Bottlenecks and Microhabitat Preference in Invasive Wall Lizard, Podarcis Muralis

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Bottlenecks and Microhabitat Preference in Invasive Wall Lizard, Podarcis muralis

A thesis submitted to the

Graduate School of the University of Cincinnati

In partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

In the Department of Biological Sciences of the College of Arts and Sciences

2013

Cassandra M. Homan

B.A., Capital University, 2010

Committee:

Dr. Kenneth Petren, Chair

Dr. Bruce Jayne

Dr. Stephen Matter

Abstract

We investigated two critical aspects of an animal invasion: the possibility of a genetic bottleneck upon introduction, and microhabitat choice during the spread into a novel environment. Many invasive species seem likely to undergo a genetic bottleneck upon introduction since only a few individuals are needed to found a population. However, bottlenecks are uncommon in invasive species, suggesting that there may be an important cost to bottlenecks. We studied an invasive population of common wall lizard (Podarcis muralis) in

Cincinnati, Ohio that is thought have been the product of a single introduction of a few individuals in the early 1950’s. Our microsatellite analysis of genetic samples collected directly from the source population and compared them to samples collected from the Cincinnati population confirmed a substantial loss of genetic diversity, indicating a genetic bottleneck.

Simulations suggest that the bottleneck was likely on the scale of three individuals. The loss of allelic diversity was so large that we were unable to confirm the Italian source of the Cincinnati population. Despite this clear loss of genetic diversity, Podarcis muralis is still thriving in the introduced range.

Previous studies have found that environmental niche modeling does not fully predict the invasion success of introduced P. muralis. We attempted to characterize microhabitat use as a more effective or complementary means to predict the extent of invasion of this species.

Cincinnati P. muralis have been noted to prefer south facing artificial rock walls. We quantified

17 variables in sites with confirmed high densities of lizards and compared those with low or zero density sites nearby to determine the components of microhabitat most contribute to the proliferation of the species. Our results indicate that the most important factor for high density is not rock substrate, but substrate crack density, which is likely a source of refuge for the species.

Other factors such as total amount of suitable substrate and overall distribution of cover types were less important. Our results suggest that this lizard is not necessarily limited to urban areas.

This ability to use a broad range of microhabitats, combined with their ability to thrive at even very low levels of genetic diversity suggests Podarcis muralis is a very robust invader, whose full impact on native species may not yet be apparent.

III

Acknowledgements

This work would not have been possible without the input and guidance of my committee members, Ken Petren, Bruce Jayne and Steve Matter. Their comments and suggestions, as well as those from my lab members, Kim Wyatt and Lucinda Lawson, have undoubtedly challenged me and helped me grow as a scientist. I would also like to thank my family and friends, who have supported me through every step of this journey.

Table of Contents

List of tables VII

List of figures IX

Overview 12

Chapter 1: The paradox survives: pronounced bottleneck in invasive lizard

Introduction 16

Materials and Methods 18

Results 20

Discussion 22

Figures 25

Chapter 2: Microhabitat preference of the common wall lizard

Introduction 31

Materials and Methods 34

Results 36

Discussion 38

Figures 42

References 52

List of tables

Chapter 1

Table1.1 Summary of genetic analysis of the Cincinnati and Italian populations. Sample

sizes are the number of total individuals successfully genotyped at each location.

Total alleles are the number of alleles found in each population, while private

alleles are the number of alleles exclusive to each population. The average

number of alleles refers to the average across all 8 markers.

Table 1.2 Effective alleles t-test. The effective alleles in the Cincinnati population

(2.40922) are significantly fewer (p=0.0078) than those in the Italian population

(7.73613).

Table 1.3 Effective heterozygosity t-test. The effective heterozygosity in the Cincinnati

population (0.57106) is significantly less (p=0.0078) than the effective

heterozygosity in the Italian population (0.89934).

Table 1.4 Life expectancy simulation. BottleSim simulated the same analysis

(exponential population growth, starting with 10 individuals, and sexual

maturation of 1 year, generational overlap of 20, dioecy with random mating for

100 iterations) changing only the years of life expectancy (2, 5, & 7 years).

Expected heterozygosity varied by 0.0061.

Table 1.5 Differences in He based on sex. In analyses with an odd number of

individuals, one sex had to be more plentiful than the other. We ran simulations

favoring both males and females, and in each situation, the effective

heterozygosity was higher with more females (by 0.0144 in a simulation of 5

VII

starting individuals at a constant population size for 10 years; by 0.0393 in a

simulation of 3 individuals reproducing exponentially immediately).

Chapter 2

Table 2.1 Sites in Cincinnati measured for microhabitat and lizard density. The number

of sub-sites that were accessible is also indicated.

Table 2.2 Spreadsheet of microhabitat and general site information used for data

collection.

Table 2.3 Substrate type and corresponding number of sub-sites for each type.

Table 2.4 Correlation matrix of all continuous microhabitat variables.

Table 2.5 Summary of the most significant elements for each individual regression. Red

values indicate a negative impact on lizard density, while blue values indicate a

positive correlation to lizard density.

VIII

List of Figures

Chapter 1

Figure 1.1 Map of introduction path. Lizards were presumably collected just south of

Lago di Guardia in the Northern part of Italy and released in the Torrence Court

area of Cincinnati. Inset to the left shows a close-up of the greater Cincinnati

area, with black crosses marking the sites of lizard collection and microhabitat

analyses. The red dot is the point of introduction, also used in microhabitat

analyses.

Figure 1.2 Effective heterozygosity simulation in BottleSim, with the average effective

heterozygosity across the approximately 60 years since the species has been

introduced. Each colored line represents a different simulation scenario, which is

indicated by the label following the line, where the first number indicates the

number of starting individuals, and the number after the underscore indicates the

number of years the population stayed at a constant size. Exp following the

underscore indicates the population grew exponentially immediately upon release.

The dotted lines indicate the actual He of the Cincinnati (0.57106) and Italian

(0.89934) populations.

Figure 1.3 Effective alleles simulation in BottleSim, with the average number of effective

alleles across the approximately 60 years since the species has been introduced.

Each colored line represents a different simulation scenario, which is indicated by

the label following the line, where the first number indicates the number of

starting individuals, and the number after the underscore indicates the number of

years the population stayed at a constant size. Exp following the underscore

indicates the population grew exponentially immediately upon release. The

dotted lines indicate the actual Ea of the Cincinnati (2.40922) and Italian

(7.73613) populations.

Chapter 2

Figure 2.1 ANOVA of substrate type, with categories of rock wall, rocks, sidewalk and

logs. One way ANOVA yielded an r-square of 0.16 and p-value of 0.015

(F=3.7808, df=3). Substrate type is highly correlated to substrate crack density

and therefore, any effects of type on density are more likely due to crack density

effects.

Figure 2.2 ANOVA of slope direction, yielding an r-square of 0.02 and p-value of 0.95

(F=.2579, df=6).

