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Article

Phylogeographic Pattern and Genetic Structure of the Cyprinid Fish Microphysogobio kachekensis (Oshima 1926) in Mainland China and Hainan Island Based on Mitochondrial and Nuclear DNA

by
Jin-Quan Yang
1,
Jiabo Chen
2,
Junjie Wang
2,
Tian-Qi Zhou
1,
Yuh-Wen Chiu
3,
Hung-Du Lin
4 and
Wen-Sheng Ou
5,*
1
Shanghai Universities Key Laboratory of Marine Animal Taxonomy and Evolution, Shanghai Ocean University, Shanghai 201306, China
2
Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, School of Life Science, South China Normal University, Guangzhou 510631, China
3
Department of Biological Resources, National Chiayi University, 300 University Road, Chiayi 600355, Taiwan
4
The Affiliated School of National Tainan First Senior High School, Tainan 70101, Taiwan
5
Department of Landscape Architecture, National Chin-Yi University of Technology, Taichung 411030, Taiwan
*
Author to whom correspondence should be addressed.
Fishes 2026, 11(2), 122; https://doi.org/10.3390/fishes11020122
Submission received: 11 January 2026 / Revised: 13 February 2026 / Accepted: 14 February 2026 / Published: 19 February 2026
(This article belongs to the Section Taxonomy, Evolution, and Biogeography)
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Abstract

South China’s freshwater biodiversity has been shaped by Quaternary climatic oscillations and persistent geological barriers. We investigated the phylogeography and conservation implications of the primary freshwater fish Microphysogobio kachekensis across mainland China and Hainan Island using mitochondrial (cyt b and control region) and nuclear (RAG2 and rpS7-1) markers from 200 individuals. Mitochondrial analyses recovered two major lineages and multiple sublineages largely structured by drainage basins, whereas nuclear data resolved four geographically concordant lineages. Population differentiation was strong (high FST), and SAMOVA/AMOVA supported major barriers restricting gene flow, including the Qiongzhou Strait, Gulf of Tonkin, Yunkai Mountains, and Nanling Mountains. Ancestral-area reconstruction inferred the Pearl River region as the most likely source area, followed by dispersal to northern Hainan and subsequent expansion to southern Hainan and the Red River, with additional northward expansion to the Zhejiang–Fujian region. Despite high haplotype diversity, within-population nucleotide diversity was low, consistent with long-term river isolation and complex demographic history. We propose six ESUs and four MUs for evolutionarily informed conservation and to guide stock enhancement in southern China.
Keywords:
DIY-ABC; Hainan Peninsular; Microphysogobio kachekensis; phylogeography
Key Contribution: This study delineates evolutionarily significant units and management units of Microphysogobio kachekensis, providing a scientific basis for conservation prioritization and stock enhancement in southern China.

1. Introduction

The rich freshwater biodiversity of South China has been profoundly shaped by its complex climatic and geological history, characterized by repeated palaeoclimatic fluctuations and sea-level oscillations that alternately connected and isolated landmasses. Because the dispersal of primary freshwater fishes is tightly constrained by drainage connectivity, their present-day distributions often retain clear imprints of historical geological events and regional zoogeographical boundaries [1].
Traditionally, the ichthyofauna of South China has been divided solely on the basis of faunal similarity, without explicit consideration of phylogenetic relationships. Under this classical zoogeographical framework, freshwater fishes were classified into five subregions: (I) Taiwan Island, (II) Zhejiang–Fujian, (III) Nujiang–Lancangjiang, (IV) Pearl River, and (V) Hainan Island [2]. In this scheme, Hainan Island is regarded as an independent zoogeographical unit within South China. Located off the southern coast of mainland China, Hainan Island is separated from the mainland to the north by the Qiongzhou Strait and from northern Vietnam to the west by the Gulf of Tonkin. The island’s heterogeneous topography, dominated by the Wuzhishan–Yinggeling Mountain Range (WY Range) rising to nearly 1800 m, provides an exceptional natural setting for investigating phylogeographic patterns shaped by geological isolation and river-basin fragmentation [3].
Despite the recognition of the Qiongzhou Strait as a major zoogeographical boundary between the Pearl River region and Hainan Island, several freshwater fish species—such as Glyptothorax hainanensis and Opsariichthys hainanensis—have been reported from both Hainan Island and mainland China, suggesting that this barrier has not uniformly restricted dispersal across taxa [3,4]. During Pleistocene glacial periods, lowered sea levels exposed the Gulf of Tonkin and large portions of the northern South China Sea, forming continuous landmasses that intermittently connected Hainan Island with mainland China and northern Vietnam [5]. These geological processes, together with uplift of the continental shelf, facilitated temporary reconnection of river systems in coastal lowlands and estuaries, thereby enabling episodic dispersal of primary freshwater fishes across otherwise isolated drainages. Among these regions, the Pearl River system has been repeatedly implicated as a major refugium and dispersal corridor for freshwater fishes during Quaternary climatic oscillations, suggesting a potentially central role in shaping regional phylogeographic patterns [6].
Previous phylogeographic studies of freshwater fishes on Hainan Island have identified three major regional assemblages corresponding to river systems draining into the Qiongzhou Strait, the South China Sea, and the Gulf of Tonkin—namely, the Nandu River region, the Wanquan–Linshui River region, and the Changhua River region [3,7,8,9,10]. These findings indicate that patterns of genetic structure and dispersal in Hainan’s freshwater fishes must be interpreted within the island’s complex geological history and drainage evolution. Collectively, available evidence suggests that the freshwater fauna of Hainan Island likely originated from northern (mainland China) and/or western (Vietnam) source regions [5], although the relative importance of these colonization routes remains unresolved for many taxa.
In recent decades, intensifying anthropogenic disturbances—including habitat destruction, pollution, and river regulation—have led to severe declines in freshwater fish diversity across China, highlighting the urgent need for effective conservation strategies targeting both native ichthyofauna and their freshwater ecosystems [11,12]. Stock enhancement has frequently been adopted as a management response to population declines; however, such practices often overlook historical genetic structure, thereby promoting secondary contact among previously isolated or deeply divergent lineages [13]. Given limited biodiversity conservation resources, it is critically important to prioritize funding toward intraspecific groups—distinct in ecological roles, genetic makeup, or evolutionary history—that warrant protection as Conservation Units (CUs). At the population level, conservation planning commonly employs two complementary frameworks: Evolutionarily Significant Units (ESUs), which emphasize long-term evolutionary independence often inferred from reciprocal monophyly or deep genetic divergence [13], and Management Units (MUs), which reflect demographically independent populations with limited contemporary gene flow [13]. Because primary freshwater fishes typically exhibit limited dispersal—particularly during early life stages—they are especially vulnerable to population fragmentation driven by drainage isolation, making the preservation of local populations critical for maintaining species-level biodiversity [14].
The genus Microphysogobio comprises small, benthic gudgeons widely distributed in East Asia, ranging from the Amur basin in eastern Russia through Korea, northern and southern China (including Hainan Island), Taiwan, Mongolia, Laos, and Vietnam. Most species inhabit upper to middle river reaches characterized by moderate to fast currents over gravel or rocky substrates. Microphysogobio kachekensis (Ōshima, 1926)—the senior taxon to which M. labeoides (Nichols & Pope, 1927), originally described from Hainan Island, is now regarded as a junior synonym—occurs on Hainan Island, throughout southern mainland China, and in northern Vietnam [15]. Owing to its broad distribution across major zoogeographical regions and its confinement to freshwater habitats, M. kachekensis represents an ideal model for examining the evolutionary and biogeographic consequences of geological processes in South China.
To reconstruct the phylogeographic history of M. kachekensis, we employed an integrative molecular approach using both mitochondrial and nuclear markers. Specifically, we analyzed mitochondrial cytochrome b (cyt b) and control-region sequences, together with nuclear sequences from the first intron of the ribosomal protein S7 gene (rpS7-1) and the second intron of the recombination-activating gene (RAG2). Mitochondrial markers have been extensively applied in phylogeographic studies of freshwater fishes, while rpS7-1 and RAG2 have increasingly been adopted as informative nuclear loci for resolving phylogenetic relationships and population structure in fishes. Specifically, this study addresses four key questions: (1) What is the extent of geographic genetic structure among populations of M. kachekensis? (2) How did M. kachekensis colonize river systems across distinct zoogeographical regions? (3) Do genetically distinct lineages occur within Hainan Island? (4) Is there evidence of major phylogeographic discontinuities associated with geological barriers among freshwater fishes in South China?

