ENVIRONMENTAL INFLUENCES ON AMPHIBIAN INNATE ...

ENVIRONMENTAL INFLUENCES ON

AMPHIBIAN INNATE IMMUNE DEFENSE TRAITS

by

KATHERINE L. KRYNAK

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Department of Biology

CASE WESTERN RESERVE UNIVERSITY

August, 2015

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Katherine L. Krynak

candidate for the degree of Doctor of Philosophy*.

Committee Chair

Michael F. Benard

Committee Member

David J. Burke

Committee Member

Jean H. Burns

Committee Member

Patricia M. Dennis

Committee Member

Brandon A. Sheafor

Date of Defense

June 25, 2015

*We also certify that written approval has been obtained

for any proprietary material contained therein.

Copyright © 2015 by Katherine L. Krynak

All rights reserved

i

Table of contents

Table of contents ................................................................................................................ i

List of Tables .................................................................................................................... iv

List of Figures .................................................................................................................... v

Acknowledgments .......................................................................................................... viii

Abstract.........................................................................................................................…11

Chapter 1: Introduction ................................................................................................. 12

1.1. Amphibian declines and disease ........................................................................... 12

1.2. Amphibian pathogen resistance and innate immune defense traits ...................... 13

1.3. Response to disease-related declines: current conservation strategies ................. 14

1.4. What influences variation in amphibian innate immune defense traits?............... 15

1.4.1. Mesocosm experiment: Larval environment alters amphibian immune

defenses differentially across life stages and populations ........................... 15

1.4.2. Observational field study: Landscape and water characteristics correlate with

immune defense traits across Blanchard’s cricket frog (Acris blanchardi)

populations .................................................................................................. 16

1.4.3. Laboratory study: Rodeo™ herbicide exposure decreases larval survival, and

alters the skin-microbiome of Blanchard’s cricket frogs (Acris blanchardi).

..................................................................................................................... 16

1.5. Research Goals ...................................................................................................... 17

Chapter 2: Larval environment alters amphibian immune defenses differentially

across life stages and populations. ................................................................ 18

2.1. In Press PLOS One ............................................................................................... 18

Authors: Katherine L. Krynaka*

, David J. Burkeb, and Michael F. Benard

a ................ 18

2.2. Abstract ................................................................................................................. 18

2.3. Introduction ........................................................................................................... 19

2.4. Methods ................................................................................................................. 23

2.4.1. Experimental set-up ....................................................................................... 23

2.4.2. Data collection and analysis .......................................................................... 26

2.5. Results ................................................................................................................... 33

2.6. Discussion ............................................................................................................. 41

2.7. Conclusions ........................................................................................................... 46

2.8. Appendices ............................................................................................................ 48

2.8.1. Table A1. ANOVA results examining treatment effects on average time to

metamorphosis. a. Referent: Northern population, No shade, Acidified pH.

b. Referent: Northern population, Shade, Acidified pH. c. Referent:

Northern population, No Shade, Un-manipulated pH. d. Referent: Northern

population, Shade, Un-manipulated pH. e. Referent: Southern population,

No shade, Acidified pH. f. Referent: Southern population, Shade, Acidified

pH. g. Referent: Southern population, No Shade, Un-manipulated pH. h.

Referent: Southern population, Shade, Un-manipulated pH. Significant

results in bold. ............................................................................................. 48

ii

2.8.2. Table A2. ANCOVA results examining treatment effects on Juvenile Mass.

a. Referent: Northern population, No shade, Acidified pH. b. Referent:

Northern population, Shade, Acidified pH. c. Referent: Northern

population, No Shade, Un-manipulated pH. d. Referent: Northern

population, Shade, Un-manipulated pH. e. Referent: Southern population,

No shade, Acidified pH. f. Referent: Southern population, Shade, Acidified

pH. g. Referent: Southern population, No Shade, Un-manipulated pH. h.

Referent: Southern population, Shade, Un-manipulated pH. Significant

results in bold. ............................................................................................. 50

2.8.3. Table A3. ANCOVA results examining treatment effects on mean AMP

production (standardized by gram body weight). a. Referent: Northern

population, No shade, Acidified pH. b. Referent: Northern population,

Shade, Acidified pH. c. Referent: Northern population, No Shade, Un-

manipulated pH. d. Referent: Northern population, Shade, Un-manipulated

pH. e. Referent: Southern population, No shade, Acidified pH. f. Referent:

Southern population, Shade, Acidified pH. g. Referent: Southern

population, No Shade, Un-manipulated pH. h. Referent: Southern

population, Shade, Un-manipulated pH. Significant results in bold. .......... 52

2.8.4. Table A4. ANCOVA results examining treatment effects on AMP bioactivity

(defined as the slope of the log-transformed growth curve). a. Referent:

Northern population, No shade, Acidified pH. b. Referent: Northern

population, Shade, Acidified pH. c. Referent: Northern population, No

Shade, Un-manipulated pH. d. Referent: Northern population, Shade, Un-

manipulated pH. e. Referent: Southern population, No shade, Acidified pH.

f. Referent: Southern population, Shade, Acidified pH. g. Referent:

Southern population, No Shade, Un-manipulated pH. h. Referent: Southern

population, Shade, Un-manipulated pH. Significant results in bold. .......... 55

2.8.5. Table A5. ANCOVA results examining treatment effects on AMP bioactivity

(defined as the Bd growth rate). a. Referent: Northern population, No shade,

Acidified pH. b. Referent: Northern population, Shade, Acidified pH. c.

Referent: Northern population, No Shade, Un-manipulated pH. d. Referent:

Northern population, Shade, Un-manipulated pH. e. Referent: Southern

population, No shade, Acidified pH. f. Referent: Southern population,

Shade, Acidified pH. g. Referent: Southern population, No Shade, Un-

manipulated pH. h. Referent: Southern population, Shade, Un-manipulated

pH. Significant results in bold. .................................................................... 58

2.8.6. Table A6. The sequence similarity of clones (out of 161 total) created from

skin swabs of R.catesbeiana using primers 926r and 338f. Identification is

based upon comparison to NCBI database entries using the FASTA

program (National Center for Biotechnology Information). The percent

identity (% ID) to best match is shown. ...................................................... 61

Chapter 3: Landscape and water characteristics correlate with immune defense

traits across Blanchard’s cricket frog (Acris blanchardi) populations ...... 65

3.1. Submitted for publication review .......................................................................... 65



a ................ 65

3.2. Abstract ................................................................................................................. 65

iii

3.3. Introduction ........................................................................................................... 66

3.4. Methods ................................................................................................................. 70

3.4.1. Site selection ................................................................................................. 70

3.4.2. Data collection ............................................................................................... 72

3.5. Results ................................................................................................................... 78

3.6. Discussion ............................................................................................................. 87

3.7. Appendices ............................................................................................................ 96

3.7.1. Table A1. The sequence similarity of clones (out of 169total) created from

skin swabs of Acris blanchardi using primers 338f and 926r. Identification

is based upon comparison to NCBI database entries using the FASTA

program (National Center for Biotechnology Information).The percent

identity (% ID) to best match is shown. Fragment size in base pairs (bp)

generated using MboI restriction enzyme. Indicator species analysis based

on community profiles. Letters designate sites with specific bacterial taxa.

..................................................................................................................... 96

Chapter 4: Rodeo™ herbicide exposure decreases larval survival and alters skin-

microbiome of Blanchard’s cricket frogs (Acris blanchardi) ..................... 99

4.1. Submitted for publication review .......................................................................... 99



a ................ 99

4.2. Abstract ................................................................................................................. 99

4.3. Introduction ......................................................................................................... 100

4.4. Methods ............................................................................................................... 105

4.5. Results ................................................................................................................. 114

4.6. Discussion ........................................................................................................... 120

4.7. Conclusions ......................................................................................................... 127

Chapter 5: Conclusion .................................................................................................. 128

5.1. Summary ............................................................................................................. 128

5.2. Environmental effects on innate immune defense traits ..................................... 128

5.3. Host effects on skin-associated microbiomes ..................................................... 129

5.4. Conservation Implications .................................................................................. 130

Bibliography .................................................................................................................. 133

iv

List of Tables

Table 2.1 MRPP results from microbial community comparisons. Significance (bold)

defined as an Affect Size (A) where A≥0.1and p≤0.05 (McCune and Grace 2002). .. 36

Table 3.1 Survey site water characteristics and number of individual Acris

blanchardi sampled. ................................................................................................... 71

Table 3.2 Response variables (NMDS axis 1, 2, and 3 scores, AMP production, AMP

bioactivity (r) were modeled as a function of each of the following predictors. ... 77

Table 3.3 Top models explaining environmental influence on Acris blanchardi

immune defense traits across sites in Ohio and Michigan based on AICc ranking. Microbial community axis scores are based on a three dimensional NMDS ordination

solution and describe the variation seen across each axis. Models were capped at six

parameters (K=6) because of the small sample size (N=11 sites). AICc score, change

in AICc (∆AICc), and the AICc model weight (⍵) for each model are shown for the

top models (∆ AICc≤ 4) for each response variable. The top 10 models are shown for

AMP bioactivity (r) and are all ∆ AICc<4. .................................................................. 80

Table 3.4 Model averaged parameter estimates, unconditional standard error (SE)

of the estimate, and 95% unconditional confidence intervals (CI) of landscape

and water characteristics on Acris blanchardi immune defense traits across sites

in Ohio and Michigan. Only parameters from top models (∆AICc ≤4 )are included. *

Indicates that only the top 10 models are represented and are all ∆AICc ≤4. Based on

95% CI, influential parameters are in bold. ................................................................. 81

Table 3.5 Models used to assess host influence (AMP production and AMP

bioactivity (r)) on Acris blanchardi skin-associated microbial community NMDS

axis scores across sites in Ohio and Michigan based on AICc ranking. AICc score,

change in AICc (∆AICc), and the AICc model weight (⍵) for each model are shown

for each response variable. ........................................................................................... 86

Table 3.6 Model averaged parameter estimates (Est.), unconditional standard error

(SE) of the estimate, and 95% unconditional confidence intervals (CI) of host

characteristics on Acris blanchardi skin-associated microbial community NMDS

axis scores across sites. ............................................................................................... 86

Table 4.1 Rodeo treatment assignments (number of replicates indicated; three

animals per replicate). Treatments originally balanced (five replicates per Rodeo™

concentration/exposure stage combination); however, due to high larval mortality

following Rodeo™ larval treatment, replicate assignments were adjusted to improve

ability to assess sub-lethal effects on Low and Medium Rodeo™ concentrations, and

the effects of Rodeo™ exposure timing. .................................................................... 106

Table 4.2 ANCOVA analysis of larval Rodeo™ concentration effects on juvenile

Acris blanchardi traits (carry-over effects). Excluded replicates with post-

metamorphic treatments due to the unbalanced design, the result of larval mortality.

.................................................................................................................................... 118

Table 4.3 ANCOVA analysis of Rodeo™ treatment effects on Acris blanchardi traits.

Treatments consisted of combinations between two exposure levels (Low, and

Medium Rodeo™) and three Rodeo™ exposure stages (larval, juvenile, or both: larval

and juvenile Rodeo™ exposure). Marginally significant treatment effects in bold. 119

v

List of Figures

Figure 2.1 Average time to metamorphosis with standard error. Both Shade and

Population were significant predictors of mean larval duration under all treatment

combinations (shade prange

=0.003 to 0.04; population p=9.5 x10-5

to 0.017). Figure

displays results of Population effects within Acidified environments. Full ANOVA

outputs can be found in Table A1. ............................................................................... 34

Figure 2.2 Population effect on mean juvenile mass (g) at sample collection with

standard error. Population and Days in lab were significant predictors of juvenile

mass in many but not all treatment environments (Population prange

=0.007 to 0.0866;

Days in lab p=0.0042). Figure displays results of Population effects within Shaded and

Acidified treatments. Full ANCOVA outputs can be found in Table A2. ................... 35

Figure 2.3 NMDS ordination plot of Rana catesbeiana larval and juvenile frog

microbial community similarity by acidification treatment. N=152 after outlier

analysis (McCune and Grace 2002). Ordination stress =20%. Axes display percentage

variance explained. Circles designate juvenile frog microbial communities, triangles

designate larval microbial communities. Open symbols designate acidified pH

treatments while closed symbols designate un-manipulated pH treatments. ............... 37

Figure 2.4 Clone library comparison between larval and post-metamorphic

(juvenile) Rana catesbeiana skin-associated bacteria. The percent of the clone

library represented by each taxonomic group is shown. (Larvae library: N=78,

Juvenile library: N=83) ................................................................................................ 38

Figure 2.5 Interaction effects on AMP production (µg/ml standardized by gram

body weight) with standard error (Acidification x Shade p=0.0272; Population x

Shade prange=0.0501 to 0.7868). A. Northern referent. B. Southern referent. C.

Acidified referent. Referent variables refer to a specific treatment environment,

indicating what two-way interaction is being displayed. Contrasts indicate significant

simple effects within each two-way interaction (p<0.05) (eg. A. indicates a significant

Acidification effect within the NoShade treatments and a significant Shade effect

within the Acidified treatments in the northern Population) (Crawley 2007; Kleinbaum

et al. 2014). Full ANCOVA outputs can be found in Table A3................................... 39

Figure 2.6 Interactive effects on AMP bioactivity in terms of slope of the log-

transformed growth curve with standard error (Shade x Population p=0.085,

Acidification x Shade x Population p= 0.12). A. Acidified referent. B. No Acid

referent. Contrast indicates significant simple effect of Shade within un-manipulated

pH (NoAcid) treatments of the Northern population (p=0.018). Full ANCOVA results

can be found in Table A4. ............................................................................................ 40

Figure 2.7 Interactive effects on AMP bioactivity in terms of Bd growth rate with

standard error (Acid x Population prange

=0.033 to 0.084, Acidification x Shade x

Population p=0.773) A. Sun referent. B. Shade referent. Contrast indicates significant

simple effect of Acidification within full sun (NoShade) treatments of the Northern

Population (p=0.018). Full ANCOVA results can be found in Table A5. ................... 41

Figure 3.1 Geographic range of Acris blanchardi and areas of documented decline

are shown in dotted dark gray (Gamble et al. 2008). .............................................. 69

vi

Figure 3.2 Survey site locations in Ohio and Michigan across a portion of Acris

blanchardi’s declining range (source: lat 40.405760 long -82.930501. Google Earth.

May 9 2013. Februrary 11, 2015). ............................................................................... 71

Figure 3.3 NMDS ordination of Acris blanchardi skin-associated microbial

communities. Points represent site averages with standard error (MRPPsite: A=0.146,

p<0.0001). A) Axis 1 and 2. B) Axis 1 and 3. Water surface area (“SA”, m2), latitude,

conductivity and the ratio of natural to managed land (N:M, m2) were predictive of

microbial community axis scores of the NMDS ordination. ....................................... 79

Figure 3.4 Frog sex and landscape characteristics interact to influence skin

microbiome variation across NMDS axis 1. A. Interaction effect of frog sex and

latitude on microbial community NMDS axis 1 scores of Acris blanchardi across sites

in Ohio and Michigan (conditional R2=0.46). B. Interaction effect of frog sex and

water surface area (“SA”, m2) on microbial community NMDS axis 1 scores of Acris

blanchardi across sites in Ohio and Michigan (conditional R2=0.48). Females=pink.

Males=aquamarine. ...................................................................................................... 82

Figure 3.5 Interaction effects of the ratio of natural to managed terrestrial habitat

(N:M) and water surface area (“SA”, m2) on microbial community NMDS axis 3

scores (represented by color shading) of Acris blanchardi (conditional R2=0.34). 83

Figure 3.6 Clone library of Acris blanchardi skin-associated bacteria. The percent of

the clone library represented by each taxonomic group is shown. (N=169). Of

Betaproteobacteria cloned (N=86 clones), 65.1% were significant indicators of site J.

Ypsillanti, MI. ............................................................................................................... 84

Figure 3.7 AMP production (in the form of natural peptide mixtures) standardized

by gram body weight (gbw) of Acris blanchardi across sites in Ohio and

Michigan. Letters correspond to Figure 3.2 site locations. ........................................ 85

Figure 3.8 Interaction effect of water surface area (“SA”, m2) and Conductivity (µS)

on AMP production (shading; AMP µg/ml per gram body weight) in Acris

blanchardi across sites in Ohio and Michigan (conditional R2=0.24). ................... 85

Figure 3.9 AMP bioactivity (r) as a function of AMPs produced (standardized by

gram body weight) from Acris blanchardi across sites in Ohio and Michigan (Estimate=4.0 x 10

-04, SE=2.0 x 10

-04, df=75, p=0.051; conditional R

2=0.04). 95%

confidence interval is displayed as the shaded region. ................................................ 87

Figure 4.1 Experimental methodology. Rodeo™ treatments were conducted at four

treatment concentrations: Control- 0.0mg a.i./L (0.0mg a.e./L), Low- 0.75mg

a.i./L(1.01mg a.e./L), Medium- 1.5mg a.i./L (2.02mg a.e./L), and High- 2.5 mg a.i./L

(3.38mg a.e./L). .......................................................................................................... 108

Figure 4.2 Larval Acris blanchardi survival in response to Rodeo™ concentration.

Low: 0.75mg a.i./L, Medium: 1.5 mg a.i./L, and High: 2.5 mg a.i./L. High Rodeo™

concentration for a period of 12 days reduced survival by 36.67% compared to Control

(Two-sample Wilcoxon test significant with Bonferroni correction: p=0.012).

N=number of replicates at beginning of the experiment. ........................................... 115

Figure 4.3 Juvenile Acris blanchardi survival in response to Rodeo™ treatments

(corrected for larval survival). There were no treatment effects between Control (C)

and treatments. Low (L): 0.75mg a.i./L, Medium (M): 1.5 mg a.i./L, and High (H): 2.5

mg a.i./L. Larvae and frog symbols correspond to stage at which the animals were

exposed to Rodeo™. Survival from metamorphosis to the end of the experiment (i.e.

vii

juvenile survival) did not significantly differ between Control and any of the

treatments. N=number of replicates at end of larval period. ...................................... 115

Figure 4.4 Acris blanchardi skin microbiome as a function of larval Rodeo™

concentration. A. Larval microbiome NMDS ordination (3D solution stress=15.87%;

Axis 3 not shown) as influenced by larval Rodeo™ concentration (mean and standard

error shown; Controln=14: 0.0mg a.i./L; Lown=8: 0.75mg a.i./L; Mediumn=10: 1.5 mg

a.i./L; Highn=5: 2.5 mg a.i./L). Rodeo™ concentration altered larval microbial

community structure along NMDS Axis 2 (F(3,33)=2.632, p=0.07). Post hoc planned

contrasts: a= not significantly different from Control; b= p<0.008 compared to

Control. B. Juvenile microbiome NMDS ordination (3D solution stress=11.2%; Axis

2 not shown) as a function of larval Rodeo™ concentration (mean and standard error

shown; Controln=6: 0.0mg a.i./L; Lown=2: 0.75mg a.i./L; Mediumn=3: 1.5 mg a.i./L;

Highn=5: 2.5 mg a.i./L). Larval Rodeo™ concentration did not affect juvenile

microbiome when excluding replicates with post-metamorphic treatments (i.e.

replicates exposed as juveniles only as well as replicates exposed as both larvae and

juveniles). Post hoc planned contrasts: a= not significantly different from Control. 117

Figure 4.5 Juvenile microbiome NMDS ordination (3D solution stress =11.2%)

indicating marginally significant effect of Rodeo™ concentration (axis 3:

F(1,19)=4.24, p=0.06). Post hoc planned contrasts did not reveal significant mean

differences between the two Rodeo™ concentrations. L= larval exposure, J= juvenile

exposure, B= exposure at both larval and juvenile life stages. .................................. 119

viii

Acknowledgments

The research presented in this manuscript could not have been completed without

the generous support and guidance I have received from many over the past five years.

I thank the National Science Foundation Graduate Research Fellowship Program,

Cleveland Metroparks, The Holden Arboretum, and Case Western Reserve University for

financial support of my research.

I thank my advisor Michael Benard, for the high bar set for me as a student and

for the scientist I have become. Thank you for the Blanding’s in a bucket and all of the

other unexpected laughs along the way. Above all, thank you for supporting my

conservation research passions and for investing in my ideas.

To my committee members David J. Burke, Jean H. Burns, Patricia M. Dennis,

and Brandon A. Sheafor, thank you all for your guidance, support, and encouragement.

David, thank you for teaching me how to tweak lab protocols like a recipe, the value of

the sacrifice to the PCR god, and for giving me a broad appreciation for The Sound of

Music. Jean, thank you for being an amazing role model and thank you for the confidence

your support and encouragement has given me. Pam, thank you for your boundless

enthusiasm and support of my research and for the long phone conversations we’ve had

over the years planning our limitless projects for the future. Brandon, I’m not sure I

would have found my research niche without you. Thank you for introducing me to this

field and encouraging me to take the leap into graduate school.

I thank Brittany Bogus, Matt Kluber, and Jeremy Rayl, for bringing laughter out

to the mesocosm field on a daily basis. Thank you for your hard work and glove sweat

while bucketing water into and out of the mesocosms approximately 8,000 times a day

for three months. You are rock stars.

ix

I thank CWRU Squire Valleevue Farm staff: Ana Locci, Jack Swartz, Allen

Alldridge, Patty Gregory, Zoey Bond, and Christopher Bond for all of your assistance,

your support, and welcoming me into your farm family. I thank the late Mark McGee for

his heart of gold under that gruff exterior and his let’s get it done way of being. Mark

was always so very helpful to me and all of the CWRU students and we all miss you

greatly.

I thank Richard Lehtinen and Edith Sonntag for their invaluable advice on A.

blanchardi. I thank Todd Farler of Madison Township Park, Gary and Diana Williamson,

John Mynheir, Lisa Lenos, Rebekah Lenos, Sarah Wolfe, Gary Sturgis, Robert and

Patricia Duffey, Edith Sonntag, Wood County Park district, St. Mary’s Fish Hatchery,

Patrick Doran, Sarah Burgess, and Jenella Hodel from The Nature Conservancy, and the

many public land managers in Ohio and Michigan for their assistance with my 2012 field

survey study.

I thank Robert Duffey, Tim Krynak, Debbie Nofzinger, Brooke Nofzinger, Kathy

Edelen, Sheryl Petersen, and Kristy Becka for field assistance, friendship, support, and

sometimes counseling during my extended field work in 2013. I thank the random folks

at the Loves truck stop for the shower vouchers. I obviously looked like I needed them. I

thank Todd Nofzinger and the Rangers of Wood County Park District for their watchful

eyes at my camp site and I thank the Nofzinger family for preventing me from being

sucked up in a tornado and giving me and my Lupine pup a place to crash during the

storms.

x

I thank Sarah Carrino-Kyker, Charlotte Hewins, Sheryl Petersen, Juliana

Medeiros, and the crew at The Holden Arboretum’s Science Center for welcoming me

into their laboratory utopia, for your assistance, friendship, and positive energy. Sheryl

Petersen, thank you for your countless hours of assistance with R, and for the teaching

me the power poses. Sarah and Charlotte thank you for the time, assistance, and the

skills you handed down to me in the laboratory. Sheryl and Juliana thank you for the gift

of the bucket.

I thank Steve Mather for assistance with landscape GIS analyses and April

Luginbuhl-Mather, Sam, and Bea for the wonderful working dinners.

I thank current and former members of the Benard Lab: Kacey Dananay, Hilary

Rollins, Mimi Guo, Laura Hill, Alex Grossman, Henry Hershey, Julia Boehler, Belle

Perez, Charlotte Yuan, Matt Kluber, Mathew Conger, Matt Boes, Catherine Osborn,

Kristen Zozulin, Pheobe Edwards, Jeremy Rayl, Brittany Bogus, and Andrew Zajac for

your laughs, encouragement, pep talks, tadpole measures, manuscript and presentation

editorial assistance, and for the yummy baked goods.

Dad (Robert Duffey), thank you for being my frog spotter, my water chemist, and

my CFC security guard. Thank you Dad, and thank you Mom (Patricia Duffey), for

telling me that I could accomplish anything, as long as I put my mind to it.

Finally, I thank my husband Tim Krynak (and our dogs Lupine and Miss Izzy).

Words will never adequately express my gratitude for your love, support, encouragement,

field assistance, and the editorial assistance (Tim, not the dogs) that you have given me

during this journey and also for making me stop, take a walk, watch a bird, and throw a

ball or two along the way. I love you!

11

Environmental Influences On

Amphibian Innate Immune Defense Traits

Abstract

By

KATHERINE LYNN KRYNAK

Disease-associated mortality is a leading cause of amphibian declines world-wide;

therefore, understanding the influence anthropogenic environmental change has on traits

which provide disease resistance is important for successful amphibian conservation.

