Genetic structure among spawning aggregations of the gulf coney Hyporthodus acanthistius

Abstract

Many large groupers form spawning aggregations, returning to the same spawning sites in consecutive spawning seasons. Connectivity between spawning aggregations is thus assured by larval dispersal. This study looks into the genetic structure and gene flow among spawning aggregations of a large grouper, the gulf coney Hyporthodus acanthistius, in the northern Gulf of California. First, using the mitochondrial control region and 11 microsatellites, we calculated FST metrics and conducted a Bayesian clustering analysis to determine structure among 5 spawning aggregations. Shallow genetic structure was found, separating the southernmost spawning aggregate from the remainder. Second, we used the results from the structure analysis and local water circulation patterns to delineate 3 distinct models of gene flow. The best-supported model, in which the southernmost spawning aggregate formed one group and all other spawning aggregates were nested into a second group, was the one that was consistent with water circulation during the species’ spawning season. Larval retention within a seasonal anticyclonic gyre that formed during the gulf coney’s spawning season may be responsible for the patterns found. This study highlights the importance of local oceanographic conditions in dictating the structure among spawning aggregations even at small geographic scales and contributes to informed management plans for this overexploited grouper.

Full text

MARINE ECOLOGY PROGRESS SERIES
Mar Ecol Prog Ser
Vol. 499: 193–201, 2014
doi: 10.3354/meps10637 Published March 3
INTRODUCTION
Knowledge of how genetic variation is partitioned
in the ocean is fundamental for understanding the
ecology, conservation and management of marine
resources (Mora & Sale 2002, Gell & Roberts 2003,
Cowen et al. 2007, Francis et al. 2007, Planes et
al. 2009). One of the strongest drivers of genetic struc-
ture is connectivity, i.e. the demographic linking of lo-
cal populations via the dispersal of larvae, juveniles
or adults (Sale et al. 2005), which influences almost
all ecological and evolutionary processes in meta -
populations (Hanski & Gaggiotti 2004). Genetic
connectivity has been shown across a range of geo-
graphical scales among different marine taxa, ranging
from virtually panmictic throughout considerably
large geographic ranges (Bowen et al. 2001, Lessios et
al. 2003, Klanten et al. 2007, Beldade et al. 2009, Leray
© Inter-Research 2014 · www.int-res.com*Corresponding author: rbeldade@gmail.com
Genetic structure among spawning aggregations of
the gulf coney Hyporthodus acanthistius
Ricardo Beldade1, 2, 3, 4,*, Alexis M. Jackson1, Richard Cudney-Bueno5, 6,
Peter T. Raimondi1, Giacomo Bernardi1
1Department of Ecology and Evolutionary Biology, University of California Santa Cruz, 100 Shaffer Road, Santa Cruz,
California 95060, USA
2USR 3278 CRIOBE, CNRS EPHE, CBETM de l’Université de Perpignan, 66860 Perpignan Cedex, France
3Laboratoire d’excellence ‘Corail’, USR 3278 CRIOBE CNRS-EPHE, 66860 Perpignan Cedex, France
4Universidade de Lisboa, Faculdade de Ciências, Centro de Oceanografia, Campo Grande, 1749-016 Lisboa, Portugal
5School of Natural Resources and the Environment, University of Arizona, Biological Sciences East, Room 325, Tucson,
Arizona 85721, USA
6Institute of Marine Sciences, University of California Santa Cruz, 100 Shaffer Road, Santa Cruz, California 95060, USA
ABSTRACT: Many large groupers form spawning aggregations, returning to the same spawning
sites in consecutive spawning seasons. Connectivity between spawning aggregations is thus
assured by larval dispersal. This study looks into the genetic structure and gene flow among
spawning aggregations of a large grouper, the gulf coney Hyporthodus acanthistius, in the north-
ern Gulf of California. First, using the mitochondrial control region and 11 microsatellites, we cal-
culated FST metrics and conducted a Bayesian clustering analysis to determine structure among
5 spawning aggregations. Shallow genetic structure was found, separating the southernmost
spawning aggregate from the remainder. Second, we used the results from the structure analysis
and local water circulation patterns to delineate 3 distinct models of gene flow. The best-sup-
ported model, in which the southernmost spawning aggregate formed one group and all other
spawning aggregates were nested into a second group, was the one that was consistent with
water circulation during the species’ spawning season. Larval retention within a seasonal anti -
cyclonic gyre that formed during the gulf coney’s spawning season may be responsible for the pat-
terns found. This study highlights the importance of local oceanographic conditions in dictating
the structure among spawning aggregations even at small geographic scales and contributes to
informed management plans for this overexploited grouper.