2. Materials and Methods

2.1. Population Sampling and Molecular Methods

A total of 202 M. kachekensis specimens were collected from nine localities across Hainan Island and mainland China between 2010 and 2015, including Huaan (HA), Luhe (LH), Heyuan (HY), Yangchun (YM), Basha (BS), Qionghai (QH), Wuzhi (WZ), Ledong (LD), and the Red River (HH) (Table 1; Figure 1). Based on major geo-historical events and regional ichthyofaunal divisions, these populations were assigned to four ichthyofaunal regions sensu Li [2]. Fish were captured using seines at each sampling site and humanely euthanized with MS-222 (Sigma-Aldrich, St. Louis, MO, USA). Specimens were subsequently fixed and preserved in 95% ethanol. All voucher specimens are deposited in the Shanghai Universities Key Laboratory of Marine Animal Taxonomy and Evolution under the care of Professor Jin-Quan Yang.
Genomic DNA was extracted from muscle tissue using a Genomic DNA Purification Kit (Gentra Systems, Valencia, CA, USA). Two mitochondrial DNA markers (cyt b and the control region) and two nuclear DNA markers (rpS7-1 and RAG2) were amplified using polymerase chain reaction (PCR). Primers for cyt b, the control region, and rpS7-1 were adopted from Xiao, Zhang & Liu [16], Zhou et al. [7], and Chow & Hazama [17], respectively. Primers for RAG2 were newly designed in this study: BF (5′-CAACTCCTGTTATCTCCCTC-3′) and BR (5′-ATTCATCCTCCTCATCTTCC-3′). Each 25 μL PCR contained 2.5 ng template DNA, 1 μL of each primer (10 nM), 9.5 μL ddH2O, and 12.5 μL of 2× Taq PCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). Amplifications were performed on an thermal cycler (Eppendorf, Hamburg, Germany) under the following conditions: an initial denaturation at 94 °C for 5 min; 35 cycles of denaturation at 94 °C for 1 min, annealing at 50–55 °C for 45 s, and extension at 72 °C for 1 min 30 s; followed by a final extension at 72 °C for 10 min and storage at 4 °C. PCR products were sequenced on an ABI 377 automated sequencer (Applied Biosystems, Foster City, CA, USA). Chromatograms were inspected using CHROMAS (Technelysium Pty Ltd., Brisbane, Australia), and sequences were manually edited in BIOEDIT v6.0.7 [18].

2.2. Sequence Alignment and Phylogenetic Inference

Allele phasing for nuclear loci was performed using PHASE v2.1.1, a Bayesian statistical method optimized for reconstructing haplotypes from diploid genotype data. Additional allele-phase reconstruction for RAG2 and rpS7-1 was conducted in DnaSP v6.0 [19] to enable independent allelic analyses prior to phylogenetic reconstruction. Sequences of the two mitochondrial genes (cyt b and control region) and the two nuclear genes (rpS7-1 and RAG2) were aligned using Clustal X v2.1 [20]. Substitution models were selected separately for mitochondrial and nuclear datasets using Smart Model Selection (SMS) in PhyML, with the GTR + Γ + I model selected under the corrected Akaike Information Criterion (AICc). Phylogenetic analyses were conducted using MEGA-X [21] for Neighbor-Joining (NJ), PhyML v3.0 [22] for Maximum Likelihood (ML), and MrBayes v3.1.2 [23] for Bayesian Inference (BI). Divergence times and times to the most recent common ancestor (TMRCA) were estimated in BEAST v1.10 [24] using a Markov chain Monte Carlo (MCMC) analysis of 107 generations, with the first 10% discarded as burn-in, assuming a strict molecular clock. A substitution rate of 5% per million years was applied to the combined mitochondrial cyt b and control region sequences [25]. The posterior tree distribution was summarized using TreeAnnotator v2.2.1 [26], and annotated trees were visualized in FigTree v1.4.4 [27]. The haplotype network was constructed using the TCS network algorithm implemented in PopART v1.7. [28].

2.3. Population Genetic Diversity and Structure

Genetic diversity within populations was quantified as the number of haplotypes (N), haplotype diversity (h) [29], and nucleotide diversity estimated as θπ (current diversity) and θω (historical diversity) following the Jukes–Cantor model [30], all calculated in DnaSP v6.0 [19]. Comparisons between θπ and θω were used to assess shifts in genetic diversity over time [31]. To evaluate phylogeographic structure, GST (based on haplotype frequencies alone) and NST (incorporating both haplotype frequencies and sequence divergence) were compared following Pons & Petit [32], as implemented in DnaSP v6.0. Pairwise FST values and hierarchical analyses of molecular variance (AMOVA) were performed in Arlequin v3.5 [33,34] using Kimura two-parameter (K2P) distances and 20,000 permutations. Genetic variation was partitioned into within-population (FST), among populations within groups (FSC), and among-group (FCT) components. Four hierarchical grouping schemes were tested to evaluate the influence of major geographic barriers: (1) two groups separated by the Qiongzhou Strait (Hainan Island vs. mainland China); (2) four ichthyofaunal regions sensu Li [2]; (3) five groups further subdivided by the Gulf of Tonkin, Shiwandashan Mountains, and Nanling Mountains; and (4) six groups refining the previous scheme by subdividing Hainan Island using the Wuzhi–Yinggeling (WY) Range, in addition to the Qiongzhou Strait, Gulf of Tonkin, Yunkai Mountains, and Nanling Mountains. Spatial analysis of molecular variance (SAMOVA) was conducted in SAMOVA v2.0 [35] using simulated annealing over 500 iterations to identify geographically adjacent population groups that maximize among-group differentiation (FCT). Values of K = 2–9 were tested, and the configuration with the highest FCT was selected as the optimal grouping.

2.4. Historical Demography and Biogeographic Reconstruction

Neutrality tests, including Tajima’s D [36] and Fu’s Fs [37], along with mismatch-distribution analyses, were performed in DnaSP v6.0 to assess departures from demographic equilibrium. Temporal changes in effective population size were inferred using Bayesian Skyline Plot (BSP) analyses implemented in BEAST v1.10 [24], based on two independent MCMC runs of 200 million generations, with the first 10% discarded as burn-in and effective sample sizes (ESS) exceeding 200. BSPs were visualized using Tracer v1.6 [38]. Ancestral area reconstruction was conducted using Bayesian Binary MCMC (BBM) analysis implemented in RASP v3.2 [39,40]. Six biogeographic regions were defined based on sampling localities and species distribution: Pearl River (P: HY, LH), southern Hainan (S: LD, WZ), northern Hainan (N: QH, BS), Zhejiang–Fujian (F: HA), Nujiang–Lancangjiang (L: HH), and Moyang River (Y: YM).

2.5. Approximate Bayesian Computation (ABC) Analyses

Approximate Bayesian computation (ABC) analyses were performed in DIYABC v2.0.4 [41] using mitochondrial cyt b and control-region sequences to investigate alternative demographic histories. Following Cabrera & Palsbøll [42], five model-based demographic scenarios were evaluated: Scenario A (CON), constant population size; Scenario B (DEC), historical bottleneck; Scenario C (INC), recent population expansion; Scenario D (DECINC), ancient bottleneck followed by expansion; and Scenario E (INCDEC), ancient expansion followed by recent decline (Figure 2). Simulations were conducted under the HKY mutation model with default priors. A total of 3,000,000 simulated datasets were generated to construct the reference table using all available summary statistics. Posterior probabilities of scenarios were estimated using a logistic regression approach based on the 1% closest simulated datasets, as implemented in DIYABC.

