ArticlePDF Available

Selection and sex-biased dispersal in a coastal shark: The influence of philopatry on adaptive variation

Authors:

Abstract and Figures

Sex-biased dispersal is expected to homogenize nuclear genetic variation relative to variation in genetic material inherited through the philopatric sex. When site fidelity occurs across a heterogeneous environment, local selective regimes may alter this pattern. We assessed spatial patterns of variation in nuclear-encoded, single nucleotide polymorphisms (SNPs) and sequences of the mitochondrial control region in bonnethead sharks (Sphyrna tiburo), a species thought to exhibit female philopatry, collected from summer habitats used for gestation. Geographic patterns of mtDNA haplotypes and putatively neutral SNPs confirmed female philopatry and male-mediated gene flow along the northeastern coast of the Gulf of Mexico. A total of 30 outlier SNP loci were identified; alleles at over half of these loci exhibited signatures of latitude-associated selection. Our results indicate that in species with sex-biased dispersal, philopatry can facilitate sorting of locally adaptive variation, with the dispersing sex facilitating movement of potentially adaptive variation among locations and environments. This article is protected by copyright. All rights reserved.
Content may be subject to copyright.
Selection and sex-biased dispersal in a coastal shark: the
influence of philopatry on adaptive variation
D. S. PORTNOY,* J. B. PURITZ,* C. M. HOLLENBECK,* J. GELSLEICHTER,D. CHAPMANand
J. R. GOLD*
*Department of Life Sciences, Marine Genomics Laboratory, Harte Research Institute, Texas A&M University-Corpus Christi,
6300 Ocean Drive, Corpus Christi, TX 78412, USA, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, USA,
Stony Brook University, Stony Brook, NY 11776, USA
Abstract
Sex-biased dispersal is expected to homogenize nuclear genetic variation relative to
variation in genetic material inherited through the philopatric sex. When site fidelity
occurs across a heterogeneous environment, local selective regimes may alter this pattern.
We assessed spatial patterns of variation in nuclear-encoded, single nucleotide polymor-
phisms (SNPs) and sequences of the mitochondrial control region in bonnethead sharks
(Sphyrna tiburo), a species thought to exhibit female philopatry, collected from summer
habitats used for gestation. Geographic patterns of mtDNA haplotypes and putatively
neutral SNPs confirmed female philopatry and male-mediated gene flow along the north-
eastern coast of the Gulf of Mexico. A total of 30 outlier SNP loci were identified; alleles
at over half of these loci exhibited signatures of latitude-associated selection. Our results
indicate that in species with sex-biased dispersal, philopatry can facilitate sorting of
locally adaptive variation, with the dispersing sex facilitating movement of potentially
adaptive variation among locations and environments.
Keywords: elasmobranchs, genome scan, localized adaptation, male-mediated gene flow
Received 13 July 2015; revision received 27 October 2015; accepted 27 October 2015
Introduction
Sex-biased dispersal arises when individuals of one sex
exhibit site fidelity (philopatry), while individuals of
the opposite sex are prone to disperse (Pusey 1987).
This occurs in a wide variety of vertebrate taxa (e.g.
birds, Clarke et al. 1997; mammals, Lawson Handley &
Perrin 2007) and is thought to result from fitness differ-
ences between the sexes associated with local competi-
tion for resources (including mates), inbreeding
avoidance, and/or parental investment (Gandon 1999;
Perrin & Mazalov 2000). There also is a relationship
between mating system and which sex is dispersive;
monogamous species feature territorial males and dis-
persive females, while polygamous species feature
female philopatry and male dispersal (Greenwood
1980).
Dispersal and resulting gene flow acts as a homoge-
nizing force across the genome, opposed by the pro-
cesses of genetic drift and disruptive selection. The
level of gene flow necessary to counteract genetic drift
can be relatively small, as large populations experience
little drift and only a few migrants are required in small
populations (Wright 1931; Slatkin 1985). Disruptive
selection, on the other hand, is capable of generating
divergence in specific genomic regions, even when gene
flow is high, if the strength of selection is high relative
to the number of immigrants and/or the patterns of
immigration are nonrandom in relation to local environ-
mental conditions (Endler 1973; Slatkin 1987; Garant
et al. 2007). Sex-biased dispersal, via gene flow through
the dispersive sex, has a homogenizing effect on bipar-
entally inherited nuclear variation; uniparentally
inherited markers not under disruptive selection (e.g.
heterologous sex chromosomes, mtDNA) sort through
the philopatric sex and may depart from homogeneity
at a greater rate through time (Avise 1994). If habitats of
a philopatric species vary in environmental conditions,
Correspondence: David S. Portnoy, Fax: 361-825-2025; E-mail:
david.portnoy@tamucc.edu
©2015 John Wiley & Sons Ltd
Molecular Ecology (2015) 24, 5877–5885 doi: 10.1111/mec.13441
the homogenizing effects of sex-mediated gene flow
may be counteracted in specific genomic regions if
localized selection leads to increased reproductive suc-
cess for the philopatric sex (Lenormand 2002). Finally,
philopatric behaviour by one of the sexes can reduce
the strength of migration, facilitating local adaptation
(Slatkin 1987).
Studies in several species of live-bearing sharks have
revealed spatial genetic patterns (homogeneity in
nuclear-encoded microsatellites and heterogeneity in
maternally inherited mtDNA) consistent with female
philopatry and male-mediated gene flow (Portnoy &
Heist 2012; Chapman et al. 2015). Females in these spe-
cies exhibit considerable parental investment, giving
birth after long gestation periods to small litters of fully
developed offspring, suggesting that return to a favour-
able habitat could enhance embryonic growth during
gestation (Economakis & Lobel 1998; Driggers et al.
2014) as well as provide predictable access to food and
shelter from predators (Heupel et al. 2007). It also is
known that habitats used by the same species for gesta-
tion and/or parturition may differ substantially, even at
small spatial scales (DiBattista et al. 2007; Feldheim et al.
2014). Based on the above, coastal philopatric sharks
represent a good model system to assess possible effects
that localized adaptation may have on genomewide pat-
terns of variation in the context of sex-asymmetric gene
flow.
We assessed spatial patterns of variation in nuclear-
encoded single nucleotide polymorphisms (SNPs) and
sequences of the mitochondrial control region in bon-
nethead sharks (Sphyrna tiburo), a species thought to
exhibit female-biased philopatry (Driggers et al. 2014).
Bonnetheads are common seasonal residents in coastal
and estuarine waters of the western Atlantic Ocean
(Atlantic), including the Gulf of Mexico (Gulf), and are
known to use nearshore habitat for gestation and partu-
rition (Compagno 1984; Driggers et al. 2014). Bonnet-
heads in the Atlantic and Gulf migrate seasonally, and
a variety of life stages are commonly found in bays,
estuaries, and nearshore waters from May to November
(Cortes et al. 1996; Ulrich et al. 2007). The species has a
short gestation period of 45 months (Parsons 1993),
with parturition occurring in the late summer to early
fall and mating occurring shortly thereafter (Manire &
Rasmussen 1997; Ulrich et al. 2007). Sperm storage is
necessary, as ovulation does not occur until spring
(Manire et al. 1995). Bonnetheads mature between
17 years (Lombardi-Carlson et al. 2003; Frazier et al.
2014) and females give birth to 214 (avg. ~9) fully
developed pups (Frazier et al. 2013). Unlike other
coastal sharks, the observed migratory behaviour does
not appear to be associated with the use of nursery
areas (Heupel et al. 2007), but instead may be related to
increasing food availability for gestating females and
gaining access to potential mates for males (Driggers
et al. 2014). Significant differences in life history among
bonnetheads across small geographic regions have been
documented in several studies; differences found
between samples from the eastern Gulf include size at
age, growth rate, and size and age at maturity (Parsons
1993; Carlson & Parsons 1997; Lombardi-Carlson et al.
2003). In addition, studies in both the Atlantic and east-
ern Gulf have shown site fidelity by adult bonnetheads
(particularly females) to particular estuaries or bays
during the summer months, on intra- and interannual
timescales (Heupel et al. 2006; Driggers et al. 2014).
We sampled adult and subadult animals from three
localities along the west coast of Florida (eastern Gulf
of Mexico) and one locality off the coast of North Caro-
lina (western Atlantic Ocean). Sample localities in the
Gulf were selected because of identified latitudinal dif-
ferences in life history parameters among bonnetheads
in the region (Lombardi-Carlson et al. 2003); the sample
from the Atlantic was included to have a sample out-
side the Gulf and because of identified differences in
life history between bonnetheads in the Gulf and Atlan-
tic (Frazier et al. 2014). We used a ddRAD approach
(Peterson et al. 2012) to genotype individuals at thou-
sands of nuclear-encoded SNPs, permitting a search for
spatial differences in genomic regions putatively under
selection; inclusion of putatively neutral SNPs and
mtDNA sequences allowed us to assess further whether
dispersal in bonnetheads is sex-biased.
