Atlantic bonito (Sarda) genomic analysis reveals population differentiation across Northeast Atlantic and mediterranean locations: Implications for fishery management

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Marine Environmental Research xxx (xxxx) xxx
Please cite this article as: Judith Ollé-Vilanova et al., Marine Environmental Research, https://doi.org/10.1016/j.marenvres.2024.106408
Available online 13 February 2024
0141-1136/© 2024 Elsevier Ltd. All rights reserved.
Atlantic bonito (Sarda) genomic analysis reveals population differentiation
across Northeast Atlantic and mediterranean locations: Implications for
shery management
Judith Oll´
e-Vilanova
a
, Ghailen Hajjej
b
, David Macias
c
, S´
amar Saber
c
, Pedro G. Lino
d
,
Rub´
en Mu˜
noz-Lechuga
d
,
e
, Sid’Ahmed Baibbat
f
, Fambaye Ngom Sow
g
,
N’guessan Constance Diaha
h
, Rosa M. Araguas
a
, Núria Sanz
a
, Jordi Vinas
a
,
*
a
Laboratori Ictiologia Genetica, Departament de Biologia, Universitat de Girona, 17003, Girona, Catalunya, Spain
b
National Institute of Marine Science and Technology, Tunisia
c
Centro Oceanogr´
aco de M´
alaga. Instituto Espanol de Oceanografía (IEO-CSIC), Spain
d
Instituto Portugues Do Mar e da Atmosfera (IPMA), Avenida 5 de Outubro S/n, 8700-305, Olh˜
ao, Portugal
e
Department of Biology, Faculty of Marine and Environmental Sciences, University of C´
adiz, 11510, Puerto Real, C´
adiz, Spain
f
Laboratoire des Peches (Dakhla), Morocco
g
Centre De Recherches Oceanographiques de Dakar, Senegal
h
Centre National de Recherches Oc´
eanologiques, Abidjan, Cote d’Ivoire
ARTICLE INFO
Keywords:
Atlantic bonito
ddRadSeq
Population genomics
Atlantic -mediterranean transition
Fishery management
Pelagic
Genetics
Conservation
ABSTRACT
The Atlantic bonito (Sarda, family Scombridae) is a pelagic species and one of the most exploited small tuna
species. Despite its economic importance, biological information is scarce with no associated management di-
rectives. In this study, using a population genomic approach resulted in a lack of panmixia of two genetic pools
with different effective population sizes (east central-tropical Atlantic and northeast Atlantic-Mediterranean) and
an intermixing zone in Atlantic Morocco. The absence of genetic heterogeneity between the locations in Atlantic -
Mediterranean transitional zone adds new evidence that challenges the Strait of Gibraltar as a phylogeographic
barrier for marine pelagic species. These results are proposed to the ICCAT Commission to establish management
areas for this species, although they are not consistent with the recently adopted pelagic ecoregions. Finally, a
panel of highly informative SNPs was developed for efcient and low-cost population assignment and the
analysis of unresolved population structures.
1. Introduction
In the epipelagic realm, assessing the population structure of marine
species is particularly challenging. The apparent lack of biogeographical
barriers and the potential homogenizing effect of marine currents,
together with the biological characteristics of epipelagic organisms,
such as migratory behavior, long lifespan, large population sizes, and
pelagic larval stages, are thought to promote low levels of genetic dif-
ferentiation (Cowen et al., 2007). However, this classical view of the
population structure of these pelagic species has been challenged in
numerous studies. A combination of several biotic and abiotic factors
contributes to the formation of genetically differentiated populations
(Oll´
e et al., 2022; R.S. Waples, 1998). Historical paleogeographic
changes at ocean levels may have contributed to the isolation of pop-
ulations, leaving a genetic ngerprint that can currently be detected
genetically (Allaya et al., 2015; Forde et al., 2023). Furthermore, species
life history traits and strategies, such as the duration of larval stages
combined with phylogeographic discontinuities caused by oceanic
fronts or gyres, could promote local retention and self-recruitment of
larvae, contributing to the genetic differentiation of populations (Pasc-
ual et al., 2017; Van Wyngaarden et al., 2017).
