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Article

A Comparative Study of Genetic Responses to Short- and Long-Term Habitat Fragmentation in a Distylous Herb Hedyotis chyrsotricha (Rubiaceae)

by
Na Yuan
1,†,
Shujing Wei
2,†,
Hans Peter Comes
3,
Sisheng Luo
2,
Ruisen Lu
4,* and
Yingxiong Qiu
5,*
1
Provincial Key Laboratory of Agrobiology, Institute of Crop Germplasm and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
2
Guangdong Academy of Forestry, Guangzhou 510520, China
3
Department of Environment & Biodiversity, Salzburg University, A-5020 Salzburg, Austria
4
Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
5
Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Plants 2022, 11(14), 1800; https://0-doi-org.brum.beds.ac.uk/10.3390/plants11141800
Submission received: 23 May 2022 / Revised: 3 July 2022 / Accepted: 5 July 2022 / Published: 7 July 2022
(This article belongs to the Special Issue Molecular Biology of Ornamental Plants)
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Abstract

:
The genetic effects of habitat fragmentation are complex and are influenced by both species traits and landscape features. For plants with strong seed or pollen dispersal capabilities, the question of whether the genetic erosion of an isolated population becomes stronger or is counterbalanced by sufficient gene flow across landscapes as the timescales of fragmentation increase has been less studied. In this study, we compared the population structure and genetic diversity of a distylous herb, Hedyotis chyrsotricha (Rubiaceae), in two contrasting island systems of southeast China. Based on RAD-Seq data, our results showed that populations from the artificially created Thousand-Island Lake (TIL) harbored significantly higher levels of genetic diversity than those from the Holocene-dated Zhoushan Archipelago (ZA) (π = 0.247 vs. 0.208, HO = 0.307 vs. 0.256, HE = 0.228 vs. 0.190), while genetic differences between island and mainland populations were significant in neither the TIL region nor the ZA region. A certain level of population substructure was found in TIL populations, and the level of gene flow among TIL populations was also lower than in ZA populations (m = 0.019 vs. 0.027). Overall, our comparative study revealed that genetic erosion has not become much stronger for the island populations of either the TIL or ZA regions. Our results emphasized that the matrix of water in the island system may facilitate the seed (fruit) dispersal of H. chrysotricha, thus maintaining population connectivity and providing ongoing resilience to the effects of habitat fragmentation over thousands of years.

1. Introduction

Over the past 200 years, human activities have altered more than 80% of the terrestrial world, leading to increasing habitat reduction and fragmentation of almost all ecosystems [1,2,3]. Theoretically, habitat fragmentation has severe negative impacts on community structures, population distributions and abundances, patterns of genetic variation, gene flow, etc. [4,5]. These changes, especially genetic alterations, can reduce the individual fitness and evolutionary potential of populations, further impeding the long-term persistence of species and increasing the risk of local extinction [6,7].
Detecting the genetic effects of habitat fragmentation on natural populations is challenging in practice, as there are many confounding factors involved [2]. For instance, life-history traits such as life span, pollination, and seed dispersal, are essential in determining the magnitude of the plant’s response to habitat fragmentation. Short-lived herbs experience more generations and may show a faster decrease in genetic diversity than longer-lived trees or shrubs over the same timescale of fragmentation [8]. In addition, plants with abiotic-mediated pollination and seed dispersal are expected to be less susceptible to habitat fragmentation than those with animal-mediated pollination and seed dispersal [9,10]. For example, Wang et al. (2011) [11] observed no significant difference in genetic diversity between pre- and post-fragmentation cohorts of a wind-pollinated tree Castanopsis sclerophylla (Lindl. & Paxton) Schottky in a recently fragmented island system. Due to the disappearance of a genetic barrier, the genetic structure of post-fragmentation cohorts was significantly weakened by increased wind speeds and easier pollen movement over water. In contrast, our previous study on the distylous herb Hedyotis chrysotricha (Palib.) Merr in the Thousand-Island Lake (TIL) region revealed that 53 years of fragmentation has led to the loss of c. 17.4% of the initial genetic diversity in the island populations [12]. Hedyotis chrysotricha is insect-pollinated, with seeds that are dispersed by wind or (and) water [13,14]. Although patterns of gene flow have not been greatly modified among the H. chrysotricha populations, it is uncertain whether extensive seed flow can counteract the negative effects of genetic drift to reach a new dynamic equilibrium and assist the long-term persistence of remnant populations.
The various island systems in southeastern China provide natural laboratories for testing the genetic responses of plants to habitat fragmentation. For example, a long-established hotspot for testing the predictions of population genetics theory on recent anthropogenic habitat fragmentation is the TIL region in southeast China (Chun’an County, Hangzhou City, Zhejiang Province, China). This artificial lake (c. 573 km2), with about 1078 islands of different sizes (0.25–1320 ha) and shapes (c. 83 km2 in total), was formed in 1959 after the construction of the Xinanjiang River hydroelectric power station [15]. In contrast, the Zhoushan Archipelago (ZA), located in the East China Sea off the northern coast of Zhejiang Province, is an ideal candidate for examining the genetic responses to long-term habitat fragmentation. It was originally an extended part of the nearby continent and was formed by rising sea levels during the early Holocene, c. 7000–9000 years ago. It consists of 1339 islands and 3306 reefs, covering about 1440 km2 in total [16,17]. The two island systems differ greatly in spatial scale and temporal origin, making them ideal systems for comparative studies detecting both short-term and long-term responses of species to habitat fragmentation. For example, Yuan et al. (2015) [18] compared the patterns of nuclear microsatellite (SSR) variation and the population structure of an evergreen shrub, Loropetalum chinense (R. Br.) Oliver in these two island systems. The results showed that this species can mitigate the negative effects of millennia-old natural habit fragmentation via water-facilitated seed dispersal. Similar results were also observed in a comparative genetic study of the perennial vine Actinidia chinensis Planch. [19]. For long-lived species, the occurrence of gene flow among populations has great potential to mitigate the genetic erosion caused by both recent and historical habitat fragmentation [20]. However, similar comparative genetic studies to determine whether the same is true for short-lived, heterostylous plants, e.g., H. chrysotricha, are still lacking.
Hedyotis chrysotricha is a short-lived, subtropical forest understory herb of southeast China, with a wide distribution in both the TIL and ZA regions [21,22]. This insect-pollinated species has two floral morphs, i.e., long-styled vs. short-styled, which differ reciprocally in the placement of stigmas and anthers to enhance outcrossing. In an ideal population, an equal morph ratio is expected for the availability of compatible pollen and reproductive success [23]. However, skewed morph ratios have been observed in small and fragmented populations of many heterostylous species as a result of demographic stochasticity [24,25]. Morph types differ not only in herkogamy, but also in self- and intramorph compatibility levels, etc. This may further affect levels of inbreeding and population genetic diversity, and the ability to cope with the effects of habitat fragmentation [26,27,28]. For instance, van Rossum and Triest (2008) [29] found that pin and thrum individuals of distylous Primula veris differed in fine-scale spatial genetic structure patterns at a small scale, which may be explained by partial self-compatibility of the pin morph combined with restricted seed dispersal and pollinator behavior. In our previous genetic study [13], we surveyed populations of H. chrysotricha from the recently fragmented TIL region only, using nuclear SSR markers. Although SSRs are highly polymorphic markers, a limited number of markers might result in low power for addressing landscape genetic questions at large spatial and temporal scales [30,31,32]. With the decreasing cost of next-generation sequencing, restriction site-associated DNA sequencing (RAD-Seq), which enables the fast identification of thousands of single nucleotide polymorphisms (SNPs) in non-model organisms without any prior information, has become a cost-effective approach in phylogeographic and population genetic studies [33]. In this study, we employed genome-wide SNPs derived from RAD-Seq data to examine and compare short-term vs. long-term effects of habitat fragmentation on H. chrysotricha in the TIL vs. ZA regions. Specifically, we aimed to: (1) investigate the effects of short-term vs. long-term habitat fragmentation on the species’ population genetic variation (diversity, inbreeding) patterns; (2) examine the genetic differentiation and genetic structure of H. chrysotricha populations at small and large spatial scales; and (3) evaluate patterns of gene flow and demographic changes of H. chrysotricha populations in the two island systems. This comparative study will increase our understanding of the genetic responses of short-lived plants with strong seed dispersal capabilities to fragmentation and provide an insight into the management and conservation of subtropical forest dwellers in fragmented habitats at both small and large spatial–temporal scales.

