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
by
Diana M. Jacinto
August 2014
i
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.
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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.
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TABLE OF CONTENTS
Signature Page
Dedication
Acknowledgments
Abstract
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iii
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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
Sample collection
DNA extraction, microsatellite amplification and genotyping
Microsatellite quality analysis
Statistical analyses
Clustering analysis
Clonal analysis
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15
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18
19
20
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RESULTS
Microsatellite loci, HWE, null alleles, and linkage disequilibria
Genetic diversity and differentiation
Clustering analysis
Clonal analysis
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23
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25
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DISCUSSION
Differentiation and connectivity of M. alcicornis
Genetic diversity
Genotypic diversity
Conclusion
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27
30
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32
TABLES AND FIGURES
35
REFERENCES
48
APPENDIX A: Multilocus genotypes in data set
APPENDIX B: Psex values
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ABSTRACT
GENETIC DIVERSITY, POPULATION STRUCTURE AND CONNECTIVITY OF
MILLEPORA ALCICORNIS (HYDROZOA: ANTHOMEDUSAE: MILLEPORIDAE) IN
THE FLORIDA REEF TRACT
by
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.
vi
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
1
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
2
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
3
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 reefframework 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
4
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.
5
Unlike scleractinians, millepores are gonochoristic hydrozoans that exhibit freeswimming 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
6
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).
7
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
8
(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
9
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
10
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
11
(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
12
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
13
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).
14
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.
15
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
16
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
17
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,
18
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
19
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
20
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
21
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.
22
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
23
(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
24
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
25
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%.
26
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
27
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.
28
(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
29
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
30
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.
31
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 heatresistant 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
32
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
33
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.
34
TABLES AND FIGURES
Figure 1. Sampling sites in the Florida Reef Tract in the middle Keys and Miami.
35
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.
36
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.
37
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).
38
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).
39
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.
Region
Middle Keys
Miami
Site
#
Site Name
Latitude (°)
Longitude (°)
Water
Depth
(m)
# Samples
Collected
Date
Collected
1
2
3
4
5
6
7
8
9
10
11
12
Long Key Bridge Rubble (LKBR)
11' Mound (11'M)
East Turtle Shoals (ETS)
Tennessee Reef (TR)
Stag Party (SP)
Stag Party East (SPE)
Coral Gardens (CG)
East of Alligator Reef (EAR)
Cheeca Rocks (CR)
Miami 1 (M1)
Miami 2 (M2)
Miami 3 (M3)
24.7268000
24.7234833
24.7250500
24.7455667
24.7581667
24.7781333
24.8422833
24.8618167
24.9033910
25.7659444
25.7512778
25.7798250
-80.8275667
-80.8616333
-80.9188667
-80.7828167
-80.7575000
-80.7362667
-80.7205667
-80.6013167
-80.6148680
-80.0893889
-80.0898056
-80.1120861
8.2
5.8
8.8
7.3
7.0
5.5
4.6
7.9
7.3
12.0
11.2
11.0
29
28
27
29
30
28
19
29
25
28
28
34
6-Aug-12
6-Aug-12
6-Aug-12
7-Aug-12
7-Aug-12
7-Aug-12
7-Aug-12
8-Aug-12
8-Aug-12
14-Dec-13
14-Dec-13
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.
