Investigation of Population Structure in the Rare Amsonia
ludoviciana Vail (Louisiana Bluestar; Apocynaceae)
Patrick A. Smallwood, Melissa D. Caspary, and James E. Russell
Southeastern Naturalist, Volume 17, Issue 3 (2018): 456–469
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2018 SOUTHEASTERN NATURALIST 17(3):456–469
Investigation of Population Structure in the Rare Amsonia
ludoviciana Vail (Louisiana Bluestar; Apocynaceae)
Patrick A. Smallwood1,*, Melissa D. Caspary2, and James E. Russell2
Abstract - Amsonia ludoviciana (Louisiana Bluestar), in the Apocynaceae family, is a rare
herbaceous perennial plant species found in the ecotones of granite rock outcrops of Georgia
as well as the pine flatwoods in Louisiana, Mississippi, and Texas. This research focused
on using fluorescently tagged primers to amplify polymorphic microsatellite loci as a way
to characterize the population genetic structure of the rare Louisiana Bluestar in Georgia.
We sampled 6 populations across the Georgia range of Louisiana Bluestar. We employed
8 microsatellite loci to detect variation within and among Louisiana Bluestar populations
and to detect patterns in population structure and dispersal. Values of the genetic-diversity
estimator Gst suggested that the majority of the sampled populations demonstrated moderate
genetic similarity. Further, Geneland software indicated the presence of 3 clusters. From
our population genetic analyses, it can also be inferred that there is substantial gene flow
between populations of Louisiana Bluestar in the state of Georgia.
Introduction
Amsonia ludoviciana Vail (Louisiana Bluestar) is a rare herbaceous perennial
in the Apocynaceae family known to occur in the shallow soils and open habitat in
the ecotones of mafic Lithonia granitic gneiss outcrops of the Piedmont of Georgia
(Weakley 2012) and Pinus (pine) flatwoods in Louisiana, Mississippi, and Texas
(Doffitt et al. 2014, Lemke 1987, Weakley 2012). In Georgia, rock outcrops are
characterized by high solar-irradiance, extreme temperatures, xeric substrates, and
an island-like geographic distribution that likely contributes to the radiation of
many of the endemic species typical of the habitat (Quarterman et al. 1993, Shure
1999, Wyatt and Allison 2000). Louisiana Bluestar occurs in the ecotone between
rock outcrop and forested habitat, growing in the thin soils and diffuse light of
the rock margin where water seeps at the rock edge (Allison 2013; Edwards et al.
2013; M.D. Caspary, pers. observ.).
Historic occurrences of Louisiana Bluestar are reported in South Carolina, but
it is presently only found in Louisiana, Mississippi, Texas, and Georgia (Doffitt
et al. 2014, Estill and Cruzan 1999, Weakley 2012). In the state of Georgia, Louisiana
Bluestar is known to occur across 33 sites in Dekalb, Rockdale, and Walton
counties, excluding historic occurrences documented in Gwinnett County (Fig. 1;
Allison 2013, GADNR 2013). All populations within Georgia are located within a
25-km2 cluster.
1Department of Plant Biology, University of Georgia, Miller Plant Sciences Building,
Athens, GA 30601. 2School of Science and Technology, Georgia Gwinnett College,
1000 University Center Lane, Lawrenceville, GA 30043. *Corresponding author -
Patrick.Smallwood25@uga.edu.
Manuscript Editor: Scott Markwith
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The ecotone-associated flora for many outcrops has shifted over time. Fire
suppression compounded by exotic plant invasion and anthropogenic impacts are
creating a dense understory layer of high competition and low light that discourage
establishment and persistence of native outcrop flora (Caspary 2011). Research
has shown that narrow endemics, such as the plant species that have specialized on
mafic Lithonia gneiss, are particularly vulnerable to extinction and are presently
being threatened by adjacent development, conversion to other land-use cover,
invasive species, dumping, fire-building, quarrying, recreational impacts, road
frontage, vehicular traffic, and grazing by farm animals (Allison 2013, Dury 1974).
