RESEARCH ARTICLE
Turtle soup, Prohibition, and the population
genetic structure of Diamondback Terrapins
(Malaclemys terrapin)
Paul E. Converse1*, Shawn R. Kuchta1,2, J. Susanne Hauswaldt3, Willem
M. Roosenburg1,2
1 Department of Biological Sciences, Ohio University, Athens, Ohio, United States of America, 2 Ohio Center
for Ecology and Evolutionary Studies, Ohio University, Athens, Ohio, United States of America, 3 Department
of Biological Sciences, University of South Carolina, Columbia, South Carolina, United States of America
* paulconverse@icloud.com
Abstract
Diamondback terrapins (Malaclemys terrapin) were a popular food item in early twentieth
century America, and were consumed in soup with sherry. Intense market demand for terra-
pin meat resulted in population declines, notably along the Atlantic seaboard. Efforts to sup-
ply terrapins to markets resulted in translocation events, as individuals were moved about to
stock terrapin farms. However, in 1920 the market for turtle soup buckled with the enactment
of the eighteenth amendment to the United States’ Constitution—which initiated the prohibi-
tion of alcoholic drinks—and many terrapin fisheries dumped their stocks into local waters.
We used microsatellite data to show that patterns of genetic diversity along the terrapin’s
coastal range are consistent with historical accounts of translocation and cultivation activi-
ties. We identified possible instances of human-mediated dispersal by estimating gene flow
over historical and contemporary timescales, Bayesian model testing, and bottleneck tests.
We recovered six genotypic clusters along the Gulf and Atlantic coasts with varying degrees
of admixture, including increased contemporary gene flow from Texas to South Carolina,
from North Carolina to Maryland, and from North Carolina to New York. In addition, Bayes-
ian models incorporating translocation events outperformed stepping-stone models. Finally,
we were unable to detect population bottlenecks, possibly due to translocation reintroducing
genetic diversity into bottlenecked populations. Our data suggest that current patterns of
genetic diversity in the terrapin were altered by the demand for turtle soup followed by the
enactment of alcohol prohibition. In addition, our study shows that population genetic tools
can elucidate metapopulation dynamics in taxa with complex genetic histories impacted by
anthropogenic activities.
Introduction
Turtle soup was a popular food item in the United States during late nineteenth and early
twentieth centuries. Although many turtle species were consumed, diamondback terrapins
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OPENACCESS
Citation: Converse PE, Kuchta SR, Hauswaldt JS,
Roosenburg WM (2017) Turtle soup, Prohibition,
and the population genetic structure of
Diamondback Terrapins (Malaclemys terrapin).
PLoS ONE 12(8): e0181898. https://doi.org/
10.1371/journal.pone.0181898
Editor: Tzen-Yuh Chiang, National Cheng Kung
University, TAIWAN
Received: January 9, 2017
Accepted: July 10, 2017
Published: August 9, 2017
Copyright: © 2017 Converse et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: Ohio University Student Enhancement
award, Ohio Center for Ecology and Evolutionary
Studies. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
(Malaclemys terrapin) were considered a delicacy and were highly sought. The historical mar-
ket price for terrapins demonstrates their popularity: a dozen larger terrapin sold for $70.00
USD during 1915–1920 [1], or ~$852 in 2017 USD. Recipes for turtle soup varied, but many
contained sherry. However, in 1920 the United States ratified the Eighteenth Amendment,
banning the production, sale, and transport of alcoholic beverages (Prohibition). The sherry
used to make turtle soup became difficult to procure, and demand for turtle soup plummeted.
After the market crashed, several terrapin farms purportedly dumped their stocks into local
waters.
Prior to Prohibition, intense demand for turtle soup resulted in the decline of terrapins
across large portions of their range, particularly along the Atlantic seaboard [2]. Due to their
larger size, female terrapins were preferred [1]. To combat extirpation and supplement the tur-
tle soup market, terrapin farms were established by private businesses and governmental agen-
cies to explore domestication and cultivation [3–6] Terrapins from the mid- and north
Atlantic were preferred for their superior taste and size. In particular, terrapins from Chesa-
peake Bay were the favorite and were sold with the moniker “Chesapeakes” [1, 3]. In 1891,
approximately 40,000 kg of terrapin were harvested from Chesapeake Bay alone, but by 1920
terrapin harvests plummeted to ~370 kg [7]. As wild stocks dwindled, terrapins were translo-
cated from other regions in an effort to maintain stock solvency [1]. Terrapins from North and
South Carolina (“Carolinas”) were frequently shipped to other states, while terrapins from
Florida were derided as too small and insipid in flavor [3]. Terrapins from Texas achieved a
desirable size but lacked the flavor of “Chesapeakes” and “Carolinas,” and some terrapin farms
hybridized terrapins with the goal of creating a quick-growing, yet flavorful, terrapin [6].