2. Results

2.1. RAD-Seq Data and SNP Filtering

Approximately 389.4 billion (389,411,194,200) raw reads were produced from 256 individuals of H. chrysotricha sampled across the TIL and ZA regions. After quality control, filtering, and trimming, an average of 1421.5 million and 1638.9 million high-quality reads were retained for the TIL and ZA samples, respectively (Table S1). A catalog containing 9,131,292 loci was constructed, and 9,067,359 loci were genotyped by GSTACKS using the data sets of all H. chrysotricha samples. The mean, minimum, and maximum values for effective per-sample coverage were 26.9×, 16.8×, and 53.0×, respectively. After filtering loci of low quality (minor allele frequency < 0.01; missing rate > 0.5), 185 and 188 unlinked SNPs were eventually identified in TIL and ZA populations, respectively, and used for all subsequent analyses (Tables S2 and S3).

2.2. Population Variation

Based on genome-wide SNP markers, we detected significantly higher levels of genetic diversity in H. chrysotricha from the TIL region than from the ZA region (π = 0.247 vs. 0.208, p < 0.01; HO = 0.307 vs. 0.256, p < 0.01; HE = 0.228 vs. 0.190, p < 0.01; Table 1 and Table 2). Within each region, there were no major differences in genetic diversity between island and mainland populations (TIL island vs. mainland populations: π = 0.257 vs. 0.237, HO = 0.313 vs. 0.302, HE = 0.237 vs. 0.219; ZA island vs. mainland populations: π = 0.208 vs. 0.207, HO = 0.256 vs. 0.255, HE = 0.189 vs. 0.192). FIS values were all negative in all H. chrysotricha populations, with a mean value of −0.078 and −0.066 in the TIL and ZA regions, respectively (Table 1 and Table 2).
Pairwise FST values between the TIL populations ranged from 0.001 to 0.393, with about 13% being significant (p < 0.05) after sequential Bonferroni correction (Table 3). Among the significant pairwise comparisons, the great majority (11 out of 16) involved population EM04. Pairwise FST values between the ZA populations ranged from 0.001 to 0.436, with about 24% being significant (p < 0.05) after sequential Bonferroni correction (Table 4). Here, a large proportion of significantly high FST values (6 out of 13) involved population ZI05. The average FST value across populations was slightly higher in the TIL (FST = 0.103) region than in the ZA (FST = 0.092) region, yet this difference was only marginally significant (p = 0.046). No IBD pattern was found in either region (TIL: r = 0.181, p = 0.174; ZA: r = 0.072, p = 0.242) (Figure S1).

2.3. Population Genetic Structure

Based on a STRUCTURE analysis, the most likely number of genetic groups was K = 3 for the TIL populations and K = 2 for the ZA populations. In the TIL region, Cluster I (‘dark blue’) was present at high frequency (80% of all local samples) in seven of eight island populations, whereas the great majority of individuals (89%) from the western mainland populations and the remaining island population (IP08) were assigned to Cluster II (‘red’) (Figure 1 and Figure S2). In the eastern mainland populations, half of the individuals from EM04 were assigned to Cluster III (‘green’); the remaining populations contained variable numbers of individuals belonging to both Cluster I and Cluster II (Figure 1). In the ZA region, nearly all populations (excepting ZI04) consisted of variable numbers of individuals with a probability of ≥ 0.5 of belonging to both Cluster I (‘blue’) and Cluster II (‘orange’), when K = 2 (Figure 2). At K = 3–5, we also observed that the population ZM02 was assigned to a separate cluster (Figure S3), suggesting potential genetic differentiation between this mainland population and the others.

2.4. Contemporary Gene Flow

In H. chrysotricha populations from the TIL region, the proportion of individuals that originated from within the same site ranged from 68% (IP04) to 81% (WM02), with an average value of 72% (see BAYESASS, Table S4). In contrast, rates of gene flow (or ‘migration’, m) between populations were low to moderate, and in the majority of populations no more than 5% of the individuals were exchanged with other populations (Table S4). Similarly, in the ZA region, populations largely consisted of individuals originating from the same site (73% on average) (see BAYESASS, Table S5). Nevertheless, the average rate of interpopulation gene flow in the ZA region (mZA = 0.027) was significantly higher than in the TIL region (mTIL = 0.019; p = 0.003). The DIVMIGRATE method also suggested that the relative migration rate was significantly higher in the ZS region than in the TIL region (rmZA = 0.349, rmTIL = 0.252, p < 0.01) (Tables S6 and S7). In addition, we observed significant asymmetric gene flow patterns in 10 out of the 16 populations in the TIL region, and 4 out of the 11 populations in the ZA region (Figure 3 and Figure 4).

