Next Article in Journal
Two Methods of Monitoring Cats at a Landscape-Scale
Previous Article in Journal
Range Sizes of the World’s Mammals, Birds, and Amphibians from the Mid-Holocene to the Industrial Period
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Animals
Volume 11
Issue 12
10.3390/ani11123560
animals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Diversity and Population Dynamics of Leptobrachium leishanense (Anura: Megophryidae) as Determined by Tetranucleotide Microsatellite Markers Developed from Its Genome

by
Chao Fu
,
Qingbo Ai
,
Ling Cai
,
Fuyuan Qiu
,
Lei Yao
and
Hua Wu
*
Institute of Evolution and Ecology, School of Life Sciences, Central China Normal University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Animals 2021, 11(12), 3560; https://doi.org/10.3390/ani11123560
Submission received: 10 November 2021 / Revised: 6 December 2021 / Accepted: 13 December 2021 / Published: 15 December 2021
(This article belongs to the Section Animal Genetics and Genomics)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

More than 41 percent of amphibians evaluated by International Union for Conservation of Nature are threatened. It is vitally important to establish scientific and effective protection strategies for these organisms. Leishan Spiny Toad is endemic to China and it has a narrow distribution area. Long-term intentional human use and habitat destruction has caused the species to suffer. Here, we developed newly reliable and efficient molecular markers based on its genome to assess its genetic diversity and population history and provided support for conservation of this toad. Our results show that this toad still possesses high genetic diversity, but population decline may increase the possibility of inbreeding, which could work against persisting survival. Recovering the toad’s habitat and strengthening the publicity and education of wildlife protection can be helpful to its sustainability.

Abstract

Persisting declination of amphibians around the world has resulted in the public attaching importance to the conservation of their biodiversity. Genetic data can be greatly helpful in conservation planning and management, especially in species that are small in size and hard to observe. It is essential to perform genetic assessments for the conservation of Leptobrachium leishanense, an endangered toad and receiving secondary protection on the list of state-protected wildlife in China. However, current molecular markers with low reliability and efficiency hinder studies. Here, we sampled 120 adult toes from the population in the Leishan Mountain, 23 of which were used to develop tetranucleotide microsatellite markers based on one reference L. leishanense genome. After primer optimization, stability detection, and polymorphism detection, we obtained 12 satisfactory microsatellite loci. Then, we used these loci to evaluate the genetic diversity and population dynamics of the 120 individuals. Our results show that there is a low degree of inbreeding in the population, and it has a high genetic diversity. Recently, the population has not experienced population bottlenecks, and the estimated effective population size was 424.3. Accordingly, stabilizing genetic diversity will be key to population sustainability. Recovering its habitat and avoiding intentional human use will be useful for conservation of this species.

1. Introduction

Amphibians have long been declining on a global scale, and this trend will continue [1]. Furthermore, some amphibians face extinction or have become extinct [2]. There have been reports of massive declines in amphibians in many places, including areas where all species have been actively conserved [3,4]. Although there has been little consensus on the causes of this phenomenon [5], we recognize that amphibian populations are under serious threat and are in desperate need of conservation.
The Leishan Spiny Toad (Leptobrachium leishanense) is an endemic amphibian to China and is mainly restricted in Leishan county of Guizhou Province. This species inhabits broadleaf forests at elevations ranging from 1100–1800 m and breeds in slow-flowing streams via larval development [6]. The toad suffers from significant habitat loss and is often harvested for local consumption [7]. Thus, the population size has declined dramatically. It is listed as an endangered species on the International Union for Conservation of Nature (IUCN) Red List and receives secondary protection on the new list of state-protected wildlife in China. Formulating scientific conservation strategies is necessary for this species.
Genetic assessment is one of the aims of the conservation of biodiversity [8] and an important measure for amphibian population conservation [9,10,11]. Estimating genetic diversity and effective population size are the main goals of genetic assessments [12]. Genetic diversity reflects the adaptive potential of populations for environmental change [13]. When genetic diversity decreases, the extinction risk of populations increases [14]. Moreover, the levels of genetic diversity are related to population size [15]. It is a consensus that determining effective population size is more vital than measuring census size in populations [16]. In theory, small populations are susceptible to genetic depletion through drift and inbreeding, with adverse consequences for viability [17,18]. Therefore, effective population size can be used to assess the viability of populations.
As next-generation sequencing technologies offer new opportunities for conservation genetics [19], microsatellite markers with high mutation rates and genome-wide distributions reveal recent changes in genetic structure and demography critical for population management [20,21]. Although several studies using 10 dinucleotide microsatellites have shown that L. leishanense has high levels of genetic diversity and has not experienced recent bottleneck events [22,23,24], genetic assessments of this species are not nearly sufficient. In addition, dinucleotide microsatellites are considered less efficient and more unreliable than tetranucleotides because of their minimal PCR stutter [25]. Moreover, the traditional methods of microsatellite isolation and characterization are quite involved, costly, and time-consuming [26]. With the publication of a number of genomes, we can obtain sufficient numbers of different types of useful microsatellite loci more efficiently [27]. The genome sequencing project of L. leishanense has provided the opportunity to isolate and characterize microsatellites at the genomic level [28].
Here, we totally sampled 120 adult toes of L. leishanense from the population in the Leishan Moutain, and 23 of them were used to develop tetranucleotide microsatellite markers with polymorphisms based on one reference L. leishanense genome. After that, we analyzed the genetic diversity and population dynamics using the microsatellite loci we identified. The goals of this study were to (1) develop microsatellite loci with high reliability and efficiency, (2) evaluate the genetic diversity of the L. leishanense population, (3) detect if the population is experiencing a population bottleneck, (4) estimate the effective population size, and (5) provide molecular support for L. leishanense conservation planning.

