Integration of genomic and ecological methods inform management of an undescribed, yet highly exploited, sardine species

Abstract

Assessing genetic diversity within species is key for conservation strategies in the context of human-induced biotic changes. This is important in marine systems, where many species remain undescribed while being overfished, and conflicts between resource-users and conservation agencies are common. Combining niche modelling with population genomics can contribute to resolving those conflicts by identifying management units and understanding how past climatic cycles resulted in current patterns of genetic diversity. We addressed these issues on an undescribed but already overexploited species of sardine of the genus Harengula. We find that the species distribution is determined by salinity and depth, with a continuous distribution along the Brazilian mainland and two disconnected oceanic archipelagos. Genomic data indicate that such biogeographic barriers are associated with two divergent intraspecific lineages. Changes in habitat availability during the last glacial cycle led to different demographic histories among stocks. One coastal population experienced a 3.6-fold expansion, whereas an island-associated population contracted 3-fold, relative to the size of the ancestral population. Our results indicate that the island population should be managed separately from the coastal population, and that a Marine Protected Area covering part of the island population distribution can support the viability of this lineage.

Full text

royalsocietypublishing.org/journal/rspb
Research
Cite this article: Coelho JFR, Mendes LF,
Di Dario F, Carvalho PH, Dias RM, Lima SMQ,
Verba JT, Pereira RJ. 2024 Integration of
genomic and ecological methods inform
management of an undescribed, yet highly
exploited, sardine species. Proc. R. Soc. B 291:
20232746.
https://doi.org/10.1098/rspb.2023.2746
Received: 5 December 2023
Accepted: 6 February 2024
Subject Category:
Global change and conservation
Subject Areas:
ecology, evolution, genomics
Keywords:
Harengula, Clupeidae, fishery genomics,
genetic diversity, marine protected areas
Author for correspondence:
Jéssica Fernanda Ramos Coelho
e-mail: jessicovsky@gmail.com
†
Co-last authors.
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.
c.7090089.
Integration of genomic and ecological
methods inform management of an
undescribed, yet highly exploited,
sardine species
Jéssica Fernanda Ramos Coelho
1
, Liana de Figueiredo Mendes
2
,
Fabio Di Dario
3
, Pedro Hollanda Carvalho
3
, Ricardo Marques Dias
4
,
Sergio Maia Queiroz Lima
1
, Julia Tovar Verba
5,†
and Ricardo J. Pereira
5,6,†
1
Departamento de Botânica e Zoologia, and
2
Departamento de Ecologia, Universidade Federal do Rio Grande do
Norte, Avenida Senador Salgado Filho S/N, Campus Universitário, 59078-970, Natal/RN, Brazil
3
Instituto de Biodiversidade e Sustentabilidade - Universidade Federal do Rio de Janeiro, Avenida São José do
Barreto, 764, 27965-045, Macaé/RJ, Brazil
4
Museu Nacional, Universidade Federal do Rio de Janeiro, Quinta da Boa Vista - São Cristóvão, 20940-040,
Rio de Janeiro/RJ, Brazil
5
Evolutionary Biology, Ludwig Maximilian University of Munich, Grosshaderner Strasse 2, 82152, Planegg-
Martinsried, Germany
6
Department of Zoology, State Museum of Natural History Stuttgart, Rosenstein 1–3, 70191, Stuttgart, Germany
JFRC, 0000-0002-3736-8912; JTV, 0000-0001-5399-6890; RJP, 0000-0002-8076-4822
Assessing genetic diversity within species is key for conservation strategies
in the context of human-induced biotic changes. This is important in
marine systems, where many species remain undescribed while being over-
fished, and conflicts between resource-users and conservation agencies are
common. Combining niche modelling with population genomics can con-
tribute to resolving those conflicts by identifying management units and
understanding how past climatic cycles resulted in current patterns of
genetic diversity. We addressed these issues on an undescribed but already
overexploited species of sardine of the genus Harengula. We find that the
species distribution is determined by salinity and depth, with a continuous
distribution along the Brazilian mainland and two disconnected oceanic
archipelagos. Genomic data indicate that such biogeographic barriers are
associated with two divergent intraspecific lineages. Changes in habitat
availability during the last glacial cycle led to different demographic
histories among stocks. One coastal population experienced a 3.6-fold
expansion, whereas an island-associated population contracted 3-fold,
relative to the size of the ancestral population. Our results indicate that
the island population should be managed separately from the coastal
population, and that a Marine Protected Area covering part of the island
population distribution can support the viability of this lineage.
1. Introduction
The current biodiversity crisis is leading to the decrease in population size and the
extinction of some species [1,2], threatening the balance of ecosystems [3,4]. The
Convention on Biological Diversity (CBD) recognizes genetic diversity within
species as one of the three main levels of biodiversity that must be monitored
and preserved, along with species and ecosystem diversity [5]. This is because gen-
etic diversity within species is the heritable variability upon which selection can
act, and thus underlies the ability for a species to adapt and persist in changing
environments [6]. Although the CBD set a target to maintain and restore genetic
diversity within species until 2030 in the Kunming–Montreal Global Biodiversity
Framework, monitoring changes of genetic diversity within species is challenging
because it requires understanding how past and current environment affect the
© 2024 The Author(s) Published by the Royal Society. All rights reserved.
