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The Effects of Logging on Primate-Habitat Interactions: A Case Study of Redtail Monkeys (Cercopithecus ascanius) in Kiba...


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THE EFFECTS OF LOGGING ON PR IMATE-HABITAT INTERACTIONS: A CASE STUDY OF REDTAIL MONKEYS ( Cercopithecus ascanius ) IN KIBALE NATIONAL PARK, UGANDA By CLAUDIA MARGRET STICKLER A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Claudia M. Stickler

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To the memory of my grandmother, who had talent and intelligence in abundance, but lacked the freedom and opportunity to follow all of her dreams, and to my parents, siblings and other family members, who have c onsistently served as bot h inspiration and support.

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ACKNOWLEDGMENTS I extend my deep thanks to my advisor, Colin Chapman, for seeing me through this sometimes difficult process, devoting many hours to helping me in innumerable ways in the field and on-campus. I thank him for providing the generosity and support I needed to accomplish this project. The other members of my committee were no less important. Michael Binford took me under his wing and spent countless hours guiding me towards appropriate methods, critical resources, and important perspectives, expressed confidence in me, and provided encouragement at crucial moments. Sue Boinski first took a chance and hired me to work as part of her field crew in Suriname and encouraged and facilitated my application to the University of Florida. Since then, she has been a constant source of professional and personal support. Graeme Cumming introduced me to the importance of a spatial approach and helped me to proceed through every step of my thesis, always reminding me of the essential elements of my research, demanding great conscientiousness, and encouraging a sense of pride and confidence in my work. Other faculty members at the University of Florida offered critical support throughout this stage of my education. Jane Southworth initially helped me to understand the applications of a spatial perspective on social and ecological questions. Since then, she has not only helped to guide and support my research, but has also been an unwavering source of encouragement and support. I want to extend my thanks to Marianne Schmink for helping me to maintain my roots in the practical and social aspects iv

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of conservation and development. Steven Humphrey helped to create a flexible and rich environment for taking on the difficult task of gaining an interdisciplinary education. Thanks are due also to Karen Kainer, Jon Dain, Robert Buschbacher and Dan Zarin for advice and encouragement on how to bridge the gap between basic and applied science and between the natural and social sciences. In the field, I was assisted by a team of incredibly skilled and knowledgeable people. First and foremost, I thank Clovice Adyeeri Kaganzi for his tireless dedication to following the redtails in all kinds of weather and vegetation and to mapping every last tree in the forest. I attribute the quality of this work in large part to the careful and thoughtful documentation of his observations and to his vast store of knowledge of Kibale. Richard Kaserengenyu was a cheerful and conscientious observer, who took every change of plans in stride and took great pride in his work. James Apuuli Magaro and Chris Kaija were invaluable in accurately inventorying each tree in the selected study sites, and entertained and educated me in innumerable ways. Lawrence Tusiime provided his impressive skills as a botanist, and proved a steadfast partner in sampling habitat characteristics for many months. Tinkasiimire Astone enthusiastically took on each new job of seeking out and planting seeds, and monitoring seedlings. He taught me a great deal about the history of Kibale and its surroundings. I extend my deep thanks to Patrick Omeija and Jackson Efitre for the all the support they provided. Last but not least, Alice Akiki provided companionship and dedicated care. My friends and colleagues at the University of Florida have been invaluable. Beyond providing friendship, they are also great sources of knowledge and experience. Tracy Van Holt, Rodrigo Vergara, Connie Clark and John Poulsen took enormous v

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amounts of time to help me with my work and through the most difficult times. Meredith Evans, Anna Prizzia, Lin Cassidy, Wendy-Lin Bartels, Franklin Paniagua, Alfredo Rios, Tom Gillespie and Cedric Worman formed an extraordinary and patient support team, without whom I would not have arrived at this point with any humor or sanity. Finally, I thank my family for setting high standards for what individuals can achieve, no matter what their vocation. They provide me with inspiration and a sense of responsibility to the broader world. My parents afforded me great opportunities and freedom to follow my dreams, even if those took me far away. My twin, Alexander, and my brother Peter and my sister Carin have cheered me on and always created a place in their homes for me. My research and education were supported through a National Science Foundation Graduate Research Fellowship. The Office of the President, Uganda, the Uganda National Council for Science and Technology, and the Uganda Wildlife Authority granted me permission to work in Uganda. Gilbert Isabirye-Basuta and John Kasenene graciously allowed me to conduct research at the Makerere University Biological Field Station. vi

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TABLE OF CONTENTS Page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................ix LIST OF FIGURES.............................................................................................................x CHAPTER 1 GENERAL INTRODUCTION....................................................................................1 2 COPING WITH LOGGING: BEHAVIORAL MECHANISMS OF REDTAIL MONKEYS (Cercopithecus ascanius) IN KIBALE NATIONAL PARK, WESTERN UGANDA.................................................................................................7 Introduction...................................................................................................................7 Methods........................................................................................................................9 Study Area.............................................................................................................9 Behavioral Observations.....................................................................................10 Resource and Habitat Assessment.......................................................................12 Analyses..............................................................................................................13 Results.........................................................................................................................16 Activity Budget...................................................................................................16 Home Range Size and Use..................................................................................17 Daily Ranging......................................................................................................18 Overall Diet.........................................................................................................18 Dietary Selection and Diversity...........................................................................19 Habitat Use..........................................................................................................20 Mixed Species Associations................................................................................21 Discussion...................................................................................................................22 Resource Availability..........................................................................................23 Conclusion..................................................................................................................25 3 THE EFFECTS OF PATCH STRUCTURE ON REDTAIL MONKEY (Cercopithecus ascanius) HABITAT USE IN UNLOGGED AND HEAVILY LOGGED AREAS OF KIBALE NATIONAL PARK, UGANDA...........................46 Introduction.................................................................................................................46 Methods......................................................................................................................49 vii

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Study Area...........................................................................................................49 Behavioral and Habitat Attributes.......................................................................50 Analyses..............................................................................................................51 Results.........................................................................................................................55 Habitat Use..........................................................................................................55 Spatial Structure of Habitat Attributes................................................................56 Path Analysis.......................................................................................................57 Discussion...................................................................................................................60 Factors Explaining Habitat Use Patterns.............................................................61 Conclusion..................................................................................................................63 4 SUMMARY AND CONCLUSIONS.........................................................................79 LIST OF REFERENCES...................................................................................................85 BIOGRAPHICAL SKETCH.............................................................................................94 viii

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LIST OF TABLES Table page 2-1 Test of independence for group activity samples pooled by 60-min or 15-min sample for redtail groups..........................................................................................28 2-2 Monthly and mean home range size or redtail groups in logged and unlogged forest in Kibale National Park, Uganda between August and December 2002..................29 2-3 Time spent eating different types of foods by redtail groups...................................30 2-4 Dietary diversity and overlap across months for redtail groups in logged and unlogged forest of Kibale National Park, Uganda...................................................32 2-5 Characteristics of the tree community in home range areas of focal redtail groups.33 3-1 Habitat attributes examined for two redtail monkey home ranges in unlogged and heavily logged forest................................................................................................68 3-2 The six components produced in Principal Components Analysis describing the major variation in habitat structure in areas of unlogged and heavily logged forest.69 3-3 Standardized direct, indirect and total effect coefficients for path analysis models (Model 1) examining habitat variables on probability of redtail occurrence...........70 3-4 Effect coefficients for path analysis models examining the effect of unripe fruit trees on feeding site selection by redtails.................................................................71 ix

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LIST OF FIGURES Figure page Figure. 2-1. The Kanyawara study site is located in Kibale National Park, western Uganda. Maps are adapted from MUBFS 2003......................................................34 Figure 2-2. Percentage of time engaged in different activities by redtails in unlogged and heavily logged forest.........................................................................................35 Figure 2-3. Monthly total grid cells entered and cumulative grid cells entered by redtails in unlogged and heavily logged areas..........................................................36 Figure 2-4. Pattern of home range use by redtail groups in heavily logged and unlogged forest.........................................................................................................37 Figure 2-5. Daily distance traveled by redtail monkeys in logged and unlogged areas. 38 Figure 2-6. Average percent of monthly feeding observations on different items by redtail monkeys in unlogged and heavily logged forest. ........................................39 Figure 2-7. Strauss selectivity index (measures selectivity vs. dominance) for top 80% of food species selected by redtails..................................................................40 Figure 2-8. Height in canopy of redtails in unlogged and heavily logged forest...............41 Figure 2-9. Distribution of number of canopy layers observed in home ranges of redtails............................................................................................42 Figure 2-10. Understory vegetation associated with redtail location in unlogged and heavily logged forest................................................................................................43 Figure 2-11. Monthly percent of time spent in mixed species associations for redtails....44 Figure 2-12. The proportion of observations in which redtails in unlogged and heavily logged areas.................................................................................................45 Figure 3-1. The study site is located in the Kanyawara field site in the northern part of Kibale National Park in western Uganda.............................................................72 Figure 3-2. Patterns of habitat utilization by redtail monkeys in unlogged and heavily logged forest area.....................................................................................................73 x

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Figure 3-3. Surface maps for six of the habitat variables measured in an area of unlogged forest.........................................................................................................74 Figure 3-4. Surface maps for six of the habitat variables measured in an area of heavily logged forest................................................................................................75 Figure 3-5. Path diagram (Model 1) describing habitat utilization by redtails in unlogged and heavily logged areas..........................................................................................76 Figure 3-6. Path diagram (Model 2) describing habitat utilization by redtails in an unlogged area...........................................................................................................77 Figure 3-7. Probability maps of trees potentially producing unripe and ripe fruits in unlogged and heavily logged forest of Kibale National Park, Uganda. ..78 xi

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science THE EFFECTS OF LOGGING ON PRIMATE-HABITAT INTERACTIONS: A CASE STUDY OF REDTAIL MONKEYS (Cercopithecus ascanius) IN KIBALE NATIONAL PARK, UGANDA By Claudia Margret Stickler May 2004 Chair: Colin A. Chapman Major Department: School of Natural Resources and Environment The scale and intensity of timber exploitation in the tropics have increased in recent decades, leaving degraded forests and deforested areas in their wake. The impact of these activities on forest-dependent vertebrates is variedranging from loss of habitat and resources to population isolation or extinction. In recognition of the inadequacy of existing protected areas to support viable populations in the long-term, the focus of wildlife conservation in tropical forests is shifting to identifying and supporting more ecologically sound timber management strategies. This study focuses on the impact of logging on primate behavior and habitat use because primates play an important role in tropical forest communities, serving as seed dispersers and as indicators of overall mammal species richness. Primates may also face a greater threat from broad-scale habitat loss and hunting than most birds do, because they are constrained to travel arboreally or terrestrially, and may not be able to move readily between areas to take advantage of greater resource availability or other suitable habitat. xii

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To better understand how logging affects primates, I first focused on contrasting the behavior of two habituated groups of redtail monkeys in unlogged and heavily logged forest in Kibale National Park, Uganda. Monkeys in the heavily logged area were found to use larger home ranges, travel further and spend more time foraging and less time feeding than conspecifics in the unlogged areas. They appear to be constrained by resource and habitat limitations. Specifically, a reduced tree density in the heavily logged area leads to a lower abundance of potential food trees and reduced arboreal pathways. Microhabitat characteristics in the heavily logged area also appeared to constrain the animals movements and foraging ability. I also examined habitat selection by redtails in the heavily logged and unlogged areas to determine whether the behavioral differences observed in the first part were due to habitat limitations or to differences in habitat preferences. I evaluated a set of topographic, forest structural, and resource attributes for each home range to determine the spatial structure of each. Tree basal area showed the least amount of patch structure in both areas and operated on the small scale, in the range of 20 to 40 m. In both areas, the majority of remaining variables grouped together with slope. In the unlogged area, the scale of these variables was relatively large, whereas in the heavily logged area, the variables showed an intermediate scale of patch structure. These results suggest that there is a common organizing structure to both areas, but that the specific characteristics of each area are a result of scale. Habitat selection by the monkeys was similar in both areas; they were primarily influenced by basal area, but understory vegetation also played an important role. xiii

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CHAPTER 1 GENERAL INTRODUCTION The scale and intensity of timber exploitation in the tropics has increased in recent decades, leaving degraded forests and deforested areas in its wake (Fimbel et al. 2001, Weber et al. 2001). The impact of these activities on forest-dependent vertebrates are variedranging from loss of habitat and resources to population isolation or extinction (Fimbel et al. 2001). In recognition of the inadequacy of existing protected areas to support viable populations in the long-term, the focus of wildlife conservation in tropical forests is shifting to identifying and supporting more ecologically sound timber management strategies (Frumhoff 1995). This shift is accompanied by a heightened awareness of the role vertebrates play in community processes. For example, in tropical areas, vertebrates role in seed dispersal is notable, with up 45 to 90% of forest trees thought to be adapted for vertebrate dispersal (Jansen and Zuidema 2001). As a result, the challenge for managers and researchers is to help in designing timber management plans that do not compromise these interactions and processes. A prerequisite for developing such suggestions, however, is a deeper understanding of how animals respond to the habitat changes caused by logging. Unfortunately, these are still not well-known. This study focuses on the impact of logging on primate behavior and habitat use for several reasons. Primates play an important role in tropical forest communities. Primates are thought to be important as seed dispersers, consuming a wide range of seeds (Chapman 1995, Garber and Lambert 1998). As a result of the high percentage of biomass they represent in many tropical rain forests (Terborgh 1983), primates services 1

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2 as seed dispersers for a great variety of fruiting trees suggests that their net impact on forest regeneration may be high (Jansen and Zuidema 2001). Thus changes in primate behavior and ranging due to logging and forest fragmentation may alter or disrupt seed dispersal processes (Chapman and Onderdonk 1998, Clark et al. 2001). Primates also serve as good indicators of mammal species richness in most tropical areas (Emmons 2000), indicating that forest management plans based on a better understanding of primate response to logging could serve a wider animal community, as well. Finally, primates may face a greater threat from broad-scale habitat loss and hunting than most birds do. Because they are constrained to travel arboreally or terrestrially, and since many are territorial, they may not be able to move readily between areas to take advantage of greater resource availability or other suitable habitat (Cannon and Leighton 1994, Putz et al. 2001). Changes in the forest structure due to logging may create barriers for primates where none exist for some bird species or larger mammals. Among vertebrate taxa, primates are one of the best-studied. The majority of research examining the effects of logging on primates focus on relationships between population density and broad vegetation changes (Skorupa 1988, Johns and Skorupa 1987, Plumptre and Reynolds 1994, Chapman et al. 2000). However, these studies have yielded few generalizations that contribute to the formulation of specific strategies for sustainable managing forests (Plumptre and Grieser-Johns 2001). As a result, interest in understanding primate behavior and the habitat changes caused by logging at a finer scale is growing. Ample evidence exists to suggest that behavioral responses to resource availability and habitat quality are important in dictating population density of organisms (Krebs and Kacelnik 1991, Lima and Zollner 1996, Pulido and Diaz 1997). Among

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3 primates, numerous studies have shown that seasonal differences in food availability result in differences in diet composition (Van Schaik et al. 1993, Kaplin et al. 1998, Poulsen et al. 2001), activity budgets, and travel patterns (Van Schaik et al. 1993, Poulsen et al. 2001) for different species. Similarly, group sizes of foraging primates can also be influenced by the size and spatial distribution of food patches (Leighton and Leighton 1982). Animals may also alter travel patterns or fruit consumption rates as a function of resource patch size and availability (Leighton and Leighton 1982, Chapman 1988). Those studies comparing primate behavior between logged and unlogged forest show that primates do make some adjustments in their behavior or group size to compensate for habitat changes (Olupot 2000, Clarke et al. 2002, Fairgrieve and Muhuzoma 2003), but they are too few in number to allow generalization from the variety of responses they describe. I addressed these issues by quantifying the differences in behavior and habitat and resources available to redtail monkeys (Cercopithecus ascanius) in heavily logged and unlogged areas of Kibale National Park, Uganda. Broadly, I focus on the following questions: Does the behavior of primates differ in heavily logged and unlogged forest? Does this reflect limitations posed by the habitat or resource availability? Do logged and unlogged forests differ in their structure and composition? Are such differences relevant to primate habitat selection? Which habitat attributes are important to primates in logged and unlogged areas in selecting suitable habitats? To better understand how logging affects primates, I first focused on contrasting the behavior of two habituated groups of redtail monkeys in unlogged and heavily logged forest. This species is of interest because redtails, and similar species across Africa, tend to adapt well to conditions of disturbed forest (Cords 1986). At Kibale, however, regular

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4 population censuses since logging show that redtail densities continue to decline 28 years after logging (Chapman et al. 2000). I collected behavioral data on each group during all-day follows, approximately 3 times a week over a 5 month time period, using 15-min scan sampling. At each scan interval, I recorded the groups activities, diet, and location. These data were analyzed to determine if and how the behavioral patterns of redtails in heavily logged and unlogged areas differs. To evaluate the observed patterns in relation to resource availability in each area, I inventoried the tree community above 10 cm diameter at breast height (DBH). Each tree was identified and mapped, and subsequently categorized as a food or nonfood tree based on the feeding observations collected from each group. In addition, I assessed the density of the understory vegetation and vertical stratification in each home range, to determine the availability of different microhabitat types. I compared these measurements with redtails canopy height preferences and associations with understory density categories. This study allowed an assessment whether redtails in the heavily logged areas were constrained by the abundance and distribution of resources and preferred microhabitats. To further understand how observed behavioral differences might be a response to differences in habitat structure, I examined patterns of habitat utilization by the monkeys relative to the habitat available through a spatial analysis. The major of objectives of this second study were to identify the variables defining areas of suitable habitat and to develop predictive models of habitat selection based on important ecological variables. I analyzed the spatial structure of a suite of variables describing the topography and forest structure and composition, to determine if the measured variables operate at the same scale, and at the same scale in each home range. The variables, including slope, aspect,

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5 canopy height, basal area, understory height, and the presence of several understory vegetation types were sampled from a set of 50 m 2 plots, spatially stratified in each groups home range. Food tree density was determined from the tree map completed in the first study. I identified which of these factors explained the most variation in each set of data through a principal components analysis (PCA). In combination with correlation analyses, the PCA helped to determine which variables to enter into a conceptual model of the interacting variables determining redtail habitat use, using path analysis. The same conceptual model was applied to the habitat attribute and monkey use data from each area, to determine if the same set of variables and interactions explains redtail habitat selection in both the logged and the unlogged area. Finally, I developed surface models predicting redtail habitat utilization in each area based on the variables determined to be important by the path analysis, using spatial interpolation. The main objective of these two studies was to link patterns of resource availability and suitable habitat to population density by examining redtail behavioral patterns. Since the focus of this objective was to examine the relationships between different variables on the basis of spatial patterns, the research relied on a spatially explicit approach. This is based on the key tenet of landscape ecologythat ecological pattern and process are linked (Turner 1989). A spatial approach has the potential to uncover important relationships that might otherwise have gone unnoticed or remained uninterpretable, by providing information about the spatial scales of interacting factors. An important part of this is the ability to evaluate variability within a dataset for the information it may reveal about the fundamental structure organizing the system (Forman and Godron 1986). The focus on the interaction of spatial patterns allows for more

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6 accurate description of the system, thus leading to more accurate models (Turner et al. 2001). Most importantly, from a management perspective, this suggests the potential to predict and thus avoid undesirable spatial configurations created by logging activities, based on a quantitative understanding of the spatial scales of basic attributes of the forest. Equally important is the fact that this type of research builds the foundation for developing predictive models over a much larger spatial scale by relating variable measurements at different scales.

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CHAPTER 2 COPING WITH LOGGING: BEHAVIORAL MECHANISMS OF REDTAIL MONKEYS (Cercopithecus ascanius) IN KIBALE NATIONAL PARK, WESTERN UGANDA Introduction Currently, tropical forests are being degraded and fragmented at increasing rates, primarily due to logging-associated activities (Chapman and Peres 2001). Wildlife may be directly affected by habitat degradation, or indirectly through increased human accessibility to forests, which can lead to increased hunting or habitat loss through land conversion (Frumhoff 1995). Although most wildlife populations dependent on closed-canopy mature forests decline as a result of logging activities, the long-term ecological responses of most vertebrates are still not well understood (Fimbel et al. 2001). The majority of studies examining the effects of timber extraction on primates compare population densities in logged and unlogged areas of forest (Skorupa 1988, White 1992, 1994, Plumptre and Reynolds 1994, Grieser-Johns and Grieser-Johns 1995, Chapman et al. 2000). Most research suggests that vegetation changes resulting from logging are responsible for differences in primate population densities, although individual species responses sometimes differ greatly (Plumptre and Reynolds 1994; Struhsaker 1997, Chapman et al. 2000). Studies are often not comparable due to differences among the methods employed or time allowed after the logging event (Chapman et al. in prep). Additionally, numerous confounding factors (e.g., hunting) can affect primate densities (Wilkie et al. 1992, 1998, Oates 1996, Struhsaker 1997, Chapman et al. in prep). Thus, although primates are one of the best-studied taxa of tropical forest 7

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8 vertebrates, few generalizations about their responses to the effects of logging have emerged. This lack of generalizability could be the result of a high degree of intraspecific variation in responses to logging among sites. In the Budongo Forest Reserve, Uganda, the increase in blue monkey (Cercopithecus mitis) populations in logged areas appears to be the result of increased fruit availability associated with a change in forest composition following logging (Fairgreave and Muhuzuma 2003). In contrast, in Kibale National Park, Uganda, less than 200 km from Budongo, blue monkey populations decrease after logging. Even among folivorous species, the variation in responses is high, with some responding positively and others negatively to the habitat changes caused by logging, due to group size requirements for resources (Chapman et al. in prep). Although research regarding primates behavioral responses to logging is growing (Clarke et al. 2000, Olupot 2000), more is needed to understand how changes induced by logging affect different species. Here, I quantify some aspects of the behavior of redtail monkeys (Cercopithecus ascanius schmidtii Matschie) in logged and unlogged forest in Kibale National Park, Uganda. This species is of particular interest because redtails and other members of their lineage (e.g., C. cephus) are generally the most adaptable of the cercopithecines, often thriving in disturbed secondary forest and on forest edges, as well as in undisturbed forest (Struhsaker and Leland 1979, Cords 1986, Butynski 1990, Thomas 1991, Naughton-Treves 1996). However, primate censuses conducted nearly 30 years after logging in Kibale indicate a continued decline in redtail population density in the heavily logged

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9 areas (Chapman et al. 2000). Redtail density is significantly lower in the logged areas (1.04 groups/km 2 ) than in the unlogged areas (11.48 groups/km 2 ; Chapman et al. 2000). The objective of this study was to determine whether redtails exhibit different behavioral patterns in logged and unlogged forest. To meet this objective, I contrast the ranging behavior and foraging ecology of two groups of redtail monkeys in logged and unlogged forest of Kibale. Furthermore, I assess the quality of habitat and abundance of resources in each area to develop possible explanations for observed differences in behavior. This type of information is important for constructing informed forest management plans. Methods Study Area Kibale National Park (766 km 2 ) is located in western Uganda (0 13-0 41N and 30 19-30 32E), near the base of the Ruwenzori Mountains (Fig. 2-1). It is classified as moist evergreen forest, transitional between lowland rain forest and montane forest (Struhsaker 1997, Chapman and Lambert 2000). Situated at 1500 m elevation, the Kanyawara study site in the northern part of the park, receives an average of 1741 mm of rainfall per year, distributed over distinct biannual wet and dry seasons (Chapman and Chapman, unpublished data). Kibales logging history, coupled with a lack of hunting, provides an ideal environment for examining primate response to logging. I contrasted the behavior of a group of redtails in a heavily logged area, to one in an unlogged area. The heavily logged area was harvested in 1969 (Struhsaker 1997), and approximately 7.4 stems/ha or 21m 3 /ha were removed (Skorupa 1988; Struhsaker 1997). However, as a result of high incidental damage associated with the felling, Skorupa (1988) estimates that approximately 50% of all trees in the area were destroyed. Since

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10 then, many areas have become dominated by dense, thorny herbaceous vegetation, frequently dominated by semi-woody Acanthaceae species (primarily, Acanthus arborescens), which surrounds emergent colonizer or remnant trees (Struhsaker 1997, Paul et al. in press). The area used by the focal monkey group also abuts smallholder agricultural fields and tea plantations. The unlogged area has never been commercially harvested. A few large stems (0.03-0.04 trees/ha) were removed before 1970; however, this low level of extraction appears to have had little effect on the structure of the forest (Skorupa and Kasenene 1984, Skorupa 1988, Struhsaker 1997, Chapman et al. 2000). There is an extensive, gridded trail system in both areas (Struhsaker 1997). Behavioral Observations Diet and behavioral data were collected from one group of redtails in each site between August and December 2002. Both groups have been previously studied and were accustomed to human presence; nevertheless, to ensure that both were adequately habituated, a field assistant and I followed each group for two months prior to collecting quantitative data. The group in the unlogged forest, group UL, consisted of 24 individuals, while the group in the heavily logged area, group HL, had 16 individuals. These numbers are representative of the mean group sizes of groups in unlogged and heavily logged forest at Kibale (unlogged: 27.70.9, n= 3, heavily logged: 15.00.5, n= 3; K. Rode, unpublished data). Behavioral scores were recorded during all-day follows using a 15-minute scan sampling regime. We attempted to carry out follows in blocks of 3 days, spaced at 1-week intervals, for each group. However, due to the often extremely dense and impassable vegetation in the heavily logged area, we often followed group HL for 4 days

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11 and group UL for 2 days to obtain comparable numbers of observations per week (UL: 2.4.76 days/week, 20.7.05 hours/week; HL: 2.4.70 days/week, 18.1.88 hours/week). Scans lasted 10 minutes or until 5 individuals had been sampled, whichever came first. Observers recorded the time and activity maintained by an individual for at least 5 sec. Individuals were never scored more than once during a scan, and we tried to avoid scoring the same individual in two consecutive scans. Twenty-one different behavioral categories were scored, but subsequently collapsed into six categories for analyses: feed, search for insects, rest, move, social, and other (which included drinking, urinating, and defecating). If an individual was feeding, observers recorded the food part (e.g., insect, fruit, leaf) and food species, if known (less than 0.01% of species could not be identified). Habitat use was quantified by estimating each individuals height in the canopy and ranking the density of the understory vegetation below the individual. Height in the canopy was scored on a scale of 1 to 5, with the scale representing the following height divisions 1 = 0-7 m (4.7.9); 2 = 7-14 m (12.9.6); 3 = 14-18 m (16.7.7); 4 = 18-23 m (20.4.6); 5 = 23 m or higher (24.8.2). Understory vegetation was also scored on a scale of 1 to 5, which ranged from 1 = very open (no or almost no vegetation, generally only tree seedlings or saplings) to 5 = very dense (generally herbaceous, often a combination of Graminaceae and Acanthaceae species, generally impassable for humans). The presence of other primate species, and other groups of redtails or potential predators within 50 m of the group was recorded at the end of each scan period. Group spread was estimated every 15 minutes as the length of the axes of an ellipse whose

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12 center was the perceived center of mass of the group. The surface area of the ellipse was calculated and averaged over all scans. I calculated the square root of this measure to obtain mean group spread. We recorded the location of the center of the group every 15 min by measuring the distance and angle to the nearest point with known coordinates. Reference points were located at 20 m intervals on the gridded trail system in each groups home range. Reference points were recorded using a hand-held global positioning system. Coordinates for group locations were calculated and plotted in the ArcView 3.2 and ArcGIS 8.3 geographic information systems (ESRI 2002). This method allows more exact measurement of day range and home range estimation because the measurements are based on geographically accurate positions. Moreover, recording monkey locations in a GIS database enables a richer and more quantitative analysis of habitat and space use by facilitating the integration of animal movements with multiple habitat layers (Hooge et al. 2001). Resource and Habitat Assessment Resources and habitat quality were assessed for an area at least as large as that considered to constitute each redtail groups stable home range, based on observations of these groups for 12 months prior to the current study (K. Rode, unpublished data). In the areas used by the focal groups, all trees above 10 cm diameter at breast height (DBH) were identified to species, measured, and plotted in the GIS. Less than 1 % of all stems mapped could not be identified to species. We measured the distance and angle to each tree from the reference points located throughout each study area and calculated geographic coordinates for each tree (UL: 15,489 trees over 48.4 ha; HL: 8433 trees over 40.1 ha). The availability of plant food resources was assessed by categorizing each tree

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13 as food or nonfood based on the feeding observations. For example, in the unlogged area only those tree species that monkeys fed on between August and December 2002 in this area were scored as food species. Stem density and total basal area were calculated for all trees, all food trees, and specific species. Shannon diversity and evenness were calculated for the entire tree community and for the suite of potential food trees in each home range. To assess other habitat characteristics likely to be important to redtails, such as vertical complexity and density of understory vegetation, I established a set of 50 m 2 plots in each home range, spatially stratified by spacing plots every 20 m along the trail grid in each area. Trails were usually no more than 50 to 100 m apart, resulting in 1484 plots in the unlogged area and 1362 plots in the heavily logged area. The plots assessed 7.42 ha (37%) and 6.81 ha (32%) of the total home range of the focal group in the unlogged and heavily logged forest. Vertical stratification (i.e., number of canopy layers) was assessed by scoring presence or absence of vegetation in each of the height categories. Analyses Home range size was estimated in two ways. I used the Animal Movement extension for ArcView (Hooge and Eichenlaub 1997) to estimate total and monthly area used by the minimum convex polygon method. The MCP method estimates the area both used and probably traversed by each group, but likely contains areas not used by the animals (Burt 1943). Although this method generally overestimates home range size, it is the most commonly employed and therefore facilitates comparison with other studies (Hooge et al. 2001). I also estimated home range sizes using grid-cell analysis (White and Garrot 1990), which has been employed in other studies of primate ranging (Kaplin

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14 2001, Di Fiore 2003). The GCA method reflects only the area the group used during observation periods, and thus, provides a conservative estimate of home range size (White and Garrott 1990). However, it is highly dependent on the size of grid cell utilized (White and Garrott 1990, Hooge et al. 2001). In this case, a 30 x 30 m cell size was determined from the observed mean group spread of each group (unlogged: x = 29.6.16; heavily logged: x = 31.4.22), and from the groups 30 m movement distance between observation intervals during foraging. The best estimate of home range area for each group is likely to fall in between the values calculated by the MCP and GCA methods. For each method, mean monthly home range sizes were compared using Mann-Whitney tests. Home range use was measured by superimposing a grid of 30 x 30 m cells over the plotted group locations. To assess home range use, I counted the number of different grid cells the troop entered each day, the total number of cells entered per day, and the cumulative number of different cells entered per month per troop. In addition, I calculated the number of times a grid cell was entered during the study for each group. Daily travel distances were calculated, by summing the straight line distances between successive location points for each day. I used only full observation days in evaluating and comparing average daily travel distances. Full days were defined as beginning by at least 0800 h and lasting until at least 1800 h. This also allows comparison with previous studies of redtails at Kibale that used the same time periods (Lambert 1997, K. Rode unpublished data). Mean daily travel distances were compared between groups using t-tests and within groups using Kruskal-Wallis tests.

