adjacent protected areas of western Uganda  – Queen Elizabeth National Park, Kibale, and Toro-Semliki Wildlife Reserve – and these areas are important for connectivity along the Albertine rift to be regained (Salerno et al., 2017). GFHs in Kibale are under threatening poaching pressure (Hance, 2015; authors pers. obs). Conserving the GFH population of Kibale and the terrestrial fauna of the park would assure that ecosystem process is maintained and the park maintains its ecological integrity. It also means that one of the largest pigs of the world would be protected and would amaze the future generations. Ugandan rainforest, as well as all eastern African rainforest, are facing great pressure from the growing human population and are being significantly encroached upon (Plumptre et al., 2007). In addition, terrestrial mammals face great pressure from snare poaching, which is known to harm species such as chimpanzees, baboons, and elephants (Hance, 2015). Studying and protecting the terrestrial mammal community of Kibale is urgently needed as it is a stronghold of population of many species that have been extirpated elsewhere. Understanding the basic ecological information such as movement patterns, occupancy rate, relative abundance, group size and behavior, and habitat and space requirements of these species is fundamental to design effective conservation measures. Movement ecology is an interesting framework to study a secretive species that is affected by high hunting pressure. Animal movements and areas needed and preferred are key ecological information that may have invaluable conservation interest. Acknowledgments  RR-H greatly appreciates the support that the National Geographic through the Committee of Research and Exploration gave to this project with two grants (No.9189-12; 9839-16). Great appreciation goes to Fondation Segré for funding 3 years of research through the project “Conservation of Giant Forest Hog in a Set of Protected Areas of Western Uganda” El Colegio de la Frontera Sur (Campeche, Mexico). The Anthropology Department of McGill University (Montreal, Quebec, Canada) provided field and laboratory equipment and support for the field season. The Uganda Wildlife Authority (UWA) help was essential for us to deploy camera traps and to guide us into some isolated sites. RR-H is indebted to four dedicated field assistants without whom this project would not have been possible: Martin Mukasa (coauthor), Patrick Kyaligonza, the late Wilson Ruikakara, and John Okwilo. Great collaborators of this project were Alex Tumukunde, Mauro Sanvicente, Sophie Calme, the late David Hyeroba, Tony Goldberg, Edith Rojas, Patrick Omeja, Kato Innocent, the late Jeremy Lwanga, and personnel from the Uganda Wildlife Authority. Thank you to Lizzi Martínez for map elaboration. Thanks to all these people and institutions.

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References Blake, S., & Maisels, F. (this volume). Chapter 3: Forest elephant movements in Central Africa – Megafauna need megaspaces. In R. Reyna-Hurtado, C. A. C. Chapman, & M. Melletti (Eds.), Movement ecology of afrotropical forest mammals. Springer. Chapman, C. A., & Lambert, J. (2000). Habitat alteration and the conservation of African primates: A case study of Kibale National Park, Uganda. American Journal of Primatology, 50, 169–185. Chapman, C. A., Chapman, L. J., Wrangham, R., Isabirye-Basuta, G., & Ben-David, K. (1997). Spatial and temporal variability in the structure of a tropical forest. African Journal of Ecology, 35, 287–302. Chapman, C. A., Galán-Acedo, C., Gogarten, J. F., Hou, R., Lawes, M. J., Omeja, P. A., Sarkar, D., Sugiyama, A., & Kalbitzer, U. (2021). A 40-year evaluation of drivers of African rainforest change. Forest Ecosystems, 8(1), 66. https://doi.org/10.1186/s40663-­021-­00343-­7 d’Huart, J.  P. (1978). Écologie de l’hylochere (Hylochoerus meinertzhageni Thomas) au Parc Nationale des Virunga (Exploration PNV, Deuxieme Série, Fsc. 25). Foundation pour favoriser les recherchers Scientifiques en Afrique. d’Huart, J. P., & Kingdon, J. (2013, in press). Hylochoerus meinertzhageni. In J. S. Kingdon, & M.  Hoofman (Eds.), The mammals of Africa. Vol. 6. Pigs, deer, giraffe, bovids and hippos. University of California Press. Gaynor, K. M., Hojnowski, C. E., Carter, N. H., & Brashares, J. S. (2018). The influence of human disturbance on wildlife nocturnality. Science, 360(6394), 1232–1235. Gillespie, T. R., & Chapman, C. A. (2001). Determinants of groups size in the red colobus monkey (Procolobus badius): An evaluation of the generality of the ecological-constraints model. Behavioral Ecology and Sociobiology, 50, 329–338. Hance, J. (2015, May 22). Chimpanzees losing lives and limbs to the landmines of the forest. Published in The Guardian. http://www.theguardian.com/environment/radical-­conservation/2015/ may/22/chimps-­losing-­lives-­and-­limbs-­to-­the-­landmines-­of-­the-­forest IUCN Red List. https://www.iucnredlist.org/ Jorge, M. L. S., Keuroghlian, A., Bradham, J., Oshima, J. E. F., & Ribeiro, M. C. (2019). White-­ lipped peccary movement and range in agricultural lands of Central Brazil. In R.  Reyna-­ Hurtado & C. Chapman (Eds.), Movement ecology of neotropical forest mammals (pp. 39–55). Springer. Kays, R., Crofoot, M. C., Jetz, W., & Wikelski, M. (2015). Terrestrial animal tracking as an eye on life and planet. Science, 348(6240), aaa2478. Kernohan, B.  J., Gitzen, R.  A., & Millspaugh, J.  J. (2001). Analysis of animal space use and movement. In J. J. Millspaugh & J. M. Marzluff (Eds.), Radio tracking and animal populations (pp. 125–166). Academic Press. Kingdon, J. (1997). The Kingdon field guide to African mammals (p.  450). Princeton University Press. Klingel, H. (1997). Social organisation of the giant forest hog Hylochoerus meinertzhageni. XXV international ethological conference in Wien 1997. Advances in Ethology, 32, 259. Klingel, H., & Klingel, U. (2004). Giant forest hog, Hylochoerus meinertzhageni, in Queen Elizabeth National Park, Uganda. Suiform Soundings, 4, 24–25. Klingel, H., Klingel, U., & Viehl, K. (1999). Social organisation of the afrotropical suidae. XXVI international ethological conference in Bangalore 1999. Advances in Ethology, 34, 159. Lawes, M.  J., & Chapman, C.  A. (2006). Does the herb Acanthus pubescens and/or elephants suppress tree regeneration in disturbed Afrotropical forests? Forest Ecology and Management, 221, 274–284. Mondol, S., Moltke, I., Hart, J., Keigwin, M., Brown, L., Stephens, M., & Wasser, S. K. (2015). New evidence for hybrid zones of forest and savanna elephants in Central and West Africa. Molecular Ecology, 24, 6134–6147. Nathan, R. (2008). An emerging movement ecology paradigm. Proceedings of the National Academy of Sciences, 105, 19050–19051.

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Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052–19059. Niedballa, J., Sollmann, R., Courtiol, A., & Wilting, A. (2016). camtrapR: An R package for efficient camera trap data management. Methods in Ecology and Evolution, 7(12), 1457–1462. Plumptre, A. T. R. B., Davenport, M., Behangana, R., Kityo, G., Eilu, P., Ssegawa, C., Ewango, D., Meirte, C., Kahindo, M., Herremans, J., Kerbis-Peterhanse, J. D., Pilgrim, M., Wilson, M., & Languy, D. M. (2007). The biodiversity of the Albertine Rift. Biological Conservation, 134, 178–194. Reyna-Hurtado, R., Rojas-Flores, E., & Tanner, G. W. (2009). Home range and habitat preferences of white-lipped peccary groups (Tayassu pecari) in a seasonal tropical forest of the Yucatan Peninsula, Mexico. Journal of Mammalogy, 90, 1199–1209. Reyna-Hurtado, R., Tumukunde, A., Chapman, C. A., Rojas-Flores, E., Sanvicente, M., Sengupta, R., & Calme, S. (2014). On the track of the giant forest hog in Kibale, National Park, Uganda: A preliminary report on studying the species. Suiform Soundings, 12, 38–41. Reyna-Hurtado, R., d’Huart, J.  P., & Turkalo, A. (2017). Chapter 11: Forest hog Hylochoerus meinertzhageni (Thomas 1904). In M. Melletti & E. Meijaard (Eds.), Ecology, conservation and management of wild pigs and peccaries (pp. 114–121). Cambridge University Press. Reyna-Hurtado, R., Sima-Pantí, D., Andrade, M., Padilla, A., Retana-Guaiscon, O., Sanchez-­ Pinzón, K., Martinez, W., Meyer, N., Moreira-Ramírez, J.  F., Carrillo-Reyna, N., Rivero-­ Hernández, C. M., Serrano Mac-Gregor, I., Calme, S., & Arias-Dominguez, N. (2019). Tapir population patterns under the disappearance of free-standing water. Therya, 10(3), 353. Reyna-Hurtado, R., Chapman, C. A., Melletti, M., Mukasa, M., & d’Huart, J. P. (this volume). Chapter 2: Movement patters and population dynamics of giant forest hog groups in Kibale National Park, Uganda. In R.  Reyna-Hurtado, C.  A. C.  Chapman, & M.  Melletti (Eds.), Movement ecology of afrotropical forest mammals. Springer. Ripple, W. J., Newsome, T. M., Wolf, C., Dirzo, R., Everatt, K. T., Galetti, M., Hayward, M. W., Kerley, G.  I., Levi, T., & Lindsey, P.  A. (2015). Collapse of the world’s largest herbivores. Science Advances, 1, e1400103. Salerno, J., Chapman, C.  A., Diem, J.  E., Dowhaniuk, N., Goldman, A., MacKenzie, C.  A., Aria-Omeja, P., Palace, M.  W., Reyna-Hurtado, R., Ryan, S.  J., & Hartter, J. (2017). Park isolation in anthropogenic landscapes: Land change and livelihoods at park boundaries in the African Albertine Rift. Regional Environmental Change, 18, 913–928. https://doi.org/10.1007/ s10113-­017-­1250-­1 Siege, L. (2011). Infanticide in giant forest hog (Hylochoerus meinertzhageni). Suiform Soundings, 11(1), 3–5. Silva-Rodríguez, E. A., Ortega-Solís, G. R., & Jiménez, J. E. (2010). Conservation and ecological implications of the use of space by chilla foxes and free-ranging dogs in a human-dominated landscape in southern Chile. Austral Ecology, 35, 765–777. Struhsaker, T. T., Lwanga, J. H., & Kasenene, J. M. (1996). Elephants, selective logging and forest regeneration in the Kibale forest, Uganda. Journal of Tropical Ecology, 12, 45–64. Tucker, M. A., Böhning-Gaese, K., Fagan, W. F., Fryxell, J. M., Van Moorter, B., Alberts, S. C., Ali, A. H., Allen, A. M., Attias, N., Avgar, T., Bartlam-Brooks, H., Bayarbaatar, B., Belant, J. L., Bertassoni, A., Beyer, D., Bidner, L., van Beest, F. M., Blake, S., Blaum, N., Bracis, C., Brown, D., de Bruyn, P. J. N., Cagnacci, F., Calabrese, J. M., Camilo-Alves, C., Chamaillé-Jammes, S., Chiaradia, A., Davidson, S. C., Dennis, T., DeStefano, S., Diefenbach, D., Douglas-Hamilton, I., Fennessy, J., Fichtel, C., Fiedler, W., Fischer, C., Fischhoff, I., Fleming, C. H., Ford, A. T., Fritz, S.  A., Gehr, B., Goheen, J.  R., Gurarie, E., Hebblewhite, M., Heurich, M., Hewison, A. J. M., Hof, C., Hurme, E., Isbell, L. A., Janssen, R., Jeltsch, F., Kaczensky, P., Kane, A., Kappeler, P.  M., Kauffman, M., Kays, R., Kimuyu, D., Koch, F., Kranstauber, B., LaPoint, S., Leimgruber, P., Linnell, J. D. C., López-López, P., Markham, A. C., Mattisson, J., Medici, E. P., Mellone, U., Merrill, E., de Miranda Mourão, G., Morato, R. G., Morellet, N., Morrison, T. A., Díaz-Muñoz, S. L., Mysterud, A., Nandintsetseg, D., Nathan, R., Niamir, A., Odden, J.,

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Chapter 3

Forest Elephant Movements in Central Africa: Megafauna Need Megaspaces Stephen Blake and Fiona Maisels Abstract  To survive, all organisms must maximize energy input and reproductive output and minimize risk. This applies to how they travel through their environment. Due to numerous mechanical and physical laws that scale allometrically, forest elephants (Loxodonta cyclotis), as the largest vertebrate inhabitants of Africa’s dense tropical forests, solve this optimization in rather different ways than the smallest, for example, shrews. In this chapter, we discuss how body size influences animal ranging and why elephants ought to have very large ranges. We then use GPS telemetry data we collected ourselves and additional data from published studies to characterize home range size and other movement metrics of forest elephants in Central Africa. We demonstrate how the availability of water, food, nutrients, social organization, sex, and personality combines to drive the movements of forest elephants. We conclude that these factors are largely trumped by a human-induced landscape of fear throughout the range of forest elephants. We explain how the combination of large body size and the extent of forest elephant movements lead to their profound ecosystem engineering impacts, which help maintain forest biodiversity and increase carbon sequestration. We then show how human activities, primarily poaching and infrastructure development, restrict elephant movements, with negative consequences for forest function that have globally relevant ramifications. We finally argue that if forest elephant movements in their present form are to be maintained, the planet’s rich nations must match and surpass the impressive legislation for protected areas made by forest elephant range states in their commitment to demand and create the economic conditions needed for the sustainable management of tropical forest resources, including elephants.

S. Blake (*) Department of Biology, Saint Louis University, Saint Louis, MO, USA Max Planck Institute for Animal Behavior, Radolfzell, Germany Wildcare Institute, Saint Louis Zoo, Saint Louis, MO, USA e-mail: [email protected] F. Maisels Wildlife Conservation Society, Bronx, NY, USA Biological and Environmental Sciences, University of Stirling, Stirling, Scotland, UK © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_3

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Keywords  Animal movement · Tropical forest · Congo Basin · Conservation · Ecosystem engineer · Home range

3.1 Introduction The movements of organisms are driven by a combination of factors internal to the organism (internal state and the capacity to move and navigate) which interact with external (environmental) factors to determine movement trajectories (Nathan et al., 2008). Variation in these factors and their interactions determines the spatial and temporal scales over which movements occur. Movement trajectories over timescales from milliseconds to lifetimes impact fitness in myriad ways. Efficiency of movement over space and time to acquire resources such as food, water, mates, and safety will maximize energy balance and the probability of survival and reproduction compared to less efficient movements. Nathan et al. (2008) argue that the same fundamental mechanisms govern the movement of slime mold in a petri dish, wildebeest on the Serengeti, and dandelion seeds in the atmosphere. Arguably, if slime mold in a petri dish is at one end of a scale of organismal movement complexity, the other terrestrial extreme could be represented by forest elephant movements in a tropical rainforest. Elephants are the largest and among the most long-lived, intelligent, and socially complex terrestrial animals (Filippi et al., 2017; Healy et al., 2014; McComb et al., 2000; Wittemyer et al., 2005; Wittemyer & Getz, 2007), and tropical rainforests are the most diverse ecosystems on the planet (Connell, 1978). As keystone species and ecosystem engineers (Berzaghi et al., 2019; Blake et al., 2009), patterns of forest elephant movements may have profound impacts on the structure and composition of their habitats, which feeds back into shaping future elephant movement trajectories in an oscillating cycle (Blake et al., 2009). This chapter summarizes movement data from the Republic of Congo, Gabon, and the Central African Republic to describe the evolutionary ecology ballet between forest elephants and their environment and the unfortunate role that humans now play as choreographers.

3.2 Big Animals Need Big Spaces The classic textbook example of the relationship between body size and energy expenditure involves a shrew at the extreme left of the body mass axis and elephants at the extreme right, with an exponential decline in mass-specific energy use over a suite of animals of increasing size (e.g., Freeman et al., 2019). The caption states that 1 kg of shrews requires a lot more metabolic energy than 1 kg of elephants. Small animals therefore must acquire more energy per unit mass than large ones (Kleiber, 1947). However, large animals must maintain large bodies and therefore

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have a larger absolute nutritional requirement than small ones. One manifestation of this is the positive correlation between body size and the scale of ranging among terrestrial vertebrate species  – generally, large animals require more space than smaller ones to find and acquire the food resources they need to maintain a positive energy balance (Peters, 1983). This broad-scale pattern of allometric scaling remains consistent across vertebrates but is modified by locomotion strategy (e.g., walking, running, jumping) and factors such as the abundance of food, trophic guild, and prey size (Tamburello et al., 2015). It is no surprise then that, among walking herbivores, forest elephants have the biggest known home ranges of forest dwelling species (Blake et al., 2008; Blake et al., 2009) (Fig. 3.1). But how big is big?

3.2.1 How Big Is a Big Home Range? Home range is a useful concept in ecology since it defines the space in which “normal activities of food gathering, mating and caring of young” occur (Burt, 1943). However, home range is difficult to define quantitatively; different interpretations of the concept and different data inputs result in different calculations of home range shape and area (Aebischer et al., 1993; Borger et al., 2006; Cagnacci et al., 2010; Laver & Kelly, 2008; Powell & Mitchell, 2012). Indeed, the very concept of “home range” may need revision as tracking technologies allow ever-more complete quantification of movement trajectories (Kays et al., 2015; Kie et al., 2010). Moreover, home range concepts are embedded in a continuum of movement strategies across different temporal and spatial extents, from fine-grained habitat selection at the scale of foraging driven by diet choice, to larger extents of patch use within a matrix of differing habitat quality, to landscapes and beyond involving dispersal, nomadism, and migration (Bastille-Rousseau et al., 2017; Bunnefeld et al., 2011; Fleming et  al., 2014; Orians, 1991; Singh et  al., 2012). Despite these complexities, when using comparable concepts, data, and methods, home range scales allometrically with body mass (M) at about M-0.75 (McNab, 1963) or slightly above (Harestad & Bunnell, 1979; Kelt & Van Vuren, 1999; Mace & Harvey, 1983) backing up the conclusion of Reiss (1988) who, with wonderful simplicity, summarized a large body of theoretical and empirical work by stating “it is hardly surprising that elephants range over larger areas than rabbits.” Until recently the movement patterns of forest elephants were completely unknown, largely due to the technical and logistical difficulties of working on African tropical forests. Dense forests are difficult to access, and because of illegal killing, forest elephants are usually only found at large distances from roads (Barnes et al., 1991; Barnes et al., 1997; Blake et al., 2007; Maisels et al., 2013). Furthermore, visibility in the forest means that movements of individual elephants cannot be determined except via telemetry, and VHF tracking of large mobile animals on foot through dense forest is often logistically unfeasible. The advent of the ARGOS satellite communications system in 1978 provided the first opportunity to track the

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Fig. 3.1 (a) The track of a single elephant (Spikey) generated from hourly GPS fixes illustrates the detail of movement, while the minimum convex polygon (MCP) home range bounding these points merely displays the maximum extent of range and does a poor job of representing space use within the polygon; (b) the 95% kernel home range which estimates the boundaries of where the animal spends 95% of its time with an improved perspective on space use; (c) the 50% kernel home range which is often classified as the “core range” revealing areas of concentration, and (d) detail of the interior of the 50% kernel, the Dzanga Bai (Central African Republic)

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movements of wide-ranging animals without vehicle support. In the early 1990s, James Powell deployed ARGOS and VHF telemetry collars to three forest elephants in southeastern Cameroon (Powell, 1997). Minimum convex polygon (MCP) home ranges (Stickel, 1954) of these elephants were between 203 and 598km2, larger than a rabbit’s, but considerably smaller than many estimates of savannah elephant ranges (e.g., Lindeque & Lindeque, 1991; Thouless, 1996; Verlinden & Gavor, 1998). Subsequent studies were developed mostly in response to the deepening conservation crisis facing forest elephants in the Congo Basin (Barnes, 1999; Barnes et al., 1995; Blake et al., 2007; Blake & Hedges, 2004; Maisels et al., 2013; Poulsen et al., 2017), with movement data collected from forested sites in Congo, Cameroon, Central African Republic, and Gabon and including forest/savannah mosaics in Gabon (Beirne et al., 2019, 2020, 2021; Blake, 2002; Blake et al., 2001, 2008; Mills et al., 2018; Molina-Vacas et al., 2020; Poulsen et al., 2021; Rosin et al., 2020). In total, at least 130 GPS collars have been successfully deployed on forest elephants since 1994. Home range sizes (MCP) vary from 25 to 2226km2 and a maximum linear distance between extremes of range of 104 km, a distance which can potentially be traversed in 5 days – forest elephants can walk up to 20 km per day linear displacement for several days (Blake, 2002). These home range estimates are small compared to the ranging behavior of some savannah elephants from west, east, and southern Africa (Wall et al., 2021) but are consistent with the expectation for a large forest dwelling mammal based on allometric scaling (bigger than a rabbit) (Blake et al., 2009).

3.2.2 The Devil Is in the Details The minimum convex polygon has been used as a crude estimator of home range for decades (Powell, 2000). It is simply a polygon bounded by the outer points of the animal’s trajectory maintaining convex angles between polygon boundaries. All the detail of space use inside the polygon is lost. It was a useful metric for early studies using radiotelemetry where researchers had a small number of relocations from an animal if they were lucky, but the advent of GPS telemetry made collection of thousands or even millions of relocations possible within a single study (Kays et  al., 2015). This level of detail renders the MCP superfluous for anything but the most basic comparisons such as the maximum range that can be delimited. However, more sophisticated analytical methods for large datasets (Calabrese et  al., 2021; Fleming & Calabrese, 2017; Silva et al., 2022) are not without problems, which can have serious impacts on home range definitions and size metrics. Inaccurate ranging metrics, which either under- or overestimate ranging, can have negative consequences for conservation (Noonan et  al., 2020). This chapter is not the place to discuss the merits and pitfalls of different home range estimators and what they mean – this is a hot theoretical topic beyond the scope of this chapter – more worthwhile is to consider the scale of variation in space use by elephants across their distribution and the environmental drivers of this variation. However, illustrating

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some gross differences between home range metrics and what they signify is merited (Fig. 3.1). The GPS tracklog of hourly points over nearly a year from a single elephant, Spikey, in the Ndoki Forest reveals the details of her trajectory, almost completely within the Nouabalé-Ndoki National Park. Spikey repeated travels north-south over a similar geographic range to the west and a single visit to the east but with destinations consistent with the western movements (Fig. 3.1a.). While the northern and southern destinations are frequently and repeatedly visited, the interior of the range is devoid of points and unused. The minimum convex polygon shows the extremes of ranging only. The 95% kernel density estimate is used frequently to better estimate home range since it excludes unused areas in a logically defensible way (Fleming & Calabrese, 2017). However, the statistical discussion on the best way to calculate kernel density estimates (KDE) continues apace (Butts et  al., 2022; Fleming et al., 2022; Silva et al., 2022). Our crude illustration reveals the 95% KDE captures nicely areas of high point density (obviously) but does not include two critical link trajectories to the east and overestimates likely space use around the movement trajectory (Fig. 3.1b). The 50% KDE (Fig. 3.1c) is often referred to as the core area of a home range (Laver & Kelly, 2008) and here clearly identified the Dzanga Bai as the area of highest point density (Fig. 3.1d). While the bai is clearly important to Spikey as a source of minerals and social interactions (Goldenberg et al., 2021; Turkalo & Fay, 2001), it is not biologically meaningful to consider the bai and surrounding area as “core range.” This region only satisfies part of the resource requirements of Spikey, who must move over much larger areas to access sufficient food resources. From a conservation perspective then, there are two clear messages from these data. First Spikey ranges over an enormous area (over 2500km2) crossing an international boundary and using two national parks in different countries. Second, if management resources are limited, ensuring the complete protection of Dzanga Bai (CAR) and its environment is an obvious priority.

3.3 The Scaffolding of Forest Elephant Movement: Elephant Trails Just as paths, roads, and rail lines define the principal terrestrial routes and destinations used by people, discovering how and why forest elephants move is greatly enhanced by understanding the structure of their permanent trail systems. Repeated movements by animals over a surface often result in trail formation in organisms as diverse as ants and elephants (Blake & Inkamba-Nkulu, 2004; Ganeshaiah & Veena, 1991; Nelson et al., 1991). Such trails are usually minimum cost pathways between high gain patches within a habitat (Ganskopp et  al., 2000; Weaver & Tomanek, 1951); thus, the geography of trails provides clues to important resources and movement trajectories between them. Forest elephant trails provide physical connections between several different resource types including fruiting trees, mineral-rich forest

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clearings (bais), and easy traverses across deep or swampy rivers (Blake & Inkamba-­ Nkulu, 2004; Klaus et al., 1998; Vanleeuwe & Gautier-Hion, 1998). Forest elephant trails become wider, and trail networks become denser with increasing proximity to bais (Blake & Inkamba-Nkulu, 2004, Vanleeuwe & Gautier-Hion, 1998). The density of fruiting trees consumed by forest elephants is correlated with proximity to trails, and trail intersections are often created around large fruit trees (Blake & Inkamba-Nkulu, 2004). Follows of fresh tracks on trails show that forest elephants rarely browse when walking on trails but veer off trails into open forest and light gaps to access forage (Blake & Inkamba-Nkulu, 2004). In forests with significant relief, elephant trails often follow ridge tops, while in flat terrain they follow the edge of swamps (Blake pers.obs.). The ease of walking along a manicured elephant trail compared to bushwhacking through dense vegetation must be experienced to be appreciated. Presumably elephants feel the same way. Trails therefore provide us with clues on the movement strategies of elephants to access important dependable/high payback resources (fruit, minerals) over a low transport cost surface. Permanent trails may serve as a sort of cultural spatial memory (Haefner & Crist, 1994; Kashetsky et  al., 2021) (Macfarlane, 2013; Moor, 2017). Thus, a naïve forest elephant or an individual with incomplete spatial memory could rely on following permanent trails to lead them efficiently to favored resources (Blake & Inkamba-Nkulu, 2004) such as enormous fruiting trees (Fig. 3.2).

3.4 What Drives Forest Elephant Movements? In maintaining consistency with the movement ecology paradigm proposed by Nathan et al. (2008), forest elephant movements are the product of internal (intrinsic) and external (extrinsic) factors over multiple temporal scales. Short term energy requirements determine when, where, and how to forage, and feeding strategies are determined by the distribution, abundance, and nutritional quality of available foods. Physiological requirements such as rest, thermoregulation, safety and stress management, as well as social and reproductive needs all interact to determine movement trajectories from seconds to lifetimes (Damuth, 1981). These factors are in turn governed by the biotic and abiotic environment, which are increasingly influenced by that difficult to categorize and the yet omnipresent and multifaceted impact resulting from anthropogenic activity (Blake et al., 2008; Tucker et al., 2018). Interactions between abiotic (topography, insolation, ambient temperature, rainfall, etc.) and biotic (life history traits, intra- and interspecific interactions) factors determine the spatiotemporal distribution of resources, which, coupled with movement capacity, establish the energy landscape over which animals move (Lempidakis et al., 2018; Shepard et al., 2013). These same interactions also generate a heterogeneous distribution of predation risk – the landscape of fear (Laundré et al., 2001). Finally, the combination of these energy and fear landscapes comprises the surface over which animals must attempt to meet their immediate energy balance needs,

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Fig. 3.2 (a) An Autranella congolensis tree at the intersection of several large elephant trails. Trees of this species produce hundreds of large succulent fruits every year. This individual may be 1000 years old and likely came from a seed that was carried from its parent tree inside an elephant’s gut and dispersed in a pile of nutrient-rich dung. (b) An elephant walking down a trail connecting favored resources such as fruit trees

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minimize risk, and ultimately maximize lifetime fitness (Gallagher et al., 2017). So, what does all this mean for forest elephants? Each of the drivers of elephant movements can be considered alone, but they interact in complex ways across the different landscapes where forest elephants still occur. Here we examine some of the most prominent one by one.

3.4.1 Water Availability Previous studies have demonstrated that forest elephants change their ranging patterns and distribution in relation to permanent water under different rainfall regimes. Declines in rainfall in Central Africa over recent decades (Bush, Jeffery, et  al., 2020a) may lead to long term changes to elephant distribution with cascading ecological impacts on the forest. Blake (2002) showed that four GPS collared forest elephants in the Ndoki Forest were strongly clumped around permanent watercourses during dry seasons, but as rainfall increased, they spent more time in uplands. This pattern was corroborated by Beirne et al. (2021) from analyses of a sample of 96 forest elephants across Gabon. Blake (2002) suggested this was not due to water limitation, as is the case for savannah elephants living in arid conditions, but rather to the increased abundance and quality of upland food resources during wetter periods. In higher rainfall periods, fruit and high nutritional value browse are readily available in upland areas, whereas in dry months, upland food is scarce but remains abundant in swamps and riverine areas. Beirne et al. (2021) also recognized this but also argued that elephants may be constrained around permanent water during dry periods because of water limitation but can expand their range into uplands in high rainfall periods because ephemeral standing water is widely available. It is worth mentioning that in Ndoki, and in most Central African tropical forests, the maximum distance between water courses is usually only 3–4 kilometers, and usually much less; thus, the risk of not accessing water every day, from anywhere in the forest, is negligible. Forest elephants in Ndoki showed a strong daily pattern in their distribution in relation to permanent water. They generally congregated in or close to rivers and streams at night and dispersed into uplands during daylight hours (Blake, 2002). We can only speculate on the reasons for this pattern; it is unlikely to be related to thermoregulation since the extensive swamps around even the smallest of rivers in Ndoki maintain cool conditions throughout the day. Forest elephants may find it easier to navigate along trails and between resource patches in daylight but restrict their movements around fallback riparian resources at night. Perhaps more likely is a manifestation of the landscape of fear (Laundré et al., 2001) – rapid silent movement away from danger is difficult in thick vegetation and swamps, but much easier in dry upland forest with a dense trail network. Conversely, we can speculate that elephants’ own experience with humans may have shown that elephants know that humans cannot move through the thick undergrowth and swampy conditions along valley bottoms at night without making considerable noise, so it may be safer for

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elephants to stay in these same valleys at night since hunter access to swamps is difficult in darkness.

3.4.2 Food Availability It is difficult to assess the impact of food availability on forest elephant movements. Data on food abundance and distribution are not available on the scales at which elephants are making movement decisions. Forest elephants are generalist herbivores and facultative frugivores that consume well over 100 plant species and hundreds of plant parts at any given site (Blake, 2002; Short, 1981; Theuerkauf et al., 2000; White et al., 1993), and the distribution and phenology of such a wide range of plants in such botanically rich forests is not known. This is likely the reason why Beirne et al. (2021) found little effect of gross metrics of vegetation productivity (the normalized difference vegetation index, NDVI) on movement parameters including home range size and travel distance. The phenology of plants in Central African forests has been quantified from few sites, of which only 11 have long-term studies (i.e., between 6 and 29  years; Tutin & Fernandez, 1993, White, 1994b, Tutin & White, 1998; https://africanphenologynetwork.online/the-­metadata-­inventory/). We know that general vegetative growth and fruit availability increase with rainfall in tropical forests (Blake, 2002; Richards, 1952; Van Schaik et al., 1993; White, 1994b) and that as a general rule annual fruiting and flowering cycles are the most common, but this is nevertheless highly variable (Adamescu et al., 2018). In general, across tropical forests, the temporal component of food availability is still poorly understood and the spatial component even less so. Consequently, correlating food production to forest elephant movements is not possible except in the crudest terms. However, some general observations can be made and discussed from both elephant movement data and distribution at the population level from survey data, though even these are based on secondary evidence using rainfall as a proxy for gross food production, which has been used in other systems (Reyna-Hurtado et al., 2016). Using telemetry data, both Blake (2002) and Beirne et al. (2020) found a positive correlation between rainfall and travel distance of forest elephants in Congo, Central African Republic, and Gabon. These authors suggest this is due to elevated fruit abundance during wet seasons. Fruit is patchily distributed, with larger distances between discrete patches of fruit than, for example, leaf availability, which is more ubiquitous. Strong correlations between fruit consumption and rainfall (Blake, 2002; White et  al., 1993) support this diet/habitat switch. Because fruit trees are more abundant in uplands compared to riparian areas (Blake, 2002), fruit availability is likely a driver of the increasing presence of elephants in uplands in the rainy seasons (Mills et al., 2018). Forest elephant survey data corroborate the impact of fruit on movement. For example, Blake (2002) found a strong spatiotemporal correlation between fruit availability and the abundance of forest elephants in the Ndoki Forest, Republic of Congo. White (1994c) assessed the role of a single fruit species (Sacoglottis gabonensis), on elephant abundance in Lope, Gabon,

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calculating that all the elephants in a 3000 km2 area moved into a 200km2 Sacoglottis forest during the fruiting season of this species. Indeed, the same tree species likely forms a keystone food for forest elephants when little else is available, as evidenced in the coastal forests of Gabon (Morgan, 2009).