Locus
Motif
#
Repeats
Primer
JLK1B
AAT
5
F
R
J3R24
AT
5
F
R
J5ZMV
AC
8
F
R
J0B6S
AG
7
F
R
F19OY
AAT
5
F
R
JPXC8
AAGT
5
F
R
J4915
AT
5
F
R
H3ZLI
AT
5
F
R
Sequence
CGA GTT TTC CCA GTC ACG ACA GGT CCC GTA
ATC TCA CCT G
GTT TCT TCC TTT GTT GCC TAA GTT TGG G
CGA GTT TTC CCA GTC ACG ACG ACG TCA CAG
TGA GTA TTG GC
GTT TCT TCC CAG TCC TCC AAA TCA TGC
CGA GTT TTC CCA GTC ACG ACT GCC TGA TAG
ATG CGT GGA G
GTT TCT TCA TAA CCA CTT CAG GCG CG
CGA GTT TTC CCA GTC ACG ACC GTT CGT GTG
GAC TAC TGA TG
GTT TCT TAC AGA GAG GCA GAA TGG TTG
CGA GTT TTC CCA GTC ACG ACT GGT GCT CCC
TCA TAC TTG TC
GTT TCT TAC AGT TGG ATC CTT GAG TTG C
CGA GTT TTC CCA GTC ACG ACA GCA TGT ATT
GTG TCA TCC TGC
GTT TCT TAT CTG GGT CTG GCT GCT AAG
CGA GTT TTC CCA GTC ACG ACA CAG GGA AGG
ACA AGT TTA GTC
GTT TCT TCC TTA TGC AAT TCC TCC ATC CC
CGA GTT TTC CCA GTC ACG ACG GTC CTA GTG
TAG TGT GGA GC
GTT TCT TTC ACT GGT TGC AAC TGA TCA C
41
Annealing
Tm (°C)
Fluorescent
Tag
67.9
6 FAM
58.5
67.9
NED
59.8
68.1
PET
60.2
67.7
VIC
56.6
67.8
PET
57.9
67.1
VIC
58.9
66.6
NED
59.4
68.5
58.6
6 FAM
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
Ho
He
HWE
FIS
4
0.133
0.533
0.00*
0.7646
5
0.286
0.400
0.00*
0.3084
5
0.381
0.459
0.08
0.1940
6
0.300
0.4525
0.01
0.3596
7
0.591
0.565
0.60
-0.0225
4
0.292
0.457
0.02
0.3808
3
0.286
0.2526
1.00
-0.0947
7
0.455
0.468
0.38
0.0519
4
0.333
0.410
0.06
0.2154
5
0.462
0.394
1.00
-0.0565
5
0.636
0.546
0.65
-0.1417
5
0.379
0.334
1.00
1.0000
8
0.378
0.439
0.00
0.1393
J3R24
Na
Ho
He
HWE
FIS
2
0.467
0.500
0.55
0.1009
2
0.667
0.499
0.97
-0.3146
2
0.524
0.495
0.72
-0.0329
2
0.400
0.495
0.30
0.2165
2
0.455
0.483
0.52
0.0830
2
0.625
0.499
0.94
-0.2321
2
0.143
0.490
0.01
0.7263
2
0.500
0.499
0.63
0.0212
2
0.278
0.498
0.06
0.4654
4
0.577
0.530
0.75
-0.0684
2
0.318
0.474
0.12
0.3496
5
0.552
0.598
0.37
0.0949
5
0.459
0.505
0.16
0.0805
J5ZMV
Na
Ho
He
HWE
FIS
4
0.267
0.527
0.02
0.5193
6
0.571
0.586
0.43
0.0203
6
0.571
0.519
0.49
0.0170
7
0.500
0.600
0.02
0.0045
6
0.591
0.595
0.02
0.0029
6
0.583
0.569
0.59
0.0228
5
0.429
0.582
0.17
0.0085
7
0.455
0.684
0.02
0.0035
6
0.611
0.537
0.63
0.0193
7
0.423
0.530
0.04
0.0072
5
0.682
0.508
1.00
0.0000
7
0.552
0.693
0.04
0.0064
8
0.520
0.577
0.01
0.1173
J0B6S
Na
Ho
He
HWE
FIS
4
0.533
0.589
0.38
0.1284
4
0.619
0.636
0.17
0.0511
4
0.524
0.666
0.07
0.2361
4
0.550
0.648
0.30
0.1755
4
0.682
0.656
0.41
-0.0161
5
0.750
0.652
0.87
-0.1296
3
0.500
0.625
0.14
0.2353
4
0.500
0.548
0.09
0.1098
5
0.556
0.674
0.09
0.2037
3
0.654
0.624
0.54
-0.0291
4
0.545
0.610
0.11
0.1280
4
0.655
0.689
0.29
0.0667
5
0.589
0.635
0.06
0.0815
F19OY
Na
2
4
2
3
2
2
2
3
3
4
4
4
4
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
Ho
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
He
--1.00
0.44
1.00
0.14
----1.00
1.00
1.00
1.00
1.00
0.23
HWE
0.0000 0.0023 0.0000 0.0017
----0.0000 0.0000 0.0000 0.0000 0.0000 0.0085
FIS
--*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.