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15 For each site, the entire group served as the unit of analysis. Therefore, the proportion that each activity type and each food part contributed to each scan was calculated. For understory vegetation density and height in canopy, scores for individual observations within each scan period were averaged. To evaluate the appropriate sampling interval, I contrasted monthly proportion of scans that each activity comprised using 15and 60-min scan intervals for each group. Based on our observation, redtails rarely maintained the same activity for 60 minutes. There was no significant difference using either scan interval (15 or 60 min) for any of the activity categories for either group (Table 2-1). As a result, each 15-min scan interval was considered to be an independent observation, and scans were pooled to determine overall and monthly activity budgets, diet composition (by food part) and overall habitat use. Monthly activity budgets between groups were compared using Mann-Whitney tests. I determined the contribution of various plant species to the monthly diets of the two groups by calculating their simple proportional representation among the set of all feeding records for each group. Diet composition and selection were compared using a variety of indices. Dietary diversity and evenness was calculated for both monthly and overall diets using the Shannon-Wiener (H) index and Shannon evenness (E). Shannon t-tests were used to compare diversity of diets by calculating a variance of H and degrees of freedom based on the proportional abundance of individual food species in each groups overall diet (Magurran 1988). Overlap between the diets was calculated using the Holmes-Pitelka index, D i = S i where D i is the total percent overlap and S i is the percent overlap between shared food items (Holmes and Pitelka 1968; Struhsaker 1975). Finally, selectivity of food items relative to their availability was assessed using the

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16 Strauss selectivity index (L): L = r i p i where r i and p i equal the percent of the food item in the diet and the environment, respectively (Strauss 1979). This index is less sensitive than many other commonly used selectivity indices to the presence of rare food items. Preference values range from to +1 and are centered on zero, which indicates random feeding; negative values indicate avoidance and positive values indicate selection. In cases where nonparametric statistical tests were used, equivalent parametric tests were carried out and found to show the same general trends (i.e., significant or non-significant probabilities). Therefore, even in cases where non-parametric tests are performed, parametric means are presented in figures because they are more comparable to other studies. Results Activity Budget The UL group spent more time engaged in feeding than the HL group (Mann-Whitney: p = 0.008). However, HL spent significantly more time searching for insects, moving, and resting than group UL (search: p = 0.016; move: p = 0.008; rest: p = 0.016). Overall, the UL group spent nearly 50% of its time feeding and 20% of its time searching for insects (Fig. 2-2). The group in the heavily logged area devoted approximately 30% of its time to feeding and 30% to searching for insects. Time spent resting differed markedly between the two groups, with UL group spending about 15% of its time resting and HL group spending 25% of the time resting. HL group was engaged in moving 3 times more frequently than UL group. Time spent engaged in social activities, such as grooming, did not differ between the groups (p = 0.69), and comprised 7.3.80% and 7.9.14% of the UL and HL groups activity budget, respectively.

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17 Home Range Size and Use The mean monthly home range size of UL group based on the minimum convex polygon (MCP) method was twice as large than when grid-cell analysis (GCA) was used (sign test paired by month, Z = 1.788, p < 0.01, Table 2-2). The total area used by the UL group was 32.5 ha, based on MCP, and 20.2 ha, based on GCA. The group used 40-57% of the total home range based on GCA in any one month, and 48-76% of total MCP calculated home range. The mean monthly home range area of the HL group based on the MCP method was more than three times larger than the GCA estimate (sign test, Z = 1.788, p < 0.01, Table 2-2). The total area used by the HL group was 49.0 ha, based on MCP, and 21.3 ha, based on GCA. The group used 49-63% of total home range based on MCP in any one month and 33-46% of total GCA-calculated home range. Monthly home range size was larger for HL group than the UL group when estimated using MCP (Mann Whitney: U = 1.0, df = 8; p = 0.016). When monthly home range size was estimated using GCA, there was no difference between the groups (U = 5.0, df = 8, p = 0.117). To explain these inconsistencies between the two methods of estimating home range size, I examined the pattern of home range use of each group (Fig. 2-3). Both groups entered new quadrats throughout the study, suggesting that the home range of each group will be larger than what was recorded over 5 months (Fig. 2-3). Nevertheless, the pattern of home range use between groups appears consistent over that time. Generally, UL group entered more grid cells per month, but HL group entered slightly more new cells per month (UL: 46.6 cells per month .26; HL: 48.4 cells per month .01). This result is predicted by an examination of spatial patterns of home range use of each of the groups (Fig. 2-4). Overall, more contiguous grid cells in the UL home range were entered at least once during the period (i.e., there are very few unshaded

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18 quadrats in ULs range use map within the outer boundary of the recorded home range), indicating that the entire bounded area was used more intensively (Fig. 2-4b). In contrast, group HLs home range use map shows a larger number of quadrats not entered, with more widely dispersed areas of use (Fig. 2-4a). Daily Ranging The mean distance traveled each day was greater for the group in the logged area than the group in the unlogged forest (UL group: 1111 m 265.0; HL group: 1332 m 387.8; t = -2.677, df = 61, p = 0.01). Mean daily travel distance for group UL did not differ among months (Kruskal-Wallis: 2 = 2.728; df = 4; p = 0.604), but varied among months for the HL group ( 2 = 10.692; df = 4; p = 0.03; Fig. 2-5). Overall Diet The majority of feeding observations for both groups were on insects, but a significantly greater proportion of feeding observations were made on insects for the HL group than the UL group (UL: 62.3%; HL: 75.5%; t = 7.042; df = 2221; p <0.001). Although insects made up the bulk of feeding observations for both groups, they consumed a wide variety of other food types, as well (Fig. 2-6). A greater proportion of feeding observations of the UL group was comprised of mature leaves, young leaves, petioles, and ripe fruits (mature leaves: t = 3.389; df = 2221; p=0.001; young leaves: t=4.806; df = 2221; p<0.001; petioles: t= 4.245; df = 2221; p<0.001; ripe fruit: t = 8.596; df = 2221; p <0.001), but this group spent significantly less time feeding on flowers than group HL (t = -6.036; df = 2221; p< 0.001). Whereas more than a third of the UL groups diet was composed of fruits, leaves, seeds, and flowers, less than 25% of the HL groups overall diet was composed of these plant foods.

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19 Dietary Selection and Diversity Consistent with previous studies (Gautier-Hion 1988), both groups plant diets were dominated by relatively few plant species. Monkeys in the UL group selected plant foods from 70 different species, but 81.8% of all feeding observations came from only 12 species (Table 2-3). Of these, one was a terrestrial herb (Marantochloa leucantha), which was not common in the heavily logged area and one was a liana, while the remaining are trees. Three of the remaining 10 tree species were small trees (mean DBH < 15cm). In contrast, the plants comprising 80.9% of the HL groups plant diet were all medium to large trees (mean DBH > 15cm). The HL groups plant diet was selected from 31 species, but only 10 tree species comprised 80% of the observations. UL groups overall plant diet, over the study, was more diverse than that of HL group (Shannon t = 5.24; df = 808; p < 0.001; Table 2-4). Mean dietary overlap across months between the groups in the different areas was 21.7% (.45 s.d.), but ranged from 15.6 to 30.2%. Redtails in the unlogged area also consumed fruit from eight different Ficus species, whereas monkeys in the heavily logged area fed from only two fig species. The tree community above 10cm DBH in the unlogged area was less diverse (Shannon t = -37.30; df = 20641; p<0.001) than that of the heavily logged area, yet had a 50% higher stem density (Table 2-5). When I considered only food species, I found that nearly 90% of the individuals inventoried in the unlogged area were potential food trees, whereas fewer than 60% of individuals fell in this category in the heavily logged area (Table 2-5). Food tree diversity also differed significantly between the two areas (Shannon t= 4.67, df = 13838, p<0.001). Stem density of potential food trees was almost 2.5 times greater in the unlogged area in comparison to the heavily logged area.

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20 The Strauss selectivity index (L) showed that, of the most preferred food species, only UL monkeys choice of Ficus exasperata was not different from random, and only Diospyros abyssinica was selected less than it was available (Fig. 2-7a). The remaining species were selected in greater proportions than they were available in the forest. However, since the herb M. leucantha and the vine of the Piper sp. were not inventoried, it was difficult to judge how much the monkeys selection of these species differed from their respective distributions in the unlogged forest. Similarly, Rothmannia urcelliformis, Craterispermum laurinum, and Tarenna pavioides are small trees, so they were likely to be underrepresented in the inventory if the mean DBH for each species population is not at least 10cm. However, it was also clear that the monkeys were not only selecting species that were among the most dominant in the forest. Linociera johnsonii and Macaranga schweinfurthii, among the most preferred food species for UL group, did not count among the most dominant species in the tree community in the unlogged forest (Table 2-3; Fig. 2-7). In the heavily logged area, monkeys selected eight of their ten most preferred food species more than they were available in the forest (Fig. 2-7b). This was particularly true for Symphonia globulifera and D. abyssinica. They selected Celtis durandii less than expected based on its availability, and did not select Celtis africana. Of the 10 most preferred food species, all were among the most abundant in the forest. Habitat Use Redtails in the heavily logged forest occupied the highest part of the canopy (22 m or higher) 86% of the time (Fig. 2-8). They were never observed to descend to the ground and were observed in lower/emerging understory vegetation (up to 7 m) less than 1% of the time. The animals in the unlogged area spent 85% of their time in a

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21 combination of three layers: the upper canopy (43.0%), the lower-canopy (11.8%) and lower/emerging understory (25.6%; Fig. 2-8). This difference is unlikely to be due to the relative availability of the different canopy layers, since an assessment of the available number of canopy layers in each area showed a similar distribution (Fig. 2-9; statistics are presented in the figure legend). Both groups occupied the two upper canopy layers more than expected and the lower layers less than expected, given the proportional distribution of each height category in the home ranges. The two areas differed in the density of understory vegetation. The heavily logged area was dominated by very dense herbaceous growth and very little open understory, whereas the unlogged area showed a more even distribution of understory vegetation density (Fig. 2-10). The groups differed in their association with different classes of understory vegetation. In the unlogged area, redtails were associated with the majority of understory vegetation classes in proportion to their distribution. The monkeys were observed to associate with the most dense vegetation less than expected, given the proportional distribution of vegetation density classes in the home range ( 2 = 26.89, df = 1, p<0.001). In contrast, redtails in the heavily logged area associated with the two most open understory vegetation classes more than expected and with the more closed understories less than expected (Fig. 2-11). Open understory was rarer in the heavily logged site, so it appears that HL redtails were selecting areas of less dense vegetation and that this habitat attribute may be more important in explaining redtail presence than the number of canopy layers. Mixed Species Associations The UL group spent more of its time (65.7%) in mixed-species associations than the HL group (36.2%) ( 2 = 503.93; df = 1; p<0.001). Of the time UL group spent in

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22 mixed-species associations, 72% was with red colobus, 31% with blue monkeys, and 20% with black-and-white colobus (Fig. 2-12). HL group spent 52% of its time in association with red colobus, 24% with blue monkeys, 24% with black-and-white colobus, and 20% with grey-cheeked mangabeys. Because the monkeys sometimes associate in groups of more than two species, these percentages do sum to 100. When percent of time observed in association was compared with the percent each species comprises of the primate community in each area (derived from Chapman et al. 2000), redtails in the both areas were found to associate with red colobus (UL: 2 = 1181.65, df = 1, p<0.001; HL: 2 = 137.16, df =1, p<0.001) and blue monkeys (UL: 2 = 1424.92, df = 1, p<0.001; HL: 2 = 1059.88, df = 1, p<0.001) more than expected. In the heavily logged area, redtails associate with black-and-white colobus less than expected ( 2 = 224.83, df = 1, p<0.001), whereas associations between these two species occur proportional to the black-and-white colobus density in the unlogged area ( 2 = 224.83, df=1, p=0.021). Association with mangabeys is more than expected for redtails in the HL group ( 2 = 189.41, df =1, p<0.001) and less than expected in the unlogged area ( 2 = 53.08, df = 1, p<0.001). Discussion Redtail monkeys inhabiting heavily logged forests used larger home ranges, had lower dietary diversity and spent less time feeding than conspecifics inhabiting unlogged forest habitats. Despite having a smaller group size, redtail monkeys in logged areas travel further and spend more time moving than redtails in unlogged habitats. These results suggest that redtails in logged forests are spending more time and expending more energy in search of food (Milton 1984; Janson 1988). The results of my habitat assessment further indicate that reduced abundance of resources and suitable habitat

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23 constrain redtails in the heavily logged forest, which may explain lower population densities. Resource Availability The quality and quantity of resources available to the monkeys is likely to strongly influence behavioral patterns and reproductive success (Janson and Chapman 2000). To compensate for the greater energy expenditure by redtails in the heavily logged area, I would predict higher feeding and foraging rates. My observations confirm that although the HL group spent more time searching for insects than group UL, it did not spend more time feeding. I explain this pattern by the decrease in potential plant food sources in the heavily logged area. Our inventory of trees indicates that there are fewer potential food trees in the heavily logged area. This corresponds with an inventory conducted in 1980-81, 11 years after logging operations ended (Skorupa 1988). In addition, the heavily logged area had a lower density of of figs, which appear to be important to redtails in both areas (Struhsaker 1997). In other forests, this loss of food trees appears to be counterbalanced by the colonization of gaps and cleared areas by important fruit-producing pioneer species, such as Musanga sp. (Johns and Skorupa 1987). At Kibale, however, pioneer tree species that colonize after large disturbances are not important food sources for frugivores (Struhsaker 1997). Finally, trees in heavily logged areas may also be less productive. For example, Skorupa (1988) reports a 25% reduction in fruiting and young leaf production in one heavily logged part of Kibale. It appears likely, then, that redtails in the heavily logged area feed less on plant foods because the latter are both less abundant and less productive. Redtails may compensate for increased energy expenditure by feeding more on insects. Skorupa (1988) provides indirect evidence that insect abundances could be

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24 higher in heavily logged areas at Kibale. He suggests that the lower production of young leaf and fruit in heavily logged areas at Kibale may be due in part to increased pressure from insects (Skorupa 1988). A better assessment of insect resources is necessary to determine to whether lack of plant resource availability leads to increased insect feeding or if increased insect abundance results in the higher number of observations of insect feeding. Habitat Quality The habitat structure in the heavily logged area may also contribute to the behavioral patterns observed in the focal group. The results indicate that habitat may be of lower quality for redtails in the heavily logged area. Redtails exploit numerous canopy strata, frequently foraging in the mid-canopy layers and even descending to the ground to forage (Struhsaker and Leland 1979, Gebo and Chapman 1995). It is likely that the insect resources comprising the bulk of redtails diets are located throughout the canopy and that their vertical locations within the canopy may vary daily or seasonally (Terborgh 1993). This may partially explain redtails general preference for mid-canopy layers in the unlogged area. Our habitat assessment shows a distinct difference in amount of mid-canopy available to redtails and in the density of understory vegetation in logged areas. Since the vegetation in many parts of the heavily logged areas consists primarily of dense thickets of herbaceous vegetation and semi-woody plants dominated by Acanthaceae species, descending to the ground or the lower canopy appears to be physically prohibitive, limiting monkeys to the upper strata. Moreover, the understory plant M. leucantha appears to be important for redtails in the unlogged areas. This plant is often found in large patches throughout the undisturbed area. By contrast, very few isolated M. leucantha plants were observed in the heavily logged area. I propose that redtails in

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25 unlogged areas can exploit smaller areasand coexist in higher densitiesat least particially because the vertical component of their environment provides more resources and habitat than in the heavily logged areas. Conclusion Overall, I conclude that both resource abundance and the amount of suitable habitat are reduced, causing redtails to expend greater effort in searching for food in the heavily logged areas of Kibale. The fact that redtail population densities in these logged areas were still declining 28 years after logging (Chapman et al. 2000), suggests that redtails in heavily logged areas are still above the carrying capacity of the forest. Whereas colobines at Kibale appear to compensate for ecological differences resulting from habitat disturbance by adjusting their group size, a reduction in redtail group sizes in logged areas has still not resulted in a stable population density. Other studies suggest that parasite loads (Gillespie et al., submitted) and predation risk (Struhsaker and Leakey 1990, Mitani et al. 2001) could also be contributing to this decline. However, the results presented here suggest that constraints on the particular habitat and resource requirements of redtails are the primary drivers of population decline in redtails. This study points to the importance not only of structure and composition of regenerating forest in predicting the response of primates to disturbance, but of the specific habitat requirements of different species. Members of the C. cephus lineage are generally assumed to be highly flexible in their dietary requirements and highly adaptable to regenerating and other disturbed forests (Thomas 1991, Kingdon 1997). However, their preference for mid-canopy vegetation seems to make them more vulnerable to the effects of logging than other species that prefer upper canopy strata. Conventional logging generally alters the character of the understory through incidental damage

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26 (Thiollay 1992, Laurance and Laurance 1996). Where timber stands are managed, plans often call for the removal of vines, which normally serve as access routes and food sources for nonvolant arboreal animals (Galletti et al. 1994, Putz et al. 2001). When these vines are removed, animals suffer the loss of functional habitat areas. For the large number of primate and other species that occupy the midand understory niche within a community (Thomas 1991, Boinski and Sirot 1997, Putz et al. 2001), these management strategies are especially harmful. Management objectives focused on both timber harvest and maintenance of healthy wildlife populations will have to mitigate the impacts of this disturbance. In the case of Kibale, restoration efforts would have to focus not only on replanting food trees and improving accessibility to those trees, but also on improving mid-canopy and understory habitat. In another example, although reduced impact logging prescribes vine cutting for target trees, the number of trees removed and the overall damage to the forest is reduced from that caused by conventional logging practices, indicating that the amount of habitat loss might be reduced as well (Holmes et al. 2002). This scheme appears to be more cost effective than conventional logging, so it presents an interesting possibility for compromise between the interests of foresters and wildlife conservationists. However, research on the effects of the disturbance caused by reduced impact logging is still too limited to unequivocally advocate the approach. At Kibale, as in many sites, long-term impacts of logging on population densities vary widely among primate species, but these differences may not be apparent when behavioral responses are not understood. Thus, the ability of management or restoration

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27 plans to take into account different species resource and habitat needs is likely to be critical in maintaining viable populations following logging disturbance.

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28 Table 2-1. Test of independence for group activity samples pooled by 60-min or 15-min sample for redtail groups in unlogged and heavily logged forest at Kibale National Park, Uganda. 2 Test of Independence a Activity Unlogged Heavily Logged 2 p 2 p Feeding 0.008 1.0 0.104 0.99 Searching 0.008 1.0 0.012 1.0 Traveling 0.32 0.99 0.32 0.99 Resting 0.014 1.0 0.083 0.99 Social 0.019 1.0 0.051 1.0 Other 0.21 0.995 0.66 0.96 a df = 4

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29 Table 2-2. Monthly and mean home range size (ha) for redtail groups in logged and unlogged forest in Kibale National Park, Uganda between August and December 2002 (UL = unlogged group, HL, heavily logged group). Home range area (ha) MCP 1 Home range area (ha) GCA 2 Month UL HL UL HL August 21.13 23.94 11.43 9.18 September 17.23 30.39 8.19 9.90 October 22.89 30.00 10.44 8.73 November 24.60 30.80 9.45 8.37 December 15.54 29.11 9.99 7.02 Mean (+/-SD) 20.28 (+/-3.81) 28.85 (+/-2.81) 9.90 (+/-1.20) 8.64 (+/-1.07) TOTAL 32.49 48.97 20.16 21.33 1 MCP=minimum convex polygon method; 2 GCA=grid-cell analysis, based on 30x30 m grid cell

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30 Table 2-3. Time spent eating different types of foods by two redtail groups in unlogged and heavily logged forest of Kibale National Park, Uganda, from tree species comprising 80% of their respective feeding time (UL (unlogged) = 81.84%; HL (heavily logged)= 80.9%). Group UL Group HL Species (Family), density Part consumed % in diet Species (Family), density Part consumed % in diet Seeds 1.2 Young leaves 0.2 Ripe Fruit 20.5 Ripe fruit 11.9 Unripe Fruit 1.4 Unripe fruit 15.1 Seeds 0.4 Celtis durandii (Celtidaceae), 41.27 Diospyros abyssinica (Ebenaceae), 12.21 Young Leaves 0.2 Flowers 16.1 Seeds 0.7 Ripe fruit 2.5 Ripe fruit 6.8 Unripe fruit 6.3 Linociera johnsonii (Oleaceae), 2.15 Symphonia globulifera (Guttiferae), 5.61 Young leaves 0.3 Ripe fruit 6.7 Seeds 0.3 Ripe fruit 5.4 Unripe fruit 2.6 Diospyros abyssinica (Ebenaceae), 16.56 Paranari excelsa (Rosaceae), 5.07 Young leaves 3.1 Young leaves 2.3 Flowers 1.2 Petioles/Pith 1.0 Petioles/Pith 2.6 Unripe fruit 2.9 Trilepsium madagascariense (Moraceae), 62.30 Trilepsium madagascariense (Moraceae), 7.26 Seeds 0.2 Ripe fruit 4.6 Ripe fruit 6.7 Unripe fruit 1.0 Unripe fruit 0.2 Seeds 0.2 Marantochloa leucantha (Marantaceae), density unknown Celtis durandii (Celtidaceae), 17.95 Young leaves 4.5 Young leaves 3.1 Unripe fruit 1.9 Rothmannia urcelliformis (Rubiaceae), 0.34 Celtis Africana (Celtidaceae), 10.63 Seeds 0.2 Young leaves 3.1 Ripe Fruit 3.8 Ripe fruit 1.9 Unripe Fruit 0.2 Unripe fruit 2.9 Mimusops bagshawei (Sapotaceae), 4.03 Blighia unijugata (Sapindaceae), 2.88 Seeds 1.6 Young leaves 0.4 Ripe fruit 3.4 Ripe fruit 0.4 Unripe fruit 0.1 Unripe fruit 3.1 Macaranga schweinfurthii (Euphorbiaceae), 3.54 Linociera johnsonii (Oleaceae), 3.77 Young leaves 2.9 Young leaves 1.7 Petioles/pith 0.1 Flowers 0.6 Craterispermum laurinum (Rubiaceae), 0.49 Millettia dura (Leguminoseae), 3.20

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31 Table 2-3. Continued Group UL Group HL Species (Family), density Part consumed % in diet Species (Family), density Part consumed % in diet Ripe fruit 1.7 Ripe fruit 2.3 Unripe fruit 0.1 Unripe fruit 0.4 Ficus exasperata (Moraceae), 7.20 Ficus sansibarica (Moraceae), 0.32 Ripe fruit 1.8 Piper gnensis (Piperaceae), density unknown Mature leaves 1.1 Young leaves 0.2 Tarenna pavetoides (Rubiaceae), density unknown Unripe fruit 0.4 Total 81.8 80.9

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32 Table 2-4. Dietary diversity and overlap across months for redtail groups in logged and unlogged forest of Kibale National Park, Uganda. Month Dietary Diversity (H) % Diet Overlap Unlogged Heavily Logged August 3.04 2.77 30.2 September 2.84 2.32 15.6 October 2.54 2.30 20.0 November 2.54 2.21 26.5 December 2.58 1.28 16.3 21.7 ( 6.45)

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33 Table 2-5. Characteristics of the tree community in home range areas of focal redtail groups in unlogged and heavily logged forest areas of Kibale National Park, Uganda, for all trees above 10cm DBH. Food trees species were identified based on feeding observations for each group during the study period from August to December 2002. Unlogged Heavily Logged All Trees Food Trees All Trees Food Trees Species richness (S) 106 49 (46.23%) 1 110 24 (21.82%) 1 Individuals (N) 13542 11958 (88.30%) 2 8433 4868 (57.73%) 2 Simpson (1/D) 13.085 10.29 27.852 11.324 Shannon (H) 3.131 2.704 3.752 2.638 Shannon evenness 0.6714 0.6984 0.7982 0.8413 Stem Density (trees/ha) 334.4 295.3 210.3 121.4 Total Basal Area (m2) 4141.86 3700.36 2645.04 1905.02 1 Figure in parentheses indicates percent of total species 2 Figure in parentheses indicates percent of total number of individuals

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34 Figure. 2-1. The Kanyawara study site is located in Kibale National Park, western Uganda. Maps are adapted from MUBFS 2003.

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35 ***0102030405060FeedForageTravelRestSocial OtherActivityPercent of observations Figure 2-2. Percentage of time engaged in different activities by redtails in unlogged (unshaded) and heavily logged (shaded) forest areas of Kibale National Park, Uganda. Bars indicate standard error, and asterisks indicate significantly greater proportions of time devoted to different activities.

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36 050100150200250AugustSeptemberOctoberNovemberDecemberNumber of grid cells Figure 2-3. Monthly total grid cells entered and cumulative grid cells entered by redtails in unlogged (unshaded) and heavily logged (shaded) areas of Kibale National Park, Uganda. The bars indicate the number of different grid cells entered per month by each group (unshaded = unlogged; shaded = heavily logged). The lines show the cumulative number of new grid cells enter over the study period (unlogged = solid line; heavily logged = dashed line).

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37 (a) Figure 2-4. Pattern of home range use by redtail groups in (a) heavily logged and (b) unlogged forest of Kibale National Park, Uganda. Darker shading indicates a greater percentage of times that the group was present in a given 30x30 m grid cell. The relative position of the grids to each other reflects the true spatial relationship of the two home ranges. (b)

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38 DecemberNovemberOctoberSeptemberAugustDaily distance traveled (m)3000200010000 Figure 2-5. Daily distance traveled by redtail monkeys in logged (shaded) and unlogged (unshaded) areas of Kibale National Park, Uganda. Boxes indicate the interquartile range of distances per month per group. The line inside each box indicates the mean distance traveled each month and the bars show the extreme values obtained each month.

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39 0.010.020.030.040.050.060.070.080.090.0100.0MLYLFLPSDRFURFInsectOtherFood TypesPercent of Observations Figure 2-6. Average percent of monthly feeding observations on different items by redtail monkeys in unlogged (unshaded) and heavily logged (shaded) forest in Kibale National Park, Uganda. Lines above bars indicate standard error. ML = mature leaves; YL = young leaves; FL = flowers; P = petioles; SD = seeds; RF = ripe fruit; URF = unripe fruit. The group in the unlogged forest was observed to consume seeds on a few occasions, but the amount was negligible relative to other food items.

