THE INFLUENCE OF SPECIES TRAITS AND LANDSCAPE ATTRIBUTES ON THE RESPONSE OF MID- AND LARGE-SIZED NEOTROPICAL MAMMALS TO FOREST

FRAGMENTATION

By

DANIEL HARRY THORNTON

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2010

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© 2010 Daniel Harry Thornton

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To Erin and Sam, Mom and Dad, and everyone else who helped along the way

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ACKNOWLEDGMENTS

The National Science Foundation, American Society of Mammalogists, Wildlife

Conservation Society-Guatemala Program, University of Florida IGERT Working

Forests in the Tropics, IDEA Wild, and the Department of Wildlife Ecology and

Conservation-University of Florida provided financial support for this research. I thank

the administration of Tikal National Park and the Consejos Nacional de Áreas

Protegidas in Guatemala for supporting this work. I am indebted to Nery Jurado and

Demetrio Cordova for invaluable help in the 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 aspects of my research. Research Protocol

E019 was approved by the UF Institutional Animal Care and Use Committee.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS.................................................................................................. 4

LIST OF TABLES............................................................................................................ 7

LIST OF FIGURES.......................................................................................................... 8

ABSTRACT ..................................................................................................................... 9

CHAPTER

1 INTRODUCTION .................................................................................................... 12

2 ANALYZING THE RELATIONSHIP BETWEEN SPECIES TRAITS AND VULNERABILITY TO FRAGMENTATION: PASSIVE SAMPLING EFFECTS AND CROSS-LANDSCAPE COMPARISONS........................................................ 17

Introduction ............................................................................................................. 17 Methods .................................................................................................................. 21

Study Site ......................................................................................................... 21 Species Ecological and Life History Traits........................................................ 23 Mammal Surveys.............................................................................................. 24 Measuring Vulnerability to Fragmentation ........................................................ 26 Influence of Species Traits on Vulnerability...................................................... 29 Cross-Landscape Comparison of Vulnerability................................................. 30

Results.................................................................................................................... 31 Vulnerability to Fragmentation.......................................................................... 32 Influence of Species Traits on Vulnerability...................................................... 32 Cross-Landscape Comparison ......................................................................... 33

Discussion .............................................................................................................. 34 Influence of Species Traits on Vulnerability...................................................... 34 Cross-Landscape Comparison ......................................................................... 38 Species-Level Patterns .................................................................................... 41 Conclusions...................................................................................................... 42

3 EVALUATING THE RELATIVE INFLUENCE OF HABITAT LOSS AND FRAGMENTATION: DO TROPICAL MAMMALS MEET THE TEMPERATE PARADIGM?......................................................................................................... 533

Introduction ........................................................................................................... 533 Methods ................................................................................................................ 555

Study Site ....................................................................................................... 555 Mammal Surveys............................................................................................ 566 Habitat Measurements: Patch-Scale Variables .............................................. 577 Habitat Measurements: Landscape-Scale Variables........................................ 58

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Additional Predictor Variables .......................................................................... 59 Relative Influence of Habitat Loss and Fragmentation ..................................... 60

Results.................................................................................................................... 63 Discussion .............................................................................................................. 66

4 A REEXAMINATION OF THE INFLUENCE OF LANDSCAPE CONTEXT ON SPECIES PRESENCE AND ABUNDANCE............................................................ 76

Introduction ............................................................................................................. 76 Methods .................................................................................................................. 80

Literature Search.............................................................................................. 80 Collection of Data From Studies....................................................................... 81 Data Analysis ................................................................................................... 83

Results.................................................................................................................... 85 Effects of Predictor Variables ........................................................................... 85 Test of Predictions............................................................................................ 86

Discussion .............................................................................................................. 87

5 CONCLUSION...................................................................................................... 104

General Conclusions ............................................................................................ 104 Conservation Recommendations.......................................................................... 105

APPENDIX

A DETAILS OF COMPILATION OF ECOLOGICAL AND LIFE HISTORY TRAITS . 111

B PARAMETER ESTIMATES FOR OCCUPANCY MODELS.................................. 114

C BEST-FIT MODEL SETS...................................................................................... 115

D AIC WEIGHTS ...................................................................................................... 119

E DATA ON REFERENCES INCLUDED IN REVIEW ............................................. 120

LIST OF REFERENCES ............................................................................................. 123

BIOGRAPHICAL SKETCH.......................................................................................... 147

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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 components analysis of ecological and life history traits.. ...................................................................................................... 46

2-3 Sampling effort employed for camera-trapping and visual censusing of mammals in forest patches and intact forest 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 fragmentation as response variables. ................. 48

3-1 Sampling effort employed for camera-trapping and visual censusing of mammals in forest patches................................................................................. 71

3-2 Patch and landscape context metrics for the 50 focal patches in my study area. ................................................................................................................... 72

3-3 Correlations between landscape metrics used to categorize habitat amount and configuration in the landscape surrounding focal patches. .......................... 73

3-4 Parameter estimates for patch-scale variables and habitat loss and fragmentation variables. ..................................................................................... 74

4-1 Example table used for coding species response to landscape context, patch, and within-patch variables. ...................................................................... 97

4-2 Covariates used in the generalized linear mixed models.................................... 98

4-3 Influence of predictor variables on probability of response to landscape context, patch, and within-patch variables. ......................................................... 99

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LIST OF FIGURES

Figure page 2-1 Location of study area in Guatemala.. ................................................................ 49

2-2 Map showing fragmented and continuous forest study areas............................. 50

2-3 Results of hierarchical partitioning analysis for 3 landscapes............................. 51

2-4 Results of hierarchical partitioning analysis for difference in occupancy between continuous forest and forest patches of the same size. ....................... 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 for least squares means of probability of response to landscape context. ........................................................................ 102

4-4 Comparison of parameter estimates for least squares means of probability of response to landscape context and patch variables based on sample size (i.e., # of patches surveyed). ............................................................................ 103

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

THE INFLUENCE OF SPECIES TRAITS AND LANDSCAPE ATTRIBUTES ON THE RESPONSE OF MID- AND 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 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.

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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.

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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.

CHAPTER 1 INTRODUCTION

The clearing of tropical forest for food production 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 agriculture, cattle pasture, and

urban lands. Until recently, the 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., Sekercioglu 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 fragmentation in tropical environments is urgently

needed.

A substantial body of literature exists detailing how loss and fragmentation of

habitat affects the distribution, abundance, richness, and demography of plants and

animals in a variety of ecosystems (see reviews in Harrison & Bruna 1999; Debinski &

Holt 2000; Ewers & Didham 2006; Fleishman & Mac Nally 2007; Laurance 2008).

Factors operating at a variety of scales have been found to strongly influence species

distribution or abundance in fragmented environments, including small-scale variations

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in patch quality or level of disturbance within 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 characteristics of patches and landscapes, species

survival within fragmented landscapes is determined by ecological and life history traits

of species (Henle et al. 2004). Numerous traits have been identified as potentially

influencing species response to fragmentation, 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 well-studied topics in general, the vast

majority of research has been done in temperate 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. Taxonomic bias in fragmentation studies also is

marked, with birds being the most commonly studied species (e.g., Fahrig 2003).

Only a limited number of studies have examined 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 & Chapman 2000; Estrada et al.

2002; Michalski & Peres 2005; Anzures-Dadda & Manson 2007; Michalski & Peres

2007; Arroyo-Rodríguez et al. 2008; Urquiza-Haas et al. 2009). This lack of research is

problematic given that mid and large-sized mammals likely cannot maintain viable

populations in a single reserve and move extensively over large areas, necessitating

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use of fragmented landscapes. Large tropical 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 significance, 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 Petén, northern Guatemala, is an important site for addressing the

effects of habitat fragmentation on mammals. The Maya Forest is the largest contiguous

tropical forest in Mesoamerica and maintaining the integrity of this forest is of critical

conservation concern. The human population of the Petén has increased 10-fold over

the last 40 years, and this population explosion 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 exists in scattered and isolated fragments

surrounded by agricultural fields and cattle ranches. The forest within the core area of

the reserve increasingly is threatened by illegal slash-and-burn agriculture and hunting,

with large portions of the western half of the reserve also heavily fragmented (Wildlife

Conservation Society 2007). This region of Guatemala contains a diverse fauna of mid-

and large-sized mammals that includes 5 felid species, 5 ungulates, 2 large rodent

species, 2 primates, and a diversity of smaller carnivores. Several of these species are

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of global conservation concern (e.g., Baird’s 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 [Mazama americana]) or are poorly known

throughout their range (e.g. margay [Leopardus wiedii], tayra [Eira barbara]).

I examined occupancy patterns of 25 mid- and large-sized mammals in forest

patches and intact forest sites of northern Guatemala to determine how these species

responded to habitat loss and fragmentation. I addressed several questions that have

general significance to fragmentation and landscape ecology theory as well as to

applied conservation management of tropical mammals in fragmented habitats. I also

reviewed 125 fragmentation studies to broaden my analysis and begin a synthesis of

the diverse literature on multi-scaled fragmentation effects.

Chapter 2 is an examination of the relationship between species traits and

vulnerability to fragmentation. Although several ecological and life history traits

commonly are associated with vulnerability to fragmentation, the combination of traits

that are most highly influential 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 mammals 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 species traits on patch occupancy rates of the

same set of mammals on two landscapes in Mexico (using previously published data) to

investigate if similar species traits successfully predict patch occupancy on distinct

landscapes.

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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 debate is whether or not, at the 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 factors on species occupancy patterns.

Chapter 4 broadens my analysis and takes advantage of the substantial 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 tested the influence of taxonomic status and

several methodological variables on the probability that species would respond

significantly to these variables. I conclude my dissertation with a chapter detailing

general conclusions and conservation recommendations.

