Next Article in Journal
Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard
Next Article in Special Issue
Phylogenetics and Biogeography of Lilium ledebourii from the Hyrcanian Forest
Previous Article in Journal
Vegetation Ecology of Debris-Covered Glaciers (DCGs)—Site Conditions, Vegetation Patterns and Implications for DCGs Serving as Quaternary Cold- and Warm-Stage Plant Refugia
Previous Article in Special Issue
Phylogeography and Genetic Structure of Sand Dune Specialist Stilpnolepis centiflora (Asteraceae) in Northwest China Revealed by Molecular Data
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diversity
Volume 14
Issue 2
10.3390/d14020115
diversity-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 thumb_up
...
Endorse textsms
...
Comment
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 Mating System of Two Mangrove Species (Rhizophora apiculata and Avicennia marina) in a Heavily Disturbed Area of China

by
Wenxun Lu
1,2,
Zhen Zou
2,
Xueying Hu
1 and
Shengchang Yang
2,3,*
1
College of Life Sciences, Peking University, Beijing 100871, China
2
College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
3
Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, Xiamen University, Xiamen 361102, China
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(2), 115; https://doi.org/10.3390/d14020115
Submission received: 28 December 2021 / Revised: 1 February 2022 / Accepted: 2 February 2022 / Published: 6 February 2022
(This article belongs to the Special Issue Ecology, Evolution and Diversity of Plants)
Browse Figures
Review Reports Versions Notes

Abstract

:
Mangrove forests are distributed in the intertidal zones of tropical and subtropical regions, and have been severely damaged by anthropogenic activities, climate change, and stochastic events. Although much progress has been made in the conservation and restoration of mangroves in China, studies of the genetic diversity of mangroves are lacking, especially for isolated populations, yet such studies are essential for guiding conservation and restoration efforts. Here, we evaluated the genetic diversity, spatial genetic structure, and mating system of two mangrove species, Rhizophora apiculata and Avicennia marina, in a heavily disturbed area in Tielu Harbor, Sanya City, Hainan Island, China, using 18 nuclear microsatellite markers. We found that the genetic diversity of R. apiculata, which is classified as ‘Vulnerable’ in the China Red List categories, was high and similar compared with the genetic diversity estimates of other populations reported in previous studies. In contrast, the genetic diversity of A. marina, which is classified as a species of ‘Least Concern’, was low compared with the genetic diversity estimates of other populations. We then evaluated the presence of genetic bottlenecks, spatial genetic structure, and the mating system to determine the effects that habitat destruction has had on these two species. Our findings indicate that distinct conservation and restoration approaches are needed for these two species. Generally, our results provide valuable information that will aid the development of conservation and restoration strategies for the mangroves of Tielu Harbor.

