POSTGLACIAL EXPANSION OF RHIZOPHORA MANGLE L. IN THE CARIBBEAN
SEA AND FLORIDA
by
John Paul Kennedy
A Thesis Submitted to the Faculty of
The Charles E. Schmidt College of Science
In Partial Fulfillment of the Requirements for the Degree of
Master of Science
Florida Atlantic University
Boca Raton, Florida
August 2014
Copyright by John Paul Kennedy 2014
ii
ACKNOWLEDGEMENTS
This work was funded by an NSF EPSCoR grant, the FAU President’s Challenge
scholarship, and a generous donation from Carolyn Stutt of the Mangrove Garden
Foundation. I would like to first thank my advisor, Dr. Donna J. Devlin, for welcoming
me into her lab and opening my eyes to the fascinating world of mangroves. I would also
like to thank my committee members (Dr. Colin Hughes, Dr. C. Edward Proffitt, and Dr.
Robert Shatters), as well as Dr. Aaron Dickey, Dr. Greg O’Corry-Crowe, and Dr. Ned
Smith, for providing insightful comments and suggestions that greatly improved this
work. Thanks to Maria Pil and Dr. Walter Boeger for their input and willingness to
include the collection sites of St. Kitts and Senegal in this study. Additional thanks go to
the O’Corry-Crowe lab for access to lab equipment, Dr. Holly Nance for providing
insight and additional primers, Laura Herren, M.S. for her expertise in map making, and
Pamela Alderman and Carla Robinson for the astounding job they do (and have done) at
the HBOI library. Also, this project would not have been possible without Ángel Dieppa,
Richard Brust, Thomas Frankovich, Scott Jones, Ilka Feller, and Jean Raffray who helped
with sample collections. Lastly, a special thanks to my lovely wife, Antonella Jara, for
her unconditional love, support, and willingness to spend long hours together in the lab.
iv
ABSTRACT
Author:
John Paul Kennedy
Title:
Postglacial expansion of Rhizophora mangle L. in the Caribbean
Sea and Florida
Institution:
Florida Atlantic University
Thesis Advisor:
Dr. Donna J. Devlin
Degree:
Master of Science
Year:
2014
The Last Glacial Maximum (LGM) was a period of massive range contraction for
numerous taxa, including the water-dispersed mangrove species, Rhizophora mangle L.
Following the LGM, R. mangle expanded poleward via propagule transport by ocean
currents. In this study, we use microsatellite loci to characterize the genetic structure of
nine R. mangle populations and compare potential expansion pathways that resulted in
the colonization of the Florida peninsula and Caribbean islands. Results show
comparatively greater genetic connectivity between the Caribbean mainland and Florida,
a similar pattern between West Africa and Caribbean islands, and substantial admixture
on the island of San Salvador, the Bahamas. We conclude that Florida and Caribbean
island R. mangle populations were likely recolonized via different expansion pathways.
Estimates of recent migration rates are low and populations are structured into three
regions (Caribbean mainland, Caribbean islands, Florida). These findings provide insight
for future management and conservation initiatives.
v
POSTGLACIAL EXPANSION OF RHIZOPHORA MANGLE L. IN THE CARIBBEAN
SEA AND FLORIDA
List of Tables ................................................................................................................... viii
List of Figures .................................................................................................................... ix
Introduction ......................................................................................................................... 1
Factors affecting Rhizophora mangle range expansion ................................................ 2
Post-LGM expansion of Rhizophora mangle from glacial refuge populations ............ 4
Potential Recolonization Pathways following the LGM .............................................. 7
Objective & Hypotheses ............................................................................................... 9
Methods............................................................................................................................. 11
Collection & DNA isolation ....................................................................................... 11
Microsatellite analysis ................................................................................................ 11
Data quality ................................................................................................................. 12
Genetic diversity ......................................................................................................... 14
Population clustering .................................................................................................. 14
Mutation models ......................................................................................................... 15
Genetic structure ......................................................................................................... 17
Genetic differentiation over geographic distances ...................................................... 18
Estimates of historic and contemporary migration rates............................................. 18
Effect of latitude on genetic diversity ......................................................................... 21
vi
Results ............................................................................................................................... 22
Data quality ................................................................................................................. 22
Genetic diversity ......................................................................................................... 23
Population clustering .................................................................................................. 24
Mutation models ......................................................................................................... 25
Genetic structure ......................................................................................................... 26
Genetic differentiation over geographic distance ....................................................... 28
Estimates of migration rates........................................................................................ 28
Effect of latitude on genetic diversity ......................................................................... 30
Discussion ......................................................................................................................... 31
Hypothesis 1: Florida recolonization pathways .......................................................... 32
Hypothesis 2: West Africa to the Caribbean islands .................................................. 37
Hypothesis 3: Genetic diversity with Latitude............................................................ 39
Population genetic structure and Implications for conservation / management ......... 41
Future work ................................................................................................................. 44
References ......................................................................................................................... 59
vii
TABLES
Table 1. Collection sites and genetic diversity indices ..................................................... 45
Table 2. Microsatellite loci ............................................................................................... 46
Table 3. Deviations from Hardy-Weinberg equilibrium (HWE) ...................................... 46
Table 4. Linkage disequilibrium ....................................................................................... 47
Table 5. MICRO-CHECKER results for potential genotyping errors .............................. 48
Table 6. Estimates of null allele frequencies .................................................................... 48
Table 7. Analysis of molecular variance (AMOVA) ........................................................ 49
Table 8. Pairwise values of population genetic differentiation......................................... 50
Table 9. Estimates of recent migration rates ..................................................................... 51
viii
FIGURES
Figure 1. Map of sampled populations and dispersal pathways. ...................................... 52
Figure 2. Determination of most likely number of population clusters (K) ..................... 53
Figure 3. Population structure among sampled populations ............................................. 54
Figure 4. Neighbor-joining tree ........................................................................................ 55
Figure 5. Principal coordinates analysis (PCoA) .............................................................. 55
Figure 6. Graphical representation of Mantel tests ........................................................... 56
Figure 7. Estimates of historic effective population sizes and migration rates ................ 57
Figure 8. Genetic diversity with latitude........................................................................... 58
ix
INTRODUCTION
Species distributions have repeatedly fluctuated with changing climatic conditions
over the course of Earth’s history. The Last Glacial Maximum (LGM), which ended
approximately 19 kya (Yokoyama et al. 2000, Clarke et al. 2009), is the most recent
period of extensive range contraction for numerous taxa and restricted many species into
refuge populations at lower latitudes for the duration of this glacial period (Hewitt 2000).
As climate warmed during the Holocene, glaciers retreated and a new period of
expansion began with many species ranges rapidly expanding into previously
uninhabitable areas (Comes & Kadereit 1998, Davis & Shaw 2001). Range expansion
continues today, driven by rapid climate warming (Parmesan & Yohe 2003, Root et al.
2003, Kordas et al. 2011).
Patterns of expansion after the LGM were species-specific due to regional
variations in land masses and ocean circulation patterns that either acted as natural
conduits or barriers to expansion, as well as each species’ life history traits (Hewitt
1996). Nonetheless, a combination of fossil evidence and molecular data points to
commonalties in expansion patterns during this warming period (Hewitt 1996, 2000).
First, leading edge expansion via founding events was rapid and dominated by longdistance dispersers that occupied available space prior to the arrival of others. This
pattern of rapid expansion resulted in a decrease in allelic diversity and an increase in
homozygosity within certain populations at higher latitudes (e.g. Schmitt & Seltz 2002,
Pil et al. 2011, Sandoval-Castro et al. 2012). Second, once established populations
1
reached high densities, subsequent migrants contributed little to the overall gene pool due
to competitive exclusion (Waters 2011, Waters et al. 2013). Thus, discontinuous
populations along the same expansion pathway may exhibit significant genetic structure.
By characterizing the genetic structure of physically disconnected populations of a
species over a geographical scale and understanding regional variables, such as ocean
circulation patterns, we can infer expansion pathways following the LGM (Pleines et al.
2009 & references within).
Mangroves are a group of pantropical tree and shrub species that exist at the
interphase of land and sea (Tomlinson 1986). These diverse plant species provide a
model for testing hypotheses of postglacial passive marine expansion pathways because
their hydrochorous offspring (i.e. propagules) are dispersed by prevailing regional surface
ocean currents (Rabinowitz 1978, Clarke et al. 2001). The present study tests hypotheses
regarding the post-LGM expansion of the red mangrove, Rhizophora mangle L., an
important constituent of Neotropical mangrove forests that is capable of long distance
dispersal and is an effective colonizer (Tomlinson 1986).
Factors affecting Rhizophora mangle range expansion
Rhizophora mangle produces large propagules that can survive 100+ days
(Rabinowitz 1978) to over a year floating in salt water (Davis 1940). Some of these
propagules are transported over long distances via ocean currents (Davis 1940, Gunn &
Dennis 1973), although most are retained within or near their natal population (McKee
1995, Lema Veléz et al. 2003, Sengupta et al. 2005, Sousa et al. 2007). Thus, long
distance dispersal (LDD) of R. mangle propagules is likely stochastic in nature, but can
2
result in genetic connectivity between disconnected populations located along
predominant surface ocean currents (Lo et al. 2014), as is also true for other Rhizophora
species (Wee et al. 2014, Yahya et al. 2014). Genetic evidence indicates relatively recent
trans-Atlantic LDD (~7000 km) via equatorial currents between R. mangle populations in
West Africa and South America (Cerón-Souza et al. 2010, Takayama et al. 2013), with a
similar pattern of LDD in other mangrove (Nettel & Dodd 2007) and mangroveassociated species (Takayama et al. 2006, 2008a).
The ability to self-fertilize is one characteristic of successful colonizing species
(Baker 1955) and this adaptation enables even a single propagule to establish a
population when outcrossing is not possible (Lowenfeld 1991). Selfing can also lead to a
reduced effective population size and greater genetic differentiation among populations
(Glémin et al. 2006). Rhizophora mangle reproduces sexually via outcrossing or selffertilization, and in many areas self-fertilization is more frequent (Lowenfeld 1991,
Lowenfeld & Klekowski 1992, Klekowski et al. 1994, Proffitt & Travis 2005, 2014).
Rhizophora mangle is also viviparous; thus, propagules (embryos) are fully developed
before abscission from the maternal tree (Juncosa 1982, Elmqvist & Cox 1996). Once
abscised, propagules are capable of rooting and beginning the seedling stage of their
development, thus avoiding a lengthy dormant period (Rabinowitz 1978, Tomlinson
1986). Estuarine habitats are naturally variable (e.g. tidal flux, storm events), so this
viviparous nature permits R. mangle propagules to rapidly establish as soon as conditions
become favorable.
Mangroves are generally associated with low energy, soft substrate shorelines
(Woodroffe 1992, Krauss et al. 2008) and drifting propagules do not typically establish
3
on steep coastlines or those exposed to increased wave activity (Woodroffe & Grindrod
1991). As a result, the availability of suitable habitat may be as important as actual
dispersal capability in delineating the range of mangroves, especially for island
populations where the size of estuaries is typically limited (Ellison 1996, Duke et al.
