Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics

Abstract

Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) is a long-lived, anadromous species that is broadly distributed along the Atlantic coast of North America. Historic overharvest and habitat degradation resulted in significant declines to Atlantic sturgeon populations and, following decades of limited recovery, the species was listed under the Endangered Species Act of the United States in 2012. Given continued threats to recovery and limited information about population demography, there is a need for new tools to assist in Atlantic sturgeon conservation. Here, we present a range-wide microsatellite genetic baseline for North American Atlantic sturgeon that is comprised of 2510 individuals from 18 genetically distinct groups collected in 13 rivers and one estuary. Analysis of this baseline suggested that populations from the northern range of Atlantic sturgeon were more highly differentiated than those from the southern extent, where patterns of differentiation were complicated by rivers with genetically distinct spring and fall spawning runs and less geographic distance separating populations. Despite significant demographic bottleneck events, all populations showed at least moderate levels of genetic diversity across a suite of metrics. Additionally, individual-based assignment tests had over 80% accuracy for assigning individuals to their river of origin, highlighting the utility of this baseline for characterizing the composition of mixed-stock aggregations and understanding stock-specific vulnerability and recovery. The expanded spatial coverage of this baseline dataset enabled novel inferences about patterns of genetic differentiation and spawning phenology in Atlantic sturgeon which can be used to support conservation and management efforts.

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Conservation Genetics
https://doi.org/10.1007/s10592-021-01390-x
RESEARCH ARTICLE
Establishment ofamicrosatellite genetic baseline forNorth American
Atlantic sturgeon (Acipenser o. oxyrhinchus) andrange‑wide analysis
ofpopulation genetics
ShannonL.White1 · DavidC.Kazyak1· TanyaL.Darden2· DanielJ.Farrae2· BarbaraA.Lubinski1·
RobinL.Johnson1· MichaelS.Eackles1· MatthewT.Balazik3· HaroldM.BrundageIII4· AdamG.Fox5·
DewayneA.Fox6· ChristianH.Hager7· JasonE.Kahn8· IsaacI.Wirgin9
Received: 20 January 2021 / Accepted: 26 July 2021
© The Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract
Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) is a long-lived, anadromous species that is broadly distributed along
the Atlantic coast of North America. Historic overharvest and habitat degradation resulted in significant declines to Atlantic
sturgeon populations and, following decades of limited recovery, the species was listed under the Endangered Species Act of
the United States in 2012. Given continued threats to recovery and limited information about population demography, there is
a need for new tools to assist in Atlantic sturgeon conservation. Here, we present a range-wide microsatellite genetic baseline
for North American Atlantic sturgeon that is comprised of 2510 individuals from 18 genetically distinct groups collected in
13 rivers and one estuary. Analysis of this baseline suggested that populations from the northern range of Atlantic sturgeon
were more highly differentiated than those from the southern extent, where patterns of differentiation were complicated by
rivers with genetically distinct spring and fall spawning runs and less geographic distance separating populations. Despite
significant demographic bottleneck events, all populations showed at least moderate levels of genetic diversity across a
suite of metrics. Additionally, individual-based assignment tests had over 80% accuracy for assigning individuals to their
river of origin, highlighting the utility of this baseline for characterizing the composition of mixed-stock aggregations and
understanding stock-specific vulnerability and recovery. The expanded spatial coverage of this baseline dataset enabled
novel inferences about patterns of genetic differentiation and spawning phenology in Atlantic sturgeon which can be used
to support conservation and management efforts.
Keywords Atlantic sturgeon· Microsatellite genetic baseline· Population genetics· Dual spawning· Assignment tests
Matthew T. Balazik, Harold M. Brundage III, Adam G. Fox,
Dewayne A. Fox, Christian H. Hager, Jason E. Kahn, Isaac I.
Wirgin authors appear in alphabetical order.
* Shannon L. White
swhite8@vt.edu
1 U.S. Geological Survey, Eastern Ecological Science Center,
Kearneysville, WV25430, USA
2 South Carolina Department ofNatural Resources, Marine
Resources Research Institute, Charleston, SC29412, USA
3 Center forEnvironmental Studies, Virginia Commonwealth
University, Richmond, VA, USA
4 Environmental Research andConsulting, Inc, Lewes,
DE19958, USA
5 Warnell School ofForestry andNatural Resources,
University ofGeorgia, Athens, GA30602, USA
6 Department ofAgriculture andNatural Resources, Delaware
State University, Dover, DE19901, USA
7 Chesapeake Scientific, Williamsburg, VA23185, USA
8 National Marine Fisheries Service, SilverSpring, MD20910,
USA
9 Department ofEnvironmental Medicine, New York
University School ofMedicine, NewYork, NY10010, USA

