Theropithecus Gelada) on an Intact Afro-Alpine Grassland at Guassa, Ethiopia ______

Home , Baboon, Chlorocebus, Crested mangabey, Gelada, Hamadryas baboon, Macaque

LONG-TERM RANGING PATTERNS OF WILD GELADA MONKEYS (THEROPITHECUS GELADA) ON AN INTACT AFRO-ALPINE GRASSLAND AT GUASSA, ETHIOPIA ______

A Thesis

Presented to the

Faculty of

California State University, Fullerton ______

In Partial Fulfillment

of the Requirements for the Degree

Master of Arts

Anthropology ______

Cha Moua

Thesis Committee Approval:

Associate Professor Peter J. Fashing, Chair Associate Professor Nga Nguyen, Department of Anthropology Associate Professor Elizabeth G. Pillsworth, Department of Anthropology

Fall, 2015

ABSTRACT

Long-term studies of animal ranging ecology are critical to understanding how animals utilize their habitat across space and time. Although gelada monkeys

(Theropithecus gelada) inhabit an unusual, high altitude habitat that presents unique ecological challenges, no long-term studies of their ranging behavior have been conducted. To close this gap, I investigated the daily path length (DPL), annual home ranges (95%), and annual core areas (50%) of a band of ~220 wild gelada monkeys at

Guassa, Ethiopia, from January 2007 to December 2011 (for total of n = 785 full-day follows). I estimated annual home ranges and core area using the fixed kernel reference

(FK REF) and smoothed cross-validation (FK SCV) bandwidths, and the minimum convex polygon (MCP) method. Both annual home range (MCP - 2007: 5.9 km2; 2008:

8.6 km2; 2009: 9.2 km2; 2010: 11.5 km2; 2011: 11.6 km2) and core area increased over the 5-year study period. The MCP and FK REF generated broadly consistent, though slightly larger estimates that contained areas in which the geladas were never observed.

All three methods omitted one to 19 sleeping sites from the home range depending on the year. Thus, neither the MCP nor fixed kernel estimators were more accurate than the other. Similarly, mean annual DPL (± SE m) increased over the study period (2007:

2,848±57 m; 2008: 3,339±65 m; 2009: 3,272±72 m; 2010: 3,835±80 m; 2011: 4,100±86 m). In general, the geladas showed remarkable variation in daily, monthly, and annual

DPL. I also investigated the effects of movement across uneven topography on DPL, and

I discuss the ecological implications of these findings. I compare the ranging behavior of geladas at Guassa to (a) geladas at other study sites, (b) to Papio (baboon) species, (c) to both terrestrial and arboreal primates, and (d) to grazing ungulates. The extensive inter- annual variability in ranging patterns in this study demonstrates the importance of long- term monitoring for wild nonhuman primates and its implications for conservation policy.

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TABLE OF CONTENTS

ABSTRACT ...... ii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

ACKNOWLEDGMENTS ...... viii

Chapter 1. INTRODUCTION ...... 1

Research in Animal Ranging Ecology ...... 1 The Importance of Long-Term Ranging Studies ...... 4 Gelada Monkeys as a Model System ...... 6 Gelada Monkeys Study Site, Guassa, Ethiopia ...... 8 Objectives of the Study ...... 9

2. METHODS ...... 11

Study site...... 11 The Qero System and its Future ...... 12 Study Subjects ...... 13 Data Collection and Analysis ...... 14 Daily Ranging Data ...... 15 Ranging Analysis: Calculation of Daily Path Lengths ...... 17 Ranging Analysis: Amending Daily Path Lengths to Account for Changes in Altitude ...... 18 Home Range Analysis ...... 19 Home Range Estimator: Minimum Convex Polygon ...... 20 Home Range Estimator: Fixed kernel ...... 23 Autocorrelation: Implications on Home Range Analysis ...... 27 Statistical Analysis ...... 30

3. RESULTS ...... 32

Annual Home Range Estimates: MCP...... 32 Annual Home Range Estimates: FK REF ...... 33 Annual Home Range Estimates: FK SCV ...... 41

Comparison of Annual Home Range Across Methods ...... 43 Trends in Annual Home Range ...... 43 Annual Core Area: Use and Trends ...... 46 Ranging Patterns: Daily, Monthly, and annual trends in DPL ...... 49 Monthly Mean DPL ...... 51 Annual Mean DPL ...... 53

4. DISCUSSION ...... 56

Summary of Findings...... 56 Evaluation of the MCP Method ...... 57 Evaluation of the Kernel Estimators ...... 60 Implications and Suggestions for Future Research ...... 63 Comparison of Gelada Monkey Ranging Behavior Across Sites ...... 67 How do the Annual Home Range Estimates of Geladas at Guassa Compare to Those for Geladas at Other Sites?...... 68 How do Geladas Utilize Their Home Range at Guassa and How Does it Compare to That of Geladas at Other Sites? ...... 70 How do the DPL of Geladas at Guassa Compare to Those of Geladas at Other Sites? ...... 71 Comparison of Gelada Monkey Ranging Behavior Across Taxa...... 71 Comparison of Gelada Ranging Behavior to Papio Species ...... 72 Comparison of Gelada Ranging Behavior to Terrestrial Nonhuman Primate Species ...... 77 Comparison of Gelada Ranging Behavior to Arboreal Nonhuman Primate Species ...... 83 Comparison of Gelada Ranging Behavior to Terrestrial Ungulate Species .. 89 Implications of Inhabiting in a Topographically Variable Environment on Calculations of Distance Traveled ...... 91 Ecological Implications of Movement Across Uneven Topography...... 92 Critiques of the Altitudinal Change Formula ...... 96 Conclusions ...... 97

APPENDIX: ADDING ERROR TO USER IDENTIFIED DUPLICATE PAIRS ...... 100

BIBLIOGRAPHY ...... 105

LIST OF TABLES

Table Page

2.1 Results of Autocorrelation Analysis ...... 30

3.1 Comparison of Annual Home Range Estimates for MCP ...... 33

3.2 Core Areas (50%) and Annual Home Ranges (95%) Based on the FK 0.6*REF ...... 34

3.3 Core Areas (50%) and Annual Home Ranges (95%) Based on the FK SCV ...... 41

3.4 Monthly Mean DPL ± SE (m), Number of Full-days, and Range of DPL for Each and all Years...... 52

4.1 Comparison of Gelada Monkey Ranging Patterns Across Sites ...... 69

4.2 DPL, Home Range, and Core Area of Papio Species...... 75

4.3 DPL, Home Range, and Core Area of Terrestrial and Arboreal Nonhuman Primates...... 78

4.4 DPL, Home Range, and Core Area of Terrestrial Ungulate Species ...... 86

LIST OF FIGURES

Figure Page

2.1 Monthly Mean Rainfall at Guassa, Ethiopia ...... 12

2.2 Monthly Mean Temperature at Guassa, Ehiopia ...... 13

2.3 Diagram Showing the Corrected DPL Based on a2 + b2 = c2...... 19

3.1 Comparison of Annual Home Ranges From 2007 to 2008 Using the MCP Method ...... 35

3.2 Comparison of Annual Home Ranges for 2007 Based on Scaling the FK REF..... 36

3.3 Comparison of Annual Home Ranges for 2008 Based on Scaling the FK REF..... 37

3.4 Comparison of Annual Home Ranges for 2009 Based on Scaling the FK REF..... 38

3.5 Comparison of Annual Home Ranges for 2010 Based on Scaling the FK REF..... 39

3.6 Comparison of Annual Home Ranges for 2011 Based on Scaling the FK REF..... 40

3.7 Comparison of Annual Home Ranges From 2007 to 2011 Using the FK SCV ..... 42

3.8 Cumulative 10-day Home Range Size Calculated Using the MCP Method (95% Solid and 100% Dotted) ...... 45

3.9 Cumulative Annual Home Range Estimates Calculated Using the MCP Method ...... 48

3.10 Comparison of the Relationship Between Time and DPL for Each and all Range Years ...... 50

3.11 Comparison of Monthly Mean DPL and for all Range Years ...... 51

3.12 Plot of Monthly Mean DPL on a Continuous Time Scale ...... 54

3.13 Comparison of Annual Mean DPL + SE (m)...... 55

vii

ACKNOWLEDGMENTS

I owe thanks and am indebted to many people who helped made this thesis possible.

First and foremost, I would like to thank my life-long partner, the love of my life, and my only best friend, Judy N. Vang, for her unconditional and unwavering support and love these last five long and arduous years. Her presence and comfort were instrumental in keeping me on the right path, and her happiness and health push me to always do better and attain great things for the betterment of our lives. My daughter,

Julianne Dej Ntshiab Moua, though she is too young to realize, has been a constant bright spot in my life, uplifting my spirit and resparking my resolve.

Next, I owe thanks to my parents, Vang Moua and Mai Lor, for giving me the opportunity to receive an education, and in essence, experience the wonders of education in their place. It is without doubt my parents’ struggles working in the fields to this day and their individual and collective strengths to stay strong and unrelenting have changed my siblings’ and my life for the better. I cannot appreciate them enough for all that they have done for my siblings and me. I would like to thank my grandparents, Wa Lee Moua and Xiong Thao, who were instrumental in raising my siblings and me during our childhood years. I only wish they could be here still to share this moment with me.

viii

Life in Fullerton was made easier thanks to my brother, Sher, who was also working on his Master’s at the time, spent some of his time in my car driving back and forth between Fullerton and Long Beach just so that I could be closer to my school. I also would like to thank my younger brother, Tao, and his girlfriend, Jennifer Lee, who opened their home up to me every time I visited them in San Diego. Furthermore, I thank my youngest brother, Yen Kong, and my younger sisters, Panglee, Gao Nou, Chamee, for helping take care of our parents while we were away home for college.

Moreover, I thank Dr. John V.H. Constable, my undergraduate advisor and mentor at Fresno State who continued to give me life lessons and guidance about graduate school.

Last but not least, I thank the wonderful people I met during my time here at CSU

Fullerton, from my peers to my professors to the office staff, in particular Tannise

Collymore, and to my Thesis Committee advisors, Drs. Elizabeth Pillsworth, Nga

Nguyen, and Peter J. Fashing. I am especially indebted to Drs. Nguyen and Fashing, two of the hardest working, devoted, and generous people I know. I will always remember and be thankful for the patience, support, friendliness, and hospitality they have given me and my family these past five years. They remained by me and were there for me whenever I needed them. I cannot thank them enough for giving me the opportunity to explore and expand my mind and develop as a scientist and a scholar. Thank you.

CHAPTER 1

INTRODUCTION

Research in Animal Ranging Ecology

Over the last half century, studies of animal ranging ecology have played an integral role in expanding our knowledge of the behavior and ecology of numerous species of animals, from ungulates (pronghorn antelope Antilocapra americana:

Buechner 1950; elk Cervus canadensis: Craighead et al. 1975), to birds (breeding, feeding, and ranging ecology reviewed in Sutherland et al. 2004 and Wiens 1989), and land mammals (giraffe Girafa camelopardalis: Dagg and Foster 1976, Leuthold and

Leuthold 1978; leopard Panthera onca: Rabinowitz and Nottingham 1986; African elephant Loxodonta africana: Sikes 1971) including nonhuman primates (chimpanzees

Pan troglodytes: Boesch and Achermann 2000; L’Hoest’s monkeys Cercopithecus lhoesti: Kaplin 2001; Bale monkey Chlorocebus djamdjamensis: Mekonnen et al. 2010; mountain gorilla Gorilla beringei beringei: Vedder 1984; Watts 1998).

Being able to monitor and document an animal’s behavior and ecology over time can clarify or reveal the role some animals have on the biological integrity of their ecosystem. African elephants (Loxodonta africana), for example, consume or destroy woody vegetation, which allows light to penetrate into the forest floor thereby facilitating light-dependent plant species to establish and diversify (Field 1971; Western 1989).

Further, organisms like bumble bees, birds (Avian spp.), and (arguably) nonhuman

2 primates engage in pollination or seed dispersal, which facilitates reproductive success and genetic diversity of plant species (Chapman et al. 1994; Dew and Wright 1998;

Howe 1977; Wallace and Trueman 1995). Lastly, predators consume prey to regulate prey population densities (Berger et al. 2001; Bergerud et al. 1983; Mills 1984).

While these studies demonstrate that animals can play an integral part in the success and health of their ecosystems, they also show that the relationship between organisms and their environment is a highly complex and deeply interconnected one.

This suggests that as resources, such as food, water, and shelter, and ecological variables, such as weather patterns, predation pressure, habitat loss, and group size vary across space and time, we can expect animals to adjust their behaviors and movements in accordance to these changes in order to maintain continued acquisition of the resources required for survival and reproduction.

In response to ecological variability, for example, terrestrial ungulates (Albon and

Langvatn 1992; Lesage et al. 2000; Luccarini et al. 2006; Marra et al. 2005) and nonhuman primates (Li et al. 2008) have been shown to migrate to or occupy temporary

(or seasonal) home ranges. Furthermore, animals may make minor or major shifts within or outside their normal home range (Asensio et al. 2012; Donaldson and Echternacht

2005; Edwards et al. 2009; Fashing et al. 2007; Ferguson et al. 1999; Li et al. 2010), or exploit alternative or fall back resources (Doran-Sheehy et al. 2009; Dunbar 1977; Li and

Rogers 2005; Pavelka et al. 2003) when primary resources become scarce. Alternatively, large groups may fission into smaller factions to mitigate the effects of within-group feeding competition while simultaneously reducing travel distance needed to search for

(more) food (Chapman and Chapman 2000; Chapman and Pavelka 2005; Dias and Strier

2003; Oluput et al. 1994). Indeed, studies of animal ranging ecology have provided researchers with a valuable tool for documenting the behavioral responses animals make in relation to changes in their surrounding environment.

In addition to information about space use and movement patterns, studies of animal ranging ecology can also provide valuable data or uncover aspects of an animal’s ranging ecology essential to making informed conservation decisions (e.g., Covert et al.

2008; Hervert et al. 2005; Heymann and Aquino 2010; Hillman 1988; Kaplin 2001;

Mekonnen et al. 2010; Laliberte and Ripple 2004; Rabinowitz and Nottingham 1986).

Recently, Mekonnen et al. (2010) completed the first study on the ranging and feeding behavior of the Bale monkey (Chlorocebus djamdjamensis). They were able to document, among other things, the monkey’s immense reliance on bamboo leaves, despite inhabiting an area where the resource is heavily exploited by the local human population (Mekonnen et al. 2010). Alternatively, information about an animal’s behavioral or ranging ecology may be lacking or unclear. In these instances, researchers may reinvestigate to gather additional data (e.g., Georgii 1980) or reevaluate the existing literature in order to arrive at more robust conclusions about an animal’s ranging behavior or habitat use preferences (e.g., Heymann and Aquino 2010). The Peruvian red uakari monkey (Cacajao calvus ucayalii), for example, was once widely considered to be apt to flooded-forest habitats. A recent review of all available data on its sightings and whereabouts by Heymann and Aquino (2010), however, left the authors to reject this notion and instead conclude that the monkeys show a preference for habitats mixed with flooded-forest, terra firme (comprised of differently sized vegetation and terrain), or palm swamps.

Indeed, the types of information obtained from studies of animal ranging, such as the types of resources an animal relies on for survival (or the foods it substitutes when conditions worsen), its habitat use patterns (over time), or its ecological role in its environment, not only represent valuable assets concerned individuals need in order to make informed conservation and management-related decisions, but also demonstrates the importance of monitoring in animal populations.

The Importance of Long-Term Ranging Studies

Despite the significance of studies of animal ranging ecology, most ranging studies (in nonhuman primates in particular) have only been carried out over several months (e.g., Baoping et al. 2009; Doran 1997; Dunbar and Dunbar 1975; Mekonnen et al. 2010; Zinner et al. 2002) or a single annual cycle (e.g., Albernaz and Magnusson

1999; Barton et al. 1992; Fashing 2001; Hunter 2001; Poulsen et al. 2001; Schreier 2010;

Willems et al. 2009). Though informative, some social activities, such as mating or births

(Carnegie et al. 2011; Janson and Verdolin 2005) and group size (Dias and Strier 2003;

Wieczkowski 2005), and ecological factors, such as food resources (Li and Walker 1986) and climatic patterns (Haile 2005; Malhi and Wright 2004), may vary seasonally or occur only during certain years, but not others. Therefore, the ability to acquire data over a long stretch of time is important because it may lend researchers the opportunity to identify behavior or movement trends that would otherwise be imperceptible with studies shorter in duration.

For example, in their investigation of the longitudinal ranging patterns of muriqui

(Brachyteles arachnoides hypoxanthus) at Estação Biolόgica de Caratinga, Minas Gerais,

Brazil, across two temporally distinct study periods 15 years apart, Dias and Strier (2003)

5 reported an increase in the home range size (from 1.68 km2 to 3.09 km2) of their group of muriquis between the study periods, which they attributed to a concomitant increase in group size (from 23-27 to 57-63 individuals) over this same period. Similarly,

Wieczkowski (2005), in her re-examination of the ranging behavior of a group of Tana

River mangabeys in Kenya, first studied by Homewood (1976) and then later by Kinnaird

(1990), spanning more than two decades, found that home range size in their group of mangabeys had increased over time (0.17 km2 to 0.19 km2 to 0.47 km2 in 1974, 1988-

1989, and 2000-2001, respectively). This pattern of increasing range use coincided with, and was likely explained by the large group sizes found during each study period (36 to

17 to 50 members in 1974, 1988-1989, and 2000-2001, respectively) (Wieczkowski

2005). (In the 1960s, habitat disturbance led to a reduction in available habitat, which is argued to explain the high density of mangabeys and their smaller range sizes in the 1970 study by Homewood [Wieczkowski 2005].) Lastly, Li et al. (2010), studying the long- term ranging patterns of the Yunnan snub-nosed monkey (Rhinopithecus bieti) at Samage

Forest in the Baimaxueshan Nature Reserve, Yunnan, China, from 1998 to 2007, found that annual home range size increased each year until it reached an asymptote after the seventh year of observation, where it decreased slightly thereafter (7.67 km2 in 1998 to

18.77 km2 in 2004 to 17.14 km2 in 2007). These findings are important because they capture the adaptive responses animals make as resources and conditions vary across space and time, and further demonstrate the value of longitudinal monitoring in wild nonhuman primate populations—and animals in general.

