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The Influence of Species Traits and Landscape Attributes on the Response of Mid- and Large-sized Neotropical Mammals to ...

Permanent Link: http://ufdc.ufl.edu/UFE0041501/00001

Material Information

Title: The Influence of Species Traits and Landscape Attributes on the Response of Mid- and Large-sized Neotropical Mammals to Forest Fragmentation
Physical Description: 1 online resource (147 p.)
Language: english
Creator: Thornton, Daniel
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: fragmentation, landscape, mammals, occupancy, patch, review, scale, traits, tropics, vulnerability
Wildlife Ecology and Conservation -- Dissertations, Academic -- UF
Genre: Wildlife Ecology and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: As tropical reserves become smaller and more isolated, the ability of species to use fragmented landscapes will be a key determinant of species survival. Although a substantial amount of research has examined species distribution, abundance, and richness in fragmented landscapes, the response of mid- and large-sized tropical mammals to fragmentation remains relatively unknown. This gap in knowledge is significant given that large mammals may be especially vulnerable to fragmentation and play key roles in the maintenance of tropical forest habitat. Species use of fragmented habitats is determined by both the ecological and life history traits of species and the physical attributes of patches and landscapes. Although several species traits commonly are associated with vulnerability to fragmentation, the combination of traits that are most highly influential, and the effectiveness of those traits in predicting vulnerability across distinct landscapes, remains poorly understood. Considerable uncertainty also exists regarding what characteristics of patches and landscapes exert the most influence on species. In particular, the relative influence of habitat loss vs. habitat fragmentation on species is currently an area of intense debate. Consensus from empirical studies in temperate areas is that habitat loss is more important than fragmentation, but this question has not been investigated for any tropical and wide-ranging fauna. Based on ecological traits (e.g., a high level of specialization in tropical faunas), these species may be expected to respond more strongly to fragmentation than temperate species studied thus far. I studied use of forest patches by 25 mid- and large-sized neotropical mammals in northern Guatemala. I determined the relationship between several species traits and vulnerability to fragmentation and assessed the effectiveness of those traits in predicting patch occupancy rates of the same mammals on two distinct landscapes in Mexico. I also determined how patch occupancy patterns of mammals were influenced by habitat loss and fragmentation in the landscape surrounding patches, as well several patch-scale variables (i.e., patch size and quality). Additionally, I reviewed 125 multi-scaled fragmentation studies to further examine the relative influence of patch and landscape-scale factors on a variety of species. For mammals in Guatemala, body size, home range size, and hunting vulnerability were related to occupancy rates, but after controlling for passive sampling effects only hunting vulnerability strongly influenced sensitivity to fragmentation. Species that were heavily hunted were less common in forest patches than intact forest sites. The cross-landscape comparison revealed both similarities and differences in the species traits that influenced patch occupancy in Guatemala and Mexico. My analysis of the influence of patch and landscape attributes on occupancy patterns revealed that a greater number of mammal species responded significantly to habitat fragmentation than to habitat loss in the landscape, and that this response generally was negative. Patch-scale factors also exerted a strong influence on occupancy of mammals. My review of the literature revealed that species from diverse taxa responded strongly to within-patch, patch, and landscape-scale variables, but the probability of response to these factors changed across taxa and according to several methodological variables. Given the ubiquity of hunting in tropical environments, my findings indicate that management efforts in fragmented landscapes that do not account for hunting pressure may be ineffective in conserving tropical mammals. My study also indicates that species traits may be useful in predicting relative patch occupancy rates and/or vulnerability to fragmentation across distinct landscapes, but that caution must be used as certain traits can become more or less influential on different landscapes, even when considering the same set of species. Finally, my results point to the need for management efforts in fragmented environments to go beyond habitat preservation and also consider prevention of habitat fragmentation per se, at least for tropical mammals.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Daniel Thornton.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Sunquist, Melvin E.
Local: Co-adviser: Branch, Lyn C.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041501:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041501/00001

Material Information

Title: The Influence of Species Traits and Landscape Attributes on the Response of Mid- and Large-sized Neotropical Mammals to Forest Fragmentation
Physical Description: 1 online resource (147 p.)
Language: english
Creator: Thornton, Daniel
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: fragmentation, landscape, mammals, occupancy, patch, review, scale, traits, tropics, vulnerability
Wildlife Ecology and Conservation -- Dissertations, Academic -- UF
Genre: Wildlife Ecology and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: As tropical reserves become smaller and more isolated, the ability of species to use fragmented landscapes will be a key determinant of species survival. Although a substantial amount of research has examined species distribution, abundance, and richness in fragmented landscapes, the response of mid- and large-sized tropical mammals to fragmentation remains relatively unknown. This gap in knowledge is significant given that large mammals may be especially vulnerable to fragmentation and play key roles in the maintenance of tropical forest habitat. Species use of fragmented habitats is determined by both the ecological and life history traits of species and the physical attributes of patches and landscapes. Although several species traits commonly are associated with vulnerability to fragmentation, the combination of traits that are most highly influential, and the effectiveness of those traits in predicting vulnerability across distinct landscapes, remains poorly understood. Considerable uncertainty also exists regarding what characteristics of patches and landscapes exert the most influence on species. In particular, the relative influence of habitat loss vs. habitat fragmentation on species is currently an area of intense debate. Consensus from empirical studies in temperate areas is that habitat loss is more important than fragmentation, but this question has not been investigated for any tropical and wide-ranging fauna. Based on ecological traits (e.g., a high level of specialization in tropical faunas), these species may be expected to respond more strongly to fragmentation than temperate species studied thus far. I studied use of forest patches by 25 mid- and large-sized neotropical mammals in northern Guatemala. I determined the relationship between several species traits and vulnerability to fragmentation and assessed the effectiveness of those traits in predicting patch occupancy rates of the same mammals on two distinct landscapes in Mexico. I also determined how patch occupancy patterns of mammals were influenced by habitat loss and fragmentation in the landscape surrounding patches, as well several patch-scale variables (i.e., patch size and quality). Additionally, I reviewed 125 multi-scaled fragmentation studies to further examine the relative influence of patch and landscape-scale factors on a variety of species. For mammals in Guatemala, body size, home range size, and hunting vulnerability were related to occupancy rates, but after controlling for passive sampling effects only hunting vulnerability strongly influenced sensitivity to fragmentation. Species that were heavily hunted were less common in forest patches than intact forest sites. The cross-landscape comparison revealed both similarities and differences in the species traits that influenced patch occupancy in Guatemala and Mexico. My analysis of the influence of patch and landscape attributes on occupancy patterns revealed that a greater number of mammal species responded significantly to habitat fragmentation than to habitat loss in the landscape, and that this response generally was negative. Patch-scale factors also exerted a strong influence on occupancy of mammals. My review of the literature revealed that species from diverse taxa responded strongly to within-patch, patch, and landscape-scale variables, but the probability of response to these factors changed across taxa and according to several methodological variables. Given the ubiquity of hunting in tropical environments, my findings indicate that management efforts in fragmented landscapes that do not account for hunting pressure may be ineffective in conserving tropical mammals. My study also indicates that species traits may be useful in predicting relative patch occupancy rates and/or vulnerability to fragmentation across distinct landscapes, but that caution must be used as certain traits can become more or less influential on different landscapes, even when considering the same set of species. Finally, my results point to the need for management efforts in fragmented environments to go beyond habitat preservation and also consider prevention of habitat fragmentation per se, at least for tropical mammals.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Daniel Thornton.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Sunquist, Melvin E.
Local: Co-adviser: Branch, Lyn C.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041501:00001


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THE INFLUENCE OF SPECIES TRAITS AND LANDSCAPE ATTRIBUT ES ON THE RESPONSE OF MIDAND LARGE-SIZED NEOTROPICAL MAMMALS TO FOREST FRAGMENTATION By DANIEL HARRY THORNTON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORID A IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010 1

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2010 Daniel Harry Thornton 2

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To Erin and Sam, Mom and Dad, and ev eryone else who helped along the way 3

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ACKNOWLEDGMENTS The National Science Foundation, Americ an Society of Mammalogists, Wildlife Conservation Society-Guatem ala Program, University of Florida IGERT Working Forests in the Tropics, IDEA Wild, and t he Department of Wildlife Ecology and Conservation-University of Fl orida provided financia l support for this research. I thank the administration of Tikal National Park and the Consejos Nacional de reas Protegidas in Guatemala for supporting this work. I am indebted to Nery Jurado and Demetrio Cordova for invaluable help in t he field. I thank Roan McNab, Rony Garcia, Jose Moreira, and the staff of WCS-Guatemala for logistical support and advice. I extend my gratitude to Alejandro Estrada and Tania Urquiza-Haas for access to their data and information regarding their study areas. Finally, I thank my committee members Dr. Lyn Branch, Dr. Mel Sunquist, Dr. Michael Binford, Dr. Emilio Bruna, and Dr. Mary Christman for advice on numerous aspec ts of my research. Research Protocol E019 was approved by the UF Instituti onal Animal Care and Use Committee. 4

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TABLE OF CONTENTS page ACKNOWLEDG MENTS..................................................................................................4 LIST OF TABLES............................................................................................................7 LIST OF FI GURES..........................................................................................................8 ABSTRACT .....................................................................................................................9 CHAPTER 1 INTRODUC TION....................................................................................................12 2 ANALYZING THE RELATIONSHIP BETWEEN SPECIES TRAITS AND VULNERABILITY TO FRAGMENTAT ION: PASSIVE SAMPLING EFFECTS AND CROSS-LANDSCAP E COMPARIS ONS........................................................17 Introducti on.............................................................................................................17 Methods ..................................................................................................................21 Study Si te.........................................................................................................21 Species Ecological and Life History Traits........................................................23 Mammal Surv eys..............................................................................................24 Measuring Vulnerability to Fragment ation........................................................26 Influence of Species Trai ts on Vulner ability......................................................29 Cross-Landscape Comparis on of Vulner ability.................................................30 Result s....................................................................................................................31 Vulnerability to Fragmentat ion..........................................................................32 Influence of Species Trai ts on Vulner ability......................................................32 Cross-Landscape Comparis on.........................................................................33 Discussio n..............................................................................................................34 Influence of Species Trai ts on Vulner ability......................................................34 Cross-Landscape Comparis on.........................................................................38 Species-Level Patterns....................................................................................41 Conclusi ons......................................................................................................42 3 EVALUATING THE RELATIVE INFLUENCE OF HABITAT LOSS AND FRAGMENTATION: DO TROPICAL MAMMALS MEET THE TEMPERATE PARADIGM? .........................................................................................................533 Introducti on...........................................................................................................533 Methods ................................................................................................................555 Study Si te.......................................................................................................555 Mammal Surv eys............................................................................................566 Habitat Measurements: Patc h-Scale Vari ables..............................................577 Habitat Measurements: Land scape-Scale Va riables ........................................58 5

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Additional Predict or Vari ables ..........................................................................59 Relative Influence of Habi tat Loss and Fragm entation.....................................60 Result s ....................................................................................................................63 Discussio n..............................................................................................................66 4 A REEXAMINATION OF THE INFLUENCE OF LANDSCAPE CONTEXT ON SPECIES PRESENCE AND ABUND ANCE............................................................76 Introducti on.............................................................................................................76 Methods ..................................................................................................................80 Literature Search..............................................................................................80 Collection of Data From Studies .......................................................................81 Data Anal ysis...................................................................................................83 Result s....................................................................................................................85 Effects of Predictor Vari ables ...........................................................................85 Test of Pr edicti ons............................................................................................86 Discussio n..............................................................................................................87 5 CONCLUS ION......................................................................................................104 General Conclusions ............................................................................................104 Conservation Reco mmendations ..........................................................................105 APPENDIX A DETAILS OF COMPILATION OF ECOLOGICAL AND LIFE HISTORY TRAITS.111 B PARAMETER ESTIMATES FOR OCCUPANCY MO DELS.................................. 114 C BEST-FIT MODE L SETS ......................................................................................115 D AIC WEIG HTS......................................................................................................119 E DATA ON REFERENCES INCLUDED IN REVIEW............................................. 120 LIST OF REFE RENCES.............................................................................................123 BIOGRAPHICAL SKETCH ..........................................................................................147 6

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LIST OF TABLES Table page 2-1 Life history and ecological traits of mammals studied in northern Guatemala....45 2-2 Factor loadings from principle co mponents analysis of ec ological and life history traits.. ......................................................................................................46 2-3 Sampling effort employed for camera-trapping and visual censusing of mammals in forest patches and intact fore st sites..............................................46 2-4 Vulnerability estimates for the 3 landscapes included in the analysis.................47 2-5 Results from regression analysis using PCA axes as predictor variables and 3 measures of vulnerability to fr agmentation as res ponse variab les..................48 3-1 Sampling effort employed for camera-trapping and visual censusing of mammals in fore st patc hes.................................................................................71 3-2 Patch and landscape context metrics for the 50 focal patches in my study area. ...................................................................................................................72 3-3 Correlations between landscape metri cs used to categorize habitat amount and configuration in the landscape surrounding foca l patches ...........................73 3-4 Parameter estimates for patch-s cale variables and habitat loss and fragmentation variables......................................................................................74 4-1 Example table used for coding species response to landscape context, patch, and within-patch variabl es.......................................................................97 4-2 Covariates used in the gener alized linear mi xed models....................................98 4-3 Influence of predictor variables on probability of response to landscape context, patch, and withinpatch vari ables. .........................................................99 7

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LIST OF FIGURES Figure page 2-1 Location of study area in Guatemala..................................................................49 2-2 Map showing fragmented and conti nuous forest study areas.............................50 2-3 Results of hierarchical parti tioning analysis fo r 3 land scapes .............................51 2-4 Results of hierarchical partitioni ng analysis for difference in occupancy between continuous forest and forest patches of the same si ze........................52 3-1 Map of a portion of the study area......................................................................75 4-1 Example of typical focal patch study.................................................................100 4-2 Characteristics of the studies, and study species, included in the review.........101 4-3 Comparison of parameter estimates fo r least squares means of probability of response to landsca pe contex t.........................................................................102 4-4 Comparison of parameter estimates fo r least squares means of probability of response to landscape context and patch variables based on sample size (i.e., # of patches surveyed) .............................................................................103 8

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Abstract of Dissertation Pr esented to the Graduate School of the University of Florida in Partial Fulf illment of the Requirements for t he Degree of Doctor of P hilosophy THE INFLUENCE OF SPECIES TRAITS AND LANDSCAPE ATTRIBUTES ON THE RESPONSE OF MIDAND LARGE-SIZED NEOTROPICAL MAMMALS TO FOREST FRAGMENTATION By Daniel Harry Thornton May 2010 Chair: Mel Sunquist Cochair: Lyn Branch Major: Wildlife Ecology and Conservation As tropical reserves become smaller and more isolated, the ability of species to use fragmented landscapes will be a key determinant of species survival. Although a substantial amount of res earch has examined species distribution, abundance, and richness in fragmented landscapes, the re sponse of midand large-sized tropical mammals to fragmentation remains relatively unknown. This gap in knowledge is significant given that large mammals may be especially vulnerable to fragmentation and play key roles in the maintenance of tropical forest habitat. Species use of fragmented habitats is determined by both the ecological and life history traits of species and the physical attributes of patches and landscapes. Although several species traits commonly are associat ed with vulnerability to fragmentation, the combination of traits that are most highly in fluential, and the effectiveness of those traits in predicting vulnerability across dist inct landscapes, remains poorly understood. Considerable uncertainty also exists r egarding what characteristics of patches and landscapes exert the most influence on species. In particular, the relative influence of habitat loss vs. habitat fragmentation on specie s is currently an area of intense debate. 9

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Consensus from empirical studies in tem perate areas is that habitat loss is more important than fragmentation, but this question has not been invest igated for any tropical and wide-ranging fauna. Based on ecol ogical traits (e.g., a high level of specialization in tropical faunas), these species may be expected to respond more strongly to fragmentation than tem perate species studied thus far. I studied use of forest patches by 25 midand large-sized neotropical mammals in northern Guatemala. I dete rmined the relationship between several species traits and vulnerability to fragmentation and assessed the effectiveness of those traits in predicting patch occupancy rates of the same mammals on two distinct landscapes in Mexico. I also determined how patch occupancy patterns of mammals were influenced by habitat loss and fragmentation in the landscape surr ounding patches, as well several patchscale variables (i.e., patch size and quality) Additionally, I reviewed 125 multi-scaled fragmentation studies to further examine the relative influence of patch and landscapescale factors on a variety of species. For mammals in Guatemala, body size, home range size, and hunting vulnerability were related to occupancy rates, but after controlling for passive sampling effects only hunting vulnerability strongly influenced sensitivit y to fragmentation. Species that were heavily hunted were less common in forest patches than intact forest sites. The cross-landscape comparison revealed both similarities and differences in the species traits that influenced patch occupancy in Guatemala and Mexico. My analysis of the influence of patch and landscape attribut es on occupancy patterns revealed that a greater number of mammal species respond ed significantly to habitat fragmentation than to habitat loss in the landscape, and that this response generally was negative. 10

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11 Patch-scale factors also exerted a strong influence on occupancy of mammals. My review of the literature revealed that species from diverse taxa responded strongly to within-patch, patch, and landscape-scale vari ables, but the probability of response to these factors changed across taxa and according to several methodological variables. Given the ubiquity of hunting in tropica l environments, my findings indicate that management efforts in fragmented landscapes that do not account for hunting pressure may be ineffective in conserving tropical mamma ls. My study also indicates that species traits may be useful in predicting relative patch occupancy rates and/or vulnerability to fragmentation across distinct landscapes, but t hat caution must be used as certain traits can become more or less influential on diffe rent landscapes, even when considering the same set of species. Finally, my result s point to the need for management efforts in fragmented environments to go beyond habit at preservation and also consider prevention of habi tat fragmentation per se at least for tropical mammals.

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CHAPTER 1 INTRODUCTION The clearing of tropical forest fo r food produc tion and urban expansion is proceeding at a tremendous rate (Asner et al. 2009), resulting in landscapes that contain small fragments of forest surrounded by a sea of agric ulture, cattle pasture, and urban lands. Until recently, t he main focus of conservation effort in the tropics has been the creation and maintenance of large tracts of uninterrupted forest. Although this strategy has resulted in reduced forest loss inside protected areas (Nepstad et al. 2006) and will remain as a key component of effective conservation in the tropics, researchers increasingly have recognized the potential conservation value of fragmented and human-dominated landscapes that exist outside of reserves (Daily et al. 2003; Chazdon et al. 2009). These altered landscapes serve as important refugia for tropical species caught outside of protected areas (e.g., Seke rcioglu et al. 2007) and act as corridors for species during seasonal, migratory, or dispersal movements (Hansen & DeFries 2007). Given that fragmented landscapes are becoming more common while at the same time tropical reserves become smaller and more isolated (deFries et al. 2005), a better understanding of species response to fragmentat ion in tropical environments is urgently needed. A substantial body of literature exists detailing how loss and fragmentation of habitat affects the distribution, abundanc e, richness, and demography of plants and animals in a variety of ecosystems (see revi ews in Harrison & Bruna 1999; Debinski & Holt 2000; Ewers & Didham 2006; Fleish man & Mac Nally 2007; Laurance 2008). Factors operating at a variety of scales have been found to strongly influence species distribution or abundance in fr agmented environments, includi ng small-scale variations 12

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in patch quality or level of disturbance withi n patches (e.g., Michalski & Peres 2007; Schooley & Branch 2009), meso-scale variations in patch size and shape (e.g., Graham & Blake 2001; Crooks 2002), and large-scale variations in the composition and configuration of habitat in the landscape or region (e.g., Bakker et al. 2002; Lee et al. 2002). In addition to the physical characte ristics of patches and la ndscapes, species survival within fragmented landscapes is dete rmined by ecological and life history traits of species (Henle et al. 2004). Numerous traits have been identified as potentially influencing species response to fragmentatio n, including body size, home range size, natural density, niche breadth, and matrix tolerance, among others (Laurance 1991; Davies et al. 2000; Henle et al. 2004). Although habitat loss and fragmentation are we ll-studied topics in general, the vast majority of research has been done in temper ate ecosystems, with relatively less work in tropical environments (see Chapter 4). Moreover, tropical studies largely have been concentrated in a few well-studied areas of Brazil and Mexico, with other regions of the tropics receiving much less attention. Taxono mic bias in fragmentation studies also is marked, with birds being the most commonly st udied species (e.g., Fahrig 2003). Only a limited number of studies have ex amined the response of larger tropical mammals to habitat fragmentation, and many of these studies were focused exclusively on primate species (Estrada et al. 1994; Onderdonk & C hapman 2000; Estrada et al. 2002; Michalski & Peres 2005; Anzures-Dadda & Manson 2007; Michalski & Peres 2007; Arroyo-Rodrguez et al. 2008; Urquiza-Haas et al. 2009). This lack of research is problematic given that mid and large-siz ed mammals likely cannot maintain viable populations in a single reserve and move ext ensively over large areas, necessitating 13

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use of fragmented landscapes. Large tropi cal mammals also are of particular conservation concern because many are threatened globally or locally, are often flagship species for conservation programs, and may play key roles in the long-term maintenance of tropical forest through their roles as top carnivores and seed predators and dispersers. Despite their conservation significanc e, we still have a poor understanding of how these species respond to the changes in patch quality, habitat area, and configuration that occur with habitat loss and fragmentation, and how species traits influence interspecies differences in response. The Maya Biosphere Reserve (MBR) and its surrounding landscape in the Department of Petn, norther n Guatemala, is an important site for addressing the effects of habitat fragmentat ion on mammals. The Maya Forest is the largest contiguous tropical forest in Mesoamerica and maintaining t he integrity of this fo rest is of critical conservation concern. The human population of the Petn has incr eased 10-fold over the last 40 years, and this population expl osion along with concurrent agricultural activity has resulted in deforestation of a large portion of the entire department since 1970 (McNab et al. 2004). The only tropical forest now left within the buffer zone and surrounding landscape of the MBR exis ts in scattered and isolated fragments surrounded by agricultural fields and cattle ra nches. The forest within the core area of the reserve increasingly is threatened by illeg al slash-and-burn agric ulture and hunting, with large portions of the western half of the reserve also heavily fragmented (Wildlife Conservation Society 2007). This region of Guatemala contai ns a diverse fauna of midand large-sized mammals that includes 5 feli d species, 5 ungulates, 2 large rodent species, 2 primates, and a diversity of sma ller carnivores. Several of these species are 14

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of global conservation concern (e.g., Bairds tapir [ Tapirus bairdii], jaguar [Panthera onca] and black howler monkey [ Alouatta pigra ]), heavily utilized by humans for food (e.g., paca [ Agouti paca ], red brocket deer [ Mazam a americana ]) or are poorly known throughout their range (e.g. margay [ Leopardus wiedii ], tayra [ Eira barbara ]). I examined occupancy patterns of 25 midand large-sized mammals in forest patches and intact forest sites of norther n Guatemala to determine how these species responded to habitat loss and fragmentation. I addressed several questions that have general significance to fragmentation and l andscape ecology theory as well as to applied conservation management of tropical mammals in fragmented habitats. I also reviewed 125 fragmentation studies to br oaden my analysis and begin a synthesis of the diverse literature on mult i-scaled fragmentation effects. Chapter 2 is an examination of the re lationship between species traits and vulnerability to fragmentation. Although seve ral ecological and life history traits commonly are associated with vulnerability to fragmentation, the comb ination of traits that are most highly infl uential for tropical mammals remains poorly understood. Moreover, the usefulness of species traits to predict response to fragmentation across diverse landscapes is virtually unstudied. I determined which of 7 species traits most strongly influence the vulnerability of ma mmals to habitat loss and fragmentation and how the results of this analysis were affected by the incorporation of passive sampling effects. I also examined the influence of s pecies traits on patch occupancy rates of the same set of mammals on two landscapes in Me xico (using previously published data) to investigate if similar species traits succe ssfully predict patch occupancy on distinct landscapes. 15

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16 Chapter 3 is an examination of how characteristics of both patches and landscapes influence patch occupancy patterns of 20 mammal species. Considerable uncertainty exists regarding which scales of patchiness tend to exert the most influence on species, and which factors at each scale are most important. One question that has generated substantial recent debat e is whether or not, at t he landscape-scale, species respond more strongly to habitat loss or habitat fragmentation per se (i.e., the breaking apart of habitat, independent of habitat loss) Empirical evidence from studies conducted in temperate environments suggests that habitat loss is much more important, but this question rarely has been investigated in tropical environments. I tested this hypothesis for tropical mammals by determining if patch occupancy patterns were influenced more strongly by habitat loss or fragmentation. I also determined the influence of patch and within-patch fact ors on species occupancy patterns. Chapter 4 broadens my analysis and takes advant age of the subst antial number of fragmentation studies that have been published over the last decade to examine in more detail the relative influence of small and large-scale factors on species distribution and abundance in patchy landscapes. I summarized the results of 125 studies that have simultaneously examined species response to within-patch, patch, and landscape-scale factors in fragmented environments. I test ed the influence of taxonomic status and several methodological variables on the probability that spec ies would respond significantly to these variables. I conclude my dissertation with a chapter detailing general conclusions and conservation recommendations.

