Nutritional Strategy and Social Environment in Redtail Monkeys (Cercopithecus Ascanius)

City University of New York (CUNY) CUNY Academic Works

All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects

2-2020

Nutritional Strategy and Social Environment in Redtail Monkeys (Cercopithecus ascanius)

Margaret Bryer The Graduate Center, City University of New York

How does access to this work benefit ou?y Let us know!

More information about this work at: https://academicworks.cuny.edu/gc_etds/3554 Discover additional works at: https://academicworks.cuny.edu

This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] NUTRITIONAL STRATEGY AND SOCIAL ENVIRONMENT IN REDTAIL MONKEYS

(CERCOPITHECUS ASCANIUS)

MARGARET A. H. BRYER

A dissertation submitted to the Graduate Faculty in Anthropology in partial fulfillment of the

requirements for the degree of Doctor of Philosophy, The City University of New York

2020

MARGARET A. H. BRYER

ii Nutritional strategy and social environment in redtail monkeys (Cercopithecus ascanius)

Margaret A. H. Bryer

This manuscript has been read and accepted for the Graduate Faculty in Anthropology in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy.

December 6, 2019 Jessica M. Rothman

Chair of Examining Committee

December 6, 2019 Jeff Maskovsky

Executive Officer

Supervisory Committee: Larissa Swedell Andrea L. Baden Marina Cords David Raubenheimer

THE CITY UNIVERSITY OF NEW YORK

iii ABSTRACT

Nutritional strategy and social environment in redtail monkeys (Cercopithecus ascanius)

Margaret A. H. Bryer

Advisor: Jessica M. Rothman

An animal’s nutritional strategy involves the complex interplay between its dynamic physiology and its environment, an environment that includes a landscape of foods that vary in nutritional composition as well as a social environment of other feeding individuals. Social behavior—cooperative or competitive, with conspecifics or with other sympatric species— influences individual feeding behavior. Investigation of social feeding by estimating individual intake of multiple nutritional components, sometimes referred to as “social nutrition,” can give insight into how social variables may lead to shifts in nutritional niche.

In this study, I examined the effects of temporal shifts in diet, reproductive status and conspecific and heterospecific social factors on the nutritional intake of an arboreal guenon. This study draws from 137 full-day focal follows of adult female redtail monkeys (Cercopithecus ascanius) in three study groups in Kibale National Park, Uganda; phenological data from the monkey group home ranges; insect abundance data via malaise trap and sweep net methods; nutritional wet chemistry or near-infrared reflectance spectroscopy (NIRS) analyses of over 600 food samples; and mineral analyses via inductively coupled plasma optical emission spectroscopy

(ICP-OES) of 101 food samples.

I first characterized the redtail nutritional strategy as it relates to fruit in the diet, monkey group and reproductive status. I found that redtail monkeys are “food composition generalists,”

iv consuming food items that are diverse in macronutrient and mineral composition. Insects were modest contributors to metabolizable energy and protein in the redtail diet, given that time spent feeding on insects was comparable to fruits, but insects contributed substantially to mineral intake. The balance of dietary nonprotein energy to available protein (NPE:AP) of redtail females was similar to other omnivorous primates. Redtail daily fat intake varied the most, while protein varied the least, indicating protein regulation. Redtail female nutritional strategy was affected by habitat variation in their home ranges, while ripe fruit in the diet or reproductive status did not have a strong effect on nutrition. One redtail group relied on the fruit and gum of

Prunus africana, a high density tree in disturbed areas, which led to higher dietary NPE:AP than the other groups. Increased switching among foods was associated with increased sodium, copper and iron intake, suggesting that diet diversity and rapid food switching enable increased consumption of minerals.

Second, I examined the relationship between redtail female agonism and nutritional strategy. Redtail monkey females exhibited low levels of within-group female agonism compared to other cercopithecines. Intragroup agonism was mostly in a feeding context, suggesting that some intragroup contest competition occurs. A relationship between agonism during feeding and daily nutritional intake could not be detected for multiple macronutrients and energy. Foods that female redtail monkeys contested over were diverse in food type and in macronutrient and mineral composition. Though nutritional effects of intragroup food competition were not detectable at the daily scale, future work should examine the level of the food patch when short-term nutritional costs may occur. Alternatively, group membership as it relates to being able to defend a food resource as a group may be more relevant to nutritional shortfalls than an individual’s ability to compete with another individual over food.

Third, I examined how polyspecific association affects female redtail daily nutritional intake. The redtail study groups varied in polyspecific association patterns and partners. I could not detect a relationship between polyspecific association with blue monkeys (Cercopithecus mitis) or grey-cheeked mangabeys (Lophocebus albigena) and redtail daily food intake, total metabolizable energy, nonprotein energy, sodium, or copper, regardless of shifts in dietary ripe fruit. An established interspecific dominance hierarchy by body size at feeding trees leading to spatial adjustments and mitigating interspecific competition may help explain this result. Food over which redtails and heterospecifics contested did not fit one nutritional profile and were instead diverse in macronutrient and mineral composition. I was unable to find a statistically clear relationship between red colobus monkey (Procolobus rufomitratus) polyspecific association and redtail female intake of macronutrients or energy, which was expected given redtail-red-colobus lower dietary overlap than frugivores. Though I could not detect a relationship between red colobus monkey polyspecific association and redtail daily intake of sodium from insects, future work will address questions of insect feeding efficiency and red colobus potential flushing of insects for redtails.

Redtail monkeys combine foods of diverse nutritional composition and use fat, digestible fiber and nonstructural carbohydrates interchangeably as energy sources. This study characterizes the diet of a generalist forest guenon, clarifies the mineral-driven role that insects play in the redtail monkey diet, and suggests that redtail females may suffer minimal daily nutritional costs of intragroup agonism and of polyspecific association.

ACKNOWLEDGEMENTS First, a huge thank you to my adviser Jessica Rothman for her guidance and support throughout this long process. She provided me with incredible opportunities in the field and in the nutritional chemistry laboratory. The attentive and thorough training she offered me on research and scientific writing, as well as her candid approach to advising her students in the ways of academia, have been crucial to my professional development. I am grateful for her guidance in completing three degrees over the past ten years.

I am also grateful to the other members of my dissertation committee who have provided insight and feedback on my dissertation: Larissa Swedell, Andrea Baden, Marina Cords, and

David Raubenheimer. I thank Larissa for her primate social behavior expertise and her feedback on my dissertation at multiple stages, including at the proposal stage. Thank you to Andrea for her thoughtful input and behavioral ecology expertise, as well as for raising landscape-level questions. Thank you to Marina for her guenon and social behavior expertise and her guidance from proposal to final product. Thank you to David for his enthusiasm in discussing my project with me and guidance on Nutritional Geometry. Thank you to additional members of my proposal committee who improved my project in preparation for grant applications: Chris Gilbert and Herman Pontzer. Thank you to Mike Steiper for his professional advice as I more recently entered the job market.

I sincerely thank the Ugandan field assistants who made my project possible with their exceptional knowledge of Kibale National Park’s flora and fauna: Hillary Musinguzi and Sabiiti

Richard showed tremendous dedication to the redtail monkey project. Thank you also to Richard

Kaseregenyu and Patrick Ahabyoona for additional assistance with behavioral data collection,

Peter Irumba for assistance in phenology data collection, and James Magaro and Francis

Katuramu for assistance in field-lab work.

vii

I am very grateful to the Uganda Wildlife Authority and the Uganda National Council for

Science and Technology for permission to conduct this research in Kibale National Park. Thank you also to Makerere University Biological Field Station for accommodations during my field seasons.

This work was made possible through support from the National Science Foundation

(Doctoral Dissertation Research Improvement Grant NSF BCS 1540369 and NSF BCS

1521528), Hunter College of the City University of New York, The Graduate Center of the City

University of New York, and the New York Consortium in Evolutionary Anthropology

(NYCEP, NSF DGE 0966166). I would like also to thank NYCEP’s intrepid leader, Eric Delson, who has developed and run a great collaborative graduate program and provided support and structure to CUNY students like me. Thank you also to Ellen DeRiso, who made the administrative aspects of the Graduate Center Anthropology program run so smoothly.

I am grateful to those whose research on redtail monkeys informed and made possible my work. Tom Struhsaker’s long-term foundational work in Kibale National Park and research on redtail monkeys inspired my research and framed my social and ecological findings. Joanna

Lambert’s detailed redtail cheek-pouching research informed how I collected data on cheek pouching and processing food in my methods and in assessing the role of the cheek pouch in redtail monkey nutrition. Colin Chapman’s work on Kanyawara monkeys, including redtail monkeys, was also key to framing and developing my dissertation. All three researchers have also been supportive and kind in collaboration or communication with me. In addition to these three Kibale researchers, I am also grateful to Marina Cords for her foundational research on redtail monkeys at Kakamega, Kenya.

viii

I am grateful to Tapani Hopkins and Isaiah Mwesige for teaching me tropical entomology methods and being willing to collaborate enthusiastically on trying to quantify insect abundance.

I would like to be an entomologist in my next life thanks to them.

Thank you to Jenny Paltan, the incredible primate nutritional ecology lab manager, who knows lab equipment and nutritional assays inside-out and is as innovative as she is organized.

Thank you to the following people who are my colleagues and friends in the Rothman lab group, past and present: Caley Johnson, Santi Cassalett, Katarina Evans, Emma Cancelliere, Allegra

DePasquale, Camille Stewart, and Kandra Cruz.

The data analyses in this dissertation were improved by meetings with and recommended literature from Christen Madsen and Safa Shehab at the Quantitative Research Consulting Center at the Graduate Center of the City University of New York.

Thank you to Roberto Delgado and Tony Di Fiore for offering me training, guidance, and patience when I was first gaining primatology field experience. Thank you to Marina Cords and

Jill Shapiro, who inspired me as an undergraduate with their classes in biological anthropology.

While in graduate school, I have been so grateful for the friendships with colleagues who both remind me how rewarding and fun biological anthropology work can be, and that academics can and should have some form of work-life balance: thank you Shahrina Chowdhury, Kris

Sabbi, Kelsey Pugh, Elissa Ludeman, Emma Finestone, Santi Cassalett, and Marian Hamilton.

A small thank you to the stranger who, at 4 a.m. the night before my defense date, repeatedly yelled for his girlfriend to let him into my Brooklyn apartment building and proceeded to ring all the apartments over and over; thank you for toughening me up with irritation prior to my defense the next morning.

I would not have been able to do this long stretch of graduate school work without the perspectives and crucial support of the following: Matt McDonough, Elisa Ignatius, Edith

Velazquez, Maciej Kasperowicz, Elizabeth Brown, Eliana Rios, Caryn Epstein, Leah Sandals,

Ilona Logvinova, Molly Heim, Anne Cécile Dewavrin, Claire Comfort, Jen Just, Gail Tirana,

Donna McDonough, Michelle Slavin, Lauren Epstein, Michelle DuPré, Patricia Bellucci and

Nivea Calico.

Thank you to my enthusiastic and supportive family: Karl Hartig, Martha Dale, Mike

Dale, Kate Bryer, Elizabeth Bryer, Jeff Bryer, Steve Zeisel, Sam Zeisel, Anna Zeisel, Jake

Zeisel, and Alice Muller. A profound thank you to my parents, Mary Hartig and Jackson Bryer; this dissertation is dedicated to them.

TABLE OF CONTENTS

LIST OF FIGURES AND TABLES…………………………………………….……xii

LIST OF APPENDICES…………………………………………….………………..xv

CHAPTER 1: INTRODUCTION……………………………………………………....1

CHAPTER 2: NUTRITIONAL ECOLOGY OF FEMALE REDTAIL MONKEYS (CERCOPITHECUS ASCANIUS): DIET COMPOSITION AND INTERGROUP VARIATION………………………………………………………………………….33

CHAPTER 3: EXTRINSIC AND INTRINSIC EFFECTS ON NUTRITIONAL STRATEGY OF FEMALE REDTAIL MONKEYS (CERCOPITHECUS ASCANIUS) …………………………………………………………………………100

CHAPTER 4: FEMALE REDTAIL MONKEY INTRAGROUP AGONISM AND NUTRITIONAL STRATEGY………………………………………………………131

CHAPTER 5: POLYSPECIFIC ASSOCIATION AND REDTAIL MONKEY NUTRITIONAL STRATEGY………………………………………………………167

CHAPTER 6: SUMMARY………………………………………………………….197

APPENDIX………………………………………………………………………….204

REFERENCES………………………………………………………………………266

FIGURES AND TABLES

CHAPTER 2 Table 2.1. Variation in daily intake of food, macronutrients and energy by redtail monkey females ...... 79 Table 2.2. Variation in redtail mineral intake and comparison to estimated NRC (2003) nonhuman primate requirements ...... 80 Table 2.3. Mean macronutrient and antifeedant composition by redtail food category ...... 81 Table 2.4. Mean mineral composition by redtail food category ...... 82 Table 2.5 Tree species composition of two phenology transects in redtail group home ranges…83 Figure 2.1. Fruit availability indices (ripe fruit and unripe fruit) for two phenology transects in redtail home ranges ...... 85 Figure 2.2. Redtail daily percentage time feeding versus daily percentage caloric intake by food category ...... 86 Table 2.6. Daily percentage time feeding versus daily percentage caloric intake by food category by redtail monkey group ...... 87 Table 2.7. Daily percentage time feeding versus daily percentage caloric intake for fruits only by redtail monkey group ...... 88 Figure 2.3. RMTs of redtail female daily intake by food part for macronutrients protein, nonstructural carbohydrates (TNC), and digestible fiber ...... 89 Figure 2.4. RMTs of redtail female daily intake by food part for macronutrients protein, fat, and carbohydrates (TNC + digestible fiber) ...... 90 Figure 2.5. RMTs of redtail female daily intake by food part for macronutrients protein, TNC+fat, and digestible fiber ...... 91 Table 2.8. Important food items (>1% intake by calories) across three redtail study groups ...... 92 Figure 2.6. RMTs of mean daily macronutrient intake by redtail group over the macronutrient composition of foods in diet ...... 94 Figure 2.7. RMTs of mean daily macronutrient intake by redtail group over the macronutrient composition of only important foods in diet ...... 95 Figure 2.8. Comparison of redtail percentage of daily time feeding and percentage of sodium (mg daily intake) by food part ...... 96 Table 2.9. Comparison for each study group of percentage of daily time feeding and percentage of sodium (mg daily intake) by food part ...... 97 Table 2.10. Top 10 sources of sodium for redtail females ...... 98 Table 2.11. Top 10 sources of copper for redtail females ...... 99 CHAPTER 3 Table 3.1. Variation in daily intake of macronutrients, energy and NPE:AP by redtail females 118 Figure 3.1. Variation in ripe fruit, unripe fruit, leaves, insects and gum in the diet (% DM intake) of female redtails...... 119 Figure 3.2. Variation in ripe fruit and unripe fruit in the diet of Kus and Suk group females ....120 Figure 3.3. Variation in fruit-of-any-ripeness and leaves in the diet of Kus and Suk group females during the study period ...... 121 Figure 3.4. Relationship between NPE:AP and ripe fruit and unripe fruit in the redtail diet ...... 122 Figure 3.5. Relationship between NPE:AP and leaves and insects in the redtail diet ...... 123 Table 3.2. Differences in daily NPE:AP by type of food in the diet of female redtails in all three study groups ...... 124

xii

Table 3.3. Differences in daily NPE:AP by type of food in the diet of redtail females in Kus and Suk groups ...... 124 Figure 3.6. Relationship between daily intake of nonprotein energy and intake of available protein by female redtails...... 125 Figure 3.7. Boxplots showing differences in daily macronutrients and energy intake during high versus low ripe fruit in diet ...... 126 Table 3.4. Differences in daily macronutrient intake by ripe fruit in the diet of redtail females in all three study groups ...... 127 Table 3.5. Differences in daily macronutrient intake by ripe fruit in the diet of redtail females in Kus and Suk groups ...... 128 Table 3.6. Differences in daily macronutrient intake by percentage of leaves in the redtail diet ...... 129 Table 3.7. Total metabolizable energy intake controlled for metabolic body mass in redtail monkeys and three other primate species ...... 130 CHAPTER 4 Table 4.1. Ethogram of female redtail monkey affiliative and agonistic behaviors ...... 152 Table 4.2. Rates of types of female redtail monkey agonism given and received in the three study groups ...... 153 Table 4.3. Comparison of rates of agonistic interaction between redtail females across primate taxa ...... 154 Table 4.4. Context of agonistic interactions between adult females redtails in the three study groups ...... 155 Table 4.5. Female redtail agonism in a feeding context by food category ...... 156 Table 4.6. Macronutrient composition of redtail female contested food items ...... 157 Table 4.7. Mineral composition of redtail female contested food items ...... 158 Table 4.8. Summary of redtail female grooming across the three study groups ...... 159 Table 4.9. Redtail monkey aggressive intergroup encounters involving one or more of the study groups ...... 160 Figure 4.1. Relationship between ripe fruit in the diet (kcal) and female redtail agonism ...... 161 Figure 4.2. Relationship between ripe fruit in the diet (kcal) and female redtail aggression ...... 162 Figure 4.3. Comparison of prediction of simulation model output by Lihoreau and colleagues (2015) showing effects of competition on nutrient balance to relationship between redtail female daily NPE:AP balance and female agonism ...... 163 Figure 4.4. Comparison of prediction of simulation model output by Lihoreau and colleagues (2015) showing effects of competition on nutrient balance to relationship between redtail female daily NPE:AP balance and female aggression ...... 165 CHAPTER 5 Figure 5.1. Redtail group differences in proportion of time spent with and in number and type of social interactions with blue monkeys ...... 186 Figure 5.2. Redtail group differences in proportion of time spent with and in number and type of social interactions with grey-cheeked mangabeys ...... 187 Figure 5.3. Redtail group differences in proportion of time spent with and in number and type of social interactions with red colobus monkeys ...... 188 Table 5.1. Results of linear mixed models testing the effect of blue monkey nearest-neighbor frequency on female redtail monkey daily fat intake ...... 189 Figure 5.4. Relationship between redtail fat intake and blue monkey polyspecific association .190

xiii

Figure 5.5. Differences in female redtail daily NPE intake by blue monkey aggression received ...... 191 Figure 5.6. Difference in daily sodium from insects in redtail monkey diets by red colobus monkey presence or absence ...... 192 Table 5.2. Macronutrient composition of contested foods between redtails and blue monkeys .193 Table 5.3. Micronutrient composition of contested foods between redtails and blue monkeys ..194 Table 5.4. Macronutrient composition of contested foods between redtails and grey-cheeked mangabeys...... 195 Table 5.5. Micronutrient composition of contested foods between redtails and grey-cheeked mangabeys...... 196

xiv

APPENDICES

APPENDIX 1 Macronutrient composition of redtail monkey foods 204

APPENDIX 2 Mineral composition of redtail monkey foods 225

APPENDIX 3 Map of redtail monkey feeding trees for three study groups 233

APPENDIX 4 Summary of female redtail monkey focal follows 234

APPENDIX 5 Redtail female diurnal variation in feeding observations 235

APPENDIX 6 Presence or absence of condensed tannins in redtail foods 236

APPENDIX 7 Comparison of plant species eaten by redtail monkeys in three study groups versus in Struhsaker’s study group (1973-74) 240

APPENDIX 8 Variation in starch composition of redtail foods by part 244

APPENDIX 9 Variation in sugar composition of redtail foods by part 245

APPENDIX 10 Comparison of redtail foods’ total nonstructural carbohydrate (TNC) content by subtraction versus TNC content via sugar and starch assays 246

APPENDIX 11 Female redtail individual differences in contribution of daily sodium by food type 251

APPENDIX 12 Comparison of macronutrient percentages of redtail diet: this study versus Conklin-Brittain et al. (1998) 252

APPENDIX 13 Comparison of macronutrient percentages of redtail diet: this study versus Rode et al. (2006) 253

APPENDIX 14 Female redtail individual differences in daily food intake 254

APPENDIX 15 Female redtail individual differences in daily protein intake and in nonprotein energy intake 255

APPENDIX 16 Female redtail individual differences in daily fiber intake (digestible NDF) 256

APPENDIX 17 Female redtail individual differences in daily sodium and copper intake 257

APPENDIX 18 Female redtail individual differences in daily iron intake 258

APPENDIX 19 Fruit availability indices (ripe fruit and unripe fruit) for two phenology transects in redtail home ranges compared to Multivariate El Niño Southern Oscillation Index 259

APPENDIX 20 Insect abundance in redtail home ranges: Total number of insects caught via monthly netting and via malaise traps 260

APPENDIX 21 Insect abundance in redtail home ranges: Composition of insect net catch by insect Order (by count) 261

APPENDIX 22 Insect abundance in redtail home ranges: Composition of malaise trap catch by insect Order (by biomass) 263

APPENDIX 23 Results of linear mixed models testing if conspecific female agonistic interactions in a feeding context predict daily intake of available protein and daily intake of nonprotein energy 264

APPENDIX 24 Results of linear mixed models testing if conspecific female agonistic interactions in a feeding context predict daily ratio of nonprotein energy:available protein (NPE:AP) balance 265

xvi

CHAPTER 1

INTRODUCTION

To survive and reproduce, animals must acquire sufficient amounts and ratios of macronutrients, energy and micronutrients (Barboza et al., 2008; Oftedal, 1991; Simpson &

Raubenheimer, 2012). An animal’s nutritional needs fluctuate depending on stage of development (Altmann, 1991,1998; Chyb & Simpson, 1990), reproductive status (Krockenberger

& Hume, 2007; Vargas et al., 2010) and health (Cotter et al., 2011; Simopoulos, 2002).

Additionally, variation over space and time in food availability (Chapman et al. 2002a;

Ganzhorn, 2002; Knott, 1998) and food nutritional composition (Carlson et al., 2013; Chapman et al., 2003; Milton, 1999; Worman & Chapman, 2005) affect nutritional intake. An animal moves through its environment seeking and avoiding a diverse array of nutritional and antifeedant components: seeking macronutrients (fat, nonstructural carbohydrates, available protein, and digestible fiber), energy and micronutrients (Altmann, 1998; Cheeke & Dierenfeld,

2010; NRC, 2003), and avoiding some components, including indigestible fiber (Milton &

Demment, 1988; Oates et al., 1980; Van Soest, 1978) and diverse plant secondary compounds

(Dearing et al., 2005; Freeland & Janzen, 1974; Glander, 1982). Adding to the complexity of meeting nutritional requirements are interactions among nutritional components; these interactions can affect nutrient availability to the animal (DeGabriel et al., 2009; Freeland &

Janzen, 1974; Robbins et al., 1987; Rothman et al., 2008b, 2009b).

Sociality is critical to both hindering and helping individuals reach their nutritional goals, has fitness consequences, and likely played an important role in primate, including human, evolution. Nutritional ecology can be used to examine links between sociality and ecology in primates by explicitly testing socioecological theory (van Schaik, 1989; Sterck et al., 1997;

1 Wrangham, 1980). Socioecological models of primates are based on the core assumption that feeding ecology explains variation in female primate social relationships, with food “quality” and distribution in the environment dictating the types of competition that females experience

(Isbell, 1991; Koenig & Borries, 2009; van Schaik, 1989; Sterck et al., 1997; Wrangham, 1980).

The relationship between sociality and ecology can be quantified through variation in the acquisition of key nutritional components across individuals. Additionally, social effects on nutrition are not confined to conspecifics; the presence of other species can also alter individual nutritional intake (Goodale, 2017; Houle et al., 2014; Thompson & Barnard, 1984). “Social nutrition” (sensu Lihoreau et al., 2015; Simpson & Raubenheimer, 2012; Simpson et al., 2015) involves testing aspects of social foraging theory with multivariate nutritional ecology. Social nutrition quantifies the complex interplay among an animal’s physiology, behavior, and environment, an environment that includes diverse nutrient and antifeedant sources as well as other foraging animals (Lihoreau et al., 2017, 2018; Simpson et al., 2015).

In this chapter, I review the effects of sociality on nutrient access and intake, and provide background for my dissertation project: specifically, I discuss (1) social foraging theory and

“social nutrition,” (2) primate socioecological theory, (3) social nutrition in invertebrate taxa, and (4) my study species’ diet, digestive physiology, and social behavior. In Chapter 2, I present my findings on female redtail monkey (Cercopithecus ascanius) diet composition and study group differences. In Chapter 3, I further explore redtail monkey nutritional strategy and how it relates to habitat variation, ripe fruit in the diet and reproductive status. In Chapter 4, I assess if intraspecific female agonism affects female redtail monkey daily nutritional intake and balance, and, in Chapter 5, I examine polyspecific association and female redtail monkey daily nutritional

2 intake and balance. Chapter 6 is a summary of my findings and how these findings inform a broader understanding of primate nutritional ecology and social behavior.

SOCIAL FORAGING THEORY

Social foraging theory broadly states that social behavior—cooperative or competitive, with conspecifics or with other sympatric species—influences individual feeding behavior

(Giraldeau & Caraco, 2000). Social foraging involves the complexity of multiple players influencing each other’s feeding decisions (Clark & Mangel, 1986; Giraldeau & Caraco, 2000).

At a basic level, the presence of an individual (or individuals) at a feeding site attracts other individuals (Chapman et al., 2014; Judd & Sherman 1996; Krebs et al., 1972; Lihoreau &

Rivault 2011). Multiple factors then influence which individuals in a social group gain access to preferred foods, and skewed food access leads to variation in fitness within the population

(Caraco & Giraldeau, 1991; Giraldeau & Dubois, 2008). The costs and benefits of social foraging fall under the costs and benefits of participating in social groups generally: though foraging with other individuals can aid in locating and accessing food—referred to as

“aggregation economy” or “local enhancement”—leading to increased individual fitness, it can also lead to increased feeding competition as part of a “dispersion economy,” leading to decreased individual fitness (Giraldeau & Caraco, 2000).

Classic optimal foraging theory (OFT), based on the economic concept of optimality, states that natural selection favors individual animals gaining as much energy (or another

“currency”) as possible without incurring net energy (or other) loss via foraging effort

(MacArthur & Pianka, 1966; Pyke et al., 1977; Stephens & Krebs, 1986). Belovsky (1978) developed an OFT model using variables including wet weight of food ingested per day, caloric

3 values of foods, estimated minimum sodium requirement, and amount of food processed in the rumen per day to predict moose (Alces alces) feeding behavior in energy-maximizing and energy-minimizing scenarios. The results of the model showed that aquatic plants are essential to the moose diet as a sodium source, and a diet of forbs and aquatic plants is inadequate to meet the energetic demands of reproduction. Both the energy maximizing model and the observed moose behavior indicated that deciduous leaves and aquatic plants were key to adequate energy and sodium intake, respectively. However, predictions of similar more detailed models of moose feeding behavior did not accurately predict consumption of particular plant species (Belovsky,

1981), indicating increased complexity in food choice perhaps due to plant secondary compounds and interactions between nutrients. OFT also does not consider multiple animals interacting and affecting each other’s foraging outcomes (Giraldeau & Dubois, 2008); as a result, social foraging is instead presented in a framework of game theory and frequency dependent models (Giraldeau & Caraco, 2000), both of which incorporate the influence of individual foragers on one another.

Food patch use in a social context

One major framework within social foraging theory is producer-scrounger decision- making, which addresses the widespread phenomenon of food patch discovery by certain individuals (“producers”) and exploitation of that discovery by other individuals (“scroungers” or “joiners”) (Barnard & Sibly, 1981; Giraldeau & Caraco, 2000). Though this framework is infrequently applied to primate behavior (but see Di Bitetti & Janson, 2001; King et al., 2009;

Lee & Cowlishaw, 2017), its predictions directly relate to contest competition; depending on food distribution, individuals in a group who find food may or may not differ from members who exploit food. Subordinate chacma baboons (Papio ursinus) were able to join/scrounge dominant

4 baboons at large food patches, but not at smaller clumps of food (“sub-patches”) that could be monopolized by dominant individuals (Lee & Cowlishaw, 2017). Producer-scrounger models acknowledge that producers and scroungers affect each other’s energetic gains and that individuals may also opportunistically switch between foraging tactics (Vickery et al., 1991).

Social patch models and primate socioecological models consider more than one scale of competition over a resource patch in a frequency dependent framework: (1) models assuming one resource patch per individual, so competition is over access to a patch and (2) models assuming one patch is exploited by multiple individuals at the same time, so competition is over resources within a patch (Janson, 1988; Livoreil & Giraldeau, 1997). Scenario (2) is complicated by factors including how many and when individuals discover the patch (producers in the producer-scrounger framework) and how many and when others join in the patch (scroungers)

(Vickery et al., 1991), as well as interactions between individuals in the patch that interfere

(“contest” or “interference” competition) with individual food intake (Giraldeau & Caraco,

2000). These two distinct types of competition for resources, referred to as “scramble” and

“contest,” stem from economic terminology applied to ecology (Nicholson, 1954) and characterize individual behavior in a group in the context of resource defense and food patch access and use (Parker, 2000; Shaw et al., 1995).

Despite the prominence of characterizing food distribution in the ecological literature using terms like “patch,” “patchiness,” and “clumped,” definitions are often inconsistent

(Giraldeau & Caraco, 2000; Isbell et al., 1998; Searle et al., 2005; Vogel & Janson, 2011; Wiens,

1976). Generally, the concepts under discussion are (1) food distribution in the environment

(how frequently an individual interacts with a finite amount of food in the environment) and (2) food density or quality (how dense the food is in the finite resource and/or how much of a

5 nutritional payoff the individual will get from a given finite food resource) (Giraldeau & Caraco,

2000). The resource defense model is an ecological model based on the economic concepts of optimality and defendability: animals will defend resources aggressively only when the energy payoff of access to that food is greater than the energy expense of defending it, which is determined by food distribution and the density of other individuals (Brown, 1964; Grant, 1993).

Some tests of the resource defense model have demonstrated that foods that are “clumped” in the environment lead to more aggression (i.e., defense) (Barton et al., 1996; Grant & Guha, 1993;

Monaghan & Metcalfe, 1985; Pruetz & Isbell, 2000), an assumption also frequently made in primate socioecological models (Isbell, 1991; van Schaik, 1989). Trying to form a universal definition of a clumped resource or a food patch is problematic because how, when, and at what scale a particular animal interacts with components of its heterogeneous environment determines what is clumped or patchy to the animal (Isbell et al., 1998; Wiens, 1976) and how this affects interactions with other individuals. Landscape ecologists further propose delineating patches hierarchically (Girvetz & Greco, 2007; Kotliar & Wiens, 1990), with animals responding to multiple scales of resource heterogeneity within the environment; however, empirical testing of these models is usually restricted to two levels (Lee & Cowlishaw, 2017).

Alternatively, food site depletion time (Isbell et al., 1998) or food site residence time

(Chancellor & Isbell, 2009) – the time it takes for an individual to handle and consume food at a food site – may be more informative than characterizing spatial characteristics of the food patch.

The concept of “food site” is specific to the movement of the individual forager and is independent of food type and from classic concepts of “clumped” versus “dispersed” foods

(Chancellor & Isbell, 2009; Isbell et al., 1998). Isbell and colleagues define food site as “any location at which an adult female stopped to eat food. Food sites could be as small as a single

6 arthropod or as large as several branches with flowers as long as all were within either arm's reach” (Isbell et al., 1998, pg. 125). Adult female grey-cheeked mangabey food site residence time was longest for bark and shortest for insects; additionally, more agonistic interactions occurred over bark than other food types and high-ranking females exhibited longer food site residence times for bark, indicating that this temporal variable is a measure of resource value and affects female social relationships (Chancellor & Isbell, 2009).

Mixed species association and social foraging theory

Because the association of multiple species within a social group or aggregation occurs in many taxa, social foraging theory has been applied to mixed species groups (also referred to as polyspecific associations). Social foraging theory in a polyspecific context must consider the added complexity of the diverse feeding and social behaviors of separate species who may or may not prefer the same foods and fear the same predators. A substantial body of literature covers the ecology of mixed species associations in birds, which form especially stable groups in forest habitats due to benefits outweighing costs related to predation risk and/or foraging (Morse,

1970; Sridar et al., 2009; Terborgh, 1990). The producer-scrounger framework has been investigated at the scale of producer (or leader) species and scrounger (or follower) species in mixed species bird groups (Camphuysen & Webb, 1999), though multiple additional roles among species have been characterized, including “core” or “nuclear” species and “sentinel” species, that do not fall into the classic producer-scrounger categories, or can fall under both

(Harrison & Whitehouse, 2011). Follower species can gain improved food access (Beauchamp,

2005; Krebs, 1973) and feeding benefits through mechanisms like flushing of insect food by the leader species (Hino, 1998; Munn, 1986), with fitness consequences for follower species demonstrated in some systems by increased survival (Jullien & Clobert, 2000) and better body

7 condition (Dolby & Grubb, 1998). Assigning a species a particular role, however, can be overly simplistic; instead, roles may be better characterized in the context of behavioral plasticity or a spectrum of roles by a particular species in mixed species association (Srinivasan et al., 2010).

Protection from predation through mixed species grouping follows the same reasoning as in conspecific groups, except that complementary alarm systems (Goodale & Kotagama, 2005), division of vigilance roles among species (Munn, 1986), and differential level of predation risk

(Goodale et al., 2017) affect the types of species that make beneficial associates. Patch use and scale in mixed species groups is complicated by spatial stratification of species who forage on similar foods (Farine & Milburn, 2013) and by merging (Powell, 1989) or diverging (Alatalo et al., 1986) shifts in foraging niche during mixed species association. In birds, heterospecific groups often lead to competition over food, with interspecific dominance hierarchies forming and dominant bird species benefiting most from the mixed species group (Cimprich & Grubb,

1994). Individual food intake (“net rate of energy intake” based on intake rates of worms of estimated size) was affected by flock composition and number of species in a mixed species flock of birds including some combination of lapwings, black-headed gulls and golden plovers

(Barnard et al., 1982). By examining the number of birds and presence and absence of species in relation to individual intake, Barnard and colleagues found that when gulls (scrounger) were present, lapwing net rate of energy intake decreased, whereas, without gulls, even if still in a mixed flock with golden plovers, lapwing net rate of energy intake was affected only by conspecific number. With the inclusion of one particular species, intraspecific competition is drowned out by interspecific competition. Golder plover net rate of energy intake was affected by the number of lapwings in the flock, unless gulls arrived, in which case the lapwings had no

8 effect on the golden plovers. Interspecific competition over food may be greater with one species than another (Barnard et al., 1982).

Diverse mammalian taxa also form mixed species groups (Goodale et al., 2010; Stensland et al., 2003) including primates (discussed further in “Polyspecific association and primate socioecological model” section below), marine mammals (Cords & Wursing, 2014, Frantzis &

Herzing, 2002), and ungulates (FitzGibbon, 1990; Kiffner et al., 2014; Pays et al., 2014), among others. Additionally, mixed species groups may not be restricted to species of just one Order

(French & Smith, 2005; Heymann & Hsia, 2015; Minta et al., 1992). Examining social foraging in a mixed species group of any combination of species is more complex than conspecific social foraging in that subtle costs and benefits of grouping vary not only over time and space and by individual forager, but also by number of and roles of species participating (Goodale et al.,

2017).

PRIMATE SOCIOECOLOGICAL MODELS

Primate socioecological models draw from the literature on social foraging and resource defense described above, with a focus on the proximate and ultimate causes of primate sociality.

At the core of the socioecological model is feeding ecology explaining variation in female primate social relationships (Isbell, 1991; Koenig & Borries, 2009; van Schaik, 1989; Sterck et al., 1997; Wrangham, 1980). Food distribution in the environment and “quality” dictate the type(s) of food competition that females experience: within-group scramble (WGS), within- group contest (WGC), and/or between-group contest (BGC) (van Schaik, 1989). Under given competitive conditions, females may be able to bias resources in their favor to increase fitness, resulting in a spectrum of systems of female social relationships. Spatial distribution of food is

9 hypothesized to influence affiliative and agonistic social interactions: when food is distributed in patches that are usurpable, as fruit often is, then WGC is predicted to occur because individual females can monopolize high-quality patches (Barton & Whiten, 1993; Isbell et al., 1998;

Koenig, 2000; Mitchell et al., 1991; Saito, 1996). More “widely distributed” resources like insects and leaves, which arguably cannot be monopolized, are hypothesized to lead to WGS or no competition (Isbell, 1991; Janson, 1988). However, these predictions are not always supported in primates due to social factors like dominance relationships and group size, as well as environmental factors like food distribution and quality. Subordinate individuals may avoid high-ranking individuals at clumped resources because the subordinates by definition will lose in a contest (Vogel, 2005) and if all individuals in a group can be accommodated by a large food patch, then contest competition will be low. These predictions are also complicated by unclear definitions of food distribution; primate foods like eusocial insects and young leaves are not in fact “widely dispersed” (Koenig, 2002; Snaith & Chapman, 2005), though defining insect abundance and distribution, let alone availability, is challenging (Gathua, 2000; Nummelin,

1996; Nummelin & Zilihona, 2004). Defining a clump or patch as one tree of one species may oversimplify the spatial and temporal distribution of the mixed diet of a primate that often includes several plant parts of varying quality in one tree or a cohesive primate group feeding from multiple trees simultaneously (Isbell et al., 1998; Vogel & Janson, 2011). Ambiguities across studies indicate “numerical estimates of the patchiness of food distribution are both difficult to measure and strongly dependent upon measurement scale and animal species. Thus, patches may be defined as individual food items at the smallest scale and as entire groves of trees at a very large scale” (Isbell et al., 1998, pg. 124). As a result, rather than how “clumped” a food is, whether or not the food is “usurpable” or “monopolizable” has become a more biologically

10 meaningful variable (Isbell et al., 1998), though effectively measuring this variable can be difficult (Vogel & Janson, 2011). When patches were characterized at two scales (“patch” and

“sub-patch”), Lee and Cowlishaw (2017) found that adult baboons competitively excluded other individuals much more frequently at the sub-patch scale than the patch scale.

Food “quality” also influences female competitive regime. The concept of food quality is often oversimplified by the assumption that ripe fruit is “high quality” and leaves are “low quality.” However, nutritional (protein, fats, nonstructural carbohydrates, digestible fiber, energy, minerals) and antifeedant components (plant secondary compounds, indigestible components of fiber, indigestible chitin) and an animal’s ability to digest these components determine the quality of food. These measures of food quality are dynamic and complex as nutritional content varies across spatial (Chapman et al., 2003; Ganzhorn, 1995; Hohmann et al.,

2010; Houle et al., 2014) and temporal (Carlson et al., 2013; Chapman et al., 2003; Hohmann et al., 2010; Masette et al., 2014; Worman & Chapman, 2005) scales. Additionally, different nutrients and toxins affect one another and can influence digestibility (Robbins, 1987; Rothman et al., 2008b; Simpson & Raubenheimer, 2012). Female social relationships, particularly dominance rank, can lead to differential consumption of food that differs in terms of “quality” and distribution (Barton & Whiten, 1993; Chancellor & Isbell, 2009; Murray et al., 2006; van

Noordwijk & van Schaik, 1987; Saito, 1996; Whitten, 1983; Wittig & Boesch, 2003) and energy intake (Janson, 1985; Vogel, 2005; but see Takahashi, 2018).

Primate behavior reflective of scramble competition, within groups and between groups, can be more difficult to detect than indicators of contest competition. Within the producer- scrounger framework, scramble competition can be considered “tolerated scrounging” when a food resource cannot be monopolized (Stevens & Cushman, 2004). Indicators of within-group-

11 scramble competition (WGS) with increased group size include the following: increase in home range and day range size (if food quality is similar) and increase in time spent traveling, time spent feeding and group spread while feeding (Isbell, 1991; van Schaik et al., 1983; Teichroeb and Sicotte 2018). Primates may only use one or some of these behavioral adjustments to mitigate WGS (Snaith & Chapman, 2007) and these behaviors vary by food availability in the environment. Red colobus monkeys (Procolobus rufomitratus) in Uganda and ursine colobus monkeys (Colobus vellerosus) in Ghana in larger groups had greater home ranges, day ranges and group spread while feeding than colobines in smaller groups, despite similar food quality

(Snaith & Chapman, 2008; Teichroeb & Sicotte, 2009). Additionally, ursine colobus use of behaviors to mitigate scramble competition varied by food availability, specifically indicating stronger WGS when food was less available (Teichroeb & Sicotte, 2018). Between-group- scramble competition (BGS) is more difficult to detect, though changes in WGS are predicted to affect BGS as the level of overlap between home ranges of different conspecific groups changes

(Janson & van Schaik, 1988).

Nonhuman primate socioecology holds implications for the evolution of human socioecology. Early Homo cooperation through group hunting, division of labor, and food sharing likely facilitated ease of access to food resources by gathering food in one location for all members of a group (Aiello & Wheeler, 1995; Milton, 1999; O’Connell et al., 1999; Ungar,

2007, 2012; Wrangham et al., 1999). Food sharing can be linked back to the producer-scrounger framework of social foraging theory, with some individuals hunting or gathering animal and plant foods as producers, while other members of the group could then join in food consumption after collection and preparation. The hunter and gatherer individuals (producers) invested substantial search time in acquiring high quality, likely less abundant, foods like meat and

12 underground storage organs (O’Connell et al., 1999; Wrangham et al., 1999). There is disagreement whether hominin food sharing is best characterized as reciprocal altruism (Gurven,

2004), tolerated scrounging (Stevens & Cushman, 2004), and/or kin selection. Increased food quality with the advent of cooking likely increased the amount of preferred food available for food sharing (Wrangham et al., 1999; Wrangham & Conklin-Brittain, 2003). These modes of increased food quality likely decreased variation in food availability across members of the group due to food sharing (Pontzer, 2012). Hominins may have experienced within-group contest competition in times of scarcity, as well as between-group contest competition, in some time frames with other sympatric hominin species.

PRIMATE POLYSPECIFIC ASSOCIATION

Mixed species association or polyspecific association occurs in primates, especially in those living in forest habitats, though the duration and stability of these groupings vary

(Platyrrhini: Garber, 1988; Heymann & Buchanan-Smith, 2000; Norconk, 1990; Peres, 1992b;

Pinheiro et al., 2011; Cercopithecidae: Chapman & Chapman, 2000a; Cords, 1987; Gautier-Hion et al., 1983; Noë & Bshary, 1997; Struhsaker, 1981; Strepsirrhini: Eppley et al., 2015; Freed,

2006). Polyspecific association among forest primate species has been defined slightly differently across some studies. Gautier-Hion and colleagues defined polyspecific association as

“one troop that includes two or more troops of different species between which there is no spatial discontinuity” (Gautier-Hion et al., 1983; pg. 326). Struhsaker defined polyspecific association as “when members of two or more social groups of different species are spatially intermingled; this usually means they are in the same or contiguous trees and within 20 m of one another”

(Struhsaker, 1981; pg. 269). Chapman defined polyspecific association as heterospecific group(s)

13 <50 m or <20 m from the focal monkey group (Chapman & Chapman, 1996; Chapman et al.,

2012). As with other taxa, polyspecific association in primates is driven by a combination of foraging and/or predation protection benefits. If dietary overlap between species in association is low, then species in the group can gain predator protection without the potential costs of feeding competition (Oates & Whitesides, 1990).

If dietary overlap is high, the question of whether dietary niche overlap increases or decreases among participants is a central ecological question in the primate polyspecific association literature. Increased diet overlap among species in polyspecific association has been observed at multiple sites (Chapman & Chapman, 2000a; Cords, 1987; Gautier Hion et al., 1983;

Struhsaker, 1981), with associates converging on preferred food resources, thereby temporarily increasing their diet overlap. During periods of low fruit availability, dietary overlap among three associating guenon species (Cercopithecus nictitans, C. pogonias, and C. cephus) decreased, but the species remained in polyspecific association as frequently as in high fruit availability periods (Gautier-Hion, 1980, 1988). Maintaining polyspecific association regardless of fruit availability suggests the anti-predator value of polyspecific groups compared to single- species groups. Increased travel by participants in a polyspecific association compared to conspecific groups suggests a potential energetic cost to dietary overlap (Chapman & Chapman

2000a; Cords, 1987; Gautier-Hion, 1988). Whether interspecific contest competition occurs is dependent on multiple factors, including the distribution of food in the habitat, group spread among individuals of different species, which species joins the mixed association when, and satiation of one species before another (Janson, 1988). Vertical stratification of species in the canopy or wider group spread help minimize interspecific contest competition (Garber, 1988;

Gautier-Hion et al., 1983; Heymann & Buchanan-Smith, 2000; Norconk, 1990). This kind of

14 spatial stratification can facilitate and even improve access to a variety of food types; for example, different spatial positions and insect-capture behaviors by two species of tamarins meant that during insect feeding moustached tamarins (Saguinus mystax pileatu) flushed insects from foliage that was then eaten by saddle-backed tamarins (S. fuscicollis avilapiresi), with both species maintaining similar insect capture rates (Peres, 1992b).

Interspecific dominance hierarchies determined by species body size do occur in some mixed species associations (Cords, 1990; Houle et al., 2010; Struhsaker, 1981). Dominant species may exclude subordinate species when food is usurpable (Houle et al., 2010, 2014; Peres,

1996). In the same two Saguinus species referred to above, both species had long food patch residence time when in large food patches (“defined as trees >10cm in DBH and lianas and epiphytes >10m in height”), whereas the dominant species excluded the subordinate species from small patches (“understory trees and treelets <10cm in DBH or other plants <10m in height”) with clumped food (“food items in a highly clustered fashion”). This exclusion of the subordinate tamarin species usually took the form of making the subordinate wait outside the patch, though contest competition over the patch did also occur less frequently (Peres, 1996).

Aggressive intergroup encounters between polyspecific groups have also been observed, indicating cases of a polyspecific group participating in between-group contest competition in defense of preferred resources (Garber, 1988; Gautier-Hion et al., 1983; Peres, 1992a). Scramble competition among individuals of different species in polyspecific association may also occur

(Rehg, 2006), but this type of competition is more difficult to detect and depends on the position of heterospecifics in group spread and on resource distribution.

Even if contest competition occurs, polyspecific association persists in many primate systems due to mixed species grouping conferring greater protection from predators than

15 conspecific groups. This reduced predation risk may be due to increased group size compared to conspecific groups, one species responding aggressively to a particular predator (Arlet & Isbell,

2009; Gautier-Hion, 1988; Stanford, 1995), and/or spreading the burden and benefits of vigilance and alarm calling across species (Gautier-Hion, 1983; Heymann & Buchanan-Smith, 2000; Noë

& Bshary, 1997). Arboreal guenon species in polyspecific association have a similar vocal repertoire which allows efficient interspecific alarm call communication (Cords, 1987; Gautier-

Hion, 1988; Marler, 1973).

Though some researchers have proposed that polyspecific association among primates is due to chance encounters (Waser, 1982), the choice to stay in a polyspecific association and the duration of that association are better indicators of polyspecific association as an adaptive behavior than likelihood of encountering a heterospecific group (Chapman & Chapman, 2000a;

Struhsaker, 1981). Additionally, models like Waser’s (1982) do not take into account that multiple species may be drawn to particular food resources (Cords, 1990). The ecological and social implications of polyspecific association are increasingly complex as more species participate and more groups of a given species participate, especially as variation in comingling of groups may occur daily, seasonally, or interannually (Gautier-Hion, 1988). The following polyspecific association scenarios are quite different: percent of time in polyspecific association with (1) any group of species X, (2) Group A of species X, (3) Group B of species X, (4) any groups of species X and species Y, and so on, or (5) any combination of these scenarios in succession or in combination—over the course of one day in some cases. As a result, the data collection workload of fully characterizing and quantifying the ecological implications of these associations can become extremely complex.

SOCIAL NUTRITION

To characterize individual nutritional strategies in the context of complex foraging and social environments, social nutrition research examines how animals balance multiple food components, including protein, fat, structural and nonstructural carbohydrates, and plant secondary compounds (Raubenheimer et al., 2009; Simpson & Raubenheimer, 2012). Empirical research on multivariate nutrition at the group and community levels has been done predominantly in insects (Cook et al., 2010; Dussutour & Simpson, 2009; Grover et al., 2007;

Lihoreau et al., 2016; Simpson & Raubenheimer, 2012). Entomology allows for direct testing of fitness effects of nutrition and determining adaptive intake targets (Eggert et al., 2008; Salomon et al., 2008); directly linking nutrition to fitness is more challenging in longer-lived organisms.

Eusocial insects provide an opportunity to examine intraspecific social behaviors in relation to nutrition. Producer-scrounger decision-making in eusocial insects incorporates both colony overall nutrition and individual insect nutrition (Mayack & Naug, 2013; Toth &

Robinson, 2005). In eusocial bees, producers seek out resources and use the waggle dance to convey information about resources, while scroungers exploit this information. When individual honeybee or whole colony nutritional state were high (defined as an individual fed sucrose and a fully populated colony, respectively), honeybees explored (produced) more and exploited

(scrounged) less (Katz & Naug, 2016). However, different nutritional pathways likely regulate the behavior of individuals engaged in these two foraging strategies (Ament et al., 2010), demonstrated by differential sensitivity to individual and colony nutritional states, with producers more sensitive to colony nutritional status over individual nutritional status if there was a mismatch (Katz & Naug, 2016). Individual eusocial insects make strategic feeding decisions for the benefit of the colony for multiple nutrients: honeybees experiencing an

17 experimental deficit in one amino acid subsequently chose food options that addressed this nutritional deficit (Hendricksma & Shafir, 2016). Similarly, worker ants, both in an experimental laboratory setting (Solenopsis invicta, Cook et al., 2010) and in the wild (Ectatomma ruidum,

Cook & Behmer, 2010), prioritized carbohydrates over protein while collecting food for all members of the colony (in which workers need a high carbohydrate diet and larvae and the queen need high protein diet). When ants (Lasius niger) were fed a high-protein diet, worker ant lifespan was ten times shorter, with resulting colony failure, than when fed a diet with a preferred carbohydrate:protein ratio (Dussutour & Simpson, 2012). Illustrating another strategic feeding decision in insects, the largest female social spiders (Stegodyphus sp.) acquire high-lipid prey through contest competition, thereby becoming breeders, while smaller females become helpers; macronutrient intake therefore dictates dominance and reproduction via body size in this spider species (Rypstra, 1993; Salomon et al., 2008).

Intraspecific social nutrition studies in non-primate taxa have also addressed social foraging questions of patch use. In field-based feeding trials, insectivorous ibis (Threskiornis moluccus) chose higher-lipid food as group size increased at a “clumped” resource (food dish), which may indicate switching to a less preferred food or less strategic feeding under scramble or contest competition (Coogan et al., 2017). Antifeedant plant secondary compounds affect food patch use in marsupials, partially driving the heterogeneity of the feeding environment and potentially population density (Foley & Moore, 2005; Moore & Foley, 2005).

Social nutrition has also been linked to microbiome (Di Stefano et al., 2019; Pasquaretta et al., 2018) and immune function (Cotter et al., 2019; Ponton et al., 2013, 2019), indicating diverse avenues for interdisciplinary social nutrition research connecting nutrition, health and social behavior.

18 Modeling social nutrition

To test complex hypotheses related to social nutrition, researchers have recently combined state-space models of nutritional geometry (Simpson & Raubenheimer, 2012) with other models, including agent-based models (Hosking et al., 2019; Senior et al., 2015, 2016b;

Simpson et al., 2010;), social network analysis (Senior et al., 2016a), phase change models

(Lihoreau et al., 2017), and landscape ecology models (Simpson et al., 2010). Using these hybrid models, researchers can test hypotheses about the relationships between social and nutritional variables that are difficult to test in wild vertebrates in complex environments.

Several model studies have examined individual nutritional strategies in a heterogenous landscape in a group context to assess patch use. Lihoreau et al. (2017) created a model based on phase change models in physics (Vicsek et al., 2009) to test predictions about nutritional intake of social animals in a heterogenous food landscape. The model included a nutritional environment with foods with different features in terms of protein to carbohydrate content

(though this can be generalized to any food components that affect fitness), overall abundance

(“proportion of cells with that food”), and patchiness (“fractal dimension of all cells containing that food”; low is isolated large patches, high is evenly distributed small patches). In this model, overlaying the nutritional space is the individual’s nutritional state (location in the nutrient space in terms of nutrient intake), intake target (the coordinates representing the goal balance of nutrients that maximizes fitness), and social variables indicating spatial alignment or repulsion from other individuals. When they ran simulations of groups of 50 individuals for 10,000 time steps, across multiple food environments, they found that social interactions improved individual nutrient balancing performance (efficiency of reaching intake target) in environments where foods are rare, in clumps, and contain different though complementary Carbohydrate:Protein

19 ratios (i.e. they can be mixed to reach the intake target). Lihoreau and colleagues (2017) also found that the model generated movement patterns that looked similar to near-far search models, which suggest that animals feed nearby, in a localized area, until the nutritional reward is not high enough and then they search for food farther away, regardless of patchiness variation

(Motro & Schmida, 1995).

Dominance relationships and nutritional intake and balance have also been explored with these models. Lihoreau and colleagues (2015) predicted, through a series of simulation models, differential nutrient balancing in the context of competition in social vertebrates: individuals better able to reach or get close to their nutritional intake target are better able to win contest competitions, gaining “winner’s advantage,” which leads to improved balance (ratios) of limiting nutritional components. Senior and colleagues (2016a) used nutritional geometry with agent- based models and social network analysis to further examine how nutritional state affects dominance relationships. They generated dominance networks over time in three different nutritional environments containing three foods (with variation in food abundance) and found that when food was more abundant, fewer contests occurred and the network was minimally connected over time. Intuitively, if food was less abundant, the dominance networks were more connected due to more contest competition, leading to increased variation in fitness across group members. Senior and colleagues’ (2016a) model also indicated that network centrality variables are good indicators of future fitness, a finding that suggests that collecting certain more limited social network centrality measures in wild populations can be as effective in linking behavior to fitness as more complex social network analyses.

Examining individual amino acids and fatty acids, instead of protein and fat, requires expanding the state-space model of nutritional geometry to fully characterize and visualize these

20 relationships in nutritional performance landscapes. Methods have been proposed like measuring angles and distances between peaks in nutritional performance landscapes (Bunning et al., 2015) or using a vector-based approach to better characterize the peaks (best diets) and valleys (worst diets) of performance landscapes (Morimoto & Lihoreau, 2019).

QUANTIFYING PRIMATE NUTRITION

The relationship between social behavior and diet generally has been extensively explored in testing primate socioecological models (Barton & Whiten, 1993; Chancellor &

Isbell, 2009; Murray et al., 2006; van Noordwijk & van Schaik, 1987; Whitten, 1983; Wittig &

Boesch, 2003), and intake of energy and, to a lesser extent, other nutrients have been explored in relation to social behavior in some primates (Janson, 1985; Vogel, 2005; Takahashi, 2018).

When restricted from meeting their nutritional goals by other extrinsic factors like food availability and/or composition, primates—like other animals—compensate by eating more of some food components over others (Felton et al., 2009; Rothman et al., 2011; Simpson &

Raubenheimer, 1995). Frugivorous spider monkeys (Ateles chamek) managed nutrients in their diet by maintaining a balance of nonprotein energy (NPE) to available protein (AP) in kcal of

8:1, prioritizing AP and allowing NPE to fluctuate over time (Felton et al., 2009). Orangutans

(Pongo pygmaeus wurmbii) similarly maintain protein intake while allowing non-protein energy intake to fluctuate with fruit availability (Vogel et al., 2017). In contrast, mountain gorillas

(Gorilla beringei) prioritize NPE when constrained by food availability, overeating AP and maintaining NPE (Rothman et al., 2011). Mountain gorillas maintained a NPE:AP of 2:1 kcal when the majority of available food was herbaceous leaves and shifted to 3:1 kcal when fruit made up 40-60% of the diet. On a shorter timescale, over the course of one month, one female

21 chacma baboon (Papio ursinus) maintained an average daily NPE:AP balance of 5:1, combining diverse natural and human foods to achieve this balance, with implications for more omnivorous primate diets (Johnson et al., 2013).

The protein-to-fiber ratio in folivorous primate foods has previously been linked to patch use and primate biomass (Chapman & Chapman, 2002b; Ganzhorn, 2002; Milton, 1979), but more recent work demonstrates that the protein-to-fiber ratio may not be as straightforward or have the predictive power once thought (Johnson et al., 2017; Wallis et al., 2012). Increases in colobine group size and changes in leaf nutritional quality interact with hormonal stress levels and parasite burden (Chapman et al., 2007, 2015). The nutrition of food patch use beyond the protein-to-fiber ratio was examined for folivorous black-and-white colobus: when the ratio of intake of nonprotein energy to protein (NPE:AP) within a patch deviated from 1.55:1, the monkey left the patch more quickly (Johnson et al., 2017). This nutritional strategy that dictates patch residence time likely explains why black-and-white colobus monkeys leave patches of leaves before they are depleted (Tombak et al., 2012). In contrast, sympatric red colobus monkeys deplete food patches before moving to the next patch (Tombak et al., 2012); this difference between these two colobine species likely influences aspects of social behavior with conspecifics as well as between these colobines (Johnson et al., 2017).

Efforts to quantify the nutritional costs of primate within-group scramble competition

(WGS) involve incorporating nutritional intake into analyses of behavioral indicators of WGS discussed earlier in this chapter (Grueter et al., 2018; Stacey, 1986). Variation in female energy intake rates in multiple mountain gorilla (Gorilla beringei) groups indicated the complexity of different types of competition occurring simultaneously: though travel distance increased with group size for most groups, for the largest group, travel distance was lower despite higher energy

22 intake rates (Grueter et al., 2018). The authors argue that this finding is likely explained by the largest group’s successes in between-group contest competition.

Socioecological models predict, in the context of contest competition, dominant individuals will either prevent subordinate individuals from entering a food resource, will eject them from the food resource, or will force them to move to a less desirable portion of the food resource patch. These behaviors by dominant individuals may result in nutritional costs for subordinate individuals. With increased rates of aggression over a food resource, dominant capuchin monkey individuals skewed energy intake in their own favor (Cebus apella: Janson,

1985; Cebus capucinus: Vogel, 2005). When minimal aggression occurred over a resource, however, rank effects were not observed in energy intake, indicating that aggression is the tool that dominant individuals use to skew nutritional intake in their favor. In contrast, dominance rank did not affect energy intake rates in Assamese macaques (Macaca assamensis) (Heesen et al., 2013) or mountain gorillas (Grueter et al., 2015), even in food distribution contexts in which contest competition would be expected and where aggression occurred. Similarly, though high ranking blue monkey (Cercopithecus mitis) females gained preferential access to fruits and had lower glucocorticoid levels than subordinates (Foerster et al., 2011), rank was unrelated to protein intake and balance of non-protein energy to protein (NPE:AP) (Takahashi, 2018).

However, higher-ranking blue monkey females fed from fewer unique foods daily (Takahashi,

2018) and, when fruit availability was low, low-ranking females spent more time feeding (Pazol

& Cords, 2005), which arguably both indicate subtle reduction in foraging efficiency in subordinates.

REDTAIL MONKEY (CERCOPITHECUS ASCANIUS) BEHAVIOR AND PHYSIOLOGY

Redtail monkey diet and nutrition

Redtail monkeys are small-bodied guenons (adult male mean weight = 3.7 kg, adult male range = 3 - 4.8 kg, N= 32, adult female mean = 2.8 kg, adult female range = 2.1 - 3.8, N= 55

[Colyn, 1994; Kingdon et al., 2013]; adult male mean weight = 4.3 kg, N = 47, adult female mean = 2.9 kg, N = 49 [Delson et al., 2000]) that eat fruits, insects, young leaves and flower parts (Chapman and Chapman 2000b; Chapman et al., 2002b; Cords, 1986, 1987; Gathua, 2000;

Rode et al., 2006a; Struhsaker, 1978, 1980, 2017; Tapper et al., 2019) depending on season and site. Redtails switch between a wide variety of food types and species (Lambert, 2002a), aided by cheek pouches (Lambert, 2005) and a long digestive retention time given body size (adult female MRT = 29.4 ± 9.8 hours [Lambert, 2002b]). Digestive retention time is the amount of time that a food remains in the gastrointestinal system and is potentially indicative of increased absorption of nutrients and increased capacity for microbial fermentation in the hindgut in primates with hindgut-fermenting digestive physiology (Lambert 1997, 2002b). Redtail monkeys living in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania, face lower availability of fruits and leaves, higher predation risk, and limitation by thermoregulation needs compared to forest redtails, and exhibit substantially larger home range and longer daily path distance than forest redtails (McLester et al., 2019a). These ecological differences between forest redtails and savanna-woodland redtails may have implications for intraspecific variation in dietary strategy.

The macronutrient and antifeedant contents of the redtail monkey diet at Kibale were previously reported to be similar to those of sympatric frugivorous blue monkeys (Cercopithecus mitis) and grey-cheeked mangabeys (Lophocebus albigena) if insects were not considered

(Conklin-Brittain et al., 1998; Wrangham et al., 1998). Redtail monkey foods also had similar sugar content to foods eaten by sympatric red colobus monkeys (Procolobus rufomitratus)

(Danish et al., 2006). Mineral intake by redtail monkeys was lower than sympatric folivores, but comparable to mineral intake of sympatric frugivores—though high-iron invertebrates may offset low frugivore iron intake (Rode et al., 2003).

Redtail monkeys are the smallest-bodied diurnal frugivores (or omnivores) at Kanyawara site in Kibale National Park, which may carry feeding costs (Houle et al., 2010; Struhsaker,

1981). Redtails fed lower in feeding trees and altered their feeding rates when larger-bodied blue monkeys or grey-cheeked mangabeys were present, regardless of whether overt interspecific aggression occurred (Houle et al., 2010). The upper crowns of four species of feeding trees in

Kibale had higher fruit density, larger fruit crops, and higher sugar concentration compared to lower strata, which may result in interspecific contest competition over higher strata fruits

(Houle et al., 2014). Possible nutritional mechanisms of coexistence include larger-bodied frugivores consuming higher-energy foods more selectively while redtails switch to the remaining medium to low quality food left behind. By looking at the related measure of giving- up density (i.e., food left behind at the end of a foraging bout), Houle and colleagues demonstrated that redtails are subordinate to and have lower giving-up densities (leave less food behind) than sympatric blue monkeys; redtails shortened their time in a feeding tree when faced with blue monkey aggression, but if no agonism occurred, they were less likely to leave and more likely to instead switch to lower strata of the feeding tree (Houle et al., 2006).

Redtail monkey digestive physiology

Redtail monkey digestion is aided by cheek pouches, buccal pockets connected to the mouth through a slit (Lambert, 2005; Murray, 1975), in which monkeys place food items temporarily, usually fruits. Pre-digestion in cheek pouches is facilitated by the production of digestive enzymes, including salivary amylase, into the pouch (Lambert, 2005; Rahaman et al.,

1975). When ready to process cheek pouched food after a variable amount of time (Lambert,

1997), redtail monkeys will move cheek-pouched food from the cheek pouch into the mouth, often pushing the outside of the full cheek pouch with the dorsal side of the wrist (Murray, 1973; this study). Processing cheek pouched food frequently involves rejection of some parts (for example, seeds, which are sometimes spit out) and digestion of others (for example, fruit pulp)

(Lambert, 1997). The potential adaptive significance of cercopithecine cheek pouches includes reduced feeding competition and reduced predation pressure (Lambert, 2005); in either instance, the use of cheek pouches in a “retrieve-and-retreat pattern” has been observed in multiple species of cercopithecines (Murray, 1975, pg. 169; Lambert, 2005; Lambert & Rothman, 2015).

Redtail monkeys have long digestive retention times, indicative of slow digestion and more microbial fermentation activity than expected given their small body size and relatively simple digestive morphology. The morphology of the guenon digestive tract is unremarkable in that it is similar to that of other primate frugivores and includes a simple stomach, a C-shaped duodenum, small-to-medium sized caecum, and an elongated colon condensed by haustration and folded taenia (Cercopithecus cephus: Chivers & Hladik, 1980; Cercopithecus mitis erythrarchus: Bruorton & Perrin, 1988). Lambert (2002b) found that, when body size was controlled for in an experimental study of retention time in chimpanzees versus multiple cercopithecine species, the cercopithecines had relatively slow retention times, despite small body size. In these experiments using markers fed to captive redtail monkeys, the redtail monkey female (weight = 4.7kg) exhibited a mean retention time =29.4 (SD=9.8), time of first marker appearance =19.4 (0.1), and time of last marker appearance =42.1 (5.7) (Lambert, 1997).

Comparison of similarly-sized frugivorous platyrrhines demonstrates how long these redtail monkey digestive time indicators are: Cebus apella first marker appeared in feces after 3.5 hours,

26 compared to C. ascanius first marker appearance at 19.4 hours. The redtail monkey long retention time has implications for increased absorption activity in the small intestine and for microbial fermentation in the colon, often referred to as “hindgut fermentation” (Lambert, 1997,

2002b). Fecal microbiota (reflective of microbiome of the hindgut) of redtail monkeys, when compared to humans and two colobine monkey species, overlapped with human microbiota more than colobines (Yildirum et al., 2010), though generally microbiota tended to be species-specific

(McCord et al., 2014; Yildirum et al., 2010). Redtail fecal microbiota include genera that have been linked to production of volatile fatty acids (Yildirum et al., 2010), suggesting a greater ability to digest fiber than expected for a primate once considered a frugivore. Given potential fermentation activity in the caecum as well as the colon, “caeco-colic fermentation” may better reflect the mid- and hindgut fermentation occurring in cercopithecines (Clemens & Phillips,

1980; Lambert, 1998).

Redtail monkey social system

Redtail monkey social groups include multiple philopatric adult females (Cords, 1984,

1987) and one or multiple adult males (Cords, 1984; Struhsaker, 1977, 1978, 1988; McLester et al., 2019a; this study). Females demonstrate aggressive behaviors during conspecific intergroup encounters that include loud vocalizations and chases, indicative of between-group contest competition (Brown, 2011, 2013; Cords, 1984, 1987; Struhsaker, 1980). Socioecological models predict, based on such between-group competition, that guenons bond together against other groups and are not aggressive within groups (Isbell, 1991; Sterck et al., 1997), though at least one species of guenon (Cercopithecus mitis) exhibits moderately despotic, moderately tolerant, and nepotistic dominance hierarchies (Klass & Cords, 2015). Rowell (1988) remarked that guenon social behavior is “discreet” in a system driven by “monitor and adjust” behaviors

27 compared to other Cercopithecines. Sterck and colleagues (1997) hypothesized that guenon social systems are “resident-egalitarian,” in the sense that a formal dominance hierarchy does not exist among females. However, empirical data on redtail female intragroup relationships are lacking. Observations of female affiliative behaviors may be indicative of intragroup female bondedness; though redtail monkey females spent a small portion of their overall activity budget grooming, and group spread was often greater than other sympatric monkeys (Struhsaker &

Leland, 1979), they spent more of their grooming time grooming other females than expected by chance (Struhsaker, 1980).

Observations of redtail monkey group fissions at Kanyawara, Kibale (Struhsaker &

Leland, 1988; personal observation of fission group that had split from Kus group prior to May

2015), Ngogo, Kibale (Windfelder & Lwanga, 2002) and at Issa Valley (McLester et al.,

2019a,b) indicate breaking points at which behavioral adjustments, like increased day range, cannot mitigate within-group competition. According to the ecological constraints model

(Chapman & Chapman, 2000b), as group size increases, individuals are unable, after a certain point, to move fast enough and feed enough to meet the nutritional needs of all individuals in the group.

Though there is variation in social relationships within and between species (Chapman &

Rothman, 2009; Strier, 2009), recent and ongoing tests of socioecological hypotheses using the blue monkey, a member of the same genus, may shed light on redtail female social relationships.

Socioecological comparisons with more closely related guenons, specifically members of the

Cephus group of the Cercopithecus genus, would be preferable, but conspecific social data on these species are lacking, though there are ecological and polyspecific association studies of

Cercopithecus cephus (Gautier-Hion, 1980; Gautier-Hion et al., 1981; Tutin et al., 1997). Long-

28 term data on blue monkeys at Kakamega, Kenya, show that blue monkey females form stable linear dominance hierarchies (Klass & Cords, 2011, 2015). Most aggressive interactions between blue monkey females were over feeding site access; and overlap in diet items between females was almost significantly correlated with rank distance, which suggests some within-group contest competition (Payne et al., 2003). High ranking blue monkey females gained preferential access to fruits and had lower glucocorticoid levels than subordinates (Foerster et al., 2011), though rank was unrelated to birthrate, time spent feeding, time spent on dispersed versus clumped foods, ingestion rates of fruit (Cords, 2000, 2002), protein intake, and balance of nonprotein energy to protein (NPE:AP) (Takahashi, 2018).

Redtail monkey polyspecific association

Redtail monkeys at multiple sites are frequently in polyspecific association with other arboreal monkey species, including other guenon species (Chapman & Chapman, 1996, 2000a;

Cords, 1987; Gautier-Hion, 1988; Struhsaker, 1981; Teelen, 2007). Redtail monkey polyspecific association patterns vary within (Chapman & Chapman, 2000a) and across sites (Cords, 1990).

Diet overlap between redtail monkeys and blue monkeys (Cercopithecus mitis) and redtail monkeys and mangabeys increased during polyspecific association in Kibale, which is likely due to blue monkeys and mangabeys joining redtail monkeys at preferred food resources (Struhsaker,

1981). Dietary overlap between redtail monkeys and red colobus monkeys was substantially lower than dietary overlap between redtail monkeys and blue monkeys or mangabeys; however, red colobus and redtail diets still overlapped by as much as 25% at Kanyawara (Chapman &

Chapman, 2000a). Percent of time spent in polyspecific association is lower and the leader- follower nature of the association is different between redtail monkeys and blue monkeys in

Kakamega, Kenya (Cords, 1987, 1990), compared to Kibale, likely driven by differences in

29 species population density (blue monkeys have a higher population density in Kakamega and a much lower population density at Kibale than redtail monkeys) that affect species home range size and group size. Across six locations within Kibale National Park, Uganda, grouping with any other species varied substantially among five species of diurnal primates with various levels of dietary overlap. However, across these locations in the park, redtail monkeys were the species most commonly seen in association with grey-cheeked mangabeys (Lophocebus albigena), red colobus monkeys (Procolobus rufomitratus), and black-and-white colobus monkeys (Colobus guereza) (Chapman & Chapman, 2000a).

