Genetic Diversity and Population Structure of the Chinese Mitten Crab (Eriocheir sinensis) from Six Different Lakes Using Microsatellites

Abstract

The Chinese mitten crab (Eriocheir sinensis) is among the most important species in China and other countries, and it contributes significantly to aquaculture and meeting protein demands for the fast-growing human population. To ensure their sustainable exploitation, management, and use in aquaculture, it is imperative to know their genetic diversity. Thus, we studied the genetic diversity of six populations of Chinese mitten crabs from six different lakes in the Yangtze River’s drainage system. A total of 180 Chinese mittens crabs were collected from six lakes in China, with 30 being collected from each lake. Then, DNA was extracted using TaKaRa Dalian, genotyping was performed by the Gene Marker software for statistical analysis and the genetic parameters such as observed number of alleles (Na)were studied. A total of 87 alleles were observed in 180 individuals of six wild Eriocheir sinensis populations. The Fis results showed that six sites had negative values, and crab20 had the largest value. The results of Fit showed that the single locus had a negative value. All the Fst values among the populations were lower than 0.50, while an AMOVA analysis showed that 0.36% of the genetic variation came from among the six populations and 94.08% of the genetic variation was between individuals in a population. The six Chinese mitten crab populations showed higher genetic variability among individuals of the same population with very low genetic variability between the populations. Therefore, this illustrates that the crabs from the six lakes have similar genetic diversity and minor genetic differences among them.

1. Introduction

The Chinese mitten crab is an important species in China, appreciated by consumers for its taste and nutritional value and its price by farmers and fishermen. In 2015, its contribution to the fisheries and aquaculture industry was approximately 820,000 tons, which accounted for more than 50 billion CNY in value [1,2]. This species will remain important towards meeting the protein needs of the ever-growing human population. Moreover, the wild population of the Chinese mitten crab serves as a broodstock resource base for aquaculture. The Chinese mitten crab (Eriocheir sinensis) is a catadromous species that is widely distributed throughout rivers, lakes, and estuaries of the eastern region of China [3,4,5]. As a result of pollution, habitat loss, and overexploitation, there has been an unprecedented decline in the population of these crabs since the 1960s, making them prone to collapse if not well managed. Furthermore, just like other aquatic organisms, the Chinese mitten crab continually needs to cope with the effects of global climate change, which is causing increases in aquatic temperatures and has led to changes in dissolved oxygen availability, salinity, and circulation patterns in aquatic environments [6,7,8].

Over the past years, efforts have been put forward by the Chinese government through the Ministry of Fisheries to ensure the long-term sustainability of aquatic biodiversity in China, including stock management and the implementation of protected areas (MPAs) [9]. Within this context, the preservation of genetic resources plays a crucial role and is one of the factors that has always been taken into consideration to ensure sustainable utilization and optimized conservation actions of aquatic organisms [10,11]. One metric that may help us understand and predict whether or not species are most likely to adapt to future conditions is genetic diversity, as it influences species’ capacity for resilience and their adaptative potential to environmental changes [12]. The patterns of population structure and genetic diversity inform us of the life histories, demography, ecology, and reproduction of a species [13,14]. Therefore, population genetic diversity and structure are particularly important for the protection and optimal use of genetic resources of E. sinensis.

So far, many scholars have studied the genetic structure and diversity of the Chinese mitten crab population in many water bodies in China. For example, Sui et al. [15] conducted a study on the genetic diversity and population structure of the Chinese mitten crab in its native range. In this study, it was found that neutral processes such as genetic drift and isolation have not resulted in significant genetic differentiation among the assessed geographic locations of this catadromous species. It was also found that a geographic population structure was absent. Another study conducted by Zhang et al. [16] assessed the genetic diversity and genetic structure of farmed and wild Chinese mitten crab (E. sinensis) populations from three major basins by mitochondrial DNA COI and Cyt b gene sequences. Since 2009, very few studies have assessed the genetic diversity and population structure of the Chinese mitten crab in its natural range in China. Moreover, it is very obvious that from that time up to the present, significant changes have occurred in these aquatic habitats. To ensure the effective and optimal utilization and conservation of the Chinese mitten crab, it is necessary to have recent genetic information about the population.