Most known populations of Louisiana Bluestar are small, from a few plants to tens
of individuals (Allison 2013), which may compromise long-term maintenance of genetic
variation. Additionally, wind and water are the dominant mechanisms for fruit
and seed dispersal on rock outcrops, so dispersal events are typically highly localized
around these habitats (Wyatt and Fowler 1977). The Louisiana Bluestar has a pair
of long, pubescent, dehiscent follicles (Woodson 1928). Once ripened, the follicle
releases its seeds, which are then predominantly gravity dispersed. This fruit type
likely leads to a low level of genetic connectivity between populations of Louisiana
Bluestar, which could result in inbreeding depression. The island-like geographic
distribution and the small size of granite outcrops, may also influence the level of genetic
variability within endemic species (MacArthur and Wilson 1967).
The pollinator of the species is not known. The blue petals and a 5–9-mm tubular
salverform corolla shape (Woodson 1928) indicate a pollination syndrome targeting
bees and butterflies. Allison (2013) documented Eurytides marcellus Kramer
Figure 1. Map of the range of Amsonia ludoviciana (Louisiana Bluestar) within the state of
Georgia. (A) Map of Georgia with the dotted line representing state boarders. (B) Zoomed
in map (GADNR 2013). Each square area represents ¼ of a USGS 7.5-minute quadrangle
map. Light gray squares indicate 20 years since the last reported sighting of the species,
while the dark gray squares have reports of the species presenc e in the last 5 years.
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(Zebra Swallowtail) and other butterflies nectaring on the Louisiana Bluestars in
Georgia, however it is not known if these plants require a specialist pollinator or
rely more heavily on a suite of generalists to serve as vectors for pollen transfer.
To our knowledge, the rate of selfing within the species has also never been investigated
in this genus, but evidence in related species suggest that selfing rates
are expected to be low. Post-zygotic self-incompatibility has been noted within
some species in Apocyanaceae, particularly those with evolutionary adaptive traits
towards pollen massing (Endress et al. 2007, Wyatt et al. 1998). A previous study
investigating microsatellite diversity within a single Apocyanaceae species found
observed frequencies of heterozygosity for polymorphic loci ranged from 0.50 to
0.80, except for a single locus which had an observed value of 0.20 (Topinka et al.
2004). This observation may be suggestive of a low rate of selfing when compared
to average expected values of plants with a similar life history (gravity dispersed,
short lived, eudicot) (Hamrick and Godt 1996).
Information about existing threats and population genetic variability of rare
plants like Louisiana Bluestar are needed to understand potential long-term survivability
of species native to such landscapes and to direct conservation efforts.
To this end, the goal of this study was to gain a deeper understanding of genetic
diversity and genetic structure within the Georgian populations of the rare Louisiana
Bluestar.
Methods
Plant material collection
We selected 6 locations in Rockdale and Walton counties (POP1, POP2, POP3,
POP4, POP5, and POP6) as representative study sites across the present-day species
range and because these sites had moderate to large, accessible populations
of Louisiana Bluestar (10–50 plants). Historically, Louisiana Bluestar was documented
in Rockdale, Dekalb, Walton, and Gwinnett counties in Georgia. However,
the majority of plants in the present-day distribution are concentrated in Rockdale
County, 4 km to the southeast of the historic distribution. All Gwinnett County
sites are suspected to be extirpated and the Dekalb County sites are small and were
inaccessible at the time of sampling. The distance between sites varied from 2.04
km to 14.60 km. Due to the small size of populations (Table 1), we sampled 12 individuals
from each location using 2 medium-sized leaves from each individual. We
collected samples during the summer months of 2014. We stored at 4 °C all leaves
obtained for DNA extraction until extraction procedures could be carried out. If the
extraction procedure was not performed within a month of collection, we stored the
sample at -20 °C until the leaves could be processed.
DNA extraction
We employed the GeneJET Plant Genomic DNA Purification Mini Kit (Thermo
Scientific, Waltham, MA) for extraction of genomic DNA (gDNA) from collected
leaf material. We followed the provided procedure with the following modifications:
liquid nitrogen was not used in the initial grinding of plant material. We
placed the leaf sample in a 1.5-ml centrifuge tube, added the provided lysis buffer A
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and lysis buffer B, and ground the sample using the prepared sterilized glass pestle
until the solution had taken on a green color. We centrifuged all samples at 17,000
x g during steps 5 and 9 of the DNA-extraction kit protocol.