A classic example of a gene ow barrier is the gateway between the
Atlantic Ocean and the Mediterranean Sea that is he Strait of Gibraltar
and its associated Almeria-Oran oceanographic gyre (Patarnello et al.,
2007). Conversely, for some species, especially large pelagic sh, the
Strait of Gibraltar does not seem to act as a barrier to gene ow. For
* Corresponding author.
E-mail address: jordi.vinas@udg.edu (J. Vinas).
Contents lists available at ScienceDirect
Marine Environmental Research
journal homepage: www.elsevier.com/locate/marenvrev
https://doi.org/10.1016/j.marenvres.2024.106408
Received 10 November 2023; Received in revised form 8 February 2024; Accepted 12 February 2024

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2
instance, in pelagic species such as Atlantic bluen and albacore tunas
(Davies et al., 2011; Rodríguez-Ezpeleta et al., 2019), it has been
observed that the genetic pool of Mediterranean populations extends
into the waters of the eastern Atlantic, crossing this elusive biogeo-
graphic barrier. Thus, it is essential to identify these biogeographical
barriers that can lead to the isolation of populations to promote the
conservation of the species. Particularly in the case of species considered
shery resources, knowledge of the population structure is essential to
propose management measures (Nielsen et al., 2023; Reiss et al., 2009).
This is even more crucial when the lack of knowledge of not only the
population structure but also basic biological traits of the species hinders
the proposal of appropriate management measures (Lucena-Fr´
edou
et al., 2021).
A paradigmatic case of a commercially important species is the
Atlantic bonito, whose existing biological information is scarce and
focused on specic areas (Kahraman et al., 2014; Macías et al., 2005).
Atlantic bonito belongs to the family Scombridae, which also includes
other highly prized species such as Bluen tuna (Thunnus thynnus), al-
bacore (T. alalunga), and Little tunny (Euthynnus alletteratus). Although
it is considered a small tuna species, it can reach up to 100 cm in length
and weigh more than 7 kg (Collette and Nauen, 1983). It is mainly
targeted by coastal and artisanal sheries, although signicant catches
are also from bycatch (Juan-Jord´
a et al., 2020), being one most
exploited small tuna sh species with more than 20,000 mt per year
(Lucena-Fr´
edou et al., 2021). Despite Atlantic bonito of this high eco-
nomic, there are currently no management measures for this species. In
this regard, the International Commission for the Conservation of
Atlantic Tunas (ICCAT) initiated a specic research plan in 2017 to
deepen knowledge of biology (focused on reproduction and growth) and
population structure, prioritizing Atlantic bonito, as well as the little
tunny and wahoo as a species objective (SCRS, 2019).
The aim of the present study is to analyze the genetic population
structure of Atlantic bonito in localities in the eastern Atlantic and
western Mediterranean, located on both sides of the Strait of Gibraltar.
In a preliminary study, using a single mitochondrial marker and with a
geographic range similar to that used in the present study, genetic dif-
ferentiation between localities in the eastern Atlantic and Mediterra-
nean areas was detected (Vi˜
nas et al., 2020). Here, we use a genomic
approach, ddRadseq, which has proven to be a highly effective tool for
determining population structure in pelagic species, particularly when a
shallow level of genetic differentiation is expected, and allows the
detection of possible genomic regions susceptible to selection (Narum
et al., 2013). Thus, the importance of this study is, at least, twofold: i) to
evaluate the possible biogeographical barrier of the Strait of Gibraltar in
this pelagic sh species, and ii) to acquire essential knowledge of the
population structure to promote sustainable sheries.
2. Material and methods
As part of the ICCAT SMT program (Fr´
edou et al., 2021), samples of
92 Atlantic bonitos were collected from the years 2018–2019 in seven
locations in the western Mediterranean and eastern Atlantic species
distribution. Locations were distributed in three ICCAT’s statistical areas
(see Table 1 and Fig. 1). A small muscle tissue or portion of the n was
excised, preserved in 96% ethanol, and shipped to the Laboratori d’Ic-
tiologia Gen`
etica of the Universitat de Girona.