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evolution of intraspecific lineages. This understanding is especially challenging in marine ecosystems, particularly in hyper-diverse
tropical regions of the globe, where species are still being described, their relative abundances and connectivity are unknown, and
yet some species are under strong human exploitation. Conflicts of interest between resource users and conservation managers start
at a local scale owing to environmental policies that disregard scientific data and users’participation in management [7]. These
local conflicts can, however, affect economy, food availability and ecosystem functioning at a broader spatialscale, demanding updated
scientific information to support better conservation laws and practices. The integrations of genomic data [8–10] and ecological mod-
elling [11,12] can potentially provide evidence-based suggestions for conservation plans and contribute to resolve such conflicts.
Determining the number of intraspecific lineages within a species of concern is an important task in conservation biology, as
evolutionarily independent units often require independent management plans [13]. Likewise, assessing relative levels of genetic
diversity within each intraspecific lineage (N
e
or effective population size) is important because it reflects both the differences in cur-
rent number of individuals (Nor census size) and the demographic history of these lineages in response to past environmental change
[14]. Both the number of intraspecific lineages and their genetic diversity depend on evolutionary processes such as genetic drift in
isolated populations, selection in divergent environments and gene flow among them. Because such processes are highly dynamic
over space and time, in order to aid conservation planning, the integration of genomic analyses and ecological niche modelling
can provide fast and useful information regarding the drivers of population size and connectivity of exploited species. These processes
are well understood in terrestrial systems [15–17], yet only recently have they begun to be studied in marine systems [18]. Despite the
high potential dispersal ability of marine taxa, recent studies have shown that several oceanographic barriers, such as oceanic currents
and depth, constrain gene flow [19], therefore enabling diversification within species. Understanding how such oceanographic
barriers condition the number of management stocks, their current levels of genetic diversity, and genetic connectivity remain
important tasks in conservation biology.
Marine systems support the livelihood of millions of people worldwide, increasing the conflict between local communities
and conservation strategies. The application of genomics to fisheries management has contributed in resolving some of these
conflicts by identifying the number of stocks within species of commercial interest, their levels of genetic connectivity, and
relative measures of diversity [20]. Yet, such efforts have been mainly restricted to marine species from the Northern Hemisphere,
where species-level diversity is lower relative to tropical regions. For example, genomic studies in the yellow-fin tuna and
cod have helped identifying divergent intraspecific lineages as distinct fishery stocks with high genetic differentiation that require
independent management [21,22]. Genomic data on species of conservation concern, such as cod, herring and European hake,
have been used to identify the fishing origin of commercial products, and thus aid against illegal, unreported and unregulated fish-
eries [23]. A study in yellow-fin tuna indicates that the populations occurring in different oceans present significant and asymmetric
gene flow [24], allowing the identification of populations that act as source or sink of migrants, another factor relevant for defining
conservation priorities. Studies in a pipefish [19] and in the Arctic charr [25] have quantified changes in effective population size
(N
e
) of different stocks, potentially driven by both their past evolution and more contemporary exploitation by fisheries. Much
less is known about species in tropical waters, even though such species play an indispensable role in the subsistence of local
communities [26].
One such economically important group of fishes worldwide is the Clupeidae, a family that includes several species of forage
fishes popularly known as sardines, herrings and shads, among others [27,28]. The genus Harengula Valenciennes 1847 comprises
four taxonomically recognized species, three of which are reported as occurring in thewestern Atlantic. Whereas Harengula humeralis
(Cuvier, 1829) is anatomically distinct and restricted mostly to the central Atlantic, Harengula clupeola (Cuvier, 1829) and Harengula
jaguana Poey, 1865 are reported as having a much broader distribution, from the US coast to southern Brazil [27]. However, recent
molecular studies indicate that specimens of Harengula off the Brazilian coast belong to a third mitochondrial lineage, which is
more divergent (3.2% in the barcoding gene CO1)thanH. clupeola and H. jaguana (1.8%) [29]. The lineage of Harengula likely endemic
to the Brazilian Biogeographic Province, herein Harengula sp., is apparentlyallopatric relative to congeners and is the only memberof
the Clupeidae present in two oceanic archipelagos of the South Atlantic (Fernando de Noronha (FNO) and Trindade-Martin Vaz;
[30]), suggesting a higher tolerance to ecological conditions than other overall similar species of sardines [31]. Although Harengula
sp. remains unrecognized taxonomically, it shows signs of decline in population size [32] and likely requires specific management
measures [33,34].
