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Article

Genetic Diversity of Five Pelodiscus sinensis Sub-Populations in the Dongting Lake Basin Based on Cytb and 12S rRNA Markers

by
Zhiliang Zuo
1,†,
Hewei Xiao
1,†,
Qifan Wu
1,
Lu Tian
1,
Feng Gao
1,
Cheng Li
1,
Jiajia Ni
2,
Zhitao Peng
1,* and
Jin Xiang
1,*
1
Hunan Fisheries Research Institute and Aquatic Products Seed Stock Station, Changsha 410153, China
2
Research and Development Center, Guangdong Meilikang Bio-Science Ltd., Foshan 528315, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diversity 2025, 17(8), 575; https://doi.org/10.3390/d17080575
Submission received: 24 June 2025 / Revised: 11 August 2025 / Accepted: 12 August 2025 / Published: 15 August 2025
(This article belongs to the Section Freshwater Biodiversity)
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Abstract

To explore the current state of genetic diversity of in the Chinese soft-shelled turtle (Pelodiscus sinensis) in the Dongting Lake basin, the genetic diversity of five sub-populations consisting of 71 turtles were analyzed through mitochondrial Cytb and 12S rRNA. Our results revealed 13 haplotypes for Cytb and 6 for 12S rRNA. The overall haplotype diversity and nucleotide diversity indices were 0.750 and 0.014, and 0.712 and 0.009, respectively. The Shaoyang sub-population showed the lowest genetic diversity according to both markers. The genetic distances between sub-populations ranged from 0.010 to 0.018 (Cytb), and from 0.002 to 0.014 (12S rRNA), with the largest distance observed between the Shaoyang and Junshan sub-populations. The Junshan sub-population was significantly different from the other sub-populations (p < 0.05), and gene exchange was weak, despite belonging to the same population. Genetic variation within the P. sinensis sub-population was much higher than that between sub-populations. There was no recent expansion event in the history of P. sinensis. Overall, the genetic diversity of P. sinensis was high, whereas it appeared to be homogenous, suggesting a potential decline in genetic diversity. This study provides valuable insights for the conservation and sustainable use of P. sinensis.

1. Introduction

Chinese soft-shell turtle (Pelodiscus sinensis) is a species of turtle belonging to the Trinychidae family [1] and is widely distributed in China, Japan, Korea, and Vietnam [2]. Due to its nutritional and medicinal value, Chinese soft-shell turtle has become an important economic culture species in China [3], with an annual output of 500,000 tons [4]. The Chinese soft-shell turtle has evolved into different geographical populations due to geographical differences and artificial selection [5]. These populations can be divided into the Yellow River turtle, Dongting turtle, Yellow Sand turtle, Huaihe turtle, and other varieties based on their water system or origin [6,7,8]. Among these, the Dongting turtle is one of the most representative local varieties of the Chinese soft-shell turtle, with good production performance and high industrial development value. It is also the first high-quality germplasm resource of the Chinese soft-shell turtle [9]. However, in recent decades, the scale of artificial breeding of the Chinese soft-shell turtle has expanded, leading to many cultured individuals escaping into natural waters and causing serious damage to wild germplasm resources [10]. Simultaneously, overfishing and habitat destruction have caused a sharp decline in the wild populations of Chinese soft-shell turtle, with some populations on the brink of extinction [11,12]. As a result, the Chinese soft-shell turtle has been listed in the United Nations Red Book on vulnerable species (http://www.iucnredlist.org/details/39620/0) (accessed on 20 October 2022). Furthermore, the industry is facing challenges such as increasing diseases, slow growth, and differences in growth specifications, which are seriously hindering its sustainable development [13]. As a representative population of Chinese soft-shell turtle, it is crucial to scientifically evaluate the current state of its germplasm resources, reveal its genetic diversity and structure, and take measures to protect these resources, breed improved varieties, and ensure the long-term development of the industry.
Mitochondrial DNA is known for its matrilineal inheritance, rapid evolution, and high copy number, making it a valuable tool in population genetics and phylogenetic studies [14,15]. The length of the mitochondrial genome in different varieties of Chinese soft-shelled turtles varies slightly, with approximately 17,000 bp and containing 37 genes that encode 13 proteins [16]. These genes are arranged in the same order and direction as most vertebrates [17,18]. Among these genes, Cytb and 12S rRNA genes have moderate evolution rates, and their variations can reveal the phylogenetic relationships among species, making them suitable for studying the genetic structure of closely related populations [19,20]. Currently, research on the Dongting turtle primarily focuses on comparing growth performance and genetic structure among different populations. For instance, Xiao et al. [21] found that the growth rate and feed utilization rate of the Green-card turtle (a hybrid of Dongting turtle ♀ × Yellow River turtle ♂) were higher than those of the Yellow River turtle and Dongting turtle. Lu et al. [8] found that the Changyong turtle is closely related to the Wu turtle, Dongting turtle, and Huangsha turtle based on the Cytb, COI, and 12S rRNA genes. Xiong et al. [22] revealed that the soft-shell turtles in five regions of East Asia can be divided into four populations based on the Cytb gene and speculated that they may have originated in the Yangtze River basin and spread to the Yellow River, Taiwan Province, and Japan through the Xijiang River basin. Liang et al. [23,24], respectively, used Super-GBS sequencing technology and SSR genetic molecular markers to show that Chinese soft-shell turtles can be divided into three subgroups, and the genetic differentiation coefficient of the Dongting and Changyong populations was extremely low (FST = 0.0088), indicating no differentiation. There were frequent gene flows among both populations, and the genetic variation mainly came from individuals. Despite the increasing research on the Dongting turtle, there is still a lack of studies on the genetic structure of the population. Therefore, this study aimed to analyze the genetic diversity and structure of 71 wild Chinese soft-shell turtles from five geographical sites in the Dongting Lake basin: the Junshan area of East Dongting Lake (JS), Hanshou area of West Dongting Lake (HS), Yuanjiang area of South Dongting Lake (YJ), Shaoyang section of Zishui River (SY), and Hengyang section of Xiangjiang River (HY). We used Cytb and 12S rRNA gene sequences to reveal the genetic evolution relationship and historical trends of the sub-populations. This study provides a theoretical basis for the protection and utilization of wild soft-shell turtle resources in the Dongting Lake basin.

