
==== Front
Ecol Evol
Ecol Evol
10.1002/(ISSN)2045-7758
ECE3
Ecology and Evolution
2045-7758
John Wiley and Sons Inc. Hoboken

10.1002/ece3.70284
ECE370284
ECE-2024-05-00950.R2
Conservation Genetics
Genetics
Genomics
Population Genetics
Research Article
Research Article
Population genetics and evolutionary history of the intertidal brittle star Ophiothrix (Ophiothrix) exigua in the northern China Sea
Zhang et al.
Zhang Qian 1 2
Hu Xuying 1 2
Deng Zongjing 3 4
Li Yixuan 5
Dong Yue 1 6
Han Chen 7
Zeng Xiaoqi 8
Xiao Ning 9
Zhang Xuelei 1 2
Xu Qinzeng https://orcid.org/0000-0003-3036-5361
1 2 xqz08@163.com
xuqinzeng@fio.org.cn

1 Key Laboratory of Marine Eco‐Environmental Science and Technology, First Institute of Oceanography MNR Qingdao China
2 Laboratory for Marine Ecology and Environmental Science, Qingdao Marine Science and Technology Center Qingdao China
3 National Museum of Nature and Science Taito‐ku Japan
4 Department of Biological Sciences, Graduate School of Science The University of Tokyo Bunkyo‐ku Japan
5 Department of Biology Hong Kong Baptist University Hong Kong China
6 College of Environmental Science and Engineering Ocean University of China Qingdao China
7 School of Ocean Sciences, China University of Geosciences Beijing China
8 Institute of Evolution & Marine Biodiversity Ocean University of China Qingdao China
9 Institute of Oceanology, Department of Marine Organism Taxonomy and Phylogeny Chinese Academy of Sciences Qingdao China
* Correspondence
Qinzeng Xu, First Institute of Oceanography, MNR, Qingdao, China.
Email: xqz08@163.com; xuqinzeng@fio.org.cn

16 9 2024
9 2024
14 9 10.1002/ece3.v14.9 e7028426 8 2024
14 5 2024
29 8 2024
© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.
https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Abstract

Ophiothrix (Ophiothrix) exigua is a common brittle star in the northwestern Pacific. As a dominant species, O. exigua inhabiting the intertidal rocky ecosystem are affected by multiple environmental stressors, but molecular insights into their genetic population structure remain poorly studied. In this study, we investigated the population genetics and evolutionary history of six O. exigua populations from the northern China Sea using mitochondrial (COI, NAD4) and nuclear (ITS2, 18S) gene markers. High haplotype diversity, low nucleotide diversity, and low rates of gene differentiation among the populations of O. exigua were detected. Pairwise genetic differentiation (ΦST) statistics between different localities were negative or low and insignificant, suggesting strong gene flow of this species over the study areas. The phylogenetic analyses showed that the populations exhibited high homogeneity between localities in our study area. Demographic analyses indicated that the populations experienced sustained expansion around 0.2 million years ago. This expansion was likely related to transgressions events in the Yellow Sea during the Pleistocene period. Additional samples of O. exigua from disparate geographical locations, especially the Japan Sea and the Korean Peninsula, will be needed to unravel the population genetic patterns and evolutionary history of this species.

This study investigated the genetic diversity, population structure, and demographic history of the brittle star Ophiothrix (Ophiothrix) exigua, a common intertidal species in the northwestern Pacific Ocean, using mitochondrial and nuclear gene markers. The results revealed high genetic diversity but remarkably low genetic differentiation among populations in the Yellow and Bohai Seas, suggesting widespread gene flow and the absence of cryptic speciation within the study area. Demographic analyses indicated that the populations experienced expansion around 0.2 million years ago, likely related to sea level changes during the Pleistocene glaciations.

demographic expansion
genetic diversity
northwestern pacific
ophiuroid
population genetic structure
National Key Research and Development Program of China 10.13039/501100012166 2022YFF0802202 National Natural Science Foundation of China 10.13039/501100001809 42176135 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:17.09.2024
Zhang, Q. , Hu, X. , Deng, Z. , Li, Y. , Dong, Y. , Han, C. , Zeng, X. , Xiao, N. , Zhang, X. , & Xu, Q. (2024). Population genetics and evolutionary history of the intertidal brittle star Ophiothrix (Ophiothrix) exigua in the northern China Sea. Ecology and Evolution, 14 , e70284. 10.1002/ece3.70284
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pmc1 INTRODUCTION

Genetic diversity is a vital component of biodiversity and a key parameter for assessing the status of biological resources (Cen et al., 2023). Rich genetic diversity helps enhance the adaptability and evolutionary potential of species (Hughes et al., 2008; Yang et al., 2016). Various processes such as divergence, isolation, and bottleneck all influence the levels of genetic diversity of population (Raman et al., 2023). However, the genetic diversity, population structure, and ecological functions of marine organisms have all been profoundly affected by global warming and human activities (Donelson et al., 2019; Eirin‐Lopez & Putnam, 2019; Törnroos et al., 2019). Rising ocean temperatures pose a significant challenge to the survival of marine organisms (Schiel et al., 2004), potentially leading to the northward migration of some benthic organisms (Xu et al., 2023). Assessing and maintaining the genetic diversity of marine organisms is crucial for preserving biodiversity and sustaining the health of marine ecosystems.

The Yellow and Bohai Seas, as important continental marginal seas in the northwestern Pacific Ocean (Liu, 2013), are sensitive to global change due to the wide, flat, and low‐gradient shelves (Liu et al., 2016, 2018). Since the late Quaternary, the frequent “transgression” and “regression” events in the Yellow and Bohai Seas have been primarily driven by the significant fluctuations in sea level caused by climate change (Liu et al., 2014; Yao et al., 2017). Sedimentary environment studies in the Yellow and Bohai Sea region have revealed that this area has experienced at least four marine transgression events since 200 Ka (Yao et al., 2022), which likely correspond to the interglacial periods indicated by Marine Isotope Stages (MIS)7, 5, 3, and 1 (Yao et al., 2022; Yi et al., 2013). These cyclic transgression and regression events have significantly impacted the population structure of marine species inhabiting the Yellow and Bohai Sea Shelf (Avise, 2000; Han et al., 2008; Shen et al., 2011; Yang & Li, 2016), leading to population growth and range expansion (Han et al., 2008; Wang, 1999) during transgression or population bottlenecks, genetic drift, and fragmented or confined to refugia during regression (Liu et al., 2006; Voris, 2000). The alternation between transgression and regression events notably affected intertidal species, likely shaping their current distribution patterns and driving their rapid adaptation to ever‐changing environments (Xue et al., 2014).