Total DNA extraction was performed using the DNeasy Blood &
Tissue Kit (Qiagen), including an RNase A digestion step. DNA quantity
and integrity were assessed using a Qubit dsDNA HS Assay Kit (Life
Technologies) and a 1% agarose gel. For RAD-seq library preparation, a
total amount of 100 ng of DNA per sample was digested with the two
endonucleases, PstI and MspI. Adaptor and padding sequences were
added to each fragment. Paired-end sequencing of 151 bp was per-
formed using Illumina NovaSeq 6000 S
4
(Genomics Center, University of
Minnesota). Once the reads were sequenced, padding sequences and
restriction sites were removed following the sequencing service di-
rections with gbstrim. pl (https://bitbucket.org/jgarbe/gbstrim/src/
master/). After removing the padding sequences and restriction sites,
reads with different lengths were trimmed to 80 bp using Process_radtags
in STACKS 2 ((Rochette et al., 2019). Process_radtags were also used to
lter reads that presented low-quality scores (sliding window 0.25 and
score threshold 20). FastQC and MultiQC (“FastQC,” 2015) were used to
assess read quality during cleansing. Reads were mapped against the
Euthynnus afnis reference genome (BPLY00000000.1 VRT
02-OCT-2021) using BWA-Mem (Li and Durbin, 2009) with default pa-
rameters. From the aligned BAM les, a loci catalog was created under
gstacks in STACKS2. The populations program from STACKS2 was used
to extract information only from biallelic loci and generate the VCF le
with the following parameters: 0.05 as the minimum rate to accept a
polymorphism; maximum observed heterozygosis for a locus 0.6, to
avoid paralogous loci; minimum percentage of individuals in a popu-
lation of 0.75, and two as the minimum populations to retain a locus.
2.1. Filtering radseq
STACK’s output data were ltered for variant depths with less than
0.05 missing data for each marker across all samples using the R package
vcfR (Knaus and Grünwald, 2017). Detection loci under selection were
realized using two independent methods, considering only loci under
selection the ones shared by both. The OutFLANK R package (Whitlock
and Lotterhos, 2015) was used to infer the F
ST
distribution of neutral loci
by trimming the extreme values. OutFLANK was used in the ltered data
set using the following parameters left and right trimming fractions of
0.05, minimum heterozygosity of 0.1, and FDR q-value of 0.01. The
second approach was realized using the pcadapt package (Luu et al.,
2017; Priv´
e et al., 2020), which infers the loci under selection based on
principal component analysis (PCA). Pcadpat determines the population
structure based on PCA and computes a vector containing K z scores to
infer the extent to which an SNP is related to the rst K principal
components. Using the command scree plot, the number of PCs to be
retained was selected when the eigenvalues were attened. Outliers
were selected using the FDR Bonferroni correction with a q-value of
0.01.
Table 1
Sample description and basic genetic diversity indices for each location. ICCAT statistical area; N, sample size; Ar, Allele richness; Obs_Het, Observed heterozygosity;
Exp_Het, Expected Heterozygosity; F
IS
, F-statistic inbreeding coefcient; HWE, Hardy-Weinberg Equilibrium.
Location Code N ICCAT area Ar Obs_Het Exp_Het F
IS
Test global HWE
East Atlantic
Abidjan Cˆ
ote D’Ivoire CIV 14 AT-SE/BIL97 1.788 (1.713–1.856) 0.220 0.210 −0.039 (−0.107–−0.045) 1.000
Hann- Senegal SEN 14 AT-NE/BIL94B 1.783 (1.710–1.842) 0.228 0.210 −0.067 (−0.135–−0.070) 1.000
Dahkla- Morocco MOR 14 AT-NE/BIL94B 1.787 (1.695–1.839) 0.233 0.214 −0.068 (−0.148–−0.067) 1.000
Peniche- Portugal North PRT_N 11 AT-NE/BIL94B 1.791 (1.733–1.848) 0.222 0.214 −0.036 (−0.104–−0.052) 1.000
Olhao- Portugal South PRT_S 14 AT-NE/BIL94B 1.809 (1.736–1.870) 0.223 0.216 −0.031 (−0.088–−0.045) 1.000
Mediterranean
Malaga- Spain ESP 14 MD/BIL95 1.792 (1.678–1.854) 0.230 0.211 −0.066 (−0.135–−0.065) 1.000
Gabes- Tunis TUN 11 MD/BIL95 1.772 (1.676–1.836) 0.232 0.214 −0.067 (−0.150–−0.077) 1.000
J. Oll´
e-Vilanova et al.

