SOCIAL BEHAVIOR AND KINSHIP IN THE FOUR-TOED SALAMANDER, HEMIDACTYLIUM SCUTATUM · 2018-12-22 ·...
Transcript of SOCIAL BEHAVIOR AND KINSHIP IN THE FOUR-TOED SALAMANDER, HEMIDACTYLIUM SCUTATUM · 2018-12-22 ·...
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
ii
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-
iii
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
iv
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.
v
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
vi
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.
vii
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.
56
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
57
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
101
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