Modeling the Morphological and Genetic Consequences of Population Introduction for Euphydryas gillettii

Jack McGregor

May 7, 2015

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Modeling the Morphological and Genetic Consequences of Population Introduction for Euphydryas gillettii

An Honors Thesis Submitted to

the Department of Biology in partial fulfillment of the Honors Program

STANFORD UNIVERSITY

by Jack McGregor

May 2015

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Acknowledgements

This project would not have been possible without the help of Dr. Carol Boggs

and Rajiv McCoy who served as my mentors for this project. Additionally, Dr. Peter

Vitousek was incredibly helpful as a second reader. Members of the Petrov lab were also

generous enough to give up lab space so that I could run my experiments. John Schroeder

was also kind enough to talk about his data for Malate Dehydrogenase with me, and also

shared his list of genes that had significant SNPs. Additional thanks goes to all of the

Boggs lab research assistants that took the pictures and collected the wing clipping

samples and to Dr. Paul Ehrlich who let me take pictures of his specimens. Finally, this

project was funded by two UAR small grants that were used to pay for the lab supplies

and sequencing fees.

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Table of Contents: Page…………………………………………………………………………Section 6…………………………………………………………………………List of Tables 7…………………………………………………………………………List of Figures 8…………………………………………………………………………Abstract 9…………………………………………………………………………Introduction 14………………………………………………………………………..Study System 15…………………………………………………………………Materials and Methods 20…………………………………………………………………………Results 25…………………………………………………………………………Discussion 33…………………………………………………………………………Bibliography 36…………………………………………………………………………Tables 42…………………………………………………………………………Figures

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List of Tables 1. Specimens Photographed 2. Specimens Sequenced 3. Genes of interest 4. PCR Primers for genes 5. Granite Creek vs. Gothic all data 6. Gothic vs. Gothic all data 7. Togwote Pass vs. Togwote Pass all data 8. Togwote Pass vs. Granite Creek all data 9. Enrichment test output for variation lost 10. Enrichment test output for variation maintained

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List of Figures: 1. Photos of study sites 2. Population size of Gothic population from 1977-2013 (Redrawn from Boggs et al.) 3. Representations of allele frequencies (Figure 3 from McCoy e al. 2014) 4. Example photograph and measurement illustration 5. Significant differences between Gothic and Granite Creek body size 6. Significant differences between Males and Females from Gothic and Granite Creek 7. Significant differences in eye diameter for Gothic and Granite Creek 8. Significant differences in eye diameter/body length for Gothic and Granite Creek 9. Significant Differences between Gothic 2011 and Gothic 2012 10. Significant Differences between Males and Females from Gothic 11. Significant Differences between Togwote Pass in 1959 and Togwote Pass in 1979 12. Significant Differences between Togwote Pass in 1979 and Granite Creek in 1979 13. Sequence Chromatograms 14. Enrichment data for SNPs where variation was lost 15. Enrichment data for SNPs where variation was maintained

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Abstract

Translocation is one method of protecting a species from extinction, however it is not well understood how populations react to being introduced to a new environment. One potential model system is Gillette’s checkerspot butterfly (Euphydryas gillettii) in Colorado. Since its introduction to Colorado in the 1970s, the population has suffered multiple severe bottlenecks, thus potentially subjecting the isolated population to evolutionary forces including strong genetic drift. Previous studies in Euphydryas and other lepidopterans suggest that the Colorado population has lost genetic variation (McCoy et al. 2014) but did not rule out the possibility of balancing selection operating on a subset of functional polymorphisms. However, studies looking at variation in E. gillettii were more broadly focused and thus prompt a more extensive look at variation in specific functional categories known to be under balancing selection in other species. The observation of severe bottlenecks raises questions about how this population has changed both morphologically and genetically since its introduction. Since there is little research looking into the morphological changes after a population introduction in Lepidoptera, the first part of this study compares 12 morphological traits from photographs of the current population and preserved specimens of the founding population. The current population is larger overall, but this is most likely due to genetic drift or higher host plant quality. There is also no significant evidence of fluctuating asymmetry, which suggest that there are less morphological effects of inbreeding than we expected. After finding suggestive evidence of selection in the population, the second part of this study sequenced a synonymous SNP in malate dehydrogenase to compare variation between the current populations in Colorado and the variation found in previous studies on a population in the natural range, specifically looking for evidence of balancing selection or genetic drift. We found that there was no longer a SNP at this position, suggesting that genetic drift had removed variation at that site. However, using a Fisher’s Exact test for enrichment on gene functional groups, we found that genes involved in superoxide metabolism were more likely to be enriched with SNPs where variation was maintained in the introduced population. Therefore, these genes represent the best candidates for future sequencing endeavors.

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Introduction: As climate change continues to pose an ever-increasing risk of extinction for

many species, scientists may choose to introduce a species to a new environment in order

to reduce this threat (Richardson et al. 2009). This process is most commonly known as

translocation or assisted migration. Unfortunately, a lack of understanding by policy

makers prevents such action from being implemented (McLachlan, Hellmann, and

Schwartz 2007). Although scholars have studied assisted migration and its effect on

community structure (Mueller and Hellmann 2008; Gray et al. 2015), none to my

knowledge have looked at the effect of assisted migration on the morphology and genetic

composition of a translocated population.

New populations are usually established by a small number of genetically similar

founder individuals and are subject to large fluctuations in population size (Liebhold and

Tobin 2008). Therefore, forces such as genetic drift can play a large role in shaping the

population’s evolution and structure. For example, genetic drift has been shown to

change morphological traits and reduce genetic diversity by causing alleles to go extinct

even if average heterozygosity is high (Merila J 2001; Nei, Maruyama, and Chakraborty

Ranajit 1971; Whitehouse and Harley 2001). While this theory is widely taught, the

reality of the population’s evolution is much more complicated because forces such as

balancing selection can also be acting in the population to maintain genetic variation.

However, the extent to which both drift and selection influence genetic diversity in a

population is highly contested, thus creating a need for more empirical studies in diverse

systems.

Studies seeking to quantify genetic drift and balancing selection across a wide

range of taxa often yield conflicting results. For example, the effects of genetic drift were

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shown to outweigh the effects of balancing selection in maintaining variation in MHC

complexes in a species of endangered robins (Petroica traversi)(Miller and Lambert

2004), but balancing selection was shown to be sufficient to maintain variation in MHC

complexes for arctic foxes (Vulpus lagopus) (Ploshnitsa et al. 2012). Contrasting findings

such as these prompt further study into the role of balancing selection after a bottleneck

for not only specific genes, but across the entire genome.