CHAPTER 2 ANALYZING THE RELATIONSHIP BETWEEN SPECIES TRAITS AND

VULNERABILITY TO FRAGMENTATION: PASSIVE SAMPLING EFFECTS AND CROSS-LANDSCAPE COMPARISONS

Introduction

Preserving biodiversity in the tropics requires integration of conservation efforts

both within and outside of reserves. Protected areas in the tropics only cover 5-10% of

remaining tropical forest (Myers 2002) and are inadequate for the protection of a large

number of species (Rodrigues et al. 2004; Ceballos 2007; Jenkins & Giri 2007).

Moreover, tropical reserves are becoming smaller 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 tropical 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 agricultural 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 conservation of biodiversity

within human-altered landscapes of tropical forest ecosystems. These patches provide

critical habitat for many forest-dependent tropical species living 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 (Laurance 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 life history or ecological traits of species (Henle et al.

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2004). An understanding of how species traits 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). Such knowledge is beneficial

to predicting and mitigating species loss in human-dominated landscapes (Laurance

1991; Davies et al. 2000).

Several species traits are commonly associated with vulnerability to fragmentation.

However, the combination of traits that are most highly influential, and the degree to

which those traits determine vulnerability on distinct landscapes, remains poorly

understood for most taxa (Henle et al. 2004). Three factors confound analyses of how

species traits influence vulnerability to fragmentation. First, studies do not always

separate “passive sampling effects” from actual effects of habitat loss and fragmentation

(Johnson 2001; Haila 2002). Passive sampling effects are apparent patterns in the

relative distribution or abundance of organisms among habitat fragments that are

merely artifacts of sampling. Such effects 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 distributed species (Bolger et al. 1991;

Johnson 2001; Haila 2002). These species are not necessarily more vulnerable to

habitat loss and fragmentation, but will be categorized that way if passive sampling

effects are not accounted for in the analysis. A second common problem is that issues

of detectability often are not considered in analyses. Species that appear to be

vulnerable could merely be those that are harder to detect (Fleishman & Mac Nally

2007). Inclusion of these elusive species in the analysis without accounting for

detectability will underestimate the actual number of patches occupied by such species

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(MacKenzie et al. 2002) and make it harder to identify the real relationships between

vulnerability 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 available to examine how the same set of

species respond to habitat loss and fragmentation 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. Neotropical 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 (Chiarello 1999; Peres 2001). These same

mammals play key roles in maintaining long-term integrity of tropical 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 mid-

and large-sized mammals to use, move through, 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 information from tropical and temperate regions

suggests that several factors may influence vulnerability of mid- and large-sized

mammals, including body size, mobility, matrix tolerance, niche breadth, nearness to

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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 fragments and intact forest sites by 25 mid- and large-

sized mammals to determine how species traits influence sensitivity to habitat loss and

fragmentation. I tested the generality of these results with a comparison of patch

occupancy patterns of all 25 mammal species 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 traits influencing vulnerability to habitat

loss and fragmentation of mammals in Guatemala?

2) Does accounting for passive sampling effects alter the relative importance of

species traits in determining vulnerability to habitat loss and fragmentation? I predicted

that traits influencing distribution of species in an unaltered landscape (i.e., body size,

home-range size, reproductive rate, trophic 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 ecological and life history traits explain interspecific

variation in vulnerability to fragmentation on different landscapes? I predicted that the

same species traits would explain vulnerability 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 Guatemalan study. To my knowledge, this is the first attempt

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to look at whether or not similar suites of ecological and life history 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 area in the Petén region of northern

Guatemala (Fig. 2-1). The northernmost part of the study area is an intact landscape of

humid subtropical forest located within Tikal National Park, which itself is situated within

the larger Maya Biosphere Reserve (an UNESCO world heritage site and the largest

contiguous tropical forest in Central 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 the south. This area was formerly contiguous

forest, but now consists of a diverse collection of primary rainforest patches embedded

in a matrix of secondary and regenerating forest, cattle pasture and agricultural land

(Fig. 2-2A). Forest destruction and fragmentation 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 scattered patches of seasonally inundated bajo forest

and a fringe of savannah forest in the south. Altitudinal variation is minimal (130 to 400

masl). Annual average temperature of this region is 21–24º C, and annual average

precipitation is 1,350 mm, with a marked dry season from December to May when the

average monthly rainfall is only 60 mm.

I selected 50 primary rainforest patches that ranged in size from 2.9 to 445.5 ha

and 12 sites in continuous forest (6 sites each of 20 ha and 100 ha; Fig. 2-2B) as my

study sites. Rainforest patches contained a diverse collection of tree species, but were

often dominated by some combination of Brosimum alicastrum, Manilkara zapota, Ficus

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sp., Vitex gaumeri, Pouteria sp., Sebastiana longicuspis, Terminalia amazonia, and

Alseis yucatánensis, 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 surrounded by uninterrupted forest cover. My

continuous forest sites were subject to low levels of hunting pressure and collection of

non-timber forest products. Based on these characteristics, I made the assumption that

these sites in continuous forest are analogous 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 park. A previous camera-trapping study of mammals in the

park found increased mammal diversity in areas closer to the main road (Kawanishi

1995).

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Species Ecological and Life History Traits

The mid- and large-sized mammal community in northern Guatemala is quite

diverse, and includes 29 species. However, four of these species are either patchily

distributed throughout the region or have habits that 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 America, the Mexican black howler monkey (Alouatta

pigra) is restricted to Guatemala, Belize, and the Yucatán peninsula of Mexico. Several

species are of high global or local conservation 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 literature used to

determine values for each trait category are described in Appendix A. These traits were

chosen because they commonly are hypothesized to influence vulnerability to

fragmentation in mammals based on empirical and theoretical evidence (Laurance

1991; Peres 2001; Henle et al. 2004; Ewers & Didham 2006). Ecological and life history

variables used in my study were correlated. 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 home-

range size prior to input in the PCA analysis. Results from the PCA indicate that 83% of

the variation in the seven traits is explained by just three axes. Based on factor

loadings, these three axes each represent a distinct aspect of mammalian biology and

23

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 generalized 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., are 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 level 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-December) because of problems with camera

performance in very wet conditions. I used camera-trapping and visual censuses to

survey for mammal presence within study sites. This combination of techniques gave us

the best chance to detect the presence of elusive 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 included 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 camera-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 walking 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

traps in approximately equal proportions within 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 detectability to account for potential biases

related to unequal sampling per unit area in the patches.

I deployed camera-traps for a 16-day period in each site. A photograph of a

species at any camera within a site was considered an indication of presence. I

recorded presence/absence for each species within 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 detection history) for use in modeling detection

probabilities for each species. Because absence 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 sampling 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 trails. I walked approximately 1 km per hour and

recorded direct observations of animals, vocalizations, and well-defined tracks as

25

indications of presence within the site. Distance walked varied with patch size (Table 2-

3). 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 survey 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 walking 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 until 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 sites, 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 multiple 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 search for mammals in the trees. I repeated

nocturnal surveys 5 times, walking a different 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 probabilities for each 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 than probability of

26

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 presentation, I

will use traditional occupancy 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 later 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 account for passive sampling effects is to

have pre- and post-fragmentation information on species distribution, such information

rarely is available and my comparative approach is appropriate given similarities

between study sites in intact and fragmented areas.

To determine the overall proportion of patches occupied by each species, I

modeled occupancy and detection probabilities using detection/non-detection data

collected from all 50 forest patches and 12 continuous forest sites for 24 species. For

kinkajous, modeling was limited to the 17 forest patches and 4 continuous forest sites

surveyed during pre-dawn hours. In program PRESENCE, I first modeled detection as a

function of patch size and a categorical variable representing fragmented vs. continuous

sites, keeping occupancy constant. I determined the best fit detection model for each

species using AICc (Burnham & Anderson 2002). Species with less than 7 total

27

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., patch size or size of

the site in continuous forest) and fragmented vs. continuous forest status. Based on this

final model, I calculated the overall proportion of patches occupied by summing the

individual occupancy estimates for each patch (excluding occupancy estimates from

continuous sites) and dividing by the total number of patches. This was my first

measure of vulnerability, with higher proportion of patches occupied indicating less

vulnerable species. For my second measure of vulnerability, I used occupancy models

for each species to estimate the probability of occupancy in a 20-ha forest site and a 20-

ha patch, and subtracted the two values. I repeated this process for 100- ha sites, which

was my third measure of vulnerability. High positive values resulting from these two

analyses indicate species that had high occupancy 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 vulnerability, 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 models with limited numbers of covariates.

28

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 between 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 contribution of each individual species trait. For the first

approach, I ran three separate regressions with 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 partitioning 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 explanation). Initial inputs into analyses are the

goodness-of-fit values (e.g., R2) obtained using regression analysis. The hierarchical

partitioning analysis estimates the independent explanatory power of a variable (i.e.,

effect on a response variable attributable solely to that particular predictor) and joint

explanatory power (i.e., effect on a response variable attributable to joint action with

other predictors) of each predictor variable. The independent explanatory power serves

as the appropriate measure of the influence of a predictor variable on a response

(Chevan & Sutherland 1991; Mac Nally 2000). The key advantage of hierarchical

29

partitioning analysis is that it can provide an accurate assessment of the independent

effect of a predictor variable, even in the presence of multicollinearity (Murray & Conner

2009; but see Smith et al. 2009 for a criticism of this approach).

Cross-Landscape 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. (2009) studied patch occupancy of mid- and

large-sized mammals in 147 fragments of tropical dry forest in Yucatán, 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 patches, and Urquiza-Haas et al. (2009) used

interviews with landowners to determine patch 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 communication). Results of these 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 Yucatán landscape, but were treated as separate species for the Los

Tuxtlas and Guatemalan landscapes. I therefore treated them as separate species for

the Yucatán landscape, and assigned each species the same patch occupancy as was

30

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 Yucatán study.