Ophiothrix (Ophiothrix) exigua Layman, 1874, is a common and dominant ophiuroids in the intertidal zone (Liao, 2004; Yi & Irimura, 1987) and is reported from the surrounding intertidal areas of Qingdao Coast (Liao, 2004), East Sea, Chindo & Cheju Island of Korean peninsula (Shin, 1995), Bousou peninsula, Inland Sea, Japan Sea (Kitazawa et al., 2015). As the ecological keystone species, O. exigua plays crucial roles in ecosystems, making significant contributions to the marine carbon cycle and calcium reservoirs (Johnston & Gruner, 2018; Lebrato et al., 2010; Lessin et al., 2019). Ophiothrix (Ophiothrix) exigua is also sensitive to temperature fluctuations, making it a good bioindicator of ecological stress (Fuad et al., 2022; Wang et al., 2023). However, O. exigua may be a cryptic species complex rather than a single species due to its highly diverse morphology, indicating various colors (gray, yellow, and brown), patterning of arms, and arrangement of spinelets around the disk (Boissin et al., 2008; Hernández‐Díaz et al., 2023; Taboada & Pérez‐Portela, 2016). That may bring difficulties to its identification (Leiva et al., 2023). Such morphological variants were subsequently made this species improperly identified as several species, for instance, O. hylodes H.L. Clark, 1911, O. marenzelleri Koehler, 1904, and O. stelligera Lyman, 1874. They were recently proved synonymised with O. exigua (Irimura, 1981). Furthermore, this species exhibits a high reproductive capacity, developing from small eggs into juvenile stages within approximately 1 month through a planktotrophic larva (Kitazawa et al., 2015).

In this study, we aim to elucidate the population genetics and evolutionary history of O. exigua from the northern China Sea using DNA sequence data from both mitochondrial and nuclear genes to (1) assess the genetic structure among different geographic populations; (2) examine the genetic diversity; and (3) infer the demographic changes of O. exigua.

2 MATERIALS AND METHODS

2.1 Sampling and DNA extraction

A total of 80 samples of O. exigua were collected from six intertidal zones of locations including Qinhuangdao (QHD), Dalian (DL), Rongcheng (RC), Langyatai (LYT), Renjiatai (RJT), and May Fourth Square (WS, Figure 1). The morphology of O. exigua exhibits slight variation between different sampling points (Table S1). After morphological identification, the collected samples were fixed with absolute ethanol and stored at −20°C. A segment of approximately 1 cm from the arm tissue of the brittle star was taken for DNA extraction. After grinding in liquid nitrogen, total genomic DNA was extracted using either the QIAamp Fast DNA Tissue Kit (QIAGEN, Germany) or the OMEGA Tissue DNA Kit (OMEGA, USA), following the specific extraction procedures provided in the respective manuals. The extracted total genomic DNA was qualified by 1% agarose gel electrophoresis before PCR amplification.

FIGURE 1 Distribution map showing sampling locations of Ophiothrix (Ophiothrix) exigua populations. This map was made via Ocean Data View software. (https://odv.awi.de).

2.2 PCR amplification and sequencing

For each sample, we conducted amplification with specific primers for two mitochondrial genes—cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunits 4 (NAD4)—and two nuclear genes—Internal Transcribed Spacer 2 (ITS2) and 18S ribosomal RNA (18S) (Table 1). The primers for the NAD4 were designed based on the complete mitochondrial genome sequence of O. exigua (GenBank number: ON729296) (Table 1). PCRs were conducted in a total volume of 25 μL mixture, comprising 1 μL of each primer (10 μM), 9.5 μL of diluted distilled H2O, 12.5 μL of 2× Taq PCR Master Mix (with blue dye, Sangon Biotech, Inc), and 1 μL of the template DNA. The PCR cycling conditions for COI, ITS2, and 18S were followed as described in the respective references (Table 1). The amplification of NAD4 used the following profile: The first step was at 95°C for 3 min, followed by 35 cycles at 95°C for 30 s, 50°C for 30 s and 72°C for 1 min, and a further extension at 72°C for 10 min. The PCR products were sequenced for both directions by Sangon Biotech, Inc. (Qingdao, China) with the same primers used in the amplification reaction. All sequences of this study were uploaded to the GenBank database (Table 2).

TABLE 1 The PCR primers for amplification of COI, NAD4, 18S and ITS2 sequences.

Destination fragment	Primer name	Primer sequence (5′‐3′)	
COI (Folmer et al., 1994)	COI‐F	TTCTACCAACCATAAGGATATAGGG	
COI‐R	TTCTACCAACCATAA GGATATAGGG	
NAD4 (This study)	NAD4‐F	CTACCACAGCCGCCATTA	
NAD4‐R	TCCGTATCCTCCCATCTT	
18S (Hunter et al., 2016)	18F997	TTCGAAGACGATCAGATACCG	
18R1779	TGTTACGACTTTTACTTCCTCTA	
ITS2 (Naughton et al., 2014)	OphITS2F	TCGTTCGTCTGAGGGTCGTTA	
OphITS2R	GTAGTCTCGCTCGATCTG	

TABLE 2 The information of GenBank accession.

Locus	GenBank accession number	
COI	OR782206‐OR782277, OR984206‐OR984213	
NAD4	OR780679‐OR780743, PP001162‐PP001168	
ITS2	OR784232‐OR784298, PP001711‐PP001718	
18S	OR775595‐OR775657, OR988137‐OR988143	

2.3 Data analysis

The sequenced fragments obtained from bidirectional sequencing were assembled using Dnastar Lasergene v7.1 (Clewley, 1995). All sequences were checked and aligned using the ClustalW algorithm (Thompson et al., 1994) available in MEGA v7 (Kumar et al., 2016). The alignment of each gene fragment was then auto‐trimmed by G‐blocks (Jose, 2000) with default settings to output conserved datasets. Genetic diversity indices, including the number of nucleotide composition, segregating sites (S), number of haplotypes (H), nucleotide diversity (π), haplotype diversity (Hd), and gene flow, were estimated for each dataset using DnaSP v6.12.03 (Librado & Rozas, 2009). The 18S dataset showed highly conserved intraspecific variation, with populations from the six locations sharing a single haplotype. Due to the lack of informative variation, the 18S marker was excluded from further analyses.

To determine the evolutionary relationships among haplotypes, we constructed phylogenetic trees using the Bayesian Inference (BI) and maximum likelihood (ML) methods. Using PAUP 4.0b10 (Swofford, 2002), the partition homogeneity test revealed no significant incongruence among the COI, NAD4, and ITS2 datasets (p = .3200), allowing them to be combined for phylogenetic analysis. We determined the best‐fit model (HKY + G) for BI and ML analyses using ModelFinder v1.5.4 (Kalyaanamoorthy et al., 2017) embedded in IQ‐TREE v1.6 (Nguyen et al., 2015). We used Ophiothrix (Acanthophiothrix) scotiosa Murakami, 1943, and Ophiothrix (Acanthophiothrix) proteus Koehler, 1905, as the outgroups. The ML analysis of concatenated COI‐NAD4‐ITS2 sequence datasets was carried out using IQ‐TREE with 10,000 ultrafast bootstrap (Hoang et al., 2018). BI analysis was implemented using MrBayes v3.2.6 (Ronquist et al., 2012) on two separate runs of Markov chains for 1 million generations with the first 25% of trees as burn‐in. Effective sample size (ESS) was determined using Tracer v1.7 (Rambaut et al., 2018) to ensure all parameter values with ESS >200. Based on the phylogenetic analyses and population delimitation, pairwise genetic distances were computed in MEGA using the Kimura 2‐parameter (K2P) model (Kimura, 1980).