Figure 2.3 Principle components analysis of continuous microhabitat variables. The red

dots are sites with low density (<0.01 lizards/meter), while the blue dots indicate

sites with higher density. The 95% density ellipses overlap significantly,

indicating that sites with high and low density are very similar. The first principle

component encompasses 17.7% of the variation and was most heavily influenced

by the distance from the road. Principle component two accounted for 13.1% of

the variation in the data and was most heavily influenced by percent grass and

overall percentage vegetation, both of which influenced density negatively.

Figure 2.4 Discriminant function analysis of continuous microhabitat variables. The red

dots are sites with low density (<0.01 lizards/meter), while the blue dots indicate

sites with higher density. The ellipses indicate the 50% confidence intervals for

high (blue) and low (red) density. Canonical one differs (p=0.5987) the two

X densities, with substrate crack density loading most heavily (-0.85) followed by substrate length (0.43), substrate width (0.42), and substrate height (0.40).

Overview

Invasive species are increasingly present in our highly-connected world, and have the potential to cause harm to human health, natural ecosystems and their services, and the economy

(Perrings et al. 2005, Pimental et al. 2005). Deserving or not, non-native species often acquire a negative reputation among people in the scientific community and beyond. Invasive species, defined here as a species that establishes its population beyond its native range and spreads beyond the initial point of introduction, pose a distinct threat to biodiversity and ecosystem services and health, outpaced only by the effects of habitat destruction (Wilcove et al. 1998).

Examples of these threats include the outcompeting of native tadpoles by the invasive cane toad in Australia (Crossland et al. 2008), kudzu overtaking much of the area in the Southeastern

United States (Grebner et al. 2011), and the threat of yellow fever as spread by an invasive mosquito (Lounibos 2002). In an effort to mitigate these effects, the U.S. government spends an estimated $120 billion per year to suppress the spread or mediate the effects of these invaders

(Pimental et al. 2005). Not all introduced species, however, are necessarily harmful to resident species or ecosystem services (Valery et al. 2008, Blackburn et al. 2011). Research exploring the classification of non-native species as either harmful or harmless is limited and often takes place only after we begin to see detrimental effects on our ecosystem. Despite this wait-and-see approach, research suggests that it would be much more cost-effective to invest in the prevention of the spread of harmful species rather than attempt to control invasive species once they become a problem (Finnoff et al. 2006).

Computer modeling is one tool that can be used to predict the spread of an invasive species, primarily utilizing geographic information systems to record invasive habitat and predict course of spread. Additionally, there are several other parameters used to estimate the potential impacts of introduced species, from economic and labor costs to control the spread of the 12 invader, to comparing biological diversity before and after an introduction, or quantifying the fitness of a native species that is being impacted by the introduced species. However, even with multiple techniques for assessing the potential problems imposed by invasive species, there is no single comprehensive method for predicting a species’ possibility for invasiveness or whether that species will have a significant negative impact on natural ecosystems, community health, or the economy.

Currently, our ability to accurately predict the spatial extent (Fitzpatrick et al. 2007,

Schulte et al. 2011) and severity (Anderson et al. 2004) of impact that an introduced species may have on a community is limited by the complexity of the problem and the focus of current work on the macro- and on species that directly impact agriculture and livestock, such as kudzu

(Grebner et al. 2011), mealybugs (Miller et al. 2002) and fire ants (Vinson 1992). This approach leaves not only many ecosystems understudied, but leads to a narrow estimate of the invasive capabilities of these species.

The study of invasive species has a heavy focus on predicting spread via some form of niche modeling, which takes a broad-scale approach, utilizing climate and range information.

This approach relies on many assumptions, chiefly that the niche of the parent population will be identical to the niche of the introduced population, which can result in inaccurate predictions of the eventual scope and extent of invasion (Fitzpatrick et al. 2007, Schulte et al. 2011). In addition, this method is based on the environmental niche, and not the full realized niche, which is harder to quantify because it includes microhabitat use, interspecies competition, potential food and shelter, and other non-climatic variables (Case & Gilpin 1974, Hurlbert 1978).

Podarcis muralis, the common wall lizard, is a small, insectivorous lacertid broadly distributed throughout Europe (Strijbosch et al. 1980, Barbault & Mou 1988, Harris et al. 2002),

13 where it has exceeded its native range (Schulte et al. 2012). It lives in walls, forests, and soil dwellings in their native range (Martin-Vellejo et al. 1995) and can be found basking or walking along walls with crevices to use as refuge (Amo et al. 2003). The lizard has a variety of known predators in their native range (Martin & Lopez 1990). Their dorsal scales range from brown to green, frequently with blue spots along the lateral portion of the abdomen. The ventral portion of the lizard can be white, yellow or red and varies in the level of mottling (Calsbeek et al. 2010).

The species is diurnal and oviparous, breeding April-June with hatchlings emerging July-

September (Barbault & Mou 1988).

The wall lizard was introduced to Cincinnati, Ohio from Italy around the year 1952

(Deichsel & Gist 2001). After vacationing in Northern Italy, George Rau brought back approximately 10 lizards, which were later released just outside of his Cincinnati home. Given the very similar climactic conditions of Cincinnati to the northern part of Italy, the lizards survived and began to spread throughout Cincinnati (Deichsel & Gist 2001), and in some cases, even beyond, reaching into parts of Kentucky (Draud & Ferner 1994) and Indiana (Etenohan, pers. comm.). With the absence of most native lizards within the city limits, P. muralis spread rapidly and can now be found throughout most of the city (Deichsel & Gist 2001) and reaching population densities as high as 1250 individuals/ha (Brown et al. 1995).

This study attempts to utilize a multi-dimensional niche-based approach to complement broad-scale environmental methods in order to allow for greater accuracy in predicting the extent and pattern of invasion. A multidimensional niche-based approach has been used in relatively few scenarios involving invasive species as a predictive tool (Moles et al. 2012). The system chosen for study is the invasion of the common wall lizard, Podarcis muralis, in the Cincinnati,

Ohio USA region, which is a tractable model system for study. Two aspects of this invasion are

14 considered in order to provide a more complete understanding of the invasive niche of P. muralis and its success as an invader. Chapter 1 investigates the potential bottleneck effect and loss of genetic variation upon introduction compared to the source population, and Chapter 2 investigates the characteristics of microhabitats that are used during range expansion in the novel environment. The goal of this fine-scale analysis is to better understand the spatial dynamics of invasion, to increase the accuracy of predictions about the extent of invasion and therefore, to better assess the impact of this invasion on native communities.

Chapter 1: The paradox survives: pronounced bottleneck in invasive lizard

Introduction

Genetic analyses are an invaluable tool for invasion biology because they can be used to trace lineages of introduction and speciation or elucidate genetic diversity within a population

(Williams et al. 2005, Ugelvig et al. 2008, Munro et al. 2011). Recent studies have used microsatellite analysis to better understand the location and timeline of species introductions

(Okada et al. 2008, Rollins et al. 2009, Guillemaud et al. 2010, Dybdahl & Drown 2011). This genetic data can reveal not only where invasive species came from, dispersal routes, and current ranges, but also can be used to predict where a species may disperse to next, as in the transportation of the round goby, whose future dispersion can be predicted by looking at shipping routes and presence of goby microsatellites in the ballast water of ships (LaRue et al.