Harengula sp. is consumed as part of traditional dishes and is the main source of live bait in several regions of the Brazilian
Exclusive Economic Zone [35]. Currently, there is no specific management strategy for Harengula sp. in Brazil, except in Marine
Protected Areas (MPAs) and their buffer zones. The fishery of this species within the MPA of the FNO archipelago led to conflict
between environmental managers, who established a no-take zone within the MPA in the 1980s, and local fishers, who claimed the
right to fish in this area [36]. In an attempt to resolve this conflict, the Brazilian government lifted restrictions to exploit sardine in
the no-take zone of FNO MPA, initially from 2020 to 2022, and later extended this lift of restrictions until 2024 [37]. Such conflicts
have an impact in the local economy and fishery strategies of the island. To date there is no scientific evaluation of how the island
population is divergent from the coastal population, what is their degree of genetic connectivity, or if there are temporal and spatial
changes in genetic diversity (N
e
).
Here, we addressed these issues by combining ecological niche models and population genomic methods in this highly
exploited species of sardine that remains largely unknown by scientists and legislators. First, we used ecological niche models to
establish predictions on how temporal and spatial changes in habitat suitability have constrained the distribution of Harengula sp.
relative to other formally recognized species of the genus, and how these changes have affected divergence and gene flow within
Harengula sp. Second, we used genomic data to test how those oceanographic barriers led to intraspecific divergence. Finally, we
used demographic modelling to estimate the magnitude and direction of gene flow between independent lineages and changes in
N
e
. These results provide science-based information to support better management strategies focused on the sustainable exploitation
of this species.
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2. Material and methods
(a) Habitat dynamics
To understand the environmental drivers of the distribution of Harengula sp., and how these have changed over time, we fitted an eco-
logical niche model (ENM) in the current climate, and then projected it to the Last Glacial Maximum (LGM), approximately 21 thousand
years ago. We used a machine-learning algorithm of maximum entropy (MaxEnt v. 3.4.4; [38]) in the dismo package 1.3–5 in R v. 4.0.5
[39,40]. MaxEnt default modelling parameters were employed (e.g. beta multiplier and feature classes) to perform the models, which were
then evaluated according to area under the receiver operating characteristic (ROC) curve (AUC) [41,42].
We retrieved 227 georeferenced occurrence records (latitude and longitude) of ‘Harengula clupeola’and ‘H. jaguana’in the Brazilian
Exclusive Economic Zone (EEZ) from online databases and from our fieldwork collection [43,44] (electronic supplementary material,
table S1). Since a previous study showed that Harengula sp.is the only species occurring in the Brazilian EEZ [29], we re-classified these obser-
vations as Harengula sp. (figure 1). To increase accuracy in our database, we only considered georeferenced records that refer to specimens
collected after 1945 [46] and that are available as vouchers in biological collections.
We downloaded four abiotic layers characterizing present and LGM climatic and geophysical aspects of the environment from
MARSPEC, using the sdmpredictors package in R [40,47]. The layers used in the models were: bathymetry (or depth; m), slope (degrees),
mean of sea surface salinity (psu) and mean of sea surface temperature (°C) [48,49]. We chose these variables because they (i) are of
general relevance for species of the Clupeidae [27], (ii) show low correlation between them (Pearson’sr< 0.8), and (iii) are available
for both present and LGM scenarios. To better delimit the area of occurrence of this species and to avoid model overfitting due to differ-
ences between occurrence points and background points [50], we cropped the layers into provinces as defined by Spalding et al.[45].
See electronic supplementary material for details.
(b) Molecular analyses
We collected 92 specimens from 12 sites representing most of the known distribution of Harengula sp., 10 of which are closely associated with
the Brazilian coast, one is located at the coastal island of Abrolhos (ABR), and another is located in the oceanic archipelago of Fernando de
Noronha (FNO) (figure 1; electronic supplementary material, table S1, licences SISBIO 76 053-1 and 74 802-1). Samples were either collected
using a beach seine or bought in local fish markets. Muscle samples were preserved in 100% ethanol and sent for DNA extraction, sequencing
and single nucleotide polymorphism (SNP) genotyping at Diversity Arrays Technology (DArT), following the automated DArTseq method,
hereafter DArTseq. DArTseq is a cost-effective approach for SNP discovery using a reduced representation of the genome [51]. It is similar to
the double digest restriction-site associated DNA sequencing (ddRAD) method as it uses one restriction enzyme that recognizes a common
sequence motif, and another that recognizes a rarer motif. By linking sequencing adaptors to genomic DNA fragments that have both restric-
tion sites and are within a target fragment size, we recover thousands of sequencing tags (or loci) scattered randomly along the genome,
which contain one or more SNPs each [52]. This methodology does not require a reference genome for identifying thousands of SNPs in
homologous sites across individuals and hence is widely applicable to undescribed species lacking such resources.
60° W
0°
10° S
20° S
30° S
Harengula sp.