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Fig. 1. Map of sampling localities. Sampling codes described in Table 1. Dotted grey lines and text represented the ICCAT’s BIL statistical areas used as references for
management. Colour of each location is maintained thorough the study.
J. Oll´
e-Vilanova et al.

Marine Environmental Research xxx (xxxx) xxx
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2.2. Genetic population analysis
Basic genetic population indices, including genetic diversity, het-
erozygosity, allelic richness (Ar), and F
IS
, were estimated using the
command basicStats from the package diveRsity (Keenan et al., 2013).
Discriminant analysis of principal components (DAPC) (Jombart et al.,
2010) was run using the adegenet (T. Jombart and Ahmed, 2011)
package. The number of PCs to retain was inferred by cross-validation
and using the a-score as described in the adegenet package. Then, to
identify the optimal number of clusters (K) it has run sequentially with
increasing values of K. The Bayesian Information Criterion (BIC) of the
different clustering solutions are compared, accepting the most optimal
solution the one with the lowest BIC. Finally, the number of clusters and
the group membership probabilities were estimated using the distribu-
tion of the discriminant functions. Additionally, to the supervised DAPC
method, the genetic structure was also validated by an unsupervised
non-model-based approach using a principal component analysis (PCA)
as implemented in the package adegenet. Pairwise F
ST
among locations
was tested using the command pairwise. neifst using the package hierfstat
(Goudet, 2005). Analysis of molecular variance (AMOVA) (Excofer
et al., 1992) testing different possible population structures was realized
using the command poppr. amova from the package Poppr (Kamvar et al.,
2014).
Several panels with the most signicant SNPs were obtained from the
genetically separated locations using the SambaR package using the
ndstructure function which detects the SNPs with the highest standard
deviation in population minor allele frequencies (de Jong et al., 2021).
Each panel was tested using a the unsupervised PCA analysis in adegenet.
The effective population size (N
e
) of each genetically differentiated
population was inferred using NeEstimatorV2.1 (Do et al., 2014) soft-
ware based on linkage disequilibrium using the nonrandom association
of alleles at different gene loci algorithm. Finally, isolation by distance
was evaluated by testing the correlation by the linearized pairwise F
ST
and geographical distance using a Mantel test with 1000 permutations
using the adegenet (Jombart, 2008) and ade4 (Dray and Dufour, 2007)
packages.
3. Results
The number of demultiplexed sequence reads ranged from 11.4 to
34.1 million per individual, with an average of 17.4 (±4.8). Once reads
were cleansed and padding and restriction enzyme sequences removed,
the remaining reads ranged from 8.4 to 25.9 million, with an average of
12.6 (±3.7) per sample, representing an average of 72.5% (±3.9) of the
reads kept per sample. The RAD loci catalog consisted of 629,186 loci
with a mean of 137.4 sites per loci, and coverage ranged from 25.6x to
68.9x with an average of 35.3x (±7.9x), which resulted in a total of
98.842 variants with 27.96% missing data. Finally, the data set with less
than 2.5% missing data consisted of 8770 SNPs.
Similar levels of genetic diversity were obtained for all locations
(Table 1), with a lack of inbreeding and an excess of heterozygotes, as
noted by signicant negative F
IS
values across locations. Of the 8770
SNPs, only 4 SNPs were detected as outliers by both approximations
(Outank and PCAadapt). BLAST results of these loci are described in
Supplementary Information Table S1. None of the loci resulted in sig-
nicant alignment with a well annotated gene. Since only 4 SNPs
(0.045% of the total) were considered under selection, we decided to
keep a unique data set including all the SNPs for all subsequent analyses.
Fig. 2. Discriminant Analysis of Principal Components (DAPC). A, Number of clusters (K) identied using the Bayesian Information Criterion (BIC). B, Individual
density plot on the rst discriminant function. C, correspondence between locations and inferred clusters. D, DAPC scatterplot where dots represent individuals,
colours denoting sampling origin and inclusion of 95% inertia ellipses. Colour codes for locations as same as Fig. 1.