Generally, within each species, genetic resources can be partitioned not only among interbreeding individuals but also among populations. Population genetic diversity and population structure may emerge from several mechanisms which include reproductive isolation, geographic distance [17], biogeographic barriers (soft boundaries associated with hydrological processes or hard barriers such as land bridges [18]), or specific behavioral traits (e.g., dependability to natal spawning grounds [18]). Understanding the patterns of connectivity and the rate of genetic exchange among populations is therefore fundamental from a resource management perspective, as the effectiveness of MPA is intrinsically dependent not only on the species’ ability to self-replenish but also on the net export of biomass (i.e., spillover) beyond their boundaries [19].

The main aim of the present study is to determine the genetic diversity and population structure of the Chinese mitten crab from six lakes that are part of the Yangtse drainage system using microsatellites. The results could provide a theoretical basis for the rational exploitation, management, and protection of natural Chinese mitten crabs, as well as valuable information for selective breeding programs.

2. Materials and Methods

2.1. Study Area and Sample Collection

A total of 180 Chinese mitten crabs were collected from six different lakes in Jiangsu province in China and brought to the Freshwater Fisheries Research Center of the Chinese Academy of Fishery Sciences. A total of 30 crabs of which 15 were females and 15 males were collected from each lake. The lakes include Changdang Lake (CD) (32°59′–31°62′ N, 119°52′–118°60′ E), Gucheng Lake (GC) (31°14′–31°18′ N, 118°53′–118°57′ E), Gaoyou Lake (GY) (32°42′–33°41′ N, 119°06′–119°25′ E), Hong-tse Lake (HZ) (33°06′–33°40′ N, 118°10′–118°52′ E), Taihu Lake (TH) (30°55′40″–30° N, 119°52′32″–120° E), and Yangcheng Lake (YC) (30°55′40″–30° N, 119°52′32″–120° E); the locations of the lakes are shown in Figure 1. Upon arrival, the crabs were prepared, labeled, and stored in a −80 °C freezer.

2.2. DNA Extraction

DNA was extracted using an animal genomic DNA extraction kit (TaKaRa, Dalian, China). The DNA was tested for purity by agarose gel electrophoresis. The OD260 and OD280 concentrations were determined by a UV spectrophotometer. The qualified DNA dilution concentration was 50.00 ng/mL at −20 °C and stored for future use.

2.3. Microsatellite Primer Amplification

The 13 pairs of primers previously developed against microsatellites were used in the present study (Table S1). The FAM label at the 5′end of the forward primer was synthesized by Wuxi Tianlin Biological Co., Ltd. (Wuxi, China). The PCR reaction cycling was as follows: pre-denaturation at 94 °C for 5 min; pre-denaturation at 94 °C for 10 s and annealing for 10 s at 72 °C for 15 s in 30 cycles; and pre-denaturation at 72 °C for 10 min. The PCR products were detected by capillary electrophoresis using ABI3730XL sequencer (ABI, Foster City, CA, USA), and genotyping was performed by Gene Marker software for statistical analysis.

2.4. Genetic Diversity

The Popgene32 (v1.3.2) software [20] was used to calculate the allele number (Na), effective allele number (Ne), observed heterozygosity (Ho), Shannon Wiener index (I), and expected heterozygosity (He), to perform the likelihood ratio test, to calculate the Hardy–Weinberg balance, genetic differentiation index, Fst (F-statistics, Fst), and Nei’s standard genetic distance (Ds), and to perform neutral analysis detection. Genepop’s contingency table method was used for a linkage disequilibrium analysis [21]. The Botstein method was used to calculate the polymorphic information content (PIC) of each locus. Finally, the Arlequin3.5 software [22] was used to complete the AMOVA analysis.