Unlike the genetic effects of a population bottleneck, which are well documented

but with conflicting results, studies looking at the morphological consequences of a

population bottleneck are rare. Usually, these studies are limited due to the long timescale

required to see significant morphological change. However, changes in morphology can

be a strong indicator of different selective or random forces acting on a population. The

most well studied trait is fluctuating asymmetry, or differences in size and shape within

an individual. Most commonly, fluctuating asymmetry is used as a proxy for

developmental instability caused by environmental and genomic stress (Parsons 1992).

Theory suggests that after stressful events like severe bottlenecks, fluctuating asymmetry

should increase in the population, but the literature suggests that asymmetry is not

affected by bottlenecks. For example, studies on the butterfly species Plebejus argus and

Parnassius apollo found no significant changes in fluctuating asymmetry despite

bottlenecks due to habitat fragmentation and isolation respectively (Brookes et al. 2015;

Habel et al. 2012). These contradictions to theory raise multiple questions. Are there

other forces that keep morphology stable despite genomic stress? Do bottlenecks affect

morphology in the same way they affect genetic variation? Finally, can we apply these

effects to all kinds of morphological analysis, or are these effects only applicable to

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indicators of stress like fluctuating asymmetry. Unfortunately, without a strong literature

base examining morphology, the answers to these questions elude us.

Fortunately, a species of checkerspot butterfly (Euphydryas gillettii), presents

itself as an ideal system to examine both the morphological and genetic consequences of

an introduced population that has suffered multiple bottlenecks.

In 1977, researchers introduced the butterfly Euphydryas gillettii from its native

range in Wyoming to a site in Gothic, Colorado (Holdren and Ehrlich 1981). When

compared with the donor site (Granite Creek, Wyoming), the Gothic site is a more

complex 3D environment with patches of trees and less open space (Boggs pers. obs, See

figure 1). Additionally, there has been some habitat change since the introduction with

more trees growing in the meadows (Boggs pers. obs). The Gothic site is also completely

isolated from all other populations in the native range, thus precluding gene flow into the

population.

As with many newly introduced species, the Gothic population suffered

significant population size fluctuations since its introduction. The population remained

below 200 individuals/ 2 ha until the early 2000s (Boggs et al. 2006, see figure 2).

Additionally, the population went through a significant bottleneck to 20 individuals/2 ha

shortly after introduction and suffered two more severe bottlenecks in the late 1980s and

1998, reducing the population to 20 individuals.

The long period of small population size and lack of gene flow suggest that

genetic drift and inbreeding likely played a pivotal role in shaping the genetic

composition of the Gothic population, and potentially also altered the species’

morphology. Indeed, genetic examination of the E. gillettii population showed that a

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large proportion of alleles went extinct 30 years after the population’s founding (McCoy

et al. 2014)(See figure 3).

There has yet to be a study on changes in Lepidoptera body size after a genetic

bottleneck. Therefore, one goal of this project is to see if there are any significant

differences in wing morphology, eye diameter, flight capabilities, or fluctuating

asymmetry between the Gothic and native populations. These traits were chosen because

they could easily be measured using imaging software and they would not be affected by

desiccation. We chose to look at wing morphology in particular because it would give us

insight into flight-related traits such as wing aspect ratio, and a proxy for wing loading

that uses body size instead of mass. Additionally, since eye diameter can used as a proxy

for brain size, we chose to measure it to see if there may be any possible differences in

neuronal development. We hypothesized that there would be significant differences in

wing morphology because we expected dispersants to be lost from the population, and

thus morphologies suited for dispersal. Similarly, we expected to see significant

differences in eye diameter because of the differences in habitat complexity between the

two sites.

These tests, in addition to addressing a knowledge gap in the understanding of

how genetic bottlenecks affect morphology, will also open up possibilities for population

genetic studies. If significant differences between the two populations are found, then

these differences could be due to either genetic drift or adaptive selection. Therefore, a

study that quantifies the effects of genetic drift and selection in this population are also

necessary.

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The second goal of my project was to see if there were any changes in genetic

variation between the native and derived populations. Specifically, I searched for

evidence of balancing selection maintaining genetic variation in the Gothic population,

because a survey of the genome by McCoy et al. (2014) on E. gillettii, showed that there

was significant allelic reduction in the derived population (See figure 3). While this

survey is limited in sample size, and is a coarse estimate of allele frequencies that limits

insights about the possible role of selection, genome-wide data obtained from this study

was used to identify candidate genes for a large-scale study. McCoy et al. also identified

2700 synonymous single nucleotide polymorphisms (SNPs), 1400 nonsynonymous

SNPs, and 2600 UTR SNPs in E, gillettii that were also used to identify candidate regions

to sequence (McCoy et al. 2014). Using these extensive data collections, I hypothesized

that although drift may have caused significant loss in variation in the derived population,

balancing selection may have acted to maintain variation in key metabolic genes.

Therefore, we expected to see SNPs that were still segregating in the Gothic population

in key metabolic genes.

While there are specific genes that are known to be under balancing selection in

lepidopterans, there may be entire functional groups that are under selection as well.

Therefore, the third goal of this study was to reanalyze the transcriptome assembly and

SNP annotations from McCoy et al. to determine whether any functional groups were

enriched for SNPs that were still segregating in the Gothic population. We hypothesized

that functional groups related to metabolic activity will be enriched for SNPs where

variation was maintained in the Gothic population.

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Study System:

The native range for E. gillettii includes meadows in Idaho, Montana, Wyoming,

British Columbia and Alberta, Canada. However, in 1977, a new population of E. gillettii

was established when Dr. Cheryl Holdren and Dr. Paul Ehrlich intentionally introduced

individuals from Granite Creek, Wyoming to Gothic, Colorado which is 4˚ 20´ of latitude

south of Granite Creek (Holdren and Ehrlich 1981). Although the Gothic site is south of

E. gillettii’s normal range, once the Gothic site’s higher altitude (2,900 m as opposed to

2,100 m) is taken into account, the climates are quite similar and both sites contain the

host plant and similar predators (Holdren and Ehrlich 1981). The Gothic site, however, is

a more complex 3D environment than Granite Creek. While the Granite Creek site is

mostly open grassland with a few streams running through it, the open meadows in

Gothic are much smaller and contain patches of willow and spruce (Boggs pers obs, See

figure 1). Additionally, there has been some habitat change in Gothic since the

introduction as there are more willow trees than at the time of introduction (Boggs pers

obs).