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The BIC criterion of the number of clusters was the lowest in the
aggrupation with K =2 (Fig. 2A). The two groups can be recognized by
plotting the densities of individuals on the membership of a discriminant
function (Fig. 2B). The rst group including the samples from the central
east Atlantic (CIV, SEN and MOR) and a second group of samples from
the northeast Atlantic and Mediterranean (PRT_N, PRT_S, ESP and TUN).
The individuals ‘s location membership to each group (in this case two
groups) can also be summarized plotting the group membership to each
individual (Fig. 2C), and also the scatter plot (Fig. 2D). This population
structure was further conrmed by unsupervised PCA by aggregating
the locations into the same two groups detected previously (Fig. 3).
Finally, the posterior probably membership probability (Fig. 4) de-
picts a situation of limited intermixing of locations. Accordingly, no
shared genotypes were found between CIV and TUN. On the other hand,
MOR is situated in an intermediate situation. Some genotype represen-
tatives from ESP can be found from TUN up to SEN (but not in CIV), with
a reduction in ESP genotypes as geographical distance increases. On the
other hand, the connection between the two localities on each side of the
strait (PRT_S and ESP) can be observed by the high number of shared
genotypes between them (Fig. 4). According to the posterior probability,
these two locations present 32% and 25% of external genotypes,
respectively. In both cases, around 70% of these external genotypes were
from the location on the other side of the strait. The pattern of the two
groups of samples was also observed in the pairwise F
ST
s results (Table 2
and Fig. 5A). As expected, the two locations with the highest F
ST
s values
correspond to the two locations most geographically separated (i.e., CIV
and TUN) (Fig. 5B). Therefore, it is not surprising to nd a slight but
positive correlation between geographical distance and genetic differ-
entiation (Mantel test of isolation by distance p value: 0.029). Further-
more, it can be observed some degree of genetic differentiation within
groups. For instance, the Tunis (TUN) location is signicantly differen-
tiated than the other three locations of the same aggregation (PRT_N,
PRT_S, ESP). In contrast, there is no clear pattern of differentiation in the
other group. The MOR locality is signicantly differentiated from SEN,
but not from CIV location.
The analysis of molecular variance (AMOVA) also supports the ag-
gregation of locations in two groups. Between groups variance (F
CT
and
Va) is maximized when the locations were grouped into the two areas
(central east Atlantic, and northeast Atlantic/Mediterranean) (Supple-
mentary Table S2). Remarkably, when the locations were grouped
following a geographical arrangement (i.e., Atlantic vs Mediterranean
locations), F
CT
and Va were not statistically signicant (Supplementary
Table S2).
Accepting the population structure of K =2, samples were grouped
according to this pattern. Subsequently, the effective population size of
each genetically differentiated unit can be estimated. The group of lo-
cations in the central east Atlantic (CIV, SEN and MOR) resulted in a
relatively low effective population size of Ne =439.0 individuals,
whereas the other group (PRT_N, PRT_S, ESP and TUN) resulted in a
larger estimated effective population size of Ne =1555.7 individuals.
Finally, the validation of the panels of the most signicant SNPs was
based on the population structure of the two groups. PCAs depicted that
using only 100 selected SNPs is adequate to detect the observed genetic
structure (See Supplementary Fig. 1). This panel of SNPs could be used
for a low-cost and efcient methodology of evaluating the population
structure in Atlantic bonito.
4. Discussion
The population structure of Atlantic bonito along the eastern
Atlantic– Mediterranean region results in a lack of panmixia, with two
differentiated genetic pools (Figs. 2 and 3). One genetic cluster includes
two locations within the Mediterranean (Tunis and Spain) and two lo-
cations along the coast of Portugal in the Northeast Atlantic. The other
genetic pool is composed of the three central-tropical Atlantic locations
of Morocco, Senegal, and Cˆ
ote d’Ivoire. Although all results indicate the
presence of two distinct populations, the relatively small sample size
may compromise these results. However, previously, in a preliminary
study using a single molecular marker (mtDNA control region), Vi˜
nas
et al., (2020) also supported the genetic structure found here using
ddRADseq. Therefore, the well-known higher resolution of genomic
approaches over conventional genetic methods, even with small sample
sizes (Andrews et al., 2016; Hohenlohe et al., 2021), conrms the pop-
ulation structure of two well-differentiated genetic pools. These results
substantiate the capability of this species to form in genetically differ-
entiated locations at large oceanic levels (between the northeast Atlantic
and northwest Atlantic (Vi˜
nas et al., 2010) and also at a more regional
scale, within the Mediterranean (Turan, 2015; Vi˜
nas et al., 2004).