2. Materials and Methods

2.1. Sampling

In 2012, 2013, 2014, 2015, and 2018, we collected 24 L. leishanense adults per year in Maoping village of Leishan County, Guizhou, China (Figure 1), sampled their toes, fixed the toes in anhydrous ethanol, and stored them in a −20 °C refrigerator. All individuals were released immediately after sampling. All experiments involving animals were approved by the Animal Ethics Committee of the School of Life Sciences, Central China Normal University (CCNU-IACUC-2019–008). We have complied with all relevant ethical regulations for animal testing and research.

2.2. DNA Extraction and Primer Selection

DNA samples were extracted using the TIANamp DNA kit (Tiangen, Beijing, China) and stored at −20 °C. MicroSatellite identification tool (MISA-web, Gatersleben, Germany) [29] was used to obtain the simple sequence repeats (SSRs) of L. leishanense from its genome. Then, we randomly selected 87 tetrabase repeat microsatellite markers that were repeated more than 10 times and designed 87 pairs of primers according to the flanking sequences at both ends of each primer. With the extracted DNA as a template, we optimized the annealing temperature of the primers and reaction system. Each polymerase chain reaction (PCR) procedure was conducted in a 10 μL volume, in which the premix was 5 μL, each primer was 0.3 μL, template DNA was 0.6 μL, and ddH2O was 3.8 μL. The procedure was performed with initial denaturation at 94 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at temperature Ta for 30 s, extension at 72 °C for 45 s, and extension at 72 °C for 5 min. PCR products were detected by 1% agarose gel electrophoresis. By adjusting the Ta temperature, a product with a clear band was obtained. The Ta temperature corresponding to the product was used as the optimum temperature for PCR amplification. Under the optimal amplification conditions, we used the DNA of three different individuals to detect the stability of primers in different individuals and screened the primers that could be amplified stably.

2.3. Polymorphic Microsatellite Verification

The screened primers were used to synthesize 5′ upstream fluorescent primers (FAM, HEX and TEMED, compounded by Tiangen, Beijing, China). DNA amplification was performed on 23 individuals collected in 2012 and 2013 by PCR with fluorescent primers, and the amplified fluorescence PCR products were sent to Tsingke Biological Company, Beijing, China for SSR scanning and sequenced by an ABI 3730xl analyzer. Then, the products were genotyped and calculated, and the evaluation criterion of the polymorphisms was a PIC value higher than 0.5 [30]. We used Genemarker 1.3 software [31] to read the lengths of alleles, genotyped the microsatellite markers, and selected the sites with obvious polymorphisms for the following analysis. The microsatellite genotyping data in Excel were transformed by using the Microsatellite Toolkit [32]. Cervus 3.0 software [33] was used to calculate the number of alleles (Na), polymorphism information content (PIC), expected heterozygosity (He), and observed heterozygosity (Ho). Micro-Checker 2.2.3 [34] was used to check large allele dropout of the microsatellite markers. GenePop 1.2 software [35] was used to detect the Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) of the screening microsatellite markers with polymorphisms, and the Bonferroni correction was used for correction. The significance level was p < 0.05.

2.4. Genetic Diversity Analysis

A total of 120 DNA samples were amplified by PCR with the screening fluorescent primers described above. The PCR products were sent to Qingke Biological Company for SSR scanning. An ABI 3730xl analyzer was used for sequencing. Data were analyzed using GenAlEx 6.502 [36] to calculate the effective number of alleles (Ne), the mean relatedness of the individuals for every year, and the per year genetic differentiation coefficient (Fst), and Cervus 3.0 software was used again to calculate the values described above. Excel and Microsatellite Toolkit v3.1.1 software were used for preliminary genetic data statistics and data format conversion. FSTAT 2.9.3.2 software [37] was used to calculate allelic richness (Ar), allelic diversity (Hs), and the inbreeding coefficient (Fis).

2.5. Population Bottleneck Identification

Bottleneck 1.2.02 software [38] was used to test whether the population had experienced population bottlenecks. Sign and Wilcoxon methods were used to test mutations through three mutation models: the infinite allele model (IAM), the stepwise mutation model (SMM), and the two-phased model of mutation (TPM). TPM was set to 95% SMM, with a variance of 30 and 1,000 iterations.

2.6. Effective Population Size Calculation

NeEstimator 2.1 [39] was used to calculate the effective population size by selecting the random mating model, and the confidence interval was 95%.

3. Results

3.1. Distribution of SSR in Genome of L. leishanense

A total of 1,454,145 microsatellite markers were obtained from the genome of L. leishanense. Monobase repeat microsatellite markers and dibase repeat microsatellite markers were the most common among all microsatellite markers. There were 874,773 monobase repeat microsatellite markers and 263,927 dibase repeat microsatellite markers, accounting for 60.16% and 18.15% of the total number of microsatellite markers, respectively, followed by 71,167 tribase repeat microsatellite markers and 23,332 tetrabase repeat microsatellite markers, accounting for 4.89% and 1.60% of the total number of microsatellite markers, respectively. The number of pentabase repeat microsatellite markers and hexabase repeat microsatellite markers was the lowest, with 909 pentabase repeat microsatellite markers and 844 hexabase repeat microsatellite markers, accounting for only 0.12% of the total microsatellite markers (Figure 2).

3.2. Polymorphism Microsatellite Loci

Eighty-seven pairs of primers randomly chosen from 23,332 tetrabase repeat microsatellite marker. After primer optimization, 64 pairs of primers were successfully amplified. Then stability detection was used, and 46 pairs of primers were obtained. Employing polymorphism detection, we obtained 12 satisfactory microsatellite loci. The Na of these loci ranged from 6–16. PIC values ranged from 0.537–0.904. Ho and He were between 0.609–0.913 and between 0.622–0.931, respectively (Table 1). All 12 loci were not significant with regard to LD (p > 0.05), and there were no loci deviated from HWE (p > 0.05). According to Micro-Checker 2.2.3, there was no large allele dropout of these microsatellite markers and no scoring error caused by the shadow peak.