Gelada Monkeys as a Model System

Gelada monkeys (Theropithecus gelada) are an ideal model system with which to employ home range estimators to investigate and uncover how these animals utilize their home range across space and time. First and foremost, gelada monkeys live in a complex fission-fusion social system (Kawai et al. 1983). Multiple one-male units can come together and form a unit called the band which consists of units that are typically seen ranging together and share a common home range (Kawai et al. 1983; Snyder-Mackler et al. 2012). Units from different bands sometimes aggregate to form a larger unit called the herd, or all the individuals seen traveling together at a particular time (Dunbar and

Dunbar 1975; Kawai et al. 1983; Ohsawa 1979; Snyder-Mackler et al. 2012). Not all of the one-male units belonging to a single band are necessarily present at any given time

(Dunbar 1980; Dunbar and Dunbar 1975; Ohsawa 1979; Snyder-Mackler et al. 2012).

Therefore, herd size can fluctuate considerably across time. Secondly, gelada monkeys live in an environment characterized by high altitude (range: 1700 – 4200 m: Dunbar

1998), cold temperatures, and rugged and mountainous topography (Ashenafi 2001;

Dunbar and Dunbar 1974, 1975; Fashing et al. 2014; Hunter 2001; Kawai 1979; Mori and

Belay 1990). We can therefore expect the physical constraints imposed by their environment, coupled with the instability of their herd sizes, to shape the decisions these monkeys make in terms of movement and habitat selection in both the short- and long- term.

Despite the wealth of literature on the social system and behavior of gelada monkeys, relatively little is known about how gelada monkeys utilize their unusual habitat, especially in regard to home range size and daily movement patterns (e.g., Hunter

2001) over an extended and continuous period of time. Prior research has indicated that gelada monkeys exhibit marked variations in both the distance they travel on a daily basis and in their use of certain parts of the home range relative to other areas over time (Crook

1966; Dunbar and Dunbar 1974, 1975; Hunter 2001; Kawai 1979), and that such movement patterns may be related to variations in resource availability and distribution, band (herd) size, and weather conditions, e.g., fog, rainfall, and hail (Dunbar and Dunbar

1975; Hunter 2001; Iwamoto and Dunbar 1983; Kawai and Iwamoto 1979). Though intriguing and informative, these findings only describe the ranging behavior of gelada monkeys over the short-term (i.e., no more than one year of continuous observation:

Hunter 2001), and more pertinent to the objectives of this study, lack detailed investigations into the home range size and use patterns of geladas in the long-term and the specific analytical tools used to estimate home range (Dunbar and Dunbar 1974,

1975; Kawai 1979).

The scarcity of reports on the ranging ecology of geladas is alarming given the number of potential challenges the species faces in the following years, including rising global temperatures (Dunbar 1998), human encroachment and hunting pressures at

Sankaber, Gich, and Bole (Dunbar 1977; but see Beehner et al. 2008), and the potentially tenuous status of long-standing traditional conservation bylaws at Guassa, the most pristine of all established gelada study sites (Ashenafi 2001; Ashanfi and Leader-

Williams 2005). In combination, these challenges threaten the integrity of the remaining gelada habitat and ultimately their long-term existence. Therefore, the need to quantify how gelada monkeys utilize their unusual habitat, including their movement patterns and spatial requirements, is at an all-time high. Critical questions include: (i) How large of an

8 area do gelada monkeys utilize on a year-to-year basis?; (ii) How do gelada monkeys use their home range and how do their home range use patterns change over time?; (iii) How far do gelada monkeys travel on a daily basis, how do their daily movements vary month- to-month and year-to-year, and how does living in an uneven and hilly habitat influence total daily distance traveled? Obtaining answers to these questions will undoubtedly expand our knowledge about their short-term and long-term ranging patterns, and more importantly, provide information essential for making informed conservation-related decisions (Beehner et al. 2008; Cowlishaw and Dunbar 2000; Dunbar 1998).

Furthermore, the information obtained as a result of these questions can facilitate comparisons of gelada ranging patterns to other species of nonhuman primates and ungulates, and possibly help evaluate their utility in hypotheses about human and nonhuman primate evolution (Jolly 1970; Jablonski 1993; Wrangham 1980; Fashing et al.

2014).

Gelada Monkey Study Site, Guassa, Ethiopia

In December 2005, Nguyen and Fashing (2009) established a new gelada monkey study site at Guassa, an ecologically intact afro-alpine grassland in the Ethiopian

Highlands (Ashenafi 2001; Ashenafi and Leader-Williams 2005). Before this research commenced at Guassa, the only sites where gelada monkeys had been studied were at three more disturbed sites in the northern Ethiopian Highlands—Sankaber and Gich, both located in the Simen Mountains, and Bole (Crook 1966; Dunbar and Dunbar 1974, 1975;

Kawai 1979)—and at one location, Arsi, in central Ethiopia south of the Rift Valley

(Mori and Belay 1990). Recently, reports by Fashing, Nguyen, and colleagues (Fashing et al. 2010, 2011, 2014; Lee 2011; Moua et al. 2012; Nguyen and Fashing 2012; Nguyen et

al. 2015; Venkataraman et al. 2014, 2015) have offered a glimpse into the behavioral ecology of a band of ~220 free-ranging gelada monkeys at this new relatively undisturbed location. For example, geladas at Guassa eat a more varied diet than geladas at more disturbed sites, incorporating not only graminoids (grasses and sedges), but also forbs (herbs), invertebrates, and occasionally bird eggs into their diet (Fashing et al.

2010, 2014). Geladas at Guassa also suffer from large parasitic swellings caused by a tapeworm (Taenia serialis) which represent a significant contributor to mortality in this population (Nguyen et al. 2015). Ethiopian wolves (Canis simensis) also sometimes form mixed-species associations with geladas at Guassa, but do not prey on the monkeys.

Wolves appear to be benefit from these associations in that they are more successful at capturing rodents when among geladas than when they are hunting for rodents solitarily

(Venkataraman et al. 2015). Thus, the geladas at Guassa are clearly an interesting, and in some ways unique, study population and are particularly ideal subjects for the study of ecology, given the relatively undisturbed nature of their habitat.

Objectives of the Study

In an effort to fill gaps in our understanding about the ecology of geladas at

Guassa—and as a species—we present data on the ranging patterns of geladas at Guassa,

Ethiopia, studied over a five-year period from January 2007 to December 2011. First and foremost, the primary objectives of this five-year study were to (a) assess the annual home range size and core area use; (b) evaluate the accuracy of the minimum convex polygon (MCP) and fixed kernel techniques for estimating home range size and core area; (c) test the relationship between sample size and home range size in the MCP method; (d) discuss the theoretical and practical implications of (b) and (c) for future

research; and (e) determine the total distance traveled daily and explore the effects of living in an environment with uneven topography on estimates of distance traveled. Our secondary objectives were to compare the ranging behavior of gelada monkeys at Guassa to (f) gelada monkeys at other study sites where similar data are available; (g) to Papio spp., terrestrial nonhuman primates (e.g., chimpanzees, patas monkeys, etc.), and both arboreal frugivorous and folivorous nonhuman primates; and (h) to terrestrial ungulate species (because of their similar gramnivorous diet to gelada monkeys). Lastly, we provide a discussion the importance of longitudinal monitoring for conservation and management purposes and suggestions for future research.

The objectives of this study will afford us the opportunity to evaluate the effectiveness of the MCP and fixed kernel methods in estimating the home range and core area use patterns in this band of gelada monkeys, and also provide us with the valuable information we have been missing about the long-term ranging behavior of gelada monkeys.

CHAPTER 2

METHODS

Study Site

The Guassa study area, ~111 km2 in area (Lat 10 15’ - 10 - 27’ N and Lon 39

45’ - 39 48’ E), is an unusually intact afro-alpine grassland located in the Central

Highlands of Ethiopia (Ashenafi 2001; Ashenafi and Leader-Williams 2005). The study site rests between 3200-3600 m above sea level on the western border of the Greater Rift

Valley (Ashenafi 2001; Fashing et al. 2010). Guassa’s unique geographic location makes the study site extremely hilly and mountainous, with steep drop offs of greater than 1 km along the eastern edge of the study area (Ashenafi 2001). Moreover, Guassa experiences highly seasonal weather patterns. Rainfall occurs throughout the year (range of monthly mean rainfall: 17 mm to 482 mm), but is mostly concentrated between July and August

(Figure 2.1). Monthly mean maximum temperatures typically range from 16 to 19 C, whereas monthly mean minimum temperatures generally range from 1 to 6 C; the overall monthly mean daily temperature ranges from 9 to 12.5 C (Figure 2.2) (Fashing et al. 2014).

Guassa’s pristine afro-alpine grassland can be categorized into distinctive vegetation zones depending on the composition of the plants in each area (Ashenafi

2001). The Festuca grassland (also known locally as guassa), the second largest vegetation zone which covers ~19.9% of Guassa, is composed of various species of

grasses, such as F. macrophylla, F. simensis, and Poa schimperina, and herbs, like

Artemesia spp. and Thymus schimperi, to name a few (Ashenafi 2001). Within the Guassa ecosystem, F. macrophylla acts as an important resource to both the wild fauna (food source: Ashenafi 2001; Fashing et al. 2014) and the surrounding human population

(source of building material [thatching] for homes and utility items [ropes and wires]

(Ashenafi 2001; Ashenafi and Leader-Williams 2005).

Figure 2.1. Monthly mean rainfall at Guassa, Ethiopia.

The Qero System and its Future

For centuries, Guassa had been protected by a locally constructed conservation agreement called the Qero system whose premise was to minimize, control, and regulate human disturbance or settlement and the extraction of resources within the Guassa area

(Ashenafi and Leader-Williams 2005). Since the 1975 Agrarian Reform, however, the

Qero system has since been replaced by a regional-based committee made up of eight

clan groups who now collectively oversee the current and future preservation of the

Guassa area (Ashenafi and Leader-Williams 2005). Due to weak leadership, unequal representation given to clan groups, and inconsistent enforcement of bylaws and fines, the shift from the Qero system to the current clan-based system may compromise the long standing relationship between the local inhabitants and the fauna and flora endemic to the Guassa area (see Ashenafi and Leader-Williams 2005 for a deeper discussion about the implications regarding the replacement of the Qero system by the clan-based committee).

Figure 2.2. Monthly mean temperature at Guassa, Ethiopia.

Study Subjects

Gelada monkeys (Theropithecus gelada), henceforth geladas, are medium-sized terrestrial primates found throughout the northern Ethiopian Highlands (Crook 1966;

Dunbar and Dunbar 1974, 1975; Hunter 2001; Kawai 1979; Nguyen and Fashing 2009)

and at one location, Arsi, in central Ethiopia south of the Rift Valley (Mori and Belay

1990; Mori et al. 1999). Geladas sleep alongside cliff edges, but conduct their daily activities on the plateau above (Dunbar and Dunbar 1974, 1975; Kawai and Iwamoto

1979). Their diet consists mostly of grasses (Crook 1966; Dunbar and Dunbar 1975;

Iwamoto 1979; Fashing et al. 2014), however, geladas have also been observed to consume herbs, roots, and insects (Dunbar 1977; Iwamoto 1993), especially at Guassa where these items play an important role in the gelada diet (Fashing et al. 2010, 2014).

The foundation of the gelada monkey multi-level social system is the one-male unit (OMU), which consists of 1-3 males, 1-9 females, juveniles, and dependent young

(Dunbar 1980; Kawai 1979; Kawai et al. 1983; Nguyen and Fashing 2009, 2012).

Alternatively, males without any alliance to an OMU may group together to form an all- male unit (Kawai et al. 1983). Multiple OMUs that tend to range within the same geographic location is called a band (Dunbar 1980; Kawai et al. 1983). A temporary mass of OMUs or bands without a social or reproductive connection is called a herd (Kawai et al. 1983). Prior research has indicated that though OMUs (with the occasional cycling of the alpha male due to male-to-male competition) and bands may remain stable over time, herds have been shown to be much more fluid and unpredictable in duration and number

(Dunbar and Dunbar 1975; Hunter 2001; Ohsawa 1979; P. Fashing, unpub.data).

Data Collection and Analysis

The data used for this study were collected by members of the Guassa Gelada

Research Project, headed by Peter Fashing and Nga Nguyen, and span a five-year period from January 2007 to December 2011.

Daily Ranging Data

Ranging data were collected on a band of approximately 220 geladas, known as

Steelers band, grouped into 16 OMUs (Nguyen et al. 2015). Fashing and Nguyen first started habituating Steelers band in December 2005 and, along with field assistants and student researchers, have continued to monitor the animals’ behaviors and movements on a near-daily basis since November 2006 (Fashing et al. 2010). Follows started at 0700-

0800 in the morning before the geladas departed their sleeping cliffs and concluded at

1730-1800 in the evening, depending on the geladas’ distance from the camp and weather conditions. The location of the Steelers band OMU currently followed was recorded every half-hour with a handheld GPS device (Garmin GPSMAP 62). During instances when the researchers had to switch to a different OMU of Steelers band during the daily follow, e.g., to carry out behavior sampling on a different OMU, the researchers selected the next OMU of Steelers band within five to 10 meters to the OMU being followed currently as the new follow unit. This was done to minimize the distance between the old and new OMU and to ensure an accurate depiction of the band’s (or herd’s) movement.

All half-hour readings were recorded with an error of less than 10 meters (m), unless striving to obtain a reading with an error of less than 10 m placed the researcher in a precarious situation, e.g., the band was at the edge of a cliff.

Data Analysis: To be considered a valid ranging day, henceforth full-day, each full-day had to have both a morning and an evening sleeping cliff reading and at least a

1600 (i.e., 4:00 PM) reading. There was no minimum number of half-hour readings so as long as the aforementioned criteria were met. Based on the criteria above, I identified a total of n = 785 full-day follows (mean = 157, range = 145 – 168) from January 2007

through December 2011, with an average of 20 ± 1.4 (SD) number of readings per day

(range = 14 – 24).

Sometimes the researchers were unable to remain with the herd until an evening sleeping cliff site was chosen. In these cases, the researchers returned the next morning before the geladas departed from their morning sleeping cliff and recorded the exact location of the current sleeping cliff and used this sleeping cliff reading as the evening sleeping cliff for the previous ranging day. Since the geladas had yet to venture from this sleeping cliff, the researchers were confident that the gelada monkeys slept on this sleeping cliff the entire night. Under this circumstance, the researchers assumed the geladas took the shortest possible route from their last known location the prior day to their sleeping cliff site that night. It is therefore likely that the animals’ path lengths and daily path length (for such days) may have been slightly underestimated for some full- day follows (e.g., Swedell 2006), though this is not expected to present any major problems to the analysis conducted here. All GPS locations were recorded in Latitude and

Longitude (Lat and Lon), Geographic World Coordinate System WGS 84 and subsequently uploaded to MapSource® (Garmin 2011) at the end of each month.

Data Analysis: Preparing the Data for ArcMap 10 I used Microsoft Excel 2010 to organize and prepare the data for ranging analysis. First, I matched each GPS location data point, identified by its waypoint number (i.e., the unique ID number indicating the order in which the GPS point was taken) and Lat and Lon coordinate, to their respective researcher notes. The researcher notes were entered into a palm device (Palm m500) in the field at the time of each reading, and describe the number and sequence of the reading, the time and date of the reading, whether the reading was a sleeping cliff or

regular half-hour reading, and any relevant information that may be used to assess the validity of that particular reading. Then, I uploaded the organized Excel documents into

ArcMap 10 (ESRI 2012) under the coordinate system Geographic World Coordinate

System, WGS 84. Thereafter, I changed the Layers Data Frame properties to the

Projected Coordinate System, UTM (i.e., Universal Trans Mercator), WGS 84, Northern

Hemisphere, WGS 84 UTM Zone 37N, the coordinate zone to which Ethiopia belongs.

This series of changes transforms the Lat and Lon decimal degree coordinates into UTM meter coordinates, making it possible to calculate the distance between consecutive half- hour readings (for daily path length) and to estimate fixed kernel home ranges. Since the coordinate transformation is not permanent using this procedure, I exported the data as a shapefile, and then implemented the addxy command in Geospatial Modeling

Environment 0.7.2 (GME; Beyer 2012) to replace the original Lat and Lon coordinates with the newly defined UTM coordinates.

Ranging Analysis: Calculation of Daily Path Lengths

I calculated all half-hour path lengths and daily path length (henceforth DPL) using GME 0.7.2 (Beyer 2012). I define DPL as the sum of all consecutive half-hour readings belonging to each unique full-day follow.

I identified two approaches in GME that can be used to calculate DPL, and I utilized both approaches to validate my estimates. The first approach utilizes the convert.pointstolines and addlength commands. The former command uses a line to connect all of the consecutive half-hour readings belonging to a unique full-day follow while the latter then calculates the total distance of that line (in a unit of distance specified by the user, such as m [meters] in this case); the final value represents the DPL.

Alternatively, the second approach utilizes the movement.pathmetrics command. Because this command calculates the distance of each half-hour reading, I first re-organized all half-hour readings that belong to the same full-day, and then I obtained the sum of all the half-hour readings to determine the DPL. Lastly, I compared the DPL estimates produced via both of these methods and verified that both techniques produced identical estimates

(Moua unpub. data).

Ranging Analysis: Amending Daily Path Lengths to Cccount for Changes in Altitude

Once I confirmed the validity of the DPL estimates, I manually reanalyzed each half-hour path length reading to account for the influence of changing altitude on distance traveled. I reasoned that the extremely rugged and mountainous topography of Guassa will cause the geladas to travel longer distances than traditionally calculated (e.g.,

Sprague 2000). To test the influence of changing altitude on distance traveled in this band of geladas, I adapted Pythagora’s theorem for the three sides of a right triangle (i.e., a2 +

2 2 b = c ) (Figure 2.3). Specifically, I assumed that: (i) x1 and x2 denotes the location of subsequent half-hour readings and a2 represents the distance, in meters, between these

2 two readings, squared; (ii) line segment x2x3, denoted as b , represents the change in

2 altitude, in meters, squared, between half-hour readings x1 and x2; and (iii) lastly, c is the sum of a2 and b2, where after solving for c2, I obtain c, the corrected path length after taking into account change in altitude. I implemented this formula to calculate the corrected path length for all half-hour readings. Then, I summed all corrected half-hour path length readings belonging to each unique full-day follow to obtain the overall corrected DPL, henceforth referred to as simply DPL (i.e., all reports of DPL henceforth refer to the corrected estimate described here, unless otherwise stated).

Figure 2.3. Diagram showing the corrected DPL based on a2 + b2 = c2.

