Assessing Bd Prevalence and Infection Intensity on Jamaica’S

ASSESSING BD PREVALENCE AND INFECTION INTENSITY ON JAMAICA’S

FROGS IN RELATION TO CANOPY COVER IN COFFEE FARMS

Jennifer Michelle Brown

A Thesis Presented to

The Faculty of Humboldt State University

In Partial Fulfillment of the Requirements for the Degree

Master of Science in Natural Resources: Wildlife

Committee Membership

Dr. Matthew Johnson, Professor, Committee Chair

Dr. Richard Brown, Committee Member

Dr. Karen Pope, Committee Member

Dr. Alison O’Dowd, Graduate Coordinator

July 2016

ABSTRACT

ASSESSING Bd PREVALENCE AND INFECTION INTENSITY ON JAMAICA’S FROGS IN RELATION TO CANOPY COVER IN COFFEE FARMS

Jennifer Brown

Populations of tropical amphibians have declined primarily from habitat loss and

chytridiomycosis, a disease caused by a fungus Batrachochytrium dendrobatidis (Bd). It

has been hypothesized that these factors negatively interact, with forest habitat loss

reducing prevalence and infection rates of the fungus over wide spatial scales. I

examined this hypothesis on twenty-one coffee farms in Jamaica by testing the prediction that Bd prevalence and infection intensity, on two widespread frogs, increases with forest

cover surrounding farms and with local canopy cover within each farm. Recent literature

suggests fungicides may clear frogs of Bd, so, I also explored the relationship between

fungicide use and Bd at the farm scale. At the farm scale, neither Bd prevalence nor

intensity was strongly associated with fungicide use or forest cover; Bd was positively

associated with elevation for the non-native species Eleutherodactylus johnstonei.

Within a farm, infection intensity increased with more shade cover as predicted for E.

johnstonei, perhaps due to favorable microclimates. However, infection intensity

decreased with shade and leaf litter depth for the native species Eleutherodactylus gossei.

Bd prevalence results were inconclusive; prevalence could depend on other factors not addressed (climate, Bd strain, etc.). Although my results were mixed, Bd does appear to

be less problematic for some frog species in disturbed habitats with less cover. Frogs that can endure forest loss could find refuge from this disease in human modified habitats, such as coffee farms. Jamaica’s Bd strain is not described, posing challenges for conservation efforts due to variability of Bd epidemiology.

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ACKNOWLEDGEMENTS

Foremost, I would like to express my sincere gratitude to my advisor and field

assistant Dr. Matt Johnson for the continuous support of my Master’s research, for his

patience, motivation, enthusiasm, and immense knowledge. His guidance helped me in

all the time of this research and thesis writing. I could not have imagined having a better

advisor for this research. In addition, I would like to thank the rest of my graduate

committee, Dr. Karen Pope and Dr. Rick Brown, for their encouragement and insightful

comments. I must also thank the members of the Johnson Lab that helped me through the years: Stephanie Eyes, Megan Garfinkel, Dawn Blake, Shannon Mendia, and Wendy

Willis. The diversity of support will forever be immensely appreciated.

A special thanks to all my field assistants, including pilot season assistants Kate

Howard, Lauren Hoyle, Brian Fugundes and Aaron Spidal, and thesis assistants Bennett

Hardy, Chris West, Carina Ibara, and Gaby Ruso. I am forever grateful to all your hard work, especially for all repetitive drawn-out hours of habitat sampling. I will never forget those ‘fun’ daytime surveys. Thank you so very much.

Funding for this project was provided by The Wildlife Society Western Section,

Sequoia Park Zoo, Marin Rod and Gun Club and National Science Foundation. Thank you for all the support you give to wildlife conservation.

Lastly, but definitely not least, I must thank my amazing family and friends.

Without the love and support of all of you I would never have gotten through. You know

who you are. I must express my very profound gratitude to my best friend, partner and

field assistant Christopher West and his family for providing me with endless support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible

without you. I love you all very much. Thank you.

TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGEMENTS ...... iv

TABLE OF CONTENTS ...... vi

LIST OF TABLES ...... viii

LIST OF FIGURES ...... ix

LIST OF APPENDICES ...... x

INTRODUCTION ...... 1

METHODS ...... 6

Study System ...... 6

Study Species ...... 8

Transects ...... 9

Frog Captures and Bd Sampling ...... 9

Habitat Variables ...... 10

Farm Scale ...... 10

Within-Farm Transect Scale ...... 11

Statistical Analysis ...... 12

Farm Scale ...... 13

Within-Farm Transect Scale ...... 14

RESULTS ...... 16

Farm Scale ...... 17

Prevalence: E. johnstonei ...... 17

Prevalence: E. gossei ...... 18

Infection Intensity: E. johnstonei ...... 18

Infection Intensity: E. gossei ...... 18

Within-Farm Transect Scale ...... 19

Prevalence: E. johnstonei ...... 19

Prevalence: E. gossei ...... 19

Infection Intensity: E. johnstonei ...... 20

Infection Intensity: E. gossei ...... 20

DISCUSSION ...... 28

Conclusions ...... 34

REFERENCES ...... 36

APPENDICES ...... 55

vii

LIST OF TABLES

Table 1: Distribution of 27 farms among vegetation cover classes, Jamaica, June-July 2013...... 22

Table 4: Top two ranked models and model averaged parameter estimates (β), standard errors, and lower and upper 95% confidence intervals (LCI,UCI) for generalized linear mixed models predicting infection intensity of Bd on Eleutherodactylus gossei relative to shade cover and leaf litter depth in coffee farms in Jamaica, June-July 2013. (* Indicates coefficient confidence does not overlap zero) ...... 26

Table 5: Summary of the most important variables predicting variation in Bd prevalence and infection intensity in E. johnstonei and E. gossei at two spatial scales among 20 coffee farms based on top supported model (see Tables 2-3 and Appendix F for model selection results). Variables in bold had coefficient confidence intervals that did not overlap zero, and the superscripts indicate directions of effects; variables in parentheses were included in the top candidate model set (ΔAICc < 2) but had confidence intervals for coefficients that overlapped zero. The top model’s McFadden’s and marginal and conditional R2 values are also shown...... 27

viii

LIST OF FIGURES

Figure 1. Locations of 27 coffee farms used for this study of Bd on frogs in Jamaica, June-July 2013...... 7

LIST OF APPENDICES

Appendix A: Names of farms and Parish locations used in study...... 55

Appendix B: All species captured, total species, number of frogs and average canopy cover (transect scale) in 27 coffee farms in Jamaica, June-July 2013. Species in order of coding in table: E. cundalis, E. fuscus, E. glaucoreius, E. gossei, E. johnstonei, E. pantone, E. planirostris, O. brunneus, O. wilderi and R. marina (Superscripts ‘G’ is “ground-dweller” and ‘A’ is “arboreal”, * denotes non-native species) ...... 56

Appendix C: Average, leaf litter (cm), percent ground cover, coffee cover, and canopy cover (Within-Farm Scale); elevation (m), percent forest cover, and fungicide use: Yes/No = 0/1 (Farm Scale) in all 27 coffee farms in Jamaica, June-July 2013...... 57

Appendix D: Jacob Kerby Lab University of South Dakota, qPCR methods...... 58

Appendix E: Average precipitation, in Jamaica, measured in inches for June-July from 2012-2014 (Data Collection: June-July, 2013). NOAA Climate Data, Station: Montego Bay SANGSTE, Jamaica...... 59

Appendix F: All model selection result tables for both spatial scale analyses...... 60

INTRODUCTION

Amphibians, widely recognized as useful indicators of ecosystem health due to their

sensitivity to alterations of natural systems, are experiencing alarming global declines

(Stuart et al. 2004). Of 6,200 species known to science 32% are listed as threatened with

extinction (IUCN 2015). The two primary drivers of population declines and extinctions

globally are chytridiomycosis, a disease caused by a pathogenic chytrid fungus

Batrachochytrium dendrobatidis (Berger et al. 1998; Lips 1999; Daszak et al. 2003;

Bosch et al. 2001; Kiesecker et al. 2004; Bd hereafter), and habitat loss resulting from, especially, agricultural development (Dodd and Smith 2003; Cushman 2006; Becker et al. 2007). These factors are believed to have played a role in the disappearance of 94 of

120 frog species since 1980 (Stuart et al. 2004; Vredenburg et al. 2010).

