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\begin{document}

\title{Threadfin porgy (\emph{Evynnis cardinalis}) haplotype pattern and
genetic structure in Beibu Gulf, South China Sea}



\author[1]{Lei Xu}%
\author[1]{Xuehui Wang}%
\author[1]{Liang Wang}%
\author[1]{Jiajia Ning}%
\author[1]{Yafang Li}%
\author[1]{Delian Huang}%
\author[1]{Shuangshuang Liu}%
\author[1]{Feiyan Du}%
\affil[1]{South China Sea Fisheries Research Institute}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Threadfin porgy (\emph{Evynnis cardinalis}) is an important commercial
fishing target of bottom trawl fisheries in the northern South China
Sea. It is mainly threatened by overexploitation and listed as
endangered in the IUCN Red List. To investigate \emph{E. cardinalis}
population demographic history and genetic structure, fragments of the
mitochondrial cytochrome c oxidase subunit I gene were sequenced for 162
individuals collected from Beibu Gulf, South China Sea. In total, 44
different haplotypes were identified, and the dominant widespread
haplotype was found in all 11 sampling sites. Across the dataset,
nucleotide diversity was low but haplotype diversity was high. Low
pairwise comparisons of \selectlanguage{greek}Φ\selectlanguage{english}ST and high gene flow between all sampling
sites revealed a genetically homogeneous population structure in Beibu
Gulf, which indicated a single panmictic stock of E. cardinalis in this
area. The star-like haplotype network, unimodal mismatch distribution,
and significantly negative Tajima's D and Fu's Fs values indicated
recent population demographic expansion of E. cardinalis. The mismatch
distribution and Bayesian skyline plot results indicated that \emph{E.
cardinalis} from Beibu Gulf experienced colonization and demographic
expansion during the late Pleistocene due to sea level fluctuations.%
\end{abstract}%



\sloppy


\textbf{Threadfin porgy (\emph{Evynnis cardinalis}) haplotype pattern
and genetic structure in Beibu Gulf, South China Sea}

Lei Xu\textsuperscript{1,2}, Xuehui Wang\textsuperscript{1,2}, Lianggen
Wang\textsuperscript{1,2}, Jiajia Ning\textsuperscript{1,2}, Yafang
Li\textsuperscript{1,2}, Delian Huang\textsuperscript{1,2}, Shuangshuang
Liu\textsuperscript{1,2}, Feiyan Du\textsuperscript{1,2*}

1 South China Sea Fisheries Research Institute, Chinese Academy of
Fishery Sciences, Guangzhou 510300, China

2 Guangdong Provincial Key Laboratory of Fishery Ecology and
Environment, Guangzhou 510300, China

*Corresponding author: feiyanegg@163.com

\section*{Abstract}

{\label{abstract}}

Threadfin porgy (\emph{Evynnis cardinalis} ) is an important commercial
fishing target of bottom trawl fisheries in the northern South China
Sea. It is mainly threatened by overexploitation and listed as
endangered in the IUCN Red List. To investigate \emph{E.
cardinalis}population demographic history and genetic structure,
fragments of the mitochondrial cytochrome c oxidase subunit I gene were
sequenced for 162 individuals collected from Beibu Gulf, South China
Sea. In total, 44 different haplotypes were identified, and the dominant
widespread haplotype was found in all 11 sampling sites. Across the
dataset, nucleotide diversity was low but haplotype diversity was high.
Low pairwise comparisons of \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} and high gene flow
between all sampling sites revealed a genetically homogeneous population
structure in Beibu Gulf, which indicated a single panmictic stock
of\emph{E. cardinalis} in this area. The star-like haplotype network,
unimodal mismatch distribution, and significantly negative Tajima's D
and Fu's Fs values indicated recent population demographic expansion
of\emph{E. cardinalis} . The mismatch distribution and Bayesian skyline
plot results indicated that \emph{E. cardinalis} from Beibu Gulf
experienced colonization and demographic expansion during the late
Pleistocene due to sea level fluctuations.

\textbf{KEYWORDS} : Haplotype pattern, Genetic differentiation,
Demographic expansion, COI, South China Sea