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Introduction
Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) is an
anadromous fish species that is broadly distributed from
Labrador, Canada to Florida, United States. There is also
a disjunct Baltic population of Atlantic sturgeon that was
originally colonized by migrants from North America about
1200years ago but is now primarily maintained by stocking
(Ludwig etal. 2008). Historic records suggest that Atlantic
sturgeon once spawned in at least 35 rivers along the east
coast of North America; however, today it is believed that
only 18 rivers still support naturally reproducing populations
(ASSRT 2007; Waldman etal. 2019). Atlantic sturgeon are
difficult to capture, resulting in little available information
regarding stock size and status (Wirgin etal. 2015), but it is
generally believed that most population sizes are critically
low, and even the largest populations in the United States are
believed to have fewer than 1000 spawning individuals per
year (Kahnle etal. 2007; Kazyak etal. 2020).
Atlantic sturgeon was the target of an unsustainable com-
mercial fishery that largely collapsed in the late 19th cen-
tury, but which continued through much of the 20th century
despite severely reduced landings (Dadswell 2006; Hilton
etal. 2016). Following decades of population declines, the
Atlantic States Marine Fisheries Commission enacted a
40-year moratorium on Atlantic sturgeon harvest in waters of
the United States in 1998 with the goal of protecting 20year
classes of spawning females (ASMFC 1998). In 2012, Atlan-
tic sturgeon was listed under the Endangered Species Act of
the United States (NOAA 2012a, b). As part of this listing,
and partially based on available genetic information at the
time, rivers in the United States were divided into five dis-
tinct population segments (DPSs). Four DPSs were classified
as endangered including the South Atlantic, Carolina, Ches-
apeake Bay, and New York Bight DPSs. The fifth DPS, the
Gulf of Maine, was classified as threatened due to limited
perceived threats to population persistence (ASSRT 2007).
In Canada, a small commercial fishery still exists in the St.
Lawrence and Saint John rivers, where Atlantic sturgeon are
designated as threatened by the Committee on the Status of
Endangered Wildlife in Canada (COSEWIC 2011).
Conservation and management of Atlantic sturgeon
are complicated by its complex life history traits. While
there is a latitudinal cline of increasing age at maturation
and decreased fecundity in northern populations (Hilton
etal. 2016), Atlantic sturgeon are long-lived (30 to at least
60years), reach sexual maturity late (10 to 30years for
females, which mature later than males), and sometimes
have a long interval between successive spawning events
(1 to 5years). Spawning adults are highly philopatric, lead-
ing to genetically distinct populations among most riv-
ers (Grunwald etal. 2008; Wirgin etal. 2012). Moreover,
several rivers have evidence of separate, genetically distinct,
spring and fall spawning runs (Balazik etal. 2017; Farrae
etal. 2017).
After hatching, juvenile Atlantic sturgeon remain in
their natal tributary for up to six years before migrating to
the ocean (Hilton etal. 2016). Once there, subadults and
non-spawning adults engage in extensive coast-wide move-
ments. The behavior of Atlantic sturgeon in marine waters
is not well-understood, but telemetry data indicate that indi-
viduals can move several hundreds of kilometers within a
few months, with some individuals moving up to 1500km
(Erickson etal. 2011; Rulifson etal. 2020). During these
periods of extensive marine movements, it is common for
high-density, mixed-stock aggregations to form in foraging
areas (Dunton etal. 2010; Dadswell etal. 2016), and for
individuals to temporarily occupy tidal sections of non-natal
rivers and estuaries (Wirgin etal. 2015; Fox etal. 2018).
Because of the range of habitats occupied and co-occurrence
with species of commercial interest, Atlantic sturgeon in
marine environments are particularly vulnerable to bycatch
with a mortality rate of captured individuals of up to 20%
(Stein etal. 2004; Dunton etal. 2015). Collectively, the
life history traits of Atlantic sturgeon coupled with their
occurrence in key fishing grounds, shipping channels, and
energy areas make them vulnerable to continued mortality.
Incidental take jeopardizes Atlantic sturgeon recovery, as
it has been estimated that 4% mortality due to anthropo-
genic activity—which includes bycatch, poaching, and ship
strikes—can result in significant declines to even the largest
Atlantic sturgeon populations (ASMFC 2007). As such, it
is important to understand and mitigate population-specific
exposure to human activities (Dunton etal. 2015).
Given the highly mobile behavior of Atlantic sturgeon
and their extensive use of marine habitats and non-natal
estuaries, there is a critical need to be able to assign indi-
viduals to specific DPSs or rivers/estuaries of origin when
they are encountered away from natal areas (Wirgin etal.
2012; Waldman etal. 2013; Kazyak etal. 2021). Information
on individual origin, along with a better understanding of
population genetic structure, is vital for gaining insights into
the ecology of Atlantic sturgeon, understanding the impacts
of anthropogenic activities on specific stocks, tracking
recovery progress, and predicting stock-specific responses
to proposed management activities (Hilton etal. 2016). To
date, several studies have used genetic data to document sig-
nificant population substructuring and to identify the natal
origin of Atlantic sturgeon caught in marine waters (Wirgin
etal. 2000, 2007; Grunwald etal. 2008). However, many
of these studies relied on the use of mitochondrial DNA,
which may mutate too slowly to detect change in genetic
diversity following relatively recent harvest restrictions or
the structure of newly established populations (Anders etal.
2011). Other studies employing nuclear microsatellites have