1. Introduction

Mangrove forests, which are dominated by plants in the Rhizophoraceae, Verbenaceae, and Combretaceae families, occur in the intertidal zones of tropical and subtropical regions [1,2]. Mangrove ecosystems are some of the world’s most biodiverse and productive forest ecosystems [3], and they provide important ecosystem services, such as carbon sequestration, wave attenuation, and refuges for organisms [4,5]. Despite the high social, economic, and ecological value of mangroves, they have been severely degraded due to anthropogenic activities [6,7], climate change [8], and stochastic events [9]. For example, many mangroves have been converted to nursery areas for fishery species and play a critically important role in sustaining production in coastal fisheries [10]. Mangrove conservation and restoration strategies have been widely implemented given the alarming rate of decline in the area of mangrove forests worldwide [10,11,12,13].
Mangrove forests in China are mainly distributed along the southeastern coast, including Hainan, Guangxi, Guangdong, Fujian, and Taiwan Provinces [14]. China’s mangrove forests have been severely fragmented in previous decades, and China has historically had one of the highest rates of mangrove loss worldwide [14,15,16]. The area of mangrove forests in China declined from 48,300 hm2 in 1950 to 22,025 hm2 in 2001 [16,17,18]. The implementation of strict protection and large-scale restoration measures after 2001 has resulted in an increase in the area of mangrove forests; by 2019, the area of mangroves in China reached 30,000 hm2 [19,20]. Despite this conservation success, an increase in the area of mangroves does not, by itself, result in the restoration of a healthy mangrove ecosystem [16], as habitat degradation, decreases in biodiversity, and biological invasions are still major problems affecting mangroves in China [21,22,23].
Reductions in population size are the most direct effect of anthropogenic activities and climate change on populations [24]; however, anthropogenic activities and climate change can also affect other characteristics of populations, such as genetic diversity, the mating system, and spatial genetic structure. Genetic diversity refers to the genetic variation among different individuals or populations within a species [25], and it buffers populations against variability in environmental conditions [26]. Thus, characterizing the genetic diversity of populations is a major goal of research in biodiversity conservation [27,28]. The effects of anthropogenically driven habitat fragmentation and destruction on the genetic diversity of populations of species have been well studied [29,30]. Habitat fragmentation reduces the size of populations and increases their isolation, which results in reduced heterozygosity, the loss of alleles, and reduced gene flow between populations. This can lead to an increase in inbreeding and reductions in effective population size and can have deleterious effects on the long-term persistence of populations [31,32]. Small population size and the isolation of populations can have negative effects on the mating system of plants [33]. Some species also possess ecological and genetic traits, such as those that prevent self-pollination, that counteract the deleterious effects of small population size and isolation on mating systems [34]. Spatial genetic structure is another important component of the population genetics of a species, as a thorough understanding of the spatial genetic structure within and among populations can aid in the development of conservation and restoration strategies [35,36]. An increasing number of studies have examined the population genetics of mangroves in recent years, and investigations of the population genetic diversity and spatial genetic structure of different species in mangroves have been conducted to guide mangrove conservation and restoration efforts [9,37,38].
Tielu Harbor Mangrove Nature Reserve is located in Sanya City in southern Hainan Island. A total of 13 true mangrove species in nine genera and six semi-mangrove species in six genera have been documented in the reserve [39]. Mangrove resources are abundant in Tielu Harbor, and the mangroves in this area are ancient and endangered [14]. Previous studies indicate that many ancient trees of the mangrove species, such as Bruguiera sexangular, Bruguiera gymnorhiza, Lumnitzera racemose, Lumnitzera littorea, and Xylocarpus granatum, occur in Tielu Harbor [14,39,40]. Increases in human activities have reduced the area of mangroves in Tielu Harbor, which is currently approximately 3–4 hm2 [14,40], and degraded existing mangrove habitat. The mangroves of Tielu Harbor are thus in a precarious state and require urgent conservation attention [14,39].
Mangroves in Tielu Harbor can be divided into four major types: Rhizophora apiculata communities, Avicennia marina communities, Lumnitzera racemosa communities, and Xylocarpus granatum communities [14,39]. Of these, R. apiculata is the dominant species, and A. marina is generally considered a pioneer species (Figure 1). R. apiculata and A. marina are listed as ‘Vulnerable’ and of ‘Least Concern’, respectively, in the China Red List categories [16]. Study of the genetic diversity, mating systems, and population genetic structure of these two important mangrove species is needed to ensure that effective conservation measures are taken for the mangrove forests in Tielu Harbor.
Here, we investigated the genetic diversity, spatial genetic structure, and mating systems of two mangrove species, R. apiculata and A. marina, in Tielu Harbor Mangrove Nature Reserve. We also conducted a comprehensive analysis of our results and recommended specific conservation measures that could be taken to guide ongoing mangrove conservation and restoration efforts in Tielu Harbor.

2. Materials and Methods

2.1. Study Area and Plant Sampling

Plant materials for this study were collected from Tielu Harbor Mangrove Nature Reserve, Sanya City, Hainan Province, China (18°15′–18°17′ N, 109°42′–109°42′ E, Figure 2). Tielu Harbor features a tropical monsoon climate, and the mean annual precipitation and temperature are 1255 mm and 25.5 °C, respectively [39]. Sampling was conducted from June to August 2016. Two quadrats were established for R. apiculata (80 × 130 m2) and A. marina (110 × 180 m2). Leaves of all adult individuals and some seedlings of the two species in quadrats were sampled. The coordinates of each sample were recorded with a Garmin GPSmap 60CSx (Garmin Ltd., Lenexa, KA, USA). A total of 167 samples (139 adults and 28 seedlings) and 210 samples (152 adults and 58 seedlings) were collected for R. apiculata and A. marina, respectively. In addition, three individuals of R. apiculata and five individuals of A. marina with propagules were randomly selected and used as mother trees for paternity analysis. To minimize the impact on the capacity for population renewal, approximately 10 propagules were randomly sampled from each mother tree. Leaves and propagules collected were desiccated in plastic zip-lock bags with silica gel and stored at room temperature until DNA extraction.

2.2. DNA Extraction, PCR Amplification, and Microsatellite Screening

The total genomic DNA was extracted from the dried leaves and propagules of each sample using a Plant Genomic DNA Kit DP305 (Tiangen Biotech, Beijing, China). Specifically, for A. marina, we collected their propagules and cultured them until new leaves appeared in the laboratory, and then collected leaves of each seedling for DNA extraction. For R. apiculate, we directly collected a part of each propagule for subsequent DNA extraction because of its large propagule. After the concentration and purity were determined, each DNA sample was diluted with TE buffer to a concentration of 5 to 20 ng/µL and stored at −20 °C until use. Eighteen nuclear microsatellite markers (nine nuclear simple-sequence repeat (nSSR) markers for each species) developed by previous studies [41,42,43,44] were amplified for R. apiculata and A. marina (Supplementary Table S1). Fluorescently labeled primers (with HEX or ROX) were synthesized (Sangon Biotech [Shanghai] Co., Ltd.), and the PCR amplification conditions used were based on a previously published protocol [41,42,43,44]. All amplified PCR products were screened using capillary electrophoresis (Sangon Biotech Co., Ltd., Shanghai, China).