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CHAPTER 2 ANAL YZING THE RELATIONSHIP BETWEEN SPECIES TRAITS AND VULNERABILITY TO FRAGMENTATION : PASSIVE SAMPLING EFFECTS AND CROSS-LANDSCAPE COMPARISONS Introduction Preserving biodiversity in the tropics requi res integration of conservation efforts both within and outside of reserves. Protect ed areas in the tropics only cover 5-10% of remaining tropical forest (Myers 2002) and ar e inadequate for the pr otection of a large number of species (Rodrigues et al. 2004; Ceballos 2007; Jenkins & Giri 2007). Moreover, tropical res erves are becoming sm aller and more isolated over time because of forest loss within park borders and in the surrounding landscape (deFries et al. 2005). Human-modified landscapes outside of tropi cal reserves therefore will serve an increasingly important role in preserving species diversity (Chazdon et al. 2009). These landscapes typically consist of remnant forest patches embedded in a matrix of agriculture, cattle pasture, and secondary forest regrowth. Although recent studies have shown that a subset of forest species use ag ricultural and pastural habitats (Daily et al. 2003; Harvey et al. 2006; Medina et al. 2007; Sekercioglu et al. 2007), remnant forest patches are probably the most important components to c onservation of biodiversity within human-altered landscapes of tropical forest ecosystems. These patches provide critical habitat for many fo rest-dependent tropical species liv ing outside of protected areas (Turner & Corlett 1996; Laurance & Bierregaard 1997). Substantial interspecies variation exists in the ability of species to occupy or use forest fragments (Lauranc e 1991; Gascon et al. 1999; Laurance 2002; Laurance et al. 2002). Survival of species within forest patches is determined by a combination of patch and landscape attributes, and the lif e history or ecological traits of species (Henle et al. 17

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2004). An understanding of how species trai ts influence distribution or abundance in forest patches is important for identifying generalities in response to habitat loss and fragmentation (Henle et al. 2004; Ewers & Didham 2006). Su ch knowledge is beneficial to predicting and mitigating species lo ss in human-dominated landscapes (Laurance 1991; Davies et al. 2000). Several species traits are commo nly associ ated with vulnerability to fragmentation. However, the combination of traits that are most highly influent ial, and the degree to which those traits determine vulnerability on distinct landscapes, remains poorly understood for most taxa (H enle et al. 2004). Three fa ctors confound analyses of how species traits influence vulnerability to fragmentation. First, studies do not always separate passive sampling effects from act ual effects of habitat loss and fragmentation (Johnson 2001; Haila 2002). Passive sampli ng effects are apparent patterns in the relative distribution or abundance of or ganisms among habitat fr agments that are merely artifacts of sampling. Such effe cts occur because less abundant or patchily distributed species would be expected by chance alone to be present in fewer fragments than more abundant or evenly dist ributed species (Bolger et al. 1991; Johnson 2001; Haila 2002). These species are not necessarily more vulnerable to habitat loss and fragmentation, but will be ca tegorized that way if passive sampling effects are not accounted for in the analysis. A second comm on problem is that issues of detectability often are not considered in analyses. Species that appear to be vulnerable could merely be those that ar e harder to detect (Fleishman & Mac Nally 2007). Inclusion of these elusive specie s in the analysis without accounting for detectability will underestimate the actual num ber of patches occupied by such species 18

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(MacKenzie et al. 2002) and make it harder to identify the real relationships between vulnerabilit y of a species and ecological or life history traits. Finally, the ability to identify influential ecological and life history traits is hindered by a lack of cross-landscape comparisons. The opportunity rarely is av ailable to examine how the same set of species respond to habitat loss and fragmentat ion on different landscapes. This limits our ability to identify generalities in how species traits influence response to habitat patchiness and to evaluate the relative role of context-specific environmental factors vs. more intrinsic biological factors in controlling response patterns. I addressed these issues with a study of mammal distribution patterns in a fragmented tropical landscape in northern Guatemala. Neotropi cal mammals are an excellent suite of species for studies of interspecies variability in response to fragmentation because they are a species-rich group that varies greatly in ecological and life history traits. Moreover, this suite of mammals includes some of the first species to disappear from fragmented habitats (C hiarello 1999; Peres 2001). These same mammals play key roles in maintaining long -term integrity of tr opical forests through their roles as top carnivores and seed predators and dispersers (Terborgh et al. 1999; Terborgh et al. 2001; Silman et al. 2003; Wright et al. 2007). Thus, the ability of midand large-sized mammals to use, move thro ugh, and survive in forest fragments is an important issue for biodiversity conservation in the tropics. Although studies focused on understanding differences in relative vulnerability of mammals to habitat loss and fragmentation remain sparse, previous info rmation from tropical and temperate regions suggests that several factors may influenc e vulnerability of midand large-sized mammals, including body size mobility, matrix toleranc e, niche breadth, nearness to 19

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range boundary, and vulnerability to hunting pressure (Laurance 1991; Chiarello 1999; Crooks 2002; Swihart et al. 2003; Michalski & Peres 2007). I studied use of forest fr agments and intact forest sites by 25 midand largesized mammals to determine how species traits influence sensitivity to habitat loss and fragmentation. I tested the gener ality of these results with a comparison of patch occupancy patterns of all 25 mammal spec ies across three distinct fragmented landscapes, and addressed several specific questions related to the influence of species traits on vulnerability: 1) What are the most important species trai ts influencing vulnerability to habitat loss and fragmentation of mammals in Guatemala? 2) Does accounting for passive sampling effe cts alter the relative importance of species traits in determining vulnerabi lity to habitat loss and fragmentation? I predicted that traits influencing distri bution of species in an unaltere d landscape (i.e., body size, home-range size, reproductive rate, trophi c level) would be less important in determining vulnerability to fragmentation once sampling effects were taken into account in the analysis. 3) Do the same set of ecol ogical and life history trai ts explain interspecific variation in vulnerability to fragmentation on different landscapes? I predicted that the same species traits would explain vul nerability across landscapes. To address this question, I re-analyzed data from two studies conducted in nearby Mexican landscapes with the same species (Estrada et al. 1994, Urquiza-Haas et al 2009) so that the results were comparable with my Guatem alan study. To my knowledge, this is the first attempt 20

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to look at whether or not similar suites of ecological and life hist ory traits determine response to fragmentation for the same set of species inhabiting different landscapes. Methods Study Site I conducted this study in a 300,000-ha ar ea in the Petn region of northern Guatemala (Fig. 2-1). The nort hernmost part of the study area is an intact landscape of humid subtropical forest located within Tik al National Park, which itself is situated within the larger Maya Biosphere Res erve (an UNE SCO world heritage site and the largest contiguous tropical forest in C entral America). The southern part of the study area is a highly fragmented landscape located within the buffer zone of the Maya Biosphere Reserve and on private lands farther to t he south. This area was formerly contiguous forest, but now consists of a diverse colle ction of primary rainforest patches embedded in a matrix of secondary and regenerating fore st, cattle pasture and agricultural land (Fig. 2-2A). Forest destruction and fragm entation began in the late 1970s. Forest patches are thus no more than 30 years old. Forest cover in this area consists of subtropical humid rainforest with sca ttered patches of seasonally inundated bajo forest and a fringe of savannah forest in the south. Al titudinal variation is minimal (130 to 400 masl). Annual average temperature of th is region is 21 C, and annual average precipitation is 1,350 mm, wit h a marked dry season from December to May when the average monthly rainfall is only 60 mm. I selected 50 primary rainforest patches t hat ranged in size from 2.9 to 445.5 ha and 12 sites in continuous forest (6 site s each of 20 ha and 100 ha; Fig. 2-2B) as my study sites. Rainforest patches contained a di verse collection of tree species, but were often dominated by some combination of Brosimum alicastrum, Manilkara zapota, Ficus 21

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sp. Vitex gaumeri, Pouteria sp. Sebastiana longicuspis, Term inalia amazonia, and Alseis yucatnensis Bursera simaruba Spondias mombin Aspidosperma megalocarpon Dendropanax arboreus Protium copal, Pimenta dioica and Cedrela odorata. I did not sample rainforest patches that were severely degraded by logging or fire, but I sampled a number of lightly degraded sites. Lightly degraded sites were those that still had a largely intact overstory of trees and where the effects of past fires were limited to less than 25% of the patch area. I did not sample patches of bajo forest, savannah forest, or secondary forest. Almost all forest patches in my study were used to some degree by local inhabitants for collection of non-timber forest products, hunting, or cattle grazing. Study sites in continuous forest were similar in tree composition and density to rainforest patches, but were su rrounded by uninterrupted forest cover. My continuous forest sites were subject to low levels of hunt ing pressure and collection of non-timber forest products. Based on these c haracteristics, I made the assumption that these sites in continuous forest are analogou s to pre-fragmentation conditions in the fragmented part of the study area. Continuous forest sites were situated greater than 500 m, but less than 3 km from the main road in Tikal National Park (Fig. 2-2B). This distance restriction was established to take advantage of the decreased hunting pressure on mammals close to the main road and archaeological ruins (which are patrolled more heavily by park guards), while minimizing disturbance related to traffic and human visitors to the par k. A previous camera-trappi ng study of mammals in the park found increased mammal diversity in areas closer to the main road (Kawanishi 1995). 22

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Species Ecological and Li fe History Traits The midand large-sized mammal communi ty in northern Guatemala is quite diverse, and includes 29 species. However, f our of these species are either patchily distributed throughout the region or have habits t hat make them very difficult to detect (grison [ Galictis vitatta ], northern naked-tailed armadillo [ Cabassous centralis ], coyote [ Canis latrans], cacomistle [Bassariscus sumichrasti ]). After excluding these species, I could adequately sample 25 species in my study area. Although most species are found elsewhere in Central and/or South Americ a, the Mexican black howler monkey ( Alouatta pigra) is restricted to Guatemal a, Belize, and the Yucatn peni nsula of Mexico. Several species are of high global or local conser vation concern, including the tapir ( Tapirus bairdii ), jaguar ( Panthera onca ), spider monkey ( Ateles geofroyii ), and white-lipped peccary (Tayassu pecari ). I determined values of 7 life history and ecological traits for each species using field guides and published literature (Table 2-1) The procedure and lit erature used to determine values for each trait category are de scribed in Appendix A. These traits were chosen because they commonly are hypothes ized to influence vulnerability to fragmentation in mammals based on empi rical and theoretical evidence (Laurance 1991; Peres 2001; Henle et al. 2004; Ewers & Didham 2006). Ecological and life history variables used in my study were correlat ed. Consequently, I performed a principle components analysis (PCA) using proc FACTOR in SAS (SAS 2008) to reduce the number of variables and remove correlations. I log-transformed body mass and homerange size prior to input in the PCA analysis. Results from the PCA indicate that 83% of the variation in the seven traits is expl ained by just three axes. Based on factor loadings, these three axes each represent a distinct aspect of ma mmalian biology and 23

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ecology (Table 2-2). PCA axis 1 (reproduction/ niche specialization axis) represents a gradient from species with low reproductive rates and specialized diets and habitats, to species with high reproductive rates and gener alized diets and habitats. PCA axis 2 (body size/hunting vulnerability axis) represents a gradient from species with small body size and low vulnerability to hunting (i.e., ar e rarely or never hunted) to species with large body size and high vulnerability to hunting (i.e., heavily hunted/persecuted species). PCA axis 3 (home range/trophic leve l axis) represents a gradient from species with small home ranges and lower trophic levels to species with large home ranges and higher trophic levels. Mammal Surveys I determined mammal presence/absence within forest patches and continuous sites from January 2006 to August of 2008. I avoided sampling during mid-late wet season (mid-September to mid-Decembe r) because of problems with camera performance in very wet conditions. I used camera-trapping and visual censuses to survey for mammal presence wit hin study sites. This combination of techniques gave us the best chance to detect the presence of el usive arboreal and terrestrial species. I placed camera-traps in a variety of locations in each study site to maximize the number of species photographed. These locations in cluded roads, small and large game trails, water holes, den sites, and other areas containing substantial signs of animal use such as tracks, digging, or scraping. I placed ca mera-traps at least 10 m from the edge of patches, with the sensor approximately 10 20 cm off the ground so that smaller species could not avoid detection by walk ing under the sensor. I used both passive (Leaf River model C-1BU, Leaf River Outdoor Products, Taylorsville, MS) and active (Trailmaster model 1500, Goodson and Associates, Inc., Lenexa, KS) infrared camera 24

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traps in approximately equal proportions wit hin each site. I placed more cameras in larger sites (Table 2-3) and spaced them farther apart in order to cover a larger area. I included patch size in assessments of detect ability to account for potential biases related to unequal sampling per unit area in the patches. I deployed camera-traps for a 16-day per iod in each site. A photograph of a species at any camera within a site wa s considered an indication of presence. I recorded presence/absence for each species withi n each site after every 4-day interval. By breaking up the 16-day period into 4-day sessions, I created a series of repeat detection/non-detection data (i.e., a detecti on history) for use in modeling detection probabilities for each species. Because absenc e of a species could be the result of either true absence or failure to detect a species, detectability must be included to avoid underestimating occupancy (MacKenzie et al. 2006). Camera-trapping was ineffective for samp ling arboreal species, as well as two terrestrial species (white-tailed deer [ Odocoileus virginianus ] and collared peccaries [ Pecari tajacu ]). In order to document presence/ absence of these species, I performed visual censuses of sites in the early morning (between sunrise and 3 hours after sunrise). Surveys were repeated 5 times within a 2-week period for each site, resulting in a series of detection/non-detection data for use in modeling detection probabilities. In order to cover as much of the site as possible and to increase my chance of encountering species, I did not cut transects for walking within each site. I surveyed sites by walking along small roads, human foot paths, and game trails, and by walking through sections without any obvious tra ils. I walked approximately 1 km per hour and recorded direct observations of animals, vocalizations, and well-defined tracks as 25

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indications of presence within the site. Dis tance walked varied with patch size (Table 23). For small sites (less than 10 ha), I was able to walk through most or all of the site during each session. For sites too large to su rvey completely in one session, I divided the site into 2-4 sections and randomly chose a section to walk each session. I repeated this process until I had 5 surveys for the site. During each survey, I chose a random compass direction to guide my general walki ng direction within the designated section. Once an edge was reached (of the section, or of the site itself), I walked a new compass direction oriented back into the section unt il the appropriate distance was reached. I walked greater distances in larger patches, but did not walk the same distance per unit area in small and large patches (Table 2-3). As with cameras, patch size was included in assessments of detectability to account for potential biases related to unequal sampling per unit area in the patches. For 17 forest patches and 4 continuous site s, I also conducted visual censuses 2 hours prior to sunrise to search for arboreal nocturnal mammals, particularly kinkajous ( Potos flavus ). I cut meter-wide transects within multip le sections of each site in order to move through the fragments during the night. I walked transects at a very slow pace (0.5km/hour), and used flashlights to sear ch for mammals in the trees. I repeated nocturnal surveys 5 times, walking a differ ent transect each night. Larger sites received greater sampling effort (more and longer transects were cut and walked; Table 2-3). Measuring Vulnerability to Fragmentation I modeled patch occupancy and detection pr obabilities for each species using logistic regression in program PRESENCE ( Hi nes 2006). Because many species in my study were capable of moving in and out of patches during sampling, the occupancy estimator is best interpreted as probability of use of a patch, rather than probability of 26

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occupancy. The detection parameter is best inte rpreted as probability of being within the patch and detected during sampling (MacKenzie et al. 2006). For ease of presentation, I will use traditional occ upancy terminology in this paper. I used three measures of vulnerability: 1) overall proportion of patches occupied (used) by each species (continuous forest sites not included), 2) difference between probability of occupancy (use) in continuous vs. fragmented forest sites of 20-ha size, and 3) difference between probability of occupancy (use) in continuous vs. fragmented forest sites of 100-ha size. These two late r measures account for passive sampling effects in the analysis by comparing expected patterns of occupancy in continuous forest sites with observed patterns in forest fragments of similar size. The two sizes (20 ha and 100 ha) were chosen to represent the size range of most patches in my study area (Table 2-3). Although the ideal way to a ccount for passive samp ling effects is to have preand post-fragmentation information on species distribution, such information rarely is available and my comparative appr oach is appropriate given similarities between study sites in in tact and fragmented areas. To determine the overall proportion of pat ches occupied by each species, I modeled occupancy and detection probabilities using detection/non-detection data collected from all 50 forest patches and 12 c ontinuous forest sites for 24 species. For kinkajous, modeling was limited to the 17 fore st patches and 4 continuous forest sites surveyed during pre-dawn hours. In program PRESENCE, I first mo deled detection as a function of patch size and a categorical va riable representing fragm ented vs. continuous sites, keeping occupancy constant. I dete rmined the best fit detection model for each species using AICc (Burnham & Anderson 2002). Species with less than 7 total 27

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detections in all sites combined were considered to have constant detection probabilities. I then took the best fit or constant detection model, and modeled probability of occupancy using two covariates: size of the site (i.e., pat ch size or size of the site in continuous forest) and fragmented vs. continuous forest status. Based on this final model, I calculated the overall proporti on of patches occupied by summing the individual occupancy estimates for each patch (excluding occupanc y estimates from continuous sites) and dividing by the tota l number of patches. This was my first measure of vulnerability, with higher proportion of patc hes occupied indicating less vulnerable species. For my second measur e of vulnerability, I used occupancy models for each species to estimate the probability of occupancy in a 20-ha forest site and a 20ha patch, and subtracted the two values. I repeat ed this process for 100ha sites, which was my third measure of vul nerability. High positive values resulting from these two analyses indicate species that had high occu pancy probabilities in continuous forest sites, but low occupancy probabilities in forest patches of the same size. These species are considered highly vulnerable to habitat loss and fragmentation. Conversely, high negative values indicate species that benefit from habitat loss and fragmentation, as they were more commonly encountered in forest patches than in continuous forest sites of the same size. For the analysis of vulner ability, I limited the number of covariates used in modeling detection and occupancy to two because of sparse datasets for several species (e.g., jaguars, tapir, puma [ Puma concolor ]). Overall patch occupancy rates using simple or more complicated models (see Chapter 3) were similar, so I only present results based on the simple model s with limited numbers of covariates. 28