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used non-synonymous marker sets (e.g., Henderson etal.
2005; Wirgin etal. 2015; Farrae etal. 2017), had different
inclusion criteria for identifying natal individuals, and/or
had limited sample sizes, particularly for populations that
spawn in both spring and fall. These differences can make
it difficult to directly compare results among studies and
ultimately limit the ability to compile individual datasets
when trying to make inferences that span longer temporal
and spatial scales (Kazyak etal. 2021).
To address these critical data gaps, our objectives for this
study were to: (1) establish a representative genetic baseline
for extant North American Atlantic sturgeon populations,
(2) broadly describe patterns of diversity and differentiation
across the species’ range, and (3) evaluate the effectiveness
of the genetic baseline for discriminating among populations
and DPSs.
Methods
Sample collection
Atlantic sturgeon were sampled in 13 rivers with putative
spawning populations and one estuary from 1980 to 2019,
with most (68%) samples obtained in 2009 or later. Sampling
was conductedduring surveys by state and federal agencies
and other permitted groups using gill nets and trawls. Some
additional samples were collected from incidental takes dur-
ing other activities. Most samples constituted of a small fin
clip that was preserved, typically in 95% non-denatured eth-
anol or RNALater™, and sent to either the U.S. Geological
Survey Eastern Ecological Science Center (USGS-EESC)
or the South Carolina Department of Natural Resources
(SCDNR). Additional details about sample collection and
associated metadata can be found in Kazyak etal. (2021)
and Supplemental TableS1.
Sub-adults and non-spawning adults are known to tem-
porarily occupy non-natal tributaries during foraging migra-
tions. Therefore, to minimize the probability of including
non-natal individuals in our dataset, we only included
river-resident juveniles (RRJ) and sexually mature adults.
All RRJs were less than 500mm total length, which cor-
responds to individuals before they emigrate away from
nursery habitats (Hilton etal. 2016). Adults were generally
greater than 1500mm total length and captured near a puta-
tive spawning site at a time of year consistent with reproduc-
tion. The only exceptions were two individuals from the Pee
Dee River and seven individuals from the Saint John River
that had a total length that was marginally lower than our
criteria (1200–1400mm) but were included due to limited
sample size in those collection locations and because the
individuals were captured at a time and location consistent
with spawning.
The Albemarle Sound collection, herein referred to as
the Albemarle Complex, in the Carolina DPS is thought to
represent different spawning populations from several adja-
cent tributaries. While sampling efforts on individual tribu-
taries were unable to collect enough individuals to analyze
each river separately, specimens from these tributaries are
assumed to be genetically similar (ASSRT 2007). As such,
pooling the data into a single collection is expected to pro-
vide a reasonable characterization of the genetic diversity for
rivers in that area (an assumption that was later supported by
results of our genetic distance analyses and individual-based
assignments; see below).
Based on behavioral observations and microsatellite anal-
yses suggesting significant genetic differentiation (Balazik
etal. 2017; Farrae etal. 2017; see Supplementary Materials
for more in-depth analyses), we also divided samples from
the James, Pee Dee, Edisto, and Ogeechee rivers into fish
that constituted separate spring and fall spawning runs. In
total, our dataset contained 2510 individuals from 18 defined
groups. These defined collections represented all five DPSs,
as well as the two Canadian rivers that are known to support
spawning (Fig.1; Supplemental TableS1).
Microsatellite genotyping andmarker validation
The majority of individuals included in the analysis were
genotyped at the USGS-EESC. Whole genomic DNA was
extracted from tissue samples using Puregene extraction kits
(Qiagen) according to manufacturers’ protocols. All samples
genotyped by USGS-EESC were screened for 12 Atlantic
sturgeon microsatellite disomic loci including LS19, LS39,
LS54, LS68, Aox12, Aox23, Aox45, AoxD44, AoxD165,
AoxD170, AoxD188, and AoxD241 (May etal. 1997; King
etal. 2001; Henderson-Arzapalo and King 2002).
A subset of individuals was genotyped by SCDNR using
similar methods and described in more detail by Farrae etal.
(2017). Prior to any analyses, a rigorous standardization
effort occurred between the two laboratories. To accomplish
this, SCDNR genotyped 94 individuals that had been previ-
ously genotyped by USGS-EESC and were known to have
genotypes that represented the majority of alleles observed
across all Atlantic sturgeon populations. Overall, 99.7% of
1128 allele calls were identical between the two laboratories
(Farrae etal. 2017; Kazyak etal. 2021). However, individu-
als genotyped by SCDNR were not screened at locus LS39.
This locus demonstrated fairly low diversity (average of 2.59
alleles per population), and at least half of all individuals
were genotyped by USGS-EESC for most collections (the
exceptions were spring spawning runs from the James and
Pee Dee rivers, where 23% and 35% of individuals were
genotyped at LS39, respectively). Accordingly, there was
a sufficient sample size for characterizing genetic diver-
sity at LS39 for all populations. Additionally, while there

Conservation Genetics
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is strong concordance between the two laboratories, there
were occasional differences that pertained to allele 187 on
locus Aox12. Because only three and two individuals from
the James and Pee Dee rivers, respectively, had this allele,
we elected to omit data for this allele (i.e., treat data at Aox12
as missing for those individuals). Ultimately, because this
affected so few individuals, we do not anticipate any effect
on our analyses. All genotypes can be found in White etal.
(2021).
Given that our collections represented individuals col-
lected across many years and included a combination
of juveniles and adults, we used the program GenAlEx
(Peakall and Smouse 2006, 2012) to identify, and subse-
quently remove, samples with identical genotypes as these
were presumed to represent the same individual. The pro-
gram MicroChecker (Van Oosterhout etal. 2004) was used
to test for errors due to stuttering and large allele dropout.
To validate the use of the microsatellite marker suite for
population genetic analyses, we used the package genepop
(Rousset 2008) in R v4.0.2 (R Core Team 2020) to test for
adherence to Hardy–Weinberg equilibrium (HWE) and link-
age disequilibrium (LD) at each collection location using
100,000 dememorizations, 1000 batches, and 20,000 itera-
tions per batch. Statistical significance of tests for HWE and
LD analyses were compared to a Bonferonni-corrected α
of 0.004 and 0.0008, respectively. Corrected α values were
determined by dividing 0.05 by the number of loci tested for
Fig. 1 Distribution of rivers that are confirmed or suspected of sup-
porting present-day Atlantic sturgeon spawning activity (ASMFC
2017), color-coded by distinct population segment (DPS) in the
United States and Canadian rivers. Rivers and river complexes
included in the present study are labeled and indicated with a thicker
line, with numbers in parentheses indicating the sample size from
each of the 18 collections