2.3. Statistical Analysis of Genetic Parameters

For each nuclear locus, the Hardy–Weinberg equilibrium (HWE), linkage disequilibrium (LD), and null alleles were tested using GenePop Volume 3.4 [45]. Genetic diversity parameters, such as the number of alleles (Na), number of effective alleles (Ne), Shannon diversity index (I), observed heterozygosity (Ho), expected heterozygosity (He), and unbiased expected heterozygosity (uHE), were calculated using GenAlex Version 6.5 [46]; ADZE Version 1.0 [47] was used to determine the allele abundance (AR) and private allele abundance (PAR). The inbreeding coefficient (Fis) and its 95% confidence interval was calculated using GDA v1.1 (http://en.bio-soft.net/dna/gda.html, accessed on 28 December 2021). The total paternity exclusion probability of the first [Pr(Ex1)] and second parent [Pr(Ex2)] was estimated using CERVUS Version 3.0 [48]. In addition, we performed the analysis of molecular variance (AMOVA) to estimate the distribution of genetic variance among adults and seedlings using Φ-statistics with GenAlEx Version 6.5 [46] for both two species.

2.4. Analysis of Bottlenecks and Spatial Genetic Structure

The likelihood of prior population bottlenecks in the two species was estimated using BOTTLENECK Version 1.2.02 [49] using three models, the two-phase model (TPM), the infinite allele model (IAA), and the stepwise mutation model (SMM), under default settings. The significance of bottlenecks was estimated using the sign test and one-tailed Wilcoxon sign-rank test.
Spatial autocorrelation analyses were carried out for the entire population and for seedlings and adults using SPAGeDi Version 1.2 [50]. Average multilocus kinship coefficients (Fij) were calculated for nine distance classes according to size of quadrats and a previous study [51]. Specifically, for A. marina, distance classes were 1, 2, 4, 8, 16, 32, 64, 128 and 300 m; and we set distance classes as 1, 2, 4, 8, 16, 32, 64, 128 and 200 m for R. apiculate. The 95% confidence interval of the different distance classes was tested using 10,000 random permutations. The Sp statistic was calculated for individuals from the first distance class following the method of Vekemans and Hardy [52].

2.5. Analysis of Mating System Parameters and Pollen Dispersal

MLTR Version 3.2 [53] was used to analyze the multilocus outcrossing rate (tm), the single-locus outcrossing rate (ts), and the biparental inbreeding rate (tm−ts) under the mixed mating model with 1000 bootstrap replications to assess the 95% confidence intervals for standard errors. The most likely pollen donor for each propagule was determined via maximum-likelihood assignment in CERVUS Version 3.0 [48]. For paternity analyses, all adult individuals were considered as candidate parents, and 10,000 simulations were conducted with 0.01 as the mistyped rate, 0.9 as the sampled candidate parent proportion, and 95% as the strict and 80% as the relaxed confidence level.

3. Results

3.1. Genetic Diversity of R. apiculata and A. marina

Two nSSR loci (RM102 and Am98) were not included in subsequent analyses because they were not polymorphic. A total of 37 alleles of eight nSSR loci and 23 alleles of eight nSSR loci were detected in both adults and seedlings of R. apiculata and A. marina, respectively. The genetic diversity parameters of each nSSR locus for all individuals of the two species are shown in Table 1. The average frequency of null alleles in R. apiculata and A. marina was 18.8% (3.5–47.1%) and 20.7% (8.4–29.3%), respectively. Only four out of 28 pairwise comparisons showed significant LD in the R. apiculata population (including seedlings, p < 0.01), whereas approximately half (13 out of 28) of the pairwise comparisons showed significant LD in the A. marina population (including seedlings, p < 0.01). Six nSSR loci (Rhst01, Rhst11, Rhst13, Rhst20, RM114, and RM116) deviated significantly from HWE in the R. apiculata population (heterozygote deficiency, p < 0.01), and all nSSR loci in the A. marina population showed heterozygote deficiency (p < 0.01).
The results of the genetic diversity analysis of different age groups (adults and seedlings) of the two species are shown in Table 2. Because the number of seedlings sampled was small, several genetic diversity parameters (Na, I) were lower in seedlings than in adults in the R. apiculata population. A similar pattern was observed in the A. marina population. Because Na is sensitive to sample size, we used ADZE Version 1.0 to calculate allele richness. In the R. apiculata population, the AR of seedlings and adults was similar, but the PAR of seedlings was lower than that of adults. The AR and PAR of seedlings were higher than that of adults in the A. marina population (Table 2). AMOVA analysis for adults and seedlings of R. apiculata revealed that the genetic differences mainly existed among different individuals (99.00%), only 1.00% occurred between different age groups (Supplementary Table S2). Similarly, AMOVA analysis in A. marina population revealed that 100% genetic variation existed within different individuals (Supplementary Table S3).