Materials and methods
Tissues (fin clips) from 134 bonnetheads sampled
between 1998 and 2000 from four nearshore localities
(Fig. 1) were used in the study. Samples were obtained
during the summer months (May to September) when
mature individuals are in areas used for gestation, par-
turition, and mating. Individuals sampled were mostly
a mix of mature females and males.
Double-digest RAD (ddRAD) libraries were prepared
following Peterson et al. (2012); details of the protocol
may be found in the Appendix S1 (Supporting informa-
tion). Libraries were sequenced on two lanes of an Illu-
mina HiSeq 2000 DNA sequencer. The first library was
sequenced as a paired-end run for reference contig
assembly in order to facilitate downstream bioinformat-
ics inference. The second library was sequenced as a
single-end run, as a cost-effective manner to genotype
SNPs. The dDocent pipeline (Puritz et al. 2014) was used
for reference contig assembly, read mapping, and SNP
genotyping. Default parameters were used for each
step, with the exception of contig assembly, where a
customized script was used to mitigate the high levels
©2015 John Wiley & Sons Ltd
5878 D. S. PORTNOY ET AL.
of repeats and duplications expected in large genomes.
The initial set of data consisted of 648 035 variant SNP
loci across 147 920 fragments.
The entire mitochondrial control region (1134 bp) was
amplified using primers Pro-L and 282H (Keeney et al.
2003); details of the protocol may be found in the
Appendix S1 (Supporting information). Electrophore-
tograms were examined by eye, aided by GENEIOUS v.7.1
(Biomatters Ltd.); all sequences were trimmed to
1064 bp due to occasional nonspecific amplification on
the 30end that made accurate base calling difficult.
Single nucleotide polymorphisms were extensively fil-
tered before further analysis. The initial raw data set
was filtered to remove all genotypes with <5 reads per
individual and loci called in <75% of all individuals.
Consequently, only the top 90% of individuals in geno-
type call rate were retained. The resulting data set con-
tained 121 individuals. SNPs were then filtered to meet
the following criteria: presence in 97.5% of individuals
across the data set, minor allele frequency >5% across
the data set, and conformance to expectations of
HardyWeinberg equilibrium (HWE). Additional
parameters considered during filtering included allele
balance within heterozygous individuals, SNP quality
to depth ratio, percentage of contribution from forward
and reverse reads, maximum mean read depth across
individuals, and removal of possible paralogs (Details
on SNP filtering are described in Appendix S1, Sup-
porting information). The final, filtered data set con-
sisted of 5914 SNPs spread across 3967 fragments.
Genetic diversity (nuclear genome) within each locality
was assessed as the mean nucleotide diversity (p) across
all SNPs, using VCFTOOLS (Danecek et al. 2011). Homo-
geneity of pacross localities was assessed using analysis
of variance (ANOVA) and TukeyKramer HSD indepen-
dent contrasts as implemented in JMP
â
v.11 (SAS Institute
Inc.). Genetic diversity (mtDNA) was assessed as mean
nucleon (h) and nucleotide diversity (p) within each
0
0.25
0.5
0.75
1
PC
TB
FB
NC
(A)
(B)
(C)
(D)
36
31
31
22
80° W86° W
27° N 32° N
Fig. 1 Samples of bonnethead sharks obtained off North Carolina (NC, blue), Florida Bay (FB, red; 18 males, 13 females), Tampa Bay
(TB, orange; 17 males, 14 females) and Panama City (PC, yellow; 15 males, 21 females). Results of discriminant analysis of principle
components for (A) putatively neutral N-SNP loci, (B) outlier O-SNP loci putatively under selection, with group membership defined
by sample locality, and (C) outlier O-SNP loci putatively under selection, with group membership based on k-means clustering.
Females are coded as circles, males as triangles, and individuals of unknown sex as squares. Representative allele frequencies (D) of
three O-SNP loci (left to right, E66074, E109425, E106435) that contributed ~24% to the distribution of individuals along the X-axis.
Colours represent sample locations for all figures. SNP, single nucleotide polymorphism.
©2015 John Wiley & Sons Ltd
PHILOPATRY AND ADAPTIVE VARIATION 5879
locality, using ARLEQUIN v.3.5.1.2 (Excoffier & Lischer
2010).
Relatedness of individuals within each locality was
assessed in VCFTOOLS, using the statistic developed by
Yang et al. (2010). Two individuals in the sample from
Florida Bay (FB) possessed high relatedness to each other
(0.61) relative to the average relatedness (0.045) across
all individuals, suggesting these two individuals shared
parents. The individual with more missing data was
removed from subsequent SNP-based analyses to avoid
possible issues with consanguinity. SNPs were then orga-
nized into haplotypes (loci), using a custom Perl script
that produces output in GENEPOP format. During haplo-
typing, a total of 23 loci were excluded from further anal-
ysis; 12 were identified as possible paralogs and 11 could
not be haplotyped in more than 90% of individuals
assayed. GENEPOP files were converted to BAYESCAN format,
using PGDSPIDER v.2.0.7 (Lischer & Excoffier 2012), and
BAYESCAN (Foll & Gaggiotti 2008) was used to identify
individual outlier loci by assessing fit to different models
of selection. The program was run with all default values,
with the exception of 30 pilot runs and a thinning interval
of 50; significance of outlier loci was determined using a
q-value that directly corresponded to a false discovery
rate (FDR) of 0.05. Loci were then divided into two sets:
one that contained putatively neutral SNPs (N-SNP loci)
and one that contained outlier SNPs (O-SNP loci) puta-
tively under selection.
Geographic homogeneity among localities in N-SNP
and O-SNP loci was tested using single-level analysis of
molecular variance (AMOVA), as implemented in GENODIVE
V.2.0 (Meirmans & van Tienderen 2004). Pairwise F
ST
values (both nuclear data sets) were estimated using
GENODIVE; significance of pairwise F
ST
values was
assessed by permuting individuals between samples
10 000 times. Homogeneity of mtDNA haplotypes
among localities was tested using single-level AMOVA,as
implemented in ARLEQUIN. Distances were calculated
using a Kimura 2-parameter model (Kimura 1980), as
selected by JMODELTEST v. 2.1.4 (Guindon & Gascuel
2003; Darriba et al. 2012). Pairwise Φ
ST
values were esti-
mated using ARLEQUIN, with significance determined by
permuting individuals between samples 10 000 times.
Correction for multiple testing was implemented using
the FDR procedure (Benjamini & Hochberg 1995).
Discriminant Analysis of Principle Components
(DAPC; Jombart et al. 2010) was carried out on both
N-SNP and O-SNP loci, using the ADEGENET package
(Jombart & Ahmed 2011) in Rv.3.0.2 (R Development
Core Team 2013), with group membership defined by
locality. DAPC also was carried out on O-SNP loci,
with group membership inferred using k-means cluster-
ing (MacQueen 1967); contribution of O-SNP loci to
genetic clustering was then inferred from loading vari-
ables used in each discriminant function. For all O-SNP
loci, the reference contig, assembled from paired-end
reads, was screened against the NCBI nucleotide-read
database, using the BLASTN algorithm (Altschul et al.
1990). The top three hits with E-values <0.01 were
recorded.
Results
Summary statistics for SNPs and mtDNA are given in
Table S1 (Supporting information); GenBank accession
numbers and geographic distribution of mtDNA haplo-
types are given in Table S2 (Supporting information).
Estimated mean nucleotide diversity (p) across all SNP
loci per sample (SE) varied from 0.296 (0.002) in the
sample from North Carolina (NC) to 0.319 (0.002) in
the sample from FB. Mean estimates of pdiffered sig-
nificantly across samples (F
3
=21.483, P<0.001), with
mean pin NC being significantly lower than in the
other samples (TukeyKramer HSDP<0.001). The
same pattern was observed in haplotype diversity of
mtDNA sequences; estimated diversity was lower in
NC (h=0.719 0.077), while hvalues did not differ
among the other three samples.
A total of 30 haplotypes, containing 49 O-SNPs, were
identified as candidate loci under selection (q<0.05);
the remaining SNPs (5865 scattered across 3910 haplo-
types) were consistent with a neutral model. A total of
72 alleles were identified among the 30 O-SNP loci; 21
loci were bi-allelic, while nine were multi-allelic
(Table S3, Supporting information). Significant hetero-
geneity among all four localities in all three marker
types was detected by AMOVA (Table S4, Supporting
information); the proportion of the total genetic vari-
ance explained by geography (locality) was 0.79%
(N-SNP loci), 7.77% (mtDNA haplotypes), and 27.07%
(O-SNP loci). Pairwise estimates of F
ST
and Ф
ST
(Table 1) revealed differences among the three marker
types. For N-SNP loci, allele frequencies in NC differed
significantly from those in FB, TB (Tampa Bay), and PC
(Panama City); allele frequencies in the latter three were
homogeneous. For mtDNA, the haplotype distribution
in NC differed significantly from those in FB, TB, and
PC; estimates of Ф
ST
between FB and PC differed signif-
icantly from one another, while those between FB and
TB and TB and PC were homogeneous. Allele frequen-
cies of O-SNP loci in both NC and PC differed signifi-
cantly from one another and from those in FB and TB,
while allele frequencies in FB and TB were homoge-
neous. Significant heterogeneity among the three locali-
ties in the Gulf also was detected by AMOVA for mtDNA
haplotypes (F
ST
=0.027, P=0.033) and O-SNP loci
(F
ST
=0.157, P=0.000), but not for N-SNP loci
(F
ST
=0.0003, P=0.151).