Genetic differentiation index (ΦST) and analysis of molecular variance (AMOVA) among geographic populations were analyzed using Arlequin v3.5.2.2 (Excoffier & Lischer, 2010), testing the distribution frequency differences of haplotypes among populations. The ΦST values were estimated using a pairwise difference distance matrix, and significance levels were assessed by 1000 permutations. Tajima'D (Tajima, 1989) and Fu's Fs (Fu, 1996) neutrality tests were used to infer the historical demography of O. exigua populations. A TCS haplotype network (Clement et al., 2002) was constructed using POPART v.1.7 (Leigh & Bryant, 2015) to determine the genealogical relationships between haplotypes, visualizing the overall structure and spatial pattern of genetic diversity from the haplotype data of O. exigua.

The mismatch distribution of populations from different locations was estimated by DnaSP to test the frequency distribution of pairwise differences under past population change. The demographic parameter tau (τ) was determined using DnaSP software to estimate the time since regional population expansion. The equation t = 2μk/τ (Rogers & Harpending, 1992) was used to determine the population size change of O. exigua, where t is the number of years since population expansion, μ is the per site per year substitution rate, and k refers to the gene fragment size. Finally, to assess changes in the size of the O. exigua population over time, we plotted the Bayesian Skyline Plots (BSP) using BEAUti v 2 and BEAST v 2.6.3 (Bouckaert et al., 2014). For both mismatch and BSP analyses, a substitution rate of 2.48 × 10−8 per site per year was utilized for the COI, in accordance with previous studies in Ophiuroidea (Naughton et al., 2014).

3 RESULTS

3.1 Sequence characterization

A total of 80 COI, 72 NAD4, 75 ITS2, and 70 18S sequences were successfully amplified for 80 individuals. The average sizes of COI, NAD4, ITS2, and 18S gene fragments were 584, 645, 586, and 731 bp, respectively. All four gene sequences showed a high similarity (>98.97%) to the complete mitochondrial genome sequence of O. exigua in GenBank (ON729296). The average frequencies of the four nucleotides in the COI and NAD4 gene regions for all samples of O. exigua were 54.54% (A + T), 45.46% (G + C), 61.70% (A + T), and 38.30% (G + C), respectively. The A + T content was higher than the G + C content, which was consistent with the characteristics of the mitochondrial base composition (Galaska et al., 2019; Scouras et al., 2004; Sun et al., 2023).

3.2 Genetic variation

We calculated genetic diversity statistics for the six locations of O. exigua using three gene fragments (Table 3). The populations presented high haplotype diversity, with Hd values ranging from 0.995 to 0.998, and low nucleotide diversity, with π values between 0.00882 and 0.01372. NAD4 exhibited the highest nucleotide diversity (π = 0.01372), followed by COI (π = 0.00985). In contrast, ITS2 appeared to be the most conserved gene in O. exigua compared with the other two fragments (π = 0.00032). Six populations all presented high genetic diversity according to their high haplotype diversities (Hd: 0.933–1). The nucleotide diversities ranged from 0.00760 to 0.01622, with the LYT population exhibiting the highest values for both single mitochondrial gene (π = 0.01119 for COI, π = 0.01622 for NAD4) and the concatenated genes (π = 0.01074).

TABLE 3 Genetic diversity statistics of Ophiothrix (Ophiothrix) exigua populations.

Locus	Location	N	S	H	π	Hd	Tajima's D	Fu's fs	
COI	QHD	15	30	15	0.01001	1.000	−1.54117	−10.85574**	
DL	11	28	11	0.00996	1.000	−1.80781*	−6.31013*	
RC	20	40	20	0.01078	1.000	−1.75973*	−16.86731**	
WS	12	25	12	0.00760	1.000	−2.06612**	−8.85344**	
LYT	10	31	8	0.01119	0.933	−1.94261*	−1.29468	
RJT	12	24	11	0.00905	0.985	−1.48734	−5.26936*	
Total	80	95	71	0.00985	0.995	−2.39742**	−8.85344*	
NAD4	QHD	15	44	15	0.01574	1.000	−1.07019	−7.36020 **	
DL	11	32	11	0.01161	1.000	−1.46104	−5.24451**	
RC	14	39	14	0.01481	1.000	−0.96158	−6.79857**	
WS	12	33	11	0.01200	0.985	−1.31715	−3.72781*	
LYT	8	34	8	0.01622	1.000	−1.07534	−2.08024	
RJT	12	31	10	0.01250	0.970	−0.87725	−2.17401	
Total	72	84	65	0.01372	0.997	−1.77614	−4.56422**	
ITS2	QHD	15	2	2	0.00046	0.133	−1.49051	−0.23493	
DL	11	0	1	0.00000	0.000	0.00000	0.00000	
RC	16	2	2	0.00043	0.125	−1.49796	−0.17695	
WS	12	2	3	0.00057	0.318	−1.45138	−1.32484*	
LYT	9	0	1	0.00000	0.000	0.00000	0.00000	
RJT	12	1	2	0.00028	0.167	−1.14053	−0.47566*	
Total	75	6	6	0.00032	0.130	−2.00119*	−6.66178*	
COI + NAD4 + ITS2	QHD	15	73	15	0.00956	1.000	−1.49051	−5.38584**	
DL	11	60	11	0.00838	1.000	−1.66233*	−3.30375*	
RC	14	61	14	0.00837	1.000	−1.35124	−5.26169*	
WS	12	58	12	0.00767	1.000	−1.68562*	−4.21960*	
LYT	8	59	8	0.01074	1.000	−1.34847	−1.17196	
RJT	12	54	12	0.00839	1.000	−1.16998	−3.91670*	
Total	72	170	69	0.00882	0.998	−2.14571*	−3.41603**	
Abbreviations: DL, Dalian; H, number of haplotypes; Hd, haplotype diversity; LYT, Langyatai; N, number of sequences; QHD, Qinhuangdao; RC, Rongcheng; RJT, Renjiatai; S, number of segregating sites; WS, May Fourth Square; π, nucleotide diversity.

*p <  .05.

**p <  .01.

3.3 Phylogenetic and network analyses

The median‐joining network (Figure 2) showed the connection among the six O. exigua populations. The ITS2 network had the simplest topology, with the highest number of shared haplotypes (H1) located at the center, while other haplotypes differed by only one or two substitutions from this main haplotype. Based on coalescent theory, H1 may represent the ancestral haplotype of O. exigua. In contrast, the COI and NAD4 networks revealed profound diverse bush‐like genealogies, with black circles indicating missing haplotypes, and numerous haplotypes broadly and evenly distributed. The haplotypes from each sampling location were homogeneously mixed in the networks, failing to show distinct geographical patterns or population differentiation.

FIGURE 2 The network based on (a) COI, (b) NAD4 and (c) ITS2 haplotypes. Numbers and each circle represent a unique haplotype; size is proportional to their frequencies; colors denote populations; dashes in the branches represent the number of mutation steps. This figure was generated using POPART v.1.7. DL, Dalian; LYT, Langyatai; QHD, Qinhuangdao; RC, Rongcheng; RJT, Renjiatai; WS, May Fourth Square.