Home Range Analysis

I estimated annual home ranges using two common techniques: the minimum convex polygon (MCP) and the fixed kernel (FK). Home ranges are defined and were calculated based on 95% of the data, or fixes. I also calculated 100% annual home ranges using the MCP to compare results with the 95% annual home range estimates of the MCP method. I used the FK method to estimate core area, defined as the 50% density contour, to identify localities of concentrated activity. Both the 50% and 95% designation for core area (e.g., Asensio et al. 2011; Donaldson and Echternacht 2005; Fashing et al. 2007;

Loveridge et al. 2009; Rowe and Dalgarn 2010; Wartman et al. 2010; but see Powell

2000) and home range (Laver and Kelly 2008; Powell 2000; Seaman and Powell 1996;

White and Garrott 1990; Worton 1989; but see Bӧrger et al. 2006; Seaman et al. 1999) are in line with the conventional method of home range analysis, and therefore facilitate comparisons across studies.

Additionally, ranging data for individual range years were combined into larger datasets to produce cumulative annual home ranges. For example, the 2007 and 2008 datasets were combined into one dataset and then analyzed to produce a cumulative

annual home range for 2007-2008. I repeated this process of adding subsequent datasets for the remaining range years. In the end, I obtained a total of five datasets, four of which contained data from subsequent years (i.e., 2007-2008, 2007-2009, 2007-2010, and 2007-

2011, except for 2007). All cumulative annual home ranges were estimated using the

MCP method only.

Lastly, I calculated cumulative home ranges at every 10 full-days. For example, the first dataset started at full-days 1-10, then full-days 1-20, then full-days 1-30, until full-days 1-785. As with the cumulative annual home ranges, all cumulative 10-day home ranges were estimated using the MCP method only.

Home Range Estimator: Minimum Convex Polygon

The MCP (Mohr 1947) is a relatively old method researchers have utilized to extrapolate home range. The MCP method uses straight lines and convex angles of less than 180 degrees to connect the outermost points in a distribution of “fixes” (i.e., telemetry or geographic location data) to produce a home range in the shape of a polygon

(Anderson 1982a; Mohr 1947). Mechanically and conceptually the MCP is simple to understand and implement, but the practical applicability of a polygon-shaped home range has engendered a variety of issues that have severely hampered its long-standing use as a home range tool (e.g., Borger et al. 2006; Laver and Kelly 2008; Powell 2000). A commonly cited drawback associated with the MCP method is its tendency to erroneously include areas the focal subject has never visited or been observed in within the home range estimate (Andreka et al. 1999; Pebsworth et al. 2012; Powell 2000). This leads to two additional problems: first, it does not accurately reflect the focal subject’s movement and home range use patterns, and second, it overestimates the actual extent of

the focal subject’s home range (e.g., Andreka et al. 1999; Pebsworth et al. 2012).

Furthermore, outliers or unusual movements, due to their location generally being on the periphery of the home range, will exacerbate the issues above. This is because the MCP connects the farthest points together, and since outliers are usually farther away from more common movements near the center of the home range, areas of space that lie between adjacent data will be inadvertently included in the home range, inflating the home range estimate. Moreover, the accuracy of the MCP has been tied to sample size such that the larger the sample size, the larger (and more accurate) the home range estimate (Bekoff and Mech 1984; Boyle et al. 2009; Girard et al. 2002; Jennrich and

Turner 1969; Schoener 1981; Seaman and Powell 1996). Lastly, the MCP fails to produce any meaningful conclusions about trends in the focal subject’s activity inside the home range. This inability to assess space use patterns within the home range is significant considering the question of how an animal utilizes its home range is equally, if not arguably more, important to how large the home range is.

Despite the aforementioned limitations of the MCP method, I constructed MCP home ranges using both 95% and 100% of the data points in Home Range Tools, version

1.1 for ArcGIS 9.3 (Rodgers et al. 2007). I calculated 95% MCP home ranges using the

“Fixed Mean” default option in HRT. I note sample size where appropriate. The scientific community has generally chosen to construct MCP home ranges using only a percentage of the data points, usually 95% (Anderson 1982; Powell 2000; Powell et al. 1997), because the MC method is highly susceptible to outliers (Andreka et al. 1982; Bekoff and

Mech 1984; Börger et al. 2006; Pebsworth et al. 2012; Powell 2000). However, several authors (e.g., Kernohan et al. 2001; White and Garrott 1990) have argued there is no

biological support for the removal of the top 5% of the data, because it can result in the loss of valuable data, e.g., removal of sleeping cliff sites from the (95%) home range estimate (Pebsworth et al. 2012). Rodgers et al. (2007) advise that researchers should use the “Remove X/Y Duplicates" command to remove all duplicate data points prior to home range analysis because calculating the distances between duplicate data can result in a “division by zero” error that can lead to a software crash. I was reluctant to implement this command to remove duplicate data for several reasons: geladas at Guassa routinely reuse sleeping cliffs, and they often remain immobile during periods of extreme weather conditions, such as hail, rainfall, and thick fog (Dunbar 1977; Dunbar and

Dunbar 1975; Hunter 2001; Kawai and Iwamoto 1979; this study). Indeed, both of these behaviors often result in duplicate or clumping of data points because the geladas are in the same location for an extended period of time. Third, I was unsure of the ramifications that removing duplicate data would have on the overall home range, both in terms of the home range area estimate and possible biological interpretations (e.g., Blundell et al.

2001; de Solla et al. 1999). To determine whether or not removing the duplicate data would have any effect on the home range estimate, I calculated home ranges with (i.e., the original datasets) and without duplicate data points using the “Remove X/Y

Duplicates” command in HRT. I compared the results and found that 95% MCP home ranges constructed without duplicate points were (1-3%) larger than those constructed with duplicate points for four of the five years (Moua, unpub. data). Further, ArcGIS 9.3 did not force quit or malfunction when the MCP command was used to calculate annual home ranges with duplicate points in the dataset. Based on the findings above, I decided to estimate all home ranges using data from the original datasets.

Though the MCP command successfully calculated all the annual and cumulative annual home ranges from the original datasets, I later discovered that the MCP command failed to produce any cumulative 10-day home range estimates for the first 100 full-days

(i.e., full-days 1-10, 1-20, 1-30, . . . 1-100) with the original datasets. In some cases,

ArcGIS 9.3 unexpectedly shut down without warning. This experience is indicative of the software crash Rodgers et al. (2007) warned that can occur because of duplicate data in the dataset. It appears that the effects of duplicate data on the home range analysis of the

MCP method in HRT is much more pronounced in smaller sample sizes than larger (since no similar issues occurred with the larger datasets). Since I was unable to calculate 10- day cumulative home ranges for the first 100 days of the study using the original data, I re-analyzed all annual, cumulative annual, and cumulative 10-day home range estimates without any duplicate data (to ensure all estimates of home range were calculated from the same data using the MCP method). Therefore, all reports of MCP annual home range, cumulative annual home range, and cumulative 10-day home range estimates have been derived from the datasets containing no duplicate data. Lastly, as I previously determined that 95% home ranges calculated without duplicate data were slightly larger than those calculated from the original data, I acknowledge that the 95% home range estimates reported in this study could be slightly overestimated, though a negligible difference.

Further, I acknowledge that MCP and fixed kernel annual home ranges will be calculated using different sample sizes, however, I anticipate the results to be negligible (above).

Home Range Estimator: Fixed Kernel

Unlike the MCP method, kernel estimators can provide information about how an animal utilizes its home range, in addition to several other features that make kernel

estimators currently the most preferred home range tool (Borger et al. 2006; Gitzen et al.

2006; Laver and Kelly 2008; Nilsen et al. 2009; Powell 2000; Seaman and Powell 1996).

Kernel estimators construct a home range, called a kernel or density estimate, based on the relative density of points in a utilization distribution (UD)—a juxtaposition of fixes

(Silverman 1986; Worton 1989). It accomplishes this placing a fixed or an adaptive kernel around each data point. A fixed kernel applies a constant smoothing factor (or bandwidth), h, to the data, whereas an adaptive kernel adjusts its bandwidth relative to the concentration of points in the regions such that more concentrated areas receive less smoothing and vice versa (Silverman 1986; Worton 1989). Seaman and Powell (1996) have demonstrated that the fixed kernel produces home range estimates that more closely reflect the UD than the adaptive kernel.

Currently, the fixed kernel is the preferred home range estimator due to its ability to generate density estimates of animal ranging behavior and produce a home range estimate that may fit the shape of the focal subject’s distribution (as opposed to the MCP which is confined to a polygon) (Laver and Kelly 2008; Powell 2000; Seaman and Powell

1999; Worton 1989). A density estimate, or density contour, represents the probability value of the focal subject being in that location relative to other areas in the home range

(Worton 1989). The density estimate is extrapolated to identify areas within the home range that are of relative importance to the focal subject, such as a core area, information critical to uncovering how animals use their home range and for conservation-related purposes. Lastly, kernels, unlike other home range estimators, such as the MCP, grid cell, and ellipse, are free from issues that constrain the home range to a rigid and fixed shape, and this characteristic allows kernels to produce a home range estimate that may captures

the focal subject’s fluid movements and dynamic home range use patterns (reviewed in

Powell 2000).

Effects of Bandwidth Estimator on Kernel Home Range Estimates Despite possessing features that are ideal to any home range estimator, the accuracy and performance of kernel estimators have been shown to be highly dependent on the bandwidth used to assess the data (Borger et al. 2006; Gitzen et al. 2006; Powell 2000;

Seaman and Powell 1996; Worton 1989). Currently, the least-squares cross validation bandwidth (LSCV) is the bandwidth of choice (Borger et al. 2006; Gitzen et al. 2006;

Powell 2000; Seaman and Powell 1999); the LSCV bandwidth, however, is limited in application and prone to errors (Blundell et al. 2001; Gitzen et al. 2006; Horne and

Garton 2006).

Recently, research by Gitzen et al. (2006) and Horne and Garton (2006) found that bandwidths such as the plug-in and solve-the-equation, and the likelihood cross- validated bandwidths, respectively, performed similarly or better than the widely considered LSCV bandwidth under identical experimental conditions. Horne and Garton

(2006) demonstrated, for example, that the likelihood cross-validated bandwidth generated density contours that were relatively more accurate and indicative of the focal subject’s home range use patterns than those estimated using the LSCV bandwidth when sample size was ≤50. (Both the LSCV and likelihood cross-validated bandwidths produced similar density estimates as sample size increased, indicating that sample size has a relatively larger impact on density estimates than the choice of smoothing parameter [Horne and Garton 2006; Seaman et al. 1999].) Indeed, these findings corroborate the push by many researchers who advocate using multiple bandwidth

estimators in an effort to gauge the performance capabilities of each relative to the other

(Börger et al. 2006; Boyle et al. 2009; Powell 2000; Seaman and Powell 1996; White and

Garron 1990; Worton 1989). Based on these suggestions, I implemented FK analysis using the reference, or ad hoc (REF); the LSCV; plug-in; and smoothed cross-validation

(SCV) bandwidth estimators. I used Home Range Tools (HRT) (Rodgers et al. 2007) to conduct fixed kernel REF and LSCV home range analyses, whereas I used GME to calculate fixed kernel LSCV, plug-in, and SCV home ranges (Beyer 2012). (GME does not possess the REF option, whereas calculating the LSCV in both HRT and GME provides comparability of results across programs.) Following the recommendation of many researchers (Gitzen et al. 2006; Pebsworth et al. 2012; Rodgers et al. 2007; Seaman and Powell 1996; Worton 1989), I multiplied the REF by a fixed proportion (e.g., 0.2,

0.4, 0.6, 0.8, and 1.0), also known as scaling the REF, which may circumvent its tendency to underestimate or overestimate the home range.

Results of Preliminary Analyses of FK Kernel Estimators I conducted a series of preliminary analyses to evaluate the performance capabilities of each bandwidth estimator.

During the preliminary analysis phase, I discovered that HRT was unable to produce any home range estimates using the LSCV bandwidth estimator, in which the following error message appeared: “Warning: the LSCV function failed to minimize between 0.5*HREF and 2.00*HREF. The bandwidth defaulted to HREF.” It appears that when the LSCV bandwidth fails to reduce the mean integrated square error to an appreciable level, it reverts to the REF bandwidth (Gitzen et al. 2006; Rodgers et al.

2007). Conversely, I found that GME successfully generated LSCV home ranges. I

compared the LSCV home ranges obtained in GME to the 1.0*REF home ranges calculated in HRT, and found that the density contours of each were remarkably similar

(Moua unpub. data). I suspect GME was also unable to process the LSCV and automatically reverted to the REF bandwidth, though without notifying the user about the underlying reasons for the change. Following the unraveling of these findings, I omitted the LSCV bandwidth estimator from this study altogether.

Both the SCV and plug-in bandwidth estimators produced home range estimates with highly disconnected and scattered density contours (Moua unpub. data). Given the similarity in the estimates produced by these two bandwidths, I report the findings for the

SCV bandwidth only. In sum, I report home range estimates for only the FK REF and

SCV bandwidths.

Autocorrelation: Implications on Ranging Analysis

Autocorrelation is defined as the aggregation of (location) data points that are spaced too close in time that their association is no longer the result of random movement

(Legendre 1983; Swihart and Slade 1985a). It is generally assumed that data are independent of one another, i.e., not autocorrelated (Legendre 1993; Swihart and Slade

1985b), because data that are autocorrelated may lead researchers to support or reject a hypothesis without a statistically significant finding (Legendre 1993). The purported impacts of autocorrelated data on estimates of animal ranging ecology are mixed at best.

Studies have shown, for example, that autocorrelated data generate MCP home ranges that underestimated and did not correctly portray the focal subject’s space use patterns, and also reduced the detail and length of travel paths (Swihart and Slade 1985b).

Conversely, numerous studies have demonstrated that eliminating autocorrelation may

actually diminish the quality and interpretational power of the findings (e.g., Blundell et al. 2001; de Solla et al. 1999; Hansteen et al. 1997; Legendre 1993; Otis and White

1990). de Solla et al. (1999) found, for instance, that measurements of movement patterns of both antler files (Protopiophila litigata) and snapping turtles (Chelydra serpentina) were negatively affected at the expense of increasing the sampling time interval (to reach independence of observations), such that a longer sampling interval resulted in a reduction in the detail of the animal’s whereabouts and thus underestimated total distance traveled. It appears that deleting data or increasing the time interval between subsequent readings to reach independence of observations (as suggested by Swihart and Slade

1985a, b) may actually do more harm to the data analysis than intended (de Solla et al.

1999; Legendre 1993), and others have shown that autocorrelated data may actually help interpret results (Hansteen et al. 1997). For example, in their examination of root vole

(Microtus oeconomus) ranging behavior, Hansteen et al. (1997) found that male root voles tended to exhibit autocorrelated movement at short sampling intervals (i.e., at 30 and 60 mins). The authors posit this phenomenon may be explained by the animals having large home ranges but not moving far enough between consecutive time intervals to reach independence of observations (Hansteen et al. 1997) (an animal with a large home range needs relatively more time between consecutive time intervals to distance itself from its previous location if independence of observations is to be met: Schoener

1981). Indeed, these results suggest that, in some cases, autocorrelation may provide researchers with added analytical and interpretational power about the behavior and ecology of the focal subject.

I have described the disadvantages and advantages of autocorrelation on estimates of animal ranging parameters, and the possible solutions to remedy autocorrelated data

(e.g., increase time interval or delete data points: Swihart and Slade 1985a, b). However,

I feel that increasing the time interval between consecutive observations or deleting data until independence of observations is met (Swihart and Slade 1985a,b) would result in the loss of crucial data and possibly inferential power about the movement patterns of the geladas at Guassa. Ranging data in this study were collected at regular 30-minute intervals throughout each full study day to insure a complete record was obtained of gelada monkey movement patterns at Guassa. Furthermore, geladas are known to remain immobile or inactive during periods of extreme weather and they frequently re-use sleeping sites (Dunbar and Dunbar 1975; Hunter 2001; Kawai and Iwamoto 1979; this study), behaviors that are likely to lead to autocorrelation (clumping of data points).

Indeed, eliminating data from the analysis for the sole purpose of reaching independence of observations could potentially diminish the quality of the estimates (e.g., de Solla et al.

1999; Hansteen et al. 1997). I feel that this was something I did not want to risk.

Prior to fixed kernel analysis, I subjected the data to both Schoener’s Index (Schoener

1981) and Swihart and Slade’s Index (Swihart and Slade 1985b) to test for serial autocorrelation with the option provided in HRT. The results (Table 2.1) of the autocorrelation analysis indicate that the data are autocorrelated. Values of <1.6 or >2.4 for Schoener’s Index or >0.6 for Swihart and Slade’s Index indicate autocorrelation.

Given the discussion on the issue of autocorrelation (above), and in spite of the autocorrelation test results (below), I opted to analyze the data without amending them to reach independence of observations.

Table 2.1 Results of autocorrelation analysis

Year n Swihart and Slade Schoener’s Index 2007 2,944 0.04 2.18 2008 3,325 0.06 2.60 2009 3,420 0.06 2.33 2010 3,078 0.06 2.40 2011 3,326 0.07 2.45

Statistical Analysis

A goal of this study was to ascertain how DPL varied over the course of the year.

To investigate the relationship between time and DPL, I employed linear regression analysis. I plotted each full-day follow (identified by its date) and its respective DPL value on the x and y axis, respectively, of a scatterplot diagram. Once the data were plotted, I obtained the line of best fit for the relationship using the least-squares method.

The least-squares method draws a straight line through the data such that the line is exactly the same distance from each data point on the diagram (Salkind 2009). I then calculated the Pearson product-moment correlation coefficient, r, for the given line of best-fit. The Pearson correlation coefficient ranges from a value of -1 to 1 and describes the strength and direction of the relationship (Salkin 2009).

Afterwards, I implemented one-way analysis of variance (ANOVA) to assess whether the observed variations in the monthly mean DPL and annual mean DPLs were significantly different within and across years. It is generally assumed in an ANOVA that each group exhibits a similar degree of variation, also known as the homogeneity of variances. I used Levene’s test of homogeneity of variances to investigate whether or not the data supported this assumption. In instances where the homogeneity of variances assumption was rejected, i.e., Levene’s statistic < 0.05, I re-tested the data using both the

Welch and Brown-Forsythe tests, ideal in cases in which the homogeneity of variances assumption has been rejected (Pallant 2010).

All statistical tests were implemented using SPSS 20 (IBM 2012) and tested with a significance level of α = 0.05, unless otherwise stated.

All figures were created using SigmaPlot 12.5.