Hybridization experiments between terrapins from Texas and the Carolinas began in 1914 at
the U.S. Fisheries Biological Station in Beaufort, North Carolina, and the first hybrid individu-
als were confirmed in 1919 [6]. However, Prohibition followed these hybridization experi-
ments. As terrapin farms along the Atlantic coast closed, they purportedly released their mixed
stocks into local waters. The population admixture that resulted is poorly known.
Previous work has detected hints of the influence of translocation on patterns of terrapin
genetic diversity [8]. Terrapins from South Carolina were shown to be more similar to Texas
populations, while Florida populations were identified as genetically distinct [9–11]. In addi-
tion, dramatic increases in contemporary gene flow into Chesapeake Bay were interpreted to
be the result of translocation [12]. Alternatively, documented patterns of genetic diversity
could be due to natural movements. For example, the Suwannee Seaway is a hypothetical
embayment that existed during the early Miocene and late Pliocene [13–16]. This seaway ran
through the northern part of present-day Florida, potentially facilitating gene flow between the
Gulf and the Atlantic while isolating populations in peninsular Florida [8].
Here we use microsatellite data to examine patterns of genetic variation across the terrapins’
range and report evidence of human-mediated gene flow that is consistent with historical
accounts of terrapin translocation. We accomplish this by: i) inferring population structure in
a Bayesian framework, as well as with discriminate analysis of principal components (DAPC);
ii) estimating both historical and contemporary levels of gene flow and comparing them to
infer changes in gene flow over time; iii) testing alternative models of population structure,
including stepping-stone models and models that include translocation events; and iv) survey-
ing for population bottlenecks. Based on previous studies and historical documentation, we
hypothesized: i) admixture between Atlantic and Gulf populations, but not with Florida popu-
lations; ii) some non-adjacent populations (e.g. Texas and New Jersey) would show increases
in contemporary gene flow, consistent with translocation; iii) population genetic models
incorporating translocation would outperform other models; and iv) genetic tests for
Human-mediated gene flow in terrapins
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principal components (DAPC) in the R package adegenet v. 1.4–2 [19]. STRUCTURE delin-
eates genetic clusters by maximizing conformity to Hardy-Weinberg equilibrium while simul-
taneously minimizing linkage disequilibrium among loci for K user-defined clusters, while
DAPC utilizes k-means to maximize variation between groups after PCA transformation. For
STRUCTURE, we ran K from 1 to 12 (sampling locations), replicating each value of K ten
times, each with a random starting seed. Individual runs were composed of a Markov chain
Monte Carlo (MCMC) algorithm of 700,000 steps, with the first 50% removed as burn-in. We
used the admixture model, the LOCprior, and LOCISPOP prior for all runs. Preferred values
of ΔK were computed with the Evanno method [20] in STRUCTURE HARVESTER v. 0.6.94
[21]. Output from STRUCTURE HARVESTER was processed in CLUMPP v. 1.1.2 [22] to
control for labeling switching and multimodality. DISTRUCT v. 1.0 [23] was used to visualize
the data. We ran STRUCTURE in a hierarchical fashion: first we ran an initial analysis to
detect basal levels of structure [20], and then we searched for additional structure within each
identified population individually.
For DAPC, we first optimized the number of PC axes to avoid under- or over-fitting our
population genetic models. We accomplished this with cross-validation, which uses stratified
random sampling and divides the dataset into a training set and a validation set. We parti-
tioned the training set to be 50% of the data and the validation set to 50%, and employed 100
replicates. The cross-validation method estimated 60 axes should be retained. The Bayesian
information criterion (BIC) was used to choose the optimum number of clusters for DAPC
analysis. Four discriminate functions (DF) were retained for each analysis.