3.3. Population Genetic Diversity

Using the 12 loci screened above, all 120 individuals were used to study the genetic diversity of the population. HWE detection of the L. leishanense population was performed. After Bonferroni correction, the significance level was p < 0.0042. The results are shown in Table 2. When we used all 120 samples for testing, loci LEA23, LEA7, LEA47, LEA2, and LEA53 deviated from HWE significantly, with Fis values as 0.179, 0.112, 0.119, 0.230, and 0.134, respectively (Table 3). When we separated the samples into each year for testing, locus LEA20 deviated from HWE significantly in 2013, with a Fis value of 0.215. Locus LEA23 and LEA7 deviated from HWE significantly in 2015 and 2018. Fis values of locus LEA23 in 2015 and 2018 are 0.132 and 0.249, respectively. Fis values of locus LEA7 in 2015 and 2018 are 0.161 and 0.369, respectively. Locus LEA47 deviated from HWE significantly in 2018 with Fis value as 0.201, and locus LEA2 deviated from HWE significantly in 2015 with Fis value as 0.218.
Then, we calculated pairwise year Fst valus in L. leishanense (Table 3). None of these values is greater than 0.05, suggesting the genetic differentiation between these years is negligible [40]. Further, we calculated the mean relatedness of the individuals for every year (Figure 3). In 2013, 2014, and 2015, mean pairwise relatedness within groups was significantly greater than zero, indicating the samples we collected in these three years have relatively close relationships.
Next, we calculated Na, Ne, PIC, Ho, He, Ar, Hs, and Fis of the population (Table 4). The results indicated that the genetic diversity of the population was still high. The positive value of Fis and that of Ho was lower than that of He, suggesting that there was a low degree of inbreeding in the population.

3.4. Population Bottleneck

The average expected heterozygosity (Heq) of the population in the IAM, SMM, and TPM models was calculated (Table 5). In the IAM model, there were 11 sites where He was significantly higher than Heq (p < 0.05), among which LEA22, LEA25, LEA20, LEA35, LEA14, LEA23, and LEA24 were extremely significantly higher than Heq (p < 0.01). In the TPM and SMM models, only He at LEA5 was significantly higher than Heq, showing heterozygote surplus. The sign test and Wilcoxon test were used to detect the heterozygosity surplus of the population under the three models of IAM, TPM, and SMM (Table 6). The mutation-drift balance of the population was detected under the IAM model, both of the sign and Wilcoxon tests showed significant deviations from the mutation-drift balance of the population. Under the TPM and SMM models, both the results of the sign test and Wilcoxon test showed that the population did not deviate from mutation-drift equilibrium.
The analysis of allele frequency distribution in the L. leishanense population showed that the allele frequency was mainly concentrated between 0.0–0.1, which was approximately 84.52% of the total allele frequency. Alleles with a frequency of 0.1–0.2 accounted for 12.69% of the total allele frequency. The proportion of the frequency distribution interval of 0.2–0.3 was 1.80%, while the allele proportions of the frequency distribution intervals of 0.3–0.4 and 0.4–0.5 were 0.5%. The allele frequency showed a typical “L” type distribution (Figure 4), suggesting that the population has not recently experienced a bottleneck effect.

3.5. Effective Population Size

According to the LD distance, the effective population size was estimated to be 424.3 (95% CI = 272.7–878.2) from NeEstimator 2.1.3.1.

4. Discussion

We isolated and characterized 12 tetrabase repeat microsatellite markers with polymorphisms from one reference genome of L. leishanense. Then, we used these loci to study the genetic diversity and population dynamics of this species. We found that the genetic diversity of the population was high and that there was a low degree of inbreeding in the population. Moreover, the population has not recently experienced bottleneck effects, and the estimated effective population size is 424.3.

4.1. Tetranucleotide Microsatellite Markers

Although the results above are similar to those of Zhang’s research [24], which used 10 dibase repeat microsatellite markers, the 12 tetranucleotide microsatellite markers we developed are more polymorphic and suitable for genetic diversity research. During PCR amplification, a biological phenomenon called stutter is generated due to chain slippage, resulting in typing errors, and the stutter product has one or more fewer duplicates than the real allele product [41,42]. In general, tetranucleotide repeats tend to stutter less than trinucleotide and dinucleotide repeats and are much more accurate and reliable [43,44]. Therefore, in different types of microsatellite systems, tetrabase repeat microsatellite markers are more common than dibase or tribase markers. Moreover, the PIC values of all 12 loci were higher than 0.5, suggesting that the loci we developed had higher polymorphism. Stable and reliable microsatellite markers are a necessary prerequisite for population estimation in the wild [45]. Thus, after primer optimization, stability detection, and polymorphism detection, we finally obtained 12 satisfactory tetranucleotide microsatellite loci.