KEY WORDS: Grouper · Dispersal · Connectivity · Sea of Cortez · Oceanography · Eddies ·
Retention · Migration models · Rooster hind · Epinephelus
Resale or republication not permitted without written consent of the publisher

Mar Ecol Prog Ser 499: 193–201, 2014
et al. 2010) to clearly structured populations at very
small scales (Sotka et al. 2004, Bernardi 2005, Barber
et al. 2006, Gerlach et al. 2007, Beldade et al. 2012).
Many fish form spawning aggregations (i.e. groups
of conspecific fish that gather for the purpose of
spawning, with densities or numbers significantly
higher than those found in the area of aggregation
during non-reproductive periods; Domeier & Colin
1997), including groupers, snappers, jacks, surgeon-
fishes, damselfishes and parrotfishes (Sala et al.
2003, Erisman et al. 2007, Gladstone 2007, Sadovy
de Mitcheson et al. 2008, Gerhardinger et al. 2009).
Some groupers return to the same spawning sites in
consecutive spawning seasons (Sala et al. 2001, Starr
et al. 2007), in some cases covering large distances
to do so (Bolden 2000). If adult spawning aggrega-
tion site fidelity is indeed ubiquitous among large
groupers, then the dispersal of the pelagic larval
stages that are subjected to transport by ocean cur-
rents should be the main driver of genetic connec -
tivity. Two elements underline the importance of
oceanographic characteristics to the dispersal of
spawning aggregation offspring. First, the specific
location of spawning aggregations appears to maxi-
mize the rapid advection of eggs and larvae away
from the reef environment (e.g. Choat 2012, Colin
2012a). Second, knowledge of the onset of sensorial
and swimming abilities of pelagic larvae, which in
the case of groupers is still largely unknown, is
essential to understand how larval abilities might
steer the dispersal process (e.g. Colin 2012b, Hamner
& Largier 2012). Larval abundance and even the
magnitude of recruitment events appear to be corre-
lated with oceanographic and climatic parameters,
such as temperature, salinity and depth (but see e.g.
Aburto-Oropeza et al. 2010, Marancik et al. 2012).
The northern Gulf of California (NGC) is home to
several fishes that aggregate to spawn and is part of
one of the most productive marine ecosystems in
the world, contributing most of Mexico’s fishery re -
sources (Arvizu-Martínez 1987, Lluch-Cota et al.
2007, Erisman et al. 2012). The NGC covers a rela-
tively small area extending from the Colorado delta
in the north to Bahia de Los Angeles and Isla Tiburon
in the south (Fig. 1). In this region, in-depth know -
ledge of water circulation patterns and other geomor-
phological characteristics (Fig. 1) provide a unique
opportunity to describe genetic structure and test
models of gene flow in locally occurring species. In
the NGC, the main oceanographic features comprise
intense tidal mixing (Argote et al. 1995) and a sea-
sonally reversing gyre, anticyclonic in summer (June
to September) (Fig. 1B) and cyclonic in winter
(Fig. 1C) (Lavín et al. 1997, Marinone et al. 2008);
strong coastal currents along the eastern Sonora
coastline (Peguero-Icaza et al. 2011); and small resid-
ual currents and small eddies in the upper gulf (Mari-
none et al. 2011). These characteristics are likely to
influence the transport of larvae in the NGC (Mari-
none et al. 2004, Cudney-Bueno et al. 2009). Both
local water circulation and bottom geomorphologic
characteristics may have important implications for
the formation of spawning aggregations as well as for
the fate of eggs or larvae released there (Cherubin et
al. 2011, Karnauskas et al. 2011). In the NGC, there
are 2 deep basins, the Delfin Basin (800 m) and the
Wagner Basin (200 m), and several sills (Lavín et al.