2011). Microsatellite data are also useful in determining the best management strategy for invasive species by providing information about what parts of the population are leading to range expansion (Rollins et al. 2009).

Because invasive populations often go undetected for extended periods of time, we have very few opportunities to identify the source of the population that was introduced and evaluate how the genetic profile of the species may have changed in response to the new environment.

Finding the source population may allow for greater understanding of how invasions work – for example, if certain genetic profiles are more apt to disperse and become successful invaders

(Estoup et al. 2004). Genetic analysis can be used to find the source of an invasive population, but without prior knowledge of the invasion path and a large native range, this can be a challenging task.

One might reason that a reduction of genetic variation is common for introduced species, given the small number of individuals that can establish a population. A loss of genetic diversity presents a paradox as to why a species with such limited genetic variation can prove successful as an invader. In principle, greater genetic diversity should allow for a population to better withstand changes in the environment, or adapt to a novel environment, while lower genetic variation would put a population at greater risk of extinction (Frankham & Ralls 1998, Bijlsma et al. 2000). This would suggest that populations with low genetic diversity would fare poorly, and that invasive species with low genetic diversity would be rare. Conversely, a severe bottleneck may even be helpful in some species like the Argentine ant, which can form supercolonies due to genetic uniformity (Tsutsui et al. 2000). Genetic bottlenecks have been identified in some introduced species, including insects (e.g. potato tuber moth, Puillandre et al. 2008), weeds

(Amsellum et al. 2000), gastropods (Martel 2004), and some vertebrates (e.g. wallabies, Le Page

2000). However in several cases where a single introduction has been suspected, genetic analysis has revealed a lack of a bottleneck, sometimes with multiple introductions and greatly increased genetic diversity in several cases (Holland 2001, Johnson & Starks 2004, Wattier et al.

2007, Kolbe et al. 2004). This raises the question of whether genetic diversity may be important for invasion success and severely bottlenecked invaders may be at a disadvantage.

An effective way to determine if a species has undergone a bottleneck is to quantify allelic diversity at microsatellite loci. Microsatellites change quickly across an evolutionary scale, but for ecological purposes, they are relatively stable and exceptionally informative

(Tessier & Bernatchez 1999, Zane et al. 2002, Schwartz et al. 2007). If the average number of alleles at a given microsatellite marker are greatly reduced in the introduced population, one may conclude that the population has undergone a genetic bottleneck.

The common wall lizard Podarcis muralis, is widespread in areas from Spain to Italy. It was reportedly introduced to Cincinnati, OH USA, where it is now locally abundant within a 150 km2 region that spans southwest Ohio, southeast Indiana and northern Kentucky. Anecdotal evidence supports a single source population from Lago di Garda, Italy, a specific time (1951), a specific number of lizards (10), and a specific point of introduction. This suggests that the species may well have undergone a genetic bottleneck. The information comes from a letter addressed to D. Gist in 1989 (Deichsel & Gist 2001). Despite some speculation about the specific circumstances (Hedeen and Hedeen 1999, Diechsel & Gist 2001), the letter from George

Rau is viewed as highly reliable.

The goals of this study are (1) to verify whether the anecdotal evidence of a single Italian origin is supported by molecular analyses of the Cincinnati population, and (2) determine whether there is evidence of a bottleneck of the population upon introduction. These two pieces of information shed light on how the population came to be established, allowing us to better understand what allows them to flourish as an invasive species. Previous analysis suggests that the population went through a bottleneck upon introduction (Lescano & Petren 2010); however no genetic data were available from the source population. We predict that genetic analysis of the Italian population will yield much higher genetic diversity than that of the Cincinnati population.

Materials and Methods

Data Collection

Wall lizard specimens for genetic analysis were collected from the hypothesized origin of the Cincinnati population, Lago di Gardia, in Northern Italy (Figure 1.1) in 2011 (n=15). Snips

18 of tails from the lizards were preserved in 70% ethanol and brought to University of Cincinnati for further analysis. Tail snips from the Cincinnati population (n=117) were also collected in

2011-2012 for comparison with the Italian population.

DNA was extracted from tail tips using a guanidine-based method (Petren 1998). DNA was diluted to 1:20 with DNA water, and then amplified using a multiplex polymerase chain reaction for eight highly variable microsatellite fragments, A7, B3, B7, D1, B6, Lv-4-alpha, Lv-

4-72, and Lv-3-19 (Boudjemadi et al 1999, Nembrini & Oppliger 2003) and amplified using standard PCR protocols (Lescano & Petren 2010). All samples were sent to Cornell University’s

DNA services center for fragment analysis.

The results from both the Cincinnati and Italian populations were viewed and genotyped together using GeneMapper 3.7 (Applied Biosystems) and summarized using GenAlEx 6.5

(Peakall & Smouse 2006, 2012). Previously genotyped specimens (n=227) were available from a previous study (Lescano & Petren 2010).

Data Analysis

The Cincinnati and Italian populations were analyzed separately in GenAlEx (Peakall &

Smouse 2006, 2012). The numbers of alleles, effective allele number, expected and observed heterozygosity, and numbers of private alleles were scored. The number of effective alleles and the expected heterozygosity were compared using a matched pairs t-test using JMP. To determine the likelihood that the population is the result of a single introduction, the number of private alleles was compared between the Cincinnati and Italian populations.

A simulation approach was used to determine the extent of a genetic bottleneck using

BottleSim (Kuo & Janzen 2003). The program is simplistic in terms of the kind of natural

19 history characteristics it can include, but we based them on the information we had on the lizard with the intent of maximizing genetic diversity. Allele frequencies from the 15 Italian specimens genotyped were used to generate propagules of various sizes and subjected to bottlenecks of varying duration. Each simulation used a diploid, multilocus population with variable population sizes (depending on which bottleneck was being simulated), with a moderate level of overlap in generations (degree of overlap=20). The assumed reproductive system was dioecy with random mating. The average longevity of the organism was 5 years and the age at sexual maturity was 1 year. Each simulation was performed for 100 iterations.

Different bottleneck scenarios were fed into the program to determine the likely level of suppression the population had to endure in order to produce the current level of genetic variation in the Cincinnati population. Because of the information regarding the introduction of the species, the simulations were started at a maximum number of individuals of 10, decreasing to as few as two individuals. The populations were then put through varying levels of bottleneck

– with some simply reproducing exponentially immediately upon introduction, and others holding at the introductory population size for 10 and 25 years. The life expectancy parameter was varied to determine the effect on simulation outcomes. The effective number of alleles and the average expected heterozygosity was compared to the observed values for the Cincinnati populations to determine the most likely bottleneck scenario.