1998). Once suitable habitat is colonized, forests can become densely populated with
reproductive trees relatively quickly as R. mangle seedlings can begin reproducing at an
age of only 2-3 years (Proffitt & Travis 2010). Therefore, subsequent migrants likely
have a negligible impact on the overall genetic structure of the population due to a lack of
open space along the forest fringe and an overwhelming number of propagules from trees
adapted to local conditions.
Post-LGM expansion of Rhizophora mangle from glacial refuge populations
Mangrove distributional limits are generally confined to the 20oC isotherm of sea
water and areas with winter air temperatures ≥ 20oC (Duke et al. 1998). Due to this
physiological constraint, Neotropical mangrove populations at higher latitudes went
extinct during the latest glacial period, while refuge populations persisted in more
equatorial regions (Woodroffe & Grindrod 1991, Triest 2008, Saintilan et al. 2014). In
North America, pollen records indicate that prior to the Pleistocene (~2.6 Mya) the
distribution of mangrove forests was similar to that of present day south Florida and
Mexico, but these forests disappeared during the subsequent glacial cycles (Sherrod &
McMillian 1985 & references within). Temperate forest species retreated southward into
areas previously inhabited by mangrove, such as the Florida peninsula (Loehle 2007). In
combination with colder temperatures, a 120 m decrease in sea level during the LGM
4
(Ionita et al. 2009) led to the extinction of mangrove populations on many Caribbean
islands because the coastal/intertidal zone consisted of exposed shallow continental shelf
breaks, habitat unsuitable for mangrove establishment (Woodroffe & Grindrod 1991,
Ellison 1996, Nettel & Dodd 2007).
Climate warming and sea level rise during the Holocene provided suitable
conditions for the expansion of mangrove refuge populations to higher latitudes and
island coastlines. As salt water intrusion forced salt intolerant plant species to retreat
landward, space opened for mangrove propagule recruitment in Florida (Gaiser et al.
2006). Expansion during this warming period led to the recolonization of the Florida
peninsula ~3-4 kya (Davis 1940, Scholl et al. 1964) and Bermuda ~3 kya (Ellison 1996)
by a limited number of mangrove species, which resulted in the observed reduction in
mangrove species richness from the equator to higher latitudes (Duke et al. 1998).
By characterizing the post-LGM expansion history of mangrove species, we gain
an understanding of how extant populations are connected and how genetic diversity is
distributed spatially. This vital insight can guide future conservation and management
initiatives and is especially important for foundation species, such as mangroves, that
provide valuable ecosystem services for estuarine communities (Nagelkerken et al. 2008,
Barbier et al. 2011). The continued persistence of mangrove forests, and the associated
faunal communities they support, is linked to some extent to among population
connectivity and within population genetic diversity.
Researchers have characterized the genetic structure of R. mangle populations
which presumably persisted during and since the LGM (Arbeláez-Cortes et al. 2007,
Cerón-Souza et al. 2012, Bruschi et al. 2013) and those that have only established since
5
this event (Pil et al. 2011, Sandoval-Castro et al. 2012, 2014). These studies identified
three findings about R. mangle population genetic structure. First, high levels of
population connectivity exist over long distances along portions of continuous mainland
coastlines likely due to propagule longevity and transport via ocean currents. Rhizophora
mangle populations exhibit low genetic differentiation along >81 km and ~500 km of the
Pacific coast of Nicaragua (Bruschi et al. 2013) and Colombia (Arbeláez-Cortes et al.
2007), respectively; as well as along ~300 km of the Caribbean coast in Panama (CerónSouza et al. 2012). Second, ocean circulation patterns and coastline morphology can act
as barriers to dispersal between R. mangle populations and result in significant genetic
structure. For example, along the coast of Brazil, the bifurcation of surface ocean currents
prevents gene flow between northern and southern populations (Pil et al. 2011); while, on
the NW coast of Mexico, the peninsula of Baja California and associated ocean currents
restrict gene flow between populations within and outside the Gulf of California
(Sandoval-Castro et al. 2012). Third, there is a pattern of decreasing genetic diversity
toward the distributional limits of this species (Pil et al. 2011, Sandoval-Castro et al.
2012, 2014). The authors of these studies conclude that this pattern is likely the result of
poleward expansion via consecutive founder events after the LGM.
At present, research is lacking for R. mangle populations from other portions of its
distributional range, specifically the Florida peninsula, Caribbean islands, and West
Africa. This study is the first to directly compare the extent of R. mangle dispersal among
the more extensive forests along the continuous Caribbean mainland and the more
restricted forests along the Caribbean Island Chain, as well as investigate potential LDD
between Northwest African and Caribbean island R. mangle populations. We explore
6
potential R. mangle post-LGM expansion pathways by utilizing multi-locus microsatellite
data. Microsatellites are DNA sequences consisting of tandem repeated units typically 15 bases in length (Jarne & Lagoda 1996, Frankham et al. 2002). The number of these
repeats is highly variable due to slippage during DNA replication that results in an
increase or decrease in the number of repeats and substantial length polymorphism
(Shinde et al. 2003). Microsatellites are considered neutral markers (Frankham et al.
2002; but see Pennisi 2012) and provide estimates for the contemporary distribution of
genetic polymorphisms (i.e. genetic structure) within populations of a species (Jarne &
Lagoda 1996, Selkoe & Toonen 2006).
Using microsatellite data, we assess the genetic structure of nine R. mangle
populations from the Caribbean mainland, Caribbean islands, the Florida peninsula, and
Northwest Africa. Based on patterns of genetic structure among these populations, we
compare two potential post-LGM dispersal pathways which led to the recolonization of
the Florida peninsula. We also assess the possibility of LDD between African and
Caribbean island populations.
Potential Recolonization Pathways following the LGM
Mangroves typically have a continuous distribution along coastlines unless
separated by geographical barriers (Tomlinson 1986, Duke et al. 1998). Following the
LGM, recolonization of higher latitudes along the Caribbean mainland likely took place
via the progressive expansion of low latitude glacial refuge populations along the
continuous coastline. However, the question remains as to how populations were founded
on the Florida peninsula and Caribbean islands. Rhizophora mangle does not span the
7
U.S. coastline of the Gulf of Mexico (Stevens et al. 2006, Osland et al. 2013); therefore,
post-LGM colonization of the Florida peninsula did not occur via continuous expansion
along the mainland Gulf coast. Instead, propagules had to have been transported to
Florida, as well as to Caribbean islands, by ocean currents.
One potential post-LGM expansion route follows the strongest regional currents,
which we have termed the Mainland pathway. This proposed expansion pathway consists
of a combination of three currents (i.e. Caribbean, Loop, Florida) that move water west in
the Caribbean Sea, through the Yucatan Channel, and into the Florida Straits along the
southeast coast of the Florida peninsula (Gyory et al. 2012) (Fig. 1). The Mainland
pathway predicts that propagules from Caribbean mainland populations recolonized the
Florida peninsula. An alternative transport pathway is along the Caribbean islands, which
we have termed the Antilles Island pathway. This alternative pathway follows the
comparatively weaker Antilles Current that flows to the northwest, east of the Greater
Antilles, and eventually joins the Florida Current north of the Bahamas (Rowe et al.
2012) (Fig. 1). The Antilles Island pathway predicts that propagules from Caribbean
island populations recolonized Florida via a stepping-stone dispersal pattern along the
island chain.
These two expansion pathways to Florida differ substantially in the volume of
water transported. The Caribbean (21 Sv; Sverdrup, 106 m3 sec-1), Loop (28 Sv), and
Florida (30 Sv) Currents transport much more water (Johns et al. 2002) than the Antilles
Current (2-7 Sv) (Rowe et al. 2012). However, oceanographic models estimate that the
120 m decrease in sea level associated with the last glacial period reduced water transport
via the Florida Current by 4-5 Sv (Ionita et al. 2009, Lynch-Stieglitz et al. 2009). A
8
shallower depth within the Florida Straits may have blocked some water transport and,
consequently, led to an increase in the volume of water transported by the Antilles
Current (Ionita et al. 2009). Hence, the relative strengths of these two currents have
undergone changes since the LGM, which could have impacted R. mangle propagule
dispersal patterns.
An additional transport pathway that we consider expands upon genetic evidence
of LDD between West African and South American R. mangle populations (Cerón-Souza
et al. 2010, Takayama et al. 2013). The North Equatorial Current (NEC) is a broad
westward flowing current, strengthened by Atlantic trade winds, that originates from the
NW coast of Africa (Bischof et al. 2004a). The NEC approaches the Americas east of the
Lesser Antilles and feeds both the Guiana and Caribbean currents (Bourlès et al. 1999a).
We hypothesize that after the LGM R. mangle propagules originating from populations in
NW Africa (Saenger & Bellan 1995, UNEP 2007) were carried by the NEC directly to
the islands of the Lesser Antilles where mangroves are presumed to have gone extinct
(Woodroffe & Grindrod 1991, Ellison 1996, Nettel & Dodd 2007). If so, then these
propagules of African origin may have played a significant role in the recolonization of
the Caribbean islands and the extent of gene flow between these two continents may be
much more extensive than previously imagined.
Objective & Hypotheses
The present study explores patterns of R. mangle range expansion following the
LGM. Within this context, we test three main hypotheses.
9
1. Rhizophora mangle recolonized the Florida peninsula via the Mainland
pathway. This pattern of expansion predicts greater genetic connectivity between
Florida and Caribbean mainland populations, and greater genetic differentiation
between Florida and Caribbean island populations.
Alternative hypothesis: Rhizophora mangle recolonized the Florida peninsula
via the Antilles Island pathway. This pattern of expansion predicts greater
genetic connectivity between Florida and Caribbean island populations, and
greater genetic differentiation between Florida and Caribbean mainland
populations.
2. Trans-Atlantic dispersal via the North Equatorial Current (NEC) has
occurred from NW Africa to Caribbean island R. mangle populations. The
direction and location of the NEC may result in gene flow between these two
areas and the contemporary genetic structure of Caribbean island populations may
resemble that of a NW African population.
3. Within-population genetic diversity decreases with latitude. This pattern may
be the result of range expansion by a small number of initial recruits from low
latitude refuge populations and competitive exclusion of subsequent migrants
once populations reached high densities.
10
METHODS
Collection & DNA isolation
A total of 237 Rhizophora mangle trees were sampled from 9 collection sites
(referred to as ‘populations’ throughout this manuscript) in the Caribbean, Florida, and
West Africa (Fig. 1, Table 1). The distance between these populations ranges from ~250
km (SFL to EFL) to ~7,735 km (WFL to SGL). Leaves were collected from 30 trees
within each population, except St. Kitts (n=12) and Senegal (n=15). Sampled trees were
at least 20 m apart from each other to avoid resampling the same individual twice. After
collection, leaves were dehydrated and stored in silica gel. Genomic DNA was isolated
from 200 mg of dry leaf tissue using the DNeasy Plant Mini Kit (Qiagen®) following the
standard protocol with modifications (i.e. extended incubation, addition of PVPP to the
precipitation buffer, precipitated lysate centrifuged twice). Isolated DNA was stored in a
freezer at -20oC until further analyses.