42
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
%
Fd.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
J3R24
J5ZMV
J0B6S
F19OY
0.002
-0.010
-0.003
-0.004
0.010
0.368
0.852
0.637
0.687
0.078
0.002
-0.011
-0.003
-0.004
0.011
0.368
0.852
0.637
0.687
0.078
0.004
-0.023
-0.008
-0.011
0.013
0.368
0.846
0.632
0.686
0.078
0.004
-0.024
-0.008
-0.012
0.014
0.368
0.846
0.632
0.686
0.078
0.002
-0.012
-0.005
-0.008
0.003
0.369
0.843
0.631
0.687
0.081
Total
-0.003
0.763
-0.003
0.763
-0.006
0.761
-0.006
0.761
-0.003
0.760
43
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
J3R24
J5ZMV
J0B6S
F19OY
0.002
-0.012
-0.004
-0.004
0.010
0.387
0.888
0.647
0.661
0.079
0.002
-0.013
-0.004
-0.004
0.011
0.387
0.888
0.647
0.661
0.079
0.003
-0.025
-0.009
-0.011
0.013
0.387
0.884
0.645
0.659
0.081
0.003
-0.026
-0.010
-0.012
0.014
0.387
0.884
0.645
0.659
0.081
0.001
-0.013
-0.006
-0.008
0.003
0.387
0.879
0.642
0.658
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
LKBR
11' M
ETS
TR
SP
SPE
CG
EAR
CR
M1
M2
M3
-0.039
-0.012
-0.025
-0.017
-0.048
-0.007
-0.016
-0.003
-0.008
-0.037
0.047
11' M
ETS
TR
SP
SPE
CG
E AR
CR
M1
M2
M3
-0.018
-0.006
-0.006
-0.012
-0.018
-0.015
-0.008
0.002
-0.006
0.005
-0.022
-0.011
0.001
-0.002
-0.002
-0.003
-0.008
-0.016
-0.009
-0.009
-0.004
-0.007
-0.006
0.005
0.002
-0.006
-0.003
-0.004
-0.001
-0.012
-0.008
-0.014
-0.001
-0.001
-0.013
-0.004
-0.004
-0.012
-0.008
-0.016
0.009
0.002
-0.009
0.003
-0.014
-0.017
-0.008
0.001
-0.011
0.008
-0.005
0.007
0.001
-0.002
-0.008
0.024
0.006
0.016
-0.003
0.026*
0.021*
0.000
0.019
0.005
0.003
0.016
-0.013
-0.036
0.003
-0.024
-0.018
-0.012
-0.024
-0.026
-0.016
0.011
-0.030
-0.011
0.002
-0.035
0.010
-0.016
-0.017
0.002
0.032
0.010
-0.005
-0.020
0.004
-0.028
-0.033
-0.023
-0.006
-0.004
-0.019
-0.012
-0.003
0.019
0.016
0.050*
-0.008
-0.006
-0.001
0.003
-0.010
0.043*
-0.009
-0.030
-0.019
0.015
-0.001
-0.007
0.006
0.002
0.036
-0.029
-0.005
0.010
-0.017
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.