PCR amplification of target microsatellites
We selected a total of 8 microsatellite loci (Ake2, Ake3, Ake5, Ake6, Ake7,
Ake8, Ake10, and Ake11; see Table 2) due to demonstrated polymorphisms in
other Amsonia species (Topinka et al. 2004). We employed PrimeSTAR® HS DNA
polymerase (Takara Bio, Mountain View, CA) to perform all PCR reactions. Each
reaction contained the following: 1X PrimeSTAR Buffer (5-mM Mg2+), 0.2 mM of
each dNTP, 0.2-μM concentration for the respective forward and reverse primers,
Table 1. Population sizes of each sampling site (small = less than 15 plants, medium = 15–20 plants,
and large = more than 20 plants observed.), observed heterozygosity (Ho), expected heterozygosity
(He) (P-value of Ho = He using a 1-sample proportions test (POP1 = 0.121, POP2 = 0.802, POP3 =
0.783, POP4 = 0.091, POP5 = 0.028, POP6 = 0.045), the calculated Fis values with 95% confidence
intervals, the total number of private alleles found at each sampling site, the computed allelic richness
(Ar) and the 95% confidence interval of Ar of each site, and the effective number of alleles (Ae) along
with its standard error. * = 95% confidence interval does not include 0. ** = P-value of a 1-sample
proportion test of He = Ho is < 0.05.
Population Fis Private Ar Ae
Site size Ho He (95% CI) alleles (95% CI) (± SE)
POP1 Large 0.479 0.489 0.006 3 3.15 2.217
(-0.203, 0.132) (2.75, 3.50) (± 0.283)
POP2 Medium 0.458 0.454 -0.005 4 3.26 2.107
(-0.205, 0.124) (2.75, 3.63) (± 0.254)
POP3 Medium 0.464 0.458 0.017 0 3.07 2.096
(-0.181, 0.118) (2.63, 3.38) (± 0.289)
POP4 Small 0.552 0.544 0.061 9 4.11 2.453
(-0.278, 0.196) (3.25, 4.75) (± 0.296)
POP5 Small 0.300 0.355** 0.129 6 2.66 1.657
(-0.056, 0.234) (2.00, 3.13) (± 0.151)
POP6 Large 0.522 0.484** -0.095 8 3.16 2.306
(-0.247, -0.014)* (2.63, 3.63) (± 0.430)
Table 2. The number of alleles (Na), effective number of alleles (Nae), the average size of produced
amplicons for each study locus, and annealing temperature used.
Locus Na Nae Amplicon size (min–max in bp) Annealing temp. (°C)
Ake2 9 2.165 (± 0.322) 200–225 56.2
Ake3 6 2.165 (± 0.215) 120–222 56.2
Ake5 7 1.255 (± 0.081) 138–274 60.9
Ake6 10 1.879 (± 0.215) 204–242 60.9
Ake7 10 2.493 (± 0.275) 193–223 60.9
Ake8 8 2.325 (± 0.143) 95–111 56.2
Ake10 10 2.802 (± 0.561) 109–169 60.9
Ake11 7 1.659 (± 0.309) 162–210 56.2
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and 5 μl of template DNA produced from the DNA extraction procedure. We
brought the reactions to a final volume of 25 μl through the addition of moleculargrade
ddH2O. PCR reactions were performed using the thermocycle protocol of
Topinka et al. (2004); however, we changed the annealing temperature to optimize
each individual microsatellite reaction (Table 2). Following amplification, we electrophoresed
all products on a 2% agarose gel at 90 V for 1.5 h to verify the presence
of the desired product.