Redtail monkeys at Kibale and at Kakamega are the smallest diurnal primate at the bottom of an interspecific primate dominance hierarchy driven by body size (Cords, 1990; Houle et al. 2010; Struhsaker, 1981). At Kanyawara, redtail monkeys fed lower in feeding trees and changed their feeding rates when dominant frugivorous species (Lophocebus albigena and/or

Cercopithecus mitis) were present, regardless of whether overt interspecific aggression occurred

(Houle et al., 2010). A similar interspecific hierarchy was proposed in the context of polyspecific association among guenons in Gabon, with Cercopithecus cephus, a small-bodied guenon closely related to the redtail monkey, at the bottom of that hierarchy (Gautier-Hion, 1988).

Cercopithecus cephus ranging and feeding behaviors were significantly different when in a conspecific group versus when in a polyspecific group with two other species (Cercopithecus nictitans and C. pogonias), with C. cephus in the polyspecific group covering a larger home range and longer daily travel distance (Gautier-Hion, 1988).

Protection from predators is an advantage of polyspecific association for redtail monkeys.

The main predators of forest redtail monkeys are crowned hawk-eagles (Stephanoaetus coronatus) and chimpanzees (Pan troglodytes schweinfurthii), while savanna-woodland redtail

30 monkeys also face large carnivore predators like leopards (Panthera pardus) (McLester et al.,

2019b). Redtail monkeys are the most common prey remains found in crowned hawk-eagle nests at Ngogo in Kibale National Park (Mitani et al., 2001) and redtail monkey remains have also been found in and near eagle nests at Kanyawara (Skorupa, 1989; Struhsaker & Leakey, 1990).

Arboreal guenon species in polyspecific association have a similar vocal repertoire which allows efficient interspecific alarm call communication (Cords, 1987, 1990; Gautier-Hion, 1988;

Marler, 1973). At Kanyawara, blue monkey and redtail monkey adult female and immature alarm calls (“chirps”) and blue monkey and redtail monkey adult male loud calls (referred to as

“barks” or “pops”) in a polyspecific group aid members of both species in predator detection

(Marler, 1973; Struhsaker, 1981). Red colobus monkeys (Leland & Struhsaker, 1979; Stanford,

1995) and grey-cheeked mangabeys (Arlet & Isbell, 2009) attack or mob predators, to the advantage of redtail monkeys with whom they are in association. Redtail monkeys frequently form polyspecific groups with red colobus monkeys, the preferred prey of chimpanzees, which may confer protection from chimpanzees and eagles for redtail monkeys (Struhsaker, 1981;

Teelen, 2007).

Polyspecific association with or among semi-terrestrial guenons is extremely rare across sites: L’Hoest’s monkeys (Cercopithecus lhoesti) in Uganda (Chapman & Chapman, 2000a;

Struhsaker, 1981) and De Brazza’s monkeys (Cercopithecus neglectus) in Gabon (Gautier-Hion,

1988) were rarely observed in polyspecific association at all, including with small-bodied arboreal Cephus group guenons (C. ascanius in Uganda, C. cephus in Gabon), and both lived in small social groups and exhibited relatively cryptic behavior, including severely reduced vocalizations compared to other guenons. This pattern is likely in part due to reduced polyspecific association with terrestrial primates generally, reflecting reduced interaction

31 because of forest strata used, as sympatric baboons also were not seen in association with redtail monkeys (Cords, 1990).

Interactions between redtail monkeys and heterospecifics are not limited to behavior associated with feeding and predator avoidance. There is typically an inverse relationship between diet overlap and interspecific grooming frequency; redtails groom and are groomed by red colobus monkeys, with whom redtails have lower dietary overlap compared to other sympatric species except black-and-white colobus (Struhsaker, 1981). The existence of redtail monkey-blue monkey hybrids (Detwiler, 2002; Struhsaker et al., 1988) also indicates successful mating behavior between those two Cercopithecus species. These close-contact affiliative interactions with heterospecifics may have social nutrition implications because of potential buffering of interspecific competition.

This study aims to test aspects of primate socioecological models and social foraging theory with detailed multivariate nutritional intake data and conspecific and polyspecific behavioral data. Despite a rich literature in non-vertebrate social nutrition, this study is among the first to examine social nutrition in a primate. In the following chapters, I first present the nutritional strategy of female redtail monkeys, and then address how conspecific agonism and polyspecific association affect nutritional intake and balance in redtail monkeys. In addition to examining intake and balance of multiple macronutrients and energy, I also examine mineral intake, which is understudied in nonhuman primates.

CHAPTER 2: NUTRITIONAL ECOLOGY OF FEMALE REDTAIL MONKEYS (CERCOPITHECUS ASCANIUS): DIET COMPOSITION AND INTERGROUP VARIATION

INTRODUCTION

To survive and reproduce, animals must interact with complex foraging environments to meet their nutritional needs (Altmann, 1998; Barboza et al., 2008; Janson & Chapman, 1999;

McArthur et al., 2014; Oftedal, 1991; Westoby, 1974). Both the animal’s physiology and environment are dynamic: the animal’s nutritional requirements and intake fluctuate depending on variables including ontogeny (Altmann, 1991,1998; Chyb & Simpson, 1990; Leibowitz et al.,

1991), reproductive status (Krockenberger & Hume, 2007; Richter et al., 1938; Vargas et al.,

2010) and health (Cotter et al., 2011; Simopoulos, 2002), as well as by spatiotemporal variation in both food availability (Chapman et al., 2002b; Ganzhorn, 2002; Knott, 1998) and food nutritional composition (Chapman et al., 2003; Carlson et al., 2013; Hohmann et al., 2010;

Milton, 1999; Norconk et al., 2009; Worman & Chapman 2005).

Nutrition includes macronutrients that provide energy (fat, nonstructural carbohydrates, available protein, and digestible fiber), water, and micronutrients (vitamins, minerals, fatty acids, and amino acids), as well as antifeedants, including indigestible fiber and plant secondary compounds. Primates rely on mineral micronutrients in small amounts for a variety of key functions, including regulating numerous reactions in the body related to hormones and enzymes, fluid balance in cells, and tissue structure (Barboza et al., 2008; FAO/WHO, 2005; NRC, 2003;

Robbins, 1993). Tropical soils tend to be sodium poor, and there is evidence that primates specifically seek sodium sources (Oates, 1978; Reynolds et al., 2009; Rode et al., 2003; Rothman et al., 2006). However, the availability of minerals in wild primate foods, and which minerals are limiting, is understudied.

Carbohydrates are important and complex sources of metabolizable energy that include monosaccharides, disaccharides, oligosaccharides and polysaccharides (NRC, 2003). Primates consume nonstructural carbohydrates via a diverse set of molecules that are more easily digestible than structural carbohydrates, including soluble sugar (Danish et al., 2006; Reynolds et al., 1998) and starch (Whiten et al., 1991; Wrangham & Conklin-Brittain, 2003). By contrast, structural carbohydrates are components of the plant cell wall, include insoluble non-starch polysaccharides (insoluble fiber) and soluble non-starch polysaccharides (soluble fiber) (NRC,

2003), and vary from being indigestible by primates to digestible via microbial fermentation

(Van Soest, 1994).

Nonhuman primates frequently choose foods high in protein, soluble carbohydrates and/or fat relative to fiber, and low in plant secondary compounds (Barton & Whiten, 1994;

Chapman & Chapman, 2002; Conklin-Brittain et al., 1998; Knott, 1998; Milton, 1979; Oates et al., 1980; Wrangham et al., 1998). Those choices have been linked to fitness: for example, protein and energy intake predicted reproductive lifespan, number of offspring and survival in baboons (Altmann, 1991). Red colobus monkeys exhibited higher cortisol levels in response to poor nutrition and increased parasite load, with implications for reduced fecundity (Chapman et al., 2007). Examining multiple nutritional components yields a more nuanced understanding of the diet composition and feeding behavior of an animal (Belovsky, 1978; Raubenheimer &

Simpson, 1997; Simpson & Raubenheimer, 2012).

Diet composition and nutritional intake fluctuate with spatiotemporal variation in food availability (Chapman et al., 2002a; Ganzhorn, 2002; Knott, 1998). Primate energy intake varies with fruit availability, with animals exploiting this rich energy source when it is available (Knott,

1998; Conklin-Brittain et al., 2006; Heesen et al., 2013; Vogel et al., 2017). Seasonal differences

34 in food availability sometimes affect macronutrient intake. For example, depending on seasonal food availability, howler monkeys (Alouatta pigra) relied on fat or nonstructural carbohydrates as sources of energy (Righini et al., 2017); sifakas (Propithecus diadema) increased consumption of leaves and thus protein intake during periods of low fruit availability (Irwin et al., 2014).

A complex characterization of animal dietary flexibility and nutritional intake (Janson &

Chapman, 1999; Pulliam, 1975; Raubenheimer & Simpson, 1997) is key to understanding where a species falls on the specialist-generalist dietary spectrum (Cui et al., 2018; Machovsky-

Capuska et al., 2016). Niche theory predicts generalists will consume a variety of food types, one aspect of their wide niche breadth, which makes them better able to adjust to environmental change (Futyuma & Moreno, 1988). However, nutritional and antifeedant components of foods complicate this definition of niche breadth. The specialist-generalist spectrum can be considered at three levels in terms of nutrition: the level of the macronutrient composition of all foods in the diet of the animal, the level of macronutrient intake as they draw from multiple foods to create their diet, and the antifeedant (chemical or physical deterrents) composition of all foods in the diet (Machovsky-Capuska et al., 2016). For example, while foods consumed by members of a population may be diverse in nutritional composition (“food composition generalist”), the amount and ratio of key macronutrients ingested may be similar regardless of foods ingested

(“macronutrient specialist”) (Machovsky-Capuska et al., 2016). Despite variation in food composition, food availability, and fiber digestibility across monkey groups, blue monkeys in three groups at Kakamega, Kenya, reached similar intakes of metabolizable energy overall, as well as similar intakes of lipid, available protein, fiber and total nonstructural carbohydrates

(Takahashi et al., 2019). Therefore, blue monkeys are an example of a “food composition

35 generalist” and a “macronutrient specialist” (Takahashi et al., 2019; Machovsky-Capuska et al.,

2016).

The aim of this study was to characterize redtail monkey (Cercopithecus ascanius) diet nutritional composition by characterizing sources of energy, macronutrients, and minerals.

Redtail monkeys are small-bodied guenons (adult male mean = 4.3 kg, adult female mean = 2.9 kg [Delson et al., 2000]; adult male mean = 3.7 kg, adult female mean = 2.8 kg [Colyn, 1994;

Kingdon et al., 2013]) that eat fruits, insects, young leaves, and flower parts, with diet variation depending on season and site (Chapman & Chapman, 2000b; Chapman et al., 2002b; Cords,

1986, 1987; Gathua, 2000; Lambert, 2002a; Rode et al., 2006a; Struhsaker, 1978, 1980, 2017;

Tapper et al., 2019). Variation in redtail monkey diet has also been observed among groups within one study site: in Kibale National Park, percentages of redtail monkey food types varied across multiple locations in the park with leaves ranging from 13-35%, fruit of any ripeness ranging from 36-60%, flowers 3-15%, and insects 15-31% (Chapman et al., 2002b). By contrast, little intergroup variation in diet was found between redtail monkey groups at Kakamega, Kenya

(Cords, 1986; Gathua, 2000). Redtail diverse diet is aided by cheek pouches with digestive enzyme activity (Lambert, 2005) and a longer digestive retention time than expected given small body size (adult female MRT = 29.4 ± 9.8 hours [Lambert, 2002b]). The fiber (NDF) content of the redtail diet at Kanyawara was previously found to be similar to the fiber content of the diets of larger-bodied sympatric blue monkeys (Cercopithecus mitis), mangabeys (Lophocebus albigena) and chimpanzees (Pan troglodytes) (Conklin-Brittain et al., 1998; Wrangham et al.,

1998), suggesting redtail monkeys can ferment substantial amounts of fiber effectively despite their small body size and simple digestive tract.

36 Given redtail monkey diverse diet (Chapman et al., 2002b; Rode et al., 2006; Struhsaker,

2017) and more frequent switching among foods (Lambert, 2002a) compared to other sympatric monkeys species, I hypothesized that that redtails are “food composition generalists” (sensu

Machovsky-Capuska et al., 2016). I predicted (Prediction 1a) that important foods (>1% of caloric intake) would be plant and insect sources diverse in species, part, and nutritional composition.

One study group ranged in disturbed habitat more than the other two groups, and this group had previously been observed feeding frequently from Prunus africana (Chapman et al.,

2002b), a tree found more in secondary rather than primary forest (Fashing et al., 2004; Owiny &

Malinga, 2014). Kyo group’s home range covered mostly areas recovering from conifer plantation clear-cutting, areas recovering from light logging, and the biological field station campus, where Prunus africana density is higher (Owiny & Malinga, 2014) than in neighboring unlogged forest where Kus and Suk group home ranges frequently overlapped. Regenerating areas like those where Kyo group ranges are also composed of more shrubs like exotic Lantana camara (Omeja et al., 2016). I therefore hypothesized that Kyo group important foods would differ from those of the other two study groups. I predicted (P1b) that Prunus africana and more shrubs would be important foods to Kyo group females, while not to the other two groups’ females.

Frugivorous primates gain energy primarily from fruit nonstructural carbohydrates and fat (when fruits are fatty). Nonstructural carbohydrates include diverse nutritional components, including sugar and starch. Redtail monkeys frequently begin digestion of food in the cheek pouch with enzymes including salivary amylase for breaking down starch (Lambert, 2005;

Rahaman et al., 1975). Though redtail monkeys do not consume starchy underground storage

37 organs, their high consumption of unripe fruits and occasional consumption of seeds is facilitated by salivary amylase in cheek pouches (Lambert, 1998). I therefore hypothesized that the redtail monkey diet would be dominated by high-sugar and high-starch fruits. I predicted (P2a) that redtail monkey important foods (>1% of caloric intake) would include ripe and unripe fruits with sugar and starch content higher than the mean across all redtail foods. I also hypothesized that habitat differences between Kyo group and the other two groups would drive intergroup differences in sugar content of foods. I predicted (P2b) Kyo group important foods would include fruits with higher sugar content, including high-sugar Prunus africana fruit (Danish et al., 2006).

Food availability in the environment often dictates primate food selection (Cui et al.,

2019; Gursky, 2000; Hill, 1999; Hladik, 1977; Lambert et al., 2004; van Schaik et al., 1993) and sometimes nutritional intake (Cui et al., 2019; Koch et al., 2017; Sterling et al., 1994; Takahashi et al., 2019; Vogel et al., 2017). I therefore hypothesized that redtail monkey female diet composition would change based on food availability. In periods of high fruit-of-any-ripeness availability, I predicted (P3a) that redtail females would eat more fruit of any ripeness (i.e. higher percentage of kcal per day from fruit); in periods of high ripe fruit availability, redtail females would eat more ripe fruits; (P3b) in periods of high young leaf availability, redtail females would eat more young leaves; (P3c) when insects were more abundant, redtails would eat more insects.

Diets consisting of tropical plants meet many, but not necessarily all, essential mineral requirements. One mineral that is not widely available in plant matter is sodium which is key to maintenance of fluid in the body and nerve activity (NRC, 2003); as a result, multiple primates seek out sodium in tropical environments (Oates et al., 1978; Rode et al., 2003; Rothman et al.,

2006). Copper, the tropical availability of which is less well understood than sodium, is a mineral

38 essential to enzymes important to metabolic and immune function, as well as formation of connective tissue (NRC, 2003). Based on observed lower copper intake than recommended requirements (Nagy & Milton, 1979; Rode et al., 2006a) and based on a correlation between redtail monkey abundance and copper intake (Rode et al. 2006a), some researchers also suggest that copper is a limiting mineral for tropical forest primates. Insects, compared to plant food items, are a greater source of multiple minerals, including sodium (Deblauwe & Janssens, 2008;

Isbell et al., 2013; O’Malley & Power, 2014). Because redtail monkeys have diverse diets that include mineral-rich insects, I hypothesized that they would gain potentially limiting minerals from diverse food types. I predicted (P4) that females would acquire sodium and copper from a variety of plant species/parts and insect morphotypes, rather than relying on one or a small number of food items for these key micronutrients.

METHODS

Study site and subjects

This study was conducted at Kanyawara site, in Kibale National Park, western Uganda

(0°13 – 0°41N and 30°19 –30°32E), a 795 km2 mid-altitude evergreen forest in a 282-ha area at an elevation of 1500 m. The research camp next to and in which this study took place is

Makerere University Biological Field Station (MUBFS), formed in 1970 by Thomas Struhsaker and subsequently run by Makerere University in Kampala, Uganda (Struhsaker, 1997). The site experiences bimodal seasonality, with rainy seasons approximately March - May and September

- November (Stampone et al., 2011; Struhsaker, 1997) with average annual rainfall of 1680 mm

(1990-2015, Chapman et al. unpub. in Chapman et al., 2018a) and interannual variation in rainfall patterns, (Chapman et al., 2018a; Struhsaker, 1997). All data for this project were

39 collected during a strong El Niño year (2015-2016) (Oceanic Niño Index, NOAA), with potential implications for rainfall and food availability. Previously monitored El Niño years in Kibale

National Park had increased rainfall overall and led to greater intermonthly variation in rainfall than non-El Niño years (Struhsaker, 1997). Additionally, after previous El Niño periods, increased annual production of ripe fruit has been reported at Kanyawara (Chapman et al.,

2018a).

Redtail monkeys (Cercopithecus ascanius) are common in the area, with a density of ca.

184 individuals/km2 (Chapman et al., 2010) with smaller home ranges but wider group spread compared to other sympatric monkey species (Struhsaker, 1978; Struhsaker & Leland, 1979, average annual home range 24 hectares). Redtail monkey abundance has increased overall in

Kibale National Park since 1970 based on long term census data (Chapman et al., 2018b), though short-term declines in redtail monkey abundance in areas recovering from logging have also been observed (Chapman et al., 2018b; Skorupa, 1986; Struhsaker, 1997). Redtail group density varies across areas of the site with different logging histories: K15 forest compartment (high intensity commercial logging 1969) redtail monkey group density = 1.04 groups per km2; K14

(low intensity logging 1969) redtail group density = 11.48 groups/km2; K30 (old growth forest with no commercial logging history) group density = 4.83 groups/km2 (Chapman & Chapman,

2000b). Unlogged forest (K30) supports more individuals (135.05 redtail monkeys/km2) in larger groups than heavily logged forest (K15) (38.12 redtail monkeys/km2) (Rode et al., 2006a).

The three redtail monkey study groups had been habituated to human presence,

Kusamerera (Kus) group since 2008 and Sukaali (Suk) and Kyomuhendo (Kyo) groups since

2011. All three groups increased in size during the study period: in 2015, each group was less than 40 individuals across all age/sex classes (Kus=34, adult females =16, adult males=5;

Kyo=31, adult females=16, adult males=3; Suk=36, adult females=15, adult males=6) and by

2017, each group approached or surpassed 50 individuals. Redtail monkey single-male groups with male influxes are described in long-term redtail monkey research at the same site

(Struhsaker, 1977, 1988) as well as at Kakamega, Kenya (Cords, 1984); however, during this study, all three study groups included multiple males on most days, rather than only in finite influx periods.

The three study groups had overlapping home ranges (Appendix 3) in forest recovering from varying levels of logging intensity (Chapman et al., 2018b; Struhsaker, 1997), with resulting variation in tree composition (Owiny et al., 2016; Valtonen et al., 2017). The three study groups ranged in parts of K14 forest compartment, which was lightly logged (25% basal area removed) in 1969 and left to regenerate naturally; Kus and Suk group also ranged in parts of

K30 forest compartment, which consists of primary forest that has never been commercially logged (Kasenene, 1987; Struhsaker, 1997). Kyo group also ranged in former conifer plantations that were clear-cut during the period 1987-1999 and left to regenerate into forest (referred to as regenerating age class area 19 [RAC19]; Nyafwono et al., 2014; Owiny et al., 2016) as well as within Makerere Biological Field Station.

Study subjects were 24 adult female redtail monkeys in the three study groups (8 females per group), all parous females except for one adult female in Kus group (who had not been seen with an infant since habituation). Adult female study animals were individually identifiable by features of their faces, tails, and/or nipples; and were observed within 5-15 m, with some variation due to monkey location in the canopy.

Feeding behavioral data collection

A three-person field team (Hillary Musinguzi, Richard Sabiiti and I; or Hillary

Musinguzi, Richard Sabiiti, and Patrick Ahabyoona) estimated nutritional intake of adult female redtail monkeys by conducting continuous focal follows (aiming for full-day focal follows from

7:00 to 19:00) of adult females in the three groups (N=8 focal females per group) from May

2015 to January 2017. We rotated among the focal females in each group, aiming for a comparable sample size of full-day focal follows across individuals; one week was devoted to each study group, with the fourth week of the month for phenological monitoring and food sample processing and preparation catch-up. Field team members rotated among work shifts of

7:00 to 19:00, 7:00 to 14:00, and 11:00 to 19:00 so that two team members were always present: one observing and reporting the focal female’s feeding behavior and a second team member recording feeding behavior and tagging feeding trees. Field assistants and I assessed inter- observer reliability every three months during the study via comparison of simultaneous focal follows. Feeding was defined as starting with ingestion of a food item into the mouth and ending when the individual stopped chewing and switched to another activity. During a feeding bout, we recorded the plant species and part and counted the number of items eaten.

When a focal female processed cheek-pouched food (for example, spitting seeds and eating pulp from fruit that had been temporarily stored in her cheek pouches), I recorded

“processing.” Processing was not counted as feeding as the female had already been recorded placing the food in her mouth/cheek pouches, but how the food was processed determined how to collect and prepare the food sample for nutritional analysis. Processing bouts had to be matched to particular fruit-feeding bouts, as (1) multiple species of fruits are processed in one day and (2) the same species of fruit may be processed in multiple ways, even over the course of one day.

42 Previous meta-analyses of guenon feeding ecology have pointed out that seed consumption has been defined differently across studies, leading to different conclusions about guenon seed consumption: “Some researchers considered the consumption of both fruit flesh and seeds as fruit eating, while others classed as seed eating both the consumption of seeds from dry fruit and immature fleshy fruit” (Chapman et al., 2002b, pp. 331-332). For this study, I defined seed eating as any ingestion of seeds both independent of the rest of the fruit or along with the rest of (or other parts of) the fruit. As per Lambert (1997) seed handling definitions, whenever a redtail monkey female ate fruit, I recorded whether the fruit’s seeds were destroyed (visibly chewed), spit (spit out one seed at a time) or swallowed (swallowed whole). Cheek pouching complicates assessing seed handling, as the monkey may place fruit in the cheek pouch first, then later spit seeds out as she processes the fruit. An additional complication was seed swallowing, which can be difficult to identify, though there are certain behavioral cues such as the tilting back of the head and not chewing while consuming food or processing cheek pouched food

(Lambert, 1999). Lambert (1997) observed in one group at the same field site that redtail monkeys spat seeds in the majority of feeding records (61%) and destroyed seeds (24%), swallowed seeds (15%), or engaged in a combination of spitting and swallowing seeds (6%) less frequently. Like Lambert (1997), I observed variation in seed handling of the same fruit species, which is likely due to chemical variation in fruit nutritional and antifeedant composition spatiotemporally. Fig (Ficus) fruit seeds were always swallowed and subsequently found in feces

(Lambert, 1997; this study), but I analyzed the whole fig in nutritional analyses because of the high quantity of small seeds in one fig (hundreds of seeds of approximately 1mm length); incorporation of a fiber digestibility coefficient into energy calculations should account for the lower digestibility of a whole fig due to fig seeds not actually being digested.

We recorded feeding on insects similarly to feeding on plants, except we were only able to identify insects to Order. If the insect could not be identified, “unidentified insect” was recorded. Feeding on insects rather than plant parts was apparent even if the insect was too small to identify, as redtail monkeys eat insect(s) off of a substrate without damaging the substrate and use specific motor patterns in capturing insects (Cords, 1986; Struhsaker, 2017).

We also recorded whether the focal female was cycling, pregnant or lactating. Adult female redtail monkeys at Kanyawara give birth throughout the year based on long-term data, with a peak in births from November - February (Butynksi, 1988; Struhsaker, 1988) and interbirth intervals (IBI) are distributed bimodally at 12 and 24 months (Struhsaker & Butynski unpublished data in Cords, 1988). Pregnancy is difficult to detect in female redtail monkeys as there is no visual signal of pregnancy; therefore, birth dates of infants and previous reports of redtail monkey gestation length were used to estimate when the female was pregnant or cycling.

An estimated redtail monkey gestation length of 6 months was used based on previous redtail monkey gestation estimates (Haddow, 1952) and time differences between redtail monkey copulations and births recorded at the same site 1973-1983 (Struhsaker, 1988). Lactation was determined through observation of a female with an infant suckling. Redtail monkey infants at

Kanyawara move independently from the mother at 6 months (Struhsaker & Pope, 1991) and are weaned between 18-20 months (Struhsaker unpublished data in Butynski, 1988). Less frequent suckling by older redtail monkey infants (eight months old and older) may be less relevant to high maternal energetic burden and lactational amenorrhea, given reduced energetic burden of later lactation found in other primates (Emery Thompson, 2012).

44 When a focal female was partially visible but I could not see her forelimbs and mouth, I recorded OS for “out of sight.” When I did not know where the focal female was because of poor visibility, I recorded “Lost.”

Plant and insect food collection

Each feeding tree with DBH > 10 cm was tagged and the locations of saplings, shrubs and vines were recorded during full-day focal follows. Within one week of the full-day behavioral follow, Hillary Musinguzi, Richard Sabiiti, field-lab assistant James Magaro, and/or I collected samples of all foods eaten by focal female. Redtails switch among foods rapidly and sometimes feed high in the canopy requiring food sample collection with a pole saw, so we could not usually collect food samples on the day of the focal follow. We collected samples of foods from the same tree, sapling, vine or shrub that the female fed from and same location on the plant—if the food part was still available. We collected samples of all foods consumed by focal females until June 2016, when only the top 10 foods consumed per month were collected at the end of the month. We collected identified insects for nutritional analysis opportunistically in undergrowth or low in trees. Collection of identified insects in the exact location of consumption by monkeys was usually not possible, except in the case of noctuid caterpillar flushes when hundreds of developing Lepidoptera larvae defoliated multiple tree species. For all foods, enough matter was collected to generate 20 g dry weight ground sample. For insects, this required collection of at least 70 individuals.

In preparing foods for sample preparation, we processed the foods as the monkeys did

(e.g., if the monkey spit out the seeds of a ripe fruit, I removed the seeds from that ripe fruits).

The collected food was weighed (field wet weight and unit weights), dried in a dehydrator to a constant weight, and weighed again (field dry weight). Dried samples were ground in a Wiley

Mill with 1 mm screen (Thomas Scientific) then exported to Hunter College of the City

University of New York’s Primate Nutritional Ecology Laboratory.

Fecal collection

Field assistants and I collected 1 g fecal samples from focal females (1) the day after a focal follow, based on redtail female digestive retention time (Lambert 1997, 2002) whenever possible and (2) opportunistically. Samples were placed in ethanol for at least 24 hours, drained, allowed to dry and exported to Hunter College of the City University of New York’s Primate

Nutritional Ecology Laboratory.

Plant phenology monitoring

Diet variation across individuals and groups is partially dictated by what food is available in the home range at a given time. Tree phenology patterns overall at Kanyawara vary by tree individual, tree species, month, year and ENSO Index (Chapman et al. 2005, 2018a; Struhsaker,

1975, 1997). Peter Irumba and another field assistant monitored two plant phenology transects monthly. Each transect was 1 km long and included all trees with DBH > 10 cm present 5 m on each side of the transect: one (622 trees, 46 species) was in the overlapping home ranges of Kus and Suk study groups in K14 (an area recovering from low-density logging) and a second (401 trees, 47 species) was in Kyo home range, on the edge of K14, into an area recovering from

1987-99 clear-cutting of conifer plantations, and into Makerere Biological Field Station (see

Table 2.5 for tree composition). The presence of young leaves (YL), mature leaves (ML), flowers (FL), unripe fruit (UF) and ripe fruit (RF) were estimated in monitored trees using a 0-4 scale (0=not present, 1=1-25% of capacity, 2=26-50%, 3=51-75%, 4=76-100%) (Chapman et al.

1994).

Food availability indices

46 Separate food availability indices were generated for each phenology transect and for fruit of any ripeness (Fruit Availability Index, FAI), ripe fruit only (Ripe Fruit Availability

Index, RFAI), young leaves (Young Leaf Availability Index, YLAI), and flowers (Flower

Availability Index, FLAI). Because redtail monkeys ate unripe and ripe fruit, the availability of each was monitored. The leaf availability index only included young leaves because redtail monkeys rarely ate mature leaves. For each transect, for each month, each food availability index was calculated as follows:

where B is the mean basal area of tree species x for forest compartment of Kanyawara relevant to monkey group (Chapman and Chapman 1997); P is the mean phenology score of the plant part of interest for tree species x in month m, added up for all occurrences of that feeding tree species, S, on transect.

For calculating food availability indices, I used phenology data from tree species on transects that we observed redtail monkeys feeding from or were included in a long-term research Kanyawara redtail monkey plant food list (Struhsaker, 2017). Any trees on the transect that died during phenological monitoring were excluded from analyses.

Because redtail monkeys eat plant exudate, assessing exudate availability would ideally also be included in redtail food availability measures. However, how to effectively assess the spatiotemporal availability of exudate in primate habitats is debated among researchers (Garber

& Porter, 2010; Genin, 2008; Joly-Radko & Zimmerman, 2010) and gum availability has been measured differently across studies: for example, a gum site may be defined as (1) one tree containing gum or (2) as one globule of gum. The multiple Prunus africana trees lining a road

47 within Makerere University Biological Field Station can be considered a reliable source of gum available to redtail monkey groups, though I did not systematically examine availability of gum on these P. africana trees. In addition to the fact that these P. africana trees were clustered,

Prunus species commonly undergo gummosis, leading to more frequent accumulation and excretion of gum than other tree species that only secrete gum when damaged (Simas et al.,

2008).

Insect abundance monitoring

As with plant foods, determining availability and abundance of insect foods is important to assessing variation in primate diet and nutritional intake, though only a few primate studies have tried to examine insect abundance (capuchin monkeys, Saimiri oerstedi: Boinski, 1988; multiple platyrrhines: Janson & Emmons, 1990; redtail monkeys: Gathua, 2000; tarsiers, Tarsius spectrum: Gursky, 2000; woolly monkeys, Lagothrix lagotricha: Fonseca et al., 2019). Most studies of insectivorous primates have assumed that insects are widely or uniformly distributed, which is an oversimplification. Measuring overall insect abundance, let alone availability to an insectivore, in a tropical forest environment is extremely challenging. Tropical rain forests contain diverse insect taxa, each with multiple developmental morphs, and one microhabitat within one tree may hold a different sample of this diversity than another (Wolda, 1978;

Nummelin, 1996). Entomological studies at Kanyawara have demonstrated variation in overall insect abundance across different forest types (unlogged and recovering from heavily and lightly commercially logged) (Nummelin, 1989), and over time (Nummelin & Zilihona, 2004).

I collaborated with PhD candidate Tapani Hopkins from Zoological Museum of Turku,

Finland, to conduct short-term malaise trapping and long-term netting insect collection. Hopkins, with the Biodiversity Unit of the University of Turku and the University of Eastern Finland,

48 monitored multiple malaise traps throughout the forest at Kanyawara site, collecting the contents of traps every two weeks (approximately 6,000 insects per collection) and preserving samples for identification. Malaise traps are tent-like traps that capture insects in a container of ethanol; they are effective at insect capture because insects tend to fly upwards when hitting a vertical surface

(Matthews & Matthews, 1971). Hopkins and I identified four malaise traps that were already located in the redtail monkey study group home ranges. In June and July 2015, Hopkins, field assistant Isaiah Mwesige and I identified relevant 2-week samples from the four relevant malaise traps and created a subsample from each trap to preserve in 80% ethanol (100% ethanol renders the insects too fragile for identification); I identified insects in each subsample to the Order level with assistance from Hopkins and the insect identification guide Castner (2000). Hopkins also shared count data for relevant malaise traps for August 2015 and biomass data from relevant traps for June-August 2015.

For monthly netting, I established three insect netting locations with flagging tape, one in each monkey home range. Between August 2015 and May 2016, once per month in the morning

(between 8:00 and 9:30), Richard Sabiiti or I started at the designated location and, moving in the same direction each month, swept back and forth 50 times with a standard insect net contacting as much vegetation as possible and not going over the same area more than once. At the end of the sweep, I deposited the contents of the net into a plastic container containing 80% ethanol and identified insects to Order.

Insect abundance indices

Following previous insect indices used by primatologists (Gathua, 2000; Gursky, 2000) and entomologists (Nummelin, 1996), insect indices were generated as follows: (1) number of insects caught via monthly netting, (2) percentage Lepidoptera by count via monthly netting (3)

49 percentage Orthoptera by count via monthly netting, and (4) percentage cicadas by number via monthly netting. These two insect orders (Lepidoptera: moths, butterflies; Orthoptera: grasshoppers, locusts, crickets) and one insect morphotype (cicadas) were chosen because they were the most common identified insects consumed by redtail monkeys. Additionally, using biomass averages of individual insects of each order provided by Hopkins (University of Turku,

Finland), the following insect abundance indices based on monthly netting data were also generated: (5) netting catch biomass Lepidoptera (grams) (estimated biomass of Lepidoptera per month = Lepidoptera count*average wet weight of Lepidoptera individual) and (6) netting biomass Orthoptera (g) (estimated of Orthoptera per month = Orthoptera count*average wet weight of Orthoptera individual). I could not generate a similar index for cicadas because biomasses were not available. Insect indices were also generated based on short-term malaise trap data. Based on malaise trap monthly insect counts and biomass for summer 2015, I calculated monthly (1) malaise trap insect count, (2) malaise trap insect biomass, and (3) malaise percentage Lepidoptera by biomass.

Neither the malaise trap nor the netting method can be considered availability measures, but instead measures of overall abundance of insects in some of the microhabitats of redtail monkey home ranges. Owlet moth larvae flushes (Order: Lepidoptera, Family: Noctuidae) were not reflected in my insect abundance methods given how they were clumped in trees for a limited period of time (personal observation).

Nutritional chemistry

Using standard AOAC methods, I analyzed plant foods (N=601) for macronutrients (fat, available protein, nonstructural carbohydrates, and digestible fiber), indigestible fiber, condensed tannins and minerals, and I analyzed insect foods (N=10) for macronutrients, chitin and minerals.

50 I measured the amount of crude fat by boiling samples in petroleum ether at 90°C for 120 min

(AOCS, 2009) in an XT15 Fat Analyzer (ANKOM, Macedon, NY). If a sample contained >5% fat, then, before conducting sequential fiber analyses, I pre-treated the fiber bag with acetone to prevent lipid interference in fiber analyses; untreated high fat samples can lead to artificially high

NDF and ADF values due to detergents being more soluble in fats than in water during the assay

(Van Soest et al., 1991). I conducted sequential fiber analyses through neutral detergent fiber

(NDF) with -amylase and acid detergent fiber (ADF) using an A200 fiber analyzer (ANKOM,

Macedon, NY) and 72% sulfuric acid treatment for lignin (ADL) (Goering & Van Soest, 1970;

Van Soest, 1963; Van Soest et al., 1991). The inclusion of -amylase in NDF is to break down starch, thereby preventing starch interference with the NDF result (Van Soest et al., 1991). These sequential detergent analyses allow me to characterize fiber components with varying levels of digestibility in samples: the NDF wash recovers lignin, cellulose and hemicellulose as residue, while other cell components are washed away; the ADF residue contains lignin and cellulose, while hemicellulose is washed away. Using this system, cellulose and hemicellulose content can be estimated by difference. Finally, the sulfuric acid treatment (ADL) residue is lignin content, while cellulose is washed away (Van Soest et al., 1991; Van Soest, 1994).

I ashed lignin fiber bags at the end of sequential fiber analyses to correct for other components that may have ash double-counted in their values, thereby affecting total nonstructural carbohydrate (TNC) by subtraction. Every time sequential fiber assays are run, the

NDF result has a minute portion of ash in it, the ADF result similarly has a minute portion of ash in it (less than NDF), and the lignin result has ash in it (less so than NDF and ADF). Ashing the

NDF, ADF and lignin residues and then subtracting these from NDF value, ADF value and lignin value respectively, is an ideal way to prevent this double counting, but is not cost-efficient.

51 Instead, I subtracted that lignin ash result from NDF, from ADF and from lignin. Typically, the

NDF-ash is less than 1% ash on a DM basis unless the sample is higher than 10% in ash, in which case it may be 1-2% DM (Rothman, unpublished data).

I estimated total nitrogen (N) via combustion (AOAC, 1995) using a Leco TruSpec

(Leco, St. Joseph, MI) and then calculated crude protein (CP) by multiplying nitrogen by 6.25

(Maynard & Loosli, 1969; Robbins, 1993). The 6.25 conversion factor may overestimate the crude protein content of tropical plants (Conklin Brittain et al., 1999; Milton & Ditzis, 1981); however, I used this value so that this study would be comparable to other primate nutritional ecology studies. The 16 g of nitrogen per 100 g of protein method of calculating crude protein content includes some indigestible protein that is bound to fiber (Conklin-Brittain et al., 1999;

Rothman et al., 2012); therefore, I analyzed protein available for digestion (Available Protein) through subtraction of [Acid Detergent Insoluble Nitrogen (ADIN)*6.25] from the crude protein value (Licitra et al., 1996; Rothman et al., 2008b).

All identified insect samples were analyzed for crude fat, crude protein, available protein, and ADF (to determine the antifeedant chitin) using the same assays used for plants. I calculated

TNC by subtraction and digestible energy intake from insects with the same equations, except excluding NDF (and replacing NDF with ADF when calculating TNC by subtraction).

Leaf galls are growths on leaves induced by insects, bacteria, viruses or other causes

(Shorthouse & Rohfritsch, 1992). Many primates, including redtail monkeys, eat leaf galls

(Bryer et al., 2015; Goodall, 1986; McGrew, 2014; Tutin, 1999) and, though some leaf galls contain insect eggs or larvae of diverse taxa (Raman, 2011), researchers cannot assume that galls contain insect matter. Given the uncertainty of the content of leaf gall samples, and previous leaf gall samples I analyzed having high ADF content (Bryer et al., 2015), they were analyzed with

52 the same assays as plant samples. Detailed research on leaf galls induced by insects in Kibale

National Park is limited to those induced on leaves and stems of Neoboutonia macrocalyx by gall midges (order Diptera, family Cecidomyiidae) and jumping plant-lice (Order Hemiptera, family

Psyllidae) (Malinga et al., 2014). I observed a redtail monkey female feeding on a large gall on a

N. macrocalyx leaf only once during the study period. Redtail monkeys at Kanyawara frequently ate galls on Vepris nobilis leaves, which are likely induced by jumping plant-lice (Order

Hemiptera, family Phacopteronidae) (Malenovsky et al., 2007). As part of insect gall induction, insects gather leaf carbohydrates to concentrated gall sites on the leaf as resources for developing insects (Castro et al., 2013), which may indicate that the nutritional payoff of eating galls is leaf carbohydrates concentrated in the small nut-like growths on the leaf. The macronutrient payoff of eating a gall may even go beyond concentrated digestible carbohydrates: some insect-induced galls include nutritive cells for developing insects that “include elevated levels of carbohydrates and lipids, soluble sugars and proteins, and show intense phosphatase activity” compared to non- gall components of the plant (Raman, 2011, pg. 527).

I estimated total nonstructural carbohydrate (TNC) by difference: TNC = [100 – (NDF +

AP + (lipid-1) + ash)] for plant parts (Rothman et al., 2012). I subtracted 1 from fat because fat results include indigestible components like wax. To more accurately characterize some of the nonstructural carbohydrates in the foods eaten by redtail monkeys, I also conducted sugar and starch analyses (N=100 samples). I measured simple sugars with the phenol-sulfuric acid assay

(Dubois et al. 1956; Hall et al. 1999) with a sucrose standard; the results were read with a spectrometer set to 490 nanometers (Danish et al. 2006). Starch content was measured via the amyloglucosidase/alpha-amylase method with a Megazyme total starch kit with a D-glucose standard and maize control (AOAC Official Method 996.11; AACC Official Method 76-13.01).

53 I measured ash by burning the sample in a muffle furnace at 550°C. If the ash result was

>10%, the sample was repeated where possible. Ash serves as a gross estimate of mineral content; however, the residue that remains after burning the food sample is an overestimate of inorganic elements in the food (NRC, 2003). Therefore, foods (n=101 unique food items) were analyzed by inductively coupled plasma optical emission spectroscopy (ICP-OES) by Dairy One

Forage Lab in Ithaca, New York for calcium, phosphorous, magnesium, potassium, sodium, iron, copper, manganese, zinc, and molybdenum. ICP-OES is a method of analyzing element composition of a food sample by exciting atoms in the sample using energy from argon plasma

(hot ionized gas). The resulting excitation and energy transitions of the atoms of the sample produce spectra reflecting the elements in the sample that are being excited (Hou et al., 2006).

To calculate fiber digestibility of the diet, fecal samples (N=69) were run through sequential fiber analyses and a mean NDF digestibility coefficient (Rothman et al., 2008a) was calculated for each monkey group as follows:

% ADL diet % NDF feces NDF Digestibility = 100 − 100 ∗ × DM % ADL feces % NDF diet where % ADL diet is the percentage of daily intake acid detergent lignin; % ADL feces is the percentage of feces sample composed of acid detergent lignin; % NDF diet and % NDF feces are defined similarly except with neutral detergent fiber content. NDF digestibility is on a Dry

Matter (DM) basis as (fecal samples and all food samples used in intake calculations were analyzed on a DM basis).

Wet chemistry assays for macronutrients described above were conducted on all plant and non-plant food samples (N=281), except for leaf samples (N=320), which were run through near-infrared reflectance spectroscopy (NIRS) using a Foss XDS spectrometer (Laurel, MD).

This method generates nutritional composition values through irradiation of samples with near-

54 infrared light, reflecting a spectrum of chemical bonds that are matched to reference values generated by wet chemistry analyses (Foley et al., 1998; Rothman et al., 2009a). First, near- infrared spectra are matched to macronutrient wet chemistry values from other relevant samples and an empirical calibration equation is created using hedge (error) parameters, math treatments and modified partial least squares regression (MPLS) (Shenk & Westerhaus, 1991; Rothman et al., 2009a). Second, to determine macronutrient composition results for an unanalyzed food sample, the unanalyzed food sample is run through the NIRS calibration equation (i.e. the unanalyzed food’s near-infrared spectra are translated into macronutrient values via the equation). An NIRS calibration equation for leaf samples from Kibale National Park was previously generated by Dr. Jessica Rothman (Rothman, unpublished data). In addition to the dried and ground leaf samples run through NIRS, a subset of leaf samples collected for this project were stored and scanned through a FOSS XDS near-infrared spectrometer in the field laboratory in Uganda. These leaf near-infrared spectra files were then exported to New York where the values were used to generate nutritional values via the leaf equation for the NIR spectrometer in New York.

I also attempted to generate an NIRS fruit equation that would enable rapid analysis for macronutrients of fruits eaten by monkeys in Kibale National Park. I first compiled relevant reference values, specifically wet chemistry values for macronutrients for fruits (any ripeness, any component of fruit, N=420) from Kibale monkey projects that are part of the Rothman

Primate Nutritional Ecology Laboratory database. These dried and ground fruit samples (N=420) were also run through the NIR spectrometer to generate near-infrared spectra corresponding to each sample. I then used these spectra and macronutrient wet chemistry values to generate empirical calibration equations with different iterations of hedge (distance from mean)

55 parameters (Global Hedge (GH) and Neighborhood Hedge (NH)) and math treatments

(derivatives, gap, smooth, second smooth), using the standard calibration method of modified partial least squares regression (MPLS) (Rothman et al., 2009a; Shenk & Westerhaus, 1991). I then ranked these calibration equations based on their standard error of cross validation (SECV) and highest coefficient of determination (R-squared value). However, I was unable to generate a calibration equation with a low enough SECV and high enough R-squared based on previously accepted parameters (Rothman et al., 2009a). The high level of variation in macronutrient composition of the fruit samples at Kibale and among different fruit parts and ripeness levels may explain my difficulty in generating an accurate equation. Separate calibration equations may need to be created for unripe fruit pulp, ripe fruit pulp, unripe fruit seeds only, and ripe fruit seeds only, each requiring a large sample size. NIRS fruit equation development difficulty likely varies by field site plant biodiversity and by how generalist the diet(s) of the primate(s) of interest is/are.

Though my research questions do not directly involve plant secondary compounds, I included the analysis of one class of plant secondary compound, condensed tannins, as a potential confounding factor affecting nutritional intake and balance. To determine presence/absence of condensed tannins in redtail monkey foods, I analyzed 95 unique samples via acid-butanol assay with one extraction (Swain & Hillis, 1959; Waterman & Mole, 1994)

(Appendix 6). Condensed tannins are the most common tannin type in vascular plants (Bernays et al., 1989) that can deter primates from consuming plant parts (Milton, 1979; Oates, 1977; though see Chapman & Chapman, 2002; Reynolds et al., 1998). Condensed tannins bind to protein thereby reducing protein available for digestion (Rothman et al., 2009b; Wallis et al.,

2010) and have been linked to reducing mineral absorption (Freeland et al., 1985).

56 Calculations of nutrient intake

I calculated metabolizable energy from plants as follows: Digestible energy (kcal) =

[(NDF × 3) × NDF digestion coefficient] + (AP × 4) + ((lipid-1) × 9) + (TNC × 4) (Conklin &

Wrangham, 1994; Conklin-Brittain et al., 2006; Rothman et al., 2012). Insect energy intake was calculated using the same equation, except excluding NDF. I calculated the intake of a macronutrient in a feeding bout based on the amount of food eaten ([average unit weight × unit count] × field-corrected dry matter) multiplied by the macronutrient composition of the food.

Field-corrected dry matter was calculated as follows: field dry matter ratio × lab dry matter ratio.

Mineral intake was also calculated using weight-based intake calculations of milligram (mg) intake based on mineral composition of food items either in ppm (mg/kg) or percentage. For unidentified insects, mean nutritional values across identified insect samples were used. For food intake data without recorded counts, I used the median intake rate for the species and food part eaten. For each focal day, all macronutrient, energy and micronutrient intake values were extrapolated for recorded Out of Sight and Lost time to better reflect daily intake.

Females from two of the study groups were observed drinking anther oil from Symphonia globulifera flowers, as previously observed in redtail monkeys at the same site (Struhsaker,

2017) and in multiple tamarin species (Garber, 1988: Saguinus mystax, Saguinus fuscicollis;

Dietz et al., 1997: Leontopithecus rosalia). Symphonia globulifera flowers are unusual because their anther oil contains the fatty acid methyl ester (methyl nervonate), with pollen suspended in it (Bittrich et al., 2013). Due to the small amount of anther oil in each flower and its rapid consumption by redtail monkeys and other species at high canopy strata during the short-term S. globulifera flowering period, I was unable to collect enough S. globulifera anther oil for

57 nutritional analyses and instead estimated nutritional composition based on high fat content inherent in an oil with a small amount of pollen.

For samples missing nutritional composition values due to (1) food item recorded in a focal follow not collected due to the plant part abundance being insufficient for collection in the field (2) sample used up during the course of multiple chemical assays, or (3) NIRS scan of a leaf sample generated values outside of the a global hedge and/or neighborhood hedge parameters (indicating distance from the mean) set by the leaf calibration equation, I used the following rules to estimate missing values. Analyzed sample values were used as estimates for the missing values based on same food item, monkey group, month and year, or season (wet vs. dry) if month/year sample was not available. If possible, the analyzed sample and the missing sample were from the same tree; if an analyzed sample from the same tree was not available, a sample from a different tree but the same species/part, monkey group, and month/year or season was used. If no analyzed sample was available for a species/part, I used values for that species and part from my Master’s project dataset of redtail monkey foods (2011-2012). Because I was unable to collect mushroom samples, I used the macronutrient composition values for a mushroom sample collected as part of other Kibale monkey projects from the Rothman Primate

Nutritional Ecology Laboratory database and the published mineral composition values for wild

Ugandan mushrooms (Nakalembe et al., 2015).

Dataset

From May 2015 - January 2017, we conducted 162 focal follows (total in-sight observation hours = 1,043 hours) of adult female redtail monkeys; however, 25 of these follows were less than 5 hours with female in sight and therefore not used in estimating daily nutritional intake. This 5-hour cutoff was chosen because (1) any focal follows with less than 5 hours in

58 sight likely do not reflect the redtail diverse foraging behavior during the span of one day and (2) a higher cutoff reduced the sample size substantially (a 6-hour cutoff reduced the sample size to

107 focal follows). The resulting 137 focal follows (total in-sight observation hours = 961 hours) of adult female redtail monkeys (N=23 females) from three groups (N=45 focal follows Kus; N =

46 focal follows Suk, and N=45 focal follows Kyo) were analyzed to characterize individual nutritional intake (Appendix 4).

Statistical analyses

Statistical models and visualizations were generated in R 3.5.3 (R Core Team 2018), using statistical packages lme4 (Bates et al., 2015), multcomp (Hothorn et al., 2016), piecewiseSEM (Lefcheck, 2016), dunn.test (Dinno, 2017), and ggplot2 (Wickham, 2016).

Nutritional geometry visualizations of macronutrient balance: To visualize the nutritional space through which redtail monkeys navigate and how they mix macronutrients, I used Right-

Angled Mixture Triangles (RMTs), which represent three-term ratios among three nutritional components (Raubenheimer, 2011; Raubenheimer et al., 2015). Each data point represents the three-term-ratio of the three macronutrients represented by the three axes, the third of which is an implicit axis (i-axis) that is interpreted as a series of negative-sloped isoclines from 0 where the i-axis intersects the x and y axes at value of 1. The value of the i-axis can easily be visualized and calculated based on the x and y values because the three components add up to 1 (or 100 if working with percentages). I constructed two iterations of RMTs: the first visualizes macronutrient composition of foods consumed by redtail females to represent the foods from which monkeys choose, or the “menu” (Figure 2.6-2.7) and the second visualizes macronutrient intake by food category, representing the extent to which various foods contribute specific macronutrients to female daily intake (Figures 2.3-2.5). For food composition, a three-term ratio

59 was generated based on three macronutrients in the food; for macronutrient intake by food category, a three-term ratio was generated based on daily macronutrient intake by females. For example, for Figure 2.6a, an RMT of mean daily intake by group and food composition for protein, TNC and NDF, the nutritional composition datapoints were generated as follows: One data point represents one food, specifically the three-term ratio of protein:TNC:fiber in that food.

The three macronutrient percentages for the food are summed, then each food is divided by the summed value to generate the ratio, which is then converted to a percentage for clarity. The average three-term-ratio of individual intake for each monkey group was also included to compare the monkeys’ “menu” versus how they combined macronutrients for intake of the average individual in each group.

Food availability and diet composition: To test the effects of food availability and study group on diet composition, linear mixed models with maximum likelihood (ML) were run with response variable of food part in the diet (percentage by kcal intake) and food availability index

(or insect abundance index) and study group as fixed effects and individual monkey as random effect. When I compared a model to the null model, the null model included only random effect.

Group could not be a random effect in the models because there were only three groups, and therefore insufficient levels for a random effect (Harrison et al., 2018). Using restricted maximum likelihood estimators (REML) in linear mixed models is preferable to ML when sample size is small (Kuznetsova et al., 2017), but likelihood ratio tests to test the significance of the change in model fit can only be run with ML. Residual distribution was checked for normality for assessing model fit.

60 RESULTS

Food diversity of redtail diet

During the mean 7 hours ± 30 minutes (SD) per day that females were in sight during focal follows (N=137 females-days with female in sight >5 hours; total in-sight observation hours = 961), females spent an average of 18% of time feeding. Females switched rapidly among an average of 160 ± 35 different food items throughout the day (feeding on Uvariopsis congensis ripe fruit in tree A and then moving and feeding on U. congensis ripe fruit in tree B counts as two food items), which included an average of 10 ± 2 different plant species. The study groups visited feeding trees in marginally overlapping areas (Appendix 3) and consumed plant foods from a total of 104 identified plant species. During the study period, females from all three groups were observed feeding at least once from 42 plant species (see Appendix 7 for full list of plant species observed eaten in the three study groups 2015-2016 and in one study group observed 1973-1974 by Struhsaker). Overall, females exploited 304 unique food items during the study period, including 287 plant food items, 6 insect morphotypes, galls on leaves from 7 plant species, along with other more rarely eaten food items, including mushrooms, soil processed by termites, and dead wood.

Redtail foods of different categories varied in macronutrient (Table 2.3; Appendix 1) and key mineral composition (Table 2.4; Appendix 2). Ripe fruits were, on average, lower in fiber and higher in total nonstructural carbohydrates than unripe fruits. Insects were over 2x higher in protein than young leaves; insects were also high in fat compared to other food types. Exudate was a source of total nonstructural carbohydrates, though the composition of complex polysaccharides in gum varies in digestibility. Stems and pith of stems were highest in fiber

(NDF). Mature leaves in the redtail diet were similar in protein composition as young leaves in

61 the diet. Flowers were sources of protein and total nonstructural carbohydrates, though also high in fiber. Standard deviations showed that ripe fruit and unripe fruit in the redtail diet varied widely in NDF, fat, and TNC. Approximately half of the samples for each of the following food categories contained condensed tannins: ripe fruits, unripe fruits, flowers and stems. Redtail food categories also varied in composition of sodium and copper (Table 2.4): insects and stems/pith of stems were higher in sodium than the other food parts and insects were higher in copper than other food parts.

Across the three groups, females spent the most feeding time on insects (35 ± 15%) and fruits (any component, any ripeness, 34 ± 18%), followed by young leaves, plant exudate, flowers (including flowers, flower buds, anther oil, or flower stalks), stems and/or pith of stems, and mature leaves (Table 2.6). Time spent feeding on fruits was mostly spent on unripe fruits

(Table 2.7). Less than 1% of daily feeding time was devoted to other items including leaf buds, leaf petioles, mushrooms, leaf galls (including those likely induced by insects on leaves of Vepris nobilis and Neoboutonia macrocalyx), termite soil, dead wood, and unidentified foods. The placement of exudate in ranking of percentage time feeding was driven by Kyo group females feeding on Prunus africana gum (Table 2.6), though Kus and Suk females also fed on exudate including from P. africana, Zanthoxylum leprieurii, and Diospyros abyssinica.

When daily diet composition is considered in terms of percentage by calories rather than time, differences emerge, especially for contributions by insects and plant exudate (Figure 2.2).

Though females in each group and across groups spent averages of >30% of feeding time on insects, total metabolizable energy gained from insects was on average <10% of daily calories consumed (Table 2.6). High exudate caloric contribution occurred in Kyo group, with females spending 10 ± 10% (ranging from 0 - 58%) of daily feeding time with a resulting 33 ± 22% of

62 daily caloric intake on Prunus africana gum, indicating that this food is energy-dense. Aside from these differences, caloric contributions followed a similar order as time contributions

(Figure 2.6). Though females spent most fruit-feeding time on unripe fruit, ripe fruit led to a higher energy payoff (Table 2.7). In a minority of feeding bouts, a female fed on two plant parts simultaneously; each plant part combination contributed <1% to daily caloric intake across groups.

Important food items

Important foods, defined as food items that contributed >1% of calories consumed by females in a group for the study period, represented the most calories consumed by each group

(Kus 75%, Kyo 80%, Suk 73%). Considering them in pairs (Table 2.8), Kus and Suk had more overlapping important foods (10) than Suk and Kyo (8) or Kus and Kyo (6). Only five food items were important for all three monkey groups during the study period: Celtis gomphophylla

(synonym is Celtis durandii) fruit (of any ripeness), Mimusops bagshawei ripe fruit, adult cicadas (Order Hemiptera, Superfamily Cicadoidea), Ficus exasperata fruit (of any ripeness), and Chionanthus africanus (synonym is Linociera johnsonii) unripe fruit; average percentage contribution by calories differed across groups, with the exception of cicadas and Ficus exasperata fruit (Table 2.8). The number of important foods was slightly lower in Suk group

(16) than Kus (20) and Kyo (20). Fruits of particular plant species were the majority of important foods for each group (Kus 70%, Kyo 65%, Suk 69%) and fruit of Prunus africana (as well as gum of Prunus africana) fell under important foods for Kyo group, but not the other two groups.

Two insect morphotypes fell under important foods: cicadas for all three groups and Lepidoptera larvae (caterpillars) for Kus group.

Carbohydrates in redtail monkey important foods

63 Important fruit species were diverse in sugar and starch composition (Table 2.8). The four important plant foods shared by the three monkey groups included one fruit that was low in both starch and sugar, Celtis gomphophylla fruit (ripe and unripe fruit), but this fruit species’ role as an important energy source was explained by its high fat content. Chionanthus africanus unripe fruit and Mimusops bagshawei ripe fruit were high in sugar, though only C. africanus seed of unripe fruit was high in starch. Ficus exasperata fruits were low in sugar and in starch, though digestible fiber as an energy source explained this fig’s role as an important food.

Overall, redtail monkey plant foods were low in starch, except for seeds of some unripe and ripe fruits (seeds eaten along with the rest of the fruit or, more rarely, extracted from matrix and eaten separately) (Appendix 8). Overall, plant foods were diverse in sugar content, even within food categories (Appendix 9). Sugar composition of important foods did not differ by study group

(Kruskal-Wallis chi-squared = 7.45, df = 17, p = 0.98), though Prunus africana fruit and gum were high outliers for Kyo and Suk group important foods. The number of important foods with above-average sugar content also varied across groups: 25% of Kus group important foods, 42% of Kyo important foods, and 33% of Suk important foods. Starch composition of important foods also did not differ by study group (Kruskal-Wallis chi-squared = 15.58, df = 29, p = 0.98). Few important foods were high in starch (>10% starch content): 15% of Kus group important foods,

20% of Kyo group important foods (all of which are high-starch seeds of fruits of shrubs), and

12% of Suk important foods.

After quantifying sugar and starch composition of redtail monkey foods, I found that unexplained total nonstructural carbohydrate (TNC, calculated by subtraction) remained

(Appendix 10). Therefore, I was unable to replace the calculation of total nonstructural

64 carbohydrates (TNC) by difference with quantified sugar and starch when calculating intake, and all subsequent intake results are presented with TNC by subtraction.

Macronutrients in the diet

Redtail monkey female average daily intake of food on a dry matter basis (grand mean) was 89.93 ± 30.64 grams. Average (grand mean) daily intake of metabolizable energy was 247.7

± 84.9 kcal (Table 2.1), where 18.7% was available protein, 10% fat, 33% total nonstructural carbohydrate, and 17.5% digestible fiber.

The right-angled mixture triangles (RMTs) of redtail monkey female daily intake by food part for macronutrients show that females drew macronutrients from diverse foods and that Kyo group differed from the other study groups in that it gained total nonstructural carbohydrates from gum (Figure 2.3). In Kus and Suk group, females gained protein from young leaves, unripe fruit and flowers, and ripe fruit varied widely in its contribution to total nonstructural carbohydrate intake. For all three groups, insects provided protein and low levels of TNC and fat.

In separate calculations, I found that across the three monkey groups, protein was gained mostly from young leaves (25 ± 18.6% of daily protein consumed [kcal) and insects (24 ± 17% of daily protein), then from unripe fruit (19.1 ± 18.9% of daily protein), then ripe fruit (11.3 ± 16.1% of daily protein), followed by smaller contributions by other foods. As shown in the RMT, all three groups gained fat from some unripe and ripe fruits and, rarely, from flowers (Figure 2.4). Ripe and unripe fruits varied more in their contribution to daily digestible fiber than young leaves did

(Figure 2.5).

The RMTs representing the groups’ menus of foods demonstrate that redtail females were choosing from foods of diverse macronutrient composition, though mostly low in fat (Figure

2.6). When the macronutrient composition of only important foods (>1% kcal intake) are

65 presented in an RMT for each study group, the nutritional space is still broad, though certain important foods contributed more to energy intake than others (Figure 2.7). Average macronutrient intake by group over all foods (Figure 2.6) and over important foods (Figure 2.7) indicates that Kus and Suk groups clustered together, while Kyo group attained less energy from fat and more energy from total nonstructural carbohydrates than the other two groups.

Food composition of diet and food availability

The percentage of daily calories from ripe fruit was not explained by ripe fruit availability (ripe fruit availability index divided by 1000; Figure 2.1; Appendix 19), β =0.0004, t(113) = 0.49, p = 0.62; a model with RFAI as a predictor was not significantly different from one with only random effect, χ2(4) = 0.25, p = 0.62. Unripe fruit in the diet (% daily kcal) also had an unclear relationship (sensu Dushoff et al., 2019) with unripe fruit availability (UFAI;

Figure 2.1; Appendix 19), β = -0.006, t(113) = -0.31, p = 0.75; a model with UFAI as a predictor was not significantly different from one with random effects only, χ2(4) = 0.09, p = 0.75. Fruit of any ripeness in the diet had an unclear relationship with fruit-of-any-ripeness availability (FrAI),

β = 0.02, t(113) = 0.81, p = 0.42; a model with FrAI as a predictor was not significantly different from one with random effect only, χ2(4) = 0.60, p = 0.44. Percentage of daily calories from young leaves was not related to young leaf availability (YLAI), β = 0.001, t(113) = 1.73, p =

0.09; a model with YLAI was not significantly different from a model with random effects only,

χ2(4) = 2.99, p = 0.08. Though insect abundance varied spatiotemporally (Appendices 20-22), no clear relationships were found between insects in the diet (percentage of daily calories) and insect abundance indices. These findings may indicate that fruit availability via phenology transects and insect abundance via malaise traps and netting do not reflect what foods are available to redtail monkeys. Alternatively, redtail females may not be sensitive to food

66 availability in the observed home range. Therefore, these measures of food availability will not be used in the subsequent chapters.

Sources of sodium and copper

Redtail female sodium intake varied widely across focal-days (Table 2.2a), but females consumed less sodium daily on average than the National Research Council’s estimated sodium requirement for nonhuman primates and a similar amount of copper daily as the NRC estimated copper requirement for rhesus macaques (Table 2.2b). Across the three groups during the study period, average percentage by time spent feeding on insects of 35 ± 15% led to 50.8 ± 29.9% daily sodium intake (by mg) (Figure 2.8), with differences across groups driven by P. africana gum being a greater source of sodium for Kyo group than for the other two groups (Table 2.9).

Female redtail monkeys relied heavily on insects, especially cicadas, for their sodium (Table

2.10) and copper intake (Table 2.11). An average of half of daily sodium and copper was gained from cicadas, and the rest was acquired from a variety of plant food species and parts. Although all females in Kus and Suk groups gained most of their daily sodium from insects, the other sources of daily sodium varied across individuals, while Kyo group daily sodium sources were primarily exudate and insects with variation in other sources across individuals (Appendix 11).

DISCUSSION

Food diversity of redtail monkey diet

Most of the energy in redtail diets comes from unripe fruit and ripe fruit, followed by young leaves. Despite time spent feeding on insects comparable to time spent feeding on fruits, metabolizable energy contributions to the diet from insects were low. Protein intake was also lower than expected given time spent feeding, but comparable to daily protein contributions from

67 young leaves. The low energy and protein payoffs given time spent feeding on insects were explained by substantial insect contribution to daily mineral intake (see Sources of sodium and copper section below).

In terms of time spent feeding on different food parts, my results concur with previous characterizations of redtail monkey diets as consisting primarily of fruits, insects, young leaves and flower parts (Chapman et al., 2002b; Cords, 1986, 1987; Gathua, 2000; Lambert 2002a;

Rode et al., 2006a; Struhsaker, 1978, 1980, 2017). As in previous comparisons, I found that redtail monkeys at Kanyawara spent less time eating fruit and more time eating insects than redtail monkeys at Kakamega, Kenya (Cords, 1990). Though most of the fruit ingested by redtail monkey females was unripe fruit based on time feeding, even low percentages of feeding time on ripe fruit led to substantial energy gains (Table 2.7).

Reliance on Prunus africana fruit and gum by females of Kyo group was an energy bonanza drawn from carbohydrates, including nonstructural carbohydrates and digestible fiber

(in addition to minerals, as explained in Sources of sodium and copper section below). Prunus africana fruits were previously determined to be the highest sugar fruit in the redtail monkey diet

(Danish et al., 2006). The digestibility of gum, which is composed of complex polysaccharides, varies across and within plant species (Hladik et al., 1980; Nash, 1986; Power, 2010); intraspecific variation in gum digestibility is partially driven by age of the gum, i.e., how recently exuded from the tree (Nash, 1986). Future work will better assess the composition and digestibility of gum consumed by redtails via soluble fiber assays. Patas monkeys ate Acacia sp. gum that was 70% soluble fiber and < 20% TNC (Isbell et al., 2013), indicating that a microbial population able to break down soluble polysaccharides is important to gaining energy from gum.

Redtail caeco-colonic fermentation abilities (Lambert, 1998) make it likely that females in Kyo

68 group gain substantial energy from P. africana gum. I observed redtail monkeys spitting or swallowing whole and defecating the seeds of P. africana unripe and ripe fruits to access only pulp and skin (as previously observed by Lambert [1997]), likely due to the high levels of cyanogenic glycosides in this species’ seeds at the same site (Chapman & Chapman, 2002; Rode et al., 2006b).

Kyo’s unique reliance on Prunus africana was explained by Kyo’s home range covering mostly disturbed forest (clear-felled recovery areas, lightly logged areas, and biological field station campus) and P. africana being a secondary forest species that is more likely to regenerate in disturbed forest because of its light requirements (Fashing, 2004; Owiny & Malinga, 2014). In

2011, Prunus africana mean stem density per hectare was higher in areas recovering from 1987-

1999 conifer plantation clear-felling (RAC19: 27.5 stems per ha) and forest recovering from light logging in 1969 (K14: 17.5) than primary forest (K30: 0.0) (Owiny & Malinga, 2014:

Supplementary Table 1). Such previous findings at the site suggest that Kus and Suk groups, that travel and forage frequently in K30 forest compartment, may less frequently encounter Prunus africana than Kyo group, which moves through more disturbed habitat. I did not generate detailed home range habitat variation data for this study and am instead relying on previous detailed long-term Kanyawara forest composition research (Kasenene, 1987; Owiny et al., 2016;

Owiny & Malinga, 2014; Struhsaker, 1997; Valtonen et al., 2017). Future work will more fully characterize vegetation variation within and across redtail monkey study group home ranges.

Important food items

Important food items varied across the three study groups, with only five important foods

(four plant foods and one insect morphotype) shared by all three groups. Two of the important foods for all three groups were low (below mean) in both sugar and starch, but high (above

69 mean) in other macronutrients (Celtis gomphophylla [synonym is Celtis durandii] fruit: fat;

Ficus exasperata fruit: fiber). Few important foods were >10% starch composition, except for seeds of ripe or unripe fruits. This starch finding is somewhat surprising given salivary amylase secretion in cercopithecine cheek pouches, but is explained by the redtail tendency to reject seeds and by the absence of starchy underground storage organs from their diet. Kyo group important foods included plant parts from shrubs (including starchy seeds), while the other groups did not; the recovering forest and anthropogenic environment with which Kyo group females interact contains more shrubs than less disturbed areas of the park (Kasenene, 1987; Chapman et al.,

1999a; Omeja et al., 2016).

Diet composition and food availability

Against my predictions, I found that fruit availability measured via phenology transects did not predict daily percentage of calories consumed from fruit, regardless of whether I examined ripe fruit, unripe fruit, or fruit of any ripeness. These findings may reflect Kanyawara site’s diverse fruiting tree population with asynchronous fruiting patterns within and between species (Struhsaker, 1997; Chapman et al., 1999b). In an international comparative study of phenological patterns of tropical forests, Kibale National Park had “a very diverse profile with sub-annual, annual and a variety of mainly supra-annual cycles” compared to other forests

(Adamescu et al., 2018, pg. 423). Additionally, potential methodological limitations of my fruit availability dataset in detecting an effect include: Kyo group females frequently fed from fruits of shrubs and vines that were not reflected in the tree phenology transect; Kus and Suk exploited fruiting vines which were also not reflected in phenological monitoring. Given variation in use of

<10 cm (DBH) resources and plants of different categories in lower strata, future studies should include phenological monitoring of shrubs and vines in the redtail diet.

No clear relationship was found between insect abundance indices and insects in the daily diet, but the likelihood that microhabitats sampled via malaise trap and netting methods do not represent the full availability of insects to redtails is high. Based on canopy traps and netting in

2015, Lyke (2018) found that arthropod abundance in redtail monkey (Kus group) and blue monkey habitat at Kanyawara was greater in the wet season than the dry season; however,

Lyke’s (2018) molecular analyses revealed more Lepidoptera in redtail monkey feces during the dry season and that overall redtail monkey insect digestion was not related to her insect abundance measures. My study and Lyke’s study may indicate that redtail monkey ingestion of insects does not vary by insect abundance due to the abundant and biodiverse insect community at Kanyawara throughout the year; however, current abundance methods were incomplete in characterizing insect variation across numerous microhabitats.

