SOCIAL BEHAVIOR AND KINSHIP IN THE FOUR-TOED SALAMANDER,

HEMIDACTYLIUM SCUTATUM

BY

ABIGAIL JOY MALEY BERKEY

DISSERTATION

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy in Ecology, Evolution, and Conservation Biology

in the Graduate College of the

University of Illinois at Urbana-Champaign, 2015

Urbana, Illinois

Doctoral Committee:

Professor Andrew V. Suarez, Chair

Associate Professor, Marlis R. Douglas, Co-Director of Research, University of Arkansas

Associate Professor Christopher A. Phillips, Co-Director of Research

Professor Jeffrey D. Brawn

Associate Professor Robert Lee Schooley

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ABSTRACT

Amphibians are among the taxa experiencing the largest global decline in biodiversity.

Many salamanders, especially North American members of the family Plethodontidae, are at risk

of local extirpation because they persist in small, isolated populations due to specialized habitat

requirements and limited dispersal ability. To effectively conserve and manage such species,

factors influencing population connectivity and dynamics, such as dispersal and behavior, must

be understood across various spatial scales. The Four-Toed Salamander, Hemidactylium

scutatum, a species of concern in eastern North America, is a small-bodied plethodontid with a

biphasic life cycle. Despite its broad geographic range, its occurrence is patchy due to specific

breeding habitat requirements. Little is known about how distance between these habitat patches

affects gene flow and which physical and biotic landscape features may act as barriers to

dispersal. The population structure, i.e. gene flow and genetic diversity, of other plethodontid

species is characterized by high levels of genetic divergence over relatively short distances and

often reflects an isolation by distance pattern. In this study, I examined dispersal and population

structure in H. scutatum from a local to a regional scale, focusing on the interaction between

relatedness and dispersal (kin discrimination) and the potential for phenotypic traits to be used as

a mechanism for kin recognition. I combined empirical observations with lab experiments to (1)

examine population structure in H. scutatum (Chapter 1), (2) determine if phenotypic similarity

between individuals is associated with genotypic similarity (Chapter 2), and (3) examine if kin

discrimination is exhibited and potentially triggered by scent (Chapter 3).

In Chapter 1, I used microsatellites to estimate genetic variation and gene flow within and

among populations, calculate effective population size, and evaluate the possibility of population

bottlenecks on both local and regional scales. The genetic data recorded a pattern of isolation-by-

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distance, characteristic of plethodontid salamanders. However, H. scutatum showed low genetic

divergence among neighboring sites (1,000-2,000m). Several explanation exist for the low

genetic divergence among populations including greater gene flow in H. scutatum compared to

other plethodontids, relatively recent range expansion following the last Pleistocene glaciation,

and a reduction in allele loss as a result of reduced whole clutch mortality in an indirect

developing species.

In Chapter 2 I examined the geographic and genotypic variation in the ventral spot

pattern of H. scutatum. Specifically, if variation in color pattern is genetically controlled, color

pattern may be used as a mechanism for kin recognition between conspecifics or be utilized in

conservation decisions as an indicator of genetic diversity within a population. Additionally, I

investigated the potential use of phenotypic traits as an indicator of stress or genetic diversity

within populations. Fluctuating asymmetry, differences in the bilateral symmetry of a character

due to disturbances in internal or external environments, influences color pattern in many in

amphibian taxa and has the potential to be used as an indicator of at-risk populations under

stress. Hemidactylium scutatum is characterized by a white ventral surface patterned with

distinctive black spots. I used quantitative image analysis and microsatellite markers to

investigate the potential influence of geographic variation on spot pattern and the potential

relationship of phenotypic and genetic similarity. I also examined the possible influence of body

condition, via fluctuating asymmetry, on spot pattern. While spot pattern exhibited significant

variation on the regional scale, no relationship was found between spot pattern similarity and

degree of kinship between individuals, suggesting that spot pattern is not used as a mechanism

for kin recognition in H. scutatum. I also found no relationship between spot pattern symmetry

and body condition. Combined, these findings suggest that, while spot pattern may not be useful

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for assessing genetic variation or population stress, it may be useful for taxonomists and allow

recent immigrants to populations to be identified.

In Chapter 3, I examined if isolated populations have increased degrees of kinship among

individuals, potentially resulting in selection for kin recognition and kin discriminatory

behaviors. Understanding the role of kinship in social behavior of at risk species such as H.

scutatum may help in conservation and management efforts, especially those focusing on

reintroduction and captive propagation programs. I investigated if there was an association

between relatedness and aggressive behavior in H. scutatum by conducting behavioral trials in

the lab and examined if related individuals may be spatially aggregated within a wild population.

No relationship was found between the straight line distance between individuals and their

relatedness. I also did not find a relationship between relatedness and aggressive behavior in the

lab. It is possible that kin discriminatory behaviors differ between demographic groups or kin

discrimination does not occur under the conditions observed in this study. Finally, the indirect

development of H. scutatum may impact juvenile dispersal, resulting in a decreased rate of

encounters between kin and decreased selection for kin recognition compared to other, direct

developing plethodontid species.

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ACKNOWLEDGEMENTS

First and foremost, I would like to acknowledge my advisers, Drs. Christopher A. Phillips

and Marlis R. Douglas. Their guidance and wisdom have been invaluable to my research. I

would also like to my thank my other committee members, Drs. Andrew V. Suarez, Jeffrey D.

Brawn, and Robert Lee Schooley who contributed greatly to the development and refining of this

project. Dr. Mark Davis, Dr. Paul Tinerella, Dr. Whitney Banning Anthonysamy, Dr. Carla

Cáceres, Sarah Baker-Wylie, Dan Wylie, Dr. Michael Douglas, Dr. Michael Dreslik, Ellen

Lawrence, John MacGregor, Susan King, Dr. Stephen Richter, Dr. Reid Harris, and Tom

Biebighauser have all lent their expertise and offered constructive feedback and ideas. Tim

Herman provided me with H. scutatum tissues invaluable information on sampling locations for

this species in Kentucky. Various agencies have been involved with this research including the

Program in Ecology, Evolution, and Conservation Biology and the Department of Integrative

Biology at the University of Illinois in Champaign-Urbana, the Museum of Vertebrate Zoology

at the University of California, the Illinois Natural History Survey, the Illinois Department of

Natural Resources, the USDA National Forest Service, and the Kentucky Fish and Wildlife

Service. Specimens were collected and handled under the Illinois Department of Natural

Resources Endangered and Threatened Species Permits #09-11S and #11-10S, the Kentucky Fish

and Wildlife Service Scientific Collecting Permit #SC1211096, and the Institutional Animal

Care and Use Committee Protocol #12016. This project greatly benefited from the help of many

field and lab assistants including Joe Frumkin, Ryan Jorgensen, Brittany Perrotta, Kevin Murray,

Alex Remigio, Courtney Romolt, David Bozman, Noah Horsley, Austin Meyers, Christopher

Maley, and Gracie Berkey. This project was generously funded by numerous sources, including

the Illinois Wildlife Preservation Fund, the Endangered Species Protection Board, the Chicago

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Herpetological Society, the University of Illinois, and Tom Beauvais. I would also like to

acknowledge Dr. Carol A. Augspurger for her kind words and guidance through the stormy

waters of graduate school. Finally I would like to thank my parents, Charles and Joyce Maley,

my aunt Gail Steck, and my husband Daniel Berkey for their support and never failing

confidence in me.

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TABLE OF CONTENTS

CHAPTER 1. POPULATION GENETICS OF THE FOUR-TOED SALAMANDER,

HEMIDACTYLIUM SCUTATUM, AT LOCAL AND REGIONAL SCALES …………..1

CHAPTER 2. SOURCES OF VARIATION IN THE VENTRAL SPOT PATTERN OF THE

FOUR-TOED SALAMANDER ………………………………………………………...36

CHAPTER 3. ROLE OF KINSHIP AND INTRASPECIFIC AGGRESSION IN THE FOUR-

TOED SALAMANDER………………………………………………………………....69

APPENDIX A.………………………………………………………………………………….103

APPENDIX B.………………………………………………………………………………….104

APPENDIX C.………………………………………………………………………………….105

APPENDIX D.………………………………………………………………………………….106

APPENDIX E.………………………………………………………………………………….107

APPENDIX F.………………………………………………………………………………….108

1

CHAPTER 1. POPULATION GENETICS OF THE FOUR-TOED SALAMANDER,

HEMIDACTYLIUM SCUTATUM, AT LOCAL AND REGIONAL SCALES

Introduction

Global declines in biodiversity place us amid a major extinction event in the geological

record. Causes implicated in this decline include climate change (Bellard et al. 2012, Hannah et

al. 2002, Thomas et al. 2004), habitat fragmentation and destruction (Fischer and Lindenmayer

2007, Krauss et al. 2010, Tilman et al. 2001), introduction of exotic species (Vellend et al. 2007,

Vilà et al. 2011), and environmental pollution (Hsu et al. 2006, Johnston and Roberts 2009,

Zvereva and Kozlov 2010). Amphibians are particularly vulnerable to these agents, as well as

additional factors associated with global change, including increased UV radiation, over

exploitation, and emerging diseases (Beebee and Griffiths 2005, Collins and Storfer 2003). As a

result, amphibians are one of the vertebrate taxa most affected by this global loss in biodiversity

(Stuart et al. 2004, Wake and Vredenburg 2008).

Persistence of a species is influenced by short- and long-term processes and depends on

its ability to cope with environmental change. Long-term, directional shifts in natural phenomena

have historically occurred slowly, allowing mobile species to disperse to more suitable regions,

such as refugia during periods of glaciation. Alternatively, a species may adapt in response to

long-term pressures, but its adaptive capacity depends largely on inherent genetic diversity

(Barrett and Schluter 2007, Lande and Shannon 1996). Genetic diversity can be eroded through

drift as populations become increasingly isolated and/or inbreeding as they decrease in size,

potentially resulting in an intensified genetic load (Reed and Frankham 2003, Rowe and Beebee

2003). This process, which ultimately leads to reduced fitness and further decreased population

size, is known as the small population paradigm (see Caughley 1994). Thus for declining species

understanding the magnitude of population isolation is important for effective conservation and

2

management and this is best accomplished by assessing genetic structure within and among

habitat patches.

Due to specialized habitat requirements and/or limited dispersal ability (Welsh and

Droege 2001) many salamander species, especially the small-bodied members of the family

Plethodontidae, persist in small, isolated populations characterized by high levels of genetic

divergence. A review by Larson et al. (1984) across 21 species found a mean FST value of 0.53

among populations, suggesting that plethodontids typically form units not connected by gene

flow. While studies on population structure generally have focused on large-scale differentiation

(>100km distance; Niemiller et al. 2008, Tilley and Mahoney 1996), genetic structuring has also

been observed on local scales (<10km; Kozak et al. 2006) and across distances as small as 200m

(Cabe et al. 2007). Moreover, individual movements in plethodontids appear to be limited, even

among nearby populations (Gillette 2003, Mathis 1991), resulting in isolation-by-distance

patterns (Crespi et al. 2003, Jackman and Wake 1994, Martínez-Solano et al. 2007). Gene flow

can be further reduced in these groups by both natural barriers, such as streams (Marsh et al.

2007), or man-made ones, such as roads (deMaynadier and Hunter 2000, Marsh et al. 2005,

Marsh et al. 2008). Additionally, genetic structure of many salamander species reflects post-

Pleistocene colonization, and is characterized by low diversity and consequently weak

population divergence (Hewitt 2004). This pattern is consistent across many salamander

families, including Plethodontidae (Highton and Webster 1976), Salamandridae (Kutcha and Tan

2005), and Ambystomatidae (Demastes et al. 2007, Phillips et al. 2000, Steele and Storfer 2006).

Life history characteristics may also modulate genetic structure in plethodontids. Most species

exhibit direct development with few retaining the ancestral mode of an aquatic larval stage.

Direct development may increase the potential for whole clutch mortality that in turn could

3

reduce genetic variation within, but increase distinctness among populations through genetic drift

(Dubois 2004). In addition, low rates of gene flow in the direct-developing Plethodon cinereus

(Cabe et al. 2007) indicateindicate low dispersal rates. Combined effects of life history

characteristics and scant dispersal could be a mechanism that led to the relatively high number of

species in the Plethodontidae.

The Four-toed Salamander, Hemidactylium scutatum, is a small-bodied plethodontid with

a biphasic life cycle and specific habitat requirements. The species has a broad geographic

distribution that extends north to Nova Scotia, south to Florida, and as far west as Oklahoma and

Missouri (Petranka 1998), but with patchy occurrences in the southern and western areas.

Populations tend to centralize around patches of forested areas surrounding spring or seep-fed

pools. Eggs are laid at the edge of ponds and larvae hatch at a late stage and wriggle into the

pond where they metamorphose within 3-6 weeks. Among 36 states, districts, and territories H.

scutatum is designated as either “imperiled” or “critically imperiled” in 9 of these regions and

“vulnerable” within an additional 13 (NatureServe 2011). Persistence of isolated populations

makes H. scutatum an intriguing candidate for investigating how distance affects gene flow and

what physical and biotic landscape features act as barriers to dispersal. Additionally, H. scutatum

is an upland, terrestrial species with indirect development, a life history unusual among

plethodontids and may exhibit unique patterns of population structure that require specific

conservation strategies.

Due to the small-body size of plethodontid salamanders, genetic studies are best

accomplished using non-lethal methods for tissue sampling. Microsatellite analysis is especially

useful to assess gene flow because it requires only small amounts of tissue (i.e. tail clips), is

relatively inexpensive, and yields generally highly polymorphic loci facilitating fine-scale

4

examination of genetic structure (Selkoe and Toonen 2006). Gene flow has been evaluated in a

variety of salamander species, but few studies used microsatellite and most focused on the

relatively large bodied ambystomatid salamanders (Giordano et al. 2007, Spear et al. 2005,

Zamudio and Wieczorek 2007), save a couple studies on the Eastern Red-backed Salamander, P.

cinereus (Cabe et al. 2007, Noël et al. 2007), a smaller plethodontid species.

This study investigated the population structure of H. scutatum on both local and regional

scales by evaluating several questions. (1) Does H. scutatum have patterns of population

structure similar to those found in other plethodontid species? If H. scutatum’s dispersal

capabilities are similar to those recorded for other plethodontid species, then comparable patterns

of population structure, including high levels of population differentiation over relatively short

distances, limited gene flow between populations, and an isolation-by-distance pattern indicated

by a positive correlation between inter-population distance and genetic divergence (Wright 1943)

should be observed. (2) Does H. scutatum have patterns of population structure that reflect re-

colonization from refugia following glaciation events? If the present patterns of genetic diversity

in H. scutatum reflect the influence of glaciation events, then genetic differences should be low

between sites located within previously glaciated regions as a result of re-colonization events. (3)

Has the unusual life history of H. scutatum resulted in patterns of population structure that differ

from those observed in other plethodontid species? If indirect development in H. scutatum results

in decreased whole clutch mortality and subsequent allele loss, then less genetic divergence

should observed between sites than recorded for other plethodontid species. I used microsatellite

markers to estimate genetic variation, gene flow, and genetic differences between populations,

and to evaluate the possibility of previous population bottlenecks on both local and regional

5

scales. I further compared these results to those previously recorded in the literature for other

plethodontid species.

Materials and Methods

Samples and Collection Sites

I selected study sites at two spatial scales: (1) the local scale consisted of two sites (I-AN

and I-AS) in the Middle Fork State Fish and Wildlife Area in Vermilion County, IL; (2) the

regional scale was represented by 11 sites in central Kentucky (K-A, K-B, K-C, K-D, K-E, K-F,

K-H, K-J, K-L, K-M, K-N) and one site in Jo Daviess County in northwestern Illinois (I-B)

(Figure 1.1). Sites I-AN and I-AS, separated by ~1,244 m and a stream, were analzyed separately

in this analysis. Previous studies on H. scutatum documented relatively small maximum straight-

line dispersal distances of only 201 m from a wetland source (Windmiller 2000) and streams

appear to be a barrier to dispersal in a similar sized plethodontid, P. cinereus (Marsh et al. 2007).

I conducted visual encounter surveys for H. scutatum at local sites from March -

November for four years (2009-2012) and at regional sites in KY from 18-23 March 2011. For

each capture, I recorded the GPS location in UTM coordinates using a Garmin GPS 12, body

size (mm) as Snout-Vent-Length (SVL), Total-Length (TL) and mass (g), and photographed the

underside of the individual to identify potential recaptures. For genetic analyses, I collected

small tail clips (1-5 mm) from individuals weighing more than 0.18 g using sterilized clippers

and tissues were stored in ethanol at -80 °C. Samples from site I-B, were collected by Tim

Herman in May 2004 using similar procedures. For the regional analyses, additional tissues from

the local site I-AN collected by Tim Herman in April-May 2005 (I-AN05) were included.

6

Molecular Methods and Genetic Analyses

I isolated whole genomic DNA from tail clips using the Qiagen DNEasy Extraction kit

following the manufacture’s protocol. For genetic analysis I screened 28 microsatellite loci

developed for four salamander species, three of which were plethodontid and one an

ambystomatid, for their potential usefulness in H. scutatum. Seven loci (HS3a, HS3b, HS5, HS7,

HS8, HS14, and HS15) were developed for H. scutatum by the Reid Harris lab at James Madison

University. The remaining loci were initially designed for other salamander species, including 11

for P. elongatus [PE0, PE1, PE3, PE4, PE5, PE7, PE8, PE9, PE10, PE11, and PE12 (Degross et

al. 2004)], seven for P. cinereus [PC1, PC2, PC3, PC4, PC5, PC6, and PC7 (Connors & Cabe

2003)], and three for Dicamptodon tenebrosus [DT4, DT5, and DT8 (Curtis & Taylor 2001)]. I

set up the final optimized PCR reactions in 10 µl volumes, each with 2 µl of the extracted DNA

solution, 10 µM of each primer, 1.25 mM of each dNTP, 25 mM MgCl2, 2 µl 1X reaction buffer,

and 1.5-2.0 units Taq and conducted amplifications on either an Applied Biosystems 2720

Thermocycler or an Applied Biosystems Veriti Thermocycler. With the exception of loci DT4

and HS7, amplifications consisted of a 3 minute denaturation step at 95 °C, 15 cycles of 45 s at

95 °C, 60 s at the annealing temperature, 45 s at 72 °C, followed by 25 cycles of 30 s at 95 °C,

45 s at the annealing temperature, 30 s at 72 °C. The amplification for DT4 consisted of a 3

minute denaturation step at 95 °C, 15 cycles of 45 s at 95 °C, 60 s at the annealing temperature,

60 s at 72 °C, followed by 25 cycles of 30 s at 95 °C, 45s at the annealing temperature, 45 s at

72 °C and the amplification for HS7 consisted of a 3 minute denaturation step at 95 °C, 15 cycles

of 45 s at 95 °C, 60 s at the annealing temperature, 90 s at 72 °C, followed by 25 cycles of 30 s at

95 °C, 45 s at the annealing temperature, 30 s at 72 °C.