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Influence of Species Traits on Vulnerability I evaluated the influence of ecological and life history traits on vulnerability to fragmentation with multiple regression and hierarchical partitioning. I used two approaches to account for correlations bet ween the species traits: 1) I used the uncorrelated PCA axes of species traits as predictor variables in a multiple regression, and 2) I used hierarchical partitioning analysis (Chevan & Sutherland 1991) to tease apart the independent contributio n of each individual spec ies trait. For the first approach, I ran three separate regressions wi th proc REG in SAS (SAS 2008) using the three PCA axes as predictor variables and my three measures of vulnerability as dependent variables. Because the PCA axes are orthogonal, partial R2 values of each axis can be used to evaluate the relative influence of each axis on the response variable. For the second approach, I employed hierarchical partitioning analysis with the original seven species traits as predictor variables using the hierpart macro in SAS (Murray & Conner 2009). Hierarchical partiti oning analysis calculates the increase in model fit associated with each predictor variable by averaging the goodness of fit increase across the hierarchy of models in which the variable appears (see [Chevan and Sutherland 1991] for additional explanati on). Initial inputs into analyses are the goodness-of-fit values (e.g., R2) obtained using regression analysis. The hierarchical partitioning analysis estimates the independent expl anatory power of a variable (i.e., effect on a response variable attributable sole ly to that particular predictor) and joint explanatory power (i.e., effect on a respons e variable attributable to joint action with other predictors) of each pr edictor variable. The indepe ndent explanatory power serves as the appropriate measure of the influenc e of a predictor variable on a response (Chevan & Sutherland 1991; Mac Nally 2000). The key advantage of hierarchical 29

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partitioning analysis is that it can prov ide an accurate assessment of the independent effect of a predictor variable, even in the presence of mult icollinearity (Murray & Conner 2009; but see Smith et al. 2009 for a criticism of this approach). Cross-Lan dscape Comparison of Vulnerability I compared results of my analysis to two other studies that involved the same species (Estrada et al. 1994; Urquiza-Haas et al. 2009). Estrada et al. (1994) studied patch occupancy of small and large mammals in 35 lowland rainforest fragments of Los Tuxtlas, Mexico, and Urquiza-Haas et al. (2 009) studied patch occupancy of midand large-sized mammals in 147 fragments of tropica l dry forest in Yucatn, Mexico. As in this study, the overall proportion of fragments occupied was determined for each species, although the methodology of the two studies differed from my own. Estrada et al. (1994) used live traps and diurnal/noctural visual surveys to determine presence/absence of mammals within patc hes, and Urquiza-Haas et al. (2009) used interviews with landowners to determine pat ch occupancy. Patch occupancy estimates were not listed in the original paper for the Los Tuxtlas data, but were obtained from the author (A. Estrada, personal co mmunication). Results of t hese two studies were not corrected for passive sampling effects, and thus are comparable only to my vulnerability measure of overall proportion of fragments occupied. I limited my cross-landscape comparison to those species shared among the three landscapes (n = 25 species). Patch occupancy patterns of common and Virginia opossums ( Didelphis marsupialis and Didelphis virginianus ) were evaluated at the genus level in the Yucatn landscape, but we re treated as separate species for the Los Tuxtlas and Guatemalan landsca pes. I therefore treated them as separate species for the Yucatn landscape, and assigned each species the same patch occupancy as was 30

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recorded for the two species combined (i.e., 100 %). Whether or not I combine data for these two species did not alter results of the subsequent analysis. For the Los Tuxtlas landscape, data were collected on Alouatta palliata and Dasyprocta mexicana instead of Alouatta pigra and Dasyprocta punctata as in my study and the Yucatn study. However, because of similarities in mo rphology and ecology of these species (Reid 1997), I included them in the analysis and tr eated them as equiva lent species. I tested for general cor relations between the rankings of species vulnerability in my study and the two Mexican studies using Sp earmans correlation coefficients. This analysis tested whether or not species that tended to be ranked lower in terms of overall patch occupancy in my study also tended to rank lower in the other two landscapes. I also performed multiple regression and hierarchical partitioning analysis to determine which combination of species traits were most important in influencing patch occupancy on each landscape. I made the a ssumption that the values used in my study for the ecological and life history traits of species could be applied to those same species in the Mexican landscapes. This assumption is likely to hold for some traits such as body size and trophic level, but may not hold for other traits such as home-range size. However, given the lack of region specific information fo r many of these traits and the geographic proximity of the three study areas, I belie ve my approach is appropriate as a first assessment of cross-landsc ape generalities in species re sponse to fragmentation. Results I detected 25 species of midand large-sized mammals within my study sites in 12,960 camera-trap nights and 400 kilometers of visual surveys. All spec ies detected in intact forest sites also were detected in 1 or more fragments, ex cept for white-lipped peccaries, which only were detected in intact forest. The number of species detected in 31

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forest patches varied between 3 and 19, with significantly more species detected in larger fragments fragments ( P < 0.01). I detected between 7 and 16 species in intact forest sites, with more species det ected in the larger 100-ha sites ( P < 0.10). Vulnerability to Fragmentation Relativ e vulnerability of species to fr agmentation as measured by proportion of patches occupied varied greatly among specie s. Occupancy of forest patches in my study area ranged from 0 (e.g., white-lipped peccaries) to near ly 100% (e.g., kinkajous, Table 2-4). Best-fit models and parameter es timates for each species are presented in Appen dix B. Inclusion of detec tability in the analysis resulted in substantial increases in overall patch occupancy for species that were difficult to detect (Table 4). For example, the estimated percentage of patches occ upied increased 20.8 % and 19.2 % from the naive estimate that did not include det ectability for the Mexican porcupine ( Coendou mexicanus ) and tayra ( Eira barbara ), respectively. Relative vulnerability as measured by diffe rences in occupation of continuous and fragmented forest sites of the same size al so varied greatly between species (Table 24). Some species were much more common in continuous forest, such as white-tailed deer, red brocket deer ( Mazama americana ), and puma, whereas others were much more commonly encountered in forest patches, including northern tamandua ( Tamandua mexicana) and northern raccoon ( Procyon lotor ). Results of the vulnerability analysis using 20-ha and 100-ha sites we re similar for all species. Influence of Species Traits on Vulnerability My measure of vulnerability that was uncorrected for sampling effects (overall proportion of patches occupied) was influ enced strongly by PCA axis 2 and 3 (Table 25). This indicates that species that are larger and more heavily hu nted, and species that 32

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have larger home ranges and hi gher trophic levels, tended to occupy fewer fragments than those species that did not have those traits. Parameter es timates and partial R2 values indicate that axis 2 (body size/hunting axis) was t he most important predictor. Overall, the model with all three predictors explained a large amount of variation in patch occupancy (R2 = 0.58). Results from the hier archical partitioning analysis generally confirm the results from the PCA regression analys is, but enabled us to look at the importance of individual predictors in more detail (Fig. 2-3A). The overall proportion of fragments occupied was influ enced most strongly by body size, home range, and to a lesser extent by hunting pressure. Accounting for passive sampling effects alte red the relative influence of species traits on vulnerability to fragmentation. My measure of vulnerability corrected for sampling effects (the difference in occupancy between fragmented and continuous forest sites of similar size) was influenced strongly only by axis 2 (Table 2-5). This result was similar when considering either 20-ha or 100-ha sites. This indicates that species that are larger and more heavily hunted tended to be much more likely to occupy continuous forest sites than fore st patches of the same size. Overall fit of these models was slightly lower, with only 41.3 % and 49.2 % of the variation explained by the full models for 20-ha and 100-ha sites, respectively Hierarchical partitioning demonstrated that this measure of vul nerability was driven largely by differences in hunting vulnerability, which accounted for almost hal f of the explained variance in both cases (Fig. 2-4A and B). Cross-Landscape Comparison In general, species that had lower levels of patch occupancy in my study also had lower levels of patch occupancy in t he Los Tuxtlas and Yucatn landscapes (r = 0.66 33

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and 0.52, respectively). This agreement between studies only holds when comparing the rankings of species in terms of their relative vulnerability; absolute values of patch occupancy differed substantially between the studies (Table 2-4). All three PCA axes had a signif icant effect on vulnerability to fr agmentation for species in Los Tuxtlas (P = 0.022, 0.026, and 0.002, respectively), and these axes explained 54.2 % of the variation in the response. In Yucatn, the reproduc tion/niche breadth and body size/hunting axes were related to vulnerability (P = 0.015 and 0.011, respectively), and all three axes explained 44.0 % of the variation in proportion of patches occupied. Hierarchical partitioning generally agr eed with the PCA analyses but with some added detail on individual predictors. For the Los Tuxtlas dataset, hom e range and reproductive rate were most influential, accounting for a la rge amount of the expl ained variation in vulnerability (Fig. 2-3B). In Yucatn, body size and habitat breadth had the highest levels of independent explanat ory power (Fig 2-3C). Discussion Influence of Species Traits on Vulnerability After accounting for passive sampling e ffects, vulnerability to hunting was the single most important species trait influenc ing how species responded to fragmentation in my Guatemalan study site. Species t hat were more heavily hunted were more vulnerable to fragmentation. The negative impacts of hunting on densities and/ or abundances of tropical mammals have been well documented in intact forest across the Americas (Bodmer et al. 1997; Carillo et al. 2000; Hill et al. 2003; Peres & Nascimento 2006). Many of the species included in my study respond negatively to hunting pressure in continuous forests of G uatemala and Mexico (Baur 1998; Po lisar et al. 1998; Naranjo & Bodmer 2007; Reyna-Hurtado & Tanner 2007). However, the effect of hunting on 34

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mammal distribution and abundance has not been documented widely in fragmented habitats (but see Cullen Jr. et al. 2000), even though the lack of escape habitat and ease of access of hunters to forest patches makes species especially vulnerable to hunting within forest remnants (Peres 2001; Parry et al. 2009). I ob served direct and indirect evidence of hunting in 20 of my forest patches during the relatively limited sampling period, and all fragments in my study were located close to human communities (< 5 km) and easily accessible vi a roads/trails. Based on models of the minimum forest area required to maintain a su stainable harvest, the majority of forest fragments i n tropical environments fall well below the size necessary to support populations of midand large-sized mammals that are experiencing hunting pressure (Peres 2001). My data provide empirical su pport for a profound impact of hunting on tropical vertebrates in fragm ented landscapes by showing t hat more heavily hunted and persecuted species were most likely to show a large reduction in their occupancy of forest patches when compared to their normal occupancy patterns in intact forest. The relative influence of particular spec ies traits changed substantially depending on whether or not I accounted for sampling effe cts in my estimate of vulnerability to fragmentation. As predicted, two traits that are important in determining the density/distribution of mammals in intact forest (body size and home-range size) were very important in driving vulnerability to fragmentation when samp ling effects were not removed. Species that have these traits are expected to be present in a smaller proportion of patches just by virtue of their natural rarity. These traits declined substantially in importance when I acc ounted for sampling effects by comparing occupancy patterns in forest patches with expected occupancy patterns in continuous 35

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forest. Thus, body size and home range may not be as important in determining vulnerabilit y to fragmentation as indicat ed by an examination of patch occupancy patterns alone. Although some studies that correlate species traits with vulnerability to fragmentation account for sampling effects (e.g., Bolger et al. 1991; Lynam & Billick 1999; Davies et al. 2000; Meyer et al. 2008) a substantial number do not and employ measures such as overall patch occupan cy or raw species-area curves to infer sensitivity to fragmentation (e.g., Onderdonk & Chapman 2000; Crooks 2002; Virgos et al. 2002; Viveiros de Castro & Fernandez 20 04; Wang et al. 2009). Other authors have pointed out the pitfalls of using patch occupancy rates or raw species-area curves to infer vulnerability to fragmentation (Bolger et al. 1991; Johnson 2001; Haila 2002; Meyer et al. 2008). My results demonstrate that, in some instances, non-removal of sampling effects could lead to incorrect conclusions regarding the importance of species traits. In particular, not accounting for passive samp ling effects could lead to an increased emphasis on the importance of traits a ssociated with natural abundance or widespread distribution such as body size, natural abundanc e, or potentially niche breadth that may not be warranted. However, in some systems, these traits exert a heavy influence on regional extinction proneness (Woodroffe & Gi nsberg 1998; Purvis et al. 2000; Kamilar & Paciulli 2008) and correlate with vulnerabili ty to fragmentation even after the removal of sampling effects (Davies et al. 2000; Shahabuddin & Ponte 2005). The importance of these traits cannot be discounted. However, removal of sampling effects will promote a better understanding of the influence of these types of traits on species vulnerability to fragmentation ( sensu Meyer et al. 2008). 36

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In addition to biases created by passive sa mpling effects, vulnerability estimates of mammals in this study would have been biased had I not accounted for differences in detectability in the analysis of occupancy and vulnerability patterns. Detectability varies as a function of species-specific abundance wit hin sites (Royle & Nichols 2003) as well as species-specific differences in the effi cacy of survey techniques. For example, my visual surveys did an excellent job of det ecting monkeys, which are generally fast moving, noisy, and likely to vocalize, but were much less effective at detecting porcupines, which move slowly and quietly through the canopy. Although camera-traps are ideal for collecting data on a large number of species, they do not detect all species equally well (Barea-Azcn et al. 2007; Tobler et al. 2008). For example, smaller species are less likely to trigger the cameras (Tobler et al. 2008). Specific characteristics of camera trap locations, such as being placed on roads, small animal trails, or den sites also may influence the probability of detection (Trolle & Kery 2005; Larrucea et al. 2008; Harmsen et al. 2010). Ocelots in my study had much higher encounter rates on roads than small animal trails, whereas the revers e pattern was true for agoutis (Thornton, unpublished data). Failure to inco rporate species-specific estimates of detectability in my analysis would have underestimated the proportion of patches occupied for a number of species that were difficult to detect (e.g., raccoons and tayra). Furthermore, because detectability was different in intact forest and forest patches for some species (e.g., species with the fragmented vs. conti nuous forest covariate included in best-fit detection models in Appendix B), failure to incorporate dectection probabilities in my analysis of occupation would have biased my calculation of differences in occupancy rates of intact and fragmented sites. 37

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Cross-Landscape Comparison Relative rankings of species with re spect to patch occupancy were in general agreement between the three stud y landscapes, although the correlations were far from perfect. Overall, species that were rank ed lower in terms of patch occupancy on one landscape tended to be ranked lower on the other landscapes, and vice versa. However, absolute levels of patch occupanc y for the same specie s differed drastically among the study sites. In general, species had the lowest levels of patch occupancy in Los Tuxtlas and highest levels in Yucatn. For example, coatis occupied 20% of the patches in Los Tuxtlas, 63% of the patches in Guatemala, and 98% of the patches in Yucatn. Some of this variation could be explained by differences in methodology. Interviews used in Yucatn may have result ed in higher estimates of patch occupancy for species because they measured both pas t and present mammal use of patches. Mammals were recorded as present if t hey had been observed within the patch in the last 5 years (Urquiza-Haas et al. 2009) The generally lower estimates of patch occupancy in Los Tuxtlas may have been influenced by the use of live traps instead of camera traps or lower levels of trapping effort. Also, the Los Tuxtlas study was done before techniques existed for the incorporat ion of detectability in patch occupancy estimates. A failure to incorporate dec tectability may have biased estimates of occupancy lower for some species. The th ree landscapes differed in climate, human population density, and physical characteristics of patches and the surrounding landscape and these context-sp ecific landscape characteristics also could drive differences in absolute levels of patch occupancy among these landscapes. Similar to the results from Guatemala, s pecies traits strongly influenced variation in overall patch occupancy patterns of midand large-sized mammals in both Mexican 38

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landscapes. Averaged across all three landscapes, species traits examined in this study explained approximately 52% of the variation in overall patch occupancy for this set of midand large-sized mammals. Intrinsic biologica l traits are thus ex tremely important in determining patch occupancy rates fo r the species considered her e. Even though I limited my comparison to almo st the exact same set of species in each landscape, the relative importance of species traits in determining patch occupancy patterns differed among the three landscapes Because this crosslandscape analysis used a measur e of fragmentation sensitivit y that did not account for passive sampling effects, my comparison is perhaps best viewed as an analysis of how species traits influence patch occupancy, rather than vulnerability to habitat fragmentation per se However, my general finding that different traits can emerge as important on different landscapes for the same set of species is applicable and relevant to studies of species vulnerabilit y to habitat loss and fragmentation. Occupancy patterns in Yucatn were influen ced primarily by body size and habitat breadth. This agrees largely with the results published in t he original study (UrquizaHaas et al. 2009), even though the authors incl uded additional species in their analysis and used a slightly different measure of habitat specificity than I did. Habitat breadth, or a related characteristic, matrix tole rance, has been found to be an important determinant of vulnerability to fragmentat ion in studies on mammals (Laurance 1991) and other taxa (Gascon et al. 1999; Sekerc ioglu et al. 2007). Species with a greater habitat breadth are expected to be less vulnerable to fragmentation because they can forage more effectively outside of forest patches, use more disturbed habitats, and may be better able to cross non-forest habitat (H enle et al. 2004). Patch occupancy rates in 39

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the Los Tuxtlas landscapes were influenced primarily by home range and reproductive rate. Home-range size and reproductive rate are correlated with abundance, and thus may be appear as important primarily because of sampling effects, or because species with lower abundance are more likely to go ex tinct in fragments. Home-range size may be also be influential because species with la rger home ranges may be more l ikely to come into contact and conflict with humans (Woodroffe & Ginsberg 1998). Reproductive rate could also exert an influence on spec ies vulnerability to fragmentation through affects on the ability of animals to rebound from loss of habitat (Henle et al. 2004). Although difficult to assess based on only three study sites, variation in the influence of species traits on patch occupancy may be influenced by context-specific differences between the landscapes. For exampl e, the relatively large influence of hunting vulnerability on patch occupancy in my Guatemalan landscape compared to the Mexican landscapes could be related to differences in hunting pressure among the three areas. Patches in my study area were heavily impacted by hunting pressure whereas this was not the case in Los Tuxt las at the time of the study (personal communication, A. Estrada). Subsistence hunting occurs in Yucatn (personal communication, T. Urquiza-Haas), but hunt ing pressure may be less intense than in northern Guatemala where a rapidly increasing and extremely poor rural population creates a large demand for wild game. The impor tance of habitat breadth in the Yucatn compared to the other landscapes may be related to differences in the type of patches studied in each area. Patches in the Yucatn were a mixture of primary, secondary, and disturbed habitats, whereas patches in Guatemala and Los Tuxtlas were largely undisturbed primary forest si tes. Because habitat breadth is important in determining 40

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use of disturbed or secondary forest habitats this trait could be a more important influence on patch occupancy patterns in Yucatn. Collectively, results from the cross-landsca pe comparison indic ate both similarities and differences in how the same species responded to habitat patchiness. The strong influence of both body size and home-range size in determining patch occupancy on all three landscapes probably accounts for the correlation between the studies in terms of the relative rankings of species according to patch occupancy rates. This suggests that in may be possible to predict with some degree of accuracy relative patch occupancy patterns of these species on novel landscape s, which is one of the major goals of analyzing the relationship between specie s traits and response to landscape change (Mac Nally & Bennett 1997). However, the influence of other variables, particularly reproductive rate, habitat breadth, and hunting vulnerability on patch occupancy patterns changed substantially across landscapes and likely accounts for the less than perfect nature of the correlations. The search for generalities in species response to habitat patchiness, and potentiall y to habitat loss and fragmentation per se based on shared ecological and life history characteri stics therefore will be made more difficult because of variability in the impor tance of traits among landscapes. Species-Level Patterns I sampled more fragments than any previous study of tropical midand large-sized mammals in fragmented environments, ex cluding studies exclusively based on interviews with landowners (i.e., Michal ski & Peres 2005; Urquiza-Haas et al. 2009). Given this substantial dataset, I conclude that forest fragments in my study area supported a diverse assemblage of mammal s. Larger fragments in my study area generally supported a greater number of species, but small forest patches were still 41

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used by a number of mammal species, some of them heavily fo rest-dependent (e.g., pacas [Agouti paca ], agoutis [ Dasyprocta punctata ], margays [ Leopardus wiedii ], spider monkeys, brocket deer). For example, I det ected 9 species in a patch of only 7 ha, including paca, margay, and ocelot (Leopardus pardalis ). The suite of species using fragments in my study area included se veral wide-ranging and/or forest dependent species that are often considered most at risk of disappeari ng from fragmented environments. However, because forest fragm ents in my study area were relatively young, this diversity of midand large-sized mammals could be the result of insufficient time for species to disappear completely from fragments (i,e., an extinction debt, Tilman et al. 1994). Although overall mammal diversity in my landscape was high, not all species were equally adept at using forest patches. Seve ral species appeared to be highly vulnerable to loss and fragmentation of habitat and had a co mbination of low patch occupancy and lower occupancy rates in forest patches than in intact forest sites (e .g., pumas, jaguars, ocelots, tapir, white-lipped peccaries, spider monkeys). Many of these species are of the highest conservation priority nationally and/or globally. In contrast, a suite of species obviously benefited from loss and fragmentation of forest and had high patch occupancy rates and/or were more commonly encountered in forest patches than continuous forest. These species included both habitat generalists (e.g., northern raccoon, jaguarundi [ Puma yagouaroundi ], hog-nosed skunk) and forest-dependent species (e.g., tayra). Conclusions My results indicate that vulnerability to hunting drives much of the interspecies differences in sensitivity to habitat lo ss and fragmentation in northern Guatemala. 42

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Because I was able to incorporate detectability in my analysis, my findings do not reflect detection differences between species but inst ead real patterns in vulnerability. Hunting pressure on midand large-sized mammals i s common in many fragmented tropical environment (Peres 2001). Reduction of hunti ng pressure may have a marked positive effect on the ability of species to use and persist within fragment ed landscapes of the tropics and thus should be a primary focus of management efforts in human-dominated environments with high levels of hunting. My work also shows that the way in which vulnerability to fragmentation is measured, and in particular whether or not passive sampling effects are accounted for in the analysis, may alter conclusions regarding the relative influence of species traits on s ensitivity to habitat loss and fragmentation. Finally, my cross-landscape comparison f ound correlations among mammals on three distinct landscapes when comparing the relati ve ranking of species in terms of patch occupancy, which suggests some degree of similarity in response that could be used to predict how the same specie s will react on novel landscapes. However, my comparison also demonstrates that the relative infl uence of certain species traits on patch occupancy patterns (and perhaps to some ex tent on vulnerability fragmentation) changes across landscapes, perhaps because of context-specific differences between landscapes. Moreover, absolute values of pat ch occupancy were markedly different on the three landscapes. I found these results even though I was considering almost the exact same set of species in all three landscapes. These findings therefore suggest some limitations in the use of species ecologic al and life history traits to predict variation in patch occupancy and/or sensitivity to loss and fragmentation of habitat across diverse 43

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44 landscapes, at least until we are able to better incorporate extrinsic factors such as landscape context into the analysis.