3.2. Bottlenecks and Spatial Genetic Structure

We next conducted bottleneck analysis to determine whether these two populations have undergone population bottlenecks. We detected a significant excess of heterozygotes in the seedlings of R. apiculata under the IAA model according to both the sign test and the one-tailed Wilcoxon sign-rank test, suggesting that the seedlings of this population may have experienced a reduction in population size (Table 3). Our results indicated that the total population (adults + seedlings) of R. apiculata has experienced a bottleneck under the IAA model (Table 3).
The spatial autocorrelation analysis indicated significant spatial genetic structure at short distance classes for both seedlings and adults in the R. apiculata population (2–4, 4–8, 8–16, and 16–32 m; Figure 3a). Significant positive Fij values were observed at short distance classes among R. apiculata adults (2–4, 4–8, 8–16, and 16–32 m; Figure 3b). Fij values decreased with distance in the R. apiculata population. Significant positive Fij values were also observed for both seedlings and adults of A. marina (until 32 m; Figure 3c), and a similar pattern was observed for A. marina adults. However, Fij values among adults were lower than those among the total A. marina population, and correlations between Fij values and distance class were weak (Figure 3d). Sp statistics for each analysis are shown in Figure 2 and reveal a strong spatial genetic structure for these two species at short distances.

3.3. Mating System Parameters

The tm, ts, and tm−ts for R. apiculata at the population level were 1.135 (0.029), 1.111 (0.051), and 0.024 (0.042), respectively. Bootstrap analysis suggested that both tm (95% CI = 1.007–1.193) and ts (95% CI = 1.009–1.213) were greater than 1, and tm−ts (95% CI = −0.06–0.108) did not differ significantly from 0. This suggested the presence of high outcrossing and a relatively low proportion of biparental inbreeding in R. apiculata. tm (1.001–1.116) and ts (1.082–1.551) were also greater than 1 for various mother trees of R. apiculata (Table 4). For A. marina, tm (95% CI = 0.448–0.980) and ts (95% CI = 0.318–0.874) were lower than 1 at the population level, which indicated that selfing has occurred in this population. tm − ts (95% CI = 0.020–0.216) significantly differed from 0. This suggested the presence of significant biparental inbreeding. There was a high degree of variation in the mating system parameters of each mother tree in the A. marina population (Table 4).
We identified pollen donors within the R. apiculata quadrat for 35% (8 of 23) of the propagules at the 80% confidence level. The remaining 65% of the propagules were likely derived from pollen donors outside the quadrat. All eight propagules originated from outcrossing. These results were consistent with the mating system parameters estimated using MLTR software. However, only three (6%) propagules were identified as pollen donors within the quadrat at the 80% confidence level for A. marina, indicating that nearly all propagules originated from outcrossing. Mother trees of these three propagules were A48 (two propagules) and A126 (one propagule), and their tm was higher compared with that of other trees in the A. marina quadrat. The above findings suggest that most of the pollen donors were located outside of the A. marina quadrat. Because most pollen donors were from outside the quadrat, we did not calculate the pollen dispersal distance.