CHAPTER 3

RESULTS

Annual Home Range Estimates: MCP

Annual home range (95%) increased in size over the five-year study period

(Figure 3.1), being smallest in 2007 (5.7 km2) and largest in 2011 (11.6 km2). The 95% and 100% annual home range and the percentage difference between them are shown in

Table 3.1. Overall, this difference in increase in home range area from 2007 to 2011 amounts to a percent of increase of more than 50% over this time period.

All annual home range estimates from 2007 to 2011 contained areas the geladas were not observed in (Figure 3.1). However, these areas of empty and unused areas were relatively fewer and reduced in the 95% estimates compared to the 100% estimates, which resulted in home ranges that were 23.4% (2007) to 48.4% (2010) smaller than their respective 100% home range estimates (Table 3.1). Despite these findings of reduced home range size, numerous sleeping sites (2007: 2; 2008: 1; 2009: 10; 2010: 9; 2011: 19), which are all located along the cliff edges that border the eastern edge of the study area, were erroneously excluded from the 95% home range estimates.

Table 3.1 Comparison of annual home range estimates for MCP.

Year n 95% 100% % DIFF 2007 2,611 5.9 7.7 23.4 2008 3,169 8.6 12.1 29.0 2009 3,071 9.2 14.7 37.4 2010 2,812 11.5 22.3 48.4 2011 3,148 11.6 16.7 30.5

Annual Home Range Estimates: FK REF

I found that multiplying the FK REF bandwidth by proportions of 0.2 to 1.0 resulted in noticeably dissimilar annual home range estimates (Figures 3.2-3.6; Table

3.2). For example, the smaller proportions of the REF (e.g. ≤0.4) produced density contours that were fragmentary and shaped irregularly, whereas the higher proportions of the REF (≥0.6) generated density contours that were relatively smoother and more continuous. However, the density contours of the former incorporated copious areas the geladas did not visit (Figure 3.2-3.6), particularly some uninhabitable areas east of the geladas’ sleeping cliffs. Indeed, these differences in the density contours are depicted in the area estimates for each annual home range. To briefly elaborate, the 2007 annual home range for the 0.2*REF, 4.1 km2, was substantially smaller (47%) than the 7.7 km2 estimate obtained for the 1.0*REF (Table 3.2). In general, the number and size of the empty, unused spaces, and the total area of each home range tended to increase as the proportion increased (i.e., 0.2  1.0). Conversely, the density contours became more fragmented and broken as the proportion decreased (i.e., 1.0  0.2). Based on the results obtained here, it appears that the 0.6*REF produced annual home ranges that had relatively continuous density contours and incorporated areas the geladas did not visit the fewest. The 0.8*REF produced much smoother and less irregularly shaped density

34 contours, but the density contours were comparatively wider and thus tended to include more areas the geladas never visited. As such, I conclude that the 0.6*REF produced annual home ranges that appear to most accurately reflect the geladas’ ranging behavior.

Table 3.3 Core Areas (50%) and Annual Home Ranges (95%) Based on the FK 0.6*REF

Year n 50% 90% 95% 99% 2007 2,944 1.7 4.7 5.7 7.9 2008 3,325 2.1 6.1 7.7 11.4 2009 3,420 2.0 6.4 8.3 12.3 2010 3,078 2.0 8.0 13.0 18.6 2011 3,326 2.2 7.7 11.6 16.0

Figure 3.1 Comparison of annual home ranges from 2007 to 2011 using the MCP method.

Figure 3.2 Comparison of annual home ranges for 2007 based on scaling the FK REF.

Figure 3.3 Comparison of annual home ranges for 2008 based on scaling the FK REF.

Figure 3.4 Comparison of annual home ranges for 2009 based on scaling the FK REF.

Figure 3.5 Comparison of annual home range for 2010 based on scaling the FK REF.

Figure 3.6 Comparison of annual home ranges for 2011 based on scaling the FK REF.

Annual Home Range Estimates: FK SCV

Similarly, annual home ranges calculated using the FK SCV bandwidth estimator generated home ranges whose density contours were also oddly shaped, highly discontinuous, and scattered like those seen in the 0.2 and 0.4 FK REF home range estimates (Figure 3.7). Annual home range sizes estimated using the FK SCV were as follows: 4.5 km2 (2007); 6.4 km2 (2008); 6.8 km2 (2009); 9.0 km2 (2010); and 8.6 km2

(2011) (Table 3.3). Overall, the FK SCV produced the smallest annual home ranges of the three methods assessed here in terms of area (size).

Table 3.3 Core areas (50%) and Annual Home Ranges (95%) Based on the FK SCV.

Year n 50% 90% 95% 99% 2007 2,944 1.2 3.8 4.5 6.0 2008 3,325 1.7 5.1 6.4 9.3 2009 3,420 1.7 5.3 6.8 9.7 2010 3,078 1.5 6.4 9.0 14.0 2011 3,326 1.8 6.6 8.6 13.4

Figure 3.7 Comparison of annual home ranges from 2007 to 2011 using the FK SCV.

Comparison of Annual Home Ranges Across Methods

Despite the considerable variation in the home range estimates produced by each method, there is evidence that illustrates some degree of commonality in the home ranges among the MCP and both fixed kernel REF and SCV methods. To being with, the FK

SCV and some proportions of the REF method (e.g., 0.2 and 0.4) generally produced small, disconnected, and incongruous density contours that resulted in small range size estimates. Conversely, the ≥0.6 FK REF generated mostly contiguous and smooth density contours, particularly at the higher density contours (e.g., >90%). Further, like the MCP method, the ≥0.6 FK REF tended to incorporate areas never used by the animals, which consequently resulted in inflated area estimates (Table 3.1 and 3.2). Additionally, each home range method omitted one to 19 sleeping sites from the 95% estimates. Lastly, despite the observed differences in the appearance of the annual home ranges estimated by these methods, the trend of increasing annual home range size over time was evident across all three home range estimate methods.

Trends in Annual Home Range

In general, annual home range size increased gradually over the five-year study period: it was smallest in 2007, largest in 2010, and dropped slightly in 2011 (except for the MCP in 2010 and 2011 where we found the reverse to be true). A closer examination of the relationship between number of study days and home range size found that growth in the home range was greatest during the first two to three years of the study period

(study days 460-470), but has since slowed down and appears to have reached an asymptote after the 2010 and 2011 range years, with an occasional peak in home range

(e.g., study days 550-560, 660-670, and 780-785) (Figure 3.8). These findings imply an underlying relationship between the number of study days and the size of the home range.

)

2 20

Home range sizerangeHome(km

0 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750

Number of study days

Figure 3.8 Cumulative 10-day home range size calculated using the MCP method (95% solid and 100% dotted). 45

Cumulative Annual Home Range Estimates In general, annual home range size increased with the addition of ranging data from subsequent years (Table 3.4). Using the

MCP method, the cumulative annual home range for 2007-2011 was slightly smaller

(11.2 km2) than the annual home range for 2011 (11.6 km2). One possible explanation for this observation is that I calculated 95% MCP home ranges using the “Fixed Mean” option in HRT, which obtains the mean of all the Lat and Lon coordinate pairs, then removes the top 5% coordinate pairs from the dataset that are farthest from this mean.

Since the cumulative annual home range for 2007-2011 contained the largest sample size and therefore most data near the center of the home range, the mean for the 2007-2011 dataset was likely smaller than the mean for the 2011 dataset, which meant that more data were considered “farther” away and thus removed.

Annual Core Area: Use and Trends

Core area use during the five-year study indicates the geladas concentrated the majority of their activities in roughly two to five regions within the home range (Figures

3.2-3.7): one to four in the northern and one in the southern region. The geladas’ space use patterns did not remain static over time. According to both the FK REF and SCV, in

2010 and 2011 the core area in the southern region of the home range underwent a westward expansion after having remained relatively stable (in size) the three years prior.

Core area size estimates varied considerably among the various proportions of the

FK REF bandwidth (Table 3.2). Roughly, annual core area for the FK REF increased in size from 2007 to 2008, and depending on the particular proportion examined, it either increased again or reduced in size until it finally increased to its largest size in 2011.

Similarly, annual core area for the FK SCV increased from 2007 (1.2 km2) to 2008 (1.7

47 km2), where it remained unchanged in 2009, but then it dipped back down in 2010 (1.5 km2), before it increased to its largest (combined) size in 2011 (1.8 km2). Overall, core area use can be characterized as being smallest in the first year of study and largest in the last year, though the number and size of the core areas changed year to year as estimated using the FK SCV method.

Figure 3.9 Cumulative annual home range estimates calculated using the MCP method. 48

Ranging Patterns: Daily, Monthly, and Annual Trends in DPL

The geladas traveled, on average, 3495 ± 1017.1 (SD) m per day (n = 785). The day of shortest travel occurred on April 24, 2008 when the geladas ranged only 690 m, while the day of furthest travel happened on November 11, 2011, when the geladas traveled 7970 m.

Daily path lengths varied considerably within years and across the five-year study period (Figure 3.10). Despite this wide variation in DPL, a modest, though statistically

2 significant increasing time trend was evident for three of the five years (2008: r adj = 0.07;

2 2 2009: r adj = 0.04; and 2011: r adj = 0.19; p < 0.001 for all three years), and for all five

2 years combined (r adj = 0.20, p < 0.01). Conversely, no discernible pattern in DPL was

2 2 evident for the 2007 (r adj = 0.01; p = 0.16) and 2010 range years (r adj = -0.01; p = 0.89).

Despite the significant increases in DPL (for three of the five range years), only some of the observed variation in DPL (between 4-20%) could be explained by time (i.e., year), which left a substantially high proportion (between 80-96%) unaccounted. We therefore presume additional as yet unidentified variables are responsible for explaining the remaining proportion of variation in DPL.

2007 2008 2009 10000 10000 10000 22 2 2 rr = = 0.01 0.20 r = 0.07 r = 0.04 adjadj adj adj p = 0.16 p < 0.0005 p < 0.05 8000 p < 0.0005 8000 8000 n = 145 days n = 162 days n = 158 days

6000 6000 6000

4000 4000 4000

Daily path length (m) path length Daily 2000 (m) path length Daily 2000 (m) path length Daily 2000

0 0 0 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

2010 2011 2007-2011 combined 10000 10000 10000 2 2 2 r = -0.01 r = 0.19 r = 0.20 adj adj adj p = 0.89 p < 0.0005 p < 0.0005 8000 8000 8000 n = 152 days n = 168 days n = 785 days

6000 6000 6000

4000 4000 4000

Daily path length (m) path length Daily 2000 (m) path length Daily 2000 (m) path length Daily 2000

0 0 0 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11

Figure 3.10 Comparison of the relationship between time and DPL for each and all range years. 50

6000

5000

4000

3000

2000 2007

Monthly mean DPL (m) DPL mean Monthly 2008 1000 2009 2010 2011 Mean 2007-2011 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 3.11 Comparison of monthly mean DPL and for all range years.

Monthly Mean DPL

The monthly average distance the geladas traveled each day (i.e., monthly mean

DPL ± SE (m)) varied considerably within and across years (Figure 3.11 and Table 3.4).

The monthly minimum and maximum values also illustrate and further bolster the widely variable daily movement patterns of the geladas at Guassa (Table 3.4). Despite these observations, plotting the monthly mean DPL data on a continuous time scale show a peak in monthly mean DPL during the latter period of each year between September and

November (Figure 3.12). We found that the observed variation in monthly mean DPLs within years was significantly different from each other for all range years (one-way

ANOVA, 2007: F(11,133) = 2.94, p < 0.01; 2008: F(11,150) = 4.10, p < 0.001; 2009: F(11,146)

= 2.75, p < 0.01; 2010: F(11,140) = 1.89, p < 0.05; 2011: F(11,156) = 5.94, p < 0.001).

Table 3.4 Monthly Mean DPL ± SE (m), Number of Full-days, and Range of DPL for Each and all Years

2007 2008 2009 Mean DPL ± Mean DPL ± Mean DPL ± Month n Range (m) Month n Range (m) Month n Range (m) SE (m) SE (m) SE (m) Jan 5 2,655 ± 616 1,223 – 4,327 Jan 15 3,293 ± 132 2,473 – 4,261 Jan 10 2,678 ± 281 1,437 – 4,409 Feb 17 2,842 ± 199 1,368, – 4,532 Feb 15 2,816 ± 193 1,577 – 3,858 Feb 15 2,832 ± 249 1,545 – 4,873 Mar 14 2,812 ± 181 1,557 – 3,639 Mar 6 2,727 ± 166 2,124 – 3,221 Mar 18 3,181 ± 229 2,022 – 5,108 Apr 13 2,916 ± 118 2,222 – 3,527 Apr 15 2,924 ± 213 690 – 4,217 Apr 16 3,139 ± 168 1,891 – 4,671 May 12 2,383 ± 137 1,631 – 3,217 May 13 3,497 ± 199 2,249 – 4,745 May 8 3,153 ± 227 2,031 – 3,929 Jun 10 2,344 ± 195 1,328 – 3,355 Jun 9 3,172 ± 225 2,244 – 4,396 Jun 11 3,454 ± 230 2,361 – 4,981 Jul 14 2,918 ± 127 2,019 – 3,606 Jul 16 3,214 ± 193 1,982 – 4,424 Jul 13 3,424 ± 140 2,551 – 4,299 Aug 15 3,022 ± 133 1,609 – 3,876 Aug 12 3,093 ± 185 2,063 – 4,251 Aug 17 3,280 ± 180 1,749 – 4,191 Sep 4 3,075 ± 323 2,484 – 3,763 Sep 18 3,889 ± 232 2,421 – 5,707 Sep 10 4,320 ± 344 3,072 – 5,748 Oct 16 3,489 ± 181 2,147 – 4,766 Oct 15 3,930 ± 155 2,952 – 4,821 Oct 13 3,544 ± 136 2,328 – 4,099 Nov 11 2,834 ± 189 1,888 – 4,033 Nov 17 3,782 ± 218 1,899 – 5,668 Nov 11 3,681 ± 167 2,803 – 4,766 Dec 14 2,614 ± 137 1,747 – 3,428 Dec 11 3,030 ± 225 2,276 – 4,460 Dec 16 2,978 ± 336 858 – 6,143 12 2,824 ± 211a 12 3,281 ± 195a 12 3,305 ± 224a 145 2,848 ± 57b 1,223 – 4,532 162 3,339 ± 65b 690 – 5,707 158 3,272 ± 72b 858 – 6,143 2010 2011 2007-2011 Jan 12 3,540 ± 188 2,629 – 4,399 Jan 17 3,233 ± 288 1,252 – 5,134 Jan 59 3,167 ± 122 1,223 – 5,134 Feb 12 3,653 ± 233 2,509 – 4,953 Feb 15 3,603 ± 239 2,137 – 5,453 Feb 74 3,120 ± 107 1,368 – 5,453 Mar 14 3,765 ± 263 2,273 – 5,149 Mar 19 3,850 ± 178 2,324 – 5,219 Mar 71 3,364 ± 110 1,557 – 5,220 Apr 14 3,955 ± 196 2,668 – 5,501 Apr 16 3,416 ± 214 2,239 – 5,380 Apr 74 3,270 ± 93 690 – 5,501 May 7 3,758 ± 256 2,721 – 4,703 May 13 3,720 ± 288 2,546 – 5,941 May 53 3,282 ± 123 1,631 – 5,941 Jun 15 4,254 ± 172 2,861 – 5,668 Jun 17 4,536 ± 148 3,525 – 5,576 Jun 62 3,735 ± 127 1,033 – 5,668 Jul 8 3,388 ± 212 2,766 – 4,530 Jul 8 3,756 ± 281 2,632 – 4,648 Jul 59 3,302 ± 87 1,972 – 4,648 Aug 6 4,214 ± 460 3,154 – 5,904 Aug 13 4,689 ± 230 2,925 – 6,209 Aug 63 3,563 ± 123 1,749 – 6,209 Sep 15 4,402 ± 333 2,420 – 6,688 Sep 14 4,330 ± 250 2,970 – 6,233 Sep 60 4,130 ± 139 2,420 – 6,688 Oct 15 4,078 ± 225 2,525 – 5,467 Oct 15 4,953 ± 206 3,765 – 7,267 Oct 74 4,004 ± 102 2,147 – 7,267 Nov 19 3,260 ± 275 1,358 – 5,393 Nov 9 5,279 ± 573 3,155 – 7,970 Nov 68 3,663 ± 151 1,358 – 7,970 Dec 15 3,798 ± 293 2,445 – 5,536 Dec 12 4,419 ± 323 2,090 – 6,302 Dec 68 3,347 ± 145 858 – 6,302 12 3,839 ± 259a 12 4,149 ± 268a 12 3,496 ± 119a

52 152 3,835 ± 80b 1,358 – 6,688 168 4,100 ± 86b 1,284 – 7,970 785 3,495 ± 36b 690 – 7,970

Annual Mean DPL

Annual mean DPL increased significantly over the course of the five-year period

(Figure 3.13; one way ANOVA: F(4,780) = 44.1, p < 0.001). Results from Scheffe’s post- hoc test reveal the annual mean DPL for 2007 was significantly different from the annual mean DPL for 2008-2011 (p < 0.001 for all cases); the annual mean DPL for both 2008 and 2009 were significantly different from 2007 and 2010-2011 (p < 0.01 for all cases); and the annual mean DPL for both 2010 and 2011 were significantly different from 2007-

2009 (p < 0.001 for all cases) (in Figure 3.6, letters that are shared among years indicate where years are not significantly different from each other, whereas letters that differ among years indicate where years are significantly different from each other). Despite the significant increases in annual mean DPL, approximately only 18% of the variation in annual mean DPL can be explained by time (i.e., year). We therefore suspect the remaining 82% of the variation in annual mean DPL to be explained by additional though currently unidentifiable variables (shaping DPL at the annual time scale).

Effect of Change in Altitude on Calculations of Distance Traveled

We found that accounting for changes in altitude on half-hourly distance traveled resulted in a 0.15% (8 August 2011) to 12.2% (1 December 2009) increase in DPL (n =

785, mean = 1.67 ± 0.9% (SD)). In terms of actual additional distance traveled, these percentages amount to an increase in DPL by 8 to 278 m (mean = 53 ± 22.4 m (SD)).

6000

5000

4000

3000

2000

Monthly mean DPL (m) DPL mean Monthly

1000

Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11

Figure 3.12 Plot of monthly mean DPL on a continuous time scale.

5000

c 4000 c

b b

3000 a

2000

Annual mean DPL + (m) DPL SE mean Annual 1000

0 2007 2008 2009 2010 2011

Figure 3.13 Comparison of annual mean DPL + SE (m).