2.5. Demographic (Bottleneck) Analyses

Irrespective of the mutation model assumed (IAM, infinite allele model; SMM, stepwise mutation model; or TPM, two-phase mutation model), we found a significant excess of heterozygosity (p < 0.05) for one island (IP05) and one mainland population (EM04) in the TIL region, and one island population (ZI01) in the ZA region (Table 5), indicating that these populations may have experienced a recent bottleneck. However, a significant excess of heterozygosity was only found under the IAM model for one TIL population (IP08) and five ZA populations (ZI02, ZI07, ZM02, ZM03, ZM04; Table 5).

3. Discussion

Habitat fragmentation changes continuous habitats into small and isolated patches, and populations inhabiting these habitats are often considered to have low genetic diversity [8,34]. In our previous study [12], we found that island populations of H. chrysotricha in the TIL region had significantly lower mean genetic diversity than those from the mainland, based on nuclear SSR markers, suggesting that anthropogenic habitat fragmentation can lead to significant loss of genetic diversity over a few decades. In the present study, we further tested whether genetic erosion increases with an increase in spatio-temporal scales of fragmentation. Based on RAD-Seq-derived SNP markers, we observed that the average estimates of genetic diversity for ZA populations were significantly lower than for TIL populations (π = 0.247 vs. 0.208, p < 0.01; HO = 0.307 vs. 0.256, p < 0.01; HE = 0.228 vs. 0.190, p < 0.01; Table 1 and Table 2), and we found no major differences in genetic diversity between island and mainland populations in either the TIL or the ZA region. Although it remains unknown how much of the initial genetic variation has been lost in each region since the formation of islands, these results may indicate that H. chrysotricha populations have retained a considerable proportion of their initial genetic diversity over either ~60 years or thousands of years of habitat fragmentation.
Generally, the genetic effects of habitat fragmentation on plants vary, depending on their life-history traits [2]. In this study, the lack of a significant effect of habitat fragmentation on the genetic diversity of H. chrysotricha may be explained by demographic factors and/or strong gene dispersal capacity. As a distylous plant species, H. chrysotricha has two contrasting flower morphs that reciprocally differ in the spatial separation of stigmas and anthers, which in turn prevents selfing and intramorph mating. Morph bias is expected to reinforce genetic drift effects and inbreeding by reducing mating opportunities [35,36]. We also found that no biased morph ratio existed in either island or mainland populations, and overall levels of inbreeding in both island regions were negative and close to zero, suggesting a sufficient within-population heterozygosity (Table 1 and Table 2). In addition, most of the populations showed no genetic signatures of recent bottlenecks (Table 5). Therefore, it is likely that population sizes of H. chrysotricha have remained sufficiently large to prevent loss of genetic diversity via inbreeding and genetic drift after fragmentation [37,38].
The gene dispersal ability via pollen or seed is another key characteristic that determines the potential of a plant species to counteract the negative effects of habitat fragmentation [8]. Hedyotis chrysotricha is an insect-pollinated plant; its flower visitors include solitary bees (Halictidae and Andrenidae), Diptera (Syrphidae and Bombyliidae), honey bees, small lepidopterans, and/or thrips [13,39]. Due to the limited foraging distances of these pollinators, they seem unlikely to be able to maintain significant levels of gene flow and population connectivity across fragmented habitats. In contrast, the indehiscent fruit of H. chrysotricha contains several very small seeds (1.5–2 × 2–2.5 mm), whose dispersal should be easily facilitated by wind [21]. In our previous study, we detected a moderate level of gene flow among 18 H. chrysotricha populations in the TIL region [12]. Based on high-quality SNP markers, our genetic analyses here have further verified the undisturbed population connectivity of H. chrysotricha populations in the TIL region, with an average of c. 28% individuals in each population being exchanged with other sites (Table S2). Given the relatively large spatial and temporal scales of habitat fragmentation in the ZA region (Figure 5), one might expect high population differentiation and low levels of gene flow between island populations. However, both BAYESASS and DIVMIGRATE analyses suggested that the average level of gene flow among populations in the ZA region was higher than in the TIL region, although the results from these two methods may not be compared directly. Similarly, populations in the ZA region exhibited marginally lower levels of genetic differentiation than those in TIL region (FST = 0.092 vs. 0.103). A previous study showed that water greatly facilitated the seed dispersal of a typical wind disperser across a fragmented landscape [40]. By referencing the population genetic study of the maritime Hedyotis strigulosa var. parviflora (Hook. et Arn.) Yamazaki [14], we speculated that water flow may also facilitate the fruit dispersal of H. chrysotricha. Although the hydration of H. chrysotricha could remain speculative due to the lack of detailed morphological and experimental evidence, the genetic evidence presented here further verified that the species has sufficient fruit (seed) dispersal capabilities to maintain moderate-to-high levels of ongoing gene flow and population connectivity across fragmented landscapes at large spatial and temporal scales.
Corresponding to the above results, little evidence of strong genetic structuring was found in H. chrysotricha within the two island systems. Based on SNP markers, the STRUCTURE analysis grouped the TIL populations basically into three clusters (Figure 1) and the ZA populations into two clusters (Figure 2). In the TIL region, most of the island populations were genetically separated from western mainland populations, which is consistent with the results of our previous nSSR analysis, whereas the genetic composition of eastern mainland TIL populations was slightly different from the former study, consisting of a mixture of all three clusters [12]. In the ZA region, the genetic divergence between the mainland and island populations was not as high as expected, and most of the island populations still feature a high percentage of genetic admixtures, even after thousands of years of isolation (Figure 2). The higher levels of population substructure in the TIL region (three clusters) than in the ZA region (two clusters) may be attributed to their contrasting landscape histories. For example, before water flooded the TIL region, mountains (e.g., the Baiji Mountains in the western part of the TIL region) may have acted as a stronger dispersal barrier than water for gene exchange between populations inhabiting the middle and western former hilltops. In the case of the ZA region, which separated from the nearby mainland 7000–9000 years ago due to a rise in sea level, water has been instrumental in facilitating gene exchange between populations for a much longer time than in the TIL region. When combined with the lack of isolation by distance in each island system, these results further suggested that population connectivity in H. chrysotricha has not been greatly modified in either island system, regardless of their significant differences in spatial–temporal characteristics.

4. Materials and Methods

4.1. Sample Collection and DNA Extraction

We collected a total of 160 H. chrysotricha individuals from eight islands and eight adjacent mainland populations in the TIL region, and 110 individuals from seven islands and four adjacent mainland populations in the ZA region (Table 1 and Table 2; Figure 5). In each population, almost equal numbers of individuals with long-styled (L-morph) or short-styled (S-morph) flowers were collected separately. We extracted the total genomic DNA from the dry leaf material of each sample using DNA Plantzol Reagent (Invitrogen, Carlsbad, CA, USA), following the manufacturer’s protocol. We then checked the quality of the DNA using 1% agarose gel electrophoresis and measured the DNA concentration using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Finally, 256 of the 270 DNA samples met the minimum quality requirements and were retained for subsequent sequencing and genotyping.