Bd is one of two members in the family, Chytridiomycota, which is known to be a pathogen to vertebrate hosts (Longcore et al. 1999; Martel et al. 2013). It is a waterborne epidermal fungus and has an expansive host range among anurans (frogs and toads,

Fisher et al. 2009). This fungus infects the mouthparts of larval amphibians and keratinized skin of post-metamorphic anurans (Berger et al. 1998). Fungal zoospores attach to keratin, reducing the ability of a host to osmoregulate, which may lead to respiratory arrest followed by death (Voyles et al. 2009; Vredenburg et al. 2010). It can only infect and persist on hosts when environmental conditions, primarily temperature and humidity are favorable. The optimal condition for survival and reproduction of Bd

occurs in cool and moist environments, and it can survive without a host in moist

substrates for up to three months (Johnson and Speare 2005). In in vitro experiments, the

optimal temperature for Bd is between 17°C and 25°C, and zoospore death occurs above

28°C (Piotrowski et al. 2004). With ongoing global climate change, tropical forest

environments will likely continue to support Bd persistence and spread (Pounds et al.

2006; Laurance 2008; Rohr et al. 2011). At a local scale, Bd spreads via amphibian host

interactions, and is dispersed through water, soil, migrating birds, humans, and other

animals associated to water bodies (Johnson and Speare 2005; Morgan et al. 2007;

Kilpatrick et al. 2010). Currently, Bd has been detected in >500 amphibian species,

mostly in tropical and subtropical regions (Olson et al. 2013), and it has been

documented to cause extinctions and population declines in >350 species (Lips et al.

2006, Blaustein et al. 2012).

The conservation of amphibians is especially urgent in the Caribbean, where more

than 80% of amphibians are threatened in the Dominican Republic, Cuba, Jamaica, and

Haiti from habitat loss and diseases (Stuart et al. 2004; Hedges 2011). In Jamaica, of the

21 endemic frog species within the genera Eleutherodactylus (17 species) and Osteopilus

(4 species), 14 have been declared endangered and have very small distributional ranges

on the island (Hedges and Diaz 2010; IUCN 2015). Recent research on Jamaican frogs,

using machine learning algorithms, Holmes et al. (2014) predicts that increased temperatures in Jamaica will put frogs occupying high-precipitation habitat at greater risk of Bd infections by expanding the optimal period for Bd spread. Limited information is

available on these endemic frogs, which poses a challenge for current conservation efforts (Young et al. 2001; Hedges 2006; Wilson 2011). The recent discovery of Bd in

Jamaica and predicted Bd pattern risks for these frogs (Holmes et al. 2012; Holmes et al.

2014) and ongoing deforestation (Evelyn and Camirand 2003) raises the importance of understanding how habitat loss and Bd interact to affect frogs on the island.

While habitat loss and chytridiomycosis probably pose the greatest threat to tropical amphibians, previous research suggests that, paradoxically, habitat loss may reduce the spread of the disease (Becker and Zamudio 2011; Becker et al. 2012, 2015;

Murrieta-Galindo et al. 2014; Saenz et al. 2015; Beyer et at. 2015). Becker and Zamudio

(2011) showed that Bd is more prevalent with higher infection intensities in pristine tropical forests compared to disturbed habitats. They attributed this effect to abiotic and biotic factors, as pristine forests have greater shade and humidity, which favor fungal growth, as well as higher host species richness and diversity, which could provide a larger host reservoir (Kapos 1989, Cushman 2006, Becker et al. 2007). Van Sluys and

Hero (2009) suggest that less forested areas could provide refugia from Bd due to habitat influences on this pathogen. Murrieta-Galindo et al. (2014) found that Bd prevalence and infection intensity was higher in cloud forests and lower in coffee farms.

In the tropics, forest loss and degradation are driven largely by farming, such as for palm, cacao, and coffee plantations (Perfecto 1996; Casson 2000; Angelsen and

Kaimowitz 2001). Coffee is a primary export crop in Jamaica, and this farming has caused habitat loss and degradation of native forests (Palmer 1968; Eyre 1987; Tole

2001; Chai et al. 2009). Coffee farms are cultivated in several different ways, which falls

on a continuum of habitat modification from the structure of tropical forest habitats they

replace (Moguel and Toledo 1999; Rappole et al. 2003). This variation provides a convenient gradient of canopy cover for ecological studies. Previous research on other species has shown declines in abundance and richness along this gradient (Estrada et al.

1993; Greenberg et al. 1997; Johnson 2000; Cassano et al. 2009); to date, some research

has been conducted on frogs in coffee (Pineda et al. 2005; Faria et al. 2007;

Gurushankara et al. 2007; Hoyos-Hoyos et al. 2012; Rathod and Rathod 2013; Murrieta-

Galindo et al. 2014), but none on Caribbean frogs in coffee.

It is critical to note that agrochemicals, specifically fungicides, are sometimes used in coffee to combat fungal diseases (Silva et al. 2006). Frogs living in these farms are likely exposed to fungicides in addition to other chemical spraying. Fungicides are commonly used across coffee farms worldwide to eliminate ‘coffee leaf rust’ (Hemileia vastatrix), a parasitic fungus to coffee plants (Avelino et al. 2012) found throughout

Jamaica although it may be more prevalent in farms with heavy shade cover (López-

Bravo et al. 2012). Interestingly, the preferred climate of coffee rust is similar to that of

Bd, that being cool and moist. Ironically, previous research has shown that direct and indirect fungicide treatments on Bd infected frogs can reduce/or clear fungal growth and enhance development of frogs (Garner et al. 2009; Martel et al. 2011; Hanlon et al. 2012;

Bosch et al 2015; Hardy et al. 2015). Thus, fungicide spraying in coffee farms may be eliminating Bd from frogs and their environment. However, their highly sensitive skin

(Lips 1998) makes frogs susceptible to environmental contaminants (Pogorzelska et al.

1981; Harris et al. 1998; Schiesari et al. 2007), so fungicide and other chemical spraying could be weakening or eliminating frogs from farms all together. Indeed, Murrieta-

Galindo et al. (2014) reported low Bd prevalence and infection intensity but encountered few frogs in heavily shaded coffee farms dosed with substantial amounts of agrochemicals (fungicide use not specified). Rathod and Rathod (2013) also reported lower species richness and abundance in coffee farms that use agrochemicals. Additional research is needed to understand how to balance potential positive and negative effects of fungicide use on tropical frogs.

In this study, I examined Bd prevalence and infection intensity on two widespread frogs in coffee farms in Jamaica. Prior to exploring any habitat and Bd interactions, I investigated potential influence of fungicide use on Bd prevalence and infection intensity at the farm scale. Subsequently, I extended Becker and Zamidio’s (2011) work by examining effects of habitat modification on prevalence and infection intensity of Bd on frogs using vegetation cover measured at two spatial scales over a gradient of shade cover in Jamaican coffee farms. Specifically, I tested the predictions that the prevalence and intensity of Bd on frogs will both increase with percent forest cover surrounding farms and with local shade cover within farms.

METHODS

Study System

This research was conducted on 27 coffee farms on the island of Jamaica: nine on the western side, four in the central region, and fourteen on the eastern side of the country

(Fig.1). Farms had coffee trees planted in rows, with variable amounts of shade tree cover (see Table 1 in Results for details of vegetation cover). Some farms were shaded by just banana or plantain trees while others included a mosaic of banana and plantain, and other fruit trees (Persea americana, Citurs sp., Mangifera indica, Artocarpus altillis,

Chrysophyllum cainito, and Blighia sapida), and timber trees (Cedrela odorata,

Swieteina mahagoni, Inga vera, Samanea saman, Terminalia latifolia, and Ficus sp.).

Fungicides, under various trade names in Jamaica, such as Topsin, Tilt and Champion are manually sprayed to coffee bushes to combat coffee rust. A common insecticide sprayed in these farms was Karate.

Figure 1. Locations of 27 coffee farms used for this study of Bd on frogs in Jamaica, June-July 2013.