North Brazil
Current
Southern
Equatorial
Current
Brazil
Current
FNO
ecoregions
North Brazil Shelf
tropical southwestern
Atlantic
warm temperate
southwestern Atlantic
river outflow
CE
RN
PB
PE
AL
BA
ABR
ES
RJ
SP
SC
other
occurrences
50° W 40° W 30° W 20° W 10° W 0°
Figure 1. Observation of Harengula sp. in the southwest Atlantic. Observations are marked by asterisks (occurrence record only), or by coloured circles for sampling
sites included in the genomic analyses. Arrows indicate the direction of main oceanic currents. Ecoregions are marked as in Spalding et al.[45]. Sampling localities:
FNO, Fernando de Noronha archipelago (oceanic island); CE, Ceará; RN, Rio Grande do Norte; PB, Paraíba; PE, Pernambuco; AL, Alagoas; BA, Bahia; ABR, Abrolhos
(continental island); ES, Espírito Santo; RJ, Rio de Janeiro, SP, São Paulo; SC, Santa Catarina.
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From the unfiltered dataset, we removed two individuals with an excessive amount of missing data (greater than 98%), and 166 loci
that are putatively sex-linked (electronic supplementary material, figure S1). We further filtered SNPs considering the assumptions of each
analysis (electronic supplementary material, table S2), using the package dartR in R [53]. For the diversity analyses, we generated a
‘linked-SNPs’dataset, which contained all SNPs across loci, irrespective of the amount of missing data, and a ‘linked-SNPs-0MD’dataset
removing all SNPs containing missing data. For population structure analyses and divergence, we generated an ‘unlinked-best-SNPs’
dataset containing the most informative (i.e. higher frequency) SNP per locus. Finally, for the demographic analyses, we generated an
‘unlinked-random-SNPs’dataset, which retained a random SNP per locus, irrespective of its frequency. See electronic supplementary
material, table S2 for details on the DArTseq service and data filtering details.
(c) Population structure and genetic diversity
To estimate the number of intraspecific lineages of Harengula sp. we used two approaches that differ in their model assumptions. Because
both approaches assume no linkage between SNPs within loci, we used the unlinked-best-SNPs dataset. First, we performed a non-model-
based principal component analysis (PCA) to visualize the genetic similarities between individuals, using the dartR package with the file
in the genlight format [53]. Second, we estimated the most likely number of clusters using the software ADMIXTURE v. 1.3.0 [54], which
estimates individual ancestry by maximizing Hardy–Weinberg and linkage equilibria within Kancestral clusters. For this, we converted
the genlight file to plink using the gl2plink function [53]. We considered models with Kfrom 1 to 12 (total number of sampling sites), and
used 10 independent runs for each K. The most likely number of clusters was estimated based on the lowest cross-validation error which
indicates the predictive accuracy of each model [55].
To estimate the level of genetic divergence within Harengula sp., we used the same unlinked-best-SNPs dataset to calculate pairwise gen-
etic differentiation (F
ST
;[56]) between all sampling sites in R (function gl.fst.pop)[40,53]. Finally, we tested for isolation by distance within the
coastal genetic cluster using the Mantel test in the dartR package (function gl.ibd), accessing its significance using 999 permutations [53,57,58].
To estimate genetic diversity within individuals, sampling sites and clusters, we used the linked-SNPs dataset and calculated nucleotide
diversity based only on variant sites (π-SNP). In contrast with the traditional measure of nucleotide diversity (π;[59]), which computes the
average number of nucleotide differences across sequences with variant and invariant sites, π-SNP specifically assesses nucleotide diversity
in variant sites only. We estimated π-SNP using pixy v. 1.2.7.beta1 [60]. For this, we converted the genlight object to vcf using plink v. 3 and
the gl2vcf function [53]. Using the linked-SNPs-0MD, we calculated inbreeding coefficient (F
IS
), i.e. the level of heterozygosity of an individ-
ual relative to the heterozygosity observed in its cluster or population, using the basic.stats function from the hierfstat package [61]inR.
It is important to note that diversity summary statistics assume that each group conforms to the expectations for Wright–Fisher population,
and thus that allelic frequencies are not influenced by gene flow between populations nor by changes in population size. In contrast, the
algorithm used in ADMIXTURE and in the following demographic analysis considers possible gene flow between clusters.
(d) Demographic history
To test whether the observed genetic variation significantly deviates from the expected variation under neutrality (e.g. due to demo-
graphic change or to selection), we calculated Tajima’sD[62] for each identified cluster, using DnaSP 6 [63] and the linked-SNPs-0MD
dataset because this software does not take missing data into consideration.
To estimate the magnitude and direction of gene flow between population clusters, we used a diffusion approximation method
implemented in δaδi v. 2.0.5 [64] and the δaδi-pipeline [65]. Because this method is based on the site frequency spectrum, we estimated
it based on the dataset unlinked-random-SNPs, which contains 34% missing data, considering variant sites only. We converted the data to
vcf using the function gl2vcf. We reduced the amount of missing data by projecting down the number of SNPs and individuals, choosing a
projection that maintained a relatively large number of segregating sites and individuals. We tested three simpler demographic model
scenarios where change in effective population occurs during population splitting: (i) ‘no migration’, with three parameters: effective
size of population 1 (N
e
1), size of population 2 (N
e
2) and time since split (T); (ii) ‘symmetric migration’, with a fourth parameter reflecting
migration (2N
e
m); and (iii) ‘asymmetric migration’, with two migration parameters in opposing directions (2N
e
m1 >2 and 2N
e
m2 >1).