2.5. Population Structure and Genetic Differentiation

In this study, Arlequin v3.5 [22] was used for the analysis of the pairwise Fst for each pair of populations. An exact test with 10,000 permutations was used to determine statistical significance. The genetic differentiation was estimated using AMOVA after analysis using Arlequin v3.5 [22]. Tests for genetic differentiation among the crab populations were carried out at three hierarchical levels of variation: among individuals, within populations (FIS), and within individuals (FIT). With the help of these findings, we evaluated population structure and mobility by using gene flow and then the BayesAss v3 [23] software. Additionally, the theory of the Bayesian method allocated samples into clusters. Different values of the length of the burn-in period (10,000–100,000) and MCMC repetitions (10,000–100,000) were used in every run and the most likely value for K was calculated using the ΔK method [24].

3. Results

3.1. Microsatellite

The information about the detected microsatellite markers is summarized in

Table 1. Genetic diversity indicators.

	Crab1	Crab20	Crab2	Crab11	Crab18	Crab15	Crab6	Crab17	Crab8	Crab12	Esin42	HLJEsa42	Crab21
AO
GC	2.00	5.00	1.00	6.00	7.00	8.00	5.00	6.00	9.00	10.00	14.00	10.00	5.00
HZ	2.00	6.00	2.00	6.00	6.00	7.00	3.00	5.00	8.00	9.00	17.00	9.00	6.00
YC	5.00	5.00	2.00	7.00	8.00	7.00	4.00	5.00	11.00	9.00	15.00	11.00	8.00
CD	2.00	5.00	1.00	8.00	7.00	7.00	4.00	5.00	8.00	6.00	13.00	15.00	6.00
GY	3.00	5.00	1.00	6.00	7.00	7.00	4.00	5.00	10.00	9.00	13.00	12.00	4.00
TH	2.00	7.00	1.00	5.00	7.00	5.00	3.00	4.00	10.00	7.00	15.00	14.00	6.00
All	5.00	9.00	2.00	10.00	13.00	8.00	5.00	6.00	15.00	13.00	19.00	19.00	8.00
AE
GC	1.98	3.25	1.00	2.03	3.86	4.05	1.70	3.32	3.75	6.14	11.11	5.59	2.85
HZ	1.86	2.89	1.06	2.98	3.44	2.31	1.18	2.86	4.09	5.80	10.84	6.33	3.57
YC	2.13	2.65	1.03	2.40	4.35	2.44	1.45	2.47	4.87	6.16	9.62	8.03	3.46
CD	1.98	2.14	1.00	2.51	2.62	3.07	1.46	2.45	3.94	4.54	9.72	8.45	3.23
GY	1.82	2.25	1.00	2.57	3.07	2.72	1.18	2.61	4.74	5.73	9.83	8.78	2.82
TH	1.89	3.61	1.00	3.19	3.60	1.92	1.22	2.31	4.23	5.57	10.05	9.00	3.44
All	1.96	2.88	1.01	2.63	3.71	2.75	1.36	2.73	4.38	6.14	11.95	8.47	3.25
HO
GC	0.90	0.46	0.00	0.53	0.40	0.33	0.33	0.60	0.76	0.76	0.83	0.40	0.56
HZ	0.73	0.43	0.06	0.83	0.40	0.36	0.16	0.53	0.70	0.76	0.83	0.53	0.80
YC	0.86	0.33	0.03	0.63	0.40	0.46	0.36	0.46	0.80	0.83	0.83	0.53	0.86
CD	0.9	0.36	0.00	0.63	0.60	0.46	0.36	0.40	0.70	0.80	0.90	0.90	0.70
GY	0.63	0.40	0.00	0.56	0.63	0.60	0.16	0.23	0.73	0.93	0.76	0.93	0.53
TH	0.70	0.33	0.00	0.70	0.66	0.46	0.20	0.43	0.80	0.90	0.66	0.93	0.80
All	0.78	0.38	0.01	0.65	0.51	0.45	0.26	0.44	0.75	0.83	0.80	0.70	0.71
HE
GC	0.50	0.70	0.00	0.51	0.75	0.76	0.42	0.71	0.74	0.85	0.92	0.83	0.66
HZ	0.47	0.66	0.06	0.67	0.72	0.57	0.15	0.66	0.76	0.84	0.92	0.85	0.73
YC	0.54	0.63	0.03	0.59	0.78	0.60	0.31	0.60	0.80	0.85	0.91	0.89	0.72
CD	0.50	0.54	0.00	0.61	0.62	0.68	0.32	0.60	0.75	0.79	0.91	0.89	0.70
GY	0.45	0.56	0.00	0.62	0.68	0.64	0.15	0.62	0.80	0.83	0.91	0.90	0.65
TH	0.48	0.73	0.00	0.69	0.73	0.48	0.18	0.57	0.77	0.83	0.91	0.90	0.72
All	0.49	0.65	0.01	0.62	0.73	0.63	0.26	0.63	0.77	0.83	0.91	0.88	0.69
HWE
GC	0.00	0.04	-	0.92	0.00	0.00	0.68	0.19	0.90	0.58	0.77	0.00	0.09
HZ	0.00	0.05	0.85	0.16	0.00	0.05	0.94	0.44	0.63	0.43	0.99	0.01	0.65
YC	0.02	0.00	1.00	0.99	0.00	0.15	0.89	0.38	0.97	0.48	0.99	0.06	0.22
CD	0.00	0.17	-	0.91	0.87	0.11	0.89	0.45	0.63	0.44	0.82	0.99	0.40
GY	0.03	0.06	-	0.39	0.42	0.39	0.99	0.00	0.91	0.86	0.45	0.74	0.00
TH	0.00	0.00	-	0.90	0.88	0.67	0.90	0.50	0.96	0.54	0.95	0.99	0.49
All	0.00	0.00	0.89	0.99	0.00	0.00	0.32	0.00	0.20	0.54	0.20	0.03	0.01