3.4.3 Nutrient Availability Forest elephants are well known to excavate and ingest mineral-rich soils or mineral-­ rich water from seep holes to supplement their nutrient intake from forage (Blake & Inkamba-Nkulu, 2004; Fishlock, 2010; Turkalo et al., 2016; Turkalo & Fay, 2001; Vanleeuwe & Gautier-Hion, 1998). In some parts of their range, forest elephant physical activities lead to the formation of large clearings called “bais” that result from long term digging and trampling in and near watercourses (Turkalo & Fay, 1995). Sometimes these clearings cover several tens of hectares and may contain more than 100 elephants at the same time. In others, elephants dig into banks and under trees, sometimes forming small caves. In the southwestern Central African Republic, bai soils are high in sodium, potassium, calcium, magnesium, phosphorus, manganese, and clay because of dolerite parent material (Klaus et al., 1998). At these sites, forest elephants often dig holes in the substrate, into which mineral-rich water seeps, which the elephants drink. Electrical conductivity of solute in holes where elephants are active is higher than at holes where they are not (Blake, 2002). Bais comprises an important component of the environmental superstructure through which elephants move and have a strong impact on movement patterns. Four GPS collared elephants in the Ndoki Forest visited a bai on 38% of days, and two of these individuals were present at a bai for over 50% of days (Blake unpub. data). Forest elephant activity in bais is mostly at night (Blake, 2002; Turkalo & Fay, 2001; Wrege et  al., 2017) which is likely due to poaching pressure. Travel speed decreases with distance from bais, and elephants often run into a bai at high speed and in a considerable state of excitement. Finally, forest elephants may display prolonged central place foraging around bais over many days or weeks (Fig. 3.3).

3.4.4 Social Organization Forest elephant core social units are small, usually consisting of one or two adult females and their dependent offspring (Merz, 1986; Turkalo & Barnes, 2013; White et  al., 1993); however, their larger-scale social organization is more complex (Goldenberg et al., 2021) involving consistent associations among core units. Unlike savannah elephants in which extended matriarchal hierarchies are the norm (McComb et al., 2000; Wittemyer et al., 2005), small core group size in forest elephants is likely driven by a combination of intraspecific competition for patchy food

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Fig. 3.3  A bull named Sue was fitted with a GPS collar at Mabalé Bai, in the Nouabalé-Ndoki National Park, Republic of Congo (a). Sue visited the bai primarily at night to drink mineral-rich water and move out into the surrounding forest during the day to forage (b). Sue’s data can be downloaded from movebank.org (https://www.movebank.org/cms/webapp?gwt_fragment=page=s tudies,path=study1818825)

resources and reduced predation risk in forests compared to African savannahs (Turkalo et al., 2016). The role of social organization in forest elephant movements is poorly known; however, two contexts merit discussion. First, in addition to offering nutritional resources, bais are well known as elephant social arenas (Fishlock et al., 2008; Turkalo & Fay, 2001) and may serve an important role in male-male

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competition for females. Repeated travel to bais, often from tens of kilometers away, may be driven by sociality, mating, as well as resource acquisition. Small sample sizes of tagged elephants preclude any inferences on social interactions and movement, except at a single site – Loango, on the Gabonese coast. Here, a sample of six female forest elephants tagged in a strip of land between a lagoon and the ocean had small, adjacent ranges with minimal range overlap between individuals (Schuttler et al., 2012). Indeed, females showed strong spatial avoidance, likely because of competition. Wittemyer et  al. (2007) found social segregation among savannah elephants, based on interactions between individuals; dominance relationships and seasonal resource availability led to competition and avoidance during food scarcity. Dominant groups selected better habitat and moved less than subordinate groups. For instance, western lowland gorillas, sympatric with forest elephants, also show movement patterns that indicate resource defense or territoriality (Morrison et al., 2020). While we cannot determine the social context of movement in forest elephants, we must acknowledge that sociality may play an important role, not only in mating systems but also in shaping movements motivated by resource acquisition, both of which will have consequences for fitness.

3.4.5 Personality and Sex The ecological and social context in which movement occurs is perceived, processed, interpreted, and manifested differently by individual forest elephants – i.e., movements reveal personality (Beirne et  al., 2021). This was first suggested by Blake et al. (2008) whose sample size of 28 forest elephants showed highly individual space use. More recently, Beirne et al.’s (2021) analysis of 96 GPS tagged elephants from across Gabon suggested that forest elephants display a range of behaviors or “behavioral syndromes” along a continuum from “idler” to “explorer” that was not dependent on geography or environmental context. Such plasticity should not be unexpected in intelligent and long-lived species with prodigious memories such as elephants. This added level of behavioral complexity has strong implications for predicting forest elephant responses to environmental change, which we will discuss below. A study using 20 collared forest elephants showed that annual male home range size was slightly larger than that of females (Wall et al., 2021). Beirne et al. (2021) in a more detailed analysis of movement characteristics with a larger sample size confirmed that sex was a “key driver” of four of five movement metrics investigated (home range size, diurnally, site fidelity and exploratory behavior, but not distance moved). Males had larger home ranges and less site fidelity between years, were less nocturnal, and showed less exploratory behavior than females. These differences were interpreted as impacts from higher poaching rates on male forest elephants than females. Thus, males move less during the day and are more restricted in their movements – perhaps responses to a heightened perception of, or a difference in the intensity of, the landscape of fear (below).

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3.4.6 The Landscape of Fear The “landscape of fear” is a relatively new term coined based on the responses of elk to newly reintroduced wolves into Yellowstone National Park (Laundré et al., 2001). The term eloquently describes an established concept in biology involving the geography of risk and the ability of organisms to perceive and respond to it. Implications to forest elephants of the landscape of fear were first reported on by Barnes et al. (1991) in their landmark paper “Man Determines the Distribution of Forest Elephants in the Rainforests of Northeastern Gabon.” This paper described the impact of roads on forest elephant abundance in one of the least inhabited parts of Central Africa, showing a strong positive correlation between distance from road and elephant abundance. This information was then used to build a model of predicted forest elephant distribution across Central Africa, based on the distance from the nearest road or navigable river (Michelmore et al., 1994). This phenomenon has been corroborated frequently across the region (e.g., Barnes et  al., 1997; Blake et al., 2007; Maisels et al., 2013). These studies however were all based on inferences of elephant abundance based on dung counts and do not demonstrate an explicit impact on movement. The decline in abundance with proximity to roads was likely due to extirpation of the local elephant population due to poaching, as well as avoidance behavior by individual elephants in response to the landscape of fear. It was not until a regional forest elephant movement ecology study was initiated in 2000 that a clearer picture of forest elephant responses to the landscape of fear was obtained, though even this was a crude interpretation of the complexity of the response (Blake et al., 2008). A total of 28 forest elephants were fitted with GPS collars in 6 sites across Central Africa (excluding DR Congo) (Fig. 3.4). The tags lasted for a mean of 372 days (range 60–619 days). The overwhelming conclusion was that the landscape of fear trumped other ecological constraints on home range. Three home range metrics (MCP, 95% and 50% kernel HR) and maximum linear displacement were all strongly correlated with the area of roadless wilderness in which the elephant occurred. A criticism of that paper was that at the smallest site, Loango (Gabon), the collared elephants were somewhat confined between the ocean and a lagoon; however, even with these data removed the relationship held. Some of the collared elephants occurred in the largest expanses of roadless wilderness remaining in Central Africa (Minkébé, Nouabalé-Ndoki (at the time of the study). Blake et al. (2008) concluded that there were no forest elephants left in the Congo Basin whose movements were not impacted by the human footprint and therefore the landscape of fear. The landscape of fear of forest elephants is most consistently built around human population centers and infrastructure (Beirne et al., 2021), but it is not the infrastructure itself that is directly responsible for the risk, but rather it is the kinds of human activities associated with the infrastructure. Indeed, forest elephants often prefer the secondary vegetation associated with roads and villages (Barnes et al., 1991; Barnes et al., 1995). Illegal killing is the flesh of the landscape of fear, whereas

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Fig. 3.4  Tracks (black dots) and MCP home ranges (gray lines) of 28 forest elephants fitted with GPS collars in the Republic of Congo, Central African Republic, and Gabon, in relation to national parks (bold black outlines) and international boundaries (fine black outlines) (from Blake et al., 2008)

the infrastructure is the bones. This was exemplified in Blake et al.’s (2008) regional study, in which from all of the 28 collared elephants that had the physiological possibility to cross roads, 17 of these individuals crossed “protected” roads (roads inside national parks), repeatedly in many cases. However, only a single elephant crossed an “unprotected” road (a road outside of a national park). An adult female crossed National Route 2 in northern Republic of Congo three times during a year. Her mean travel speed during road crossing events was 14 times higher than her mean background speed. Moreover, she crossed the road at the furthest point between any two villages on the road, and while precise time is not available, extrapolations of travel time indicate that she crossed the road each time between 2 and 4 am, when human activity is at its lowest. These roads all had laterite surfaces, vegetated edges, and minimal traffic (up to ca. 20 logging trucks per day) (Blake, pers. obs.). The relationship between the landscape of fear and infrastructure can be inverted if law enforcement is carried out on and around human infrastructure. In the Rabi oil field of southwest Gabon, two elephants were collared in the heart of the oil field, and two more in the nearby oil town of Gamba. The oil company carries out anti-­ poaching activity within its concessions. In these sites, rather than the elephants avoiding human infrastructure, their movements were concentrated around infrastructure (Kolowski et  al., 2010), and the elephants had very small home ranges

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with their core areas focused in the heart of the oil extraction infrastructure. Moreover, the Rabi elephants did not move into either of the adjacent national parks. Thus, the landscape of fear for elephants at these industrial sites was reversed by managing human behavior toward elephants (reducing/eliminating poaching). An interaction between the landscape of fear, forage quality, and the high mobility of forest elephants may be driving one of the most pressing problems facing the future of forest elephant conservation – crop damage and elephant-human conflict. If the threat from poaching is high throughout the forest, even in remote areas, fear may be omnipresent for elephants, in which case access to high-quality resources may be the main driver of elephant ranging. Secondary forest near villages and especially crops provide high-quality forage to which forest elephants are attracted (Breuer & Ngama, 2021; Shaffer et  al., 2019). Furthermore, in areas where law enforcement is active such as around protected areas and major population centers, proximity to villages may present a lower risk to elephants than deep into the forest. These factors likely combine to drive elephants to crop raid and induce conflict.

3.5 The Ecological Consequences of Forest Elephant Movement Patterns Forest elephants have been described as bulldozers (Kortlandt, 1984), pruners (White et  al., 1993), tree planters (Blake et  al., 2009), and “mega-gardeners” (Campos-Arceiz & Blake, 2011). These authors and others agree that the ecological impacts of forest elephants on their environment are suitably “elephantine.” Two key factors contribute to their robust effects: biomass and body size. Forest elephants can make an extraordinary contribution to mammalian biomass (over 80% in some cases) (White, 1994a) which in itself means their ecological interactions are pervasive to sustain such mass. Secondly, large body size has direct consequences (Galetti et  al., 2018; Owen-Smith, 1988) including physical damage to trees and other plants on a scale no other organisms can replicate, which destroy existing microhabitats and create others (e.g., forest gaps), and to soils through compaction, digging, and nutrient exchange. Thirdly, impacts of large body size are manifest through generalist diets and prodigious ranging. As generalists, forest elephants browse on potentially hundreds of plant species (Blake, 2002; Short, 1981; Tchamba & Seme, 1993; Theuerkauf et al., 2000; White et al., 1993). This stimulates a plethora of community-wide fitness consequences for plants and animals. These include reduced fitness of browsed species, potentially increased fitness of competitors of browsed species, reduced fitness for consumers that share diet items with elephants, increased fitness for their competitors, and so on through the web of interactions. This influences the balance of competitive interactions among trees in Central African forests toward slow growing, high wood density species, with globally relevant implications for carbon sequestration and climate change (Berzaghi et al., 2019).

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As large-bodied generalists, forest elephants consume a wide variety of fruit species and disperse the seeds (Blake et al., 2009; White et al., 1993), creating a suite of cascading ecological impacts through the community. The role of elephants as long-­ distance seed dispersers increases their profound role in shaping the ecological trajectory of the forest (Blake et al., 2009; Cain et al., 2000; Nathan, 2006). By moving seeds further than any other disperser, forest elephants maintain connectivity of gene pools among distant populations and increase the probability of germination by overcoming density-dependent mortality (Connell, 1971; Janzen, 1970). These mutualistic interactions have led to the coevolution of many tree species and elephants as their primary disperser (Beaune et  al., 2013; Campos-Arceiz & Blake, 2011; Feer, 1995; Guimarães et al., 2008; Hawthorne & Parren, 2000). There is an enormous literature on mechanisms of seed dispersal and seed dispersal effectiveness (summarized by Murray, 2012). However, in tropical forest ecology, there remain few studies that have reported on the impact of seed dispersal on vegetation structure. However, Blake et al. (2009) not only were able to demonstrate the scale of elephant dispersal in terms of number of seeds dispersed by species and estimate the distance over which seeds are dispersed but were also able to report on how dispersal shapes forest structure. Apart from overcoming Janzen-Connell effects (discussed above), trees dispersed primarily by elephants showed no decrease in similarity over space, i.e., these trees were distributed randomly throughout the forest up to a linear distance of 67 km, while trees of all other dispersal guilds showed a decay in similarity over space. This implies that forest elephants are successfully dispersing seeds over large spatial extents. Many of the tree species dispersed by elephants are consumed by other members of the rich fauna of frugivores, including primates, ungulates, birds, and myriad community members from diverse taxa from all domains of life. A single forest elephant probably consumes about 150 kg of forage every day, of which much is indigestible lignin and cellulose. Forest elephants also defecate between 10 and 20 times per day and have digesta retention times of 42  hours (Poulsen et al., 2021) and average dispersal distance of digesta of about five kilometers. Sometimes, however, forest elephants disperse seeds and nutrients over prodigious distances of tens of kilometers (Poulsen et al., 2021). Forest elephants eat fruit and browse heavily in uplands on both monocots and dicots in riparian areas and swamps, moving repeatedly between the two. There are no data on quantities or nutrient content of urine, but nitrogenous waste production is undoubtedly high. Given the diurnal patterns of movement discussed earlier (propensity to move from lowlands to uplands), the different foraging strategies in different habitats, forest elephants are recycling nutrients in quantities and over spatial extents far beyond any other species of vertebrate in African tropical forests. Indeed, it has been suggested that the loss of megaherbivores (which included elephant-like proboscideans) from the Americas – likely due to hunting by the newly arrived humans over 13,000 years ago – has resulted in a severe loss in lateral nutrient transportation, resulting in much poorer soils away from the sources of the major nutrients [the base of the Andes, along the principal rivers, and along the coast (Doughty et al., 2013a, 2013b, Doughty, Wolf, Baraloto, & Malhi, 2016a, Doughty, Wolf,

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Morueta-Holme, et al., 2016b)]. These same authors even posit that the collapse of a number of human civilizations – including in North Africa – may be due to the removal of, specifically, elephants and the subsequent loss of soil fertility (Doughty et al., 2013a).

3.6 The Implications of Forest Elephant Movement Patterns for Biodiversity Conservation This last section consists of two main themes: first, the implications of movement for conservation of elephants as a species of conservation concern and, second, the implications of elephant movement for the conservation of ecological processes that maintain diversity of tropical forests.

3.6.1 Forest Elephant Movement and Elephant Conservation The conservation problems associated with animals having large home ranges are well known, particularly for species that either threaten human livelihoods or have high economic value (Tucker et  al., 2018; Woodroffe & Ginsberg, 2000). Forest elephants fall squarely into both categories via their potential to destroy crops and occasionally even kill people and the economic value of their ivory and meat. Animals with large ranges require large blocks of habitat, particularly true for migratory species which are disappearing around the world (Malpeli, 2022; Sawyer et al., 2016; Wilcove & Wikelski, 2008). Species conservation plans – and one exists for Central African forest elephants, albeit out of date (AfESG., 2005)  – require information on the distribution and abundance of the species and similar information on the intensity, geography, and trends in threats to that species (e.g., Soule & Wilcox, 1980). The interaction of the distribution of threats, for example, the spatial distribution of hunting intensity, and the spatial structure of the target species has a large influence on the impact of hunting on prey populations. Wide-ranging species will come into contact with areas of high hunting intensity more than prey species with small home ranges (Yackulic et al., 2011 and Fig. 3.5). Similarly, large-bodied animals living primarily in protected areas are more likely to encounter the boundary of the protected area than small ones. Given that abundance scales with body size, the negative consequences of increased mortality from high-risk areas are likely to be greater for populations of large-bodied animals than smaller bodied animals, and because larger-bodied animals tend to have slower reproductive rates than smaller taxa (and forest elephants have extremely slow reproductive rates (Turkalo et al., 2018)), this effect is even more pronounced.

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Fig. 3.5  A conceptual model taken from Yackulic et al. (2011). Animals such as forest elephants with large home ranges are more vulnerable to hunting than animals with smaller home ranges. Home range hexagons that touch the hunting shadow have been eliminated to illustrate the difference in the extent of potential hunting impact

Telemetry data on the movements of forest elephants in Central Africa highlight these issues clearly. The largest elephant home range recorded (in the Nouabalé-­ Ndoki National Park) is larger than the area of half of the national parks in the Congo Basin (Blake et  al., 2008). Most elephants collared inside national parks spend a considerable portion of their time outside the protected areas, often in areas of high poaching intensity. Soberingly, 3 of 96 forest elephants collared in Gabon were killed by poachers over a 4-year period (Beirne et al., 2021). If elephants respond to the landscape of fear by restricting their ranges, their access to resources will necessarily be limited. Some resources may be “nonnegotiable” such as bais, which may account for the hundreds of elephants sometimes killed at these sites. Forage quality will likely decline, and if many elephants respond in similar ways, intraspecific competition will increase. There is no direct evidence for either compression of ranges or for overexploitation of resources as there is for savannah elephants when ranges are restricted (Van Aarde & Jackson, 2007; Whyte et  al., 2003). However, forest elephants in Gabon are already known to be food stressed due to climate change (Bush, Whytock, et al., 2020b), and limiting access to forage will likely be detrimental to elephant nutritional balance across their range. Coupled with competition, this could increase physiological stress, decrease energy budgets, and reduce reproductive output. Moreover, overconsumption of resources in the deep forest could drive elephants toward high nutritional value foods in village fields and other agricultural areas, increasing human-elephant conflict, which is already a major conservation problem (Atsri et  al., 2020; Barnes, 1996; Ngama et al., 2016; Ngama et al., 2018; Ngama et al., 2019; Terada et al., 2021). Restricting forest elephant movements through barriers and/or fear will also increase isolation and reduce mating opportunities and gene flow within and between populations. Two seminal papers by Richard Barnes, published when knowledge of elephant movement patterns was in its infancy, illustrated the dangers of range restriction coupled with poaching for forest elephants (Barnes, 1996, 1999). In the first, Barnes discussed the conflict between forest elephants and humans as one of resource competition and predation. In the second, Barnes describes the decline of forest elephants and their habitats from once large

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contiguous tracts of both forest and forest elephants, to the present situation of small, isolated elephant populations living in fragmented landscapes because of the growth of human populations and the unsustainable ivory trade. Barnes discussed the loss of genetic viability and stochastic extinction processes to which small populations are prone. The relatively small nation of Gabon, holding only 13% of Central Africa’s forests, has become the main stronghold for the species, containing around 50% of the global forest elephant population, as they have been disproportionately poached throughout the rest of their range (Maisels et al., 2013). The most recent assessment of Gabon’s elephants suggested that most of them live outside the protected areas and still occur at high density in both protected areas and logging concessions (Laguardia et al., 2021). It seems almost inconceivable that the vast forests of the Congo Basin and their elephants could be similarly impacted, yet elephants are all but extinct in the Democratic Republic of Congo (Blake et al., 2007; Maisels et al., 2013) and declined dramatically in northeast Gabon (Poulsen et al., 2017). Forest elephants are now listed by the IUCN Red List as Critically Endangered (Gobush et al., 2021). The thirst for natural resources is driving roads and exploitative industries deeper into remaining forests every year. Although China banned domestic ivory trade at the end of 2017, and demand is declining within the country (Meijer et  al., 2020), there is nevertheless sufficient ongoing demand to drive a highly illegal ivory trade which is visible, for example, along the road from Thailand via Myanmar into China (Vigne & Nijman, 2022). Combined with the level poverty in Central Africa, this remaining demand continues to drive illegal ivory consumption. The large home ranges of elephants and their low population density, both consequences of large body size, exacerbate the potential for decline compared to smaller bodied, less mobile species.

3.6.2 Forest Elephants and the Conservation of Ecosystem Processes The ongoing decline in forest elephant numbers – mainly due to poaching – will also cause declines in the level and effectiveness of their role in “ecosystem engineering.” In addition, the decline in home range size seen across Central Africa because of the expanding human footprint and landscape of fear also has strong implications for forest function. The spatial scale of the ecosystem impacts of forest elephants is a critical part of their effectiveness – elephants ranging over large areas, sometimes over 100 km of linear distance (Blake, 2002), are important to maintaining the diversity of Africa’s tropical forests. The loss of moderate length and long-­ distance seed dispersal by elephants will likely tip the competitive balance in favor of the species-poor guild of abiotically dispersed tree species, leading to reduced tree species richness and overall community diversity (Blake et  al., 2009). Since elephant dispersed trees are among the largest forest trees and have high wood density (Berzaghi et  al., 2023), loss of this long-distance dispersal will eventually degrade the carbon sequestration potential of the forest (Berzaghi et al., 2023).

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The impact of restricting the ranging of elephants into small habitat patches has been widely documented in east and southern Africa (e.g. Barnes, 1980, 1983; Barnes, 1985; Owen-Smith et  al., 2006; Van Aarde et  al., 2006; Van Aarde & Jackson, 2007; Whyte et al., 1998; Whyte et al., 2003). Overabundance caused by range restriction leads to increasing browsing and grazing pressure, increased mortality of favored trees and browse, overgrazing causing soil degradation, the loss of vegetative biomass for other herbivores, and knock-on impacts across all trophic levels. There is currently no evidence that this is occurring in Africa’s tropical forests, perhaps because of the difficulty of conducting this kind of research in tropical forests and perhaps because there are no situations in which elephant abundance does not decline through poaching as range is restricted. As discussed earlier, it is not infrastructure development per se that restricts forest elephant range (as fences and other physical barriers do to savannah elephants), but rather it is the landscape of fear, coupled with high mortality, that restricts forest elephant movements and distribution (Beirne et al., 2021; Blake et al., 2007; Blake et al., 2008; Maisels et al., 2013; Wall et  al., 2021). However, there is little doubt that restricting the movements of a population of forest elephants will increase genetic isolation of fragmented populations, decrease seed dispersal distances, and increase foraging rates per unit area, necessarily concentrating damage to the plant community, increasing compaction, and increasing intra- and interspecific competition. In addition, lateral nutrient transportation would grind to a halt – or at least would be restricted to very short distances, resulting in an Amazon–like paucity of important soil nutrients across much of Central Africa.

3.7 What to Do? The Same Old Wish List? Current knowledge of forest elephant movements offers no novel solutions for conservation. Promising methods in landscape ecology such as network theory to identify critical connection hubs (Bastille-Rousseau et al., 2018) and other related novel methods (e.g., Movescape, Bastille-Rousseau & Wittemyer, 2021) require considerably higher sampling intensity of movement and more complete habitat classification than are yet available in the range of forest elephants as many forest dwelling mammals (this book). We remain in the infancy of understanding patterns of forest elephant movements and the spatiotemporal dynamics of their habitats. Low sample sizes, the scale and complexity of movement strategies, and the variability among individuals merely highlight the urgency of long stated management objectives (Blake et al., 2007; Maisels et al., 2013; Poulsen et al., 2017; Turkalo et al., 2016), namely, effective protection of multiple large tracts of interconnected high-quality forest throughout the range of forest elephants. But once again, how large is large? A single forest elephant home range can be ca. 2500km2 – larger than half of the national parks in Central Africa and almost all those in forested west Africa. Many of these national parks have low and declining elephant populations (Maisels et al., 2013; Poulsen et al., 2017). Mean forest elephant home range size from a sample of

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elephants in Gabon was 195km2 (Beirne et  al., 2021); however, most of the elephants in this sample were likely constricted in their movements by a landscape of fear in forest blocks bounded by roads (Blake et al., 2008; Tucker et al., 2018; Wall et al., 2021). Thus, we cannot adequately answer the questions: How large is large? How much space is enough? How many elephants do we need in what configuration to maintain ecosystem processes? What we do recognize, know, and do, includes the following: Habitat fragmentation almost always has negative ecological consequences (Simberloff & Abele, 1982; Synes et al., 2020), and wide-ranging species are usually the first to be impacted (Woodroffe & Ginsberg, 2000). Central Africa still has large tracts of relatively intact forest, though these are declining rapidly as the last remote areas are accessed for extractive industries and agriculture (Aquilas et al., 2022). There is an urgent need to establish regional land use plans which locate any new infrastructure, agroindustry, and industry in general in places where environmental damage can be minimized. Well-designed management plans can have a positive impact on habitat loss and illegal killing of elephants and other wildlife (Clark et al., 2009; Tritsch et al., 2020). As human populations and per capita consumption continue to grow, demand for natural resources is constantly increasing (Oberle et  al., 2019), and resource exploitation plans must involve strategic long term spatially explicit design. At a more fundamental level, we also know that eliminating elephant poaching through law enforcement, education, and providing alternative livelihoods will stop and then reverse population declines. Against this backdrop, and the economic insecurity and limited governance capacity reality of most forest elephant range states, how can politicians and institutions implement the basic recommendations so easily made on paper by biologists such as “protect elephants effectively,” “reduce infrastructure expansion,” “preserve core habitat,” and “maintain connectivity”? Interestingly, some Central African nations have legislated significant proportions of their terrestrial surface area as protected areas; for example, nearly 37% of the land mass of Congo is in some form of legally designated protected area (https://data.worldbank.org/indicator/ER.LND. PTLD.ZS) and lies in the 25th place in world rankings. The Democratic Republic of Congo has a greater percentage of its terrestrial area (13.8%) under protected area status than the United States (13.0%) and Canada (11.9%) (https://data.worldbank. org/indicator/ER.LND.PTLD.ZS). Moreover, most North American protected areas are in marginal and unproductive habitats with limited economic value, whereas many protected areas in forest elephant range states are in high-quality habitats that contain among the highest levels of biodiversity on the planet (Richards, 1996). Protected area designation has also been somewhat driven by the distribution of large populations of charismatic fauna in remote areas (CBFP, 2007). However, parks remain poorly managed in general due to lack of investment, corruption, and limited technical expertise (Laurance et al., 2012). Both legal and illegal exploitation of natural resources is occurring up to the borders of, and into, protected areas and therefore into the home ranges of elephants that range on and out of protected areas. Throughout industrial concessions, buffer zone and landscape management plans need to create softer, wider borders between protected

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areas and the industrial matrix within which they are located. In addition, management plans should aim to provide connectivity between the remote core areas of protected areas, to enable forest elephant populations to thrive and to continue to provide the multitude of ecological services described earlier. Beyond park and landscape management for target species comes national land use planning that integrates sustainable economic development, biodiversity conservation, climate mitigation, and other societal benefits. In 2017, Gabon completed a comprehensive national planning exercise, which included implementation budgets and spatially explicit zoning to support a national development strategy with forest management at its core. Forest elephant range states, at least in Central Africa, have paid a significant opportunity cost by locking up large areas of exploitable forest for conservation purposes, and Gabon for prioritizing forest resource management in its future. Gabon has recently taken a hugely important step to maintaining its forests, by trading carbon credits. By selling credits for standing, healthy rainforests, the trees could be worth more alive than dead – and maintain the huge diversity of flora and fauna within them, including the forest elephants. Moreover, forests containing functional populations of forest elephants sequester more carbon that forests that do not, at globally relevant levels (Berzaghi et  al., 2019). The forest elephant range states are rich in natural resources but are among the lowest ranking nations according to the human development index – indeed, the highest-ranking forest elephant range state ranks 119th (out of 189 countries) on that index (UNDP, 2020). These nations have commitments to their human populations and need encouragement and genuine support from the rest of the world to realize the full benefits of forests and the elephants they contain toward the future stability of the planet. Acknowledgments  Thanks go to the governments of the Republic of Congo, Central African Republic, and Gabon for permission to conduct the studies on which this chapter is based.

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Chapter 4

Elephant Movements, Abundance, and Use of Water Sources in Kibale National Park, Uganda Rafael Reyna-Hurtado , Mario Melletti , Martin Mukasa, Patrick A. Omeja, David Keeble, Alexander V. Georgiev, Graeme Shannon, and Colin A. Chapman

Abstract  Large herbivores, such as elephants, have been eradicated from large swaths of their historic ranges due to habitat loss and intense hunting pressure. However, in areas where they are still alive, they frequently engage in conflicts with humans due to crop raiding or because their natural habitats are being fragmented, limiting the natural movements of groups of this species. Three years of camera trap data coupled with records of elephant presence from transects recorded over 23  years reveal complex movement patterns of this species across the Kibale National Park, Uganda. We tested if elephant movement was influenced by seasonal changes, by the presence and distance of mature crops in surrounding farmland, or by rainfall or temperature changes. We describe occupancy rate, detection probability, daily movement habits, and group size and structure when visiting water sources. Elephants showed high probability of occupancy and low detection probability at R. Reyna-Hurtado (*) Department of Biodiversity Conservation, El Colegio de la Frontera Sur, Campeche, Mexico IUCN Wild Pigs Specialist Group, Campeche, Mexico e-mail: [email protected] M. Melletti African Buffalo Initiative Group and Wild Pig Specialist Group IUCN, Rome, Italy M. Mukasa Giant Forest Hog Project, Kibale National Park, Uganda P. A. Omeja Makerere University, Kampala, Uganda D. Keeble · A. V. Georgiev · G. Shannon School of Natural Sciences, Bangor University, Bangor, UK e-mail: [email protected]; [email protected] C. A. Chapman Biology Department, Vancouver Island University, Nanaimo, BC, Canada Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_4

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water sources and potentially preferred water sources near crop fields that are located outside the park. Kibale’s elephants move in small family groups and visit water sources at all times of the day but with a preference for sunset and on days with less rain. Understanding forest-living elephants’ movement is crucial to minimize conflicts between elephants and farmers and to understand the dynamics between elephant herd movement and forest dynamics. Keywords  Loxodonta africana · Loxodonta cyclotis · Occupancy models · Group size · Daily activity patterns · Family groups

4.1 Introduction The world’s largest herbivores are important ecosystem engineers and play a key role in shaping local plant and wildlife communities through their foraging and trampling behavior (Ripple et al., 2015; Terborgh et al., 2016; Poulsen et al., 2018). These animals perform ecological roles that cannot be replaced by other species, yet they are experiencing dramatic population declines (Ripple et al., 2015). Elephants present a clear example of how humans are reducing populations of large herbivores. African forest elephant (Loxodonta cyclotis) populations have declined by 86% in the last 31 years and African savanna elephant (Loxodonta africana) by 60% in the last 50 years (IUCN, 2021). Indeed, it is estimated that more than 100,000 African elephants were killed between 2010 and 2012 (Wittemyer et al., 2014). Elephants can have dramatic impacts on their environment when populations reach high densities (Smart et al., 1985). However, this is not always the case for all savanna elephant populations, and densities do not always explain the negative impacts on vegetation (Guldemond et al., 2017). In some cases, changes in elephant densities can significantly alter vegetation over large areas (Buechner & Dawkins, 1961; Laws, 1970; Smart et  al., 1985; Hawthorne & Parren, 2000; Lawes & Chapman, 2006). For example, when elephants in Murchison Falls National Park, Uganda, were protected from organized hunting in the 1930s, their population growth was associated with a 55–59% reduction in the number of large trees (Buechner & Dawkins, 1961). Conversely, illegal hunting during the Ugandan civil war in the 1970s and 1980s dramatically reduced elephant numbers in Murchison, followed by a corresponding increase in the park’s woodland area (Brooks & Buss, 1962; Buss & Savage, 1966; Douglas-Hamilton et al., 1980; Eltringham & Maplas, 1980; Ferry et al., 2021). The impact of elephants on regeneration and recruitment of plant species may reduce dispersal and distribution of some tree and bush species. Their role in forest dynamics was verified in experiments excluding elephants (Hatton & Smart, 1984; Smart et al., 1985). Similar evidence was obtained from studies of an increasing elephant population in Kruger National Park, South Africa (Shannon et al., 2008; Smit & Ferreira, 2010). These conditions of exclusion can be inducted by alterations caused by people confining the presence of elephants in

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restricted areas where the impact of this megaherbivore on the vegetation structure and ecosystem functioning may be relevant (O’Connor et  al., 2007; Asner et al., 2016). Elephants are also important seed dispersers, with the ability to transport seeds over long distances (Campos-Arceiz & Blake, 2011; Terborgh et al., 2016; Beirne et al., 2021). They disperse seeds far from the parent tree (Campos-Arceiz & Blake, 2011; Bunney et al., 2017), giving the seedlings higher survival rates, as they are not competing with the parent and escape density dependent pathogens that congregate near parent trees (Chapman & Chapman, 1995; Cochrane, 2003; Terborgh, 2020). In addition, passing through the elephant’s digestive tract often improves germination success (Chapman et  al., 1992; Babweteera et  al., 2007; Blake et  al., 2009; Campos-Arceiz & Blake, 2011). The elephants’ large size results in them being the sole dispersers of certain tree species, and the local extirpation of elephants will likely lead to a decrease in the abundance of these species (Chapman et al., 1992; Campos-Arceiz & Blake, 2011; Tweheyo et al., 2013). For example, elephants in Kibale National Park, Uganda, are the sole disperser of Balanites wilsoniana seeds, as these are too large for other species to consume (Chapman et al., 1992; Cochrane, 2003; Campos-Arceiz & Blake, 2011). Elephants are also important for nutrient cycling making previously stored nutrients readily available through their considerable demand for forage resources and comparatively rapid digestive physiology (Poulsen et al., 2018; Kalbitzer et al., 2019). Movement ecology is a recently developed discipline dealing with animal movement and integrates all aspects of it to better understand its causes and consequences (Nathan, 2008). Given the important ecological roles that elephants play, research into their movement ecology will play a central role in comprehending the functioning, dynamics, and conservation of the systems they inhabit. Movement patterns of a species can be explored with sophisticated telemetry systems that use satellite information to track individuals with a GPS integrated into a device attached to an animal’s body (Kays et  al., 2015). However, camera traps are also a very useful technique that allows researchers to obtain information on animal movement by recording species, individuals, or groups of animals passing through the angle of view of a camera (Nichols & Karanth, 2011). Analysis of camera trap data has advanced substantially in the last 15 years. By using camera traps, researchers are able to estimate occupancy rate, detection probability, density for some species, daily habits, speed of movement, group age structure, group sex composition, group size, and social behavior, among other aspects (O’Connell et al., 2011; Burton et al., 2015). Elephants inhabiting forest systems are very difficult to study, because while savanna elephants can be approached and observed from a vehicle, this is rarely possible in the forest environment and it is extremely dangerous for researchers to approach elephants on foot. As a result, camera traps represent one of the best opportunities to collect information on elephant movement patterns in forest systems (Gessner et al., 2014). Our research had two objectives. First, we used camera traps to investigate elephant movement patterns within Kibale National Park (Kibale hereafter), which is a

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forested park in western Uganda. We use 3 years of camera trap data to examine if elephant movement is associated with seasonal changes, by the presence and distance of mature crops in surrounding farmlands, or rainfall or temperature changes. We described occupancy rate, detection probability, daily movement habits, and group size and structure when visiting water sources. We explored who arrived first and who was the first to leave water sources. Second, we report on changes in elephant relative abundance at eight sites across Kibale studied over a period of between 11 and 23  years, to evaluate overall changes in abundance and shifting patterns of use of the forest in Kibale. Recent genetic studies showed that the elephant populations in Kibale comprise three distinct groups: savanna elephants, forest elephants, and hybrids between the two species (Mondol et al., 2015; Wasser et al., 2015). Forest elephants have ranged into Uganda in the past (Brooks & Buss, 1962), but they have now settled in Kibale, possibly because movement through the humanized landscape is no longer possible, social groups have been disrupted by poaching, and forest elephants moved into Uganda from the Democratic Republic of the Congo during periods of civil unrest (Keigwin et al., 2016). Elephants in Kibale affect ecosystem regeneration by foraging on seedlings and vegetation, favoring to eat some species such as Acanthus pubescens (Omeja et  al., 2016; Wheeler et  al., 2016), which has inhibited forest regeneration in logged sections of the park (Lawes & Chapman, 2006). Elephants not only have profound impacts on Kibale’s forest dynamics but often cause significant damage to farmers’ crops outside of the park (Sarkar et al., 2021). Elephant have been classified as one of the more damaging species for crops around Kibale (MacKenzie, 2012; Mackenzie & Ahabyona, 2012; MacKenzie et al., 2017), and people have developed several strategies to deter elephants to approach fields. These strategies include drumming, shouting, a phone system to alert Uganda Wildlife Authority to scare-shoot and drive elephants back into the park, and the digging of deep trenches that elephants cannot cross (Sarkar et al., 2016; Muchwampaka et al., in press). Given that elephant crop raiding can severely damage relations between parks and people (Mackenzie & Ahabyona, 2012; Shaffer et al., 2019; Sarkar et al., 2021), a greater understanding of elephant movement patterns offers managers useful information that could significantly advance conservation efforts. Furthermore, people can be injured and killed when trying to deter elephants from eating their crops; thus, movement data may help save human lives.

