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. CHAPMAN‡and
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
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
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
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:
©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
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
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 4–5 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
1–7 years (Lombardi-Carlson et al. 2003; Frazier et al.
2014) and females give birth to 2–14 (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
Hardy–Weinberg 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 Tukey–Kramer HSD indepen-
dent contrasts as implemented in JMP
v.11 (SAS Institute
Inc.). Genetic diversity (mtDNA) was assessed as mean
nucleon (h) and nucleotide diversity (p) within each
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.18.104.22.168 (Excofﬁer & Lischer
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
values (both nuclear data sets) were estimated using
GENODIVE; signiﬁcance of pairwise F
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
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
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
=21.483, P<0.001), with
mean pin NC being signiﬁcantly lower than in the
other samples (Tukey–Kramer HSD—P<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
(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
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
=0.027, P=0.033) and O-SNP loci
=0.157, P=0.000), but not for N-SNP loci
©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, north–south
(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 (P≤0.05)
with latitude and explained 56.9% of the variation
along the primary axis, while 18 O-SNP loci had r
ues ≥0.90 and explained 75.6% of the variation along
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
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
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
values for putatively neutral SNP loci (N-SNP) and for outlier SNP loci putatively under
selection (O-SNP), and pairwise Φ
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 host–parasite/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).
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.
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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.
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.
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
Appendix S1 Methods.
©2015 John Wiley & Sons Ltd
PHILOPATRY AND ADAPTIVE VARIATION 5885