Results

Genotyping was successful for 344 of the individuals in Cincinnati population and 14 individuals from the Italian population, though two were missing ~half of the data. Of 108 total alleles found in the two populations, 73 alleles were specific to the Italian population and 15

20 were specific to the Cincinnati population, and only 20 alleles were shared. The number of private alleles in the Italian population (73) greatly exceeded that of the Cincinnati population

(15) as one would expect based on a hypothesized bottleneck (Table 1.1). However, because 15 of 35 total alleles found in the Cincinnati population could not be traced to the Italian population, and many of these were common alleles with frequencies greater than 40%, we cannot state definitely that the Cincinnati population came from Lago di Guarda.

The average number of effective alleles in the Cincinnati (Ea=2.409, n=349) and Italian

(Ea=7.736, n=14) populations differed significantly (p<0.0078, Table 1.2). There was also a highly significant difference in the expected heterozygosity (Cincinnati=0.571, Italy=0.899, p<0.0078, Table 1.3). This shows that the level of genetic variation in the introduced populations was drastically reduced and strongly suggests a single introduction that was likely severely bottlenecked upon introduction to Cincinnati.

Our bottleneck analyses appeared robust to assumptions about average life expectancy.

The effective heterozygosity varied by less than 0.006 when life expectancy was varied (2, 5 and

7 years were compared, Table 1.4). Only two bottleneck scenarios approached the reduction in heterozygosity that approached the levels observed in the Cincinnati population (1) a propagule of 5 individuals with no population growth for 10 years and (2) a propagule of 3 individuals (1 female and 2 males), and exponential reproduction immediately upon introduction (Figure 1.2).

Generally, in scenarios where the number of individuals in the starting population is odd, the population with more females than males retains a slightly higher level of heterozygosity, revealing that at very low population sizes, females contribute more to genetic diversity than males (Table 1.5).

BottleSim also modeled the average effective alleles based on the input of population parameters. For effective alleles, the population sizing parameter that was most similar to that of the Cincinnati population (2.409) was the population that started with 4 individuals and retained a population sized of 4 individuals for 10 years, as well as the one that started with 2 individuals and had exponential reproduction (Figure 1.3).

Discussion

Our results confirm that the common wall lizard (P. muralis) underwent a severe genetic bottleneck upon introduction to Cincinnati. Our molecular results and simulations suggest that while 10 individuals may very well have been released in 1951, of those 10, maybe only 3 or 4 contributed to the genetic diversity we see in the population today. This is comparable to other bottlenecks, like the Florida panther, estimated at a low of 2-6.2 individuals (Culver et al. 2008) and the black-footed ferret, with only 7 captive breeding individuals (Wisely et al. 2002).

Although we could not confirm the identity source population, the high genetic diversity of the potential source population studied here and two other native populations reported in the literature (Boujemadi et al. 1999, Nembrini & Opplinger 2003) it is highly unlikely that more than one successful introduction occurred.

The bottleneck experienced by the Cincinnati population appears to have been rather dramatic give the drop from 90% to 58% heterozygosity and the loss of more than half of the alleles (7.7 to 2.4). Other populations of common wall lizards in France and Switzerland showed similarly high levels of expected heterozygosity (0.74-0.94; Boujemadi et al. 1999, Nembrini &

Opplinger 2003). This is unusual, in that many successfully established non-native populations, such as ragweed (Genton et al. 2005), the brown anole (Kolbe et al. 2004) and the paper wasp

(Johnson & Starks 2004), are the result of multiple introductions and an increase, rather than decrease, in the level of genetic diversity.

The bottleneck experienced by the introduced population was particularly strong, it which complicates determining the exact location of origin using genetic data. While we could not confirm the source, we also cannot rule it out. The high levels of variation in Italy suggest that a larger sample size would be needed to more effectively sample the population in the native range. Additional sampling might very well reveal more alleles, including some of those found only in the Cincinnati population. The most common effect of bottlenecks is the loss of rarer alleles (Maruyama & Fuerst 1985, Luikart et al. 1998), and the bottleneck studied here no doubt resulted in the loss of many uncommon alleles. However, due to the severity of the bottleneck, a few rarer alleles could by chance be driven to high frequencies in Cincinnati. This might account for the common private alleles in Cincinnati. Alternatively, the missing alleles might be found in another location in Italy, or they may have evolved some time after introduction. A process known as “allele surfing” can cause a rare mutation to rise dramatically in frequency, purely by chance, in an expanding population (Hallatschek et al. 2007, Excoffier & Ray 2008).

Additional sampling in the native European range of P. muralis and perhaps other kinds of markers (e.g. mtDNA) will be needed to determine the source of the Cincinnati introduction

(Downie 2002, Havill et al. 2006, Corin et al. 2007, Puillandre et al. 2008). Though determining exactly where the lizards came from would be helpful, it may be nearly impossible to compare such drastically reduced genetic information to Lago Guardia, or anywhere else for that matter.

A major conclusion of our study is that some species may become successful invaders despite having gone through a severe genetic bottleneck and having very low levels genetic

23 variation. This may be attributable to several processes. Enemies and competitors may be reduced in introduced ranges, as implied by the phrase “enemy release”, and for which there is some evidence in P. muralis (Burke et al. 2007). Alternatively, the genetic variation measured by neutral markers may not be closely related to the genetic variation needed to respond to natural selection (McKay & Latta 2002, Akey et al. 2004, Zaldivar et al. 2004). Regardless, the notion that even a few founders can cause a widespread invasion is a reminder to maintain measures to prevent even a small propagule from gaining a foothold after either intentional or unintentional relocation. Species movement needs to be highly monitored, and, especially in ecosystems that are sensitive to disruptions in environment, such as islands, extra precautions may be necessary to prevent spread (e.g. chitrid).

Conversely, if a species like the wall lizard can thrive on such low genetic diversity after a bottleneck, this presents some level of hope for endangered species that have suffered from genetic bottlenecks as well. Further research should be done to discern exactly what makes a species successful, despite low genetic variation, and this information could be used to better understand a path to preventing the extinction of species with low genetic diversity.

Table1.1 Summary of genetic analysis of the Cincinnati and Italian populations. Sample sizes are the number of total individuals successfully genotyped at each location. Total alleles are the number of alleles found in each population, while private alleles are the number of alleles exclusive to each population. The average number of alleles refers to the average across all 8 markers.

Italy Cincinnati sample sizes 15 349 total alleles 73 15 private alleles 73 15 average alleles 11.625 4.375 effective alleles 7.736 2.409 unbiased expected heterozygosity 0.899 0.571

Table 1.2 Effective alleles t-test.

Wilcoxon signed rank t-test Ea Cincinnati 2.40922 Ea Italy 7.73613 Mean difference -5.3269 Std error 0.60094 N 8 Test Statistic S -18 Prob>lSl 0.0078 Prob>S 0.9961 Prob

~~Table 1.3 Effective heterozygosity t-test.~~

~~Wilcoxon signed rank t-test He Cincinnati 0.57106 He Italy 0.89934 Mean difference -0.3283 Std error 0.02432 N 8 Test Statistic S -18 Prob>lSl 0.0078 Prob>S 0.9961 Prob~~

Table 1.4 Life expectancy simulation. BottleSim simulated the same analysis (exponential population growth, starting with 10 individuals, and sexual maturation of 1 year, generational overlap of 20, dioecy with random mating for 100 iterations) changing only the years of life expectancy (2, 5, & 7 years). Expected heterozygosity varied by .0061.