Amphibians are protected from pathogens by two skin-associated immune defense traits:

the microbial communities which inhabit their skin (microbiome) and the antimicrobial

peptides (AMPs) produced by the skin. Utilizing experimental and observational studies,

I investigated the relationships between the environment and amphibian skin-associated

immune defense traits. I found that small pH shifts (i.e. from ~ 7 to 6) in the larval

environment caused changes in Rana catesbeiana larval microbiome structure, an effect

which disappeared after metamorphosis. Additionally, I found post-metamorphic AMP

production and bioactivity were significantly affected by interactions between population,

pH, and the presence or absence of shade in the larval environment. In an observational

field survey I found that Acris blanchardi populations across Ohio and Michigan differed

in microbiomes and AMP production, but not AMP bioactivity against Bd

(Batrachochytrium dendrobatidis). Microbiomes were associated with water

conductivity, ratio of natural to managed land, and latitude. Additionally the

microbiomes were affected by interactions between frog sex and latitude, between frog

sex and water surface area, and between the ratio of natural to managed land and water

surface area. AMP production was influenced by the interaction between water surface

12

area and conductivity. Finally, I examined the influence of a glyphosate-based herbicide

on A. blanchardi skin-associated immune defense traits across life stages and at differing,

environmentally relevant concentrations. I found a 37% decrease in survival of larvae

exposed to 2.5mg/L of active ingredient (glyphosate) compared to control, but no effects

on juvenile survival. Larval herbicide concentration did alter the larval microbiome, but

did not alter larval duration and did not carryover to alter post-metamorphic traits.

Furthermore, herbicide concentration only marginally affected juvenile mass and the

juvenile microbiome. I did not find evidence of effects of the host’s AMPs affecting the

skin microbiome in any of my studies, indicating that the environment external to the

amphibian is relatively more influential on the amphibian skin-associated microbiome

compared to this physiological trait of the host.

Chapter 1: Introduction

In this first chapter, 1) I provide a broad overview of global amphibian declines,

with focus on disease-related declines, 2) I define the amphibian innate immune defense

traits which provide disease resistance, 3) I discuss current amphibian conservation

initiatives in response to disease-related declines, and 4) I introduce my research goals

and my three research chapters in relation to amphibian conservation.

1.1. Amphibian declines and disease

It has been estimated that amphibians are declining at a rate upwards of 2700

times the background extinction rate (Roelants et al. 2007). The International Union for

the Conservation of Nature states that 41% of species are currently threatened with

extinction (AmphibiaWeb 2015; IUCN 2014). Declines have been attributed to habitat

loss and fragmentation, chemical contamination, climate change, over-exploitation, and

13

disease (Collins and Storfer 2003; IUCN 2014; Wake and Vredenburg 2008). Disease-

related mortality is a leading cause of rapid declines globally and is expected to increase

due to the ease of transportation (Daszak et al. 2003). Viruses of the Family Iridoviridae

(Cunningham et al. 1996; Jancovich et al. 1997), the bacterial pathogen Aeromonus

hydrophila (Bradford 1991; Carey 1993), parasitic trematode infections (Johnson et al.

2002; Johnson and Sutherland 2003; Kiesecker 2002; Rohr et al. 2008b), and fungal

pathogens including saprophilic water molds (Kiesecker and Blaustein 1997; Romansic et

al. 2009), Batrachochytrium salamandrivorans (Bsal) and B. dendrobatidis (Bd) (Berger

et al. 1998; Daszak et al. 2003; Martel et al. 2013), have all been implicated in amphibian

disease-related declines (AmphibiaWeb 2015). In particular, Batrachochytrium

dendrobatidis (Bd), a skin-associated fungal pathogen has caused rapid declines,

extirpations, and is responsible for more than 200 amphibian extinctions globally (Wake

and Vredenburg 2008). It is largely accepted that disease-related mortality is a function of

complex interactions between anthropogenic environmental change and disease (Hayes et

al. 2010; Kiesecker et al. 2001; Pounds et al. 2006; Rohr and Raffel 2010; Rohr et al.

2008a); however, mechanisms by which the environment influences disease susceptibility

are not well understood (Hayes et al. 2010).

1.2. Amphibian pathogen resistance and innate immune

defense traits

While there is some evidence of acquired immune response to pathogens in

amphibians (McMahon et al. 2014; Richmond et al. 2009; Rollins-Smith et al. 1992),

there are two innate skin-associated immune defense traits which provide amphibians

with the first line of defense against disease 1) the microbial communities which inhabit

their skin (microbiome), and 2) the antimicrobial peptides (AMPs) produced by granular

14

glands in the skin (Belden and Harris 2007; Harris et al. 2006; Rollins-Smith and Conlon

2005; Rollins-Smith et al. 2011; Rollins-Smith et al. 2005). These traits are known to

vary between amphibian species (McKenzie et al. 2012; Woodhams et al. 2007a), yet

little is known regarding the degree to which these traits vary across populations, what

may influence trait variation across populations and across life-stages, and if trait

variation results in differential disease resistance (Rollins-Smith 2009; Rollins-Smith et

al. 2011; Woodhams et al. 2011).

1.3. Response to disease-related declines: current conservation

strategies

Current conservation efforts utilized in response to rapid disease-related mortality

focus largely on population monitoring, development of amphibian rescue centers which

house captive assurance colonies in cases where extinction is eminent, and direct

manipulations of the skin-associated microbiomes of amphibians (i.e. bio-augmentation)

in attempt to bolster immune function against pathogens (Bletz et al. 2013; Young et al.

2001). While bio-augmentation strategies are an exciting new approach to amphibian

conservation, we do not yet understand the relative influence of host (amphibian skin)

versus the environment external to the host in regulating these microbial communities

over time. Broadening our understanding of the relationship between these skin-

associated microbial communities, potential regulatory traits of the amphibian host, and

the regulatory effects of the environment external to the host will offer direction to

improve conservation strategies.

15

1.4. What influences variation in amphibian innate immune

defense traits?

Utilizing an experimental mesocosm study, an observational field study, and an

experimental indoor laboratory study, I examined the influence of the environment on the

amphibian skin-associated microbiome and antimicrobial peptides. By examining these

traits in unison, I was also able to investigate the regulatory influence of AMPs produced

by the host on the skin-associated microbiome. Additionally, I examined the influence of

the environment on these two traits across life stages in order to improve our

understanding of the relative influence of the larval environment compared to the post-

metamorphic environment on amphibian immune defense traits post-metamorphosis. In

the following subsections I briefly outline the goals of each of my three dissertation

studies.

1.4.1. Mesocosm experiment: Larval environment alters amphibian immune

defenses differentially across life stages and populations

Utilizing the American bullfrog, Rana catesbeiana, a species with an introduced

world-wide distribution and high degree of environmental tolerance (Ficetola et al. 2007),

I investigated how common changes in the larval habitat (pH shift from 7 to 6 and the

presence or absence of pond shading) can influence the skin-associated microbiome and

the antimicrobial peptides (AMPs) produced in the skin. Understanding how common

fluctuations in the environment can influence these traits is important for relative

comparison to potentially less benign anthropogenic changes, such as chemical

contamination, which may also alter these traits.

16

1.4.2. Observational field study: Landscape and water characteristics correlate

with immune defense traits across Blanchard’s cricket frog (Acris blanchardi)

populations

I surveyed Acris blanchardi skin-associated immune defense traits of adult frogs

from 11 populations across the northern edge of the species’ geographic range. I

correlated landscape and water characteristics with trait differences across sites to

determine what aspects of the environment explain the observed trait variation. This

field-based survey allows us to examine the relative influence of natural variation and

anthropogenic influence on innate immune defense traits in nature. I utilized a declining

amphibian species as my model to assist with species-specific conservation efforts.

1.4.3. Laboratory study: Rodeo™ herbicide exposure decreases larval survival,

and alters the skin-microbiome of Blanchard’s cricket frogs (Acris blanchardi).

Finding differences between A. blanchardi immune defense traits across

populations in association with anthropogenic environmental influence in my

observational field study compelled me to investigate a particular land-management

practice commonly used in habitats where A. blanchardi occur: herbicide use. I tested

whether a commercially available glyphosate-based herbicide, Rodeo™, which is

designated for use in and around aquatic sites by the US Environmental Protection

Agency, has sub-lethal effects on A. blanchardi, including effects on the innate immune

defense traits. Studies which determine toxicity of pesticides are typically focused on

lethal effects; however, sub-lethal effects can have long-term negative effects on wildlife

fitness and population persistence (Desneux et al. 2007; Fleeger et al. 2003; Rohr and

McCoy 2010). In this study, I examined the influence of Rodeo™ herbicide on A.

blanchardi across life-stages and at differing, environmentally relevant Rodeo™

concentrations.

17

1.5. Research Goals

My intention with these three studies was to investigate how the environment

influences amphibian innate immune defense traits as well as how host characteristics

(AMP production and AMP bioactivity) may influence the skin-associated microbiome.

It is my hope that by improving our understanding of influences on these traits we may be

able to prevent some disease-related declines via changes to conservation strategies

including bio-augmentation initiatives and changes to land management practices to

better protect amphibian immune health.

18

Chapter 2: Larval environment alters amphibian

immune defenses differentially across life stages and

populations.

2.1. In Press PLOS One



a

a. Department of Biology, Case Western Reserve University, 2080 Adelbert Road,

Cleveland, Ohio, 44106 USA

b. Research Department, The Holden Arboretum, 9500 Sperry Road, Willoughby,

OH 44094 USA

*Corresponding author: Address: Department of Biology, Case Western Reserve

University, 2080 Adelbert Road, Cleveland, Ohio, 44106 USA. Tel.: +1 216 368

5430.

E-mail addresses:

[email protected] (K.L. Krynak), [email protected] (M.F. Benard),

[email protected] (D.J. Burke)

2.2. Abstract

Recent global declines, extirpations and extinctions of wildlife caused by newly

emergent diseases highlight the need to improve our knowledge of common

environmental factors that affect the strength of immune defense traits. To achieve this

goal, we examined the influence of acidification and shading of the larval environment on

amphibian skin-associated innate immune defense traits, pre and post-metamorphosis,

across two populations of American bullfrogs (Rana catesbeiana), a species known for

its wide-ranging environmental tolerance and introduced global distribution. We assessed

treatment effects on 1) skin-associated microbial communities and 2) post-metamorphic

antimicrobial peptide (AMP) production and 3) AMP bioactivity against the fungal

pathogen Batrachochytrium dendrobatidis (Bd). While habitat acidification did not affect

survival, time to metamorphosis or juvenile mass, we found that a change in average pH

from 7 to 6 caused a significant shift in the larval skin microbial community, an effect

which disappeared after metamorphosis. Additionally, we found shifts in skin-associated

mailto:[email protected]



19

microbial communities across life stages suggesting they are affected by the

physiological or ecological changes associated with amphibian metamorphosis.

Moreover, we found that post-metamorphic AMP production and bioactivity were

significantly affected by the interactions between pH and shade treatments and interactive

effects differed across populations. In contrast, there were no significant interactions

between treatments on post-metamorphic microbial community structure suggesting that

variation in AMPs did not affect microbial community structure within our study. Our

findings indicate that commonly encountered variation in the larval environment (i.e.

pond pH and degree of shading) can have both immediate and long-term effects on the

amphibian innate immune defense traits. Our work suggests that the susceptibility of

amphibians to emerging diseases could be related to variability in the larval environment

and calls for research into the relative influence of potentially less benign anthropogenic

environmental changes on innate immune defense traits.

2.3. Introduction

Although it is well accepted that phenotypes vary between populations and are

influenced by environmental conditions, there is increasing interest in the effects of

environmental change on traits that affect resistance to newly emerging pathogens

(Bradley and Altizer 2007; Engering et al. 2013; Pounds et al. 2006). There is a large

body of evidence indicating that environmental change, including human induced

changes such as increasing temperatures, deforestation, and acidification, can alter an

organism’s growth, development and survival (Fockedey et al. 2005; Gruwez et al. 2014;

Schlosser et al. 2000; Scott et al. 2006; reviewed by Skelly 2001; Williams et al. 2013).

However, relatively few studies have experimentally examined the effects of

20

environmental change on immune defense traits, which may greatly affect individual

health (Dittmar et al. 2014; Groner et al. 2013; Loudon et al. 2014; reviewed by Norris

and Evans 2000). Given the rapid global spread of infectious diseases, a better

understanding of the environmental factors that govern the expression of immune defense

traits, and how this response to environmental change varies between populations and

across life-stages, is increasingly needed.

Amphibians are an excellent study group for examining the role of the

environment in regulating immune defense traits. Many of the diseases associated with

amphibian declines either enter the amphibian through the dermal tissue (i.e. skin), or

directly affect the dermal tissue (e.g. chytridiomycosis caused by the fungus

Batrachochytrium dendrobatidis) (Gray et al. 2009; Longcore et al. 1999; Raffel et al.

2008). Many amphibians possess two innate traits which resist pathogen infection of the

skin. First, amphibian adults and larvae harbor diverse microbial communities on their

skin (McKenzie et al. 2012). Some amphibian skin-associated microbial species produce

metabolites that suppress and eliminate some amphibian diseases (Belden and Harris

2007; Brucker et al. 2008; Harris et al. 2009; Harris et al. 2006; Lauer et al. 2007;

Woodhams et al. 2007b). Second, antimicrobial peptides (AMPs) produced by the

granular glands of amphibian skin provide an effective defense against a variety of

pathogens by disrupting pathogen cell and viral membranes (Rollins-Smith 2009;

Rollins-Smith and Conlon 2005; Rollins-Smith et al. 2005). How changes in the

environment affect skin-associated microbial communities and AMPs has not been

widely examined (Rollins-Smith et al. 2011).

21

The small number of studies which have examined the effect of the environment

on amphibian innate immune defense traits are largely correlative (Kueneman et al. 2014;

McKenzie et al. 2012; Woodhams et al. 2007a) and few have applied experimental

manipulations to examine how environmental factors affect these traits (Davidson et al.

2007; Groner et al. 2013; Groner et al. 2014; Kung et al. 2014; Loudon et al. 2014).

Experimental studies provide conflicting evidence on the degree to which the

environment may influence amphibian immune defense traits; some studies have found

that immune defenses are not altered by the environment (Groner et al. 2013; Loudon et

al. 2014; McKenzie et al. 2012) whereas other studies have found environmental effects

on immune defenses (Davidson et al. 2007; Kung et al. 2014). Even commonly

encountered variations to amphibian habitat may alter immune defense traits as has been

routinely found in studies examining traits associated with growth and development

(Benard 2004; Tejedo et al. 2010). Additionally, commonly encountered variations in the

environment may affect immune defense traits across life stages. Several studies have

found alterations to larval habitat including changes in canopy cover, pond ephemerality,

pollutants, predator exposure, and competitor densities can have long-term effects on

amphibian growth, survival, and performance (Benard 2004; Boes and Benard 2013;

Boone 2005; Goater 1994; Hagman et al. 2009; Relyea 2009; Webber et al. 2010);

however, only two studies, that we are aware of, have examined carry-over effects of the

environment on amphibian innate immune defense traits (Groner et al. 2013; Groner et al.

2014). These two studies found significant effects of larval exposure to predators and

competitors on post-metamorphic AMP production; however, the skin-associated

microbial community was not examined. To improve our understanding of environmental

22

influence on amphibian innate immune defense traits, additional studies are needed which

1) manipulate other commonly encountered amphibian environmental conditions 2)

examine multiple immune defense traits in unison, and 3) assess the influence of the

environment across life stages and populations. Knowledge of intraspecific differences in

response to environmental change will improve our understanding of the relative

importance of genetics and the environment on this aspect of amphibian health.

To test if commonly encountered variations in the environment simultaneously

alter amphibian immune defense traits (i.e. skin-associated microbial communities and

AMPs), we used the American bullfrog, Rana catesbeiana (also known as Lithobates

catesbeianus, sensu Frost et al (Frost et al. 2006)), as our model organism. We chose the

American bullfrog because of its high degree of environmental tolerance and introduced

global distribution (Ficetola et al. 2007). In our study, we hypothesized that commonly

encountered variation in the larval habitat, small pH shifts (i.e. from ~ 7 to 6) and the

presence or absence of pond shading (similar to canopy cover), can alter the microbial

communities and AMPs of R. catesbeiana skin with little change to traditional correlates

of amphibian fitness (survival, time to metamorphosis, and juvenile mass). Additionally

we predicted that the treatment effects may differ between R. catesbeiana populations

and that microbial community structure would change with ontogeny. Expecting that

these common environmental variations may affect these innate immune defense traits in

concert, we predicted that treatments affecting AMPs would similarly influence the post-

metamorphic (juvenile) microbial community.

23

2.4. Methods

2.4.1. Experimental set-up

We conducted our experiment in 80 circular polyethylene tanks (1,100 liter),

hereafter called “mesocosms”, located at Case Western Reserve University’s Squire

Valleevue Farm (Hunting Valley, Ohio). On June 2, 2011 we filled each mesocosm with

local pond water. Pond water was filtered using Phiefer Pet Screen to prevent predacious

macro-invertebrates from being transferred into the mesocosms. We added approximately

one gallon of dry leaves, collected from the mixed temperate hardwood forest floor

adjacent to the mesocosm field, to each mesocosm to provide substrate for microbial

growth and shelter for larvae. To prevent invasion by other species, tight-fitting screen

lids made of 60% shade cloth covered each mesocosm.

We used a randomized block design with three treatments, each of which had two

levels: population (larvae collected from two sites, one from southern Ohio and one from

northern Ohio), acidification (acidified or un-manipulated pH), and canopy cover (shade

or full sun) for a total of eight treatment combinations in five spatial blocks across the

mesocosm field. We replicated each treatment ten times, for a total of 80 experimental

units. To compare the effects of canopy cover, tent canopies (approximately three x three

meters) were randomly placed (by block) above half of the mesocosms on June 15, 2011.

To acidify the larval habitat we manipulated water pH so that the acidified treatment had

a pH of 5.5-6.5 (mean ±SE=6.0 ±0.6), while the un-manipulated treatment had a pH of

7.0-7.5 (mean ±SE =7.1 ± 0.5). To generate and maintain the lower pH we added

hydrochloric acid (HCl) and lowered the pH by approximately 0.2 pH units per day

beginning on 17 June. Acidification had three steps. First, five buckets each containing

approximately 12 liters of water were acquired from each mesocosm. Second, we added

24

30% HCL to the buckets of water via micropipetter based on pH reading of the water

(e.g. if 5ml of 30%HCL was to be added to the mesocosm, 1ml was administered to each

bucket). Third, we slowly and gently poured each bucket of acidified water back into the

mesocosm. Buckets were poured so as to thoroughly disperse the acid, mixing the

solution into the entire mesocosm water volume, which prevented direct exposure of

larvae to the concentrated acid product. To equalize disturbance across all mesocosms,

we also removed and replaced the same volume of water in the un-manipulated

treatments. We monitored pH on a daily basis using an Extech pH meter (model

#PH100). “Acidified” mesocosms reached the desired level of difference from “un-

manipulated” mesocosms ten days after experimental acidification initiation. Once pH

equilibration was reached, acidification procedure was decreased to one-two times per

week.

We collected 2000 hatchling R. catesbeiana larvae (Gosner stage 24-26; Gosner

1960) during the first two weeks of June 2011from each of two Ohio pond sites: southern

Ohio (Butler Co.) and northern Ohio (Wood Co.). The sites differ dramatically in terms

of anthropogenic influence. The southern Ohio site receives water from treated domestic

waste-water effluent, is largely unshaded with little canopy cover, and is presumed to

experience higher levels of pH instability due to runoff from the adjacent golf course and

the chemicals used by pond owners to control pond algal blooms (e.g. copper sulfate).

We measured pH at the southern Ohio site on June 6, 2011 and found it was 9.74

(ExTech model #PH100). In contrast, the rural northern Ohio pond site is a protected

pond (Wood County Park District) that is partially shaded from the sun with forest

surrounding the pond’s north, east, and west regions of the pool, with long-term fallow

25

fields of prairie plants and hawthorn trees at the pond’s southern side. We measured pH

at the northern Ohio site on June 11, 2011 and found it was 8.95 (ExTech model

#PH100). The southern Ohio population is located approximately 220km south of the

northern Ohio population’s collection site. Site access was permitted by landowners.

We added 50 R. catesbeiana larvae to each of the 80 mesocosms. Southern Ohio

larvae were collected on June 6, transported on June 7, and added to mesocosms on June

8, 2011 (a random sample of 10 tadpoles were all Gosner 25; Gosner 1960). Northern

Ohio larvae were collected on June 11 and 12, transported on June 12 and added to

mesocosms on June 13, 2011 (average Gosner Stage±SE: 25.1 ±0.11, N=10; Gosner

1960). Larval diet was supplemented with rabbit chow (Kaytee) throughout the duration

of the larval period to maintain adequate food availability for all larvae. Supplemental

food was administered equally across all mesocosms twice weekly (3.5g/mesocosm).

When larvae reached Gosner Stage 42 (i.e. when front legs erupt; Gosner 1960),

we transferred the first three metamorphosed juvenile frogs from each mesocosm to an

indoor animal room maintained at 28° C with a 12 hour light/dark cycle. While R.

catesbeiana commonly overwinter as larvae and may take up to three years to reach

metamorphosis (Wright and Wright 1949), high temperatures, low densities, and

associated higher food availability can facilitate rapid growth and development (Benitez-

Mandujano and Florez-Nava 1997; Collins 1979; Provenzano and Boone 2009). Rana

catesbeiana in the Midwestern United States are known to reach metamorphosis within a

single season (Collins 1979). We did not manipulate pH and shade in post-metamorphic

habitats. After three juvenile R. catesbeiana individuals had been transferred from each

mesocosm to the indoor laboratory facility, the remaining larvae in that mesocosm were

26

collected and counted. Three larvae were swabbed for microbial community analyses (see

sample collection description below) to examine treatment effects on the skin-associated

microbial communities of larvae. Larvae were subsequently euthanized using MS-222.

Unfortunately, across the 4000 larvae that were introduced to the mesocosms, eleven

were determined to be another species (Acris crepitans); however, all survival data

(percent survival) was corrected for this error. At the indoor laboratory facility, juvenile

R. catesbeiana were housed in 15L polyethylene boxes held at a slant (~15 degrees) so

that the 1L of de-chlorinated water in each box was deeper at one end providing both

terrestrial and aquatic regions. Plastic cups provided shelter. Juvenile R. catesbeiana were

fed five crickets per animal three times per week and water was changed three times

weekly (100% water change).

2.4.2. Data collection and analysis

Percent larval survival was determined by counts of larvae remaining at the end of

the larval rearing period and was log transformed. Due to unexplained, extremely high

mortality in a single mesocosm (only one animal reached the end of the experiment), this

mesocosm was eliminated from all analyses. Average survival in all other mesocosms

was 95.3% ± 1.3% SE. Average time to metamorphosis per mesocosm was found by

determining the average number of days from experiment beginning (date of larval

addition) until date of metamorphosis for the three juvenile frogs transferred to the indoor

laboratory facility. We log transformed average time to metamorphosis to meet

normality. We assessed treatment effects on percent survival and average time to

metamorphosis utilizing ANOVA (Type III sums of squares). Each response variable was

regressed on to all treatments (Acidification, Population, Shade), interactions between

treatments, and block. Mass (g) was obtained for each juvenile frog immediately post

27

euthanasia (post microbial community sampling and AMP collections) and was averaged

by mesocosm. Mass was cubed to meet normality (Tukey 1977). Treatment effects on

juvenile mass were assessed with ANCOVA to account for possible confounding effects

of when (age in days) mass data was collected in respect to date of metamorphosis

(predictor variable called “Days in lab” hereafter). All three post-metamorphic animals

from a single mesocosm died during the laboratory rearing portion of the study, and

subsequently, this mesocosm was excluded from all post-metamorphic trait analyses

(juvenile survival was 100% after excluding this mesocosm).

We collected microbial community samples of larvae and juvenile frogs using

sterile swabs (product # MW113, Advantage Bundling), pre-rinsing animals in sterile

water and subsequently gently rubbing the swab across the animal’s skin in a

standardized manner (McKenzie et al. 2012). Microbial samples taken from juvenile

frogs were collected immediately prior to AMP collection. Swabs containing skin

microbial community samples were subsequently frozen at -80° C in 2ml cryovials until

processed.

To avoid pseudo-replication, we pooled swabs by mesocosm and developmental

stage (i.e. swabs from animals contained in the same mesocosm were analyzed as a single

unit and larval swabs were analyzed separately from juvenile swabs). We extracted

microbial DNA from the skin swabs using a bead beating and phenol chloroform

extraction method (Burke et al. 2006a; Burke et al. 2006b). Negative PCR results using

two different primer sets (58A2F and NLB4, 58A2Fand ITS4) targeting the ITS2 gene

region of fungal DNA suggested that fungal communities did not contribute significantly

to the microbial community on the skin of the animals used in this study; therefore

28

further fungal community analyses were not performed. If fungal communities had

significantly contributed to the skin microbiome of animals in this study at either stage of

development (larvae or juvenile), quantification of Bd, Batrochochytrium dendrobatidis,

would have been conducted as a likely contributor to the fungal microbial community.

We amplified bacterial DNA using 16S rRNA gene primers: 338f and 926r (Muyzer et al.

1993) according to the Burke et al. (Burke et al. 2006b) protocol.