Study Species

In Jamaica there are 21 endemic amphibian species in the genera Eleutherodactylus and

Osteopilus (possibly 22 counting an undescribed Osteopilus sp., Wilson 2011.). Seven are “critically endangered” (E. alticola, E. cavernicola, E. fuscus, E. griphus, E. junori,

E. orcutti [possibly extinct], and E. sisyphodemus), 8 are “endangered” (E. andrewsi, E. grabhami, E. jamaicensis, E. luteolus, E. nubicola, O. crucialis, O. marianae, and O. wilderi), 2 are “near threatened” (E. glaucoreius and E. pantoni), 2 are “vulnerable” (E. cundalli and E. pentasyringos), and 2 are of “least concern” (E. gossei and O. brunneus)

(IUCN 2015). Members of Jamaican Eleutherodactylus spp. are primarily land dwellers and they deposit eggs terrestrially and undergo direct development. Jamaican Osteopilus sp. are canopy dwellers, they deposit eggs in tank bromeliads and undergo metamorphosis. There are four introduced amphibian species on the island: Rhinella marina, Eleutherodactylus johnstonei, Eleutherodactylus planirostris, and Lithobates catesbianus; E. johnstonei is widespread and common in human disturbed areas.

Previous research by Holmes et al. (2012) on Bd prevalence in Jamaican forests, showed relatively low prevalence on species overall. Of particular interest was her prevalence figures for E. johnstonei (23%) and E. gossei (16%), the most commonly captured species in my study.

Transects

This study was conducted from 1 June - 19 July 2013. To locate frogs at each farm, I

used nocturnal visual searches on 4 to 6 50 m transects, depending on farm size (Pearman

et al. 1995). At each site, the first transect starting point was randomly located 5 m in

from and oriented perpendicular to the farm’s perimeter, with the other transects located

systematically 10 m from, and parallel to, the first. Vegetation data were collected during

daylight (10:00-14:00) and transects were surveyed from night into early morning (19:00-

01:00) when frog activity increased and choruses were loudest (Pearman et al. 1995;

Rocha et al. 2000; Fogarty and Vilella 2001).

Frog Captures and Bd Sampling

There were two surveyors on each transect to maximize capture success, one person in

front of the other. Both observers performed visual encounter surveys (VES) with

headlamps at the highest beam setting, to widen spotting ability (Corben and Fellers

2001). All frogs detected within roughly one meter of each transect side were captured, placed into labeled plastic bags, weighed and measured (snout-vent-length SVL), then identified to species.

Each captured frog was swabbed ten times on the bottom of each foot, under each thigh, along the stomach, and on the seat patch (Brem et al. 2007). All gear was thoroughly disinfected between each farm with a 10% bleach solution, followed by air drying. Fieldwork was conducted in high heat and humidity; therefore swab samples

were stored in self-sealing vials with 70% ethanol to reduce possible denaturing of DNA

(Brem 2007) and sent to Jacob Kerby’s Laboratory at University of South Dakota for

quantitative PCR analysis (qPCR). Current Bd analyses using qPCR are based upon

zoospore equivalents if the strain has been well studied or genomic equivalents if the

strains are undescribed (Longo 2013). Given that Bd strain or strains on Jamaica are

currently uncharacterized, numbers of genomic equivalents were used to quantify

infection intensity. For additional details on the qPCR methods, see Appendix D.

Habitat Variables

Farm Scale

Land-cover information was acquired from ESA CCI with 30 m pixel resolution. Using

ArcGIS 10.3, percentage forest cover was calculated within 600 meter diameter buffers

on each farm. ESA CCI defines forest cover as, "Lands covered with trees, with

vegetation cover over 30%, including deciduous and coniferous forests, and sparse

woodland with cover 10-30%, etc.”. Forest cover ranged from 10% to 100% among the

27 farms in the study. Farm elevation collected on ground was confirmed by inputting

UTMs into Google Earth Pro version 7.1.5.1557, and ranged from 150 to 1200 meters.

To determine fungicide use in farms, farmers were asked for details of fungicide

application on their farms during the year 2013 (~6 months leading up to field data

collection). Response details varied considerably, so responses were simplified to either

“yes” or “no”, applied in 2013.

Within-Farm Transect Scale

Habitat variables were measured on each farm at 18 locations distributed systematically among transects. Habitat locations were distributed at three intervals (0, 25, 50 meters) on farms containing six transects, while farms with four or five transects had three (0, 25,

50 meters) or four (0, 25, 35, 50 meters) intervals (summing to 18 locations on each farm). Variables recorded at all habitat locations: leaf litter depth (mm) and percent (0-

100%) shade cover at 3 different vegetation strata. I used Schlaepfer and Gavin’s (2000) gradient methods to define cover strata:

• “Ground” = understory vegetation < 2 meters from soil

• “Coffee” = coffee shrub cover from 2-5 meters

• “Canopy” = tree cover > 5 meters

At each point, field workers measured ground and coffee shrub cover (%, visually estimated within a 5 m radius), and canopy cover (%, estimated with a spherical densiometer).

Statistical Analysis

I analyzed infection data from E. johnstonei and E. gossei separately for several reasons.

First, E. johnstonei tended to be captured on coffee shrubs while E. gossei was most commonly captured on the ground, which might cause differences in Bd prevalence, intensity, and their responses to cover (Becker et al. 2015). Second, initial investigation showed that Bd prevalence and intensity differed between the species, being somewhat higher in E. johnstonei (see Results). Third, previous research has shown that prevalence and intensity can vary among different Bd strains (Berger et al. 2005; Martel et al. 2013;

Rosenblum et al. 2013) and amphibian species (Woodham et al. 2007; Gervasi et al.

2014; 2013), which could confound results of habitat analyses if capture rates of species varied between farms with different vegetation cover. I only included data in the analysis from transects or farms from which I captured and sampled at least five frogs of a given species. For the farm scale analyses, all frogs caught on transects as well as frogs caught incidentally on a farm were included in the analysis because they all experienced the same vegetation at this scale. However, for the within farm scale, only frogs captured along transects were used in the analysis because local canopy was variable and only measured along transects. Nine farms had E. johnstonei alone, one farm had just E. gossei, while 11 farms had both species, so reported frog sample sizes vary among analyses depending on species and scale. My focus was on obtaining a representative sample of frogs for Bd analyses, therefore I do not here report extensive analyses on factors associated with species abundance or richness. However, the overwhelmingly

most common species captured were E. johnstonei and E. gossei (82% of all captures),

captured on 21 farms (see Results).

Farm Scale

To investigate effects of forest cover, fungicide use, and elevation on Bd prevalence and

infection intensity, I used generalized linear models (GLM) with farm as the unit of

analysis. I used the predictor variables fungicide use (yes or no), percent forest cover

within 600 m, and elevation (m). Response variables included the mean of Bd prevalence

and infection intensity on both species per farm for all farms in which the target species

were found (20 farms with E. johnstonei and 12 farms with E. gossei). I calculated Bd

prevalence as the number of Bd infected frogs per farm divided by the total frogs sampled

per farm (1 = Bd positive frog, 0 = Bd negative frog). To assess Bd infection intensity at the farm scale, I used only samples classified as Bd positive, which removed four farms from the analysis of E. gossei (8 farms left) but none for E. johnstonei (still 20 farms).

Infection intensity ranged from 1 to 225 genomic equivalents per farm, therefore the

Poisson error was selected in these GLMs. For both response variables, I used the statistical software R (R Development Core Team 2014) to build a candidate set of 8 models using all combinations of habitat predictor variables: elevation and percent forest cover all as fixed effects. I compared the models using Akaike's Information Criterion for small samples (AICc) in order to select the best models and assessed model fit by computing McFadden’s R2.

Within-Farm Transect Scale

ANOVA was performed to determine if the relationship between vegetation structures varied among transects. These exploratory statistics revealed that vegetation data differed significantly among transects within farms, owing to different shade management, pruning histories, or natural variation in vegetative growth among areas within a farm. Therefore, I used transect as the unit of analysis among 20 farms,

averaging vegetation data (predictors) and Bd data for each species (response) measured

along a transect. This yielded 64 data points from 19 farms for E. johnstonei and 36 data

points from 12 farms for E. gossei. Farm was included as a random effect in all models

to account for variation between farms not captured by the measured vegetation

variables.

I used generalized linear mixed models (GLMM) with error structure as described

above for the farm scale analyses. For both Bd prevalence and intensity variables, I built

a candidate set of 15 models (Appendix F.5 – F.8) using all biologically relevant additive

combinations of habitat predictor variables: three gradients of percent cover (ground,

coffee and canopy) and leaf litter depth all as fixed effects, with farm as a random effect.

