California State University, Northridge Genetic

Home , Anthoathecata, Millepora alcicornis, Millepora complanata, Millepora dichotoma, Tennessee Reef

CALIFORNIA STATE UNIVERSITY, NORTHRIDGE

GENETIC DIVERSITY, POPULATION STRUCTURE AND CONNECTIVITY OF

MILLEPORA ALCICORNIS (HYDROZOA: ANTHOMEDUSAE: MILLEPORIDAE) IN

THE FLORIDA REEF TRACT

A thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in Biology

Diana M. Jacinto

August 2014

The thesis of Diana M. Jacinto is approved:

Peter J. Edmunds, Ph.D. Date

Jeanne Robertson, Ph.D. Date

Elizabeth Torres, Ph.D. Date

Steve Dudgeon, Ph.D., Chair Date

California State University, Northridge

ii DEDICATION

This thesis is dedicated to my parents for providing me with the constant love and encouragement to achieve academic success. To my mom, thank you for always supporting and encouraging me throughout my life. To my dad, you have always nurtured my inquisitive mind and for that I am forever grateful. Thank you for being not only the best father but also a best friend.

iii ACKNOWLEDGMENTS

I would like to thank my committee members who supported and encouraged my efforts in completing this thesis. To my advisor Dr. Steve Dudgeon, you have impacted my growth as a scientist and my perspective on the world around me, for that I am grateful. To Dr. Jeanne Robertson, thank you for the time you invested in guiding this project and the positivity you provided me with throughout my academic journey. To Dr. Peter Edmunds, thank you for input and guidance in writing my thesis. To Dr. Elizabeth Torres, thank you for providing me with the scientific foundation and support that has led to the completion of this thesis. I would especially like to thank the individuals who helped with the collection of the samples used in this study, Sylvia Zamudio and William Precht. I would also like to thank the various individuals who have provided input and advice that led to the completion of this thesis. I am also grateful for my labmate Lareen Smith, who has been supportive and positive throughout my time at CSUN. I am grateful for the invaluable friendships I have formed with my fellow graduate students and wish them nothing but success. I would also like to thank my friends and Daniel Gray Longino, who have been supportive and understanding throughout my academic journey.

This research was supported by funding from the National Science Foundation California State University Louis Stokes Alliance for Minority Participation Bridge to the Doctorate (CSU-LSAMP BD) Award (HRD-1139803), CSUN-Graduate Equity Fellowship, CSUN Thesis Support, and CSU California Pre-Doctoral honorable mention fund.

iv TABLE OF CONTENTS

Signature Page ii Dedication iii Acknowledgments iv Abstract v

INTRODUCTION 1 Millepora 4 Southern Florida ocean current patterns 8 Genetic diversity, population structure and connectivity of M. alcicornis in the FRT 11 Hypotheses 12

METHODOLOGY 15 Sample collection 15 DNA extraction, microsatellite amplification and genotyping 16 Microsatellite quality analysis 18 Statistical analyses 19 Clustering analysis 20 Clonal analysis 21

RESULTS 23 Microsatellite loci, HWE, null alleles, and linkage disequilibria 23 Genetic diversity and differentiation 23 Clustering analysis 25 Clonal analysis 26

DISCUSSION 27 Differentiation and connectivity of M. alcicornis 27 Genetic diversity 30 Genotypic diversity 31 Conclusion 32

TABLES AND FIGURES 35

REFERENCES 48

APPENDIX A: Multilocus genotypes in data set 57 APPENDIX B: Psex values 62

v ABSTRACT

GENETIC DIVERSITY, POPULATION STRUCTURE AND CONNECTIVITY OF

MILLEPORA ALCICORNIS (HYDROZOA: ANTHOMEDUSAE: MILLEPORIDAE) IN

THE FLORIDA REEF TRACT

Diana M. Jacinto

Master of Science in

Biology

Coral reefs are experiencing global declines due to changing environmental conditions triggered by climate change and anthropogenic effects impacting important reef-building organisms and their inhabitants. Millepores are calcareous hydrocorals found on shallow reefs worldwide, however little information is known about their genetic diversity and population biology. The present study sought to determine the population structure and genetic diversity of Millepora alcicornis, a branching fire coral, in reefs found in the Florida Reef Tract (FRT) and population connectivity was inferred.

Five microsatellite markers were used to detect genetic differentiation between 12 sampling sites from reefs from the middle Keys and Miami within the FRT. A single panmictic population of M. alcicornis in the FRT (K=1; FST=0.001) was found with moderate levels of genetic diversity (Ho=0.426, SE=0.023; Na=6.0, SE=0.763) inferring high connectivity and gene flow among reefs in the FRT. High connectivity of M. alcicornis in the FRT along with moderate levels of genetic diversity is a hopeful indication that M. alcicornis will be better able to acclimate to changing environmental conditions.

INTRODUCTION

Coral reefs are not only a great source of biodiversity in marine habitats, but a great source of biodiversity on the planet (Knowlton 2001a). Coral reef-building organisms create habitats for thousands of species, with diversity estimates of marine inhabitants ranging from 600,000 to more than 9 million (Knowlton 2001b; Reaka-Kudla

1997; Knowlton et al. 2010). Globally, reefs have experienced declines in cover (the percentage of hard substrate covered by living coral tissue; Selig and Bruno 2010) due to climate change and human impacts such as coral bleaching, habitat destruction, overfishing and pollution from agriculture and land development (Hughes et al. 2003;

Hughes et al. 2010). The decline in coral cover is in large part due to the loss of important reef-framework builders, scleractinians in addition to reef-building octocorals and hydrocorals (Carpenter et al. 2008).

Understanding the genetic population structure, the partitioning of putative populations based on allele frequencies (Freeland et al. 2011), and connectivity, the genetic exchange of individuals among geographically separated populations (Cowen et al. 2007), of reef-building species is crucial to develop and implement appropriate management strategies that could prevent further decline of coral cover in reef ecosystems. Marine Protected Areas (MPAs) are currently the best management tool for conserving these threatened reef systems (Hughes et al. 2003). Exploring the extent of genetic connectivity within and among coral reefs provides MPA managers with the correct information to determine the spatial management and appropriate placement of

MPAs in coral reef habitats (Palumbi 2003; McCook et al. 2009; Cvitanovic et al. 2013).

High levels of gene flow or connectivity promotes high genetic diversity (the amount of

genetic variation contained within population; Freeland et al. 2011) and genotypic diversity (the number of unique multilocus genotypes present in a population; Baums et al. 2006a) within a species, which results in an increased potential ability to adapt to and recover from environmental changes (Markert et al. 2010; Hughes et al. 2003). Exploring the degree of connectivity among reefs can determine how broad (regional; i.e., involving two or more countries) or localized (only encompassing certain areas over a short distance) MPAs should be, based on conserving reef-building organisms with high genotypic diversity (Hughes et al. 2003). MPAs are unlikely to prevent mortality of corals due to bleaching because MPAs cannot control rising water temperatures (Hughes et al. 2003); however, MPAs will facilitate a partial recovery of reefs that are populated by different reef-building organisms with diverse genotypes (Hughes et al. 2003). High genotypic diversity suggests high rates of gene flow in which various alleles into are introduced into a population creating new gene combinations on which selection can potentially act (van Oppen and Gates 2006).

Investigating population connectivity in a marine environment remains a challenge due to the technical limitations of tracking large numbers of small propagules

(i.e., larvae) from an organism in a vast fluid environment (larval dispersal; Selkoe and

Toonen 2011; van Oppen and Gates 2006). Connectivity among marine populations can be inferred by estimation of genetic differentiation, the magnitude of genetic divergence among and within putative populations (Bird et al. 2011), through the use of molecular markers. Molecular markers, such as microsatellites (short tandem repeats of DNA motifs; Freeland et al. 2011), not only provide estimates of genetic diversity but also allow for the identification of clones caused by asexual reproduction or unique genotypes

produced by sexual reproduction. Determining levels of connectivity among coral reefs can help to determine how populations will respond to natural and anthropogenic disturbances. Low levels of genetic differentiation would infer gene flow (high connectivity) between reefs causing a population to be more likely replenished by migrating individuals between reefs after a disturbance (Jones et al. 2009). Contrary, high levels of genetic differentiation and low levels of connectivity can lead to habitat loss of a reef after a disturbance, since it is unlikely that there would be population replenishment from migrating individuals (Jones et al. 2009).

Studies of genetic population structure and connectivity of reef-building organisms have focused on scleractinians (i.e., stony corals, Class: Anthozoa; Baums et al. 2005; Baums et al. 2006a; Baums et al. 2010; Goffredo et al. 2004; Hemond and

Vollmer 2010; Ayre and Hughes 2004; Nakajima et al. 2010; Mackenzie et al. 2004) and little attention has been given to sympatric hydrocorals in the genus Millepora (Class:

Hydrozoa), which can also be prominent reef-framework builders (Lewis 1989; Zankl and Schroeder 1972; Loya 1976; Adey 1977; Adey and Burke 1977; Dustan 1985;

Amaral et al. 2008). Unlike scleractinians, millepores exhibit an alternation of generations involving a planktonic medusa with a short pelagic life (<12 hrs; Soong and

Cho 1998) and little is known about the duration of the pelagic larva (Lewis 2006).

Determining the genetic structure and genetic connectivity among individuals of millepores over a geographic scale (i.e., between islands or adjacent reefs) provides an indirect means to estimate the organism’s pelagic larval duration and dispersal capability.

The present study developed and utilized 5 microsatellite loci to determine the genetic structure of Millepora alcicornis, a calcareous hydrocoral abundant on shallow coral

reefs (Lewis 1989; Edmunds 1999; Dustan 1985; Amaral et al. 2008), along the Florida

Reef Tract (FRT). Inferences regarding population connectivity and the role of dispersing larvae of the species were made as result of this study. The null hypothesis of panmixia in

M. alcicornis in the FRT was tested. The expectation for no population structure is based on the potential of broad distribution of reproductive propagules (larval dispersal; Lewis

1989; Edmunds 1999), and the strong and consistent surface currents running through the

FRT (Lee et al. 1992).

Millepora

Millepores are colonial polypoidal hydrozoans in the benthos of tropical seas that secrete a calcareous skeleton (Lewis 1989). Two types of polyps protrude from their skeleton, short feeding gastrozooids and long defensive dactylozooids (Kruijf 1975).

Millepores are often referred to as “fire corals” due to their highly toxic defensive polyps, which cause painful epidermal swellings when inflicted on humans (Middlebrook et al.

1971; Lewis 2006). Millepores are found on coral reefs worldwide in shallow waters to depths of 40 m (Lewis 1989). Eighteen species of millepores have been distinguished, mostly based on morphometric parameters. Seven species are unique to the Western

Atlantic (4 Caribbean and 3 endemic to Brazil; Lewis 2006; Amaral et al. 2008), and the other 11 have been found in the Indo-Pacific (Goldberg 2013). In shallow tropical seas, millepore colonies can be prominent on coral reefs and can serve as important reef- framework builders, second to scleractinians (Bahamas: Zankl and Schroeder 1972; N.

Gulf of Eilat, Red Sea: Loya 1976; West Indies and Lesser Antilles: Adey 1977; Adey and Burke 1977; Key Largo, FL: Dustan 1985; Brazil: Amaral et al. 2008). Like

scleractinians, millepores are heterotrophic, feeding on zooplankton (Lewis 1992), and autotrophic, relying on dinoflagellate symbiotic Symbiodinium (Lewis 2006). Colony morphology can be highly plastic within a species (Weerdt 1981) depending on their geographic distribution in the water (i.e., shallow vs. deeper waters). Upright, delicate branching forms of different species of millepores are found in calm, sheltered waters.

