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Article

Variation in the Local Grey Mullet Populations (Mugil cephalus) on the Western Pacific Fringe

by
Chien-Hsien Kuo
1,
Sin-Che Lee
2,
Shin-Yi Du
2,
Chao-Shen Huang
3 and
Hung-Du Lin
4,*
1
Department of Aquatic Bioscience, National Chiayi University, Chiayi 60004, Taiwan
2
Institute of Cellular and Organismic Biology, Academia Sinica, Taipei 115, Taiwan
3
Fisheries Research Institute Kaohsiung Branch, Kaohsiung 806, Taiwan
4
The Affiliated School of National Tainan First Senior High School, Tainan 701, Taiwan
*
Author to whom correspondence should be addressed.
Genes 2024, 15(10), 1280; https://doi.org/10.3390/genes15101280 (registering DOI)
Submission received: 23 August 2024 / Revised: 21 September 2024 / Accepted: 24 September 2024 / Published: 29 September 2024
(This article belongs to the Special Issue DNA Taxonomy, Molecular Phylogeny and Population Genetics of Cartilaginous Fishes and Teleost Fishes)
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Abstract

:
Background: Understanding population genetic structures is crucial for planning and implementing conservation programmes to preserve species’ adaptive and evolutionary potential and thus ensure their long-term persistence. The grey mullet (Mugil cephalus) is a globally distributed coastal fish. Its populations in waters surrounding Taiwan on the western Pacific fringe are divided into at least two stocks (migratory and residential), but questions remain regarding their genetic divergence and gene flow. Methods and Results: To cast more light on this, allozyme variations at 21 presumptive gene loci of 1217 adult grey mullets from 15 localities in Japan, Taiwan and mainland China, and four gene loci from 1470 juveniles from three localities in Taiwan were used to investigate patterns of genetic variation. The mean expected heterozygosity (He) was 0.128—ranging from 0.031 (Matsu) to 0.442 (Kaoping)—and the mean observed heterozygosity (Ho) was 0.086—ranging from 0.017 (Kaohsiung) to 0.215 (Kaoping). Both AMOVA and the high overall mean FST of 0.252 indicated enormous genetic differentiation among populations and the positive mean value of FIS was 0.328, indicating a deficiency of heterozygotes. PCoA indicated that the samples of M. cephalus could be split into three groups and STRUCTURE analysis showed that all individuals were grouped into three genetic clusters. The results of mutation-drift equilibrium tests did not suggest that the populations experienced any recent genetic bottleneck. The results from all localities in the present investigation showed significant change in the GPI-A genotype frequencies with latitudes—e.g., increases in GPI-A*135/135 homozygote frequencies and GPI-A*100/100 frequencies were highly correlated with latitudinal cline. All migratory populations with the GPI-A genotype were almost exclusively the GPI-A*100/100 homozygote. During the life history of M. cephalus, the GPI-A*100/135 heterozygote frequency significantly decreases with age. Conclusions: Based on these data, we suggest that each GPI-A genotype represents trait combinations of higher fitness in some portions of the environment. Furthermore, the genotypic frequencies change in accordance with life stages, suggesting that selection occurs throughout the life span.

1. Introduction

Complex geological processes, climatic history, and diverse coastal habitats across different regions of the world create opportunities that shape the current phylogeographic patterns of marine organisms [1,2]. The northwest Pacific margin waters include the Sea of Japan, Yellow Sea, East China Sea, and South China Sea. Ocean currents, ancestral habitat discontinuity, and climatic constraints in the northwest Pacific may play an important role in shaping the contemporary genetic and population structures of marine organisms [3]. During the Pleistocene glacial periods, the Taiwan Strait emerged due to the decline of sea levels, and it has acted as a barrier to the movement of marine organisms across the two sides of this strait [4,5]. These historical geological events, combined with complex coastal habitats, have been linked to significant genetic barriers between two evolutionary lineages of marine species due to behavioural or oceanographic constraints, such as in Chinese black sleeper (Bostrychus sinensis) [6] and shimofuri goby (Tridentiger bifasciatus) [7]. Conversely, some species exhibit low genetic structure, likely due to higher migration rates, as seen in cutlassfish (Lepturacanthus savala) [8], prawn (Macrobrachium japonicum) [9], and hairtail (Trichiurus japonicus) [10]. The genetic structure of fish populations has attracted a great deal of attention, not just because of basic interest in the evolution of biotic organisms but mainly because of its importance for fishery management.
The grey mullet (Mugil cephalus) is a globally distributed marine fish, inhabiting coastal waters and estuaries in tropical and subtropical seas between latitudes 42° N and 42° S [11,12,13]. Spawning occurs offshore near the surface, with buoyant eggs hatching approximately 48 h after fertilization. Larvae are dispersed across the continental shelf by ocean currents, spending the first 2–3 months in a planktonic stage. During this phase, they grow to a standard length (Ls) of 16–20 mm and form dense schools that migrate toward inshore waters and estuaries [14]. Young recruits first appear in the surf zone before moving into the shallow areas of sounds, bays, and estuaries. Here, juveniles (40–69 mm Ls) spend their first year in waters with salinities ranging from 0 to 35‰ [15]. Adult grey mullets primarily feed on detritus and reach sexual maturity in their third year, at which point they form large migratory schools [16,17].
In Taiwan’s coastal waters and estuaries, two types of grey mullet are observed: migratory and resident [18]. The migratory type originates from the northern East China Sea, travelling along mainland China’s coast to the Taiwan Strait during the spawning season [19]. Migratory adults and juveniles then return to the mainland coast, aided by the Kuroshio Current, while juveniles remain in Taiwanese estuaries until late April. In contrast, the resident type inhabits Taiwanese estuaries year-round, with minimal migration. The grey mullet’s life history involves both passive and active dispersal mechanisms, primarily along the shoreline, which likely shape population subdivisions. Understanding the population dynamics and genetic structure of this species is crucial, given its significance to coastal fisheries and aquaculture in many countries worldwide [13].
Previous genetic work revealed that the populations of M. cephalus have two to three different lineages in the western Pacific fringe [18,20]. Analyzing the temporal patterns of lineages can provide insight into the contemporary and historical genetic connectivity of M. cephalus. For example, a COI phylogenetic tree revealed three lineages: the NWP1 lineage has a northward distribution from Taiwan to Russia, the NWP2 lineage is distributed along the warm Kuroshio Current, and the NWP3 lineage has a distributional range that suggests tropical affinities [18]. There are two life histories of M. cephalus (migratory and residential populations) in Taiwan, and gene flow occurs between them. Population genetic structures differ with estimated genetic distances and the genetic markers used. For example, Sun et al. [20] clustered M. cephalus in the China Sea into two groups based on mitochondrial DNA—one for the populations from the Bohai and East China Seas and the other from the Yellow and South China Seas—while Jamandre et al. [21] indicated that M. cephalus in the northwest Pacific belongs to two highly divergent lineages, with the inferred population structure being closely associated with the distribution of both lineages. According to Livi et al. [22], M. cephalus appears to consist of highly isolated populations characterized by specific mitochondrial lineages.
Huang et al. [23] previously employed the GPI-A locus as a genetic marker to distinguish between at least two grey mullet stocks—migratory and residential—within the western Pacific fringe, indicating that natural selection might influence allelic variation. In this study, we applied allozyme electrophoresis to investigate the potential genetic structure of grey mullet populations around Taiwan. Our objectives were threefold: first, to assess genetic divergence and gene flow among western Pacific populations of Mugil cephalus; second, to evaluate whether local populations are subject to selective pressures; and third, to determine the timing of selection during the grey mullet’s life cycle, specifically whether genotypic mortality occurs progressively or results from intense selection at a particular developmental stage.