\section*{1 INTRODUCTION}

{\label{introduction}}

Marine fish are generally assumed to have high dispersal potential
because of their high dispersal capability at both larval and adult
stages, and the absence of physical barriers to dispersal (Caley et al.,
1996; Hellberg, 2009). Theoretically, the higher dispersal capability of
a species, the lower its inter-population genetic structure. Therefore,
in marine fish species, especially migratory species, the signal of
population differentiation is weak and difficult to detect because of
high levels of gene flow (Gandra, Assis, Martins, \& Abecasis, 2020).
Understanding the distribution of genetic diversity of important
commercial species is vital to implementing protection policies and
management regulations (Araki \& Schmid, 2010). Genetic diversity
(within and among populations) greatly influences species adaptive
potential to environmental changes, and ultimately determines their
long-term resilience to ecological disturbances (Pauls, Nowak, B\selectlanguage{ngerman}álint,
\& Pfenninger, 2013). Additionally, knowledge of genetic structure is
useful for understanding fish spawning migration routes, areas, and
seasons. Such information can help fisheries managers define the
spatiotemporal scales over which they can implement effective stock
management and conservation plans (Bradbury, Laurel, Snelgrove, Bentzen,
\& Campana, 2008). Therefore, from a resource management perspective,
understanding the patterns of genetic diversity and levels of gene flow
among populations is a fundamental issue. The effectiveness of marine
protected areas depends on both their ability to self-recruit
(reproductive potential) and the spillover of adults and export of
larvae to nearby fished areas (Harrison et al., 2012; Le Port et al.,
2017). Over the last 30 years, genetic studies have become an essential
tool for stock management and conservation of estuarine, coral reef, and
coastal populations because genetic studies are useful for estimating
genetic diversity and the ability to survive anthropogenic activities
(e.g., overfishing, habitat degradation, eutrophication, invasive
species, and pollution) (Gaither, Toonen, Robertson, Planes, \& Bowen,
2010; Gill \& Kemp, 2002; Machado, da Silva Cortinhas, Proietti, \&
Haimovici, 2020; Ruzzante, Taggart, \& Cook, 1998; Ryman, Utter, \&
Laikre, 1995). Mitochondrial DNA (mtDNA) markers are widely employed to
detect population structure in marine species because they have large
number of copies, high mutation rates, generally maternal inheritance,
and almost nonexistent recombination. The primary advantages of mtDNA
are the inheritance pattern and nonexistent recombination: clonal
inheritance through the maternal line allows tracing of speciation
events over multiple generations, and male dispersal does not homogenize
the population (Prugnolle \& de Meeus, 2002). These factors make mtDNA
markers particularly appropriate indicators of population genetic
structure in marine organisms such as zooplankton and migratory fishes,
which generally have high gene flow (Machado et al., 2020; L. Wang et
al., 2013; Xu, Li, Wang, \& Du, 2019).

Threadfin porgy, \emph{Evynnis cardinalis} (Lacepède 1802), occurs in
the Indo-West Pacific from Japan, Korea, and China to Vietnam and
Indonesia (Z. Chen \& Qiu, 2005). This species is primarily distributed
from 30--60 m depth but also can occur to 100 m depth (Iwatsuki \&
Carpenter, 2014). This species is found over a wide range of bottom
types, but is more common close to reefs or on rough bottoms. Small
individuals are very common at some localities in shallow, sheltered
bays; larger fish often occur in deeper water (Eggleston,
1974).\emph{Evynnis cardinalis} is one of the main commercial fishing
targets in bottom trawl fisheries in the northern South China Sea and is
considered to have three geographical stocks in the northern South China
Sea: the Taiwan Strait, South China Sea, and Beibu Gulf
stocks.\emph{Evynnis cardinalis} is a migratory fish that is captured
year-round with significant seasonal differences among fishing areas. In
Beibu Gulf, \emph{E. cardinalis} undergoes seasonal migration and is
found toward the northeastern shallow area of the gulf in late autumn
and winter. Spawning takes place in northwest Weizhou Island in the
spring, and the recruits disperse widely in the northern area of the
gulf and begin to disperse south in late spring (Iwatsuki \& Carpenter,
2014). However, this species exhibits life history characteristics,
including late maturity and longevity, that predispose it to impacts
from heavy exploitation. In the northern South China Sea, the Beibu Gulf
stock of \emph{E. cardinalis} declined 58\% from 2001 to 2005, whereas
the Taiwan Strait stock declined 62\% from 1993 to 2008. Therefore, a
recent IUCN Red List assessment reported \emph{E. cardinalis} as
endangered, and this species is mainly threatened by overexploitation
(Iwatsuki \& Carpenter, 2014). Previous studies on this species in Beibu
Gulf have focused on feeding habits, growth and mortality, ecological
distribution, phylogeny, and stock density (Cai, Chen, Xu, \& Zhang,
2017; Z. Chen \& Qiu, 2005; Z. Z. Chen \& Qiu, 2003; D. Zhang, Shao, Su,
\& Jiang, 2007; K. Zhang et al., 2020; Y. Zhang, Dai, Yan, Yang, \& Lu,
2014). However, no information is available to date on the genetic
structure of \emph{E. cardinalis} in the northern South China Sea, and
this prevents reliable stock assessments and protection policy
formulation. The objective of this study was to provide a population
genetic analysis using a portion of the mitochondrial cytochrome c
oxidase subunit I (COI) gene to assess the population genetic diversity
pattern and historical demography, and estimate \emph{E. cardinalis}
expansion time in Beibu Gulf.

\section*{2 MATERIALS AND METHODS}

{\label{materials-and-methods}}

\subsection*{2.1 Study area}

{\label{study-area}}

Beibu Gulf (17°--22°N, 105°--110°E) is in the northwestern part of the
South China Sea and has a long coastline that belongs to China and
Vietnam. It is a semi-closed gulf of approximately 128,000
km\textsuperscript{2} that ranges from Leizhou Peninsula, Qiongzhou
Strait, and Hainan Island to Vietnam, and extends to the Guangxi coast
in the north (Ma et al., 2010). The bottom of Beibu Gulf is flat and
slopes from the northwest to the southeast, and the water depth in the
gulf is typically less than 100 m (average depth, 42 m). The surrounding
climate of this gulf is subtropical and monsoonal. Moreover, this gulf
contains numerous estuaries from which rivers discharge nutrients. Beibu
Gulf is a traditional fishing ground and main source of fishery products
for coastal areas because of its high productivity and rich biological
diversity, which benefit from its geographic location and climatic
conditions (Z. Chen, Xu, Qiu, Lin, \& Jia, 2009).