44
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
J3R24
J5ZMV
J0B6S
F19OY
0.567
0.519
0.555
0.612
0.067
0.413
0.507
0.601
0.652
0.224
0.473
0.507
0.531
0.686
0.286
0.468
0.511
0.618
0.667
0.271
0.578
0.496
0.609
0.671
0.171
0.471
0.507
0.582
0.664
0.042
0.261
0.522
0.610
0.654
0.071
0.479
0.511
0.706
0.562
0.246
0.425
0.520
0.551
0.698
0.208
0.401
0.540
0.542
0.635
0.248
0.557
0.489
0.516
0.626
0.174
0.339
0.610
0.708
0.702
0.194
0.453
0.520
0.594
0.652
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
LKBR
11' M
ETS
TR
SP
SPE
CG
EAR
CR
M1
M2
M3
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.024*
11' M
ETS
TR
SP
SPE
CG
EAR
CR
M1
M2
M3
4.360
9.223
5.783
4.978
8.330
13.93
7.889
11.20
16.71
2.915
10.84
14.04
19.36
5.933
3.086
16.78
19.42
23.89
12.46
10.07
7.307
27.33
30.45
35.48
22.42
19.53
16.49
12.23
29.09
31.95
36.53
24.40
21.63
18.55
12.64
4.821
137.3
139.5
142.7
133.2
130.7
127.7
120.7
112.9
109.5
136.0
138.2
141.3
131.8
129.2
126.2
119.3
111.5
108.0
1.631
137.4
139.6
142.6
133.3
130.8
127.8
120.9
113.3
109.8
2.747
3.880
0.000
0.000
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.005
0.000
0.000
0.001
0.000
0.005
0.000
0.000
0.001
0.016
0.005
0.000
0.000
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.010
0.008
0.024*
0.000
0.000
0.000
0.002
0.000
0.022*
0.000
0.000
0.000
0.009
0.000
0.000
0.003
0.001
0.018*
0.000
0.000
0.005
0.000
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.
45
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
N
G
G (Psex)
R
R(Psex)
Long
Key
Bridge
Rubble
11'
Mound
East
Turtle
Shoals
Tennessee
Reef
Stag
Party
Stag
Party
East
Coral
Gardens
East of
Alligator
Reef
Cheeca
Rocks
Miami
1
Miami
2
Miami
3
Across
All
Sites
16
15
15
0.933
0.933
21
18
21
0.850
1.000
22
22
22
1.000
1.000
20
19
19
0.947
0.947
22
20
22
0.905
1.000
24
23
24
0.957
1.000
14
14
14
1.000
1.000
22
22
22
1.000
1.000
18
18
18
1.000
1.000
25
24
25
0.958
1.000
22
20
22
0.905
1.000
32
29
29
0.903
0.903
259
207
254
0.798
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).
To:
LKBR
11' M
ETS
TR
SP
SPE
CG
EAR
CR
M1
M2
M3
*p<0.05
From:
LKBR
0.6856*
0.0128
0.0148
0.0147
0.0117
0.013
0.0134
0.0124
0.0131
0.014
0.0112
0.0107
11' M
0.0423
0.7004*
0.0356
0.044
0.0438
0.0651*
0.045
0.0501
0.0399
0.0547
0.0558
0.0383
ETS
0.0378*
0.0268
0.7029*
0.0402*
0.0307
0.0294
0.0484*
0.0203
0.0278
0.0369
0.0215
0.0238
TR
0.019
0.022*
0.025
0.6855*
0.0187
0.0166
0.0192
0.0229
0.0226
0.0196
0.0161
0.0235
SP
0.0227*
0.0179
0.0209
0.0196*
0.6842*
0.0229
0.0191
0.0208
0.017
0.0164
0.0167
0.0138
SPE
0.0644*
0.0793*
0.0415
0.0540*
0.0548*
0.7083*
0.0471*
0.0661*
0.0477*
0.068*
0.0878*
0.0399*
CG
0.0128*
0.0131
0.0128
0.0128
0.0123
0.0118
0.6855*
0.0124
0.0124
0.0112
0.0113
0.0123
EAR
0.0193
0.0233
0.0189
0.0187
0.0252
0.0296
0.021
0.6902*
0.021
0.0179
0.0184
0.0152
CR
0.0132*
0.0161
0.0172
0.0153
0.0179
0.014
0.0158
0.0157
0.6872*
0.0172
0.0131
0.0185
M1
0.0334
0.0501*
0.0619*
0.0441*
0.0625*
0.0417
0.0406
0.0517*
0.0784*
0.6905*
0.0497
0.0738*
M2
0.037
0.0253
0.0328
0.0321
0.0244
0.0351
0.0248
0.0244
0.02
0.0293
0.6853*
0.0217
M3
0.0125*
0.0128
0.0158
0.0189*
0.0138*
0.0125
0.0203
0.0131*
0.0131
0.0235*
0.0131
0.7085*
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.