Microsatellite analysis
Individuals from Panama, Belize, Puerto Rico, the Bahamas, and the three Florida
sites were genotyped at seven DNA microsatellite loci, four (RM11, RM19, RM38,
RM41) previously developed by Rosero-Galindo et al. (2002) and three (RM05, RM50,
RM86) developed by Takayama et al. (2008b). Individuals from St. Kitts and Senegal
were genotyped at six of these loci (all except RM11). Therefore, RM11 was included for
within population diversity indices (Table 1), but all additional statistical analyses
utilized only the six shared microsatellite loci. Initially, we also utilized two additional
11
loci (RM21, RM46) developed by Rosero-Galindo et al. (2002), but these loci were
found to be monomorphic for all individuals from Belize, Puerto Rico, and South Florida.
Due to the lack of information provided by these loci, they were not pursued further in
this study.
We used a three-primer protocol (Hauswauldt & Glenn 2003; Nance et al. 2009)
for polymerase chain reactions (PCR). Each reaction contained a total volume of 25 µL
with 10x PCR buffer, 1.25 µM fluorescently labelled forward primer tag, 1 µM forward
primer with an additional sequence at the 5’ end that corresponds to the fluorescently
labelled forward primer tag, 2.5 µM unlabeled reverse primer, 0.2 mM dNTPs, 1.5 mM
MgCl2, 0.5 U Platinum Taq DNA polymerase (Invitrogen), and 20 ng of genomic DNA.
Amplifications were conducted on a 9800 Fast Thermal Cycler (Applied Biosystems)
with an initial denaturation at 94 oC for 2 minutes, followed by 28 cycles of denaturation
at 94 oC for 30 s, annealing at 57 oC for 59 s, and extension at 72 oC for 59 s, then 8
cycles at 94 oC for 30 s, 53 oC for 59 s, and 72 oC for 59 s to allow the fluorescently
labelled tag to anneal to the amplified fragments, and a final extension at 72 oC for 10
minutes. PCR products for up to three microsatellite loci were pseudo-multiplexed
(Guichoux et al. 2011) and separated via capillary electrophoresis on an ABI 310 Genetic
Analyzer using GeneScan 600 LIZ size standard (Applied Biosystems), then visualized
and allele sizes were scored in GeneMapper v.3.7 (Applied Biosystems).
Data quality
We tested for deviations from Hardy-Weinberg equilibrium (HWE) and linkage
disequilibrium (LD) for each population-locus pairwise combination with the program
Arlequin v.3.5 (Excoffier & Lischer 2010). Exact tests of HWE were performed with
12
1x106 steps in the Markov chain and 1x105 dememorisation steps. Within each
population, LD between loci was evaluated by a likelihood-ratio test with 1.6x104
permutations and five initial conditions for the Expectation-Maximization (EM)
algorithm. For these tests, we controlled for type I error inflation associated with multiple
comparisons by adjusting for the false discovery rate (FDR) (Benjamini & Hochberg
1995). This approach controls type I error, but not at the expense of increased type II
error (Garcia 2003, Nakagawa 2004, Verhoeven et al. 2005). To adjust for the FDR, we
calculated adjusted p-values (termed q-values) with the program QVALUE (Storey 2002,
Storey & Tibshirani 2003, Storey et al. 2004) that is run with the statistical software R (R
Core Team 2013).
We further tested for potential genotyping errors with the program MICROCHECKER 2.2.3 (van Oosterhout et al. 2004). When potential errors were identified for
population-locus combinations, we utilized the program’s Oosterhout algorithm to
calculate adjusted allele and genotype frequencies. We used these calculated frequencies
to manually adjust observed (HO) and expected (HE) heterozygosity and inbreeding
coefficients (FIS) for populations with potential genotyping errors (see Genetic diversity
section). Values were calculated as HO =
1−
HO
HE
# heterozygotes
total genotypes
, HE = 1 − ∑ki=1 pi 2 , and FIS =
(Hartl & Clarke 2007). As the MICRO-CHECKER results indicated potential
null alleles for certain population-locus combinations, we calculated a maximum
likelihood estimate of null allele frequencies for each population and locus with the
expectation maximization algorithm of Dempster et al. (1977) in the program FreeNA
(Chapuis & Estoup 2007). We also assessed levels of genotyping errors by randomly
selecting 5% of the total individual-locus pairs previously amplified (85 of 1,632
13
individual-locus pairs) to amplify and genotype a second time. We used these repeat
amplifications to calculate an overall error rate for this study (see Bonin et al. 2004).
Genetic diversity
We assessed genetic diversity in terms of the number of polymorphic loci (PL),
number of alleles (A), and observed (HO) and expected heterozygosity (HE) with the
program Arlequin v.3.5 (Excoffier & Lischer 2010). We also calculated allelic richness
(AR) and inbreeding coefficients (FIS) with the program FSTAT 2.9.3.2 (Goudet 2002).
Allelic richness was standardized to the minimum sample size and within-population
deviations from HWE, indicated by FIS values significantly greater than zero, were
evaluated with 1,260 randomizations. Each population with a significant FIS value was
further tested for inbreeding while accounting for null alleles with the program INEst
(Chybicki & Burczyk 2009). As an additional means of measuring genetic diversity, we
determined the number of unique multi-locus genotypes (MLG) within each population
with the program Microsatellite Toolkit (Park 2001) and then calculated a measure of
within-population genotypic richness (R; Dorken & Eckert 2001) which varies from 0 (all
trees have the same MLG) to 1 (all trees have a distinct MLG).
Population clustering
We utilized the Bayesian clustering program InStruct (Gao et al. 2007) to
estimate population structure among the nine sampled populations. InStruct is analogous
to the widely used program STRUCTURE (Pritchard et al. 2000), but allows for
departures from HWE and partial inbreeding. STRUCTURE assumes individuals are
either fully outcrossing or haploid, so applying this algorithm to partially selfing species
can produce spurious results (Gao et al. 2007). As R. mangle is capable of self-
14
fertilization and we find deviations from HWE in certain populations (see Results
section), we used InStruct for this analysis. Using the multilocus genotypes of each
sampled individual, InStruct determines the most likely number of population clusters
(K) within the data set and then assigns each genotyped individual to 1+ of these clusters
based upon allele frequencies and estimated selfing rates, without a priori knowledge of
the geographic location of each individual. Using the admixture model, a preliminary
analysis included five independent runs at each K value from 1 to 9. Based on run lengths
from other published studies, each of these runs was 5x105 Markov chain Monte Carlo
(MCMC) iterations after an initial burn-in of 2.5x105 steps with a thinning interval of ten
iterations. To determine the most likely K value, we used the Deviance Information
Criteria (DIC) which performs better than other popular approaches (Gao et al. 2011). At
this most likely K value, we ran five longer independent chains of 1x106 MCMC
iterations after a 5x105 step burn-in. We averaged the resulting matrices of individual
membership coefficients (Q-matrices) across these longer runs and calculated the average
pairwise similarity of these matrices (H’) to quantify run consistency with the program
CLUMPP (Jakobsson & Rosenberg 2007) using the FullSearch algorithm. Although the
FullSearch algorithm is the slowest of three potential options within the program
CLUMPP, Jakobsson & Rosenberg (2007) found that it will always determine the
optimal alignment of clusters across multiple runs. The averaged Q-matrix was then
visualized with the program DISTRUCT (Rosenberg 2004).
Mutation models
Prior to calculating levels of among population genetic differentiation, we utilized
an allele size permutation test (Hardy et al. 2003) to determine whether variation in allele
15
sizes (i.e. stepwise mutations) among our sampled populations is an informative means of
quantifying population differentiation compared to the effects of migration and genetic
drift. The Stepwise Mutation Model (SMM, Kimura & Ohta 1978) assumes that mutation
rate is the major determinant of genetic structure and, therefore, utilizes differences
among allele fragment lengths to determine levels of differentiation among populations.
The index of genetic differentiation RST (Slatkin 1995) assumes the SMM. In contrast, the
Infinite Allele Model (IAM, Kimura & Crow 1964) assumes that more recent processes
(i.e. migration and genetic drift) determine levels of genetic structure and utilizes allele
identity to determine population differentiation. The widely used index of genetic
differentiation FST (Wright 1965) assumes the IAM. The program SPAGeDi version 1.4
(Hardy & Vekemans 2002) compares the multi-locus observed RST value (before
permutation) to the distribution of permutated RST values (ρRST) calculated by randomly
reassigning allele sizes within each locus. If >5% of the ρRST values are larger than the
observed RST, the null hypothesis that FST = RST is not rejected. This indicates that
variation in allele sizes (RST) is not an informative means of quantifying population
differentiation compared to allele identity (FST). Thus, FST will likely will be a better
indicator of genetic differentiation because stepwise mutations have not significantly
contributed to population differentiation compared to migration and genetic drift, as well
as the high variance associated with RST estimates (Hardy et al. 2003). After modifying
our data set from allele fragment lengths in base pairs to the number of repeat units, we
ran a series of 20,000 permutations for all nine sampled populations, the subset of five
populations along the Mainland pathway, and the subset of six populations along the
Antilles Island pathway.
16
Genetic structure
We evaluated genetic structure by analysis of molecular variance (AMOVA)
(Excoffier et al. 1992) with the program Arlequin v.3.5 (Excoffier & Lischer 2010)
assuming the IAM and the SMM by using FST and RST coefficients, respectively. We
conducted an AMOVA with three hierarchical levels (among groups, among populations
within groups, and within populations). Groups consist of the population clusters defined
by the clustering analysis in the program InStruct. Because the results of this analysis
differed for the IAM and SMM (see Results section), we performed an additional set of
AMOVA including only those sampled populations along the Mainland and Antilles
Island pathways, respectively. We included these additional analyses to help us better
understand these contradictory results. In addition, we estimated levels of genetic
differentiation between all population pairs with FST and RST coefficients in Arlequin
v.3.5 (Excoffier & Lischer 2010). Significance levels for these analyses were determined
with 20,000 permutations. We calculated FDR adjusted p-values for all pairwise
population FST and RST values as described earlier (see Data Quality section) although
this did not change the number of significant differences found prior to these adjustments.
To further visualize genetic structure among the nine R. mangle populations, we
constructed a neighbor-joining (NJ) tree and performed a principal coordinate analysis
(PCoA). With the program Microsatellite Toolkit (Park 2001), we calculated allele
frequencies within each population. For populations with potential genotyping errors, we
used the estimated values from MICRO-CHECKER (see Data Quality section) to
manually adjust these allele frequencies. These frequency data were input into the
program POPTREE2 (Takezaki et al. 2010) to visualize genetic relationships among the
17
nine sampled populations by generating an unrooted neighbor-joining (NJ) tree with
pairwise DA genetic distances (Nei et al. 1983). Takezaki and Nei (1996, 2008) found DA
genetic distance to be more efficient than other measures in obtaining the correct tree
topology from microsatellite data. We determined significance levels for the best tree
topology with 1x104 bootstrap replicates. As an additional means of visualizing
population differentiation, we performed a PCoA with Nei’s genetic distances (Nei 1972)
calculated and plotted with the program GenAlEx 6.5 (Peakall & Smouse 2012).