\subsection*{2.2 Sample collection}

{\label{sample-collection}}

All fish specimens were collected from fishery surveys carried out by
the South China Sea Fisheries Research Institute; these surveys were
conducted by the commercial fishing vessel ``Beiyu60011'' in the
northern South China Sea using bottom trawler nets in September 2018.
Eleven sampling sites from Beibu Gulf were investigated in the study
area, which covered over 45,000 km\textsuperscript{2} (Fig. 1). In
total, 11--16 specimens of each sampling site were used for the DNA
analyses after morphological identification. The dorsal fin was removed
from each specimen and preserved in absolute ethanol at \selectlanguage{english}-20degC.

\subsection*{2.3 DNA extraction, amplification, and
sequencing}

{\label{dna-extraction-amplification-and-sequencing}}

Genomic DNA was extracted from dorsal fin tissue using the TIANamp
Marine Animals DNA Kit (TIANGEN, China) following the manufacturer's
protocol. The concentration for use as a PCR template was adjusted to an
A260 of approximately 0.05--0.2. Fragments of the mtDNA COI gene were
amplified from total genomic DNA using the polymerase chain reaction.
The primers FishF1 and FishR1 (Ward, Zemlak, Innes, Last, \& Hebert,
2005) were used for COI PCR amplification. Each 50 \selectlanguage{greek}µ\selectlanguage{english}l PCR consisted of
31.25 \selectlanguage{greek}µ\selectlanguage{english}l ddH\textsubscript{2}O, 5 \selectlanguage{greek}µ\selectlanguage{english}l PCR buffer, 5 \selectlanguage{greek}µ\selectlanguage{english}l CoralLoad
concentrate, 4 \selectlanguage{greek}µ\selectlanguage{english}l of 25 \selectlanguage{greek}µ\selectlanguage{english}M MgCl\textsubscript{2}, 1 \selectlanguage{greek}µ\selectlanguage{english}l of 10 \selectlanguage{greek}µ\selectlanguage{english}M dNTPs,
0.5 \selectlanguage{greek}µ\selectlanguage{english}l of 25 \selectlanguage{greek}µ\selectlanguage{english}M solution of each primer, 2.5 \selectlanguage{greek}µ\selectlanguage{english}l DNA template, and 0.25
\selectlanguage{greek}µ\selectlanguage{english}l TopTaq DNA polymerase (QIAGEN, Germany). The PCR conditions for
amplification were: an initial step of 2 min at 95\selectlanguage{ngerman}°C; 35 cycles of 0.5
min at 95°C (denaturation), 0.5 min at 54°C (annealing), and 1 min at
72°C (extension); followed by 7 min at 72°C (final extension) on a 2720
Thermal Cycler (Applied Biosystems, USA). PCR products were visualized
on 1.5\% agarose gels and the most intense products were selected for
sequencing. Products were bidirectionally sequenced on an ABI 3730XL
automated sequencer with both forward and reverse primers following
manufacturer's instructions.

\subsection*{2.4 Genetic analysis}

{\label{genetic-analysis}}

The sequences were assembled and edited in Bioedit (Hall, 1999) and
aligned using the CLUSTALW multiple algorithm under default options.
Ambiguous sequences were trimmed after alignment. The authenticity of
our COI sequences was verified by a BLAST search in GenBank (BLASTn,
megablast algorithm) and compared with the highest match (99\%--100\%).
Molecular diversity from COI sequences was measured using DnaSP 5.0
(Librado \& Rozas, 2009) with the following variables: number of
haplotypes (H), polymorphic sites (S), haplotype diversity (h), and
nucleotide diversity (\selectlanguage{greek}π\selectlanguage{english}).

To visually represent the relationships among the mtDNA haplotypes of
the sampled \emph{E. cardinalis} individuals, we performed a network
analysis using HAPLOVIEWER, which turns trees built from traditional
phylogenetic methods into haplotype genealogies (Salzburger, Ewing, \&
Von Haeseler, 2011). The phylogenetic tree used for this graphical
representation was obtained by employing a maximum likelihood approach
in PhyML 3.0 (Guindon et al., 2010) using a GTR model with four
gamma-distributed rate categories as the substitution model.

Population genetic differentiation was inferred by estimating pairwise
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values among 11 sampling sites using the Tamura--Nei
model of nucleotide substitution in Arlequin 3.5 (Excoffier \& Lischer,
2010). This model was assessed as most suitable for our data using
jModeltest 2.1 (Darriba, Taboada, Doallo, \& Posada, 2012). The
significance of \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values was tested by 10,000
permutations. To estimate migration rates between sampling sites, we
performed maximum likelihood analysis using the coalescence method in
Migrate 3.2.1 (Beerli \& Felsenstein, 1999). The estimated parameters
were ?\textsubscript{ij} = \selectlanguage{greek}θ\selectlanguage{english}\textsubscript{i}M\textsubscript{ij}, where
?\textsubscript{ij} is the number of effective migrants from i to j, \selectlanguage{greek}θ
\selectlanguage{english}is mutation-scaled population size, and M\textsubscript{ij} is
m\textsubscript{ij}/\selectlanguage{greek}μ \selectlanguage{english}(where m\textsubscript{ij} is the immigration rate
from population i into j, and \selectlanguage{greek}μ \selectlanguage{english}is the mutation rate per generation).

