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. 2025 Aug 4;7(3):606–618. doi: 10.1007/s42995-025-00312-x

Latitudinal–environmental variations driving the local adaptation of Pampus argenteus stocks along the Chinese coast

Jiehong Wei 1,2,#, Yongshuang Xiao 1,#, Kar-Hoe Loh 3, Angel Herrera-Ulloa 4, Jing Liu 1,✉, Kuidong Xu 1,2,✉
PMCID: PMC12413356  PMID: 40919467

Abstract

The distribution of Pampus argenteus (Euphrasen, 1788) spans a pronounced latitudinal–environmental gradient from the subtropical to the subpolar zones. The species is reported to have multiple stocks along coastal China, exhibiting different spawning behaviors and habitat preferences. Such ecological variations might imply potential genetic divergence and local adaptation. We resequenced 117 genomes from six coastal stocks of P. argenteus in China. Although no hierarchical genetic structure was identified, over 50% of the single-nucleotide polymorphisms (SNPs) indicated moderate to strong divergence in at least two stocks. The Mantel test identified 21 100-kb sliding windows with significant isolation by distance and environment, while a majority did not. Given the lack of genome-wide isolation by distance, the 21 windows may be under selection pressure from the latitudinal–environmental variations. Among the 21 windows, certain genes were linked to circadian clock regulation and thermal stress response, suggesting sea surface temperature and sunshine duration as selective forces. A total of 17 genes regulated neuron activity; variations near these genes might subsequently shape the different spawning and migratory behaviors among the stocks. Additionally, 1204 SNPs were mapped to non-coding regions; 14 transcriptional and translational factors were identified in the 21 windows. These findings imply that alterations in gene expression might contribute to the local adaptation of the P. argenteus stocks.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-025-00312-x.

Keywords: Pampus argenteus, Whole-genome resequencing, Population structure, Sunshine duration, Sea surface temperature, Local adaptation

Introduction

Variations in habitat environments can influence the fitness of marine organisms, leading to spatially divergent selection and local geographic adaptations among populations (Kenchington et al. 2015; Leggett et al. 1984). The correlations among phenotypic, environmental, and genetic variations help identify loci undergoing local adaptation and natural selection. Numerous genes associated with growth performance, muscle contraction, and vision are identified under positive selection among populations of the spotted sea bass, Lateolabrax maculatus (McClelland, 1844), along the Chinese coast (Chen et al. 2023). Their genetic variations are significantly correlated with habitat sea surface temperature (SST), suggesting a case of thermal adaptation (Chen et al. 2023). Likewise, variations in syne2 are closely linked to the photoperiod and diverged spawning timings among Pacific herring batches (Petrou et al. 2021). In the populations of three salmonid spp., the lengths of clock1a and clock1b are proposed to control spawning time, enabling the adaptation of their spawning behavior to varied habitat photoperiods across latitudes (O'Malley et al. 2010).

Variations in the non-coding regions of chromosomes may also account for phenotypic changes, which have been highlighted as an alternative route for the adaptative evolution of fishes. These regions, including introns, intergenic regions, and 3′ and 5′ untranslated regions (UTRs), contain various regulatory elements that mediate transcription and translation, e.g., enhancers, silencers, and intronic short conserved elements (Majewski and Ott 2002). In populations of the yellowtail clownfish, Amphiprion clarkii (Bennett, 1830), signals involved in local adaptation were found in the 5′ and 3′ UTR loci of genes related to cold stress response, which were remarkably associated with minimum SST (Clark et al. 2021). Mutations in the cis-regulatory elements induce the biased expression of hpdb between the surface water and cave-resident populations of the Mexican tetra, Astyanax mexicanus (De Filippi, 1853) (Krishnan et al. 2022). The elevated expression of hpdb allows the cave-resident populations to convert excessive tyrosine for energy production, an adaptation to the nutrient-deprived environment (Krishnan et al. 2022). Similarly, interspecific variations are most pronounced in the introns and 5′ and 3′ UTRs of the sight-related genes among the twilight-zone cichlids, Diplotaxodon spp. (Hahn et al. 2017). Transcriptional regulation is thus proposed as a key mechanism for differential adaptation in these species (Hahn et al. 2017).