As detailed above, the Gothic population fell to below 20 individuals/2 ha

multiple times and remained around 200 individuals/ 2 ha until the population reached a

peak size of 3000 individuals in 2002 (Boggs et al. 2006). The population then crashed to

150 individuals/2 ha in 2005 (Boggs et al. 2006) (See figure 2). The population has

fluctuated between 100 and 10,000 individuals in 2 hectares since then (Boggs unpubl.

data). While there is data suggesting that E. gillettii is highly susceptible to population

fluctuations in the native range and that populations have declined, it is hard to quantify

this in the native range, because the populations operate as typical metapopulations

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(Williams 2012). Therefore, different populations are continually going extinct and

reestablishing, so gene flow is very high within the native range. However, the Gothic

population is completely isolated from other populations, so there is no gene flow into the

population.

Materials and Methods:

Morphological Study

Specimens

I photographed pinned E. gillettii specimens from Togwote Pass and

Granite Creek between 1957 and 1979 from Dr. Paul Ehrlich’s collection (Table 1). A

team at the Rocky Mountain Biological Laboratory photographed the specimens from

Gothic in 2011 and 2012. For all the specimens, I photographed their dorsal side with

their wings spread so that the forewings were clearly visible. I used a Canon Powershot

A4000 for all the photographs.

Morphological Traits Measured

Using the imaging software ImageJ (rsbweb.nih.gov), I measured the right and

left forewing length (from base to forewing tip), right and left forewing area, right and

left eye diameter as viewed dorsally (proxy for brain size), dorsal thorax area, and body

length from head to abdomen (See figure 4). The ratio of eye diameter to body length was

calculated as well to control for differences in eye diameter due to body size. I used these

measured values to calculate the aspect ratio of the left and right forewings, wing loading,

and adjusted wing loading. Normally, wing loading is calculated using body mass,

however, the specimens from the Ehrlich collection were too desiccated to weigh, so I

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calculated wing loading by substituting thorax area for dry mass in the wing loading

equation. This resulted in the equation being (Thorax Area)/(Forewing Area). The value

for adjusted wing loading was calculated by substituting body length for dry mass into

the equation, giving the equation, (Body Length)/(forewing area). For aspect ratio, I used

the average data in the equation [4*(Forewing Length)2]/(Forewing Area). Fluctuating

asymmetry was calculated as the absolute value of the value for the left structure minus

the value for the right structure to avoid negative values in fluctuating asymmetry.

Statistical Analysis

Data were analyzed using Systat 11.0 (Systat software inc.). I averaged left and

right forewing length, forewing area and eye diameter, respectively. Data were ln-

transformed to achieve normality and then, I took four separate comparisons. The four

comparisons were: Granite Creek (1970s) vs Gothic (2010s), Togwote Pass (1950s) vs

Togwote Pass (1970s), Granite Creek (1970s) vs Togwote Pass (1970s), and Gothic

(2011) vs Gothic (2012). Granite Creek (1970s) vs Gothic (2010s) is the comparison of

interest, however, Granite Creek (1970s) vs Togwote Pass (1970s) examines variation in

morphology in the native range, Gothic (2011) vs Gothic (2012) examines variation

along short time scales, and Togwote Pass (1950s) vs Togwote Pass (1970s) examines

variation over longer time scales with migration.

For all traits in the four comparisons, we checked for homoscedasticity with a

Bartlett’s test, and natural log transformed any parameter that failed (p<0.05). Histograms

that were excessively skewed or kurtotic were also ln transformed. In all four

comparisons, thorax area, wing loading, aspect ratio, and all fluctuating asymmetry

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parameters were ln transformed, while the rest of the parameters had acceptable

histograms. 1-way ANOVAS were performed for all traits looking at Site or Year, and

Sex, and then 2-way ANOVAS were performed to see any interaction between site/year

and sex.

Population Genetic Study

Genes of Interest

Using SNP data from McCoy et al. (2014), I identified candidate genes by

aligning the E. gillettii transcriptome with the SNP database using the program GMAP

(msi.unm.edu). This allowed me to determine whether the SNPs of interest fell too close

to an intron-exon boundary to design viable primers.

From this, I identified 12 genes of interest: nine with SNPs and 3 genes with no

SNPs identified by McCoy et al. (2014) but that are functionally important and may

contain low frequency variation that was missed by the study. The 9 genes with SNPs

are: phosphoglucoisomerase (Pgi), phosphoglycerate mutase (Pgm), superoxide

dismutase 2 (SOD2), alcohol dehydrogenase (Adh), glycogen phosphorylase, malate

dehydrogenase, transaldolase, pyruvate carboxylase, and monoacylglycerate lipase (See

Table 2). Pgi, Pgm, and SOD2 were chosen due to the high frequency of isozyme

variation in either the Gothic or Wyoming population in 2004 (Boggs unpub. data).

Additionally, Pgi had strong heterozygote advantage in the butterflies Colias meadii and

Melitaea cinxia (Watt et al. 2003). The rest of the genes had SNPs that were significantly

associated with flight metabolic rate in Euphydryas editha (Schroeder unpub. data). The

three genes that lack any identified SNPs, but were considered for sequencing are

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succinate dehydrogenase subunit D (Sdhd), isocitrate dehydrogenase, and

phosphofructokinase (pfk). Sdhd was also shown to have a high frequency of

heterozygotes in the Gothic population, while both isocitrate dehydrogenase, and pfk

have SNPs in E. editha (Boggs unpub data, Schroeder unpub data).

Specimens

The specimens from our genetic study came from wing clippings taken by field

assistants at the Rocky Mountain Biological Laboratory (RMBL) in Gothic, Colorado

from the years 2011 and 2012 (Table 3). Wing clippings were around 2mm in size, were

stored in 70% ethanol, and then frozen. For the 2010 data, we used allele frequencies

compiled by McCoy et al. (2014) from 8 larval samples taken from the Togwote Pass

population and 8 larval samples from the Gothic population. The Granite Creek

population went extinct in the early 2000s, therefore, for the population genetic study, we

used specimens taken from other areas in Wyoming as a proxy. It is appropriate to

substitute Togwote Pass for Granite Creek because, unlike the Gothic population, which

is isolated from any migrants entering the population, the Wyoming populations do have

gene flow between them.