~~Life expectancy He 2 0.7235 5 0.7238 7 0.7296~~

Table 1.5 Differences in He based on sex. In analyses with an odd number of individuals, one sex had to be more plentiful than the other. We ran simulations favoring both males and females, and in each situation, the effective heterozygosity was higher with more females (by

~~.0144 in a simulation of 5 starting individuals at a constant population size for 10 years; by .0393 in a simulation of 3 individuals reproducing exponentially immediately).~~

~~5_10 3_exp More females 0.579 0.619 More males 0.5646 0.5797~~

~~Figure 1.1~~

~~Figure 1.2~~

~~Figure 1.3~~

~~Chapter 2: Microhabitat preference of the common wall lizard~~

~~Introduction~~

Particularly in a world connected by several modes of transportation, it is important to determine which species pose a risk of establishing in non-native areas. Organisms are constantly being brought across borders and out of native range, stuck to the bottom of a boot or hiding in a shipment of ornamental plants; some organisms are even intentionally introduced, without the foresight of realizing the species may establish and broaden their range and impact

(Pimental et al. 2005, Xu et al. 2005, Dextrase & Mandrak 2006, Martin et al. 2009). While some of these species cause relatively minimal changes to the local ecosystem, others are more destructive, and the total cost to control these invasive species has exceeded $100 billion in the

United States annually (Pimental et al. 2005). While not all species will readily establish outside their native habitat, it is a major goal of invasion biology to determine if a species has kind of niche plasticity necessary for invasion in order to control the spread of invasive species (Sexton et al. 2002, Richards et al. 2006, Broennimann et al. 2007, Jeschke & Strayer 2008).

~~Along with determining the factors that govern initial establishment success, predicting the extent of invasion is a primary goal of invasion biology (Hill et al. 1998, Buckley et al. 2003,~~

~~Gallien et al. 2010). Range expansion may be affected by habitat fragmentation, the vagility and dispersal ability of the species, or even the amount of contact with humans (Paris 1965, Gates &~~

~~Gysel 1978, Andren 1994, Barros et al. 2009). An association with human activity, and a greater tolerance to human disturbance, may increase a species’ ability to establish in a new region~~

~~(Gherghel et al. 2009), and also provide additional opportunities to expand their distribution via intentional human dispersal or inadvertent hitch-hiking (Strugariu et al. 2008, Oliveiri 2009,~~

~~Wichmann et al. 2009, Daumer 2012).~~

Another factor that is important factor in determining the dynamics of invasive species is habitat suitability. Habitat use affects patterns at the level of the community, population and individual (Hoss et al. 2010). Two spatial tools are employed in studying large-scale habitat effects on species: (1) GIS, which is used to model and characterize the current distribution of a species through comparisons of species distributions and landscape features such as rivers and land-use maps (Liu et al. 1995, Dettmers & Bart 1999) and (2) ecological niche modeling, which incorporates aspects of the environmental niche such as precipitation, temperature and vegetation in order to predict areas of current and potential occupancy (Peterson & Vieglais 2001, Peterson

2003, Raxworthy 2007, Kearney & Porter 2009). These tools have proven useful, however, they only consider coarse-grained aspects of a species niche, and do not provide a full picture of the ecology and biology of an invasive species. Behavior, in particular, can be an important component of a successful invader (Holway & Suarez 1999, Labrie et al. 2006, Weis 2010,

~~Chapple et al. 2012).~~

A more complete picture of the habitat use, and microhabitat use of an invasive species would clearly add to the picture provided by the coarse-grained approaches. Key features such as slope, density of living or breeding spaces, competitor distributions, etc., may further extend our ability to determine the course and extent of spread of an invasive species (Menke 2003,

~~Amo et al. 2007, Gracceva et al. 2008). In some instances, microhabitat could be as important~~

~~(and maybe more important) than coarse environmental variables in determining a species success (Menke 2003).~~

~~Despite the potential that microhabitat has for creating a better model for predicting species establishment and spread, quantifying microhabitat is a complicated process (Beyer et al.~~

~~2010). Complications with methods range from sampling efficacy and design, to bias and~~

32 correlated data (Fieberg et al. 2010, Frair et al. 2010) as well as the difficulty in collecting this fine-scale data (Brouat 2003, Baraloto & Coulteron 2010). Studies of microhabitat currently focus largely on stream ecosystems (Grossman & Ratajczak 1998, Urabe & Nakano 1999,

Crowder & Diplas 2006) and soil communities (Ettema & Wardle 2002). Attempts at quantifying microhabitat in larger, more terrestrial species often focus on only part of picture, detailing the kind of cover or the type of substrate that the organism chooses without giving a full picture of the microhabitat. However, given more inclusive parameters, including type, size and details about refuge space, slope direction, type and relative abundance of cover, etc., microhabitat could be a good parameter for modeling organisms’ distribution and abundance

~~(Boyce & McDonald 1999, Johnson & Seip 2008), how a range will shift during an introduction~~

~~(Aldridge et al. 2008), and whether a species will initially establish itself during an introduction~~

~~(Boyce et al. 2003, Aldridge & Boyce 2007).~~

~~Podarcis muralis, the common wall lizard, has been introduced repeatedly throughout~~

Germany and North America. Their preferred microhabitat in the introduced range is unmortared limestone walls on south-facing slopes (Hedeen 1984, Brown et al. 1995). Podarcis muralis may be opportunistic in habitat selection (Ioannidis & Bousbouras 1997). The goal of this study was to attempt to quantify several habitat variables in places inhabited by invasive P. muralis in

Cincinnati, Ohio, USA to identify the key variables required by this species. It is important to understand whether this lizard is indeed limited to limestone walls, or whether general habitat features are provided by other types of structures, which would lead to a greater extent of invasion outside the urban realm. We ask two main questions: (1) is lizard abundance associated with south-facing slopes and (2) is there a correlation between rock walls and lizard abundance.

~~Materials and Methods~~

~~Data Collection~~

~~Data on microhabitat and density of P. muralis lizards in a given area were collected for~~

~~15 central Cincinnati sites, chosen based on known high lizard density (Lescano & Petren 2010).~~

Sites were chosen based on previous history as opposed to simple random sampling within their range to ensure that the lizards had adequate time to arrive at each location, given that much of their distribution can be attributed to human-aided dispersal (Lescano & Petren 2010). Varying numbers of sub-sites (n=1-6) at each central site, dependent on accessibility, for each location were also chosen, bringing the total number of sites from which data were collected to 60 (Table

~~2.1).~~

~~Eighteen variables of microhabitat were measured at each of the sub-sites based on microhabitat details measure in previous experiments (Vitt 1983, Ioannidis 1997, Diego-Rasilla~~

2003). These variables fell under the categories of substrate details, amount and type of cover, and area features (Table 2.2). Each variable and site was measured by the same individual to assure that there would be no variation within experimenters’ results.