However, because of similarities in morphology and ecology of these species (Reid

1997), I included them in the analysis and treated them as equivalent species.

I tested for general correlations between the rankings of species vulnerability in my

study and the two Mexican studies using Spearman’s 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 assumption 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 for many of these traits and the geographic

proximity of the three study areas, I believe my approach is appropriate as a first

assessment of cross-landscape generalities in species response to fragmentation.

Results

I detected 25 species of mid- and large-sized mammals within my study sites in

12,960 camera-trap nights and 400 kilometers of visual surveys. All species detected in

intact forest sites also were detected in 1 or more fragments, except for white-lipped

peccaries, which only were detected in intact forest. The number of species detected in

31

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 detected in the larger 100-ha sites (P < 0.10).

Vulnerability to Fragmentation

Relative vulnerability of species to fragmentation as measured by proportion of

patches occupied varied greatly among species. Occupancy of forest patches in my

study area ranged from 0 (e.g., white-lipped peccaries) to nearly 100% (e.g., kinkajous,

Table 2-4). Best-fit models and parameter estimates for each species are presented in

Appendix B. Inclusion of detectability 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 occupied increased 20.8 % and 19.2 % from the

naive estimate that did not include detectability for the Mexican porcupine (Coendou

mexicanus) and tayra (Eira barbara), respectively.

Relative vulnerability as measured by differences in occupation of continuous and

fragmented forest sites of the same size also varied greatly between species (Table 2-

4). 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 were 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 influenced strongly by PCA axis 2 and 3 (Table 2-

5). This indicates that species that are larger and more heavily hunted, and species that

32

have larger home ranges and higher trophic levels, tended to occupy fewer fragments

than those species that did not have those traits. Parameter estimates and partial R2

values indicate that axis 2 (body size/hunting axis) was the 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 hierarchical partitioning analysis

generally confirm the results from the PCA regression analysis, but enabled us to look

at the importance of individual predictors in more detail (Fig. 2-3A). The overall

proportion of fragments occupied was influenced most strongly by body size, home

range, and to a lesser extent by hunting pressure.

Accounting for passive sampling effects altered 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 forest 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 vulnerability was driven largely by differences in hunting

vulnerability, which accounted for almost half 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 the Los Tuxtlas and Yucatán landscapes (r = 0.66

33

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 significant effect on vulnerability to fragmentation 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 Yucatán, the reproduction/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 agreed with the PCA analyses but with some added detail on

individual predictors. For the Los Tuxtlas dataset, home range and reproductive rate

were most influential, accounting for a large amount of the explained variation in

vulnerability (Fig. 2-3B). In Yucatán, body size and habitat breadth had the highest

levels of independent explanatory power (Fig 2-3C).

Discussion

Influence of Species Traits on Vulnerability

After accounting for passive sampling effects, vulnerability to hunting was the

single most important species trait influencing how species responded to fragmentation

in my Guatemalan study site. Species that 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 Guatemala and Mexico (Baur 1998; Polisar et al. 1998; Naranjo

& Bodmer 2007; Reyna-Hurtado & Tanner 2007). However, the effect of hunting on

34

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 observed 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 via roads/trails. Based on models of the

minimum forest area required to maintain a sustainable harvest, the majority of forest

fragments in tropical environments fall well below the size necessary to support

populations of mid- and large-sized mammals that are experiencing hunting pressure

(Peres 2001). My data provide empirical support for a profound impact of hunting on

tropical vertebrates in fragmented landscapes by showing that 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 species traits changed substantially depending

on whether or not I accounted for sampling effects 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 sampling 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 accounted for sampling effects by comparing

occupancy patterns in forest patches with expected occupancy patterns in continuous

35

forest. Thus, body size and home range may not be as important in determining

vulnerability to fragmentation as indicated 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 occupancy 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 2004; 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 sampling effects could lead to an increased

emphasis on the importance of traits associated with natural abundance or widespread

distribution such as body size, natural abundance, or potentially niche breadth that may

not be warranted. However, in some systems, these traits exert a heavy influence on

regional extinction proneness (Woodroffe & Ginsberg 1998; Purvis et al. 2000; Kamilar

& Paciulli 2008) and correlate with vulnerability 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

In addition to biases created by passive sampling 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 within sites (Royle & Nichols 2003) as well

as species-specific differences in the efficacy of survey techniques. For example, my

visual surveys did an excellent job of detecting 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-Azcón 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 reverse pattern was true for agoutis (Thornton,

unpublished data). Failure to incorporate 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. continuous 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

Cross-Landscape Comparison

Relative rankings of species with respect to patch occupancy were in general

agreement between the three study landscapes, although the correlations were far from

perfect. Overall, species that were ranked 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 occupancy for the same species differed drastically

among the study sites. In general, species had the lowest levels of patch occupancy in

Los Tuxtlas and highest levels in Yucatán. For example, coatis occupied 20% of the

patches in Los Tuxtlas, 63% of the patches in Guatemala, and 98% of the patches in

Yucatán. Some of this variation could be explained by differences in methodology.

Interviews used in Yucatán may have resulted in higher estimates of patch occupancy

for species because they measured both past and present mammal use of patches.

Mammals were recorded as present if they 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 incorporation of detectability in patch occupancy

estimates. A failure to incorporate dectectability may have biased estimates of

occupancy lower for some species. The three landscapes differed in climate, human

population density, and physical characteristics of patches and the surrounding

landscape and these context-specific landscape characteristics also could drive

differences in absolute levels of patch occupancy among these landscapes.

Similar to the results from Guatemala, species traits strongly influenced variation

in overall patch occupancy patterns of mid- and large-sized mammals in both Mexican

38

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

mid- and large-sized mammals. Intrinsic biological traits are thus extremely important in

determining patch occupancy rates for the species considered here.

Even though I limited my comparison to almost 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 cross-

landscape analysis used a measure of fragmentation sensitivity 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’ vulnerability to habitat loss and fragmentation.

Occupancy patterns in Yucatán were influenced primarily by body size and habitat

breadth. This agrees largely with the results published in the original study (Urquiza-

Haas et al. 2009), even though the authors included additional species in their analysis

and used a slightly different measure of habitat specificity than I did. Habitat breadth, or

a related characteristic, matrix tolerance, has been found to be an important

determinant of vulnerability to fragmentation in studies on mammals (Laurance 1991)

and other taxa (Gascon et al. 1999; Sekercioglu 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 (Henle et al. 2004). Patch occupancy rates in

39

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 extinct in fragments. Home-range size may

be also be influential because species with larger home ranges may be more likely to

come into contact and conflict with humans (Woodroffe & Ginsberg 1998). Reproductive

rate could also exert an influence on species 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 example, 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 Tuxtlas at the time of the study (personal

communication, A. Estrada). Subsistence hunting occurs in Yucatán (personal

communication, T. Urquiza-Haas), but hunting 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 importance of habitat breadth in the Yucatán

compared to the other landscapes may be related to differences in the type of patches

studied in each area. Patches in the Yucatán were a mixture of primary, secondary, and

disturbed habitats, whereas patches in Guatemala and Los Tuxtlas were largely

undisturbed primary forest sites. Because habitat breadth is important in determining

40

use of disturbed or secondary forest habitats, this trait could be a more important

influence on patch occupancy patterns in Yucatán.

Collectively, results from the cross-landscape comparison indicate 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 landscapes, which is one of the major goals of

analyzing the relationship between species 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 potentially to habitat loss and fragmentation per se, based on

shared ecological and life history characteristics therefore will be made more difficult

because of variability in the importance of traits among landscapes.

Species-Level Patterns

I sampled more fragments than any previous study of tropical mid- and large-sized

mammals in fragmented environments, excluding studies exclusively based on

interviews with landowners (i.e., Michalski & 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 mammals. Larger fragments in my study area

generally supported a greater number of species, but small forest patches were still

41

used by a number of mammal species, some of them heavily forest-dependent (e.g.,

pacas [Agouti paca], agoutis [Dasyprocta punctata], margays [Leopardus wiedii], spider

monkeys, brocket deer). For example, I detected 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 several wide-ranging and/or forest dependent

species that are often considered most at risk of disappearing from fragmented

environments. However, because forest fragments in my study area were relatively

young, this diversity of mid- and 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. Several species appeared to be highly vulnerable

to loss and fragmentation of habitat and had a combination 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 loss and fragmentation in northern Guatemala.

42

Because I was able to incorporate detectability in my analysis, my findings do not reflect

detection differences between species but instead real patterns in vulnerability. Hunting

pressure on mid- and large-sized mammals is common in many fragmented tropical

environment (Peres 2001). Reduction of hunting pressure may have a marked positive

effect on the ability of species to use and persist within fragmented 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 sensitivity to habitat loss and fragmentation.

Finally, my cross-landscape comparison found correlations among mammals on three

distinct landscapes when comparing the relative 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 species will react on novel landscapes. However, my comparison

also demonstrates that the relative influence of certain species traits on patch

occupancy patterns (and perhaps to some extent on vulnerability fragmentation)

changes across landscapes, perhaps because of context-specific differences between

landscapes. Moreover, absolute values of patch 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 ecological and life history traits to predict variation

in patch occupancy and/or sensitivity to loss and fragmentation of habitat across diverse

43

44

landscapes, at least until we are able to better incorporate extrinsic factors such as

landscape context into the analysis.

Table 2-1. Life history and ecological traits 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 Yucatán 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 Baird’s 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 frugivore, 2 = omnivore, 3 = primarily 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

Table 2-2. Factor loadings from principle 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 variance of the three axes = 84%.

Variable 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 for 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

Table 2-4. Vulnerability estimates for the 3 landscapes included in the analysis.