©2015 John Wiley & Sons Ltd
5880 D. S. PORTNOY ET AL.
Analysis of N-SNP loci, using DAPC and with prior
group membership defined by locality, revealed two
distinct clusters along the primary (X) axis (Fig. 1A);
one was comprised of individuals from NC, while the
other contained individuals from the three localities in
the Gulf. Analysis of O-SNP loci, with prior group
membership defined by locality, revealed a different
pattern along the primary axis (Fig. 1B). Twelve
individuals from PC clustered with individuals in the
sample from NC, while the remaining individuals
formed a second cluster; both clusters were more
diffuse than in the analysis of N-SNP loci. When prior
group membership of O-SNP loci was inferred using k-
means clustering, three distinct clusters were revealed
in DAPC analysis (Fig. 1C). One cluster contained pri-
marily individuals from NC and PC and one individual
from TB; one cluster contained individuals from the
Gulf, primarily from PC; and one cluster contained
mostly individuals from FB and TB and one individual
from PC. The primary (X) axis described 99.6% of the
variance. Allele frequencies at three representative O-
SNP loci (Fig. 1D) clearly reveal a clinal, northsouth
(latitudinal) pattern in allele frequencies. The correla-
tion between allele (haplotype) frequencies at each O-
SNP locus and latitude was then evaluated using stan-
dard least squares regression as implemented in JMP
v.11. Alleles at 17 O-SNP loci were correlated (P0.05)
with latitude and explained 56.9% of the variation
along the primary axis, while 18 O-SNP loci had r
2
val-
ues 0.90 and explained 75.6% of the variation along
the X.
Eight of the 30 O-SNP loci had no sequence counter-
part in GenBank; the remaining 22 were highly similar
(E-value <0.01) to several DNA sequences (Table S5,
Supporting information). Frequent ‘hits’ included
sequence similarities to clones or contigs in other spe-
cies, and to annotated genomic regions of known
immune response proteins (e.g. cytokines MIP-3 and
interleukin-1band a T cell receptor), putative regulatory
elements (e.g. zinc-finger proteins, Hox genes), and
SINE-type sequences.
Discussion
The significant difference in N-SNP loci between bon-
netheads from the Atlantic and Gulf indicates geneti-
cally distinct populations with little to no gene flow
between the two regions. This geographic pattern has
been observed in other marine taxa (Avise 1992; Gold &
Richardson 1998; Gold et al. 2009) including coastal
sharks (Portnoy et al. 2014) and supports results from a
recent mtDNA assessment of population structure in
the bonnethead (Escatel-Luna et al. 2015). This pattern
is hypothesized to stem from biogeographic processes
associated with the Florida Current and/or narrowing
of the continental shelf in southeastern Florida (Portnoy
et al. 2014). The absence of significant divergence in
N-SNP loci among the three localities in the Gulf is con-
sistent with gene flow occurring between the Florida
Keys (FB) and north-central Florida (PC).
Asymmetry in geographic patterns of variation
between N-SNP loci (homogeneous) and mtDNA haplo-
types (heterogeneous) among bonnetheads from the
Gulf is consistent with female philopatry and male-
biased dispersal (Melnick & Hoelzer 1992). Because
mtDNA is haploid and uniparentally inherited, a
greater magnitude of divergence at mtDNA compared
to nuclear loci is to be expected (Birky 2001). Similar
patterns are documented in several shark species (Port-
noy & Heist 2012; Chapman et al. 2015) and interannual
tag-and-recapture studies of bonnetheads (Driggers
et al. 2014) demonstrate strong site fidelity of females to
specific estuaries. The pattern of mtDNA haplotype
variation among bonnetheads in the Gulf indicates an
isolation-by-distance effect rather than complete isola-
tion as mtDNA haplotypes in the intermediate sample
locality (TB) did not differ significantly from those in
sample localities (PC and FB) at the geographic
extremes. This also suggests that female bonnetheads
may stray from preferred localities but most likely to
neighbouring ones.
The largest proportion of the genetic variance explained
by locality (geography) was due to O-SNP loci. In theory,
Table 1 Below diagonal: pairwise F
ST
values for putatively neutral SNP loci (N-SNP) and for outlier SNP loci putatively under
selection (O-SNP), and pairwise Φ
ST
values for mtDNA haplotypes (mtDNA), between samples of bonnetheads obtained off North
Carolina (NC), Florida Bay (FB), Tampa Bay (TB) and Panama City (PC). Above diagonal: probability (P) values; those significant
after correction for multiple comparisons are given in bold
N-SNP O-SNP mtDNA
NC FB TB PC NC FB TB PC NC FB TB PC
NC <0.001 <0.001 <0.001 NC <0.001 <0.001 <0.001 NC <0.001 0.001 0.014
FB 0.019 0.317 0.038 FB 0.543 0.382 <0.001 FB 0.234 0.158 0.011
TB 0.021 0.000 0.344 TB 0.462 0.000 <0.001 TB 0.161 0.014 0.406
PC 0.021 0.001 0.000 PC 0.180 0.244 0.177 PC 0.064 0.055 0.000
©2015 John Wiley & Sons Ltd
PHILOPATRY AND ADAPTIVE VARIATION 5881
outlier loci can reflect genomic regions associated with
local adaptive differences (Nielsen et al. 2009; Allendorf
et al. 2010) or genomic regions that have diverged more
than expected over time via a nonadaptive process such
as genetic drift (Hedrick 2011). However, genetic drift is a
genomewide effect (Luikart et al. 2003) and the significant
correlations between allele frequencies at O-SNP loci and
latitude and the complete absence of any clinal pattern in
N-SNP loci indicate that the observed geographic pattern
of O-SNP loci stems from localized divergent selection.
The greater similarity in allele frequencies at outlier
O-SNP loci between PC and NC also supports divergent
selection associated with latitude as the two localities are
situated at more northerly latitudes yet are at the geo-
graphic extreme of possible (homogenizing) gene flow
among the localities studied.
Signatures of latitude-driven selection are common
given that natural phenomena (e.g. climate, diurnal
cycle) impact distributions of biological organisms, and
that selection is imposed by the local biotic environment
and interactions between a focal population and other
organisms (Kawecki & Ebert 2004). Examples of well-
known latitude-specific effects on marine fish include
demographic traits such as growth rate (Conover & Pre-
sent 1990) and hostparasite/pathogen systems (Poulin
& Morand 2000). A few of the O-SNP loci found in this
study did have sequence similarities to regions of genes
putatively involved in regulation and development, and
there are significant latitudinal differences in growth rate
and size at age among bonnetheads in the region of the
Gulf sampled (Lombardi-Carlson et al. 2003). A larger
proportion of the O-SNP loci had sequence similarities to
regions of genes involved in immune response. This
result might reflect latitudinal variation in parasite infec-
tivity (Poulin & Morand 2000) and increased infectivity
of parasites to sympatric hosts rather than allopatric
hosts of the same species (Morand et al. 1996). Some cau-
tion in interpreting these data, however, is advisable, in
part because the O-SNP loci sequences were small in size,
and in part because the majority of SNPs recovered using
a ddRAD approach are not within protein-coding genes
(Baxter et al. 2011). Further, while we found a general
correlation of allele frequencies with latitude for O-SNPs,
this does not demonstrate causation as other factors may
be equally or more important. As an example, the spatial
sampling encompasses both the warm-temperate and
tropical provinces along the Florida coast, and differ-
ences in allele frequencies could reflect differences in
ecology and climate.
Occurrence of philopatry in association with a non-
random pattern of geographic variation in small geno-
mic regions was reported recently (Stiebens et al. 2013)
in a study of variation in MHC alleles among philopa-
tric loggerhead turtles in the Cape Verde Archipelago.
Both mtDNA haplotypes and MHC alleles were struc-
tured genetically among nesting islands, but only
nuclear-encoded microsatellites followed a geographic
pattern, in this case one of isolation by distance indica-
tive of restricted male dispersal. In our study, only
females appeared structured geographically along the
western coast of Florida. These and tagging data
(Driggers et al. 2014) where >95% of interannual bonnet-
head returns to the same estuary were female indicate
that bonnethead males are less philopatric than females,
and that maintenance of localized adaptive alleles in
bonnetheads may occur through female matrilines.
Thus, selection and sex-specific philopatry can interact
to sort adaptive nuclear alleles across geographic space.