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40 TaPaPiGnFiExCrLaMaScRoUrMaLeBoPhDiAbMiBaLiJoCeDu-0.15-0.1-0.0500.050.10.150.2 (a) LiJoCeDuCeAfBlUnBoPhPaExSyGlDiAbMiDuFiBr-0.0500.050.10.150.20.250.3 Figure 2-7. Strauss selectivity index (measures selectivity vs. dominance) for top 80% of food species selected by redtails in (a) unlogged and (b)heavily logged areas of Kibale National Park, Uganda. Abbreviations: BlUn = Blighia unijugata; BoPh = Bosqueia phoberos; CeAf = Celtis africana; CeDu = Celtis durandii; CrLa = Craterispermum laurinum; DiAb = Diaspyros abyssinica; FiBr = Ficus brachyleps; FiEx = Ficus excelsa; LiJo = Linociera johnsonii; MaLe = Marantochloa leucantha; MaSc = Macaranga schweinfurthii; MiDu = Millettia dura; MiBa = Mimusops bagshaweii; PaEx = Paranari excelsa; PiGn = Piper gnensis; RoUr = Rothmannia urcelliformis; SyGl = Symphonia globulifera; TaPa = Tarenna pavioides. (b)

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41 Height in canopy12345Percent of individual observations100806040200 GROUP UL HL 98717261243 Figure 2-8. Height in canopy of redtails in unlogged and heavily logged forest at Kibale National Park, Uganda, by percent of observations in each category. Height category scale: 1 = 0-7m (x=4.7.9); 2 = 7-14m (x = 12.9.6); 3 = 14-18m (x = 16.7.7); 4 = 18-23m (x= 20.4.6); 5 = 23m or higher (x = 24.8.2).

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42 Number of vegetated height categories54321Percent of habitat plots50403020100 932411718422810 Figure 2-9. Distribution of number of canopy layers observed in home ranges of redtails in heavily logged and unlogged areas of Kibale National Park, Uganda. Plots were scored as having vegetation in from 1 to all 5 of the possible height categories. Height categories were as follows: 0-7m; 7-14m; 14-18m; 18-23m; 23m or higher. Height in canopy of redtails relative to vegetated height categories in each area: Unlogged: Class 1: 2 = 5379.033; df =1; p < 0.001, Class 2: 2 = 239.889; df = 1; p < 0.001, Class 3: 2 = 104.21; df = 1; p < 0.001, Class 4: 2 = 273.489; df = 1; p < 0.001, Class 5: 2 = 140.688; df = 1; p < 0.001; Heavily Logged: Class 1: 2 = 80,004.19; df = 1; p < 0.001, Class 2: 2 =13.2129; df = 1; p < 0.001, Class 3: 2 = 769.0676; df = 1; p < 0.001, Class 4: 1265.837; df = 1; p < 0.001, Class 5: n/a (0 observations).

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43 ******0510152025303540455012345Understory vegetation density rankingPercent of Observations Figure 2-10. Understory vegetation associated with redtail location in unlogged (unshaded) and heavily logged (shaded) forest at Kibale National Park, Uganda, by percent of observations in each category. Understory vegetation was scored on a scale of 1 to 5, where 1 = very open (no or almost no vegetation, generally only tree seedlings or saplings) and 5 = very dense (generally herbaceous, often a combination of Graminaceae and Acanthaceae species). Lines indicate the distribution of understory vegetation density across categories, observed in the unlogged (solid line) and heavily logged (dashed line) home ranges. Association of redtails with understory vegetation classes in each area: Unlogged: Class 1: 2 = 0.194; df =1; p < 0.95, Class 2: 2 = 1.817; df = 1; p > 0.10, Class 3: 2 = 3.613; df = 1; p = 0.06, Class 4: 2 = 3.466; df = 1; p = 0.07, Class 5: 2 = 26.893; df = 1; p < 0.001; Heavily Logged: Class 1: 2 = 994.73; df =1; p < 0.001, Class 2: 2 = 168.04; df = 1; p < 0.001, Class 3: 2 = 5.825; df = 1; p = 0.04, Class 4: 2 = 5.825; df = 1; p = 0.04, Class 5: 2 = 86.331; df = 1; p < 0.001. Asterisks indicate that redtails association with an understory vegetation class was different than expected.

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44 0102030405060708090100RCBMBWCMBSpeciesPercent of observations Figure 2-11. Monthly percent of time spent in mixed species associations for redtails in unlogged (unshaded) and heavily logged (shaded) areas of Kibale National Park Uganda. RC = red colobus (Procolobus badius), BM = blue monkey (Cercopithecus mitis), BWC = black and white colobus (Colobus guereza), MB = grey-cheeked mangabey (Lophocebus albigena).

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45 (a) Unlogged 72.120.530.72.625.617.85.09.20.010.020.030.040.050.060.070.080.090.0100.0RCBWCBMGCMPercent (b) Heavily Logged 52.223.624.220.028.057.72.25.50.010.020.030.040.050.060.070.080.090.0100.0RCBWCBMGCMPercent Figure 2-12. The proportion of observations in which redtails in (a) unlogged and (b) heavily logged areas of Kibale National Park, Uganda were associated with each of four other prominent species in the diurnal primate community (unshaded), compared with each species proportional representation in the community (shaded). RC = red colobus (Procolobus badius), BM = blue monkey (Cercopithecus mitis), BWC = black and white colobus (Colobus guereza), MB = grey-cheeked mangabey (Lophocebus albigena).

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CHAPTER 3 THE EFFECTS OF PATCH STRUCTURE ON REDTAIL MONKEY (Cercopithecus ascanius) HABITAT USE IN UNLOGGED AND HEAVILY LOGGED AREAS OF KIBALE NATIONAL PARK, UGANDA Introduction The timber industry is of central importance to many tropical countries, providing sources of income and links to regional and global markets, among other things (Naughton-Treves and Weber 2001). However, logging practices frequently damage and degrade forests to the point of compromising their ability to provide important ecological services, such as nutrient and water cycling and habitat for wildlife (FAO 2003). As a result, recent years have seen a dramatic rise in efforts toward more sustainable logging practices (Frumhoff 1995, Lindenmayer et al. 1999). However, neither the impact of these schemes, nor the more conventional plans, on forest wildlife is not well-known. In general, logging primarily affects habitat structure and the resource base (Fimbel et al. 2001), but it can also lead to forest fragmentation, which results in reduced landscape connectivity, smaller patch sizes, increased edge effects, and population isolation (Laurance and Bierregaard 1997). The occurrence and the magnitude of fragmentation effects, as well as their effect on wildlife, are determined by the spatial extent and intensity of the logging operations. Clear-cutting is generally the most detrimental to forest species, but conventional selective logging operations can often have significant negative impacts, as well (Fimbel et al. 2001). In a comparison of the simulated effects of alternative timber harvest plans on landscape structure, Gustafson and Crow (1996) found that a plan prescribing group 46

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47 selection (the removal of small groups of trees) created nearly as much edge as a plan calling for clear-cutting of a much larger area of forest. The more limited size of harvest areas in the group selection plan affected forest spatial pattern more than the greater intensity of harvest in the clearcut plan, implying that the selective logging plan may have more negative consequences for forest dwelling animals. In an example from Venezuela, enrichment strips (a silvicultural treatment in which strips are cleared and replanted with commercial seedlings following selective logging) negatively affected understory and terrestrial birds (Mason 1996). Strip enrichment was more damaging than selective logging because it opened larger areas of canopy and created barriers within the forest. Such spatial discontinuity in forested landscapes can have a number of consequences for wildlife, including local extinction and changes in species composition. The effects can extend to community and ecosystem processes insofar as animals play an important role in seed dispersal and pollination and serve other functions. In the tropics, up to 90% of all plant species are adapted for seed dispersal by vertebrates (Howe and Smallwood 1982, Jansen and Zuidema 2001). As a result, the ability of vertebrates to persist in and move around tropical forests is of great importance for natural regeneration processes. There is, thus, a need to carefully assess the effects of the spatial pattern of logging on forest continuity. In developing a forest management plan that supports such different vertebrates, it is necessary to determine the amount and configuration of suitable habitat that is important for different animal species. This is often not well known in the tropics (Fimbel et al. 2001), so a first step must be to identify the combination of attributes that defines suitable habitat for different species.

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48 In this study, I examine the degree to which an analysis of habitat use creates a better understanding of the variables that are important to forest-dwelling vertebrates, and evaluate whether this information can contribute to the formulation of timber extraction plans that are more compatible with the persistence wildlife populations. This study focuses on the response of redtail monkeys (Cercopithecus ascanius schmidtii Matschie) to habitat change as a result of logging 30 years earlier, in Kibale National Park, Uganda. Redtails are part of a rich primate community in the park, consisting of 13 species and one of the highest densities of primates in Africa (Struhsaker 1997). Periodic population censuses since the time of the logging indicate a continued decline in redtail population density in the heavily logged areas since the time of logging (Chapman et al. 2000). Redtail density is significantly lower in the logged areas (1.04 groups/km 2 ) than in the unlogged areas (11.48 groups/km 2 ; Chapman et al. 2000). Kibale provides a good model for testing primate response to logging alone, since direct human disturbance through other factors such as hunting is not a factor. The heavily logged site in Kibale has half the stem density and basal area of trees of the unlogged area (Stickler and Chapman, in prep); thus, total basal area will likely be important in dictating redtail habitat use. Moreover, in the heavily logged area, 60% of the trees were potential food sources for the monkeys, whereas nearly 90% of the trees in the unlogged area were potential food sources (Stickler and Chapman, in prep). Thus the spatial distribution of potential food trees will likely influence differences in habitat use. I also predict that the type and quality of the understory will influence redtail habitat use. Specifically, I expect that monkeys will avoid areas dominated by a dense understory characterized by thorny vines or herbs because it reduces the vertical foraging area

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49 (Fimbel et al. 2001), and since they exploit both the understory and the higher canopy within forests (Struhsaker and Leland 1979; Cords 1986), I expect an intermediate amount of understory vegetation will be preferred. To identify the factors important in redtail habitat selection, I examined habitat use by redtail monkeys in heavily logged and unlogged areas and examined the role of spatial structure of habitat attributes and resources in determining behavioral differences. Additionally, I compared habitat use models for monkeys in unlogged and heavily logged areas to examine the extent of overlap in critical habitat characteristics. Methods Study Area Kibale National Park (766 km 2 ) is located in western Uganda (0 13-0 41N and 30 19-30 32E), near the base of the Ruwenzori Mountains (Fig. 3-1). It is classified as moist evergreen forest, transitional between lowland rain forest and montane forest (Struhsaker 1997, Chapman and Lambert 2000). Situated at 1500 m elevation, the Kanyawara study site in the northern part of the park receives an average of 1741 mm of rainfall per year, distributed over distinct biannual wet and dry seasons (Chapman and Chapman, unpublished data). I contrasted habitat use by a group of redtails in a heavily logged area (16 individuals), with that of a group in an unlogged area (24 individuals). The heavily logged area was harvested in 1969 (Struhsaker 1997). Approximately 7.4 stems/ha or 21 m 3 /ha were removed (Skorupa 1988, Struhsaker 1997). However, as a result of high incidental damage associated with the felling, Skorupa (1988) estimates that approximately 50% of all trees in the area were destroyed. Since then, dense, thorny herbaceous vegetation has grown in many areas, which are frequently dominated by semi-woody Acanthaceae species (primarily, Acanthus arborescens) (Struhsaker 1997,

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50 Paul et al., in press). The area used by the focal group in the logged forest also abuts smallholder agricultural fields and tea plantations. The unlogged area has never been commercially harvested. A few large stems (0.03-0.04 trees/ha) were removed before 1970; however, this low level of extraction appears to have had little effect on forest structure (Skorupa and Kasenene 1984, Skorupa 1988, Struhsaker 1997, Chapman et al. 2000). There is an extensive, gridded trail system in both areas. Behavioral and Habitat Attributes Behavioral data were collected on one group of redtails in each site between August and December 2002. The group in the unlogged area had 24 individuals and the group in the logged area had 16. Group locations were recorded every 15 minutes during all-day follows, in 3-day blocks at 1-week intervals, yielding 1563 observations for the group in the unlogged area and 1301 observations for the group in the heavily logged area. We recorded the location of the center of the group at every scan interval by measuring the distance and angle to the nearest point with known coordinates. Reference points were located at 20 m intervals on the gridded trail system in each groups home range. Reference points were recorded using a hand-held global positioning system (Garmin 12 XL). Coordinates for group locations were calculated and plotted in the ArcView 3.2 and ArcGIS 8.3 geographic information systems (ESRI 2002). Further details concerning behavioral data collection are included in Stickler and Chapman (in prep). To measure habitat characteristics, a set of spatially stratified set of 50 m 2 plots were selected by spacing plots every 20 m along the trail grid in each area (Table 3-1). The plots assessed 37% (n = 1484) and 32% (n = 1362) of the area in unlogged and heavily logged forest, respectively. In each plot, slope and canopy height were measured

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51 using a clinometer. Three categories of understory vegetation were scored as being present or absent. The understory type was scored as being present if it was present in at least 25% of the plot. The categories were selected based on their association with different forest structures and/or their importance to the monkeys as food sources or as obstacles. In the areas used by the focal groups, all trees above 10 cm diameter at breast height (DBH) were identified to species, measured, and plotted in the GIS. The geographic coordinates for each tree were calculated from the distance and angle from the reference points (unlogged: 15,489 trees in 48.4 ha; heavily logged: 8433 trees in 40.1 ha). The availability of plant food resources was assessed by categorizing each tree as food or nonfood, and as providing ripe fruits, unripe fruits, flowers or young leaves, based on the feeding observations. Analyses Variograms were constructed for group locations to determine the distance at which observations of redtail group presence were spatially independent. The variogram gives a geostatistical measure of the spatial scale over which patterns are dependent, defining a distance or spatial extent (= range) over which spatial dependence can be dependent (Isaaks and Srivastava 1988). Spatial independence occurred after 32 m in the unlogged area and after 30 m in the heavily area. Therefore, to derive appropriate and comparable sampling units for the two areas, I superimposed a grid of 30 x 30 m cells over the group locations, habitat plots, and tree locations in the GIS. The grid contained 531 cells for the unlogged area and 466 cells for the heavily logged area. The number of times the group entered a cell was used to calculate probabilities of redtail use. Home range size was calculated using an adaptive kernel function for the point distribution in each area, using the Animal Movement extension for ArcView 3.2(Hooge and Eichenlaub 1997). I

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52 assessed the spatial pattern of habitat use with a coefficient of dispersion (CD) statistic (Upton and Fingleton 1985). The CD calculates the ratio of the variance against the mean of the number of times the group entered a grid cell. A value less than one indicates a uniform dispersion, a value greater than one indicates a clumped dispersion, and a value near one indicates a random pattern. To determine the scale of variation for each variable, spatial structure was analyzed using variogram analysis in the Geostatistical Analyst extension for ArcGIS 8.3 (ESRI 2002). For each variable, a semivariogram produced a range value, which specifies the distance at which spatial independence is achieved, and a nugget value, which represents independent error, measurement error or microscale variation at scales too fine to detect (Isaaks and Srivastava 1988). Nugget values closer to 0 indicate that the amount of error and/or undetected patch structure in the dataset are low (Isaaks and Srivastava 1988). The spatial stationarity of each variable was evaluated using variogram analysis (Cressie 1993). As the assumption of stationarity was met for each variable, Morans I statistic was calculated as a measure of global autocorrelation for each variable over the extent of each home range. Morans I can be thought of similarly to Pearsons r (Cressie 1993). Typically, Morans I varies on a scale from to +1. Occasionally, the statistic can take on a value outside this range, due to an unusual weight matrix or averaging within a distance class (Fotheringham et al. 2000). A large I value indicates that the variable exhibits spatial dependence (Cressie 1993). To characterize the gradients of variation in each area and evaluate if they were the same, I conducted a principal components analysis (PCA). PCA assesses the relationships within a set of variables, in this case the set of ecological variables (Table 3

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53 1) to identify the most important sources of variability within the set (McGarigal et al. 2000). For further analyses of the relationships between ecological variables, I assigned values derived from the habitat plots to the grid in the GIS. Canopy and understory vegetation heights, slope, and aspect were averaged for each cell, and basal area was summed. For the understory vegetation types, scores of presence or absence were summed over each cell to derive a count of the number times a vegetation type occurs in a cell. Similarly, the number of trees (in all categories) was summed by grid cell. For both home range datasets, I calculated probabilities of such count variables occurrence in each grid cell. Variables were transformed when necessary to ensure normality. Tree counts and probabilities of understory vegetation types, group presence, and activity site selection were overdispersed, and thus log-transformed. Habitat selection models were created and evaluated using path analysis in the AMOS 5 software (Arbuckle 2003). Path analysis is a subset of structural equation modelling that allows more than one dependent variable and the effects of dependent variables on one another to be considered (Everitt and Dunn 1991). This method is appropriate when correlations that are not easily explained exist among the dependent variables and an experimental approach is not possible. Moreover, it is often more interpretable than a partial correlation matrix because the relationships are expressed in terms of a path diagram. In path analysis, the model specified by the user (default model) is compared to a saturated model (the most general model, which fits any dataset) and an independence model (in which the observed variables are assumed to be completely uncorrelated) (Mitchell 2001). Model fit is assessed by comparing the default model with

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54 the saturated model using a 2 goodness-of-fit index. If the expected correlations (default model) do not deviate significantly from the saturated model, the hypothesized model is judged to fit well (Shipley 1997). As a result, the best path analysis model typically has a p-value higher than 0.05, unlike standard regression models where the best model has a p-value below 0.05. Path analysis assumes linearity among variables, large sample size, and the inclusion of all important variables in the model (Everitt and Dunn 1991). I used the results of patch structure and principal components analyses, as well as previous research and my understanding of the system, as a guide in determining the components and likely structure of the hypothetical model. I constructed several default models testing different hypothesized relationships among the independent and dependent variables. Each of the default models was compared to the saturated model to determine which best represented the relationships among the variables. The ability of different models to accurately reflect system interactions was also evaluated by examining individual relationships within each model on the basis of coefficient value significance. To avoid problems resulting from potential remaining spatial autocorrelation or non-normally distributed data, I report as significant only those relationships for which the conventionally reported p-values were <0.001. Here, the desirable p-value for showing that a significant relationship between two variables exists is again standard, p<0.05. Each variable in the model also has direct and indirect effects on the response variable, described by the correlation coefficient for each pair of variables (Mitchell 2001). Because some variables have no direct connection to the response variable, the direct effect in these cases is 0. The total effect is simply the sum of the direct and indirect effects, and gives an indication of the importance and overall

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55 direction of correlation of the variable in the system as a whole (Mitchell 2001). Each model also contains a set of error terms (or disturbance terms) that reflect the unexplained variance and the measurement error resulting from the regression of each set of independent variables on their respective dependent variables, as specified in the default model (Shipley 1997). Each variables error term is assumed to be uncorrelated with the other variables internal to the default model. Once the best model to explain general habitat use was identified, I also constructed hypothetical models to examine habitat effect on feeding, foraging (searching for insects), and resting site selection in each site. These models aided in understanding the relationship between habitat attributes and the probability of any one of the redtails major activities occurring, to determine if different activities could be explained by different habitat qualities. Results Habitat Use In both the unlogged and logged areas, redtails exhibit a clustered pattern of habitat use (unlogged: CD = 11.12; heavily logged: CD = 9.36). However, an examination of the spatial distribution of grid cell use suggests that redtails in the unlogged area use their home range more uniformly than their counterparts in the heavily logged area (Fig. 3-2). In addition, some areas within the outer bounds of the home range are used infrequently or not at all in the heavily logged area, whereas in the unlogged area, almost all areas are used at least once. Overall, the range of frequencies of grid cell use was greater for the group in the unlogged area (0 to 55 observations) than in the heavily logged area (0 to 33 observations). The area described by the 95% probability contour is 29.24 ha in the unlogged area and 39.76 ha in the heavily logged area (Fig. 3-2).

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56 Spatial Structure of Habitat Attributes In the unlogged area, Morans I statistic shows that slope is strongly autocorrelated, and that this pattern deviates significantly from random (Fig. 3-3). The remaining variables also exhibited positive autocorrelation, but the results of z-tests do not allow us to infer that the respective spatial patterns are non-random. The spatial scale of the habitat attributes shows a great range of variation among variables. Analysis with semivariograms showed that Marantochloa leucantha (21 m) and basal area (38 m) had the smallest patch sizes, and that both had nugget (intercept) values close to 0, suggesting that these variables were sampled at an appropriate scale (Fig. 3-3). By contrast, slope (17.41) and canopy height (32.75) had high nugget values, indicating that there may be some degree of variation that the sampling does not detect. Understory height, A. arborescens, and sapling presence scaled between approximately 180 and 230 m. In the heavily logged area, the majority of measured variables exhibited strong and highly significant autocorrelation, and thus, patchiness (Fig. 3-4). As in the unlogged area, basal area exhibited the smallest patch size (range = 29 m) and was sampled at a fine enough scale to detect most spatial structure. Slope and A. arborescens scaled similarly, 88 m and 85 m, respectively. However, whereas A. arborescens presence appears to have been measured at a suitable resolution, slope may not have been (nugget = 23.09). As in the unlogged area, canopy height in the disturbed area had a high nugget value (23.12). Understory height and sapling presence scaled at 115 m and 111 m, respectively, and had nugget values close to 0. M. leucantha scaled at nearly 250 m, but I attribute this result, and the low Morans I statistic value, to the extreme rarity of this vegetation type in the understory (i.e., M. leucantha was scored as present in 5 sample plots).

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57 In each area, there appeared to be three different scales of patchiness for habitat attributes: (1) 20 to 40 m; (2) 85 to 135 m; and (3) 180 to 250 m. In both areas, basal area falls in the first category, as does M. leucantha in the unlogged forest only. In the unlogged forest canopy height falls in the second category. In the heavily logged area, slope, canopy height, understory vegetation height and A. arborescens and sapling presence fall in the second category. Finally, in the unlogged area, slope, understory vegetation height, and A. arborescens and sapling presence group in the third category. In both areas, the first four variability gradients extracted in the PCA described approximately 80% of the variation in the set of habitat measurements (Table 3-2). Furthermore, the variables or groups of variables contributing most to each of these components was similar among between the two areas. In each case, component 1 appeared to describe high, open forest (described by positive relationships between basal area, saplings and canopy height), whereas component 2 described a low understory, as well as a dense, high A. arborescens-dominated understory. All of the variables loaded at least moderately on component 1 in the unlogged area, indicating that the habitat structure may be more variable than in the heavily logged area. Slope had the highest loading on component 3, and component 4 described high forest, with a sapling-dominated understory. In the heavily logged area, basal area and A. arborescens-dominated understory loaded heavily on components 5 and 6, respectively. The opposite was true for the unlogged area, with the A. arborescens understory defining component 5, and canopy height and basal area defining component 6. Path Analysis When the same basic conceptual model (Model 1) was applied to both cases, the path analysis reveals both similarities and differences in the drivers of redtail habitat use

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58 (Fig. 3-5). The variables and design of Model 1 were based on the analyses of spatial structure and the principal components analysis, and on a general understanding of the system Model 1, including average percent slope, total basal area and mean canopy and understory vegetation heights, fits well in the heavily logged case; the hypothesized model does not differ significantly from the saturated model (Fig. 3-5). When this Model 1 is applied to the unlogged area, the path analysis shows that the default model does differ significantly from the saturated model (Fig. 3-5). In both the heavily logged and the unlogged area, total basal area of trees above 10 cm DBH was important in explaining patterns of habitat use. Basal area was a stronger predictor of habitat use in the unlogged area than in the heavily logged area ((Table 3-3). Slope had a strong indirect effect on habitat use, and had a significant effect on all other explanatory components of the model in the unlogged area. Only its relationships to canopy and understory height were significant in the heavily logged area. Understory vegetation height had a strong effect on habitat use in the heavily logged area, but not in the unlogged area. For both models, canopy height has neither a strong nor a significant effect on monkey occurrence, but when links from basal area or understory height to canopy height are removed, model fit decreases in both cases (e.g., when link from basal area to canopy height is removed: unlogged: 2 = 144.95, df = 3, p < 0.001; heavily logged: 2 = 182.59, df = 3, p < 0.001). Since Model 1 deviated significantly from the observed correlations in the unlogged area, I developed a second model (Model 2) that better explained monkey occurrence. The best model of general habitat variables included probability of A. arborescens presence instead of understory vegetation height, although A. arborescens did not have a significant effect on habitat use within the default model (Fig. 3-6). Slope

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59 remained an important factor in influencing both canopy height and total basal area. Thus, the results show that total basal area and slope are the most important factors explaining redtail habitat used in the unlogged area. I determined whether this combination of variables also explained each of the groups major activities. In the unlogged area, model fit improved markedly over the general habitat use model when feeding, foraging, and resting were included as the response variables, respectively (Table 3-4). In each case, slope and basal area had the only significant effects. The significance of A. arborescens as an explanatory variable decreased, although replacing this variable with understory height yielded even poorer models. When the general habitat use model was applied to explaining feeding, foraging, and resting site choice in the heavily logged area, models improved when abundance of food trees and A. arborescens were included, even though A. arborescens had no direct effect on the activity site selection. The best models, however, were produced when abundance of potential fruit trees was entered into the model with A. arborescens (Table 3-4). Since the abundance of fruit trees appeared to be important, I examined their spatial structure in the two home range areas with respect to fruit trees (Fig. 3-7). Trees potentially offering ripe fruits occur exhibit a weaker patch structure and occur at a larger scale in the unlogged than the heavily logged area. This is consistent with the basal area of trees in both areas. Aspect, presence of saplings and M. leucantha, and the density of trees providing young leaves and flowers as a potential preferred food did not have a strong direct or indirect effect on any of the other variables, so they did not enter into the final models.

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60 Discussion Patterns of habitat use by redtail monkeys in unlogged and heavily logged areas of Kibale National Park differed in their spatial scale, but could be explained by a similar suite of habitat attributes. Further analysis of the spatial structure of the habitat characteristics revealed that these patterns of use are a reflection of the underlying local environment. I compared the spatial structure of habitat characteristics within and between the two areas. My analysis showed that heavily logged forest exhibited a higher degree of patchiness for most habitat attributes over the spatial extent examined. In general, most variables in the unlogged area tended towards a more random spatial pattern, with the exception of slope. Nevertheless, positive autocorrelation was apparent for the other variables, including cumulative basal area, understory and canopy height, and two understory vegetation types, A. arborescens and saplings. In the disturbed forest, only basal area and the understory herb M. leucantha lacked significant spatial structure. The variables appeared to reflect three scales of spatial structure among both areas. Basal area varied over the shortest distance in both areas. The scale of variation appears to be consistent between the areas, despite the fact that the basal area in the heavily logged area is less than 65% that of the unlogged area (Stickler and Chapman, in prep). This suggests that changes due to logging do not alter the fundamental pattern of how tree size varies with distance. In both areas, slope, understory vegetation height, and the presence of A. arborescens and saplings group together in the same scale range. However, this group of variables fell into the large scale category in the unlogged area, as opposed to the middle distance category in the heavily logged area. Finally, canopy height scaled with slope and the other variables in the heavily logged area, while in the

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61 unlogged area, the distance over which canopy height showed patchiness was intermediate relative to basal area and slope. The principal components analysis corroborates the suggestion that there is an underlying structure common to both areas. In both areas, the analysis extracted four distinct gradients of variation describing habitat conditions. Factors Explaining Habitat Use Patterns Path analysis showed that redtails are most strongly influenced by basal area in both areas. Consistent with my expectations at the outset of the study, this indicates that the animals directly respond to the fine-scale patch structure represented by the combination of all trees in their environment. Similarly, monkeys were also negatively influenced by the height of the understory vegetation in both areas, although this relationship was not statistically significant in the unlogged area. Particularly in the heavily logged area, understory height was as an important factor as basal area in determining the extent to which redtails used an area. In the unlogged area, it was understory dominated by A. arborescens that was an important negative estimator of reduced habitat use. Understory height and A. arborescens are often positively correlated, particularly in the heavily logged area (Stickler, unpublished data). However, the composition of the understory is more diverse in the unlogged area than the heavily logged, which may explain why redtail habitat use is more influenced by the presence of A. arborescens than understory in general. This provides support for my initial prediction that redtails would prefer areas with intermediate density of understory, since this increases the vertical foraging area within the canopy. This result also emphasizes the importance of the type of understory vegetation. This is demonstrated in the heavily logged area where A. arborescens and similar plants are largely dominant.

