Genetic Differentiation Among Florida Populations of Diadema Antillarum

University of Central Florida STARS

Honors Undergraduate Theses UCF Theses and Dissertations

2016

Genetic Differentiation Among Florida Populations of Diadema antillarum

Luke M. Chandler University of Central Florida

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Recommended Citation Chandler, Luke M., "Genetic Differentiation Among Florida Populations of Diadema antillarum" (2016). Honors Undergraduate Theses. 44. https://stars.library.ucf.edu/honorstheses/44 GENETIC DIFFERENTIATION AMONG FLORIDA POPULATIONS OF DIADEMA ANTILLARUM

LUKE MILO CHANDLER

A thesis submitted in partial fulfillment of the requirements for the Honors in the Major Program in Biology in the College of Sciences and in the Burnett Honors College at the University of Central Florida Orlando, Florida

Spring Term 2016

Thesis Chair: Eric Hoffman, PhD.

ABSTRACT

This project used molecular genetic markers (microsatellites) to determine the amount

of genetic diversity within populations and whether significant differentiation exists

among Florida populations of the long-spined sea urchin, Diadema antillarum.

Specifically, this project aimed to (1) compare genetic diversity of D. antillarum from six

populations in south Florida ranging from Biscayne Bay, the Florida Keys, and Dry

Tortugas, and (2) determine whether two broodstock populations of D. antillarum

contain variation indicative of native Florida populations. Together, these questions can

address whether broodstock populations contain the genetic variation necessary to

meet the Florida Fish and Wildlife Conservation Commission’s (FWC’s) genetic policies

for reintroduction throughout south Florida. Global FST among native populations was

0.0004 with a highest pairwise FST of 0.0025 between the Upper Keys and the area

west of Key West, showing an overall trend of little natural differentiation between

populations. Global FST for all populations inclusive of the broodstock samples was

0.0019 with a highest pairwise FST between a native population and broodstock of

0.0066 between Dry Tortuga and Mote’s broodstock, indicating little differentiation

resulting from captive breeding. Average allelic richness and heterozygosity ranged from 22.6–24.4 and 0.937–0.956, respectively, for each population. Two-way ANOVAs comparing genetic diversity between native and broodstock populations showed no statistical difference in allelic richness (F= 3.892, p= 0.0535) or heterozygosity (F=1.43, p=0.237). The computer program STRUCTURE estimated the most likely number of

genetic clusters to be k=1, inclusive of broodstock populations, further indicating a lack of differentiation either among native populations or between native and broodstock

iii populations. These data suggest that captive-bred individuals of D. antillarum could be used for reintroduction as part of a plan to re-establish healthy urchin populations throughout the Florida Keys.

Acknowledgements

This research was funded by the Florida Fish and Wildlife Conservation Commission, grant 2403-7036. I would like to thank the UCF Office of Undergraduate Research, the Hoffman Lab, the PHaSeD Lab, Dr. Eric Hoffman, Dr. Linda Walters, Dr. William C. Sharp, and all the people who helped collect the sea urchins.

Table of Contents

Introduction ...... 1

Methods ...... 6

Sampling ...... 6

Molecular methods ...... 6

Statistical analysis ...... 7

Results ...... 9

Discussion ...... 11

Appendix A: Figures ...... 16

Appendix B: Tables ...... 23

Literature Cited ...... 28

List of Figures

Figure 1: Map of Florida showing the native populations used in this study ...... 18

Figure 2:Two-way ANOVA box-plot for differences in average allelic richness between native and broodstock populations ...... 19

Figure 3: Two-way ANOVA box-plot for differences in average heterozygosity between native and broodstock populations ...... 20

Figure 4: Output of STRUCTURE analysis for clusters a) K=2, b) K=6, c) K=8. No clear assignment per population is seen and thus no genetic differentiation exists ...... 21

Figure 5: Mean likelihood scores for values of K (clusters) from Structure Harvester .. 22

vii

List of Tables

Table 1: Forward and Reverse primers used for amplification of microsatellites in PCR

...... 24

Table 2: Allelic richness across loci and averaged per population...... 25

Table 3: Heterozygosity across all loci and averaged per population ...... 26

Table 4: Evanno table from Structure Harvester showing the likelihood of each value of

K (clusters). The largest Delta K generally corresponds with the true value of K...... 27

viii

Introduction

Between 1977 and 2001, coral cover declined by 80% throughout the Caribbean

(Gardner et al. 2003; Bellwood et al. 2004). During the past few decades, coral reefs

have been increasingly threatened by overfishing and other human activities that cause

excess pollution (Lessios 1988a; Brown 1987; Done 1992). Outside of human influence,

hurricanes and competition have also caused significant declines in coral abundance

(Hughes 1989). There is direct evidence that certain species of macroalgae significantly

reduced the survival and recruitment of coral larvae (Kuffner et al. 2006; Paul et al.