As a result of these findings, for subsequent chapters, I will not use plant part availability via phenology transects or insect abundance via malaise traps and netting to assess temporal changes in food availability for redtail monkeys.

Diet at the macronutrient level

Macronutrient contributions to female redtail monkey diet were, for some nutritional components, similar to previous nutritional studies of the redtail monkey diet at this site (see

Appendix 12 for comparison to Conklin-Brittain et al. [1998] and Appendix 13 for comparison to Rode et al. [2006a]), but differences and new insights are due to the following: we conducted full-day follows of individuals; I included collected insects in weight-based intake calculations; I analyzed fiber intake and digestible fiber; and I analyzed available protein. Because digestible fiber is a potential major component of energy gain for redtail monkeys (Lambert, 1997, 1998),

71 incorporating a measure of digestible fiber was crucial for calculating accurate metabolizable energy intake estimates in this monkey species.

Compared to the nutritional intake of another forest guenon, the female blue monkey

(Cercopithecus mitis) at Kakamega Forest, Kenya (Takahashi, 2018), the daily metabolizable energy intake of female redtail monkeys at Kanyawara had lower contributions from fat and

TNC, but a higher contribution from digestible NDF. Blue monkeys have a shorter average digestive retention time than redtail monkeys (Lambert, 1997, 2002), which may indicate that more microbial fermentation occurs in the redtail monkey gut than in the blue monkey gut, leading to higher redtail energy gains from fiber. The redtail monkey fecal microbiome has previously been compared directly with fecal microbiomes of colobine species (Clayton et al.,

2017; McCord et al., 2014; Yildirim et al., 2010), but comparison among guenon species has not been conducted.

Except for primates that are seed predators (Norconk & Conklin-Brittain, 2004), primate diets are almost always lower in fat than in other macronutrients; however, this low fat content still contributes the majority of essential fatty acids to the diet (Reiner et al., 2014), as opposed to only some essential fatty acids. Across the three study groups, redtail monkey females consumed a low-fat diet with some exceptions (Figure 2.4). Celtis gomphophylla ripe fruits are high in fat, though fat content of this species’ ripe fruits varies seasonally (Worman & Chapman, 2005).

Another high-fat food eaten by redtail monkeys is anther oil from Symphonia globulifera flowers, which is composed of nervonic acid, a long-chain monounsaturated fatty acid rare in plants (Bittrich et al., 2013; Murphy & Mukherjee, 1988), more often found in fish oil, and important to nerve cell production (Parrish et al., 1997). Due to the small amount of anther oil in each flower and its rapid consumption over the S. globulifera flowering period by redtail

72 monkeys at high strata, I was unable to collect enough S. globulifera anther oil for nutritional analysis. Instead, I estimated anther oil nutritional intake based on high fat content inherent in a pure fatty acid containing a small amount of pollen. S. globulifera anther oil consumed by females in Kyo and Suk groups contributed an average of 18% daily fat intake during only 4% daily feeding time, indicating a unique fat source for these primates. I did not have a large enough anther oil feeding sample size to test the hypothesis that this tree species’ anther oil is a resource exploited when fruit is less available (Garber, 1988).

Redtail females drew macronutrients from diverse foods and Kyo group differed from the other study groups in that it gained total nonstructural carbohydrates from gum (Figure 2.3). For all three groups, insects provided protein and low amounts of TNC and fat, and fruits varied widely in contributions to fat and digestible fiber intake. Like two other generalist primates

(Macaca mulatta: Cui et al., 2018; Cercopithecus mitis: Takahashi et al., 2019), redtail monkeys combine foods of diverse nutritional composition and use fat, nonstructural carbohydrates and digestible fiber interchangeably as energy sources.

Sources of sodium and copper

My finding that redtail females relied on insects, especially cicadas, for half of daily sodium and copper, with the rest of daily sodium intake comprised of diverse plant sources, adds to studies indicating that primates exploit a wide range of food items (Cancelliere et al., 2014;

Behie & Pavelka, 2012; Rode et al., 2003, 2006a), including some especially mineral-rich resources (Rode et al. 2006a; Rothman et al., 2006), to obtain multiple essential minerals.

Sodium is limiting for most primates in tropical environments because tropical soil is sodium- poor (Oates, 1978; Reynolds et al., 2009; Rode et al., 2003; Rothman et al., 2006). Though copper intake (mg/day) by redtails has been positively correlated with redtail monkey population

73 density (Rode et al., 2006a) across four locations in Kibale National Park, further information is needed on copper availability in the environment to assess if copper is limiting. Insectivory has previously been linked to mineral acquisition in primates (Rode et al., 2003; Rothman et al.,

2014), and this study demonstrates how mineral acquisition (and, to a lesser extent, protein acquisition) can drive insectivory in some species. Female redtail monkeys’ low metabolizable energy gains from insects contrast with female capuchin monkeys’ (Cebus capucinus imitator) reliance on insectivory for energy, especially during low fruit availability months and during lactation (Bergstrom et al., 2019).

Prunus africana gum contributed the most dietary sodium after insects for Kyo group: an average of 10% of feeding time spent on P. africana gum led to an average of almost 30% of daily sodium intake for Kyo group females. Though plant exudates are diverse in nutritional composition by species, various gums eaten by primates are often higher in essential mineral content than other plant foods (Nash & Whitten, 1989; Ushida et al., 2006). Potential additional sources of sodium in the redtail monkey foods that I observed at this site but was unable to collect samples for include decaying wood, and plants and water sources in swamps. Decaying wood provides sodium for other primates in Uganda (Rothman et al., 2006). Additionally, swamp plants and swamp water at Kibale have previously been found to contain high levels of sodium; some swamp plants in Kibale National Park contained 700-6400 ppm sodium (Oates,

1978).

Limitations and future directions

Given the challenges of identifying and collecting small insects consumed by arboreal animals, behavioral and molecular methods enable complementary identification of insects

(Bohmann et al., 2011; Lyke et al., 2019; Mallott et al., 2014; Pickett et al., 2012). Molecular and

74 observational studies should be done together, as molecular studies may also miss relevant information due to some taxa being too degraded to identify or due to inadequate reference DNA for certain species—as was the case with cicadas, which I observed but which were not represented in Lyke’s (2018) molecular results. In addition, molecular methods may identify insect remains or insect webs on leaves that monkeys eat, leading to the incorrect conclusion that the monkey has eaten that insect taxon.

In 55% of focal follows greater than 5 hours, the female focal subject either (1) had visibly full cheek pouches at the start of the focal follow, (2) processed cheek-pouched food when fruit intake had not yet been recorded within the first 30 min of the follow, and/or (3) at the end of the focal follow was still feeding on fruits. Though focal sampling is, by definition, a sample of behavior, evidence of early morning and late evening fruit feeding (cheek pouching other food items besides fruit was observed rarely) when visibility is lower for behavioral observation, suggests under-estimation of daily fruit intake. Diurnal distribution of focal time females were observed feeding and processing food also suggests underestimation of feeding time in early a.m. and later p.m. hours (Appendix 5).

Future work on the nutrition of exudivory in guenons should analyze gum samples via soluble fiber assays to better characterize carbohydrates consumed. Gums consumed by primates vary in digestibility and composition within and across species (Hladik, 1980; Nash, 1986;

Power, 2010). The fermentation of gum in redtail monkey caeco-colonic fermentation may not result in the same end products as those of the soluble sugar assay, so the usefulness in characterizing gum content with a sugar assay may be minimal, with variation across plant species (Hall, 2007).

75 As with other macronutrients, plant food items vary spatiotemporally in sugar and starch composition, which was not addressed by my analyses of unique food items. Long- and short- term temporal variation in sugar and starch content of plants occurs due to changes in nonstructural carbohydrate storage and use during phases of plant phenology (Würth et al., 2005) and plant response to environmental stress (Martínez-Vilalta et al., 2016). Additionally, starch and sugar composition of a plant part can be interdependent: some fruits ripen via starch hydrolyzing into sugar, leading to ripe fruit high in sugar content because it is low in unconverted starch content (Marriott et al., 1981; Shiga et al., 2011). Diurnal changes in plant chemistry may be important to primate feeding; one study of chimpanzees noted that young leaves increase in sugar as the day progresses and chimpanzees eat them later when sugar is higher (Carlson et al., 2013).

Though sugar composition analysis of fruits consumed by redtail monkeys has been conducted (Danish et al., 2006; this study), the use of a sucrose standard may not be appropriate for analyzing the sugar content of these Ugandan plant foods. Analyses by high-performance liquid chromatography (HPLC) of sugar content of fruits, leaves and bark consumed by chimpanzees at Budongo, Uganda, showed that, on average, fructose and glucose content was greater than sucrose content (Reynolds et al., 1998), suggesting the inaccuracy of a sucrose standard. Characterizing nonstructural carbohydrates via multiple assays will require at least accurate assays for starch, sugar and pectin (see Appendix 10 for sugar and starch composition results compared to TNC by subtraction for this study).

Mineral content varies spatiotemporally due to soil composition and variation in the ability of roots to absorb elements (Van Soest, 1994; Whitehead et al., 1985); I analyzed a subset of food items (one of each plant species/part and one of each insect morphotype) for this study

76 and further characterization of intraspecific variation in mineral content is needed. Additionally, though my analyses quantified mineral composition of foods consumed and weight-based mineral intake by redtail monkeys, they did not address mineral absorption and how much of those minerals are lost, to feces or urine, for example (Van Soest 1994). Future research should address mineral absorption for minerals for which non-invasive absorption studies can be conducted. Absorption can also be affected by multiple interactions among nutrients, which is more challenging to quantify, especially in a wild population. Rode and colleagues (2006a) hypothesized that redtail monkeys may face reduced copper absorption in high fiber diets. Kyo group’s higher fiber intake than the other two study groups may have implications for copper absorption, though the relationship between reduced copper bioavailability and fiber ingestion is not straightforward because fiber is complex and the relationship may be indirect and related to how multiple micronutrients, including zinc and vitamin C, also affect copper bioavailability

(Adams, 2018).

Comparative work between forest and savanna-woodland redtail monkeys has shown variation in ranging behaviors in dealing with dramatically different habitats (McLester et al.,

2019a); future comparative work can further tease apart how redtails adjust to these two different habitats and the resulting phenotypic plasticity in feeding behavior.

Conclusion

This study confirmed that the redtail monkey diet is diverse and flexible at the food and macronutrient levels and clarified the role of insects as modest contributors to energy and protein intake, but substantial contributors to essential mineral intake. Redtail females gained most of their calories from fruit of any ripeness and from young leaves, with one monkey group then gaining the third-most amount of energy from Prunus africana gum. In better characterizing the

77 nonstructural carbohydrate portion of the redtail monkey diet, I found, with the exception of rarely exploited high-starch seeds, that starch played a minor role in redtail non-structural carbohydrate consumption, while soluble sugar was gained from a variety of important (>1% kcal intake) fruits. Additionally, redtail monkey important foods included some high-fat or high- fiber fruits that are low in starch and sugar, indicating that redtail monkeys switch between nonprotein macronutrient sources for energy. Cicadas were the number one source of sodium and copper for redtail monkeys and, after cicadas, redtails drew these two essential minerals from diverse plant sources, especially various species of unripe fruit, ripe fruit and young leaves.

Insectivory, or exudivory in the case of Kyo group, provided a substantial part of redtail monkey’s daily sodium intake, but they then sought out a variety of foods to gain the rest of this limiting mineral. Redtail females are “food composition generalists,” pulling from nutritionally diverse food items and “macronutrient specialists,” combining foods to reach similar daily ratios of macronutrients. One caveat to “macronutrient specialists” is the difference between Kyo group and the other two groups: through heavy reliance on the fruit and gum of Prunus africana and reliance on structural and nonstructural carbohydrates for energy, and on P. africana gum for sodium, Kyo group demonstrated subtle macronutrient generalism, adjusting intake away from the other two study groups. Redtail ability to exploit foods of diverse composition and make slight adjustments to macronutrient ratios helps explain this species’ success in diverse habitats

(forest fragments: Onderdonk and Chapman 2000; savanna-woodland mosaic: McLester et al.

2019a; Tapper et al. 2019).

78 CHAPTER 2 FIGURES AND TABLES

Table 2.1 Variation in daily intake of food, macronutrients and energy, and variation in percentage of diet by macronutrient. Total metabolizable energy includes intake in kcal from Fat, NDF, TNC (calculated by subtraction) and Available Protein, using modified Atwater values (see Methods). Standard deviation (SD) and coefficient of variation (CV) also presented; CV here shows a unit-less measure of dispersion in the variable. Mean (N=137 focal days) Grand mean (N=23 females) Mean SD CV Mean SD CV Food (grams) 88.13 64.31 0.73 89.93 30.64 0.34 Available protein 46.59 27.96 0.60 46.21 11.09 0.24 (kcal) NDF (kcal)* 42.03 36.29 0.86 43.32 18.51 0.43 NDF (grams) 34.37 28.71 0.83 35.10 13.35 0.38 Fat (kcal) 24.09 25.73 1.07 24.98 12.49 0.50 TNC (kcal) 124.37 108.19 0.87 128.93 58.07 0.45 Total metabolizable 241.32 169.71 0.70 247.67 84.92 0.34 energy (kcal) % NDF 37.73 6.34 0.17 37.81 1.72 0.05 % Fat 5.63 4.82 0.85 5.61 2.07 0.37 % Available Protein 15.23 6.10 0.40 14.82 3.40 0.23 % TNC 32.70 9.22 0.28 33.38 5.87 0.18 *NDF intake calculated by multiplying NDF intake by the Atwater value of 3 as well as the NDF digestibility coefficient (except for insect intake for which NDF is not relevant).

79 Table 2.2 (A) Variation in daily intake of food on a dry matter basis (grams) and daily intake of minerals (grams or milligrams as indicated). (B) Comparison between estimated requirements mineral intake in primate diets according to the National Research Council’s Nutrient Requirements of Nonhuman Primates (2003) and average daily mineral intake for redtail monkey females in this study. Values shown below from this study are based on mean (not grand mean) intake of minerals and food for 137 focal follows. A. Mean (N=137 focal days) Grand mean (N=23 females) Mean SD CV Mean SD CV Food (g) 88.13 64.31 0.73 89.93 30.64 0.34 Calcium (g) 1.15 2.96 2.58 1.31 1.47 1.12 Phosphorous (g) 0.56 1.48 2.64 0.82 1.55 1.89 Magnesium (g) 0.60 1.71 2.85 0.90 1.80 2 Potassium (g) 3.52 9.70 2.76 5.22 10.10 1.93 Sodium (mg) 9.57 14.28 1.49 11.58 12.13 1.05 Iron (mg) 22.19 57.49 2.59 19.37 19.40 1 Zinc (mg) 2.63 1.90 0.72 2.60 0.87 0.34 Copper (mg) 1.41 0.97 0.69 1.41 0.46 0.32 Manganese (mg) 6.95 5.63 0.81 6.54 2.16 0.33 Molybdenum (mg) 0.21 0.21 1.03 0.21 0.15 0.72

B. Mineral Estimated micronutrient requirements Average daily diet according to NRC (2003)* (DM basis) this Nonhuman primate Rhesus macaque study for redtail diets (Macaca mulatta) monkey females diet Calcium, % 0.8 0.55 1.3 Phosphorous†, % 0.6 0.33 0.6 Magnesium, % 0.08 0.04 0.09 Potassium, % 0.4 - 4 Sodium, % 0.2 - 0.01 Iron‡, mg/kg 100 100 251.8 Copper, mg/kg 20 15 16 Manganese, mg/kg 20 44 78.9 Zinc, mg/kg 100 13-20 29.9 * National Research Council (2003) estimations for nonhuman primates overall were based on “diets containing conventional feed ingredients intended for post-weaning nonhuman primates, accounting for potential differences in nutrient bioavailabilities and adverse nutrient interactions, but not accounting for potential losses in feed processing and storage” (NRC 2003, Table 11-2, page 193). Estimations for rhesus macaques were based on monkeys “fed purified or semipurified diets” (NRC 2003, Table 11-1, page 192). †This estimation of phosphorous intake includes phytate phosphorous which has limited bioavailability (NRC 2003); the same limitation is true of this study’s measure of phosphorous intake. ‡ Only some of iron ingested is bioavailable due to different types of iron in food and because some plant secondary compounds bind iron (NRC 2003).

- - CT 0% 0% 50% 45% 100% 45.5% 54.5% 38.5% containing containing Percentage Percentage of samples

11 11 7.5 3.9 9.9 2.3 8.6 SD

13.4 18.1 13.4

TNC 35 53.6 22.2 31.2 28.6 32.9 28.1 49.6 27.3 25.5 Mean

1 1 4.3 2.6 1.2 2.1 2.3 7.3 7.1 0.7 SD

Fat

4 2.6 7.4 6.6 2.6 1.8 9.7 3.6 2.8 2.5 Mean

of food categories eaten by female redtail monkeys redtail eaten by female categories of food 0 7

5.1 7.1 2.8 7.9 9.3 5.2 5.3 5.5 SD ein

Prot Available Available

16 13 58 2.2 7.1 10.5 20.8 15.8 17.2 12.4 Mean

on a subset of samples (N=99 samples) subset (N=99 food on a of samples 8

10 0.4 8.5 6.4 2.4 7.0 3.7 4.7 SD NA on dry matter basis basis matter on dry

Lignin chitin content. content. chitin

8.1 3.3 NA 13.2 14.1 15.3 15.3 12.9 13.9 13.3 Mean

composition 14 8.4 7.8 2.9 5.8 8.1 5.9 8.7 7.3 SD

14.3

approximates

ADF 27 5.3 27.7 40.6 37.6 25.3 31.6 28.7 16.5 29.1 Mean

and antifeedant antifeedant and 12 6.1 9.3 9.4 8.1 3.7 9.8 SD NA 18.8 15.1

NDF Total nonstructural carbohydrates (TNC) calculated by subtraction and condensed tannins (CT) analyzed for analyzed tannins (CT) for condensed carbohydrates and (TNC) calculated by subtraction nonstructural Total

7.1 NA 43.5 50.1 46.6 3 38.6 43.7 41.8 36.4 38.8 Mean period.

udy

Mean macronutrient macronutrient Mean )

. (6)

s 2 g leaves

number of number Stems Pith of stems (13) on leaves Galls (2) (12) Flowers (38) leaves Mature (12) (112) Youn (308) Exudate (3) Insect analyzed Ripe fruits (90) fruits Unripe Food part Food ( samples *Condensed tannins presence/absence wasanalysis conducted tannins presence/absence *Condensed during the st during the ADF For only. insects, presence/absence Table

- - by SD

0.76 1.01 2.71 5.37 2.49 2.08 0.07 0.21

(ppm)

0.7 Molybdenum 1.11 0.93 1.43 4.30 2.78 1.76 0.75 1.45 3.20 Mean

- - 03

0.90 1.61 0.72 0.35 1.32 0.78 0.71 1.56 - - SD erals and (B) trace(B) and erals (ppm) 63.84 84.79 62.93 18.86 67.18

113. 260.47 374.06

‡

Potassium (%) Potassium 7 1.76 1.80 2.22 0.71 2.21 2.62 1.62 3.59 3.72 0.30 171 562 04.56 67.51 62.19 46.50 60.67 85.50 Mean 285.50 Manganese 1

- -

61 - - SD 0.09 0.20 0.20 0.04 0.06 0.22 0.13 0.01 SD 1.41 7.78 with (A) macromin with (A) ation of average. of ation 15.60 14.42 12.36 47.10 18.44 10. , (ppm)

Zinc 2 0.21 0.29 0.39 0.22 0.22 0.44 0.39 0.20 0.66 0.11 37 15 Mean Magnesium (%) Magnesium 23.52 21.43 32.69 36.27 19.50 33.50 Mean 172.17

- - - - SD SD 0.12 0.12 0.12 0.01 0.32 0.18 0.16 0.23 71.04 35.21 40.75 45.25 41.01 47.38 228.52 200.64 (ppm)

†

000 YL = 1,140 ppm) removed from calculation of average. calculation from removed ppm) = 1,140 YL Iron ,

73 77 35 126 98.14 Mean 0.25 0.26 0.41 0.02 1.31 0.40 0.20 0.27 0.43 0.07 38 112.08 246.17 115.50 189.30 Mean 100 ppm) removed from calcul from removed ppm) 100 Phosphorous (%) Phosphorous ,

- - eaten by female by redtailfemale monkeys eaten

4.2 4.6 4.4 2.8 5.7 2.1 9.2 SD - - 68.6 SD ry 0.26 3.33 0.78 0.05 0.11 0.50 0.15 0.04

flowers = 27 = flowers

4 10 11 9.3 8.9 5.5 11.3 67.7 12.6 12.5 Mean Copper (ppm) Copper Calcium (%) Calcium 0.38 1.40 0.96 0.48 0.27 0.63 1.64 0.37 0.80 0.16 Mean

food catego food

Craterispermum laurinum Craterispermum - - by

7.1 SD 35.4 39.6 percentage to be converted can but composition copper with comparison easier for (mg/kg) in ppm 31.2 11.5 279.8 233.3 259.4

Piper capensis Piper

ere

5 45 90

7.3 170 235 39.1 92.1 21.4

296.7 Mean Sodium (ppm)* Sodium composition (1)

000. e leaves (2) leaves e , presented h presented

samples analyzed) samples is is

Flowers (11) Flowers Matur Stems (2) only stems Pith of (1) Soil Food type Food type ( (29) Ripe fruit (21) fruit Unripe (26) leaves Young (2) Exudate (6) Insects

mineral

for flower Fe content ( content Fe flower for

Mean Mean (11)

.4 2 samples analyzed) samples

# odium composition composition odium

Mature leaves (2) leaves Mature (1) Stems (2) only stems Pith of (1) Soil Ripe fruit (29) Ripe fruit (21) fruit Unripe (26) leaves Young (2) Exudate (6) Insects Flowers Food type ( S Major outlier Major ( content leaf Mn young for outlier Major † ‡ * 10 by value ppm dividing B. A. Table separately. presented minerals,

82 Table 2.5 Tree species composition of two 1000-meter phenology transects. One transect was in overlapping home range of study groups Kus and Suk and the second transect was in the home range of Kyo group.

Tree species Phenology transect in monkey group Observed redtail home range food? Kus and Suk Kyo (number of trees of (number of trees species) of species) Trilepisium madagascariense 113 1 Yes Funtumia sp. 81 77 Yes Celtis gomphophylla 77 42 Yes Markhamia lutea 41 3 Yes Vepris nobilis 40 3 Yes Celtis africana 20 25 Yes Diospyros abyssinica 17 27 Yes Chaetachme aristata 15 1 Yes Mimusops bagshawei 11 3 Yes Cordia africana 10 1 Yes Ficus exasperata 8 2 Yes Blighia unijugata 6 3 Yes Fagaropsis angolensis 6 4 Yes Parinari excelsa 6 1 Yes Dombeya kirkii 5 3 Yes Olea welwitschii 4 33 Yes Casearia sp. 3 1 Yes Millettia dura 3 3 Yes Neoboutonia macrocalyx 2 6 Yes Premna angolensis 2 3 Yes Zanthoxylum leprieurii 2 2 Yes Allophylus sp. 1 2 Yes Ilex mitis 1 1 Oxyanthus sp. 1 1 Yes Trema orientalis 1 9 Yes Leptonychia mildbraedii 46 Yes Strombosia scheffleri 44 Yes Uvariopsis congensis 11 Yes Pouteria altissima 9 Yes Lepisanthes senegalensis 6 Yes Rauvolfia vomitoria 5 Yes Chrysophyllum 4 Yes gorungosanum Myrianthus arboreus 3 Yes Pancovia turbinata 2 Yes Antiaris toxicaria 1 Yes

83 Tree species Kus and Suk Kyo transect Redtail food? transect (# trees of (# trees of species) species) Beilschmiedia sp. 1 Cassia sp. 1 Cassipourea sp. 1 Yes Englerophytum sp. 1 Eudema sp. 1 Ficus natalensis 1 Yes Lovoa sp. 1 Monodora myristica 1 Yes Phyllanthus sp. 1 Pseudospondias microcarpa 1 Yes Scolopia sp. 1 Strychnos mitis 1 Yes Unknown sp. 1 Prunus africana 63 Yes Bridelia sp. 10 Yes Erythrina abyssinica 10 Yes Sapium sp. 10 Albizia grandibracteata 9 Yes Spathodea campanulata 9 Yes Maesa lanceolata 5 Yes Lepisanthes senegalensis 4 Yes Polyscias sp. 4 Albizia gummifera 2 Yes Cordia millenii 2 Yes Chionanthus africanus 2 Yes Margaritaria sp. 2 Persea americana 2 Rothmannia urcelliformis 2 Yes Vernonia sp. 2 Apodytes sp. 1 Yes Bassania sp. 1 Clausena sp. 1 Ficus saussureana 1 Ficus mucuso 1 Yes Kigelia africana 1 Yes subsp. moosa

84 A.

Figure 2.1 Fruit-of-any-ripeness Availability Indices for (A) Kus and Suk groups’ overlapping home range and (B) Kyo home range. Unripe fruit and ripe fruit were scored separately, so Fruit- of-any-ripeness Availability Index is sum of unripe fruit availability index and ripe fruit availability index for a given month. See Methods for details of phenology transects and calculation of indices.

85 Figure 2.2 Comparison daily % calories ingested and % time feeding by food item across all focal follows in the three groups (N = 137 focal-days). Food items ordered by percentage of diet by kilocalories. “Fruit” here includes fruits of any ripeness and any component of fruit, and any combination of different maturity levels or components. “Flowers” include flowers at any stage of maturity and any component of the flower. Soil, dead wood, lichen and water are only reported in percentage by time.

15 3.7 1.3 1.2 0.7 0.6 5.9 1.7 0.4 7.4 6.6 3.8 SD 18.5 11.8 time)

and any

by ( 17 35 1.5 0.3 0.3 0.3 0.1 2.2 0.7 0.1 3.7 3.6 1.1 33.6 Mean for each group each for

17 2.5 2.4 1.6 0.8 2.6 7.9 4.6 SD 0.09 28.1 20.5 11.8 Across 3 groups Combinationsof plant fruit + flower + fruit

Food parts ordered by ordered parts Food Mean kcal) kcal)

( 5 0.9 0.5 0.3 0.2 0.5 8.1 1.7 NA NA 0.01 50.8 18.4 12.6 Mean

2 6 N Suk. 46 = 1.8 2.1 1.8 1.5 0.6 0.8 0.3 2.7 7.9 SD by food part. 15.4 1 13.4 , Lantana camara time)

( 1 1 0 6.5 3.9 1.9 0.4 0.3 0.2 0.1 0.9 38.6 1 34.1 0.07 Mean N=45 Kyo N=45

Suk group 7.6 6.4 1.6 1.9 0.6 0.5 SD 26.4 14.5 10.5 15.2 0.08 Kus

akdown of % kcal and % time for fruits of different of kcal and fruits akdown time % % for kcal) kcal) intake; only the intake;

by ( 2 0 17 3.9 8.7 7.3 0.7 0.4 0.1 0.2 NA NA 58.7 0.01 Mean

0 10 9.7 5.4 2.4 4.1 0.7 1.4 0.7 0.1 5.9 0.3 SD 17.9 14.6 daily percentage of time feeding feeding of time percentage daily time)

2 by

( 2 0 10 4.4 1.2 0.2 0.5 0.3 2. 0.1 26.6 14.9 33.3 0.02 Mean

intake intake

: N=45 female focal days focal female : N=45

7 c Kyo group 9.5 7.2 3.6 2.9 1.4 2.6 0.3 4.5 SD 25.4 22.4 0.01 kcal) kcal)

Kyo group.

by ( 37 7.1 3.7 1.9 1.4 0.3 0.8 0.1 1.4 for NA NA 12.8 33.1

Mean <0.01

male in sightmale

food

2.2 0.4 6.3 1.2 4.3 1.1 0.5 0.9 0.6 SD 20.2 13.3 0.98 16.8 evels or components (see next table for table next for or componentsbre (see evels time)

percentage of calori percentage by unique 0 (

1.1 0.1 0.2 2.6 0.5 1.5 0.4 0.1 0.3 0.2 y were excluded as they made up <1% of up <1% kcal as excluded y were they made 35.5 19.4 37.7 a Mean

>5 hours fe hours >5

daily daily

9 Kus group 6.3 2.9 2.7 3.4 0.4 1.3 0.1 SD 27.8 22.2 11.3 kcal) kcal)

by 0 4 ( 1.3 8.5 1.1 0.8 0.7 0.1 0.3 NA NA 56.4 25.2 0.03 Mean flowers flowers and fruits consumed simultaneously by Kyo group females.

Comparison of Comparison

6 e .

Food part % drinking water % soil % leaf buds % mushrooms % galls on leaf % petiole of leaf % flower + fruit* % insects % flowers % matur leaves % stems and/orpith % fruit % young leaves % exudate Lantana camara * contribution based on calories across three groups. “Fruit” here includes fruits of any ripeness and includes fruit, here any of ripeness fruits groups. component three any of across “Fruit” on calories based contribution different of maturity l combination the of flower. component of maturity flowers any and include any stage simplicity,at “flowers” For ripeness). simultaneousl consumed parts as kept it was is combination Table days across focal calculated

8.6 7.5 6.2 SD . 16.4 18.5

time)

by ( 5.1 2.5 2.6 pulp of an 18.6 33.6 Mean

N 46 = Suk

, 24 SD 21.8 11.2 11.2 28.1 Across 3 groups kcal) kcal)

by ( 5.1 4.8 17.2 17.6 50.8 Mean N=45 Kyo N=45

6 6.9 2.7 SD 15.2 15.4 Kus,

time)

by ( 3.1 0.7 3.2 19.1 38.6 Mean

. Suk group SD 15.5 26.2 13.9 10.6 26.4 kcal) kcal)

by ( 20 6.7 7.3 5.1 58.7 Mean

N=45 female focal days focal female N=45

13 6.7 7.1 SD 10.4 17.9 time)

by ( 9.4 3.5 2.2 14.6 26.6 Mean roup g daily percentage of time feeding for for (out of feeding of time fruits percentage daily ripeness only, by fruit

to 9.5 SD Kyo 14.3 20.9 10.5 25.4 kcal) kcal)

by deviation of N=137 focal days (focal follows >5 hours in sight). For simplicity, follows in sight). hours (focal N=137 days of focal For >5 deviation ( intake intake 37 4.5 3.3

13.3 15.9 Mean c

6.6 5.3 SD 19.9 10.6 20.2 time)

by ( 2.9 3.3 2.4 21.8 35.5 Mean ipe fruit, both go under “unripe fruit” below). The minority of cases in which fruits were were fruits in which eaten minority of cases below). The fruit” ipe fruit, “unripe both go under

9.6 SD 24.7 12.5 27.8 25.7 Kus group percentage of calori percentage kcal) kcal)

ily

by da ( 3.6 6.1 16.8 56.4 31.6 Mean

ups calculated as mean ups calculated standard mean and as

ripeness

Comparison of Comparison

7 . 2 ipe fruit or ate just pulp of an unr or ate just pulp of an ipe fruit undetermined % Fruit total % unripe fruit % ripe fruit % unripe and ripefruit eaten at same time % fruit of Fruit by ripeness Across three gro three Across some if seed and includes ate of theseshe (for of consumption fruitexample, part fruit the ripeness categories any of each unr not included are primarily (observed in parts Kyo group) plant simultaneouslywith other Table days: group calculated for focal across each Mean time daily total feeding).

od part for macronutrients Protein x Protein od part macronutrients for

Only top 5 food parts are included group. each included for Only top 5 food parts are

angled mixture triangles (RMTs) of redtail monkey by fo monkey intake daily redtail female of (RMTs) triangles mixture angled - Right .3 2 term ratios for macronutrients were converted to percentages forterm for converted clarity. were ratios to percentages macronutrients - Figure Figure Suk Kus group, (C) group. for (B) Kyo and group (A) x digestibleTNC NDF Three

for for

triangles (RMTs) of redtail monkey female daily intake by food part for macronutrients Protein x Fat Protein part macronutrients by food monkey for intake daily redtail female of (RMTs) triangles tios for macronutrients were converted to percentages for clarity. for to percentages converted were macronutrients tios for term term ra - angled mixture mixture angled - ght Ri .4 2 roup. roup. Three x Carbohydrates (TNC + digestible NDF) for (A) Kus group, (B) Kyo group and (C) Suk Only (A) group included (C) group. are + Kyo for (B) and top 5 food parts (TNC Kus group, digestible NDF) x Carbohydrates g each Figure Figure

90 .

Only top 5 food parts are included for each are group Only top 5 food parts .

group and (C) Suk (C) and group group oup, (B) Kyo for for (A) Kus gr

angled mixture triangles (RMTs) of redtail monkey female daily intake by food part for macronutrients Protein x Protein part macronutrients by food monkey for intake daily redtail female of (RMTs) triangles mixture angled - Right .5 2 term ratios for macronutrients were converted to percentages forterm for converted clarity. were ratios to percentages macronutrients - TNC+Fat x digestible NDF TNC+Fat Three Figure Figure

91 Table 2.8 Important food items, defined ≥ 1% intake (kcal), by monkey group. Top foods shared by all three groups are listed first, then shared by different combinations of two groups, then unique to a group. Number indicates percentage contribution (calories of food item out of total calories consumed by each group). “Fruit” indicates both ripe and unripe fruit were consumed. Sugar and starch composition of these food items also presented with ranges indicate multiple samples of different components and/or ripeness analyzed. Food items Important food items overlap Important food sugar and (% contribution of kcal starch composition intake) Kus Kyo Suk Sugar Starch (%) (%) Celtis gomphophylla fruit 18.01 2.46 15.64 3.79-8.47 0.61-4.65 Mimusops bagshawei ripe 7.06 2.93 1.09 15.56 1.47 fruit Cicada adult (Hemiptera: 5.03 5.42 5.86 - - Cicadoidea) Ficus exasperata fruit 1.30 1.13 1.83 4.97-6.78 0.50-0.69 Chionanthus africanus unripe 1.03 4.25 5.08 15.16-19.67 5.77-18.44 fruit Olea welwitschii fruit 2.08 2.41 - - Trilepisium madagascariense 8.30 1.57 3.42 2.63 young leaf Ficus brachylepis fruit 6.97 24.30 7.92 1.57 Chaetachme aristata fruit 2.38 1.18 2.14 2.20 Strychnos mitis fruit 2.33 1.25 2.34 1.66 Diospyros abyssinica fruit 1.97 1.83 13.20 2.31 Prunus africana exudate 33.46 3.08 49.06* 1.77* (gum) Prunus africana fruit 8.11 2.30 - - Macaranga sp. fruit 1.01 1.38 3.81 1.46 Diospyros abyssinica flower 4.82 - - Oxyanthus sp. unripe fruit 2.74 6.73 1.31 Funtumia sp. unripe fruit 2.50 7.45 26.42 Pancovia turbinata young 2.22 - - leaf Fagaropsis angolensis fruit 1.53 3.58 0.33 Marantachloa sp. fruit 1.46 5.37 20.40 Dovyalis sp. fruit 1.13 19.41 0.13 Caterpillars (Lepidoptera 1.11 2.52 1.12 larvae) Craterispermum laurinum 1.07 0.84 0.67 young leaf Aframomum sp. ripe fruit 3.42 6.80 32.83 Rubus pinnatus fruit 2.17 12.45 1.15

92 Kus Kyo Suk Sugar Starch (%) (%) "Bean plant" ripe fruit 1.91 6.10 15.74 Urera cameroonensis young 1.80 2.94 0.94 leaf Lantana camara flower and 1.71 4.13-17.60 0.98-2.89 fruit Diospyros abyssinica young 1.52 15.26 5.08 leaf Palisota sp. fruit 1.33 18.51 31.54 Maesa lanceolata fruit 1.24 5.56-13.47 0.83-3.45 Olea welwitschii mature leaf 1.19 7.35 1.48 Chionanthus africanus young 1.18 6.06 1.97 leaf Ficus mucuso fruit 1.02 7.29 1.07 Uvariopsis congensis fruit 3.32 7.40-13.20 2.23-13.95 Apodytes sp. flower 2.55 - - Chaetachme aristata young 1.11 3.37 0.77 leaf (-) indicates no food sample available for sugar and starch analyses *Soluble fiber in exudates like gums interferes with sugar analysis via acid-butanol assay and potentially with starch analysis.

digestible - non

represents

. was excluded it excluded as was composition NDF, (B) AP x Fat x Carbohydrates and (C) AP x TNC+Fat x AP x TNC+Fat (C) and x Carbohydrates (B) x Fat AP NDF, dig intake by group (large diamonds) nutritional by group composition over the intake (large of were multiplied by NDF digestibility coefficient to better reflect what amount of in reflect to fiber better digestibilitymultiplied coefficient were by NDF macronutrients (A)macronutrients x AP x TNC angled mixture triangle (RMT) of mean daily daily (RMT) triangle of mean mixture angled - values for composition food

term ratios for macronutrients were converted to percentages to percentages clarity were ratios macronutrients for term converted for - (small circles) for for circles) (small

lable to the monkey (except for insect monkey ADF to the for foods, which lable (except for Right

Fiber .6

. Three 2

. NDF dig is avai food chitin) Figure Figure foods in diet

foods (small grey circles with number centered on top) (defined ≥ 1% intake centered on top) (defined circles with number grey foods (small NDF. The number food data contribution important The point indicates over each in NDF.

dig of mean daily intake (large diamonds) by (A) Suk (C) groupsKus, intake by (A) daily diamonds) (large Kyo and over (B) of mean term ratios for macronutrients were converted to percentages for macronutrients for to percentages termclarity. for converted were ratios - at xat important

Three of each of of each

angled mixture triangle (RMT) (RMT) triangle mixture angled - Right nutritional composition

.7 2 ric percentage by that percentage important food. ric mean the by monkey group) x TNC+F Protein for [kcal], calo Figure Figure

95 Figure 2.8 Comparison of percentage of daily time feeding and percentage of sodium by milligrams daily intake by food part. Mean calculated across three groups (N = 137 focal follows). Food items ordered by percentage of daily sodium in grams. “Fruit” here includes fruits of any ripeness and any component of fruit, and any combination of different maturity levels or components. “Flowers” include flowers at any stage of maturity and any component of the flower. Not included are the rare cases of multiple plant parts consumed simultaneously.

ent ent

15 0.7 3.8 1.3 1.7 7.4 3.7 6.6 0.4 SD 18.5 11.8 d any d any time) soil).

y an

consuming

by ( 35 17 0.3 1.1 0.3 0.7 3.7 1.5 3.6 0.1 33.6 Mean that that by food part.

1 0.8 0.1 4.9 4.8 4.4 3.1 SD ) 29.9 27.3 20.2 Across 3 groups feeding feeding

by mg

have indicated (

N = 46 Suk, and N=45 46 Suk, Kyo. and N = time

* ssed by (“termite termites soil”) , 0.4 0.2 2.4 1.7 1.1 0.5 0.03 50.8 27.6 11.9 Mean Kus daily

6 2 hlychewing and then outelephantspitting 1.8 ays 0.6 1.8 2.7 2.1 7.9 0.3 SD 13.4 15.4 1 time)

( 1 1 6.5 3.9 0.2 1.9 0.4 0.9 34.1 38.6 1 0.07 Mean percentage of percentage to

Suk group 4.5 4.4 0.1 4.5 0.6 0.7 0.1 SD ) 29.3 25.5 14.2

by mg : N=45 female focal d focal female : N=45

( * 4.8 2.3 1.7 0.9 0.2 0.2 58.7 24.7 0.03 0.04 Mean

10 9.7 4.1 5.4 0.7 2.4 0.7 0.3 SD 14.6 17.9 by milligrams (mg)

time)

by ( 2 0 10 4.4 0.3 1.2 0.2 0.1 33.3 26.6 14.9 Mean intake

1 Kyo group 4.3 4.9 3.7 1.1 SD ) , previous analyses of water and plants in swamps at the site same inking from swamp water sources directly and thoroug 26.6 17.3 22.5 >5 hours female in sight hours >5

collected one samplefrom soil consumed by monkeysproce that was

I by mg ( 0 0 * 21 1.8 1.9 1.5 0.3 0.3 41.6 29.8 Mean

daily sodiumdaily (Na) 4.3 6.3 0.4 0.9 1.2 1.1 2.2 SD 13.3 16.8 20.2 0.98 for mineral content

time)

observed drfemales

by ( 1.5 2.6 0.1 0.3 0.5 0.4 1.1 0.2 19.4 37.7 35.5 Mean

7 ) Kus group 5.7 5.1 1.2 0.3 5.2 SD 31.7 34.2

by mg ( 0 * 1 52 3.2 1.5 2.4 0.7 0.5 0.1 37.1 Mean lect variationthe in composition consumedsoils of by infemales Kus and groupsSuk (Kyowerefemales not observed consuming

may besodium source.a

Comparison of percentage of percentage Comparison

didnot analyze water samples

9 I .

2 †

for each group calculated across focal days group calculated for across focal each

d parts ordered by contribution sodium by mg across three groups. “Fruit” here includes componincludes of any by ripeness fruits “Fruit”and groups. any three ordered here contributiond parts across sodium by mg Food part exudate

% drinking water and/orpith % flowers % soil % galls on leaf % mature leaves % leaf buds % insects % fruit % % young leaves % stems Soil at this diverse is site in mineral composition and which does not ref *Though swamp water grass on the edge swampof habitat. † Foo of maturit include any stage “Flowers” maturity at flowers or components. any and levels combination different of fruit, of the flower. of component Table Mean

97 Table 2.10 Top 10 sodium (Na) sources (food items) ranked from high to low percentage contribution to intake (by mg) by group. Possible other top sodium sources based on previous research but not included here because of lack of sample: swamp water, elephant grass thoroughly chewed and spat out, and decaying/dead wood. Abbreviations for plant parts as follows: unripe fruit (any component, unless specified) = UF, ripe fruit (any component, unless specified) = RF, flower (any component) = FL, young leaf = YL.

Top 10 sodium sources, ranked by % contribution to Na intake Kus Kyo Suk Food item % Na Food item % Na Food item % Na intake intake intake 1. Funtumia sp. 25.8 Cicadas 44.2 Cicadas 39.5 UF* 2. Cicadas 23.4 Prunus africana 28.4 Ficus 7.6 gum brachylepis RF 3. Marantachloa sp. 3.7 Prunus africana 8.5 Prunus 2.2 RF fruit† africana gum 4. Ficus brachylepis 1.5 Aframomum sp. 2.2 Prunus 1.9 RF RF africana fruit† 5. Caterpillars 1.3 Caterpillars 1.6 Uvariopsis 1.6 congensis RF 6. Chaetachme 1 Lantana camara 1 Marantachloa 1 aristata YL UF sp. RF 7. Mimusops 0.4 “Bean plant” RF 0.7 Ficus 1 bagshawei RF exasperata RF 8. Dovyalis 0.3 Ficus 0.6 Salacia elegans 0.8 macrocalyx UF exasperata RF seeds 9. Macaranga sp. 0.3 Macaranga sp. 0.4 Chaetachme 0.5 pith of stem seeds of RF aristata YL 10. Ficus exasperata 0.2 Mimusops 0.4 Macaranga sp. 0.5 RF bagshawei RF seeds of RF * Funtumia sp. unripe fruit sample collected (and fruit eaten by females) was pulp with orange larvae of unidentified insect species, which likely increased the sodium content of the fruit compared to when the larvae are not present. †Prunus africana fruit sodium intake estimated using sodium composition values from Rode et al. 2003 Appendix A. Prunus africana is an endangered species, so I could not collect P. africana fruit samples for this study. (I used a previously collected P. africana gum sample for all mineral analyses for this study.)

98 Table 2.11 Top 10 copper (Cu) sources (food items) ranked from high to low percentage contribution to intake (by mg) by group. Abbreviations for plant parts as follows: unripe fruit (any component, unless specified) = UF, ripe fruit (any component, unless specified) = RF, flower (any component) = FL, young leaf = YL.

Top 10 copper sources, ranked by % contribution to Cu intake Kus Kyo Suk Food item % Cu Food item % Cu Food item % Cu intake intake intake 1. Cicadas 43.3 Cicadas 52.7 Cicadas 47.8 2. Celtis 6.9 Prunus africana 4.5 Ficus 9.9 gomphophylla UF gum brachylepis RF 3. Chaetachme 3.9 Prunus africana 4.3 Rothmannia sp. 5.1 aristata YL fruit† YL 4. Trilepisium 3.7 Chionanthus 3.2 Celtis 4 madagascariense YL africanus YL gomphophylla UF 5. Ficus brachylepis 2.9 Caterpillars 2 Chionanthus 2.8 RF africanus UF 6. Caterpillars 2.4 Lantana camara 1.8 Uvariopsis 2.3 FL + UF congensis RF 7. Funtumia sp. UF* 2.2 Urera 1.4 Chaetachme 1.4 cameroonensis aristata YL YL 8. Marantachloa sp. 1.8 Aframomum sp. 1.2 Macaranga sp. 1.2 RF RF seeds UF 9. Oxyanthus sp. UF 1.2 Palisota sp. RF 1.1 Prunus 1 africana fruit† 10. Chionanthus 1.1 “Bean plant” RF 1.1 Celtis africana 0.7 africanus YL YL * Funtumia sp. unripe fruit sample collected (and fruit eaten by females) was pulp with orange larvae of unidentified insect species, which likely increased the copper content of the fruit compared to when the larvae are not present. †Prunus africana fruit copper intake estimated using copper composition values from Rode et al. 2003 Appendix A. Prunus africana is an endangered species, so I could not collect P. africana fruit samples for this study. (I used a previously collected P. africana gum sample for all mineral analyses for this study.)

99 CHAPTER 3: EXTRINSIC AND INTRINSIC EFFECTS ON NUTRITIONAL STRATEGY OF FEMALE REDTAIL MONKEYS (CERCOPITHECUS ASCANIUS)

INTRODUCTION

An animal’s nutritional requirements and intake fluctuate often with spatiotemporal variation in food availability (Chapman et al., 2002a; Ganzhorn, 2002; Knott, 1998) and food nutritional composition (Carlson et al., 2013; Chapman et al., 2003; Milton, 1999; Rothman et al., 2014; Worman & Chapman, 2005) as well as with intrinsic variables like reproductive status

(Krockenberger & Hume, 2007; Richter et al., 1938; Vargas et al., 2010) and health (Cotter et al., 2011; Simopoulos, 2002). Experimental studies demonstrate that organisms choose amounts and ratios of nutritional components that can be directly linked to fitness and lifespan (Fanson &

Taylor, 2012; Jensen et al., 2012; Lee et al., 2008; Maklakov et al., 2008; Simpson et al., 2004;

Solon-Biet et al., 2014), indicating that reaching species-specific nutritional goals is adaptive.

These nutritional components are diverse: macronutrients include fat, nonstructural carbohydrates, available protein, and digestible fiber; energy is gained via fat, nonstructural carbohydrates and digestible fiber (Altmann, 1998; Cheeke & Dierenfeld, 2010; FAO/WHO,

1985; NRC, 2003). Micronutrients are nutritional components needed in small amounts but relied on for regulating reactions in the body related to hormones and enzymes, fluid balance in cells, and tissue structure (Barboza et al., 2008; FAO/WHO, 2005; NRC, 2003; Robbins, 1993).

Rarely eaten foods sometimes contain key minerals not found in other foods (Nie et al., 2015;

Reynolds et al., 2019; Rothman et al., 2006) and insects are a greater source of some minerals than other foods items (Deblauwe & Janssens, 2008; Isbell et al., 2013; O’Malley & Power,

2014; Rode et al., 2003; Rothman et al., 2008a).

Indigestible fiber (Milton, 1979; Milton & Demment, 1988; Oates, et al.,1980; Van Soest,

1978) and diverse plant secondary compounds (Bernays et al., 1989; Dearing et al., 2005;

100 Freeland & Janzen, 1974; Glander, 1982) are antifeedant components of foods. These components reduce digestibility or are toxic and therefore can influence access to key nutrients and energy. The effects of plant secondary compounds on nutrition are complex, as shown by studies of one class of compounds: tannins bind to protein, reducing protein digestibility

(Robbins et al., 1987) and may also affect mineral absorption (Theil, 2004), but studies investigating whether animals avoid plant secondary compounds show mixed results by species and study site (DeGabriel et al., 2014; Milton, 1998; Oates et al., 1977; Reynolds et al., 1998).

Multiple wild primates have been shown to regulate nutritional components on a daily basis (Johnson et al., 2013; Rothman et al., 2011; Takahashi, 2018), but when restricted from meeting their nutritional goals by food availability or composition, animals compensate by prioritizing some components over others (Coogan et al., 2014; Cui et al., 2018; Felton et al.,

2009; Raubenheimer & Jones, 2006; Raubenheimer & Simpson, 2003; Rothman et al., 2011).

Wild primates select these foods to achieve a balance of nutritional components and often adjust their feeding behavior to changes in food availability or composition (Cui et al., 2018; Dröscher et al., 2016; Felton et al., 2009; Irwin et al., 2015; Rothman et al., 2011; Vogel et al., 2017). The ratio of nonprotein energy (NPE, defined as intake of energy from fat, nonstructural carbohydrates and digestible fiber) to protein in animal diets has been found to be a powerful predictor of variables related to development, reproduction, aging, obesity and immune function

(Blumfield et al., 2012; Lee et al., 2008; Ponton et al., 2011; Simpson & Raubenheimer, 2009;

Solon-Biet et al., 2014). Highly frugivorous spider monkeys (Ateles chamek) managed nutrients in their diet by maintaining a balance of NPE:AP of 8:1 kcal; as preferred food availability varied, NPE fluctuated while AP was regulated (Felton et al., 2009). Blue monkeys

(Cercopithecus mitis) also prioritized AP intake while allowing NPE intake to fluctuate, and

101 maintained a mean balance of NPE:AP of 5.2:1 (when NPE:AP balance was predicted via linear mixed model with individual monkey as random effect, the balance was 3.8:1; Takahashi, 2018;

Takahashi et al., in prep). By contrast, mountain gorillas (Gorilla beringei) maintained NPE:AP of 2:1 kcal when the majority of food consumed was herbaceous leaves and shifted to 3:1 kcal when fruit made up 40-60% of the diet. Unlike spider monkeys, gorillas prioritized NPE when constrained by food availability, overeating AP and maintaining NPE (Rothman et al., 2011).

Nutrient balancing can also vary by sex and age (Rothman et al., 2011; Vogel et al. 2017) and reproductive status (Cui et al., 2018; Koch et al., 2017; but see Dröscher et al., 2016; Takahashi,

2018).

The pressure to switch between foods occurs at multiple scales: with fluctuations in preferred food availability or composition, an animal will switch to other food types or species

(Bogart & Pruetz, 2011; Hanya, 2004; Hill, 1997; Isbell et al., 2013; Peres, 1994; Wrangham et al., 1998), which can increase dietary breadth, or—in cases when one food is relied on when preferred foods are not available—decrease dietary breadth. The term “fallback food” for foods switched to oversimplifies food switching because the distribution and nutritional composition of these foods vary widely and they are not necessarily “lower quality” foods on which to fall back

(Lambert & Rothman, 2015), though multiple definitions (and types) of fallback foods have been proposed (Marshall et al., 2009). Food switching also often occurs on a short timescale: during a day of feeding, a primate may switch foods in response to feeding competition (Barton, 1993;

Janson, 1988; Lambert, 2002a). Food switching at these different scales facilitates access to diverse macronutrients and micronutrients (Milton et al., 2003; Westoby, 1978) and minimizes exposure to toxic doses of plant secondary compounds (Cork & Foley, 1991; Freeland & Janzen,

1974; Wiggins et al., 2006).

102 The aim of this study was to examine factors that may affect macronutrient, energy and mineral intake regulation in an omnivorous monkey. Redtail monkeys are small-bodied guenons

(adult male mean = 4.3 kg, adult female mean = 2.9 kg [Delson et al., 2000]; adult male mean =

3.7 kg, adult female mean = 2.8 kg [Colyn, 1994; Kingdon et al., 2013]) that primarily eat fruits, insects, young leaves, and flower parts, relying on food components to differing degrees depending on season and site (Chapman et al., 2002b; Cords, 1986, 1987; Gathua, 2000;

Lambert, 2002a; Rode et al., 2006a; Struhsaker, 1978, 1980, 2017). Variation in redtail monkey diet has also been observed among groups within one study site (Chapman et al., 2002b; Chapter

3; but see Cords, 1986; Gathua, 2000).

Ripe fruit availability (measured via phenology transects) at this site did not predict redtail macronutrient intake in previous studies (Conklin-Brittain et al., 1998; Wrangham et al.,

1998) or percentage of ripe fruit in the diet (Chapter 2), indicating: (1) the complexity in food availability introduced by asynchronous fruiting schedules of trees, shrubs and vines redtails fed from, (2) phenology scoring as our research team and other Kibale researchers conducted it does not reflect food availability, and/or (2) the redtail ability to maintain a diverse diet pulling macronutrients from multiple sources (Chapter 2), potentially buffering the effects of fluctuations in ripe fruit availability. Therefore, in this chapter, percentage of ripe fruit in the diet

(dry matter intake) will be used as a variable indicating temporal shifts in diet (Rothman et al.,

2008).

On a daily basis, animals regulate macronutrient intake, energy intake, and ratios among nutritional components (Cui et al., 2018; Johnson et al., 2013; Rothman et al., 2011; Vogel et al.,

2017), and I hypothesized that redtail monkeys were no exception. Given redtail monkeys’ diverse diet, I predicted (Prediction 1) that the mean balance of non-protein energy (NPE) to

103 available protein (AP) (NPE:AP) would place redtails between frugivorous primates (Felton et al., 2009; Vogel et al., 2017) and omnivorous primates (Cui et al., 2019; Johnson et al. 2013).

Second, I examined how temporal shifts in diet affect macronutrient and energy intake. I hypothesized that fluctuations in the amount of ripe fruit in the diet would affect macronutrient intake and NPE:AP balance. I predicted (P2a) that when ripe fruit made up a low percentage of the diet (<20%, a threshold in the distribution of the dry matter intake data), redtail monkeys would consume more fiber and protein and less nonprotein energy, lowering the NPE:AP balance; when ripe fruit makes up a high percentage of the diet (>20%), I predicted that redtails would consume more fat, nonstructural carbohydrates and nonprotein energy. I also predicted

(P2b) that when leaf parts made up a higher percentage of the diet, redtails would consume more fiber and protein, lowering the NPE:AP balance. I also hypothesized that intake of macronutrients would be affected by percentage of ripe fruit in the diet. I predicted (P2c) that more ripe fruit in the diet would lead to increased TNC and fat intake, and lower protein and fiber intake.

I hypothesized that reproductive status would affect macronutrient intake and NPE:AP balance. I predicted (P3) the nutritional demands of lactation (Oftedal, 1991; McCabe &

Fedigan, 2007; Morehouse et al., 2010) would lead to increased daily NPE intake, and therefore increased daily NPE:AP, by lactating females compared to cycling and pregnant females, regardless of the percentage of ripe fruit in the diet.

I found differences in diet composition and in sources of macronutrients among the monkey study groups, specifically that Kyo group consumed Prunus africana gum and fruit more than the other two study groups (Chapter 2). I hypothesized that this difference in diet composition resulting from habitat variation would lead to a difference in NPE:AP between Kyo

104 and the other two groups. I predicted that Kyo group’s higher nonstructural carbohydrate gains from gum and fruit of Prunus africana (Chapter 2) would translate into higher NPE:AP in Kyo group than the other two groups.

Redtail monkeys switch among a variety of food types and species (Lambert, 2002a;

Chapter 2) during consumption of a diverse diet physiologically made possible by digestive enzymes in cheek pouches (Lambert, 2005) and a longer digestive retention time than expected given small body size (adult female MRT = 29.4 ± 9.8 hours [Lambert 2002b]). Redtails at the same site as this study switched between foods more frequently than sympatric blue monkeys

(Cercopithecus mitis) and red colobus monkeys (Procolobus rufomitratus), and redtail nearest- neighbor distance predicted the rate of switching between foods (Lambert et al., 2002a).

Therefore, food switching may be in response to feeding competition with neighboring individuals. In addition to potentially reducing feeding competition (Janson, 1988; Lambert,

2002), food switching also increases access to diverse nutrients in a biodiverse tropical forest. By feeding from and switching between many different food items each day, an animal reduces the possibility of overeating particular plant secondary compounds (Freeland & Janzen, 1974;

Wiggins et al., 2006) and increase the likelihood of consuming foods of diverse macronutrient and micronutrient composition (Cancelliere et al., 2014; Milton, 2003; Westoby, 1978). Sodium and copper may be limiting minerals for redtail monkeys (Rode et al., 2006). Though tropical plant foods can exhibit high iron composition (Cancelliere et al., 2014; Milton, 2003), iron in plants is largely unavailable for digestion due to multiple factors (Frossard et al., 2000; Glahn et al., 2002), making sources of iron less plentiful than they seem. Iron is an essential component of proteins that transport and store oxygen, specifically hemoglobin and myoglobin, though iron must be in the heme form, which is found in animal proteins, to be fully absorbed for this

105 function (NRC, 2003). Insects provide key minerals more consistently than plant foods (Chapter

2; Deblauwe & Janssens, 2008; Isbell et al., 2013; O’Malley & Power, 2014), though, for example, iron in insects is mostly non-heme as well (Mwangi et al., 2018; Nichol & Locke,

1990). I predicted (Prediction 5a) that number of times per day switching between food items would predict intake of sodium, copper, and iron, regardless of amount of fruit in the diet. I also predicted (Prediction 5b) more variation (higher standard deviation and coefficient of variation) in daily mineral intake than macronutrient and energy intake because smaller amounts of minerals are required than amounts of macronutrients, so daily regulation of minerals may be unnecessary.

METHODS

The study site and subjects, along with the feeding and behavioral data collection and food and fecal collection are described in Chapter 2. Additionally, the nutritional chemistry and nutrient intake calculations are also described in Chapter 2 (see subheadings: Nutritional chemistry; Calculations of nutrient intake; and Dataset).

Temporal shifts in diet

Because ripe fruit availability (measured via phenology transects) at this site did not predict percentage of energy from ripe fruit (Chapter 2), percentage of ripe fruit in the diet

(percentage of dry matter intake from fruit) was instead used as an indicator of temporal shifts in diet (as in other nutritional ecology studies, including Rothman et al., 2008). Percentages in the diet (dry matter intake) for unripe fruit, fruit of any ripeness, young leaves, and insects were also calculated. These values indicate shifts by the redtail females in terms of food choice, enabling analyses of how shifts in food choice affect macronutrient and energy intake and strategy.

106 Statistical analyses

Nutrient and energy intake and regulation

To examine the ratio of nonprotein energy to available protein (NPE:AP), I ran a linear mixed model with maximum likelihood (ML) using NPE as the response variable and AP as the explanatory variable. Individual (monkey ID) was included as a random effect; monkey group could not be included as a random effect with only three monkey groups. The linear mixed model’s slope was the NPE:AP balance with repeated measures taken into account. A likelihood ratio test compared the model and a null model (which contained only the random effect); a marginal R-squared value and a conditional R-squared value were generated. Marginal R- squared indicates how much of the response variation is explained by the fixed effect, and conditional R-squared indicates how much of the response variation is explained by the fixed and random effects (Nakagawa et al., 2017). Grand mean of NPE:AP was also presented for direct comparison with other nutrient balancing primate studies that generated a mean or grand mean daily NPE:AP.

Extrinsic and intrinsic factors affecting nutritional strategy

Linear mixed models with ML were run with response variables of daily intake of total metabolizable energy, daily intake of each macronutrient, daily intake of nonprotein energy, and daily ratio of NPE:AP and fixed effects of ripe fruit in the diet (percentage daily dry matter intake from ripe fruit), leaves in the diet, or insects in the diet. Models were run with individual

107 monkey as random effect. Residual distribution was checked for normality for assessing model fit.

For those linear mixed models that included reproductive status as a fixed effect, focal days for the one female in Kus group who had never observed with an infant were excluded; it was unclear if she is unable to become pregnant or unable to carry an infant to term, i.e., if she is always cycling or sometimes pregnant. Models were run with two versions of reproductive categories as fixed effects: (1) cycling, pregnant, lactation and (2) cycling, pregnant, lactation 1

(suckling less than 6 months from birth) and lactation 2 (suckling more than 6 months from birth). Suckling can last up to 18 months after birth in redtail monkeys (Struhsaker unpublished data in Butynski, 1988), though whether this suckling is comfort suckling or is nutritional is unclear.

To examine the relationship between food switching and mineral intake, I ran linear mixed models with ML with response variable daily sodium intake, fixed effects of number of switches between food items per hour (to control for differences in full-day focal length), fruit in diet, reproductive status, and group, and random effect of individual. Similar LMMs were then run for copper and iron. Switching between food items may better reflect diversity of diet than unique food items eaten due to intraspecific variation in nutritional content.

RESULTS

Macronutrient and mineral intake

Daily intake of macronutrients and energy (Table 3.1) varied less (CV<1, with exception of fat) than daily intake of multiple minerals, include calcium, phosphorous, magnesium, potassium, sodium and iron (Chapter 2: Table 2.2a). For daily macronutrient intake, daily fat

108 varied the most and protein varied the least. For daily mineral intake, daily magnesium varied the most and daily copper varied the least; for all minerals, except for zinc, copper and manganese

(and molybdenum with grand mean CV), CV >1, indicating high variance in daily mineral intake

(Chapter 2: Table 2.2a). Individual differences in food intake (Appendix 14) and in intake of some macronutrients and minerals were detected within the study groups (Appendices 15-18).

Redtail monkey NPE:AP balance

The mean balance of nonprotein energy to available protein (NPE:AP) was 4.6:1 for focal follows greater than 5 hours (SD=3.14, CV=0.68, N = 137 focal follow days; Table 3.1). Grand mean NPE:AP was 4.7:1 (Table 3.1) regardless of inclusion of one major outlier for dry matter intake. NPE:AP varied from 1.1:1 to 19.8:1 (N=137). Based on the results of a linear mixed model with random effect of individual, redtail monkey female NPE:AP was 3.2:1 (Figure 3.6).

Protein intake was positively related to nonprotein energy intake, β = 3.23, t(113) = 9.98, p <

0.0001, 95% CI 2.52 to 3.94, N=137, and explained 34% of nonprotein energy intake (marginal

R-squared of linear mixed model). The fixed (intake AP) and random effects (individual) explained 47% of intake of NPE.

Extrinsic and intrinsic factors affecting nutritional strategy

During the study period, the amount of ripe fruit in the redtail diet varied (Figure 3.1), though complementary dry matter intake of unripe fruit and ripe fruit (Figure 3.2) and fruit-of- any-ripeness and leaves (Figure 3.3) were more apparent in Kus and Suk group than in Kyo group. Kyo group exploited gum when ripe fruit in the diet was low (Figure 3.1b).

As ripe fruit in the diet increased, daily NPE:AP also increased, though the effect was greater when I tested just Kus and Suk groups (Table 3.3) versus all three groups (Table 3.2;

109 Figure 3.4). As leaf matter increased in the diet and as insect matter increased in the daily diet, daily NPE:AP decreased (Figure 3.5; Table 3.2).

Ripe fruit in the diet had a significant effect on daily intake of fat (kcal), NDF (grams), digestible NDF (kcal), ADF (grams) and lignin (grams), though effect sizes were small (Figure

3.7; Table 3.4). Because Kyo group’s diet composition is distinct from the other two groups

(Figure 3.1b), Kus and Suk were also tested separately: for Kus and Suk group females, more ripe fruit in the diet led to increased intake of nonprotein energy (kcal), TNC (kcal), NDF (g), digestible NDF (kcal), ADF (g) and ADL (g) (Table 3.5). As leaves in the diet increased, daily intake of metabolizable energy, nonprotein energy, TNC, digestible NDF (kcal), NDF (g), and fat decreased, though these decreases were small (Table 3.6).

Reproductive status was a significant predictor of daily intake of protein, χ2(6) = 10.78, p

= 0.01; contrasts revealed that females in early lactation (<6 months) ate less protein (β = -0.15, t(100) = -3.20, p =0.002) than cycling females, but the effect was small (marginal R-squared =

0.09). Reproductive status did not have a significant effect on daily intake of metabolizable energy (kcal), nonprotein energy (kcal), NDF (kcal), TNC (kcal) or fat (kcal).

Group had a significant effect on NPE:AP, χ2(5) = 28.41, p < 0.001 (marginal R-squared

= 0.27), specifically Kyo group had a higher NPE:AP, β = 0.30, t(20) = 5.56, p < 0.001, because

Kyo females ingested more NPE daily than females of the other groups. Group had a significant effect on daily intake of NPE (with Kus as reference, χ2(5) = 20.56, p<0.001), but not intake of

AP, χ2(5) = 0.45, p = 0.80.

The hourly number of switches among foods was a predictor of daily copper intake (χ2(4)

= 6.74, p = 0.01), daily iron intake (χ2(4) = 8.75, p = 0.003), and daily sodium intake (χ2(4) =

16.25, p < 0.0001). Copper (β = 0.006, t(112) = 2.65, p = 0.01) and iron (β = 0.01, t(112) = 2.98,

110 p = 0.003) increased with the number of switches per hour, though the effect was small for both

(copper: marginal R-squared = 0.05; iron: marginal R-squared = 0.06). Sodium increased with the number of switches per hour (β = 0.05, t(112) = 4.12, p < 0.0001), with a larger effect than the other two minerals (marginal R-squared = 0.11). The positive relationship between sodium intake and number of switches was not linked to increased sodium from insects with increased switching: the hourly number of switches among foods was not a predictor of daily percentage of sodium from insects (χ2(4) = 0.001, p = 0.97).

DISCUSSION

Redtail monkey mean daily NPE:AP placed them with other omnivorous primates (Cui et al., 2018; Johnson et al., 2013; Takahashi, 2018). The mean NPE:AP balance for redtail monkeys was similar to, though slightly lower than, that reported for blue monkeys (Takahashi, 2018;

Takahashi et al., in prep). This difference may be driven by higher insect protein consumption, leading to higher protein intake relative to nonprotein energy intake, in redtail monkeys at

Kanyawara compared to blue monkeys in Kakamega Forest; additionally, blue monkey intake of high-fat oil palm (Elaeis sp.) fruits may contribute to a higher NPE:AP in blue monkeys compared to redtails. Redtail female nutritional strategy was affected by habitat variation more than ripe fruit in the diet or reproductive status. Prunus africana density was greater in disturbed areas (Owiny & Malinga 2014: Supplementary Table 1) in which Kyo group ranged than in primary forest in which Kus and Suk groups ranged, suggesting that P. africana was available mostly to Kyo group. Food switching in this generalist feeder facilitated increased daily sodium, copper and iron intake; though the increase was small for copper and iron, small increases in mineral intake, especially for trace minerals (needed in small quantities) can be biologically significant.

111 Temporal shifts in food choice and nutritional strategy

Ripe fruit in the diet did not dictate macronutrient and energy intake in the ways expected. When all three study groups are considered, the effects of ripe fruit in the diet on

NPE:AP balance was weak; though NPE:AP increased with ripe fruit in the diet, ripe fruit in the diet only explained 3% of the variation in NPE:AP. Ripe fruit in the diet did not predict metabolizable energy intake, another indicator that redtails are getting their energy from diverse sources. Additionally, against expectation, fiber intake increased with increased ripe fruit in the diet (Table 3.4). This result may reflect that (1) redtails are forced to consume relatively fibrous fruits even when those fruits are ripe due to interspecific competition with larger-bodied sympatric monkeys or (2) redtail monkeys can exploit fibrous fruits of any maturity thanks to digestive enzymes in their cheek pouches and caeco-colonic fermentation capabilities (Lambert,

1998). Leaves in the diet and insects in the diet, as comparable source of protein to each other, drove down NPE:AP. One caveat to the insect finding is that days when the majority of dry matter intake consisted of insects were rare and were due mostly to caterpillar flushes, when redtails were stuffing their cheek pouches with developing caterpillars. Regardless, these rare caterpillar flush days led to low daily NPE:AP ratios.

Daily intake by redtail monkey females was lower in both grams and calories than those reported for blue monkeys (Cercopithecus mitis) at Kakamega, Kenya, the only other guenon whose nutritional strategy has been fully characterized. Blue monkey females in Kakamega,

Kenya, ate on average 2.5x as much food in grams and in calories (Takahashi, 2018; Takahashi et al., 2019) as redtail monkey females in this study. These differences in intake were likely driven by factors including: (1) blue monkey female larger body size (adult female blue monkey

112 mean = 4.2 kg, adult female redtail monkey mean = 2.9 kg [Delson et al., 2000]) leading to higher energy needs, (2) Kakamega blue monkey access to energy-dense “non-natural foods” with one blue monkey study group gaining over 50% of their daily energy from these human- introduced sources, for example guava (Psidium guajava) fruit (Takahashi, 2018; Takahashi et al., 2019), and (3) Kanyawara redtail female higher intake of insects, which are low in grams dry weight compared to other foods. I also found a difference in total metabolizable energy intake between blue monkeys and redtail monkeys when I controlled for metabolic body mass (Table

3.7); the reason why redtail energy needs per kg body mass were lower than for blue monkeys requires further investigation, but may be due to factors like energetic needs of more daily travel by blue monkeys at Kakamega compared to redtail monkeys at Kanyawara. Alternatively, the lower value may reflect that my study underestimates daily energy intake by female redtail monkeys.