4.2. Genetic Diversity

We speculated that several loci deviated from HWE (Table 2) mainly caused by sampling from the same family (Figure 3). The sharp decline of the population size has increased the possibility in sampling individuals of same family. The genetic diversity of a population is a long-term process, the population of L. leishanense does not have significant genetic differentiation among these five years (Table 3); accordingly, we considered that these deviating loci were still effective in estimating population genetic diversity. Then, a series of indices were used to measure the genetic diversity of the toad, including Na, Ar, Ne, PIC, Ho, He, and Hs. According to our results, the toad still has high genetic diversity. Threatened species usually have small or declining populations and are prone to loss of genetic diversity due to inbreeding or genetic drift [14]. As an endangered and narrowly distributed toad, the population shows the opposite result. Several studies investigating endangered or narrowly distributed species have obtained similar results [45,46,47,48,49], indicating that endangered species or species with a narrow distribution may also have high levels of genetic diversity. When the earth was in an ice age, some areas with a stable ecological environment became the refuge of organisms, and the populations living in the refuge survived and accumulated rich genetic diversity [50]. The Leishan Spiny Toad is a relatively primitive species, and its formation dates back to the Miocene [22]. The toad survived by staying on Leigong Mountain and retained rich genetic diversity when the ice age came. In addition, two additional distribution sites were found by Zheng et al. [51], suggesting that the toad is not strictly a narrowly distributed species. We may have underestimated the genetic diversity of the species.
Although the Fis value of the population is on the low degree, this does not mean that there is no inbreeding between the individuals in the population. According to our year-by-year field work, its population size is declining. This undoubtedly increases the possibility of its inbreeding. Inbreeding has a negative effect on the fitness of the population, including fertility and viability [52], which is not conducive to the long-term development of the population. We could not find more obvious molecular evidence of inbreeding, possibly due to our restricted sampling size and the relatively high number of alleles found. As with high number of alleles, the probability of obtaining homozygote hgenotypes in one locus is very low. Thus, it will influence our detection of inbreeding.

4.3. Population Dynamics

Combining the results of model simulation with allele frequency distribution, we find that the population has not recently experienced a bottleneck effect. We tested three models, and IAM was significant both in the sign test and the Wilcoxon test. Both SMM and TPM were not significant in the sign test and Wilcoxon test (Table 5). IAM assumes that there is only one mutation of an allele in a population, and each mutation produces a new allele, which is generally used in isozyme or DNA sequencing data. SMM supposes that alleles can mutate upward or downward into new alleles. TPM is the synthesis of the previous two models, and the probability of occurrence of two kinds of mutations can be determined. The principle of allelic mutation in microsatellite data is the increase or absence of repeating units, which is represented by the change in sequence length. Some studies believe that TPM is more suitable for microsatellite data [53]. Therefore, we accept the result of TPM that there is no significant excess heterozygosity in this population. That is, the rate of heterozygosity decrease is approximately the same as the rate of allele loss in L. leishanense, indicating that the population has not recently experienced a bottleneck.
However, the ability to detect the population bottleneck based on heterozygosity is limited, and the number of alleles is more sensitive to population fluctuation, so it is more reliable to analyze the distribution of allele rates in the case of heterozygous residues to determine whether the population has experienced the bottleneck effect [54]. To enhance the adaptability to environmental changes, species tend to accumulate many rare alleles with low frequency. Therefore, the frequency distribution of alleles in mutation–drift equilibrium shows an “L” shape. If the species recently experienced a genetic bottleneck, the distribution of alleles with low frequency (0.0–0.1) will change to a mid-frequency distribution (0.1–0.2); thus, the allele frequency distribution will deviate from “L” [55]. In this study, the frequencies of the allele were generally in a typical “L” (Figure 3), suggesting that the population did not experience bottleneck effects.
The LD distance between microsatellite markers can be used to estimate effective population size, and this method has been applied to mammals, fish, amphibians, and other animals [56]. Effective population size is a valuable method in population conservation and management research. Maintaining an effective population of sufficient size is a key factor to maintain the rich genetic diversity of the population. Based on the microsatellite loci we developed, the estimated effective population size of L. leishanense is 424.3. Nei et al. [57] deemed that the population size should be 4–10 times of the effective population size to maintain the stability of population genetic diversity. Therefore, to maintain the stability of the population, the number of Leishan Spiny Toads should be 1697.2–4243. However, while LD information is used to estimate the effective population size, the accuracy of the results is significantly correlated with the sample size [58]. More samples may be needed to obtain more reliable and accurate results in L. leishanense.

5. Conclusions

Our study has provided 12 reliable tetranucleotide microsatellite loci with polymorphisms, enriching the information regarding the genetic diversity and population dynamics of L. leishanense. Although the genetic diversity is still high based on our results, a low degree of inbreeding indicates that the population is declining. Avoiding habitat fragmentation and intentional human use will be key to the conservation of this species. Furthermore, recovering the streams and woodlands where the species once existed abundantly will also help to stabilize its genetic diversity.

Author Contributions

Conceptualization, C.F. and H.W.; formal analysis, Q.A.; funding acquisition, H.W.; investigation, L.Y.; methodology, L.C.; resources, F.Q.; software, Q.A.; supervision, H.W.; writing—original draft, C.F.; writing—review and editing, C.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Biodiversity Survey, Monitoring and Assessment Project of the Ministry of Ecology and Environment, China (2019–2023).

Institutional Review Board Statement

All experiments involving animals were approved by the Animal Ethics Committee of the School of Life Sciences, Central China Normal University (CCNU-IACUC-2019–008). We have complied with all relevant ethical regulations for animal testing and research.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