\subsection*{2.5 Inferring the historical
demography}

{\label{inferring-the-historical-demography}}

Signatures of population demographic changes (bottlenecks or expansions)
in \emph{E. cardinalis} were first examined by Tajima' D and Fu's Fs
(Fu, 1997; Tajima, 1989) statistics with 10,000 permutations in Arlequin
3.5 to determine whether \emph{E. cardinalis} in the Beibu Gulf data
conformed to or departed from neutral theory model expectations because
of factors such as a population bottleneck or expansion. For neutral
markers, significant negative Tajima's D and Fu's Fs values can be
expected under population expansion.

Next, we calculated the mismatch distribution among haplotypes under the
sudden expansion model. This measure quantifies the smoothness of the
observed mismatch distribution. We employed parametric bootstrapping
(1,000 replicates) as implemented in Arlequin 3.5 to test the goodness
of fit of the observed mismatch distribution to that expected under the
sudden expansion model using the sum of squared deviations and
raggedness index (R index) (Excoffier, 2004; Ray, Currat, \& Excoffier,
2003; Schneider \& Excoffier, 1999). Populations that underwent
substantial expansion are expected to exhibit unimodal mismatch
distributions with a low R index.

In addition, we used two methods to estimate the time of \emph{E.
cardinalis} population expansion in Beibu Gulf. First, the expansion
time was directly estimated from the mismatch distribution with the
statistic \selectlanguage{greek}τ \selectlanguage{english}(tau) and translated into absolute time in years (t) using
the equation \selectlanguage{greek}τ \selectlanguage{english}= 2ut, where u is the mutation rate of the sequence and
is calculated as u = 2\selectlanguage{greek}µ\selectlanguage{english}k; k is the number of nucleotides in the sequence
and \selectlanguage{greek}µ \selectlanguage{english}is the mutation rate of the mtDNA gene per generation (Rogers \&
Harpending, 1992). Following Cantatore et al. (1994), mutation rates
between 1\% and 3\% per million years were selected for our
mitochondrial analysis.

Subsequently, we inferred historical demography from effective
population size estimates over time using the Bayesian skyline plot
(BSP) method in BEAST 1.8.0 (Alexei J. Drummond \& Rambaut, 2007; A. J.
Drummond, Rambaut, Shapiro, \& Pybus, 2005). An HKY model with
among-site rate heterogeneity across all branches that assumed a strict
molecular clock was used for this calculation. Markov chains were run
for 2.5\selectlanguage{ngerman}×10\textsuperscript{7} generations and sampled every 1,000
generations, with the first 2,500 samples discarded as burn-in. Three
replicates were run and combined to separately analyze the COI datasets.
Other parameters were set as default values. TRACER 1.5 was used to
visualize the posterior probabilities of the Markov chain statistics and
to calculate a statistical summary of the genetic parameters.

\section*{3 RESULTS}

{\label{results}}

\subsection*{3.1 Genetic diversity}

{\label{genetic-diversity}}

In total, 162 high-quality sequences were obtained after alignment and
trimmed to 618 bp. The average base composition was A = 22.3\%, C =
28.8\%, G = 18.8\%, T = 30.1\%. In total, we found 44 different
haplotypes in our 11 sampling sites (Table 1, Fig. 2). All haplotype
sequences were deposited in GenBank (accession numbers
MW881382--MW881425). Overall, the nucleotide diversity was low (average,
0.00232; range, 0.00185--0.00277), whereas the haplotype diversity was
relatively high and heterogeneous (average, 0.788; range, 0.657--0.886)
(Table 1).

The haplotype network analyses of the mtDNA from \emph{E. cardinalis} in
Beibu Gulf showed a typical star-like pattern, with the most frequent
haplotype, H1, located in the central position of the haplotype network
and surrounded by many low-frequency haplotypes that were divergent from
H1 by only one or two mutations (Fig. 2). Most of the haplotypes were
low-frequency, and 27 haplotypes were unique to only one sampling site.
Only 11 out of the 44 haplotypes were observed at more than two sampling
sites. However, several of the abundant haplotypes were found at
multiple sampling sites. For example, the dominant widespread haplotype
H1 was found in all 11 sampling sites (Fig. 3); the largest distance
between two sampling sites harboring this haplotype was more than 300
km. H6 and H8 were distributed at eight and seven sampling sites,
respectively. Moreover, between six and 10 haplotypes were found per
sampling site, but there was no clear pattern of geographical variation
(Fig. 3). The highest number of haplotypes (H = 10) was observed at
sites 8 and 10 (Table 1).

The average genetic differentiation, \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST}, of \emph{E.
cardinalis} within Beibu Gulf was 0.031 (range, -0.0657 to 0.0671). All
pairwise \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values among the sampling sites were small
and not significant (Table 2). The effective number of migrants per
generation among 11 sampling sites was from 5.98 to 45.76. The migration
rates from S7 to S1 were the lowest, whereas those from S1 to S3 were
the highest (Table 3).

\subsection*{3.2 Demographic history}

{\label{demographic-history}}

The Tajima's D and Fu's Fs tests of \emph{E. cardinalis} in Beibu Gulf
were significantly negative (Tajima's D = -2.446, P \textless{} 0.01;
Fu's Fs = -28.378, P \textless{} 0.01); this showed that \emph{E.
cardinalis} may have experienced population expansion (Table 4). The
mismatch distribution appeared to be unimodal (Fig. 4), which was
consistent with the expected distribution under a sudden expansion model
(R index = 0.055, P = 0.26).