2. Materials and Methods

2.1. Sample Collection

The study was approved by the Ethics Committee of Hunan Fisheries Research Institute and Aquatic Products Seed Stock Station (procedure approval 17 July 2023, approval number HNFI20230717). A total of 71 samples of wild Chinese soft-shell turtles were collected from various locations in the Dongting Lake and the surrounding rivers, including JS (112.8477° E, 29.4488° N; n = 16), HS (111.979° E, 28.920° N; n = 10), YJ (112.770° E, 29.079° N; n = 18), SY (111.744° E, 27.229° N; n = 11), and HY (112.284° E, 26.982° N; n = 16) (Figure 1). The rear skirt tissues of the wild samples were collected and stored in anhydrous ethanol at −4 °C.

2.2. DNA Extraction, PCR Amplification and Sequencing

The skirt tissue that was preserved in anhydrous ethanol was washed three times with sterile water before extracting turtle DNA using a genomic DNA kit (TianGen, Beijing, China). The mitochondrial Cytb and 12S rDNA genes of the turtle were amplified using the primers CytbF (5′-AGC CAT ACA TTA CTC ACC-3′) and CytbR (5′-GTG AAG GAT GGA GGA TGT-3′), and 12SA850 (5′-AAA CTG GGA TTA GAT ACC CCA CTA T-3′) and 12SB1270 (5′-GAG GGT GAC GGG CGG TGT GT-3′), as previously described by Xiong et al. [22] and Engstrom et al. [25]. The PCR products were then visualized on a 1.2% agarose gel and bidirectionally sequenced using an ABI PRISM® 3700 Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA).

2.3. Data Analysis

After manually removing low-quality sequences based on the peak plot, the remaining sequences were concatenated using ContigExpress 9.1.0 (Invitrogen, Carlsbad, CA, USA) [26]. Next, the spliced sequences of the same gene were aligned using ClustalW [27], and maximum likelihood phylogenetic trees were constructed using MEGA11 with 1000 bootstrap replicates [28]. Variable and parsimony-informative sites were identified, and the nucleotide base composition, pairwise genetic distances, and transition/transversion ratios were calculated using MEGA11. The haplotype number (H), haplotype diversity (Hd), segregating sites (S), nucleotide diversity (Pi), and average nucleotide difference (K) of the samples were counted using DnaSP 5.0 [29]. Finally, the haplotype network diagram was constructed using Popart 1.7 [30]. Analysis of molecular variance (AMOVA) and nucleotide mismatch analysis were performed using Arlequin 3.5 [31].

3. Results

3.1. Sequence Characteristics of Cytb and 12S rRNA Genes

Except for one sample from the HS sub-population, one sample from the SY sub-population, and three samples from the HY sub-population that were failed to amplify the Cytb gene sequence, all other samples successfully amplified the required target sequences. In total, homologous sequences of the Cytb gene with 659 bp from 66 individuals and the 12S rRNA gene with 406 bp from 71 individuals across five sub-populations were obtained. The proportions of T, C, A, and G bases in the Cytb gene were 31.1%, 11.9%, 27.5%, and 29.5%, respectively, with A+T accounting for 58.6% and G+C accounting for 41.4% (Table 1). Similarly, the proportions of T, C, A, and G bases in the 12S rRNA gene were 22.2%, 22.6%, 37.7%, and 17.6%, respectively, with A+T accounting for 59.9% and G+C accounting for 40.1% (Table 1). These results indicated that the Cytb and 12S rRNA genes of the Chinese soft-shell turtles exhibit a clear base bias, consistent with the base composition of mitochondrial genes in other turtle species.