Encrusting and plate-like forms of different species of millepores are typically found in more turbulent waters (Vago et al. 1994; Meroz-Fine et al. 2003; Lewis 2006).

Millepores exhibit a dimorphic life cycle with asexual and sexual modes of reproduction (Lewis 2006). Asexual reproduction occurs through sympodial growth by the production of new skeleton and soft tissue, by fission (Lewis 1992), and through the reattachment, regeneration, and repair of colony fragments (fragmentation; Edmunds

1999; Lewis 1991a). Edmunds (1999) found that 79% of colony branches of M. alcicornis in St. John, U.S. Virgin Islands were broken off after Hurricane Gilbert (1988) at a depth of 3 meters. Of the 1252 broken fragments found either attached or detached from a substrate, 4.1% were found reattached to rock, 0.5% attached to sand, 8.4% fused to underlying millepore tissue, and the remaining 87% of fragments were unattached. The potential for proliferation through fragmentation was estimated to be at least 0.5 colonies m-2 from a single disturbance, which is quite high due to the large number of fragments produced in relation to the small percentage (4%) of broken branches that formed new colonies (Edmunds 1999). Similarly, Lewis (1991a) found 35% of broken M. complanata fragments reattached across 3 fringing reefs after a storm in Barbados, West Indies. Both of these studies show how fragments of colonies can act as asexual propagules and potentially increase population size after a physical disturbance.

Unlike scleractinians, millepores are gonochoristic hydrozoans that exhibit free- swimming medusae (approximately 0.05-1.0 mm diameter length; Lewis 2006) during sexual reproduction (Hickson 1891; Lewis 2006). The medusae, encased in depressions in the skeleton known as ampullae, have a suggested limited role of enclosing, protecting, and dispersing gametes (Lewis 1991b; Soong and Cho 1998) due to the short amount of time spent in the water column (<12 hours; Soong and Cho 1998). Development of medusae inside ampullae occurs within 20-30 days (Soong and Cho 1998). The ampullae appear as swollen bumps on the colony surface right before medusae are released. After the medusae pulse out of the ampullae, these depressions later become filled with skeletal growth (Lewis 2006). Soong and Cho (1998) investigated the sexual reproduction of fire corals on the coast of southern Taiwan by observing medusae development and release.

Once the medusae escape from the ampullae of the colony, the medusae pulsate to the ocean surface to aggregate and synchronize with other medusae. Male medusae, containing a single sperm sac, are the first to be released followed by female medusae containing approximately 3-5 eggs, which contain numerous Symbiodinium (Lewis

1991b). Female medusae are only active in the water column for an hour after releasing their gametes and then quickly sink to the ocean floor. Male medusae empty their sperm sacs over the span of an hour and remain active for approximately 6-12 h before disintegrating. Fertilization in vitro occurred within a few hours, however no embryo lived longer than 24 h (Soong and Cho 1998).

From the Soong and Cho (1998) study, it can be inferred that the role of medusae is limited to carry gametes to the sea surface for fertilization to occur. There is currently no information in the literature regarding millepore planula larvae, however, comparisons

of planula larvae could be made to related stylasterid (Hydrozoa: Anthoathecata:

Stylasteridae; lack medusa stage) and scleractinian larvae. Fritchman (1974) observed in vitro the rapid settlement of the hydrocoral Allopora petrograpta larvae found in San

Juan Islands, Washington, in which larvae quickly settled at the bottom of a petri dish once the developed planulae were released from the ampullae. Brooded larvae from the scleractinian, Pocillopora damicornis, which are released with maternally inherited

Symbiodinium, have the potential to disperse in the plankton for more than 100 d

(Richmond 1987; Harii et al. 2002); however, Isomura and Nishihira (2001) have reported settlement of P. damicornis planulae in vitro 96 h after release. Larvae from broadcast spawning scleractinians, which acquire Symbiodinium after release, have reported maximum pelagic larval durations ranging from 195 to 244 d (Graham et al.

2008). Pelagic larval duration and larval mortality is greatly dependent on numerous factors such as ultraviolet radiation (Wellington and Fitt 2003), elevated seawater temperatures (Randall and Szmant 2009), water flow/ocean current patterns (Botsford et al. 2009; Roberts 1997), predation (Fabricius and Metzner 2004), and available energy reserves/starvation (Graham et al. 2013), which in turn determine the success of settlement (attachment and transition into juvenile state; Gleason and Hoffman 2011).

Reproductive seasons of millepores, in which several batches of medusae are released, have been listed as occurring from June to March in Curaçao and between April and July in Barbados for M. complanata (Lewis 1991b), March to June (highest frequency of fertile colonies in April to May) in Taiwan for M. dichotoma, M. murrayi, and M. platyphylla (Soong and Cho 1998), June to August for M. alcicornis in Brazil

(Amaral et al. 2008), and March to June for M. braziliensis in Brazil (Amaral et al. 2008).

However, no record of the reproductive season of M. alcicornis in Florida has been reported and further observations and experiments are required to definitively identify the reproductive season. Hybridization of millepore species is unlikely due to synchronization of medusae between colonies of individual species and different spawning dates among species (Soong and Cho 1998).

Scleractinians and millepores exhibit similar responses to natural and anthropogenic disturbances due to occupying the same habitats. Colonies undergo severe fragmentation during major storm events (Edmunds 1999). Both stony corals and millepores expel their symbionts during bleaching events (Lewis 2006), revealing similar sensitivities to variable water temperatures. Studies have shown that although millepores face the same environmental stresses as scleractinians, they appear to have a shorter recovery period to bleaching events (Lewis 1989; Loya 1976). After a mass-bleaching event of 1988 in Bermuda, Cook et al. (1990) stated that M. alcicornis was the species with the highest percentage of bleached tissue, but it was also the first species to recover by the re-colonization of Symbiodinium in host tissue. Although their ecology and biology is similar to scleractinians (i.e., ability to secrete calcareous skeleton, presence of

Symbiodinium, plankton feeding strategies, susceptibility to bleaching; Lewis 1989), studies on millepores are limited in numbers and have received little attention in comparison to scleractinian studies (Lewis 2006).

Southern Florida ocean current patterns

The Florida Reef Tract (FRT) extends from Martin County on the Atlantic coast, to the Dry Tortugas in the Gulf of Mexico covering a distance of approximately 530 km

(NOAA Coral Reef Information System 2014). The Florida coastal system is mainly comprised of the connected subregions: Florida Bay, Southwest Florida Shelf, and the

Keys Coastal Zone. Each region has different physical characteristics and flow properties, but are strongly connected by their circulation and exchange processes and by oceanic boundary currents to remote upstream regions of the Gulf of Mexico (Lee et al.

2002). Subtidal currents, which are produced through interactions of local winds, along with the larger Gulf Stream, the Florida Current, and benthic topography comprise South

Florida’s unique water circulation system (Lee et al. 2002). The main current that runs throughout the FRT is the Florida Current, a strong surface current (velocity exceeding

150 cm s-1; Jaap 1984) formed by the convergence of the Gulf of Mexico Loop Current and the Yucatan Current (Lee and Smith 2002). The Florida Current is a key component for reef development and thriving tropical marine biota by transporting warm water from the Caribbean, which moderates winter temperatures allowing reef biota to flourish (Jaap

1984).

The Keys coastal zone consists of a narrow, curved continental shelf with shallow coral reef formations (Jaap 1984; Lee and Williams 1999; Lee et al. 2002). The Keys curved shoreline causes different current patterns to occur along the Keys. The western and lower Keys have westward currents due to winds from the east; however, these same winds do not have the same effect on the upper Keys. The middle Keys experience the most seasonal current variation with northward flows in the summer and southward flows in the fall through spring (Lee and Williams 1999). The Keys Coastal Zone are heavily influenced by the formation of eddies (circular currents of water) that travel along the shore. Eddies spawned from the boundary of the Loop Current move into the Florida

Straits, which become trapped between the Florida Current in the south and the Dry

Tortugas to the north. These eddies can remain stationary for approximately 50-140 d before being pushed out by a newly formed gyre (larger circular systems of surface currents) or the Florida Current (Lee et al. 1992). The eddies decrease in size from 100 to

200 km off the Dry Tortugas to tens of km in the middle and upper Keys, and increase in forward speed from 5-15 km day-1 off the western Keys to 15-30 km day-1 off the upper

Keys (Lee et al. 1995; Lee and Smith 2002). Eddies can retain coastal-derived larvae that would otherwise be carried out by the Florida Current (Cowen et al. 2006). These eddies also help to intensify westward countercurrents, which enhance interactions with coastal waters to maintain the low-nutrient conditions needed for reef survival (Lee et al. 2002).

Lee and Smith (2002) deployed near surface drifters (satellite-tracked ocean surface drifting buoys; Lumpkin and Pazos 2006) from September 1994 to November

1999 off of the Shark River discharge plume in the Everglades to observe the ocean currents connecting the coastal waters of Southern Florida. All of the drifters entered the

Keys coastal waters and most recirculated in the coastal countercurrent and eddies (Lee and Smith 2002). Surface trajectories revealed Southern Florida’s coastal waters are highly connected by ocean currents. Surface drifters took approximately one to two months to reach the Keys region and then another two weeks to reach the Tortugas or if strong southern winds were present, drifters became entrained in the Florida Current and headed north towards Miami. Regardless of prevailing seasonal winds, all surface trajectories recirculated in offshore eddies and wind-driven countercurrents for one to three month periods before being removed from the coastal system through the Florida

Current (Lee and Smith 2002; Lee et al. 2002). Currents are known for maintaining

connectivity among coral reefs by influencing dispersal distance and direction of coral larvae through providing vectors of gene flow (Botsford et al. 2009; Roberts 1997); therefore, with the high degree of connectivity among regions in the FRT by current patterns (Lee and Smith 2002; Lee et al. 2002) it is possible that, assuming passive transport of larvae, coral reef species in the FRT are connected by high levels of gene flow.

Genetic diversity, population structure and connectivity of M. alcicornis in the FRT

Currently there is a lack of genetic information on M. alcicornis along the FRT, which this study aims to resolve through use of DNA microsatellite markers.

Microsatellites were chosen due to their high mutation rates, which can result in high allelic diversity that can be used to infer demographic or connectivity patterns (Freeland et al. 2011). Microsatellites also have the ability of distinguishing ramets (each physical sampled individual) from genets (clonal lineage encompassing all ramets derived from the same zygote), which provides a means of excluding clones from genetic diversity analyses.

The sampling sites used in the present study are centered in the FRT, the largest continuous barrier reef in the U.S. and is central to U.S. coral research (NOAA Coral

Reef Information System 2014; Hemond and Vollmer 2010). Two main regions in the

FRT were used in the present study to include reefs found in the middle Keys and Miami

(Fig. 1). Coral reefs in the middle Keys are exposed to a higher input of water flow from the Florida Bay and represent a transition area for subtidal currents due to the curving shoreline converging with the Florida Current, increasing the speed of the frontal eddies

(Lee and Williams 1999; Lee et al. 2002; Lee and Smith 2002). Miami sits on the edge of the reef tract and is closer to the mainland; therefore reefs found off of Miami are subjected to more anthropogenic effects (Lee at al. 1992). Reefs found off of Miami are mainly subjected to the Florida current, which at times can reach a velocity of 200 cm s-1 within 25 km off of the coast (Lee et al. 2000). With these two sampling regions (reefs from the middle Keys and Miami) being approximately 110 km apart and being exposed to different environmental conditions (i.e., current/flow patterns), genetic differentiation is to be expected to occur between the sampling regions if larval dispersal is limited.