Using terminal restriction fragment length polymorphism profiling (TRFLP), we

examined microbial community structure across treatments (Burke et al. 2008; Burke et

al. 2006a; Burke et al. 2005, 2006b). This profiling procedure provides results

comparable to 454 pryosequencing when sampling across local spatial scales such as in

this study (van Dorst et al. 2014). We used restriction enzymes MspI and HaeIII

(Promega) to prepare samples for TRFLP profile analyses subsequently generated at the

Life Sciences Core Laboratory Center (Cornell University) using a GS600 LIZ size

standard (Applied Biosystems). We used Peak Scanner TM Software (version 1.0,

Applied Biosystems 2006) for our analyses. Only peaks which accounted for >1% of the

relative peak area were included in sample analyses (Burke et al. 2008). Only TRFs

produced by MspI restriction enzyme with the reverse primer were included in analyses

because HaeIII digests did not produce adequate fragment numbers. We used nonmetric

multi-dimensional scaling analyses (NMDS) and multi-response permutation procedures

(MRPP) to assess treatment effects on bacterial community structure in PC-ORD

(Version 5.0; Bruce McCune and MJM Software, 1999) for larvae and for juvenile frogs.

MRPP is a non-parametric discriminant function analysis which tests for difference

between two or more groups of entities. TRFLP profiles were arcsine-square root

29

transformed prior to analysis (McCune et al. 2002). We utilized a cloning and sequencing

approach to identify dominant members of the larval and juvenile frog skin-associated

microbial community (Qiagen PCR Cloning Plus) constructing two clone libraries

(Larvae N=78, Juveniles N=83) for larval and juvenile frog bacterial communities. We

archived resulting cloned sequences in GenBank (Appendix A; Accessions HF947349-

HF947509). Indicator species analyses were conducted on terminal restriction fragments

which were identified to taxa using predicted TRFs from the clone libraries. Indicator

species analysis (a monte carlo test) was completed using PC-ORD (version 5.0) and

determines whether bacterial species on R. catesbeiana skin differed between treatments

or life stages.

We collected AMP samples from juvenile frogs on September 15-17, 2011 using

a modified protocol by Rollins-Smith (Rollins-Smith et al. 2002) utilizing a 0.01% nor-

epinephrine bath to elicit the secretion of AMPs by juvenile frogs (Sheafor et al. 2008).

AMP samples were collected grouping frogs by mesocosm to avoid pseudo-replication.

Each group of frogs was placed in the nor-epinephrine bath (500µl of 20mM nor-

epinephrine hydrochloride in 50ml of collection buffer; collection buffer consists of

2.92g NaCl, 2.05g sodium acetate and 1L of HPLC grade water). The bath covered the

frogs’ bodies. Collection vessels were swirled to wash proteins from the frogs' skin and to

prevent frogs from climbing out of the bath. After 15 minutes the solution was removed

from the collection vial. The collected buffer (and secretions contained within) was then

immediately acidified with 100% TFA and filtered using a C-18 Sep-Pak Classic

Cartridge (Waters Corporation) and Sep-Paks were subsequently rinsed with 1%TFA

before storing. All juvenile frogs had completely absorbed tails prior to sample

30

collection. Samples (C-18 Sep-Paks) were frozen at -80° C until sample elution in

parafilm sealed falcon tubes to prevent desiccation. Eluted samples were dried at 15° C in

an Eppendorf VacufugeTM.

Samples were reconstituted in 1ml of sterile water (HPLC

grade) and syringe filtered (13mm Pall Acrodisc with Tuffryn™ membrane and 0.2µm

pore size). We utilized a Micro BCA ™ Protein Assay Kit (product # 23235) for analysis

of total protein concentration from our AMP sampling. We used 100µl reactions to

measure optical density at 562nm (absorbance) with a BioTek Synergy HT plate reader.

Absorbance measures were used to estimate concentration of the protein (µg/ml) using

Bradykinen as the protein standard (i.e. AMP production). Each sample and standard was

run in triplicate. The concentrations of the protein standard were log transformed and a

linear model was used to estimate protein concentration within each sample. AMP

production was averaged by mesocosm and standardized by total frog mass (i.e. mass of

the three juvenile frogs sampled was summed and µg/ml AMP was divided by this total

mass) and log transformed to meet normality. We standardized the measure of AMP

production by frog mass because larger frogs have more skin and therefore are likely to

produce more secretions. Standardizing by frog mass allows for cross treatment

comparisons without the potential confounding effects of the size of the frogs on this

measure of AMP production. We analyzed AMP production with ANCOVA (Type III)

by regressing AMP production (µg/ml per gram body weight) onto all predictor variables

(Acidification, Population, Shade), block and AMP collection time in respect to date of

metamorphosis (number of “Days in lab” before AMP sampling), including interactions

between Acidification, Population and Shade. Heteroscedasticity of the model was

31

quantitatively assessed via a Breusch-Pagan test, and the assumption of homogenous

variances was confirmed.

We conducted assays against Batrochocytrium dendrobatidis (Bd strain JEL 404,

originally isolated from a R. catesbieana larva in Oxford Co. Maine) in culture to

determine bioactivity of AMP samples. Based upon the BCA assay results, standardized

concentrations of each AMP sample were made. Final concentrations of 40µg/ml,

20µg/ml, 10µg/ml, 5µg/ml, and 1µg/ml were tested against Bd using a microplate

technique. 50µl of Bd zoospore solution at a concentration of 2 x 106 zoospores/ml (in

1% tryptone broth) was added to each well of a 96 well flat-bottom sterile plate. 50µl of

AMPs at the aforementioned concentrations was then added to each well (each

concentration for each sample replicated three times). We prepared positive and negative

controls on each 96 well plate (three replicates per control on each plate). Positive

controls consisted of 50ul of 2 x 106 Bd zoospores and 50ul of sterile PCR grade water

and negative controls contained 50µl of heat killed Bd zoospores of the same

concentration and 50µl of sterile PCR grade water (Gibble and Baer 2011; Gibble et al.

2008). We read optical density (OD; BioTek Synergy HT) of wells at 490nm on days 0

(immediately after plating), day 1(13 hours post plating), day 2, day 3, day 5, day 7, day

9, and day 11. Zoospore growth of all samples had plateaued by day 9. Percent growth

was determined for each sample (mesocosm) by subtracting mean OD490nm on day 9

from mean OD490nm on day 1 and multiplying by 100 for each sample. Bioactivity was

defined as the slope of the best fit line calculated from the log transformed growth curve

for each sample (Gibble and Baer 2011). We could not determine minimal inhibitory

concentration (MIC) in our bioassay because it was greater than 40 µg/ml; for this reason,

32

our log transformed growth curves are linear, allowing for bioactivity to be assessed

using the slope of the log transformed growth curves as the response variable in our

models. We suspect our inability to assess the MIC is because recently metamorphosed

juvenile bullfrogs produce relatively few AMPs. It is unknown at what point in post-

metamorphic development that amphibians are capable of producing their full repertoire

of AMPs (Holden et al. 2015; Schadich et al. 2010). We analyzed bioactivity (slope) with

ANCOVA (Type III) by regressing slope onto all predictor variables (Acidification,

Population, Shade), block, and Days in lab, including interactions between Acidification,

Population and Shade.

Due to the fact that not all bioassay samples show plateaued growth (OD490) on

the same day (range Day 3-Day 9), we examined potential treatment effects on a second

measure of bioactivity, growth rate. A logistic growth model was fit to data using a self-

starting nls logistic model function (R Development Core version 3.0.2, stats package,

José Pinheiro and Douglas Bates) for all samples at a concentration of 20µg/ml using a

reparameterized version of the logistic growth model (Formula A: below), where “P” is

the population size, “Po” is the original population size (population sizes measured as

OD490nm), “t” is time in days, “K” is the carrying capacity (plateau point of Bd growth),

and “r” is the growth rate.

Equation 1

Twenty µg/ml was the highest peptide concentration in which all samples were

represented. Growth rate “r” was then assigned as the response variable and regressed

onto all predictor variables (Acidification, Population, Shade), block and Days in lab,

33

including interactions between Acidification, Population and Shade in an ANCOVA

(Type III) model.

Unless otherwise stated, we completed statistical analyses using R (R Core Team

2013). All ANOVA and ANCOVA models were assessed using referent cell coding

(treatment contrasts as opposed to helmert contrasts); (Crawley 2007) examining the

effects of each treatment combination on each response variable as a separate model. This

methodology provides assessment of treatment effects within three-way interaction

models by conducting ANOVA/ANCOVA for each treatment combination

independently, comparing within-group means (Kleinbaum et al. 2014). Results are

described using prange

indicating a range of p values for each response across treatments.

This study was carried out in strict accordance with guidelines of the Ohio

Department of Natural Resources (permit number 14-222) and approved by Case

Western Reserve University’s Institutional Animal Care and Use Committee (IACUC

permit number 2011-0073).

2.5. Results

While we found no significant treatment effects on larval survival (Mean: 95.3%

±1.3% SE), there were treatment effects on the other larval traits. Shade significantly

delayed average time to metamorphosis (mean larval duration: shaded mesocosms 75.12

±0.56 days SE, unshaded mesocosms 69.35 ±0.83 days SE; prange

=0.0033 to 0.0395;

Figure 2.1, Table A1); however Acidification did not have a significant effect on average

time to metamorphosis (prange

=0.4767 to 0.9766). The southern population had

significantly longer larval duration than the northern population (mean larval duration:

southern Ohio 75.92± 0.75 days SE, northern Ohio 68.38 ±0.55 days SE; prange

=9.5 x 10-5

34

to 0.0165; Figure 2.1, Table A1). Population also significantly affected juvenile mass

(Mean: southern Ohio 4.28 ± 0.04g SE, northern Ohio 3.90 ± 0.06g SE, prange

=0.0072 to

0.0866; Figure 2.2, Table A2), even when taking duration of time between

metamorphosis and sample collection into account (Days in lab p=0.0042). In other

words, juvenile frogs held in the indoor laboratory facility for a longer period of time

were greater in mass. Acidification and Shade treatments did not significantly affect

juvenile mass at sample collection. No interactions were significant for any of these

models.

Figure 2.1 Average time to metamorphosis with standard error. Both Shade and Population were

significant predictors of mean larval duration under all treatment combinations (shade prange

=0.003 to 0.04;

population p=9.5 x10-5

to 0.017). Figure displays results of Population effects within Acidified

environments. Full ANOVA outputs can be found in Table A1.

35

Figure 2.2 Population effect on mean juvenile mass (g) at sample collection with standard error. Population and Days in lab were significant predictors of juvenile mass in many but not all treatment

environments (Population prange

=0.007 to 0.0866; Days in lab p=0.0042). Figure displays results of

Population effects within Shaded and Acidified treatments. Full ANCOVA outputs can be found in Table

A2.

NMDS and MRPP analyses indicated differences in microbial community

structure between developmental stages (larvae and juvenile frogs) (A=0.10, p<0.0001,

Table 2.1, Figure 2.3). Within the larval stage, acidification of the larval habitat altered

skin microbial communities (A=0.14, p<0.0001, Table 2.1, Figure 2.3). Our examination

of juvenile frog microbial community structure did not reveal any significant treatment

affects (Table 2.1). Clone library comparisons highlight the large difference in skin-

associated microbiota between larvae and juveniles most notably in terms of a shift from

a Bacteriodetes dominated (73%) larval flora to a Betaproteobacteria dominated (83%)

juvenile frog flora (Figure 2.4, Table A6). Multiple indicator species of developmental

stage (using predicted terminal restriction fragment size) were also found including the

genus Herbaspirillum which is only represented in the juvenile frog clone library and

Cetobacterium only represented in the larval clone library. Ideonella sp. was an indicator

36

of acidified treatment while Niastella sp. was an indicator of non-acidified treatment

within the larval clone library.

Table 2.1 MRPP results from microbial community comparisons. Significance (bold) defined as an

Affect Size (A) where A≥0.1and p≤0.05 (McCune and Grace 2002).

Grouping Factor Treatment A p

Combined samples

(larvae and metamorphs)

Developmental Stage 0.1 <0.0001

Acidification 0.04 <0.0001

Shade 0.004 0.0588

Population 0.0007 0.2819

Block 0.004 0.1651

Larvae

Acidification 0.140 <0.0001

Shade 0.0129 0.0365


Block 0.0021 0.3658

Juvenile Frogs

Acidification -0.0056 0.9596

Shade -0.0029 0.7071


Block 0.0276 0.0062

Acidification x Shade -0.0077 0.8442

Population x Shade -0.0084 0.872

Population x Acidification -0.0088 0.8866

37

Figure 2.3 NMDS ordination plot of Rana catesbeiana larval and juvenile frog microbial community

similarity by acidification treatment. N=152 after outlier analysis (McCune and Grace 2002). Ordination

stress =20%. Axes display percentage variance explained. Circles designate juvenile frog microbial

communities, triangles designate larval microbial communities. Open symbols designate acidified pH

treatments while closed symbols designate un-manipulated pH treatments.

38

Figure 2.4 Clone library comparison between larval and post-metamorphic (juvenile) Rana

catesbeiana skin-associated bacteria. The percent of the clone library represented by each taxonomic

group is shown. (Larvae library: N=78, Juvenile library: N=83)

Antimicrobial peptide (AMP) production analyses revealed significant

Acidification x Shade (p= 0.0272) and Population x Shade (p= 0.0501) interactions

across many, but not all treatment combinations (Figure 2.5; Table A3). These results

indicate that the populations utilized in our study responded differently to larval habitat

acidification and shading.

39

Figure 2.5 Interaction effects on AMP production (µg/ml standardized by gram body weight) with

standard error (Acidification x Shade p=0.0272; Population x Shade prange=0.0501 to 0.7868). A.

Northern referent. B. Southern referent. C. Acidified referent. Referent variables refer to a specific

treatment environment, indicating what two-way interaction is being displayed. Contrasts indicate

significant simple effects within each two-way interaction (p<0.05) (eg. A. indicates a significant

Acidification effect within the NoShade treatments and a significant Shade effect within the Acidified

treatments in the northern Population) (Crawley 2007; Kleinbaum et al. 2014). Full ANCOVA outputs can

be found in Table A3.

Antimicrobial peptide (AMP) bioactivity analyzed as slope of the log transformed

growth curves showed significant main effects of Shade (p=0.0175) and marginal

Population x Shade interaction effects (p=0.085) in some but not all environments; again,

indicating that the populations utilized in this study are responding differently in terms of

40

AMP bioactivity (slope), though our detection of a three-way interaction was marginal

(p=0.118; Figure 2.6, Table A4). When bioactivity was assessed using Bd growth rate “r”

calculated from the logistic growth model, we found significant (or marginally

significant) Population x Acidification interactions (prange

=0.0327 to 0.0839; Figure 2.7,

Table A5). This final measure of bioactivity in terms of Bd growth rate indicates

population level differences in response to larval habitat pH change.

Figure 2.6 Interactive effects on AMP bioactivity in terms of slope of the log-transformed growth

curve with standard error (Shade x Population p=0.085, Acidification x Shade x Population p= 0.12). A. Acidified referent. B. No Acid referent. Contrast indicates significant simple effect of Shade within un-

manipulated pH (NoAcid) treatments of the Northern population (p=0.018). Full ANCOVA results can be

found in Table A4.

41

Figure 2.7 Interactive effects on AMP bioactivity in terms of Bd growth rate with standard error

(Acid x Population prange

=0.033 to 0.084, Acidification x Shade x Population p=0.773) A. Sun referent.

B. Shade referent. Contrast indicates significant simple effect of Acidification within full sun (NoShade)

treatments of the Northern Population (p=0.018). Full ANCOVA results can be found in Table A5.

2.6. Discussion

Recent disease-associated declines, extirpations, and extinctions of amphibians

world-wide have resulted in numerous studies which examine relationships between

disease resistance and innate immune defense traits (Harris et al. 2009; Rollins-Smith

2009), but little is known about the influence of the environment on these traits, or how

consistent responses to environmental variations may be across populations (Belden and

Harris 2007; Rollins-Smith et al. 2011). Our findings support the hypothesis that common

variation in the larval environment can significantly alter amphibian immune defense

42

traits. By measuring both skin-associated microbial communities and antimicrobial

peptides we gain additional information to assess amphibian fitness beyond the

commonly measured correlates of fitness, traits such as survival, time to metamorphosis

and juvenile mass. While larval duration and juvenile mass were affected by pond

shading and population, these traits were not affected by larval habitat acidification.

Larval survival was not affected by any of our treatments. Microbial community structure

was affected by our small changes to larval habitat pH (i.e 1 pH unit), but this effect of

pH did not carry-over post-metamorphosis. We did not find effects of pond shading or

population on microbial community structure in either larvae or juvenile animals. Post-

metamorphic AMP production and bioactivity however revealed complex interactions

between these larval habitat changes and population in addition to indicating that the

larval environment has a legacy effect on AMPs expressed after metamorphosis.

We found that a pH change of 1 unit, near neutral, did not alter the commonly

measured correlates of fitness (e.g. survival, time to metamorphosis, juvenile mass). The

effects of pH changes near neutral (pH 7) have not been shown to affect survival, but can

cause changes in larval growth (Kiesecker 1996; Relyea 2006). However, low pH (≤4.7)

has been shown to negatively affect survival, larval duration, juvenile mass, and can

indirectly alter these traits through interspecific interactions (reviewed by Lacoul et al.

2011; Leuven et al. 1986; Pierce 1985; Rowe et al. 1992). In our study, an average

change from pH of 7 to pH of 6 in the larval habitat yielded surprising strong effects on

the microbial community inhabiting the skin of larval R. catesbeiana. The mechanism by

which these composition shifts occur is unknown. It is also unknown if this change in the

microbial community results in functional differences and if this change in microbial

43

community affects the larvae’s ability to resist disease. However, if skin-associated

microbial communities are an important defense against pathogens, it is conceivable that

the changes we observed could influence disease resistance. Meta-transcriptomic

approaches may assist future studies in assessing functional differences between skin-

associated microbial communities that develop from changes in pond water pH (Loudon

et al. 2014). Bacteria isolated from amphibian skin can produce metabolites that inhibit

pathogens (Brucker et al. 2008) and previous studies have noted that multiple bacterial

species from the Class Betaproteobacteria and Phylum Bacteriodetes, the dominate taxa

present in our samples, can provide amphibians with pathogen resistance (Becker et al.

2009; Lauer et al. 2007). Microbial species could also contribute to immune defense by

providing a physical barrier to infection or by stimulating the amphibians’ production of

antimicrobial peptides (AMPs) which constitutes the second innate immune defense trait;

therefore, environments which alter microbial community structure may also alter

resistance to pathogens through AMP production. Conversely, environments which alter

AMP production or relative proportions of AMP constituents may alter the microbial

associations of the amphibian skin. While no studies have examined this in amphibians,

similar relationships have been previously documented in human studies of skin-

associated microbial communities and AMP production (Grice and Segre 2011).

Microbial communities may also provide other benefits beyond disease resistance. For

example, as has been documented with plants, microbial communities could be assisting

their host organisms in processes such as osmoregulation and nutrient uptake (Bressan et

al. 2001; Lucio et al. 2013); therefore knowledge of how common variations in the

44

environment alter these communities may be important for understanding amphibian

health in ways that have yet to be explored.

Unlike the microbial community shift observed in our larval samples, pH of the

larval environment did not have a significant effect on the microbial community structure

of the juvenile frog skin. In other words, there was no evidence of carry-over effects of

the larval habitat pH on the juvenile frog skin-associated microbial community. Our

study did find significant shifts in the microbial community between larvae and newly

post-metamorphic juvenile frogs. These results are similar to those found by Kueneman

et al. (2014) which is the only other published study examining ontogenetic effects on the

amphibian skin-associated microbiome. In that field-based study, microbial community

structure differed between larvae and juvenile Rana cascadae, within a single site. The

difference in skin-associated microbial communities between larval and post-

metamorphic amphibians may be due to physiological changes undergone during

metamorphosis or are associated with the more terrestrial behavior of the post-

metamorphic frogs. It has been hypothesized that AMPs produced after metamorphosis

may regulate microbial community structure (Kung et al. 2014; Rollins-Smith 2009). If

microbial community structure is regulated by the AMPs after metamorphosis, we would

expect to see both AMPs and microbial community structure affected in similar ways by

our treatments. However, our treatments did not affect post-metamorphic microbial

community structure, suggesting that we can reject the hypothesized link between

microbial community structure and AMPs in this case. It is important to consider that if

AMP production was affected to a much greater extent, it is possible that this may shift

the skin-associated microbial community.

45

Multiple hypotheses could explain the differences between populations in AMP

production and bioactivity in response to our experimental treatments including

differential ability of populations to plastically respond to our environmental

perturbations, differences in maternal investment between populations, carry-over effects

from early life-experiences prior to larval collection, or local adaptation (Bashey 2006;

Chapman et al. 2010; Murphy et al. 2014; Stillwell and Fox 2005). We found significant

increases in AMPs produced by animals from the northern population, which is in stark

contrast to the lack of response by the southern Ohio population to our treatments. We

suspect the southern Ohio collection site to be highly variable in terms of water quality as

it is receiving water for treated residential sewage and is located next to a chemically

treated golf course. Our mesocosm environments would therefore be more different from

the native environment for the northern Ohio population (little natural variation in water

quality) than the southern Ohio population (high variability in water quality). Consistent

with the hypothesis of local adaptation or carry-over effects of early life experience,

increased AMP production by the northern population may indicate a stress response

caused by the relatively large change in environmental conditions in respect to the stable

conditions the population has adapted to (Rollins-Smith 2009). On the other hand, the

lack of response by the southern population may reflect adaptation to highly variable and

potentially stressful water quality conditions stemming from chemical contamination of

the pond by human activities. Future studies may need to measure levels of corticosteroid

or other stress associated hormones to elucidate potential mechanistic relationships

between environmental change and stress response in terms of AMP production. AMP

bioactivity of these natural peptide mixtures may also be decreased with increasing AMP

46

production because of changes in the relative proportion of AMPs produced (Gibble and

Baer 2011; Rollins-Smith 2009). Future research should examine effects of such

commonly encountered variations in the environment on AMP constituents as could be

measured by high pressure liquid chromatography (HPLC) analyses (Conlon and

Sonnevend 2010). This would allow us to examine how commonly encountered

variations in the environment alter relative proportions of AMPs produced by different

populations.

Our finding that common larval habitat changes carried-over to alter post-

metamorphic AMP bioactivity was surprising and supports the hypothesis that the larval

environment can have long-term effects on amphibian health. Few studies have examined

the potential carry-over effects of the larval habitat on post-metamorphic immune defense

traits (Groner et al. 2013; Groner et al. 2014). While our two measures of AMP

bioactivity provide somewhat conflicting results, this may be explained by a lack of

statistical power to detect the three-way interaction between acidification, shade and

population. This finding provides future researchers with rational for careful

consideration of the likely complicated interactive effects on amphibian immune defense

traits.

2.7. Conclusions

We found that commonly encountered variation in environmental conditions can

alter amphibian innate immune defense traits differentially across populations and life-

stages. Natural environmental variation in soil chemistry (e.g. pH, alkalinity) is expected

at a landscape level, due to changes in geology, climate or land cover. If immune defense

traits, as found in this study, are affected by these natural changes, our results have

47

implications for our understanding of differences in the magnitude of disease outbreaks

and mortality between populations at the landscape level. Our research also has

implication for our understanding of how anthropogenic change may differentially affect

population immune defense traits and response to disease pressure. Global climate

change, agrochemical usage and run-off, and invasive species interactions with native

wildlife all have the potential to alter immune defense traits either directly or indirectly

and quite possibly to a greater degree than our treatments induced, but studies of the

effects of anthropogenic influence on immune defense traits and correlated responses of

populations to disease pressure are currently lacking. In addition, our work suggests that

future studies should incorporate multiple developmental stages in such analyses, for as

we have shown, changes to larval habitat may have long-term effects on traits not

measureable until later developmental stages. Many previous studies have shown species

level differences in skin-associated microbial communities and AMPs (Kueneman et al.

2014; McKenzie et al. 2012; Rollins-Smith and Conlon 2005; Woodhams et al. 2007a)

but population level variation of these traits and the influence of the environment on these

traits across populations is an area of research which needs further exploration (Rollins-

Smith et al. 2011). Such research programs have the potential to identify unforeseen

direct and indirect effects of anthropogenic environmental changes to species’ immune

defense traits and disease resistance capabilities, providing an opportunity to prevent

future catastrophic declines associated with newly emergent disease via changes to our

land-management practices.

48

2.8. Appendices

2.8.1. Table A1. ANOVA results examining treatment effects on average time to

metamorphosis. a. Referent: Northern population, No shade, Acidified pH. b.

Referent: Northern population, Shade, Acidified pH. c. Referent: Northern

population, No Shade, Un-manipulated pH. d. Referent: Northern population, Shade,

Un-manipulated pH. e. Referent: Southern population, No shade, Acidified pH. f.

Referent: Southern population, Shade, Acidified pH. g. Referent: Southern

population, No Shade, Un-manipulated pH. h. Referent: Southern population, Shade,

Un-manipulated pH. Significant results in bold.

a. ANOVA results examining treatment effects on average time to metamorphosis.

Significant results in bold. Referent: Northern population, No shade, Acidified pH.