1997) whose putative part in limiting dispersal of lar-
vae or concentrating prey for early larval stages
remains unclear (e.g. Karnauskas et al. 2011).
The gulf coney Hyporthodus acanthistius (formerly
Epinephelus acanthistius; Craig & Hastings 2007) is a
194
Fig. 1. Northern Gulf of California (NGC) including (A) bathymetry (depth in meters) and named sampled spawning aggrega-
tions of the gulf coney Hyporthodus acanthistius (PLI, Puerto Libertad; PLO, Puerto Lobos; STO, Santo Tomas; PPE, Puerto
Peñasco; and SLG, San Luiz Gonzaga); (B) ocean circulation in the summer (only the month of July is represented); and (C) in
the winter (only the month of January is represented). Ocean circulation reproduced from Marinone (2003) by permission of the
American Geophysical Union

Beldade et al.: Genetic structure among grouper spawning aggregations
tropical and subtropical large grouper that occurs
from southern California to Peru (Heemstra & Ran-
dall 1993), including the Gulf of California (or Sea of
Cortez) (Cudney-Bueno & Turk-Boyer 1998, Aburto-
Oropeza et al. 2008). It is found at depths greater
than ~45 m usually in silty areas adjacent to rocky
reefs (Thomson et al. 2000), and spawns in aggre -
gations on muddy bottoms during the spring and
summer months (Cudney-Bueno & Turk-Boyer 1998).
During the spawning period, artisanal fishermen
heavily target this species (Aburto-Oropeza et al.
2008). Indeed, the high commercial value and tempo-
ral and spatial predictability of their mass gatherings
make groupers a prime target for fisheries. Despite
its present ‘Least Concern’ conservation status (IUCN
2012), the abundance of the gulf coney in the NGC
has been rapidly declining over the past 2 decades
(Aburto-Oropeza et al. 2008). Elsewhere, there are
many examples of collapsed grouper spawning
aggregations because of overfishing such as the Nas-
sau grouper E. striatus (e.g. Sala et al. 2001, Aguilar-
Perera 2006) and the gulf grouper Mycteroperca jor-
dani (Sáenz-Arroyo et al. 2005). Given the threat of
overfishing to fish that form spawning aggregations
(Sadovy de Mitcheson et al. 2013), it is imperative to
provide connectivity data to devise informed man-
agement plans.
In this study, we integrate molecular evidence from
highly variable molecular markers (control region
and 11 microsatellites) to assess genetic structure
and connectivity among spawning aggregations of
the gulf coney in the NGC. Oceanographic and geo-
morphological regional characteristics are used to
delineate particular models of gene flow across the
spawning aggregation network. This study provides
essential information for the management and re -
covery of this threatened fishery in the NGC.
MATERIALS AND METHODS
Sampling and DNA extraction
Fin clips of Hyporthodus acanthistius were collected
in 2003 aboard fishing boats that operated at 5 spawn-
ing locations in the NGC: Puerto Libertad, Puerto
Lobos, Santo Tomás, Puerto Peñasco, and San Luiz
Gonzaga (Fig. 1). Immediately after collection, fin
clips were placed in 95% ethanol and stored at ambi-
ent temperature in the field and then at 4°C in the lab.
Total genomic DNA was extracted from 20 mg of fin
tissue by Proteinase K digestion in lysis buffer (10 mM
Tris, 400 mM NaCl, 2 mM EDTA, 1% sodium dodecyl
sulfate) overnight at 55°C. This was followed by
purification using phenol/chloroform ex tractions and
alcohol precipitation (Sambrook et al. 1989).
mtDNA and microsatellites
We amplified the 5’ end of the hyper-variable por-
tion of the mitochondrial control region using the
universal primers CR-A and CR-E (Lee et al. 1995).
Each 100 µl reaction contained 10 to 100 ng of DNA,
10 mM Tris HCl (pH 8.3), 50 mM KCl, 1.5 mM MgCl2,
2.5 units of Taq DNA polymerase (Perkin-Elmer),
150 mM of each dNTP, and 0.3 mM of each primer
and was amplified with a cycling profile of 45 s at
94°C, 1 min at 52°C and 1 min at 72°C for 35 cycles.