3.2. Genetic Diversity of P. sinensis Sub-Population

A total of 25 segregating sites (S) were detected in the Cytb gene of the five soft-shell turtle sub-populations, accounting for 3.79% of the total variable sites. This included five singleton sites and 20 parsimony-informative sites. All variable sites were either transitions or transversions, with a ratio of 6:1. There were no base insertions or deletions. The haplotype number (H) was 13, the haplotype diversity (Hd) was 0.750, the nucleotide polymorphism (Pi) was 0.014, and the average nucleotide difference number (K) was 6.547. The highest Hd (0.889) was found in the HS group. The YJ sub-population had the highest S (30), H (10), Pi (0.016), and K (10.706), whereas the SY sub-population had the lowest H (3), Hd (0.378), and Pi (0.008) (Table 2).
A total of 14 S were detected in the 12S rRNA gene of the five sub-populations, accounting for 3.45% of the total variable sites. This included seven singleton sites and seven parsimony-informative sites. All variable sites were either transitions or transversions, with a ratio of 12:1. The H was 6, the Hd was 0.609, the Pi was 0.010, and the K was 3.23. The HS sub-population had the highest Hd (0.844). The HY sub-population had the highest S (19), H (5), Pi (0.011), and K (4.283), whereas the SY sub-population had the lowest H (4), Hd (0.491), and Pi (0.006). The S, H, Hd, Pi, and K calculated based on the Cytb gene were all higher than those calculated based on the 12S rRNA gene (Table 2). Additionally, all five sub-populations had high Pi (Pi > 0.005), with the SY sub-population having the lowest Hd (Hd < 0.5) and the other sub-populations having high Hd (Hd > 0.5; Table 2). Overall, the genetic diversity of the five Chinese soft-shell turtle sub-populations in the Dognting Lake basin was high, as indicated by high Hd and Pi. The HS, YJ, and HY sub-populations had relatively high genetic diversity, whereas the SY sub-population had relatively low genetic diversity.

3.3. Genetic Structure of P. sinensis Sub-Population

The genetic distances of the Cytb gene among the five sub-populations ranged from 0.010 to 0.018. The lowest genetic distance was observed between the SY and HY sub-populations (0.010), whereas the highest was between the SY and JS sub-populations (0.018). The genetic differentiation FST between the JS sub-population and the other sub-populations was found to be significant (p < 0.05). The largest FST was observed between the JS and SY sub-populations (0.514), whereas the lowest gene flow (Nm = 0.23) was found between these two sub-populations (Table 3). No significant genetic differentiation was observed among the other sub-populations, and negative values were found between the HY sub-population and the HS, SY, YJ, and HS sub-populations, indicating no genetic differentiation. The highest gene flow was observed between the SY and HS sub-populations (8.54) (Table 3).
The genetic distances of the 12S rRNA gene among the five sub-populations ranged from 0.002 to 0.014. The lowest genetic distance was between the HY and JS sub-populations (0.002), whereas the highest was between the SY and JS sub-populations (0.014). The genetic differentiation between the JS sub-population and other sub-populations was significant (p < 0.05). The genetic differentiation index FST (0.558) between the JS and SY sub-populations was the largest, whereas the Nm (0.19) was the lowest. However, the genetic differentiation among the other sub-populations was not significant. The HY sub-population and the HS, SY, YJ, and HS sub-populations exhibited negative values, indicating no genetic differentiation. The Nm (17.46) between the SY and HS sub-populations was the highest (Table 3). These results suggest that there was weak gene exchange between the JS and SY sub-populations, but strong gene exchange between the SY and HS sub-populations. AMOVA showed that 16.35% of the total genetic variation was due to inter-population variations based on the Cytb gene, whereas 83.65% was due to intra-population variations. For the 12S rRNA gene, 20.85% of the total variation was due to inter-population variations, whereas 79.15% was due to intra-population variations (Table 4). This indicates that most of the genetic variation in the five sub-populations of Chinese soft-shell turtles came from individuals within the sub-population, and the JS sub-population has the highest genetic differentiation from the other sub-populations.