The silver pomfret, Pampus argenteus (Euphrasen, 1788), is one of the most fished and economically vital species in the West Pacific (Wei et al. 2022). Coastal China is one of its dominant habitats, including four marginal seas that span a broad latitude range, i.e., the South China Sea, East China Sea (ECS), Yellow Sea, and Bohai Sea (BS), with the maximum harvest in China of 373 kilotons (www.fao.org/fishery/en/statistics/software/fishstatj). The broad area makes various environmental factors and geographic distances among habitats crucial in shaping the population structures of marine organisms (Shao et al. 2016; Sun et al. 2022; Wang et al. 2013). Temperature and salinity are proposed as the most significant factors affecting distribution, migration, and reproduction in P. argenteus. The species never occurs in bottom waters with temperatures < 10 ℃ and starts overwintering migration at 14–15 ℃ by the end of autumn (Zheng et al. 2003). Pampus argenteus usually spawns in estuaries with lower salinity; its spawning peak in the BS occurs 1 month later than in the warmer southern waters of Lvsi and Haizhou Bay (Zheng et al. 2003). Li et al. (2022) detected 329 single-nucleotide polymorphisms (SNPs) among the coastal stocks of P. argenteus, which were significantly associated with SST, supporting potential local adaptation driven by temperature. Furthermore, the coastal stocks of P. argenteus at different latitudes exhibit distinct preferences in migratory patterns and habitat uses (Zhao et al. 2011). The P. argenteus stocks that inhabit ECS never utilize spawning grounds north of the Changjiang estuary, which is geographically isolated from the Bohai-Yellow Sea (BYS) stocks (Zhao et al. 1990). These fixed ecological variations between ECS and BYS stocks reflect potential divergence within P. argenteus. However, their genomic bases are still unclear. Recently, we proposed the first chromosome-level reference genome of P. argenteus, which laid a solid foundation for its whole-genome resequencing (Wei et al. 2024). In this study, we resequenced and analyzed the genomes of 117 silver pomfrets from the coastal habitats of China. We aimed to explore the potential local adaptations and genetic mechanisms underlying the different spawning and migratory behaviors among the coastal stocks of P. argenteus in China.

Materials and methods

Sample collection

From 2021 to 2022, 117 juvenile silver pomfret were obtained from six sampling sites to represent the coastal stocks of major fishing grounds in China, viz, Zhuhai (ZH, the Pearl River Estuary fishing ground), Xiamen (XM, the Southern Fujian fishing ground), Shengsi (SS, the Zhoushan fishing ground), Lianyungang (LYG, the Haizhou Bay fishing ground), Weihai (WH, the Shidao fishing ground), and Qinhuangdao (QHD, the Liaodong Bay fishing ground), covering 2728.9 km and a latitude range of 21.8–39.9° N. All samples were collected from September to November before the overwintering migration of P. argenteus (Zheng et al. 2003). All specimens (n = 117) were either caught by fishermen using gill nets (SS and QHD) or collected from the local fishery landing sites (ZH, XM, WH, and LYG). To ascertain the sequencing quality, only endemic batches with scales and golden luster were collected from the landing sites. A piece of muscle tissue was isolated from each specimen and frozen in liquid N2 for at least 3 h immediately after collection. The samples were then sent for whole-genome resequencing (WGR) at Biozeron Ltd., Shanghai, China.

DNA sequencing and genotyping

The total genomic DNA of the samples was extracted using the E.Z.N.A® Tissue DNA kit (Omega BioTek Inc., GA, USA). Libraries for whole-genome resequencing were prepared with the TruSeq™ Nano DNA Sample Prep Kit (Illumina Inc., CA, USA) following the instructions provided. The qualities of the libraries were assessed utilizing the TBS-380 Mini-Fluorometer (Turner BioSystems Inc., CA, USA) and the Picogreen assay kit (Invitrogen Ltd., MA, USA). The libraries were then subjected to next-generation sequencing in a Novaseq 6000 platform (Illumina Inc., CA, USA), generating 150-bp paired-end reads with 10 × sequencing depth. Adapters and low-quality reads were removed from the raw reads employing the Illuminaclip tool of the Trimmomatic v0.36 software (adapters.fa:2:30:10 SLIDINGWINDOW:4:15 MINLEN:75; Bolger et al. 2014).

The clean reads were mapped against the chromosome-level reference genome of P. argenteus (GCA_036321115; Wei et al. 2024) utilizing BWA v0.7.12 (Li and Durbin 2010). PCR duplicates were identified and filtered using Picard v.1.129 (https://broadinstitute.github.io/picard). SNPs and indels were then called by employing Haplotype-Caller of GATK v4.1.2.0 (stand-call-conf 50 –max-genotype-count 500; McKenna et al. 2010). A hard filtering was performed in GATK to remove the low-quality variants (non-biallelic loci; genotype quality < 20; mean number of reads per individual < 4). Using the VCFtools v0.1.12b (Danecek et al. 2011), we excluded the SNPs based on the following conditions: depth distribution of all sites < 2.5% or > 97.5%, minor allele frequency (MAF) < 0.05, and missing data > 20% among all individuals, markedly deviating from the Hardy–Weinberg equilibrium (p < 0.001). A total of 6,347,341 informative SNPs were retained in the final dataset and annotated with ANNOVAR v2024Oct14 based on the Nr (non-redundant protein database) against the reference genome of P. argenteus (Wei et al. 2024; Yang and Wang 2015).