Hypotheses

In the present study, the null hypothesis of panmixia for M. alcicornis from reefs from 9 sampling sites in the middle Keys and 3 sampling sites off Miami was tested. Due to the potential of passive transport of larvae through known current patterns (i.e. the strong Florida Current), I expected to find little evidence of genetic structure across sampling sites in the FRT. Based upon the limited role of pelagic dispersal of fire coral medusae found in vitro (simply a short-lived vehicle to get gametes to the surface; Soong and Cho 1998), insights on pelagic larval dispersal (such as distances and patterns of dispersal) could be estimated through the use of genetic metrics based on molecular markers to determine the degree of connectivity found between regions (or sampling sites; Selkoe and Toonen 2011). It is possible that the larvae could be traveling to adjacent reefs or even greater distances (>100 km) by becoming entrained in currents

(i.e., the Florida Current).

Alternatively, the formation of larges eddies could create unique potential genetic

sinks that would inhibit the transportation of medusae or planular larvae (Cowen et al.

2006) over distances greater than tens of km (based on estimated eddie size found near sampling sites/regions; Lee and Smith 2002), resulting in strong genetic structure. These eddies can remain stationary up to 140 days (Lee et al. 2002), which is presumably longer than the planktonic lifespan of millepora medusae and larvae in situ considering the possible environmental (ultraviolet radiation: Wellington and Fitt 2003; elevated seawater temperature: Randall and Szmant 2009; water flow/ocean current patterns:

Botsford et al. 2009; Roberts 1997; predation: Fabricius and Metzner 2004) and biological factors (energy reserves/metabolic rates; Graham et al. 2013; genetic abnormalities and disease: Rumrill 1990) that could contribute to larval mortality. It is possible that genetic differentiation of M. alcicornis only exists among regions (>100 km apart), from which it could be inferred that the larvae are not capable of dispersing great distances. Population structure of M. alcicornis could also be found between sampling sites over smaller distances, suggesting that larvae are not even dispersing to adjacent reefs (local retention).

This study also investigated genetic diversity, the amount of genetic variation contained within a population (Freeland et al. 2011), and genotypic diversity, the number of unique multilocus genotypes present in a population (Baums et al. 2006a), of M. alcicornis. High levels of genetic and genotypic diversity are associated with a species ability to adapt to altered environments (Markert et al. 2010). In the face of climate change, genetic and genotypic diversity could be a determining factor for the persistence of M. alcicornis and other reef-framework builders. Populations of structural species, such as the seagrass Zostera marina, with high genotypic diversity showed a shorter

recovery time (repair of damaged tissue) after a warming event in Germany (Reusch et al.

2005). Oliver and Palumbi (2011) found that high genotypic diversity (non-clonal genets), in conjunction with heat-resistant lineages of Symbiodinium, could be contributing to the survival of Acropora hyacinthus colonies in thermally variable lagoon pools in American Samoa. Populations with low genotypic diversity are vulnerable to pathogens and parasites (Booth and Grime 2003), which are common causes of mortality in corals (Weil et al. 2006) and are expected to increase due to rising water temperatures

(Rosenberg and Ben-Haim 2002).

METHODOLOGY

Sample collection

Millepora alcicornis colony samples were collected inside the NOAA Florida

Keys National Marine Sanctuary (FKNMS) in August 2012 and outside the Biscayne

National Park off the Miami coast between December 2012 and February 2013 (Fig.1;

Table 1). Approximately 20-30 M. alcicornis samples per site were collected from 9 sites in the middle Keys inside the FKNMS (region 1) and 3 sites outside the Biscayne

National Park in Miami (region 2) for a total of 12 sites and 334 samples (Table 1), covering 145 km of the Florida Reef Tract (FRT: 530 km). The minimum distance between the two sampling regions (site 9, Cheeca Rocks to site 10, Miami 1) was 109.5 km.

Once a sampling reef was located, a tape measure was drawn out from the middle of the reef to reach a radius of 10 m and samples of M. alcicornis were collected haphazardly from colonies within that radius using SCUBA with depths ranging from 4.6

– 12 m (Table 1). To minimize the chance that the same colony was repeatedly sampled, only colonies approximately 3-4 m apart were collected. A radius of 10 m was chosen to provide a scale large enough to collect enough samples from colonies further than a few meters apart from each other, in order to not compromise the number of samples (sample size) collected per site, while still maintaining a radius small enough to feasibly sample.

A colony (ramet) was defined as a continuous upright entity of skeleton with a clean stalk from an encrusting base attached to a substrate (rock, sand, underlying coral tissue;

Baums et al. 2006a). Tips of the colony (< 40 mm) were manually snapped off and placed inside a Ziploc bag. Upon returning to the boat, samples were preserved in 70% ethanol.

Samples were then shipped back to California State University, Northridge and stored at -

80°C prior to DNA extraction.

DNA extraction, microsatellite amplification and genotyping

DNA extractions were carried out using a DNeasy Blood and Tissue Extraction

Kit (QIAGEN, Valencia, CA, USA), incubating cells overnight at 56°C to ensure complete lysis. DNA was quantified with a NanoDrop Spectrophotometer (Thermo

Scientific) by recording 260/280 ratios, 260/230 ratios, and ng/µl. A microsatellite library with 917,751 sequences was obtained through Roche/454 sequencing (courtesy of I.

Baums and D. Ruiz-Ramos, Pennsylvania State University). MSATCOMMANDER

(Faircloth 2008) was used to identify microsatellite repeats and design primers for 30 candidate loci. A 3 primer method (Brownstein et al. 1996) was used to label the amplicons, in which a 20-base “long tag” (5'-CGAGTTTTCCCAGTCACGAC-3’) was added to the 5’ end of the forward locus-specific primer and a “pig-tail” tag (5’-

GTTTCTT-3’) was added to the 5’ end of the reverse primer. The third primer was a fluorescently labeled 6-FAM long labeled tag. These primers were tested on a small subset of samples to determine amplification. An Eppendorf Mastercycler™ AG 22331

(Eppendorf, Hamburg, Germany) was used to carry out PCR reactions of 10 µl final volume containing 1 µl of template DNA (>2ng/µl), 1.8 µl of sterile water, 0.15 µl of long-tailed forward primer, 2 µl of the pig tailed reverse primer, 0.05 µl of the FAM labeled primer (10 µM concentration of each primer), and 5 µl of Apex Taq RED Master

Mix (Genessee Scientific, San Diego, CA, USA). A touchdown PCR protocol was applied with an initial denaturation of 95°C for 5 min, 6 cycles of 40 s at 95°C, 45 s at

61°C (with a temperature decrement of 1°C each cycle), 45 s at 72°C followed by 28 cycles of 40 s at 95°C, 45 s at an annealing temperature of 58°C, 45 s at 72°C, and a 5 min extension at 72°C, followed by a hold at 4°C. PCR products were visualized on a

3.5% agarose gel.

Successfully amplified products were prepared for fragment analysis. Two µl of each PCR product (diluted 1:10) was added to 14 µl of Hi-Di formamide (Applied

Biosystems, Foster City, CA, USA) and 0.2 µl of size standard (GeneScan—600 Liz;

Applied Biosystems, Foster City, CA, USA). The prepared samples were denatured for 5 min at 95°C followed by 4 min at 4°C prior to fragment analyses on an ABI 3130XL

DNA Analyzer (Applied Biosystems, Carlsbad, CA, USA). The resulting electropherograms were analyzed using GeneMarker (SoftGenetics, State College, PA,

USA) to determine if the loci were polymorphic and to score alleles based on amplicon size. Re-synthesized forward primers with a 5’ fluorescent dye (PET, NED, 6FAM, VIC,

Applied Biosystems, Carlsbad, CA, USA) were created for 8 polymorphic loci and used for genotyping (Table 2). To determine if the fluorescent tags created a shift in allele size call, loci were amplified and plated separately before multiplexing.

I conducted multiplex PCR using Type-It Microsatellite PCR kits (QIAGEN,

Valencia, CA, USA). The PCR reactions were scaled to 10 µl reactions with 5 µl Type-It

Multiplex Master Mix, 1 µl 10X primer mix (Multiplex 1 loci: JLK1B, J5ZMV, J3R24, and JPXC8; Multiplex 2 loci: H3ZLI, F19OY, J49I5, and J0B6S), 3 µl sterile water, and

1 µl template DNA. The same PCR touchdown protocol previously stated was used. Loci were plated using an internal size standard (Gene Scan 600-Liz; Applied Biosystems,

Carlsbad, CA, USA) and visualized with an ABI 3130XL DNA Analyzer (Applied

Biosystems, Carlsbad, CA, USA). The resulting electropherograms were analyzed with

GeneMarker (SoftGenetics, State College, PA, USA) using automated binning and scoring was manually confirmed. Samples that failed to amplify were re-amplified separately using the corresponding primer annealing temperature and were scored again.

Microsatellite quality analysis

The presence of null alleles, alleles that failed to amplify in a PCR due to faulty

PCR conditions or mutations in the primer binding regions (Selkoe and Toonen 2006), was determined using MICROCHECKER v2.2.3 (van Oosterhout et al. 2004). Loci that resulted in null alleles in the majority of the sampling populations were dropped from further analysis. Brookfield allele frequency corrections (Brookfield 1996) were applied to loci that contained null alleles in one, or a few, populations. Brookfield corrections were chosen because they take into account relatively low heterozygosity levels, which are common in clonal organisms. GENEPOP v3.4 (Raymond and Rousset 1995) was used to test for deviations from Hardy-Weinberg Equilibrium (HWE) by comparing observed genotype frequencies to expected frequencies in an ideal population with random mating, no mutation, drift, selection, nor migration based on Weir and

Cockerham’s FIS estimates (Weir and Cockerham 1984). GENEPOP default Markov

Chain parameters were used with the alternative hypothesis of heterozygote deficiency.

FSTAT v2.9.3 (Goudet 2001) was used to test for linkage disequilibrium (or genotypic equilibrium) between loci.

To determine if these microsatellite markers were statistically able to detect genetic differentiation at various levels of FST when used against any other data set,

POWSIM v4.1 (Ryman and Palm 2006) was implemented. POWSIM simulates sampling from a specified number of populations that have diverged to predefined levels to estimate statistical power when testing the null hypothesis of genetic homogeneity for various combinations of samples sizes, number of loci, number of alleles, and allele frequencies for any hypothetical degree of differentiation (FST; Ryman and Palm 2006).

Default parameters were used with a 2000 effective population size (Ne) over 40 generations of drift (t) with 500 simulation runs/replications.

Statistical analyses

FSTAT v2.9.3 (Goudet 2005) was used to measure genetic diversity based on the number of alleles sampled (Na) per locus and population and to estimate Nei’s H values of genetic diversity (Nei 1987). For any given locus, H represents the probability that two alleles randomly chosen from the population will be different from one another (Freeland et al. 2011). GENALEX v6.5 (Peakall and Smouse 2012) was used to calculate G''ST,

Jost’s Dest, observed heterozygosity (Ho), expected heterozygosity (He), and Analysis of

Molecular Variance (AMOVA) based on 999 permutations. G''ST values are used to quantify genetic differentiation between populations based on heterozygosity. G''ST was used rather than FST because it is capable of interpreting multiallelic markers, whereas

FST was developed for biallelic markers (Meirmans and Hedrick 2011). G''ST is adjusted for small sampling populations and standardizes G'ST relative to the mean within population heterozygosity (Bird et al. 2011). Jost’s Dest, or true diversity, uses true allelic diversity to derive a measure of genetic differentiation (Bird et al. 2011). AMOVA estimates population differentiation based on molecular data using squared Euclidean

distance matrices yielding sum of squares for hierarchical levels of the population

(Excoffier et al. 1992). AMOVA estimated the amount of variation among regions

(region 1: sampling sites 1-9, middle Keys; region 2: sampling sites 10-12, Miami), among populations, among individuals and within individuals. BayesAss v3.0 (Wilson and Rannala 2003) was used to determine migration rates between sites to infer gene flow. Parameter values following a burn-in of 2,000,000 with 20,000,000 iterations, deltas set to 0.3, 0.6, and 0.6 for allele frequency, inbreeding coefficient, and migration rate to achieve rates between 20-40% (as recommended by Rannala 2011), respectively.