We thank Chenliang Li, Shichao Wei, and Wei Zhang for helping with sample collection.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Houlahan, J.E.; Findlay, C.S.; Schmidt, B.R.; Meyer, A.H.; Kuzmin, S.L. Quantitative evidence for global amphibian population declines. Nature 2000, 404, 752–755. [Google Scholar] [CrossRef] [PubMed]
  2. Stuart, S.N.; Chanson, J.S.; Cox, N.A.; Young, B.E.; Rodrigues, A.S.; Fischman, D.L.; Waller, R.W. Status and trends of amphibian declines and extinctions worldwide. Science 2004, 306, 1783–1786. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. McMenamin, S.K.; Hadly, E.A.; Wright, C.K. Climatic change and wetland desiccation cause amphibian decline in Yellowstone National Park. Proc. Natl. Acad. Sci. USA 2008, 105, 16988–16993. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Whitfield, S.M.; Bell, K.E.; Philippi, T.; Sasa, M.; Bolaños, F.; Chaves, G.; Savage, J.M.; Donnelly, M.A. Amphibian and reptile declines over 35 years at La Selva, Costa Rica. Proc. Natl. Acad. Sci. USA 2007, 104, 8352–8356. [Google Scholar] [CrossRef] [Green Version]
  5. Kiesecker, J.; Blaustein, A.; Belden, L. Complex causes of amphibian population declines. Nature 2001, 410, 681–684. [Google Scholar] [CrossRef]
  6. Fei, L.; Ye, C.Y. Amphibians of China (Ⅰ); Science Press: Beijing, China, 2016. (In Chinese) [Google Scholar]
  7. The IUCN Red List of Threatened Species. Available online: https://www.iucnredlist.org/species/57627/63864909 (accessed on 7 September 2021).
  8. Convention on Biological Diversity. Available online: https://www.cbd.int/sp/targets/ (accessed on 7 September 2021).
  9. Arens, P.; Bugter, R.; Van’t Westende, W.; Zollinger, R.; Stronks, J.; Vos, C.C.; Smulders, M.J. Microsatellite variation and population structure of a recovering Tree frog (Hyla arborea L.) metapopulation. Conserv. Genet. 2006, 7, 825–835. [Google Scholar] [CrossRef] [Green Version]
  10. Perl, R.B.; Geffen, E.; Malka, Y.; Barocas, A.; Renan, S.; Vences, M.; Gafny, S. Population genetic analysis of the recently rediscovered Hula painted frog (Latonia nigriventer) reveals high genetic diversity and low inbreeding. Sci. Rep. 2018, 8, 5588. [Google Scholar] [CrossRef] [Green Version]
  11. Yu, D.; Zheng, R.; Lu, Q.; Yang, G.; Fu, Y.; Zhang, Y. Genetic diversity and population structure for the conservation of giant spiny frog (Quasipaa spinosa) using microsatellite loci and mitochondrial DNA. Asian Herpetol. Res. 2016, 7, 75–86. [Google Scholar]
  12. Yun, S.A.; Son, H.D.; Im, H.T.; Kim, S.C. Genetic diversity and population structure of the endangered orchid Pelatantheria scolopendrifolia (Orchidaceae) in Korea. PLoS ONE 2020, 15, e0237546. [Google Scholar] [CrossRef]
  13. Allentoft, M.E.; O’Brien, J. Global amphibian declines, loss of genetic diversity and fitness: A review. Diversity 2010, 2, 47–71. [Google Scholar] [CrossRef] [Green Version]
  14. Frankham, R. Genetics and extinction. Biol. Conserv. 2005, 126, 131–140. [Google Scholar] [CrossRef]
  15. Frankham, R. Relationship of Genetic Variation to Population Size in Wildlife. Conserv. Biol. 1996, 10, 1500–1508. [Google Scholar] [CrossRef] [Green Version]
  16. Beebee, T.J.C. Conservation genetics of amphibians. Heredity 2005, 95, 423–427. [Google Scholar] [CrossRef]
  17. Lynch, M.; Conery, J.; Burger, R. Mutation Accumulation and the Extinction of Small Populations. Am. Nat. 1995, 146, 489–518. [Google Scholar] [CrossRef] [Green Version]
  18. Ralls, K.; Brugger, K.; Ballou, J. Inbreeding and juvenile mortality in small populations of ungulates. Science 1979, 206, 1101–1103. [Google Scholar] [CrossRef]
  19. Primmer, C.R. From conservation genetics to conservation genomics. Ann. N. Y. Acad. Sci. 2009, 1162, 357–368. [Google Scholar] [CrossRef] [PubMed]
  20. DeFaveri, J.; Viitaniemi, H.; Leder, E.; Merilä, J. Characterizing genic and nongenic molecular markers: Comparison of microsatellites and SNPs. Mol. Ecol. Resour. 2013, 13, 377–392. [Google Scholar] [CrossRef]
  21. Wilson, A.B.; Ashe, J.; Padron, M.; Hamilton, H. Comprehensive genus-wide screening of seahorse microsatellite loci identifies priority species for conservation assessment. Conserv. Genet. Resour. 2021, 13, 221–230. [Google Scholar] [CrossRef]
  22. Li, J.; Wei, S.; Hu, M.; Luo, Z.; Zhao, M.; Wu, H. Reflection of paleoclimate oscillations and tectonic events in the phylogeography of moustache toads in southern China. J. Zool. 2018, 305, 17–26. [Google Scholar] [CrossRef] [Green Version]
  23. Wang, J.; Cheng, J.; Zhang, R.P.; Wang, Y.Z.; Li, C.L.; Wu, H. Microsatellite DNA markers for mating systems analysis in the Leishan moustache toad (Vibrissaphora leishanensis). Conserv. Genet. Resour. 2011, 3, 761–763. [Google Scholar] [CrossRef]