Diversity indices and population structures

Nucleotide diversity (π), Watterson’s estimator (θ), and Tajima’s D values of each chromosome were calculated with a 100-kb sliding window using VCFtools (Danecek et al. 2011). The concatenated sequence of all the SNPs for each individual was generated with the Python script vcf2fasta (https://github.com/santiagosnchez/vcf2fasta). A neighbor-joining (NJ) tree of all the individuals was generated with the concatenated sequences utilizing FastTree v2.1.10 and the general time reversible model (Price et al. 2009). Population structure visualization used principal component analysis (PCA) with six principal components by employing the GCTA v1.93.2 (Yang et al. 2013). Genetic admixture of the six coastal stocks was estimated with ADMIXTURE v1.3.0, with clusters (K) ranging from 2 to 6 (Alexander et al. 2009). Cross-validation (CV) errors for the assumed K values were estimated with 200 bootstrap replicates, and the K value with the lowest CV error was considered optimal. TREEMIX v1.13 was employed to identify the splitting patterns and subsequent gene flow among the six stocks (Pickrell and Pritchard 2012). Although zero to six migration edges were allowed during the analysis, the optimal number was determined based on the residual covariance matrix (Pickrell and Pritchard 2012).

Candidate genome regions and loci under selection

The Mantel test was performed using the R package vegan v2.5.6 to identify the genomic regions and loci under local adaptation and analyze the linear correlations among geographic distance, environmental factors, and genetic variations (Oksanen et al. 2019). The standardized Mantel test statistic (r) and significance of linear regressions (p) were evaluated utilizing Pearson’s product–moment correlation with 1000 permutations. SST and salinity data for the six sampling sites, the most pronounced factors affecting P. argenteus (Zheng et al. 2003), were collected from the Copernicus Marine Services database (https://marine.copernicus.eu/) to evaluate the annual differences. Furthermore, the variations in sunshine duration among habitats could affect the circadian and circannual rhythms, thereby also accounting as the candidate factor (O’Malley et al. 2010; Petrou et al. 2021). The mean monthly sunshine duration of the sites for each month was obtained from the Weather Altas (https://www.weather-atlas.com/zh/china/weihai-climate). The annual sunshine duration range (L) was defined as the difference between the maximum and minimum mean monthly sunshine durations. The geographic distances and latitudinal differences among the sites were estimated by employing Google Maps (https://www.google.com/maps). We first ascertained the correlations of geographic distance with the latitudinal and environmental factor differences among the sites (i.e., SSTmax, SSTmin, Smax, Smin, and L) using the Mantel test. The results indicated a significant and strong linear correlation (p < 0.05, r > 0.90) between these (see “Results”). Given their similar latitudinal gradient, the impacts of these environmental factors on the population structure of P. argenteus could not be statistically distinguished from the geographic distance. Therefore, we used isolation by distance (IBD), i.e., increasing genetic differentiation among stocks with their geographic distance (Slatkin 1993). It was utilized as a potential signal to detect genome regions and loci under selection and inferred the relations between environmental factors and local adaptation based on gene functions.

To avoid potential deviation from the IBD hypothesis, only genome sliding windows with the top 5% pairwise fixation index (Fst) values between the farthest site pair QHD and ZH were employed for the Mantel test. Pairwise Fst values among the six stocks were estimated with 100-kb sliding windows and a 50-kb step size using the R package PopGenome (Pfeifer et al. 2014). Finally, the IBDs of these windows were tested with geographic distance and linearized pairwise Fst, i.e., Fst/(1-Fst), using the Mantel test. Protein-coding genes in the candidate windows were annotated with ANNOVAR and then subjected to Gene Ontology (GO) and KEGG Orthology (KO) term enrichment analyses by employing TBtools v2.041 with default settings (Chen et al. 2020). GO and KO annotations of the reference genome were used as a background for enrichment analyses. Only significant terms and pathways were considered after false discovery rate correction (Q < 0.05). Putative gene functions were identified by accessing the UniProt website (https://www.uniprot.org; accessed 15.11.2023) and searching Google Scholar with the gene names. Furthermore, we also calculated the linearized pairwise Fst values of the SNPs in the candidate windows using VCFtools v0.1.12b (Danecek et al. 2011). The IBDs of these SNPs were examined by applying the Mantel test. The candidate windows and SNPs with remarkable IBD patterns are henceforth referred to as the IBD windows and SNPs, respectively. Full names for all the gene (lowercase and italicized) and protein (capitalized and non-italic) abbreviations are given in Supplementary Table S1.