Genetic differentiation over geographic distances
We tested for a pattern of isolation by distance along both the Mainland and
Antilles Island pathways with two Mantel (1967) tests of correlation between matrices of
the previously calculated pairwise DA genetic distances and geographic distances
between each pair of sampled populations in the program Arlequin v.3.5 (Excoffier &
Lischer 2010). Significance levels were determined with 20,000 permutations. Because
R. mangle propagules float passively in the water, we calculated geographic distances
between each pair of populations as the distance along the coastline and/or the track of
the proposed dispersal pathway in Google Earth 7.1.2.2041. We fit linear regression lines
to the corresponding figures for graphical representation.
Estimates of historic and contemporary migration rates
We estimated historic and more recent gene flow along the Mainland and Antilles
Island pathways with the programs Migrate 3.2.1 (Beerli & Felsenstein 1999, 2001) and
BayesAss3 (Wilson & Rannala 2003), respectively. Migrate 3.2.1 estimates mutationscaled historical effective population sizes (Θ = 4Neµ) and migration rates (M = m/µ)
using a coalescence approach (Beerli & Felsenstein 1999). We made numerous attempts
18
to estimate these parameter values for each of the eight sampled populations along the
two dispersal pathways, but many of the final estimates did not converge (i.e. large 95%
confidence intervals) even after lengthy run times. Therefore, we ultimately estimated
these values for four population clusters: Mainland (Panama, Belize), Florida (West,
South, East Florida), the Bahamas, and Caribbean Islands (St. Kitts, Puerto Rico). These
groupings reflect the results of the InStruct analysis with the exception of the Bahamas
being analyzed as a separate population cluster. Based on our findings of extensive
admixture within the Bahamas population (see Results section), we determined that
analyzing this island as a separate population cluster would provide the most detail
possible of historic gene flow patterns along the proposed pathways.
We used the maximum likelihood approach (Beerli & Felsenstein 1999, 2001)
and an a priori migration model to infer the post-LGM recolonization history of this
species along the two proposed dispersal pathways (see Fig. 7). Based on ocean
circulation patterns discussed in the Introduction, the migration model allows for
dispersal from the Caribbean mainland to Florida (i.e. the Mainland pathway) and from
the islands of St. Kitts / Puerto Rico to the Bahamas and then to Florida (i.e. the Antilles
Island pathway). Also, the model allows for dispersal from the islands of St. Kitts /
Puerto Rico to the Caribbean mainland and vice versa, as well as dispersal from Florida
to the Bahamas. All runs used the Brownian motion approximation, allowed for varying
mutation rates among loci, and did not include a distance matrix between population
clusters. After an initial burn-in of 1x104 steps, preliminary runs consisted of 10 shortchains (1x103 recorded steps) and 3 long-chains (1x104 recorded steps), both at an
increment of 100 steps, across the six shared microsatellite loci. We used uniform priors
19
for both Θ (0, 100, 10) and M (0, 10000, 1000) and conducted three independent runs,
each with a different initial seed number to verify that results were consistent across runs.
These initial runs resulted in comparatively consistent parameter values (Θ, M), so we
used the results from the run with the highest maximum likelihood estimate as starting
values for our final, longer runs. These longer runs consisted of 10 short-chains (1x104
recorded steps) and 3 long-chains (1x105 recorded steps), both at an increment of 100
steps, after an initial burn-in of 1x104 steps. For each locus, a total of 4x107 genealogies
were visited. To increase MCMC efficiency, we used a heating scheme (1, 1.5, 3, 1x105)
that runs four parallel chains and swaps between them during the analysis. We conducted
three independent runs, each with a different initial seed number. The resulting parameter
values were again comparatively consistent across runs and we present the results from
the run with the highest maximum likelihood estimate.
BayesAss3 (BA3) is a Bayesian MCMC method that estimates recent migration
rates over the last several generations by using assignment methods (Manel et al. 2005)
and does not assume that sampled populations are in Hardy-Weinberg equilibrium
(Wilson & Rannala 2003). The program does, however, assume that migration rates are
relatively low, which we expect to be true for our sampled populations as they are
separated by large water bodies over relatively long distances. Also, previous researchers
found convergence problems with the underlying algorithm (Faubet et al. 2007,
Meirmans 2014); to address potential issues with this program, we adhered to
recommendations published by Meirmans (2014) when possible. For this analysis, we
were able to estimate recent migration rates for all pairwise combinations of the eight
sampled populations along the two hypothesized dispersal pathways to the Florida
20
peninsula. We used the program Formatomatic (Manoukis 2007) to convert our genotype
data to the BA3 input file format. Each run consisted of 1x107 MCMC iterations, with the
first 1x106 discarded as a burn-in, and sampling every 100 iterations. We conducted ten
independent runs, each with a different initial seed number to verify that results were
consistent across runs. To obtain optimal acceptance rates, between 20-60% (Rannala
2007), MCMC mixing parameters for migration rates, allele frequencies, and inbreeding
coefficients were adjusted to 0.25, 0.80, and 1.0, respectively. These adjustments resulted
in acceptance rates of 30%, 27%, and 43%, respectively. With the statistical software R
(R Core Team 2013), we used a script provided by Meirmans (2014) to calculate the
Bayesian deviance for each independent run and our final results correspond to the run
with the lowest deviance value.
Effect of latitude on genetic diversity
We tested for a pattern of reduced genetic diversity within R. mangle populations
with latitude by means of separate linear regressions in the program SAS (version 9.2).
We regressed levels of within population genetic diversity (i.e. allelic richness, observed
heterozygosity) on the northern latitude corresponding to each sampled population.
21
RESULTS
Data quality
Expected heterozygosity is greater than observed heterozygosity for all seven
polymorphic loci (Table 2). Observed heterozygosity per locus varies from 0.22 (RM19)
to 0.49 (RM05). Significant deviations from Hardy-Weinberg equilibrium (HWE) vary
from zero loci in five populations (SGL, StK, BAH, EFL, WFL) to 3 loci in two
populations (PAN, BLZ) after false discovery rate (FDR) corrections. In total, we find
these population-locus deviations from HWE at a frequency of 0.13 (8 of 61; Table 3).
Evidence of linkage disequilibrium (LD) for population-locus pairs is found at a
frequency of 0.03 (6 of 177) after FDR corrections (Table 4). MICRO-CHECKER finds
evidence for potential stutter and null alleles at frequencies of 0.08 (5 of 61) and 0.15 (9
of 61), respectively (Table 5). However, there is not a consistent pattern of deviation
from HWE, LD, or potential genotyping errors for a particular locus, or pair of loci, to
suggest a systematic bias across all sampled populations. Estimated null allele
frequencies per population range from 0.01 (SGL) to 0.10 (PR), and per locus from 0.02
(RM-50) to 0.12 (RM-19) (Table 6). Across all population-locus combinations, null
alleles are estimated at a frequency of 0.05 and, at this frequency, should not introduce
substantial bias to our analyses (Dakin & Avise 2004, Carlsson 2008). Repeat
amplifications of a random subset of our samples (5% of total individual-locus pairs)
finds three genotyping errors out of a total of 121 allele comparisons (two individuals
expressed an extra allele, one lost an allele) and an estimated error rate of 2.5% (3 errors
22
out of 121 comparisons). These three genotypes were removed from the dataset for
subsequent analyses.
As these previous analyses indicate that genotyping errors are neither systematic
nor present at high frequencies within our data set, we use all seven loci to quantify levels
of within population genetic diversity and the six shared loci in all other statistical
analyses. We also, whenever possible, adjust allele and genotype frequencies with
estimates from MICRO-CHECKER to account for potential genotyping errors. Because
we do detect deviations from HWE within certain populations, we attempt to utilize
analyses that do not make this assumption.
Genetic diversity
The level of genetic diversity is low across seven polymorphic loci, with a total of
35 alleles among 237 individuals. Alleles per locus range from 2 (RM 11, 05) to 9 (RM
41) and average allelic richness is 2.47, with a range from 1.97 (StK) to 3.00 (HS). The
number of polymorphic loci varies by population, with 2 of 6 loci in St. Kitts, 4 of 6 in
Senegal, 5 of 7 in Puerto Rico, and all seven loci are polymorphic in the remaining 6
populations. The proportion of distinct multi-locus genotypes (MLG) per population (i.e.
genotypic richness) varies from 0.55 in St. Kitts (7 MLG in 12 individuals) and 0.66 in
Belize (20 MLG in 30 individuals) to 1.00 in Panama and East Florida (all 30 individuals
possess unique MLG). Private alleles are present in all but two of the collection sites
(BAH & SFL). The frequency of private alleles is low (≥0.05), with the exception of one
allele in St. Kitts (0.32) and another in Senegal (0.12). Inbreeding coefficients (FIS) range
from 0.02 in Senegal to 0.26 in Puerto Rico. Within four populations (PAN, BLZ, PR,
SFL) FIS values are significantly greater than zero, suggesting an excess of homozygosity
23
and deviation from HWE. However, after null alleles are accounted for, only two of these
populations (PR, SFL) still possess significantly positive FIS values.
Population clustering
For the preliminary clustering analysis in the program InStruct, DIC values
increase substantially from K=1-3, appear to reach a relative plateau at K=3, and then
continue to increase much more gradually until reaching the highest DIC value at the
maximum number of clusters at K=9 (Fig. 2A). Increasing K from 2 to 3 results in ∆DIC
= 25%; whereas, continuing to increase K further never results in a ∆DIC >6% (Fig. 2B).
Therefore, because of the comparatively minor rate of change after K=3, we accept K=3
as the most likely number of population clusters, an approach suggested by Durand et al.
(2009) and François & Durand (2010). At K=3, we average individual membership
coefficients across five additional independent runs of longer duration than the
preliminary analysis and the average pairwise similarity (H’=0.998) indicates that these
runs are highly congruent. All of our sampled populations are assigned to one of these
three clusters with a probability ≥85%, except for the Bahamas (65%). Of the 237
genotyped individuals in this study, 203 (86%) are assigned to one of these three clusters
with a probability ≥85%. Within populations, >85% of the sampled individuals are
assigned to a cluster with a probability ≥85%, except Panama (70%) and the Bahamas
(40%). If K=4 had been accepted as the solution, the Bahamas would have separated out
as a discrete population cluster.
The three tightly defined clusters coincide with the geographical location of our
sampled populations and delineate this species into three genetically differentiated
population clusters (Fig. 3A).
24
1.
Caribbean mainland (i.e. Panama & Belize) individuals are assigned to the Blue
Cluster, with some connectivity to the other clusters within the region.
2.
Florida individuals are assigned to the Orange Cluster. West and South Florida
show almost no connectivity to the other two clusters; whereas, East Florida
shows low, but comparatively higher levels of admixture.
3.
Caribbean island (i.e. St. Kitts, Puerto Rico, the Bahamas) and West Africa (i.e.