The genetic structure of the population obtained here highlights the
absence of genetic heterogeneity between localities on both sides of the
Strait of Gibraltar with (PRT_S and ESP) as indicated by the large
number of genotypes shared between these two locations (Fig. 4) lack of
FST signicance (Table 2). This indicates that the west Mediterranean
genetic pool extends into the northeastern Atlantic Ocean, as suggested
by the migratory movement of this species across Strait of Gibraltar (Rey
et al., 1984; Rey and Cort, 1981). The inuence of the Mediterranean
population on the eastern Atlantic in a highly vagile species was also
Fig. 3. Principal component analysis (PCA), using the whole RAD-seq dataset,
showing individuals and 95% inertia ellipses on the coordinates of the rst two
components. Colour codes for locations as same as Fig. 1.
Fig. 4. Posterior probability membership for each individual grouped by lo-
cations. Colour codes for locations as same as Fig. 1.
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Marine Environmental Research xxx (xxxx) xxx
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observed in swordsh (Xiphias gladius) (Smith et al., 2015), with a
strikingly similar pattern to the Atlantic bonito. In swordsh, the Med-
iterranean genetic pool extends to the Atlantic coast of Morocco.
Concordance with this pattern has also been observed in other small
tuna species: Bullet tuna (Auxis rochei) (Oll´
e-Vilanova et al., 2022),
Frigate tuna (Auxis thazard) (Oll´
e et al., 2019) and Little tunny
(Euthynnus alletteratus) (Oll´
e et al., 2022), where no genetic differenti-
ation was detected between localities on either side of the Strait of
Gibraltar. However, in these cases, the geographical location of the gene
ow boundary in the Atlantic could not be determined due to the lack of
appropriate samples in the Atlantic region. All these results, together
with the well-established migratory movement of these species through
the strait, fuel the controversy about whether the Strait of Gibraltar
represents a phylogeographic barrier for marine species (Patarnello
et al., 2007). In most of these cases, the population structure is more
dependent on oceanographic conditions (such as gyres and others) and
philopatric behavior of the species (Oll´
e-Vilanova et al., 2022; Patar-
nello et al., 2007; Smith et al., 2015), rather than the Strait of Gibraltar
acting as a biogeographical barrier.
Additional evidence for the existence of two population units is the
different demography of each genetic differentiated pool. The estima-
tion of the effective population size was approximately threefold higher
for the Mediterranean northeast Atlantic population (Ne =1555.7) than
for the central tropical Atlantic population (Ne =439.0). Assessment of
Ne in marine species can be extremely misleading (Marandel et al.,
2019; R. S. Waples, 2016), although it can be overcome when hundreds
or thousands of markers are used (F. Marandel et al., 2020; R. K. Waples
et al., 2016), as was done in this study. However, considering that in
recent studies on large pelagic species, the Ne/N ratio has been esti-
mated to be as low as 0.25 (R. S. Waples et al., 2018), it would imply that
both populations, but especially the centraol-tropical population, could
be compromised by not having enough genetic diversity to adapt to
environmental and anthropogenic threats.
The result of genetic differentiation in these two units seems to
contradict the positive IBD correlation also detected here. However, the
identication of a correlation between genetic differentiation and
geographic distance does not exclude the presence of genetically
differentiated structures. Previously, a population structure based on
IBD was detected in this species in the Mediterranean (Turan, 2015;
Vi˜
nas et al., 2004). Thus, given all the results, it can be inferred that
population structure based on IBD for Atlantic bonito would encompass
an extensive area from the eastern Mediterranean to the Atlantic African
coast. It remains to be determined whether this IBD pattern extends into
the western Atlantic, as well as what occurs in other unsampled regions
such as the Cantabrian and North Sea, where this species is also present.