Response Treatment df F p

Larval

Duration

(days)

Acidification 1,67 0.5121 0.4767

Shade 1,67 9.2956 0.0033

Population 1,67 11.6104 0.0011

Block 4,67 1.5853 0.1884

b. ANOVA results examining treatment effects on average time to metamorphosis. Significant results in bold. Referent: Northern population, Shade, Acidified pH.


Larval

Duration

(days)


Shade 1,67 9.2956 0.0033

Population 1,67 6.0467 0.0165

Block 4,67 1.5853 0.1884

c. ANOVA results examining treatment effects on average time to metamorphosis. Significant results in bold. Referent: Northern population, No Shade, Un-manipulated

pH.


Larval

Duration

(days)


Shade 1,67 8.5333 0.0047

Population 1,67 17.2434 9.5x 10-5

Block 4,67 1.5853 0.1884

d. ANOVA results examining treatment effects on average time to metamorphosis. Significant results in bold. Referent: Northern population, Shade, Un-manipulated pH.


Larval

Duration

(days)


Shade 1,67 8.5333 0.0047

Population 1,67 13.8682 0.0004

Block 4,67 1.5853 0.1884

e. ANOVA results examining treatment effects on average time to metamorphosis. Significant results in bold. Referent: Southern population, No shade, Acidified pH.


Larval

Duration

(days)


Shade 1,67 4.4120 0.0395

Population 1,67 11.6104 0.0011

Block 4,67 1.5853 0.1884

49

f. ANOVA results examining treatment effects on average time to metamorphosis. Significant results in bold. Referent: Southern population, Shade, Acidified pH.


Larval

Duration (days)


Shade 1,67 4.4120 0.0395

Population 1,67 6.0467 0.0165

Block 4,67 1.5853 0.1884

g. ANOVA results examining treatment effects on average time to metamorphosis.

Significant results in bold. Referent: Southern population, No Shade, Un-manipulated

pH.


Larval

Duration

(days)


Shade 1,67 7.2110 0.0091

Population 1,67 17.2434 9.5 x 10-5

Block 4,67 1.5853 0.1884

h. ANOVA results examining treatment effects on average time to metamorphosis. Significant results in bold. Referent: Southern population, Shade, Un-manipulated pH.


Larval

Duration

(days)


Shade 1,67 7.2110 0.0091

Population 1,67 13.8682 0.0004

Block 4,67 1.5853 0.1884

50

2.8.2. Table A2. ANCOVA results examining treatment effects on Juvenile Mass.

a. Referent: Northern population, No shade, Acidified pH. b. Referent: Northern

population, Shade, Acidified pH. c. Referent: Northern population, No Shade, Un-

manipulated pH. d. Referent: Northern population, Shade, Un-manipulated pH. e.

Referent: Southern population, No shade, Acidified pH. f. Referent: Southern

population, Shade, Acidified pH. g. Referent: Southern population, No Shade, Un-

manipulated pH. h. Referent: Southern population, Shade, Un-manipulated pH.

Significant results in bold.

a. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Northern population, No shade, Acidified pH.


Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 .03182 0.5747

Population 1,65 7.6957 0.0072

Block 4,65 0.5596 0.6928

b. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Northern population, Shade, Acidified pH.


Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 0.3182 0.5747

Population 1,65 3.0282 0.0866

Block 4,65 0.5596 0.6928

c. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Northern population, No Shade, Un-manipulated pH.


Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 1.3172 0.2553

Population 1,65 5.6603 0.0203

Block 4,65 0.5596 0.6928

d. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Northern population, Shade, Un-manipulated pH.

Response Treatment df F P

Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 1.3172 0.2553

Population 1,65 3.6277 0.0613

Block 4,65 0.5596 0.6928

51

e. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Southern population, No shade, Acidified pH.


Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 0.1811 0.6718

Population 1,65 7.6957 0.0072

Block 4,65 0.5596 0.6928

f. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Southern population, Shade, Acidified pH.


Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 0.1811 0.6718

Population 1,65 3.0282 0.0866

Block 4,65 0.5596 0.6928

g. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Southern population, No Shade, Un-manipulated pH.


Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 0.4909 0.4860

Population 1,65 5.6603 0.0203

Block 4,65 0.5596 0.6928

h. ANCOVA results examining treatment effects on Juvenile Mass. Significant results

in bold. Referent: Southern population, Shade, Un-manipulated pH.


Juvenile mass

(g)

Days in lab 1,65 8.7818 0.0042


Shade 1,65 0.4909 0.4860

Population 1,65 3.6277 0.0613

Block 4,65 0.5596 0.6928

52

2.8.3. Table A3. ANCOVA results examining treatment effects on mean AMP

production (standardized by gram body weight). a. Referent: Northern population,

No shade, Acidified pH. b. Referent: Northern population, Shade, Acidified pH. c.

Referent: Northern population, No Shade, Un-manipulated pH. d. Referent: Northern

population, Shade, Un-manipulated pH. e. Referent: Southern population, No shade,

Acidified pH. f. Referent: Southern population, Shade, Acidified pH. g. Referent:


population, Shade, Un-manipulated pH. Significant results in bold.

a. ANCOVA results examining treatment effects on mean AMP production

(standardized by gram body weight). Significant results in bold. Referent: Northern

population, No shade, Acidified pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 4.1441 0.0459

Population 1,65 0.5392 0.4654

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 5.1084 0.0272

Acid x Population 1,65 0.3452 0.5589

Shade x Population 1,65 3.9849 0.0501

Acid x Shade x

Population 1,65 1.4104 0.2393

b. ANCOVA results examining treatment effects on mean AMP production


population, Shade, Acidified pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 4.1441 0.0459

Population 1,65 4.3546 0.0408

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 5.1084 0.0272



Acid x Shade x

Population 1,65 1.4104 0.2393

53

c. ANCOVA results examining treatment effects on mean AMP production


population, No Shade, Un-manipulated pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 0.8808 0.3515

Population 1,65 0.0100 0.9205

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 5.1084 0.0272



Acid x Shade x

Population 1,65 1.4104 0.2393

d. ANCOVA results examining treatment effects on mean AMP production


population, Shade, Un-manipulated pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 0.8808 0.3515

Population 1,65 0.2252 0.6367

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 5.1084 0.0272



Acid x Shade x

Population 1,65 1.4104 0.2393

e. ANCOVA results examining treatent effects on mean AMP production

(standardized by gram body weight). Significant results in bold. Referent: Southern

population, No shade, Acidified pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 0.3543 0.5538

Population 1,65 0.5392 0.4654

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 0.3094 0.5799


Shade x

Population 1,65 3.9849 0.0501

Acid x Shade x

Population 1,65 1.4104 0.2393

54

f. ANCOVA results examining treatment effects on mean AMP production


population, Shade, Acidified pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 0.3543 0.5538

Population 1,65 4.3546 0.0408

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 0.3094 0.5710


Shade x

Population 1,65 3.9849 0.0501

Acid x Shade x

Population 1,65 1.4104 0.2393

g. ANCOVA results examining treatment effects on mean AMP production


population, No Shade, Un-manipulated pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 1.4712 0.2296

Population 1,65 0.0100 0.9205

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 0.3094 0.5799



Acid x Shade x

Population 1,65 1.4104 0.2393

h. ANCOVA results examining treatment effects on mean AMP production


population, Shade, Un-manipulated pH.


Mean AMP

production

(ug/ml)

Days in lab 1,65 0.4087 0.5249


Shade 1,65 1.4712 0.2296

Population 1,65 0.2252 0.6367

Block 4,65 0.4132 0.7985

Acid x Shade 1,65 0.3094 0.5799



Acid x Shade x

Population 1,65 1.4104 0.2393

55

2.8.4. Table A4. ANCOVA results examining treatment effects on AMP

bioactivity (defined as the slope of the log-transformed growth curve). a.

Referent: Northern population, No shade, Acidified pH. b. Referent: Northern

population, Shade, Acidified pH. c. Referent: Northern population, No Shade, Un-

manipulated pH. d. Referent: Northern population, Shade, Un-manipulated pH. e.

Referent: Southern population, No shade, Acidified pH. f. Referent: Southern

population, Shade, Acidified pH. g. Referent: Southern population, No Shade, Un-

manipulated pH. h. Referent: Southern population, Shade, Un-manipulated pH.

Significant results in bold.

a. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

slope of the log-transformed growth curve). Significant results in bold. Referent:

Northern population, No shade, Acidified pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 0.9599 0.3309

Population 1,65 0.1112 0.7399

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.3047 0.2575



Acid x Shade x

Population 1,65 2.4998 0.1187

b. ANCOVA results examining treatment effects on AMP bioactivity (defined as the


Northern population, Shade, Acidified pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 0.9599 0.3309

Population 1,65 0.1131 0.7378

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.3047 0.2575



Acid x Shade x

Population 1,65 2.4998 0.1187

56

c. ANCOVA results examining treatment effects on AMP bioactivity (defined as the


Northern population, No Shade, Un-manipulated pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 5.9510 0.0175

Population 1,65 2.6192 0.1104

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.3047 0.2575



Acid x Shade x

Population 1,65 2.4998 0.1187

d. ANCOVA results examining treatment effects on AMP bioactivity (defined as the


Northern population, Shade, Un-manipulated pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 5.9510 0.0175

Population 1,65 0.7188 0.3996

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.3047 0.2575



Acid x Shade x

Population 1,65 2.4998 0.1187

e. ANCOVA results examining treatment effects on AMP bioactivity (defined as the


Southern population, No shade, Acidified pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 2.7261 0.1036

Population 1,65 0.1112 0.7399

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.1987 0.2776



Acid x Shade x

Population 1,65 2.4998 0.1187

57

f. ANCOVA results examining treatment effects on AMP bioactivity (defined as the


Southern population, Shade, Acidified pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 2.7261 0.1036

Population 1,65 0.1131 0.7378

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.1987 0.2776



Acid x Shade x

Population 1,65 2.4998 0.1187

g. ANCOVA results examining treatment effects on AMP bioactivity (defined as the


Southern population, No Shade, Un-manipulated pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 0.0132 0.9089

Population 1,65 2.6192 0.1104

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.1987 0.2776



Acid x Shade x

Population 1,65 2.4998 0.1187

h. ANCOVA results examining treatment effects on AMP bioactivity (defined as the


Southern population, Shade, Un-manipulated pH.


Bioactivity

(slope)

Days in lab 1,65 2.9846 0.0888


Shade 1,65 0.0132 0.9089

Population 1,65 0.7188 0.3996

Block 4,65 0.8954 0.4719

Acid x Shade 1,65 1.1987 0.2776



Acid x Shade x

Population 1,65 2.4998 0.1187

58

2.8.5. Table A5. ANCOVA results examining treatment effects on AMP

bioactivity (defined as the Bd growth rate). a. Referent: Northern population, No

shade, Acidified pH. b. Referent: Northern population, Shade, Acidified pH. c.

Referent: Northern population, No Shade, Un-manipulated pH. d. Referent: Northern

population, Shade, Un-manipulated pH. e. Referent: Southern population, No shade,

Acidified pH. f. Referent: Southern population, Shade, Acidified pH. g. Referent:


population, Shade, Un-manipulated pH. Significant results in bold.

a. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Northern population, No shade,

Acidified pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 0.3013 0.5850

Population 1,65 1.4540 0.2323

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.3860 0.5366



Acid x Shade x

Population 1,65 0.0840 0.7729

b. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Northern population, Shade,

Acidified pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 0.3013 0.5850

Population 1,65 1.8659 0.1767

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.3860 0.5366



Acid x Shade x

Population 1,65 0.0840 0.7729

59

c. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Northern population, No Shade,

Un-manipulated pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 1.8023 0.1841

Population 1,65 3.4687 0.0671

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.3860 0.5366



Acid x Shade x

Population 1,65 0.0840 0.7729

d. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Northern population, Shade, Un-

manipulated pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 1.8023 0.1841

Population 1,65 1.2395 0.2697

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.3860 0.5366



Acid x Shade x

Population 1,65 0.0840 0.7729

e. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Southern population, No shade,

Acidified pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 0.1788 0.6738

Population 1,65 1.4540 0.2323

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.0417 0.8388



Acid x Shade x

Population 1,65 0.0840 0.7729

60

f. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Southern population, Shade,

Acidified pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 0.1788 0.6738

Population 1,65 1.8659 0.1767

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.0417 0.8388



Acid x Shade x

Population 1,65 0.0840 0.7729

g. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Southern population, No Shade,

Un-manipulated pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 0.36926 0.5331

Population 1,65 3.4687 0.0671

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.0417 0.8388



Acid x Shade x

Population 1,65 0.0840 0.7729

h. ANCOVA results examining treatment effects on AMP bioactivity (defined as the

Bd growth rate). Significant results in bold. Referent: Southern population, Shade, Un-

manipulated pH.


Bioactivity (Bd

growth rate)

Days in lab 1,65 2.6786 0.1065


Shade 1,65 0.3926 0.5331

Population 1,65 1.2395 0.2697

Block 4,65 0.2306 0.9202

Acid x Shade 1,65 0.0417 0.8388



Acid x Shade x

Population 1,65 0.0840 0.7729

61

2.8.6. Table A6. The sequence similarity of clones (out of 161 total) created from

skin swabs of R.catesbeiana using primers 926r and 338f. Identification is based

upon comparison to NCBI database entries using the FASTA program (National

Center for Biotechnology Information). The percent identity (% ID) to best match is

shown.

Clone

ID

Clone

Accession

ID

Best Match

Accession ID Best Match %ID Division

BF_M01 HF947349 HE993549.1 Ideonella sp. 99 Betaproteobacteria



BF_M05 HF947352 JX177698.1 Limnobacter sp. 99 Betaproteobacteria

BF_M06 HF947353 HE614874.1 Vogesella perlucida 99 Betaproteobacteria

BF_M07 HF947354 HE653237.1 Flavobacterium sp. 99 Bacteriodetes

BF_M08 HF947355 KC294078.1 Comamonas sp. 98 Betaproteobacteria




BF_M12 HF947359 GQ284439.1 Limnobacter thioxidans 96 Betaproteobacteria



BF_M15 HF947362 HQ396921.1 Acineto bacterjunii 100 Gammaproteobacteria




BF_M19 HF947366 FM886888.1 Pelomonas saccharophila 100 Betaproteobacteria

BF_M20 HF947367 AB627080.1 Clostrdium sensustricto 100 Firmicutes


BF_M23 HF947369 KC294078.1 Comamonas sp. 100 Betaproteobacteria

BF_M24 HF947370 NR_043315.1 Brevundimonas

kwangchunensis

100 Alphaproteobacteria

BF_M25 HF947371 NR_044326.1 Vogesella sp. 100 Betaproteobacteria

BF_M26 HF947372 FM886864.1 Comamonadaceae

bacterium

99 Betaproteobacteria


BF_M29 HF947374 GQ284439.1 Limnobacter sp. 100 Betaproteobacteria


BF_M31 HF947376 AY308840.1 Flectobacillus sp. 99 Bacteriodetes




BF_M35 HF947380 AJ556799.1 Comamonadaceae 98 Betaproteobacteria


BF_M37 HF947382 JQ317253.1 Bacteriodes sp. 99 Bacteriodetes


BF_M39 HF947384 DQ854973.1 Ideonella sp. 93 Betaproteobacteria




BF_M44 HF947388 HE648174.1 Undibacterium sp. 99 Betaproteobacteria

BF_M45 HF947389 M99574.1 Epulopiscium sp. 99 Firmicutes

BF_M46 HF947390 AB698738.1 Methylotenera mobilis 99 Betaproteobacteria


62

















BF_M68 HF947408 AB076853.1 Comamonas sp. 99 Betaproteobacteria



BF_M72 HF947411 HQ288929.1 Azospirillum lipoferum 99 Alphaproteobacteria

BF_M73 HF947412 FJ906694.2 Rhodobacter sp. 99 Alphaproteobacteria


BF_M75 HF947414 HQ538615.1 Herbaspirillum sp. 95 Betaproteobacteria









BF_M86 HF947423 JQ995475.1 Zoogloea resiniphila 96 Betaproteobacteria

BF_M87 HF947424 AB696863.1 Ideonella sp. 99 Betaproteobacteria




BF_M91 HF947428 HE616175.1 Rhizobacter sp. 97 Gammaproteobacteria




BF_T01 HF947432 JF102672.1 Chitinophaga

ginsengisegetis

91 Bacteriodetes

BF_T02 HF947433 HE600686.1 Limnohabitans sp. 99 Betaproteobacteria

BF_T03 HF947434 JF710262.1 Chitinophaga sp. 91 Bacteriodetes







BF_T13 HF947441 AB353123.1 Cetobacterium somerae 97 Fusobacteriales


BF_T16 HF947443 JF824804.1 Alistipes sp. 93 Bacteriodetes




63



BF_T23 HF947449 AB793710.1 Clostridium sp. 90 Firmicutes













BF_T39 HF947462 NR 025421.1 Limnobacter thiooxidans 91 Betaproteobacteria




BF_T43 HF947466 GQ140629.1 Alistipes sp. 89 Bacteriodetes














BF_T57 HF947480 NR 029213.2 Burkholderia graminis 85 Betaproteobacteria


BF_T59 HF947482 AB688628.1 Rickettsiaceae endo-

symbiont of

carteriacerasiformes

100 Alphaproteobacteria



















64





BF_T86 HF947505 AB360415.1 Niastella sp. 92 Bacteriodetes

BF_T88 HF947506 JQ317253.1 Bacteriodes sp. 91 Bacteriodetes

BF_T89 HF947507 AB360415.1 Niastella sp. 91 Bacteriodetes



65

Chapter 3: Landscape and water characteristics

correlate with immune defense traits across

Blanchard’s cricket frog (Acris blanchardi) populations

3.1. Submitted for publication review



a




OH 44094 USA



5430.

E-mail addresses:



3.2. Abstract

Amphibians are protected from pathogens by two skin-associated immune

defense traits: the skin microbiome and the antimicrobial peptides (AMPs) produced

within the skin. Although environmental change alters amphibian traits such as growth,

development, and behavior, we know little about how geographic variation and

environmental characteristics may affect amphibian immune defense traits and disease

resistance. An excellent model to investigate this is the Blanchard’s cricket frog (Acris

blanchardi), a species suspected to be in decline due to a variety of anthropogenic

environmental changes. We conducted a field survey across the northern edge of the

species’ range where it has undergone severe declines. We surveyed the skin-associated

microbial communities (microbiome) and natural peptide secretions (AMPs) at each site

and utilized an AICc model selection and model averaging approach to test for potential

environmental influence on these traits. We found that populations differed in

microbiomes and AMP production, but not AMP bioactivity against Bd




66

(Batrachochytrium dendrobatidis). The microbiome was associated with water

conductivity, ratio of natural to managed land, and latitude. Additionally the microbiome

was affected by interactions between frog sex and latitude, between frog sex and water

surface area, and between the ratio of natural to managed land and water surface area.

AMP production was influenced by an interaction between water surface area and

conductivity. Host characteristics (AMPs) did not influence the microbiome; however,

Bd growth rate in culture was positively associated with AMP production. This study

indicates environmental characteristics can influence amphibian immune defense traits

and may explain population differences in pathogen resistance.

3.3. Introduction

Amphibian populations have experienced large declines over the last several

decades as a result of anthropogenic disturbance including habitat destruction,

environmental contamination and the introduction of invasive pathogens (Daszak et al.

2003). Amphibians with small effective population size and limited dispersal capabilities

may be particularly vulnerable to disease-associated mortality and subsequent decline if

changes in their environment depress immune function. The Blanchard’s cricket frog,

Acris blanchardi, is one such species (Gray 1983, Burkett 1984). This species has

undergone dramatic declines over the past four decades (Beauclerc et al. 2010; Gray and

Brown 2005) and a variety of anthropogenic environmental alterations including habitat

loss, fragmentation, acidification, and chemical contamination have been hypothesized to

have caused these declines (Lehtinen and Skinner 2006; Reeder et al. 2005; Russell et al.

2002). In addition, disease outbreaks, including those caused by Batrachochytrium

dendrobatidis (Bd), a fungal pathogen associated with global amphibian declines and

67

extinctions, have been suspected as having a potential role in these declines (Gray et al.

2009; Steiner and Lehtinen 2008). However, synergistic interactions between

environmental change and disease are likely (Hayes et al. 2010). Acris blanchardi also

have highly vascularized skin, which may enhance the effects of chemical contamination

and disease susceptibility (Beasley et al. 2005). This potential sensitivity, suspected

disease susceptibility and declining status make A. blanchardi an excellent model for

examining environmental influence on skin-associated immune defense traits.

Amphibians are protected from pathogens in the environment via two skin-

associated immune defense traits: the microbial communities (microbiome) inhabiting the

skin surface (Harris et al. 2006) and the anti-microbial peptides (AMPS) produced by

granular glands within the host’s skin (Rollins-Smith et al. 2005). These traits act as a

first line of defense against pathogen invasion (Rollins-Smith 2009), therefore

understanding environmental factors which cause differences in these traits between

populations is important for understanding disease resistance and susceptibility. It is

known that the structure of the amphibian skin microbiome correlates strongly with host

species (Kueneman et al. 2014; McKenzie et al. 2012) and there is also evidence that

microbiome structure changes with host ontogeny (Krynak et al. In Press; Kueneman et

al. 2014). In contrast, few studies test for differences in microbiome structure across

amphibian populations and little is known about what components of the environment

influence interpopulation variation in the amphibian microbiome (Becker et al. 2014;

Fitzpatrick and Allison 2014; Krynak et al. In Press). Similarly, there are few tests for

intraspecific variation in AMPs (Tennessen et al. 2009) and little information regarding

potential host or environmental characteristics which may account for these population

68

level differences (Groner et al. 2013; Groner et al. 2014; Krynak et al. In Press;

Woodhams et al. 2007a). Even common environmental variation, such as small shifts in

pH (7 to 6) and degree of pond shading, can alter amphibian skin microbiome and AMP

production (Krynak et al. In Press). Studies which assess the influence of environmental

characteristics on these traits across populations can improve our understanding of

differential disease resistance, and provide rationale for altering land-management

practices to better protect wildlife health.

Variation in water characteristics including pond pH, alkalinity, total phosphate

levels, and conductivity, may explain skin-associated immune-defense trait differences

across amphibian populations. Environmental pollutants which alter these water

characteristics have been associated with increased rates of amphibian skeletal

deformities and parasitic infections (Hopkins et al. 2013; Hopkins et al. 2000). Water

quality characteristics have also been associated with effects on other more traditional

fitness correlates including survival (Dobbs et al. 2012; Karraker and Ruthig 2009), larval

duration (Ling et al. 1986), and post-metamorphic mass (Brand et al. 2010; Rowe et al.

1992; Smith and Burgett 2012). Landscape-level environmental characteristics such as

amount of residential and agricultural habitat are also associated with effects on these

traditional fitness correlates. Land management practices are associated with changes in

amphibian abundance, growth rate and body size (Barrett et al. 2010; Gray and Smith

2005; Gray et al. 2004). Although growth and development are correlated with amphibian

fitness (Semlitsch et al. 1988; Stephens et al. 2013), their assessment alone may give an

incomplete picture of the effect of environmental change on amphibian population

persistence and disease resistance capabilities (Gervasi and Foufopoulos 2008).

69

To determine the effect of water quality and landscape characteristics on A.

blanchardi skin-associated immune defense traits, we conducted a field survey across

pond sites in Ohio and Michigan. Our sites extended in a latitudinal transect across the

northern edge of the species’ geographic range (Figure 3.1).We surveyed the skin-

associated microbiome and AMPs (in the form of natural peptide secretions) of multiple

individuals at each site. We hypothesized that 1) environmental variation across sites

correlate with differences in immune defense traits among populations 2) that pond site

would explain differences in microbiome structure, AMP production, and AMP

bioactivity and 3) that trait differences would correlate to differences between sites in

terms of water and landscape characteristics.

Figure 3.1 Geographic range of Acris blanchardi and areas of documented decline are shown in

dotted dark gray (Gamble et al. 2008).

70

3.4. Methods

3.4.1. Site selection

Between May 30 and June 28 of 2012, we assessed 52 potential sampling sites,

including a mix of historic and predicted (based on habitat type) A. blanchardi

populations (Lehtinen 2002). We chose sampling sites based on A. blanchardi population

size and accessibility. We assumed populations to be independent if they were greater

than 2km from other sites based on the low dispersal distance in Acris sp. (Gray 1983;

Gray and Brown 2005). Since many A. blanchardi populations are experiencing dramatic

declines, if a population was deemed small (<100 calling males), we did not include the

site in the immune defense trait survey. Only 11 sites had large enough populations and

occurred in terrain conducive for animal capture (Figure 3.2).We sampled sites after the

main breeding period to avoid removing animals from the populations before they had

reproduced (Gray 1983). These sites varied in water and landscape characteristics (Table

3.1) and some sites may be highly influenced by anthropogenic factors. Consequently,

our study sites span a range from relatively undisturbed habitat to habitat greatly affected

by human activities including chemical treatment.

71

Figure 3.2 Survey site locations in Ohio and Michigan across a portion of Acris blanchardi’s declining

range (source: lat 40.405760 long -82.930501. Google Earth. May 9 2013. Februrary 11, 2015).