Senegal) individuals are assigned to the Yellow Cluster. With the exception of the
Bahamas which shows considerable admixture, the other three populations show
almost no connectivity with the other clusters.
Mutation models
An allele permutation test (Hardy et al. 2003) allows us to assess the contribution
of stepwise mutations compared to migration and genetic drift to population
differentiation. Among all nine sampled populations, the observed RST (0.67) is
significantly greater than the permutated RST (pRST = 0.35) (p=0.001); therefore, the null
hypothesis (FST = RST) is rejected. This result indicates that stepwise mutations have
contributed to genetic differentiation among our nine sampled populations and, thus, RST
is an informative means of quantifying genetic differentiation among these sampled
populations. This pattern is expected at larger scales, such as the present study, because
geographically distant populations may have diverged for a sufficiently long time and/or
currently exchange migrants at a considerably low rate (i.e. ≤ the mutation rate) (Hardy et
al. 2003). This result does not allow RST to be discarded in favor of FST; instead, we
present both values for further genetic structure analyses. Incidentally, we find that
25
pairwise FST values are significantly correlated with RST values (r = 0.58, P<0.001, SAS
9.2).
Interestingly, the contribution of stepwise mutations varies along the two
dispersal pathways to Florida. Among populations along the Mainland pathway (i.e.
PAN, BLZ, WFL, SFL, EFL) observed RST (0.27) is not significantly greater than the
pRST (0.36) (P=0.745), which indicates that stepwise mutations have a negligible effect
and that migration and genetic drift are primarily responsible for the observed
differentiation among Caribbean mainland and Florida populations along the
comparatively stronger ocean currents of the Mainland pathway. In contrast, among
populations along the Antilles Island pathway (i.e. StK, PR, BAH, EFL, SFL, WFL)
observed RST (0.62) is significantly greater than pRST (0.35) (P=0.007). This result
indicates that stepwise mutations have a non-negligible effect on population
differentiation among Caribbean island and Florida populations, and that migration rates
are significantly low and divergence times are significantly long along the Antilles Island
pathway to the Florida peninsula.
Genetic structure
The three hierarchical level AMOVA (Table 7) detects significant differentiation
among the three population clusters defined by the InStruct analysis for both the IAM
(FCT = 0.34, P<0.05) and the SMM (RCT = 0.17, P<0.05). However, the distribution of
genetic variation differs for the two mutation models. Based on the IAM, most genetic
variation is within populations (57%), while based on the SMM most genetic variation is
among populations within groups (51%). These contradictory results are not surprising,
considering the results of the allele permutation test which indicate that different
26
processes (i.e. migration and drift versus stepwise mutations) contribute differently to the
observed patterns of genetic differentiation along the Mainland and Antilles Island
pathways, respectively. An additional three hierarchical level AMOVA for populations
along the Mainland pathway indicates a similar distribution of genetic variation for both
the IAM and the SMM, with most genetic variation within populations for both models
(63% and 62%, respectively). Whereas, an additional three hierarchical AMOVA for
populations along the Antilles Island pathway indicates a different distribution of genetic
variation for the IAM and the SMM, with most genetic variation within populations for
the IAM (52%) and among populations within groups for the SMM (52%). The results of
these AMOVA corroborate the findings of the allele permutation test as the distribution
of genetic variation is relatively the same for both the IAM and SMM along the Mainland
pathway (i.e. stepwise mutations have not significantly contributed to genetic
differentiation), but the distribution of genetic variation is different for the IAM and
SMM along the Antilles Island pathway (i.e. stepwise mutations have significantly
contributed to genetic differentiation).
Pairwise FST comparisons between populations range from 0.04-0.67, have lower
variance (SD=0.17), and all combinations are significantly different (q<0.05); whereas,
pairwise RST comparisons range from 0.00-0.94, have higher variance (SD=0.33), and 33
of 36 combinations are significantly different (q<0.05) (Table 8). Non-significant RST
values correspond to West Florida - South Florida, South Florida – East Florida, and
surprisingly Senegal – St. Kitts. The non-significant result for Senegal – St. Kitts should
be viewed with caution, however, as fewer individuals were sampled from these
populations (Senegal, n=15; St. Kitts, n=12).
27
The NJ tree constructed with DA genetic distances (Fig. 4) highlights the
significant genetic differentiation among the nine sampled R. mangle populations, but the
comparatively greater genetic connectivity between the Caribbean mainland and Florida
populations. At one extreme, West Africa and the Caribbean island populations of St.
Kitts and Puerto Rico cluster together, while the Caribbean mainland and Florida
populations share the other extreme. The Bahamas population is at a position
intermediate to all other populations. The PCoA (Fig. 5) provides additional support for a
separation into three discrete population clusters, as indicated by the InStruct analysis,
with the Bahamas again located at an intermediate position between the three population
clusters.
Genetic differentiation over geographic distance
Mantel tests indicate a significant relationship between genetic and geographic
distance along the Antilles Island pathway (r2 = 0.90, P=0.006, Fig. 6A), but not along the
Mainland pathway (r2 = 0.39, P=0.088, Fig. 6B). Genetic distance, therefore, cannot be
explained solely by geographic distance along the Mainland pathway. Instead, it is highly
likely that an additional factor influences genetic distance among Caribbean mainland
and Florida populations.
Estimates of migration rates
As predicted, estimates of historic migration rates from the program Migrate 3.2.1
indicate that the Mainland pathway was the primary source of gene flow to the Florida
peninsula (Fig. 7). The historic migration rate along the Antilles Island pathway from St.
Kitts / Puerto Rico to the Bahamas (M = 4.52) is greater than along the Mainland
pathway from the Caribbean mainland to the Florida peninsula (M = 3.16), but gene flow
28
ceases almost entirely along the last portion of the Antilles Island pathway from the
Bahamas to Florida (M = 0.17). As a result, the Antilles Island pathway appears to have
transported propagules from the source populations of St. Kitts / Puerto Rico to the
Bahamas, but then contributed very little past the Bahamas to the Florida peninsula.
Surprisingly, substantial migration is also found from Florida to the Bahamas (M = 6.30)
and from the Caribbean mainland to the Bahamas (M = 2.12). Thus, the Bahamas appears
to be a location which receives gene flow from Florida, St. Kitts / Puerto Rico, and the
Caribbean mainland, but little gene flow from the Bahamas reaches other populations.
Estimated historic effective population sizes support postglacial expansion of R.
mangle from more equatorial regions to higher latitudes. Population sizes are larger for
lower latitude populations that presumably persisted during the LGM or were recolonized
earlier during the expansion process (Caribbean mainland, Θ=1.56; St. Kitts / Puerto
Rico, Θ=1.36). In contrast, population sizes are significantly lower (i.e. non-overlapping
95% confidence intervals) for higher latitude populations that were recolonized later
during the expansion process (Florida Θ=1.01; the Bahamas Θ=0.39).
Estimates of recent migration rates in the program BayesAss3 indicate that there
is contemporary gene flow (i.e. over the last several generations) between populations
within certain population clusters defined by the InStruct analysis, but not between
populations in different clusters (Table 9). In the Caribbean Mainland population cluster,
an estimated 16% (± 11%) of individuals from Panama are migrants derived from Belize.
In the Florida population cluster, an estimated 11% (± 8%) of South Florida are from
West Florida, and an estimated 18% (± 11%) of East Florida are from South Florida.
There is no evidence of contemporary gene flow among Caribbean island populations.
29
Effect of latitude on genetic diversity
There is no significant pattern of decreasing allelic richness (r2 = 0.003, P=0.88)
or observed heterozygosity (r2 = 0.044, P=0.59) with increasing latitude (Fig. 8). Instead,
populations from the Caribbean mainland (i.e. Panama, Belize), East Florida, and the
Bahamas have comparatively higher values for both measures of genetic diversity
compared to all other sampled populations.
30
DISCUSSION
The principal objective of this study is to elucidate the relative importance of
three postglacial Rhizophora mangle recolonization pathways in shaping the genetic
structure of this species within the Caribbean and Florida and to build upon the growing
body of knowledge about the population genetic structure of this species at different
points across its distributional range. Our results indicate that the Mainland pathway,
from the Caribbean mainland to the Florida peninsula, is the primary dispersal route that
led to the postglacial recolonization of the Florida peninsula. Surprisingly, Florida
populations are highly differentiated from the Caribbean mainland source populations
(i.e. Panama, Belize) even though the Florida peninsula was likely recolonized only ~3-4
kya (Davis 1940, Scholl et al 1964). We also find evidence for long distance dispersal
(LDD) across the Atlantic Ocean, likely via the North Equatorial Current (NEC) between
a population at the northern extreme of this species’ range in Africa (Saenger & Bellan
1995, UNEP 2007) and populations along the Caribbean Island Chain. The genetic
structure of these Caribbean island populations (i.e. St. Kitts, Puerto Rico) is more similar
to that of an Africa population (i.e. Senegal) than to that of Caribbean mainland
populations (i.e. Panama, Belize). Unexpectedly, we find a high level of admixture within
a population from the island of San Salvador, in the Bahamas, likely because of gene
flow from both the Mainland and Antilles Island pathways to this island. Lastly, diversity
measurements indicate that latitude does not have a significant effect on population
genetic diversity for the nine R. mangle populations in this study.
31
Hypothesis 1: Florida recolonization pathways
When comparing two dispersal pathways to the Florida peninsula there is a clear
pattern of greater genetic connectivity along the Mainland pathway. Estimates of
pairwise genetic differentiation (Table 8) and DA genetic distances (Fig. 6) indicate lower
divergence between Florida and potential source populations along the Caribbean
mainland when compared to potential source populations along the Caribbean islands
(i.e. St. Kitts, Puerto Rico). A neighbor-joining tree (Fig. 4) provides a visual
representation of the comparatively lower genetic distance between populations from
Florida and the Caribbean mainland compared to all other populations sampled in this
study. Thus, the Mainland pathway is likely the primary dispersal pathway that led to the
recolonization of the Florida peninsula following the Last Glacial Maximum (LGM). The
comparatively greater strength of the ocean currents along the Mainland pathway versus
the Antilles Island pathway is presumably the reason why the former pathway has
contributed more to the current genetic landscape on the Florida peninsula. Propagules
originating from Caribbean mainland forests were likely transported northward along the
Mainland pathway by the Caribbean Current (21 Sv), then to the Florida Straits via the
Loop Current (28 Sv), and finally along the SE coast of the Florida peninsula by the
Florida Current (30 Sv) (Johns et al. 2002). These three currents are substantially
stronger than the Antilles Current which flows NW along the Greater Antilles (2-7 Sv)
(Rowe et al. 2012).