I compared the models using Akaike's Information Criterion for small samples (AICc) in

order to select the best models (Anderson 2008). I then assessed model fit by computing

both marginal (containing only fixed effects) and conditional model (containing both

fixed and random effects) R2 (Nakagawa and Schielzeth 2013) using the MuMIn package

in R (Barton 2015). While the marginal R2 represents the amount of variance explained

by fixed effects, the conditional R2 can be interpreted as the variance explained by the entire model, with the difference between the two reflecting how much variability is in

random effects (Nakagawa and Schielzeth 2013). Lastly, I used linear regression to

examine associations of habitat variables among farms, and two-sample t-tests to

compare habitat variables of farms that were or were not treated with fungicide.

RESULTS

The 27 coffee farms in this study provided a range of ground, coffee, and shade tree

canopy cover. The majority of farms had 25-75 percent vegetative ground cover. Most farms had between 0-50 percent coffee cover (Table 1). Shade canopy cover decreased

2 with increasing elevation (F1,26 = 13.52, P < 0.01, R = 0.35), and leaf litter increased

2 with increasing shade cover (F1,26 = 5.55, P = 0.03, R = 0.18), but there were otherwise no significant correlations among habitat variables. Fungicide use occurred on 17 of 27 farms, although details on the amount of fungicide used by farmers during the timeframe

of my study are not known. The most common fungicide used was Champion (Copper

hydroxide as active ingredient). Farms with applied fungicide had lower ground

vegetation cover (t26 = 2.43, P = 0.02) than those without, but there were otherwise no differences in habitat variables among farms with and without fungicide application.

I sampled 836 frogs among 27 farms, including seven native species: E. cundalli,

E. fuscus (critically endangered), E. gossei, E. glaucoreius, E. pantoni, O. brunneus,

O.wilderi, and three non-native species: E. johnstonei, E. planirostris, R.marina . Not all species occurred at all survey locations due to species’ ranges. Of the 21 farms used in this analysis, the most common species included E. johnstonei (non-native) on 20 farms, and E. gossei (native) on 12 farms. In total I detected 471 E. johnstonei (52% Bd positive) and 213 E. gossei (37% Bd positive) among these farms. Genomic equivalents for Bd infection intensity ranged from 1 to 5715 for E. johnstonei (averaged 102 genomic

equivalents) and 1 to 486 for E. gossei (averaged 28 genomic equivalents). Frogs

sampled did not show any evidence of clinical chytridiomycosis, such as lethargy, skin

lesions, or sloughing.

Farm Scale

Prevalence: E. johnstonei

At the farm scale, Bd prevalence on E. johnstonei was positively associated with

elevation. The two top models (< 2 ΔAICc, cumulative weight = 0.72) describing Bd prevalence on E. johnstonei included the variables elevation and forest cover (Table 2, full candidate set in Appendix F). The null model ranked fifth in the model set and received very little support. The top model AIC received more than half of the weight in the model set (AICc weight = 0.51) and suggested Bd prevalence was positively associated with elevation with McFadden’s R2 of 0.45 and confidence intervals for the

coefficient did not overlap zero (0.001 to 0.006), indicating strong support for this effect.

The second best model included a positive correlation to elevation and negative to forest cover, but both coefficient’s confidence intervals overlapped zero (-0.001 to 0.007; -0.05 to 0.02) and received less support as a predictor model in the model set (AICc weight =

0.21). Models including fungicide use received little support in the candidate set (the sum of all AICc weights for models with fungicide use equaled 0.24).

Prevalence: E. gossei

The prevalence of Bd on E. gossei was not well predicted by any variables at the farm

scale in this study. The two top models (< 2 ΔAICc, cumulative weight = 0.67)

describing Bd prevalence on E. gossei included the variable elevation (Table 2, full candidate set in Appendix F), but the null model ranked first in the model set and received most of the support (AICc weight = 0.37). The second best model included

elevation but only received minor support (AICc weight = 0.3). Estimated coefficients consistently overlapped zero, indicating weak support for effects of these variables.

There was very little evidence for an effect of fungicide use or forest cover.

Infection Intensity: E. johnstonei

Infection intensity of Bd on E. johnstonei was only weakly associated with forest cover at

the farm scale. The top model (AICc weight = 0.34) describing Bd infection intensity on

E. johnstonei included a negative association with forest cover (Table 2), but confidence

intervals for the coefficients overlapped zero (-0.017 to 0.002) and with an R2 of 0.2 in

the top model, there was weak support for this effect. In addition to confidence intervals

suggesting weak support for this top model, the null model placed second best in the set

(AICc weight = 0.22). There was very little evidence for an effect of fungicide use.

Infection Intensity: E. gossei

The infection intensity of Bd on E. gossei was not well predicted by any variables at the

farm scale in this study. The top model (AICc weight = 0.63) describing Bd infection

intensity on E. gossei was the null model (Table 2). The second best model, although

with little weight (AICc weight = 0.13) included only elevation. There was little

evidence for effects of forest cover or fungicide use.

Within-Farm Transect Scale

Prevalence: E. johnstonei

Within farms, Bd prevalence on E. johnstonei was not strongly associated with any

habitat variables. The seven top models (< 2 ΔAICc, cumulative weight = 0.70)

describing Bd prevalence on E. johnstonei included the variables canopy, coffee, ground

cover and leaf litter (Table 3); however, the null model ranked second in model set, so the model set received little support. The top model ranked less than one AICc unit above

the null model, but suggested Bd prevalence was negatively associated with canopy

cover, though confidence intervals overlapped zero (-0.08 to 0.007), indicating weak

2 2 support for this effect. The R GLMMm and R GLMMc for the top model were 0.11 and

0.62 respectively, suggesting that inclusion of the random effect variable, farm, provides a better fit than the model with only fixed effects (Nakagawa and Schielzeth 2013).

Prevalence: E. gossei

The prevalence of Bd on E. gossei was also not well predicted by habitat variables within

a farm. The four top models (< 2 ΔAICc, cumulative weight = 0.53) describing Bd

prevalence on E. gossei included the variables canopy and coffee cover (Table 3), but the

null model ranked first in the model set and received most of the support. The second

best model included canopy cover, the third included coffee cover, and the fourth model

included both canopy and coffee cover; all with little support. Estimated coefficients consistently overlapped zero, indicating weak support for effects of these variables.

Infection Intensity: E. johnstonei

Within farms, infection intensity of Bd on E. johnstonei was positively associated with canopy cover. The seven top models (< 2 ΔAICc, cumulative weight = 0.71) describing

Bd infection intensity on E. johnstonei included the variables canopy, coffee cover, ground cover, and leaf litter depth (Table 3). The null model ranked seventh in the model set and received little support. The top model included canopy cover and confidence intervals for the coefficient did not overlap zero (0.001 to 0.01) indicating support for

2 2 these effects. The R GLMMm and R GLMMc for the top model were 0.01 and 0.15, respectively. The second, third and fourth models included leaf litter paired with canopy, coffee or ground cover.

Infection Intensity: E. gossei

The infection intensity of Bd on E. gossei was negatively associated with leaf litter depth and weakly associated with other habitat variables. The four top models (< 2 ΔAICc, cumulative weight = 0.68) describing Bd infection intensity on E. gossei included the variables canopy, coffee, ground cover and litter (Table 3). The null model ranked fourth in model set and received little support. The top ranked model included the variables canopy cover and leaf litter and suggested a negative association of both to Bd infection intensity. Confidence intervals for the variable leaf litter did not overlap zero (-0.65 to

-0.09), indicating support for this effect. However, confidence intervals for the variable

2 2 canopy did overlap zero (-0.03 to 0.01). The R GLMMm and R GLMMc for the top

model were 0.06 and 0.26, respectively. The second model included the variables coffee cover and leaf litter, both negatively associated to Bd infection intensity where only confidence intervals for leaf litter did not overlap zero. The top two models shared equivalent support (AICc weights = 21) and both included leaf litter paired with canopy or coffee cover. Therefore, model averaging was performed on these two models (Table

4). Model averaging results did not alter the sign indication of effects on variables leaf litter, coffee cover or canopy cover. The third top model included leaf litter and ground cover but indicated weak support.

Table 1: Distribution of 27 farms among vegetation cover classes, Jamaica, June-July 2013.