3. Results

3.1. Genetic Diversity of Microphysogobio Kachekensis

We analyzed a total of 2304 base pairs (bp) from 200 specimens, comprising 1141 bp of mitochondrial cyt b and 1163 bp of the control region, alongside 1886 bp from 95 specimens, including 984 bp of nuclear RAG2 and 902 bp of nuclear rpS7 sequences. The mitochondrial DNA sequences were notably A + T rich (60.0%), comprising 30.7% adenine, 29.3% thymine, 15.0% guanine, and 25.0% cytosine, whereas the nuclear DNA exhibited a moderate A + T bias (54.1%), with 26.5% adenine, 27.6% thymine, 23.3% guanine, and 22.6% cytosine. A similar pattern of A + T enrichment is commonly observed in vertebrate mitochondrial genomes and reflects typical base-composition biases across coding regions. We identified 99 mitochondrial haplotypes across 158 variable sites, of which 128 were phylogenetically informative, and 83 nuclear haplotypes across 251 variable sites, with 182 being phylogenetically informative (Table 1). Among the mtDNA haplotypes, only one (H13) was shared between populations BS and QH on Hainan Island, while three (H18, H21, and H22) were shared by mainland China populations HA and HY (Table S1). In contrast, among the nuDNA haplotypes, only one (H02) was shared between LD and WZ on Hainan Island, and two (H23 and H24) were shared between HA and HY in mainland China (Table S1).
Mitochondrial DNA exhibited high haplotype diversity (Hh = 0.980), ranging from 0.667 in HH to 1.000 in HA, whereas nucleotide diversity (π) was relatively low (0.0147), ranging from 0.001 (HA, LH, HY, YM, HH, BS) to 0.003 (WZ) (Table 1). At the regional scale, Zhejiang–Fujian (HA) showed the highest haplotype diversity (1.000), followed by Hainan Island (0.971) and the Pearl River region (0.940). In nuclear DNA, haplotype diversity was similarly high (Hh = 0.994), ranging from 0.895 (HY) to 1.000 (LH, YM, BS, HH), while nucleotide diversity remained low (0.0155), varying from 0.001 (HA, HY) to 0.021 (BS, WZ). Hainan Island exhibited the highest regional haplotype diversity (0.996), followed by the Pearl River (0.973) and Zhejiang–Fujian (HA) (0.933) (Table 1). Comparisons between current (θπ) and historical (θω) nucleotide diversity indicated that θπ values were generally lower than θω in most populations, reflecting reduced contemporary genetic diversity relative to historical levels. In contrast, the Pearl River region exhibited θπ values exceeding θω, indicating a distinct demographic pattern relative to other regions. When all samples were considered together, overall diversity metrics also reflected this pattern (Table 1).

3.2. Phylogenetic Reconstruction and Genetic Structure

A comparison between NST and GST indices showed that NST was significantly higher than GST (0.875 vs. 0.122), indicating a pronounced phylogeographic structure in M. kachekensis [32]. Phylogenetic analyses based on mtDNA recovered two major lineages—Lineage A (A1–A4) and Lineage B (B1–B3)—corresponding to geographic distributions across populations (Figure 3). Consistent topologies were obtained using Neighbor-Joining (NJ), Maximum Likelihood (ML), and Bayesian Inference (BI) methods, as well as haplotype network analyses. Lineage A1 occurred primarily in LD and WZ populations in southern Hainan Island, with additional individuals in BS and QH. Lineage A2 was largely restricted to BS, with a single individual in WZ. Lineage A3 was exclusive to the HH population in the Nujiang–Lancangjiang region, whereas Lineage A4 was mainly associated with QH, with two individuals also detected in WZ. Lineage B1 was confined to YM, and B2 to LH in the Pearl River region. Lineage B3 occurred in both HA (Zhejiang–Fujian) and HY (Pearl River), suggesting some level of connectivity between these regions. The haplotype network further grouped mtDNA haplotypes into seven major sublineages (A1–A4 and B1–B3), with A2 and A3 occupying interior positions and the remaining sublineages located at network tips (Figure 4). Phylogenetic analyses of nuDNA recovered four well-supported lineages (I–IV). Lineages I and II were restricted to Hainan Island, Lineage III included individuals from both Hainan Island and the Red River, and Lineage III was confined to mainland China (Figure S1). The nuDNA haplotype network also resolved four corresponding sublineages (I–IV). Differences in the number of lineages recovered between mtDNA and nuDNA datasets likely reflect variation in marker resolution. Shared nuDNA haplotypes were observed only between HA and HY, and among QH, WZ, and LD populations (Figure S2).
Overall FST based on mtDNA was 0.886, with pairwise values ranging from 0.054 (between HA and HY) to 0.935 (between LD and LH). In nuDNA, overall FST was 0.404, and pairwise values ranged from −0.045 (between HA and HY) to 0.882 (between HA and HH). Pairwise FST values between populations from different regions were generally high and statistically significant, indicating strong genetic differentiation (Table 2). Hierarchical AMOVA based on mtDNA under the six-region configuration (Scenario IV) showed that 32.65% of the variation was attributable to differences among groups (FCT = 0.326, p = 0.034), 52.67% among populations within groups (FSC = 0.782, p < 0.001), and 14.68% within populations (FST = 0.853, p < 0.001) (Table 3). Alternative grouping schemes produced similarly high among-group variation. For nuDNA, AMOVA under the two-region model (Scenario I) attributed 10.14% of the variation to among-group differences (FCT = 0.101, p = 0.009), 30.36% among populations within groups (FSC = 0.338, p < 0.001), and 59.50% within populations (FST = 0.405, p < 0.001) (Table 3). SAMOVA identified the highest FCT values when populations were partitioned into K = 8 groups for both mtDNA (0.847, p < 0.01) and nuDNA (0.452, p < 0.01), with only HA and HY clustering together, whereas all other populations formed distinct units. This spatial arrangement further underscores the influence of the Qiongzhou Strait, Gulf of Tonkin, Yunkai Mountains, and Nanling Mountains as key geographic barriers restricting gene flow in M. kachekensis (Table 3).

3.3. Historical Demography, Molecular Dating, and Historical Biogeography

Neutrality tests for both clades and the total population yielded positive Tajima’s D values and significantly negative Fu’s Fs values (total population: Tajima’s D = 0.44085, p > 0.10; Fu’s Fs = −15.645, p < 0.001), and mismatch-distribution analyses showed multimodal patterns. Although neutrality tests and mismatch distributions did not support population expansion, Fu’s Fs values were consistently negative. Bayesian skyline plot analyses revealed an increase in effective population size around 20 ka (Figure 5). The estimated time to the most recent common ancestor (TMRCA) of M. kachekensis was 0.209 Myr. Divergence times were estimated at 0.104 Myr for sublineages A1 + A2, 0.124 Myr for A3 + A4, and 0.175 Myr for Lineage B. BBM analyses indicated that the most recent common ancestor of M. kachekensis was distributed in the Pearl River region with an occurrence probability of 100%. Two major dispersal–vicariance events were inferred. A southward dispersal from the Pearl River region to northern Hainan was followed by vicariance associated with the formation of the Qiongzhou Strait, giving rise to Lineage A. A northward dispersal from the Pearl River region to the Zhejiang–Fujian region resulted in Lineage B. Subsequent geological events further structured lineages: uplift of the Wuzhi–Yinggeling Range subdivided Lineage A into A1 and A2, the opening of the Gulf of Tonkin separated Hainan Island and Red River populations (A3 and A4), and uplift of the Lianhua Mountains subdivided Lineage B into B2 and B3 (Figure 6).

3.4. Approximate Bayesian Computation

The ABC analysis was conducted to infer the demographic history of M. kachekensis. Among the five demographic scenarios tested using DIYABC, Scenario A (CON) was strongly supported as the most likely model, exhibiting the highest posterior probability (PP = 0.9552; 95% CI: 0.9084–1.0000). This scenario was clearly favored over Scenario E (INCDEC; PP = 0.0436; 95% CI: 0.0000–1.0000), Scenario B (DEC; PP = 0.0012; 95% CI: 0.0000–1.0000), Scenario C (INC; PP = 0.0000; 95% CI: 0.0000–1.0000), and Scenario D (DECINC; PP = 0.0000; 95% CI: 0.0000–0.0033). Overall, these results indicate that M. kachekensis has experienced a demographic history characterized by a constant population size.

4. Discussion

4.1. High Population Differentiation and Low Genetic Diversity of Microphysogobio Kachekensis

Given that primary freshwater fish are isolated by watershed barriers, genetic diversity has been used for several decades in conservation planning to assess populations’ adaptive potential to environmental change [43]. Generally, freshwater fish populations in southern China are characterized by high haplotype diversity and low nucleotide diversity, such as Pterocryptis cochinchinensis [44], Channa gachua [5] and Barbodes semifasciolatus [10]. Similarly, our analysis of M. kachekensis, using both mitochondrial and nuclear markers, revealed the same pattern: elevated haplotype diversity paired with diminished nucleotide diversity. The overall haplotype diversity across all samples of M. kachekensis was very high (h = 0.980), reflecting substantial genetic variation comparable to other freshwater fishes in Hainan Island and southern mainland China, such as B. semifasciolatus (h = 0.944) [10], P. cochinchinensis (h = 1.000) [44], Garra orientalis (h = 0.981) [45], and O. hainanensis (h = 0.946) [3]. The nucleotide diversity (θπ) of M. kachekensis (0.0147) was similar to that observed in locally co-distributed species—O. hainanensis (0.0149) [3], Aphyocypris normalis (0.0149) [46], and B. semifasciolatus (0.0134) [10]—yet lower than that of C. gachua (0.0318) [5]. When compared to its congeners, Microphysogobio kachekensis also displays relatively high genetic diversity: for example, M. brevirostris has h = 0.847 and θπ = 0.0087 [47], and Microphysogobio alticorpus exhibits h = 0.896 and θπ = 0.0050 [48].
In general, species with broader geographic distributions tend to maintain higher genetic diversity [49,50]. M. kachekensis occurs across both Hainan Island and mainland China, whereas M. brevirostris and M. alticorpus are endemic to Taiwan with narrower distribution ranges and accordingly lower genetic diversity. Furthermore, our study revealed that M. kachekensis exhibits low genetic diversity within individual populations (θπ = 0.001–0.003), likely a consequence of genetic drift resulting from reduced effective population sizes. Paradoxically, at the species-wide scale, genetic diversity is relatively high (θπ = 0.0147). This pattern aligns with findings in other regional freshwater fishes—B. semifasciolatus [10], C. gachua [5], G. orientalis [45] and P. cochinchinensis [44]. As noted in these studies, effective population size reductions in freshwater fishes have been driven not only by overfishing but also by widespread habitat destruction on mainland China [11,51]. Similarly, overexploitation and anthropogenic disturbances are likely the principal factors behind declines in M. kachekensis populations on both Hainan Island and the mainland [11,51]. Collectively, the pattern of high haplotype diversity coupled with low nucleotide diversity in M. kachekensis is consistent with the combined effects of historical population bottlenecks associated with Pleistocene climatic fluctuations, persistent river-system fragmentation, and subsequent demographic recovery in the face of recent anthropogenic pressures.

4.2. Demographic History of Microphysogobio Kachekensis

In our study, neutrality tests (including Tajima’s D) and mismatch-distribution analyses both failed to detect a classic signal of range expansion in M. kachekensis. Nonetheless, Fu’s Fs test produced a signal consistent with demographic expansion—a method often regarded as more sensitive than Tajima’s D in detecting population growth [36]. Moreover, the Bayesian Skyline Plot revealed an increase in effective population size during the Late Pleistocene (~20 ka). Taken together, these results imply that M. kachekensis has endured a complex demographic history rather than a simple, monotonic expansion. During the Pleistocene, glacial–interglacial cycles strongly shaped the demographic trajectories of freshwater fishes on the Chinese mainland. Species such as Rhodeus ocellatus [52], Squalidus argentatus [53], G. orientalis [45], and O. hainanensis [3] each show evidence of population expansions. In contrast, other taxa appear comparatively unaffected by glacial fluctuations and exhibit no signature of demographic growth—for example, C. gachua [5]. Notably, C. gachua is a headwater/rivulet species, whereas—like G. orientalis [45] and O. hainanensis [3]—it inhabits mid- to lower-stream environments. This contrast supports the inference that M. kachekensis has experienced a more intricate expansion history.
Furthermore, on more recent timescales our ABC (Approximate Bayesian Computation) analysis compared five alternative demographic scenarios, and the best-supported model indicates that M. kachekensis underwent a constant population size through time. This is a very interesting result, because previous studies in this region have tended to document an ancient population contraction followed by a more recent expansion in freshwater fishes of southern mainland China and Hainan Island (e.g., O. hainanensis [9]; B. semifasciolatus [10]). In light of past research showing that demographic histories of freshwater species in this region were strongly impacted by sea-level fluctuations and land–sea dynamics during Late Pleistocene glacial cycles, we propose that M. kachekensis—as a large river-system inhabitant, differing from the tributary-type species studied previously—may have been less influenced by habitat changes and thus maintained a comparatively stable population size. The demographic patterns inferred from the Bayesian Skyline Plot and the ABC analysis should be interpreted as complementary rather than contradictory. These methods differ in their temporal sensitivity and underlying assumptions, with the Bayesian Skyline Plot reflecting coalescent-based changes in effective population size and ABC comparing alternative demographic scenarios across longer timescales. As such, the combined results are consistent with a complex demographic history in M. kachekensis that cannot be adequately described by a single expansion or contraction model.

4.3. Population Differentiation and Geographic Barriers

The population analysis of M. kachekensis revealed a strong phylogeographic structure, evidenced by a significant correlation between phylogeny and geography (NST = 0.875; GST = 0.122). M. kachekensis exhibited a high degree of genetic differentiation (FST = 0.886). The majority of haplotypes were confined to individual river systems, with only one haplotype shared between the BS and QH river basins, and three other haplotypes found in both HA and HY (Figure 3). This pronounced genetic structuring is attributable to the distinct haplotype compositions observed across different populations and regions. Freshwater fish species inhabiting upstream sections of rivers typically exhibit higher levels of genetic differentiation compared to downstream populations, due to prolonged effects of spatial isolation and genetic drift [54]. Our study revealed that genetic differentiation is relatively high compared to other sympatric freshwater species, regardless of whether they inhabit upstream regions. For example, FST values are 0.801 for Glyptothorax hainanensis [4], 0.750 for O. lepturum [7], 0.876 for C. gachua [5], and 0.936 for P. cochinchinensis [44]. In contrast, downstream species in this area show markedly lower population differentiation—for instance, A. normalis (FST = 0.530) [46] and Carassioides cantonensis (FST = 0.036) [55]. These findings suggest that geographic barriers exert a stronger influence on upstream species than on their downstream counterparts [5]. When compared to congeneric species in Taiwan, both M. alticorpus (FST = 0.876) [48] and M. brevirostris (FST = 0.956) [47] exhibit high levels of population differentiation. This highlights the profound impact of river system isolation on the genetic structure of Microphysogobio species, likely due to their specific habitat preferences.
According to the SAMOVA results, the vast majority of genetic variation (FCT = 0.843) in M. kachekensis was explained by differences among the eight geographic regions, whereas only 0.44% of the variation occurred among populations within groups. Consistent with this pattern, the AMOVA results indicate that the Qiongzhou Strait, Gulf of Tonkin, Yunkai Mountains, and Nanling Mountains serve as major geographic barriers limiting gene flow in M. kachekensis. The pronounced genetic differentiation observed among river basins contrasts sharply with patterns reported for other freshwater fishes inhabiting the same region. For example, the Gulf of Tonkin does not constitute a barrier to dispersal in C. gachua [5], and the Qiongzhou Strait is not a barrier for A. normalis [46]. Taken together, the consistently high levels of genetic differentiation revealed by FST, SAMOVA, and AMOVA analyses indicate that population structure in M. kachekensis is shaped by geographic barriers operating over long evolutionary timescales. These barriers—such as major mountain systems and marine straits—not only restrict contemporary gene flow but have also imposed persistent historical isolation among river basins, resulting in deeply structured genetic patterns across both spatial and temporal scales.

4.4. Phylogeography of Microphysogobio Kachekensis

In our study, we focused on inferring the historical phylogeography of M. kachekensis in mainland China and Hainan Island. Freshwater fishes serve as living archives of past landscape transformations because their dispersal is constrained by the connectivity of river-drainage networks [56]. Phylogenetic analyses based on mtDNA revealed two major lineages: using the Qiongzhou Strait as a boundary, Lineage A is primarily distributed on Hainan Island and in the Red River, whereas Lineage B occurs on mainland China. Consistent with this pattern, the nuDNA phylogenetic tree also recovered a comparable geographical break, with Lineages I, II, and III distributed on Hainan Island and in the Red River, and Lineage IV restricted to mainland China. This pattern contrasts with previous studies of many freshwater fishes occurring on both Hainan Island and mainland China, in which the Qiongzhou Strait did not constitute a major geographic barrier (e.g., G. orientalis [45], A. normalis [46], and P. cochinchinensis [44]). We propose that this difference is linked to the rapid lineage-sorting undergone by M. kachekensis [57]. Interestingly, Lineage A encompasses populations from both Hainan Island and the Red River. Although the Red River population forms a monophyletic group, this pattern indicates a closer phylogenetic relationship between the Hainan and Red River freshwater-fish faunas. This finding is consistent with previous studies on freshwater fishes such as C. gachua [5] and B. semifasciolatus [10]. Lineage B is distributed across mainland China: the YM population (south of the Yunkai Mountains) and the LH population (east of the Lianhua Mountains) each form distinct monophyletic clades, highlighting the barrier effect of these two mountain systems. This result is in agreement with findings for P. cochinchinensis [44]. Meanwhile, the HY population in the Pearl River system and the HA population in the Jiulong River system together form a unique monophyletic group. We propose that, because these two river systems belong to large drainage networks and likely maintain larger effective population sizes, lineage sorting in these populations remains incomplete.
Owing to the complex geohistorical history of the region, ancestral area reconstruction was performed using Bayesian Binary MCMC (BBM) analysis implemented in RASP v3.2 [39]. The BBM results indicate that the Pearl River region represents the most likely ancestral range of M. kachekensis. The estimated divergence time of M. kachekensis (~0.209 Ma, Pleistocene) suggests that ancestral populations initially dispersed from the Pearl River region to northern Hainan Island, and subsequently expanded southwards to southern Hainan Island and the Red River.
This dispersal pattern contrasts with many previous studies on freshwater fishes in Hainan Island, such as B. semifasciolatus [10], O. hainanensis [3,58], and C. gachua [5], which revealed northward dispersal from southern Hainan Island. We propose that this discrepancy is likely attributable to differences in biogeographic origins: M. kachekensis appears to have originated in the north and expanded southward, whereas B. semifasciolatus, O. hainanensis, and C. gachua originated in southern Hainan and dispersed northwards. A second dispersal route of M. kachekensis was inferred from the Pearl River drainage northward to the Zhejiang–Fujian region. Quaternary climatic oscillations—particularly recurrent glacial–interglacial cycles—profoundly shaped the spatial distribution and genetic structure of freshwater fishes [6,59,60]. Northward dispersal from the paleo-Pearl River system has also been documented in numerous taxa, such as Hemibagrus guttatus [6] and B. semifasciolatus [10]. Collectively, the reconstructed dispersal routes of M. kachekensis—including southward expansion to Hainan Island and the Red River, as well as northward dispersal from the Pearl River system to the Zhejiang–Fujian region—highlight the pivotal role of large river systems as both sources and corridors for freshwater fish diversification in southern China. These pathways, operating in concert with Quaternary climatic oscillations and regional geomorphic barriers, have jointly shaped the present-day phylogeographic structure of the species.

4.5. Implications for Conservation

In recent years, freshwater fishes in southern China have experienced severe survival pressures due to environmental pollution and increasing anthropogenic disturbances [11]. Consequently, species conservation in this region has become an urgent priority. Evolutionary significant units (ESUs) are commonly delineated based on reciprocal monophyly inferred from mitochondrial markers. The six reciprocally monophyletic lineages identified in M. kachekensis therefore provide strong support for managing these lineages independently and recognizing them as six distinct ESUs. This hierarchical delineation of ESUs, management units (MUs), and conservation units (CUs) broadly conforms to established conservation-genetic frameworks, in which ESUs are defined by reciprocal monophyly and long-term evolutionary independence [61], MUs reflect geographically structured populations, and CUs emphasize regions of particular evolutionary importance, such as inferred centers of origin [62,63].
With respect to management units (MUs), we recommend that protection measures be implemented concurrently across units owing to their pronounced genetic uniqueness. Specifically, we propose that Hainan Island, the Red River region, the Pearl River region of mainland China, and the Zhejiang–Fujian region be designated as four MUs. Furthermore, because the Pearl River region represents the inferred center of origin for this species, we recommend that it be accorded the highest conservation priority and designated as a Conservation Unit (CU).
In summary, the genetic diversity, demographic history, population structure, and phylogeographic patterns of M. kachekensis collectively indicate a complex evolutionary history shaped by interacting processes across multiple spatial and temporal scales. The coexistence of high haplotype diversity and low nucleotide diversity reflects repeated demographic contractions during Pleistocene climatic oscillations, long-term fragmentation of river systems, and subsequent demographic recovery under increasing anthropogenic pressure. Demographic inferences derived from coalescent-based and model-based approaches further suggest that short-term population fluctuations were superimposed on an overall pattern of long-term demographic stability. Pronounced genetic differentiation among river basins underscores the persistent role of geographic barriers in limiting gene flow and structuring populations over evolutionary timescales. Finally, reconstructed dispersal routes highlight the central role of major river systems—particularly the Pearl River—as both centers of origin and dispersal corridors, linking mainland China, Hainan Island, and adjacent regions. Together, these findings emphasize the importance of integrating phylogeography, demographic history, and conservation genetics to understand freshwater fish diversification and to inform effective, evolutionarily informed conservation strategies in southern China.

5. Conclusions

This study integrates mitochondrial and nuclear markers to clarify the genetic diversity, population structure, demographic history, and phylogeography of M. kachekensis across mainland China and Hainan Island. The species exhibits high haplotype diversity but low nucleotide diversity, reflecting long-term isolation among river systems, historical demographic fluctuations during Pleistocene climatic oscillations, and subsequent demographic persistence under increasing anthropogenic pressures. Genetic analyses reveal strong phylogeographic structure and deep differentiation among regions, with the Qiongzhou Strait, Gulf of Tonkin, Yunkai Mountains, and Nanling Mountains acting as persistent barriers to gene flow. Mitochondrial and nuclear datasets recover broadly congruent geographic patterns, highlighting long-term evolutionary isolation among river basins.
Demographic inferences indicate a complex history, with short-term population fluctuations during the Late Pleistocene superimposed on overall long-term stability. Ancestral-area reconstruction identifies the Pearl River region as the most likely center of origin, followed by southward dispersal to Hainan Island and the Red River and northward expansion to the Zhejiang–Fujian region, emphasizing the role of large river systems as centers and corridors of diversification. From a conservation perspective, multiple reciprocally monophyletic lineages support the recognition of six evolutionarily significant units (ESUs), while pronounced regional differentiation justifies the delineation of four management units (MUs). These results underscore the value of integrating phylogeography and conservation genetics to inform effective management of freshwater fishes in southern China.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes11020122/s1, Figure S1: Phylogenetic tree reconstructed from nuDNA RAG2 and rpS7-1 sequences of Microphysogobio kachekensis. Tree topology is consistently supported by Neighbor-Joining (NJ) analyses. Values at each node are shown in the order NJ bootstrap.; Figure S2: Minimum-spanning haplotype network inferred from nuDNA RAG2 and rpS7-1 sequences of Microphysogobio kachekensis. Each circle represents a unique haplotype, with circle size proportional to haplotype frequency. Colors within circles denote haplotypes from different geographic sampling locations.; Table S1: Analysis of molecular variance (AMOVA) for Mesophysogobio kachekensis based on nuDNA.

Author Contributions

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

Funding

This research was supported by the National Chin-Yi University of Technology, Taiwan (Grant Nos. NCUT24-T-HL-004 and NCUT25-T-HL-003). Additional funding was provided by the National Natural Science Foundation of China (Grant No. 31872207) and the Project of Financial Funds of the Ministry of Agriculture and Rural Affairs, Investigation of Fishery Resources and Habitat in the Pearl River Basin (Grant No. ZJZX-06). The APC was funded by W.-S.O.

Institutional Review Board Statement

All animal procedures were approved by the Animal Research and Ethics Committee of Shanghai Ocean Universities (permissions, 20171014; approval date: 13 March 2017), and conducted in accordance with institutional guidelines and the ARRIVE guidelines.

Data Availability Statement

Genetic sequences related to the analyses conducted during this study are available on GenBank under accession numbers. cyt b: PX557050-PX557249; control region: PX557250-PX557449; RAG1: PX557450-PX557546; S7: PX557547-PX557643.

Acknowledgments

We sincerely thank Zhuo-Cheng Zhou for his assistance with sample collection.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Economidis, P.S.; Bănărescu, P.M. The distribution and origins of freshwater fishes in the Balkan Peninsula, especially in Greece. Int. Rev. Gesamten Hydrobiol. 1991, 76, 257–283. [Google Scholar] [CrossRef]
  2. Li, S. Studies on Zoogeographical Divisions for Freshwater Fishes of China; Science Press: Beijing, China, 1981. [Google Scholar]
  3. Zhang, W.J.; Wang, J.J.; Li, C.; Chen, J.Q.; Li, W.; Jiang, S.Y.; Hsu, K.C.; Zhao, M.; Lin, H.-D.; Zhao, J. Spatial genetic structure of Opsariichthys hainanensis in South China. Mitochondrial DNA Part A 2020, 31, 98–107. [Google Scholar] [CrossRef]
  4. Chen, X.L.; Chiang, T.Y.; Lin, H.D.; Zheng, H.S.; Shao, K.T.; Zhang, Q.; Hsu, K.C. Mitochondrial DNA phylogeography of Glyptothorax fokiensis and Glyptothorax hainanensis in Asia. J. Fish Biol. 2007, 70, 75–93. [Google Scholar] [CrossRef]
  5. Wang, J.J.; Li, C.; Chen, J.Q.; Jin, J.J.; Jiang, S.Y.; Yan, L.B.; Lin, H.-D.; Zhao, J. Phylogeographic structure of the dwarf snakehead (Channa gachua) around the Gulf of Tonkin: Historical biogeography and pronounced effects of sea-level changes. Ecol. Evol. 2021, 11, 12583–12595. [Google Scholar] [CrossRef]
  6. Yang, L.; He, S. Phylogeography of the freshwater catfish Hemibagrus guttatus: Implications for South China biogeography and influence of sea-level changes. Mol. Phylogenet. Evol. 2008, 49, 393–398. [Google Scholar] [CrossRef]
  7. Zhou, T.Q.; Lin, H.D.; Hsu, K.C.; Kuo, P.H.; Wang, W.K.; Tang, W.Q.; Liu, D.; Yang, J.Q. Spatial genetic structure of the cyprinid fish Onychostoma lepturum on Hainan Island. Mitochondrial DNA Part A 2017, 28, 901–908. [Google Scholar] [CrossRef] [PubMed]
  8. Zhao, J.; Hsu, K.C.; Luo, J.Z.; Wang, C.H.; Chan, B.P.; Li, J.; Kuo, P.H.; Lin, H.-D. Genetic diversity and population history of Tanichthys albonubes: Implications for conservation. Aquat. Conserv. 2018, 28, 422–434. [Google Scholar] [CrossRef]
  9. Wang, J.J.; Zhang, W.J.; Wu, J.X.; Li, C.; Ju, Y.M.; Lin, H.-D.; Zhao, J. Multilocus phylogeography and population genetic analyses of Opsariichthys hainanensis reveal Pleistocene isolation followed by high gene flow around the Gulf of Tonkin. Genes 2022, 13, 1908. [Google Scholar] [CrossRef] [PubMed]
  10. Wang, J.J.; Wu, J.X.; Yang, J.Q.; Chen, J.B.; Yang, J.M.; Li, C.; Lin, H.-D.; Zhao, J. Phylogeography and demographic history of the cyprinid fish Barbodes semifasciolatus. Front. Ecol. Evol. 2023, 11, 1193619. [Google Scholar] [CrossRef]
  11. Chen, W.; Liu, L.; Wang, J.; Zhou, L. Threatened freshwater fish need protection. Science 2021, 374, 164. [Google Scholar] [CrossRef] [PubMed]
  12. Cao, L.; Shao, W.-H.; Yi, W.-J.; Zhang, E. A review of conservation status of freshwater fish diversity in China. J. Fish Biol. 2024, 104, 345–364. [Google Scholar] [CrossRef] [PubMed]
  13. Sušnik, S.; Berrebi, P.; Dovč, P.; Hansen, M.M.; Snoj, A. Genetic introgression between wild and stocked salmonids and the prospects for using molecular markers in population rehabilitation: The case of the Adriatic grayling (Thymallus thymallus). Heredity 2004, 93, 273–282. [Google Scholar] [CrossRef]
  14. Carim, K.J.; Vindenes, Y.; Eby, L.A.; Barfoot, C.; Vøllestad, L.A. Life history, population viability, and the potential for local adaptation in isolated trout populations. Glob. Ecol. Conserv. 2017, 10, 93–102. [Google Scholar] [CrossRef]
  15. Sun, Z.X.; Tang, W.Q.; Zhao, Y.H. Generic revision on the Biwia–Microphysogobio complex (Cypriniformes, Gobionidae) with descriptions of three new genera. Zoosyst. Evol. 2025, 101, 2013–2037. [Google Scholar] [CrossRef]
  16. Xiao, W.; Zhang, Y.; Liu, H. Molecular systematics of Xenocyprinae (Teleostei: Cyprinidae): Taxonomy, biogeography, and coevolution of a special group restricted in East Asia. Mol. Phylogenet. Evol. 2001, 18, 163–173. [Google Scholar] [CrossRef] [PubMed]
  17. Chow, S.; Hazama, K. Universal PCR primers for S7 ribosomal protein gene introns in fish. Mol. Ecol. 1998, 7, 1255–1256. [Google Scholar]
  18. Hall, T.A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 1999, 41, 95–98. [Google Scholar]
  19. Rozas, J.; Ferrer-Mata, A.; Sánchez-DelBarrio, J.C.; Guirao-Rico, S.; Librado, P.; Ramos-Onsins, S.E.; Sánchez-Gracia, A. DnaSP 6: DNA Sequence Polymorphism Analysis of Large Data Sets. Mol. Biol. Evol. 2017, 34, 3299–3302. [Google Scholar] [CrossRef]
  20. Thompson, J.D.; Gibson, T.J.; Plewniak, F.; Jeanmougin, F.; Higgins, D.G. The CLUSTAL_X windows interface: Flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 1997, 25, 4876–4882. [Google Scholar] [CrossRef]
  21. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef]
  22. Guindon, S.; Dufayard, J.-F.; Lefort, V.; Anisimova, M.; Hordijk, W.; Gascuel, O. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst. Biol. 2010, 59, 307–321. [Google Scholar] [CrossRef] [PubMed]
  23. Huelsenbeck, J.P.; Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 2001, 17, 754–755. [Google Scholar] [CrossRef]
  24. Suchard, M.A.; Lemey, P.; Baele, G.; Ayres, D.L.; Drummond, A.J.; Rambaut, A. Bayesian Phylogenetic and Phylodynamic Data Integration Using BEAST 1.10. Virus Evol. 2018, 4, vey016. [Google Scholar] [CrossRef] [PubMed]
  25. Dowling, T.E.; Tibbets, C.A.; Minckley, W.L.; Smith, G.R. Evolutionary relationships of the plagopterins (Teleostei: Cyprinidae) from cytochrome b sequences. Copeia 2002, 2002, 665–678. [Google Scholar] [CrossRef]
  26. Rambaut, A.; Drummond, A.J. TreeAnnotator v2.2.1. 2015. Available online: http://beast.community/treeannotator (accessed on 29 December 2025).
  27. Rambaut, A. FigTree v1.4.4. Available online: http://tree.bio.ed.ac.uk/software/figtree/ (accessed on 4 February 2026).
  28. Leigh, J.W.; Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 2015, 6, 1110–1116. [Google Scholar] [CrossRef]
  29. Nei, M. Molecular Evolutionary Genetics; Columbia University Press: New York, NY, USA, 1987. [Google Scholar]
  30. Jukes, T.H.; Cantor, C.R. Evolution of protein molecules. In Mammalian Protein Metabolism; Munro, H.N., Ed.; Academic Press: New York, NY, USA, 1969; Volume 3, pp. 21–132. [Google Scholar]
  31. Templeton, A.R. The “Eve” hypotheses: A genetic critique and reanalysis. Am. Anthropol. 1993, 95, 51–72. [Google Scholar] [CrossRef]
  32. Pons, O.; Petit, R.J. Measuring and testing genetic differentiation with ordered vs. unordered alleles. Genetics 1996, 144, 1237–1245. [Google Scholar] [CrossRef]
  33. Excoffier, L.; Smouse, P.E.; Quattro, J.M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 1992, 131, 479–491. [Google Scholar] [CrossRef]
  34. Excoffier, L.; Lischer, H.E.L. 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]
  35. Dupanloup, I.; Schneider, S.; Excoffier, L. A simulated annealing approach to define the genetic structure of populations. Mol. Ecol. 2002, 11, 2571–2581. [Google Scholar] [CrossRef]
  36. Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 1989, 123, 585–595. [Google Scholar] [CrossRef]
  37. Fu, Y.X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 1997, 147, 915–925. [Google Scholar] [CrossRef]
  38. Rambaut, A.; Suchard, M.A.; Xie, D.; Drummond, A.J. Tracer v1.6. 2014. Available online: http://beast.community/tracer (accessed on 29 December 2025).
  39. Yu, Y.; Harris, A.J.; He, X.J. S-DIVA (Statistical Dispersal-Vicariance Analysis): A tool for inferring biogeographic histories. Mol. Phylogenet. Evol. 2010, 56, 848–850. [Google Scholar] [CrossRef] [PubMed]
  40. Yu, Y.; Harris, A.J.; Blair, C.; He, X.J. RASP (Reconstruct Ancestral State in Phylogenies): A tool for historical biogeography. Mol. Phylogenet. Evol. 2015, 87, 46–49. [Google Scholar] [CrossRef] [PubMed]
  41. Cornuet, J.-M.; Pudlo, P.; Veyssier, J.; Dehne-Garcia, A.; Gautier, M.; Leblois, R.; Marin, J.-M.; Estoup, A. DIYABC v2.0: A software to make approximate Bayesian computation inferences about population history using SNP, DNA sequence and microsatellite data. Bioinformatics 2014, 30, 1187–1189. [Google Scholar] [CrossRef]
  42. Cabrera, A.A.; Palsbøll, P.J. Inferring past demographic changes from contemporary genetic data: A simulation-based evaluation of the ABC approach. Mol. Ecol. Resour. 2017, 17, 45–59. [Google Scholar] [CrossRef]
  43. Vrijenhoek, R.C. Conservation genetics of freshwater fish. J. Fish Biol. 1998, 53, 394–412. [Google Scholar] [CrossRef]
  44. Huang, R.; Chen, J.; Zhang, R.; Chen, W. Phylogeography and demography of Pterocryptis cochinchinensis (Siluriformes: Siluridae) in southern China. Glob. Ecol. Conserv. 2025, 62, e03728. [Google Scholar] [CrossRef]
  45. Yang, J.Q.; Hsu, K.C.; Liu, Z.Z.; Su, L.W.; Kuo, P.H.; Tang, W.Q.; Lin, H.-D. The population history of Garra orientalis using mitochondrial DNA and microsatellite data with approximate Bayesian computation. BMC Evol. Biol. 2016, 16, 73. [Google Scholar] [CrossRef]
  46. Huang, Y.; Wang, J.; Chen, J.; Wu, Y.; Lin, H.-D.; Chiang, T.-Y. Phylogeography and population genetic structure of Aphyocypris normalis in South China. Mitochondrial DNA Part A 2019, 30, 771–780. [Google Scholar] [CrossRef]
  47. Chang, H.-Y.; Wang, W.-K.; Chen, K.-N.; Su, J.-K.; Hsin, C.-Y.; Li, J.; Lin, H.-D. Phylogeography and genetic structure of the endemic cyprinid fish Microphysogobio brevirostris in northern Taiwan. Biochem. Syst. Ecol. 2016, 65, 176–184. [Google Scholar] [CrossRef]
  48. Jean, C.-T.; Wu, C.-Y.; Tsai, K.-C.; Wang, W.-K.; Hsu, Y.-Y.; Chang, Y.-M.; Lin, H.-D. Population genetic structure in the endemic cyprinid fish Microphysogobio alticorpus in Taiwan: Evidence for a new phylogeographical area. Biochem. Syst. Ecol. 2014, 57, 108–116. [Google Scholar] [CrossRef]
  49. Manel, S.; Guerin, P.E.; Mouillot, D.; Blanchet, S.; Velez, L.; Albouy, C.; Pellissier, L. Global determinants of freshwater and marine fish genetic diversity. Nat. Commun. 2020, 11, 692. [Google Scholar] [CrossRef]
  50. De Kort, H.; Prunier, J.G.; Ducatez, S.; Honnay, O.; Baguette, M.; Stevens, V.M.; Blanchet, S. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 2021, 12, 516. [Google Scholar] [CrossRef]
  51. Kang, B.; Deng, J.; Wu, Y.; Chen, L.; Zhang, J.; Qiu, H.; Lu, Y.; He, D. Mapping China’s freshwater fishes: Diversity and biogeography. Fish Fish. 2014, 15, 209–230. [Google Scholar] [CrossRef]
  52. Yang, J.Q.; Hsu, K.C.; Kuo, P.H.; Li, L.L.; Tang, W.Q.; Liu, D.; Lin, H.-D. Mitochondrial and nuclear genetic structure in Rhodeus ocellatus with approximate Bayesian computation. Environ. Biol. Fishes 2018, 101, 829–841. [Google Scholar] [CrossRef]
  53. Yang, J.-Q.; Tang, W.-Q.; Liao, T.-Y.; Sun, Y.; Zhou, Z.-C.; Han, C.-C.; Liu, D.; Lin, H.-D. Phylogeographical Analysis on Squalidus argentatus Recapitulates Historical Landscapes and Drainage Evolution on the Island of Taiwan and Mainland China. Int. J. Mol. Sci. 2012, 13, 1405–1425. [Google Scholar] [CrossRef] [PubMed]
  54. Gouthier, L.; Duval, E.; Blanchet, S.; Loot, G.; Veyssière, C.; Galan, M.; Jacquin, L. Spatial patterns of neutral and functional genetic variations along dendritic networks of riverscape in brown trout populations. Diversity 2023, 15, 784. [Google Scholar] [CrossRef]
  55. Deng, C.-X.; Zhou, W.-Y.; Gao, X.-C.; Xu, Z.-N.; Zhang, Q. Genetic diversity of Carassioides cantonensis from Hainan Island based on mitochondrial control region sequence analysis. Guangdong Agric. Sci. 2014, 41, 151–155. (In Chinese) [Google Scholar]
  56. Wang, J.P.; Lin, H.D.; Huang, S.; Pan, C.H.; Chen, X.L.; Chiang, T.Y. Phylogeography of Varicorhinus barbatulus (Cyprinidae) in Taiwan based on nucleotide variation of mtDNA and allozymes. Mol. Phylogenet. Evol. 2004, 31, 1143–1156. [Google Scholar] [CrossRef]
  57. Waters, J.M.; Burridge, C.P.; Craw, D. River capture and freshwater biological evolution: A review of galaxiid fish vicariance. Diversity 2020, 12, 216. [Google Scholar] [CrossRef]
  58. Lin, H.-D.; Kuo, P.-H.; Wang, W.-K.; Chiu, Y.-W.; Ju, Y.-M.; Lin, F.-J.; Hsu, K.-C. Speciation and differentiation of the genus Opsariichthys (Teleostei: Cyprinidae) in East Asia. Biochem. Syst. Ecol. 2016, 68, 92–100. [Google Scholar] [CrossRef]
  59. Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 2000, 405, 907–913. [Google Scholar] [CrossRef] [PubMed]
  60. Hewitt, G.M. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. Lond. B 2004, 359, 183–195. [Google Scholar] [CrossRef] [PubMed]
  61. Moritz, C. Defining “Evolutionarily Significant Units” for conservation. Trends Ecol. Evol. 1994, 9, 373–375. [Google Scholar] [CrossRef] [PubMed]
  62. Moritz, C. Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst. Biol. 2022, 51, 238–254. [Google Scholar] [CrossRef]
  63. Palsbøll, P.J.; Bérubé, M.; Allendorf, F.W. Identification of management units using population genetic data. Trends Ecol. Evol. 2007, 22, 11–16. [Google Scholar] [CrossRef]
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Figure 1. Microphysogobio kachekensis sample locations in the south of Yangtze River. All localities used in this study are indicated by •.
Figure 1. Microphysogobio kachekensis sample locations in the south of Yangtze River. All localities used in this study are indicated by •.
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Figure 2. Schematic representation of five demographic scenarios for Microphysogobio kachekensis tested using approximate Bayesian computation (ABC). Time and effective population size are not to scale; t indicates time points, boxes represent population sizes, and N1–Ne denote different effective population sizes.
Figure 2. Schematic representation of five demographic scenarios for Microphysogobio kachekensis tested using approximate Bayesian computation (ABC). Time and effective population size are not to scale; t indicates time points, boxes represent population sizes, and N1–Ne denote different effective population sizes.
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Figure 3. Phylogenetic tree reconstructed from mitochondrial cyt b and control region sequences of Microphysogobio kachekensis. Tree topology is consistently supported by Neighbor-Joining (NJ), Maximum Likelihood (ML), and Bayesian Inference (BI) analyses and is rooted along the midpoint of the longest branch. Values at each node are shown in the order: BI posterior probability/ML bootstrap/NJ bootstrap.
Figure 3. Phylogenetic tree reconstructed from mitochondrial cyt b and control region sequences of Microphysogobio kachekensis. Tree topology is consistently supported by Neighbor-Joining (NJ), Maximum Likelihood (ML), and Bayesian Inference (BI) analyses and is rooted along the midpoint of the longest branch. Values at each node are shown in the order: BI posterior probability/ML bootstrap/NJ bootstrap.
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Figure 4. TCS haplotype network inferred from mitochondrial cyt b and control region sequences of Microphysogobio kachekensis. Each circle represents a unique haplotype, with circle size proportional to haplotype frequency. Colors within circles denote haplotypes from different geographic sampling locations.
Figure 4. TCS haplotype network inferred from mitochondrial cyt b and control region sequences of Microphysogobio kachekensis. Each circle represents a unique haplotype, with circle size proportional to haplotype frequency. Colors within circles denote haplotypes from different geographic sampling locations.
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Fishes 11 00122 g005
Figure 5. Bayesian skyline plot illustrating temporal dynamics of effective population size in Microphysogobio kachekensis. The y-axis shows the log-transformed product of effective population size (Ne) and generation length, and the x-axis represents time before present (million years ago, Ma).
Figure 5. Bayesian skyline plot illustrating temporal dynamics of effective population size in Microphysogobio kachekensis. The y-axis shows the log-transformed product of effective population size (Ne) and generation length, and the x-axis represents time before present (million years ago, Ma).
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Fishes 11 00122 g006
Figure 6. Time-calibrated phylogeny and ancestral area reconstruction of Microphysogobio kachekensis inferred using Bayesian Binary MCMC (BBM) analysis. Branch lengths are scaled to time (million years ago, Ma), and colored circles at nodes indicate posterior probabilities of ancestral area states. The asterisk (*) denotes ancestral ranges with a posterior probability of less than 5%.
Figure 6. Time-calibrated phylogeny and ancestral area reconstruction of Microphysogobio kachekensis inferred using Bayesian Binary MCMC (BBM) analysis. Branch lengths are scaled to time (million years ago, Ma), and colored circles at nodes indicate posterior probabilities of ancestral area states. The asterisk (*) denotes ancestral ranges with a posterior probability of less than 5%.
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Table 1. Location, number and genetic marker sampled used in the present study. The haplotype (h) and nucleotide diversity [θπ and θω (%)] of mtDNA (mt) and nuclear (nu) genes.
Table 1. Location, number and genetic marker sampled used in the present study. The haplotype (h) and nucleotide diversity [θπ and θω (%)] of mtDNA (mt) and nuclear (nu) genes.
Locations
(Abbreviation)		Longitude
Latitude		Sample Size
(mt/nu)		Haplotype Size
(mt/nu)		mtDNAnuDNA
Haplotype Diversity (h)Nucleotide DiversityHaplotype Diversity (h)Nucleotide Diversity
θπθω θπ θω
Zhejiang-Fujian
      1. Huaan (HA)24.99 N
117.52 E		7/67/51.0000.0010.0020.9330.0080.009
Pearl River84/2935/240.9400.0100.0070.9590.0070.011
      2. Luhe (LH)23.34 N
115.63 E		34/510/50.7770.0010.0011.0000.0090.010
      3. Heyuan (HY)23.74 N
114.69 E		33/1514/100.8710.0010.0020.8290.0010.002
      4. Yangchun (YM)22.17 N
111.77 E		17/911/90.8820.0010.0021.0000.0050.006
Hainan Island 106/5756/530.9710.0090.0070.9930.0180.026
      5. Basha (BS)19.22 N
109.45 E		31/1818/180.8670.0010.0041.0000.0200.025
      6. Qionghai (QH)19.23 N
110.41 E		36/1520/140.9480.0020.0050.9710.0030.004
      7. Wuzhi (WZ)18.44 N
109.32 E		20/1110/100.8370.0030.0060.9820.0160.017
      8. Ledong (LD)18.50 N
109.18 E		19/1311/50.8300.0010.0010.9870.0180.015
Nujiang-Lancangjiang
      9. Red River (HH)23.13 N
101.84 E		3/32/30.6670.0010.0011.0000.0010.001
Total 200/9597/830.9800.01470.01290.9910.01470.0291
Table 2. Pairwise genetic differentiation (FST) values among populations of Mesophysogobio kachekensis. Values below the diagonal are FST values calculated from mitochondrial DNA (mtDNA), and values above the diagonal are calculated from nuclear DNA (nuDNA). An asterisk (*) indicates statistical significance (p < 0.05).
Table 2. Pairwise genetic differentiation (FST) values among populations of Mesophysogobio kachekensis. Values below the diagonal are FST values calculated from mitochondrial DNA (mtDNA), and values above the diagonal are calculated from nuclear DNA (nuDNA). An asterisk (*) indicates statistical significance (p < 0.05).
PopulationBSHAHHHYLDLHQHWZYM
BS 0.4010.4500.398 *0.3130.3850.3670.2320.310
HA0.749 * 0.882−0.0450.4620.4780.7250.4050.563
HH0.3960.838 0.8620.4710.6120.5910.4270.611
HY0.852 *0.0540.679 * 0.449 *0.473 *0.707 *0.390 *0.547 *
LD0.769 *0.884 *0.7330.887 * 0.4210.3710.0180.377
LH0.885 *0.843 *0.806 *0.882 *0.935 * 0.5330.3880.459
QH0.692 *0.657 *0.221 *0.813 *0.686 *0.851 * 0.3110.472
WZ0.657 *0.692 *0.3790.798 *0.311 *0.840 *0.584 * 0.300
YM0.817 *0.8150.7150.843 *0.893 *0.886 *0.755 *0.771 *
Table 3. Analysis of molecular variance (AMOVA) for Mesophysogobio kachekensis based on mitochondria DNA.
Table 3. Analysis of molecular variance (AMOVA) for Mesophysogobio kachekensis based on mitochondria DNA.
Scheme Category Description%Var.Statisticp
Scenario I, two groups divided primarily by the Qiongzhou Strait—Hainan Island (BS, QH, LD, WZ) versus mainland China (HA, HH, HY, LH, YM)
Among groups30.47FCT = 0.3050.011
Among populations within groups56.06FSC = 0.8060.000
Within populations13.47FST = 0.8650.000
Scenario II, four groups as defined by Li (1981) [2]—Zhejiang–Fujian (HA), Nujiang–Lancangjiang (HH), Pearl River (HY, LH, YM), and Hainan Island (BS, QH, LD, WZ)
Among groups27.35FCT = 0.2730.001
Among populations within groups58.77FSC = 0.8090.000
Within populations13.88FST = 0.8610.000
Scenario III, five groups separated by the Qiongzhou Strait (BS, QH, LD, WZ), Gulf of Tonkin, Shiwandashan Mountains (HH), Yunkai Mountains (YM) and Nanling Mountains (HY, LH), Wuyi Mountains (HA)
Among groups31.66FCT = 0.3170.000
Among populations within groups54.38FSC = 0.7960.000
Within populations13.96FST = 0.8600.000
Scenario IV, six groups that further refine the Scenario III by subdividing the Hainan region with the WY Range into northern Hainan (BS, QH) and southern Hainan (LD, WZ)
Among groups32.65FCT = 0.3260.000
Among populations within groups52.67FSC = 0.7820.000
Within populations14.68FST = 0.8530.000
Scenario V, best group divided by SAMOVA [(BS), (HA, HY), (HH), (LD), (LH), (YM), (QH), (WZ)]
Among groups84.29FCT = 0.8430.029
Among populations within groups0.44FSC = 0.0280.000
Within populations15.27FST = 0.8470.000
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Yang, J.-Q.; Chen, J.; Wang, J.; Zhou, T.-Q.; Chiu, Y.-W.; Lin, H.-D.; Ou, W.-S. Phylogeographic Pattern and Genetic Structure of the Cyprinid Fish Microphysogobio kachekensis (Oshima 1926) in Mainland China and Hainan Island Based on Mitochondrial and Nuclear DNA. Fishes 2026, 11, 122. https://doi.org/10.3390/fishes11020122

AMA Style

Yang J-Q, Chen J, Wang J, Zhou T-Q, Chiu Y-W, Lin H-D, Ou W-S. Phylogeographic Pattern and Genetic Structure of the Cyprinid Fish Microphysogobio kachekensis (Oshima 1926) in Mainland China and Hainan Island Based on Mitochondrial and Nuclear DNA. Fishes. 2026; 11(2):122. https://doi.org/10.3390/fishes11020122

Chicago/Turabian Style

Yang, Jin-Quan, Jiabo Chen, Junjie Wang, Tian-Qi Zhou, Yuh-Wen Chiu, Hung-Du Lin, and Wen-Sheng Ou. 2026. "Phylogeographic Pattern and Genetic Structure of the Cyprinid Fish Microphysogobio kachekensis (Oshima 1926) in Mainland China and Hainan Island Based on Mitochondrial and Nuclear DNA" Fishes 11, no. 2: 122. https://doi.org/10.3390/fishes11020122

APA Style

Yang, J.-Q., Chen, J., Wang, J., Zhou, T.-Q., Chiu, Y.-W., Lin, H.-D., & Ou, W.-S. (2026). Phylogeographic Pattern and Genetic Structure of the Cyprinid Fish Microphysogobio kachekensis (Oshima 1926) in Mainland China and Hainan Island Based on Mitochondrial and Nuclear DNA. Fishes, 11(2), 122. https://doi.org/10.3390/fishes11020122

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