Dispersal along the Mainland pathway to Florida does, however, vary by location
along the peninsula as evidenced by estimates of pairwise genetic differentiation (Table
8), DA genetic distances and a non-significant pattern of isolation by distance along the
32
Mainland pathway (Fig. 6B). These analyses indicate greater dispersal to the East Florida
population although this location is geographically the farthest from the Caribbean
mainland (see Fig. 1). A Bayesian clustering analysis in the program InStruct (Fig. 3) and
the neighbor-joining tree (Fig. 4) corroborate this finding as East Florida shows
comparatively greater connectivity with the Caribbean mainland compared to the other
two Florida populations. This observed pattern is likely because of the topography of the
Florida peninsula and the location of the ocean currents along the Mainland pathway. The
shallow continental shelf off the west coast of Florida maintains the aforementioned
currents of the Mainland pathway far offshore of the west and southwest coasts of the
peninsula, but these same waters are later funneled through the Florida Straits towards the
Florida Keys and then northward along the southeast coast of Florida (Ichiye et al. 1973).
As a result, propagules transported by these currents would rarely be expected to reach
west and southwest Florida, but would presumably reach the southeast coast much more
frequently. Populations located closer to the ocean currents of the Mainland pathway
(e.g. East Florida) would, therefore, have experienced greater gene flow from Caribbean
mainland populations following the LGM, while populations farther from this pathway
would have experienced greater isolation (e.g. West, South Florida). A similar pattern of
greater genetic exchange among populations located along regional ocean currents also
exists for Indonesian populations of Rhizophora apiculata (Yahya et al. 2013), which
produces propagules similar to those of R. mangle (Tomlinson 1986). As post-LGM
expansion of R. mangle likely occurred via progressive founding events as climate
gradually became favorable for recruitment (Hewitt 1996, 2000), we hypothesize that
other areas located along the Mainland pathway, such as the Yucatan peninsula, NW
33
Cuba and the Florida Keys, may have acted as stepping stones between Caribbean
mainland and East Florida populations. Characterization of the genetic structure of R.
mangle populations from these additional areas along the Mainland pathway would
address this hypothesis.
Although our findings indicate that the Florida peninsula was recolonized
primarily by propagules transported by the Mainland pathway, Florida R. mangle
populations have diverged substantially from Caribbean mainland populations (Fig. 3, 5)
since the peninsula was recolonized ~3-4 kya (Davis 1940, Scholl et al 1964). One
potential explanation for this degree of population divergence is that dispersal to Florida
from other parts of the region is dependent on stochastic events. Post-LGM
recolonization of the Florida peninsula likely began with a small number of initial
recruits transported long distances from lower latitude populations. Subsequent dispersal
to the peninsula and/or successful establishment must be rare, because gene flow has not
been sufficient to prevent genetic differentiation from these low latitude source
populations. Propagules may continue to reach Florida, but contribute very little to the
overall gene pool because of competitive exclusion (Waters 2011, Waters et al. 2013).
Once Florida populations became established, subsequent migrants may have
encountered very little open space and had to compete with an overwhelming number of
locally adapted propagules. In either case, a combination of initial founder events and the
ability of this species to both self-fertilize and reproduce at a young age (Proffitt & Travis
2010) is likely the reason for the significant population divergence between Florida and
Caribbean mainland R. mangle populations over the relatively short time period since the
postglacial recolonization of the Florida peninsula.
34
Contrary to our alternative hypothesis, we do not find evidence for dispersal along
the Antilles Island pathway to Florida. Instead, the InStruct analysis indicates genetic
connectivity along the Antilles Island pathway, but not past the Bahamas to the Florida
peninsula (Fig. 3). Estimated historic migration rates support genetic connectivity from
the islands of St. Kitts and Puerto Rico to the Bahamas (M = 4.52), but almost no
subsequent migration from the Bahamas to Florida (M = 0.17) (Fig. 7). The significant
pattern of isolation by distance along the Antilles Island pathway further indicates limited
dispersal from the Caribbean islands to Florida (Fig. 6A). Based on these analyses,
dispersal along the Antilles Island pathway likely contributed to the recolonization of the
island of San Salvador in the Bahamas, but did not contribute significantly to the
recolonization of the Florida peninsula. Recolonization of Bahamian islands, such as San
Salvador, occurred after the LGM when the strength of the Antilles Current is estimated
to have been greater than at present due to a substantially lower sea level (Ionita et al.
2009). This transient increase in the Antilles Current presumably resulted in greater
dispersal rates along the islands and an increased probability of propagules from
Caribbean island populations farther south reaching the Bahamas during the initial
recolonization period of these islands. However, during this same time period, estimated
water transport along the Florida Current was still considerably greater than the Antilles
Current (Ionita et al. 2009, Lynch-Stieglitz et al. 2009). Therefore, the south-north
direction of the Florida Current may have always acted as a barrier to propagule dispersal
from the Bahamas to the Florida peninsula.
An unexpected finding from this study is the high level of admixture within the
Bahamas population from the island of San Salvador (Fig. 3). Based upon the
35
predominant ocean currents within this region, we predicted that propagules transported
by the Antilles Island pathway would recolonize this island population and we did not
expect gene flow from the Caribbean mainland or Florida to the Bahamas. Surprisingly,
estimates of genetic differentiation between Caribbean mainland populations (i.e.
Panama, Belize) and San Salvador is comparable to that between the Caribbean mainland
populations and East Florida (Table 8). Moreover, estimates of historic migrate rates
(Fig. 7) indicate substantial gene flow from both the Caribbean mainland (M = 2.12) and
Florida (M = 6.30) to this Bahamian island, but very little gene flow from the Bahamas to
Florida (M = 0.17). This poses the question: How did propagules from Caribbean
mainland and Florida populations reach this island in the Bahamas? One potential
explanation is that our proposed Mainland pathway extends past Florida and reaches the
islands of the Bahamas. Ocean dispersal simulations show that particles released from SE
Florida in August-September are carried northward by the Florida Current into the West
Atlantic, with a number of these particles drifting to the southeast and reaching the
islands of the Bahamas (Putman & He 2013). Rhizophora mangle propagules in Florida
generally abscise from paternal trees in August-September (D.J. Devlin, personal
observation) and presumably could be transported by these same ocean currents to the
Bahamas. Another potential explanation is dispersal from Caribbean mainland
populations to the island of Cuba, where expansion could occur eastward along the
northern coast, followed by northward transport to both SE Florida and the islands of the
Bahamas. Cactus species from the genus Harrisia experienced a similar pattern of
expansion, with initial colonization of west Cuba, expansion along the northern coastline
to east Cuba, and then dispersal to neighboring islands in the Greater Antilles, including
36
the Bahamas (Franck et al. 2013). Characterizing the genetic structure of additional
Bahamian and Cuban populations will be necessary to better understand the
recolonization history of R. mangle on these islands.
Hypothesis 2: West Africa to the Caribbean islands
We find the genetic structure of populations from the islands of St. Kitts and
Puerto Rico to be similar to that of the West African population of Senegal. A Bayesian
clustering analysis in the program InStruct groups all Caribbean island populations with
Senegal (Fig. 3) and both the neighbor-joining tree (Fig. 4) and PCoA (Fig. 5) indicate
genetic connectivity between Senegal and the Caribbean islands of St. Kitts and Puerto
Rico. Long distance dispersal (LDD) from NW Africa to these island populations, a
distance of ~5,000 km, likely occurs via the North Equatorial Current (NEC). The NEC
flows westward from NW Africa, off the coast of Senegal, directly to the Lesser Antilles
(Bourlès et al. 1999b, Bischof et al. 2004a). An alternative explanation would be
dispersal from these Caribbean islands to West Africa via the North Equatorial
Countercurrent (NECC), but this seems less probable because the NECC is located much
farther south than the sampled populations in this study (Bischof et al. 2004b). TransAtlantic dispersal along the NEC must have taken place relatively recently (i.e. postLGM) because mangroves are presumed to have gone extinct on many Caribbean islands
during the last glacial cycles due to a combination of arid conditions and a drop in sea
level (Woodroffe & Grindrod 1991, Ellison 1996, Nettel & Dodd 2007). Therefore, it is
possible that West African propagules were among the first to recolonize certain
Caribbean islands. If northward dispersal from South America to the islands of the Lesser
37
Antilles were not common, then initial founding propagules of West African origin may
have colonized, reproduced, and occupied most, if not all, of the available mangrove
habitat on these islands over a fairly short period of time. This pattern of colonization
would explain our observed similarities in the contemporary genetic structure of
Caribbean island populations and an African population. This scenario seems plausible as
water entering the Caribbean basin flows west to east (Johns et al. 2002) and may impede
south to north gene flow from South America to Lesser Antilles populations. Moreover,
the islands of the Lesser Antilles possess relatively small areas that mangroves can
inhabit (Imbert et al. 2000, Angelelli & Saffache 2013), so even a few initial founding
propagules from West Africa could relatively quickly occupy a significant portion of the
available habitat.
Although some caution is warranted as sample sizes are considerably smaller for
both the Senegal (n=15) and St. Kitts (n=12) populations, we feel that more extensive
sampling would not likely substantially change the pattern of genetic connectivity
described in this study. First, the Puerto Rico population consists of a sample size
equivalent to our other populations (n=30) and demonstrates a genetic structure more
similar to these less extensively sampled populations than to the other sampled
populations (Fig. 3, 4, 5). Second, both Senegal and St. Kitts were generally
characterized by only 1-2 frequent alleles at each genotyped locus and a small number of
low frequency alleles. If more individuals were sampled, we may have encountered more
of these uncommon alleles, but this would probably not modify our characterization of
the overall genetic structure of these populations.
38
Genetic evidence indicates LDD of R. mangle propagules between West African
and South American populations (Cerón-Souza et al. 2010, Takayama et al. 2013) most
likely via the South Equatorial Current (SEC). Our study expands our knowledge
regarding trans-Atlantic R. mangle dispersal patterns by exploring dispersal along the
North Equatorial Current (NEC) from NW Africa to the Caribbean Island Chain. Our
data suggest that LDD of propagules across the Atlantic Ocean occurs often enough to
have played an important role in recolonizing certain Caribbean islands. However, the
question still remains as to whether there are two separate pathways connecting West
Africa and the Neotropics, the SEC (West Africa to South America; Cerón-Souza et al.
2010, Takayama et al. 2013) and the NEC (NW Africa to the Caribbean Islands; this
study), or there is simply one broad equatorial dispersal pathway. A more extensive
sampling regime and the use of additional types of molecular markers may be necessary
to better understand the extent of connectivity between R. mangle populations separated
by the Atlantic Ocean.
Hypothesis 3: Genetic diversity with Latitude
Contrary to expectation, we do not find a significant pattern of decreasing genetic
diversity with latitude (Table 1, Fig. 8). Genetic diversity is greatest within the Caribbean
mainland populations of Panama and Belize. Observed values are relatively similar to
other R. mangle populations from the center of this species’ range in Pacific Colombia
(AR = 4.3-5.7, Ho = 0.39-0.64; Arbeláez-Cortes et al. 2007), Pacific and Atlantic Panama
(5.5-11.7 alleles locus-1; Ho = 0.26-0.57, Cerón-Souza et al. 2012), and Pacific Nicaragua
(AR = 5.9-6.9, Ho = 0.32-0.78; Bruschi et al. 2013). We find lower diversity at higher
39
latitudes (25-27o N) within South and West Florida populations, but we also find
comparable values within lower latitude populations from Senegal (13o N) and Puerto
Rico (17o N). Lowest genetic diversity is present within the island population of St. Kitts
(17o N) with values comparable to populations above 20o N on the Pacific coast of
Mexico (AR = 1.3-2.5, Ho = 0.01-0.4; Sandoval-Castro et al. 2012) and above 20o S in
Brazil (AR = 1.25-1.37, Ho = 0.03-0.12; Pil et al. 2011). Surprisingly, we find
comparatively higher genetic diversity within higher latitude populations from the
Bahamas (23o N) and East Florida (27o N). The latter population possesses levels
comparable to the potential LGM refuge population of Panama.