7

I evaluated the amplification success of loci on agarose gels stained with GelGreen

(Biotium, Inc., Hayward CA) and visualized fragments using a DarkReader Transluminator DR-

88M (Clare Chemical Research, Inc., Dolores CO). For efficient screening of genetic diversity, I

grouped selected loci into pool-plex panels where the amplification products of multiple loci for

the same individual are combined for data generation. I labeled forward primers with a

fluorescent dye and further evaluated each locus in terms of polymorphism, amplification

specificity, and signal strength. Genotyping was conducted on an Applied Biosystems 3730xl

Analyzer at the W. M. Keck Center for Comparative and Functional Genomics, University of

Illinois. An internal size standard (LIZ 500) was included with each sample to determine

fragment length. I scored alleles using GENEMAPPER v5.0 and assessed genotype scoring errors,

the presence of null alleles, and the occurrence of large allele dropout via MICROCHECKER v2.2.3

(Oosterhout et al. 2004). I also employed POWSIM v4.1 to determine the statistical power of

using this set of microsatellites for analyses (Ryman and Palm 2006).

Local Analysis

For evaluation of genetic structure at the local scale, I analyzed 157 samples collected

between 2009-2012 from Vermilion County, Illinois (sites I-AN and I-AS). I used GENEPOP v3.4

(Raymond and Rousset 1995) to test each locus for Hardy-Weinberg equilibrium and linkage

disequilibrium and to assess the overall allelic richness and heterozygosity (genetic diversity)

using Markov-chain parameters with 10000 dememorization steps, 999 batches, and 9999

iterations per batch. GENEPOP was also used to assess the number of migrants (Nm) per

generation between populations following the private allele method outlined by Barton and

Slatkin (1986) and correcting for sample size. Population structure was evaluated by determining

8

numbers of distinct gene pools employing a Bayesian assignment test as implemented in

STRUCTURE v2.3.4 (Hubisz et al. 2009) using uncorrelated allele frequencies, the LOCPRIOR

model, a 10000 burn in period with 10000 reps, and testing K values from 1 to 6 with 10 runs at

each K. Preliminary runs with STRUCTURE suggested that λ= 0.5 would be appropriate for this

system and STRUCTURE HARVESTER v0.6.94 (Earl and vonHoldt 2012) was used toassess the

appropriate number of clusters following the Evanno method (Evanno et al. 2005). Under K=2,

STRUCTURE identified most individuals as belonging solely to one main gene pool across the I-

AN and I-AS sites with a second group of individuals with whole or partial membership in a

second distinct gene pool. To determine whether the individuals with membership in the second

gene pool represented recent migrants, I conducted a two-tailed t-test with unequal variances

comparing the mean relatedness value of individuals, calculated by following the estimate of

relatedness (r) defined by Queller and Goodnight (1989), in the main gene pool, to the mean

relatedness value of these potential immigrants. I used LDNE v1.31 (Waples and Do 2008) to

estimate the effective population size (Ne) for gene pools identified by these analyses using a

minimum allele frequency 0.01 and to construct confidence intervals for each Ne estimate using

parametric methods. I performed an Analysis of Molecular Variance (AMOVA) using ARLEQUIN

v3.5.1.2 (Excoffier and Lischer 2010) to examine population structure and to investigate the

proportion of genetic variation contributed by variation between the two sampling sites (among

populations), within each sampling site (within populations), and among individuals and to

calculate F-statistics.

9

Regional Analysis

For evaluation of genetic structure at the regional scale, I analyzed the entire set of

samples, including all individuals from the local analysis, as well as tissues collected from

Vermillion County in 2005, from Jo Daviess County in 2004, and Kentucky in 2011. I used

GENEPOP v3.4 to check for deviations from Hardy-Weinberg expectations and linkage

disequilibrium using Markov-chain parameters with 10,000 dememorization steps, 999 batches,

and 9,999 iterations per batch. I used STRUCTURE v2.3.4 to examine overall population structure

using uncorrelated allele frequencies, the LOCPRIOR model, a 10,000 burn in period with 10,000

reps, testing K values from 1 to 13 with 10 runs at each K, and using λ= 0.5. STRUCTURE

HARVESTER v0.6.94 was again used to determine the appropriate number of population clusters.

In order to evaluate population designations, I used RXC (www.marksgeneticsoftware.net) to

conduct multilocus contingency tests of allele frequency heterogeneity following Waples and

Gaggioti (2006) with 99 batches, 9,999 demorization runs, and 9,999 replicates for each

randomization test. I used ARLEQUIN v3.5.1.2 to perform an AMOVA examining relative genetic

variation regionally (among populations), within populations, and within individuals and to

calculate F-statistics. To test for an isolation-by-distance pattern of genetic differences among

sampling sites, I conducted a Mantel test examining association between linear distance and FST

value for each pairwise comparison. I used BOTTLENECK v1.2.02 (Cornuet and Luikart 1997) to

test for recent population bottlenecks at sites with more than 10 individuals (K-A, K-D, K-L, and

I-A) using 10,000 repetitions to conduct Wilcoxon rank sign tests under the infinite alleles

model.

10

Results

Samples and Collection Sites

To assess genetic structure at the local level, 157 salamanders were genotyped, with 111

from site I-AN and 46 from I-AS (Table 1.1). Most sampled individuals were adults; only 13

were juveniles. Sample size for each site is listed by year in Appendix A. For the regional

analysis 279 unique samples were used, including the 157 individuals from the local analysis,

seven from I-B, eight from I-A collected in 2005, and 107 samples collected from central

Kentucky.

Microsatellite Loci

Of the 28 microsatellite loci screened (Table 1.2), 20 initially amplified fragments in H.

scutatum and were tested in pool-plex panels. When screened using dye-labeled primers, nine

loci (Table 1.2), showed sufficient variability to assess genetic diversity in H. scutatum. The

remaining 13 loci were excluded from analyses due to scoring problems, inconsistent or weak

amplification or artifacts. The size range, final pool-plex panels, dye assignments, and number of

alleles for each of the nine loci are listed in Table 1.2. Additional optimization could potentially

render the excluded loci useful in future studies of H. scutatum. Linkage disequilibrium was

found for the locus pair HS7 and HS8 in both I-AN and I-AS populations. While no large allele

dropout was detected for any of the nine loci, homozygote excess, often suggestive of null

alleles, was detected for DT8, HS3a, HS3b, HS7, HS8, and PC1. This homozygote excess is

likely due to the Wahlund effect, wherein the admixture of two or more populations with

differing allele frequencies results in heterozygote deficiency if analyzed as a combined

11

population (Hartl 2000). Because such a high proportion of the loci were out of Hardy-Weinberg

Equilibrium, it is likely that sampling issues, rather than scoring errors and null alleles, are

responsible. Because many plethodontid salamanders utilize underground retreats [i.e. P.

cinereus (Jaeger 1980)], it is likely that the individual H. scutatum sampled at a given time

represent only a subset of the proportion of the population active and above ground during the

sampling period. In addition, it is possible that a large proportion of samples represent closely

related individuals, also affecting Hardy-Weinberg expectations and linkage disequilibrium.

However, locus HS7 was excluded from further analyses because it was found to be in linkage

disequilibrium with HS8. Tests for the statistical power of these eight loci using POWSIM gave

an expected Type I error rate of α=0.048.

Local Analysis

Hardy-Weinberg expectations for allele frequencies were violated, with a Bonferroni

corrected p-value needed for significance of p<0.003, for all loci but HS3b in I-AN, and for four

(out of eight) loci in I-AS (i.e., DT4, HS3b, HS8, and HS5). Substantial amounts of immigration

and emigration could explain these violations of Hardy-Weinberg equilibrium. It is more likely,

however, that these deviations from HWE are a result of the Wahlund effect, wherein the

admixture of two or more populations with differing allele frequencies results in heterozygote

deficiency if analyzed as a combined population (Hartl 2000). Indeed, the subsequent Bayesian

assignment analysis suggests that some population admixture has occurred from recent

immigrants into the population.

Genetic Diversity. —Expected heterozygosity (HE) was high for both I-AN and I-AS,

with 0.64 ± 0.18 and 0.65 ± 0.17, respectively. Loci originally developed for H. scutatum

12

showed the greatest genetic diversity with per loci heterozygosity ranging from 0.82 to 0.87

compared to a maximum heterozygosity among the cross-amplified loci of 0.54 (Table 1.2,

Appendix B). The overall FIS value was 0.137 ± 0.047, suggesting that inbreeding occurs

infrequently. Expected heterozygosity for I-AN and I-AS varied very little between years

(Appendix C), with a range of 0.61 to 0.66 and 0.61 to 0.71 respectively.

Genetic Structure. —The results of the Bayesian Assignment test conducted in

STRUCTURE indicated that the best overall value for number of gene pools was K=1, with a Ln

likelihood = -3571.1. This suggests that individuals from the two sites, I-AN and I-AS, are

actually part of one larger population without subdivision. STRUCTURE runs conducted with K=2

indicated that most individuals belong solely to one large gene pool (cluster 1) with a handful of

samples either partially or fully being assigned to another gene pool (cluster 2) (Figure 1.2). The

t-test comparison of the mean relatedness (Queller and Goodnight 1989) of individuals belonging

to these two gene pools indicated a significantly greater relatedness of individuals assigning to

cluster 1 than those assigning to cluster 2 with a p-value of 0.027, indicating individuals in

cluster 2 represent recent immigrants (or their offspring) from various source populations. The

results of the AMOVA indicate that most of the observed variance (86%) is the result of within

individual variation with the remaining variance (14%) attributed to among individual variation

(Table 1.3). The calculated pairwise FST value between the I-AN and I-AS sites was very low

and indicated no divergence (FST=0.004, p-value=0.11). The mean number of migrants per

generation (Nm) between these two localities was 7.78. The effective population size, NE, for the

combined I-AN and I-AS sites (i.e. site I-A) was 593 with a 95% confidence interval of 263 to

5151.

13

Regional Analysis

Genetic Diversity.—The I-AN and I-AS sites exhibit low genetic divergence, therefore I

lumped them together (I-A) for the regional analyses focusing on within-population genetic

diversity. However, I continued to analyze them separately for analyses focusing on population

differences and genetic structure. This approach allowed the local I-AN and I-AS data to be

included in analyses focusing on the role of distance in population structure in H. scutatum.

Expectations for Hardy-Weinberg genotype frequencies were violated in all populations except

K-F and K-H (Bonferroni corrected p-value <0.003). Expected heterozygosity (HE) was high for

populations in the regional analysis and ranged from 0.52 ± 0.04 to 0.74 ± 0.08.

Genetic Structure.—The STRUCTURE assignment test indicated that four gene pools (K=4)

best reflect the genetic structure in the regional analysis (ln likelihood = -7333.7, Figure 1.3).

Under this scenario, I-AN and I-AS are grouped together, with K-D, K-F, and K-N forming a

second, and K-H, K-L, and K-M comprising a third group. The remainder of the Kentucky sites

(K-A, K-B, K-C, K-E, K-J) and I-B showed a high proportion of admixed gene pools. Unlike

AMOVA results for the local analysis, the regional AMOVA analysis indicated that 9% of the

overall genetic diversity is due to among population variation, 18% due to among individual

variation, and 73% due to within individual variation (Table 1.3). The overall FIS value for the

regional analysis was 0.202 ± 0.044. Significant genetic differences were detected between all

three regions (I-A, I-B, and KY) (Table 1.4). Within the KY sites, the only significant

differences were found between site K-D and sites K-J, K-L, and K-M (Appendix D). The results

of the RxC analysis found four major gene pools: cluster 1 contains individuals from I-AN and I-

AS, cluster 2 from I-B, cluster 3 from K-D, and cluster 4 from the remaining Kentucky sampling

sites (K-A, K-B, K-C, K-E, K-F, K-H, K-J, K-L, K-M, and K-N) (Figure 1.4 and Appendix E).

14

The Mantel test found a significant positive relationship between geographic distance and FST (p-

value = 0.01, Figure 1.5). The results of the Wilcoxon tests for population bottlenecks suggest a

recent population bottleneck for population K-L (p-value = 0.004), but not for populations K-A,

K-D, or I-A (Table 1.5).

Discussion

The global decline in amphibian biodiversity has created a need for a better

understanding of how short- and long-term processes impact the population genetics of

amphibian species at different spatial scales. I examined the genetic structure of 13 populations

of H. scutatum to test alternative hypotheses for local and regional scale patterns. Specifically, I

investigated whether H. scutatum exhibits limited gene flow and isolation-by-distance patterns

that characterize most other plethodontid species. I also examined the potential impact of post-

glacial range expansion on genetic structure in H. sctuatum. Finally, I evaluated the hypothesis

that life history characteristics in H. scutatum, especially developmental mode, may be

influencing the genetic structure of this species. I found evidence for high levels of gene flow

between neighboring populations (1000-2000 m), but an isolation-by-distance pattern on a

larger, regional scale. Genetic differences between populations, i.e. FST values, were low relative

to those observed in other plethodontid species.

Population structure and dispersal at the local scale

On a local scale, dispersal appears to be much greater in H. scutatum than in other

plethodontids. Neither FST values or the RxC analysis indicated significant genetic differences

between I-AN and I-AS. The mean number of migrants between these two areas is relatively

15

high suggesting extensive gene flow - a surprising result considering the relatively large distance

(1,224 m) that separates these two sites. Interestingly, a similar pattern was observed among

northern populations in Kentucky; while some of these populations are separated by short

distances (e.g., K-A and K-H), and therefore would be expected to show little divergence, most

are distant from the others by several kilometers (Appendix F). Small sample sizes could

influence these results, but stochastic sampling issues would be expected to result in

exaggerated, rather than lower genetic divergence. In addition, this pattern is repeated among

sites with large sample sizes (i.e. I-AN and I-AS). Phylogeographic work on H. scutatum has

suggested that the patterns of genetic structure observed in this species reflect a post-glacial

expansion from refugia in the Appalachian Mountains (Herman 2009). A post-glacial

recolonization would account for the lack of genetic differences observed between I-AN and I-

AS, but does not explain the similar pattern observed among the northern populations in

Kentucky, an area that remained unglaciated during the Pleistocene.

Potential for high dispersal is further supported by other signals in my data. Bayesian

Assignment tests conducted in STRUCTURE (Figure 1.2) identified two distinct gene pools.

However, they were not associated with the two sampling sites. Instead, the population appears

to be quite homogenous across the entire area, with the exception of some individuals that have

very distinct genotypes. These individuals are likely recent immigrants or the offspring of recent

immigrants and thus provide evidence that migration to these sites from an outside source is

occurring. Hence, while I find support for the post-glacial hypothesis, I cannot exclude the

possibility that H. scutatum possesses greater dispersal abilities than expected.

16

Population structure and dispersal at the regional scale

Results of my regional analyses are more congruent with observations in other

plethodontid species and best fit an isolation-by-distance model, a common pattern among

members of Plethodontidae [Batrachoseps attenuatus (Martínez-Solano et al. 2007),

Desmognathus ochrophaeus (Tilley and Mahoney 1996), D. wrighti (Crespi et al. 2003),

Ensantina sp. (Jackmann and Wake 1994), and P. cinereus (Cabe et al. 2007)]. Thus it is not

surprising that such a pattern was observed in this study and this supports the hypothesis that H.

scutatcum follows a pattern of genetic structure similar to that of other plethodontids on a

regional scale (Figure 1.5). In general, salamanders from sites in Kentucky were not genetically

divergent from each other, with the exception of site K-D that was genetically distinct from the

remaining populations in Kentucky. Divergence of the K-D population likely represents a deep

biogeographic signal and can be explained by vicariance due to topography and underlying

geology of Kentucky; the site is located on the Mississippian Plateau, whereas the remainder of

the sampling sites is located on the Cumberland Plateau. These two plateaus are separated by the

Pottsville escarpment, a series of sandstone cliffs and steep-sided valleys (Newell 1986).

While the Bayesian assignment and RxC analyses are largely in agreement, some

differences do exist in the population clusters identified by each method. Specifically, the RxC

analysis indicates K-D as distinct from all other populations, whereas the Bayesian assignment

analysis clusters K-D with K-C, K-F, and K-N. These seemingly contradictory results may be

due to the isolation-by-distance pattern. Bayesian assignment analysis is known to overestimate

population structure when there is an isolation-by-distance signal in the data (Frantz et al. 2009)

and is sensitive to small sample sizes (Waples and Gaggioti 2006), such as those in populations

K-C, K-F, and K-N (Table 1.1).

17

Population Size and Recent Bottlenecks

The estimate of effective population size of 593, for site I-A is low relative to that

recorded for other plethodontid species, but the upper limit of the 95% confidence interval for

this estimate (5151) places Ne estimates for H. scutatum within range of other plethodontids (i.e.

Ne ranging from 4000 to 64000, see review by Larson et al. 1984). Among the four sites with

sufficient sample size for testing (K-A, K-D, K-L, and I-A), only site K-L was identified as

having recently undergone a population bottleneck. It is unlikely that this is due to population

size reduction during the last Pleistocene glaciation that ended about 10000 years ago (see Black

1974) as site only I-A was located within the limits of the last glacial maximum (Figure 1.1,

Inset B). It is possible that some site specific, local variable is responsible for this pattern, as any

factor at a regional scale, such as post-Pleistocene climatic fluctuations, would have also

impacted the nearby (~9 km) site K-A.

Statistical validity of data

Shallow divergence could potentially be an artifact due to insufficient power of the

markers used to detect genetic structure. However, results based on the eight loci should be

relatively robust as suggested by the POWSIM expected Type I error rate of α=0.048 and should

be sufficient to assess population structure. While additional loci would likely be informative,

microsatellite cross-amplification is notoriously difficult in amphibians as a result of their large

genome size (Garner 2002). It is not surprising that so many of the loci from species other than

H. scutatum failed to amplify or could not be reliably scored. Additionally, a number of other

studies have successfully used similar numbers of microsatellites to assess salamander

population structure. Cabe et al. (2007) and Noël et al. (2007) investigated genetic diversity in P.

18

cinereus populations and found observed levels of heterozygosity ranging from 0.32 to 0.85

(based on 6 loci) and from 0.38 to 0.69 (based on 7 loci), respectively. Among members of the

genus Ambystoma, Giordano et al. (2007) and Spear et al. (2005) found a negative relationship

between elevation and genetic differences with observed heterozygosity ranging from 0.03 to

0.81 for A. tigrinum (derived from 7 loci) and of 0.32 for A. macrodactylum (from 8 loci),

respectively. In contrast, Purrenhage et al. (2009) found no isolation-by-distance patterns and

strong overall connectivity among populations of A. maculatum using 8 loci with observed

heterzygosity ranging from 0.49 to 0.70.

Conclusions

Small isolated populations may be a common occurrence in plethodontid species and may

have contributed to the long-term evolution of this group. My study found that H. scutatum

follows a similar pattern of genetic structuring as other plethodontid species on a regional scale.

On a local scale, however, very little genetic divergence was observed between sites. This may

be a result of high levels of gene flow and dispersal, reduced genetic variation between

populations due to post-glacial recolonization from a common source, and/or a reduction in allele

loss as a result of reduced whole clutch mortality in an indirect developing species. While

genetic patterns of post-glacial recolonization have been found in other studies on H. sctutatum

(Herman 2009), increased dispersal cannot be ruled out because reduced genetic divergence was

similarly found in populations from unglaciated areas and Bayesian assignment analyses

suggested the presence of recent immigrants in sites I-AN and I-AS. Moreover, it is possible that

indirect development, the ancestral condition in plethodontids, persists in H. scutatum because it

prevents the loss of genetic variation and counters any loss of gene flow this species experiences

19

due to its need for specialized breeding habitat. Whether as a result of increased dispersal or

developmental mode, reduced loss in genetic variation could translate into a reduced

susceptibility in H. scutatum to the small population paradigm. Future conservation and

management decisions for this species should consider the implications of life history traits such

as developmental mode on the short-term survival of populations and the long-term survival of

species.