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Table 2-1. Life history and ecological trai ts of mammals studied in northern Guatemala. Species Common name Body mass (kg) Home range (ha) Trophic levela Repro. rateb Dietary breadthc Habitat breadthc Hunting vuln.d Didelphis marsupialis Common opossum 1.5 12 2 12 7 7 1 Didelphis virginianus Virginia opossum 1.8 12 2 12 7 7 1 Dasypus novemcinctus Nine-banded armadillo 3.0 6 2 4 5 6 3 Tamandua mexicana Northern tamandua 6.2 25 3 1 1 5 1 Alouatta pigra Yucatn black howler 6.9 17 1 0.5 2 2 2 Ateles geoffroyi Central American spider monkey 7.0 250 1 0.33 1 1 2 Leopardus pardalis Ocelot 9.7 1695 3 0.5 6 3 2 Leopardus wiedii Margay 3.6 1095 3 0.5 6 3 2 Panthera onca Jaguar 65.9 5600 3 1 3 3 3 Puma concolor Puma 45.0 6500 3 1.33 3 4 3 Puma yagouaroundi Jaguarundi 5.2 1065 3 2.5 7 5 2 Urocyon cinereoargenteus Gray Fox 2.7 95 2 4 7 6 1 Conepatus semistriatus Striped hog-nosed skunk 2.5 103 2 4 4 4 1 Eira barbara Tayra 4.5 1375 2 2.5 5 4 1 Nasua narica White-nosed coati 4.6 60 2 3.5 4 3 2 Potos flavus Kinkajou 3.3 23 1 1 4 2 1 Procyon lotor Northern raccoon 5.6 50 2 3.5 8 6 2 Tapirus bairdii Bairds Tapir 240 125 1 0.5 3 3 3 Pecari tajacu Collared peccary 19.0 249 2 2 6 3 3 Tayassu pecari White-lipped peccary 33.5 2387 2 2 5 1 3 Mazama americana Red brocket deer 22.0 52 1 1 3 2 3 Odocoileus virginianus White-tailed deer 34.0 284 1 1.5 3 5 3 Coendou mexicanus Mexican porcupine 2.0 19 1 1 3 2 1 Agouti paca Paca 8.5 2 1 1.5 4 4 3 Dasyprocta punctata Central American agouti 3.5 2 1 1.5 4 3 3 a Trophic level: 1 = primarily browser/grazer or frugiv ore, 2 = omnivore, 3 = prim arily carnivore/myrmecophage. b Calculated as # of young/year. cValues for dietary and habitat breadth based on number of food or habitat categories used higher values indicate more generalized diets or habitats. See Appendix A for full description of trait categories. d Hunting vulnerability 1 = rarely/never hunted or killed, 2 = occasionally hunted, 3 = often hunted (e.g., a preferred game species). 45

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Table 2-2. Factor loadings from princi ple components analysis of ecological and life history traits. Factor loadings indicate the correlation coefficients between the original variables and the new PCA variables (see text for interpretation of axes). Total explained varianc e of the three axes = 84%. Variab le PCA Axis 1 PCA Axis 2 PCA Axis 3 Body mass -0.32a 0.84 0.23 Home range -0.21 0.24 0.89 Trophic level 0.29 -0.09 0.87 Reproductive rate 0.82 -0.30 -0.15 Dietary breadth 0.80 -0.12 0.23 Habitat breadth 0.89 -0.15 0.00 Hunting vulnerability -0.13 0.94 -0.05 Eigenvalues 3.07 1.79 0.98 Variation explained (%) 43.82 25.50 14.04 Table 2-3. Sampling effort employed fo r camera-trapping and visual censusing of mammals in forest patches and intact forest sites. Distances listed were walked 5 times in each fragment. Patch size # of patches surveyed # of cameras placed in patch Distance walked per session for daytime surveys (km)* Distance walked per session for nighttime surveys (km)* 2.9-10 12 7 0.8 0.5 >10-20 7 10 1 0.8 >20-40 12 14 1.2 1 >40-80 5 17 1.5 1 >80-160 7 20 1.8 1.2 >160-320 4 25 2 1.5 >320 3 28 2 1.5 *Distances listed were walked 5 times in each fragment 46

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Table 2-4. Vulnerability estimates for the 3 landscapes included in the analysis. Species Nave PFOa Overall PFOb Diff. occ. 20 ha sitesc Diff. occ. 100 ha sitesc PFO Yucatand PFO Los Tuxtlase Didelphis marsupialis 66.0 76.5 -33.5 -33.9 100 54.3 Didelphis virginianus 36.0 31.9 15.3 16.8 100 40.0 Dasypus novemcinctus 70.0 70.4 -0.9 -0.8 100 14.3 Tamandua mexicana 62.0 86.9 -77.4 -77.5 95.9 17.1 Alouatta pigra* 80.0 80.7 23.9 13.0 32.0 45.7 Ateles geoffroyi 38.0 38.3 79.7 63.8 55.1 2.9 Leopardus pardalis 34.0 45.5 24.9 10.5 81.0 2.9 Leopardus wiedii 52.0 57.1 -11.3 -10.6 64.0 5.7 Panthera onca 6.0 9.0 14.5 28.0 55.1 0.0 Puma concolor 6.0 14.7 66.4 84.0 66.7 2.9 Puma yagouaroundi 24.0 36.1 -17.5 -28.1 79.6 14.3 Urocyon cinereoargenteus 42.0 45.7 17.8 17.8 100 0.0 Conepatus semistriatus 56.0 60.2 0.1 0.1 100 8.6 Eira barbara 24.0 43.2 -25.7 -28.3 94.6 14.3 Nasua narica 60.0 62.4 7.3 7.4 98.0 20.0 Potos flavus 88.0 96.9 -25.0 -21.2 93.2 17.1 Procyon lotor 48.0 71.0 -36.0 -38.3 97.3 17.1 Tapirus bairdii 2.0 2.3 26.7 47.9 12.2 0.0 Pecari tajacu 14.0 16.2 50.2 38.6 91.2 14.3 Tayassu pecari 0.0 0.0 0.0 16.7 4.1 0.0 Mazama americana 30.0 31.1 50.2 46.3 84.4 2.9 Odocoileus virginianus 20.0 24.4 74.3 64.2 91.8 0.0 Coendou mexicanus 36.0 56.8 -28.4 -31.3 92.5 22.9 Agouti paca 66.0 66.4 30.3 20.5 98.0 40.0 Dasyprocta punctataf 44.0 44.3 52.5 45.2 98.0 28.6 a Estimated percentage of patches occupied in Guatemala not corrected for detectability (number of fragments where species was detected at least once/total number of fragments). b Estimated percentage of patches occupied in Guatemala corrected for detectability (= sum of individual occupancy probabilities from each patch/total number of patches). c Occupancy probability (expressed as percentage) of continuous forest site occupancy probability in forest patch of same size. d Percentage of patches occupied in Yucatan based on interviews with landowners (Urquiza-Haas et al. 2009). e Percentage of patches occupied in Los Tuxtlas based on live trapping and visual censuses (Estrada et al. 1994). f For Los Tuxtlas dataset, data were collected on Alouatta palliata and Dasyprocta mexicana. 47

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Table 2-5. Results from regression analysis using PCA axes as predictor variables and 3 measures of vulnerability to fr agmentation as response variables. Predictor variables Parameter estimate SE P-value Partial R2 Response variable = Proportion of fragments occupied Axis 1 (reproduction/specializati on) 0.01 0.04 0.76 0.01 Axis 2 (body size/hunting) -0.18 0.04 <0.01 0.45 Axis 3 (home range/trophic level) -0.09 0.04 0.03 0.12 Response variable = Difference in occupancy of 20 ha intact sites and 20 ha patches Axis 1 (reproduction/specializati on) -0.08 0.06 0.24 0.04 Axis 2 (body size/hunting) 0.23 0.06 <0.01 0.34 Axis 3 (home range/trophic level) -0.07 0.06 0.30 0.03 Response variable = Difference in occupancy of 100 ha intact sites and 100 ha forest patches: Axis 1 (reproduction/specializati on) -0.08 0.06 0.22 0.04 Axis 2 (body size/hunting) 0.25 0.06 <0.01 0.44 Axis 3 (home range/trophic level) -0.04 0.06 0.49 0.01 48

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Figure 2-1. Location of study area in Guat emala. Inset map shows location of the Maya Biosphere Reserve in northern Guatem ala. Enlarged portion shows location of my study sites in relation to the Maya Biosphere Reserve. Intact forest sites (upper black box) were located in the south-central portion of Tikal National Park, and forest patches (lower black box ) were located in the buffer zone of the Maya Biosphere Reserve and priv ate lands farther to the south. 49

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igure 2-2. Map showing fragmented and continuous forest study areas. A) Map n rk F showing a small portion of my fragment ed study area, with forest cover i black, and matrix habitat (ag/pasture and regenerating forest) in white. B) Map showing my intact forest study area, with black boxes indicating the position of 20 and 100 ha study sites in Tikal National Park. The central pa road is also shown in black. These study sites were embedded within completely intact forest cover. 50

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Figure 2-3. Results of hier archical partitioning analysis fo r 3 landscapes. A) proportion of patches occupied in G uatemala, B) proportion of patches occupied in Los Tuxtlas, Mexico based on data in Es trada et al. (1994), C) proportion of patches occupied in Yucatn, Mexico based on data in Urquiza-Haas et al. (2009). BS = body size, HR = home range size, TL = tropic level, RR = reproductive rate, DB = dietary breadt h, HB = habitat breadth, HV = hunting vulnerability 51

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52 Figure 2-4. Results of hier archical partitioning analysis for difference in occupancy between continuous forest and forest patc hes of the same size. A) difference in occupancy of intact sites and forest patches of 20-ha size, B) difference in occupancy of intact sites and forest patches of 100-ha size. BS = body size, HR = home range size, TL = tropic level, RR = reproductive rate, DB = dietary breadth, HB = habitat breadth, HV = hunting vulnerability.

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CHAPTER 3 EVAL UATING THE RELATIVE INFLUENCE OF HABITAT LOSS AND FRAGMENTATION: DO TROPICAL MAMMALS MEET THE TEMPERATE PARADIGM? Introduction Given the rapid landscape transformation occurring in the tropics, knowledge of efficient actions for reducing biodiversity loss is urgently needed. Although prev ention of habitat loss is an important and obvious way to protect biodiversi ty, the effect of reducing habitat fragmentation per se (i.e., breaking apart of habitat independent of habitat loss) on biodiversity is still unclear. Some theoretical studies predict strong effects of fragmentation (e.g., With & King 1 999), whereas others predi ct small effects of fragmentation compared with habitat loss (e.g., Fahrig 1997). Consensus from numerous empirical studies is that habitat fr agmentation has relatively weak effects on species distribution and abundance compared with habitat loss, and that this effect is at least as likely to be positive as negative (reviewed in Fahrig 2003; Smith et al. 2009). Results from these studies are used to sugges t that conservationists should be more concerned with overall habitat loss in the l andscape than with how remaining habitat is arranged (Fahrig 2003; Ritchie et al. 2009; St-Laurent et al 2009). Consequently, management plans and design of protected ar eas should focus on preservation and restoration of target amounts of habitat, rather than on the details of habitat arrangement (Fahrig 1997). However, these conclusions ar e based on empirical studies of temperate fauna, and this issue rarely has been investigated in tropical systems. Moreover, most studies have focused on bird s, insects, and small mammals, and to my knowledge this question has not been investi gated for any wide-ranging or large-bodied species such as large mammals. Lack of st udies on tropical species and wide-ranging 53

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species is significant because two of t he major results of habitat fragmentation per se are an increase in amount of edge and a reducti on in mean patch size. Tropical faunas contain a high percentage of edge-sensitive species (Laurance et al. 2002) and therefore may be influenced more strongly by habitat fr agmentation than temperate faunas studied thus far (Fahrig 2003). Wide-r anging or large bodied species that require large areas of habitat for persistence may re spond more strongly to reductions in patch size that occur with habitat fragmentation t han less wide-ranging species (Villard et al. 1999). Often the influence of habitat loss and fragm entation is assessed empirically by determining presence or abundance of species within focal patches or sites, and then relating these patterns to variables measured in the surrounding landscape that serve as correlates of habitat loss (i.e., amount of habitat in the landscape) and habitat fragmentation (i.e., configuration of habitat in the landscape) (e.g., Drolet et al. 1999; Klingbeil & Willig 2009; Ritchie et al. 2009). I dentifying the proper spatial scale at which to measure habitat loss and fragmentation is a key challenge for reliable estimates of the influence of these factors. Different species respond most strongly to landscapescale variables measured at different spatial extents and theref ore one spatial scale often is not sufficient in multi-species st udies (Holland et al. 2005). Moreover, a single species may respond most strongly to habita t loss at a different spatial scale than habitat fragmentation or other landscape-scale variables (Boscolo & Metzger 2009). Studies that examine a species response to a single spatial scale may find a weak response to habitat loss or fragmentation bec ause the proper scale was not used in the analysis. A second challenge in determining influence of landscape-scale variables 54

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such as habitat loss and fragmentation is cont rol of site or patch -scale variables. Studies that do not simultaneously evaluate t he influence of both small and large scale factors may attribute a species response to either habitat loss or fragmentation in the landscape when in fact the response is driven by site or patch-scale variables (Koper & Schmiegelow 2006). I examined responses of 20 midand la rge-sized neotropical mammals to habitat loss and fragmentation at multiple spatial ex tents and assessed their response to patch size and measures of patch quality in a fr agmented landscape of northern Guatemala. I tested the currently accepted hy pothesis developed from temperate studies that habitat loss is more important than habitat fragmentat ion in determining distribution patterns of species If this hypothesis is true, then patch occupancy patterns of mammals should be influenced more strongly by habitat loss than habi tat fragmentation, after controlling for the influence of patch-scale variables. Methods Study Site I conducted this study in a 250,000-ha ar ea in the Petn region of northern Guatemala (Fig. 3-1). The nor thernmost part of the study area is located within the buffer zone of the Maya Biosphere Reserve (a UNESCO world heritage site, and the largest contiguous tropical forest in Cent ral America), and the remainder on private lands further to the south. This area was fo rmerly contiguous forest, but now consists of primary rainforest patches embedded in a matr ix of regenerating fo rest, cattle pasture and agricultural land. Forest cover consists primarily of subtropical humid rainforest. Forest destruction and fragmentation began in the late 1970s. Forest patches are thus no more than 30 years old. Annual average tem perature of this region is 21 C, and 55

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annual average precipitation is 1350 mm, with a marked dry season from late December to May. My study sites were 50 primary rainforest patches (hereafter focal patches) that ranged in si ze from 2.9 to 445.5 ha. I did not sample rainforest patches that were severely degraded by logging or fire, but I sampled a number of lightly degraded sites. Almost all forest patches in my study were used to some degree by local inhabitants for collection of non-timber forest pro ducts, hunting, or shade for cattle. Mammal Surveys I used camera traps and visual censuses to determine presence/absence of mammals weighing > 1 kg within focal fore st patches from January 2006 to August 2008. I avoided sampling during mid-late wet season (mid-September to midDecember) because of poor camera-trap performance in wet conditions. I deployed camera traps for a 16-day period in each fo cal patch. A photograph of a species at any camera within a patch was considered an indication of presence. I recorded presence/absence for each species within each patch after every 4-day interval. By breaking up the 16-day period into 4-day sessions, I created a series of repeat detection/non-detection data (i.e., a det ection history) for modeling detection probabilities for each species (MacKenzie et al. 2002). Camera trapping was ineffective for samp ling arboreal species and two terrestrial species ( Odocoileus v irginianus and Pecari tajacu ). In order to document presence/absence of these species, I performed visual censuses of focal patches between sunrise and 3 hours after sunrise. Su rveys were repeated 5 times within a 2week period for each patch, and presence-absence was assessed after each survey to create a detection history. I surveyed patc hes by walking along human foot paths and 56

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game trails, and by walking through secti ons without any obvious trails. I walked approximately 1 km per hour and recorded direct observations of animals, vocalizations, and well-defined tracks as indications of presence. I placed more cameras in larger patches, and walked greater distances during visual censuses in larger patches (Table 31). Because of the wide range in patch sizes, I could not sample with the same intensity per unit area in large and small patches. I included patch size in assessments of detectabi lity to account for potential biases in unequal sampling per unit area. Habitat Measurements: Patch-Scale Variables For each focal patch, I calculated focal pat ch size and patch quality (Table 3-2). I chose patch quality measures that have been found to affect patch use of midand large-sized mammals in other studies (e.g., bas al area of fruiting trees, fire disturbance), or factors that I thought w ould affect use of patches in my system (e.g., cattle disturbance, basal area of small trees/saplin gs). I determined basal area of trees using the point-centered quarter method (PCQM) along randomly placed transects within each focal patch. I spaced sample points fu rther apart, and sampled more points, in larger patches (Table 3-1). In each quadrat of the PCQM samp le, I recorded distance to the nearest stem in 3 distinct size-classe s: 0-10-cm diameter-at-breast-height (dbh), >10-30-cm dbh, and >30-cm dbh. Fo r the two largest size cla sses, I identified stems to species level when possible and to family level otherwise. I det ermined basal area of fruiting trees for the two lar gest size classes combined (> 10-cm dbh). Fruiting tree species were those identified as being import ant to larger mammals in the area based on published literature (Ponce-Santizo et al. 2006), lo cal knowledge, and personal observation. I also determined total basal ar ea of small trees and saplings (stems less 57

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than 10-cm dbh). I determined presence/absence of cattle within each focal patch by looking for signs of cattle presence during ot her sampling activities. I assessed effects of past fire along the edge and interior of the patch and cr eated a binary fire variable (0=no fire effects, 1=fire along edge and/or less than 25% of interi or) for the analysis. Patches with greater than 25% of the interior disturbed by fire were not surveyed. Habitat Measurements: Landscape-Scale Variables I created a vegetation map for my study site by pe rforming an unsupervised classification of 2003 Landsat ET M+ images using ERDAS imagine 9.0. The original unsupervised classification consisted of 40 cl asses, which I combined into 4 classes: water, pasture/agriculture, regenerating forest 15 years old, primary forest/secondary forest > 15 years old. Primar y forest and older secondary forest were combined into one class because of my inability to separate these classes with Landsat imagery. Classes were assigned with the aid of ground-truthed points, examinat ion of .5m resolution 2001 aerial photographs of the study site, as well as my knowledge of the study area. I used an additional 500 ground-truthed points and 200 random points from aerial photographs to assess classification accuracy of the l andcover classes. Overall classification accuracy was 80.6 %. Forest was classified with an accuracy of 91.3%, pasture at an accuracy of 80.5 % and regenerat ing forest at 70.0 %. I measured habitat loss and fragmentation within buffers of 6 different sizes (500, 1000, 1500, 2000, 2500, 3000 m) from the edge of each focal pat ch (Fig. 3-1). Within each buffer, I used FRAGSTATS software (M cGarigal et al. 2002) to calculate proportion of forest in the landscape to r epresent habitat loss and 4 other metrics to represent habitat fragmentat ion: mean nearest-neighbor di stance of forest patches, density of forest/non-forest edge, mean size of forest patches, and density of forest 58

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patches (Table 3-2). Substantial correlati ons among these 4 fragmentation metrics suggested that variables were redundant. Moreover, fragmentation metrics were highly correlated with proportion of forest (Table 3-3). I chose density of patches as my measure of habitat fragmentation because it was uncorrelated with proportion of forest within all buffers except the 500-m buffer, where there was a moderate correlation between the variables (r = 0.49) Proportion of forest and densit y of patches thus served as largely independent measures of habitat l oss and fragmentation, respectively. A second analysis in which I statistically re moved the correlation between proportion of forest and fragmentation measures resulted in qualitatively similar results to those presented here. Density of forest patches was correlated negatively with mean nearest neighbor distance and mean patch size, and positively with density of forest/non forest edge. This variable thus represents a gradient from landscapes containing a few, larger, highly isolated patches and a low total amount of forest edge to landscapes containing many, smaller, less isolated patches and a hi gh total amount of forest edge. I also calculated proportion of regenerat ing forest within each buffe r as an additional measure of landscape context that could affect ma mmal occupancy by providing low quality habitat between patches (Table 3-2). Additional Predictor Variables I calculated distance from the focal patch to the Maya Biosphere Reserve boundary as a measure of potential effect of a nearby source. I also calculated distance from the focal patch to the nearest hum an community as a measure of hunting pressure. Distance to nearest village is commonl y used as an index of hunting pressure because hunting tends to be more intense closer to villages in the tropics (Parry et al. 2009). I recorded season of sampling (dry vs. early wet) for each patch. Correl ation 59

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coefficients between all conti nuous potential predictor vari ables included in analyses (patch, landscape, and additional variables) were small (r < 0.5). Relative Influence of Habi tat Loss and Fragmentation I used detection/non-detection data for the 50 focal forest patches to examine patch occupancy and detection probabilities for eac h species using logistic regression in program PRESENCE (Hines 2006). Because many species in my study were capable of moving in and out of patches during sampling, the occupancy estimator is best interpreted as probability of use of a patch, rather t han probability of occupancy. The detection parameter is best interpreted as probability of being within the patch and detected during sampling (MacKenzie et al. 2006). For ease of pres entation, I will use traditional occupancy termi nology in this paper. I built occupancy models for each species b y first determining best-fit detection models holding occupancy constant. I tested tw o covariates for detection: patch size and season of survey. The best fit detection model for each species, determined with AICc (Burnham & Anderson 2002), then was used in a ll subsequent analyses of occupancy. I next determined the spatial scale at which each species responded most strongly to habitat loss (i.e., proportion of forest in the landscape), habitat fragmentation (i.e., density of forest patches in the land scape), and proportion of regenerating forest in the landscape by considering each of these landscape-scale variables independently in occupancy models. I fit these models for each of the 6 buffer size classes, and determined, for each variable, the best-fit buffer size class (i.e., the best-fit spatial scale). I used best-fit detection models and best-fi t spatial scale(s) for each species to examine relative influence of habitat loss and fragmentation on patch occupancy 60