Species Naïve 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

Table 2-5. Results from regression analysis using PCA axes as predictor variables and 3 measures of vulnerability to fragmentation as response variables.

Predictor variables Parameter estimate ± SE

P-value Partial R2

Response variable = Proportion of fragments occupied

Axis 1 (reproduction/specialization) 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/specialization) -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/specialization) -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

Figure 2-1. Location of study area in Guatemala. Inset map shows location of the Maya

Biosphere Reserve in northern Guatemala. 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 private lands farther to the south.

49

igure 2-2. Map showing fragmented and continuous forest study areas. A) Map n

rk

Fshowing a small portion of my fragmented study area, with forest cover iblack, 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 paroad is also shown in black. These study sites were embedded within completely intact forest cover.

50

Figure 2-3. Results of hierarchical partitioning analysis for 3 landscapes. A) proportion

of patches occupied in Guatemala, B) proportion of patches occupied in Los Tuxtlas, Mexico based on data in Estrada et al. (1994), C) proportion of patches occupied in Yucatán, 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 breadth, HB = habitat breadth, HV = hunting vulnerability

51

52

Figure 2-4. Results of hierarchical partitioning analysis for difference in occupancy between continuous forest and forest patches 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.

CHAPTER 3 EVALUATING 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 prevention of

habitat loss is an important and obvious way to protect biodiversity, 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 1999), whereas others predict small effects of

fragmentation compared with habitat loss (e.g., Fahrig 1997). Consensus from

numerous empirical studies is that habitat fragmentation 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 suggest that conservationists should be more

concerned with overall habitat loss in the landscape 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 areas should focus on preservation and

restoration of target amounts of habitat, rather than on the details of habitat

arrangement (Fahrig 1997). However, these conclusions are based on empirical studies

of temperate fauna, and this issue rarely has been investigated in tropical systems.

Moreover, most studies have focused on birds, insects, and small mammals, and to my

knowledge this question has not been investigated for any wide-ranging or large-bodied

species such as large mammals. Lack of studies on tropical species and wide-ranging

53

species is significant because two of the major results of habitat fragmentation per se

are an increase in amount of edge and a reduction 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 fragmentation than temperate

faunas studied thus far (Fahrig 2003). Wide-ranging or large bodied species that require

large areas of habitat for persistence may respond more strongly to reductions in patch

size that occur with habitat fragmentation than less wide-ranging species (Villard et al.

1999).

Often the influence of habitat loss and fragmentation 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). Identifying 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 landscape-

scale variables measured at different spatial extents and therefore one spatial scale

often is not sufficient in multi-species studies (Holland et al. 2005). Moreover, a single

species may respond most strongly to habitat 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 because the proper scale was not used in the

analysis. A second challenge in determining influence of landscape-scale variables

54

such as habitat loss and fragmentation is control of site or patch-scale variables.

Studies that do not simultaneously evaluate the 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 mid- and large-sized neotropical mammals to habitat

loss and fragmentation at multiple spatial extents and assessed their response to patch

size and measures of patch quality in a fragmented landscape of northern Guatemala. I

tested the currently accepted hypothesis developed from temperate studies that habitat

loss is more important than habitat fragmentation 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 habitat fragmentation, after controlling for

the influence of patch-scale variables.

Methods

Study Site

I conducted this study in a 250,000-ha area in the Petén region of northern

Guatemala (Fig. 3-1). The northernmost 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 Central America), and the remainder on private

lands further to the south. This area was formerly contiguous forest, but now consists of

primary rainforest patches embedded in a matrix of regenerating forest, 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 temperature of this region is 21–24º C, and

55

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 size 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 products, 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 forest patches from January 2006 to August

2008. I avoided sampling during mid-late wet season (mid-September to mid-

December) because of poor camera-trap performance in wet conditions. I deployed

camera traps for a 16-day period in each focal 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 detection history) for modeling detection

probabilities for each species (MacKenzie et al. 2002).

Camera trapping was ineffective for sampling arboreal species and two terrestrial

species (Odocoileus virginianus 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. Surveys were repeated 5 times within a 2-

week period for each patch, and presence-absence was assessed after each survey to

create a detection history. I surveyed patches by walking along human foot paths and

56

game trails, and by walking through sections 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 3-1). 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 detectability to account for potential biases in

unequal sampling per unit area.

Habitat Measurements: Patch-Scale Variables

For each focal patch, I calculated focal patch size and patch quality (Table 3-2). I

chose patch quality measures that have been found to affect patch use of mid- and

large-sized mammals in other studies (e.g., basal area of fruiting trees, fire disturbance),

or factors that I thought would affect use of patches in my system (e.g., cattle

disturbance, basal area of small trees/saplings). 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 further apart, and sampled more points, in

larger patches (Table 3-1). In each quadrat of the PCQM sample, I recorded distance to

the nearest stem in 3 distinct size-classes: 0-10-cm diameter-at-breast-height (dbh),

>10-30-cm dbh, and >30-cm dbh. For the two largest size classes, I identified stems to

species level when possible and to family level otherwise. I determined basal area of

fruiting trees for the two largest size classes combined (>10-cm dbh). Fruiting tree

species were those identified as being important to larger mammals in the area based

on published literature (Ponce-Santizo et al. 2006), local knowledge, and personal

observation. I also determined total basal area of small trees and saplings (stems less

57

than 10-cm dbh). I determined presence/absence of cattle within each focal patch by

looking for signs of cattle presence during other sampling activities. I assessed effects

of past fire along the edge and interior of the patch and created a binary fire variable

(0=no fire effects, 1=fire along edge and/or less than 25% of interior) 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 performing an unsupervised

classification of 2003 Landsat ETM+ images using ERDAS imagine 9.0. The original

unsupervised classification consisted of 40 classes, which I combined into 4 classes:

water, pasture/agriculture, regenerating forest ≤ 15 years old, primary forest/secondary

forest > 15 years old. Primary 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, examination 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 landcover classes. Overall classification

accuracy was 80.6 %. Forest was classified with an accuracy of 91.3%, pasture at an

accuracy of 80.5 % and regenerating 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 patch (Fig. 3-1). Within

each buffer, I used FRAGSTATS software (McGarigal et al. 2002) to calculate

proportion of forest in the landscape to represent habitat loss and 4 other metrics to

represent habitat fragmentation: mean nearest-neighbor distance of forest patches,

density of forest/non-forest edge, mean size of forest patches, and density of forest

58

patches (Table 3-2). Substantial correlations 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 density of patches thus served

as largely independent measures of habitat loss and fragmentation, respectively. A

second analysis in which I statistically removed 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 high total amount of forest edge. I also

calculated proportion of regenerating forest within each buffer as an additional measure

of landscape context that could affect mammal 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 human community as a measure of hunting

pressure. Distance to nearest village is commonly 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. Correlation

59

coefficients between all continuous potential predictor variables included in analyses

(patch, landscape, and additional variables) were small (r < 0.5).

Relative Influence of Habitat Loss and Fragmentation

I used detection/non-detection data for the 50 focal forest patches to examine

patch occupancy and detection probabilities for each 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 than 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 presentation, I will use

traditional occupancy terminology in this paper.

I built occupancy models for each species by first determining best-fit detection

models holding occupancy constant. I tested two 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 all 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 landscape), 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-fit spatial scale(s) for each species to

examine relative influence of habitat loss and fragmentation on patch occupancy

60

patterns. I fit occupancy models in program PRESENCE, limiting the number of

occupancy covariates in models to 5 because of sample size constraints. Models

included proportion of forest and density of forest patches in the landscape, and 3

patch-scale variables: patch size, basal area 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 predictor 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 coefficients have been found to be one of the best

methods for estimating the influence of habitat loss and fragmentation variables, even

when the variables are highly correlated, which was not the case in my analysis (Smith

et al. 2009). I explicitly included 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 quality (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

I calculated autocorrelation in the residuals of my occupancy models using

Moran’s I. To calculate residuals, I subtracted the observed occupancy state from the

probability that a species was present and detected at least once according to the

occupancy models (Moore & Swihart 2005). I converted residuals to Pearson’s

residuals, and used the “autocorr” macro in SAS (SAS 2008) to calculate Moran’s 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 Moran’s I: 0-4 km, >4-8 km, >8-

12 km, <12-16 km, >16-20 km. A Monte Carlo randomization test with 999 permutations

was used to determine the probability of obtaining an I value as large as the observed

value for each species in each lag category. Significance of Moran’s I values were

tested using Bonferonni corrections, where the ith lag was test at 0.05/I (Fortin & Dale

2005). For species found to have autocorrelated residuals based on Moran’s I values (n

= 3), I calculated an index presented in Moore and Swihart (2005):

autocovi = ij

Jj

iijJj

w

ywi

i

1

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 index as a covariate for those species with

autocorrelated residuals and re-ran the occupancy 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, the index decreased autocorrelation in

residuals for the three species.

62

I performed an additional analysis within a multi-model testing framework to

develop best-fit models with all 11 habitat variables from my study patches (patch,

landscape, and additional variables). This enabled us to examine if the relative influence

of habitat loss vs. fragmentation 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 influence on mammal occupancy patterns. I

developed a priori a candidate list of 56 models to test that were constrained to a

maximum of 6 variables influencing occupancy. These models included all univariate

models and more complex models with patch size, habitat loss and fragmentation in

combination with other variables. I calculated AIC weights for variables to provide a

measure of the relative influence of these variables on patch occupancy (Burnham &

Anderson 2002). I assessed overdispersion in the data for each species with a

goodness-of-fit test implemented in program PRESENCE (MacKenzie & Bailey 2004).