To study the phylogenetic relationship of O. exigua in six regions, we constructed the haplotype phylogenetic trees using the concatenated COI‐NAD4‐ITS2 gene set for all populations. ML and BI analyses produced phylogenetic trees with consistent topology but with different supporting values (Figure S1). The phylogenetic analysis of O. exigua populations revealed a lack of stable geographical differentiation. Despite the presence of unique haplotypes within each population, individuals from different locations were intermixed in the phylogenetic tree, without any obvious group clustering observed.

3.4 Genetic structure

The AMOVA results showed that a large proportion of molecular genetic variation was present within populations (99.25%, p < .001), and the remaining variation was found among populations (0.75%, p < .001) for the concatenated sequences. The overall fixation index (FST) observed for all populations was nonsignificant (0.00753, p > .05, Table 4). Consistent with the concatenated dataset, the genetic variance percentages were high for all three markers: 99.94% for COI, 100.12% for NAD4, and 99.25% for ITS2 (Table 5). Pairwise ΦST values between populations varied from −0.009 (QHD‐WS) to 0.081 (DL‐RJT) for the concatenated dataset. Population pairwise ΦST analysis among all populations demonstrated no significant genetic differentiation. The mean K2P distance was between 0.008 and 0.010 for all populations (Table 5).

TABLE 4 AMOVA analysis for the population of Ophiothrix (Ophiothrix) exigua.

Source of variation	Df	Sum of squares	Variance components	Percentage of variation (%)	FST	
COI	
Among populations	5	14.488	0.00175 Va	0.06	0.00061	
Within populations	74	212.724	2.87465 Vb	99.94		
Total	79	227.213	2.87640			
NAD4	
Among populations	5	21.824	−0.00537 Va	−0.12	−0.00121	
Within populations	66	292.301	4.42880 Vb	100.12		
Total	71	314.125	4.42343			
ITS2	
Among populations	5	0.388	−0.00132 Va	−1.43	−0.01426	
Within populations	69	6.492	0.09408 Vb	101.43		
Total	74	6.880	0.09276			
COI + NAD4 + ITS2	
Among populations	5	37.955	0.05281 Va	0.75	0.00753	
Within populations	66	459.462	6.96155 Vb	99.25		
Total	71	497.417	7.01436			

TABLE 5 Mean K2P genetic distance (below diagonal) and pairwise ΦST values (above diagonal) between populations based on the concatenated genes.

Locality	QHD	DL	RC	WS	LYT	RJT	
QHD		−0.020	−0.017	−0.009	−0.028	0.052	
DL	0.009		−0.013	−0.012	−0.021	0.081	
RC	0.009	0.008		−0.018	−0.019	0.025	
WS	0.009	0.008	0.008		−0.028	0.061	
LYT	0.010	0.009	0.009	0.009		0.065	
RJT	0.010	0.009	0.009	0.009	0.010		
Abbreviations: DL, Dalian; LYT, Langyatai; QHD, Qinhuangdao; RC, Rongcheng; RJT, Renjiatai; WS, May Fourth Square.

3.5 Historical dynamics of the population

BSP analysis, mismatch distribution, and neutrality tests were employed to investigate the historical demography of the O. exigua population. The overall negative neutrality tests (Tajima's D and Fu's Fs) suggest that O. exigua encountered a population bottleneck followed by expansion (Table 3). The mismatch distribution curves drawn based on COI and NAD4 mitochondrial gene sequences showed unimodal distribution, which was consistent with the population expansion model (Figure 3). Using the formula t = 2μk/τ (τ = 5.6), the time of the expansion event was calculated as 193,327 years before present (BP). BSP analysis of the total population also showed an increase in the effective population size, indicating that the populations experienced sustained expansion at about 0.2 Ma (Figure 4) and that the population rapidly grew to its current size.

FIGURE 3 The mismatch distributions for all individuals in Northern China Sea. (a) COI and (b) NAD4. This figure was generated using DnaSP v6.12.03.

FIGURE 4 BSP Analysis of COI sequence datasets for all individuals in Northern China Sea. The X‐axis represents time in millions of years before the present. The Y‐axis represents the effective population size. The solid line indicates the median population size, and the 95% interval of HPD is shown in blue. This was generated using BEAUti v 2 and BEAST v 2.6.3.

4 DISCUSSION

In the present study, we found high population homogeneity across the Yellow and Bohai Seas. The populations of O. exigua in the studied areas exhibit a relatively high level of genetic diversity, but no population divergence (Figure 2 and Table 3). Our results demonstrated that the different morphs present in O. exigua correspond to a single evolutionary unit, with no evidence of cryptic species, as we observed no genetic divergence was observed between those variant individuals. The homogeneity in O. exigua contrast with the high number of cryptic speciation events detected in other ophiothrix, such as Ophiothrix fragilis f. nuda Madsen, 1970 (Pérez‐Portela et al., 2013; Taboada & Pérez‐Portela, 2016) and Ophiothrix angulata Say, 1825 (Hernández‐Díaz et al., 2023). The difference in morphological variation of O. exigua may be the result of phenotypic plasticity due to adaptation to the environment. Furthermore, the extremely high genetic diversity found in O. exigua seems to be a characteristic of some abundant ophiuroids likely related to large population sizes (Hunter & Halanych, 2010), which makes populations less susceptible to genetic drift and helps maintain a high‐level of low‐frequency haplotypes within populations (Muths et al., 2009; Pérez‐Portela et al., 2013). A higher genetic diversity also helps to enhance the adaptability of O. exigua to the environment (Bonin et al., 2007; Cen et al., 2023).

The lack of significant differences among various geographical areas in O. exigua may result from the combination of life‐history traits and ocean current circulation patterns (Leiva et al., 2023; Pérez‐Portela et al., 2017; Taboada & Pérez‐Portela, 2016). One key factor is the pelagic larval duration (PLD), which has a significant impact on gene flow and genetic structure in benthic organisms with a larval stage (Scheltema, 1986, 1995; Selkoe & Toonen, 2011). The planktotrophic larva of O. exigua remains in the water column for approximately 1 month (Kitazawa et al., 2015), exhibiting a strong dispersal ability that allows it to cross geographical barriers, thus significantly enhancing gene exchange. Additionally, ocean currents play a crucial role in larval dispersal (Scheltema, 1986). For instance, northward currents such as the Yellow Sea Warm Current and the Liaonan Coastal Current facilitate the northward dispersal of planktonic larvae from the DL population. Meanwhile, southward currents such as the Bohai Bay Coastal Current, Liaodong Bay Coastal Current, and Lubei Coastal Current promote the southward migration of planktonic larvae from QHD and RC populations, thereby enabling gene exchange (Chen et al., 2020; Li, Yang, et al., 2020; Ramírez Ayala, ; Yang et al., 2019). Frequent gene flow leads to the homogenization of genetic resources, resulting in the absence of significant genetic differentiation and typical geographical structure among O. exigua populations in the northern China Sea.