“Substrate” for each population was defined as the primary feature on which lizards were visibly active (Table 2.3). The substrate details category was comprised of substrate type, its level of crack density, and substrate size. The substrate was categorized as landscape rock wall, forest, rocks, and sidewalk. Level of crack density, the number of visible cracks of the appropriate size for the lizards to escape into, was recorded on a scale of 1-3, with 1 having little or no visible cracks and 3 having abundant cracks. Substrate size was estimated in meters, measuring length as well as height and width.

~~Cover relative abundance was estimated as a percentage of the area within 5 meters of the substrate. Categories of cover included grass, groundcover, pavement, shrubs/trees, mulch and bare ground.~~

Slope of the ground for the ~10 m surrounding the main substrate was estimated with visual approximation by comparing the highest and lowest points of the area and getting an average slope. Additionally, the distance from the walkway and roadway were recorded, as well as the level of traffic from both cars and people, with 1 representing little to no traffic and 3 representing a high and consistent level of traffic. Level of sun exposure was measured in regards to the south-facing part of the substrate. This was measured for the southeast, south and southwest portion of the site, at intervals of 30, 60 and 90 degrees above the horizon. These were recorded on a scale of 0 to 2, with 0 having complete shade, 1 having partial sun exposure, and 2 having full sun exposure. The values for each quadrat are then added together to give an overall exposure score for the site, with a maximum possible score of 18.

In addition to the various components of microhabitat, the density of lizards was also estimated at each site. In order to control for the various sizes of substrate, lizards were counted in a 1 meter wide strip along the length of the substrate and then averaged per meter. Lizards were counted while walking at a slow pace (12.5 meters/minute) along the perimeter, and they were counted on at least 3 separate occasions to establish an average for the site and to control for varying weather effects. Sites were subsequently separated into presence and absence sites based on the lizards’ existence at each site. Because this presence/absence data may be thrown off by a lizard simply wandering through a location, we also separated sites into high and low

~~(less than 0.1 lizards/meter) density locations.~~

~~Data Analysis~~

Density data were square root transformed using Excel to provide normality for the distribution. Two one-way ANOVAs were run using the substrate type and slope direction as the independent factor and density as the dependent variable in JMP. A principle components analysis and discriminant function analysis were run using the high versus low density sites.

Multiple regressions were performed using JMP to determine which of the microhabitat variables accounted for the largest amount of variation in density of the lizards, using all continuous microhabitat data (all variables but slope direction and substrate type). A linear regression was run using density per meter as a dependent variable. After each regression, the least significant factor was removed and then the regression was repeated until only the significant factors remained. Similar methods were used in logistic regressions with the lizard presence as the dependent factor and another with lizard abundance (high/low) as the dependent factor.

~~Results~~

Lizard density ranged from zero to 0.65 lizards per meter across 60 sites, with an average density of 0.16 lizards per meter. Within those 60 sites, 13 had an apparent complete absence of lizards, 47 had some lizards present, and 35 were classified as high density sites (greater than or equal to 0.1 lizards/meter) and 25 had low density (<0.1 lizards/meter). The lizards were counted across a range of 5 to 40 meters, depending on the availability of substrate at the site, with a mean length of 15.1 meters.

~~To address the hypothesis that rock walls were important to the density of the lizards, we ran a one way ANOVA of substrate type, which yielded an r-square of 0.16 and a p-value of .015~~

~~(Figure 2.1). Rock walls were not significantly different in lizard density then rocks (p=0.9721) or logs (p=0.3747), but were significantly different then sidewalk (p=0.0139). However, the~~

36 hypothesis of rock walls being correlated with high lizard density was not supported because several types of substrate had high density of lizards, while 15 sites with either rock piles or rock walls as substrate conversely yielded low density of lizards.

~~An ANOVA of slope direction by density yielded an r-square of 0.02 and a p-value of~~

0.95 suggesting slope direction was not important (Figure 2.2). A correlation matrix revealed that substrate height and width are correlated at 0.87, while percent vegetation and percent pavement are negatively correlated at 0.68 (Table 2.4).

Principal components analysis showed high overlap between the low density and high density sites (Figure 2.3). The first principle component encompassed 17.7% of the variation directionality, and was most heavily influenced by the distance from the road. Principle component two accounted for 13.1% of the variation in the data and was most heavily influenced by percent grass and overall percentage vegetation, both of which influenced density negatively.

A discriminant function analysis further confirmed a high degree of overlap between both high and low density sites, with only canonical one separating the two site types to any degree, and not significantly so (p=0.5987). 2 outliers were excluded from analysis. Misclassified sites accounted for 28 percent of the total sites, 7 low density sites and 9 high density. Substrate crack density loaded most heavily and positively affects lizard density, followed by components of substrate size (Figure 2.4).

We used regression analysis to further refine the factors contributing to the extreme differences in density. Each regression started with the 17 nominal variables of microhabitat, and one variable was removed each step. A nominal logistic regression of lizard presence indicated that cover was the most important factor, with percent pavement, percent grass, percent trees/bushes, percent groundcover and percent mulch coming up as significant (Table 2.5). An

37 ordinal logistic regression of high versus low density of lizards indicated that percent pavement, percent mulch, percent overall vegetation, substrate width and substrate crack density were significant factors in determining lizard density. Linear regression analysis based on the continuous variable lizards per meter indicated that the components responsible for determining lizard density were the substrate height and substrate crack density (Table 2.5).

~~Discussion~~

The ANOVAs of substrate type and slope direction indicate that neither of these have a big influence on the density of the lizards, giving little support for our initial hypothesis that lizards will be at higher density where there is rock wall on a south-facing exposure. However, despite the fact that there appears to be no correlation with density and substrate type or slope direction, there is still a large amount of variation in lizard density.

The principle components analysis showed extreme overlap in sites that were labeled as high and low density, indicating that sites can be very similar and still have very different lizard densities. This suggests that the majority of variation among our variables had little predictive value for density, and instead, a small, key, fraction of variables might best predict lizard density.

The DFA attempted to discern this set of variables, but the fact that a significant proportion of the sited were misclassified suggests that complex interactions are occurring, or unmeasured habitat features are involved. Nevertheless, substrate crack density emerged as the most important factor in determining density.

Crack density and substrate size are broadly supported by the regression analyses to be significant factors in determining lizard density. Overall, the higher number of crevices in a substrate seems to correlate directly with lizard density, placing the importance on contrasts in

38 hardness rather than the exact kind of substrate surface. Lizards don’t seem to have a preference for the type of substrate, so long as it is suitable for basic needs that might include escaping from predators, escaping from temperature extremes and locating good temperatures, and a matrix in which to dig chambers for eggs. Size of substrate appears to be negatively associated with lizard density; this was unexpected, and may be a byproduct of reduced crack density at larger sizes, reduced accessibility of the researcher to lizard location, or complexities of sampling a 3- dimensional space. In a logistic regression of presence versus absence of lizards, as well as a logistic regression of high versus low density, multiple different components of cover were significant. Pavement was positively correlated with density; pavement offers flat foraging areas that can increase the forging efficiency of lizards that make foraging strikes to capture visible prey (Petren and Case 1998). Thus pavement may enhance foraging and grass may retard it, as long as there are insects in the local area to be captured.