Results

Resequencing data and population genetics statistics

A total of 752.61 Gb clean reads were obtained from the WGR of the 117 silver pomfrets. The average clean reads of the six stocks ranged from 37,084,299 to 48,381,902. The average sequencing depth was 11.13 × compared to the reference genome size, with an average of 95.62% of the clean reads mapped to the reference genome, covering ~ 98.23% of it (Supplementary Table S2). An average of 2,313,311 single-nucleotide variants were called from each individual by comparing with the reference genome (Supplementary Table S2). After filtering, 6,347,341 SNPs shared by the six stocks were retained and divided into 10,049 100-kb windows for subsequent analyses. The SNPs were nearly randomly distributed among the 24 chromosomes of P. argenteus, although the windows near the telomeres of some chromosomes possessed a lower number of SNPs (Supplementary Fig. S1).

The π and θ values of each 100-kb window showed nearly identical frequency distributions among the six stocks. Many windows near the telomeres had lower π and θ values (e.g., Chr1–3, Chr5–7, Chr13, and Chr17; Supplementary Fig. S2 and S3). The WH, XM, and ZH stocks had slightly lower Tajima’s D values than QHD, LYG, and SS throughout the 24 chromosomes (Supplementary Fig. S2). Nevertheless, these values of the six stocks fell between – 2.00 and 2.00 in most windows, with only windows 19–38 having values of 2.00–3.00. Pairwise Fsts of most sliding windows among the sites were low (0.00–0.05). However, 124 windows indicated Fst values of 0.05–0.15 in at least one stock pair, supporting moderate genetic differentiations in them (Wright 1984). After filtering, 131 windows were retained for the Mantel test, with the pairwise Fst values between the QHD and ZH stocks ranging from 0.0025 to 0.0615. Among the identified SNPs, 3,310,088 (52.15%), 452,043 (7.12%), and 28,372 (0.45%) showed Fst values over 0.05, 0.15, and 0.25 in at least one pair of the coastal stocks, respectively, suggesting a moderate to strong differentiation in the corresponding stock pairs (Wright 1984).

Population structure, gene flow, and demographic history

Overall, the NJ tree indicated chaotic relationships for the 117 silver pomfrets (Fig. 1A; Supplementary Fig. S4). Among them, 13 individuals from XM were resolved as a monophyletic group, sister to a clade composed of all WH and LYG individuals. However, the external branches of the NJ tree were much deeper than the internal ones (Supplementary Fig. S4). Although PCA separated the WH and LYG individuals from the other specimens, PC1 and PC2 represented less than 2% of the total genetic variation (Fig. 1B). Similarly, the Admixture analysis at K = 2 indicated the lowest CV error (Supplementary Fig. S5), with no hierarchical genetic structure among the six sampling localities (Fig. 1C). The maximum likelihood tree generated in TreeMix recognized SS as the outermost stock. The best-fit model with the lowest residual covariances indicated six optimal migration edges (Fig. 2A) and a genetic drift between SS and the other five stocks (Fig. 2C). The strongest migration edge was observed in the gene flow from QHD to SS (Fig. 2B).

Fig. 1.

Fig. 1

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Clustering analyses indicating the genetic structures of the six Pampus argenteus stocks. A NJ trees of the 117 P. argenteus individuals (NJ tree with actual branch length is given in Supplementary Fig. S4); B PCA plot indicating the first (1.00%) and second (0.88%) principal components; (C) ADMIXTURE analysis with the optimal K value (K = 2)

Fig. 2.

Fig. 2

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Results of the TreeMix analysis. A Map indicating migration edges among the six coastal stocks; B heat map indicating migration weights among stocks; C maximum likelihood tree of the six stocks reconstructed in TreeMix, with branch lengths (drift parameters) modified to indicate the migration edges (true drift parameters were shown in Supplementary Fig. S6)

Environmental factors utilized for the Mantel test

The SST, salinity, annual sunshine duration range, and geographic distance data used in the Mantel analysis are shown in Supplementary Tables S3 and S4. The differences in the first three factors among the sample sites showed a significant correlation with their geographical distances (p < 0.05, Fig. 3), with all the r values exceeding 0.90, indicating a robust positive association between environmental factors and geographical distances (Schober et al. 2018).

Fig. 3.

Fig. 3

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Mantel test results indicating the linear correlations between environmental factors and geographic distances among the sampling sites