On the basis of the mutation rate of 1\%--3\% per million years for
mitochondrial genes (Cantatore et al., 1994) and \selectlanguage{greek}τ \selectlanguage{english}value of all data
(1.531, 95\% confidence intervals: 1.281--1.941), the expansion time
for\emph{E. cardinalis} in Beibu Gulf was estimated to have occurred
from approximately 62--21 ka.

The BSP analysis indicated that the \emph{E. cardinalis} haplotypes in
Beibu Gulf coalesced approximately 40 ka when a mutation rate of 2\% per
million years was used for analysis. However, the BSP pattern revealed a
coalescence time of 30--70 ka when using a mutation rate of 1\%--3\% per
million years (Fig. 5). Despite these different coalescence times, the
BSP patterns showed similar tendencies and indicated that a steady
population expansion took place between 5--30 ka.

\section*{4 DISCUSSION}

{\label{discussion}}

Studies of population connectivity with mitochondrial markers provide
critical information on gene flow and genetic relationships between
neighboring populations (Garc\selectlanguage{ngerman}ía, Vergara, \& Gutiérrez, 2008; Turner,
McPhee, Campbell, \& Winemiller, 2004). Many studies showed that
mitochondrial markers are highly effective for revealing marine fish
genetic diversity and population connectivity (T. Gao et al., 2019;
Lavergne et al., 2014; Machado et al., 2020). In this study on\emph{E.
cardinalis} , mtDNA sequence analysis of specimens from Beibu Gulf
revealed no significant genetic differentiation among sampling sites,
with low \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST}values indicating genetic homogeneity.

In contrast to freshwater species, marine fish are usually expected to
show low genetic differentiation across their distribution. This is
mainly attributed to genetic exchange being maintained by adult mobility
throughout Beibu Gulf during reproduction, and through the passive
dispersal of eggs and larvae due to the lack of noticeable physical
barriers in ``open'' oceans (Grant \& Bowen, 1998; Hellberg, 2009;
Machado et al., 2020). The dominant widespread haplotype H1 was found in
all 11 sampling sites, which also indicated high dispersal potential of
planktonic egg, larval, or adult stages of \emph{E. cardinalis} in Beibu
Gulf.

Previous studies suggested that \emph{E. cardinalis} breed once a year
in Beibu Gulf (Z. Z. Chen \& Qiu, 2003; Hou, Feng, Lu, \& Zhu,
2008).\emph{Evynnis cardinalis} gonads begin to develop in November and
spawning occurs from December to February. The population is
concentrated in the northern Beibu Gulf during spawning. In the early
spring, spawned fish mainly occur in the northeast of the gulf, and
juveniles concentrate in shallow nearshore of this area in the late
spring. Then, juveniles gradually migrate southwest and widely disperse
in deep waters of Beibu Gulf in summer or early autumn (K. Zhang et al.,
2020).

In addition, the dispersal pattern of \emph{E. cardinalis} was also
impacted by circulation in Beibu Gulf. In spring, the density gradient
and monsoon wind drive the ocean current from northeast to southwest in
the gulf. The surface current velocity reaches 30 cm/s, and the current
in the middle layer is approximately 5--10 cm/s (J. Gao, Wu, \& Ya,
2017). The direction of the spring currents roughly coincides with the
migration of \emph{E. cardinalis} . Therefore, the seasonal migration
and ocean current may be responsible for gene exchange among different
locations, and therefore why \emph{E. cardinalis} shows low levels of
genetic differentiation in Beibu Gulf. If we refer to the biological
description of a stock as given by Ihssen et al. (1981), ``a stock is an
intraspecific group of randomly mating individuals with temporal and
spatial integrity,'' then the lack of distinct spatial boundaries and
genetic substructure (low \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values) revealed by
genetic analyses indicated that \emph{E. cardinalis} in Beibu Gulf
belong to a single stock.

The presence of a single stock in Beibu Gulf indicates that geographical
isolation might block gene exchange between the Beibu Gulf stock and the
other two \emph{E. cardinalis} stocks, the Taiwan Strait and South China
Sea stocks. In Beibu Gulf, the circulation, Hainan Island, and Leizhou
Peninsula could act as barriers that impede free dispersal of \emph{E.
cardinalis} into this gulf from other areas of the South China Sea (J.
Gao et al., 2017). The findings from our study and similar
investigations conducted elsewhere demonstrated that marine fish that
inhabit coastal waters usually constitute a single panmictic stock. For
example, Rodrigues et al. (2008) revealed that \emph{Cynoscion
acoupa}from northern Brazil represents a single stock, even though it
occupies at least 1260 km of coastline. T. Gao et al. (2019) reported a
high level of genetic homogeneity in the \emph{Pholis fangi} population
around Bohai Sea and Yellow Sea, and suggested it should be considered
as a single panmictic stock. Hoolihan, Anandh, and van Herwerden (2006)
also reported a homogeneous distribution of Spanish mackerel
(\emph{Scomberomorus commerson} ) throughout the Arabian Gulf, Gulf of
Oman, and Arabian Sea on the basis of mtDNA analyses.