4.2 Methods 4.2.1 Study Site Kibale National Park is a 795 km2 park (1500 asl) at the foothill of the Ruwenzori mountains in southwestern Uganda (0°13′–0°41′N and 30°19′–30°32′E; Fig.  4.1; Chapman et  al., 1997; Chapman & Lambert, 2000). The main

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Uganda

All boundaries are approximate

Legend 0

5

10

20 km

N

Papyrus,elephant grass Shrub Short grass Forest

Notes: Land cover classification derived from Lansat ETM+, acquired January 31, 2003 Map by: Joel Harter, University of New Hampshre

Fig. 4.1  The location of Kibale National Park within Uganda and Africa, a satellite image of the region illustrating that the forested park (forest is dark green) is surrounded by deforested agricultural land, and two images of the forest in the park (regenerating and old-growth forest)

vegetation type is tall-closed canopy rainforest (57%) with a mosaic of swamp (4%), grasslands (15%), former pine plantations (1%), and colonizing forest (19%) (Chapman & Lambert, 2000). The average annual rainfall from 1970 until 2020 was 1646 mm and ranged from 1197 mm in 1993 to 2214 mm in 1996 (measured at Makerere University Biological Field Station; Chapman unpublished data). Annual average monthly maximum temperature from 1970 until 2020 was 27.9 °C, and the annual average minimum temperature was 15.8 °C (Chapman et  al., 2021). The study was based out of Makerere University Biological Field Station at Kanyawara, and thus camera traps were typically set in the northern half of the park (Fig. 4.2). Kibale is the last large stretch of mid-elevation forest remaining in East Africa, and it supports the highest biomass of primates ever recorded (Chapman et  al., 1999, 2010b). The 13 primate species include the endangered red colobus (Piliocolobus rufomitratus) and chimpanzee (Pan troglodytes). Kibale is also home to savanna and forest elephants and hybrids between the two, giant forest hogs (Hylochoerus meinertzhageni), bush pigs (Potamochoerus larvatus), and several forest antelopes and carnivore species, among them the golden cat (Profelis (Caracal) aurata).

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Fig. 4.2  Kibale National Park, Uganda, with pins indicating the locations where camera traps were set

4.2.2 Data Collection Methods Camera traps: water is a key factor driving the distribution and abundance of savanna elephants (Chamaille-Jammes et  al., 2007; Shannon et  al., 2009). However, for African forest elephants, the seasonal variation in rainfall in forests is not as pronounced as that in savannas, and water is generally more readily available; thus, permanent water sources are likely to be more accessible. The movement of forest elephants, instead, tends to be more influenced by food ­availability and fruiting seasons of trees (Blake & Inkamba-Nkulu, 2004; Beirne et al., 2021). Given the importance of water sources for savanna elephants and the widespread distribution of elephant forage (Rode et  al., 2006), and the mix of elephant groups we may have in Kibale, we elected to set camera traps near water sources (Fig. 4.3). We set up 9 camera traps (Reconyx Inc. M800) in 12 sites between February 2019 and October 2021. These cameras were programmed to take photos continuously and in the rapid-fire mode and for 24 h without delay between photos. The cameras were attached at 50 cm above the ground approximately, to the base of the nearest tree of selected water sources where wildlife came to drink and sometimes to feed or lick the soil for minerals. The cameras were attended by a team of dedicated field assistants who changed batteries, replaced memory cards, and ensured

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Fig. 4.3  Elephants arriving to a water source in Kibale National Park, Uganda

the camera remained in place. Some cameras malfunctioned, sometimes due to humidity or to animals that moved them, and in a couple of cases, some were stolen by poachers.

4.2.3 Elephant Abundance and Distribution Based on Census Data We sampled transects that were along a set route of approximately 4 km in length at eight sites. The monitoring was done by a team of five experienced observers whose membership remained the same each year, except for one replacement in 2014. Surveys were typically conducted once a month for each survey year, spanning the years 1996–1997, 2005–2006, 2008–2009, 2014–2015, and 2018–2019 (Omeja et al., 2014, 2016; Sarkar et al., 2021). We sampled three sites in all years and added an additional five sites in 2008. A description of the sites can be found in Sarkar et al. (2021). A total of 2010 km was walked. These surveys recorded dung and track counts, any direct elephant observations, sounds, or other indications of elephant presence. Dung and tracks were removed during each census walk after they were recorded, so that they would not be counted the following month. The sum of all signs was subsequently used to determine relative elephant habitat use per kilometer for each transect to standardize across all sites.

4.2.4 Data Analysis We obtained 19,824 photos of elephants during the period of February 2019 to October 2021. These records were reduced to 173 independent records. Independence time was set at 60  minutes to be comparable with other studies (Reyna-Hurtado

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et al., 2019). We estimated the time of day that elephants visited the water sources along a 24-h continuum by constructing a circular plot using CamtrapR (R studio package; Niedballa et al., 2016). We estimated the occupancy rate (ψ) and detection (P) probability that herds of elephants showed up in the water sources for the three years and we also ran analyses separated by periods of each year (2019, 2020, 2021). We used R studio CamtrapR (R studio package) for testing occupancy rate, and we separated our data by 7-day periods to detect presence or absence of the ­species in those periods. General linear models were used to explore drivers of site visitation by family groups. We considered the following variables: Distance to crops: Here we measured the Euclidean distance (straight distance) from each site to the nearest crop field using Google Earth. Crop maturity: Here we assigned each record to one of two options according to the surrounding crop maturity stage; we assign either crop maturity low season or crop maturity high season as determined by the number and time of crops that were ready to be harvested by farmers. We determined this by interviewing several local people, and we asked the monthly maturity status of crops elephants frequently eat. We found that June–July and December–January are high crop seasons for 12 crops. Group size: We counted the minimum number of elephants that arrived at each site. We defined different individuals when the herd arrived in a formation, and we could identify different animals by sex, tusk, ear shape and size, and sometimes scars on the body. When possible, we assessed the social structure of the herd by counting adults and juveniles and determining the elephant’s sex. The sex and age of the first and the last elephant that entered and leave the water source respectively, were recorded. We determined a group when we have elephants passing or wallowing in the water points continuously or when they showed with less than 1 h of difference. Time spent in the sites: We recorded the time that groups of elephants spent in each site (minimum, maximum, and average time) to the nearest minute. Rainfall and temperature: We obtained daily records of rainfall and maximum and minimum temperature collected at Makerere University Biological Field Station located near the Kanyawara village (Fig. 4.4). We associated each record of an elephant herd visit to any point with the maximum temperature and the monthly and daily amount of rainfall. We ran linear models and general linear models using the lm package (R studio version 1.3.1093) and obtained individual p value and adjusted R-squared estimations for each variable affecting the number of visits elephant herds had to each site. We also constructed a model with distance to crops, group size, and time spent in each site to explain the number of visits per site. We ran several models and progressively eliminated the least significant variable. We estimated the Akaike Information Criteria (AIC) to select the best model.

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350 300 250 200 150 100 50 0

Jan

Feb Mar Apr May Jun Year 2019

Jul

Year 2020

Aug

Sep

Oct

Nov Dec

Year 2021

Fig. 4.4  Rainfall patterns (measured in millimeters) in Kibale National Park, Uganda Table 4.1  The occupancy and detection probability of elephants visiting water sources in Kibale National Park, Uganda Year 2019 2020 2021

Occupancy rate (ψ) 0.671 0.850 0.485

SE 0.71 0.20 0.35

Detection probability (P) 0.133 0.113 0.04

SE 0.03 0.02 0.03

4.3 Results 4.3.1 Occupancy Models Elephants had a high occupancy rate in all the years at all the water sources sampled with a ψ of 0.999 (SE  =  0.0142) and a low probability of detection P of 0.0955 (SE  =  0.0135). 2021 was the year with the lowest occupancy rate, and elephant herds occurred in more sites in 2020 (Table 4.1).

4.3.2 Diurnal Patterns Elephants, moving either in family groups or individually, did not show any preference for particular times of the day to visit water sources. There was a peak of activity at sunset but were no clear preferences for either diurnal or nocturnal visitations (Fig. 4.5).

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Fig. 4.5  Temporal patterns of water source visitation by elephants in Kibale National Park, Uganda

4.3.3 Group Size We recorded a maximum group size of 27 different animals. However, given the limitation of camera traps, herds may have been larger. The average herd size was 3.4 animals. Sixty-three instances (36% of the independent records) were solitary individuals. Excluding solitary individuals, the average group size was 5.2 elephants; usually the herds consisted of one or two adult females and one or two juveniles.

4.3.4 Arrival and Departures Adults typically entered the water source before juveniles – 115 times (90%) an adult entered first and only 12 (10%) times a juvenile entered first. Of the 115 times an adult entered first, 39% were females, 12% were adult males, and 48% were unidentified adults. When leaving the water source (n = 126), adults were typically the last to leave (93.7%, 38.1% females, 12.7% males, unidentified 42.9%), and juveniles were the last to leave in only eight instances (6.3%).

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4.3.5 Variables Explaining Movements The best GLM model to explain the presence of elephant herds at water sources was the combination of the group size and the time the group spent in each site (Table 4.2). This result indicates that large herds visit water sources more frequently and spend more time there (Fig. 4.6). No single variable significantly influenced the frequency with which elephants visited particular water sources. However, the most frequently visited water source was close to crop fields (distance to crops: p value  =  0.20; adjusted R-squared: 0.1071; Fig.  4.7). Furthermore, there was a very weak tendency for elephants to spend more time in the sites close to the crop sites (p value  =  0.14; adjusted R-squared: 0.170; Fig. 4.8). Table 4.2  Generalized models predicting the number of visits of elephant herds to some specific sites in Kibale National Park, Uganda Variables explaining the number of visits to each Model site 1 Distance to the nearest crops Average group size Average time in each site 2 Average group size Average time in each site 3 Average group size 4 Average time in each site 5 Distance to the nearest crops

Fig. 4.6  Number of independent visits of elephant herds to water sources in Kibale National Park, Uganda. Each location is a separate water source (i.e., C1–C9). Black points represent the number of visits of elephants to each water source

P value 0.94 0.02 0.00 0.00 0.00 0.55 0.02 0.2

Adjusted R-squared 0.78

AIC 77.93

0.81

75.90

−0.08 0.48 0.10

91.40 84.66 89.7

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Distance to the Nearest Crop Field (km)

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Number of Elephant Visits Fig. 4.7  Relationship between number of visits of elephant herds to water sources and the distance to the nearest crop field in Kibale National Park, Uganda

Distance to the Nearest Crop Field (km)

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Average Time Elephant Stayed (Min) Fig. 4.8  Relationship between the distance to the nearest crop field and the average time (min) an elephant group stayed at a water source in Kibale National Park, Uganda

Average Time Elephant Stayed (Min)

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Number of Elephant Visits

Fig. 4.9  Relationship between number of visits of elephant herds to water sources and the average time (min) the herds stayed at the site in Kibale National Park, Uganda

The number of visits to the water sources was positively correlated with the average time the herds stayed at the site (p value = 0.02; adjusted R-squared = 0.48; Fig. 4.9). This suggests elephants have some favorite sites where they stay for long periods with a high rate of visits. Furthermore, the larger the family group, the longer they stayed at the water source (p value = 0.00; adjusted R-squared = 0.762; Fig. 4.10). Elephant herd size was not correlated with rainfall (p value  =  0.42; adjusted R-squared = −0.00), nor did they spend more time at water sources as a function of daily rainfall (p value = 0.25; adjusted R-squared = 0.00).

4.3.6 Elephant Abundance and Distribution Based on Census Data Four hundred and three observations of elephant signs were recorded across the eight sites over the duration of the study. Elephant abundance increased rapidly across the park between 1996 and 2008 but is increasing more slowly in recent surveys (Fig.  4.11). Elephant abundance in Sebitoli (a heavily logged site) and Nyakatojo (a site regenerating following the clearing of a pine plantation) was high in 2014 but decreased in the latest 2019 survey (Fig. 4.12).

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400

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0 0

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Elephant Groups Maximum Size Fig. 4.10  Relationship between elephant herd size and the duration of their stay (min) at a water source in Kibale National Park, Uganda

Fig. 4.11  Change in the relative abundance of elephants (signs per kilometer) in Kibale National Park, Uganda

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Fig. 4.12  Relative abundance of elephants (sign per kilometer) in Kibale National Park recorded across the different sites

4.4 Discussion Elephants of Kibale move in small herds visiting water sources frequently across the day and night, although they often visit these sites at dusk. There is no clear evidence that elephants select water sources to be near crops. Elephant occupancy rate was generally high, but detection probability was low. This suggests that elephants visit and know all the water sources (occupancy rate) and have a few preferred sources but visit them infrequently (detection probability). However, we did not monitor all water sources, so their infrequent visitation may simply represent them going to unmonitored sources. Elephants often eat soil near water sources. In fact, they often spend more time licking the bare soils than drinking water. Thus, it seems likely that the soil near some water sources is an important source of minerals for elephants, or it may act to aid in digestion (Reyna-Hurtado et al., this volume). This relationship between water-soil and animal visits is worth exploring as these sites can be very important for several species as a source of drinking water, wallowing, and minerals (Sitienei et al., 2012). There is evidence that at least for giant forest hog, an endangered species living in Kibale, these sites are the main and favorite sites, and some of the daily activities, such as foraging and resting, occur around these sites (Reyna-­ Hurtado et al., this volume). Elephants of Kibale travel in small herds, averaging five individuals, and many individuals travel alone. However, there were instances when large herds of more than 20 individuals were observed. Moving in small herds is a characteristic of forest-living animals. Ungulates form large groups in open habitats, such as

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buffalo (Syncerus caffer), bison (Bison bison), and even elephants living in savanna habitats, where herds of 100 individuals can be found (IUCN, 2021). In Kibale, small herd size could be an adaptation of the forest habits, which would imply that savanna elephants can adapt well to forested habitats, although the elephants of Kibale are a mix of savanna, forest, and hybrid elephants. In recent years, elephants and most ungulate populations have increased in the park (Chapman et al., 2010a; Omeja et al., 2014; Hou et al., 2021); this is having profound consequences on the forest regeneration and has led to increased conflict with farmers. Monitoring elephant movement and herd size is important to predict and hopefully prevent conflicts with local communities and to understand forest dynamics, especially in patches that are regenerating and that seem to be favored by elephant herds (Omeja et al., 2016). This chapter was an attempt to describe movement patterns of elephant herds in Kibale by collecting data through camera traps. Camera traps are fixed devices that record a small window of space and time of the forest and thus have their limitations. These devices also fail frequently or get stolen, or sometimes the same species destroys them (that happened to us several times). Photography of animals that cannot be identified individually also has several limitations as we do not know if the same group is returning to the same sites or different groups are visiting the same site. Thus, future studies should consider using telemetry collars. Monitoring elephant movements in detail would provide evidence of when, how, and where elephants crop raid in the surroundings and what resources within the park are important for them, or they lack, and at what time. Telemetry collars also provide a useful approach to recognize individuals and sometimes entire groups or herds (Reyna-­Hurtado et al., 2009). Elaboration of conservation actions such as reduction of conflicts with farmers would be a win-win action that is urgently needed. Already there has been an incidence of retribution killing, where farmers killed an elephant because of the anger they felt toward them for the loss of their crops and livelihood (Chapman unpublished data). Conserving elephant populations within the park and preventing conflict with humans are key and urgent conservation actions. Movement and population ecology are key frameworks to obtain sound ecological information to advance in science and to prevent conflicts between wildlife and human. Acknowledgments  We thank Claire Hemingway and Dipto Sarkar for helpful ideas and comments on this project. We thank Edith Rojas, Martin Mukasa, Patrick Kyaligonza, John Okwuilo, and Jean Pierre d’Huart, for unvaluable help on the field, and to Lizzi Martínez for the map elaboration. RRH thanks the National Geographic through the Committee of Research and Exploration that gave to this project a grant No. 9839-16. RRH also thanks Fondation Segré for funding to investigate wildlife in Kibale National Park through the project “Conservation of Giant Forest Hog in a Set of Protected Areas in Western Uganda” and El Colegio de la Frontera Sur for help to write this chapter. The funding that helped us develop some of these ideas was the IDRC grant “Climate Change and Increasing Human-Wildlife Conflict” to CAC.  CAC was supported by the Wilson Center while writing this paper.

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Chapter 5

Movement Ecology and Evolutionary History of Forest Buffalo Lisa Korte, Mario Melletti

, and Nathalie Smitz

Abstract  Movement ecology framework is particularly insightful for forest buffalo (Syncerus caffer nanus) because we can examine it in the context of buffalo movement across Africa at different spatial and temporal scales. Despite being mainly a sedentary species, buffalo are distributed widely throughout Africa. They occur throughout the Congo Basin forest region of Central Africa and in the residual blocks of forest in West Africa. Unlike for the well-studied savanna buffalo (S.c. caffer, S.c. brachyceros, S.c. aequinoctialis), few data exist on movement ecology of forest buffalo. Although limited, these data are sufficient to elaborate optimal plans for forest buffalo conservation. Relative to savanna buffalo, forest buffalo have smaller home ranges, shorter daily movement, no seasonal movement, and smaller group sizes. While movement ecology is often based on real-time tracking data, genetic studies allow for the understanding of demographic changes shaping the pattern of divergence and distribution on an evolutionary timescale. Genetic information is an important parameter to take into consideration when developing management practices in a conservation context. First, it evaluates the genetic health of a species (especially in the present context of land and climatic changes and overexploitation, resulting in a mosaic of isolated populations) but respects the genetic integrity of the species populations, which can display local genetic adaptations. Over the last decades, the investigation of the phylogeography and evolutionary history of the African buffalo revealed the existence of only two main lineages within the species (incongruency with the accepted taxonomy), one encompassing the buffalo from West and Central (WC) Africa (S.c. nanus, S.c. brachyceros, S.c. The views expressed are solely those of the authors and do not necessarily represent the views of the institutions represented. L. Korte (*) International Affairs, Africa Branch, US Fish & Wildlife Service, Falls Church, VA, USA e-mail: [email protected] M. Melletti WPSG (Wild Pig Specialist Group) and AfBIG (African Buffalo Interest Group) IUCN SSC, Rome, Italy N. Smitz Royal Museum for Central Africa (Biology Department), Tervuren, Belgium e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_5

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aequinoctialis) and another one including all savanna buffalo from eastern and southern (ES) Africa (S.c. caffer). Nowadays, despite the lack of contemporary barriers to gene flow, lineages appear conserved, and they should therefore be ascribed to evolutionarily significant units. Additionally, it is important to recognize that the forest buffalo subspecies is more vulnerable compared to its savanna counterparts. To assure the species’ long-term survival, sustainable management to maintain the species’ resilience to environmental changes and diseases is of prime importance, via, for example, the establishment of protected corridors allowing gene flow between isolated populations. Keywords  Forest buffalo · Savanna buffalo · Home range · Movement · Habitat use · Activity pattern · Spatial distribution · Wildlife corridors · Molecular tools · Evolutionary history · Conservation unit

5.1 Introduction Of the many different ecosystems and species around the world, there is a great gap of knowledge from the tropical regions. Biodiversity and species numbers are the highest in these regions; however, they face great habitat loss and degradation. In the Afrotropical regions, one-third of species richness and abundance could potentially be lost due to the expansion and the intensification of land use (Kehoe et al., 2017; Chapman et al., 2022). It is a particularly susceptible region since it is at the crossroads of demographic and agricultural growth. Indeed, Africa’s human population is expected to almost triple by 2050, reaching about 1.34 billion individuals (https://www.weforum.org/agenda/2020/01/the-­children-­s-­continent/). The increasing human population has led to a growing demand for natural resources, resulting in the unsustainable use of species and ecosystems and also reducing habitats and space for wildlife and increasing human-wildlife conflicts (Malhi et  al., 2013; Perrings & Halkos, 2015; Vancutsem et  al., 2020). This human pressure is also exacerbated by the additional effects of climate change. In addition, the use and trade, both legal and illegal, of wildlife is a major cause leading to biodiversity loss. These threats are all direct or indirect effects of human perturbations. Despite these threats, Africa is home to eight of the world’s biodiversity hotspots and supports the Earth’s largest intact assemblages of large mammals still freely roaming in many countries. In this assemblage, the charismatic African buffalo (Syncerus caffer) is one of Africa’s big five safari animals. The species exhibits extreme morphological variability across its sub-Saharan distribution range, with the forest-dwelling buffalo assigned to one subspecies (S.c. nanus; IUCN, 2019). While it is morphologically the most divergent, it is also the least studied subspecies, probably due to its elusive behavior, and low population density, occurring often in dense and inaccessible forest habitat. On the contrary, the Cape buffalo subspecies of eastern and southern African savannas is well studied with at least three books on its ecology (Sinclair, 1977; Mloszewski, 1983; Prins, 1996).

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In this chapter, the movement ecology (real-time movement patterns) and evolutionary history (demographic changes on an evolutionary timescale) of forest buffalo are summarized and compared to the more largely investigated savanna buffalo subspecies.

5.1.1 African Buffalo Taxonomy, Distribution, and Population Estimates Although there are considerable morphological variations in body size, fur color, horn shape, and size throughout the species range, with several intermediate forms (mixed morphological characteristics) in areas of overlap between the four recognized subspecies, the African buffalo is currently considered as a single species by various authorities (Prins & Sinclair, 2013;  Cornélis et  al., 2014; IUCN, 2019). Historically, the African buffalo inhabited almost the whole of sub-Saharan Africa, with its distribution range limited by the availability of permanent sources of water. It is also physically able to disperse through a wide range of habitats. Three subspecies inhabit African savannas, including the Cape buffalo of East and southern Africa (S.c. caffer), the West African savanna buffalo (S.c. brachyceros), and the Central African savanna buffalo (S.c. aequinoctialis). The last subspecies (S.c. nanus) is a forest-dweller, inhabiting the rainforests of West and Central Africa. Additionally, some authors claim the presence of a mountain form as a distinct species (Virunga buffalo, S. mathewsi; Groves & Grubb, 2011; Wilson & Mittermeier, 2011) that was also described in East Africa and may be distinct (Kingdom 1982). However, this new species is not accepted by all taxonomists for the lack of genetic data on the origin of this form that might be a mixed form between savanna and forest buffalo adapted to forested mountain regions. Forest buffalo distribution includes two main regions in West and Central Africa. In West Africa, isolated populations occur in the remaining rainforest blocks with a stronghold of the population in the Congo Basin (Cornélis et  al., 2014; IUCN, 2019). Forest buffalo are associated with forest clearings and riverine forests within the rainforest, which represents a habitat particularly sensitive to human-induced alterations (Prins & Reitsma, 1989;  Blake, 2002;  Melletti et  al., 2007; Bekhuis et al., 2008; Korte, 2008a). Forest buffalo populations are absent or occur at low densities in dense rainforest far from clearings (Blake, 2002; Melletti et al., 2007; Hickey et al., 2019; Madidi et al., 2019; Tiedoue et al., 2020). West African savanna buffalo occur in the Sahel-Sudanian savanna from Senegal to the eastern regions of Central African Republic (CAR). In the last few years, it was only observed in some protected areas (PAs) (e.g., W-Arly-Pendjari (WAP) Complex; Antoninova et al., 2019). Its range most probably contracted, concentrating the main remaining populations in the last strongholds such as the transboundary WAP Complex (Burkina Faso, Niger, and Benin). Central African savanna buffalo occur in Central African countries within the Sahelo-Sudanian belt (savannas and gallery forests). This subspecies has

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experienced a dramatic collapse of its population in particular in North CAR. The last strongholds remain in Zakouma NP (Chad), Chinko (CAR), and Garamba NPs (DRC) where effective conservation measures increased buffalo population sizes within these parks (Spies et al., 2017; Potgieter et al., 2019). Cape buffalo is the most abundant and widespread subspecies occurring in East and southern Africa. Several Cape buffalo populations exceed 10,000 individuals. One of the main strongholds (> 60,000 buffalo) is located in the Serengeti grasslands of northern and Selous-Mikumi ecosystems of southern Tanzania (Tawiri, 2019). Other large populations occur in Botswana with over 28,000 buffalo in Moremi Game Reserve (GR), Chobe, Makgadikgadi, and Nxai Pan NPs and in the Kruger NP (South Africa) with over 38,000 individuals (Ferreira et al., 2019). Over the last century, the African buffalo has suffered substantial population decline, though uneven over its distribution range and between subspecies. Importantly, over the last three generations, the rates of decline of some populations were evaluated to be very close to the threshold for threatened status (IUCN, 2019). Although it has decreased in several regions, overall, the total estimate for the three African savanna buffalo subspecies is 675,000 individuals in 2021. This does not consider forest buffalo (see also Cornélis et al., 2023). Based on the compilation of wildlife survey reports (aerial, pedestrian, camera-trapping) from 39 countries hosting this species, this estimate suggests that the overall savanna buffalo population is slightly increasing or stable from the estimate of East (1998) of 627,000 individuals. While the last estimate includes populations surveyed in PAs (e.g., national parks and reserves of different statutes) and hunting concessions, it does not count buffalo outside PAs, where numbers are probably low due to several negative factors heavily impacting buffalo. Also, this estimate does not include approximately 75,000 Cape buffalo privately owned in about 2800 private game ranches and farms in South Africa (Peter Oberem, pers. comm. based on 2017 data). In contrast, forest buffalo estimates are low, and populations are likely isolated from each other and from other subspecies. The only estimates available for forest buffalo are from the forest of Agoua (Central Benin) with about 100 individuals (Natta et al., 2014); Campo-Ma’an NP (650 km2; Cameroon), where population was estimated at 20 individuals (Bekhuis et al., 2008); Dzanga sector of Dzanga-Ndoki NP (CAR) with population estimated in 2002–2004 at about 32–38 individuals (Melletti et al., 2007); Lopé NP (north sector 70 km2) where Korte (2008b) estimated about 300 individuals; and Odzala-­ Kokoua NP (Republic of Congo) where Chamberlan et  al. (1995) estimated the forest buffalo population at around 500 individuals. Although forest buffalo occur at low densities, there is insufficient evidence to state that this subspecies is nearly threatened at continental scale. Presently, habitat loss, bushmeat extraction, and poaching are the main challenges threatening the species. With the unprecedented rates of human population growth, which is expected to further increase in the near future, buffalo numbers are predicted to continue to decline (IUCN, 2019). Innovative strategies are required for their conservation. In particular, forest buffalo are and will continue to be dependent

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on conservation action due to the small size of populations, the lack of connectivity between populations, limited grass available in their forest range, and increasing human pressures (Cornélis et al., 2023).

5.1.2 Movement Ecology Movement ecology is a relatively new and rapidly expanding discipline that has played a notable role in our understanding of movements of wild animals as well as associated ecological processes this movement generates (Nathan, 2008; Nathan et al., 2022). With the advent of modern GPS telemetry, many ecological aspects of animal movement have been more deeply studied (Rodgers & Anson, 1994). All species move at different stages of their life, and their movements are driven by several factors, including social interactions, predation, reproduction, disease risk, competition, habitat, human pressure, weather conditions, and food resources to name a few (Nathan et al., 2022). In the last decades, the advancement of new technologies has improved our ability to answer important questions related to animal movement such as how and why to move? Where and when to move? What are the ecological effects of animals moving from one place to another? What are the evolutionary causes that push a species to move? (Nathan, 2008, Nathan et al., 2022). Relative to mammal species dwelling in open habitat, such as grasslands, forest mammals tend to be lesser known species regarding several ecological aspects, including movement ecology. The most commonly used methods (e.g., radio-­ tracking and traditional aerial surveys) to study movement of animals in open habitats are challenging, limited, or impossible to use in the rainforest (for more details, see Reyna-Hurtado & Chapman, 2019). Savanna buffalo are often visible in large herds in open areas, whereas forest buffalo are rarely seen in the continuous forest with limited observations confined to scattered forest clearings. Thus, in comparison with the detailed descriptions of movement ecology in savanna buffalo (Naidoo et al., 2012, 2014), little is known about the ecology and behavior of forest buffalo with few studies and limited data on their movement ecology (Melletti et  al., 2007; Korte, 2008a).

5.1.3 Conservation Genetics Similar to movement ecology, conservation genetics is a young field of research that aims at understanding the past and recent genetic dynamic between populations to guide policy makers in the development and the implementation of management practices. Since the advent of molecular biology, there has been a huge evolution in the possibilities of producing accurate genetic resources. Investigating the genetic diversity (i.e., genomic variation within a species) of a species in a conservation framework is essential as a species’ capability to adapt to its changing environment

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and to pressures depends on its genetic diversity and to the gene flow between the species’ populations within its distribution range. With the modification of the natural habitat, species distribution range usually gets highly fragmented, leading to the isolation of populations often associated with a disruption of natural admixture. This is especially true for species displaying large distribution ranges and/or which cannot coexist with humans. The resulting genetic erosion associated with genetic drift and diversity loss can affect the species’ resilience to environmental changes and to diseases (Frankham et al., 1999; Hedrick, 2005). Populations are even more vulnerable if genetic drift, inversely related to the effective population size, is accompanied with inbreeding depression. Decreased genetic diversity has already been associated with reduced fitness, such as high juvenile mortality or reduced immunity, as, for example, within the cheetah (Acinonyx jubatus; Schmidt-Küntzel et al., 2018). Investigating the genetic health of a species is, therefore, an essential parameter for conservation planning. Additionally, it is important to respect the genetic integrity of a species, which can display local genetic adaptations to its environment. Especially with regard to reallocation of individuals (as is often the case with endangered flagship species), a proper understanding of the genetic structure and composition at the different spatial scales is required. In addition, genetic erosion, inbreeding depression, and reduction of genetic health are phenomena that are becoming apparent after a certain time lag. While a population may appear to be “healthy” based on actual numbers or life cohorts, the impact of the underlying genetic deterioration will only be visible at a later stage when the deterioration is often irrevocable. Studying genetic diversity over time is, therefore, crucial. Conservation practices should mitigate the genetic erosion and assure the long-term survival of the species. Over the last decades, dozens of studies involving molecular tools investigated the migration patterns, evolution, and phylogenetic relatedness between African buffalo populations. However, the access to DNA material from the forest-dwelling subspecies has been limited. On the other hand, large amounts of genetic data up to whole genomes are available for its savanna counterparts. With the available data on the savanna counterparts, the conservation genetics and evolutionary history provide a valuable context for understanding how a relatively sedentary species is found across the majority of the continent (i.e., how it could maintain high genetic diversities and low population differentiation over time).

5.2 Forest Buffalo vs. Savanna Buffalo Home Ranges and Daily Movements Smaller home ranges and short movements of buffalo are generally reported in forest habitat, compared to open, drier habitats (Cornélis et al., 2014). In forest buffalo, the maximum home range size recorded was 8 km2, during a 2-year study in Dzanga-­ Ndoki NP (CAR; Melletti et al., 2007), while the maximum daily distance covered

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by a buffalo herd was 4 km (Melletti et al., 2008). At Lopè NP (Gabon), average home range size was 5 km2 with a range of 2–8 km2 for seven radio-collared adult female buffalo (Korte, 2008b). Herds were shown to maintain stable home range size and location over 2 years with little overlap between neighboring herds, suggesting a strong spatial segregation. At Dzanga-Ndoki NP, buffalo were observed to move within a clearing for feeding, using all places with intermediate areas between forest edge and the center of glades used more frequently (Melletti et al. 2007). Moreover, buffalo tracking in this area was carried out at least once per week for a total of 96 tracking days with buffalo daily distances ranging between 0.5 and 4 km (Melletti et al., 2007, 2008). In Dzanga-Ndoki NP, 19 forest clearings were used on a rotational basis by one forest buffalo herd as the main food resources, centering its small home range in this important habitat type. In the Central African Republic and in the Republic of Congo, researchers found that over 10 years of observations in the forest clearings, the same herds used the same area (Turkalo & Fay, 2001;  Blake, 2002; Breuer, 2008; Geßner, 2008). While feeding mainly occurs within the clearings in Dzanga-­ Ndoki NP and in Nouabale-Ndoki NP (Blake, 2002; Melletti et al., 2007), at sites that lack open areas such as in Cameroon and Ivory Coast reported signs of feeding activities and movements along the edges of roads and riverbanks (Hoppe-Dominik, 1992; Bekhuis et al., 2008). Savanna buffalo can travel twice the daily distance of forest buffalo, covering distances up to 8 km in 24 hours (Sinclair, 1977; Stark, 1986). Daily average distances for savanna buffalo of 3 and 6  km have been recorded in Zimbabwe (Conybeare, 1980; Taylor, 1985) and of 3.35 km in Kruger NP (Ryan & Jordaan, 2005). Savanna buffalo annual home range sizes generally average between 50 and 350 km2 with the largest exceeding 1000 km2 (Cornélis et al., 2014), sizes much larger than the home ranges recorded in forest buffalo. In addition, far-reaching seasonal herd movements are reported by several studies. For example, in Kruger NP before the 1960s, Cape buffalo moved westward in the dry season to take advantage of the rains (Sinclair, 1977; Witkowski, 1983; Ryan et al., 2006). Naidoo et al. (2012) in the Caprivi Strip (northeastern Namibia) using radio-collared Cape buffalo showed that a part of the buffalo population undertook seasonal movements. These tracked buffalo moved at distances of maximum 115 km from water sources, a greater distance than that recorded for forest buffalo (0.5–4 km in 2 years’ study; Melletti et al., 2007, 2008; Korte, 2008a, 2008b). These large distances recorded in Cape buffalo are explained by multiple factors: environmental conditions (e.g., rainfall, fires, woodland cover), distance to the nearest barriers (e.g., rivers, fences, cultivated areas), and social factors (e.g., age, herd size) (Cornélis et al., 2014). Naidoo et al. (2012) also observed that buffalo in larger herds moved greater distances than those in small ones in the Caprivi Strip (Namibia). A similar study undertaken in the southeastern part of the Okavango Delta (Botswana) also emphasized different movement strategies in two buffalo herds (Bennitt, 2012). The author claimed that these differences were probably driven by the availability of different resources. Also, radio-collared savanna buffalo in West Africa (WAP Complex) are known to

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cover longer distances (35 ± 10 km) at the beginning of the rain season (Cornélis et al., 2011) than forest buffalo.

5.2.1 Forest vs. Savanna Buffalo Habitat Use African buffalo lives in several types of habitats, ranging from open savannas to rainforests (Prins & Sinclair, 2013; Cornélis et al., 2014). Buffalo may reach high elevation ranging from lowland areas up to mountains. This species doesn’t occur in areas with rainfall less than 250 mm (e.g., deserts and Sahel). African buffalo persist in semiarid environments, as long as surface water is available year-round within 20–40 km (Naidoo et al., 2012; Prins & Sinclair, 2013). Both home range size and movement of buffalo are influenced by habitat type and the spatiotemporal distribution of key resources (i.e., food and water). Savanna and forest buffalo are mainly found in habitats with a high herbaceous biomass. Water is a crucial resource for savanna buffalo and is considered the main constraint affecting their movements and spatial distribution. Water is less of a limiting factor for forest buffalo because it is more evenly distributed in the rainforest. Savanna buffalo must drink at least every 2 days and can cover several kilometers back and forth from the water places (Prins & Sinclair, 2013). In contrast, forest buffalo movement is constrained by the availability of herbaceous vegetation, rather than by water. For example, in Western African savanna buffalo (S.c. brachyceros), a GPS tracking study showed that buffalo herds spend 95% of their time within 5.3 km from the permanent water area and around 50% of their time within 1.4 km (Cornélis et al., 2011). A few studies in different areas of Central Africa show a high correlation between the presence of buffalo and forest clearings or savanna patches (Blake, 2002; Korte, 2008a, b; Melletti et al., 2007, 2008; Tiedoue et al., 2020). For example, Melletti et  al. (2007, 2008, 2009) found a strong correlation between buffalo and grassy glades at Dzanga-Ndoki NP (CAR) where clearings were the center of the buffalo home range. No signs of buffalo presence were recorded more than 500 m away from clearings during a 2-year study in Dzanga-Ndoki NP (Melletti et al., 2007). Buffalo positively selected these clearings, although the surface area represented only 1% of the study area. Similar results were reported in Cameroon by Bekhuis et al. (2008), where buffalo rarely penetrated into the forest more than 300 m from logging roads that were the main feeding spots. Korte (2008a) at Lopè NP collared nine adult female forest buffalo from different herds to quantify habitat preference. Buffalo selected proportionally more open habitat (marsh and savanna) and used less forest than would be expected based on habitat availability. Habitat use within home ranges varies with season. Between March and August, > 60% of locations of individuals were in forest habitats that represented  ind.; Sinclair, 1977; Mloszewski, 1983; Prins, 1996), and small suitable areas may also determine small home ranges. Furthermore, the forest habitat limits buffalo movement and size of its home range as shown in some studies in Central Africa (Blake, 2002; Melletti et  al., 2007;  Korte, 2008a, b;  Bekhuis et al., 2008). The few studies available on forest buffalo show that this subspecies lives in small and potentially isolated populations from each other that are particularly vulnerable to human impact with few corridors connecting sites that permit the movement and the exchange of individuals between different populations, limiting the gene flow (Melletti et al., 2007; Korte, 2008a, b). This is exacerbated in the residual forest blocks of West Africa. Thus, the future of forest buffalo populations will depend on maintaining connections (i.e., gene flow) between populations. Because rainforests support small, scattered herds of forest buffalo, maintaining connections

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is vital for the long-term survival of forest buffalo. Protecting small areas may have some advantages in terms of resources employed for the conservation management of small buffalo populations or in terms of preventing disease transmissions. On the contrary, small areas may be more susceptible to human-induced alterations than larger ones, and this poses some serious conservation issues on species highly dependent on this habitat type. Unfortunately, the lack of conservation measures specific to this subspecies has left forest buffalo vulnerable. Such situations occur when the threats are difficult to tackle or are not addressed properly, notably when the human pressure is high due to large areas deforested and an exponential increase of poaching and non-sustainable subsistence hunting that fuels the bushmeat trade in West Africa and in the Congo Basin (Cornélis et al., 2023). Habitat loss and degradation is a major threat to forest buffalo populations. Formerly, intact rainforests in West Africa and the Congo Basin have been, and continue to be, lost at a dramatic rate (Hansen et al., 2013) due to agricultural expansion and extractive industries (Malhi et al., 2013). While the vastness of the Congo Basin seems to safeguard forest buffalo, in West Africa it is clearly gravely threatened. Unfortunately, there are no conservation activities specific to forest buffalo in West Africa. Thus, existing legal protection seems to be what allows these few remaining forest buffalo populations to survive in West Africa. Past and current bushmeat trade pressure and loss of habitat will result in population declines. Conservation activities and enforcement of laws to protect these small, disconnected populations are crucial.

5.4.1 Wildlife Corridors Wildlife corridors improve habitat connectivity facilitating animal movement and other important ecological processes (Newmark, 1993). These wildlife areas can be designed to keep local migration/movement of species from one protected area to another or from encroaching human populations in areas of high interaction between the two. These corridors can also be designed to keep animals away from highways, busy roads, and other areas where their traditional migratory patterns intersect with potentially dangerous man-made infrastructures. Furthermore, corridors may play an important role in maintaining gene flow and thus genetic diversity between different animal populations preventing inbreeding and local extinctions. Another role of these areas is the maintenance of a bridge between different habitats; otherwise, they would be small and isolated and more susceptible to human-induced alterations. They are also crucial in maintaining long-term viable populations. Forest buffalo occur in small, scattered, and isolated populations through the Congo Basin and West Africa (Korte, 2008a, b; Cornélis et al., 2023) making them vulnerable to local extinction induced by habitat loss and human pressure. Thus, corridors will be important. Forest buffalo can cross stretches of forest, but they do not penetrate deep into the forest (Blake, 2002; Melletti et  al., 2007; Bekhuis et  al., 2008). Indeed, in

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Dzanga-Ndoki NP, the maximum distance travelled by buffalo, crossing a forest stretch from one clearing to another, was 1 km (Melletti et al., 2007); this distance is in line with the data reported of 0.5 km from clearings and of 0.3 km from logging road (Bekhuis et al., 2008). On the basis of a 2-year study in Dzanga-Ndoki NP, forest buffalo used mainly a network of water courses as natural corridors to move from one glade to another (Melletti et al., 2007, 2008). So, preserving intact stretches of forest along rivers may be important. Wildlife corridors are often overlooked by governments and local authorities, but they play a crucial role as we have seen in connecting isolated protected areas and patches of different habitats. For example, in Kibale NP (Uganda), the 180 km of Dura corridor connecting Queen Elizabeth NP with Kibale NP plays an important role for animal movement between these two parks. Although this corridor is threatened by human encroachment, poaching, mining, and habitat loss, it permits the movement of wildlife as observed by camera trap study (R.  Reyna, J.P. d’Huart, M. Melletti pers. obs.). Observations of savanna species (e.g., Uganda kob, waterbuck, warthog, savanna buffalo) deep in the Kibale’s forest show the important role of these corridors for animal movement and for increasing gene flow. In Kibale NP, there are no recent records of forest buffalo, and the only observations made by camera traps from 2013 to 2021 (R. Reyna pers. comm.) come from savanna buffalo adapted to forest environments. Savanna buffalo from Queen Elizabeth NP penetrate into Kibale NP through the Dura corridor. It is not clear if these buffalo live permanently inside the forest of Kibale NP or if they just wander for short periods in this park.

5.4.2 Translocation When Corridors Fail When corridors are lost, translocation may be needed. In South Africa, Cape buffalo have been reintroduced in many private reserves and farms, where they were formerly extirpated (Cornélis et  al., 2014). Several other countries have restocked savanna buffalo populations that were isolated and lost connections with other herds. Although savanna buffalo translocation is possible, it depends on sufficient funding for the operation as well as long-term maintenance of the herds at these locations. While there are no translocation projects to date for forest buffalo, this may be needed in the future for West Africa. It is likely that young and subadult individuals might translocate well, but further investigation is needed.

5.5 Conclusion The African buffalo is mainly a sedentary species yet is distributed throughout sub-­ Sahara with the exception of deserts and arid regions. Although forest buffalo have received little attention by the scientific community and local authorities, what is

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known about its movement ecology allows its management for successful conservation, but further genetic resources should be generated to deepen our knowledge about its genetic health. Relative to savanna buffalo, forest buffalo have smaller home ranges, which are centered on food sources and increasingly isolated due to human activities, shorter daily and seasonal distances travelled, and smaller group size. Forest buffalo are, and will continue to be, dependent on conservation action due to the small size of populations, the lack of connectivity between populations, limited food resources available in their forest range, and increasing human pressure in countries where they are present. The most cost-effective and efficient approach to maintaining connectivity is likely wildlife corridors based on water courses used by forest buffalo, but in some areas, this will be impossible. Furthermore, an effective anti-poaching strategy will be critical to maintain buffalo populations. Most importantly, conservation efforts should recognize forest buffalo as a distinct conservation unit to maintain its genetic integrity and allow the exchange of individuals among populations through the implementation of wildlife corridors. Understanding animal movement ecology has important consequences in wildlife management and conservation. Forest buffalo is a species with scattered populations strongly connected to clearings that are sparsely distributed, and its movements are very limited. These ecological traits make buffalo more vulnerable to habitat loss and degradation and poaching affecting these crucial habitats. In this case, understanding the movement ecology of a poorly known species can provide insights for conservation.

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Chapter 6

Site Fidelity and Home Range Shifts in a Leaf-Eating Primate Urs Kalbitzer, Martin Golooba, and Colin A. Chapman Abstract  The ability to move allows animals to optimize their fitness by responding to spatial variability in their environment, such as changes in the location of food resources, predators, parasites, competitors, or reproductive partners. However, many animals remain within a limited area throughout their lives. Such site fidelity has been reported for social groups of nonhuman primates and other mammals over many years or even decades. The few studies that have addressed and confirmed long-term site fidelity in primates have focused on species feeding on presumably unevenly distributed food resources, such as fruit, which indicates heterogeneous habitat quality, a factor often considered to decrease site fidelity. However, if we want to test and extend theories about the ecological factors linked to site fidelity, including the role of habitat quality heterogeneity, studies about species feeding on more evenly distributed foods, such as leaves, are essential. Therefore, we here investigate the long-term ranging pattern of the leaf-eating (and endangered) red colobus (Piliocolobus tephrosceles) in Kibale National Park. For the period from 2010 until 2019, we established home range density estimates (HRDE) for our main study group for annual, 6-month, and 3-month periods via “autocorrelated kernel density estimation.” Then, we compared changes in these HRDEs across time using Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-­3-­031-­27030-­7_6. U. Kalbitzer (*) Department of Biology, University of Konstanz, Constance, Germany Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Constance, Germany e-mail: [email protected] M. Golooba Makerere University Biological Field Station, Fort Portal, Uganda C. A. Chapman Biology Department, Vancouver Island University, Nanaimo, BC, Canada Wilson Center, Washington, DC, USA School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_6

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three metrics: (1) the “overlap” of HRDEs, which is a measure of similarity between (probability) distributions that reflect the estimated home ranges, (2) the distance between the centroids of the HRDEs, and (3) the distances between the points of maximum density of HRDEs. Our results indicate high home site fidelity for red colobus, as our study group was still using the same area after 10 years. Although these results are as expected, it is important to note that this pattern is similar in primates with other types of diet (including fruit). Thus, going forward, it will be crucial to quantitatively assess heterogeneity in food distribution for different types of diet and study additional factors that may lead to such a high site fidelity in many primates. Keywords  Territory quality · Habitat quality · Social group · Folivore · Frugivore · Kibale · Uganda

6.1 Introduction The ability to move allows animals to respond to environmental variability by changing their spatial location (Nathan et  al., 2008). Changes in position can be crucial to maximize fitness by responding to spatial and temporal variability in food resources; avoiding predators, parasites (Bonnell et al., this volume), and competitors; or adjusting to the position of conspecifics as social or reproductive partners. However, while movement behavior over short timescales has received considerable research interest, the long-term ranging patterns, and the underlying evolutionary and ecological factors that drive these patterns, are less well known. A common pattern of ranging is that animals, or social groups of animals, remain in a specific area over long periods, although individuals may disperse from their natal areas shortly before or after reaching adulthood (Clutton-Brock & Lukas, 2012) or change their primary home range between different seasons (i.e., migrate; e.g., Hurme et al., 2022). Such site fidelity has been suggested to improve fitness for several reasons (Edwards et al., 2009; Switzer, 1993, 1997). It can allow animals to optimize foraging efficiency if they gain knowledge of the timing and location of food resources in their usual ranging area. In addition, it can help animals to reduce predation risk by knowing dangerous areas and places to escape to in case of predator attacks (Bonnell et  al., this volume). Unfortunately, there are only a limited number of long-term studies on site fidelity and, on the other side of the equation, home range shifts. This limits our understanding of the determinants of site fidelity and when it may become beneficial for animals to change their common ranging area. For example, when is it advantageous to shift home range in response to changes in environmental conditions caused by natural or anthropogenic factors? Such understanding is not only important from a theoretical perspective, but it will also help us to improve our understanding of the vulnerability of animals to

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changing environments and, therefore, to manage and conserve wild-ranging population of endangered, or potentially endangered, animals (Kalbitzer & Chapman, 2018). For example, the knowledge obtained from such studies can inform managers when dispersal corridors will be needed. A common observation for nonhuman primates and many nonmigrating mammals is that individuals and social groups show high site fidelity, though the evidence for this is largely anecdotal and quantitative studies are few. For example, social groups of mangabeys (Lophocebus albigena) in Kibale National Park, Uganda, retain their home ranges over 10 years (Janmaat et al., 2009), and there are reports that the groups are still in the same areas as of this year, 2022, more than 10  years later (personal communication, Clovis Kaganzi and Richard Mutegeki). Also, ring-tailed lemurs (Lemur catta) investigated over several decades mostly remained within the same home ranges (Jolly & Pride, 1999). Site fidelity over multiple years has also been documented for other primate species, including Bornean southern gibbons (Hylobates albibarbis, Cheyne et al., 2019), spider monkeys (Ateles geoffroyi; Ramos-Fernandez et  al., 2013), or owl monkey (Aotus azarae; Wartmann et al., 2014). High site fidelity can also be limited to several years, after which animals suddenly change their home range. For example, meerkats (Suricata suricatta) in the Kalahari desert, South Africa, usually have stable home ranges (or territories) for several years before they exhibit sudden shifts in their space use (Kranstauber et al., 2020). Also, groups of mountain gorillas (Gorilla gorilla beringei) in Rwanda sometimes exhibited stable home ranges for several years before dramatic shifts in their home ranges linked to changes in group composition and major intermale conflicts (Watts, 1998). In addition to evidence from such systematic long-term analyses, site fidelity also appears to be anecdotally confirmed by long-term studies that observed the same social groups over long periods within the same area without specifically analyzing location data. However, quantitative investigations still need to be more extensive both in terms of number and in terms of diversity of species, because the required datasets with regular location data collected for over long periods are very difficult to obtain. For example, for nonhuman primates, the studies that quantitatively assessed site fidelity focused on species that rely on temporally and spatially variable resources and are often only available during short periods, such as fruit or insects (see references above; with the exception of gorillas, all other primate species listed above have a considerable proportion of fruit in their diet). However, studying long-term movement patterns in a diversity of species, specifically with regard to diet, is essential to investigate the general links between different life histories, behavioral strategies, ecological environments, and site fidelity. One of the important ecological factors that may change the benefits of remaining at the same location is variation in the distribution and quality of food resources. For example, when the locations of high-quality food resources with restricted spatial distributions change over time, it may also be beneficial for animals to change their ranging areas. On the other hand, when the primary diet exhibits less variation in quality and spatial location (i.e., it is relatively evenly and widely distributed), the advantages of shifting home ranges would be expected to be less.

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Environmental, including territory quality, and individual factors potentially affecting the degree of site fidelity were theoretically explored by Switzer (1993) using the construction of mathematical models. His models predict that site fidelity should increase with the costs of changing territories, an individual’s current age, and probability of adult mortality, and site fidelity should decrease with maximum life span and heterogeneity in territory quality. While Switzer uses the term “territory,” which is often defined as an area where the resources are exclusively used by the owner (Morrison et al., 2020), the same factors should apply to any regularly used home range area. Thus, his models provide theoretical support for the notion that food resource distribution, an important facet of territory quality, should be linked to site fidelity. However, to obtain empirical evidence for this idea and therefore to improve theoretical considerations about the ecological and evolutionary factors leading to site fidelity, investigations of site fidelity in species with different diets are required, including species feeding on widely and evenly distributed resources. Leaf-eating primates presumably represent such a group, and, therefore, the investigation of home range use and site fidelity in such primates and evaluations of the dynamics that affect shifts in their home range are critical. We investigated home range use and site fidelity of the leaf-eating red colobus (Piliocolobus tephrosceles) in Kibale National Park, Uganda, over a 10-year period including 8 distinct years with location data (2010–2016 plus 2019). Red colobus in Kibale primarily feed on young leaves, generally a widely distributed resource in the tropical rainforest of Kibale. However, leaves of different plant species show considerable variation in their nutritional content, such as their protein-to-fiber content, and red colobus show strong preferences for specific plant species (Chapman et  al., 2002; Chapman & Chapman, 2002). In addition, the availability of young leaves varies seasonally, with higher availability during the two rainy seasons (unpublished data), and previous research indicated that red colobus adapt their movement behavior according to season (Reyna-Hurtado et al., 2018). Thus, while their diet is generally widely available, there are indications for some spatial and temporal variability, and the quantitative assessment and comparison of food resource distributions for different food categories should receive much more scientific attention in behavioral ecology. Until then, we will have to rely on simplified assessments, such as that a leaf diet is more evenly distributed than a ripe fruit diet. Here, we investigate the temporal consistency of the home range use of a social group of red colobus by estimating home range use for periods of different lengths (annual, 6-month, 3-month) over the 10 years and then using different metrics to compare home range estimates over time. Given the presumably relatively evenly distributed food resource distribution (i.e., high homogeneity in territory quality), we predict high site fidelity over long periods, even though we expect less fidelity for the comparisons across seasons. Beyond insights into site fidelity, our results also provide information about areas where this endangered primate focuses their activities, which may help conservation managers and policy makers to install and maintain appropriate protected areas for their protection.

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6.2 Methods 6.2.1 Study Site and Data Collection Behavioral data were collected between July 2010 and December 2016 and between March 2019 and October 2019 from a group of red colobus in Kibale National Park, Uganda (0° 13′ – 0° 41′ N and 30° 19′ – 30° 32′ E). This group included, at each point in time, about 30 adult females and 15 adult males (for details, see Kalbitzer & Chapman, 2021). This group ranged in the moist, evergreen forests of the Kanyawara area near Makerere University Biological Field Station (Chapman & Lambert, 2000), where rainfall is bimodal with two wet and two dry seasons each year and an annual average rainfall of 1646 mm (Chapman et al., 2021). The group ranged in what are locally known as Forestry Compartments K-30 and K-14. The K-30 site is a 282-ha area of old-growth forest that has never been commercially harvested, but a few large stems (0.03–0.04 trees/ha) were removed by people cutting timbers before 1970. The K-14 area is a 405-ha forest block that was logged at low intensity (14  m3/ha or 5.1 stems/ha) from May through December 1969. Approximately 25% of all trees in compartment K-14 were destroyed by logging and incidental damage, but the area the group used was very lightly affected (Chapman et al., 2010; Chapman & Lambert, 2000; Skorupa, 1988; Struhsaker, 1999). Two to three Ugandan field assistants spent several days per month with the red colobus and recorded scan samples every 15–30 min, with the frequency depending on other data collection going on at the time (i.e., when much other data collection was going on, the scan samples were only recorded every 30 min; details in Kalbitzer and Chapman (2021). For each scan sample, the field assistants selected five adults and recorded various data for each of these individuals, including their identity and behavior. In addition, the GPS location at the approximate center of the group was recorded during each scan using coordinates in UTM N 36. For the entire study, we had 545 days with location data, which included between 0 and 17 days of data per month (mean = 6 days/month; Table 6.S1).

6.2.2 Data Analysis We created 4 distinct datasets for our analysis: 1 comprising all the range use data (2010 until 2016 and 2019) as a single dataset, 1 consisting of 8 annual subsets of data (1 for each year of the study), 1 consisting of 15 6-month subsets, and 1 consisting of 27 3-month subsets. For the 6-month dataset, we divided each year into two periods: one from January until June and one from July until December, including one dry and one wet season each (for details of the climate, see Chapman et al., 2021). For the 3-month dataset, we split the data into four different seasons, two dry and two wet seasons: December until February (dry), March until May (wet), June until August (dry), and September until November (wet). Each annual subset

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included range use data stemming from between 26 (in 2019) and 101  days of observation (in 2015; mean = 68 days/year); for the 6-month dataset, each subset included range use data collected on between 13 and 61  days of observation (mean = 36 days/6-month period); and the for 3-month dataset, each subset comprised data collected on between 3 and 31 days of observation (mean = 19 days/3-­ month period; for details, Table 6.S1). For each of these four datasets, we then followed the same procedure to estimate and compare home ranges across all periods included into each dataset (i.e., comparing all annual periods with each other, all 6-month periods with each other, and all 3-month periods with each other). To estimate home ranges, we followed the framework described in Calabrese et al. (2016) using the R package ctmm version 1.0.0 (Fleming & Calabrese, 2022) in R version 4.2.1. (R Core Team, 2022). In contrast to more conventional methods to estimate home ranges, this approach can separate the movement from the sampling process and account for various types of autocorrelation within the used movement data (namely, “position autocorrelation,” “velocity autocorrelation,” and the correlation linked to the “tendency to remain in a defined home range” (Calabrese et al., 2016). As a result, this approach is relatively robust for working with irregularly sampled data and has much less tendency to underestimate home range areas for autocorrelated movement data. To obtain such home range estimates, we first calculated variograms using the ctmm-function “variogram.” These variograms were then used to “guesstimate” initial parameter values (using the function “guess.ctmm”) to automatically fit and rank (according to AICc values) a set of isotropic and anisotropic versions of continuous-­time stochastic process models with different autocorrelation structures (using the function “ctmm.select”). At first, we proceeded with the most informative model (i.e., with the lowest AICc value) for each period and derived home range estimates via “autocorrelated kernel density estimation” (using the function “akde”). The resulting home range density estimates (hereafter HRDE) indicate, for each point in the area, the probability (i.e., density) that the group can be found at that location. These densities can be further summarized, for example, to the cumulative 95% density area, where the model predicts a 95% probability that the group can be found within that area (the 95% contour). However, because some of the selected models exhibited issues estimating the correct home ranges (i.e., the 95% contours were far too large considering the actual observation data) and all these models were anisotropic, we refitted all models using the full autocorrelation structure but isotropic versions of the models. We then recalculated the HRDE, and here we report and discuss the result from these isotropic models. We then compared the resulting HRDEs across all periods within each dataset (i.e., annual, 6-month, and 3-month datasets). For these comparisons, we included three different metrics: (1) the “overlap” between HRDEs using the “Bhattacharyya coefficient” (using the “overlap” function; Winner et al., 2018), which compares the similarity of distributions (hereafter OverlapHRDE); (2) the distance (in meter) between the centroids of the cumulative 95% density area (hereafter Dcentroids); and (3) the distance between the points of the highest density of the HRDE (hereafter Dmaxdens).

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These points of highest density can be considered the points where the group was most likely to be observed during that respective period, according to the home range model. While (2) is dependent on the 95% threshold that we set for the contour, (1) and (3) are taking the entire distribution into account and are therefore independent of such a threshold. For OverlapHRDE, the values ranged from 0 to 1, with 0 indicating that the two home ranges share no common area and 1 indicating that the home ranges are identical. In contrast, for the two distance metrics Dcentroids and Dmaxdens, larger values (in meters) indicate more distinct home ranges.

6.3 Results Using all data from 2010 to 2016 and 2019 combined resulted in a home range estimate with an area of 0.915 km2 (Fig. 6.1 and Table 6.S1). When splitting this dataset into the annual, 6-month, and 3-month periods, we generally observed a great deal of overlap across periods. Regarding the annual estimates, the 95% cumulative density areas ranged from 0.679 to 1.603 km2 (Fig. 6.2a and Table 6.S1; note that such estimates can be larger than the estimate for the entire period because with fewer data, some areas may become more plausible to be used by the animals – according to the model – that were implausible when considering all data. However, the area size is generally not correlated with the number of included data points; see also Table  6.S1). While there were some differences in home range use across years, they tended to be relatively similar (Fig.  6.2a–d). According to OverlapHRDE, the most distinct home ranges were 2010 vs. 2011, 2012, 2013, 2014, and 2016 (Fig. 6.2b; all OverlapHRDE values 0.83) and probably driven by the small area estimate for 2010 (Fig. 6.2a). The centroids and the locations of the maximum HRDE values tended to be within a few 100 m (Fig. 6.2c, d) with the largest Dcentroid values for 2010 vs. 2012; 2011 vs. 2014 and 2019; and 2012 vs. 2014, 2015, 2016, and 2019, which were all between 150 m and 195 m. For Dmaxdens, the largest values were observed for 2010 vs. 2011, 2013, 2014, 2015, and 2016; 2011 vs. 2012 and 2019; 2012 vs. 2013, 2014, 2015, and 2016; and 2019 vs. 2013, 2014, 2015, and 2016, with distances between 300 m and 561 m. Taken together, the three metrics indicate no clear pattern of changes in home range use across years. For the analysis of 6-month periods, the estimated areas (i.e., 95% cumulative density contours) ranged from 0.679 km2 to 2.028 km2 (Fig. 6.3a and Table 6.S1). As for the annual plots, the estimated home ranges for all 6-month periods tended to be similar, and the three metrics did not indicate a clear pattern of distinct home ranges (Fig. 6.3b–d). Finally, regarding the 3-month periods, the estimated areas (i.e., 95% cumulative density contours) ranged from 0.387 to 2.991 km2 (Fig. 6.4a and Table 6.S1). While home range estimates appeared to be different across some periods, the different

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Fig. 6.1  The home range density estimate (HRDE) of our red colobus study group for the entire dataset, which included data from 2010 until 2016 and from 2019. These density estimates indicate the probability of observing the group at a specific point according to the autocorrelated kernel density estimate (akde, see methods), with darker blue colors indicating higher probabilities and lighter colors indicating lower probabilities. The red contour line outlines the cumulative 95% density, which, in combination with the white-blue color gradient, should provide the reader with a good idea about the model estimates. The white circles indicate the actual location data, with the size proportional to the number of times the group was recorded at that specific location. The red triangle indicates the location of the maximum HRDE value (which was used to calculate Dmaxdens). The black area indicates area outside the park

metrics did not agree about such differences (Fig. 6.4b–d). For example, according to OverlapHRDE, the period June to August 2010 was different from many other periods, including those with values close to 0.5 (Fig. 6.4b). However, both distance metrics (Fig.  6.4c, d) did not indicate such differences, presumably because the estimate for June to August 2010 suggests a smaller home range than for the other periods but with a similar center of ranging activity (i.e., it was located within the other home range estimates; Fig. 6.4a).

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Fig. 6.2  Annual home range density estimates (HRDEs) and comparisons across years. For the annual maps of the HRDEs (a), symbols and colors are as in Fig. 6.1. For the heatmaps, the color indicates the similarity/difference between annual HRDEs considering OverlapHRDE (b), Dcentroids (c), and Dmaxdens (d). Lighter colors indicate more similar home ranges and darker colors more distinct home ranges with values close to 1 for (b) and with values close to 0 for (c) and (d) indicating the most similar home ranges

6.4 Discussion We estimated home range use and site fidelity of a group of red colobus monkeys in Kibale National Park from 2010 until 2019. The red colobus group barely changed their primary ranging area as comparisons across years, 6-month periods, and 3-month periods indicate high overlaps in home range use and similar locations of home range centers. Thus, our results support our predictions that red colobus have a high site fidelity over long periods even though we had expected more seasonal changes (i.e., across 3-month periods) than we observed.

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Fig. 6.3  Six-month home range density estimates (HRDEs) and comparisons across these periods. Colors and symbols as in Figs. 6.1 and 6.2

These observations align with several of Switzer’s (1993) predictions, mainly that site fidelity increases with “territory” homogeneity. What is surprising is that several nonhuman primate species (e.g., mangabeys, ring-tailed lemurs, spider monkeys) that feed on ripe fruit and other food resources considered to occur in patches have been described as showing a high site fidelity as well (Janmaat et al., 2009; Jolly & Pride, 1999; Ramos-Fernandez et al., 2013). Such a diet is expected to be associated with more significant heterogeneity of territory quality, which should lead to lower site fidelity. However, one major drawback in all such considerations is that assumptions about food resource distribution are often based on the type of diet (e.g., fruit vs. leaves) and rarely on quantitative data considering the actual quantity and location of available food. Thus, to further explore the link between territory/habitat heterogeneity and site fidelity in primates, we need to learn much more about foraging behavior in relation to temporal and spatial variability in food resource abundance in species with different types of diet. Another factor that would increase site fidelity according to Switzer is the cost of changing territories. For red colobus in Kibale, we assume that they benefit from the knowledge of their home ranges and, therefore, that such costs are relatively high. One of the main predators of red colobus in Kibale are chimpanzees (Pan troglodytes), and while hunting events do not appear to be as frequent as in other parts of Kibale (Lwanga et al., 2011; Watts & Mitani, 2015) and hunting rate (hunt attempts/

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Fig. 6.4  Three-month home range density estimates (HRDEs) and comparisons across these periods. Colors and symbols as in Figs. 6.1 and 6.2

encounters) by chimpanzees in our study area is much lower (7.9%) than at other comparable sites where similar data are available (64.7%, 48.0%; Gilby et  al., 2015), all three coauthors have observed such events at our field site. Chimpanzees primarily feed on ripe fruit (Chapman et al., 1995), and the combination of the spatial scarcity of most fruit-producing trees and their short fruiting periods (Chapman et al., 2018) means that ripe fruit is usually only found in some areas within the forest (i.e., ripe fruit is patchily distributed; Chapman et al., 1995). Knowing where ripe fruit occurs during different periods of the year may therefore help red colobus to avoid such areas. On the other hand, Switzer (1993) also predicts that site fidelity decreases with a longer maximum life span and a low probability of adult mortality. Since red

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colobus (like most nonhuman primates) have relatively long life spans and low probability of adult mortality (Gogarten et al., 2012), this seems to be at odds with these predictions. However, these predictions are made at the level of individuals. For example, for an individual with a long life span, it may be beneficial to change their territory because even minor differences in territory quality may pay off if much of this life span is remaining (Switzer, 1993). However, it is not straightforward how to apply the concept of life span to social group of animals. Thus, while some of Switzer’s predictions may be more easily adapted to groups (e.g., territory quality), applying other criteria to social groups may require an extension of this modeling framework. Furthermore, all criteria, including territory quality or cost of changing territories, are measured by reproductive outcome. Thus, proper testing of these predictions requires assessing and comparing reproductive outcomes across territories (Switzer, 1993, 1997). Some additional factors that may play an important role in the site fidelity of red colobus and other animals are the location of rare and infrequently eaten yet essential resources, such as salt. Red colobus appear to seek out foods (e.g., Markhamia lutea petioles, Eucalyptus bark) that have high salt content (Rode et al., 2003) and eat soil from very specific locations (i.e., repeatedly eat the soil from a few square meter area over decades – C. Chapman unpublished data). Knowing the locations of such critical resources may require groups to remain within the same area. This high level of site fidelity was also found despite the fact that Kibale is changing. Changes that have been documented over the years include increasing temperatures (Chapman et  al., 2021), declining nutritional quality of leaves (Rothman et al., 2015), changes in tree fruiting phenology (Chapman et al., 2018; Kalbitzer et al., n.d., Invited for Resubmission), a decline in some of the preferred foods of colobines (Chapman et al., 2013a, b, 2021), and a decrease in the abundance of the often nutritious light-demanding plant species (Chapman et al., 2021). Furthermore, levels of disease infections have changed (Chapman et  al., 2012, 2015), and insect and snail populations have declined (Opito et  al., 2023). Surprisingly, these environmental changes correspond to a general increase in primate abundance (Sarkar et al., 2022) and an increase in group size for all common species (Gogarten et al., 2015). The high site fidelity, thus, suggests that the advantages of remaining in a known site are likely powerful for species such as the red colobus. In conclusion, our results support our expectation of high site fidelity for red colobus in Kibale. However, to better understand interspecific differences in site fidelity, we require better approaches to quantify heterogeneity in home range/territory quality in combination with foraging and fitness assessments. Furthermore, additional theoretical work about the benefits and costs of site fidelity for social groups vs. individuals, considering the dispersal from and immigration into such groups by individuals, would greatly benefit our understanding of site fidelity and movement behavior in general. Finally, it would be interesting to explore why site fidelity is such a widespread phenomenon, specifically for primates.

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Acknowledgments  First of all, we thank all the people who made the collection of the analyzed long-term data possible, specifically the project managers and field assistants of the Kibale Fish and Monkey Project, including Dennis Twinomugisha, Patrick Omeja, Emmanuel Aliganyira, Robert Basaija, Clovis Kaganzi, Tusiime Laurence, and Peter Tuhairwe. Furthermore, we thank the many people who, over the last 10+ years, have made significant contributions to entering and cleaning up the dataset. We also thank Rafael Reyna-Hurtado and Mario Melletti for encouraging us to contribute to the volume “Movement Ecology of Afrotropical Forest Mammals” and two anonymous reviewers for highly valuable suggestions to improve our manuscript. Funding was provided by the Canada Research Chairs Program, the Natural Sciences and Engineering Research Council of Canada, the Faculty of Arts of McGill University, National Geographic, Leakey Foundation, the International Development Research Centre (Canada), the Max Planck Society (Germany), the Center for the Advanced Study of Collective Behavior (CASCB) at the University of Konstanz (Germany), and the Young Scholar Fund (YSF) of the University of Konstanz.

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Lwanga, J. S., Struhsaker, T. T., Struhsaker, P. J., Butynski, T. M., & Mitani, J. C. (2011). Primate population dynamics over 32.9 years at Ngogo, Kibale National Park, Uganda. American Journal of Primatology, 73, 997–1011. https://doi.org/10.1002/ajp.20965 Morrison, R. E., Hirwa, J. P., Mucyo, J. P. S., Stoinski, T. S., Vecellio, V., & Eckardt, W. (2020). Inter-group relationships influence territorial defence in mountain gorillas. Journal of Animal Ecology, 89(12), 2852–2862. https://doi.org/10.1111/1365-­2656.13355 Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052–19059. https://doi.org/10.1073/ pnas.0800375105 Opito, E. A., Alanko, T., Kalbitzer, U., Nummelin, M., Omeja, P., Valtonen, A., & Chapman, C. A. (2023). 30 years brings changes to the arthropod community of Kibale National Park, Uganda. Biotropica. https://doi.org/10.1111/btp.13206 R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-­project.org/ Ramos-Fernandez, G., Aguilar, S. E. S., Schaffner, C. M., Vick, L. G., & Aureli, F. (2013). Site fidelity in space use by spider monkeys (Ateles geoffroyi) in the Yucatan Peninsula, Mexico. PLoS One, 8(5), e62813. https://doi.org/10.1371/journal.pone.0062813 Reyna-Hurtado, R., Teichroeb, J. A., Bonnell, T. R., Hernández-Sarabia, R. U., Vickers, S. M., Serio-Silva, J. C., Sicotte, P., & Chapman, C. A. (2018). Primates adjust movement strategies due to changing food availability. Behavioral Ecology, 29(2), 368–376. https://doi.org/10.1093/ beheco/arx176 Rode, K. D., Chapman, C. A., Chapman, L. J., & McDowell, L. R. (2003). Mineral resource availability and consumption by colobus in Kibale National Park, Uganda. International Journal of Primatology, 24(3), 541–573. https://doi.org/10.1023/A:1023788330155 Rothman, J.  M., Chapman, C.  A., Struhsaker, T.  T., Raubenheimer, D., Twinomugisha, D., & Waterman, P. G. (2015). Long-term declines in nutritional quality of tropical leaves. Ecology, 96(3), 873–878. https://doi.org/10.1890/14-­0391.1 Sarkar, D., Bortolamiol, S., Gogarten, J. F., Hartter, J., Hou, R., Kagoro, W., Omeja, P., Tumwesigye, C., & Chapman, C. A. (2022). Exploring multiple dimensions of conservation success: Long-­ term wildlife trends, anti-poaching efforts and revenue sharing in Kibale National Park, Uganda. Animal Conservation, 25(4), 532–549. https://doi.org/10.1111/acv.12765 Skorupa, J. P. (1988). The effect of selective timber harvesting on rain forest primates in Kibale Forest, Uganda (PhD). University of California, Davis. Struhsaker, T.  T. (1999). Ecology of an African rain forest: Logging in Kibale and the conflict between conservation and exploitation. University Press of Florida. Switzer, P. V. (1993). Site fidelity in predictable and unpredictable habitats. Evolutionary Ecology, 7(6), 533–555. https://doi.org/10.1007/BF01237820 Switzer, P. V. (1997). Factors affecting site fidelity in a territorial animal, Perithemis tenera. Animal Behaviour, 53(4), 865–877. https://doi.org/10.1006/anbe.1996.0352 Wartmann, F.  M., Juárez, C.  P., & Fernandez-Duque, E. (2014). Size, site fidelity, and overlap of home ranges and core areas in the socially monogamous owl monkey (Aotus azarae) of Northern Argentina. International Journal of Primatology, 35(5), 919–939. https://doi. org/10.1007/s10764-­014-­9771-­7 Watts, D.  P. (1998). Long-term habitat use by mountain gorillas (Gorilla gorilla beringei). Consistency, variation, and home range size and stability. International Journal of Primatology, 19(4), 651–680. https://doi.org/10.1023/A:1020324909101 Watts, D. P., & Mitani, J. C. (2015). Hunting and prey switching by chimpanzees (Pan troglodytes schweinfurthii) at Ngogo. International Journal of Primatology, 36(4), 728–748. https://doi. org/10.1007/s10764-­015-­9851-­3 Winner, K., Noonan, M. J., Fleming, C. H., Olson, K. A., Mueller, T., Sheldon, D., & Calabrese, J. M. (2018). Statistical inference for home range overlap. Methods in Ecology and Evolution, 9(7), 1679–1691. https://doi.org/10.1111/2041-­210X.13027

Chapter 7

Primate Movements Across the Nutritional Landscapes of Africa Emma G. Thurau, Brynn E. Lowry, John Bosco Nkurunungi, and Jessica M. Rothman

Abstract  To find suitable food, organisms must navigate through a matrix of resources that vary in their concentrations of nutrients, toxins, and digestion inhibitors while also avoiding multiple hazards such as food competitors and predators. Wild primates eat a variety of foods that vary in their availability and quality both spatially and seasonally, and their movements differ accordingly. Here, we review the movement ecologies of primates in African forests with a focus on nutrient acquisition. We discuss how primates find different nutrients using a variety of sensory adaptations and adapt their movement to meet specific nutritional needs. We discuss future directions for research in this area. Keywords  Nutrition · Movement · African primates · Ecology · Sensory ecology · Plant secondary metabolites · Brownian movement

7.1 Primates and African Forests Africa has about 2,070,000 km2 or 22% of its landmass as forest, which is disappearing at a rapid rate (Vancutsem et al., 2021). For example, in just 30 years, there has been a 24% decline in forest, with a 10% loss over the last decade (Vancutsem E. G. Thurau (*) · B. E. Lowry · J. M. Rothman Department of Anthropology, Hunter College of the City University of New York, New York, NY, USA Anthropology Program, The Graduate Center, City University of New York, New York, NY, USA New York Consortium in Evolutionary Primatology, New York, NY, USA e-mail: [email protected]; [email protected]; [email protected] J. B. Nkurunungi Biology Department, Mbarara University of Science and Technology, Mbarara, Uganda e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_7

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et al., 2021). Forests in Africa tend to have less tree diversity than South American and Asian forests (Couvreur, 2015), though some areas are particularly species rich, such Albertine Rift and in some regions of Madagascar (Linder et  al., 2012). However, despite lower overall diversity, African rainforests have an aboveground biomass that is substantially higher than rainforests in the Amazonian region and in Borneo (Lewis et al., 2013). This is interesting to consider from the perspective of foraging primates who rely on multiple food sources to meet their nutritional needs (Lambert & Rothman, 2015) and must move through these forests to obtain adequate resources. The distribution and abundance of plant nutrients and toxins in a forest can have a strong effect on foraging movement of animals (Marshall et  al., 2014). Plant responses to seasonal variation in the amount of sunlight, rainfall, and temperature can have profound effects on where animals are accessing nutrients (Conklin-­ Brittain et al., 1998; Ganzhorn, 2002; Scharf et al., 2019), such as the canopy layer, part of the tree crown, and area of the forest. Tropical forests, though having less seasonal fluctuations in comparison with temperate forests, still experience phenological cycles of flowering, fruiting, and leaf flushing, and there are subsequent behavioral shifts in how primary consumers access nutrients (van Schaik et  al., 1993; Hanya et al., 2013) and therefore how they travel within their landscape. Movement is often adjusted in response to these changes in nutrient availability (Fernandez-Duque & van der Heide, 2013; Marshall et al., 2014), and this response will be based on primate diet among other factors (Fig.  7.1). For example, most frugivores tend to prefer consuming fleshy fruits when available and may be more constrained by fruit availability during the non-fruiting season. Therefore, they may adjust their behavior by traveling longer distances to find available fruits (Milton, 1980; Terborgh & Stern, 1987; Lambert & Rothman, 2015). Western lowland gorillas (Gorilla gorilla) change their activity patterns in response to seasonal frugivory (Masi et al., 2009); they spend more time traveling and less time resting when eating fruit. In the Cape Peninsula of South Africa, chacma baboon (Papio ursinus) groups that eat anthropogenic foods in some seasons traveled less than those who foraged on natural foods (Lewis & O’Riain, 2017), and these anthropogenic foods are of superior nutritional quality and density than those that are natural. More folivorous primates may also be constrained by seasonal fluctuations. During low availability of young leaves, these primates tend to increase their intake of harder to digest mature leaves (Milton, 1980; Snaith & Chapman, 2007) and therefore may move less. The structure of the forest environment will also affect the distribution and availability of plant foods. In dense tropical rainforest, sunlight can be a limiting factor for the growth and reproduction of plants (Niinemets, 2007). Because of this, trees in forest gaps or on forest edges that have access to more sunlight could be more productive and even produce leaves and fruits with higher nutritional quality (Thomas, 1991; Niinemets et al., 1998; Chapman et al., 2003). These plants also invest in rapid growth rather than digestion inhibitors (Coley, 1983). Similarly, emergent trees of the high canopy will have more access to sunlight in their higher leaves, creating a vertical gradient in nutritional quality within individual trees as

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Fig. 7.1  The potential interactive relationships among ecological factors, food selection, and predation that affect primate movement. For example, food selection of macronutrients depends on food availability, as well as primate adaptations such as sensory adaptations for detecting foods within their habitat, and in turn these factors affect where primates will move within a tree and more generally their habitat

well as an offset in the timing of fruiting (Frankie et al., 1974; Houle et al., 2007, 2014). The upper canopy is also characterized by high nutrient content (Niinemets, 2007; Houle et al., 2007, 2014) as well as some secondary metabolites, for example, phenolics tend to increase in high-light conditions, like in the highest part of the canopy (Bryant et  al., 1983; Waterman et  al., 1984; Dudt & Shure, 1994). This variation could also influence vertical stratification of primate foraging behavior as primates position themselves in the canopy based on nutritional needs or dominance rank (Houle et al., 2010; Ruivo et al., 2017). For example, high-ranking chimpanzees had higher rates of energy intake than lower-ranking individuals, and this was accomplished through the control of food sites higher in the canopy (Houle & Wrangham, 2021); based on preliminary data, the same may be true for leaf eating monkeys (Rothman and Chapman, unpublished data). Most primates forage in groups. While larger group sizes can be beneficial for reducing the risk of predation, intergroup competing for better food resources, and increasing genetic diversity, there are costs to foraging with a large group, especially when resources are limited. Large group sizes increase within-group scramble competition (when a resource is available to all competitors) for resources (van Schaik & Janson, 1988; Wrangham et al., 1993). Larger groups will deplete food patches more quickly and will travel longer distances to meet their nutritional needs, especially during seasons of low food availability (Terborgh, 1983; Janson, 1988; Chapman, 1990). For example, mountain gorillas (Gorilla beringei) in Uganda traveled a longer daily distance, and their home ranges were larger when they were in larger groups and when they were eating more fruit than leaves (Ganas & Robbins, 2005). Ursine colobus monkeys (Colobus vellerosus) in Ghana also traveled further

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and had larger home ranges in bigger groups than smaller groups (Teichroeb & Sicotte, 2009).

7.2 Plant Compounds Important to Primates The two types of plant compounds that most often impact primate feeding behavior are nutrients and plant secondary metabolites. Nutrients are thought to drive selection (Oftedal, 1991; Raubenheimer et al., 2015), and plant secondary metabolites are thought to drive avoidance (Windley et al., 2022). Nutrients are essential compounds necessary for an animal’s growth, maintenance, and reproduction (Oftedal et al., 1991) and have clear effects on primate food selection (Takahashi et al., 2019). Primate food selection is driven by both protein and energy. For example, colobus monkeys (Colobus badius) in Korup National Park, Cameroon, tend to select protein-rich young leaves (Usongo & Amubode, 2001), while other primates, like chimpanzees (Pan troglodytes), select calorie-rich ripe fruit to meet their energetic needs (Uwimbabazi et al., 2016). However, rather than only selecting specific nutrients, most primates seem to balance their intakes of nutrients by consuming a mixture of food of varying amounts of nutrients rather than maximizing or minimizing a particular nutrient (Raubenheimer et al., 2015). For example, blue monkeys (Cercopithecus mitis) rather than focusing on intaking a particular nutrient (e.g., carbohydrates) vary their fruit consumption to maintain a similar daily intake of protein and energy (Takahashi et al., 2019). Following this strategy, primates also exhibit movements with the goal to balance their nutrient intake. For example, the probability of patch departure by black and white colobus monkeys (Colobus guereza) in Uganda was best predicted by the protein/nonprotein energy (fats and carbohydrates) ratio of a particular patch (Johnson et al., 2017). Primate movement is also affected by plant secondary metabolites, which are chemical defenses used by plants to reduce herbivore consumption due to their toxic and antinutritional effects (Glander, 1982). For example, some plant secondary metabolites, like tannins, reduce protein digestibility, while others like alkaloids are associated with toxicity (Jan et  al., 2021). Primates exhibit variable responses to plant secondary metabolites that depend on the type of plant secondary metabolite consumed. For example, black and white colobus and olive colobus monkeys (Procolobus verus) avoid leaves with high tannin content but are seemingly unaffected by alkaloids (Oates et al., 1977). Primate movement is also likely affected by their ability to detoxify plant secondary metabolites. Detoxification involves the use of existing enzymes within the primate’s body to detoxify plant secondary metabolites when they are consumed (Marsh et al., 2005; Windley et al., 2022). A primate may be more likely to eat a generalist diet to avoid overtaxing particular detoxification pathways (Moore et al., 2013). This generalist diet implies more time moving in their landscape. Primates therefore must navigate their landscape carefully to adequately balance their diet and avoid the consumption of toxic compounds.

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However, the balancing of nutrients and avoidance of plant toxins are difficult because of the environmental variation in nutrient and plant secondary metabolite content found across plants and landscapes (Bryant et al., 1983; Moore et al., 2013; Coley et al., 2018). Both plant secondary metabolites and nutrient content vary by plant part, between forest patches, canopy height, seasonality, and space (Oftedal et al., 1991; Coley & Barone, 1996; Reyna-Hurtado et al., 2018), and as a result have specific effects on primate movement. Primates achieve a balanced intake of nutrients by consuming different plant parts. If a primate is seeking out fruit, they may move farther in their landscape since fruit tends to be more patchily distributed, and if a primate is seeking out leaves, they may move less since leaves are found throughout their landscape, though they may be patchy in quality (Snaith & Chapman, 2007). Similarly in response to high plant secondary metabolite content in unripe fruits and mature leaves, primates are more likely to seek out young leaves and ripe fruits (Garber, 1987; McKey, 1974; Coley & Barone, 1996). For example, gray-cheeked mangabeys (Lophocebus albigena) avoid seeds with high amounts of tannins (Masette et  al., 2015). Both the nutrient and plant secondary metabolite content will also affect activity levels. For example, a population of Angolan colobus monkeys (Colobus angolensis) when consuming high-quality foods such as fruit (high calorie) and young leaves (high protein) exhibit higher amounts of activity when compared to another population of Angola colobus monkeys due to differences in food distribution between the two populations (Arseneau-Robar et al., 2021). Primate movement will also vary based on the patch quality and time to patch depletion. If a nutritionally valuable patch is located far away, a primate may travel long distances (Janson and Goldsmith 1995; Grueter et al. 2013). For example, black and white colobus monkeys are more likely to quickly depart from a patch when their consumption of food items in the patch would put them out of balance in their nonprotein and protein consumption (Johnson et al., 2017). Distance traveled between patches, time spent foraging in patches, and length of feeding bouts are determined by how widely distributed and how abundant food patches are. To access more widely dispersed resources, primates are expected to travel over longer distances, have longer feeding bouts, and stop more often than when resources are more clumped (Isbell et al., 1998). Primates are also likely to move vertically in the canopy in order to access and balance their nutrient intakes while avoiding plant secondary metabolites. Primates may choose to forage in the high canopy in order to reach these high-quality food items (Houle et al. 2007). However, primates also risk the intake of secondary metabolites present at higher amounts in highest part of the canopy (Bryant et al., 1983; Ganzhorn 1995; Brenes-Arguedas & Coley 2005). Therefore, primates may be more likely to depart faster from the highest part of the canopies to avoid plant secondary metabolites. Seasonal and spatial variation in food availability within a forest will affect how primates move. For example, chimpanzees consume both figs and drupes, but they vary in their consumption with some months associated with a majority diet of figs and others associated with a majority diet of drupes. In both seasons, chimpanzees

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consume similar levels of food and metabolizable energy, but during “fig months” when drupe availability is low, chimpanzees spend more time feeding (Uwimbabazi et al., 2019). Based on this, primates may move more frequently and farther during months when food availability decreases (Oftedal et al., 1991; Felton et al., 2009).

7.3 Sensing Food and Toxins Primates are able to navigate their environment using their senses to assess the many foods available in their landscape. This assessment is accomplished through the association of sensory information (e.g., plant color) with positive and negative post-ingestion feedback (Provenza et al., 2000; Dominy et al., 2001). Visual information includes plant part size, shape, brightness, and color. Plant color is particularly important for determining nutritional quality. For example, leaf color can be used to discriminate between protein-rich and protein-poor leaves by trichromatic monkeys (Dominy & Lucas, 2001), suggesting primates can use color to select their leaves based on the leaf’s nutrient content. Further, Nevo et al. (2018) found that fruits have a higher contrast against the leaf background in the red-green channel and therefore trichromatic primates are better adapted for locating those fruits within this dense canopy. Olfactory information includes plant odors, and the types most associated with feeding behavior are volatile organic compounds (Peñuelas & Llusià, 2004; Nevo & Ayasse, 2020). Volatile organic compounds are hydrophobic compounds that can repel or attract animals (Nevo & Ayasse, 2020). They have been found to be related to sugar content of fruits consumed by lemurs (Nevo et  al., 2019), suggesting primates can use volatile organic compounds to select for high-sugar foods. The hardness of a plant food refers to resistance to plastic deformation, while toughness is the resistance to crack propagation (Wright et al., 2008; Lucas et al., 2012). Hardness has been associated with both nutrient and plant secondary metabolite content (Kinzey & Norconk, 1993). Toughness is positively correlated to the amount of fiber within a food. Primates seem to select foods with low toughness, likely due to the increased costs of digesting tough foods (Elgart-Berry, 2004; Dunham & Lambert, 2016; Matsuda et al., 2017). Taste is a major indicator of both the nutrient and plant secondary metabolite content of foods and therefore a key determinant of the acceptance or rejection of foods once the food enters the mouth (Dominy et al., 2001; Sánchez-Solano et al., 2022). Different senses are used as different distances. Visual information and odor act as long-distance cues that allow initial tree or patch selection, while touch and taste act as short-distance cues to select a particular plant part within a patch or tree (Stutz et  al., 2017). Long-distance cues help primates to efficiently navigate their landscape without the possible loss of energy by determining from a far distance if a food source would help them meet their nutritional needs (Dominy & Lucas, 2001). Short-distance cues are likely most important for detecting variations in nutrient and plant secondary metabolite content and thus act as fine-tuned tool primates use for

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meeting their nutritional needs. Taste may be a risky cue to use to assess new resources since they may contain plant secondary metabolites that can be poisonous or antinutritional to an animal (Dominy et al., 2001; Iaconelli & Simmen, 2002). Primates may be more likely to move toward commonly consumed items or items that appear to be safe for consumption using long-distance cues but will move in a patch or tree using taste and touch cues. Based on this, long-distance cues are likely associated with longer distanced movement, while short-distance cues are associated with small movements within a patch.

7.4 Locomotor Behavior Primates are known to have highly variable locomotor and positional behavior (Fleagle, 2013), and these movements vary depending on the specific goals of the movement, such as feeding (Napier & Walker, 1967; Isbell et  al., 1998; Larson, 2018). Locomotion associated with feeding is shorter and tends to be more precise (Fleagle & Mittermeier, 1980). Accordingly, primates rely on larger supports during brachial travel and smaller supports during foraging (McGraw, 1998a). However, chimpanzees will only use manual suspension when feeding among small branches (Hunt, 1992). Most primates, arboreal or terrestrial, spend a significant portion of their time sitting, either during feeding, resting, sleeping, or even social behavior (McGraw, 1998b).

7.4.1 Arboreal Locomotion Climbing and leaping are thought to be some of the most common locomotor behaviors seen in arboreal primates; however, there is substantial interspecific variation in the amount of time spent devoted to either behavior (Napier & Walker, 1967; McGraw, 1998a). Large-bodied primates are regularly shown to rely on larger branches and spend more time in the middle and upper canopies where supports are more horizontally continuous (Fig. 7.2). Smaller primates can use smaller branches and spend more time in the lower forest canopy where leaping is advantageous for moving through discontinuous forest cover (Fleagle & Mittermeier, 1980). However, recent studies of sympatric cercopithecoid monkeys of equatorial Africa have found that frequency of leaping is not always predicted by body size or height in the canopy (Gebo & Chapman, 1995; McGraw, 1998a). Studies from both Taï National Park, Ivory Coast, and Kibale National Park, Uganda, found that the largest colobines, Colobus guereza and Colobus badius, leap more during travel and feeding than many of the smaller monkeys of the same forest (Gebo & Chapman, 1995, McGraw, 1998a). Frequent leaping in larger-bodied colobines may be explained by their consumption of leaves: leaping may be an adaptation for efficiently consuming

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Fig. 7.2  A subadult mountain gorilla (Gorilla beringei) in Bwindi Impenetrable National Park, Uganda, moving arboreally through its rainforest habitat. Mountain gorillas are the largest living primate (100–200 kg) but still spend a significant time traveling in the trees

leaves or moving across significant gaps; however, leaping can be potentially costly due to the risk of falling associated with this movement (McGraw, 1998a). Arboreal primates also use a variety of postures while resting in the discontinuous environment of the canopy. They may sit or stand on horizontal branches, hang below branches, lie on supports, or cling to a vertical or subvertical support (Hunt et al., 1996). Species differ in the postures they use and time spent resting. Studies have found that colobines, a highly folivorous species, spend more time sitting than arboreal cercopithecines which are often more frugivorous or insectivorous (McGraw, 1998b). This may be due to spatial distributions of food resources; leaves are more abundant and continuously available throughout the canopy and can be better exploited by sitting in one place than fruits or insects, which are more patchily distributed and require more constant movement for acquisition (Clutton-Brock, 1973; Terborgh, 1983). Further, colobine monkeys use foregut fermentation to digest their highly fibrous diet. This process is energetically costly, and often colobines will rest while digesting their food (Dasilva, 1992; Kay & Davies, 1994). Frugivorous cercopithecine primates tend to stand more while feeding, so they can

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continuously move between more dispersed food patches, even storing fruit in their cheek pouches to increase their mobility (Fleagle & Mittermeier, 1980; McGraw, 1998b). This behavior is even more pronounced while feeding on invertebrates for which feeding bouts are shorter and resources are more patchily distributed (Bryer et al., 2015). Like locomotion, support use during positional behavior is affected by body size, with smaller species spending more time feeding while resting on twigs than branches and all species spending more time on more stable boughs and larger branches while resting or socializing (Clutton-Brock, 1973; McGraw, 1998b).

7.4.2 Terrestrial Locomotion Terrestrial primates share many generalized locomotor behaviors with arboreal primates. But moving along a relatively continuous substrate has important implications. Living on the ground allows primates to reach larger body sizes which often allow them to live in larger groups, have larger home ranges, and be less vulnerable to predators (Milton & May, 1976; Clutton-Brock & Harvey, 1977; Janson & Goldsmith, 1995). Food availability greatly affects movements for terrestrial primates. A classic example arises from a comparison of patas (Erythrocebus patas) and the vervet monkeys (Cercopithecus pygerythrus), two closely related “generalized quadrupeds” that cohabit the dry, seasonal woodlands of Eastern Africa (Disotell, 1996; Tosi et al., 2005). Both monkeys consume gums, but vervet monkeys tend to consume more plant materials, while patas monkeys consume more insects. Because of this, patas monkeys exhibit more continuous movement while foraging to catch their moving prey (Chism & Rowell, 1988; Nakagawa, 1999; Isbell & Chism, 2007; Isbell et al., 2013). This may explain why their home ranges are nearly 100 times larger than those of vervet monkeys (Chism & Rowell, 1988; Isbell, 1998). Their dispersed food source may cause patas monkeys to travel farther distances with a more meandering ranging pattern, have shorter feeding bouts, and stop more often than vervets while foraging (Isbell et  al., 1998, 1999; Isbell et al., 2013).

7.5 Primate Cognition and Movement Compared to other mammals, primates exhibit disproportionately large brains relative to body size. One major hypothesis for the evolution of these large brains is the ecological intelligence hypothesis (Clutton-Brock & Harvey, 1980), which posits that the need to remember the spatiotemporal patterning of primate foods have selected for large brain sizes (Milton, 1981; Rosati 2017). Therefore, frugivores should exhibit larger brain sizes due to the patchy distribution of fruit (Milton, 1981). Primates are thought to use spatial and temporal memory of resource availability and distribution in the past to inform their movement decisions (Janson &

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Byrne, 2007). Chimpanzees monitor trees and use long-term spatial memory while foraging. For example, chimpanzees moved toward some trees in a goal-directed way without prior monitoring, suggesting chimpanzees are using spatial memory to select some of their fruits (Janmaat et al., 2013). This is a cognitively demanding task. Primates must be aware of what is available and where and when it is available in variable environments while balancing this with competing groups, complex landscapes, and predator avoidance (Trapanese et al., 2019). For example, studies on baboons living in different environments have shown that differences in motion may depend more on resource distribution and environment (Schreier & Grove, 2010; Sueur, 2011).

7.6 Lévy vs. Brownian Motion vs. Reuse The classic theory of Brownian motion describes how an animal moves randomly through its environment in pursuit of foods (Brown, 1828). When food resources are abundant and evenly distributed, animals are expected to exhibit Brownian movement, characterized by a more random, regular motion with a relatively constant step length and random turning angles (Turchin, 1996; Viswanathan et al., 1996). When resources are rare or more patchily distributed, however, animals are expected to use a Lévy walk strategy characterized by interspersed short and long steps which allow them to maximize distance covered and encounters with food (Bartumeus et al., 2002). The type of motion is expected to be influenced by the abundance and distribution of the food target as well as size ratios between searcher and target (Bartumeus et al., 2002; Humphries et al., 2010). Folivores are expected to use a more Brownian-type motion when moving through their environment due to the more continuous distribution and availability of leaves, while frugivores are expected to move in a more non-Brownian way as their food sources are more patchily distributed and require more precise spatial movement (Terborgh, 1983; Reyna-Hurtado et al., 2018). The gray-cheeked mangabey of Kibale National Park, Uganda, forages with a more Lévy-like movement pattern during the dry season, when fruit consumption increases (Reyna-Hurtado et al., 2018). Other movement strategies are reusing routes and landmark navigation. For example, chimpanzees reuse parts of their routes in Kibale National Park, Uganda (Bertolani, 2013). By traveling familiar paths, primates are likely easing the cognitive load of spatial navigation but may risk missing opportunities to access new resources. Landmark navigation involves the use of a visual cue to determine the distance to a goal and requires awareness of the distance the landmark is from the goal (Trapanese et al., 2019). Neither path reuse nor landmark navigation is well-­ studied in primates.

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7.7 Future Directions To understand primate movement, we must integrate nutritional ecology into movement ecology. Primates like all animals must navigate a complex landscape of variable nutrients and toxins, and by understanding their nutritional requirements, we can better understand their movements. Nutrient landscape maps could be created to demonstrate the concentrations and amounts of nutrients in the environment. These have been used in human nutrition to demonstrate the spatial distribution of nutrients in urban environments in relation to human food security (Vonthron et al., 2020). These kinds of maps have also been used to determine hotspots of animal abundance based on soil and foliar concentrations of nitrogen (Anderson et  al., 2010). We have limited knowledge of the spatial aspects of plant secondary metabolites, but we do know they likely drive the avoidance of particular foods and therefore movement. Without incorporating plant secondary metabolites into movement ecology, we will miss an important factor driving primate movement. New techniques to measure the large diversity of plant compounds in primate foods should be incorporated into movement studies to consider how these metabolites impact primate movement decisions (Moore et  al., 2004; Windley et  al., 2022). Movement studies should also consider taking an integrative approach to studying movement ecology as not only will nutrients or plant secondary metabolites impact primate movement, but so will conspecific groups, predators, geographical barriers, disease, and human disturbance. Movement studies must consider and integrate all of these parameters to understand primate movement. Studying movement ecology in an integrative manner will help unravel the different aspects of movements as well as how movement may be affected by the global change  – including deforestation, temperature, and forest fragmentation – that affects African primate habitat today.

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Chapter 8

Conditions Facilitating a “Landscape of Fear from Disease” in African Forest Mammals Tyler R. Bonnell

, James Robert Ochieng, and Colin A. Chapman

Abstract  Alongside the direct cost of predation, predation risk itself can be costly to mammals as it increases prey vigilance, induces avoidance behaviors, and changes movement patterns. Predation attempts can create learned associations between landscape contexts and predation risk, producing landscapes of fear of predation – areas that individuals avoid or show increased vigilance. Evidence suggests that disease could have similar effects, yet this is poorly researched. In this paper, we explore the potential of a “landscape of fear from disease,” which is when individuals show avoidance or increased vigilance of disease threats in specific locations. We present a framework that can identify and link elements responsible for the development of a landscape of fear from disease. We use this framework to pinpoint combinations of pathogen characteristics and host movement behaviors that are likely to facilitate learned associations between landscapes and disease threats. Some of these combinations will occur in the context of African forest mammals and thus could influence their conservation. Given the potential population consequences of a landscape of fear from disease, we discuss the possibility for

T. R. Bonnell (*) Department of Psychology, University of Lethbridge, Lethbridge, AB, Canada Department of Mathematics & Computer Science, University of Lethbridge, Lethbridge, AB, Canada e-mail: [email protected] J. R. Ochieng Department of Zoology, Entomology and Fisheries Sciences, College of Natural Sciences, Makerere University, Kampala, Uganda C. A. Chapman Biology Department, Vancouver Island University, Nanaimo, BC, Canada Wilson Center, Washington, DC, USA School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_8

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human induced changes to climate and landscapes to alter the development of a landscape of fear from disease. Keywords  Fear of disease · Pathogen transmission · Learning to avoid pathogens · Host movement · Pathogen avoidance behavior

8.1 Introduction The landscape of fear model is useful to understand the spatial-temporal use of landscapes by prey (Laundré et al., 2010; Gaynor et al., 2019). Under this model, risk of predation is thought to vary across the landscape, and prey are thought to respond by altering movement or time allocation patterns based on the level of fear it has of being injured or killed (Laundré et al., 2010). A major assumption of this model is that animals learn associations between locations, or landscape characteristics, and the risk of predation (Laundré et  al., 2010; Gallagher et  al., 2017). Predators, in general, have low success rates (e.g., 8–26%: Mech, 1966, Temple, 1987, Longland & Price, 1991). Thus, individuals escaping from a predator can associate landscapes and predation risk through associative learning. This information can then be transmitted to offspring if they range together during a period of extended infant dependency. The fear of acquiring a disease could similarly help explain spatial-temporal use of the landscape by hosts (Fritzsche & Allan, 2012). For disease risk to influence host movement, however, this assumes that individuals could learn to associate disease risk with certain locations or landscape characteristics. And there is empirical support showing a link between spatial-temporal variation in pathogen risk and movement/behavioral patterns in hosts, suggesting that these associations are possible. For example, Fritzsche and Allan (2012) found that mammals were more likely to abandon food when the risk of infection from the lone star tick (Amblyomma americanum) was high. There is also evidence of between individual variations in the avoidance of pathogens. For instance, individual woolly monkeys (Lagothrix lagotricha) vary in their level of fecal avoidance (Philippon et al., 2021). Mandrills (Mandrillus sphinx) are suggested to distinguish parasitized group members via fecal odors and avoid grooming conspecifics infected with parasites that can be transmitted by contacting feces (Poirotte et al., 2017), and groups move longer distances when group members showed high richness in short-life cycle parasites, like protozoans, a strategy that could allow them to escape contaminated habitats (Brockmeyer et al., 2015). See Hongo this volume (Hongo, 2023) for a greater discussion of the influence of parasites on mandrill movement. Little theoretical or empirical work has explored the hypothesized mechanisms that would allow a landscape of fear of disease to develop, namely, learning associations between landscape locations or characteristics and disease risk. With a landscape of fear of predation, it is easy to see how predation attempts could create learned associations between landscape contexts and predation risk, as there is an

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immediate shock of a predation attempt associated with a location. However, with a landscape of fear of disease, the ability of an animal to learn landscape associations from pathogens is not as straightforward, as there is typically a delay between infection by a pathogen and the negative detectable consequence – illness. This is particularly difficult given (1) the large number of pathogens that can cause the same symptoms (e.g., nausea), (2) the substantial variation in pathogen life cycles (e.g., environmentally or directly transmitted, latency period, sensitivity to microclimates, severity of symptoms), and (3) the large temporal and spatial variation in host movement patterns (e.g., rates of revisiting food and water sources or use of sleeping sites). Similarly, the physical and social environments can play a role in shaping the spatial and temporal distribution of hosts and pathogens and the probability of transmission (Day, 2001; Nunn & Altizer, 2006; MacIntosh et al., 2012). This calls for careful consideration and research to identify the conditions under which a landscape of fear from disease could develop. Here we first examine the potential costs of disease to hosts and evidence for animals altering their movements or exhibiting spatial-temporal variation in anti-­ pathogen behavior associated with what could be high disease-risk areas. We then propose a framework, extended from the landscape of fear from predation model, to clearly identify the elements responsible for the development of a landscape of fear from disease. We use this framework to identify conditions where a landscape of fear from disease is likely to occur. We discuss these conditions along two axes: (1) the movement behavior of the host and (2) the characteristics of the pathogen. In particular, we consider how host movement patterns alter the ability of an animal to learn patterns of disease risk in the environment (Lewis et  al., 2021). We aim to establish a priori what combinations of landscape-pathogen-host characteristics are likely to lead to a host developing a landscape of fear from disease. Finally, we use these identified conditions to better understand landscapes of fear from disease in forest dwelling African mammals and to consider the possibility for human induced changes to climate and landscapes to alter the development of a landscape of fear from disease.

8.2 Pathogen Characteristics and Effects on Animal Host Pathogens live temporarily or permanently in or on another organism (host) from which they are physically or physiologically dependent. Pathogens include such organisms as protozoans, nematodes, cestodes, trematodes, and arthropod, such as lice, ticks, and mites (Cheng, 1973). At low levels of infections, pathogens can have minimal effect, but under some conditions, they can significantly affect hosts’ fitness by depressing their metabolism, thus suppressing their physiological and immunological responses, increasing their vulnerability to predation and other diseases which may result in death, and this can significantly affect populations. For example, based on more than three decades of observations, researchers have shown that rainforest anthrax caused widespread deaths for a broad range of mammalian

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hosts and predicted that it will accelerate the decline and possibly result in the extirpation of local chimpanzee (Pan troglodytes verus) populations (Hoffmann et al., 2017). Therefore, understanding their role in wildlife communities is critical for wildlife conservation. Pathogen transmission can occur directly; thus, the pathogen only requires a single host to complete its life cycle or indirectly involving biological vectors and/ or one or more intermediate hosts. Horizontal pathogen transmission occurs through ingesting the infective stage of the pathogen in contaminated food or water (e.g., raw meat (e.g., Taenia) or water vegetation (e.g., Fasciolopsis flukes), in water (e.g., Entamoeba, Cryptosporidium, Guinea worm – Dracunculus medinensis)), through sexual intercourse (e.g., Trichomonas vaginalis), or by direct skin penetration (e.g., Strongyloides stercoralis, and biting arthropods: Trypanosoma species, Plasmodium, Leishmania, Babasia) (Esch & Fernandez, 2013). Vertical pathogen transmission from the mother to child occurs either through congenital/transplacental (e.g., Toxoplasma gondii) or by breast milk (e.g., S. stercoralis) route (Cheng, 1973; Esch & Fernandez, 2013) and is not considered here. Pathogens have far-reaching implications for the conservation of African tropical rainforest faunas. The most pathogenic parasites can confer a combination of pathogenic effects including increased abnormal behaviors like reduced movement and increased inactivity, reduced appetite, inability to avoid predators, and reduction in breeding success. Other pathogenic effects are intestinal ulceration, anemia, tissue damage, delay in puberty, spontaneous abortion, congenital malformation, and mortality (Lilly et al., 2002; Chapman et al., 2005a). Some disease outbreaks severely affect populations. Anthrax is commonly associated with arid ecosystems, particularly African savannahs like the Serengeti (Hampson et al., 2011), but also occurs in forested systems (Leendertz et al., 2006; Gogarten et al., in press). Major outbreaks typically cause high mortality in wild ungulate species and usually exhibit strong seasonal and interannual variation (Hoffmann et  al., 2017). For example, in the 1890s, in East and South Africa, rinderpest caused the deaths of approximately 50–90% of wildebeest (Connochaetes spp.), buffalo (Syncerus caffer), and giraffe (Giraffa camelopardalis) (Dobson et al., 2011). Also, die-offs in kudus (Tragelaphus strepsiceros) and impalas (Aepyceros melampus) occur in the dry season with a 10-year periodicity in Krüger National Park, South Africa (De Vos, 1990). These examples highlight the cost of pathogens on hosts and the potential selection pressures for avoiding infection.

8.3 Evidence That Animals Alter Behavior and Movement in Fear of Pathogens Animals move to find food, water, shelter, sleeping sites, and suitable mates and avoid predators. During this travel, they will encounter pathogens. Behavioural mechanisms employed by wildlife that appear to function to avoid pathogens have been documented both by opportunistic field observations and quantitative field

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experiments. Mechanisms are associated with feeding, elimination, grooming, social, sexual, maternal, and sleeping behaviors. Wildlife typically has strategies to avoid eating foods contaminated with fecal material, potentially to avoid fecal-orally transmitted pathogens. For example, with bonobos (Pan paniscus) in the forests of the Democratic Republic of Congo, the avoidance of contaminated food correlated negatively with Balantidium coli infection, which is a potentially pathogenic protozoan transmitted through the fecal-oral route (Sarabian et  al., 2021a). Under experimental conditions, chimpanzees (Pan troglodytes) tend to maintain greater distances from contaminants and/or refuse to consume contaminated foods (Sarabian et  al., 2017). Similarly, carnivores avoid foraging on other carnivore carcasses and rarely engage in cannibalism, even though these appear to be easily available energy sources (Weinstein et al., 2018). This may limit chances of contracting similar parasites. Wild ungulates avoid eating grass near recently dropped feces even though the grass may be quite luxurious (Ezenwa, 2004a; Ezenwa et al., 2006). Denning canids defecate and urinate away from the den and rest areas. Newborns, however, have no mobility; hence, mothers keep the den clean by consuming the fresh feces and avoid infections themselves as parasite ova take several days to hatch into infective larvae (Sarabian et al., 2018). In Kibale National Park, Uganda, the gray-cheeked mangabeys (Lophocebus albigena) travel further and exhibit less day-to-day overlap in feeding sites during dry weather than during wet days. This may minimize contact with fecal contaminated foliage, because possibly in the dry season rain does not wash fecal material off foliage (Freeland, 1980). Also, in the forests of Kibale, baboons (Papio anubis) frequently change sleeping trees which possibly functions to minimize exposure to infectious parasite larvae (Freeland, 1980; Bezjian et  al., 2008). Baboons in Amboseli National Park, Kenya, typically use a sleeping site for only two nights and wait about 10 days before returning to the same site (Hausfater & Meade, 1982). During their absence, fecal material is likely to have been washed away or degraded. Animals also move to avoid ectoparasites (Fritzsche & Allan, 2012). Birds avoid nests infected with ectoparasites (Oppliger et  al., 1994), and cervids avoid areas with blood sucking insects (Nelson et al., 1977). To avoid pests and vectors, some animals seek microhabitats where there are fewer insects (Hart, 1990). To avoid intense mosquito bites, antelopes and other ungulates move toward higher altitudes during wet seasons, and arboreal primates move to the tree canopies (Dudley & Milton, 1990; Hart, 1990). Also, windy ridges are advantageous to hosts since they limit mosquito and fly bites.

8.4 Landscapes of Fear from Disease Framework To better understand how landscapes of fear from disease develop, we modified the mechanistic framework proposed by Gaynor et al. (2019) for landscape of fear from predation to consider the avoidance of pathogens. In our framework, the main elements are the (1) physical/social environment, (2) spatial variation in risk of disease,

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(3) perception of risk, and (4) behavioral response of the animal (Fig. 8.1). By taking each element in turn, it is possible to describe how a landscape of fear from disease could develop. 1. Starting with the physical/social environment, many physical environmental characteristics (e.g., temperature, moisture, soil type) strongly influence the survival of pathogens. This is especially so for environmentally transmitted pathogens. For example, changes in climate can alter the spatial-temporal distribution of helminths (Bowman, 1999; Bonnell et  al., 2010; Chapman et  al., 2010a) (Fig. 8.1 i). Similarly, the social environment and group size of the hosts can influence propagation of directly transmitted pathogens (Snaith et  al., 2008; Nunn et al., 2011; Nunn et al., 2015; Gogarten et al., in press) (Fig. 8.1 ii). For example, for Japanese macaque (Macaca fuscata), infections of some pathogens are positively associated with how central a female is within her group, as well as with her dominance (MacIntosh et al., 2012). Similarly, higher ranking male

Fig. 8.1  A framework for understanding the development of a landscape of fear from disease. This framework is based on a similar framework developed for the landscape of fear from predation by Gaynor et al. (2019)

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chimpanzees have higher testosterone levels and greater helminth burden (Muehlenbein & Watts, 2010). The study of social and physical environment impact on infectious disease is an interdisciplinary field often combining pathogen biology, landscape ecology, climate, and host behavioral ecology (Ostfeld et al., 2005) (Fig. 8.1 iii). 2. The interaction of social and physical environments with pathogens creates spatial-­temporal variation in risk across space and time (Fig. 8.1 iv). For example, water sources are foci of animal activity; thus, not surprisingly, the concentration of fecal-oral pathogens in the environment near water holes is up to two orders of magnitude greater than away from them (Titcomb et  al., 2021). Correspondingly, forest primates that range in the low valley bottoms that are wet, a condition that allows infective stage pathogens to persist in the environment longer, have elevated indices of pathogen infections compared to those that range in immediately adjacent upland areas (Chapman et al., 2010b). 3. The strength of cues of risk, the spatial-temporal predictability of the risk, and the sensory ability of the animal (Fig. 8.1 v) all determine the extent to which an animal can perceive variation in pathogen risk (Fig. 8.1 vi). Animals clearly have the ability to perceive characteristics of the environment that could be associated with predictably elevated risk of pathogen infection (e.g., they perceive the presence of a water source). They can also detect fecal material that could be contaminated with pathogens and biting arthropods that can transmit diseases (Sarabian et al., 2021b). See also Hongo (2023, this volume). 4. If an animal can perceive variation in risk and the cost of response is low compared to the potential benefits (Fig. 8.1 vii), the spatial-temporal distribution of hosts could be modified by pathogen risk (Fig. 8.1 viii). For example, the animal could avoid dry season water holes in the rainy season when other more temporary water sources area available, or animals can avoid dispersing to social groups with high levels of infection when other groups are available (Baudouin et  al., 2019). Additionally, anti-pathogen behavior could similarly vary with pathogen risk (Fig. 8.1 ix). Finally, the resulting changes in host behavior can directly influence both the environment and the risk of pathogens, creating a feedback loop (Fig. 8.1 x). For example, changes in the spatial-temporal distribution of a host in response to perceived pathogen risk can directly alter the subsequent distribution of pathogens, especially in cases where a mobile host sheds pathogens. Similarly, changes in social behavior in response to perceived risk can alter the social environment of the hosts on which transmission of many directly transmitted pathogens relies. This framework brings clarity to the terms used to describe the landscape of fear from disease. Under this framework, a match between risk maps of pathogen transmission (iv) and behavioral or distributional response of the host in question (viii or ix), along with evidence that the host has the ability to detect the pathogen (directly or indirectly), would be evidence of a landscape of fear from disease (vi). This framework also provides a better understanding of potential mismatches between risk maps (iv) and response maps (viii or ix). For example, if costs of anti-­ pathogen behavior are low, a “play it safe” behavioral strategy, though not

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necessarily a conscious strategy, might result in a host performing anti-pathogen behavior relatively homogenously across the landscape. Anti-pathogen behavior is just one of many competing factors driving movement and distribution behavior – e.g., finding food, mates, and shelter and avoiding predation. These other factors when imposing a higher cost/benefit could outweigh any anti-pathogen behavior. Similarly, the specific condition of the host likely matters in terms of an individual’s response to risk (e.g., a starving animal will lower their response to a landscape of fear from disease or predation). These potential mismatch conditions can help define the case where a landscape of fear from disease is likely to develop: (1) where costs of acquiring the pathogen are high compared to competing factors, (2) the cost of anti-pathogen behavior is not negligible, and (3) the host in question is in a healthy condition. In cases where a landscape of fear from disease develops, the mechanisms driving the observed anti-pathogen behavior or distribution of host could be innate or learned. Though both cases can be seen as learned responses, one is just being learned within a generation and the other between generation learning. The predominance of one or the other is likely tied to the predictability/stability of the stimuli that the animal is using to detect the risk of pathogen transmission. If the stimuli are constant and predictable, learning an innate response is likely more efficient (e.g., avoiding others who show sickness behavior). Meanwhile, a more subtle and changing stimuli essentially require within generation learning (e.g., learning to avoid regions/conditions where a pathogen is detected through indirect stimuli  – such as conspecifics getting sick at a particular water hole). Learning how an animal perceives its environment and the pathogen risks (vi), as well as the degree to which behavioral responses are learned or innate, will help understand when invasive pathogens or landscape changes might result in ecological traps, where fear response reduces the ability of the animal in question to adapt to changes. When ecological traps are primarily the result of innate behavioral responses, it is likely much harder for populations to adapt. A potentially useful goal, when/if evidence for a landscape of fear from diseases is found, might be to measure how “fix” the behavioral response to perceived risk is. As with forest mammals, the prediction would be that behavioral response would likely show some/ high amount of behavioral flexibility, being relatively long-lived mammals living in dynamic environments.

8.5 Learning Associations Between Landscapes and Disease Risk 8.5.1 Pathogen Characteristics A long delay between the infection of a host and when the host experiences symptoms of illness (i.e., incubation period) will decrease the possibility of associative learning (Mitchell et  al., 2009). If the animal becomes ill with perceptible

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symptoms soon after experiencing a stimulus associated with infection, the more likely the animal will learn to avoid that stimulus. Possibly, the most easily learned stimuli associated with transmission would involve diseases transmitted by biting insects and ticks, as the animal could easily learn to avoid the pain of the bite. In contrast, it would be difficult to learn that Trichuris infections were associated with a sleeping site because after the eggs are shed in the feces of an infected animal, it is only after 15–30 days that the embryonated eggs become infective and another 60–70 days until the female starts to oviposit between 3000 and 20,000 eggs each day (Cheng, 1973). Furthermore, unless the animal is infected by a large number of Trichuris adults, it will not become ill with perceptible symptoms (Bowman, 1999; Gillespie et al., 2005). The presence of stable cues associated with acquiring a pathogen is likely to play an important role in the development of a landscape of fear of disease. In the case of an environmentally transmitted pathogens, if the pathogen is consistently tied to particular landscape characteristics for its survival (e.g., cool, moist, dark soil for helminths), there is an increased chance that an association between illness and landscape characteristics could be learned. Water holes are one particular landscape feature that is associated with increased survival and concentration of the infective stage of pathogens (Chapman et al., 2010b; Ndlovu et al., 2018). A very consistent cue is the odors, and visual cues associated with feces and many infectious organisms, including bacteria, viruses, parasitic protozoa, and helminths, are found in animal dung. Feces elicit avoidance behavior in a wide range of animals including African ungulates, like the dik-dik (Madoqua kirkii), chimpanzees, bonobos, and African elephants (Loxodonta africana) (Ezenwa, 2004b; Sarabian et  al., 2017; Ndlovu et  al., 2018; Sarabian et  al., 2021b). Ndlovu et  al. (2018) suggested that elephants avoid water holes with high levels of fecal contamination. In the case of directly transmitted pathogens, animals may learn to avoid individuals with particular traits that are associated with elevated risk of infections. Corresponding to this suggestion, mandrills groom heavily parasitized group members less frequently than less parasitized individuals (Poirotte et  al., 2017). For social group-living mammals, one would expect that animals avoid unfamiliar individuals as they may harbor novel pathogens not occurring in the group. Sifakas (Propithecus verreauxi) follow this expectation and newly encountered individuals are groomed less than long-term residents. Finally, the virulence of the pathogen is likely to increase selection pressures for behaviors or learning mechanisms that lead to lower infections, but only up to a point. Pathogens with extreme virulence (i.e., almost always fatal) would end any selection pressure on behaviors. A disease with extreme virulence is Ebola. Census data following an Ebola outbreak in Minkebe Forest in Gabon indicate catastrophic declines in the chimpanzee and gorilla (Gorilla gorilla) populations of greater than 90% (Huijbergts & Wachter, 2003). This suggests that mid-virulent pathogens would facilitate associative learning the most. However, in species with social learning, it is possible that individuals learn from the examples of conspecifics who become infected and avoid behaviors which might become associated with becoming infected.

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Thus, we suggest these pathogen characteristics (P) can be viewed as three axes in a landscape of fear of disease: • (Axis P1) How tied to landscape characteristics is the pathogen? • (Axis P2) What is the delay between infection and illness? • (Axis P3) How virulent is the pathogen?

8.5.2 Movement Characteristics Host movement patterns also influences the chance of learning associations that could reduce the risk of disease. The frequency that the host revisits areas is key to both learning and pathogen transmission. Repeated exposure to similar conditions is required for associative learning to take place, allowing selection to act on variation in behavior in those conditions (Mitchell et al., 2009). In many species, such as species with high home range fidelity, the animal occupies a home range and can have varying degrees of predictability in use, e.g., specific route used at predictable times throughout the day (Di Fiore & Suarez, 2007). Predictability is particularly high in species that repeatedly use fixed sleeping sites, such as emergent trees or cliffs (Anderson, 1984; Chapman, 1989; Mekonnen et al., 2021), and is a pattern often found in baboons that use open habitats (Hamilton, 1982; Abie et al., 2017). Similarly, predictability in movement can be high for species that return to the same feeding sites (e.g., fig trees (Shanahan et  al., 2001)) or watering holes (Shannon et al., 2009). For example, the most heavily used travel routes of elephants in Tembe Elephant Park, South Africa, were associated with water holes (Shannon et  al., 2009). Similarly, baboons (Papio ursinus) travel along highly repetitive routes among their favorite fig trees (Noser & Byrne, 2006; Noser & Byrne, 2009). The frequency of revisits is also key to pathogen transmission as an infection can only occur if the animal visits a site when the pathogen is infective. For example, Oesophagostomum bifurcum is a gastrointestinal parasite occurring in ungulates, including the giant forest hog (Hylochoerus meinertzhageni) and bush pigs (Potamochoerus porcus), and primates of Kibale National Park, Uganda (Reyna-­ Hurtado et al., 2023, this volume). It causes intestinal obstruction, abdominal pain, and fever (Chapman et al., 2005b, Chapman unpublished data). It only takes 3 days for a larva to be infective after defecation ­(https://www.cdc.gov/dpdx/oesophagostomiasis/index.html). In contrast, Trichuris sp. that is found in the same species and can, when acute, cause severe abdominal pain and severe anemia takes between 15 and 30 days to become infective (Ghai et al., 2014; Ochieng et al., 2021). This difference in parasite life cycle could impact the ability of an animal to learn to avoid infected areas. For example, learning to avoid repeatedly returning to a fruiting tree that typically bears ripe fruit for only a couple of weeks, e.g., many figs (Janzen, 1979), would be more likely in the case of Oesophagostomum compared to Trichuris infections. Thus, the possibility of becoming reinfected by revisiting a site

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will be a function of both how frequently the animal returns to a specific location and the parasite life cycle. Some species show little home range fidelity and encounter many different conditions, but rarely the same conditions repeatedly, such as the herds of ungulates on the Serengeti (Sinclair et al., 2007). If the host movements cover a wide range of conditions, where the host experiences a heterogeneous landscape, there is more of a chance that variation in the landscape can be associated with variation in risk. That is, if the host home range is homogenous, it becomes more difficult to develop an association between a specific location and risk of parasitism. These two movement characteristics of the host (M) might then be thought of as two axes: • (Axis M1) How repeatable are movement patterns of the host (revisit rates)? • (Axis M2) How heterogenous is the landscape within the host home range? More generally, movement behavior influenced by the landscape of fear from disease can be considered as an element of the relatively new paradigm in ecology, movement ecology (Nathan et al., 2008), which deals with causes and consequences of animal movement. This chapter attempts to highlight the importance of pathogens within this paradigm.

8.6 African Forest Mammals Given the proposed framework, we can apply the identified axes to understand when a landscape of fear from disease might develop in African forest mammals and the consequences of landscape modification and climate changes. As more empirical and theoretical work would be needed to fully validate and understand the implications of a landscape of fear from disease, the application of this framework remains speculative. Parasite characteristics that increase the chance of associative learning (Axis P1– P3) suggest that mid-virulent parasites that are heavily tied to landscape features and take little time to develop the infective stage are more likely to facilitate associative learning. This suggests that in the African mammal context, Schistosoma mansoni, Ascaris lumbricoides, Necator americanus, Enterobius vermicularis, and Strongyloides species among other parasites are likely candidates. Hosts with high site fidelity that are reliant on clumped resources (e.g., fruiting trees, water holes) and species that cover heterogeneous terrain (forest edges, water holes, fragmented landscapes) are most likely to develop associations between risk of parasitism and landscape characteristics (Axis M1–M2). Similarly, species which do not have fixed home ranges, or who have large home ranges, but return to specific locations repeatedly (e.g., forest elephants returning to water holes) are also likely candidates for developing a landscape of fear from disease (Axis M1) (Fig. 8.2).

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Fig. 8.2  Display of the proposed five axes that are likely to influence the development of a landscape of fear from disease. An optimal line is drawn, alongside two examples of host-pathogen combinations. M1) How repeatable are movement patterns of the host (revisit rates)? M2) How heterogenous is the landscape within the host home range? P1) How tied to landscape characteristics is the pathogen? P2) What is the delay between infection and illness? P3) How virulent is the pathogen?

8.7 Applying the Landscape of Fear from Disease to African Conservation Humanity faces unprecedented environmental challenges, and nowhere are these challenges greater than in Africa, the poorest and second most populous continent in the world (UN, 2015). Already, 20% of the continent’s land surface (6.6 million km2) is degraded, an area twice the size of India (Archer et al., 2018), and Africa’s population is predicted to quadruple by 2100 (UN, 2015). Climate model predict that the effects of climate change will be severe in Africa (Niang et al., 2014); thus, environmental conflict is projected to rise sharply (Laurance et  al., 2014). These changes will severely impact biodiversity, and it is predicted that by 2100, more than half of Africa’s bird and mammal species could be lost (Archer et al., 2018). Grappling with these challenges requires mobilizing as much information as possible and strengthening Africa’s research capacity (Atickem et  al., 2019; Mekonnen et al., 2022). Considering how climate and landscape changes in Africa

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may impact the development of a landscape of fear from disease may prove useful in the development of informed management plans for some species. According to predictions (Masson-Delmotte et al., 2021), all of Africa will get warmer, but changes in rainfall patterns will vary across the continent. Some areas are projected to become wetter, such as much of Uganda, while others will experience drying effects, including much of southern Africa (Kalbitzer & Chapman, 2018; Ahmadalipour et al., 2019). However, generally the climate will become more variable (Ahmadalipour et al., 2019). This increased variability in climate is likely to impact an animals’ ability to develop associations between landscape characteristics and parasitism risk. In particular, an animal’s ability to assess spatial risk of parasitism could be reduced if climate variability leads to high spatial-temporal variation in where environmentally transmitted parasites are found (Axis P1) or increased variability in host movement (Axis M1). Alternatively, if climate variability leads to increased landscape heterogeneity (Axis M2), an increased distinction between landscape patches might facilitate an animal learning to associate certain landscape patches with high/low disease risk. With respect to climate change induced variation in resource availability, there are few records of sufficient duration to be helpful to understand what the future will bring for the conservation of African mammals. However, 32 years of tree phenology data from Lope National Park, Gabon, reveal an 81% decline in fruiting that corresponded to an 11% decline in body condition of fruit-dependent forest elephants in the last decade (Bush et al., 2020). In Kibale National Park, Uganda, a 40-year climate and phenological record reveals that the proportion of the tree community fruiting predictably declined with ENSO and Indian Ocean Dipole events, and both of these climatic phenomena are increasing in frequency and duration (Kalbitzer and Chapman unpublished data). However, individual species respond differently to climate variables, thus making clear predictions of how individual host species will respond will be challenging. If these patterns play out, and landscapes of fear of disease do not form, disease levels will increase further endangering Africa’s wildlife. It is possible that with climate change there will be a convergence on rare resources and food shortages. This would facilitate associative learning. However, under such a scenario, animals likely have no option but to converge on these rare resources, so learning could likely not be put to use. Globally, forests are being reduced in size and fragmented into smaller and smaller blocks. In fact, areas of continuous tracts of forest larger than 500 km2 suitable for large animals (possibly including naturally treeless areas, with no remotely detected signs of human activity) comprise only 20% of remaining tropical forests, and these forests are disappearing at a rate of 7.2% each year (Potapov et al., 2017). Unfortunately, only 12% of these areas are protected (Potapov et  al., 2017). Furthermore, it is predicted that with 50 years the number of fragments will increase 33-fold and the mean size of fragments will decline to between 0.25 and 17  ha (Taubert et al., 2018). Increased habitat fragmentation is likely to facilitate the association between landscape characteristics and risk of parasitism.

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8.8 Conclusion The landscape of fear from disease model has the potential to be an important ecological theory integrating the fields of ecology, parasitology, behavior, movement ecology, and population biology. Given that developing a landscape of fear from disease can reduce pathogenic infections, any anthropogenic change that causes a reduction of the ability of animals to form associations between landscape characteristics and parasite risk has the potential to increase parasite burdens within populations and cause population declines. Thus, refining the concept of the landscape of fear of disease will have useful implications for the conservation of endangered species. Within the wider paradigm of movement ecology, the incorporation of landscapes of fear from disease also offers another important driver for understanding animal movement patterns. We suggest that parasite avoidance is far more prevalent than is currently appreciated (Buck et al., 2018). However, this area of research is in its infancy; thus, we encourage more research to identify the conditions that promote the landscape of fear from disease to develop. Only with a firm understanding of these conditions will it be possible to understand the consequences of humans reshaping landscapes of fear through habitat modification, climate change, and resource extraction. Acknowledgments  We would like to thank Rafael Reyna-Hurtado and Mario Melletti for inviting us to contribute to this chapter and overseeing the review process. We thank Claire Hemingway and Dipto Sarkar for helpful ideas and comments on this project. The funding that helped us develop some of these ideas is the IDRC grant “Climate Change and Increasing Human-Wildlife Conflict.” CAC was supported by the Wilson Center while writing this paper.

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Chapter 9

Do Seasonal Frugivory and Cognition Shape Foraging Movements in Wild Western Gorillas? Benjamin Robira, Simon Benhamou, Terence Neba Fuh, and Shelly Masi

Abstract  Tropical forests show high spatiotemporal seasonal variation in food availability, especially for fruits. To forage efficiently, frugivorous primates are expected to have higher spatiotemporal knowledge of food availability than folivorous primates. Here, we compiled published and new evidence to shed light on the foraging strategies and the underpinning cognition in the seasonally frugivorous western gorilla (G. gorilla) in response to seasonal changes in resources (high- and low-fruit seasons). Specifically, we assessed how western gorillas decide where to feed (movement heuristic and spatial knowledge), how to move (e.g., movement speed and straightness), and when to go (temporal knowledge) and come back to feeding sites (recursion pattern) when feeding mostly on fruits or leaves. Based on GPS tracks continuously recorded on three habituated groups in Central African Republic (May 2016 to November 2017), we found that western gorillas rely on spatiotemporal knowledge to decide where to go and when in both dietary seasons. B. Robira (*) Centre d’Ecologie Fonctionnelle et Evolutive, CNRS et Université de Montpellier, Montpellier, France Current affiliation for Benjamin Robira: Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Trentino-Alto Adige, Italy Eco-anthropologie, Muséum National d’Histoire Naturelle, CNRS, Université de Paris, Musée de l’Homme, Paris, France e-mail: [email protected] S. Benhamou Centre d’Ecologie Fonctionnelle et Evolutive, CNRS et Université de Montpellier, Montpellier, France T. N. Fuh Dzanga-Sangha Protected Areas, Central African Republic, Current affiliation for Terence Fuh: World Wide Fund for Nature – Germany, Berlin, Germany S. Masi (*) Eco-anthropologie, Muséum National d’Histoire Naturelle, CNRS, Université de Paris, Musée de l’Homme, Paris, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_9

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Space-use patterns (daily path length and weekly range) were larger during the high-fruit season because of changes in food spatial distribution. However, the foraging strategies barely changed with seasons in terms of speed, straightness, and recursion patterns. Our results highlight how spatiotemporal cognition may buffer the effects of changes in food availability in seasonal frugivorous species. Given their role in seed dispersal, characterizing the cognition underlying tropical frugivore movements may provide insights into understanding large-scale ecological processes underpinning tropical forest regeneration and dynamics. Keywords  Gorilla gorilla · Optimal foraging · Seasonality · Space use · Spatial memory · Temporal memory

9.1 Introduction Perhaps because of the tales of the first world explorers showcasing the lushness of the tropical forests, these forests have been considered as an invariant source of food in the collective imagination. However, tropical forests are very heterogeneous environments, both in space and time (Terborgh, 1986). In particular, food availability oscillates along the year, dictated by rain patterns (van Schaik et al., 1993). The availability of fruit changes not only seasonally within a year but also across years (Chapman et al., 2005; Brockman and van Schaik, 2005). To cope with seasonal variations in food availability, forest animals may have two responses: to leave (i.e., to migrate) or to stay and be flexible (Avgar et  al., 2014). While migration is an option for some tropical forest birds (Sekercioglu, 2010), mammals living in tropical forests are most commonly site-faithful (e.g., primates: Africa – Janmaat et al., 2009; Central America – Ramos-Fernandez et al., 2013; South America – Wartmann et al., 2014; Continental Asia – José-Domínguez et al., 2015; Asian Islands: Cheyne et al., 2019). Frugivorous primates are thus challenged by periods of fruit scarcity. As a consequence, many frugivorous primates seasonally adjust their behavior in terms of activity budget (e.g., Pavelka & Knopff, 2004; Masi et al., 2009), diet (e.g., Conklin-Brittain et al., 1998; Wrangham et al., 1998; Remis et al., 2001; Rothman et al., 2008; Masi et al., 2015), or ranging patterns (e.g., Ganas & Robbins, 2005; Campera et al., 2014; Reyna-Hurtado et al., 2018; Volampeno et al., 2011). By choosing where and how to move, primates may increase the likelihood to find a fruit(ing) tree (e.g., de Guinea et al., 2019). Simultaneously, through a process of trials and errors, they may increase their knowledge on what resources can be found and when (Trapanese et  al., 2018). Given the number and complexity of information needed to forage efficiently, frugivorous primates inhabiting seasonal habitats are thus expected to have a higher spatiotemporal knowledge of food distribution and availability than more herbivorous/folivorous primates (Ecological

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Intelligence Hypothesis: Milton, 1981, Rosati, 2017). Indeed, while spatial memory should have been selected in animals facing highly heterogeneous habitats with limited visibility (e.g., dense vegetation; Boyer and Walsh, 2010; Grove, 2013; Reyna-Hurtado et al., 2012; Bracis et al., 2015), some form of temporal memory should have been selected in animals facing temporally variable but fairly predictable resources (e.g., seasonal food; Robira et al., 2021). Frugivorous primates living in tropical forests are thus likely candidates for displaying advanced spatiotemporal skills. On that subject, comparative studies on primates showed an association between frugivory and specific movement heuristics relying on advanced memory of the fruit spatiotemporal distribution (Milton, 1981; Teichroeb & Vining, 2019; Trapanese et al., 2019). Yet, as most traits, the level of folivory/frugivory can vary greatly among or within animal species. The level of folivory/frugivory may even alternate from one extreme to the other depending on availability of resources. For instance, western gorillas (Gorilla gorilla) are mainly (70% of the feeding time) frugivorous when fruit, richer in carbohydrates and soluble sugars than herbs or leaves, is highly available in the forest and become mainly folivorous/herbivorous when fruit is scarce (Masi et al., 2015). Despite this dietary change, western gorillas are capable of fully balancing their energy budget across the entire year, even when feeding on less nutritive vegetative parts (Masi et al., 2015). While western gorillas benefit from physiological adaptations, such as a long gut hosting specific ciliates able to efficiently degrade cellulose (Chivers & Hladik, 1980), the role played by foraging strategies and cognition in the possible optimization of the energy balance is currently unknown. Given the high energetic costs involved by having a large brain, species living in a highly seasonal environment may have smaller brains (relative to body mass) than species experiencing little seasonality (expensive brain hypothesis, e.g., in primates: van Woerden et al., 2010, 2012, 2014). Yet, cognition may also help buffering the effect of seasonality in food availability (cognitive buffer hypothesis, Allman et al., 1993; Sol, 2009). In large-brained primate species, the seasonality experienced in terms of energy intake is considerably less marked than the seasonality in resource availability (van Woerden et al., 2012, 2014). Interestingly, there is evidence that the brain architectures of western gorillas and mountain gorillas (Gorilla beringei beringei) differ, the latter being considerably more folivorous/herbivorous (Barks et al., 2015). In particular, the size of the cerebellum and hippocampus are larger in western than in mountain gorillas (Barks et al., 2015). These two areas are key for efficient foraging, as cerebellum supports immediate information processing (Koziol et al., 2014; Sokolov et al., 2017), while the hippocampus is home to spatial mapping (Burgess et al., 2002). This suggests that frugivory may trigger cognitive development. As cognition, frugivory, and movement are linked, we aimed to assess whether, and how, western gorillas adjust their movement patterns and foraging heuristic in response to the seasonal changes in diet, i.e., between the high- and low-fruit seasons (see Masi et  al., 2009 for seasonal definitions). In this chapter, we compile published and new evidence to quantify space-use (where to go) and movement

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properties (how to go) to shed light on the foraging strategies of western gorillas and the underpinning cognition during the contrasting dietary seasons (frugivory vs. folivory/herbivory). Investigating how western gorillas decide where to go (movement heuristic and spatial knowledge), how to go (e.g., movement speed and straightness), and when to go (or revisit some places; recursion pattern) should allow us to infer how preponderant in movement decision and how advanced spatiotemporal cognition is (Janmaat et al., 2021). This current study thus aims to provide an appraisal of western gorilla space-use patterns and its ecological and cognitive correlates, within the framework of movement ecology (Nathan et al., 2008). It also aims to forge the links between theories involving animal movement, diet, and cognition in the light of primate cognition evolutionary history. For this purpose, we summarized information from the primary literature to which we associated new findings based on tracking data from three habituated groups of wild western gorillas (Nindividuals  =  7, 7–9, and 9–10), ranging in the Dzanga-Sangha Protected Areas, in Central African Republic (for further details on the study site, see Fuh et al., 2022; for details on the composition of the three study groups, see Table 9.1). We analyzed with R software (v4.2.1, R Core Team, 2022) the daily GPS tracks recorded with a location every 15 m while simultaneously following the three gorilla groups from May 2016 to November 2017. This corresponded to 469, 337, and 380 tracking days, respectively, for the three groups (284, 125, and 127 days when counting only those with an observation time larger than 6 h, for which we could reliably estimate the daily path length). While tracking the gorillas during the high-fruit season (from June to October, with seasons defined as in Masi et al., 2009), we also collected the main activities (feeding, foraging, resting, travelling, socializing, and other minor activities) and the food eaten (species and food type, such as fruit, young leaves, all leaves, insects, etc.) using continuous focal animal sampling (Altmann, 1974; for details on the activity definitions, see Miglietta et al., 2021). This corresponded to 88, 37, and 133 days, respectively, for each group, for which we also had the behavioral data. Further details on data analyses are provided in the main text and the figure legends. For all linear models, inspection of residuals (based on DHARMa R package, Hartig, 2022) indicated no major violation of required statistical assumptions. The R code used to perform the analyses is available at https://github.com/benjaminrobira/African_mammal_ movement_gorilla_chapter.

9.2 Seasonality and Space Use Animal movements are the result of a plethora of drivers and constraints (Nathan et al., 2008; Teitelbaum & Mueller, 2019; Riotte-Lambert & Matthiopoulos, 2020; Shaw, 2020) in which humans are now playing a growing role (Tucker et al., 2018). In remote areas, such as many tropical forests, food remains often the main factor shaping animal movements. As such, some components of western gorilla space-­ use pattern vary across seasons, reflecting flexibility linked to seasonal changes in

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Table 9.1  Group composition and hours of focal follows in terms of age/sex class Group ID CAR 1 CAR 1 CAR 1 CAR 1 CAR 1 CAR 1 CAR 1 CAR 2 CAR 2 CAR 2 CAR 2 CAR 2 CAR 2 CAR 2 CAR 2 CAR 2 CAR 3 CAR 3 CAR 3 CAR 3 CAR 3 CAR 3 CAR 3 CAR 3 CAR 3 CAR 3

Age/sex class SB AF AF SAM JUV INF INF SB AF AF AF AF SAM JUV INF INF SB AF AF BB SAM JUV JUV JUV INF INF

Hours of focal 67.68 46.27 54.48 43.47 40.18 30.55 42.48 96.26 17.68 Not followed Not followed Not followed Not followed 22.00 Not followed Not followed 70.47 69.79 52.65 34.61 51.30 52.35 56.54 37.84 44.23 9.26

First observation – – – – – – – – – – – – – – – – – – – – – – – – 2016-11-01

Last observation – – – – – – – – – – – – 2016-07-01 2017-03-01 – – – – – – – – – – – –

Age/sex classes following Breuer et al. (2009) SB silverback, AF adult female, SAM subadult male, JUV juvenile, INF infant First and last observation columns provide information about the changes in group composition. Symbol “-” in these columns indicates that gorillas were present before and after the study period. Dates follow the year/month/day format. “Not followed” indicates individuals present in the group but not focal followed, since they were not fully habituated yet

food availability and distribution. For example, the distance western gorillas travel each day is consistently affected by season (independently from the habituation level of the study groups; studies reviewed in Fig. 9.1), except for a study on unhabituated cross river gorillas (Gorilla gorilla diehli). From the data on our three study groups, the daily travelled distance of western gorillas is on average longer by 836 m during the high-fruit season, when compared to the low-fruit season (linear mixed model fitted with the “lmer” function of the lme4 R package, Bates et al., 2015, on the logarithm of the daily path length (DPL) – in m – as a function of season with group as a random effect, CI95% = [383, 1708]; Fig. 9.1). This difference in travel distance stems likely from the need to search for fruiting trees dispersed in the

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Fig. 9.1  Comparison of daily path length (DPL) from different western gorilla studies. The dots indicate the mean value. The dot size is proportional to the square root of the number of observation days. The segments indicate the range (min-max). The color depends on whether a seasonal effect was observed or not. For our data specifically, we contrasted the high-fruit (orange; June, July, August, and September months) and the low-fruit (green; November, December, January, February, March, April,) seasons (see Masi et al., 2015). We also indicated the standard error. It is worth noting that the measure of DPL likely depends on the recording methods as well as the habituation level of the group (see, e.g., comparison in Cipolletta, 2004). In this figure, location recording methods were based on different methods: indirect assessment (e.g., nest, feces, and other space-use signs; Etiendem & Tagg, 2013), direct following with a pedometer (Goldsmith, 1999), a grid-based location record (Tutin, 1996; Cipolletta, 2004; Wangue et al., 2015), or direct following with handheld GPS recorder (Doran-Sheehy et al., 2004; Seiler & Robbins, 2020; this study). Even when a same method is used (e.g., GPS tracking), its parameterization (e.g., sampling frequency) can influence the measurement obtained (McCann et al., 2021). In our study, we specifically focused on days for which monitoring covered almost entirely the gorilla active time (observational time > 6 h)

forest (Goldsmith, 1999; Tutin, 1996; Cipolletta, 2004; Doran-Sheehy et al., 2004; Seiler & Robbins, 2020). Indeed, only a small portion of trees of a given species actually fruit each year, while all trees bear young and mature leaves in a year (Chapman et al., 2005; Janmaat et al., 2016). The additional travel cost is energetically compensated by the higher energetic richness of fruit compared with other vegetative plant parts (e.g., Masi et  al., 2015). In addition, during the high-fruit season, the weekly range of western gorillas is on average 71 ha larger than during the low-fruit season (CI95% = [43, 99]; linear mixed model with the area 95% of the Utilisation Distribution, UD, as a function of season, with the cumulative hours of gorilla following as an offset term and study group as random factor for weeks with at least seven days of monitoring, Nweeks-group  =  62; UDs were estimated with

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movement-­based kernels using the “BRB” function of the adehabitatHR R package, Calenge 2006, with a smoothing parameter set to 100 m). Despite their large dietary breadth, western gorillas remain food selective. Some plant species are eaten principally for their bark, stem (mature or young), leaves, or fruit (Doran et al., 2002; Rogers et al., 2004; Masi et al., 2015). In addition, some of the plant species eaten during the period of fruit scarcity are found in different vegetation types (i.e., the aquatic herbs in clearings or swamps or terrestrial herbaceous vegetation in secondary forest patches), as opposed to the fleshy fruits eaten in the high-fruit season mostly found in mixed forest. The way our study groups of western gorillas exploited the environment varied between seasons in terms of locations, duration, and number of (re)visits (Fig. 9.2). If the areas visited for the longest time were also those visited most often in both seasons, they did not match between seasons (Fig. 9.2). This reflects a difference in habitat selection along the year in accordance with the variation in food availability in the different types of vegetation and diet choices.

9.3 Seasonality and Spatiotemporal Memory The spatial and temporal seasonal variations in food distribution, characterized by different degree of spatial aggregation and temporal predictability, may lead to different foraging strategies (Boyer and Walsh, 2010; Bracis et al., 2015). For instance, some fruiting events are predictable only in the short term, such as Dialium spp. trees (mainly found in mixed forest or its combination with lianas of Haumania danckelmaniana). Even though these trees synchronously produce fruits for a month-period only once every few years (Bai Hokou, long-term data), they are crucial for western gorillas’ nutrition because they fruit at the start of the season of fruit scarcity (Masi & Breuer, 2018). However, tree phenology of most fruit species eaten by primates during periods of fruit abundance remains fairly predictable (Chapman et al., 2005; Janmaat et al., 2013a, 2016). As any animal that displays home range behavior (as oppose to nomadic species), western gorillas certainly possess a good spatial memory that enables them to navigate through their home ranges (Salmi et al., 2020; Benhamou, 2010). However, using memory for foraging is not just a matter of large-scale navigation by relying on permanent spatial features. Often, food locations are ephemeral, thus requiring specific temporal knowledge to be used at the right time (Plante et al., 2014). As memory of food abundance and distribution comes at a cost, we questioned here to which level of accuracy western gorillas need to memorize environmental features to forage efficiently and whether this level depended on the seasonal diet or not.

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Fig. 9.2  Intensity distribution (ID) and recursion distribution (RD) for the three gorilla groups for each dietary season (low fruit, green, and high fruit, orange). The light color (tending toward yellow) indicates a higher relative value. The intensity distribution corresponds to the distribution of the mean length duration of visits, while the recursion distribution corresponds to the distribution of the number of visits (Benhamou & Riotte-Lambert, 2012). They were estimated using the BRB MKDE algorithm (freely available at https://www.cefe.cnrs.fr/fr/recherche/bc/dpb/216-­simon-­ benhamou), with the minimum distance to consider absence of movement set to 10 m, the smoothing parameter and the radius of the circle running along the path to compute the residence time and the number of visits set to 30 m, and the time out the circle required to consider a revisit set to 240 min. Visits shorter than 15 min were ignored. As data were acquired using continuous tracking, the maximum time allowed between successive locations was set to 2 min and the diffusion coefficient was set to 0

9.4 Straightness of Movement In the theoretical framework of optimal foraging, animals are often expected to minimize travelling distances, i.e., to avoid unfruitful detours. In numerous species, animals indeed tend to avoid backtracking at short term by showing a local forward tendency (Bovet & Benhamou, 1988). In addition, using some form of spatial

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memory also makes it possible to avoid backtracking at a larger scale, e.g., at the daily scale, to avoid overexploitation of the same feeding areas. The straightness of the path reaching a specific goal can be a measure of the orientation efficiency (Benhamou, 2004). Moving straight toward a distant (i.e., beyond perceptual reach) location is often interpreted as evidence for the use of spatial memory (e.g., Parada et al., 2017; Trapanese et al., 2018; Janmaat et al., 2021). However, straight movements may result from two foraging strategies with markedly different efficiencies. On the one hand, an animal may move straight in a random direction until it finds an appropriate food site (random ballistic walking; Bartumeus et al., 2016). On the other hand, it may plan its movement toward a potentially productive feeding patch based on a spatiotemporal memory. Our data indicate that western gorillas tend to move in straight line at moderate speed (Fig. 9.3a, b). This holds when they move toward feeding sites located far beyond their visual range (> 30 m; Salmi et al., 2020), which is severely limited by the dense foliage of the forest. Furthermore, for both seasons, we found a linear relationship between the daily net displacement (straight-line distance between the first and last locations of the day) and DPL (Fig. 9.3c, d). This suggests that western gorillas tended to keep a certain constant movement orientation at the daily scale, which results in an advective movement. Although weak (i.e., slope < 1), this linear relationship is not coherent with a ballistic walk with random reorientations at every feeding site. As a result, like in other primates (chimpanzees: Normand et al., 2009; mangabeys: Janmaat et al., 2012; mantled howler monkeys: Hopkins, 2016), the daily movements of western gorillas allowed them to visit more trees than expected under the null hypothesis of a ballistic movement, either with or without random reorientation at every feeding site (simple straight-line movement across the home range for the whole day; Fig. 9.3e). Clearly, the daily number of exploited trees of a given species is just a proxy for primate foraging success (e.g., Janmaat et al., 2012). In the future, the combination of drones and laser technology (Salas, 2021) combined with modern sampling techniques should make it possible to obtain an exhaustive day-by-day picture of available resourceful trees. The longest daily movements for which the linear relationship between net movement and DPL is particularly striking are generally associated with gorillas visiting rare and distant areas, sometimes a few kilometers away from their core home range, such as flooded clearings and swamps (Doran-Sheehy et  al., 2004; Seiler & Robbins, 2020; Magliocca & Gautier-Hion, 2002). These distant areas are thus worth remembering: despite being highly scattered, they provide unique aquatic food and as such are valuable. Keeping a straight course over long distances (i.e., several kilometers) is usually attributed to a migratory process. These findings raise the question on the mechanisms supporting navigation during these long-­distance movements (e.g., sun orientation, as for naive Aka people; Jang et al., 2019).

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Fig. 9.3 (a–e). Forward tendency, speed, and foraging efficiency as a function of dietary season. The mean is shown by a white dot. The statistics provided come from linear models fitted with the “lm” function of the stats R package (R Core Team, 2022), and we compared the model with and without the test predictor with a likelihood ratio-test using the “anova” function of the stats R package (R Core Team, 2022). We verified the absence of major deviations from statistical assumptions with visual inspections using the DHARMa R package (Hartig, 2022). The sample sizes are indicated below each boxplot. For these analyses, gorilla GPS tracks were rediscretized with a constant step length of 30 m using the “make_track” function of the amt R package (Signer et al., 2011), and (continued)

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9.5 Decision Rules and Recursions The high foraging efficiency of western gorillas should thus rely on a much more sophisticated rule than simply moving forward in a random direction until a feeding site is found, as a naive forager may do. Comparing mechanistic modeling and observations (e.g., Hopkins, 2016; Janson, 1998, 2007, 2016) provides a reliable way to attempt to infer the underlying decision rules. We applied it to western gorillas (Robira et  al., in review) and showed that, while foraging, gorillas tended to prioritize the closest trees with the highest probability to provide ripe food over other characteristics, such as the differences in the long-term interest of the feeding sites (approximated by the total time gorillas spent there during the study period). Food resource can be diverse in terms of phenology: it can be more or less ephemeral, and the predictability of its occurrence depends on numerous factors, such as the existence of serial correlations (temporal contingency; Colwell, 1974) or cross-correlations, i.e., correlations between the phenologies of different species. Hence, it has been suggested that primates may anticipate which places will become productive based on phenological knowledge at the individual tree or plant species level (Janmaat et al., 2016). However, an animal unable to use phenology information to forage can be almost as efficient simply by targeting the fruiting or leafing trees in the environment (Robira et al., 2021). As suggested for other forest primates (Janmaat et  al., 2012, 2013a, b), western gorillas might rely on immediate Fig. 9.3  (continued) only complete observation days (> 6 h) were considered. (a) We calculated the local forward tendency as the mean cosine of turning angles for each tracking day. We compared the values obtained for each season within group using a linear regression with the daily mean cosine as predicted variable and the season as categorical test predictor while taking the number of steps considered per day into account (in terms of square root) as a control predictor for each group separately. (b) We filtered out steps likely to correspond to resting or feeding (speed 4 revisits), revisits occurred in bursts within the season during which they provide food, such as fruit or young leaves (Fig. 9.4b). Indeed, western gorillas revisited a few times the same tree every few days, thus in a very short time window. Likely, this foraging pattern reflects food renewal pattern on the trees. As fruiting and leafing are continuous processes, full food depletion may require several visits,

Fig. 9.4  Recursion patterns as a function of food type and dietary season. We mapped the different feeding locations associated to a same food type (fruit, young leaves, all leaves) by barycentring the locations of feeding observations of a same food type that were within 30 m of each other. We computed the recursion history for each group at those locations using the “getRecursionsAtLocations” function of the recurse R package (Bracis et al., 2018). We considered a circular 30-m buffer around each location to estimate entrance and exit time and considered new visits if the time spent outside the area was more than 12 h. For each location with sufficient revisits (> 4 revisits) within the season of highest interest of the associated food type (i.e., in the high-fruit season for fruit-­ associated locations, in the low-fruit season for leaf-associated locations), we calculated a within season regularity index (Martin et al., 2015) to estimate whether visits occurred randomly in time (index = 1), in burst (index 1) within a given season. (a) Density distribution of visit rate (per day) to feeding sites along the study period depending on whether the feeding site is associated to feeding on fruit, young leaves, or all leaves. (b) Regularity index within the appropriate season for each food type, with the data from the different groups pooled to increase the sample size. We analyzed differences among food types as a whole using an ANOVA (“anova” function of the stats R package, R Core Team, 2022)

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in particular for large trees. Thus, western gorillas seem to keep track that a given place is likely to provide edible resource in a few days (e.g., more fruits are ripening). This ability is widespread even in less frugivorous species, like Asian forest elephants where recursions are shaped by the growth speed of grass species (English et al., 2014).

9.6 Absence of Seasonal Differences in the Level of Cognition Used for Foraging Because fruit is rarer both in time and space but much more nutritional than herbs and leaves, one can assume that they act as pivotal resources for the development of cognition in primate evolutionary history (Potts, 2004). While this rationale is usually invoked to explain cognitive differences between frugivorous and folivorous species, one can question whether this difference holds true within species that alternatively exploit different resource types. In other words, one can wonder whether seasonal frugivores such as western gorillas rely on the same level of cognition to forage during the high- and low-fruit seasons (Hypothesis 1) or whether they search more at random for leaves and more widely distributed herbs when fruit is scarce in the forest (Hypothesis 2). The classical dichotomy established in primate ecology and evolution between the difficulty to find fruit and leaves might be oversimplistic (Sayers, 2013). For instance, young leaves are a transient and limited food which may favor memory emergence like fruit (see also the folivore paradox; Snaith & Chapman, 2007). Furthermore, memory is generally associated to increased energetic demands mostly because of the maintenance cost of the cognitive machinery supporting memories (Raichle, 2006). The energy-based selection pressure between a strictly folivorous species and a transient folivorous species might thus largely differ because of a kind of carryover effect (Harrison et al., 2011). Thus, western gorillas may use their cognitive skills to forage in periods of fruit scarcity as much as during periods of high-fruit availability because their use may still provide some benefits or at least does not cause additional costs (e.g., using them in low-fruit periods simply because of its efficiency in high-fruit period). Although we did not provide a proper test of these two hypotheses, our results underlined that the seasonal differences in western gorilla foraging strategies appeared quite weak. In particular, the markers of use of a relatively high level of cognition for foraging were equivalent between seasons, with gorillas travelling as straight and as fast in both seasons and showing the same global forward tendency (Fig. 9.3). Interestingly, while no seasonal differences were found in these markers, the food type eaten at the feeding sites affected how straight and fast western gorillas travelled, with an increase in straightness and speed when fruit and aquatic herbs were targeted (Salmi et  al., 2020). The similarity in the seasonal foraging movement patterns may therefore be the consequence of the overall search for rare and difficult-to-find food (e.g., trees with young leaves or fruit) in both seasons which generally end up being the most

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nutritional ones for a given season (Masi et al., 2015; Masi & Breuer, 2018; Lodwick and Salmi, 2019). In addition, during the low-fruit season, western gorillas increased the consumption of staple food such as stems of herbs, e.g., Aframomum spp., Palisota spp., and Haumania danckelmaniana (Masi et al., 2015). These terrestrial herbs are present year-round but are concentrated in high density in widely scattered patches of secondary forest. It may be profitable for western gorillas to use spatial memory (used for fruit; Salmi et al., 2020) to target particularly large and herb-rich patches during the low-fruit season. Remembering these areas may also be profitable all year-round because they are favorite areas for western gorilla night nests (Mehlman & Doran, 2002). How western gorillas may specifically adjust their foraging strategies in response to each food spatiotemporal specificities remains to be further elucidated, as well as the degree of sophistication of attribute, spatial, and temporal memory used.

9.7 Conclusions In this chapter, we highlighted how spatiotemporal cognition may buffer the effect of seasonality, allowing western gorillas to maximize their foraging efficiency through minimizing travelling costs. Space-use patterns were greatly affected by changes in resource type between seasons as a consequence of changes in food location and nutritional quality (fruit vs. herbs/leaves). However, western gorillas’ strategy to find food was barely affected by it. Western gorillas seem to remember where the key food areas are and the type of food they provide. They seem to be able to infer when food is available independently of the food type (i.e., fruit, mature or young leaves, stems, bark, etc.) and the seasonal variations in food availability. Like other primates (Zuberbühler & Janmaat, 2010), gorilla knowledge is likely multifactorial and multi-scale. The current global climate change, as well as anthropogenic deforestation and land encroachment, may exacerbate seasonal fruit shortage for forest animals by affecting tropical tree phenology (Bush et  al., 2020). In particular, increasingly irregular rainfall patterns, increasing temperature, and logging have been affecting forest phenology, diversity, and dynamics (Wright, 2005). The response to current changes seems also highly heterogeneous, both at the individual tree and at the tree species/community level (Chapman et  al., 2005; Polansky & Boesch, 2013). Unpredictable periods of fruit scarcity also have become more frequent in the Central African tropical forest where the study groups range (Masi, pers. obs). As food density, variability, and predictability are all key notions to mobilize when and how to use memory, the extent in the next future to which cognition will remain useful to forage efficiently for tropical mammals is questionable. Environmental changes might be associated with the alteration of current movement strategies, cascading onto the environment itself through seed dispersal. Along with other large forest mammals, primates greatly contribute to shape the spatial patterns of forest plant species by dispersing fruit seeds (Chapman et al., 2013). Primates thus play a

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crucial role in tropical forest regeneration (Chapman & Onderdonk, 1998). Understanding primate movement ecology and associated cognition therefore stands as a path for better understanding larger ecological processes underlying tropical forest dynamics. Acknowledgments  We would like to thank Mario Melletti, Rafael Reyna-Hurtado, and Colin Chapman for inviting us to contribute to this chapter. We thank the two reviewers, Kathy Slater and Sophie Calmé, for constructive comments on earlier versions of the manuscript. We are grateful to the Ministry of Higher Education and Scientific Research of CAR for the permission to conduct this research. We deeply thank the Dzanga-Sangha Protected Areas and WWF CAR for allowing us to carry out fieldwork at their sites. Special thanks go to the Bai-Hokou and Mongambe staff for assistance in the field, especially the local Ba’Aka trackers, for their exceptional tracking skills and incredible forest knowledge. We are greatly thankful to Action Transversal du Muséum and the Department of Human and Environment for the Project Federateur of the National Museum of Natural History (MNHN, Paris) for their financial support for this study. We also deeply thank the UMR 7206 and the MNHN for the institutional and the additional financial support. BR was funded by a PhD grant from the French Ministry of Higher Education and Research to the École Normale Supérieure in Paris.

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Chapter 10

Females Move in Tight Crowds, Males Roam: Socioecology and Movement Ecology of Mandrills Shun Hongo

Abstract  Mandrills (Mandrillus sphinx) have a unique social system for primates, with huge groups of hundreds of individuals and males moving in and out of the group seasonally. Despite intensive field studies conducted at several sites in the Congo Basin rainforests, the mechanisms and adaptation of their social organization are still poorly understood. How do groups maintain their huge size while moving around in the forest with poor visibility? How do solitary males find groups in the vast forests? And what are the adaptive advantages of these behaviors? In this review, I summarize what we know surrounding these questions and compare mandrill ecology with that of Neotropical social mammals, offering potential explanations for these questions. Group crowdedness and frequent exchange of long-distance calls could be keys to the collective movement of large groups that engage in regular subgrouping. The adaptive benefits of the large group size possibly lie in female tactics relating to infanticide avoidance and polyandrous mating. While very little is known about how solitary males find groups at the onset of the mating season, the adaptive function of their seasonal influxes can be relatively well explained as foraging and mating tactics. Since the major questions of mandrill social organization are strongly related to their movement ecology, intensive movement research using GPS telemetries and remote sensing is crucially needed to disentangle the social system of this intriguing monkey. Further, broader comparisons among the social movement of rainforest mammals will be essential to comprehensively understand their movement ecology. Keywords  Group crowdedness · Group size · Long-distance call · Male influx · Mandrillus sphinx · Social organization

S. Hongo (*) The Center for African Area Studies, Kyoto University, Kyoto, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_10

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10.1 Introduction 10.1.1 Mandrill: A Fascinating Primate The mandrill (Mandrillus sphinx) is a diurnal, semiterrestrial primate living exclusively in the rainforests near the coast of the Gulf of Guinea (Fig. 10.1). Distribution range of this cercopithecine monkey is dominated by lowland forests with canopy heights of about 30–40  m, covered by evergreen and semi-deciduous trees and dense undergrowth. Some areas, including the Lopé National Park and Moukalaba-­ Doudou National Park in Gabon, also have small-scale savannas surrounded by riverside forests, forming landscapes called the forest-savanna mosaic. Annual precipitation of the mandrill range falls between 1200 and 2200 mm, with one clear dry season lasting 3–4 months and one rainy season of 8–9 months. In some areas, there is another “small” dry season in the middle of the rainy season. As Darwin (1871) noted, mandrills exhibit prominent sexual dimorphism (Fig.  10.2): Adult males weigh about 30  kg—more than three times as heavy as adult females (Setchell et al., 2001); males also have long canine teeth (Leigh et al., 2008) and display bright red-and-blue coloration on their faces, genitalia, and buttocks (Setchell & Dixson, 2001). These striking characteristics have been naturally regarded as a model of sexual selection. Consequently, their sexual behavior and physiology are now well understood, mainly as products of intensive research conducted on the semi-free ranging groups at the Centre International de Recherches Médical de Franceville (CIRMF), Gabon (Dixson, 2015; Setchell, 2016). This interesting creature has also attracted researchers struggling with the deep forest. Since the 1970s, field ecologists have studied mandrills in rainforests of Cameroon, Equatorial Guinea, and Gabon (Sabater Pi, 1972; Jouventin, 1975; Hoshino et al., 1984; Harrison, 1988). Despite poor visibility and limited mobility in rainforests, they have persistently followed the movement of mandrill groups, gradually unraveling their ecology and society. The pioneer field studies revealed, for example, that mandrills are highly omnivorous in their diet. They prefer the pulp of ripe fruits when available, but other foods—seeds, monocotyledonous herbs, barks, roots, and invertebrates (mainly ants and termites)—are also regularly consumed (Hoshino, 1985; Lahm, 1986). They also eat vertebrates occasionally: Hoshino (1985) and Lahm (1986) sometimes found vertebrate matter in mandrill feces; Kudo and Mitani (1985) observed an adult male killing a juvenile bay duiker (Cephalophus dorsalis) and eating its meat. More recent studies have clarified the flexibility of mandrill groups to respond to seasonal fruit production. During the long dry season (generally for 3–4 months), when fruit production becomes very low, group members spend more time foraging (Nsi Akoue et al., 2017) and increase dietary diversity by relying more on less preferred foods, particularly seeds and wooden tissues buried in the leaf litter (Hongo et al., 2018). However, this fruit-based omnivorous diet and its seasonal variation are observed in many African primates (Hemingway & Bynum, 2005). The feeding ecology is thus not a point that fully illustrates the uniqueness of wild mandrills— the big mysteries lie in their social organization and movement ecology.

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Fig. 10.1  Distribution range of wild mandrills (Mandrillus sphinx) (pink area) derived from the IUCN Red List of Threatened Species (Abernethy & Maisels, 2019). Yellow circles represent their intensive research sites

10.1.2 Mysteries of Large Groups and Seasonal Male Influxes First of all, their extremely large group size is a curiosity in itself. In the Lopé National Park, central Gabon, Abernethy et al. (2002) counted wild mandrill groups crossing the savannah between gallery forests 20 times by video recording. The observed group sizes ranged from 338 to 845, with a mean of 620 individuals,

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Fig. 10.2  Sexual size dimorphism in mandrills in the Moukalaba-Doudou National Park, Gabon: (a) a subadult male inspecting genital sexual swelling of an adult female; (b) an adult male mate-­ guarding an adult female with sexual swelling

representing the largest size of stable groups observed in wild primates. Hongo (2014) subsequently conducted a field study in the Moukalaba-Doudou National Park, southern Gabon, and filmed a group of 350 mandrills and 2 subgroups of 169 and 442 individuals, suggesting that group size of hundreds is a general social feature of this species. The evolution of such large groups in a rainforest-dwelling primate is surprising—it seems to challenge existing theories for the classic socioecological model, which argues that the primate group size is determined by food distribution and predation pressure (van Schaik & van Hooff, 1983). Mandrills are considered to have historically been confined to Central African rainforests (Dixson, 2015), where dense vegetation would complicate the coordination of ground movement by large groups. Additionally, their dietary preference for fruits is generally less compatible with very large groups than folivorous diets: Many rainforest fruits are patchily distributed, which would lead to intense competition for foods between group members directly (called interference or contest competition) and indirectly (exploitation or scramble competition) (van Schaik & van Noordwijk, 1988; Chapman &

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Chapman, 2000). Further, predation risk in rainforests is usually considered lower than in open habitats, so the benefit of large groups in avoiding predators seems limited. All the environmental conditions predict mandrills living in small groups. However, they form larger groups than savanna-dwelling baboons—why? Second, male sociality deviates from the standard of group-living primates, in which males stay with females all year round and typical adult sex ratios in a group are 2 to 10 females per male (Clutton-Brock et al., 1977). Mandrill groups include multiple males and females, but the adult sex ratio was largely female-biased: The Lopé groups had less than 17 adult males (i.e., >10-year-old males), and the mean adult sex ratio was 24.6 (Abernethy et al., 2002); the Moukalaba group of 125 adult females included just 5 adult males (adult sex ratio = 25) (Hongo, 2014). Moreover, this female-biased composition is more pronounced in fruit-rich, rainy seasons, when many females give birth and most males presumably live in solitary (Brockmeyer et  al., 2015; Hongo et  al., 2016). During dry seasons, on the other hand, many solitary males join groups after the number of sexually active females increases, competing with each other for mating (Hongo et al., 2016). This temporary immigration of many males into the group is termed male influx. In mandrills, the male influx is thought to occur during every dry season (Hongo et  al., 2016). Therefore, most males would move around in the forest yearly in search of groups. Due to their large group home ranges of about 50  km2 (White et al., 2010) and the low group density (White, 1994), however, this annual search for groups at the right time of year for mating seems quite challenging for solitary males. How do they navigate themselves to rejoin a group, and why do they leave it?

10.1.3 Purpose of This Chapter As we have seen in the previous section, critical questions about mandrill social and movement behaviors are summarized as follows: • How do female groups move around in the forest with poor visibility while maintaining their huge size? • How do solitary males find groups in the vast forests? • What are the adaptive advantages of these behaviors? Unfortunately, all the questions are not entirely resolved yet, and this review may not provide clear answers. So instead, I summarize and analyze the evidence provided by a variety of mandrill studies while comparing and contrasting them with Neotropical social forest mammals (Reyna-Hurtado & Chapman, 2019) to find potential explanations and identify further questions. This is because, like mandrills, they form social groups in tropical rainforests and have interesting similarities with mandrills in their socioecology and movement ecology.

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10.2 Movement Coordination and Adaptive Significance of Large Groups 10.2.1 How Do Group Members Coordinate Their Movement? Mandrills predominantly move on the rainforest floor, where visibility is only about 20–30  m. Large mandrill groups forage without prolonged interruption for 10 to 11 hours from dawn to dusk, ranging for long distances up to 10 km per day (White, 2007; Hongo et  al., 2022). Nutritional, energetic, and social demands are most likely different for each individual, depending on its age, reproductive status, and individual history—if so, how can members of mandrill large groups travel together without scattering? 10.2.1.1 Group Crowdedness Observing small wild groups of 15–95 mandrills in the Campo Faunal Reserve (now part of the Campo-Ma’an National Park), southern Cameroon, Hoshino et al. (1984) reported that group members keep very close interindividual distances during group movement. Hongo (2016) hypothesized that this proximity among group members—he termed it the high crowdedness of the group—contributes to the coordination of group movement. Highly crowded groups are observed not only when animals are on alert but also when undisturbed. At Moukalaba-Doudou, for example, an unalarmed subgroup of 169 individuals passed on a fallen tree crossing a river in only 4 min 20 s (Hongo, 2014). Camera traps also recorded often crowded groups in the forest (Fig. 10.3). Similarly to mandrills, crowded social groups are also observed in Neotropical ungulates. Collared peccaries (Pecari tajacu) and white-lipped peccaries (Tayassu pecari) are rainforest-dwelling social animals, although group size and home range are, in general, much larger in the latter (Keuroghlian et al., 2004). Groups of both species move with close interindividual distance in dense forests (Byers & Bekoff, 1981; Fragoso, 1998; Reyna-Hurtado et al., 2009; Biondo et al., 2014). These observations suggest that mandrills and peccaries coordinate their speed and direction of movement with neighboring individuals to maintain crowded groups, as seen in locust and fish swarms (Hemelrijk & Hildenbrandt, 2008; Ariel & Ayali, 2015). Byers and Bekoff (1981) hypothesized that collared peccaries rely on olfaction to determine their spatial position relative to other group members. And white-lipped peccaries are considered to use togetherness vocalizations to keep close interindividual distances (Mayer & Wetzel, 1987). On the other hand, I speculate that mandrills, a primate species supposed to have good eyesight, may visually measure the distance from neighboring animals in the group. Regardless of the type of sensory cue, crowded groups may be adaptive for mammals living in large groups to move collectively across the dense rainforest floor. Since

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Fig. 10.3  Crowded mandrill groups observed in the Moukalaba-Doudou National Park, Gabon: (a) a subgroup of 169 individuals traveling on a tree crossing a river; (b) a group traveling on the ground; (c) a subgroup of 442 individuals crossing a logging road. Photos (a) and (c) are captured from video recordings used in Hongo (2014)

behavioral mechanisms and adaptive benefits of group crowdedness have been understudied in terrestrial mammals, future work should focus on their communication for maintaining interindividual distances and the relationship between group size and crowdedness.

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10.2.1.2 Subgrouping and Long-Distance Calls Although mandrill groups keep crowded during the movement, they also frequently engage in fission and fusion. All the groups studied in the wild have been observed to split into two or more subgroups regularly but temporarily during movement, keep apart for several hours to a few days, and eventually reunite together (Hoshino et al., 1984, Abernethy et al., 2002, Shun Hongo unpublished data). Since the social composition and membership stability of the subgroups are still unknown, we cannot conclude whether they are stable social units or more flexible, temporary aggregations. If the former is the case, then mandrills are suggested to live in a unique multilevel society: Multilevel societies that are not based on one-male reproduction units are not observed in the other primates (Grueter et al., 2020). If the latter is true, then the fission-fusion dynamics of mandrills will be highly variable in subgroup composition and size, as known in many social mammals (Aureli et  al., 2008), including white-lipped peccaries (Keuroghlian et al., 2004). In any case, the subgrouping would be beneficial to efficient foraging between fruiting trees. Auditory communication seems to play an essential role in the fission-fusion of mandrill subgroups. Kudo (1987) followed the movement of the Campo groups while recording their vocal exchanges and identified 11 vocal types. Among them, two long-distance calls—two-phase grunt and crowing—were emitted during the group movement and were much more frequently vocalized than the other types. Kudo (1987) also discussed the differences in their functions. The two-phase grunts are continuously emitted only by adult males, probably helping coordinate the group movement, whereas the crowing is vocalized by all group members except adult males. Since group members emit crowing mainly before and after feeding behavior, this call may have the function of coordinating the formation of subgroups and reintegrating subgroups into a large group. Interestingly, the other savanna-­ dwelling African papionins (baboons and geladas) do not have vocalizations phonetically equivalent to crowing, implying that this long-distance call reaching >500  m has evolved with frequent and dynamic fission-fusion behavior in dense rainforests. Future research should test Kudo’s above hypotheses on the functions of long-distance calls.

10.2.2 Why Do Mandrills Move in Extremely Large Groups? 10.2.2.1 Potential Disadvantages of Large Groups Mandrills appear to be paying a great cost to maintain their large groups. For example, a huge group of ca. 700 individuals at Lopé forage in a large home range of 118  km2, with 46  km2 of forested area (White et  al., 2010). The large size of its home range could be a result of a patchy habitat with forest-savanna mosaic, where mandrills need to cross many gallery forests and bosquets to forage. However, the group home range fitted the predicted relationship between primate group mass and

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home range size demonstrated by Clutton-Brock and Harvey (1977), suggesting that mandrills pay an energetic cost of increasing group size at a similar rate as other primates. Also, mandrill groups need to move extended distances to meet food requirements. At Moukalaba-Doudou, groups move 6–7 km on average during the day (Hongo et al., 2022). Even a small group of ca. 120 animals at Lékédi Park, southeastern Gabon, ranges a mean of 2.4  km per day within a home range of 8.7  km2 (Brockmeyer et  al., 2015). The ecological constraints model in primate groups (Chapman & Chapman, 2000) predicts that increased group size will lead to an increased home range and an extended day range, particularly in fruit-eating species, which apparently seems to be the case for large mandrill groups. In addition, large, crowded groups of mandrills may generate particularly fertile ground for parasite transmission, possibly imposing additional travel costs through the need for parasite infection avoidance. Brockmeyer et al. (2015) found that the Lékédi group moved longer distances when group members showed high richness in short-life cycle parasites (e.g., protozoans) and suggested a strategy to escape contaminated habitats on a local scale. Protozoan richness in female mandrills at Lékédi varies seasonally, with more protozoa in the early gestation period (i.e., ~2 months after fertilization) (Poirotte et al., 2016). Interestingly, this parasite-rich period at Lékédi corresponds to the dry season, when groups at Moukalaba-Doudou forage through much broader areas (Hongo et  al., 2018). The Lékédi group also avoided returning to areas with high contamination levels of gastrointestinal parasites, particularly during the dry season (Poirotte et  al., 2017a). Since mandrills seem to distinguish parasitized group members via fecal odors and avoid grooming conspecifics infected with orofecally transmitted parasites (Poirotte et al., 2017b), large mandrill groups may shift locations frequently to avoid foraging in areas with their own feces containing high parasite loads. 10.2.2.2 Possible Adaptive Benefits As seen in the above section, large mandrill groups have to travel long distances and use large home ranges to find enough food, and their high crowdedness may increase the risk of parasite infections. Although they display temporary subgrouping and seasonal diet changes to efficiently forage in large groups (Hongo et al., 2018), such behavioral flexibility would have been unnecessary if they lived in smaller groups. So, what adaptive benefits would trump the disadvantages? Of course, the large group size is well known to be generally beneficial in predation avoidance. Leopards (Panthera pardus) and Central African pythons (Python sebae) are known predators of mandrills (Henschel et al., 2011; Abernethy & White, 2013), and crowned eagles are also likely to kill mandrills (Shun Hongo, personal observation). In fact, mandrill groups avoid traveling through open savannas, sleep high in trees, and almost wholly avoid terrestrial activity at night (Brockmeyer et al., 2015; Hongo et al., 2022), all suggesting predator avoidance. However, as I discussed in the Introduction, a counterstrategy against predators alone is insufficient to explain the formation of groups of hundreds in rainforests, where the

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predation risk should be generally lower than in open habitats. Interestingly, Kiltie and Terborgh (1983) have asked similar questions about large herds of white-lipped peccaries. They argued that an increased predator-detection rate and per-capita predator avoidance were the most likely adaptive benefits of forming large herds. But it is still questionable whether peccaries weighing 30–40  kg have to live in groups of up to 300 animals just to avoid solitary carnivores such as cougars (Puma concolor) and jaguars (Panthera onca). Here, I would like to discuss the two social benefits of large groups in female reproduction, although the evidence is still inadequate. First, larger groups might be adaptive in reducing the infanticide risk. Female mandrills are seasonal breeders, where frequent infanticide is generally considered unlikely, but killing unrelated infants may nevertheless be beneficial for most males performing seasonal influxes to enhance their siring probability. Indeed, cases of highly suspected infanticide have been reported from the CIRMF colony: Three infants were found dead with injuries following the introduction of unrelated adult males into the colony (Setchell et  al., 2006b). Extremely large groups may prevent the infanticidal males from detecting and approaching infants through the dilution effect and increased vigilance. The high crowdedness of the group may also allow infants and their mothers to avoid male aggression through the confusion effect, an antipredatory strategy found in fish schools (Chivers et  al., 1995). Moreover, large groups may benefit females in coalitionary attacks against unfavorable males (Morelli et  al., 2009; Cords & Fuller, 2010). A coalitionary attack by multiple females on a recently immigrated male was observed at CIRMF (Setchell et al., 2006a). Female mandrills may form large coalitions to cope with males with much larger bodies and longer canines (Treves & Chapman, 1996). Altogether, forming large, crowded groups may be adaptive for female mandrills as a counterstrategy against infanticide by males. Second, large groups may increase the possibility of polyandrous mating for females. In mandrills, polyandrous mating would be difficult in small groups because dominant males rigorously guard females when receptive (Setchell et al., 2005), and females can’t synchronize their ovulation cycles precisely (Setchell et  al., 2011). Charpentier et  al. (2005) reported that the paternity skew by alpha males decreased as the number of females increased, even in the small CIRMF colonies. Moreover, forming large groups would lower group densities (White, 1994), which may, in turn, result in a certain number of solitary adult males being unable to find and join groups at the most appropriate time for mating. This delay in adult male influxes makes the ratio of receptive females to adult males higher than 1 (Hongo et al., 2016), possibly allowing low-ranking males to mate with receptive females. Therefore, females in large groups can mate with many males, including low-ranking and subadult males. This polyandrous mating may confuse the paternity of infants and increase the chances for females to choose males, both of which should be adaptive for females. Further, female mate choice is suggested to be beneficial in terms of the immune system, such as major histocompatibility complex (MHC) diversity (Setchell et al., 2010).

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10.3 Seasonal Influxes of Solitary Males 10.3.1 How Do Solitary Males Find Groups? Most male mandrills live alone during the birth (rainy) season, so they must find groups to mate with females at the onset of the mating (dry) season. So far, it is not at all clear how the males meet this challenge—here, I present several keys that may be relevant to their navigation capacity (Nathan et al., 2008). First, female long-distance calls (crowing) may be key for males to localize groups. As mentioned above, the exchange of crowing vocalization by many female foraging groups can reach more than hundreds of meters in dense forests (Kudo, 1987). Indeed, female crowing has almost always triggered researchers to find mandrill groups. Second, the nomadic movement of groups in the mating season observed at Moukalaba-Doudou may also help solitary males join the groups (Hongo et al., 2018). During the dry season, group members have a more diverse dietary repertory and travel more widely than during the rainy season. This home range expansion may favor seasonal male influxes. Future studies should compare the movement patterns of groups and solitary males to test these hypotheses.

10.3.2 Why Do Males Join and Leave Groups Seasonally? The adaptation of seasonal male influx can be relatively well explained. First, living as a solitary male will substantially reduce the energetic costs of traveling and the time spent foraging. Because of their much larger body size compared to females, living in groups may be more costly for males than for females in terms of long-­ distance movements and intragroup food competition. Second, group living enhances the risk of intensive male competition and resulting injuries (Setchell et al., 2006b). In particular, the mating season, when males live in the group, corresponds to the dry season with lower fruit production. By feeding on energy-rich fruits alone during the rainy season, males may need to compensate for the large energetic loss due to intragroup food competition and intermale mate competition during the dry season. Nonetheless, Hongo et  al. (2016) reported that camera traps at Moukalaba-­ Doudou observed adult males in the group all year round, although the percentage of adult males among all individuals decreased from 5.2% to 0.9% during the rainy (birth) season. This suggests that a small number of adult males stay in groups even during the birth season. Moreover, these males tended to position near females with sexual swellings, implying that they are dominant males capable of mating with a few receptive females outside the mating season to increase their offspring (Hongo et al., 2016). Long-term studies with individual identification and behavioral observation are indispensable to examine the above hypotheses.

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An interesting example of concordance with male mandrills is found in a Neotropical carnivore—the coatis. The white-nosed coati (Nasua narica) is a social carnivore mainly living in Central America’s forests (Cuarón et al., 2016). Similarly to mandrills, this terrestrial procyonid is sexually dimorphic in body size, with males larger than females. In addition, males are solitary most of the year and enter social groups composed of females and immatures only during the mating season. Solitary males enjoy high foraging success compared to social females and subadult males (Gompper, 1996), supporting my hypothesis on the benefits of solitary living in male mandrills. Moreover, a few adult males who join groups in non-mating season are also reported in white-nosed coatis (Gompper & Krinsley, 1992). Clarifying the social behavior of these exceptional male coatis may provide clues to the puzzle of the sociality of male mandrills.

10.4 Potential Keys to Unraveling the Puzzle Wild mandrill groups are extremely difficult to locate, identify, and follow for direct behavioral observation due to their large size and low group density. On the other hand, tracking male movement is nearly impossible without using GPS telemetries as they seasonally leave the group and range alone. As I have discussed through this chapter, major questions of mandrill social organization are strongly related to their movement ecology: group crowdedness, patterns and frequency of subgrouping, intermale variations in the seasonal male influx, and fission-fusion dynamics between female-led large groups and solitary males. Therefore, intensive research of movement and positioning behavior using GPS telemetries and remote sensing is crucially needed to disentangle the social system of this intriguing monkey. In addition, comparing mandrill ecology with a broader range of taxa, as I briefly attempted in this chapter with some Neotropical forest mammals, will help unravel the mandrill mysteries. Taking a broader perspective, we’ll be able to comprehensively understand the various characteristics of the social movement in forest mammals, with signposts of the socioecological models and the movement ecology framework (Nathan et al., 2008). Acknowledgments  I appreciate the Institut de Recherche en Écologie Tropicale (IRET) for the long-lasting collaboration. Etienne François Akomo-Okoue and Fred Loïque Mindonga-Nguelet worked with me for long-term camera trapping. I am also grateful to the Centre National de la Recherche Scientifique et Technologique (CENAREST) and Agence National de Parcs Nationaux (ANPN) in Gabon for the permission to conduct the study. I sincerely thank all researchers, staff, and field assistants of the PROCOBHA project for providing advice and assistance. Namely, no work of mine would have been possible without the help and cooperation of Yoshihiro Nakashima, Yuji Takenoshita, Shiho Fujita, Chieko Ando, and Juichi Yamagiwa. My gratitude also goes to Rafael Reyna-Hurtado, Colin Chapman, and Mario Melletti for inviting me to contribute to this exciting edited volume. Jessica Rothman and an anonymous reviewer provided many constructive comments on an earlier version of this chapter. Lastly, I express my sincere appreciation and condolences to Jiro Hoshino, a Japanese pioneer in mandrill research who passed away in June 2022. My studies on mandrills in the Moukalaba-Doudou National Park were funded by JSPS KAKENHI (grant numbers JP19107007 and JP12J01884), Kyoto University Global COE Program (A06), and JST/JICA-SATREPS (PROCOBHA).

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Chapter 11

Linking Movement Ecology to Conservation Biology Colin A. Chapman

, Rafael Reyna-Hurtado

, and Mario Melletti

11.1 Movement Ecology and Why It Is Needed for Conservation Movement is an essential component of all life. It determines evolutionary pathways, shapes ecological processes, affects individual fitness, and strongly shapes how populations are affected by anthropogenic perturbations (Nathan, 2008; Nathan et al., 2022). As a result, the field of movement ecology has become central to both ecology and conservation biology. New technological advances [e.g., telemetry systems (Kays et al., 2015), drones (Wich & Koh, 2018; Beaver et al., 2020; He et al., 2020; Corcoran et al., 2021), AI to identify species and individuals from camera trap photos (Guo et al., 2020; Tuia et al., 2022)] have greatly enhanced the quality of the movement data that can be obtained and changed the very questions that could be asked. As a result, movement ecology’s role in conservation should increase.

C. A. Chapman (*) Biology Department, Vancouver Island University, Nanaimo, BC, Canada Wilson Center, Washington, DC, USA School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China R. Reyna-Hurtado Department of Biodiversity Conservation, El Colegio de la Frontera Sur, Campeche, Mexico IUCN Wild Pigs Specialist Group, Campeche, Mexico IUCN Peccary Specialist Group, Campeche, Mexico e-mail: [email protected] M. Melletti Wild Pig Specialist Group and African Buffalo Initiative Group IUCN SSC, Rome, Italy © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7_11

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Another reason movement ecology’s research is needed concerns the current state of the global environment. Globally, ~60 million ha of tropical primary forest were lost from 2002 to 2019 (Weisse & Gladman, 2020), and 21% of this loss occurred in Africa (Estrada et al., 2020; Chapman & Peres, 2021). The forests of the Congo Basin cover 200 million ha, but it lost 16 million ha between 2000 and 2014, mostly to small scale agriculture (Reiche et  al., 2021). As road infrastructure improves in the Democratic Republic of Congo and the Republic of Congo, forest loss in Africa is expected to increase dramatically. A very important factor that will drive future forest loss is Africa’s growing human population. The continent’s population is currently 1.4 billion and it is projected to quadruple by 2100 (UN, 2015). This growing population will need energy and wood supplies 80% or more of domestic energy needs across Africa (Chapman et  al., 2022). In the Democratic Republic of Congo (DRC), fuelwood contributes 95% of energy needs, which amounts to an estimated 70 million m3 of wood each year (Mayaux et al., 2013). The effect of climate change on Africa’s forests remains to be determined. However, climate change projections for Africa’s rainforest regions indicate a 3–4  °C increase in temperature by 2100 (Zelazowski et  al., 2011; Malhi et  al., 2013), approximately double the estimated mean surface temperature increase for the Earth in general (IPCC, 2021). These projections are supported by meteorological data (Bush et al., 2020). For example, in the highlands of Uganda, the maximum monthly temperature has risen by 1.05  °C over the last 50  years (Chapman et al., 2021).

11.2 What Movement Ecology Research Can Provide Knowing what shapes animal movements can provide critical insights to manage endangered species and population. If animals repeatedly return to locations with specific resources, it suggests these resources are critical to manage, if conservationists want to facilitate the maintenance or recovery of a population. For example, movement studies have shown that water sources are a key factor driving their distribution and abundance of savanna elephants (Loxodonta africana) (Chamaille-­ Jammes et  al., 2007) and savanna buffalo (Cornelis et  al., 2014). Such water resources are important for some elephant populations living in forest (L. africana, L. cyclotis, and their hybrids) (Reyna-Hurtado et  al., this volume-b), but not for other forest elephants (L. cyclotis) and for forest buffalo (Syncerus c. nanus) whose movement, instead, tends to be more influenced by food availability and fruiting seasons of trees (Blake & Inkamba-Nkulu, 2004; Beirne et al., 2021; Melletti et al., 2007; Korte, 2008; Blake & Maisels, this volume). This suggests that in some situations, constructed water sources would be a useful conservation tool (Loarie et al., 2009). Forest buffalo (Syncerus caffer nanus) rely on forest clearings and riverine forests and are absent or occur at low densities in dense rainforest far from clearings

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(Korte et al., this volume). Thus, to manage forest buffalo populations, protecting, maintaining, or even creating forest clearings may promote population recovery or establishment. Important food and mineral resources can be inferred from animal movement data (Reyna-Hurtado et al., this volume-a; Thurau et al., this volume). Using this information in conservation efforts may be particularly important in Africa for a series of reasons. First, the United Nations has coined this the Decade of Restoration; thus, funding for restoration efforts has greatly increased. Second, Africa will be a prime target for these restoration efforts because 20% of Africa’s land surface (6.6 million km2) is degraded, an area twice the size of India (Archer et  al., 2018). Finally, many of Africa’s national parks had large sections logged or converted to agriculture, often during periods of political unrest. As the areas are still legally national parks and in several instances the people who converted the forest to agriculture have been resettled, these are prime areas for reforestation efforts. For example, the Kibale National Park, Uganda, is 796 km2, but over 200 km2 of forest was deforested for agriculture or logged prior to the area becoming a national park in 1993 (Chapman & Lambert, 2000; Omeja et  al., 2012). As the park is now well protected and people no longer reside within its boundaries, restoration efforts are major components of the Uganda Wildlife Authority (UWA) efforts to protect endangered species. In fact, the FACE Foundation started a carbon offset reforestation program in Kibale in collaboration with the UWA in 1995. The regenerating area was protected from fire and planted with native seedlings (Omeja et al., 2016; Wheeler et al., 2016). To date, 1.76 million seedlings have been planted, and 6500 ha of former agricultural land has been reforested. This has been a huge success for wildlife, and the abundance of many of the primate species (Chapman et al., 2018) and ungulates (Hou et al., 2021) in the reforested area are comparable to the adjacent old-growth forest. Movement data that determines the home range size of a species is important in conservation planning as this tells managers the size of a protected area needed to sustain a viable population. Forest buffalo have a surprisingly small home range, when compared to their savanna counterparts (Korte et al., this volume). Red colobus monkeys (Piliocolobus tephrosceles) that have growing population sizes (Chapman et  al., 2010; Sarkar et  al., 2021) have home ranges that appear to be remarkably stable over many years (Kalbitzer et  al., this volume), suggesting if habits can be protected from disturbance, populations will prosper. This of course assumes that hunting is not occurring at an unsustainable level and influencing movement patterns, which the study by Blake and Maisels (this volume) demonstrates it can. Reyna-Hurtado et al. (this volume-a) used evaluations of home range and group sizes to estimate that the population size of giant forest hogs (Hylochoerus meinertzhageni) in Kibale could be as high as 300 individuals. However, given the high rate of poaching in some areas, they suggested a conservative estimate of approximately 150 animals should be used in conservation planning.

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11.3 Next Steps It is, however, extremely difficult to conduct animal movement studies in tropical forests. Telemetry offers great opportunities but capturing animals can be extremely difficult. For example, Reyna-Hurtado et al. (this volume-a) spent between 1 and 2 months per year over 5 different years trying to capture and deploy a telemetry system on giant forest hogs and failed. Trying to dart and put a telemetry system on elephants within the forest (i.e., in populations that do not come to a bai or use open grassland areas) would at best be extremely dangerous. When one encounters elephants in the forest, the wise thing to do is retreat as startled elephants can charge. It is quite easy to get within 15 m of an elephant in the forest and not detect the animal. Following the darted elephant would be difficult, and one would never know what the other elephants in the group would do. Similarly, having sufficient camera traps in a forest to monitor wide ranging forest mammals is both logistically and financially challenging. Also, camera traps works better when animals can be individually identified, which allows individuals to be monitored over large spatial and temporal scales. Individually recognizable tapir (Tapirus bairdii) were followed for 4 years using a grid of camera traps in Mexico (Reyna-Hurtado et al., 2016). New technologies are however providing exciting new opportunities to contribute to the field of movement ecology and conservation. For example, thermal imaging techniques were used to estimate the group size of primates in degraded riparian forest of Sabah, and this technique detected almost twice the number of animals than counting by eye (Jumail et al., 2021). The use of drones with appropriate sensors offers exciting new opportunities to census forest dwelling mammals and examine movement patterns (Wich & Koh, 2018). Drones have been used to count orangutan nests (Wich et  al., 2015) and estimate spider monkey subgroup size (Spaan et al., 2019). Exciting new genetic techniques offer great potential for conservation. By simply sampling the DNA in air from the Copenhagen Zoo, researchers were able to detect 49 vertebrate species through metabarcoding of airborne eDNA (Lynggaard et al., 2021). If this technique can be refined to work in natural forest to detect what species are in an area of a specific size, or even track individuals, this could revolutionize movement ecology in the difficult to study forest systems. The use of airborne eDNA techniques is currently being tested in the Kibale National Park, Uganda. There is a clear need for developing better conservation strategies, particularly in Africa which will face great challenges in the coming decades. The field of movement ecology provides information that can be extremely useful for conservation. New technologies are offering exciting new avenues to gather data on animal movement. As a result, now is the time to put increased efforts into research that can lead to conservation to ensure the future holds increasing hope for African forest mammals. Acknowledgments  We would like to thank many of the staff of the Uganda Wildlife Authority for countless constructive discussions that we have had over the year about conservation strategies; this has added useful perspectives to what we have presented. Colin Chapman was supported by

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the Wilson Foundation while writing this chapter. Rafael Reyna-Hurtado would like to thank El Colegio de la Frontera Sur for help while writing this chapter and to the National Geographic Committee of Research and Exploration and to the Fondation Segré for funding fieldwork in movement ecology. The Anthropology Department of McGill University (Montreal, Quebec, Canada) provided field and laboratory equipment and support for the field season.

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Index

A Activity patterns, 116 African primates, 125, 172 Animal movements, 2–4, 6, 10, 61, 83, 91–93, 143, 146, 154, 188–190 B Brownian movements, 124 C Congo Basin, 4, 21, 31, 40, 45, 46, 81, 91, 188 Conservation, 2, 4–6, 10, 21, 23, 31, 32, 42, 44, 45, 47, 49, 61, 62, 74, 82–84, 88–93, 102, 136, 144–146, 187–190 Conservation unit, 89 D Daily activity patterns, 15 E Ecologies, 2–5, 10, 12, 23, 28, 29, 33, 40, 43, 47, 61, 74, 80, 81, 83, 88, 89, 93, 102, 125, 139, 143, 146, 154, 163, 165, 172–182, 187–190 Ecosystem engineers, 28, 60 Evolutionary history, 81, 84, 88, 90, 154, 163

F Family groups, 3, 4, 11, 66, 67, 71 Fear of disease, 134, 135, 141, 142, 145, 146 Folivores, 124 Forest buffalo, 4, 81–93, 188, 189 Frugivores, 36, 43, 116, 123, 124, 162, 163 G Gorilla gorilla (G. gorilla), 2, 4, 101, 116, 141, 153, 155 Group crowdedness, 5, 177, 182 Group sizes, 3–5, 12, 15, 19, 21, 23, 37, 61, 62, 66, 68, 69, 87, 88, 93, 110, 117, 138, 173, 174, 176, 177, 179, 189, 190 H Habitat quality, 29 Habitat use, 13, 65, 86 Home ranges, 3–5, 12–20, 22, 29–32, 36, 39–41, 44–48, 84–86, 90, 93, 100–102, 104–110, 117, 118, 123, 142–144, 157, 159–161, 175, 176, 178, 179, 181, 189 Host movements, 4, 134, 135, 142, 145 Hylochoerus meinertzhageni, 3, 11, 16–18, 22, 63, 142, 189 K Kernel density estimator (KDE), 14, 15, 32

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Reyna-Hurtado et al. (eds.), Movement Ecology of Afrotropical Forest Mammals, https://doi.org/10.1007/978-3-031-27030-7

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196 Kibale, 2–4, 10–23, 60–74, 92, 101–103, 107, 108, 110, 121, 124, 137, 142, 145, 189, 190 L Learning to avoid pathogens, 136 Long distance call, 5, 178, 181 Loxodonta africana, 11, 60, 141, 188 Loxodonta cyclotis (L. cyclotis), 2, 3, 11, 60, 188 M Male influxes, 175, 180–182 Mandrillus sphinx, 5, 134, 172, 173 Minimum convex polygon (MCP), 15, 30–32, 40, 41 Molecular tools, 84 Movements, 2–5, 10, 19, 21, 23, 28–33, 35–41, 43–45, 47, 48, 61, 62, 64, 74, 81, 83–93, 100–102, 104, 110, 116–119, 121–125, 134–136, 140, 142–144, 146, 153, 154, 158–159, 163–165, 172–182, 187–190 N Nutrition, 125, 157 O Occupancy models, 67 Optimal foraging, 158

Index P Pathogen avoidance behavior, 141 Pathogen transmission, 136, 139, 140, 142 Plant secondary metabolites, 118–121, 125 S Savanna buffalo, 4, 81–83, 85–88, 90, 92, 93, 188 Seasonality, 119, 153–157, 164 Sensory ecology Social groups, 62, 100–102, 110, 139, 175, 176, 182 Social organization, 5, 37, 38, 172, 182 Space use, 30–32, 101, 153, 154, 156, 164 Spatial distributions, 44, 86, 88, 101, 122, 125 Spatial memory, 33, 124, 153, 157–159, 164 T Temporal memory, 123, 153 Territory quality, 102, 108, 110 Tropical forests, 1–6, 11, 29, 35, 36, 43, 44, 46, 47, 90, 116, 145, 152–154, 164, 165, 190 U Uganda, 2–4, 6, 11–14, 16–18, 20–23, 60–65, 67–72, 92, 101–103, 117, 118, 121, 122, 124, 137, 142, 145, 188–190 W Wildlife corridors, 4, 10, 91–93