2011). This negative relationship between macroalgae and corals suggests that coral

recovery may not be possible until macroalgal biomass is reduced on reefs (Hughes &

Connell 1999). In order to restore coral reef systems in this sense, the trophic structure

and associated keystone species that have been lost due to anthropogenic or

catastrophic causes may need to be reestablished (Hughes 1994). When population restoration is considered, three basic criteria must be met. The first two are fundamental for the survival of the population; sufficient habitat protection (Soule & Gilpin 1986;

Armstrong & Seddon 2008) and awareness of life history stages critical for survival and

reproduction (Marcot & Holthausen 1987; Lande 1988). The third requirement of

instating a restoration policy is to determine how the genetic variation of the

reintroduced population compares to the population being supplemented (Bowles &

Whelan 1994).

In recent times, the steepest decline of coral in the Caribbean occurred in 1983-1984 and coincided with the massive die-off of a dominant herbivore, the long-spined sea urchin Diadema antillarum (Gardner et al. 2003). Mature D. antillarum can reach up to

500 mm in diameter with their spines ranging another 300-400 mm. They have been seen to gather in groups around the base of coral reefs for protection from their few predators, the queen triggerfish, two species of toadfish, and a couple shellfish species.

With a typical lifespan from 4 to 8 years, this external fertilizer is seen to have a long pelagic larval duration of 30-60 days, giving the sea urchin a wide dispersal rate, especially when strong ocean currents are present (Karlson & Levitan, 1990; Lessios

1988b; White et al. 2010).The D. antillarum die-off expedited the coral’s decline by allowing macroalgae to flourish (Hughes et al. 1987; Lessios 1988a, b; Levitan 1988;

Carpenter 1990; Gardner et al. 2003). Diadema antillarum was well documented in tropical systems as an indiscriminate grazer of macroalgae due to its ability to consume large amounts of algal biomass on coral reef ecosystems (Sammarco et al. 1974;

Carpenter 1986; Morrison 1988; Carpenter 1990). Carpenter (1990) estimated that D. antillarum consumed 60 - 97% of all macroalgae in St. Croix before the 1980s die-off. In

1983, when this unidentified waterborne pathogen caused the mass mortality of 97% of the D. antillarum population, macroalgae proliferated over the coral reefs (Lessios

2001a; Lessios 2005). Within 16 months of the die-off, macroalgal biomass had increased 439% (Carpenter 1990).

A comparative study of coral versus sea urchin abundances in Belize, St. Croix,

Barbados, Jamaica, Bonaire, and Grenada showed that D. antillarum-grazed areas

2 increased coral abundance 10-fold to densities of 5 – 32 individuals/m2 (Carpenter &

Edmunds 2006). Although the sea urchins occurred in varying densities, D. antillarum was generally abundant with common densities ranging from 3 up to 71 individuals/m2

(Ogden & Zieman 1977; Bauer 1980; Sammarco 1980; Hawkins and Lewis 1982). Post- mortality densities were found to be 97-100% less than pre-mortality densities (Bak et al. 1984). Chiappone and colleagues (2008) documented that recovery of D. antillarum in the Florida Keys has been much slower than expected, with densities still well below one individual/m2. During seven different annual sampling periods (2000-2007) the maximum site-level density in the Florida Keys was only 0.33 individuals/m2, with the highest densities of larger (> 50 mm diameter) individuals reported from only a few locations (Chiappone et al. 2008). The most recent census of D. antillarum in 2011 shows even more of a decline in the Florida Keys, with an average density of 0.02 individuals/m2 (Lessios 2016).