2. Materials and Methods

In total, 1291 adult grey mullets 15.1–54.0 cm Ls were collected from 15 localities in three regions: (1) Japan’s archipelago: Nagasaki (NA); (2) the coast of mainland China: Shanghai (SH), Tachen (TC), and Matsu (MS); and (3) Taiwan’s main island: Tanshui (TS), Dashi (DS), Hualien (HL), Wuchi (WC), Tadu (TD), Peimen (PM), Anping (AP), Chiding (CD), Kaohsiung (KS), Kaoping (KP), and Tapong (TP). An additional 1470 juveniles were collected from three localities in Fulung (FLJ), Tanshui (TSJ), and Linbien (LBJ) in Taiwan estuaries (Figure 1). The population of M. cephalus in waters adjacent to Taiwan migrate annually from the feeding grounds in coastal waters of China to offshore waters of both southwestern and northeastern Taiwan to spawn in December at 3–4 years old [24]. From December to January, samples from the spawning population of M. cephalus collected from Wuchi (WC), Anping (AP), Chiding (CD), and Kaohsiung (KS) were defined as being migratory. These specimens were frozen at −75 °C immediately after being transported live to the laboratory. All studies in animals were conducted by ethical committee guidelines and approved by the Animal Research and Ethics Committee of department of Aquatic Bioscience, National Chiayi University (Taiwan) (Protocol Number: D20200318-01), and the study was carried out in compliance with the ARRIVE guidelines. All surgeries were performed under MS-222 anesthesia, and all efforts were made to minimize suffering. These specimens were frozen at −75 °C immediately after being transported live to the laboratory. After examining the enzyme tissue-specific distribution among the brain, eye, heart, skeletal muscle, liver, gill, kidney, and gonad, we chose to use the skeletal muscle and heart in the subsequent experiments. We used the following buffers: TVB [25], TC 7.0, TC 8.0 [20], and LiOH [26]. Tissue was homogenized with 2 to 3 volumes of buffer (0.1 mM Tris-HCl pH 7.0, 1 mM Na2 EDTA, and 0.05 mM NADP+) and centrifuged at 17,000× g for 40 min at 4 °C. Electrophoresis was run on a 12% (w/v) starch gel (Sigma Chemical Company, St. Louis, MO, USA) and stained with the recipes following the methods of Jean et al. [27]. Thirteen loci nomenclature followed recommendations from Shaklee et al. [28]. Alleles at each locus encoded were identified according to the migration mobility of the protein; the most common allele present was scored 100.
Allozyme analysis is a technique used in population genetics and evolutionary biology, but it has limitations, such as limited detection of genetic variability and susceptibility to environmental influences [29]. Allelic and genotypic frequencies of examined loci in M. cephalus populations were obtained by counting phenotypes directly from the gels. The mean number of alleles per population (Na), the proportion of polymorphic loci at the 95% level (P95), the observed heterozygosity (Ho), and the expected heterozygosity (HE) were computed by ARLEQUIN 3.5 [30]. All loci were tested for proximity to the Hardy–Weinberg equilibrium (HWE), and all pairwise combinations of loci were tested for linkage disequilibrium using ARLEQUIN 3.5 [30]. The allelic richness (AR) and inbreeding coefficient (FIS) for each population were estimated using FSTAT v.2.9.3 software [31] and GenePop Web Version 4.0.10 [32], respectively. F-statistics (FIS, FIT, and FST) for the polymorphic allozyme loci were estimated according to Weir and Cockerham [33] using the software FSTAT v.2.9.3 [31].
To identify candidate loci potentially influenced by the selection of M. cephalus based on the allozyme loci, we implemented the Fdist approach of Beaumont and Nichols [34] in LOSITAN v. 1.6 [35] and the BayeScan v. 2.1 [36] software based on the FST outlier approach. In LOSITAN, a neutral distribution of FST with 50,000 iterations was simulated with a false discovery rate of 0.1 and confidence interval of 0.95. All loci putatively identified by the LOSITAN program were removed from the dataset to generate a panel of allozyme loci, conform to assumptions of neutrality, and avoid misleading signals in population structure. We used the BayeScan software [36] and the R function “plot_bayescan” (https://cmpg.unibe.ch/software/BayeScan/download.html; accessed on 21 November 2023) to detect loci under selection. Pairwise FST values and a hierarchical analysis of molecular variance (AMOVA), based on allele frequency information, were calculated to evaluate the amount of population genetic structure using ARLEQUIN Ver 3.5 with 1000 permutations [30]. For the hierarchical AMOVA, the populations were grouped according to seven scenarios: (1) no groups (Scenario I, N = 14); (2) the Taiwan, Japan, and mainland China groups (Scenario II, N = 3); (3) the phylogeny results (Scenario III, N = 3), including NA—WC, CD, KS, AP, SH, TC—and MS—TS, DS, TD, HL, KP, PM and TP; (4) the Taiwan and other population groups (Scenario IV, N = 2); (5) 22 samples when GPI 100/100 and GPI 135/135 are treated separately (Scenario V, N = 2); (6) the Taiwan, Japan, and mainland China groups when GPI loci are removed (Scenario VI, N = 2); and (7) Taiwan and Japan in a migratory group (Scenario VII, N = 2). Tests for isolation by distance (IBD) were carried out in order to determine whether genetic differentiation increased with geographic distance using a Mantel test (10,000 permutations) in ARLEQUIN 3.5 [30]. We used the information on the coordinates from Google Earth for measuring the distance between the sampling sites.
To determine the relationships among the populations based on Nei [37], the genetic distance between all pairs of populations was examined. A dendrogram was also created using the unweighted pair grouping method with the arithmetic mean (UPGMA) method with bootstrap values calculated using POPULATIONS ver.1.2. [34], which was viewed on TreeView. Based on the allozyme loci, both principal component analysis (PCoA) and a Bayesian clustering method were used to explore the genetic clusters of M. cephalus and its relatives. A principal component analysis (PCoA) based on the standardized covariance of genetic distances between populations was performed using the program GenAlEx v 6.5 [38]. Bayesian assignment tests were applied to estimate the number of genetic clusters and to evaluate the degree of admixture among them using STRUCTURE v2.3.3 [39] based on microsatellite data. An estimation of the number of subpopulations (K) was completed using 10 independent runs with K = 1–25 (assuming no prior population delineation information) at 1,000,000 MCMC repetitions combined with a 100,000-repetition burn-in period. The posterior probability of each K value was calculated using an estimated log-likelihood, and the likelihood ratio was tested to determine the optimal numbers of subgroups. To obtain the most appropriate number of genetic groups in our dataset, the most likely K-value was determined in Structure Harvester Web 0.6.94 [40,41] using the log posterior probability of the data for a given K, Ln Pr (XjK) [42]. To determine whether the M. cephalus populations had experienced a recent shrinkage in effective population size using the software BOTTLENECK 1.2.02 [43], the observed distribution of allele frequency was compared to that of a population in a mutation-drift equilibrium assuming the IAM (infinite allele model).

3. Results

3.1. Genetic Variations and Population Structures of Adult Grey Mullets

A total of 2761 grey mullets—1291 adults and 1470 juveniles—were investigated based on 21 loci scored from 13 isozyme markers (mAAT, CK-A, GPI-A, GPI-B, IDH-A, IDH-B, LDH-A, LDH-B, sMDH-A, MPI, PGDH, PGM-A, and PGM-B). Their genotypic frequencies are presented in Tables S1 and S2. The number of samples (Na), number of alleles per population (Ab), allelic richness (AR), expected heterozygosity (HE), observed heterozygosity (HO), and inbreeding coefficient (FIS) for each sample are shown in Table 1. The average number of alleles in each population ranged from 2.000 (DS, HL, CD, AP, WC, TC) to 3.400 (TSJ, juveniles) (average = 2.367). The mean allelic richness for each population ranged from 1.035 (CD) to 1.382 (SH) (average = 1.188). The mean expected heterozygosity was 0.128 (ranging from 0.031 (MS) to 0.442 (KP)), and the mean observed heterozygosity was 0.086 for each sample (ranging from 0.017 (KS) to 0.215 (KP)). The positive FIS (heterozygote deficiencies) observed in most populations ranged from 0.000 (CD and TC) to 0.698 (KS), except those from the WC (−0.017) and MS (−0.007) populations (Table 1).
The results of Weir and Cockerham’s (1984) estimates of F-statistics showed that the highest values of FIS (0.643) and FIT (0.652) occurred at the locus mAAT, whereas the highest value of FST (0.253) occurred at the PGDH locus (Table 2). The jackknife resampling procedure allowed us to calculate a standard deviation of global F-values over loci (global FIS = 0.452 ± 0.124; global FIT = 0.578 ± 0.130; global FST = 0.218 ± 0.044). Overall, the jackknifing and the bootstrapping over the locus (for a 95% and a 99% confidence interval, respectively) showed higher FST levels than the FIS or FIT levels (Table 2). The marked positive global FIS indicates a likely heterozygote deficiency within M. cephalus samples.
The number of alleles for each of the 13 isozyme loci (average = 3.00) ranged from two (CK-A, IDH-A, IDH-B, LDH-B, MDH-A, and PGM-B) to five (GPI-A) in M. cephalus (Table 2). The average allelic richness per locus was 1.249, ranging from 1.003 (MDH-A) to 2.415 (GPI-A). The observed heterozygosity (HO) was 0.029, ranging from 0.000 (MDH-A) to 0.185 (GPI-A), and the expected heterozygosity (HE) was 0.043, ranging from 0.000 (MDH-A) to 0.296 (GPI-A). The mean value of FIS was 0.328, and five of the thirteen studied isozyme loci (mAAT, GPI-A, MDH-A, PGM-B, PGDH) had a positive FIS value, higher than that predicted by the HWE.
Table S3 shows the pairwise FST values (−0.010 (between DS and HL) to 0.608 (between KP and MS), with a mean value of 0.252). The pairwise FST values among the NA (Nagasaki) and other samples were significantly different in all pairwise comparisons (Table S3). The analysis of variance (AMOVA) was applied to test the probable factors shaping genetic structure according to geographical barriers. The AMOVA results indicated that most of the genetic variation was within individuals, i.e., one group (80.70%), three groups (Scenario I, 60.91%), three groups (Scenario II, 59.64%), and four groups (Scenario III, 75.62%) (Table 3). When the populations were divided into one group, three groups (Scenario I), three groups (Scenario II), and four groups (Scenario III), 0%, 27.20%, 34.71%, and 19.85% of the total variation was found among the group divisions, respectively (Table 3). No significant correlation was found between geographic distance and genetic differentiation of populations when all populations were included in the IBD analyses (r = 0.193, p = 0.107). Furthermore, the significant correlation between pairwise genetic and geographic distances (Mantel correlation; r = 0.478) only used in the non-migratory residential group provided support for the isolation by distance hypothesis.
Accordingly, the subsequent analyses of the 18 total localities including juveniles resulted in an enormous genetic divergence, suggesting that there were at least two widely differing populations depending on the allelic frequencies of GPI-A locus (Table S1). However, they were provisionally categorized into three arbitrary groups, as shown on the map in Figure 1 (Japan, the East China Sea, and Taiwan inshore waters). The UPGMA trees based on allozyme markers on Figure 2a show four clades: A, B, C, and D. Clade A comprises the fish captured from Southern Taiwan: Tapong (TP), Kaoping (KP), and Peimen (PM). Clade B, located in the Northern Taiwan, includes Tadu (TD), Tansui (TS), Dashi (DS), and Hualien (HL). Clade C only comprises the sample from the Japanese coast (Nagasaki, NA). Finally, Clade D comprises samples from the coastal waters near Taiwan—Kaohsiung (KS), Chiding (CD), Anping (AP), and Wuchi (WC)—and off the coast of mainland China—Matsu (MS), Tachen (TC), and Shanghai (SH)—all of which were caught by gill nets on their way southbound from nearby coastal waters. These migratory populations partially mix with residential populations in midwinter during the migratory population’s annual southward movement. An NJ tree, constructed with Cavalli-Sforza and Edward’s chord distance models (Figure 2b), clearly shows that the Japanese NA sample clustered closer to the offshore migratory types than to the inshore residential type. Once the samples with the GPI-A locus were removed from the data, the position of NA became more tightly clustered with DS (Dashi, northern Taiwan) than to others (Figure 2c). This suggests that the GPI-A locus plays an important role in population differentiation.
PCoA was performed using the first two principal coordinates to investigate the population patterns using the genetic distances among samples. The variances in the first and second principal components were 77.46% and 13.24%, respectively, summing to 90.70% in total variation. PCoA indicated that the samples of M. cephalus could be split into three groups (Figure 3): fish from (1) PM, KP, TP, LBJ, TS, TD, DS, HL, TSJ, and FLJ; (2) NA; and (3) SH, TC, MS, WC, AP, CD, and KS. We then used the genetic STRUCTURE clustering algorithm previously adopted from allozyme data; the results indicated the presence of three distinct genetic clusters (K = 3) (Lnp(D) = −5730.13)) according to the ΔK metric developed by Evano et al. [35] (Figure 4a), which could test if the GPI locus and some other metabolic genetic loci might affect the population structuring. Figure 4b reveals three groups when all the loci remained, with four groups appearing once the GPI-A locus was removed. Similarly, the following LOSITAN test shown in Figure 5 could easily identify the outlier allozyme locus as GPI-A. Furthermore, BayeScan analysis indicated that GPI-A locus was under selection. For M. cephalus, the above 15 samples displayed a normal L-shaped allele frequency distribution in the mode-shift indicator, which indicated a stable population. None of the samples appeared have the significant heterozygosity excess that was observed by the Wilcoxon test under IAM, indicating that genetic bottlenecks were not detected in M. cephalus due to mutation-drift equilibrium. Therefore, the studied population of M. cephalus did not experience a recent genetic bottleneck.

3.2. Selection on GPI and PGDH

The overall offshore migratory samples (AP, CD, WC, MS, SH, TC, and KS) revealed had strikingly greater FST values on the GPI (0.489) and PGDH (0.202) loci (Table S1). In addition, the frequency of GPI-A*100 and PGDH*100 decreased significantly between localities along with altitude (Figure 6, Table S1); such a directional change in allelic frequencies was not found in other loci. As mentioned previously, the heterozygous genotype of GPI-A*100/135 alone in all juveniles (27.89%) was 1.5 times higher than that of residential adults (18.96%) (Table S2). To clarify the differences between juveniles and adults, all samples were pooled to determine if the proportion of GPI-A*100/135 decreased with age. We found that the GPI-A*100/100 increased in older fish, while GPI-A*135/135 remained steady at all ages. The proportion of GPI-A*100/135 decreased from larvae (27.89%) to 4-year-old adults (5.7%), and the GPI-A*100/100 increased from larvae (20.20%) to 4-year-old adults (37.1%). This suggests that a negative effect of isozyme heterozygosity (GPI-A*100/135) may be maintained by selection (Figure 7).

4. Discussion

4.1. Genetic Diversity and Population Differentiation in Mugil cephalus

Isozymes are commonly used genetic markers that provide additional information about the genetic structure of the species. The present study used a 13-polymorphic isozyme system to detect genetic variability in M. cephalus. The genetic diversity changes in time and space within species have been recognized as a fundamental part of biodiversity conservation, enabling populations to evolve in response to environmental changes [44]. Previous studies have found that populations with higher heterozygosity at locus-encoding enzymes tend to have significantly less fluctuating asymmetry and provide evidence that more heterozygous individuals within random mating populations are more developmentally stable [45]. The average heterozygosity (0.025) calculated from 21 isozyme loci for 15 local samples was far lower than that estimated for 10 samples from around the world [46] and Florida [47]. This is similar to the results from other animals—e.g., giant tiger prawn (0.027; range 0.018–0.046) [48]—but lower than those of the inshore sparid fish Acanthopagrus schlegeli (0.066; range 0.059–0.082) [27].
Levels of heterozygosity vary with ecological adaptability. Marine fish are generally found to have heterozygote deficiencies, which is probably caused by the occurrence of rare homozygous genotypes, inbreeding, selection, and the Wahlund effect [49]. The present investigation suggests that, for M. cephalus, overfishing is also an explanation for these deficiencies. Since 1986, catches of M. cephalus have sharply dropped due to intense fishing [50]. The previous study showed that M. cephalus experienced several demographic crashes due to decreases in surface water temperatures [18]. Although, M. cephalus did not exhibit a recent genetic bottleneck by isozyme markers. In the inshore large-scale mullet Liza macrolepis, the Ho values for the populations in the enclosed lagoon and estuaries (0.043–0.044) were much higher than those from open coasts (0.028–0.029) [51]. Populations with high gene flow also have a lower HE than those living in restricted areas. Mugil cephalus in the waters surrounding Taiwan was primarily subdivided into migratory and residential populations using GPI-A genotypes as a distinguishable criterion 18, with Ho values of 0.022 and 0.037, respectively. The wide difference is presumably due to the residential population being isolated, leading to low gene flow. In marine fish, loss of genetic diversity in wild populations has been reported in the northwest Pacific Ocean: the effective population size was reduced due to overfishing by commercial fisheries (e.g., Chinese pomfret, Pampus chinensis [52]; cutlassfish, L. savala [8]). The low heterozygosity values and significant number of polymorphisms found in grey mullets from the offshores of KS, CD, WC, AP, MS, SH, and TC (i.e., the migratory population) may be explained either by genetic drift, the founder effect, or strong directional selection due to geographic isolation (Table S1). According to the pairwise FST, higher population differentiation was detected between the migratory and residential populations (Table 3). We argue that this is probably caused by restricted gene flow between the migratory and residential populations.
In general, population differentiation in M. cephalus may also be due to poor dispersal ability (Figure 3). This pattern is well supported by the results of the STRUCTURE analysis, in which these two groups or populations showed different patterns under K = 2 (Figure 4). Previous studies suggested that M. cephalus in the northwest Pacific Ocean is divided into two or three groups based on mtDNA and microsatellite (msat) loci [18,53]. The migratory and residential populations in our results resemble the NPW1 and NWP2 lineages, respectively, set by Shen et al. [18]. Our results show reduced genetic diversity in migratory populations. The Pleistocene climatic oscillations were likely less severe in southern latitudes, where the Kuroshio Current remained a stabilizing influence [18]. This aligns with previous studies suggesting that NWP2 has higher genetic diversity than NWP1, based on mtDNA COI gene analysis [18,53]. Many previous studies suggested that the major drop in sea level in the Taiwan Strait acted as a biogeographic barrier during major falls in sea level during the Pleistocene, which might have cut off migration on either side of the strait, forming two lineages in marine animals—e.g., the genera Helice [54], B. sinensis [6] and L. savala [8]. The results of the AMOVA test based on three geographical groups (Scenario II) indicate that there are significant differences among groups, suggesting a differentiation between migratory and residential populations.
During the last glacier maximum (LGM) in the late Pleistocene, the Taiwan Strait acted as a geographical barrier and presented two lineages for marine fishes due to the descending sea level (e.g., L. savala [8]; T. nanhaiensis [55]). We suggested that migratory and residential populations probably diverged after the last glacial event. Growth rates differ slightly between these two populations. Temperature used to be considered one of the variables that affects growth, and growth may increase with rising temperatures in other species. However, a higher growth rate in migratory populations than in residential populations would appear to contradict the slower growth rate in species with a migratory population growing in habitats with lower temperatures. Since changes in allelic frequencies are not temperature dependent [23], selection is the more likely explanation for why these two populations are separate. The tendency for the genotypic frequencies of GPI-A*100/100 to increase with age implies that the migratory population is well adapted to its environments. On the contrary, decreasing frequencies of the heterozygous allele suggest that allozyme variation may result from directional selection occurring in 100/135 in older fish, indicating strong selection within the residential population (Figure 6).
There are several selective mechanisms that can account for both widespread polymorphisms and geographic clines. One is divergent directional selection, with the intensity of selection varying in parallel with the relevant environmental variables along a geographic gradient [56]. According to LOSITAN and BayeScan, the outlier GPI-A locus (subject to positive directional selection) is a significant contributor to the genetic differentiation among populations; this suggests that natural selection (potentially resulting from genetic variation in environmental conditions) could be involved in driving genetic heterogeneity within M. cephalus. By itself, such a mechanism should lead to monomorphism at the geographic extremes. Our results reveal that the frequency of the GPI-A*100 allele declined with decreasing latitude, and only the migratory populations (i.e., AP, CD, MS, WC, SH, TC, and KS) contained the GPI-A*100 allele. Directional shifts in the allelic frequency of the GPI-A locus observed here suggest that GPI-A*100 is favoured in higher-latitude habitats, while GPI-A*135 is favoured in lower-latitude habitats. Thus, we argue that sea water temperature correlated with decreasing latitude might have promoted patterns of local adaptation and, therefore, genetic differentiation among M. cephalus populations (Figure 6). PGDH is the only allozyme locus in which this firm connection between latitudinal differences in the ambient temperature and allelic frequency has been found. The results of the AMOVA test based on the Taiwan, Japan, and mainland China groups when GPI loci were removed indicate that there were no significant differences at the group level. In general, when a single locus shows a pattern of variation that is highly discordant with other loci, it is likely that natural selection is acting on that locus [57]. Previous studies on fishes and other organisms have proposed that PGI (e.g., GPI) is a key enzyme for temperature adaptation [23,58]. Population genetic studies have revealed unusual patterns of variation at PGI in a wide range of organisms [59,60,61]. Temperature affects PGI allozyme functional properties such as the Michaelis–Menten binding constant, Km, and thermal stability in a diverse array of species [52,62]. In the grey mullet, the GPI-A*100/100 homozygote exhibits a lower Km value and thermal stability compared to the GPI-A*135/135 homozygote, suggesting that the GPI-A*100 allele may function better than the GPI-A*135 one at low temperatures. A similar case in a montane insect was reported by Dahlhoff and Rank [63]. It is proposed that the major role that GPI plays in glycolysis is especially critical for temperature adaptation [64].
Like with GPI, the PGDH data in this report demonstrate widespread polymorphism and a latitudinal cline. However, PGDH has fewer polymorphisms than GPI (Figure 6), but otherwise has a much less pronounced latitudinal cline. The PGDH frequency was observed from 0.94 to 0.99 in the migratory population (AP + CD + WC + KS), MS, TC, and SH to about 0.67 in TP. Therefore, while data support a hypothesis that the selective gradient is able to maintain a cline in the longitudinal direction for PGDH, it may be argued that clinal variation in the relative fitness of the homozygotes is more important than that of GPI. Isozyme loci that significantly deviate from neutral expectations of differentiation, or outlier loci, may be closely linked to the target of natural selection or even be directly under selection themselves [65]. The results of the present analyses clearly support the hypothesis that the latitudinal clines for GPI and PGDH are caused by negative correlations between the latitude and the frequencies of the GPI-A*100 and PGDH*100 alleles (Figure 6).

4.2. Genetic Variation among the Migratory Groups

The samples collected from the coastal waters of Nagasaki (NA) were similar to offshore samples collected from Taiwan, indicating a closer affiliation between migratory populations from either the coast of mainland China or Taiwan Island proper than to those from the inshore waters of Taiwan (Figure 2b). The same results showed that the Nagasaki samples were unique to those from other locations with the migratory element caught exclusively in the midwinter. In fact, the Nagasaki samples consisted of 17% GPI-A 100/100, 46% GPI-A 130/130, and 34% GPI-A 100/130 hybrids and 2.5% GPI-A 117/130 hybrids (Table S1). These two hybrid forms had never been found in migratory populations leading toward the south. The above samples with a GPI-A 100/100 locus among the annually migratory stocks were only slightly different from those with the same locus that resided in the inshore waters, per the FST (0.16042) and percent variation (3.18%) (Table 3VII). We found that some annual migrants settled in the inshore waters, because hybrids were found inside the near shore or estuaries. GPI-A 100/100 and GPI-A 135/135 alleles were used to delimit the above two populations, and Figure 2b shows that GPI-A 100/100 and GPI-A 135/135 had a surprisingly closer affiliation to the same inshore samples—e.g., TP with 100 allele vs. TPR with 135 alleles—than to the typical southward migratory schools carrying GPI-A 100/100 alleles. A minority of the above migratory schools might have colonized the inshore waters along with the existing residential population. They did not return to the traditional nursery ground farther north. Nevertheless, the higher FST (0.57504) and 48.75% variation reported earlier (Table 3V) illustrated a striking separation between these two populations.
To confirm that the GPI-A locus dominates the changes in population structuring, a new, reorganized dataset was treated by removing all the samples with the entire locus. The new analysis resulted in a much lower FST (0.12855) and 1.34% variation (Table 3VI), which could only shallowly distinguish these two populations. The population structure of 15 samples with the lower FST (0.38472) and 18.70% variance was obtained once GPI-A 100/100 and GPI-A 135/135 were pooled (Table 3IV). The subsequent UPGMA tree newly constructed with Cavalli-Sforza and Edward’s chord distance models did not quite match with the above fishing localities, as indicated in Figure 2a, suggesting that intermingling might happen between migratory and residential populations. Further comparing the pairwise FST and AMOVA test results among 22 samples—including migratory GPI-A 100/100 and the residential GPI-A 135/135 genotypes—revealed significant differences: a higher variance of 48.75% and FST of 0.57504 compared to the variance of 18.707% and FST of 0.38472 obtained from the undivided 15 samples above (Table 3IV, V).

4.3. Genetic Variation among Non-Migratory Populations

The non-migratory residential group around the inshore waters of Taiwan is designated as the fish with GPI 135/135 locus. A comparison between the semi-enclosed lagoon at Tapong and six other inshore sites (Kaoping, Peimen, Tadu, Tanshui, Dashi, and Hualien) yielded an FST of 0.103–0.239. TP is geographically closer to KP (FST = 0.103) than to the other sites (0.192–0.239). These results resemble those expressed in genetic distance. The population sub-structuring and dynamic models of grey mullets living in the inshore waters of Taiwan were exemplified by the semi-enclosed lagoon, Tapong Bay, in SW, Taiwan, where the fish reside year-round. The grey mullets in that lagoon consist of six GPI-A genotypes, predominately 135/135 (60.7%), followed by 100/135 (16.7%), 100/100 (16.1%), 117/135 (4.6%), 100/75 (1.1%), and 100/117 (0.74%). Their ages ranged from 0 to 5 years. Within the lagoon, the fish were only found in the age group 3–5 years during October and the following March (Figure 8A–C), indicating that the spawning ground was localized in the lagoon proper and probably extended into adjacent areas. The composition of the population in the lagoon was predominated by genotypes GPI-A*135/135 (60.7%) and GPI-A*100/135 (16.7%), summing to a total of 77.4% during the migration period and 82.83% during the non-migration period; this indicates that fish stocks moved slightly into the lagoon for feeding. On the other hand, the slight increase in the GPI-A*100/100 genotype from 17.17% to 22.46% from moving inshore toward the lagoon represents the settlement of partially solitary migrating schools in the lagoon. Among the residential groups distributed in the seven localities above, the sample from KP is the most highly diversified with the remaining samples (FST 0.452–0.692), except TP (0.103).

4.4. Occurrence of Juvenile Grey Mullet

The occurrence of juveniles corresponded to the spawning seasons of the populations they belonged to, e.g., October–January for the residential population and December–March for the migrating population. The earliest catchable residential juveniles attained were 15 mm Ls in the Tanshui estuary and 90 mm Ls in the Linbien estuary near the outskirt of Tapong Bay, and they subsequently disappeared from the estuaries by May. The smallest migratory juveniles (20–25 mm Ls) with the GPI-A*100/100 genotype caught in November grew to a peak size of 55–60 mm Ls in April in the Linbien estuary and then had disappeared by May. The large-sized residential population suggests that the residential population spawned earlier than the migratory population. Some juveniles with the migratory genotype (GPI-A*100/100) appeared in November and December and were partially produced by interbreeding with the heterozygous (GPI-A*100/135) residential population. A total of 1470 juveniles contained seven GPI-A genotypes—100/100, 100/135, 135/135, 100/117, 135/117, 100/75, and 135/75—with the following percentage compositions: 20.2%, 27.89%, 49.32%, 0.07%, 2.11%, 0.20%, and 0.20%, respectively (Table S2). The genotypes with 135 alleles made up 79.32% of the composition and 64.42% of the allelic frequency. The latter was close to that of residential adults (63.26%). However, the heterozygous genotype of 100/135 alone in all juveniles (27.89%) was 1.5 times greater than the residential adults (18.96%), a result of selection in the course of growth (Table S2).

4.5. Selection during Life Stages

Selection is key to the evolutionary process and has already been invoked to explain the differentiation in genetic variability (detected with allozymes) among marine populations from heterogeneous environments [66]. The proportion of the GPI-A*100/135 heterozygous genotype in juveniles was higher than that in adults (27.89% vs. 18.96%). Therefore, data on all residential populations were pooled to test whether selection can act at a specific stage in time or is simply a result of a continuous process throughout the organism’s entire life span. The present analysis shows no selection that acts at a specific stage. Genotypic frequencies in juvenile samples closely match those observed for later age 0 juveniles. Furthermore, the genotypic frequencies change across life stages (Figure 7), suggesting that selection occurs throughout the life span.
In general, genetic polymorphisms being maintained in natural populations suggest that a population has adapted to environmental heterogeneity [67]. This process does not depend on local populations adapting to their environment. If selection favours a homozygous genotype for one type of environmental parameters and other homozygotes in another, then the heterozygous genotype may represent the highest arithmetic or harmonic mean fitness [68]. Thus, the heterozygous state may act as a buffer against environment variation, and the presence of environmental heterogeneity results in the realization of a higher heterozygote fitness potential [69]. However, our results differ from this description in that the heterozygous genotype acts against a strong selection force.

4.6. Linkage Disequilibrium between GPI-A and PGDH

Linkage disequilibrium and non-random associations between alleles or groups of nucleotides may indicate epistatic relationships. The pattern of pairwise linkage disequilibrium is consistent between populations, indicating that strong evidence of selection drives the extent of linkage disequilibrium (LD) [70]. Linkage disequilibrium between individual locus is very scarce in fish, except when genes extremely closely linked or chromosomal inversions are often associated. When significant associations have been observed, it is often not at all clear whether they are due to non-random sampling of haplotype, random genetic drift, or natural selection. Significant disequilibrium can indeed arise without epistasis from random genetic drift within a given population in subdivided populations and by gene migration or the founder effect [71]. In Drosophila, numerous examples of significant linkage disequilibrium have been discovered between specific allozyme and chromosomal inversions, which have been explained as reflecting selection for favoured multilocus allelic combinations. In general, linkage disequilibrium is mostly associated with closely linked genes but may involve distantly linked genes when special cytological mechanisms allow it to exist [72]. We do not know the positions of the GPI-A and PGDH genes in the chromosome. The significant linkage disequilibrium of GPI-A and PGDH genes identified in the present study is presumably a result of special cytological mechanisms. However, it remains unclear what combination of environmental variables could result in a decrease in the frequency of the GPI-A*100/135 genotype. The proportion of PGDH*100/100 slightly declined with age, but PGDH*125/125 otherwise increased with age (Figure 7). The proportion of PGDH*125/125 increased from larvae (9.2%) to 4-year-old adults (25.7%) The genotypic frequency distribution patterns were similar between GPI-A*100/135 and PGDH*100/100 (Figure 7). The distinct selection regime of environmental stresses cannot be fully explained. Further examination of biochemical and physiological properties of the GPI and PGDH genotypes in grey mullet under a variety of environmental conditions may help resolve this problem.

5. Conclusions

Our results indicate that all populations are clustered into three distinct genetic groups in the PCoA analysis. In this study, migratory and resident populations were classified as the NWP1 and NWP2 lineages, respectively, based on isozyme data. The differences between migratory and residential populations suggest that the major drop in sea level in the Taiwan Strait acted as a biogeographic barrier during major falls in sea level in the Pleistocene, when the fish’s migration route was cut off on either side of the strait to form two lineages of marine fishes. Mugil cephalus was detected in the migratory and residential populations with Ho values of 0.022 and 0.037, respectively. The wide difference is presumably due to the residential population being isolated, leading to low gene flow. AMOVA and the high overall mean FST of 0.252 indicated enormous genetic differentiation among populations. The significant contribution of the outlier GPI-A locus (subject to positive directional selection) to the genetic differentiation among populations based on LOSITAN and BayeScan suggests that natural selection (potentially resulting from genetic variation in environmental conditions) could be involved in driving genetic heterogeneity within M. cephalus. Heterozygote deficiencies and the loss of the genetic diversity found in M. cephalus are probably the results of overfishing by commercial fisheries. During the life history of M. cephalus, the GPI-A*100/135 heterozygote frequency significantly decreases with age. Based on these data, we suggest that each GPI-A genotype represents trait combinations of higher fitness in some portions of the environment. While this study offers valuable insights into the genetic differentiation of Mugil cephalus populations, several key gaps remain that need to be addressed to strengthen the findings and guide future research. These include limited geographic sampling and a sufficient focus on environmental variables.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/genes15101280/s1, Table S1: Genotype frequencies and Nei’s (1977) F-statistics of loci for all Mugil cephalus sampled. Localities in bold type indicate nonmigrating local populations while those in normal type indicate migrating populations; Table S2: Genotype frequencies of Mugil cephalus juveniles, with some metabolic allozymes; Table S3: Matrix of pairwise Fst (below diagonal) and RST (above diagonal) among 15 populations based on allozyme loci in Mugil cephalus. Bold type letters indicate statistically significant results.

Author Contributions

Conceptualization, C.-H.K. and S.-C.L.; methodology, C.-H.K. and S.-C.L.; software, C.-H.K., S.-Y.D. and H.-D.L.; formal analysis, S.-Y.D.; investigation, C.-H.K. and C.-S.H.; data curation, C.-H.K. and S.-Y.D.; writing—original draft preparation, C.-H.K., S.-C.L. and H.-D.L.; writing—review and editing, S.-C.L. and H.-D.L.; project administration, S.-C.L.; funding acquisition, C.-H.K. and S.-C.L. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by Professor Chien-Hsien Kuo.

Institutional Review Board Statement

All studies on animals were conducted by ethical committee guidelines and approved by the Animal Research and Ethics Committee of department of Aquatic Bioscience, National Chiayi University (Taiwan) (Protocol Number: D20200318-01), and the study was carried out in compliance with the ARRIVE guidelines. All surgeries were performed under MS-222 anesthesia, and all efforts were made to minimize suffering.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset was deposited in Tables S1 and S2. Voucher specimens are housed at the Institute of Cellular and Organismic Biology, Academia Sinica, Taiwan.

Acknowledgments

This work was carried out in the laboratory of Lee under financial support from Institute of Zoology (now renamed as the Institute of Cellular and Organismic Biology), Academia Sinica, where genetic markers such as isozymes and DNA sequences are used to investigate the population differentiation and phylogeny of fishes.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bowen, B.W.; Gaither, M.R.; DiBattista, J.D.; Iacchei, M.; Andrews, K.R.; Grant, W.S.; Toonen, R.J.; Briggs, J.C. Comparative phylogeography of the ocean planet. Proc. Natl. Acad. Sci. USA 2016, 113, 7962–7969. [Google Scholar] [CrossRef] [PubMed]
  2. Conover, D.O.; Clarke, L.M.; Munch, S.B.; Wagner, G.N. Spatial and temporal scales of adaptive divergence in marine fishes and the implications for conservation. J. Fish Biol. 2006, 69, 21–47. [Google Scholar] [CrossRef]
  3. Chiu, Y.W.; Bor, H.; Wu, J.X.; Shieh, B.S.; Lin, H.D. Population Structure and Phylogeography of Marine Gastropods Monodonta labio and M. confusa (Trochidae) along the Northwestern Pacific Coast. Diversity 2023, 15, 1021. [Google Scholar] [CrossRef]
  4. Liu, J.X.; Gao, T.X.; Wu, S.F.; Zhang, Y.P. Pleistocene isolation in the Northwestern Pacific marginal seas and limited dispersal in a marine fish, Chelon haematocheilus (Temminck & Schlegel, 1845). Mol. Ecol. 2007, 16, 275–288. [Google Scholar] [CrossRef] [PubMed]
  5. Chen, X.; Wang, J.J.; Ai, W.M.; Chen, H.; Lin, H.D. Phylogeography and genetic population structure of the spadenose shark (Scoliodon macrorhynchos) from the Chinese coast. Mitochondrial DNA Part A 2018, 29, 1100–1107. [Google Scholar] [CrossRef] [PubMed]
  6. Qiu, F.; Li, H.; Lin, H.D.; Ding, S.; Miyamoto, M.M. Phylogeography of the inshore fish, Bostrychus sinensis, along the Pacific coastline of China. Mol. Phylogenet. Evol. 2016, 96, 112–117. [Google Scholar] [CrossRef] [PubMed]
  7. Wang, J.J.; Hsu, K.C.; Chen, Y.H.; Zhao, J.; Tang, W.Q.; Liu, D.; Yang, J.Q.; Lin, H.D. Phylogeography of Tridentiger bifasciatus (Gobiidae) in the northwestern Pacific. Front. Ecol. Evol. 2022, 10, 935251. [Google Scholar] [CrossRef]
  8. Gu, S.; Yi, M.R.; He, X.B.; Lin, P.S.; Liu, W.H.; Luo, Z.S.; Lin, H.D.; Yan, Y.R. Genetic diversity and population structure of cutlassfish (Lepturacanthus savala) along the coast of mainland China, as inferred by mitochondrial and microsatellite DNA markers. Reg. Stud. Mar. Sci. 2021, 43, 101702. [Google Scholar] [CrossRef]
  9. Han, C.C.; Lai, C.H.; Huang, C.C.; Wang, I.C.; Lin, H.D.; Wang, W.K. Phylogeographic Structuring of the Kuroshio-Type Prawn Macrobrachium japonicum (Decapoda: Palaemonidae) in Taiwan and Ryukyu Islands. Diversity 2022, 14, 617. [Google Scholar] [CrossRef]
  10. Hsu, K.C.; Yi, M.R.; Gu, S.; He, X.B.; Luo, Z.S.; Kang, B.; Lin, H.D.; Yan, Y.R. Composition, demographic history, and population structures of Trichiurus. Front. Mar. Sci. 2022, 9, 875042. [Google Scholar] [CrossRef]
  11. Thomson, J.M. Synopsis of Biological Data on the Grey Mullet (Mugil cephalus Linnaeus 1758) Fishery Synopsis 1, Division of Fisheries and Oceanography; CSIRO: Mebourne, Australia, 1963. [Google Scholar]
  12. Thomson, J.M. The grey mullet. Oceanogr. Mar. Biol. Rev. 1966, 4, 301–335. [Google Scholar]
  13. Whitfield, A.K.; Panfili, J.; Durand, J.D. A global review of the cosmopolitan flathead mullet Mugil cephalus Linnaeus 1758 (Teleostei: Mugilidae), with emphasis on the biology, genetics, ecology and fisheries aspects of this apparent species complex. Rev. Fish Biol. Fish. 2012, 22, 641–681. [Google Scholar] [CrossRef]
  14. Anderson, W.W. Larval development, growth, and spawing of striped mullet (Mugil cephahis) along the south Atlantic coast of the United States. Fish Bull. 1958, 58, 501–519. [Google Scholar]
  15. Nordile, F.G.; Szelistowski, W.A.; Nordile, W.C. Ontogenesis of osmotic regulation in the striped mullet, Mugil cephalus L. J. Fish Biol. 1982, 20, 79–86. [Google Scholar] [CrossRef]
  16. Robins, C.R.; Ray, G.C.; Douglass, J.; Freund, R. A Field Guide to Atlantic Coast Fishes of North America; Houghton and Mifflin: Boston, MA, USA, 1986. [Google Scholar]
  17. Hsu, C.C.; Han, Y.S.; Tzeng, W.N. Evidence of flathead mullet Mugil cephalus L. spawning in waters northeast of Taiwan. Zool. Stud. 2007, 46, 717–725. [Google Scholar]
  18. Shen, K.N.; Jamandre, B.W.; Hsu, C.C.; Tzeng, W.N.; Durand, J.D. Plio-Pleistocene sea level and temperature fluctuations in the northwestern Pacific promoted speciation in the globally-distributed flathead mullet Mugil cephalus. BMC Evol. Biol. 2011, 11, 83. [Google Scholar] [CrossRef]
  19. Su, W.C.; Kawasaki, T. Characteristics of the life history of gray mullet from Taiwan waters. Fish. Sci. 1995, 61, 377–381. [Google Scholar] [CrossRef]
  20. Sun, P.; Shi, Z.H.; Yin, F.; Peng, S.M. Genetic variation analysis of Mugil cephalus in China Sea based on mitochondrial COI gene sequences. Biochem. Genet. 2012, 50, 180–191. [Google Scholar] [CrossRef]
  21. Jamandre, B.W.; Durand, J.D.; Tzeng, W.N. Phylogeography of the flathead mullet Mugil cephalus in the north-west Pacific as inferred from the mtDNA control region. J. Fish Biol. 2009, 75, 393–407. [Google Scholar] [CrossRef] [PubMed]
  22. Livi, S.; Sola, L.; Crosetti, D. Phylogeographic relationships among worldwide populations of the cosmopolitan marine species, the striped gray mullet (Mugil cephalus), investigated by partial cytochrome b gene sequences. Biochem. Syst. Ecol. 2011, 39, 121–131. [Google Scholar] [CrossRef]
  23. Huang, C.; Weng, C.; Lee, S. Distinguishing two types of gray mullet, Mugil cephalus L. (Mugiliformes: Mugilidae), by using glucose-6-phosphate isomerase (GPI) allozymes with special reference to enzyme activities. J. Comp. Physiol. B 2001, 171, 387–394. [Google Scholar] [CrossRef] [PubMed]
  24. Wang, C.H.; Hsu, C.C.; Chang, C.W.; You, C.F.; Tzeng, W.N. The migratory environmental history of freshwater resident flathead mullet Mugil cephalus L. in the Tanshui River, northern Taiwan. Zool. Stud. 2010, 49, 504–514. [Google Scholar]
  25. Siciliano, M.J.; Shaw, C.R. Separation and visualization of enzymes on gels. In Chromatographic and Electrophoretic Techniques. Vol. II. Zone Electrophoresis (ed. Smith I); Year Book Medical Publishers: Chicago, IL, USA, 1976; pp. 185–209. [Google Scholar]
  26. Redfield, J.A.; Salini, J.P. Techniques of starch-gel electrophoresis of penaeid prawn enzymes (Penaeus spp. and Metapenaeus spp.). CSIRO Div. Fish. Oceanogr. 1980, 116, 1–20. [Google Scholar]
  27. Jean, C.T.; Lee, S.C.; Hui, C.F.; Chen, C.T. Tissue-specific isozymes in fishes of the subfamily Sparinae (Perciformes: Sparidae) from the coastal waters of Taiwan. Zool. Stud. 1995, 34, 164–169. [Google Scholar]
  28. Shaklee, J.B.; Allendorf, F.W.; Morizot, D.C.; Whitt, G.S. Gene nomenclature for protein-coding loci in fish. Trans. Am. Fish Soc. 1990, 119, 2–15. [Google Scholar] [CrossRef]
  29. Bossart, J.L.; Prowell, D.P. Genetic estimates of population structure and gene flow: Limitations, lessons and new directions. Trends Ecol. Evol. 1998, 13, 202–206. [Google Scholar] [CrossRef] [PubMed]
  30. Excoffier, L.; Lischer, H.E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 2010, 10, 564–567. [Google Scholar] [CrossRef]
  31. Goudet, J. FSTAT, a Program to Estimate and Test Gene Diversities and Fixation Indices, Version 2.9.3. 2001. Available online: http://www2.unil.ch/popgen/softwares/fstat.htm (accessed on 21 November 2023).
  32. Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 2008, 8, 103–106. [Google Scholar] [CrossRef] [PubMed]
  33. Weir, B.S.; Cockerham, C.C. Estimating F-statistics for the analysis of population structure. Evolution 1984, 1984, 1358–1370. [Google Scholar] [CrossRef]
  34. Beaumont, M.A.; Nichols, R.A. Evaluating loci for use in the genetic analysis of population structure. Proc. R. Soc. B Biol. Sci. 1996, 263, 1619–1626. [Google Scholar] [CrossRef]
  35. Antao, T.; Lopes, A.; Lopes, R.J.; Beja-Pereira, A.; Luikart, G. LOSITAN: A workbench to detect molecular adaptation based on a FST-outlier method. BMC Bioinform. 2008, 9, 323. [Google Scholar] [CrossRef] [PubMed]
  36. Foll, M.; Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 2008, 180, 977–993. [Google Scholar] [CrossRef] [PubMed]
  37. Nei, M. Genetic distance between populations. Am. Nat. 1972, 106, 283–292. [Google Scholar] [CrossRef]
  38. Peakall, R.O.D.; Smouse, P.E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 2006, 6, 288–295. [Google Scholar] [CrossRef]
  39. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. [Google Scholar] [CrossRef]
  40. Earl, D.A. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  41. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef] [PubMed]
  42. Pritchard, J.K.; Wen, W.; Falush, D. Documentation for the Structure Software, Version 2; Department of Human Genetics, University of Chicago: Chicago, IL, USA, 2004. [Google Scholar]
  43. Piry, S.; Luikart, G.; Cornuet, J.M. Computer note. BOTTLENECK: A computer program for detecting recent reductions in the effective size using allele frequency data. J. Hered. 1999, 90, 502–503. [Google Scholar] [CrossRef]
  44. Miller, J.M.; Burke, J.S.; Fitzhugh, G.R. Early life history patterns of Atlantic North American flatfish: Likely (and unlikely) factors controlling recruitment. Neth. J. Sea Res. 1991, 27, 261–275. [Google Scholar] [CrossRef]
  45. Leary, R.F.; Allendorf, F.W.; Knudsen, K.L. Superior developmental stability of heterozygotes at enzyme loci in salmonid fishes. Am. Nat. 1984, 124, 540–551. [Google Scholar] [CrossRef]
  46. Rossi, A.R.; Capula, M.; Crosetti, D.; Sola, L.; Campton, D.E. Allozyme variation in global populations of striped mullet, Mugil cephalus (Pisces: Mugilidae). Mar. Biol. 1998, 131, 203–212. [Google Scholar] [CrossRef]
  47. Campton, D.E.; Mahmoudi, B. Allozyme variation and population structure of striped mullet (Mugil cephalus) in Florida. Copeia 1991, 1991, 485–492. [Google Scholar] [CrossRef]
  48. Sugama, K.; Benzie, J.A.H.; Bailment, E. Genetic variation and population structure of the giant tiger prawn, Penaeus monodon, in Indonesia. Aquaculture 2002, 205, 37–48. [Google Scholar] [CrossRef]
  49. Mamuris, Z.; Apostolidis, A.P.; Triantaphyllidis, C. Genetic protein variation in red mullet (Mullus barbatus) and striped red mullet (M. surmuletus) populations from the Mediterranean Sea. Mar. Biol. 1998, 130, 353–360. [Google Scholar] [CrossRef]
  50. Lan, K.W.; Zhang, C.I.; Kang, H.J.; Wu, L.J.; Lian, L.J. Impact of fishing exploitation and climate change on the grey mullet Mugil cephalus stock in the Taiwan Strait. Mar. Coast Fish 2017, 9, 271–280. [Google Scholar] [CrossRef]
  51. Lee, S.C.; Cheng, H.L.; Chang, J.T. Allozyme variation in the large-scale mullet Liza macrolepis (Periformes: Mugilidae) from coastal water of western Taiwan. Zool. Stud. 1996, 35, 85–92. [Google Scholar]
  52. Sun, P.; Tang, B.J. Low mtDNA variation and shallow population structure of the Chinese pomfret Pampus chinensis along the China coast. J. Fish Biol. 2018, 92, 214–228. [Google Scholar] [CrossRef]
  53. Bae, S.E.; Kim, J.K.; Li, C. A new perspective on biogeographic barrier in the flathead grey mullet (Pisces: Mugilidae) from the northwest Pacific. Genes Genom. 2020, 42, 791–803. [Google Scholar] [CrossRef]
  54. Yin, W.; Fu, C.; Guo, L.; He, Q.; Li, J.; Jin, B.; Wu, Q.; Li, B. Species delimitation and historical biogeography in the genus Helice (Brachyura: Varunidae) in the Northwestern Pacific. Zool. Sci. 2009, 26, 467–475. [Google Scholar] [CrossRef]
  55. Gu, S.; Yan, Y.R.; Yi, M.R.; Luo, Z.S.; Wen, H.; Jiang, C.P.; Lin, H.D.; He, X.B. Genetic pattern and demographic history of cutlassfish (Trichiurus nanhaiensis) in South China Sea by the influence of Pleistocene climatic oscillations. Sci. Rep. 2022, 12, 14716. [Google Scholar] [CrossRef] [PubMed]
  56. Endler, J.A. Gene flow and population differentiation. Science 1973, 179, 243–250. [Google Scholar] [CrossRef] [PubMed]
  57. Slatkin, M. Gene flow and geographic structure of natural populations. Science 1987, 236, 787–792. [Google Scholar] [CrossRef] [PubMed]
  58. Riddoch, B.J. The adaptive significance of electrophoretic mobility in phosphoglucose isomerase (PGI). Biol. J. Linn. Soc. 1993, 50, 1–17. [Google Scholar] [CrossRef]
  59. Katz, L.A.; Harrison, R.G. Balancing selection on electrophoretic variation of phosphoglucose isomerase in two species of field cricket: Gryllus veletis and G. pennsylvanicus. Genetics 1997, 147, 609–621. [Google Scholar] [CrossRef] [PubMed]
  60. Hoffmann, R.J. Evolutionary genetics of Metridium senile. I. Kinetic differences in phosphoglucose isomerase allozymes. Biochem. Genet. 1981, 19, 129–144. [Google Scholar] [CrossRef] [PubMed]
  61. Johannesson, K.; Kautsky, N.; Tedcngren, M. Genotypic and phenotypic differences between Baltic and North Sea populations of Mytilus edulis evaluated through reciprocal transplantations II. Genetic variation. Mar. Ecol. Prog. Ser. 1990, 59, 211–220. [Google Scholar] [CrossRef]
  62. Mitton, J.B. Selection in Natural Populations; Oxford University Press: New York, NY, USA, 1997. [Google Scholar]
  63. Dahlhoff, E.P.; Rank, N.E. Functional and physiological consequences of genetic variation at phosphoglucose isomerase: Heat shock protein expression is related to enzyme genotype in a montane beetle. Proc. Natl. Acad. Sci. USA 2000, 97, 10056–10061. [Google Scholar] [CrossRef]
  64. Watt, W.B. Adaptation at specific loci. II. Demographic and biochemical elements in the maintenance of the Colias Pgi polymorphism. Genetics 1983, 103, 691–724. [Google Scholar] [CrossRef] [PubMed]
  65. Wojnicka-Półtorak, A.; Celiński, K.; Chudzińska, E. Genetic differentiation between generations of Pinus sylvestris natural population: A case study from the last European primeval forest. Aust. J. Forest. Sci. 2017, 134, 261–280. [Google Scholar]
  66. Deli, T.; Guizeni, S.; Ben, A.L.; Said, K.; Chatti, N. Chaotic genetic patchiness in the pelagic teleost fish Sardina pilchardus across the Siculo-Tunisian Strait. Mar. Biol. Res. 2020, 16, 280–298. [Google Scholar] [CrossRef]
  67. Hedrick, P.W.; Cockerham, C.C. Partial inbreeding: Equilibrium heterozygosity and the heterozygosity paradox. Evolution 1986, 40, 856–861. [Google Scholar] [CrossRef] [PubMed]
  68. Gillespie, J.H. A general model to account for enzyme variation in natural population. II. Characterization of the fitness function. Am. Nat. 1976, 110, 809–821. [Google Scholar] [CrossRef]
  69. Maynard Smith, J. Evolutionary Genetics; Oxford University Press: Oxford, UK; New York, NY, USA, 1998. [Google Scholar]
  70. Lewontin, R.C. The Basis of Evolutionary Change; Columbia University Press: New York, NY, USA, 1974. [Google Scholar]
  71. Balakirev, E.S.; Balakirev, E.I.; Rodrguez-Trelles, F.; Ayala, F.J. Molecular evolution of two linked genes, Est-6 and Sod, in Drosophila melanogaster. Genetics 1999, 153, 1357–1369. [Google Scholar] [CrossRef]
  72. Ayala, F.J.; Balakirev, E.S.; Sdez, A.G. Genetic polymorphism at two linked loci, Sod and Est-6, in Drosophila melanogaster. Gene 2002, 300, 19–29. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Map showing the 18 sampling localities of Mugil cephalus. Collection sites (circles) correspond to locations given in the text and Table 1. The map showing the 18 sampling localities of Mugil cephalus © 2022 by Hung-Du Lin is licenced under Attribution 4.0 International. The map was created using the Microsoft Paint app in Windows 10.
Figure 1. Map showing the 18 sampling localities of Mugil cephalus. Collection sites (circles) correspond to locations given in the text and Table 1. The map showing the 18 sampling localities of Mugil cephalus © 2022 by Hung-Du Lin is licenced under Attribution 4.0 International. The map was created using the Microsoft Paint app in Windows 10.
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Figure 2. (a) UPGMA dendrogram of pooled Mugil cephalus collected among 15 localities based on Cavalli-Sforza and Edward’s chord distance. AP, Anping; CD, Chiding; WC, Wuchi, TC, Tachen; DS, Dashi; HL, Hualien; KP, Kaoping estuary; KS, Kaohsiung; MS, Matsu; SH, Shanghai; NA, Nagasaki (Japan); PM, Peimen; TD, Tadu; TP, Tapong; TS, Tamshui. (b) The NJ tree constructed with Cavalli-Storza and Edward’s chord distance models. The GPI-A 100/100 and GPI-A 135/135 genotypes were independently counted on the inshore samples yielded. The letter R behind the localities indicates inshore samples. (c) A new NJ tree constructed using Cavalli-Sforza and Edward’s chord distance models when the entire GPI-A locus is removed.
Figure 2. (a) UPGMA dendrogram of pooled Mugil cephalus collected among 15 localities based on Cavalli-Sforza and Edward’s chord distance. AP, Anping; CD, Chiding; WC, Wuchi, TC, Tachen; DS, Dashi; HL, Hualien; KP, Kaoping estuary; KS, Kaohsiung; MS, Matsu; SH, Shanghai; NA, Nagasaki (Japan); PM, Peimen; TD, Tadu; TP, Tapong; TS, Tamshui. (b) The NJ tree constructed with Cavalli-Storza and Edward’s chord distance models. The GPI-A 100/100 and GPI-A 135/135 genotypes were independently counted on the inshore samples yielded. The letter R behind the localities indicates inshore samples. (c) A new NJ tree constructed using Cavalli-Sforza and Edward’s chord distance models when the entire GPI-A locus is removed.
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Figure 3. Principal component analysis (PCoA) of the Mugil cephalus populations in Japan, mainland China, and Taiwan based on the allozyme dataset. For each axis, eigenvalues, variance (% variance), and cumulative variance (% cum variance) are provided. The letter J behind the localities represents juveniles. NA: Nagasaki population in Japan.
Figure 3. Principal component analysis (PCoA) of the Mugil cephalus populations in Japan, mainland China, and Taiwan based on the allozyme dataset. For each axis, eigenvalues, variance (% variance), and cumulative variance (% cum variance) are provided. The letter J behind the localities represents juveniles. NA: Nagasaki population in Japan.
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Figure 4. (a) Clustering of individuals using Structure at K = 3 (with all loci included). (b) Clustering of individuals using Structure at K = 4 (with the GPI-A locus removed). Individuals are represented by vertical bars, with each colour representing one cluster and the length of the coloured segment indicating the individual’s estimated degree of kinship to that cluster (Y-axis). The different colours correspond to the population designations given at the bottom of the figure (X-axis). Populations are separated by black bars, and abbreviation are defined in Table 1.
Figure 4. (a) Clustering of individuals using Structure at K = 3 (with all loci included). (b) Clustering of individuals using Structure at K = 4 (with the GPI-A locus removed). Individuals are represented by vertical bars, with each colour representing one cluster and the length of the coloured segment indicating the individual’s estimated degree of kinship to that cluster (Y-axis). The different colours correspond to the population designations given at the bottom of the figure (X-axis). Populations are separated by black bars, and abbreviation are defined in Table 1.
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Figure 5. Test for neutrality using LOSITAN based on 13 allozyme loci. Observed value of heterozygosity vs. FST at each locus (black dots) and 95% confidence envelope expected under neutrality.
Figure 5. Test for neutrality using LOSITAN based on 13 allozyme loci. Observed value of heterozygosity vs. FST at each locus (black dots) and 95% confidence envelope expected under neutrality.
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Figure 6. Frequencies of GPI-A 100/100 and PGDH 100/100 genotypes at different latitudes.
Figure 6. Frequencies of GPI-A 100/100 and PGDH 100/100 genotypes at different latitudes.
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Figure 7. Genotype frequency distribution of GPI-A (A) and PGDH (B) over the grey mullet life history.
Figure 7. Genotype frequency distribution of GPI-A (A) and PGDH (B) over the grey mullet life history.
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Figure 8. Gonad maturity stages in relation to body sizes and ages of Tapong grey mullets classified into GPI-A 100/100 (A), GPI-A 100/135 (B), and GP1-A 135/135 (C) groups. Gonad stages: ●, I; ○, II; △, III; ▲, IV; ×, V. Note: The x-axis represents a period (monthly), and the y-axis shows body size (cm). 0+, 1+, 2+, 3+, 4+, 5+ represents the age.
Figure 8. Gonad maturity stages in relation to body sizes and ages of Tapong grey mullets classified into GPI-A 100/100 (A), GPI-A 100/135 (B), and GP1-A 135/135 (C) groups. Gonad stages: ●, I; ○, II; △, III; ▲, IV; ×, V. Note: The x-axis represents a period (monthly), and the y-axis shows body size (cm). 0+, 1+, 2+, 3+, 4+, 5+ represents the age.
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Table 1. Genetic diversity measures and the exact test for Hardy–Weinberg equilibrium in grey mullet Mugil cephalus samples, including some juveniles.
Table 1. Genetic diversity measures and the exact test for Hardy–Weinberg equilibrium in grey mullet Mugil cephalus samples, including some juveniles.
PopulationAbbreviationN aA bAR cHo dHe eFis f
Adult
NagasakiNA412.5001.2590.1640.1980.174
ShanghaiSH222.2861.3820.0580.0930.383
TachenTC92.0001.2310.1110.1110.000
MatsuMS482.1671.1250.0310.031−0.007
Dashi DS502.0001.2010.1320.1430.083
TanshuiTS1872.2501.2140.0550.0920.402
WuchiWC252.0001.1190.0660.065−0.017
TaduTD422.2001.1860.0800.1320.390
PeimenPM732.1671.2470.1000.1310.235
AnpingAP312.0001.1410.0530.0920.423
ChidingCD202.0001.0350.0500.0500.000
KaoshiungKS1312.5001.1230.0170.0560.698
KaopingKP443.0001.2070.2150.4420.515
TapongTP5402.3331.2830.0490.0830.407
HualienHL282.0001.1550.1420.2150.340
Juvenile
FulungFLJ4262.8001.1590.0590.1270.532
TanshuiTSJ6213.4001.1530.0760.1210.367
LinbienLBJ4233.0001.1600.0990.1290.231
Mean 2.3671.1880.0860.128
a Number of specimens; b mean number of alleles; c mean allelic richness with standard deviation; d mean observed heterozygosity with standard deviation; e mean expected heterozygosity with standard deviation; f inbreeding coefficient.
Table 2. Summary of F-statistics (FIS, FIT, and FST) at 13 polymorphic loci for 18 populations of Mugil cephalus. The estimation was carried out according to Weir and Cockerham (1984).
Table 2. Summary of F-statistics (FIS, FIT, and FST) at 13 polymorphic loci for 18 populations of Mugil cephalus. The estimation was carried out according to Weir and Cockerham (1984).
LocusNa aAR bHO cHE dFISFITFST
mAAT31.1530.0070.0280.6430.6520.025
CK-A21.1020.0240.023−0.031−0.0020.029
GPI-A52.4150.1850.2960.4540.5700.214
GPI-B41.4810.0310.031−0.029−0.0180.011
IDH-A21.0070.0010.001−0.0020.0000.003
IDH-B21.0190.0080.008−0.0160.0020.017
LDH-A41.0230.0130.013−0.0210.0030.023
LDH-B21.0320.0040.004−0.005−0.0000.004
MDH-A21.0030.0000.0000.002−0.000−0.003
MPI41.090.0260.025−0.016−0.0010.015
PGM-A41.0550.0030.003−0.002−0.002−0.000
PGM-B21.0260.0010.0020.2460.2500.004
PGDH31.840.0700.1230.4400.5820.253
Jackknifing over loci
Total 0.452 ± 0.1240.578 ± 0.1300.218 ± 0.044
Bootstrapping over loci (95% confidence interval)
−0.008 ± 0.4470.007 ± 0.5640.012 ± 0.223
Bootstrapping over loci (99% confidence interval)
−0.024 ± 0.453−0.012 ± 0.5690.011 ± 0.233
a: Number of alleles; b: Allelic richness; c: Observed heterozygosity; d: Expected heterozygosity.
Table 3. Analysis of molecular variance (AMOVA) for 15 Mugil cephalus populations based on allozyme loci.
Table 3. Analysis of molecular variance (AMOVA) for 15 Mugil cephalus populations based on allozyme loci.
Scheme Category Description% Var.Statisticp
One geographical group
Among populations within groups19.30FST = 0.1930.000
Within populations80.70
Scenario I: Three geographical groups (Taiwan, Japan, and mainland China)
Among groups27.20FSC = 0.1630.000
Among populations within groups11.90FST = 0.3900.000
Within populations60.91FCT = 0.2710.009
Scenario II: Three geographical groups (NA); (WC, CD, KS, AP, SH, TC, and MS); (TS, DS, TD, HL, KP, PM, TP, TSJ, FLJ, and LBJ)
Among groups34.71FSC = 0.0860.000
Among populations within groups5.66FST = 0.4030.000
Within populations59.64FCT = 0.3470.000
Scenario III: Four ecological groups (NA); (WC, CD, KS, AP, SH, TC, and MS); (TS, DS, TD, HL, KP, PM, and TP); (TSJ, FLJ, and LBJ)
Among groups19.85FSC = 0.0560.000
Among populations within groups4.53FST = 0.2430.000
Within populations75.62FCT = 0.1980.000
Scenario IV: Three geographical groups (Taiwan vs. other populations)
Among groups18.70FSC = 0.2430.000
Among populations within groups19.77FST = 0.3840.000
Within populations61.53FCT = 0.1860.000
Scenario V: Residential and migratory, among 22 samples when GPI 100/100 and GPI 135/135 are treated separately
Among groups48.75FSC = 0.1700.000
Among populations in group8.75FST = 0.5750.000
Within population42.50FCT = 0.4870.000
Scenario VI: Taiwan, Japan, and mainland China groups when GPI loci are removed
Among groups1.34FSC = 0.1160.000
Among populations in group11.52FST = 0.1280.000
Within population87.15FCT = 0.0130.000
Scenario VII: Taiwan and Japan migratory groups
Among groups3.18FSC = 0.1320.000
Among populations in group12.86FST = 0.1600.000
Within population83.96FCT = 0.0310.000
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Kuo, C.-H.; Lee, S.-C.; Du, S.-Y.; Huang, C.-S.; Lin, H.-D. Variation in the Local Grey Mullet Populations (Mugil cephalus) on the Western Pacific Fringe. Genes 2024, 15, 1280. https://doi.org/10.3390/genes15101280

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Kuo C-H, Lee S-C, Du S-Y, Huang C-S, Lin H-D. Variation in the Local Grey Mullet Populations (Mugil cephalus) on the Western Pacific Fringe. Genes. 2024; 15(10):1280. https://doi.org/10.3390/genes15101280

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Kuo, Chien-Hsien, Sin-Che Lee, Shin-Yi Du, Chao-Shen Huang, and Hung-Du Lin. 2024. "Variation in the Local Grey Mullet Populations (Mugil cephalus) on the Western Pacific Fringe" Genes 15, no. 10: 1280. https://doi.org/10.3390/genes15101280

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