When overdispersion was present, I used QAICc 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 occurred in the spatial scale of response to

habitat loss and fragmentation (Appendix C). For example, spider monkeys (Ateles

63

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 fragmentation at the same spatial scale

(e.g., agouti [Dasyprocta punctata]), others responded at markedly 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 effect on occupancy for only 4 species, and a

marginally significant effect for 2 (Table 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 increasing habitat loss in the landscape.

According to standardized effect sizes, 10 of 20 mammals responded more strongly to

habitat fragmentation in the landscape than habitat loss (Table 3-4).

Results from analysis of all 11 variables generally support conclusions 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 variables 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 appeared in highly competitive models

64

(< ∆1 AICc unit of best fit model) for agoutis. In contrast, habitat amount appeared in

best-fit models for only tayras, spider monkeys, Mexican anteaters (Tamandua

mexicana) and highly competitive models 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 largely to the influence of focal patch size.

Eleven species responded significantly to focal patch size (Table 3-4). Focal patch size

also appeared as one of the top two most influential 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 exerted a more limited influence on species, with

basal area of small tree and saplings having 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 Biosphere Reserve appeared in best-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 regenerating forest in the

landscape surrounding patches appeared in the best-fit model for nine-banded

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armadillo, and cattle presence appeared in the best 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 temperate studies of a relatively weak

effect of habitat fragmentation, tropical mid- and large-sized mammals in northern

Guatemala responded strongly to habitat fragmentation independent of habitat loss. A

larger number of species responded in a significant manner to my measure of habitat

fragmentation (density of patches) than to habitat loss (proportion of forest). The effect

of fragmentation on species was apparent even after accounting for effects of patch-

scale factors on response patterns. My data therefore do not support the hypothesis

developed from temperate studies that habitat loss is substantially more important than

fragmentation. Importantly, the effect of fragmentation on species was negative for 75%

of the mammals when considering only those 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 effects of fragmentation on patch occupancy

patterns of two species (tayras, 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 mid- and large-sized mammals, instead of smaller

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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 certain large-bodied bird species respond more

strongly to fragmentation (Villard et al. 1999). However, of the 5 species in my study

with the largest home ranges, only 1 (margay) responded in a negative manner to

fragmentation, casting some doubt on this explanation. An alternative explanation for

mammals responding strongly to fragmentation is related to the location of my study site

in a tropical ecosystem. Habitat fragmentation 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 these 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 analyzed, even though species were not

particularly large or wide-ranging. Collectively, these finding indicate that prevention or

management of habitat fragmentation per se may be an important component of

strategies to preserve some tropical species. This subject warrants increased attention,

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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 habitat loss and fragmentation measured at a

variety of spatial extents. I echo concerns expressed by other authors (Holland et al.

2005) that investigations using only one landscape size or spatial extent to represent

the “landscape-scale” are problematic. Such studies 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 requirements of these species and has been

documented elsewhere (Michalski & Peres 2007). Patch quality also was influential in

determining patch occupancy patterns for a subset of species. Fruiting tree basal area

exerted a positive influence on patch occupancy by collared peccaries, suggesting that

this species was preferentially using patches with higher quality or more abundant fruit

resources. Basal area of small 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 cover. Cattle presence did not exert a strong

influence on occupancy for most species, but when this variable appeared in best-fit

models or model sets for species, cattle always exerted a negative influence on

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occupation probability. Cattle may reduce available browse and cover or disturb resting

or denning sites and have been found to exert a negative influence on other species

inhabiting fragments (Hannah et al. 2007). The lack of an influence of fire disturbance

within patches on mammal distribution contrasts with another recent study (Michalski &

Peres 2007). Unlike the Michalski and Peres (2007) investigation, I specifically 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 (Novaro et al. 2000). Given the relatively

recent nature of forest loss and fragmentation in my study area, 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 forest were apparent for species that are

considered habitat generalists or are highly adaptable to human disturbance such as

stripped hog-nosed skunks. Distance 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, because all fragments in my study area were

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relatively easy to access through roads or trails, hunting pressure was likely high and

fairly equal in intensity across the entire landscape.

Although conservation strategies will continue to focus on prevention of habitat

loss, my results also highlight the importance 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 (Fahrig 2003). A greater number of studies with

a broader taxonomic and geographic scope are necessary to inform the debate on the

relative influence of habitat loss and fragmentation and for understanding how much of

the pattern observed in this study is driven by the wide-ranging habitats of mid- and

large-sized mammals versus the tropical environment. Other recent studies on birds

and bats in tropical environments suggest that tropical fauna generally may be more

responsive to fragmentation (Develey & Metzger 2006; Klingbeil & Willig 2009). Given

that deforestation and fragmentation of remaining habitat are significant problems inside

and outside protected areas in the tropics (deFries et al. 2005; Broadbent et al. 2008), a

broader understanding of the effects of fragmentation on species distributions and

population persistent is vitally important for conservation.

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Table 3-1. Sampling effort employed for 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-quarter-method

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Table 3-2. Patch and landscape context metrics for the 50 focal patches in my study area.

Variable Mean (SD) Description Patch-scale

Focal patch size 68.5 (98.7) Area 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 primary 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

73

Table 3-3. Correlations between landscape metrics used to categorize habitat amount and configuration in the landscape surrounding focal patches. Values listed are for the 2000-m buffer (different buffers have different values for the correlation coefficients, though the relationships between variables are qualitatively similar for all buffers except the 500 m buffer – 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

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 trees/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

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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 study site in northern Guatemala. Larger map shows a small portion of the total 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.

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CHAPTER 4 A REEXAMINATION OF THE INFLUENCE OF LANDSCAPE CONTEXT ON SPECIES

PRESENCE AND ABUNDANCE

Introduction

Identifying conditions under which large-scale factors strongly influence local

processes is a central focus of landscape ecology (Levin 1992; Turner 2005). In

spatially heterogeneous or “patchy” landscapes, 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) variables, 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 scales 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 should consider the landscape surrounding

patches, or instead focus solely on managing patch-scale factors such as patch size or

quality, is an important decision that is directly informed by these types of studies

(Mazerolle & Villard 1999).

Despite the applied conservation significance, 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 published studies in patchy

landscapes presents opportunities to synthesize information across studies and arrive

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at a better understanding of species response to landscape- and patch-scale

characteristics. A review of 61 studies in the late 1990s (Mazerolle & Villard 1999)

detected a significant response to landscape context in 59% of studies examined, and

concluded that local environmental conditions alone were inadequate for predicting

species distributions. This effect was especially pronounced for vertebrate taxa, with

fewer studies on invertebrates finding a response to landscape factors. However,

almost all studies in the review found a response 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 excellent 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 example, many methodological 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 attention in the literature. Such methodological

considerations may be particularly relevant for studies in patchy landscapes given the

diversity of approaches taken to investigate the relative influence of patch- and

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 species for drastically different reasons.

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I reviewed 125 focal patch studies published from 1998 to 2009 to determine the

probability of a species’ response to landscape context, patch, and within-patch factors.

Focal patch studies were defined as empirical field-based studies that examined

species response (e.g., presence-absence) within discrete focal patches, and then

related that response to characteristics of the focal patches and the surrounding

landscape via statistical analysis. I also determined 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 lower 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, large-scale factors will not be as important

in determining their distribution or abundance. This prediction tests the generality of one

of the findings of Mazerolle 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 factors) because of the

greater specialization of tropical species. Highly specialized species may be particularly

sensitive to habitat fragmentation and/or heterogeneity at a variety of scales (Henle et

al. 2004; Devictor et al. 2008).

3a) Studies that measured 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

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landscape context by only considering simple Euclidean distance or connectivity

measures of isolation of focal patches (Fig. 4-1). Modeling studies have shown that

buffer measures of isolation are more highly 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 predetermined buffer to measure landscape context.

Recent studies indicate that even closely related species respond to landscape context

at different spatial extents (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 landscape 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 factors. Abundance data should be a more

sensitive measure of species response than presence-absence (Cushman & McGarigal

2004b).

5) Sample size (number of focal patches surveyed) would exert a positive

influence on the probability of finding a significant response of a species to landscape

context, patch, and within-patch variables because of increases in statistical power

(Quinn & Keough 2002).

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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 literature. My search yielded 3830 articles. I then scanned through abstracts

to further narrow my search to only those articles that were field-based, focal patch

studies. For this review, I only included focal patch studies that measured presence-

absence, 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 primarily terrestrial systems. This included

studies of forest, open (e.g., prairie, meadows), and aquatic (e.g. marshes, wetlands,

ponds) habitat patches embedded in anthropogenically 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, also were included. I did not include studies

where the patch was an island, and I excluded studies conducted on plants. I also

excluded studies that used Principle Components 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.

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Collection of Data From Studies

For each article in the final set for review (Appendix 1), I determined if the species

in the study displayed a significant response to landscape context, patch, and within-

patch variables. Individual species in the studies were used as the unit of analysis. The

number of species investigated in a single study ranged from 1 to 62. Landscape

context variables included habitat composition and configuration measured in buffers

around focal patches, and/or distance-based measures 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., Hanski’s connectivity index) (Fig.

4-1). Patch variables were patch size and measures of patch shape, including

perimeter, perimeter/area ratio, and fractal dimension. Within-patch variables were

measures related to patch quality such as vegetation 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 not evaluate, or held constant statistically,

the influence of patch and within-patch factors. Exclusion of these studies does not alter

the results of subsequent analysis, so they are included.

Because authors used a diversity of statistical approaches to evaluate the

influence of landscape, patch, and within-patch factors on species response, I

developed a set of rules for defining a “significant” response. A particular variable was

defined as having a significant influence on species response if it: 1) had a significant

(p< 0.05) univariate or multivariate influence 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 the best fit model, or a highly competitive

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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, patch, 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 patch size, but not measures of within-

patch quality, it would be coded as “1” for both response to landscape context and patch

factors, but a “0” for response to within-patch factors. These data then formed the basis

for three separate generalized linear mixed models that evaluated the influence of

covariates on response to landscape context, patch, and within-patch factors (described

below).