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62 For redtails and some other members of the Kibale primate community, such areas are largely inaccessible due to their growth form and thorny leaves (Struhsaker 1997). The least expected result was the finding that slope plays such an important role in habitat use. Slope was a critical indirect estimator of habitat use in both areas. In the unlogged area, it had a strong positive effect on basal area and canopy height. The only part of this area that had a relatively flat slope was in the valley bottom at the north end of the home range. This area is characterized by wet, swampy conditions, a predominance of A. arborescens and a lower tree density, than in higher areasa combination that appears to be less preferred by redtails. As a result, it is not unusual that in this area, slope can be used as an indicator of monkey habitat use. Moreover, examining the habitat characteristics patch structure demonstrates that slope operates at a larger scale than the other variables, revealing its importance in the system as a whole. In the heavily logged area, slope had a strong positive influence on canopy height and understory height, but a non-significant positive effect on basal area. I explain these relationships both by the natural characteristics of this area and the effect of logging. As in the unlogged area, slopes are flatter in two swampy valleys. Throughout the Kanyawara site, tree cover in such areas tends to be sparse, and the understory tends to be dense and dominated by A. arborescens and herbs and vines. Average canopy height also tends to be lower. These valleys bound a higher, flatter ridge that might be predicted to have similar characteristics to the upland areas in the unlogged area. However, this area was subjected to intensive logging in the early 1970s. As a result, tree density is low. Perhaps because of this, canopy height is sometimes higher in areas with steeper slopes than in the flatter areas. Nevertheless, because of the often very steep slopes between the

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63 high ridge and the valley bottoms, tree cover is higher in flatter areas than on the slopes. This may explain why the effect of slope on basal area is not so strong. It may also explain the positive relationship between slope and understory height; steeper slopes may encourage more dense, taller herbaceous growth if trees cannot establish or survive as well on these slopes. Results suggest fruiting trees are important determinants of redtail habitat use. When feeding, foraging, and resting site selection were examined, trees potentially providing ripe or unripe fruits in the heavily logged areas significantly improved the path analysis models. Potential fruit trees have a restricted and highly clumped distribution in the heavily logged area that coincides with the areas of highest basal area. This is probably related to the fact that only 60% of all trees in the heavily logged area were potential food trees. In the unlogged area, by contrast, fruit trees (which represent nearly 90% of all trees in the area) appear to be much more uniformly distributed, although the relationship to high basal area is also strong. This result provides support for the importance of high basal area in the monkeys overall habitat use patterns because areas with a higher density and size of trees are more likely to have a higher number of fruit trees. Conclusion A combination of natural and logging-induced factors contribute to differences in behavior and differences in population density among redtails at Kibale. The availability of suitable habitat appears to be more restricted and more patchy in the logged than in the unlogged area. The differences in activity patterns, diet, and ranging behavior between the groups observed in a previous study (Stickler and Chapman, in prep) appear to be a function of habitat constraints. It seems that the interaction of logging with the local

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64 environmental conditions has lowered the carrying capacity of the affected forest. As a result, I suggest that redtails reduce their group size and use larger areas to compensate for the reduction and fragmentation of preferred habitat. At the population level, this likely translates to the decline in numbers observed by Chapman et al. (2000). The example of redtails in Kibale National Park illustrates the importance of including landscape characteristics in models describing small-scale habitat preferences. Typically, explanations of habitat selection focus on vegetation-related components as these variables are hypothesized to directly affect species abundance at larger spatial scales (Morrison et al. 1998). However, wildlife-habitat relationships are often hierarchical and thus inconsistent across spatial scales (Schulz and Joyce 1992, Cushman and McGarigal 2002, Johnson et al. 2002). There is good evidence that both small-scale and landscape-scale variables are important in the movement and distribution of a variety of animals (Kotliar and Wiens 1990, Mazerolle and Villard 1999, Johnson et al. 2002, Thompson and McGarigal 2002). The current study provides further support for the importance of variables at multiple scales in explaining and predicting habitat preferences, even over a relatively small spatial extent and fine resolution at the organismal level. These findings emphasize the importance of a multi-scale approach in order to better interpret the seemingly conflicting results of habitat studies conducted at different scales (Thompson and McGarigal 2002). Equally important is the spatial distribution of resources and habitat (Fahrig and Paloheimo 1988). In this study, a comparison of the spatial structure of neighboring forest stands that were subjected to different management plans highlights the importance of habitat availability and distribution for primates. At Kibale, as in many other sites, the

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65 underlying topography appears to be critical in structuring vegetation-related elements of habitat (Bormann and Likens 1979, Forman and Godron 1986). This observation suggests that slope may play an important role in forest regeneration (expressed by finer-scale variables), and that the combination of this interaction with the effect of disturbance caused by logging may partially explain the fact that more than 30 years has not been a sufficient time period for the heavily logged forest to return to its pre-harvest structure (Paul et al. in press). As a consequence, the amount of suitable habitat for redtails in heavily logged areas is lower than in the unlogged area. Moreover, the spatial pattern of potential habitat is such that some resources and habitat are inaccessible, reducing functional habitat even further (Orians and Wittenberger 1991). Thus, from a management or restoration perspective, management interventions to increase the abundance of high-quality habitat may not automatically lead to an equal increase in availability of such habitat (Gustafson and Crow 1994 Storch 2002). These conclusions have important implications for forest management and conservation strategies and policies. They provide further support for the need for management plans to take into account the spatial structure of ecosystem characteristics and the spatial pattern of disturbance (Poiani et al. 2000, Putz et al. 2000, Sanderson et al. 2002). Some timber extraction schemes, such as reduced impact logging, already incorporate spatial considerations to lower the damage done to the forest (Holmes et al. 2002). It seems that such an approach could be effective if it took into account the spatial requirements of resident wildlife. This could be done relatively easily, since it already takes into account topographic and forest structural variables in determining where roads and skid trails should be placed to decrease damage and increase efficiency. In this case,

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66 for example, the spatial discontinuities caused by logging should avoid segregating areas such that they become inaccessible to the primates. In addition, gaps or other damage caused by logging must be at scales smaller than the home range size and location constraints imposed on organisms by their social ecology or their physical size and requirements. Although Putz et al. (2001) recommend silvicultural enrichments as one possible way of restoring habitat or mitigating the impacts of logging, they also point out that some of these methods are incompatible with maintaining the pathways and patch sizes necessary to support nonvolant arboreal animals and, temporarily, for birds. For example, when a forest stand is thinned, by removing competing trees and vines or understory trees and vegetation (Smith 1986), the size of gaps caused by the removal of target trees alone may increase to the point of being insurmountable by some species of animals (Putz et al. 2001). Where conventional extraction is followed by strip enrichment (Mason 1996), strips must be planned to be short enough in length that nonvolant animals can circumvent them and still access resources and habitat on the other side. In addition, such strips must be far enough apart so as not to create functionally isolated forest patches because they are not large enough to accommodate a group of foraging primates, for example. In countries dependent on forestry but interested in maintaining viable and diverse wildlife communities, policies regarding harvestable size and harvest intensities must be altered to accommodate the habitat and space use requirements of forest wildlife. The integration of spatial and behavioral ecology provides an important tool for modeling the effects of different management schemes on vertebrates, providing for the basis for

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67 scientifically-sound decisions regarding both the economic and ecological future of an area.

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68 Table 3-1. Habitat attributes examined for two redtail monkey home ranges in unlogged and heavily logged forest in Kibale National Park, western Uganda, and expected relationships. The attributes were measured in a set of spatially stratified plots. Measures of Resource Availability are derived from an inventory of all trees above 10cm diameter at breast height (DBH) in each home range. Habitat Attribute Description of attribute and relationship to other forest characteristics Topography Slope Percent slope Aspect Direction of slope (in degrees) Forest Structure Canopy Structure Height Maximum height (m) estimated in each habitat plot. Higher canopy height is related to a better developed arboreal habitat Total Basal Area A function of abundance and size (diameter at breast height) of each tree, measured in cm 2 A higher total basal area is related to a more connected canopy and a higher resource base. Understory Vegetation Height The average height (m) of the understory vegetation. A higher understory height is related to greater density of the understory. Acanthus Acanthus arborescens and related plants constitute a dense, herbaceous vegetation that often dominates regenerating or swampy areas. This vegetation is avoided by redtails. Marantachloa Marantachloa leucantha is an understory herb characteristic of upland forest light gaps and is a preferred food of redtails Saplings/Seedlings Saplings and/or seedlings are typically present in closed forests with an open understory. Resource Availability Food Trees The density (individuals/m 2 ) of tree species fed on by redtails in each area. A higher density of redtail food items indicates a higher carrying capacity of the forest. Unripe Fruit The density (individuals/m 2 ) of tree species from which monkeys were observed to feed on unripe fruits Ripe Fruit The density (individuals/m 2 ) of tree species from which monkeys were observed to feed on ripe fruits Young leaves The density (individuals/m 2 ) of tree species from which monkeys were observed to feed on young leaves Flowers The density (individuals/m 2 ) of tree species from which monkeys were observed to feed on flowers

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69 Table 3-2. The six components produced in Principal Components Analysis describing the major variation in habitat structure in areas of unlogged (UL) and heavily logged (HL) forest in Kibale National Forest, Uganda. The loading values describe the correlation of the habitat variables with the respective component. Loading values Component 1 Component 2 Component 3 Component 4 Component 5 Component 6 UL HL UL HL UL HL UL HL UL HL UL HL Importance (%) 39.85 27.41 21.04 25.43 14.27 16.99 9.81 12.31 8.72 10.99 6.29 6.86 Cumulative (%) 39.85 27.41 60.89 52.84 75.16 69.83 84.98 82.14 93.70 93.13 100.0 100.0 Variable Basal Area 0.454 0.578 -0.333 -0.215 0.327 0.153 -0.470 -0.278 0.662 0.239 0.389 Slope 0.306 -0.186 -0.918 -0.969 -0.139 0.187 0.171 Understory Height -0.315 -0.116 -0.628 -0.658 -0.118 -0.108 0.335 0.692 -0.420 0.114 0.501 Canopy Height 0.515 0.534 -0.299 -0.155 -0.144 -0.699 -0.448 -0.783 Acanthus -0.326 -0.124 -0.605 -0.700 0.357 -0.127 -0.624 0.270 -0.102 -0.637 Sapling 0.478 0.593 0.200 -0.149 0.787 0.597 0.224 -0.277 0.239 -0.433

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70 Table 3-3. Standardized direct, indirect and total effect coefficients for path analysis models (Model 1) examining habitat variables on probability of occurrence of redtails in unlogged and heavily logged areas of Kibale National Park for general habitat utilization model (Fig. 3-2). Effect on Unlogged Heavily Logged Habitat Utilization Habitat Utilization Variable Direct Effect Indirect Effect Total Effect Direct Effect Indirect Effect Total Effect Slope 0.000 0.144 0.144 0.000 -0.064 -0.064 Basal Area 0.362 (p = 0.001) 0.004 0.366 0.278 (p = 0.009) -0.075 0.203 Canopy Height -0.003 0.012 0.010 -0.053 -0.083 -0.136 Understory Height -0.044 0.000 -0.044 -0.249 (p = 0.001) 0.000 -0.249 r 2 0.140 0.105

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71 Table 3-4. Effect coefficients for path analysis models examining the effect of unripe fruit trees on feeding site selection by redtails in unlogged and heavily logged areas of Kibale National Park. Overall model fit and coefficient significance and values are similar in each to models examining the effect of unripe or ripe fruit trees on the following response variables: feeding site selection, foraging site selection, and resting site selection. Variables in correlation Direct effect Indirect effect Total effect Model fit Unlogged Slope 0.000 0.144 0.144 Canopy Height 0.014 0.000 0.014 Acanthus 0.146 0.000 0.147 Unripe food trees 0.442 (p<0.001) 0.006 0.448 r 2 0.183 2 = 13.671 df = 2 p = 0.001 Heavily Logged Slope 0.000 0.034 0.034 Canopy Height -0.142 0.018 -0.124 Acanthus 0.054 0.000 0.054 Unripe food trees 0.254 (p=0.002) 0.024 0.278 r 2 0.090 2 = 0.080 df = 2 p = 0.961

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72 Figure 3-1. The study site is located in the Kanyawara field site in the northern part of Kibale National Park in western Uganda. Map of Uganda and Kibale National Park area adapted from MUBFS (2003).

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73 Figure 3-2. Patterns of habitat utilization by redtail monkeys in unlogged (a, c) and heavily logged (b, d) forest areas of Kibale National Park, western Uganda. Graphs (a) and (b) depict the number of times the focal monkey group entered a grid cell in study area. Units are in meters, in the Universal Transverse Mercator (UTM) coordinate system, Zone 36 North. Graphs (c) and (d) show the probability distribution of habitat use by groups in each area. Outer bounds describe the 95% confidence limit of groups space use and are shown in light blue (unlogged: 29.24 ha; heavily logged: 39.76 ha). Bright pink areas indicate most intensively used areas.

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74 Figure 3-3. Surface maps for six of the habitat variables measured in an area of unlogged forest in Kibale National Park, western Uganda. Darker shading corresponds to a higher value of the variable. Cutoff values for shading levels were chosen to produce approximately equal frequency quintiles. The Morans I statistic describes the degree of spatial autocorrelation in the variable; significant (p<0.001) autocorrelations are marked with an asterisk. The range describes distance over which the variable show autocorrelation, and a nugget value close to 0 indicates that most of the spatial variation in the variable values has been captured by the sampling design.

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75 Figure 3-4. Surface maps for six of the habitat variables measured in an area of heavily logged forest in Kibale National Park, western Uganda. Darker shading corresponds to a higher value of the variable. Cutoff values for shading levels were chosen to produce approximately equal frequency quintiles. Morans I statistic describes the degree of spatial autocorrelation in the variable; significant (p<0.001) autocorrelations are marked with an asterisk. The range describes distance over which the variable show autocorrelation, and a nugget value close to 0 indicates that most of the spatial variation in the variable values has been captured by the sampling design.

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76 Figure 3-5. Path diagram (Model 1) describing habitat utilization by redtails in unlogged and heavily logged areas of Kibale National Park, western Uganda. Standardized regression weights are shown for each pair of variables. Significant correlations (p<0.01) are indicated by thick arrows. Model goodness-of-fit is described by the 2 statistic and associated p-value. A p-value above 0.05 indicates that the model does not differ significantly from the saturated model, which indicates that the model has a good fit. Components denoted Es, Et, Eu, Ec, and Em are error terms corresponding to each variable in the model.

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77 Figure 3-6. Path diagram (Model 2) describing habitat utilization by redtails in an unlogged area of Kibale National Park, western Uganda. Standardized regression weights are shown for each pair of variables. Significant correlations (p<0.01) are indicated by thick arrows. Model goodness-of-fit is described by the 2 statistic and associated p-value. A p-value above 0.05 indicates that the model does not differ significantly from the saturated model, which indicates that the model has a good fit. Components denoted Es, Et, Eu, Ec, and Em are error terms corresponding to each variable in the model.

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78 Figure 3-7. Probability maps of trees potentially producing unripe and ripe fruits in unlogged and heavily logged forest of Kibale National Park, Uganda, during the period from August to December, 2002. Tree species were identified as potential fruit trees in each area on the basis of feeding observations on two groups of redtail monkeys whose home ranges coincided with these areas. Darker shading corresponds to a higher number of fruit trees. Range indicates the scale of each variables patch size. Nugget values close to zero indicate that the variable has been sampled at the appropriate spatial resolution.

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CHAPTER 4 SUMMARY AND CONCLUSIONS This study contributes to our understanding of the variables important in determining the behavioral patterns of one primate species, redtail monkeys (Cercopithecus ascanius) in response to logging as a link to population density. Most importantly, however, it serves as a model for applying a spatial approach to investigating primate behavior in relationship to the habitat changes caused by anthropogenic disturbance to gain a better understanding of the mechanisms underlying population change in relation to large-scale disturbance, such as timber extraction. This is critical for planning ecologically sound conservation strategies. In the first study, I contrasted the behavior of redtail monkeys in unlogged and heavily logged forest in Kibale National Park, Uganda. My results indicate that redtails in the heavily logged area use larger home ranges, travel further and spend more time foraging and less time feeding than conspecifics in the unlogged areas. They appear to be constrained by resource and habitat limitations. Specifically, a reduced tree density in the heavily logged area leads to a lower abundance of potential food trees and reduced arboreal pathways. Microhabitat characteristics in the heavily logged area also appeared to constrain the animals movements and foraging ability. Using measurements obtained from a set of sampling plots spatially stratified throughout the home range of each focal group, I determined that understory density was high in a much larger proportion of the heavily logged area than the unlogged area. In cases where density was high, the understory tended to be dominated by a thorny, herbaceous plant surrounding emergent 79

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80 trees. Vertical stratification was also reduced over much of the heavily logged area. These results suggest that the forest in the heavily logged area is structurally limiting to redtails. Since they typically forage in both the upper and mid-canopy layers and sometimes descend to the ground, as well, having access to understory vegetation that is passable and provides resources may be critical. Moreover, such understory helps to create pathways throughout the forest. The finding that fewer canopy layers and a denser understory dominate the heavily logged area, suggests that travel for redtails is constrained by patches of inaccessible vegetation, limiting both the vertical and horizontal foraging space of the monkeys. These differences likely contribute to the lower group sizes, higher travel distances and larger home ranges of redtails in the heavily logged areas, and may explain the reduction in population density of redtails in logged areas, as well. In the second study, I examined habitat selection by redtails in the heavily logged and unlogged areas to determine whether the behavioral differences observed in the first part were due to habitat limitations or to differences in habitat preferences. I evaluated a set of topographic, forest structural, and resource attributes for each home range to determine the spatial structure of each. In particular, I was interested in determining whether the variables showed distinct patch structures and whether variables operated on similar spatial scales. I found that the variables tended to arrange themselves spatially, but that these scales varied among the two areas. Basal area showed the least amount of patch structure in both areas and operated on the small scale, in the range of 20 to 40 m. In both areas, the majority of remaining variables grouped together with slope. In the unlogged area, the scale of these variables was relatively large, whereas in the heavily

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81 logged area, the variables showed an intermediate scale of patch structure. These results suggest that there is a common organizing structure to both areas, but that the specific characteristics of each area are a result of scale. Conducting a path analysis to test the relationship between these variables and to the response variable (redtail habitat use) revealed that slope is most likely the major structuring variable in each area. Both the location of disturbance from logging (typically in high flat areas) and the nature of regeneration (dominated by an aggressive, colonizing herb: Acanthus arborescens) in the heavily logged study appears to interact with this structuring variable to define the characteristics of the area. Habitat selection by the monkeys was similar in both areas, but reflected this difference in the specific characteristics of both areas. That is, redtails were primarily influenced by basal area, but understory vegetation also played an important role. In the heavily logged area, the height of the understory (which is correlated with density) was the most critical direct factor, in addition to basal area, negatively correlating with monkey habitat use. In the unlogged area, A. arborescens, contributed to a better model of habitat selection. I interpret this result as a reflection of the more diverse understory in the area, where the correlation between understory height and understory density is not as strong as in the heavily logged area. A. arborescens is far less common in the unlogged than the heavily logged area; thus, the results of this analysis suggest that the monkeys avoid areas dominated by A. arborescens, which are also low in basal area. I also interpret this result as an indication that monkeys in the unlogged area have greater flexibility in selecting suitable habitat, since it appears to be more common and more accessible than in the unlogged area. When feeding, foraging, and resting site selection,

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82 were examined, potential fruit trees were the strongest estimator of these response variables only in the heavily logged area. A subsequent analysis of fruit tree spatial structure showed that fruit trees are patchier in the heavily logged area than the unlogged. Most importantly, however, slope had a strong indirect effect on the monkeys in both areas through its relationship to the other variables. This study highlights the complex interaction of variables characterizing forest habitats. In particular, it shows how the spatial structure of each variable is important in defining its relationship to the other variables, and how these interactions lead to the unique characteristics defining different areas. This analysis allowed me to begin to separate the effects of underlying topography from those of disturbance from logging on primate response to timber harvesting. In a broader sense, this highlights the importance of structuring variables in determining ecosystem trajectories. This combination of variables is then reflected in the distribution and abundance of species over the larger landscape. Thus, sustainable forest management plans must identify the general set of variables describing the system and determine the relationships between them as a prerequisite for deciding the level and spatial pattern of timber extraction that will best maintain the integrity of the system. Using quantitative spatial methods, it should be possible to model the cascading effects of different plans and make informed decisions about which plan best fits economic and ecological goals. Considering the important role primates are thought to play in seed dispersal, this study also has implications for forest regeneration. In the particular case of Kibale, better planning prior to logging might have considered the way in which timber extraction reconfigured primate foraging and travel spaces, and thus the spatial pattern of

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83 regeneration. Where primates cannot access trees, the suite of dispersers for an individual tree is decreased and the likelihood that seeds remains closer to the parent tree and become more vulnerable to density-dependent mortality increases. Moreover, because of the recolonizing vegetation of the area, even those trees that do manage to attract dispersers or those that rely on autodispersal will have to contend with inhospitable conditions for germination, seedling growth, and sapling survival. Thus, the relationship between primates, logging, and seed dispersal using spatial analysis is an important avenue for further research. Furthermore, this research also reinforces the importance of planning conservation and forest management at multiple scales. Current wisdom holds that biodiversity conservation plans must integrate human settlements, natural resource exploitation, and protection across the landscape (Poiani et al. 2000, Sanderson et al. 2002). Because of the large home range needs of some vertebrates, focusing on the maintenance of population viability of these species has been suggested as a way of ensuring for the protection of large, ecologically diverse areas (Wilcox 1984, Lambeck 1997, Caro and ODoherty 1999, Miller et al. 1999). In light of the range of scales over which organisms interact with the environment, more recent plans expand this concept to account for the scale and grain of habitat requirements of these large vertebrates (Sanderson et al. 2002). However, as the current study shows, organisms with relatively small, restricted, and stable home ranges can be negatively affected by resource extraction that occurs on a scale that coincides with their habitat requirements and territorial constraints, even if this level of extraction does not affect larger vertebrates. In Kibale, although elephants and chimpanzees are affected by logging-induced habitat

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84 changes (Struhsaker 1997), they are not arboreally restricted and can travel further to seek out suitable resources and habitat. Thus, vertebrates with smaller spatial requirements should form part of the suite of landscape species (Sanderson et al. 2002) chosen for conservation planning because they may be more directly affected by finer-scale changes in forest spatial structure caused by logging.

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LIST OF REFERENCES Arbuckle, J. 2003. AMOS 5 Path Analysis Software. Chicago, IL: Sweetwaters Corp. Boinski, S. and Sirot, L. 1997. Uncertain conservation status of squirrel monkeys in Costa Rica, Saimiri oerstedi citrinellus. Folia Primatologica 68: 181-193. Burt, W.H. 1943. Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24: 346-352. Butynski, T.M. 1990. Comparative ecology of blue monkeys (Cercopithecus mitis) in highand low-density subpopulations. Ecological Monographs 60(1): 1-26. Bormann, F.H., and Likens, G.E. 1979. Pattern and Process in a Forested Ecosystem. New York: Springer Verlag. Cannon, C.H. and Leighton, M. 1994. Comparative locomotor ecology of gibbons and macaques: selection of canopy elements for crossing gaps. American Journal of Physical Anthropology 93: 505-524. Caro, T.M. and ODoherty, G. 1999. On the use of surrogate species in conservation biology. Conservation Biology 13: 804-814. Chapman, C.A. 1988. Patterns of foraging and range use by three species of neotropical primates. Primates 29:177-194. Chapman, C.A. 1995. Primate seed dispersal: coevolution and conservation implications. Evolutionary Anthropology 4: 74-82. Chapman, C.A. and Onderdonk, D.A. 1998. Forests without primates: primate/plant codependency. American Journal of Primatology 45: 127-141. Chapman, C.A., Balcomb, S.R., Gillespie, T.R., Skorupa, J.P. and Struhsaker, T.T. 2000. Long-term effects of logging on African primate communities: a 28-year comparison from Kibale National Park, Uganda. Conservation Biology 14: 207-217. Chapman, C.A. and Lambert, J.E. 2000. Habitat alteration and the conservation of African primates: case study of Kibale National Park, Uganda. American Journal of Primatology 50: 169-185. 85

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86 Chapman, C.A. and Peres, C. 2001. Primate conservation in the new millennium: the role of scientists. Evolutionary Anthropology 10: 16-33. Chapman, C.A., M.D. Wasserman, and T.R. Gillespie. Behavioural patterns of colobus in logged and unlogged forests: The conservation value of harvested forests. In V. Reynolds, H. Notman, and N. Newton-Fisher (Eds.). Primates of Uganda. (Submitted). Clarke, M.R., Collins, D.A., and Zucker, E.L. 2002. Responses to deforestation in a group of mantled howlers (Alouatta palliata) in Costa Rica. International Journal of Primatology 23: 365-381. Clark, C.J., J.R. Poulsen and V.T. Parker. 2001. The role of arboreal seed dispersal groups on the seed rain of a lowland tropical forest. Biotropica 33: 606-620. Cords, M. 1986. Interspecific and intraspecific variation in diet of two forest guenons, Cercopithecus ascanius and C. mitis. Journal of Animal Ecology 55: 811-827. Cowlishaw, G. and Dunbar, R. 2000. Primate Conservation Biology. Chicago: The University of Chicago Press. Cressie, N.A.C. 1993. Statistics for Spatial Data. New York: John Wiley and Sons, Inc. Cushman, S. A., and K. McGarigal. 2002. Hierarchical, multi-scale decomposition of species-environment relationships. Landscape Ecology 17:637-646. Di Fiore, A. 2003. Ranging behavior and foraging ecology of lowland woolly monkeys (Lagothrix lagotricha poeppiggi) in Yasuni National Park, Ecuador. American Journal of Primatology 59: 47-66. Emmons, L.H. 2000. Of mice and monkeys: Primates as predictors of mammal community richness. In J.G. Fleagle, C.H. Janson, K.E. Reed (Eds), Primate Communities. Cambridge: Cambridge University Press. Pp. 171-188. Environmental Systems Research Institute (ESRI). 2002. ArcView 3.3 and ArcGIS 8.3 Geographic Information Systems Software. Redlands, CA. Everitt, B. S. and Dunn, G. 1991. Applied Multivariate Data Analysis. London: Edward Arnold. Fahrig, L. and Paloheimo, J. 1988. Effect of spatial arrangement of habitat patches on local population size. Ecology 69: 468-475. Fairgrieve, C. and Muhumuza, G. 2003. Feeding ecology and dietary differences between blue monkey (Cercopithecus mitis stuhlmanni Matschie) groups in logged and

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87 unlogged forest, Budongo Forest Reserve, Uganda. African Journal of Ecology 41: 141-149. Fimbel, R.A., Grajal, A., and Robinson, J.G, eds. 2001. Logging-wildlife issues in the tropics: an overview. In R.A. Fimbel, A. Grajal, and J.G. Robinson (Eds.), The Cutting Edge: Conserving Wildlife in Logged Tropical Forests, Eds. New York: Columbia University Press. Pp. 3-9. Food and Agriculture Organization (FAO). 2003. State of the Worlds Forests 2003. Rome: FAO Information Division. Forman, R.T.T., and Godron, M. 1986. Landscape Ecology. New York: John Wiley and Sons. Fotheringham, A.S., Brunsdon, C., and Charlton, M. 2002. Quantitative Geography: Perspectives on Spatial Data Analysis. London: Sage Publications. Frumhoff, P.C. 1995. Conserving wildlife in tropical forests managed for timber. Bioscience 45: 456-464. Galletti, M., Pedroni, F., and Morrellato, L.P.C. 1994. Diet of the brown howler monkey (Alouatta fusca) in a forest fragment in southeastern Brazil. Mammalia 48: 111-118. Garber, P.A. and Lambert, J.E. 1998. Primates as seed dispersers: Ecological processes and directions for future research. American Journal of Primatology 45: 3-8. Gautier-Hion, A. 1988. Food niches and co-existence in sympatric primates in Gabon. In D.J.Chivers and J.Herbert (Eds.), Recent Advances in Primatology. London: Academic Press. Pp. 269-286. Gebo, D. and Chapman, C.A. 1995. Positional behavior in five species of old world monkeys. American Journal of Physical Anthropology 97: 49-76. Gillespie, T.R., Chapman, C.A. and Greiner, E.C. Long-term effects of logging on parasite dynamics in African primate populations. Unpublished manuscript. Grieser-Johns, A. 1997. Timber Production and Biodiversity Conservation in Tropical Rain Forests. Cambridge: Cambridge University Press. Grieser-Johns, A. and Grieser-Johns, B. 1995. Tropical forest primates and logging: long term co-existence? Oryx 29: 205-211. Gustafson, E.J. and Crow, T.R. 1996. Simulating the effects of alternative forest management strategies on landscape structure. Journal of Environmental Management 46: 77-94.