Furthermore, in addition to IBD, adaptation could contribute to the
genetic differentiation of populations, as has been seen in other small
pelagic species, such as anchovy (G. Catanese et al., 2017; Montes et al.,
2016) or horse-mackerel (Fuentes-Pardo et al., 2023). This possibility
could not be ruled out; however, it should be noted that only four (out of
800 SNPs) were detected under selection. Thus, and putting all the re-
sults together, Atlantic bonito stands out as a canonical example of
population structure in a marine pelagic species, where the combination
of the assumed absence of barriers is expected to produce a population
structure of isolation by distance. However, it does not exclude the
formation of discrete genetic groups, probably as a consequence of the
combination of elusive biogeographic barriers with philopatric behavior
(Pascual et al., 2017).
Another signicant aspect is the intermediate situation of the
Moroccan location. Although genetically grouped with Ivory Coast and
Senegal, Morocco is the location that shares more genotypes with other
areas (approximately 33.3% of the genotypes come from other locations
(Fig. 4), suggesting that the inuence of the northern genetic pool,
although diluted, could reach up to Morocco. Studies on the migratory
movements of Atlantic bonito are scarce, and although its high migra-
tory capacity of more than 35 km/day has been reported (Rey et al.,
1984), the species seems to show a philopatric behavior toward its
breeding areas, with an evidence that some individuals reside
year-round in the western Mediterranean (Sabat´
es and Recasens, 2001).
In the case of Morocco, this region seems to act as a breeding ground, as
Table 2
Pairwise FSTs values and associated p values among all locations. Below diagonal F
ST
s values; above diagonal, p values. Locations codes as Table 1.
CIV SEN MOR PRT_N PRT_S ESP TUN
CIV – 0.00E+00 1.27E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00
SEN 0.003 – 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
MOR 0.001 0.003 – 0.00E+00 0.00E+00 0.00E+00 0.00E+00
PRT_N 0.010 0.008 0.007 – 5.46E-02 0.00E+00 0.00E+00
PRT_S 0.008 0.007 0.005 0.001 – 1.00E-04 0.00E+00
ESP 0.010 0.009 0.006 0.004 0.002 – 0.00E+00
TUN 0.015 0.013 0.012 0.005 0.004 0.006 –
Fig. 5. A, Heatmap of the pairwise F
ST
s values; colour gradients represent F
ST
s values, from lowest (dark green) to highest (dark red). B, Boxplot of the F
ST
s values for
each location when compared to the rest of locations. Colour codes for locations as same as Fig. 1.
J. Oll´
e-Vilanova et al.

Marine Environmental Research xxx (xxxx) xxx
7
indicated by the presence of larvae on the Moroccan coast (Arkhipov and
Pak, 2019; Rodríguez-Roda and Di Centa, 1981), together with the
capture of actively reproductive individuals in this region (Baibbat et al.,
2017; Petukhova, 2020). Migratory philopatric behavior toward this
breeding ground seems to be conrmed by tagging, where at the
beginning of the reproductive season, sh from the western Mediterra-
nean were tagged in the tuna trap of Ceuta and then recovered in both
the South and North Atlantic areas of the Strait (Rey et al., 1984; Rey
and Cort, 1981). Within the Mediterranean, the Tunis location seem to
be genetically differentiated from rest of locations of the same group.
Adding new evidence of the capability of this species to form genetically
differentiated populations on a relatively small scale. However, for this
location there is little biological information to support this putative
distinct population, but with some evidence ruling out the Tunisian
region as a breeding area (Koched et al., 2016). Therefore, further
analysis is needed to establish the population structure of this species in
the southern Mediterranean.
5. Applications
Fishery management should be based on the identication of
demographically independent genetic units (Hauser and Carvalho,
2008; R. S. Waples et al., 2008). Despite its economic importance, this
species lacks a specic management directive or stock structure. This is
probably a consequence of the deciency of knowledge of life-history
traits and historical catches (Lucena-Fr´
edou et al., 2021). To ll this
gap, in 2017, ICCAT prioritized biological research on the reproduction,
growth, and structure of Atlantic bonito and two other economically
relevant small tuna species (i.e., little tunny tuna and wahoo) (SCRS,
2019).