The recovery of D. antillarum in many locations over the last 35 years has not been as successful as anticipated, and likewise, the reduction in macroalgal biomass has been lower than predicted (Lee 2006). Genetic variation and the problems associated with small population size, such as inbreeding, may have contributed to the delayed recovery of D. antillarum (Stephens & Sutherland 1999; Charlesworth 1998). High inbreeding coefficients have been found in a microsatellite study of a different sea urchin, Strongylocentrotus droebaciensis, in a population affected by disease (Addison

& Hart 1994). Inbreeding, however, has not been documented in recovering populations of D. antillarum since the massive die-off (Lessios et al. 2001a). Another potential

reason for the delayed recovery of D. antillarum includes the Allee effect, where the

density of individuals is not high enough to reproduce successfully (Knowlton 1992;

Courchamp et al. 1999; Carpenter & Edmunds 2006). Recent advances in the ability to

culture D. antillarum ex-situ have given rise to the concept that hatchery-produced sea

urchins can be used to augment census size of naturally occurring populations in the

Florida Keys (Harris & Eddy 2015). Many specific management actions involving D.

antillarum restoration have been suggested; even some contradictory measures

regarding whether reintroductions should be with implemented as aggregated clumps or

dispersed individuals (compare Miller et al. 2007 and Macia et al. 2007). Successful

reintroductions of these broodstock populations would increase sea urchin numbers

such that Allee effects or inbreeding depression are not a problem and an abundant,

ecologically competent population can be re-establish (Idrisi et al. 2003).

Previous research on D. antillarum examined phylogeographic variation of mitochondrial

DNA at a global scale and found regional differences within Diadema between the western Atlantic (Caribbean) and the eastern Atlantic (Canary Islands) (Lessios et al.

2001a, b). Caribbean populations exhibited surprisingly high genetic diversity and little

variation was found among populations (Lessios et al. 2001a, b). Unfortunately, the

marker utilized in these studies would not necessarily resolve fine-scale differentiation

within and among populations from a common region (e.g. the Caribbean) (Johnson et al. 2003). Studies of many hypervariable microsatellite loci are known to be able to

depict the fine-scale genetic structure of populations (Jarne & Lagoda 1996; Anderson

et al. 2010; Hess et al. 2011; Chambers & MacAvoy 2000). For this reason,

4 microsatellite DNA analysis was used in my study of south Florida populations.

Specifically, this project aimed to (1) compare natural genetic diversity and differentiation of D. antillarum from six populations in south Florida ranging from

Biscayne Bay, the Florida Keys, and Dry Tortugas and (2) determine whether two broodstock populations of D. antillarum contain genetic variation indicative of native

Florida populations. Together, these questions can address whether broodstock populations contain the genetic variation necessary to meet the Florida Fish and Wildlife

Conservation Commission’s (FWC’s) genetic policies for reintroduction throughout south Florida. These policies account for potential genetic impacts by requiring captive organisms to be released into areas of the same genetic unit and to not alter the natural genetic diversity within and among wild populations (FWC Rule 68B-8.003, F.A.C.).

Methods

Sampling

Sea urchin tissue samples were collected by non-invasive sampling of regenerative

tube feet as described by Addison and Hart (2004). One hundred and ninety samples were collected from six native populations of D. antillarum: Upper Keys (UPPK; n=34),

Middle Keys (MIDK; n=31), Lower Keys (LOWK; n=30), West of Key West (WKYW;

n=33), Dry Tortugas (DRTO; n=30), and Biscayne Bay (BBAY; n=32) (Figure 1).

Additionally, 66 captive-bred samples were obtained from two broodstock populations;

housed at Mote Marine Laboratory (MTBK; n=30) originally collected from Jeff’s Patch

Reef and Martin Moe’s laboratory (MOBK; n=36) originally collected from Jeff’s Patch

Reef and Boot Key. Tissue samples were stored in Drie-riteTM desiccant as a

preservative.

Molecular methods

DNA was extracted from sea urchin tissue via QIAGEN DNeasy Blood and Tissue

Extraction Kit protocol (Qiagen Inc., Germany). Utilizing loci designed and optimized in

the Hoffman Laboratory at UCF (Table 1), microsatellite amplification was performed

through polymerase chain reaction (PCR). Reactions took place using 1µL DNA (about

50 ng total DNA), 2µL 10x PCR buffer, 1.3µL 25mM MgCl2, 1.6µL 10mM dNTPs, 1µL

10mM florescent dye, 1µL 10µM reverse primer, 0.3µL 10µM forward primer, and 0.2µL

Taq polymerase. PCRs included an initial denaturation step at 94°C for 4 min., 35

cycles at 94°C for 30 s., annealing at 55°C for 35 s., and 72°C for 45 s., followed by a

final extension period at 72°C for 7 min. Results of each PCR were viewed on a 2%

agarose gel processed through gel electrophoresis. Positively amplified microsatellite

PCR product was sent to the University of Arizona Genetics Core (Tuscan, AZ, USA) to

be genotyped. Allele sizes were appropriately scored using the program GeneMarker v2.6.3 (SoftGenetics, LLC).