Association of spatially discrete matrilines and local-
ized genomic regions under selection suggest that
female genotype and philopatry to gestational areas
may increase offspring fitness as a maternal effect
(Mousseau & Fox 1998; Badyaev & Uller 2009). This is
consistent with a review of parental effects in species
with sex-asymmetric dispersal and a model that
showed that selective pressure to develop locally adap-
tive parental effects is high when dispersal is sex-biased
(Revardel et al. 2010). Unfortunately, studies of parental
effects in sharks are limited (Hussey et al. 2010) despite
a female reproductive biology (long gestation, live
birth) in several species that suggests occurrence of
important maternal effects.
Sex-specific philopatry reduces overall dispersal and
consequently may redistribute genetic diversity among
rather than within subpopulations or demes. In bonnet-
head sharks, homogeneity of N-SNP loci across geo-
graphic localities within the Gulf demonstrates that
genetic diversity was partitioned equally within and
among demes, indicating that extensive male dispersal
was enough to overcome drift processes. In contrast,
strong differentiation at a small subset of nuclear genes
among samples collected at gestational areas indicates
that localized selection was sufficiently strong to out-
weigh the homogenizing force of dispersal and gene
flow. Thus, while female philopatry in bonnethead
sharks may promote maintenance of adaptive alleles in
specific localities, gene flow mediated by males or stray-
ing females could move potentially adaptive variation
among environments (Slatkin 1987; Garant et al. 2007).
Given local environmental heterogeneity on larger tem-
poral scales, the maintenance and movement of poten-
tially adaptive variation across the landscape likely
facilitates species persistence (Bowen & Roman 2005).
Acknowledgements
We thank C.A. Manire for providing several samples from
Florida and P. Bentzen and two anonymous reviewers for
©2015 John Wiley & Sons Ltd
5882 D. S. PORTNOY ET AL.
helpful comments and suggestions. This work was supported
by funds provided by the College of Science and Engineering
at Texas A&M University-Corpus Christi and by the Environ-
mental Protection Agency (Award #R826128-01-0 to C.A. Man-
ire). Although the research described in this article was
supported in part by the United States Environmental Protec-
tion Agency, it has not been subjected to the agency’s required
peer and policy review and therefore does not necessarily
reflect the views of the Agency and no official endorsement
should be inferred. Field sampling in Florida was conducted
under research permits issued by the Florida Fish and Wildlife
Conservation Commission to Mote Marine Laboratory. This
article is publication number 10 of the Marine Genomics
Laboratory at Texas A&M University-Corpus Christi and
number 104 in the series Genetic Studies in Marine Fishes.
References
Allendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and
the future of conservation genetics. Nature Reviews Genetics,
111, 697709.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990)
Basic local alignment search tool. Journal of Molecular Biology,
215, 403410.
Avise JC (1992) Molecular population structure and the biogeo-
graphic history of a regional fauna: a case history with les-
sons for conservation biology. Oikos,63,6276.
Avise JC (1994) Molecular Markers, Natural History and Evolu-
tion. Chapman & Hall, Inc., New York, New York.
Badyaev AV, Uller T (2009) Parental effects in ecology and
evolution: mechanisms, processes and implications. Philo-
sophical Transactions of the Royal Society of London B: Biological
Sciences,364, 11691177.
Baxter SW, Davey JW, Johnston JS et al. (2011) Linkage map-
ping and comparative genomics using next-generation RAD
sequencing of a non-model organism. PLoS ONE,6, e19315.
Benjamini Y, Hochberg Y (1995) Controlling the false discovery
rate: a practical and powerful approach to multiple testing.
Journal of the Royal Statistical Society B (Statistical Methodol-
ogy),57, 289300.
Birky CWJR (2001) The inheritance of genes in mitochondria
and chloroplasts: laws, mechanisms, and models. Annual
Review of Genetics,35, 125148.
Bowen BW, Roman J (2005) Gaia’s handmaidens: the Orlog
model for conservation biology. Conservation Biology,19,
10371043.
Carlson JK, Parsons GR (1997) Age and growth of the bonnet-
head shark, Sphyrna tiburo, from northwest Florida, with
comments on clinal variation. Environmental Biology of Fishes,
50, 331341.
Chapman DC, Feldheim KA, Papastamatiou YP, Hueter RE
(2015) There and back again: a review of residency and
return migrations in sharks, with implications for population
structure and management. Annual Review of Marine Science,
7, 547570.
Clarke AL, Sæther B-E, Røskaft E (1997) Sex biases in avian
dispersal: a reappraisal. Oikos,79, 429438.
Compagno LJV (1984) FAO Species Catalogue. Vol. 4. Sharks
of the world. An annotated and illustrated catalogue of
shark species known to date. Part 2Carcharhiniformes.
FAO Fisheries Synopsis,125, 251655.
Conover DO, Present TMC (1990) Countergradient variation in
growth rate: compensation for length of the growing season
among Atlantic silversides from different latitudes. Oecologia,
83, 316324.
Cortes E, Manire CA, Hueter RE (1996) Diet, feeding habits
and diel feeding chronology of the bonnethead shark,
Sphyrna tiburo, in southwest Florida. Bulletin of Marine
Science,58, 353367.
Danecek P, Auton A, Abecasis G et al. (2011) The variant call
format and VCFtools. Bioinformatics,27, 21562158.
Darriba D, Taboada GL, Doallo R, Posada D (2012) jMODELTEST
2: more models, new heuristics and parallel computing. Nat-
ure Methods,9, 772.
DiBattista JD, Feldheim KA, Gruber SH, Hendry AP (2007)
When bigger is not better: selection against large size, high
condition and fast growth in juvenile lemon sharks. Journal
of Evolutionary Biology,20, 201212.
Driggers WB III, Frazier BS, Adams DH et al. (2014) Site fide-
lity of migratory bonnethead sharks Sphyrna tiburo (L. 1758)
to specific estuaries in South Carolina, USA. Journal of Experi-
mental Marine Biology and Ecology,459,6169.
Economakis AE, Lobel PS (1998) Aggregation behavior of the
grey reef shark, Carcharhinus amblyrhynchos, at Johnston
Atoll, Central Pacific Ocean. Environmental Biology of Fishes,
51, 129139.
Endler JA (1973) Gene flow and population differentiation.
Science,19, 243250.
Escatel-Luna E, Adams DH, Uribe-Alcocer M, Islas-Villanueva
V, D
ıaz-Jaimes P (2015) Population genetic structure of the
bonnethead shark, Sphyrna tiburo, from the western North
Atlantic Ocean based on mtDNA sequences. Journal of Hered-
ity,106, 355365.
Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new
series of programs to perform population genetics analyses
under Linux and Windows. Molecular Ecology Resources,10,
564567.
Feldheim KA, Gruber SH, DiBattista JD et al. (2014) Two dec-
ades of genetic profiling yields first evidence of natal
philopatry and long-term fidelity to parturition sites in
sharks. Molecular Ecology,23, 110117.
Foll M, Gaggiotti O (2008) A genome-scan method to iden-
tify selected loci appropriate for both dominant and
codominant markers: a Bayesian perspective. Genetics,180,
977993.
Frazier B, Gelsleichter J, Gonzalez De Acevedo M (2013) Prelimi-
nary data on the reproductive biology of the bonnethead (Sphyrna
tiburo) from the southeast U.S. Atlantic coast. SEDAR34-WP-22.
SEDAR, Charleston, South Carolina, pp. 11.
Frazier BS, Driggers WB III, Adams DH, Jones CM, Loefer JK
(2014) Validated age, growth and maturity of the bonnethead
Sphyrna tiburo in the western North Atlantic Ocean. Journal of
Fish Biology,85, 688712.
Gandon S (1999) Kin competition the cost of inbreeding and
the evolution of dispersal. Journal of Theoretical Biology,200,
345364.
Garant D, Forde SE, Hendry AP (2007) The multifarious effects
of dispersal and gene flow on contemporary adaptation.
Functional Ecology,21, 434443.
Gold JR, Richardson LR (1998) Population structure in greater
amberjack, Seriola dumerili, from the Gulf of Mexico and the
western Atlantic Ocean. Fisheries Bulletin,96, 767778.
©2015 John Wiley & Sons Ltd
PHILOPATRY AND ADAPTIVE VARIATION 5883
Gold JR, Saillant E, Ebelt ND, Lem S (2009) Conservation
genetics of gray snapper (Lutjanus griseus) in U.S. waters of
the northern Gulf of Mexico and western Atlantic Ocean.
Copeia,2009, 277286.
Greenwood PJ (1980) Mating systems, philopatry and dispersal
in birds and mammals. Animal Behaviour,28, 11401162.
Guindon S, Gascuel O (2003) A simple, fast, and accurate algo-
rithm to estimate large phylogenies by maximum likelihood.
Systematic Biology,52, 696704.
Hedrick PW (2011) Genetics of Populations. Jones and Bartlett,
Sudbury, Massachusetts.
Heupel MR, Simpfendorfer CA, Collins AB, Tyminski JP (2006)
Residency and movement patterns of bonnethead sharks,
Sphyrna tiburo, in a large Florida estuary. Environmental Biol-
ogy of Fishes,76,4767.