Conservation Genetics
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HWE equilibrium (12) and the number of pairwise combina-
tions among loci that were tested for LD (66).
Identification ofdistinct populations fromsample
collections
As a predominance of family structure can bias results of
population genetic structure analyses in the program STRU
CTU RE, we used the program COLONY 2.0.6.2 (Jones and
Wang 2010) to identify the number of full-sibling families
in each collection. For this analysis, each collection site was
analyzed separately using the full-likelihood method with a
medium run length, medium precision, updated allele fre-
quency, and sibship scaling. We assumed a model of polyga-
mous mating and no inbreeding, a genotype error rate of
0.005, and family size estimates were obtained with maxi-
mum likelihood estimation. We also investigated pairwise
sibship probability and considered two samples to represent
full siblings if the probability of sibship was > 0.95 and used
the program SPAGeDi (Hardy and Vekemans 2002) to esti-
mate the within-collection relatedness coefficient between
individuals (rxy; Queller and Goodnight 1989).
We used the program GenAlEx (Peakall and Smouse
2006, 2012) to estimate pairwise
F′
ST
among collection loca-
tions (Meirmans and Hedrick 2011). To test for potential
effects of isolation-by-distance we performed a Mantel test
on the relationship between spatial distance between two
sample locations and their pairwise
F′
ST
. In this analysis,
distance was measured as the shortest waterway distance
between river mouths, as measured using the Corridor Tool
in ArcGIS.
We visualized patterns of population genetic differentia-
tion using a hierarchical STRU CTU RE analysis (Pritchard
etal. 2000; Janes etal. 2017). In this analysis, we first ran
STRU CTU RE with all 18 collections. Based on this analy-
sis, we grouped collections with similar patterns of popula-
tion differentiation, and then performed another STRU CTU
RE analysis on each of those groups independently. We con-
tinued this iterative process until each collection was repre-
sented as a unique genetic cluster or there was no evidence
of further substructuring within the group. Occasionally a
collection did not strongly cluster with a single group and
many individuals appeared to cluster equally with two dif-
ferent groups. In these cases, the collection was included in
both groups in the next stage of the analysis.
For all STRU CTU RE analyses, we used an admixture
model with each pre-defined collection as a location prior.
We retained 200,000 repetitions after a burn-in of 100,000
and ran 10 replicates for each value of K = 1 to G + 1 (where
K was the number of genetic clusters and G was the num-
ber of groups in the analysis). Results from STRU CTU RE
were visualized using STRU CTU RESelector (Li and Liu
2018). For most analyses we used ΔK (Evanno etal. 2005)
to subjectively select the most appropriate value for K. How-
ever, in cases where it appeared to better match the ecology
of the system, we chose an alternate value for K for further
exploration (Janes etal. 2017). This generally occurred when
K = 2 had the most support, but a single collection was rep-
resented by one cluster and all remaining collections in the
analysis grouped together in the other cluster. In this case,
choosing K = 3 combined two hierarchical levels by iden-
tifying the unique collection as one cluster and detecting
additional patterns of differentiation in the remaining col-
lections. Thus, in all cases using a different value for K only
minimized the number of hierarchies in the analysis and did
not influence any inferences from how collections grouped.
To investigate potential biases due to unequal sample
size among populations (Puechmaille 2016; Wang 2017),
we repeated the above analysis using a subsampled dataset.
This dataset consisted of all individuals from collections that
had a sample size less than 100, and a randomly sampled
100 individuals from collections that had a sample size that
exceeded this threshold. Results obtained using the subsam-
pled dataset were similar to those from the full dataset. As
such, we present only the results from the analysis of the
full dataset as it more thoroughly characterizes intra- and
inter-collection genetic variability.
One criticism of STRU CTU RE is that the clustering algo-
rithm assumes conformance to HWE and LD. Therefore, we
also performed a discriminate analysis of principal compo-
nents (DAPC) which, like STRU CTU RE, seeks to identify
clusters of genetically similar individuals in multivariate
space. However, DAPC does not make assumptions about
HWE or LD and is instead based simply on allelic composi-
tion. We performed the DAPC analysis using the R package
adegenet (Jombart 2008) and included collection site as a
prior in the analysis. We retained 80 principal components
and 16 discriminant axes and used the R package plotly to
visualize results in 3-dimensional space (Sievert etal. 2017).
We also used the R package adegenet (Jombart 2008)
to estimate the chord distance (Cavalli-Sforza and Edwards
1967) among collection locations. Distances were assessed
in a neighbor-joining tree framework, with nodes boot-
strapped over 1000 replicates.
Characterization ofpopulation genetic diversity
Having identified sample collections that constituted distinct
populations, we sought to characterize the genetic diversity
of each population. We used the program ADZE (Szpiech
etal. 2008) to estimate rarefied allelic richness (AR) and
rarefied private allelic richness (PAR), standardized to 60
alleles (equivalent to a sample of 30 individuals). Unbiased
expected heterozygosity (uHE) was estimated in the program
GenAlEx (Peakall and Smouse 2006, 2012), and we used the

Conservation Genetics
1 3
program SPAGeDi (Hardy and Vekemans 2002) to estimate
within-population inbreeding coefficients (FIS; Nei 1987).
An estimate of effective population size (
̂
N
e) was
obtained from the program NeEstimator v.2.01 (Do etal.
2014) using the linkage disequilibrium method and jackknife
95% confidence intervals with a rare allele exclusion ratio
of 0.02. Importantly, because each population was sampled
at varying temporal scales and intensities and represented a
mixture of single and mixed-cohort samples, our estimates
for
̂
N
e should be interpreted with reservation as they tech-
nically represent a value between true Ne and the effective
number of breeders, Nb. Therefore, while we find our esti-
mates for
̂
N
e valuable for comparing the general magnitude
of difference among populations, it should not be used to
make inferences about long-term population viability.
We used the program BOTTLENECK (Cornuet and Lui-
kart 1996) to determine the likelihood of a recent bottleneck
event using a two-phase mutation model with 1000 itera-
tions and assuming 95% single step mutations and a variance
among mutational steps of 12 (Piry etal. 1999). Inferences
were based on the two-tailed Wilcoxon signed rank test with
Bonferroni-corrected α of 0.003.
Accuracy ofclassification andassignment testing
To determine the specificity of the population genetic struc-
ture of each population, we performed individual-based
assignment tests in the program GeneClass2 (Piry etal.
2004) using the Bayesian algorithm of Rannala and Moun-
tain (1997). Assignment tests were performed by assigning
individuals to populations, which was used to determine the
likelihood of assigning an individual to the river in which
it was collected. We then took the population assignments
and collapsed the actual and predicted populations into their
associated DPSs to evaluate the effectiveness of the baseline
for DPS-level assignments.
We also performed a 100% simulations analysis in
ONCOR (Kalinowski etal. 2007), which evaluates the
ability to perform genetic stock identification by simulat-
ing samples from a single population and determining the
probability that those samples assign back to the population
of origin (Anderson etal. 2008). Populations comprised of
individuals that accurately assign to their population of ori-
gin at or near 100% have high assignment sensitivity and are
therefore good candidates for use in genetic stock identifica-
tion analyses. In this analysis, we simulated 100 samples of
200 individuals from each of the reference collections. As
with the individual-based assignments, we performed the
100% simulations at the population- and DPS- level, which
determines the ability to assign individuals to the population
and DPS of origin, respectively.
Results
Microsatellite genotyping andmarker validation
There were 2510 individuals successfully genotyped and
included in project analyses. Overall, there were 30 sig-
nificant deviations from HWE and 236 significant tests for
LD in the dataset. These deviations were generally spread
across loci and collection locations, but 53% (16) of sig-
nificant tests for HWE and 41% (97) of significant tests for
LD occurred in collections from the York and Satilla rivers.
It is likely that these deviations are due to significant fam-
ily structure, as the sample size from these rivers is large
relative to the presumed population size and there was also
evidence of multiple full-sibling pairs (see below). There-
fore, we retained all loci in the marker suite for evaluation of
genetic population structure and characterization.
Identification ofdistinct populations fromsample
collections
We detected between 0 and 262 full sibling pairs in the 18
individual collections (Supplemental TableS2). The collec-
tion locations with the most full-sibling pairs included the
York River (n = 262; 1.2% of all pairs), Edisto River spring
run (n = 55; 0.7% of all pairs), Pee Dee River spring run
(n = 41; 1.9% of all pairs), and Satilla River (n = 30; 1.1%
of all pairs). Despite the large number of full-sibling pairs
in some collections, average rxy ranged from -0.03 to 0.00
suggesting that, when family structure was present, the num-
ber of individuals in each family was generally small. In
concordance with the above suggestion of small family size
based on relatedness, COLONY estimated that full-sibling
family sizes ranged from 1 to 14 individuals with between 26
and 384 families represented in each collection. Given that
the potential benefits of including all data have been dem-
onstrated to offset the risk of retaining small family groups
in analyses of population genetics (Waples and Anderson
2017), we chose to include all individuals in our analyses.
Across all collection locations, pairwise
F′
ST
ranged from
0.03 to 0.45 and average pairwise
F′
ST
for a single collection
ranged from 0.17 to 0.34 (Supplemental TableS3). In gen-
eral, pairwise
F′
ST
was lower between collection locations in
the same DPS than among populations from different DPSs,
which reflects the finding of significant isolation-by-distance
(p < 0.001). The only exception was in the Chesapeake Bay
DPS, where pairwise
F′
ST
for the York River population
was lower with populations from the Carolina DPS, and
pairwise
F′
ST
in both spawning runs from the James River
was lower with populations from the New York Bight DPS
(Fig.2). Nonetheless, the relatively high pairwise
F′
ST
values