Table 3.1 Survey site water characteristics and number of individual Acris blanchardi sampled.

Site Co., State (N=♂.♀) pH

CaCO3

(mg/L)

Conductivity

(µS) PO4 (mg/L) N:M

Water

SA

(m2)

A.Mynheir Site

Butler Co.

OH 5.3 9.75 100 239 5 0.2 1191.4

B.Williamson

Site

Butler Co.

OH 5.5 9.74 100 232 0 0 6871.1

C.Madison

Township Park

Butler Co.

OH 5.3 8.06 180 448 19 0.7 1371.4

D.Kiser Lake Champaign

Co. OH 5.5 9.12 200 380 10 2.5 49753.6

E.St. Mary's Auglaize

Co. OH 2.5 9.45 105 570 3 0 65983.1

F.CricketFrog

Cove

Wood Co.

OH 5.5 8.9 90 163 5 7.7 2270.5

G.Neal's Site Wood Co.

OH 5.5 8.36 180 337 0.1 0.4 8787

H.W.W.Knight

Nature Center

Wood Co.

OH 5.1 7.9 90 405 15 0.3 9941.6

I.TheNature

Conservancy,

Lenawee

Co. MI 5.5 9.02 120 417 0 10.7 5242.5

J.Ypsillanti Washtenaw

Co. MI 3.1 8.24 180 604 0 6.3 79206.2

K.Grand Mere Berrien Co.

MI 3.4 8.03 200 619 0 2.3 50344.0

72

3.4.2. Data collection

With the exception of a single site, we hand-captured six-ten adult A. blanchardi,

targeting five males and five females, during daylight hours, at each of the 11 sites (Table

3.1). We maintained frogs in individual air-filled plastic bags, in a cooler until sample

collection (within six hours of capture). We collected skin-associated microbiome

samples from pre-rinsed animals via a standardized swabbing technique (McKenzie et al.

2012) using sterile synthetic swabs, stored in 95% ETOH in 2ml cryovials on ice until

transferred to a -80oC freezer (within four days of sample collection). Preserved swab

samples remained frozen at -80oC until DNA extraction. We collected natural peptide

secretions (called AMPs here after) immediately after microbial community samples

utilizing a 0.01% nor-epinephrine (20mM norepinephrine hydrochloride) bath to elicit the

secretion of the proteins (Krynak et al. In Press; Sheafor et al. 2008). We euthanized

animals in MS-222 immediately after AMP sample collection, weighed each frog,

collected a tissue sample which was preserved in 95% ETOH, and formalin fixed the

body of each frog for museum donation. We acidified AMP samples with 100%

trifluoroacetic acid (TFA) and purified samples using C-18 SepPak Classic Cartridge

(Waters Corporation), saving the acidified collection buffer for a second AMP

purification event at the time of sample elution. We stored AMP samples on ice until

transferred to a -80oC freezer.

We measured pH, alkalinity (methyl orange), conductivity, and total phosphate at

each pond site using a HACH Stream Survey test kit (Table 3.1).Water samples for

analysis were collected at the frog collection site (pond edge). We collected data on

landscape characteristics including latitude, the ratio of natural (prairie and forest) to

managed (agricultural and residential land) terrestrial cover (referred to as N:M

73

hereafter), and water surface area (m2; “water SA” hereafter) within a 200m buffer of

each pond site digitizing open layers Google satellite imagery in Qgis (Quantum QGIS

Development Team 2015).We chose a 200m buffer size based on the limited dispersal

capabilities of the species and the desire to assess immediate environmental influences at

each collection site (Gray 1983).

We extracted microbial DNA from the skin swabs using a bead beating (2x 40

seconds) and phenol chloroform extraction method (Burke et al. 2008; Burke et al.

2006a). Negative PCR results using two different primer sets (58A2F and NLB4,

58A2Fand ITS4) targeting the ITS2 gene region of fungal DNA suggested that fungal

communities did not contribute significantly to the microbial community on the skin of

the animals used in this study; therefore further fungal community analyses were not

performed (Krynak et al. In Press). We amplified bacterial DNA using 16S rRNA gene

primers: 338f and 926r (Muyzer et al. 1993) according to the Carrino-Kyker et al

(Carrino-Kyker et al. 2012) protocol. Using terminal restriction fragment length

polymorphism profiling (TRFLP), we examined microbiome structure across sites (Burke

et al. 2008; Carrino-Kyker et al. 2012; Krynak et al. In Press). We used the restriction

enzyme MboI (Promega) to prepare samples for TRFLP profile analyses subsequently

generated at the Life Sciences Core Laboratory Center (Cornell University) using a

GS600 LIZ size standard (Applied Biosystems). We used Peak Scanner TM

Software

(version 1.0, Applied Biosystems 2006) and R (R version 3.0.2, 2013) for our analyses.

TRFLP profiles were processed using the TRFLPR package in R (Petersen et al. 2015; R

version 3.0.2, 2013). Only peaks which accounted for >1% of the relative peak area were

included in sample analyses (Burke et al. 2008).We used nonmetric multi-dimensional

74

scaling analyses (NMDS) and multi-response permutation procedures (MRPP) to assess

bacterial community structure across sites in PC-ORD (Version 5.0; Bruce McCune and

MJM Software, 1999). MRPP is a non-parametric discriminant function analysis which

tests for differences between two or more groups of entities (McCune et al. 2002).

TRFLP profiles were arcsine-square root transformed prior to analysis (McCune et al.

2002).We used axis scores from resulting NMDS ordination solution to assess influence

of environmental and host characteristics on the variation across each NMDS axis

independently (see analysis description below). We utilized a cloning and sequencing

approach to identify dominant members of skin-associated microbiome (Qiagen PCR

Cloning Plus) constructing a single clone library (N=169 clones produced). We archived

resulting cloned sequences in the European Bioinformatics Institute (EMBL; Cambridge,

UK), DNA DataBank of Japan (DDBJ), and GenBank (Table A1; LN794355-

LN794520).We performed TRFLP on the clones to determine actual TRF size for each

clone, again using the MboI restriction enzyme (Promega).We conducted indicator

species analyses on terminal restriction fragments from the microbiome profiles and

identified taxa using TRFs from the clone library. We completed indicator species

analysis (a monte carlo test for group prediction) using PC-ORD (version 5.0) to examine

site specific bacterial taxa from A. blanchardi skin.

We eluted AMPs from the C-18 SepPaks, and subsequently passed the saved,

acidified collection buffer through the SepPaks for a second collection attempt (Sheafor

et al. 2008).This second pass of AMPs was then immediately eluted from the SepPaks.

We dried eluted samples at 15°C in an Eppendorf VacufugeTM

and reconstituted samples

in 500µl of sterile water (HPLC grade) and syringe filtered them (13mm Pall Acrodisc

75

with Tuffryn membrane and 0.2m pore size) prior to analysis. We utilized a Micro

BCA TM

Protein Assay Kit (product # 23235) for analysis of total protein concentration

from our AMP sampling. We used 100µl reactions to measure optical density at 562nm

(absorbance) with a BioTek Synergy HT plate reader. We used absorbance measures to

estimate concentration of the protein (µg/ml) using Bradykinen as the protein standard

(referred to as AMP production). Each sample and standard was run in triplicate and we

standardized AMP production by frog mass (µg/ml per gram body weight). Site influence

on AMP production was assessed via ANOVA.

We measured AMP bioactivity by determining pathogen growth rate in culture

when challenged by AMPs from individuals across sites. We conducted assays against

Batrochochytrium dendrobatidis (Bd strain JEL 404, originally isolated from a Rana

catesbeiana larva in Oxford Co. Maine) in culture. Based upon the BCA assay results, a

standardized concentration (100µg/ml stock, 50µg/ml in assay) of each AMP sample was

made. 50µl of Bd zoospore solution at a concentration of approximately 2 x 106

zoospores/ml (in 1% tryptone broth) was added to each well of a 96 well flat-bottom

sterile plate. 50µl of AMPs at the aforementioned concentration was then added to each

well (each sample replicated 3 times).We prepared positive and negative controls on each

96 well plate (three replicates per control on each plate). Positive controls consisted of

50µl of 2 x 106

Bd zoospores/ml and 50µl of sterile PCR grade water. Negative controls

contained 50µl of heat-killed Bd zoospores of the same concentration and 50µl of sterile

PCR grade water (Gibble and Baer 2011; Gibble et al. 2008).We read optical density

(OD; BioTek Synergy HT) of wells at 490nm on days 0 (immediately after plating), day

1(13 hours post plating), day 2, day 3, day 4, day 6, day 7, and day 8. A logistic growth

76

model was fit to data using a self-starting nls logistic model function (R version 3.0.2,

stats package, José Pinheiro and Douglas Bates), and the growth rate (r) of Bd was

determined (Krynak et al. In Press). Site influence on Bd growth rate (called AMP

bioactivity hereafter) was assessed via ANOVA.

We used variance inflation factor (VIF) to assess collinearity between explanatory

variables and we excluded variables if their VIF was greater than five. pH was the only

variable which was excluded from our statistical analyses as having a VIF greater than

five. We used an AICc model selection approach to compare linear mixed models, with

site held as the random factor in every model to assess 1) environmental factors

influencing the immune defense traits (microbial community variation along NMDS axes

(axis 1, 2, and 3 scores), AMP production, and AMP bioactivity) and 2) host factors

(AMP production and AMP bioactivity) influencing microbial community NMDS axis

scores (Burnham and Anderson 2002). Environmental models included main effects

(alkalinity, total phosphate, conductivity, N:M, water SA, latitude, and sex of the frog)

and interactions perceived to be biologically important: water SA x N:M, water SA x

conductivity, water SA x alkalinity, N:M x alkalinity, and latitude x alkalinity as well as

interactions between the sex of the animal sampled and each of the main environmental

predictors for a total of 23 environmental models (Table 3.2). We included all 23

environmental models in assessment of each of the response variables (microbial

community NMDS axis 1, 2, and 3 scores, AMP production, and AMP bioactivity). Host

models included those examining potential main effects of AMP production and AMP

bioactivity (r), their additive effects, and their interaction effects on microbial community

NMDS axis scores, for a total of four models for each response variable (NMDS axis

77

1,2,and 3 scores). Model fit for environmental and host models was assessed using

conditional R2, which describes the proportion of variance explained by both the fixed

and random factors (Nakagawa and Schielzeth 2013).We used a model averaging

(Burnham and Anderson 2002) approach to assess predictor influence on every response

variable examining both environmental and host influences on these immune defense

traits. The influence of AMP production on AMP bioactivity (r) was assessed separately

via linear regression mixed-model analysis; AMP bioactivity (r) as a function of AMP

production. All analyses, unless otherwise stated, were conducted in R (R version 3.0.2,

2013).

Table 3.2 Response variables (NMDS axis 1, 2, and 3 scores, AMP production, AMP bioactivity (r)

were modeled as a function of each of the following predictors.

Model number Predictors

1 N:M

2 Alkalinity

3 Conductivity

4 Total phosphate

5 Latitude

6 Water SA

7 Sex

8 N:M + water SA

9 N:M * Water SA

10 Water SA +conductivity

11 Water SA * conductivity

12 Water SA + alkalinity

13 Water SA * alkalinity

14 N:M +alkalinity

15 N:M * alkalinity

16 Latitude + alkalinity

17 Latitude * alkalinity

18 Sex * N: M

19 Sex * conductivity

20 Sex * alkalinity

21 Sex * total phosphate

22 Sex * water SA

23 Sex * latitude

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3.5. Results

A three-dimensional ordination solution for NMDS analysis of A. blanchardi

microbiome revealed a significant site effect on microbiome structure (MRPP: A=0.146,

p<0.0001; Figure 3.3).The variation observed across each of the NMDS axes was

explained by environmental parameters. AICc model selection found multiple

environmental models which had similar model weights and ∆AICc≤4 (Table 3.3) to

explain the variation across each NMDS axis. Model averaged parameter estimates on the

variation observed across NMDS axis 1 indicated a main effect of N:M, and interaction

effects of frog sex x latitude, and frog sex x water SA (Table 3.4). As N:M increased,

axis 1 scores also increased (conditional R2=0.44). Female frogs from the northern

latitudes had different microbial communities than females from southern latitudes, while

male frogs’ microbial communities did not differ with latitude (conditional R2=0.46;

Figure 3.4).The microbial communities of males and females responded in opposite ways

to water SA but only when surface area was large (≥50,000 m2); under conditions of

small water SA (≤ 10,000 m2)

, the microbial communities on the skin of males and

females were similar (conditional R2=0.48; Figure 3.4). Model averaged estimates of

parameter influence on the variation in microbial communities across axis 2 revealed

significant main effects of conductivity, water SA, and latitude (Table 3.4). Axis 2 scores

increased with latitude and conductivity, but decreased as water SA increased

(conditional R2=0.22, 0.25, and 0.25 respectively).Variation in microbial communities

across axis 3 was associated with an interaction effect of N:M and water SA across sites

(conditional R2=0.34; Table 3.4). Axis 3 scores were similar under conditions of high

N:M and small water SA and also when N:M was low but water SA was large. Those

79

microbial communities differed from those where N:M was high and water SA was large;

however, this later condition was only represented by a single site (Figure 3.5).

Figure 3.3 NMDS ordination of Acris. blanchardi skin-associated microbial communities. Points

represent site averages with standard error (MRPPsite: A=0.146, p<0.0001). A) Axis 1 and 2. B) Axis 1

and 3. Water surface area (“SA”, m2), latitude, conductivity and the ratio of natural to managed land (N:M,

m2) were predictive of microbial community axis scores of the NMDS ordination.

80

Table 3.3 Top models explaining environmental influence on Acris blanchardi immune defense traits

across sites in Ohio and Michigan based on AICc ranking. Microbial community axis scores are based

on a three dimensional NMDS ordination solution and describe the variation seen across each axis. Models

were capped at six parameters (K=6) because of the small sample size (N=11 sites). AICc score, change in

AICc (∆AICc), and the AICc model weight (⍵) for each model are shown for the top models (∆ AICc≤ 4)

for each response variable. The top 10 models are shown for AMP bioactivity (r) and are all ∆ AICc<4.

Response Model K AICc ∆AICc AICc⍵

NMDS Axis 1

N:M 4 9.54 0.00 0.20

N:M+total phosphate 5 130.77 1.23 0.11

sex*latitude 6 130.82 1.28 0.11

N:M * water SA 6 131.32 1.77 0.08

latitude 4 131.57 2.03 0.07

N:M + water 5 131.80 2.25 0.07

total phosphate 4 132.42 2.88 0.05

N:M * total phosphate 6 132.81 3.26 0.04

alkalinity * latitude 5 132.89 3.35 0.04

sex * water SA 6 132.96 3.41 0.04

sex * N:M 6 133.04 3.49 0.04

NMDS Axis 2

water SA + conductivity 5 132.25 0.00 0.32

water SA * conductivity 6 133.62 1.37 0.16

water SA 4 134.76 2.51 0.09

water SA + total phosphate 5 135.77 3.52 0.06

latitude 4 135.81 3.56 0.05

N:M + water SA 5 135.85 3.60 0.05

NMDS Axis 3 N:M * water SA 6 140.47 0.00 0.98

AMP production

(µg/ml per gbw) water SA*conductivity 6 1257.0 0.00 0.94

Bd growth rate in vitro

conductivity 4 183.45 0.00 0.12

alkalinity 4 183.46 0.02 0.11

total phosphate 4 183.95 0.51 0.09

sex 4 184.12 0.67 0.08

water SA 4 184.73 1.28 0.06

N:M 4 184.83 1.38 0.06

latitude 4 184.84 1.39 0.06

alkalinity + latitude 5 184.89 1.44 0.06

water SA + total phosphate 5 185.13 1.68 0.05

water + conductivity 5 185.63 2.18 0.04

81

Table 3.4 Model averaged parameter estimates, unconditional standard error (SE) of the estimate,

and 95% unconditional confidence intervals (CI) of landscape and water characteristics on Acris

blanchardi immune defense traits across sites in Ohio and Michigan. Only parameters from top models

(∆AICc ≤4 )are included. * Indicates that only the top 10 models are represented and are all ∆AICc ≤4.

Based on 95% CI, influential parameters are in bold.

Response Predictor Est. SE 95% CI

NMDS

Axis 1

N:M 0.067 0.028 0.011 to 0.122

total phosphate -0.020 0.017 -0.054 to 0.014

sex*latitude -0.103 0.048 -0.196 to- 0.001

N:M * water SA -2.0 x 10-06 1.1 x 10-06 -4.2 x 10-06 to 2.0 x 10-06

latitude 0.201 0.109 -0.013 to 0.415

water -1.0 x 10-06 4 x 10-06 -9.0 x 10-06 to 8.0 x 10-06

N:M * total phosphate -0.005 0.009 -0.023 to 0.013

alkalinity * latitude -0.002 0.002 -0.007 to 0.002

sex * water SA -4.9 x 10-06 2.1 x 10-06 -9.0 x 10-06 to -8.0 x 10-06

sex * N:M -0.006 0.012 -0.032 to 0.019

NMDS

Axis 2

water SA -1.1 x 10-05 4.8 x 10-06 -2.0 x 10-05 to -1.6 x 10-06

conductivity 0.002 7.8 x 10-04 8 x 10-05 to 0.003

water SA x Conductivity 3.0 x 10-08 3.0 x 10-08 -3.0 x 10-08 to 8.0 x 10-08


latitude 0.167 0.078 0.014 to 0.321

N:M 0.024 0.023 -0.020 to 0.068

NMDS

Axis 3 N:M * water SA -4.8 x 10-06 9.0 x 10-07 -6.5 x 10-06 to -3.0 x 10-06

AMP

production

(µg/ml per

gbw)

water*conductivity 4.69 x 10 -05 1.38x 10-05 1.99 x 10-05 to 7.4 x 10-05

Bd growth

rate in

vitro*

conductivity -7.4 x 10-04 5.7 x 10-04 -0.002 to 3.8 x 10-04

alkalinity -0.002 0.002 -0.006 to 8.9 x 10-04


sex 0.079 0.071 -0.06 to 0.219

water SA -2.0 x 10-06 4.0 x 10-06 -9.0 x 10-06 to 5.0 x 10-06

N:M 0.012 0.019 -0.026 to 0.050

latitude 0.058 0.071 -0.082 to 0.198

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Figure 3.4 Frog sex and landscape characteristics interact to influence skin microbiome variation

across NMDS axis 1. A. Interaction effect of frog sex and latitude on microbial community NMDS axis 1

scores of Acris blanchardi across sites in Ohio and Michigan (conditional R2=0.46). B. Interaction effect of

frog sex and water surface area (“SA”, m2) on microbial community NMDS axis 1 scores of Acris

blanchardi across sites in Ohio and Michigan (conditional R2=0.48). Females=pink. Males=aquamarine.

83

Figure 3.5 Interaction effects of the ratio of natural to managed terrestrial habitat (N:M) and water

surface area (“SA”, m2) on microbial community NMDS axis 3 scores (represented by color shading)

of Acris blanchardi (conditional R2=0.34).

Cloning and sequencing of microbial communities across A. blanchardi

populations revealed that Betaproteobacteria (51.8%) make up the major division of

bacteria found on the frogs’ skin, followed by Gammaproteobacteria (15.7%; Figure 3.6).

Of the 51.8% of Betaproteobacteria sequenced from the clone library, 65% of these were

significant indicators of a single site, Ypsillanti, Michigan (J; Table A1). These

Betaproteobacteria were largely represented by members of the order Burkholderiales,

including the genera Acidovorax, Aquabacterium, Polynucleobacter and Pelamonas, and

the genus Vogesella of the order Neisseriale. Multiple other indicators of site included

Microbacterium as an indicator of The Nature Conservancy site (I). Cloacibacterium and

Hymenobacter of the class Flavobacteria and Zoogloea of the order Rhodocyclales were

indicators of Madison Township Park (C). Pedobacter of the class Sphingobacteriia was

an indicator of a residential Butler County, Ohio site (A). Rhizobium, Methylobacterium,

84

and Ochrobactrum of the order Rhizobiales (division Alphaproteobacteria), were

indicators of another residential Butler Co. Ohio site (B). Porphyrobacter of the order

Sphingomonadales (division Alphaproteobacteria), was an indicator of residential Butler

County, Ohio site (A) and St. Mary’s fish hatchery in Auglaize Co. Ohio (E).

Figure 3.6 Clone library of Acris blanchardi skin-associated bacteria. The percent of the clone library

represented by each taxonomic group is shown. (N=169). Of Betaproteobacteria cloned (N=86 clones),

65.1% were significant indicators of site J. Ypsillanti, MI.

Site significantly predicted AMP production (F(10,76)= 3.377, p=0.001; Figure

3.7).We found a single best environmental model to explain the variation in AMP

production across sites (Table 3.3; AICc⍵=0.94).We found an interaction effect of water

SA x conductivity on the amount of AMPs produced across sites (conditional R2=0.24;

Table 3.4; Figure 3.8). AMP production was highest from frogs at sites with larger water

SA and high conductivity, and AMP production was lower from frogs at sites with

smaller water SA and low conductivity. Site did not significantly predict AMP bioactivity

(r) (F(10,76) =0.593, p=0.815), and we did not find any water or landscape characteristics

Actinobacteria

4.2% Alphaproteobacteria

7.8%

Bacteroidetes

1.2%

Bacteroidia

6.0%

Betaproteobacteria

51.8%

Chloroplast

1.2%

Cytophagia

0.6%

Deltaproteobacteria

1.2%

Flavobacteria

1.8%

Gammaproteobacteria

15.7%

Other Proteobacteria

0.6%

Sphingobacteria

7.8%

85

that predicted AMP bioactivity (r) (Table 3.3; Table 3.4). Host characteristics, AMP

production and AMP bioactivity (r), did not predict microbial community NMDS axis

scores (Table 3.5; Table 3.6).

Figure 3.7 AMP production (in the form of natural peptide mixtures) standardized by gram body

weight (gbw) of Acris blanchardi across sites in Ohio and Michigan. Letters correspond to Figure 3.2

site locations.

Figure 3.8 Interaction effect of water surface area (“SA”, m2) and Conductivity (µS) on AMP

production (shading; AMP µg/ml per gram body weight) in Acris blanchardi across sites in Ohio and

Michigan (conditional R2=0.24).

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Table 3.5 Models used to assess host influence (AMP production and AMP bioactivity (r)) on Acris

blanchardi skin-associated microbial community NMDS axis scores across sites in Ohio and

Michigan based on AICc ranking. AICc score, change in AICc (∆AICc), and the AICc model weight (⍵)

for each model are shown for each response variable.

Response Model K AICc ∆AICc AICc ⍵

NMDS Axis 1

AMP bioactivity (r) 4 133.99 0.00 0.42

AMP production 4 134.16 0.18 0.39

AMP production + r 5 136.17 2.19 0.14

AMP production * r 6 138.47 4.48 0.05

NMDS Axis 2

AMP production 4 138.39 0.00 0.45


AMP production + r 5 140.54 2.15 0.15

AMP production * r 6 142.78 4.38 0.05

NMDS Axis 3


AMP production * r 6 154.80 0.55 0.31

AMP production 4 156.29 2.04 0.15

AMP production + r 5 156.41 2.16 0.14

Table 3.6 Model averaged parameter estimates (Est.), unconditional standard error (SE) of the

estimate, and 95% unconditional confidence intervals (CI) of host characteristics on Acris blanchardi

skin-associated microbial community NMDS axis scores across sites.

Response Predictor Est. SE 95% CI

NMDS Axis 1

AMP production 5.0 x 10-05 1.6 x 10-04 -2.6 x 10-04 to 3.6x 10-04

AMP bioactivity (r) 0.04 0.07 -0.10 to 0.18

AMP production * r 4.0 x 10-05 2.7 x 10-04 -5.7 x 10-04 to 5.0 x 10-04

NMDS Axis 2

AMP production -1.2 x 10-04 1.6 x 10-04 -4.4x 10-04 to 2.0x 10-04


AMP production * r 7.7x 10-05 2.9x 10-04 -4.9 x 10-04 to 6.4 x 10-04

NMDS Axis 3

AMP production -3.2 x 10-05 1.8 x 10-04 -3.8 x 10-04 to 3.2 x 10-04


AMP production * r 6.1 x 10-04 3.0 x 10-04 -1.0 x 10-05 to 1.2x 10-03

A linear regression which examined the influence of AMP production on AMP

bioactivity (r) (i.e. Bd growth rate) indicated a marginal positive relationship, meaning as

more AMPs were produced by the frogs, the faster Bd grew in vitro (Estimate= 4.0 x 10-

04, SE=2.0 x 10

-04, df=75, t =1.979, p =0.051; conditional R

2=0.04; Figure 3.9).

87

Figure 3.9 AMP bioactivity (r) as a function of AMPs produced (standardized by gram body weight)

from Acris blanchardi across sites in Ohio and Michigan (Estimate=4.0 x 10-04

, SE=2.0 x 10-04

, df=75,

p=0.051; conditional R2=0.04). 95% confidence interval is displayed as the shaded region.