The population homogeneity found in O. exigua could be also the result of historical recolonization events, including habitat expansion and demographic growth during the Pleistocene period. In this study, the results of haplotype diversity, the neutral theory test (Tajima's D and Fu's Fs), mismatch distribution and BSP all support the occurrence of a rapid population expansion event for O. exigua in the northern China Sea. The expansion time were estimated using different methods in this study. Mismatch analysis estimated the expansion time to be around 193,327 years ago, while BSP analysis dated the expansion event to 0.2–0.1 Ma. This timing closely aligns with three significant marine transgressions in the studied area since the Pleistocene. These transgressions correspond to high sea‐level periods during MIS7a (197–191 ka, an interglacial period), MIS6 (130–191 ka, possibly influenced by regional tectonic activity), and MIS5e (130–115 ka, the Eemian Interglacial) (Bintanja et al., 2002; Wang et al., 2020; Yi et al., 2012, 2013). During glacial maxima, with a sea level drop of 130–150 m, the Bohai Sea and Yellow Sea Shelf were completely exposed (Liu et al., 2007; Xie et al., 1995). Therefore, we have sound reason to infer that different geographical populations of O. exigua might coalesce together due to the geographical range contraction during the glacial period and then expand population range when favorable conditions emerged during interglacial periods (Xi et al., 2023). The periodic coalescence of populations could enhance historical gene flow among populations (Xue et al., 2014). Expansion events in this period were well‐documented for other dominant species in the same regions, such as Nibea albiflora Richardson, 1846 (170–85 ka) (Han et al., 2008), Ophiura sarsii vadicola Djakonov, 1954 (111.8–60.1 ka) (Li, Dong, et al., 2020), and Atrina pectinata Linnaeus, 1767 (ca. 160 ka) (Xue et al., 2014). The genetic population patterns of those species were similar to O. exigua, with high genetic diversity and low population divergence.

The natural geographic distribution of O. exigua includes the China Seas (Liao, 2004), the Japan Sea (Kitazawa et al., 2015; Okanishi et al., 2024), the Korean Peninsula (Shin, 1995), and other northwestern Pacific areas. Although O. exigua is widely distributed, this study focused solely on the northern China Sea. To unravel the population genetic patterns and evolutionary history of O. exigua in the northwestern Pacific areas, more samples from disparate geographical locations, especially the Japan Sea and the Korean Peninsula, need to be collected. The southern China Sea was not considered in our study because the southern specimens of this species in earlier records may have been misidentified. We confirmed significant morphological differences between our fresh O. exigua specimens and museum samples from southern China Sea (Marine Biological Museum, Chinese Academy of Sciences). Compared with our O. exigua specimens, the southern samples identified in Liao (2004) exhibit more sparsely distributed and elongated club‐shaped spines on the disk, less densely covered radial shields with clearly visible outlines, more slender and transparent arm spines, and distinctive longitudinal black and white stripes on their dorsal arm plates. Based on morphological characteristics, the southern specimens should be assigned to Ophiothrix (Ophiothrix) ciliaris Lamarck, 1816 (Liao, 2004).

AUTHOR CONTRIBUTIONS

Qian Zhang: Resources (equal); writing – original draft (supporting). Xuying Hu: Resources (equal). Zongjing Deng: Resources (equal). Yixuan Li: Writing – review and editing (equal). Yue Dong: Resources (equal); writing – review and editing (equal). Chen Han: Resources (equal). Xiaoqi Zeng: Writing – review and editing (equal). Ning Xiao: Resources (equal). Xuelei Zhang: Conceptualization (equal). Qinzeng Xu: Conceptualization (equal); funding acquisition (supporting); writing – review and editing (equal).

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supporting information

Figure S1.

Table S1.

ACKNOWLEDGEMENTS

Heartfelt thanks to Wenge Shi, Yuyao Sun, Jing Mo, and Xiaomei Liao from First Institute of Oceanography, Ministry of Natural Resources for their enthusiastic participation in sample collection. This work was financially supported by the National Key Research and Development Program of China (2022YFF0802202) and the National Natural Science Foundation of China (42176135).

DATA AVAILABILITY STATEMENT

The sequences newly generated are deposited in GenBank (accession numbers OR782206‐OR782277 & OR984206‐OR984213for COI, OR780679‐OR780743 & PP001162‐PP001168 for NAD4, OR784232‐OR784298 & PP001711‐PP001718 for ITS2, and OR775595‐OR775657 & OR988137‐OR988143 for 18S).
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REFERENCES