20

Literature Cited

Barrett, R., and D. Schluter. 2007. Adaptation from standing genetic variation. Trends in Ecology

& Evolution 23:38-44.

Barton, N. H. and M. Slatkin. 1986. A quasi-equilibrium theory of the distribution of rare alleles

in a subdivided population. Journal of Heredity 56:409-415.

Beebee, T. J. and R. A. Griffiths. 2005. The amphibian decline crisis: a watershed for

conservation biology? Biological Conservation 125:271-285.

Bellard, C., C. Bertelsmeier, P. Leadley, W. Thuiller, and F. Courchamp. 2012. Impacts of

climate change on the future of biodiversity: Biodiversity and climate change. Ecology

Letters 15:365-377.

Black, R. F. 1974. Geology of the Ice Age National Scientific Reserve of Wisconsin. National

Park Service Monograph Series 2:1-234.

Cabe, P. R., R. B. Page, T. J. Hanlon, M. E. Aldrich, L. Connors, and D. M. Marsh. 2007. Fine-

scale population differentiation and gene flow in a terrestrial salamander (Plethodon

cinereus) living in continuous habitat. Heredity 98:53-60.

Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63:215-244.

Collins, J. P. and A. Storfer. 2003. Global amphibian declines: sorting the hypotheses. Diversity

and Distributions 9:89-98.

Connors, L. M. and P. R. Cabe. 2003. Isolation of dinucleotide microsatellite loci from red-

backed salamander (Plethodon cinereus). Molecular Ecology Notes 3:131-133.

Cornuet, J. M. and G. Luikart. 1997. Description and power analysis of two tests for detecting

recent population bottlenecks from allele frequency data. Genetics 144:2001-2014.

Crespi, E. J., L. J. Rissler, and R. A. Browne. 2003. Testing Pleistocene refugia theory:

phylogeographical analysis of Desmognathus wrighti, a high-elevation salamander in the

southern Appalachians. Molecular Ecology 12:969–984.

Curtis, J. M. R. and E. B. Taylor. 2001. Isolation and characterization of microsatellite loci in the

Pacific giant salamander Dicamptodon tenebrosus. Molecular Ecology Notes 9:116-118.

Degross, D. J., L. S. Mead, and S. J. Arnold. 2004. Novel tetranucleotide microsatellite markers

from the Del Norte salamander (Plethodon elongates) with application to its sister species

the Siskiyou Mountain salamander (P. stormi). Molecular Ecology Notes 4:352-354.

21

Demastes, J. W., J. M. Eastman, J. S. East, and C. Spolsky. 2007. Phylogeography of the blue-

spotted salamander, Ambystoma laterale (Caudata: Ambystomatidae). The American

Midland Naturalist 157:149–161.

deMaynadier, P. G. and M. L. Hunter, Jr. 2000. Road effects on amphibian movements in a

forested landscape. Natural Areas Journal 20:56-65.

Dubois, A. 2004. Developmental pathway, speciation, and supraspecific taxonomy in

amphibians. 1. Why are there so many frog species in Sri Lanka? Alytes 22: 19-37

Earl, D. A. and B. M. vonHoldt. 2012. STRUCTURE HARVESTER: a website and program for

visualizing STRUCTURE output and implementing the Evanno method. Conservation

Genetics Resources 4:359-361.

Ehlers, J., P. L. Gibbard, and P. D. Hughes. (eds.) 2011. Quaternary glaciations - extent and

chronology: a closer look. Developments in Quaternary Science, Vol. 15. Elsevier,

Amsterdam.

Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals

using the software STRUCTURE: a simulation study. Molecular Ecology 14:2611-2620.

Excoffier, L. and H. E. L. Lischer. 2010 Arlequin suite ver 3.5: a new series of programs to

perform population genetics analyses under Linux and Windows. Molecular Ecology

Resources 10:564-567.

Fischer, J. and D. B. Lindenmayer. 2007. Landscape modification and habitat fragmentation: a

synthesis. Global Ecology and Biogeography 16:265-280.

Frantz, A. C., S. Cellina, A. Krier, L. Schley, and T. Burke. 2009. Using spatial Bayesian

methods to determine the genetic structure of a continuously distributed population:

clusters or isolation by distance? Journal of Applied Ecology 46:493-505.

Garner, T. W. J. 2002. Genome size and microsatellites: the effect of nuclear size on

amplification potential. Genome 45:212-215.

Gillette, J. R. 2003. Population ecology, social behavior, and intersexual differences in a natural

population of red-backed salamanders: a long-term field study. PhD Dissertation:

University of Louisiana.

Giordano, A. R., B. J. Ridenhour, and A. Storfer. 2007. The influence of altitude and topography

on genetic structure in the long-toed salamander (Ambystoma macrodactylum). Molecular

Ecology 16:1625-1637.

Hannah, L., G. F. Midgley, T. Lovejoy, W. J. Bond, M. Bush, J. C. Lovett, D. Scott, and F. I.

Woodward. 2002. Conservation of biodiversity in a changing climate. Conservation

Biology 16:264-268.

22

Hartl, D. L. 2000. A Primer of Population Genetics. 3rd Edition. Sinauer Associates, Inc.,

Sunderland, M.A., USA.

Herman, T. A. 2009. Range-wide phylogeography of the four-toed salamander (Hemidactylium

scutatum): out of Appalachia and into the glacial aftermath. Master’s Dissertation:

Bowling Green State University.

Hewitt, G. M. 2004. Genetic consequences of climatic oscillations in the Quaternary.

Philosophical Transactions of the Royal Society B: Biological Sciences, 359:183–195.

Highton, R. and T. P. Webster, T. P. 1976. Geographic protein variation and divergence in

populations of the salamander Plethodon cinereus. Evolution 30:33.

Hubisz, M. J., D. Falush, M. Stephens, and J. K. Pritchard. 2009. Inferring weak population

structure with the assistance of sample group information. Molecular Ecology Resources

9:1322-1332.

Hsu, M. J., K. Selvaraj, and G. Agoramoorthy. 2006. Taiwan’s industrial heavy metal pollution

threatens terrestrial biota. Environmental Pollution. 143:327-334.

Jackman, T. R. and D. B. Wake. 1994. Evolutionary and historical analysis of protein variation

in the blotched forms of salamanders of the Ensatina complex (Amphibia:

Plethodontidae). Evolution 48:876.

Jaeger, R. G. 1980. Microhabitat of a terrestrial forest salamander. Copeia 1980:265-268.

Johnston, E. L. and D. A. Roberts. 2009. Contaminants reduce the richness and evenness of

marine communities: A review and meta-analysis. Environmental Pollution 157:1745-

1752.

Kozak, K. H., R. A. Blaine, and A. Larson. 2005. Gene lineages and eastern North American

palaeodrainage basins: phylogeography and speciation in salamanders of the Eurycea

bislineata species complex: drainage history and salamander phylogeography. Molecular

Ecology 15:191–207.

Krauss, J., R. Bommarco, M. Guardiola, R. K. Heikkinen, A. Helm, M. Kuussaari, R. Lindborg,

E. Öckinger, M. Pärtel, J. Pino, J. Pöyry, K. M. Raatikainen, A. Sang, C. Stefanescu, T.

Teder, M. Zobel, and I. Steffan-Dewenter. 2010. Habitat fragmentation causes immediate

and time-delayed biodiversity loss at different trophic levels: Immediate and time-

delayed biodiversity loss. Ecology Letters 13:597-605.

Kuchta, S. R. and A. M. Tan. 2004. Isolation by distance and post-glacial range expansion in the

rough-skinned newt, Taricha granulosa: phylogeography of the rough-skinned newt.

Molecular Ecology 14:225–244.

23

Lande, R. and S. Shannon. 1996. The role of genetic variation in adaptation and population

persistence in a changing environment. Evolution 50:434.

Larson, A., D. B. Wake, and K. P. Yanev. 1984. Measuring gene flow among populations having

high levels of genetic fragmentation. Genetics 106:293-308.

Marsh, D. M., G. S. Milam, N. P. Gorham, and N. G. Beckman. 2005. Forest roads as partial

barriers to terrestrial salamander movement. Conservation Biology 19:2004-2008.

Marsh, D. M., R. B. Page, T. J. Hanlon, H. Bareke, R. Corritone, N. Jetter, N. G. Beckman, K.

Gardner, D. E. Seifert, and P. R. Cabe. 2007. Ecological and genetic evidence that low-

order streams inhibit dispersal by Red-backed Salamanders (Plethodon cinereus).

Canadian Journal of Zoology 85:319-327.

Marsh, D. M., R. B. Page, T. J. Hanlon, R. Corritone, E. C. Little, D. E. Seifret, and P. R. Cabe.

2008. Effects of roads on patterns of genetic differentiation in red-backed salamanders,

Plethodon cinereus. Conservation Genetics 9:603-613.

Martínez-Solano, I., E. L. Jockusch, and D. B. Wake. 2007. Extreme population subdivision

throughout a continuous range: phylogeography of Batrachoseps attenuatus (Caudata:

Plethodontidae) in western North America. Molecular Ecology 16:4335-4355.

Mathis, A. 1991. Large male advantage for access to females: evidence of male-male

competition and female discrimination in a territorial salamander. Behavioral Ecology

and Sociobiology 29:133–138.

McGrath, K. M. 1996. Analysis of trinucleotide microsatellite DNA in the genome of the four-

toed salamander, Hemidactylium scutatum. Undergraduate Thesis: James Madison

University.

NatureServe. 2011. NatureServe Explorer: An online encyclopedia of life [web application].

Version 7.1. NatureServe, Arlington, Virginia. Available

http://www.natureserve.org/explorer. (Accesssed: January 2, 2012).

Newell, W. L. 1986. Physiography. In R. C. McDowell (Ed.), The geology of Kentucky. United

States Government Printing Office, Washington, D.C. USA.

Niemiller, M. L., B. M. Fitzpatrick, and B.T. Miller. 2008. Recent divergence with gene flow in

Tennessee cave salamander (Plethodontidae: Gyrinophilus) inferred from gene

genealogies. Molecular Ecology 17:2258-2275.

Noël, S. M. Ouellet, P. Galois, and F. J. Lapointe. 2007. Impact of urban fragmentation on the

genetic structure of the eastern red-backed salamander. Conservation Genetics 8:599-606.

24

Oosterhout, C. V., W. F. Hutchinson, D. P. M. Wills, and P. Shipley. 2004. Micro-checker:

software for identifying and correcting genotyping errors in microsatellite data.

Molecular Ecology Notes 4:535-538.

Petranka, J. W. 1998. Salamanders of the United States and Canada. Smithsonian Institution

Press, Washington, D.C., USA

Phillips, C. A., G. Suau, and A. R. Templeton. 2000. Effects of Holocene climate fluctuation on

mitochondrial DNA variation in the ringed salamander, Ambystoma annulatum. Copeia,

2000:542-545.

Purrenhage, J. L., P. H. Niewiarowski, and F. B. G. Moore. 2009. Population structure of spotted

salamanders (Ambystoma maculatum) in a fragmented landscape. Molecular Ecology

18: 235-247.

Queller, D. C. and K. F. Goodnight. 1989. Estimating relatedness using genetic markers.

Evolution 43:258-275.

Raymond, M. and F. Rousset. 1995. GENEPOP (version 1.2): population genetics software for

exact tests and ecumenicism. Journal of Heredity 86:248-249.

Reed, D. H. and R. Frankham. 2003. Correlation between fitness and genetic diversity.

Conservation Biology 17:230-237.

Reid, L. A. 1994. Characterization of a microsatellite DNA locus in the four-toed salamander,

Hemidactylium scutatum. Undergraduate Thesis: James Madison University.

Rowe, G. and T. J. Beebee. 2003. Population on the verge of a mutational meltdown? Fitness

costs of genetic load for an amphibian in the wild. Evolution 57:177-181.

Ryman, N. and S. Palm. 2006. POWSIM: a computer program for assessing statistical power

when testing for genetic differentiation. Molecular Ecology Notes. 6:600-602.

Schrecengost, A. C. 1998. The isolation and characterization of genetic markers for use in a

kinship study of the four-toed salamander. Undergraduate Thesis: James Madison

University.

Selkoe, K. A. and R. J. Toonen. 2006. Microsatellites for ecologists: a practical guide to using

and evaluating microsatellite markers. Ecology Letters 9:615-629.

Spear, S. F., C. R. Peterson, M. D. Matocq, and A. Storfer. 2005. Landscape genetics of the

blotched tiger salamander (Ambysoma tigrinum melanostictum). Molecular Ecology

14:2553-2564.

25

Steele, C. A. and A. Storfer. 2006. Coalescent-based hypothesis testing supports multiple

Pleistocene refugia in the Pacific Northwest for the Pacific giant salamander

(Dicamptodon tenebrosus): testing Pleistocene refugia hypotheses. Molecular Ecology

15:2477–2487.

Stuart, S. N., J. S. Chanson, N. A. Cox, B. E. Young, A. S. L. Rodrigues, D. L. Fischman, and R.

W. Waller. 2004. Status and trends of amphibian declines and extinctions worldwide.

Science 306:1783-1786.

Thomas, C. D., A. Cameron, R. E. Green, M. Bakkenes, L. J. Beaumont, Y. C. Collingham, B. F.

Erasmus, M. F. De Siqueira, A. Grainger, L. Hannah, L. Hughes, B. Huntley, A. S. van

Jaarsveld, G. F. Midgley, L. Miles, M. A. Ortega-Huerta, A. Townsend Peterson, O. L.

Phillips, and S. E. Williams. 2004. Extinction risk from climate change. Nature 427:145-

148.

Tilley, S. G. and M. J. Mahoney. 1996. Patterns of genetic differentiation in salamanders of the

Desmognathus ochrophaeus complex (Amphibia: Plethodontidae). Herpetological

Monographs 10:1.

Tilman, D., J. Fargione, B. Wolff, C. D’Antonio, A. Dobson, R. Howarth, D. Schindler, W. H.

Schlesinger, D. Simberloff, and D. Swackhammer. 2001. Forecasting agriculturally

driven global environmental change. Science 292:281-284.

Vilà, M., J. L. Espinar, M. Hejda, P. E. Hulme, V. Jarošík, J. L. Maron, J. Pergl, U. Schaffner, Y.

Sun, P. Pyšek. 2011. Ecological impacts of invasive alien plants: a meta-analysis of their

effects on species, communities and ecosystems: Ecological impacts of invasive alien

plants. Ecology Letters 14:702-708.

Vellend, M., L. Harmon, J. Lockwood, M. Mayfield, A. Hughes, J. Wares, and D. Sax. 2007.

Effects of exotic species on evolutionary diversification. Trends in Ecology & Evolution

22:481-488.

Wake, D. B. and V. T. Vredenburg. 2008. Are we in the midst of the sixth mass extinction? A

view from the world of amphibians. Proceedings of the National Academy of Sciences

105:11466-11473.

Waples, R. S. and C. Do. 2008. LDNE: a program for estimating effective population size from

data on linkage disequilibrium. Molecular Ecology Resources 8:753-756.

Waples, R. S. and O. Gaggiotti. 2006. What is a population? An empirical evaluation of some

genetic methods for identifying the number of gene pools and their degree of

connectivity. Molecular Ecology 15:1419-1439.

Welsh, H. H., Jr. and S. Droege. 2001. A case for using plethodontid salamanders for monitoring

biodiversity and ecosystem integrity of North American forests. Conservation

Biology15:558-569.

26

Windmiller, B. 2000. Study of Ecology and Population Response to Construction in Upland

Habitat by Vernal Pool Amphibians. Report for the Massachusetts Natural Heritage

Program. Sudbury, MA.

Wright, S. 1943. Isolation by distance. Genetics 28:114.

Zamudio, K. R. and A. M. Wieczorek. 2007. Fine-scale spatial genetic structure and dispersal

among spotted salamander (Ambystoma maculatum) breeding populations. Molecular

Ecology 16:257-274.

Zvereva, E. L. and M. V. Kozlov M.V. 2010. Responses of terrestrial arthropods to air pollution:

a meta-analysis. Environmental Science and Pollution Research 17:297-311.

27

Figures

Figure 1.1. Encountered individual and site locations for the Four-toed Salamander,

Hemidactylium scutatum, plotted in ArcGIS 10.2.1 using GPS coordinates. Inset A shows

locations of encountered individuals at sites I-AN and I-AS at the Middle Fork State Fish and

Wildlife Area, Vermilion County, IL from 2009 to 2012. Inset B depicts 14 sampling locations in

North America with respect to the last glacial maximum (Ehlers et al. 2011). Inset C shows a

magnified view of seven sampling sites in northern. Sample size, GPS coordinates, and acronym

for each site are provided in Table 1.1.

28

Figure 1.2. Probabilistic allocation of 157 H. sctuatum to two gene pools as determined by

STRUCTURE (K=2). Colors represent gene pools. Data are based on eight microsatellite loci.

Samples were collected from two sites in Illinois I-AN and I-AS (see Figure 1.1). Sample size

and acronym for each site is provided in Table 1.1.

Figure 1.3. Probabilistic allocation of genotypes of 279 H. scutatum to four gene pools as

determined by STRUCTURE (K=4). Colors represent gene pools. Data are based on eight

microsatellite loci. Samples were collected from 14 sites across Illinois and Kentucky (see Figure

1.1 and 1.2). Sample size and acronyms for each site is provided in Table 1.1.

29

Figure 1.4. Results from a RxC analysis of 279 H. scutatum genotypes. Data are based on eight

microsatellite loci. Samples were collected from 14 sites across Illinois and Kentucky (see Figure

1.1). Samples size and acronyms for each site are provided in Table 1.1. Each circle represents

site or cluster of sites that the RxC analysis indicated as belonging to the same population. Lines

between circles indicate nonsignificant results for a multilocus contingency test of heterogeneity

of allele frequencies among pairs of samples. Sites that can be linked via a series of multiple

nonsignificant tests are considered to be part of the same population.

30

Figure 1.5. Relationship of pairwise geographic distance (km) and genetic divergence (FST)

between 14 sample sites of H. scutatum located across Illinois and Kentucky (see Figure 1.1).

Sample size for each site is provided in Table 1.1.

31

Tables

Table 1.1. Summary of 279 individual H. scutatum tissue samples collected during visual

encounter surveys across 14 sites in central Illinois and Kentucky. Samples from I-AN and I-AS

were examined for patterns at the local scale, whereas all samples were included in the regional

analysis. Listed are: Code = site acronym, N = number of samples, State = site state, KY Cluster

= membership of the site in the north or south cluster in Kentucky, County = site county, UTM

Coordinates = coordinates of mean centroid calculated from all sampled individual locations.

Geographic location of sites is depicted in Figure 1.1.

Code N State KY Cluster County UTM Coordinates

Local I-AN 111 IL N/A Vermilion 433114 4454580

I-AS 46 IL N/A Vermilion 433033 4453804

Regional I-AN05 8 IL N/A Vermilion 433114 4454580

I-B 7 IL N/A Jo Daviess 718487 4687128 K-A 12 KY North Powell 264610 4187946 K-B 4 KY North Menifee 273080 4198054 K-C 2 KY South Laurel 745448 4101073 K-D 29 KY South Adair 668775 4119508 K-E 7 KY North Powell 265866 4188298 K-F 3 KY North Powell 264949 4187610 K-H 5 KY North Powell 264672 4187874 K-J 6 KY North Powell 263948 4189143

K-L 23 KY North Wolfe 272711 4183552 K-M 7 KY North Wolfe 272752 4187911 K-N 9 KY North Menifee 264754 4191931

32

Table 1.2. Amplification success of microsatellite loci for application in H. scutatum. Species =

taxon for which the locus was developed. GenBank # = accession number for locus sequence in

GenBank. Locus = locus name. Citation = publication originally describing the locus.