3.6. Discussion

Amphibians have undergone dramatic disease-associated declines in recent years

and these declines are expected to increase due to the ease of global transportation and

introduction of novel diseases (Daszak et al. 2003). This hypothesized increase in

pathogen introduction, coupled with changing climate and other anthropogenic

environmental stressors make understanding how amphibian immune defense traits are

altered by changing environments crucial for successful long-term conservation efforts

(Lips et al. 2008; Rohr et al. 2008a).This is particularly important for species with small

populations which are restricted in their ability to disperse to new habitats, like A.

blanchardi (Gray and Brown 2005). Our study has shown that multiple environmental

factors including the ratio of natural to managed land, water conductivity, water surface

88

area, and latitude can influence the skin-associated microbiome of A. blanchardi.

Additionally, we found interactions between frog sex and latitude, frog sex and water

surface area, as well as the ratio of natural to managed land and water surface area can all

influence the microbiome of this species. These results are in accordance with previous

work which has shown inter-population differences in skin microbiome of amphibians

(Kueneman et al. 2014), including an experimental study in which we found that

environmental characteristics can drive those differences (Krynak et al. In Press). We

also found that the environment altered another important component of immune

defenses; the antimicrobial peptides produced by granular glands in the frog’s skin

(Rollins-Smith et al. 2005).Water surface area and conductivity interacted to influence

the amount of antimicrobial peptides produced. We did not find evidence that host

characteristics, AMP production and bioactivity, influenced the microbiome. We did find

some evidence for a positive relationship between AMP production and growth rate of

Bd challenged with AMPs from A. blanchardi. Across sites, as A. blanchardi produced

more AMPs, Bd growth rate (AMP bioactivity (r)) increased. We found that A.

blanchardi antimicrobial peptides, regardless of the amount produced, were not able to

depress growth of Bd based on our in vitro analysis of bioactivity, which is in agreement

with previously published findings (Conlon 2011).

The hypothesis that the environment may alter microbial community structure is

not new; however, few have tested whether the environment alters the skin-associated

microbiome of amphibians (Kueneman et al. 2014; Loudon et al. 2014; McKenzie et al.

2012). Microbial studies conducted in culture have shown that environment affects which

bacterial species can persist on a particular media, at differing temperatures, pH, and

89

nutrient concentrations (Vartoukian et al. 2010). Bacterial species compete for space and

nutrients in these environments and this in turn can shift the relative proportions of

species present (Nichols et al. 2008; Vartoukian et al. 2010). In nature, habitat disruption

could cause a change in the local pool of microbial colonists, thereby affecting the

microbiome of the amphibian skin (Fitzpatrick and Allison 2014), or habitat disruption

may elicit selection pressure on the relative proportions of the host’s microbial colonists.

Alternatively, physiological changes in the frog skin could be associated with stress from

habitat disruption (e.g. mowing of lawns and plowing of fields in more managed lands)

and could result in microbiome shifts. Stress associated with habitat disruption causes

immune suppression across many taxa (Morimoto et al. 2011) and stress from habitat

disruption, which can include habitat degradation or other changes in the habitat, such as

competitor and predator abundance, can alter physiological traits like corticosterone

levels (Homan et al. 2003; Liesenjohann et al. 2013).These physiological changes may

make the skin less habitable for some bacterial species, but more habitable for others,

shifting the microbiome structure.

We found that frogs from similar habitats had similar microbiome structure;

furthermore, environmental conditions of the habitat correlated with microbiome

structure. For example, the ratio of natural to managed land influenced the variation in

frog microbiome structure across NMDS axis 1. The microbiome on frogs from

populations in more natural habitats was most similar to the microbiome on frogs from

other populations in more natural habitats; however, these microbiomes differed from the

microbiomes of frogs from populations in more managed habitats. The observed

relationship between land use and amphibian microbiome agrees with studies which have

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found land use influences the microbial communities in soil and water (Carrino-Kyker et

al. 2011; Yao et al. 2000), suggesting that the differences in frog skin microbial

communities could be due to differences in available colonizing microbes, rather than

differences in frog physiology. Alternatively the environment external to the frog host

may select for particular bacterial taxa persistence on the host frog’s skin (Vartoukian et

al. 2010).

We also found water conductivity was associated with variation in the amphibian

microbiome across sites; frogs from ponds with similar conductivity had similarly

structured microbiomes. Pond conductivity is affected by both natural and anthropogenic

factors (Carrino-Kyker et al. 2011). Furthermore, residential and agricultural runoff can

alter microbial communities in vernal pools (Carrino-Kyker et al. 2011). Water

conductivity could therefore be directly altering the relative proportions of bacterial taxa

on the frogs skin though selective pressures or indirectly by altering the bacterial taxa

available in the habitat to colonize the amphibian. Residential and agricultural run-off

alter traditional measures of amphibian fitness (Gallagher et al. 2014; Hua and Pierce

2013), but our results indicate that additional measures of amphibian health, including the

immune defense traits need be examined.

The relationship between water surface area and microbiome variation indicate

that the size of the pond can affect microbiome structure (Figure 3.3). We found that

water surface area also interacted with the ratio of natural to managed land to affect the

A. blanchardi skin microbiome (Figure 3.5). We found greater inter-pond variation in

frog microbiome structure between large water bodies than between small water bodies.

This leads us to suggest that this variability is influenced by surrounding terrestrial land

91

use or differences in relative spatial heterogeneity of pond water chemistry. Large ponds

may display greater habitat heterogeneity and localized differences may exist in water

chemistry, which could affect within pond variability in frog skin microbial communities.

Small ponds may display lesser habitat heterogeneity, and therefore less within-site

variability in skin microbiome. Differences in surrounding land use and within pond

spatial heterogeneity between large and small ponds could influence differences in

variability in frog microbiome structure. Although the cause of differences in variability

between small and large ponds is unknown, our data suggest that surrounding land use,

which is known to affect water chemical quality, may be partly responsible for these

differences.

The microbiome structure of A. blanchardi skin also changed with latitude. The

latitudinal differences in microbiome of A. blanchardi may reflect differences in

pathogen resistance among populations across the species’ range, particularly in northern

latitudes (Gray and Brown 2005). Declines have resulted in Acris blanchardi being listed

as a species of concern in Michigan, while declines have lessened in Ohio in recent years

(Lehtinen and Witter 2014). If microbiome structural differences caused depressed

immune function, this may have led to the declines observed in the northern latitudes

including Michigan and Ohio.

We also observed an interaction between the frogs’ sex and latitude and frogs’ sex

and water surface area indicating that the microbiome response is partially dependent on

the sex of the individual animal. This differential response in microbiome structure across

environments between the sexes may help to explain the sex ratio differences that have

been documented across populations; males largely outnumbering females or females

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largely outnumbering males at particular locations (Gray 1983; Reeder et al. 1998).

Previous studies have linked amphibian sex ratio shifts to chemical contamination of the

habitat (Boegi et al. 2003; Hayes et al. 2010; Reeder et al. 2005). However, our results

suggest an alternative hypothesis for interpopulation variation in sex ratios. If the

differences in microbiome observed in our study do affect frog immune defense (Harris

et al. 2009), then males and females may differ in pathogen resistance at different

latitudes and among different-sized ponds. Therefore, differential mortality of the sexes

due to differences in pathogen resistance could cause interpopulation variation in sex

ratio.

Although microbiome structure differs between populations, it is possible that the

function of different microbial communities is the same (Lear et al. 2014). In the present

study, we documented the structure of microbial communities, but did not conduct

functional experiments to determine if particular skin microbiome structures confer

stronger immune defense than other skin microbiome structures. Culture-based studies

have found that particular microbial taxa produce metabolites which are capable of

providing resistance to amphibian pathogens (Becker et al. 2009; Brucker et al. 2008;

Harris et al. 2006). However, relative to the number of taxa estimated to be associated

with amphibian skin from studies utilizing sequencing approaches (McKenzie et al.

2012), few taxa have been investigated in pure culture in terms of disease resistance due

to limitations of culture-based techniques. Additionally, it has been discovered that once

microbial taxa are incorporated into a community, emergent metabolites can be produced,

which are not produced by individual microbial taxa as found in pure culture (Raes and

Bork 2008; Xavier 2011); therefore microbial taxa functionality needs to be investigated

93

on a community basis. Our study provides evidence that the relative proportions of

bacterial taxa present on the skin of A. blanchardi are affected by environmental

characteristics; however, functional properties of these communities across environments,

as related to pathogen resistance, will require meta-transcriptomic techniques and will be

an important next step in amphibian conservation research.

Our study also found that the environment influenced other components of the A.

blanchardi immune defense system: the production of AMPs. This is similar to what we

found during an experimental study which showed environmental variation in larval

habitat pH and degree of pond shading had long-term (post-metamorphic) effects on

AMP production in Rana catesbeiana (Krynak et al. In Press). Predators and competitors

also alter AMP production in amphibians (Groner et al. 2013; Groner et al. 2014). We

found that environmental variation in conductivity and water surface area interacted to

affect AMP production in A. blanchardi. Specifically, AMP production increased with

water surface area and conductivity. The cause of this pattern is unknown, however, it is

possible that larger water bodies have a larger surface water catchment within the

surrounding landscape, and this leads to greater surface water runoff into these ponds.

This would increase the concentration of chemical constituents within the pond, leading

to greater stress on individual animals and possibly higher AMP production. This pattern

may also reflect other unmeasured factors which may influence AMP production, such as

disease presence or unmeasured chemical contamination that may be interacting with

these landscape characteristics (Rollins-Smith 2009).

Surprisingly, we found that AMP production was positively associated with Bd

growth rate in vitro, though this effect is marginal. Other studies have found species

94

which produce more AMPs, or particular types of AMPS, are more protected from Bd

(Rollins-Smith and Conlon 2005; Tennessen et al. 2009); however, in the case of A.

blanchardi, Bd growth was not inhibited by the AMPs (Conlon 2011), regardless of the

amount of AMPs produced. The effect size of the relationship between AMP production

and AMP bioactivity in our study is small; however, the importance of this potential

relationship gives cause for attention. A positive relationship between AMP production

and Bd growth rate may be particularly detrimental to amphibian populations if AMP

production, which has presumably evolved to provide broad pathogen resistance, instead

stimulates the growth of this non-native pathogen (Rollins-Smith et al. 2005; Weldon et

al. 2004). Our study indicates that AMPs of some amphibian species or populations may

actually promote an increase in Bd zoospore formation. Though our study suggests that

AMPs from A. blanchardi do not provide effective protection against Bd, they may

reduce growth rate or cure other pathogen infections of the skin, and therefore

understanding the influence of environmental conditions on AMP production is important

for understanding the role of these proteins on disease resistance of A. blanchardi

populations.

Lastly, the lack of latitudinal effect on A. blanchardi AMP production and

bioactivity along the species declining range can be explained by multiple hypotheses.

This may suggest that AMPs in this species are not bioactive against any pathogens

which may be associated with latitudinal declines in the species and therefore, we do not

see evidence of selection on these traits. It also could be that historic A. blanchardi

declines in the northern regions of the species’ geographic range are not related to disease

(Steiner and Lehtinen 2008). It may also be that these traits are not genetically

95

determined, but are instead environmentally induced by factors not associated with

latitude, or it could be that environmental characteristics interact with the genetic

expression of these immune defense traits. An interaction between a population’s genes

and the environment could lower heritability of traits (Dutilleul et al. 2015) and thereby

reduce heritable expression of disease resistance by AMPs. In other words,

environmental factors may limit a population’s ability to evolve resistance to pathogens.

Therefore, to understand population level differences in disease susceptibility and to

improve success of long-term conservation strategies, we must first understand the direct

effects of the environment on amphibian immune defense traits, but then we must also

examine potential interactions between environmental and genetic factors on the

expression of immune defense traits to protect amphibians from disease threats in the

future.

96

3.7. Appendices

3.7.1. Table A1. The sequence similarity of clones (out of 169total) created from

skin swabs of Acris blanchardi using primers 338f and 926r. Identification is based

upon comparison to NCBI database entries using the FASTA program (National

Center for Biotechnology Information).The percent identity (% ID) to best match is

shown. Fragment size in base pairs (bp) generated using MboI restriction enzyme.

Indicator species analysis based on community profiles. Letters designate sites with

specific bacterial taxa.

Clone

ID

Clone

Accession ID Best Match ID Division/Phylum

Fragment Size (bp) Indicator

(p<0.05) 38f 926r

A1 LN794355 Stenotrophomonas 100 Gammaproteobacteria 44.9 221.1 A2 LN794356 Pedobacter 100 Sphingobacteriia 158.1 381.6

A3 LN794357 Pedobacter 100 Sphingobacteriia 158.4 381.7

A4 LN794358 Pedobacter 100 Sphingobacteriia 158.1 381.5 A5 LN794359 Cloacibacterium 100 Flavobacteriia 578.9 577.0 C

A6 LN794360 Burkholderiales 100 Betaproteobacteria 46.5 534.8 J

A7 LN794361 Pedobacter 100 Sphingobacteriia 157.9 381.7 A8 LN794362 Vogesella 100 Betaproteobacteria 45.4 534.5 J


A11 LN794364 Rhizobium 100 Alphaproteobacteria 45.0 509.9 B A12 LN794365 Pseudoxanthomonas 100 Gammaproteobacteria 45.0 221.0

A13 LN794366 Burkholderiales 95 Betaproteobacteria 45.7 535.4

A14 LN794367 Stenotrophomonas 100 Gammaproteobacteria 44.9 221.0 A15 LN794368 Stenotrophomonas 99 Gammaproteobacteria 44.9 221.0

A16 LN794369 Proteobacteria 100 Gammaproteobacteria 46.5 218.7

A17 LN794370 Actinomycetales 96 Actinobacteria 537.7 17.8 A18 LN794371 Sphingobium 99 Alphaproteobacteria 45.2 511.6

A19 LN794372 Microbacterium 100 Actinobacteria 375.6 192.0 I

A20 LN794373 Stenotrophomonas 99 Gammaproteobacteria 45.0 221.0 A21 LN794374 Aquabacterium 100 Betaproteobacteria 45.1 535.0 J

A22 LN794375 Stenotrophomonas 100 Gammaproteobacteria 45.0 221.0

A23 LN794376 Burkholderiales 98 Betaproteobacteria 46.5 534.9 J A24 LN794377 Vogesella 100 Betaproteobacteria 45.5 534.5 J


A26 LN794379 Alistipes 100 Bacteroidia 158.2 217.8 A27 LN794380 Bradyrhizobiaceae 100 Alphaproteobacteria 44.9 274.3

A28 LN794381 Bacteroides 100 Bacteroidia 157.6 381.3

A29 LN794382 Aquabacterium 100 Betaproteobacteria 45.2 535.0 A30 LN794383 Vogesella 100 Betaproteobacteria 45.7 534.6 J

A31 LN794384 Parabacteroides 100 Bacteroidia 77.1 145.7

A32 LN794385 Burkholderiales 100 Betaproteobacteria 46.5 535.0 A33 LN794386 Burkholderiales 99 Betaproteobacteria 46.6 534.8 J

A34 LN794387 Aquabacterium 100 Betaproteobacteria 45.3 534.8 J

A35 LN794388 Acidovorax 100 Betaproteobacteria 45.6 535.4 A36 LN794389 Vogesella 92 Betaproteobacteria 45.6 535.0




A41 LN794393 Actinomycetales 97 Actinobacteria 581.1 579.9 A42 LN794394 Vogesella 100 Betaproteobacteria 45.6 534.9 J


A44 LN794396 Stenotrophomonas 100 Gammaproteobacteria 45.1 221.1 A45 LN794397 Burkholderiales 98 Betaproteobacteria 47.0 534.9 J


A47 LN794399 Variovorax 96 Betaproteobacteria 45.7 535.2 A48 LN794400 Bradyrhizobium 92 Alphaproteobacteria 46.8 275.4

A49 LN794401 Aquabacterium 100 Betaproteobacteria 45.2 534.0 J

A50 LN794402 Comamonadaceae 100 Betaproteobacteria 45.6 534.9 J A51 LN794403 Aquabacterium 100 Betaproteobacteria 45.0 534.6 J

A52 LN794404 Aquabacterium 93 Betaproteobacteria 45.1 535.1

97

A53 LN794405 Dechloromonas 98 Betaproteobacteria 46.9 536.7

A54 LN794406 Burkholderiales 92 Betaproteobacteria 46.5 534.9 J A55 LN794407 Stenotrophomonas 100 Gammaproteobacteria 45.0 221.0


A57 LN794409 Burkholderiales 97 Betaproteobacteria 46.5 534.8 J A58 LN794410 Pelomonas 100 Betaproteobacteria 45.1 534.9 J


A60 LN794412 Chloroplast 100 Chloroplast 563.4 562.6 A61 LN794413 Stenotrophomonas 99 Gammaproteobacteria 42.8 221.0

A62 LN794414 Burkholderialesincert

aesedis

93 Betaproteobacteria 46.5 534.7 J


A64 LN794416 Desulfobacteraceae 100 Deltaproteobacteria 587.0 585.8

A65 LN794417 Bacteroidetes 100 Bacteroidia 577.7 577.1 A A66 LN794418 Phenylobacterium 100 Alphaproteobacteria 558.5 557.7

A67 LN794419 Comamonas 100 Betaproteobacteria 45.7 535.4

A68 LN794420 Comamonadaceae 90 Betaproteobacteria 46.4 534.9 J A70 LN794421 Stenotrophomonas 99 Gammaproteobacteria 44.9 221.0

A71 LN794422 Deltaproteobacteria 88 Proteobacteria 137.5 448.7

A72 LN794423 Acidovorax 100 Betaproteobacteria 45.6 535.3 A73 LN794424 Comamonadaceae 100 Betaproteobacteria 45.5 537.3 C

A74 LN794425 Aeromonas 100 Gammaproteobacteria 367.6 219.1

A75 LN794426 Burkholderiales 100 Betaproteobacteria 46.8 534.8 J A76 LN794427 Pedobacter 97 Sphingobacteriia 381.7 158.2 A


A78 LN794429 Sanguibacter 100 Actinobacteria 566.8 566.0 A79 LN794430 Burkholderiales 98 Betaproteobacteria 45.0 534.8 J

A81 LN794431 Phyllobacteriaceae 94 Alphaproteobacteria 44.9 74.0 A82 LN794432 Burkholderiales 97 Betaproteobacteria 46.4 534.8 J

A84 LN794433 Cloacibacterium 100 Flavobacteriia 579.1 577.7

A85 LN794434 Stenotrophomonas 100 Gammaproteobacteria 45.0 220.9 A86 LN794435 Betaproteobacteria 91 Betaproteobacteria 45.5 534.0

A87 LN794436 Acidovorax 100 Betaproteobacteria 45.6 535.5

A88 LN794437 Aquabacterium 100 Betaproteobacteria 44.9 534.9 J A89 LN794438 Aquabacterium 100 Betaproteobacteria 44.8 534.9 J


A91 LN794440 Porphyrobacter 97 Alphaproteobacteria 46.5 511.1 A92 LN794441 Burkholderialesincert

aesedis

92 Betaproteobacteria 45.1 534.7 J

A93 LN794442 Bacteroides 100 Bacteroidia 158.8 381.4 A94 LN794443 Stenotrophomonas 100 Gammaproteobacteria 45.0 221.0


A96 LN794445 Comamonadaceae 99 Betaproteobacteria 45.5 534.6 J A97 LN794446 Aquabacterium 94 Betaproteobacteria 45.0 534.7 J

A98 LN794447 Bacteroidetes 95 Bacteroidetes 578.7 578.1 C

A100 LN794448 Comamonadaceae 100 Betaproteobacteria 45.1 448.1 A101 LN794449 Aquabacterium 99 Betaproteobacteria 45.0 534.9 J


A103 LN794451 Burkholderiales 93 Betaproteobacteria 46.8 534.7 J A104 LN794452 Zoogloea 100 Betaproteobacteria 45.5 537.2 C

A105 LN794453 Proteobacteria 100 Betaproteobacteria 46.6 534.9 J

A106 LN794454 Burkholderialesincertaesedis

96 Betaproteobacteria 46.6 535.0

A107 LN794455 Parabacteroides 100 Bacteroidia 77.1 145.7

A108 LN794456 Burkholderiales 90 Betaproteobacteria 46.5 534.8 J A109 LN794457 Stenotrophomonas 100 Gammaproteobacteria 45.0 221.0

A110 LN794458 Acidovorax 100 Betaproteobacteria 584.4 583.8 J

A111 LN794459 Comamonadaceae 100 Betaproteobacteria 44.9 535.3 A112 LN794460 Variovorax 98 Betaproteobacteria 45.6 535.3

A113 LN794461 Betaproteobacteria 87 Betaproteobacteria 46.5 534.8 J

A114 LN794462 Stenotrophomonas 97 Gammaproteobacteria 45.0 221.0 A115 LN794463 Comamonadaceae 100 Betaproteobacteria 45.2 448.1


A118 LN794465 Deltaproteobacteria 95 Deltaproteobacteria 586.2 585.4 J A119 LN794466 Burkholderiales 99 Betaproteobacteria 45.1 534.9 J

A120 LN794467 Dechloromonas 99 Betaproteobacteria 45.6 535.6

A122 LN794468 Burkholderiales 99 Betaproteobacteria 46.5 534.7 J A123 LN794469 Burkholderiales 97 Betaproteobacteria 46.5 534.9 J



98

A126 LN794472 Ochrobactrum 100 Alphaproteobacteria 45.0 509.6 B

A127 LN794473 Vogesella 99 Betaproteobacteria 45.7 534.8 J A128 LN794474 Chitinophagaceae 100 Sphingobacteriia 414.7 17.8

A129 LN794475 Chitinophagaceae 89 Sphingobacteriia 46.0 145.7

A130 LN794476 Aquabacterium 100 Betaproteobacteria 45.0 535.0 A131 LN794477 Methylobacterium 100 Alphaproteobacteria 45.0 509.3 B

A132 LN794478 Bradyrhizobium 99 Alphaproteobacteria 46.7 275.5

A133 LN794479 Erythrobacteraceae 100 Alphaproteobacteria 45.4 510.0 A, E A134 LN794480 Bacteroides 100 Bacteroidia 157.6 381.3


A136 LN794482 Burkholderiales 99 Betaproteobacteria 46.4 534.8 J A137 LN794483 Pedobacter 100 Sphingobacteriia 158.2 381.7

A138 LN794484 Acidovorax 100 Betaproteobacteria 45.6 535.5

A139 LN794485 Aquabacterium 93 Betaproteobacteria 45.2 535.2 A140 LN794486 Vogesella 98 Betaproteobacteria 85.02 500.2 J

A141 LN794487 Actinomycetales 100 Actinobacteria 98.01 476.01

A142 LN794488 Stenotrophomonas 96 Gammaproteobacteria 45.0 221.0 A143 LN794489 Stenotrophomonas 100 Gammaproteobacteria 45.0 221.1


A145 LN794491 Delftia 90 Betaproteobacteria 45.6 221.0 A146 LN794492 Pedobacter 95 Sphingobacteriia 157.9 381.7


A148 LN794494 Burkholderiales 97 Betaproteobacteria 46.7 534.8 J A149 LN794495 Burkholderiales 97 Betaproteobacteria 46.5 534.8 J


A151 LN794497 Hymenobacter 100 Cytophagia 578.4 577.4 C A153 LN794498 Stenotrophomonas 100 Gammaproteobacteria 45.0 221.1

A154 LN794499 Vogesella 100 Betaproteobacteria 367.0 218.8 A155 LN794500 Microbacteriaceae 93 Actinobacteria 374.7 192.1 I

A156 LN794501 Comamonadaceae 85 Betaproteobacteria 45.6 535.5

A157 LN794502 Stenotrophomonas 99 Gammaproteobacteria 44.8 220.9 A158 LN794503 Burkholderiales 98 Betaproteobacteria 46.4 534.8 J


A160 LN794505 Aquabacterium 100 Betaproteobacteria 45.0 535.0 J A161 LN794506 Porphyrobacter 100 Alphaproteobacteria 46.3 510.9 A, E


A164 LN794508 Burkholderiales 97 Betaproteobacteria 46.4 534.9 J A165 LN794509 Bacteroidetes 100 Bacteroidetes 549.8 17.2 C


A167 LN794511 Bacteroidetes 100 Betaproteobacteria 46.4 535.3 A168 LN794512 Aquabacterium 100 Betaproteobacteria 45.1 535.0

A170 LN794513 Flavobacterium 100 Flavobacteriia 44.4 379.6

A171 LN794514 Streptophyta 100 Chloroplast 96.01 413.01 A172 LN794515 Stenotrophomonas 99 Gammaproteobacteria 44.9 221.0

A173 LN794516 Novosphingobium 100 Alphaproteobacteria 45.7 511.1

A175 LN794517 Rhodococcus 100 Actinobacteria 492.1 74.3 A176 LN794518 Vogesella 100 Betaproteobacteria 45.5 534.8 J


A178 LN794520 Polynucleobacter 100 Betaproteobacteria 46.8 534.5 J

1. Predicted TRF based on MboI cut site. Actual TRF not available.

99

Chapter 4: Rodeo™ herbicide exposure decreases

larval survival and alters skin-microbiome of

Blanchard’s cricket frogs (Acris blanchardi)

4.1. Submitted for publication review



a




OH 44094 USA



5430.