Avise, J. C. (2000). Phylogeography: The history and formation of species. Harvard university press.
Bintanja, R. , van de Wal, R. , & Oerlemans, J. (2002). Global ice volume variations through the last glacial cycle simulated by a 3‐D ice‐dynamical model. Quaternary International, 95 , 11–23. 10.1016/S1040-6182(02)00023-X
Boissin, E. , Féral, J. P. , & Chenuil, A. (2008). Defining reproductively isolated units in a cryptic and syntopic species complex using mitochondrial and nuclear markers: The brooding brittle star, Amphipholis squamata (Ophiuroidea). Molecular Ecology, 17 (7 ), 1732–1744. 10.1111/j.1365-294X.2007.03652.x 18266624
Bonin, A. , Nicole, F. , Pompanon, F. , Miaud, C. , & Taberlet, P. (2007). Population adaptive index: A new method to help measure intraspecific genetic diversity and prioritize populations for conservation. Conservation Biology, 21 (3 ), 697–708. 10.1111/j.1523-1739.2007.00685.x 17531048
Bouckaert, R. , Heled, J. , Kühnert, D. , Vaughan, T. , Wu, C.‐H. , Xie, D. , Suchard, M. A. , Rambaut, A. , & Drummond, A. J. (2014). BEAST 2: A software platform for Bayesian evolutionary analysis. PLoS Computational Biology, 10 (4 ), e1003537. 10.1371/journal.pcbi.1003537 24722319
Cen, X. , Zhang, G. , Liu, H. , Yao, G. , Xiong, P. , He, M. , & Liu, W. (2023). Analysis of genetic diversity in two different shell colors of the giant triton snail (Charonia tritonis) based on mitochondrial COI sequences. Frontiers in Marine Science, 9 , 1066750. 10.3389/fmars.2022.1066750
Chen, J. , Shi, X. , Liu, Y. , Qiao, S. , Yang, S. , Yan, S. , Lv, H. , Li, J. , Li, X. , & Li, C. (2020). Holocene vegetation dynamics in response to climate change and hydrological processes in the Bohai region. Climate of the Past, 16 (6 ), 2509–2531. 10.5194/cp-16-2509-2020
Clement, M. , Snell, Q. , Walke, P. , Posada, D. , & Crandall, K. (2002). TCS: Estimating gene genealogies. Paper presented at the proceedings 16th international parallel and distributed processing symposium (p. 7). Parallel and Distributed Processing Symposium, International.
Clewley, J. P. (1995). Macintosh sequence analysis software: DNASTAR's Lasergene. Molecular Biotechnology, 3 (3 ), 221–224. 10.1007/BF02789332 7552691
Donelson, J. M. , Sunday, J. M. , Figueira, W. F. , Gaitán‐Espitia, J. D. , Hobday, A. J. , Johnson, C. R. , Leis, J. M. , Ling, S. D. , Marshall, D. , & Pandolfi, J. M. (2019). Understanding interactions between plasticity, adaptation and range shifts in response to marine environmental change. Philosophical Transactions of the Royal Society B, 374 , 20180186. 10.1098/rstb.2018.0186
Eirin‐Lopez, J. M. , & Putnam, H. M. (2019). Marine environmental epigenetics. Annual Review of Marine Science, 11 , 335–368. 10.1146/annurev-marine-010318-095114
Excoffier, L. , & Lischer, H. E. L. (2010). Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and windows. Molecular Ecology Resources, 10 (3 ), 564–567. 10.1111/j.1755-0998.2010.02847.x 21565059
Folmer, O. , Black, M. , Hoeh, W. , Lutz, R. , & Vrijenhoek, R. (1994). DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology, 3 , 294–299.7881515
Fu, Y.‐X. (1996). New statistical tests of neutrality for DNA samples from a population. Genetics, 143 (1 ), 557–570. 10.1093/genetics/143.1.557 8722804
Fuad, M. T. I. , Shi, W. , Liao, X. , Li, Y. , Sharifuzzaman, S. M. , Zhang, X. , Liu, X. , & Xu, Q. (2022). Transcriptomic response of intertidal brittle star Ophiothrix exigua to seasonal variation. Marine Genomics, 64 , 100957. 10.1016/j.margen.2022.100957 35580505
Galaska, M. P. , Li, Y. , Kocot, K. M. , Mahon, A. R. , & Halanych, K. M. (2019). Conservation of mitochondrial genome arrangements in brittle stars (Echinodermata, Ophiuroidea). Molecular Phylogenetics and Evolution, 130 , 115–120. 10.1016/j.ympev.2018.10.002 30316947
Han, Z. Q. , Gao, T. X. , Yanagimoto, T. , & Sakurai, Y. (2008). Genetic population structure of Nibea albiflora in yellow sea and east China sea. Fisheries Science, 74 , 544–552. 10.1016/j.ympev.2006.04.019
Hernández‐Díaz, Y. Q. , Solis, F. , Beltrán‐López, R. G. , Benítez, H. A. , Díaz‐Jaimes, P. , & Paulay, G. (2023). Integrative species delimitation in the common ophiuroid Ophiothrix angulata (Echinodermata: Ophiuroidea): Insights from COI, ITS2, arm coloration, and geometric morphometrics. PeerJ, 11 , e15655. 10.7717/peerj.15655 37483979
Hoang, D. T. , Chernomor, O. , von Haeseler, A. , Minh, B. Q. , & Vinh, L. S. (2018). UFBoot2: Improving the ultrafast bootstrap approximation. Molecular Biology and Evolution, 35 (2 ), 518–522. 10.1093/molbev/msx281 29077904
Hughes, A. R. , Inouye, B. D. , Johnson, M. T. , Underwood, N. , & Vellend, M. (2008). Ecological consequences of genetic diversity. Ecology Letters, 11 (6 ), 609–623. 10.1111/j.1461-0248.2008.01179.x 18400018
Hunter, R. L. , Brown, L. M. , Alexander Hill, C. , Kroeger, Z. A. , & Rose, S. E. (2016). Additional insights into phylogenetic relationships of the class Ophiuroidea (Echinodermata) from rRNA gene sequences. Journal of Zoological Systematics and Evolutionary Research, 54 (4 ), 269–275. 10.1111/jzs.12135
Hunter, R. L. , & Halanych, K. M. (2010). Phylogeography of the Antarctic planktotrophic brittle star Ophionotus victoriae reveals genetic structure inconsistent with early life history. Marine Biology, 157 , 1693–1704. 10.1007/s00227-010-1443-3
Irimura, S. (1981). Ophiurans from Tanabe Bay and its vicinity, with the description of a new species of Ophiocentrus. Publications of the Seto Marine Biological Laboratory, 26 (1–3 ), 15–49. 10.5134/176023
Johnston, C. A. , & Gruner, D. S. (2018). Marine fauna sort at fine resolution in an ecotone of shifting wetland foundation species. Ecology, 99 (11 ), 2546–2557. 10.1002/ecy.2505 30168591
Jose, C. (2000). Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular Biology and Evolution, 17 (4 ), 540–552. 10.1093/oxfordjournals.molbev.a026334 10742046
Kalyaanamoorthy, S. , Minh, B. Q. , Wong, T. K. , von Haeseler, A. , & Jermiin, L. S. (2017). ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature Methods, 14 (6 ), 587–589. 10.1038/nmeth.4285 28481363
Kimura, M. (1980). A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution, 16 , 111–120. 10.1007/BF01731581 7463489
Kitazawa, C. , Akahoshi, S. , Sohara, S. , Noh, J. T. , Tajika, A. , Yamanaka, A. , & Komatsu, M. (2015). Development of the brittle star Ophiothrix exigua Lyman, 1874 a species that bypasses early unique and typical planktotrophic ophiopluteus stages. Zoomorphology, 134 (1 ), 93–105. 10.1007/s00435-014-0233-8