Statistical analysis

Once accurate allele sizes were scored for all individuals at each locus and each

population, Genepop v4.2.1 (Raymond & Rousset 1995; Rousset 2008) was used to

calculate deviations from Hardy-Weinberg Equilibrium (HWE) using exact tests with a

Bonferroni correction (Rice 1989) to account for multiple comparisons. Genepop was

also used to test population differentiation by estimating global FST and pairwise FST among native populations. Moreover, we compared estimates of allelic richness and

heterozygosity, calculated with FSTAT v1.2 (Goudet 1995), among populations to

determine whether any outliers occurred. In order to evaluate whether levels of genetic

diversity in broodstock samples were comparable to natural sites, a two-way analysis of

variance (ANOVA) was run in R (R Core Team 2012), testing for the effects of locus

and diversity. Finally, the program STRUCTURE v2.3.4 (Pritchard et al. 2000), a

Bayesian-based clustering method for multilocus data, was used to determine the most

likely number of clusters (K) supported by the data, with 10 runs for each cluster (K)

from 1 to 8 (100,000 iterations with 10,000 burn-in period). The program STRUCTURE

HARVESTER was used to evaluate the most likely number of cluster (K) and to determine if any substructure exists among sample sites (Earl and vonHoldt 2012).

Results

Each population at each locus was found to be in HWE after Bonferroni correction, with the exception of two comparisons (LOWK at KS11 and MOBK at KS24), confirming the overall utility of these loci as genetic markers. To address the question of whether or not there were differences among native populations we estimated both global and pairwise

Fst (Rutz et al. 2012; Butlin 2010). Global Fst, including all native populations, was

0.0004, showing little differentiation among populations. Pairwise FST, among the native populations, ranged from 0.0004 – 0.0025 (with the highest estimate between West of

Key West and Upper Keys) suggesting a high amount of gene flow among all populations. Additionally, average allelic richness per population ranged from 23.1

(West of Key West) to 24.4 (Lower Keys) (Table 2) and average heterozygosity per population ranged from 0.941 (Dry Tortugas) to 0.956 (Lower Keys) (Table 3), showing high similarity among these estimates of genetic diversity among the native populations.

To determine whether differences exist between the native populations and the two broodstock populations, we estimated genetic differentiation within the broodstock populations. Overall, global FST, accounting all populations and all loci, was estimated to be 0.0019, suggesting a lack of differentiation between native and broodstock populations. Pairwise FST between the two broodstock populations was 0.0079 and the greatest value of FST between native and broodstock was 0.0066 between Dry Tortugas and Mote’s broodstock. We found the average allelic richness (Table 2) and heterozygosity (Table 3) of the broodstock populations to be 22.58 and 0.94,

9 respectively. When analyzed via a two-way ANOVA, both allelic richness (F= 3.892, p=

0.0535; Figure 2) and heterozygosity (F=1.43, p=0.237; Figure 3) were not significantly different between natural versus broodstock populations.

The bar-plots for STRUCTURE analysis including all 8 populations for the assumptions

K=2, K=6, and K=8 (Figure 4) show no clear assignment for any clustering across all populations. Furthermore, the mean likelihood values at the lowest values of K are at their maximum and do not increase as the number of clusters increases (Figure 5). Data that fail to show an increase in mean likelihood with increasing values of K can be indicative of a single genetic cluster, K=1. In contrast, STRUCTURE HARVESTER estimated the critical delta K value to be highest (and thus the most likely number of clusters) at K=6 (Table 4).

Discussion

The resolution of both within- and between-population levels of genetic variation of a

single species was appropriately addressed by assessing diversity at eight

hypervariable microsatellite loci. My results showed that high gene flow exists

throughout south Florida and native populations should be treated as panmictic.