Historical and contemporary gene flow
We used MIGRATE v. 3.6.5 [24] to estimate historical gene flow levels (M = mh/μ: proportion
of migrants per generation, scaled by mutation rate). For these analyses we treated sampling
localities as populations with the exception of South Carolina. Because South Carolina lacked
structure among its sampling localities (Fig 1G; see results), we treated all South Carolina sam-
pling localities as a single population. For each population, we randomly subsampled 40 indi-
viduals for gene flow estimates. If a population had fewer than 40 individuals, we included all
individuals in gene flow estimates. For each run, we slice sampled the posterior distribution
with a MCMC of 5,000,000 steps with the first 50% removed as burn-in. Each MCMC con-
sisted of four statically heated parallel chains sampled every 500 iterations. Five independent
replicates were run, totaling 25,000,000 MCMC steps. Estimates of θ (= 4Neμ: effective popula-
tion size, scaled by mutation rate) were modeled under a uniform distribution bounded
between 0.0001 and 100, and historical gene flow estimates were bounded between 0 and 2000.
For estimates of contemporary gene flow (m: proportion of migrants per generation), we
used BAYESASS v. 3.0 [25]. We ran 10 independent MCMC simulations with random starting
seeds [26] for 30,000,000 steps, and sampled every 3,000 steps. We discarded the first 50% as
burn-in. We used TRACER v. 1.5 [27] to visualize MCMC simulations, and used R scripts to
calculate a Bayesian deviancy measure to determine the run that best fit the data [28, 29].
Temporal changes in gene flow
Because MIGRATE uses the coalescent, it estimates gene flow over long periods of time,
approximately 4Ne generations (several thousand years) in the past [24]. Thus, its gene flow
estimates pre-date translocation events in the early twentieth century. By contrast, BAYESASS
estimates gene flow “. . .over the last several generations” [25], where “several” is commonly
interpreted as roughly five generations (e.g. [12, 30] With a generation time of 12 years
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(Roosenburg, unpublished data), the contemporary gene flow estimates of BAYESASS are
within the last 60 years or so. These estimates post-date known terrapin translocation events.
To estimate temporal changes in gene flow, we compared historical and contemporary esti-
mates. First, we multiplied the historical gene flow estimates from MIGRATE (M = mh/μ) by a
mutation rate (μ) of 2.72 x 10−3 mutations site-1 generation-1. This mutation rate was estimated
explicitly for the microsatellites used in previous studies [31]. The resulting values are sub-
tracted from the contemporary gene flow estimates (m) generated by BAYESASS. Thus,
Δm = m—mh. Positive values of Δm indicate increased levels of contemporary gene flow, nega-
tive values indicate reduced contemporary gene flow, and values near zero indicate no tempo-
ral change in gene flow.
Testing coastal population structure
We compared eight models of gene flow in MIGRATE, generated with the methods described
above (Fig 2). Our initial model was a linear stepping-stone (Model A), which restricted gene
flow to adjacent populations. We then added or removed gene flow routes from Model A
based on results from STRUCTURE, DAPC, and our Δm estimates. Models B–F incorporated
gene flow from translocation events with varying connectivity to Florida. Models G and H
modeled the Suwannee Seaway. We used approximate Be´zier scores and log Bayes factors
(LBF) to determine which model best explained coastal population structure.
Bottlenecks
BOTTLENECK v. 1.2.02 [32] was used to test for bottlenecks. Locus mutation was modeled
with the stepwise-mutation model (SMM) and the two-phase model (TPM). The TPM mod-
eled 95% of mutations as single-step while multi-step mutations were modeled with 12% vari-
ance [32]. Each test consisted of 20,000 permutations. We tested for bottlenecks by sampling
locality and by STRUCTURE cluster. We used the Wilcoxon signed-rank test, which detects
bottlenecks 25–250 generations in the past [33], and a mode-shift test, which detects bottle-
necks “. . .within the past few dozen generations” [34].
Results
Population structure—STRUCTURE
Initial runs of STRUCTURE found ΔK = 2 best describes coastal population structure (Fig
1B). However, ΔK scores of 4 and 7 are comparable, and we also report them for comparative
purposes (Fig 1C and 1D). As ΔK identifies basal levels of hierarchical structure [20], we also
tested for substructure within groups for ΔK = 2, which included northern and southern
groups. Inference of population substructure within the northern group (NC, MD, NJ, NY)
revealed K = 2 or K = 3 subclusters (Fig 1E and 1F). This is due to two solutions of K having
overlapping likelihood scores (S1 Fig). If K = 2 is adopted, NJ and NY constitute a cluster (Fig
1E), while if K = 3 is adopted, they form separate subclusters (Fig 1F). The southern group
(TX, FL, SC) included ΔK = 3 subclusters (Fig 1G), one for each state. We did not detect sub-
structure among SC sampling localities. Thus, in total there are either five or six genotypic
clusters, dependent upon K = 2 or K = 3 for north Atlantic terrapins (S1 Fig).