CHAPTER 4

DISCUSSION

Summary of Findings

Annual home ranges estimated using the MCP method included spacious areas the geladas never used, which were exacerbated by outliers and ultimately inflated home range area estimates. Additionally, several sleeping sites were omitted from the 95% home range estimates.

Like the MCP, both the fixed kernel SCV and REF bandwidths omitted several sleeping sites from the 95% estimate. Both the SCV and (0.2 and 0.4) REF bandwidths constructed annual home ranges with discontinuous and broken density contours.

Conversely, the >0.6*REF bandwidth produced continuous density contours, though these density contours included areas the geladas did not and could not use.

In general, home range estimates estimated with the MPC were larger in area than those estimated using both the REF and SCV bandwidths.

Gelada monkey annual home range and core area use increased in size from year to year and were generally larger at the end of the study than at the beginning of the study, patterns evident across all home range estimation techniques.

Daily path length (DPL), on the other hand, varied considerably within years, though the total distance the geladas covered on any given day tended to increase from

January through to December. Monthly mean DPLs also varied extensively within and

57 between years, though it can be said that monthly mean DPLs generally increased over the five-year study period. Overall, annual mean DPL increased significantly between years, being shortest in 2007 and longest in 2011.

Evaluation of the MCP Method

Annual home ranges calculated using the MCP method were generally larger in size than those estimated using both fixed kernel methods (Pimley et al. 2005; but see

Boyle et al. 2009), and tended to include areas the geladas never visited (Andreka et al.

1999; Fashing et al. 2007; Pebsworth et al. 2012; Powell 2000), trends that are evident across the literature. A likely explanation for these observations is that the MCP method uses straight lines and convex angles to connect the outermost data points to create a home range whose shape is confined to a rigid and static figure that is unable to capture the fluid and dynamic movements and space use patterns of animals. All annual home ranges, for instance, contained a sizeable pocket of empty space in the western region of the home range, despite the lack of evidence suggesting the geladas were observed in this area over the five-year study period.

The decision to exclude a small portion of data from home range analysis, typically the top 5%, has become common practice among studies of animal ranging ecology (e.g., Andreka et al. 1999; Fashing et al. 2007; Pebsworth et al. 2012; Pimley et al. 2005; Powell 2000; but see Borger et al. 2006), since this technique has demonstrated consistently the ability to remove outliers or unusual movements that can have detrimental impacts on the home range estimate, or at least mitigate their effects. Indeed, removal of the top 5% from home range analysis reduced the size of the 95% annual home ranges by 23.4% to 48.4% relative to their respective 100% estimates. The largest

58 reduction occurred between the annual home ranges for the 2010 range year, which saw the 100% MCP drop from 22.3 km2 to 11.5 km2 in the 95% MCP, a decrease of 48.4%.

Comparisons of the 95% and 100% annual home ranges for all five years show a decrease in the inclusion of data located on the periphery of the home range, which in turn reduced the size and prevalence of empty, unused areas within the home range. This was most evident for the 2010 range year when the geladas made two separate and uncharacteristically long excursions to the far north of the Guassa study area. The removal of these extreme data points explains the significant reduction in the home range area for the 95% annual home range for 2010, and further highlights the susceptibility of home range estimators such as the MCP to outliers.

Several authors have questioned the decision to exclude data from the home range analysis (Kernohan et al. 2001; Powell 2000; White and Garrott 1990), and though the findings reported here appear to show that this strategy of excluding the top 5% is effective at eliminating unusual data points and producing smaller and relatively more accurate home ranges, there is evidence that supports the concerns raised by these authors. Specifically, several sleeping sites, from one to 19 depending on the range year, were omitted from the 95% annual home range estimates (e.g., Pebsworth et al. 2012).

Recently, Pebsworth et al. (2012), in their investigation of the ranging ecology of chacma baboons (Papio hamadryas ursinus) at Wildcliff Nature Reserve in Western Cape, South

Africa, reported the loss of two of seven sleeping sites from their 95% MCP estimates. It is difficult to assess how often biologically important data such as sleeping sites get removed from the 95% home range estimate, but it appears to be related to the practice of excluding some portion of the top data from the home range analysis. Presumably, the top

5% generally represent the data located on the boundary or fringes of the home range, movements considered to have relatively little biological significance. The findings here show, however, that data on the fringes of the home range, such as sleeping sites, can present critical challenges on the MCP method because such data may not only be considered an outlier and therefore are more likely to excluded from the home range analysis, but they can also represent areas essential to the focal subject’s ecology, in this case sleeping sites (Powell 2000). Indeed, the findings of loss of sleeping sites reported here and in Pebsworth et al. (2012) question the merit behind the removal of data from the home range analysis and stress the need for researchers to consider the advantages and disadvantages of their analytical decisions (e.g., Kernohan et al. 2001; Powell 2000;

White and Garrott 1990).

Home ranges estimated using the MCP method tend to increase as sample size increases (Bekoff and Mech 1984; Boulander and White 1990; Boyle et al. 2009;

Jennrich and Turner 1969; Schoener 1981). The home ranges estimated at 10-day intervals—a proxy measure of sample size—demonstrated that the geladas’ home range increased in size as the number of study days increased, until home range size reached an asymptote after approximately 570 days of study, a sample size of n = 12,244. Studies using simulation data (Bekoff and Mech 1984) and telemetry data (Girard et al. 2002) found 100-200 and 100-300 data points, respectively, could be enough to produce reliable and accurate home range estimates. The finding that the geladas’ home range continued to increase well beyond the 200-300 data point threshold suggests that the relationship between home range size and sample size is much more complex and most likely involves research-related components, such as sampling regime and analytical methods,

60 and species-specific variables, such as behavior and ecology (Bekoff and Mech 1984;

Boyle et al. 2009). The current study, however, did not categorically attempt to test the relationship between home range size and sample size in the MCP method (e.g., Boyle et al. 2009), and thus the conclusions reached here are derived from the analysis of the home range size at 10-day intervals. Nonetheless, this finding of increasing home range size with increasing study days is important for two reasons: firstly, it appears to support the conclusion that home range size is directly correlated with sample size in the MCP method; and secondly, but more importantly, it demonstrates the value of long-term studies in discovering and illuminating the nuances in animal ranging behavior attainable only through extended and continuous research. Lastly, this finding of increasing home range size with increasing sample days invokes the need to investigate the determinants of this phenomena in geladas at Guassa, Ethiopia.

Evaluation of the Kernel Estimators

Both fixed kernel estimators produced widely disparate results. The fixed kernel

SCV, for example, generated annual home ranges whose density contours were relatively more disconnected, fragmented (i.e., islands), and shaped irregularly. As this is the first study to utilize the fixed kernel SCV bandwidth in a practical setting (see Duong and

Hazelton 2005 for multivariate SCV; see Hall et al. 1992 for univariate SCV for full descriptions of the bandwidth; see Dobrovidov and Rud’ko 2009 for the SCV bandwidth applied in statistical and theoretical settings), it is difficult to explain the observed density contours. Similar findings of fragmented and disconnected density contours have been reported for the LSCV bandwidth (Amstrup et al. 2004; Blundell et al. 2001; Gitzen et al.

2006), which occur when the LSCV fails to find an appropriate smoothing value for the

61 given dataset (Gitzen et al. 2006; Rodgers et al. 2007; Seaman and Powell 1996). Perhaps a similar issue may explain the fragmentary contours observed in the SCV estimates, though additional research is required to confirm whether or not this is the case.

Whereas the MCP incorporated empty, unused areas predominantly in the western region of home range, the fixed kernel REF, and to a certain extent the fixed kernel SCV, included inhospitable areas east of the geladas’ sleeping sites that line the eastern border of the Guassa study site. This finding suggests that data such as sleeping sites that are located near the periphery of the home range pose an issue to all three home range estimators as each included areas the animals were never observed in, and thus produced annual home range values that were overestimates. One possible explanation for these results is that the REF bandwidth has been reported to produce wide and expansive density contours—that is, it tends to overestimate the boundaries of the home range— when used to analyze utilization distributions with more than one center of activity (i.e., core area) (Gitzen et al. 2006; Seaman and Powell 1996; Seaman et al. 1999). The findings reported here suggest that the geladas utilized more than one core area (based on the 50% density contour), which supports the conclusion that the REF bandwidth is not an appropriate bandwidth estimator for distributions with multiple centers of activity

(Gitzen et al. 2006; Seaman and Powell 1996; Seaman et al 1999).

Not all annual home ranges estimated using the REF bandwidth included areas the geladas neither used nor visited. Rather, we found that scaling—i.e., multiplying—the

REF bandwidth by a fixed proportion (i.e., 0.2, 0.4, 0.6, 0.8, and 1.0) (e.g., Gitzen et al.

2006; Pebsworth et al. 2012; Seaman and Powell 1996; Worton 1989) produced annual home ranges with density contours that varied in degree of continuity and

62 disconnectivity. To determine which proportion produced the most accurate and reliable home range, Pebsworth et al. (2012) assessed each home range on its ability to include all sleeping sites and major centers of activity, and the least amount of empty, unused areas.

Based on these criteria, Pebsworth et al. (2012) concluded that a proportion of 0.65 of the

REF generated the most reliable and accurate home range estimate for their study group of chacma baboons. Using these criteria to assess the results reported here, I conclude that a proportion of 0.6 of the REF produced a home range that had a combination of smooth and continuous density contours and density contours that incorporated relatively smaller degree of areas the geladas were never observed in. Unfortunately, none of the proportions I analyzed produced an annual home range that contained all sleeping sites for all five study years while meeting the two criteria above. The findings reported here and in Pebsworth et al. (2012) demonstrate that the practice of scaling the REF bandwidth by a fixed proportion affords the user the ability to decide the appropriate bandwidth value for the given utilization distribution.

Kernel estimators are widely purported to be superior to the MCP method in home range estimation (Borger et al. 2006; Laver and Kelly 2008), but the findings here do not definitively support this popular supposition. The results show that both kernel estimators, like the MCP, incorporated areas the geladas did not utilize and omitted sleeping sites from the annual home range estimates. In turn, these results demonstrate the value of utilizing multiple home range tools to estimate home range as results may not conform to previous findings (Boyle et al. 2009).

Implications and Suggestions for Future Research

Studies of animal ranging ecology are imperative for conservation-related purposes, and the analytical techniques and tools researchers utilize to investigate and understand aspects of animal movement and home range use patterns are equally critical for developing sound and effective conservation plans. The results reported here have wide implications for future research. I therefore propose the following recommendations based on the observations reported here.

For the choice of home range estimators, I strongly agree the MCP should be still utilized as a home range estimator despite its widely documented drawbacks and the growing consensus against its continued use (Borger et al. 2006; Gitzen et al. 2006; Laver and Kelly 2008). I contend that using the MCP to estimate home range affords researchers the invaluable ability of making comparisons between methods (Boyle et al.

2009; Pimley et al. 2005; Pebsworth et al. 2012) and within the same study or across studies and taxa (Biebouw et al. 2009; Fashing et al. 2007; Grueter et al. 2009; Robbins and McNeilage 2003; Strier 2003; Wartman et al. 2010; Wieczkowski 2005). Moreover, I recommend using the fixed kernel method to supplement the MCP method (Borger et al.

2006; Boyle et al. 2009; Gitzen et al. 2006; Laver and Kelly 2008; Powell 2000; Seaman and Powell 1999). The choice of bandwidth will likely vary depending on the nature of the data being analyzed (Gitzen et al. 2006; Powell 2000), though I recommend using the

LSCV and REF bandwidth estimators. The LSCV, despite its drawbacks (Blundell et al.

2001; Boyle et al. 2009; Gitzen et al. 2006; Pebsworth et al. 2012), is the bandwidth of choice among researchers (e.g., Powell 2000) and its use is worth attempting. The REF bandwidth should be implemented with the scaling option at consistent intervals of 0.05,

64 or an interval the researcher finds suitable for the given dataset (e.g., 0.1). Though the results reported here regarding the home range estimating power of the SCV bandwidth is inconclusive at best, additional research is needed to ascertain whether or not the SCV bandwidth will have a place as a reliable home range estimator. Indeed, this sentiment applies to many bandwidth estimators that have seen little application in home range studies, e.g., the plug-in methods and cross-validation bandwidths (Beyer 2012; Gitzen et al. 2006), and thus I recommend researchers test various bandwidth estimators to assess its efficacy as a home range estimator.

On the topic of data analysis, the decision to subject the entire dataset (i.e., 100%) or a majority of the data (e.g., 90% or 95%) to home range analysis should be driven largely by the research question(s) being asked and the behavioral ecology of the focal subject. As I have demonstrated, in focal subjects, like the geladas at Guassa, where the periphery of the home range is the location of critical data essential to understanding the behavioral ecology of the species, the researcher must select a data analysis procedure that is line with the research question but will simultaneously produce results that are reliable and accurate. Nonetheless, I strongly advise researchers to conduct home range analysis using both 95% and 100% of the dataset, since doing so will allow for comparisons across techniques and the assurance of making informed and sound decisions supported by quantitative analysis and field notes.

Additional components to consider include sample size and sampling regime.

With regard to sample size, the number of data points needed to produce a reliable and accurate home range estimate is contingent upon a variety of factors, such as home range estimator (e.g., Anderson 1982; Bekoff and Mech 1984; Boyle et al. 2009; Seaman and

Powell 1999), behavior and ecology of the focal subject (see Bekoff and Mech 1984 for examples; Girard et al. 2002), and body size (mass) (Clutton-Brock and Harvey 1977).

Research has shown, for example, that the MCP method can underestimate (Girard et al.

2002) and overestimate home range at small sample sizes (Glessener and Britt 2004), whereas the fixed kernel method can produce a home range that provides a general, but accurate account of the focal subject’s space use patterns with a small sample size

(sample size of 10 in Borger et al. 2006; sample size of 30-50 in Seaman and Powell

1999; but see Boyle et al. 2009). Whether or not the focal subject lives independently or in a group can possibly have an effect on the number of data points required to reproduce an accurate depiction of its home range. Group living may invoke greater instances of intra-specific competition for resources, which in turn will force the group to make additional or further movements in search of food (Chapman and Chapman 2000; Dunbar and Dunbar 1975; Dunbar and Iwamoto 1983; Fashing et al. 2007). Similarly, diet, such as fruigivory, insectivory, and gummivory, has been shown to influence home range size

(Clutton-Brock and Harvey 1977; Isbell 1998). In light of these variables, the ability to find the “optimal” sample size may be difficult given the number of factors that influence the relationship between sample size and home range estimation. I therefore recommend a sample size that falls within the means of the research goal(s), taking into account each of the variables described above, including the likelihood and practicality of gathering such data and the associated costs (i.e., time and money). Finally, it may be more strategic to gather a plethora of data at the onset and then sub-sample or condense the data later whenever needs change.

The sampling regime, defined as the time interval between subsequent data points, the length of the study, and which locations should be recorded, is arguably influenced by the same factors that affect sample size as discussed above. For instance, a short time between subsequent data points, such as 15 to 30 minutes, may be ideal for focal subjects that cover long distances in short periods of time. Conversely, a longer time between subsequent data points of one hour or greater may be more appropriate for focal subjects that remain immobile in a general area over an extended period of time or do not travel far enough between sampling intervals (e.g., Hansteen et al. 1997). If the time interval between subsequent data points is too short, this may lead to data being autocorrelated, which has been argued to undermine the integrity of the results (Swihart and Slade 1985a,b; but see Fieberg 2007; de Solla et al. 1999). Depending on the focal subject, some locations may be more critical than others, e.g., sleeping sites, water holes, territorial zones, etc., and thus their acquisition should take precedence over other location data, particularly in the event that time or cost may prevent ample data acquisition. Where sufficient time and funding are available, researchers beginning detailed studies of animal ranging ecology should plan to undertake several years of continuous observation as this will result in (a) more complete estimations of overall home range size and (b) the identification of any inter-annual differences in movement and home range use patterns.

In sum, the recommendations described above provide the researcher with the greatest degree of comparability of results across studies and confidence in research method supported by extensive research (e.g., Borger et al. 2006; Pimley et al. 2009;

Pebsworth et al. 2012; Powell 2000).

Comparison of Gelada Monkey Ranging Behavior Across Sites

One purpose of this study was to compare the DPL, home range size, core area, and furthest distance traveled from a sleeping cliff site (edge) for geladas at Guassa,

Ethiopia, to those at Sankaber, Gich, and Bole (no ranging data exist for geladas inhabiting the study area at Arsi, Ethiopia: Mori and Belay 1990; Mori et al. 1999). Both

Sankaber and Gich are located within 15 km of each other in the Simien Mountains

National Park in central Ethiopia (Dunbar and Dunbar 1975; Iwamoto and Dunbar 1983;

Kawai 1979). They are approximately ~305 km and ~320 km northwest of Guassa, respectively. The study site of Bole, on the other hand, lies ~ 100-200 (south)west of

Guassa, near Addis Ababa, the capital of Ethiopia (Dunbar and Dunbar 1974; Iwamoto and Dunbar 1983).

One obstacle to comparing home range estimates across sites or species (here and below) concerns the types of method(s) used to estimate the home range. For example, I estimated home range size using both the MCP and fixed kernel methods, whereas

Hunter (2001) utilized the grid-cell method with 200 x 200 m quadrats (Dunbar and

Dunbar 1975 and Kawai 1979 did not specify which methods they used). Because each method employs different assumptions or criteria to construct a home range, the use of different techniques makes it problematic to make direct and robust comparisons about space use patterns. In addition to using different methods to calculate home range size, the time scale in which the ranging data are collected is another obstacle to consider.

Home ranges estimated from data gathered over only a few weeks or several months, for example, are not the same as home ranges estimated using data obtained from an entire year or more, because each estimate reflects the habitat and space use patterns of the

68 animal during different times of the year and under (presumably) different environmental conditions.

How do the Annual Home Range Estimates of Geladas at Guassa Compare to Those for gGeladas at Other Sites?

Mean home range size of the geladas at Guassa during the five-year period was

7.06 km2 (fk SCV), 9.12 km2 (fk LSCV), and 9.28 km2 (MCP). The five-year mean home range estimates reported for the fk LSCV and MCP, but not the fk SCV, methods are similar to the home range size reported for geladas at Sankaber, 9.28 km2 (Hunter 2011), studied over a one-year period. Meanwhile, mean home ranges of both the geladas at

Guassa (this study) and at Sankaber (Hunter 2011) are much larger than those reported for geladas at Bole and Gich (Dunbar 1977; Dunbar and Dunbar 1975; Iwamoto and

Dunbar 1983; Kawai and Iwamoto 1979).