Amplification = success (+) or failure (-) of loci to amplify in initial tests evaluated on agarose

gels. Genotyping = success (+) or failure (-) of genotyping loci resulting from variation in primer

specificity, signal strength, and occurrence of locus polymorphism. Dye =fluorescent dye used to

label the primer for sequencing. The remaining parameters are derived from 279 samples

analyzed in this study, including: Allele Size Range (bp) = size range of alleles in base pairs, N =

observed number of alleles, Na = expected number of alleles, HO = observed heterozygosity, HE

= expected heterozygosity.

Sp

ecie

s

Gen

Ba

nk

#

Lo

cus

Cit

ati

on

Am

pli

fica

tio

n

Gen

oty

pin

g

Pa

nel

Dy

e

All

ele

Siz

e

Ra

ng

e (b

p)

N

Na

HO

HE

Hem

idact

yliu

m s

cuta

tum

N/A HS3a

McG

rath

(1996) +

+ A PET 199-268 22 7.4 0.59 0.85

N/A HS3b + + A PET 149-197 16 7.2 0.66 0.86

N/A HS5

Sch

rece

ngost

(1998)

+ + B VIC 224-256 26 8.5 0.48 0.87

N/A HS7 + + B 6-FAM 93-178 35 8.2 0.42 0.86

N/A HS8 + + B 6-FAM 109-188 28 8.0 0.54 0.84

N/A HS14 + + C NED 158-205 17 6.7 0.71 0.82

N/A HS15

Rei

d

(1994)

+ ?

Ple

tho

do

n e

long

atu

s

AY532595 PE0

Deg

ross

et

al. (2

00

4)

+ ?

AY532596 PE1 - -

AY532597 PE3 + ?

AY532598 PE4 - -

AY532599 PE5 - -

AY532600 PE7 + ?

AY532601 PE8 + ?

33

AY532602 PE9 + -

AY532603 PE10 + -

AY532604 PE11 + -

AY532605 PE12 - -

P.

ciner

eus

AY151377 PC1 C

onnors

& C

abe

(20

03

) + + C VIC 427-455 10 3.1 0.45 0.54

AY151374 PC2 + ?

AY151380 PC3 - -

AY151379 PC4 - -

AY151372 PC5 + -

AY151376 PC6 - -

AY151373 PC7 - -

Dic

am

pto

don

teneb

rosu

s AF149305 DT4

Curt

is &

Tay

lor

(2001) + + A NED 265-291 8 2.5 0.24 0.32

AF150725 DT5 + ?

AF150728 DT8 + + A 6-FAM 101-135 11 3.5 0.38 0.52

Table 1.2 (continued)

34

Table 1.3. Distribution of genetic variance in H. scutatum at both local and regional scales as

determined by Hierarchical Analysis of Molecular Variance (AMOVA). Results are based on

genotypes across eight microsatellite loci of (A=Local Analysis) 157 samples collected from two

sites (I-AN and I-AS) in Illinois, and (B=Regional Analysis) 279 samples collected from 13 sites

in Illinois and Kentucky. Variance was evaluated at three hierarchical levels (Source of

Variation). Listed are: d.f. = degrees of freedom, S.S. = sums of squares, Variance Components

= amount of variance due to each source of variation, % = percent of total variance. Sample size

and acronyms for each site is provided in Table 1.1. Geographic locations of all 13 sites are

shown in Figure 1.1.

(A)

(B)

Local Analysis

Source of Variation d.f. S.S. Variance Components %

Among populations 1 4.01 0.01 Va 0.32

Among individuals 155 454.10 0.35 Vb 13.67

Within individuals 157 349.00 2.22 Vc 86.01

Total 313 807.11 2.58 100.00

Regional Analysis

Source of Variation d.f. S.S. Variance Components %

Among populations 12 131.76 0.27 Va 9.04

Among individuals 266 856.64 0.54 Vb 18.37

Within individuals 279 596.50 2.14 Vc 72.59

Total 557 1584.90 2.95 100.00

35

Table 1.4. Pairwise population FST values of H. scutatum between three Illinois sampling sites (I-

AN, I-AS, and I-B) and 11 combined Kentucky sampling sites (KY). Data are based on eight

microsatellite loci. Geographic locations of these sites are provided in Figure 1.1. Acronyms and

samples size for each site are provided in Table 1.1. FST values are below diagonal. P-values are

above diagonal. To reduce the chance of Type I error, the Bonferroni corrected p-value needed

for significance is p<0.008. Significant p-values indicated in bold.

I-AN I-AS I-B KY

I-AN 0.106 0.000 0.000

I-AS 0.004 0.000 0.000

I-B 0.135 0.103 0.000

KY 0.076 0.074 0.100

Table 1.5. Results of Wilcoxon tests (p-values) for recent bottlenecks in H. scutatum calculated

under the Infinite Alleles Model (IAM) for four sampling sites of (K-A, K-D, K-L, and I-A).

Data are based on eight microsatellite loci. Geographic locations of these sites are provided in

Figure 1.1. Acronyms and sample size for each site are provided in Table 1.1. To reduce the

chance of Type I error, for each model, the Bonferroni corrected p-value needed for significance

is p<0.0125.

Wilcoxon Test

K-A 0.195

K-D 0.250

K-L 0.004

I-A 0.055

36

CHAPTER 2. SOURCES OF VARIATION IN THE VENTRAL SPOT PATTERN OF

THE FOUR-TOED SALAMANDER

Introduction

Variation in color pattern, including the hue, brightness, number, size, shape, and location

of stripes, patches, or spots, is a common phenomenon in many taxa. The functions of this

variation are diverse and include predator avoidance through cryptic, disruptive, or aposematic

coloration, mimicry, and startle or distracting coloration (eye spots), as well as ornamental

coloration for attraction of mates, photoprotection, structural support, microbial resistance,

thermoregulation, and communication (see reviews in Hubbard et al. 2010, Protas and Patel

2008, Roulin 2004). In addition, color pattern may be a mechanism for the recognition of

conspecifics (i.e. Secondi et al. 2010) and possibly kinship as intraspecific variation in color

pattern has been detected on a fine enough spatial scale to allow populations (Costa et al.2009),

genders (i.e. Davis and Grayson 2008, Pokhrel 2009, Todd and Davis 2007), and even

individuals to be distinguished (Breitenbach 1982, Carafa and Biondi 2004, Eitam and Blaustein

2002). Geographic variation in color pattern, moreover, is widespread among amphibians and

has been attributed to the interaction between environment and predation (Storfer et al. 1999),

the interaction between predation pressures and morphotype frequencies (Hegna et al. 2013),

environmental characteristics (Fernandez and Collins 1988), resource partitioning (Denoël et al.

2001) or random events (Gray 1983).

In intraspecific interactions, color pattern may be used as an indicator of kinship. Many

studies have found color pattern in salamanders to be heritable (Highton 1975, Lipsett and Piatt

1936, Twitty 1961) and visual displays and cues are used in many interactions (i.e. Kohn and

Jaeger 2009, Secondi et al. 2010, Thaker et al. 2006). These characteristics have the potential to

create selection pressure for the use of color pattern in kinship mediated social interactions in

37

salamanders. Kinship mediated behaviors have been implicated in a number of salamander

species, including A. opacum (Hokit et al. 1996, Walls and Blaustein 1995, Walls and

Roudebush 1991), A. tigrinum (Pfennig and Collins 1993, Pfennig et al. 1999), Hemidactylium

scutatum (Harris et al. 2003), Hynobius retardatus (Wakahara 1997), Notophthalmus viridescens

(Gabor 1996), and Plethodon cinereus (Gibbons et al. 2003, Wareing 1997). While some

evidence exists that chemical cues play an important role in kin recognition among larvae

(Chivers et al. 1997, Pfennig et al. 1994, Wakahara 1997), no work has attempted to untangle the

role of color pattern in kin recognition among terrestrial adults.

Intraspecific variation in color pattern might also be an indicator of variation in health

among individuals, especially if the color pattern is costly to produce, normally symmetrical, and

sensitive to conditional or fluctuating asymmetry. Fluctuating asymmetry (Ludwig 1932),

differences in the right and left measurements of a normally symmetrical bilateral character, is

generally considered to be the result of disturbances in intrinsic (e.g., cellular development) or

extrinsic (e.g. habitat fragmentation, pollution, parasite load) environments (Møller and Swaddle

1997). Increasing asymmetry in dorsal spot pattern has been correlated with decreasing body

condition and increasing habitat disturbance in A. maculatum (Davis and Maerz 2007, Wright

and Zamudio 2002) and asymmetry in dorsal pigmentation in A. opacum has been interpreted as

an indicator of differences in stress response between the sexes (Pokhrel 2009, Pokhrel et al.

2013).

The Four-toed Salamander, H. scutatum, is a relatively small plethodontid salamander

(~5-10cm Total Length; Petranka 1998), exhibiting a white ventral surface patterned with

distinctive black spots (Figure 2.1). This ventral skin pigmentation pattern has been well

documented as a variable character and has been used reliably to identify individuals in field

38

studies (Breitenbach 1982, Carreno and Harris 1998, Harris et al. 1995, Harris and Gill 1980,

Harris and Ludwig 2004). While it has been considered aposematic coloration in this species

Brodie et al. (1974), spot pattern has not been quantified in H. scutatum. Because aposematic

color patterns experience selection for symmetry (Forsman and Herrström 2004, Forsman and

Merilaita 1999), it would be expected that the ventral spot pattern in H. scutatum is, under ideal

conditions, symmetrical. Additionally, H. scutatum has a broad geographic distribution that

extending north to Nova Scotia, south to Florida, and as far west as Oklahoma and Missouri

(Petranka 1998), but with patchy occurrences in the southern and western areas. Because

populations tend to be disjunct and centralize around patches of forested areas surrounding

spring or seep-fed pools, population structure in this species should lead to increased geographic

variation in phenotypic traits. These specific habitat requirements, coupled with the small body

size and susceptibility to desiccation of H. scutatum, are predictors of limited lifetime dispersal.

This should increase the rate of encounters between relatives within a population and create

conditions where selection for mechanisms of kin recognition, such as the use of the ventral spot

pattern, would arise. Finally, as H. scutatum is designated as either ‘imperiled’ or ‘critically

imperiled’ throughout much of its range (NatureServe 2011), the ability to identify at risk

populations (i.e. those experiencing heightened levels of stress) by a simple, phenotypic

characteristic such as increased asymmetry of spot pattern, would be a useful conservation and

management tool

This study examined ventral spot pattern in H. scutatum across different spatial scales to

ask the following questions: (1) Does spot pattern vary geographically? If spot pattern varies

geographically or among populations, then symmetry and/or overall abundance of spotting

should vary between regions across the range of H. scutatum. Geographic variation in this

39

feature would make it a useful tool for taxonomists. (2) Is spot pattern similarity within

populations correlated with genetic relatedness among individuals? If ventral spot pattern of H.

scutatum is genetically determined, spot pattern similarity should increase with increased

relatedness (i.e., genetic similarity). (3) Does spot pattern vary with body condition? If spot

pattern is an indicator of body condition, bilateral asymmetry in spot pattern should increase as

body condition decreases. I used Quantitative Image Analysis (QIA) to quantify spot pattern in

terms of proportion of spots and symmetry and examined how these variables vary regionally. I

used microsatellite markers to estimate relatedness between individuals and examined the

potential relationship between genotypic similarity (relatedness) and phenotypic similarity

(similarity in spot pattern) between individuals. Finally, I estimated body condition using a

residual index and investigated the potential relationship between spot pattern symmetry and

body condition.

Methods

Samples and Collection Sites

I sampled from March to November over four consecutive years (2009-2013) while H.

scutatum was active at two sampling site (I-AN and I-AS) in the Middle Fork State Fish and

Wildlife Area in Vermilion County, Illinois and from 18-23 March 2011 at eight sites (K-A, K-

D, K-E, K-H, K-J, K-L, K-M, K-N) in central Kentucky (Figure 2.2). At each site, I searched for

individuals along haphazardly placed transects by looking under potential shelter objects. For

each encountered individual, I recorded the GPS location in UTM coordinates using a Garmin

GPS 12, and measured the body size (mm) as Snout-Vent-Length (SVL), Total-Length (TL) and

40

mass (g). For genetic analyses, I collected small tail clips (1-5 mm) from individuals weighing

more than 0.18 g using sterilized clippers and tissues were stored in ethanol at -80 °C.

I photographed the ventral spot pattern of each salamander for use in individual

identification and to avoid repeated tissue samples from the same individual (Breitenbach 1982).

To immobilize individuals for photographs with a digital camera, I placed each salamander

within a re-sealable plastic bag and gently flattened the bag so that the animal was held between

the layers of plastic. Photographic sampling has many benefits as outlined by Todd et al. (2001)

including speed, creation of an instant, permanent record, its non-invasive or destructive quality,

and the opportunity for repeat measurements in the future. Digital photography allowed me to

document the spot pattern of each individual for later scoring in the lab and to create a record of

sampled individuals for the identification of recaptures.

Molecular Methods and Genetic Analyses

To infer relatedness, I used microsatellite analysis to determine genetic similarity of

individuals using eight microsatellite markers as described in Chapter 1. I calculated relatedness

r as proposed by Queller and Goodnight (1989) using the methodology described in Chapter 3.

Quantifying Body Condition

To quantify body condition for sampled individuals, I used the residuals from a

regression of log-transformed mass on log-transformed total length as an index (Harris 2007,

Harris and Ludwig 2004, Schulte-Hostedde et al. 2005). This approach to measuring body

condition, originally proposed by Gould (1975), controls for variation across body sizes (Jakob

41

et al. 1996) and satisfies more assumptions than indices based on adjusting body mass by length

(Schulte-Hostedde et al. 2005).

Photographic Analyses

I used the plugin STRAIGTEN (Kocsis et al. 1991) for the IMAGEJ photo analysis software

(Abramoff et al. 2004) to straighten the image for each individual along an approximated medial

line. STRAIGHTEN uses an algorithm to fit a non-uniform cubic spline along user defined nodes

along the approximated medial line. I rotated the images so that all were oriented vertically with

the head of the salamander at the top of the image. Because missing or regrown tails would alter

the ventral skin pigmentation pattern recorded for an individual, I chose a subset of the ventral

area for my region of interest (ROI). This included the ventral area below the points where the

gular fold reaches the lateral sides of the salamander and above the most anterior portion of the

vent. The ROI width matched the maximum width of the salamander’s ventral surface as

bordered by the edges of the lateral coloration. The ventral spot pattern was then quantified

following Todd et al. (2005) using quantitative image analysis (QIA). This approach, QIA or the

assessment of image data, is useful for examining the size, shape, number, and placement of

patches or spots in a color pattern and has successfully been used in amphibian species [i.e.

spectacled salamanders, Salamandrina sp. (Angelini et al. 2010) and fire-bellied toads, Bombina

sp. (Biancardi and Di Cerbo 2010)]. To remove the factor of body size from analysis I shrunk/fit

the ROI to fit a standard lattice of 10 x 65 cells. I scored each cell of the grid visually and

subjectively as a 1 or 0 representing the presence (>50% covered) or absence (<50% covered) of

a ventral spot respectively. The completed lattice for a given individual was copied to a

spreadsheet and I rearranged the original grid into a single spreadsheet column so that each

42

succeeding column of the grid was stacked below the one that preceded it. The total of the values

in this column gives the phenotype score for an individual and represents the amount of spotting

present in the ROI and reflects the lightness (low phenotype score) or darkness (high phenotype

score) of an individual.

To assess the phenotypic similarity of two individuals, I summed the absolute values of

the differences of corresponding cells representing the same grid cell/region for each salamander.

I divided this sum by the total number of cells to find the proportion of cells with identical

scores, i.e. the phenotypic similarity score, for each pair of individuals. This score ranges from 0

to 1 with lower values indicating greater similarity in terms of presence or absence of spotting in

a given grid cell.

I evaluated the symmetry of an individual by finding the sum of the absolute values of the

difference between each cell on the left side of the scoring grid with the corresponding cell in the

mirrored location on the right side of the grid. I divided this value by the total number of cells to

provide a proportion of cells, i.e. the symmetry score, for which an individual was bilaterally

symmetrical. This score ranged from 0 (symmetrical) to 1 (asymmetrical).

Statistical Analysis

To validate the efficacy of this method of phenotypic scoring, a subset of individuals was

photographed multiple times and these photos scored independently. I then calculated an F-

statistic to compare the variance with individual phenotypic similarity scores with the variance

between individual phenotypic similarity scores. To assess whether spot pattern varies

geographically, I used two-tailed t-tests with unequal variances to compare the mean phenotype

and symmetry scores between the combined populations from Illinois and combined populations

43

from Kentucky sites. I also used a two-tailed t-test with unequal variances to compare the mean

body condition for individuals with complete tails in Illinois vs. individuals with complete tails

in Kentucky. For the two sampling sites within Illinois (I-AN and I-AS) and using only

individuals with complete tails, I modeled the impacts of SVL, mass, and sampling site as a

function of phenotype scores using the LME4: LINEAR MIXED-EFFECTS MODELS USING EIGEN AND

S4 PACKAGE V. 1.1-7 (Bates et al. 2014) in R V3.1.1 (R Core Team 2014). Because I was

interested in the effect of body size on phenotypes, I modeled SVL and mass as fixed effects and

sampling site as a random effect. The fit of this full model was compared to a null model which

contained only the site variable by calculating AIC values corrected for small sample size (AICc,

Burnham and Anderson 2002) using AICCMODAVG: MODEL SELECTION AND MULTIMODEL

INFERENCE BASED ON (Q)AIC(C) PACKAGE V. 2.0-3 (Mazerolle 2015) in R. I also calculated the

likelihood of each model (-2 log likelihood), differences in AICc values (Δi) and the Akaike

weights (Δwi) for these models. Models with Δi<2 should be considered competing models

(Burnham and Anderson 2002). An Aikaike weight is interpreted as the variability accounted for

by the model, i.e. the best model among those evaluated will have the highest Δwi. The impact of

SVL, mass, and sampling site were similarly modeled as a function of phenotype symmetry

score. To investigate the potential for allometric changes in the phenotype score, i.e. spot

density, and symmetry score, I assessed potential correlations between individual size and spot

pattern, using both salamander SVL and mass measurements. I conducted a Mantel test using the

VEGAN: COMMUNITY ECOLOGY PACKAGE V2.0-10 (Oksanen et al. 2013) in R V3.1.1 (R Core

Team 2014) to examine the potential relationship between phenotypic (phenotypic similarity

score) and genotypic similarity (relatedness, r). I investigated the potential for fluctuating

44

asymmetry in H. scutatum by assessing potential correlations between symmetry score and body

condition and between phenotype score and body condition.

Results

I collected 180 tissue samples with their corresponding photos (Figure 2.2). Of these, 86

(85 with complete tails) came from site I-AN and 48 (47 with complete tails) came from site I-

AS in Illinois and 46 (with complete tails) from 8 sites in Kentucky. An additional 44 tissue

samples of individuals with complete tails but no photo data were collected from Kentucky for

use in the comparison of body condition indices. Because of small sample sizes representing

sites in Kentucky (range = 1–18) samples were pooled for analysis.

Variation in phenotypic similarity scores between redundant photos of the same

individual (range = 2-4 photos for each of 11 individuals) was significantly lower than scores of

photos taken of different individuals (F=233.24, p-value=0.00). This indicates that this method

of QIA should allow reliable quantification of phenotype.