Additionally, we tested three more complex models allowing additional changes of effective population size after population splitting
(T2): (iv) ‘symmetric migration with size change in one population’, with six parameters (N
e
1a,N
e
1b,N
e
2,2N
e
m,T1 and T2); (v) ‘sym-
metric migration with size change in both populations’, with a seventh parameter describing change in the second population (N
e
2b); and
(vi) ‘asymmetric migration with size change in both populations’, with an eighth parameter describing different migration rates (2N
e
m1 >
2 and 2N
e
m2 >1). Since mutation rate and generation time are unknown for this species, we were not able to estimate absolute parameter
values. Therefore, all parameters were estimated relative to the effective population size of the ancestral population [64]. We selected the
model with a combination of lowest Akaike information criterion (AIC) and higher parameters convergence, accounting for the different
number of parameters in competing models [66,67]. To verify how well the selected model and estimated parameters fitted our data,
we visually inspected the residuals between empirical and model site frequency spectra (SFSs) plotted using the δaδi-pipeline [64].
We considered the parameter values estimated under the model and replicate with lowest AIC score.
3. Results
(a) Habitat dynamics
Our ecological niche model (ENM) for Harengula sp. shows good ability to discriminate between areas where the species occurs and
areas where it does not occur (AUC 0.964, s.d. 0.017). Bathymetry and mean sea surface salinity showed the highest percentage
contribution to model performance, of 73.5 and 23.7%, respectively (electronic supplementary material, figure S2 and table S3).
The ENM for the present ( figure 2b) shows continuous, although heterogeneous, habitat suitability alongca 6100 km of the Brazilian
continental shelf, bounded by the Amazon–Orinoco Barrier in the north and the La Plata River in the south. The suitable habitat at the
oceanic archipelagos of FNO (included in the genetic sampling of this study) and of Trindade-Martin Vaz (not sampled here) are sep-
arated from the coast by approximately 400 and 1000 km of unsuitable habitat, respectively. Seamounts between these islands and the
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coast provide patchy suitable area. All the islands located on the continental shelf, such as Abrolhos (ABR),are connected to the coast by
highly suitable habitat.
The ENM for the LGM (figure 2a) shows that the suitable habitat was narrower than in the present, although still continuous
along the Brazilian coast. Habitat suitability at the oceanic archipelagos of FNO and Trindade-Martin Vaz was similar to the
present model. Islands in the continental shelf had lower suitability than present and were exposed owing to the lower sea level.
(b) Molecular analyses
Our unfiltered dataset comprised 91 individuals genotyped for 66 639 loci, with 98 313 binary SNPs (on average, two SNPs per
locus), a total of 30.35% missing data and an average read depth across all markers of 20 reads per locus. Individuals had an aver-
age of 28.84% missing data, while SNPs had 69%. Five individuals had more than 40% missing data and were excluded from
downstream analyses. After filtering for genotype call consistency (call rate) and locus quality (reproducibility) (electronic sup-
plementary material, table S2), we kept 86 individuals genotyped for 62 295 loci, containing 88 639 SNPs and 29.5% missing
data (linked-SNPs dataset). Further removal of missing data retained 9369 SNPs in the linked-SNPs-0MD dataset. The
unlinked-best-SNPs and unlinked-random-SNPs datasets had monomorphic loci removed, resulting in each dataset consisting
of 59 992 SNPs with 34% missing data.
(c) Population structure and genetic diversity
The first dimensional axis of the PCA (figure 3b) explains 4% of the genetic variance and separates individuals from the coast of
Brazil (hereafter ‘coastal’population), including those from Abrolhos Island on the continental shelf, from individuals collected in
the oceanic FNO archipelago (hereafter ‘island’population). The second dimensional axis explains 1.8% of the variance and further
divides the individuals from the coastal population latitudinally.
The ADMIXTURE analysis shows the highest likelihood for the model assuming two ancestral clusters (K=2,figure 3a), which
separated all the island individuals from all the coastal individuals. Individuals from the island do not share ancestry with the
coastal population. Conversely, individuals from the coast share some ancestry with the island population, with the population
closest to the island CE showing up to 32.4% of island ancestry, and the furthest population (SC) showing no island ancestry.
Genetic differentiation (F
ST
) between the island population and every locality from the coastal population was significant and
above 0.17 (electronic supplementary material, table S4); F
ST
between the island cluster and the coastal cluster was 0.14 (p< 0.05).
Between coastal sites, F
ST
was relatively low and was higher between the extremes of the costal distribution (CE–SC: 0.04, p< 0.05;
electronic supplementary material, table S4). Consequently, there is significant isolation by distance in the coastal population
(y= 0.0054 + 7.1×10
−09x
,R² = 0.318, p= 0.001; figure 4a).