. The 13 microsatellite loci tested were found to be highly polymorphic across the six populations. A total of 87 alleles were detected in 180 individuals of the six wild Eriocheir Sinensis populations. No null alleles were detected across the populations. The observed allele number (A_O) for the 13 detected microsatellite markers ranged from 1 for crab2 to 19 for Esin42 and HLJEsa42, with an average of 6.69 alleles per locus (Table 1). The effective number of alleles (A_E) ranged from 1.02 for Crab2 to 11.96 for Esin42. The average observed heterozygosity (H_O) was 0.56, with a range of 0.01 for Crab2 to 0.79 for Crab1 (Table 1). The average expected heterozygosity was 0.62, with a range of 0.01 for Crab2 and 0.92 for Esin42 (H_E).

3.2. Genetic Diversity

The analysis of genetic diversity indicators such as the observed number of alleles (A_O), the number of effective alleles (A_E), Hardy Weinberg’s balanced likelihood ratio test outcome (HWE), observed heterozygosity (H_O), and expected heterozygosity (H_E) are presented in Table 1, while the levels and patterns of genetic diversity for the six crab populations are summarized in Table 1. The observed heterozygosity and expected heterozygosity were similar among the six crab populations. Crabs from CD had the highest observed heterozygosity (0.59) while those from GC had the lowest (0.53). The expected heterozygosity was highest (0.64) in crabs from GC while those from CD had the lowest (0.61).

A total of 87 alleles were observed in 180 individuals of the six wild Eriocheir sinensis populations with an overall mean of 6.69 alleles per locus ranging from 1 to 15. The number of effective alleles ranged from 1 to 11.95 with the most being at Esin 42. The numbers of observed and expected loci in six populations were obtained so that correlations with possible variations could be observed in the populations in the six lakes.

3.3. Hardy–Weinberg Equilibrium and Mutation–Drift Equilibrium Significant Test Using IAM, TPM, and SMM Model

The Hardy–Weinberg equilibrium test and linkage disequilibrium (LD) test are widely used in analyzing population evolution and structure, and locating important functional genes [25]. In the detection of the Hardy–Weinberg linkage disequilibrium in the six crab populations, a total of 78 gene locus pairs were detected, of which 10 were detected in the linkage disequilibrium state, including crab11/crab18, crab20/crab15, crab15/crab6, crab20/crab12, crab20/Esin42, crab8/Esin42, crab12/Esin42, crab12/HLJEsa42, Esin42/HLJEsa42, crab12/crab21 (Table S2).