Mean phenotype scores (p-value = 0.00, Figure 2.3) and mean individual symmetry

scores (p-value = 0.00, Figure 2.4) were significantly lower for individuals from Kentucky

compared to individuals from Illinois. This geographic variation was characterized by lower

levels of spotting and higher pattern symmetry in samples from Kentucky. This variation does

not appear to be due to differences in body condition between these two areas as individuals

from Kentucky did not differ significantly in body condition from individuals from Illinois (p-

value=1.00, Figure 2.5). Size of individuals, measured by SVL or mass does not appear to play a

significant role in their phenotype (Figures 2.6 and 2.7) or symmetry (Figures 2.8 and 2.9)

scores. The AIC analysis supports this, suggesting that the null models, i.e. the models

45

containing on the site variable, as the most parsimonious model for both phenotype and

symmetry score model sets (Tables 2.1 and 2.2). Combined results suggest that individuals do

not experience significant ontogenetic changes in spot pattern over time.

I found no relationship between phenotypic similarity scores and inter-individual

relatedness (p-value=0.71, Figure 2.10). Finally, I found no significant relationship between

phenotype score (Figure 2.11) or symmetry score (Figure 2.12) and body condition, indicating

that spot pattern, in terms of overall amount of spots or lateral symmetry, is not influenced by

body condition.

Discussion

This study found significant geographic variation in the ventral spot pattern of H.

scutatum, both in terms of overall amount of spotting and degree of bilateral symmetry. No

evidence was found, however, to suggest that spot pattern is influenced by relatedness between

individuals, or by body condition. This lack of association between spot pattern and these

variables could be due to (a) a lack of heritability in spot pattern, (b) the use of alternative signals

to assess relatedness between individuals, or (c) violations in the assumptions made by the

fluctuating asymmetry analysis. Each of these scenarios is discussed in detail below.

Geographic Variation

The ventral spot pattern of H. scutatum could be a potential tool for identifying

populations of taxonomic or conservation importance as it shows significant variation across the

portion of its range sampled in this study. This may be due to differences in the underlying

genetic variation. While low genetic diversity, i.e. stresses resulting from inbreeding, could

46

account for the increased asymmetry in spot scores observed at sites in Illinois, recent population

genetics work suggest high levels of heterozygosity at these sites (Chapter 1). Alternatively,

because no relationship between symmetry and body condition was found and because the mean

body condition, as measured by the residual index, did not differ between Illinois and Kentucky,

this might simply reflect genetic variation between the two regions (Chapter 1). The differences

in phenotype and symmetry scores between Illinois and Kentucky, therefore, may have been

induced by genetic drift influencing alleles responsible for spot pattern (Costa et al. 2009). Rudh

et al. (2007) suggested that variation in natural or sexual selection could be responsible for the

geographic variation in dorsal spot pattern they observed in Dendrobates pumilio, but it seems

unlikely that these explanations apply to H. scutatum whose ventral spot pattern is less likely to

be seen by predators and conspecifics.

Influence of Size and Age

No relationship was found between individual size, either measured by SVL or mass, and

spot pattern, either measured by phenotype or symmetry score, suggesting that spot pattern in

this species does not change with age. This was expected and reinforces the utility of these spot

patterns for individual identification over long time periods (Harris and Ludwig 2004). Work on

other plethodontid species has also found relatively high consistency in spot pattern over time

(Bendik et al. 2013).

Relatedness and Phenotypic Similarity

In my analyses, phenotypic similarity scores and relatedness between pairs of individual

H. scutatum were not correlated. A primary explanation could be that ventral spot pattern is

47

influenced by the environment. If spot pattern has low heritability, and assuming kin recognition

occurs among adult H. scutatum, individuals must assess the identity of relatives using

alternative characteristics. In H. scutatum, larvae are capable of kin recognition (Harris et al.

2003) and it is probable that, like other salamander larvae (Chivers et al. 1997, Pfennig et al.

1994, Wakahara 1997), they use chemical signals to do so. Among terrestrial plethodontids,

chemosensory cues are used in different contexts to communicate a variety of information (i.e.

Jaeger and Forester 1993) and have been implicated in kin recognition (Wareing 1997). Thus, it

is likely that the ability to assess kinship via chemical cues is retained in adult H. scutatum.

Additionally, the ventral location of the spot pattern may make this trait less useful in

intraspecific communication. The spot pattern in H. scutatum may serve as aposematic coloration

as this species produces noxious skin secretion (Brodie 1977) and, when encountered by a

predator, often remains immobile and allows itself to be turned over with the ventral spot pattern

exposed (Brodie et al. 1974).

Alternatively, a relationship between phenotypic and genotypic similarity may exist, but

was not detected with the QIA techniques used in this study. While variation in photos taken of

the same individual was significantly lower than variation in photos taken of different

individuals, the degree of resolution obtained through these methodologies may only be capable

of detecting broad scale patterns such as the geographic variation observed between the Illinois

and Kentucky sampling sites.

Fluctuating Asymmetry

The usefulness of fluctuating asymmetry as a measure of stress or fitness in an organism

varies with the trait examined (e.g. Møller and Swaddle 1997). In this study, no association was

48

observed between ventral spot pattern symmetry and body condition. There are a number of

explanations for this observation. First, fluctuating asymmetry may not be associated with color

pattern in H. scutatum. While fluctuating asymmetry has been observed in the dorsal spot and

pigmentation patterns in some amphibian species, including A. maculatum (Davis and Maerz

2007, Pokhrel 2009, Wright and Zamudio 2002) and A. opacum (Pokhrel 2009, Pokhrel et al.

2013), a lack of fluctuating asymmetry with respect to dorsal spot pattern has been observed in

Notophthalmus viridescens (Sherman et al. 2009). Secondly, the assumption that ventral spot

pattern in H. scutatum is symmetrical may have been violated. Third, spot pattern in H.

sctutatum forms very early on, i.e. shortly after metamorphoses (pers. obs.). This methodology

assumes that the measured body condition of an individual at the time of the study is indicative

of the body condition of an individual at the time of spot formation. If an individual’s body

condition has changed from the time of spot formation to the time when measured, this

assumption would be violated. Finally, the relationship between fluctuating asymmetry and

fitness is stronger when measured on traits undergoing growth (Aparicio 2001) or on traits that

are costly to grow (Aparicio and Bonal 2002). Most of the individuals in this study were well

past the point of metamorphosis and spot formation and this may have obscured any existing

relationship between symmetry score and body condition.

Conclusion

In conclusion, I found that spot pattern varies geographically in H. scutatum. The

significant variation in spot pattern between regions and the lack of relationship between

individual spot pattern and size reinforces the practicality of using this trait to identify

individuals. If variation in spot pattern exists on a finer scale (i.e. between populations), this trait

49

could also assist in conservation and management decisions by allowing recent immigrants in a

population to be identified and the degree of gene flow between populations to be estimated. I

did not, however, find support for the hypothesis that phenotypic similarity between individuals

is an indicator of their genotypic similarity, i.e. relatedness. Spot pattern does not appear to be a

viable substitute for molecular methods in evaluating relatedness among individuals. I also found

no support for the hypothesis that the ventral spot pattern in H. scutatum varies with body

condition (i.e. stress or fitness) of individuals or populations via the phenomenon of fluctuating

asymmetry. This suggests that spot pattern is also not a useful tool for assessing relative stress

among populations.

50

Literature Cited

Abramoff, M. D., P. J. Magalhaes, and S. J. Ram. 2004. Image Processing with ImageJ.

Biophotonics International 11:36-42.

Angelini, C., C. Costa, S. Raimondi, P. Menesatti, and C. Utzeri. 2010. Image analysis of the

ventral colour pattern discriminates between Spectacled salamanders, Salamandrina

perspicillata and S. terdigitata (Amphibia, Salamandridae). Amphibia-Reptilia 31:273–

282.

Aparicio, J. M. 2001. Patterns of growth and fluctuating asymmetry: the effects of asymmetrical

investment in traits with determinate growth. Behavioral Ecology and Sociobiology

49:273-282.

Aparicio, J. M. and R. Bonal 2002. Why do some traits show higher fluctuating asymmetry than

others? A test of hypotheses with tail feathers of birds. Heredity 99:139-144.

Bates, D., M. Maechler, B. Bolker, and S. Walker. 2014. lme4: Linear mixed-effect models using

Eigen and S4. R package version 1.1-7. http://CRAN.R-project.org/package=lmer4.

Bendik, N. F., T. A. Morrison, A. G. Gluesenkamp, M. S. Sanders, and L. J. O’Donnell. 2013.

Computer-Assisted Photo Identification Outperforms Visible Implant Elastomers in an

Endangered Salamander, Eurycea tonkawae. PloS One 8:e59424.

Biancardi, C. M. and A. R. Di Cerbo. 2010. Quantitative pattern analysis methodology in

amphibians. Atti VIII Congresso Nazionale SHI (Chieti, 22-26 September 2010):383-

390.

Brede, E. 2000. A morphometric study of a hybrid newt population (Triturus cristatus/T.

carnifex): Beam Brook Nurseries, Surrey, U.K. Biological Journal of the Linnean Society

70:685–695.

Breitenbach, G. L. 1982. The frequency of communal nesting and solitary brooding in the

salamander, Hemidactylium scutatum. Journal of Herpetology 16:341-346.

Brodie, E. D., Jr. 1977. Salamander antipredator postures. Copeia 1977:523-535.

Brodie, E. D., Jr., J. A. Johnson, and C. K. Dodd, Jr. 1974. Immobility as a defensive behavior in

salamanders. Herpetological 30:79-85.

Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a

practical information-theoretic approach. 2nd ed. Springer-Verlag, New York.

Carafa, M. and M. Biondi. 2004. Application of a method for individual photographic

identification during a study on Salamandra salamandra gigliolii in central Italy. Italian

Journal of Zoology 71:181–184.

51

Carreno, C. A. and R. N. Harris. 1998. Lack of nest defense behavior and attendance patterns in

a joint nesting salamander Hemidactylium scutatum (Caudata: Plethodontidae). Copeia

1998:183-189.

Chivers, D. P., E. L. Wildy, and A. R. Blaustein. 1997. Eastern long-toed salamander

(Ambystoma macrodactylum columbianum) larvae recognize cannibalistic conspecifics.

Ethology 103:187–197.

Costa, C., C. Angelini, M. Scardi, P. Menesatti, and C. Utzeri. 2009. Using image analysis on the

ventral colour pattern in Salamandrina perspicillata (Amphibia: Salamandridae) to

discriminate among populations. Biological Journal of the Linnean Society 96:35–43.

Davis, A. K. and K. L. Grayson. 2008. Spots of adult male red-spotted newts are redder and

brighter than in females: evidence for a role in mate selection? The Herpetological

Journal. 18:83–89.

Davis, A. K. and J. C. Maerz. 2007. Spot symmetry predicts body condition in spotted

salamanders, Ambystoma maculatum. Applied. Herpetology 4:195-205.

Denoël, M., P. Poncin, and J. C. Ruwet. 2001. Sexual compatibility between two heterochronic

morphs in the alpine newt, Triturus alpestris. Animal Behaviour 62:559–566.

Eitam, A. and L. Blaustein. 2002. Noninvasive individual identification of larval Salamandra

using tailfin spot patterns. Amphibia-Reptilia 23:215-219.

Fernandez, P. J., Jr., and J. P. Collins. 1988. Effect of environment and ontogeny on color pattern

variation in arizona tiger salamanders (Ambystoma tigrinum nebulosum Hallowell).

Copeia, 1988:928–938.

Forsman, A. and S. Merilaita. 1999. Fearful symmetry: pattern size and asymmetry affects

aposematic signal efficacy. Evolutionary Ecology 13:131-140.

Forsman, A., and J. Herrström. 2004. Asymmetry in size, shape, and color impairs the protective

value of conspicuous color patterns. Behavioral Ecology 15:141-147.

Gabor, C. R. 1996. Differential kin discrimination by red-spotted newts (Notophthalmus

viridescens) and smooth newts (Triturus vulgaris). Ethology 102:649-659.

Gibbons, M. E., A. M. Ferguson, D. R. Lee, and R. G. Jaeger. 2003. Mother-offspring

discrimination in the red-backed salamander may be context dependent. Herpetologica

59:322-333.

Gould S. 1975. Allometry in primates, with emphasis on scaling and evolution of the brain. Pp.

244–292. In F.S. Szalay (Ed.), Approaches to Primate Paleobiology. Basel: Karger.

52

Gray, R. H. 1983. Seasonal, annual, and geographic variation in color morph frequencies of the

cricket frog, Acris crepitans, in Illinois. Copeia 1983:300.

Harris, R. N. 2007. Body condition and order of arrival affect cooperative nesting behaviour in

four-toed salamanders Hemidactylium scutatum. Animal Behaviour 75:229-233.

Harris, R. N. and D. E. Gill. 1980. Communal nesting, brooding behavior, and embryonic

survival of the four-toed salamander Hemidactylium scutatum. Herpetologica 36: 141-

144.

Harris, R. N., W. W. Hames, I. T. Knight, C. A. Carreno, and T. J. Vess. 1995. An experimental

analysis of joint nesting in the salamander Hemidactylium scutatum (Caudata:

Plethodontidae): the effects of population density. Animal Behaviour 50:1309-1316.

Harris, R. N., T. J. Vess, J. I. Hammond, and C. J. Lindermuth. 2003. Context-dependent kin

discrimination in larval four-toed salamanders Hemidactylium scutatum (Caudata:

Plethodontidae). Herpetologica 59:164-177.

Harris, R. N. and P. M. Ludwig. 2004. Resource level and reproductive frequency in female

four-toed salamander, Hemidactylium scutatum. Ecology 85:1585-1590.

Hegna, R. H., R. A. Saporito, and M. A. Donnelly. 2013. Not all colors are equal: predation and

color polytypism in the aposematic poison frog Oophaga pumilio. Evolutionary Ecology

27:831–845.

Highton R. 1975. Geographic variation in genetic dominance of the color morphs of the red-

backed salamander, Plethodon cinereus. Genetics 80:363–374.

Hokit, D. G., S. C. Walls, and A. R. Blaustein. 1996. Context-dependent kin discrimination in

larvae of the marbled salamander, Ambystoma opacum. Animal Behaviour 52:17-31.

Hubbard, J. K., J. A. C. Uy, M. E. Hauber, H. E. Hoekstra, and R. J. Safran. 2010. Vertebrate

pigmentation: from underyling genes to adaptive function. Trends in Genetics 26:231-

239.

Lipsett J. C. and J. Piatt. 1936. Experimental Hybridization of Triturus v. viridescens and

Triturus v. symmetrica. Copeia. 1936:1.

Ludwig, W. 1932. Das Rechts-Links Problem im Tierreich und beim Menschen. Springer,

Berlin.

Jaeger, R. G. and D. C. Forester. 1993. Social behavior of plethodontid salamanders.

Herpetologica 49:163-175.

Jakob, E. M., S. D. Marshall, and G. W. Uetz. 1996. Estimating fitness: a comparison of body

condition indices. Oikos 77:61.

53

Kocsis, E., B. L. Trus, C. J. Steer, M. E. Bisher, and A. C. Steven. 1991. Journal of Structural

Biology 107:6-14.

Kohn N. R., and R. G. Jaeger. 2009. Male salamanders remember individuals based on chemical

or visual cues. Behaviour 146:1485–1498.

Mazerolle, M. J. 2015. AICcmodavg: Model selection and multimodel inference based on

(q)AIC(c). R package version 2.0-3. http://CRAN.R-project.org/package=AICcmodavg.

Møller, A. P. and J. P. Swaddle. 1997. Asymmetry, Developmental Stability, and Evolution.

Oxford University Press, Inc., New York, USA.

NatureServe. 2011. NatureServe Explorer: An online encyclopedia of life [web application].

Version 7.1. NatureServe, Arlington, Virginia. Available

http://www.natureserve.org/explorer. (Accesssed: January 2, 2012).

Oksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, P. R. Minchin, R. B. O’Hara, G. L. Simpson,

P. Solymos, M. H. H. Stevens, and H. Wagner. 2013. vegan: community ecology

package. R Package version 2.0-10. http://CRAN.R-project.org/package=vegan.

Petranka, J. W. 1998. Salamanders of the United States and Canada. Smithsonian Institution

Press, Washington, D.C., USA.

Pfennig, D. W. and J. P. Collins. 1993. Kinship affects morphogenesis in cannibalistic

salamanders. Nature 362: 836-838.

Pfennig, D. W., J. P. Collins, and R. E. Ziemba. 1999. A test of alternative hypotheses for kin

recognition in cannibalistic tiger salamanders. Behavioral Ecology 10:436-443.

Pfennig, D. W., P. W. Sherman, and J. P. Collins. 1994. Kin recognition and cannibalism in

polyphenic salamanders. Behavioral Ecology 5:225–232.

Pokhrel, L.R. 2009. Mapping the Dorsal Skin Pigmentation Patterns of Two Sympatric

Populations of Ambystomatid Salamanders, Ambystoma opacum and A. maculatum from

Northeast Tennessee. Thesis. Eastern Tennessee State University, Johnson City,

Tennessee.

Pokhrel, L. R., I. Karsai, M. K. Hamed, and T. F. Laughlin. 2013. Dorsal pigmentation and

sexual dimorphism in the marbled salamander (Ambystoma opacum). Ethology, Ecology,

and Evolution 25:214-226.

Protas, M. E. and N. H. Patel. 2008. Evolution of coloration patterns. Annual Review of Cell and

Developmental Biology 24:425-446.

54

Queller, D. C. and K. F. Goodnight. 1989. Estimating relatedness using genetic markers.

Evolution 43:258-275.

R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. http://www.R-project.org/.

Roulin, A. 2004. The evolution, maintenance and adaptive function of genetic colour

polymorphism in birds. Biological Reviews 79:815-48.

Romano A., M. Mattoccia, S. Marta, S. Bogaerts, F. Pasmans, and V. Sbordoni. 2009.

Distribution and morphological characterization of the endemic Italian salamanders

Salamandrina perspicillata (Savi, 1821) and S. terdigitata (Bonnaterre, 1789) (Caudata:

Salamandridae). Italian Journal of Zoology 76:422–432.

Rudh, A., B. Rogell, and J. Höglund. 2007. Non-gradual variation in colour morphs of the

strawberry poison frog Dendrobates pumilio: genetic and geographical isolation suggest a

role for selection in maintaining polymorphism. Molecular Ecology 16:4284-4294.

Secondi J., A. Johanet, O. Pays, F. Cazimajou, Z. Djalout, and C. Lemaire. 2010. Olfactory and

visual species recognition in newts and their role in hybridization. Behaviour. 147:13–14.

Schulte-Hostedde, A. I., B. Zinner, J. S. Millar, and G. J. Hickling. 2005. Restitution of mass–

size residuals: validating body condition indices. Ecology 86:155–163.

Sherman, E., K. Tock, and C. Clarke. 2009. Fluctuating asymmetry in Ichthyophonus sp. infected

newts, Notophthalmus viridescens, from Vermont. Applied Herpetology 6:369-378.

Storfer, A., J. Cross, V. Rush, and J. Caruso. 1999. Adaptive coloration and gene flow as a

constraint to local adaptation in streamside salamander, Ambystoma barbouri. Evolution

53:889.

Thaker M., C. R. Gabor, and J. N. Fries. 2006. Sensory cues for conspecific associations in

aquatic San Marcos salamanders. Herpetologica 62:151–155.