After purification of amplified DNA genes following
the manufacturer’s protocol (ABI, Perkin-Elmer), we
sequenced on an ABI 3100 automated sequencer
(Applied Biosystems).
All individuals were genotyped for 13 microsatel-
lites following protocols described in Molecular Eco -
logy Resources Primer Development Consortium et
al. (2009). Each individual was genotyped using
GENE MAPPER 3.7 (Applied Biosystems). To estimate
potential genotyping errors, we re-amplified and re-
scored 21 randomly picked samples and evaluated
concordance between the first and second score.
Overall, the genotyping error rate was less than 2%,
which is reasonable for population differentiation
studies based on allele frequencies (Bonin et al. 2004),
and less than 2% of data were missing for any given
locus. Data were scanned for null alleles and stutter-
ing using MICROCHECKER 2.2.3 (van Oosterhout et
al. 2004) and for deviations from Hardy-Weinberg
equilibrium (HWE) and linkage disequilibrium after
10 000 permutations using ARLEQUIN 3.5 (Excoffier
& Lischer 2010). Two micro satellites, EAC_ A08 and
EAC_B08, were dropped from the ana lysis because of
the putative presence of null alleles.
Genetic diversity and genetic structure among
spawning aggregations
Genetic diversity measures for each population
including number of haplotypes, haplotype diver-
sity and nucleotide diversity were calculated with
DNAsp 5 (Librado & Rozas 2009). To assess popula-
tion structure, we used 2 separate approaches. In the
first, more classical approach, fixation indices (FST)
relying on allele frequencies were calculated using
ARLEQUIN 3.5 (Excoffier & Lischer 2010). We calcu-
195

Mar Ecol Prog Ser 499: 193–201, 2014
lated 95% confidence intervals around the FST esti-
mates using GDA 1.1 (Lewis & Zaykin 2001), rather
than just p-values, as these may not be good indica-
tors of differentiation between populations and are
dependent on sample size and variability (Jost 2008).
In the second approach, we used a Bayesian model-
based clustering method using microsatellite data im -
plemented in STRUCTURE 2.3.2 (Falush et al. 2007).
STRUCTURE assumes that there are knumber of
groups within which samples have compatible multi-
locus genotypes. Convergence of parameters (α, Fand
likelihood) in preliminary runs was used to deter-
mine the burn-in (500 000) and length (1 000 000) of
each run. For our analysis, we used an admixture
model, which allows individuals to have mixed
ancestry, and added sampling location as a weak
prior (Falush et al. 2007). Then we performed 10
replicate runs for each cluster kvarying between 1
and 6. To determine the correct number of clusters
in the sample, we followed Evanno et al. (2005), who
proposed the use of an ad hoc statistic Δkbased on
the rate of change in the log probability of data
between successive kvalues. STRUCTURE HAR-
VESTER was used to calculate Evanno’s Δkand illus-
trate the differences in likelihood and Δkfor each k
(Earl & VonHoldt 2012). A Q plot was chosen to illus-
trate differences between populations, where each
single vertical line (representing 1 individual) is par-
titioned into k-colored segments that represent that
indi vidual’s estimated membership fraction in each
of the k-inferred clusters (Pritchard et al. 2000).
Direction and magnitude of gene flow among
spawning aggregations
To determine the pervasive migration pattern in
the study area, we used MIGRATE-N 3.2.16 (Beerli
& Palczewski 2010) to contrast 3 migration models.
In all 3 models, spawning aggregates were nested
according to the genetic structure suggested by the
FST and structure analysis. Based on well-described
local oceanographic circulation, we defined the di -
rection of gene flow for each model as follows: (1) an
unrestricted full migration model, (2) a model consid-
ering 2 population sizes and unidirectional north-
ward gene flow and (3) a model considering 2 pop -
ulation sizes and unidirectional southward gene
flow. Model 2 was delineated taking into account
that Hyporthodus acanthistius spawns in the spring-
summer, during which time anticyclonic circulation
forces the water to flow northward on the eastern
side of the NGC (see Fig. 1B) (Marinone 2012). Model
3 depicts the autumn-winter cyclonic water circula-
tion pattern, which forces the water to flow south-
ward (see Fig. 1C) (Marinone 2012).