Convergence was confirmed through Tracer v.1.3 (Rambaut and Drummond 2005) by examining trace files for consistent oscillations.

Clustering Analysis

Population structure was estimated using STRUCTURE software (Pritchard et al.

2000), which employs a Bayesian clustering method to determine the number of genetically different populations (K) by clustering together individuals with similar multilocus genotypes. The ‘admixture ancestry model’ was implemented under the assumption of ‘correlated allele frequencies’ (due to the high probability of interconnected populations) to improve clustering (Falush et al. 2003; Baums et al. 2005).

A burn-in length of 100,000 was used with 1,000,000 Markov chain Monte Carlo

(MCMC) simulations. Fifteen replicate runs were carried out, using a random number seed for each run, for each K value tested from 2-20. Missing data were included in this analysis, resulting in data from 311 individuals. STRUCTURE outputs were visualized using STRUCTURE HARVESTER (Earl and vonHoldt 2011). The Evanno method, an

interpretation of STRUCTURE data to determine the optimal number of clusters (K) using an ad hoc statistic based on the rate of change in the log probability of data between successive K values (Evanno et al. 2005), was used.

Clonal analysis

In order to ensure that genets were collected in this study for genetic diversity analyses, GENCLONE 2.0 (Arnaud-Haond and Belkhir 2007) was used to determine the number of genets (genetic individuals or clones) by detecting the number of multilocus genotypes (MLG) and the incidence of repeated MLGs in the data set. Repeated MLGs can occur in a data set for three reasons. The first is due to sampling error in the field; the same colony sampled twice during the collection. Asexual reproduction by colony fragmentation also results in repeated MLGs. Finally, a repeated MLG can result from distinct sexual reproductive events in which gametic pairs fuse that share identical genotypes with other gametic pairs. I estimated the probability that identical MLG’s were separate sexual events by calculating its probability, (Psex(FIS)), in GENCLONE. Psex(FIS) is defined as the probability for a given multilocus genotype to be observed in N samples as a consequence of different sexual reproductive events (Arnaud-Haond and Belkhir

2007). Psex(FIS) smaller than 0.05 for a given MLG is considered to be a ramet of a single zygote (i.e., genet) and in those cases all additional ramets beyond the first incidence were excluded from further analysis to avoid bias (Arnaud-Haond and Belkhir 2007;

Krueger-Hadfield et al. 2013). Psex(FIS) takes into account departures from HWE by implementing FIS using allelic frequencies estimated with the round-robin method to obtain a more conservative estimate of Psex (Arnaud-Haond and Belkhir 2007). Clonal

richness, or genotypic richness, was estimated as R = (G – 1)/(N – 1), where G is the number of MLGs and N is the number of ramets sampled (Dorken and Eckert 2001). R ranges from 0, indicating a monoclonal population, to 1, when all different ramets result from distinct clonal lineages. R values were calculated for each sampling site and across all sites, with and without the consideration of Psex(FIS) values.

RESULTS

Microsatellite loci, HWE, null alleles, and linkage disequilibria

Of the 30 potential loci, only 24 resulted in successful amplification. Eight were polymorphic and fluorescently tagged (Table 2). Locus JPXC8 was contaminated and subsequently dropped from the data set. Locus H3ZLI resulted in null alleles in 8 of the

12 populations and locus J4915 failed to amplify in a majority of the samples, therefore both loci were dropped from the data set. Only samples that successfully amplified across all remaining 5 loci were used in the analysis. There was no evidence of linkage disequilibrium between pairs of loci after a Bonferroni correction (adjusted P-value for

5% nominal level, 3600 permutations: 0.000278) was applied. Two of the 12 populations at locus JLK1B significantly deviated from HWE expectations after a Bonferroni correction was applied (P<0.01, Table 3). These deviations are consistent with a type I error of 0.05 (given 50 analyses run [12 populations X 5 loci]) and each locus overall was regarded to be within HWE expectations. When HWE analysis was performed again using Brookfield corrected genotypes, there were only three deviations found in locus

JKL1B and the rest of the populations were still within HWE expectations.

Genetic diversity and differentiation

Negative FIS values observed in 7 populations for 3 loci, indicated an excess of heterozygotes, however the majority of FIS values were positive indicating a deficit in heterozygotes (Table 4). Across 5 loci and 12 putative populations (sampling sites), the mean number individuals sampled was 21.167 (SE=0.526), the mean number of alleles

(Na) was 6.00 (SE=0.763), the observed and expected heterozygosity was 0.426

(SE=0.023) and 0.467 (SE=0.022), respectively, and an overall gene diversity (H) of

0.481 (Tables 4, 8). The AMOVA revealed no population genetic differentation among regions or among sites. Ninety percent of the variance occurred within individuals, and the remaining 10% was attributed to individuals (Table 4). I found little evidence of genetic differentiation between each site compared to the regions and between each site compared to the total population (FSR= -0.004, p>0.05; FST= 0.001, p>0.05; Table 4), indicating a single connected population.

No evidence of population genetic structure was found using G''ST or Dest values with or without Brookfield (1996) adjusted frequencies (Table 5, 6). The highest amount of genetic differentiation (G''ST) and true diversity (Dest) occurred between sites Stag

Party and Miami 3 (G''ST = 0.05, p<0.05, Dest= 0.026, p<0.05; Table 7); however, the total

G''ST and Dest over all sites were not different from zero (Table 5). Other G-statistical analyses such as GST and G'ST were concordant with those for G''ST (Table 5). POWSIM analysis revealed that these particular 5 loci were sufficient to resolve significant population structure with FST as low as 0.01 (Average FST over all the 500 runs = 0.01;

Expected FST = 0.01).

I used BayesAss v3.0 (Wilson and Rannala 2003) to infer gene flow among sites by estimated mean migration rates (Nm; Table 11). However, it should be noted that more than one distinct population is needed to correctly quantify gene flow. I have no evidence of population structure for the sites sampled in the present study, the migration rates were estimates between sampling sites (not genetic populations). A majority of the Nm values were not significant, but most of the Nm values of individuals migrating from the sampling site Stag Party East (in the middle Keys) and entering other sampling sites were

significant. Seven sampling sites were donor sources of migrants into site Long Key

Bridge Rubble, one of the most southern sampling sites (Fig. 1). No migrants were found leaving Miami sites and entering sites downstream in the middle Keys. Highest Nm values resulted from individuals not leaving site they were sampled from, indicating high levels of self-recruitment (Table 11). To estimate emigration rates, the proportion of individuals leaving the source site but not necessarily entering any of the sampling sites, all of the significant Nm values for a donor site were summed and the resulting value was subtracted from 1 (Table 12).

Clustering analysis

Further evidence for a lack of genetic differentiation was found using the

Bayesian genetic clustering software STRUCTURE (Pritchard et al. 2000). The Evanno method, an interpretation of STRUCTURE data to determine the optimal number of clusters (K) using an ad hoc statistic based on the rate of change in the log probability of data between successive K values (Evanno et al. 2005), was unable to evaluate a model of null panmixia in which K is 1. Therefore, the heuristic method of identifying the optimal number of genetic clusters as the value of K achieves the highest posterior probability while maximizing the average cluster membership coefficients was used

(Pritchard et al. 2010). Ln(K) revealed a single population (K=1) based on the highest value of the mean LnP(K) (Fig. 4; Table 12). Further evidence of a single population was seen in the symmetry of the proportion of individuals assigned to each population (1/K;

Fig. 5), and the large variance of alpha (Dirichlet parameter for the degree of admixture with a small alpha implying that most individuals are from one population, while alpha>1

implies that most individuals are admixed) during the course of the run (Pritchard et al.

2000). The overall FST was 0.001 (Table 4) and only three pairwise FST values were above

0.02 (Table 9). FST, G''ST, Dest values and a cluster (K) of 1 all equate to a lack of genetic structure resulting in a panmictic population.

Clonal analysis

The high levels of genotypic diversity found in the present study give confidence to the sampling regime of collecting colonies further than 3-4 m apart. GENCLONE 2.0

(Arnaud-Haond and Belkhir 2007) detected 207 unique multilocus genotypes (MLGs) in the set of 259 colonies samples (Appendix Table 14). The least amount of clonal richness, 85%, was found at 11’ Mound, with 18 MLGs out of 21 individuals. A clonal richness of 100% was found in four of the 12 sites (Table 10). When compared across all sites, a clonal richness of 80% was found with 207 MLGs from 259 individuals indicating 52 ramets were present in the total number of samples. However, when the probability of ramets resulting from a different reproductive event (Psex(FIS)) was taken into account, the actual amount of MLGs increased to 254 (Appendix Table 15). In other words, five individuals (Psex(FIS) < 0.05) were actual ramets that were descendants of their respective zygotes. The increase in clonal richness to 100% was seen in five sites.

With Psex the least amount of clonal richness of 90% was found at Miami 3 and nine of the 12 sites had 100% clonal richness. Across all sites, the clonal richness assessed by analyses incorporating Psex increased the total clonal richness to 98%.

DISCUSSION

The analyses of population structure assessed in the present study illustrate a single panmictic population of M. alcicornis along the FRT based on five microsatellite loci. Across 12 sampling sites, there was no genetic differentiation between sites and the majority of genetic variation resided within individuals. Based on sampling of colonies greater than 3-4 meters apart (within a site defined by a 20 meter diameter), there was a high percentage of clonal (genotypic) richness indicating that nearly all M. alcicornis individuals sampled in this study resulted from sexual reproduction. These data suggest that there is a high level of population connectivity of among M. alcicornis along the

FRT.

Differentiation and connectivity of M. alcicornis

Based on the lack of differentiation reported in this study, the null hypothesis that

M. alcicornis in the FRT constitutes a single, interbreeding population throughout the range of sites cannot be rejected. Low levels of genetic differentiation detected among 12 sampling sites in relation to the total population is in line with the amount of genetic variation found among sampling sites (FST=0.001; AMOVA: 0% variation among sites;

Table 4). Further confirmation of a single panmictic population of M. alcicornis was found via clustering analysis. Latch et al. (2006) found that STRUCTURE cannot correctly identify K below FST values of 0.02; therefore, FST values found in this study were too low for clustering analysis to optimally detect any genetic clustering. However, indices of FST, G''ST, Dest, provide strong evidence of a panmictic population.

The present study found on average 69% of retention (self-recruitment) within a

single sampling site (non-migrants) and 31% of individuals found leaving a single site.

Due to the limitations of this study not every reef along the FRT was sampled, therefore not every migrant could be quantified, however, these numbers reveal that individuals are leaving their source locality and are either entering other sites not included in this study or are being pushed out of the reef tract by currents (Table 12). These levels of emigration account for the low levels of differentiation between sites inferring high gene flow hence indicating the ability for larvae to disperse to nearby reefs potentially

>100km.

A similar lack of genetic structure in the FRT is reported for other reef-building corals such as Acropora cervicornis (lower and upper Keys: Hemond and Vollmer 2010; lower and upper Keys and off the coast of Ft. Lauderdale, Broward County: Baums et al.