Although I recognize that the vote-counting methodology that I employed has

many limitations as a method for synthesizing 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 sizes 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. Authors used a large diversity of

metrics to represent landscape, patch, and within-patch factors, making it difficult to get

estimates of the same metric for all studies. 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.

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Several other factors potentially may have biased my review. Given that I had to

sort through so many studies containing the 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 contain these keywords. A more significant

potential problem is whether my review is biased toward finding effects of landscape

context on species because only those studies finding an effect were published.

However, several factors may serve to ameliorate this bias. First, the vast majority of

studies simultaneously considered smaller-scale variables (within-patch and patch), and

thus were not specifically focused on just examining response to landscape context.

Moreover, the majority of studies (60%) examined the response of multiple species,

which increased the chance that both significant and non-significant results would be

published. I cannot rule out the possibility 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 and Villard (1999) review in that I used the

species, instead of the study, as the unit of analysis. The advantage of my approach is

that not all species in a single study will respond the same to patch and landscape

factors, and thus we do not lose this information by summarizing data at the study level.

The disadvantage is that because I examined the response of multiple species within

individual studies, correlations in the response of species to landscape, patch, or within-

patch variables may be present (e.g., all the species in a particular study may have

been more or less likely to respond to landscape context or patch or within-patch

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variables because of the selection of certain metrics, or unaccounted for peculiarities of

sampling, analysis, or study system). To account for these correlations, I used

generalized linear mixed models in Proc GLIMMIX (SAS 2008) to examine patterns in

how species responded to landscape, patch, and within-patch variables. GLMMs

provide a flexible framework for analyzing non-normal data when correlations among

observations are present. GLMMs account for 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 factors 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-Rogers method (Littell et al. 2006). I used F-

tests to determine the overall significance of each of the predictor variables, and Tukey-

Kramer adjustment for multiple comparisons to test for significant differences between

levels of each predictor variable. I also ran the GLMMs without any covariates 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 employing multiple buffers had a greater chance of

finding a response to landscape context, I restricted my dataset to focal patch studies

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that used buffers and re-ran the GLMM with response to landscape context as the

dependent variable. Instead of using the predictor variable “type of study” (Table 4-1), I

used a binomial variable that distinguished between 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 the total number of study species (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 account 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 focus 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 total number of studies were located in

temperate areas (88%).

Based on results of GLMMs without covariates, the mean probability of a species

responding to landscape context was 0.56. The 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 landscape context was influenced by taxonomic

category of the species, type of study, location of study, and sample size of the study,

but not by type of response variable. The probability of a species responding

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significantly to patch variables was only influenced 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 prediction, invertebrates did not have a significantly lower

probability of responding to landscape context than most other vertebrate taxa (Fig. 4-

3A). Tukey adjusted multiple comparison tests indicated that invertebrates, birds, and

herpetofauna did not have significantly different probabilities of responding to landscape

context. However, mammals had a significantly higher probability of response to

landscape context than invertebrates and birds (p < 0.01, p = 0.04, respectively).

Contrary to my second prediction, the probability of tropical species responding to

landscape context was less likely than for 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). However, 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 buffers around the focal patch did not increase the

likelihood of detecting a response to landscape context (p = 0.97).

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Studies using abundance or density were more likely to detect a response to

within-patch factors than those 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 presence-

absence 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 variables 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 landscape, patch, and within-patch factors varies

greatly across study sites and between species, my synthesis of focal patch studies

revealed several 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 closely 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 individual 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 important 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

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patchy landscapes. However, in contrast with 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 having a significantly lower probability of response to landscape context than

mammals, and herpetofauna falling between these two extremes.

The relative high probability of mammals responding to landscape context could

be driven by several factors. Mammals generally may perceive and interact with their

landscape at larger scales than birds or invertebrates, and thus be more responsive to

habitat variables measured at landscape scales. In contrast, birds and invertebrates

may be more strongly influenced by habitat variables in their immediate vicinity than by

diffuse variables acting at coarse scales (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 & Stutchbury 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, this 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 & Desrochers 2002; Gobeil & Villard 2002;

Robertson & Radford 2009). The higher probability of detecting a response of mammals

to landscape context also may indicate that the coarse-scale habitat maps used to

estimate landscape context variables in focal patch studies (usually vegetation maps

derived from aerial or satellite imagery) were better at depicting habitat for mammals

than for birds or invertebrates, and, consequently, were more accurate representations

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of landscape context for mammals. The inadequacy of using vegetation types to depict

habitat for butterflies is well known (Dennis 2003; Dennis et al. 2006; Vanreusel & Van

Dyck 2007), and butterflies accounted for 59% of the invertebrate species included in

my review. However, whether or not this same 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 marginally significant. A recent modeling

study has shown that patch isolation metrics (whether distance or buffer based) tend to

have lower predictive power for specialist than generalist species because specialist

species are influenced more strongly by the 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 incorporate 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 unrecorded 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 environment. 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 nature 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 with the results of the Mazerolle and Villard

(1999) review. However, because Mazerolle and Villard did not separate out patch and

within-patch variables, they could not assess differences in how species responded to

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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 probability 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 increased isolation of patches and a

reduction in patch size, but also changes in habitat 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 variables that I found in my review also highlights the need to

incorporate patch quality in metapopulation models (e.g, Schooley & Branch 2009).

My review of the literature indicates that several methodological variables had

pronounced effects on the probability of detecting 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 buffers around the focal patch had a higher

probability of detecting a response to landscape context than studies that measured

landscape variables by calculating Euclidean distance or connectivity measures of the

focal patch to surrounding patches. Buffer measures of isolation have been found to be

more highly correlated with animal movement in modeling studies than Euclidean

distance or connectivity measures (Bender et al. 2003; Tischendorf et al. 2003) and

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thus may be better proxies of overall isolation 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 context. Studies that employ buffers also are

more likely to analyze a variety of variables related to habitat composition and

configuration that go beyond measures 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 measures as predictors of connectivity were

sensitive to measures of the buffer radius (Moilanen & Nieminen 2002). Thus, all buffers

are not equivalent and studies that use multiple 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 species. However, authors are unlikely to know

beforehand what scales may be most important, especially given that even closely

related species can respond most strongly 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

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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 2008; Yamaura et al. 2008). A more likely

explanation for the lack of relationship between use of multiple buffers 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 take 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 response 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 (Ward 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 the influence of sample size on detection of a

response to fragmentation. Although not a surprising finding, as the power of statistical

tests to find a significant effect 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

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almost 55% of the studies included in this review examined species response 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 probability of detecting a significant response to within-

patch variables, but not patch or landscape context variables. Studies were more likely

to detect a response of species to within-patch 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 more of the total variation in species-

environment 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 within-

patch factors. However, I noted several common 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 residuals of regression

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models). Autocorrelation is problematic for classic statistical test such as regression that

rely on independently distributed errors (Legendre 1993) and may lead to erroneous

conclusions regarding the significance of covariates in studies of species-environment

relationships (Lichstein et al. 2002; Christman 2008). A large separation distance

between focal patches may have led authors to conclude that there was no need to

estimate spatial autocorrelation and may explain the low number of studies testing for

autocorrelation. I cannot address this issue 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 multicollinearity 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 species-environment relationships are likely

commonplace and can lead to incorrect conclusion regarding the importance of

predictors (Koper & Schmiegelow 2006).

Finally, only 34% of studies that employed 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 context, use of single vs. multiple buffers

may have a major effect on the 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

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studies, with the caveat that more work must be done to determine the utility of multiple

vs. single buffers in analyzing the effect of landscape context on species.

My review is limited by the use of a vote-counting procedure to synthesize

results, which cannot account for differences in power between studies to find

significant effects and ignores information on effect sizes. The fact that sample size had

a significant effect on probability of detecting response to landscape and patch factors

indicates the importance of synthesizing information across studies in a more rigorous

manner. Meta-analysis of effect sizes would provide a better way to combine

information from multiple studies on the relative 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 commonly published with standardized effect sizes, instead of

just significance tests of no effect, or with raw data available in appendices for a re-

analysis of datasets in a form more amenable to meta-analytical approaches (Osenberg

et al. 1999b). Additionally, more work could be done to narrow down the diverse set of

landscape metrics (e.g., Cushman et al. 2008) used to just a few that are of primary

relevance and that could be applied repeatedly across studies and ecosystems. My

review suggests issues that would be interesting to address in a meta-analytic

framework, such as a comparison of effect 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 substantial opportunities exist to synthesize information in order

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to address particular questions that cannot be resolved with a single study and to look

for general trends in species response across ecosystems (Lindenmayer & Fischer

2006). My review revealed that 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 focal patch studies, as species

responded at high rates to heterogeneity measured at the landscape, patch, and within-

patch scales. Landscape context was generally important in determining distribution or

abundance of species within focal patches, and conservation efforts in patchy

landscape will need to consider characteristics 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 perception or movement behavior 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 fragmentation 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.

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 within-patch 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 “1” indicates that a species was found to respond significantly to landscape context (or patch/within patch factors), whereas a “0” indicates no significant response found.

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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) Studies conducted in tropical regions

2) Temperate (TEMP) Studies 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

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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

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Figure 4-1. Example of typical focal patch study. Forest patches are in gray, and matrix habitat (which may or may not be homogenous) in white. Animal presence-absence, abundance or density is recorded within the focal patch, along with within-patch variables such as vegetation structure or level 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 nearest patch (A in figure), distance to nearest occupied patch (B in figure), distance to nearest source, or connectivity measures (e.g., Hanski’s 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/configuration, 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) exert a significant effect on species presence or abundance.