4. Discussion

Protections for mangroves have strengthened in China over the past two decades, and many mangrove reserves have been established [16,17,18]. Although much conservation effort has been devoted to increasing the area of mangrove forests, the genetic health of mangroves has been largely ignored by comparison. Small and isolated populations are often characterized by lower genetic diversity, engage in higher levels of inbreeding, and experience bottlenecks, all of which affect their resilience against a backdrop of anthropogenic threats and pressures and climate change [9,54]. Assessing the genetic diversity and mating system of such small and isolated populations of mangroves is critically important for setting reasonable restoration goals for maintaining and improving levels of genetic diversity of remaining mangrove populations [55,56,57].
We evaluated the genetic diversity of two mangrove species, R. apiculata and A. marina, in Tielu Harbor Mangrove Nature Reserve. There were no significant differences in the genetic diversity (Ho = 0.357, He = 0.472) of the R. apiculata population in Tielu Harbor with other R. apiculata populations. Yahya et al. [58] characterized the genetic variation of 15 R. apiculata populations in the Greater Sunda Islands of Indonesia using five microsatellite loci and found that Ho and He values were 0.338 (0.117–0.457) and 0.378 (0.123–0.482), respectively. The genetic diversity of R. apiculata in Malaysia was found to be particularly low, as the Ho and He values were 0.299 (0.194–0.483) and 0.325 (0.247–0.503), respectively [59]. Although the genetic diversity of R. apiculata in our study population was not low, we found that the AR and PAR values of seedlings were lower than those of adults (Table 2). Given that it takes nearly three years for propagules of R. apiculata to develop from flower bud primordia to maturity [60], the low genetic diversity of these seedlings may reflect the impact of recent anthropogenic activities on the population.
The genetic diversity of the A. marina population in our study (Ho = 0.195, He = 0.384) was lower compared with that of other populations examined in a previous study [61]. Maguire et al. [58] detected high levels of genetic diversity of A. marina in 14 populations across its global range using three microsatellite loci. Nearly all of the populations examined in Maguire et al. [61] had higher Ho values compared with the A. marina population in Tielu Harbor; the one exception was a Japanese population (Ho = 0.000). Hou et al. (unpublished data) investigated the genetic diversity of an A. marina population along the Sanya River, which is located close to Tielu Harbor, and the Ho and He values of this population were 0.532 and 0.575, respectively. Our data indicate that habitat degradation might have had a significant effect on the genetic diversity of the A. marina population in Tielu Harbor. Similarly, Salas-Leiva et al. [55] reported that the low genetic diversity (Ho = 0.277) of another Avicennia species (A. germinans) along the Colombian Pacific coast was associated with habitat degradation and fragmentation processes. However, we found that the AR and PAR of the seedlings of A. marina were higher than that of adults. The propagules of A. marina are small and light and can spread by sea currents [62]; thus, some of these seedlings might have originated from outside the quadrat, such as populations near Tielu Harbor (e.g., the Sanya River population and Qingmei Harbor population [39]).
We found that A. marina populations in Tielu Harbor had significant positive Fis values. High levels of inbreeding have been documented in several mangrove species, such as Rhizophora mangle [63], Rhizophora stylosa [43], A. marina [61], and Kandelia candel [64]. The high level of inbreeding observed in A. marina can be explained by the habitat degradation and fragmentation [55]. Considering that the population was not detected to have experienced bottleneck (Table 3) and results of the mating system and paternity analyses, we speculate that the significant positive Fis values and low genetic diversity in A. marina reflect mating between close relatives rather than a reduction in effective population size. The results of the genetic bottleneck analysis indicate that the R. apiculata population in Tielu Harbor may have experienced a reduction in population size induced by habitat fragmentation. Although previous studies have shown that habitat fragmentation can have significant deleterious effects on the genetic health and mating system of wind-pollinated trees [65], the outcrossing rates observed for R. apiculata in our study were higher than those reported for other mangrove trees [51,66]. The continuous distribution of R. apiculata trees in Tielu Harbor might facilitate the exchange of pollen among plants and explain the high outcrossing rate [67].
The effect of seedlings was greater on the spatial genetic structure of A. marina than that of R. apiculata (Figure 3). Pronounced spatial genetic structure has been detected over short distances within populations of mangrove species given that many propagules become established around the mother tree [51]. The pollen and propagule dispersal of R. apiculata, a viviparous species, is limited; similar observations have been made for another viviparous mangrove plant, K. candel [51,68]. However, because each R. apiculata individual occupied a large space, virtually no seedlings can grow further up within 2 m of the mother tree. As discussed above, this creates significant genetic structure over short distances, as was observed in adults of R. apiculata (2–32 m, Figure 3b). The spatial genetic structure of A. marina within 2 m changed significantly depending on whether seedlings were included in the analysis. This can be explained by the amount of space occupied by adult trees. Adult A. marina trees are small, and seedlings can grow around their mother trees. Thus, when seedlings were excluded from the analysis, genetic structure was not significant within 2 m, but when they were included, genetic structure was significant.
We studied the genetic diversity, spatial genetic structure, and mating system of two mangrove species, R. apiculata and A. marina, in a heavily disturbed area in Tielu Harbor, China, using nuclear microsatellite markers. Microsatellite markers are widely used in population genetic studies of mangrove plants, such as R. apiculata [58,59], K. candel [51,68], A. germinans [55,56,57] and A. marina [61], to estimate genetic diversity, genetic structure and mating system. This indicates the usefulness of microsatellites in the genetic analysis of mangrove plants. The findings from our studies and field investigations suggest that these two mangrove species require distinct restoration and rehabilitation approaches. Given that the diversity of the R. apiculata population in Tielu Harbor is similar to that of other populations [58,59], the propagules produced by local adult trees could be used for mangrove restoration. However, for this approach to succeed, the connectivity among individuals needs to be maintained, and this includes the connectivity between newly restored mangroves with existing mangroves, to minimize the effects of inbreeding on future generations. Connectivity among individuals is also considered important in the in-situ conservation of another mangrove plant, A. germinans [57]. The low genetic diversity and significant inbreeding of A. marina in Tielu Harbor suggest that the genetic health of the A. marina population is poorer than that of the R. apiculata population [61]. Consequently, the use of propagules from the A. marina population in Tielu Harbor for mangrove restoration is likely insufficient for ensuring the long-term persistence of restored A. marina populations. In a previous study, Salas-Leiva et al. [56] considered that reforestation using propagules from different populations would improve the maintenance of genetic diversity and the viability of the reforested population in the short and medium term. Propagules from numerous populations with high genetic diversity are needed to ensure that restored A. marina populations are resilient enough to persist in the face of anthropogenically driven habitat degradation and climate change [28,69].
Mangrove conservation and restoration efforts in China have generally been successful over the last several decades. Approximately 67% of mangroves are now within nature reserves, and 43 mangrove protected areas have been established [16]. Studies of the genetic diversity, fine-scale genetic structure, and mating system of mangroves are needed to ensure the success of mangrove conservation efforts. In this study, we only selected limited seedlings and propagules to minimize the impact on the capacity for population renewal. We believed that if more seedling and propagule samples can be taken, the results should be more reliable. Furthermore, additional studies are needed to evaluate the genetic diversity of populations following mangrove restoration, as well as monitor the status of populations. Such studies of genetic diversity and structure would provide valuable insight into whether restoration efforts are having their intended effect of increasing the resilience of restored areas.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/d14020115/s1, Table S1: Information of 18 nSSR primer pairs, Table S2: Summary of the AMOVA results for adults and seedlings in R. apiculata population, Table S3: Summary of the AMOVA results for adults and seedlings in A. marina population.