3.4. Phylogenetic Tree and Haplotype Network Diagram of P. sinensis Sub-Populations

The maximum-likelihood tree constructed based on the Cytb gene exhibited similar results to those of Xiong et al. (2019) [22], and the distribution of the sequences obtained by us in the Xijiang population and Japanese population decreased in turn, which more clearly confirmed the conclusion of Xiong et al. (2019) that the Japanese population spreads from the Dongting population through the Xijiang population. The phylogenetic trees indicated that the five sub-populations did not exhibit significant geographical aggregation and that the haplotypes were intermixed (Figure 2). Out of the 13 haplotypes identified using the Cytb gene, Hap2 and Hap4 were the most common, accounting for 42.25% and 16.9% of the individuals, respectively. All five sub-populations shared Hap1 and Hap2, whereas Hap9 was unique to the SY sub-population; Hap5 and Hap6 was unique to the HY sub-population; Hap7 was unique to the JS sub-population; and Hap10, Hap11, Hap12, and Hap13 were unique to the YJ sub-population. Hap3 was shared by the HS, SY, and YJ sub-populations; Hap4 was shared by the HS, JS, and YJ sub-populations; and Hap8 was shared by the JS and YJ sub-populations (Figure 2A). Similarly, out of the six haplotypes identified using the 12S rRNA gene, Hap1 and Hap3 were the most common, accounting for 53.52% and 32.39% of the individuals, respectively. All five sub-populations shared Hap1 and Hap3, whereas Hap5 was unique to the HY sub-population; Hap6 was unique to the JS sub-population; Hap4 was unique to the YJ sub-population; and Hap2 was shared by the HS, SY, and YJ sub-populations (Figure 2B).
The haplotype network diagrams for the five sub-populations were similar to phylogenetic trees, with the haplotypes scattered across different geographical populations. However, there was no clear genetic and geographic association observed (Figure 3A,B). The analysis of the Cytb gene showed that Hap1 and Hap2 were located in the center of the network and were shared by all five sub-populations, suggesting that they may be ancestral haplotypes from which other haplotypes evolved (Figure 3A). Similarly, the analysis of the 12S rRNA gene revealed that Hap1 and Hap3 were shared by all five sub-populations and located in the center of the network, indicating that they may also be ancestral haplotypes (Figure 3B).

3.5. Historical Dynamics of P. sinensis Sub-Populations

Based on the analysis of the Cytb and 12S rRNA genes, a neutrality test was conducted on the Chinese soft-shell turtle populations. The results revealed that both Tajima’s D and Fu’s Fs were positive (Table 2), and the distribution of nucleotide mismatch was multimodal (Figure S2). This suggests that the Chinese soft-shell turtle populations in the Dongting Lake basin adhere to the hypothesis of neutral evolution and have not experienced any population expansion in their history. However, the Tajima’s D value for the SY sub-population based on the Cytb gene was negative, and there was no significant difference (p > 0.05). Similarly, the Fu’s Fs values for the SY and HY sub-populations based on the 12S rRNA gene were negative, with no significant difference (p > 0.05). The remaining sub-populations showed positive Tajima’s D and Fu’s Fs, with no significant difference (p > 0.05).