Population structure—DAPC
BIC indicated the optimum number of clusters is six (Fig 3). DAPC for the six-cluster analysis
is shown in Fig 4, and membership probabilities are provided in Fig 5. Mid- and north Atlantic
terrapins form clusters 1, 3, and 6; cluster 4 diagnoses Chesapeake Bay (MD) terrapins, and
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cluster 2 includes populations located in the Gulf (TX, FL). Cluster 5 is composed of SC terra-
pins and links cluster 4 with clusters 1, 3, and 6. Cluster 2 exhibits no overlap with any cluster.
If DAPC is run on sampling localities (Fig 1A), an alternative population genetic structure
is delimited that resembles the spatial distribution of our sampling localities (Figs 6 and 7).
Clusters 1–6 (SC1–6) overlap heavily. Cluster 7 (NC) deviates slightly from clusters 1–6. Clus-
ter 8 (MD) overlaps with cluster 7 and clusters 1–6. Cluster 9 (NJ) falls above cluster 8, and
cluster 10 (NY) falls above cluster 9. Cluster 11 (FL) exhibits no overlap with any cluster, while
cluster 12 (TX) overlaps with SC populations.
Historical and contemporary gene flow
The highest levels of historical gene flow were from NJ to NC (Table 1; mh = 0.0775) and the
lowest were from NY to FL (mh = 0.0128). Contemporary gene flow estimates show SC to NC
A
B
C
D
E
F
NY
NJ
MD
NC
SC
FL
TX
NY
NJ
MD
NC
SC
FL
TX
NY
NJ
MD
NC
SC
FL
TX
NY
NJ
MD
NC
SC
FL
TX
NY
NJ
MD
NC
SC
FL
TX
NY
NJ
MD
NC
SC
FL
TX
NY
NJ
MD
NC
SC
FL
TX
NY
NJ
MD
NC
SC
FL
TX
G
H
Linear stepping stone
(LSS)
Translocation
Translocation


(isolated FL)
Translocation


(sink FL)
Translocation
(Atlantic exchange)
Translocation
(Gulf exchange)
Suwanne Seaway
(isolated FL)
Suwanne Seaway

(sink FL)
Fig 2. The eight models of population structure derived from STRUCTURE, DAPC, andΔm results. Black lines denote naturally occurring gene flow
while red lines indicate gene flow possibly arising from translocation. Model A is a linear stepping-stone; Model B added gene flow from TX to SC and from
NC to NY; Model C removed all gene flow to and from FL while retaining translocation; Model D treated FL as a sink population; Model E allowed gene flow
with FL on the Atlantic coast; Model F allowed gene flow with FL on the Gulf Coast; Model G depicts the Suwannee Seaway with completely isolated FL
populations; Model H depicts the Suwannee Seaway with FL as a sink population.
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2
4
6
8
10
12
305310315320Value of BIC
versus number of clusters
Number of clusters
BICFig 3. BIC scores indicating k = 6 is the preferred value of genetic clusters when retaining 60 PC axes.
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exhibited the highest levels of gene flow (Table 2; m = 0.1497), while SC to FL demonstrated
the lowest levels (m = 0.0037).
Of the 42 gene flow routes, six exhibited increased levels of contemporary gene flow
(Table 3; +Δm> 0.010), 22 demonstrated reduced levels of contemporary gene flow (-Δm<
-0.010), and 14 were relatively stable over time (-0.010 < Δm< 0.010). Four of the six gene
flow routes estimated to have increased contemporary gene flow are found between adjacent
populations. The two routes between non-adjacent populations to show higher levels of con-
temporary gene flow are from TX to SC (Δm = +0.0567) and from NC to NY (Δm = +0.0320).