Heupel MR, Carlson JK, Simpfendorfer CA (2007) Shark nurs-
ery areas: concepts, definition, characterization and assump-
tions. Marine Ecology Progress Series,337, 287297.
Hussey NE, Wintner SP, Dudley SFJ, Cliff G, Cocks DT, Mac-
Neil MA (2010) Maternal investment and size-specific repro-
ductive output in carcharhinid sharks. Journal of Animal
Ecology,79, 184193.
Jombart TS, Ahmed I (2011) ADEGENET 1.3-1: new tools for the
analysis of genome-wide SNP data. Bioinformatics,27,3070
3071.
Jombart T, Devillard S, Balloux F (2010) Discriminant analysis
of principal components: a new method for the analysis of
genetically structured populations. BMC Genetics,11, 94.
Kawecki TJ, Ebert D (2004) Conceptual issues in local adapta-
tion. Ecology Letters,7, 12251241.
Keeney D, Heupel MR, Heuter RE, Heist EJ (2003) Genetic
heterogeneity among blacktip shark, Carcharhinus limbatus,
continental nurseries along the U.S. Atlantic and Gulf of
Mexico. Marine Biology,143, 10391046.
Kimura M (1980) A simple method for estimating evolutionary
rates of base substitutions through comparative studies of
nucleotide sequences. Journal of Molecular Evolution,16,111120.
Lawson Handley LJ, Perrin N (2007) Advances in our under-
standing of mammalian sex-biased dispersal. Molecular Ecol-
ogy,2007, 15591578.
Lenormand T (2002) Gene flow and the limits to natural selec-
tion. Trends in Ecology and Evolution,17, 183189.
Lischer HEL, Excoffier L (2012) PGDSpider: ab automated data
conversion tool for connecting population genetics and geno-
mics programs. Bioinformatics,28, 298299.
Lombardi-Carlson LA, Cort
es E, Parsons GR, Manire CA
(2003) Latitudinal variation in life-history traits of bonnet-
head sharks, Sphyrna tiburo (Carcharhiniformes: Sphyrnidae)
from the eastern Gulf of Mexico. Marine and Freshwater
Research,54, 875883.
Luikart G, England PR, Tallmon D, Jordan S, Taberlet P (2003)
The power and promise of population genomics: from geno-
typing to genome typing. Nature Reviews Genetics,4, 981994.
MacQueen JB (1967) Some methods for classification and analy-
sis of multivariate observations. Proceedings of the 5th Berkeley
Symposium on Mathematical Statistics and Probability,1, 281297.
Manire CA, Rasmussen LE (1997) Serum concentrations of ster-
oid hormones in the mature male bonnethead shark, Sphyrna
tiburo.General and Comparative Endocrinology,107, 414420.
Manire CA, Rasmussen EL, Hess DL, Hueter RE (1995) Serum
steroid hormones and the cycle of the female bonnethead
shark, Sphyrna tiburo.General and Comparative Endocrinology,
97, 366376.
Meirmans PG, van Tienderen PH (2004) GENOTYPE and GEN-
ODIVE: two programs for the analysis of genetic diversity of
asexual organisms. Molecular Ecology Notes,4, 792794.
Melnick DJ, Hoelzer GA (1992) Differences in male and female
macaque dispersal lead to contrasting distributions of
nuclear and mitochondrial DNA variation. International Jour-
nal of Primatology,13, 379393.
Morand S, Manning SD, Woolhouse MEJ (1996) Parasite-host
coevolution and geographic patterns of parasite infectivity
and host susceptibility. Proceedings of the Royal Society of
London B: Biological Sciences,263, 119128.
Mousseau TA, Fox CW (1998) The adaptive significance of
maternal effects. Trends in Ecology and Evolution,13, 403406.
Nielsen EE, Hemmer-Hansen J, Larsen PF, Bekkevold D
(2009) Population genomics of marine fishes: identifying
adaptive variation in space and time. Molecular Ecology,18,
31283150.
Parsons GR (1993) Geographic variation in reproduction
between two populations of the bonnethead shark, Sphyrna
tiburo.Environmental Biology of Fishes,38,2535.
Perrin N, Mazalov V (2000) Local competition, inbreeding and
the evolution of sex-biased dispersal. The American Naturalist,
155, 116127.
Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE
(2012) Double digest RADseq: an inexpensive method for de
novo SNP discovery and genotyping in model and non-
model species. PLoS ONE,7, e37135.
Portnoy DS, Heist EJ (2012) Molecular markers: progress and
prospects for understanding reproductive ecology in elasmo-
branchs. Journal of Fish Biology,80, 11201140.
Portnoy DS, Hollenbeck CM, Belcher CN et al. (2014) Contem-
porary population structure and post-glacial genetic demog-
raphy in a migratory marine species, the blacknose shark,
Carcharhinus acronotus.Molecular Ecology,23, 54805495.
Poulin R, Morand S (2000) The diversity of parasites. Quarterly
Review of Biology,75, 277293.
Puritz JB, Hollenbeck CM, Gold JR (2014) DDOCENT: a RADseq,
variant-calling pipeline designed for population genomics of
non-model organisms. PeerJ,2, e431.
Pusey AE (1987) Sex-biased dispersal and inbreeding avoid-
ance in birds and mammals. Trends in Ecology and Evolution,
2, 295299.
R Development Core Team (2013) R: A Language and Environ-
ment for Statistical Computing. R Foundation for Statistical
Computing, Vienna, Austria. Available from http://www.R-
project.org.
Revardel E, Franc A, Petit RJ (2010) Sex-biased dispersal promotes
adaptive parental effects. BMC Evolutionary Biology,10,217.
Slatkin M (1985) Gene flow in natural populations. Annual
Review of Ecology and Systematics,16, 393430.
Slatkin M (1987) Gene flow and the geographic structure of
natural populations. Science,236, 787792.
Stiebens VA, Merino SE, Roder C, Chain FJJ, Lee PLM,
Eizaguirre C (2013) Living on the edge: how philopatry
maintains adaptive potential. Proceedings of the Royal Society
of London B: Biological Sciences,280, 20130305.
Ulrich G, Jones CM, Driggers WB III, Drymon M, Oakley D,
Riley C (2007) Habitat utilization, relative abundance and
seasonality of sharks in the estuarine and near shore waters
©2015 John Wiley & Sons Ltd
5884 D. S. PORTNOY ET AL.
of South Carolina. American Fisheries Society Symposium,50,
125139.
Wright S (1931) Evolution in Mendelian populations. Genetics,
16,97159.
Yang J, Benyamin B, McEvoy BP et al. (2010) Common SNPs
explain a large proportion of the heritability for human
height. Nature Genetics,42, 565569.
D.S.P., J.B.P. and J.R.G. had responsibility for data col-
lection and analysis and primary responsibility for writ-
ing of the manuscript. All other authors have reviewed
and contributed to the current version of the manu-
script. C.M.H. participated in data collection and
analysis, and J.G. and D.C. obtained the samples.
Data accessibility
GenBank accession numbers for mtDNA sequences may
be found in Table S2 (Supporting information). Demulti-
plexed, raw sequencing reads: Short Read Archive (Bio-
project accession #PRJNA286089). The final SNP data set,
in VCF format, the neutral and outlier haplotype data
sets, in GENEPOP format, and a script to reproduce bioin-
formatic filtering: Dryad doi:10.5061/dryad.7k4c1.
Supporting information
Additional supporting information may be found in the online ver-
sion of this article.
Table S1 Summary of diversity statistics for 5914 SNPs and
sequences (1064 base pairs) of the mitochondrial control region
for samples of bonnetheads from North Carolina (NC) and
three localities along the Gulf Coast of Florida: Florida Bay
(FB), Tampa Bay (TB), and Panama City (PC).
Table S2 Distribution of haplotypes and GenBank Accession
numbers for mitochondrial control region sequences from sam-
ples of bonnetheads off North Carolina (NC) and three loca-
tions along the Gulf Coast of Florida: Florida Bay (FB), Tampa
Bay (TB) and Panama City (PC).
Table S3 Results of standard least squares regression of allele
frequencies at outlier loci by latitude: loci are organized as bi-
allelic and multi-allelic.
Table S4 Results of analysis of molecular variance (AMOVA) for
all three data sets.
Table S5 Results of BLAST search for sequence similarity of SNP
containing loci.
Appendix S1 Methods.
©2015 John Wiley & Sons Ltd
PHILOPATRY AND ADAPTIVE VARIATION 5885
... Although these distinct genetic lineages occasionally hybridize, they should be managed as separate stocks with unique management needs, especially since the eastern Pacific lineage may have been extirpated from the Gulf of California in recent years (Pérez-Jiménez, 2014). Bonnethead populations also exhibit genetic divergence across the Atlantic and Gulf coasts of Florida (Díaz-Jaimes et al., 2021;Escatel-Luna et al., 2015;Fields et al., 2016;Portnoy et al., 2015). However, while migration in other hammerhead species is well-studied (Gallagher & Klimley, 2018), little is known about migration in bonnethead sharks. ...