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patterns. I fit occupancy models in progr am PRESENCE, limiting the number of occupancy covariates in models to 5 becaus e of sample size constraints. Models included proportion of forest and density of forest patch es in the landscape, and 3 patch-scale variables: patch size, basal ar ea of fruiting trees >10 cm dbh, and basal area of small trees and saplings (< 10 cm dbh). I standardized all predictor variables prior to entrance in the analysis, so that resulting coefficients for predi ctor variables represent the influence on the response of a one standard deviation change in the predictor, holding all other predictors in the model constant. Standardized partial coeffici ents have been found to be one of the best methods for estimating the influence of habi tat loss and fragmentation variables, even when the variables are highly correlated, whic h was not the case in my analysis (Smith et al. 2009). I explicitly incl uded patch-scale variables in the analyses to be confident that responses attributed to habitat loss or fragmentation in the landscape were not driven by response to patch size or qualit y (Koper & Schmiegelow 2006). The relative influence of habitat loss and fragmentation was compared by examining values of the standardized predictors. This analysis also enabled us to assess the relative influence of patch-scale factors on occupancy patterns. Significance of each of the 5 variables was assessed using a likelihood ratio test of the full model vs. a reduced model without the variable. For 3 species, I reduced the number of variables considered in modeling occupancy to 4 variables ( Pecari tajacu, Tamandua mexicana ) and 2 variables ( Procyon lotor ) because 5-variable occupancy models could not be fit without obtaining unrealistically large parameter estimates. 61

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I calculated autocorrelation in the residuals of my occupancy models using Morans I To calculate residuals, I subtracted the observed occupan cy state from the probability that a species was present and det ected at l east once according to the occupancy models (Moore & Swihart 2005). I converted residuals to Pearsons residuals, and used the autocorr macro in SAS (SAS 2008) to calculate Morans I values. Because focal patches in my study were spread over such a large study area, I used relatively large lag distances for the calculation of Morans I : 0-4 km, >4-8 km, >812 km, <12-16 km, >16-20 km. A Monte Carlo randomization test with 999 permutations was used to determine the probability of obtai ning an I value as large as the observed value for each species in each lag category. Significance of Morans I values were tested using Bonferonni corrections, where the i th lag was test at 0.05/ I (Fortin & Dale 2005) For species found to have autocorrelated residuals based on Morans I values (n = 3), I calculated an index presen ted in Moore and Swihart (2005): autocovi = ij J j iij J jw ywi i1 1 where yj = 1 for all occupied patches in a set Ji that are defined as neighbors of patch i, and wij = 1/hij, where hij was the distance between patches i and j (see Moore & Swihart 2005 for additional description). I added this inde x as a covariate for those species with autocorrelated residuals and re-ran the o ccupancy model. I could not use a more traditional autocovariate (e.g., Augustin et al. 1996), because I did not sample all surrounding patches for presence-absence, nor could I estimate occupancy for unsampled patches. Although not ideal, t he index decreased autocorrelation in residuals for the three species. 62

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I performed an additional analysis within a multi-model testing framework to develop best-fit models with all 11 habitat variables from my st udy patches (patch, landscape, and additional variable s). This enabled us to examine if the relative influence of habitat loss vs. fragment ation qualitatively changed when I considered these variables in combinations with all predictor variables and to test a larger overall set of variables that I thought could exert an in fluence on mammal occupancy patterns. I developed a priori a candidate list of 56 models to te st that were constrained to a maximum of 6 variables influenc ing occupancy. These models included all univariate models and more complex models with pat ch size, habitat loss and fragmentation in combination with other variables. I calculat ed AIC weights for variables to provide a measure of the relative influence of t hese variables on patch occupancy (Burnham & Anderson 2002). I assessed overdispersion in the data for each species with a goodness-of-fit test implem ented in program PRESENCE (MacKenzie & Bailey 2004). When overdispersion was present, I used QA ICc to compare models and calculate AIC weights (Burnham & Anderson 2002). Results I photographed 25 species in over 8800 camera -trap nights. I recorded presence of 6 species of arboreal mammals within forest fragments in 320 km of visual censusing. After excluding species with a very small number of detections, I could adequately analyze 20 species from my study area (Table 3-4). Detectability varied greatly among species (Table 3-4). Patch size and season of survey had effects on detectability for a subset of species (Table 3-4). Substantial interspecies variability occurr ed in the spatial scale of response to habitat loss and fragmentation (Appendix C). For example, spider monkeys ( Ateles 63

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geoffroyi ) responded most strongly to habitat fragmentation within a 500-m buffer and tayra ( Eira barbara ) responded most within a 3000-m buffer. Although some species responded most strongly to habitat loss and fr agmentation at the same spatial scale (e.g., agouti [ Dasyprocta punctata ]), others responded at markedl y different scales to these two variables. (e.g., nine-banded armadillo [ Dasypus novemcinctus ]). Based on results from the 5-variable models, habitat fragmentation (i.e., density of patches) had a significant effect ( p < 0.05) on occupancy for 6 species, and a marginally significant effect ( p < 0.10) for 2 other species. In contrast, habitat loss (i.e., proportion of forest) had a significant effe ct on occupancy for only 4 species, and a marginally significant effect for 2 (T able 3-4). Of those species that responded significantly to habitat fragmentation, 6 responded negatively and 2 positively to increasing fragmentation in the landscape surrounding focal patches. Three species responded negatively and 3 positively to incr easing habitat loss in the landscape. According to standardized effect sizes, 10 of 20 mammals respond ed more strongly to habitat fragmentation in the landscape than habitat loss (Table 3-4). Results from analysis of all 11 variables generally support c onclusions from the 5-variable models. According to AIC weights (Appendix D), habitat fragmentation was one of the top two most influential variables for 10 of 20 species, whereas habitat loss was one of the top two most influential vari ables for only 5 of 20 species. Habitat fragmentation appeared in best-fit models for 7 species: spider monkeys, Virginia opossums ( Didelphis virginianus ), tayras, margay (Leopardus wiedii ), coatis ( Nasua narica), northern raccoons ( Procyon lotor ), and gray foxes ( Urocyon cinereoargenteous ). Habitat fragmentation also appear ed in highly competitive models 64

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(< 1 AICc unit of best fit model) for agoutis In contrast, habitat amount appeared in best-fit models for only tayras, spi der monkeys, Mexican anteaters (Tamandua mexicana ) and highly competitive model s for coatis (Appendix C). Patch-scale variables exerted a strong influence on species. For the 18 species that were modeled with all 5 variables, 13 responded more strongly to at least one patch-scale variable than either habitat loss or fragmentation based on standardized effect sizes. This response was related la rgely to the influence of focal patch size. Eleven species responded signific antly to focal patch size (Table 3-4). Focal patch size also appeared as one of the top two most infl uential variables according to AIC weights for 11 of 20 species (Appendix D), and appeared in best-fit models for 10 of 20 species (Appendix C). Patch quality measures exert ed a more limited influence on species, with basal area of small tree and saplings hav ing a significant negative influence on 2 species (pacas [ Agouti paca ], and white-tailed deer) and a marginally negative influence on one other species (tayras) in the 5-variable models. Basal area of large fruiting trees within the patch had a significant positive effect on one species (collared peccaries; Table 3-4). Other factors not considered in the 5-variable model exerted a strong influence on patch occupancy patterns of a few species (Appendix C and D). Distance to the boundary of the Maya Biosph ere Reserve appeared in be st-fit models and had a negative influence on 1 species (Mexican porcupine [ Coendou mexicanus ] and a positive influence on 2 species (stripped hog-nosed skunk [ Conepatus semistriatus ], common opossum [ Didelphis marsupialis ]). Proportion of regenerat ing forest in the landscape surrounding patches appeared in the best-fit model for nine-banded 65

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armadillo, and cattle presence appeared in the bes t fit model for coati. Fire disturbance and distance to nearest community did not appear in any best-fit models and had low overall AIC weights for all species. Discussion In contrast to the dominant paradigm from temper ate studies of a relatively weak effect of habitat fragmentation, tropical midand large-s ized mammals in northern Guatemala responded strongly to habitat fragmentation independent of habitat loss. A larger number of species responded in a si gnificant manner to my measure of habitat fragmentation (density of patches) than to habitat loss (proporti on of forest). The effect of fragmentation on species was apparent even after accounting for effects of patchscale factors on response patterns. My data therefore do not support the hypothesis developed from temperate studies that habitat loss is substant ially more important than fragmentation. Importantly, t he effect of fragmentation on species was negative for 75% of the mammals when considering only thos e species that displayed a significant response to fragmentation. Thus, landscapes with habitat distributed in a larger number of smaller patches and higher density of edge tended to be less suitable for mammals than landscapes with habitat distributed in a few, larger patches and a lower density of edge. These negative effects were apparent for both forest specialists (e.g., spider monkeys, margays) and surprisingly for several more generalist species (e.g., armadillos and gray foxes). Positive effe cts of fragmentation on patch occupancy patterns of two species (tay ras, anteaters) could be driven by positive response to edges or to decreased isolation between patches. The strong response to fragmentation found in my study may be related to the fact that I analyzed response of midand large-sized mammals, instead of smaller 66

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species such as birds that have been the focus of the majority of studies to date. These mammals, with their relatively large area and energetic requirements, may respond strongly to changes in patch size that occur with increasing fragmentation. Large area requirements also may be a reason why certai n large-bodied bird s pecies respond more strongly to fragmentati on (Villard et al. 1999). However, of the 5 species in my study with the largest home ranges, only 1 (mar gay) responded in a negative manner to fragmentation, casting some doubt on this ex planation. An alternative explanation for mammals responding strongly to fragmentation is related to the location of my study site in a tropical ecosystem. Habitat fragment ation may be of greater importance in determining distribution or abundance of species in tropical than in temperate systems because tropical species tend to show strong responses to edges (Laurance et al. 2002; Fahrig 2003). A major result of increasing fragmentation is a higher density of edges. Edge density may exert effects on species by increased exposure to edge-altered habitat (Laurance et al. 2002), increased time spent in matrix habitat (Fahrig 2003), or increased exposure to human persecution (Woodroffe & Ginsberg 1998). Additional support for this idea comes from two other recent studies conducted in tropical areas with bats and birds (Develey & Metzger 2006; Klingbeil & Willig 2009; but see Zurita & Bellocq 2010). In both of t hese cases, the authors found strong relationships (both positive and negative) between fragmentation per se and species distribution and/or abundance for a subset of the species ana lyzed, even though species were not particularly large or wide-rangi ng. Collectively, these finding indicate that prevention or management of habitat fragmentation per se may be an import ant component of strategies to preserve some tropical species. This subject warrants increased attention, 67

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with more studies focused on both wide-ranging and more sedentary species in tropical environments. Increased emphasis on studies of wide-ranging taxa in temperate regions also would help to untangle whether increased sensitivity of species to habitat fragmentation found in this study is a product of species characteristics or biome of the study site. Species in my study responded to habi tat loss and fragmentation measured at a variety of spatial extents. I echo concerns expressed by other authors (Holland et al. 2005) that investigations usi ng only one landscape size or sp atial extent to represent the landscape-scale are problematic. Such st udies do not account for the fact that different species may respond most strongly to habitat variables measured at different spatial extents, or that a given species may respond to one type of landscape metric at a different scale than another landscape metric (e.g., Boscolo & Metzger 2009). The strong positive influence of focal patch size on occupancy patterns of mammals was likely driven by large area r equirements of these species and has been documented elsewhere (Michalski & Peres 2007) Patch quality also was influential in determining patch occupancy patterns for a subs et of species. Fruiting tree basal area exerted a positive influence on patch occ upancy by collared peccaries, suggesting that this species was preferentially using patches with higher quality or more abundant fruit resources. Basal area of sm all trees and sapling within a patch exerted a negative influence on 3 species, which was counter to my expectations that species would be more likely to use patches with more cove r. Cattle presence did not exert a strong influence on occupancy for most species, bu t when this variable appeared in best-fit models or model sets for species, cattle always exerted a negative influence on 68

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occupation probability. Cattle ma y reduce available browse an d cover or disturb resting or denning sites and have been found to exert a negative influence on other species inhabiting fragments (H annah et al. 2007). The lack of an influence of fire disturbance within patches on mammal distribution contrast s with another recent study (Michalsk i & Peres 2007). Unlike the Michalski and Peres (2007) investigation, I s pecifically excluded patches from my study that were severely impacted by fire, which likely explains the discrepancy between the two studies. Distance of focal patches from continuous forest was an influential factor for only a small subset of species, even though the presence of sources often is important in maintaining tropical mammal populations (Nov aro et al. 2000). Given the relatively recent nature of forest loss and fragmentation in my study ar ea, the lack of influence of a potential nearby source may be related to insufficient time for species to respond fully to landscape change (i.e., an extinction debt; Tilman et al. 1994). Positive effects of increasing distance from continuous fore st were apparent for species that are considered habitat generalists or are highly adaptable to human disturbance such as stripped hog-nosed skunks. Dist ance to the nearest community, my measure of hunting pressure, did not strongly influence patch occupancy patterns of mammals, which is surprising considering the importance of hunting pressure in determining mammal distributions in the tropics in general (e.g., Peres & Palacios 2007 ), as well as overall vulnerability to fragmentation for mammals in this particular landscape (D.H.T., unpublished data). However, distance to nearest community perhaps is too coarse a measure of hunting pressure and was inadequate for capturing variation in hunting pressure on my landscape. Moreover, becaus e all fragments in my study area were 69

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relatively easy to access through roads or trails, hunting pressure was likely high and fairly equal in intensity ac ross the entire landscape. Although conservatio n strategies will continue to focus on prevention of habitat loss, my results also highlight the import ance of increased attention to prevention of habitat fragmentation per se To date, managing landscape pattern to prevent fragmentation or ameliorate the negative effects of habitat loss has been deemed as an ineffective approach to biodiversity conservation based on studies conducted in temperate environments on a few taxa (Fahri g 2003). A greater number of studies with a broader taxonomic and geograp hic scope are necessary to inform the debate on the relative influence of habitat loss and fragment ation and for understanding how much of the pattern observed in this study is driven by the wide-ranging habitats of midand large-sized mammals versus the tropical env ironment. Other recent studies on birds and bats in tropical environments suggest th at tropical fauna generally may be more responsive to fragmentation (Develey & Me tzger 2006; Klingbeil & Willig 2009). Given that deforestation and fragmentation of remaining habitat are significant problems inside and outside protected areas in the tropics (deF ries et al. 2005; Broadbent et al. 2008), a broader understanding of the e ffects of fragmentation on species distributions and population persistent is vitally im portant for conservation. 70

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Table 3-1. Sampling effort employed fo r camera-trapping and visual censusing of mammals in forest patches. Patch size (ha) # of patches surveyed # of cameras within patch Total distance walkeda # of vegetation sample sitesb 2.9-10 12 7 4.0 8 >10-20 7 10 5.0 13 >20-40 12 14 5.0 16 >40-80 5 17 7.5 19 >80-160 7 20 9.0 22 >160-320 4 25 10.0 25 >320 3 28 10.0 28 a Total distance walked over 5 visits to the patch. b Sampling sites for point-centered-quartermethod 71

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Table 3-2. Patch and landscape context metr ics for the 50 focal patches in my study area. Variable Mean (SD) Description Patch-scale Focal patch size 68.5 (98.7) Ar ea of focal forest patch (ha) Basal area of large fruiting trees 20.0 (10.1) Basal area per ha of all important fruiting tree species within the patch (stems 10-cm dbh) Basal area of small trees/saplings 2.9 (1.2) Basal area per ha of all shrubs/saplings within the patch (stems < 10-cm dbh) Fire N/A Ranking of fire disturbance on 3-point scale Cattle N/A Presence-absence of cattle in fragment Landscape-scale* Proportion of forest 0.21 (0.11) Proportion of prim ary and secondary forest (15+ years regrowth) in the landscape Proportion of regenerating forest 0.32 (0.11) Proportion of regenerating forest (< 15 years regrowth) in the landscape Density of patches 2.6 (0.94) Density of forest patches in the landscape (#/km2) Mean patch size 8.9 (6.6) Mean size of all forest patches in the landscape (ha) Mean nearest neighbor distance 0.15 (0.05) Mean Euclidean nearest neighbor distance of all patches in the landscape (km) Density of edge 0.32 (0.12) Total density of forest/non-forest edge in the landscape (km/ha) Means of landscape-scale variables calculated based on 2000-m buffers from the edge of each sampled patch. Buffers of other sizes resulted in different values for these categories. 72

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73 Table 3-3. Correlations between landscape metrics used to categorize habitat amount and configuration in the landscape surr ounding focal patches. Values listed are for the 2000-m buffer (different bu ffers have different values for the correlation coefficients, though the relationships between variables are qualitatively similar for all buffers ex cept the 500 m buf fer see text). PF* DFP MNN DE MPS PF 1.00 -0.08 -0.61 0.72 0.88 DFP 1.00 -0.42 0.47 -0.37 MNN 1.00 -0.74 -0.35 DE 1.00 0.46 MPS 1.00 PF = Proportion of forest, DFP = Density of forest patches, MNN = Mean nearest neighbor distance of forest patches, DE = Density of forest/non-forest edge, MPS = Mean forest patch size

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Table 3-4. Parameter estimates for patch-scale variables and habitat loss and fragmentation variables. Speciesa Detection modelb Mean pc FPSd BALFT BASTS PF DFP Agouti paca constant 0.73 0.92* 0. 00 -0.69** 0.41 -0.37 Alouatta pigra fps+sea 0.65 0.63 -0.07 0.08 0.36 0.31 Ateles geoffroyi constant 0.61 1.19** 0.02 -0.52 1.07** -1.50** Coendou mexicanus constant 0.19 0.29 0. 08 0.22 1.11* -0.64 Conepatus semistriatus constant 0.50 0.51 -0.11 -0.43 -0.94** -0.42 Dasyprocta punctata sea 0.87 0.62* 0.22 -0.43 0.08 -0.11 Dasypus novemcinctus fps+sea 0.67 0.76 -0.33 0.64 -1.09** -1.02** Didelphis marsupialis fps+sea 0.53 -0.03 0.72 -1.57 -0.30 0.11 Didelphis virginianus constant 0.33 1.35** 0.16 -0.15 -0.23 -0.69 Eira barbara constant 0.29 0.91 -0. 25 -1.05* 0.95 1.01* Leopardus pardalis fps 0.30 3.14** 0.01 -0.26 0.36 0.16 Leopardus wiedii constant 0.42 1.81** 0.05 0.54 0.45 -1.16** Mazama americana constant 0.53 1.15** -0.15 -0.03 0.41 0.37 Nasua narica fps 0.49 1.18** 0.59 0.47 0.75 -1.14** Odocoileus virginianus constant 0.31 1.46** 0.51 -1.88** -0.55 -0.01 Pecari tajacu fps 0.29 1.50** 1.33** -0.09 0.24 Procyon lotor constant 0.27 ----0.52 -1.35** Puma yagouaroundi constant 0.29 2.81* 1.00 0.39 1.16* -0.84 Tamandua mexicana constant 0.31 -0.88 -1.86 --1.56** 3.28* Urocyon cinereoargenteus sea 0.34 1.32 0.57 0.95 1.10 -1.80** a I did not detect two species in patches (Tayassu pecari, Bassariscus sumichrasti ). Seven species with sparse data were excluded from analysis (Panthera onca, Puma concolor, Tapirus bairdii, Spilogale putorius, Galictis vitatta, Philander opossum, Potos flavus ). b Lists the best-fit detection model for each species using focal patch size (fps) and wet/dry season (sea) as possible covariates. c Mean detection probabilities per survey for each species. d FPS = Focal patch size, BALFT = Basal area of large fruiting trees, BASTS = Basal area of small tr ees/saplings, PF = Proportion of forest in the landscape (my measure of habitat loss), DFP = Density of forest patches in the landscape (my measure of habitat fragmentation). p < .10 based on likelihood ratio tests of full vs. reduced model without each variable. **p < .05 based on likelihood ratio tests of full vs. reduced model without each variable 74

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Figure 3-1. Map of a portion of the study area. Rectangle in the inset map in left corner indicates the location of my overall st udy site in northern Guatemala. Larger map shows a small portion of the tota l study area. Arrows indicate the location of two focal forest patches where species presence-absence was measured. Buffers used to calculate landscape context variables are shown around the two focal patches. Buffers were measured from the edge of focal forest patches and did not include area of the focal patch in calculation of landscape metrics. Buffers of 2500 and 3000 m are not shown. 75

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CHAPTER 4 A REEXAMINATION OF THE INFLUENCE OF LANDSCAPE CONTEXT ON SPECIES PRE SENCE AND ABUNDANCE Introduction Identifying conditions under which large-sca le factors strongly influence local processes is a central focus of l andscape ecology (Levin 1992; Turner 2005). In spatially heterogeneous or patchy landscape s, studies often focus on determining the relative influence of small-scale factors such as within-patch (e.g., vegetation structure) or patch (e.g., patch size and shape) vari ables, and large-scale factors such as landscape context (e.g., amount of habitat in the landscape) on the distribution, abundance, and richness of organisms. Results of such multi-scale studies have been used to test theories regarding which scale s exert the strongest influence on species (Cushman & McGarigal 2004a), and because the vast majority of these studies are conducted in anthropogenically fragmented landscapes, results also may be used to guide management decisions regarding conservation of species in fragmented environments (Bakker et al. 2002; Banks et al. 2005; Holland & Bennett 2009). In particular, whether management efforts sh ould consider the landscape surrounding patches, or instead focus solely on managing pat ch-scale factors such as patch size or quality, is an important decis ion that is directly inform ed by these types of studies (Mazerolle & Villard 1999). Despite the applied conservation signifi cance, we still have a poor understanding of whether species respond more strongly to small or large-scale heterogeneity in habitat (Kotliar & Wiens 1990) and little work has focused on the assessment of trends in response across taxa or regions. The large number of publishe d studies in patchy landscapes presents opportunities to synthesize information across studies and arrive 76