In addition, mtDNA sequence regions are particularly appropriate to
infer historical processes that might be responsible for the
contemporary geographic distribution of marine species because they are
more prone to genetic drift than nuclear markers and have a smaller
effective population size (Avise, 1994; Slatkin \& Hudson, 1991). In our
study, the haplotype network of \emph{E. cardinalis} from Beibu Gulf
exhibited a star-like and unstructured pattern with a predominance of
scattered. The dominant haplotype (carried by 45\% of the specimens) was
in the central position of the haplotype network and surrounded by many
haplotypes that diverged from the dominant haplotype by only few
mutations. Most surrounding haplotypes were unique to each sampling site
and showed few differences between them (Fig. 2). Similar star-like
haplotype networks have been observed for other species in different
coastal areas: \emph{Terapon jarbua} , which consists of a panmictic
stock from the Socotra Archipelago to the Hadhramout Coast along the
wider Gulf of Aden (Lavergne et al., 2014); and \emph{Pogonias courbina}
, which did not display distinct structure along the coast of the
southwestern Atlantic Ocean (Machado et al., 2020).

A star-like haplotype network pattern, high haplotype diversity, and low
nucleotide diversity are often considered consequences of recent
population expansion linked to the Pleistocene environmental changes
(Craig, Eble, Bowen, \& Robertson, 2007; Liu et al., 2011; Pereira,
M\selectlanguage{ngerman}árquez, Marin, \& Marin, 2009). The recent demographic expansion
of\emph{E. cardinalis} from Beibu Gulf is also supported by the unimodal
mismatch distribution and significantly negative Tajima's D and Fu's Fs
values. The population expansion of \emph{E. cardinalis} in Beibu Gulf,
which was directly estimated from the mismatch distribution, started
62--21 ka before present, which was during the late Pleistocene. BSP
analysis indicated steady population expansion that started around 30
ka. Both two methods of estimated period of population expansion are
consistent with the environment changes of the northern South China Sea
in the Pleistocene.

\emph{Evynnis cardinalis} is primarily distributed from 30--60 m depth,
and spawns in coastal habitats and shallow shorelines. Therefore,
the\emph{E. cardinalis} distribution is closely correlated with
fluctuating sea levels. When sea levels fell 120 m below the present
level during the last glacial maximum of the Pleistocene, the northern
South China Sea included Beibu Gulf, which was part of the South China
continent, Hainan Island and Taiwan Island were connected to mainland
China, and the entire South China Sea was separated from the Indian
Ocean to form a semi-closed basin (Voris, 2000; P. Wang \& Sun, 1994).
Similar to other terrestrial species, \emph{E. cardinalis} may have
moved and survived in a potential glacial refuge during this period,
such as the semi-closed South China Sea (Hewitt, 1999). In the late
Pleistocene, the sea level was still 30 m below the present level, but
the glaciation began to disappear and the sea water gradually poured
into Beibu Gulf via the Qiongzhou Strait (Lu, Huang, Li, \& Zhang,
2003). An initial population of \emph{E. cardinalis} may have immigrated
to Beibu Gulf from neighboring areas after it was filled with sea water
and sufficiently deep. This initial panmictic stock quickly colonized
the empty novel environment under the founder and priority effects, and
experienced rapid population expansion when favorable conditions
occurred (Boileau, Hebert, \& Schwartz, 1992; Shulman et al., 1983).

\section*{5 CONCLUSION}

{\label{conclusion}}

A homogeneous population structure with low genetic diversity and
star-like haplotype pattern was revealed for \emph{E. cardinalis} in
Beibu Gulf. Furthermore, pairwise comparisons of \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} and
the effective number of migrants between all sampling sites in Beibu
Gulf indicated a single panmictic stock of \emph{E. cardinalis} . This
is mainly attributed to the free genetic exchange of \emph{E.
cardinalis}during reproduction. However, \emph{E. cardinalis} from the
eastern South China Sea cannot disperse into Beibu Gulf because of the
circulation patterns and ocean geographical features such as the
semi-closed sea of Beibu Gulf. This molecular evidence revealed
that\emph{E. cardinalis} from Beibu Gulf experienced colonization and
population expansion during the late Pleistocene due to sea level
fluctuations.

\section*{ACKNOWLEDGMENTS}

{\label{acknowledgments}}

We thank all colleagues and students for their help with sampling. This
study was funded by Science and Technology Basic Resources Investigation
Program of China (2018FY100105) and Fund of Guangdong Provincial Key
Laboratory of Fishery Ecology and Environment (FEEL-2019-9). We thank
Mallory Eckstut, PhD, from Liwen Bianji (Edanz)
(https://www.liwenbianji.cn), for editing the language of a draft of
this manuscript.

\section*{DISCLOSURE STATEMENT}

{\label{disclosure-statement}}

None of the co-authors has any conflict of interest to declare. The
authors alone are responsible for the content and writing of the paper.

\section*{AUTHOR CONTRIBUTIONS}

{\label{author-contributions}}

\textbf{Lei Xu} : Conceptualization, Methodology, Formal analysis, Data
curation, Writing - original draft, Writing - review \&
editing.\textbf{Kay Van Damme} : Writing - original draft.

\textbf{Lianggen Wang} , \textbf{Jiajia Ning} , \textbf{Delian Huang}
,\textbf{Yafang Li} , \textbf{Shuangshuang Liu} and \textbf{Hong Li} :
Methodology, Formal analysis. \textbf{Feiyan Du} : Funding acquisition,
Project administration, Resources.

\section*{DATA ACCESSIBILITY}

{\label{data-accessibility}}

DNA sequences: GenBank accessions: MW881382--MW881425.