  24. Zhang, W.; Luo, Z.; Zhao, M.; Wu, H. High genetic diversity in the endangered and narrowly distributed amphibian species Leptobrachium leishanense. Integr. Zool. 2015, 10, 465–481. [Google Scholar] [CrossRef]
  25. Archie, E.A.; Moss, C.J.; Alberts, S.C. Characterization of tetranucleotide microsatellite loci in the African savannah elephant (Loxodonta africana africana). Mol. Ecol. Notes 2003, 3, 244–246. [Google Scholar] [CrossRef]
  26. Hu, L.; Xia, Y.; Zheng, Y.; Zeng, X. Isolation and characterization of ten microsatellite loci for the Chong’an moustache toad Leptobrachium liui (Anura: Megophryidae). Conserv. Genet. Resour. 2012, 4, 877–879. [Google Scholar] [CrossRef] [Green Version]
  27. Xu, Z.C.; Jin, Y.C.; Milne, R.I.; Xiahou, Z.Y.; Qin, H.T.; Ye, L.J.; Gao, L.M.; Liu, J.; Li, D.Z. Development of 32 novel microsatellite loci in Juglans sigillata using genomic data. Appl. Plant. Sci. 2020, 8, e11328. [Google Scholar] [CrossRef] [Green Version]
  28. Li, J.; Yu, H.; Wang, W.; Fu, C.; Zhang, W.; Han, F.; Wu, H. Genomic and transcriptomic insights into molecular basis of sexually dimorphic nuptial spines in Leptobrachium leishanense. Nat. Commun. 2019, 10, 5551. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Beier, S.; Thiel, T.; Münch, T.; Scholz, U.; Mascher, M. MISA-web: A web server for microsatellite prediction. Bioinformatics 2017, 33, 2583–2585. [Google Scholar] [CrossRef] [Green Version]
  30. Botstein, D.; White, R.L.; Skolnick, M.; Davis, R.W. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 1980, 32, 314–331. [Google Scholar]
  31. Holland, M.M.; Parson, W. GeneMarker® HID: A reliable software tool for the analysis of forensic STR data. J. Forensic Sci. 2011, 56, 29–35. [Google Scholar] [CrossRef]
  32. Park, S. Microsatellite Toolkit; Trinity College: Dublin, Ireland, 2001. [Google Scholar]
  33. Kalinowski, S.T.; Taper, M.L.; Marshall, T.C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 2007, 16, 1099–1106. [Google Scholar] [CrossRef]
  34. Van Oosterhout, C.; Hutchinson, W.F.; Wills, D.P.; Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 2004, 4, 535–538. [Google Scholar] [CrossRef]
  35. Raymond, M. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 1995, 86, 248–249. [Google Scholar] [CrossRef]
  36. Peakall, R.O.D.; Smouse, P.E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 2006, 6, 288–295. [Google Scholar] [CrossRef]
  37. Goudet, J. FSTAT (Version 2.9.3.2): A Program to Estimate and Test Gene Diversities and Fixation Indices. 2002. Available online: http://www.unil.ch/izea/software/fstat.html (accessed on 17 November 2019).
  38. Cornuet, J.M.; Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 1996, 144, 2001–2014. [Google Scholar] [CrossRef]
  39. Do, C.; Waples, R.S.; Peel, D.; Macbeth, G.M.; Tillett, B.J.; Ovenden, J.R. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 2014, 14, 209–214. [Google Scholar] [CrossRef]
  40. Wright, S. Variability within and among Natural Populations. In Evolution and the Genetics of Populations; The University of Chicago Press: Chicago, IL, USA, 1978; Volume 4. [Google Scholar]
  41. O’Reilly, P. Isolation of twenty low stutter di- and tetranucleotide microsatellites for population analyses of walleye pollock and other gadoids. J. Fish. Biol. 2000, 56, 1074–1086. [Google Scholar] [CrossRef]
  42. Schlötterer, C.; Tautz, D. Slippage synthesis of simple sequence DNA. Nucleic. Acids. Res. 1992, 20, 211–215. [Google Scholar] [CrossRef] [Green Version]
  43. Edwards, A.; Civitello, A.; Hammond, H.A.; Caskey, C.T. DNA typing and genetic mapping with trimeric and tetrameric tandem repeats. Am. J. Hum. Genet. 1991, 49, 746. [Google Scholar] [PubMed]
  44. Walsh, P.S.; Fildes, N.J.; Reynolds, R. Sequence analysis and characterization of stutter products at the tetranucleotide repeat locus vWA. Nucleic. Acids. Res. 1996, 24, 2807–2812. [Google Scholar] [CrossRef]
  45. Huang, K.; Guo, S.; Cushman, S.A.; Dunn, D.W.; Qi, X.; Hou, R.; Zhang, J.; Li, Q.; Zhang, Q.; Shi, Z.; et al. Population structure of the golden snub-nosed monkey Rhinopithecus roxellana in the Qinling Mountains, central China. Integr. Zool. 2016, 11, 350–360. [Google Scholar] [CrossRef]
  46. Finger, A.; Kettle, C.J.; Kaiser-Bunbury, C.N.; Valentin, T.; Doudee, D.; Matatiken, D.; Ghazoul, J. Back from the brink: Potential for genetic rescue in a critically endangered tree. Mol. Ecol. 2011, 20, 3773–3784. [Google Scholar] [CrossRef] [PubMed]
  47. Jian, H.; Li, S.; Guo, J.; Li, S.; Wang, Q.; Yan, H.; Qiu, X.; Zhang, Y.; Cai, Z.; Volis, S.; et al. High genetic diversity and differentiation of an extremely narrowly distributed and critically endangered decaploid rose (Rosa praelucens): Implications for its conservation. Conserv. Genet. 2018, 19, 761–776. [Google Scholar] [CrossRef]