Conservation Genetics
1 3
observed among all collections provided evidence that each
collection represented a genetically distinct population.
The first level of the hierarchical STRU CTU RE analy-
sis indicated broad patterns of differentiation that clustered
populations from the northern (Canadian rivers, Gulf of
Maine, and New York Bight DPSs) and southern (Carolina
and South Atlantic DPSs) extents of the range of Atlantic
sturgeon. Collections from the Chesapeake Bay DPS were
split at this level, with the York River primarily clustering
with the southern populations and the James River spring
and fall runs clustering primarily with the northern popula-
tions (Fig.3, level 1A). Subsequent STRU CTU RE runs on
the northern cluster generally replicated the current DPS
classification scheme, and then clearly identified each river
as a genetically distinct population. The only exception was
the Delaware River, which was not as clearly distinguished
from the Hudson River (i.e., the New York Bight DPS was
well-resolved at level 2 of the analysis, but a significant pro-
portion of individuals in the Delaware River had intermedi-
ate Q-scores at level 3).
Additional STRU CTU RE runs on the southern cluster
produced more complex patterns of population genetic dif-
ferentiation. The York River collection clustered indepen-
dently from the other southern populations at level 2 of the
analysis. Populations in the Carolina and South Atlantic
DPSs initially clustered together in level 3 of the analy-
sis, with two clusters generally corresponding to fall and
spring spawning runs rather than DPS structure. In level
3, the Albemarle Complex and Pee Dee fall run popula-
tions appeared to cluster with populations from both fall
and spring spawning groups (Fig.3, Level 3c), and were
included in both clusters in level 4 of the analysis. In level
4, populations in the Carolina DPS appeared to cluster sepa-
rately from the South Atlantic DPS (Fig.3, Levels 4c, 4d).
Subsequent analyses on the Carolina DPS suggested that
each of the three collections in the Carolina DPS were dis-
tinct, but with some evidence of genetic exchange among the
populations that would perhaps be better characterized with
better defined collections in the Albemarle Complex (Fig.3,
Levels 5b, 6a). Subsequent analyses on the two clusters in
the South Atlantic DPS showed low levels of admixture
among most populations; however, the Edisto River fall run
and Ogeechee River spring and fall runs had shared ancestry
with several populations (Fig.3, levels 5d, e).
Results from DAPC were generally consistent with results
produced by the STRU CTU RE analysis (Fig.4). The first
axis, which explained 48.5% of total variance, resembled the
first level of the hierarchical STRU CTU RE analysis in that
it grouped individuals by DPS and separated populations
into northern and southern clusters. The second axis, which
explained 14.1% of variation, mostly highlighted the York
River as being genetically distinct from all other collections.
Finally, the third axis, which explained 7.7% of variation,
reflected the observed differences noted at level 5 of the
STRU CTU RE analysis that separated populations from the
South Atlantic DPS into groups that roughly corresponded
to spring and fall spawning seasons. An interactive 3-dimen-
sional plot of the results is available at: https:// chart- studio.
plotly. com/ ~swhit e8/ 1/#/.
The resulting neighbor-joining tree of chord distance
was generally well-resolved (i.e., support for most nodes
was > 75%) and in accordance with results from STRU CTU
RE and DAPC. Specifically, populations from the Canadian
rivers and the Gulf of Maine and New York Bight DPSs
appeared in isolated clades of the tree. As before, the Chesa-
peake Bay DPS was divided, with the York River population
grouping most closely to populations from the south, and
the James River fall and spring runs grouping most closely
with populations from the north. Additionally, the neighbor-
joining tree supported the grouping of populations in the
Carolina and South Atlantic DPSs by spawning phenology,
rather than DPS. Specifically, the Carolina DPS did not form
a monophyletic clade, rather the Pee Dee fall and spring runs
each grouped to separate clades that contained populations
from the South Atlantic DPS, and these clades generally
separated populations by spawning season (Fig.5). Thus, the
neighbor-joining tree suggested that fall and spring spawn-
ing runs from different DPSs generally grouped more closely
to each other than they did to other populations from the
same river or DPS.
Fig. 2 Pairwise
F′
ST
among 18 Atlantic Sturgeon collections plotted
against shortest waterway distance between river mouths. Pairwise
values from the same distinct population segment (DPS) are color-
coded, while values from populations from different DPSs appear in
grey. Points surrounded by a circle represent
F′
ST
for fall- and spring-
run populations within the same river with James, Pee Dee and
Ogeechee rivers appearing in a solid line and Edisto River a dashed
line