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

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Figure 4-3. Comparison of parameter estimates for least squares means of probability

of response to landscape context. A) Taxonomic category as the predictor variable B) Type of study as predictor variable. The y-axis provides the least squares mean estimate of the response on the logit 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 category would respond significantly to landscape context. Means with different letters above the error bars are significantly different.

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Figure 4-4. Comparison of parameter estimates 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 logit 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 category would respond significantly to landscape context. Means with different letters above the error bars are significantly different.

CHAPTER 5 CONCLUSION

General Conclusions

My study demonstrates that species traits were important determinants 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 influenced vulnerability to fragmentation. Heavily hunted

species were much less common in forest patches than in continuous forest sites.

Given the ubiquity of hunting in tropical environments, my work suggests that

management efforts in fragmented landscapes that do not account for hunting pressure

may be ineffective in conserving tropical mammals. 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 sensitivity to fragmentation. My

cross-landscape analysis for sites in Mexico and Guatemala 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.

Characteristics of patches and landscapes 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 greater influence on

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mammal occupancy patterns as habitat loss runs counter to the current paradigm

developed from temperate studies. My results point to the need to go beyond habitat

preservation and also consider prevention of habitat fragmentation per se, at least for

tropical mammals.

When broadening my analysis and considering a larger collection of

fragmentation literature, my study confirms the general importance of the multi-scaled

approach in focal patch studies. Species from several different taxa responded at high

rates to variables measured at the landscape, patch, and within-patch scales.

Landscape context was generally important in determining distribution or abundance of

species within focal patches, and conservation efforts in patchy landscape will need to

consider characteristics of the surrounding landscape, or risk missing important

influences for approximately half the species in a given system. My data also suggest

that methodological variables, such as sample size or type of landscape metric, could

heavily influence the findings of any particular study. Such variables must be considered

more frequently in the literature as potential confounding factors for why certain species

or groups of species do or do not exhibit responses to within-patch, patch and

landscape factors.

Conservation Recommendations

Given that hunted mammals are highly vulnerable to fragmentation in northern

Guatemala, an increased emphasis by government 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).

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However, restriction of hunting through enforcement, sustainability education, or

provision of alternative protein has not been particularly successful in northern

Guatemala, even in well-protected intact forest of the Maya Biosphere Reserve (R.

McNab, personal communication). Reduction of hunting would likely be even more

difficult in privately owned, fragmented landscapes. An alternative approach would be to

encourage individual landholders that own forest patches to try and limit hunting on their

property, as long as the risks and costs associated 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, rather 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 Incentivos Forestales), which pays private

landowners for the preservation and maintenance of primary rainforest patches or for

reforestation efforts. Through this program or similar government incentives,

landowners (solely, or in cooperation with surrounding landholders) could be

encouraged to maintain patches of forest habitat, or to reforest, in a manner that

maintains forest cohesion across the entire landscape. However, only a small number of

landholders that maintain primary forest patches 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 effectiveness of this program in addressing

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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 influence potentially could be limited through better management of forest

patches. Cattle presence in patches exerted 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 increase 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 Guatemala. Low basal area of fruiting trees was detrimental for

one mammal in our study, as well as a large ground dwelling bird (Crax rubra,

unpublished data). Selective logging 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 landholders to maintain forest patches in

their current state. Although we did not find an influence of limited fire 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 evidence from our study

area suggests that patches with extensive fire damage have completely altered

vegetation structure. Moreover, 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 impacts of fire on forest patches, such as

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fire breaks, may be one of the most important steps to the long-term maintenance of

forest cover in the fragmented zones of northern Guatemala.

Although detailed accounts of how each individual species responded to

fragmentation is not discussed in this dissertation, several species-level patterns are of

particular note from an applied conservation perspective. Margays were detected in a

majority of the patches studied, including several small (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 referred to as being highly vulnerable to

habitat loss and fragmentation, little information 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 which 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 northern Guatemala also have failed to detect the presence

of jaguars (López et al. 2008), which indicates that jaguars may have difficulty moving

or dispersing across fragmented landscapes. This is of concern given the emphasis

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being placed on connecting jaguar populations throughout their range (Salom-Pérez 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 difficult 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 serve as corridors for this wide-ranging

carnivore.

Two species of conservation concern, white-lipped peccaries and Baird’s tapir,

were extremely uncommon in my study area. White-lipped peccaries were the only

species completely absent from forest patches 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 fragmentation. The vulnerability of this species to

hunting (Peres 1996; Cullen Jr. et al. 2000), and its unique ecology (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. Baird’s 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 that these species struggle greatly to use

fragmented landscapes outside of the reserve 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 should perhaps receive more attention at

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the local level, as these species also are highly vulnerable to fragmentation in northern

Guatemala. These species are important for other reasons, such 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.

APPENDIX A DETAILS OF COMPILATION OF ECOLOGICAL AND LIFE HISTORY TRAITS

Body mass: We determined body mass as the average of male and female body

mass. When only a range was given, we took the midpoint of the range. Body mass

values were taken from Reid (1997) for all 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 2002) and recently published telemetry studies (Sanchez-

Rojas et al. 1997; Saenz & Vaughan 1998; Beck-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 forest 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 were taken from field guides (Emmons & Feer

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1997; Reid 1997; Nowak 1999; Sunquist & Sunquist 2002). When data from seasonal

and non-seasonal environments were listed, we used data from seasonal areas

because of the marked dry season in Guatemala. 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 values. When data were not available for a species

(e.g., Alouatta pigra), we used data from closely related species. For those species with

data from many regions, we used information 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 mast, small mammals, large

mammals, reptiles/amphibians, birds, carrion, and domestic crops.

Habitat breadth: We calculated habitat breadth based 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 (Reyna-Hurtado & Tanner 2005; Michalski et al.

2006; Parry et al. 2007). Habitat categories were limited to those commonly

encountered in northern Gautemala: Tropical/sub-tropical forest,

secondary/regenerating forest, marsh/wet savanna, dry savanna/grassland, pasture,

cropland, and urban environments.

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Hunting vulnerability: Hunting vulnerability was categorized as follows: 1 =

rarely/never hunted or killed, 2 = occasionally hunted or killed, but not a preferred game

species or actively persecuted species, 3 = often hunted or killed – 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 median value from the 3 rankings was used

as the estimate of hunting vulnerability for this 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 shown to be the most highly preferred game

species in two studies of hunting off-takes 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 species, but instead a product 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 affected by several intrinsic characteristics,

such as body size, mobility, escape characteristics, trophic level, and palatability.

APPENDIX B PARAMETER ESTIMATES FOR OCCUPANCY MODELS

These models were used to calculate the proportion of fragments occupied and

the difference in occupancy between intact and fragmented forest sites. SZ = size of the

site or patch, FR = forest patch vs intact forest status (0 = forest patch, 1 = intact forest

site), p = probability of detection, ψ = probability of occupation

Species Model Agouti paca p(0.96)ψ(0.79+0.74*SZ+1.82*FR) Alouatta pigraa p(0.78+0.84*SZ)ψ(1.59+0.83*SZ) Ateles geoffroyia p(0.43)ψ(-0.90+.90*SZ) Coendou mexicana p(-1.54)ψ(0.33+0.66*SZ-1.30*FR) Conepatus semistriatus p(-0.41-1.10*FR)ψ(0.44+.42*SZ+0.00*FR) Dasyprocta punctata p(1.0)ψ(-0.23+0.47*SZ+2.77*FR) Dasypus novemcinctus p(1.13+0.42*SZ-0.77*FR)ψ(0.89+0.38*SZ-0.04*FR) Didelphis marsupialis p(0.37+0.34*SZ)ψ(1.18-0.06*SZ-1.47*FR) Didelphis virginianus p(-0.33) ψ(-0.79+0.47*SZ+0.69*FR) Eira barbara p(-1.53+0.61*SZ) ψ(-0.28+0.26*SZ-1.40*FR) Leopardus pardalis p(-0.89+0.17*SZ) ψ(0.50+2.95*SZ+1.08*FR) Leopardus wiedii p(-0.31) ψ(0.46+1.09*SZ-0.47*FR) Mazama americana p(0.25) ψ(-0.83+1.22*SZ+2.27*FR) Nasua narica p(0.09+0.68*SZ) ψ(-0.13+0.84*SZ+0.31*FR) Odocoileus virginianus p(-0.87-0.01*SZ+0.07*FR) ψ(-1.11+0.95*SZ+3.92*FR) Panthera onca p(-1.22) ψ(-2.95+1.07*SZ+1.94*FR) Pecari tajacu p(-0.76) ψ(-0.19+0.92*SZ+2.33*FR) Potos flavus p(-0.51-0.86*FR) ψ(3.51+0.34*SZ-2.40*FR) Procyon lotor p(-1.08-1.71*FR) ψ(0.97-0.78*SZ-1.62*FR) Puma concolor p(-1.58+0.64*FR) ψ(-3.00+2.90*SZ+5.25*FR) Puma yagouaroundi p(-1.13) ψ(-0.57+0.99*SZ-1.40*FR) Tamandua mexicana p(-0.90+0.40*SZ) ψ(2.06-0.57*SZ-4.17*FR) Tapirus bairdii p(-0.62) ψ(-4.89+1.07*SZ+4.47*FR) Tayassu pecarib Urocyon cinereoargenteus p(-0.13) ψ(-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.