2010), A. palmata (Dry Tortugas, lower and upper Keys: Baums et al. 2005), and

Montastraea faveolata (lower and upper Keys: Baums et al. 2010). The absence of population genetic structure within Florida may indicate that gene flow is high across the

FRT and reefs are connected. This could be largely due to the physical processes (current patterns) that would impact particle dispersal in the FRT. If millepore larvae are positively buoyant, as with M. faveolata (Gleason and Hofmann 2011), and have the potential to disperse in the plankton for months after fertilization (like P. damicornis;

Richmond 1987; Harii et al. 2002) population genetic structure could be greatly influenced by currents. With the high degree of connectivity among regions in the FRT by current patterns (Lee and Smith 2002; Lee et al. 2002) it is possible that, assuming passive transport of larvae, the current patterns are maintaining connectivity among coral reefs in the FRT by influencing dispersal distance and direction of M. alcicornis larvae.

(Botsford et al. 2009; Roberts 1997).

Eddies have the potential of retaining coastal-derived larvae (Cowen et al. 2006).

Eddies that propagate between the Dry Tortugas and lower Keys have been reported as sources of larvae retention for the spiny lobster Panulirus argus (Yeung and Lee 2002).

However, if eddies in the FRT restricted larval dispersal of M. alcicornis, genetic structure among regions would have been evident. If the pelagic larval duration of M. alcicornis is longer than the time interval that eddies are stagnant in the water column, larvae could be easily entrained in the Florida Current creating large scales of dispersal.

It is possible that the extent of this single panmictic population involves regions in the western Caribbean. Baums et al. (2005) found that Florida was only one locality in a single population of Acropora palmata in the western Caribbean, including Panama,

Mexico, the Bahamas, and Navassa. Oceanographic models of connectivity based on simulated damselfish larval exchange between geographical locations in the Caribbean revealed northern Central American and Cuban reefs are the most likely sources of immigrant larvae and propagules into Florida (Cowen et al. 2006). The link between the western Caribbean and Florida resides in prevailing currents, with the Caribbean Current flowing into the Yucatan Current, which flows into the Loop and Florida Current. Baums et al. (2006b) simulated exchanges of computer generated A. palmata larvae among localities in the Caribbean and found larvae that settle in Florida were from a source locality in Mexico, again suggesting that current patterns, along with larval dispersal capabilities, are largely contributing to gene flow within the western Caribbean and

Florida. Further sampling of millepore colonies in the Caribbean would need to be carried out in order to definitively determine any historical and genetic linkage between the

regions of the western Caribbean and the FRT for M. alcicornis.

Genetic diversity

Genetic diversity for the sampled sites of M. alcicornis in the present study was quantified using various measures. The number of alleles in M alcicornis was within range of exhibited values for other cnidarians (Fig.2; Mackenzie et al. 2004; Andras et al.

2013; Baums et al. 2005; Baums et al. 2010; Goffredo et al. 2004). Similarly, the observed heterozygosity (Ho) of M. alcicornis was comparable with Ho levels from other studies of cnidarians (Fig. 3; Mackenzie et al. 2004; Andras et al. 2013; Baums et al.

2005; Baums et al. 2010; Goffredo et al. 2004). Combined, the number of alleles and levels of observed heterozygosity indicate M. alcicornis in the FRT is not genetically depauperate, rather this species exhibits moderate levels of genetic diversity in the FRT.

High connectivity between coral reefs results in an exchange of larvae creating genetic diversity, which is important in terms of resilience against disturbances caused by environmental changes (Jones et al. 2009; van Oppen and Gates 2006). With the global decline of reef-building corals (Hughes et al. 2003; Bellwood et al. 2004), moderate

(preferentially high) levels of genetic diversity created by gene flow from migrating individuals provides new alleles that can be integrated into a population providing new gene combinations which selection can act upon (van Oppen and Gates 2006; Williams et al. 2014; Frankham 1995). A study by Markert et al. (2010) demonstrated in vitro the effects of genetic diversity on fitness for the estuarine crustacean Americamysis bahia.

Populations with low genetic diversity (50% decrease in heterozygosity relative to starting population) had reduced fitness and under stressful conditions of decreased

salinity, 73% of the population died during the experimental duration. Conversely, populations with high genetic diversity were able to survive stressful conditions (Markert et al. 2010). Globally, coral reefs are experiencing environmental stresses ranging from thermal extremes to the increased severity of hurricanes (Hughes et al. 2003; Gardener et al. 2003), both resulting in damage to these fragile ecosystems. Maintaining moderate levels of genetic diversity of M. alcicornis and other reef-building organisms is crucial for the persistence of coral reefs and their inhabitants.

Genotypic diversity

High levels of genotypic diversity were observed in the present study, ensuring that the sampling strategy employed successfully minimized the sampling of the same clonal lineage by only sampling colonies >3-4 m apart favoring sexually produced colonies (genets). The importance of estimating the probability of repeated multilocus genotypes resulting from independent sexual reproduction events (Psex; Arnaud-Haond and Belkhir 2007) was seen in the difference of clonal richness (R) calculated with and without Psex. If Psex was not taken into account in the present study of M. alcicornis, the overall genotypic richness would have been reduced 18% (Table 10). Therefore, it is important to adjust for this probability in detecting population structure in clonal organisms by taking into account that the same MLG can occur in a different reproductive event. The lowest estimate of clonal richness was found in Miami 3 (90%;

Table 10), which is likely due to sampling colonies less than 3-4 meters apart from each other.

High genotypic richness does not indicate that the rate of fragmentation is low.

With increasing frequency and severity of hurricanes (Hughes et al. 2003), it is evident that breakage of colonies resulting in fragments is potentially high, but the rates of successful establishment of the fragments are low (Edmunds 1999) and are not contributing to an overall decrease in clonal richness. M. alcicornis colonies in the FRT seem to be utilizing sexual reproduction, beyond a distance of a few meters, to contribute to the effective population size.

Like genetic diversity, a higher level of genotypic diversity enhances ecosystem recovery by increasing species ability to adapt to altered environments (Reusch et al.

2005). However, with species such as reef-building corals, it is important to not only take into account the genotypic diversity of the coral but also the genotypic diversity of their endosymbiotic algae. Corals have been shown to modify the levels of Symbiodinium genotypes in response to environmental change by favoring types that are thermally tolerant (Jones et al 2008; Mieog et al. 2007; Baker et al. 2004). Oliver and Palumbi

(2011) found that high genotypic diversity (non-clonal genets) and endosymbiotic heat- resistant clades of Symbiodinium helped Acropora hyacinthus colonies from thermally variable lagoon pools in American Samoa survive an increase in thermal stress. The endosymbiotic algae of M. alcicornis was not investigated in this study, but should be genotyped in future studies to determine how well M. alcicornis will be able to handle the thermal stress caused by climate change.

Conclusion

A single panmictic population of M. alcicornis in the FRT was found with low levels of genetic differentiation and moderate levels of genetic diversity. Indices of

genetic diversity and differentiation provide evidence that subpopulations of M. alcicornis in the FRT are exchanging larvae between sampling sites. With limited information available on millepore medusae and larvae, the present study provides insight into the dispersal potential of sexual propagules of M. alcicornis. It is possible that the medusae may have a larger dispersal capability than previously seen in a laboratory setting (Soong and Cho 1998). Alternatively, it is possible that an extended pelagic larval duration is primarily responsible for the high levels of gene flow found among sites.

Further studies would need to be carried out to provide more information on the dispersal potential of both fire coral medusa and larvae.

Due to the sampling method employed in the present study, it can only be stated with certainty that colonies greater than 3-4 meters apart resulted from sexual reproduction. Future studies should estimate abundance of fire corals as well as genotyping all samples present within a sampling radius on a reef to avoid restricting ramets to ascertain a definite estimate of clonal richness. The high levels of genotypic diversity and moderate levels of genetic diversity found in this study is a hopeful indication that M. alcicornis will be able to adapt to changing environmental conditions due to climate change.

The spatial extent of the population of M. alcicornis is unknown and further sampling in the Caribbean needs to be carried out to provide a phylogenetic connection among localities by determining the amount of genetic differentiation between regions.

Including samples from the Caribbean would offer insight into the direction of gene flow and could test hypotheses of the western Caribbean being a historical source of genetic diversity in Florida. The direction of gene flow would also shed light into the impact of

the main currents found in the regions (i.e., Caribbean, Yucatan, Loop, and Florida

Currents). This study, in line with other studies of reef-building organisms along the

FRT, supports the indication that the FRT is a single region of highly connected reefs.

The successful protection and management of these ecosystem builders depends on the management of the FRT as a whole.

TABLES AND FIGURES

Figure 1. Sampling sites in the Florida Reef Tract in the middle Keys and Miami.

Gorgonia ventalina Acropora palmata

Acropora cervicornis

Acropora nasuta

Balanophyllia europaea

Figure 2. Rarefaction curve showing the average number of alleles (Na) per locus for five species of cnidarians (Acropora nasuta: Mackenzie et al. 2004; Gorgonia ventalina: Andras et al. 2013; Acropora palmata: Baums et al. 2005; Acropora cervicornis: Baums et al. 2010; Balanophyllia europaea: Goffredo et al. 2004) in relation to the average Na found for M. alcicornis in the present study.

Acropora palmata

Acropora cervicornis

Gorgonia ventalina

Acropora nasuta

Balanophyllia europaea

Figure 3. Rarefaction curve showing the average observed and expected heterozygosity (Ho, He) for five species of cnidarians (Acropora nasuta: Mackenzie et al. 2004; Gorgonia ventalina: Andras et al. 2013; Acropora palmata: Baums et al. 2005; Acropora cervicornis: Baums et al. 2010; Balanophyllia europaea: Goffredo et al. 2004) in relation to the Ho, He found in M. alcicornis in the present study.

Figure 4. The mean likelihood of K, population clusters, by the mean estimation of the natural log of the probability of data with standard deviations. Graph generated using the web-based program STRUCTURE HARVESTER (Earl and vonHoldt 2012).

Figure 5. Bar plots aligning STRUCTURE runs for K=2 to K=7 (not shown K=8-12). Each plot was created using data from 311 individuals, and is sorted by q values (the proportion of the individual’s ancestry from population K; Pritchard et al 2000). The plots are read from left to right in order of southern sampling sites (middle Keys) to northern sampling sites (Miami), each bar represents an individual, and the color of the bar represents the proportion of the individual’s coefficient of membership to K (q).