... One study of bonnethead populations from South Carolina recaptured individuals in the winter as far south as Florida , suggesting the possibility of a southward migration that aligns with that seen in other shark species (Ashe et al., 2015;Dimens et al., 2019;Kajiura & Tellman, 2016). However, a large bonnethead recapture study that sampled both the Gulf and Atlantic coasts of Florida did not observe any individuals who traveled between coasts (Bethea & Grate, 2013 (Fields et al., 2016;Portnoy et al., 2015), microsatellite (Díaz-Jaimes et al., 2021), and genomic data (Portnoy et al., 2015). However, divergent mtDNA and genomes were identified on the Gulf and Atlantic coasts of Florida (Díaz-Jaimes et al., 2021;Escatel-Luna et al., 2015;Fields et al., 2016;Portnoy et al., 2015). ...
... One study of bonnethead populations from South Carolina recaptured individuals in the winter as far south as Florida , suggesting the possibility of a southward migration that aligns with that seen in other shark species (Ashe et al., 2015;Dimens et al., 2019;Kajiura & Tellman, 2016). However, a large bonnethead recapture study that sampled both the Gulf and Atlantic coasts of Florida did not observe any individuals who traveled between coasts (Bethea & Grate, 2013 (Fields et al., 2016;Portnoy et al., 2015), microsatellite (Díaz-Jaimes et al., 2021), and genomic data (Portnoy et al., 2015). However, divergent mtDNA and genomes were identified on the Gulf and Atlantic coasts of Florida (Díaz-Jaimes et al., 2021;Escatel-Luna et al., 2015;Fields et al., 2016;Portnoy et al., 2015). ...
Article
Full-text available
Gene flow is important for maintaining the genetic diversity required for adaptation to environmental disturbances, though gene flow may be limited by site fidelity in small coastal sharks. Bonnethead sharks (Sphyrna tiburo)—a small coastal hammerhead species—demonstrate site fidelity, as females are philopatric while males migrate to mediate gene flow. Consequently, bonnetheads demonstrate population divergence with distance, and Atlantic populations are genetically distinct from those of the Gulf of Mexico. Indeed, Florida forms a vicariant zone between these two bodies of water for many marine species, including some sharks. However, while bonnetheads are expected to have limited dispersal, the extent and rate of bonnethead migration remain uncertain. Thus, we aimed to determine their dispersal capacity by evaluating connectivity between disparate populations from the Gulf of Mexico and Atlantic Ocean. Using 10,733 SNPs derived from 2bRAD sequences, we evaluated genetic connectivity between Tampa Bay on the Gulf Coast of Florida and Biscayne Bay on the Atlantic coast of Florida. While standard analyses of genetic structure revealed slight but significant differentiation between Tampa Bay and Biscayne Bay populations, demographic history inference based on the site frequency spectrum favored a model without divergence. However, we also estimate that if population divergence occurred, it would have been recent (between 1500 and 4500 years ago), with continuous unidirectional gene flow from Tampa Bay to Biscayne Bay. Our findings support the hypothesis that bonnetheads can migrate over relatively large distances (>300 miles) to find mates. Together, these results provide optimism that under proper management, a small‐bodied globally endangered shark can undergo long migrations to sustain genetic diversity.
... While there has been considerable focus on certain species such as dolphins (Barcelo et al., 2022;Pratt et al., 2022;Wittwer et al., 2023) and sailfish (da Silva Ferrette et al., 2023), seascape genomics has seen limited application in elasmobranchs, with only two studies focusing on the blue skate Dipturus batis (Delaval et al., 2022) and the copper shark Carcharhinus brachyurus (Klein et al., 2024). Nonetheless, these studies highlight the potential of highly heterogeneous seascapes to contribute to adaptive divergence, especially in populations exhibiting resident and philopatric tendencies, driven by ecologically divergent selection pressures (Portnoy et al., 2015;Pratt et al., 2022). ...
... Indeed, some evidence of regional panmixia has been observed in several circumglobally distributed species, revealing only minimal genetic differentiation across ocean basins (Rus Hoelzel et al., 2006;Vignaud et al., 2014;Taguchi et al., 2015;Verissimo et al., 2017;Junge et al. 2019). Conversely, certain elasmobranch species exhibit a wide range of behaviors influenced by habitat preference (e.g., residency, site fidelity, and philopatry) (Chapman et al., 2015), as well as environmental factors and biogeographic features (Schlaff et al., 2014;Yates et al., 2015;Lee et al., 2018), often contributing to unexpected patterns of genetic structure (Hirschfeld et al., 2021), and evidence of local adaptation (Momigliano et al., 2017), most notably within species of Sphyrnidae (Portnoy et al., 2015;Diaz-Jaimes et al., 2021;Felix-Lopez et al., 2024). ...
... Indeed, some evidence of regional panmixia has been observed in several circumglobally distributed species, revealing only minimal genetic differentiation across ocean basins (Rus Hoelzel et al., 2006;Vignaud et al., 2014;Taguchi et al., 2015;Verissimo et al., 2017;Junge et al. 2019). Conversely, certain elasmobranch species exhibit a wide range of behaviors influenced by habitat preference (e.g., residency, site fidelity, and philopatry) (Chapman et al., 2015), as well as environmental factors and biogeographic features (Schlaff et al., 2014;Yates et al., 2015;Lee et al., 2018), often contributing to unexpected patterns of genetic structure (Hirschfeld et al., 2021), and evidence of local adaptation (Momigliano et al., 2017), most notably within species of Sphyrnidae (Portnoy et al., 2015;Diaz-Jaimes et al., 2021;Felix-Lopez et al., 2024). ...
Preprint
Globally, hammerhead sharks have experienced severe declines owing to continued overexploitation and anthropogenic change. The smooth hammerhead shark Sphyrna zygaena remains comparatively understudied compared to other members of the family Sphyrnidae, and despite its Vulnerable status, a comprehensive understanding of its genetic landscape remains lacking. The present study aimed to conduct a fine-scale genomic assessment of Sphyrna zygaena within the highly dynamic marine environment of South Africa’s coastline, using thousands of single nucleotide polymorphisms (SNPs) derived from restriction site-associated DNA sequencing (3RAD). A combination of differentiation-based outlier detection methods (OUTFlank and pcadapt) and Genotype-Environment Association (GEA) (Redundancy Analysis) analysis in Sphyrna zygaena were employed. Subsequent assessments of putatively adaptive loci revealed a distinctive south to east genetic cline. Amongst these, notable correlations between adaptive variation and sea-surface dissolved oxygen and salinity, in addition to spatial factors were evident. Conversely, analysis of 110, 965 neutral SNP markers revealed a lack of regional population differentiation, a finding that remained consistent across various analytical approaches, including an assessment of isolation-by-distance (IBD) and isolation-by-environment (IBE), genetic clustering analyses (LEA, fastSTRUCTURE, and find.clusters), and a discriminant analysis of principal components (DAPC). These results provide evidence for the presence of differential selection pressures within a limited spatial range, despite high gene flow implied by the selectively neutral dataset. This study offers notable insights regarding the potential impacts of genomic variation in response to fluctuating environmental conditions in the circumglobally distributed Sphyrna zygaena.
... Sphyrna tiburo seasonally migrates offshore and across latitudes and exhibits site fidelity to specific estuaries [21][22][23]. The population genetic structure of this species was confirmed by female philopatry to nursery areas and the presence of male-mediated gene flow along the northeastern coast of the Gulf of Mexico, which was apparent in mtDNA, microsatellite loci, and neutral single nucleotide polymorphism (SNP) data [24][25][26]. In particular, outlier SNP data reflected latitudinal selection and indicated that this selection was strong enough to outweigh the homogenizing pressure of male migration [24]. ...
... The population genetic structure of this species was confirmed by female philopatry to nursery areas and the presence of male-mediated gene flow along the northeastern coast of the Gulf of Mexico, which was apparent in mtDNA, microsatellite loci, and neutral single nucleotide polymorphism (SNP) data [24][25][26]. In particular, outlier SNP data reflected latitudinal selection and indicated that this selection was strong enough to outweigh the homogenizing pressure of male migration [24]. From this example, it is apparent that female philopatry may act to maintain the genetic diversity present in specific locations, whereas male-mediated gene flow may promote adaptive variation across the landscape, thus facilitating species persistence at local and regional scales [24]. ...
... In particular, outlier SNP data reflected latitudinal selection and indicated that this selection was strong enough to outweigh the homogenizing pressure of male migration [24]. From this example, it is apparent that female philopatry may act to maintain the genetic diversity present in specific locations, whereas male-mediated gene flow may promote adaptive variation across the landscape, thus facilitating species persistence at local and regional scales [24]. ...