A comparison of annual home ranges, on the other hand, indicates that though annual home range in the geladas at Guassa started out smaller (from 2007-2009), it eventually eclipsed (in 2010 and 2011) the estimate reported by Hunter (2001). This indicates that the range use patterns of geladas at Guassa exhibit extreme inter-annual variability, and demonstrates the value of long-term monitoring of nonhuman primate ranging patterns, particularly for gelada populations.

Table 4.1 Comparison of Gelada Monkey Ranging Patterns Across Sites

Farthest Home Home Mean DPL Core distance Elevation Duration range size range size Study site DPL (km), area traveled Source (m) of study (km2), (km2), (km) range (km2) from cliff mean range edge (km)

7.06a, Guassa, 3,200 – 0.7 – 60 mo. 3.5 9.12b, 4.50 – 12.30 1.95 2.5 this study Ethiopia 3,600 8.0 9.28c

Simien Mountains 1,700 – 1.0 – 12 mo. 2.1 9.28d n.r. n.r. 1.6 Hunter 2001 (Sankaber), 4,200 3.5 Ethiopa Simien Dunbar 1977; Dunbar Mountains 1,700 – 1.5 – and Dunbar 1975; 10 mo.1 2.5 2.99e 2.15 – 3.44 n.r. n.r. (Sankaber), 4,200 3.5 Iwamoto and Dunbar Ethiopia 1983 Simien Mountains 1,700 – 1.8 – Iwamoto and Dunbar 9 mo.2 1.9 1.78e 1.70 – 1.90 n.r. 1.0 (Gich), 4,200 2.0 1983, Iwamoto 1979 Ethiopia

Bole Dunbar 1977; Dunbar Valley, 1,700 6 mo. 0.6 n.r. 0.84e 0.78 – 0.90 n.r. n.r. and Dunbar 1974, Ethiopia 1975

1During the 10 month study period, ranging data were collected for a period of only two weeks during the wet and dry seasons (four weeks total of observation). 2During the nine month study period, ranging data were collected for a period of only three months. aMean annual home range size estimated using the 95% fk SCV method over the five-year period. bMean annual home range size estimated using the 95% fk LSCV method over the five-year period. cMean annual home range size estimated using the 95% MCP method over the five-year period. dHome range size estimated using the grid-cell method with 200 x 200 m quadrats. eDid not report home range estimation technique. 69

How do Geladas Utilize Their Home Range at Guassa and How Does It Compare to That of Geladas at Other Sites?

Geladas, like many other species of non-human primates, (e.g., spider monkeys:

Asensio et al. 2011; chimpanzees: Basabose 2005; sifakas: Gerber et al. 2011; mountain gorillas: Watts 1998), do not utilize their home range in a uniform manner, and instead exhibit preferential use of some areas relative to others (Dunbar 1977; Dunbar and

Dunbar 1975; Hunter 2001; Kawai and Iwamoto 1979; Ohsawa 1979; this study). The

Guassa area consists of a blend of numerous habitat types interspersed within its boundaries (Ashenafi 2001; Fashing et al. 2014). It is conceivable that the distribution of the main food source of geladas, green grass blades (Dunbar and Dunbar 1975; Hunter

2001; Iwamoto 1979; Fashing et al. 2014), within these patchily distributed habitats is likely to result in differential space use patterns (core areas) over time, because animals are expected to situate themselves in areas where they can maximize energy acquisition while simultaneously minimizing energy expenditure (Stephen and Krebs 1986). Indeed, as Dunbar and others (Dunbar 1977; Dunbar and Dunbar 1975; Hunter 2001; Kawai

1979) have indicated in their shorter duration studies of gelada monkey ranging ecology, the space use patterns of geladas appear to be related to the spatial and temporal availability and distribution of resources and weather conditions, e.g., thick fog or rainfall. The fact that the geladas at Guassa are exhibiting preferential space use, as indicated by the core areas, lends credence to the hypothesis that the animals may be selecting and concentrating most of their activities in areas of relatively higher resource availability. Future research should seek to obtain detailed data on resource availability and distribution over time to further investigate this hypothesis.

How do the DPL of Geladas at Guassa Compare to Those of Geladas at Other Ses?

Geladas at Guassa travel, on average, substantially further per day than those at

Sankaber, Gich, and Bole (Dunbar 1977; Dunbar and Dunbar 1974, 1975; Hunter 2001;

Iwamoto and Dunbar 1983; Kawai and Iwamoto1979). Furthermore, the longest DPL observed at Guassa was 8.0 km, more than twice the longest distance (3.5 km) recorded for geladas at Sankaber (Hunter 2001) and four times the distance (2.0 km) for geladas at

Gich (Kawai and Iwamoto 1979). No minimum or maximum DPL estimates were reported for geladas at Bole.

Comparison of Ranging Behavior Across Taxa

A secondary objective of this study was to compare the DPL, annual home range, and core area of geladas to the ranging behavior reported for (various) species of nonhuman primates. I separated my analysis into four categories: Papio spp.

(phylogenetic relationship) in Table 4.2; terrestrial nonhuman primates (mode of locomotion); arboreal frugivores; and arboreal folivores (mode of locomotion and dietary profile), all in Table 4.3. Dividing the numerous species of nonhuman primates in this manner helped facilitate direct comparisons among the different ecological groups to which nonhuman primates make up (e.g., Clutton-Brock and Harvey 1977). Ideally, I would have liked to base the comparisons on all species of nonhuman primates for which ranging data are available, because this would provide us with a (near) complete and thorough analysis of nonhuman primate ranging behavior (please refer to Campbell et al.

2011 and the various chapters in this text for a complete list of ranging studies on nonhuman primates). However, I decided to only include data from nonhuman primate populations studied over a period of one year or greater, because the data are more likely

72 to depict the animals’ ranging patterns over the course of (at least) an entire annual cycle

(which also corresponds with the data presented in our study). Lastly, as I have discussed above, making comparisons across species is problematic due to the differential use of methods to estimate home range in each study. Therefore, I focus my attention only on the value(s) reported and do not make any assumptions beyond that.

Comparison of Gelada Ranging Behavior to Papio Species

Baboons (Papio spp.), travel, on average, further per day and occupy home ranges larger in size than geladas (see Table 4.2). If I compare the ranging behavior of gelada monkeys to individual species of Papio, however, different relationships emerge.

Geladas, particularly the Guassa geladas (mean DPL = 3.5 km), exhibit a mean

DPL comparable to, though slightly shorter than chacma baboons (P. cyncephalus ursinus) studied at the Drakensberg Mountains, Natal Province, South Africa (4.1 km:

Henzi et al. 1992; Whiten et al. 1987) and at the Suikerbosrand Nature Reserve,

Transvaal, South Africa (4.1 km: Anderson 1981, 1982). No study populations of geladas, however, travel as far per day as those chacma baboons at Tshipise, Transvaal,

South Africa (8.5 km: Stoltz and Saayman 1970). Despite their fairly similar DPLs, home range size in chacma baboons (17.2 – 24.6 km2) are several times larger than those reported for geladas (0.84 – 12.3 km2).

The mean DPL reported for yellow (P. cynacephalus) (5.6 km: Barton et al.

1992), olive (P. anubis) (5.0 km: Bronikowski and Altmann 1996), and hamadyras (P. hamadryas) (7.5 – 13.2 km) baboons are longer than those observed for all studied groups of geladas. Similarly, home range sizes for olive (4.1 – 43.8 km2) and hamadyras

(28 – 30 km2) baboons are (considerably) larger than those reported for geladas in general.

What can explain the observed differences and similarities in the ranging behavior of geladas and Papio spp.? Research has indicated that variations in group (herd) size, temperature, food availability and distribution, access to water, weather patterns, and proximity or access to sleeping cliff sites have been suggested to influence both the movement and space use patterns among Papio (Barton et al. 1992; Bronikowski and

Altmann 1996; Henzi et al. 1992; Kunz and Lisenmair 2008; Stoltz and Saayman 1970;

Schreier 2010; Smuts 1985; Swedell 2006, 2011; Whiten et al. 1987) and geladas

(Dunbar and Dunbar 1975; Hunter 2001; Kawai 1979). Here, I elaborate on two of these factors and their effects on ranging behavior in baboons and geladas: access to sleeping sites and waterholes.

Whiten et al. (1987) have postulated that access to multiple sleeping cliff sites and the differential use of sleeping cliff sites on a nightly basis may explain the shorter day ranges observed in their population of chacma baboons at the Drakensberg Mountains,

Natal Province, South Africa, than compared to other species of baboons (e.g., hamadryas and olive baboons: Kunz and Lisenmair 2008; Sigg and Stolba 1981; Smuts

1985; Swedell 2006). Having access to multiple (and suitable) sleeping sites reduces the amount of travel an animal has to invest in when searching for a place to sleep (Whiten et al. 1987). Like chacma baboons, geladas have access to multiple sleeping sites and also tend to use different sleeping sites on a nightly basis, though repeated use of a sleeping cliff site on consecutive days has been observed many times in this band of geladas as well (Moua unpubl. data). Conversely, hamadryas baboons regularly use the same

74 sleeping cliff site for numerous days on end (Sigg and Stolba 1981; Swedell 2006), and coupled with the lack of potential sleeping cliff sites (at Filoha: Swedell 2006; and

Comoé National Park, Ivory Coast: Kunz and Linsenmair 2008), this may explain the longer day ranges in these animals relative to chacma baboons and geladas.

In addition to the impact access to sleeping sites can have on the movement and space use patterns, access to water has also been considered a factor in determining how far baboons travel on a daily basis, which areas of their habitat are used, and the size of the home range (Barton et al. 1992; Hamilton et al. 1976; Kunz and Linsenair 2008;

Smuts 1985; Stoltz and Saayman 1970; Swedell 2011). For example, chacma baboons inhabiting the study site at Tshipise, Transvaal, South Africa, traveled long distances to waterholes widely dispersed throughout their home range (Stoltz and Saayman 1970).

Furthermore, limited access to water can result in larger home ranges (olive baboons:

Kunz and Linsenmair 2008), because the animals would have to incorporate a home range of relatively larger area to compensate for the lack of available waterholes. In geladas, such as those at Gich (Kawai and Iwamoto 1979), the high availability of water sources within the home range facilitates minimal movement to and from waterholes; however, this implies that where access to water is (becomes) limited, it is conceivable geladas, like baboons, are likely to increase movement or expand their home range in search for additional sources of water. Future research should aim to obtain more information about the geographic distribution of waterholes within the home range of the geladas at Guassa, and record the behavior of the Guassa geladas in relation to drinking water to better understand the relationship between access to water and movement and space use patterns.

Table 4.2 DPL, Home Range, and Core Area of Papio Species

Home Home DPL Core Duration of # of DPL (m), range range Study site (m), area Source Study groups range (km2), (km2), mean (km2) mean range Hamadryas Fihola, baboons (Papio 14 mo. 1 7.5 3.2 – 11.2 301 n.r. n.r. Swedell 2002 Ethiopia hamadryas) Erer-Gota, Sigg and Stolba 18 mo. 1 9.5 n.r. 282 n.r. n.r. Ethiopia 1981 Erer-Gota, 12 mo. 1 13.2 4.1 – 19.2 n.r. n.r. n.r. Kummer 1968 Ethiopia

Chacma baboons Tshipise, Stoltz and Saayman (P. cynocephalus Transvaal, 16 mo.a 2 8.5 2.4 – 14.5 17.21 12.9 – 23.3 n.r. 1970 ursinus) South Africa

Drakensberg Mountains, Henzi et al. 1992; Natal 18 mo. 2 4.1 1.5 – 8.0 233 n.r. n.r. Whiten et al. 1987 Province, South Africa Suikerbosran d Nature Anderson 1981, Reserve, 18 mo. 4 3.6 2.3 – 4.6 24.62,c 20.5 – 28.3 n.r. 1982a Transvaal, South Africa Wildcliff Nature 19.1 – 23.14, Reserve, Pebsworth et al. 12 mo. 1b n.r. n.r. n.r. 15.4 – 16.75, n.r. Western 2012 10.2 – 14.36 Cape, South

Africa 75

Yellow baboons Amboseli, Bronikowski and 108 mo. 2 5.0 3.0 – 6.9 n.r. n.r. n.r. (P. cynacephalus) Kenya Altmann 1996

Olive (anubis) Laikipia baboons (P. Plateau, 12 mo. 1 5.6 n.r. 43.82 n.r. n.r. Barton et al. 1992 anubis) Kenya

Comoé, Cȏte Kunz and 20 mo. 1 n.r. n.r. 4.17 n.r. 0.208 d’Ivoire Linsenmair 2008

Comoé, Cȏte Kunz and 14 mo. 1 n.r. n.r. 16.67 n.r. 1.68 d’Ivoire Linsenmair 2008 n.r. = not reported aOf the 16 months in the field, the researchers obtained behavioral and ranging data for only eight of the 16 months. bHome ranges calculated based on radio-collared data of one juvenile male in the group. cCumulative home range estimated from combining individual home range estimate of four troops of baboons. dFifteen months of continuous observation combined with two shorter periods of three months and four months. 1Did not specify method used to estimate home range. 2Home range estimated using grid-cell method with 250 x 250 m quadrats. 3Home range estimated using grid-cell method with 200 x 200 m quadrats. 4Home range estimated using 95% MCP method (with different screening protocols: see Pebsworth et al. 2012). 5Home range estimated using 95% fixed kernel reference bandwidth (with different screening protocols: see Pebsworth et al. 2012). 6Home range estimated using 95% LoCoH method (with different screening protocols: see Pebsworth et al. 2012). 7Home range estimated using 100% MCP method. 8Core area estimated using 70% fixed kernel LSCV bandwidth.

Comparison of Gelada Ranging Behavior to Terrestrial Nonhuman Primate Species

A comparison of the DPL and home range of geladas to various terrestrial primates, such as patas monkeys (Erythrocebus patas pyrrhonotus), vervet monkeys

(Cercopithecus aethiops), gorillas (Gorilla beringei beringei), and chimpanzees (Pan troglodytes), show geladas are somewhere intermediate among those terrestrial primates

(Table 4.3). Geladas, particularly the Guassa geladas, and patas monkeys, for example, exhibit similar day journey lengths (600 – 3,495 m and 3,830 – 4,220 m, respectively), however, patas monkeys live in much larger home ranges (28.5 km2: Chism and Rowell

1988; Isbell 1998; Isbell et al. 1999) than do geladas (0.78 km2 – 9.28 km2). Vervet monkeys (Isbell et al. 1999), on the other hand, do not occupy home ranges (0.15 – 1.15 km2) as large as those reported for geladas. In comparison to specific chimpanzee populations, geladas, particularly those at Guassa and Sankaber, utilize home ranges similar in area to the chimpanzees studied at Kahuzi (mean: 7.55 km2; total area: 12.81 km2: Basabose 2005), half the area compared to the chimpanzees at Taї National Park (27 km2: Boesch and Boesch 1989), and significantly smaller than the chimpanzees at Mt.

Assirik, Senegal (278 – 333 km2: Baldwin et al. 1982). Lastly, in comparison to mountain gorillas, geladas (at Guassa and Sankaber) occupy home ranges similar in size to the group studied by Vedder (1984: 8.56 km2) and Watts (1998: 8.1 km2), but much smaller than the group studied recently by Robbins and McNeilage (2003: mean = 27.7 km2, range = 21.1 – 40.1 km2, using the MCP method).

Overall, our findings with regards to the relationship between DPL and home range and feeding ecology in nonhuman primates are mostly consistent with conclusions reached earlier by the influential work of Clutton-Brock and Harvey (1977). Despite

Table 4.3 DPL, Home range, and Core Area of Terrestrial and Arboreal Nonhuman Primates

Home Home Duration # of DPL (m), DPL (m), range range Core area Study site Source of study groups mean range (km2), (km2), (km2) mean range Terrestrial

primates Patas monkeys Segera Ranch, (Erythrocebus 17 mo. 1 n.r. n.r. 28.514 n.r. n.r. Isbell 1998 Laikipia, Kenya patas pyrrhonotus) Mutara Ranch, Chism and Rowell -- -- 3,830 n.r. 23.4? n.r. n.r. Kenya 1988 Chism and Rowell 4,220 n.r. 32? n.r. n.r. 1988 Amboseli National 26 mo. 6 n.r. n.r. 0.151 0.05 – 0.25 n.r. Isbell et al. 1990 Park, Kenya Homewood Tana River 1,184 (1978), Kinnaird mangabey Tana Forest, Kenya 32 mo.a 2 1,290 – 0.282 0.17 – 0.47 n.r. (1990), (Cercocebus 1,395 Wieczkowski galeritus galeritus) (2005) Kahuzi-Biega Nat’l Chimpanzee (Pan Park, Democratic 60 mo. 1 n.r. n.r. 7.65 7.1 – 8.3 0.7 Basabose 2005 troglodytes) Republic of Congo

Mt. Assirik, Baldwin et al. 48 mo. 1 n.r. n.r. 3069,g 278 – 333 n.r. Senegal 1982

6.88, 3.2 – 5.98, 1.1 Budongo Forest Newton-Fisher 15 mo. 1e n.r. n.r. 6.912, – 4.912, 5.0 – n.r. Reserve, Uganda 2003 14.513 13.213 78

Bwindi Mountain gorilla Impenetrable 16.3 – 286, Robbins and (Gorilla beringei 36 mo. 1 n.r. n.r. 206, 289 9.5b-c National Park, 21.1 – 40.19 McNeilage 2003 beringei) Uganda

Bwindi Impenetrable 84 mo. 3 – 5 n.r. n.r. 8.15 3.1 – 15.6 2.9 Watts 1998 National Park, Rwanda

Bwindi Impenetrable 18 mo. 1 n.r. n.r. 8.565 n.r. n.r. Vedder 1984 National Park, Rwanda