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at a better understanding of species re sponse to landscapeand patch-scale characteristics. A review of 61 studies in the late 1990s (Mazerolle & Villard 1 999) detected a significant response to landscape context in 59% of studies examined, and concluded that local envir onmental conditions alone were inadequate for predicting species distributions. This effect was es pecially pronounced for vertebrate taxa, with fewer studies on invertebrates finding a response to landscape factors. However, almost all studies in the review found a re sponse of species to patch-scale factors, which suggested that the influence of landscape context was complementary to that of patch characteristics. Although this review served as an excelle nt first look at variation in species response to landscape and patch factors, a large number of new studies have been conducted over the last ten years that may add additional insights and allow for a broadening of the scope of the analysis. For ex ample, many methodolog ical factors that may influence whether or not a species responds to landscape context or patch variables (such as sample size or type of response variable) were not considered in the previous review and have received little att ention in the literature. Such methodological considerations may be particularly relevant for studies in patchy landscapes given the diversity of approaches taken to investigat e the relative influence of patchand landscape-scale variables. The previous review also did not include any studies on tropical species, which may react differently than temperate species, nor did the authors separate the effects of patch variables from within-patch variables, even though these two scales of heterogeneity may influence specie s for drastically different reasons. 77

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I reviewed 125 focal patch studies pub lished from 1998 to 2009 to determine the probability of a species response to landsc ape context, patch, and within-patch factors. Focal patch studies were defined as em pirical field-based studies that examined species response (e.g., presence-absence) within discrete focal patches, and then related that response to characteristi cs of the focal patches and the surrounding landscape via statistical analysis. I also dete rmined if the probability of response to landscape, patch, and within-patch factors were influenced by taxa, tropical/temperate affinity, and several methodological variables. I compared the results of my review to those of Mazerolle and Villard (1999), and tested the following predictions: 1) Invertebrates would have a lowe r probability of responding to landscape context than vertebrate taxa. Because of small body size and relatively low vagility, invertebrates may perceive and interact with their landscape at a much smaller-scale than other taxa (Mech & Zollner 2002). Thus, la rge-scale factors will not be as important in determining their distributi on or abundance. This predicti on tests the generality of one of the findings of Mazero lle and Villard (1999). 2) Tropical species would be more likely than temperate species to respond to all 3 categories of variables (landscape, patch and within-patch fact ors) because of the greater specialization of tropica l species. Highly specialized species may be particularly sensitive to habitat fragmentation and/or het erogeneity at a variety of scales (Henle et al. 2004; Devictor et al. 2008). 3a) Studies that measur ed landscape context by examining the composition and/or configuration of the landscape in a buffer around the focal patch would be more likely to detect an influence of landscape context on species than studies that measured 78

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landscape context by only considering simple Euclidean distance or connectivity measures of isolation of focal patches (F ig. 4-1). Modeling studi es have shown that buffer measures of isolation are more hi ghly correlated with animal movement than distance-based measures, and thus reflect isolation in a more realistic manner (Bender et al. 2003; Tischendorf et al. 2003). 3b) I further predicted that studies with buffers around focal patches would be more likely to detect an influence of landscape context on species if they tested multiple buffers than if they only used one predete rmi ned buffer to measure landscape context. Recent studies indicate that even closely related species respond to landscape context at different spatial extent s (e.g., Cooper & Walters 2002 ; Schmidt & Tscharntke 2005; Boscolo & Metzger 2009). Thus studies that only consider one spatial extent (i.e., one buffer) may find little response to landsc ape context for some species because the wrong spatial extent was used in analysis. 4) Studies using abundance data would be more likely to find a significant effect of landscape, patch, and within-patch fact ors. Abundance data should be a more sensitive measure of species response t han presence-absence (Cushman & McGarigal 2004b). 5) Sample size (number of focal pat ches surveyed) would exert a positive influence on the probability of finding a signif icant response of a species to landscape context, patch, and within-patch variables bec ause of increases in statistical power (Quinn & Keough 2002). 79

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Methods Literature Search I used the ISI Web of Science database to search for articles published between January 1998 and October 2009 with the search terms patch and landscape. I used this range of publication dates to prevent overlap of sources with the Mazerolle and Villard (1999) review. I confined results to articles in the ecology, conservation, biology, or forestry l iterature. My search yielded 3830 articles. I then scanned through abstracts to further narrow my search to only those ar ticles that were field-based, focal patch studies. For this review, I only included fo cal patch studies that measured presenceabsence, abundance, or density of species within patches. Focal patch studies examining species richness or diversity as the response variable were excluded. I only considered focal patch studies conducted in pr imarily terrestrial systems. This included studies of forest, open (e.g., prairie, meadows), and aquatic (e.g. marshes, wetlands, ponds) habitat patches embedded in ant hropogenically fragmented environments. These antropogenically fragmented environments had a matrix of agricultural, urban, or managed forest. Several studies (10) conducted in naturally fragmented landscapes, but where patches could be clearly defined, al so were included. I did not include studies where the patch was an island, and I excl uded studies conducted on plants. I also excluded studies that used Principle Compo nents Analysis to reduce dimensionality of predictors when it resulted in variables that were not easily interpretable as solely belonging to within-patch, patch, or landscape context categories. After excluding articles based on my criteria, I had a set of 125 focal patch studies on 970 species for use in the final analysis. 80

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Collection of Data From Studies For each article in the final set for revi ew (Appendix 1), I determined if the species in the study displayed a significant res ponse to landscape context, patch, and withinpatch variables. Individual species in the stud ies were used as the unit of analysis. The number of species investi gated in a single study ranged from 1 to 62. Landscape context variables included habitat compositi on and configuration measured in buffers around focal patches, and/or distance-based meas ures of isolation of the focal patch such as simple Euclidean distance (e.g., distance to the nearest patch, distance to nearest occupied patch) or connectivity metrics (e .g., Hanskis connectivity index) (Fig. 4-1). Patch variables were patch size and measures of patch shape, including perimeter, perimeter/ar ea ratio, and fractal dimension. Within-patch variables were measures related to patch quality such as v egetation structure, level of disturbance, availability of food or water, temperature, pH, etc. Most of the studies included in my review simultaneously evaluated species response to all three sets of variables, however, a small number of studies (7) did no t evaluate, or held constant statistically, the influence of patch and withinpatch factors. Exclusion of these studies does not alter the results of subsequent analysi s, so they are included. Because authors used a diversity of st atistical approaches to evaluate the influence of landscape, patch, and within-pat ch factors on species response, I developed a set of rules for defining a signi ficant response. A particular variable was defined as having a significant influence on spec ies response if it: 1) had a significant (p< 0.05) univariate or multivariate influenc e on the response (I chose only multivariate analyses when both were given), 2) appeared in a best-fit model based on stepwise multiple regression analysis, 3) appeared in t he best fit model, or a highly competitive 81

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model when given (within 2 AIC or AICc units of the best fit model), based on information-theoretical approaches. I used a binomial response variable to indicate whether species showed a significant response to landscape context, pat ch, and within-patch factors, considering each category of variables separately (Table 4-1) For example, if a particular species in a study responded to landscape context and pat ch size, but not measures of withinpatch quality, it would be coded as for both response to landscape context and patch factors, but a for response to within-patch factors. These data then formed the basis for three separate generalized linear mix ed models that evaluat ed the influence of covariates on response to landscape context, patch, and within-patch factors (described below). Although I recognize that the vote-c ounting methodology that I employed has many limitations as a method for synthesizin g the results of multiple studies (Hedges & Olkin 1980, 1985), the data from the studies in my review were not amendable to a more rigorous meta-analysis of effect size s for several reasons. First, studies included in my review often only reported results of significance tests and did not list effect sizes for parameters. Also, descriptive statistics for predictor variables were usually not reported, preventing standardization of effect sizes. Au thors used a large diversity of metrics to represent landscape, patch, and withinpatch factors, making it difficult to get estimates of the same metric for all studi es. Furthermore, authors often tested variables in a multiple regression framework. Because effect sizes of variables in multiple regression depend on other variables included in the analysis, such data are difficult to use in meta-analysis. 82

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Several other factors potentially may hav e biased my review. Given that I had to sort through so many studies containing t he keywords patch and landscape, I may have overlooked several appropriate focal patch studies. I also may have missed several focal patch studies that did not c ontain these keywords. A more significant potential problem is whether my review is biased toward finding effects of landscape context on species because only those studi es finding an effect were published. However, several factors may serve to ameliora te this bias. First, t he vast majority of studies simultaneously considered smaller-sca le variables (within-patch and patch), and thus were not specifically focused on ju st examining response to landscape context. Moreover, the majority of studies (60%) ex amined the response of multiple species, which increased the chance that both signifi cant and non-significant results would be published. I cannot rule out the possibilit y that some studies may not have been published that did not find a significant effect for any within-patch, patch, or landscape variables for all species examined, although I think this unlikely to account for a large number of studies. Data Analysis My review differs from the Mazerolle an d Villard (1999) review in that I used the species, instead of the study, as the unit of analysis. The ad vantage of my approach is that not all species in a single study wil l respond the same to patch and landscape factors, and thus we do not lose this informa tion by summarizing data at the study level. The disadvantage is that because I examined the response of multiple spec ies within individual studies, correlations in the respons e of species to landscape, patch, or withinpatch variables may be present (e.g., all th e species in a particular study may have been more or less likely to respond to l andscape context or patch or within-patch 83

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variables because of the selection of certai n metrics, or unaccounted for peculiarities of sampling, analysis, or study system). To account for these correlations, I used generalized linear mixed models in Proc GL IMMIX (SAS 2008) to examine patterns in how species responded to landscape, pat ch, and within-patch variables. GLMMs provide a flexible framework for anal yzing non-normal data when correlations among observations are present. GLMMs account fo r these correlations by incorporating a random effect variable when analyzing the data, which in my analysis was a variable denoting the citation from which the data were collected. The response variable in each GLMM was the presence, or lack thereof, of a significant response to landscape context, patch, and within-patch factor s for each species (Table 4-1). I considered five predictor variables (i.e ., fixed effects) as potentially influencing the probability that a species would display a significant response to landscape, patch, and within-patch variables (Table 4-2). To test for effects of these variables on the probability of response, I ran the GLMMs with all predictor variables simultaneously in the model. I used a residual pseudo-liklihood as the estimation method and assessed degrees of freedom using the Kenward-Roge rs method (Littell et al. 2006). I used Ftests to determine the overall significance of each of the predictor variables, and TukeyKramer adjustment for multiple comparisons to test for significant differences between levels of each predictor variable. I also r an the GLMMs without any co variates to get an estimate of the overall mean probability of species responding to landscape context, patch, and within-patch variables. To test whether or not studies employi ng multiple buffers had a greater chance of finding a response to landscape context, I restricted my dataset to focal patch studies 84

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that used buffers and re-ran the GLMM with response to landscape context as the dependent variable. Instead of usi ng the predictor variable type of study (Table 4-1), I used a binomial variable that distinguished be tween studies employing single or multiple buffers. Results Birds were by far the most common study species in the articles of my review, accounting for almost 69% of t he total number of study specie s (Fig. 4-2). Although this bias is not as pronounced when examining the percentage of the total number of studies that focused on birds, birds still acc ount for the largest number of studies, followed closely by mammals. Herpetofauna were the least common study species, only accounting for 6% of the total number of study species. In particular, reptiles were almost completely unstudied and were the fo cus of only 3 focal patch studies. Species from temperate climates also accounted for the vast majority of study species (79%) and an even greater percentage of the tota l number of studies were located in temperate areas (88%). Based on results of GLMMs without covari ates, the mean probability of a species responding to landscape context was 0.56. T he mean probability of species response to patch and within-patch factors was 0.59 and 0. 71, respectively. Probability of species response to either patch or within patch factors was 0.85. Effects of Predictor Variables When considering all predictor variables simultaneously in GLMMs, the probability of a species responding significantly to l andscape context was influenced by taxonomic category of the species, type of study, locati on of study, and sample size of the study, but not by type of response variable. The probability of a species respond ing 85

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significantly to patch variables was only infl uenced by sample size. The probability of a species responding significantly to within-patch variables was only influenced by type of response variable (Table 4-3). Test of Predictions Contrary to my first predi ction, invertebrates did not have a significantly lower probability of responding to landscape context than most other vertebrate taxa (Fig. 43A). Tukey adjusted multiple comparison test s indicated that invertebrates, birds, and herpetofauna did not have significantly different probabilities of responding to landscape context. However, mammals had a significa ntly higher probability of response to landscape context than invertebrates and bird s (p < 0.01, p = 0.04, respectively). Contrary to my second prediction, the pr obability of tropical species responding to landscape context was less likely than fo r temperate species (0.42 and 0.59, respectively). This difference was marginally significant (p = 0.07). In agreement with my third prediction, studies that measured landscape context variables within buffers around the focal patch had a higher probability of detecting an effect of landscape context than studies that only measured landscape context via isolation measures. Studies measuring landscape context using both buffer and isolation measures had a similar probability of finding a response to landscape context as studies measuring landscape context with buffers only, and both of these types of studies had a significantly greater chance to detect a response to landscape context than isolation only studies (Fig. 4-3B). Howe ver, the second part of my prediction was not supported by my analysis. When considering only studies that measured landscape context in buffers, the use of multiple buffe rs around the focal patch did not increase the likelihood of detecting a response to landscape context (p = 0.97). 86

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Studies using abundance or density were more likely to detect a response to within-patch factors than thos e using presence-absence as a response variable (p = 0.04), which supports my fourth prediction. Probabilities of detecting a response to within-patch variables were 0.81 and 0.60 for abundance or density and presenceabsence studies, respectively. I did not find an effect of response variable on likelihood of detecting a response to landscape context or patch factors. In agreement with my fifth prediction, the probability of detecting species response to landscape context and patch va riables was influenced by sample size. Studies that surveyed a large number of patches (> 60) were more likely to find a response to landscape context and patch variables than studies based on smaller sample sizes (Fig. 4-4). The probability of detecting a response to within-patch variables was not affected by sample size. Discussion Although the relative influence of landsc ape, patch, and within-patch factors varies greatly across study sites and between species, my synthesis of focal patch studies revealed s everal overarching trends. When considered together, over half of the species included in this review (56%) were influenced significantly by at least one measure of landscape context. This agrees clos ely with the results of the early review by Mazerolle and Villard (1999), which found that 59% of studies detected a response to landscape context. Moreover, none of the indi vidual taxa analyzed in my review had a probability of response to landscape context less than 0.4. In agreement with Mazerolle and Villard (1999), I conclude that landscape context is an im portant factor influencing species distribution and abundance, and that models based on local-scale variables only will be insufficient for many species in predicting distribution or abundance in 87

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patchy landscapes. However, in contrast wit h the earlier review, I did not find that invertebrates have a lower probability of response to landscape context than vertebrates. My findings indicate a more complicated picture, with both invertebrates and birds ha ving a significantly lower probability of response to landscape context than mammals, and herpetofauna falling betwe en these two extremes. The relative high probability of mamma ls responding to landscape context could be driven by several factors. Mammals gener ally may perceive and interact with their landscape at larger scales than birds or inve rtebrates, and thus be more responsive to habitat variables measured at landscape scales. In contrast, bird s and invertebrates may be more strongly influenced by habitat variables in their immediate vicinity than by diffuse variables acting at coarse sca les (Cushman & McGarigal 2004a). Another possibility for the difference in probability of response of birds and mammals is that the higher mobility of birds may enable them to move freely among fragments in some cases, (e.g., Fraser & Stutc hbury 2004; e.g., Dale et al. 2009; Churchill & Hannon 2010) resulting in a low response to isolation, which was the most common measure of landscape context in my review. However, th is clearly does not apply to all birds, as some species are reluctant to cross small gaps and show altered movement behavior in fragmented landscapes (e.g., Belisle & De srochers 2002; Gobeil & Villard 2002; Robertson & Radford 2009). The higher probabili ty of detecting a response of mammals to landscape context also may indicate that the coarse-scale hab itat maps used to estimate landscape context variables in foca l patch studies (usually vegetation maps derived from aerial or satellit e imagery) were better at depicting habitat for mammals than for birds or invertebrates, and, consequent ly, were more accurate representations 88

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of landscape context for mammals. The i nadequacy of using vegetation types to depict habitat for butterflies is well known (Dennis 2 003; Dennis et al. 2006; Vanreusel & Van Dyck 2007), and butterflies accounted for 59% of the invertebrate species inc luded in my review. However, whether or not this sa me problem would apply to birds or other invertebrates is unknown. Tropical species were less likely to respond to landscape context than temperate species, although this difference was only ma rginally significant. A recent modeling study has shown that patch isolation metri cs (whether distance or buffer based) tend to have lower predictive power for specialist than generalist species because specialist species are influenced more strongly by t he matrix composition (Tischendorf et al. 2003). Given that most studies in my review used isolation as the sole measure of landscape context and did not in corporate matrix quality in estimations of isolation, studies may not have detected the response of these species to landscape context. Another possibility is that some other unr ecorded methodological variable that has an effect on probability of response to landscape context may have been covarying with the location of the study in a tropical or temperate environm ent. A larger number of focal patch studies conducted in tropical regions are needed to address this issue and gain a better understanding of the multi-scaled nat ure of species response to habitat fragmentation. Species in all taxa also responded at high rates to patch and within-patch variables in my review, which also agrees wit h the results of the Mazerolle and Villard (1999) review. However, because Mazeroll e and Villard did not separate out patch and within-patch variables, they could not asse ss differences in how species responded to 89

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these two scales of heterogeneity. My review indicates that although the probability of species response to landscape context and patch variables (patch size and shape) was similar, species had an especially high probabili ty of response to within-patch variables. A high likelihood of response to within-patch variables is notable given the increasing recognition that fragmentation results in not only increas ed isolation of patches and a reduction in patch size, but also changes in habi tat quality of patches over time (Holland & Bennett 2009). Long term patch quality changes in fragmented landscapes, such as edge-related vegetation changes (Laurance et al. 2002), or degradation due to cattle, logging, or fire can have a marked effect on species presence or abundance (e.g., Hannah et al. 2007; Michalski & Peres 2007). The high frequency of response of species to within-patch variabl es that I found in my review also highlights the need to incorporate patch quality in metapopulati on models (e.g, Schooley & Branch 2009). My review of the literature indicates that several methodological variables had pronounced effects on the probability of detecti ng a significant response of species to landscape, patch, and within-patch factors. This finding suggests that focal patch studies may fail to find an effect at a particular scale because of the approach that was used, not necessarily because species were not responding at that scale. Studies that measured landscape variables within buffe rs around the focal patch had a higher probability of detecting a response to landscape context than studies that measured landscape variables by calculat ing Euclidean distance or c onnectivity measures of the focal patch to surrounding patches. Buffer measures of isolation have been found to be more highly correlated with animal movem ent in modeling st udies than Euclidean distance or connectivity measures (Bender et al. 2003; Tischendorf et al. 2003) and 90

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thus may be better proxies of overall isolatio n of the focal patch. If this is the case, studies employing these buffer measures of isolation would be more likely to find that species respond significantly to landscape cont ext. Studies that employ buffers also are more likely to analyze a variety of vari ables related to habitat composition and configuration that go beyond me asures of isolation (e.g., edge density, matrix quality) that may increase the chance of detecting a response to one or more landscape-scale variables. The use of multiple buffers did not appear to have a marked effect on the probability of detecting species response to landscape context, which was surprising considering that many studies have found that spatial scale influences the strength of response of species to landscape variables (e.g., Boscolo & Metzger 2009; e.g., Cooper & Walters 2002; Schmidt & Tscharntke 2005). Moreover, a recent simulation study found that the performance of buffer measur es as predictors of connectivity were sensitive to measures of t he buffer radius (Moilanen & Niemi nen 2002). Thus, all buffers are not equivalent and studies that use mult iple buffers should have a better chance of detecting a significant response to landscape context. The lack of effect of multiple buffers on probability of detecting a response to landscape context may indicate that authors employing single buffers did a good job of identifying the most influential spatial scale of influence on their particular specie s. However, authors are unlikely to know beforehand what scales may be most important, especially given that even closely related species can respond most strongl y to landscape variables measured at drastically different scales (Kolozsvary & Swihart 1999; Rizkalla & Swihart 2006; Kadoya et al. 2008; Klingbeil & Willig 2009). For example, over 50% of single buffer 91

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studies of birds in my review used either a 1 or 2 km buffer. However, focal patch studies employing multiple buffers consistently show that birds respond most strongly to both larger and smaller buffers than this size (Hinsley et al. 1995; Ribic & Sample 2001; Sallabanks et al. 2006; Renfrew & Ribic 2 008; Yamaura et al. 2008). A more likely explanation for the lack of re lationship between use of multip le buf fers and detection of response to landscape context is the binary nature of my measure of response (i.e., significant or non-significant), which does not ta ke into account variation in the strength of response to landscape context. Measures of landscape context within multiple buffers often are correlated because larger buffers encompass smaller buffers. A significant response of a species to landscape context therefore may be found at more than one scale. However, the overall strength of the res ponse could be quite different (Hedges & Olkin 1980, 1985). Studies that measured species response in a large number of focal patches (> 60 patches) were more likely to detect effects of landscape context and patch variables on species presence or abundance than studies that measured response in a smaller number of patches. Sample size has been found to influence the detection of changes in species prevalence or population size (War d et al. 2008; Nielsen et al. 2009) and the accuracy of large-scale species distribution modeling (Wisz et al. 2008), but to my knowledge this is the first indication of t he influence of sample size on detection of a response to fragmentation. Although not a surp rising finding, as the power of statistical tests to find a significant effe ct of a variable is related to sample size (Quinn & Keough 2002), this is rarely discussed as a possible reason for lack of a response of species to patch or landscape variables in the literature. This may be a significant oversight, as 92