4. Discussion

Genetic diversity is typically defined as the total genetic variation among individuals within a species [32]. It is an important indicator of a species or population ability to adapt to its environment. A high level of genetic diversity can enhance a population adaptability, while low diversity can lead to bottlenecks or even extinction of the population [33]. Hd and Pi are commonly used parameters to measure genetic diversity in aquatic animals [34,35]. In this study, the average values of Hd and Pi for the Cytb and 12S rRNA genes in the five sub-populations were 0.750 and 0.609, and 0.016 and 0.010, respectively. These values indicate that the genetic diversity of Chinese soft-shell turtle populations was relatively high (Hd > 0.5, Pi > 0.005) [36], and that they have strong breeding potential. Previous studies have reported low genetic diversity of Taihu turtles, Taiwan turtles, and Yellow River turtles (Hd < 0.5, and Pi < 0.005) [6,37], whereas Yellow-sand turtles and Japanese turtles have shown high genetic diversity (Hd > 0.5) [38,39]. These results suggest that the genetic diversity of Chinese soft-shell turtle populations in different aquatic systems can vary significantly. Our results showed that the genetic diversity of Chinese soft-shell turtles in the Dongting Lake basin was similar to those of the populations in the East China (Hd = 0.688 ± 0.098, Pi = 0.027 ± 0.098) [40] and Yangtze River basin (Hd = 0.882, Pi = 0.010) [41] based on the Cytb gene. However, this differed from the results of a previous study on the Dongting population using microsatellite markers [42]. This discrepancy may be due to differences in study methods or turtle samples. The Chinese soft-shell turtles used in our study were all wild individuals, which may contribute to the observed high genetic diversity. Additionally, the mitochondrial molecular markers used in our study are inherited maternally, whereas microsatellite markers are inherited from both parents and undergo gene recombination [43]. Furthermore, the level of genetic diversity of the 12S rRNA gene was lower than that of the Cytb gene, which was consistent with the findings of Lu et al. [8] and Chen et al. [16]. This may be due to conservative nature of the 12S rRNA gene, which faces greater pressure from natural selection and has a slower evolution rate than the Cytb gene [44]. Both mitochondrial genes exhibited that the Hd of the HS sub-population was the highest, reaching above 0.8, and Pi > 0.01. This indicates that the HS sub-population had a high degree of genetic diversity and variation, which is consistent with the findings of Zhang et al. [45] on the genetic diversity of Hanshou turtles. Conversely, both genes showed that the Hd and Pi of the SY sub-population were the lowest, with Hd < 0.5, indicating that the genetic diversity of the SY sub-population was relatively low. This may be related to human influence on the habitat and germplasm resources of the SY sub-population [46]. The SY sub-population is located in the transition zone between the Xuefeng Mountain and Nanling Mountain systems, which is a relatively closed small group with limited communication with the outside world. This may have led to inbreeding and a decrease in population diversity. Additionally, environmental pollution, overfishing, and other human activities have contributed to a decline in Chinese soft-shell turtle resources in this habitat, further reducing population diversity [47]. Grant and Bowen [36] have classified historical event patterns into four sub-populations based on Hd = 0.5 and Pi = 0.005. According to this classification, the genetic diversity of the HS, HY, JS, and YJ groups in our study should be classified as pattern IV (Hd > 0.5, Pi > 0.05), suggesting that these sub-population may have been large and stable in the past. The genetic diversity of the SY sub-population should be classified as pattern III (Hd < 0.5, Pi > 0.05).
The A+T base contents in the mitochondrial Cytb and 12S rRNA gene sequences of Chinese soft-shell turtle were 58.6% and 59.9%, respectively. This indicates a clear preference for A and T bases, which is in line with the common occurrence of high A + T content in the mitochondrial DNA of turtles [16,48]. Xu et al. [49] found that the A + T base contents in the 12S rRNA gene of Japanese populations of Chinese soft-shell turtle and Qingxi Wu turtle were 59.1% and 59.54%, respectively, which is consistent with our findings.
Based on the Cytb and 12S rRNA gene sequences, the genetic diversity, FST, and Nm of five Chinese soft-shell turtle sub-populations in the Dongting Lake basin were analyzed. According to the population classification standard proposed by Nei [50], the genetic distance between populations ranged from 0 to 0.05, and the genetic distance between subspecies ranged from 0.02 to 0.20. Our results of the Cytb and 12S rRNA gene sequences of Chinese soft-shell turtle sub-populations showed that the genetic distance between populations measured by the two genes was similar. The largest genetic distance between populations was between the SY and JS sub-populations, but they were less than 0.02, which did not reach the level of subspecies differentiation. The SY sub-population is located in the Zishui basin in the middle of Hunan Province (the upper tributary of the Dongting Lake), which is far away from the main lake area of the Dongting Lake (the JS sub-population). The presence of multi-level dams (such as Zhexi Reservoir) affects the gene exchange between the two sub-populations, resulting in the largest genetic distance between them. However, due to geographical isolation, the differentiation time has not been long enough to reach the level of the subspecies differentiation, so it still belongs to interspecific variation. FST can reflect the level of genetic differentiation among populations. The degrees of genetic differentiation were divided into three grades: weak (FST < 0.05), moderate (0.05 ≤ FST ≤ 0.25), and high (FST > 0.25) differentiation [51]. Both genes showed that the FST of the JS sub-population was greater than 0.15, indicating significant differentiation (p < 0.05) and weak gene exchange. This was similar to the findings of Luo et al. [52], who observed unique genetic differentiation characteristics in the population collected from the junction of the Yangtze River and Poyang Lake compared to other populations in the Yangtze River. This suggests that the JS sub-population may have some spatial isolation from other sub-populations. It may also be due to the fact that the JS sub-population is located at the intersection of Dongting Lake and the Yangtze River, which has significantly different habitat conditions (such as water fluctuation and sediment deposition), driving the adaptive evolution of the JS sub-population at certain gene mutant sites and leading to genetic differentiation. The genetic differentiation index of JS and SY was the largest (p > 0.5), indicating a high level of genetic differentiation, whereas the genetic differentiation among other sub-populations was moderate or low, with extensive gene exchange. The Nm of the HS and SY sub-populations was the largest (>4), indicating that individuals from these two sub-populations can freely mate at random [53]. Therefore, although the Chinese soft-shell turtle sub-populations in the Dongting Lake basin exhibited a certain level of genetic differentiation, different geographical populations should be considered as one subspecies, similar to the findings of Li et al. [37] and Han et al. [54] for the Taihu Hua turtle and Taihu turtle populations. The geographical distance between the HS and JS sub-populations was close, yet the FST reached 0.180. However, the geographical distance between the HS and HY sub-populations was far, yet the FST was negative, indicating no genetic differentiation. This suggests that geographical distance may not be the main reason for genetic differentiation, and instead, the founder effect and habitat differences are more likely to have caused the current genetic differentiation pattern [55]. AMOVA of both genes showed that the variation among individuals within the sub-population was the main source of genetic variation in the Chinese soft-shell turtle population in the Dongting Lake basin, similar to the findings of Chen et al. [56].
In this study, we constructed phylogenetic trees and haplotype networks for five sub-populations in the Dongting Lake basin using the Cytb and 12S rRNA genes. Our results showed that there was no clear geographical clustering or pedigree structure among the samples from each sub-population. This is consistent with previous studies by Yu et al. [41] and Zhang et al. [57], which found no distinct geographical patterns among soft-shell turtle sub-populations in different regions. The lack of geographical isolation or the existence of shared habitats within the same water area may be important factors contributing to the differentiation of populations. Dongting Lake is a typical water-handling lake, with four rivers in the south (Xiangjiang River, Zijiang River, Yuanjiang River, and Lishui River) and three inlets in the north (Songzi, Taiping, and Ouchi) that flow into the Yangtze River. The lake water is frequently updated and exchanged [58]. Additionally, Chinese soft-shell turtles have strong mobility and can move both on land and in water. Therefore, whereas there was some genetic differentiation among populations in the Dongting Lake basin, it is not significant and is limited to differences within populations. The open waters of the lake weaken the differentiation trend and promote extensive gene exchange, preventing the formation of distinct subspecies based on geographical location. Analysis of haplotype composition revealed a significant difference in the number of haplotypes. The combined frequency of Hap2 and Hap4 of the Cytb gene was 59.15%, whereas the combined frequency of Hap1 and Hap3 of the 12S rRNA gene was 85.91%. This suggests that the genetic composition of the turtle populations in the Dongting Lake basin tends to be homogenous, possibly due to differences in adaptability to the environment among haplotypes. As a result, the number of individuals with strong adaptability is increasing, leading to genetic homogenization within populations [35]. This highlights the need for increased monitoring and protection of wild soft-shell turtle populations in the Dongting Lake basin, with a focus on identifying and preserving endangered rare haplotypes for future ecological restoration.
Nucleotide mismatch analysis and Tajima’s and Fu’s neutral tests are scientific methods used to determine whether a biological population has undergone expansion. According to Tajima [59], a negative value for Tajima’s D or Fu’s Fs suggests that the population has experienced expansion events in past. Additionally, a single peak in the Poisson distribution of the nucleotide mismatch distribution also indicates a historical population expansion. Conversely, a bimodal or multimodal distribution suggests that the population has remained relatively stable [60]. In this study, the distribution of nucleotide mismatches and the results of the neutral tests indicated that there was no significant population expansion in the history of the Chinese soft-shell turtle populations in the Dongting Lake basin. This finding was consistent with the conclusions of Yu et al. [41] and Wang et al. [58] regarding the historical dynamics of Chinese soft-shell turtle populations in the middle and lower reaches of the Yangtze River and the Corbicula fluminea population in Dongting Lake, respectively. However, it contradicts the conclusion of Xiong et al. [22] that the Chinese soft-shelled turtle population has expanded in five East Asian regions. This inconsistency may be attributed to the different sources of Chinese soft-shell turtle populations.