Todd P. A., P. G. Sanderson, and L. M. Chou. 2001. Photographic technique for the study of

coral morphometrics. Raffles Bulletin of Zoology. 49:191–196.

Todd, P. A., R. J. Ladle, R. A. Briers, and A. Brunton. 2005. Quantifying two-dimensional

dichromatic patterns using a photographic technique: case study on the shore crab

(Carcinus maenas L.). Ecological Research 20:497–501.

Todd B. D., and A. K. Davis. 2007. Sexual dichromatism in the marbled salamander, Ambystoma

opacum. Canadian Journal of Zoology 85:1008–1013.

Twitty, V. C. 1961. Second-generation hybrids of the species of Taricha. Proceedings of the

National Academy of Sciences 47:1461-1486.

55

Walls, S. C. and A. R. Blaustein. 1995. Larval marbled salamanders, Ambystoma opacum, eat

their kin. Animal Behaviour 50:537-545.

Walls, S. C. and R. E. Roudebush. 1991. Reduced aggression toward siblings as evidence of kin

recognition in cannibalistic salamanders. American Naturalist 138: 1027-1038.

Wakahara, M. 1997. Kin recognition among intact and blinded, mixed-sibling larvae of a

cannibalistic salamander Hynobius retardatus. Zoological Science 14:893-899.

Wareing, K. 1997. Aspects of the mother-offspring bond in the red-backed salamander

(Plethodon cinereus): maternal care and recognition. Thesis. Binghamton University,

Vestal, New York.

Wright, A. N., and K. R. Zamudio. 2002. Color pattern asymmetry as a correlate of habitat

disturbance in spotted salamanders (Ambystoma maculatum). Journal of Herpetology

36:129–133.

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Figures

Figure 2.1. Example of a Four-toed Salamander, Hemidactylium scutatum image being prepared

for scoring. Stage 1 shows the raw image. Stage 2 shows the image manipulated to remove the

background and converted to grayscale. Stage 3 shows the image after straightening with a

10x65 grid for spot pattern scoring superimposed.

Stage 1 Stage 2 Stage 3

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Figure 2.2. Encountered individual and site locations for the H. scutatum plotted in ArcGIS

10.2.1 using GPS coordinates. Inset A shows locations of encountered individuals at sites I-AN

and I-AS at the Middle Fork State Fish and Wildlife Area, Vermilion County, IL from 2009 to

2012. Inset B depicts fourteen sampling locations in North America. Inset C shows a magnified

view of seven sampling sites in northern Kentucky.

58

Figure 2.3. Comparison of mean phenotype scores for individuals from sites in central Kentucky

(KY) vs. those collected from sites I-AN and I-AS in Illinois. Scores range from 0 to 1 with

patternless individuals possessing a value of 0 and completely patterned (dark) individuals

possessing a value of 1. Whiskers represent the lowest and maximum values of observations

(excluding outliers), edges of the boxes indicate the 25% and 75% quartiles. T-test p-

value<0.000. Sampling locations shown in Figure 2.2.

59

Figure 2.4. Comparison of mean symmetry scores for individuals from sites in central Kentucky

(KY) vs. those collected from sites I-AN and I-AS in Illinois. Scores range from 0 to 1 with

laterally symmetrical individuals possessing a score of 0 and laterally asymmetrical individuals

possessing a score of 1. Whiskers represent the lowest and maximum values of observations

(excluding outliers), edges of the boxes indicate the 25% and 75% quartiles. T-test p-

value=0.000. Sampling locations shown in Figure 2.2.

60

Figure 2.5. Comparison of mean body condition scores for individuals from sites in central

Kentucky (KY) vs. those collected from sites I-AN and I-AS in Illinois. Body condition was

calculated using a residual index based on the log transformed variables of mass on total length

(TL). Whiskers represent the lowest and maximum values of observations (excluding outliers),

edges of the boxes indicate the 25% and 75% quartiles. T-test p-value=1.0. Sampling locations

shown in Figure 2.2.

61

Figure 2.6. Relationship of phenotype scores and snout-vent length or SVL (mm) for H.

scutatum collected from sites I-AN and I-AS in central Illinois. Scores range from 0 to 1 with

patternless individuals possessing a value of 0 and completely patterned (dark) individuals

possessing a value of 1. Sampling locations shown in Figure 2.2.

62

Figure 2.7. Relationship of phenotype scores and mass (mg) for H. scutatum collected from sites

I-AN and I-AS in central Illinois. Scores range from 0 to 1 with patternless individuals

possessing a value of 0 and completely patterned (dark) individuals possessing a value of 1.

Sampling locations shown in Figure 2.2.

63

Figure 2.8. Relationship of phenotype symmetry scores and snout-vent length or SVL (mm) for

H. scutatum collected from sites I-AN and I-AS in central Illinois. Scores range from 0 to 1 with

laterally symmetrical individuals possessing a score of 0 and laterally asymmetrical individuals

possessing a score of 1. Sampling locations shown in Figure 2.2.

64

Figure 2.9. Relationship of phenotype symmetry scores and mass (mg) for H. scutatum collected

from sites I-AN and I-AS in central Illinois. Scores range from 0 to 1 with laterally symmetrical

individuals possessing a score of 0 and laterally asymmetrical individuals possessing a score of

1. Sampling locations shown in Figure 2.2.

65

Figure 2.10. Relationship of phenotypic similarity scores and relatedness between pairs of 134

individual H. scutatum collected from sites I-AN and I-AS in central Illinois. Scores range from

0 to 1 with identically patterned pairs of individuals possessing a value of 0 and entirely

dissimilarly patterned pairs of individuals possessing a value of 1. Relatedness calculated

following Queller and Goodnight (1989) using eight microsatellite loci (see Chapter 1).

Sampling locations shown in Figure 2.2. Using a subset of 93 individuals with complete data, the

Mantel test p-value=0.85.

66

Figure 2.11. Relationship of phenotype scores and body condition for individual H. scutatum

collected from sites I-AN and I-AS in central Illinois. Scores range from 0 to 1 with patternless

individuals possessing a value of 0 and completely patterned (dark) individuals possessing a

value of 1. Body condition was calculated using a residual index based on the log transformed

variables of mass on total length (TL). Sampling locations shown in Figure 2.2.

67

Figure 2.12. Relationship of symmetry scores and body condition for individual H. scutatum

collected from sites I-AN and I-AS in central Illinois. Scores range from 0 to 1 with patternless

individuals possessing a value of 0 and completely patterned (dark) individuals possessing a

value of 1. Body condition was calculated using a residual index based on the log transformed

variables of mass on total length (TL). Sampling locations shown in Figure 2.2.

68

Tables

Table 2.1. Relationship of phenotype score and individual characteristics for 132 individual H.

scutatum from the Middle Fork State Fish and Wildlife Area in Vermilion Co., Illinois. Samples

sizes by year and population cluster are provided in Table 2.1. Site refers to the effect of

population cluster an individual was sampled from, SVL indicates the impact of an individual’s

snout-vent length, and mass is the effect of total body mass. Models were ranked using Akaike’s

information criterion (AIC) adjusted for small sample sizes (AICc), number of parameters, the -2

Log Likelihood for a given model, the difference between the AICc value for a given model and

the lowest AICc value overall (Δi), and Akaike weights (wi) calculated using the AICc values.

Model # of Parameters AICc -2 Log Likelihood Δi Δwi

Site 1 -383.12 194.69 0 0.385919

SVL + Site 2 -381.83 195.14 1.2889 0.202589

Mass + Site 2 -382.64 195.54 0.4824 0.303211

SVL + Mass + Site 3 -380.58 195.62 2.5418 0.108281

Table 2.2. Relationship of symmetry score and individual characteristics for 132 individual H.

scutatum from the Middle Fork State Fish and Wildlife Area in Vermilion Co., Illinois.

Distribution of samples sizes by year and population cluster are provided in Table 2.1. Site refers

to the effect of population cluster an individual was sampled from, SVL indicates the impact of

an individual’s snout-vent length, and mass is the effect of total body mass. Models were ranked

using Akaike’s information criterion (AIC) adjusted for small sample sizes (AICc), number of

parameters, the -2 Log Likelihood for a given model, the difference between the AICc value for a

given model and the lowest AICc value overall (Δi), and Akaike weights (wi) calculated using the

AICc values.

Model # of

Parameters

AICc -2 Log

Likelihood

Δi Δwi

Site 1 -419.6706 212.97 0 0.319418

SVL + Site 2 -418.6602 213.55 1.0104 0.192732

Mass + Site 2 -419.8996 214.17 -0.229 0.358167

SVL + Mass + Site 3 -417.8678 214.27 1.8028 0.129684

69

CHAPTER 3. ROLE OF KINSHIP AND INTRASPECIFIC AGGRESSION IN THE

FOUR-TOED SALAMANDER

Introduction

Kin recognition, the ability to distinguish between relatives and non-relatives, plays a role

in the ecology and evolution of many species. The extent and significance of its influence

(through kin selection) is subject to intense debate (e.g. Abbot et al. 2011, Breed 2014, Nowak et

al. 2010) and the occurrence and importance of this process varies among species. Two forces

are thought to drive the evolution of kin recognition: kin selection and mate choice. Hamilton

(1964) proposed that kin selection, the indirect fitness gained by assisting close relatives,

explains the evolution of kin recognition. In social taxa, or organisms with limited dispersal,

neighboring individuals are expected to be more closely related than in solitary or contrast,

Bateson (1983) suggested that mate choice might drive the evolution of kin recognition if

individuals choose mates based on relatedness in order to avoid inbreeding depression.

Kin recognition is inferred when actions of an individual towards conspecifics are

modulated based on relatedness (i.e., kin discrimination; Barnard 1990, Barnard and Aldhous

1991, Byers and Bekoff 1986, and Waldman et al. 1988). A failure to observe kin discrimination

may either indicate that no mechanism for kin recognition exists, or kin recognition is occurring

but discrimination is not (Beecher 1991). It is important to realize that the occurrence of kin

recognition may vary with developmental stages during an individual’s lifetime (Waldman 1991)

and the context of intraspecific encounters may influence the occurrence of kin discrimination.

Thus, the lack of kin discrimination at certain life stages may indicate a lack of selection for kin

recognition at that life stage (Armitage 1989).

Salamanders of the family Plethodontidae are particularly suited for studies of kin

recognition. Many plethodontid species have low vagility and high desiccation risk compared to

70

taxa with larger overall body size, and are therefore characterized by restricted gene flow among

populations (see review by Larson et al. 1984). Adults rely on cover objects and, in indirect

developing species, populations are centered around breeding ponds. Together, these aspects of

their biology increase the likelihood of encounters between related individuals and create

conditions conducive to evolution of kin discrimination in intraspecific interactions.

Among plethodontids, territoriality and courtship/mate choice are two common post-

metamorphic interactions observed between conspecifics. If salamanders in high-quality

territories share resources with close relatives, they increase the fitness of kin and thereby their

own inclusive fitness (Waldman 1991). While no studies to date have examined the role of kin

recognition in intraspecific interactions between adult plethodontids, research in other taxa

suggests that the influence of kin recognition may be complex. In Rainbow Trout, Oncorhynchus

mykiss, kin remained “better neighbors” in terms of aggressive territorial behavior than non-kin

even when territory quality decreased so long as the territories of neighboring individuals were

of similar quality (Brown and Brown 1993a, 1993b). Evidence for kin structuring within

populations has been found for a diverse array of taxa (i.e. Coltman et al. 2003, MacColl et al.

2000, Støen et al. 2005) and appears to be an important factor in fitness and reproductive success

(i.e. MacColl et al. 2000).

The role of demographic class in direct aggressive encounters between conspecifics in

other plethodontid species is complex and it is difficult to predict what role it may play in H.

scutatum. While differences in the level of aggression between the sexes, generally with males as

the more aggressive sex, has been documented for many species, (i.e. Staub 1993, Wiltenmuth

1996, Wrobell et al. 1980), a number of studies have also found no difference in aggression

71

between the sexes for some plethodontid species (i.e. Jaeger et al. 1982, Ovaska 1993, Staub

1993).

The life history traits and conservation/management needs of the Four-toed Salamander,

Hemidactylium scutatum, make it an excellent candidate for this research. The Four-toed

Salamander is a widespread, indirect-developing species that occurs in 36 states, districts, and

territories in the eastern United States and Canada (Petranka 1998). It is designated as either

‘imperiled’ or ‘critically imperiled’ in nine of these regions with status of ‘vulnerable’ in another

13 areas (NatureServe 2011). Despite being widespread, H. scutatum exhibits a patchy

distribution due, in part, to its requirement for a breeding habitat featuring fishless seeps, springs,

or marshes (Petranka 1998). Because of its small body size, susceptibility to desiccation, and

specific habitat requirements, lifetime dispersal in H. scutatum should be limited. Consequently,

the probability of encounters between highly related individuals within a population is increased,

creating conditions conducive to the evolution of kin recognition and kin discrimination.

The indirect development of this species may also be causing a lack of viscosity within

populations, i.e. inertia to disperse from natal locations. In direct developing species, sibling

groups disperse from a single, central nest location and should, all else being equal, result in

clusters of related individuals, i.e. “family groups”, across a landscape. In contrast, for indirect

developing species, many females in a population use the same breeding pool. Consequently, all

larvae metamorphose and disperse from more or less the same location causing all individuals to

be distributed at random within the population. Therefore, the assumed clustering patterns within

direct developing plethodontid species, such as P. cinereus, could cause increased selection

pressure for kin recognition and discrimination (Gibbons et al. 2003, Liebgold and Cabe 2008,

Wareing 1997).

72

This study evaluated the potential role of kinship in intraspecific encounters between

post-metamorphic H. scutatum by asking several questions. (1) Does kinship, especially via the

mechanism of kin selection, influence how individuals distribute themselves within a

population? If closely related H. scutatum are more tolerated than non-relatives, kin should be

spatially aggregated within a population. Alternatively, if H. scutatum utilize spatial distribution

as a mechanism for inbreeding avoidance, then the distances between kin in a population should

be greater than expected by chance. (2) Does kinship mediate antagonistic encounters between

conspecifics? If relatedness mediates aggression among females, then an individual should

display fewer aggressive behaviors for shorter amounts of time toward the scent of a related

female compared to the scent of an unrelated female. Alternatively, if differences exist in social

behavior between demographic groups, i.e. if female salamanders are nonaggressive, then there

should be no difference in the number and duration of aggressive behaviors displayed by a

female individual toward the scent of a female conspecific compared to an unscented control. (3)

Does indirect development, as a result of reduced within population viscosity, lead to reduced

selection for kinship mediated social behaviors than that found in direct developing species? If

indirect development results in reduced selection for kin recognition and kin discrimination, then

no role of kinship should be observed in the spatial distribution of individuals across a landscape

or in aggressive behaviors displayed by an individual toward the scent of conspecifics.

To explore these questions, I evaluated genetic relatedness (as determined from microsatellite

DNA analysis) in the context of spatial distribution among individuals encountered in the wild. I

also recorded the behavior of females in the laboratory when exposed to scent/chemosensory

signals of other females and tested for an association between level of relatedness and extent of

aggressive behavior. Chemosensory signals, such as pheromones, are an important mode of

73

communication and may be the primary mechanism of kin recognition in plethodontid

salamanders and transmit a great deal of information (i.e. Gautier et al. 2006, Jaeger and Gergits

1979). The potential for chemical signals to indicate genetic similarity, i.e. kinship, has been

suggested in several studies (Madison 1975, Wakahara 1997)

Methods

Spatial Distribution of Individuals within a Population

Samples and Collection Sites.—Sampling was conducted from March to November over

four consecutive years (2009-2013) while H. scutatum was active in the Middle Fork State Fish

and Wildlife Area in Vermilion County, Illinois (Figure 3.1). I conducted visual surveys for H.

scutatum along haphazardly placed transects by looking under potential shelter objects. For each

encountered individual, I recorded the GPS location in UTM coordinates using a Garmin GPS

12, and measured the body size (mm) as Snout-Vent-Length (SVL) and Total-Length (TL), and

mass (g). For genetic analyses, I collected small tail clips (1-5 mm) from individuals weighing

more than 0.18 g using sterilized clippers and tissues were stored in ethanol at -80 °C. The

ventral spot pattern of each salamander was photographed for use in individual identification and

to avoid repeated tissue samples from the same individual (Breitenbach 1982).

Molecular Methods and Genetic Analyses.—To infer relatedness, I determined genetic

similarity of individuals using eight microsatellite markers (Chapter 1). Because pedigree data

was unavailable, relatedness between each pair of individuals was determined by calculating

relatedness (r) (Queller and Goodnight 1989). Relatedness is defined as the proportion of alleles

shared between focal individuals relative to the proportion of alleles shared between these

individuals and the entire population. While this measure of relatedness has the advantage of

74

reducing bias for small sample sizes, it also is influenced by the genetic variation within the

reference population. Tsutsui and Suarez (2003) noted that the group used as the reference

population can influence the estimate of r if the reference population is not representative of the

genetic composition across the entire range. In cases where genetic variation in the reference

population is decreased compared to the variation present across the entire range, especially

when most individuals are related as a result of inbreeding, estimates of r may be reduced and

suggestive of a low degree of kinship even between close relatives. To avoid this potential bias, I

calculated relatedness using additional samples from the extended range of this species collected

from other sites in Illinois and Kentucky (Chapter 1).

Statistical Analyses.—Samples consisted of two geographically distinct clusters

distributed around two breeding pools separated by a distance of 1,224 m (Figure 3.1). While

these clusters are not genetically distinct (Chapter 1), it is unlikely that inter-site interactions

between individuals occur frequently. Previous work by Windmiller (2000) observed a

maximum straight-line distance of only 201 m from a wetland source and suggested that H.

scutatum dispersal is limited. Additionally, the presence of a stream separating the two

geographic clusters (Figure 3.1) is expected to further restrict movement as low order streams act

as a barrier to dispersal in the similarly sized plethodontid P. cinereus (Marsh et al. 2007). Thus I

analyzed the data for the north population cluster, I-AN, and the south population cluster, I-AS,

separately by year. I modeled the impact of relatedness, population cluster, and year as random

effects, as a function of straight-line inter-individual distance using the LME4: LINEAR MIXED-

EFFECTS MODELS USING EIGEN AND S4 PACKAGE V. 1.1-7 (Bates et al. 2014) in R V3.1.1 (R Core

Team 2014). Because I was interested in the effect of relatedness on within population

distribution of individuals, I modeled relatedness as a fixed effect and population cluster and

75

year as random effects. The fit of this full model was compared to a null model which omitted

the relatedness variable by calculating AIC values corrected for small sample size (AICc,

Burnham and Anderson 2002) using AICCMODAVG: MODEL SELECTION AND MULTIMODEL

INFERENCE BASED ON (Q)AIC(C) PACKAGE V. 2.0-3 (Mazerolle 2015) in R. I also calculated the

likelihood of each model (-2 log likelihood), differences in AICc values (Δi) and the Akaike

weights (Δwi) for these models. Models with Δi<2 should be considered competing models

(Burnham and Anderson 2002). An Akaike weight is interpreted as the variability accounted for

by the model, i.e. the best model among those evaluated will have the highest Δwi. To also

investigate the potential association between relatedness and spatial distribution, I conducted

Mantel tests using the VEGAN: COMMUNITY ECOLOGY PACKAGE V2.0-10. (Oksanen et al. 2013)

in R V3.1.1 and compared relatedness versus geographic distance for each possible dyad of

individuals for each population cluster by year. Because of repeated comparisons created by the

population cluster/year combinations, the Bonferroni corrected level of significance for these

tests was α=0.00625.