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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 rescaled based 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 patch, 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 regenerating 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 = autocovariate index (see text for 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(.)ψ(-BASTS) 3 0.00 0.18 p(.)ψ(-CAT) 3 0.50 0.14 p(.)ψ(FPS) 3 0.55 0.13 p(.)ψ(.) 2 0.65 0.13 p(.)ψ(FPS+SEA) 4 1.26 0.09 p(.)ψ(SEA) 3 1.34 0.09 p(.)ψ(PREG) 3 1.35 0.09 p(.)ψ(FPS+PREG) 4 1.38 0.09 p(.)ψ(PF) 3 1.90 0.07

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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)ψ(.) 4 0.00 0.25 p(fps+sea)ψ(PF) 5 1.09 0.15 p(fps+sea)ψ(-CAT) 5 1.10 0.15 p(fps+sea)ψ(FPS) 5 1.44 0.12 p(fps+sea)ψ(DFP) 5 1.56 0.12 p(fps+sea)ψ(DISTS) 5 1.58 0.11

Ateles geoffroyi 500 500 500 p(.)ψ(FPS+PF-DFP+AUTO) 6 0.00 0.54 p(.)ψ(FPS+PF-PREG-DFP+AUTO) 7 1.48 0.26

p(.)ψ(FPS-DFP) 5 1.90 0.21 Coendou mexicanus 3000 2000 500

p(.)ψ(FPS -DISTS) 4 0.00 1.00 Conepatus semistriatus 3000 500 3000

p(.)ψ(DISTS) 3 0.00 0.17 p(.)ψ(FPS+DISTS-DISTC) 5 0.38 0.14 p(.)ψ(FPS+DISTS) 4 0.75 0.12 p(.)ψ(-PF) 3 1.04 0.10 p(.)ψ(FPS -PF) 4 1.05 0.10 p(.)ψ(-DFP+DISTS-DISTC) 4 1.13 0.10 p(.)ψ(FPS-PF-DFP) 5 1.21 0.10

Dasyprocta punctata 500 500 1500 p(sea)ψ(FPS) 4 0.00 0.22 p(sea)ψ(FPS-DFP) 5 0.48 0.18 p(sea)ψ(.) 3 0.87 0.15 p(sea)ψ(-BASTS) 4 1.19 0.12 p(sea)ψ(-CAT) 4 1.30 0.12 p(sea)ψ(-DISTC) 4 1.37 0.11 p(sea)ψ(-DFP) 4 1.60 0.10

Dasypus novemcinctus 500 2000 2000 p(fps+sea)ψ(-PREG+SEA) 6 0.00 1.00

Didelphis marsupialis 1000 1000 500 p(fps-sea)ψ(BALFT-BASTS+DISTS)

7 0.00 0.51

p(fps-sea)ψ(DISTC+DISTS-SEA)

7 1.27 0.27

p(fps-sea)ψ(DISTS) 5 1.68 0.22 Didelphis virginianus 3000 1000 1000

p(.)ψ(FPS-DFP) 4 0.00 0.54 p(.)ψ(DISTS) 3 1.66 0.23 p(.)ψ(FPS) 3 1.70 0.23

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Model #Par ∆ AICc/ QAICc weight

Buffer size PF

Buffer size DFP

Buffer size

PREG Eira barbara 500 3000 3000

p(.)ψ(PF+DFP+SEA) 5 0.00 1.00 Leopardus pardalis 2500 2500 1500

p(fps)ψ(FPS) 4 0.00 0.38 p(fps)ψ(FPS+PREG) 5 0.19 0.34 p(fps)ψ(FPS-DISTS) 5 1.94 0.14 p(fps)ψ(FPS+PF) 5 1.95 0.14

Leopardus wiedii 2000 2000 3000 p(.)ψ(FPS-DFP) 4 0.00 0.70 p(.)ψ(FPS) 3 1.65 0.30

Mazama americana 500 1500 500 p(.)ψ(FPS) 3 0.00 0.29 p(.)ψ(FPS+PREG) 4 0.63 0.21 p(.)ψ(FPS+DFP) 4 1.34 0.15 p(.)ψ(FPS+PF) 4 1.44 0.14 p(.)ψ(FPS+DISTS) 4 1.87 0.11 p(.)ψ(FPS-CAT+FIRE) 5 1.95 0.11

Nasua narica 2500 500 1000 p(fps)ψ(-CAT-DFP+AUTO) 6 0.00 0.23 p(fps)ψ(-CAT-DFP+PF+AUTO) 7 0.03 0.23

p(fps)ψ(-CAT+AUTO) 5 0.03 0.23 p(fps)ψ(-CAT-DFP+FIRE+AUTO) 7 0.56 0.17

p(fps)ψ(-CAT+FIRE+AUTO) 6 0.93 0.14 Odocoileus virginianus 500 1000 500

p(.)ψ(FPS+BALFT-BASTS+AUTO) 6 0.00 0.71

p(.)ψ(-BASTS+AUTO) 4 1.82 0.29 Pecari tajacu 500 1500 500

p(.)ψ(FPS+BALFT-BASTS) 5 0.00 1.00 Procyon lotor 500 2500 500

p(.)ψ(-DFP) 3 0.00 0.39 p(.)ψ(-DFP-CAT+FIRE) 5 0.86 0.25 p(.)ψ(-PF-PREG-DFP) 5 1.24 0.21 p(.)ψ(-PF-DFP-CAT+FIRE) 6 1.87 0.15

Puma yagouaroundi 3000 2500 1500 p(.)ψ(FPS+SEA) 4 0.00 0.22 p(.)ψ(FPS) 3 0.21 0.20 p(.)ψ(SEA) 3 1.06 0.13 p(.)ψ(FPS-DISTS) 4 1.11 0.13 p(.)ψ(FPS+PREG) 4 1.17 0.12 p(.)ψ(.) 2 1.53 0.10

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Model #Par ∆ AICc/ QAICc weight

Buffer size PF

Buffer size DFP

Buffer size

PREG p(.)ψ(FPS+PF) 4 1.56 0.10

Tamandua mexicana 1500 1500 3000 p(.)ψ(-PF-PREG+DFP) 5 0.00 0.40 p(.)ψ(CAT+FIRE+DISTS) 5 1.29 0.21 p(.)ψ(-PF+DFP) 4 1.36 0.20 p(.)ψ(DFP-SEA) 4 1.61 0.18

Urocyon cinereoargenteus 1500 500 500 p(sea)ψ(-DFP) 3 0.00 0.45 p(sea)ψ(-DFP+SEA) 5 1.65 0.20 p(sea)ψ(.) 3 1.75 0.19 p(sea)ψ(SEA) 4 1.99 0.17

APPENDIX D AIC WEIGHTS

AIC weights calculated from 56 patch occupancy models fit using combinations of 11 variables. AIC weights for each

variable were calculated by summing the weights of the first ten models in which the variable appears.

Species FPS* PF DFP PREG BALFT BASTS CAT FIRE DISTS DISTC SEAA. paca 0.31 0.13 0.13 0.16 0.13 0.19 0.15 0.11 0.11 0.05 0.21A. pigra 0.18 0.18 0.14 0.12 0.06 0.06 0.12 0.9 0.15 0.10 0.15A. geoffroyi 0.87 0.68 0.86 0.28 0.06 0.07 0.03 0.03 0.03 0.01 0.06C. mexicanus 0.60 0.04 0.03 0.02 0.04 0.04 0.02 0.02 0.93 0.28 0.12C.semistriatus 0.30 0.38 0.35 0.20 0.08 0.08 0.11 0.11 0.50 0.28 0.13D. punctata 0.38 0.15 0.24 0.09 0.08 0.12 0.11 0.09 0.15 0.13 0.12D. novemcinctus 0.00 0.23 0.23 0.99 0.00 0.00 0.00 0.00 0.02 0.00 0.99D. marsupialis 0.14 0.04 0.04 0.10 0.40 0.44 0.03 0.03 0.73 0.31 0.25D. virginianus 0.47 0.18 0.28 0.12 0.05 0.06 0.05 0.05 0.23 0.12 0.11E. barbara 0.35 0.79 0.68 0.25 0.03 0.03 0.08 0.08 0.07 0.04 0.39L. pardalis 0.73 0.20 0.13 0.22 0.05 0.05 0.08 0.07 0.15 0.06 0.10L. wiedii 0.90 0.28 0.53 0.11 0.03 0.03 0.06 0.06 0.08 0.03 0.07M. americana 0.88 0.23 0.23 0.19 0.04 0.04 0.09 0.09 0.12 0.04 0.07N. narica 0.09 0.25 0.44 0.15 0.02 0.02 0.64 0.39 0.07 0.04 0.05O. virginianus 0.67 0.09 0.08 0.06 0.44 0.56 0.13 0.07 0.11 0.08 0.11P. tajacu 0.75 0.13 0.13 0.12 0.67 0.66 0.05 0.05 0.14 0.04 0.23P. lotor 0.10 0.36 0.69 0.30 0.05 0.07 0.20 0.21 0.12 0.07 0.20P. yagouaroundi 0.51 0.14 0.12 0.16 0.06 0.06 0.08 0.07 0.17 0.10 0.28T. mexicana 0.10 0.46 0.50 0.29 0.07 0.06 0.13 0.13 0.37 0.22 0.22U. 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 small trees and saplings, CAT = cattle presence, FIRE = fire, DISTS = distance to source, DISTC = distance to nearest community, SEA = season of survey (wet vs. dry).

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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-Rodríguez 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 Castellón & Sieving (2006) Aves 1 Chandler et al. (2009) Aves 9 Cox et al. (2003) Mammalia 3 Cozzi et al. (2008) Insecta 3 Cristóbal-Azkarate et al. (2005) Mammalia 1 Crooks (2002) Mammalia 11 Crozier & Niemi (2003) Aves 7 Denoël & Lehmann (2006) Amphibia 1 Díaz 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 Fernández-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 González-Varo et al. (2008) Aves 1

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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 Jaquiéry 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) Insecta, 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

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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 bacelor’s 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 his master’s degree in wildlife ecology and

conservation. In August 2003, he continued his graduate program at the University of

Florida. He received his Ph.D. in wildlife ecology and conservation in May of 2010.

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