4.2. RAD Sequencing and Data Processing

RAD libraries were prepared using the restriction enzyme EcoRI and sample-specific barcodes. Samples in the libraries were pooled and sequenced on an Illumina HiSeq 2500 to generate 150 bp pair-end reads, at the Novogene Bioinformatics Institute (Beijing, China). Standard quality control (QC) pipelines were used to process the raw sequencing data. Reads with > 5% of unknown nucleotides, low-quality reads (quality value ≤ 5), and reads that did not contain sample-specific barcodes were discarded. After filtering procedures, the quality-filtered reads data for each individual were then assembled into de novo loci, and SNP calling was performed using the following core programs: USTACKS, CSTACKS, SSTACKS, GSTACKS, and POPULATIONS, implemented in the STACKS pipeline v2.2 [41]. Briefly, all sequences were firstly processed in USTACKS to produce consensus sequences of RAD tags. The program USTACKS takes a set of short-read sequences as input and aligns them into exactly matching stacks. The minimum depth of coverage required for creating a stack was set at three (–m: 3) sequences, and the maximum distance allowed between stacks was set at four (–M: 4) nucleotides. The program CSTACKS was used to build a catalog of consensus loci containing all the loci from all the H. chrysotricha samples and merging all alleles together, with the number of mismatches allowed between sample loci set to four (–n: 4). Next, sets of stacks (i.e., putative loci) created in USTACKS were compared against the catalog using SSTACKS. Then, GSTACKS was used to align the reads to the locus and to call SNPs. Finally, the catalog of reads was filtered using the POPULATIONS program to produce a data set for downstream analyses. To identify high-credibility SNPs, polymorphic RAD loci that were present in at least 75% of the individuals in each population were retained (–r: 0.75). Potential homologs were excluded by removing loci showing heterozygosity of >0.5 within samples [42], and one SNP per RAD locus was kept, to reduce the impact of linkage disequilibrium.

4.3. Genetic Diversity and Population Structure

We calculated the observed heterozygosity (HO), expected heterozygosity (HE), nucleotide diversity (π), and inbreeding coefficient (FIS) for each population using the POPULATIONS program in STACKS. Pairwise F-statistics (FST) (1000 permutations) among populations were calculated using ARLEQUIN v3.5.2 [43]. Population structure was analyzed using the program STRUCTURE v2.3.5 [44]. The number of genetic clusters (K) was set from 1 to 16 for TIL populations and from 1 to 11 for ZA populations, corresponding to the number of sampled populations in each region. Ten independent iterations were conducted for each K, with a burn-in of 10,000 and 100,000 Markov chain Monte Carlo replicates, by assuming the admixture model and independent allele frequencies. The optimal K value was determined from the ΔK values calculated by STRUCTURE HARVESTER [45]. A Mantel test for genetic differentiation [FST/(1 − FST)] against geographic distance (log10 transformed) was performed in GENALEX v6.1 [46] for each region, to test the pattern of isolation by distance (IBD). Statistical significance was determined with 1000 permutations.

4.4. Gene Flow and Demographic History Analyses

For each region, we estimated the levels of interpopulation gene flow using the program BAYESASS v3.03 [47]. First, we ran BAYESASS with the default delta values for allelic frequency, migration rates, and inbreeding coefficients. Then, subsequent runs were adjusted with different delta values to ensure that the acceptance rate ranged between 40 and 60% for each parameter [47]. We performed 10 independent runs (1 × 107 iterations with a burn-in of 106 generations), each with a different initial seed. Model convergence was assessed by the program TRACER v1.5 [48]. In addition, we also used the DIVMIGRATE function from the DIVERSITY package v1.9.90 in R [49] to calculate the relative direction of the gene flow between island and mainland populations, using Nei’s GST method. To test for asymmetric flow (significantly higher in one direction than the other), 95% confidence intervals were calculated from 1000 bootstrap replicates [50].
We used the program BOTTLENECK v1.2.02 [51] to determine whether H. chrysotricha populations underwent significant reductions in effective population size (Ne). We used Wilcoxon’s signed rank test, which examines whether populations exhibit a greater level of heterozygosity than predicted in a population at mutation-drift equilibrium, to detect bottlenecks occurring over approximately the last 2–4 Ne generations. For each H. chrysotricha population, we performed 10,000 simulations for each of three mutation models (IAM, infinite allele model; SMM, stepwise mutation model; and TPM, two-phase mutation model, with 95% single-step and 5% multi-step mutations). Statistical significance was set at the 0.05 level.

5. Conclusions

Based on high-resolution SNPs, we compared the genetic response of H. chrysotricha to short-term and long-term habitat fragmentation. The levels of genetic diversity and the population substructure of H. chrysotricha populations were both higher in the TIL region than in the ZA region, while gene flow was higher and less asymmetric among ZA populations. The differences in genetic diversity between island and mainland populations in both regions were not significant, suggesting that genetic erosion of island populations does not increase as the spatial and temporal scales of fragmentation increase. Population connectivity in H. chrysotricha has also not been greatly modified by either short-term or long-term habitat fragmentation. The strong fruit (seed) dispersal capacity of H. chrysotricha, facilitated by the matrix of water in the island system, could buffer against the negative effects of habitat fragmentation and maintain the population connectivity over thousands of years.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/plants11141800/s1. Table S1: Raw statistical data for Hedyotis chrysotricha samples from the Thousand-Island Lake (TIL) and Zhoushan Archipelago (ZA) regions, generated by Illumina sequencing; Table S2: Genotype information for the 185 filtered SNPs from the TIL dataset; Table S3: Genotype information for the 188 filtered SNPs from the ZA dataset; Table S4: Migration rates (the fraction of individuals in population i (pop i) from population j (pop j)) and confidence intervals between populations of H. chrysotricha from the TIL region. Bold values represent the proportion of non-migrants within a population, and italic values indicate asymmetrical migration rates between populations; Table S5: Migration rates (the fraction of individuals in population i (pop i) from population j (pop j)) and confidence intervals between populations of H. chrysotricha from the ZA region. Bold values represent the proportion of non-migrants within a population, and values in italics indicate asymmetrical migration rates between populations; Figure S1: Analysis of isolation by distance based on FST/(1 − FST) and the geographic distance (log10 transformed) in (A) the Thousand-Island Lake (TIL) region and (B) the Zhoushan Archipelago (ZA) region; Figure S2: Histogram of the STRUCTURE analysis in the TIL region for the model with K = 2, 4, and 5. A vertical bar represents a single individual, and each color corresponds to a suggested cluster; Figure S3: Histogram of the STRUCTURE analysis in the ZA region for the model with K = 3–5. A vertical bar represents a single individual, and each color corresponds to a suggested cluster.