Tagging of PCR products with fluorophores
We used the microsatellite amplicons as template DNA for a second PCR
procedure, hereafter referred to as the PCR-tagging reaction (PCRtag), in order to
generate fluorescently tagged DNA fragments. We diluted the PCR products from
the first reaction to a 1:100 concentration using molecular grade ddH2O and used
these diluted PCR products as the template DNA in the PCRtag reaction. We carried
out PCRtag reactions using the same concentration of reagents as in the original
PCR, with the exception of the forward primer. The PCRtag required 2 forward
primers: (1) the original forward primer with a tagging sequence attached to the 5'
end (5'-CAGGACCAGGCTACCGTG-original primer sequence for target loci-3';
Blacket et al. 2012), and (2) the tagging sequence with 1 of 4 fluorophores—either
FAM, VIC, NED, or PET—attached at the 5' end. PCRtag cycle 1 created the
attached tagging strand (forward primer 1) that was replicated in cycle 2. The replicated
strand was subsequently used as the template strand for the incorporation of
the fluorophore (forward “tagging” primer 2) in PCRtag cycle 3. The final concentration
of the forward primer was 0.06 μM, whereas the tagging primer was 0.08 μM,
allowing a 0.4 tagging primer: 0.3 forward primer: 1 reverse primer-volume ratio to
be maintained, as suggested by Blacket et al. (2012). We verified successful amplification
through use of a 2% agarose gel using electrophoresis as described above.
All fluorescently tagged oligonucleotides were synthesized by Eurofins Genomics
(Huntsville, AL).
The 2-step PCR reaction methodology employed here was developed by others
to lower costs associated with the development process of primers (Culley
et al. 2013). The 2-step PCR methodology used with Louisiana Bluestar, however,
incorporated the use of the normal, nonfluorophore-containing primers to
initially detect non-specific binding of primers to other regions of the genome.
We employed this modification to the 2-step PCR amplification used by Culley
et al. (2013) due to the observation that when all 3 primers involved in the tagging
process were used together, multi-banding resulted from non-specific primer
annealing. The use of a normal forward primer, lacking the tagging sequence,
allowed the amplification of only the locus of interest. Once we verified the success
of the first PCR, the observed non-specific binding could be overcome by the
massive number of copies.
DNA fragment analysis of amplicons
After verification of a successful PCR reaction, both Ake7 and Ake8 amplifications
from a respective individual were multiplexed in a 1:1 ratio. The fluorescently
tagged Ake2, Ake3, and Ake4 were also multiplexed together; the Ake5, Ake6,
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Ake10, and Ake11 amplicons were multiplexed into a separate sample. We diluted
the multiplexed samples by a 1:10 volume ratio using molecular-grade ddH2O.
After sample preparation, we added to the Ake 7:Ake 8 multiplex sample a GGF
500 ROX labeled size standard produced by Georgia Genomics Facility (Athens,
GA). The remaining multiplex samples had a Gene Scan LIZ 600 standard added,
followed by analysis through use of an ABI 3730XL Genetic Analyzer (Thermo-
Fisher Scientific, Waltham, MA). To determine the genotype of each individual, we
analyzed the produced .FSA files using the open-source program STRand (Toonen
and Hughes 2001).
Analysis of differentiation and gene flow
Once we had determined individual genotypes, we calculated allelic richness
(Ar), observed and expected heterozygosity (Ho and He, respectively), inbreeding
coefficient (Fis), and genetic differentiation (Gst) in the R package diveRsity
(Keenan et al. 2013). We used 9999 bootstraps to determine the 95% confidence
interval for Fis and Gst and calculated the effective number of alleles (Ae) for each
population, along with standard errors in GenAlEx 6.5 (Peakall and Smouse 2006,
2012). We also employed this program to calculate genetic distances between all
pairwise individuals, which we subsequently used in a principal coordinates analysis
(PCoA).
We conducted a mantel test (GenAlEx) using 9999 permutations to analyze
isolation by distance. We examined how populations were clustered using a
100,000-step Markov Chain Monte-Carlo after a burn-in period of 50,000 steps in
the R package Geneland (Guillot et al 2012). The following relationship was used
to determine the number of individuals migrating between each pairwise set of
population (Nm):
Gst = 1 / (4Nm + 1)
We determined the 95% confidence intervals for Nm estimates by using the above
relationship on the upper and lower bounds of the 95% confidence interval for the
Gst for the respective pair of populations.