The other factors, percent mulch and vegetation are all negatively correlated with lizard density according to high versus low density regression. Because the percent vegetation and percent pavement were negatively correlated, we ran the model once without percent vegetation and once without percent pavement to see if they were confounding each other. With either variable removed, all other variables ceased to be significant factors except for substrate crack density. This demonstrates an extremely complex interaction between the other variables; together, they have a significant effect on lizard density, but separately, they explain nothing.

The logistic regression of presence versus absence of lizards also indicated that other components of cover are important for lizards, with vegetative groundcover positively associated with lizard presence and grass and mulch negatively associated with lizard presence. The fifth cover component, percent tree/bushes showed a pattern where intermediate values had highest

39 densities. Too few bushes may provide little cover and insects, while too many bushes may negatively affect sun exposure and thermoregulation. However, because these factors are only supported by the presence/absence regression, they may be less important for lizard density.

~~Additionally, these correlations may just be by-products of low sample levels, with only 13 sites that were labeled as absent of lizards.~~

One limitation of this study is that the lizards may still be limited by in their ability to disperse to different locations. In some situations, sites with no lizards occurred across a very busy roadway, which may have prevented the lizards from dispersing to the secondary location.

We attempted to account for this by mapping distance to nearest road and the level of traffic, but the nearest road was not always the busiest or the one that may be limiting dispersal. Future experiments, perhaps performing introductions to one side of a busy road and monitoring if the lizards disperse the opposite side, to address this problem could be very informative.

~~Our results suggest that substrate crack density is the most important factor in determining lizard presence and, ultimately, the dispersion patterns and range of P. muralis.~~

This implies that the range of suitable microhabitat is fairly broad, and will not exclude this lizard from forest or other rural habitats as long as suitable crevices or digging sites are available for refuge. This presents a unique challenge for predicting the overall range of this lizard, as substrate crack density can be hard to quantify at a large scale, un-like other predictive variable, such as precipitation, that can easily be quantified via available maps and data.

Despite these challenges, it is important to note that the high level of significance in some of the microhabitat variables, particularly crack density, indicate that microhabitat may be a useful addition to a general habitat analysis. GIS and macrohabitat analyses and predictions would still be the most efficient way to estimate a species’ dispersion, but microhabitat analysis

40 can aid in this and even potentially be a useful tool for preventing species spread (if a lizard is very dependent on having a high level of crack density in some substrate for refuge purposes, remove or patch heavily cracked walls, piles of refuge, etc.). Microhabitat could be a key determinant in responding to invasive species (Menke 2003, Gracceva et al. 2008).

Additionally, because the hypothesis of high lizard density at rock-walls on south-facing slopes based on anecdotal evidence lacked support from this study, it points out the need to scientifically test data rather than rely on casual observation.

Aside from an importance on crack density of the substrate, P. muralis seems to be relatively unconstrained by microhabitat parameters. This indicates that the species is more of a generalist in habitat choice (Ioannidis & Bousbouras 1997), a trait which has been suspected to be a key component of invasion success.

~~Table 2.1 Sites in Cincinnati measured for microhabitat and lizard density. The number of sub- sites that were accessible is also indicated.~~

~~Site Sub-sites Fairview 4 Ault 6 Alms 3 Eden 6 Lunken 1 Beechmont 3 Monfort 2 Delhi 4 Fairmont 5 Torrence 5 Norwood 3 Milford 5 Devou 4 Park Hills 3 Ft. Thomas 6~~

~~Table 2.2 Spreadsheet of microhabitat and general site information used for data collection.~~

Site: Date: Substrate Time: Type: Lizards Porosity: Adults: Size: Juveniles: Slope Angle: Exposure SE S SW Direction: 30 Cover 60 pavement: 90 grass: tree/bush: groundcover: mulch: bareground: Disturbance Distance to road: Road traffic: Distance to walkway: Walkway traffic: Additional Notes:

~~Table 2.3 Substrate type and corresponding number of sub-sites for each type.~~

~~Substrate type Number of sub-sites landscape rock wall 35 rocks 11 sidewalk 9 woods 5~~