Through the mutation–drift balance test of the three models, it was found that the crabs in Yangcheng Lake (YC) had the highest number of gene loci with the expected number of loci having an excess of heterozygosity, followed by crabs from Hongze Lake. For the observed number of loci with an excess of heterozygosity, crabs in Yangcheng Lake (YC) were also showed the least. Only under the infinite alleles model (IAM) model, the crabs in Yangcheng Lake (YC) were in a drifting equilibrium state. Regardless of the model, the crabs of GC, GY, and TH confirmed the drift equilibrium state (

Table 2. Mutation–drift equilibrium significant test using IAM, transcript per million (TPM), and single-step mutation (SMM) model.

	IAM	TPM	SMM
Expected number of loci with excess of heterozygosity	42.67	42.86	43.11
Observed number of loci with excess of heterozygosity	55.00	40.00	21.00
Observed number of loci with deficient heterozygosity	19.00	34.00	53.00
All loci fit mutation–drift equilibrium	1.30	2.09	0.37

).

Genetic variability and identity have many contributions to the domestication process, and the more variations there are in populations, the greater an individual is likely to adapt and pass traits on to the next generation. From the Shannon index (

Table 3. Shannon’s information index of six crab populations.

	Pop	Crab1	Crab2	Crab6	Crab8	Crab11	Crab12	Crab15	Crab17	Crab18	Crab21	Esin42	HLJEsa42
Shannon’s Information index	GC	0.68	0.00	0.85	1.62	1.09	1.99	1.66	1.36	1.58	1.26	2.51	1.94
	HZ	0.65	0.14	0.34	1.62	1.32	1.88	1.25	1.20	1.45	1.44	2.56	2.00
	YC	0.89	0.08	0.61	1.88	1.20	1.95	1.25	1.05	1.64	1.51	2.42	2.21
	CD	0.68	0.00	0.65	1.61	1.33	1.63	1.44	1.07	1.33	1.33	2.39	2.38
	GY	0.73	0.00	0.37	1.81	1.26	1.88	1.34	1.12	1.42	1.16	2.41	2.29
	TH	0.66	0.00	0.37	1.74	1.36	1.81	0.96	0.98	1.52	1.38	2.47	2.39
	All	0.74	0.04	0.61	1.82	1.33	1.97	1.43	1.19	1.63	1.39	2.62	2.40

), the genetic diversity is in the order of Yangcheng Lake hairy crabs, Gucheng Lake hairy crabs, Hongze Lake hairy crabs, Taihu hairy crabs, Changdang Lake hairy crabs, and Gaoyou Lake hairy crabs. From the perspective of polymorphic information content, in crabs from six different lakes, 10 sites were at a high polymorphism level, 2 were at a moderate polymorphism level, and 1 was at a low polymorphism level (

Table 4. PIC index of six crab populations.

	Pop	Crab1	Crab2	Crab6	Crab8	Crab11	Crab12	Crab15	Crab17	Crab18	Crab21	Esin42	HLJEsa42
PIC	GC	0.37	0.00	0.31	0.74	0.60	0.77	0.67	0.58	0.62	0.69	0.90	0.88
	HZ	0.37	0.00	0.41	0.73	0.51	0.83	0.75	0.69	0.73	0.64	0.90	0.81
	YC	0.45	0.00	0.14	0.79	0.61	0.82	0.63	0.62	0.67	0.64	0.90	0.88
	CD	0.35	0.05	0.14	0.75	0.66	0.82	0.57	0.65	0.70	0.71	0.90	0.84
	GY	0.36	0.00	0.17	0.76	0.68	0.81	0.48	0.56	0.72	0.70	0.90	0.89
	TH	0.52	0.03	0.30	0.79	0.58	0.83	0.59	0.59	0.77	0.71	0.89	0.87

).

The Fis results showed that six sites had negative values while crab20 had the largest value (

Table 5. Summary of F-Statistics and gene flow for six crab populations.