Article
Populations of highly mobile species that undertake long distance migrations are typically considered to be panmictic. Nonetheless, mechanisms related to behavior or local environmental conditions promote genetic isolation in the absence of physical barriers. Highly migratory shark species exhibit varying levels of fidelity to specific regions, shaping the genetic architecture of different populations and resulting in geographically based genetic variation with potential adaptive value. An understanding of the genetic variation of highly migratory species is needed to develop effective conservation strategies. This study aimed to assess the neutral and adaptive variation of the smooth hammerhead shark (Sphyrna zygaena) in the northern Mexican Pacific (NMP) via single nucleotide polymorphisms (SNPs). We analyzed 1480 SNPs in 92 individuals from four geographic regions in the NMP, of which 1469 SNPs were neutral loci (n-SNP), and 11 were putatively under selection (o-SNP) using four genoma scan methods. Genetic diversity was geographically similar among regions (Ho = 0.275). The neutral variation showed panmixia (n-SNPs; FST = 0.0012, p = 0.44), which may be associated with the high dispersal capacity of S. zygaena. A pattern of adaptive variation between individuals from the Gulf of California and Pacific coast was revealed using o-SNPs FST-based methods (24 oSNPs; FST = 0.061, p < 0.001), which may be promoted by individual preferences based on physiological limitations. The estimated effective population size (Ne) of S. zygaena was 1390 individuals, which is theoretically optimal for the population to persist over time.
... It is also possible that philopatry might be sex biased as has been observed in Blacktip Sharks Carcharhinus limbatus (Keeney et al. 2005), lesser kestrels Falco naumanni (Alcaide et al. 2009), ringed salamanders Ambystoma annulatum (Williams et al. 2021), and several other animals. If one sex does not display philopatry, sufficient gene flow may be maintained between breeding habitats by the nonphilopatric sex, genetically homogenizing the population (Blundell et al. 2002;Portnoy et al. 2015). Channel Catfish may display male-biased philopatry because males build nests alone, mate monogamously, and provide uniparental care after driving off the female (Tatarenkov et al. 2006). ...
... Channel Catfish may display male-biased philopatry because males build nests alone, mate monogamously, and provide uniparental care after driving off the female (Tatarenkov et al. 2006). These conditions favor dispersal in females, potentially reducing breeding site fidelity and habitat segregation (Greenwood 1980;Portnoy et al. 2015). To test for sex-biased habitat preferences, we could estimate the genetic structure of Channel Catfish populations using sex-linked gene markers or observe differences between mitochondrial DNA (uniparental inheritance) and nuclear DNA (biparental inheritance) (Lawson Handley and Perrin 2007;Portnoy et al. 2015). ...
... These conditions favor dispersal in females, potentially reducing breeding site fidelity and habitat segregation (Greenwood 1980;Portnoy et al. 2015). To test for sex-biased habitat preferences, we could estimate the genetic structure of Channel Catfish populations using sex-linked gene markers or observe differences between mitochondrial DNA (uniparental inheritance) and nuclear DNA (biparental inheritance) (Lawson Handley and Perrin 2007;Portnoy et al. 2015). ...
Article
Full-text available
Objective Individual habitat preference can reduce intraspecific competition for resources and may differ between age groups, sexes, and adult phenotypes. The Channel Catfish Ictalurus punctatus is a widespread species occurring in diverse freshwater habitats. This species displays breeding philopatry, returning to nesting sites occupied in previous years. Larger Channel Catfish tend to nest in the main channels of large rivers, whereas smaller fish tend to prefer smaller tributaries. The purpose of our study was to determine whether this habitat segregation potentially associated with habitat preference affects the genetic structure of a population. We hypothesized that spatial segregation of breeding sites in the Ottawa River and its smaller tributaries at Lac des Chats reduced gene flow within the population, resulting in genetically differentiated demes associated with lacustrine‐like and fluvial habitats. Methods Microsatellite allelic data was collected from 162 Channel Catfish. Result We found little genetic variation between the Ottawa, Mississippi, and Madawaska rivers. Furthermore, our analyses suggested that the sampled specimens comprised one panmictic population. Fish from one site in the Ottawa River, however, were significantly differentiated from fish from a nearby site also in the Ottawa River as well as from fish from the Mississippi River tributary. Conclusion Given that fish from sites further up the Ottawa River were not differentiated from fish from these sites, it is unlikely that geography can account for the differences observed; rather, assortative mating may explain the differentiation. We propose that panmixia within the population is caused by ontogenetic changes in habitat selection, straying individuals, or sex‐biased dispersal and philopatry.
... Studies aimed at characterizing genetic populations in the Atlantic cluster of S. tiburo have demonstrated that the species has high genetic diversity and has experienced recent expansions (Escatel-Luna et al., 2015;Fields et al., 2016). Furthermore, the subdivision of the Atlantic lineage into at least three regions, namely the southeastern US Atlantic, western Florida, and southern Gulf of Mexico, has been suggested by both SNP markers and life-history analyses (Díaz-Jaimes et al., 2021;Frazier et al., 2023;Portnoy et al., 2015). The pattern of mitochondrial divergence was primarily attributed to female philopatric behavior. ...
Article
Full-text available
The apparent lack of physical barriers in the marine realm has created the conception that many groups have a constant gene flow. However, changes in ocean circulation patterns, glacial cycles, temperature, and salinity gradients are responsible for vicariant events in many fish species, including sharks. The bonnethead shark, Sphyrna tiburo, is an endangered small coastal shark species. Although considerable efforts have recently been undertaken, little remains known about the possible biogeographic scenario that can explain its actual distribution within the western Atlantic (WA). Here, we used 599 mitochondrial sequences to assess the phylogeographic structure and implement Bayesian phylogenetic analyses to obtain divergence times and reconstruct the ancestral geographic range. This allowed us to infer processes responsible for the diversification of S. tiburo into major divergent lineages. Our results indicated that S. tiburo in the WA represents three independent lineages, with Brazilian samples differentiated into a distinct genetic cluster. The posterior probability of ancestral range analysis indicated that the species likely originated in the northern region (Carolina Province and the southern Gulf of Mexico), where it colonized southward through the uplifting of the Central American Isthmus (CAI). The Northern and Caribbean genetic clusters appear to have arisen from the intensification of the Loop Current, which currently flows northward passing the Yucatan Peninsula, Gulf of Mexico, and east Florida. Following initial colonization, the Northeastern Brazil group differentiated from the Caribbean region due to the sediment and freshwater discharge of the Amazon‐Orinoco Plume. Thus, the evolutionary history of the S. tiburo complex can be explained by a combination of dispersal and vicariance events that occurred over the last ~5 million years (MY). We established and confirmed the species and population limits, demonstrating that the Amazon‐Orinoco Plume constitutes a significant dispersal barrier for coastal sharks. Finally, we discuss some recommendations for the conservation of the bonnethead shark.
... The nuclear data for S. dumeril in the western North Atlantic has a very clear signal of structure with the largest difference between the Atlantic and Gulf, corresponding to the known faunal break for multiple taxa along southern Florida (Neigel 2009) such as bonnethead (Fields et al. 2016;Portnoy et al. 2015), blacknose sharks (Portnoy et al. 2014), and blacktip sharks (Swift et al. 2023). Within the Gulf of Mexico, significant population structure was detected as well, aligning with the distribution break seen along the Mississippi Canyon (Driggers et al. 2018); however, no evidence was found to indicate cryptic speciation in the genetic data. ...
Article
Full-text available
While dorsal-ventrally compressed chondrichthyans are among the most imperiled fishes in the world, there is still limited knowledge of the biology of many of these species, even in well-studied ocean basins. In the western North Atlantic Ocean, the population structure of the Atlantic angel shark (Squatina dumeril) is not fully understood; therefore, the portioning of genetic variation was assessed among individuals caught along the east coast of the United States (Atlantic) and on the northern Gulf of Mexico (Gulf) using reduced representation genomics and mitochondrial sequencing. Three distinct groups were delineated with nuclear data, the Atlantic, the eastern Gulf, and the western Gulf, along boundaries described by previous research. Mitochondrial data only resolved two groups, with the western Gulf separated from the eastern Gulf and Atlantic combined. Demographic modeling suggested that the Atlantic population separated from a single Gulf population which subsequently split into eastern and western populations. Additionally, there was evidence that adjacent populations experienced gene flow after splitting, which may explain the incongruence between results based on nuclear and mtCR data. Correlations between environmental variables and allele frequencies at 873 loci indicated potential local adaptation. Therefore, the preservation of all three groups is necessary for the conservation of long-term adaptive variation important for species persistence.
... Ecological barriers among the three regions where divergent lineages were observed seem to be the most plausible explanation as it has been supported in earlier studies on various elasmobranchs species, for example the blacktip shark Carcharhinus limbatus (Keeney et al., 2005), the bonnethead shark Sphyrna tiburo (Escatel-Luna et al., 2015;Portnoy et al., 2015;Fields et al., 2016), the whitespotted eagle ray Aetobatus narinari (Sellas et al., 2015), and the southern stingray Hypanus americanus (Richards, DeBiasse & Shivji, 2019), indicating limited gene flow between these areas. Even among large coastal shark species, genetic differences at larger spatial scales between western North Atlantic and western South Atlantic populations have been reported using mitochondrial markers, for example scalloped hammerhead (Sphyrna lewini, Chapman, Pinhal & Shivji, 2009;Pinhal et al., 2020), bull shark (Carcharhinus leucas, Karl et al., 2011), Caribbean sharpnose shark, (Rhizoprionodon porosus, Mendonça, Oliveira & Gadig, 2011), nurse shark (Ginglymostoma cirratum, Karl, Castro & Garla, 2012), Brazilian sharpnose shark (Rhizoprionodon lalandii, Mendonça et al., 2013), silky shark (C. ...