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\textbf{Table 1} . Descriptive statistics of genetic diversity
of\emph{Evynnis cardinalis} sampled from Beibu Gulf based on COI
sequence data.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Sampling sites & \emph{N} & \emph{H} & \emph{S} & \emph{h} &
\selectlanguage{greek}\emph{π}\selectlanguage{english}\tabularnewline
\midrule
\endhead
S1 & 15 & 8 & 9 & 0.79\selectlanguage{ngerman}±0.105 & 0.00231\selectlanguage{ngerman}±0.0006\tabularnewline
S2 & 15 & 9 & 9 & 0.886\selectlanguage{ngerman}±0.069 & 0.0025\selectlanguage{ngerman}±0.0005\tabularnewline
S3 & 15 & 9 & 10 & 0.886\selectlanguage{ngerman}±0.069 & 0.00253\selectlanguage{ngerman}±0.0005\tabularnewline
S4 & 15 & 6 & 7 & 0.705\selectlanguage{ngerman}±0.114 & 0.00185\selectlanguage{ngerman}±0.0005\tabularnewline
S5 & 15 & 8 & 7 & 0.79\selectlanguage{ngerman}±0.105 & 0.00203\selectlanguage{ngerman}±0.0004\tabularnewline
S6 & 15 & 7 & 7 & 0.657\selectlanguage{ngerman}±0.138 & 0.00188\selectlanguage{ngerman}±0.0005\tabularnewline
S7 & 15 & 8 & 9 & 0.79\selectlanguage{ngerman}±0.105 & 0.0025\selectlanguage{ngerman}±0.0006\tabularnewline
S8 & 15 & 10 & 12 & 0.857\selectlanguage{ngerman}±0.09 & 0.00277\selectlanguage{ngerman}±0.0006\tabularnewline
S9 & 15 & 7 & 9 & 0.724\selectlanguage{ngerman}±0.121 & 0.00231\selectlanguage{ngerman}±0.0007\tabularnewline
S10 & 16 & 10 & 11 & 0.825\selectlanguage{ngerman}±0.098 & 0.00222\selectlanguage{ngerman}±0.0005\tabularnewline
S11 & 11 & 7 & 7 & 0.873\selectlanguage{ngerman}±0.089 & 0.00277\selectlanguage{ngerman}±0.0006\tabularnewline
Overall & 162 & 44 & 44 & 0.788\selectlanguage{ngerman}±0.033 & 0.00232\selectlanguage{ngerman}±0.0002\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\emph{N} , sample size; \emph{H} , number of haplotypes; \emph{S} ,
number of segregating sites; \emph{h} , haplotype diversity (±S.D.);
\selectlanguage{greek}\emph{π} , \selectlanguage{english}nucleotide diversity (\selectlanguage{ngerman}±S.D.)

\textbf{Table 2} . Pairwise differentiation \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST}
of\emph{Evynnis cardinalis} sampled from Beibu Gulf based on COI
sequence data.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
& S1 & S2 & S3 & S4 & S5 & S6 & S7 & S8 & S9 & S10 & S11\tabularnewline
\midrule
\endhead
S1 & 0 & & & & & & & & & &\tabularnewline
S2 & -0.0597 & 0 & & & & & & & & &\tabularnewline
S3 & -0.0203 & -0.0456 & 0 & & & & & & & &\tabularnewline
S4 & -0.0213 & -0.0364 & -0.0322 & 0 & & & & & & &\tabularnewline
S5 & 0.0041 & -0.039 & -0.0386 & -0.0397 & 0 & & & & & &\tabularnewline
S6 & -0.0204 & -0.0442 & -0.0421 & -0.0424 & -0.0567 & 0 & & & &
&\tabularnewline
S7 & -0.033 & -0.0403 & -0.0276 & -0.0246 & -0.0079 & -0.0509 & 0 & & &
&\tabularnewline
S8 & -0.0283 & -0.0275 & -0.0197 & -0.0505 & -0.0113 & -0.0239 & -0.0323
& 0 & & &\tabularnewline
S9 & -0.008 & -0.0312 & -0.0295 & -0.0212 & -0.0228 & -0.0588 & -0.0657
& -0.0266 & 0 & &\tabularnewline
S10 & -0.0274 & -0.0289 & -0.0205 & -0.0443 & -0.0337 & 0.006 & -0.0098
& -0.0249 & 0.0014 & 0 &\tabularnewline
S11 & -0.0476 & -0.0141 & 0.0107 & 0.0281 & 0.0367 & 0.0671 & 0.0335 &
0.0302 & 0.0598 & 0.0037 & 0\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} . Migration rates (number of migrants per generation)
among \emph{Evynnis cardinalis} sampling sites from Beibu Gulf
(receives/exports).\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
& S1 & S2 & S3 & S4 & S5 & S6 & S7 & S8 & S9 & S10 & S11\tabularnewline
\midrule
\endhead
S1 & 0 & & & & & & & & & &\tabularnewline
S2 & 19.34/12.78 & 0 & & & & & & & & &\tabularnewline
S3 & 17.37/45.76 & 13.73/43.34 & 0 & & & & & & & &\tabularnewline
S4 & 20.67/8.33 & 25.16/16.59 & 25.61/10.87 & 0 & & & & & &
&\tabularnewline
S5 & 17.26/12.19 & 11.62/10.72 & 38.16/7.41 & 9.08/14.31 & 0 & & & & &
&\tabularnewline
S6 & 15.36/18.03 & 23.55/19.19 & 42.58/18.06 & 17.02/22.18 & 6.01/16.28
& 0 & & & & &\tabularnewline
S7 & 5.98/30.87 & 17.41/45.36 & 21.88/30.68 & 12.38/22.22 & 12.13/37.6 &
18.83/30.66 & 0 & & & &\tabularnewline
S8 & 15.67/39.67 & 20.4/36.59 & 14.36/36.54 & 7.69/35.23 & 11.57/18.65 &
22.88/33.07 & 24.08/9.91 & 0 & & &\tabularnewline
S9 & 20.77/22.94 & 25.72/9.85 & 31.27/19.78 & 20.88/15.8 & 14.99/33.7 &
34.08/14.1 & 41.58/36.78 & 37.02/29.12 & 0 & &\tabularnewline
S10 & 23.62/40.04 & 7.89/30.56 & 41.27/28.81 & 8.65/38.6 & 6.98/36.74 &
20.98/26.3 & 22.18/28.17 & 31.06/24.21 & 22.95/30.58 & 0
&\tabularnewline
S11 & 9.6/45.44 & 23.9/32.68 & 37.6/38.86 & 11.78/30.71 & 10.46/36.42 &
13.41/18.61 & 28.33/33.01 & 16.41/40.41 & 15.93/27.67 & 34.03/31.68 &
0\tabularnewline
\bottomrule
\end{longtable}