Candidate genomic regions and loci under local adaptation

The Mantel test results for all the sliding windows are given in Supplementary Table S5. Under selection, 21 IBD windows were candidates (Fig. 4; Supplementary Fig. S7). Their r values were 0.43–0.79, with the pairwise Fst between ZH and QHD varying from 0.0025 to 0.0615 (Supplementary Table S5). A total of 127 genes were found in the IBD windows, with 112 being functionally annotated based on the reference genome (Table 1; Supplementary Table S6). No GO term or KO pathway was markedly enriched after the false discovery rate (Q < 0.05) correction. Of these windows, two adjacent ones situated at 20.5–20.7 Mb in Chr 13 exhibited the steepest slope (Fig. 4), which included four protein-coding genes, i.e., dlgap1, ptf1, rtjk, and an unidentified one. Dlgap1 was the largest, spanning across the two windows (Fig. 5). In the 20.6–20.7 Mb region of Chr 13, only two SNPs were mapped to a dlgap1 exon, while the other 438 belonged to the downstream, non-coding region of dlgap1 (Fig. 5). Following a comprehensive search of Google Scholar and UniProt, we identified 54 genes within the IBD windows that play roles in cell-signaling processes and gene expression regulation (Supplementary Table S6). Specifically, 17 were associated with neuroactivity (Table 2), among which 11 regulate synaptic signaling and plasticity (Fig. 6). Another 6 were linked to circadian rhythm regulation (Table 2), and 14 were identified to encode transcriptional or translational factors. Additionally, four genes, dnajc4, hspd1, pten, and fas, were associated with thermal stress responses (Table 2). Within the 21 selected windows, 23,825 SNPs were identified, with 1607 (6.74%) exhibiting significant IBD patterns, i.e., IBD SNPs (r values: 0.3761–0.9321; Supplementary Table S7). The 1204 IBD SNPs indicated Fst values > 0.05 (0.05–0.326) between the QHD and ZH stocks, suggesting moderate to strong differentiation (Wright 1984). The 1543 IBD SNPs were within the non-coding sequences, including introns, non-coding regions, and UTRs (Supplementary Table S7). Conversely, only 28 SNPs were non-synonymous mutations in protein-coding genes. Additionally, six SNPs had r values over 0.90, suggesting their explicit linear correlation with geographic distance. These six SNPs were located within the non-coding sequences (Supplementary Fig. S8, Table S7).

Fig. 4.

Fig. 4

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IBD windows exhibiting the top six r values. Relationship between the linearized pairwise fixation index (Fst) values and geographic distance for pairwise comparisons among the sites

Table 1.

Protein-coding genes within the isolation by distance (IBD) windows

Chr Position (Mb) Annotated genes
Chr 1 8.55–8.65 rbl2, chd9, unidentified (5)
Chr 2 5.45–5.55

tle1, smad4, mex3b, tmed7, fem1c,

ccdc112, pggt1b, unidentified (1)
Chr 4 11.75–11.85

slc45a1, prxl2b, mmel1, miip, tnfrsf1b,

trim62, znf362, unidentified (3)
Chr 5 0.25–0.35 abca1b, tbc1d12a, matr3, paip2, exosc3, slc44a1, tgfbi
11.35–11.45

kcnk10, cenpf, rd3, stx5a, vegfb, nudt22,

dnajc4, hnrnpul2, tmem179, hs6st2, mbnl3
Chr 6 19.05–19.15 galnt18, unidentified (1)
Chr 7 2.85–2.95 Itga5, or6n2, unidentified (4)
2.90–3.00 rab3gap1, unidentified (3)
6.50–6.60 coq10b, hspd1, mob4, rftn2, plcl1
Chr 8 0.10–0.20 syt11, mtx1a, thbs3a, hjv, met27, vsig2, unidentified (2)
Chr 9 2.80–2.90 cacng2, syngr1, ift27, tab1, mgat3, ctrp1
Chr 10 7.10–7.20 il12rb2, klhl26, crtc1, comp, tmem38a, smim7, med26, crygm3, slc35e1, ptger4, dab1, fyb1, unidentified (1)
Chr 12 13.50–13.60

col17a1, sfr1, gsto1, cfap43, itprip, ch25h,

fas, pten, papss2, sgms1, minpp1
19.00–19.10 macrod2
Chr 13 0.30–0.40

wdr33, pex5l, erfe, mybl1, sft2d3,

mfn1, unidentified (1)
14.60–14.70 pde4b, fzr1, muc5ac, ipo13, mgst3, lrrc52, zyg11b, angptl1, tada1, jun, fggy, plpp6, prdx6, myoc, dyn3, unidentified (1)
20.50–20.60 ptf1a, dlgap1, rtjk, unidentified (1)
20.60–20.70 dlgap1
Chr 14 4.65–4.75 auts2, unidentified (1)
4.70–4.80 ints2, unidentified (1)
Chr 21 7.80–7.90 epm2a, aloxe3, fbxo30, grm1, tle1
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Full names of the gene abbreviations can be found in Supplementary Table S1

Fig. 5.

Fig. 5

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Scatter plot showing the r values of the SNPs within the 20.5–20.7 Mb window of Chr 13. The exons of ptf1, dlgap1, rtjk, and an unidentified gene irfn4b are shown below the x-axis

Table 2.

Genes related to regulation of neuroactivity, circadian rhythm, transcription, and translation within the isolation by distance (IBD) windows

Function Genes
Neuron activity auts2, cacng2, dab1, dyn3, dlgap1, grm1, jun, kcnk10, mob4, rab3gap1, rd3, syngr1, syt11, plcl1, pten, pde4b, paip2
Circadian rhythm crtc1, grm1, paip2, pde4b, plcl1, pten
Transcriptional and translational factors auts2, chd9, crtc1, ints2, jun, matr3, mbnl3, med26, mex3c, mybl1, paip2, tle1, wdr33, znf362, zyg11
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Detailed functions of the 127 genes are given in Supplementary Table S6

Fig. 6.