	Crab1	Crab20	Crab2	Crab11	Crab18	Crab15	Crab6	Crab17	Crab8	Crab12	Esin42	HLJEsa42	Crab21
Fis	−0.62	0.38	−0.02	−0.06	0.26	0.27	−0.03	0.28	0.01	−0.01	0.10	0.18	−0.03
Fit	−0.60	0.40	−0.01	−0.04	0.29	0.29	0.00	0.29	0.02	0.01	0.12	0.200	−0.02
Fst	0.01	0.03	0.01	0.01	0.03	0.03	0.03	0.02	0.01	0.02	0.02	0.01	0.01
Nm *	20.78	7.04	12.50	13.64	7.08	7.84	6.77	11.04	24.85	13.06	15.33	13.42	35.27

* Nm = gene flow estimated from Fst = 0.25 (1 − Fst)/Fst.

). The results of Fit revealed that the single locus had a negative value, which was also the largest at crab20 locus. All the Fst values among the populations were lower than 0.50. The gene flow value between populations was between 6.77 and 35.27 (Table 5).

The neutral test analysis results of 1000 repeated simulations of 13 microsatellites showed that all 12 sites were within the 95% confidence interval and belonged to neutral sites. Only the Esin42 site was not within the 95% confidence interval (

Table 6. Ewens–Watterson test for neutrality in six crab populations.

Locus	Obs. F	Min F	Max F	Mean	SE	L95	U95
crab1	0.50	0.20	0.97	0.57	0.03	0.29	0.93
crab20	0.34	0.11	0.95	0.38	0.02	0.18	0.72
crab2	0.98	0.50	0.99	0.84	0.02	0.50	0.99
crab11	0.37	0.10	0.95	0.34	0.017	0.17	0.69
crab18	0.26	0.07	0.93	0.27	0.01	0.14	0.53
crab15	0.36	0.12	0.96	0.41	0.02	0.21	0.78
crab6	0.73	0.20	0.97	0.56	0.03	0.28	0.92
crab17	0.36	0.16	0.97	0.50	0.03	0.25	0.88
crab8	0.22	0.06	0.92	0.24	0.00	0.12	0.51
crab12	0.16	0.07	0.93	0.27	0.01	0.14	0.56
Esin42	0.08	0.05	0.90	0.19	0.00	0.10	0.38
HLJEsa42	0.11	0.05	0.90	0.18	0.00	0.10	0.38
crab21	0.30	0.12	0.96	0.40	0.02	0.20	0.78

).

3.4. Population Structure of Six Crab Populations

The pairwise Fst values across all six populations are given in Table 5. The pairwise Fst values ranged from −0.00 (CD vs. GY) to 0.01 (GC vs. TH). All the pairwise Fst values were not statistically significant (p > 0.05). The genetic differentiation between the six populations was very low. Although not statistically significant, the genetic differentiation between GC and TH (0.002) was the highest and the lowest was between CD and TH (−0.00). To further understand the genetic differentiation among the six crab populations, we performed a genetic identity and distance analysis (

Table 7. Genetic identity and genetic distance of six crab populations.

	GC	HZ	YC	CD	GY	TH
GC		0.95	0.95	0.95	0.94	0.94
HZ	0.04		0.97	0.96	0.97	0.96
YC	0.04	0.02		0.95	0.96	0.95
CD	0.05	0.04	0.04		0.97	0.95
GY	0.05	0.02	0.03	0.02		0.97
TH	0.05	0.03	0.04	0.04	0.02

Nei’s genetic identity (above diagonal) and genetic distance (below diagonal).

). All the populations were highly similar, with similarities ranging from 94.54% for GY vs. GC to as high as 97.57% for GY vs. CD. Furthermore, STRUCTURE v 2.3.4 [26] was used to study the population structure and genetic relations of the six different populations. The model-based clustering analysis suggested k values ranging from two to six to possibly identify population structure; however, no evidence of population genetic structure could be observed, and the genetic structure of crabs in six different water bodies was found to be not significantly different (Figure 2). Furthermore, an analysis of molecular variance (AMOVA) enabled us to understand the nature and level of genetic variance and differentiation within and among the crab populations. The results showed very little (0.36%) variation among the crab populations but a high variation (94.08%) between individuals in the populations (

Table 8. AMOVA analysis of six crab populations.