Models of population structure
As the diamondback terrapin has a linear distribution (Fig 1), we used a stepping stone process
as our null model of population connectivity (Fig 2A). To model translocation events, we mod-
ified the stepping stone model to include unidirectional gene flow between TX and SC, and
NC and NY; these models differed only in their relative isolation of FL (Fig 2B–2F). We mod-
eled the Suwannee Seaway by modeling bidirectional gene flow between TX and SC, with FL
either completely isolated (Fig 2G) or a sink population (Fig 2H). We found that the Atlantic
1
2
3
4
5
6
MD NY
SC
SC
MD SC
NJ NC
SC
TX FL
SC
NJ MD
SC
DA eigenvalues
PCA eigenvalues
Fig 4. DAPC for 60 retained axes and four discriminate functions. Six clusters are recovered with this model. The top half contains populations along
the Atlantic coast while Gulf populations are found on the right half of the diagram. SC terrapins are present within each cluster.
https://doi.org/10.1371/journal.pone.0181898.g004
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Exchange model (Fig 2E), which modeled translocation events from TX to SC and NC to NY
(gene flow between non-adjacent populations), and which included bidirectional gene flow
between SC and FL but unidirectional gene flow between TX and FL, vastly outperformed
all other models, including the linear stepping-stone model (Be´zier scores = -13,7232 vs.
-72,456.30). Converting approximate Be´zier scores into posterior probabilities confirmed the
Atlantic Exchange model as the best-fit model (PP = 100%). All other models had estimated
posterior probabilities near 0%.
Bottlenecks
We recovered no evidence for genetic bottlenecks for any sampling locality with the TPM or
SMM, although the TPM for SC6 approached significance (Table 4; P = 0.055). Mode-shift
tests also failed to detect bottlenecks for any sampling locality. In addition, neither the TPM,
SMM, nor a mode-shift test detected bottlenecks for any STRUCTURE cluster (Table 4).
SC1
SC2
SC3
SC4
SC5
SC6
NC
MD
NJ
NY
FL
TX
SC1
2
3
4
5
6
Cluster
Fig 5. Membership probabilities for 60 retained PC axes and six genetic clusters. Warmer colors denote more certainty in membership probabilities to
each respective cluster. Cluster numbers correspond with populations denoted in Fig 4.
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Discussion
Over the last two centuries, the relationship between humans and terrapins has been complex,
and the terrapin’s population genetic structure reflects this relationship. The demand for turtle
soup resulted in historical population contractions and extirpations, and culminated in the
construction of terrapin farms [3–6]. To get flavorful terrapins to market quickly, Texas and
Carolina terrapins were hybridized at the North Carolina terrapin farm [6]. Then, in 1920, the
enactment of Prohibition restricted access to sherry, which drastically cut demand for turtle
soup. Consequently, many terrapins were released into local waters, which promoted popula-
tion admixture and may have resulted in the reintroduction of genetic diversity.
We documented population genetic structure that is consistent with historical accounts of
terrapin translocation during the twentieth century [1, 3]. We recovered two or three geno-
typic clusters in the mid- and north Atlantic (MD, NJ-NY) and three genotypic clusters in the
DA eigenvalues
PCA eigenvalues
TX
NY
Fig 6. DAPC for 60 retained axes on sampling localities (k = 12). Clusters in the model resemble the spatial distribution of sampling localities along the
Gulf and Atlantic seaboards (see Fig 1). FL exhibits no admixture with any cluster while TX shows overlap with populations found in SC.
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SC1
SC2
SC3
SC4
SC5
SC6
NC
MD
NJ
NY
FL
TX
SC1
2
3
4
5
6
7
8
9
10
11
12
Cluster
S. Figure 5

















Fig 7. Membership probabilities for 60 retained PC axes and 12 genetic clusters (sampling localities). Warmer colors denote more certainty in
membership probabilities to each respective cluster. Cluster numbers correspond with populations denoted in Fig 6.
https://doi.org/10.1371/journal.pone.0181898.g007
Table 1. Historical gene flow (mh) estimates from MIGRATE among coastal populations; gene flow is measured by the proportion of migrants per
generation, ranging from 0.0–1.0. Populations on the left are sending gene flow into recipient populations listed above columns.
Historical gene flow estimates.