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almost 55% of the studies included in this review examined species respons e in less than 60 focal patches, my cut-off for large sample size. Although I used an arbitrary cut-off to determine large vs. medium vs. small sample sizes in my analysis, I do not think this biased my analysis. I re-ran the GLMMs using sample size as a continuous variable and found a similar result of increasing probability of response to patch and landscape variables with increasing sample size. For example, the odds of finding a significant response to landscape and patch factors increased (holding all other variables in the model constant) by 2.4% and 3.0%, respectively, for every additional 10 focal patches sampled. Whether or not species response was measured as presence-absence or abundance had an effect on the pr obability of detecting a significant response to withinpatch variables, but not patch or landscape context variables Studies were more likely to detect a response of species to withinpatch factors when abundance or density were used as the response variable. My findings support a recent community-level study on birds that found that abundance data explained mo re of the total variation in speciesenvironment relationships than did presence-absence data (Cushman & McGarigal 2004b), particularly when looking at the relationship between species response and small-scale heterogeneity in vegetation among plots. Focal patch studies in my review employed a variety of approaches to analyze data and reach conclusions regarding the importance of landscape, patch, and withinpatch factors. However, I noted several co mmon analytical problems that could easily be addressed in future studies of this kind. Only 20 of the 125 studies in my review tested for spatial autocorrelation in their datasets (i.e., the re siduals of regression 93

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models). Autocorrelation is problematic for classi c statistical test such as regression that rely on independently distributed errors (Legendre 1993) and may lead to erroneous conclusions regarding the significance of co variates in studies of species-environment relationships (Lichstein et al. 2002; Ch ristman 2008). A large separation distance between focal patches may have led authors to conclude that t here was no need to estimate spatial autocorrelation and may explain the low number of studies testing for autocorrelation. I cannot address this i ssue because most studies did not provide information on separation distances of patches However, even study sites that are far apart (e.g., focal patches with non-overlapping buffers) may still show autocorrelation (Lichstein et al. 2002; Schooley 2006). Forty of the 125 studies in my review did not measure correlations between predictor variables (landscape context, patch and within-patch factors), although the problem of multicollinearit y for regression models in ecology and conservation biology has been acknowledged for quite some time (Mac Nally 2000). Correlations between predictors in multi-scaled studies of s pecies-environment relationships are likely commonplace and can lead to incorrect co nclusion regarding the importance of predictors (Koper & Schmiegelow 2006). Finally, only 34% of studies that em ployed a buffer approach to measuring landscape context variables used multiple buffers. Although my own analysis showed that use of single vs. multiple buffers does not have a major impact on the probability of finding a significant response to landscape c ontext, use of single vs. multiple buffers may have a major effect on t he strength of the response to landscape context (i.e., overall effect size). I thus list this as a potential deficiency in the analysis of focal patch 94

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studies, with the caveat that more work mu st be done to determine the utility of multiple vs. single buffers in analyzing the effe ct of landscape context on species. My review is limited by the use of a vote-counting procedure to synthesize results, which cannot account for differ ences in power between studies to find significant effects and ignores information on e ffect sizes. The fact that sample size had a significant effect on probabi lity of detecting response to landscape and patch factors indica tes the importance of synthesizing in formation across studies in a more rigorous manner. Meta-analysis of effect sizes wo uld provide a better way to combine information from multiple studies on the re lative influence of landscape, patch, and within-patch factors (Osenberg et al. 1999a). If data could be published in a form more amenable to meta-analysis, this would greatly aid future reviews. For example, focal patch studies could be co mmonly published with standardized effect sizes, instead of just significance tests of no effect, or with raw data available in appendices for a reanalysis of datasets in a form more amenable to meta-analytical approaches (Osenberg et al. 1999b). Additionally, more work coul d be done to narrow down the diverse set of landscape metrics (e.g., Cushman et al. 2008) used to just a few t hat are of primary relevance and that could be applied repeatedly across studi es and ecosystems. My review suggests issues that would be in teresting to address in a meta-analytic framework, such as a comparison of effe ct sizes of certain landscape variables when assessed in single or multiple buffers or a comparison of effect sizes of patch isolation when isolation is measured in different ways. The literature on species response to patchiness and fragmentation already is so large and diverse that substant ial opportunities exist to syn thesize information in order 95

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96 to address particular questions that cannot be resolved with a single study and to look for general trends in species response ac ross ecosystems (Lindenmayer & Fischer 2006). My review revealed t hat the probability of species responding to landscape context, as well as patch and within-patch factors, was influenced by a variety of methodological aspects of the studies. Such study design issues are rarely discussed by authors as reasons why a particular study did not find an effect, but should be given more consideration because of their relatively large influence. My study also confirmed the general importance of the multi-scaled approach in foca l patch studies, as species responded at high rates to heterogeneity meas ured at the landscape, patch, and withinpatch scales. Landscape context was generally im portant in determining distribution or abundance of species within focal patches and conservation efforts in patchy landscape will need to consider characterist ics of the surrounding landscape, or risk missing important influences for approximately half the species in a given system. Landscape context appears to be especially important for mammals, and whether this is driven by differences in scales of percept ion or movement behavio r of taxa, or the accuracy of habitat maps (or other methodological differences) in studies of mammals vs. other taxa remains unknown. My review also indicates that more multi-scaled studies of species response to fragmentat ion or spatial heterogeneity should be done that focus on herpetofauna, as well as tropical species of all taxa, given the under representation of these groups in my review.

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Table 4-1. Example table used for coding species response to landscape context, patch, and within-patch variables. These data form the basis of three separate general linear mixed models. Citationa Species Common name Significant response to landscape contextb Significant response to patch variables Significant response to withinpatch variables 1 Andropadus latirostris Yellow-whiskered Greenbul 1 0 1 1 Andropadus virens Little Greenbul 0 0 1 1 Aviceda cuculoides African Cuckoo-Hawk 1 0 1 1 Baeopogon clamans Sjostedt's Greenbul 1 1 1 2 Mniotilta varia Black-and-white Warbler 0 0 0 2 Momotus momota Blue-crowned Motmot 0 1 0 2 Myiozetetes similis Social Flycatcher 1 1 0 3 Climacteris affinis White-browed Treecreeper 0 1 1 4 Parus major Great Tit 1 1 1 4 Poecile montana Willow Tit 1 0 1 4 Sitta europaea Eurasian Nuthatch 0 1 1 a Citation identifies the reference used to obtain the data for each species. This variable was used as the random effect in generalized linear mixed models to account for correlations present in the dataset because I recorded the response of multiple species from single studies (e.g., citations 1, 2, and 4 in the table).b A indicates that a species was found to respond significantly to landscape context (or patch/within patch factors), whereas a indicates no significant response found. 97

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Table 4-2. Covariates used in the generalized linear mixed models. Covariates Categories Description 1) Birds (B) Class Aves 2) Mammals (M) Class Mammalia 3) Herpetofauna (H) Includes Class Amphibia and Reptilia Taxonomic category of study species 4) Invertebrates (I) Includes Class Insecta, Arachnida, and Gastropoda Type of study 1) Focal patch buffer study (FPBS) Landscape context variables were measured in buffers around the focal patch (distances of buffers varied between studies) and included composition and configuration metrics 2) Focal patch buffer and isolation study (FPBIS) Landscape context variables were measured in buffers around the focal patch, and also included isolation metrics from the focal patch (Euclidean distances or connectivity measures) 3) Focal patch isolation study (FPIS) Landscape context variables were only isolation metrics from the focal patch (Euclidean distances or connectivity measures) Tropical/temperate affinity 1) Tropical (TROP) Stud ies conducted in tropical regions 2) Temperate (TEMP) Stud ies conducted in temperate regions Sample size 1) Small (SM) Studies that examined 30 or less patches 2) Medium (MD) Studies that examined between 31 and 60 patches 3) Large (LG) Studies that examined greater than 60 patches Response variable* 1) Presence-Absence (P) Species presence-absence was assessed within patches 2) Abundance/Density (A) Species abundance or density was assessed within patches *For studies that measured and analyzed both abundance/density and presence-absence of the same species within patches, I randomly chose one of the response variables to consider in the review for each species and excluded the other 98

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Table 4-3. Influence of predictor variables on probability of response to landscape context, patch, and within-patch variables. Values listed based on three separate generalized linear mixed models. Variable Landscape context F-value P-value Patch F-value P-value Within-patch F-value P-value Taxonomic category of study species 4.39 >0.01 0.52 0.67 1.04 0.38 Type of study 5.58 >0. 01 2.18 0.12 0.06 0.94 Tropical/temperate affinity 3. 62 0.06 0.02 0.90 1.90 0.17 Sample size 3.53 0.04 3.61 0.03 1.13 0.33 Response variable 0.51 0.48 0.20 0.65 4.41 0.04 99

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Figure 4-1. Example of typical focal patch st udy. Forest patches are in gray, and matrix habitat (which may or may not be hom ogenous) in white. Animal presenceabsence, abundance or density is recorded within the focal patch, along with within-patch variables such as vegetati on structure or leve l of disturbance. Size and/or shape of the focal patch is measured, as well as landscape context variables. Landscape context variables may be based on isolation measures such as distance to the neares t patch (A in figure), distance to nearest occupied patch (B in figure), distance to nearest source, or connectivity measures (e.g., Hanskis connectivity index). Landscape context variables also may be based on composition and/or configuration of habitat in the landscape surrounding the focal patch measured within buffers of various distances from the focal patch (C and D in figure). Studies may just use one buffer to determine composition/configurat ion, or may use multiple buffers at different distances and chose the buffer that provides the most explanatory power. Statistical test are used to determine which factors (within-patch, patch, and landscape context variables) ex ert a significant effect on species presence or abundance. 100

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Figure 4-2. Characteristics of the studies, and study specie s, included in the review. Data are organized according to categorie s of predictor variables tested in the generalized linear mixed models. Becaus e single studies sometimes included multiple species, the proportions of s pecies and studies represented by each category differ somewhat. Abbreviations of predictor variable categories are given in Table 1. 101

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Figure 4-3. Comparison of parameter estima tes for least squares means of probability of response to landscape context. A) Taxonomic category as the predictor variable B) Type of study as predictor va riable. The y-axis provides the least squares mean estimate of the res ponse on the logit scale. Error bars represent 95 % confidence interv als around the mean. Numbers in parentheses have been converted from the logit scale to probabilities and indicate the mean probability that a s pecies in each category would respond significantly to landscape context. Means with different letters above the error bars are significantly different. 102

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103 Figure 4-4. Comparison of parameter estima tes for least squares means of probability of response to landscape context and patch variables based on sample size (i.e., # of patches surveyed). A) Landscape context as the response B) Patch variables as the response. The y-axis provides the least squares mean estimate of the response on the l ogit scale. Error bars represent 95 % confidence intervals around the mean. Numbers in parentheses have been converted from the logit scale to probabilities and indicate the mean probability that a species in each cat egory would respond significantly to landscape context. Means with different letters above the error bars are significantly different.

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CHAPTER 5 CONCLUS ION General Conclusions My study demonstrates that species traits were important dete rminants of mammal response to fragmentation. Body size, home range size, and hunting pressure were related to occupancy rates, but after controlling for passive sampling effects only hunting pressure strongly influenc ed vulner ability to fragmentat ion. Heavily hunted species were much less common in forest pa tches than in continuous forest sites. Given the ubiquity of hunting in tropica l environments, my work suggests that management efforts in fragmented landscapes that do not account for hunting pressure may be ineffective in conserving tropical mamma ls. My work also shows that the way in which vulnerability to fragmentation is measured, and in particular whether or not passive sampling effects are accounted for in the analysis, may alter conclusions regarding the relative influence of species tr aits on sensitivity to fragmentation. My cross-landscape analysis for sites in Mexico and Guatemala indicates that species traits may be useful in predicting relative pat ch occupancy rates and/or vulnerability to fragmentation across distinct landscapes, but t hat caution must be used as certain traits can become more or less influential on diffe rent landscapes, even when considering the same set of species. Characteristics of patches and land scapes also heavily influenced mammal response to fragmentation. Mammal use of forest fragments was heavily influenced by small-scale variables such as patch size and quality, as well as large-scale variables such as loss and fragmentation of habitat in the surrounding landscape. My finding that habitat fragmentation in the landscape exerted an equal or gr eater influence on 104

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mammal occupancy patterns as habitat loss runs counter to the current paradigm developed from temperate st udies. My results point to the need to go beyond habitat preservation and also consider pr evention of habitat fragmentation per se at least for tropical mammals. When broadening my analysis and cons idering a larger collection of fragmentation literature, my study confirms the general im portance of the multi-scaled approach in focal patch studies. Species from several different taxa responded at high rates to variables measured at the l andscape, patch, and within-patch scales. Landscape context was generally important in determining distribution or abundance of species within focal patches, and conservati on efforts in patchy landscape will need to consider characteristics of the surrounding landscape, or risk missing important influences for approximately half the specie s in a given system. My data also suggest that methodological variables, such as samp le size or type of landscape metric, could heavily influence the findings of any particula r study. Such variables must be considered more frequently in the literature as potential confounding fact ors for why certain species or groups of species do or do not exhi bit responses to within-patch, patch and landscape factors. Conservation Recommendations Given that hunted mammals ar e highly vulnerable to fragmentation in northern Guatemala, an increased emphasis by gov ernment agencies on reduction of hunting and enforcement of existing hunting laws in fragmented landscapes of Guatemala would be highly beneficial to the continued persistence of a number of mammal species in forest fragments. Hunting for personal consumption or for bush meat markets in northern Guatemala is quite common (Baur 1998; Polisar et al. 1998; McNab 1999). 105

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However, restriction of hunting through enforcement, sustainabi lity education, or provision of alternative protein has not been particularly successful in northern Guatemala, even in well-prot ected intact forest of the Maya Biosphere Reserve (R. McNab, personal communication). Reduction of hunting would likely be even more difficult in privately owned, fragmented land scapes. An alternative approach would be to encourage individual landhol ders that own forest patches to try and limit hunting on their property, as long as the risks and costs asso ciated with active patrolling of forest fragments is not too high. Several landholders in my study site already have begun to do this as they try to develop successful ecotourism operations. My finding of the strong negative effect of habitat fragmentation per se on mammal use of forest patches suggests that in additional to the preservation of as much habitat as possible, management efforts should encourage the retention of habitat in fewer, larger patches of forest habitat, ra ther than in a greater number of smaller patches. In privately held lands in Guatemala, this could be achieved, in part, through programs such as PINFOR (Programa de Inc entivos Forestales), which pays private landowners for the preservati on and maintenance of primary rainforest pa tches or for reforestation efforts. Through this progr am or similar government incentives, landowners (solely, or in cooperation with surrounding landholders) could be encouraged to maintain patches of forest habi tat, or to reforest, in a manner that maintains forest cohesion across the entire l andscape. However, only a small number of landholders that maintain primary forest pat ches on their property take advantage of PINFOR (e.g., only 9 of 50 landholders in my study were enrolled in PINFOR at the time of my study), which may limit the effe ctiveness of this program in addressing 106

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conservation issues on private lands at the landscape-scale, at least until it is more widely adopted. Several patch quality factors influenced mammal use of forest patches, and their negative inf luence potentially could be limit ed through better management of forest patches. Cattle presence in patches exer ted a negative influence on a subset of mammals in our study. Preventing cattle from seeking shelter within forest fragments through fencing or other means would be an easy way to incr ease the value of forest patches for mammals. This approach may have the added benefit to landholders of reducing conflicts with large carnivores (de Azevedo & Murray 2007), which still occurs frequently in northern G uatemala. Low basal ar ea of fruiting trees was detrimental for one mammal in our study, as well as a large ground dwelling bird ( Crax rubra unpublished data). Selective l ogging that frequently occurs in forest patches in Guatemala therefore may be detrimental to some species and could be reduced through programs such as PINFOR that pay l andholders to maintain forest patches in their current state. Although we did not find an influence of limited fi re disturbance within patches on mammal occupancy patterns, we could not address the detrimental influence of more widespread fire within patches as we did not sample any patches that had been burned over more than 25% of their area. Anecdotal evid ence from our study area suggests that patches with extensive fire damage have completely altered vegetation structure. Moreov er, these patches have a large standing crop of dead trees, and therefore will likely disappear over time. Fire frequently enters into forest patches during the dry season, and efforts to reduce im pacts of fire on fore st patches, such as 107

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fire breaks, may be one of the most impor tant steps to the long-term maintenance of forest cover in the fragmented z ones of northern Guatemala. Although detailed accounts of how eac h individual species responded to fragmentation is not discussed in this dissertat ion, several specieslevel patterns are of particular note from an applied conservation perspective. Margays were detected in a majority of the patches st udied, including several sma ll (e.g., 5 ha) and isolated patches, and had similar occupancy rates in large intact and fragmented forest sites. Although the arboreal margay is commonly re ferred to as being highly vulnerable to habitat loss and fragmentation, little informati on exists on its ecology in intact or fragmented habitats (Sunquist & Sunquist 2002). Limited data suggest that it is absent from highly altered habitats such as shade coffee, pasture, or disturbed fragments as well as heavily settled areas (Estrada et al. 1994, Daily et al 2003, Urquiza-Haas et al 2009). My data suggest the margay is quite capable of persisting in forest patches of northern Guatemala (at least in the short-term), and hence may be more adaptable to disturbance than previously thought. Tayras were similarly resilient to fragmentation, even though this species also is thought to be heavily forest dependent and highly arboreal. Jaguars only occupied 3 patches, all of wh ich were located close to the reserve boundary. This is true even though I sampled several large patches located far away from the Maya Biosphere Reserve. Two other studies of relatively large, isolated forest fragments in other parts of nor thern Guatemala also have fa iled to detect the presence of jaguars (Lpez et al. 2008), which indicate s that jaguars may have difficulty moving or dispersing across fragmented landscapes. This is of concern given the emphasis 108

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being placed on connecting jaguar populations throughout their range (Salom-Prez et al. 2007; Rabinowitz & Zeller 2010). Many of the proposed jaguar corridors in Guatemala, Mexico and Honduras cross highly fragmented landscapes ( Rabinowitz & Zeller 2010 ) that may be very di fficult for jaguars to utilize. Increased attention to jaguar ecology in fragmented habitats may help to achieve a better understanding of the usefulness of fragmented landscapes to se rve as corridors for this wide-ranging carnivore. Two species of conservation concern, white-lipped peccaries and Bairds tapir, were extremely uncommon in my study ar ea. White-lipped peccaries were the only species completely absent from forest pat ches in my landscape. Even though this species also was rare in intact forest, and thus did not appear to be overly vulnerable to fragmentation according to my analysis, the scale of my study was likely inadequate to assess the response of this species to fragmentat ion. The vulnerability of this species to hunting (Peres 1996; Cullen Jr. et al. 2000), and its unique ecol ogy (living in very large social groups and ranging over large areas) suggest that it will not be able to persist within, or move through fragmented landscapes. Bairds tapirs were only present within one forest patch, and were much more common in intact forest. Current management efforts by NGOs and government agencies within the intact forest of the Maya Biosphere Reserve that emphasize work on jaguar, white-lipped peccaries, and tapir, are well-focused given t hat these species struggle greatly to use fragmented landscapes outside of the reserv e and are species of local and regional conservation concern. I would suggest that other species, such as puma, white-tailed deer, red brocket deer, and spider monkeys s hould perhaps receive more attention at 109

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110 the local level, as these species also are high ly vulnerable to fragmentation in northern Guatemala. These species are important for other reasons, su ch as their roles as top carnivores (puma), prey species for large carnivores and people (white-tailed deer and red brocket deer), seed dispersers (spider monkeys), or as ecotourism attractions for private enterprises located outside the reserve.

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APPENDIX A DETAILS OF COMPILATION OF ECOLOGICAL AND LIFE HISTORY TRAITS Body mass : We determined body mass as the av erage of male and female body mass. When only a range was given, we t ook the midpoint of the range. Body mass values were taken from Reid (1997) for a ll non-felid mammals. Values for the 5 cat species were taken from Sunquist and Sunquist (2002). Home range size : We determined home range size from field guides (Walker 1999, Sunquist and Sunquist 200 2) and recently published telemetry studies (SanchezRojas et al. 1997; Saenz & Vaughan 1998; Be ck-King & von Helversen 1999; Carrillo et al. 2000; Foerster & Vaughan 2002; Maffei & Taber 2003; Michalski & Peres 2007; Aliaga-Rossel et al. 2008; Dillon & Kelly 2008). We took the average of male and female ranges and the average of wet and dry ranges when listed separately. When multiple home range sizes were given for a species, we preferentially used ranges from adjacent forest in Belize and Mexico or tropical fo rest environments in general when available. For species that only had home range estimates from North America ( Urocyon cinereoargenteus, Dasypus novemcinctus), we used data from the most southernly range. For species that did not have a home range listed ( Conepatus semistriatus, Coendou mexicana ), we took home range estimates from closely related species. Trophic level : We categorized trophic levels as follows based on dietary habits listed in Reid (1997) and Sunquist and Sunquist (2002): 1 = primarily browser/grazer or frugivore, 2 = omnivore, 3 = primarily carnivore/myrmecophage. Reproductive rate : We calculated reproductive rate as the number of young produced per year. This was determined by multiplying the average litter size by average number of litters per year. Data we re taken from field guides (Emmons & Feer 111

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1997; Reid 1997; Nowak 1999; Sunquist & Sunqui st 2002). When data from seasonal and non-seasonal environments we re listed, we used data from seasonal areas because of the marked dry season in Guatem ala. When not specified, we assumed one litter per year. When multiple litter sizes were listed, we only used data from wild populations and averaged listed va lues. When dat a were not available for a species (e.g., Alouatta pigra ), we used data from closely relate d species. For those species with data from many regions, we used informati on preferentially from southern/tropical climates. Dietary breadth: Dietary breadth was calculated as the number of categories of different prey types eaten by each species. When we could determine relative contributions of prey items, we only counted items that made up at least 5% of the diet. Data were taken from Reid (1997), Emmons and Feer (1997) and Sunquist and Sunquist (2002). Dietary categories included in the analysis were: grass, browse, crustacean/fish, insects/arthropods, hard mast, soft ma st, small mammals, large mammals, reptiles/amphibians, bird s, carrion, and domestic crops. Habitat breadth : We calculated habitat breadth bas ed on information on general habitat preferences given in Reid (1997) Emmons and Feer (1997), and Sunquist and Sunquist (2002). For additional information on use of secondary and open habitats, we also consulted additional literature (Rey na-Hurtado & Tanner 2005; Michalski et al. 2006; Parry et al. 2007). H abitat categories were limited to those commonly encountered in northern Gautemala: Tropical/sub-tropical forest, secondary/regenerating forest, marsh/wet sa vanna, dry savanna/grassland, pasture, cropland, and urban environments. 112

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113 Hunting vulnerability : Hunting vulnerability was categorized as follows: 1 = rarely/never hunted or killed, 2 = occasionally hunted or kill ed, but not a preferred game species or actively persecuted species, 3 = often hunted or kill ed a preferred game species (e.g., Mazama americana ) or actively persecuted species (e.g., Panthera onca ) species. Rankings of the species were determined independently by 2 biologists from the area with a long history of work in the region (Garcia-Anleu, personal communication, Radachowsky, personal communication) and one former hunter (Cordova, personal communication). The medi an value from the 3 rankings was used as the estimate of hunting vulnerability for th is study. Anecdotal data from 2 years in the field by the PI support this general ranking of species. Furthermore, game species listed as highly vulnerable in our study were s hown to be the most highly preferred game species in two studies of hunting off-take s of local communities within the Maya Biosphere Reserve (Baur 1998; Polisar et al. 1998). Although hunting vulnerability is not an intrinsic biological characteristic of a s pecies, but instead a produc t of region-specific hunter preference, we feel its inclusion as a species trait is appropriate. The likelihood of a species being hunted or persecuted is affe cted by several intrinsic characteristics, such as body size, mobility, escape characte ristics, trophic leve l, and palatability.