Table 1. Sampling information by site. Distance between two regions (site 9, Cheeca Rocks and site 10, Miami 1) = 109.5 km. Sites covering a distance of 145 km in the Florida Reef Tract. Water Site # Samples Date Region Site Name Latitude (°) Longitude (°) Depth # Collected Collected (m) Middle Keys 1 Long Key Bridge Rubble (LKBR) 24.7268000 -80.8275667 8.2 29 6-Aug-12 2 11' Mound (11'M) 24.7234833 -80.8616333 5.8 28 6-Aug-12

3 East Turtle Shoals (ETS) 24.7250500 -80.9188667 8.8 27 6-Aug-12

4 Tennessee Reef (TR) 24.7455667 -80.7828167 7.3 29 7-Aug-12

5 Stag Party (SP) 24.7581667 -80.7575000 7.0 30 7-Aug-12

6 Stag Party East (SPE) 24.7781333 -80.7362667 5.5 28 7-Aug-12

7 Coral Gardens (CG) 24.8422833 -80.7205667 4.6 19 7-Aug-12

8 East of Alligator Reef (EAR) 24.8618167 -80.6013167 7.9 29 8-Aug-12

9 Cheeca Rocks (CR) 24.9033910 -80.6148680 7.3 25 8-Aug-12

Miami 10 Miami 1 (M1) 25.7659444 -80.0893889 12.0 28 14-Dec-13 11 Miami 2 (M2) 25.7512778 -80.0898056 11.2 28 14-Dec-13

12 Miami 3 (M3) 25.7798250 -80.1120861 11.0 34 9-Feb-14

Table 2. Summary of loci information including primer sequences. Forward primers (F) had a long-label added to the 5’ end and reverse (R) primers had a ‘pig-tail’ sequence attached to the 5’ end. Annealing temperatures were based on reaction containing 50mM NaCl. Loci JPXC8, J4915, and H3ZLI were excluded from the final data analysis. # Annealing Fluorescent Locus Motif Primer Sequence Repeats Tm (°C) Tag CGA GTT TTC CCA GTC ACG ACA GGT CCC GTA JLK1B AAT 5 F 67.9 6 FAM ATC TCA CCT G R GTT TCT TCC TTT GTT GCC TAA GTT TGG G 58.5

CGA GTT TTC CCA GTC ACG ACG ACG TCA CAG J3R24 AT 5 F 67.9 NED TGA GTA TTG GC R GTT TCT TCC CAG TCC TCC AAA TCA TGC 59.8

CGA GTT TTC CCA GTC ACG ACT GCC TGA TAG J5ZMV AC 8 F 68.1 PET ATG CGT GGA G R GTT TCT TCA TAA CCA CTT CAG GCG CG 60.2

CGA GTT TTC CCA GTC ACG ACC GTT CGT GTG J0B6S AG 7 F 67.7 VIC GAC TAC TGA TG R GTT TCT TAC AGA GAG GCA GAA TGG TTG 56.6

CGA GTT TTC CCA GTC ACG ACT GGT GCT CCC F19OY AAT 5 F 67.8 PET TCA TAC TTG TC R GTT TCT TAC AGT TGG ATC CTT GAG TTG C 57.9

CGA GTT TTC CCA GTC ACG ACA GCA TGT ATT JPXC8 AAGT 5 F 67.1 VIC GTG TCA TCC TGC R GTT TCT TAT CTG GGT CTG GCT GCT AAG 58.9

CGA GTT TTC CCA GTC ACG ACA CAG GGA AGG J4915 AT 5 F 66.6 NED ACA AGT TTA GTC R GTT TCT TCC TTA TGC AAT TCC TCC ATC CC 59.4

CGA GTT TTC CCA GTC ACG ACG GTC CTA GTG H3ZLI AT 5 F 68.5 6 FAM TAG TGT GGA GC R GTT TCT TTC ACT GGT TGC AAC TGA TCA C 58.6

Table 3. Summary of sample size (N), number of different alleles (Na), observed (Ho) and expected (He) heterozygosity for each site and across all sites by locus and HWE p-values based on FIS values. If only one allele was present (homozygous), HWE and FIS values were not calculated. Site LKBR 11' M ETS TR SP SPE CG EAR CR M1 M2 M3 Overall Locus N 15 21 21 20 22 24 14 22 18 26 22 29 254

JLK1B Na 4 5 5 6 7 4 3 7 4 5 5 5 8 Ho 0.133 0.286 0.381 0.300 0.591 0.292 0.286 0.455 0.333 0.462 0.636 0.379 0.378 He 0.533 0.400 0.459 0.4525 0.565 0.457 0.2526 0.468 0.410 0.394 0.546 0.334 0.439 HWE 0.00* 0.00* 0.08 0.01 0.60 0.02 1.00 0.38 0.06 1.00 0.65 1.00 0.00 FIS 0.7646 0.3084 0.1940 0.3596 -0.0225 0.3808 -0.0947 0.0519 0.2154 -0.0565 -0.1417 1.0000 0.1393

J3R24 Na 2 2 2 2 2 2 2 2 2 4 2 5 5 Ho 0.467 0.667 0.524 0.400 0.455 0.625 0.143 0.500 0.278 0.577 0.318 0.552 0.459 He 0.500 0.499 0.495 0.495 0.483 0.499 0.490 0.499 0.498 0.530 0.474 0.598 0.505 HWE 0.55 0.97 0.72 0.30 0.52 0.94 0.01 0.63 0.06 0.75 0.12 0.37 0.16 FIS 0.1009 -0.3146 -0.0329 0.2165 0.0830 -0.2321 0.7263 0.0212 0.4654 -0.0684 0.3496 0.0949 0.0805

J5ZMV Na 4 6 6 7 6 6 5 7 6 7 5 7 8 Ho 0.267 0.571 0.571 0.500 0.591 0.583 0.429 0.455 0.611 0.423 0.682 0.552 0.520 He 0.527 0.586 0.519 0.600 0.595 0.569 0.582 0.684 0.537 0.530 0.508 0.693 0.577 HWE 0.02 0.43 0.49 0.02 0.02 0.59 0.17 0.02 0.63 0.04 1.00 0.04 0.01 FIS 0.5193 0.0203 0.0170 0.0045 0.0029 0.0228 0.0085 0.0035 0.0193 0.0072 0.0000 0.0064 0.1173

J0B6S Na 4 4 4 4 4 5 3 4 5 3 4 4 5 Ho 0.533 0.619 0.524 0.550 0.682 0.750 0.500 0.500 0.556 0.654 0.545 0.655 0.589 He 0.589 0.636 0.666 0.648 0.656 0.652 0.625 0.548 0.674 0.624 0.610 0.689 0.635 HWE 0.38 0.17 0.07 0.30 0.41 0.87 0.14 0.09 0.09 0.54 0.11 0.29 0.06 FIS 0.1284 0.0511 0.2361 0.1755 -0.0161 -0.1296 0.2353 0.1098 0.2037 -0.0291 0.1280 0.0667 0.0815

F19OY Na 2 4 2 3 2 2 2 3 3 4 4 4 4 Ho 0.067 0.238 0.238 0.300 0.091 0.042 0.071 0.273 0.222 0.269 0.182 0.207 0.183 He 0.064 0.219 0.278 0.265 0.165 0.041 0.069 0.241 0.202 0.244 0.170 0.191 0.179 HWE --- 1.00 0.44 1.00 0.14 ------1.00 1.00 1.00 1.00 1.00 0.23 FIS --- 0.0000 0.0023 0.0000 0.0017 ------0.0000 0.0000 0.0000 0.0000 0.0000 0.0085 *p< 0.01 (adjusted p-value with Bonferroni correction) LKBR: Long Key Bridge Rubble; 11’M: 11’ Mound; ETS: East Turtle Shoals; TR: Tennessee Reef; SP: Stag Party; SPE: Stag Party East; CG: Coral Gardens; EAR: East of Alligator Reef; CR: Cheeca Rocks; M1: Miami 1; M2: Miami 2; M3: Miami 3.

Table 4. Summary AMOVA table with corresponding F-statistics and probability values of a random value greater than equal to the observed data value. Source of Sum of Mean Estimated % F- d.f Value Probability variation squares squares variance variation statistics Among Regions 1 2.317 2.317 0.006 0% FRT 0.005 0.040 Among Pops 10 11.377 1.138 0.000 0% FSR -0.004 0.857 Among Indiv 242 320.295 1.324 0.116 10% FST 0.001 0.363 Within Indiv 254 277.000 1.091 1.091 90% FIS 0.097 0.001

Total 507 610.990 1.213 100% FIT 0.097 0.001

Table 5. Summary of G-statistics with corresponding probability (P) values. GST is an FST analog adjusted for bias; G'STN is Nei’s standardized GST; G'STH is Hedrick’s standardized GST; G''ST is Hedrick’s standardized GST corrected for bias when the number of populations is small; Dest is Jost’s estimate of differentiation.

Locus GST P (GST) G'STN P G'STN G'STH P G'STH G''ST P G''ST Dest P Dest JLK1B 0.002 0.368 0.002 0.368 0.004 0.368 0.004 0.368 0.002 0.369 J3R24 -0.010 0.852 -0.011 0.852 -0.023 0.846 -0.024 0.846 -0.012 0.843 J5ZMV -0.003 0.637 -0.003 0.637 -0.008 0.632 -0.008 0.632 -0.005 0.631 J0B6S -0.004 0.687 -0.004 0.687 -0.011 0.686 -0.012 0.686 -0.008 0.687 F19OY 0.010 0.078 0.011 0.078 0.013 0.078 0.014 0.078 0.003 0.081

Total -0.003 0.763 -0.003 0.763 -0.006 0.761 -0.006 0.761 -0.003 0.760

Table 6. Summary of G-statistics (with corresponding probability (P) values) calculated using Brookfield (1996) adjusted allele frequencies. GST is an FST analog adjusted for bias; G'STN is Nei’s standardized GST; G'STH is Hedrick’s standardized GST; G''ST is Hedrick’s standardized GST corrected for bias when the number of populations is small; Dest is Jost’s estimate of differentiation.

Locus GST P (GST) G'STN P G'STN G'STH P G'STH G''ST P G''ST Dest P Dest JLK1B 0.002 0.387 0.002 0.387 0.003 0.387 0.003 0.387 0.001 0.387 J3R24 -0.012 0.888 -0.013 0.888 -0.025 0.884 -0.026 0.884 -0.013 0.879 J5ZMV -0.004 0.647 -0.004 0.647 -0.009 0.645 -0.010 0.645 -0.006 0.642 J0B6S -0.004 0.661 -0.004 0.661 -0.011 0.659 -0.012 0.659 -0.008 0.658 F19OY 0.010 0.079 0.011 0.079 0.013 0.081 0.014 0.081 0.003 0.089

Total -0.003 0.796 -0.004 0.796 -0.007 0.792 -0.007 0.792 -0.003 0.792

Table 7. Pairwise population matrix of G''ST values (below the diagonal) and Dest values (above the diagonal). LKBR 11' M ETS TR SP SPE CG E AR CR M1 M2 M3 -0.018 -0.006 -0.012 -0.008 -0.022 -0.003 -0.007 -0.001 -0.004 -0.017 0.024 LKBR -0.039 -0.006 -0.018 0.002 -0.011 -0.008 -0.006 -0.012 -0.012 -0.008 0.006 11' M -0.012 -0.013 -0.015 -0.006 0.001 -0.016 0.005 -0.008 -0.008 0.001 0.016 ETS -0.025 -0.036 -0.030 0.005 -0.002 -0.009 0.002 -0.014 -0.016 -0.011 -0.003 TR -0.017 0.003 -0.011 0.010 -0.002 -0.009 -0.006 -0.001 0.009 0.008 0.026* SP -0.048 -0.024 0.002 -0.005 -0.004 -0.004 -0.003 -0.001 0.002 -0.005 0.021* SPE -0.007 -0.018 -0.035 -0.020 -0.019 -0.008 -0.004 -0.013 -0.009 0.007 0.000 CG -0.016 -0.012 0.010 0.004 -0.012 -0.006 -0.009 -0.004 0.003 0.001 0.019 EAR -0.003 -0.024 -0.016 -0.028 -0.003 -0.001 -0.030 -0.007 -0.014 -0.002 0.005 CR -0.008 -0.026 -0.017 -0.033 0.019 0.003 -0.019 0.006 -0.029 -0.008 0.003 M1 -0.037 -0.016 0.002 -0.023 0.016 -0.010 0.015 0.002 -0.005 -0.017 0.016 M2 M3 0.047 0.011 0.032 -0.006 0.050* 0.043* -0.001 0.036 0.010 0.005 0.032 *p<0.05 **p<0.01 LKBR: Long Key Bridge Rubble; 11’M: 11’ Mound; ETS: East Turtle Shoals; TR: Tennessee Reef; SP: Stag Party; SPE: Stag Party East; CG: Coral Gardens; EAR: East of Alligator Reef; CR: Cheeca Rocks; M1: Miami 1; M2: Miami 2; M3: Miami 3.