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BIOGRAPHICAL SKETCH Claudia Stickler received her B.S. degree in biology and international political economy from the University of Puget Sound, Tacoma, Washington, in May 1996. She spent three years in Cameroon, Central Africa, first as an agroforestry extension agent with the Peace Corps and then as a research assistant on a San Francisco State University project focusing on tropical forest regeneration and plant-animal interactions. In 2000, she worked in Suriname, South America, as a research assistant on a long-term University of Florida study of primate behavior and ecology. In the fall of 2001, she began work towards her M.S. degree in the College of Natural Resources and Environment, University of Florida. She is affiliated with the Department of Zoology, the Tropical Conservation and Development Program, and the Land Use and Environmental Change Institute at the University of Florida. 94


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Title: The Effects of Logging on Primate-Habitat Interactions: A Case Study of Redtail Monkeys (Cercopithecus ascanius) in Kibale National Park, Uganda
Physical Description: Mixed Material
Copyright Date: 2008

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THE EFFECTS OF LOGGING ON PRIMATE-HABITAT INTERACTIONS:
A CASE STUDY OF REDTAIL MONKEYS (Cercopithecus ascanius)
IN KIBALE NATIONAL PARK, UGANDA














By

CLAUDIA MARGRET STICKLER


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2004

































Copyright 2004

by

Claudia M. Stickler

































To the memory of my grandmother, who had talent and intelligence in abundance, but
lacked the freedom and opportunity to follow all of her dreams, and to my parents,
siblings and other family members, who have consistently served as both inspiration
and support.















ACKNOWLEDGMENTS

I extend my deep thanks to my advisor, Colin Chapman, for seeing me

through this sometimes difficult process, devoting many hours to helping me in

innumerable ways in the field and on-campus. I thank him for providing the generosity

and support I needed to accomplish this project. The other members of my committee

were no less important. Michael Binford took me under his wing and spent countless

hours guiding me towards appropriate methods, critical resources, and important

perspectives, expressed confidence in me, and provided encouragement at crucial

moments. Sue Boinski first took a chance and hired me to work as part of her field crew

in Suriname and encouraged and facilitated my application to the University of Florida.

Since then, she has been a constant source of professional and personal support. Graeme

Cumming introduced me to the importance of a spatial approach and helped me to

proceed through every step of my thesis, always reminding me of the essential elements

of my research, demanding great conscientiousness, and encouraging a sense of pride and

confidence in my work.

Other faculty members at the University of Florida offered critical support

throughout this stage of my education. Jane Southworth initially helped me to understand

the applications of a spatial perspective on social and ecological questions. Since then,

she has not only helped to guide and support my research, but has also been an

unwavering source of encouragement and support. I want to extend my thanks to

Marianne Schmink for helping me to maintain my roots in the practical and social aspects









of conservation and development. Steven Humphrey helped to create a flexible and rich

environment for taking on the difficult task of gaining an interdisciplinary education.

Thanks are due also to Karen Kainer, Jon Dain, Robert Buschbacher and Dan Zarin for

advice and encouragement on how to bridge the gap between basic and applied science

and between the natural and social sciences.

In the field, I was assisted by a team of incredibly skilled and knowledgeable

people. First and foremost, I thank Clovice Adyeeri Kaganzi for his tireless dedication to

following the redtails in all kinds of weather and vegetation and to mapping every last

tree in the forest. I attribute the quality of this work in large part to the careful and

thoughtful documentation of his observations and to his vast store of knowledge of

Kibale. Richard Kaserengenyu was a cheerful and conscientious observer, who took

every change of plans in stride and took great pride in his work. James Apuuli Magaro

and Chris Kaija were invaluable in accurately inventorying each tree in the selected study

sites, and entertained and educated me in innumerable ways. Lawrence Tusiime provided

his impressive skills as a botanist, and proved a steadfast partner in sampling habitat

characteristics for many months. Tinkasiimire Astone enthusiastically took on each new

job of seeking out and planting seeds, and monitoring seedlings. He taught me a great

deal about the history of Kibale and its surroundings. I extend my deep thanks to Patrick

Omeija and Jackson Efitre for the all the support they provided. Last but not least, Alice

Akiki provided companionship and dedicated care.

My friends and colleagues at the University of Florida have been invaluable.

Beyond providing friendship, they are also great sources of knowledge and experience.

Tracy Van Holt, Rodrigo Vergara, Connie Clark and John Poulsen took enormous









amounts of time to help me with my work and through the most difficult times. Meredith

Evans, Anna Prizzia, Lin Cassidy, Wendy-Lin Bartels, Franklin Paniagua, Alfredo Rios,

Tom Gillespie and Cedric Worman formed an extraordinary and patient support team,

without whom I would not have arrived at this point with any humor or sanity.

Finally, I thank my family for setting high standards for what individuals can

achieve, no matter what their vocation. They provide me with inspiration and a sense of

responsibility to the broader world. My parents afforded me great opportunities and

freedom to follow my dreams, even if those took me far away. My twin, Alexander, and

my brother Peter and my sister Carin have cheered me on and always created a place in

their homes for me.

My research and education were supported through a National Science

Foundation Graduate Research Fellowship. The Office of the President, Uganda, the

Uganda National Council for Science and Technology, and the Uganda Wildlife

Authority granted me permission to work in Uganda. Gilbert Isabirye-Basuta and John

Kasenene graciously allowed me to conduct research at the Makerere University

Biological Field Station.
















TABLE OF CONTENTS

Page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES ..................................................... ix

L IST O F FIG U RE S .............. ......................... ........................... ....................... .. .. .... .x

CHAPTER

1 GEN ER AL IN TR OD U CTION ...................................... ...................... ...............1...

2 COPING WITH LOGGING: BEHAVIORAL MECHANISMS OF REDTAIL
MONKEYS (Cercopithecus ascanius) IN KIBALE NATIONAL PARK,
W E STERN U G A N D A ...................... ...............................................................7......

Introduction ................................................................................ ..........................7
M eth o d s ...................................................................................................... .......... 9
S tu d y A re a ..................................................................................................... 9
B ehavioral O observations ...................................... ....................... ............... 10
R source and H habitat A ssessm ent.................................................. ............... 12
A n a ly se s .............................................................................................................. 1 3
R results ............................................................................ .................... 16
A activity B budget .......................................................................................... 16
H om e R ange Size and U se ..................................... ..................... ............... 17
D aily R an g in g ...................................................................................................... 18
Overall D iet ...................................... ..................... ... .... ............... 18
D ietary Selection and D iversity..................................................... 19
H ab itat U se .............. ........................................................................................... 2 0
M ixed Species A associations ........................................................... ................ 2 1
D isc u ssio n ................................................................................................................ .. 2 2
R source A availability .. .. ...... ............ .............................................. 23
C o n c lu sio n ............................................................................................................... .. 2 5

3 THE EFFECTS OF PATCH STRUCTURE ON REDTAIL MONKEY
(Cercopithecus ascanius) HABITAT USE IN UNLOGGED AND HEAVILY
LOGGED AREAS OF KIBALE NATIONAL PARK, UGANDA ...........................46

In tro d u ctio n ................................................................................................................ 4 6
M e th o d s ...................................................................................................................... 4 9









Study A rea ...........................................................................................................49
B ehavioral and H habitat A ttributes.................................................... ................ 50
A naly ses .............. ..................................................................... ......5 1
R e su lts.. . ........ ........................................................... ......................................5 5
H habitat U se ........................................................................................................55
Spatial Structure of Habitat Attributes ..............................................................56
P ath A analysis ................................................................................................. 57
D iscussion........................................... ..................... 60
Factors Explaining Habitat Use Patterns .......................................... ............... 61
C conclusion .............. ........................................................................ . ..... 63

4 SUM M ARY AND CONCLUSION S .................................. .............. ................ 79

L IST O F R E FE R E N C E S ........................................................... ............... ................ 85

BIOGRAPH ICAL SKETCH ................... ............................................................... 94





































viii















LIST OF TABLES


Table page

2-1 Test of independence for group activity samples pooled by 60-min or 15-min
sam ple for redtail groups ......................................... ........................ ................ 28

2-2 Monthly and mean home range size or redtail groups in logged and unlogged forest
in Kibale National Park, Uganda between August and December 2002...............29

2-3 Time spent eating different types of foods by redtail groups...............................30

2-4 Dietary diversity and overlap across months for redtail groups in logged and
unlogged forest of Kibale National Park, Uganda. .............................................32

2-5 Characteristics of the tree community in home range areas of focal redtail groups.33

3-1 Habitat attributes examined for two redtail monkey home ranges in unlogged and
heavily logged forest ......................... ........................ ................................... 68

3-2 The six components produced in Principal Components Analysis describing the
major variation in habitat structure in areas of unlogged and heavily logged forest.69

3-3 Standardized direct, indirect and total effect coefficients for path analysis models
(Model 1) examining habitat variables on probability of redtail occurrence...........70

3-4 Effect coefficients for path analysis models examining the effect of unripe fruit
trees on feeding site selection by redtails ........................................... ................ 71















LIST OF FIGURES


Figure page

Figure. 2-1. The Kanyawara study site is located in Kibale National Park, western
Uganda. Maps are adapted from MUBFS 2003.................................................34

Figure 2-2. Percentage of time engaged in different activities by redtails in unlogged
and heavily logged forest ......................................... ........................ ................ 35

Figure 2-3. Monthly total grid cells entered and cumulative grid cells entered by
redtails in unlogged and heavily logged areas..................................... ................ 36

Figure 2-4. Pattern of home range use by redtail groups in heavily logged and
u n lo g g ed fo rest......................................................................................................... 3 7

Figure 2-5. Daily distance traveled by redtail monkeys in logged and unlogged areas. 38

Figure 2-6. Average percent of monthly feeding observations on different items by
redtail monkeys in unlogged and heavily logged forest. ...................................39

Figure 2-7. Strauss selectivity index (measures selectivity vs. dominance) for top
80% of food species selected by redtails............................................. ................ 40

Figure 2-8. Height in canopy of redtails in unlogged and heavily logged forest...............41

Figure 2-9. Distribution of number of canopy layers observed in
hom e ranges of redtails. .............. .............. ............................................ 42

Figure 2-10. Understory vegetation associated with redtail location in unlogged and
heavily logged forest.. ............. ................ .............................................. 43

Figure 2-11. Monthly percent of time spent in mixed species associations for redtails....44

Figure 2-12. The proportion of observations in which redtails in unlogged and
heavily logged areas. ............. ................ .............................................. 45

Figure 3-1. The study site is located in the Kanyawara field site in the northern part
of Kibale National Park in western Uganda........................................ ................ 72

Figure 3-2. Patterns of habitat utilization by redtail monkeys in unlogged and heavily
log g ed forest area. .................................................................................................... 7 3









Figure 3-3. Surface maps for six of the habitat variables measured in an area of
u n lo g g ed fo rest ......................................................................................................... 7 4

Figure 3-4. Surface maps for six of the habitat variables measured in an area of
heavily logged forest.. ............. ................ .............................................. 75

Figure 3-5. Path diagram (Model 1) describing habitat utilization by redtails in unlogged
and heavily logged areas ..................................................................... ................ 76

Figure 3-6. Path diagram (Model 2) describing habitat utilization by redtails in an
u n lo g g ed are a .......................................................................................................... 7 7

Figure 3-7. Probability maps of trees potentially producing unripe and ripe fruits in
unlogged and heavily logged forest of Kibale National Park, Uganda. ..............78















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

THE EFFECTS OF LOGGING ON PRIMATE-HABITAT INTERACTIONS: A CASE
STUDY OF REDTAIL MONKEYS (Cercopithecus ascanius) IN KIBALE NATIONAL
PARK, UGANDA

By

Claudia Margret Stickler

May 2004

Chair: Colin A. Chapman
Major Department: School of Natural Resources and Environment

The scale and intensity of timber exploitation in the tropics have increased in recent

decades, leaving degraded forests and deforested areas in their wake. The impact of these

activities on forest-dependent vertebrates is varied-ranging from loss of habitat and

resources to population isolation or extinction. In recognition of the inadequacy of

existing protected areas to support viable populations in the long-term, the focus of

wildlife conservation in tropical forests is shifting to identifying and supporting more

ecologically sound timber management strategies. This study focuses on the impact of

logging on primate behavior and habitat use because primates play an important role in

tropical forest communities, serving as seed dispersers and as indicators of overall

mammal species richness. Primates may also face a greater threat from broad-scale

habitat loss and hunting than most birds do, because they are constrained to travel

arboreally or terrestrially, and may not be able to move readily between areas to take

advantage of greater resource availability or other suitable habitat.









To better understand how logging affects primates, I first focused on contrasting

the behavior of two habituated groups of redtail monkeys in unlogged and heavily logged

forest in Kibale National Park, Uganda. Monkeys in the heavily logged area were found

to use larger home ranges, travel further and spend more time foraging and less time

feeding than conspecifics in the unlogged areas. They appear to be constrained by

resource and habitat limitations. Specifically, a reduced tree density in the heavily

logged area leads to a lower abundance of potential food trees and reduced arboreal

pathways. Microhabitat characteristics in the heavily logged area also appeared to

constrain the animals' movements and foraging ability.

I also examined habitat selection by redtails in the heavily logged and unlogged

areas to determine whether the behavioral differences observed in the first part were due

to habitat limitations or to differences in habitat preferences. I evaluated a set of

topographic, forest structural, and resource attributes for each home range to determine

the spatial structure of each. Tree basal area showed the least amount of patch structure

in both areas and operated on the small scale, in the range of 20 to 40 m. In both areas,

the majority of remaining variables grouped together with slope. In the unlogged area,

the scale of these variables was relatively large, whereas in the heavily logged area, the

variables showed an intermediate scale of patch structure. These results suggest that

there is a common organizing structure to both areas, but that the specific characteristics

of each area are a result of scale. Habitat selection by the monkeys was similar in both

areas; they were primarily influenced by basal area, but understory vegetation also played

an important role.














CHAPTER 1
GENERAL INTRODUCTION

The scale and intensity of timber exploitation in the tropics has increased in recent

decades, leaving degraded forests and deforested areas in its wake (Fimbel et al. 2001,

Weber et al. 2001). The impact of these activities on forest-dependent vertebrates are

varied-ranging from loss of habitat and resources to population isolation or extinction

(Fimbel et al. 2001). In recognition of the inadequacy of existing protected areas to

support viable populations in the long-term, the focus of wildlife conservation in tropical

forests is shifting to identifying and supporting more ecologically sound timber

management strategies (Frumhoff 1995). This shift is accompanied by a heightened

awareness of the role vertebrates play in community processes. For example, in tropical

areas, vertebrates' role in seed dispersal is notable, with up 45 to 90% of forest trees

thought to be adapted for vertebrate dispersal (Jansen and Zuidema 2001). As a result,

the challenge for managers and researchers is to help in designing timber management

plans that do not compromise these interactions and processes. A prerequisite for

developing such suggestions, however, is a deeper understanding of how animals respond

to the habitat changes caused by logging. Unfortunately, these are still not well-known.

This study focuses on the impact of logging on primate behavior and habitat use

for several reasons. Primates play an important role in tropical forest communities.

Primates are thought to be important as seed dispersers, consuming a wide range of seeds

(Chapman 1995, Garber and Lambert 1998). As a result of the high percentage of

biomass they represent in many tropical rain forests (Terborgh 1983), primates' services









as seed dispersers for a great variety of fruiting trees suggests that their net impact on

forest regeneration may be high (Jansen and Zuidema 2001). Thus changes in primate

behavior and ranging due to logging and forest fragmentation may alter or disrupt seed

dispersal processes (Chapman and Onderdonk 1998, Clark et al. 2001). Primates also

serve as good indicators of mammal species richness in most tropical areas (Emmons

2000), indicating that forest management plans based on a better understanding of

primate response to logging could serve a wider animal community, as well. Finally,

primates may face a greater threat from broad-scale habitat loss and hunting than most

birds do. Because they are constrained to travel arboreally or terrestrially, and since

many are territorial, they may not be able to move readily between areas to take

advantage of greater resource availability or other suitable habitat (Cannon and Leighton

1994, Putz et al. 2001). Changes in the forest structure due to logging may create barriers

for primates where none exist for some bird species or larger mammals.

Among vertebrate taxa, primates are one of the best-studied. The majority of

research examining the effects of logging on primates focus on relationships between

population density and broad vegetation changes (Skorupa 1988, Johns and Skorupa

1987, Plumptre and Reynolds 1994, Chapman et al. 2000). However, these studies have

yielded few generalizations that contribute to the formulation of specific strategies for

sustainable managing forests (Plumptre and Grieser-Johns 2001). As a result, interest in

understanding primate behavior and the habitat changes caused by logging at a finer scale

is growing. Ample evidence exists to suggest that behavioral responses to resource

availability and habitat quality are important in dictating population density of organisms

(Krebs and Kacelnik 1991, Lima and Zollner 1996, Pulido and Diaz 1997). Among









primates, numerous studies have shown that seasonal differences in food availability

result in differences in diet composition (Van Schaik et al. 1993, Kaplin et al. 1998,

Poulsen et al. 2001), activity budgets, and travel patterns (Van Schaik et al. 1993,

Poulsen et al. 2001) for different species. Similarly, group sizes of foraging primates can

also be influenced by the size and spatial distribution of food patches (Leighton and

Leighton 1982). Animals may also alter travel patterns or fruit consumption rates as a

function of resource patch size and availability (Leighton and Leighton 1982, Chapman

1988). Those studies comparing primate behavior between logged and unlogged forest

show that primates do make some adjustments in their behavior or group size to

compensate for habitat changes (Olupot 2000, Clarke et al. 2002, Fairgrieve and

Muhuzoma 2003), but they are too few in number to allow generalization from the

variety of responses they describe.

I addressed these issues by quantifying the differences in behavior and habitat and

resources available to redtail monkeys (Cercopithecus ascanius) in heavily logged and

unlogged areas of Kibale National Park, Uganda. Broadly, I focus on the following

questions:

* Does the behavior of primates differ in heavily logged and unlogged forest?
* Does this reflect limitations posed by the habitat or resource availability?
* Do logged and unlogged forests differ in their structure and composition?
* Are such differences relevant to primate habitat selection?
* Which habitat attributes are important to primates in logged and unlogged areas in
selecting suitable habitats?

To better understand how logging affects primates, I first focused on contrasting

the behavior of two habituated groups of redtail monkeys in unlogged and heavily logged

forest. This species is of interest because redtails, and similar species across Africa, tend

to adapt well to conditions of disturbed forest (Cords 1986). At Kibale, however, regular









population censuses since logging show that redtail densities continue to decline 28 years

after logging (Chapman et al. 2000). I collected behavioral data on each group during

all-day follows, approximately 3 times a week over a 5 month time period, using 15-min

scan sampling. At each scan interval, I recorded the group's activities, diet, and location.

These data were analyzed to determine if and how the behavioral patterns of redtails in

heavily logged and unlogged areas differs. To evaluate the observed patterns in relation

to resource availability in each area, I inventoried the tree community above 10 cm

diameter at breast height (DBH). Each tree was identified and mapped, and subsequently

categorized as a "food" or "nonfood" tree based on the feeding observations collected

from each group. In addition, I assessed the density of the understory vegetation and

vertical stratification in each home range, to determine the availability of different

microhabitat types. I compared these measurements with redtails' canopy height

preferences and associations with understory density categories. This study allowed an

assessment whether redtails in the heavily logged areas were constrained by the

abundance and distribution of resources and preferred microhabitats.

To further understand how observed behavioral differences might be a response to

differences in habitat structure, I examined patterns of habitat utilization by the monkeys

relative to the habitat available through a spatial analysis. The major of objectives of this

second study were to identify the variables defining areas of suitable habitat and to

develop predictive models of habitat selection based on important ecological variables. I

analyzed the spatial structure of a suite of variables describing the topography and forest

structure and composition, to determine if the measured variables operate at the same

scale, and at the same scale in each home range. The variables, including slope, aspect,









canopy height, basal area, understory height, and the presence of several understory

vegetation types were sampled from a set of 50 m2 plots, spatially stratified in each

group's home range. Food tree density was determined from the tree map completed in

the first study. I identified which of these factors explained the most variation in each set

of data through a principal components analysis (PCA). In combination with correlation

analyses, the PCA helped to determine which variables to enter into a conceptual model

of the interacting variables determining redtail habitat use, using path analysis. The same

conceptual model was applied to the habitat attribute and monkey use data from each

area, to determine if the same set of variables and interactions explains redtail habitat

selection in both the logged and the unlogged area. Finally, I developed surface models

predicting redtail habitat utilization in each area based on the variables determined to be

important by the path analysis, using spatial interpolation.

The main objective of these two studies was to link patterns of resource

availability and suitable habitat to population density by examining redtail behavioral

patterns. Since the focus of this objective was to examine the relationships between

different variables on the basis of spatial patterns, the research relied on a spatially

explicit approach. This is based on the key tenet of landscape ecology-that ecological

pattern and process are linked (Turner 1989). A spatial approach has the potential to

uncover important relationships that might otherwise have gone unnoticed or remained

uninterpretable, by providing information about the spatial scales of interacting factors.

An important part of this is the ability to evaluate variability within a dataset for the

information it may reveal about the fundamental structure organizing the system (Forman

and Godron 1986). The focus on the interaction of spatial patterns allows for more






6


accurate description of the system, thus leading to more accurate models (Turner et al.

2001). Most importantly, from a management perspective, this suggests the potential to

predict and thus avoid undesirable spatial configurations created by logging activities,

based on a quantitative understanding of the spatial scales of basic attributes of the forest.

Equally important is the fact that this type of research builds the foundation for

developing predictive models over a much larger spatial scale by relating variable

measurements at different scales.














CHAPTER 2
COPING WITH LOGGING: BEHAVIORAL MECHANISMS OF REDTAIL
MONKEYS (Cercopithecus ascanius) IN KIBALE NATIONAL PARK, WESTERN
UGANDA

Introduction

Currently, tropical forests are being degraded and fragmented at increasing rates,

primarily due to logging-associated activities (Chapman and Peres 2001). Wildlife may

be directly affected by habitat degradation, or indirectly through increased human

accessibility to forests, which can lead to increased hunting or habitat loss through land

conversion (Frumhoff 1995). Although most wildlife populations dependent on closed-

canopy mature forests decline as a result of logging activities, the long-term ecological

responses of most vertebrates are still not well understood (Fimbel et al. 2001).

The majority of studies examining the effects of timber extraction on primates

compare population densities in logged and unlogged areas of forest (Skorupa 1988,

White 1992, 1994, Plumptre and Reynolds 1994, Grieser-Johns and Grieser-Johns 1995,

Chapman et al. 2000). Most research suggests that vegetation changes resulting from

logging are responsible for differences in primate population densities, although

individual species responses sometimes differ greatly (Plumptre and Reynolds 1994;

Struhsaker 1997, Chapman et al. 2000). Studies are often not comparable due to

differences among the methods employed or time allowed after the logging event

(Chapman et al. in prep). Additionally, numerous confounding factors (e.g., hunting) can

affect primate densities (Wilkie et al. 1992, 1998, Oates 1996, Struhsaker 1997, Chapman

et al. in prep). Thus, although primates are one of the best-studied taxa of tropical forest









vertebrates, few generalizations about their responses to the effects of logging have

emerged.

This lack of generalizability could be the result of a high degree of intraspecific

variation in responses to logging among sites. In the Budongo Forest Reserve, Uganda,

the increase in blue monkey (Cercopithecus mitis) populations in logged areas appears to

be the result of increased fruit availability associated with a change in forest composition

following logging (Fairgreave and Muhuzuma 2003). In contrast, in Kibale National

Park, Uganda, less than 200 km from Budongo, blue monkey populations decrease after

logging. Even among folivorous species, the variation in responses is high, with some

responding positively and others negatively to the habitat changes caused by logging, due

to group size requirements for resources (Chapman et al. in prep). Although research

regarding primates' behavioral responses to logging is growing (Clarke et al. 2000,

Olupot 2000), more is needed to understand how changes induced by logging affect

different species.

Here, I quantify some aspects of the behavior of redtail monkeys (Cercopithecus

ascanius schmidtii Matschie) in logged and unlogged forest in Kibale National Park,

Uganda. This species is of particular interest because redtails and other members of their

lineage (e.g., C. cephus) are generally the most adaptable of the cercopithecines, often

thriving in disturbed secondary forest and on forest edges, as well as in undisturbed forest

(Struhsaker and Leland 1979, Cords 1986, Butynski 1990, Thomas 1991, Naughton-

Treves 1996). However, primate censuses conducted nearly 30 years after logging in

Kibale indicate a continued decline in redtail population density in the heavily logged









areas (Chapman et al. 2000). Redtail density is significantly lower in the logged areas

(1.04 groups/km2) than in the unlogged areas (11.48 groups/km2; Chapman et al. 2000).

The objective of this study was to determine whether redtails exhibit different

behavioral patterns in logged and unlogged forest. To meet this objective, I contrast the

ranging behavior and foraging ecology of two groups of redtail monkeys in logged and

unlogged forest of Kibale. Furthermore, I assess the quality of habitat and abundance of

resources in each area to develop possible explanations for observed differences in

behavior. This type of information is important for constructing informed forest

management plans.

Methods

Study Area

Kibale National Park (766 km2) is located in western Uganda (0 13'-0o 41'N and

300 19'-300 32'E), near the base of the Ruwenzori Mountains (Fig. 2-1). It is classified as

moist evergreen forest, transitional between lowland rain forest and montane forest

(Struhsaker 1997, Chapman and Lambert 2000). Situated at 1500 m elevation, the

Kanyawara study site in the northern part of the park, receives an average of 1741 mm of

rainfall per year, distributed over distinct biannual wet and dry seasons (Chapman and

Chapman, unpublished data). Kibale's logging history, coupled with a lack of hunting,

provides an ideal environment for examining primate response to logging.

I contrasted the behavior of a group of redtails in a heavily logged area, to one in

an unlogged area. The heavily logged area was harvested in 1969 (Struhsaker 1997), and

approximately 7.4 stems/ha or 21m3/ha were removed (Skorupa 1988; Struhsaker 1997).

However, as a result of high incidental damage associated with the felling, Skorupa

(1988) estimates that approximately 50% of all trees in the area were destroyed. Since









then, many areas have become dominated by dense, thorny herbaceous vegetation,

frequently dominated by semi-woody Acanthaceae species (primarily, Acanthus

arborescens), which surrounds emergent colonizer or remnant trees (Struhsaker 1997,

Paul et al. in press). The area used by the focal monkey group also abuts smallholder

agricultural fields and tea plantations. The unlogged area has never been commercially

harvested. A few large stems (0.03-0.04 trees/ha) were removed before 1970; however,

this low level of extraction appears to have had little effect on the structure of the forest

(Skorupa and Kasenene 1984, Skorupa 1988, Struhsaker 1997, Chapman et al. 2000).

There is an extensive, gridded trail system in both areas (Struhsaker 1997).

Behavioral Observations

Diet and behavioral data were collected from one group of redtails in each site

between August and December 2002. Both groups have been previously studied and

were accustomed to human presence; nevertheless, to ensure that both were adequately

habituated, a field assistant and I followed each group for two months prior to collecting

quantitative data. The group in the unlogged forest, group UL, consisted of 24

individuals, while the group in the heavily logged area, group HL, had 16 individuals.

These numbers are representative of the mean group sizes of groups in unlogged and

heavily logged forest at Kibale (unlogged: 27.70.9, n= 3, heavily logged: 15.00.5, n=

3; K. Rode, unpublished data).

Behavioral scores were recorded during all-day follows using a 15-minute scan

sampling regime. We attempted to carry out follows in blocks of 3 days, spaced at 1-

week intervals, for each group. However, due to the often extremely dense and

impassable vegetation in the heavily logged area, we often followed group HL for 4 days









and group UL for 2 days to obtain comparable numbers of observations per week (UL:

2.40.76 days/week, 20.7+7.05 hours/week; HL: 2.40.70 days/week, 18.1+6.88

hours/week). Scans lasted 10 minutes or until 5 individuals had been sampled, whichever

came first. Observers recorded the time and activity maintained by an individual for at

least 5 sec. Individuals were never scored more than once during a scan, and we tried to

avoid scoring the same individual in two consecutive scans. Twenty-one different

behavioral categories were scored, but subsequently collapsed into six categories for

analyses: feed, search for insects, rest, move, social, and other (which included drinking,

urinating, and defecating). If an individual was feeding, observers recorded the food part

(e.g., insect, fruit, leaf) and food species, if known (less than 0.01% of species could not

be identified).