Author Contributions

N.Y. and Y.Q. conceived the study; N.Y., R.L. and S.L. collected the samples. N.Y. extracted the DNA samples. N.Y., S.W. and R.L. analyzed the genetic data and wrote the manuscript. H.P.C., R.L. and Y.Q. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Chinese National Natural Science Foundation (No. 31800312), the Jiangsu Innovative and Enterpreneurial Talent Programme (JSSCBS20211311), the Natural Science Foundation of Guangdong Province, China (No. 2021A1515010946), and the Forestry Science and Technology Innovation Project of Guangdong Province, China (No. 2020KJCX003).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

SNPs in VCF files across the Thousand-Island Lake (TIL) and the Zhoushan Archipelago (ZA) populations were deposited in Mendeley Data, V1, https://0-doi-org.brum.beds.ac.uk/10.17632/wsx8swxd27.1 (accessed on 22 May 2022).

Acknowledgments

Special thanks go to the anonymous referees for providing valuable and constructive comments to improve the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fischer, J.; Lindenmayer, D.B. Landscape modification and habitat fragmentation: A synthesis. Glob. Ecol. Biogeogr. 2007, 16, 265–280. [Google Scholar] [CrossRef]
  2. Aguilar, R.; Quesada, M.; Ashworth, L.; Herrerias-Diego, Y.; Lobo, J. Genetic consequences of habitat fragmentation in plant populations: Susceptible signals in plant traits and methodological approaches. Mol. Ecol. 2010, 17, 5177–5188. [Google Scholar] [CrossRef] [PubMed]
  3. Laurance, W.F.; Sayer, J.; Cassman, K.G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 2014, 29, 107–116. [Google Scholar] [CrossRef] [PubMed]
  4. Saunders, D.A.; Hobbs, R.J.; Margules, C.R. Biological consequences of ecosystem fragmentation: A review. Conserv. Biol. 1991, 5, 18–32. [Google Scholar] [CrossRef]
  5. Tilman, D.; May, R.M.; Lehman, C.L.; Nowak, M.A. Habitat destruction and the extinction debt. Nature 1994, 371, 65–66. [Google Scholar] [CrossRef]
  6. Fahrig, L.; Merriam, G. Conservation of fragmented populations. Conserv. Biol. 1994, 8, 50–59. [Google Scholar] [CrossRef] [Green Version]
  7. Frankham, R. Genetics and extinction. Biol. Conserv. 2005, 126, 131–140. [Google Scholar] [CrossRef]
  8. Young, A.; Boyle, T.; Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 1996, 11, 413–418. [Google Scholar] [CrossRef]
  9. Sato, Y.; Kudoh, H. Fine-scale genetic differentiation of a temperate herb: Relevance of local environments and demographic change. AoB Plants 2014, 6, plu070. [Google Scholar] [CrossRef]
  10. Fontúrbel, F.E.; Jordano, P.; Medel, R.; Bartomeus, I. Scale-dependent responses of pollination and seed dispersal mutualisms in a habitat transformation scenario. J. Ecol. 2015, 103, 1334–1343. [Google Scholar] [CrossRef]
  11. Wang, R.; Compton, S.G.; Chen, X.Y. Fragmentation can increase spatial genetic structure without decreasing pollen-mediated gene flow in a wind-pollinated tree. Mol. Ecol. 2011, 20, 4421–4432. [Google Scholar] [CrossRef] [PubMed]
  12. Yuan, N.; Comes, H.P.; Mao, Y.R.; Qi, X.S.; Qiu, Y.X. Genetic effects of recent habitat fragmentation in the thousand-island lake region of southeast china on the distylous herb Hedyotis chrysotricha (Rubiaceae). Am. J. Bot. 2012, 99, 1715–1725. [Google Scholar] [CrossRef] [PubMed]
  13. Riveros, G.M.; Barría, O.R.; Humaña, P.A.M. Self-compatibility in distylous Hedyotis salzmannii (Rubiaceae). Plant Syst. Evol. 1995, 194, 1–8. [Google Scholar] [CrossRef]
  14. Maki, M.; Matsumua, S.; Yamshiro, T. Allozyme diversity and history of distribution expansion in the maritime perennial plant Hedyotis strigulosa (Rubiaceae), distributed over the wide latitudes in the Japanese Archipelago. Biol. J. Linn. Soc. 2008, 93, 679–688. [Google Scholar] [CrossRef] [Green Version]
  15. Wang, Y.; Zhang, J.; Feelfy, K.J.; Jiang, P.; Ding, P. Life-history traits associated with fragmentation vulnerability of lizards in the Thousand-Island Lake, China. Anim. Conser. 2009, 12, 329–337. [Google Scholar] [CrossRef]
  16. Wang, J.T.; Wang, P.X. The relationship between sea level rising and climate change in East China. Chin. J. Geogr. 1980, 35, 299–313, (In Chinese with English abstract). [Google Scholar] [CrossRef]
  17. Zhou, J.L. The Comprehensive Agriculture Programme of Zhoushan City; Zhejiang Peoples Press: Hangzhou, China, 1987. [Google Scholar]
  18. Yuan, N.; Comes, H.P.; Cao, Y.N.; Guo, R.; Zhang, Y.H.; Qiu, Y.X. A comparative study on genetic effects of artificial and natural habitat fragmentation on Loropetalum chinense (Hamamelidaceae) in Southeast China. Heredity 2015, 114, 544–551. [Google Scholar] [CrossRef]
  19. Yu, W.; Wu, B.; Wang, X.; Yao, Z.; Li, Y.; Liu, Y. Scale-dependent effects of habitat fragmentation on the genetic diversity of Actinidia chinensis populations in China. Hortic. Res. 2020, 7, 172. [Google Scholar] [CrossRef]
  20. Fuller, M.R.; Doyle, M.W. Gene flow simulations demonstrate resistance of long-lived species to genetic erosion from habitat fragmentation. Conserv. Genet. 2018, 19, 1439–1448. [Google Scholar] [CrossRef]
  21. Chen, T.; Zhu, H.; Chen, J.R.; Charlotte, M.T.; Friedrich, E.; Henrik, L.; Michele, F.; Christian, P. Rubiaceae. In Flora of China; Wu, Z.Y., Raven, P.H., Eds.; Science Press: Beijing, China; Missouri Botanical Garden Press: St. Louis, MO, USA, 2011; Volume 19, p. 159. [Google Scholar]
  22. Yuan, N.; Sun, Y.; Nakamura, K.; Qiu, Y.X. Development of microsatellite markers in heterostylous Hedyotis chrysotricha (Rubiaceae). Am. J. Bot. 2012, 99, e43–e45. [Google Scholar] [CrossRef]
  23. Shibayama, Y.; Kadono, Y. Floral morph composition and pollen limitation in the seed set of Nymphoides indica populations. Ecol. Res. 2003, 18, 725–737. [Google Scholar] [CrossRef]