Table 8. Nei’s estimate of genetic diversity (H) per locus and population and across all sites. Site Locus LKBR 11' M ETS TR SP SPE CG E AR CR M1 M2 M3 All sites JLK1B 0.567 0.413 0.473 0.468 0.578 0.471 0.261 0.479 0.425 0.401 0.557 0.339 0.453 J3R24 0.519 0.507 0.507 0.511 0.496 0.507 0.522 0.511 0.520 0.540 0.489 0.610 0.520 J5ZMV 0.555 0.601 0.531 0.618 0.609 0.582 0.610 0.706 0.551 0.542 0.516 0.708 0.594 J0B6S 0.612 0.652 0.686 0.667 0.671 0.664 0.654 0.562 0.698 0.635 0.626 0.702 0.652 F19OY 0.067 0.224 0.286 0.271 0.171 0.042 0.071 0.246 0.208 0.248 0.174 0.194 0.184 Total H = 0.481 LKBR: Long Key Bridge Rubble; 11’M: 11’ Mound; ETS: East Turtle Shoals; TR: Tennessee Reef; SP: Stag Party; SPE: Stag Party East; CG: Coral Gardens; EAR: East of Alligator Reef; CR: Cheeca Rocks; M1: Miami 1; M2: Miami 2; M3: Miami 3.

Table 9. Pairwise population FST values below the diagonal; geographic distances (km) between sites above the diagonal. LKBR 11' M ETS TR SP SPE CG EAR CR M1 M2 M3 LKBR 4.360 9.223 4.978 7.889 10.84 16.78 27.33 29.09 137.3 136.0 137.4 11' M 0.000 5.783 8.330 11.20 14.04 19.42 30.45 31.95 139.5 138.2 139.6 ETS 0.000 0.000 13.93 16.71 19.36 23.89 35.48 36.53 142.7 141.3 142.6 TR 0.000 0.000 0.000 2.915 5.933 12.46 22.42 24.40 133.2 131.8 133.3 SP 0.000 0.002 0.000 0.005 3.086 10.07 19.53 21.63 130.7 129.2 130.8 SPE 0.000 0.000 0.001 0.000 0.000 7.307 16.49 18.55 127.7 126.2 127.8 CG 0.000 0.000 0.000 0.000 0.000 0.000 12.23 12.64 120.7 119.3 120.9 EAR 0.000 0.000 0.005 0.002 0.000 0.000 0.000 4.821 112.9 111.5 113.3 CR 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 109.5 108.0 109.8 M1 0.000 0.000 0.000 0.000 0.010 0.002 0.000 0.003 0.000 1.631 2.747 M2 0.000 0.000 0.001 0.000 0.008 0.000 0.009 0.001 0.000 0.000 3.880 M3 0.024* 0.005 0.016 0.000 0.024* 0.022* 0.000 0.018* 0.005 0.003 0.016 *p<0.05 LKBR: Long Key Bridge Rubble; 11’M: 11’ Mound; ETS: East Turtle Shoals; TR: Tennessee Reef; SP: Stag Party; SPE: Stag Party East; CG: Coral Gardens; EAR: East of Alligator Reef; CR: Cheeca Rocks; M1: Miami 1; M2: Miami 2; M3: Miami 3.

Table 10. Summary of clonal richness, R, found in each site and across all sites based on multilocus genotypes (G) and the number of samples (N) per site. R and G were calculated with and without taking Psex into account. Site Long East Stag East of Across Key 11' Tennessee Stag Coral Cheeca Miami Miami Miami Turtle Party Alligator All Bridge Mound Reef Party Gardens Rocks 1 2 3 Shoals East Reef Sites Rubble N 16 21 22 20 22 24 14 22 18 25 22 32 259 G 15 18 22 19 20 23 14 22 18 24 20 29 207

G (Psex) 15 21 22 19 22 24 14 22 18 25 22 29 254 R 0.933 0.850 1.000 0.947 0.905 0.957 1.000 1.000 1.000 0.958 0.905 0.903 0.798

R(Psex) 0.933 1.000 1.000 0.947 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.903 0.981

Table 11. Mean migration rates, Nm, of individuals into each site. The migration rate from a sampling site into the same sampling site is defined as the proportion of individuals in each generation that are not migrants and are bolded (diagonal values). From: To: LKBR 11' M ETS TR SP SPE CG EAR CR M1 M2 M3 LKBR 0.6856* 0.0423 0.0378* 0.019 0.0227* 0.0644* 0.0128* 0.0193 0.0132* 0.0334 0.037 0.0125* 11' M 0.0128 0.7004* 0.0268 0.022* 0.0179 0.0793* 0.0131 0.0233 0.0161 0.0501* 0.0253 0.0128 ETS 0.0148 0.0356 0.7029* 0.025 0.0209 0.0415 0.0128 0.0189 0.0172 0.0619* 0.0328 0.0158 TR 0.0147 0.044 0.0402* 0.6855* 0.0196* 0.0540* 0.0128 0.0187 0.0153 0.0441* 0.0321 0.0189* SP 0.0117 0.0438 0.0307 0.0187 0.6842* 0.0548* 0.0123 0.0252 0.0179 0.0625* 0.0244 0.0138* SPE 0.013 0.0651* 0.0294 0.0166 0.0229 0.7083* 0.0118 0.0296 0.014 0.0417 0.0351 0.0125 CG 0.0134 0.045 0.0484* 0.0192 0.0191 0.0471* 0.6855* 0.021 0.0158 0.0406 0.0248 0.0203 EAR 0.0124 0.0501 0.0203 0.0229 0.0208 0.0661* 0.0124 0.6902* 0.0157 0.0517* 0.0244 0.0131* CR 0.0131 0.0399 0.0278 0.0226 0.017 0.0477* 0.0124 0.021 0.6872* 0.0784* 0.02 0.0131 M1 0.014 0.0547 0.0369 0.0196 0.0164 0.068* 0.0112 0.0179 0.0172 0.6905* 0.0293 0.0235* M2 0.0112 0.0558 0.0215 0.0161 0.0167 0.0878* 0.0113 0.0184 0.0131 0.0497 0.6853* 0.0131 M3 0.0107 0.0383 0.0238 0.0235 0.0138 0.0399* 0.0123 0.0152 0.0185 0.0738* 0.0217 0.7085* *p<0.05 LKBR: Long Key Bridge Rubble; 11’M: 11’ Mound; ETS: East Turtle Shoals; TR: Tennessee Reef; SP: Stag Party; SPE: Stag Party East; CG: Coral Gardens; EAR: East of Alligator Reef; CR: Cheeca Rocks; M1: Miami 1; M2: Miami 2; M3: Miami 3.

46 Table 12. Mean emigration rates, proportion of individuals leaving the source sampling site but not found migrating to the other sampling sites, estimated by subtracting all significant migration rates (Nm) from one. Nm determined using BayesAss v3.0 (Wilson and Rannala 2003). Site Mean emigration rate Long Key Bridge Rubble 0.1737 11' Mound 0.1482 East Turtle Shoals 0.2352 Tennessee Reef 0.1377 Stag Party 0.1847 Stag Party East 0.2266 Coral Gardens 0.2190 Alligator Reef 0.1789 Cheeca Rocks 0.1867 Miami 1 0.2180 Miami 2 0.2269 Miami 3 0.1778

Table 13. Summary of STRUCTURE results showing the number of K (clusters), the number of repetitions, and the mean of the natural log of the probability of K with standard deviations. Evanno method (2005) calculations of delta K are also provided. Mean Stdev # K Reps Ln'(K) |Ln''(K)| Delta K LnP(K) LnP(K)

1 15 -2746.1133 0.0352 ------2 15 -2917.2533 154.387 -171.14 206.566667 1.33798 3 15 -2881.8267 21.9805 35.426667 77.913333 3.544658 4 15 -2924.3133 20.2432 -42.486667 152.513333 7.534054 5 15 -3119.3133 53.2843 -195 77.526667 1.454962 6 15 -3236.7867 38.3184 -117.473333 54.14 1.4129 7 15 -3300.12 51.6828 -63.333333 35.226667 0.681593 8 15 -3328.2267 59.5841 -28.106667 29.34 0.492413 9 15 -3385.6733 47.8175 -57.446667 44.993333 0.940938 10 15 -3398.1267 36.9786 -12.453333 7.126667 0.192724 11 15 -3417.7067 35.9866 -19.58 48.98 1.361064 12 15 -3388.3067 67.3014 29.4 522.033333 7.756647 13 15 -3880.94 1853.3326 -492.633333 951.353333 0.51332 14 15 -3422.22 64.3022 458.72 426.98 6.640213 15 15 -3390.48 38.0282 31.74 200.353333 5.268553 16 15 -3559.0933 565.6219 -168.613333 357.886667 0.632731 17 15 -3369.82 35.6513 189.273333 200.293333 5.618129 18 15 -3380.84 50.4584 -11.02 591.206667 11.716712 19 15 -3983.0667 1612.2879 -602.226667 1105.64 0.685758 20 15 -3479.6533 423.4029 503.413333 ------

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56 APPENDIX A

Table 14. Number of multilocus genotypes (MLG) based on a data set of 259 individuals. Samples with a Psex <0.05 were considered to be ramets and were excluded from further analysis. Distinct MLG Sampling unit designation designation 1 8011

2 101

3 2017

4 908

5 12031

6 1206 1203*

7 4016

8 403

9 901

10 10025

11 1103

12 10026

13 3019

14 12017

15 8019

16 9010

17 9017

18 10013

19 9016

20 5020

21 12032

22 1008

23 808

24 2025

25 11021

26 12034

27 201

28 8027

29 109

30 4020

31 1019

32 6026

33 12029

34 6014 104 12025

35 10010

36 8014 9024

57 37 909 502 5029 3023

38 7018

39 2015

40 1106 406

41 11016 1101

42 12023 8024

43 9023

44 106 701

45 10019

46 12026

47 907 505

48 306

49 12024

50 9020

51 3027

52 301

53 1205

54 6011

55 10021

56 809

57 4021

58 303

59 6012

60 2012 4027*

61 1006

62 503

63 1001 906

64 4014

65 12020 12019

66 2013

67 2026

68 9011

69 404 2022 3025 102 6010

70 6020

71 2016 305

72 8012

73 6018

74 6013 802 108

75 4011

76 2028 1009 707 1014 6028 2023 10014 77 12016

78 407

79 10015 8020

58 80 12033

81 3022

82 905

83 1007

84 4013

85 609

86 8017

87 6016

88 10017

89 12014

90 307

91 7010

92 708

93 10023

94 2020 202

95 608 1104 602 11014

96 5021

97 1005

98 3014

99 5014

100 7014

101 9013

102 4012 409

103 902

104 7012

105 8028 10012

106 9018 1015 7015

107 1012 103* 3015*

108 8025

109 702 308 5025 209 6015

110 7020

111 5023 1108

112 12015

113 4019

114 1202

115 1209

116 12012

117 12018 1201*

118 12011

119 408

120 807

121 11013

122 9015

59 123 6017

124 12028

125 11017

126 5017

127 12010

128 10027

129 3024

130 10028

131 5015

132 4010

133 705

134 8016

135 5010

136 11015

137 1004

138 7016

139 601

140 6019

141 1207

142 506

143 508

144 204

145 7011

146 8023

147 11010 10018

148 1109

149 11012

150 801

151 11020

152 2019

153 205 8018 206

154 302

155 605

156 5019 5027 309

157 1003

158 203

159 8029

160 509

161 1105

162 5011

163 12030

164 6025

165 12021

60 166 402

167 11018

168 10024

169 10016 2024

170 706

171 1107

172 1102

173 5026

174 3010

175 3012

176 6022

177 12027

178 10022

179 11022

180 12013

181 11019

182 10011

183 3017

184 3016

185 12022

186 805

187 5024

188 9021

189 6027

190 2021

191 8010

192 4024

193 1010

194 1026

195 6021

196 504

197 6024

198 11011 401

199 1023

200 3011

201 8015

202 3020

203 5013

204 107

205 4022

206 1011

207 6023 *p of Psex <0.05

61 APPENDIX B

Table 15. GENCLONE output showing the Psex(Fis) values per repeated MLG. Psex(Fis) values less than 0.05 were considered to be ramets from the same genet and were excluded from further analysis. # Psex 1 Psex (Fis) 1 MLG Pgen Pgen (Fis) Reencounter Ramets reencounter reencounter 1 1.64E-05 2.41E-05