TX
FL
SC
NC
MD
NJ
NY
TX
-
0.0295
0.0282
0.0235
0.0272
0.0228
0.0520
FL
0.0502
-
0.0224
0.0300
0.0243
0.0232
0.0262
SC
0.0424
0.0263
-
0.0489
0.0287
0.0294
0.0729
NC
0.0354
0.0207
0.0215
-
0.0221
0.0438
0.0254
MD
0.0180
0.0277
0.0719
0.0249
-
0.0451
0.0583
NJ
0.0606
0.0202
0.0421
0.0775
0.0493
-
0.0388
NY
0.0336
0.0128
0.0335
0.0374
0.0280
0.0214
-
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Gulf and southern populations (TX, FL SC), for a total of six genotypic clusters (Fig 1B–1G;
Fig 3). We also recovered the North American Gulf/Atlantic phylogeographic divide previ-
ously described in the terrapin and other taxa (Fig 1C) [8, 31, 35–37]. Recent work [38] has
suggested that sampling localities with uneven sampling may lead to erroneous STRUCTURE
results. While this is a concern, we do not think it is affecting our data. The population of con-
cern in our study (Texas) has been recovered in previous studies [9], and is supported by our
DAPC results (Figs 6 and 7) and an additional PCA analysis (S2 Fig).
In addition to delineating population structure and quantifying population connectivity,
we also found that a modified stepping-stone model with genetic exchange along the Atlantic
seaboard and unidirectional gene flow from TX to SC and from NC to NY best describes terra-
pin population structure (Fig 2E). This model of population connectivity outperformed a lin-
ear stepping-stone model, as well as models of the Suwannee Seaway, a natural conduit of gene
flow between Gulf and Atlantic populations (Fig 2). Furthermore, we found Florida popula-
tions to be divergent from neighboring populations (Fig 1B–1D and 1G; Figs 4 and 6; Table 3),
which complements accounts that Florida terrapins were not translocated due to their inferior
size and taste [3], as well as other studies that reported FL terrapins to be genetically distinct
[9–11].
Our Bayesian model comparisons supported bidirectional gene flow between SC and FL,
and unidirectional gene flow from TX to FL (the Atlantic Exchange model, Fig 2E), but did
not support alternative models of connectivity, such as the Suwannee Seaway (Fig 2).
We found that NC is highly admixed with other mid- north Atlantic populations (Fig 1C–
1F), which could be the result of human-mediated gene flow. For example, relative to historical
levels of gene flow, we documented increased contemporary connectivity from NC to NY
Table 2. Contemporary gene flow (m) estimates from BAYESASS among coastal populations; gene flow is measured by the proportion of
migrants per generation, ranging from 0.0–1.0. Populations on the left are sending gene flow into recipient populations listed above columns.
Contemporary gene flow estimates.
TX
FL
SC
NC
MD
NJ
NY
TX
-
0.0268
0.0849
0.0184
0.0191
0.0207
0.0197
FL
0.0207
-
0.0233
0.0181
0.0192
0.0172
0.0195
SC
0.0042
0.0037
-
0.0047
0.0054
0.0077
0.0051
NC
0.0139
0.0133
0.1497
-
0.0511
0.0245
0.0574
MD
0.0169
0.0080
0.0518
0.0101
-
0.0145
0.0129
NJ
0.0108
0.0109
0.0389
0.0154
0.0630
-
0.1125
NY
0.0142
0.0128
0.0324
0.0157
0.0286
0.0303
-
https://doi.org/10.1371/journal.pone.0181898.t002
Table 3. Temporal changes in gene flow (Δm) among all populations; values in italics denote gene flow routes under a linear stepping-stone;
bolded values denote an increase in contemporary gene flow (+Δm) of >0.010. Populations on the left are sending gene flow into recipient populations
listed above columns.
Changes in gene flow over time.