DNA Extraction and Amplification

Using the Qiagen DNeasy Blood and Tissue Kit (69504), I eluted 100 uL of DNA

solution. While the extraction mostly followed the prescribed protocol, I hand-ground the

samples with an electric macerator, and eluted 100 uL instead of 200 uL. Using a Qubit

fluorescence assay, I obtained measureable amounts of DNA from 96 of 100 samples.

Concentrations ranged from .03 ug/mL to .8 ug/mL.

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PCR primers were designed using the primer3 software (biotools.umassmed.edu)

and in silico PCR was run to check specificity of the primers using isPCR software

(UCSC). For genes with identified SNPs, I amplified a 200bp region of DNA

surrounding the SNP. For the three genes without SNPs, we amplified a ~1000 bp exon

region. In the case of Pgi and Pgm, primers were designed using the Melitaea cinxia

genome because these two genes were absent from the GMAP alignment file due to the

low coverage of the gillettii transcriptome database. Primers were ordered from

Integrated DNA technologies (IDt). See Table 4 for primer sequences.

In order to amplify the genes of interest, I used the Qiagen taq master mix kit

(Product #201445). I followed the prescribed protocol, however, instead of having 100uL

solutions, I scaled the amount down to 25uL. The success of my reactions was verified

using a gel electrophoresis assay in which the gel had a gelRed DNA stain. Ethidium

bromide can also be used in lieu of the gelRed stain. Products were then purified using

the Qiagen qiaquick clean-up kit (Product #28104). Purified products were then sent to

the Protein and Nucleic Acid Facility at Stanford (PAN) for Sanger sequencing.

Statistical Analysis

Sequence chromatograms were manually curated with 4Peaks

(Nucleobytes) and aligned using ClustalW2 (Ebi.ac.uk). These alignments were used to

call genotypes and calculate population allele frequencies, which were compared with the

corresponding frequencies from McCoy et al. (2014).

In order to test for enrichment in gene functional groups for SNPs where

variation was maintained, the E. gillettii transcriptome compiled by McCoy et al. (2014)

was annotated using the software Blast2Go (Blast2go.com). A fasta file of all of the

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contigs with SNPs was created from the transcriptome database, and a list of all SNPs

where variation was lost in Colorado or variation was maintained Colorado were created

using the SNP database. These lists were then used as test sets for a Fisher’s Exact test

for enrichment with all other SNPs being used as the reference set.

Results: Morphological Study: A total of 11 traits were measured or calculated for each organism, and can be

split into four main categories: body size, eye diameter, flight capabilities, and fluctuating

asymmetry. For body size, forewing length, forewing area, thorax area, and body length

were measured (See figure 4). Eye diameter was measured as a proxy for brain size

(Snell-Rood pers. comm). Flight capabilities were approximated using wing loading and

forewing aspect ratio. Finally, fluctuating asymmetry served as our proxy for

developmental instability, and was calculated for forewing length, forewing area,

forewing aspect ratio, and eye diameter.

Comparison 1: Variation Between the Source and Derived Population

For this comparison, Gothic specimens from 2011 and 2012 were considered

together, and compared to Granite Creek specimens from 1977.

For all measures of body size, Gothic individuals were significantly larger than

the Granite Creek specimens (figure 5). On average, Gothic individuals had forewings

that were 1.62mm longer and 19.35mm2 larger in area than Granite Creek specimens

(F1,219=31.8, p=2.00E-07, F1,219=21.9, p=6.07E-06 respectively). Gothic specimens were

also larger by 2.84mm2 and 2.5mm for thorax area and body length respectively

(F1,219=37.5, p=1.5E-08, F1,219=131.7, p=2.2E=-16).

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Similarly, females were significantly larger than males for all parameters of body

size, which is expected for this species (figure 6). Females had 1.64mm longer and

24.39mm2 larger forewings (F1,219=55.5, p=8.0E-13, F1,219=80.5, p=3.9E-15), 1.07mm2

larger thoraxes (F1,219=3.45 ,p=.05), and .86mm longer bodies (F1,219=14.8 ,p=6.8E-05).

These differences in size also included eye diameter (figure 7). Gothic specimens

had eyes that were .14mm larger than Granite Creek specimens (F1,219= 24.3, p=1.59E-

06). There were no significant differences in eye diameter due to sex (F1,219=0.002,

p=0.70). There was, however, a significant sex by site interaction (p=0.02). Granite

Creek females were shown to have smaller eye diameters than all other groups.

However, when I corrected eye diameter for body size by taking the average eye

diameter divided by body length, I found that Granite Creek specimens had a larger

relative eye diameter (F1,219= 7.5, p=0.006) and that there was a significant sex by site

interaction where Granite Creek males were driving this larger eye diameter (p=.001)

(figure 8). In line with our uncorrected findings, males had significantly larger relative

eye size (F1,219=6.5, p=0.01).

For our estimates of flight capabilities, there were no statistically significant

differences between sites for either wing loading or aspect ratio (F1,219=2.0, p=0.15,

F1,219=0.15, p=.67). Between sexes however, females had a higher wing loading and

aspect ratios (F1,219=93.0, p=2.00E-16, F1,219=36.5, p=2.24E=-09) (Figure 6).

For all of the fluctuating asymmetry measurements, there were no significant

differences either for site or sex. However, there was a trend for a sex by site interaction

where Granite Creek females had less asymmetry (p=0.08). (See Table 5 for all data)

Comparison 2: Variation Across Short Time Scales

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For this comparison, we compared Gothic specimens from 2011 and Gothic

specimens from 2012 to see if there was any significant variation from year to year or

between sexes.

In terms of body size, while there were statistically significant differences that

suggested individuals from 2011 were larger than individuals from 2012, the differences

were quite small. Individuals from 2011 had a 1.1mm2 greater thorax area, and a 1mm

greater body length (F1,177=6.2, p=0.01, F1,177=8,5, p=0.004 respectively). All other

parameters were not statistically significantly different. When comparing body size with

regard to sexes, females were statistically significantly larger than males for all

measurements except eye diameter, which is expected. Additionally, there were no sex by

site interactions for body size (Figure 9).

Similarly, aspect ratios were different between years, but the difference was

negligible (F1,177=20.5, p=1.00E-05, Figure 9). However, between males and females,

females had larger wing loading and larger aspect ratios (F1,177=80.2, p=8.08E-16,

F1,177=21.4, p=2.4E-07)(Figure 10).