Fig. 6

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Graph indicating the functions of some key genes (in red color) within the isolation by distance (IBD) windows, depicted based on the descriptions by Kanematsu et al. (2007), Dityatev et al. (2010), Nakamura et al. (2015), Rasmussen et al. (2017), Arriagada-Diaz et al. (2020), and Pan et al. (2021)

Discussion

Genetic structure of the P. argenteus stocks in coastal China

All the analyses of all the SNPs congruently showed an overall lack of lineage divergence among the stocks of P. argenteus along the China’s coast. The π and θ values of the genomes were similar among the six stocks, with the pairwise Fst values being close to zero in most sliding windows. Although the individuals from LYG and WH seem to have a closer phylogenetic relationship as indicated by the NJ tree and PCA, the short internal branch lengths in the NJ tree, as well as the low contributions of PC1 and PC2, suggest these differentiations to comprise only a minuscule proportion of the total genetic variations (Fig. 1B). Similarly, admixture analysis of all the SNPs also suggested a non-hierarchical population structure at K = 2 (Fig. 1C). The Mantel test did not identify any IBDs in most genome regions among the P. argenteus stocks, suggesting that isolation by geographic distance was not the primary factor in shaping genetic structure in P. argenteus. Pampus argenteus actively migrates among numerous spawning, feeding, and wintering grounds in the ECS shelf, which facilitates gene flow, leading to genetic homogeneity (Li et al. 2022; Zhao et al. 2011). The coastal BYS stocks share the wintering ground east of Jeju Island, leading to their mixing (Zhao et al. 1990). TreeMix analyses indicated a southward gene flow from QHD to WH and SS, which is consistent with the southward overwintering migration of the BYS stock (Fig. 2). A part of the ECS stocks might migrate northward across the Changjiang River estuary, resulting in gene flow between the ECS and BYS stocks (Zhao et al. 1990). Additionally, P. argenteus produces buoyant eggs, with its planktonic larval stage lasting for 15 days after hatching (Oh et al. 2009). These eggs and planktonic larvae might be transported by coastal currents and dispersed among different coastal nurseries and feeding grounds, which further facilitates the mixing of stocks (Li et al. 2022). The genome-wide low genetic structure suggested that the coastal stocks of P. argenteus in China constitute a single genetic population, which might have originated from a more recent common ancestor.

In contrast, several sliding windows and over 50% of the SNPs had moderate to strong differentiations (Fst > 0.05, Wright 1984). This finding supports the existence of genetic divergence in certain loci despite a low genome-wise differentiation. Since the coastal stocks of P. argenteus demonstrated latitudinal variations in spawning and migratory habits (Zhao et al. 1990; Zheng et al. 2003), we further focused on the genomic regions and SNPs with latitudinal variations. Thus, we identified 21 windows with significant IBD patterns (Fig. 4), which included 1204 IBD SNPs with moderate to strong genetic differentiation between QHD and ZH (Fst values 0.0500–0.3256, Supplementary Table S7). Given the robust correlation between geographic distance and environmental variables (Fig. 3), such IBD patterns could be equivalently regarded as isolation by environment (IBE; Wang and Bradburd 2014). IBD occurs when interpopulation gene flow becomes too low to homogenize genetic changes caused by genetic drift and selection, which is usually confounded with IBE, i.e., increasing genetic differences with environmental distance (Wang and Bradburd 2014). Gene flow and drift, as selectively neutral processes, account for genome-wide IBD patterns (Davis et al. 2023; Petrou et al. 2021). Conversely, natural selection is often confined to certain genes and loci rather than accounting for genome-wide changes, e.g., selective sweep (Kim and Nielsen 2004). In the P. argenteus stocks, only 21 of the 10,049 sliding windows demonstrated significant IBD patterns (Table 1). Such localized IBD patterns might be the result of selection by environmental gradients across latitudes, i.e., SST, salinity, and annual sunshine duration range (Fig. 3). In fact, four genes related to thermal stress responses, i.e., dnajc4, hspd1, pten, and fas were found in the IBD windows (Qian et al. 2020; Song et al. 2018; Yan et al. 2021; Table 4), supporting the hypothesis that drastic temperature differences across latitudes might account for the local adaptation of the six coastal stocks.