Author Contributions

Conceptualization, W.L. and S.Y.; methodology, W.L. and S.Y.; validation, W.L. and Z.Z.; formal analysis, W.L.; investigation, W.L. and S.Y.; data curation, W.L. and Z.Z.; writing—original draft preparation, W.L.; writing—review and editing, W.L., X.H. and S.Y.; visualization, W.L.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2017YFC0506103); Ocean Public Fund Research Project Grants of State Oceanic Administration (No. 201305021-2); and National Natural Science Foundation of China (No. 30972334).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We thank editors and three anonymous reviewers for their suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alongi, D.M. Present state and future of the world's mangrove forests. Environ. Conserv. 2002, 29, 331–349. [Google Scholar] [CrossRef] [Green Version]
  2. Kathiresan, K.; Bingham, B.L. Biology of mangroves and mangrove ecosystems. Adv. Mar. Biol. 2001, 40, 81–251. [Google Scholar]
  3. Tomlinson, P.B. The Botany of Mangroves, 2nd ed.; Cambridge University Press: Cambridge, UK, 2016; pp. 11–28. [Google Scholar]
  4. Costanza, R.; d’Arge, R.; de Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; O’Neill, R.V.; Paruelo, J.; et al. The value of ecosystem service and nature capital in the world. Nature 1997, 387, 235–260. [Google Scholar] [CrossRef]
  5. Alongi, D.M. Carbon sequestration in mangrove forests. Carbon. Manag. 2012, 3, 313–322. [Google Scholar] [CrossRef]
  6. Polidoro, B.A.; Carpenter, K.E.; Collins, L.; Duke, N.C.; Ellison, A.M.; Ellison, J.C.; Farnsworth, E.J.; Fernando, E.S.; Kathiresan, K.; Koedam, N.E.; et al. The loss of species: Mangrove extinction risk and geographic areas of global concern. PLoS ONE 2010, 5, e10095. [Google Scholar] [CrossRef] [PubMed]
  7. Eddy, S.; Milantara, N.; Sasmito, S.D.; Kajita, T.; Basyuni, M. Anthropogenic drivers of mangrove loss and associated carbon emissions in South Sumatra, Indonesia. Forests 2021, 12, 187. [Google Scholar] [CrossRef]
  8. Field, C.D. Impact of expected climate change on mangroves. Hydrobiologia 1995, 295, 75–81. [Google Scholar] [CrossRef]
  9. Bologna, P.; Campanella, J.J.; Restaino, D.J.; Fetske, Z.A.; Lourenco, M.; Smalley, J.V. Lingering impacts of hurricane Hugo on Rhizophora mangle (red mangrove) population genetics on St. John, USVI. Diversity 2019, 11, 65. [Google Scholar] [CrossRef] [Green Version]
  10. Lee, S.-Y.; Primavera, J.H.; Dahdouh-Guebas, F.; McKee, K.; Bosire, J.O.; Cannicci, S.; Diele, K.; Fromard, F.; Koedam, N.; Marchand, C.; et al. Ecological role and services of tropical mangrove ecosystems: A reassessment. Glob. Ecol. Biogeogr. 2014, 23, 726–743. [Google Scholar] [CrossRef]
  11. FAO. The World's Mangroves 1980–2005; Food and Agriculture Organization of the United Nations: Rome, Italy, 2007. [Google Scholar]
  12. Fan, H.; Wang, W. Some thematic issues for mangrove conservation in China (in Chinese). J. Xiamen Univ. Nat. Sci. 2017, 56, 323–330. [Google Scholar]
  13. Duke, N.C.; Meynecke, J.-O.; Dittmann, S.; Ellison, A.M.; Anger, K.; Berger, U.; Cannicci, S.; Diele, K.; Ewel, K.C.; Field, C.D.; et al. A world without mangroves? Science 2007, 317, 41–42. [Google Scholar] [CrossRef] [Green Version]
  14. Wang, W.Q.; Wang, M. The Mangrove of China, 1st ed.; Science Press: Beijing, China, 2007; pp. 1–186. [Google Scholar]
  15. SFA. National Mangrove Resource Inventory Report; The State Forestry Administration of the People’s Republic of China: Beijing, China, 2002. [Google Scholar]
  16. Wang, W.; Fu, H.; Lee, S.-Y.; Fan, H.; Wang, M. Can strict protection stop the decline of mangrove ecosystems in China? From rapid destruction to rampant degradation. Forests 2020, 11, 55. [Google Scholar] [CrossRef] [Green Version]
  17. Yang, S.-C.; Lu, W.-X.; Zou, Z.; Li, S. Mangrove Wetlands: Distribution, Species Composition and Protection in China (in Chinese). Subtrop. Plant Sci. 2017, 46, 301–310. [Google Scholar]