We determined the population genetic structure of A. ludoviciana in the program
STRUCTURE version 2.3.4 using a burn in of 10,000 steps and 100,000 MCMC
steps (Pritchard et al. 2000). We found the optimal number of clusters to be K = 4
through the use of STRUCTURE HARVESTER version 0.6.94 (Earl et al. 2012),
which follows the methods of Evanno et al. (2005). The ancestry model used in
the analysis was the admixed model with the assumption of independent allele frequency.
We employed STRUCTURE PLOT version 2.0 to visualize the resulting Q
matrices (Ramasamy et al. 2014).
Results
Microsatellite diversity
The average number of alleles (Na) was 8.36 and the average number of effective
alleles (Nae) was 2.139 (± 0.120) across the 8 loci used (Table 2). Expected
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heterozygosity (He) varied from 0.355 to 0.544, with population 5 producing the
lowest He and population 4 the highest (Table 1). Observed values of Ar fell between
2.66 (95% CI = 2.00–3.13) and 4.11 (95% CI = 3.25–4.75). Fis values varied from
-0.095 (95% CI = -0.247 to -0.014) to 0.129 (95% CI = -0.056–0.234), with POP6
the only 95% confidence interval to not include 0. The number of private alleles at
each site was 3 for POP1, 4 for POP2, 0 for POP3, 9 for POP4, 6 for POP5, and 8
for POP6. Ae values varied from 1.657 (SE = ±0.151) to 2.453 (SE = ±0.296). All
population-level values of Ae (including the range from the standard error) existed
within the 95% confidence interval for Ar.
Population differentiation and gene flow
Values of Gst varied from 0.0450 (95% CI = 0.0212–0.0806) and 0.1817 (95% CI
= 0.1262–0.2448) with the global value 0.1918 (95% CI = 0.1615–0.2262), and Nm
varied from 1.13 (95% CI = 0.77–1.73) to 5.31 (95% CI = 2.85–11.54) (Table 3).
The mantel test revealed a pattern of isolation by distance ( P < 0.0001).
We observed no strong clustering in the PCoA (Fig. 2). However, some patterns
among populations emerged within the plot. Individuals from POP2, POP3, and
POP5 were freely interspersed together in the negative region of principal coordinate
analysis (PCA) 1. Individuals from POP1 were in this area as well, though
some individuals were also placed on the positive side of PCA 1 intermingled with
POP4 and POP6. Points from POP4 were heavily skewed towards the positive side
of PCA2. POP4 appears to be the most isolated population, with few individuals
from other populations interspersed within the area which POP4 occupies. Points
Table 3. Calculated values of Gst (below the diagonal) and the calculated values for Nm, based upon
the values of the pairwise Gst (above the diagonal). Numbers within the parentheses are the lower and
upper bounds of the 95% confidence interval, respectively.
POP1 POP2 POP3 POP4 POP5 POP6
POP1 3.09 5.31 2.78 2.02 4.39
(1.85, 5.72) (2.85, 11.54) (1.63, 5.46) (1.27, 3.38) (2.37, 9.22)
POP2 0.0748 3.18 1.69 4.42 1.87
(0.0419, (1.92, 6.19) (1.19, 2.48) (2.29, 11.65) (1.24, 2.83)
0.1193)
POP3 0.045 0.0729 2.27 1.56 1.96
(0.0212, (0.0388, (1.48, 3.70) (0.98, 2.59) (1.24, 3.25)
0.0806) 0.1152)
POP4 0.0826 0.1289 0.0994 1.13 1.87
(0.0438, (0.0917, (0.0633, (0.77, 1.73) (1.25, 2.93)
0.1330) 0.1734) 0.1449)
POP5 0.1102 0.0535 0.1379 0.1817 1.49
(0.0688, (0.0210, (0.0879, (0.1262, (1.00, 2.27)
0.1640) 0.0986) 0.2034) 0.2448)
POP6 0.0539 0.1179 0.1132 0.1180 0.1436
(0.0264, (0.0813, (0.0715, (0.0787, (0.0994,
0.0954) 0.1675) 0.1679) 0.1666) 0.1995)
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from POP6 are intermixed with others from POP1, POP3, and a solitary point near
the POP4 region.