Kin Discrimination in Antagonistic Encounters

Sampling and Maintenance of Salamanders.—Since H. scutatum is state-threatened in

Illinois, but relatively common in Kentucky, I collected individuals for experimental trials from

18-23 March 2011 from ten sites (K-A, K-B, K-C, K-E, K-F, K-H, K-J, K-L, K-M, K-N) in

Kentucky (Figure 3.1). The target sample size from a given site was two adult females and I

processed these individuals as described above. Each individual was housed separately in a clear

plastic container kept in a Percival environmental chamber model I-35 LL, maintained at 20-

24 °C on a 12/12 LD cycle. Dampened, unbleached paper toweling was used as a substrate and

76

crumpled unbleached paper toweling provided as a shelter. Provosoli media (Wyngaard and

Chinnappa 1982), a synthetic pond water substitute, was used to mist and dampen these

individuals as needed. I fed individuals fruit flies and pinhead crickets ad lib such that all

individuals were offered the same diet each time.

Experimental Setup.—Each experimental trial utilized an acting salamander whose

behavior and movements I observed in response to the chemosensory cues provided by a

stimulus salamander. Bedding was scented by the stimulus salamander for a full six days before

being used in an experiment (Jaeger and Gergits 1979, Mathis 1990). The experimental chamber

used for each trial consisted of a clear plastic dome, 8 cm x 8 cm, placed over a square plastic

base. I set this arrangement inside a four-sided, white cardboard blind in order to eliminate

external visual stimuli and to maximize the visibility of the salamander on the video footage. I

conducted trials at 18-25 °C under a red light to minimize stress to the salamander (Dubois 1890,

Graber 1883, Pearse 1910). Each acting salamander was only used once per day and each

bedding sample used only once before being discarded. For each trial, I covered the square

plastic base with scented bedding, placed the acting salamander on it, covered this individual

with an opaque shelter, and contained the whole setup within the transparent plastic dome. I

allowed the salamander to acclimate for a period of 5 minutes in order to minimize the impact of

handling stress. After the acclimation period, I removed the shelter and allowed the individual to

move freely within the plastic dome for 30 minutes. I recorded the movements and behavior of

the individual using a high-resolution digital video camera (WiLife Indoor Surveillance Camera,

Logitech, Fremont, CA, USA) that remotely recorded the video to a laptop computer (WiLife

Indoor Master System, Logitech). I repeated these trials so that each salamander was used as an

actor against the scent of all other individuals (“conspecific-scented”), allowing every possible

77

dyad combination of actor-scent to be tested. For control conditions, each individual was used as

an acting salamander in two additional trials using bedding scented only with Provosoli media

(“Provosoli-scented”) and its own bedding (“self-scented”) respectively.

Video Analysis. —I reviewed the video footage for each trial using SITANDWAIT Event

Recorder software (Szabo and Kis 2012) to score the behaviors and measure the activity of each

individual blindly with respect to the relatedness between individuals. The following behaviors

following Jaeger (1984) were recorded: (1) rate of nose taps (NT: pressing the tip of the nose to

the substrate) over entire trial period, (2) duration of time spent posturing in all trunk raised

(ATR) with the trunk lifted entirely off of the substrate, (3) duration of time spent posturing in

flattened position (FLAT) with the entire trunk in contact with the substrate, (4) duration of time

spent posturing in front trunk raised (FTR) where the anterior portion of the trunk was raised off

of the substrate while the posterior remained in contact with it, and (5) the occurrence of

defecating (DEF: individuals defecated during the trial). The activity of the salamander was

scored as follows: (1) active (ACTD: duration of walking across the substrate), (2) inactive

(INACTD: duration of not moving at all, starting after an individual had not changed position for

>=10 seconds), and (3) climbing (CLIMB: duration of time where individuals attempted to climb

the side of the experimental chamber, beginning whenever a salamander had placed both front

feet on the side and ending when at least one front foot had returned to the substrate.

Statistical Analysis.—I modeled the impact of relatedness and the straight-line distance

between population clusters, first as a function the proportion of time spent in ATR, secondly as

a function of the proportion of time spent walking, and finally as a function of the rate of nose

taps displayed by the acting individual as before, modeling relatedness, the variable of interest,

as a fixed effect and straight-line distance as a random effect.

78

As a secondary test of the role of kinship in mediating aggression, I conducted Mantel

tests to examine the influence of relatedness on each of the three main response variables: 1)

percent of the trial period spent in an aggressive posture (ATR), 2) percent of the trial period

spent walking, and 3) on the amount of chemosensory behavior displayed (rate of nose taps). I

used Mann-Whitney U tests in R V3.1.1 (R Core Team 2014) to compare the reactions of

individuals toward conspecifics to their reactions toward the control conditions, Provosoli- or

self-scented bedding, and because these response variables exhibited non-normal distributions.

Results

Spatial Analysis

I first examined if there was an association between spatial distances among the capture

location of individuals and their relatedness. For this spatial analysis, 128 tissue samples were

collected from the Middle Fork State Wildlife Area over four years, with each individual

represented by a unique GPS location (Figure 3.1). Samples were compiled separately for I-AN

and I-AS (Table 3.1). Mantel tests indicated no significant relationship in the relatedness

between individuals and their spatial proximity (Figure 3.2). The AIC analysis supported these

results, suggesting the null model, i.e. the model containing only the population cluster and year

variables, as the most parsimonious model (Tables 3.2). These combined results indicated that

fine scale distribution of individuals within a population is not influenced by kinship.

Behavioral Analysis

I investigated the relationship between the intraspecific behavior and relatedness within

pairs of individuals. Seventeen female adult H. scutatum were collected from the 10 sampling

sites in Kentucky (Table 3.3). The relatedness between these individuals is included in Figure

79

3.11. Behavioral responses to the scent of conspecifics varied greatly within and between acting

salamanders. In some trials the actor spent the entire period walking while in an ATR posture but

when paired with a different scent, spent the entire period still and in a FLAT posture (Figure 3.6

and Figure 3.7). Mantel tests assessing the influence of kinship on activity (Figure 3.3), posture

(Figure 3.4), and rate of nose taps (Figure 3.5) showed no significant relationship with the

responses of all individuals pooled. This conclusion is supported by the AIC analysis, as the

most parsimonious models for all response variables were the null models (Table 3.4). Within

the Mantel tests for each response variable conducted on the individual basis, only one individual

showed a significant relationship for the activity (Figure 3.6) and posture variables (Figure 3.7),

but not the rate of nose tapping (Figure 3.8). It is likely that this is an artifact of the data and does

not represent a true impact of kinship on intraspecific interactions. The results of the Mann-

Whitney U tests (Table 3.5) indicated that the salamanders also did not exhibit significantly

different behaviors when their reactions to conspecific- vs. Provosoli-scented toweling or

conspecific- vs. self-scented toweling were compared for either activity and posture (Figure 3.9)

or nose tap rate (Figure 3.10).

Discussion

This study found no strong evidence of association between kinship and social

interactions of post-metamorphic H. scutatum, either from the indirect measurement of fine-scale

spatial distribution or direct observations of the behavior exhibited by adult females in response

to conspecific cues. Adult female H. scutatum, furthermore, showed no difference in response

during conspecific- vs. self-scented trials or conspecific- vs. Provosoli-scented toweling. The

lack of association between kinship and evaluated characteristics in this study can be interpreted

80

with several potential scenarios: (a) differences in the social behavior among demographic

groups, (b) influence of developmental mode and sex on juvenile dispersal, and (c) possibility

that the study did not target conditions under which kin discrimination occurs. Each of these

scenarios is discussed in more detail below.

Demographic Differences in Social Behavior

It is possible that the failure to observe kin discrimination in this study is not due to lack

of such behavior (i.e., kin discrimination is occurring), but instead the result of our inability to

differentiate among behaviors of different demographic groups, such as males versus females.

For one, it is difficult to sex H. scutatum in the field and adults were examined as a single group

comprising of both males and females. In addition, tissue samples were only obtained from

individuals of sufficient size. Consequently, the evaluation of spatial distribution and genetic

relatedness was limited to a general analysis with all demographic classes pooled. Male-biased

dispersal, inferred from a sex-based difference in genetic and spatial association, has been found

in P. cinereus (Liebgold et al. 2011), but the potential for sex-biased dispersal has not yet been

investigated in H. scutatum. It is thus possible that spatial aggregation of kin does occur in this

species, but might be only observable in non-dispersing sexes and/or ontogenetic stages.

The results from the experimental trials also support this hypothesis that intraspecific

behavior may vary among demographic groups in H. scutatum. Individuals in these trials

exhibited no differences in the frequency or duration of aggressive behaviors displayed toward

the scent of a conspecific vs. an unscented controls. Because female salamanders are frequently

less aggressive than their male counterparts (i.e. Staub 1993, Wiltenmuth 1996, Wrobell et al.

1980), the restriction of these trials to the use of female individuals may have led to the lack of

81

increased aggression toward the scent of conspecifics. It is possible that, if females are the least

or non-aggressive sex in H. scutatum, that kinship may mediate interactions involving male

individuals.

Influence of Developmental Mode and Sex on Juvenile Dispersal

I failed to reject the hypothesis that developmental mode in H. scutatum has resulted in

reduced selection for kin recognition and kin discrimination. Because of their inherent need for

dispersal form and migration to a breeding pool, indirect developing species like H. scutatum

may have greater dispersal abilities than direct developing species. Genetic analyses on this

species have revealed higher degrees of gene flow between populations of H. scutatum than

would be expected for similarly sized direct developing plethodontid species (Chapter 1). This

increased vagility could result in a reduced rate of encounters between close relatives, reduced

selection for kin discriminatory behaviors, and ultimately be responsible for the lack of influence

of kinship on the within population distribution and antagonistic interactions of individuals

observed in this study.

Lack of Kin Discrimination

The lack of kin discrimination in this study may not necessarily be an indicator of a lack

of kin recognition. Kin recognition has been observed in H. scutatum, occurring between

mothers and their nests (Carreno and Harris 1998) as well as at larval stages, although kin

discrimination in the latter instance appears to be context dependent and only occurs in

environments with a high predator density (Carreno et al. 1996, Harris et al. 2003). It is possible

that kin recognition does play an important role in the interactions between post-metamorphic

82

individuals, but only under the conditions outside the scope of this experiment. While kin

discrimination between post metamorphic individuals was not observed in this study, H.

scutatum has been shown to exhibit some degree of territoriality (Grant 1955), but individuals

are frequently found sharing cover objects (pers. obs.). This suggests that under certain

conditions, possibly when kin are involved, H. scutatum may allow other individuals into their

territories. The design of this study also precludes the possibility that kin recognition may be a

facilitator of mate choice and the avoidance of inbreeding/outbreeding depression in this species.

Madison (1975) determined that female P. jordani preferentially associate with unfamiliar males

during the mating season and that familiarity might act as a surrogate for kinship.

While Grant (1955) suggested the existence of territoriality in H. scutatum, his research

did not note the sex of individuals used and so no information is available on the role of sex in

intraspecific aggression for this species. The lack of detecting kin discriminatory behaviors

during the experimental portion of this study may also reflect that females are non- or less

aggressive than males, or that individuals in general are non- or less aggressive toward females.

Finally, a lack of observed kin discrimination can occur when levels of relatedness

between individuals are too low to illicit kin-related responses. Hamilton (1964) argued that kin

selection should be observed when the benefit from interaction multiplied by the relatedness

between the individuals is greater than the cost of interaction. This inequality, Hamilton’s Rule,

depends on the degree of relatedness between the individuals involved. This estimate of

relatedness ranges from 0, where individuals share no alleles and are therefore “unrelated,” to 1,

where individuals share all alleles and are “closely related.” While the mean relatedness for

individuals in the behavioral experiments was close to 0 (Figure 3.11), there was much greater

variation in relatedness for the spatial analysis and some individuals had relatively high estimates

83

of relatedness. Therefore, although the behavioral experiments may have been hampered by very

low relatedness, results for the spatial analysis that included some relatively closely related

individuals, still failed to indicate that kinship does influence intraspecific encounters between

post-metamorphic individuals in H. scutatum.

Conclusion

In conclusion, this study neither detected a correlation between relatedness and spatial

distribution of individuals within a wild population of H. scutatum, nor between relatedness and

behavior towards conspecifics in adult females. However, I did not assess if intraspecific

behavior differs between demographic groups or that developmental mode may influence

selection for kin recognition and kin discrimination. Genetic analyses of H. scutatum populations

revealed that this species might have some mechanism for maintaining within population

variation (Chapter 1). While increased genetic variation should result in a decreased rate of

encounters between close relatives and decreased selection for kinship mediated behaviors,

demographic differences in behavior remains a possibility and may explain the patterns observed

in this study. Further investigation into the role of developmental mode in the life history and

evolution of this species may assist in understanding the patterns of behavior observed across the

Plethodontidae.

84

Literature Cited

Abbot, P., J. Abe, J. Alcock, S. Alizon, J. A. Alpedrinha, M. Andersson, J. B. Andre, M. van

Baalen, F. Balloux, S. Balshine, N. Barton, L. W. Beukeboom, J. M. Biernaskie, T. Bilde,

G. Borgia, M. Breed, S. Brown, R. Bshary, A. Buckling, N. T. Burley, M. N. Burton-

Chellew, M. A. Cant, M. Chapuisat, E. L. Charnov, T. Clutton-Brock, A. Cockburn, B. J.

Cole, N. Colegrave, L. Cosmides, I. D. Couzin, J. A. Coyne, S. Creel, B. Crespi, R. L.

Curry, S. R. X. Dall, T. Day, J. L. Dickinson, L. A. Dugatkin, C. El Mouden, S. T.

Emlen, J. Evans, R. Ferriere, J. Field, S. Foitzik, K. Foster, W. A. Foster, C. W. Fox, J.

Gadau, S. Gandon, A. Gardner, M. G. Gardner, T. Getty, M. A. D. Goodisman, A.

Grafen, R. Grosberg, C. M. Grozinger, P. H. Gouyon, D. Gwynne, P. H. Harvey, B. J.

Hatchwell, J. Heinze, H. Helantera, K. R. Helms, K. Hill, N. Jiricny, R. A. Johnstone, A.

Kacelnik, E. T. Kiers, H. Kokko, J. Komdeur, J. Korb, D. Kronauer, R. Kümmerli, L.

Lehmann, T. A. Linksvayer, S. Lion, B. Lyon, J. A. R. Marshall, R. McElreath, Y.

Michalakis, R. E. Michod, D. Mock, T. Monnin, R. Montgomerie, A. J. Moore, U. G.

Mueller, R. Noë, S. Okasha, P. Pamilo, G. A. Parker, J. S. Pedersen, I. Pen, D. Pfennig,

D. C. Queller, D. J. Rankin, S. E. Reece, H. K. Reeve, M. Reuter, G. Roberts, S. K. A.

Robson, D. Roze, F. Rousset, O. Rueppell, J. L. Sachs, L. Santorelli, P. Schmid-Hempel,

M. P. Schwarz, T. Scott-Phillips, J. Shellmann-Sherman, P. W. Sherman, D. M. Shuker,

J. Smith, J. C. Spagna, B. Strassmann, A. V. Suarez, L. Sundström, M. Taborsky, P.

Taylor, G. Thompson, J. Tooby, N. D. Tsutsui, K. Tsuji, S. Turillazzi, F. Úbeda, E. L.

Vargo, B. Voelkl, T. Wenseleers, S. A. West, M. J. West-Eberhard, D. F. Westneat, D. C.

Wiernasz, G. Wild, R. Wrangham, A. J. Young, D. W. Zeh, J. A. Zeh, and A. Zink. 2011.

Inclusive fitness theory and eusociality. Nature 471:E1-E4.

Armitage, K. B. 1989. The function of kin discrimination. Ethology Ecology & Evolution 1:111-

121.

Barnard, C. J. 1990. Kin recognition: problems, prospects and the evolution of discrimination

systems. Advances in the Study of Behavior 19:29-81.

Barnard, C. J. and P. Aldhous. 1991. Kinship, kin discrimination, and mate choice. Pp. 125-147.

In P. G. Hepper (Ed.), Kin Recognition. Cambridge: Cambridge University Press.

Bates, D., M. Maechler, B. Bolker, and S. Walker. 2014. lme4: Linear mixed-effect models using

Eigen and S4. R package version 1.1-7. http://CRAN.R-project.org/package=lmer4.

Bateson, P. G. 1983. Optimal outbreeding. Pp. 257-309. In P. G. Bateson (Ed.), Mate Choice.

Cambridge: Cambridge University Press.

Beecher, M. D. 1991. Successes and failures of parent-offspring recognition in animals. Pp. 94-

124. In P. G. Hepper (Ed.), Kin Recognition. Cambridge: Cambridge University Press.

Breed, M. D. 2014. Kin and nestmate recognition: the influence of W. D. Hamilton on 50 years

of research. Animal Behavior 92:271-279.

85

Breitenbach, G. L. 1982. The frequency of communal nesting and solitary brooding in the

salamander, Hemidactylium scutatum. Journal of Herpetology 16:341-346.

Brown, G. E. and J. A. Brown. 1993a. Social dynamics in salmonid fishes: do kin make better

neighbors? Animal Behaviour 45:863-871.

Brown, G. E. and J. A. Brown. 1993b. Do kin always make better neighbors? The effects of

territory quality. Behavioral Ecology and Sociobiology 33:225-231.

Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a

practical information-theoretic approach. 2nd ed. Springer-Verlag, New York.

Byers, J. A. and M. Bekoff. 1986. What does “kin recognition” mean? Ethology 72:342-345.

Carreno, C. A. and R. N. Harris. 1998. Lack of nest defense behavior and attendance patterns in

a joint nesting salamander Hemidactylium scutatum (Caudata: Plethodontidae). Copeia

1998:183-189.

Carreno, C. A., T. J. Vess, and R. N. Harris. 1996. An investigation of kin recognition abilities in

larval four-toed salamanders, Hemidactylium scutatum (Caudata: Plethodontidae).

Herpetologica 52:293-300.

Coltman, D. W., J. G. Pilkington, and J. M. Pemberton. 2003. Fine-scale genetic structure in a

free-living ungulate population. Molecular Ecology 12:733-742.

Dubois, R. 1890. Sur la perception des radiations lumineuses par la peau, chez les Protées

aveugles des grottes de la Carniole. Comptes Rendus de l’Académie des Sciences

110:358-361.

Gautier, P., K. Olgun, N. Uzum, and C. Miaud. 2006. Gregarious behaviour in a salamander:

attraction to conspecific chemical cues in burrow choice. Behavioral Ecology and

Sociobiology 59:836-841.

Graber, V. 1883. Fundamentalversuche über die Helligkeits- und Farbenempfindlichkeit

augenloser und geblendeter Thiere. Sitzungsberichte der Akademie der Wissenschaften in

Wien, Mathematisch-Naturwissenschafliche Klasse 87:201-236.

Grant, W. C. 1955. Territorialism in two species of salamanders. Science 121:137-138.

Gibbons, M. E., A. M. Ferguson, D. R. Lee, and R. G. Jaeger. 2003. Mother-offspring

discrimination in the red-backed salamander may be context dependent. Herpetologica

59:322-333.

Hamilton, W. D. 1964. The genetical evolution of social behaviour. International Journal of

Theoretical Biology 7:1-16.

86

Harris, R. N., T. J. Vess, J. I. Hammond, and C. J. Lindermuth. 2003. Context-dependent kin

discrimination in larval four-toed salamanders Hemidactylium scutatum (Caudata:

Plethodontidae). Herpetologica 59:164-177.