Source of Variation	d.f	Sum of Squares	Variance Components	Percentage of Variation
Among populations	5	22.91	0.013	0.36
Among individuals Within populations	174	662.62	0.20	5.56
Within individuals	180	613.00	3.41	94.08
Total	359	1298.53	3.62

). This shows that the populations were not reasonably genetically differentiated based on the lake of origin, and that genetic variation was among individuals within each population. This is in agreement with pairwise Fst, genetic identity analysis, distance analysis, and model-based clustering analysis results showed that no reasonable genetic differentiation and structure could be observed among the six populations.

4. Discussion

Genetic diversity, species diversity, and ecosystem diversity are constituents of biodiversity, which is the basis of the evolution of life and species diversity [27,28]. Exploring genetic diversity enables us to know the origin of species diversity, variation, and evolution [29,30,31]. Several studies have been conducted on the genetic diversity of the Chinese mitten crab mainly for different populations in China [16,32,33]. The present study was aimed at assessing the genetic diversity of the Chinese mitten crab populations by microsatellite makers. The results obtained showed that the average Fst value at eight microsatellite loci, which revealed low genetic differentiation among the six crab populations and high variation within individuals in the populations and a low genetic flow [34].

4.1. Chinese Mittens Crab Diversity

By considering different genetic diversity parameters, a pairwise study of loci, the Shannon index, and the linkage disequilibrium, we found that 10 pairs of loci confirmed the linkage disequilibrium, which means that the populations deviated from the Hardy–Weinberg principle. Since the pairs of the loci are in linkage disequilibrium, there is a likelihood of genetic drift in the Chinese mitten crab population which can affect gene flow. The Fst values showed a moderate genetic difference among the different loci. The low genetic variation observed can be attributed to the geographic connectivity of the six crab populations, based on the genetic distance in the Chinese mitten crab population [34].

4.2. Mutation–Drift Equilibrium Significant Test Using IAM, TPM, and SMM Model

Three evolutionary models were used for the analysis of the mutation–drift equilibrium. In addition, the IAM model based on multi-step mutations is more suitable than the single-step mutation (SMM) model of genes [35]. However, TPM has both single-step and multi-step mutations, so researchers believe that microsatellites are more suitable for bottleneck analysis using TPM [36]. Previous studies have shown that environmental temperature has a great influence on animals [37,38,39]. However, the crabs in the six lakes are found in the Yangtze River drainage systems, and thus there are very little variations in the temperatures between them (Figure 1). Since temperature differences are not a large influence, there is little difference in the size of adult crabs. At the same time, the results show low genetic differentiation between the six crab populations and high variation within individuals in the populations. Nonetheless, judging from the experimental data collected, Yangcheng Lake crabs have richer biodiversity and better reproductive potential and growth performance.

4.3. Genetic Distance of Chinese Mitten Crabs Population

Previous studies showed that there was weak genetic differentiation among populations of crabs for several reasons [15,40,41], including their low migratory capacity, which hinders gene flow. In addition, the market demand for crabs can have significant impacts on genetic variability among Chinese mittens crabs [40], as market demand increases the translocation of crabs from one province to another. From our results, a higher genetic distance was found in HZ and YC, which means gene flow in their population is lower compared to that of other Chinese mittens crabs in other lakes. However, crab populations from YC and HC showed no significant genetic distance because the geographical distance between them is small; thus, they had more to mix genetically. The reason is presumably due to the absence of physical barriers to migration between the two lakes.

5. Conclusions

In conclusion, the study has shown that the six Chinese mittens crab populations have high genetic variability among individuals of a population and low genetic diversity between the six populations. In addition, no population structure was observed. Genetic diversity studies are necessary to assess the genetic diversity of Chinese mitten crabs after being subjected to mass selection and environmental change. The of the present study are important towards sustainable exploitation of the Chinese mitten crab. They also provides information for the selection process of germplasm resource for breeding Chinese mitten crabs.