Individuals within POP3 and POP4 were predominantly assigned to the first
cluster in the STRUCTURE analysis, with the exception of 2 individuals within
POP4 (Fig. 3). These 2 individuals were the only ones that were assigned to the 4th
cluster within the data set. STRUCTURE also showed that POP2 and POP5 mostly
Figure 3. The plotted results from the STRUCTURE analysis. The bars along the bottom of
the figure display which vertical bars are representative of individuals from a given sampling
site. The shading of the bars represents which genetic cluster to which STRUCTURE
assigned an individual. Individuals can be assigned to multiple genetic clusters, with part
of the genotype suggesting one cluster and another part of the genotype suggesting the individual
belongs to a different cluster. In total there were 4 clusters which were inferred from
the Evanno method. However, 1 cluster only had 2 individuals.
Figure 2. Principal coordinate analysis based upon the pairwise genetic distances between
all individuals. Principle coordinate 1 described 17.32% of the variation in the data, while
principle coordinate 2 described 12.89%.
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contained individuals from the same cluster as well. The 2 remaining populations
(POP1 and POP6) were not dominated by a single cluster and instead showed a
mixed cluster composition.
The analysis using Geneland produced 3 separate spatial clusters (Fig. 4). The
3 western populations (POP3, POP6, and POP1) were placed into a single cluster.
The eastern POP4 was placed into a singular cluster while the 2 central populations
(POP2 and POP5) were clustered together.
Figure 4. The generated map of cluster membership using the R package Geneland. Three
clusters of populations were inferred form this analysis. The map in panel A shows the inferred
boarders of the 3 clusters. Within this map, cluster 1 is the darkest grey, cluster 2 is
the medium grey, and cluster 3 is the lightest grey. The remaining maps show the probability
that an individual found in a given location is a member of the respective cluster, with panel
B representing cluster 1, panel C representing cluster 2, and panel D representing cluster
3. The lighter the color the higher the probability of belonging to that cluster. Lines within
these maps show the probability topology. Axis units have been omitted to protect the locations
of the sampled populations.
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Discussion
When compared to other species that are endemic to the granite outcrops in
the Southeastern US, the Louisiana Bluestar displays higher levels of gene flow
between populations and higher genetic diversity. Most outcrop species capable
of self-pollination have high levels of differentiation, with Fst and Gst values varying
from 0.217 to 0.693 and He values of 0.020–0.069 (Koelling et al. 2011, Wyatt
1997, Wyatt et al. 1992). The data presented here for the Louisiana Bluestar, however,
seem more in line with the self-incompatible species that occur on outcrop
sites, which have Fst and Gst values from 0.077 to 0.235 and He values between
0.142 and 0.681 (Gevaert et al. 2013, Godt and Hamrick 1993). While the mating
system for the Louisiana Bluestar is not currently known, this is evidence that there
may be a self-incompatibility system present.
Louisiana Bluestar produces a follicle, which dehisces to release seed. Seeds
dispersed through this method would typically not move far from the maternal
plant, thus having little impact on the movement of genes between populations.
Populations of the Louisiana Bluestar appear to be much less differentiated when
compared to other perennial plants that utilize a gravity seed-dispersal strategy.
Such plants have been reported to have an average Gst of 0.248, which is higher
than the upper bound of the 95% confidence interval for the Louisiana Bluestar
(0.2262; Hamrick and Godt 1996), and which may mean that pollinators are supporting
genetic connectivity between populations. Louisiana Bluestar also appears
to be more genetically diverse with an estimated He of 0.464, compared to an average
estimated He of 0.174 among ecological cohorts (Hamrick and Godt 1996). This
finding, along with the high Nm values, is suggestive of notable levels of gene flow
between populations.
The majority of the loci investigated in this study showed much lower values
of Nae in comparison to the number of observed alleles (Na), indicating that there
is a high number of rare alleles. This result could be suggestive of a higher level
of diversity throughout the species’ range in Georgia because we sampled only 6
exterior populations for this study (Slatkin 1985). Further investigations into the
range of alleles at these loci within the populations of Louisiana Bluestar throughout
Georgia may be a worthy avenue of future research. There is also the possibility
of intragenic hybridization. We observed no co-occurring Amsonia species at these
sites, suggesting that this hybridization could only occur through long-distance pollen
flow.