5. Conclusions

In this study, we analyzed the genetic diversity of five geographic sub-populations of Chinese soft-shell turtles in the Dongting Lake basin using the Cytb and 12S rRNA genes. Our findings showed that whereas there was low genetic distance among the sub-populations, there was high genetic diversity among individuals and potential for improved breeding. However, there was a certain trend of genetic differentiation, although all sub-populations belong to the same subspecies. Due to the open waters of the Dongting Lake, there was increased gene exchange, and we did not observe obvious genetic branches or geographical group clustering within each sub-population. We also found that the populations of Chinese soft-shell turtles had not experienced expansion in their history and had remained relatively stable. However, there was a concern that the genetic composition of the populations was becoming more homogeneous, which could lead to a decline in genetic diversity. Therefore, it is crucial to regularly monitor the genetic diversity of soft-shell turtle resources in the Dongting Lake. Unfortunately, the ecological environment of the Dongting Lake has been severely damaged, leading to increase pressure from invasive species, intensified water pollution, and fragmented habitats. This poses a potential threat to the wild resources of Chinese soft-shell turtles. To address this issue, we recommend standardizing proliferation and release activities, strictly prohibiting the spread of exotic soft-shell turtle varieties, and strengthening the protection and restoration of Chinese soft-shell turtle habitats. Given the multi-regional and multi-departmental management of the Dongting Lake, it is essential to establish a cross-regional and cross-departmental collaborative protection mechanism. Simultaneously, we believe that it is crucial to effectively protect the germplasm resources of soft-shell turtles in the Dongting Lake while also utilizing their genetic diversity advantages. This will provide a high-quality resource base for the breeding and industrial development of Chinese soft-shell turtles, ultimately achieving a balance between ecological and economic benefits. This will also contribute to the stable, healthy, and sustainable development of the Chinese soft-shell turtle industry.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d17080575/s1, Figure S1: Maximum-likelihood tree constructed based on the Cytb gene show the phylogenetic relationship of Pelodiscus sinensis; Figure S2: Mismatch distribution analysis of five P. sinensis populations.