#K
Reps
Mean
LnP(K)
Stdev
LnP(K)
Ln'(K)
|Ln''(K)|
Delta 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
19
20
15
15
15
-3380.84
-3983.0667
-3479.6533
50.4584
1612.2879
423.4029
-11.02
-602.226667
503.413333
591.206667
1105.64
---
11.716712
0.685758
---
47
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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
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
909
502
7018
2015
1106
406
11016 1101
12023 8024
9023
106
701
10019
12026
907
505
306
12024
9020
3027
301
1205
6011
10021
809
4021
303
6012
2012 4027*
1006
503
1001
906
4014
12020 12019
2013
2026
9011
404
2022
6020
2016
305
8012
6018
6013
802
4011
2028 1009
12016
407
10015 8020
5029
3023
3025
102
6010
1014
6028 2023 10014
108
707
58
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
12033
3022
905
1007
4013
609
8017
6016
10017
12014
307
7010
708
10023
2020
202
608
1104
602 11014
5021
1005
3014
5014
7014
9013
4012
409
902
7012
8028 10012
9018 1015 7015
1012 103* 3015*
8025
702
308
5025
209 6015
7020
5023 1108
12015
4019
1202
1209
12012
12018 1201*
12011
408
807
11013
9015
59
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
6017
12028
11017
5017
12010
10027
3024
10028
5015
4010
705
8016
5010
11015
1004
7016
601
6019
1207
506
508
204
7011
8023
11010 10018
1109
11012
801
11020
2019
205
8018
302
605
5019 5027
1003
203
8029
509
1105
5011
12030
6025
12021
206
309
60
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
*p of Psex <0.05
402
11018
10024
10016
706
1107
1102
5026
3010
3012
6022
12027
10022
11022
12013
11019
10011
3017
3016
12022
805
5024
9021
6027
2021
8010
4024
1010
1026
6021
504
6024
11011
1023
3011
8015
3020
5013
107
4022
1011
6023
2024
401
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.
MLG
Pgen
Pgen (Fis)
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
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
#
Ramets
Reencounter
Psex 1
reencounter
Psex (Fis) 1
reencounter
2
1
0.005406579
0.00595533*
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
37
0.00458893
0.003992409
2
1
0.734930389
0.754614867
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
2
1
0.04138965
0.03007417*
2
1
0.48098467
0.603405425
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
61
0.001005087
0.001801655
62
8.44E-05
0.00015234
63
0.002528928
0.003564446
63
0.002528928
0.003564446
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
95
0.00682447
0.006029417
95
0.00682447
95
0.00682447
95
2
1
0.505118675
0.546394775
0.006029417
2
1
0.830278505
0.791193173
0.006029417
3
2
0.528228044
0.463138508
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
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
153
154
2
1
0.412299419
0.341224925
0.006206072
2
1
0.86123533
0.800587772
0.00759639
0.006206072
3
2
0.586130908
0.478056362
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
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
199
0.000413676
0.000813968
2
1
0.340745538
0.336306915
2
1
0.156659314
0.240999847
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
68