  18. Jia, M.; Wang, Z.; Zhang, Y.; Mao, D.; Wang, C. Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China. Int. J. Appl. Earth Obs. Geoinf. 2018, 73, 535–545. [Google Scholar] [CrossRef]
  19. Hamilton, S.E.; Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 2016, 25, 729–738. [Google Scholar] [CrossRef]
  20. Wang, M.; Wang, W.; Lin, G.; Ma, W.; Fu, R. The Mangroves of Sanya, 1st ed.; Science Press: Beijing, China, 2019; pp. 1–212. [Google Scholar]
  21. Ma, Z.; Melville, D.S.; Liu, J.; Chen, Y.; Yang, H.; Ren, W.; Zhang, Z.; Piersma, T.; Li, B. Rethinking China’s new great wall. Science 2014, 346, 912–914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Wang, Q.; Song, L.; Agusti, S.; Duarte, C.; Christakos, G.; Wu, J. Changes of the Macrobenthos Community with Non-native Mangrove Rehabilitation (Kandelia obovata) and Salt Marsh Invasion (Spartina alterniflora) in Ximen Island, Zhejiang, China. Ocean Sci. J. 2021, 56, 395–405. [Google Scholar] [CrossRef]
  23. Maiti, S.K.; Chowdhury, A. Effects of anthropogenic pollution on mangrove biodiversity: A review. J. Environ. Prot. 2013, 4, 1428–1434. [Google Scholar] [CrossRef] [Green Version]
  24. Feeley, K.J.; Silman, M.R. Land-use and climate change effects on population size and extinction risk of Andean plants. Glob. Chang. Biol. 2010, 16, 3215–3222. [Google Scholar] [CrossRef]
  25. Çalişkan, M. Genetic Diversity in Plants, 1st ed.; InTech: Rijeka, Croatia, 2012; pp. 1–493. [Google Scholar]
  26. Verma, A.K. Genetic diversity as buffer in biodiversity. Indian J. Biol. 2017, 4, 61–63. [Google Scholar]
  27. Rao, V.R.; Hodgkin, T. Genetic diversity and conservation and utilization of plant genetic resources. Plant Cell Tissue Organ Cult. 2002, 68, 1–19. [Google Scholar]
  28. Kahilainen, A.; Puurtinen, M.; Kotiaho, J.S. Conservation implications of species–genetic diversity correlations. Glob. Ecol. Conserv. 2014, 2, 315–323. [Google Scholar] [CrossRef] [Green Version]
  29. Dixo, M.; Metzger, J.P.; Morgante, J.S.; Zamudio, K.R. Habitat fragmentation reduces genetic diversity and connectivity among toad populations in the Brazilian Atlantic Coastal Forest. Biol. Conserv. 2009, 142, 1560–1569. [Google Scholar] [CrossRef]
  30. Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 2003, 34, 487–515. [Google Scholar] [CrossRef] [Green Version]
  31. Tambarussi, E.V.; Boshier, D.H.; Vencovsky, R.; Freitas, M.L.M.; Di-Dio, O.J.; Sebbenn, A.M. Several small: How inbreeding affects conservation of Cariniana legalis Mart. Kuntze (Lecythidaceae) the Brazilian Atlantic Forest's largest tree. Int. For. Rev. 2016, 18, 502–510. [Google Scholar] [CrossRef]
  32. Coelho, N.H.P.; Tambarussi, E.V.; Aguiar, B.I.; Roque, R.H.; Portela, R.M.; Braga, R.C.; Sanson, D.; Silva, R.A.R.; Ferraz, E.M.; Moreno, M.A.; et al. Understanding genetic diversity, spatial genetic structure, and mating system through microsatellite markers for the conservation and sustainable use of Acrocomia aculeata (Jacq.) Lodd. Ex Mart. Conserv. Genet. 2018, 19, 879–891. [Google Scholar] [CrossRef] [Green Version]
  33. Eckert, C.G.; Kalisz, S.; Geber, M.A.; Sargent, R.; Elle, E.; Cheptou, P.O.; Goodwillie, C.; Johnston, M.O.; Kelly, J.K.; Moeller, D.A.; et al. Plant mating systems in a changing world. Trends Ecol. Evol. 2010, 25, 35–43. [Google Scholar] [CrossRef]
  34. Hopper, S.D. OCBIL theory: Towards an integrated understanding of the evolution, ecology and conservation of biodiversity on old, climatically buffered, infertile landscapes. Plant Soil 2009, 322, 49–86. [Google Scholar] [CrossRef]
  35. Miller, M.P.; Haig, S.M.; Wagner, R.S. Phylogeography and spatial genetic structure of the southern torrent salamander: Implications for conservation and management. J. Hered. 2006, 97, 561–570. [Google Scholar] [CrossRef] [Green Version]