  48. Li, B.; Wang, A.; Zhang, P.; Li, W. Genetic diversity and population structure of endangered Glehnia littoralis (Apiaceae) in China based on AFLP analysis. Biotechnol. Biotechnol. Eq. 2019, 33, 331–337. [Google Scholar] [CrossRef] [Green Version]
  49. Yue, X.; Chen, J.; Guo, Y.; Wang, Q. Population genetic structure of Sagittaria natans (Alismataceae), an endangered species in China, revealed by nuclear SSR loci analyses. Biochem. Syst. Ecol. 2011, 39, 412–418. [Google Scholar] [CrossRef]
  50. Tzedakis, P.C.; Lawson, I.T.; Frogley, M.R.; Hewitt, G.M.; Preece, R.C. Buffered tree population changes in a Quaternary refugium: Evolutionary implications. Science 2002, 297, 2044–2047. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Zheng, Y.; Hu, J.; Zeng, X. Examining the interglacial high-elevation refugia scenario in East Asian subtropical mountain systems with the frog species Leptobrachium liui. Ecol. Evol. 2018, 8, 9326–9340. [Google Scholar] [CrossRef]
  52. Curik, I.; Zechner, P.; Solkner, J.; Achmann, R.; Bodo, I.; Dovc, P.; Kavar, T.; Marti, E.; Brem, G. Inbreeding, microsatellite heterozygosity, and morphological traits in Lipizzan horses. J. Hered. 2003, 94, 125–132. [Google Scholar] [CrossRef] [Green Version]
  53. Kosman, E.; Jokela, J. Dissimilarity of individual microsatellite profiles under different mutation models: Empirical approach. Ecol. Evol. 2019, 9, 4038–4054. [Google Scholar] [CrossRef]
  54. Nei, M.; Maruyama, T.; Chakraborty, R. The bottleneck effect and genetic variability in populations. Evolution 1975, 29, 1–10. [Google Scholar] [CrossRef]
  55. Luikart, G.; Allendorf, F.W.; Cornuet, J.M.; Sherwin, W.B. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. 1998, 89, 238–247. [Google Scholar] [CrossRef] [PubMed]
  56. Zhao, Y.; Zhu, X.; Li, Z.; Xu, W.; Dong, J.; Wei, H.; Li, Y.; Li, X. Genetic diversity and structure of Chinese grass shrimp, Palaemonetes sinensis, inferred from transcriptome-derived microsatellite markers. BMC Genet. 2019, 20, 75. [Google Scholar] [CrossRef]
  57. Nei, M.; Chakraborty, R.; Fuerst, P.A. Infinite allele model with varying mutation rate. Proc. Natl. Acad. Sci. USA 1976, 73, 4164–4168. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Hill, W.G. Estimation of linkage disequilibrium in randomly mating populations. Heredity 1974, 33, 229–239. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Animals 11 03560 g001 550
Figure 1. Sampling site of L. leishanense. The red triangle represents the sampling site: Maoping village of Leishan County, Guizhou Province, China.
Figure 1. Sampling site of L. leishanense. The red triangle represents the sampling site: Maoping village of Leishan County, Guizhou Province, China.
Animals 11 03560 g001
Animals 11 03560 g002 550
Figure 2. Distribution of SSR repeat types in the genome of L. leishanense.
Figure 2. Distribution of SSR repeat types in the genome of L. leishanense.
Animals 11 03560 g002
Animals 11 03560 g003 550
Figure 3. Mean within-year pairwise relatedness estimates for 5 years in L. leishanense. Red lines represent permuted 95% confidence intervals around the null hypothesis of zero relatedness and error bars represent bootstrapped confidence intervals around the mean.
Figure 3. Mean within-year pairwise relatedness estimates for 5 years in L. leishanense. Red lines represent permuted 95% confidence intervals around the null hypothesis of zero relatedness and error bars represent bootstrapped confidence intervals around the mean.
Animals 11 03560 g003
Animals 11 03560 g004 550
Figure 4. Allele frequency distribution in the population of L. leishanense.
Figure 4. Allele frequency distribution in the population of L. leishanense.
Animals 11 03560 g004
Table
Table 1. Detailed information on 12 microsatellite loci developed from 23 L. leishanense.
Table 1. Detailed information on 12 microsatellite loci developed from 23 L. leishanense.
LocusPrimer Sequence (5′-3′)
(F, Forward; R, Reverse)		Repeat MotifPCR Production (μL)Labelling DyeTa (°C)Size Range (bp)NaHoHePIC
LEA22F:TGCGACTACGTAACCCTGTG
R:AGGAAATGAGCCTTTGCCTC		(AGAT)1635′ FAM56 °C216–29260.6090.6220.537
LEA25F:GTGGTTGGTTGGTTGGGTC
R:TGGTCAGGATGTGAGGAGTG		(TGGT)1365′ HEX58 °C226–282140.8260.8890.856
LEA20F:ATTTGATGGTGTCTGGGAGG
R:CTAAGAGAGCCGAAACGTCG		(GATA)1395′ TAMRA58 °C195–263160.8700.9300.903
LEA35F:GCGGGAGTTTGAGCTGTATC
R:CAGCTTACATTGTGTGCAGC		(CTAT)1435′ FAM62 °C192–260160.9130.9250.897
LEA14F:ATAAGCTAAACAGGCGTGGG
R:TTTCATATCAGGGGAGAGCG		(TTTC)1865′ HEX62 °C150–234140.8700.8820.851
LEA23F:CCAGGAACAAGGTCAGTGGT
R:CCCATGTTCGAGAGGAGAAG		(TCTA)1895′ TAMRA64 °C178–258100.7390.8500.812
LEA5F:TCAACTCAACTCTCCCCCTG
R:AACGCACATCCCTAGTGGTC		(CTTT)1435′ FAM60 °C183–199110.8260.8590.823
LEA7F:ACCATCAATTTTAGGGGTGC
R:TGGGATTTCCCAGTCATTTC		(AGAT)2065′ HEX60 °C178–246140.8260.9080.879
LEA47F:GACAAATGGGGAGATGATGG
R:AAAACGTCAGTGGCAAATCC		(AGAT)1795′ TAMRA62 °C162–261110.7390.8860.853