Kumar, S. , Stecher, G. , & Tamura, K. (2016). MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Molecular Biology and Evolution, 33 (7 ), 1870–1874. 10.1093/molbev/msw054 27004904
Lebrato, M. , Iglesias‐Rodríguez, D. , Feely, R. A. , Greeley, D. , Jones, D. O. , Suarez‐Bosche, N. , Lampitt, R. S. , Cartes, J. E. , Green, D. R. , & Alker, B. (2010). Global contribution of echinoderms to the marine carbon cycle: CaCO3 budget and benthic compartments. Ecological Monographs, 80 (3 ), 441–467. 10.1890/09-0553.1
Leigh, J. W. , & Bryant, D. (2015). PopART: Full‐feature software for haplotype network construction. Methods in Ecology and Evolution, 6 (9 ), 1110–1116. 10.1111/2041-210X.12410
Leiva, C. , Pérez‐Sorribes, L. , González‐Delgado, S. , Ortiz, S. , Wangensteen, O. S. , & Pérez‐Portela, R. (2023). Exceptional population genomic homogeneity in the black brittle star Ophiocomina nigra (Ophiuroidea, Echinodermata) along the Atlantic‐Mediterranean coast. Scientific Reports, 13 (1 ), 12349. 10.1038/s41598-023-39584-7 37524805
Lessin, G. , Bruggeman, J. , McNeill, C. L. , & Widdicombe, S. (2019). Time scales of benthic macrofaunal response to pelagic production differ between major feeding groups. Frontiers in Marine Science, 6 , 15. 10.3389/fmars.2019.00015
Li, J. , Yang, S. , Li, R. , Shu, J. , Chen, X. , Meng, Y. , Ye, S. , & He, L. (2020). Vegetation history and environment changes since MIS 5 recorded by pollen assemblages in sediments from the western Bohai Sea, northern China. Journal of Asian Earth Sciences, 187 , 104085. 10.1016/j.jseaes.2019.104085
Li, Y. , Dong, Y. , Xu, Q. , Fan, S. , Lin, H. , Wang, M. , & Zhang, X. (2020). Genetic differentiation and evolutionary history of the circumpolar species Ophiura sarsii and subspecies Ophiura sarsii vadicola (Ophiurida: Ophiuridae). Continental Shelf Research, 197 , 104085. 10.1016/j.csr.2020.104085
Liao, Y. (2004). Fauna Sinica Invertebrata Echinodermata Ophiuroidea (Vol. 40 ). Science Press.
Librado, P. , & Rozas, J. (2009). DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics, 25 (11 ), 1451–1452. 10.1093/bioinformatics/btp187 19346325
Liu, J. (2013). Status of marine biodiversity of the China seas. PLoS One, 8 (1 ), e50719. 10.1371/journal.pone.0050719 23320065
Liu, J. , Liu, Q. , Zhang, X. , Liu, J. , Wu, Z. , Mei, X. , Shi, X. , & Zhao, Q. (2016). Magnetostratigraphy of a long quaternary sediment core in the South Yellow Sea. Quaternary Science Reviews, 144 , 1–15. 10.1016/j.quascirev.2016.05.025
Liu, J. , Shi, X. , Liu, Q. , Ge, S. , Liu, Y. , Yao, Z. , Zhao, Q. , Jin, C. , Jiang, Z. , & Liu, S. (2014). Magnetostratigraphy of a greigite‐bearing core from the South Yellow Sea: Implications for remagnetization and sedimentation. Journal of Geophysical Research: Solid Earth, 119 (10 ), 7425–7441. 10.1002/2014JB011206
Liu, J. , Zhang, X. , Mei, X. , Zhao, Q. , Guo, X. , Zhao, W. , Liu, J. , Saito, Y. , Wu, Z. , & Li, J. (2018). The sedimentary succession of the last~ 3.50 Myr in the western South Yellow Sea: Paleoenvironmental and tectonic implications. Marine Geology, 399 , 47–65. 10.1016/j.margeo.2017.11.005
Liu, J. X. , Gao, T. X. , Wu, S. F. , & Zhang, Y. P. (2007). Pleistocene isolation in the northwestern Pacific marginal seas and limited dispersal in a marine fish, Chelon haematocheilus (Temminck & Schlegel, 1845). Molecular Ecology, 16 (2 ), 275–288. 10.1111/j.1365-294X.2006.03140.x 17217344
Liu, J.‐X. , Gao, T.‐X. , Yokogawa, K. , & Zhang, Y.‐P. (2006). Differential population structuring and demographic history of two closely related fish species, Japanese sea bass (Lateolabrax japonicus) and spotted sea bass (Lateolabrax maculatus) in northwestern Pacific. Molecular Phylogenetics and Evolution, 39 (3 ), 799–811. 10.1016/j.ympev.2006.01.009 16503171
Muths, D. , Jollivet, D. , Gentil, F. , & Davoult, D. (2009). Large‐scale genetic patchiness among NE Atlantic populations of the brittle star Ophiothrix fragilis . Aquatic Biology, 5 (2 ), 117–132. 10.3354/ab00138
Naughton, K. M. , O'Hara, T. D. , Appleton, B. , & Cisternas, P. A. (2014). Antitropical distributions and species delimitation in a group of ophiocomid brittle stars (Echinodermata: Ophiuroidea: Ophiocomidae). Molecular Phylogenetics and Evolution, 78 , 232–244. 10.1016/j.ympev.2014.05.020 24875252
Nguyen, L.‐T. , Schmidt, H. A. , von Haeseler, A. , & Minh, B. Q. (2015). IQ‐TREE: A fast and effective stochastic algorithm for estimating maximum‐likelihood phylogenies. Molecular Biology and Evolution, 32 (1 ), 268–274. 10.1093/molbev/msu300 25371430
Okanishi, M. , Yoshigou, H. , Tagawa, K. , & Shibata, N. (2024). An eDNA metabarcoding of brittle stars: Examples from the Seto Inland Sea, a semi‐closed marine area. Regional Studies in Marine Science, 74 , 103515. 10.1016/j.rsma.2024.103515
Pérez‐Portela, R. , Almada, V. , & Turon, X. (2013). Cryptic speciation and genetic structure of widely distributed brittle stars (Ophiuroidea) in Europe. Zoologica Scripta, 42 (2 ), 151–169. 10.1111/j.1463-6409.2012.00573.x
Pérez‐Portela, R. , Rius, M. , & Villamor, A. (2017). Lineage splitting, secondary contacts and genetic admixture of a widely distributed marine invertebrate. Journal of Biogeography, 44 (2 ), 446–460. 10.1111/jbi.12917
Raman, G. , Choi, K. S. , & Park, S. (2023). Population structure and genetic diversity analyses provide new insight into the endemic species Aster spathulifolius maxim. And its evolutionary history. Plants, 13 (1 ), 88. 10.3390/plants13010088 38202396
Rambaut, A. , Drummond, A. J. , Xie, D. , Baele, G. , & Suchard, M. A. (2018). Posterior summarization in Bayesian phylogenetics using tracer 1.7. Systematic Biology, 67 (5 ), 901–904. 10.1093/sysbio/syy032 29718447
Rogers, A. R. , & Harpending, H. (1992). Population growth makes waves in the distribution of pairwise genetic differences. Molecular Biology and Evolution, 9 (3 ), 552–569. 10.1093/oxfordjournals.molbev.a040727 1316531
Ronquist, F. , Teslenko, M. , van der Mark, P. , Ayres, D. L. , Darling, A. , Höhna, S. , Larget, B. , Liu, L. , Suchard, M. A. , & Huelsenbeck, J. P. (2012). MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology, 61 (3 ), 539–542. 10.1093/sysbio/sys029 22357727
Scheltema, R. S. (1986). On dispersal and planktonic larvae of benthic invertebrates: An eclectic overview and summary of problems. Bulletin of Marine Science, 39 (2 ), 290–322.
Scheltema, R. S. (1995). The relevance of passive dispersal for the biogeography of Caribbean mollusks. American Malacological Bulletin, 11 (2 ), 99–115.
Schiel, D. R. , Steinbeck, J. R. , & Foster, M. S. (2004). Ten years of induced ocean warming causes comprehensive changes in marine benthic communities. Ecology, 85 (7 ), 1833–1839. 10.1890/03-3107
Scouras, A. , Beckenbach, K. , Arndt, A. , & Smith, M. J. (2004). Complete mitochondrial genome DNA sequence for two ophiuroids and a holothuroid: The utility of protein gene sequence and gene maps in the analyses of deep deuterostome phylogeny. Molecular Phylogenetics and Evolution, 31 (1 ), 50–65. 10.1016/j.ympev.2003.07.005 15019608
Selkoe, K. , & Toonen, R. J. (2011). Marine connectivity: A new look at pelagic larval duration and genetic metrics of dispersal. Marine Ecology Progress Series, 436 , 291–305. 10.3354/meps09238
Shen, K.‐N. , Jamandre, B. W. , Hsu, C.‐C. , Tzeng, W.‐N. , & Durand, J.‐D. (2011). Plio‐Pleistocene sea level and temperature fluctuations in the northwestern Pacific promoted speciation in the globally‐distributed flathead mullet Mugil cephalus . BMC Evolutionary Biology, 11 , 1–17. 10.1186/1471-2148-11-83 21194491
Shin, S. (1995). Echinodermata from Chindo Island, Korea. Animal Systematics, Evolution and Diversity, 11 (1 ), 115–124.
Sun, S. E. , Xiao, N. , & Sha, Z. (2023). Mitogenomes provide insights into the phylogeny and evolution of brittle stars (Echinodermata, Ophiuroidea). Zoologica Scripta, 52 (1 ), 17–30. 10.1111/zsc.12576
Swofford, D. (2002). PAUP*. Phylogenetic analysis using parsimony (*and other methods), version 4.0b10 (4th ed.).
Taboada, S. , & Pérez‐Portela, R. (2016). Contrasted phylogeographic patterns on mitochondrial DNA of shallow and deep brittle stars across the Atlantic‐Mediterranean area. Scientific Reports, 6 (1 ), 32425. 10.1038/srep32425 27585743
Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123 (3 ), 585–595. 10.1093/genetics/123.3.585 2513255
Thompson, J. D. , Higgins, D. G. , & Gibson, T. J. (1994). CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position‐specific gap penalties and weight matrix choice. Nucleic Acids Research, 22 (22 ), 4673–4680. 10.1093/nar/22.22.4673 7984417
Törnroos, A. , Pecuchet, L. , Olsson, J. , Gårdmark, A. , Blomqvist, M. , Lindegren, M. , & Bonsdorff, E. (2019). Four decades of functional community change reveals gradual trends and low interlinkage across trophic groups in a large marine ecosystem. Global Change Biology, 25 (4 ), 1235–1246. 10.1111/gcb.14552 30570820
Voris, H. K. (2000). Maps of Pleistocene sea levels in Southeast Asia: Shorelines, river systems and time durations. Journal of Biogeography, 27 (5 ), 1153–1167. 10.1046/j.1365-2699.2000.00489.x
Wang, P. (1999). Response of Western Pacific marginal seas to glacial cycles: Paleoceanographic and sedimentological features. Marine Geology, 156 (1–4 ), 5–39. 10.1016/S0025-3227(98)00172-8
Wang, X. , Li, Y. , & Meng, F. (2023). Transcriptomic response study of brittle star Ophiothrix exigua to sediment burial. Hydrobiologia, 850 (16 ), 3645–3654. 10.1007/s10750-023-05271-x
Wang, Z. , Zhang, J. , Mei, X. , Chen, X. , Zhao, L. , Zhang, Y. , Zhang, Z. , Li, X. , Li, R. , & Lu, K. (2020). The stratigraphy and depositional environments of China's sea shelves since MIS5 (74‐128) ka. Geology in China, 47 (5 ), 1370–1394. 10.12029/gc20200506
Xi, L. , Sun, Y. , Xu, T. , Wang, Z. , Chiu, M. Y. , Plouviez, S. , Jollivet, D. , & Qiu, J. W. (2023). Phylogenetic divergence and population genetics of the hydrothermal vent annelid genus Hesiolyra along the East Pacific rise: Reappraisal using multi‐locus data. Diversity and Distributions, 29 (1 ), 184–198. 10.1111/ddi.13653
Xie, C. , Jian, Z. , & Zhao, Q. (1995). Paleogeographic maps of the China seas at the last glacial maximum (p. 75). WESTPAC Paleogeographic Maps. UNESCO/IOC Publications.
Xu, Y. , Ma, L. , Sui, J. , Li, X. , Wang, H. , & Zhang, B. (2023). Potential impacts of climate change on the distribution of echinoderms in the Yellow Sea and East China Sea. Marine Pollution Bulletin, 194 , 115246. 10.1016/j.marpolbul.2023.115246 37453168
Xue, D.‐X. , Wang, H.‐Y. , Zhang, T. , & Liu, J.‐X. (2014). Population genetic structure and demographic history of Atrina pectinata based on mitochondrial DNA and microsatellite markers. PLoS One, 9 (5 ), e95436. 10.1371/journal.pone.0095436 24789175
Yang, C. , Lian, T. , Wang, Q.‐X. , Huang, Y. , & Xiao, H. (2016). Preliminary study of genetic diversity and population structure of the relict Gull Larus relictus (Charadriiformes Laridae) using mitochondrial and nuclear genes. Mitochondrial DNA Part A DNA Mapping, Sequencing, and Analysis, 27 (6 ), 4246–4249. 10.3109/19401736.2015.1022759 25812050
Yang, M. , & Li, X. (2016). Molecular phylogeography of marine animals in marginal seas of the northwestern pacific: A summary and prospectiveness. Guangxi Sciences, 23 (4 ), 299–306. 10.13656/j.cnki.gxkx.20160913.012
Yang, S. , Song, B. , Ye, S. , Laws, E. A. , He, L. , Li, J. , Chen, J. , Zhao, G. , Zhao, J. , & Mei, X. (2019). Large‐scale pollen distribution in marine surface sediments from the Bohai Sea, China: Insights into pollen provenance, transport, deposition, and coastal‐shelf paleoenvironment. Progress in Oceanography, 178 , 102183. 10.1016/j.pocean.2019.102183
Yao, Z. , Liu, J. , Wan, S. , Liu, Y. , Yi, L. , Liu, J. , Shan, X. , Qiao, S. , Zhao, D. , & Xiao, G. (2022). Progress and prospects of research on the quaternary sedimentary environment in the eastern shelf of China. Marine Geology & Quaternary Geology, 42 (5 ), 42–57. 10.16562/j.cnki.0256-1492.2022063001
Yao, Z. , Shi, X. , Qiao, S. , Liu, Q. , Kandasamy, S. , Liu, J. , Liu, Y. , Liu, J. , Fang, X. , & Gao, J. (2017). Persistent effects of the Yellow River on the Chinese marginal seas began at least~ 880 ka ago. Scientific Reports, 7 (1 ), 2827. 10.1038/s41598-017-03140-x 28588261
Yi, L. , Lai, Z. , Yu, H. , Xu, X. , Su, Q. , Yao, J. , Wang, X. , & Shi, X. (2013). Chronologies of sedimentary changes in the south bohai sea, China: Constraints from luminescence and radiocarbon dating. Boreas, 42 (2 ), 267–284. 10.1111/j.1502-3885.2012.00271.x
Yi, L. , Yu, H. , Ortiz, J. D. , Xu, X. , Qiang, X. , Huang, H. , Shi, X. , & Deng, C. (2012). A reconstruction of late Pleistocene relative sea level in the south Bohai Sea, China, based on sediment grain‐size analysis. Sedimentary Geology, 281 , 88–100. 10.1016/j.sedgeo.2012.08.007
Yi, S. K. , & Irimura, S. (1987). A taxonomic study on the Ophiuroidea from the Yellow Sea. Animal Systematics, Evolution and Diversity, 3 (2 ), 117–136.