Eight of the Ake microsatellite loci described by Topinka et al. (2004) appear to
be polymorphic within Louisiana Bluestar. Topinka’s 4 remaining Ake loci (Ake1,
Ake4, Ake9, and Ake12) either failed to demonstrate polymorphism or failed to
amplify. To our knowledge, this is the first study to identify polymorphic microsatellite
loci for the Louisiana Bluestar. Interestingly, the Louisiana Bluestar showed
a higher number of alleles at the microsatellite loci used within this study than any
of the other 6 Amsonia species in which these microsatellites have been examined
(Topinka et al. 2004). For these other species, most loci contained 2 to 4 alleles, in
comparison to the 6 to 10 alleles observed for Louisiana Bluestar.
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While the Evanno method inferred that there were 4 clusters, Geneland only
detected 3. It should be noted however that only 2 individuals were actually assigned
to the fourth cluster in the STRUCTURE analysis. These individuals could
be migrants from one of the populations not surveyed within this study. When these
2 samples are ignored, then both methods are in agreement with only 3 genetic
clusters existing. It also appears as though these 3 clusters are split geographically,
with a western, eastern, and central cluster. The STRUCTURE plot also suggested
that the migration events are coming from the eastern and central clusters to the
western ones, and not in the opposite direction.
POP1 appears to be the population most involved in interpopulation gene movement,
with the highest mean value of Nm. Further, the PCoA analysis also shows
that individuals from POP1 are intermingled with individuals from the other populations
in ordination space, suggesting there are individuals within this population
which are genetically similar to other individuals across all populations. This situation
could be the result of migratory pollen coming into this population, which
would also explain why it is composed of such a mixture of STRUCTURE-based
genetic clusters. Another population that appears to function as a genetic migratory
destination is POP6; this may just be a result of the high level of genetic intermixing
between geographically proximate POP1 and POP6 (Gst = 0.0539, Nm = 4.39).
Our analyses seem to be in disagreement over the interaction between POP3
and POP4. Although the STRUCTURE plot shows that all of the individuals in
these 2 populations should be from the same genetic cluster, these are 2 of the
more geographically distant populations. Further, both the PCoA and Geneland
analyses seem to agree that these 2 populations are genetically dissimilar. The
PCoA seems to be in agreement with Geneland and suggests that individuals
in POP3 are more similar to POP5. It is also strange that POP3 is more similar
to POP4 in the STRUCTURE analysis than it is to the geographically adjacent
POP5. Why would such a strong barrier exist between these 2 populations, but allow
the more distant populations to be similar to each other? While interspersed
populations of Louisiana Bluestar may act as genetic stepping stones between
POP3 and POP4, why would they not also allow genetic homogenization between
all 3 populations?
Overall, there appears to be a high level of diversity and connectivity within
the Georgian populations of Louisiana Bluestar. However, throughout the species’
range, there is a marked increase in urbanization and land-use change (Caspary
2011). The question of how the genetic makeup of the Louisiana Bluestar will
change with time remains. One of the populations resides a short distance from a
neighborhood that was under development and undergoing expansion during the
time of collection. Amsonia species are perennial plants; thus, the data presented
here are likely more representative of the historic trends than a snapshot of current
events. The relative contributions of seed and pollen movement cannot be determined
from the data presented here. Chloroplast-DNA data would be useful for
future study to determine the relative extent of seed dispersal (Ennos 1994).
Southeastern Naturalist
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P.A. Smallwood, M.D. Caspary, and J.E. Russell
2018 Vol. 17, No. 3
Acknowledgments
The authors acknowledge Dr. Fengjie Sun for his expertise and assistance and the Georgia
Gwinnett College Seed Fund for supporting this research. We also acknowledge Chris
Doffitt, with the Louisiana Heritage Program, and Theo Witsell, with the Arkansas Natural
Heritage Commission, for generously sharing plant material for this research. The Georgia
Department of Natural Resources, Non-game Conservation Section also provided collection
permits and location information for the work to continue.
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