LEA24F:GTGAAACTTGCATCCACTGC
R:AAAATTAGCTATGGGTGGCG		(TATC)2035′ FAM62 °C205–289150.9130.9310.904
LEA2F:CACCCCGTGACAATATACCC
R:TGAGGGATCATTCTTCTGGC		(GATA)1165′ HEX62 °C207–251110.9130.8920.859
LEA53F:ATGGATAGATGGATGGCTGG
R:CAACGCGGAAAAAGAAACAT		(TAGA)1395′ TAMRA62 °C210–254130.9130.9180.889
Table
Table 2. Hardy–Weinberg equilibrium test of 12 loci in L. leishanense.
Table 2. Hardy–Weinberg equilibrium test of 12 loci in L. leishanense.
Locus2012–201820122013201420152018
PHWEPHWEPHWEPHWEPHWEPHWE
LEA220.0610.4410.0730.1570.2820.031
LEA250.0440.7370.0090.1390.5090.073
LEA200.0191.0000.003*0.5410.2050.335
LEA350.4350.2290.6070.2070.9170.151
LEA140.0700.2660.0250.4790.3950.091
LEA230.000 *0.1080.0230.0380.000 *0.003 *
LEA50.2370.6570.0530.8430.9840.208
LEA70.004 *0.5410.3300.4260.004 *0.004 *
LEA470.000 *0.8620.1000.6950.0140.004 *
LEA240.0370.1330.9140.8440.4470.086
LEA20.000 *0.0060.0100.0120.000 *0.008
LEA530.001 *0.0170.2800.4910.7270.019
Note: * Indicated significant deviations from Hardy–Weinberg equilibrium after Bonferroni correction.
Table
Table 3. Pairwise year Fst values in L. leishanense.
Table 3. Pairwise year Fst values in L. leishanense.
Year20122013201420152018
20120.0000.0060.0100.0110.000
20130.0060.0000.0100.0320.017
20140.0100.0100.0000.0380.019
20150.0110.0320.0380.0000.009
20180.0000.0170.0190.0090.000
Table
Table 4. Genetic diversity indices of L. leishanense in 120 individuals.
Table 4. Genetic diversity indices of L. leishanense in 120 individuals.
LocusNaArNePICHoHeHsFis
LEA2225.00025.00014.3710.9260.8580.9340.9350.082
LEA2516.00016.0009.6580.8880.7920.9000.9010.121
LEA2021.00021.00013.0970.9190.8670.9280.9280.066
LEA3519.00019.00011.9500.9100.9080.9200.9200.013
LEA1416.00016.00010.1190.8930.8580.9050.9050.052
LEA2323.00023.00014.1800.9250.7670.9330.9340.179
LEA511.00011.0003.0340.6150.5830.6730.6740.134
LEA717.00017.0008.1770.8670.7830.8810.8820.112
LEA4725.00025.00012.9910.9180.8170.9270.9270.119
LEA2420.00020.00012.0100.9110.8670.9210.9210.059
LEA216.00016.0008.5180.8730.6830.8860.8870.230
LEA5312.00012.0007.2340.8470.7500.8650.8660.134
Mean18.41718.41710.4450.8740.7940.8900.8900.107
Notes: number of alleles (Na), allelic richness (Ar), effective number of alleles (Ne), polymorphism information content (PIC), observed heterozygosity (Ho), expected heterozygosity (He), allelic diversity (Hs), and the inbreeding coefficient (Fis).
Table
Table 5. Bottleneck test of 12 microsatellite loci in L. leishanense.
Table 5. Bottleneck test of 12 microsatellite loci in L. leishanense.
LocusSample SizeHeIAMTPMSMM
HeqpHeqpHeqp
LEA221200.9340.8770.005 *0.9300.4530.9370.336
LEA251200.9000.7920.004 *0.8850.2960.8990.473
LEA201200.9280.8500.000 *0.9140.2280.9230.502
LEA351200.9200.8290.002 *0.9020.1760.9160.501
LEA141200.9050.7960.003 *0.8840.1860.8990.456
LEA231200.9330.8640.001 *0.9230.2900.9330.495
LEA51200.6730.7000.3180.8250.010 *0.8480.000 *
LEA71200.8810.8080.0800.8930.2600.9050.091
LEA471200.9270.8760.028 *0.9300.3710.9370.144
LEA241200.9210.8360.007 *0.9100.3320.9200.448
LEA21200.8860.7930.030 *0.8840.4760.9000.205
LEA531200.8650.7270.018 *0.8440.2960.8620.469
Notes: * Significant difference between He and Heq (p < 0.05).
Table
Table 6. Bottleneck significance test on populations of L. leishanense.
Table 6. Bottleneck significance test on populations of L. leishanense.
TestPIAMPTPMPSMM
Sign test0.0230 *0.18870.5983
Wilcoxon test0.0002 *0.05490.7651
Notes: * Significant deviation from mutation-drift equilibrium at p < 0.05.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Fu, C.; Ai, Q.; Cai, L.; Qiu, F.; Yao, L.; Wu, H. Genetic Diversity and Population Dynamics of Leptobrachium leishanense (Anura: Megophryidae) as Determined by Tetranucleotide Microsatellite Markers Developed from Its Genome. Animals 2021, 11, 3560. https://doi.org/10.3390/ani11123560

AMA Style

Fu C, Ai Q, Cai L, Qiu F, Yao L, Wu H. Genetic Diversity and Population Dynamics of Leptobrachium leishanense (Anura: Megophryidae) as Determined by Tetranucleotide Microsatellite Markers Developed from Its Genome. Animals. 2021; 11(12):3560. https://doi.org/10.3390/ani11123560

Chicago/Turabian Style

Fu, Chao, Qingbo Ai, Ling Cai, Fuyuan Qiu, Lei Yao, and Hua Wu. 2021. "Genetic Diversity and Population Dynamics of Leptobrachium leishanense (Anura: Megophryidae) as Determined by Tetranucleotide Microsatellite Markers Developed from Its Genome" Animals 11, no. 12: 3560. https://doi.org/10.3390/ani11123560

APA Style

Fu, C., Ai, Q., Cai, L., Qiu, F., Yao, L., & Wu, H. (2021). Genetic Diversity and Population Dynamics of Leptobrachium leishanense (Anura: Megophryidae) as Determined by Tetranucleotide Microsatellite Markers Developed from Its Genome. Animals, 11(12), 3560. https://doi.org/10.3390/ani11123560

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop