SOCIAL AND ECOLOGICAL INFLUENCES ON  
SPOTTED HYENA (CROCUTA CROCUTA) FORAGING ECOLOGY 

By 

Julie C. Jarvey 

A DISSERTATION 

Submitted to 
Michigan State University 
in partial fulfillment of the requirements 
for the degree of 

Integrative Biology – Doctor of Philosophy 
Ecology, Evolutionary Biology and Behavior – Dual Major 

2024 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

In my dissertation, I use agent-based modeling and observational long-term behavioral data 

to elucidate how social and ecological factors affect foraging behavior of spotted hyenas, a highly 

social carnivore, in the Maasai Mara National Reserve, Kenya. 

Group hunting is common among social carnivores, but in societies structured by linear 

dominance hierarchy, access to rewards from hunting are not evenly distributed among group 

members. In my first chapter, I explore how the inequality of resource distribution due to social 

rank influences the evolution of cooperative hunting in a digital evolution experimental world. The 

system reflects spotted hyena society, where an individuals’ dominance rank affects their priority of 

access to critical resources with dominant individuals receiving greater benefits. I found that group 

hunting evolved at different rates depending on how equitably rewards were shared and high-

ranking agents that benefited the most from group hunting were more likely to group hunt. These 

results provide insights into mechanisms that may promote cooperation in animal societies 

structured by dominance hierarchies. 

In my second chapter, I use long-term behavioral data to investigate hyena hunting patterns 

and how attributes of both hunters and prey influence hunting behavior. Dominance rank, age, and 

sex strongly influence hyena behavior and access to resources. Additionally, prey attributes can 

also influence hunt success for predators. I examine how attributes of spotted hyenas and their 

prey affect hunting success and hunting group sizes and compare these results to previous work in 

this same region from 1988–1995, representing a period with lower human activity, a smaller 

hyena population, and more abundant prey. Prey species and prey age affected hunt success, as well 

as the age of the hunter when hunting alone. Hyenas hunted prey that were harder to capture in 

groups more often, and mean hunting group size varied by social rank and age. Group hunting was 

more common during this study period, possibly reflecting the increasing hyena population and 

decreases in easier to capture prey. These results demonstrate that attributes of predators and 

 
their prey influence hunt success and hunt grouping behavior in spotted hyenas and are important 

to consider in predation studies. 

In my third chapter, I investigate dietary flexibility in hyenas by comparing feeding 

behavior (1) contemporaneously among hyenas inhabiting areas with different ecological 

conditions, and (2) longitudinally across the same population as ecological conditions change in a 

region that has experienced decreasing wild prey and increasing livestock presence. I found that in 

both sites, hyenas primarily consumed the prey type that is most available. In my longitudinal 

comparison, I found that hyenas at one site ate more livestock, and became less reliant on resident 

prey, as livestock increased there over time. Long-term changes in hyena diets reflected declines in 

wild prey abundance and increases in livestock presence. This work demonstrates the flexibility of 

hyena foraging across seasons and in the face of long-term changes in prey availability. 

By using agent-based simulations and empirical data, this dissertation deepens our 

understanding of the mechanisms influencing hunting decisions, as well as the complex social and 

ecological aspects that shape foraging behavior in spotted hyenas across various temporal scales. I 

discuss how observed changes in behavior could impact broader ecological interactions, ecosystem 

processes, and community ecology. 

 
To Maisie, thank you for being my best teacher and putting everything into perspective.  

May you grow up to be more decisive than your mother. 

And to those who have made it here, I tip my hat to you. 

iv 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ACKNOWLEDGEMENTS 

I am extremely fortunate and grateful for the immense amount of support I received from 

my community throughout my Ph.D. program. This dissertation would not have been possible 

without their love and support.  

Thank you to my advisor, Dr. Kay Holekamp, for her guidance, support of my intellectual 

pursuits, and contributing to my professional development. I am so grateful for the opportunity to 

learn from her and be a part of the Mara Hyena Project. I am also thankful for the guidance and 

support from my committee members, Dr. Thomas Getty, Dr. Elizabeth Tinsley Johnson, Dr. Gary 

Roloff, and my past committee member, Dr. Margaret Ostrom. I am also grateful for the teaching 

and mentorship from Dr. Tracy Montgomery, Dr. Eila Roberts, and Dr. Terri McElhinny. Thank you 

to my mentors from the University of Michigan, Dr. Bobbi Low and Dr. Jacinta Beehner, and the U.S. 

Fish and Wildlife Service, Heidi Keuler, Jeena Koenig, Louise Mauldin, who have supported and 

encouraged me throughout the years.  

I am grateful for the encouragement, support, and thoughtful feedback from the members of 

the Holekamp lab including Julia Greenberg, Lily Johnson-Ulrich, Zachary Laubach, Kenna Lehmann, 

Kevin McCormick, Tracy Montgomery, Connie Rojas, Maggie Sawdy, Eli Strauss, Julie Turner, Dee 

White, Jana Woerner, and, especially Olivia Spagnuolo. I would like to especially thank our lab 

managers, Hadley Couraud, Erin Person, and Sabrina Salome. The collaborative and positive 

environment created by this group has shaped me as a scientist and collaborator and enriched my 

time at MSU. 

I am thankful to my collaborators for their expertise, partnership, and friendship. From 

collecting data, designing protocols and digital evolution experiments, to data analysis and 

manuscript writing, my collaborators have helped me learn and grow in ways I could not have 

previously imagined. 

v 

 
This dissertation would not be possible without the decades of work which began in 1988 

by Kay Holekamp and Laura Smale, and continued through the efforts of numerous research 

assistants, graduate students, and post docs. I am especially thankful for the hard work, dedication, 

and thoughtfulness of my undergraduate research assistants Annica Brocker, Grace Werner, 

Campbell Melton, Allison Strassburg, Martha Dawson, Kaelynn Seestadt, Mitchell McBride, and 

Jamie Raupp.  

I am profoundly grateful to my Kenyan colleagues for all the many ways they contribute to 

the research project and for welcoming me into Fisi Camp. This includes Jackson Kamaamia, Joseph 

Kamaamia, Samuel Kamaamia, Steven Karkar, George Kilenyet, Wilson Kilong, Philimon Naiguran, 

Moses Naiguran, Lesinko Naurori, Malit Ole Pion, and Dickson Pion. I am especially thankful to Malit 

for his mentorship and patience with me as he taught me about hyena behavior and data collection, 

and to Joseph for his leadership, wisdom, and making Fisi Camp feel like home. 

The students, staff, post docs, and faculty of the Department of Integrative Biology and the 

Ecology, Evolution, and Behavior Program have contributed immensely to my professional 

development, helped me navigate my degree program, and I am so thankful for the support and 

mentorship of this amazing community. My friends near and far have all been there for me in so 

many ways and sustained me throughout my time at MSU.  

I am so grateful to the animals who have been with me through this journey. Lomi and 

Spynner have patiently, and sometimes impatiently, been there with me, cuddled up by my side as I 

spent hours analyzing data and writing. This dissertation would not be possible without the hyenas. 

I am thankful to them for teaching me so much and giving me a window into their lives. 

Thank you to my family, especially my parents, Gerald and Danise Jarvey, who have been a 

constant source of support and encouragement from the very beginning. Al and Mary Smith have 

also been extremely gracious and supportive of me. I am exceptionally grateful for AJ Smith and his 

unwavering support, positivity, patience, and confidence in me. I could not have finished this 

vi 

 
dissertation without his partnership and support. Thank you to Maisie for teaching me to use my 

time wisely, take care of myself, take time to play, and treasure every moment. 

Thank you to the National Science Foundation, International Society for Behavioral Ecology, 

and MSU Graduate School, College of Natural Science, Department of Integrative Biology, and the 

Ecology, Evolution, and Behavior Program for funding various stages of my research. I am so 

grateful to the Office of the President of Kenya, National Commission for Science, Technology and 

Innovation, Kenya Wildlife Service, Narok County Government, and the Mara Conservancy for 

allowing me to conduct research on spotted hyenas in the Maasai Mara National Reserve.  

vii 

 
TABLE OF CONTENTS 

INTRODUCTION ...................................................................................................................................................... 1 

CHAPTER 1: THE EFFECTS OF SOCIAL RANK AND PAYOFF STRUCTURE ON THE EVOLUTION 
OF GROUP HUNTING ............................................................................................................................................. 7 

CHAPTER 2: THE INFLUENCE OF INDIVIDUAL ATTRIBUTES ON THE HUNTING BEHAVIOR OF 
SPOTTED HYENAS .............................................................................................................................................. 25 

CHAPTER 3: HYENA FEEDING BEHAVIOR ACROSS VARYING ECOLOGICAL CONDITIONS ......... 59 

BIBLIOGRAPHY .................................................................................................................................................... 89 

viii 

 
 
 
 
 
 
 
INTRODUCTION 

Feeding is foundational to animal survival, exerting one of the strongest forces on individual 

fitness. For large carnivores that must hunt their food, feeding involves many challenges. 

Successfully finding, capturing, and consuming prey incurs many costs, such as search time and 

energy expenditure, the risk of injury or loss from kleptoparasites, and requires skills that may take 

time to learn (Creel & Creel, 1995; Kruuk, 1972; Ritwika et al., 2023; Tucker et al., 2016). 

Individuals must also navigate environmental variation which causes changes in prey availability 

and demography across short and long temporal scales and thus must exhibit flexibility to acquire 

food (Ho  ner et al., 2002; Navarro et al., 2017; Vettorazzi et al., 2022). The hunting decisions 

individuals must make, including prey selection and whether to hunt cooperatively, affect hunting 

success and ultimately fitness (Pettorelli et al., 2015). 

Social carnivores must navigate additional challenges that come with group living, such as 

increased competition with conspecifics (Jordan et al., 2022) and increased detection by additional 

group members (Smith et al., 2008), scavengers, and kleptoparasites (Vucetich et al., 2004, Smith et 

al., 2008). There are also benefits to group living including increased hunting success, ability to hunt 

larger prey, and improved defense against kleptoparasites (Carbone et al., 2005; Cooper, 1991; 

Pe riquet et al., 2015). However, the costs and benefits of sociality are not felt equally by group 

members when societies are structured by linear dominance hierarchies. Social rank plays an 

important role in shaping behavior (Boydston et al., 2003; Hofer & East, 1993; Smith et al., 2008), 

and can have profound impacts on health, reproductive success, and longevity (Creel et al., 1997; 

Holekamp et al., 1996; Sapolsky, 2005; Tung et al., 2016). 

In this dissertation, I investigate the foraging ecology of a gregarious carnivore, the spotted 

hyena (Crocuta crocuta), a highly flexible forager that lives in complex fission-fusion societies 

structured by linear dominance hierarchies. Spotted hyenas are effective hunters, and readily shift 

their diet to prey that are most abundant (Cooper, 1990; Cooper et al., 1999; Holekamp et al., 

1 

 
 
1997b; Kruuk, 1972). They inhabit a wide range of habitats, from arid landscapes, montane forests, 

and savanna grasslands to urban areas with foraging behavior ranging from primarily hunting 

native ungulates to scavenging anthropogenic food sources in urban areas (Abay et al., 2011; Yirga 

et al., 2013; Yirga & Bauer, 2010). They compete with other sympatric large carnivores for food, 

primarily lions (Lehmann and Montgomery et al. 2017). Additionally, they live in societies with 

linear dominance hierarchies (Frank 1986). Dominance confers many benefits to high-ranking 

hyenas, including access to critical resources such as carcasses (Tilson & Hamilton 1984). Low-

ranking individuals have lower priority of access to these resources, which leads to divergent 

behavior, including altered space use patterns (Boydston et al., 2003; Ho  ner et al., 2005), hunting 

rates (Holekamp et al., 1997b), and association patterns with groupmates (Smith et al., 2007).   

I use agent-based simulations and empirical data from long-term behavioral observations to 

investigate how hunting and feeding behavior varied across individual attributes and ecological 

conditions. Specifically, I investigate (1) how social rank and feeding tolerance influence which 

hunting strategies evolve in a digital system, (2) how hunter and prey attributes influence hunt 

success and hunting group size, and (3) how feeding behavior varies across ecological conditions in 

space and time. 

In Chapter 1, I use agent-based modeling to explore how inequality of resource distribution 

due to social rank influences the evolution of cooperative hunting in a digital evolution 

experimental world. I designed a system that reflects the dynamics of spotted hyena societies, in 

which an individual’s social rank affects their priority of access to critical resources, with dominant 

individuals receiving greater benefits. I investigate whether cooperative hunting evolves under 

varying levels of despotism among agents of different ranks and whether social rank influences 

individual agents’ probabilities of hunting in groups. At the population level, I found that group 

hunting rates increased (1) as the rewards from hunting were shared more equally among hunters, 

and (2) as the relative payoff from group hunting compared to solo hunting increased. At the agent 

2 

 
 
level, higher-ranking agents were more likely to group hunt than lower-ranking agents when 

rewards were shared unequally. These results provide insights into mechanisms that may promote 

cooperative behaviors among individuals with varying social ranks in animal societies structured by 

dominance hierarchies. 

In Chapter 2, I use long-term behavioral data to investigate hyena hunting patterns to 

investigate how individual attributes of both hunter and prey influence hunting behavior. Hunting 

behavior represents a critical component of predator-prey dynamics and attributes of both 

predator and prey affect prey selection and hunting success and are important to consider in 

predation studies (Pettorelli et al., 2015). Prey selection and hunting success rates can vary based 

on individual attributes such as age, sex, and social rank of predators (Cooper et al., 2007; Litvaitis 

et al., 1986; Ross et al., 1997; Saulitis et al., 2000) and of their prey (Fitzgibbon, 1990b; Hilborn et 

al., 2012; Holekamp et al., 1997b; Ho  ner et al., 2002; Karanth & Sunquist, 1995; Stander & Albon, 

1993). Among social carnivores, group hunting is common and individual attributes such as 

dominance rank, age, and sex can also strongly influence access to shared rewards from hunting. 

Thus, the differential benefits from group hunting may lead to divergent hunting behavior among 

individuals (as I found in agent-based simulations in Chapter 1). I investigate how individual 

attributes of both hunter and prey influence hunting behavior in the spotted hyena, a social 

carnivore that lives in fission-fusion societies that hunts both alone and with groupmates. Since 

spotted hyena societies have linear dominance hierarchy, where priority of access is determined by 

social rank, individual benefits from group hunting vary based on rank.  

I investigate how prey species, prey age class, and hunting strategy influence hunting 

success of hyenas in the Maasai Mara National Reserve, Kenya. I also ask how prey selection 

influences hunting group size and how individual attributes of hunters impact solo hunt success 

and group hunting behavior. I then compare these results (1996-2018) to previous research on 

hyenas in this same region from 1988-1995 (Holekamp et al., 1997b), a period where there was less 

3 

 
 
human activity, a smaller hyena clan size, and higher prey abundance. I found that hunting success 

varied based on the species and age class of prey and age class of the hunter. Hunting strategies 

varied by prey species; hyenas tended to hunt in larger groups for species more difficult to capture. 

Hunting behavior also varied among individual attributes. Low-ranking hyenas tended to hunt in 

smaller groups than high-ranking individuals. Juveniles tended to hunt in larger groups than adults. 

The differences in these strategies reflect the tradeoffs individuals experience – low-ranking 

individuals experience higher competition in groups and juveniles have lower odds of hunting 

success when they hunt alone. I compare these results to previous research on hyenas in this same 

region from 1988-1995 (Holekamp et al., 1997b). Hunt success based on species and grouping 

patterns by rank and age were similar between study periods, but group hunting occurred more 

frequently during the later study period (1996-2018). The shift towards increased group hunting 

may relate to changing ecological conditions that occurred in the region over the past 3 decades. 

Future work in other systems investigating how predator and prey attributes affect hunting 

behavior and how these dynamics may change over time, due to fluctuations in both predator and 

prey populations and in ecological factors, will increase our understanding of the relationship 

between predator-prey dynamics. 

In Chapter 3, I investigate how ecological conditions influence spotted hyena diets across 

space and time. Large carnivores are critically important in maintaining healthy ecosystems but are 

also highly vulnerable to anthropogenic change and face many threats globally (Ripple et al., 2014). 

Global ecological changes such as habitat loss, declining prey populations, conflict with humans, and 

climate change are all occurring at faster rates than extant species have previously experienced. 

Large carnivores are particularly vulnerable to anthropogenic change and come into frequent 

conflict with humans when livestock overlap with carnivore habitat (Inskip & Zimmermann, 2009; 

Treves & Karanth, 2003; Wolf & Ripple, 2017; Woodroffe & Ginsberg, 1998). Species exhibiting 

flexibility in behaviors (Wong & Candolin, 2015) including space use, diel activity, and diet (Farias & 

4 

 
 
Kittlein, 2008; Gaynor et al., 2018; Oriol-Cotterill et al., 2015) often cope best with rapid 

environmental changes (Smith et al., 2012; Suraci et al., 2019). 

I use long-term observational behavioral data to compare feeding observations (1) among 

hyenas inhabiting two sites with different ecological conditions, and (2) in the same population of 

hyenas longitudinally across 31 years during a period of rapid ecological change. I compare the 

feeding behavior of hyenas inhabiting two areas of the Maasai Mara with different management 

practices: one where livestock grazing is present (Talek Region) and one without livestock grazing 

(Mara Conservancy). Next, I examine long-term patterns of hyena diets across 31 years in the Talek 

Region of the Maasai Mara. This region has undergone substantial changes in the landscape 

including large fluctuations in livestock grazing intensity, varying weather patterns, and changes in 

native herbivore abundance. I summarize prey availability, livestock presence, temperature, and 

rainfall to understand how the two sites compare and to look at the broader ecological changes 

through time. We then quantify diet using observational records of feeding behavior to examine 

how hyena diets compare between the two sites, and across time in the Talek Region. 

Hyenas in both populations responded to seasonal changes in prey availability, consuming 

primarily the prey type that is most available. Hyenas in the Talek Region consumed domestic 

livestock and less diverse diets of resident prey species than in the Mara Conservancy. 

Longitudinally, in the Talek Region over 31 years, hyenas were seen feeding on resident prey less 

often and feeding on livestock more often. These diet changes reflected prey availability. Resident 

prey density declined through time and livestock counts increased.  Our results complement 

previous work documenting the decline of herbivore populations (Green, 2015; Ogutu et al., 2005, 

2009, 2011; Ottichilo et al., 2000), and adds to the body of evidence that hyenas are flexible foragers 

and switch to consuming prey that is most available over seasons and years. This flexibility likely 

facilitates hyenas’ ability to persist in landscapes with high human use. We discuss the implications 

of dietary flexibility in large carnivores and future avenues for research.  

5 

 
 
This dissertation broadens our understanding of how social carnivores navigate complex 

social and ecological factors in their environment across various temporal scales. By using both 

simulation-based and empirical data, this work adds to our understanding of the tradeoffs faced by 

individuals across different attributes that impact their ability to acquire and access food. Variation 

among individuals and populations through time could impact broader ecological interactions and 

ecosystem processes.  

Writing style 

My research is based on the contributions and collaborations with other researchers. 

Chapter 1 is the product of a collaboration between researchers who are co-authors on the 

published manuscript. Chapters 2 and 3 arise from long-term data collected from the Mara Hyena 

Project, founded by Kay Holekamp and Laura Smale in 1988. These datasets were collected over 

decades by numerous researchers. Therefore, I use first-person plural throughout this dissertation.  

6 

 
 
THE EFFECTS OF SOCIAL RANK AND PAYOFF STRUCTURE ON THE EVOLUTION OF GROUP 

CHAPTER 1: 

ABSTRACT 

HUNTING 

Group hunting is common among social carnivores, and mechanisms that promote this 

behavior are a central topic in evolutionary biology. Increased prey capture success and decreased 

losses from competitors are often invoked as factors promoting group hunting. However, many 

animal societies have linear dominance hierarchies where access to critical resources is determined 

by social rank, and group-hunting rewards are shared unequally. Despite this inequality, animals in 

such societies cooperate to hunt and defend resources. Game theoretic models predict that rank 

and relative rewards from group hunting vs. solitary hunting affect which hunting strategies will 

evolve. These predictions are partially supported by empirical work, but data needed to evaluate 

these predictions are difficult to obtain in natural systems. We use digital evolution to assess how 

social rank and tolerance by dominants of subordinates feeding while sharing spoils from group 

hunting influence which hunting strategies evolve in digital organisms. We created a computer-

simulated world to reflect social and hunting dynamics of spotted hyenas (Crocuta crocuta). We 

found that group hunting increased as tolerance increased and as the relative payoff from group 

hunting increased. Also, top-ranking agents were more likely to group hunt than lower-ranking 

agents under despotic sharing conditions. These results provide insights into mechanisms that may 

promote cooperation in animal societies structured by dominance hierarchies.  

This work has been published as: Jarvey, J.C., Aminpour, P., Bohm, C., 2022. The effects of 

social rank and payoff structure on the evolution of group hunting. PLoS ONE. 17(6): e0269522. 

https://doi.org/10.1371/journal.pone.0269522 

7 

 
 
 
INTRODUCTION  

Understanding mechanisms that lead to evolution and maintenance of group hunting in 

animal societies is a central topic in behavioral and evolutionary ecology and the subject of much 

theoretical and empirical work (e.g., Carbone et al., 2005; Mesterton-Gibbons & Dugatkin, 1992; 

Packer, 1988; Packer & Ruttan, 1988; Stander, 1992; Tennie et al., 2009; Vucetich et al., 2004; Watts 

& Mitani, 2002). Group hunting occurs in diverse taxa including birds (e.g., Bednarz, 1988; Hector, 

1986; Yosef & Yosef, 2010), mammals (e.g., Benoit-Bird & Au, 2009; Busse, 1978; Pitman & Durban, 

2012), fish (e.g., Schmitt & Strand, 1982; Strübin et al., 2011), and arachnids (e.g., Rypstra, 1985; 

Tizo-Pedroso & Del-Claro, 2007). It is especially common among social carnivores (e.g., Bailey et al., 

2013; Bowen, 1981; Fanshawe & Fitzgibbon, 1993; Kruuk, 1972; Stander, 1992). Group hunting can 

lead to improved individual energy return through a variety of mechanisms, including increased 

prey capture success (Creel & Creel, 1995; Fanshawe & Fitzgibbon, 1993; Holekamp et al., 1997b; 

Kruuk, 1972; Stander & Albon, 1993), the ability to capture larger prey items when hunting in 

groups (Holekamp et al., 1997b; Kruuk, 1972), and higher success at defending kills against 

kleptoparasites (Caraco & Wolf, 1975; Carbone et al., 2005; Cooper, 1991; Creel & Creel, 1995; 

Kruuk, 1972; Packer, 1986; Périquet et al., 2015; Stander & Albon, 1993; Vucetich et al., 2004). 

Group hunting has many benefits compared to solitary hunting among social carnivores. 

However, this does not mean that each individual hunter benefits equally. Many carnivore societies 

are structured by dominance hierarchies and rewards from group hunting are unequally 

distributed based on dominance rank (Atwood & Gese, 2008; Frank, 1986; Gese et al., 1996a; 

Kruuk, 1972; Smith et al., 2008; Tilson & Hamilton, 1984). This occurs in spotted hyenas (Crocuta 

crocuta) in which, an individuals’ social rank falls within a strict linear dominance hierarchy that 

determines its priority of access to critical resources and therefore confers significant fitness 

advantages to high-ranking individuals (Frank, 1986; Holekamp et al., 1996; Holekamp & Strauss, 

2020; Kruuk, 1972; Smith et al., 2007; Tilson & Hamilton, 1984). Furthermore, like most large 

8 

 
 
gregarious carnivores, spotted hyenas live in fission-fusion societies, such that individuals spend 

most of their time alone or in small subgroups that change multiple times per day (Smith et al., 

2008). Therefore, individuals have opportunities to hunt both solitarily and in groups as demanded 

by ecological and social conditions and thus are not obligate group hunters. Spotted hyenas 

typically hunt alone for most prey items, and although mean hunting group size is 1.5 hyenas 

(Holekamp et al., 1997b), they also commonly hunt in groups to capture larger or more challenging 

prey (Holekamp et al., 1997b; Kruuk, 1972). Because high-ranking individuals can use aggression to 

displace subordinates from kills, rewards are unequally distributed, with high-ranking individuals 

receiving most of the nutritional and energetic rewards from group hunts whereas low-ranking 

individuals receive little reward (Frank, 1986; Kruuk, 1972; Smith et al., 2008). Thus, rewards for 

cooperating are unequally distributed based on dominance rank (Gese et al., 1996a; Smith et al., 

2008). Despite this inequality and these fission-fusion dynamics, individual hyenas, even those with 

low ranks, still hunt in groups. Research on social cognition has revealed that many animals 

respond negatively to inequality in reward distribution (reviewed by Brosnan & de Waal, 2014). 

Why do low-ranking carnivores participate in group hunts if they receive disproportionately small 

shares of the hunting rewards or no reward at all?  

Because spotted hyenas live in fission-fusion societies in which benefits of collective action 

are likely to vary with social rank, these societies offer an interesting system in which to investigate 

the dynamics of group hunting. However, hunting dynamics are difficult to observe in nature and 

spotted hyenas are too long-lived to study evolution in action. Although empirical data from 

observational studies provide important information about the factors influencing group hunting, a 

deeper understanding of the evolution of this phenomenon and individual fitness outcomes is 

difficult to attain by observing animal behaviors in their natural habitat. Thus, theoretical and 

computational approaches may yield novel insights into mechanisms promoting group hunting. 

9 

 
 
Game theory improved our understanding of conditions that facilitate evolution of group 

hunting. Game theoretical models predict that social rank and relative rewards from cooperative vs 

solitary hunting affect whether group hunting is an evolutionary stable strategy (Axelrod & 

Hamilton, 1981; Packer & Ruttan, 1988). Although these models have some support from empirical 

data (reviewed by Packer & Ruttan, 1988), the game theoretical framework cannot account for 

complexities of biological systems such as spatial interactions, stochasticity, mutation rates, and 

evolutionary dynamics (Adami et al., 2016). In contrast, agent-based methods allow for such 

complexities to be explored and can yield insights into evolutionary dynamics that cannot be 

achieved by game theory alone (Adami et al., 2016). Using computational techniques such as agent-

based digital evolution can enhance our understanding of natural systems to help generate and test 

predictions about collective behavior. Although previous digital evolution experiments have 

already enhanced our understanding of factors that affect the evolution of cooperative behavior in 

spotted hyenas (e.g., fluctuating prey availability, communication, and interspecific competitors,  

(Rajagopalan et al., 2011, 2019), variation in social rank has not been considered in silico until now. 

Here we use digital evolution to examine factors influencing the evolution of group hunting 

in a society where access to resources is rank-based and individuals have options of hunting alone 

or in groups. We investigated how social rank and variation, based on differential tolerance among 

high-ranking hyenas for feeding concurrently with lower-ranking individuals, in the equity with 

which hunting rewards are shared among group members affect which strategies evolve in agents 

in a digital system. As also occurs among other animals living in hierarchical societies (Sapolsky & 

Share, 2004), there is a great deal of variation in how tolerant dominant hyenas are with respect to 

allowing subordinates access to critical resources (Smith et al., 2015). Here we asked specifically: 1) 

How does tolerance during the sharing of rewards from cooperative hunting affect the evolution of 

group hunting? 2) Are individuals more likely to group hunt when they are higher- or lower-

ranking than other members of their group? We hypothesized that 1) group hunting would 

10 

 
 
decrease or fail to evolve altogether with decreasing tolerance (i.e., increasing rank influence on 

payoffs) because the per capita payoff from solo hunting would come to outweigh the group-

hunting payoff for a greater proportion of agents within the system as tolerance decreases and, 2) 

higher-ranking individuals would group hunt more often than lower-ranking individuals because 

they should always receive greater per capita rewards from group hunting.  

MATERIALS AND METHODS  

The game environment 

We used a custom-built digital environment created in MABE (Modular Agent Based 

Evolver) (Bohm et al., 2017) designed to reflect the social structure of spotted hyenas. The digital 

environment was comprised of a 60 x 60 toroidal grid where each location in the grid was occupied 

by an agent (3600 agents in total). Agents in the game could not move but could produce offspring 

after accumulating enough resources to reproduce. Each agent in the initial population was 

randomly assigned a unique rank, and thereafter, offspring were assigned the rank immediately 

below that of their parent, reflecting the maternal rank inheritance of spotted hyenas (Engh et al., 

2000). Rank was used to determine resource distribution after successful group hunts (described 

below).  

Hunting games 

We ran experiments for 1,000,000 updates. Before each world update, each agent played in 

5 hunting games in groups with 4 close neighbors. In one game, each agent was the central agent, 

playing in a game with their immediately adjacent agents, in the other four games, agents were 

adjacent to the north, south, east, or west of the central agent (Fig 1.1A). In the hunting games each 

agent made a single choice, either to group hunt for large prey, or to solo hunt for small prey. If it 

chose to solo hunt, the agent got the entire small payoff; if it chose to group hunt, they got a larger 

average per capita payoff, but shared the reward with the other agents that also chose to group 

hunt. Agents that solo hunted had a 50% chance of receiving the solo-hunt payoff. The success rate 

11 

 
 
for a group hunt was also 50% unless only one agent chose to group hunt; in which case the success 

rate was 5%. Agents that group hunted had a chance to receive a share of an accumulated group-

hunt payoff based on the number of hunters who chose to hunt for large prey. Failed hunts (both 

solo and group) resulted in no resource gain. These success rates reflect the possibilities 1) that a 

single individual can choose to hunt a larger prey animal, and 2) that others in the group may not 

join in the hunt, and 3) the odds of succeeding when spotted hyenas attempt to hunt large prey. 

Hyenas have a low probability of capturing large prey, such as zebra, when hunting alone, but odds 

of success increase substantially with a second hunter, although hunting success does not improve 

much with >2 hunters (Holekamp et al., 1997b).  We limited our experiments to a system where 

hunters capture only a single prey item at a time, because we were interested in the binary choice 

between the consistent payoff of a small prey vs. the payoff of a large prey (where the chance of 

success depends on whether others choose to cooperate). Additionally, we assumed that the social 

system of the agents did not evolve due to benefits of group hunting, but rather that group living 

preceded the evolution of group hunting, which is most likely true for spotted hyenas as it is for 

lions, in which group hunting was likely a consequence, but not a cause, of the evolution of sociality 

(Packer, 1986; Packer & Ruttan, 1988). 

Agent hunting decisions were determined by values encoded in their 5-site genome. The 

value at each genome site represented the probability that an agent would group hunt given an 

agents’ relative rank in its hunting group (e.g., if an agent is the third-ranking agent in the hunting 

group, the value at the third genome site represents the probability of group hunting in that 

hunting game). 

Fitness 

An agents’ reproductive success depended on how many resource points it earned from 

hunting. Before each world update, each agent participated in 5 hunting games. The number of 

resource points each agent received was equal to the average number of resource points that agent 

12 

 
 
  
earned from the 5 hunting games. When an agent received enough resource points, it produced an 

offspring at the next update. The reproduction cost was set at 50 points, the solo-hunt payoff was 

set at 5, and the group-hunt payoff was variable. Agents accumulated resource points until they 

achieved enough to reproduce. When they accumulated 50 points, they produced an offspring at the 

next update, after hunting games were resolved. If agents did not receive enough resource points to 

reproduce at that update, they carried over their points from previous updates and continued to 

accumulate points until they could reproduce. When an agent reproduced, it paid the reproduction 

cost and produced an offspring that was placed randomly into one of the 24 neighboring locations 

within a 2-grid cell radius of the parent agent (Fig 1.1B). We choose this design to reflect the fact 

that female hyenas typically remain in their natal groups throughout their lives (Frank, 1986), and 

that they associate most closely with their kin (Holekamp et al., 1997a). The agent at the new 

offspring’s location died and any resources it had collected were lost so that the phenotypes 

reproducing faster would tend to outcompete slower-reproducing phenotypes in the population. 

The offspring inherited a mutated copy of the parent’s genome and the rank directly below the 

parents’ rank, following rules of maternal rank inheritance in spotted hyenas (Engh et al., 2000).  

All agents ranking below the new offspring were reassigned to one rank lower to maintain the 

linear dominance hierarchy. Mutations occurred when new agents were born at a 0.05% 

probability per genome site. If a mutation occurred, mutated sites were set to a new random 

probability (i.e., probability of group hunting, in the range [0-1]). After new agents were born, the 

numeric ranks of all agents in the population were updated to maintain unique rank ordering; that 

is, the entire population was structured by a linear dominance hierarchy where no two agents had 

the same rank. 

Reward structure 

The reward from group hunting was distributed to participating agents based on their 

relative rank among the group hunters. We manipulated two variables that affected the payoff 

13 

 
 
distribution: tolerance (i.e., how evenly the reward was shared among group hunters based on 

rank) and the payoff ratio of solo hunts to group hunts. The payoff for a successful solo hunt was 5 

points for all experiments. We used two group-hunt payoffs to test the effect of the solo-hunt to 

group-hunt payoff ratio on which strategies evolve. The group-hunt payoff conditions were set at 2 

and 1.2 times the size of the solo-hunt payoff (i.e., per capita average = 10 and 6 points, 

respectively). The total group-hunt payoff depended on the total number of group hunters so that 

the average per capita reward remained constant to approximate the relative energy gain 

individuals would accrue from multiple hunting bouts alone or in a group. We tested five different 

“tolerance” values: 1.0, 0.96, 0.88, 0.76, 0.64. Tolerance of 1 meant the payoff was shared equally 

among all agents who group hunted. As tolerance decreased, the payoff became increasingly 

skewed by social rank. For example, if three agents group hunted and tolerance was 0.64, the 

second-ranking agent got 64% of the payoff received by the first-ranking agent, and the third-

ranking agent got 64% of the payoff received by the second-ranking agent. Note that the rank skew 

was determined by the relative ranks of group hunters, such that, if only the first-, third-, and fifth-

ranking agents choose to group hunt, their ranks for sharing payoffs would be 1, 2, and 3 (Fig 1.2). 

We ran simulations for each tolerance factor for the two group-hunt payoff schemes, resulting in 10 

total conditions. We ran 200 replicate simulations for each condition for 1,000,000 updates. At each 

update, we tracked the rate of group hunting based on relative social ranks of agents within each 

hunting group. We sampled each replicate every 1,000 updates and took the average group-hunt 

rate of the last 100,000 updates (n=101 samples). We then took the mean across the 200 replicates 

to calculate the final mean group-hunt rate for each experimental condition. 

All data post-processing, analysis, and plotting were conducting in R (R Core Team, 2021) 

version 4.0.5 and RStudio (RStudio Team, 2021) version 2021.9.1.372 using the packages ‘here’ 

(Mu  ller, 2020) version 1.0.1, ‘tidyr’ (Wickham & Girlich, 2022) version 1.2.0, ‘dplyr’ (Wickham et al., 

14 

 
 
2022) version 1.0.8, ‘ggplot2’ (Wickham, 2016) version 3.3.6, and ‘gridExtra’ (Auguie, 2017) 

version 2.3. 

RESULTS  

Group-hunting rates in our population of agents were strongly influenced by the tolerance 

shown by dominant hyenas (Fig 1.3). When the reward was divided evenly (i.e., tolerance = 1), 

agents group hunted nearly 100% of the time under both payoff scenarios. As the reward from 

group hunting became less evenly distributed (i.e., increasingly skewed by social rank), group-

hunting rates decreased within the population.  

Hunting decisions were also strongly affected by relative social rank. When tolerance was 

low, higher-ranking agents generally group hunted at higher rates than did lower-ranking agents. 

When the group-hunt payoff was twice the magnitude of the solo-hunt payoff, the difference 

between the group-hunting rates of the highest-ranking agents and all other agents increased with 

decreasing tolerance, with the greatest difference at the lowest tolerance level of 0.64 (Fig 1.3A). 

The relative size of the group-hunt payoff compared to the solo-hunt payoff had a 

substantial effect on hunting decisions (Fig 1.3). When the relative payoff from group hunting was 

reduced (i.e., from 2 to 1.2 times the solo-hunt payoff), agents group-hunted at similar rates only 

when the reward was divided evenly. However, at all other tolerance levels, group-hunting rates 

were lower among agents of all relative ranks compared to when the payoff was twice the size of 

solo hunting. At this smaller group-hunt payoff, the variance in group-hunting rates based on 

relative social ranks was also reduced (Fig 1.3B). There were small differences in group-hunting 

rates among agents of different social ranks at the lowest tolerance levels (0.64 and 0.74). The 

differences in group-hunting rates between agents with rank 1 and agents with ranks 2 to 5 were 

larger at moderate tolerance levels (0.88-0.94).  

15 

 
 
 
 
DISCUSSION  

In our agent-based digital evolution system, the evolution of group hunting was strongly 

influenced by tolerance, social rank, and relative per capita reward from group hunting and solo 

hunting. These results both support previous game theoretical predictions and emulate findings 

from empirical studies (Packer & Ruttan, 1988). Our results also reflect tradeoffs faced by social 

carnivores.  

Tolerance and social rank 

Tolerance during distribution of hunting rewards had a strong effect on group-hunting rates 

in our experiments. As tolerance decreased, group hunting decreased, especially among lower-

ranking agents. These results support previous experimental work. In other species, individuals are 

sensitive to inequity in rewards for performing the same task (Brosnan & de Waal, 2014; de Waal & 

Davis, 2003; Wascher & Bugnyar, 2013). Mechanisms that increase tolerance in resource sharing 

and improve benefits for lower-ranking individuals should promote cooperation in societies 

structured by linear dominance hierarchies. Further work investigating variation in social bonds 

among individuals could improve our understanding of cooperation in hierarchical societies. High-

ranking individuals may share resources more equitably in exchange for other benefits. There is 

some evidence of such mechanisms in animal societies. For example, low-ranking spotted hyenas 

gain social and feeding tolerance by associating with high-ranking hyenas (Smith et al., 2007). Male 

chimpanzees share meat with males who are preferred social partners and who provide 

coalitionary support (Mitani & Watts, 2001; Nishida et al., 1992; Samuni et al., 2018). This 

increased feeding tolerance may promote participation in cooperative tasks by low-ranking 

individuals who form stronger bonds with, or provide more social support to, high-ranking 

individuals. Future work investigating how partner choice, effort, and variation in equity influence 

participation in cooperative tasks will further our understanding of the conditions under which 

cooperation evolves (Brosnan & de Waal, 2014). 

16 

 
 
Although group-hunting rates decreased with decreasing tolerance, low-ranking agents still 

group hunted despite the low payoff these agents received. Kin selection is another mechanism that 

might promote cooperation and could potentially explain why low-ranking agents sometimes 

cooperate in highly despotic societies  (Shimoji & Dobata, 2022). Individuals that cooperate in 

groups containing their close relatives receive additional inclusive fitness benefits by helping kin 

(Hamilton, 1964). Furthermore, feeding tolerance can increase when feeding with kin (de Waal & 

Davis, 2003), and lower-ranking individuals may gain access to resources through higher-ranking 

relatives. Our experiments did not test this hypothesis; however, we set the distance offspring were 

placed in the world from their parent at a maximum of two cells away, meaning that offspring 

stayed relatively close to their parent, and were likely in one or multiple subgroups with their 

parent and other relatives. This close physical distance could generate a cluster of closely related 

agents, all of which inherited similar strategies from their parent agent, and concurrently increase 

inclusive fitness benefits from cooperating with kin. We expect that setting this reproductive 

distance farther from the parents would have a negative effect on evolution of cooperation, but 

more experiments are needed to test this hypothesis. Modifying the distance at which offspring can 

travel from their parents in digital evolution systems may provide more insights into how kin 

selection influences group hunting in animal societies. 

There may be some conditions under which it is worth cooperating some percentage of time 

for lower-ranking agents. For example, if the fifth-ranking agent group hunts and the fourth- and 

third-ranking agents solo hunt, the fifth-ranking agent’s relative rank improves among the group 

hunters and thus it will receive a better payoff than if all five agents group hunted. Situations such 

as these may explain why, in our experiments, there was little to no difference between the group-

hunt rates of the second- to fifth-ranking agents across tolerance levels under both payoff 

conditions. Further work is needed to investigate how actions taken by individual agents vary 

17 

 
 
across hunting groups and as their relative rank and the number of group hunters vary in hunting 

games.  

Reward from solo hunting 

The relative per capita reward from group hunting vs. solo hunting also influenced group-

hunting rates. As occurs in other species (e.g., Creel & Creel, 1995; Packer & Ruttan, 1988), we 

found that when there is greater potential for a higher per capita payoff from group hunting, this 

strategy should be more common. In our digital system, we kept the group- and solo-hunting 

success rates consistent throughout the experiments. In natural systems, the group- and solo-

hunting success rates can vary spatially and temporally within and among populations due to 

varying ecological conditions. Chimpanzees in the Gombe forest hunt cooperatively less than 

chimpanzees in the Taï forest, most likely because solo hunting at Gombe is more successful than 

solo hunting in Taï (Boesch, 1994). Relative solo- and group-hunting success can also vary within 

populations due to seasonal changes in prey availability. In the Maasai Mara, Kenya, zebra (Equus 

burchelli) and wildebeest (Connochaetes taurinus) abundance peaks for several months during the 

annual migration (Cooper et al., 1999). When these large prey are widely available, spotted hyenas 

have higher hunting success and are more gregarious (Holekamp et al., 1997b; Smith et al., 2008), 

which may affect the relative returns from alternative hunting strategies, particularly for low-

ranking individuals. Solo- and group-hunt success rates can also vary with prey type (Holekamp et 

al., 1997b), affecting relative payoffs from hunting strategies (Packer & Ruttan, 1988). For instance, 

solo-hunting spotted hyenas in the Kalahari Desert have low success hunting adult gemsbok (Oryx 

gazella) but high success hunting calves (Mills, 1985), and thus should profit more by hunting in 

groups for adults but hunting alone for calves. In contrast, spotted hyenas in Tanzania have greater 

success when hunting in groups for wildebeest calves but group hunting does not increase hunt 

success when hunting for adult wildebeest (Kruuk, 1972). Future research is needed to understand 

18 

 
 
how varying prey availability and capture success over space and time influence evolutionary 

dynamics of cooperative hunting.  

Our results agree with game theoretical models that the reward from cooperating must be 

greater than the reward from solo hunting for individuals to hunt cooperatively (Packer & Ruttan, 

1988). In societies where rank determines priority of access to food, this reward must be even 

larger for low-ranking individuals to cooperate (Packer & Ruttan, 1988; Tilson & Hamilton, 1984). 

Thus, as solo-hunting success decreases, cooperation should be favored (Packer & Ruttan, 1988). 

Here, we tested two group-hunt payoff scenarios; one where the average per capita payoff from 

group hunting was twice that of the solo-hunt payoff, and one where the average per capita payoff 

was 1.2 times the solo-hunt payoff. Even when the mean group-hunt per capita payoff was only 1.2 

times the solo-hunt payoff, group hunting still occurred, albeit at low rates, under the most despotic 

conditions for resource sharing. How much inequity will agents tolerate before group hunting 

disappears from this system?  

Social carnivores face a tradeoff between relative costs and benefits gained from solo 

hunting vs. group hunting. Solitary foragers can be overwhelmed by groups of intra- and inter-

specific competitors once a kill has been made, and thus lose control of food resources (Cooper, 

1991; Fanshawe & Fitzgibbon, 1993), making solo hunting riskier and potentially less profitable 

than group hunting. However, hunting alone can attract less attention, and successful solo hunters 

may enjoy more time feeding on their prey before attracting competitors. For example, in the 

Maasai Mara, on average, 6 or more additional spotted hyenas were present at kills made by pairs 

within 10 minutes of a successful hunt, whereas hunting alone rarely attracted other hyenas (Smith 

et al., 2008). In our system, we did not include the potential for lost resources due to cheaters or 

kleptoparasites, but future studies could investigate the relationship between group size and losses 

due to competitors and how this tradeoff influences individuals’ decisions.  

19 

 
 
 
CONCLUSIONS 

We conducted a digital evolution experiment to investigate mechanisms promoting group 

hunting in a social system where reward from cooperative hunting is unequally distributed. 

Although this experiment presents a simplified model of a spotted hyena social system, it 

demonstrates the utility of agent-based digital evolution in examining complex evolutionary and 

behavioral dynamics across time scales and ranges of variables that are difficult or impossible to 

study in natural systems. This method also allows us to manipulate variables and track fitness 

outcomes to extents that are difficult or impossible to do with living animals. For example, our 

simulations ran for 1,000,000 updates which approximated roughly 20,000-50,000 generations, 

representing a time scale that would be impossible to measure in extant long-lived organisms. In 

this system, we manipulated how rewards were divided among group hunters and the per capita 

reward agents received from hunting based on hunting strategy, group size, and social rank. In 

natural systems, observers are limited to natural-occurring behaviors and cannot experimentally 

manipulate factors this precisely or across these ranges. Thus, digital evolution offers a powerful 

complement to empirical and theoretical work. In these experiments, we considered the effect of 

tolerance of the reward from group hunting. However, many other factors can influence the 

evolution and stability of group hunting such as communication, potential to cheat and punish 

cheaters, group size, role/effort in hunt, learned behaviors, kin selection, and partner choice 

(Brosnan & de Waal, 2014; Campbell et al., 2020). Thus, we close with the caveat that other factors 

merit investigation in future research to elucidate effects of other social and ecological variables. 

We suggest that doing so will further our understanding of the evolution of group hunting and of 

cooperative behavior more broadly.  

ACKNOWLEDGMENTS 

We are grateful to Christoph Adami and Wolfgang Banzhaf for their advice and feedback 

throughout the project development. We also thank Kay Holekamp, Kenna Lehmann, Tracy 

20 

 
 
Montgomery, Helen McCreery, and Christina Anaya for their thoughtful comments when reviewing 

this manuscript, Eli Strauss for assistance with coding, and Jory Schossau for valuable discussions 

and insight throughout the project. We are grateful for the support of the Michigan State University 

Institute for Cyber-Enabled Research. 

21 

 
 
 
 
FIGURES 

Figure 1.1. Setup of groups for hunting games and possible offspring agent locations. (A) N1 
(black squares) is the location of a single agent. Grey squares represent the neighboring agents that 
N1 will play the 5 hunting games with during each world update. (B) The locations (grey squares) 
labeled with * show the possible offspring locations for a parent at location N1 (black square), as 
we assume offspring remain closer to their parent than do unrelated hyenas. Offspring replace the 
agent at their location, effectively killing off that agent. 

22 

 
 
 
 
 
 
Figure 1.2. Per capita payoffs under different experimental conditions. Payoff scheme agents received for group hunting and solo 
hunting under different tolerance conditions (when the group-hunting payoff is 2 times the per capita payoff from solo hunting). Agents 
hunted in groups with four other agents and each agent decided whether to group hunt (i.e., hunt for large prey and share the reward with 
other agents that choose to group hunt) or solo hunt (i.e., hunt for small prey and the agent gets a smaller reward that is not shared). If an 
agent chose to group hunt, the payoff it received depended on its relative rank among other group hunters, the tolerance value, and how 
many other agents chose to group hunt. Agents received equal shares of the group-hunting payoff when tolerance = 1. As tolerance 
decreased, the payoff was increasingly skewed by dominance rank, with the highest-ranking agent receiving a greater proportion of the 
payoff. Agents received the same payoff from solo hunting regardless of rank under all tolerance conditions. 

23 

 
 
 
 
 
 
Figure 1.3. Group-hunting rates under varying tolerance conditions. The average rate of group hunting in the agent population 
across different tolerance levels when the group-hunting payoff is (A): 2 times the solo-hunt payoff per capita and (B): 1.2 times the solo-
hunt payoff per capita. Tolerance determined how equally the reward from group hunting was shared (i.e., 1 = shared equally, 0.64 = each 
agent gets 64% of the reward as the agent with the rank immediately above it). Each point represents the average group-hunting rate of 
the agents in their respective relative rank positions within hunting subgroups (e.g., rank 1 = highest ranking agent in the subgroup). Each 
tolerance condition was run for 1,000,000 updates (sampled every 1,000 updates), plots represent the mean of the last 100,000 updates 
(n=101 samples) averaged across 200 replicates per condition. Bars represent ±2 standard errors of the mean group-hunting rates among 
agents of each relative rank across each tolerance condition.  

24 

 
 
 
 
THE INFLUENCE OF INDIVIDUAL ATTRIBUTES ON THE HUNTING BEHAVIOR OF SPOTTED 

CHAPTER 2: 

ABSTRACT 

HYENAS 

Hunting behavior represents a critical component of predator-prey dynamics and exerts 

important influences on survival and reproduction of predators. Attributes of both predators and 

prey affect prey selection and hunting success and are important to consider in predation studies. 

Among social carnivores, group hunting is common and individual attributes such as social 

dominance rank, age, and sex can strongly influence access to shared rewards from hunting. In this 

chapter, we investigated how attributes of hunters and prey influence hunting behavior in the 

spotted hyena, a social carnivore that lives in fission-fusion societies that hunts both alone and with 

groupmates. Spotted hyena societies have linear dominance hierarchies, where priority of access is 

determined by social rank, and individual benefits from group hunting vary based on rank. We 

investigated how prey attributes and hunting strategy influence hunting success. We then asked 

how prey selection influences hunting group sizes, and how individual attributes of hunters impact 

solo hunt success and group hunting behavior. We compared these results from 1996-2018 to 

previous research on hyenas in this same region from 1988-1995, a period when there was less 

human activity, smaller hyena clan sizes, and higher prey abundance. We show that hunting success 

varied based on prey species, prey age class, and hunter age class. Hyena hunting strategies varied 

by prey species. Hyenas tended to hunt in larger groups for more difficult to hunt prey species (i.e., 

prey species with which hunters had lower hunting success rates). Hunting behavior also varied 

among individual hyena attributes, with low-ranking hyenas hunting in smaller groups than high-

ranking individuals. Juveniles tended to hunt in larger groups than adults. Differences in these 

strategies reflect tradeoffs experienced by individual hunters – low-ranking individuals 

experienced higher competition in groups, and juveniles had lower odds of hunting success when 

25 

 
 
they hunted alone. We observed more group hunting from 1996-2018 than in previous years 

(1988-1995), and more observations of hyenas hunting more difficult to capture prey. These 

differences may be due to the larger clan size during our study period and declines of easier to 

capture prey. We demonstrate that attributes of predators and their prey exert influences on hunt 

success and hunt grouping behavior in spotted hyenas. We also show that hunting behavior can 

change in a population through time, which may be in response to demographic and ecological 

changes. Future work investigating how predator and prey attributes affect hunting behavior in 

other systems and how these dynamics may change over time will increase our understanding of 

the relationship between predator-prey dynamics and broader ecosystem processes. 

 INTRODUCTION  

Predator-prey dynamics have profound consequences for ecosystem processes, community 

structure, food web dynamics, and for the demography and life history of prey animals, which in 

turn shape future predator-prey dynamics (Beschta & Ripple, 2009; Lima, 1998). Predators, 

particularly apex predators like large carnivores, play an especially important role in shaping 

communities through top-down effects via their choices of which prey species to target, and effects 

of predation risk on behavioral decisions made by prey animals (Creel & Christianson, 2008; Fryxell 

& Lundberg, 1994; Lima & Dill, 1990). Thus, hunting behavior represents a critical component of 

predator-prey dynamics. Hunting behavior also plays a foundational role in the lives of large 

carnivores, exerting important influences on their survival and reproduction. Hunting decisions, 

including prey selection and whether to hunt with groupmates affect hunting success and ultimately 

fitness.  

Predator and prey attributes are important to consider in predation studies. For example, 

prey attributes such as age, sex, body condition, and personality can influence risk of predation 

(Aanes & Andersen, 1996; Fitzgibbon, 1990a&b; Murray, 2002; Re ale & Festa-Bianchet, 2003). 

Hunting success rates and prey selection by predators also vary with prey species, sex, and age class 

26 

 
 
(Fitzgibbon, 1990b; Hilborn et al., 2012; Holekamp et al., 1997b; Ho  ner et al., 2002; Karanth & 

Sunquist, 1995; Stander & Albon, 1993). Factors that influence hunting success may be highly 

dynamic; they are often influenced by ecological, social, and physical attributes of the species within 

the prey community, and may also vary with attributes of the hunters themselves (Pettorelli et al., 

2015). Individual attributes of hunters, such as age, sex, and social status (Cooper et al., 2007; 

Litvaitis et al., 1986; Ross et al., 1997; Saulitis et al., 2000) influence hunting behavior and rates of 

hunting success. For example, research on bottlenose dolphins demonstrated that females had more 

variable diets than males, whereas males were more specialized predators (Rossman et al., 2015). 

Stander (1992) found that stalker roles played by lionesses during hunts varied with their 

morphology; slower, more heavyset lions typically assumed prey-catcher positions during hunts 

whereas slimmer, faster individuals functioned more often as chasers of prey, herding them toward 

catchers. Hunting is dangerous and requires practice and, as a result, young hunters tend to be less 

successful than mature adults (Caro, 1994; Hayward et al., 2007; Holekamp et al., 1997b). Across 

species and environments, clearly individual characteristics of the predator-prey dyad critically 

affects hunting success. 

Group hunting, which is common among social carnivores (Bailey et al., 2013), introduces 

additional factors that impact both hunting success and individual fitness. While social carnivores 

have an enhanced ability to capture prey by hunting cooperatively (Fanshawe & Fitzgibbon, 1993; 

Stander & Albon, 1993), and defending prey against kleptoparasites (Creel & Creel, 1995; Pe riquet 

et al., 2015), they are also surrounded by conspecific competitors, which are their groupmates 

(Smith et al., 2008). As a result, both attributes of individual participants in a hunt (e.g., age, sex, 

dominance rank) as well as characteristics of the group (e.g., hunting party size, composition) 

impact individual success. For example, in carnivore societies structured by social rank, competitive 

ability and access to food at carcasses is not equal among individuals (Atwood & Gese, 2008; Gese et 

al., 1996b; Smith et al., 2008; Tilson & Hamilton, 1984). For a hunt to be successful for an individual 

27 

 
 
predator, the per capita energy intake must outweigh the energetic demands associated with prey 

capture and defense (Macarthur & Pianka, 1966). Although group hunting has been shown to 

increase hunt success and allow hunters to capture much larger prey than they could alone (Creel & 

Creel, 2002; Packer, 2023), the per capita benefit for low-ranking individuals may fail to outweigh 

the cost of inadequate nutritional gain from the kill (Holekamp & Dloniak, 2010).  

Here, we use long-term behavioral data to investigate hunting patterns of spotted hyenas 

(Crocuta crocuta) and inquire how individual prey and hunter attributes influence hunting success 

in the Maasai Mara National Reserve, Kenya. Spotted hyenas are social carnivores that inhabit a 

wide range of habitats, including landscapes with diverse, seasonally variable prey bases (Kruuk, 

1972; Trinkel et al., 2004). Spotted hyenas are effective hunters and most of their food is acquired 

through active hunting rather than scavenging (Cooper, 1990; Holekamp & Dloniak, 2010; Kruuk, 

1972). Cursorial hunters who run their prey to exhaustion, spotted hyenas hunt both alone and in 

groups (Kruuk, 1975; Mills & Harvey, 2001). Previous work has demonstrated that hyenas 

cooperate to hunt in groups to take down prey much larger than themselves (Kruuk, 1975; 

Holekamp et al., 1997b, Hofer & East, 1993), and to defend carcasses against kleptoparasitism from 

lions (Lehmann & Montgomery et al., 2017). Hyena societies are structured by linear dominance 

hierarchies (Frank, 1986; Kruuk, 1972; Tilson & Hamilton, 1984), and dominance rank, age, and sex 

strongly influence hyena behavior and access to resources. High-ranking individuals have 

preferential access to these critical resources (Engh et al., 2000; Smith et al., 2008), and they enjoy 

greater fitness than their low-ranking peers (Frank, 1986; Holekamp et al., 1996; Tilson & Hamilton, 

1984). Our long-term study of spotted hyenas allows an assessment of temporal variation in 

hunting behavior as ecological conditions change. 

Using a long-term dataset gathered from a single clan of hyenas across 23 years, we first 

describe typical prey items and hunting scenarios for this clan of hyenas. We then examine 

predictors of hunting success, and model how prey attributes, predator attributes, and hunting 

28 

 
 
strategy (group vs. solo) influenced success of a given hunt. Next, we investigated predictors of 

hyena hunting strategy, including how prey and hunter attributes affect hunting group size. Finally, 

we compared our findings to previous work on hunting by hyenas from the same clan (Holekamp et 

al., 1997b) when the surrounding human population density was lower and ecological conditions 

were different from those documented more recently. 

METHODS  

Study site 

We conducted this study in the Maasai Mara National Reserve, Kenya (henceforth “the Mara”). The 

Mara is a tropical savanna, primarily composed of rolling grasslands dotted with trees and riverine 

habitats (Sinclair & Norton-Griffiths, 1979). Precipitation follows a bimodal pattern annually, with 

most precipitation occurring in November–December and March–May (Green et al., 2019b; Ogutu et 

al., 2008). Prey availability also varies seasonally in the Mara. A diverse community of large 

herbivores are present year-round, and for a portion of the year (June – November), large migratory 

herds of wildebeest (Connochaetes taurinus) and zebra (Equus quagga) occupy the Mara (Bell, 1971; 

Craft et al., 2015; Sinclair & Norton-Griffiths, 1979). We followed one study clan, the Talek Clan, 

inhabiting the region near the north-eastern border of the Reserve, near the town of Talek (Fig 2.1). 

This clan has been under continuous observation since June 1988.  

Hyena hunting behavior 

Field observers collected behavioral data during twice-daily observation periods from 

~0500-0830 h and again from ~1630-2000 h. Observation sessions (hereafter “sessions”) were 

initiated when observers encountered one or more hyenas separated from other hyenas by at least 

200 m. A session ended when interactions among hyenas ceased and observers left the individual or 

group, or when observers lost sight of the hyenas (Lehmann et al., 2017); session duration was 

recorded in minutes, lasting from five minutes to several hours. Observers recorded all instances of 

observed hyena hunting behavior during observation sessions. Field observers followed the 

29 

 
 
definition used by Kruuk (1972) and Holekamp et al. (1997b) to classify hunting behavior - “a chase 

by one or more hyenas of a selected prey animal that covered at least 50 m” (Holekamp et al., 

1997b, p. 3). If the chase ended in prey capture, the hunt was classified as successful. If the end of 

the hunt was not observed (e.g., due to darkness, hyenas running into vegetation), the hunt outcome 

was classified as unknown. Hyenas often “test chase” prey before attempting to hunt (Kruuk, 1972). 

Field observers considered test chases to be slower-paced chases that were shorter than 50 m or 

short rushes towards groups of prey that were not followed by longer, faster chases of a single prey 

animal (Holekamp et al., 1997b; Kruuk, 1972); test chases were not included in our analyses.  

For each hunting attempt, field observers recorded the date, time, prey species and age class 

(juvenile or adult), identities of individual hyenas that participated in the hunt, and outcome of the 

hunt (i.e., success, fail, or unknown). Prey animals were considered adults unless observers 

indicated prey animal was a juvenile. Prey listed as infant, newborn, young, juvenile, or subadult 

were considered juvenile.  For each identifiable hyena involved in each hunt, field observers 

assigned age, sex, and dominance rank using data collected from long-term behavioral observations. 

Yearly dominance rank was calculated using the outcomes of dyadic agonistic interactions via 

methods described in (Strauss & Holekamp, 2019). Ranks were calculated separately for natal 

individuals and immigrant males, then combined such that immigrants ranked below all natal 

individuals. We used the standardized dominance rank for individual hyenas during the year in 

which hunting was observed for analyses involving social rank. Hyenas less than three years of age 

were defined as juveniles, and individuals three years or older adults (Holekamp et al., 1997b).  

We analyzed observations of hyena hunting behavior from January 1996 – December 2018. 

We excluded hunting attempts classified as unknown, and those for which the prey animal was not 

an ungulate (Rodentia, n=2; Tubulidentata, n=1), was domestic livestock (because observers 

intervened, when possible, to prevent livestock loss; n=3), or was unknown (n=1).  Because the 

number of hyenas participating in a hunt often changes during a hunt (Kruuk, 1972), we used 

30 

 
 
number of hyenas present at the end of the hunt as hunting group size (Holekamp et al., 1997b), 

and we assumed all hunters listed in each hunting record were present until the end of the hunt. We 

designated each hunt as a “solo hunt” or a “group hunt” to look at the effect of hunting alone or in a 

group for some analyses. Hunting records with one or more unidentified hyenas were only included 

in analyses of solo versus group hunting, not hunting group size. For these analyses, we included 

hunts with unidentified group numbers because we could assign these hunts as group hunts. 

Data analysis & software 

First, we summarized total attempted and successful hunts by prey species, and 

summarized solo and group hunts by hunter age, sex, and rank. We then quantified hunting success 

for prey species with > 9 observed hunts and fit a generalized linear model to investigate how 

hunting success varied with prey species, prey age, and hunting strategy. The outcome variable was 

whether or not the hunt was successful (binomial distribution), and the predictor variables were 

prey species, prey age class (adult or juvenile), and hunting strategy (solo or group hunt). We 

conducted pairwise post hoc tests among species pairs to test for differences in hunting success 

rates. To test for evidence of temporal autocorrelation, we ran a generalized linear mixed model 

which included the same outcome and predictor variables in the previous model, and added two 

additional predictor variables: (1) Julian date as a quadratic term to account for seasonal variation 

in prey availability,  and (2) observation session as a random variable to account for both repeated 

measures of the hunts observed within the same session and any unmeasured variability individual 

observation sessions might possess. We then focused on the top two prey species, Thomson’s 

gazelle (Eudorcas thomsoni) and wildebeest, and investigated how prey age class, hunt type (solo or 

group), and the interaction between these variables influenced hunt success. We ran separate 

models for Thomson’s gazelles and wildebeests. 

We then examined whether hunter attributes influenced solo hunting success by fitting a 

generalized linear mixed model. The outcome variable was hunting success (or not; binomial 

31 

 
 
distribution), and predictor variables were hunter age class (adult or juvenile), sex, and 

standardized dominance rank, and prey age class (adult or juvenile). We included individual 

identity as a random effect to account for repeated measures of the same individual over time and 

any unmeasured variability among individual hunters. For this model, we included hunts of any 

identifiable prey type that was not domestic livestock. 

Next, we investigated how hunting group size varied by prey species and hunter attributes. 

We first tested whether mean hunting group size varied for the top six prey species using a Kruskal-

Wallis test and a Dunn post hoc test with Bonferroni correction to assess species pairwise 

comparisons. Next, we fit a generalized linear model to determine whether hunt type (solo or 

group) varied across prey species and age class for the top six prey species. The outcome variable 

was whether the hunt was a group hunt (or not; binomial distribution), and predictor variables 

were prey species and prey age class (adult or juvenile). We next ran a post hoc test to assess 

pairwise differences. We also tested whether mean hunting group size varied by hunter age class, 

sex, and rank using a Kruskal-Wallis test. To compare across ranks, we assigned standardized ranks 

to quartiles (low, mid-low, mid-high, and high), and used a Dunn post hoc test with Bonferroni 

correction to test rank quartile pairwise comparisons. 

We performed all analyses using R version 4.3.3 (R Core Team, 2024) in RStudio version 

2023.12.1.402 (Posit team, 2024). We used the ‘lme4’ package version 1.1-35.2 (Bates et al., 2015) 

to fit all models, and the ‘performance’ package version 0.11.0 (Lu  decke et al., 2021) to test for 

collinearity among predictor variables. We used the ‘emmeans’ package version 1.8.5 (Lenth, 2023) 

to run pairwise post hoc tests to assess differences in hunting success and group sizes between each 

prey species. We only included predictors that had a variance inflation factor < 5. We used the 

‘asbio’ package version 1.9-7 (Aho, 2024) to run the Dunn post hoc test for multiple pairwise 

comparisons for the Kruskal-Wallis test. We used ‘tidyr’ version 1.3.1 (Wickham et al., 2024), ‘dplyr’ 

version 1.1.4 (Wickham et al., 2023), ‘lubridate’ version 1.9.2 (Grolemund & Wickham, 2011), and 

32 

 
 
‘stringr’ version 1.5.1 (Wickham, 2023) packages to manipulate and summarize data in R. We used 

‘ggplot2’ version 3.5.0 (Wickham, 2016), ‘sjPlot’ version 2.8.14 (Lu  decke, 2023), ‘ggpubr’ version 

0.6.0 (Kassambara, 2023), and ‘gridExtra’ version 2.3 (Auguie, 2017) to visualize data. We created 

the study area map using ArcGIS Pro (ESRI, 2021). 

RESULTS 

General hunting summary 

Field observers documented 302 hunts by members of the hyena clan, of which 272 hunts 

had known outcomes (Table 2.1). Field observers identified 221 hyenas in 250 hunts (107 female, 

114 male). Of the 272 hunts with known outcomes, 120 were group hunts and 152 were solo hunts. 

Thomson’s gazelle was the most common prey species, followed by wildebeest and zebra (Table 

2.1). Field observers could determine hunting group size for 263 hunts; the mean hunting group 

size was 2.3 (SE=0.15) hyenas. Adults made up the majority of solo hunters, while juveniles made 

up a larger proportion of group hunters (Table 2.2). Females made up a larger portion of observed 

solo and group hunters than males, and the mean rank of group hunters was higher than that of solo 

hunters (Table 2.2). 

Hunting success 

Prey attributes: Hunting success varied among prey species, with highest success observed for 

Thomson’s gazelle, wildebeest, and impala, and lowest success observed for warthog, topi, and 

zebra (Fig 2.2). From the generalized linear model, predicted probabilities of hunting success, 

irrespective of hunting type or prey age, were highest for Thomson’s gazelle and wildebeest, and 

lowest for warthog (Table 2.3). In pairwise post hoc tests, the probability of success was higher for 

Thomson’s gazelle than warthogs (Odds Ratio=10.51, SE=11.85, p = 0.04), topi (Odds Ratio=3.92, 

SE=2.45, p=0.03), or zebra (Odds Ratio=3.43, SE=2.09, p=0.04). Hyenas also had better odds of 

success when hunting wildebeest than warthogs (Odds Ratio=0.10, SE=0.11, p=0.04) or topi (Odds 

Ratio=0.27, SE=0.18, p=0.04). Hyenas had better chances of success hunting juvenile than adult 

33 

 
 
prey in our model including all top 6 prey species (Table 2.4). For all top prey species, group hunting 

did not significantly increase the odds of success over solo hunting (Table 2.4). Julian date and 

observation session (as a random variable) did not influence hunting success in the model results 

(Table S1), and thus were not included in our final model. 

When hunting Thomson’s gazelles, group hunting affected hunt success for adult prey but 

not juvenile prey (Table 2.5a). In addition, in pairwise post hoc tests, the probability of successfully 

hunting a juvenile Thomson’s gazelle did not differ significantly when comparing solo with group 

hunts (Odds ratio=1.389, SE=0.880, p=0.604). However, group hunting significantly increased 

hunting success for adult Thomson’s gazelles (Odds ratio=0.165, SE=0.113, p=0.008) (Fig 2.3). 

When hunting wildebeest, the interaction between prey age and hunt type was not significant 

(Table 2.5b). Hunting success for wildebeest did not differ significantly between solo and group 

hunts. Hyenas were more likely to capture juvenile wildebeest than adults (Fig 2.3).  

Hyena attributes: In solo hunts, both hunter age class and prey age class influenced hunting success 

(Table 2.6). Juvenile hunters had lower odds of success than adults, and hyenas had higher odds of 

successfully hunting juvenile prey than adult prey (Fig 2.4). There was no significant effect of either 

hunter rank or sex on solo hunt success (Table 2.6).  

Hunting group size 

Prey attributes: Mean hunting group size varied across prey types (Kruskal-Wallis χ2 =72.32, 

p<0.001), with the highest average hunting group sizes observed for warthogs and zebra (Fig 2.5). 

Our generalized linear model showed that the odds of group hunting compared to solo hunting also 

varied significantly with prey species, but not with prey age class; hyenas were more likely to hunt 

warthogs, wildebeest, and zebra in groups than they were to hunt Thomson’s gazelles (Table 2.7). 

Prey age class did not significantly affect the likelihood of group hunting (Table 2.7).  

When comparing mean hunting group size across prey types between this study and that of 

Holekamp et al. (1997b), we saw differences for Thomson’s gazelles, topi, and zebra (Table 2.8). 

34 

 
 
Mean hunting group size was smaller for Thomson’s gazelle and zebra and larger for topi in our 

study compared to Holekamp et al. (1997b). Furthermore, the relative proportion of hunts that 

were group hunts was larger for every top prey species in our study than in that of Holekamp et al. 

(1997b) (Table 2.8).  

Hyena attributes: Mean hunting group size varied among rank quartiles (Kruskal-Wallis χ2 

=50.02, p<0.001; Fig 2.6; n=524). In post hoc tests of pairwise comparisons, low-ranking hyenas 

hunted on average in smaller groups than hyenas in the mid-high rank quartile (Dunn post hoc test; 

Diff= -82.11, p<0.001). On average, low-ranking hyenas also hunted in smaller groups than high-

ranking hyenas (Dunn post hoc test; Diff = -129.53, p< 0.001). Finally, hyenas in the mid-low rank 

quartile tended to hunt in smaller groups than high-ranking hyenas (Dunn post hoc test; Diff = -

85.24, p< 0.001). Mean hunting group size also differed significantly between hunter age classes 

(Kruskal-Wallis χ2 = 18.2, p< 0.001; Fig 2.7; n=528), with juveniles tending to hunt in larger groups 

than adults. Mean hunting group size did not differ significantly between males and females 

(Kruskal-Wallis χ2 =2.7, p=0.1; Fig 2.8; n=541).  

DISCUSSION 

Characteristics of both prey and predator predicted hunting success and hunting group size. 

For spotted hyenas, we found that hunter age, prey species and prey age, influenced hunting 

success. Group hunting resulted in greater success when hyenas hunted adult Thomson’s gazelle, 

but group hunting did not enhance success when considering all common prey species together. 

Finally, individual attributes of both hunter and prey influenced hunting group sizes. Specifically, 

both hunter age and social rank, and both prey species and prey age class, affected group size 

during hunts. Overall, our results lend support to previous work cautioning against oversimplifying 

predator-prey dynamics (Pettorelli et al., 2015) and add to our understanding of how hunting 

behavior varies with age and social rank.  

35 

 
 
Overall, the effects of prey species and prey age class on hunting success had significant 

impacts on hunting success. For example, hunting success was higher for juvenile prey than adult 

prey for all top prey species, as seen in other systems (lions, Hayward et al., 2007; cheetahs, Hilborn 

et al., 2012). Two of the most common prey species for hyenas in this study, topi and warthog, had 

no successful hunts for adult prey. We did not find that hunting with groupmates significantly 

improved hunting success over hunting alone when modeling all top prey species together. This 

may be because the effects of prey species and prey age class on hunting success were much larger 

than hunting group size. We did not have enough observed hunts per prey species, age class, or 

hunting group size to investigate interactions among these variables. As hyenas are more likely to 

hunt prey species with lower hunting success rates in groups than alone, and as juvenile prey are 

easier to capture than adults, these effects were difficult to disentangle with our small sample sizes. 

For example, of the 30 observed hunts for zebra, only four were solo hunts, and none of these solo 

hunts were successful. Of the 14 observed warthog hunts, only one was a solo hunt, which was 

unsuccessful. 

For the most common prey species, Thomson’s gazelle, hunting group size significantly 

affected hunting success, but only when hunting adults. Hyenas had more success hunting adult 

Thomson’s gazelles with clan-mates than when hunting alone but hunting success for juveniles did 

not improve when hunts involved multiple clan-mates. This is most likely because hunting for 

juvenile gazelles usually involved a solitary hunter walking in zig-zag pattern upwind to flush 

resting juveniles out of hiding, and clan-mates only join hunts of juveniles after the fawn has been 

flushed. Surprisingly, for the second most common prey type, wildebeest, group hunting did not 

significantly improve hunting success for adults over hunting alone. Given this, we would expect to 

observe less group hunting for wildebeest, since the expected per capita benefit would be reduced 

in a group hunt, but we found that hyenas had almost equal odds of hunting wildebeest alone or in a 

group. The seasonal ecology of the Mara may influence rates of group hunting. This may be 

36 

 
 
especially the case for wildebeest, since they migrate into the Mara in massive herds. When masses 

of wildebeest are present, hyenas are the most gregarious (Smith et al., 2008). Therefore, group 

hunting may increase during this time in response to high prey density while, at the same time, 

attempting to hunt alone may become more difficult because of hyena gregariousness.  

Juvenile hyenas were less likely than adults to capture prey when they hunted alone. This is 

consistent with previous findings on hyena development. Their skulls are not fully developed until 

~35 months (Tanner et al., 2010), and it appears to take several years to learn how to become an 

effective hunter (Holekamp et al., 1997b). Neither sex nor dominance rank had a significant 

influence on hunting success, which is consistent with Holekamp et al. (1997b). We did not have the 

sample size to investigate whether prey selection varied with hunter attributes, but hunter age and 

dominance rank may influence which prey hyenas attempt to hunt alone. Younger hyenas may 

choose smaller, non-ungulate prey that are easier to capture (Holekamp et al., 1997b; Mills, 1990). 

Since lower-ranking individuals face greater competition when feeding in groups, they may benefit 

more from selecting smaller prey, that is relatively easier to capture, and that they can quickly 

consume, to avoid attracting competitors.  

Consistent with previous hunting studies (Kruuk 1972; Holekamp et al., 1997b; Trinkel, 

2010), hyenas hunted more difficult prey (those with low rates of successful capture) in larger 

groups than they did for prey that yielded higher success. Hunting group size varied not only with 

attributes of prey, but also with those of the hunters themselves. Lower-ranking hyenas hunted in 

smaller groups than high-ranking hyenas, and juveniles hunted in larger groups than adults, 

consistent with results obtained by Holekamp et al. (1997b). Since juvenile hyenas are less effective 

hunters, they benefit more by joining clan-mates when hunting. The larger mean hunting group 

sizes we observed among high-ranking individuals may reflect the fact that high-ranking individuals 

can easily usurp food from their clan-mates, so there is little cost for them to hunt with others. In 

addition, high-ranking females enjoy higher reproductive success than their lower-ranking peers, 

37 

 
 
and thus have more relatives as potential hunting partners (Holekamp et al., 1997b). Furthermore, 

since low-ranking hyenas face greater risks of losing during feeding competition (Smith et al., 

2008), low-ranking hyenas benefit less from group hunting, particularly when group-hunting does 

not improve the odds of prey capture over solo hunting. Earlier work revealed that low-ranking 

hyenas spend more time alone (Smith et al., 2008), and range farther from communal dens than do 

high-ranking individuals (Boydston et al., 2003), and they also forage more outside of their clan 

territories (Ho  ner et al., 2005). However, when females have young cubs, social rank does not 

influence their space use, they become central place foragers, and they have smaller home ranges, 

regardless of social rank (Boydston et al., 2003). Future work investigating how reproductive state 

influences hunting decision among females will provide more insight into the trade-offs faced by 

females with den-dependent cubs.  

Thomson’s gazelle and wildebeest made up the majority of the observed hunts from 1996-

2018, which is consistent with previous results from 1988-1995 (Holekamp et al., 1997b). However, 

field observers saw fewer topi hunts and more hunts of challenging prey (e.g., warthog and buffalo) 

in our study than did Holekamp et al. (1997b) in their earlier study of this same hyena clan. 

Specifically, we saw 14 warthog hunts (making them the 6th most popular prey species) and five 

buffalo hunts, whereas Holekamp et al. (1997b) saw only one of each. These changes may reflect the 

declines of resident prey species in the Talek Region since the early 1990s (Chapter 3, Green, 2015; 

Ogutu et al., 2005, 2009, 2011; Ottichilo et al., 2000). In Tanzania, long-term declines in easier to 

capture prey availability caused hyenas to consume more buffalo calves and adult wildebeest, which 

were more difficult to capture (Ho  ner et al., 2002). 

The overall mean hunting group size of our study was larger (2.3 hunters), than the mean 

hunting group size reported by Holekamp et al. (1997b) (1.5 hunters). When comparing mean 

hunting group sizes by species, we found that hyenas hunted Thomson’s gazelle and zebra in 

smaller groups than they did in the late 1980s and early 1990s (as reported in Holekamp et al., 

38 

 
 
1997b), but the group sizes in which they hunted topi increased between these same periods. These 

differences may be due to small sample size, particularly for zebra and topi. We cannot directly 

compare characteristics of groups hunting warthogs since Holekamp et al. (1997b) observed only 

one warthog hunt.  

While solo hunts constituted the majority of hunts during both time periods, Holekamp et 

al. (1997b) reported a higher percentage of solo hunts (76%) than we did (56%). When comparing 

species, Holekamp et al. (1997b) found that zebras were the only species hunted more often in 

groups than by solitary hyenas. We found that zebra and warthogs were both hunted more often in 

groups, and the relative proportion of hunts that were group hunts increased for every species, with 

the most drastic increases for wildebeest, topi, and zebra. This may reflect the larger clan sizes 

found during our study period than during the late 1980s and early 1990s; when hyena population 

density is higher, it may be more difficult to separate from clan-mates such that it becomes more 

difficult to hunt alone.  

Despite equivalent sampling effort intensities across the two study periods, field observers 

documented 275 hunts with known outcomes across 23 years, whereas Holekamp et al. (1997b) 

observed 272 hunts in just 7.5 years. This decrease in the frequency of observed hunts may reflect 

the greater nocturnality of hyenas in recent years due to higher pastoralist population density and 

increased human activity along the edge of the Reserve (Kolowski et al., 2007). Field observers were 

not able to observe hunts before dawn or past dusk in the current study. Globally, animals – 

especially apex carnivores – are responding to anthropogenic disturbance by becoming increasingly 

nocturnal, (Gaynor et al., 2018), and these responses to anthropogenic change can vary due to 

individual attributes (Hertel et al., 2017; Kolowski et al., 2007; Nevin & Gilbert, 2005). Changing 

activity patterns can also make prey more susceptible to predation and alter prey selection by 

predators, which can alter population dynamics (Brook et al., 2012; Kilgo et al., 1998; Ordiz et al., 

2017). 

39 

 
 
Overall, the temporal changes we observed in hunting behavior may reflect changes in the 

ecology of the Talek Region. Prey populations have declined, (Ogutu et al., 2005, 2009, 2011; 

Ottichilo et al., 2000), temperatures have risen, and rainfall patterns have become more variable, 

including more periods of drought and extreme rainfall (Green et al., 2019b; Ogutu et al., 2007). 

Human activity and livestock grazing pressure in the Talek Region has increased during this time as 

well, altering habitat characteristics and herbivore grouping and populations (Green et al., 2018). 

Concurrent with these ecological changes, hyena populations have grown larger along the edge of 

the reserve, evidently in response to the decreasing lion population in this area and supplemental 

food in the form of livestock left behind in the Reserve unattended (Green et al., 2018). Future work 

comparing hunting success and prey selection between day and night would further improve our 

understanding of hyena foraging behavior and, more broadly, of predator-prey dynamics, 

particularly in systems modified by human activity. 

We used observational data documenting hunting attempts in this study. These events are 

rare and extremely difficult to observe directly in large carnivores. For the species and 

environments where hunting can be observed, hunt observations are biased towards the times of 

day, habitats, and prey sizes where events can be most easily seen. This introduces the potential to 

over- and under-estimate the importance of different prey types in their diet. However, 

observational studies do not require invasive methods. GPS technology has greatly improved 

predation studies (Elbroch et al., 2018) and has been applied to a wide range of species (e.g., Miller 

et al., 2013; Pitman et al., 2014; Svoboda et al., 2013; Tambling et al., 2010), allowing observers to 

detect kill sites without direct observational methods. However, this method is expensive, requires 

invasive methods, and is constrained by the battery life of devices; investigators using GPS 

technology must balance the amount information collected gain against battery life. A combination 

of observational and GPS data could improve our understanding of carnivore hunting behavior at 

40 

 
 
short and long temporal scales, and improve parameter estimates for the effects of social and 

environmental variables on hunt success. 

Together, these results demonstrate how hunting behavior and success can be influenced by 

traits of prey and predators and can change over time. Moreover, the changes in hunting behavior 

documented here reflect ecological changes in the region and demographic changes in the hyena 

population during this time period. Future work in other systems investigating how predator and 

prey attributes affect hunting behavior and how these dynamics may change over time, due to 

fluctuations in both predator and prey populations and in ecological factors, will increase our 

understanding of the relationship between predator-prey dynamics and broader ecosystem 

processes and community ecology. 

41 

 
 
 
 
TABLES 

Table 2.1. Summary of observed ungulate hunting attempts by hyenas in the Talek clan with known outcomes, Jan 1996 – Dec 2018, 
Maasai Mara, Kenya. 

Solo hunts 

Group hunts 

% 

Success  Total   Success 
28 
35 
7 
6 
26 
0 
13 
4 
0 
0 
0 
0 
1 

42.5 
41.5 
30.0 
44.4 
0.0 
50.0 
0.0 
0.0 
0.0 
0.0 
0.0 
0.0 
- 

17 
15 
2 
3 
5 
0 
1 
1 
0 
0 
0 
0 
0 

% 
Success 
60.7 
42.9 
28.6 
50.0 
19.2 
- 
7.7 
25.0 
- 
- 
- 
- 
0.0 

Genus species 

Prey type  
Thomson's gazelle  Eudorcas thomsoni 
Blue wildebeest 
Topi 
Impala 
Plains zebra 
Grant's gazelle 
Warthog 
African buffalo 
Kirk's Dikdik 
Duiker 
Eland 
Waterbuck 
Reedbuck 

Connochaetes taurinus 
Damaliscus lunatus 
Aepyceros melampus 
Equus quagga 
Nanger granti 
Phacochoerus africanus 
Syncerus caffer 
Madoqua kirkii 
Sylvicapra grimmia 
Taurotragus oryx 
Kobus ellipsiprymnus 
Redunca redunca 

Total   Success 

80 
41 
10 
9 
4 
2 
1 
1 
1 
1 
1 
1 
0 

34 
17 
3 
4 
0 
1 
0 
0 
0 
0 
0 
0 
0 

42 

 
 
 
 
 
 
   
 
 
 
 
 
Table 2.2. Hyena age class, sex, and rank by solo and group hunts observed for the Talek clan, 
Maasai Mara, Kenya, January 1996 – December 2018. 

% juvenile 
% adult 
% female 
% male 
mean rank (± SE) 

Solo hunts 
20.1% 
78.1% 
52.9% 
47.1% 
0.02 (± 0.05) 

Group hunts 

56.0% 
44.0% 
62.2% 
37.8% 
0.40 (± 0.03) 

43 

 
 
 
  
 
 
Table 2.3. Results from generalized linear model of the effects of hunt strategy (solo vs. group), 
prey age, and prey species on the probability of hunt success. Prey species are compared to 
Thomson’s gazelle as the reference level. Group hunting (2 or more hunters) is compared to solo 
hunting. Juvenile prey are compared to adult prey. Odds ratios > 1 indicate increased odds of hunt 
success based on that respective predictor, while odds ratios < 1 indicate decreased odds of hunting 
successfully. 

Predictors 

Group hunt 
Juvenile prey 
Impala 
Warthog 
Topi 
Wildebeest 
Zebra 
Observations 

Odds Ratios 
1.6 
7.41 
0.41 
0.1 
0.26 
0.94 
0.29 

260 

CI 
0.85 – 3.05 
4.07 – 13.94 
0.12 – 1.34 
0.00 – 0.61 
0.07 – 0.83 
0.47 – 1.87 
0.08 – 0.91 

p 
0.144 
<0.001 
0.137 
0.037 
0.029 
0.86 
0.043 

44 

 
 
 
  
  
 
 
Table 2.4. Predicted probability of hunting success for each prey species, averaged across group 
and solo hunts and adult and juvenile prey. 

Prey species 

Thomson's gazelle 
Impala 
Warthog 
Topi 
Wildebeest 
Zebra 

Probability 
of success 
0.50 
0.29 
0.09 
0.21 
0.49 
0.23 

SE 

0.06 
0.12 
0.09 
0.09 
0.07 
0.10 

Asymptotic 
lower CL 
0.39 
0.12 
0.01 
0.08 
0.36 
0.09 

Asymptotic 
upper CL 
0.62 
0.55 
0.45 
0.45 
0.62 
0.46 

45 

 
 
 
 
 
 
Table 2.5. Results from generalized linear model of the effects of hunt type, prey age, and the 
interaction between hunt type and prey age on the probability of hunt success for (a) Thomson’s 
gazelle and (b) wildebeest. Group hunting (2 or more hunters) is compared to solo hunting. Juvenile 
prey are compared to adult prey. Odds ratios > 1 indicate increased odds of hunt success based on 
that respective predictor, while odds ratios < 1 indicate decreased odds of hunting successfully. 

(a) Thomson’s gazelle  

Predictors 

Group hunt 
Juvenile prey 
Group hunt x juvenile prey 
Observations 

(b) Wildebeest 

Odds Ratios 
6.04 
7.87 
0.12 

108 

CI 
1.64 – 24.67 
2.98 – 22.58 
0.02 – 0.73 

p 
0.008 
<0.001 
0.022 

Predictors 

Odds Ratios 

CI 

Group hunt 
Juvenile prey 

Group hunt x juvenile prey 

Observations 

76 

0.83 
6.97 

5.45 

0.21 – 3.23 
1.82 – 30.86 

0.46 – 142.41 

p 

0.791 
0.007 

0.214 

46 

 
 
 
 
 
 
 
Table 2.6. Results from generalized linear model of the effects of hunter age class, sex, dominance 
rank, and prey age on the probability of successfully solo hunting. Juvenile hunters are compared to 
adult hunters. Male hunters and compared to female hunters. Juvenile prey are compared to adult 
prey. Odds ratios > 1 indicate increased odds of hunt success based on that respective predictor, 
while odds ratios < 1 indicate decreased odds of hunting successfully. 

Predictors 

Hunter age (juvenile) 

Hunter sex (male) 

Standardized rank 

Prey age (juvenile) 

Odds Ratios 
0.24 

1.82 

0.8 

8.16 

CI 
0.06 – 0.95 

0.56 – 5.88 

0.30 – 2.15 

p 
0.041 

0.316 

0.656 

3.11 – 21.38 

<0.001 

Observations 

129 

47 

 
 
 
  
  
 
 
Table 2.7. Results from generalized linear model of the effects of prey age and prey species on the 
probability of group hunting. Prey species are compared to Thomson’s gazelle as the reference 
level. Juvenile prey are compared to adult prey. Odds ratios > 1 indicate increased odds of group 
hunting that respective prey type or age class.  

Predictors 
Juvenile prey 
Impala 
Warthog 
Topi 
Wildebeest 
Zebra 

Observations 

Odds Ratios 
1.09 
1.84 
38.34 
1.97 
2.47 
19.18 

260 

CI 
0.61 – 1.97 
0.56 – 5.70 
7.05 – 716.22 
0.66 – 5.66 
1.32 – 4.66 
6.63 – 70.55 

p 
0.76 
0.295 
0.001 
0.21 
0.005 
<0.001 

48 

 
 
 
 
 
 
Table 2.8. Mean hunting group sizes and percentage of hunts that were group hunts by prey 
species for this study and Holekamp et al. 1997b. This study includes hunting data from January 
1996 – December 2018; Holekamp et al. 1997b includes hunting data from June 1988 – December 
1995 in the Talek region of the Maasai Mara, Kenya.  

Prey species 
Thomson's gazelle 
Impala 
Warthog 
Topi 
Wildebeest 
Zebra 

This study 

Holekamp et al. 1997b 

Mean hunting 
group size 
1.42 
1.69 
4.07 
2.47 
2.15 
5.52 

% group 
hunts 

25.9% 
40.0% 
92.9% 
41.2% 
46.1% 
86.7% 

SE 
0.1 
0.5 
0.5 
0.7 
0.2 
0.7 

Mean hunting 
group size 
2.08 
1.7 
- 
1.2 
2.92 
9.1 

% group 
hunts 
20.3% 
25.0% 
- 
6.3% 
26.2% 
68.4% 

SE 
0.1 
0.3 
- 
0.1 
0.3 
0.5 

49 

 
 
 
 
 
 
 
FIGURES 

Figure 2.1 

Maasai Mara National Reserve, Kenya, and estimated territory of Talek hyena clan that were 
observed for hunting behaviors, 1996 – 2018. 

50 

 
 
 
 
 
 
 
 
Figure 2.2. Proportion of successful hunts for top 6 prey species for Talek hyenas from January 
1996 – December 2018. Species are organized from left to right by increasing mean adult body 
mass.  

51 

 
 
 
 
 
 
Figure 2.3. Predicted probabilities of hunt success for Thomson’s gazelle (left) and wildebeest 
(right) by hunt type (solo or group) and adult (red) and juvenile (blue) prey. Whiskers indicate 
95% confidence intervals. 

52 

 
 
 
 
 
Figure 2.4. Predicted probability of successfully solo hunting by hunter age class and prey age 
class. 

53 

 
 
 
 
 
 
 
 
Figure 2.5. Mean hunting group size for top 6 prey species for Talek hyenas from 1996 – 2018 for 
hunts where group size could be determined precisely. Bars represent standard error of the mean. 
Species are organized from left to right by mean adult body mass.  

54 

 
 
 
 
 
 
 
Figure 2.6. Boxplot of hunting group size based on individual hunters’ rank quartile. **** indicates 
p-value <0.0001 for post hoc pairwise comparisons.  

55 

 
 
 
 
 
 
Figure 2.7. Boxplot of hunting group size based on individual hunters’ age class. **** indicates p-
value <0.0001 for Kruskal-Wallis test. 

56 

 
 
 
 
 
 
 
Figure 2.8. Boxplot of hunting group size based on individual hunters’ sex. ns indicates p-value was 
not significant for Kruskal-Wallis test. 

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
SUPPLEMENTARY INFORMATION  

Table S1. Results from generalized linear mixed model of the effects of hunt strategy (solo vs. 
group), prey age, prey species, and Julian date (modeled as a quadratic term) on the probability of 
hunt success. Observation session is included as a random variable. Each prey species is compared 
to Thomson’s gazelle as the reference level. Group hunting (two or more hunters) is compared to 
solo hunting, which is the reference level for hunt strategy. Juvenile prey are compared to adult 
prey, which is the reference level for age. Odds ratios > 1 indicate increased odds of hunt success for 
the specified predictor, compared to the reference level, while odds ratios < 1 indicate decreased 
odds of hunt success. 

Predictors 

Group hunt 
Juvenile prey 
Impala 
Warthog 
Topi 
Wildebeest 
Zebra 
Julian date (1st degree) 
Julian date (2nd degree) 

Observations 

Odds Ratios 
1.58 
7.08 
0.37 
0.09 
0.21 
0.77 
0.28 
47.72 
0.68 

260 

CI 
0.83 – 2.99 
2.95 – 17.01 
0.11 – 1.29 
0.01 – 0.87 
0.05 – 0.82 
0.36 – 1.63 
0.08 – 0.95 
0.27 – 8510.67 
0.00 – 107.91 

p 
0.165 
<0.001 
0.119 
0.037 
0.025 
0.496 
0.041 
0.144 
0.882 

58 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CHAPTER 3: 

HYENA FEEDING BEHAVIOR ACROSS VARYING ECOLOGICAL CONDITIONS 

ABSTRACT 

Large carnivores play an important role in maintaining healthy ecosystems, yet are globally 

threatened. They are particularly vulnerable to anthropogenic change and come into frequent 

conflict with humans when livestock overlap with large carnivore habitat. At the same time, native 

prey populations are declining for many large carnivores. Dietary flexibility can allow carnivores to 

cope with changes to their environment but, at the same time, may increase human-wildlife conflict. 

In this chapter, we investigate how ecological conditions influence the diet of spotted hyenas, a 

highly flexible forager and most abundant large carnivore in sub-Saharan Africa. We use long-term 

observational behavioral data to compare feeding observations across space and time. We compare 

feeding behavior of hyenas inhabiting areas of the Maasai Mara with different management 

practices: one where livestock grazing (Talek Region) is present and one without livestock grazing 

(Mara Conservancy). We then examine long-term hyena diet across 31 years in the Talek Region of 

the Maasai Mara; a region that has undergone changes in the landscape, including fluctuations in 

livestock grazing intensity, varying weather patterns, and changes in wild herbivore abundance. We 

summarize prey availability, livestock presence, temperature, and rainfall to understand how the 

two sites compare and to look at the broader ecological changes through time. We then quantify diet 

using observational records of feeding behavior to examine how hyena diets compare between the 

two sites, and across time in the Talek Region. Hyenas in both populations responded to seasonal 

changes in prey availability, consuming primarily the prey type that is most available. Hyenas in the 

Talek Region consumed domestic livestock and had a less diverse diet of resident prey species than 

in the Mara Conservancy. Longitudinally, in the Talek Region, hyenas were seen feeding on livestock 

more often as livestock presence increased. When livestock feeding increased, feeding on resident 

prey decreased. These changes reflect the observed declines of wild herbivores in the region. Our 

59 

 
 
results complement previous work documenting the declines of herbivore populations and adds to 

the body of research showing that hyenas are flexible foragers that switch to consuming prey that 

are most available both over seasonal time scales and across long-term ecological change. This 

flexibility likely facilitates hyenas’ ability to persist in landscapes with high human use. Further 

work on dietary flexibility and behavioral plasticity on species in changing environments will 

improve our understanding of how species cope with ecological change.  

INTRODUCTION 

Large carnivores are critically important in regulating and maintaining healthy ecosystems 

(Ale & Whelan, 2008; Berger et al., 2001; Beschta & Ripple, 2009; Estes et al., 2011), but large 

carnivore populations are seriously threatened worldwide (Ripple et al., 2014). Global ecological 

changes such as habitat loss, shifting and/or declining prey populations, conflict with humans, and 

climate change are all occurring at faster rates than extant species have previously experienced. 

Phenotypic plasticity helps to facilitate persistence in the face of rapid or novel environmental 

change (Losos et al., 2001; Renaud et al., 2015; Winchell et al., 2016), and this is especially true for 

species with long generation times, whose environments may change significantly within a few 

generations. Behavior underlies much phenotypic plasticity, and it often represents animals’ first 

response to cope with changing environmental conditions and stressors (Wong & Candolin, 2015). 

Examples of behavioral responses to anthropogenic change include shifts in diel activity patterns 

(Gaynor et al., 2018), space use (Kolowski & Holekamp, 2009; Oriol-Cotterill et al., 2015), and diet 

(Farias & Kittlein, 2008).  

Large carnivores are particularly vulnerable to effects of anthropogenic change due to their 

high trophic level, which makes them sensitive to shifts in native wildlife and vegetative 

communities and depletion of their native prey (Dorresteijn et al., 2015; Muhly et al., 2013; Ripple 

et al., 2014). However, dietary flexibility is thought to play a large role in a species’ ability to cope 

with changing environments. Species with specialized diets are more susceptible to decreases in 

60 

 
 
abundance of their primary food sources, whereas species with broader and more flexible diets can 

readily shift their diet in response to changes in availability of particular types of food (Navarro et 

al., 2017; Scholz et al., 2020). Nevertheless, maladaptive changes in foraging behavior sometimes 

occur because anthropogenic change outpaces evolutionary responses, decoupling links between 

environmental cues and effects on fitness (Lamb et al., 2017; Nisi et al., 2022). 

Habitat loss and fragmentation have also created overlap between native carnivore habitat 

and anthropogenic land use, leading to increased rates of livestock depredation (Inskip & 

Zimmermann, 2009; Treves & Karanth, 2003; Wolf & Ripple, 2017; Woodroffe & Ginsberg, 1998). 

Anthropogenic change will continue to exacerbate the problem of livestock depredation, because 

the expanding human population will further restrict carnivore home ranges, increase proximity to 

human settlements (Ugarte et al., 2019), and deplete natural prey populations (Ripple et al., 2015; 

Wolf & Ripple, 2016). Livestock depredation is associated with reduced fitness for carnivores, and 

retaliatory killing of carnivores in response to livestock depredation has emerged as a key threat on 

a global scale (Ripple et al., 2014). As the effects of human-induced environmental changes increase, 

large carnivores that cannot readily modify their diet will face more challenges and their 

populations may be threatened.  

Understanding how foraging behavior is altered in the face of rapid ecological change will 

improve our predictions regarding which species may be challenged by such changes, and thus 

where to target conservation efforts. Specifically, identifying how spatial and temporal variation in 

wild prey and livestock abundance affect prey selection in large carnivores will broaden our 

understanding of dietary flexibility and how it may enable other species to cope with future large-

scale ecological changes. It will also provide insight into the environmental conditions that increase 

livestock depredation and other human-wildlife conflict, and it will thus inform conflict mitigation 

practices and other wildlife management strategies (Abay et al., 2011; Khanal et al., 2020; 

Khorozyan et al., 2015; Nelson et al., 2016; Suryawanshi et al., 2017). 

61 

 
 
Spotted hyenas (Crocuta crocuta) are the most abundant large carnivore in sub-Saharan 

Africa and exhibit high behavioral flexibility under changing habitat conditions (Holekamp & 

Dloniak, 2010). Hyenas are highly gregarious, generalist predators who successfully inhabit a wide 

range of habitat types including forests, savannas, deserts, and cities. These hyenas are highly 

flexible foragers that generally hunt whichever herbivore species are most available (Cooper, 1990; 

Cooper et al., 1999; Holekamp et al., 1997b; Kruuk, 1972). They readily switch prey types as the 

relative availability of local prey species changes seasonally (Cooper et al., 1999; Holekamp et al., 

1997b) and across longer time scales (Ho  ner et al., 2002).They can also persist exclusively on 

domestic prey or even primarily on refuse (Abay et al., 2011; Yirga et al., 2013; Yirga & Bauer, 

2010). Spotted hyenas’ extreme dietary flexibility makes them an excellent species in which to 

investigate how ecological variation affects carnivore foraging ecology, and to understand whether 

there are limits to this flexibility. Furthermore, hyenas are often implicated in livestock depredation 

but are underrepresented in livestock depredation studies (Hoffmann & Montgomery, 2022), 

highlighting the need for more information on how hyenas interact with livestock. 

Here, we investigate feeding behavior and dietary flexibility across space and time in a 

population of spotted hyenas inhabiting the Maasai Mara National Reserve, Kenya (henceforth “the 

Mara”). The Mara is a tropical savanna with seasonal variations in prey: it both supports a diverse 

community of large resident mammals and, for a portion of the year, hosts large migratory herds of 

wildebeest and zebra (Bell, 1971; Craft et al., 2015; Sinclair & Norton-Griffiths, 1979). 

Anthropogenic disturbance varies considerably within the Mara: the Mara River divides the Mara 

into two separate management areas characterized by different ecological conditions and 

contrasting livestock management practices. In this study, we leverage a long-term 31-year foraging 

dataset collected at multiple sites within the Mara to assess predictors of dietary flexibility in 

spotted hyenas across the Mara landscape. We inquire how hyena feeding behavior varies (1) 

between two areas with different ecological conditions, one characterized by intensive livestock 

62 

 
 
grazing and one where livestock are prohibited, and (2) within a single social group across three 

decades of ecological change. 

METHODS 

Maasai Mara National Reserve  

The Mara is primarily composed of rolling grasslands dotted with trees and riverine habitats 

(Sinclair & Norton-Griffiths, 1979). Precipitation follows a bimodal pattern annually, with most 

precipitation occurring in November–December and March–May (Green et al., 2019b; Ogutu et al., 

2008). Temperature has less seasonal variation (Green et al., 2019b). Due to climate change, 

temperature is increasing, and rainfall is becoming less seasonally predictable (Green et al., 2019b; 

Ogutu et al., 2007). Together, these changing climatic conditions threaten primary production of 

grasslands, exacerbate woody plant expansion, and reduce herbivore population density and 

diversity (Li et al., 2020). The Mara is also under immense pressure from many anthropogenic 

sources (e.g., agribusiness, fencing, firewood harvesting, tourism, livestock grazing), causing 

population declines of resident and migratory herbivores (Green, 2015; Ogutu et al., 2005, 2009, 

2011; Ottichilo et al., 2000). The declining herbivore populations have changed the prey base for 

large carnivores, increasing the likelihood of livestock depredation and conflict with humans, and 

threatening human safety and livelihood as well as carnivore populations.  

Management practices differ significantly within the Mara between the west and east sides 

of the Mara River, which influences local community ecology (Green et al., 2018). The Eastern side 

of the Mara is bordered by the growing town of Talek, (hereafter “the Talek Region,” Fig 3.1), and the 

hyenas in this region interact with humans and livestock frequently (Green et al., 2018; Kolowski & 

Holekamp, 2006). Patterns and intensity of livestock grazing in and near the Talek Region have 

varied throughout time due to the complex interactions among ecological, social, and political 

influences (Butt, 2014), but overall grazing has increased in the past several decades (Green et al., 

2019b). On the western side of the Mara River in “the Mara Conservancy”, livestock grazing is 

63 

 
 
prohibited and strictly enforced, such that hyenas encounter no livestock in the Reserve, and do not 

interact with humans except those in tour vehicles. Due partially to ecology and partially to 

livestock grazing practices, the grass height throughout the Mara Conservancy is generally 

considerably higher than that in the Talek Region (Spagnuolo et al., 2020). The differences between 

these regions make the Mara an excellent site for comparing concomitant dietary flexibility under 

different ecological conditions, and inquiring how livestock (i.e., domestic cows, sheep, and goats) 

influence hyena foraging ecology.  

Study site and climate 

We conducted our study at two sites, one in the Talek Region and one in the Mara 

Conservancy (Fig 3.1). The Mara Hyena Project has been monitoring the Talek clan in the Talek 

Region since mid-1988 and has been monitoring 3 clans (Happy Zebra, Serena North, and Serena 

South clans) in the Mara Conservancy since mid-2008, using the same methods at each site. Site-

level weather data, including minimum and maximum temperatures and total precipitation, were 

recorded daily at weather stations located at each study site (Figs 3.2-3.3).  

Hyena feeding behavior 

We collected hyena behavioral data during twice-daily observation periods from (~0500-0830 h) 

and (~1600-2000 h). During these periods, observation sessions (hereafter “sessions”) were 

initiated when observers encountered one or more hyenas separated from other hyenas by at least 

200 m. A session ended when interactions among hyenas ceased and observers left the individual or 

group, or when observers lost sight of the hyenas (Lehmann et al., 2017); session duration was 

recorded in minutes, lasting from 5 minutes to several hours.  

Observers recorded all instances of hyena feeding behavior seen during sessions. A hyena 

was considered feeding if it was observed actively chewing and swallowing food, but not if it was 

carrying food, interacting with but not ingesting food, or exhibiting other signs of having fed (e.g., 

hyena was bloody). An observation session could contain multiple feeding observations if the 

64 

 
 
session included multiple carcasses or successful hunts. Within each session where at least one 

hyena fed, observers tried to collect information on the prey species, which was determined based 

on carcass size and coloration, horn shape, hoof shape, and other cues. However, prey species were 

often difficult to identify as observers needed to see the carcass prior to its decimation, and 

identification to species was only possible in around 50% of feeding sessions due to dense 

vegetation or darkness (see Results). This had the potential to over-represent observations of large 

prey since these carcasses take longer to consume and draw attention of more hyenas and 

scavengers.  

To calculate monthly hyena diet profiles, we used all observations of hyenas actively feeding 

on identified prey species. For each month, we summarized the total feeding observations for the 

Talek clan in the Talek Region and all feeding observations across the three study clans in the Mara 

Conservancy. We then calculated monthly relative percentages of each prey species and prey type 

(e.g., migratory, resident) on which the hyenas in each region were observed to feed.  

Prey availability 

To quantify wild prey availability, we drove prey transects biweekly at 10 km/hr between 

0800-1000 h. In the Talek Region, two 4-km transects were monitored within the Talek clan’s 

territory from 1988-2019, and an additional transect was also monitored from 2005-2019. In the 

Mara Conservancy, six transects of variable lengths (1.5 km to 5.4 km), two in the territory of each 

study clan, were monitored from 2008-2019. Full details of transect methods are available in (Green 

et al., 2019a; Holekamp et al., 1997b). Briefly, we used a range finder to count any individual of the 

prey species listed in Table 3.1 that was within 100 meters of the vehicle on either side of the 

transect. To estimate monthly herbivore availability, we calculated the mean monthly density 

(animals/km2) of resident and migratory herbivores by dividing the number of each herbivore type 

by the transect area (transect length * 0.2 km) for each transect, then averaging across transects 

within each month and region.  

65 

 
 
To estimate the number of livestock grazing in the Talek Region, we performed livestock 

counts by systematically driving through the Talek clan’s territory, recording all cows, sheep, and 

goats seen within park boundaries (Green, 2015). Between May 2000 and May 2008, observers 

performed counts up to twice daily, once between 0500-1000 h and again between 1600-2000 h. 

After May 2008, livestock counts were performed biweekly between 1600-2000 h (Green, 2015). To 

estimate livestock abundance, we summed the total livestock counted each day, then averaged 

across all days on which livestock counts occurred within each month.  

Data analysis 

To compare prey availability to hyena diet profiles, we first grouped hyena prey species into 

four categories: migratory herbivores, resident herbivores, livestock, and other prey (Table 3.1). 

Migratory herbivores include wildebeest and zebra. Although Thomson’s gazelles migrate 

elsewhere in the Mara-Serengeti ecosystem (Bell 1971; Said et al., 2003), they are non-migratory in 

our study region (Green et al., 2019b) and are therefore considered resident here, as in earlier 

studies (Ogutu et al., 2011; Ottichilo et al., 2000). Livestock include domestic cows, goats, and sheep. 

Prey species that were consumed by hyenas but were not included in herbivore prey transects (e.g., 

small/cryptic antelope, birds, fish, other carnivores) were categorized as “other prey” and comprise 

only 2.6% of the feeding dataset. 

We first described how hyena feeding observations relate to prey availability across both (1) 

space, using the 2008-2019 datasets from our study sites on both sides of the Mara, and (2) time, 

using the 1988-2019 dataset from the Talek Region. We calculated the percent of feeding 

observations involving each prey category by month, and we related this to the mean monthly prey 

density of each prey category. For the Talek Region, we also compared annual percent of livestock in 

hyena diets to mean annual livestock counts. We then compared relative proportions of resident 

prey in diets and tested whether the resident prey diets 1) differed between the Talek Region and 

the Mara Conservancy from 2008-2019 and 2) between 3 study decades (1988-1998; 1999-2008; 

66 

 
 
2009-2019) in the Talek Region using a Fisher’s exact test. We used generalized linear models to 

investigate whether feeding on specific prey species varied across time in Talek. We fit three logistic 

models using the hyena feeding dataset, where one of the top three resident prey species in the 

Talek hyena diet was the outcome variable in each model. Our outcome variable was binomial: 

whether or not the feeding observation was on that particular prey species, and our predictor 

variable was in which decade the feeding observation occurred.  

Finally, we used the hyena feeding dataset to fit three generalized linear mixed models to 

investigate hyena prey selection and test how hyena feeding behavior related to prey density. Our 

outcome variable for each model was prey category (i.e., migratory prey, resident prey, and 

livestock) and was binomial: whether or not the feeding observation involved that particular prey 

category. Our predictor variables were the scaled mean monthly prey density for each prey 

category, and we included year as a random variable to account for interannual variation. All 

analyses were run using R version 4.3.3 (R Core Team, 2024) in RStudio version 2023.12.1.402 

(Posit team, 2024). We ran models using the lme4 package version 1.1-35.2 (Bates et al., 2015). We 

tested for collinearity among predictor variables using the ‘performance’ package version 0.11.0 

(Lu  decke et al., 2021), and removed collinear predictors until all variance inflation factors were < 5.  

We used the ‘tidyr’ version 1.3.1 (Wickham et al., 2024), ‘dplyr’ version 1.1.4 (Wickham et 

al., 2023), ‘lubridate’ version 1.9.2 (Grolemund & Wickham, 2011; Wickham, 2023), and ‘stringr’ 

version 1.5.1 (Wickham, 2023) packages to manipulate and summarize data in R. We used ‘ggplot2’ 

version 3.5.0 (Wickham, 2016), ‘sjPlot’ version 2.8.14 (Lu  decke, 2023), ‘ggpubr’ version 0.6.0 

(Kassambara, 2023), and ‘gridExtra’ version 2.3 (Auguie, 2017) to visualize data. We created the 

study area map using ArcGIS Pro (ESRI, 2021). 

67 

 
 
 
 
RESULTS 

Data summary 

The dataset included 7603 instances of feeding observed during 7573 observation sessions 

from the Talek clan (in the Talek Region) from June 1988 through December 2019, and 1950 

instances of feeding during 1896 sessions from the three Mara Conservancy clans from June 2008 

through December 2019. Prey type was unidentified in 44.6% (n=3390) of feeding observations in 

the Talek Region and in 54.7% (n=1067) of feeding observations in the Mara Conservancy. Of the 

observations where prey type was identifiable, wildebeest was the most common prey type on both 

sides of the Mara (Table 3.2). Thomson’s gazelle, topi, and impala were the most common resident 

prey in the Talek Region, and buffalo, topi, warthog, and hippo were the most common resident prey 

in the Mara Conservancy (Table 3.2). Domestic cows made up 4.4% and sheep and goats 0.4% of 

identifiable feeding observations in the Talek Region (Table 3.2a), but livestock were never 

observed in the Mara Conservancy. 

Cross-sectional comparison of feeding observations (2008-2019) 

Hyena diets in the Talek Region and the Mara Conservancy both followed the same general 

pattern, where hyenas ate mostly resident prey in the first half of the year and switched to 

consuming mostly migratory prey when the migratory herbivores arrived during the second half of 

the year (Fig 3.4). When the migratory herbivores were absent, resident prey made up a larger 

percentage of the feeding observations in the Mara Conservancy than in the Talek Region. In the 

Talek Region, livestock were consumed more often when migratory prey density was lower (Fig 

3.4a). In both regions, other prey made up only a small proportion of the diet and were consumed 

slightly more often when the migration was absent (Fig 3.4).  

Observations of hyenas feeding on migratory and resident prey tended to vary with 

respective prey densities (Fig 3.5). Migratory herbivore density peaked from June–November in the 

Talek Region and from July–November in the Mara Conservancy (Fig 3.5 a & c). Feeding events 

68 

 
 
involving migratory prey were more frequent in the months when higher densities of migratory 

prey were present. Feeding events involving resident prey were more frequent in months with 

higher densities of resident prey, although resident prey density was less variable than migratory 

prey density (Fig 3.5 b & d). In the Talek Region, resident prey densities were higher from January–

July than in August–December (Fig 3.5 b); in the Mara Conservancy, resident prey density had very 

little monthly variation across the year (Fig 3.5 d). The resident prey species consumed by hyenas in 

the Talek Region were significantly different than those consumed by hyenas in the Mara 

Conservancy (Fig 3.6; Fisher’s exact test, p < 0.001).  

Longitudinal feeding observations in the Talek Region (1988-2019) 

Over the last three decades in the Talek Region, hyena diets followed the same seasonal 

pattern described above, where hyenas ate mostly resident prey in the first half of the year and 

mostly migratory prey during the second half of the year (Fig 3.7). However, both migratory and 

resident prey densities appear to have decreased in recent years: prey densities in 1999-2008 and 

2009-2019 were on average lower than in 1988-1998. Relative proportions of observations of 

hyenas feeding on migratory prey remained relatively consistent through time, whereas relative 

proportions of observations of hyenas feeding on resident prey declined across the three decades. 

Observations of hyenas feeding on livestock were more common in 2009-2019 than in previous 

years, and livestock counts were also highest during this same decade (Fig 3.8). As seen earlier (Fig 

3.4), livestock counts tended to be higher in months when migratory prey densities and feeding 

observations were lower (Fig 3.7).  

The resident prey species consumed by Talek hyenas have also changed over time (Fig 3.9; 

Fisher’s exact test, p < 0.001). Consumption of Thomson’s gazelle has decreased over time: 

Thomson’s gazelle made up a larger proportion of hyena feeding observations on resident prey in 

1988-1998 than in 1999-2008 (β = -0.391, p = 0.003) and 2009-2019 (β = -0.730, p < 0.001). 

Consumption of impala has increased over time: impala made up a smaller proportion of hyena 

69 

 
 
feeding observations on resident prey in 1988-1998 than in 1999-2008 (β = 1.022, p < 0.001) and 

2009-2019 (β = 0.537, p = 0.011). Relative proportions of topi in hyena diets did not significantly 

vary across time (1988-1998 versus 1999-2008: β = -0.232, p = 0.172; 1988-1998 versus 2009-

2019: β = 0.217, p = 0.179). 

Prey selection model results 

In both regions, observations of hyenas feeding on migratory and resident prey tended to 

vary with respective prey densities. In the Talek Region, hyenas were more likely to consume 

migratory prey, resident prey, and livestock when those respective prey types were more abundant 

(Fig 3.10). The strongest association between feeding behavior and prey availability was with 

migratory prey. Migratory prey were consumed more often when there were higher densities of 

migratory prey present locally, and consumed less often when there were higher densities of either 

resident prey or livestock. Resident prey were consumed more often when there were higher 

densities of resident prey and less often when there were higher densities of migratory prey. 

Livestock were consumed more often when livestock counts were higher and less often when 

migratory prey density was higher (Fig 3.10).  

In the Mara Conservancy, feeding on migratory prey was positively associated with 

migratory prey densities, but there was no significant relationship between feeding on migratory 

prey and resident prey density (Fig 3.11). Feeding on resident prey was positively associated with 

resident prey densities and negatively associated with migratory prey densities (Fig 3.11). As in the 

Talek Region, the relationship between migratory prey density and hyenas feeding on migratory 

prey had the largest effect size (Fig 3.11).  

DISCUSSION  

Our results demonstrate that hyena foraging behavior responds to ecological change across 

three distinct scales: seasonally, spatially, and longitudinally over three decades. Seasonally, hyenas 

in both regions consumed primarily migratory prey when the wildebeest and zebra migration was 

70 

 
 
present, and consumed resident prey when migratory prey were scarcer. Spatially, hyenas exposed 

to greater anthropogenic activity in the Talek Region consumed a less diverse range of resident prey 

species than did hyenas in the better protected habitat of the Mara Conservancy. Talek hyenas were 

the only individuals observed to regularly consume livestock. Longitudinally, the representation of 

livestock in the diet of Talek hyenas has increased over the last 30 years, reflecting increasing 

livestock grazing intensity in the region (Green, 2015). As expected, hyenas largely consume the 

prey category that is most available at any given time. This is consistent with previous studies 

demonstrating that hyenas show a functional response to local prey availability across seasons 

(Holekamp et al., 1997b, Cooper et al., 1999) and changes in long-term prey availability (Ho  ner et 

al., 2002). In the Ngorongoro Crater, Tanzania, Ho  ner et al. (2002) found that hyenas ate more 

buffalo calves and adult wildebeest as preferred prey that was easier to capture decreased. 

Hyenas at both sites predominately ate migratory wildebeest and zebra during the months 

when the migration was present in their area. In the Talek Region, feeding on migratory prey was 

positively associated with migratory prey density, and negatively associated with both resident prey 

density and livestock count (Fig 3.10). In the Mara Conservancy, feeding on migratory prey was only 

positively associated with migratory prey density (Fig 3.11). Unlike in the Talek Region, where 

resident prey densities vary seasonally, resident prey in the Mara Conservancy are at consistently 

low densities year-round (Fig 3.5). In both regions, feeding on resident prey was positively 

associated with resident prey density and negatively associated with migratory prey density (Figs 

3.10 & 3.11). Among resident prey, Thomson’s gazelles were involved in a large portion of Talek 

hyena feeding observations, whereas Mara Conservancy hyenas were seen eating a more diverse 

range of resident prey species (Fig 3.6). This difference was observed despite a longitudinal shift in 

the Talek clan’s diet away from primarily Thomson’s gazelles over the last 30 years (Fig 3.9).  

The impact of changing climatic factors on wildlife populations may explain some of the 

observed longitudinal variation in hyena foraging behavior. Our data show an increase in 

71 

 
 
temperature in the Talek region in the past 30 years (Fig 3.3), corroborating previous studies in the 

Mara documenting increasing temperature (Green et al., 2019b; Ogutu et al., 2007) and more 

extreme rainfall patterns (Bartzke et al., 2018). These changing weather patterns might have both a 

direct impact on hyena foraging behavior by affecting prey susceptibility to predation (Mills et al., 

1995), and an indirect impact on prey densities by affecting herbivore population dynamics (Ogutu 

et al., 2008). Perhaps as a result, our data indicate a long-term decrease in both migratory and 

resident herbivore density (Fig 3.7), corroborating previous work (Green et al., 2019b; Ogutu et al., 

2009, 2011, 2016; Ottichilo et al., 2000). Future work investigating how temperature and rainfall 

impact hyena foraging and prey availability in the Mara is important to get a more complete 

understanding of how current conditions influence foraging and to make future predictions.  

Our data show that hyenas relied more on livestock as a food source as livestock presence 

increased. In the Talek Region, feeding on livestock was positively associated with livestock counts 

and negatively associated with migratory prey density (Fig 3.10), indicating that hyenas may 

replace resident herbivores with livestock when migratory herbivore density is low (Fig 3.4). The 

largest change in the Talek clan’s diet in the last 30 years was the increase in livestock feeding (Fig 

3.7 & 3.8), likely due to the increase in livestock grazing intensity in that region. Livestock loss due 

to carnivore depredation is a global issue, which causes major income loss and poses serious safety 

risks to people living near carnivores (Treves & Karanth, 2003). Livestock movement influences 

herbivore space use, but the impacts can vary by herd size and seasonality (Masiaine et al., 2021, 

Odadi et al., 2011, Kimuyu et al., 2017), therefore the complex dynamics between livestock, 

herbivores, and carnivores vary depending on regionally specific conditions. Research and 

management actions to promote human-carnivore coexistence should be location-specific, 

culturally sensitive, and should include knowledge, leadership, goals and perspectives from local 

people (Parmisa & Reid, 2021; van Eeden et al., 2018; Wilkinson et al., 2021).   

72 

 
 
In this study, we focused on differences among hyena populations across space and time, but 

diet can also vary widely among individuals within a social group. Hyena space use varies by age, 

sex, and reproductive status (Boydston et al., 2003, 2005), and individual access to critical resources 

such as carcasses varies with social rank (Frank, 1986; Kruuk, 1972; Smith et al., 2008; Tilson & 

Hamilton, 1984). These factors may influence individuals’ perceptions and knowledge of their 

environment and lead to high inter-individual variation in foraging decisions and diet. For example, 

certain individual hyenas may be more likely to depredate livestock, as shown in other species 

(Linnell et al., 1999; Mizutani, 1993; Morehouse et al., 2016). A fecal DNA study of hyenas in the 

Ngorongoro Crater found that overall livestock was relatively rare in the diet, but older hyenas were 

more likely to eat livestock, but detections included livestock that could have been scavenged or 

depredated (Dheer et al., 2023). Future studies using molecular techniques such as stable isotope 

analysis and DNA metabarcoding could provide more highly resolved diet data to compare among 

individuals across different temporal scales (e.g., Masse  et al., 2023; Scholz et al., 2020), and can be 

applied to large carnivore systems to better understand patterns of livestock depredation (Dheer et 

al., 2023; Thuo et al., 2020). While every method has benefits and drawbacks, incorporating 

multiple methods can improve diet estimates (Chiaradia et al., 2014; Nielsen et al., 2018) and 

elucidate foraging behavior.  

Ultimately, top predators such as spotted hyenas are biodiversity indicators (Natsukawa & 

Sergio, 2022), and exert strong influences on ecosystem function. Their prey choices and capture 

success rates have cascading direct and indirect effects on sympatric populations of other large 

predators, mesopredators, herbivores, and primary producers as well as abiotic components of the 

ecosystem (Estes et al., 2011; Ripple et al., 2014). Therefore, ecological and anthropogenic changes 

that cause top predators to alter their diet can have profound effects on ecosystem function. Dietary 

niche breadth can help facilitate predator survival in changing habitats. For example, species with 

more dietary flexibility are more able to cope with human activities by exploiting anthropogenic 

73 

 
 
food sources (Maibeche et al., 2015; Yirga et al., 2013). Hyenas have a high niche breathe compared 

to sympatric carnivores (Vissia et al., 2023); other carnivores with lower niche breadth or lower 

tolerance for human activities may have less flexible foraging strategies. Further work on dietary 

flexibility and behavioral plasticity should be conducted on other species in changing environments.  

74 

 
 
 
TABLES 

Table 3.1. Herbivore species included in prey transect counts, Talek Region (1988 – 2018) and 
Mara Conservancy (2008-2019), Kenya. Zebra and wildebeest were categorized as migratory prey, 
and all other herbivores counted were categorized as resident prey. 

Common name 
Migratory herbivores  
  Plains zebra 
  Blue wildebeest 
Resident herbivores  
  Thomson's gazelle 

Impala 

  Topi 
  Warthog 
  Hartebeest 
  Grant's gazelle 
  African buffalo 
  Hippo 
  Giraffe 
  Eland 
  African elephant 
  Duiker 
  Oribi 
  Reedbuck 
  Waterbuck 
  Olive baboon 
   Bushbuck 

Genus species 

Equus quagga 
Connochaetes taurinus 

Eudorcas thomsoni 
Aepyceros melampus 
Damaliscus lunatus 
Phacochoerus africanus 
Alcelaphus buselaphus 
Nanger granti 
Syncerus caffer 
Hippopotamus amphibius 
Giraffa camelopardalis 
Taurotragus oryx 
Loxodonta africana 
Sylvicapra grimmia 
Ourebia ourebi 
Redunca redunca 
Kobus ellipsiprymnus 
Papio anubis 
Tragelaphus scriptus 

75 

 
 
 
 
 
 
 
 
 
 
Table 3.2. Summary of hyena feeding observations where prey type was identified in the (a) Talek 
Region (June 1988 – December 2019) and (b) Mara Conservancy (June 2008 – December 2019), 
Kenya. Hyenas in the Mara Conservancy were never observed consuming livestock. 

(a) Talek Region 
Prey type 
Blue wildebeest 
Thomson's gazelle 
Plains zebra 
Topi 
Cow 
Impala 
Giraffe 
African buffalo 
Savanna elephant 
Common warthog 
Invertebrate 
Grant's gazelle 
Common eland 
Sheep/goat 
Bird 
Rodent/hare 
Spotted hyena 
Bat-eared fox 
Nile crocodile 
Reptile 
Hippo 
Hartebeest 
Olive baboon 
Jackal 
Mongoose 
Domestic dog 
Kirk's Dikdik 
Common Reedbuck 
Waterbuck 
Domestic cat 
Catfish 
Lion 
*highest identifiable taxonomic level 

Genus species 
Connochaetes taurinus 
Eudorcas thomsoni 
Equus quagga 
Damaliscus lunatus 
Bos taurus 
Aepyceros melampus 
Giraffa camelopardalis 
Syncerus caffer 
Loxodonta africana 
Phacochoerus africanus 
Arthropoda, Taenia* 
Nanger granti 
Taurotragus oryx 
Ovis aries, Capra hircus 
Aves* 
Rodentia, Lagomorpha* 
Crocuta crocuta 
Otocycon melgalotis 
Crocodylus niloticus 
Testudines, Squamata* 
Hippopotamus amphibius 
Alcelaphus buselaphus 
Papio anubis 
Canis spp.* 
Herpestidae* 
Canis familiaris 
Madoqua kirkii 
Redunca redunca 
Kobus ellipsiprymnus 
Felis catus 
Clarias gariepinus 
Panthera leo 

n obs  % obs 
50.32 
2120 
14.38 
606 
9.76 
411 
6.79 
286 
4.39 
185 
4.25 
179 
1.88 
79 
1.52 
64 
1.14 
48 
1.12 
47 
0.83 
35 
0.38 
16 
0.36 
15 
0.36 
15 
0.33 
14 
0.31 
13 
0.28 
12 
0.21 
9 
0.21 
9 
0.19 
8 
0.17 
7 
0.14 
6 
0.12 
5 
0.12 
5 
0.12 
5 
0.09 
4 
0.07 
3 
0.05 
2 
0.05 
2 
0.02 
1 
0.02 
1 
0.02 
1 

76 

 
 
 
 
 
 
 
  
 
 
 
Table 3.2 (cont’d). 

(b) Mara Conservancy 
Prey type 
Blue wildebeest 
Plains zebra 
African buffalo 
Topi 
Common warthog 
Hippo 
Impala 
Thomson's gazelle 
Invertebrate 
Giraffe 
Savanna elephant 
Spotted hyena 
Bird 
Olive baboon 
Hartebeest 
Lion 
Nile crocodile 
Common eland 
Rodent/hare 
Waterbuck 
*highest identifiable taxonomic level 

Genus species 
Connochaetes taurinus 
Equus quagga 
Syncerus caffer 
Damaliscus lunatus 
Phacochoerus africanus 
Hippopotamus amphibius 
Aepyceros melampus 
Eudorcas thomsoni 
Arthropoda* 
Giraffa camelopardalis 
Loxodonta africana 
Crocuta crocuta 
Aves* 
Papio anubis 
Alcelaphus buselaphus 
Panthera leo 
Crocodylus niloticus 
Taurotragus oryx 
Rodentia, Lagomorpha* 
Kobus ellipsiprymnus 

n obs  % obs 
51.76 
457 
11.55 
102 
6.57 
58 
6.00 
53 
4.64 
41 
4.08 
36 
3.74 
33 
2.83 
25 
2.38 
21 
2.04 
18 
1.59 
14 
0.91 
8 
0.68 
6 
0.23 
2 
0.23 
2 
0.23 
2 
0.23 
2 
0.11 
1 
0.11 
1 
0.11 
1 

77 

 
 
 
 
 
 
 
 
 
  
FIGURES 

Figure 3.1. Maasai Mara National Reserve, Kenya, and study area locations for quantifying hyena 
feeding and prey availability, 1988 – 2019. 

Talek Region 

78 

 
 
 
 
 
 
Figure 3.2. Mean monthly maximum (solid line) and minimum (dashed line) temperature ± SE and mean monthly precipitation ± SE (grey 
bars) from 2009 – 2019 for the (a) Talek Region and (b) Mara Conservancy, Kenya.  

79 

 
 
 
 
 
Figure 3.3. Mean annual maximum (solid line) and minimum (dashed line) temperature ± SE and 
cumulative annual precipitation (grey bars) for the (a) Talek Region from 1989 – 2019 and (b) 
Mara Conservancy from 2009 – 2019, Kenya. Data from the first years of study in the Talek Region 
(1988) and the Mara Conservancy (2008) were excluded from annual weather summaries because 
data collection began mid-year and did not contain a full year of data. 

80 

 
 
 
 
 
 
Figure 3.4. Monthly proportion of hyena feeding observations on migratory herbivores (red), resident herbivores (orange), livestock 
(light blue), and other prey (dark blue) from July 2008 to December 2019 in the (a) Talek Region and (b) Mara Conservancy, Kenya. 
Migration season is indicated by the black brackets (June – November for the Talek Region; July – November for the Mara Conservancy). 

81 

 
 
 
 
 
 
Figure 3.5. Monthly mean prey density ± SE (dashed line) and proportion of hyena feeding 
observations (solid line) from July 2008 to December 2019, separated by region and prey type. 
Feeding events of migratory herbivores (solid red) and mean monthly migratory herbivore density 
(dashed red) in the (a) Talek Region and (b) Mara Conservancy, Kenya. Feeding events of resident 
herbivores (solid orange) and mean monthly resident herbivore density (dashed orange) in the (c) 
Talek Region and (d) Mara Conservancy. 

82 

 
 
 
 
 
 
 
 
 
Figure 3.6. Monthly proportion of hyena feeding observations on different resident prey species from July 2008 to December 2019 in the 
(a) Talek Region and (b) Mara Conservancy, Kenya. The category “other antelope” included dik dik, hartebeest, reedbuck, and waterbuck. 

83 

 
 
 
 
 
Figure 3.7. Monthly mean prey density and proportion of hyena feeding observations in the Talek 
Region, Kenya: 1988 – 1998 (light blue), 1999 – 2008 (medium blue), and 2009 – 2019 (dark blue): 
(a) migratory herbivore density, (b) migratory herbivore feeding observations, (c) resident 
herbivore density, (d) resident herbivore feeding observations, (e) livestock counts, and (f) livestock 
feeding observations. Error bars represent ± SE of mean monthly prey density and livestock counts.  

84 

 
 
 
 
 
Figure 3.8. Mean annual livestock counts ± SE (dashed line) and annual proportion of hyena 
feeding observations on livestock (solid line) in the Talek Region, Kenya from 2000 – 2019. 

85 

 
 
 
 
 
 
Figure 3.9. Monthly proportion of hyena feeding observations on different resident prey species by 
decade in the Talek Region, Kenya. The category “other antelope” includes dik dik, hartebeest, 
reedbuck, and waterbuck. 

86 

 
 
 
 
 
Figure 3.10. Forest plot showing odd ratios and 95% confidence intervals of the fixed effects from models of observed hyena feeding 
behavior on migratory herbivores and resident herbivores in the Talek Region, Kenya. Odds ratios > 1 indicate increased odds of feeding 
on that respective prey type, while odds ratios < 1 indicate decreased odds of feeding on that prey type. *** indicates p-value of < 0.001,   
** indicates p-value of < 0.01, and * indicates p-value of < 0.1. 

87 

 
 
 
 
 
Figure 3.11. Forest plot showing odd ratios and 95% confidence intervals of the fixed effects from models of observed hyena feeding 
behavior on migratory herbivores and resident herbivores in the Mara Conservancy, Kenya. Odds ratios > 1 indicate increased odds of 
feeding on that respective prey type, while odds ratios < 1 indicate decreased odds of feeding on that prey type. *** indicates p-value of     
< 0.001, ** indicates p-value of < 0.01, and * indicates p-value of < 0.1. 

88 

 
 
 
 
 
 
 
 
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