Author Contributions

Conceptualization, Z.Z., Z.P. and J.X.; methodology, Z.Z. and J.X.; software, Z.Z. and J.X.; validation, Q.W., F.G. and Z.P.; formal analysis, Z.Z., H.X., J.N. and J.X.; investigation, Z.Z., H.X., Q.W., F.G., L.T., C.L., Z.P. and J.X.; resources, Z.P. and J.X.; data curation, Z.P.; writing—original draft preparation, Z.Z.; writing—review and editing, J.N. and J.X.; visualization, Z.Z. and H.X.; supervision, L.T.; project administration, Z.P. and J.X.; funding acquisition, Z.P. and J.X. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Aquatic Seed Industry Plan of Department of Agriculture and Rural Affairs of Hunan Province (HNAP2024).

Institutional Review Board Statement

The study was approved by the Ethics Committee of Hunan Fisheries Research Institute and Aquatic Products Seed Stock Station (procedure approval 17 July 2023, approval number HNFI20230717).

Data Availability Statement

The data will be made available upon request.

Acknowledgments

The authors would like to thank Qiwen Feng at Guangdong Meilikang Bio-Science Ltd. (Foshan, China) for his assistance with DNA sequencing data analysis.

Conflicts of Interest

Author Jiajia Ni was employed by the company Guangdong Meilikang Bio-Science Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