  24. Kéry, M.; Matthies, D.; Schmid, B. Demographic stochasticity in population fragments of the declining distylous perennial Primula veris (Primulaceae). Basic Appl. Ecol. 2003, 4, 197–206. [Google Scholar] [CrossRef]
  25. Washitani, I. Creeping “fruitless falls”: Reproductive failure in heterostylous plants in fragmented landscapes. In The Biology of Biodiversity; Kato, M., Ed.; Springer: Tokyo, Japan, 2000; pp. 133–145. [Google Scholar] [CrossRef]
  26. Rossum, F.V.; Sousa, S.; Triest, L. Morph-specific differences in reproductive success in the distylous primula veris in a context of habitat fragmentation. Acta Oecolog. 2006, 30, 426–433. [Google Scholar] [CrossRef]
  27. Ågren, J.; Ericson, L. Population structure and morph-specific fitness differences in tristylous Lythrum salicaria. Evolution 1996, 50, 126–139. [Google Scholar] [CrossRef]
  28. Jacquemyn, H.; van Rossum, F.; Brys, R.; Endes, P.; Hermy, M.; Triest, L.; de Blust, G. Effects of agricultural land use and fragmentation on genetics, demography and population persistence of the rare Primula vulgaris, and implications for conservation. Belg. J. Bot. 2003, 136, 5–22. [Google Scholar]
  29. Van Rossum, F.; Triest, L. Fine-scale spatial genetic structure of the distylous Primula veris in fragmented habitats. Plant Biol. 2007, 9, 374–382. [Google Scholar] [CrossRef]
  30. 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]
  31. Landguth, E.L.; Fedy, B.C.; Oyler-McCance, S.J.; Garey, A.L.; Emel, S.L.; Mumma, M.; Wagner, H.H.; Fortin, M.J.; Cushman, S.A. Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern. Mol. Ecol. Resour. 2012, 12, 276–284. [Google Scholar] [CrossRef]
  32. Hoban, S.M.; Gaggiotti, O.E.; Bertorelle, G. The number of markers and samples needed for detecting bottlenecks under realistic scenarios, with and without recovery: A simulation based study. Mol. Ecol. 2013, 22, 3444–3450. [Google Scholar] [CrossRef]
  33. Savolainen, O.; Lascoux, M.; Meril, J. Ecological genomics of local adaptation. Nat. Rev. Genet. 2013, 14, 807–820. [Google Scholar] [CrossRef]
  34. Leimu, R.; Vergeer, P.; Angeloni, F.; Ouborg, N.J. Habitat fragmentation, climate change, and inbreeding in plants. Ann. N. Y. Acad. Sci. 2010, 1195, 84–98. [Google Scholar] [CrossRef] [PubMed]
  35. Byers, D.L.; Meagher, T.R. Mate availability in small populations of plant species with homomorphic sporophytic self-incompatibility. Heredity 1992, 68, 353–359. [Google Scholar] [CrossRef] [Green Version]
  36. Washitani, I. Predicted genetic consequences of strong fertility selection due to pollinator loss in an isolated population of Primula sieboldii. Conserv. Biol. 1996, 10, 59–64. [Google Scholar] [CrossRef]
  37. Schaal, B.A.; Leverich, W.J. Molecular variation in isolated plant populations. Plant Species Biol. 1996, 11, 33–40. [Google Scholar] [CrossRef]
  38. Ewers, R.M.; Didham, R.K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 2006, 81, 117–142. [Google Scholar] [CrossRef]
  39. Ornduff, R. Heterostyly, Population Composition, and Pollen Flow in Hedyotis caerulea. Am. J. Bot. 1980, 67, 95–103. [Google Scholar] [CrossRef]
  40. Soomers, H.; Karssenberg, D.; Soons, M.B.; Verweij, P.A.; Verhoeven, J.T.A.; Wassen, M.J. Wind and water dispersal of wetland plants across fragmented landscapes. Ecosystems 2013, 16, 434–451. [Google Scholar] [CrossRef] [Green Version]
  41. Catchen, J.; Hohenlohe, P.A.; Bassham, S.; Amores, A.; Cresko, W.A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 2013, 22, 3124–3140. [Google Scholar] [CrossRef] [Green Version]
  42. Hohenlohe, P.A.; Amish, S.J.; Catchen, J.M.; Allendorf, F.W.; Luikart, G. Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Mol. Ecol. Resour. 2011, 11, 117–122. [Google Scholar] [CrossRef]
  43. Excoffier, L.; Laval, G.; Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinform. 2007, 23, 47–50. [Google Scholar] [CrossRef] [Green Version]
  44. Pritchard, J.K.; Stephens, P.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. [Google Scholar] [CrossRef] [PubMed]
  45. Evanno, G.S.; Regnaut, S.J.; Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Peakall, R.; 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]
  47. Wilson, G.A.; Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 2003, 163, 1177–1191. [Google Scholar] [CrossRef]
  48. Rambaut, A.; Drummond, A.J.; Xie, D.; Baele, G.; Suchard, M.A. Posterior summarisation in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 2018, 67, 901–904. [Google Scholar] [CrossRef] [Green Version]
  49. Keenan, K.; McGinnity, P.; Cross, T.F.; Crozier, W.W.; Prodohl, P.A. diveRsity: An R package for the estimation of population genetics parameters and their associated errors. Methods Ecol. Evol. 2013, 4, 782–788. [Google Scholar] [CrossRef] [Green Version]
  50. Sundqvist, L.; Keenan, K.; Zackrisson, M.; Prodöhl, P.; Kleinhans, D. Directional genetic differentiation and relative migration. Ecol. Evol. 2016, 6, 3461–3475. [Google Scholar] [CrossRef] [Green Version]
  51. Piry, S.; Luikart, G.; Cornuet, J.M. Bottleneck: A computer program for detecting recent reductions in the effective size using allele frequency data. J. Hered. 1999, 90, 502–503. [Google Scholar] [CrossRef]
Plants 11 01800 g001 550
Figure 1. STRUCTURE analysis of 155 individuals (16 populations) of Hedyotis chrysotricha from the Thousand-Island Lake (TIL) region, based on RAD-Seq-derived single nucleotide polymorphism (SNP) data. (A) Plots of the mean posterior probability [LnP(D)] values of each K and (B) the corresponding ΔK statistics. (C) Histogram of the STRUCTURE analysis for the model with K = 3 (showing the highest ΔK). A vertical bar represents a single individual, and each color corresponds to a suggested cluster (Cluster I: dark blue; Cluster II: red; Cluster III: green). The x-axis corresponds to population codes. The y-axis presents the estimated membership coefficient (Q) for each individual in the three clusters.