Conservation Genetics
1 3
Fig. 3 Results of hierarchical STRU CTU RE analysis for 18 Atlantic
Sturgeon collections. Level 1 of the analysis included all 18 collec-
tions and generally divided populations into northern and southern
clusters. Subsequent STRU CTU RE analyses (Levels 2 to 6) were
independently conducted on these two clusters, continuing until all
populations showed no evidence of further genetic structuring. At
each level populations with similar Q scores were grouped together
for analysis at the next level, with colored circles next to population
names indicating populations that belonged to the same group in the
next level. Circles with two colors indicate populations that had inter-
mediate Q scores and were included in multiple groups in the next
level of the analysis. Absence of a colored circle indicates the final
level in which a population was included in the analysis. Alphanu-
meric labels next to each subplot correspond with ΔK plots in Sup-
plemental Fig. 6. Plot magnification and the order of collections
change across levels; however, data (and thus sample sizes) for each
collection remained static throughout the analysis
Fig. 4 Discriminant principal components analysis (DAPC) ordina-
tion plots for 18 Atlantic sturgeon collections. Each point represents
an individual, color-coded by Distinct Population Segment (DPS)
and symbolized by collection. A full interactive plot can be found at
https:// chart- studio. plotly. com/ ~swhit e8/ 1/#/

Conservation Genetics
1 3
Population genetic characterization
Estimates of rarefied AR were similar among populations and
ranged from 5.08 to 8.75 (Fig.6; Supplemental Table2).
Thirteen populations contained at least one private allele
across all loci, and average rarefied PAR ranged from 0 to
0.26. Overall, populations of Atlantic sturgeon showed at
least moderate levels of genetic diversity, with estimates of
uHE ranging from 0.54 to 0.73, and there was little evidence
of inbreeding across all populations with FIS ranging from
−0.12 to 0.08. None of the genetic metrics (AR, PAR, uHE,
or FIS) appeared to show trends by latitude or DPS.
Estimates for
̂
N
e ranged from 9.3 in the York River
(95% CI 6.9–11.8) to 154.5 in the Savannah River (95%
CI 99.6–287.7) across populations (Fig.6; Supplemental
Table2). In rivers where population size has been estimated,
the magnitude of differences observed in
̂
N
e among popula-
tions roughly corresponded to the differences in total popula-
tion size (Hilton etal. 2016). For example, the Hudson and
Altamaha populations are thought to be some of the largest
extant populations of Atlantic sturgeon and, likewise, had
some of the largest estimates of
̂
N
e. Conversely, the Dela-
ware populations, which was nearly extirpated in the early
1990s had one of the smallest estimates of
̂
N
e. For all four
rivers with separate spring and fall spawning runs,
̂
N
e was
lower in the spring-run population. No population showed
significant signs of a recent bottleneck event (Supplemental
Table2), but the test for the Delaware population was mar-
ginally insignificant (p = 0.008).
Classification andassignment testing
Individual assignment tests were able to assign individu-
als to the correct population and DPS with high efficiency
(Table1). On average, 81.7% of individuals were assigned
to the correct population of origin, and 94.0% of individuals
were assigned to the correct DPS of origin. Most misclassifi-
cation at the population-level occurred in the South Atlantic
DPS, which was not surprising given that the populations
in this DPS exhibit the highest levels of genetic similarity
and lowest
F′
ST
.
On average, the 100% simulations analysis suggested that
97.7% of simulated individuals sampled from a population
were natal to that population, and only one population had a
composition < 90% (Ogeechee River fall run; 87.1%). Aver-
age percent composition at the DPS level was 98.1%, and
only one population had an assignment composition < 95%
(Pee Dee fall run; 87.2%). Collectively, the individual-
based assignments and 100% simulations suggest that the
Fig. 5 Neighbor-joining tree based on Edward’s chord distance for
18 Atlantic Sturgeon collections. Collections are color-coordinated
by distinct population segment (DPS), and numbers at nodes indicate
percent bootstrap support values calculated over 1000 iterations
Fig. 6 Population genetic diversity metrics for 18 Atlantic Sturgeon
collections. A = average rarefied allelic richness (AR; ± standard
error), B = unbiased expected heterozygosity (uHE; ± standard error),
C = average inbreeding coefficient (FIS; ± standard error), D = esti-
mated effective population size (
̂
N
e; ± 95% confidence interval). Pop-
ulations are color-coded by distinct population segment (DPS)

Conservation Genetics
1 3
Table 1 Classification confusion matrix for individual-based assignments using GeneClass2 for 18 Atlantic Sturgeon populations
Overall, 81.7% of individuals were assigned to the correct population of origin (bold numbers) and 94.0% were assigned to the correct distinct population segment (DPS; grey highlighted)