Moreover, the comparison of broodstock to native populations revealed no

differentiation, even though the table given by STRUCTURE HARVESTER implied

differently; the likelihood of only one genetic cluster cannot fundamentally be tested

(Hubisz et al. 2009). Other supporting data, such as an assignment bar-plot, the trend of

likelihood values and FST estimates, provide strong evidence that the native Florida and

broodstock populations are not genetically differentiated. Coupled with similar levels of

heterozygosity and allelic richness, it is ascertained that individuals obtained from Jeff’s

patch reef, Summerland Key, Matecumbe Key, or Boot Key can be reared ex-situ be released safely into Florida waters as a restoration effort. Such increases in the number of viable individuals could help to increase numbers of naturally occurring sea urchins and approach historic D. antillarum population sizes.

The primary advantages of restoring D. antillarum populations to their historic

population sizes include the rise of stable sea urchin populations and the return to the

historic trophic structure of Caribbean coral reefs. The return of D. antillarum should

restrict macroalgal growth and return the coral reef ecosystem back to its more

historically natural state (Lessios et al. 2001; Tuya et al. 2005). In turn, this would

impact species reliant on a healthy coral ecosystem, including the diversity of reef

associated fish and invertebrates. In addition to the biological benefits, restored coral

reef ecosystems would have significant anthropogenic effects improving societies both

economically and aesthetically (Cesar et al. 2003; Yeo 2004).

As part of a comprehensive coral reef restoration strategy, the return of D. antillarum

has drastically improved coral abundance in other Caribbean locations. A comparative

study of coral versus sea urchin populations in Belize, St. Croix, Barbados, Jamaica,

Bonaire, and Grenada showed that D. antillarum-grazed areas increased coral

abundance 10-fold (Carpenter & Edmunds 2006). Thus, it is pertinent for Florida’s coral

reef restoration that populations of D. antillarum are supplemented by many individuals.

Repatriation into natural habitats is often accompanied by captive-breeding programs

whose goal is to reduce the probability of extinction by increasing the number of viable

individuals in small populations (Scott & Carpenter 1987; Dodd & Seigel 1991). These

captive bred individuals are then supplemented into the wild as an attempt to assist the

recovery of the species. Approximately 700 translocations occur each year in the

United States, about 90 percent being native game species. Game species tend to have a much higher success rate (86%) than the reintroductions of threatened, endangered or sensitive species (46%) (Griffith et al. 1989). The potential genetic concerns when

reintroducing threatened species grown ex-situ to the wild include the loss of wild

population fitness and viability, altered natural genetic diversity within and among wild

populations, and the reduced long-term adaptive potential of wild populations (FWC

2007). Reynolds et al. (2013) demonstrated that, after accounting for sufficient genetic

diversity, decimated populations of seagrass were replenished by active restoration

within 10 years, which would have otherwise taken between 125 and 185 years to

achieve through natural recruitment events.

When recoveries of small populations are attempted through captive-breeding, many

limitations can affect the overall success rate. Problems such as establishing self-

sufficient captive populations, high costs, domestication, disease outbreaks, and

maintaining genetic continuity are all management concerns for species that are already

near extinction (Snyder et al. 1996). However, many improvements in breeding

efficiency and species consideration are showing a return of naturally occurring

ecosystems through captive-breeding and reintroduction plans, as is demonstrated by

the restoration of coniferous, rhizobial and actinorhizal species to revive the barren and

stunted, open birch–maple woodland in Sudbury, Ontario (Bradshaw & Chadwick 1980;

Winterhalder 1996; Dobson et al. 1997; Seddon et al. 2007). Supplementation and

restoration of keystone species are essential for maintaining the foundation of a

fluctuating ecosystem and preventing unwanted shifts in the environment.

Additionally, introductions of non-native species are purposefully done with the intent to

improve habitat or increase food production in a certain area. These introductions do

not always provide the desired outcome. One example of an invasive introduction is the

table potato,Solanum tuberosum (Suttles 1987). This potato was brought over by settlers into Fraser River Valley of British Columbia and competed against a native root

vegetable wapato, Sagittaria latifolia (Spurgeon 2001). In this case, the table potato

greatly reduced and, in some cases, virtually eliminated wapato growth, leading to the

alteration of the wetlands to European-style agricultural fields (Garibaldi & Turner 2004).

No restoration efforts were enforced to save the wapato and as a result, the entire

ecosystem changed, permanently affecting the indigenous people of the Fraser River

Valley.