Cover Class Percent Cover 0-25% 26-50% 51-75% 76-100% Ground 7 8 11 1 Coffee 15 9 3 0 Canopy 4 9 11 3

Table 2: Top models (ΔAICc < 2) for the prevalence and infection intensity of Bd on Eleutherodactylus johnstonei and Eleutherodactylus gossei analyzed at the farm scale in relation to forest cover, elevation and fungicide use in coffee farms in Jamaica, June-July 2013. For full candidate model sets, see Appendix F. Analyses were generalized linear models using a binomial error structure (prevalence) or a Poisson error structure (for intensity); see Methods for details. Variables in bold had coefficient confidence intervals that did not overlap zero, and superscripts indicate direction of effects. Response Cumulative Species Model k AICc ΔAICc wi LL variable wi Prevalence E. johnstonei elevation+ 2 24.32 0.00 0.51 0.51 -9.81 elevation + forest cover 3 26.15 1.83 0.21 0.72 -9.33 E. gossei null 1 19.02 0.00 0.37 0.37 -8.31 elevation 2 19.44 0.42 0.30 0.67 -7.05 Intensity E. johnstonei forest cover 2 63.47 0.00 0.34 0.34 -29.38 null 1 64.39 0.92 0.22 0.56 -31.08 E. gossei null 1 31.07 0.00 0.63 0.63 -14.20

Models are ranked based on Akaike's Information Criterion (AICc), ∆AICc, and Akaike weights (wi). Akaike's Information Criterion is based on 2 x log likelihood (LL) and the number of parameters (K) in the model. Cumulative model weights (Cumulative wi) are also reported.

Table 3: Top models (ΔAICc < 2) for the prevalence and infection intensity of Bd on Eleutherodactylus johnstonei and Eleutherodactylus gossei analyzed at the within-farm transect scale in relation to shade cover, coffee cover, ground cover, and leaf litter depth on coffee farms in Jamaica, June-July 2013. For full candidate model sets, see Appendix F. Analyses were generalized linear models using a binomial error structure (prevalence) or a Poisson error structure (for intensity); see Methods for details. Variables in bold had coefficient confidence intervals that did not overlap zero, and the superscripts indicate directions of effects. Response Cumulative Species Model k AICc ΔAICc wi LL Variable wi Prevalance E. johnstonei canopy + litter 6 78.98 0.00 0.16 0.16 -35.15 Null 7 79.35 0.37 0.13 0.30 -37.58 canopy 5 79.62 0.64 0.12 0.41 -36.61 ground 6 80.46 1.48 0.08 0.49 -37.03 canopy + ground + litter 6 80.59 1.62 0.07 0.56 -34.78 coffee 6 80.63 1.65 0.07 0.63 -37.11 ground + litter 5 80.70 1.73 0.07 0.70 -36.01 E. gossei null 2 53.27 0.00 0.23 0.23 -24.45 canopy 3 54.49 1.23 0.12 0.35 -23.87 coffee 3 55.11 1.84 0.09 0.44 -24.18 canopy + coffee 4 55.15 1.88 0.09 0.53 -22.93 Intensity E. johnstonei canopy+ 3 209.29 0.00 0.16 0.16 -101.37 canopy + litter 4 209.67 0.38 0.13 0.29 -100.37 coffee + litter 4 210.21 0.92 0.10 0.38 -100.64 ground + litter 4 210.30 1.01 0.09 0.48 -100.68 ground 3 210.44 1.15 0.09 0.57 -101.95 canopy + ground 4 210.69 1.40 0.08 0.64 -100.88 Null 2 210.91 1.62 0.07 0.71 -103.32

Response Cumulative Species Model k AICc ΔAICc wi LL Variable wi E. gossei canopy + litter- 4 100.01 0.00 0.21 0.21 -44.67 coffee + litter 4 100.06 0.04 0.21 0.42 -44.70 ground+ litter 4 100.79 0.77 0.14 0.56 -45.06 Null 2 101.24 1.23 0.11 0.68 -48.27

intercept 2.4 ± 0.76 0.91 3.89* 2.18 ± 0.63 0.85 3.53* canopy -0.01 ± 0.01 -0.03 0.01 -0.01 ± 0.11 -0.03 0.01 coffee NA NA NA -0.01 ± 0.01 -0.03 0.01 litter -0.37 ± 0.14 -0.65 -0.09* -0.35 ± 0.14 -0.65 -0.05*

Farm E. johnstonei elevation+ 0.45 (forest cover-) 0.2

E. gossei _ _ _ _

2 2 2 2 R GLMMm R GLMMc R GLMMm R GLMMc Within- E. (canopy-), farm 0.11 0.62 canopy+ 0.01 0.15 johnstonei (litter+)

E. gossei _ _ _ litter-, (canopy-) 0.06 0.26

DISCUSSION

My results provide relatively little support for Becker and Zumidio’s (2011) hypothesis that Bd is positively associated with forest canopy habitat for Eleutherodactylus frogs in

Jamaica coffee farms. Among tests at two spatial scales (farm and within-farm), on two response variables (Bd prevalence and infection intensity), for two species (E. johnstonei and E. gossei), Bd was strongly associated with only canopy cover at the within-farm scale for E. johnstonei, and only for infection intensity (Table 4). At the farm scale, the prevalence of Bd on E. johnstonei was positively associated with elevation, and at the within-farm scale the infection intensity of Bd on E. gossei was negatively associated with leaf litter depth. The use of fungicide by farmers did not show any consistent association with Bd prevalence or infection intensity.

Although results were overall inconsistent, the effect of canopy cover on Bd infection intensity on E. johnstonei is noteworthy. This effect was strong, with a coefficient suggesting that for every 10% increase in canopy cover, Bd infection intensity increases by approximately three genomic equivalents. Becker and Zamudio (2011) suggested two mechanisms for a relationship between Bd and forest cover. First, the loss of canopy cover may create sub-optimal microclimate conditions for Bd resulting in a positive association between canopy cover and Bd. In addition to creating less optimal

Bd habitat, the warmer temperatures can aid infected frogs by preventing, reducing or eliminating fungal growth (Murphy et al. 2011; Rowley and Alford 2013). Bd requires cool and humid environments (i.e., pathogen hosts and soil) to flourish and is presumably

less widespread on substrates in warmer drier habitats and during droughts (Johnson et al.

2003; Piotrowski et al. 2004). Microclimate data were not incorporated into my study because I surveyed just one day per farm; however studies have shown that heavily shaded farms (e.g. coffee, cacao, and tea) are cooler than those without shade (Rice and

S.M.B Center 1996; Perfecto et al. 1996; Beer 1987; Lin et al. 2008; De Souza et al.

2012) making warmer farms less optimal for Bd growth. Based on my results, this mechanism may be operating for E. johnstonei in this study system.

The second mechanism suggested by Becker and Zamudio, and other colleagues

(Becker and Zumidio 2011, Becker et al. 2012, 2015) relates to disturbed habitats with lower host density and richness; in other words, canopy habitat disturbance lowers species richness and community complexity; thus indirectly reducing Bd infections.

Becker and Zamudio (2011) suggested that higher diversity and host density might amplify Bd infections (Skerratt et al. 2007; Fisher et al. 2009). The host density hypothesis was further supported in Becker et al. (2012). Becker et al. (2015) also found that Bd infection intensity was elevated when host density was high in temperate forests

(low species richness) with closed canopies when after the variable microclimate was held constant. It has been shown in previous studies that higher infection intensity will likely occur in denser host populations by promoting continuous host reinfections (Briggs et al. 2010). Although my study was not designed to rigorously quantify abundance and species richness, exploratory statistics suggest that frog encounter rate was positively associated with Bd infection intensity (J. Brown, unpubl.), suggesting the host density mechanism is potentially operating in this study system and merits further study. In

tropical forests, Becker et al. (2015) found lower Bd infection intensity in pristine forests

with high species richness, holding the variable microclimate constant. They suggested that their result was an indicator of host diversity reducing Bd, supporting the ‘dilution effect’. In other words, higher host richness reduced pathogen intensity (LoGiudice et al.

2003; Keesing et al. 2006). In more recent studies, Bd systems exhibited a similar dilution effect, showing an increased number of amphibian species in systems with reduced Bd infection intensity (Searle et al. 2011; Venesky et al. 2014a; Han et al. 2015).

This ‘dilution effect’ is not supported by my results, perhaps due to the general low species richness found in my study system.