Conservation Genetics
1 3
probability of correctly assigning individuals to the popula-
tion and/or DPS of origin with the baseline dataset is high
(Supplemental TableS4).
Discussion
Our baseline represents the most comprehensive, range-wide
analysis of Atlantic sturgeon population genetics to date. As
we have entered the second half of the 40-year moratorium on
Atlantic sturgeon harvest in the United States, development
of this baseline is timely as managers require more informa-
tive tools for assessing the status of recovering populations.
In particular, the larger sample size of individuals from more
populations (total n = 2510 individuals collected from 18
populations) and the use of consistent, well-defined criteria
for identifying natal individuals provided the basis for an
informative description of range-wide population genetic
diversity. Moreover, this baseline will be a valuable resource
for mixed-stock analyses that seek to identify the natal sources
of subadult and adult Atlantic sturgeon that are encountered in
coastal waters and/or away from spawning rivers so that the
impacts of human activities on stocks can be assessed.
Given results from previous studies and the highly
philopatric behavior of Atlantic sturgeon (Grunwald etal.
2008), it is unsurprising that we detected significant patterns
of population differentiation and isolation by distance. How-
ever, it is worth noting that
F′
ST
was generally lower among
the six populations in the South Atlantic DPS (Supplemental
TableS3), a finding that has been previously reported by oth-
ers (Wirgin etal. 2007; Waldman etal. 2019). The average
distance among rivers surveyed in the South Atlantic DPS
is only 117km, and so the lower levels of differentiation
could simply be the realization of a system that conforms
to a model of isolation-by-distance at small spatial scales.
However, compared to rivers in the northern extent of the
range, rivers in the South Atlantic DPS have relatively unde-
fined, braided channels that lead to a common estuary, and
most rivers are connected by the Intracoastal Waterway. This
complex hydromorphology may facilitate movement of juve-
nile Atlantic sturgeon between rivers, particularly during wet
years when the tidal freshwater zone expands and a larger
proportion of the river has tolerable salinity levels (Fox and
Peterson 2019). It is also possible that the proximity of the
rivers increases the probability of straying. Ultimately, while
our data presently suggest limited connectivity among rivers,
future research could benefit from more direct study of Atlan-
tic sturgeon behavior in the South Atlantic DPS as well as a
general study on the relationship between river geomorphol-
ogy and population connectivity in other systems.
Expanding on the discovery of dual spawning runs in
the James and Edisto rivers (Balazik etal. 2017; Farrae
etal. 2017), we found evidence to suggest that there are
also separate, genetically distinct spring and fall spawning
runs within the Pee Dee and Ogeechee rivers. Moreover,
the dichotomous assignment of individuals from the Satilla
River in STRU CTU RE analyses and deviations from HWE
and LD is suggestive of possible dual spawning runs in this
population, though data limitations prevented us from test-
ing this hypothesis with the current baseline and deviations
could also be explained by the presence of non-natal indi-
viduals or other genetic phenomena (e.g., Wahlund effects).
The prevalence of spring and fall spawning runs throughout
the range of Atlantic sturgeon has not been well-assessed;
however, the growing number of rivers demonstrating dual
spawning suggests that the behavior may be more wide-
spread than previously thought, particularly in the middle
of the range where fall and winter temperatures are warm
enough to promote growth and survival but do not regularly
reach supraoptimal conditions (Markin and Secor 2020).
Similar hypotheses have been posited for the closely related
Gulf sturgeon (A. o. desotoi; D. Fox, personal communica-
tion), and many Acipenser species exhibit dual-spawning in
at least a portion of their range (Balazik and Musik 2015),
suggesting the behavior may be well-conserved across taxa.
It will be important for future efforts to remain vigilant in
seasonal survey efforts to continue resolving complex pat-
terns of population differentiation and understand how
observed behavioral patterns relate to timing of reproduc-
tion (Nelson etal. 2013; Vine etal. 2019).
Documenting separate spring and fall spawning in more
rivers may be particularly informative for understanding the
evolutionary history of spawning phenology. Although our
sample size for these comparisons is currently limited to
four rivers, we provide evidence suggesting Atlantic stur-
geon spawning in the same season may be more genetically
similar than are Atlantic sturgeon that spawn in the same
river during different seasons. For example,
F′
ST
between
spring and fall spawning runs in the Edisto River was 0.22,
whereas
F′
ST
for spring runs from the Edisto and Pee Dee
rivers was 0.15. Overall, this may suggest that dual spawn-
ing seasons may not have evolved independently in each
population through processes such as parallel evolution or
temporal restriction in gene flow [isolation-by-time; Hendry
and Day (2005)], but may be a divergent trait that radiated
through Atlantic sturgeon populations. Similar uncertainties
about the phylogenetic basis of spawning season phenol-
ogy have been addressed in anadromous salmon through the
integration of genetic, genomic, and behavioral data (Taylor
etal. 1996; Waples etal. 2004; Prince etal. 2017), with
some suggesting difference in run time may be the result of
mutations at relatively few quantitative trait loci (Hess etal.
2016; Thompson etal. 2020). Analogous studies for Atlantic
sturgeon may be useful for understanding evolutionary rela-
tionships among populations and for identifying evolution-
ary significant sources of genetic diversity.