The result of habitat loss is that natural communities may become restricted to

disconnected habitat fragments and normal development may be disrupted (Wilson

1988). Small fragmented habitats have a higher likelihood of showing increased rates of

extinction and translocation would be required to maintain community composition,

especially for communities with limited dispersal abilities (MacArthur & Wilson 1967).

For example, to restore exploited fisheries in Hong Kong, an artificial reef program has

been implemented which deploys reefs in selected locations to establish new nurseries

and enhanced spawning-ground protected areas (Wilson et al. 2002). Likewise, the

clear destruction of filter feeding oyster reefs by boating and overharvesting (Newell

1988; Grizzle et al. 2002; Wall et al. 2005), as well as diseases, such as dermo caused

by the pathogen Perkinsus marinus (Andrews 1988), is known to cause eutrophication

and the overall loss of the normal marine ecosystem (Mann & Powell 2007; Cerco &

Noel 2007). The rehabilitation of major species, such as the eastern oyster Crassostrea

virginica, would show a return in major environmental services; beyond just control of

phytoplankton blooms, these oyster beds provide seston filtration, refuge from

predation, benthic-pelagic coupling, formation of nesting habitats, and the establishment

of feeding grounds for mobile species and for sessile stages of species that attach to

molluscan shells (Boudreaux 2006; Coen et al. 2007). D. antillarum should be

considered similarly influential at returning the historic trophic structure and revitalizing

the coral reef ecosystem.

For the case of the Caribbean coral reefs, macroalgae was kept under control by major

herbivores like the parrotfish and sea urchins (Ogden 1976; Hay & Taylor 1985). Since

many reef fishes are highly selective in the algae they consume and sea urchins,

conversely, tend to base their selection on relative abundance, estimated to have once consumed over 60% of available macroalgae, sea urchins are a good species to focus

on for coral reef recovery (Ogden & Lobel 1978; Carpenter 1990). While factors like

overfishing and pollution are detrimental to all species, the pathogen-induced die off of

D. antillarum left a specific niche too large to be filled by other herbivores, (Ogden &

Carpenter 1987; McClanahan et al. 1994; Mumby et al. 2006). Diadema antillarum’s

high genetic variability and success in restoring balance to the reef systems provide a clear distinction for why it is the best species to focus repopulation efforts through

captive-breeding and reintroduction (Carpenter 1985).

In conclusion, successfully reared D. antillarum can, and should, be implanted into

south Florida reef habitats as a means to supplement the wild populations. With a

successful broodstock program here in Florida, we can supplement numbers of native

D. antillarum and see the benefits of this species with regard to coral abundance as

seen elsewhere (Hughes 1989; Carpenter & Edmunds 2006; Lee 2006).

Appendix A: Figures

Population Originating reef(s) Biscayne Bay (BBAY) Marker 3 Upper Keys (UPPK) Pickle’s Reef Middle Keys (MIDK) E. Washer Woman, Sandbank’s, Jeff’s Patch Reef Lower Keys (LOWK) Nearshore Newfound Harbor, Wonderland, Big Pine area West of Key West (WKYW) Patch Reef NW of Sand Key Dry Tortugas (DRTO) Garden Key, Pinnacle Reef Moe’s Broodstock (MOBK) Boot Key, Jeff’s Patch Reef Mote’s Broodstock (MTBK) Jeff’s Patch Reef

Figure 1: Map of Florida showing the native populations used in this study

Figure 2:Two-way ANOVA box-plot for differences in average allelic richness between native and broodstock populations

Figure 3: Two-way ANOVA box-plot for differences in average heterozygosity between native and broodstock populations

Figure 4: Output of STRUCTURE analysis for clusters a) K=2, b) K=6, c) K=8. No clear assignment per population is seen and thus no genetic differentiation exists

Figure 5: Mean likelihood scores for values of K (clusters) from Structure Harvester

Appendix B: Tables

Table 1: Forward and Reverse primers used for amplification of microsatellites in PCR

Primer Locus Sequence name

EAH908 KS3 F 5' TGTAAAACGACGGCCAGTCTTCCCGTTTTGTTGCATTT 3'

EAH909 KS3 R 5' CCGAACATGGATCCCTAAAA 3'

EAH912 KS9 F 5' TGTAAAACGACGGCCAGTTTTGCCAATGAGCTGTCAAG 3'