Habitat use was quantified by estimating each individual's height in the canopy

and ranking the density of the understory vegetation below the individual. Height in the

canopy was scored on a scale of 1 to 5, with the scale representing the following height

divisions 1 = 0-7 m (4.72.9); 2 = 7-14 m (12.94.6); 3 = 14-18 m (16.74.7); 4 = 18-23

m (20.43.6); 5 = 23 m or higher (24.82.2). Understory vegetation was also scored on a

scale of 1 to 5, which ranged from 1 = very open (no or almost no vegetation, generally

only tree seedlings or saplings) to 5 = very dense (generally herbaceous, often a

combination of Graminaceae and Acanthaceae species, generally impassable for

humans).

The presence of other primate species, and other groups of redtails or potential

predators within 50 m of the group was recorded at the end of each scan period. Group

spread was estimated every 15 minutes as the length of the axes of an ellipse whose









center was the perceived center of mass of the group. The surface area of the ellipse was

calculated and averaged over all scans. I calculated the square root of this measure to

obtain mean group spread.

We recorded the location of the center of the group every 15 min by measuring

the distance and angle to the nearest point with known coordinates. Reference points

were located at 20 m intervals on the gridded trail system in each group's home range.

Reference points were recorded using a hand-held global positioning system.

Coordinates for group locations were calculated and plotted in the ArcView 3.2 and

ArcGIS 8.3 geographic information systems (ESRI 2002). This method allows more

exact measurement of day range and home range estimation because the measurements

are based on geographically accurate positions. Moreover, recording monkey locations

in a GIS database enables a richer and more quantitative analysis of habitat and space use

by facilitating the integration of animal movements with multiple habitat layers (Hooge et

al. 2001).

Resource and Habitat Assessment

Resources and habitat quality were assessed for an area at least as large as that

considered to constitute each redtail group's stable home range, based on observations of

these groups for 12 months prior to the current study (K. Rode, unpublished data). In the

areas used by the focal groups, all trees above 10 cm diameter at breast height (DBH)

were identified to species, measured, and plotted in the GIS. Less than 1 % of all stems

mapped could not be identified to species. We measured the distance and angle to each

tree from the reference points located throughout each study area and calculated

geographic coordinates for each tree (UL: 15,489 trees over 48.4 ha; HL: 8433 trees over

40.1 ha). The availability of plant food resources was assessed by categorizing each tree









as "food" or "nonfood" based on the feeding observations. For example, in the unlogged

area only those tree species that monkeys fed on between August and December 2002 in

this area were scored as "food" species. Stem density and total basal area were calculated

for all trees, all food trees, and specific species. Shannon diversity and evenness were

calculated for the entire tree community and for the suite of potential food trees in each

home range.

To assess other habitat characteristics likely to be important to redtails, such as

vertical complexity and density of understory vegetation, I established a set of 50 m2

plots in each home range, spatially stratified by spacing plots every 20 m along the trail

grid in each area. Trails were usually no more than 50 to 100 m apart, resulting in 1484

plots in the unlogged area and 1362 plots in the heavily logged area. The plots assessed

7.42 ha (37%) and 6.81 ha (32%) of the total home range of the focal group in the

unlogged and heavily logged forest. Vertical stratification (i.e., number of canopy layers)

was assessed by scoring presence or absence of vegetation in each of the height

categories.

Analyses

Home range size was estimated in two ways. I used the Animal Movement

extension for ArcView (Hooge and Eichenlaub 1997) to estimate total and monthly area

used by the minimum convex polygon method. The MCP method estimates the area both

used and probably traversed by each group, but likely contains areas not used by the

animals (Burt 1943). Although this method generally overestimates home range size, it is

the most commonly employed and therefore facilitates comparison with other studies

(Hooge et al. 2001). I also estimated home range sizes using grid-cell analysis (White

and Garrot 1990), which has been employed in other studies of primate ranging (Kaplin









2001, Di Fiore 2003). The GCA method reflects only the area the group used during

observation periods, and thus, provides a conservative estimate of home range size

(White and Garrott 1990). However, it is highly dependent on the size of grid cell

utilized (White and Garrott 1990, Hooge et al. 2001). In this case, a 30 x 30 m cell size

was determined from the observed mean group spread of each group (unlogged: x =

29.60.16; heavily logged: x = 31.40.22), and from the groups' 30 m movement

distance between observation intervals during foraging. The best estimate of home range

area for each group is likely to fall in between the values calculated by the MCP and

GCA methods. For each method, mean monthly home range sizes were compared using

Mann-Whitney tests.

Home range use was measured by superimposing a grid of 30 x 30 m cells over the

plotted group locations. To assess home range use, I counted the number of different grid

cells the troop entered each day, the total number of cells entered per day, and the

cumulative number of different cells entered per month per troop. In addition, I

calculated the number of times a grid cell was entered during the study for each group.

Daily travel distances were calculated, by summing the straight line distances

between successive location points for each day. I used only full observation days in

evaluating and comparing average daily travel distances. Full days were defined as

beginning by at least 0800 h and lasting until at least 1800 h. This also allows

comparison with previous studies of redtails at Kibale that used the same time periods

(Lambert 1997, K. Rode unpublished data). Mean daily travel distances were compared

between groups using t-tests and within groups using Kruskal-Wallis tests.









For each site, the entire group served as the unit of analysis. Therefore, the

proportion that each activity type and each food part contributed to each scan was

calculated. For understory vegetation density and height in canopy, scores for individual

observations within each scan period were averaged. To evaluate the appropriate

sampling interval, I contrasted monthly proportion of scans that each activity comprised

using 15- and 60-min scan intervals for each group. Based on our observation, redtails

rarely maintained the same activity for 60 minutes. There was no significant difference

using either scan interval (15 or 60 min) for any of the activity categories for either group

(Table 2-1). As a result, each 15-min scan interval was considered to be an independent

observation, and scans were pooled to determine overall and monthly activity budgets,

diet composition (by food part) and overall habitat use. Monthly activity budgets

between groups were compared using Mann-Whitney tests.

I determined the contribution of various plant species to the monthly diets of the

two groups by calculating their simple proportional representation among the set of all

feeding records for each group. Diet composition and selection were compared using a

variety of indices. Dietary diversity and evenness was calculated for both monthly and

overall diets using the Shannon-Wiener (H') index and Shannon evenness (E). Shannon

t-tests were used to compare diversity of diets by calculating a variance of H' and degrees

of freedom based on the proportional abundance of individual food species in each

group's overall diet (Magurran 1988). Overlap between the diets was calculated using

the Holmes-Pitelka index, Di = ZSi, where Di is the total percent overlap and Si is the

percent overlap between shared food items (Holmes and Pitelka 1968; Struhsaker 1975).

Finally, selectivity of food items relative to their availability was assessed using the









Strauss selectivity index (L): L = ri pi, where ri and pi equal the percent of the food

item in the diet and the environment, respectively (Strauss 1979). This index is less

sensitive than many other commonly used selectivity indices to the presence of rare food

items. Preference values range from -1 to +1 and are centered on zero, which indicates

random feeding; negative values indicate avoidance and positive values indicate

selection.

In cases where nonparametric statistical tests were used, equivalent parametric

tests were carried out and found to show the same general trends (i.e., significant or non-

significant probabilities). Therefore, even in cases where non-parametric tests are

performed, parametric means are presented in figures because they are more comparable

to other studies.

Results

Activity Budget

The UL group spent more time engaged in feeding than the HL group (Mann-

Whitney: p = 0.008). However, HL spent significantly more time searching for insects,

moving, and resting than group UL (search: p = 0.016; move: p = 0.008; rest: p = 0.016).

Overall, the UL group spent nearly 50% of its time feeding and 20% of its time searching

for insects (Fig. 2-2). The group in the heavily logged area devoted approximately 30%

of its time to feeding and 30% to searching for insects. Time spent resting differed

markedly between the two groups, with UL group spending about 15% of its time resting

and HL group spending 25% of the time resting. HL group was engaged in moving 3

times more frequently than UL group. Time spent engaged in social activities, such as

grooming, did not differ between the groups (p = 0.69), and comprised 7.31.80% and

7.94.14% of the UL and HL groups' activity budget, respectively.









Home Range Size and Use

The mean monthly home range size of UL group based on the minimum convex

polygon (MCP) method was twice as large than when grid-cell analysis (GCA) was used

(sign test paired by month, Z = 1.788, p < 0.01, Table 2-2). The total area used by the

UL group was 32.5 ha, based on MCP, and 20.2 ha, based on GCA. The group used 40-

57% of the total home range based on GCA in any one month, and 48-76% of total MCP

calculated home range. The mean monthly home range area of the HL group based on

the MCP method was more than three times larger than the GCA estimate (sign test, Z =

1.788, p < 0.01, Table 2-2). The total area used by the HL group was 49.0 ha, based on

MCP, and 21.3 ha, based on GCA. The group used 49-63% of total home range based on

MCP in any one month and 33-46% of total GCA-calculated home range. Monthly home

range size was larger for HL group than the UL group when estimated using MCP (Mann

Whitney: U = 1.0, df= 8; p = 0.016). When monthly home range size was estimated

using GCA, there was no difference between the groups (U = 5.0, df= 8, p = 0.117).

To explain these inconsistencies between the two methods of estimating home

range size, I examined the pattern of home range use of each group (Fig. 2-3). Both

groups entered new quadrats throughout the study, suggesting that the home range of

each group will be larger than what was recorded over 5 months (Fig. 2-3). Nevertheless,

the pattern of home range use between groups appears consistent over that time.

Generally, UL group entered more grid cells per month, but HL group entered slightly

more new cells per month (UL: 46.6 cells per month 49.26; HL: 48.4 cells per month

35.01). This result is predicted by an examination of spatial patterns of home range use

of each of the groups (Fig. 2-4). Overall, more contiguous grid cells in the UL home

range were entered at least once during the period (i.e., there are very few unshaded









quadrats in UL's range use map within the outer boundary of the recorded home range),

indicating that the entire bounded area was used more intensively (Fig. 2-4b). In

contrast, group HL's home range use map shows a larger number of quadrats not entered,

with more widely dispersed areas of use (Fig. 2-4a).

Daily Ranging

The mean distance traveled each day was greater for the group in the logged area

than the group in the unlogged forest (UL group: 1111 m + 265.0; HL group: 1332 m

387.8; t = -2.677, df= 61, p = 0.01). Mean daily travel distance for group UL did not

differ among months (Kruskal-Wallis: x2 = 2.728; df = 4; p = 0.604), but varied among

months for the HL group (X2 = 10.692; df= 4; p = 0.03; Fig. 2-5).

Overall Diet

The majority of feeding observations for both groups were on insects, but a

significantly greater proportion of feeding observations were made on insects for the HL

group than the UL group (UL: 62.3%; HL: 75.5%; t = 7.042; df= 2221; p <0.001).

Although insects made up the bulk of feeding observations for both groups, they

consumed a wide variety of other food types, as well (Fig. 2-6). A greater proportion of

feeding observations of the UL group was comprised of mature leaves, young leaves,

petioles, and ripe fruits (mature leaves: t = 3.389; df = 2221; p=0.001; young leaves:

t=4.806; df= 2221; p<0.001; petioles: t= 4.245; df= 2221; p<0.001; ripe fruit: t = 8.596;

df = 2221; p <0.001), but this group spent significantly less time feeding on flowers than

group HL (t = -6.036; df= 2221; p< 0.001). Whereas more than a third of the UL

group's diet was composed of fruits, leaves, seeds, and flowers, less than 25% of the HL

group's overall diet was composed of these plant foods.









Dietary Selection and Diversity

Consistent with previous studies (Gautier-Hion 1988), both group's plant diets

were dominated by relatively few plant species. Monkeys in the UL group selected plant

foods from 70 different species, but 81.8% of all feeding observations came from only 12

species (Table 2-3). Of these, one was a terrestrial herb (Marantochloa leucantha),

which was not common in the heavily logged area and one was a liana, while the

remaining are trees. Three of the remaining 10 tree species were small trees (mean DBH

< 15cm). In contrast, the plants comprising 80.9% of the HL group's plant diet were all

medium to large trees (mean DBH > 15cm). The HL group's plant diet was selected

from 31 species, but only 10 tree species comprised 80% of the observations.

UL group's overall plant diet, over the study, was more diverse than that of HL

group (Shannon t = 5.24; df= 808; p < 0.001; Table 2-4). Mean dietary overlap across

months between the groups in the different areas was 21.7% (+6.45 s.d.), but ranged from

15.6 to 30.2%. Redtails in the unlogged area also consumed fruit from eight different

Ficus species, whereas monkeys in the heavily logged area fed from only two fig species.

The tree community above 10cm DBH in the unlogged area was less diverse

(Shannon t = -37.30; df = 20641; p<0.001) than that of the heavily logged area, yet had a

50% higher stem density (Table 2-5). When I considered only food species, I found that

nearly 90% of the individuals inventoried in the unlogged area were potential food trees,

whereas fewer than 60% of individuals fell in this category in the heavily logged area

(Table 2-5). Food tree diversity also differed significantly between the two areas

(Shannon t= 4.67, df= 13838, p<0.001). Stem density of potential food trees was almost

2.5 times greater in the unlogged area in comparison to the heavily logged area.









The Strauss selectivity index (L) showed that, of the most preferred food species,

only UL monkeys' choice ofFicus exasperata was not different from random, and only

Diospyros abyssinica was selected less than it was available (Fig. 2-7a). The remaining

species were selected in greater proportions than they were available in the forest.

However, since the herb M. leucantha and the vine of the Piper sp. were not inventoried,

it was difficult to judge how much the monkeys selection of these species differed from

their respective distributions in the unlogged forest. Similarly, Rothmannia urcelliformis,

Craterispermum laurinum, and Tarennapavioides are small trees, so they were likely to

be underrepresented in the inventory if the mean DBH for each species' population is not

at least 10cm. However, it was also clear that the monkeys were not only selecting

species that were among the most dominant in the forest. Linocierajohnsonii and

Macaranga /I ei/ lu thii i/il, among the most preferred food species for UL group, did not

count among the most dominant species in the tree community in the unlogged forest

(Table 2-3; Fig. 2-7).

In the heavily logged area, monkeys selected eight of their ten most preferred

food species more than they were available in the forest (Fig. 2-7b). This was

particularly true for Symphonia globulifera and D. abyssinica. They selected Celtis

durandii less than expected based on its availability, and did not select Celtis africana.

Of the 10 most preferred food species, all were among the most abundant in the forest.

Habitat Use

Redtails in the heavily logged forest occupied the highest part of the canopy (22 m

or higher) 86% of the time (Fig. 2-8). They were never observed to descend to the

ground and were observed in lower/emerging understory vegetation (up to 7 m) less than

1% of the time. The animals in the unlogged area spent 85% of their time in a









combination of three layers: the upper canopy (43.0%), the lower-canopy (11.8%) and

lower/emerging understory (25.6%; Fig. 2-8). This difference is unlikely to be due to the

relative availability of the different canopy layers, since an assessment of the available

number of canopy layers in each area showed a similar distribution (Fig. 2-9; statistics

are presented in the figure legend). Both groups occupied the two upper canopy layers

more than expected and the lower layers less than expected, given the proportional

distribution of each height category in the home ranges.

The two areas differed in the density of understory vegetation. The heavily

logged area was dominated by very dense herbaceous growth and very little open

understory, whereas the unlogged area showed a more even distribution of understory

vegetation density (Fig. 2-10). The groups differed in their association with different

classes of understory vegetation. In the unlogged area, redtails were associated with the

majority of understory vegetation classes in proportion to their distribution. The

monkeys were observed to associate with the most dense vegetation less than expected,

given the proportional distribution of vegetation density classes in the home range (x =

26.89, df = 1, p<0.001). In contrast, redtails in the heavily logged area associated with

the two most open understory vegetation classes more than expected and with the more

closed understories less than expected (Fig. 2-11). Open understory was rarer in the

heavily logged site, so it appears that HL redtails were selecting areas of less dense

vegetation and that this habitat attribute may be more important in explaining redtail

presence than the number of canopy layers.

Mixed Species Associations

The UL group spent more of its time (65.7%) in mixed-species associations than

the HL group (36.2%) (x2 = 503.93; df= 1; p<0.001). Of the time UL group spent in









mixed-species associations, 72% was with red colobus, 31% with blue monkeys, and

20% with black-and-white colobus (Fig. 2-12). HL group spent 52% of its time in

association with red colobus, 24% with blue monkeys, 24% with black-and-white

colobus, and 20% with grey-cheeked mangabeys. Because the monkeys sometimes

associate in groups of more than two species, these percentages do sum to 100. When

percent of time observed in association was compared with the percent each species

comprises of the primate community in each area (derived from Chapman et al. 2000),

redtails in the both areas were found to associate with red colobus (UL: 2 = 1181.65, df

= 1, p<0.001; HL: x2 = 137.16, df =1, p<0.001) and blue monkeys (UL: x2 = 1424.92, df

= 1, p<0.001; HL: 2 = 1059.88, df= 1, p<0.001) more than expected. In the heavily

logged area, redtails associate with black-and-white colobus less than expected (x =

224.83, df= 1, p<0.001), whereas associations between these two species occur

proportional to the black-and-white colobus' density in the unlogged area (x2 = 224.83,

df=l, p=0.021). Association with mangabeys is more than expected for redtails in the HL

group (x2= 189.41, df =1, p<0.001) and less than expected in the unlogged area (2 =

53.08, df = 1, p<0.001).

Discussion

Redtail monkeys inhabiting heavily logged forests used larger home ranges, had

lower dietary diversity and spent less time feeding than conspecifics inhabiting unlogged

forest habitats. Despite having a smaller group size, redtail monkeys in logged areas

travel further and spend more time moving than redtails in unlogged habitats. These

results suggest that redtails in logged forests are spending more time and expending more

energy in search of food (Milton 1984; Janson 1988). The results of my habitat

assessment further indicate that reduced abundance of resources and suitable habitat









constrain redtails in the heavily logged forest, which may explain lower population

densities.

Resource Availability

The quality and quantity of resources available to the monkeys is likely to strongly

influence behavioral patterns and reproductive success (Janson and Chapman 2000). To

compensate for the greater energy expenditure by redtails in the heavily logged area, I

would predict higher feeding and foraging rates. My observations confirm that although

the HL group spent more time searching for insects than group UL, it did not spend more

time feeding. I explain this pattern by the decrease in potential plant food sources in the

heavily logged area. Our inventory of trees indicates that there are fewer potential food

trees in the heavily logged area. This corresponds with an inventory conducted in 1980-

81, 11 years after logging operations ended (Skorupa 1988). In addition, the heavily

logged area had a lower density of of figs, which appear to be important to redtails in

both areas (Struhsaker 1997). In other forests, this loss of food trees appears to be

counterbalanced by the colonization of gaps and cleared areas by important fruit-

producing pioneer species, such as Musanga sp. (Johns and Skorupa 1987). At Kibale,

however, pioneer tree species that colonize after large disturbances are not important

food sources for frugivores (Struhsaker 1997). Finally, trees in heavily logged areas may

also be less productive. For example, Skorupa (1988) reports a 25% reduction in fruiting

and young leaf production in one heavily logged part of Kibale. It appears likely, then,

that redtails in the heavily logged area feed less on plant foods because the latter are both

less abundant and less productive.

Redtails may compensate for increased energy expenditure by feeding more on

insects. Skorupa (1988) provides indirect evidence that insect abundances could be









higher in heavily logged areas at Kibale. He suggests that the lower production of young

leaf and fruit in heavily logged areas at Kibale may be due in part to increased pressure

from insects (Skorupa 1988). A better assessment of insect resources is necessary to

determine to whether lack of plant resource availability leads to increased insect feeding

or if increased insect abundance results in the higher number of observations of insect

feeding.

Habitat Quality

The habitat structure in the heavily logged area may also contribute to the

behavioral patterns observed in the focal group. The results indicate that habitat may be

of lower quality for redtails in the heavily logged area. Redtails exploit numerous canopy

strata, frequently foraging in the mid-canopy layers and even descending to the ground to

forage (Struhsaker and Leland 1979, Gebo and Chapman 1995). It is likely that the

insect resources comprising the bulk of redtails' diets are located throughout the canopy

and that their vertical locations within the canopy may vary daily or seasonally (Terborgh

1993). This may partially explain redtails' general preference for mid-canopy layers in

the unlogged area. Our habitat assessment shows a distinct difference in amount of mid-

canopy available to redtails and in the density of understory vegetation in logged areas.

Since the vegetation in many parts of the heavily logged areas consists primarily of dense

thickets of herbaceous vegetation and semi-woody plants dominated by Acanthaceae

species, descending to the ground or the lower canopy appears to be physically

prohibitive, limiting monkeys to the upper strata. Moreover, the understory plant M

leucantha appears to be important for redtails in the unlogged areas. This plant is often

found in large patches throughout the undisturbed area. By contrast, very few isolated M.

leucantha plants were observed in the heavily logged area. I propose that redtails in









unlogged areas can exploit smaller areas-and coexist in higher densities-at least

particially because the vertical component of their environment provides more resources

and habitat than in the heavily logged areas.

Conclusion

Overall, I conclude that both resource abundance and the amount of suitable habitat

are reduced, causing redtails to expend greater effort in searching for food in the heavily

logged areas of Kibale. The fact that redtail population densities in these logged areas

were still declining 28 years after logging (Chapman et al. 2000), suggests that redtails in

heavily logged areas are still above the carrying capacity of the forest. Whereas

colobines at Kibale appear to compensate for ecological differences resulting from

habitat disturbance by adjusting their group size, a reduction in redtail group sizes in

logged areas has still not resulted in a stable population density. Other studies suggest

that parasite loads (Gillespie et al., submitted) and predation risk (Struhsaker and Leakey

1990, Mitani et al. 2001) could also be contributing to this decline. However, the results

presented here suggest that constraints on the particular habitat and resource requirements

of redtails are the primary drivers of population decline in redtails.

This study points to the importance not only of structure and composition of

regenerating forest in predicting the response of primates to disturbance, but of the

specific habitat requirements of different species. Members of the C. cephus lineage are

generally assumed to be highly flexible in their dietary requirements and highly adaptable

to regenerating and other disturbed forests (Thomas 1991, Kingdon 1997). However,

their preference for mid-canopy vegetation seems to make them more vulnerable to the

effects of logging than other species that prefer upper canopy strata. Conventional

logging generally alters the character of the understory through incidental damage









(Thiollay 1992, Laurance and Laurance 1996). Where timber stands are managed, plans

often call for the removal of vines, which normally serve as access routes and food

sources for nonvolant arboreal animals (Galletti et al. 1994, Putz et al. 2001). When

these vines are removed, animals suffer the loss of functional habitat areas. For the large

number of primate and other species that occupy the mid- and understory niche within a

community (Thomas 1991, Boinski and Sirot 1997, Putz et al. 2001), these management

strategies are especially harmful.

Management objectives focused on both timber harvest and maintenance of

healthy wildlife populations will have to mitigate the impacts of this disturbance. In the

case of Kibale, restoration efforts would have to focus not only on replanting food trees

and improving accessibility to those trees, but also on improving mid-canopy and

understory habitat. In another example, although reduced impact logging prescribes vine

cutting for target trees, the number of trees removed and the overall damage to the forest

is reduced from that caused by conventional logging practices, indicating that the amount

of habitat loss might be reduced as well (Holmes et al. 2002). This scheme appears to be

more cost effective than conventional logging, so it presents an interesting possibility for

compromise between the interests of foresters and wildlife conservationists. However,

research on the effects of the disturbance caused by reduced impact logging is still too

limited to unequivocally advocate the approach.

At Kibale, as in many sites, long-term impacts of logging on population densities

vary widely among primate species, but these differences may not be apparent when

behavioral responses are not understood. Thus, the ability of management or restoration






27


plans to take into account different species' resource and habitat needs is likely to be

critical in maintaining viable populations following logging disturbance.







28


Table 2-1. Test of independence for group activity samples pooled by 60-min or 15-min
sample for redtail groups in unlogged and heavily logged forest at Kibale
National Park, Uganda.

j Test of Independencea

Unlogged Heavily Logged
Activity
z p z p

Feeding 0.008 1.0 0.104 0.99
Searching 0.008 1.0 0.012 1.0
Traveling 0.32 0.99 0.32 0.99
Resting 0.014 1.0 0.083 0.99
Social 0.019 1.0 0.051 1.0
Other 0.21 0.995 0.66 0.96
adf = 4







29


Table 2-2. Monthly and mean home range size (ha) for redtail groups in logged and
unlogged forest in Kibale National Park, Uganda between August and
December 2002 (UL = unlogged group, HL, heavily logged group).

Home range area (ha) MCP1 Home range area (ha) GCA2
Month UL HL UL HL
August 21.13 23.94 11.43 9.18
September 17.23 30.39 8.19 9.90
October 22.89 30.00 10.44 8.73
November 24.60 30.80 9.45 8.37
December 15.54 29.11 9.99 7.02
Mean (+/-SD) 20.28 (+/-3.81) 28.85 (+/-2.81) 9.90 (+/-1.20) 8.64 (+/-1.07)
TOTAL 32.49 48.97 20.16 21.33
1MCP=minimum convex polygon method; 2GCA=grid-cell analysis, based on 30x30 m grid cell











Table 2-3. Time spent eating different types of foods by two redtail groups in unlogged
and heavily logged forest of Kibale National Park, Uganda, from tree species
comprising 80% of their respective feeding time (UL (unlogged) = 81.84%;
IL (heavily logged)= 80.9%).


Species (Family),
density
Celtis durandii
(Celtidaceae),
41.27


Linociera
johnsonii
(Oleaceae), 2.15


Diospyros
abyssinica
(Ebenaceae),
16.56


Trilepsium
madagascariense
(Moraceae),
62.30
Marantochloa
leucantha
(Marantaceae),
density unknown
Rothmannia
urcelliformis
(Rubiaceae), 0.34

Mimusops
bagshawei
(Sapotaceae), 4.03


Macaranga
schweinfurthii
(Euphorbiaceae),
3.54
Craterispermum
laurinum
(Rubiaceae), 0.49


Group UL
Part consumed

Seeds
Ripe Fruit
Unripe Fruit


Young Leaves
Seeds
Ripe fruit
Unripe fruit

Young leaves
Seeds
Ripe fruit
Unripe fruit

Young leaves
Flowers
Petioles/Pith

Seeds
Ripe fruit
Unripe fruit

Young leaves



Seeds
Ripe Fruit
Unripe Fruit


Seeds
Ripe fruit
Unripe fruit

Young leaves
Petioles/pith


% in
diet
1.2
20.5
1.4


Species
(Family), density
Diospyros
abyssinica
(Ebenaceae),
12.21


Symphonia
globulifera
(Guttiferae), 5.61


Paranari excelsa
(Rosaceae), 5.07



Trilepsium
madagascariense
(Moraceae), 7.26

Celtis durandii
(Celtidaceae),
17.95


4.5 Celtis Africana
(Celtidaceae),
10.63


Blighia
unijugata
(Sapindaceae),
2.88

Linociera
johnsonii
(Oleaceae), 3.77

Millettia dura
(Leguminoseae),
3.20


Group HL
Part
consumed
Young leaves
Ripe fruit
Unripe fruit
Seeds


% in
diet
0.2
11.9
15.1
0.4


Flowers 16.1
Ripe fruit 2.5



Ripe fruit 6.7


Young leaves
Petioles/Pith
Unripe fruit

Ripe fruit
Unripe fruit
Seeds

Young leaves
Unripe fruit


Young leaves
Ripe fruit
Unripe fruit


Young leaves
Ripe fruit
Unripe fruit

Young leaves
Flowers


--7


r-










Table 2-3. Continued


Group UL Group HL
Species (Family), Part consumed % in Species Part % in
density diet (Family), density consumed diet
Ficus exasperata Ripe fruit 1.7 Ficus Ripe fruit 2.3
(Moraceae), 7.20 Unripe fruit 0.1 sansibarica Unripe fruit 0.4
(Moraceae), 0.32
Piper gnensis Ripe fruit 1.8
(Piperaceae),
density unknown
Tarenna Mature leaves 1.1
pavetoides Young leaves 0.2
(Rubiaceae), Unripe fruit 0.4
density unknown

Total 81.8 80.9







32


Table 2-4. Dietary diversity and overlap across months for redtail groups in logged and
unlogged forest of Kibale National Park, Uganda.

Month Dietary Diversity (H') % Diet Overlap
Unlogged Heavily Logged
August 3.04 2.77 30.2
September 2.84 2.32 15.6
October 2.54 2.30 20.0
November 2.54 2.21 26.5
December 2.58 1.28 16.3
21.7 (+ 6.45)










Table 2-5. Characteristics of the tree community in home range areas of focal redtail
groups in unlogged and heavily logged forest areas of Kibale National Park,
Uganda, for all trees above 10cm DBH. Food trees species were identified
based on feeding observations for each group during the study period from
August to December 2002.