Figure 1. STRUCTURE analysis of 155 individuals (16 populations) of Hedyotis chrysotricha from the Thousand-Island Lake (TIL) region, based on RAD-Seq-derived single nucleotide polymorphism (SNP) data. (A) Plots of the mean posterior probability [LnP(D)] values of each K and (B) the corresponding ΔK statistics. (C) Histogram of the STRUCTURE analysis for the model with K = 3 (showing the highest ΔK). A vertical bar represents a single individual, and each color corresponds to a suggested cluster (Cluster I: dark blue; Cluster II: red; Cluster III: green). The x-axis corresponds to population codes. The y-axis presents the estimated membership coefficient (Q) for each individual in the three clusters.
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Plants 11 01800 g002 550
Figure 2. STRUCTURE analysis of 101 individuals (11 populations) of Hedyotis chrysotricha from the Zhoushan Archipelago (ZA) region, based on RAD-Seq-derived single nucleotide polymorphism (SNP) data. (A) Plots of the mean posterior probability [LnP(D)] values of each K and (B) the corresponding ΔK statistics. (C) Histogram of the STRUCTURE analysis for the model with K = 2 (showing the highest ΔK). A vertical bar represents a single individual, and each color corresponds to a suggested cluster (Cluster I: blue; Cluster II: orange). The x-axis corresponds to population codes. The y-axis presents the estimated membership coefficient (Q) for each individual in the two clusters.
Figure 2. STRUCTURE analysis of 101 individuals (11 populations) of Hedyotis chrysotricha from the Zhoushan Archipelago (ZA) region, based on RAD-Seq-derived single nucleotide polymorphism (SNP) data. (A) Plots of the mean posterior probability [LnP(D)] values of each K and (B) the corresponding ΔK statistics. (C) Histogram of the STRUCTURE analysis for the model with K = 2 (showing the highest ΔK). A vertical bar represents a single individual, and each color corresponds to a suggested cluster (Cluster I: blue; Cluster II: orange). The x-axis corresponds to population codes. The y-axis presents the estimated membership coefficient (Q) for each individual in the two clusters.
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Plants 11 01800 g003 550
Figure 3. (a) The migration network among the 16 Hedyotis chrysotricha populations in the Thousand-Island Lake (TIL) region based on GST (coefficient of gene differentiation) values and (b) significant relative migration relationships among populations (after 1000 bootstraps). Arrow thickness and color tone are proportional to the magnitude of the gene flow.
Figure 3. (a) The migration network among the 16 Hedyotis chrysotricha populations in the Thousand-Island Lake (TIL) region based on GST (coefficient of gene differentiation) values and (b) significant relative migration relationships among populations (after 1000 bootstraps). Arrow thickness and color tone are proportional to the magnitude of the gene flow.
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Plants 11 01800 g004 550
Figure 4. (a) The migration network among the 11 Hedyotis chrysotricha populations in the Zhoushan archipelago (ZA) region based on GST (coefficient of gene differentiation) values and (b) significant relative migration relationships among populations (after 1000 bootstraps). Arrow thickness and color tone are proportional to the magnitude of the gene flow.
Figure 4. (a) The migration network among the 11 Hedyotis chrysotricha populations in the Zhoushan archipelago (ZA) region based on GST (coefficient of gene differentiation) values and (b) significant relative migration relationships among populations (after 1000 bootstraps). Arrow thickness and color tone are proportional to the magnitude of the gene flow.
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Plants 11 01800 g005 550
Figure 5. Sample localities of Hedyotis chrysotricha in the Thousand-Island Lake (TIL) region and the Zhoushan Archipelago (ZA) region of southeast China. Population codes are identified in Table 1 and Table 2.
Figure 5. Sample localities of Hedyotis chrysotricha in the Thousand-Island Lake (TIL) region and the Zhoushan Archipelago (ZA) region of southeast China. Population codes are identified in Table 1 and Table 2.
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Table
Table 1. Genetic characteristics of island and mainland populations of Hedyotis chrysotricha in the Thousand-Island Lake (TIL) region.
Table 1. Genetic characteristics of island and mainland populations of Hedyotis chrysotricha in the Thousand-Island Lake (TIL) region.
TIL Population IDIsland Size (ha)LatitudeLongitudeSample SizeπHOHEFIS
IslandIP01132029.5340 118.8818 100.236 0.312 0.219 −0.119
IP0247.9829.5254 118.9395 80.254 0.316 0.231 −0.080
IP0327.4929.5365 118.9402 90.250 0.308 0.231 −0.063
IP041.3329.5625 118.8990 100.255 0.326 0.236 −0.095
IP050.8629.5487 118.9154 100.274 0.317 0.254 −0.053
IP062.5629.5019 118.8319 100.270 0.320 0.250 −0.040
IP072.1729.4862 118.9098 100.252 0.308 0.234 −0.055
IP088.9829.4949 118.8610 100.263 0.294 0.244 −0.008
MainlandWM01-29.5789 118.8671 100.240 0.302 0.223 −0.063
WM02-29.5563 118.8154 100.227 0.306 0.211 −0.136
WM03-29.4894 118.6907 100.221 0.300 0.206 −0.126
WM04-29.4056 118.6367 80.219 0.296 0.200 −0.127
EM01-29.3867 118.7484 100.235 0.308 0.218 −0.117
EM02-29.4104 118.8441 100.256 0.306 0.237 −0.035
EM03-29.4615 118.9056 100.221 0.296 0.206 −0.121
EM04-29.4816 119.0097 100.275 0.302 0.255 −0.017
Mean (All) 0.247 0.307 0.228 −0.078