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Diversity 17 00575 g001
Figure 1. Distribution of sampling sites. JS, Junshan District (112.8477° E, 29.4488° N; n = 16); HS, Hanshou County (111.979° E, 28.920° N; n = 10); YJ, Yuanjiang City (112.770° E, 29.079° N; n = 18); SY, Shaoyang City (111.744° E, 27.229° N; n = 11); HY, Hengyang County (112.284° E, 26.982° N; n = 16).
Figure 1. Distribution of sampling sites. JS, Junshan District (112.8477° E, 29.4488° N; n = 16); HS, Hanshou County (111.979° E, 28.920° N; n = 10); YJ, Yuanjiang City (112.770° E, 29.079° N; n = 18); SY, Shaoyang City (111.744° E, 27.229° N; n = 11); HY, Hengyang County (112.284° E, 26.982° N; n = 16).
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Figure 2. Maximum likelihood phylogenetic trees and heatmaps of Cytb (A) and 12S rRNA (B) gene haplotypes in five sub-populations of Pelodiscus sinensis. JS, samples (n = 16) collected from Junshan District (112.8477° E, 29.4488° N); HS, samples (n = 10) collected from Hanshou County (111.979° E, 28.920° N); YJ, samples (n = 18) collected from Yuanjiang City (112.770° E, 29.079° N); SY, samples (n = 11) collected from Shaoyang City (111.744° E, 27.229° N); HY, samples (n = 16) collected from Hengyang County (112.284° E, 26.982° N).
Figure 2. Maximum likelihood phylogenetic trees and heatmaps of Cytb (A) and 12S rRNA (B) gene haplotypes in five sub-populations of Pelodiscus sinensis. JS, samples (n = 16) collected from Junshan District (112.8477° E, 29.4488° N); HS, samples (n = 10) collected from Hanshou County (111.979° E, 28.920° N); YJ, samples (n = 18) collected from Yuanjiang City (112.770° E, 29.079° N); SY, samples (n = 11) collected from Shaoyang City (111.744° E, 27.229° N); HY, samples (n = 16) collected from Hengyang County (112.284° E, 26.982° N).
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Figure 3. Haplotype network of Cytb (A) and 12S rRNA (B) gene sequences of Pelodiscus sinensis. JS, samples (n = 16) collected from Junshan District (112.8477° E, 29.4488° N); HS, samples (n = 10) collected from Hanshou County (111.979° E, 28.920° N); YJ, samples (n = 18) collected from Yuanjiang City (112.770° E, 29.079° N); SY, samples (n = 11) collected from Shaoyang City (111.744° E, 27.229° N); HY, samples (n = 16) collected from Hengyang County (112.284° E, 26.982° N).
Figure 3. Haplotype network of Cytb (A) and 12S rRNA (B) gene sequences of Pelodiscus sinensis. JS, samples (n = 16) collected from Junshan District (112.8477° E, 29.4488° N); HS, samples (n = 10) collected from Hanshou County (111.979° E, 28.920° N); YJ, samples (n = 18) collected from Yuanjiang City (112.770° E, 29.079° N); SY, samples (n = 11) collected from Shaoyang City (111.744° E, 27.229° N); HY, samples (n = 16) collected from Hengyang County (112.284° E, 26.982° N).
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Table 1. Base compositions of Cytb and 12S rRNA genes in five P. sinensis sub-populations.
Table 1. Base compositions of Cytb and 12S rRNA genes in five P. sinensis sub-populations.
GeneBase Content (%)
TCAGA+TC+G
Cytb31.111.927.529.558.641.4
12S rRNA22.222.637.717.659.940.1
Table 2. Genetic diversity parameters and neutrality tests of Cytb and 12S rRNA genes in five P. sinensis sub-populations. S, number of segregating sites; H, number of haplotypes; Hd, haplotype diversity; Pi, nucleotide diversity; K, average number of nucleotide differences.
Table 2. Genetic diversity parameters and neutrality tests of Cytb and 12S rRNA genes in five P. sinensis sub-populations. S, number of segregating sites; H, number of haplotypes; Hd, haplotype diversity; Pi, nucleotide diversity; K, average number of nucleotide differences.
GeneSub-PopulationNSHHdPiKTajima’s DPFu’s FsP
CytbHS102460.8890.0159.7780.6980.7524.0940.978
SY112030.3780.0085.400−0.7440.2193.9050.965
HY161140.6150.0105.0001.6600.9593.9460.954
JS161970.7500.0095.6170.1470.6382.4080.892
YJ1830100.8760.01610.7060.8530.7860.9860.740
Total7125130.7500.0146.5470.5230.6713.0680.906
12S rRNAHS10950.8440.0104.0891.8450.9823.1670.957
SY111040.4910.0062.4−0.7260.2621.7050.839
HY161950.6000.0114.283−0.7560.1994.5020.986
JS16730.5080.0062.1830.1230.5453.1090.936
YJ18740.7120.0093.152.4440.9952.9850.929
Total711460.6090.0103.230.5860.5973.0940.929
Table 3. Genetic differentiation index (FST)/gene flow (Nm) (above diagonal) and pairwise genetic distances (below diagonal) among five P. sinensis sub-populations. * p < 0.05.
Table 3. Genetic differentiation index (FST)/gene flow (Nm) (above diagonal) and pairwise genetic distances (below diagonal) among five P. sinensis sub-populations. * p < 0.05.
GeneSub-PopulationHSSYHYJSYJ
CytbHS-0.073/8.54−0.019/−4.740.180 */0.99−0.063/−4.36
SY0.012-−0.033/−8.510.514 */0.230.113/1.83
HY0.0130.010-0.378 */0.420.051/4.10
JS0.0150.0180.016-0.177 */1.02
YJ0.0150.0140.0150.016-
12S rRNAHS-0.023/17.46−0.026/−9.740.288 */0.68−0.053/−5.97
SY0.009-−0.047/−8.110.558 */0.190.152/1.36
HY0.0110.009-0.424 */0.340.085/2.82
JS0.0120.0140.002-0.189 */1.08
YJ0.0100.0100.0120.011-
Table 4. Analysis of molecular variance (AMOVA) of five P. sinensis sub-populations.
Table 4. Analysis of molecular variance (AMOVA) of five P. sinensis sub-populations.
GeneSource of VariationdfSum of SquaresVariance ComponentPercentage of Variation (%)Fixation Index
CytbAmong sub-populations440.4560.552 Va16.35
Within sub-populations62175.0672.824 Vb83.65
Total66215.5223.3761000.164
12S rRNAAmong sub-populations425.0450.351 Va20.85
Within sub-populations6687.9971.333 Vb79.15
Total70113.0421.6851000.209
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Zuo, Z.; Xiao, H.; Wu, Q.; Tian, L.; Gao, F.; Li, C.; Ni, J.; Peng, Z.; Xiang, J. Genetic Diversity of Five Pelodiscus sinensis Sub-Populations in the Dongting Lake Basin Based on Cytb and 12S rRNA Markers. Diversity 2025, 17, 575. https://doi.org/10.3390/d17080575

AMA Style

Zuo Z, Xiao H, Wu Q, Tian L, Gao F, Li C, Ni J, Peng Z, Xiang J. Genetic Diversity of Five Pelodiscus sinensis Sub-Populations in the Dongting Lake Basin Based on Cytb and 12S rRNA Markers. Diversity. 2025; 17(8):575. https://doi.org/10.3390/d17080575

Chicago/Turabian Style

Zuo, Zhiliang, Hewei Xiao, Qifan Wu, Lu Tian, Feng Gao, Cheng Li, Jiajia Ni, Zhitao Peng, and Jin Xiang. 2025. "Genetic Diversity of Five Pelodiscus sinensis Sub-Populations in the Dongting Lake Basin Based on Cytb and 12S rRNA Markers" Diversity 17, no. 8: 575. https://doi.org/10.3390/d17080575

APA Style

Zuo, Z., Xiao, H., Wu, Q., Tian, L., Gao, F., Li, C., Ni, J., Peng, Z., & Xiang, J. (2025). Genetic Diversity of Five Pelodiscus sinensis Sub-Populations in the Dongting Lake Basin Based on Cytb and 12S rRNA Markers. Diversity, 17(8), 575. https://doi.org/10.3390/d17080575

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