Finally, while there were no significant differences in fluctuating asymmetry due

to sex, in both forewing length and forewing area, 2012 specimens had significantly

greater fluctuating asymmetry (Figure 9). A weakly significant sex by site interaction

driven by 2012 females in forewing length was also present. (See Table 6 for all data)

Comparison 3: Variation across long timescales

For this comparison, Togwote Pass specimens from the 1950s were compared

with Togwote Pass specimens from the 1970s. As there were only female specimens from

Togwote Pass, we only looked at the differences between years. Only forewing area,

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thorax area, and aspect ratio returned any statistically significant results (F1,21=4.2,

p=.05, F1,21=7.7, p=.01, F1,21= 8.2, p=.009) (Figure 11). Specimens from the 1950s had

slightly larger thorax areas, but also had wings that were on average, roughly 20mm2

larger. Additionally, specimens from the 1970s had very slightly larger aspect ratios. (See

table 7 for all data)

Comparison 4: Variation across the native range

For this comparison, we compared specimens from 1970s Togwote Pass with

specimens from 1970s Granite Creek. As there were only female specimens from

Togwote Pass, the only comparison is between sites.

Once again, there were only a small number of parameters that had any

statistically significant differences between the two sites. These were forewing length and

aspect ratio (F1,31=4.5, p=0.04, F1,31=5.7, p=0.02). In addition, while Granite Creek

specimens did have larger aspect ratios, there was only a difference of .066. However,

Granite Creek specimens had, on average, 2.15mm longer forewings. It is also important

to note that there was a weakly significant trend (F1,31=3.5, p=0.07) in terms of body

length, where Granite Creek specimens were 1.8mm larger (Figure 12). (See Table 8 for

all data)

Population Genetic Study:

Our goal was to amplify a number of SNPs in genes that code for proteins in

central metabolic pathways. We started out with 12 candidate genes and were able to

successfully design PCR primers for 6 of them. Reasons for being unable to design

usable primers ranged from the SNP falling too close to an intron/exon boundary to

getting too much nonspecific priming in in silico PCR (Jim Kent, UCSC). After running

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PCR with these 6 sets of primers, we were left with one usable synonymous SNP in the

gene encoding Malate Dehydrogenase at position 233 of the spliced mRNA. The other 5

sets of primers either produced multiple bands in electrophoresis assays, which is

indicative of nonspecific priming not caught by isPCR, or failed to produce any product.

After aligning the usable sequence chromatograms (16 out of 94), we found that

all of the individuals that were sequenced had an A at the SNP’s position (Figure 13). In

Wyoming, A was present at a frequency of .8125, and G was present at a frequency of

.1875. Therefore, the G allele was lost in the Gothic population. Similarly, there was no

described SNP at this position for E. editha either (Schroeder unpub. data). However,

there was another SNP in E. editha that corresponded to position 217 in our gene. In E.

gillettii, the A allele was fixed, while in E. editha there was either an A allele or a T allele

that were both at intermediate frequency (Schroeder unpub. data). This SNP was not

associated with either high or low metabolic rate.

In contrast, a Fisher’s Exact test on our gene functional group data returned many

interesting results. Firstly, we asked whether any gene functional groups were enriched

for SNPs that were variable in the Wyoming populations, but lost variation in the

Colorado population. We observed enrichment for 9 gene functional group categories:

nucleotide metabolic process (p=.011), organic substance biosynthetic process (p=.028),

regulation of transcription (DNA-templated, p=.03), ATP binding (p=.036), monovalent

inorganic cation transport (p=.037), ribose phosphate metabolic process (p=.037), ion

membrane transport (p=.045), hydrogen ion transmembrane transporter activity (p=.045),

and cell part (p=.047). See Figure 14 for graphs and table 3 for tabular format. While a

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Bonferroni correction may render these P-values insignificant, these functional categories

may still be enriched for true positives.

Second, we tested SNPs that were still segregating in the Colorado population for

enrichment and found that 8 gene functional groups were more likely to contain SNPs

that were still variable. These functional groups were, integral component of membrane

(p=.016), cellular protein modification process (p=.022), superoxide metabolic process

(p=.031), aspartic-type endopeptidase activity (p=.031), alpha-amino acid biosynthetic

process (p=.031), extracellular space (.033), kinase activity (p=.035), and transmembrane

transport (p=.039). See Figure 15 for graphs and Table 4 for tabular format.

Discussion:

Morphological Study:

At the beginning of the study, we hypothesized that individuals from the derived

population in Gothic, Colorado would have some morphological differences when

compared with the ancestral population from Granite Creek. We had no predictions about

body size due to the lack of studies of this nature, but we did predict that Gothic

individuals would have larger eyes, and lower aspect ratios that favor maneuverability

over gliding. Indeed, some of our hypotheses were supported by our data; however, other

data were the opposite of what was expected.

First and foremost, the body size differences between males and females were in

line with the species description where females are described as having forewings that are

on average 3mm longer than males’ (Williams 1981). Unfortunately, not having any male

specimens from Togwote Pass prevents us from making any comments on how the

difference in size between males and females has changed from the 1950s, but since there

26

were no sex by site interactions between the Granite Creek and Gothic populations for

body size, it is likely that there has not been a significant change.

The differences in body size between the Granite Creek and Gothic specimens

however, are unexpected. The difference in forewing length could be explained by

naturally occurring variation, as we saw a similar difference between Togwote Pass and

Granite Creek specimens. However, the differences in forewing area seem to be a matter

of change over generations. In the comparison between Togwote Pass specimens from

the 1950s and 1970s, we observed an average difference in forewing area of ~20mm2

with individuals from the 1950s being larger. We observed Gothic individuals having

~20mm2 larger forewings as well, which suggests that over tens of generations, average

forewing areas can shift significantly. These findings then raise the question of why is

there growth in the time since introduction, when there was gradual shrinking over a

similar timeframe in Togwote Pass. One potential answer outside of natural fluctuation is

dispersal. The Gothic population is completely isolated from any other population of E.

gillettii, so any dispersants are lost from the population and there are no migrants coming

into the population. However, this still does not explain the growth of Gothic individuals

because generally, insects with larger wings are better at dispersal (Harrison 1980).

Instead, another plausible explanation is that whatever caused the population bottlenecks

in the 1980s/1990s put strong selective pressure for larger individuals. Finally, two other

likely explanations are that host plant quality is better in Gothic, so the larvae are better

nourished, or that shifting climate conditions have allowed better development

conditions. Without data on the host plant in the founding population, we cannot make

any definitive conclusions on whether host plant quality is driving this change in size.