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APPENDIX B PARAMETER ESTIMATES FOR OCCUPANCY MODELS These models were used to calculate t he proportion of fragments occupied and the difference in occupancy between intact and fragmented forest sites. SZ = size of the site or patch, FR = fore st patch vs intact forest status (0 = forest patch, 1 = intact forest site), p = probability of detection, = probability of occupation Species Model Agouti paca p(0.96) (0.79+0.74*SZ+1.82*FR) Alouatta pigraa p(0.78+0.84*SZ) (1.59+0.83*SZ) Ateles geoffroyia p(0.43) (-0.90+.90*SZ) Coendou mexicana p(-1.54) (0.33+0.66*SZ-1.30*FR) Conepatus semistriatus p(-0.41-1.10*FR) (0.44+.42*SZ+0.00*FR) Dasyprocta punctata p(1.0) (-0.23+0.47*SZ+2.77*FR) Dasypus novemcinctus p(1.13+0.42*SZ-0.77*FR) (0.89+0.38*SZ-0.04*FR) Didelphis marsupialis p(0.37+0.34*SZ) (1.18-0.06*SZ-1.47*FR) Didelphis virginianus p(-0.33) (-0.79+0.47*SZ+0.69*FR) Eira barbara p(-1.53+0.61*SZ) (-0.28+0.26*SZ-1.40*FR) Leopardus pardalis p(-0.89+0.17*SZ) (0.50+2.95*SZ+1.08*FR) Leopardus wiedii p(-0.31) (0.46+1.09*SZ-0.47*FR) Mazama americana p(0.25) (-0.83+1.22*SZ+2.27*FR) Nasua narica p(0.09+0.68*SZ) (-0.13+0.84*SZ+0.31*FR) Odocoileus virginianus p(-0.87-0.01*SZ+0.07*FR) (-1.11+0.95*SZ+3.92*FR) Panthera onca p(-1.22) (-2.95+1.07*SZ+1.94*FR) Pecari tajacu p(-0.76) (-0.19+0.92*SZ+2.33*FR) Potos flavus p(-0.51-0.86*FR) (3.51+0.34*SZ-2.40*FR) Procyon lotor p(-1.08-1.71*FR) (0.97-0.78*SZ-1.62*FR) Puma concolor p(-1.58+0.64*FR) (-3.00+2.90*SZ+5.25*FR) Puma yagouaroundi p(-1.13) (-0.57+0.99*SZ-1.40*FR) Tamandua mexicana p(-0.90+0.40*SZ) (2.06-0.57*SZ-4.17*FR) Tapirus bairdii p(-0.62) (-4.89+1.07*SZ+4.47*FR) Tayassu pecarib Urocyon cinereoargenteus p(-0.13) (-0.17+0.00*SZ+0.72*FR) a A. pigra and A. geoffroyi had 100% occupancy in intact forest, and therefore the FR variable could not be included in the occupancy models without obtaining unrealistically large parameter and standard error estimates. We fit models without this variable, and assumed 100% occupancy in intact forest sites for the purposes of calculating estimates of vulnerability. b Occupancy models for T. pecari could not be calculated because it was not detected in any patches, and only detected in 1 intact 100 ha forest site. We used a value of 0.0 for proportion of patches occupied, 0.0 for difference between occupancy in intact sites and forest patches of 20 ha, and 0.17 (=1 occupied site/6 total 100 ha sites) for the difference between occupancy in intact and forest sites of 100 ha. 114

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APPENDIX C BEST-FT MODEL SETS Models shown are all models that fall within 2 AICc or 2 QAICc units of the best-fit model. AIC weights are rescal ed bas ed only on models that fall within 2 AICc units of the best-fit model. # Par = # of parameters in the model, FPS = size of the sampled p atch, BALFT = basal area of large fruiting trees, BASTS = basal area of small trees and saplings, CAT = presence of cattle within patch (1/0 variable), FIRE = evidence of fire within patch (1/0 variable), PF = proportion of forest in the landscape (our measure of habitat loss), DFP = density of forest patches in the landscape (our measure of habitat fragmentation), PREG = proportion of regenerat ing forest in the landscape, DISTC = distance to nearest community, DISTS = distance to source (boundary of the Maya Biosphere Reserve), SEA = season of occupancy survey (1/0 variable, wet season = 1), AUTO = autocovaria te index (see text fo r explanation). Buffer size lists the buffer size (in meters) at which each species responded most strongly to proportion of forest, density of patches, and proportion of regenerating forest in the landscape surrounding focal patches (see text for explanation). These were the scales used in the occupancy modeling for each species. Model #Par AICc/ QAICc weight Buffer size PF Buffer size DFP Buffer size PREG Agouti paca 3000 500 1500 p(.) (-BASTS) 3 0.00 0.18 p(.) (-CAT) 3 0.50 0.14 p(.) (FPS) 3 0.55 0.13 p(.) (.) 2 0.65 0.13 p(.) (FPS+SEA) 4 1.26 0.09 p(.) (SEA) 3 1.34 0.09 p(.) (PREG) 3 1.35 0.09 p(.) (FPS+PREG) 4 1.38 0.09 p(.) (PF) 3 1.90 0.07 115

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Model #Par AICc/ QAICc weight Buffer size PF Buffer size DFP Buffer size PREG Alouatta pigra 2000 500 500 p(fps+sea) (.) 4 0.00 0.25 p(fps+sea) (PF) 5 1.09 0.15 p(fps+sea) (-CAT) 5 1.10 0.15 p(fps+sea) (FPS) 5 1.44 0.12 p(fps+sea) (DFP) 5 1.56 0.12 p(fps+sea) (DISTS) 5 1.58 0.11 Ateles geoffroyi 500 500 500 p(.) (FPS+PF-DFP+AUTO) 6 0.00 0.54 p(.) (FPS+PF-PREGDFP+AUTO) 7 1.48 0.26 p(.) (FPS-DFP) 5 1.90 0.21 Coendou mexicanus 3000 2000 500 p(.) (FPS -DISTS) 4 0.00 1.00 Conepatus semistriatus 3000 500 3000 p(.) (DISTS) 3 0.00 0.17 p(.) (FPS+DISTS-DISTC) 5 0.38 0.14 p(.) (FPS+DISTS) 4 0.75 0.12 p(.) (-PF) 3 1.04 0.10 p(.) (FPS -PF) 4 1.05 0.10 p(.) (-DFP+DISTS-DISTC) 4 1.13 0.10 p(.) (FPS-PF-DFP) 5 1.21 0.10 Dasyprocta punctata 500 500 1500 p(sea) (FPS) 4 0.00 0.22 p(sea) (FPS-DFP) 5 0.48 0.18 p(sea) (.) 3 0.87 0.15 p(sea) (-BASTS) 4 1.19 0.12 p(sea) (-CAT) 4 1.30 0.12 p(sea) (-DISTC) 4 1.37 0.11 p(sea) (-DFP) 4 1.60 0.10 Dasypus novemcinctus 500 2000 2000 p(fps+sea) (-PREG+SEA) 6 0.00 1.00 Didelphis marsupialis 1000 1000 500 p(fps-sea) (BALFTBASTS+DISTS) 7 0.00 0.51 p(fps-sea) (DISTC+DISTSSEA) 7 1.27 0.27 p(fps-sea) (DISTS) 5 1.68 0.22 Didel phis virginianus 3000 1000 1000 p(.) (FPS-DFP) 4 0.00 0.54 p(.) (DISTS) 3 1.66 0.23 p(.) (FPS) 3 1.70 0.23 116

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Model #Par AICc/ QAICc weight Buffer size PF Buffer size DFP Buffer size PREG Eira barbara 500 3000 3000 p(.) (PF+DFP+SEA) 5 0.00 1.00 Leopardus pardalis 2500 2500 1500 p(fps) (FPS) 4 0.00 0.38 p(fps) (FPS+PREG) 5 0.19 0.34 p(fps) (FPS-DISTS) 5 1.94 0.14 p(fps) (FPS+PF) 5 1.95 0.14 Leopardus wiedii 2000 2000 3000 p(.) (FPS-DFP) 4 0.00 0.70 p(.) (FPS) 3 1.65 0.30 Mazama americana 500 1500 500 p(.) (FPS) 3 0.00 0.29 p(.) (FPS+PREG) 4 0.63 0.21 p(.) (FPS+DFP) 4 1.34 0.15 p(.) (FPS+PF) 4 1.44 0.14 p(.) (FPS+DISTS) 4 1.87 0.11 p(.) (FPS-CAT+FIRE) 5 1.95 0.11 Nasua narica 2500 500 1000 p(fps) (-CAT-DFP+AUTO) 6 0.00 0.23 p(fps) (-CATDFP+PF+AUTO) 7 0.03 0.23 p(fps) (-CAT+AUTO) 5 0.03 0.23 p(fps) (-CATDFP+FIRE+AUTO) 7 0.56 0.17 p(fps) (-CAT+FIRE+AUTO) 6 0.93 0.14 Odocoileus virginianus 500 1000 500 p(.) (FPS+BALFTBASTS+AUTO) 6 0.00 0.71 p(.) (-BASTS+AUTO) 4 1.82 0.29 Pecari tajacu 500 1500 500 p(.) (FPS+BALFT-BASTS) 5 0.00 1.00 Procyon lotor 500 2500 500 p(.) (-DFP) 3 0.00 0.39 p(.) (-DFP-CAT+FIRE) 5 0.86 0.25 p(.) (-PF-PREG-DFP) 5 1.24 0.21 p(.) (-PF-DFP-CAT+FIRE) 6 1.87 0.15 Puma yagouaroundi 3000 2500 1500 p(.) (FPS+SEA) 4 0.00 0.22 p(.) (FPS) 3 0.21 0.20 p(.) (SEA) 3 1.06 0.13 p(.) (FPS-DISTS) 4 1.11 0.13 p(.) (FPS+PREG) 4 1.17 0.12 p(.) (.) 2 1.53 0.10 117

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118 Model #Par AICc/ QAICc weight Buffer size PF Buffer size DFP Buffer size PREG p(.) (FPS+PF) 4 1.56 0.10 Tamandua mexicana 1500 1500 3000 p(.) (-PF-PREG+DFP) 5 0.00 0.40 p(.) (CAT+FIRE+DISTS) 5 1.29 0.21 p(.) (-PF+DFP) 4 1.36 0.20 p(.) (DFP-SEA) 4 1.61 0.18 Urocyon cinereoargenteus 1500 500 500 p(sea) (-DFP) 3 0.00 0.45 p(sea) (-DFP+SEA) 5 1.65 0.20 p(sea) (.) 3 1.75 0.19 p(sea) (SEA) 4 1.99 0.17

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APPENDIX D AIC WEIGHTS AIC weights calculated from 56 patch occupancy models fit us ing combinations of 11 vari ables. AIC weights for each variable were calculated by summing the weights of the fi rs t ten models in which the variable appears. Species FPS* PF DFP PREG BALFT BASTS CAT FIRE DISTS DISTC SEA A. paca 0.31 0.130.130.16 0.13 0.19 0.15 0.11 0.11 0.05 0.21 A. pigra 0.18 0.180.140.12 0.06 0.06 0.12 0.9 0.15 0.10 0.15 A. geoffroyi 0.87 0.680.860.28 0.06 0.07 0.03 0.03 0.03 0.01 0.06 C. mexicanus 0.60 0.040.030.02 0.04 0.04 0.02 0.02 0.93 0.28 0.12 C.semistriatus 0.30 0.380.350.20 0.08 0.08 0.11 0.11 0.50 0.28 0.13 D. punctata 0.38 0.150.240.09 0.08 0.12 0.11 0.09 0.15 0.13 0.12 D. novemcinctus 0.00 0.230.230.99 0.00 0.00 0.00 0.00 0.02 0.00 0.99 D. marsupialis 0.14 0.040.040.10 0.40 0.44 0.03 0.03 0.73 0.31 0.25 D. virginianus 0.47 0.180.280.12 0.05 0.06 0.05 0.05 0.23 0.12 0.11 E. barbara 0.35 0.790.680.25 0.03 0.03 0.08 0.08 0.07 0.04 0.39 L. pardalis 0.73 0.200.130.22 0.05 0.05 0.08 0.07 0.15 0.06 0.10 L. wiedii 0.90 0.280.530.11 0.03 0.03 0.06 0.06 0.08 0.03 0.07 M. americana 0.88 0.23 0.23 0.19 0.04 0.04 0.09 0.09 0.12 0.04 0.07 N. narica 0.09 0.25 0.44 0.15 0.02 0.02 0.64 0.39 0.07 0.04 0.05 O. virginianus 0.67 0.09 0.08 0.06 0.44 0.56 0.13 0.07 0.11 0.08 0.11 P. tajacu 0.75 0.13 0.13 0.12 0.67 0.66 0.05 0.05 0.14 0.04 0.23 P. lotor 0.10 0.36 0.69 0.30 0.05 0.07 0.20 0.21 0.12 0.07 0.20 P. yagouaroundi 0.51 0.14 0.12 0.16 0.06 0.06 0.08 0.07 0.17 0.10 0.28 T. mexicana 0.10 0.46 0.50 0.29 0.07 0.06 0.13 0.13 0.37 0.22 0.22 U. cinereoargenteus 0.14 0.14 0.40 0.08 0.08 0.09 0.14 0.14 0.08 0.05 0.23*FPS = size of sampled patch, PF = proportion of forest in the landscape (i.e., habitat loss), DFP = density of forest patches in the landscape (i.e., habitat fragmentation), PREG = proportion of regenerating forest in landscape, BALFT = basal area of large fruiting trees, BASTS = basal area of sm all trees and saplings, CAT = cattle presence, FIRE = fire, DISTS = distance to source, DISTC = distance to nearest community, SEA = season of survey (wet vs. dry). 119

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APPENDIX E DATA ON REFERENCES INCLUDED IN REVIEW Citation Taxonomic class studied Number of species studied Anderson et al. (2007) Mammalia 1 Anzures-Dadda & Manson (2007) Mammalia 1 Arroyo-Rodrguez et al. (2008) Mammalia 1 Askins et al. (2007) Aves 7 Attum et al. (2008) Reptilia 4 Austen et al. (2001) Aves 48 Bakermans & Rodewald (2006) Aves 1 Bakker et al. (2002) Aves 10 Banks et al. (2005) Mammalia 1 Barbaro et al. (2005) Aves, Insecta, Arachnida 34, 7,8 Bauerfeind et al. (2009) Insecta 1 Biedermann (2004) Insecta 2 Bonte et al. (2003) Arachnida 1 Bradford et al. (2003) Amphibia 1 Brotons & Herrando (2001) Aves 26 Brouwers & Newton (2009) Insecta 1 Brown (2007) Aves 41 Cagle (2008) Reptilia 7 Calme & Desrochers (2000) Aves 18 Cassel-Lundhagen et al. (2008) Insecta 1 Castelln & Sieving (2006) Aves 1 Chandler et al. (2009) Aves 9 Cox et al. (2003) Mammalia 3 Cozzi et al. (2008) Insecta 3 Cristbal-Azkarate et al. (2005) Mammalia 1 Crooks (2002) Mammalia 11 Crozier & Niemi (2003) Aves 7 Denol & Lehmann (2006) Amphibia 1 Daz et al. (1998) Aves 30 Elzinga et al. (2007) Insecta 4 Estades & Temple (1999) Aves 21 Estrada et al. (2002) Mammalia 1 Eyre (2006) Mammalia 1 Fahrig & Jonsen (1998) Insecta 6 Fedriani et al. (2002) Mammalia 1 Fernndez-Juricic (2004) Aves 8 Ficetola & Bernardi (2004) Amphibia 5 Fitzgibbon et al. (2007) Mammalia 1 Fleishman et al. (2002) Insecta 1 Forys & Humphrey (1999) Mammalia 1 Gonzlez-Varo et al. (2008) Aves 1 120

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Citation Taxonomic class studied Number of species studied Graham & Blake (2001) Aves 36 Gray et al. (2004) Amphibia 4 Grundel & Pavlovic (2007) Insecta 1 Guadagnin & Maltchik (2007) Aves 37 Guerry & Hunter (2002) Amphibia 9 Habel et al. (2007) Insecta 1 Hanser & Huntly (2006) Mammalia 1 Harper et al. (2008) Mammalia 2 Haynes et al. (2007) Insecta 1 Heisswolf et al. (2009) Insecta 2 Hinsley et al. (1995) Aves 22 Hokit et al. (1999) Reptilia 2 Holland & Bennett (2009) Mammalia 5 Hurme et al. (2008) Mammalia 1 Incoll et al. (2001) Mammalia 2 James et al. (2003) Insecta 1 Jansson & Angelstam (1999) Aves 1 Jaquiry et al. (2008) Mammalia 1 Johnson & Collinge (2004) Mammalia 1 Kappes et al. (2009) Gastropoda 17 Knapp et al. (2003) Amphibia 1 Kolozsvary & Swihart (1999) Amphibia 9 Koper & Schmiegelow (2006) Aves 10 Krauss et al. (2005) Insecta 1 Kurosawa & Askins (2003) Aves 25 Lawes et al. (2006) Aves 1 Lawes et al. (2000) Mammalia 3 Lee et al. (2002) Aves 3 Lomolino & Perault (2001) Mammalia 9 MacDonald & Rushton (2003) Aves 28 Maclean et al. (2006) Aves 6 Maes & Bonte (2006) In secta, Arachnida 3,2 Magle & Crooks (2009) Mammalia 1 Manu et al. (2007) Aves 62 Mapelli & Kittlein (2009) Mammalia 1 Matter et al. (2009) Insecta 1 McAlpine et al. (2006) Mammalia 1 Michalski & Peres (2005) Mammalia 18 Miller & Cale (2000) Aves 8 Moore & Swihart (2005) Mammalia 5 Mortelliti & Boitani (2007) Mammalia 2 Mortelliti & Boitani (2008) Mammalia 2 Naugle et al. (1999) Aves 3 121

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122 Citation Taxonomic class studied Number of species studied Nunes & Galetti (2007) Aves 1 Nupp & Swihart (2000) Mammalia 7 Onderdonk & Chapman (2000) Mammalia 3 Otto et al. (2007) Amphibia 1 Perkins et al. (2003) Aves 30 Piha et al. (2007) Amphibia 3 Pita et al. (2007) Mammalia 1 Rabasa et al. (2008) Insecta 1 Radford & Bennett (2004) Aves 1 Radford & Bennett (2006) Aves 1 Renfrew & Ribic (2008) Aves 3 Ribic & Sample (2001) Aves 4 Rickman & Connor (2003) Insecta 8 Riffell et al. (2003) Aves 15 Rizkalla & Swihart (2006) Reptilia 5 Rukke (2000) Insecta 5 Rukke & Midtgaard (1998) Insecta 1 Sallabanks et al. (2006) Aves 24 Santos et al. (2002) Aves 37 Scharine et al. (2009) Mammalia 1 Schooley & Branch (2009) Mammalia 1 Silva et al. (2005) Mammalia 5 Sun et al. (2003) Aves 1 Swihart et al. (2007) Mammalia 2 Thomas et al. (2001) Insecta 3 Thornton (unpublished data) Mammalia 20 Uezu et al. (2005) Aves 5 Urquiza-Haas et al. (2009) Aves, Mammalia 4,9 Vergara & Armesto (2009) Aves 20 Vergara & Marquet (2007) Aves 1 Virgos (2001) Mammalia 1 Virgos & Garcia (2002) Mammalia 2 Virgos et al. (2002) Mammalia 2 Walker et al. (2003) Mammalia 1 WallisDeVries (2004) Insecta 1 Weyrauch & Grubb (2004) Amphibia 9 Wilson et al. (2002) Insecta 2 Wilson et al. (2009) Aves 3 Winter et al. (2006) Aves 3 Yamaura et al. (2008) Aves 8 Zabel & Tscharntke (1998) Insecta 24

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BIOGRAPHICAL SKETCH Daniel Thornton received his bacelors degree in geology from Carleton College (Northfield, MN) in 1998. He enrolled in the graduate program at the University of Florida in 2000. In May 2003, he received hi s masters degree in wildlife ecology and conservation. In August 2003, he continued hi s graduate program at the University of Florida. He received his Ph.D. in wildlife ecology and conservation in May of 2010. 147