Estimates of individual nucleotide diversity estimated only using variant sites (π-SNP) ranged from 0.047 to 0.069 for the coast,
and from 0.044 to 0.054 for the island (figure 4b). The sampling sites at the known extremes of the species distribution showed
(a)LGM
0°
10° S
20° S
30° S
50° W 40° W 30° W 50° W 40° W 30° W
present
FNO
ABR
habitat suitability
0 0.98
land exposed today
land exposed LGM
(b)
Figure 2. Ecological niche model for the occurrence of the scaled sardine Harengula sp. in (a) the Last Glacial Maximum (LGM, approx. 21 ka) and (b) present
climates. The magnified area in the upper inset shows suitability among seamounts connecting the oceanic archipelago of Fernando de Noronha (FNO) to the
Brazilian coast in the LGM, and that in the lower inset highlights the increase in habitat suitability at the continental shelf Abrolhos Island (ABR). The contour
of the models reflects marine provinces as described in Spalding et al.[45].
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average levels of diversity. Overall π-SNP for the coast was 0.06 while in FNO it was 0.05 (figure 4b; electronic supplementary
material, table S5). The inbreeding coefficient (F
IS
) was 0.033 in FNO and ranged from 0.024 to 0.109 for coastal sampling sites
(electronic supplementary material, table S6 and figure S3).
(d) Demographic history
Values of Tajima’sDwere negative for every coastal locality (between −0.24 and −0.85; electronic supplementary material, table S5),
as well as for the whole coastal clusterconsidered together (−1.50, p> 0.1). Tajima’sDwas also negative for the FNO population, yet
closer to neutral expectations (−0.29, p> 0.1). These results are consistent with deviations from the assumptions from a Wright–
Fisher population [68]. Some such deviations, such as change in population size and migration, are accounted for in the demographic
models below.
After down-projecting the SNP data, we considered 19 905 SNPs, 62 individuals from the coastal populations and seven indi-
viduals from FNO. All models including gene flow have lower AIC score than the model without gene flow (electronic
supplementary material, figure S4). The lowest AIC values were observed for both the ‘symmetric migration’and ‘asymmetric
migration’models (electronic supplementary material, table S7; figure 3c). However, only the simpler model of ‘symmetric
migration’showed a complete convergence of the estimated parameters across replicates and presented low residuals (electronic
supplementary material, figures S4 and S5). N
e
estimation indicated that the population from the coast experienced an expansion
after splitting (N
e
1 = 3.597 relative to the ancestral population), while the island population experienced a contraction (N
e
2 = 0.324;
electronic supplementary material, table S7; figure 3d). The estimated symmetric migration (2N
e
m) is 2.785.
4. Discussion
(a) How does habitat suitability generate intraspecific divergence?
Ecological niche models contribute to uncovering environmental corridors and locations of potential population settlement. For
species with high dispersal ability, like fishes, these models identify potential corridors connecting distantly located habitats, as
well as zones of environmental inadequacy isolating them, aiding the detection of population structure that can be tested with
other tools, such as genomics. Our ecological models indicate that depth and salinity explain most the distribution of Harengula
sp. (contribution is 73.5 and 23.7%, respectively; electronic supplementary material, table S3). The present-day model (figure 2b)
shows a continuous suitable habitat covering the continental shelf of most of the Brazilian coast, whereas suitable habitats in the
two oceanic archipelagos (i.e. Fernando de Noronha (FNO) and Trindade-Martin Vaz) are disconnected from the coast. During
the LGM, chains of seamounts provided suitable habitat that connected the coast to the islands (inset; figure 2a); such a putative
Ne coast = 3.6 Ne FNO = 0.3
(a) genetic admixture
(b) genetic divergence (c) demographic models
(d) demographic parameters
anc
Ne1Ne2
anc
Ne1Ne2
anc
Ne1Ne2
2Nem2Nem1
2Nem2
M1. no migration M2. symmetric
migration
M3. asymmetric
migration
divergence
time
anc
coast: north
FNO
coast: south
0-
5-
–5 -
0–5 5 10 15 20
axis 1 (4.0%)
axis 2 (1.8%)
2Nem = 2.8
ADMIXTURE
proportion
0.8
0.4
0FNO CE RN PB PE AL BA ABR ES RJ SCSP
FNO
CE
RN
PB
PE
AL
BA
ABR
ES
RJ
SP
SC
Figure 3. Genomic divergence and gene flow between intraspecific lineages of the scaled sardine Harengula sp. (a) ADMIXTURE analyses assuming two ancestral
clusters (model with highest likelihood). (b) Principal component analysis. (c) Competing demographic models depicting divergence between the coastal and island
clusters. The coloured model is most likely to fit the data according to Akaike information criterion (electronic supplementary material, table S7), but parameters are
not to scale. (d) Demographic parameters estimated using the best demographic model (symmetric migration). The width of the population boxes reflects effective
population (N
e
) relative to the ancestral N
e
(anc). For explanation of abbreviations for site names, see figure 1.