Table 4 Mismatch distribution, Tajima's D and Fu's Fs statistics for

\emph{Evynnis cardinalis}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
sites & Tajima's D & Tajima's D & Fu's Fs & Fu's Fs & Mismatch
distribution & Mismatch distribution & Mismatch distribution & Mismatch
distribution & Mismatch distribution\tabularnewline
\midrule
\endhead
& D & p & Fs & p & \selectlanguage{greek}τ \selectlanguage{english}(95\% CI) & R index & R index p & SSD & SSD
p\tabularnewline
S1 & -1.471 & 0.05 & -6.441 & 0 & 2.719 (0.758, 3.865) & 0.151 & 0.27 &
0.042 & 0.2\tabularnewline
S2 & -1.591 & 0.04 & -8.46 & 0 & 2.379 (0.84, 3.629) & 0.221 & 0.08 &
0.072 & 0.04\tabularnewline
S3 & -1.844 & 0.015 & -8.333 & 0 & 2.455 (0.955, 3.832) & 0.235 & 0.05 &
0.08 & 0.04\tabularnewline
S4 & -1.39 & 0.044 & -3.927 & 0.001 & 2.607 (0.852, 4.15) & 0.178 & 0.4
& 0.051 & 0.34\tabularnewline
S5 & -1.359 & 0.102 & -7.415 & 0 & 2.234 (0.932, 3.813) & 0.352 & 0.07 &
0.105 & 0.04\tabularnewline
S6 & -0.863 & 0.248 & -5.146 & 0 & 2.658 (1.123, 4.184) & 0.15 & 0.33 &
0.041 & 0.25\tabularnewline
S7 & -1.219 & 0.132 & -6.157 & 0.001 & 3.006 (1.061, 4.217) & 0.115 &
0.41 & 0.024 & 0.36\tabularnewline
S8 & -1.796 & 0.014 & -9.165 & 0 & 2.893 (0.963, 4.479) & 0.163 & 0.23 &
0.05 & 0.13\tabularnewline
S9 & -1.319 & 0.12 & -4.663 & 0 & 2.945 (0.459, 4.707) & 0.068 & 0.74 &
0.009 & 0.78\tabularnewline
S10 & -1.944 & 0.003 & -10.011 & 0 & 2.428 (0.781, 3.863) & 0.275 & 0.05
& 0.096 & 0.06\tabularnewline
S11 & -0.73 & 0.26 & -4.09 & 0.004 & 2.617 (1.654, 4.047) & 0.154 & 0.37
& 0.042 & 0.25\tabularnewline
total & -2.446 & 0 & -28.378 & 0 & 1.531 (1.281, 1.941) & 0.055 & 0.26 &
0.002 & 0.24\tabularnewline
\bottomrule
\end{longtable}

SSD = sum of squared deviation; R index = Raggedness index;

\section*{Figure Captions}

{\label{figure-captions}}

Fig. 1. Map showing study area and 11 sampling locations in Beibu Gulf,
South China Sea.

Fig. 2. Haplotype network for \emph{Evynnis cardinalis} on the basis of
nucleotide variation at the cytochrome c oxidase subunit I (COI) gene.
Each circle represents a unique haplotype, and its size is proportional
to the number of individuals sharing that specific haplotype. For each
branch with more than one mutational step each mutational step is
labelled.

Fig. 3. Pie diagrams showing the relative abundance of different
haplotypes across the eleven sampling sites (total number of different
haplotypes = 44). The size of each pie fraction is proportional to the
fraction of individuals with a given haplotype.

Fig. 4. Observed mismatch distributions (bars) and expected mismatch
distributions under sudden expansion model (line) of COI
for\emph{Evynnis cardinalis} from Beibu Gulf

Fig. 5. Bayesian skyline plot revealing the demographic trends
of\emph{Evynnis cardinalis} in Beibu Gulf. The dark solid line is median
estimate under the assumption of per site mutation rate of 2\% per
million years. The dark gray solid line shows the median obtained under
the assumption of per site mutation rate of 1\% per million years, while
the light gray bottom line shows the median obtained under the
assumption of per site mutation rate of 3\% per million years, and the
dashed lines show the 95\% highest posterior density (HPD) limits.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig1/fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig2/fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig3/fig3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig4/fig4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig5/fig5}

\end{center}
\end{figure}

\selectlanguage{english}
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\end{document}