While the microclimate, host density, and richness mechanisms may explain higher Bd infection intensity with increased canopy cover on E. johnstonei, it remains unclear why the same pattern did not emerge for E. gossei. Conflicting results of infection intensity are difficult to explain, but differences in life history strategies of the species may offer some insight. It appeared that E. johnstonei displayed more arboreal tendencies (captured most often in coffee and banana shrub layer) than E. gossei

(captured most often on the ground). Becker et al. 2015 suggest that frogs displaying arboreal lifestyles might avoid Bd exposure and that those exposed lack evolved resistance compared to non-arboreal species, which could result in higher infection intensities. The prevalence of Bd on E. johstonei was almost 50 percent higher than on E.

gossei (52% versus 37%, respectively), but mean intensity was over three times higher on

E. johnstonei than on E. gossei (102 and 28 genomic equivalents per infected frog,

respectively). Some species, particularly those with greater rates of interspecific contact,

can evolve resistance or tolerance to Bd (Ellison et al., 2014; Gervasi et al., 2014;

McMahon et al., 2014; Venesky et al., 2014b). Perhaps a lower immune response in the

more arboreal E. johnstonei may be contributing to a stronger effect of canopy on Bd. In contrast, as a native ground dweller, E. gossei may have greater resistance or tolerance

since they are interacting with frogs where exposure to Bd is higher (soil and leaf litter),

thus explaining why lower infection intensity was possibly exhibited in shade farms.

Leaf litter depth is potentially an important variable associated negatively with

variation in Bd intensity on E. gossei (Table 4). Being a parasitic fungus to amphibians,

Bd cannot complete its life cycle in leaf litter; however, leaf litter might provide habitat

for Bd dormancy. Johnson and Speare (2003) speculated that Bd can survive without a

host for three to six weeks in moist soil and on aquatic plant debris. A previous study

suggested that leaf litter did not directly affect Bd infection intensity, but it provides a good indication of shade which might be the true driver of higher intensities (Raffel et al.

2010). In my study, I suggest that more leaf litter is just an indication of more shade cover.

My results suggest that elevation was an important predictor for Bd prevalence on

E. johnstonei at the farm scale. For the non-native E. johnstonei, Bd prevalence was positively associated to higher elevation and model coefficients did not overlap zero suggesting a strong effect. Bd prevalence is often lower at lower elevation sites, in tropical regions, primarily due to less favorable climate conditions for Bd growth

(Zumbado-Ulate et al. 2014; Sapsford et al. 2013; Brem and Lips 2008). Becker and

Zamudio (2011) found that Bd occurrence was higher in higher elevation sites. They

indicated that more deforestation occurred in lowland areas rather than in inaccessible

highlands, where natural vegetation persists; confounding the effects of elevation and

canopy loss on Bd occurrence. In other words, the effect of elevation on Bd was indirect

through habitat loss. My study sites varied from 150 meters to 1200 meters in elevation,

but all of these landscapes were heavily disturbed. Therefore, Becker and Zamudio’s

(2011) ‘nonrandom habitat loss’ theory is not likely confounding the effect of elevation in my analysis. In my results, a potential explanation for a positive correlation of Bd

prevalence on E. johnstonei and elevation is that climatic conditions were more favorable

to Bd transmission at higher elevation farms.

Interestingly, fungicide did not appear to have a strong effect on Bd prevalence or infection intensity for either frog species. This is inconsistent with other reports, especially those from studies of frogs in captive settings (Garner et al. 2009; Martel et al.

2011; Hanlon et al. 2012; Hardy et al. 2015). Fungicides on the farms in my study were applied to the environment, but not directly to infected frogs. Nonetheless, combined antifungal treatments and environmental fungicide application has been found to successfully eliminate Bd in a field setting; over a five year study, Bd prevalence and infection intensity dropped to zero in a single host system (Bosch et al. 2015). The fungicidal compound in my study system did not match those used in previous studies

(copper hydroxide vs. potassium peroxymonosulfate), perhaps explaining why my results were different. Although several different fungicides have been found to benefit frogs from Bd infections, the effects of fungicide types used in Jamaican farms on Bd has yet to be reported. Data was not available on timing of spraying and quantities used on farms in

my study, so were not able to assess differing levels of fungicide use. Importantly,

insecticides, potentially harmful to frogs, were applied in tandem with fungicides at several farms, but the effects of these other agrochemicals on frogs are unknown in my

system.

Data collection was conducted during a typical rainy season in Jamaica; however,

Jamaica was experiencing a dry spell during my study (Appendix E). Microclimate in these coffee farms was likely less ideal for Bd survival; if so, Bd loading of the environment may have been atypically low, and thus might have resulted in lower infection intensity on frogs. Interestingly, previous research suggested that Bd infection intensity is high on Eleutherodactylus frogs in intact forests during driest months but low during rainy seasons (Longo et al. 2010; Holmes et al. 2014). This is thought to occur during rains when forest temperatures increase along with humidity thus presenting less ideal microclimates for Bd. This mechanism could be occurring in coffee farms but with opposing results. In contrast to forest temperatures being cooler during droughts, coffee farms are much warmer, because they lack the canopy structure mosaics of forests that retain cooler temperatures (Kumagai et al. 2001; Madigosky 2004). During nighttime when frogs are most active, coffee farms (especially more shaded farms) are cooler and more humid than in the daytime (Rice 1996); thus potentially preventing desiccation of

Bd on frogs and allowing more fungal transmission. Frogs living in coffee farms may avoid dehydration during daytime, when the air is warmer and less humid, by residing in moist pockets associated with bromeliads, banana trees, and under cover objects. Given that both adult and juvenile Eleutherodactylus spp. exhibit site fidelity (Ovaska 1992),

and prior Bd infection can influence rates of host reinfection (Raffel et al. 2010), these refuge pockets could potentially promote high rates of Bd reinfection on E. johnstonei.

Conclusions

In summary, infection intensity for E. johnstonei was higher at farms with heavy canopy cover and lower for E. gossei at farms with less leaf litter. Prevalence was greater in higher elevation for non-native E. johnstonei. Although the analyses identified variables that were associated with variation in Bd prevalence and infection intensity, even the best models failed to account for the majority of the variation observed. As a result, there remain other confounding variables (e.g., climate, farming practices, Bd strain epidemiology, frog skin microbiota, etc.) that might be more important in driving the severity and spread of Bd on Jamaican frogs in coffee farms.

Although Holmes et al. (2012) prevalence figures for E. johnstonei (23%) and E. gossei (16%) were lower than in my study (52% and 37%, respectively), differences between these results might be affected by sample sizes. Holmes et al. (2012) sampled

43 E. johnstonei among ten study sites and 124 E. gossei far fewer than I am reporting

(roughly 10% and 50% of my samples sizes, respectively). Holmes et al. (2014) predicts that Jamaican frogs inhabiting high-precipitation forest habitats are at a great risk increasing infection rates as global warming raises local forest temperatures.

My study incorporated only two months of surveys (one day per farm), so additional studies of longer duration might capture seasonal differences of Bd epidemiology in Jamaica’s coffee farms. Variation in results of this study and Becker

and Zamudio (2011) potentially arose from differences in species life history strategies,

tolerance to Bd, chemical use in farms, or microclimate. Becker and Zamudio (2011)

examined Bd on frog species associated with watersheds, whereas this study only

included direct development frogs that are not reliant of water sources for reproduction.

Bd is highly transmittable via watersheds increasing risks for semi-aquatic species

(Rowley and Alford 2007; Kilpatrick et al. 2010; Bancroft et al. 2011, Hauselberger and

Alford 2012; Olson et al. 2013). Therefore, the species in Becker and Zamudio’s (2011)

previous work were likely exposed to more Bd than Eleutherodactylus spp. in my study.

In my study, Bd in Jamaica is less problematic in disturbed habitats with less

cover, so species that can tolerate the loss of forest canopy could find refuge from this

disease in human modified habitats, such as coffee farms. Puschendorf et al. (2009)

found that warmer or drier areas can serve as refugia from Bd. My research, though not

designed to incorporate species diversity indices, appeared to lack high richness,

suggesting that habitat loss might be problematic to native specialist species. Ultimately,

this reduced Bd – habitat loss tradeoff suggested by Becker and Zamudio (2011), if

occurring in my study system, will likely apply to generalist species with marginal

conservation pressures. The progression of Bd-caused disease varies with the local strain

and life-history strategies of host species. That said, many Bd strains have been

identified worldwide and there is considerable variation in the virulence among strains

(Berger et al. 2005; Martel et al. 2013; Rosenblum et al. 2013); the strain or strains in

Jamaica have not been genetically identified, which poses a challenge for conservation

efforts on these endemic frogs in Jamaica.

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APPENDICES

Appendix A: Names of farms and Parish locations used in study.