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ecological corridor has low relative suitability in the present-day model. Studies on co-distributed species dependent on shallow
waters, such as parrotfishes [69], octopuses [70], moray eels, mullets and other animals [30], highlight the importance of seamounts
for the colonization of oceanic islands, during glacial periods of lower sea level.
Our results also reveal the effectiveness of oceanographic barriers in the evolution of Harengula sp. The population structure
analyses indicate that Harengula sp. is structured into a coastal population, including the Abrolhos Island on the continental shelf,
and into an island population at FNO (figure 3a,b). Some co-distributed coastal species likely originally from the Brazilian coast
that colonized FNO also show this genetic divergence, as in the case of corals [71], lobster [72] and the reef-associated rockpool
blenny [73]. However, other species that colonized FNO do not show this pattern of divergence, such as parrotfishes [74], octo-
puses [70] and snappers [75], indicating that species occurring in similar habitats can have different evolutionary responses to
the same oceanographic barriers. Future studies can clarify if such idiosyncratic patterns of genetic divergence are related to
dispersal traits, such as the duration of larval stage, type of reproduction and body size.
In accordance with our ENM showing continuous suitability along the 3700 km of the coastal range sampled here, we find low
genetic differentiation between coastal localities (F
ST
< 0.04; electronic supplementary material, table S4) and significant isolation
by distance (figure 4a). These levels of genetic differentiation are similar to values found in other pelagic species with large dis-
persal capacity, such as the dog snapper (F
ST
= 0.037) [75]. Thus, our results suggest that continuous habitat allows gene flow along
the coastal range, but limited dispersal rate of this species also resulted in clinal divergence along the coast.
(b) How does demographic history condition current levels of genetic variability?
We tested if changes in habitat suitability during glacial cycles are consistent with temporal changes in effective population size
(N
e
), a measure of genetic variability. Ecological and demographic models of Harengula sp. show congruent scenarios. Our ENMs
suggest that the suitable habitats for Harengula sp. strongly increased at the coast but remained stable at the oceanic islands, includ-
ing the sampled archipelago FNO (figure 2). Similar spatial patterns have been detected for multiple co-distributed coastal species
[76,77], raising the hypothesis that marine species restricted to shallow habitats might have contrasting demographic histories at
oceanic islands relative to the coast, a hypothesis that is supported by our demographic analyses of Harengula sp.
Our demographic modelling shows that a relatively simple model of split between coastal and island populations with instan-
taneous change in population size, followed by symmetric gene flow (i.e. ‘symmetric migration’model with four parameters), is
the best model explaining the demographic history of these populations and is a good representation of the observed genomic data
(a)
(b)
isolation by distance
genetic diversity
0.04
R2 = 0.318, p = 0.001
FST / (1 – FST)
0.03
0.02
0.01
0
0.070
0.065
0.060
0.055
0.050
0.045
SC SP RJ ES ABR BA AL PE PB RN CE FNO
nucleotide diversity (π-SNP)
01 × 1062 × 106
distance (km)
3 × 106
Figure 4. Genetic diversity of Harengula sp. (a) Isolation by distance, considering mainland coastal sites only (excluding FNO), and (b) individual nucleotide diversity
(π-SNP) grouped per sampling site (mainland coastal sites in orange and oceanic island in dark grey). For explanation of abbreviations for site names, see figure 1.
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(electronic supplementary material, figure S5). This model, along with other models allowing gene flow, clearly rejects a ‘no migration’
model (electronic supplementary material, figure S4), showing that, after the colonization of the oceanic island from the coast (i.e.
population split), the two divergent sardine populations evolved in the presence of genetic connectivity across the oceanographic
barriers described above. More complex models including asymmetric migration or a second change in population size do not
show convergence in parameter estimates and AIC scores across replicates, and therefore could not be interpreted (electronic sup-
plementary material, figure S4). This genetic connectivity is congruent with our ENM for Harengula sp., suggesting that seamount
chains between the Brazilian coast and the oceanic archipelago of FNO might act as an effective ecological corridor between
differentiated populations, particularly during the glacial periods, when the sea level was lower (figure 2).
Under the best model of ‘symmetric migration’, we find that the coastal population experienced an expansion to 3.6 times the
size of the ancestral population, while the island population experienced a contraction to one-third of the ancestral population
(electronic supplementary material, table S7). Such contrasting demographic histories recapitulate the results from our ENM,
suggesting that the observed changes in genomic variability and effective population size (N
e
) reflect long-term evolutionary pro-
cesses associated with the glacial cycles rather than shorter-term processes associated with human induced over-exploitation. The
magnitude of the gene flow (2N
e
m, or the effective number of gene migrations received by a population per generation) detected is
also surprising. In the absence of selection, 2N
e
m values equal to or higher than 1 prevent populations from accumulating or main-
taining divergence [68]. Therefore, finding higher values of 2N
e
m between populations that maintain genomic differentiation
implies that selection, at least in part of the genome, must counteract gene flow [78]. In Harengula sp. we find that 2N
e
m is 2.7,
suggesting that selection is maintaining divergence between coastal and island populations in the face of such high migration
rates. Yet, it is unclear which forms of selection could maintain divergence between these populations. Interestingly, Harengula
sp. is the only species among more than 400 species of the Clupeiformes that occurs in the archipelago of FNO [30], a situation
that indicates that oceanic islands in Brazil are generally unsuitable for sardines and herrings. Future studies (of e.g. diet, mor-
phology, physiology, development) are necessary for understanding which factors are associated with divergence and
persistence of the oceanic lineage of Harengula sp. in such an unusual habitat. In addition, increasing the genomic data by
using high-coverage whole-genome sequencing can provide insights about underlying genetic adaptation in the island population.