Arboreal

primates Bare-ear marmoset Alter de Chão, Albernaz and (Callithrix Central Amazonia, 12 mo. 4 n.r. n.r. 0.112 0.04 – 0.24 n.r. Magnusson 1999 argentata) Brazil White-headed langur LGS, Fusui 12 mo. 9 n.r. n.r. 0.299 0.16 – 0.48 n.r. Li and Rogers 2005 (Trachypithecus Reserve, China leucocephalus) Santa Rosa 1.14 Howler monkey National Park, 24 mo. 1 n.r. n.r. (mean 0.81 – 0.91 0.13 Chapman 1988 (Alouatta palliate) Costa Rica 0.86) Red howler Yotoco Reserve, monkey (Alouatta 12 mo. 1 431 n.r. 0.112 n.r. n.r. Palma et al. 2011 Colombia seniculus) Yotoco Reserve, 12 mo. 1 458 n.r. 0.172 n.r. n.r. Palma et al. 2011 Coloumbia Santa Rosa 1.14 Capuchin (Cebus National Park, 24 mo. 1 n.r. n.r. (mean 0.78 – 0.89 0.13 Chapman 1988 capucinus) Costa Rica 0.84) Woolly spider Fazenda Montes monkey Claros, Minas 14 mo. 1 1,283 -- 1.68? -- -- Strier 1987 (Brachyteles Gerais, Brazil arachnoides) Santa Rosa Spider monkey National Park, 24 mo. 1 n.r. n.r. 1.474 n.r. n.r. Chapman 1988 (Ateles geoffroyi) Costa Rica

Cocha Cashu Black spider Biological Station, 465 – 21 mo.b 1 1,977 1.93 1.5 – 2.3 n.r. Symington 1988 monkey (Ateles Manu National 4,070 paniscus chamek) Park, Peru

Angolan black- and-white colobus Nyungwe Forest, 20.71, 21 mo. 1 1,700 n.r. n.r. 3.2 Fashing et al. 2007 (Colobus Rwanda 24.410 angolensis) Jozani Forest Red colobus Siex and Struhsaker Reserve, Unguja 13 mo. 3 n.r. n.r. 0.23 n.r. n.r. (Procolobus kirkii) 1999 island, Zanzibar Shambas situated along the border of Siex and Struhsaker 13 mo. 4 n.r. n.r. 0.19 n.r. n.r. Jozani Forest 1999 Reserve, Zanzibar Black-and-white Samage Forest, snub-nosed Gehuaqing, 578 – monkey 14.5 mo. 1 1,620f 32.315 n.r. 1.81 Grueter et al. 2008 Yunnan Province, 4,216 (Rhinopithecus China bieti) Yuhuangmiao, Sichuan snub- Zhouzhi National nosed monkey (R. Nature Reserve, 17 mo. 1 n.r. n.r. 22.56 n.r. n.r. Li et al. 2000 roxellana Milne- Shaanxi Province, Edwards) China Zhouzhi National Nature Reserve, 750 – 12 mo. 1 2,100 18.35 n.r. 7.4 Tan et al. 2007 Shaanxi Province, 5,000 China

Guizhou snub- Fanjingshan 523 – nosed monkey (R. National Nature 12 mo. >1 935 n.r. n.r. n.r. Niu et al. 2010 1,672 brelichi) Reserve, China

Milne-Edward’s Ranomafana sifaka (Propithecus National Park, 12 mo. 4c 747 n.r. 0.4211 0.32 – 0.46 0.14 Gerber et al. 2012 edwardsi) Madagascar

3d 818 n.r. 0.2711 0.23 – 0.33 0.61 Gerber et al. 2012 81

n.r. = not reported aCombined study durations of Homewood (1978) (7 mo.), Kinnaird (1990) (15 mo.), and Wieczkowski (2005) (12 mo.) because same groups were studied across all three study periods. bStudied over a four-year period from June 1982 to June 1986. cA total of 9 adults were sampled from 4 different groups inhabiting the ‘logged site’. dA total of 6 adults were sampled from 3 different groups inhabiting the ‘unlogged site’. eHome ranges estimated for male chimpanzees. Values in ‘HOME RANGE SIZE (km2), MEAN’ represent a composite home range estimated using ranging data for all males, whereas values in ‘HOME RANGE SIZE (km2), RANGE’ represent the variation in the home range size of individual male chimpanzees. fFull data for ranging were only available for the month of September, and thus DPL for entire study period was extrapolated based on data in this month only, however, Grueter et al. (2008) indicate September ranging was representative of the group’s movement patterns throughout the entire year. gUsed the minimum convex polygon method to estimate initial home range (did not specify percentage used), but also used nest sites and density of chimpanzees to extrapolate home range size (Baldwin et al. 1982). 1Home range estimated using the grid-cell method with 33 x 33 m quadrats. 2Home range estimated using the grid-cell method with 50 x 50 m quadrats. 3Home range estimated using the grid-cell method with 100 x 100 m quadrats. 4Home range estimated using the grid-cell method with 120 x 120 m quadrats. 5Home range estimated using the grid-cell method with 250 x 250 m quadrats. 6Home range estimated using the grid-cell method with 500 x 500 m quadrats. 7Home range estimated using 95% minimum convex polygon method. 8Home range estimated using 100% minimum convex polygon method. 9Home range estimated using minimum convex polygon method (did not specify percentage used). Li and Rogers (2005) used the MCP method, but selectively removed unused or inhabitable areas, e.g., flat land between hills. 10Home range estimated using the 95% fixed kernel with least squares cross-validation bandwidth. 11Home range estimated using the 95% fixed kernel with root-n bandwidth. 12Home range estimated using the 99% fixed kernel with least squares cross-validation bandwidth. 13Home range estimated using the 99% adaptive kernel with least squares cross-validation bandwidth. 14Did not specify home range method used. ?Method of home range technique unknown. 82

83 these general conclusions about the relationship between ranging behavior and feeding ecology in nonhuman primates, it is intriguing to see, for example, why geladas and patas monkeys exhibit such disparate ranging behaviors given that they are both terrestrial and live in relatively large group sizes. One possible explanation concerns the (primary) food item(s) that makes up each animal’s respective dietary profile. It is considered, for example, that the consumption of fruits or invertebrates would result in longer DPLs and larger home ranges due to the wide spatial and temporal variability of these resources

(Clutton-Brock and Harvey 1977). Though patas monkeys primarily consume insects and gum (Chism and Rowell 1988; Isbell 1998), a study by Isbell (1998) found that access to waterholes, and not invertebrates or gums, was the main contributing factor for the large home range size reported in her study group of patas monkeys.

Comparison of Gelada Ranging Behavior to Arboreal Nonhuman Primate Species

In comparison to both arboreal frugivores and folivores, with the exception of the

Angolan black-and-white colobus (Colobus angolensis ruwenzorii) group studied by

Fashing et al. (2007) at Nyungwe Forest, Rwanda, geladas appear to exhibit day journey lengths and home ranges that are generally longer and larger, respectively, than their arboreal fruit and non-fruit eating counterparts (see Table 4.3). For example, both fruit and non-fruit eating arboreal primates, such as muriquis (Brachyteles arachnoides hypoxanthus) (Dias and Strier 2003), red colobus (Procolobus badius), black-and-white colobus (Colobus guereza) (Chapman and Pavelka 2005), spider monkeys (Ateles geofroyi) (Chapman 1990), and Javan gibbons (Hyobates moloch) (Kim et al. 2011), to name a few, all occupy home ranges similar to (e.g., Gich and Bole) or smaller (e.g.,

Sankaber and Guassa) in size than geladas. Some species of arboreal nonhuman primates,

84 however, do appear to exhibit mean DPLs similar to or longer than the populations of geladas at Gich, Bole, and Sankaber, but not those at Guassa: black spider monkey

(Ateles geoffroyi, 1,977 m: Chapman 1988; Angolan black-and-white colobus (Colobus angolensis, 1,700 m: Fashing et al. 2007); black-and-white snub-nosed monkey

(Rhinopithecus bieti, 1,620 m: Grueter et al. 2008; and the Sichuan snub-nosed monkeys

(R. roxellana Milne-Edwards, 2,100 m: Tan et al. 2007).

Furthermore, as mentioned above, in their investigation of the ranging behavior of

Angolan black-and-white colobus (Colobus angolensis ruwenzorii) at Nyungwe, Fashing et al. (2007) found these monkeys occupied a home range size of 20.7 to 24.4 km2

(estimated using the 95% fixed kernel with LSCV and MCP methods, respectively), considerably larger than the home range reported for any other species of colobus

(Fashing et al. 2007) and numerous species of nonhuman primates reported here, including geladas (see Tables 4.1-4.3). Despite being primarily leaf consumers and living in a habitat shown to have high resource availability, the authors suggest that the monkeys’ uncharacteristically large group size (> 300 individuals) may be creating a situation in which even the abundant supply of resources is insufficient to support a group size of that magnitude (Fashing et al. 2007). Therefore, to compensate for such a large group size, the study group invested more time in moving and feeding at the expense of resting time, while simultaneously increasing their DPL and home range (Fashing et al.

2007). Dunbar and Dunbar (1975) and Iwamoto and Dunbar (1983) have indicated gelada monkeys travel further when there are more animals present in the herd. A similar argument has been made for various species of nonhuman primates (Barton et al. 1992;

Chapman and Pavelka 2005; Schreier 2010). Future research should investigate the relationship between herd size and DPL and home range in this band of gelada monkeys.

Table 4.4. DPL, Home Range, and Core Area of Terrestrial Ungulate Species

Core Duration DPL (m), DPL (m), Home range Home range Study site # ind. area Source of study mean range (km2), mean (km2), range (km2) Norris Junction & Elk (Cervus Old Faithful, 185 – Craighead et al. 24 mo. 3a 2,278 241 15.54 – 30.56 n.r. canadensis) Yellowstone Nat’l 10,000 1973 Park Comoé Nat’l Park, Fischer and Kob antelope 2,400(m), Ivory Coast, West 15 mo. 23 n.r. 0.92(m), 2.46(f)2 n.r. n.r. Linsenmair (Kobus kob kob) 2,300 (f) Africa (2001) Gettysburg National Military White-tailed deer Park & Eisenhower 600 – (Odocoileus 24 mo. n.r. >2,500 n.r. n.r. n.r. Frost et al. 1997 National Historic 1,200 virginianus) Site, Pennsylvania, USA Pohénégamook, 9.10(f,su), Lesage et al. 48 mo. 1b n.r. n.r. 9.10 – 12.47 n.r. Quebec, Canada 12.47(m,su)3 2000 Lake Témiscouata, 11.44(m,w) Lesage et al. 48 mo. 1c n.r. n.r. 11.44 – 34.12 n.r. Quebec, Canada 28.12(f,w)3 2000 Red deer (Cervus Bavarian Alps, 0.65(w), 1.67(a,sp), 22 mo. 10d n.r. n.r. 0.65 – 1.67 n.r. Georgii 1980 elaphus L.) Germany 1.21(su)4

Roe deer .40(m,w), (Capreolus Lier, Norway 48 mo. 41 n.r. n.r. 1.02(w,su), 0.32 – 1.02 n.r. Mysterud 1999 capreolus) .32(f,w), .47(f,su)6 Pampas deer Samborombon (Ozotocerus Bay, Buenos Aires, 72 mo. 12 n.r. n.r. 8.983 2.47 – 23.96 1.98 Vila et al. 2008 bezoarticus celer) Argentina

Mule deer San Bernadino 3 3 5 2.30 – 7.67 Nicholson et al. (Odocoileus Mountains, CA, 22 mo. 29 n.r. n.r. 4.44 , 7.89 5 n.r. 3.92 – 13.57 1997 86 hemionus) USA

50.80(m,w), 19.80(m,s), Desert bighorn 43.40(m,su), 3.20 – Little Harquahala sheep (Ovis 46.90(m,au), 129.10(m), Krausman et al. mountains, AR, 80 mo. 34 n.r. n.r. n.r. canadensis 38.50(f,w), 5.30 – 1989 USA mexicana) 40.10(f,s), 102.30(f) 29.60(f,su), 44.10(f,au)1 35.40(m,w), 29.30(m,s), 40.00(m,su), Harquahala 0.80 – 30.70(m,au), Krausman et al. mountains, AR, 80 mo. 34 n.r. n.r. 182.7(m), n.r. 8.80(f,w), 1989 USA 0.50 – 56.7(f) 12.10(f,s), 9.70(f,su), 10.00(f,au)1 Agassiz National 17.9(f,su,a), 2.6 – Moose (Alces 463 – Phillips et al. Wildlife Refuge, 48 mo. 36 n.r. 14.5(m,su,a), 39.1(su,a), n.r. alces) 1,111 1978 Minnesota, USA 3.6(f,w), 3.1(m,w)1 0.8 – 7.5(w) Sonoran pronghorn (Antilocapra Hervert et al. Arizona, USA 96 mo. 35 n.r. n.r. 5115 43 - 2873 n.r. americana 2010 sonoriensis) Pronghorn antelope 4,282 – (Antilocapra Trans-Pecos, Texas 15 mo. n.r. 5,632 n.r. n.r. n.r. Buechner 1950 6,437 americana) Nairobi National Eland (Taurotragus Park & Athi Kapiti 30 mo. 23 n.r. n.r. 411 21 – 60 n.r. Hillman 1988 oryx Pallas 1766) plains, Kenya Tsavo East African buffalo National Park, 13 mo. 1e n.r. n.r. 857 n.r. n.r. Leuthold 1972 (Syncerus caffer) Kenya Gerenuk Tsavo East (Litocranius National Park, 29 mo. 13 n.r. n.r. n.r. 1.5 – 3.57 n.r. Leuthold 1979 walleri) Kenya 87

n.r. = not reported w = winter home range, su = summer home range, sp = spring home range, a = autumn home range m = adult male, f = adult female aData collected on and presented for cow (female) elk only. bData collected on white-tailed deer from high-density site (Lesage et al. 2000). cData collected on white-tailed deer from low-density site (Lesage et al. 2000). dData collected on and presented for (female) red deer hind only. eData collected on herd of African buffalo. 1Home range estimated with MCP method (did not specify percentage used). 2Home range estimated with 90% MCP method. 3Home range estimated with 95% MCP method. 4Home range estimated with grid-cell method with 200 x 200 m quadrats. 5Home range estimated with 95% adaptive kernel. 6Home range estimated with 90% kernel method (did not specify whether fixed or adaptive or bandwidth used). 7Did not specify method used to estimate home range.

Comparison of Gelada Ranging Behavior to Terrestrial Ungulate Species

Geladas are unique among non-human primates in that they are primarily grass consumers (Crook 1966; Dunbar and Dunbar 1975; Iwamoto 1979; Nguyen and Fashing

2012). Therefore, the graminivorous diet and terrestrial nature of geladas make them an intriguing species to compare to grazing terrestrial ungulate species in terms of ranging ecology (e.g., Iwamoto 1979). Earlier work by Iwamoto (1979) described briefly the similarities and differences in the feeding behavior between geladas and ungulates, however, his discussion focused primarily on how a diet consisting mainly of grasses could (continue to) sustain a large population of geladas. To my knowledge, this is the first study to compare the ranging behavior of gelada monkeys to that of terrestrial ungulate species (Table 4.4).

Based on the data in this analysis, geladas appear to occupy home ranges similar to (Bole and Gich) or larger (Sankaber and Guassa) than those reported for various species of deer, e.g., red deer (Cervus elaphus), roe deer (Capreolus capreolus), white- tailed deer (Odocoileus virginianus), and rusa deer (Rusa timorensis), and other species of ungulates, such as the gerenuk (Litocranius walleri) and Kob antelope (Kobus kob kob). Conversely, ungulates such as the Sonoran pronghorn (Antilocapra americana sonoriensis), elk (Cervus elaphus), wild eland (Taurotragus oryx Pallas 1766), moose

(Alces alces), gerenuk (Litocranius walleri), and the African buffalo (Syncerus caffer), occupy home ranges several times larger than those reported for geladas across all study sites.

Few data are available on the daily movement distances of ungulates. Where such data do exist for ungulates, e.g., white-tailed deer, kob antelope, and pronghorn antelope,

90 geladas, depending on the site, exhibit day journey lengths that are similar to (Bole and

Gich), longer (Sankaber and Guassa), or shorter (all sites) than those reported for these species of ungulates (see Table 4.4).

Reports of home range for ungulates are generally derived from observations made by radio-tracking of individuals (e.g., Lesage et al. 2000; Mysterud 1999; Tufto et al. 1996). Due to the disparate behavioral ecology of male (e.g., territorial) and female

(e.g., movement in relation to parturition) ungulates and the impact of seasonal patterns on space use patterns (e.g., migration between winter and summer ranges) (Luccarini et al. 2006), home ranges are further—and appropriately—categorized by sex (e.g., Hillman

1988; Lesage et al. 2000) or season, such as summer vs. winter ranges, pre-parturition vs. post-parturition, etc., (Anderson et al. 2005; Girard et al. 2002; Lesage et al. 2000), and in some cases, based on the movements of entire groups (herd) (Hervert et al. 2010;

Leuthold 1972; Luccarini et al. 2006). Furthermore, where the annual or total home range estimate was not reported (e.g., Anderson et al. 2005; Lesage et al. 2000), space use patterns were described using seasonal home ranges. In some cases, home range sizes for juveniles or younglings were also reported (e.g., Hillman 1988; Lesage et al. 2000)— however, I did not include these estimates in the analysis. The use of seasonal home ranges or home ranges based on behavioral or reproductive events (when an annual home range was not reported) often made it difficult to compare to the home ranges estimated for the geladas. Despite the difficulties with the home range estimates I encountered above, the use of the 95% MCP and fixed kernel (LSCV bandwidth in many cases) methods in studies of ungulate ranging ecology facilitated direct comparisons of home range estimates in those studies to this study (but not with estimates reached by Dunbar

91 and others due to methodological differences). In some cases, ungulate researchers (e.g.,

Mysterud 1999; Tufto et al. 1996) estimated kernel home ranges using the 90% density contour; however, I did not consider the 5% difference to have a major impact on comparisons (Börger et al. 2006; Seaman and Powell 1996).

Implications of Inhabiting in a Topographically Variable Environment on Calculations of Distance Traveled

One goal of this study was to assess how living in a topographically variable environment would affect estimates of distance traveled. I have shown mathematically that movement across an altitudinal gradient can result in longer DPLs by up to 8% (a mean increase of up to 3% was found for monthly mean DPL). This finding corroborates an earlier conclusion reached by Sprague (2000) who found similar, though higher increases (mean: 9.5%; range: 2.5 – 21.5%) in the DPL of the Yaku monkey (Macaca fuscata yakui) at Kirishima-Yaku National Park, Japan, after he accounted for changes in altitude. Prior reports by Whiten et al. (1987) and Hunter (2001), and more recently Niu et al. (2010), also investigated the effects of topography on distance traveled in chacma baboons (P. ursinus), geladas (at Sankaber), and Guizhou snub-nosed monkeys

(Rhinopithecus brelichi), respectively; however, these authors did not specify the amount of percentage difference they found between the original and corrected DPLs, which makes it difficult to make any meaningful comparisons. Furthermore, another factor limiting comparability of results is the manner in which each study assessed the impact of change in altitude on DPL. This study and Whiten et al.’s (1987), for instance, utilized

Pythagora’s theorem of the three sides of a right triangle to account for the net change in elevation between consecutive readings, though in Whiten et al.’s (1987) study, vertical travel was calculated based on the number of 50 m contour lines the animals passed

92 through (whereas we found the exact change in altitude by subtracting the altitude reading between consecutive path lengths: see Methods). Alternatively, Sprague (2000) and Niu et al. (2010) used the mean slope angle of their respective study sites to examine the influence of topography on movement. Lastly, Hunter (2001) combined the distance the geladas traveled horizontally and the distance they traveled vertically (e.g., Whiten et al. 1987) to determine the observed difference in altitude for individual day journey lengths. The degree of difference between the original and corrected DPL readings reported in each study, therefore, may simply be a by-product of the differences inherent in each method. Despite the different methods employed in each study, the results are clear: topography can have a small, but noticeable effect on estimates of distance traveled and should therefore be accounted for whenever possible.