A possible explanation for these observed values may be the relative sizes of the
sampled R. mangle forests. Island populations typically harbor lower genetic diversity
than their mainland counterparts due to smaller sizes and isolation that can prevent the
accumulation of greater genetic diversity (Frankham et al. 2002). Areas of suitable
mangrove habitat along both the Caribbean mainland and Florida peninsula are much
more extensive than their counterparts on Caribbean islands (Spalding et al. 2010) and, as
a result, may have the potential to accumulate greater genetic diversity even at higher
latitudes. This same pattern may also be the reason for the difference in genetic diversity
within the East Florida versus West Florida populations. East Florida mangrove forests
are much more continuous than their counterparts in West Florida that are more
fragmented and restricted to separate estuaries (Kangas & Lugo 1990).
A second explanation may be the geography of this region and the resulting
expansion pathways discussed earlier. Other studies have focused on R. mangle
populations along a continuous coastline (e.g. Pil et al. 2011, Sandoval-Castro et al.
40
2012) and, therefore, post-LGM range expansion could follow a stepwise pattern from
lower to higher latitudes as climate warmed. This type of linear expansion would likely
result in decreased genetic diversity with latitude. However, our results indicate that
within the Caribbean region expansion occurred via both a continuous mainland coastline
and island chain. Although the East Florida population is located at higher latitude (27o
N), gene flow from genetically diverse Caribbean mainland sources has likely resulted in
the maintenance of higher diversity at this higher latitude. In contrast, we find that island
populations at lower latitudes are effectively isolated from these same genetically diverse
Caribbean mainland populations and, therefore, would be much less genetically diverse.
An exception is the island of San Salvador in the Bahamas that possesses greater genetic
diversity than the lower latitude islands of St. Kitts and Puerto Rico. The Bahamas is
located at the end of the Caribbean Island Chain and is presumably the farthest set of
islands from mainland LGM refuge populations. Islands located farther from continental
sources are expected to be more isolated and less biologically diverse (MacArthur &
Wilson 1967); nevertheless, our results indicate that San Salvador has experienced gene
flow from lower latitude Caribbean island, Caribbean mainland and Florida populations.
Propagule dispersal along two pathways, rather than just one, to the Bahamas likely
explains this unexpected pattern of genetic diversity along the Caribbean Island Chain.
Population genetic structure and Implications for conservation / management
Although R. mangle propagules can survive extended periods floating in salt
water (Rabinowitz 1978), our study finds that populations from the Caribbean mainland,
Caribbean islands and Florida are not panmictic (Table 7). Gene flow among our sampled
41
populations is infrequent enough to separate the region into at least three discrete
population clusters (Fig. 3, 5), with different patterns of genetic connectivity within each
of these clusters.
The Caribbean mainland may consist of one relatively continuous population as
indicated by low genetic differentiation between Panama and Belize (Table 8) and
evidence of recent migration (Table 9) over a relatively large geographic distance (~1,430
km). Cerón-Souza et al. (2012) and Sandoval-Castro et al. (2014) found a similar pattern
of low population differentiation between two Panamanian estuaries and along the
Atlantic coast of Mexico, respectively. Propagule dispersal via ocean currents appears to
link these geographically distant populations along the Caribbean mainland.
In contrast, the three Caribbean island populations in this study demonstrate much
higher levels of genetic structuring (Table 8) over a similar geographic distance (~1,455
km, StK to BAH). This pattern of greater differentiation is expected along an island chain
due to the inherent geographic isolation which results in lower rates of colonization
(Losos & Ricklefs 2009) compared to populations along a continuous coastline. Our
results also indicate that the extent of genetic connectivity varies depending on the
location of the population along the Caribbean Island Chain. DA genetic distances
between St. Kitts - Puerto Rico and between Puerto Rico - the Bahamas are relatively
similar (Fig. 6A) despite a substantially greater geographic distance between the latter
two island populations (365 and 1,090 km, respectively). The Antilles Current flows
northwest from Puerto Rico directly to the islands of the Bahamas (Rowe et al. 2012) and
may explain why the greater geographic distance between these two Greater Antilles
populations does not translate into a greater genetic distance.
42
We find weak genetic structure for the comparatively younger Florida populations
(Table 8). Estimates of recent migration rates suggest that the three sampled populations
are connected by gene flow from west to south to east Florida (Table 9). Water flow
along the apex of the Florida peninsula is predominately from the west to the east coast
(Smith 1994, Lee & Smith 2001) and likely transports propagules in this same direction.
A similar pattern of gene flow from west to east Florida also exists for oyster (C.
virginica) populations, likely due to gamete or larvae dispersal by the Florida Current
(Reeb & Avise 1990). In addition, our data indicate lower genetic differentiation between
the south and east Florida populations than between those from west and south Florida
(Table 8). The strength and unidirectional nature of the Florida Current along the SE
coast of Florida (Johns et al. 2002) may be the cause of this comparatively greater genetic
connectivity between south and east Florida.
Population genetic data can provide vital insight necessary for the conservation
and management of natural populations (Pearse & Crandall 2004, Schwartz et al. 2007).
By understanding patterns of population differentiation, demographically isolated
populations known as management units (MUs) can be identified and then monitored
(Palsbøll et al. 2007). Takayama et al. (2013) recommended two management units for
Caribbean and Atlantic R. mangle populations: West Africa / Brazil and the rest of the
Atlantic region. Our study adds to their initial findings by delineating this Atlantic region
into the three population clusters outlined in the previous paragraphs (Fig. 3). Therefore,
Caribbean and Atlantic R. mangle appear to consist of at least four separate MUs, each of
which will require a unique set of management and conservation strategies based on local
conditions to ensure the preservation of extant genetic diversity.
43
Future work
The present study finds evidence for a complex post-LGM expansion history of
the foundation species, Rhizophora mangle. Our results indicate that regional ocean
circulation patterns resulted in genetic connectivity between Caribbean mainland and
Florida populations, as well as between NW Africa and Caribbean islands. The pathways
described in this work provide a model for further investigation with other Neotropical
species dispersed via water. For instance, are populations of other mangrove species, with
different life histories, connected by these same dispersal pathways? Research indicates
that the black mangrove, Avicennia germinans, followed separate post-LGM western and
eastern dispersal routes with propagules from Caribbean island populations recolonizing
the Florida peninsula (Sherrod & McMillan 1985, McMillan 1986). Black mangroves
may have not recolonized Florida via the Mainland pathway because of their smaller
propagule size and shorter dispersal times (Rabinowitz 1978). Further research may focus
on other Neotropical mangroves also found in Florida, such as Laguncularia racemosa
(white mangrove) and Conocarpus erectus (buttonwood).
44
Table 1. Collection sites, geographic location, genetic diversity indices, and Hardy-Weinberg equilibrium (HWE). N, sample size; L,
number of loci; PL, number of polymorphic loci; A, number of alleles; AL, average number of alleles per locus; AR, allelic richness;
PA, private alleles; G, number of unique multi-locus genotypes; R, measure of genotypic richness; HE, expected heterozygosity; HO,
observed heterozygosity; a, values adjusted for potential null alleles; FIS, inbreeding coefficient; statistically significant *(p<0.05),
**(p<0.01), ***(p<0.001); ns = not significant; b, not significant after accounting for null alleles
Population
1 Bocas del Toro, Panama
Code
Latitude
Longitude
N
L
PL
A
AL
AR
PA
G
R
PAN
08 o59’54.72’’N
30
7
7
23
3.29
2.96
1
30
1.00
15
6
4
12
2.00
2.00
1
12
30
7
7
23
3.29
2.98
2
12
6
2
12
2.00
1.97
30
7
5
17
2.43
30
7
7
21
30
7
7
30
7
30
7
HE
HO
FIS
HWE
0.60 a 0.56 a
0.06
ns b
0.79
0.27
0.26
0.02
ns
20
0.66
0.58 a 0.53 a
0.10
ns b
1
7
0.55
0.15
0.15
0.06
ns
2.26
1
28
0.93
0.35 a 0.26 a
0.26
***
3.00
2.76
-
29
0.97
0.41
0.39
0.07
ns
18
2.57
2.15
-
27
0.90
0.36 a 0.27 a
0.23
**
7
22
3.14
3.00
1
30
1.00
a
0.51 a 0.48
0.07
ns
7
19
2.71
2.15
1
22
0.72
0.27
0.26
0.03
ns
18.6
2.71
2.47
0.89
22.8
0.83
0.39
0.35
0.10
o
81 42’46.10’’W
2 Diorom Boumak, Senegal
SGL
13 o50'1.38''N
16 o29'49.56''W
3 Twin Cays, Belize
BLZ
16 o49'28.90''N
o
88 06'7.61''W
45
4 Muddy Pond, St. Kitts
StK
17 o18'4.64''N
62 o41'11.71''W
5 Jobos Bay, Puerto Rico
PR
17 o56'7.08''N
o
66 15'17.22''W
6 San Salvador, Bahamas
BAH
23 o57'1.26''N
74 o31'21.11''W
7 South Everglades, FL
SFL
25 o10'30.84''N
o
80 46'13.32''W
8 Hobe Sound, FL
EFL
27 o07'27.99''N
o
80 08'55.81''W
9 St. Petersburg, FL
WFL
27 o42'5.76''N
o
82 30'11.28''W
mean
Table 2. Microsatellite loci characterized in this study. Locus #1-4; 8-9 (Rosero-Galindo
et al. 2002), #5-7 (Takayama et al. 2008). A, number of alleles; Avg. A, average number
of alleles; Size range, allele sizes in base pairs; HE, expected heterozygosity; HO,
observed heterozygosity
#
Locus
1
RM11
2
Repeated motif
A
Avg. A
Size range
HE
HO
(CT) 16(CA) 3
2
2.00
170-172
0.38
0.30
RM19
(AG) 26
4
2.46
135-152
0.63
0.22
3
RM38
(CA) 8
4
2.27
231-237
0.66
0.30
4
RM41
(GA) 25
9
3.27
152-196
0.62
0.32
5
RM05
(AC) 6(AG) 5N(AG) 5
2
2.00
220-222
0.50
0.49
6
RM50
(AC) 6(AG) 17
6
2.64
210-226
0.68
0.35
7
RM86
(TC)6(AC) 7
8
4.09
198-224
0.64
0.35
8
9
RM21
RM46
(CT) 12
1
1
-
186
198
-
-
(AT) 4(GCGT) 8(GT) 8
(GGAA) 2
Table 3. Deviations from Hardy-Weinberg equilibrium (HWE) by locus and by
population. PAN: Panama, SGL: Senegal, BLZ: Belize, StK: St. Kitts & Nevis, PR:
Puerto Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida. *(q<0.05), **(q<0.01),
***(q<0.001); q is the FDR adjusted p value; M, monomorphic; (-) SGL, StK not
genotyped at RM11
Pop
PAN
SGL
BLZ
StK
PR
BAH
SFL
EFL
WFL
RM11
**
-
RM19
***
M
***
M
M
RM38
RM41
RM05
**
RM50
RM86
M
***
M
M
*
M
**
M
*
46
Table 4. Linkage disequilibrium between microsatellite loci pairs by population. PAN: Panama, SGL: Senegal, BLZ: Belize, StK: St.