E-mail addresses:



4.2. Abstract

Disease-associated mortality is a leading cause of amphibian declines and

extinctions world-wide. Understanding the influence of land-management practices, like

herbicide use, on amphibian immune defense traits could improve conservation

outcomes. Amphibians are protected from pathogens by two skin-associated immune

defense traits: the microbial communities which inhabit their skin (microbiome), and the

antimicrobial peptides (AMPs) produced by the skin. Utilizing the Blanchard’s cricket

frog (Acris blanchardi), a declining North American amphibian species as our model, we

manipulated Rodeo™ concentration and the life stage at which exposure to Rodeo™

occurred. We assessed the influence of Rodeo™ concentration and life stage at exposure

on larval and juvenile survival, larval duration, juvenile mass, the larval and juvenile skin

microbiomes, juvenile AMP production and AMP bioactivity against Batrachochytrium




100

dendrobatidis in vitro. We found a 37% decrease in survival of larvae exposed to 2.5mg

a.i/L (active ingredient; glyphosate) compared to Control. We did not find effects on

survival of juveniles. Additionally, larvae exposed to 2.5 mg a.i./L Rodeo ™ had

structurally different larval skin microbiomes compared to Control. Effects of larval

Rodeo™ exposure did not carryover to alter traits after metamorphosis and an assessment

of additive effects did not find evidence of Rodeo™ concentration or life stage at

exposure affecting any post-metamorphic trait.

4.3. Introduction

As our dependence on herbicides for invasive plant management increases, so

should our understanding of effects of herbicide use on the biota of the lands we are

attempting to manage. Effects of herbicide use on amphibians are of particular interest

because of the dramatic amphibian population declines observed in recent years (Collins

and Storfer 2003). Previous studies have determined that herbicide use can alter

amphibian survival as well as fitness correlates such as growth, and development (Howe

et al. 2004; Lanctot et al. 2014; Relyea 2005), but herbicide use may also alter other

important amphibian traits such as their immune defenses (Rollins-Smith et al. 2011;

Woodhams et al. 2011). Disease is a leading cause of amphibian declines (Daszak et al.

2003) and therefore understanding how land management practices may alter traits which

provide amphibians with pathogen resistance is crucial for conservation efforts.

Amphibians are protected against pathogens by two innate skin-associated immune

defense traits: the microbial communities which inhabit their skin (microbiome) and the

anti-microbial peptides (AMPs) produced by the skin (Harris et al. 2006; Rollins-Smith et

al. 2011; Rollins-Smith et al. 2005). It is plausible that in cases where herbicides do not

101

alter amphibian survival or other more traditionally measured correlates of fitness such as

growth and development, exposure may still alter amphibian immune defense traits.

Herbicide use, therefore, could have long-term effects on amphibian resistance to disease,

which may lead to decreased fitness and increased risk of local population decline.

Many amphibians undergo metamorphosis, the process by which aquatic larvae

transform into more terrestrial adults (Gosner 1960). Herbicide exposure may

differentially affect larval and post-metamorphic amphibians, therefore, it is important to

assess the effects of herbicide exposure across stages of the amphibian life-cycle (Distel

and Boone 2010; Edginton et al. 2004). Herbicide exposure alters amphibian hatching

success (Berrill et al. 1994; Bishop et al. 2010; Olivier and Moon 2010), developmental

rates (Navarro-Martin et al. 2014), and post-metamorphic mass (Boone and James 2003;

Diana et al. 2000), but there is limited evidence on whether herbicide exposure may alter

amphibian immune defenses, and whether exposure effects differ across life stages

(Paetow et al. 2012; Rohr et al. 2014). Effects of exposure to herbicides at the larval stage

may or may not carry-over after metamorphosis (Rohr and Palmer 2005; Rohr et al.

2014), while exposure after metamorphosis may be relatively benign or may negatively

affect amphibian fitness (Relyea 2005). Repeated exposure to herbicides may facilitate

increased resilience to herbicide exposure over the life of the amphibian and at least one

study has found evidence of evolution towards resistance to a common agricultural

pesticide by amphibian populations (Cothran et al. 2013). Herbicide exposures may also

stimulate immune function, indirectly increasing resistance to some diseases. Mortality

associated with Batrachochytrium dendrobatidis (Bd), a fungal pathogen which has

caused global amphibian declines, decreased in Rana sylvatica and Hyla versicolor with

102

exposure to sub-lethal concentrations of a glyphosate-based herbicide, though the

mechanism by which this may occur is unknown (Gahl et al. 2011; Hanlon and Parris

2014). Paetow et al. (2012) examined the potential interaction between amphibian

herbicide exposure and Bd susceptibility and found no evidence of interactive effects on

acquired immune defenses; however, there have been no studies which examine herbicide

effects on amphibian innate immune defense traits. Herbicide exposure may alter the

microbiome on the amphibian skin which may affect protection against Bd or other

pathogens (Harris et al. 2006), or herbicide exposure may alter AMP production and

AMP bioactivity of the amphibian host, also altering resistance to infection (Gibble and

Baer 2011). Studies which assess both lethal and sub-lethal effects of herbicide exposure

and the life stage at which exposure occurs, including effects on innate immune defense

traits, would provide valuable information which could be used to prevent or minimize

potentially negative effects of herbicide use.

Utilizing environmentally-relevant concentrations and exposure durations across

life-stages, we assessed the influence of a commonly used, commercially available

glyphosate-based herbicide (Rodeo™) on Acris blanchardi, the Blanchard’s cricket frog.

Acris blanchardi has been in precipitous decline in the northern portions of its range over

the past several decades (Gamble et al. 2008; Gray and Brown 2005; Lehtinen and

Skinner 2006). These declines have been hypothesized to be related to a variety of

anthropogenic environmental factors including habitat loss, fragmentation, acidification,

and chemical contamination (Lehtinen and Skinner 2006; Reeder et al. 2005; Russell et

al. 2002). Acris blanchardi commonly inhabit permanent ponds in residential and

agricultural areas where emergent aquatic plants such as narrow-leaved cattail (Typha

103

angustifolia) and common reed (Phragmities australis) are often managed with

herbicides. Consequently, understanding the effects of herbicide exposure on A.

blanchardi is of great importance for long-term conservation of the species. In addition,

disease outbreaks, including those caused by Bd, have been suspected to have contributed

to A. blanchardi declines (Gray et al. 2009; Steiner and Lehtinen 2008). Acris

blanchardi also have highly vascularized skin, which may enhance the effects of

herbicide exposure and disease susceptibility (Beasley et al. 2005). Together, these

factors make A. blanchardi an excellent model to assess lethal and sub-lethal effects of

herbicide use on amphibians.

Rodeo™ is a glyphosate-based product used to control emergent aquatic

vegetation and is considered relatively non-toxic based on the acute exposure studies

which indicate a concentration of >100 mg/L of the active ingredient, glyphosate, is

required to elicit mortality in 50% of the most sensitive species used in the studies

(LC50/EC50/EE50/LL50; Rodeo™ Material Safety Data Sheet; Dow Agrosciences

2015). While amphibians are often considered sensitive to environmental pollutants

based on their highly permeable skin (Jung 1996), amphibians were not included in these

studies used to assess toxic effects of Rodeo™ on aquatic organisms (Rodeo™ Material

Safety Data Sheet; Dow Agrosciences 2015). While many amphibians, like A.

blanchardi, are dependent on the control of invasive aquatic plants; it is important that

cost assessment of control measures reflect true effects on non-target species including

amphibians. Left uncontrolled, species like common reed (Phragmites australis) can

essentially drain a wetland, and thereby exclude amphibian species which depend on

permanent wetlands for population persistence (Hershner and Havens 2008; Lishawa et

104

al. 2014; Mitchell et al. 2011). However, control of such plants via chemical means may

have negative effects on amphibian populations, rendering chemical eradication of

invasive plants ineffective for protecting the wetland ecosystem. Rodeo™ is advertised as

a highly effective control of cattail (Typha spp.) and common reed (Phragmites spp;

Rodeo™ specimen label; Dow Agrosciences 2013), species which commonly co-occur

with A. blanchardi. Acris blanchardi breed in the spring and early summer, which puts

their larvae at risk of Rodeo™ exposure when treatment of cattail (Typha sp.) occurs as

recommended by the manufacturer (Rodeo™ specimen label Dow Agrosciences 2013;

Gray and Brown 2005; Wright and Wright 1949). Acris blanchardi juveniles are

metamorphosing from the larval stage to the more terrestrial stage of development during

the late summer and early fall (Wright and Wright 1949), which puts newly-

metamorphosed individuals at risk of Rodeo™ exposure when treating common reed

(Phragmites sp.) as recommended (Dow Agrosciences 2013). This common exposure

regime provides realistic rational for investigating the effects of Rodeo™ exposure across

life stages on A. blanchardi.

We hypothesized that Rodeo™ exposure alters A. blanchardi traits which are

expected to be correlated with amphibian fitness. We assessed the influence of Rodeo™

exposure on A. blanchardi traits including: larval and juvenile survival, larval duration,

juvenile mass, larval and juvenile skin-associated microbiomes, juvenile AMP

production, and juvenile AMP bioactivity against Bd in vitro. We predicted that while

our environmentally relevant concentrations of Rodeo™ would not affect larval or

juvenile survival, less common measures of larval duration, juvenile mass and the skin-

associated immune defense traits, would be altered. We also predicted that effects of

105

early-life stage (larval) Rodeo™ exposure would differ from the effects of post-

metamorphic (juvenile) Rodeo™ exposure.

4.4. Methods

We obtained larvae from 12 A. blanchardi families collected from a single pond

site in Wood Co. Ohio (Wood County Park District). Adult males and females were

collected and haphazardly assigned as pairs to one gallon buckets containing pond water

and plastic aquarium plants (a single male and a single female per bucket). Pairs

produced between 20 and100 eggs. Larvae hatched between June 15, 2013 and June 22,

2013. One family hatched under indoor laboratory conditions while all others hatched

under field conditions. Larvae were randomly assigned to treatments on June 27, 2013

(Table 4.1). Treatments consisted of combinations of four exposure concentrations

(Control: no Rodeo™, Low, Medium, and High Rodeo™; see details below) and four

Rodeo™ exposure stages (Control: not exposed, larval exposure, post-metamorphic

juvenile exposure, or exposed as both larvae and juveniles). Treatments were originally

assigned with five replicates for each exposure concentration/exposure stage

combination. There was significant larval mortality in the High Rodeo™ treatment,

which left few replicates available for testing the effects of multiple exposures at the

High Rodeo™ concentration. We altered treatment assignments to account for this, and

to maximize our ability to detect carry-over effects of High Rodeo™ exposure during the

larval stage on post-metamorphic traits (Table 4.1). Those replicates originally assigned

as High Rodeo™ to be exposed only at juvenile stage became “Control” replicates, and

those replicates originally assigned as High Rodeo™ to be exposed at both larval and

juvenile stages became High Rodeo™ exposed only at larval stage.

106

Table 4.1 Rodeo treatment assignments (number of replicates indicated; three animals per

replicate). Treatments originally balanced (five replicates per Rodeo™

concentration/exposure stage combination); however, due to high larval mortality

following Rodeo™ larval treatment, replicate assignments were adjusted to improve

ability to assess sub-lethal effects on Low and Medium Rodeo™ concentrations, and the

effects of Rodeo™ exposure timing.

Exposure Stage

Rodeo Concentrations

Control

0.0mg a.i./L

(0.0mg a.e./L)

Low

0.75mg a.i./L

(1.01mg a.e./L)

Medium

1.5mg a.i./L

(2.02mg a.e./L)

High

2.5 mg a.i./L

(3.38mg a.e./L)

Control (not exposed) 10 - - -

Larvae - 5 5 10

Juvenile - 5 5 0

Larvae and Juvenile - 5 5 0

We conducted the experiment in an indoor laboratory facility at Case Western

Reserve University. We housed 3 larvae per tank. Larval tanks consisted of 15L

Sterilite™ containers filled with 10L of de-chlorinated water, and floating plastic

aquarium plants were provided for cover (50 tanks in total). We conducted 50% water

changes every other day for the duration of the larval rearing period. We fed larvae ad

libitum TetraMin™ sinking tropical tablets daily (0.08g per tank) and we siphoned all

remaining food and fecal material from the tanks on a daily basis. Upon metamorphosis

(Gosner 42; Gosner 1960), after swabbing for microbial communities (description

below), we moved individuals to ventilated 1L plastic cups containing 100ml of de-

chlorinated water in the bottom, and plastic aquarium plants for cover. We performed

100% water changes every other day on juvenile frog holding cups throughout the

duration of the experiment. We fed newly-metamorphosed frogs Colembola sp. ad

libitum until the juveniles’ tail had been completely absorbed. We fed juvenile frogs

Drosophila melanogaster dusted with RepCal™ vitamin supplement ad libitum on a

daily basis for the duration of the experiment. We maintained the animal room at 25.5-

107

27.7oC with a 12hr/12hr light/dark cycle for the duration of the experiment for both larval

and juvenile A. blanchardi.

Rodeo™ treatments (Table 4.1) included four exposure concentrations based on

mg/L of the active ingredient, glyphosate. Our concentrations all represent glyphosate

levels which have been documented in natural environments (Feng et al. 1990; Newton et

al. 1984; Thompson et al. 2004) and are below the maximum level to be expected when

spraying emergent aquatic vegetation in nature (Giesy et al. 2000). We report glyphosate

concentration as both active ingredient (a.i.) and acid equivalent (a.e.) for easy

comparison across the body of literature on glyphosate toxicity. Our treatments were as

follows: Control- 0.0mg a.i./L (0.0mg a.e./L), Low- 0.75mg a.i./L (1.01mg a.e./L),

Medium- 1.5mg a.i./L (2.02mg a.e./L), and High- 2.5 mg a.i./L (3.38mg a.e./L). We

conducted exposures for 12 day periods. As a conservative approach, we chose 12 day

exposure durations because glyphosate has a half-life between 12 days and 10 weeks

(U.S. Environmental Protection Agency. Pesticide tolerance for glyphosate. Fed. Regist.

57: 8739 40, 1992.10-98). Exposures were conducted at the following stages of A.

blanchardi development: 1) Control: not exposed during experiment, 2) larval period:

exposures began 6 days after larvae were randomly assigned to tanks, 3) juvenile period:

exposures began 10 days after the final larvae in each tank reached metamorphosis, or 4)

during both developmental stages (Figure 4.1).

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Figure 4.1 Experimental methodology. Rodeo™ treatments were conducted at four treatment

concentrations: Control- 0.0mg a.i./L (0.0mg a.e./L), Low- 0.75mg a.i./L(1.01mg a.e./L),

Medium- 1.5mg a.i./L (2.02mg a.e./L), and High- 2.5 mg a.i./L (3.38mg a.e./L).

We conducted the Rodeo™ larval exposures (Low, Medium, High) as follows.

On day 1 (July 2, 2013), we added Rodeo™ formulated product (53.8 % glyphosate,

confirmed by Mississippi State Chemical Laboratory) to each of the assigned tanks

bringing concentration to 50% of assigned treatment concentration on this first day of

Rodeo™ exposure; 8ul, 16ul, or 26ul of Rodeo™ formulated product (Low, Medium,

and High exposures respectively) was added to the 10L water in assigned tanks and water

was mixed thoroughly, equalizing disturbance across all tanks. On day two (July 3, 2013)

we repeated this process which brought Rodeo™ levels to prescribed treatments

assignments: 0mg a.i./L (Control), 0.75mg a.i./L (Low), 1.5mg a.i./L (Medium), and 2.5

mg a.i./L (High). Beginning on day 4, we conducted 50% (5L) water changes via static

renewal every other day until July 16, 2013. On day 4 (July 5, 2013) a mistake was made

109

during the water change that resulted in Rodeo™ concentrations being elevated

temporarily (low: 1.125 mg a.i./L, medium: 2.25 mg a.i./L, high: 3.75mg a.i./L); this

error was caught (July 7, 2013) and remedied with the appropriate water changes which

brought concentrations to the intended levels. It should be noted however, this elevated

glyphosate concentration (highest being 3.75 mg a.i./L) is the highest concentration to be

expected when spraying aquatic vegetation in nature (Giesy et al. 2000; Relyea 2005). On

July 16, 2013, prior to the water change, we tested pH and ammonia levels in all larval

tanks. Rodeo™ addition significantly decreased water pH in the tanks compared to

Control; however, this small difference in pH was not suspected to be a biologically

influential (mean ± standard error: Control= 7.61 ±0.01 , Low= 7.54 ±0.01, Medium=

7.52 ±0.02, High= 7.51 ±0.01; F(3,46) =14.15, p=1.15e-06; Pierce 1985). Rodeo™ larval

treatments (medium and high) were associated with a significant increase in ammonia

levels compared to Control (mean±SE ammonia; Control: 0.34 ±0.03 mg/L; Low: 0.40

±0.04mg/L, W=125, p=0.209; Medium: 0.50 ±0.00 mg/L, W=165, p < 0.001; High: 0.50

±0.00 mg/L, W=165, p < 0.001; two-sample Wilcoxon test with Bonferroni correction).

For juvenile Rodeo™ treatments, we added 16ul, 28ul and 52ul of Rodeo™ concentrate

to 10L of water to use for low, medium, and high Rodeo™ water treatments respectively.

We conducted 100% water changes on juvenile cups every other day (100ml/cup) and

Rodeo™ exposures persisted for 12 days.

We collected skin-associated microbial community samples from larvae at time of

transfer into juvenile housing; which is the point of transition between aquatic and

terrestrial life (Gosner stage 42; Gosner 1960). We collected skin-associated microbial

community samples from juveniles at time of experimental end (22 to 28 days after the

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last larvae metamorphosed from the tank and immediately prior to collection of natural

peptide secretions (AMPs)). We collected microbial community samples and AMPs from

juvenile frogs following the Krynak et al (In Press) protocol. We collected AMPs from

all individuals in each tank with a single collection to avoid pseudo-replication. We

determined larval duration (in days) for individual frogs, from June 27, 2013 until the day

when the individual reached Gosner 42 (front legs erupt; Gosner 1960), and averaged

larval duration by tank. Juvenile mass was collected immediately following AMP

collection and subsequent euthanasia and was averaged by tank.

We extracted bacterial DNA from the skin swabs, pooling swabs by tank, using a

bead beating and phenol chloroform extraction method (Burke et al. 2008; Burke et al.

2006b). We amplified bacterial DNA using 16S rRNA gene primers: 338f and 926r

(Muyzer et al. 1993) according to the Carrino-Kyker et al. protocol (Carrino-Kyker et al.

2012) protocol. Using terminal restriction fragment length polymorphism profiling

(TRFLP), we examined bacterial community structure across treatments (Krynak et al. In

Press). We used the restriction enzyme Mbo1 (Promega) to prepare samples for TRFLP

profile analyses subsequently generated at the Life Sciences Core Laboratory Center

(Cornell University) using a GS600 LIZ size standard (Applied Biosystems). We used

Peak Scanner TM

Software (version 1.0, Applied Biosystems 2006) and R (R Core Team

2013) for our data preparation. TRFLP profiles were processed using the TRFLPR

package (Petersen et al. 2015; R Core Team 2013). Only peaks which accounted for >1%

of the relative peak area were included in sample analyses (Burke et al. 2008). We used

nonmetric multi-dimensional scaling analyses (NMDS) and multi-response permutation

procedures (MRPP) to assess bacterial community structure across treatments in PC-

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ORD (Version 5.0; Bruce McCune and MJM Software, 1999). MRPP is a non-parametric

discriminant function analysis which tests for differences between two or more groups of

entities (McCune et al. 2002). TRFLP profiles were arcsine-square root transformed prior

to analysis (McCune et al. 2002). We used axis scores from resulting NMDS ordination

solution to assess influence of treatments on the variation across each NMDS axis

independently to maximize statistical power with our small sample size (see statistical

analysis description below).

We eluted AMPs from C-18 SepPaks, and subsequently passed the saved,

acidified collection buffer through the SepPaks for a second collection attempt (Krynak et

al. In Review; Sheafor et al. 2008). This second pass of AMPs was then immediately

eluted from the SepPaks. We dried eluted samples at 15°C in an Eppendorf VacufugeTM

.

We reconstituted samples in 500µl of sterile water (HPLC grade) and syringe filtered

them (13mm Pall Acrodisc with Tuffryn membrane and 0.2m pore size). We utilized a

Micro BCA TM

Protein Assay Kit (product # 23235) for analysis of total protein

concentration from our AMP sampling. We used 100µl reactions to measure optical

density at 562nm (absorbance) with a BioTek Synergy HT plate reader. We used

absorbance measures to estimate concentration of the protein (referred to as AMP

production; µg/ml) using Bradykinen as the protein standard. Each sample and standard

was run in triplicate. We standardized AMP production by total frog mass because larger

frogs have more skin and therefore are likely to produce more secretions. Standardizing

by total frog mass allows for cross treatment comparisons without the potential

confounding effects of the size of the frogs on this measure of AMP production.

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We measured AMP bioactivity by determining pathogen growth rate in culture

when challenged by AMPs from frogs across treatments. We conducted assays against

Batrochochytrium dendrobatidis (Bd strain JEL 404, originally isolated from a Rana

catesbieana larva in Oxford Co. Maine) in culture. Based upon the AMP production

assay results, a standardized concentration (100µg/ml stock, 50µg/ml in assay) of each

AMP sample was made. 50µl of Bd zoospore solution at a concentration of

approximately 2 x 106 zoospores/ml (in 1% tryptone broth) was added to each well of a

96 well flat-bottom sterile plate. 50µl of AMPs at the aforementioned concentration were

then added to each well, with each sample replicated 3 times. We prepared positive and

negative controls on each 96 well plate (three replicates per control on each plate).

Positive controls consisted of 50µl of 2 x 106 Bd zoospores/ml and 50µl of sterile PCR

grade water. Negative controls contained 50µl of heat-killed Bd zoospores of the same

concentration and 50µl of sterile PCR grade water (Gibble and Baer 2011; Gibble et al.

2008). We read optical density (OD; BioRad Imark) of wells at 490nm on day 0

(immediately after plating), day 1(13 hours post plating), day 2, day 3, day 4, day 5, day

6, day 7, day 8, and day 9. A logistic growth model was fit to data using a self-starting nls

logistic model function (R Development Core version 3.0.2, stats package, José Pinheiro

and Douglas Bates), and Bd growth rate (r) was determined (Krynak et al. In Press). Bd

growth rate (r) was used as our proxy for AMP bioactivity; rapid Bd growth rate

indicated less bioactive AMPs.

We tested if our treatments affected larval and juvenile percent survival utilizing

a two-sample Wilcoxon test. We compared survival in each treatment to survival in the

Control group. To account for multiple comparisons, we applied Bonferroni correction.

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We used ANOVA to test if Rodeo™ treatments applied during the larval stage affected

larval duration or any the three axes of the NMDS ordination of the larval microbiome.

In these models, each response variable was analyzed with a single predictor variable

(larval Rodeo™ concentration) with four levels (0.00 mg a.i./L, 0.75 mg a.i./L, 1.50 mg

a.i./L, and 2.50 mg a.i./L) via ANOVA. Replicates which underwent post-metamorphic

(juvenile) treatments were included in these analyses of larval traits; replicates which

received juvenile Rodeo™ exposures were incorporated into the Control group, and

replicates which received both larval and juvenile Rodeo™ exposures were incorporated

into the larval group. We also tested whether larval stage Rodeo™ exposure (four levels:

0.00 mg a.i./L, 0.75 mg a.i./L, 1.50 mg a.i./L, and 2.50 mg a.i./L) on its own affected

post-metamorphic (juvenile) traits (average juvenile mass, log-transformed AMP

production, log-transformed AMP bioactivity, and each of the three NMDS ordination

axes describing juvenile microbiome structure) using ANCOVA. Average age (in days)

post-metamorphosis was included as a covariate in each model to account for the possible

confounding factor of age at time of juvenile sampling. We included this analysis because

the high mortality in the 2.5mg a.i./L larval Rodeo™ treatment created an unbalanced

design. Finally, we used ANCOVA to test if Rodeo™ treatments affected post-

metamorphic (juvenile) traits, including average age post-metamorphosis as a covariate

in each model. In these ANCOVA models we assessed each of the responses (average

juvenile mass, log-transformed AMP production, log-transformed AMP bioactivity, and

each of the three NMDS ordination axes describing juvenile microbiome structure) as a

function of the additive effects of stage at which the animals were exposed to Rodeo™

(three levels: larval exposure, juvenile exposure, or both larval and juvenile exposure)

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and the concentration of Rodeo™ they were exposed to (two levels: 0.75 or 1.50 mg

a.i./L), including the age post-metamorphosis as the co-variate in each model.

Interactions were not included due to low statistical power associated with our small

sample size. We utilized Type III sums of squares for all ANCOVA analyses. Planned

contrasts were used to compare treatment means in all ANOVA/ANCOVA models.