Article
Full-text available
Cownose rays Rhinoptera bonasus and R. brasiliensis , are species distributed along the coastal waters from eastern United States, Gulf of Mexico to Brazil. This study represents the most extensive evaluation to date of the genetic diversity and population genetic structure of R. bonasus across its distribution, and it is the first to investigate the population genetics of R. brasiliensis . We analyzed sequences of COI and Cytb genes for Rhinoptera bonasus ( COI : 230, Cytb : 108) and R. brasiliensis ( COI : 181, Cytb : 105) to investigate the genetic diversity and their relationship with environmental variables, genetic structure, as well as demographic parameters. We found that benthic temperature and current velocity were the most important environmental variables in genetic diversity. The global population structure reveals the presence of significant population genetic structure in both species. Bayesian clusters in BAPS were consistent with the segregation pattern observed for haplotype networks based on COI and Cytb markers for both species, which may respond to philopatry and temperature. These results will further improve management and conservation efforts for theses species of ecological and economic importance.
... detected in other marine taxa (e.g., fishes:Bradbury et al., 2010; corals: Thomas et al., 2017; bivalves: Vendrami et al., 2019), including in the Gulf of Mexico (sharks:Portnoy et al., 2015). ...
Article
Full-text available
Patterns of genetic variation reflect interactions among microevolutionary forces that vary in strength with changing demography. Here, patterns of variation within and among samples of the mouthbrooding gafftopsail catfish (Bagre marinus, Family Ariidae) captured in the U.S. Atlantic and throughout the Gulf of Mexico were analyzed using genomics to generate neutral and non‐neutral SNP data sets. Because genomic resources are lacking for ariids, linkage disequilibrium network analysis was used to examine patterns of putatively adaptive variation. Finally, historical demographic parameters were estimated from site frequency spectra. The results show four differentiated groups, corresponding to the (1) U.S. Atlantic, and the (2) northeastern, (3) northwestern, and (4) southern Gulf of Mexico. The non‐neutral data presented two contrasting signals of structure, one due to increases in diversity moving west to east and north to south, and another to increased heterozygosity in the Atlantic. Demographic analysis suggested that recently reduced long‐term effective population size in the Atlantic is likely an important driver of patterns of genetic variation and is consistent with a known reduction in population size potentially due to an epizootic. Overall, patterns of genetic variation resemble that of other fishes that use the same estuarine habitats as nurseries, regardless of the presence/absence of a larval phase, supporting the idea that adult/juvenile behavior and habitat are important predictors of contemporary patterns of genetic structure.
... With the increased availability of genome-wide SNP data for nonmodel species, fine-scale population genomics assessments have been conducted in a number of shark species, including the application of genome scans to identify patterns of local adaptation (Bernard et al., 2021;Boussarie et al., 2022;Marie et al., 2019;Momigliano et al., 2017;Pazmiño et al., 2018;Portnoy et al., 2015). Here, we provide the first seascape genomics assessment of a shark species along the highly heterogeneous coastline of southern Africa. ...
Article
Full-text available
Adaptive divergence in response to environmental clines are expected to be common in species occupying heterogeneous environments. Despite numerous advances in techniques appropriate for non‐model species, gene–environment association studies in elasmobranchs are still scarce. The bronze whaler or copper shark (Carcharhinus brachyurus) is a large coastal shark with a wide distribution and one of the most exploited elasmobranchs in southern Africa. Here, we assessed the distribution of neutral and adaptive genomic diversity in C. brachyurus across a highly heterogeneous environment in southern Africa based on genome‐wide SNPs obtained through a restriction site‐associated DNA method (3RAD). A combination of differentiation‐based genome‐scan (outflank) and genotype–environment analyses (redundancy analysis, latent factor mixed models) identified a total of 234 differentiation‐based outlier and candidate SNPs associated with bioclimatic variables. Analysis of 26,299 putatively neutral SNPs revealed moderate and evenly distributed levels of genomic diversity across sites from the east coast of South Africa to Angola. Multivariate and clustering analyses demonstrated a high degree of gene flow with no significant population structuring among or within ocean basins. In contrast, the putatively adaptive SNPs demonstrated the presence of two clusters and deep divergence between Angola and all other individuals from Namibia and South Africa. These results provide evidence for adaptive divergence in response to a heterogeneous seascape in a large, mobile shark despite high levels of gene flow. These results are expected to inform management strategies and policy at the national and regional level for conservation of C. brachyurus populations.
... Baiz et al., 2019;Combs et al., 2018;de Jong et al., 2020;Farleigh et al., 2021; Fritz et al., 2018;Ivanov et al., 2018;Maigret et al., 2020;Portnoy et al., 2015;Ryan et al., 2017;Schley et al., 2020;Termignoni- García et al., 2017;Trense et al., 2021) where size-selection was carried out using Pippin Prep or BluePippin before the PCR step. ...
Article
Full-text available
Double‐digest Restriction‐site Associated DNA sequencing (ddRADseq) is widely used to generate genomic data for non‐model organisms in evolutionary and ecological studies. Along with affordable paired‐end sequencing, this method makes population genomic analyses more accessible. However, multiple factors should be considered when designing a ddRADseq experiment, which can be challenging for new users. The generated data often suffer from substantial read overlaps and adaptor contamination, severely reducing sequencing efficiency and affecting data quality. Here, we analyse diverse datasets from the literature and carry out controlled experiments to understand the effects of enzyme choice and size selection on sequencing efficiency. The empirical data reveal that size selection is imprecise and has limited efficacy. In certain scenarios, a substantial proportion of short fragments pass below the lower size‐selection cut‐off resulting in low sequencing efficiency. However, enzyme choice can considerably mitigate inadvertent inclusion of these shorter fragments. A simple model based on these experiments is implemented to predict the number of genomic fragments generated after digestion and size selection, number of SNPs genotyped, number of samples that can be multiplexed and the expected sequencing efficiency. We developed ddgRADer – http://ddgrader.haifa.ac.il/ – a user‐friendly webtool and incorporated these calculations to aid in ddRADseq experimental design while optimizing sequencing efficiency. This tool can also be used for single enzyme protocols such as Genotyping‐by‐Sequencing. Given user‐defined study goals, ddgRADer recommends enzyme pairs and allows users to compare and choose enzymes and size‐selection criteria. ddgRADer improves the accessibility and ease of designing ddRADseq experiments and increases the probability of success of the first population genomic study conducted in labs with no prior experience in genomics.
Article
Full-text available
Recently, the adaptive significance of maternal effects has been increasingly recognized. No longer are maternal effects relegated as simple `troublesome sources of environmental resemblance' that confound our ability to estimate accurately the genetic basis of traits of interest. Rather, it has become evident that many maternal effects have been shaped by the action of natural selection to act as a mechanism for adaptive phenotypic response to environmental heterogeneity. Consequently, maternal experience is translated into variation in offspring fitness.
Article
Full-text available
Population genomics has the potential to improve studies of evolutionary genetics, molecular ecology and conservation biology, by facilitating the identification of adaptive molecular variation and by improving the estimation of important parameters such as population size, migration rates and phylogenetic relationships. There has been much excitement in the recent literature about the identification of adaptive molecular variation using the population-genomic approach. However, the most useful contribution of the genomics model to population genetics will be improving inferences about population demography and evolutionary history.
Article
The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
Book
Preface. Part I: Background: 1. Introduction. Why Employ Molecular Genetic Markers? Why Not Employ Molecular Genetic Markers? 2. History of Molecular Phylogenetics. Debates and Diversions from Molecular Systematics. Molecular Phylogenetics. 3. Molecular Tools. Protein Assays. DNA Assays. References to Laboratory Protocols. 4. Interpretative Tools. Categorical Subdivisions of Molecular Genetic Data. Molecular Clocks. Procedures for Phylogeny Reconstruction. Gene Trees versus Species Trees. Part II: Applications: 5. Individuality and Parentage. Genetic Identity versus Non-Identity. Parentage. 6. Kinship and Intraspecific Phylogeny. Close Kinship and Family Structure. Geographic Population Structure and Gene Flow. Phylogeography. Microtemporal Phylogeny. 7. Speciation and Hybridization. The Speciation Process. Hybridization and Introgression. 8. Species Phylogenies and Macroevolution. Rationales for Phylogeny Estimation. Special Approaches to Phylogeny Estimation. Prospectus for a Global Phylogeny. Special Topics in Molecular Phylogenetics. 9. Conservation Genetics. Issues of Heterozygosity. Issues of Phylogeny. Literature Cited. Index to Taxonomic Genera. General Index.