Abbreviations: π, nucleotide diversity; HO, observed heterozygosity; HE, expected heterozygosity; FIS, within-population inbreeding coefficient.
Table
Table 2. Genetic characteristics of island and mainland populations of Hedyotis chrysotricha in the Zhoushan Archipelago (ZA) region.
Table 2. Genetic characteristics of island and mainland populations of Hedyotis chrysotricha in the Zhoushan Archipelago (ZA) region.
ZS Population IDIsland Size (ha)LatitudeLongitudeSample SizeπHOHEFIS
IslandZI01863530.0399 121.8784 40.221 0.256 0.181 −0.053
ZI0250,26530.1021 121.1167 100.209 0.261 0.194 −0.075
ZI0350,26530.0040 122.2927 100.206 0.263 0.191 −0.085
ZI04417029.8266 122.3036 100.208 0.253 0.194 −0.064
ZI05618229.8573 122.3857 100.198 0.250 0.184 −0.068
ZI06118529.9790 122.3782 80.213 0.254 0.194 −0.053
ZI0711,51230.2637 122.1850 100.202 0.255 0.187 −0.077
MainlandZM01-29.8350 121.8706 100.195 0.243 0.181 −0.058
ZM02-29.8490 121.7392 100.215 0.258 0.200 −0.064
ZM03-30.0183 121.5079 100.206 0.257 0.191 −0.074
ZM04-30.0685 121.4286 90.213 0.260 0.197 −0.055
Mean (All) 0.208 0.256 0.190 −0.066
Abbreviations: π, nucleotide diversity; HO, observed heterozygosity; HE, expected heterozygosity; FIS, within-population inbreeding coefficient.
Table
Table 3. Pairwise FST values among the 16 populations of Hedyotis chrysotricha in the Thousand-Island Lake (TIL) region.
Table 3. Pairwise FST values among the 16 populations of Hedyotis chrysotricha in the Thousand-Island Lake (TIL) region.
IP01IP02IP03IP04IP05IP06IP07IP08WM01WM02WM03WM04EM01EM02EM03EM04
IP01
IP020.028
IP030.015 0.090
IP040.022 0.010 0.018
IP050.200 0.006 0.066 0.185
IP060.093 0.007 0.006 0.004 0.073
IP070.031 0.029 0.050 0.017 0.1600.031
IP080.092 0.004 0.019 0.008 0.077 0.085 0.001
WM010.047 0.079 0.085 0.002 0.058 0.015 0.019 0.020
WM020.003 0.047 0.008 0.026 0.226 0.1050.013 0.089 0.024
WM030.092 0.002 0.039 0.030 0.201 0.0940.018 0.085 0.047 0.014
WM040.083 0.014 0.024 0.038 0.172 0.004 0.044 0.002 0.051 0.028 0.064
EM010.069 0.047 0.008 0.004 0.211 0.085 0.030 0.083 0.032 0.076 0.049 0.083
EM020.011 0.060 0.079 0.048 0.102 0.056 0.067 0.073 0.049 0.014 0.018 0.058 0.007
EM030.028 0.038 0.012 0.040 0.223 0.1040.034 0.093 0.021 0.030 0.001 0.007 0.021 0.018
EM040.3700.2380.2510.2850.2040.108 0.2870.066 0.2900.3930.3660.2880.376 0.196 0.386
Values in bold are significantly different from zero after sequential Bonferroni correction.
Table
Table 4. Pairwise FST values among the 11 populations of Hedyotis chrysotricha in the Zhoushan Archipelago (ZA) region.
Table 4. Pairwise FST values among the 11 populations of Hedyotis chrysotricha in the Zhoushan Archipelago (ZA) region.
ZI01ZI02ZI03ZI04ZI05ZI06ZI07ZM01ZM02ZM03ZM04
ZI01
ZI020.147
ZI030.157 0.075
ZI040.096 0.066 0.013
ZI050.199 0.3030.2700.067
ZI060.162 0.082 0.089 0.057 0.348
ZI070.055 0.053 0.036 0.094 0.3150.014
ZM010.139 0.031 0.065 0.053 0.3520.087 0.010
ZM020.014 0.034 0.056 0.078 0.1430.085 0.1740.158
ZM030.002 0.059 0.038 0.1670.4360.011 0.051 0.001 0.242
ZM040.125 0.073 0.096 0.063 0.3620.123 0.006 0.083 0.1280.001
Values in bold are significantly different from zero after sequential Bonferroni correction.
Table
Table 5. Bottleneck analyses for a total of 27 populations of Hedyotis chrysotricha, including 16 from the Thousand-Island Lake (TIL) region and 11 from the Zhoushan Archipelago (ZA) region. The p-values are shown for the Wilcoxon signed rank tests, as evaluated under the infinite alleles model (IAM), the two-phase mutation model (TPM), and the stepwise mutation model (SMM), respectively.
Table 5. Bottleneck analyses for a total of 27 populations of Hedyotis chrysotricha, including 16 from the Thousand-Island Lake (TIL) region and 11 from the Zhoushan Archipelago (ZA) region. The p-values are shown for the Wilcoxon signed rank tests, as evaluated under the infinite alleles model (IAM), the two-phase mutation model (TPM), and the stepwise mutation model (SMM), respectively.
TIL Regionp (Wilcoxon Signed Rank Test)ZS Regionp (Wilcoxon Signed Rank Test)
PopulationIAMTPMSMMPopulationIAMTPMSMM
IP010.6910.9840.864ZI010.0000.0010.007
IP020.9490.9990.997 ZI020.0280.216 0.915
IP030.8010.9950.958 ZI030.2020.428 0.977
IP040.9820.9990.997 ZI040.1750.628 0.977
IP050.0000.0030.000ZI050.1370.469 0.958
IP060.2620.9730.786 ZI060.2110.611 0.966
IP070.8590.9990.959 ZI070.0190.242 0.844
IP080.0170.6370.220 ZM010.331 0.694 0.981
WM010.9920.9990.999 ZM020.0210.203 0.835
WM020.4360.9890.786 ZM030.0210.206 0.889
WM030.6540.9960.860 ZM040.0340.331 0.909
WM040.9420.9990.996
EM010.3580.9830.660
EM020.8730.9990.981
EM030.7440.9960.926
EM040.0000.0010.000
Bold values indicate statistical significance (p < 0.05).
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Yuan, N.; Wei, S.; Comes, H.P.; Luo, S.; Lu, R.; Qiu, Y. A Comparative Study of Genetic Responses to Short- and Long-Term Habitat Fragmentation in a Distylous Herb Hedyotis chyrsotricha (Rubiaceae). Plants 2022, 11, 1800. https://0-doi-org.brum.beds.ac.uk/10.3390/plants11141800

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Yuan N, Wei S, Comes HP, Luo S, Lu R, Qiu Y. A Comparative Study of Genetic Responses to Short- and Long-Term Habitat Fragmentation in a Distylous Herb Hedyotis chyrsotricha (Rubiaceae). Plants. 2022; 11(14):1800. https://0-doi-org.brum.beds.ac.uk/10.3390/plants11141800

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Yuan, Na, Shujing Wei, Hans Peter Comes, Sisheng Luo, Ruisen Lu, and Yingxiong Qiu. 2022. "A Comparative Study of Genetic Responses to Short- and Long-Term Habitat Fragmentation in a Distylous Herb Hedyotis chyrsotricha (Rubiaceae)" Plants 11, no. 14: 1800. https://0-doi-org.brum.beds.ac.uk/10.3390/plants11141800

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