With no appropriate stock differentiation, assessment and manage-
ment ICCAT uses the BIL (Billsh areas) areas only as a starting point to
collect sheries catch and statistical data on what is called small tuna
species. Remarkably, there is incongruence between the results pre-
sented here and these statistical areas (see Table 1 and Fig. 1 for the
assignation of locations to the ICCAT’s statistical areas). The genetically
differentiated Mediterranean northeast Atlantic pool comprises two
different ICCAT statistical areas (Stocks): the Mediterranean BIL 95 and
the northeast Atlantic BIL 94B. Similarly, the other genetic pool also
comprises two ICCAT statistical areas, Morocco, and Senegal, in the BIL
94B area, whereas Cˆ
ote d’Ivoire is in BIL 97.
Recently, ICCAT, along with other tuna shery management com-
missions, committed to adopting an ecosystem approach to sheries
management in which spatial management units should be ecologically
meaningful (Todorovi´
c et al., 2019). In a recent paper, Todorovi´
c et al.
(2019) proposed up to seven pelagic ecoregions in the Atlantic – Med-
iterranean area based on the existing knowledge of biogeographic
classications of the pelagic environment, together with the spatial
distribution and dynamics of the main shing eets species. Our results
also challenge the boundaries of these regions. For instance, the
continuous genetic pool between the Mediterranean and the northeast
Atlantic comprises two Todorovi´
c’s ecoregions: Mediterranean and
Northern Atlantic Temperate. The other genetic pool is completely
included within the Atlantic tropical ecoregion. It should be noted,
however, that these ecoregions are formulated without considering data
from small tuna species. Thus, the results presented here should serve as
a starting point for shery managers to delimit the boundaries of man-
agement stocks.
In this study, we developed a panel of a few highly informative SNPs
that, with their genotyping, are potentially sufcient to infer the pop-
ulation structure detected by more than 8000 SNPs. This is a streamlined
application for inferring possible population structure and population
assignment of individuals using low-cost genotyping tools (such as
Fluiding, Massarray, TaqMan among others) that have been successfully
applied in other sh species (Catanese et al., 2016; King and Stevens,
2021). As mentioned before, at least two open questions remain
unresolved: the complexity of population structure within the Mediter-
ranean Sea (especially in the south), and the population genetics of the
intermixing zone between the two genetically differentiated pools (the
area between southern Morocco and Mauritania). In addition, this panel
of SNPs could help in inferring the population structure of other
non-sampled areas. For instance, using mitochondrial DNA and nuclear
markers, Vi˜
nas et al. (2010) described a genetically differentiated pop-
ulation in the West Atlantic. Thus, the application of this panel of
markers could help resolve all these questions efciently.
CRediT authorship contribution statement
Judith Oll´
e-Vilanova: Formal analysis, Investigation, Writing –
original draft. Ghailen Hajjej: Resources. David Macias: Validation,
Writing – original draft. Sama Saber: Validation, Writing – original
draft. Pedro G. Lino: Validation, Writing – original draft. Rub´
en
Mu˜
noz-Lechuga: Validation, Writing – original draft. Sid’Ahmed
Baibbat: Resources. Fambaye Ngom Sow: Resources. N’guessan
Constance Diaha: Resources. Rosa M. Araguas: Formal analysis,
Validation. Núria Sanz: Formal analysis, Validation. Jordi Vinas:
Conceptualization, Formal analysis, Funding acquisition, Project
administration, Supervision, Writing – original draft, Writing – review &
editing.
Declaration of competing interest
The authors declare the following nancial interests/personal re-
lationships which may be considered as potential competing interest.
Jordi Vinas reports nancial support and administrative support
were provided by International Commission for the Conservation of
Atlantic Tunas. If there are other authors, they declare that they have no
known competing nancial interests or personal relationships that could
have appeared to inuence the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
‘‘This work was carried out under the provision of the ICCAT Small
Tunas Year Program (SMTYP). The contents of this paper do not
necessarily reect the point of view of ICCAT, which has no re-
sponsibility over them, and in no ways anticipate the Commission’s
future policy in this area.’’ We would like to thank all the members of
the ICCAT Small Tuna Species Group for their comments during the
meetings that really helped in the construction of this study.
This research was funded by ICCAT Small Tunas Year Program
(SMTYP) and partially by the European Union through the EU Grant
Agreement No. S12.819116—Strengthening the scientic.
basis for decision-making in ICCAT.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.marenvres.2024.106408.
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