Ecological Implications of Movement Across Uneven Topography

Nonhuman primates, such as geladas (Ashenafi 2001; Dunbar 1998), some populations of chacma baboons (Henzi et al. 1992; Whiten et al. 1987), Japanese macaques (Wada and Ichiki 1989), black-crested gibbons (Fan and Jiang 2010), and snub-nosed monkeys (Kirkpatrick et al. 1999; Li et al. 2008; Long et al. 1994) live in high altitude, montane environments characterized by cold temperatures, strongly seasonal weather patterns, and wide temporal variability of resources (Ashenafi 2001;

Hanya et al. 2003; Li and Walker 1986; Nguyen and Fashing 2012; Whiten et al. 1987).

In spite of the documented effects of temperature and climate patterns on energy expenditure and thermoregulation (Bronikowski and Altmann 1996; Fan and Jiang 2010;

Henzi et al. 1992; Hill 2006; Iwamoto and Dunbar 1983; Stelzner 1988; Wada et al.

2007; Yang 2003), these animals may traverse across altitudinal gradients of more than

30 m on a daily basis to satisfy their basic biological needs, e.g., acquire food, return to sleeping sites, etc. (Dunbar and Dunbar 1975; Fan and Jiang 2010; Hanya et al. 2003;

Kawai and Iwamoto 1979; Niu et al. 2010; Whiten et al. 1987; Yang 2003; Moua unpubl. data). Furthermore, not only does upslope movement result in longer (than expected)

DPLs (Sprague 2000; this study), but an animal moving across uneven topography expends relatively more energy (related to locomotion) than it would if moving across horizontal or downslope landscape (researched in small ruminants and grazing mammals:

Dailey and Hobbs 1989; Lachica and Aguilera 2000; Lachica et al. 1997; reviewed by

Lachica and Aguilera 2005). It is therefore conceivable that the cumulative effects of upslope travel, longer travel distances, and thermoregulatory responses in relation to existing temperature and weather conditions can be expected to impose (considerable) energetic constraints on an animal’s overall energy expenditure and behavioral ecology.

In their investigation of the altitudinal ranging patterns of Guizhou snub-nosed monkeys at Fanjinshan National Nature Reserve, China, Niu et al. (2010) uncovered that the monkeys would travel from lower to higher elevations to feed and then return to lower elevations to sleep at night. This type of ‘oscillatory’ (Niu et al. 2010: 241) behavior in movement is similar to the ranging behavior reported for geladas (Dunbar and Dunbar 1975; Kawai and Iwamoto 1979; Moua unpubl. data) and possibly chacma baboons (Henzi et al. 1992; Stoltz and Saayman 1970), where the animals sleep alongside cliff edges down on the slopes below but conduct the rest of their daily activities on higher elevation above the sleeping sites. Fittingly, Niu et al. (2010) have questioned why nonhuman primates instead simply adopt a routine where the animals ‘sleep where they eat’ (Niu et al. 2010: 241) to minimize traveling up and down slopes and therefore

94 conserve energy. Niu et al. (2010) have suggested that such altitudinal movement patterns in the Guizhou snub-nosed monkey mark a trade-off between acquiring resources (in the higher elevations where food is more abundant) and avoiding predators (in the lower elevations where the animals can hide in the dense foliage and where snowfall and temperatures are thwarted by the canopy cover). Perhaps this trade-off of increased energy costs related to movement across an altitudinal gradient and reduced predation risk may also explain the similar up and down movement patterns and use of sleeping cliffs in geladas (and maybe chacma baboons).

Though the trade-off between resource acquisition and anti-predatory defense may potentially explain why nonhuman primates such as geladas and Guizhou snub- nosed monkeys exhibit such energy costly movement patterns over the course of a day, the trade-off explanation does not explain the energetic costs the monkeys experience at the time of their of upslope travels, or the impact of current environmental conditions on their energy budget (i.e., thermoregulatory responses). For example, black-crested gibbons studied at Mt. Wuliang, central Yunnan, China, tended to range at lower elevations in the morning when it was cold but moved to higher elevations in the afternoon when temperatures got warmer (Fan and Jiang 2010). Alternatively, during snow storms, for example, Japanese macaques (Macaca fuscata) in the Shiga Heights,

Japan, huddled closely to one another in order to conserve energy and create heat (Wada et al. 2007). Lastly, laboratory experiments using rats showed that the animals increased energy intake and metabolic activity (e.g., become more active or mobile) during low temperatures in order to provide the body with (additional) energy to create heat to reduce hypothermia (Brobeck 1948). These findings indicate that animals may adopt or

95 develop a variety of responses to conserve energy or maintain optimal body temperature under energetically costly situations. It is in this regard that I discuss the ecological implications of the ‘shuffle’ technique in gelada monkeys (Wrangham 1980).

In his study of gelada monkey ranging behavior, Wrangham (1980) noted that geladas often shuffle while feeding and that shuffling accounted for between 14-30% of actual distance traveled per day (cited in Iwamoto and Dunbar 1983). During a ‘shuffle’, geladas slide their hind legs back and forth with the legs maintaining continuous contact with the ground (Hunter 2001). It therefore appears that geladas should ‘shuffle’ in situations where they only wish to move a short distance, e.g., within a food patch, whereas quadruped locomotion should be used in circumstances where long(er) distance movement is required or where speed is a factor (e.g., running away from a predator). If this assumption is correct, it implies that a ‘shuffle’ would be energetically more efficient than quadruped locomotion, otherwise it would be impractical from an evolutionary and energetic perspective to ‘shuffle’ when moving on all four limbs covers more ground for the same amount of energy used. Therefore, I hypothesize that the ‘shuffle’ technique in geladas could have evolved as a more (energetically) efficient form of short movement bout in an environment characterized by cold temperatures and low nutrient quality resources. However, further more detailed data on the movement behavior of geladas, such as when geladas shuffle and when they use quadruped location and the ecological context in which each behavior was observed, are required in order to fully test this hypothesis.

Critiques of Altitudinal Change Formula

There are a couple shortcomings associated with the altitudinal change formula applied here that warrant mention. First, the exact geographic location of the herd was recorded only once every half-hour. Since it is conceivable the geladas could have traveled multiple times upslope or downslope between subsequent half-hour readings, I cannot know for certain the exact change in elevation that occurred between subsequent readings, and it is therefore possible that the estimates of half-hour path lengths (and ultimately DPL) may have been underestimated. Moreover, the monkeys could have moved exactly the same distance upslope and downslope between consecutive half-hour readings, thereby resulting in a net change in altitude of 0 m when in fact movement across an altitudinal cline had occurred. In fact, the mean net change in altitude between the very first and last readings (the difference in elevation between the morning and evening sleeping site locations, respectively) on the same full-day was only +5 m, but I was able to demonstrate the high degree of movement across uneven topography in our study group of geladas (Moua unpub. data). The finding that the mean net change in elevation on a daily basis was +5 m suggests it may be important to record the animals’ whereabouts in a time frame capable of capturing their movement across uneven landscape, because of the possibility that such (small) changes in elevation may be lost in consecutive readings with longer time intervals. Despite the simplicity of the formula, the corrected path length values obtained as a result of altitudinal effects nonetheless provide an estimate that reflects the influence of change in altitude on distance traveled. The findings presented here suggest that the conventional method of calculating DPL may be unsuitable for study sites where the topography is heterogeneous or when a percentage of

97 the animals’ movement involves upslope or downslope travel, because of the likelihood that path lengths and DPL will be underestimated (Sprague 2000). Thus, I advise that future studies of primates and other terrestrial animals that range over rugged terrain consider the influence of elevation and topographic variation when calculating movement parameters.

Conclusions

The continual expansion of the annual home range reported in this study has considerable implications for the conservation and management of the geladas and their natural habitat at Guassa (and for geladas elsewhere), and further demonstrates the importance of long-term monitoring in wild animal populations. The increasing trend in annual home range suggests that one or two years of observation would not have provided sufficient data to make an accurate conclusion about the geographic extent of the geladas’ range at Guassa. Moreover, this pattern also indicates that the home range estimates reported for the gelada populations at Sankaber, Gich, and Bole (Dunbar and

Dunbar 1974, 1975; Hunter 2001; Kawai and Iwamoto 1979), which reflect data gathered from a few weeks to a single annual cycle, may not be entirely representative of the animals’ space use patterns over a longer period of time (i.e., it is possible that home range size may be larger than what was reported at these sites had the animals been observed for a longer period of time).

Furthermore, most, if not all, of the expansions in the annual range over the five- year period have occurred primarily in the southern and western regions of the Guassa area. Though the geladas are understandably unable to expand any further east because of the cliff edges that are situated along this side of their range, movement into the northern

98 region of the range, though plausible, has been rare (except for two unusually far excursions in May and August of 2010). Why the geladas seldom range in this region remains unknown, though a likely explanation may have to do with the local inhabitants who reside in this area. During both trips to this region in 2010, for example, researchers were unable to remain with the geladas because it was part of a different administrative unit in which the study team did not have permission to work. Therefore, it is presently unknown the type of relationship that exists between the locals in this region and the geladas. Similarly, recent studies by Li et al. (2008, 2010) on the ranging ecology of the

Yunnan snub-nosed monkeys (R. bieti) and the black-and-white snub nosed monkeys (R. bieti) at Samage Forest, Baimaxue-shan National Nature Reserve, Yunnan, China, found evidence to suggest that human encroachment and disturbance into the surrounding habitat may be limiting the monkeys’ ability to expand their home range and completely avoiding ranging in areas disturbed by humans.

For geladas and animals in general, the restriction of range expansion or space use related to human activity may present a serious (and compounding) issue for the integrity of the species and their natural habitat going into the future. Computer simulations developed by Dunbar (1998), for example, demonstrate the potential implications of rising world temperatures on the distribution and viability of grasslands throughout the geladas’ home ranges in the northern Ethiopian Highlands. Human encroachment and agricultural cultivation have already penetrated other gelada monkey study sites (e.g.,

Sankaber, Gich, and Bole: Dunbar and Dunbar 1974, 1975; Hunter 2001; Kawai 1979), while Guassa remains relatively intact ecologically in large part because of the (Qero) conservation system put in place there hundreds of years ago (Ashenafi 2001; but see

Ashenafi and Leader-Williams 2005 for the implications of the recent change in management regime for the Guassa area). Should rising world temperatures result in the demise of the gelada monkeys’ grassland ecosystem as Dunbar (1998) projected, and coupled with continuing human encroachment, the monkeys can be expected to be pushed to higher elevations with far reduced available habitat (Dunbar 1998). I advise researchers to continue monitoring the ranging behavior and habitat quality of gelada monkeys at these study sites and at other locations where such observations are possible.

Doing so should provide valuable data about the monkeys’ space use patterns (over an extended period) in an environment that is likely to change due to global climate change and human activity.

In sum, I advise that ongoing and future studies of animal ranging ecology attempt to invest several years of continuous observation for the highest possibility of acquiring sufficient data about the space use patterns of animals in relation to ecological variability across space and time. The information obtained from these studies can prove crucial in helping us make informed conservation and management-related decisions.

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APPENDIX A

ADDING ERROR TO USER-IDENTIFIED DUPLICATE PAIRS

Using a kernel bandwidth, such as the least-squares cross validation (LSCV), to analyze a utilization distribution with duplicate data or data that clump can cause it to fail

(Beyer pers. comm.; Gitzen et al. 2006; Tufto et al. 1996). Some authors suggest (e.g.,

Beyer pers. comm.; Rogers et al. 2007) that adding error to duplicate or clumped data can resolve bandwidth issues associated with duplicate or clumped data, however, little is known about the ramifications of adding error to duplicate data (Rogers et al. 2007). In accordance with the recommendations of several researchers, I added error to duplicate coordinate pairs I identified using the procedure below to address the issues associated with duplicate data on kernel home range analysis.

I used Microsoft 2010 to organize, sort, identify, and add error all duplicate data. I outline this procedure below.

Step 1: Identifying Duplicates in the Lat Coordinates First, I sorted the Lat coordinates into ascending order. (Ensure the corresponding Lon coordinate stays with its corresponding Lat coordinate.) Next, I implemented Equation 1 to identify all Lat coordinates that are a duplicate. Equation 1 compares a specified Lat coordinate to the

Lat coordinate in the cell directly above and below it and expresses a value to indicate if a duplicate does or does not exist.

= 퐼퐹(푂푅(퐸[푥] = 퐸[푥 − 1], 퐸[푥] = 퐸[푥 + 1]), 푎, 푏) Equation 1

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, where E[x] is the cell number of the specified Lat coordinate; E[x-1] represents the cell number of the Lat coordinate immediately before (above) the specified Lat coordinate;

E[x+1] represents the cell number of the Lat coordinate immediately after (below) the specified Lat coordinate; a and b denote a value (≥0) (chosen by the user) to indicate whether the “if_or” statement is true or false, respectively (Figure 1).

Figure 1. Hypothetical example showing how Equation 1 is being applied.

Upon the completion of Equation 1, I immediately copied the output values in the

“Lat duplicate?” column (Column C, Figure 1) and (re)pasted the “Values” over the originals, essentially eliminating the formula and leaving only the value in the cell. (This is critical because it ensures that each Lat coordinate retains its proper identification of

“1” or “0” even after they get rearranged in Step 2.)

Step 2: Identifying Duplicates in the Lon Coordinates. Similarly, I used Equation

1 to identify duplicates in the Lon coordinates (Figure 2). After the duplicates for the Lon coordinates were identified, I copied the output values in the “Lon duplicate?” column

(Column E, Figure 2) and (re)pasted the “Values” over the originals.

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Figure 2. Hypothetical data showing how Equation 1 is being applied.

Step 3: Validating the Lat and Lon Duplicates, and Identifying Duplicates of Lat and Lon Coordinate Pairs The purpose of Steps 1 and 2 is to establish which Lat and Lon coordinate possess a duplicate, while the purpose of Step 3 is to utilize this newfound information to identify duplicate Lat and Lon coordinate pairs. To begin this process, I reorganized the data in ascending order by the Lat coordinate, making to ensure its corresponding Lon coordinate moved with it. Then I implemented Equation 2 (below) to determine whether or not both of the Lat and Lon coordinates of the same coordinate pair shared a duplicate with any other Lat and Lon coordinate pair (Figure 3). I prompted the equation to output a “1” for true and a “0” for false (or any value specified by the user).

= 퐼퐹(퐴푁퐷(퐸[푥] = 푎, 퐸[푦] = 푎), 푐, 푑) Equation 2

, where E[x] denotes the cell of the output for the Lat coordinate; E[y] denotes the cell of the output for the Lon coordinate; a denotes the value indicating if that particular Lat or

Lon coordinate was a duplicate (i.e., a or b in Equation 1); and c indicates that the

“if_and” statement is true (i.e., both E[x] and E[y] = a), whereas d indicates that the statement is false (i.e., one or neither E[x] and E[y] = a).

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It is important to organize the data in ascending order relative to the Lat coordinate, not by the Waypoint #, as this will facilitate identifying duplicates.

Furthermore, at this juncture the data of greatest import are those in the “Lat+Lon duplicate?” column with the “1,” which indicate that the Lat and Lon coordinates (may) share a duplicate with another coordinate pair. The word “may” is used here because, according to Figure 3, even though both the Lat and Lon coordinates in Waypoint #7 share duplicates, Waypoint #7 is the only one of its kind; in other words, even though the

Lat coordinate of Waypoint #7 shares a duplicate with the Lat coordinate of Waypoint

#5, the corresponding Lon coordinate of each is different, which means the coordinate pairs of Waypoints #7 and #5 do not share any duplicates with any other coordinate pair.

Waypoints #2 and #10 paint a similar situation.

It is critical to be fully aware of instances such as this one because failure to pay careful attention may result in accidentally, and needlessly, adding error when there is no need. Indeed, this means that the user will need to use the information in the “Lat+Lon duplicate?” column to manually identify duplicate coordinate pairs and add error to any and all such duplicate pairs (Step 4).

Figure 3. Hypothetical data showing how Equation 2 is being applied.

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Step 4: Adding Error to User-Identified Duplicate Coordinate Pairs I added error in increments of two meters) to both the Lat and Lon coordinates of each duplicate coordinate pair, beginning with the second duplicate coordinate pair. (I always left one duplicate coordinate pair in its original state.) To elaborate, if a Lat and Lon coordinate pair had a total of three duplicates, I (i) left one of the three duplicates in its original state;

(ii) then added two m of error to the Lat and Lon coordinate of the second of three duplicates; and (iii) lastly added four m of error to the Lat and Lon coordinate of the remaining duplicate pair. (Similarly, I added six m of error would be added to the fourth duplicate pair, eight m of error would added to the fifth duplicate pair, etc.) I continued this process of adding error to each of the remaining duplicate coordinate pairs until error had been added to all user-identified duplicate coordinate pairs. The most random error added this way was 20 m (most likely to a sleeping cliff coordinate value as the geladas regularly re-used sleeping cliff sites throughout the five-year study). The new coordinate pairs will then replace the original coordinate pairs.

Step 5: Re-analysis of data for (inadvertent) duplicate data It is quite conceivable that coordinate data may be inadvertently duplicated during the error adding phase of

Step 4. This is plausible because the user, by adding error in the form of a distance, changes the makeup of each coordinate pair, creating a new Lat and Lon that may in turn share the same value as another Lat and Lon coordinate pair. To ensure this phenomenon of creating inadvertent duplicate data does not happen, I repeated Steps 1 through 3 to identify any duplicate data, and where there were duplicate pairs, I added error to them. I continued this process (of rearranging and adding random error) until there were no longer any duplicate pairs in the dataset.

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