TX
FL
SC
NC
MD
NJ
NY
TX
-
-0.0027
+0.0567
-0.0051
-0.0081
-0.0021
-0.0323
FL
-0.0209
-
+0.0009
-0.0118
-0.0051
-0.0060
-0.0067
SC
-0.0382
-0.0026
-
-0.0441
-0.0233
-0.0217
-0.0678
NC
-0.0215
-0.0074
+0.1282
-
+0.0290
-0.0193
+0.0320
MD
-0.0911
-0.0197
-0.0201
-0.0148
-
-0.0306
-0.0453
NJ
-0.0498
-0.0093
-0.0032
-0.0621
+0.0137
-
+0.0738
NY
-0.0194
-0.0172
-0.0011
-0.0217
+0.0006
+0.0089
-
https://doi.org/10.1371/journal.pone.0181898.t003
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(Table 3), which is consistent with accounts of translocation [1]. North Carolina was the loca-
tion of a terrapin breeding operation [4–6], while NY was the location of a large terrapin mar-
ket [1]. With the volume of terrapins brought to market, it is possible some terrapins escaped
or were released into local waters. North Carolina also exhibited increased contemporary gene
flow into SC and Chesapeake Bay, MD (Table 3), consistent with a previous study that detected
large increases of contemporary gene flow into Chesapeake Bay [12]. Finally, we observed
admixture between TX and SC populations (Fig 1D and 1G; Fig 6), as well as increased levels
of contemporary gene flow from TX into SC (Table 3), consistent with chronicled transloca-
tion and hybridization experiments [1, 6, 8].
Although we documented population genetic evidence of translocation between some non-
adjacent populations, our study failed to find genetic evidence for some known instances
of translocation. In particular, we did not detect increases in contemporary gene flow or
admixture between TX and NC (Fig 1B���1D; Figs 4 and 6; Table 3). There are several possible
explanations: translocation can fail [39, 40], and released terrapins from TX may not have suc-
cessfully interbred with local populations in NC. Alternatively, our sampling in NC may have
not included admixed localities (Fig 1A). We also did not find increased contemporary gene
flow from MD to NY, although we detected admixture between these populations (Fig 1B–1F;
Figs 4 and 6; Table 3). Terrapins from MD may not have been released into local waters in NY,
or they may have failed to interbreed. Despite current weak demand for turtle soup, terrapins
from the Chesapeake Bay region continue to be sold at markets in New York, often illegally
[41, 42]. Although our Bayesian model tests cast doubt on the Suwannee Seaway (Fig 2) as a
viaduct for genetic transmission in the southern portion of the terrapin’s range, it remains
possible that long distance (coastal) dispersal and gene flow are structuring a portion of our
populations. Despite a variety of potential biogeographic barriers, the Balkan Pond Turtle
Table 4. BOTTLENECK results by sampling locality and STRUCTURE cluster. Shown are P-values for
a genetic bottleneck under the SMM and TPM. An “L-Shaped” distribution under a Mode-Shift test indicates a
bottleneck was not detected.
Bottleneck tests along the seaboards.
SMM
TPM
Mode-Shift
Sampling Locality
SC1
0.578
0.344
L-Shaped
SC2
0.281
0.922
L-Shaped
SC3
0.422
0.945
L-Shaped
SC4
0.500
0.281
L-Shaped
SC5
0.656
0.500
L-Shaped
SC6
0.281
0.055
L-Shaped
NC
0.422
0.219
L-Shaped
MD
1.000
1.000
L-Shaped
NJ
0.922
0.719
L-Shaped
NY
0.945
0.922
L-Shaped
TX
0.719
0.781
L-Shaped
FL
0.961
0.961
L-Shaped
STRUCTURE Cluster
SC
0.961
0.422
L-Shaped
MD
1.000
1.000
L-Shaped
NJ/NY/NC
0.961
0.719
L-Shaped
TX
0.719
0.781
L-Shaped
FL
0.961
0.961
L-Shaped
https://doi.org/10.1371/journal.pone.0181898.t004
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(Mauremys rivulata) exhibits a paucity of genetic variation across its range [43], suggesting it
is capable of transoceanic gene flow. Infrequent but long-range coastal gene flow may also be
occurring between Diamondback Terrapin populations.
It is well documented that terrapin populations historically underwent severe contractions
[2], but we failed to detect any population bottlenecks in any region (Table 4). For example,
the decline of terrapins from Chesapeake Bay is documented by shrinking harvests after
decades of overexploitation [7], but we did not detect bottlenecks in this region. Indeed, previ-
ous work in Chesapeake Bay indicated terrapins exhibit relatively high levels of genetic diver-
sity [9, 12]. It is possible that we failed to detect population bottlenecks because tests of
heterozygosity excess have weak statistical power [44]. Alternatively, the failure to detect bot-
tlenecks may be the consequence of terrapin translocation events reintroducing genetic diver-
sity into populations. If true, the enactment of Prohibition may have inadvertently benefited
the terrapin in two ways. The first was the collapse of the turtle soup market, which slowed the
harvesting of natural populations. The second was the closure of terrapin farms and the release
of translocated individuals into local populations, which may have reintroduced genetic diver-
sity and increased population viability [45].