MIGRATE-N provides the ratio of the marginal
likelihoods (Bayes factors) of each model, which can
subsequently be compared to select the most sup-
ported model (Beerli & Palczewski 2010). The best-
supported model will have the highest log Bayes fac-
tors. This approach is particularly suited to our data
because local hydrodynamics allow for a clear expec-
tation of unidirectional gene flow in the study area
and the nesting of aggregates reduces the number
of parameters to be estimated from the data, thus
increasing the power of the approach. A random sub-
set of 30 samples from each of the 2 populations
identified previously was used to compare the mod-
els. The mitochondrial locus was not used to test the
models of gene flow because of its limited capability,
as it is a single locus, for distinguishing the models.
A series of preliminary runs using Model 1, the
unrestricted model, were used to determine con -
vergence of posterior probabilities for each of the
parameters. Running conditions chosen included
1 000 000 recorded steps, 10 long chains and 15
heated chains, a static heating scheme with the
inverse of the temperature regularly spaced between
0 and 1 and a tree swapping interval of one; finally,
the upper prior boundary for northward migration
was set to vary between 0 and 10 000. The natural
logarithm of Bayes factors with a Bezier approxima-
tion was calculated following Beerli & Palczewski
(2010) as well as each model’s probability by dividing
each marginal likelihood by the sum of the marginal
likelihoods of both models used. The best-supported
model will be the one with the highest probability
(Beerli & Palczewski 2010).
RESULTS
Genetic diversity
We obtained 232 sequences for a 361 bp frag -
ment of the mitochondrial control region (Genbank
KF425014 to KF425245). The sequences analyzed
here had 133 polymorphic sites, 54 of which were
informative. Genetic diversity was high for almost all
spawning aggregations (Table 1). Genetic diversity
was also calculated for the microsatellites from 246
individuals and included number of alleles, ratio of
homozygotes to heterozygotes per locus, as well as
HWE tests (Table S1 in the Supplement, available at
www.int-res.com/articles/suppl/m499p193_supp.pdf).
196

Beldade et al.: Genetic structure among grouper spawning aggregations
Genetic structure of spawning aggregations
Low pairwise FST values were found across both
genetic markers (Table 2). In spite of the significant
differences found between pairs of FST estimates
derived from microsatellites, 95% confidence inter-
vals precluded any conclusion regarding the struc-
ture between Puerto Libertad and either Santo
Tomás or Puerto Peñasco population pairs (Table 2).
Differentiation between the southernmost aggrega-
tion of Puerto Libertad and the remaining aggrega-
tions was identified through the Bayesian clustering
method (Fig. 2). Evanno’s Δkbased on the mean and
standard deviation of likelihoods, L(k), for each kwas
highest for k= 2 (Fig. 3), confirming that k= 2 is the
best representation of the genetic partitioning in the
data. Assessment of convergence examples of skyline
plots of log(α) are given in the Supplement (Fig. S1).
Gene flow between spawning aggregations
Model 2 was the most supported migration model,
as demonstrated by the highest value for the natural
logarithm of the Bayes’ factors (Table 3). Estimates
of population size and number of migrants between
the defined populations as well as parameter conver-
gence are given in the Supplement (Table S2). Our
analysis aligns well with the summer anticyclonic
gyre used to delineate Model 2, which is consistent
with both magnitude and direction of the gulf coney’s
gene flow in the NGC. This
period coincides with the
pelagic phase of the Hypor -
tho dus acan thistius larvae.
The full migration model
comes second to Model 2
because of the unrestricted
mi gra tion between the pop-
ulations to the north of
Puerto Libertad. Finally,
Model 3, in which gene flow
follows the winter cyclonic
water movement patterns
described for the area,
scored the lowest in ex -
plaining larval mi gration in
the area.