27

Similarly, a survey looking at climate patterns in both Gothic and the native range would

be useful in seeing how climate affects larvae survival and growth.

One striking set of results was the significant sex by site interaction in diameter,

which was driven by Granite Creek females. Granite Creek females were shown to have

smaller eye diameters and all other groups did not show any significant differences

between them. Given these data alone, they fall in line with our hypothesis that the more

complex 3D environment in Gothic may be a selective force for larger brains and thus

larger eye diameters (Snell-rood pers comm.). Under normal conditions, we might expect

males to have a larger eye diameter than females due to the mating systems of E. gillettii.

Normally, males seek out mates in this species by either perching and surveying the area

or by actively searching for mates (Williams 1981). Therefore, it would be logical to

conclude that there is greater selective pressure for males to have larger eyes. However,

since Gothic females and males did not show any significant differences in eye diameter,

there must be a selective pressure for females to have larger eyes in Gothic. Under our

hypotheses, this pressure would have come from the more complex 3D environment.

However, to accurately test this hypothesis, we would need to look at the effect of 3D

environment on neuron activity in butterflies.

Looking at the ratio of eye diameter to body length, however, paints an entirely

different picture. Here, instead of seeing that Gothic females saw in increase in eye

diameter between the two sites, we see that Gothic males had decreased eye diameter to

body length ratios. This suggests that the results found from looking at just eye diameter

may be due to simple growth: as males’ bodies grew, so did their eyes. However, because

males had lower eye diameter/body length ratios, this may suggest that males and females

28

in Gothic are allocating resources in different proportions to body size and neuronal

development. In this case, males are allocating more resources to body size.

Finally, our findings regarding fluctuating asymmetry were also different than

expected. We hypothesized that fluctuating asymmetry would increase in the Gothic

population because of the higher proportion of inbreeding in the population. Fluctuating

asymmetry has long been used as a proxy for developmental instability, and it is well

established that inbreeding leads to developmental instability (Jun 2007; Lens et al. 2000;

Parsons 1992). However, studies in butterflies have shown that after a genetic bottleneck,

fluctuating asymmetry does not necessarily increase (Habel et al. 2012). However, it is

important to note that the population studied by Habel. et al. was a population that was

not isolated from other metapopulations, so migration could have reduced the effect of

inbreeding. Despite still being isolated from other populations, we found that the Gothic

population did not have any significant changes in fluctuating asymmetry, and that for

almost all of the comparisons, fluctuating asymmetry was quite constant. Reasons for this

could be that inbreeding is much less than previously thought or that there has not been

enough time for the negative effects of inbreeding to take place.

Population Genetic Study

The most salient result from our sequencing endeavors is that this SNP in Malate

Dehydrogenase must have been segregating at low frequency in the Wyoming population

and thus was removed quickly from the Gothic population due to genetic drift or was not

even sampled at all when the population was derived. Therefore, our findings on Malate

Dehydrogenase suggest that SNPs segregating at high frequency are rare in Gothic. This

falls in line with population genetic theory which assumes that the fate of all mutation is

29

either fixation or loss, though variants under strong balancing selection may survive over

disproportionately long time spans. Additionally, since E. editha was also fixed for the A

allele at this position, it is likely that this SNP is unique to E. gillettii and arose after the

species’ divergence from a common ancestor (Schroeder unpub data). Since Schroeder is

examining whether certain SNPs are associated with high or low flight metabolic rate for

E. editha, his data provide some insights into how variation that we may see in E. gillettii

affect metabolism. For example, about 12 base pairs away from the gillettii SNP, there is

an A to T mutation in E. editha that is segregating at about 50% frequency in both

individuals with high metabolic rate and low metabolic rate, which means that it could

possibly be under balancing selection (Schroeder unpub data). In E. gillettii, this position

is fixed for the A allele, so we cannot conclude anything about its effect on metabolism.

However, seeing a likely candidate for balancing selection so close to our SNP in a close

relative means that Malate Dehydrogenase should not be ruled out from consideration as

a candidate gene for future sequencing experiments.

Future sequencing studies will be necessary to better understand how these SNPs

are segregating in the Gothic population. First steps would be to optimize PCR protocol

to amplify the other 11 candidate SNPs originally proposed in the introduction. However,

even better candidate genes can be identified from our gene functional group data.

The results from our gene functional group enrichment tests both support and

contradict our original hypotheses that we expected to see enrichment in metabolically

important genes for SNPs where variation remained in the Colorado population. Indeed,

all 4 genes in the superoxide metabolic processes group had variation maintained in the

population. Even more interesting, Superoxide Dismutase isoform 1 (SOD1) was among

30

these genes. Not only was SOD1 one of our original candidate genes for study with its

three synonymous and one nonsynonymous SNPs, but it was also found to have two

isozymes that have been maintained at a frequency of .03 in Granite Creek since the

1970s, and was found to be segregating at a frequency of .06 in Gothic in 2004 (Boggs

unpub data). The fact that this isozyme has been maintained at a low frequency for so

long in both populations make SOD1 the ideal candidate for a test for balancing

selection. Furthermore, mutations in SOD1 have been associated with reduced longevity,

and SOD1 is known to be a key reducer of oxidative stress (Phillips et al. 1989; Sohal,

Arnold, and Orr 1990). Therefore, for future sequencing endeavors, SOD1 is of utmost

importance. As for the other enriched groups, a more detailed annotation of the E. gillettii

genome is necessary if we are to better understand which specific genes have maintained

variation, as many of the groups that were enriched for these SNPs do not have an

obvious connection to metabolic capabilities.

A similar story unfolds for the functional groups enriched for SNPs where

variation was lost in the Colorado population. Once again, we observe a number of

functional groups that we cannot make any conclusions about without an annotated

genome. However, the enrichment for the nucleotide metabolic processes functional

group is quite intriguing as it goes against our original hypothesis. Since metabolically

important genes have been shown to be under balancing selection in butterflies, we

expect to see enrichment for SNPs where variation remains in the population as we saw

with the superoxide metabolic activity functional group (Watt et al. 2003). There are

three possible explanations for this discrepancy. First, from an adaptationist perspective,

variation in nucleotide metabolism may not be as vital to the species’ success as variation

31

in SODs is, so the force of any balancing selection was not enough to maintain variation

in the presence of strong genetic drift. However, since balancing selection is very

uncommon, it is more likely that these SNPs were held at low frequency in Wyoming due

to purifying selection, and in Colorado went extinct due to the strong genetic drift.

Finally, the third explanation is that our genome database is too coarse of an estimate for

variation in the population. This database was compiled from 16 individuals (8 from

Wyoming and 8 from Gothic), so it is highly likely that some variation was missed for a

number of alleles.

Overall, our gene functional group data are a good first step into unraveling the

evolutionary forces at play within this population. Our list of functional groups that were

enriched for SNPs with continued variation in Colorado provide a number of different

avenues to look for balancing selection in this population and provide another long list of

genes that can be sequenced. Additionally, an annotated genome would be of great use to

any future studies in this system. While it is inevitable that many of these future

sequencing endeavors will result with no variation just as our Malate Dehydrogenase

experiment did, finding a high degree of polymorphism will be a good sign that balancing

selection is acting in E. gillettii.

At the onset of this project, we had two major goals: examine whether

morphology is stable despite a bottleneck, and to see whether balancing selection was

acting in key metabolic genes to maintain variation. However, acting in all of these

disparate experiments was the question of whether E. gillettii is an adequate model for

examining the consequences of population introduction. We maintain our earlier

assertions that E. gillettii can serve as an important model for these consequences. Our

32

morphology data suggest that selection or drift is influencing this population in some

profound ways, while our gene functional group enrichment data suggest that balancing

selection, purifying selection, and drift are acting on different sets of genes to maintain or

remove variation. Our data show that there are many complicated forces at play shaping

the population’s evolution, but the vast number of resources available can help unravel

and identify the key players. An annotated transcriptome, and ongoing studies for E.

editha and E. aurinia provide a good supporting base for future studies on shared SNPs

or for identifying candidate genes. There is still much that can be done in this system, but

the myriad of possibilities are promising.

33

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36

Appendix: Tables: Table 1: Specimens Photographed

Site Togwote Pass 1950s

Togwote Pass 1970s

Gothic Granite Creek

Year 1959 1977 2011, 2012

1977-1979

Sexes Females Only

Females Only

Both Both

Sample Size

9 13 98, 81 42

Table 2: Specimens Sequenced Gothic 2011 Gothic 2012 Wyoming 2010

18 F 18 M 35 F 35 M 8 F 8 M

37

Table 3: Candidate Genes for sequencing Gene SNP

Phosphoglucoisomerase Syn

Phosphoglucomutase Nonsyn

Superoxide Dismutase 1 Nonsyn

Alcohol Dehydrogenase Syn

Succinate Dehydrogenase None

Isocitrate Dehydrogenase None

Glycogen Phosphorylase Nonsyn

Malate Dehydrogenase Syn

Phosphofructo Kinase None

Transaldolase Nonsyn

Pyruvate Carboxylase Syn

Monoacylglycerol Lipase Syn

38

Table 4: PCR Primers used for sequencing

Gene SNP Location Left Primer Right Primer SOD1 550 TCCTTCGTCGAAAT

ATCAGTTG CGAATTCGTTTGTCGAGATG

Malate Dehydrogenase

233 TCGATAAAATGCAATAACCAAAGA

CGAATTCGTTTGTCGAGATG

Transaldolase-1 193 TATCGTTGAGCGAGGATGACT

CGTTTTGCTTGAGGTTCACT

Monoacyl Glycerol Lipase

570 GTGAATGTCCCCAGACGAAT

TAACGTGACACCAAGCGAAG

Pyruvate Carboxylaese

609 TCAATAGATCAGTTCAAAGATTGAGAA

CCAACGGGCAGAAGTTAGAG

Alcohol Dehydrogenase

282 GGACCAGAAGCAGCAAAGAC

ATTCGATTAATGCCCAGACG

Table 5: All Data for Granite Creek vs. Gothic Comparison

Trait   p-­‐value   Df,  F   difference  

MvsF  P  value  

Df,  F   M  vs  F  difference  

Forewing  Length   2.00E-­‐07   1,  219,  31.8   1.62   8.04E-­‐13   1,  219,  55.5   1.64  Forewing  Area   5.57E-­‐06   1,  219,  25.9   19.35   3.90E-­‐15   1,  219,  80.5   24.39  Eye  Diameter   1.59E-­‐06   1,  219,  24.3   0.14   0.7   1,  219,  .002   N/A  Ln  Thorax  Area   1.50E-­‐08   1,  219,  37.5   2.84   0.05   1,  219,  3.45   1.07  Body  Length   2.20E-­‐16   1,  219,  

131.7  2.5   6.80E-­‐05   1,  219,  14.8   0.86  

Ln  Wing  Loading   1.50E-­‐01   1,  219,  2.04   N/A   2.00E-­‐16   1,  219,  93.0   0.02  Ln  Forewing  Aspect  Ratio  

6.70E-­‐01   1,  219,  .15   N/A   2.24E-­‐09   1,  219,  36.5   0.58  

Ln  FA  Forewing  Length  

3.00E-­‐03   1,  219,  8.8     3.34E-­‐01   1,  219,  .80   N/A  

Ln  FA  Forewing  Area  

7.30E-­‐01   1,  219,  .11   N/A   8.90E-­‐01   1,  219,    .001   N/A  

Ln  FA  Eye  Diameter  

3.30E-­‐01     N/A   8.90E-­‐01     N/A  

Ln  FA  Aspect  Ratio  

6.00E-­‐01   1,  219,  1.9   N/A   6.30E-­‐01   1,  219,  3.2   N/A  

Eye  Diameter/Body  Length  

6.00E-­‐03   1,  219,  7.5     1.00E-­‐02   1,  6.5    

39

Table 6: All Data for Gothic 2011 vs. Gothic 2012

Trait   p-­‐value   Df,  F   MvsF  P  value   Df,  f  Forewing  Length   0.07   1,  177,  3.2   6.00E-­‐08   1,  177,  

47.7  Forewing  Area   0.9   1,  177,  

.007  8.53E-­‐14   1,  177,  

65.7  Eye  Diameter   0.7   1,  177,  

.078  0.3   1,  177,  .88  

Ln  Thorax  Area   0.01   1,  177,  6.2   0.005   1,  177,  3.8  Body  Length   0.004   1,  177,  8.5   7.78E-­‐05   1,  177,  

16.3  Ln  Wing  Loading   0.15   1,  177,  2.0   8.80E-­‐16   1,  177,  

80.2  Ln  Forewing  Aspect  Ratio  

1.00E-­‐05   1,  177,  20.5  

2.40E-­‐07   1,  177,  21.4  

Ln  FA  Forewing  Length   0.0149   1,  177,  6.04  

9.05E-­‐01   1,  177,      1E-­‐04  

Ln  FA  Forewing  Area   0.008   1,  177,  7.2   2.30E-­‐01   1,  177,  1.1  Ln  FA  Aspect  Ratio   0.93   1,  177,  1.3   7.40E-­‐02   1,  177,  3.2  

Table 7: All Data for Togwote Pass 1959 vs. Togwote Pass 1979 Trait   P-­‐value   Df,  F  Forewing  Length   0.1   1,  21,  1.9  Forewing  Area   0.05   1,  21,  4.2  Eye  Diameter   0.85   1,  21,  0.034  Thorax  Area   0.01   1,  21,  7.7  Body  Length   0.08   1,  21,  3.2  Wing  Loading   0.44   1,  21,  0.61  Aspect  Ratio   0.009   1,  21,  8.2  FA  Forewing  Length  

0.52   1,  21,  0.42  

FA  Forewing  Area   0.99   1,  21,  0  FA  Eye  Diameter   0.71   1,  21,  .14  FA  Aspect  Ratio   0.33   1,  21,  0.99  

40

Table 8: All Data for Togwote Pass vs. Granite Creek Trait   P-­‐value   Difference   Df,  F  Forewing  Length   0.04   2.15   1,  31,  4.5  Forewing  Area   0.1   N/A   1,  31,  2.8  Eye  Diameter   0.14   N/A   1,  31,  2.2  Thorax  Area   0.15   N/A   1,  31,  2.1  Body  Length   0.07   1.8   1,  31,  3.5  Wing  Loading   0.9   N/A   1,  31,  .015  Aspect  Ratio   0.02   0.066   1,  31,  5.7  FA  Forewing  Length  

0.8   N/A   1,  31,  0.24  

FA  Forewing  Area   0.9   N/A   1,  31,  0.03  FA  Aspect  Ratio   0.1   N/A   1,  31,  2.2  

41

Table 9: Enrichment Data for SNPs where variation was lost

Table 10: Enrichment Data for SNPs where variation was maintained

42

Figures:

Figure 1: Left) A photo of Granite Creek, Wyoming in 2004 taken by Dr. Carol Boggs. Right) A photo of Gothic, Colorado in 2012 taken by Dr. Carol Boggs.

Figure 2: Population size of Gothic population from 1977-2013 (Redrawn from Boggs et al. 2006 and unpubl data). Inset depicts population size from 1977-2005.

0  

1000  

2000  

3000  

4000  

5000  

6000  

7000  

8000  

9000  

10000  

1977   1982   1987   1992   1997   2002   2007   2012  

N

YEAR

43

Figure 3: (Figure 3 from McCoy et al. 2014) Representations of allele frequencies. (A) Joint allele frequency spectrum composed of all SNPs segregating in Wyoming (WY), Colorado (CO) or both populations. The frequency spectrum illustrates the loss of ancestral genetic variation in the CO population due to genetic drift during the bottleneck. Frequencies range from 0 to 16 chromosomes per population. The spectrum, displayed as a heatmap, is folded (i.e. unpolarized), as the state of the ancestral allele is unknown. (B) Individual samples plotted according to the first two principal components of the genotype matrix of all SNPs. Populations are indicated with different plotting symbols. Upon stratifying data by SNP class (synonymous, nonsynonymous, UTR), results were qualitatively similar and are not depicted. Principal component 1 separates samples according to population membership, while principal component 2 separates individuals within the WY population (within which the CO samples are nested, but tightly clustered).

44

Figure 4: Image of butterfly and all of the measurements taken. Red line is Forewing Length. Blue line is body length. Yellow circle is thorax area. Green line is forewing area.

Figure 5: Significant results for body size when comparing Granite Creek and Gothic. For all graphs, the X-axis has the site and the Y-axis has the value in mm or mm2 A) Significant differences in forewing length B) Significant differences in forewing area C) Significant differences in thorax area D) Significant differences in body length.

45

Figure 6: Significant results when comparing the sexes in Granite Creek and Gothic. For all graphs, the X-axis has the sex (M or F) and the Y-axis has the value in mm, mm2, or is the log of a quantitative trait. A) Differences in forewing length B) Differences in forewing area C) Differences in thorax area D) Differences in body length E) Differences in wing loading F) Differences in average aspect ratio

Figure 7: Significant results for Eye Diameter when comparing Granite Creek and Gothic. A) Differences between sites B) Differences between sexes C)Testing for interaction effects

46

Figure 8: Significant differences in Eye Diameter/Body Length ratios when comparing Granite Creek and Gothic. A) Differences between sites B) differences between sexes C) Testing for interaction effects

47

Figure 9) Significant Differences for the comparison between Gothic in 2011 and Gothic in 2012. A) Differences in thorax area B) Differences in body length C) Differences in average aspect ratio

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Figure 10) Significant results when comparing the sexes in Gothic. For all graphs, the X-axis has the sex (M or F) and the Y-axis has the value in mm, mm2, or is the log of a quantitative trait. A) Differences in forewing length B) Differences in forewing area C) Differences in thorax area D) Differences in body length E) Differences in wing loading F) Differences in average aspect ratio

Figure 11) Significant Differences for the comparison of Togwote Pass in 1959 and Togwote Pass in 1977. A) Differences in forewing area (mm2) B) Differences in thorax area (mm2) C) Differences in average aspect ratio

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Figure 12) Significant Differences for the comparison of Togwote Pass and Granite Creek in 1977. A) Differences in forewing length (mm) B) Differences in aspect ratio

Figure 13) Sequence Chromatograms for 6 samples of Malate Dehydrogenase. The nucleotide highlighted in blue is the position of interest

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Figure 14) Fisher’s Exact test results for SNPs that had variation in Wyoming but no longer had variation in Colorado. X-axis is the percentage of sequences and Y-axis is the gene functional group term. Blue bars represent SNPs where variation was lost. Red bars represent all other SNPs

Figure 15) Fisher’s Exact test results for SNPs that had variation in both Wyoming and Colorado. X-axis is the percentage of sequences and Y-axis is the gene functional group term. Blue bars represent SNPs where variation remained. Red bars represent all other SNPs