Thus, our study suggests that population genetic structure in the diamondback terrapin
possesses the signature of historical translocation events. Translocation among natural popula-
tions is known to increase levels of population admixture and genetic diversity [46, 47]. At
least in some cases, increased levels of genetic diversity save populations from the negative
consequences of inbreeding depression and lowered mean population fitness [48–52]. How-
ever, translocated populations may exhibit high levels of genetic diversity but have low effec-
tive population sizes, suggesting several population genetic metrics are required to judge the
efficacy of translocation [53]. An alternative outcome of translocation is that it may harm pop-
ulations by causing outbreeding depression, harm locally adapted populations by moving
them away from an adaptive peak [54, 55], or introduce diseases [56]. Nonetheless, given the
extent of the current biodiversity crisis [57–60], including increasing population fragmenta-
tion [61–64] conservation-oriented translocation has become increasingly pivotal to maintain-
ing population viability [65].
Historically, terrapins have been divided into seven subspecies [8], but recent genetic studies
are incongruent with extant taxonomy [9–11]. While our analyses also cast doubt on current ter-
rapin taxonomy, we do not make recommendations for future taxonomic changes. Our study
lacked sampling in the Gulf of Mexico, where there may be more population structure than cur-
rently documented. Our study also used six microsatellite loci; more thorough geographical sam-
pling and additional loci are required to make recommendations on taxonomic changes.
Our study shows that convoluted genetic histories can be disentangled with modern population
genetic tools and that translocation can leave an indelible fingerprint in populations. Anthropo-
genic influences increasingly disrupt population dynamics [12, 61, 62, 66]; however, the indirect
consequences of social and political activities are not always predictable. Our study suggests the
population genetic structure in the Diamondback Terrapin may be the byproduct of an interaction
between market demand for turtle soup during the late nineteenth and early twentieth centuries,
followed by the enactment of Prohibition in 1920, which resulted in the large scale release of cap-
tive terrapins into local waters.
Supporting information
S1 Fig. Likelihood scores for north Atlantic populations. Overlapping likelihood scores for
the number of population in the north Atlantic (Fig 1E and 1F).
(TIFF)
Human-mediated gene flow in terrapins
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14 / 18
S2 Fig. PCA analysis of terrapin genotypes. PCA analysis of sampling localities. Location
number corresponds with Figs 4 and 6.
(EPS)
S1 Text. Raw data. Dataset with all terrapin genotypes.
(XLSX)
S2 Text. Population structure files. STRUCTURE/adegent input files.
(STRU)
S3 Text. Contemporary gene flow. BAYESASS input file.
(TXT)
S4 Text. Historical gene flow. MIGRATE input file.
(TXT)
S5 Text. Bottleneck file. BOTTLENECK input file, sampling localities.
(TXT)
Acknowledgments
We thank B. Roumillat and the Inshore Fisheries Group, M. Lee, D. Owens, K. Hart, R. Wood,
M. Draud, and M. Forstner for sample collection. We thank the Hooper lab at Ohio University
for access to their computer server. We thank the KRW lab meeting at Ohio University for
input and suggestions for this manuscript.
Author Contributions
Conceptualization: Paul E. Converse, Shawn R. Kuchta, J. Susanne Hauswaldt.
Data curation: J. Susanne Hauswaldt.
Formal analysis: Paul E. Converse.
Funding acquisition: Paul E. Converse, J. Susanne Hauswaldt, Willem M. Roosenburg.
Investigation: J. Susanne Hauswaldt.
Methodology: Paul E. Converse, Shawn R. Kuchta, J. Susanne Hauswaldt, Willem M.
Roosenburg.
Project administration: J. Susanne Hauswaldt, Willem M. Roosenburg.
Resources: J. Susanne Hauswaldt, Willem M. Roosenburg.
Supervision: Shawn R. Kuchta, Willem M. Roosenburg.
Validation: Paul E. Converse, Shawn R. Kuchta.
Visualization: Paul E. Converse.
Writing – original draft: Paul E. Converse.
Writing – review & editing: Paul E. Converse, Shawn R. Kuchta, J. Susanne Hauswaldt,
Willem M. Roosenburg.
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