Significant genetic variation across latitudes and migratory/circannual rhythms in P. argenteus

The nervous system, which perceives and integrates external environmental cues, plays a vital role in fish navigation during migration (Salvanes et al. 2013; Satou et al. 2005). Dramatic changes in synapse-related genes observed in the European eel during seaward migration are believed to be closely linked to their environmental assessment and spatial cognition abilities (Podgorniak et al. 2015, 2016). Components of the SNARE complex mediating synaptic exocytosis are upregulated in the olfactory bulb of O. keta during its migration, which facilitates olfactory imprinting and memory formation (Abe and Kudo 2019; Abe et al. 2018). In this study, 17 neuron-related genes were identified within the IBD windows, with multiple SNPs near them exhibiting latitudinal variations (Table 2; Fig. 6). Many of them encode synaptic components, which have essential roles in synaptic plasticity and memory formation (36). Dyn3, Rab3gap1, and Syt11 mediate the exo and endocytosis of neurotransmitters at presynaptic densities (Arriagada-Diaz et al. 2020; Fernández-Chacón and Südhof 1999; Li et al. 2021; Sakane et al. 2006). Cacng2, Dlgap1, Dab1, Grm1, Itga5, and Plcl1 are postsynaptic proteins that control activities and recycling of Ampar, Nmdar, and GABAA receptors to mediate plasticity and signaling of glutamatergic and GABAergic synapses (Arriagada-Diaz et al. 2020; Dityatev et al. 2010; Kanematsu et al. 2007; Nakamura et al. 2015; Rasmussen et al. 2017). These genetic differentiations might alter neuroactivity in the coastal stocks of P. argenteus, which might subsequently affect their preferences for a particular combination of environmental cues and account for biased dispersal, which in turn reinforce the IBE patterns observed in these genomic regions and loci (Wang and Bradburd 2014). The BYS stock migrates along with the Yellow Sea Warm Current to spawning grounds after overwintering (Zheng et al. 2003). This current is characterized by warmth and salinity but lower dissolved O2 (Liu et al. 2015), which might be significant recognition cues for the stock. Factors changing along with latitude (e.g., sunshine duration, salinity, and water temperature) might assist the species in locating their preferred habitats, accounting for biased dispersal. Nevertheless, the neurological mechanism underlying fish migration is complicated, and the exact role of these genes in fish migration requires further validation.

Genes involved in circadian clock regulation, i.e., crtc1, grm1, paip2, pde4b, plcl1, and pten (Table 2), were also identified in the IBD windows, implying potential adaptation of circadian rhythms among silver pomfret stocks at different latitudes. Pampus argenteus shows an identical diel vertical migration with its zooplanktonic prey—the species remains in bottom waters during the day and ascends to the top at night (Zheng et al. 2003)—suggesting that this diel behavior is likely associated with food availability. The oscillation of endogenous circadian clock gene and melatonin are crucial in mediating the diel vertical migration of aquatic animals, which synchronizes the timing of migration and metabolic activities with the external light–dark zeitgeber (Falcón et al. 2011; Gleiss et al. 2017). In China, the annual sunshine duration varies by 5.6 h in QHD, which in ZH is much smaller (2.9 h, Supplementary Table S3). This pattern might require a more flexible circadian clock for the QHD stock to synchronize with the more fluctuating photoperiod. Significant latitudinal variations were identified in genes adjacent to the circadian clock regulators, implying a potential divergence of circadian rhythm among the coastal P. argenteus stocks. Of these genes, crtc1 and paip2 participate in the light-induced entrainment of the circadian clock gene expression (Alvarez-Saavedra et al. 2011; Jagannath et al. 2013). Pde4b and Pten can negatively regulate the expression of clock—and melatonin synthesis-related genes through the cAMP and mTOR signaling pathways (Kim et al. 2007; Matsumoto et al. 2016; Zagni et al. 2017). Paracrine interaction between astrocytes and pinealocytes through Grm1 and TNFα is crucial to generating the daily rhythm by melatonin secretion (Villela et al. 2013). Variations in plcl1 correlated markedly with human sleeping habits, supporting its potential role in circadian rhythm regulation (Hu et al. 2016; Jones et al. 2016). Furthermore, melatonin secretion, oscillating with seasonal changes in nocturnal duration, constitutes the intrinsic calendar for fishes, which mediates their circannual rhythm, e.g., growth, migration, and spawning (Wehr 1997; West et al. 2021). Latitudinal variations in genes near these circadian clock regulators might also alter the circannual rhythms of the P. argenteus stocks. The spawning season duration varies among silver pomfret stocks from different spawning grounds; the eastern Fujian stock (i.e., XM) spawns from March to July, whereas the BS stock (i.e., QHD) only from May to July (Zhao et al. 1990). This might reflect changes in the intrinsic circannual rhythms of these stocks, which might be associated with changes in genes near the circadian clock regulators. Nevertheless, the various spawning periods of P. argenteus could be a result of acclimation to differences in external environmental factors, e.g., light–dark cycles and effective accumulative temperature (Wang et al. 2010). This hypothesis needs to be further verified with comparative studies on circadian and circannual rhythms among different P. argenteus stocks.