Farms denoted with * were not used in final analysis due to inadequate number of samples Mt.Peto, Hanover James Hill, Clarendon Kew Park, Westmoreland Greenwich, St. Andrew Hedley, Westmoreland Bloomfield, St. Andrew Oswald, Westmoreland Forres Park, St. Andrew * Seven Rivers 1, St. James Sherwood, St. Andrew * Seven Rivers 2, St. James Ramble, St. Thomas Arnold, St. James * Wilson, St. Thomas Hutchinson, St. James Berger, St. Thomas Montpelier, St. James Davis, St. Thomas McCloud, St. James Whitfield Hall, St. Thomas * Carey, St. James Abby Green 1, St. Thomas * Baronhall 1, St. Ann Abby Green 2, St. Thomas * Baronhall 2, St. Ann Mt. Horeb, Portland Oxford, Manchester

Abby Green 1 0 0 1 0 0 0 0 0 0 0 1 1 Abby Green 2 0 0 0 0 0 0 0 0 0 0 0 0 Arnold 0 0 0 0 0 0 0 0 0 0 0 0 Baronhall 1 0 0 0 0 34 13 0 0 0 0 2 47 Baronhall 2 0 0 0 0 30 18 0 0 0 0 2 48 Berger 0 0 0 0 39 0 0 0 0 0 1 39 Bloomfield 0 0 0 1 26 0 0 0 0 0 2 27 Carey 0 0 0 30 5 8 0 0 0 0 3 43 Davis 0 0 0 4 30 0 0 0 0 0 2 34 Forres Park 0 0 0 7 0 0 0 0 0 2 2 9 Greenwich 0 0 2 8 4 0 0 0 2 0 4 16 Hedley 0 18 0 21 7 0 0 0 0 0 3 46 Hutchinson 0 0 0 16 35 0 0 1 0 0 3 52 James Hill 0 0 0 0 42 0 0 0 0 0 1 42 Kew Park 0 0 0 22 16 9 0 1 0 0 4 48 McCloud 1 0 0 12 4 0 0 1 0 0 4 18 Montpelier 0 0 0 0 25 0 0 0 0 0 1 25 Mt. Horeb 0 0 0 2 30 5 0 1 0 0 4 38 Mt.Peto 6 0 0 19 5 1 0 0 1 1 6 33 Oswald 0 20 0 34 28 0 0 0 0 0 3 82 Oxford 0 0 0 1 18 0 2 0 0 0 3 21 Ramble 0 0 0 6 31 0 0 0 0 0 2 37 Seven Rivers 1 0 0 0 3 31 0 3 0 0 0 3 37 Seven Rivers 2 0 0 0 3 32 1 18 0 0 0 4 54 Sherwood 0 0 0 2 0 0 0 0 0 0 1 2 Whitfield Hall 0 0 0 0 0 0 0 0 0 0 0 0 Wilson 0 0 0 6 31 0 0 0 0 0 2 37 57

Average Average Average Average Forest Leaf Litter Ground Coffee Canopy Elevation Fungicide Cover (cm) Cover Cover Cover

Abby Green 1 1.56 15 47 24 10 1283 1 Abby Green 2 1.17 43 23 21 86 1240 1 Arnold 2.18 29 17 87 93 375 1 Baronhall 1 2.22 18 41 56 12 558 1 Baronhall 2 3.39 6 45 55 17 561 1 Berger 2.50 46 8 46 96 846 1 Bloomfield 0.78 53 16 25 74 1152 1 Carey 1.06 34 39 74 100 299 0 Davis 0.56 67 4 51 96 607 0 Forres Park 3.66 16 8 67 57 746 1 Greenwich 0.56 70 50 44 96 1165 0 Hedley 1.00 28 52 76 44 406 0 Hutchinson 2.13 18 20 50 73 852 1 James Hill 0.50 25 30 50 76 635 1 Kew Park 1.95 31 37 55 50 353 1 McCloud 1.11 51 28 60 81 335 1 Montpelier 1.50 68 59 53 88 156 1 Mt. Horeb 0.17 87 11 11 93 1201 0 Mt. Peto 3.13 56 15 69 82 304 0 Oswald 1.00 50 15 63 55 392 0 Oxford 0.11 16 19 26 66 208 1 Ramble 3.76 44 32 84 24 236 0 Seven Rivers 1 2.32 53 21 37 23 251 0 Seven Rivers 2 2.03 43 36 49 90 152 0 Sherwood 2.67 50 11 33 62 654 1 Whitfield Hall 2.44 61 21 48 11 1269 1 Wilson 2.56 53 17 43 77 857 1

Appendix D: Jacob Kerby Lab University of South Dakota, qPCR methods.

DNA was extracted from all samples using Qiagen DNEasy spin columns following the manufacturer’s protocols. Once extracted, samples were run in triplicate using a verified qPCR methodology (Kerby et al. 2013). In summary, reactions were run in triplicate using 10 μL volumes. These are composed of: Bd forward primer ITS1-3 Chytr at a concentration of 0.9 μM, Bd reverse primer 5.8S Chytr at a concentration of 0.9 μM, Bd FAM-labeled probe Chytr MGB2 at a concentration of 0.15 μM (Boyle et al. 2004), with 5.0 μL of Taqman Fast Master Mix (Life Technologies) and 3 μl DNA template. A standard curve was utilized using gene blocks (IDT gblocks) composed of the target pathogen template to provide clear and reliable quantification of pathogen template copy number rather than zoospore equivalent estimate. As strain type was unknown, previous work (Longo et al. 2013) show that using a template based approach is a more conservative in estimating pathogen load. Samples were determined to be positive if two of the three wells exhibited estimates. Samples were rerun when only one of the replicates was positive, and were determined positive if subsequent replicates indicated the presence of template DNA.

Appendix E: Average precipitation, in Jamaica, measured in inches for June-July from 2012-2014 (Data Collection: June-July, 2013). NOAA Climate Data, Station: Montego Bay SANGSTE, Jamaica.

2.5 2.45 2

1.5 July - 1.51 1 June

0.5 0.71 Average Precipitation (in.) Precipitation Average

0 2012 2013 2014

Appendix F: All model selection result tables for both spatial scale analyses.

Appendix F.1: Support for set of generalized linear models, using Binomial error structure, predicting prevalence of Bd on Eleutherodactylus johnstonei relative to forest cover, elevation and fungicide use in coffee farms in Jamaica, June-July 2013. Model k AICc ΔAICc wi Cumulative wi LL elevation 2 24.32 0.00 0.51 0.51 -9.81 elevation + forest cover 3 26.15 1.83 0.21 0.72 -9.33 elevation + fungicide 3 26.99 2.67 0.14 0.86 -9.74 fungicide 2 29.12 4.79 0.05 0.90 -12.20 elevation + forest cover + fungicide 4 29.30 4.98 0.04 0.94 -9.32 Null 1 29.75 5.43 0.03 0.98 -13.76 forest cover + fungicide 3 31.90 7.58 0.01 0.99 -12.20 forest cover 2 32.23 7.91 0.01 1.00 -13.76 Models are ranked based on Akaike's Information Criterion (AICc), ∆AICc, and Akaike weights (wi). Akaike's Information Criterion is based on 2 x log likelihood (LL) and the number of parameters (K) in the model. Cumulative model weights (Cumulative wi) are also reported.

Appendix F.2: Support for set of generalized linear models, using Binomial error structure, predicting prevalence of Bd on Eleutherodactylus gossei relative to forest cover, elevation and fungicide use in coffee farms in Jamaica, June-July 2013. Model k AICc ΔAICc wi Cumulative wi LL Null 1 19.02 0.00 0.37 0.37 -8.31 elevation 2 19.44 0.42 0.30 0.67 -7.05 fungicide 2 21.13 2.11 0.13 0.80 -7.90 forest cover 2 21.84 2.82 0.09 0.89 -8.26 elevation + forest cover 3 23.15 4.13 0.05 0.93 -7.07 elevation + fungicide 3 23.29 4.27 0.04 0.98 -7.15 forest cover + fungicide 3 24.86 5.84 0.02 1.00 -7.93 elevation + forest cover + fungicide 4 28.03 9.01 0.00 1.00 -7.16 Models are ranked based on Akaike's Information Criterion (AICc), ∆AICc, and Akaike weights (wi). Akaike's Information Criterion is based on 2 x log likelihood (LL) and the number of parameters (K) in the model. Cumulative model weights (Cumulative wi) are also reported.