Reproductive status and nutritional strategy

I was unable to find a statistically clear (sensu Dushoff et al., 2019) relationship between reproductive status and daily NPE:AP, daily food intake or daily intake of NPE, which may reflect females using stored fat during gestation and lactation rather than adjusting energy intake

(Allen & Ullrey, 2004). The small decrease in protein intake in lactating females is likely not biologically significant. Another forest guenon, blue monkeys in Kakamega Forest, also did not adjust NPE:AP by reproductive status (Takahashi, 2018). However, primates in habitats with more dramatic seasonal shifts (than Kibale or Kakamega) with corresponding changes in temperature, rainfall and food availability, have been shown to consume more food, and therefore macronutrients and metabolizable energy, during energetically demanding reproductive stages (Koch et al., 2017).

113 Group differences in nutritional strategy

The difference in nutritional strategy by females in one of the study groups, Kyo group, appeared to be linked to reliance on a particular tree species’ resources (Prunus africana). While

Kyo group females ingested the same amount of protein as females of the other two groups, they ingested more nonprotein energy, resulting in a higher daily NPE:AP. As part of nonprotein energy intake, Kyo females also consumed more fiber (kcal). Ripe fruit of endangered P. africana has previously been found to have the highest sugar content (39%) of any fruit of the redtail monkey diet (compared to an average sugar content of 16%) (Danish et al., 2006). Kyo group reliance on P. africana fruit as well as gum, which was more frequently available due to frequent gummosis by P. africana, together provided nonstructural and structural carbohydrates for energy. Starchy seeds of fruiting shrubs (Chapter 2) consumed by Kyo females also contributed to nonstructural carbohydrate gains. Rode and colleagues (2006a) hypothesized that redtail monkeys may face reduced copper absorption in high fiber diets. Kyo group’s higher

NDF intake (kcal) than the other groups may have implications for copper absorption; however, the relationship between reduced copper bioavailability and fiber ingestion is not straightforward because fiber is complex and the relationship may be indirect and related to how multiple micronutrients, including zinc and vitamin C, also affect copper bioavailability (Adams, 2018).

Mineral intake and food switching

Sodium is the only mineral established to be limiting to animals, with evidence of animals seeking out sodium sources across vertebrate taxa (Oates, 1978; Belovsky & Jordan,

1981; McNaughton, 1988; Rothman et al., 2006). Though copper intake has been correlated with redtail monkey abundance at this study site (Rode et al., 2006a), the availability of copper in the environment has not been quantified and therefore it is unknown if this mineral is actually

114 limiting. The high variation in redtail female daily intake of multiple minerals including calcium, phosphorous, magnesium, potassium, and iron (CV>2; Chapter 2: Table 2.2a) and the fact that several of them are ingested above requirements estimated for nonhuman primates (Chapter 2:

Table 2.2b) may indicate that these minerals are more than adequately attained incidentally in a diet composed primarily of fruits, young leaves and insects and that minerals are therefore not regulated on a daily basis because they do not need to be. Unlike Rode and colleagues (2006), who found that both copper and sodium intake by redtails were below NRC (2003) requirements,

I found this true only of sodium intake. The amount of these minerals absorbed after consumption by redtail monkeys is unknown and complicates what this daily variation in mineral intake means in terms of health outcomes. For example, non-heme iron in plants is much less bioavailable than heme iron in animals, and even insects contain iron mostly in the non-heme form (Mwangi et al., 2018; Nichol & Locke, 1990); therefore, the bioavailability of iron consumed by redtail monkeys likely varies dramatically by food item.

Redtail female intake of sodium, copper and iron increased with number of switches among foods per hour. The impact of food switching on mineral intake reflects the diverse set of foods fed from for minerals and agrees with a previous study in which higher redtail dietary diversity translated into higher mineral intake (Rode et al., 2006a). The relationship between sodium and food switching was not explained by rapid opportunistic consumption of insects when switching among foods. Redtail female daily sodium intake varies only slightly less widely

(CV=1.49) than Ca, P, Mg, K and Fe and approximately half of daily sodium comes from insects

(Chapter 2). Potential additional sources of sodium in the redtail monkey diet that I observed included decaying wood, swamp water and swamp plants. Swamp plants in Kibale National Park were previously found to contain high levels of sodium (700-6400 ppm; Oates, 1978). Decaying

115 wood is a noted sodium source across tropical forest taxa (Chaves et al., 2011; Iwata et al., 2015;

Reynolds et al., 2009; Rothman et al., 2006).

The diverse set of foods redtails consumed with high iron content included soil processed by termites at the base of a decaying tree; however, how much of the iron in the termite soil consumed was accessible to the monkey’s digestive system is unknown and variation in soil trace mineral composition within and across sites makes establishing the drivers of geophagy challenging. Previous studies in primates suggest that iron has low bioavailability when ingested in soil (Abrahams, 1997; Pebsworth et al., 2013; Seim et al., 2013), though some soil sites that chimpanzees at Budongo, Uganda, fed from contained high levels of bioavailable iron

(Pebsworth et al., 2019). Kanyawara site’s diverse soil composition (Struhsaker, 1997) likely means that redtail monkeys seek soil for diverse reasons.

Limitations and future directions

Mineral content varies spatiotemporally due to soil composition and variation in the ability of roots to absorb elements (Van Soest, 1994; Whitehead et al., 1985); unique food items

(N=101) were analyzed in this study and further characterization of intraspecific variation in mineral content is needed. Additionally, though my analyses quantify mineral composition of foods consumed and weight-based mineral intake by redtail monkeys, they cannot address mineral absorption (Van Soest, 1994). Future research should address mineral absorption for minerals for which non-invasive absorption studies can be conducted. Absorption can also be affected by multiple interactions among nutrients, which is more challenging to quantify, especially in a wild population.

Redtail monkeys living in a savanna-woodland mosaic habitat at the Issa Valley,

Tanzania, face lower availability of fruits and leaves, higher predation risk, and limitation by

116 thermoregulation needs compared to forest redtails in Kibale, Uganda and Kakamega, Kenya, and Issa Valley redtails exhibit substantially larger home ranges and longer daily path distances than forest redtails (McLester et al., 2019a). All these factors hold implications for intraspecific variation in redtail monkey nutritional strategy between Issa and forest sites.

Other extrinsic factors that may affect nutritional strategy include predators, disease ecology, and social environment. Chapters 4 and 5 address two aspects of social environment: intraspecific agonism and mixed species association with sympatric primates.

Conclusions

Redtail monkey balance of nonprotein energy to available protein places them with other omnivorous primates. Redtail female nutritional strategy was affected by habitat variation

(specifically disturbance level by clear-cutting history or human settlement) more than ripe fruit in the diet or reproductive status. Food switching in this generalist primate may have enabled increased daily sodium, copper and iron intake. Though they rely on insects for minerals, redtail females also exploit a variety of plant foods for mineral intake, made possible by rapid food switching throughout the day. Intake of other minerals, for which there is minimal information about whether they are limiting, varied widely daily, suggesting that daily regulation is irrelevant for minerals because of low requirements. My findings suggest that redtail monkeys tolerate variation in macronutrient intake and NPE:AP ratio due to the diverse food items they draw from and their physiological abilities to digest foods of diverse composition.

117 CHAPTER 3 FIGURES AND TABLES Table 3.1 Variation in daily intake of macronutrients and energy, as well as ratio of nonprotein energy: available protein (NPE:AP). Ratio of nonprotein energy to available protein (kcal) was calculated by dividing the sum of kcal intake of fat + digestible NDF + TNC by kcal intake of AP. Total metabolizable energy includes intake in kcal from fat, NDF, TNC and AP, using modified Atwater values (see Methods). Standard deviation (SD) and coefficient of variation (CV) also presented. Mean (N=137 focal days) Grand mean (N=23 females) Mean SD CV Mean SD CV Food (grams) 88.13 64.31 0.73 89.93 30.64 0.34 Available protein 46.59 27.96 0.60 46.21 11.09 0.24 (kcal) NDF (kcal)* 42.03 36.29 0.86 43.32 18.51 0.43 NDF (grams) 34.37 28.71 0.83 35.10 13.35 0.38 Fat (kcal) 24.09 25.73 1.07 24.98 12.49 0.50 Total metabolizable 241.32 169.71 0.70 247.67 84.92 0.34 energy (kcal) NPE:AP 4.60 3.14 0.68 4.74 1.96 0.41 *NDF intake calculated by multiplying NDF intake by the Atwater value of 3 as well as the NDF digestibility coefficient.

118 A.

Figure 3.1 Variation in consumption by food category by month of study period for (A) Kus and Suk group females (N=92 focal days) and (B) Kyo group females (N=44 focal days). For clarity, no error bars shown. Kyo group considered separately because of reliance on Prunus africana gum.

119 A.

Figure 3.2 Kus and Suk group female variation in (A) ripe fruit (B) unripe fruit consumption by month of the study period (N=92 focal days). Kyo group considered separately because reliance on Prunus africana gum makes it distinct from the other two groups.

120 Figure 3.3 Kus and Suk group female variation in fruit (of any ripeness) and leaf (any maturity) consumption by month of the study period (N=92 focal days). Kyo group considered separately because reliance on Prunus africana gum makes it distinct from the other two groups.

121 A.

Figure 3.4 Relationship between balance of nonprotein energy to available protein (NPE:AP) and (A) ripe fruit in diet (percentage of daily dry matter intake from ripe fruit) or (B) unripe fruit in the diet (percentage of daily dry matter intake from unripe fruit) for females of the three study groups (N=136 focal days). NPE:AP log transformed for normality. Lines show predicted slopes and intercepts of linear mixed models.

122 A.

Figure 3.5 Relationship between balance of nonprotein energy to available protein (NPE:AP) and (A) leaves in diet (percentage of daily dry matter intake from leaves of any maturity) or (B) insects in the diet (percentage of daily dry matter intake from insects) for females of the three study groups (N=136 focal days). NPE:AP log transformed for normality. Lines show predicted slopes and intercepts of linear mixed models.

123 Table 3.2 All three study groups: Results of linear mixed models (LMMs) predicting daily NPE:AP by type of food in the diet (i.e., percentage of daily dry matter intake by food type). LMMs were run with random effect of individual. One outlier for intake was removed so these models included 136 focal follows of 23 females in three study groups. Marginal R2 value describes the proportion of variance in the response variable explained by the fixed effect.

Response variables Fixed effect Estimate SE 95% CI df t-value p-value marginal R2 Lower Upper NPE:AP* Ripe fruit in 0.002 0.0008 0.0005 0.004 112 2.60 0.01 0.03 the diet (% dm intake) NPE:AP* Leaves of -0.006 0.0009 0.05 0.16 112 -6.53 <0.0001 0.28 any maturity in the diet NPE:AP* Insects in -0.02 0.002 0.08 0.17 112 -9.03 <0.0001 0.37 the diet *log transformed

Table 3.3 Kus and Suk group females only: Results of linear mixed models (LMMs) predicting daily NPE:AP by type of food in the diet (i.e., percentage of daily dry matter intake by food type). LMMs were run with random effect of individual. One outlier for intake was removed so these models included 92 focal follows of 13 females in two study groups. Marginal R2 value describes the proportion of variance in the response variable explained by the fixed effect.

Response variables Fixed effect Estimate SE 95% CI df t-value p-value marginal R2 Lower Upper NPE:AP* Ripe fruit in 0.003 0.0008 0.001 0.004 76 3.44 0.0009 0.12 the diet (% dm intake) NPE:AP* Leaves of -0.005 0.0009 - - 76 -5.71 <0.0001 0.27 any maturity in the diet NPE:AP* Insects in -0.02 0.003 - - 76 -7.28 <0.0001 0.38 the diet *log transformed

124 A.

Figure 3.6 Overall balance of nonprotein energy to available protein, or relationship between intake of nonprotein energy to intake of available protein (A) for all focal days except one outlier removed (N = 136 focal days), shown with equal and unequal axes (for easier comparison to other studies) and (B) for mean by female (23 females), shown only with equal axes. Line in (A) shows predicted slope and intercept of linear mixed model with response variable daily intake of NPE and predictor variable daily intake of AP, with random effect of individual. Nonprotein energy (kcal) was calculated by sum of kcal intake of fat + digestible NDF + TNC.

125

ted to ripe fruit (<20%) (<20%) versus low versus

take) n es only for digestible NDF, NDF, digestible NDF, only for ADF, es c (>20% daily dry matter i matter dry (>20% daily

Daily macronutrient and energy intake by differences in high intake and energy by differences macronutrient Daily .7 3 in the diet periods. One outlier removed (N=136 focal follows, 23 females, 3 monkey groups). Metabolizable energy is abbrevia follows, energy focal 23 females, (N=136 Metabolizable outlier 3 monkey groups). One removed diet periods. in the both presented. Significant differen (grams) (kcal) are digestible NDF and ME. Total NDF fat intake. and ADL, Figure Figure

126 Table 3.4 All three study groups: Results of linear mixed models (LMMs) predicting daily macronutrient intake by ripe fruit in the diet (i.e., percentage of daily dry matter intake from ripe fruit). LMMs were run with random effect of individual. One outlier for intake was removed so these models included 136 focal follows of 23 females in three study groups. Marginal R2 value describes the proportion of variance in the response variable explained by the fixed effect.

Response variables Fixed effect Estimate SE 95% CI df t-value p-value marginal R2 Lower Upper Digestible NDF RF in the 0.01 0.006 0.001 0.03 112 2.16 0.03 0.03 (kcal) † diet (% dm intake) NDF intake (g) † RF in the 0.01 0.006 0.002 0.03 112 2.30 0.02 0.04 diet (% dm intake) ADF intake (g) † RF in the 0.02 0.005 0.01 0.03 112 4.29 <0.0001 0.12 diet (% dm intake) ADL intake (g) † RF in the 0.01 0.003 - - 112 4.23 <0.0001 0.12 diet (% dm intake) Fat intake (kcal) † RF in the 0.02 0.007 - - 112 2.89 0.005 0.06 diet (% dm intake) † square root transformed

127 Table 3.5 Kus and Suk group females only: Results of linear mixed models (LMMs) predicting daily macronutrient intake by ripe fruit in the diet (i.e., percentage of daily dry matter intake from ripe fruit). LMMs were run with random effect of individual. One outlier for intake was removed so these models included 92 focal follows of 13 females in two study groups. Marginal R2 value describes the proportion of variance in the response variable explained by the fixed effect.

Response variables Fixed effect Estimate SE 95% CI df t-value p-value marginal R2 Lower Upper Nonprotein energy RF in the 0.002 0.001 0.0002 0.005 76 2.16 0.03 0.05 intake (kcal)* diet (% dm intake) TNC (kcal) † RF in the 0.03 0.01 0.006 0.06 76 2.37 0.02 0.06 diet (% dm intake) Digestible NDF RF in the 0.02 0.008 - - 76 2.76 0.007 0.08 (kcal) † diet (% dm intake) NDF intake (g) † RF in the 0.02 0.007 0.005 0.03 76 2.61 0.01 0.07 diet (% dm intake) ADF intake (g) † RF in the 0.02 0.006 - - 76 3.42 0.001 0.12 diet (% dm intake) ADL intake (g) † RF in the 0.01 0.004 0.006 0.02 76 3.29 0.002 0.11 diet (% dm intake) *log transformed due to positive skew (and square root transformation did not render data normally distribute) † square root transformed

128 Table 3.6 Results of linear mixed models (LMMs) predicting daily macronutrient intake by leaves in the diet (percentage of daily dry matter intake from leaves). LMMs were run with random effect of individual. One outlier for intake was removed so these models included 136 focal follows of 23 females in three study groups.

Response variables Fixed Estimate SE 95% CI df t- p-value marginal effect value R2 Lower Upper Total metabolizable Leaves in -0.003 0.001 -0.005 -0.0008 112 -2.72 0.008 0.05 energy intake the diet (% (kcal)* dm intake) Nonprotein energy Leaves in -0.004 0.001 -0.007 -0.002 112 -3.61 0.0005 0.09 intake (kcal)* the diet (% dm intake) TNC intake (kcal) Leaves in -0.05 0.02 -0.08 -0.02 112 -3.04 0.003 0.06 † the diet (% dm intake) Digestible NDF Leaves in -0.02 0.009 -0.04 -0.002 112 -2.17 0.03 0.03 (kcal) † the diet (% dm intake) NDF intake (g) † Leaves in -0.02 0.008 -0.03 -0.001 112 -2.11 0.04 0.03 the diet (% dm intake) ADL intake (g) † Leaves in -0.01 0.005 -0.02 -0.001 112 -2.22 0.03 0.04 the diet (% dm intake) Fat intake (kcal) † Leaves in -0.04 0.009 - - 112 -4.31 <0.0001 0.12 the diet (% dm intake)

129 Table 3.7 Differences in total metabolizable energy intake across four primate species adult females when intake was controlled for metabolic body mass (MBM). MBM calculated with Pontzer et al. (2014)’s M0.73. Female daily metabolizable energy intake drawn from this study for redtail monkeys, Takahashi (2018) for blue monkeys, Vogel et al. (2017) for Bornean orangutans, and Rothman et al. (2008) for mountain gorillas.

Female primate Redtail monkey Blue monkey Mountain gorilla Bornean orangutan Estimated body mass (kg)* 2.8 4.2 100 38.8 Daily metabolizable 116.48 223.44 283.73 209.11 energy intake (kcal per unit of MBM) *Body mass estimates: female redtail average DRC (Colyn 1994; Kingdon et al. 2013); blue monkey female average W. Kenya (Delson et al. 2000; Kingdon et al. 2013); female mountain gorillas average from McNeilage (1995); and female orangutans average from Markham and Groves (1990).

130 CHAPTER 4: FEMALE REDTAIL MONKEY INTRAGROUP AGONISM AND NUTRITIONAL ECOLOGY

INTRODUCTION

Food distribution and nutritional composition are factors in dictating when and how social animals interact. These food-related interactions include individuals leading each other to food, and group members excluding each other or fighting over food, with resulting fitness consequences, including effects on reproductive output and mortality (Giraldeau & Caraco,

2000; Janson & van Schaik, 1988). Generally, preferred foods that can be monopolized lead to agonism, including aggression, among group members (Grant, 1993; Isbell & Young, 2002; van

Schaik, 1989; Wrangham, 1980). Food distribution and value can be more complex than

“clumped” energy-dense foods (Dubois et al., 2003; Vogel & Janson, 2011) or food that takes longer to process or deplete (Chancellor & Isbell, 2009; Isbell et al., 1998) leading to food competition. Social foraging theory predicts that as the value of a resource increases (dictated by the quantity of, nutritional composition of, and/or time spent consuming a food), aggression over that food increases. However, if there are enough co-feeders in the patch and high-value alternative food patches exist, individuals choose to leave the crowded patch, reducing competition; as a result, the relationship between resource value and aggression follows a concave parabolic relationship (Dubois & Giraldeau, 2003; Vogel & Janson, 2007). The size of a food resource in relation to animal group spread also dictates rates of agonism (Mitchell et al.,

1991; Peres, 1996). Squirrel monkeys (Saimiri sciureus) that fed in large Ficus fruiting trees exhibited low rates of agonism likely due to the large food patch accommodating the group in which “monkeys concentrate on consuming as many fruits as possible, rather than compete for feeding space” (Mitchell et al., 1991, pg. 59). The scale of effects of competition may also go beyond large versus small food patch in relation to group size: multiple feeding sites (Isbell et

131 al., 1998) or nested sub-patches (Lee & Cowlishaw, 2017) may occur in one tree and the distance at which an animal defends food may vary by species (Vogel & Janson, 2007). Shifts in preferred resource availability also can affect intensity of competition and rates of agonism

(Janson, 1988; Barton & Whiten, 1993). Additionally, aggression does not always occur during feeding competition due to social factors like established steep dominance hierarchies, in which relationship dynamics are already established (Thouless, 1990; Vogel, 2005).

According to primate socioecological models, female social organization emerges out of combinations of multiple types of feeding competition within and between groups (Isbell, 1991;

Janson & van Schaik, 1988; Koenig & Borries, 2009; van Schaik, 1989; Sterck et al., 1997;

Wrangham, 1980). These different types of competition dictate how females interact with one another within and between social groups; strong between-group contest competition (BGC), where conspecific groups compete over a resource, which is predicted to lead to less within- group contest competition (WGC) because of the need for intragroup alliances (Isbell, 1991; van

Schaik, 1989; Sterck et al., 1997; Wrangham, 1980). Empirical studies demonstrate that social animals increased intragroup affiliative behavior after intergroup conflict (Cheney, 1992; Jaeggi et al., 2018; Radford, 2008). When food is distributed patchily in an environment (i.e., distributed in a way in which one animal can monopolize the food) then WGC is predicted to occur (Barton & Whiten, 1993; Isbell et al., 1998; Koenig, 2000; Mitchell et al., 1991; Saito,

1996). The temporal variable of how long a female spends handling and consuming a food also affects levels of WGC (Chancellor & Isbell, 2009; Isbell et al., 1998). Under some competitive conditions, females may be able to bias resources in their group’s favor (BGC) or in their own individual favor (WGC) to increase individual fitness, resulting in a spectrum of systems of female social relationships.

132 Empirical research on “social nutrition” (sensu Lihoreau et al., 2015; Simpson &

Raubenheimer, 2012; Simpson et al., 2015), or socially mediated nutrition, has been done predominantly in insects (Cook et al., 2010; Dussutour & Simpson, 2009; Grover et al., 2007;

Lihoreau et al., 2016; Simpson & Raubenheimer, 2012) because entomology allows for direct testing of fitness effects of nutrition and for determining adaptive intake targets (Eggert et al.,

2008; Salomon et al., 2008). However, simulation models offer predictions of how contest competition may affect the nutritional ecology of social vertebrates (Lihoreau et al., 2015).

Lihoreau and colleagues’ simulation models based in nutritional geometry (Raubenheimer et al.,

2009; Simpson & Raubenheimer, 2012) predicted that individuals better able to reach or get close to their nutritional intake target better able to win contest competitions, gaining “winner’s advantage,” which leads to improved balance (ratios) of limiting nutritional components

(Lihoreau et al., 2015). As a result of competition, individual variation in intake and balance of nutritional components is predicted to emerge based on these models.

The aim of this chapter was to characterize intragroup contest competition in a guenon, the redtail monkey (Cercopithecus ascanius), by examining the relationship between female agonism and nutritional strategy. Part of this aim was testing the predictions of simulation models of how vertebrate social behavior affects multivariate nutritional intake (Lihoreau et al.,

2015). Female redtail monkeys are frequently aggressive during conspecific intergroup encounters that are over high-use food sites (Brown, 2013). These aggressive behaviors include loud vocalizations and chases and related increases in cortisol excretion (Jaeggi et al., 2018), suggesting strong between-group contest competition (Brown, 2011, 2013; Cords, 1984, 1987;

Struhsaker, 1980). Sterck and colleagues’ socioecological model (1997) predicts, based on arboreal guenon female philopatry and high between-group contest competition, that guenon

133 social systems are “resident-egalitarian,” with low within-group competition and no formal dominance hierarchy among female group members. However, at least one guenon species, the blue monkey (Cercopithecus mitis), exhibits moderately despotic, moderately tolerant, and nepotistic linear female dominance hierarchies (Klass & Cords, 2015). Empirical data on intragroup female relationships for other guenons, including redtail monkeys, are lacking. Adult female redtails spent more time grooming other adult females than expected by percentage of females in a redtail group (Struhsaker & Leland, 1979; Struhsaker, 1980) and “grooming frenzies” occur among redtail group members after aggressive intergroup encounters (Cords,

1987; Jaeggi et al., 2018), though the form and function of intragroup female affiliative behaviors in redtails has yet to be explored beyond general ideas of bonded or coalitionary within-group behavior when faced with intergroup competition. Redtail female agonistic interaction research is also lacking, though intragroup contest and scramble competition likely occur in at least subtle forms because redtail group fissions occur at large group size (Struhsaker

& Leland, 1988; Windfelder & Lwanga, 2002; personal observation of fission group).

Redtail monkeys rely on fruits, insects, young leaves, and flower parts (Chapman et al.,

2002b; Cords, 1986, 1987; Gathua, 2000; Lambert, 2002a; Struhsaker, 1978, 1980, 2017; Rode et al., 2006a) and switch between a variety of foods and species of diverse nutritional composition (Lambert, 2002a; Chapter 2-3). Though the ability to switch among diverse food resources may “blunt” feeding competition (Lambert et al., 2002a; Pazol & Cords, 2005), the relatively modest protein and energy contributions of insects given time spent feeding on insects

(Bryer et al., 2015; Chapter 2) may reflect that some foods frequently switched to by redtail monkeys are inadequate nutritionally. Lending further support to forest guenons facing intragroup feeding competition, another omnivorous guenon, the blue monkey, exhibited the

134 most female agonism during feeding and water drinking, and female agonistic interactions were over fruit 1.7 times more than expected given time spent feeding on fruit (Cords 2000). I therefore predicted (Prediction 1a) that redtail monkey agonism between females would occur disproportionately in a feeding context. Using general food categories sometimes oversimplifies whether foods are contestable (Wheeler et al., 2013) and unripe and ripe fruits consumed by redtail monkeys are diverse in nutritional composition (Chapter 2), so rather than predicting that agonism would occur over fruits, I narrowed the prediction to agonism over high metabolizable energy source fruits. I predicted (P1b) that foods contested would be mostly fruit high in nonstructural carbohydrates and fat composition. Because some foods consumed for minerals can also lead to agonistic behavior in primates (Chancellor & Isbell, 2009; Rothman et al., 2006),

I also predicted (P1c) that contested foods would include some foods with high concentrations of minerals.

I also hypothesized that agonistic encounters would lead to differential daily nutritional intake, as certain females would skew resources towards themselves after winning intragroup agonistic interactions. I predicted (P2a) that receiving more agonism during feeding during a focal day (more agonistic interactions received during feeding per hour) would lead to lower intake on that day of TNC (kcal) and fat (kcal) and higher intake of NDF (kcal). I predicted

(P2b) that winning more agonistic encounters (more agonistic interactions given during feeding per hour) during a focal day would lead to higher intake on that day of TNC (kcal) and fat (kcal).

I predicted (P3a) that receiving more agonism during feeding during a focal day would lead to lower NPE intake relative to AP, lowering NPE:AP. I predicted (P3b) that winning more agonistic encounters during feeding during a focal day would lead to more NPE intake relative to

AP intake, resulting in a higher NPE:AP, regardless of ripe fruit in the diet. Given that insects,

135 usually a widely distributed non-monopolizable rapidly depleted food, were major sources of Na and Cu (Chapter 2), I predicted (P4) that rates of agonism given or received in a feeding context would not affect daily intake of Na and Cu.

METHODS

Social behavioral data collection

During full-day feeding focal follows, the field team also recorded the category of and partners participating in adult female agonistic interactions. In addition to agonistic behaviors, we also recorded adult female affiliative behaviors (Table 4.1). Agonistic interactions include approach-retreat interactions and aggressive interactions; aggressive interactions include non- contact aggression (i.e., chases) and contact aggression (i.e., aggressive interactions that involve physical contact).

All occurrences of intergroup encounters between redtail monkey groups were recorded.

An intergroup encounter was defined as presence of another redtail monkey group <50 meters, which was mostly, though not always, made evident by vocalizations, non-contact and contact aggression between individuals in the focal group and in the other group(s) in the interaction.

Calculating rates of female agonism

Overall rates of agonism were calculated as rate of interactions per hour on a daily basis, based on adult female full-day focal follows greater than 5 hours in sight (i.e., number of

136 agonistic interactions in one focal follow divided by the number of hours focal female was in sight for that focal follow). Focal days > 5 hours in sight during which no agonism was given or received by the focal female were included as a rate of 0. For approach-retreat interactions, agonism given was defined as displacing and being avoided; agonism received was defined as being displaced and avoiding. For directionality to be determined, the recipient of the agonistic or aggressive behavior had to behave submissively (flee, vocalize submissively, etc.) and only decided interactions were used to calculate rates.

Descriptive statistics of female grooming

I defined grooming bouts as grooming between partner A and B continuing without any ≥

2 minutes interruptions. If ≥ 2 minutes elapsed when the individuals had not groomed each other, a grooming bout was terminated. When the partners involved in the grooming changed, a new bout was demarcated, even if less than two minutes had elapsed, because of the start of a new dyad. Reciprocity of grooming was defined simply here as reciprocity during a grooming bout: if

A groomed B and B groomed A during a grooming bout, then the bout was reciprocal.

Statistical analyses

To evaluate the effect of agonism on macronutrient and energy intake, I ran linear mixed models with response variables daily intake of nonprotein energy (NPE), protein (AP), TNC, Fat,

NDF, Na, Cu, and daily NPE:AP ratio. Fixed effects in models were (1) measures of adult female agonism rate per hour on daily basis, including hourly rate agonism during feeding on focal day (given and received), hourly rate agonism given during feeding on focal day, and

137 hourly rate agonism received during feeding on focal day and (2) ripe fruit in the diet (percentage of daily dry matter intake from ripe fruit). Models were also run with rates of aggression (non- contact and contact aggressive behaviors only) given and received as fixed effects. When comparing a model to the null model, the null model included only random effect. Monkey group could not be a random effect in the models because there are only three groups, which provided insufficient levels for a random effect (Harrison et al., 2018). Using restricted maximum likelihood estimators (REML) in linear mixed models is preferable to ML when sample size is small (Kuznetsova et al., 2017), but likelihood ratio tests to test the significance of the change in model fit can only be run with ML. Residual distribution was checked for normality for assessing model fit. Only females with three or more focal follows greater than 5 hours were included in these repeated measure analyses, and one additional focal follow was removed as it was a major outlier for intake (N=134 focal days).

To visualize the nutritional space in relation to agonism, I also plotted NPE by AP, grouping by agonism variables. Lihoreau and colleagues (2015) predicted via simulation models that when two nutritional components are plotted against each other, competition between individuals in a social group leads to a change in nutritional status (NS), whereby individuals combining foods can no longer reach or get close to the nutritional goal (or intake target) (Figure

4.3a).

To examine the relationship between agonism and ripe fruit in the diet (calories from ripe fruit per day), I ran Mann Whitney-U tests and generated estimation plots to examine effect size for presence and absence of agonism and aggression in relation to kilocalories consumed from ripe fruit in the daily diet.

138 RESULTS

Rates and context of agonism between adult females

When female focal animals were in sight during the study period (1,043 hours; 162 focal- days), we observed 147 female-female agonistic interactions total (Kus group: 36 interactions,

Kyo: 55, Suk: 56). To investigate the relationship between nutritional intake and agonism, I looked only at focal-days >5 hours in sight (961 hours; 137 focal-days): during an average focal- day (mean = 7 hours ± 30 minutes in sight of the observer), females engaged in 0.9 female- female agonistic interactions in any context. The grand mean rate of agonistic interactions per hour was similar across the three groups (Table 4.2). For each of the three study groups, females showed higher rates of agonism received than rates of agonism given (Table 4.2).

Agonistic interactions were observed disproportionately in the context of feeding or drinking water (Table 4.4). In terms of category of agonism, 69 observed interactions were displacement or avoidance, 50 interactions were non-contact aggression, mostly chases, and 1 interaction was contact aggression, specifically a push (Table 4.4).

When only agonism in a feeding context were considered: Kus group females participated in female feeding agonism a mean of 0.090 interactions/female/hour, Kyo 0.100 interactions/female/hour, and Suk females 0.084 interactions/female/hour; the groups did not differ in feeding agonism rates (Kruskal-Wallis chi-squared = 50.3, df = 55, p = 0.65). The groups also did not differ by agonism received in a feeding context (Kruskal-Wallis chi-squared

= 36.96, df = 41, p = 0.65; Kyo group females received agonism in a feeding context: 0.083 interactions/hour; Kus=0.069 interactions/hour; Suk:=0.049 interactions/hour). Rates of female- female feeding agonism given were lower than rates of female feeding agonism received

(Kus=0.021 interactions/hour; Kyo=0.013 interactions/hour; Suk=0.035 interactions/hour) and

139 did not differ by group (Kruskal-Wallis chi-squared = 23.70, df = 23, p = 0.42). Rates of female- female aggression in a feeding context were also similar across groups (Kruskal-Wallis chi- squared = 37.32, df = 35, p = 0.36; Kus=0.048 interactions/hour; Kyo=0.036 interactions/hour;

Suk=0.038 interactions/hour).

Nutritional composition of contested foods

Contested foods varied in macronutrient (Table 4.6) and mineral (Table 4.7) composition.

Two contested fruits (Celtis gomphophylla and Lindackeria sp.) were high in fat (compared to diet overall and compared to other fruits in the diet). One contested fruit, Chionanthus africanus unripe fruit, was exceptionally high in TNC; contested gum (Prunus africana) was also high in

TNC. The two contested insect morphotypes, cicadas and Noctuid caterpillars during a caterpillar flush, were high in protein and key minerals (P, Na, Fe, Cu, Zn and Mo) compared to other foods in the diet and compared to other contested foods. Young leaves contested over had the average amount of protein of young leaves in the diet and one species (Rothmannia urcelliformis) had high manganese (Mn) composition compared to other foods in the diet.

Intragroup affiliative behaviors between females

Adult female members of all study groups participated in female-female grooming and other affiliative behaviors. (Table 4.8). High variation in time spent grooming was observed, with females not grooming at all some days and grooming >10% of time (for Kyo and Suk females) on other days. Across the three groups the mean rate of female-female grooming bouts per hour was 0.45 ± 0.17 (Table 4.8), and no group differences were detected (Kruskal-Wallis chi-squared = 112.58, df = 110, p = 0.41). The percentage of grooming bouts per day that were reciprocal varied widely by individual, but on average over 50% of daily grooming bouts were reciprocal in each of the three groups (Table 4.8). Another, much rarer, affiliative behavior

140 observed was tail twining when two individuals wrap their tails together while sitting next to one another (N=15 female-female tail twines).

Intergroup encounters

The three study groups engaged in aggressive intergroup encounters (Table 4.9) with each other and five other redtail monkey groups (including a small group that fissioned from Kus group in early 2015). Kyo engaged in fewer intergroup encounters during the study period than the other two groups (Table 4.9).

Nutritional strategy and rates of agonism during feeding

Agonism during feeding and energy from ripe fruit

Focal days when agonism was received during feeding by the focal female did not differ in kilocalories from ripe fruit in the diet from days when she did not receive agonism (Figure

4.1a). Focal days when the female gave agonism (won an agonistic encounter) were different in kilocalories from ripe fruit (Figure 4.1b), though the small sample size for agonism given should be considered when interpreting this result. Focal days when the female was aggressive towards another female during feeding also had higher mean kilocalories from ripe fruit (Figure 4.2b).

Macronutrient intake and agonism during feeding

Daily agonism participated in during feeding (χ2(4) = 1.56, p = 0.21), agonism received

(χ2(4) = 3.59, p = 0.06) or agonism given (χ2(4) = 0.57, p = 0.45) did not predict daily intake of total nonstructural carbohydrates (TNC, kcal). Aggression received (χ2(4) = 0.05, p = 0.81) and given (χ2(4) = 0.003, p = 0.96) did not predict daily intake of TNC. Agonism received (χ2(4) =

0.02, p = 0.90) or given (χ2(4) = 0.0002, p = 0.99) did not predict daily intake of Fat (kcal).

Aggression received (χ2(4) = 0.41, p = 0.52) and given (χ2(4) = 0.06, p = 0.80) did not predict daily fat intake. Agonism received (χ2(4) = 0.44, p = 0.51) or given (χ2(4) = 0.43, p = 0.61) did

141 not predict daily intake of NDF (kcal). Aggression received (χ2(4) = 0.03, p = 0.87) or given

(χ2(4) = 0.31, p = 0.58) did not predict daily intake of NDF (kcal).

Balance of NPE:AP and agonism during feeding

Hourly rate of agonism participated in (given and received) in a feeding context was not a significant predictor of daily NPE intake. Hourly agonism received in a feeding context or hourly agonism given in a feeding context also did not predict daily NPE intake, regardless of percentage ripe fruit in the diet (% dry matter intake from ripe fruit) (Appendix 23). These agonism variables also did not predict daily AP intake (Figure 4.3b-d; Appendix 23) or the ratio of NPE:AP (Figure 4.3b-d; Appendix 24). Aggression rates also did not affect daily intake of

AP, NPE and NPE:AP (Figure 4.4b-c), regardless of percentage ripe fruit in the diet.

Mineral intake and agonism during feeding

Agonism participated in during feeding (χ2(4) = 0.83, p = 0.36), agonism received (χ2(4)

= 0.26, p = 0.61) or agonism given (χ2(4) = 1.11, p = 0.29) did not predict daily intake of sodium.

Aggression received (χ2(4) = 0.45, p = 0.50) and given (χ2(4) = 0.16, p = 0.69) did not predict daily intake of sodium. Agonism participated in during feeding (χ2(4) = 1.93, p = 0.16), agonism received (χ2(4) = 1.40, p = 0.24) or agonism given (χ2(4) = 0.71, p = 0.40) did not predict daily intake of copper. Aggression received (χ2(4) = 0.01, p = 0.90) and given (χ2(4) = 1.45, p = 0.23) did not predict daily intake of copper. Non-significant results were found regardless of percentage of ripe fruit in diet (% dry matter intake from ripe fruit).

DISCUSSION

Redtail monkey rates of female-female agonism

142 Adult female redtail monkeys engaged in low rates of agonism with each other, lower than other wild cercopithecines (Table 4.3). Mean rate of female-female agonism was lower than blue monkey female-female agonism (Klass & Cords, 2015); while blue monkey females in

Kakamega Forest, Kenya, participated in female-female agonism once every 3-4 hours, redtail females at Kanyawara, Kibale, participated in female-female agonism once every 7-8 hours.

Redtail monkey female-female agonism rate was comparable to rates found for some lemur species (Varecia verigata: 0.11 interactions/hour [Morland, 1991; Wheeler et al., 2013]) and

Thomas’s langurs (Presbytis thomasi: 0.15 interactions per hour and female [Sterck &

Steenbeek, 1997]); however, comparisons of redtail monkey social organization to these lemurs and colobines are limited by phylogenetic distance and differences including lack of female philopatry and lack of female aggression during intergroup encounters.

Redtail monkey rates of female-female aggression

Redtail monkey rates of female-female aggression were comparable or slightly higher

(Kus: 0.063 interactions/day; Kyo: 0.042 interactions/day; Suk: 0.048 interactions/day) than those found for the samango monkey (Payne et al., 2003: Cercopithecus mitis erythrarchus,

0.034 events/individual/hour); in both species, the majority of female-female aggressive interactions were over food. In contrast to these low rates of within-group female aggression, redtail females engaged in aggressive between-group interactions (Table 4.9). Kus, Kyo and Suk groups engaged in aggressive intergroup encounters with each other and five other redtail groups in the area. Previous work at Ngogo, Kibale, showed that redtail females were aggressive in 67% of intergroup encounters (Brown, 2013). Additionally, Ngogo redtail females were more likely to engage aggressively during an intergroup encounter at a “high-intensity feeding site,” indicating that redtail monkeys engage in food patch defense throughout their home range, rather than

143 defending a peripheral or core position (Brown, 2013). Differences in redtail monkey group density between Ngogo (5.6 groups/km2 [Jaeggi et al., 2018]) and Kanyawara (group density in lightly logged areas = 11.48 groups/km2; group density in unlogged areas = 4.83 groups/km2

[Chapman & Chapman, 2000a]) may lead to differences in intergroup dynamics.

Female intragroup affiliative behaviors

Redtail monkey females groomed each other more than they engaged in any other affiliative behavior, though the percentage of time spent female-female grooming during a follow was low compared to other activities like feeding and traveling (Table 4.8), with females engaging in approximately 2-3 female-female grooming bouts per day, with variation across females. Females in Kyo group showed the same grooming bout rate as blue monkey combined adult and subadult female grooming bout rate (Cords, 2000), indicating Kanyawara redtail monkey adult females may groom each other more than adult female Kakamega blue monkeys.

Previous research at Kanyawara suggests that redtail female-female grooming may serve a general intragroup bonding purpose (Struhsaker, 1980) and increased grooming (sometimes referred to as “grooming frenzies”) observed in arboreal guenons immediately after intergroup encounters may increase group cohesion in reaction to aggressive interactions with other conspecific groups (Cords, 2002; Payne et al., 2003; Jaeggi et al., 2018).

Context of female agonism & contested foods

Most redtail monkey female-female agonistic interactions were in the context of feeding

(or drinking water from a tree knot hole), followed by interactions over access to social partners, then over access to infants; the contexts of 10% of agonistic interactions observed were unclear because of reduced visibility before the interaction. In terms of intensity, female-female agonistic behaviors were primarily displacement or avoidance, followed by non-contact aggression,

144 consisting mostly of chases, and, very rarely, contact aggression (<1% of agonistic interactions)

(Table 4.4).

Food contested in agonistic interactions varied in food category (Table 4.5) and the macronutrient (Table 4.6) and mineral (Table 4.7) composition of foods contested were diverse, with some high in fat, some high in TNC, and some high in protein and/or minerals. Because only sodium is known to be limiting in tropical forests (Oates, 1978; Reynolds et al., 2009; Rode et al., 2003; Rothman et al., 2006), some high mineral composition values of contested foods may be incidental (when they choose a food for other nutritional component[s]): for example, are

Rothmannia urcelliformis young leaves and Craterispermum laurinum ripe fruit contested because they contain high levels of manganese? And is manganese a mineral that redtail monkeys seek out, or do they reach requirements through their mixed diet throughout the year?

Compared to estimated requirements for nonhuman primates, the mean daily intake of manganese by redtail females is higher than the estimated requirement for nonhuman primates

(NRC, 2003), indicating that this mineral may not be scarce in the environment, though this has yet to be explicitly tested (Chapter 2: Table 2.2b).

The spatial distribution of the foods contested by redtail females also varied. Some typically assumed to be widely distributed resources were clumped when contested over. Some young leaf species contested over were contested when in saplings, indicating a context in which the number of young leaves was finite. Caterpillars during a noctuid caterpillar flush covered defoliated trees on which they were maturing, creating an especially clumped insect resource that was also not rapidly depleted. These examples point to the complexity of which foods are contestable and how contestability varies within food types and within food species depending on spatial distribution and how rapidly a food is consumed. It was surprising that cicadas were

145 contested foods because their handling time is short and therefore unlikely to invite agonism

(Chancellor & Isbell, 2009), though sometimes the redtail prolonged this time by removing the wings of the cicada before consumption.

Rates of agonism and nutritional strategy

I was unable to find statistically clear relationships (sensu Dushoff et al., 2019) between agonism during feeding and nutritional intake of multiple macronutrients and energy at the scale of daily intake and balance, regardless of percentage of fruit in the diet. Redtail monkey females drew from a variety of plant and insect sources for their daily nonprotein energy (NPE) and protein (AP) intake (Chapter 2), and these foods vary in spatiotemporal distribution and resulting contestability. Likely due to this complexity, rate of agonism given in a feeding context did not predict daily intake of NPE, AP, or NPE:AP, and rate of agonism received in a feeding context did not predict daily intake of NPE, AP, or NPE:AP. Rates of aggression given or received also did not predict daily intake of NPE, AP, or NPE:AP (Appendices 23-24). Daily intake of macronutrients Fat, TNC and NDF were also not predicted by agonism or aggression rates.

Lihoreau and colleagues (2015) predicted via simulation models using Nutritional

Geometry (Simpson & Raubenheimer, 2012) that when two nutritional components are plotted against each other, competition between individuals in a social group leads to a change in nutritional status (NS), whereby individuals combining foods can no longer reach or get close to the nutritional goal (or intake target). Their simulations predicted that competition leads to variable intake among individuals, stretching individual NS along the nutritional rail representing the balance (or ratio) of the two nutritional components (Figure 4.3a). The balance of nonprotein energy to protein is a biologically important relationship with fitness implications (Simpson &

Raubenheimer, 2012) with which to test Lihoreau’s prediction. I found, however, that agonism

146 (Figure 4.3b-d) and aggression (Figure 4.4b-c) may not stretch individuals’ nutritional status in a plot of NPE by AP. Alternatively, my finding that focal females received more agonism than they gave may indicate that my agonism sample is skewed towards lower-ranking females and does not accurately reflect the relationship between female redtail nutritional intake and agonism.

No clear relationship was found between daily intake of sodium or copper and agonism or aggression rates. To better examine the mechanisms underlying these relationships, however, the daily scale for examining mineral intake may not be appropriate as micronutrients may be regulated on different timescales than macronutrients (Chapter 3).

The daily timeframe with which I examined agonistic interactions and nutritional intake may not be appropriate to detect intraspecific social effects on nutrition. Members of a large group may not suffer nutritionally except on very short-term scales (Janson, 1988) at the level of the food patch. Redtail monkeys at the same field site were observed reducing their intake rates after losing an aggressive interaction with conspecifics, especially in lower strata where fruits of some species in the diet were found to be lower in sugar content (Houle et al., 2010). Examining potential losses (or gains) at the patch level by looking at nutritional intake and balance at the level of feeding tree while controlling for tree size, may elucidate this contest competition in redtails further. However, nutritional losses at the patch or food site level may be less relevant to individual fitness if by the end of the day the individual has caught up nutritionally.

Alternatively, feeding competition among redtail monkey groups may be more relevant to nutritional shortfalls and differential fitness effects for individuals within these groups than within-group competition: long-term skew between redtail groups in food resources (measured by size and quality of food in home range) was driven by food defense during intergroup

147 encounters that males and females participated in equally at Ngogo, Kibale (Brown, 2011, 2013).

As a member of a group that gains access to large high-value resources through between-group contest competition, an individual redtail monkey may not lose nutritionally on a daily basis even if agonism occurred during feeding in a few resources during the course of the day.

Additional factors that may reduce female redtail monkey overt feeding competition (and therefore its detectability), besides switching between foods and wide group spread, include cheek pouch use (Lambert, 2005) and sequential fruiting tree use by an individual (Giraldeau &

Caraco, 2000). Redtail monkey cheek pouching of foods serves both anti-predation and reduction of feeding competition roles, though previous work suggests that the anti-predation role of cheek pouches in guenons is stronger (Lambert, 2005; Lambert & Rothman, 2015; Smith et al., 2008).

At the patch level, use of cheek pouching when resources are high value and clumped and group spread is not wide may reduce contest and scramble competition; instances where females were observed carrying a small branch of food to a new location may serve a similar function. Redtail females were also observed feeding from multiple trees of the same species in succession: for example, an individual fed from multiple Uvariopsis congensis fruiting trees of small to medium

DBH in a row without remaining in one long enough to deplete it of fruits. Individuals moving rapidly among feeding trees of the same species sequentially, rather than focusing on one tree for a longer feeding bout with multiple conspecifics always present, reduces the number of individuals feeding in a tree, and likely reduces resulting competition. However, rapid sequential patch use is facilitated by multiple feeding trees of similar value being close together, which is not always the case; when multiple feeding trees are not available in spatial succession, agonism over food increases (Janson & Vogel, 2006).

Future directions

148 Because the effect of agonism can be rank-dependent, if future work detects dominance hierarchies in redtail monkeys, agonism by rank may be more informative in detecting costs and benefits. The interaction of rank and agonism may mask the role of agonism in feeding competition. High-ranking primate individuals can use agonism as a tool with which to gain advantages over subordinates within the group. With increased rate of aggression over a food resource, high-ranking capuchin monkey individuals skewed energy intake in their own favor

(Cebus apella, Janson, 1985; Cebus capucinus, Vogel, 2005). Too few interactions prevented me from drawing conclusions about the presence or absence of female dominance hierarchies in female redtail monkeys. Previous research on members of the same genus point to longer-term datasets providing enough data to detect linear dominance hierarchies in populations once considered to have unstable or weak female social organization. Cords (2000) stated about another arboreal guenon: “Because rates of agonism are low, data accumulate slowly, and many dyads are seen to interact rarely if at all. Finding a hierarchy among wild blue monkeys is therefore something of a struggle, whereas hierarchies are obvious in the better-known cercopithecine species” (Cords, 2000, pg. 474). Subsequently, with long-term data, Klass and

Cords (2015) found that blue monkeys show moderately tolerant, moderately despotic, and nepotistic dominance hierarchies. Blue monkey female hierarchies were more despotic than predicted by Sterck and colleagues’ model (1997) prediction about guenons, which may be due to phylogenetic constraints (Klass & Cords, 2015); such phylogenetic constraints may also be relevant to redtail monkeys. The role of female kinship in philopatric redtail monkey female social organization may also be integral to affiliative and agonistic interactions and should be considered in future studies to look at the potential role of nepotism in this guenon.

149 A separate issue is whether rank, if present, confers fitness benefits. Though blue monkey dominance ranks exist, the advantages of being high ranking are subtler than expected: high- ranking blue monkey females at Kakamega enjoyed more access to fruits (Foerster et al., 2011), fed from fewer unique foods per day (Takahashi, 2018), and received less agonism (Klass &

Cords, 2015). However, no nutritional advantage of rank was found in terms of daily intake of energy, protein, NPE:AP, or in deviation from NPE:AP mean balance (Takahashi, 2018;

Takahashi et al., in prep). Therefore, even if future studies find dominance hierarchies in redtail monkey groups, the nutritional advantages of rank may be limited, nuanced, or vary across groups.

Substantial variation in social organization has previously been shown within species

(Isbell, 1998; Koenig et al., 1998; Nakagawa, 2008; Pruetz & Isbell, 2000; Wikberg et al., 2013) and between closely related species, due to differences in factors like group size and food distribution, so there may be limitations to my comparison of redtail monkeys with blue monkeys. One limitation of comparing redtail monkeys at Kanyawara to blue monkeys at

Kakamega is the larger role of insectivory in the redtail monkey diet in Kanyawara, which likely affects social organization due to differences in spatiotemporal distribution between insects and fruits. Almost comparable amounts of time were spent feeding on fruits and feeding on insects in the redtail groups, and though the primary energy source for redtails in the three groups was still fruit, almost as much protein was gained from insects as from young leaves (Chapter 2). The distribution of these insects varied (a caterpillar flush in one tree versus individual cicadas more widely distributed, for example), but was undoubtedly different in contestability from the ripe fruit or unripe fruit tree patches. Kyo group females’ reliance on gum on trunks and branches of

Prunus africana also may have implications for Kyo group female social organization. As

150 previously suggested for patas monkeys (Erythrocebus patas), globules of gum, like most insects, are eaten quickly because they are small and do not involve as much handling or processing time as fruits (Isbell, 1998; Isbell et al., 1998), making them less contestable.

Conclusion

Redtail monkey females exhibit low levels of within-group female agonism compared to other cercopithecines. These low levels of within-group agonism are primarily in feeding contexts, indicating that some intragroup contest competition occurs. Foods contested are diverse in macronutrient and mineral composition and in food type. At the scale of daily nutritional intake and balance, nutritional costs to received agonism and nutritional benefits of given agonism cannot be detected. These results may indicate that any nutritional shortfalls are occurring at a shorter timescale (individual losses or gains at the patch level) or a larger spatial scale (monkey group defense of food patches dictates nutritional gains more than interindividual agonism). Females also may use certain behaviors to reduce intragroup feeding competition and resulting agonistic interactions: frequently wide group spread, sequential patch feeding by individual monkey in small to medium sized trees, and cheek pouch use. An additional factor which may influence intragroup agonism in a feeding context is the added layer of competition among monkey species when a redtail monkey group is in polyspecific association. Polyspecific association and female redtail monkey nutrition is explored in Chapter 5.

151 CHAPTER 4 FIGURES AND TABLES Table 4.1 Ethogram of social behaviors observed in redtail monkey adult females. Behavior Code Definition Social States sitting fur to fur with another or close enough that observer Sit close/in contact SC cannot necessarily tell whether they are touching or not grooming another individual; must occur for at least 2 seconds to qualify as grooming; a grooming bout ends after a Giving Groom GG pause of 3 seconds or longer receiving grooming from another individual; must occur for at least 2 seconds to qualify as grooming; ends after a pause of 3 seconds or longer Receiving Groom RG when two individuals wrap their tails together, sometimes loosely, sometimes tightly Tail Twining TT

Affiliative Events A changes position or moves a part of its body near B, in a position that it would not normally be in if A were alone; often Solicit grooming SG solicitation occurs in the form of a tilted head present

Approach-Retreat Events A moves within 1m of B (an approach) and B moves within 2 Displace DI seconds of A's approach B moves away from A when A moves in a direct line towards Avoid AV B's location and is still >1m away.

Aggression & Threat Events Aggressive A stares at B and makes aggressive growl vocalization vocalization AG towards B A makes body rigid and bobs head up and down at B; Head bob HB sometimes accompanied by aggressive vocalization A makes body rigid and bobs head at B, then lunges towards Head bob lunge HL B, eliciting submissive vocalization “gecker” or flight from B Chase CH A runs behind B, following B, while B is running away from A a series of back and forth grabs, lunges, and aggressive Fight FI vocalizations that happen too quickly to record individually

Miscellaneous Events A approaches B and the two touch noses or A touches nose to B's mouth, usually when B is eating (also called NN, nose Nose-to-mouth NM to nose) A shows an interest in B's infant by touching it or coming close to it with the hand or face; infant's mother recorded as Infant inspect II recipient

Other OT any social behavior not listed above

152 Table 4.2 Rates of types of female-female agonism given and received in three study groups (grand mean) based on focal follows >5 hours (N=137). Category and Agonism rate (interactions per hour) direction of agonism Kus group females Kyo group females Suk group females Grand SD Grand SD Grand SD mean mean mean Showing aggression 0.02 0.02 0.01 0.02 0.03 0.01 (threatening, chasing, contact fight) Receiving aggression 0.06 0.05 0.02 0.03 0.02 0.03 (being threatened, chased, or physically attacked) Receiving submission 0.01 0.02 <0.01 0.01 0.02 0.03 (displacing another, being avoided or geckered* at) Showing submission 0.03 0.02 0.08 0.05 0.05 0.04 (being displaced, avoiding, or geckering at another) Overall agonism 0.03 0.04 0.02 0.02 0.05 0.04 given Overall agonism 0.09 0.05 0.10 0.07 0.07 0.05 received Overall agonism 0.12 0.04 0.13 0.07 0.12 0.06 participated in *Like blue monkeys (Cords, 2000), redtail monkeys also use a “gecker” vocalization as a submissive signal.

153 Table 4.3 Comparison of rates of agonistic interaction between females, across primate taxa (adapted from Klass and Cords 2015 Table III and Wheeler et al. 2013 Table S1), shown from lowest to highest rate. Redtail monkey female range in agonism rate (calculated by grand mean) based on full-day focal follows >5 hours in sight of adult females in three study groups indicated in bold.

Species Taxon Rate of agonism Sources (interactions/hour) Alouatta pigra Platyrrhini 0.01 Van Belle et al. 2011; Wheeler et al. 2013 Eulemur fulvus Strepsirhini 0.02-0.05 Kappeler and Fichtel 2012; Wheeler et al. 2013 Procolobus rufomitratus Colobinae 0.03-0.05 Tombak et al. 2019 Colobus vellerosus Colobinae 0.04 Wikberg et al. 2013 Varecia verigata Strepsirhini 0.11 Morland 1991; Wheeler et al. 2013 Cercopithecus ascanius Cercopithecinae 0.12-0.13 this study Trachypithecus phayrei Cercopithecinae 0.08-0.24 Koenig et al. 2004; Wheeler et al. 2013 Cercopithecus mitis Cercopithecinae 0.28 Klass and Cords 2015 Chlorocebus tantalus Cercopithecinae 0.31 Nakagawa 2003 Macaca cyclopis Cercopithecinae 0.33 Su 2003; Wheeler et al. 2013 Erythrocebus patas Cercopithecinae 0.44 Nakagawa 2003, 2008 Papio anubis Cercopithecinae 0.52 Barton and Whiten 1993; Barton 1989 Macaca fuscata Cercopithecinae 0.64 Saito 1996; Agetsuma and Nakagawa 1998 Lophocebus albigena Cercopithecinae 0.42-0.71 Olupot and Waser 2001; Chancellor and Isbell 2009 Presbytis thomasi Colobinae 1.21 Sterck and Steenbeek 1997; Wheeler et al. 2013 Cebus capucinus Platyrrhini 0.58-1.46 Rose 1998; Bergstrom 2009; Bergstrom and Fedigan 2010 Macaca fascicularis Cercopithecinae 1.52 van Noordwijk and van Schaik 1987; Reed 1999; Wheeler et al. 2013 Gorilla beringei Hominoidea 0.66-2.20 Watts 1984; Tuttle and Watts 1985; Watts 1994 Papio ursinus Cercopithecinae 0.81-2.9 Hamilton et al. 1978; Gaynor 1994; Ron et al. 1996; Hill 1999; Silk et al. 1999; Barrett et al. 2002; Henzi and Barrett 2003; Huchard and Cowlishaw 2011; Wheeler et al. 2013

154 Table 4.4 Across three study groups, based on focal days >5 hours in sight (N=137), category and context of agonistic interactions between adult females. Category of agonism Context of agonism across three study groups (N = 137 focals) Feeding or Access to Access to Unclear drinking water infants social partners Displacement or 50 1 11 7 avoidance Non-contact aggression 39 2 3 6 (chasing, threatening) Contact aggression 0 0 1 0

155 Table 4.5 Across three study groups, based on focal days >5 hours in sight, female-female agonism in a feeding context category and type of food in context. Contested food item determined by what the focal or focal’s partner were eating before and/or during the agonistic interaction. Drinking water is excluded. The high number of agonistic interactions occurring in the context of insect feeding may actually reflect exclusion from nearby other resources but this could not be determined. Category of Contested food type agonism RF UF UF+ Fr FL FL+ Ins Exu Ins UnID Ambiguous RF UF +cp food Displacement 2 6 4 1 5 0 8 5 6 0 4 or Avoidance Non-contact 3 4 0 1 0 2 19 2 5 3 0 aggression (chase) RF = ripe fruit, UF = unripe fruit, UF+RF=unripe and ripe fruit eaten simultaneously, Fr=fruit of undetermined ripeness, FL=flower, FL+UF=flower and unripe fruit eaten simultaneously, Ins=insect, Exu=exudate, Ins+cp food= insect consumption and processing cheek pouched food simultaneously, UnID=unidentified food, Ambiguous=the feeding context was ambiguous (for example, I could not tell which foods eaten in rapid succession were contested)

156 Table 4.6 Macronutrient composition of identified contested foods during female-female agonism observed across three study groups, based on focal days >5 hours in sight (N=137 focal- days). Unripe fruit = UF, ripe fruit=RF, flowers=FL, young leaves=YL.

Food item contested Group DBH or NDF ADF ADL AP Fat TNC over plant type Displacement or Avoidance Apodytes sp. FL Suk 57 ------Celtis gomphophylla UF Kus 55 28.38 20.11 8.32 19.14 12.11 29.97 Ficus brachylepis RF Suk strangler 49.83 38.95 17.47 5.99 6.57 29.45 Lantana camara UF Kyo shrub ------Chionanthus africanus Kyo 35 44.16 10.80 5.61 2.18 1.02 51.12 UF C. africanus UF Suk 14 31.03 9.85 5.04 4.11 0.81 62.71 C. africanus YL Suk 24 49.30 34.23 21.27 13.62 2.55 24.88 Prunus africana gum Kyo >10* 37.15 5.06 3.7 0.81 0.63 59.05 P. africana UF+RF Kyo, >10* ------Suk Randia sp. YL Kus sapling 36.75 30.86 18.39 17.79 3.19 34.75 Rothmannia Suk sapling 38.55 26.62 13.68 21.90 2.23 24.03 urcelliformis YL Strychnos mitis FL Kus 37 ------Tarenna sp. UF Kyo shrub 59.66 38.48 11.79 10.17 1.85 21.76 Mean food DIS/AV 41.65 23.88 11.70 10.63 3.44 37.52 Non-contact aggression (chase) Albizia grandibracteata Kyo 19+24 ------gum Apodytes sp. RF Kyo 45 ------Celtis gomphophylla UF Suk 48 34.88 25.24 10.94 19.32 4.37 32.46 Craterispermum Suk 14 53.27 38.56 15.86 5.12 2.70 32.25 laurinum RF Lantana camara FL+UF Kyo shrub 43.79 33.88 22.88 8.17 2.56 36.09 Lindackeria sp. RF Kus sapling 32.09 20.33 10.13 19.80 26.62 16.01 seeds Prunus africana UF Kus, >10* ------Suk Caterpillar (Lepidoptera: Kyo NA 20.31 NA 50.20 6.54 31 Noctuidae) Cicadas Kus, NA 12.60 NA 65.51 6.37 9.28 Suk Mean food CH 41.01 25.15 14.95 28.02 8.19 26.18 *We did not tag or measure DBH of endangered Prunus africana, only took GPS waypoint.

157

------0.8 2.1 0.3 1.4 0.8 4.4 7.1 2.1 0.6 0.3 0.6 8.1 0.5 3.44 2.68 14.5 Mo ppm

- - - - - 8 2

19 26 50 57 34 74 26 23 15 86 547 364 67.88 131.33 Mn ppm

- - - - - 8 4 8 7 2 9 12 19 10 38 11 22 13 182 10.50 43.50 Cu ppm

- - - - - 9 1 18 15 24 24 18 22 12 35 55 25 239 215 22.13 89.17 Zn ppm

3 - - - - - 94 15 58 45 73 50 94 62 91 200 70 140 178 177 81.63 221.17 Fe ppm

------0 0 0 0 0 0 0 30 10 70 10 15 30 200 840 178.33 Na ppm female agonism observed across three study three groups, based observed across female agonism -

------4.57 1.16 2.07 1.95 1.59 0.65 1.92 0.95 3.66 1.84 1.37 1.74 1.95 1.31 2.41 1.05 K %

------0.2 0.2 0.4 0.3 0.31 0.27 0.30 0.22 0.06 0.18 0.25 0.44 0.22 0.26 0.23 0.34 Mg %

------0.7 P % 1.83 1.01 0.37 0.16 0.12 0.33 0.02 0.83 0.38 0.16 0.30 0.37 0.12 0.38 0.49

------% Ca Ca 0.31 0.48 0.65 0.94 0.41 0.11 0.39 0.45 0.65 0.45 0.31 0.46 0.94 0.78 0.58 0.83 days). Unripe fruit = UF, young flowers=FL, fruit UF, leaves=YL. ripe days). = fruit=RF, Unripe -

57 55 35 24 37 45 48 14 >10 >10 >10 DBH shrub shrub shrub 19+24 sapling sapling sapling strangler

, s, Suk Suk Suk Suk Suk Suk Suk Suk Suk Suk Kus Kus Kus Kus Kyo Kyo Kyo Kyo Kyo Kyo Kyo Ku Kus Kyo, Kyo, Group

UF UF

F d over

R um FL+UF UF UF g

RF YL sp. seeds RF

Mean food CH

UF+RF Mineral composition of identified contested foods during female foods contested composition identified of Mineral

mitis mitis

sp. sp. FL

sp. UF s ment

sp. YL sp. Mean food DIS/AV 4.7 ance contact aggression

Noctuidae) Cicadas Craterispermum laurinum RF Lantana camara Lindackeria Prunus africana Caterpillar (Lepidoptera: Non Albizia grandibracteata gum Apodytes gomphophyllaCeltis Randia Rothmannia urcelliformis YL Strychno Tarenna Ficus brachylepis Chionanthus africanus C. africanus Lantana camara Prunus africana P. africana Food item conteste Displace Avoid Apodytes gomphophyllaCeltis Table days in hours focal >5 on focal sight (N=137

158 Table 4.8 Summary of adult female-adult female grooming based on full-day focals >5 hours of adult females in the three groups. For each group, grand mean was calculated (mean values were calculated for each individual female and then a grand mean was calculated across individuals). Focal female >5 hr in sight Kus group Kyo group Suk group N = 137 focal follows Mean SD Mean SD Mean SD Percentage of daily time in 1.8 0.7 3.2 1.7 2.3 0.9 sight spent female-female grooming Female-female grooming 0.4 0.1 0.5 0.2 0.4 0.1 bouts* per hour based on daily time in sight Number of adult female 1.9 0.7 2.7 0.6 2.1 0.5 grooming partners per day Percentage of daily 54.5 6.8 53.4 18.9 54 12.2 grooming bouts that were reciprocal† *A “grooming bout” was defined as when grooming between partner A and B continued without any interruptions ≥ 2 minutes. If ≥ 2 minutes elapsed when the individuals had not groomed each other, a grooming bout was terminated. When the partners involved in the grooming changed, a new bout was also demarcated. †I defined reciprocity on a short timescale at the level of bout: A grooms B and B grooms A within one grooming bout. Longer-term reciprocity, over a period of hours or days, is beyond the scope of this analysis.

159 Table 4.9 Aggressive intergroup encounters (IGEs) observed during the study period for the three study groups. Study group # of IGEs Group size Observation hours 2015 2016 Kus 29 34* 52 308 Kyo 14 31 40 332 Suk 26 36 42 321 *In early 2015, prior to study period, Kus group experienced a fission during which Kus group decreased by approximately 10 individuals.

160 A.

Figure 4.1 Gardner-Altman estimation plots showing the relationship between ripe fruit in the daily diet (daily kcal from RF) and whether agonism was (A) received during feeding and (B) given during feeding (N=134 focal-days). The mean difference is plotted on floating axes on the right as a bootstrap sampling distribution The mean is shown as a black dot and the 95% confidence interval is shown with the vertical error bar.

161 A.

Figure 4.2 Gardner-Altman estimation plots showing the relationship between ripe fruit in the daily diet (daily kcal from RF) and whether aggression was (A) received during feeding and (B) given during feeding (N=134 focal-days). The mean difference is plotted on floating axes on the right as a bootstrap sampling distribution. The mean is shown as a black dot and the 95% confidence interval is shown with the vertical error bar. Aggressive interactions are a subset of the agonistic interactions shown in Figure 4.1.

162 163

e focal focal e individuals (AP) versusno prevent prevent to to available protein to available

by whether focal female female focal by whether effects of competition of competition effects (NPE)

D) relationship redtail female D) between individuals reaching close to the intake intake to the close individuals reaching -

predicted predicted

to (B . Nonprotein energy (kcal) was was by sum . calculated energy (kcal) Nonprotein

) is shown grouped is shown grouped days - s that competition from other social individuals social other from s that competition (red data points and red arrow) and data and red pointsarrow) and (red

are hypothetical are focal

4 2015) showing ( (A) show

alance of energy alance nonprotein ata points ata Figure of available protein) of available blue d ); (D), the b (D), Lihoreau et et al. Lihoreau - uals with the same intake target intake with the same uals from

Each data point represents one focal day. one point represents focal Each data he focal female. female. focal he of individ

Figure 3c Figure . For figures (B) figures . For balance black is diamond target adapted adapted of nonprotein energy to intake of nonprotein to intake energy

stible NDF TNC. stible+ NDF intake and and intake female agonism in the context of feeding (N = 13 (N = feeding female of in agonism the context - intake ( intake target the on nutrient on nutrient Comparison of (A) of (A) Comparison

.3 4

in the context of no of context in the competition

reaching reaching (relationship between intake between (relationship in female participated + dige of intake fat of kcal daily NPE:AP balance and agonism rates; (B) is overall rate of agonism participated in, (C) is rate of agonism received by th by of received (C) is rate participated in, agonism rate of agonism overall (B) is and rates; agonism NPE:APdaily balance is rate and of by t given (D) agonism female, individuals to greater across to lead variation in nutritional intake predicted from target Figure competition

164 165

get (black get (black in the in the

aggression female female - C) relationship between redtail female C) relationship between - (relationship between intake of(relationship intake nonprotein between predicted effects of competition versus of competition no effects predicted

to (B sum of kcal intake of fat + digestible NDF + intake + + digestible NDF of fat sum of kcal (AP)

to available protein

he same intake target intake same he (NPE) with t

by whether focal female participated in female participated female by whether focal from other social individuals is predicted to lead to greater variation in to greater to lead individualsis predicted social other from from Lihoreau et et (2015)fromal. showing Lihoreau tition

. othetical individuals reaching close to the intake target in the context of no competition. of in target the context intake to the othetical close individuals reaching . Nonprotein energy (kcal) was. Nonprotein calculated (kcal) by energy Figure 3c Figure ) rates; (B) is rate of aggression received by the focal female, and (C) is rate of of female, andaggression is rate received given focal by the (C) of aggression rates; (B) is rate

is shown grouped re hyp re days - alance of nonprotein energy alance focal

aggression 4 resents one focal day focal one resents able protein) able protein) n of (A) adapted n of (A) Figure (A) shows that compe (A)Figure shows Compariso

4 . 4 Each data point rep data Each energy to intake of to intake avail energy 13 = (N feeding of context TNC. daily NPE:AP balance and NPE:APdaily balance female. by the focal intake the tar to individuals points arrow) from individualsand data and red reaching across prevent (red nutritional intake blue points a data diamond is target); b (B) and figures (C), the For Figure Figure of balance individuals and intake on nutrient competition

166 CHAPTER 5: POLYSPECIFIC ASSOCIATION AND REDTAIL MONKEY NUTRITIONAL STRATEGY

INTRODUCTION

Polyspecific association, when two or more species are intermingled during daily activities like traveling and feeding, alters the extent and nature of feeding competition and protection from predators within a group (Goodale et al., 2017; Stensland et al., 2003; Terborgh,

1990). These mixed-species associations have been linked to benefits (including protection from predators) outweighing costs (including feeding competition) across taxa (birds: Barnard et al.,

1982; Goodale & Kotagama, 2005; Morse, 1970; Sridhar et al., 2009; Terborgh, 1990; Thiollay,

1999; cetaceans: Cords & Würsig, 2014; Frantzis & Herzing, 2002; ungulates: FitzGibbon, 1990;

Kiffner et al., 2014), including in primates (Platyrrhini: Garber, 1988; Heymann & Buchanan-

Smith, 2000; Norconk, 1990; Peres, 1992b; Pinheiro et al., 2011; Cercopithecidae: Chapman &

Chapman, 2000a; Cords, 1987; Gautier-Hion et al., 1983; Noë & Bshary, 1997; Struhsaker,

1981; Strepsirrhini: Eppley et al., 2015; Freed, 2006). Living in a social group carries costs and benefits that grow more complex with polyspecific associations. Members of a polyspecific association can gain information from each other about predator presence and location of food resources; however, variation in diet and in predation risk among species affects who benefits by participating in these associations (Goodale et al., 2017).