2 4.86E-07 3.67E-07

3 6.71E-07 3.42E-06

4 6.66E-06 4.96E-06

5 9.33E-05 6.94E-05

6 2.09E-05 2.31E-05

6 2.09E-05 2.31E-05 2 1 0.005406579 0.00595533* 7 0.000113285 8.43E-05

8 8.26E-05 0.000119838

9 4.55E-05 4.15E-05

10 0.000569415 0.000560252

11 1.35E-05 1.98E-05

12 0.0001905 0.000143058

13 0.001462768 0.001453214

14 0.000111765 8.78E-05

15 0.000176893 0.00013284

16 1.22E-05 7.54E-06

17 7.56E-05 5.71E-05

18 8.42E-07 2.10E-06

19 1.31E-05 9.92E-06

20 2.46E-05 1.52E-05

21 3.28E-06 2.88E-06

22 0.000117938 0.000112712

23 2.56E-06 2.24E-06

24 2.46E-06 3.20E-06

25 0.000619594 0.000685557

26 0.000129915 0.000113027

27 0.000980648 0.001037484

28 0.000197451 0.000254489

29 0.000288416 0.000250924

30 0.001068655 0.000929739

31 0.000548475 0.001184048

32 0.000779051 0.00132241

33 0.003230741 0.003574689

34 0.008128962 0.007072268

34 0.008128962 0.007072268 2 1 0.879246639 0.840900465

62 34 0.008128962 0.007072268 3 2 0.622928779 0.547399365 35 0.003647611 0.003173454

36 0.005113379 0.00540974

36 0.005113379 0.00540974 2 1 0.734930389 0.754614867 37 0.00458893 0.003992409

37 0.00458893 0.003992409 2 1 0.69616434 0.645165449 37 0.00458893 0.003992409 3 2 0.333380875 0.276783729 37 0.00458893 0.003992409 4 3 0.117633176 0.08629899 38 0.000713495 0.000754847

39 0.00017482 0.000257621

40 0.004757598 0.006964018

40 0.004757598 0.006964018 2 1 0.709211339 0.836344278 41 0.011970732 0.013777813

41 0.011970732 0.013777813 2 1 0.955804224 0.972491613 42 0.001005416 0.001164993

42 0.001005416 0.001164993 2 1 0.229359799 0.260594641 43 0.005371482 0.006182352

44 0.007529976 0.010538966

44 0.007529976 0.010538966 2 1 0.858809277 0.935692405 45 0.000711494 0.001002521

46 0.000248313 0.000287725

47 0.006757671 0.007777797

47 0.006757671 0.007777797 2 1 0.827296209 0.867652518 48 0.000143258 0.000245912

49 0.001499018 0.001304159

50 0.000910602 0.000963378

51 6.81E-05 5.92E-05

52 6.29E-05 5.47E-05

53 0.000101361 7.32E-05

54 0.000238071 0.000171983

55 0.00179705 0.001578643

56 1.21E-06 4.47E-06

57 0.001556616 0.001124503

58 0.000289086 0.000208836

59 0.002182132 0.001916923

60 0.000163194 0.000117891

60 0.000163194 0.000117891 2 1 0.04138965 0.03007417* 61 0.001005087 0.001801655

62 8.44E-05 0.00015234

63 0.002528928 0.003564446

63 0.002528928 0.003564446 2 1 0.48098467 0.603405425 64 9.53E-05 0.000135241

65 0.00142762 0.002012187

63 65 0.00142762 0.002012187 2 1 0.309278014 0.406478311 66 0.00026513 0.000373692

67 0.005920373 0.005439269

68 0.000497249 0.000459921

69 0.014896423 0.010761206

69 0.014896423 0.010761206 2 1 0.979497298 0.939327056 69 0.014896423 0.010761206 3 2 0.899198147 0.768382749 69 0.014896423 0.010761206 4 3 0.742558838 0.528497157 69 0.014896423 0.010761206 5 4 0.539644506 0.304946554 70 0.001241369 0.000896767

71 0.00937033 0.008231494

71 0.00937033 0.008231494 2 1 0.91269588 0.882436896 72 0.008409271 0.006074874

73 0.000900714 0.000650678

74 0.008718358 0.010596492

74 0.008718358 0.010596492 2 1 0.896476383 0.936653522 74 0.008718358 0.010596492 3 2 0.660658452 0.760937685 75 0.000823782 0.001007993

76 0.021936513 0.020964404

76 0.021936513 0.020964404 2 1 0.996800627 0.995861844 76 0.021936513 0.020964404 3 2 0.978215511 0.972911421 76 0.021936513 0.020964404 4 3 0.924443594 0.909515043 76 0.021936513 0.020964404 5 4 0.821127509 0.793220227 76 0.021936513 0.020964404 6 5 0.672825012 0.633843696 76 0.021936513 0.020964404 7 6 0.503188558 0.459791868 77 0.002072741 0.001994242

78 0.013798774 0.016036155

79 0.001158952 0.001355948

79 0.001158952 0.001355948 2 1 0.259435605 0.296318148 80 0.012383515 0.011834744

81 0.001170096 0.001125782

82 0.000102092 0.000145515

83 0.006121818 0.004422413

84 0.002746969 0.001984416

85 6.42E-05 4.64E-05

86 0.001054313 0.000968637

87 0.002652788 0.001916379

88 9.16E-05 6.61E-05

89 4.32E-05 3.12E-05

90 0.000631629 0.00070971

91 0.000897163 0.000792643

92 0.000201287 0.000263455

93 4.31E-05 3.81E-05

64 94 0.002712289 0.003047578

94 0.002712289 0.003047578 2 1 0.505118675 0.546394775 95 0.00682447 0.006029417

95 0.00682447 0.006029417 2 1 0.830278505 0.791193173 95 0.00682447 0.006029417 3 2 0.528228044 0.463138508 95 0.00682447 0.006029417 4 3 0.260488757 0.206431524 96 1.20E-05 9.00E-06

97 0.003062262 0.002705507

98 0.004292811 0.004612039

99 0.000715469 0.000632116

100 0.000378459 0.000425243

101 0.000243139 0.000284182

102 0.000377398 0.00056477

102 0.000377398 0.00056477 2 1 0.093137487 0.136116053 103 0.000335465 0.000502018

104 0.010049733 0.011746186

105 0.000844072 0.000993207

105 0.000844072 0.000993207 2 1 0.19644285 0.226916675 106 0.006321606 0.008984928

106 0.006321606 0.008984928 2 1 0.806502953 0.903442221 106 0.006321606 0.008984928 3 2 0.487675522 0.676705764 107 0.000597317 0.000854693

107 0.000597317 0.000854693 2 1 0.143371849 0.198652082 107 0.000597317 0.000854693 3 2 0.010767874 0.02110962* 108 0.000530949 0.000759727

109 0.005673236 0.006630911

109 0.005673236 0.006630911 2 1 0.770889767 0.821492614 109 0.005673236 0.006630911 3 2 0.43232171 0.512876549 109 0.005673236 0.006630911 4 3 0.183127801 0.247128053 109 0.005673236 0.006630911 5 4 0.061326754 0.095162464 110 0.001272841 0.002203947

111 0.000764473 0.000821322

111 0.000764473 0.000821322 2 1 0.179691098 0.191690158 112 3.72E-05 4.17E-05

113 0.000123082 0.000438266

114 0.00018094 0.000130712

115 0.000113817 1.00E-04

116 1.90E-05 1.37E-05

117 7.94E-06 2.31E-05

117 7.94E-06 2.31E-05 2 1 0.002054548 0.00597013* 118 1.60E-06 5.67E-06

119 0.000344691 0.000256359

120 0.000507593 0.000499424

65 121 5.21E-06 3.90E-06

122 0.00063165 0.000390077

123 5.26E-05 3.25E-05

124 0.00093017 0.000759927

125 0.000585107 0.000581286

126 1.06E-06 5.17E-06

127 5.35E-07 1.10E-06

128 0.000162153 0.000120599

129 0.000526375 0.000325064

130 7.32E-05 6.02E-05

131 0.00043758 0.000357493

132 2.29E-06 2.09E-06

133 0.000539858 0.000401512

134 0.000269777 0.000391472

135 0.00010609 7.94E-05

136 0.001770711 0.00160144

137 0.000148721 0.000135411

138 1.67E-05 1.25E-05

139 0.004145342 0.004078632

140 0.002340112 0.002302454

141 0.000434592 0.000427599

142 8.24E-05 5.09E-05

143 0.000125299 0.000114632

144 0.000678147 0.000418791

145 0.000875742 0.001055179

146 0.00055087 0.000807131

147 0.002050165 0.001610181

147 0.002050165 0.001610181 2 1 0.412299419 0.341224925 148 0.005158479 0.003185629

149 0.000487415 0.000303034

150 0.003244849 0.002436761

151 0.002912044 0.001798339

152 0.000719788 0.000444506

153 0.00759639 0.006206072

153 0.00759639 0.006206072 2 1 0.86123533 0.800587772 153 0.00759639 0.006206072 3 2 0.586130908 0.478056362 154 1.70E-05 1.20E-05

155 0.000175583 0.000175613

156 0.004778374 0.004747167

156 0.004778374 0.004747167 2 1 0.710779299 0.708420893 156 0.004778374 0.004747167 3 2 0.351121498 0.348209645 157 0.004288284 0.003503428

158 7.72E-05 4.80E-05

66 159 0.000252369 0.000452229

160 0.000226485 0.000333747

161 0.000939238 0.000902172

162 0.001334089 0.001007595

163 1.56E-05 1.19E-05

164 0.001383124 0.001757562

165 6.80E-06 5.17E-06

166 1.40E-05 1.36E-05

167 0.000686602 0.000620967

168 0.000138246 0.000152319

169 0.001607377 0.00158151

169 0.001607377 0.00158151 2 1 0.340745538 0.336306915 170 0.001011092 0.001209734

171 0.00044857 0.000333618

172 2.11E-05 1.57E-05

173 0.0002413 0.000181207

174 0.000293007 0.000220037

175 3.21E-05 3.89E-05

176 0.001258207 0.000944867

177 0.001170663 0.001216337

178 0.000278319 0.000228913

179 0.000155619 0.000155645

180 3.24E-05 6.63E-05

181 0.0005173 0.0003907

182 4.96E-05 4.99E-05

183 0.000149669 0.000143762

184 1.41E-05 1.37E-05

185 1.26E-05 1.22E-05

186 0.000105275 6.50E-05

187 7.10E-05 7.10E-05

188 2.84E-07 2.46E-06

189 2.10E-06 1.30E-06

190 2.76E-07 2.22E-06

191 5.02E-08 4.61E-08

192 5.04E-06 4.63E-06

193 3.01E-05 9.14E-05

194 4.28E-05 0.000102135

195 7.95E-06 1.90E-05

196 0.000446582 0.000546221

197 3.92E-05 5.83E-05

198 0.000657638 0.001064118

198 0.000657638 0.001064118 2 1 0.156659314 0.240999847 199 0.000413676 0.000813968

67 200 8.33E-05 0.000199661

201 1.11E-05 1.36E-05

202 2.85E-05 5.67E-05

203 0.000346329 0.000213876

204 0.000233648 0.000233452

205 5.40E-05 0.000187346

206 9.89E-05 0.000285068

207 4.53E-05 0.000159721

*p of Psex <0.05