Jaeger, R. G. 1984. Agonistic behavior of the red-backed salamander. Copeia 2:309-314.

Jaeger, R. G. and W. F. Gergits 1979. Intra- and interspecific communication in salamanders

through chemical signals on the substrate. Animal Behaviour 27:150–156.

Jaeger, R. G., D. Kalvarsky, and N. Shimizu. 1982. Territorial behaviour of the red-backed

salamander: expulsion of intruders. Animal Behaviour 30:490-496.

Larson, A., D. B. Wake, and K. P. Yanev. 1984. Measuring gene flow among populations having

high levels of genetic fragmentation. Genetics 106:293-308.

Liebgold, E. B. and P. R. Cabe. 2008. Familiarity with adults, but not relatedness, affects the

growth of juvenile red-backed salamanders (Plethodon cinereus). Behavioral Ecology

and Sociobiology 63:277-284.

Liebgold, E. B., E. D. Brodie, and P. R. Cabe. 2011. Female philopatry and male-biased

dispersal in a direct-developing salamander, Plethodon cinereus. Molecular Ecology

20:249-257.

MacColl, A. D. C., S. B. Piertney, R. Moss, and X. Lambin. 2000. Spatial arrangement of kin

affects recruitment success in young male red grouse. Oikos 90:261-270.

Madison, D. M. 1975. Intraspecific odor preferences between salamanders of the same sex:

dependence on season and proximity of residence. Canadian Journal of Zoology 53:1356-

1361.

Mathis, A. 1990. Territorial salamanders assess sexual and competitive information using

chemical signals. Animal Behaviour 40:953–962.

Marsh, D. M., R. B. Page, T. J. Hanlon, H. Bareke, R. Corritone, N. Jetter, N. G. Beckman, K.

Gardner, D. E. Seifert, and P. R. Cabe. 2007. Ecological and genetic evidence that low-

order streams inhibit dispersal by red-backed salamanders (Plethodon cinereus).

Canadian Journal of Zoology 85:319-327.

Mazerolle, M. J. 2015. AICcmodavg: Model selection and multimodel inference based on

(q)AIC(c). R package version 2.0-3. http://CRAN.R-project.org/package=AICcmodavg.

NatureServe. 2011. NatureServe Explorer: An online encyclopedia of life [web application].

Version 7.1. NatureServe, Arlington, Virginia. Available

http://www.natureserve.org/explorer. (Accesssed: January 2, 2012).

87

Nowak, M. A., C. E. Tarnita, and E. O. Wilson. 2010. The evolution of eusociality. Nature

466:1057-1062.

Oksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, P. R. Minchin, R. B. O’Hara, G. L. Simpson,

P. Solymos, M. H. H. Stevens, and H. Wagner. 2013. Vegan: community ecology

package. R Package version 2.0-10. http://CRAN.R-project.org/package=vegan.

Ovaska, K. 1993. Aggression and territoriality among sympatric western plethodontid

salamanders. Canadian Journal of Zoolology 71:901-907.

Pearse, A. S. 1910. The reactions of amphibians to light. Proceedings of the American Academy

of Arts and Sciences 45:161.

Petranka, J. W. 1998. Salamanders of the United States and Canada. Smithsonian Institution

Press, Washington, D.C., USA.

Queller, D. C. and K. F. Goodnight. 1989. Estimating relatedness using genetic markers.

Evolution 43:258-275.

R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. http://www.R-project.org/.

Staub, N. L. 1993. Intraspecific agonistic behavior of the salamander Aneides flavipunctatus

(Amphibia: Plethodontidae) with comparisons to other plethodontid species.

Herpetologica 1993:271-281.

Støen, O., E. Bellemain, S. Sæbø, and J. E. Swenson. 2005. Kin-related spatial structure in

brown bears Ursus arctos. Behavioral Ecology and Sociobiology 59:191-197.

Szabo, P. and J. Kis. 2012. Sit and Wait. Available http://sitwait.sourceforge.net/. Accessed:

June 23, 2014. (Archived by WebCite® at http://www.webcitation.org/6QYI5nKaA).

Tsutsui, N. D. and A. V. Suarez. 2003. The colony structure and population biology of invasive

ants. Conservation Biology 17:48-58.

Waldman, B. 1991. Kin recognition in amphibians. Pp. 162-219. In P. G. Hepper (Ed.), Kin

Recognition. Cambridge: Cambridge University Press.

Waldman, B., P. C. Frumhoff, and P. W. Sherman. 1988. Problems of kin recognition. Trends in

Ecology and Evolution 3:8-13.

Wakahara, M. 1997. Kin recognition among intact and blinded, mixed-sibling larvae of a

cannibalistic salamander Hynobius retardatus. Zoological Science 14:893-899.

88

Wareing, K. 1997. Aspects of the mother-offspring bond in the red-backed salamander

(Plethodon cinereus): maternal care and recognition. Unpublished Thesis. Binghamton

University, Vestal, New York.

Wiltenmuth, E. B. 1996. Agonistic and sensory behaviour of the salamander Ensantina

eschscholtzii during assymetrical contests. Animal Behaviour 52:841-850.

Windmiller, B. 2000. Study of ecology and population response to construction in upland habitat

by vernal pool amphibians. Report for the Massachusetts Natural Heritage Program,

Sudbury, MA.

Wrobell, D. J., W. F. Gergits, and R. G. Jaeger. 1980. An experimental study of interference

competition among terrestrial salamanders. Ecology 61:1034.

Wyngaard, G. A. and C. C. Chinnappa. 1982. General biology and cytology of cyclopoids. Pp

495-533. In R. W. Harrison and R. C. Cowden (Eds.), Developmental Biology of

Freshwater Invertebrates. A. R. Liss, New York, New York.

89

Figures

Figure 3.1. Sampling locations representing 128 individual H. scutatum tissue samples collected

from the Middle Fork State Fish and Wildlife Area (MFSFWA), Vermilion County, IL from

2009 through 2012. GPS coordinates of each individual collected were plotted in ArcGIS 10.2.1.

Distribution of samples sizes by year and population cluster are provided in Table 3.1. Upper left

inset shows location of sampling sites in Illinois and Kentucky in relation to North America.

Upper right inset shows the location of 9 locations from which individuals were sampled in

northern Kentucky. Sample sizes for sampling sites in Kentucky are provided in Table 3.3.

90

Figure 3.2. Relationship of relatedness and straight-line geographic distance (m) between pairs of

128 individual H. scutatum. Relatedness calculated following Queller and Goodnight (1989)

using eight microsatellite loci (see Chapter 1). Data is plotted by year and divided between the I-

AN and I-AS population clusters (see Figure 3.1). Results of Mantel tests comparing relatedness

and geographic distance are included as p-values in the corresponding panels. Bonferroni

corrected p-value required for significance is p<0.00625. Distribution of samples sizes by year

and population cluster are provided in Table 3.1.

91

Figure 3.3. Relationship of relatedness and activity (i.e. proportion of time spent walking) during

experimental trials between pairs of 17 H. scutatum. Relatedness calculated following Queller

and Goodnight (1989) using eight microsatellite loci (see Chapter 1). Mantel test p-value =0.66.

92

Figure 3.4. Relationship of relatedness and posture [i.e. proportion of time spent in the all trunk

raised (ATR) posture (Jaeger 1984)] during experimental trials between pairs of 17 H. scutatum.

Relatedness calculated following Queller and Goodnight (1989) using eight microsatellite loci

(see Chapter 1). Mantel test p-value=0.606.

93

Figure 3.5. Relationship of relatedness and rate of nose tapping [i.e. mean nose taps/second

(Jaeger 1984)] during experimental trials between pairs of 17 H. scutatum. Relatedness

calculated following Queller and Goodnight (1989) using eight microsatellite loci (see Chapter

1). Mantel test p-value =0.606.

94

Figure 3.6. Individual responses of 17 individual H. scutatum in terms of proportion of time

spent walking, and their relatedness to conspecifics during experimental trials. Relatedness

calculated following Queller and Goodnight (1989) using eight microsatellite loci (see Chapter

1). Codes for individuals are listed at the top of each panel. P-values for Mantel tests of

individual responses and individual regression equations and R2 values are included in each

panel. Bonferroni corrected p-value required for significance is p<0.00294.

95

Figure 3.7. Individual responses of 17 individual H. scutatum in terms of proportion of time

spent in the all trunk raised (ATR) posture (Jaeger 1984), and their relatedness to conspecifics

during experimental trials. Relatedness calculated following Queller and Goodnight (1989) using

eight microsatellite loci (see Chapter 1). Codes for individuals are listed at the top of each panel.

P-values for Mantel tests of individual responses and individual regression equations and R2

values are included in each panel. Bonferroni corrected p-value required for significance is

p<0.00294.

96

Figure 3.8. Individual responses of 17 individual H. scutatum in terms of mean rate of nose taps

(Jaeger 1984), measured in nose taps/second, and their relatedness to conspecifics during

experimental trials. Relatedness calculated following Queller and Goodnight (1989) using eight

microsatellite loci (see Chapter 1). Codes for individuals are listed at the top of each panel. P-

values for Mantel tests of individual responses and individual regression equations and R2 values

are included in each panel. Bonferroni corrected p-value required for significance is p<0.00294.

97

Figure 3.9. Impact of stimulus type on posture [i.e. proportion of time (mean ± SE) spent in the

all trunk raised (ATR) posture (Jaeger 1984)] and activity [i.e. proportion of time (mean ± SE)

spent walking] during experimental trials using 17 H. scutatum.

98

Figure 3.10. Impact of stimulus type on rate of nose tapping (mean ± SE) [i.e. nose taps/second

(Jaeger 1984)] during experimental trials using 17 H. scutatum.

99

Figure 3.11. Distribution of relatedness values for 17 individual H. scutatum used in the

experimental analysis and the 128 individual H. scutatum used in the spatial analysis.

Relatedness was calculated following Queller and Goodnight 1989 and using eight microsatellite

loci (see Chapter 1).

100

Tables

Table 3.1: List of Hemidactylium scutatum collected from population clusters I-AN and I-AS

within the Middle Fork State Fish and Wildlife Area by year. *Drought conditions prevented the

collection of samples at cluster I-AS during 2012.

Year I-AN I-AS Total

2009 29 15 44

2010 18 12 30

2011 35 10 45

2012* 9 0 9

Total 91 37 128

Table 3.2. Relationship of relatedness and straight-line geographic distance (m) between pairs of

128 individual H. scutatum from the Middle Fork State Fish and Wildlife Area in Vermilion Co.,

Illinois. Relatedness calculated following Queller and Goodnight (1989) using eight

microsatellite loci (see Chapter 1). Distribution of samples sizes by year and population cluster

are provided in Table 3.1. Null model includes sampling year and population cluster as random

effects. Relatedness model includes these and the fixed effect of relatedness. Models were

ranked using Akaike’s information criterion (AIC) adjusted for small sample sizes (AICc),

number of parameters, the -2 Log Likelihood for a given model, the difference between the AICc

value for a given model and the lowest AICc value overall (Δi), and Akaike weights (wi)

calculated using the AICc values.

Model # of Parameters AICc -2 Log Likelihood Δi Δwi

Year + Site 2 17295.03 -8643.50 0.00 0.73

Relatedness + Year + Site 3 17297.03 -8643.49 2.00 0.27

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Table 3.3. Summary of 17 individual H. scutatum collected during visual encounter surveys

across 10 sites in Kentucky. Listed are: Code = site acronym, N = number of samples, County =

site county, UTM Coordinates = coordinates of mean centroid calculated from all sampled

individual locations. Geographic location of sites is depicted in Figures 3.1.

Code N County UTM Coordinates

K-A 1 Powell 264610 4187946

K-B 2 Menifee 273080 4198054

K-C 2 Laurel 745448 4101073

K-E 2 Powell 265866 4188298

K-F 2 Powell 264949 4187610

K-H 2 Powell 264672 4187874

K-J 2 Powell 263948 4189143

K-L 1 Wolfe 272711 4183552

K-M 1 Wolfe 272752 4187911

K-N 2 Menifee 264754 4191931

102

Table 3.4. Relationship of the responses of 17 adult H. scutatum to chemosensory cues provided

by conspecifics during experimental trials and the relatedness between individuals. The response

variables investigated include the proportion of time in an aggressive, “all trunk raised” posture

(% time ATR), proportion of time an individual was active (% time walking), and the rate of

nose tapping (nose taps/second). Relatedness calculated following Queller and Goodnight (1989)

using 8 microsatellite loci (see Chapter 1). Null models include straight-line distance between the

individual sampling sites as a random effect. Relatedness models include this and the fixed effect

of relatedness. Models were ranked using Akaike’s information criterion (AIC) adjusted for

small sample sizes (AICc), number of parameters, the -2 Log Likelihood for a given model, the

difference between the AICc value for a given model and the lowest AICc value overall (Δi), and

Akaike weights (wi) calculated using the AICc values.

Response Model

# of

Parameters AICc

-2 Log

Likelihood Δi Δwi

% Time ATR Distance 1 128.08 -61.00 0.00 0.49

Relatedness +

Distance 2 127.99 -59.93

-0.09 0.51

% Time

Walking Distance 1 89.93 -41.92

0.53 0.43

Relatedness +

Distance 2 89.40 -40.63

0.00 0.57

Nose

Taps/Second Distance 1 -2283.96 1145.02

0.00 0.59

Relatedness +

Distance 2 -2283.20 1145.67

0.75 0.41

Table 3.5. Results of Mann-Whitney U Tests comparing the behavior of 17 individual H.

scutatum in response to bedding scented with conspecific chemosensory cues vs. self-scented

bedding or bedding dampened only with Provosoli media respectively. Behaviors measured

included the proportion of time an individual spent in an aggressive, “all trunk raised” posture

(% time ATR), proportion of time an individual was active (% time walking), and the rate of

nose tapping (nose taps/second). Bonferroni corrected p-value required for significance is

p<0.008.

Comparison % Time ATR % Time Walking Nose Taps/Second

Conspecific vs. Self 0.585 0.185 0.773

Conspecific vs. Provosoli 0.593 0.059 0.305

103

APPENDICES

Appendix A. Results of visual encounter surveys for H. scutatum for central Illinois sites I-AN

and I-AS by year. Locations of individual encounters are shown in Figure 1.1. Total = total

number of individuals sampled in both sites.

Year I-AN I-AS Total

2009 33 14 47

2010 27 17 44

2011 34 14 48

2012 17 1 18

Total 111 46 157

104

Appendix B. Overall allelic diversity in terms of frequency of each allele at each locus for eight

microsatellite loci in H. scutatum based on 279 sampled individuals from 14 sites in Illinois and

Kentucky (see Figure 1.1). Sample sizes for each site are included in Table 1.1. Acronyms and

size ranges for each locus are provided in Table 1.1.

105

Appendix C. Expected heterozygosity of H. scutatum at the central sampling Illinois sites (I-AN

and I-AS) listed by year. Data are based on eight microsatellite loci. Sample sizes for each site

by year are provided in Table 1.1.

Year I-AN I-AS

2009 0.65 0.71

2010 0.61 0.61

2011 0.66 0.62

2012 0.62 N/A

106

Appendix D. Pairwise population FST values for eleven sampling sites for H. scutatum in

Kentucky. Data are based on eight microsatellite loci. Acronyms and sample sizes for each site

are provided in Table 1.1. Geographic locations of sites are shown in Figure 1.1. FST values are

below diagonal. P-values are above diagonal. To reduce the chance of Type I error, the

Bonferroni corrected p-value needed for significance is p<0.0009. Significant p-values indicated

in bold.

K-A K-B K-C K-D K-E K-F K-H K-J K-L K-M K-N

K-A 0.5398 0.3503 0.0063 0.8047 0.2361 0.7520 0.1368 0.0011 0.0056 0.6228

K-B 0.0277 0.8007 0.0288 0.9806 0.9162 0.6838 0.8708 0.1666 0.1561 0.4076

K-C 0.0489 0.1179 0.0487 0.8595 0.6998 0.0954 0.8164 0.0759 0.2564 0.3916

K-D 0.0366 0.0764 0.1060 0.0033 0.0097 0.0010 0.0004 0.0000 0.0000 0.0101

K-E 0.0088 0.0118 0.0597 0.0644 0.6500 0.6500 0.5452 0.0036 0.0493 0.6873

K-F 0.0399 0.0265 0.0248 0.1042 0.0483 0.1966 0.9312 0.1756 0.1870 0.5301

K-H 0.0028 0.0314 0.0942 0.0899 0.0229 0.0407 0.1184 0.5958 0.2214 0.2848

K-J 0.0328 0.0190 0.0231 0.0935 0.0335 -0.0086 0.0400 0.1112 0.1563 0.3480

K-L 0.0416 0.0413 0.0982 0.1050 0.0558 0.0405 0.0058 0.0291 0.0852 0.0009

K-M 0.0565 0.0773 0.0943 0.1352 0.0680 0.0540 0.0302 0.0426 0.0287 0.0170

K-N 0.0079 0.0403 0.0448 0.0432 0.0168 0.0219 0.0223 0.0250 0.0491 0.0553

107

Appendix E. Pairwise population p-values for an RxC analysis (multilocus contingency tests for

heterogeneity of allele frequencies among pairs of samples) above diagonal and standard error

values below diagonal for 279 individual H. scutatum from 14 sampling sites (see Figure 1.1).

Data are based on eight microsatellite loci. Acronyms and sample sizes for each site are provided

in Table 1.1. Significant values indicate a pair of populations should be considered distinct from

one another. To reduce the chance of Type I error, the Bonferroni corrected p-value needed for

significant is p<0.0003.

I-AN I-AS I-B K-A K-B K-C K-D K-E K-F K-H K-J K-L K-M K-N

I-AN 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

I-AS 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

I-B 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00

K-A 0.00 0.00 0.00 0.40 0.20 0.00 0.20 0.43 0.98 0.07 0.00 0.03 0.27

K-B 0.00 0.00 0.00 0.01 0.02 0.00 0.50 0.41 0.17 0.24 0.00 0.01 0.05

K-C 0.00 0.00 0.00 0.01 0.00 0.00 0.17 0.53 0.03 0.60 0.00 0.02 0.10

K-D 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

K-E 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.18 0.29 0.04 0.00 0.00 0.03

K-F 0.00 0.00 0.00 0.02 0.02 0.02 0.00 0.01 0.20 0.84 0.00 0.10 0.34

K-H 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.01 0.03 0.50 0.25 0.22

K-J 0.00 0.00 0.00 0.01 0.01 0.02 0.00 0.00 0.01 0.00 0.00 0.05 0.08

K-L 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.01 0.00

K-M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00

K-N 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.00 0.00

108

Appendix F. Straight-line distances (km) between H. scutatum sampling sites in Illinois and

Kentucky (see Figure 1.1).

I-AN I-AS I-B K-A K-B K-C K-D K-E K-F K-H K-J K-L K-M

I-AS 1

I-B 316 316

K-A 447 447 759

K-B 448 447 758 13

K-C 473 472 789 101 114

K-D 411 410 726 143 155 79

K-E 448 448 760 1 12 102 144

K-F 448 448 760 0 13 100 143 1

K-H 448 447 759 0 13 101 143 1 0

K-J 446 446 758 1 13 101 143 2 2 1

K-L 457 456 768 9 15 102 148 8 9 9 10

K-M 454 453 765 8 10 105 150 7 8 8 9 4

K-N 445 445 757 4 10 104 145 4 4 4 3 12 9