Gene expression regulation and local adaptation in P. argenteus

In the 21 IBD windows, 1607 SNPs exhibited significant IBD patterns, with 96.02% mapping to non-coding sequences. Of these, six non-coding SNPs exhibited r values over 0.90 in the Mantel test, suggesting their explicitly linear relationship with geographic distance (Supplementary Fig. S8). In the IBD window with the second greatest r value, i.e., 20.6–20.7 Mb of Chr 13 (Fig. 5), most SNPs belong to the downstream non-coding region of dlgap1. The non-coding genomic regions contain various regulatory elements, which mediate the transcription and translation of genes, e.g., enhancers, silencers, and intronic short conserved elements (Majewski and Ott 2002). A large proportion of the non-coding variations within the IBD windows may alter gene expression in different P. argenteus stocks and play an important role in developing their adaptive phenotypes across latitudes. Furthermore, 14 transcriptional and translational factors were identified in the IBD windows. For example, Crtc1 and Creb mediate the expression of numerous genes crucial for brain and neuronal activities (Parra-Damas et al. 2017). Tada1 is a component of the SAGA coactivator complex, which controls chromatin remodeling during transcription (Grant et al. 2021). Paip2 inhibits Pabp to destabilize the poly(A) tail, culminating in mRNA decay and translation termination (Yoshida et al. 2006). Wdr33 is a core subunit of the cleavage and polyadenylation specificity factor, a major component of the pre-mRNA 3′ end processing machinery (Zhang et al. 2020). Genetic changes in these transcriptional and translational factors might also affect gene expression in the six P. argenteus stocks, which could be the potential mechanism underlying local adaptations. Further transcriptomic comparisons among the P. argenteus stocks might provide valuable evidence to support this hypothesis.

Conclusion

In this study, we resequenced and analyzed the genomes of six coastal silver pomfret stocks to explore potential local adaptation and genetic mechanisms behind their different ecologies. Significant IBD patterns were found only in 21 100-kb sliding windows, which could be equivalently regarded as IBE patterns due to a marked correlation between geographic distance. Comparatively, most genome regions showed no remarkable IBD patterns, suggesting that these 21 windows might be under selection pressure. Many genes within them were linked to circadian clock regulation and thermal stress response, suggesting SST and L as selective forces for P. argenteus. Additionally, 17 genes in the windows regulate neuron activity, and variations near these genes might shape the spawning and migratory behaviors among P. argenteus stocks. The presence of IBD windows and the close relation of gene function with the environmental factors and ecology of P. argenteus provided robust evidence for the local adaptation of Chinese stocks. Furthermore, numerous non-coding SNPs and transcriptional/translational factors were identified in the 21 windows, implying that alterations in gene expression might contribute to the local adaptation of the P. argenteus stocks.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 5232 KB) (5.1MB, docx)
Supplementary file2 (DOCX 84 KB) (83.9KB, docx)
Supplementary file3 (TXT 1624 KB) (1.6MB, txt)

Acknowledgements

We would like to thank Captain Desheng Zhou from Shengsi for his contribution to obtaining fresh silvery pomfret samples, as well as Liang Guo and Huang Xin from Hunan Normal University and Institute of Zoology, Chinese Academy of Sciences, for their technical advice on the genetic analyses. We are also very grateful to Mr. Ji Li and Ms. Tingting Wang for their help in retrieving environmental data from the Copernicus Marine Services database. This work was supported by the National Natural Science Foundation of China (grant number 32270472) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDB42000000).

Author contributions

JW, YX, and JL designed the study; JW and KHL conducted the experiments; JW, YX, and AHU conducted the molecular analyses; JW wrote the manuscript; YX, JL, and KX revised the manuscript. The final version was approved by all the authors.

Data availability

All data in this study can be accessed in the paper or the Supplementary materials. The VCF file of the whole-genome resequencing data used in this study has been deposited in the figshare database (doi: https://doi.org/10.6084/m9.figshare.25771272).

Declarations

Conflicts of interest

The authors declare no competing interest.

Animal and human rights statement

All animal procedures were approved by the Ethics Committee of the Institute of Oceanology, Chinese Academy of Sciences, and carried out according to the appropriate guidelines.

Footnotes

Special Topic: Ecology & Environmental Biology.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jiehong Wei and Yongshuang Xiao have contributed equally to this work.

Contributor Information

Jing Liu, Email: jliu@qdio.ac.cn.

Kuidong Xu, Email: kxu@qdio.ac.cn.

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Supplementary Materials

Supplementary file1 (DOCX 5232 KB) (5.1MB, docx)
Supplementary file2 (DOCX 84 KB) (83.9KB, docx)
Supplementary file3 (TXT 1624 KB) (1.6MB, txt)

Data Availability Statement

All data in this study can be accessed in the paper or the Supplementary materials. The VCF file of the whole-genome resequencing data used in this study has been deposited in the figshare database (doi: https://doi.org/10.6084/m9.figshare.25771272).

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