How similar two species are in the foods they exploit varies, as does their resulting converging or diverging feeding behavior during polyspecific association. If dietary overlap between species is low, then species in the group can gain predator protection without potential costs of feeding competition (Goodale et al., 2017; Oates & Whiteside, 1990; Rasa, 1983). Direct

(Galef & Giraldeau, 2001) or indirect (Fontaine, 1980; Heymann & Hsia, 2015; Hino, 1998;

Peres, 1992b; Rasa, 1983) facilitation of food access may also occur during polyspecific

167 association; animals lead each other to preferred resources (direct) or expose prey relevant to only one polyspecific associate (indirect). If Species A in a conspecific group (i.e. monospecific group) feeds on similar foods to Species B in a conspecific group, when A and B form a mixed species group together, diet overlap may decrease in order to reduce conflict (Alatalo et al.,

1986; Rubenstein et al., 1977; Powell, 1989) or diet overlap may increase as members of both species converge on a preferred food resource (Chapman & Chapman, 2000a; Cords, 1987;

Gautier Hion et al., 1983; Struhsaker, 1981). Niche convergence or divergence during polyspecific association is sometimes dictated by seasonal fluctuations in food availability

(Develey & Peres, 2000; Gautier-Hion, 1980). Dietary overlap among three guenon species

(Cercopithecus nictitans, C. pogonias, and C. cephus) in polyspecific association in Gabon decreased during periods of low fruit availability (Gautier-Hion, 1980, 1988).

Behavioral responses to mitigate interspecific competition include increased travel in polyspecific association compared to when in conspecific groups (Chapman & Chapman, 2000a;

Cords, 1987; Gautier-Hion, 1988), spatial stratification of species in polyspecific association

(Farine & Milburn, 2013; Gautier-Hion et al., 1983; Garber, 1988; Heymann & Buchanan-Smith,

2000; Norconk, 1990), and interspecific dominance hierarchies dictated by body size

(Camphuysen & Webb, 1999; French & Smith, 2005; Houle et al., 2006; Hudson & Furness,

1988; Struhsaker, 1981). Increased travel by participants in a polyspecific association compared to conspecific groups indicates a potential energetic cost to dietary overlap (Chapman &

Chapman, 2000a; Cords, 1987; Gautier-Hion, 1988). Vertical stratification of species in the canopy or wider group spread help minimize interspecific contest competition (Garber, 1988;

Gautier-Hion et al., 1983; Heymann & Buchanan-Smith, 2000; Houle et al., 2006; Norconk,

1990). This kind of spatial stratification can facilitate and even improve access to a variety of

168 food types; for example, different spatial positions and insect capture behaviors by two species of tamarins led to one species flushing insects out of foliage to the benefit of the other species, with both species maintaining similar insect capture rates (Peres, 1992b). However, dominant species may instead exclude subordinate species when food is usurpable (Cimprich & Grubb,

1994; French & Smith, 2005; Houle et al., 2010, 2014; Peres, 1996).

Even if interspecific contest competition occurs, polyspecific association persists in many systems because it confers greater protection from predators than conspecific association. This reduced predation risk may be due to increased group size compared to conspecific groups, one species responding aggressively to a particular predator (Arlet & Isbell, 2009; Gautier-Hion,

1988; Stanford, 1995), and spreading the burden and benefits of vigilance and alarm calling across species (Gautier-Hion, 1983; Goodale & Kotagama 2005; Heymann & Buchanan-Smith,

2000; Munn, 1986; Noë & Bshary, 1997).

Feeding with some polyspecific partners may be more nutritionally costly than feeding with others. For example, in birds, individual food intake (“net rate of energy intake” based on intake rates of worms) was affected by flock composition and number of species in a mixed species flock, and interspecific competition—demonstrated by energy losses—was greater for one species dyad over another (Barnard et al., 1982). In multi-species polyspecific association in forest primates, different nutritional costs may be associated with association with particular species. Niche overlap during polyspecific association characterized at the nutritional level, sometimes referred to as the nutritional niche (Machovsky-Capuska et al., 2016), can also show how participating species may adjust their feeding behavior even if they are feeding on the same food type (Houle et al., 2014).

169 Redtail monkeys are a suitable species with which to investigate the effects of polyspecific association on nutritional niche because they are frequently in polyspecific association with other arboreal monkey species (Bryer et al., 2013; Chapman & Chapman, 1996,

2000a; Cords, 1987; Struhsaker, 1981; Teelen, 2007), with variation in association patterns within (Chapman & Chapman, 2000a) and across sites (Cords, 1990). Diet overlap between redtail monkeys and blue monkeys (Cercopithecus mitis) and redtail monkeys and grey-cheeked mangabeys (Lophocebus albigena) increased during polyspecific association in Kibale because blue monkeys and mangabeys joining redtail monkeys at preferred food resources (Struhsaker,

1981). Though blue monkeys and grey-cheeked mangabeys have previously been classified as frugivorous, suggesting a reliance on ripe fruit, they consume diverse diets that also include leaves, unripe fruit and seeds (Conklin-Brittain et al., 1998; Struhsaker & Leland, 1979). The diets of redtail monkeys, blue monkeys and mangabeys at Kanyawara were previously found to be similar in macronutrient—including fiber—composition despite differences in body size

(Conklin-Brittain et al., 1998; Wrangham et al., 1998).

As small-bodied primates, redtail monkeys are lowest in an interspecific dominance hierarchy at Kibale and at Kakamega (Cords, 1990; Houle et al., 2010; Struhsaker, 1981). At

Kanyawara, in four species of fruiting trees, redtail monkeys fed lower and altered their feeding rates when blue monkeys and/or mangabeys were present, regardless of whether interspecific aggression occurred (Houle et al., 2010). When interspecific aggression did occur, usually in a feeding context, redtails lost, i.e. showed submission (Cords, 1987; Houle et al., 2010;

Struhsaker, 1981).

Redtail monkeys at Kibale also form associations with red colobus monkeys (Procolobus rufomitratus): 92% of polyspecific redtail-red colobus associations observed in one study were

170 initiated by redtails (Struhsaker, 1981). Dietary overlap between redtail monkeys and red colobus monkeys is substantially lower than dietary overlap between redtail monkeys and blue monkeys or mangabeys because of red colobus reliance on young leaves; however, red colobus and redtail plant-part diets overlapped as much as 25% at Kanyawara (Chapman & Chapman, 2000a). Red colobus and redtail monkey polyspecific groups travel farther per day than single-species groups, which suggests interspecific food competition (Chapman & Chapman, 2000a), though this polyspecific association dyad is also driven by anti-predator benefits (Struhsaker, 1981).

The predators of redtail monkeys are crowned hawk-eagles (Stephanoaetus coronatus) and chimpanzees (Pan troglodytes schweinfurthii) (Hashimoto et al., 2000; Mitani & Watts,

1999; Mitani et al., 2001; Skorupa, 1989; Struhsaker & Leakey, 1990). Arboreal guenon species in polyspecific association have similar vocal repertoires (Cords 1987, 1990; Gautier-Hion,

1988; Marler, 1973), which allow for an efficient interspecific alarm system between blue monkeys and redtail monkeys. Red colobus monkeys (Leland & Struhsaker, 1979; Stanford,

1995) and grey-cheeked mangabeys (Arlet & Isbell, 2009) attack or mob predators, to the advantage of redtail monkeys with whom they are in association. Another advantage for redtails of forming polyspecific groups with red colobus monkeys is that red colobus are the preferred prey of chimpanzees (Struhsaker, 1981), which may confer protection from chimpanzees.

The aim of this study was to examine potential nutritional costs of polyspecific associations for female redtail monkeys (Cercopithecus ascanius) in Kibale National Park,

Uganda. Redtail monkeys at Kanyawara have previously been found to feed lower in feeding trees and to alter their feeding rates when in polyspecific association with larger-bodied blue monkeys and grey-cheeked mangabeys, regardless of whether interspecific aggression occurred

(Houle et al., 2010). The upper crowns of some fruiting tree species at Kibale have higher fruit

171 density, larger fruit crops, and higher sugar concentrations compared to lower strata, which may indicate that lower strata fruit feeding by subordinate redtails has a nutritional cost, though this was not explicitly tested (Houle et al., 2014). I hypothesized that polyspecific association with blue monkeys and mangabeys would be nutritionally costly to redtail monkey females, regardless of the amount of ripe fruit in the diet. I predicted (Prediction 1) that on days with more polyspecific association with blue monkeys or mangabeys while feeding, redtail females would decrease daily intake of dry matter food, metabolizable energy, nonprotein energy, and fat compared to feeding alone or with other monkey species, regardless of the percentage of daily dry matter intake from ripe fruit. Given the lower dietary overlap with red colobus monkeys, I predicted no decrease in dry matter food, metabolizable energy or nonprotein energy intake would result from association with red colobus monkeys, regardless of amount of ripe fruit in the diet.

Redtail monkeys lose most interspecific agonistic interactions (Cords 1990; Struhsaker,

1981) and most aggression between redtails and blue monkeys or mangabeys has previously been observed to be over fruit (Struhsaker, 1981). Agonistic interactions between redtail monkeys and blue monkeys at Kakamega also frequently occurred in feeding trees and resulted in redtails leaving a feeding tree (Cords, 1987, 1990). I hypothesized that interspecific aggression would occur over foods of high nutritional value and negatively impact redtail daily nutritional intake. I predicted (Prediction 2a) that interspecific aggression between redtail monkeys and blue monkeys or mangabeys would occur over foods high in TNC or fat and that (Prediction 2b) interspecific aggression between redtails and blue monkeys or redtails and mangabeys would result in decreased redtail NPE intake compared to no-aggression days.

172 Insects are key mineral sources for redtail monkeys (Chapter 3) and spatial distribution of most insects makes them less likely to induce contest competition within or between species, though we observed exceptions in intraspecific agonism (Chapter 4). I hypothesized that polyspecific association with larger-bodied sympatric arboreal omnivores would not affect redtail female mineral intake, but that polyspecific association with larger-bodied sympatric folivores would. I predicted (Prediction 3) that polyspecific association with blue monkeys and/or mangabeys while feeding would not affect daily Na intake, daily Cu intake, or daily Na intake from insects. However, I predicted that red colobus monkey polyspecific association would increase daily Na intake, daily Cu intake, and daily Na intake from insects because colobus would flush insects when moving through the canopy, to the benefit of redtail monkeys, an indirect feeding benefit previously observed in Platyrrhines (Peres, 1992b).

METHODS

Polyspecific associates of redtail monkeys

Monkey species included as polyspecific associates were blue monkeys (Cercopithecus mitis), grey-cheeked mangabeys (Lophocebus albigena), black-and-white colobus monkeys

(Colobus guereza), red colobus monkeys (Procolobus rufomitratus), olive baboons (Papio anubis) and L’Hoest’s monkeys (Cercopithecus lhoesti). These species vary in abundance at

Kanyawara and each species also varies in abundance depending on intensity of logging history:

173 in 2014, along a 4 km transect, 0.15 blue monkey groups were encountered per km in unlogged

K30 and 0.18 blue monkey groups in lightly logged K14; 0.27 grey-cheeked mangabey groups per km in K30 and 0.18 mangabey groups per km in K14; 0.27 black-and-white colobus monkey groups per km in K30 and 0.56 black-and-white colobus monkey groups in K14; 0.65 red colobus monkey groups per km in K30 and 0.51 red colobus monkey groups per km in K14; 0.02 olive baboon groups per km in K30 and 0.00 olive baboon groups per km in K14 (Chapman et al., 2018). L’Hoest’s monkeys were not included in Chapman and colleague’s long-term census data analyses because they were observed too infrequently via census method.

Individuals of other species were not individually identifiable to members of our research team, though age/sex classes were identified. Group size varied across and within these monkey species at Kanyawara (Gillespie & Chapman, 2001; Harris & Chapman, 2007; Struhsaker &

Leland, 1979). Levels of habituation varied across species and groups: one blue monkey group that frequently traveled and fed with Kus group was semi-habituated (individuals did not flee from our research team and fecal samples were easily collected by Martha Lyke and colleagues from Kus group and the blue monkey group in 2015); other blue monkey groups were unhabituated; grey-cheeked mangabey groups were fully habituated because of ongoing nutritional ecology research by the Rothman research group; some of the red colobus groups were fully habituated because of long-term and ongoing research by the Chapman research group; one olive baboon group was habituated because of ongoing research by the Rothman research group; L’Hoest’s monkey groups were unhabituated.

Chimpanzees (Pan troglodytes schweinfurthii) were not recorded as polyspecific associates, as done previously (Houle et al., 2010), because redtail monkeys respond to chimpanzees as they do other predators.

174 Polyspecific association data collection

During full-day feeding follows of redtail female focal animals, for each feeding bout, the field team recorded nearest heterospecific neighbor species less than 20 m and whether the closest polyspecific associate was in the same feeding tree. We also recorded all occurrences when one or multiple other monkey species individuals were present less than 20 m, as well as any instances of physical interaction (affiliative or agonistic) between redtail focal and heterospecifics. We defined polyspecific association between heterospecific groups as less than

20 meters (Struhsaker, 1981).

Predation risk data collection

We recorded any attempted or successful attacks on redtail monkeys by crowned hawk- eagles (Stephanoaetus coronatus) or chimpanzees (Pan troglodytes schweinfurthii). We also recorded the following much more common behaviors as indicators of predation risk, which likely cause chronic physiological response (Sheriff & Thaler, 2014): (1) alarm calls by redtail monkeys and/or monkey species when an eagle was observed flying overhead or sitting in a tree

<50 m from the mixed-species monkey group (indicative of sit-and-wait hunting [Schultz 2001]),

(2) alarm calls followed by monkeys moving quickly downward in tree strata when no predator was observed; (3) monkeys moving up in tree strata and hiding, with initial vocalizations and then silence, with chimpanzees <50 m from the monkey group.

Statistical analyses: Statistical models and visualizations were generated in R 3.5.3 (R

Core Team 2018), using statistical packages lme4 (Bates et al., 2015), multcomp (Hothorn et al.,

2016), piecewiseSEM (Lefcheck, 2016), dunn.test (Dinno, 2017), and ggplot2 (Wickham, 2016).

I calculated polyspecific association indices as follows: proportion of focal feeding day

(time in sight during feeding or processing of food) during which a blue monkey was the nearest

175 polyspecific neighbor to the focal female; proportion of focal feeding day during which a mangabey was the nearest polyspecific neighbor; proportion of focal feeding day during which a red colobus monkey was the nearest polyspecific neighbor. Kruskal-Wallis rank-sum tests were conducted to look at variation in polyspecific association proportion across the redtail monkey groups, with Dunn’s test (Dunn, 1964) for post hoc nonparametric pairwise comparisons allowing for unequal samples sizes.

Linear mixed models were constructed with response variables daily intake dry matter food (grams), metabolizable energy (kcal), nonprotein energy (kcal), fat (kcal) daily Na intake, daily Cu intake, or daily Na intake from insects. Fixed effects included proportion relevant heterospecific nearest neighbor and ripe fruit in the diet (percentage dry matter intake from ripe fruit) and interactions. Individual (monkey ID) was set as a random effect. When a model with an interaction term was not significantly different from a model without an interaction term, the interaction term was removed. Model fit was assessed by comparison to the null model and likelihood ratio indicated whether better fit of the model was significant. Residual distribution was checked for normality for assessing model fit.

RESULTS

The proportion of daily focal time with blue monkeys as nearest neighbors varied by redtail study group (Kruskal-Wallis chi-squared = 52.92, df = 2, p < 0.0001; Figure 5.1), with pairwise comparisons revealing that Kus group associated with blue monkeys more than the other two groups (Dunn’s test, Kyo-Kus z =56.02, adjusted p-value < 0.0001; Suk-Kus z = 20.04, adjusted p-value = 0.03) and Kyo associated least with blue monkeys (Kyo-Suk z = -35.98, adjusted p-value < 0.0001). The proportion of daily focal time with mangabeys as nearest

176 neighbor differed across the groups (Kruskal-Wallis chi-squared = 8.78, df = 2, p = 0.01; Figure

5.2), with pairwise comparisons revealing the difference was driven by Kyo group associating less with mangabeys than Kus group (Kyo-Kus z =14.71, adjusted p-value = 0.01). The proportion of red colobus as nearest neighbor time also differed across groups (Kruskal-Wallis chi-squared = 15.20, df = 2, p = 0.001; Figure 5.3) because Kyo group associated more with red colobus than the other two groups (Kus-Kyo z = -26.40, adjusted p-value = 0.002; Suk- Kyo z =

25.82, adjusted p-value = 0.002). These differences among groups also led to variation across groups in agonistic interactions (and affiliative interactions in the case of red colobus) between redtail females and other species (Figures 5.1-5.3).

No clear relationship (sensu Dushoff et al., 2019) could be found between blue monkey polyspecific association and daily intake of food (grams in dry matter) (χ2(4) = 0.17, p = 0.68), metabolizable energy (kcal) (χ2(4) = 0.10, p = 0.74), or nonprotein energy intake (kcal) (χ2(4) =

0.22, p = 0.64), regardless of percentage of ripe fruit in the diet. Redtail daily fat intake increased with blue monkey polyspecific association (χ2(4) =13.33, p = 0.0003; Table 5.1), but the increase was small (Figure 5.4; Figure 5.4a marginal r-squared = 0.10). The effect was statistically unclear of grey-cheeked mangabey polyspecific association on redtail female daily intake of dry matter food (grams) (χ2(4) = 0.36, p = 0.55), metabolizable energy (kcal) (χ2(4) = 0.89, p = 0.35), or nonprotein energy (kcal) (χ2(4) = 0.95, p = 0.33), regardless of percentage of ripe fruit in the diet. Red colobus monkey polyspecific association did not have a statistically clear effect on redtail daily intake of dry matter food (g) (χ2(4) = 0.31, p = 0.58), metabolizable energy (kcal)

(χ2(4) = 0.77, p = 0.38), nonprotein energy (kcal) (χ2(4) = 1.10, p = 0.29), regardless of percentage of ripe fruit in the diet.

177 Contested foods among heterospecifics were diverse in nutritional composition, but all high in one or more macronutrient (Table 5.2 and Table 5.4) and/or in one or more essential mineral (Table 5.3 and Table 5.5). Contested fruits were high in fat or protein or TNC, and some were high in calcium (Celtis gomphophylla and Ficus exasperata). Blue monkeys displaced and chased redtail females to gain access to anther oil of Symphonia globulifera flowers. Another contested food item was noctuid caterpillars in flushes, which were clumped insect resources that offer protein and key minerals, including sodium, iron and zinc. Contested foods between redtails and heterospecifics were from a variety of substrates, including large (DBH >50) fruiting trees. Feeding aggression from blue monkeys (presence or absence during a focal day) did not affect redtail daily intake of nonprotein energy (kcal) (Mann-Whitney test, p = 0.38; Figure 5.5).

I was unable to find a statistically clear relationship between blue monkey polyspecific association and redtail daily intake of sodium (mg) (χ2(6) = 0.82, p = 0.84) or copper (χ2(6) =

2.02, p = 0.57), regardless of percentage of ripe fruit in the diet. The association between mangabey polyspecific association and redtail daily intake of sodium (mg) (χ2(6) = 1.95, p =

0.58) or copper (χ2(6) = 6.83, p = 0.07), was statistically unclear, regardless of percentage ripe fruit in the diet. The sample size for mangabey aggression towards redtail monkeys was too small to test the relationship between aggression received from mangabeys and redtail nonprotein energy intake. I was unable to find a statistically clear relationship between red colobus polyspecific association and daily intake of sodium (χ2(6) = 3.60, p = 0.31) or copper

(χ2(6) = 4.08, p = 0.25), regardless of season shifts in ripe fruit in the diet. I also found no effect of redtail female association with red colobus on sodium intake from insects in the redtail diet

(Figure 5.6).

178 DISCUSSION

Nutritional costs of polyspecific association were not detected for redtail monkeys for multiple key nutritional components. At the daily timescale, redtail monkey females did not adjust their nutritional strategy when in the presence of other monkey species.

The redtail monkey study groups varied in polyspecific association patterns, as previously found among redtail groups at the site (Bryer et al., 2013; Chapman and Chapman

2000a). Kyo group’s frequent interactions with red colobus monkeys and minimal interactions with blue monkeys stem from Kyo group ranging mostly outside unlogged and lightly logged forest where the low-density blue monkey groups range. Kus group was frequently in association with one specific blue monkey group.

As with contested foods among conspecifics (Chapter 4), contested foods among heterospecifics were diverse in food category and in nutritional composition. Fruits contested included those in large fruiting trees, despite the argument that large-crowned trees reduce competition because all individuals can feed (Janson, 1988; Peres, 1996). Reduction of overt competition in a large fruiting tree crown may be constrained in the context of polyspecific association: as more individuals of larger body size participate, lack of competition may reach a breaking point (Clark & Mangel, 1986). Several contested resources of diverse types contained high levels of manganese, an essential mineral with functions related to metabolism, development and immune function (Aschner & Aschner, 2005; Avila et al., 2013). The mean daily intake of manganese by redtail females was higher than the estimated requirement for nonhuman primates (NRC, 2003), though daily intake varied (Chapter 2: Table 2.2). However, the implications of manganese-rich foods are unclear because only sodium is known to be limiting in tropical forests (Oates, 1978; Reynolds et al., 2009; Rode et al., 2003; Rothman et al.,

179 2006); some high mineral content in contested foods may be incidental (i.e., the animal chose the food for other nutritional components).

Blue monkey polyspecific association may not affect redtail female nutritional intake, which may be because of redtail food switching and the pre-existing interspecific hierarchy that leads to spatial stratification even when converging on a food resource (Houle et al., 2010).

Redtail monkeys’ wider group spread (Struhsaker & Leland, 1979), demonstrates another spatial adjustment that may reduce competition with polyspecific associates. Increase in redtail fat intake with blue monkey polyspecific association, initially interpreted as convergence on preferred fatty fruits, was a small increase and therefore likely not biologically meaningful: the median intake of fat was the same for days with a blue monkey nearest neighbor and days without a blue monkey nearest neighbor (Figure 5.4b).

Polyspecific association with mangabeys may not affect redtail female daily nutritional intake, and a redtail female having a mangabey as a heterospecific nearest neighbor was rarer than having a blue monkey or a red colobus. Food competition that redtail female experience with mangabeys may be qualitatively different from competition with blue monkeys, likely driven by mangabey larger body size (adult male grey-cheeked mangabey mean weight = 8.7 kg

[Olupot, 2000; Kingdon et al., 2013]; adult female grey-cheeked mangabey mean weight = 6.4 kg [Kingdon et al., 2013]) compared to both redtail monkeys (adult male redtail monkey mean =

3.7 kg, adult female redtail mean = 2.8 kg [Colyn, 1994; Kingdon et al., 2013]) and blue monkeys (adult male blue monkey mean = 6.8 kg, adult female blue monkey mean = 4.2 kg

[Delson et al., 2000; Kingdon et al., 2013]). Co-feeding with mangabeys may involve larger spatial shifts as part of the interspecific dominance hierarchy dictated by body size (Houle et al.,

2010). Exclusion of a subordinate species can take the form of overt competition at a food

180 resource, spatial stratification at a food resource (Farine & Milburn 2013; Houle et al., 2010), or the dominant species making the subordinate species wait outside the food patch (Peres, 1996).

As the largest arboreal monkeys at the site, mangabeys interact less with redtails (the smallest) and likely engage in larger-scale exclusion; the nearest-neighbor measure of proximity may be too small a measure to detect any nutritional costs to redtails of mangabey presence.

Redtails were mostly the recipients of heterospecific aggression, rather than actor (Table

5.1) driven by their small body size (Struhsaker, 1981; Norconk, 1990; Houle et al., 2006, 2010).

Chasing between blue monkeys and redtail females was observed during feeding but no clear effect could be found on redtail daily nutritional intake. These aggressive interactions may have a shorter-term nutritional cost than daily, may be more indicative of crowded convergence on a preferred food resource than indicative of actual nutritional cost, or may be of minimal cost due to redtail food switching, spatial stratification (Houle et al., 2010) or tendency to return to a feeding tree soon after conflict (Cords, 1990; personal observation).

Red colobus dietary overlap and feeding competition with redtail monkeys is lower than with blue monkeys and mangabeys (Struhsaker & Leland, 1979), which is reflected in my rare observations of redtail-red-colobus contested food items and suggested by red colobus grooming redtail monkeys. The rare contested foods included high-protein mature leaves of Olea welwitschii and high-starch seeds of ripe fruits of a shrub (locally called “bean plant”). The effect of red colobus monkey polyspecific association on redtail daily intake of macronutrients or energy was statistically unclear. I did not find that daily sodium from insects increased with red colobus polyspecific association; however, this finding does not rule out that red colobus flush out insects during their raucous movements in the tree canopy to the benefit of insectivorous redtail monkeys because I did not investigate the efficiency with which redtails consume insects

181 when in polyspecific association with red colobus. The red colobus monkey role when in polyspecific association with redtail monkeys may be predominantly an anti-predation role, as previously suggested (Struhsaker, 1981; Teelen, 2007), but future work will look into potential short-term (than daily) increases in insect foraging during red colobus polyspecific association.

Across taxa, associating with a species that mobs predators is highly valuable (Nolen &

Lukas, 2009; Turcotte & Desrochers, 2002); adult male mangabeys attack crowned hawk-eagles

(Arlet & Isbell, 2009), and red colobus monkeys mob or aggressively display at crowned hawk- eagles and chimpanzees (Leland & Struhsaker, 1979; Stanford, 1995). As a result, redtail monkeys have much to gain in predator defense when in polyspecific association with mangabeys or red colobus, regardless of any level of feeding competition. Redtail polyspecific associations with blue monkeys are also partially driven by predator detection and defense, in addition to food-related costs and benefits. Blue monkeys and redtail monkeys, as with other guenons, have similar vocalizations (Cords 1987, 1990; Marler, 1973), enabling an efficient alarm system in a heterospecific group.

Limitations and Future Directions

Whether interspecific contest competition occurs is dependent on multiple factors, including the distribution of food in the habitat, group spread among individuals of different species, which species joins the mixed association when, and satiation of one species before another (Janson, 1988). The benefits and costs of heterospecific grouping vary over space and time, by participating species composition, and can be subtle (Goodale et al., 2017). As a result, the role(s) a species plays in a mixed-species group, as a competitor over food or a collaborator in finding food or protecting from predators, is also flexible (Srinivasan et al., 2010). Examining social foraging in a mixed species group of any combination of species is more complex than

182 conspecific social foraging in that subtle costs and benefits of grouping vary not only over time and space and by individual forager, but also by number of and roles of species participating.

Redtail monkeys at the same field site were observed reducing their intake rates after losing an aggressive interaction with a heterospecific, especially in lower strata where fruits of some species in the diet were found to be lower in sugar content (Houle et al., 2010). Though a short-term nutritional shortfall was not explicitly tested by Houle and colleagues, the implication remains that for some feeding trees, feeding behavior changes after receiving aggression from other individuals. Examining potential losses (or gains) at the patch level during polyspecific association by looking at nutritional intake at the level of feeding tree while controlling for tree size, may elucidate this further. That said, the fitness consequences of nutritional losses at the patch level may be minimal if by the end of the day the animal is able to catch up in terms of daily nutritional goals.

No agonistic or affiliative interactions were observed between female redtails and

L’Hoest’s monkeys or olive baboons. However, redtail females co-fed with both species on particular foods. L’Hoest’s monkeys and one of the redtail study groups co-fed on noctuid caterpillar flushes. I also observed redtail females co-feeding with olive baboons on Prunus africana fruit, though redtail monkey group spread was wide (>150 m) while co-feeding in multiple P. africana trees with baboons. Future work on polyspecific association between redtail monkeys and these semi-terrestrial and terrestrial species can address questions about flexibility of diet and nutrition across locomotor modes.

Redtail interactions with other monkey species go beyond behavior associated with feeding and predator avoidance. Red colobus grooming redtail focal females follows the predicted inverse relationship between grooming frequency and diet overlap (Struhsaker, 1981).

183 The existence of redtail monkey-blue monkey hybrids (Detwiler, 2002; Struhsaker et al., 1988) indicates successful mating behavior between the two Cercopithecus species. These close- contact affiliative interactions with heterospecifics may have social nutrition implications for interspecific competition and bondedness with some heterospecifics over others within polyspecific groups of three or more species.

Conclusion

Polyspecific association with two sympatric species with high diet overlap (blue monkeys and grey-cheeked mangabeys) may not affect redtail daily intake of food, total metabolizable energy, nonprotein energy, sodium, or copper, regardless of shifts in amount of ripe fruit in the redtail diet. Though a positive significant relationship was found between redtail daily fat intake and polyspecific association with blue monkeys, the effect was small and likely not biologically meaningful. The redtail study groups varied in polyspecific association patterns and partners.

Food items over which redtails and heterospecifics contested did not fit one nutritional composition profile and were instead diverse in macronutrient and mineral composition. A low nutritional cost to polyspecific association with blue monkeys and mangabeys may be explained by interspecific spatial stratification by body size at feeding trees (Houle et al., 2010). Feeding- tree-level/patch-level costs to redtails of associating with blue monkeys and mangabeys are yet to be explored. The question also arises whether the “cost” to redtails is a consistent lack of access to the “best” foods due to the interspecific dominance hierarchy; perhaps no nutritional costs to polyspecific association can be detected because frequent polyspecific association confers a frequent cost that has become the redtail feeding strategy. I could not detect an effect of red colobus monkey polyspecific association on redtail female daily intake of macronutrients or energy, which was expected given lower dietary overlap, and though I found that colobus

184 polyspecific association had an unclear relationship with daily sodium intake from insects, future work will address whether feeding efficiency of insects increases via red colobus flushing insects.

185 CHAPTER 5 FIGURES AND TABLES

Figure 5.1 Redtail monkey group differences in proportion of time with blue monkey nearest- neighbors (figure) and number and type of interactions between focal female redtail monkeys and blue monkeys (table; not all social interactions were during feeding context).

186 Figure 5.2 Redtail monkey group differences in proportion of time with grey-cheeked mangabey nearest-neighbors (figure) and number and type of interactions between focal female redtail monkeys and grey-cheeked mangabeys (table; not all social interactions were during feeding context).

187 Figure 5.3 Redtail monkey group differences in proportion of time with red colobus nearest- neighbors (figure) and number and type of interactions between focal female redtail monkeys and red colobus monkeys (table; not all social interactions were during feeding context).

188 Table 5.1 Results of linear mixed models testing the effect of blue monkey nearest-neighbor frequency on daily intake of fat.

Response variables Fixed effect Estimate SE 95% CI df t-value p-value Lower Upper Fat intake (kcal) † Proportion 10.18 3.17 3.89 16.46 110 3.21 0.002 feeding day with blue monkey NN Ripe fruit in 0.01 0.009 -0.008 0.03 110 1.17 0.24 the diet (% dm intake from RF) BM NN 0.01 0.12 -0.23 0.26 110 0.08 0.93 any*RF availability Intercept 3.81 0.24 3.33 4.29 110 15.80 0 Fat intake (kcal) † Proportion 10.44 2.81 4.92 15.96 112 3.72 0.0003 feeding day with blue monkey NN Intercept 3.99 0.19 3.61 4.37 112 20.67 0 †Data square root transformed due to positive skew.

189 A.

Figure 5.4 Daily redtail intake of fat (kcal) by (A) proportion of feeding day with a blue monkey nearest neighbor and (B) days with blue monkeys as nearest neighbor and days without blue monkeys as nearest neighbor.

190 Figure 5.5 The mean difference between daily intake of nonprotein energy (kcal) when the focal redtail female was chased by a blue monkey while feeding that day compared to daily NPE intake when the focal female received no aggression from blue monkeys that focal-day. In the Gardner-Altman estimation plot, both groups are plotted on the left axes and the mean difference is plotted on floating axis on the right as a bootstrap sampling distribution. The mean difference is depicted as a black dot; the 95% confidence interval is indicated by the ends of the vertical error bar. Only Kus and Suk group females are included in this figure because only one aggressive interaction was recorded in Kyo group between redtail female and blue monkeys due to rare Kyo polyspecific association with blue monkeys.

191 Figure 5.6 An estimation plot showing mean difference between daily percentage of sodium from insects when the focal redtail female had a red colobus nearest neighbor while feeding compared to when the focal female did not have a red colobus nearest neighbor that focal-day. Both groups are plotted on the left axes and the mean difference is plotted on floating axis on the right as a bootstrap sampling distribution. The mean difference is depicted as a black dot; the 95% confidence interval is indicated by the ends of the vertical error bar.

192 Table 5.2 Macronutrient composition of identified contested foods during redtail focal (RT) agonism with blue monkeys (BM), observed across three study groups, based on redtail focal days >5 hours in sight (N=137 focal-days). Unripe fruit = UF, ripe fruit=RF, flowers=FL, young leaves=YL.

Contested food item Group DBH or NDF ADF ADL AP Fat TNC plant type BM displaced (DIS) RT:

Celtis gomphophylla RF Suk 51 16.52 9.15 3.48 20.63 30.60 24.10 Celtis gomphophylla UF Kus 30 43.08 31.41 15.76 20.10 4.86 20.52 Celtis gomphophylla UF Suk 25 29.45 20.42 8.51 22.13 13.59 24.65 Chaetachme aristata YL Kus sapling 44.61 29.77 14.40 20.43 2.38 23.97 Macaranga sp. pith of Kus 27 45.91 40.59 18.82 7.46 2.36 29.95 young stem Symphonia globulifera Suk 67 - - - - >90* <10 anther oil of FL

Mean food BM DIS RT 35.91 26.27 12.19 18.15 23.96 22.20 BM chased (CH) RT: Blighia unijugata FL Kus 122 ------Trilepisium Kus 18 22.04 9.76 2.48 12.45 1.12 59.30 madagascariense seeds of UF Trilepisium Kus 16 47.88 32.85 17.81 13.68 2.96 27.31 madagascariense YL Trilepisium Kus sapling 54.99 40.89 24.47 16.94 2.45 9.67 madagascariense YL Celtis gomphophylla UF Kus 65 34.88 25.24 10.94 19.32 4.37 32.46 Celtis gomphophylla UF Kus 25 36.01 25.96 12.27 18.91 6.88 28.31 Celtis gomphophylla UF Kus 36 22.52 15.43 6.90 13.98 24.48 29.48 Celtis gomphophylla UF Suk 25 29.45 20.42 8.51 22.13 13.59 24.65 Celtis gomphophylla UF Suk 30 43.97 33.58 12.46 21.79 2.53 23.98 Celtis gomphophylla RF Suk >10 16.52 9.15 3.48 20.63 30.60 24.10 Diospyros abyssinica YL Kus sapling 36.59 26.01 14.98 21.60 2.25 34.96 Ficus exasperata UF Kus 90 32.37 22.82 7.10 16.70 6.52 32.78 Ficus exasperata UF Kus 64 42.98 34.78 14.80 16.24 5.57 21.98 Ficus exasperata UF Suk 62 42.73 29.44 12.17 16.28 6.64 23.46 Prunus africana gum Kyo >10 37.15 5.06 3.7 0.81 0.63 59.05 Symphonia globulifera Suk 67 - - - - >90* <10 anther oil of FL Mean food BM CH RT 35.72 23.67 10.86 16.53 13.37 29.43 RT chased BM: Salacia elegans fruit Suk vine 48.36 29.04 24.38 6.65 2.96 38.91 Uvariopsis congensis RF Kus 20 57.58 17.92 3.42 13 2.81 23.83 Mean food RT CH BM 52.97 23.48 13.90 9.83 2.89 31.37 *Anther oil from Symphonia globulifera flowers is composed of nervonic acid, a long-chain monounsaturated fatty acid rare in plants, with a small amount of pollen suspended in it. Too little sample to conduct nutritional analyses on anther oil.

193

- - - 0.5 0.2 2.1 1.8 0.7 0.4 8.1 0.6 0.8 0.7 1.8 2.1 0.6 1.6 1.97 1.53 Mo ppm

- - - 2 45 26 29 33 14 71 29 26 50 139 228 128 550 71.71 163.75 Mn ppm

- - - 7 6 8 7 8 2 7 7 8 12 16 19 19 7.14 11.5 nripe fruit = UF, ripe UF, ripe nripe = fruit=RF, fruit 13.25 Cu ppm

- - - 8 1 days). days). U 17 17 18 22 26 13 17 18 17 29 42 - 12.5 19.57 20.75 Zn ppm

- - - 68 18 43 78 94 79 82 35 94 78 64 45 122 130 81.14 83.25 Fe ppm

- - - 0 0 0 0 0 0 0 70 70 70 40 70 20 70 12.86 27.50 Na ppm

- - - 2.5 1.87 1.03 1.44 1.24 1.88 1.95 2.65 2.61 2.27 1.71 2.43 1.95 1.88 1.67 0.95 K %

- - - 0.2 0.2 0.2 0.2 0.2 0.12 0.18 0.15 0.56 0.65 0.40 0.25 0.38 0.17 0.37 0.25 Mg %

- - - .36 0.2 0.4 P % 0.31 0.13 0.17 0 0.37 0.29 0.27 0.32 0.27 0.37 0.36 0.41 0.34 0.02

- - - 0.14 1.03 0.27 1.21 0.45 0.74 0.11 0.16 0.84 0.39 0.87 0.94 1.03 0.94 0.61 0.77 Ca Ca %

Suk Suk Suk Suk Suk Suk Suk Suk Kus Kus Kus Kus Kus Kus Kus Kyo Kus, Kus, Kus, Group

YL RF

YL UF RF UF

RT: YL

FL seeds of RF UF RT:

gum fruit

oung leaves=YL. One sample per food item analyzed for minerals. for sample oung leaves=YL. per minerals. One food analyzed item (DIS) lobulifera sp. pithsp. of (CH)

Mineral composition of identified contested foods during redtail focal female (RT) female (RT) agonismduring redtail focal foods with (BM) monkeys blue contested composition identified of Mineral

of FL of of FL of

3 . elegans

Mean food BM RT CH Mean food BM RT CH Mean food BM DIS RT

gomphophylla madagascariense . . Salacia Uvariopsis congensis seeds Prunus africana Symphonia globulifera anther oil RT chased BM: T gomphophyllaCeltis C Diospyros abyssinica Ficus exasperata BM BM chased Blighia unijugata Trilepisium madagascariense UF Celtis gomphophyllaCeltis Chaetachme aristata Macaranga young stem Symphonia g anther oil Contested food item BM displaced gomphophyllaCeltis flowers=FL, y flowers=FL, Table focal study across three in sight focal on hours (N=137 based >5 days observed groups,

194 Table 5.4 Macronutrient composition of identified contested foods during redtail (RT) agonism received from grey-cheeked mangabeys (MNG), observed across three study groups, based on redtail focal days >5 hours in sight (N=137 focal-days). Unripe fruit = UF, ripe fruit=RF, flowers=FL, young leaves=YL.

Contested food item Group DBH or NDF ADF ADL AP Fat TNC plant type MNG displaced RT: Celtis gomphophylla UF Kus 54 34.88 25.24 10.94 19.32 4.37 32.46 MNG chased RT: Celtis gomphophylla UF Kus >10 22.52 15.43 6.90 13.98 24.48 29.48 Caterpillar flush Kus NA 20.31 NA 50.20 6.54 31 (Lepidoptera: Noctuidae) Mean MNG CH RT 22.52 17.87 6.90 32.09 15.51 30.24

195

ppm

2.1 2.1 4.4 3.25 Mo

cheeked mangabeys - ppm

26 26 34 30 Mn

ppm 8 8

38 23 days). Unripe fruit = UF, fruit UF, ripe days). = Unripe - Cu

ppm

18 18 239 128.50 Zn gonism received fromgonism received grey

ppm 94 94

200 147 Fe

ppm 0 0

200 100 Na

1.95 1.95 4.57 3.26 K

0.2 0.2 0.31 0.26 Mg

1.1 P 0.37 0.37 1.83

0.31 0.63 0.94 0.94 Ca

Suk Suk Kus Kus, Kus, Group

UF UF

s=FL, young leaves=YL. One sample per food item analyzed for minerals. for sample analyzed item s=FL, young One food leaves=YL. per

Mineral composition of identified contested foods during redtail a (RT) foods contested composition identified of Mineral

5 . Mean MNG RT CH 5 chased RT: MNG gomphophyllaCeltis Caterpillar (Lepidoptera: Noctuidae) Contested food item MNG displaced RT: gomphophyllaCeltis fruit=RF, flower fruit=RF, Table based days study across in three observed groups, hours focal >5 (MNG) on focal sight (N=137

196 CHAPTER 6: SUMMARY

This dissertation characterizes the nutritional ecology of redtail monkeys and provides new insight into social nutrition in a primate. This study draws from 137 full-day focal follows of adult females in three study groups, phenological data from two transects in the monkey group home ranges, insect abundance data from malaise trapping and netting methods, nutritional wet chemistry or NIRS analyses of 601 food samples, 69 fecal samples, and mineral analyses via

ICP-OES of 101 food samples.

Chapter 1 first reviews social foraging theory (SFT) to place primate nutrition and socioecology in a broader social animal ecology context. SFT areas of research that have been incorporated into primate socioecology and nutrition include the producer-scrounger framework and patch use (Di Bitetti & Janson, 2001; Janson, 1988; Johnson et al., 2017; King et al., 2009;

Lee & Cowlishaw, 2017), as well as the social foraging context of mixed species associations

(Chapman & Chapman 2000a; Cords, 1987; Garber, 1988; Gautier-Hion et al., 1983; Heymann

& Buchanan-Smith, 2000; Noë & Bshary, 1997; Norconk, 1990; Peres, 1992b; Struhsaker,

1981). “Social nutrition” (sensu Lihoreau et al., 2015; Simpson & Raubenheimer, 2012) involves testing aspects of social foraging theory with multivariate nutritional ecology. To date, social nutrition work has quantified the interplay among animal’s physiology, nutritional environment, and behavior mostly in invertebrates (Cook et al., 2010; Dussutour & Simpson, 2009; Grover et al., 2007; Lihoreau et al., 2016). Additionally, social nutrition predictions have been generated based on agent-based models (Hosking et al., 2019; Senior et al., 2015, 2016b; Simpson et al.,

2010), phase change models (Lihoreau et al., 2017), and landscape ecology models (Simpson et al., 2010). The redtail monkey, as a small-bodied guenon with a diverse diet, with frequent participation in polyspecific association and with a less well understood intragroup structure, was

197 a suitable study animal with which to test some predictions of social nutrition and the primate socioecological model.

Chapter 2 characterizes redtail monkey macronutrient, sodium and copper sources in the diet. In agreement with previous studies, redtail monkey diet was diverse in food type, plant species, and macronutrient composition. Redtails spent equal time feeding on insects and fruits but gained most of their daily calories from fruits and young leaves. I clarified the role of insects in the redtail diet as modest contributors to energy and protein intake (given time spent feeding), but substantial contributors to sodium and copper intake. Redtail females are “food composition generalists,” pulling from food items diverse in macronutrient and mineral composition.

Important foods (>1% of diet by calories) were diverse in macronutrient content—for example, one fruit high in fat, while another was high in starch—indicating that redtails use fat, nonstructural carbohydrates, and digestible fiber interchangeably as energy sources. Redtails are

“macronutrient specialists,” combining diverse foods to reach similar macronutrient intake and ratios across study groups (Machovsky-Capuska et al., 2016). One caveat to redtail monkeys as

“macronutrient specialists” is the difference in macronutrient intake between Kyo group and the other two study groups: through heavy reliance on the structural and nonstructural carbohydrates in Prunus africana fruit and gum, Kyo group demonstrated that redtails can practice

“macronutrient generalist” shifts. This shift is likely dictated by the higher stem density of

Prunus africana in the disturbed habitat (Makerere University Biological Field Station and areas recovering from clear-felled conifer plantations or light logging) through which Kyo group ranges.

Chapter 3 demonstrates that redtail monkey balance of nonprotein energy to available protein falls with other omnivorous primates (Cui et al., 2018) and is only slightly lower than the

198 balance found for another guenon, the blue monkey (Takahashi, 2018; Takahashi, in prep). For daily macronutrient intake, daily fat intake varied the most, while protein varied the least, indicating protein regulation. Daily intake of macronutrients and energy varied less than daily intake of essential minerals, which supports greater daily regulation of macronutrients than micronutrients (Trumper & Simpson, 1993). Redtail female nutritional strategy was affected by habitat variation (the differences in plant composition in Kyo group home range compared to the home ranges of the other two study groups) more than it was affected by ripe fruit in the diet or reproductive status. One study group’s reliance on Prunus africana led to higher NPE:AP than the other study groups, indicating intergroup variation in nutritional strategy. More food switching by redtail monkeys facilitated increased sodium, copper and iron intake, suggesting that rapid switching among foods enables increased micronutrient access.

Chapter 4 first characterizes redtail female intragroup agonistic and affiliative behaviors and then examines the relationship between daily nutritional intake and balance and agonism.

Redtail females exhibited low rates of agonism for a cercopithecine and most agonism was observed in a feeding context. Social foraging theory and social nutrition models (Lihoreau et al.,

2015) predict that competition among individuals in a social foraging context will lead to differential food and nutritional intake. However, in my study, a relationship between agonism during feeding and nutritional intake could not be detected for multiple macronutrients and energy at the scale of daily intake and balance, regardless of amount of fruit in the diet.

Nutritional effects of intragroup food competition were not detectable at the daily scale, but future work will examine if redtail nutritional costs are detectable at the level of the food patch, as previously proposed for other species (Janson, 1988); that said, the fitness implications of patch-level losses or gains are unclear if by the end of the day the individual has nutritionally

199 “caught up.” Furthermore, between-group contest competition may be more relevant to nutritional shortfalls than individual agonistic ability: if a female is a member of a group that cannot defend preferred food resources in the group’s home range during intergroup encounters, then that female may suffer nutritionally compared to females in the group that succeeds at food defense. Long-term skew in redtail group access to food resources at Ngogo, Kibale was driven by aggressive intergroup encounters in which females participated (Brown, 2011, 2013).

I also examine what foods females contested during agonistic interactions: foods were diverse in macronutrient and mineral composition. While some contested fruits were high in fat, others were high in nonstructural carbohydrates. Caterpillar flushes were a mineral- and protein- rich clumped resource that led to female agonistic interactions. Cicadas and gum were also contested items, which is puzzling because these foods are not clumped resources or resources that take longer to handle and consume, so they would not be predicted to lead to agonism.

Possible explanations include that redtails sometimes remove cicada wings before consumption, which increases handling time of a cicada and the distribution of gum on Prunus africana trees undergoing gummosis as perceived by a redtail monkey is not straightforward. Variation in the distribution, content, and depletion time of contested foods points to the importance of considering the scale at which an animal interacts with aspects of its heterogeneous environment

(Wiens, 1976; Isbell et al., 1998) and how this affects interactions with other individuals.

Chapter 5 examines how polyspecific association affects female redtail daily nutritional intake. Polyspecific association with blue monkeys and mangabeys (the sympatric species with the diets most overlapping with the redtail diet) did not affect redtail daily intake of dry matter food, total metabolizable energy, nonprotein energy, sodium, or copper, regardless of shifts in amount of ripe fruit in the diet. Though a positive significant relationship was found between

200 blue monkey association and redtail fat intake, the effect was weak and therefore not biologically meaningful. The redtail study groups varied in polyspecific association patterns and food items over which redtails and heterospecifics contested did not fit one nutritional composition profile.

The relationship between redtail daily nutritional intake and grey-cheeked mangabey polyspecific association was unclear (sensu Dushoff et al., 2019), likely due to spatial stratification resulting from mangabey large body size mitigating competition (Houle et al.,

2010). Though I did not detect a relationship between red colobus monkey polyspecific association and daily intake of sodium from insects, further research on insect feeding efficiency is needed to examine if red colobus flush insects for redtail monkeys when the two species are in association.

In conclusion, this study fully characterizes the nutritional strategy of a small-bodied omnivorous forest guenon, clarifies the role of insects in their diet as micronutrient—and to a lesser extent, protein—sources, and demonstrates a low daily nutritional cost of conspecific and heterospecific competition. Similarly to other generalist primates (Macaca mulatta: Cui et al.,

2018; Cercopithecus mitis: Takahashi et al., 2019), redtail monkeys combine foods of diverse nutritional composition and use fat, nonstructural carbohydrates, and digestible fiber interchangeably as energy sources. Being a dietary generalist (dietary mixing) holds implications of reduced between-individual difference in fitness compared to being a dietary specialist (Senior et al., 2015). Diet diversity also has implications for mineral intake, as redtails consumed more sodium, copper and iron when they switched foods more, despite a heavy reliance on insects for essential minerals.

Intergroup variation in nutritional strategy gives insight into intraspecific variation in ecology and behavior caused by small-scale habitat variation. One of my study groups relied on

201 one plant species’ exudate and fruit much more than the other two groups, due to this tree species greater availability in their home range, which resulted in increased fiber and nonstructural carbohydrate intake. This intergroup shift in nutritional strategy shows that redtails are resilient when faced with environmental change; further evidence of this flexibility includes redtail populations persisting in savanna-woodland mosaic habitat (McLester et al., 2019a,b) and in fragmented habitat (Onderdonk & Chapman, 2000).

Social nutrition models predict differential nutrient intake and balance due to contest competition (Lihoreau et al., 2015). However, in this study, I could not detect effects of conspecific agonism on daily macronutrient, micronutrient, and energy intake. One explanation for my findings may be that nutritional costs of conspecific competition may be detectable at the level of the patch, not per day, though the fitness implications of patch-level losses without daily losses are unclear. Additionally, strong intergroup contest competition for resources may drive individual redtail female nutritional gains and losses more than intragroup competition (Brown,

2013; Jaeggi et al., 2018). Future longer-term studies may establish whether dominance ranks can be detected in redtail females and enable testing of how rates of agonism and rank do

(Janson, 1985; Vogel, 2005) or do not (Takahashi, 2018) affect nutritional intake.

Interspecific dominance hierarchies determined by species body size occur in mixed species associations (Cords, 1990; Houle et al., 2010; Struhsaker, 1981) and affect how competition plays out among different species. Patterns of interspecific aggression observed in this study confirm redtails as low-ranking in the interspecific diurnal primate hierarchy at

Kanyawara. Spatial stratification of species in the canopy and wider group spread help minimize interspecific contest competition (Garber, 1988; Gautier-Hion et al., 1983; Heymann &

Buchanan-Smith, 2000; Norconk, 1990). How diet mediates interactions between individuals and

202 between species should be considered at multiple scales in order to better characterize the dynamic interplay among an animal’s physiology and social and nutritional environments.

203

0.00 7.95 5.95 6.03 TNC 19.39 24.06 23.74 21.13 20.26 24.27 42.98 57.50 27.62 12.07 43.51 21.79 16.01 28.55 32.10 17.87 10.15

Ash 9.11 8.98 5.94 7.27 5.23 7.58 6.47 9.49 8.94 12.93 17.47 40.85 27.57 17.47 13.22 11.87 13.88 17.17 14.51 19.25 15.62

AP 4.38 29.99 29.87 27.55 16.19 15.09 19.11 18.17 21.97 17.44 14.41 25.50 17.96 16.96 19.80 15.66 17.62 25.90 18.70 32.18 31.30

All values represent represent All values CP otal nonstructural otal nonstructural 34.04 34.74 32.27 22.19 21.09 25.10 24.17 23.88 20.72 11.44 19.05 31.99 20.02 22.70 24.33 21.34 22.78 30.57 24.62 36.63 35.16

Fat 2.72 2.54 3.77 4.40 4.97 4.53 4.32 5.37 1.42 5.33 4.10 3.27 5.79 7.39 2.29 2.51 2.66 2.86 2.10 3.07 26.62

DM 0.91 0.94 0.90 0.95 0.91 0.91 0.89 0.88 0.89 0.90 0.92 0.88 0.89 0.90 0.94 0.88 0.90 0.92 0.92 0.92 0.92

4.59 7.16 5.20 2.88 5.04 7.54 7.26 5.62 6.97 7.10 8.80 ADL 19.10 12.75 10.13 21.40 21.56 18.17 16.94 13.77 17.13 20.06 foods and insect insect listed foods Plant morphotypes. foods and

etergent lignin, dry detergent ADL DM = fiber, acid etergent = ADF 29.30 20.25 32.34 20.33 36.61 33.52 32.61 29.26 26.25 24.76 26.93 20.34 14.90 30.25 29.85 28.01 27.58 28.80 17.39 20.26 33.97

NDF 48.29 26.16 40.99 32.09 45.00 39.84 49.32 47.06 41.44 40.86 39.78 35.56 33.01 41.82 41.64 41.85 40.79 43.46 25.94 26.51 49.63

pulp

Part

YL FL stalks FL stalks RF seedsRF YL YL YL YL YL YL YL YL YL fruit fruit pulp pulpRF pulpRF UF RF FL YL

ias microcarpa

sp.

Species sp. sp. Uvariopsis congensis Uvariopsis congensis Uvariopsis congensis Funtumia Funtumia Pleiocarpa pycnantha Pleiocarpa pycnantha Tabernaemontana pachysiphon Tabernaemontana pachysiphon Pseudospond Monodora myristica Monodora myristica Monodora myristica Monodora myristica Monodora myristica Monodora myristica Monodora myristica Uvariopsis congensis Uvariopsis congensis Lindackeria Pseudospondias microcarpa

R.Br.

Juss.

Harms Juss.

Macronutrient composition of redtail monkey foods, includingplant composition foods, redtail of monkey Macronutrient

. Family 1 naceae Apocy Achariaceae Anacardiaceae Annonaceae Appendix foods. morphotypes plant by plant family, listed plant then after part. alphabetically species, then Insect dry fiber, on a = neutral ADF = acid matter NDF d basis. detergent percentages TNC insoluble detergent t and protein, = protein acid (via Nitrogen), CP crude AP = available matter, = or analysis. boxes NA Grey for not enough indicate sample carbohydrates.

204

2.13 1.71 2.04 8.88 TNC 23.19 13.29 22.40 18.42 28.21 45.35 25.83 39.80 16.22 14.61 23.91 34.16 20.94 29.54 34.41 40.30 21.51 16.81 16.22 18.02 17.17 40.59 18.98

Ash 7.56 5.69 7.11 9.75 9.13 4.92 9.14 8.25 15.93 11.78 17.25 14.51 12.73 50.38 48.15 52.58 43.46 28.91 34.02 26.54 13.65 10.13 17.02 18.48 13.16 14.03 13.55

AP 8.89 2.65 7.22 7.62 8.75 3.23 19.87 22.43 19.16 21.43 16.32 23.59 11.70 12.48 13.93 14.94 24.62 17.61 18.88 16.03 10.77 29.14 28.30 31.62 30.72 35.32 23.84

CP 25.43 29.43 21.38 27.21 19.57 17.82 27.89 11.20 13.88 12.05 12.21 17.07 15.99 14.05 17.66 29.59 22.81 20.98 18.02 11.52 34.03 34.77 34.19 33.76 37.83 10.38 28.49

Fat 2.50 1.75 2.97 1.94 2.94 2.46 2.54 1.64 5.03 1.32 1.46 5.39 5.94 0.60 0.91 2.26 5.97 5.75 5.73 4.54 1.93 1.64 2.18 2.09 2.09 1.71 2.83

DM 0.94 0.94 0.92 0.92 0.93 0.91 0.94 0.89 0.90 0.90 0.91 0.91 0.91 0.91 0.90 0.88 0.90 0.92 0.90 0.89 0.92 0.91 0.90 0.94 0.90 0.92 0.94

1.06 6.23 7.96 6.25 4.26 4.23 6.04 2.12 8.85 8.02 5.08 8.56 7.20 ADL 16.73 21.98 12.51 12.81 10.65 14.13 11.64 1 23.78 12.58 23.96 19.80 21.44 14.88 10.23

DF 8.64 A 24.54 20.25 21.52 20.83 16.77 26.68 24.53 21.04 18.78 31.17 22.22 20.89 1 36.78 25.76 27.63 35.25 23.49 32.23 20.85 30.76 28.39 39.13 19.22 29.18 27.24 24.91

NDF 37.63 37.63 37.61 33.72 24.46 39.54 29.81 59.27 36.10 43.71 37.81 36.14 32.87 46.33 47.09 47.88 47.60 44.68 41.96 39.02 38.61 36.32 49.80 17.67 42.17 37.76 32.21

Part

UF UF UF UF UF YL FL stalks RF YL YL YL YL YL pith of young stem YL YL YL YL YL YL YL YL young stems RF UF FL stalks FL stalks

moosa

subsp

Species sp.

ricana africana

elia pathodea campanulata Spathodea campanulata S africanaCeltis africanaCeltis af Celtis africanaCeltis africanaCeltis africanaCeltis africanaCeltis africanaCeltis Markhamia lutea Markhamia lutea Spathodea campanulata Spathodea campanulata Spathodea campanulata Spathodea campanulata Spathodea campanulata Spathodea campanulata Spathodea campanulata Spathodea campanulata Tabernaemontana pachysiphon Tabernaemontana pachysiphon Tabernaemontana pachysiphon Dracaena Basella alba Kig Markhamia lutea

Juss. Martinov Juss.

Juss.

Raf.

Family Cannabaceae Bignoniaceae Asparagaceae Basellaceae Apocynaceae

205

TNC 31.52 24.10 26.07 15.03 17.56 18.01 21.60 16.35 25.03 19.00 23.98 20.52 22.06 25.72 32.88 30.39 22.99 24.51 25.88 22.33 33.50 27.54 33.89 38.44 42.49 32.02 29.83 29.75 40.52 32.02 32.35

Ash 9.15 9.07 7.89 7.88 9.13 8.01 9.72 8.73 9.24 9.45 9.34 7.69 6.36 9.73 9.56 9.12 7.16 7.74 10.96 10.43 11.15 12.44 11.75 11.00 10.16 12.45 11.35 12.72 15.78 12.01 10.29

8 AP 15.69 20.63 15.95 22.05 20.30 20.28 18.85 23.17 16.97 23.31 21.79 20.10 24.78 18.87 14.96 19.94 20.37 27.17 28.35 29.58 21.0 20.94 19.25 20.13 16.67 26.49 29.09 29.20 19.13 31.63 31.59

CP 19.41 23.30 17.98 24.95 23.44 24.24 22.06 26.11 24.79 26.24 24.27 23.38 27.38 21.80 19.66 22.16 23.20 31.19 31.64 33.58 26.49 27.23 26.18 25.53 21.63 31.38 31.66 34.03 24.08 34.61 36.31

Fat 1.64 1.76 1.86 2.07 3.04 1.94 1.74 2.53 4.86 1.80 5.53 4.40 3.21 8.23 2.37 2.65 2.25 2.99 2.71 2.52 2.34 4.14 2.26 2.08 3.12 2.87 3.16 3.04 22.61 30.60 35.50

DM 0.92 0.93 0.92 0.92 0.94 0.93 0.88 0.89 0.89 0.88 0.90 0.92 0.89 0.89 0.91 0.90 0.95 0.95 0.96 0.93 0.93 0.92 0.93 0.93 0.93 0.93 0.93 0.91 0.91 0.91 0.89

6.69 9.27 9.62 6.13 3.48 4.05 7.39 ADL 15.76 12.35 13.44 11.91 10.36 11.94 15.43 18.87 15.18 15.80 12.30 12.14 16.07 14.17 13.84 14.24 13.70 14.39 13.29 13.39 12.46 10.69 11.17 14.17

53 9.15 ADF 31.41 31.34 29.22 26.04 25.10 24.88 23.11 29.30 25.40 24.57 21.95 15.01 20.52 24.44 16.65 17.55 14. 10.10 38.79 38.77 40.11 38.34 36.54 34.36 34.41 33.58 19.73 19.66 18.25 25.03

4 NDF 43.08 40.61 39.88 39.53 37.31 36.95 34.04 33.33 32.39 31.3 30.49 29.71 29.37 29.36 27.03 26.29 20.23 16.52 14.41 54.39 53.51 51.70 50.46 48.01 47.36 45.80 43.97 35.60 34.68 34.12 34.09

Part

UF UF UF UF UF UF YL YL YL YL YL YL YL YL YL YL RF RF RF UF UF UF UF UF UF UF UF YL YL YL YL

Species gomphophylla

Celtis gomphophyllaCeltis Celtis gomphophyllaCeltis Celtis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis Celtis africanaCeltis africanaCeltis africanaCeltis africanaCeltis africanaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis Celtis africanaCeltis africanaCeltis africanaCeltis africanaCeltis africanaCeltis africanaCeltis africanaCeltis africanaCeltis africanaCeltis

Martinov

Family Cannabaceae

206

41 TNC 27.99 29.97 23.73 25.24 34.35 20.24 16.26 29.48 17.74 29.22 31.57 35.82 36.22 19.65 17.99 18.06 18.86 26.57 28.31 27.35 24.30 32.46 31.75 32.51 28.54 23. 33.36 30.30 33.12 27.27 24.65

Ash 9.89 9.93 9.45 9.59 9.90 9.38 7.96 8.65 9.77 9.98 9.77 9.70 9.71 9.51 11.40 10.62 10.48 10.53 10.01 16.00 13.55 10.11 12.56 10.90 11.41 10.34 10.89 11.39 12.07 11.38 11.19

AP 9.34 15.78 19.14 19.00 17.21 19.69 25.03 17.68 13.98 25.73 15.17 15.90 10.58 11.46 19.55 21.32 20.11 18.93 18.91 22.38 22.65 19.32 19.94 16.42 18.53 20.81 15.29 16.97 17.92 20.27 22.13

CP 18.11 21.44 21.79 18.89 21.49 27.00 21.20 17.47 29.74 18.19 19.17 14.21 14.34 19.65 23.62 25.37 24.81 23.35 21.43 25.72 26.01 22.48 23.26 18.88 20.63 23.66 17.40 19.96 20.02 23.27 25.00

.71 Fat 1.07 8.35 1.95 1.73 2.10 2.06 6.57 6.88 4.03 8.47 4.37 6 9.03 7.74 18.92 12.11 19.83 21.51 10.46 20.39 32.51 24.48 26.99 26.58 24.42 12.22 15.60 12.02 11.59 12.46 13.59

DM 0.92 0.91 0.87 0.91 0.91 0.91 0.93 0.92 0.93 0.93 0.93 0.92 0.96 0.92 0.92 0.94 0.93 0.93 0.93 0.93 0.92 0.93 0.94 0.96 0.94 0.94 0.95 0.92 0.92 0.92 0.92

9.05 9.76 9.21 8.19 9.58 9.31 8.93 8.51 8.33 8.32 8.33 8.09 6.84 8.87 7.34 6.90 6.36 6.30 5.92 9.27 ADL 19.22 25.87 19.75 12.54 14.20 10.94 10.29 10.58 11.17 12.27 10.62

ADF 31.67 34.15 40.32 32.59 29.87 32.66 25.24 22.89 23.28 21.43 20.86 20.89 22.21 21.26 20.45 20.42 19.48 20.11 19.10 18.18 17.88 17.61 16.36 15.43 14.04 13.49 13.35 25.19 25.96 23.60 24.80

NDF 37.53 39.13 54.39 51.62 50.86 50.20 34.88 32.83 32.13 32.02 31.47 30.83 30.74 30.15 29.62 29.45 28.42 28.38 27.83 27.12 26.02 25.90 24.96 22.52 20.64 20.02 19.73 36.37 36.01 35.83 35.23

Part

UF UF pulp YL YL YL YL UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF UF

aristata Species phophylla Chaetachme aristata Celtis gomphophyllaCeltis gomphophyllaCeltis gomCeltis gomphophyllaCeltis gomphophyllaCeltis Chaetachme aristata Chaetachme Chaetachme aristata Chaetachme aristata Chaetachme aristata Celtis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis Celtis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis gomphophyllaCeltis

Martinov

Family Cannabaceae

207

0 TNC 25.84 21.81 28.62 23.63 18.07 27.3 38.91 18.48 10.92 27.82 36.33 33.29 22.44 17.25 16.99 14.43 18.52 29.25 25.53 23.92 21.85 21.70 23.97 30.97 23.83 21.91 21.26 16.44

52 52 Ash 9.58 6.45 4.12 7.03 4.14 4.05 3.97 4.72 8.69 9.45 8.47 8.97 9. 9.61 9.15 10.11 10.13 11. 14.32 16.85 25.87 10.49 14.00 10.65 10.14 10.48 10.24 11.00

AP 9.53 6.65 0.00 0.46 0.90 21.37 25.51 18.10 25.44 15.97 15.23 32.36 11.35 16.06 22.74 22.75 23.61 13.68 18.37 19.48 21.70 20.97 20.43 13.16 21.06 22.04 24.90 29.36

CP 1.06 3.94 8.57 25.55 29.37 24.77 29.16 21.06 11.40 10.29 22.66 38.02 19.15 22.70 27.83 27.46 27.67 19.12 23.13 23.65 26.00 26.30 25.57 19.81 25.71 27.52 29.58 33.73

Fat 2.16 2.36 2.55 2.52 1.80 8.79 2.96 3.35 3.40 2.48 9.65 2.05 1.68 2.15 1.47 2.35 2.11 2.43 2.66 2.21 2.27 2.38 2.38 2.80 2.72 2.43 2.53 15.12

DM 0.92 0.93 0.92 0.91 0.92 0.91 0.91 0.91 0.91 0.91 0.88 0.91 0.91 0.91 0.91 0.88 0.91 0.88 0.91 0.93 0.90 0.90 0.91 0.92 0.91 0.90 0.91 0.88

.38 9.43 ADL 18.97 14.99 31.30 19.56 15.95 14.54 12.89 12.76 16.98 14.40 17.23 14.44 17.73 14.02 11.23 11.61 12.50 14.03 11.89 10.46 35.19 24.38 26.47 22.15 15.03 12.67 14

ADF 55.51 33.43 46.06 35.18 29.02 31.38 28.42 29.71 30.84 29.77 33.28 27.33 30.13 27.59 24.19 26.95 25.53 28.77 24.24 40.04 44.56 29.04 43.54 41.14 31.74 30.04 26.34 32.39

NDF 66.65 45.12 52.44 48.31 40.15 46.20 45.96 45.73 45.40 44.61 44.36 44.16 43.85 42.17 41.68 41.52 41.19 40.22 39.83 50.83 48.94 48.36 56.92 50.18 48.63 48.35 47.84 46.51

Part

oung stem FL FL stlks RF RF YL YL YL YL YL YL YL YL YL YL YL YL YL YL pith y. stem RF spit RF seed spit RF skin YL y YL YL YL YL

tata

aristata

Species sp. sp.

elegans Salacia elegans Salacia elegans Salacia elegans Salacia elegans Salacia Symphonia globulifera Symphonia globulifera Palisota Palisota Chaetachme aristata Chaetachme aris Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Trema orientalis Chaetachme Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata Chaetachme aristata

Mirb.

Martinov R.Br.

Lindl.

Family Commelinaceae Clusiaceae Celastraceae Cannabaceae

208

.64 8.56 TNC 32.74 26.06 34.96 33.38 12.59 14.57 19.77 29.83 32.40 19.74 17.64 25.70 27.12 27.41 41.91 25.42 37.65 54.68 55.85 59.75 52.41 37.88 17.95 23.39 38.28 17.72 24 12.57 25.98 27.18

9 Ash 8.03 6.14 5.59 5.22 8.64 9.35 5.08 7.56 8.56 7.35 7.25 8.29 4.03 1.42 5.96 6.38 4.93 17.95 16.35 15.3 13.30 15.12 15.82 13.63 11.29 13.33 16.98 14.86 14.43 10.47 15.05

AP 5.37 6.80 5.40 33.67 18.12 28.05 21.60 22.57 34.64 33.63 33.26 26.94 25.40 33.80 36.08 30.94 31.90 32.33 29.49 33.78 17.73 11.26 10.36 11.43 11.45 18.07 30.69 27.74 20.58 15.04 24.08

.01 CP 5.63 9.43 6.46 36.64 23.19 33.21 24.10 26.79 36.40 36.35 35.25 31.76 30.26 35.12 37.75 32.50 33.48 32.97 30.76 34.25 21.61 14.61 13.51 14.92 14.31 22.39 34.14 32 29.07 20.04 32.08

39 .66 Fat 2.05 3.68 3.71 2.25 4.25 2.12 2.48 2.59 3.61 3.79 2.86 2.32 2.71 3.00 2.85 2.04 2. 0 1.75 2.04 1.60 1.19 0.17 2.96 3.23 2.60 1.99 2.03 3.36 3.13 1.84

94 DM 0.90 0.90 0.91 0.91 0.94 0.90 0.91 0.92 0.91 0.90 0.92 0.92 0.90 0.91 0.91 0.93 0.90 0.92 0.91 0. 0.92 0.90 0.90 0.90 0.92 0.91 0.90 0.93 0.89 0.90 0.89

.24 8 8.53 7.18 9.97 9.47 6.47 0.90 4.12 5.56 7.39 9.78 7.90 7.00 7.14 ADL 11.00 10.56 12.79 25.00 18.23 15.74 17.14 13.84 12.80 14.98 14.43 13.79 13.54 10.87 11.09 10.38 10.22

8.24 ADF 18.91 20.31 17.19 16.75 13.82 16.90 14.54 18.35 20.28 21.15 47.12 51.51 43.33 36.89 25.94 27.25 27.02 24.30 26.01 24.59 23.10 23.62 21.03 21.28 20.78 18.57 21.25 14.03 12.64 13.61

NDF 29.14 28.02 27.69 25.08 22.48 22.43 28.71 30.45 38.93 38.35 39.71 70.46 67.51 58.53 50.47 42.97 38.77 38.44 37.03 36.59 35.58 35.31 34.93 32.08 31.99 30.06 29.48 30.10 25.75 24.19 20.86

Part

YL YL YL YL YL YL RF sd RF UF YL YL gum UF UF YL YL YL YL YL YL YL YL YL YL YL YL YL YL RF RF RF RF

sinica

Species abyssinica sp. sp. sp. sp. sp. sp. sp. sp.

Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros Diospyros abys Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinic Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros abyssinica Palisota Palisota Palisota Palisota Palisota Palisota Ipomoea Ipomoea Diospyros abyssinica

Juss. Mirb.

Gürke Gürke

Family Ebenaceae Convolvulaceae Ebenaceae Commelinaceae

209

.96 7.93 TNC 29.95 15.09 42.87 46.16 43.67 53.71 65.30 26.59 41.18 45.50 23.74 19.04 20.89 20.28 22.29 30.18 36.88 37.84 32 29.46 47.82 16.92 33.01 38.39

Ash 2.82 6.12 7.42 6.12 4.62 4.23 9.79 8.62 6.70 8.92 6.80 5.67 7.58 3.81 5.54 4.40 4.80 15.31 11.42 11.99 17.86 10.95 12.05 11.66 10.77

AP 7.46 6.30 8.80 5.49 6.15 10.22 10.88 10.12 14.57 12.51 11.85 10.48 16.48 20.00 28.03 28.05 31.80 25.89 23.30 25.48 29.24 31.98 24.11 25.87 19.17

CP 8.70 9.66 10.38 11.88 18.23 17.74 18.28 13.66 13.52 12.84 12.08 22.72 25.75 32.69 32.13 33.60 29.20 28.08 29.45 31.84 34.04 26.01 31.12 24.41 10.62

.55 Fat 2.36 2.42 2.16 2.42 2.42 1.47 2 1.03 2.31 2.73 2.40 2.77 3.39 3.24 3.13 3.16 3.49 3.04 2.72 8.87 0.62 26.92 3.127 24.57 31.50

0.9 DM 0.92 0.93 0.91 0.90 0.89 0.90 0.91 0.92 0.90 0.95 0.92 0.93 0.91 0.92 0.89 0.88 0.92 0.92 0.90 0.91 0.91 0.92 0.91 0.89

2 9.58 9.74 7.82 6.46 6.76 3.66 5.15 2.43 4.88 5.30 6.84 ADL 14.06 17.84 16.32 16.11 15.44 19.61 15.32 18.8 34.85 14.27 11.79 11.43 16.19 10.03

ADF 32.55 32.54 33.74 30.55 21.64 17.79 13.20 14.99 10.45 28.23 24.77 43.82 40.16 40.59 40.95 32.51 30.20 24.50 20.22 12.84 45.26 20.31 20.10 17.84 21.89

NDF 36.88 35.77 46.06 41.71 38.37 28.51 28.18 25.18 22.14 37.08 29.01 48.82 45.07 45.91 45.94 38.71 35.14 34.22 27.74 18.16 55.76 37.24 36.97 32.89 30.85

Part

L stems young stems ML YL YL YL YL YL YL RF RF pith of young stem pith of young stem pith of young stems seedsRF YL YL YL YL YL young stems young YL YL Y YL

sp. sp.

sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. Species

sp. sp. sp. sp. sp. sp. sp. sp. sp.

caranga Ma Macaranga Macaranga Macaranga Macaranga Macaranga Neoboutonia macrocalyx Neoboutonia macrocalyx Neoboutonia macrocalyx Erythrococca Macaranga Macaranga Macaranga Macaranga Macaranga Macaranga Acalypha Acalypha Acalypha Acalypha Acalypha Acalypha Acalypha Acalypha Erythrococca

Juss. Juss.

Family Euphorbiaceae Euphorbiaceae

210

.10 4.86 9.89 TNC 15.53 20.91 13.03 12.88 22.28 23.49 10.12 39 30.85 30.55 33.03 34.87 16.01 44.09 22.00 15.86 18.38 13.68 28.21 35.08 24.83 33.80 22.29 29.51 29.49 20.45 13.26 22.40

95 4.91 Ash 9.56 8.24 9.05 9.05 9.42 8.79 8.39 4.28 4.21 6.39 5.34 8.07 5.87 4. 4.41 8.14 10.25 10.97 12.88 1 12.90 10.27 12.75 10.08 10.06 12.20 10.38 11.67 12.62 10.11

AP 8.34 30.03 24.01 34.66 41.21 38.47 40.26 21.55 14.96 13.24 13.71 13.87 11.00 13.71 18.56 10.55 26.87 23.66 25.62 26.07 24.49 18.92 27.58 22.54 32.07 27.75 20.30 33.60 25.32 46.20

CP 34.89 27.94 38.53 44.41 42.15 42.68 25.78 21.71 18.62 18.43 19.53 19.63 22.66 18.43 28.21 14.34 30.90 31.28 31.77 33.70 28.55 23.84 31.39 27.82 36.07 31.82 26.94 38.27 32.15 48.50

2.7 Fat 1.75 2.11 3.07 3.13 3.62 2.81 3.44 2.98 0.90 4.42 3.44 5.81 4.30 4.21 1.19 1.38 2.35 2.78 2.06 2.20 2.48 2.48 2.53 2.84 2.59 1.98 2.62 2.99 2.43

DM 0.91 0.91 0.90 0.88 0.94 0.90 0.89 0.89 0.89 0.89 0.90 0.90 0.90 0.90 0.93 0.91 0.91 0.90 0.90 0.91 0.91 0.92 0.90 0.93 0.93 0.92 0.91 0.91 0.89 0.89

1.57 7.09 7.65 8.33 ADL 12.95 18.20 12.03 17.19 12.25 18.41 18.48 15.73 16.83 10.90 15.51 20.92 13.23 20.46 11.75 10.25 10.15 11.45 17.38 20.87 14.53 11.64 20.94 21.45 24.09 23.58

ADF 27.61 30.89 27.88 34.25 22.36 29.42 31.02 26.85 28.32 23.08 24.99 36.50 22.39 36.06 13.76 32.29 27.60 24.54 18.32 19.57 19.79 27.82 33.74 51.14 26.65 37.76 21.20 34.08 34.89 36.15

NDF 40.72 40.46 39.16 54.86 35.56 41.55 40.30 39.50 36.79 35.73 35.28 44.29 39.91 49.31 21.83 43.44 43.00 37.37 36.88 36.48 35.15 43.46 46.82 68.51 34.73 48.29 38.11 49.91 46.54 46.44

Part

FL FL RF YL FL YL YL YL YL YL YL YL YL FL FB FL FL YL YL YL YL YL YL pith y. stem F FL YL YL YL YL

sp.

schweinfurthii

Species opposita

abyssinica sp.

sp. sp. sp. sp. sp. sp. sp. sp. sp.

ndia Strychnos mitis Anthocleista Apodytes Clerodendrum Hoslundia opposita Hoslundia opposita Hoslundia opposita Hoslundia opposita Hoslu Premna angolensis Acacia Albizia grandbracteata Albizia grandbracteata Erythrina Millettia dura Millettia dura Millettia dura Millettia dura Millettia dura Millettia dura Millettia dura Neoboutonia macrocalyx Acacia Acacia Acacia Acacia Acacia Acacia Acacia Acacia

Jus

Juss.

R.Br. R.Br. ex

Miers

Martinov Martinov

Lindl.

Family

cacinaceae Lamiaceae Loganiaceae Mart. Lamiaceae Gentianaceae I Fabaceae Euphorbiaceae

211

9.67 3.71 TNC 21.55 25.60 32.09 59.30 14.51 15.71 11.32 20.49 17.91 20.72 17.44 19.09 22.72 61.54 40.72 53.15 59.93 36.48 41.16 18.52 28.83 12.87 14.31 23.06

Ash 9.44 8.17 6.10 5.67 4.85 5.01 6.35 5.07 1.56 8.17 9.70 12.07 16.95 15.64 13.03 16.22 13.66 11.93 10.58 10.60 12.87 11.89 10.07 12.25 12.88 11.77

08 AP 7.59 6.94 4.29 4.60 9.03 8.10 9.22 7. 10.87 12.45 16.94 14.14 16.89 18.60 12.90 18.04 15.87 19.65 15.79 13.68 20.61 17.76 14.67 13.64 24.68 20.42

CP 9.55 5.85 6.20 10.31 15.19 15.73 23.84 22.28 22.22 24.55 19.84 23.46 21.57 24.63 21.52 20.04 21.79 19.04 21.15 18.0 29.89 26.20 12.73 11.58 13.46 10.71

Fat 0.90 1.21 4.79 1.12 2.45 2.39 2.67 2.69 2.78 1.96 2.77 2.65 2.95 2.56 2.77 1.71 1.34 1.53 2.15 4.55 0.54 2.42 1.23 1.84 1.52 1.52

DM 0.88 0.89 0.88 0.91 0.90 0.88 0.92 0.94 0.91 0.93 0.88 0.89 0.90 0.91 0.90 0.90 0.90 0.88 0.93 0.92 0.90 0.90 0.90 0.90 0.94 0.91

1 7.51 2.81 2.48 2.46 1.50 ADL 19.41 12.80 15.98 17.67 17.27 18.28 16.29 16.14 13.23 19.29 19.1 14.66 13.14 10.52 10.38 13.91 12.97 24.47 25.75 18.25 20.08

.35 8.71 9.76 9.55 9.56 ADF 36.09 32.77 31.86 32.51 33.85 33.34 14.86 31.33 23.02 32.21 31.31 37.79 32.83 32.45 26.02 25.10 29.89 32 40.89 40.31 35.55 36.81

9 3 NDF 51.17 51.16 51.06 50.66 50.30 50.16 20.8 15.42 42.62 40.10 47.19 41.15 74.78 65.30 64.18 59.65 61.52 59.09 41.18 22.04 54.99 54.3 52.71 52.18 26.73 49.12

Part

YL YL YL YL YL YL and pulp UF seeds and pulp YL YL UF YL seedsRF seedsRF seedsRF seedsRF seeds RF and pulp seeds RF and pulp leaf petioles seeds of UF YL YL YL YL UF UF seeds and pulp UF seeds

sp. sp. sp. sp. sp. sp.

Species mitis Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Leptonychia mildbraedii Marantachloa Marantachloa Marantachloa Marantachloa Marantachloa Marantachloa Trilepisium madagascariense Strychnos Strychnos mitis Strychnos mitis Strychnos mitis Strychnos mitis Strychnos mitis Leptonychia mildbraedii

R.Br. R.Br. R.Br. ex

Juss.

Gaudich.

Family

Moraceae Malvaceae Marantaceae Loganiaceae Mart.

212

6.14 TNC 24.60 23.99 26.29 28.24 30.97 28.50 32.18 30.59 29.67 27.86 22.33 29.45 55.08 14.15 21.98 11.22 20.15 17.48 22.65 19.46 27.32 27.52 25.99 18.79 19.79 27.31 19.96 25.91 22.96 19.91

Ash 9.38 6.97 8.03 9.42 9.65 9.34 3.91 9.16 7.83 8.62 9.18 11.36 10.59 10.57 11.00 17.89 18.28 14.23 13.17 11.53 12.24 11.39 12.89 10.39 11.08 11.80 11.47 12.59 11.04 11.30 12.25

76 AP 5.04 5.99 7.13 15.49 17.03 16.02 16.63 14.11 14.84 12.04 13.80 15.57 16. 10.28 10.19 16.24 10.58 16.42 18.38 16.02 17.05 12.45 13.86 13.44 19.06 18.70 13.68 18.02 13.70 16.02 18.59

.66 CP 8.51 8.90 20.38 20.81 20.32 20 18.54 18.83 16.64 19.10 19.85 20.56 10.33 15.66 20.03 21.24 17.22 22.06 24.12 20.25 23.21 18.74 19.09 18.66 24.56 23.43 18.42 22.72 19.35 21.90 24.03

08 Fat 2.81 2.77 3.58 3.68 3.23 2.47 2.76 2.72 2.97 3.22 3.24 6.57 2.05 8.38 6.93 5.57 6.67 2.93 3.13 2.12 3.02 2.27 2.66 2.17 3.11 3. 2.96 2.71 2.67 3.19 2.76

DM 0.95 0.94 0.92 0.91 0.92 0.93 0.88 0.88 0.88 0.88 0.91 0.91 0.91 0.88 0.91 0.91 0.89 0.90 0.91 0.92 0.89 0.89 0.88 0.89 0.89 0.88 0.91 0.91 0.91 0.90 0.91

6.95 ADL 17.47 36.09 18.77 14.80 29.44 14.11 14.52 14.84 13.84 17.26 17.81 16.16 17.20 18.99 18.49 15.72 13.83 16.23 15.33 14.01 13.18 15.77 17.20 14.25 13.66 24.87 18.65 19.64 14.50 21.25

ADF 38.95 23.37 52.64 40.53 34.78 48.31 31.91 30.56 31.80 29.40 31.14 32.85 32.02 32.10 33.38 32.31 28.06 25.76 30.27 27.66 28.74 27.51 28.78 29.45 26.67 25.13 53.49 34.55 33.56 29.97 35.22

NDF 49.83 28.92 58.31 51.45 42.98 59.36 48.57 48.34 48.32 48.24 47.96 47.88 47.73 47.68 47.54 47.50 46.74 46.62 45.73 45.47 44.66 44.62 44.60 44.24 43.44 42.16 66.48 49.97 49.77 48.81 48.58

Part

F RF pulpRF R RF RF UF YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL RF YL YL YL YL

madagascariense madagascariense Species brachylepis Ficus exasperata Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Ficus brachylepis Ficus Ficus brachylepis Ficus exasperata Ficus exasperata Ficus exasperata Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium Trilepisium madagascariense Trilepisium Trilepisium Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense Trilepisium madagascariense

Gaudich.

Family Moraceae

213

9.80 TNC 32.34 31.40 29.78 28.69 32.93 32.94 27.40 34.34 33.08 35.14 30.20 27.36 31.22 36.65 35.61 31.53 28.68 23.46 24.71 23.34 32.78 23.40 28.75 20.81 24.85 15.84 14.73 30.15 30.84 24.40

Ash 6.32 6.89 8.53 4.06 7.21 7.53 8.64 7.58 6.88 7.85 6.95 6.34 8.12 8.08 5.19 8.81 5.83 8.63 9.41 14.81 10.47 11.13 11.17 13.30 11.88 12.23 11.46 12.63 13.68 13.96 16.25

5 AP 0.7 0.00 2.54 3.39 3.81 9.55 18.14 20.25 24.38 24.80 24.22 22.33 25.56 20.98 22.25 21.18 23.01 30.10 25.36 22.21 23.22 23.53 29.29 12.55 16.28 16.03 18.41 16.70 15.51 21.72 19.82

CP 8.03 6.31 6.57 6.80 8.34 19.83 22.44 27.13 29.27 27.52 26.41 28.29 24.92 26.64 25.25 27.38 33.10 28.78 25.78 27.03 26.84 31.41 16.77 22.62 21.89 21.74 21.12 14.92 19.45 23.01 22.87

Fat 7.47 2.25 3.13 2.87 3.90 3.06 2.29 3.20 2.64 3.61 3.25 3.28 3.25 2.88 3.58 2.62 3.50 7.80 6.64 6.20 8.51 6.52 3.35 2.94 2.20 4.40 2.21 1.76 2.23 2.02 6.84

DM 0.92 0.91 0.89 0.89 0.89 0.91 0.93 0.92 0.88 0.91 0.90 0.91 0.90 0.88 0.91 0.92 0.93 0.91 0.92 0.89 0.92 0.89 0.91 0.89 0.89 0.88 0.89 0.93 0.91 0.92 0.92

91 07 .20 9.80 6.20 3.17 3.79 3.49 7 7.10 0.99 0.68 5.47 3.55 4.57 6. 5.70 9.09 6.04 7.42 4.92 6.47 5. 2.01 ADL 40.75 35.55 32.64 23.23 33.42 19.42 30.64 12.17 11.26 10.50

ADF 18.97 19.85 19.91 18.41 24.11 17.29 22.82 62.23 58.13 57.58 49.35 59.86 45.97 21.90 20.35 26.51 21.29 23.38 21.63 22.43 19.51 22.09 21.74 21.58 22.77 21.14 20.31 46.52 29.44 29.98 28.83

NDF 33.38 33.33 32.31 32.25 32.15 31.42 32.37 65.42 64.11 66.63 62.52 70.51 61.28 39.15 37.01 33.68 28.24 40.78 36.82 36.11 35.90 35.45 35.28 34.94 34.39 33.48 33.42 57.55 42.73 41.84 39.28

Part

YL YL YL YL YL YL UF RF RF RF RF UF YL ML ML UF UF YL YL YL YL YL YL YL YL YL YL UF UF UF UF

Species asperifolia asperifolia exasperata Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus Ficus asperifolia Ficus asperifolia Ficus natalensis Ficus spongii Ficus Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus asperifolia Ficus exasperata Ficus exasperata Ficus exasperata Ficus exasperata Ficus Ficus mucuso Ficus mucuso Ficus natalensis Ficus natalensis

Gaudich.

Family Moraceae

214

.32 TNC 24.63 24.36 22.34 28.82 24.88 18.50 26.52 30.01 33.20 27.42 30.34 22.14 25.66 26.03 28.69 19.53 34.21 36.31 51.12 62.71 64.41 66.07 42.60 18 20.74 27.29 24.31 19.01 28.13

Ash 8.03 8.86 6.53 8.19 8.74 9.22 8.09 7.73 9.97 9.70 8.36 5.74 2.52 2.34 2.75 2.75 4.81 8.12 9.77 7.90 8.71 9.85 7.77 10.65 10.92 10.73 10.55 10.63 13.18

62 AP 8.85 2.18 4.11 4.22 4.60 4.08 6.60 9.11 12.86 13.28 17.70 11.36 13. 21.54 13.36 11.58 13.26 10.78 18.69 15.27 17.35 13.24 21.87 20.14 26.18 12.11 10.22 11.37 15.79

CP 5.89 7.02 8.83 8.22 20.42 22.21 24.03 19.69 23.74 28.55 22.01 18.58 17.02 22.25 19.45 28.10 24.62 24.38 22.30 29.43 27.28 28.71 10.17 24.77 19.47 14.82 20.43 24.26 17.71

Fat 2.77 2.96 3.16 2.97 2.55 3.08 2.91 2.85 2.88 2.64 2.75 2.46 2.70 3.06 2.39 2.90 2.30 3.81 1.02 0.81 1.05 0.70 2.57 1.87 2.63 2.40 2.59 2.56 2.75

DM 0.90 0.89 0.92 0.90 0.92 0.92 0.91 0.92 0.93 0.88 0.89 0.92 0.90 0.91 0.90 0.92 0.92 0.92 0.90 0.90 0.92 0.89 0.89 0.89 0.89 0.89 0.94 0.92 0.93

11 5.04 5.54 4.54 3.46 4.43 5.61 ADL 19.62 16.70 20.48 16.90 19.96 21.77 19.64 17.89 18.39 19.29 23.24 17.85 20.47 21. 19.81 15.63 21.27 19.55 16.52 11.77 15.59 18.39 15.62

9.85 8.49 ADF 31.65 30.05 30.74 30.72 29.09 10.80 38.14 41.78 40.75 38.86 38.15 38.10 36.46 33.40 33.41 35.10 32.93 34.23 31.73 32.34 30.82 33.41 32.85 31.48 21.18 10.57 10.80

78 NDF 46. 46.65 46.20 46.13 46.07 31.03 28.57 26.87 46.93 60.58 57.64 56.81 54.02 53.79 53.24 52.70 51.54 51.27 49.65 49.30 49.14 48.98 48.47 48.34 47.71 47.42 31.17 28.95 44.16

Part

YL YL YL YL YL UF seeds UF seeds UF seeds UF skin and pulp YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL UF seeds

anus

africanus Species anthus africanus Chionanthus africanus Chionanthus africanus Chion Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus afric Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus Chionanthus africanus Ficus asperifolia Ficus asperifolia Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus

Gaudich.

Hoffmanns.

Family

Oleaceae & Link Moraceae

215

7 6.53 TNC 30.93 36.93 39.64 31.83 37.18 34.26 36.64 37.47 19.67 34.44 30.90 25.75 32.86 15.99 24.07 22.83 36.31 33.92 28.99 34.43 30.86 25.65 28.72 26.82 34.42 32.4 31.43 38.22 38.38

0.86 Ash 4.82 2.91 3.01 3.72 4.44 5.09 4.23 6.35 5.86 7.97 7.75 5.85 9.19 8.29 1.77 5.64 2.32 1.85 5.33 3.95 2.72 12.79 13.77 11.31 15.66 14.54 12.21 42.09 12.08 1

AP 8.39 6.97 9.58 16.13 12.69 10.34 18.90 13.97 16.37 14.60 13.29 22.76 20.01 24.24 24.21 30.90 28.06 16.77 22.68 18.74 10.43 10.40 15.48 15.03 15.05 24.74 14.41 13.42 11.85 13.28

CP 21.53 18.66 16.94 23.73 18.16 21.72 20.00 20.69 27.16 25.94 27.78 28.13 30.93 30.23 19.49 26.46 27.55 19.88 26.66 22.10 23.25 23.39 30.98 19.13 18.11 15.53 17.29 17.80 11.67 13.81

Fat 4.33 5.27 5.57 5.33 4.32 4.33 5.06 5.06 5.00 4.05 2.64 3.45 4.78 3.17 2.60 3.12 2.59 2.65 3.12 3.41 2.52 2.83 3.03 4.97 4.42 5.29 4.81 4.89 4.45 4.60

5 DM 0.91 0.91 0.91 0.9 0.90 0.92 0.91 0.91 0.92 0.93 0.92 0.91 0.91 0.92 0.93 0.93 0.93 0.91 0.91 0.93 0.92 0.93 0.94 0.90 0.93 0.94 0.93 0.90 0.90 0.89

38 6.84 7.71 6.36 8.84 8.21 ADL 11.09 12.17 17.67 16.15 15.72 16.80 17.07 19.48 23.62 15.88 16.13 19.76 19.12 20. 14.89 14.84 17.76 20.58 14.05 13.21 18.84 10.29 15.63 17.17 15.41

ADF 19.49 20.13 18.30 19.43 25.24 27.99 27.84 28.14 21.40 31.43 32.14 37.08 33.16 28.48 30.32 28.15 31.56 27.88 28.62 21.31 24.95 29.62 24.41 22.75 20.58 29.09 22.03 28.91 31.34 29.76

NDF 26.78 25.03 24.69 23.55 39.04 45.38 43.17 43.06 39.28 51.13 50.71 50.58 50.02 46.07 47.40 45.72 44.78 43.19 42.43 41.22 41.08 40.94 40.46 38.84 53.92 43.49 28.37 45.97 45.74 45.60

Part

FL FL FL FL RF YL YL YL YL ML ML ML ML ML YL YL YL YL YL YL YL YL YL YL FL FL FL YL YL YL

Species us africanus welwitschii welwitschii Olea welwitschii Olea Olea welwitschii Piper capense Piper capense Piper capense Piper capense Piper capense Piper capense Piper capense Piper guineense Olea welwitschii Olea welwitschii Olea welwitschii Olea welwitschii Olea welwitschii Olea welwitschii Olea welwitschii Olea welwitschii Olea welwitschii Olea welwitschii Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanth Chionanthus africanus Chionanthus africanus Olea Olea welwitschii

Giseke

Hoffmanns.

Family

Piperaceae Oleaceae & Link

216

TNC 60.22 66.37 63.98 67.49 65.66 60.65 67.12 27.80 35.16 36.38 23.69 15.83 17.83 31.18 16.78 21.04 43.70 40.95 43.89 52.79 56.40 61.96 56.40 63.20 66.00 60.06 62.51 64.21 61.78 64.27

Ash 8.83 6.53 6.12 8.09 6.80 8.65 3.93 7.54 6.85 8.88 8.56 8.92 9.65 9.32 9.76 7.01 9.20 9.21 6.76 9.15 7.92 8.23 9.97 11.00 11.10 12.52 10.11 10.69 12.17 10.59

AP 7.50 8.83 8.73 7.19 9.54 9.34 6.10 7.17 5.99 8.22 9.66 8.88 7.49 9.93 9.17 9.68 7.49 9.26 9.72 11.14 11.23 14.12 18.35 10.44 11.67 15.71 11.49 12.46 12.91 11.38

68 CP 2.50 9. 9.87 15.21 11.29 11.43 12.81 11.75 12.12 21.86 23.97 15.70 12.70 10.33 12.38 17.81 12.17 13.12 20.80 16.81 14.95 15.20 1 11.05 12.69 10.34 13.14 11.59 12.05 12.68 12.30

Fat 6.30 6.38 5.84 7.15 7.72 5.67 5.86 3.63 3.91 5.44 8.20 6.60 9.19 6.94 8.15 9.67 7.09 4.31 8.85 7.19 7.62 5.93 3.92 17.40 19.09 22.22 19.69 19.57 13.34 13.38

4 DM 0.91 0.9 0.93 0.91 0.95 0.94 0.90 0.93 0.89 0.90 0.91 0.93 0.92 0.92 0.91 0.90 0.92 0.92 0.91 0.92 0.92 0.92 0.93 0.91 0.91 0.90 0.89 0.95 0.95 0.94

4.28 4.86 4.85 3.61 4.41 4.08 3.89 4.34 3.37 2.63 3.42 3.82 3.81 3.28 3.38 5.82 3.76 5.03 8.96 7.61 5.35 ADL 19.87 21.80 21.54 22.33 22.34 19.69 32.73 20.42 19.30

8.74 9.77 9.81 9.76 7.87 9.35 8.55 8.54 8.34 8.06 8.32 ADF 35.39 41.16 37.50 35.55 37.64 34.65 11.00 10.62 10.82 10.31 10.70 15.24 42.96 33.89 33.58 13.39 18.07 16.48 12.47

00 9.40 NDF 43.73 51.10 45.22 41.03 46.01 42.51 15.83 14.99 14.35 14.01 14. 13.77 13.70 13.45 13.44 13.13 12.35 11.92 11.25 11.10 10.27 1 11.68 46.80 39.66 41.20 24.88 24.53 21.95 16.34

Part

RF UF UF UF UF UF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF seedsRF UF YL YL FL RF RF RF RF

Species

sp. sp.

uineense lanceolata guineense a lanceolata iper guineense Maes Piper guineense Bridelia Pollinia Maesa Maesa lanceolata Maesa lanceolata Maesa lanceolata Maesa lanceolata Maesa lanceolata Piper guineense Piper guineense Piper guineense P Piper guineense Piper guineense Piper guineense Piper guineense Piper Piper guineense Piper guineense Piper guineense Piper g Piper guineense Piper guineense Piper guineense Piper guineense Piper guineense Piper guineense Piper guineense

Batsch ex

Giseke

Barnhart

Family

Poaceae Primulaceae Borkh. Phyllanthaceae Martinov Piperaceae

217

5.36 TNC 30.46 36.90 32.93 37.68 36.33 38.99 47.50 29.96 23.19 25.84 23.83 27.10 22.00 27.32 31.82 32.67 21.91 59.05 43.71 40.17 27.15 32.25 18.19 22.48 23.27 28.51 31.34 30.97 20.20

.95 Ash 4.33 3.79 2 6.87 4.47 6.21 3.10 7.57 5.09 8.70 9.63 3.36 3.46 8.20 7.66 9.65 9.35 7.84 13.07 10.58 16.73 15.02 10.85 12.06 11.41 11.57 11.09 11.25 16.47 15.34

9 AP 4.15 5.64 3.71 6.83 7.16 8.46 6.98 6.72 8.55 7.76 0.81 6.62 7.50 5.12 7.56 8.1 6.83 16.86 15.60 14.36 12.40 11.58 22.76 21.19 17.15 28.04 18.04 17.41 14.79 19.53

CP 9.05 9.76 8.54 9.60 9.12 1.48 8.24 9.63 7.57 19.98 18.66 18.00 15.04 20.15 10.04 10.32 10.23 10.72 30.91 26.82 11.32 18.46 10.03 10.96 30.95 10.25 22.00 21.11 17.55 23.68

Fat 1.54 1.96 1.94 1.54 1.82 2.50 1.17 1.76 5.47 4.96 4.86 6.20 5.01 6.20 2.41 4.30 0.63 1.80 3.38 2.70 2.59 2.28 1.63 1.89 2.34 2.29 2.50 1.23 22.62 10.56

.91 DM 0.90 0.93 0.91 0.93 0.92 0.92 0 0.91 0.90 0.89 0.91 0.92 0.87 0.87 0.89 0.90 0.86 0.86 0.90 0.87 0.92 0.93 0.93 0.92 0.93 0.92 0.91 0.94 0.90 0.92

00 4.52 7.56 3.57 7.27 6.83 6. 3.70 3.68 ADL 19.86 14.64 16.61 15.86 21.32 18.44 19.17 17.43 19.36 17.80 13.73 13.69 15.56 13.74 12.32 11.57 22.18 20.44 17.99 25.21 11.59 25.51

5.06 ADF 32.11 37.73 28.31 30.78 22.97 32.24 40.99 38.56 48.34 44.54 46.08 42.33 34.64 32.67 26.07 29.93 31.33 29.97 25.12 25.84 40.97 40.14 36.09 38.30 36.39 37.93 35.19 32.41 19.07

NDF 57.62 59.69 53.85 35.31 30.78 40.19 54.78 53.27 63.01 58.71 58.13 57.44 41.03 38.71 36.27 44.71 39.08 35.96 35.05 34.36 54.37 50.08 45.66 46.86 58.64 58.57 57.63 39.07 37.15 26.93

Part

m RF RF RF FB FL RF RF RF UF UF UF UF YL YL YL YL YL YL YL YL UF UF UF FL RF RF RF UF gu pith rinum

Species sp. sp. sp. sp. sp. sp. sp. sp. sp. sp.

permum laurinum sp. sp.

pinnatus Oxyanthus Oxyanthus Psychotria Psychotria Psychotria Psychotria Psychotria Psychotria Psychotria Randia Randia Craterispermum laurinum Craterispermum laurinum Craterispermum laurinum Craterispermum laurinum Craterispermum laurinum Crateris Craterispermum laurinum Craterispermum laurinum Craterispermum lau Oxyanthus Maesa lanceolata Prunus africana Rubus pinnatus Rubus Craterispermum laurinum Craterispermum laurinum Craterispermum laurinum Craterispermum laurinum Craterispermum laurinum

Batsch ex

Juss.

Family

Rosaceae Rubiaceae Primulaceae Borkh.

218

9.55 6.50 9.96 TNC 22.94 13.79 15.95 22.33 21.73 31.22 10.43 11.74 32.12 21.91 25.52 24.03 23.27 24.76 22.45 26.48 36.94 33.18 34.75 31.94 41.27 41.31 20.66 25.21 11.22 15.06 18.02 18.37

Ash 5.30 6.29 6.85 4.79 6.22 7.64 5.26 8.20 6.91 6.10 7.06 9.19 9.62 8.53 8.64 5.99 7.94 8.21 7.06 8.30 6.56 5.63 15.27 14.68 17.04 14.29 12.93 14.35 10.77 13.57 11.43

16 AP 14.81 21.64 21.63 18. 19.96 33.26 17.00 29.32 34.90 29.83 17.53 28.72 27.04 21.90 28.59 23.60 25.20 20.72 11.18 16.83 17.79 24.49 22.15 20.74 24.54 23.31 19.82 20.61 17.33 16.26 18.55

CP 26.52 31.46 32.40 28.09 28.01 39.07 24.45 34.05 39.86 34.05 25.98 31.66 29.98 25.40 31.40 27.58 27.91 26.70 21.22 23.87 24.80 31.86 27.15 28.17 28.29 26.76 34.24 34.13 31.28 25.95 32.43

.24 Fat 2 4.16 2.25 2.76 3.17 1.96 2.52 2.13 2.11 1.83 2.74 3.39 3.67 2.23 3.61 2.57 1.98 3.05 2.54 3.38 3.19 3.12 2.38 2.98 2.77 3.05 2.64 1.90 2.67 2.87 2.26

DM 0.90 0.90 0.90 0.90 0.90 0.90 0.92 0.93 0.92 0.90 0.90 0.93 0.92 0.94 0.93 0.92 0.93 0.93 0.94 0.93 0.93 0.89 0.93 0.89 0.90 0.90 0.92 0.91 0.92 0.92 0.92

7.59 5.39 7.39 8.50 ADL 13.68 12.52 13.30 16.87 12.29 13.02 11.90 12.37 26.17 29.34 25.95 23.28 25.86 27.25 26.15 24.64 26.30 23.90 21.86 15.31 14.28 16.00 19.04 23.07 18.61 19.92 18.39

09 ADF 20.61 19.52 26.62 19.46 25.45 26. 27.01 21.12 23.28 25.37 23.97 42.24 45.41 42.38 38.85 41.30 41.66 39.25 39.96 41.77 36.13 24.05 33.34 26.77 25.78 26.79 33.60 34.99 32.55 31.83 30.86

NDF 40.07 38.66 38.55 38.47 37.14 37.03 32.81 29.21 28.03 39.47 38.01 60.37 60.21 57.64 57.29 56.19 55.72 55.11 54.32 52.96 49.92 48.59 45.01 43.85 42.81 40.56 40.42 41.55 39.57 38.00 36.75

Part

L YL YL YL YL YL YL YL YL YL ML M YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL

urcelliformis

Species

sp. sp. sp. sp. sp. sp. sp.

Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Randia Randia Randia Randia Randia Randia Randia Rothmannia urcelliformis Rothmannia urcelliformis

Juss.

Family Rubiaceae

219

TNC 23.31 22.05 24.53 29.15 25.84 30.70 30.12 35.35 44.39 63.41 17.30 47.43 48.33 18.50 23.54 23.93 24.77 21.78 25.54 25.13 17.58 26.64 27.33 31.81 20.09 29.40 29.83 34.38 34.56 36.22 21.76

69 Ash 6.28 7.45 6.41 6.25 6.68 3.67 6.69 4.86 3.15 2.77 8.58 4.59 7.06 8.38 7.71 6.12 7.50 8.98 3.56 5.72 6.52 7.56 10.53 13.40 13.39 14.98 12. 12.62 11.90 15.17 11.03

AP 9.47 8.22 9.50 9.47 0.09 0.30 9.11 8.76 10.49 10.77 10.69 10.69 21.96 15.43 15.89 22.94 18.41 21.61 24.82 27.14 27.87 24.65 30.78 23.49 23.21 20.80 30.33 22.14 21.04 11.17 10.17

.52 CP 3.55 0.52 14.88 13.55 13.69 13.45 14.62 12.03 13.26 13.91 24.56 17.17 19.06 26.19 21.73 26.44 27.03 30.31 29.95 27.75 33.72 26.89 26.36 23.14 31.88 27.14 23.70 13.50 12 11.95 13.59

57 Fat 2.31 1.76 2.05 1.90 2.37 2.34 2.19 1.29 1. 2.19 6.80 5.99 2.13 2.30 1.28 1.99 1.93 3.81 2.07 1.98 2.53 2.63 2.46 1.99 3.26 2.81 1.75 2.51 2.05 1.85 13.19

DM 0.91 0.94 0.90 0.92 0.89 0.92 0.90 0.90 0.90 0.90 0.89 0.90 0.90 0.92 0.92 0.93 0.91 0.93 0.92 0.92 0.90 0.90 0.94 0.90 0.85 0.87 0.92 0.90 0.90 0.90 0.90

5.66 5.47 9.42 7.12 8.97 9.84 9.62 0.76 5.02 ADL 12.03 13.52 13.36 12.51 13.24 11.40 10.17 12.01 10.56 11.34 11.79 12.96 10.01 14.80 12.42 12.06 10.01 34.31 22.89 12.64 12.44 10.68

69 2.49 ADF 16.88 15.10 31.70 31.39 28.82 25.05 26.26 26.29 25.22 20.30 26.28 18.37 33.52 32.40 30.43 38.48 38.20 35. 34.64 36.76 35.68 33.58 32.85 30.20 50.75 33.94 23.70 19.04 25.48 21.77

NDF 26.77 23.73 49.05 49.04 43.64 43.30 35.65 35.20 34.02 33.42 37.22 36.29 50.14 49.09 47.46 59.66 59.63 59.24 57.24 55.47 55.42 54.79 52.53 48.81 51.81 32.33 39.97 36.74 36.28 35.77 35.68

Part

RF UF YL YL YL YL YL YL YL YL ML ML RF RF RF UF UF UF UF UF UF UF UF UF UF pulp gum RF YL YL YL YL

Species sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp.

nna Vepris nobilis Tarenna Tarenna Vangueriaapiculata Zanthoxylum leprieurii Fagaropsis angolensis Vepris nobilis Vepris nobilis Vepris nobilis Vepris nobilis Vepris nobilis Tarenna Tarenna Tarenna Tarenna Tarenna Tarenna Tarenna Tarenna Tarenna Tarenna Tare Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Rothmannia urcelliformis Tarenna

Juss.

Family Rutaceae Rubiaceae

220

6.97 7.58 8.41 0.00 TNC 25.40 40.31 31.38 37.69 33.46 17.53 3 10.26 13.67 18.52 28.04 19.49 22.82 24.71 13.98 22.67 20.98 24.60 21.90 66.87 60.36 26.93 31.11 34.59 28.07

Ash 3.01 6.25 2.39 2.21 5.94 4.12 7.00 8.65 7.36 2.63 4.48 6.02 11.99 23.23 20.39 14.50 14.86 21.61 12.91 11.60 12.59 14.04 12.45 14.53 14.96 14.24 10.61 14.70

AP 1.48 2.22 1.40 2.65 1.88 0.39 20.80 11.35 11.90 18.02 13.67 13.90 15.02 14.58 26.00 23.36 26.27 30.55 24.58 24.80 24.15 31.83 12.88 14.08 12.45 12.48 18.94 33.93

79 CP 7.20 8.28 5. 9.05 6.39 26.55 12.54 14.34 15.94 21.46 16.34 17.01 18.64 17.51 29.54 26.93 31.13 33.55 28.1 28.59 27.32 34.42 14.11 14.87 16.61 16.90 23.68 37.73

Fat 1.91 3.75 5.47 3.85 4.61 2.83 3.62 7.76 5.55 1.87 1.77 1.08 2.18 1.96 1.78 1.56 1.78 3.19 3.00 2.64 3.10 12.47 11.94 10.73 12.14 12.36 16.74 17.62

0.9 DM 0.93 0.92 0.93 0.93 0.93 0.91 0.91 0.91 0.91 0.89 0.89 0.92 0.91 0.91 0.92 0.92 0.94 0.92 0.91 0.91 0.94 0.91 0.95 0.91 0.91 0.89 0.90

7.63 1.68 2.10 ADL 18.79 17.52 20.16 20.12 20.75 11.82 12.21 11.53 19.75 14.45 19.91 13.50 12.86 32.01 35.65 35.65 30.69 49.44 34.69 15.10 16.16 11.64 12.43 10.71 14.00

4.92 6.49 ADF 38.93 35.98 42.20 40.81 40.59 24.64 24.69 23.44 16.86 27.91 23.11 30.96 24.58 31.94 46.29 51.02 51.02 52.52 70.17 54.12 33.12 35.66 25.82 25.38 26.12 25.83

16 7.46 8.88 NDF 49.33 46.66 53.22 46.79 45.83 39.34 38.89 35.74 31.24 37.53 37. 40.35 29.88 40.90 52.46 55.68 55.68 58.07 72.82 55.90 43.69 49.20 42.04 40.46 40.29 40.24

Part

RF UF UF UF UF YL YL YL YL RF RF RF RF YL YL YL pulpRF pulpRF seeds pulpRF seeds pulpRF seeds UF UF pulp seeds RF RF YL YL YL YL

sp.

Species sp. sp. sp. bagshawei

bilis sp. sp. sp. sp. sp. sp. sp. sp.

yalis macrocalyx Mimusops Mimusops bagshawei Mimusops bagshawei Solanum Solanum Solanum Solanum Solanum Solanum Solanum Dovyalis macrocalyx Allophylus Allophylus Allophylus Cardiospermum Pancovia turbinata Mimusops bagshawei Mimusops bagshawei Mimusops bagshawei Vepris nobilis Vepris no Vepris nobilis Vepris nobilis Vepris nobilis Vepris nobilis Vepris nobilis Vepris nobilis Dov

Juss.

Juss. Juss.

Mirb.

Juss.

Family Solanaceae Sapotaceae Salicaceae Sapindaceae Rutaceae

221

6.36 TNC 13.67 24.77 18.03 20.49 21.94 19.26 23.38 20.20 14.26 22.74 27.12 20.61 24.67 35.64 28.43 30.14 16.90 14.89 19.07 29.26 24.46 25.61 23.07 10.97 20.70 17.86 12.99

.89 6.06 Ash 8.34 18.54 15.02 17.19 14.26 14.81 16.34 14.35 14.09 16.93 1 13.06 14.31 17.54 11.01 13.39 13.60 15.21 11.40 16.34 12.09 12.18 11.19 11.49 19.02 18.18 17.06 17

AP 8.75 9.82 6.97 5.86 8.71 16.03 13.54 14.00 12.00 13.86 11.77 15.44 19.12 11.73 11.07 16.41 11.76 11.03 19.50 14.85 14.13 12.64 12.39 17.19 17.89 17.15 12.97 17.36

CP 25.74 17.18 22.92 22.68 21.12 23.18 20.39 25.57 26.66 21.63 20.63 26.35 20.99 16.41 21.16 21.08 29.86 22.74 27.90 20.63 19.61 23.89 25.00 11.56 25.99 18.48 22.12 25.99

Fat 1.30 1.54 1.36 1.63 1.64 1.26 1.44 1.81 1.31 2.06 1.70 1.92 1.98 2.06 2.41 1.64 1.73 2.32 1.70 2.02 1.40 2.67 5.50 5.10 1.19 1.46 1.39 1.20

DM 0.87 0.86 0.86 0.88 0.86 0.88 0.92 0.88 0.91 0.89 0.89 0.89 0.89 0.89 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.87 0.89 0.86 0.86 0.90 0.88 0.89

4 3 ADL 24.84 29.02 26.80 25.21 26.22 15.07 17.02 14.31 15.30 24.43 25.5 27.40 26.67 24.51 29.38 25.14 27.94 26.65 26.61 29.47 25.2 30.51 21.23 29.05 28.32 29.10 31.89 33.17

.20 ADF 40.59 44.03 41.74 38.03 40.14 41.13 37.61 33.10 34.63 43.54 44.12 49.01 44.90 42.69 46.67 43.70 46.33 42.25 42.83 46.18 41.97 44.47 39.12 42.72 43.92 44.78 42.41 46

NDF 47.74 46.99 45.32 45.01 44.59 55.17 45.78 45.48 40.51 58.64 52.67 51.95 51.73 51.57 51.45 50.92 50.88 50.63 50.62 50.29 50.06 49.47 49.38 48.41 48.05 58.21 55.53 53.94

Part

YL YL YL YL YL stems pith y. stems pith y. stems pith y. stems UF YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL YL FL FL pith y.

ensis nensis Species cameroonensis cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera Urera cameroo Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera Urera cameroonensis Urera cameroonensis Urera cameroon Urera cameroonensis Myrianthus arboreus Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis

Juss.

Family Urticaceae

222

15 TNC 35.13 33.61 36.09 41.65 42.72 47.33 42.53 46.24 17.35 44.62 27.96 30.70 31.36 16.57 25.91 27.20 35. 30.77 33.21 24.87 26.03 30.78 38.63 33.64 37.72 31.26 42.74 23.44

Ash 9.89 8.96 8.73 6.42 8.97 9.10 5.40 0.55 0.96 3.16 7.28 9.66 8.88 6.30 5.86 5.48 3.82 5.88 5.11 11.08 10.39 13.61 15.44 14.40 13.81 14.88 12.36 11.48

.32 AP 8.22 9.35 8.17 8.36 8.16 7.93 4.00 4.94 2.97 6.93 8.41 5.37 11.21 10.44 16.72 14.43 14.14 14.68 13.44 14.98 19.80 29.72 29.62 23.08 28 30.47 35.72 26.22

P C 7.68 5.87 8.89 15.80 16.68 16.27 14.84 14.99 13.79 17.10 15.52 10.76 18.66 17.15 23.89 23.66 17.84 21.64 23.29 21.62 32.09 32.11 25.93 32.17 32.33 37.93 29.28 19.49

Fat 2.56 2.68 2.56 3.41 2.86 2.43 3.23 3.04 0.37 3.24 1.88 5.32 2.15 7.08 1.81 2.02 1.98 1.91 1.78 0.81 2.57 2.58 2.98 3.23 3.67 3.35 3.06 2.29 14.56

DM 0.89 0.89 0.91 0.92 0.95 0.87 0.94 0.88 0.90 0.90 0.90 0.91 0.92 0.90 0.92 0.94 0.92 0.91 0.92 0.93 0.93 0.92 0.92 0.94 0.89 0.87 0.87 0.86 0.88

.68 6.54 9.43 9.85 8.57 9.11 7.08 8.92 4.98 4.58 7.79 ADL 22.64 25.73 22.50 10.24 28.37 19.93 24.99 22.88 18.30 16.47 15.60 12.22 12.68 25.12 10 26.94 21.34 26.64 22.07

9.92 ADF 33.95 33.53 33.46 26.86 38.47 33.99 36.70 21.88 20.19 19.70 20.62 12.30 16.65 45.33 33.70 34.32 33.88 29.65 29.54 28.58 26.21 24.60 60.87 17.87 40.08 34.71 39.07 33.76

07 NDF 67.64 57.09 40.79 35.19 55.64 38.67 45.86 33.81 31.72 30.45 30.34 25.32 24.79 23.87 58.42 45.21 44.27 43.79 38.62 38.53 36.89 35. 32.17 73.89 34.58 43.70 42.71 41.65 40.00

Part

seeds seeds of RF pith seedsRF stems YL pith y. stem YL YL YL YL YL YL YL FL FL FL FL FL FL FL FL FL fruits seeds of RF YL YL YL YL

sp.

Species vine ntana camara Lantana camara Lantana camara Lantana camara Lantana camara Aframomum "Bean plant" "Bean plant" Unknownvine Unknown Unknownvine Urtica massaica Urtica massaica Urtica massaica Urtica massaica La Lantana camara Lantana camara Lantana camara Lantana camara Lantana camara Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urera cameroonensis Urtica massaica Urtica massaica Urtica massaica Urtica massaica

Hil. -

J.St.

Juss.

Family Zingiberaceae Martinov Unknown Verbenaceae Urticaceae

223

9.28 0.00 TNC 51.25 29.76 31.00 16.13 21.68 24.55 22.30 14.74 31.53 31.02 19.78 48.05

Ash 7.32 7.80 7.33 6.23 6.23 5.34 4.31 5.02 7.25 13.27 10.80 16.91 12.51 86.94

AP 2.35 0.67 16.00 50.20 51.54 46.83 54.94 55.65 65.51 63.13 64.67 69.46

CP 8.63 1.35 19.12 58.68 58.04 55.97 61.50 61.71 72.65 72.65 71.11 76.57 78.88

Fat 3.33 1.06 6.54 9.71 9.85 9.54 6.37 6.37 5.74 0.54 1.90 15.62 17.71

DM 0.93 0.94 0.94 0.94 0.94 0.93 0.93 0.94 0.98 0.92 0.92 0.89 0.93 0.95 0.95

NA NA NA NA NA NA NA NA NA NA NA 6.66 7.82 8.39 ADL 23.23

8.91 6.44 ADF 14.46 25.87 15.45 12.60 17.65 20.58 16.50 10.75 31.25 19.38 41.31 20.31

NA NA NA NA NA NA NA NA NA NA NA NDF 14.72 43.80 30.43 60.51

Part

leaf

sp. leaf

Species

ers Neoboutonia Vepris nobilis

Grasshopp Grasshoppers Grasshoppers Soil processed by termites Galls on Galls on Unknown shrub Caterpillars (Noctuidae) Caterpillars Caterpillars Caterpillars Caterpillars Caterpillars Cicadas Cicadas

/Category

ls galls

Family Soil Leaf gal Leaf Insect morphotypes

224

0.8 0.5 2.5 0.6 0.9 0.8 0.8 0.6 0.6 2.5 Mo ppm

14 41 41 14 57 28 22 16 Mn 119 261 ppm

plant food

- 5 6 16 13 14 15 10 18 10 16 Cu Cu ppm

17 27 67 13 23 11 24 26 24 46 Zn ppm

18 68 71 67 Fe 91 89 273 749 110 100 ppm , by non followed

nalyses) consumed by redtail nalyses) consumed monkey 0 0 rcentage. Sodium (Na), iron (Fe), iron rcentage. Sodium zinc (Na), 70 80 70 30 50 60 Na Na 110 ppm 1420

1.3 2.38 1.05 3.22 2.75 3.24 1.44 2.15 4.05 1.81 K % K

0.3 0.57 0.11 0.43 0.17 0.29 0.18 0.23 0.23 0.23 Mg %

0.2 P % 0.26 0.18 0.49 0.37 0.15 0.27 0.27 0.19 0.18

21 0.5 0. 0.83 0.82 0.37 0.28 0.16 0.33 0.59 0.36 Ca % Ca

ripe ripe ripe ripe

f ripe

eds of ripe ripe of eds flowers fruit ripe whole Part se fruit young leaves husk of fruit pulp o fruit ripe of seeds fruit fruit unripe whole and seeds pulp of fruit flowers

sp.

sp. sp. sp.

Plant parts listed alphabetically by family, species, and plant part and by family, species, listedPlant alphabetically parts Funtumia Funtumia Funtumia Tabernaemontana pachysiphon Dracaena myristica Uvariopsis congensis Uvariopsis congensis Uvariopsis congensis Uvariopsis congensis Species Lindackeria Monodora

Juss.

Juss. Mineral composition of unique foods (with sample remaining for micronutrient a micronutrient remaining for sample (with composition foods unique of Mineral

Harms Juss.

. 2 alcium (Ca), phosphorous (P), magnesium (Mg), and potassium (K) reported as pe as phosphorous reported potassium (Ca), (K) and magnesium (P), alcium (Mg),

Asparagaceae Apocynaceae Annonaceae Family Achariaceae samples. C samples. parts million molybdenum (Mn) as mg/kg). (ppm or per and manganese (Mo) reported (Cu), copper (Zn), Appendix three study in the females groups.

225

0.6 0.6 0.6 2.2 0.7 0.7 1.7 0.7 1.2 0.9 1.8 2.1 Mo ppm

7 21 50 98 21 54 91 29 26 Mn 128 259 126 ppm

5 7 8 5 9 7 8 19 14 11 11 13 Cu Cu ppm

8 16 22 11 17 24 29 15 13 58 17 18 Zn ppm

84 79 68 75 86 Fe 82 35 87 78 94 279 203 ppm

0 0 0 0 0 0 0 40 70 70 Na Na 130 170 ppm

1.2 1.26 3.59 1.57 1.49 1.88 1.95 1.85 2.65 1.03 2.07 2.04 K % K

0.2 0.8 0.2 0.2 0.16 0.49 0.89 0.46 0.56 0.12 0.06 0.23 Mg %

0.4 P % 0.07 0.25 0.27 0.24 0.39 0.36 0.37 0.25 0.29 0.13 0.17

0.25 0.49 0.37 3.33 1.03 0.94 0.73 0.61 0.11 0.18 0.56 Ca % Ca 15.72

flowers fruit ripe whole young stems fruit unripe whole young leaves fruit ripe whole fruit unripe whole fruit unripe whole young leaves spit fruit ripe seed spit fruit ripe skin Part young leaves

sp.

elegans elegans

Salacia Salacia Salacia Symphonia globulifera Palisota Celtis Celtis gomphophylla Celtis gomphophylla Chaetachme aristata Chaetachme aristata Species Spathodea campanulata Spathodea campanulata africana Celtis africana Celtis

Mirb.

Juss. Martinov R.Br ae

Lindl.

Clusiaceae Commelinace Celastraceae Cannabaceae Bignoniaceae Family

226

1 1.1 0.8 2.3 0.5 0.6 1.2 0.8 1.9 0.3 0.7 1.4 1.4 1.6 Mo ppm

21 38 48 49 60 65 14 Mn 177 228 105 147 228 101 550 ppm

5 9 4 8 21 11 15 17 21 13 11 17 13 19 Cu Cu ppm

27 22 35 27 29 45 45 44 27 12 29 27 67 26 Zn ppm

66 69 89 Fe 62 64 82 101 153 124 181 168 133 189 104 ppm

0 0 0 0 0 0 50 10 50 10 20 90 70 Na Na 110 ppm

3.91 3.59 1.67 3.18 1.27 2.61 0.61 1.26 2.03 1.85 1.42 3.02 3.16 1.03 K % K

34 0.3 0.3 0.1 0.34 0.17 0.74 0.65 0.25 0. 0.73 0.19 0.14 0.32 0.46 Mg %

P % 0.63 0.44 0.41 0.43 0.52 0.27 0.48 0.27 0.53 0.41 0.36 0.64 0.52 0.21

2.5 0.14 0.87 0.27 0.11 0.77 0.58 0.66 1.21 0.16 0.12 0.11 0.63 0.26 Ca % Ca

flowers flowers whole young leaves young leaves fruit ripe whole ofpith young stem ripe of seeds fruit young leaves young leaves young leaves young leaves buds flower Part young leaves fruit unripe

sp.

sp. sp. sp.

cca sp. sp.

sp. a

sp.

ia dura ia dura ia t t Acacia Albizia grandibracteata Millet Millet Hoslundi opposita Erythroco Macaranga Macaranga Macaranga Neoboutonia macrocalyx Species Ipomoea Diospyros abyssinica Diospyros abyssinica Acalypha

Juss.

Martinov Gürke

Lindl.

Lamiaceae Fabaceae Euphorbiaceae Convolvulaceae Ebenaceae Family

227

1 0.5 0.2 0.6 0.6 0.4 0.6 0.7 0.8 0.5 1.4 1.2 Mo ppm

45 23 20 33 11 85 42 75 Mn 139 133 115 126 ppm

7 6 7 8 7 12 12 12 12 13 15 11 Cu Cu ppm

13 17 22 23 42 23 39 26 61 69 20 23 Zn ppm

35 46 Fe 68 93 88 122 140 166 130 116 121 124 ppm

0 0 0 30 70 20 10 20 30 40 80 Na Na 190 ppm

2.5 2.07 2.29 2.27 2.32 0.44 0.61 1.71 2.43 1.59 2.97 3.35 K % K

g % 0.3 0.37 0.23 0.27 0.51 0.46 0.16 0.21 0.25 0.38 0.22 0.26 M

0.4 P % 0.34 0.19 0.34 0.52 0.42 0.25 0.23 0.27 0.16 0.33 0.46

0.1 1.21 0.39 0.68 1.19 0.97 0.05 0.39 0.87 0.41 0.86 0.56 Ca % Ca

unripe fruit fruit unripe whole fruit ripe whole young leaves fruit unripe whole young leaves and seeds ripe pulp of fruit ripe of seeds fruit of seeds fruit unripe young leaves fruit ripe whole fruit ripe whole Part young leaves

sp. sp

brachylepis

tonychia tonychia Ficus exasperata Ficus exasperata Ficus Ficus mucuso Marantachloa Marantachloa Trilepisium madagascariense Trilepisium madagascariense Species Premna angolensis mitis Strychnos Lep mildbraedii

R.Br. R.Br. ex ex R.Br.

Martinov Juss.

Gaudich.

Moraceae Malvaceae Marantaceae Lamiaceae Loganiaceae Mart. Family

228

1 1 1 1 0.8 0.6 7.7 3.5 0.7 1.1 0.3 0.6 0.6 Mo ppm

8 38 15 18 44 44 83 57 10 15 Mn 242 238 133 ppm

4 6 8 6 4 5 7 14 13 10 11 14 22 Cu Cu ppm

7 14 19 50 39 18 19 25 44 27 12 17 35 Zn ppm

93 69 34 Fe 97 93 15 38 58 155 291 136 153 ppm 27100

0 0 0 0 0 0 10 30 40 50 90 40 10 Na Na ppm

2.5 2.4 2.69 3.01 2.29 0.65 1.36 1.92 1.12 1.55 2.44 2.04 2.12 K % K

73 0.17 0.17 0. 0.43 0.06 0.11 0.18 0.29 0.12 0.33 0.63 0.19 0.48 Mg %

0.4 0.4 P % 0.42 0.51 0.12 0.18 0.33 0.15 0.24 0.44 0.45 0.14 0.24

0.6 0.61 2.56 1.77 0.11 0.63 0.39 1.53 0.45 0.58 1.03 0.19 1.74 Ca % Ca

leaves

uit ripe fruit fruit ripe whole fruit unripe whole unripe fruit fruit unripe whole young of seeds fruit unripe and pulp skin of unripe fr young leaves mature leaves young leaves young leaves flowers fruit ripe whole Part mature leaves

sp.

lanceolata asperifolia asperifolia asperifolia Bridelia capense Piper guineense Piper Maesa lanceolata Maesa Chionanthus Chionanthus africanus Chionanthus africanus welwitschii Olea welwitschii Olea Species Ficus Ficus Ficus Chionanthus africanus

Martinov

Batsch ex Batsch

Giseke

Gaudich.

Hoffmanns. & Hoffmanns.

Primulaceae Borkh. Phyllanthaceae Piperaceae Oleaceae Link Moraceae Family

229

0.8 1.1 0.5 0.7 0.8 0.1 8.1 1.2 0.3 0.6 0.8 0.9 Mo 14.5 ppm

2 50 48 86 20 19 27 17 Mn 364 154 547 918 ppm 1140

0 8 9 4 2 7 4 9 6 1 13 10 12 16 Cu Cu ppm

7 9 3 1 32 38 55 25 18 15 29 14 80 Zn ppm

57 84 73 60 50 60 Fe 45 57 62 33 178 192 142 ppm

0 0 0 0 0 0 0 0 10 10 50 70 20 Na Na ppm

1.74 1.31 0.79 1.54 2.16 0.86 3.24 3.66 1.84 1.42 1.37 0.95 1.17 K % K

0.2 0.06 0.34 0.62 0.14 0.95 0.27 0.34 0.44 0.22 0.26 0.25 0.31 Mg %

7 P % 0.08 0.12 0.16 0.16 0.36 0.25 0.57 0.83 0.38 0.1 0.16 0.02 0.25

0.5 0.07 0.78 2.03 0.23 0.92 0.33 0.65 0.45 0.41 0.31 0.45 0.59 Ca % Ca

whole pulp of fruit unripe whole fruit unripe whole young leaves fruit unripe whole flowers fruit ripe whole buds flower young leaves young leaves fruit ripe whole fruit unripe Part exudate fruit ripe

sp. sp. sp.

sp. sp.

africana sp. sp.

Rothmannia urcelliformis Tarenna Tarenna Vangueria apiculata Oxyanthus Psychotria Psychotria Randia Randia Species Prunus Rubus pinnatus Craterispermum laurinum Craterispermum laurinu

. Juss.

Juss

Rosaceae Rubiaceae Family

230

1 pm 0.2 0.3 1.6 4.5 3.1 1.3 0.5 0.8 0.7 0.6 1.4 Mo p

38 60 15 37 64 21 91 23 84 70 15 Mn 129 ppm

2 2 4 6 6 6 7 8 5 21 12 13 Cu Cu ppm

4 3 10 12 44 31 41 32 20 33 32 34 Zn ppm

31 60 47 67 Fe 73 111 149 109 107 106 105 109 ppm

0 0 0 0 0 0 10 20 10 20 80 Na Na 400 ppm

2.7 4.82 2.72 1.61 2.81 2.15 1.42 0.98 1.56 2.49 2.66 0.46 K % K

0.1 0.66 0.26 0.19 0.22 0.13 0.43 0.17 0.13 0.18 0.56 0.19 Mg %

0.4 P % 0.59 0.38 0.23 0.42 0.18 0.39 0.07 0.13 0.37 0.41 0.01

0.7 0.1 0.82 0.52 0.58 0.72 0.31 0.62 0.21 0.27 1.82 0.52 Ca % Ca

ng leaves pith of young ofpith young stem ripe fruit fruit ripe whole fruit ripe whole you fruit ripe whole young leaves ripe pulp of fruit fruit unripe whole fruit ripe whole fruit unripe who flowers Part exudate

sp.

sp. sp.

Solanum Urera cameroonensis Urera cameroonensis Dovyalis Dovyalis macrocalyx Allophylus Mimusops bagshawei Mimusops bagshawei Solanum Species Zanthoxylum leprieurii Fagaropsi angolensis nobilis Vepris nobilis Vepris

Juss.

Juss. Juss. Juss.

Mirb.

Juss.

Urticaceae Solanaceae Sapindaceae Sapotaceae Salicaceae Rutaceae Family

231

1 0.3 0.8 3.2 0.9 0.9 4.4 1.3 7.1 2.1 2.5 0.9 1.4 0.1 0.3 Mo ppm

97 16 51 48 34 79 74 78 49 56 50 29 Mn 562 101 155 ppm

9 5 9 8 9 5 10 10 12 10 38 45 19 Cu Cu 119 182 ppm

9 7 19 37 23 32 29 24 19 3 Zn 133 123 239 179 215 144 ppm

pm 64 45 72 Fe 94 92 48 182 200 138 703 182 112 107 177 p 38000

0 0 30 40 90 80 20 10 90 50 Na Na 110 200 230 840 320 ppm

0.3 1.01 1.16 1.59 2.66 1.99 2.41 1.63 2.51 0.73 1.01 2.63 2.31 4.57 2.42 K % K

0.4 0.2 0.22 0.27 0.13 0.45 0.55 0.25 0.15 0.15 0.28 0.11 0.21 0.31 0.47 Mg %

1 0.4 P % 1.18 1.0 0.98 0.51 0.38 0.22 0.17 0.12 0.29 0.07 1.41 1.46 1.83 0.37

1 0.2 0.48 0.25 2.16 1.33 0.58 0.39 0.07 0.17 0.16 0.21 0.16 0.31 0.95 Ca % Ca

young leaves young leaves flowers fruit unripe whole ripe of seeds fruit ripe of seeds fruit fruit unripe whole Part fruit unripe whole

sp.

massaica momum a Caterpillars Caterpillars Caterpillars Caterpillars Cicadas Grasshoppers Lantana camara Lantana Afr "Bean plant" shrub Unknown soil Termite Species Urera cameroonensis Urera cameroonensis Urtica camara Lantana

Hil. -

Martinov

J.St.

Juss.

Soil morphotypes Insect Zingiberaceae Unidentified Unknown Verbenaceae Urticaceae Family

232 Appendix 3. Map of redtail monkey feeding trees with DBH >10, May 2015 – April 2016 for three study groups, Kusamererwa (Kus), Sukaali (Suk), and Kyomudendo (Kyo). Part of Makerere University Biological Field Station (MUBFS) referred to as “middle camp,” large trail roads, and border of Kibale National Park also indicated for reference. This map represents only a sample of feeding trees >10 DBH during this time period because some feeding trees could not be mapped because of satellite receptivity issues due to canopy cover or inclement weather.

233 Appendix 4. Summary of 137 focal follows greater than 5 hours focal female in sight for adult female redtail monkeys in three study groups. Grand mean values presented (mean values were calculated for each individual female and then each group). Focal female >5 hr in sight Kus Kyo Suk All groups N = 137 focal follows N=7 females N=9 females N=7 females N=23 females Mean SD Mean SD Mean SD Mean SD Time feeding (hours) 1.2 0.2 1.4 0.3 1.1 0.2 1.2 0.3 Time not feeding (hours) 5.4 0.6 5.8 0.2 5.9 0.5 5.8 0.6 Time in sight (Time feeding 6.6 0.6 7.2 0.57 7 0.5 7 0.5 + not feeding) (hours) Out of sight* + Lost† (hours) 3.3 0.5 3.2 0.57 3.2 0.4 3.2 0.5 Out of sight + Lost + feeding 9.9 0.3 10.4 0.4 10.2 0.3 10.2 0.4 + not feeding (hours) % time feeding out of Time 18.5 2.8 19 4.3 16.3 2.8 18 3.6 in sight Daily number of species of 8.1 1.7 11.3 2.1 9 1.1 9.6 2.2 plant fed from Daily number of sequential 159.4 24.7 175.2 48 143.5 12.5 160.8 35 switches between foods *Out of sight = when a focal female was partially visible but we could not see her forelimbs and mouth (and therefore specifics of feeding behavior). †Lost = we could not see focal female at all and did not know where she was.

234 Appendix 5. Diurnal variation in observed feeding and processing (of cheek-pouched food) (137 focal follows): hours of focal female feeding and processing by time of day (hour intervals, 7:00 – 19:00).

235 Appendix 6. Condensed tannin content via acid butanol assay of plant food samples unique across monkey groups with remaining sample available for tannin analysis. Plant parts listed alphabetically by family, species, and plant part. These results are based on one extraction and can only indicate presence (at any level) (+) and absence (-) of condensed tannins. Species and parts for which presence of tannins was detected are in bold. Samples listed as (e) indicate error due to color interference by pigments such as chlorophyll or anthocyanins, which prevents presence/absence determination.

Family Species Plant part Condensed tannins presence/absence Achariaceae Harms Lindackeria sp. ripe fruits whole (+) Annonaceae Juss. Monodora myristica young leaves (-) Apocynaceae Juss. Funtumia sp. ripe fruit (e) Funtumia sp. flowers (+) Tabernaemontana flower stalks (-) pachysiphon Asparagaceae Juss. Dracaena sp. ripe fruit whole (+) Bignoniaceae Juss. Spathodea campanulata stems (-) Spathodea campanulata young leaves (-) Cannabaceae Martinov Celtis africana ripe fruit whole (-) Celtis africana unripe fruit whole (-) Celtis africana young leaves (+) Celtis gomphophylla ripe fruit whole (-) Celtis gomphophylla unripe fruit whole (-) Chaetachme aristata unripe fruit (+) whole Chaetachme aristata young leaves (+) Celastraceae R.Br. Salacia elegans ripe fruit spit (+) seed Salacia elegans ripe fruit spit (+) skin Salacia elegans stems (+) Clusiaceae Lindl. Symphonia globulifera flowers (+) Commelinaceae Mirb. Palisota sp. ripe fruit (e) Convolvulaceae Juss. Ipomoea sp. young leaves (-)

236 Family Species Plant part Condensed tannins presence/absence Ebenaceae Gürke Diospyros abyssinica unripe fruit whole (e) Diospyros abyssinica young leaves (e) Euphorbiaceae Juss. Acalypha sp. young leaves (-) Macaranga sp. pith of y. stem (+) Macaranga sp. seeds of ripe fruit (+) Macaranga sp. young leaves (e) Macaranga sp. young stems (+) Neoboutonia macrocalyx mature leaves (-) Fabaceae Lindl. Acacia sp. young leaves (+) Albizia sp. young leaves (+) Millettia dura flower buds (+) Millettia dura flowers (-) Lamiaceae Martinov Hoslundia opposita flowers (-) Clerodendrum sp. stems (-) Loganiaceae R.Br. ex Mart. Strychnos mitis seeds and pulp (-) unripe fruit Strychnos mitis young leaves (-) Malvaceae Juss. Leptonichia mildbraedii unripe fruit whole (-) Leptonichia mildbraedii young leaves (+) Marantaceae R.Br. Marantachloa sp. seeds of ripe fruit (+) Moraceae Gaudich. Trilepisium seeds of unripe (+) madagascariense fruit Trilepisium young leaves (+) madagascariense Ficus brachylepis ripe fruit whole (+) Ficus exasperata unripe fruit whole (-) Ficus mucuso ripe fruit whole (+) Ficus asperifolia mature leaves (-) Ficus asperifolia young leaves (-)

237 Family Species Plant part Condensed tannins presence/absence Oleaceae Hoffmanns. & Chionanthus africanus seeds of unripe (+) Link fruit Chionanthus africanus unripe fruit (-) Chionanthus africanus young leaves (-) Olea welwitschii mature leaves (-) Olea welwitschii young leaves (-) Phyllanthaceae Martinov Bridelia sp. young leaves (+) Piperaceae Giseke Piper capensis flowers (+) Piper guineense ripe fruit whole (-) Primulaceae Batsch ex Maesa lanceolata ripe fruit (+) Borkh. Maesa lanceolate unripe fruit (+) Rosaceae Juss. Rubus pinnatus ripe fruit (+) Rubiaceae Juss. Craterisperum laurinum unripe fruit whole (e) Craterisperum laurinum young leaves (-) Oxyanthus sp. unripe fruit (+) whole Psychotria sp. flowers (e) Psychotria sp. ripe fruit whole (e) Randia sp. flower buds (e) Randia sp. young leaves (e) Rothmannia young leaves (-) urcelliformis Tarenna sp. ripe fruit whole (+) Tarenna sp. unripe fruit (+) whole Vangueria apiculata unripe fruit (+) whole Rutaceae Juss. Zanthoxylum leprieurii exudate (-) Fagaropsis angolensis unripe fruit whole (-)

238 Family Species Plant part Condensed tannins presence/absence Vepris nobilis ripe fruit whole (e) Vepris nobilis young leaves (e) Salicaceae Mirb. Dovyalis sp. ripe fruit whole (-) Sapindaceae Juss. Allophylus sp. young leaves (+) Cardiospermum sp. young leaves (-) Sapotaceae Juss. Mimusops bagshawei pulp and seeds of (-) ripe fruit Mimusops bagshawei unripe fruit (+) whole Solanaceae Juss. Solanum sp. ripe fruit whole (-) Solanum sp. unripe fruit whole (-) Urticaceae Juss. Urera cameroonensis flowers (+) Urera cameroonensis pith of young (+) stem Urera cameroonensis unripe fruit (+) whole Urera cameroonensis young leaves (+) Urtica massaica young leaves (-) Verbenaceae J.St.-Hil. Lantana camara flowers (-) Zingiberaceae Martinov Aframomum sp. seeds of ripe fruit (+)

239 Appendix 7. Comparison of plant species observed eaten (any plant part) at least once (indicated Y for yes) in each of my three study groups during study period and in TTK group in Kanyawara in the 1970s-80s (Struhsaker 2017). Plant species listed alphabetically by family, then species name.