(c) How does evolutionary genomics provide insights into conservation and management of fishery stocks?
Previous studies using catch data of Harengula sp. from the coast of Brazil have shown that the species already presents signs of over-
exploitation [32], suggesting the need for protection measures. Yet, it was unclear to what extent the coastal population is
representative of the whole species range, making it difficult to establish recommendations for sustainable exploitation of this species
in its entire distribution. Our study provides the first evidence that this undescribed, yet exploited, sardine species encompasses one
unique coastal lineage, and at least another isolated lineage in the oceanic archipelago of FNO. It remains to be tested if there is a third
lineage in the isolated oceanic archipelago of Trindade-Martin Vaz. Levels of genomic differentiation between lineages (F
ST
= 0.14;
electronic supplementary material, table S4) are similar to or above those distinguishing fishery stocks such as the European sardine
(minimum F
ST
between stocks > 0.05; [79]), the European hake (min F
ST
> 0.058; [80]) and yellow-fin tuna (min F
ST
> 0.15; [22]) esti-
mated using similar data. The coastal and oceanic populations of Harengula sp. should therefore be managed as independent
stocks, with strategies considering the particularities of each location, including the different fishing activities and their economic
dependence on this natural resource. The FNO population, in particular, is likely locally adapted to environmental conditions that
are exceptional for species of the Clupeidae. The ecological and social importance of the island lineage, in addition to its independent
evolution, suggests that the no-take zone of the Marine Protected Area in FNO, which covers only part of thedistribution of the island
population, is important for supporting the viability of this population of Harengula sp. This reinforces the need for monitoring and
management of this likely sensitive resource in order to maintain exploitation at sustainable levels in the long term, ensuring not only
the livelihoods of the local fishing population, but also the proper functioning of the archipelago’s marine ecosystem, which is highly
dependent on these forage fishes. More generally, the framework presented here can be applied to other species in need of urgent
management decisions but where ecological and genetic information remains limited. Such limitation is disproportionally affecting
less developed areas of the world, particularly tropical regions of the Southern Hemisphere, where human dependence on natural
resources is stronger, scientific resources are more limited, and a significant proportion of biodiversity remains to be described.
Thus, the integration of ecological and genomic methods can offer a low cost and efficient solution to resolve the accelerating conflicts
between management and local communities that depend on undescribed biodiversity.
Ethics. This work did not require ethical approval from a human subject or animal welfare committee.
Data accessibility. Data used in the ecological niche model and genomic analyses are available at the Dryad Digital Repository and can be accessed
here: https://doi.org/10.5061/dryad.np5hqbzzg [81].
Supplementary material is available online [82].
Declaration of AI use. We have not used AI-assisted technologies in creating this article.
Authors’contributions. J.F.R.C.: conceptualization, data curation, formal analysis, investigation, visualization, writing—original draft, writing—review
and editing; L.d.F.M.: data curation; F.D.D.: data curation, review and editing; P.H.C.: data curation; R.M.D.: data curation; S.M.Q.L.: conceptualiz-
ation, resources, supervision, writing—review and editing; J.T.V.: conceptualization, formal analysis, funding acquisition, investigation, project
administration, supervision, visualization, writing—original draft, writing—review and editing; R.J.P.: conceptualization, project administration,
resources, supervision, visualization, writing—original draft, writing—review and editing.
All authors gave final approval for publication and agreed to be held accountable for the work performed herein.
Conflict of interest declaration. The authors have no conflict of interest to declare.
Funding. J.F.R.C. was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior –Brasil (CAPES) –finance code 001.
Financial support to F.D.D. was provided by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico –PROTAX 443302/2020).
S.M.Q.L. receives a Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq) productivity research grant (no. Proc 312066/
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 291: 20232746
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2021-0). This study was developed in the context of the ‘Projeto MULTIPESCA –Ciência para a sustentabilidade da pesca, pescado e pescadores do
Rio de Janeiro’, which received support from the ‘Marine and Fisheries Research Project’. The Marine and Fisheries Research Project is an offset
measure established under a consent decree agreed between the company PRIO and the Federal Public Prosecutors’Office in Rio de Janeiro. It is
implemented by FUNBIO.
Acknowledgments. We thank Ricardo Betancur-R (Scripps Institution of Oceanography) for donating samples.
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