EAH913 KS9 R 5' CCACCTCAACCACATCTGAG 3'

EAH914 KS11 F 5' TGTAAAACGACGGCCAGTTGCTCCTCCGAAACCTAGAA 3'

EAH915 KS11 R 5' GATGGTGAATTGCGTGAATG 3'

EAH916 KS17 F 5' TGTAAAACGACGGCCAGTTCCAAGAAGATTATTATCTCAAATGAA 3'

EAH917 KS17 R 5' GCATGCAGACTTTGCAGATG 3'

EAH918 KS24 F 5' TGTAAAACGACGGCCAGTAAGACATTTCGGTTTTTGTTTTG 3'

EAH919 KS24 R 5' ATTTCTGCGTGGAAGAGGTG 3'

EAH920 KS26 F 5' TGTAAAACGACGGCCAGTTCTATGCTTCCAAAACGGAAA 3'

EAH921 KS26 R 5' CAATGCTCAGGTAAGATTTCG 3'

EAH922 KS29 F 5' TGTAAAACGACGGCCAGTCTTCCCGTTTTGTTGCATTT 3'

EAH923 KS29 R 5' AGTTGGAAGGGACGATGTTG 3'

EAH924 KS30 F 5' TGTAAAACGACGGCCAGTGGCGAAGATTCTCACAACCA 3'

EAH925 KS30 R 5' TTGCAATTGACTTTACTGATTATTG 3'

Table 2: Allelic richness across loci and averaged per population.

Mote’s Moe’s Biscayne Bay West of Key West Dry Tortugas Upper Keys Middle Keys Lower Keys Broodstock Broodstock

KS3 29.26 29.016 27.533 31.625 29.652 29.981 28.804 28.907

KS9 27.845 28.359 28.03 29.05 26.448 26.822 24.961 27.567

KS11 17.352 18.919 20.692 17.706 20.149 19.404 20.238 17.642

KS17 25.889 22.495 25.565 22.357 24.132 26.445 26.737 24.023

KS24 23.993 25.939 26.251 20.787 23.787 23.886 21.982 21.094

KS26 18.094 19.298 17.744 13.57 19.058 17.488 16.767 18.143

KS29 28.245 26 28.305 31.415 30.347 30.67 25.428 26.783

KS30 14.778 14.443 15.057 20.432 17.13 20.856 15.747 16.485

Average 23.182 23.059 23.647 23.368 23.838 24.444 22.583 22.581

Table 3: Heterozygosity across all loci and averaged per population

Mote’s Moe’s Biscayne Bay West of Key West Dry Tortugas Upper Keys Middle Keys Lower Keys Broodstock Broodstock

KS3 0.982 0.982 0.972 0.984 0.982 0.98 0.978 0.978

KS9 0.977 0.979 0.979 0.982 0.974 0.975 0.971 0.978

KS11 0.933 0.947 0.942 0.93 0.949 0.955 0.952 0.938

KS17 0.974 0.966 0.97 0.961 0.972 0.975 0.969 0.969

KS24 0.961 0.958 0.971 0.957 0.971 0.972 0.958 0.943

KS26 0.951 0.956 0.944 0.928 0.945 0.948 0.936 0.918

KS29 0.978 0.975 0.977 0.984 0.985 0.981 0.972 0.972

KS30 0.782 0.766 0.773 0.812 0.795 0.867 0.762 0.842

Average 0.9423 0.9411 0.941 0.9423 0.9466 0.9566 0.9373 0.9423

Table 4: Evanno table from Structure Harvester showing the likelihood of each value of K (clusters). The largest Delta K generally corresponds with the true value of K.

# K Reps Mean LnP(K) Stdev LnP(K) Ln'(K) |Ln''(K)| Delta K

1 10 -13350.66 1.3176 N/A N/A N/A

2 10 -13483.68 141.2043 -133.02 0.62 0.004391

3 10 -13616.08 260.8428 -132.4 186.31 0.714262

4 10 -13934.79 873.1946 -318.71 413.36 0.473388

5 10 -13840.14 600.3508 94.65 966.46 1.609825

6 10 -14711.95 1683.7279 -871.81 3023.38 1.795646

7 10 -18607.14 4035.4979 -3895.19 2613.4 0.647603

8 10 -19888.93 10609.8689 -1281.79 N/A N/A

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