Unlogged Heavily Logged
All Trees Food Trees All Trees Food Trees
Species richness (S) 106 49 (46.23%)' 110 24 (21.82%)1
Individuals (N) 13542 11958 (88.30%)2 8433 4868 (57.73%)2
Simpson (1/D) 13.085 10.29 27.852 11.324
Shannon (H') 3.131 2.704 3.752 2.638
Shannon evenness 0.6714 0.6984 0.7982 0.8413
Stem Density (trees/ha) 334.4 295.3 210.3 121.4
Total Basal Area (m2) 4141.86 3700.36 2645.04 1905.02
1 Figure in parentheses indicates percent of total species
2 Figure in parentheses indicates percent of total number of individuals







34









JI I


-o pwer

















NO UGA-WA





M '0 5 10 kilometers




Figure. 2-1. The Kanyawara study site is located in Kibale National Park, western
Uganda. Maps are adapted from MUBFS 2003.














60


50


u 40
40


8 30 *

20
& 20


10



Feed Forage Travel Rest Social Other
Activity


Figure 2-2. Percentage of time engaged in different activities by redtails in unlogged
unshadedd) and heavily logged (shaded) forest areas of Kibale National Park,
Uganda. Bars indicate standard error, and asterisks indicate significantly
greater proportions of time devoted to different activities.













250


200


o 150



E 100


50


0
August September Ocbber November December


Figure 2-3. Monthly total grid cells entered and cumulative grid cells entered by redtails
in unlogged unshadedd) and heavily logged (shaded) areas of Kibale National
Park, Uganda. The bars indicate the number of different grid cells entered per
month by each group unshadedd = unlogged; shaded = heavily logged). The
lines show the cumulative number of new grid cells enter over the study
period (unlogged = solid line; heavily logged = dashed line).















percent of Observations


1 25
126-50
I 51-75
I 76-100


N
WtE
9


0 250 500 Meters
II I


Figure 2-4. Pattern of home range use by redtail gri (b) a (a) heavily logged and (b)
unlogged forest of Kibale National Park, Uganda. Darker shading indicates a
greater percentage of times that the group was present in a given 30x30 m grid
cell. The relative position of the grids to each other reflects the true spatial
relationship of the two home ranges.











3000



-

2000-

CD



1000-





0


\


\


P,
0\-


C
0
10,


Figure 2-5. Daily distance traveled by redtail monkeys in logged (shaded) and unlogged
unshadedd) areas of Kibale National Park, Uganda. Boxes indicate the
interquartile range of distances per month per group. The line inside each box
indicates the mean distance traveled each month and the bars show the
extreme values obtained each month.















100 0

90 0

80 0

70 0

600 -

50 0
0
400

E- 300 -
20 05

100
00

ML YL FL P SD RF URF Insect Other

Food Types


Figure 2-6. Average percent of monthly feeding observations on different items by redtail
monkeys in unlogged unshadedd) and heavily logged (shaded) forest in Kibale
National Park, Uganda. Lines above bars indicate standard error. ML =
mature leaves; YL = young leaves; FL = flowers; P = petioles; SD = seeds;
RF = ripe fruit; URF = unripe fruit. The group in the unlogged forest was
observed to consume seeds on a few occasions, but the amount was negligible
relative to other food items.































Oa)


Cepu e


-0.1 -0.05


0 0.05 0.1 0.15


-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3
(b)
Figure 2-7. Strauss selectivity index (measures selectivity vs. dominance) for top 80% of
food species selected by redtails in (a) unlogged and (b)heavily logged areas
of Kibale National Park, Uganda. Abbreviations: BlUn = Blighia unijugata;
BoPh = Bosqueia phoberos; CeAf= Celtis africana; CeDu = Celtis durandii;
CrLa = Craterispermum laurinum; DiAb = Diaspyros abyssinica; FiBr =
Ficus brachyleps; FiEx = Ficus excelsa; LiJo = Linocierajohnsonii; MaLe =
Marantochloa 'let i/lha, MaSc = Macaranga % /I ei//n thlii, MiDu =
Millettia dura; MiBa = Mimusops bagshaweii; PaEx = Paranari excelsa;
PiGn = Piper gnensis; RoUr = Rothmannia urcelliformis; SyGl = Symphonia
globulifera; TaPa = Tarenna pavioides.


TaPa
PiGn
FiEx
CrL
Ma c
RoUr
MaLe
M BoPh
DiAL
MCiBa
L iJo
1CeDu


SFiBr

MiDu
LiJo


CeAf
BlUn
BoPh

aEx

ISyGI
DiAb







41





100-


S87
c 80-



0 60-



C 40- 43
4-
0

S- 26 GROUP

17 UL

0 HL
5 4 3 2 1

Height in canopy


Figure 2-8. Height in canopy of redtails in unlogged and heavily logged forest at Kibale
National Park, Uganda, by percent of observations in each category. Height
category scale: 1 = 0-7m (x=4.72.9); 2 = 7-14m (x = 12.94.6); 3 = 14-18m
(x = 16.74.7); 4 = 18-23m (x= 20.43.6); 5 = 23m or higher (x = 24.82.2).







42





50



40- 41 42


0--
S30 32
S28
t-
20
| 20-
5 --18
17

10-
10 9


0
1 2 3 4 5

Number of vegetated height categories



Figure 2-9. Distribution of number of canopy layers observed in home ranges of redtails
in heavily logged and unlogged areas of Kibale National Park, Uganda. Plots
were scored as having vegetation in from 1 to all 5 of the possible height
categories. Height categories were as follows: 0-7m; 7-14m; 14-18m; 18-
23m; 23m or higher. Height in canopy of redtails relative to vegetated height
categories in each area: Unlogged: Class 1: 2 = 5379.033; df =l1; p < 0.001,
Class 2: 2 = 239.889; df= 1; p < 0.001, Class 3: 2 = 104.21; df= 1; p <
0.001, Class 4: X2 = 273.489; df= 1; p < 0.001, Class 5: X2 = 140.688; df= 1;
p < 0.001; Heavily Logged: Class 1: x2 = 80,004.19; df= 1; p < 0.001, Class
2: X2 =13.2129; df= 1; p < 0.001, Class 3: 2 = 769.0676; df= 1; p < 0.001,
Class 4: 1265.837; df= 1; p < 0.001, Class 5: n/a (0 observations).












50
45 /
40
S35
30 *
o25
0
20
15 -
10
5
0
1 2 3 4 5
Understory vegetation density ranking


Figure 2-10. Understory vegetation associated with redtail location in unlogged
unshadedd) and heavily logged (shaded) forest at Kibale National Park,
Uganda, by percent of observations in each category. Understory vegetation
was scored on a scale of 1 to 5, where 1 = very open (no or almost no
vegetation, generally only tree seedlings or saplings) and 5 = very dense
(generally herbaceous, often a combination of Graminaceae and Acanthaceae
species). Lines indicate the distribution of understory vegetation density
across categories, observed in the unlogged (solid line) and heavily logged
(dashed line) home ranges. Association of redtails with understory vegetation
classes in each area: Unlogged: Class 1: 2 = 0.194; df =l; p < 0.95, Class 2:
2 = 1.817; df= 1; p > 0.10, Class 3: 2 = 3.613; df= 1; p = 0.06, Class 4: 2
3.466; df= 1; p = 0.07, Class 5: X2 = 26.893; df= 1; p < 0.001; Heavily
Logged: Class 1: x2 = 994.73; df=l; p < 0.001, Class 2: 2 = 168.04; df= 1; p
< 0.001, Class 3: X2 = 5.825; df= 1; p = 0.04, Class 4: X2 = 5.825; df= 1; p
0.04, Class 5: 2 = 86.331; df= 1; p < 0.001. Asterisks indicate that redtails'
association with an understory vegetation class was different than expected.














100

90

80

U) 70
0
60

S50
40

30

20




RC BM BWC MB
Species



Figure 2-11. Monthly percent of time spent in mixed species associations for redtails in
unlogged unshadedd) and heavily logged (shaded) areas of Kibale National
Park Uganda. RC = red colobus (Procolobus badius), BM = blue monkey
(Cercopithecus mitis), BWC = black and white colobus (Colobus guereza),
MB = grey-cheeked mangabey (Lophocebus albigena).











(a) Unlogged
1000 .


S205 17




RC BWC


307




F


92
26E


(b) Heavily Logged


BM GCM


Figure 2-12. The proportion of observations in which redtails in (a) unlogged and (b)
heavily logged areas of Kibale National Park, Uganda were associated with
each of four other prominent species in the diurnal primate community
unshadedd), compared with each species' proportional representation in the
community (shaded). RC = red colobus (Procolobus badius), BM = blue
monkey (Cercopithecus mitis), BWC = black and white colobus (Colobus
guereza), MB = grey-cheeked mangabey (Lophocebus albigena).














CHAPTER 3
THE EFFECTS OF PATCH STRUCTURE ON REDTAIL MONKEY (Cercopithecus
ascanius) HABITAT USE IN UNLOGGED AND HEAVILY LOGGED AREAS OF
KIBALE NATIONAL PARK, UGANDA

Introduction

The timber industry is of central importance to many tropical countries, providing

sources of income and links to regional and global markets, among other things

(Naughton-Treves and Weber 2001). However, logging practices frequently damage and

degrade forests to the point of compromising their ability to provide important ecological

services, such as nutrient and water cycling and habitat for wildlife (FAO 2003). As a

result, recent years have seen a dramatic rise in efforts toward more sustainable logging

practices (Frumhoff 1995, Lindenmayer et al. 1999). However, neither the impact of

these schemes, nor the more conventional plans, on forest wildlife is not well-known. In

general, logging primarily affects habitat structure and the resource base (Fimbel et al.

2001), but it can also lead to forest fragmentation, which results in reduced landscape

connectivity, smaller patch sizes, increased edge effects, and population isolation

(Laurance and Bierregaard 1997).

The occurrence and the magnitude of fragmentation effects, as well as their effect

on wildlife, are determined by the spatial extent and intensity of the logging operations.

Clear-cutting is generally the most detrimental to forest species, but conventional

selective logging operations can often have significant negative impacts, as well (Fimbel

et al. 2001). In a comparison of the simulated effects of alternative timber harvest plans

on landscape structure, Gustafson and Crow (1996) found that a plan prescribing group









selection (the removal of small groups of trees) created nearly as much edge as a plan

calling for clear-cutting of a much larger area of forest. The more limited size of harvest

areas in the group selection plan affected forest spatial pattern more than the greater

intensity of harvest in the clearcut plan, implying that the selective logging plan may

have more negative consequences for forest dwelling animals. In an example from

Venezuela, enrichment strips (a silvicultural treatment in which strips are cleared and

replanted with commercial seedlings following selective logging) negatively affected

understory and terrestrial birds (Mason 1996). Strip enrichment was more damaging than

selective logging because it opened larger areas of canopy and created barriers within the

forest.

Such spatial discontinuity in forested landscapes can have a number of

consequences for wildlife, including local extinction and changes in species composition.

The effects can extend to community and ecosystem processes insofar as animals play an

important role in seed dispersal and pollination and serve other functions. In the tropics,

up to 90% of all plant species are adapted for seed dispersal by vertebrates (Howe and

Smallwood 1982, Jansen and Zuidema 2001). As a result, the ability of vertebrates to

persist in and move around tropical forests is of great importance for natural regeneration

processes. There is, thus, a need to carefully assess the effects of the spatial pattern of

logging on forest continuity. In developing a forest management plan that supports such

different vertebrates, it is necessary to determine the amount and configuration of suitable

habitat that is important for different animal species. This is often not well known in the

tropics (Fimbel et al. 2001), so a first step must be to identify the combination of

attributes that defines suitable habitat for different species.









In this study, I examine the degree to which an analysis of habitat use creates a

better understanding of the variables that are important to forest-dwelling vertebrates, and

evaluate whether this information can contribute to the formulation of timber extraction

plans that are more compatible with the persistence wildlife populations. This study

focuses on the response of redtail monkeys (Cercopithecus ascanius schmidtii Matschie)

to habitat change as a result of logging 30 years earlier, in Kibale National Park, Uganda.

Redtails are part of a rich primate community in the park, consisting of 13 species and

one of the highest densities of primates in Africa (Struhsaker 1997). Periodic population

censuses since the time of the logging indicate a continued decline in redtail population

density in the heavily logged areas since the time of logging (Chapman et al. 2000).

Redtail density is significantly lower in the logged areas (1.04 groups/km2) than in the

unlogged areas (11.48 groups/km2; Chapman et al. 2000). Kibale provides a good model

for testing primate response to logging alone, since direct human disturbance through

other factors such as hunting is not a factor.

The heavily logged site in Kibale has half the stem density and basal area of trees

of the unlogged area (Stickler and Chapman, in prep); thus, total basal area will likely be

important in dictating redtail habitat use. Moreover, in the heavily logged area, 60% of

the trees were potential food sources for the monkeys, whereas nearly 90% of the trees in

the unlogged area were potential food sources (Stickler and Chapman, in prep). Thus the

spatial distribution of potential food trees will likely influence differences in habitat use.

I also predict that the type and quality of the understory will influence redtail habitat use.

Specifically, I expect that monkeys will avoid areas dominated by a dense understory

characterized by thorny vines or herbs because it reduces the vertical foraging area









(Fimbel et al. 2001), and since they exploit both the understory and the higher canopy

within forests (Struhsaker and Leland 1979; Cords 1986), I expect an intermediate

amount of understory vegetation will be preferred.

To identify the factors important in redtail habitat selection, I examined habitat use

by redtail monkeys in heavily logged and unlogged areas and examined the role of spatial

structure of habitat attributes and resources in determining behavioral differences.

Additionally, I compared habitat use models for monkeys in unlogged and heavily logged

areas to examine the extent of overlap in critical habitat characteristics.

Methods

Study Area

Kibale National Park (766 km2) is located in western Uganda (0 13'-0o 41'N and

300 19'-300 32'E), near the base of the Ruwenzori Mountains (Fig. 3-1). It is classified as

moist evergreen forest, transitional between lowland rain forest and montane forest

(Struhsaker 1997, Chapman and Lambert 2000). Situated at 1500 m elevation, the

Kanyawara study site in the northern part of the park receives an average of 1741 mm of

rainfall per year, distributed over distinct biannual wet and dry seasons (Chapman and

Chapman, unpublished data). I contrasted habitat use by a group of redtails in a heavily

logged area (16 individuals), with that of a group in an unlogged area (24 individuals).

The heavily logged area was harvested in 1969 (Struhsaker 1997). Approximately 7.4

stems/ha or 21 m3/ha were removed (Skorupa 1988, Struhsaker 1997). However, as a

result of high incidental damage associated with the felling, Skorupa (1988) estimates

that approximately 50% of all trees in the area were destroyed. Since then, dense, thorny

herbaceous vegetation has grown in many areas, which are frequently dominated by

semi-woody Acanthaceae species (primarily, Acanthus arborescens) (Struhsaker 1997,









Paul et al., in press). The area used by the focal group in the logged forest also abuts

smallholder agricultural fields and tea plantations. The unlogged area has never been

commercially harvested. A few large stems (0.03-0.04 trees/ha) were removed before

1970; however, this low level of extraction appears to have had little effect on forest

structure (Skorupa and Kasenene 1984, Skorupa 1988, Struhsaker 1997, Chapman et al.

2000). There is an extensive, gridded trail system in both areas.

Behavioral and Habitat Attributes

Behavioral data were collected on one group of redtails in each site between

August and December 2002. The group in the unlogged area had 24 individuals and the

group in the logged area had 16. Group locations were recorded every 15 minutes during

all-day follows, in 3-day blocks at 1-week intervals, yielding 1563 observations for the

group in the unlogged area and 1301 observations for the group in the heavily logged

area. We recorded the location of the center of the group at every scan interval by

measuring the distance and angle to the nearest point with known coordinates. Reference

points were located at 20 m intervals on the gridded trail system in each group's home

range. Reference points were recorded using a hand-held global positioning system

(Garmin 12 XL). Coordinates for group locations were calculated and plotted in the

ArcView 3.2 and ArcGIS 8.3 geographic information systems (ESRI 2002). Further

details concerning behavioral data collection are included in Stickler and Chapman (in

prep).

To measure habitat characteristics, a set of spatially stratified set of 50 m2 plots

were selected by spacing plots every 20 m along the trail grid in each area (Table 3-1).

The plots assessed 37% (n = 1484) and 32% (n = 1362) of the area in unlogged and

heavily logged forest, respectively. In each plot, slope and canopy height were measured









using a clinometer. Three categories of understory vegetation were scored as being

present or absent. The understory type was scored as being present if it was present in at

least 25% of the plot. The categories were selected based on their association with

different forest structures and/or their importance to the monkeys as food sources or as

obstacles. In the areas used by the focal groups, all trees above 10 cm diameter at breast

height (DBH) were identified to species, measured, and plotted in the GIS. The

geographic coordinates for each tree were calculated from the distance and angle from

the reference points (unlogged: 15,489 trees in 48.4 ha; heavily logged: 8433 trees in 40.1

ha). The availability of plant food resources was assessed by categorizing each tree as

"food" or "nonfood", and as providing ripe fruits, unripe fruits, flowers or young leaves,

based on the feeding observations.

Analyses

Variograms were constructed for group locations to determine the distance at which

observations of redtail group presence were spatially independent. The variogram gives a

geostatistical measure of the spatial scale over which patterns are dependent, defining a

distance or spatial extent (= range) over which spatial dependence can be dependent

(Isaaks and Srivastava 1988). Spatial independence occurred after 32 m in the unlogged

area and after 30 m in the heavily area. Therefore, to derive appropriate and comparable

sampling units for the two areas, I superimposed a grid of 30 x 30 m cells over the group

locations, habitat plots, and tree locations in the GIS. The grid contained 531 cells for the

unlogged area and 466 cells for the heavily logged area. The number of times the group

entered a cell was used to calculate probabilities of redtail use. Home range size was

calculated using an adaptive kernel function for the point distribution in each area, using

the Animal Movement extension for ArcView 3.2(Hooge and Eichenlaub 1997). I









assessed the spatial pattern of habitat use with a coefficient of dispersion (CD) statistic

(Upton and Fingleton 1985). The CD calculates the ratio of the variance against the

mean of the number of times the group entered a grid cell. A value less than one

indicates a uniform dispersion, a value greater than one indicates a clumped dispersion,

and a value near one indicates a random pattern.

To determine the scale of variation for each variable, spatial structure was analyzed

using variogram analysis in the Geostatistical Analyst extension for ArcGIS 8.3 (ESRI

2002). For each variable, a semivariogram produced a range value, which specifies the

distance at which spatial independence is achieved, and a nugget value, which represents

independent error, measurement error or microscale variation at scales too fine to detect

(Isaaks and Srivastava 1988). Nugget values closer to 0 indicate that the amount of error

and/or undetected patch structure in the dataset are low (Isaaks and Srivastava 1988).

The spatial stationarity of each variable was evaluated using variogram analysis (Cressie

1993). As the assumption of stationarity was met for each variable, Moran's I statistic

was calculated as a measure of global autocorrelation for each variable over the extent of

each home range. Moran's I can be thought of similarly to Pearson's r (Cressie 1993).

Typically, Moran's Ivaries on a scale from -1 to +1. Occasionally, the statistic can take

on a value outside this range, due to an unusual weight matrix or averaging within a

distance class (Fotheringham et al. 2000). A large I value indicates that the variable

exhibits spatial dependence (Cressie 1993).

To characterize the gradients of variation in each area and evaluate if they were the

same, I conducted a principal components analysis (PCA). PCA assesses the

relationships within a set of variables, in this case the set of ecological variables (Table 3-









1) to identify the most important sources of variability within the set (McGarigal et al.

2000).

For further analyses of the relationships between ecological variables, I assigned

values derived from the habitat plots to the grid in the GIS. Canopy and understory

vegetation heights, slope, and aspect were averaged for each cell, and basal area was

summed. For the understory vegetation types, scores of presence or absence were

summed over each cell to derive a count of the number times a vegetation type occurs in

a cell. Similarly, the number of trees (in all categories) was summed by grid cell. For

both home range datasets, I calculated probabilities of such count variables' occurrence

in each grid cell. Variables were transformed when necessary to ensure normality. Tree

counts and probabilities of understory vegetation types, group presence, and activity site

selection were overdispersed, and thus log-transformed.

Habitat selection models were created and evaluated using path analysis in the

AMOS 5 software (Arbuckle 2003). Path analysis is a subset of structural equation

modelling that allows more than one dependent variable and the effects of dependent

variables on one another to be considered (Everitt and Dunn 1991). This method is

appropriate when correlations that are not easily explained exist among the dependent

variables and an experimental approach is not possible. Moreover, it is often more

interpretable than a partial correlation matrix because the relationships are expressed in

terms of a path diagram. In path analysis, the model specified by the user (default model)

is compared to a saturated model (the most general model, which fits any dataset) and an

independence model (in which the observed variables are assumed to be completely

uncorrelated) (Mitchell 2001). Model fit is assessed by comparing the default model with









the saturated model using a 2 goodness-of-fit index. If the expected correlations (default

model) do not deviate significantly from the saturated model, the hypothesized model is

judged to fit well (Shipley 1997). As a result, the best path analysis model typically has a

p-value higher than 0.05, unlike standard regression models where the best model has a

p-value below 0.05. Path analysis assumes linearity among variables, large sample size,

and the inclusion of all important variables in the model (Everitt and Dunn 1991).

I used the results of patch structure and principal components analyses, as well as

previous research and my understanding of the system, as a guide in determining the

components and likely structure of the hypothetical model. I constructed several default

models testing different hypothesized relationships among the independent and

dependent variables. Each of the default models was compared to the saturated model to

determine which best represented the relationships among the variables. The ability of

different models to accurately reflect system interactions was also evaluated by

examining individual relationships within each model on the basis of coefficient value

significance. To avoid problems resulting from potential remaining spatial

autocorrelation or non-normally distributed data, I report as significant only those

relationships for which the conventionally reported p-values were <0.001. Here, the

desirable p-value for showing that a significant relationship between two variables exists

is again standard, p<0.05. Each variable in the model also has direct and indirect effects

on the response variable, described by the correlation coefficient for each pair of

variables (Mitchell 2001). Because some variables have no direct connection to the

response variable, the direct effect in these cases is 0. The total effect is simply the sum

of the direct and indirect effects, and gives an indication of the importance and overall









direction of correlation of the variable in the system as a whole (Mitchell 2001). Each

model also contains a set of error terms (or disturbance terms) that reflect the unexplained

variance and the measurement error resulting from the regression of each set of

independent variables on their respective dependent variables, as specified in the default

model (Shipley 1997). Each variable's error term is assumed to be uncorrelated with the

other variables internal to the default model.

Once the best model to explain general habitat use was identified, I also

constructed hypothetical models to examine habitat effect on feeding, foraging (searching

for insects), and resting site selection in each site. These models aided in understanding

the relationship between habitat attributes and the probability of any one of the redtails'

major activities occurring, to determine if different activities could be explained by

different habitat qualities.

Results

Habitat Use

In both the unlogged and logged areas, redtails exhibit a clustered pattern of habitat

use (unlogged: CD = 11.12; heavily logged: CD = 9.36). However, an examination of the

spatial distribution of grid cell use suggests that redtails in the unlogged area use their

home range more uniformly than their counterparts in the heavily logged area (Fig. 3-2).

In addition, some areas within the outer bounds of the home range are used infrequently

or not at all in the heavily logged area, whereas in the unlogged area, almost all areas are

used at least once. Overall, the range of frequencies of grid cell use was greater for the

group in the unlogged area (0 to 55 observations) than in the heavily logged area (0 to 33

observations). The area described by the 95% probability contour is 29.24 ha in the

unlogged area and 39.76 ha in the heavily logged area (Fig. 3-2).









Spatial Structure of Habitat Attributes

In the unlogged area, Moran's I statistic shows that slope is strongly autocorrelated,

and that this pattern deviates significantly from random (Fig. 3-3). The remaining

variables also exhibited positive autocorrelation, but the results of z-tests do not allow us

to infer that the respective spatial patterns are non-random. The spatial scale of the

habitat attributes shows a great range of variation among variables. Analysis with

semivariograms showed that Marantochloa leucantha (21 m) and basal area (38 m) had

the smallest patch sizes, and that both had nugget (intercept) values close to 0, suggesting

that these variables were sampled at an appropriate scale (Fig. 3-3). By contrast, slope

(17.41) and canopy height (32.75) had high nugget values, indicating that there may be

some degree of variation that the sampling does not detect. Understory height, A.

arborescens, and sapling presence scaled between approximately 180 and 230 m.

In the heavily logged area, the majority of measured variables exhibited strong and

highly significant autocorrelation, and thus, patchiness (Fig. 3-4). As in the unlogged

area, basal area exhibited the smallest patch size (range = 29 m) and was sampled at a

fine enough scale to detect most spatial structure. Slope and A. arborescens scaled

similarly, 88 m and 85 m, respectively. However, whereas A. arborescens presence

appears to have been measured at a suitable resolution, slope may not have been (nugget

= 23.09). As in the unlogged area, canopy height in the disturbed area had a high nugget

value (23.12). Understory height and sapling presence scaled at 115 m and 111 m,

respectively, and had nugget values close to 0. M. leucantha scaled at nearly 250 m, but I

attribute this result, and the low Moran's I statistic value, to the extreme rarity of this

vegetation type in the understory (i.e., M. leucantha was scored as present in 5 sample

plots).









In each area, there appeared to be three different scales of patchiness for habitat

attributes: (1) 20 to 40 m; (2) 85 to 135 m; and (3) 180 to 250 m. In both areas, basal

area falls in the first category, as does M. leucantha in the unlogged forest only. In the

unlogged forest canopy height falls in the second category. In the heavily logged area,

slope, canopy height, understory vegetation height and A. arborescens and sapling

presence fall in the second category. Finally, in the unlogged area, slope, understory

vegetation height, and A. arborescens and sapling presence group in the third category.

In both areas, the first four variability gradients extracted in the PCA described

approximately 80% of the variation in the set of habitat measurements (Table 3-2).

Furthermore, the variables or groups of variables contributing most to each of these

components was similar among between the two areas. In each case, component 1

appeared to describe high, open forest (described by positive relationships between basal

area, saplings and canopy height), whereas component 2 described a low understory, as

well as a dense, high A. arborescens-dominated understory. All of the variables loaded at

least moderately on component 1 in the unlogged area, indicating that the habitat

structure may be more variable than in the heavily logged area. Slope had the highest

loading on component 3, and component 4 described high forest, with a sapling-

dominated understory. In the heavily logged area, basal area and A. arborescens-

dominated understory loaded heavily on components 5 and 6, respectively. The opposite

was true for the unlogged area, with the A. arborescens understory defining component 5,

and canopy height and basal area defining component 6.

Path Analysis

When the same basic conceptual model (Model 1) was applied to both cases, the

path analysis reveals both similarities and differences in the drivers of redtail habitat use









(Fig. 3-5). The variables and design of Model 1 were based on the analyses of spatial

structure and the principal components analysis, and on a general understanding of the

system Model 1, including average percent slope, total basal area and mean canopy and

understory vegetation heights, fits well in the heavily logged case; the hypothesized

model does not differ significantly from the saturated model (Fig. 3-5). When this Model

1 is applied to the unlogged area, the path analysis shows that the default model does

differ significantly from the saturated model (Fig. 3-5). In both the heavily logged and

the unlogged area, total basal area of trees above 10 cm DBH was important in explaining

patterns of habitat use. Basal area was a stronger predictor of habitat use in the unlogged

area than in the heavily logged area ((Table 3-3). Slope had a strong indirect effect on

habitat use, and had a significant effect on all other explanatory components of the model

in the unlogged area. Only its relationships to canopy and understory height were

significant in the heavily logged area. Understory vegetation height had a strong effect

on habitat use in the heavily logged area, but not in the unlogged area. For both models,

canopy height has neither a strong nor a significant effect on monkey occurrence, but

when links from basal area or understory height to canopy height are removed, model fit

decreases in both cases (e.g., when link from basal area to canopy height is removed:

unlogged: x2 = 144.95, df= 3, p < 0.001; heavily logged: x2 = 182.59, df= 3, p < 0.001).

Since Model 1 deviated significantly from the observed correlations in the

unlogged area, I developed a second model (Model 2) that better explained monkey

occurrence. The best model of general habitat variables included probability of A.

arborescens presence instead of understory vegetation height, although A. arborescens

did not have a significant effect on habitat use within the default model (Fig. 3-6). Slope









remained an important factor in influencing both canopy height and total basal area.

Thus, the results show that total basal area and slope are the most important factors

explaining redtail habitat used in the unlogged area.