4.5. Results

Survival from the start of the experiment to metamorphosis (i.e. larval survival) in

the High Rodeo™ treatment was reduced 37% compared to Control treatment (Figure

4.2; Low: W=105, p =0.84; Medium: W=101, p=0.98; High: W=155, p=0.012); however,

survival from metamorphosis to the end of the experiment (i.e. juvenile survival) did not

significantly differ between Control and any of the treatments (Figure 4.3, Low

concentration larval exposure: W=10.50, p =1.00; Low concentration juvenile exposure:

W=16, p =0.76; Low concentration larval and juvenile exposure: W=20, p =0.73;

Medium concentration larval exposure: W=19, p=0.86; Medium concentration juvenile

exposure: W=17, p=0.62; Medium concentration larval and juvenile exposure: W=10,

p=0.14; High concentration larval exposure: W=10, p =0.14).

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Figure 4.2 Larval Acris blanchardi survival in response to Rodeo™ concentration. Low: 0.75mg a.i./L,

Medium: 1.5 mg a.i./L, and High: 2.5 mg a.i./L. High Rodeo™ concentration for a period of 12 days

reduced survival by 36.67% compared to Control (Two-sample Wilcoxon test significant with Bonferroni

correction: p=0.012). N=number of replicates at beginning of the experiment.

Figure 4.3 Juvenile Acris blanchardi survival in response to Rodeo™ treatments (corrected for larval

survival). There were no treatment effects between Control (C) and treatments. Low (L): 0.75mg a.i./L,

Medium (M): 1.5 mg a.i./L, and High (H): 2.5 mg a.i./L. Larvae and frog symbols correspond to stage at

which the animals were exposed to Rodeo™. Survival from metamorphosis to the end of the experiment

(i.e. juvenile survival) did not significantly differ between Control and any of the treatments. N=number of

replicates at end of larval period.

N

=7

N

=3

N

=5

N

=5

N

=4

N

=4

N

=5

N

=5

N

=20

N

=10

N

=10

N

=10

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Larval microbiome structure was marginally affected by our larval Rodeo™

concentrations along NMDS axis 2 (axis 1: F(3,33)=1.63, p=0.20; axis 2: F(3,33)=2.63,

p=0.07 ; axis 3 F(3,33)=0.41, p=0.75; Figure 4.4A). Post hoc planned contrasts indicated a

significant difference in larval microbiome structure between our high Rodeo™

concentration (2.5 mg a.i./L) and Control (axis 2:T=2.8, p= 0.009; Figure 4.4A), but no

differences were found between the Low and Medium Rodeo™ concentrations and

Control (Figure 4.4A). Larval duration was not affected by larval exposure to Rodeo™

(mean ±SE= 77.29±2.27days; Rodeo™ concentration: F(3,33)= 0.16, p=0.92).

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Figure 4.4 Acris blanchardi skin microbiome as a function of larval Rodeo™ concentration. A. Larval

microbiome NMDS ordination (3D solution stress=15.87%; Axis 3 not shown) as influenced by larval

Rodeo™ concentration (mean and standard error shown; Controln=14: 0.0mg a.i./L; Lown=8: 0.75mg a.i./L;

Mediumn=10: 1.5 mg a.i./L; Highn=5: 2.5 mg a.i./L). Rodeo™ concentration altered larval microbial

community structure along NMDS Axis 2 (F(3,33)=2.632, p=0.07). Post hoc planned contrasts: a= not

significantly different from Control; b= p<0.008 compared to Control. B. Juvenile microbiome NMDS

ordination (3D solution stress=11.2%; Axis 2 not shown) as a function of larval Rodeo™ concentration

(mean and standard error shown; Controln=6: 0.0mg a.i./L; Lown=2: 0.75mg a.i./L; Mediumn=3: 1.5 mg a.i./L;

Highn=5: 2.5 mg a.i./L). Larval Rodeo™ concentration did not affect juvenile microbiome when excluding

replicates with post-metamorphic treatments (i.e. replicates exposed as juveniles only as well as replicates

exposed as both larvae and juveniles). Post hoc planned contrasts: a= not significantly different from

Control.

We found no evidence of carry-over effects of larval Rodeo™ concentration on

juvenile mass, AMP production, AMP bioactivity, or any of the juvenile microbiome

NMDS ordination axes in our ANCOVA models which included average age post-

metamorphosis as a covariate (Table 4.2; Figure 4.4B). When examining possible

additive effects of our treatments, including average age post-metamorphosis as a

covariate in the models, we found a marginal effect of Rodeo™ concentration on juvenile

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mass; however, if controlling for multiple comparisons, the effect is not significant. Our

medium Rodeo™ concentration tended to produce larger juveniles than our low

concentration (Low: 0.30 ± 0.02 grams; Medium: 0.38 ± 0.02 grams; F(1,19)= 4.43, p=

0.05; Table 4.3). We did not find significant effects of Rodeo™ concentration or the

timing of Rodeo™ exposure on AMP production or bioactivity (AMP production

mean/SE: 252.84 ± 30.24 µg/ml per gram body weight; AMP bioactivity mean±SE: 1.00

± 0.05; Table 4.3). We did not find evidence of a strong effect of Rodeo™ concentration

or life stage of exposure on juvenile microbiome structure (Table 4.3; Figure 4.5), but we

did find a marginal effect of Rodeo™ concentration on the juvenile microbiome structure

along NMDS axis 3 (axis 3: F(1,19)=4.24, p=0.06), however post hoc planned contrasts did

not indicate significant differences between the two Rodeo™ concentrations (Low and

Medium).

Table 4.2 ANCOVA analysis of larval Rodeo™ concentration effects on juvenile Acris blanchardi

traits (carry-over effects). Excluded replicates with post-metamorphic treatments due to the unbalanced

design, the result of larval mortality.


Juvenile Mass (g) Rodeo™ concentration 3,12 0.18 0.91

Average Days Post-metamorphosis 1,12 1.24 0.29

AMP production

(ug/ml per gbw)

Rodeo™ concentration 3,12 0.42 0.74


AMP bioactivity

(Bd growth rate r)



Juvenile Microbiome

NMDS axis 1



Juvenile Microbiome

NMDS axis 2



Juvenile Microbiome

NMDS axis 3



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Table 4.3 ANCOVA analysis of Rodeo™ treatment effects on Acris blanchardi traits. Treatments

consisted of combinations between two exposure levels (Low, and Medium Rodeo™) and three Rodeo™

exposure stages (larval, juvenile, or both: larval and juvenile Rodeo™ exposure). Marginally significant

treatment effects in bold.


Juvenile Mass (g)


Exposure stage 2,19 0.50 0.61


AMP production

(ug/ml per gbw)




AMP bioactivity

(Bd growth rate r)




Juvenile Microbiome

NMDS axis 1




Juvenile Microbiome

NMDS axis 2




Juvenile Microbiome

NMDS axis 3 Rodeo™ concentration 1,19 4.24 0.06



Figure 4.5 Juvenile microbiome NMDS ordination (3D solution stress =11.2%) indicating marginally

significant effect of Rodeo™ concentration (axis 3: F(1,19)=4.24, p=0.06). Post hoc planned contrasts did

not reveal significant mean differences between the two Rodeo™ concentrations. L= larval exposure, J=

juvenile exposure, B= exposure at both larval and juvenile life stages.

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4.6. Discussion

Glyphosate toxicity as measured in acute analyses (48-96hr) does not reflect true

effects on non-target species (Bradberry et al. 2004; Relyea and Hoverman 2006). While

our laboratory manipulation is not a natural system, we utilized environmentally relevant

concentrations of glyphosate from Rodeo™ formula herbicide (Relyea 2005; Saunders

and Pezeshki 2014) and administered this to A. blanchardi larvae for a conservative

duration of time based on glyphosate half-life estimates (Colombo and Masini 2014).

Furthermore, we assessed effects across life stages, an important factor missing in acute

exposure studies (Distel and Boone 2010; Edginton et al. 2004). We found a nearly 37%

decrease in average survival of A. blanchardi larvae exposed to 2.5mg a.i./L compared to

Control. This lethal effect would not have been predicted based on the results of acute

analyses of glyphosate toxicity (Dow Agrosciences 2015). In fact, our experiment was

originally designed with the assumption that our Rodeo™ concentrations would not be

lethal, for it was our goal to examine potential sub-lethal effects on immune defense traits

over A. blanchardi life stages. The fact that our Rodeo™ treatments did not decrease

juvenile survival highlights the importance of understanding effects of herbicide exposure

across life-stages.

When assessing sub-lethal effects of Rodeo™ on A. blanchardi, we found that the

skin-associated microbiomes of larvae were altered by exposure to 2.5 mg a.i./L Rodeo™

(NMDS axis 2; Figure 4.4A), and there was some evidence of Rodeo™ concentration

affecting juvenile microbiome structure. Our Low and Medium Rodeo™ concentrations

(0.75and 1.5 mg a.i./L respectively) tended to differentially alter the juvenile skin

microbiome (NMDS axis 3; Figure 4.5). Together these results indicate that disease

resistance could be affected if amphibians are exposed to Rodeo™ herbicide at

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concentrations recommended by the manufacturer (Dow Agrosciences 2013). We did not

find effects of larval Rodeo™ concentration on larval duration nor did we find evidence

of carry-over effects of larval Rodeo™ concentration on our other post-metamorphic

traits (juvenile mass, AMP production and bioactivity). We did find that Rodeo™

concentration of 1.5mg a.i./L (medium Rodeo™) marginally differed from 0.75 mg a.i./L

(Low Rodeo™) in terms of effects on juvenile mass when assessing additive effects of

Rodeo™ concentration and the timing of exposure across developmental stages. While

not all A. blanchardi traits were affected by our treatments, the assessment of these traits

together provides clues towards understanding how glyphosate-based herbicides may

affect amphibian populations.

The finding that our Rodeo™ concentration of 2.5mg a.i./L had a negative effect

on larval survival was counter to what would be expected based on description of the

product’s environmental safety (Dow Agrosciences 2013). Acute toxicity studies which

report a LC50 of >100mg/L of glyphosate suggested to us that the concentrations used in

this study would not affect survival (Dow Agrosciences 2015). However, the increased

mortality in our highest Rodeo™ concentration was in agreement with others who have

found negative effects on survival across a variety of amphibian species at similarly

environmentally relevant concentrations of glyphosate (Relyea and Hoverman 2006;

Relyea 2005). Furthermore, factors in the natural environment may exacerbate negative

effects of glyphosate on amphibian survival across life-stages, such as differing densities

of competitors, or predators, and temperature shifts as associated with climate change

(Jones et al. 2011; Loetters et al. 2014; Relyea et al. 2005). Acris blanchardi have a

central North American distribution (Gamble et al. 2008) and habitats vary in terms of

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numerous environmental conditions including temperature and co-habiting species;

therefore some populations may be more sensitive to glyphosate exposure than others,

dependent on the environmental context. Moreover, as a declining species which is

largely annual, with an estimated complete population turnover within 16 months

(Burkett 1984), it is imperative for conservation of A. blanchardi that we thoroughly

examine potential mortality effects across life stages associated with land management

practices including the use of glyphosate-based herbicides. Our results suggest that a

single early-season (spring) Rodeo™ treatment (A. blanchardi larval stage) has the

capacity to severely decrease local population size.

In our study, Rodeo™ exposure did not alter larval duration, but marginally

affected juvenile mass. Previous studies have found that these measures are affected by

other forms of glyphosate-based herbicides. Round-up Original™ alters growth and

development in Rana pipiens (Howe et al. 2004), VisionMax™ slows developmental

rates in Rana sylvatica possibly by means of altering the expression of genes involved in

development (Navarro-Martin et al. 2014) and Round-up WeatherMax™ may alter

development by means of disrupting hormonal pathways in R. sylvatica (Lanctot et al.

2013). Shifts in larval duration can have negative effects on amphibians via desiccation

due to seasonal pond drying and increased predation or competition due to changes in

densities or size of co-habiting species over time (Bridges 2002; Newman 1988; Van

Buskirk and Saxer 2001). Juvenile mass is strongly correlated with amphibian survival to

reproduction (Semlitsch et al. 1988) and therefore careful consideration should be given

to potential effects on juvenile mass found in our study. Surprisingly, juveniles exposed

to our Medium Rodeo™ concentration were larger than those exposed to our Low

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Rodeo™ concentration, suggesting that herbicide may be causing increased growth. This

is in agreement with Lanctot et al.’s (2014) finding that sub-lethal exposure to Round-up

WeatherMax™ and Vision™ increased larval body condition (increased mass relative to

body length) in Rana sylvatica; however, this effect was dependent on larval

developmental stage and sex. Such increases in mass resulting from herbicide exposure

may indicate a compensatory effect such as increased mass counter-balancing depressed

immune function on fitness. In conjunction with our other findings which included a lack

of effect of Rodeo™ on A. blanchardi juvenile mortality, a lack of effect on larval

duration, and this possible increase in mass associated with Rodeo ™ concentration,

delaying applications of glyphosate-based herbicide products until after metamorphosis

may increase A. blanchardi fitness.

When assessing effects of glyphosate-based herbicides on amphibians, it is also

important to consider the additives in each formulation for cross comparison. Round-

up™ and Vision™ products differ from Rodeo™ in one key aspect: these formulas

contain an added surfactant (either as an undisclosed proprietary formula or

polyethoxylated tallowamine, POEA). It is commonly thought that the surfactant is the

source of direct effects on amphibians (Annett et al. 2014). Since glyphosate formulations

labeled as safe for use in and around aquatic habitats do not contain surfactants, the

negative effects of herbicide treatment may not be as pronounced in aquatic formulations.

However, Dow Agrosciences recommends (2013) to mix Rodeo™ with a non-ionic

surfactant to improve efficacy. While our study did not assess the addition of a surfactant

to the Rodeo™ formula, it is important that future studies also examine this

recommended surfactant addition on amphibian traits which correlate with fitness.

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We did not find effects of Rodeo™ on A. blanchardi antimicrobial peptides in

terms of the amount produced, or the ability of the proteins to inhibit Bd in vitro. In a

previous study, we determined that A. blanchardi populations in the states of Ohio and

Michigan (USA) differ in the amount of AMPs they produce, and this variation is

correlated to environmental characteristics including land-use and water quality (Krynak

et al. In Review). In our present study, we used a single Ohio population. It is possible

that Rodeo™ may alter AMP production in some A. blanchardi populations not included

in our present study. In agreement with previous studies on the bioactivity of A.

blanchardi AMPs against Bd, we found that AMPS from frogs used in this study were

not bioactive against Bd (Conlon 2011; Krynak et al. In Review). Pathogens not tested in

this study such as iridoviruses or Batrachochytrium salamandrivorans (Bsal) may be

inhibited by A. blanchardi AMPs and such inhibition may be altered by herbicide

exposure (Forson and Storfer 2006; Martel et al. 2013; Pearman and Garner 2005).

Therefore, while results of this study indicate that Rodeo™ herbicide may not affect this

component of the innate immune defense system, investigation of interactions between

exposure to Rodeo™ and exposure to pathogens other than Bd, across populations is

warranted.

While our study did not find effects of Rodeo™ on A. blanchardi AMPs, we did

find effects on the other important component of the amphibian innate immune system:

the skin-associated microbiomes. We found that the larval skin microbiome of A.

blanchardi was altered by our 2.5mg a.i./L concentration of Rodeo™, but this effect did

not carry-over to alter the juvenile microbiome. Additionally, the microbiome of post-

metamorphic juveniles showed a trend suggesting Rodeo™ concentration alters juvenile

125

microbiome structure; microbiomes of juveniles exposed to 1.5 mg a.i./L Rodeo™

marginally differ from those exposed to 0.75 mg a.i./L Rodeo™. Together these results

suggest that early season Rodeo™ treatment of emergent aquatic plants may differ from

late season treatment in terms of the influence on A. blanchardi disease resistance. This is

particularly important due to the commonly used regime of cattail (Typha angustifolia)

treatment in the spring when A. blanchardi larvae are present and common reed

(Phragmites australis) treatment in the late summer when larvae are metamorphosing

(Dow Agrosciences 2013; Wright and Wright 1949). Previous studies have found that

particular bacterial species found on amphibian skin are capable of producing metabolites

which suppress or cure pathogen infection of the skin (Becker et al. 2009; Harris et al.

2006; Lauer et al. 2007), but if the microbiomes are disrupted, such functions may not be

possible. Conversely, the changes to the microbiomes on A. blanchardi skin caused by

Rodeo™ may not result in functional changes. The structurally different communities,

such as seen in this study, may be functionally redundant (Kung et al. 2014; Lear et al.

2014).

In the present study, we found that AMP production and bioactivity are not

affected by Rodeo™, yet Rodeo™ does affect the skin microbiome. By investigating

these traits in unison, we can begin to tease apart the relative influence of the amphibian

host (in this case, the AMPs that the host produces) versus the environment on amphibian

skin-associated microbiomes. Previous studies have found skin microbiomes to be

species specific (McKenzie et al. 2012), but there is a lack of information on intraspecific

variation in the skin-associated microbiomes (Fitzpatrick and Allison 2014; Kueneman et

al. 2014) and even less known about the relative roles of the amphibian host and the

126

environment external to the host in regulating this microbiome structure (Krynak et al. In

Press, In Review). The AMPs which amphibians secrete onto their skin surface have the

potential to shape the microbiome by disrupting microbial membranes (Rollins-Smith

2009; Rollins-Smith et al. 2011) potentially shifting the relative proportions of microbial

taxa surviving on the skin. If the host’s AMPs were regulating the skin microbiome, we

would have expected to find similar patterns of environmental effects across both

immune defense traits. We did not find similar environmental effects across both traits;

therefore our results support the idea that amphibian skin-associated microbiomes are

relatively more affected by the environment external to the host than they are by the

AMPs of the amphibian host, which is in agreement with the findings from our previous

studies (Krynak et al. In Press, In Review).

Finally, our findings which indicate that Rodeo™ influences the skin-associated

microbiome on A. blanchardi raises the question as to if glyphosate exposure can

significantly alter the microbiomes of soil and water in the environment over time with

increased or regular use (Sviridov et al. 2015). Rodeo™ is formulated to be broken down

in the environment by microbial organisms (Rodeo™ Specimen Label; Dow

Agrosciences LLC), but it has been shown that glyphosate can accumulate in soil and

water environments (Eberbach 1999; Sviridov et al. 2015); therefore, in such cases, a

microbiome shift in the environment provides a plausible mechanism by which such

accumulation could occur (Dick and Quinn 1995; Quinn et al. 1988). Recently,

bioremediation by means of bacterial addition has been proposed to help expedite

glyphosate breakdown in the environment (Sviridov et al. 2015). This bioremediation

concept poses a plethora of questions as to how the addition of certain bacterial taxa may

127

alter the microbial structure and function in the environment, and how this relates to

wildlife health and ecosystems as a whole.

4.7. Conclusions

Our study supports the idea that acute toxicity measures are inadequate

assessments of the effects of Rodeo™ use. In agreement with multiple other studies of

glyphosate-based herbicide effects on amphibians, our study found that Rodeo™

exposure increased mortality in A. blanchardi, a species which has already suffered

population declines and extirpations in the northern portions of its range (Gamble et al.

2008; Gray and Brown 2005; Lehtinen and Skinner 2006). Additionally, we show that

Rodeo™ could be indirectly decreasing amphibian fitness by means of changes to the

skin-associated microbiome structure. Improving our knowledge of the influence

herbicide use has on amphibians across life stages provides opportunity for changes to

application strategies to protect amphibian health or at minimum, lessen negative effects

of the practice.

128

Chapter 5: Conclusion

5.1. Summary

My research goal was to assess the potential influence of the environment on the

amphibian skin-associated microbiome and the antimicrobial peptides (AMPs) produced

in the skin. Additionally, by examining these traits in unison, I was able to assess the

influence of AMPs on the amphibian skin microbiome. In all three studies, there was

evidence for environmental influence on these traits and no evidence of the AMPs from

the host influencing the amphibian skin microbiome.

5.2. Environmental effects on innate immune defense traits

In the first study (Chapter 2), I found that commonly observed variation in larval

habitat pH and shading can alter the skin microbiome and AMPs of amphibians;

however, the relationships are complex. I found the pH change from an average of 7 to 6

resulted in a significant shift in the larval skin microbiome of Rana catesbeiana, but I

found no evidence of carry-over effects on the post-metamorphic juvenile microbiome.

Post metamorphic AMP production and bioactivity were affected by interactions between

pH and shade of the larval environment, and effects differed between the two R.

catesbeiana populations used in the study.

In the second study (Chapter 3), I found that Acris blanchardi collected from sites

across the northern edge of the species geographic range differed in skin microbiome

structure and AMP production; however, AMP bioactivity revealed no significant

differences between populations. Multiple main and interacting landscape and water

characteristics predicted the trait variation I observed. The microbiome was associated

with water conductivity, the ratio of natural to managed land, and latitude. Additionally

there were interaction effects on the microbiome between frog sex and latitude, between

129

frog sex and water surface area, and between the ratio of natural to managed land and

water surface area. AMP production was influenced by the interaction between water

surface area and conductivity. Additionally, I found a negative relationship between

AMP production and resistance to Bd; the more AMPs produced by A. blanchardi, the

faster Bd grew in culture.

In the third study (Chapter 4), I found that an environmentally relevant

concentration of a glyphosate based herbicide (Rodeo™; 2.5mg a.i./L ) significantly

decreased survival of Acris blanchardi larvae, but Rodeo™ exposure did not alter

juvenile survival. Larval Rodeo™ exposure did alter the larval microbiome; 2.5mg a.i./L

Rodeo™ caused a shift in larval microbiome structure compared to control. Larval

Rodeo™ exposure did not alter larval duration, and did not carryover to alter post-

metamorphic traits. However, an assessment of additive effects of Rodeo™ concentration

and the developmental stage at which A. blanchardi was exposed to Rodeo™ indicated a

marginal effect of Rodeo™ concentration on juvenile mass and the juvenile microbiome

structure.

5.3. Host effects on skin-associated microbiomes

While it is possible that some environmental effects may indirectly cause

microbiome shifts via changes to AMPs produced by amphibians, I found no evidence of

AMPs influencing the skin microbiome in these three studies. In the first study (Chapter

2), I found carry-over effects of the larval environment on post-metamorphic Rana

catesbeinana AMP production and AMP bioactivity; however, there were no effects on

the post-metamorphic skin microbiome. If AMPs were regulating the skin microbiome

structure, I would expect to have seen similar treatment effects on the skin microbiome as

130

was observed on the AMPs. However, the direct effects of the environment versus

influence of the AMPs would not have been distinguishable. In the second study (Chapter

3), main, additive, and interactive effects of AMP production and AMP bioactivity were

not found to predict the skin microbiome structure across Acris blanchardi populations.

Finally, in the third study (Chapter 4), while I found herbicide treatment effects on skin

microbiome structure, I did not see treatment effects on the AMPs. If AMPs were

regulating the skin microbiome structure, again, I would have expected similar treatment

effects on both innate immune defense traits, but the effects of the host versus effects of

the environment external to the host would have been indistinguishable. Future studies

which directly manipulate the AMPs of amphibian skin and assess the effects on the skin

microbiome over time will further our understanding of the potential effects of host traits

on the skin microbiome.

5.4. Conservation Implications

With amphibian disease-related mortality expected to increase due to the ease of

global transportation and the introduction novel diseases (Daszak et al. 2003) it is

imperative that we improve our understanding potential influences on the traits which

provide amphibians with pathogen resistance. My research suggests that the environment

external to the amphibian host can significantly affect the skin microbiome structure and

the AMPs produced by the host. This knowledge can be used to inform current

conservation initiatives including bio-augmentation programs and regulation of land-

management practices to better protect amphibian health.

While bio-augmentation focused conservation strategies present an exciting

approach to protecting amphibians from disease, the success of such programs will

131

require enhanced emphasis on understanding relative influences of host versus the

environment on augmented microbiomes over time. My research suggests that changes in

the environment may alter augmented skin microbiomes, potentially rendering this

conservation technique less effective in some environments. Additionally, my findings of

ontogenetic effects and carry-over effects of the larval environment on post-metamorphic

skin microbiome structure indicate that augmented microbiomes may be altered over time

due to potential interactive effects of amphibian development, behavior, and the

environment external to the amphibian. Further study will be required to also assess the

relationship between skin microbiome structure and function as associated to

environmental and host influences. Furthermore, we must also assess the influence of

augmented skin microbiomes on the environment external to the amphibian. Introduction

of microbial taxa to naïve ecosystems may have long-term effects of which our current

understanding does not allow us to comprehend. Therefore, research which improves our

understanding of how amphibian microbiomes relate to the environment external to the

amphibian will enhance our abilities to utilize bio-augmentation strategies successfully

and responsibly.

My research also suggests that conservation strategies with focus on enhanced

understanding of potential negative effects of particular land-management practices on

amphibian innate immune defense traits may prove fruitful for amphibian conservation.

My research found that common environmental variation in landscape and water

characteristics influence the amphibian skin-associated immune defense traits. Future

research is needed to elucidate potentially negative effects of anthropogenic

environmental change on these traits including effects from chemical contamination,

132

invasive species, and warming temperatures. Research on both structural and functional

changes to innate immune defense traits as related to environmental change is needed. By

protecting immune defense traits which broadly provide amphibians with pathogen

protection via changes to detrimental land-management practices, we may be able to

prevent some disease-related amphibian declines and extinctions in the future.

133

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