197
Sampling site n nhhdπ
Puerto Libertad 53 18 0.839 0.0061
Puerto Lobos 62 27 0.911 0.0072
Santo Tomás 21 10 0.914 0.0068
Puerto Peñasco 55 17 0.902 0.0061
San Luiz Gonzaga 41 16 0.871 0.0065
Total 232 47 0.886 0.0066
Table 1. Collection sites, number of mitochondrial control
region sequences used (n) and molecular diversity indices
(number of haplotypes, nh; haplotype diversity, hd; and
nucleotide diversity, π) for Hyporthodus acanthistius
PLI PLO STO PPE SLG
PLI 0.003 0.003 0.002 0.005
[0.000 to 0.007] [−0.005 to 0.009] [0.000 to 0.018] [0.002 to 0.020]
PLO 0.04254 0 0.001 0.003
[−0.001 to 0.009] [−0.003 to 0.003] [−0.001 to 0.005]
STO 0.03838 0.00049 −0.003 −0.005
[−0.005 to 0.001] [−0.008 to −0.002]
PPE 0.07154 0.00045 0.0052 −0.001
[−0.003 to 0.002]
SLG 0.00561 0.01327 0.01133 0.02289
Table 2. Population structure estimated by FST between Hyporthodus acanthistius popu-
lations calculated from the mitochondrial control region (below left) and from 11 mi-
crosatellites (above right) with 95% confidence intervals between brackets. Significant
pairwise FST (at p < 0.05) after 10 000 permutations shown in bold. PLI, Puerto Libertad;
PLO, Puerto Lobos; STO, Santo Tomás; PPE, Puerto Peñasco; SLG, San Luiz Gonzaga
Fig. 2. Q-plot of the Bayesian population assignment test based on 11 microsatellite loci. Each vertical line represents a single
Hyporthodus acanthistius individual; black/gray in each vertical line represent the likelihood of belonging to each of the
clusters. Black vertical lines separate the spawning aggregations; population acronyms are defined in Fig. 1

Mar Ecol Prog Ser 499: 193–201, 2014
DISCUSSION
Genetic structure, hydrodynamics and site fidelity
In groupers, genetic differentiation of spawning
aggregations has been observed but usually at large
geographic scales (e.g. Rhodes et al. 2003, Rivera et
al. 2004, Zatcoff et al. 2004). In this study, FST statistics
based on mtDNA and microsatellites as well as
Bayesian analysis were consistent in showing weak
structure at a much smaller scale. In scenarios of weak
genetic partitioning, FST metrics and Bayesian analysis
have limited power in asserting structure (e.g. Waples
& Gaggiotti 2006). In the present study, the inclusion
of zero within the calculated FST 95% confidence in-
tervals for 2 of the pairwise comparisons between
Puerto Libertad and the other populations, and the
limited capacity for Evanno’s Δkto distinguish be-
tween a panmictic population (k= 1) and the genetic
structure suggested (k= 2) as well as similar posterior
probabilities for the same kvalues, have to be ac-
knowledged. Nonetheless, the concordance between
markers and analysis in showing that within the NGC,
the southernmost spawning aggregation sampled,
Puerto Libertad, was slightly distinct from all others
lends support to this conclusion. Hence, we present
evidence for structure between spawning aggrega-
tions at a small geographic scale. Furthermore, while
unimportant over evolutionary time scales, weak ge-
netic structure can have important implications in
ecology and conservation biology (Jones & Wang 2012).
Given that the NGC covers such a small geo-
graphic area, it was unexpected to observe even
weak population structure among spawning aggrega-
tions that are so close geographically. The spawning
aggregation at Puerto Libertad was only 40 km from
the closest spawning aggregation at Puerto Lobos.
Hydrodynamic features of the study area during the
dispersive stage of the gulf coney’s larvae are also
consistent with our results, as the southernmost
spawning aggregation is the only one that sits just
outside of the summer anticyclonic gyre. This sea-
sonal gyre may trap the larvae originating from all
other spawning aggregations, preventing them from
travelling south. Eddies may entrap fish eggs and
larvae both in the open ocean (e.g. Holliday et al.
2011) and in enclosed seas (e.g. Contreras-Catala et
al. 2012). Simultaneously, the northward currents
described for the Puerto Libertad spawning aggrega-
tion site will transport larvae northward. The genetic
differentiation of Puerto Libertad may also be recon-
ciled with some level of site fidelity. While there are
no published accounts of site fidelity or home range
for Hyporthodus acanthistius, several grouper spe-
cies such as Epinephelus tauvina (Kaunda-Arara &
Rose 2004) have some degree of site fidelity. Other
examples have come from tagging studies of E. stria-
tus, in which distances traveled to spawning sites
ranged from 30 km (Sala et al. 2001) to upwards of
100 km (Carter et al. 1994, Bolden 2000). Movement
at even the smallest of these scales could be very rel-
evant within the NGC. The potential homogenizing
effects of dispersing larvae are best explained by the
study of gene flow.