Conservation Genetics
1 3
Given that our large, range-wide dataset enabled novel
inferences about patterns of isolation-by-distance and the
effect of spawning phenology on genetic differentiation, it
is not surprising that our results are somewhat incongruous
with previous efforts. While our results support previous
findings that populations from Canadian rivers and the Gulf
of Maine and New York Bight DPSs are genetically distinct
(Wirgin etal. 2000; Waldman etal. 2002; Grunwald etal.
2008), we found less genetic differentiation among popula-
tions in the Chesapeake Bay, Carolina, and South Atlantic
DPSs. In particular, results from STRU CTU RE, DAPC, and
the neighbor-joining tree, which were all generally in agree-
ment with one another, suggest that the genetic structure of
the three southernmost DPSs may be more complex than
previously realized and likely warrants more focused studies
of individual systems. While these patterns of genetic dif-
ferentiation may raise new questions about Atlantic sturgeon
phylogenetic relationships, the overall high individual-based
assignment accuracy to natal population and DPS suggest
the current management units capture much of the coastwide
genetic variation.
Atlantic sturgeon population sizes have been slow to
recover following decades of overharvest in the late 19th
and early 20th centuries (Secor and Waldman 1999), as
evidenced by the overall modest estimates for
̂
N
e. Small
contemporary census population sizes are frequently associ-
ated with continued habitat degradation and slow population
recovery due to a combination of ongoing anthropogenic
threats (bycatch and ship strikes) and unique life history
traits including long life span, late age at sexual maturity,
and long intervals between consecutive spawning events
(Peterson etal. 2008). However, while these characteris-
tics may extend the timeline for recovery, they, along with
the polyploid genome (Waldman etal. 2019), may also be
responsible for greatly slowing the loss of genetic variation
while populations are suppressed. While we do not have
historic comparisons, all 18 populations surveyed showed
reasonably high levels of contemporary genetic diversity and
low inbreeding despite relatively recent and severe demo-
graphic bottleneck events. We found overall low estimates
for
̂
N
e that were similar to estimates of Ne provided by oth-
ers (Balazik etal. 2017; Farrae etal. 2017; O’Leary etal.
2014; Waldman etal. 2019; but see above for caution in
interpreting
̂
N
e within and across studies); however, Atlantic
sturgeon life history (iteroparity, high longevity, and many
overlapping generations) may make the species resilient to
genetic bottlenecks (Chapman etal. 2011; Lippé etal. 2006).
Ultimately, while high genetic diversity is encouraging for
future recovery efforts, it would be inappropriate to assume
that diversity is stable in contemporary Atlantic sturgeon
populations. The extent to which a polyploid genome and
resilient life history can slow erosion of genetic diversity
is not well-understood. If populations exist in a state of
hysteresis then even minimal losses to genetic diversity, as
would be expected with increased mortality or declines in
population size and individual fitness and/or survival, could
significantly reduce the genetic resiliency of populations and
contribute to declines in demography that will be difficult
to recover. There is already evidence to suggest that
̂
N
e is
smaller in spring-run populations compared to fall-run popu-
lations from the same river (e.g.,
̂
N
e was 82.0 and 13.5 in
the Pee Dee fall and spring runs, respectively) which could
be the result of smaller population sizes from more inten-
sive fishing pressure on spring-run populations (Balazik and
Musick 2015).
For anadromous, philopatric species like Atlantic stur-
geon, the ability to assign individuals to a natal source popu-
lation is critical for understanding and quantifying threats to
specific management units (Dunton etal. 2012; Wirgin etal.
2015). Our present genetic baseline expands on previous
efforts and includes 18 genetically distinct populations, each
of which can be identified to their river of origin through
individual-based assignment tests. Accuracy of individual-
based assignments was consistently lower for populations
in the South Atlantic DPS, indicating that it may be harder
to identify population-specific threats and estimate recovery
trajectories in the species’ southern range. However, high
assignment accuracy at the level of the DPS in both individ-
ual-based and mixture analyses suggests that the DPS system
can be informative for setting region-specific management
goals and objectives, provided populations within a DPS are
demographically similar and can assume the same level of
conservation risk.
While the present genetic baseline adds to the tools avail-
able to assist in the recovery and restoration of Atlantic stur-
geon populations, it also helped identify several key uncer-
tainties. As previously mentioned, future sampling efforts
will be needed to test hypotheses regarding the evolution of
spawning phenology; however, results of our analyses may
be useful for developing initial hypotheses about the timing
of spawning in some rivers. For example, at present, spawn-
ing in the Altamaha River has only been documented in fall
(Ingram and Peterson 2016). Clustering of the Altamaha
River with other fall-run populations would lend support to
the potential for Atlantic sturgeon to only spawn in fall in
the river; however, the presence of dual spawning in neigh-
boring rivers suggests that environmental conditions could
make a spring run plausible. Additionally, resolving range-
wide patterns of population diversity and differentiation will
be aided by the incorporation of additional rivers into the
baseline, particularly those in the Carolina DPS where lim-
ited sample sizes forced us to aggregate data into a single
collection, and in other populations that presently lacked suf-
ficient data for inclusion in our analyses. For example, it has
also been hypothesized that the York River may be part of a
unique metapopulation with other rivers in the Chesapeake

Conservation Genetics
1 3
Bay and incorporation of those sites could be informative
for explaining the deviations from HWE and LD that we
observed.
Overall, future efforts will be best achieved through
large, range-wide collaborations; however, the present
genetic baseline demonstrates the need for future research
to standardize field and laboratory methods to maximize the
potential of future collections. We also look towards future
opportunities to integrate this genetic baseline with telem-
etry data to better understand the life history and behavior
of Atlantic sturgeon and further refine inclusion criteria used
for identifying natal individuals.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s10592- 021- 01390-x.
Acknowledgements This paper is dedicated to Tim King, who passed
away unexpectedly during its preparation. Tim’s vision and dedication
were indispensable to the development of earlier versions of the genetic
baseline and left a lasting impact on his coauthors and our understand-
ing of Atlantic sturgeon. We thank Andy Herndon for comments on a
previous version of the manuscript and express our sincere gratitude
towards the many technicians, collaborators, personnel, and volunteers
that helped collect or coordinate the tissue samples used in this study
including Delaware Division of Fish and Wildlife, New York State
Department of Environmental Conservation, Bill Post, Doug Peterson,
Ramsey Noble, Carter Griggs, Gabriel Irigaray, Kirk Moore, Noelle
Mathies, Jay Russo, Tracy Massey, Craig Marcusson, Maddie Speirs,
April Deacy, Desiree Nuckols, Matthew Fisher, John O’Herron, and
Carter Watterson- without their efforts in the field, we would not have
been able to complete this study. We apologize to anyone we may
have inadvertently missed who helped during the many years this study
has been ongoing. We would also like to thank the NOAA-National
Marine Fisheries Service for providing much of the funding to sup-
port this research project. Any use of trade, product, or firm names is
for descriptive purposes only and does not imply endorsement by the
U.S. Government. We would like to thank the entire SCDNR Popula-
tion Genetics Laboratory, located in the Hollings Marine Laboratory.
This publication represents contribution #839 from the SCDNR Marine
Resources Research Institute. Possession of tissue samples is permitted
under ESA scientific research permit No. 21858.
Author contributions SLW performed statistical analyses and drafted
the manuscript, DCK, TLD, and DJF designed the study, performed
laboratory analyses, assisted with statistical analyses, and drafted the
manuscript, BAL, RLJ, and MSE located samples and performed labo-
ratory analyses, MTB, HMB, AGF, DAF, CHH, JEK, and JIW contrib-
uted tissue samples and assisted in manuscript preparation.
Funding This study was funded by National Marine Fisheries Service
and United States Army Corps of Engineers.
Data availability Metadata and multilocus genotypes for Atlantic stur-
geon included in the baseline are available at https:// doi. org/ 10. 5066/
P9W46 E5Q.
Declarations
Conflict interest The authors have no conflicts of interests to declare
that are relevant to the content of this article.
Ethical approval Possession of tissue samples is permitted under ESA
scientific research permit No. 21858.
Consent to publish All authors consent to publication.
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