Appendix F.3: Support for set of generalized linear models on Poisson error structure predicting infection intensity of Bd on Eleutherodactylus johnstonei relative to forest cover, elevation and fungicide use in coffee farms in Jamaica, June-July 2013. Model k AICc ΔAICc wi Cumulative wi LL forest cover 2 63.47 0.00 0.34 0.34 -29.38 Null 1 64.39 0.92 0.22 0.56 -31.08 fungicide 2 65.62 2.15 0.12 0.68 -30.46 forest cover + fungicide 3 65.70 2.24 0.11 0.79 -29.10 elevation + forest cover 3 66.23 2.77 0.09 0.87 -29.37 elevation 2 66.67 3.20 0.07 0.94 -30.98 elevation + fungicide 3 68.39 4.92 0.03 0.97 -30.44 elevation + forest cover + fungicide 4 68.48 5.02 0.03 1.00 -28.91

Appendix F.4: Support for set of generalized linear models, using Poisson error structure, predicting infection intensity of Bd on Eleutherodactylus gossei relative to forest cover, elevation and fungicide use in coffee farms in Jamaica, June-July 2013. Model k AICc ΔAICc wi Cumulative wi LL Null 1 31.07 0.00 0.63 0.63 -14.20 elevation 2 34.19 3.12 0.13 0.77 -13.90 forest cover 2 34.69 3.61 0.10 0.87 -14.14 fungicide 2 34.75 3.68 0.10 0.97 -14.18 elevation + fungicide 3 39.25 8.17 0.01 0.98 -13.62 elevation + forest cover 3 39.54 8.46 0.01 0.99 -13.77 forest cover + fungicide 3 40.28 9.21 0.01 1.00 -14.14 elevation + forest cover + fungicide 4 48.54 17.47 0.00 1.00 -13.60

Appendix F.5: Support for set of generalized linear mixed effects models, using Binomial error structure, predicting prevalence of Bd on Eleutherodactylus johnstonei relative to shade cover and leaf litter depth in coffee farms in Jamaica, June-July 2013. Model k AICc ΔAICc wi Cumulative wi LL canopy + litter 6 78.98 0.00 0.16 0.16 -35.15 Null 7 79.35 0.37 0.13 0.30 -37.58 canopy 5 79.62 0.64 0.12 0.41 -36.61 ground 6 80.46 1.48 0.08 0.49 -37.03 canopy + ground + litter 6 80.59 1.62 0.07 0.56 -34.78 coffee 6 80.63 1.65 0.07 0.63 -37.11 ground + litter 5 80.70 1.73 0.07 0.70 -36.01 canopy + coffee + litter 6 81.15 2.17 0.05 0.75 -35.06 canopy + ground 3 81.21 2.24 0.05 0.81 -36.27 coffee + litter 4 81.24 2.26 0.05 0.86 -36.28 canopy + coffee 5 81.58 2.61 0.04 0.90 -36.45 coffee + ground 5 82.21 3.23 0.03 0.94 -36.77 ground + coffee + litter 3 82.68 3.70 0.03 0.96 -35.82 canopy + coffee + ground + litter 2 82.97 4.00 0.02 0.98 -34.75 canopy + coffee + ground 4 83.41 4.44 0.02 1.00 -36.19 Models are ranked based on Akaike's Information Criterion (AICc), ∆AICc, and Akaike weights (wi). Akaike's Information Criterion is based on 2 x log likelihood (LL) and the number of parameters (K) in the model. Cumulative model weights (Cumulative wi) are also reported.

Appendix F.6: Support for set of generalized linear mixed effects models, using Binomial error structure, predicting prevalence of Bd on Eleutherodactylus gossei relative to shade cover and leaf litter depth in coffee farms in Jamaica, June- July 2013. Model k AICc ΔAICc wi Cumulative wi LL Null 2 53.27 0.00 0.23 0.23 -24.45 canopy 3 54.49 1.23 0.12 0.35 -23.87 coffee 3 55.11 1.84 0.09 0.44 -24.18 canopy + coffee 4 55.15 1.88 0.09 0.53 -22.93 canopy + litter 4 55.51 2.24 0.07 0.61 -23.11 coffee + litter 4 55.53 2.26 0.07 0.68 -23.12 ground 3 55.55 2.28 0.07 0.75 -24.40 canopy + coffee + litter 5 55.94 2.68 0.06 0.81 -21.97 ground + litter 4 56.38 3.11 0.05 0.86 -23.54 canopy + ground 4 56.91 3.64 0.04 0.90 -23.81 ground + coffee 4 57.59 4.32 0.03 0.92 -24.15 canopy + coffee + ground 5 57.80 4.54 0.02 0.95 -22.90 coffee + ground + litter 5 58.19 4.92 0.02 0.97 -23.09 canopy + ground + litter 5 58.22 4.95 0.02 0.99 -23.11 canopy + coffee + ground + litter 6 58.76 5.50 0.01 1.00 -21.93 Models are ranked based on Akaike's Information Criterion (AICc), ∆AICc, and Akaike weights (wi). Akaike's Information Criterion is based on 2 x log likelihood (LL) and the number of parameters (K) in the model. Cumulative model weights (Cumulative wi) are also reported.

Appendix F.7: Support for set of generalized linear mixed effects models on Poisson error structure, predicting infection intensity of Bd on Eleutherodactylus johnstonei relative to shade cover and leaf litter depth in coffee farms in Jamaica, June- July 2013. Model k AICc ΔAICc wi Cumulative wi LL canopy 3 209.29 0.00 0.16 0.16 -101.37 canopy + litter 4 209.67 0.38 0.13 0.29 -100.37 coffee + litter 4 210.21 0.92 0.10 0.38 -100.64 ground + litter 4 210.30 1.01 0.09 0.48 -100.68 ground 3 210.44 1.15 0.09 0.57 -101.95 canopy + ground 4 210.69 1.40 0.08 0.64 -100.88 Null 2 210.91 1.62 0.07 0.71 -103.32 canopy + coffee 4 211.47 2.18 0.05 0.77 -101.27 coffee 3 211.70 2.41 0.05 0.81 -102.58 canopy + ground + litter 5 211.85 2.56 0.04 0.86 -100.21 canopy + coffee + litter 5 211.94 2.65 0.04 0.90 -100.26 coffee + ground + litter 5 212.41 3.12 0.03 0.93 -100.49 coffee + ground 4 212.42 3.13 0.03 0.96 -101.74 canopy + coffee + ground 5 213.14 3.86 0.02 0.99 -100.86 canopy + coffee + ground + litter 6 214.36 5.07 0.01 1.00 -100.15 Models are ranked based on Akaike's Information Criterion (AICc), ∆AICc, and Akaike weights (wi). Akaike's Information Criterion is based on 2 x log likelihood (LL) and the number of parameters (K) in the model. Cumulative model weights (Cumulative wi) are also reported.

Appendix F.8: Support for set of generalized linear mixed effects models on Poisson error structure, predicting infection intensity of Bd on Eleutherodactylus gossei relative to shade cover and leaf litter depth in coffee farms in Jamaica, June-July 2013. Model k AICc ΔAICc wi Cumulative wi LL canopy + litter 4 100.01 0.00 0.21 0.21 -44.67 coffee + litter 4 100.06 0.04 0.21 0.42 -44.70 ground+ litter 4 100.79 0.77 0.14 0.56 -45.06 Null 2 101.24 1.23 0.11 0.68 -48.27 coffee 3 102.64 2.62 0.06 0.73 -47.57 ground 3 102.64 2.63 0.06 0.79 -47.57 canopy 3 103.39 3.38 0.04 0.83 -47.95 canopy + ground + litter 5 103.53 3.52 0.04 0.87 -44.62 canopy + coffee + litter 5 103.56 3.55 0.04 0.90 -44.64 coffee + ground +litter 5 103.66 3.65 0.03 0.94 -44.69 ground + coffee 4 104.15 4.13 0.03 0.96 -46.74 canopy + ground 4 105.33 5.32 0.01 0.98 -47.33 canopy + coffee 4 105.73 5.72 0.01 0.99 -47.53 canopy + coffee + ground 5 107.43 7.41 0.01 1.00 -46.57 canopy + coffee + ground + litter 6 107.68 7.66 0.00 1.00 -44.61 Models are ranked based on Akaike's Information Criterion (AICc), ∆AICc, and Akaike weights (wi). Akaike's Information Criterion is based on 2 x log likelihood (LL) and the number of parameters (k) in the model. Cumulative model weights (Cumulative wi) are also reported.