~~Table 2.4 Correlation matrix of all continuous microhabitat variables.~~

~~1.0000~~

~~0.0601~~

~~0.1966~~

~~0.0184~~

~~0.0313~~

~~0.1608~~

~~0.1731~~

~~0.1249~~

~~0.1802~~

~~0.1775~~

~~-0.1462~~

~~-0.1517~~

~~-0.1360~~

~~-0.3268~~

~~-0.0560 -0.0756 -0.0539~~

~~Exposurescore~~

~~0.0601~~

~~1.0000~~

~~0.2018~~

~~0.1660~~

~~0.1752~~

~~0.4043~~

~~0.2187~~

~~0.3122~~

~~0.1285~~

~~0.1614~~

~~-0.2948~~

~~-0.0498~~

~~-0.0108~~

~~-0.0835~~

~~-0.2724~~

~~-0.0712~~

~~-0.3015~~

~~Wkwy trafficWkwy~~

~~0.1966~~

~~0.2018~~

~~1.0000~~

~~0.5847~~

~~0.0782~~

~~0.0435~~

~~0.2518~~

~~0.0197~~

~~0.2889~~

~~0.3124~~

~~0.0495~~

~~-0.0396~~

~~-0.1260~~

~~-0.0128~~

~~-0.0939~~

~~-0.0229~~

~~-0.0061~~

~~Distance from walkwayfromDistance~~

~~1.0000~~

~~0.0102~~

~~0.0570~~

~~0.1809~~

~~0.0375~~

~~0.1374~~

~~0.1342~~

~~0.2894~~

~~-0.1462~~

~~-0.2948~~

~~-0.0396~~

~~-0.1033~~

~~-0.1061~~

~~-0.1086~~

~~-0.0002~~

~~-0.2098~~

~~-0.0098~~

~~Rd traffic Rd~~

~~0.0184~~

~~0.1660~~

~~0.5847~~

~~1.0000~~

~~0.4539~~

~~0.0923~~

~~0.2852~~

~~0.3047~~

~~0.0941~~

~~0.2283~~

~~0.2774~~

~~-0.1033~~

~~-0.0233~~

~~-0.0451~~

~~-0.4932~~

~~-0.2077~~

~~-0.0139~~

~~Distance from Rd from Distance~~

~~0.0313~~

~~0.0782~~

~~0.4539~~

~~1.0000~~

~~0.1383~~

~~0.2942~~

~~0.5073~~

~~0.1096~~

~~0.1018~~

~~0.1108~~

~~-0.0498~~

~~-0.1061~~

~~-0.2743~~

~~-0.2824~~

~~-0.6837~~

~~-0.2208~~

~~-0.0334~~

~~% vegetation%~~

~~0.1752~~

~~1.0000~~

~~0.0651~~

~~0.0883~~

~~0.0004~~

~~0.1450~~

~~-0.1517~~

~~-0.1260~~

~~-0.1086~~

~~-0.0233~~

~~-0.2743~~

~~-0.0628~~

~~-0.1982~~

~~-0.1465~~

~~-0.3066~~

~~-0.0301~~

~~-0.0785~~

~~% mulch%~~

~~0.0435~~

~~0.0923~~

~~1.0000~~

~~0.0379~~

~~0.0287~~

~~0.0137~~

~~0.1155~~

~~0.0530~~

~~0.1724~~

~~-0.1360~~

~~-0.0108~~

~~-0.0002~~

~~-0.2824~~

~~-0.0628~~

~~-0.2800~~

~~-0.2192~~

~~-0.2467~~

~~%bareground~~

~~0.1608~~

~~0.2518~~

~~0.0102~~

~~0.2852~~

~~0.1383~~

~~0.0379~~

~~1.0000~~

~~0.1333~~

~~0.0729~~

~~0.1513~~

~~-0.0835~~

~~-0.1982~~

~~-0.1620~~

~~-0.4285~~

~~-0.0336~~

~~-0.1683~~

~~-0.0883 % groundcover%~~

~~0.4043~~

~~0.3047~~

~~0.2942~~

~~0.0651~~

~~0.0287~~

~~1.0000~~

~~0.0828~~

~~0.2165~~

~~0.2134~~

~~0.1710~~

~~-0.3268~~

~~-0.0128~~

~~-0.2098~~

~~-0.1620~~

~~-0.3986~~

~~-0.3543~~

~~-0.1551~~

~~% tree/bushes%~~

~~0.1731~~

~~0.0570~~

~~0.5073~~

~~1.0000~~

~~-0.2724~~

~~-0.0939~~

~~-0.0451~~

~~-0.1465~~

~~-0.2800~~

~~-0.4285~~

~~-0.3986~~

~~-0.2724~~

~~-0.1239~~

~~-0.0109~~

~~-0.1677~~

~~-0.0877~~

~~-0.0056~~

~~% grass %~~

~~0.1249~~

~~0.1809~~

~~1.0000~~

~~0.1495~~

~~0.0394~~

~~-0.0712~~

~~-0.0229~~

~~-0.4932~~

~~-0.6837~~

~~-0.3066~~

~~-0.2192~~

~~-0.0336~~

~~-0.3543~~

~~-0.2724~~

~~-0.1617~~

~~-0.2281~~

~~-0.0196~~

~~% pavement%~~

~~0.1802~~

~~0.2187~~

~~0.0883~~

~~0.0137~~

~~0.0828~~

~~0.1495~~

~~1.0000~~

~~0.0412~~

~~0.0283~~

~~0.0414~~

~~-0.0061~~

~~-0.0098~~

~~-0.2077~~

~~-0.2208~~

~~-0.1683~~

~~-0.1239~~

~~-0.0019~~

~~Slope~~

~~0.1775~~

~~0.3122~~

~~0.0197~~

~~0.0375~~

~~0.0941~~

~~0.1096~~

~~0.2165~~

~~0.0394~~

~~0.0412~~

~~1.0000~~

~~0.0075~~

~~-0.0301~~

~~-0.2467~~

~~-0.0883~~

~~-0.0109~~

~~-0.0540~~

~~-0.2039~~

~~Substrate LengthSubstrate~~

~~0.1285~~

~~0.2889~~

~~0.1374~~

~~0.2283~~

~~0.1018~~

~~0.0004~~

~~0.1155~~

~~0.1333~~

~~0.2134~~

~~0.0075~~

~~1.0000~~

~~0.8738~~

~~0.2226~~

~~-0.0560~~

~~-0.1677~~

~~-0.1617~~

~~-0.0019~~

~~Substrate widthSubstrate~~

~~0.1614~~

~~0.3124~~

~~0.1342~~

~~0.2774~~

~~0.1108~~

~~0.1450~~

~~0.0530~~

~~0.0729~~

~~0.1710~~

~~0.0283~~

~~0.8738~~

~~1.0000~~

~~0.2132~~

~~-0.0756~~

~~-0.0877~~

~~-0.2281~~

~~-0.0540~~

~~Substrate heightSubstrate~~

~~0.0495~~

~~0.2894~~

~~0.1724~~

~~0.1513~~

~~0.0414~~

~~0.2226~~

~~0.2132~~

~~1.0000~~

~~-0.0539~~

~~-0.3015~~

~~-0.0139~~

~~-0.0334~~

~~-0.0785~~

~~-0.1551~~

~~-0.0056~~

~~-0.0196~~

~~-0.2039~~

~~Porosity~~

~~Exposurescore~~

~~Wkwy trafficWkwy~~

~~Distance from walkwayfromDistance~~

~~Rd. trafficRd.~~

~~Distance from road from Distance~~

~~Percentvegetation~~

~~Percentmulch~~

~~Percentground bare~~

~~Percentgroundcover~~

~~Percenttree/bushes~~

~~Percentgrass~~

~~Percentpavement~~

~~Slope~~

~~Substrate LengthSubstrate~~

~~Substrate widthSubstrate~~

~~Substrate heightSubstrate Substrate PorositySubstrate~~

Table 2.5 Summary of the most significant elements for each individual regression. Red values indicate a negative impact on lizard density, while blue values indicate a positive correlation to lizard density.

~~0.2938~~

~~0.0214~~

~~0.0034~~

~~0.1421~~

~~Prob>ltl~~

~~1.06~~

~~3.06~~

~~-2.37~~

~~-1.49~~

~~t ratio t~~

~~Linear fitLinear ofdensity~~

~~0.0257~~

~~0.0147~~

~~0.0056~~

~~0.0254~~

~~0.0165~~

~~Prob>Chisq~~

~~4.98~~

~~5.95~~

~~7.68~~

~~5.75~~

~~Chisquare~~

~~Ordinallogistic high/lowfor density~~

~~0.0063~~

~~0.0234~~

~~0.0157~~

~~0.0086~~

~~0.0185~~

~~Prob>Chisq~~

~~7.45~~

~~5.14~~

~~5.84~~

~~6.89~~

~~5.55~~

~~Chisquare~~

~~Nominallogistic oflizardpresence~~

~~Distance from walkwayfromDistance~~

~~Substrate heightSubstrate~~

~~Percentvegetation~~

~~Substrate widthSubstrate~~

~~Substrate porositySubstrate~~

~~Percentmulch~~

~~Percentgroundcover~~

~~Percenttree/bushes Percentgrass Percentpavement 47~~

~~Figure 2.1~~

~~Figure 2.2~~

~~Figure 2.3~~

~~Figure 2.4~~

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