Gulf coney’s gene flow in the NGC
The most supported model for the genetic ex -
change among spawning aggregations of gulf coney
198
Model No. Bezier Harmonic Choice Model
lmL lML (Bezier) probability
1 −4482.37 −4297.46 2 0.000
2 −4353.63 −4134.17 1 1.000
3 −5018.29 −4871.79 3 0.000
Table 3. Natural log Bayes factors (lmL) and log marginal
likelihoods (lML) for each gene flow model estimated by
thermodynamic integration using 11 microsatellite markers.
Model details are explained in ‘Materials and methods’
Fig. 3. Mean and standard deviation of log likelihoods, L(k)
(d); and Evanno’s Δk(j) for each kvalue estimated in 10 in-
dependent runs in STRUCTURE. The highest Δkindicates
the choice of k= 2 as the one that best describes the genetic
partitioning in the data

Beldade et al.: Genetic structure among grouper spawning aggregations
depicts the anticyclonic summer gyre, which coin-
cides with the pelagic stage of this species’ larvae. In
the NGC, using a 3-dimensional baroclinic numerical
model, Marinone et al. (2008) followed particles re -
leased in this gyre and found the same south-north
direction of dispersal. Calderon-Aguillera et al. (2003)
showed how hydrodynamics in the NGC influenced
the dispersal of blue shrimp Litopenaeus stylirostris
larvae and more recently Cudney-Bueno et al. (2009)
reported similar circulation patterns and enhanced
recruitment of rock scallop Spondyllus calcifer larvae
and black murex Hexaplex nigritus larvae coming
from the south of the NGC and settling into marine
reserves located to the north. The currents along the
eastern shore of the NGC may reach speeds of up to
0.06 m s–1 during the summer (Cudney-Bueno et al.
2009), which, considering a planktonic larval dura-
tion of approximately 30 d for the gulf coney in the
NGC (K. Rowell, Biology Department, University of
Washington, pers. comm.), would easily allow them
to disperse between adjoining spawning aggre gates
and even beyond. Within 4 wk, larvae may travel the
148 km distance that separates Puerto Libertad and
Puerto Peñasco, the southernmost and northernmost
spawning aggregations.
Management of spawning aggregations
Our study highlights the importance of detailing
the genetic structure and gene flow between spawn-
ing aggregations even at small geographic scales
where panmixia is expected. The weak genetic
structure found here can have important implications
in ecology and conservation biology of this species.
Management programs for the gulf coney in the
NGC should reinforce protection of the southernmost
spawning aggregation near Puerto Libertad. Both
shallow genetic structure and an anticyclonic direc-
tion of gene flow suggest that Puerto Libertad is a
source population, primarily exporting larvae north
into the NGC. Oceanographic data suggest that
Puerto Libertad may also play an important role as a
gateway for larvae and gene flow originating from
the middle gulf (e.g. Danell-Jiménez et al. 2009). If
this pattern proves to be true, overfishing in the mid-
dle gulf region may have dire consequences for the
populations in the NGC.
Acknowledgements. We thank the Intercultural Center for
the Study of Deserts and Oceans (CEDO), especially R.
Loiaza, I. Martinez and A. Sanchez; as well as Community
and Biodiversity, AC (COBI) especially N. Encinas, C.
Moreno and M. Rojo. We also thank 2 anonymous reviewers
who provided helpful comments on an earlier version of
the manuscript. Funding for this study was provided by the
David and Lucile Packard Foundation, grant award #2008-
32210. R.B. was also funded by the Fundação para a Ciência
e a Tecnologia (SFRH/BPD/26901/2006). Collecting permits
for this study were issued by SAGARPA under the National
Commission for Aquaculture and Fisheries, permit #2885.
This is a scientific contribution to the PANGAS project (www.
pangas.arizona.edu).
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Editorial responsibility: Per Palsbøll,
Groningen, The Netherlands
Submitted: January 24, 2013; Accepted: November 3, 2013
Proofs received from author(s): February 15, 2014
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