Kanyawara Kanyawara 2015-2016 1973-1987 Family Species Kus Kyo Suk TTK Acanthaceae Juss. Mimulopsis sp. Y Achariaceae Harms Lindackeria sp. Y Y Anacardiaceae R.Br. Pseudospondias microcarpa Y Y Y Annonaceae Juss. Monodora myristica Y Y Y Y Uvariopsis congensis Y Y Y Y Apocynaceae Juss. Funtumia elastica Y Y Y Funtumia africana (syn. is Y Y Funtumia latifolia) Pleiocarpa pycnantha Y Y Y Rauvolfia vomitoria Y Tabernaemontana Y Y Y Y pachysiphon Asparagaceae Juss. Dracaena sp. Y Basellaceae Raf. Basella alba Y Bignoniaceae Juss. Kigelia africana subsp. Y Y moosa Markhamia lutea Y Y Y Y Spathodea campanulata Y Y Boraginaceae Juss. Cordia africana (syn. is Y Y Cordia abyssinica) Cordia millenii Y Y Ehretia cymosa Y Y Cannabaceae Martinov Celtis africana Y Y Y Y Celtis gomphophylla Y Y Y Y Chaetachme aristata Y Y Y Y Trema orientalis Y Y Y Celastraceae R.Br. Salacia elegans vine Y Y Y Chrysobalanaceae R.Br. Parinari excelsa Y Y Clusiaceae Lindl. Symphonia globulifera Y Y Y Y Commelinaceae Mirb. Palisota sp. Y Y Y Convolvulaceae Juss. Ipomoea sp. Y Y Ebenaceae Gürke Diospyros abyssinica Y Y Y Y

240 Family Species Kus Kyo Suk TTK Euphorbiaceae Juss. Acalypha sp. Y Y Y Y Croton macrostachyus Y Erythrococca sp. Y Macaranga sp. Y Y Y Neoboutonia macrocalyx Y Y Y Fabaceae Lindl. Acacia brevispica Y Y Y Acrocarpus sp. Y Albizia grandibracteata Y Y Albizia gummifera Y Y Erythrina abyssinica Y Millettia dura Y Y Y Y Gentianaceae Juss. Anthocleista schweinfurthii Y Icacinaceae Miers Apodytes sp. Y Y Y Lamiaceae Martinov Hoslundia opposita Y Y Premna angolensis Y Y Y Y Clerodendrum sp. Y Y Loganiaceae R.Br. ex Mart. Strychnos mitis Y Y Malvaceae Juss. Dombeya kirkii (syn. is Y Y Dombeya mukole) Leptonychia mildbraedii Y Y Marantaceae R.Br. Marantachloa sp. Y Y Y Moraceae Gaudich. Antiaris toxicaria Y Trilepisium Y Y Y Y madagascariense Ficus brachylepis Y Y Y Ficus exasperata Y Y Y Y Ficus mucuso Y Ficus natalensis Y Y Ficus ottoniifolia Y Ficus conraui (syn. is Ficus Y stipulifera) Ficus thonningii Y Y Ficus asperifolia (syn. is Y Y Y Ficus urceolaris) Olacaceae Juss. ex R.Br. Strombosia scheffleri Y Y Y Oleaceae Hoffmanns. & Link Chionanthus africanus Y Y Y Y Olea welwitschii Y Y Y Y

241 Family Species Kus Kyo Suk TTK Phyllanthaceae Martinov Bridelia sp. Y Piperaceae Giseke Piper capense Y Y Y Piper guineense Y Y Y Y Poaceae Barnhart Pennisetum purpureum Y ("Elephant grass") Pollinia sp. Y Y Primulaceae Batsch ex Borkh. Maesa lanceolata Y Rhizophoraceae Pers. Cassipourea sp. Y Y Y Rosaceae Juss. Prunus africana Y Y Y Rubus pinnatus Y Rubiaceae Juss. Coffea sp. Y Craterispermum laurinum Y Y Y Mitragyna sp. Y Oxyanthus sp. Y Psychotria sp. Y Y Y Randia sp. Y Y Y

Rothmannia urcelliformis Y Y Y Y Tarenna sp. Y Y Y Vangueria apiculata Y Y Y Y Rutaceae Juss. Zanthoxylum leprieurii Y Y Y Fagaropsis angolensis Y Y Vepris nobilis Y Y Y Y Zanthoxylum sp. Y Y Salicaceae Mirb. Casearia sp. Y Dovyalis macrocalyx Y Y Y Sapindaceae Juss. Allophylus sp. Y Lepisanthes senegalensis Y (syn. is Aphania senegalensis) Blighia unijugata Y Y Cardiospermum sp. Y Y Pancovia turbinata Y Y Y Y Sapotaceae Juss. Pouteria altissima (syn. is Y Y Aningeria altissima) Chrysophyllum Y Y gorungosanum

242 Family Species Kus Kyo Suk TTK Sapotaceae Juss. Mimusops bagshawei Y Y Y Y Solanaceae Juss. Solanum sp. Y Y Y Stilbaceae Kunth Nuxia congesta Y Urticaceae Juss. Myrianthus arboreus Y Y Urera cameroonensis Y Y Y Urtica massaica ("stinging Y Y Y nettle") Verbenaceae J.St.-Hil. Lantana camara Y Y Zingiberaceae Martinov Aframomum sp. Y Y Unidentified "Bean plant" shrub Y Y Unidentified “Hypocreatia” vine Y Unidentified "Rukolekole" vine Y Unidentified “Superthodea” Y

243 Appendix 8. Variation in starch composition of redtail monkey foods (A) by part (N=96 samples) and (B) for fruits only (N=48) with fruit component indicated. Exudate samples not included due to ambiguity of starch assay in measuring the content of gums. Food items codes in (A): INS=insect, YL=young leaf, ML=mature leaf, UF=unripe fruit (any component), FB=flower bud, FL=flower, PYS=pith of young stem, YS=young stem, and RF=ripe fruit (any component). Fruit codes in (B): UFW=unripe fruit whole, RFW=ripe fruit whole, RFP=ripe fruit pulp, RFH=ripe fruit husk, UFHP=unripe fruit husk and pulp, RFS=ripe fruit seed, RFHP=ripe fruit husk pulp, UFS=unripe fruit seed, RFPS=ripe fruit pulp and seed.

244 Appendix 9. Variation in sugar composition of redtail monkey foods by part (N=96 food samples analyzed for soluble sugar content), with food items ordered by median. Sugar results for exudate samples are not included because soluble fiber in exudates may interfere with sugar analyses via acid-butanol assay, potentially leading to erroneously high sugar values. Food items codes are as follows: INS=insect, YL=young leaf, ML=mature leaf, FB=flower bud, UF=unripe fruit (any component), FL=flower, PYS=pith of young stem, YS=young stem, and RF=ripe fruit (any component). The three outliers for young leaf sugar content were Diospyros abyssinica, Olea welwitchii, and Randia sp. The two outliers for flower sugar content were Lantana camara and Funtumia sp. (Diospyros abyssinica flowers had high total nonstructural carbohydrate and likely high sugar content, but we did conduct sugar analysis on this species/part.)

245

.79 – (%) 9.12 6.99 6.19 12.83 38.47 33.54 11.00 21.73 11.95 26.38 15.17 10.60 11.95 15.25 56 49.70 50.43 Remainder Remainder Plant parts

, and starch , andstarch (%) 3.86 3.41 1.45 0.48 3.60 4.27 0.96 0.50 4.65 1.17 0.61 4.48 0.18 2.35 2.23 26.42 13.95 Starch Starch estimation of estimation

glucose standard glucose and -

7.40 7.45 8.52 7.80 3.31 5.15 4.29 8.47 3.79 5.34 2.44 versus 15.62 14.03 13.80 12.82 13.20 11.79 Soluble Soluble sugar (%) sugar

%) (

32.02 24.10 20.24 16.35 16.01 17.87 71.96 65.13 76.17 24.10 42.98 57.50 43.51 21.51 22.40 39.80 16.22 TNC by TNC subtraction

sulfuric acid assay assay standard sulfurica sucrose with acid

ripe fruit ripe phenol

leaves

lant part lant young leaves who fruit ripe whole fruit unripe whole fruit unripe P fruit ripe of seeds young leaves fruit ripe husks of fruit ripe pulp of of seeds whole fruit unripe seed and pulp fruit ripe flowers stalks flower whole fruit ripe young young stems whole fruit ripe , followed by insect samples. All values reported on dry matter basis. matter samples. on dry reported by insect All values , followed

t of TNC by subtraction unexplained by sugar and starch percentages. t of starch and percentages. unexplained by sugar by subtraction TNC amylase method with a Megazyme total starch kit total starch amylase D with a Megazyme method with a -

sp.

campanulata sp. total nonstructural carbohydrate (TNC) content by subtraction nonstructural (TNC) carbohydrate total content sp. sp.

. Sugar was analyzed via analyzed . Sugar was ]

species, and plant and part species, foods’

Dracaena campanulata Spathodea Spathodea africana Celtis africana Celtis gomphophylla Celtis gomphophylla Celtis gomphophylla Celtis Monodora myristica Monodora congensis Uvariopsis congensis Uvariopsis congensis Uvariopsis congensis Uvariopsis Funtumia Funtumia Tabernaemontana pachysiphon Species Lindackeria oglucosidase/alpha

. omparison of omparison

C via the via the amyl Juss . Remainder indicates the percen the . Remainder indicates . Juss. Martinov

Juss.

Harms Juss.

al carbohydrate content via sugar and starch assays. TNC by subtraction is calculated follows: 100 as = is calculated starch [TNC assays. via sugar subtraction by and TNC carbohydrate content al

Cannabaceae Asparagaceae Bignoniaceae Apocynaceae Family Achariaceae Annonaceae (NDF+Fat+Available Protein+Ash) (NDF+Fat+Available analyzed was control maize family, by listed alphabetically Appendix nonstructur

246

(%) 9.59 9.82 6.70 9.71 9.13 19.19 25.44 59.69 15.72 29.05 23.18 14.80 20.57 12.09 31.49 24.49 12.99 19.41 24.91 25.58 16.75 17.96 21.57 Remainder Remainder

.68 (%) 1.31 2.37 2.51 1.46 2.46 0.39 1.23 0.45 1.49 1.43 3.57 1.02 2.20 0.77 1.01 0.22 2.27 1 2.93 2.31 5.08 14.17 31.54 Starch Starch

1.79 4.35 3.81 3.15 4.77 3.52 5.86 6.11 7.40 6.40 2.90 2.14 3.37 4.48 5.33 1.63 8.48 4.96 10.43 18.51 17.38 13.20 15.26 Soluble Soluble sugar (%) sugar

(%)

22.40 15.53 30.55 16.01 18.48 38.91 26.76 36.33 59.75 23.39 38.28 24.64 41.91 22.29 16.31 38.39 15.09 65.30 20.89 33.80 29.49 35.82 28.62 TNC by TNC subtraction

leaves lant part lant xudate flower buds flower flowers flowers young leaves ripe fruit seed destroy seed fruit ripe seed spit fruit ripe young stems flowers whole fruit ripe young e whole fruit unripe young leaves young leaves whole fruit ripe stem ofpith young fruit ripe of seeds young leaves young leaves young leaves young leaves P whole fruit unripe young leaves

rocalyx

sp.

sp. sp. sp.

opposita

sp. me aristata aristata me

sp. sp.

elegans elegans elegans sp.

sp.

ia dura ia t dura tia t Macaranga mac Neoboutonia Acacia Albizia Millet Mille Hoslundia Premna angolensis Palisota Ipomoea abyssinica Diospyros abyssinica Diospyros abyssinica Diospyros Acalypha Erythrococca Macaranga Macaranga Species Chaetach aristata Chaetachme Salacia Salacia Salacia globulifera Symphonia

Mirb. Juss.

Juss.

R.Br.

Lindl. Martinov Gürke

Lindl.

iaceae Lam Fabaceae Euphorbiaceae Clusiaceae Commelinaceae Convolvulaceae Ebenaceae Celastraceae Family

247

(%) 3.13 0.00 0.47 16.06 14.09 25.06 27.33 13.01 21.68 16.60 19.89 21.46 25.72 26.57 32.48 23.37 25.99 11.39 12.84 Remainder Remainder

40 (%) 0.50 1.07 0.80 0.39 5.77 1.97 1.48 3.45 0.45 1.64 1.66 1.08 2.16 2.63 1.57 0.69 18.44 20. 22.23 Starch Starch

6.78 7.29 4.98 5.36 6.06 7.35 1.63 4.65 2.34 4.89 3.30 5.37 3.42 7.92 4.97 19.67 15.16 11.73 11.08 Soluble Soluble sugar (%) sugar

(%)

6.14 9.10 28.72 36.64 27.80 32.86 28.83 25.60 59.30 17.44 22.33 23.34 22.44 30.84 33.08 51.12 42.60 24.63 36.48 TNC by TNC subtraction

es hole

leaves

lant part lant mature leav mature young leaves young leaves flowers young leaves ripe of seeds pulp and fruit fruit of unripe seeds young leaves whole fruit ripe w fruit ripe whole fruit unripe whole fruit ripe mature young leaves fruit of unripe seeds unripe of and pulp skin fruit young leaves P young leaves whole fruit unripe

sp.

brachylepis asperifolia asperifolia Chionanthus africanus Chionanthus africanus Chionanthus africanus Chionanthus welwitschii Olea welwitschii Olea Bridelia capense Piper madagascariense Trilepisium madagascariense Ficus exasperata Ficus exasperata Ficus Ficus mucuso Ficus Ficus Species mitis Strychnos mildbraedii Leptonychia mildbraedii Leptonychia Marantachloa Trilepisium

Martinov

R.Br. R.Br. ex ex R.Br.

Giseke Juss.

Gaudich.

Hoffmanns. & Hoffmanns.

eae

Phyllanthaceae Piperaceae Oleac Link Marantaceae Moraceae Loganiaceae Mart. Malvaceae Family

248

(%) 9.25 5.54 6.77 9.44 7.82 26.04 11.99 11.60 19.30 34.54 13.39 25.47 20.94 50.93 40.81 29.82 26.57 29.46 28.28 23.92 15.77 23.52 Remainder Remainder

92 (%) 1.72 0.91 3.07 3.50 5.06 0.32 0.33 8.05 0.98 0. 0.13 1.17 3.45 0.83 1.77 1.15 0.67 1.31 1.71 1.14 1.44 37.85 Starch Starch

3.05 3.58 2.01 3.60 5.56 0.84 6.73 4.32 6.86 13.51 19.71 17.95 20.03 28.56 13.91 14.61 19.41 20.60 13.47 49.06 12.45 10.19 Soluble Soluble sugar (%) sugar

(%)

3 ction 23.93 66.46 60.36 34.59 15.8 58.65 40.17 30.97 36.33 29.96 27.10 31.82 41.27 15.95 34.38 30.70 44.39 63.41 17.30 47.43 63.98 23.69 TNC by TNC subtra

1 1

lant part lant xudate xudate young leaves whole fruit ripe whole fruit ripe young leaves unripe fruit whole fruit unripe e whole fruit ripe young leaves whole fruit unripe flowers whole fruit ripe buds flower young leaves young leaves whole fruit ripe whole fruit unripe whole fruit unripe e whole fruit unripe whole fruit ripe P whole fruit ripe whole fruit ripe

angolensis sp. sp.

sp. sp.

Vangueria apiculata Vangueria leprieurii Zanthoxylum Fagaropsis nobilis Vepris nobilis Vepris macrocalyx Dovyalis macrocalyx Dovyalis Allophylus Oxyanthus Psychotria Psychotria Randia Randia urcelliformis Rothmannia Tarenna Tarenna Species guineense Piper lanceolata Maesa lanceolata Maesa Prunus africana Rubus pinnatus laurinum Craterispermum

Juss. Batsch ex Batsch

Giseke Mirb.

Juss.

Juss. Juss.

Sapindaceae Salicaceae Rutaceae Rubiaceae Piperaceae Primulaceae Borkh. Rosaceae Family

249

L. (%) 8.86 0.50 4.99 21.18 18.66 15.30 23.28 16.41 13.07 17.95 22.42 16.32 35.76 21.16 11.03 Remainder Remainder

(%) 0.42 1.12 0.78 1.47 1.02 0.22 0.98 1.59 0.62 0.94 0.52 2.89 0.98 15.74 32.83 Starch Starch

6.10 8.16 2.52 3.70 2.76 7.69 2.86 4.92 0.03 2.94 2.35 4.13 6.80 15.56 17.60 Soluble Soluble sugar (%) sugar

(%)

8.41 16.90 24.46 23.07 20.20 38.63 41.65 16.14 44.62 30.70 29.76 22.30 19.78 40.31 20.18 TNC by TNC subtraction butanol assay. butanol -

lant part lant ripe fruit whole fruit ripe flowers stem ofpith young whole fruit unripe young leaves young leaves flowers whole fruit unripe fruit ripe of seeds fruit ripe of seeds whole fruit unripe P whole fruit ripe whole fruit unripe

sp.

ops bagshawei momum ra cameroonensis a antana camara antana Urera cameroonensis Urera massaica Urtica L camara Lantana Afr "Bean plant" shrub Unknown Caterpillars Grasshoppers Species Mimus bagshawei Mimusops Solanum cameroonensis Urera Ure cameroonensis Urera

ipe fruit incorrectly collected as whole fruit; redtail females observed cheek pouching and later spitting seeds of of seeds spitting later and pouching cheek observed females redtail fruit; whole as collected incorrectly fruit ipe

Hil. unr -

Martinov

J.St.

Juss. Juss. Juss.

unripe fruits. unripe

known Soluble fiber in exudates like gums may interfere with sugar analyses via acid via analyses sugar with interfere may gums like exudates in fiber Soluble camara Lantana camara 1 2 Zingiberaceae Unidentified Un Verbenaceae Sapotaceae Solanaceae Urticaceae Family

250 females in (A) in females Kus across focal adult across focal

Mean percent contribution to daily sodium intake (mg) by identified food type food by identified to contribution (mg) sodiumdaily intake percent Mean Appendix 11. up to unidentified not that do foods. unknown add group, Kyo and reflect (B) and 100% Percentages Sukgroup, (C) group.

251 Appendix 12. Comparison of macronutrient percentages of diet on dry matter basis: our study versus Conklin-Brittain et al. (1998). Our values are means of daily contribution of macronutrient contributions to daily intake in grams. Conklin-Brittain and colleagues 1992-1993 values are means of 12 monthly percentages of "chemical fractions in the diet" for two redtail monkey groups at Kanyawara in K14 and K30 forest compartments, and their observation method was 10-minute instantaneous, focal-animal sampling. Conklin-Brittain and colleagues (1998) measured crude protein (CP) and only included plant foods in nutritional intake analyses. This study’s crude protein and available protein (AP) results are presented for comparison. To compare NDF intake values to Conklin-Brittain, included are this study’s NDF intake values without an NDF digestibility coefficient. Monkey group CP (AP)* Lipid TNC NDF ADF ADL

Redtail group in K14 1992-1993 Mean 17.6 3.6 36.5 31.3 19.7 8.1 SD 1.3 2.3 3.6 4 3.3 1.6 Redtail group in K30 1992-1993 Mean 16.6 3.4 37.6 31.7 19 8.1 SD 1.8 2.5 4.4 3.1 2.5 1.2 Kyo group in K14 and MUBFS camp 2015-2016 Mean 14.4 (11.2) 4.1 39.9 37.8 21.1 11.7 SD 5.7 (4.8) 2.8 7.7 4 7.3 4.7 Kus group in K14 and K30 2015-2016 Mean 21.3 (17.1) 6.1 28.2 38.4 28 13.8 SD 5.5 (5.1) 5.3 7.4 7.1 6.4 5.1 Suk group in K14 and K30 2015-2016 Mean 21.5 (17.4) 6.7 30 37 26.5 12.6 SD 6.5 (6.2) 5.6 7.9 7.4 6.8 3.9 *Conklin-Brittain et al. 1998 measured crude protein and do not include insects in nutritional intake. This study’s protein intake values are available protein (via calculation using Acid Detergent Insoluble Nitrogen assay), though included here are both CP and AP, and our intake values included insect nutritional intake.

252 Appendix 13. Average contributions of protein, fat and acid detergent fiber (ADF) to redtail monkey diet in our study (3 monkey groups) and Rode et al. 2006 study (3 monkey groups each for unlogged and heavily logged areas at Kanyawara site May 2001-May 2002). To make this study’s result’s comparable, ADF is presented here because Rode and colleagues used ADF as a measure of fiber intake; and crude protein rather than available protein are presented. Rode and colleagues conducted scan sampling every 15 minutes and recorded intake rates opportunistically, without individual identification of monkeys. Though Rode et al. 2006 included insects in their calculations of nutritional intake of redtail monkeys, they used published values from Barker et al. (1998) and Nakagawa (2003) to calculate insect fat intake.

*Rode et al. 2006 measured crude protein, while our study measured available protein.

253 Appendix 14. Variation in daily dry matter food intake (grams) across adult female redtail monkeys, with N=3-10 focal days per adult female. Monkey group membership indicated by color. Individual females ordered by median dry matter food intake. Two females with only one focal day and one major outlier for intake were removed, resulting in N=134 focal days.

254 Appendix 15. Variation in daily intake of (A) protein (AP, kcal) and (B) nonprotein energy (NPE, kcal) across adult female redtail monkeys, with N=3-10 focal days per adult female. Two females with only one focal day were removed, as was one major outlier for intake, resulting in N=134 focal days. A.

255 Appendix 16. Variation in daily fiber intake (kcal) across adult females, with N=3-10 focal days per adult female. Daily digestible NDF intake was calculated using NDF digestibility coefficients derived from fecal analyses (see Methods). Group membership indicated by color. Individual females ordered on the x axis by median daily digestible NDF intake. Two females with only one focal day were removed, as was one outlier for intake, resulting in N=134 focal days.

256 Appendix 17. Variation in daily intake of (A) sodium (mg) and (B) copper (mg) across adult females, with N=3-10 focal days per adult female. Group membership indicated by color. Individual females ordered on the x axis by median daily copper intake. Two females with only one focal day were removed, as was one outlier for intake, resulting in N=134 focal days. A.

257 Appendix 18. (A) Variation in daily iron intake (mg) across adult females, with N=3-10 focal days per adult female. Group membership indicated by color. Individual females ordered on the x axis by median daily iron intake. Two females with only one focal day were removed, as was one outlier for intake, resulting in N=134 focal days. Major outliers are due to ingestion of high- iron termite soil and noctuid moth larvae during flush. For clarity, (B) is the same figure presented with the four major outliers removed. A.

258 Appendix 19. Fruit Availability Indices (ripe fruit and unripe fruit combined) for two phenology transects compared to Multivariate El Niño Southern Oscillation Index (MEI), as 2015-2016 was a strong El Niño year. MEI data source: https://www.esrl.noaa.gov/psd/enso/mei/table.html

259 Appendix 20. Total number of insects caught (A) by monthly netting August 2015 – May 2016 and (B) by malaise traps in collaboration with Tapani Hopkins and Biodiversity Unit of the University of Turku, and the University of Eastern Finland for June – August 2015. Catch number for each malaise trap in is quarter of catch, which we counted, multiplied by four to approximate the number of insects per 2-week malaise trap catch. Netting catch number is substantially lower than malaise catch number as is expected given differences between methods. (A)

(B)

260 Appendix 21. Percent of monthly netting catch by insect Order (based on count of insects) in (A) Kyo redtail monkey group home range between Trails 12 and 13 above Lower Camp and (B) in K14 quadrant near Census Road and (C) in K14 quadrant edge of Macaranga swamp, both in Kus and Suk monkey groups’ overlapping home range. (A)

(B)

261 (C)

262 Appendix 22. Percent of malaise trap catch by biomass (grams) by insect Order over three months (June – August 2015) in (A) Kus and Suk groups overlapping home range (trap at Census and Karambi roads) and (B) Kyo group home range (between trails 12 and 13 in area recovering from being clear-cut in the 1990s, next to Lower Camp). Biomass data identified to fewer insect orders than count data. (A)

(B)

263 Appendix 23. Results of linear mixed models testing if agonistic interactions in a feeding context predict daily intake of protein and daily intake of nonprotein energy.

Response variables Fixed effect Estimate SE 95% CI df t-value p-value Lower Upper Daily nonprotein Rate of agonism 0.11 0.21 -0.30 0.53 112 0.53 0.59 energy intake received in a (kcal) † feeding context Intercept 2.19 0.04 2.11 2.26 112 58.76 0 Daily nonprotein Rate of agonism -0.35 0.39 -1.12 0.42 112 -0.89 0.37 energy intake given in a feeding (kcal) † context Intercept 2.20 0.04 2.13 2.27 112 61.25 0 Daily nonprotein Rate of aggression -0.09 0.38 -0.84 0.66 112 -0.24 0.81 energy intake received in a (kcal) † feeding context Intercept 2.20 0.04 2.12 2.27 112 60.31 0 Daily nonprotein Rate of aggression -0.02 0.44 -0.90 0.85 112 -0.05 0.96 energy intake given in a feeding (kcal) † context Intercept 2.19 0.04 2.12 2.26 112 60.96 0 Daily available Rate of agonism 0.04 0.17 -0.31 0.39 112 0.23 0.82 protein intake received in a (kcal) † feeding context Intercept 1.60 0.03 1.55 1.65 112 61.90 0 Daily available Rate agonism given 0.13 0.33 -0.51 0.78 112 0.41 0.68 protein intake in a feeding context (kcal) † Intercept 1.60 0.02 1.55 1.65 112 64.76 0 Daily available Rate aggression 0.19 0.32 -0.43 0.82 112 0.61 0.54 protein intake received in a (kcal) † feeding context Intercept 1.60 0.02 1.55 1.64 112 65.46 0 Daily available Rate of aggression 0.36 0.37 -0.37 1.10 112 0.97 0.33 protein intake given in a feeding (kcal) † context Intercept 1.59 0.02 1.55 1.64 65.50 0 †Log transformed due to positive skew

264 Appendix 24. Results of linear mixed models testing if agonistic interactions in a feeding context predict daily ratio of nonprotein energy:available protein (NPE:AP) balance (ratio). When reproductive status was included, the one female who has never been observed with an infant since habituation in 2008 was excluded.

Response variables Fixed effect Estimate SE 95% CI df t-value p-value Lower Upper Daily ratio Rate of agonism 0.03 0.17 -0.30 0.37 112 0.20 0.84 NPE:AP † received in a feeding context Intercept 0.59 0.04 0.52 0.68 112 15.27 0 Daily ratio Rate agonism given -0.36 0.33 -1.02 0.30 112 -1.08 0.28 NPE:AP † in a feeding context Intercept 0.60 0.03 0.53 0.67 112 16.98 0 Daily ratio Rate aggression -0.05 0.05 -0.16 0.06 112 -0.92 0.40 NPE:AP † received in a feeding context Intercept 0.60 0.04 0.53 0.68 112 15.87 0 Daily ratio Rate aggressive -0.32 0.36 -1.02 0.38 112 -0.90 0.37 NPE:AP † given in a feeding context Intercept 0.60 0.04 0.53 0.67 112 16.09 0 Daily ratio Rate agonism 0.32 0.22 -0.12 0.76 110 1.43 0.15 NPE:AP † received in a feeding context Ripe fruit in diet 0.002 0.0009 0.0006 0.004 110 2.60 0.01 (% dm intake RF) Rate agonism -0.005 0.006 -0.02 0.006 110 -0.91 0.37 received per hour*RF in diet Intercept 0.54 0.04 0.46 0.62 110 13.56 0 Daily ratio Rate agonism 0.28 0.23 -0.17 0.73 98 1.18 0.24 NPE:AP † received in a feeding context Reproductive status (compared to Cycling): 98 Pregnant 0.05 0.06 -0.06 0.15 98 0.80 0.43

Lactation 1 0.13 0.07 -0.01 0.26 98 1.75 0.08 Lactation 2 -0.03 0.09 -0.21 0.15 98 -0.33 0.74 Agonism 0.044 0.73 -1.34 1.44 98 0.06 0.95 received*Preg Agonism -0.60 0.40 -1.37 0.18 98 -1.48 0.14 received*Lac1 Agonism -0.88 0.59 -2.03 0.25 98 -1.50 0.14 received*Lac2 Intercept 0.56 0.54 0.46 0.66 98 10.47 0 †Log transformed due to positive skew

265 REFERENCES

Abrahams, P. W. (1997). Geophagy (soil consumption) and iron supplementation in Uganda. Tropical Medicine & International Health, 2(7), 617-623.

Adamescu, G. S., Plumptre, A. J., Abernethy, K. A., Polansky, L., Bush, E. R., Chapman, C. A., ... & Ndangalasi, H. J. (2018). Annual cycles are the most common reproductive strategy in African tropical tree communities. Biotropica, 50(3), 418-430.

Adams, S., Sello, C., Qin, G. X., Che, D., & Han, R. (2018). Does dietary fiber affect the levels of nutritional components after feed formulation? Fibers, 6(2), 29.

Afshar, M., & Giraldeau, L. A. (2014). A unified modelling approach for producer–scrounger games in complex ecological conditions. Animal Behaviour, 96, 167-176.

Aiello, L. C., & Wheeler, P. (1995). The expensive-tissue hypothesis: the brain and the digestive system in human and primate evolution. Current Anthropology, 36(2), 199-221.

Alatalo, R. V., Gustafsson, L., & Lundberg, A. (1986). Interspecific competition and niche changes in tits (Parus spp.): evaluation of nonexperimental data. The American Naturalist, 127(6), 819-834.

Allen, M. E., & Ullrey, D. E. (2004). Relationships among nutrition and reproduction and relevance for wild animals. Zoo Biology, 23(6), 475-487.

Altmann, S. A. (1991). Diets of yearling female primates (Papio cynocephalus) predict lifetime fitness. Proceedings of the National Academy of Sciences, 88(2), 420-423.

Altmann, S. A. (1998). Foraging for survival: yearling baboons in Africa. University of Chicago Press.

Ament, S. A., Wang, Y., & Robinson, G. E. (2010). Nutritional regulation of division of labor in honey bees: toward a systems biology perspective. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2(5), 566-576.

Anderson, D. P., Nordheim, E. V., Moermond, T. C., Gone Bi, Z. B., & Boesch, C. (2005). Factors influencing tree phenology in Taï National Park, Côte d'Ivoire. Biotropica, 37(4), 631-640.

AOAC (1995). Association of Official Agricultural Chemists–Official Methods of Analysis, 16th Edition. Cuniff, P. (Ed.), AOAC International, Washington, p. 7 (Chapter 12; Tecn. 960.52)

AOCS (2009). American Oil Chemists’ Society – AOCS Official Methods and Recommended Practices of the AOCS (6th ed.), Denver.

266 Arlet, M. E., & Isbell, L. A. (2009). Variation in behavioral and hormonal responses of adult male gray-cheeked mangabeys (Lophocebus albigena) to crowned eagles (Stephanoaetus coronatus) in Kibale National Park, Uganda. Behavioral Ecology and Sociobiology, 63(4), 491.

Aschner, J. L., & Aschner, M. (2005). Nutritional aspects of manganese homeostasis. Molecular Aspects of Medicine, 26(4-5), 353-362.

Asp, N. G. (1993). Nutritional importance and classification of food carbohydrates. In Meuser, F. D. J. Manners and W. Seibel (eds), Plant Polymeric Carbohydrates. Royal Society of Chemistry, Cambridge, UK.

Avila, D. S., Puntel, R. L., & Aschner, M. (2013). Manganese in health and disease. In Interrelations between essential metal ions and human diseases (pp. 199-227). Springer, Dordrecht.

Baker, M. C., Belcher, C. S., Deutsch, L. C., Sherman, G. L., & Thompson, D. B. (1981). Foraging success in junco flocks and the effects of social hierarchy. Animal Behaviour, 29(1), 137-142.

Barboza, P. S., Parker, K. L., & Hume, I. D. (2008). Common themes across diverse taxa. In Integrative wildlife nutrition (pp. 1-15). Springer Science & Business Media.

Barnard, C. J., & Sibly, R. M. (1981). Producers and scroungers: a general model and its application to captive flocks of house sparrows. Animal Behaviour, 29(2), 543-550.

Barnard, C. J., Thompson, D. B. A., & Stephens, H. (1982). Time budgets, feeding efficiency and flock dynamics in mixed species flocks of lapwings, golden plovers and gulls. Behaviour, 80(1-2), 44-69.

Barta, Z., Liker, A., & Mónus, F. (2004). The effects of predation risk on the use of social foraging tactics. Animal Behaviour, 67(2), 301-308.

Barton, R. A. (1993). Sociospatial mechanisms of feeding competition in female olive baboons, Papio anubis. Animal Behaviour, 46(4), 791-802.

Barton, R. A., & Whiten, A. (1993). Feeding competition among female olive baboons, Papio anubis. Animal Behaviour, 46(4), 777-789.

Barton, R. A., & Whiten, A. (1994). Reducing complex diets to simple rules: food selection by olive baboons. Behavioral Ecology and Sociobiology, 35(4), 283-293.

Barton, R. A., Byrne, R. W., & Whiten, A. (1996). Ecology, feeding competition and social structure in baboons. Behavioral Ecology and Sociobiology, 38(5), 321-329.

Basset Y., Springate N.D., Aberlenc H.-P. and Delvare G. 1997. A review of methods for collecting arthropods in tree canopies. In: Stork N.E., Adis J.A. and Didham R.K. (eds), Canopy Arthropods. Chapman & Hall, London, pp. 27-52.

267 Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1–7. 2014.

Beauchamp, G. (2005). Does group foraging promote efficient exploitation of resources?. Oikos, 111(2), 403-407.

Beauchamp, G. (2016). The spatial distribution of foragers and food patches can influence antipredator vigilance. Behavioral Ecology, 28(1), 304-311.

Beauchamp, G., & Giraldeau, L. A. (1997). Patch exploitation in a producer-scrounger system: test of a hypothesis using flocks of spice finches (Lonchura punctulata). Behavioral Ecology, 8(1), 54-59.

Behie, A. M., & Pavelka, M. S. (2012). The role of minerals in food selection in a black howler monkey (Alouatta pigra) population in Belize following a major hurricane. American Journal of Primatology, 74(11), 1054-1063.

Belovsky, G. E. (1978). Diet optimization in a generalist herbivore: the moose. Theoretical Population Biology, 14(1), 105-134.

Belovsky, G. E. (1981). Food plant selection by a generalist herbivore: the moose. Ecology, 62(4), 1020-1030.

Belovsky, G. E., & Jordan, P. A. (1981). Sodium dynamics and adaptations of a moose population. Journal of Mammalogy, 62(3), 613-621.

Bergstrom, M. L., Hogan, J. D., Melin, A. D., & Fedigan, L. M. (2019). The nutritional importance of invertebrates to female Cebus capucinus imitator in a highly seasonal tropical dry forest. American Journal of Physical Anthropology, 170(2), 207-216.

Bernays, E. A., Driver, G. C., & Bilgener, M. (1989). Herbivores and plant tannins. Advances in Ecological Research, 19, 263-302).

Bittrich, V., do Nascimento-Junior, J. E., Maria do Carmo, E. A., & de Lima Nogueira, P. C. (2013). The anther oil of Symphonia globulifera Lf (Clusiaceae). Biochemical Systematics and Ecology, 49, 131-134.

Blumfield, M. L., Hure, A. J., MacDonald-Wicks, L. K., Smith, R., Simpson, S. J., Giles, W. B., ... & Collins, C. E. (2012). Dietary balance during pregnancy is associated with fetal adiposity and fat distribution. The American Journal of Clinical Nutrition, 96(5), 1032- 1041.

Bogart, S. L., & Pruetz, J. D. (2011). Insectivory of savanna chimpanzees (Pan troglodytes verus) at Fongoli, Senegal. American Journal of Physical Anthropology, 145(1), 11-20.

Bohmann, K., Monadjem, A., Noer, C. L., Rasmussen, M., Zeale, M. R., Clare, E., ... & Gilbert, M. T. P. (2011). Molecular diet analysis of two African free-tailed bats (Molossidae) using high throughput sequencing. PloS one, 6(6), e21441.

268 Boinski, S. (1988). Sex differences in the foraging behavior of squirrel monkeys in a seasonal habitat. Behavioral Ecology and Sociobiology, 23(3), 177-186.

Bortolamiol, S., Cohen, M., Potts, K., Pennec, F., Rwaburindore, P., Kasenene, J., ... & Krief, S. (2014). Suitable habitats for endangered frugivorous mammals: small-scale comparison, regeneration forest and chimpanzee density in Kibale National Park, Uganda. PloS one, 9(7), e102177.

Brown, J. L. (1964). The evolution of diversity in avian territorial systems. The Wilson Bulletin, 160-169.

Brown, M. (2011). Intergroup encounters in grey-cheeked mangabeys (Lophocebus albigena) and redtail monkeys (Cercopithecus ascanius): form and function. PhD dissertation, Columbia University.

Brown, M. (2013). Food and range defence in group-living primates. Animal Behaviour, 85(4), 807-816.

Bruorton, M. R., & Perrin, M. R. (1988). The anatomy of the stomach and cecum of the samango monkey, Cercopithecus mitis erythrarchus, Peters 1852. Zeitschrift für Säugetierkunde, 53(4), 210-224.

Bryer, M. A., Chapman, C. A., & Rothman, J. M. (2013). Diet and polyspecific associations affect spatial patterns among redtail monkeys (Cercopithecus ascanius). Behaviour, 277- 293.

Bryer, M. A., Chapman, C. A., Raubenheimer, D., Lambert, J. E., & Rothman, J. M. (2015). Macronutrient and energy contributions of insects to the diet of a frugivorous monkey (Cercopithecus ascanius). International Journal of Primatology, 36(4), 839-854.

Bunning, H., Rapkin, J., Belcher, L., Archer, C. R., Jensen, K., & Hunt, J. (2015). Protein and carbohydrate intake influence sperm number and fertility in male cockroaches, but not sperm viability. Proceedings of the Royal Society B: Biological Sciences, 282(1802).

Butynski, T. M. (1988). Guenon birth seasons and correlates with rainfall and food. In: A. Gautier-Hion, F. Bourlière, J.-P. Gautier, and J. Kingdon, (eds.), A Primate Radiation: Evolutionary Biology of the African Guenons, pp. 284-322. Cambridge University Press, Cambridge, UK.

Camphuysen, C. J., & Webb, A. (1999). Multi-species feeding associations in North Sea seabirds: jointly exploiting a patchy environment. Ardea, 87(2), 177-198.

Cancelliere, E. C., DeAngelis, N., Nkurunungi, J. B., Raubenheimer, D., & Rothman, J. M. (2014). Minerals in the foods eaten by mountain gorillas (Gorilla beringei). PloS ONE, 9(11), e112117.

269 Campbell, J. L., Williams, C. V., & Eisemann, J. H. (2004). Use of total dietary fiber across four lemur species (Propithecus verreauxi coquereli, Hapalemur griseus griseus, Varecia variegata, and Eulemur fulvus): does fiber type affect digestive efficiency?. American Journal of Primatology, 64(3), 323-335.

Cannon, C. H., Curran, L. M., Marshall, A. J., & Leighton, M. (2007). Long‐term reproductive behaviour of woody plants across seven Bornean forest types in the Gunung Palung National Park (Indonesia): suprannual synchrony, temporal productivity and fruiting diversity. Ecology Letters, 10(10), 956-969.

Caraco, T., & Giraldea, L. A. (1991). Social foraging: producing and scrounging in a stochastic environment. Journal of Theoretical Biology, 153(4), 559-583.

Carlson, B. A., Rothman, J. M., & Mitani, J. C. (2013). Diurnal variation in nutrients and chimpanzee foraging behavior. American journal of primatology, 75(4), 342-349.

Castner, J. L. (2000). Photographic atlas of entomology and guide to insect identification (No. C/595.7 C3).

Castro, A. C., Oliveira, D. C., Moreira, F. P., Silvia, A., & Isaias, R. (2013). Synchronism between Aspidosperma macrocarpon (Apocynaceae) resources allocation and the establishment of the gall inducer Pseudophacopteron sp. (Hemiptera: Psylloidea). Revista de Biologia Tropical, 61(4), 1891-1900.

Chancellor, R. L., & Isbell, L. A. (2009). Food site residence time and female competitive relationships in wild gray-cheeked mangabeys (Lophocebus albigena). Behavioral Ecology and Sociobiology, 63(10), 1447-1458.

Chapman, C. A., & Chapman, L. J. (1996). Mixed-species primate groups in the Kibale Forest: ecological constraints on association. International Journal of Primatology, 17(1), 31.

Chapman, C. A., & Chapman, L. J. (1997). Forest regeneration in logged and unlogged forests of Kibale National Park, Uganda. Biotropica, 29(4), 396-412.

Chapman, C. A., & Chapman, L. J. (2000a). Interdemic variation in mixed-species association patterns: common diurnal primates of Kibale National Park, Uganda. Behavioral Ecology and Sociobiology, 47(3), 129-139.

Chapman, C. A., & Chapman, L. J. (2000b). Constraints on group size in red colobus and red- tailed guenons: examining the generality of the ecological constraints model. International Journal of Primatology, 21(4), 565-585.

Chapman, C. A., & Chapman, L. J. (2002). Foraging challenges of red colobus monkeys: influence of nutrients and secondary compounds. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 133(3), 861-875.

Chapman, C. A., Chapman, L. J., Wangham, R., Hunt, K., Gebo, D., & Gardner, L. (1992). Estimators of fruit abundance of tropical trees. Biotropica, 24(4), 527-531.

270

Chapman, C. A., Wrangham, R., & Chapman, L. J. (1994). Indices of habitat-wide fruit abundance in tropical forest. Biotropica, 160-171.

Chapman, C. A., Chapman, L. J., Kaufman, L., & Zanne, A. E. (1999a). Potential causes of arrested succession in Kibale National Park, Uganda: growth and mortality of seedlings. African Journal of Ecology, 37(1), 81-92.

Chapman, C. A., Wrangham, R. W., Chapman, L. J., Kennard, D. K., & Zanne, A. E. (1999b). Fruit and flower phenology at two sites in Kibale National Park, Uganda. Journal of Tropical Ecology, 15(2), 189-211.

Chapman, C. A., Chapman, L. J., & Gillespie, T. R. (2002a). Scale issues in the study of primate foraging: red colobus of Kibale National Park. American Journal of Physical Anthropology, 117(4), 349-363.

Chapman, C. A., Chapman, L. J., Cords, M., Gathua, J. M., Gautier-Hion, A., Lambert, J. E., ... & White, L. J. (2002b). Variation in the diets of Cercopithecus species: differences within forests, among forests, and across species. In M. Glenn & M. Cords (Eds.), The guenons: Diversity and adaptation in African monkeys (pp. 325-350). Springer, Boston, MA.

Chapman, C. A., Chapman, L. J., Rode, K. D., Hauck, E. M., & McDowell, L. R. (2003). Variation in the nutritional value of primate foods: among trees, time periods, and areas. International Journal of Primatology, 24(2), 317-333.

Chapman, C. A., Chapman, L. J., Struhsaker, T. T., Zanne, A. E., Clark, C. J., & Poulsen, J. R. (2005). A long-term evaluation of fruiting phenology: importance of climate change. Journal of Tropical Ecology, 21(1), 31-45.

Chapman, C. A., Saj, T. L., & Snaith, T. V. (2007). Temporal dynamics of nutrition, parasitism, and stress in colobus monkeys: implications for population regulation and conservation. American Journal of Physical Anthropology, 134(2), 240-250.

Chapman, C. A., & Rothman, J. M. (2009). Within-species differences in primate social structure: evolution of plasticity and phylogenetic constraints. Primates, 50(1), 12-22.

Chapman, C. A., Bowman, D. D., Ghai, R. R., Gogarten, J. F., Goldberg, T. L., Rothman, J. M., ... & Walsh, C. (2012). Protozoan parasites in group-living primates: testing the biological island hypothesis. American Journal of Primatology, 74(6), 510-517.

Chapman, C. A., Schoof, V. A., Bonnell, T. R., Gogarten, J. F., & Calmé, S. (2015). Competing pressures on populations: long-term dynamics of food availability, food quality, disease, stress and animal abundance. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1669), 20140112.

Chapman, C. A., Valenta, K., Bonnell, T. R., Brown, K. A., & Chapman, L. J. (2018a). Solar radiation and ENSO predict fruiting phenology patterns in a 15-year record from Kibale National Park, Uganda. Biotropica, 50(3), 384-395.

271

Chapman, C. A., Bortolamiol, S., Matsuda, I., Omeja, P. A., Paim, F. P., Reyna-Hurtado, R., ... & Valenta, K. (2018b). Primate population dynamics: variation in abundance over space and time. Biodiversity and Conservation, 27(5), 1221-1238.

Chapman, C. A., Valenta, K., & Bortolamiol, S. (2018c). How variable is a primate’s world: spatial and temporal variation in potential ecological drivers of behaviour? In: Kalbitzer U., Jack K. (eds) Primate Life Histories, Sex Roles, and Adaptability (pp. 359-374). Springer, Cham.

Chapman, T. L., Holcomb, M. P., Spivey, K. L., Sehr, E. K., & Gall, B. G. (2015). A test of local enhancement in amphibians. Ethology, 121(3), 308-314.

Chaves, O. M., Stoner, K. E., Ángeles-Campos, S., & Arroyo-Rodríguez, V. (2011). Wood consumption by Geoffroyi’s spider monkeys and its role in mineral supplementation. PloS One, 6(9), e25070.

Cheeke, P. R., & Dierenfeld, E. S. (2010). Comparative animal nutrition and metabolism. CABI.

Cheney, D. L. (1992). Intragroup cohesion and intergroup hostility: the relation between grooming distributions and intergroup competition among female primates. Behavioral Ecology, 3(4), 334-345.

Chivers, D. J., & Hladik, C. M. (1980). Morphology of the gastrointestinal tract in primates: comparisons with other mammals in relation to diet. Journal of Morphology, 166(3), 337- 386.

Chyb, S., & Simpson, S. J. (1990). Dietary selection in adult Locusta migratoria. Entomologia experimentalis et applicata, 56(1), 47-60.

Cimprich, D. A., & Grubb Jr, T. C. (1994). Consequences for Carolina chickadees of foraging with tufted titmice in winter. Ecology, 75(6), 1615-1625.

Clark, C. W., & Mangel, M. (1986). The evolutionary advantages of group foraging. Theoretical Population Biology, 30(1), 45-75.

Clayton, J. B., Gomez, A., Amato, K., Knights, D., Travis, D. A., Blekhman, R., ... & Glander, K. E. (2018). The gut microbiome of nonhuman primates: Lessons in ecology and evolution. American Journal of Primatology, 80(6), e22867.

Clemens, E., & Phillips, B. (1980). Organic acid production and digesta movement in the gastrointestinal tract of the baboon and Sykes monkey. Comparative Biochemistry and Physiology, A, 66(3), 529-532.

Collignon, B., & Detrain, C. (2009). Distributed leadership and adaptive decision-making in the ant Tetramorium caespitum. Proceedings of the Royal Society B: Biological Sciences, 277(1685), 1267-1273.

272

Colyn, M. (1994). Données pondérales sur les primates Cercopithecidae d’Afrique Centrale (Bassin du Zaïre/Congo). Mammalia, 58(3), 483-488.

Conklin, N. L., & Wrangham, R. W. (1994). The value of figs to a hind-gut fermenting frugivore: a nutritional analysis. Biochemical Systematics and Ecology, 22(2), 137-151.

Conklin-Brittain, N. L., Wrangham, R. W., & Hunt, K. D. (1998). Dietary response of chimpanzees and cercopithecines to seasonal variation in fruit abundance. II. Macronutrients. International Journal of Primatology, 19(6), 971-998.

Conklin-Brittain, N. L., Dierenfeld, E. S., Wrangham, R. W., Norconk, M., & Silver, S. C. (1999). Chemical protein analysis: a comparison of Kjeldahl crude protein and total ninhydrin protein from wild, tropical vegetation. Journal of Chemical Ecology, 25(12), 2601-2622.

Conklin-Brittain, N. L., Knott, C. D., & Wrangham, R. W. (2006). Energy intake by wild chimpanzees and orangutans: Methodological considerations and a preliminary comparison. In G. Hohmann, M. M. Robbins, & C. Boesch (Eds.), Feeding ecology in apes and other primates. Ecological, physical and behavioral aspects (pp. 445–571). Cambridge, UK: Cambridge University Press

Coogan, S. C., Raubenheimer, D., Stenhouse, G. B., & Nielsen, S. E. (2014). Macronutrient optimization and seasonal diet mixing in a large omnivore, the grizzly bear: a geometric analysis. PLoS One, 9(5), e97968.

Coogan, S. C., Machovsky-Capuska, G. E., Senior, A. M., Martin, J. M., Major, R. E., & Raubenheimer, D. (2017). Macronutrient selection of free-ranging urban Australian white ibis (Threskiornis moluccus). Behavioral Ecology, 28(4), 1021-1029.

Cook, S. C., Eubanks, M. D., Gold, R. E., & Behmer, S. T. (2010). Colony-level macronutrient regulation in ants: mechanisms, hoarding and associated costs. Animal Behaviour, 79(2), 429-437.

Cook, S. C., & Behmer, S. T. (2010). Macronutrient regulation in the tropical terrestrial ant Ectatomma ruidum (Formicidae): a field study in Costa Rica. Biotropica, 42(2), 135-139.

Cords, M. (1984). Mating patterns and social structure in redtail monkeys (Cercopithecus ascanius). Zeitschrift für Tierpsychologie, 64(3-4), 313-329.

Cords, M. (1986). Interspecific and intraspecific variation in diet of two forest guenons, Cercopithecus ascanius and C. mitis. Journal of Animal Ecology, 811-827.

Cords, M. (1987). Mixed species associations of Cercopithecus monkeys in Kakamega Forest, Kenya. Univ. Ca. Publ. Zool., 117, 1-109.

273

Cords, M. (1988). Mating systems of forest guenons: A preliminary review. In: A. Gautier-Hion, F. Bourlière, J.-P. Gautier, and J. Kingdon, (eds.), A Primate Radiation: Evolutionary Biology of the African Guenons, pp. 323-339. Cambridge University Press, Cambridge, UK.

Cords, M. (1990). Mixed-species association of East African guenons: General patterns or specific examples? American Journal of Primatology, 21(2), 101-114.

Cords, M. (2000). The agonistic and affiliative relationships of adult females in a blue monkey group. In: P. F. Whitehead & C. J. Jolly (Eds.), Old world monkeys (pp. 453-479). Cambridge: Cambridge University Press.

Cords, M. (2002). Friendship among adult female blue monkeys (Cercopithecus mitis). Behaviour, 139(2-3), 291-314.

Cords, M., & Wursig, B. (2014) A Mix of species: Associations of heterospecifics among primates and dolphins. In: Yamagiwa, J., & Karczmarski, L. (eds.). Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies, pp. 409- 432. Springer Science & Business Media.

Cork, S. J., & Foley, W. J. (1991). Digestive and metabolic strategies of arboreal mammalian folivores in relation to chemical defenses in temperate and tropical forests. In ed. R. T. Palo, C. T. Robbins (eds.), Plant Defenses Against Mammalian Herbivory (pp. 133–66). Boca Raton: CRC.

Cotter, S. C., Simpson, S. J., Raubenheimer, D., & Wilson, K. (2011). Macronutrient balance mediates trade‐offs between immune function and life history traits. Functional Ecology, 25(1), 186-198.

Cotter, S. C., Pincheira-Donoso, D., & Thorogood, R. (2019). Defences against brood parasites from a social immunity perspective. Philosophical Transactions of the Royal Society B, 374(1769), 20180207.

Cui, Z. W., Wang, Z. L., Shao, Q., Raubenheimer, D., & Lu, J. Q. (2018). Macronutrient signature of dietary generalism in an ecologically diverse primate in the wild. Behavioral Ecology, 29(4), 804-813.

Cui, Z., Shao, Q., Grueter, C. C., Wang, Z., Lu, J., & Raubenheimer, D. (2019). Dietary diversity of an ecological and macronutritional generalist primate in a harsh high-latitude habitat, the Taihangshan macaque (Macaca mulatta tcheliensis). American Journal of Primatology, 81(4), e22965.

Danish, L., Chapman, C. A., Hall, M. B., Rode, K. D., & Worman, C. (2006). The role of sugar in diet selection in redtail and red colobus monkeys. In G. Hohmann, M. M. Robbins, & C. Boesch (Eds.), Feeding ecology in apes and other primates: Ecological, physical, and behavioural aspects (pp. 471-487). Cambridge: Cambridge University Press.

274

David, M., Le Hô, M., Laskowski, K. L., Salignon, M., Gillingham, M. A., & Giraldeau, L. A. (2014). Individual differences in behavioral consistency are related to sequential access to resources and body condition in a producer-scrounger game. Frontiers in Ecology and Evolution, 2, 19.

Dearing, M. D., Foley, W. J., & McLean, S. (2005). The influence of plant secondary metabolites on the nutritional ecology of herbivorous terrestrial vertebrates. Annu. Rev. Ecol. Evol. Syst., 36, 169-189.

Deblauwe, I., & Janssens, G. P. (2008). New insights in insect prey choice by chimpanzees and gorillas in southeast Cameroon: the role of nutritional value. American Journal of Physical Anthropology, 135(1), 42-55.

DeGabriel, J. L., Moore, B. D., Foley, W. J., & Johnson, C. N. (2009). The effects of plant defensive chemistry on nutrient availability predict reproductive success in a mammal. Ecology, 90(3), 711-719.

DeGabriel, J. L., Moore, B. D., Felton, A. M., Ganzhorn, J. U., Stolter, C., Wallis, I. R., ... & Foley, W. J. (2014). Translating nutritional ecology from the laboratory to the field: milestones in linking plant chemistry to population regulation in mammalian browsers. Oikos, 123(3), 298-308.

Delson, E., Terranova, C. J., Jungers, W. L., Sargis, E. J., Jablonski, N. G., & Dechow, P. C. (2000). Body mass in Cercopithecidae (Primates, Mammalia): estimation and scaling in extinct and extant taxa. Anthropological Papers of the American Museum of Natural History, 83, 1-159.

Detrain, C., & Deneubourg, J. L. (2008). Collective decision-making and foraging patterns in ants and honeybees. Advances in Insect Physiology, 35, 123-173.

Detwiler, K. M. (2002). Hybridization between red-tailed monkeys (Cercopithecus ascanius) and blue monkeys (C. mitis) in East African forests. In The guenons: Diversity and adaptation in African monkeys (pp. 79-97). Springer, Boston, MA.

Detwiler, K. M. (2004). Hybridization between red-tailed monkeys (Cercopithecus ascanius) and blue monkeys (C. mitis) in East African forests. In M. Glenn & M. Cords (Eds.), The guenons: Diversity and adaptation in African monkeys (pp. 79-97). Springer, Boston, MA.

Develey, P. F., & Peres, C. A. (2000). Resource seasonality and the structure of mixed species bird flocks in a coastal Atlantic forest of southeastern Brazil. Journal of Tropical Ecology, 16(1), 33-53.

Di Bitetti, M. S., & Janson, C. H. (2001). Social foraging and the finder's share in capuchin monkeys, Cebus apella. Animal Behaviour, 62(1), 47-56.

275

Dietz, J. M., Peres, C. A., & Pinder, L. (1997). Foraging ecology and use of space in wild golden lion tamarins (Leontopithecus rosalia). American Journal of Primatology, 41(4), 289- 305.

Dinno, A. (2017). dunn.test: Dunn’s test of multiple comparisons using rank sums. R package version, 1(4).

Di Stefano, A., Scatà, M., Vijayakumar, S., Angione, C., La Corte, A., & Liò, P. (2019). Social dynamics modeling of chrono-nutrition. PLoS computational biology, 15(1), e1006714.

Dolby, A. S., & Grubb Jr, T. C. (1998). Benefits to satellite members in mixed-species foraging groups: an experimental analysis. Animal Behaviour, 56(2), 501-509.

Dornhaus, A., & Chittka, L. (2004). Information flow and regulation of foraging activity in bumble bees (Bombus spp.). Apidologie, 35(2), 183-192.

Dröscher, I., Rothman, J. M., Ganzhorn, J. U., & Kappeler, P. M. (2016). Nutritional consequences of folivory in a small-bodied lemur (Lepilemur leucopus): Effects of season and reproduction on nutrient balancing. American Journal of Physical Anthropology, 160(2), 197-207

Dubois, M., Gilles, K., Hamilton, J., Rebers, P., & Smith, F. (1956). Colorimetric method based on phenol sulfuric acid. Analytical Chemistry, 28, 356.

Dubois, F., & Giraldeau, L. A. (2003). The forager’s dilemma: food sharing and food defense as risk-sensitive foraging options. The American Naturalist, 162(6), 768-779.

Dubois, F., Giraldeau, L. A., & Grant, J. W. (2003). Resource defense in a group-foraging context. Behavioral Ecology, 14(1), 2-9.

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6, 241–252.

Dushoff, J., Kain, M. P., & Bolker, B. M. (2019). I can see clearly now: Reinterpreting statistical significance. Methods in Ecology and Evolution, 00,1-4.

Dussutour, A., & Simpson, S. J. (2009). Communal nutrition in ants. Current Biology, 19(9), 740-744.

Dussutour, A., & Simpson, S. J. (2012). Ant workers die young and colonies collapse when fed a high-protein diet. Proceedings of the Royal Society B: Biological Sciences, 279(1737), 2402-2408.

Eggert, A. K., Otte, T., & Müller, J. K. (2008). Starving the competition: a proximate cause of reproductive skew in burying beetles (Nicrophorus vespilloides). Proceedings of the Royal Society B: Biological Sciences, 275(1650), 2521-2528.

Emery Thompson, M., Muller, M. N., & Wrangham, R. W. (2012). The energetics of lactation and the return to fecundity in wild chimpanzees. Behavioral Ecology, 23(6), 1234-1241.

276

Eppley, T. M., Hall, K., Donati, G., & Ganzhorn, J. U. (2015). An unusual case of affiliative association of a female Lemur catta in a Hapalemur meridionalis social group. Behaviour, 152(7-8), 1041-1061.

FAO/WHO (1985). Energy and protein requirements; eport of a Joint Expert Consultation. WHO Technical report 724. Geneva: World Health Organization.

FAO/WHO (2004). Vitamin and mineral requirements in human nutrition, report of a Joint FAO/WHO Expert Consultation, Bangkok, Thailand. pp. 194–216. Geneva: World Health Organization.

Fanson, B. G., & Taylor, P. W. (2012). Additive and interactive effects of nutrient classes on longevity, reproduction, and diet consumption in the Queensland fruit fly (Bactrocera tryoni). Journal of Insect Physiology, 58(3), 327-334.

Farine, D. R., & Milburn, P. J. (2013). Social organisation of thornbill-dominated mixed-species flocks using social network analysis. Behavioral Ecology and Sociobiology, 67(2), 321- 330.

Fashing, P. J. (2004). Mortality trends in the African cherry (Prunus africana) and the implications for colobus monkeys (Colobus guereza) in Kakamega Forest, Kenya. Biological Conservation, 120(4), 449-459.

Felton, A. M., Felton, A., Raubenheimer, D., Simpson, S. J., Foley, W. J., Wood, J. T., ... & Lindenmayer, D. B. (2009). Protein content of diets dictates the daily energy intake of a free-ranging primate. Behavioral Ecology, 20(4), 685-690.

Finke, M. D. (2007). Estimate of chitin in raw whole insects. Zoo Biology, 26(2), 105-115.

FitzGibbon, C. D. (1990). Mixed-species grouping in Thomson's and Grant's gazelles: the antipredator benefits. Animal Behaviour, 39(6), 1116-1126.

Foerster, S., Cords, M., & Monfort, S. L. (2011). Social behavior, foraging strategies, and fecal glucocorticoids in female blue monkeys (Cercopithecus mitis): potential fitness benefits of high rank in a forest guenon. American Journal of Primatology, 73(9), 870-882.

Foerster, S., Kithome, K., Cords, M., & Monfort, S. L. (2015). Social status and helminth infections in female forest guenons (Cercopithecus mitis). American Journal of Physical Anthropology, 158(1), 55-66.

Foley, W. J., & Moore, B. D. (2005). Plant secondary metabolites and vertebrate herbivores– from physiological regulation to ecosystem function. Current Opinion in Plant Biology, 8(4), 430-435.

Foley, W. J., McIlwee, A., Lawler, I., Aragones, L., Woolnough, A. P., & Berding, N. (1998). Ecological applications of near infrared reflectance spectroscopy – a tool for rapid, cost- effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia, 116, 293–305.

277

Fonseca, M. L., Cruz, D. M., Rojas, D. C. A., Crespo, J. P., & Stevenson, P. R. (2019). Influence of arthropod and fruit abundance on the dietary composition of highland Colombian woolly monkeys (Lagothrix lagotricha lugens). Folia Primatologica, 1-18.

Fontaine, R. (1980). Observations on the foraging association of double-toothed kites and white- faced capuchin monkeys. The Auk, 97(1), 94-98.

Frantzis, A., & Herzing, D. L. (2002). Mixed-species associations of striped dolphins (Stenella coeruleoalba), short-beaked common dolphins (Delphinus delphis), and Risso's dolphins (Grampus griseus) in the Gulf of Corinth (Greece, Mediterranean Sea). Aquatic Mammals, 28(2), 188-197.

Freed, B. Z. (2006). Polyspecific associations of crowned lemurs and Sanford's lemurs in Madagascar. In: Gould L., Sauther M.L. (eds), Lemurs (pp. 111-131). Springer, Boston, MA.

Freeland, W. J., & Janzen, D. H. (1974). Strategies in herbivory by mammals: the role of plant secondary compounds. The American Naturalist, 108(961), 269-289.

Freeland, W. J., Calcott, P. H., & Geiss, D. P. (1985). Allelochemicals, minerals and herbivore population size. Biochemical Systematics and Ecology, 13(2), 195-206.

French, A. R., & Smith, T. B. (2005). Importance of body size in determining dominance hierarchies among diverse tropical frugivores 1. Biotropica, 37(1), 96-101.

Frossard, E., Bucher, M., Mächler, F., Mozafar, A., & Hurrell, R. (2000). Potential for increasing the content and bioavailability of Fe, Zn and Ca in plants for human nutrition. Journal of the Science of Food and Agriculture, 80(7), 861-879.

Futuyma, D. J., & Moreno, G. (1988). The evolution of ecological specialization. Annual Review of Ecology and Systematics, 19(1), 207-233.

Galef Jr, B. G., & Giraldeau, L. A. (2001). Social influences on foraging in vertebrates: causal mechanisms and adaptive functions. Animal Behaviour, 61(1), 3-15.

Ganzhorn, J. U. (1995). Low-level forest disturbance effects on primary production, leaf chemistry, and lemur populations. Ecology, 76(7), 2084-2096.

Ganzhorn, J. U. (2002). Distribution of a folivorous lemur in relation to seasonally varying food resources: integrating quantitative and qualitative aspects of food characteristics. Oecologia, 131(3), 427-435.

Garber, P. A. (1988). Diet, foraging patterns, and resource defense in a mixed species troop of Saguinus mystax and Saguinus fuscicollis in Amazonian Peru. Behaviour, 105(1-2), 18- 34.

278

Garber P.A., Porter L.M. (2010) The ecology of exudate production and exudate feeding in Saguinus and Callimico. In: Burrows A., Nash L. (eds) The Evolution of Exudativory in Primates (pp. 89-108). Springer, New York, NY

Gardner, M. J., & Altman, D. G. (1986). Confidence intervals rather than P values: estimation rather than hypothesis testing. British Medical Journal (Clinical Research Ed.), 292(6522), 746-750.

Gathua, J. M. (2000). Intraspecific variation in foraging patterns of redtail monkeys (Cercopithecus ascanius) in the Kakamega Forest, Kenya. PhD dissertation Columbia University.

Gautier-Hion, A. (1980). Seasonal variations of diet related to species and sex in a community of Cercopithecus monkeys. The Journal of Animal Ecology, 237-269.

Gautier-Hion, A. (1988). Polyspecific associations among forest guenons: ecological, behavioral and evolutionary aspects. In: A. Gautier-Hion, F. Bourlière, J.-P. Gautier, and J. Kingdon, (eds.), A Primate Radiation: Evolutionary Biology of the African Guenons, pp. 452-476. Cambridge University Press, Cambridge, UK.

Gautier-Hion, A., Gautier, J. P., & Quris, R. (1981). Forest structure and fruit availability as complementary factors influencing habitat use by a troop of monkeys (Cercopithecus cephus). Revue d'ecologie, 35, 511-536.

Gautier-Hion, A., Quris, R., & Gautier, J. P. (1983). Monospecific vs polyspecific life: a comparative study of foraging and antipredatory tactics in a community of Cercopithecus monkeys. Behavioral Ecology and Sociobiology, 12(4), 325-335.

Génin, F. (2008). Life in unpredictable environments: first investigation of the natural history of Microcebus griseorufus. International Journal of Primatology, 29(2), 303-321.

Gillespie, T. R., & Chapman, C. A. (2001). Determinants of group size in the red colobus monkey (Procolobus badius): an evaluation of the generality of the ecological-constraints model. Behavioral Ecology and Sociobiology, 50(4), 329-338.

Giraldeau, L. A., & Caraco, T. (2000). Social foraging theory. Princeton University Press.

Giraldeau, L. A., & Dubois, F. (2008). Social foraging and the study of exploitative behavior. Advances in the Study of Behavior, 38, 59-104.

Giraldeau, L. A., Hogan, J. A., & Clinchy, M. J. (1990). The payoffs to producing and scrounging: what happens when patches are divisible? Ethology, 85(2), 132-146.

Girvetz, E. H., & Greco, S. E. (2007). How to define a patch: a spatial model for hierarchically delineating organism-specific habitat patches. Landscape Ecology, 22(8), 1131-1142.

279

Glahn, R. P., Wortley, G. M., South, P. K., & Miller, D. D. (2002). Inhibition of iron uptake by phytic acid, tannic acid, and ZnCl2: studies using an in vitro digestion/Caco-2 cell model. Journal of Agricultural and Food Chemistry, 50(2), 390-395.

Glander, K. E. (1982). The impact of plant secondary compounds on primate feeding behavior. American Journal of Physical Anthropology, 25(S3), 1-18.

Goering, H. K., & Van Soest, P. J. (1970). Forage fiber analyses (apparatus, reagents, procedures, and some applications). USDA Agricultural Handbook.

Goodale, E., Beauchamp, G., & Ruxton, G. D. (2017). Mixed-species groups of animals: behavior, community structure, and conservation. Academic Press.

Goodale, E., & Kotagama, S. W. (2005). Alarm calling in Sri Lankan mixed-species bird flocks. The Auk, 122(1), 108-120.

Goodale, E., Beauchamp, G., Magrath, R. D., Nieh, J. C., & Ruxton, G. D. (2010). Interspecific information transfer influences animal community structure. Trends in ecology & evolution, 25(6), 354-361.

Goodall, J. (1986). The chimpanzees of Gombe: Patterns of behavior. Cambridge, MA: Belknap Press of Harvard University Press.

Gordon, D. M., Guetz, A., Greene, M. J., & Holmes, S. (2011). Colony variation in the collective regulation of foraging by harvester ants. Behavioral Ecology, 22(2), 429-435.

Goss-Custard, J. D., Cayford, J. T., & Lea, S. E. (1998). The changing trade-off between food finding and food stealing in juvenile oystercatchers. Animal Behaviour, 55(3), 745-760.

Grant, J. W. (1993). Whether or not to defend? The influence of resource distribution. Marine & Freshwater Behaviour & Physiology, 23(1-4), 137-153.

Grant, J. W., & Guha, R. T. (1993). Spatial clumping of food increases its monopolization and defense by convict cichlids, Cichlasoma nigrofasciatum. Behavioral Ecology, 4(4), 293- 296.

Grover, C. D., Kay, A. D., Monson, J. A., Marsh, T. C., & Holway, D. A. (2007). Linking nutrition and behavioural dominance: carbohydrate scarcity limits aggression and activity in Argentine ants. Proceedings of the Royal Society B: Biological Sciences, 274(1628), 2951-2957.

Grueter, C. C., Robbins, A. M., Abavandimwe, D., Vecellio, V., Ndagijimana, F., Ortmann, S., Stoinski, T. S., & Robbins, M. M. (2015). Causes, mechanisms, and consequences of contest competition among female mountain gorillas in Rwanda. Behavioral Ecology, 27(3), 766-776.

280

Grueter, C. C., Robbins, A. M., Abavandimwe, D., Vecellio, V., Ndagijimana, F., Stoinski, T. S., & Robbins, M. M. (2018). Quadratic relationships between group size and foraging efficiency in a herbivorous primate. Scientific Reports, 8(1), 16718.

Gursky, S. (2000). Effect of seasonality on the behavior of an insectivorous primate, Tarsius spectrum. International Journal of Primatology, 21(3), 477-495.

Gurven, M. (2004). To give and to give not: the behavioral ecology of human food transfers. Behavioral and Brain Sciences, 27(4), 543-559.

Haddow, A. J. (1952). Field and laboratory studies on an African monkey, Cercopithecus ascanius schmidti Matschie. Proceedings of the Zoological Society of London, 122(2), 297-394. Oxford, UK: Blackwell Publishing Ltd.

Hall, M. B., Hoover, W. H., Jennings, J. P., & Webster, T. K. M. (1999). A method for partitioning neutral detergent-soluble carbohydrates. Journal of the Science of Food and Agriculture, 79(15), 2079-2086.

Hall, M. B. (2007). Methodological challenges in carbohydrate analyses. Revista Brasileira de Zootecnia, 36, 359-367.

Hansen, M. J., Ward, A. J., Fürtbauer, I., & King, A. J. (2016). Environmental quality determines finder-joiner dynamics in socially foraging three-spined sticklebacks (Gasterosteus aculeatus). Behavioral Ecology and Sociobiology, 70(6), 889-899.

Hanya, G. (2004). Seasonal variations in the activity budget of Japanese macaques in the coniferous forest of Yakushima: effects of food and temperature. American Journal of Primatology, 63(3), 165-177.

Harris, T. R., & Chapman, C. A. (2007). Variation in diet and ranging of black and white colobus monkeys in Kibale National Park, Uganda. Primates, 48(3), 208-221.

Harrison, N. M., & Whitehouse, M. J. (2011). Mixed-species flocks: an example of niche construction?. Animal Behaviour, 81(4), 675-682.

Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E., ... & Inger, R. (2018). A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ, 6, e4794.

Harten, L., Matalon, Y., Galli, N., Navon, H., Dor, R., & Yovel, Y. (2018). Persistent producer- scrounger relationships in bats. Science Advances, 4(2), e1603293.

Hashimoto, C., Furuichi, T., & Tashiro, Y. (2000). Ant dipping and meat eating by wild chimpanzees in the Kalinzu Forest, Uganda. Primates, 41(1), 103-108.

Heesen, M., Rogahn, S., Ostner, J., & Schülke, O. (2013). Food abundance affects energy intake and reproduction in frugivorous female Assamese macaques. Behavioral Ecology and Sociobiology, 67(7), 1053-1066.

281

Hendriksma, H. P., & Shafir, S. (2016). Honey bee foragers balance colony nutritional deficiencies. Behavioral Ecology and Sociobiology, 70(4), 509-517.

Heymann, E. W., & Buchanan-Smith, H. M. (2000). The behavioural ecology of mixed-species troops of callitrichine primates. Biological Reviews, 75(2), 169-190.

Heymann, E. W., & Hsia, S. S. (2015). Unlike fellows–a review of primate–non-primate associations. Biological Reviews, 90(1), 142-156.

Hill, D. A. (1997). Seasonal variation in the feeding behavior and diet of Japanese macaques (Macaca fuscata yakui) in lowland forest of Yakushima. American Journal of Primatology, 43(4), 305-320.

Hino, T. (1998). Mutualistic and commensal organization of avian mixed-species foraging flocks in a forest of western Madagascar. Journal of Avian Biology, 17-24.

Hladik, C. M., Charles-Dominique, P., & Petter, J. J. (1980). Feeding strategies of five nocturnal prosimians in the dry forest of the west coast of Madagascar. In P. Charles-Dominique, HM Cooper, A Hladik, Cm Hladik (eds.), Nocturnal Malagasy primates: ecology, physiology, and behavior (pp. 41-73). New York, Academic.

Ho, J., Tumkaya, T., Aryal, S., Choi, H., & Claridge-Chang, A. (2019). Moving beyond P values: Data analysis with estimation graphics. Nature Methods,16, 565-566.

Hohmann, G., Potts, K., N'Guessan, A., Fowler, A., Mundry, R., Ganzhorn, J. U., & Ortmann, S. (2010). Plant foods consumed by Pan: exploring the variation of nutritional ecology across Africa. American Journal of Physical Anthropology, 141(3), 476-485.

Hosking, C. J., Raubenheimer, D., Charleston, M. A., Simpson, S. J., & Senior, A. M. (2019). Macronutrient intakes and the lifespan-fecundity trade-off: a geometric framework agent- based model. Journal of the Royal Society Interface, 16(151), 20180733.

Hothorn, T., Bretz, F., Westfall, P., Heiberger, R. M., Schuetzenmeister, A., Scheibe, S., & Hothorn, M. T. (2016). Package ‘multcomp’. Simultaneous inference in general parametric models. Project for Statistical Computing, Vienna, Austria.

Hou, X., Amais, R. S., Jones, B. T., & Donati, G. L. (2006). Inductively coupled plasma optical emission spectrometry. Encyclopedia of Analytical Chemistry: Applications, Theory and Instrumentation, 1-25.

Houle, A., Vickery, W. L., & Chapman, C. A. (2006). Testing mechanisms of coexistence among two species of frugivorous primates. Journal of Animal Ecology, 75(4), 1034- 1044.

Houle, A., Chapman, C. A., & Vickery, W. L. (2010). Intratree vertical variation of fruit density and the nature of contest competition in frugivores. Behavioral Ecology and Sociobiology, 64(3), 429-441.

282

Houle, A., Conklin-Brittain, N. L., & Wrangham, R. W. (2014). Vertical stratification of the nutritional value of fruit: macronutrients and condensed tannins. American Journal of Primatology, 76(12), 1207-1232.

Hudson, A. V., & Furness, R. W. (1988). Utilization of discarded fish by scavenging seabirds behind whitefish trawlers in Shetland. Journal of Zoology, 215(1), 151-166.

Irwin, M. T., Raharison, J. L., Raubenheimer, D., Chapman, C. A., & Rothman, J. M. (2014). Nutritional correlates of the “lean season”: effects of seasonality and frugivory on the nutritional ecology of diademed sifakas. American Journal of Physical Anthropology, 153(1), 78-91.

Irwin, M. T., Raharison, J. L., Raubenheimer, D. R., Chapman, C. A., & Rothman, J. M. (2015). The nutritional geometry of resource scarcity: Effects of lean seasons and habitat disturbance on nutrient intakes and balancing in wild sifakas. PloS one, 10(6), e0128046.

Isbell, L. A. (1991). Contest and scramble competition: patterns of female aggression and ranging behavior among primates. Behavioral Ecology, 2(2), 143-155.

Isbell, L. A. (1998). Diet for a small primate: insectivory and gummivory in the (large) patas monkey (Erythrocebus patas pyrrhonotus). American Journal of Primatology, 45(4), 381-398.

Isbell, L. A., & Young, T. P. (2002). Ecological models of female social relationships in primates: similarities, disparities, and some directions for future clarity. Behaviour, 139(2), 177-202.

Isbell, L. A., Pruetz, J. D., & Young, T. P. (1998). Movements of vervets (Cercopithecus aethiops) and patas monkeys (Erythrocebus patas) as estimators of food resource size, density, and distribution. Behavioral Ecology and Sociobiology, 42(2), 123-133.

Isbell, L. A., Rothman, J. M., Young, P. J., & Rudolph, K. (2013). Nutritional benefits of Crematogaster mimosae ants and Acacia drepanolobium gum for patas monkeys and vervets in Laikipia, Kenya. American Journal of Physical Anthropology, 150(2), 286- 300.

Iwata, Y., Nakashima, Y., Tsuchida, S., Nguema, P. P. M., Ando, C., Ushida, K., & Yamagiwa, J. (2015). Decaying toxic wood as sodium supplement for herbivorous mammals in Gabon. Journal of Veterinary Medical Science, 77(10), 1247-1252.

Jaeggi, A. V., Trumble, B. C., & Brown, M. (2018). Group-level competition influences urinary steroid hormones among wild red–tailed monkeys, indicating energetic costs. American Journal of Primatology, 80(5), e22757.

Janson, C. (1985). Aggressive competition and individual food consumption in wild brown capuchin monkeys (Cebus apella). Behavioral Ecology and Sociobiology, 18(2), 125- 138.

283

Janson, C. H. (1988). Intra-specific food competition and primate social structure: a synthesis. Behaviour, 105(1-2), 1-17.

Janson, C. H., & Chapman, C. A. (2000). Primate resources and the determination of primate community structure. In: Fleagle JG, Janson CH, Reed K (eds). Primate communities (pp. 237-267). Cambridge: Cambridge University Press.

Janson, C. H., & Emmons, L. H. (1990). Ecological structure of the nonflying mammal community at Cocha Cashu biological station, Manu National Park, Peru. In A. H. Gentry (ed.), Four neotropical rainforests (pp. 314-338). Yale University Press, New Haven.

Janson, C. H., & van Schaik, C. P. (1988). Recognizing the many faces of primate food competition: methods. Behaviour, 105(1/2), 165-186.

Janzen, D. H. (1973). Sweep samples of tropical foliage insects: effects of seasons, vegetation types, elevation, time of day, and insularity. Ecology, 54(3), 687-708.

Jensen, K., Mayntz, D., Toft, S., Clissold, F. J., Hunt, J., Raubenheimer, D., & Simpson, S. J. (2012). Optimal foraging for specific nutrients in predatory beetles. Proceedings of the Royal Society B: Biological Sciences, 279(1736), 2212-2218.

Johnson, C. A., Raubenheimer, D., Rothman, J. M., Clarke, D., & Swedell, L. (2013). 30 Days in the life: daily nutrient balancing in a wild chacma baboon. PLoS One, 8(7), e70383.

Johnson, C. A., Raubenheimer, D., Chapman, C. A., Tombak, K. J., Reid, A. J., & Rothman, J. M. (2017). Macronutrient balancing affects patch departure by guerezas (Colobus guereza). American Journal of Primatology, 79(4), 1-9.

Jullien, M., & Clobert, J. (2000). The survival value of flocking in Neotropical birds: Reality or fiction?. Ecology, 81(12), 3416-3430.

Judd, T. M., & Sherman, P. W. (1996). Naked mole-rats recruit colony mates to food sources. Animal Behaviour, 52(5), 957-969.

Joly-Radko, M., & Zimmermann, E. (2010). Seasonality in gum and honeydew feeding in gray mouse lemurs. In: Burrows A., Nash L. (eds) The Evolution of Exudativory in Primates (pp. 141-153). Springer, New York, NY.

Kasenene, J. M. (1987). The influence of mechanized selective logging, felling intensity and gap- size on the regeneration of a tropical moist forest in the Kibale Forest Reserve, Uganda. Ph.D. dissertation, Michigan State University,

Katz, K., & Naug, D. (2016). Dancers and followers in a honeybee colony differently prioritize individual and colony nutritional needs. Animal Behaviour, 119, 69-74.

Kiffner, C., Kioko, J., Leweri, C., & Krause, S. (2014). Seasonal patterns of mixed species groups in large East African mammals. PloS one, 9(12), e113446.

284

King, A. J., Isaac, N. J., & Cowlishaw, G. (2009). Ecological, social, and reproductive factors shape producer–scrounger dynamics in baboons. Behavioral Ecology, 20(5), 1039-1049.

Kingdon, J., Happold, D., Butynski, T., Hoffmann, M., Happold, M., & Kalina, J. (2013). Mammals of Africa (Vol. II: Primates). A&C Black.

Klass, K., & Cords, M. (2011). Effect of unknown relationships on linearity, steepness and rank ordering of dominance hierarchies: simulation studies based on data from wild monkeys. Behavioural Processes, 88(3), 168-176.

Klass, K., & Cords, M. (2015). Agonism and dominance in female blue monkeys. American Journal of Primatology, 77(12), 1299-1315.

Knott, C. D. (1998). Changes in orangutan caloric intake, energy balance, and ketones in response to fluctuating fruit availability. International Journal of Primatology, 19(6), 1061-1079.

Koch, F., Ganzhorn, J. U., Rothman, J. M., Chapman, C. A., & Fichtel, C. (2017). Sex and seasonal differences in diet and nutrient intake in Verreaux's sifakas (Propithecus verreauxi). American Journal of Primatology, 79(4), 1-10.

Koenig, A. (2000). Competitive regimes in forest-dwelling Hanuman langur females (Semnopithecus entellus). Behavioral ecology and sociobiology, 48(2), 93-109.

Koenig, A. (2002). Competition for resources and its behavioral consequences among female primates. International Journal of Primatology, 23(4), 759-783.

Koenig, A., & Borries, C. (2009). The lost dream of ecological determinism: time to say goodbye?… or a white queen's proposal? Evolutionary Anthropology: Issues, News, and Reviews, 18(5), 166-174.

Koenig, A., Beise, J., Chalise, M. K., & Ganzhorn, J. U. (1998). When females should contest for food–testing hypotheses about resource density, distribution, size, and quality with Hanuman langurs (Presbytis entellus). Behavioral Ecology and Sociobiology, 42(4), 225- 237.

Kotliar, N. B., & Wiens, J. A. (1990). Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity. Oikos, 253-260.

Krebs, J. R. (1973). Social learning and the significance of mixed-species flocks of chickadees (Parus spp.). Canadian Journal of Zoology, 51(12), 1275-1288.

Krebs, J. R., MacRoberts, M. H., & Cullen, J. M. (1972). Flocking and feeding in the great tit Parus major–an experimental study. Ibis, 114(4), 507-530.

Krockenberger, A. K., & Hume, I. D. (2007). A flexible digestive strategy accommodates the nutritional demands of reproduction in a free-living folivore, the Koala (Phascolarctos cinereus). Functional Ecology, 21(4), 748-756.

285

Kurvers, R. H., Prins, H. H., van Wieren, S. E., van Oers, K., Nolet, B. A., & Ydenberg, R. C. (2009). The effect of personality on social foraging: shy barnacle geese scrounge more. Proceedings of the Royal Society B: Biological Sciences, 277(1681), 601-608.

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: tests in linear mixed effects models. Journal of Statistical Software, 82(13).

Lambert, J. E. (1997). Digestive strategies, fruit processing, and seed dispersal in the chimpanzees (Pan troglodytes) and redtail monkeys (Cercopithecus ascanius) of Kibale National Park, Uganda. Ph.D. dissertation, University of Illinois at Urbana-Champaign.

Lambert, J. E. (1998). Primate digestion: interactions among anatomy, physiology, and feeding ecology. Evolutionary Anthropology: Issues, News, and Reviews: Issues, News, and Reviews, 7(1), 8-20.

Lambert, J. E. (1999). Seed handling in chimpanzees (Pan troglodytes) and redtail monkeys (Cercopithecus ascanius): Implications for understanding hominoid and cercopithecine fruit-processing strategies and seed dispersal. American Journal of Physical Anthropology, 109(3), 365-386.

Lambert, J. E. (2002a). Resource switching and species coexistence in guenons: A community analysis of dietary flexibility. In M. Glenn & M. Cords (Eds.), The guenons: Diversity and adaptation in African monkeys (pp. 309-323). New York: Plenum Press.

Lambert, J. E. (2002b). Digestive retention times in forest guenons (Cercopithecus spp.) with reference to chimpanzees (Pan troglodytes). International Journal of Primatology, 23(6), 1169-1185.

Lambert, J. E. (2005). Competition, predation, and the evolutionary significance of the cercopithecine cheek pouch: the case of Cercopithecus and Lophocebus. American Journal of Physical Anthropology, 126(2), 183-192.

Lambert, J. E., & Rothman, J. M. (2015). Nutritional and energetic correlates of cheek pouch use in cercopithecinae. Journal of East African Natural History, 104(1-2), 31-41.

Lee, K. P., Simpson, S. J., Clissold, F. J., Brooks, R., Ballard, J. W. O., Taylor, P. W., ... & Raubenheimer, D. (2008). Lifespan and reproduction in Drosophila: new insights from nutritional geometry. Proceedings of the National Academy of Sciences, 105(7), 2498- 2503.

Lee, A. E., & Cowlishaw, G. (2017). Switching spatial scale reveals dominance-dependent social foraging tactics in a wild primate. PeerJ, 5, e3462.

Lefcheck, J. S. (2016). piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods in Ecology and Evolution, 7(5), 573-579.

286

Leibowitz, S. F., Lucas, D. J., Leibowitz, K. L., & Jhanwar, Y. S. (1991). Developmental patterns of macronutrient intake in female and male rats from weaning to maturity. Physiology and Behavior, 50(6), 1167-1174.

Lendvai, A. Z., Barta, Z., Liker, A., & Bókony, V. (2004). The effect of energy reserves on social foraging: hungry sparrows scrounge more. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271(1556), 2467-2472.

Licitra, G., Hernandez, T. M., & Van Soest, P. J. (1996). Standardization of procedures for nitrogen fractionation of ruminant feeds. Animal Feed Science and Technology, 57(4), 347-358.

Lihoreau, M., & Rivault, C. (2011). Local enhancement promotes cockroach feeding aggregations. PloS one, 6(7), e22048.

Lihoreau, M., Buhl, J., Charleston, M. A., Sword, G. A., Raubenheimer, D., & Simpson, S. J. (2015). Nutritional ecology beyond the individual: a conceptual framework for integrating nutrition and social interactions. Ecology Letters, 18(3), 273-286.

Lihoreau, M., Clarke, I. M., Buhl, J., Sumpter, D. J., & Simpson, S. J. (2016). Collective selection of food patches in Drosophila. Journal of Experimental Biology, 219(5), 668- 675.

Lihoreau, M., Charleston, M. A., Senior, A. M., Clissold, F. J., Raubenheimer, D., Simpson, S. J., & Buhl, J. (2017). Collective foraging in spatially complex nutritional environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1727), 20160238.

Lihoreau, M., Gómez-Moracho, T., Pasquaretta, C., Costa, J. T., & Buhl, J. (2018). Social nutrition: an emerging field in insect science. Current Opinion in Insect Science, 28, 73- 80.

Liker, A., & Barta, Z. (2002). The effects of dominance on social foraging tactic use in house sparrows. Behaviour, 139(8), 1061-1076.

Livoreil, B., & Giraldeau, L. A. (1997). Patch departure decisions by spice finches foraging singly or in groups. Animal Behaviour, 54(4), 967-977.

Lyke, M. M. (2018). A molecular analysis of insectivory by sympatric, omnivorous guenons (Cercopithecus ascanius, C. mitis) in Kibale National Park, Uganda. Ph.D. dissertation, University of Texas at San Antonio.

Lyke, M. M., Di Fiore, A., Fierer, N., Madden, A. A., & Lambert, J. E. (2019). Metagenomic analyses reveal previously unrecognized variation in the diets of sympatric Old World monkey species. PloS one, 14(6), e0218245.

MacArthur, R. H., & Pianka, E. R. (1966). On optimal use of a patchy environment. The American Naturalist, 100(916), 603-609.

287

Machovsky-Capuska, G. E., Senior, A. M., Simpson, S. J., & Raubenheimer, D. (2016). The multidimensional nutritional niche. Trends in Ecology & Evolution, 31(5), 355-365.

Maklakov, A. A., Simpson, S. J., Zajitschek, F., Hall, M. D., Dessmann, J., Clissold, F., ... & Brooks, R. C. (2008). Sex-specific fitness effects of nutrient intake on reproduction and lifespan. Current Biology, 18(14), 1062-1066.

Malenovský, I., Burckhardt, D., & Tamesse, J. L. (2007). Jumping plant-lice of the family Phacopteronidae (Hemiptera: Psylloidea) from Cameroon. Journal of Natural History, 41(29-32), 1875-1927.

Malinga, G. M., Valtonen, A., Nyeko, P., Vesterinen, E. J., & Roininen, H. (2014). Bottom-up impact on the cecidomyiid leaf galler and its parasitism in a tropical rainforest. Oecologia, 176(2), 511-520.

Mallott, E. K., Malhi, R. S., & Garber, P. A. (2015). High‐throughput sequencing of fecal DNA to identify insects consumed by wild Weddell's saddleback tamarins (Saguinus weddelli, Cebidae, Primates) in Bolivia. American journal of physical anthropology, 156(3), 474- 481.

Markham, R., & Groves, C. P. (1990). Brief communication: Weights of wild orang utans. American Journal of Physical Anthropology, 81(1), 1-3.

Marler, P. (1973). A comparison of vocalizations of red-tailed monkeys and blue monkeys, Cercopithecus ascanius and C. mitis, in Uganda 1. Zeitschrift für Tierpsychologie, 33(3‐ 4), 223-247.

Marriott, J., Robinson, M., & Karikari, S. K. (1981). Starch and sugar transformation during the ripening of plantains and bananas. Journal of the Science of Food and Agriculture, 32(10), 1021-1026.

Marshall, A. J., & Leighton, M. (2006). How does food availability limit the population density of white-bearded gibbons? In G. Hohmann, M. M. Robbins, & C. Boesch (Eds.), Feeding ecology in apes and other primates: Ecological, physical, and behavioural aspects (pp. 313-332). Cambridge: Cambridge University Press.

Marshall, A. J., & Wich, S. A. (2013). Characterization of primate environments through assessment of plant phenology. In: E. Sterling, M. Blair, & N. Bynum (eds.), Primate ecology and conservation: A handbook of techniques (pp. 103–127). Oxford: Oxford University Press

Marshall, A. J., Boyko, C. M., Feilen, K. L., Boyko, R. H., & Leighton, M. (2009). Defining fallback foods and assessing their importance in primate ecology and evolution. American Journal of Physical Anthropology, 140(4), 603-614.

Martínez‐Vilalta, J., Sala, A., Asensio, D., Galiano, L., Hoch, G., Palacio, S., ... & Lloret, F. (2016). Dynamics of non‐structural carbohydrates in terrestrial plants: a global synthesis. Ecological Monographs, 86(4), 495-516.

288

Masette, M., Isabirye-Basuta, G., Baranga, D., Chapman, C. A., & Rothman, J. M. (2015). The challenge of interpreting primate diets: mangabey foraging on Blighia unijugata fruit in relation to changing nutrient content. African Journal of Ecology, 53(3), 259-267.

Matthews, R. W., & Matthews, J. R. (1971). The Malaise trap: its utility and potential for sampling insect populations. The Great Lakes Entomologist, 4(4), 4.

Mathot, K. J., & Giraldeau, L. A. (2007). Increasing vulnerability to predation increases preference for the scrounger foraging tactic. Behavioral Ecology, 19(1), 131-138.

Mayack, C., & Naug, D. (2013). Individual energetic state can prevail over social regulation of foraging in honeybees. Behavioral Ecology and Sociobiology, 67(6), 929-936.

Maynard, L. A., & Loosli, J. K. (1969). Animal Nutrition. McGraw-Hill, New York.

McArthur, C., Banks, P. B., Boonstra, R., & Forbey, J. S. (2014). The dilemma of foraging herbivores: dealing with food and fear. Oecologia, 176(3), 677-689.

McCabe, G. M., & Fedigan, L. M. (2007). Effects of reproductive status on energy intake, ingestion rates, and dietary composition of female Cebus capucinus at Santa Rosa, Costa Rica. International Journal of Primatology, 28(4), 837-851.

McLester, E., Brown, M., Stewart, F. A., & Piel, A. K. (2019a). Food abundance and weather influence habitat‐specific ranging patterns in forest‐and savanna mosaic‐dwelling red‐ tailed monkeys (Cercopithecus ascanius). American Journal of Physical Anthropology, 170(2), 217-231.

McLester, E., Sweeney, K., Stewart, F. A., & Piel, A. K. (2019b). Leopard (Panthera pardus) predation on a red-tailed monkey (Cercopithecus ascanius) in the Issa Valley, western Tanzania. Primates, 60(1), 15-19.

McCord, A. I., Chapman, C. A., Weny, G., Tumukunde, A., Hyeroba, D., Klotz, K., ... & Leigh, S. R. (2014). Fecal microbiomes of non-human primates in Western Uganda reveal species‐specific communities largely resistant to habitat perturbation. American Journal of Primatology, 76(4), 347-354.

McCormack, J. E., Jablonski, P. G., & Brown, J. L. (2007). Producer-scrounger roles and joining based on dominance in a free-living group of Mexican jays (Aphelocoma ultramarina). Behaviour, 144(8), 967-982.

McGrew, W. C. (2014). The ‘other faunivory’ revisited: Insectivory in human and non-human primates and the evolution of human diet. Journal of Human Evolution, 71, 4–11.

McNaughton, S. J. (1988). Mineral nutrition and spatial concentrations of African ungulates. Nature, 334(6180), 343.

McNeilage, A. J. (1995). Mountain gorillas in the Virunga Volcanoes: ecology and carrying capacity. Ph.D. dissertation, University of Bristol.

289

Melin, A. D., Young, H. C., Mosdossy, K. N., & Fedigan, L. M. (2014). Seasonality, extractive foraging and the evolution of primate sensorimotor intelligence. Journal of Human Evolution, 71, 77-86.

Milton, K. (1979). Factors influencing leaf choice by howler monkeys: a test of some hypotheses of food selection by generalist herbivores. The American Naturalist, 114(3), 362-378.

Milton, K. (1998). Physiological ecology of howlers (Alouatta): energetic and digestive considerations and comparison with the Colobinae. International Journal of Primatology, 19(3), 513-548.

Milton, K. (1999). A hypothesis to explain the role of meat-eating in human evolution. Evolutionary Anthropology: Issues, News, and Reviews: Issues, News, and Reviews, 8(1), 11-21.

Milton, K. (2003). Micronutrient intakes of wild primates: are humans different? Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 136(1), 47-59.

Milton, K., & Demment, M. W. (1988). Digestion and passage kinetics of chimpanzees fed high and low fiber diets and comparison with human data. The Journal of Nutrition, 118(9), 1082-1088.

Milton, K., & Dintzis, F. R. (1981). Nitrogen-to-protein conversion factors for tropical plant samples. Biotropica, 13(3), 177-181.

Minta, S. C., Minta, K. A., & Lott, D. F. (1992). Hunting associations between badgers (Taxidea taxus) and coyotes (Canis latrans). Journal of Mammalogy, 73(4), 814-820.

Mitani, J. C., & Watts, D. P. (1999). Demographic influences on the hunting behavior of chimpanzees. American Journal of Physical Anthropology, 109(4), 439-454.

Mitani, J. C., Sanders, W. J., Lwanga, J. S., & Windfelder, T. L. (2001). Predatory behavior of crowned hawk-eagles (Stephanoaetus coronatus) in Kibale National Park, Uganda. Behavioral Ecology and Sociobiology, 49(2-3), 187-195.

Mitchell, C. L., Boinski, S., & Van Schaik, C. P. (1991). Competitive regimes and female bonding in two species of squirrel monkeys (Saimiri oerstedi and S. sciureus). Behavioral Ecology and Sociobiology, 28(1), 55-60.

Monaghan, P., & Metcalfe, N. B. (1985). Group foraging in wild brown hares: effects of resource distribution and social status. Animal Behaviour, 33(3), 993-999.

Moore, B. D., & Foley, W. J. (2005). Tree use by koalas in a chemically complex landscape. Nature, 435(7041), 488.

Morand-Ferron, J., Wu, G. M., & Giraldeau, L. A. (2011). Persistent individual differences in tactic use in a producer–scrounger game are group dependent. Animal Behaviour, 82(4), 811-816.

290

Morehouse, N. I., Nakazawa, T., Booher, C. M., Jeyasingh, P. D., & Hall, M. D. (2010). Sex in a material world: why the study of sexual reproduction and sex-specific traits should become more nutritionally-explicit. Oikos, 119(5), 766-778.

Morimoto, J., & Lihoreau, M. (2019). Quantifying nutritional trade-offs across multidimensional performance landscapes. The American Naturalist, 193(6), E168-E181.

Morse, D. H. (1970). Ecological aspects of some mixed-species foraging flocks of birds. Ecological Monographs, 40(1), 119-168.

Motro, U., & Shmida, A. (1995). Near—Far search: An evolutionarily stable foraging strategy. Journal of Theoretical Biology, 173(1), 15-22.

Munn, C. A. (1986). Birds that ‘cry wolf’. Nature, 319(6049), 143.

Murphy, D. J., & Mukherjee, K. D. (1988). Biosynthesis of very long chain monounsaturated fatty acids by subcellular fractions of developing seeds. FEBS letters, 230(1-2), 101-104.

Murray, P. A. (1973). The anatomy and adaptive significance of cheek pouches (Bursae buccales) in the Cercopithecinae, Cercopithecoidea. Ph.D. dissertation, University of Chicago.

Murray, P. A. (1975). The role of cheek pouches in cercopithecine monkey adaptive strategy. In: Tuttle, R. (ed.) Primate Functional Morphology and Evolution, pp. 151-194. Mouton Publishers, Chicago, IL.

Murray, C. M., Eberly, L. E., & Pusey, A. E. (2006). Foraging strategies as a function of season and rank among wild female chimpanzees (Pan troglodytes). Behavioral Ecology, 17(6), 1020-1028.

Mwangi, M. N., Oonincx, D. G., Stouten, T., Veenenbos, M., Melse-Boonstra, A., Dicke, M., & Van Loon, J. J. (2018). Insects as sources of iron and zinc in human nutrition. Nutrition Research Reviews, 31(2), 248-255.

Nagy, K. A. (1994). Field bioenergetics of mammals-what determines field metabolic rates. Australian Journal of Zoology, 42(1), 43-53.

Nagy, K. A., & Milton, K. (1979). Aspects of dietary quality, nutrient assimilation and water balance in wild howler monkeys (Alouatta palliata). Oecologia, 39(3), 249-258.

Nakagawa, N. (2008). Despotic wild patas monkeys (Erythrocebus patas) in Kala Maloue, Cameroon. American Journal of Primatology, 70(3), 238-246.

Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.

291

Nakalembe, I., Kabasa, J. D., & Olila, D. (2015). Comparative nutrient composition of selected wild edible mushrooms from two agro-ecological zones, Uganda. SpringerPlus, 4(1), 433.

Nash, L. T. (1986). Dietary, behavioral, and morphological aspects of gummivory in primates. American Journal of Physical Anthropology, 29(S7), 113-137.

Nash, L. T., & Whitten, P. L. (1989). Preliminary observations on the role of Acacia gum chemistry in Acacia utilization by Galago senegalensis in Kenya. American Journal of Primatology, 17(1), 27-39.

National Research Council (NRC) (2003). Nutrient requirements of nonhuman primates. National Academies Press.

Nichol, H., & Locke, M. (1990). The localization of ferritin in insects. Tissue and Cell, 22(6), 767-777.

Nicholson, A. J. (1954). An outline of the dynamics of animal populations. Australian Journal of Zoology, 2(1), 9-65.

Nie, Y., Zhang, Z., Raubenheimer, D., Elser, J. J., Wei, W., & Wei, F. (2015). Obligate herbivory in an ancestrally carnivorous lineage: the giant panda and bamboo from the perspective of nutritional geometry. Functional Ecology, 29(1), 26-34.

Noë, R., & Bshary, R. (1997). The formation of red colobus–Diana monkey associations under predation pressure from chimpanzees. Proceedings of the Royal Society of London. Series B: Biological Sciences, 264(1379), 253-259.

Nolen, M. T., & Lucas, J. R. (2009). Asymmetries in mobbing behaviour and correlated intensity during predator mobbing by nuthatches, chickadees and titmice. Animal Behaviour, 77(5), 1137-1146. van Noordwijk, M. A., & Van Schaik, C. P. (1987). Competition among female long-tailed macaques, Macaca fascicularis. Animal Behaviour, 35(2), 577-589.

Norconk, M. A. (1990). Mechanisms promoting stability in mixed Saguinus mystax and S. fuscicollis troops. American Journal of Primatology, 21(2), 159-170.

Norconk, M. A., & Conklin-Brittain, N. L. (2004). Variation on frugivory: the diet of Venezuelan white-faced sakis. International Journal of Primatology, 25(1), 1-26.

Norconk, M. A., Wright, B. W., Conklin-Brittain, N. L., & Vinyard, C. J. (2009). Mechanical and nutritional properties of food as factors in platyrrhine dietary adaptations. In South American Primates (pp. 279-319). Springer, New York, NY.

Nummelin, M. (1989). Seasonality and effects of forestry practices on forest floor arthropods in the Kibale Forest, Uganda. Fauna Norvegica, Series B, 36(1), 17-25.

292

Nummelin, M. (1996). The community structure of arthropods in virgin and managed sites in the Kibale Forest, Western Uganda. Tropical Ecology, 37(2), 203-213.

Nummelin, M., & Nshubemuki, L. (1998). Seasonality and structure of the arthropod community in a forested valley in the Uluguru Mountains, eastern Tanzania. Journal of East African Natural History, 87(1), 205-213.

Nummelin, M., & Zilihona, I. J. (2004). Spatial variation of arthropod communities in virgin and managed sites in the Kibale Forest, western Uganda. Forest Ecology and Management, 195(1-2), 107-114.

Nyafwono, M., Valtonen, A., Nyeko, P., & Roininen, H. (2014). Butterfly community composition across a successional gradient in a human‐disturbed afro‐tropical rain forest. Biotropica, 46(2), 210-218.

Oates, J. F. (1978). Water-plant and soil consumption by guereza monkeys (Colobus guereza): a relationship with minerals and toxins in the diet? Biotropica, 241-253.

Oates, J. F., & Whitesides, G. H. (1990). Association between olive colobus (Procolobus verus), Diana guenons (Cercopithecus diana), and other forest monkeys in Sierra Leone. American Journal of Primatology, 21(2), 129-146.

Oates, J. F., Swain, T., & Zantovska, J. (1977). Secondary compounds and food selection by colobus monkeys. Biochemical Systematics and Ecology, 5(4), 317-321.

Oates, J. F., Waterman, P. G., & Choo, G. M. (1980). Food selection by the south Indian leaf- monkey, Presbytis johnii, in relation to leaf chemistry. Oecologia, 45(1), 45-56.

O’Connell, J. F., Hawkes, K., & Jones, N. B. (1999). Grandmothering and the evolution of Homo erectus. Journal of Human Evolution, 36(5), 461-485.

Oftedal, O. T. (1991). The nutritional consequences of foraging in primates: the relationship of nutrient intakes to nutrient requirements. Phil. Trans. R. Soc. Lond. B, 334(1270), 161- 170.

Olupot, W. (2000). Mass differences among male mangabey monkeys inhabiting logged and unlogged forest compartments. Conservation Biology, 14(3), 833-843.

O'Malley, R. C., & Power, M. L. (2014). The energetic and nutritional yields from insectivory for Kasekela chimpanzees. Journal of Human Evolution, 71, 46-58.

Omeja, P. A., Lawes, M. J., Corriveau, A., Valenta, K., Sarkar, D., Paim, F. P., & Chapman, C. A. (2016). Recovery of tree and mammal communities during large‐scale forest regeneration in Kibale National Park, Uganda. Biotropica, 48(6), 770-779.

Onderdonk, D. A., & Chapman, C. A. (2000). Coping with forest fragmentation: the primates of Kibale National Park, Uganda. International Journal of Primatology, 21(4), 587-611.

293

Owiny, A. A., & Malinga, G. M. (2014). Population structure and regeneration status of Prunus africana (Hoof. f.) Kalkm. after selective and clear felling in Kibale National Park, Uganda. International Journal of Biodiversity and Conservation, 6(12), 869-875.

Owiny, A. A., Valtonen, A., Nyeko, P., Malinga, G. M., & Roininen, H. (2016). Tree communities of different aged logged areas in an Afrotropical rainforest. African Journal of Ecology, 54(2), 207-216.

Parker, G. A. (2000). Scramble in behaviour and ecology. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 355(1403), 1637-1645.

Parrish, C. C., Myher, J. J., Kuksis, A., & Angel, A. (1997). Lipid structure of rat adipocyte plasma membranes following dietary lard and fish oil. Biochimica et Biophysica Acta (BBA)-Biomembranes, 1323(2), 253-262.

Pasquaretta, C., Gómez-Moracho, T., Heeb, P., & Lihoreau, M. (2018). Exploring interactions between the gut microbiota and social behavior through nutrition. Genes, 9(11), 534.

Payne, H. F., Lawes, M. J., & Henzi, S. P. (2003). Competition and the exchange of grooming among female samango monkeys (Cercopithecus mitis erythrarchus). Behaviour, 140(4), 453-472.

Pays, O., Ekori, A., & Fritz, H. (2014). On the advantages of mixed-species groups: impalas adjust their vigilance when associated with larger prey herbivores. Ethology, 120(12), 1207-1216.

Pazol, K., & Cords, M. (2005). Seasonal variation in feeding behavior, competition and female social relationships in a forest dwelling guenon, the blue monkey (Cercopithecus mitis stuhlmanni), in the Kakamega Forest, Kenya. Behavioral Ecology and Sociobiology, 58(6), 566-577.

Pebsworth, P. A., Seim, G. L., Huffman, M. A., Glahn, R. P., Tako, E., & Young, S. L. (2013). Soil consumed by Chacma baboons is low in bioavailable iron and high in clay. Journal of Chemical Ecology, 39(3), 447-449.

Pebsworth, P. A., Hillier, S., Wendler, R., Glahn, R., Ta, C. A. K., Arnason, J. T., & Young, S. L. (2019). Geophagy among East African Chimpanzees: consumed soils provide protection from plant secondary compounds and bioavailable iron. Environmental Geochemistry and Health, 1-17.

Peres, C. A. (1992a). Consequences of joint-territoriality in a mixed-species group of tamarin monkeys. Behaviour, 123(3-4), 220-246.

Peres, C. A. (1992b). Prey-capture benefits in a mixed-species group of Amazonian tamarins, Saguinus fuscicollis and S. mystax. Behavioral Ecology and Sociobiology, 31(5), 339- 347.

294

Peres, C. A. (1994). Primate responses to phenological changes in an Amazonian terra firme forest. Biotropica, 98-112.

Peres, C. A. (1996). Food patch structure and plant resource partitioning in interspecific associations of Amazonian tamarins. International Journal of Primatology, 17(5), 695.

Pickett, S. B., Bergey, C. M., & Di Fiore, A. (2012). A metagenomic study of primate insect diet diversity. American Journal of Primatology, 74(7), 622-631.

Pinheiro, T., Ferrari, S. F., & Lopes, M. A. (2011). Polyspecific associations between squirrel monkeys (Saimiri sciureus) and other primates in eastern Amazonia. American Journal of Primatology, 73(11), 1145-1151.

Ponton, F., Wilson, K., Holmes, A. J., Cotter, S. C., Raubenheimer, D., & Simpson, S. J. (2013). Integrating nutrition and immunology: a new frontier. Journal of Insect Physiology, 59(2), 130-137.

Ponton, F., Morimoto, J., Robinson, K., Kumar, S., Cotter, S., Wilson, K., & Simpson, S. (2019). Macronutrients modulate resistance to infection and immunity in Drosophila. Functional Ecology.

Pontzer, H. (2012). Ecological energetics in early Homo. Current Anthropology, 53(S6), S346- S358.

Pontzer, H., Raichlen, D. A., Gordon, A. D., Schroepfer-Walker, K. K., Hare, B., O’Neill, M. C., ... & Burkart, J. (2014). Primate energy expenditure and life history. Proceedings of the National Academy of Sciences, 111(4), 1433-1437.

Powell, G. V. (1989). On the possible contribution of mixed species flocks to species richness in neotropical avifaunas. Behavioral Ecology and Sociobiology, 24(6), 387-393.

Power, M. L. (2010). Nutritional and digestive challenges to being a gum-feeding primate. In The evolution of exudativory in primates (pp. 25-44). Springer, New York, NY.

Pruetz, J. D., & Isbell, L. A. (2000). Correlations of food distribution and patch size with agonistic interactions in female vervets (Chlorocebus aethiops) and patas monkeys (Erythrocebus patas) living in simple habitats. Behavioral Ecology and Sociobiology, 49(1), 38-47.

Pulliam, H. R. (1975). Diet optimization with nutrient constraints. The American Naturalist, 109(970), 765-768.

Pyke, G. H., Pulliam, H. R., & Charnov, E. L. (1977). Optimal foraging: a selective review of theory and tests. The Quarterly Review of Biology, 52(2), 137-154.

Radford, A. N. (2008). Duration and outcome of intergroup conflict influences intragroup affiliative behaviour. Proceedings of the Royal Society B: Biological Sciences, 275(1653), 2787-2791.

295

Rahaman, H., Srihari, K., & Krishnamoorthy, R. V. (1975). Polysaccharide digestion in cheek pouches of the bonnet macaque. Primates, 16(2), 175-180.

Raman, A. (2011). Morphogenesis of insect-induced plant galls: facts and questions. Flora- Morphology, Distribution, Functional Ecology of Plants, 206(6), 517-533.

Rasa, O. A. E. (1983). Dwarf mongoose and hornbill mutualism in the Taru Desert, Kenya. Behavioral Ecology and Sociobiology, 12(3), 181-190.

Raubenheimer, D. (2011). Toward a quantitative nutritional ecology: the right-angled mixture triangle. Ecological Monographs, 81(3), 407-427.

Raubenheimer, D., & Jones, S. A. (2006). Nutritional imbalance in an extreme generalist omnivore: tolerance and recovery through complementary food selection. Animal Behaviour, 71(6), 1253-1262.

Raubenheimer, D., & Simpson, S. J. (1997). Integrative models of nutrient balancing: application to insects and vertebrates. Nutrition Research Reviews, 10(1), 151-179.

Raubenheimer, D., & Simpson, S. J. (2003). Nutrient balancing in grasshoppers: behavioural and physiological correlates of dietary breadth. Journal of Experimental Biology, 206(10), 1669-1681.

Raubenheimer, D., Simpson, S. J., & Mayntz, D. (2009). Nutrition, ecology and nutritional ecology: toward an integrated framework. Functional Ecology, 23(1), 4-16.

Raubenheimer, D., Machovsky-Capuska, G. E., Chapman, C. A., & Rothman, J. M. (2015). Geometry of nutrition in field studies: an illustration using wild primates. Oecologia, 177(1), 223-234.

Rehg, J. A. (2006). Seasonal variation in polyspecific associations among Callimico goeldii, Saguinus labiatus, and S. fuscicollis in Acre, Brazil. International Journal of Primatology, 27(5), 1399.

Reiner, W. B., Petzinger, C., Power, M. L., Hyeroba, D., & Rothman, J. M. (2014). Fatty acids in mountain gorilla diets: implications for primate nutrition and health. American Journal of Primatology, 76(3), 281-288.

Reynolds, V., Plumptre, A. J., Greenham, J., & Harborne, J. (1998). Condensed tannins and sugars in the diet of chimpanzees (Pan troglodytes schweinfurthii) in the Budongo Forest, Uganda. Oecologia, 115(3), 331-336.

Reynolds, V., Lloyd, A. W., Babweteera, F., & English, C. J. (2009). Decaying Raphia farinifera palm trees provide a source of sodium for wild chimpanzees in the Budongo Forest, Uganda. PloS one, 4(7), e6194.

296

Reynolds, V., Pascual-Garrido, A., Lloyd, A. W., Lyons, P., & Hobaiter, C. (2019). Possible mineral contributions to the diet and health of wild chimpanzees in three East African forests. American Journal of Primatology, e978.

Richter, C. P., Holt Jr, L. E., & Barelare Jr, B. (1938). Nutritional requirements for normal growth and reproduction in rats studied by the self-selection method. American Journal of Physiology, 122(3), 734-744.

Righini, N., Garber, P. A., & Rothman, J. M. (2017). The effects of plant nutritional chemistry on food selection of Mexican black howler monkeys (Alouatta pigra): the role of lipids. American Journal of Primatology, 79(4), 1-15

Robbins, C. T., Hanley, T. A., Hagerman, A. E., Hjeljord, O., Baker, D. L., Schwartz, C. C., & Mautz, W. W. (1987). Role of tannins in defending plants against ruminants: reduction in protein availability. Ecology, 98-107.

Robbins, C. T. (1993). Wildlife feeding and nutrition. San Diego, CA: Academic Press.

Rode, K. D., Chapman, C. A., Chapman, L. J., & McDowell, L. R. (2003). Mineral resource availability and consumption by colobus in Kibale National Park, Uganda. International Journal of Primatology, 24(3), 541-573.

Rode, K. D., Chapman, C. A., McDowell, L. R., & Stickler, C. (2006a). Nutritional correlates of population density across habitats and logging intensities in redtail monkeys (Cercopithecus ascanius). Biotropica, 38(5), 625-634.

Rode, K. D., Chiyo, P. I., Chapman, C. A., & McDowell, L. R. (2006b). Nutritional ecology of elephants in Kibale National Park, Uganda, and its relationship with crop-raiding behaviour. Journal of Tropical Ecology, 22(4), 441-449.

Rothman, J. M., Van Soest, P. J., & Pell, A. N. (2006). Decaying wood is a sodium source for mountain gorillas. Biology Letters, 2(3), 321-324.

Rothman, J. M., Dierenfeld, E. S., Hintz, H. F., & Pell, A. N. (2008a). Nutritional quality of gorilla diets: consequences of age, sex, and season. Oecologia, 155(1), 111-122.

Rothman, J. M., Chapman, C. A., & Pell, A. N. (2008b). Fiber-bound nitrogen in gorilla diets: implications for estimating dietary protein intake of primates. American Journal of Primatology, 70(7), 690-694.

Rothman, J. M., Chapman, C. A., Hansen, J. L., Cherney, D. J., & Pell, A. N. (2009a). Rapid assessment of the nutritional value of foods eaten by mountain gorillas: applying near- infrared reflectance spectroscopy to primatology. International Journal of Primatology, 30(5), 729-742.

Rothman, J. M., Dusinberre, K., & Pell, A. N. (2009b). Condensed tannins in the diets of primates: a matter of methods? American Journal of Primatology, 71(1), 70-76.

297

Rothman, J. M., Raubenheimer, D., & Chapman, C. A. (2011). Nutritional geometry: gorillas prioritize non-protein energy while consuming surplus protein. Biology Letters, 7(6), 847-849.

Rothman, J. M., Chapman, C. A., & Van Soest, P. J. (2012). Methods in primate nutritional ecology: A user’s guide. International Journal of Primatology, 33(3), 542-566.

Rothman, J. M., Raubenheimer, D., Bryer, M. A., Takahashi, M., & Gilbert, C. C. (2014). Nutritional contributions of insects to primate diets: implications for primate evolution. Journal of Human Evolution, 71, 59-69.

Rowell, T. E. (1988). The social system of guenons, compared with baboons, macaques and mangabeys. In: A. Gautier-Hion, F. Bourlière, J.-P. Gautier, and J. Kingdon (eds.), A Primate Radiation: Evolutionary Biology of the African Guenons, pp. 439-451. Cambridge University Press, Cambridge, UK.

Rowell, T. E., & Mitchell, B. J. (1991). Comparison of seed dispersal by guenons in Kenya and capuchins in Panama. Journal of Tropical Ecology, 7(2), 269-274.

Rubenstein, D. I., Barnett, R. J., Ridgely, R. S., & Klopfer, P. H. (1977). Adaptive advantages of mixed‐species feeding flocks among seed‐eating finches in Costa Rica. Ibis, 119(1), 10- 21.

Rypstra, A. L. (1993). Prey size, social competition, and the development of reproductive division of labor in social spider groups. The American Naturalist, 142(5), 868-880.

Saito, C. (1996). Dominance and feeding success in female Japanese macaques, Macaca fuscata: effects of food patch size and inter-patch distance. Animal Behaviour, 51(5), 967-980.

Salomon, M., Mayntz, D., & Lubin, Y. (2008). Colony nutrition skews reproduction in a social spider. Behavioral Ecology, 19(3), 605-611. van Schaik, C. P. (1986). Phenological changes in a Sumatran rain forest. Journal of Tropical Ecology, 2(4), 327-347. van Schaik, C. P. (1989). The ecology of social relationships amongst female primates. Comparative Socioecology, 195-218. van Schaik, C. P., & Brockman, D. K. (2005). Seasonality in primate ecology, reproduction, and life history. In Brockman, D. K., & Van Schaik, C. P. (eds.), Seasonality in primates: studies of living and extinct human and non-human primates, 44, 3-20. Cambridge University Press. van Schaik, C. P., van Noordwijk, M. A., de Boer, R. J., & den Tonkelaar, I. (1983). The effect of group size on time budgets and social behaviour in wild long-tailed macaques (Macaca fascicularis). Behavioral Ecology and Sociobiology, 13(3), 173-181.

298

Shenk, J. S., & Westerhaus, M. O. (1991). Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Science, 31(2), 469-474.

Sheriff, M. J., & Thaler, J. S. (2014). Ecophysiological effects of predation risk; an integration across disciplines. Oecologia, 176(3), 607-611.

Schmickl, T., & Crailsheim, K. (2004). Costs of environmental fluctuations and benefits of dynamic decentralized foraging decisions in honey bees. Adaptive Behavior, 12(3-4), 263-277.

Shultz, S. (2001). Notes on interactions between monkeys and African crowned eagles in Tai National Park, Ivory Coast. Folia Primatologica, 72(4), 248-250.

Searle, K. R., Thompson Hobbs, N., & Shipley, L. A. (2005). Should I stay or should I go? Patch departure decisions by herbivores at multiple scales. Oikos, 111(3), 417-424.

Seeley, T. D., Camazine, S., & Sneyd, J. (1991). Collective decision-making in honey bees: how colonies choose among nectar sources. Behavioral Ecology and Sociobiology, 28(4), 277- 290.

Seim, G. L., Ahn, C. I., Bodis, M. S., Luwedde, F., Miller, D. D., Hillier, S., Tako, E., Glahn, R. P., & Young, S. L. (2013). Bioavailability of iron in geophagic earths and clay minerals, and their effect on dietary iron absorption using an in vitro digestion/Caco-2 cell model. Food & Function, 4(8), 1263-1270.

Senior, A. M., Charleston, M. A., Lihoreau, M., Buhl, J., Raubenheimer, D., & Simpson, S. J. (2015). Evolving nutritional strategies in the presence of competition: a geometric agent- based model. PLoS Computational Biology, 11(3), e1004111.

Senior, A. M., Lihoreau, M., Buhl, J., Raubenheimer, D., & Simpson, S. J. (2016a). Social network analysis and nutritional behavior: an integrated modeling approach. Frontiers in Psychology, 7, 18.

Senior, A. M., Lihoreau, M., Charleston, M. A., Buhl, J., Raubenheimer, D., & Simpson, S. J. (2016b). Adaptive collective foraging in groups with conflicting nutritional needs. Royal Society Open Science, 3(4), 150638.

Shaw, J. J., Tregenza, T., Parker, G. A., & Harvey, I. F. (1995). Evolutionarily stable foraging speeds in feeding scrambles: a model and an experimental test. Proceedings of the Royal Society of London. Series B: Biological Sciences, 260(1359), 273-277.

Shiga, T. M., Soares, C. A., Nascimento, J. R., Purgatto, E., Lajolo, F. M., & Cordenunsi, B. R. (2011). Ripening‐associated changes in the amounts of starch and non-starch polysaccharides and their contributions to fruit softening in three banana cultivars. Journal of the Science of Food and Agriculture, 91(8), 1511-1516.

299

Shorthouse, J. D., & Rohfritsch, O. (1992). Biology of insect-induced galls. Oxford University Press.

Simas, F. F., Gorin, P. A., Wagner, R., Sassaki, G. L., Bonkerner, A., & Iacomini, M. (2008). Comparison of structure of gum exudate polysaccharides from the trunk and fruit of the peach tree (Prunus persica). Carbohydrate Polymers, 71(2), 218-228.

Simopoulos, A. P. (2002). Omega-3 fatty acids in inflammation and autoimmune diseases. Journal of the American College of Nutrition, 21(6), 495-505.

Simpson, S. J., & Raubenheimer, D. (1995). The geometric analysis of feeding and nutrition: a user's guide. Journal of Insect Physiology, 41(7), 545-553.

Simpson, S. J., & Raubenheimer, D. (2009). Macronutrient balance and lifespan. Aging, 1(10), 875.

Simpson, S. J., & Raubenheimer, D. (2012). The nature of nutrition: a unifying framework from animal adaptation to human obesity. Princeton University Press.

Simpson, S. J., Sibly, R. M., Lee, K. P., Behmer, S. T., & Raubenheimer, D. (2004). Optimal foraging when regulating intake of multiple nutrients. Animal Behaviour, 68(6), 1299- 1311.

Simpson, S. J., Raubenheimer, D., Charleston, M. A., & Clissold, F. J. (2010). Modelling nutritional interactions: from individuals to communities. Trends in Ecology & Evolution, 25(1), 53-60.

Simpson, S. J., Clissold, F. J., Lihoreau, M., Ponton, F., Wilder, S. M., & Raubenheimer, D. (2015). Recent advances in the integrative nutrition of arthropods. Annual Review of Entomology, 60, 293-311.

Skorupa, J. P. (1986). Responses of rainforest primates to selective logging in Kibale Forest, Uganda: a summary report. In: Benirschke K. (eds) Primates, pp. 57-70, Proceedings in Life Sciences. Springer, New York, NY.

Skorupa, J. P. (1989). Crowned eagles Strephanoaetus coronatus in rainforest: observations on breeding chronology and diet at a nest in Uganda. Ibis, 131(2), 294-298.

Smith, R. J., & Jungers, W. L. (1997). Body mass in comparative primatology. Journal of Human Evolution, 32(6), 523-559.

Snaith, T. V., & Chapman, C. A. (2005). Towards an ecological solution to the folivore paradox: patch depletion as an indicator of within-group scramble competition in red colobus monkeys (Piliocolobus tephrosceles). Behavioral Ecology and Sociobiology, 59(2), 185- 190.

300

Snaith, T. V., & Chapman, C. A. (2007). Primate group size and interpreting socioecological models: do folivores really play by different rules?. Evolutionary Anthropology: Issues,News, and Reviews: Issues, News, and Reviews, 16(3), 94-106.

Snaith, T. V., & Chapman, C. A. (2008). Red colobus monkeys display alternative behavioral responses to the costs of scramble competition. Behavioral Ecology, 19(6), 1289-1296.

Solon-Biet, S. M., McMahon, A. C., Ballard, J. W. O., Ruohonen, K., Wu, L. E., Cogger, V. C., ... & Gokarn, R. (2014). The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice. Cell metabolism, 19(3), 418-430.

Sridhar, H., Beauchamp, G., & Shanker, K. (2009). Why do birds participate in mixed-species foraging flocks? A large-scale synthesis. Animal Behaviour, 78(2), 337-347.

Srinivasan, U., Raza, R. H., & Quader, S. (2010). The nuclear question: rethinking species importance in multi-species animal groups. Journal of Animal Ecology, 79(5), 948-954.

Stacey, P. B. (1986). Group size and foraging efficiency in yellow baboons. Behavioral Ecology and Sociobiology, 18(3), 175-187.

Stampone, M. D., Hartter, J. N., Chapman, C. A., & Ryan, S. J. (2011). Trends and variability in localized precipitation around Kibale National Park, Uganda, Africa. Research Journal of Environmental and Earth Sciences, 3(1): 14-23.

Stanford, C. B. (1995). The influence of chimpanzee predation on group size and anti-predator behaviour in red colobus monkeys. Animal Behaviour, 49(3), 577-587.

Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton University Press.

Stensland, E. V. A., Angerbjörn, A., & Berggren, P. E. R. (2003). Mixed species groups in mammals. Mammal Review, 33(3-4), 205-223.

Stevens, J. R., & Cushman, F. A. (2004). Cognitive constraints on reciprocity and tolerated scrounging. Behavioral and Brain Sciences, 27(4), 569-570.

Sterck, E. H., Watts, D. P., & van Schaik, C. P. (1997). The evolution of female social relationships in nonhuman primates. Behavioral Ecology and Sociobiology, 41(5), 291- 309.

Strier, K. B. (2009). Seeing the forest through the seeds: Mechanisms of primate behavioral diversity from individuals to populations to beyond. Current Anthropology, 50(2), 213- 228.

Struhsaker, T. T. (1977). Infanticide and social organization in the redtail monkey (Cercopithecus ascanius schmidti) in the Kibale forest, Uganda. Zeitschrift für Tierpsychologie, 45(1), 75-84.

301

Struhsaker, T. T. (1978). Food habits of five monkey species in the Kibale Forest, Uganda. In D. J. Chivers & J. Herbert (Eds.), Recent Advances in Primatology (Vol. 1: Behaviour, pp. 225-247). London: Academic Press.

Struhsaker, T. T. (1980). Comparison of the behaviour and ecology of red colobus and redtail monkeys in the Kibale Forest, Uganda. African Journal of Ecology, 18(1), 33-51.

Struhsaker, T. T. (1981). Polyspecific associations among tropical rain-forest primates. Zeitschrift für Tierpsychologie, 57(3-4), 268-304.

Struhsaker, T. T. (1988). Male tenure, multi-male influxes, and reproductive success in redtail monkeys (Cercopithecus ascanius). In: A. Gautier-Hion, F. Bourlière, J.-P. Gautier, and J. Kingdon (eds.), A Primate Radiation: Evolutionary Biology of the African Guenons, pp. 340–363. Cambridge University Press, Cambridge, UK.

Struhsaker, T. T. (1997). Ecology of an African rain forest: logging in Kibale and the conflict between conservation and exploitation. University Press of Florida.

Struhsaker, T. T. (2017). Dietary variability in redtail monkeys (Cercopithecus ascanius schmidti) of Kibale National Park, Uganda: the role of time, space, and hybridization. International Journal of Primatology, 38(5), 914-941.

Struhsaker, T. T., & Leakey, M. (1990). Prey selectivity by crowned hawk-eagles on monkeys in the Kibale Forest, Uganda. Behavioral Ecology and Sociobiology, 26(6), 435-443.

Struhsaker, T. T., & Leland, L. (1979). Socioecology of five sympatric monkey species in the Kibale Forest, Uganda. In Advances in the Study of Behavior (Vol. 9, pp. 159-228). Academic Press.

Struhsaker, T. T., and Leland, L. (1988). Group fission in redtail monkeys (Cercopithecus ascanius) in the Kibale Forest, Uganda. In: A. Gautier-Hion, F. Bourlière, J.-P. Gautier, and J. Kingdon (eds.), A Primate Radiation: Evolutionary Biology of the African Guenons, pp. 364–388. Cambridge University Press, Cambridge, UK.

Struhsaker, T. T., Butynski, T. M., & Lwanga, J. S. (1988). Hybridization between redtail (Cercopithecus ascanius schmidti) and blue (C. mitis stuhlmanni) monkeys in the Kibale Forest, Uganda. A primate radiation: Evolutionary biology of the African guenons, 477- 497.

Struhsaker, T. T., & Pope, T. R. (1991). Mating system and reproductive success: a comparison of two African forest monkeys (Colobus badius and Cercopithecus ascanius). Behaviour, 182-205.

Struhsaker, T. T., Butynski, T. M., and Lwanga, J. (1988). Hybridization between redtail monkeys (Cercopithecus ascanius schmidti) and blue (C. mitis stuhlmanni) monkeys in the Kibale Forest, Uganda. In: A. Gautier-Hion, F. Bourlière, J.-P. Gautier, and J. Kingdon, (eds.), A Primate Radiation: Evolutionary Biology of the African Guenons, pp. 477–497. Cambridge University Press, Cambridge, UK.

302

Swain, T., & Hillis, W. E. (1959). The phenolic constituents of Prunus domestica. I.—The quantitative analysis of phenolic constituents. Journal of the Science of Food and Agriculture, 10(1), 63-68.

Takahashi, M. (2018). The Nutritional ecology of adult female blue monkeys, Cercopithecus mitis, in the Kakamega Forest, Kenya. Ph.D. dissertation, Columbia University.

Takahashi, M. Q., Rothman, J. M., Raubenheimer, D., & Cords, M. (2019). Dietary generalists and nutritional specialists: Feeding strategies of adult female blue monkeys (Cercopithecus mitis) in the Kakamega Forest, Kenya. American Journal of Primatology, 81(7), e23016.

Tapper, S., Johnson, C., Lenoël, A., Vining, A., Stewart, F., & Piel, A. (2019). Riverine red-tails: Preliminary data on forest guenons in a savanna woodland habitat in the Issa Valley, Ugalla, western Tanzania. In K. Nowak, A. Barnett, & I. Matsuda (Eds.), Primates in flooded habitats: Ecology and conservation (pp. 270–275). Cambridge, UK: Cambridge University Press.

Teelen, S. (2007). Influence of chimpanzee predation on associations between red colobus and red-tailed monkeys at Ngogo, Kibale National Park, Uganda. International Journal of Primatology, 28(3), 593-606.

Teichroeb, J. A., & Sicotte, P. (2009). Test of the ecological-constraints model on ursine colobus monkeys (Colobus vellerosus) in Ghana. American Journal of Primatology, 71(1), 49-59.

Teichroeb, J. A., & Sicotte, P. (2018). Cascading competition: the seasonal strength of scramble influences between-group contest in a folivorous primate. Behavioral ecology and sociobiology, 72(1), 6.

Terborgh, J. (1990). Mixed flocks and polyspecific associations: costs and benefits of mixed groups to birds and monkeys. American Journal of Primatology, 21(2), 87-100.

Theil, E. C. (2004). Iron, ferritin, and nutrition. Annual Review of Nutrition, 24, 327-343.

Thiollay, J. M. (1999). Frequency of mixed species flocking in tropical forest birds and correlates of predation risk: an intertropical comparison. Journal of Avian Biology, 282- 294.

Thompson, D. B. A., & Barnard, C. J. (1984). Prey selection by plovers: optimal foraging in mixed-species groups. Animal Behaviour, 32(2), 554-563.

Thouless, C. R. (1990). Feeding competition between grazing red deer hinds. Animal Behaviour, 40(1), 105-111.

Tombak, K. J., Reid, A. J., Chapman, C. A., Rothman, J. M., Johnson, C. A., & Reyna-Hurtado, R. (2012). Patch depletion behavior differs between sympatric folivorous primates. Primates, 53(1), 57-64.

303

Toth, A. L., & Robinson, G. E. (2005). Worker nutrition and division of labour in honeybees. Animal Behaviour, 69(2), 427-435.

Turcotte, Y., & Desrochers, A. (2002). Playbacks of mobbing calls of Black-capped Chickadees help estimate the abundance of forest birds in winter. Journal of Field Ornithology, 73(3), 303-308.

Tutin, C. E., Ham, R. M., White, L. J., & Harrison, M. J. (1997). The primate community of the Lopé Reserve, Gabon: diets, responses to fruit scarcity, and effects on biomass. American Journal of Primatology, 42(1), 1-24.

Tutin, C. E. (1999). Fragmented living: Behavioural ecology of primates in a forest fragment in the Lopé Reserve, Gabon. Primates, 40(1), 249–265.

Ungar, P. S. (Ed.). (2007). Evolution of the human diet: the known, the unknown, and the unknowable. Oxford University Press.

Ungar, P. S. (2012). Dental evidence for the reconstruction of diet in African early Homo. Current Anthropology, 53(S6), S318-S329.

Ushida, K., Fujita, S., & Ohashi, G. (2006). Nutritional significance of the selective ingestion of Albizia zygia gum exudate by wild chimpanzees in Bossou, Guinea. American Journal of Primatology, 68(2), 143-151.

Vahl, W. K., Lok, T., Van der Meer, J., Piersma, T., & Weissing, F. J. (2005). Spatial clumping of food and social dominance affect interference competition among ruddy turnstones. Behavioral Ecology, 16(5), 834-844.

Valtonen, A., Malinga, G. M., Nyafwono, M., Nyeko, P., Owiny, A., & Roininen, H. (2017). The successional pathway of the tree community and how it shapes the fruit-feeding butterfly community in an Afrotropical forest. Journal of Tropical Ecology, 33(1), 12-21.

Van Soest, P. J. (1963). Use of detergents in the analysis of fibrous feeds. 2. A rapid method for the determination of fiber and lignin. Journal of the Association of Official Agricultural Chemists, 46, 829-835.

Van Soest, P. J. (1978). Dietary fibers: their definition and nutritional properties. The American Journal of Clinical Nutrition, 31(10), S12-S20.

Van Soest, P. J. (1994). Nutritional ecology of the ruminant. Cornell university press.

Van Soest, P. J., Robertson, J. B., & Lewis, B. A. (1991). Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science, 74(10), 3583-3597.

Vargas, M. A., Luo, N., Yamaguchi, A., & Kapahi, P. (2010). A role for S6 kinase and serotonin in postmating dietary switch and balance of nutrients in D. melanogaster. Current Biology, 20(11), 1006-1011.

304

Vickery, W. L., Giraldeau, L. A., Templeton, J. J., Kramer, D. L., & Chapman, C. A. (1991). Producers, scroungers, and group foraging. The American Naturalist, 137(6), 847-863.

Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 75(6), 1226.

Vogel, E. R. (2005). Rank differences in energy intake rates in white-faced capuchin monkeys, Cebus capucinus: the effects of contest competition. Behavioral Ecology and Sociobiology, 58(4), 333-344.

Vogel, E. R., & Janson, C. H. (2007). Predicting the frequency of food‐related agonism in white‐ faced capuchin monkeys (Cebus capucinus), using a novel focal‐tree method. American Journal of Primatology, 69(5), 533-550.

Vogel, E. R., & Janson, C. H. (2011). Quantifying primate food distribution and abundance for socioecological studies: an objective consumer-centered method. International Journal of Primatology, 32(3), 737-754.

Vogel, E. R., Alavi, S. E., Utami-Atmoko, S. S., van Noordwijk, M. A., Bransford, T. D., Erb, W. M., ... & Rothman, J. M. (2017). Nutritional ecology of wild Bornean orangutans (Pongo pygmaeus wurmbii) in a peat swamp habitat: Effects of age, sex, and season. American Journal of Primatology, 79(4), 1-20.

Wallis, I. R., Nicolle, D., & Foley, W. J. (2010). Available and not total nitrogen in leaves explains key chemical differences between the eucalypt subgenera. Forest Ecology and Management, 260(5), 814-821.

Wallis, I. R., Edwards, M. J., Windley, H., Krockenberger, A. K., Felton, A., Quenzer, M., ... & Foley, W. J. (2012). Food for folivores: nutritional explanations linking diets to population density. Oecologia, 169(2), 281-291.

Waser, P. M. (1982). Primate polyspecific associations: do they occur by chance? Animal Behaviour, 30(1), 1-8.

Waterman, P. G., & Mole, S. (1994). Analysis of phenolic plant metabolites. Blackwell Scientific.

Watts, D. P., & Mitani, J. C. (2015). Hunting and prey switching by chimpanzees (Pan troglodytes schweinfurthii) at Ngogo. International Journal of Primatology, 36(4), 728- 748.

Westoby, M. (1974). An analysis of diet selection by large generalist herbivores. The American Naturalist, 108(961), 290-304.

305

Westoby, M. (1978). What are the biological bases of varied diets? The American Naturalist, 112(985), 627-631.

Wheeler, B. C., Scarry, C. J., & Koenig, A. (2013). Rates of agonism among female primates: a cross-taxon perspective. Behavioral Ecology, 24(6), 1369-1380.

Whiten, A., Byrne, R. W., Barton, R. A., Waterman, P. G., & Henzi, S. P. (1991). Dietary and foraging strategies of baboons. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 334(1270), 187-197.

Whitehead, D. C., Goulden, K. M., & Hartley, R. D. (1985). The distribution of nutrient elements in cell wall and other fractions of the herbage of some grasses and legumes. Journal of the Science of Food and Agriculture, 36(5), 311-318.

Whitten, P. L. (1983). Diet and dominance among female vervet monkeys (Cercopithecus aethiops). American Journal of Primatology, 5(2), 139-159.

Wich, S. A., & Van Schaik, C. V. (2000). The impact of El Niño on mast fruiting in Sumatra and elsewhere in Malesia. Journal of Tropical Ecology, 16(4), 563-577.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis, Springer.

Wiens, J. A. (1976). Population responses to patchy environments. Annual Review of Ecology and Systematics, 7(1), 81-120.

Wiggins, N. L., McArthur, C., & Davies, N. W. (2006). Diet switching in a generalist mammalian folivore: fundamental to maximising intake. Oecologia, 147(4), 650-657.

Wikberg, E. C., Teichroeb, J. A., Bădescu, I., & Sicotte, P. (2013). Individualistic female dominance hierarchies with varying strength in a highly folivorous population of black- and-white colobus. Behaviour, 295-320.

Windfelder, T. L., & Lwanga, J. S. (2002). Group fission in red-tailed monkeys (Cercopithecus ascanius) in Kibale National Park, Uganda. In M. Glenn & M. Cords (Eds.), The guenons: Diversity and adaptation in African monkeys (pp. 147-159). Springer, Boston, MA.

Wittig, R. M., & Boesch, C. (2003). Food competition and linear dominance hierarchy among female chimpanzees of the Tai National Park. International Journal of Primatology, 24(4), 847-867.

Wolda, H. (1978). Fluctuations in abundance of tropical insects. The American Naturalist, 112(988), 1017-1045.

Worman, C. O. D., & Chapman, C. A. (2005). Seasonal variation in the quality of a tropical ripe fruit and the response of three frugivores. Journal of tropical ecology, 21(6), 689-697.

306

Wrangham, R. W. (1980). An ecological model of female-bonded primate groups. Behaviour, 262-300.

Wrangham, R., & Conklin-Brittain, N. (2003). Cooking as a biological trait. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 136(1), 35-46.

Wrangham, R. W., Conklin-Brittain, N. L., & Hunt, K. D. (1998). Dietary response of chimpanzees and cercopithecines to seasonal variation in fruit abundance. I. Antifeedants. International Journal of Primatology, 19(6), 949-970.

Wrangham, R. W., Jones, J. H., Laden, G., Pilbeam, D., Conklin-Brittain, N., Brace, C. L., ... & Blurton Jones, N. G. (1999). The raw and the stolen: cooking and the ecology of human origins. Current Anthropology, 40(5), 567-594.

Wright, S. J., & Calderón, O. (2006). Seasonal, El Niño and longer term changes in flower and seed production in a moist tropical forest. Ecology Letters, 9(1), 35-44.

Würth, M. K., Pelaez-Riedl, S., Wright, S. J., & Körner, C. (2005). Non-structural carbohydrate pools in a tropical forest. Oecologia, 143(1), 11-24.

Yildirim, S., Yeoman, C. J., Sipos, M., Torralba, M., Wilson, B. A., Goldberg, T. L., ... & Nelson, K. E. (2010). Characterization of the fecal microbiome from non-human wild primates reveals species specific microbial communities. PloS one, 5(11), e13963.

307