Kitts & Nevis, PR: Puerto Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida. *(q<0.05), **(q<0.01), ***(q<0.001); q is the
FDR adjusted p value; (-) SGL, StK not genotyped at RM11
Pop 19-41 19-38 19-86 19-05 19-50 19-11 41-38 41-86 41-05 41-50 41-11 38-86 38-05 38-50 38-11 86-05 86-50 86-11 05-50 05-11 50-11
PAN
SGL
***
BLZ
*
*
*
StK
PR
*
BAH
SFL
EFL
*
WFL
47
Table 5. MICRO-CHECKER results for potential genotyping errors by locus and by
population. PAN: Panama, SGL: Senegal, BLZ: Belize, StK: St. Kitts & Nevis, PR:
Puerto Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida. S, stutter; N, null
alleles; M, monomorphic; (-) SGL, StK not genotyped at RM11
Pop
PAN
SGL
BLZ
StK
PR
BAH
SFL
EFL
WFL
RM11
S/N
-
RM19
S/N
M
S/N
M
M
RM38
RM41
N
M
N
M
M
RM05
RM50
M
S/N
M
RM86
N
N
S/N
Table 6. Estimates of null allele frequencies. Underlined values are monomorphic
population-locus combinations not included in overall mean value calculation. Bold
value is overall mean for study. PAN: Panama, SGL: Senegal, BLZ: Belize, StK: St. Kitts
& Nevis, PR: Puerto Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida; (-) SGL,
StK not genotyped at RM11
Pop
PAN
SGL
BLZ
StK
PR
BAH
SFL
EFL
WFL
Mean
RM-11 RM-19 RM-38 RM-41 RM-05 RM-50 RM-86
0.22
0.25
0.00
0.12
0.00
0.00
0.01
0.00
0.00
0.05
0.00
0.00
0.00
0.01
0.23
0.11
0.19
0.00
0.00
0.01
0.00
0.00
0.08
0.00
0.00
0.00
0.00
0.00
0.00
0.08
0.24
0.10
0.08
0.00
0.07
0.00
0.10
0.02
0.00
0.05
0.06
0.04
0.11
0.00
0.00
0.09
0.14
0.00
0.01
0.00
0.12
0.00
0.00
0.03
0.00
0.12
0.00
0.00
0.00
0.00
0.08
0.04
0.12
0.04
0.08
0.03
0.02
0.04
48
Mean
0.08
0.01
0.08
0.04
0.10
0.03
0.06
0.02
0.03
0.05
Table 7. Three hierarchical level analysis of molecular variance (AMOVA) calculated with the Infinite Allele Model (IAM) and the
Stepwise Mutation Model (SMM). Groups consist of the population clusters defined by the InStruct analysis: (1) Caribbean mainland,
(2) Caribbean island / West Africa, (3) Florida. *Statistically significant (P<0.05)
49
Table 8. Pairwise values of population genetic differentiation, with FST values below the diagonal and RST values above. PAN:
Panama, SGL: Senegal, BLZ: Belize, StK: St. Kitts & Nevis, PR: Puerto Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida.
Bold values are not statistically significant (q>0.05); q is the FDR adjusted p value.
50
Pop
PAN
BLZ
SGL
StK
PR
BAH
SFL
EFL
WFL
PAN
0.07
0.32
0.38
0.33
0.25
0.38
0.27
0.44
BLZ
0.05
0.41
0.46
0.41
0.33
0.39
0.26
0.44
SGL
0.87
0.87
0.19
0.13
0.25
0.59
0.46
0.64
StK
0.92
0.92
0.01
0.24
0.32
0.64
0.51
0.67
PR
0.31
0.38
0.68
0.76
0.18
0.56
0.45
0.59
BAH
0.27
0.32
0.86
0.91
0.33
0.36
0.27
0.39
SFL
0.36
0.40
0.85
0.90
0.40
0.15
0.04
0.15
EFL
0.25
0.29
0.83
0.88
0.35
0.08
0.003
0.11
WFL
0.52
0.55
0.88
0.94
0.46
0.17
0.03
0.06
-
Table 9. (A) Estimates of recent migration rates with source populations in columns and
populations receiving migrants in rows. For each pairwise population combination, the
first value is the migration rate (the fraction of individuals that are migrants from the
corresponding source population) and the second value is the standard deviation. Bold
values along the diagonal axis are proportions of local recruitment and underlined values
are significant migration rates (i.e. 95% confidence intervals greater than zero). (B)
Significant migration rates between sampled populations with corresponding geographic
distance. PAN: Panama, SGL: Senegal, BLZ: Belize, StK: St. Kitts & Nevis, PR: Puerto
Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida.
(A)
To
PAN
BLZ
PR
BAH
StK
SFL
EFL
WFL
From
PAN
0.77 0.06
0.02 0.02
0.01 0.01
0.03 0.03
0.02 0.01
0.01 0.01
0.01 0.01
0.01 0.01
BLZ
0.16 0.06
0.91 0.03
0.01 0.01
0.01 0.01
0.01 0.01
0.01 0.01
0.01 0.01
0.01 0.01
PR
0.01 0.01
0.01 0.01
0.92 0.03
0.02 0.02
0.02 0.02
0.01 0.01
0.01 0.01
0.01 0.01
BAH
0.01 0.01
0.01 0.01
0.02 0.01
0.88 0.04
0.02 0.01
0.01 0.01
0.01 0.01
0.01 0.01
StK
0.01 0.01
0.01 0.01
0.01 0.01
0.01 0.01
0.73 0.08
0.01 0.01
0.01 0.01
0.01 0.01
(B)
From
BLZ
SFL
WFL
To
PAN
EFL
SFL
Migration rate ±
95% confidence
Geographic
distance (km)
0.16 ± 0.11
0.18 ± 0.11
0.11 ± 0.08
1430
250
380
51
SFL
0.01 0.01
0.01 0.01
0.01 0.01
0.01 0.01
0.02 0.01
0.82 0.04
0.19 0.06
0.03 0.02
EFL
0.02 0.02
0.02 0.01
0.01 0.01
0.02 0.02
0.01 0.01
0.01 0.01
0.71 0.03
0.02 0.02
WFL
0.01 0.01
0.01 0.01
0.01 0.01
0.02 0.01
0.02 0.01
0.11 0.04
0.06 0.05
0.90 0.03
Figure 1. Nine sampled Rhizophora mangle populations from the Caribbean, Florida, and West Africa (PAN = Panama, BLZ =
Belize, SGL = Senegal, StK = St. Kitts, PR = Puerto Rico, BAH = Bahamas, W/S/EFL = West, South, East Florida). Two potential
propagule dispersal pathways to Florida (Mainland pathway, Antilles Island pathway) and one potential pathway from West Africa to
the Caribbean islands (North Equatorial Current).
52
(A)
(B)
K
1
2
3
4
5
6
7
8
9
C1
5678.5
4304.0
3439.1
3256.5
3135.8
3030.1
2947.7
2901.9
2852.7
C2
5678.5
4303.7
3433.5
3258.6
3132.0
3037.1
2954.5
2903.1
2848.6
C3
5678.4
4306.0
3427.8
3258.5
3131.9
3027.4
2944.7
2904.6
2841.5
C4
5678.4
4309.9
3438.0
3259.1
3136.1
3024.9
2943.4
2897.7
2846.6
C5
5678.4
4300.7
3428.6
3251.2
3139.6
3030.0
2948.7
2902.1
2843.7
lowest
-5678.4
-4300.7
-3427.8
-3251.2
-3131.9
-3024.9
-2943.4
-2897.7
-2841.5
ΔDIC
0.32
0.25
0.05
0.04
0.04
0.03
0.02
0.06
Figure 2. Determination of most likely number of population clusters (K) from the
InStruct analysis using changes in deviance information criteria (ΔDIC). (A) Graphical
representation of ΔDIC with increasing K values from 1-9. (B) DIC values from five
independent chains for each potential K value (K=1-9) and the corresponding ΔDIC as K
increases.
53
(A)
(B)
Figure 3. Population structure among nine sampled populations from InStruct analysis.
Samples were assigned to three (K=3) population clusters (blue, Caribbean Mainland;
orange, Florida; and yellow, Caribbean Islands & West Africa). (A) Population cluster
assignments for sampled individuals. Averaged assignments for each collection site are
indicated as a pie chart. PAN: Panama, SGL: Senegal, BLZ: Belize, StK: St. Kitts, PR:
Puerto Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida. (B) Averaged
assignments visualized geographically.
54
Figure 4. Neighbor-joining tree of nine R. mangle populations generated with DA
distances (Nei et al. 1983). Bootstrap probabilities are indicated next to the branches.
W/S/EFL: west, south, east Florida.
WFL
BAH
SFL
PR
StK
SGL
Axis 2 (21.4%)
EFL
PAN
BLZ
Axis 1 (71.1%)
Figure 5. Principal coordinates analysis (PCoA) of nine sampled populations. The first
two factors cumulatively explain 92.43% of the variation. W/S/EFL: west, south, east
Florida, BLZ: Belize, PAN: Panama, BAH: Bahamas, PR: Puerto Rico, SGL: Senegal,
StK: St. Kitts.
55
(A)
(B)
Figure 6. Graphical representation of Mantel tests; (A) Populations along Antilles Island
pathway: St. Kitts (StK), Puerto Rico (PR), the Bahamas (BAH), Florida populations
(S/W/EFL), (B) Populations along Mainland pathway: Panama (PAN), Belize (BLZ),
Florida populations.
56
Figure 7. Estimates of historic effective population sizes (Θ = 4Neµ) and migration rates
(M = m/µ) for a priori migration model of four R. mangle population clusters (ML =
Panama & Belize, FL = three Florida sites, BAH = Bahamas, ISL = St. Kitts & Puerto
Rico). 95% confidence intervals are indicated in parentheses. Arrow thickness is
indicative of the relative level of gene flow (thicker arrows indicate higher gene flow).
57
(A)
(B)
Figure 8. Genetic diversity with latitude: (A) Allelic richness (AR), (B) Observed
heterozygosity (HO). PAN: Panama, SGL: Senegal, BLZ: Belize, StK: St. Kitts & Nevis,
PR: Puerto Rico, BAH: Bahamas, S/E/WFL: south, east, west Florida.
58
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