I determined whether this combination of variables also explained each of the

groups' major activities. In the unlogged area, model fit improved markedly over the

general habitat use model when feeding, foraging, and resting were included as the

response variables, respectively (Table 3-4). In each case, slope and basal area had the

only significant effects. The significance of A. arborescens as an explanatory variable

decreased, although replacing this variable with understory height yielded even poorer

models. When the general habitat use model was applied to explaining feeding, foraging,

and resting site choice in the heavily logged area, models improved when abundance of

food trees and A. arborescens were included, even though A. arborescens had no direct

effect on the activity site selection. The best models, however, were produced when

abundance of potential fruit trees was entered into the model with A. arborescens (Table

3-4). Since the abundance of fruit trees appeared to be important, I examined their spatial

structure in the two home range areas with respect to fruit trees (Fig. 3-7). Trees

potentially offering ripe fruits occur exhibit a weaker patch structure and occur at a larger

scale in the unlogged than the heavily logged area. This is consistent with the basal area

of trees in both areas.

Aspect, presence of saplings and M. leit nilith, and the density of trees providing

young leaves and flowers as a potential preferred food did not have a strong direct or

indirect effect on any of the other variables, so they did not enter into the final models.









Discussion

Patterns of habitat use by redtail monkeys in unlogged and heavily logged areas of

Kibale National Park differed in their spatial scale, but could be explained by a similar

suite of habitat attributes. Further analysis of the spatial structure of the habitat

characteristics revealed that these patterns of use are a reflection of the underlying local

environment. I compared the spatial structure of habitat characteristics within and

between the two areas. My analysis showed that heavily logged forest exhibited a higher

degree of patchiness for most habitat attributes over the spatial extent examined. In

general, most variables in the unlogged area tended towards a more random spatial

pattern, with the exception of slope. Nevertheless, positive autocorrelation was apparent

for the other variables, including cumulative basal area, understory and canopy height,

and two understory vegetation types, A. arborescens and saplings. In the disturbed

forest, only basal area and the understory herb M. leucantha lacked significant spatial

structure.

The variables appeared to reflect three scales of spatial structure among both areas.

Basal area varied over the shortest distance in both areas. The scale of variation appears

to be consistent between the areas, despite the fact that the basal area in the heavily

logged area is less than 65% that of the unlogged area (Stickler and Chapman, in prep).

This suggests that changes due to logging do not alter the fundamental pattern of how

tree size varies with distance. In both areas, slope, understory vegetation height, and the

presence of A. arborescens and saplings group together in the same scale range.

However, this group of variables fell into the large scale category in the unlogged area, as

opposed to the middle distance category in the heavily logged area. Finally, canopy

height scaled with slope and the other variables in the heavily logged area, while in the









unlogged area, the distance over which canopy height showed patchiness was

intermediate relative to basal area and slope. The principal components analysis

corroborates the suggestion that there is an underlying structure common to both areas.

In both areas, the analysis extracted four distinct gradients of variation describing habitat

conditions.

Factors Explaining Habitat Use Patterns

Path analysis showed that redtails are most strongly influenced by basal area in

both areas. Consistent with my expectations at the outset of the study, this indicates that

the animals directly respond to the fine-scale patch structure represented by the

combination of all trees in their environment.

Similarly, monkeys were also negatively influenced by the height of the understory

vegetation in both areas, although this relationship was not statistically significant in the

unlogged area. Particularly in the heavily logged area, understory height was as an

important factor as basal area in determining the extent to which redtails used an area. In

the unlogged area, it was understory dominated by A. arborescens that was an important

negative estimator of reduced habitat use. Understory height and A. arborescens are

often positively correlated, particularly in the heavily logged area (Stickler, unpublished

data). However, the composition of the understory is more diverse in the unlogged area

than the heavily logged, which may explain why redtail habitat use is more influenced by

the presence of A. arborescens than understory in general. This provides support for my

initial prediction that redtails would prefer areas with intermediate density of understory,

since this increases the vertical foraging area within the canopy. This result also

emphasizes the importance of the type of understory vegetation. This is demonstrated in

the heavily logged area where A. arborescens and similar plants are largely dominant.









For redtails and some other members of the Kibale primate community, such areas are

largely inaccessible due to their growth form and thorny leaves (Struhsaker 1997).

The least expected result was the finding that slope plays such an important role in

habitat use. Slope was a critical indirect estimator of habitat use in both areas. In the

unlogged area, it had a strong positive effect on basal area and canopy height. The only

part of this area that had a relatively flat slope was in the valley bottom at the north end

of the home range. This area is characterized by wet, swampy conditions, a

predominance of A. arborescens and a lower tree density, than in higher areas-a

combination that appears to be less preferred by redtails. As a result, it is not unusual

that in this area, slope can be used as an indicator of monkey habitat use. Moreover,

examining the habitat characteristics' patch structure demonstrates that slope operates at

a larger scale than the other variables, revealing its importance in the system as a whole.

In the heavily logged area, slope had a strong positive influence on canopy height

and understory height, but a non-significant positive effect on basal area. I explain these

relationships both by the natural characteristics of this area and the effect of logging. As

in the unlogged area, slopes are flatter in two swampy valleys. Throughout the

Kanyawara site, tree cover in such areas tends to be sparse, and the understory tends to be

dense and dominated by A. arborescens and herbs and vines. Average canopy height also

tends to be lower. These valleys bound a higher, flatter ridge that might be predicted to

have similar characteristics to the upland areas in the unlogged area. However, this area

was subjected to intensive logging in the early 1970s. As a result, tree density is low.

Perhaps because of this, canopy height is sometimes higher in areas with steeper slopes

than in the flatter areas. Nevertheless, because of the often very steep slopes between the









high ridge and the valley bottoms, tree cover is higher in flatter areas than on the slopes.

This may explain why the effect of slope on basal area is not so strong. It may also

explain the positive relationship between slope and understory height; steeper slopes may

encourage more dense, taller herbaceous growth if trees cannot establish or survive as

well on these slopes.

Results suggest fruiting trees are important determinants of redtail habitat use.

When feeding, foraging, and resting site selection were examined, trees potentially

providing ripe or unripe fruits in the heavily logged areas significantly improved the path

analysis models. Potential fruit trees have a restricted and highly clumped distribution in

the heavily logged area that coincides with the areas of highest basal area. This is

probably related to the fact that only 60% of all trees in the heavily logged area were

potential food trees. In the unlogged area, by contrast, fruit trees (which represent nearly

90% of all trees in the area) appear to be much more uniformly distributed, although the

relationship to high basal area is also strong. This result provides support for the

importance of high basal area in the monkeys' overall habitat use patterns because areas

with a higher density and size of trees are more likely to have a higher number of fruit

trees.

Conclusion

A combination of natural and logging-induced factors contribute to differences in

behavior and differences in population density among redtails at Kibale. The availability

of suitable habitat appears to be more restricted and more patchy in the logged than in the

unlogged area. The differences in activity patterns, diet, and ranging behavior between

the groups observed in a previous study (Stickler and Chapman, in prep) appear to be a

function of habitat constraints. It seems that the interaction of logging with the local









environmental conditions has lowered the carrying capacity of the affected forest. As a

result, I suggest that redtails reduce their group size and use larger areas to compensate

for the reduction and fragmentation of preferred habitat. At the population level, this

likely translates to the decline in numbers observed by Chapman et al. (2000).

The example of redtails in Kibale National Park illustrates the importance of

including landscape characteristics in models describing small-scale habitat preferences.

Typically, explanations of habitat selection focus on vegetation-related components as

these variables are hypothesized to directly affect species abundance at larger spatial

scales (Morrison et al. 1998). However, wildlife-habitat relationships are often

hierarchical and thus inconsistent across spatial scales (Schulz and Joyce 1992, Cushman

and McGarigal 2002, Johnson et al. 2002). There is good evidence that both small-scale

and landscape-scale variables are important in the movement and distribution of a variety

of animals (Kotliar and Wiens 1990, Mazerolle and Villard 1999, Johnson et al. 2002,

Thompson and McGarigal 2002). The current study provides further support for the

importance of variables at multiple scales in explaining and predicting habitat

preferences, even over a relatively small spatial extent and fine resolution at the

organismal level. These findings emphasize the importance of a multi-scale approach in

order to better interpret the seemingly conflicting results of habitat studies conducted at

different scales (Thompson and McGarigal 2002).

Equally important is the spatial distribution of resources and habitat (Fahrig and

Paloheimo 1988). In this study, a comparison of the spatial structure of neighboring

forest stands that were subjected to different management plans highlights the importance

of habitat availability and distribution for primates. At Kibale, as in many other sites, the









underlying topography appears to be critical in structuring vegetation-related elements of

habitat (Bormann and Likens 1979, Forman and Godron 1986). This observation

suggests that slope may play an important role in forest regeneration (expressed by finer-

scale variables), and that the combination of this interaction with the effect of disturbance

caused by logging may partially explain the fact that more than 30 years has not been a

sufficient time period for the heavily logged forest to return to its pre-harvest structure

(Paul et al. in press). As a consequence, the amount of suitable habitat for redtails in

heavily logged areas is lower than in the unlogged area. Moreover, the spatial pattern of

potential habitat is such that some resources and habitat are inaccessible, reducing

functional habitat even further (Orians and Wittenberger 1991). Thus, from a

management or restoration perspective, management interventions to increase the

abundance of high-quality habitat may not automatically lead to an equal increase in

availability of such habitat (Gustafson and Crow 1994 Storch 2002).

These conclusions have important implications for forest management and

conservation strategies and policies. They provide further support for the need for

management plans to take into account the spatial structure of ecosystem characteristics

and the spatial pattern of disturbance (Poiani et al. 2000, Putz et al. 2000, Sanderson et al.

2002). Some timber extraction schemes, such as reduced impact logging, already

incorporate spatial considerations to lower the damage done to the forest (Holmes et al.

2002). It seems that such an approach could be effective if it took into account the spatial

requirements of resident wildlife. This could be done relatively easily, since it already

takes into account topographic and forest structural variables in determining where roads

and skid trails should be placed to decrease damage and increase efficiency. In this case,









for example, the spatial discontinuities caused by logging should avoid segregating areas

such that they become inaccessible to the primates. In addition, gaps or other damage

caused by logging must be at scales smaller than the home range size and location

constraints imposed on organisms by their social ecology or their physical size and

requirements. Although Putz et al. (2001) recommend silvicultural enrichments as one

possible way of restoring habitat or mitigating the impacts of logging, they also point out

that some of these methods are incompatible with maintaining the pathways and patch

sizes necessary to support nonvolant arboreal animals and, temporarily, for birds. For

example, when a forest stand is thinned, by removing competing trees and vines or

understory trees and vegetation (Smith 1986), the size of gaps caused by the removal of

target trees alone may increase to the point of being insurmountable by some species of

animals (Putz et al. 2001). Where conventional extraction is followed by strip

enrichment (Mason 1996), strips must be planned to be short enough in length that

nonvolant animals can circumvent them and still access resources and habitat on the other

side. In addition, such strips must be far enough apart so as not to create functionally

isolated forest patches because they are not large enough to accommodate a group of

foraging primates, for example.

In countries dependent on forestry but interested in maintaining viable and diverse

wildlife communities, policies regarding harvestable size and harvest intensities must be

altered to accommodate the habitat and space use requirements of forest wildlife. The

integration of spatial and behavioral ecology provides an important tool for modeling the

effects of different management schemes on vertebrates, providing for the basis for







67


scientifically-sound decisions regarding both the economic and ecological future of an

area.










Table 3-1. Habitat attributes examined for two redtail monkey home ranges in unlogged
and heavily logged forest in Kibale National Park, western Uganda, and
expected relationships. The attributes were measured in a set of spatially
stratified plots. Measures of Resource Availability are derived from an
inventory of all trees above 10cm diameter at breast height (DBH) in each
home range.


Habitat Attribute


Topography
Slope
Aspect
Forest Structure
Canopy Structure
Height

Total Basal Area


Understory

Height

Acanthus


Marantachloa

Saplings/Seedlings

Resource Availability
Food Trees


Unripe Fruit

Ripe Fruit

Young leaves

Flowers


Description of attribute and relationship to other forest
characteristics


Percent slope
Direction of slope (in degrees)


Maximum height (m) estimated in each habitat plot. Higher canopy
height is related to a better developed arboreal habitat
A function of abundance and size (diameter at breast height) of each
tree, measured in cm2. A higher total basal area is related to a more
connected canopy and a higher resource base.


The average height (m) of the understory vegetation. A higher
understory height is related to greater density of the understory.
Acanthus arborescens and related plants constitute a dense,
herbaceous vegetation that often dominates regenerating or swampy
areas. This vegetation is avoided by redtails.
Marantachloa leucantha is an understory herb characteristic of
upland forest light gaps and is a preferred food of redtails
Saplings and/or seedlings are typically present in closed forests with
an open understory.

The density (individuals/m2) of tree species fed on by redtails in
each area. A higher density of redtail food items indicates a higher
carrying capacity of the forest.
The density (individuals/m2) of tree species from which monkeys
were observed to feed on unripe fruits
The density (individuals/m2) of tree species from which monkeys
were observed to feed on ripe fruits
The density (individuals/m2) of tree species from which monkeys
were observed to feed on young leaves
The density (individuals/m2) of tree species from which monkeys
were observed to feed on flowers








69



Table 3-2. The six components produced in Principal Components Analysis describing
the major variation in habitat structure in areas of unlogged (UL) and heavily logged
(HL) forest in Kibale National Forest, Uganda. The loading values describe the
correlation of the habitat variables with the respective component.
Loading values
Component 1 Component 2 Component 3 Component 4 Component 5 Component 6
UL HL UL HL UL HL UL HL UL HL UL HL
Importance 39.85 27.41 21.04 25.43 14.27 16.99 9.81 12.31 8.72 10.99 6.29 6.86

Cumulative 39.85 27.41 60.89 52.84 75.16 69.83 84.98 82.14 93.70 93.13 100.0 100.0


Variable
Basal Area 0.454 0.578 -0.333 -0.215 0.327 0.153 -0.470 -0.278 0.662 0.239 0.389
Slope 0.306 -0.186 -0.918 -0.969 -0.139 0.187 0.171
Understory -0.315 -0.116 -0.628 -0.658 -0.118 -0.108 0.335 0.692 -0.420 0.114 0.501
Height
Canopy 0.515 0.534 -0.299 -0.155 -0.144 -0.699 -0.448 -0.783
Height
Acanthus -0.326 -0.124 -0.605 -0.700 0.357 -0.127 -0.624 0.270 -0.102 -0.637
Sapling 0.478 0.593 0.200 -0.149 0.787 0.597 0.224 -0.277 0.239 -0.433







70


Table 3-3. Standardized direct, indirect and total effect coefficients for path analysis
models (Model 1) examining habitat variables on probability of occurrence of redtails in
unlogged and heavily logged areas of Kibale National Park for general habitat utilization
model (Fig. 3-2).

Effect on
Unlogged Heavily Logged
Habitat Utilization Habitat Utilization
Variable Direct Indirect Total Direct Indirect Total
Effect Effect Effect Effect Effect Effect
Slope 0.000 0.144 0.144 0.000 -0.064 -0.064
Basal Area 0.362 0.004 0.366 0.278 -0.075 0.203
(p = 0.001) (p = 0.009)
Canopy Height -0.003 0.012 0.010 -0.053 -0.083 -0.136
Understory -0.044 0.000 -0.044 -0.249 0.000 -0.249
Height (p = 0.001)
r2 0.140 0.105











Table 3-4. Effect coefficients for path analysis models examining the effect of unripe
fruit trees on feeding site selection by redtails in unlogged and heavily logged
areas of Kibale National Park. Overall model fit and coefficient significance
and values are similar in each to models examining the effect of unripe or ripe
fruit trees on the following response variables: feeding site selection, foraging
site selection, and resting site selection.


Variables in
correlation


Heavily Logged


Slope
Canopy Height
Acanthus
Unripe food trees
r2


Slope
Canopy Height
Acanthus
Unripe food trees
r2


Direct effect


0.000
0.014
0.146
0.442 (p<0.001)


0.000
-0.142
0.054
0.254 (p=0.002)


Indirect Total
effect effect


Unlogged


Model fit

2 = 13.671
df= 2
p = 0.001


X2 = 0.080
df= 2
p = 0.961


0.144
0.000
0.000
0.006


0.034
0.018
0.000
0.024


0.144
0.014
0.147
0.448
0.183


0.034
-0.124
0.054
0.278
0.090






























SUDAN
KENYA
ZAIRE



il -e
Nabonal UGANDA



A LPANDA







Figure 3-1. The study site is located in the Kanyawara field site in the northern part of
Kibale National Park in western Uganda. Map of Uganda and Kibale
National Park area adapted from MUBFS (2003).







73




Unlogged Heavily Logged












j Paer,:ert Uhelikehocf c e Use


=61-70
431-75

JK E35 9S
T-T



(c) + (d) +



Figure 3-2. Patterns of habitat utilization by redtail monkeys in unlogged (a, c) and
heavily logged (b, d) forest areas of Kibale National Park, western Uganda.
Graphs (a) and (b) depict the number of times the focal monkey group entered
a grid cell in study area. Units are in meters, in the Universal Transverse
Mercator (UTM) coordinate system, Zone 36 North. Graphs (c) and (d) show
the probability distribution of habitat use by groups in each area. Outer
bounds describe the 95% confidence limit of groups' space use and are shown
in light blue (unlogged: 29.24 ha; heavily logged: 39.76 ha). Bright pink areas
indicate most intensively used areas.















/


4

A


I= 0.589*
Range = 26 8 m
Nugget= 17 407


Understory Height


I = 0 265
Range = 178.27 m
Nugget= 1.073


Acanthus presence Canopy Height /








1 I 0.233 =0.238
Range = 231.72m Range= 98.24
Nugget= 0.0746 Nugget= 32749
Basal Area Sapling presence ......... +









S -0.061 I 0.183
Range= 38.15 m Meters Range 229.09 mrn
Nugget < 0.oo 1 0 175 350 525 700 Nugget= 0.1359

Figure 3-3. Surface maps for six of the habitat variables measured in an area of unlogged
forest in Kibale National Park, western Uganda. Darker shading corresponds
to a higher value of the variable. Cutoff values for shading levels were chosen
to produce approximately equal frequency quintiles. The Moran's I statistic
describes the degree of spatial autocorrelation in the variable; significant
(p<0.001) autocorrelations are marked with an asterisk. The range describes
distance over which the variable show autocorrelation, and a nugget value
close to 0 indicates that most of the spatial variation in the variable values has
been captured by the sampling design.















Slope I= 1.516B Understory Height I= 1.491m
Range = 88.0 m Range= 114.48 m
Nugget= 23.089 ...... Nugget = 1.2246











Acanthus presence 1=0.700- Canopy Height I= 1 474*
Range =84 67 m Range =134 17 rnm
S\ Nugget= 0.1345 .. Nugget= 23 122












Basal Area I= -0.036 Sapling presence I=0 572"
Range = 28 81 m .-. Range = 111 15 n
.. Nugget < 00001 Nugget=0.1251










0 175 350 525 700


Figure 3-4. Surface maps for six of the habitat variables measured in an area of heavily
logged forest in Kibale National Park, western Uganda. Darker shading
corresponds to a higher value of the variable. Cutoff values for shading levels
were chosen to produce approximately equal frequency quintiles. Moran's I
statistic describes the degree of spatial autocorrelation in the variable;
significant (p<0.001) autocorrelations are marked with an asterisk. The range
describes distance over which the variable show autocorrelation, and a nugget
value close to 0 indicates that most of the spatial variation in the variable
values has been captured by the sampling design.














Heavily Logged


)2= 7.38
df= 2
p = 0.025
r2= 0.140


(2= 3.055
df= 2
p= 0.217
r2= 0.105


Figure 3-5. Path diagram (Model 1) describing habitat utilization by redtails in unlogged
and heavily logged areas of Kibale National Park, western Uganda.
Standardized regression weights are shown for each pair of variables.
Significant correlations (p<0.01) are indicated by thick arrows. Model
goodness-of-fit is described by the X2 statistic and associated p-value. A p-
value above 0.05 indicates that the model does not differ significantly from
the saturated model, which indicates that the model has a good fit.
Components denoted Es, Et, Eu, Ec, and Em are error terms corresponding to
each variable in the model.


Unlogged
































2= 6.016
df= 2
p = 0.049


Figure 3-6. Path diagram (Model 2) describing habitat utilization by redtails in an
unlogged area of Kibale National Park, western Uganda. Standardized
regression weights are shown for each pair of variables. Significant
correlations (p<0.01) are indicated by thick arrows. Model goodness-of-fit is
described by the X2 statistic and associated p-value. A p-value above 0.05
indicates that the model does not differ significantly from the saturated model,
which indicates that the model has a good fit. Components denoted Es, Et, Eu,
Ec, and Em are error terms corresponding to each variable in the model.







78



Range = 220.74 m Range = 212.2 m
Nugget 0.1468 Nugget= 0.146














Unlogged Unlogged
Unripe Fruit Ripe Fruit


Range = 147.19 m Range = 149.37 m
Nugget= 0.1079 Nugget= 0 1641














Heavily Logged Heavily Logged
Unripe Fruit Ripe Fruit



Figure 3-7. Probability maps of trees potentially producing unripe and ripe fruits in
unlogged and heavily logged forest of Kibale National Park, Uganda, during
the period from August to December, 2002. Tree species were identified as
potential fruit trees in each area on the basis of feeding observations on two
groups of redtail monkeys whose home ranges coincided with these areas.
Darker shading corresponds to a higher number of fruit trees. Range indicates
the scale of each variable's patch size. Nugget values close to zero indicate
that the variable has been sampled at the appropriate spatial resolution.














CHAPTER 4
SUMMARY AND CONCLUSIONS

This study contributes to our understanding of the variables important in

determining the behavioral patterns of one primate species, redtail monkeys

(Cercopithecus ascanius) in response to logging as a link to population density. Most

importantly, however, it serves as a model for applying a spatial approach to

investigating primate behavior in relationship to the habitat changes caused by

anthropogenic disturbance to gain a better understanding of the mechanisms underlying

population change in relation to large-scale disturbance, such as timber extraction. This

is critical for planning ecologically sound conservation strategies.

In the first study, I contrasted the behavior of redtail monkeys in unlogged and

heavily logged forest in Kibale National Park, Uganda. My results indicate that redtails

in the heavily logged area use larger home ranges, travel further and spend more time

foraging and less time feeding than conspecifics in the unlogged areas. They appear to be

constrained by resource and habitat limitations. Specifically, a reduced tree density in the

heavily logged area leads to a lower abundance of potential food trees and reduced

arboreal pathways. Microhabitat characteristics in the heavily logged area also appeared

to constrain the animals' movements and foraging ability. Using measurements obtained

from a set of sampling plots spatially stratified throughout the home range of each focal

group, I determined that understory density was high in a much larger proportion of the

heavily logged area than the unlogged area. In cases where density was high, the

understory tended to be dominated by a thorny, herbaceous plant surrounding emergent









trees. Vertical stratification was also reduced over much of the heavily logged area.

These results suggest that the forest in the heavily logged area is structurally limiting to

redtails. Since they typically forage in both the upper and mid-canopy layers and

sometimes descend to the ground, as well, having access to understory vegetation that is

passable and provides resources may be critical. Moreover, such understory helps to

create pathways throughout the forest. The finding that fewer canopy layers and a denser

understory dominate the heavily logged area, suggests that travel for redtails is

constrained by patches of inaccessible vegetation, limiting both the vertical and

horizontal foraging space of the monkeys. These differences likely contribute to the

lower group sizes, higher travel distances and larger home ranges of redtails in the

heavily logged areas, and may explain the reduction in population density of redtails in

logged areas, as well.

In the second study, I examined habitat selection by redtails in the heavily logged

and unlogged areas to determine whether the behavioral differences observed in the first

part were due to habitat limitations or to differences in habitat preferences. I evaluated a

set of topographic, forest structural, and resource attributes for each home range to

determine the spatial structure of each. In particular, I was interested in determining

whether the variables showed distinct patch structures and whether variables operated on

similar spatial scales. I found that the variables tended to arrange themselves spatially,

but that these scales varied among the two areas. Basal area showed the least amount of

patch structure in both areas and operated on the small scale, in the range of 20 to 40 m.

In both areas, the majority of remaining variables grouped together with slope. In the

unlogged area, the scale of these variables was relatively large, whereas in the heavily









logged area, the variables showed an intermediate scale of patch structure. These results

suggest that there is a common organizing structure to both areas, but that the specific

characteristics of each area are a result of scale. Conducting a path analysis to test the

relationship between these variables and to the response variable (redtail habitat use)

revealed that slope is most likely the major structuring variable in each area. Both the

location of disturbance from logging (typically in high flat areas) and the nature of

regeneration (dominated by an aggressive, colonizing herb: Acanthus arborescens) in the

heavily logged study appears to interact with this structuring variable to define the

characteristics of the area.

Habitat selection by the monkeys was similar in both areas, but reflected this

difference in the specific characteristics of both areas. That is, redtails were primarily

influenced by basal area, but understory vegetation also played an important role. In the

heavily logged area, the height of the understory (which is correlated with density) was

the most critical direct factor, in addition to basal area, negatively correlating with

monkey habitat use. In the unlogged area, A. arborescens, contributed to a better model

of habitat selection. I interpret this result as a reflection of the more diverse understory in

the area, where the correlation between understory height and understory density is not as

strong as in the heavily logged area. A. arborescens is far less common in the unlogged

than the heavily logged area; thus, the results of this analysis suggest that the monkeys

avoid areas dominated by A. arborescens, which are also low in basal area. I also

interpret this result as an indication that monkeys in the unlogged area have greater

flexibility in selecting suitable habitat, since it appears to be more common and more

accessible than in the unlogged area. When feeding, foraging, and resting site selection,









were examined, potential fruit trees were the strongest estimator of these response

variables only in the heavily logged area. A subsequent analysis of fruit tree spatial

structure showed that fruit trees are patchier in the heavily logged area than the unlogged.

Most importantly, however, slope had a strong indirect effect on the monkeys in both

areas through its relationship to the other variables.

This study highlights the complex interaction of variables characterizing forest

habitats. In particular, it shows how the spatial structure of each variable is important in

defining its relationship to the other variables, and how these interactions lead to the

unique characteristics defining different areas. This analysis allowed me to begin to

separate the effects of underlying topography from those of disturbance from logging on

primate response to timber harvesting. In a broader sense, this highlights the importance

of structuring variables in determining ecosystem trajectories. This combination of

variables is then reflected in the distribution and abundance of species over the larger

landscape. Thus, sustainable forest management plans must identify the general set of

variables describing the system and determine the relationships between them as a

prerequisite for deciding the level and spatial pattern of timber extraction that will best

maintain the integrity of the system. Using quantitative spatial methods, it should be

possible to model the cascading effects of different plans and make informed decisions

about which plan best fits economic and ecological goals.

Considering the important role primates are thought to play in seed dispersal, this

study also has implications for forest regeneration. In the particular case of Kibale, better

planning prior to logging might have considered the way in which timber extraction

reconfigured primate foraging and travel spaces, and thus the spatial pattern of









regeneration. Where primates cannot access trees, the suite of dispersers for an

individual tree is decreased and the likelihood that seeds remains closer to the parent tree

and become more vulnerable to density-dependent mortality increases. Moreover,

because of the recolonizing vegetation of the area, even those trees that do manage to

attract dispersers or those that rely on autodispersal will have to contend with

inhospitable conditions for germination, seedling growth, and sapling survival. Thus, the

relationship between primates, logging, and seed dispersal using spatial analysis is an

important avenue for further research.

Furthermore, this research also reinforces the importance of planning

conservation and forest management at multiple scales. Current wisdom holds that

biodiversity conservation plans must integrate human settlements, natural resource

exploitation, and protection across the landscape (Poiani et al. 2000, Sanderson et al.

2002). Because of the large home range needs of some vertebrates, focusing on the

maintenance of population viability of these species has been suggested as a way of

ensuring for the protection of large, ecologically diverse areas (Wilcox 1984, Lambeck

1997, Caro and O'Doherty 1999, Miller et al. 1999). In light of the range of scales over

which organisms interact with the environment, more recent plans expand this concept to

account for the scale and grain of habitat requirements of these large vertebrates

(Sanderson et al. 2002). However, as the current study shows, organisms with relatively

small, restricted, and stable home ranges can be negatively affected by resource

extraction that occurs on a scale that coincides with their habitat requirements and

territorial constraints, even if this level of extraction does not affect larger vertebrates. In

Kibale, although elephants and chimpanzees are affected by logging-induced habitat






84


changes (Struhsaker 1997), they are not arboreally restricted and can travel further to

seek out suitable resources and habitat. Thus, vertebrates with smaller spatial

requirements should form part of the suite of "landscape species" (Sanderson et al. 2002)

chosen for conservation planning because they may be more directly affected by finer-

scale changes in forest spatial structure caused by logging.















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