  36. Caballero, A.; Rodríguez-Ramilo, S.T.; Ávila, V.; Fernández, J. Management of genetic diversity of subdivided populations in conservation programmes. Conserv. Genet. 2010, 11, 409–419. [Google Scholar] [CrossRef]
  37. Al-Qthanin, R.N.; Alharbi, S.A. Spatial structure and genetic variation of a mangrove species (Avicennia marina (Forssk.) Vierh) in the Farasan Archipelago. Forests 2020, 11, 1287. [Google Scholar] [CrossRef]
  38. Zhang, Y.; Zhang, J.-W.; Yang, Y.; Li, X.-N. Structural and comparative analysis of the complete chloroplast genome of a mangrove plant: Scyphiphora hydrophyllacea Gaertn. f. and related Rubiaceae species. Forests 2019, 10, 1000. [Google Scholar] [CrossRef] [Green Version]
  39. Yao, Y.-F.; Liao, W.-B.; Song, X.-Y.; Li, C.-S. Current status and conservation of mangrove resources in Tielu Harbor, Sanya, Hainan (in Chinese). Mar. Sci. Bull. 2010, 29, 150–155. [Google Scholar]
  40. Tao, L.-P.; Huang, S.-M. Research on Mangrove Resources and Communities in Sanya (in Chinese). Nat. Sci. J. Hainan Univ. 2004, 22, 70–74. [Google Scholar]
  41. Arnaud-Haond, S.; Teixeira, S.; Massa, S.I.; Billot, C.; Saenger, P.; Coupland, G.; Duarte, C.M.; Serrão, E.A. Genetic structure at range edge: Low diversity and high inbreeding in Southeast Asian mangrove (Avicennia marina) populations. Mol. Ecol. 2006, 15, 3515–3525. [Google Scholar] [CrossRef]
  42. Geng, Q.F.; Lian, C.L.; Tao, J.M.; Li, S.Q.; Hogetsu, T. Isolation and characterization of 10 new compound microsatellite markers for a mangrove tree species, Avicennia marina (Forsk.) Vierh. (Avicenniaceae). Mol. Ecol. Notes 2007, 7, 1208–1210. [Google Scholar] [CrossRef]
  43. Islam, M.S.; Lian, C.; Kameyama, N.; Wu, B.; Hogetsu, T. Development of microsatellite markers in Rhizophora stylosa using a dual-suppression-polymerase chain reaction technique. Mol. Ecol. Notes 2004, 4, 110–112. [Google Scholar] [CrossRef]
  44. Shinmura, Y.; Wee, A.K.S.; Takayama, K.; Meenakshisundaram, S.H.; Asakawa, T.; Onrizal, O.; Adjie, B.; Ardli, E.R.; Sungkaew, S.; Malekal, N.B.; et al. Isolation and characterization of 14 microsatellite markers for Rhizophora mucronata (Rhizophoraceae) and their potential use in range-wide population studies. Conserv. Genet. Resour. 2012, 4, 951–954. [Google Scholar] [CrossRef]
  45. Raymond, M.; Rousset, F. GENEPOP (Version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 1995, 86, 248–249. [Google Scholar] [CrossRef]
  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. Szpiech, Z.A.; Jakobsson, M.; Rosenberg, N.A. ADZE: A rarefaction approach for counting alleles private to combinations of populations. Bioinformatics 2008, 24, 2498–2504. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Slate, J.; Marshall, T.; Pemberton, J. A retrospective assessment of the accuracy of the paternity inference program CERVUS. Mol. Ecol. 2000, 9, 801–808. [Google Scholar] [CrossRef] [PubMed]
  49. Piry, S.; Luikart, G.; Cornuet, J.-M. Computer note. 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]
  50. Geng, Q.; Lian, C.; Goto, S.; Tao, J.; Kimura, M.; Islam, M.S.; Hogetsu, T. Mating system, pollen and propagule dispersal, and spatial genetic structure in a high-density population of the mangrove tree Kandelia candel. Mol. Ecol. 2008, 17, 4724–4739. [Google Scholar] [CrossRef]
  51. Hardy, O.J.; Vekemans, X. SPAGeDi: A versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2002, 2, 618–620. [Google Scholar] [CrossRef] [Green Version]
  52. Vekemans, X.; Hardy, O.J. New insights from fine-scale spatial genetic structure analyses in plant populations. Mol. Ecol. 2004, 13, 921–935. [Google Scholar] [CrossRef]
  53. Ritland, K. Extensions of models for the estimation of mating systems using n independent loci. Heredity 2002, 88, 221–228. [Google Scholar] [CrossRef]
  54. Thomas, E.; Jalonen, R.; Loo, J.; Boshier, D.; Gallo, L.; Cavers, S.; Bordács, S.; Smith, P.; Bozzano, M. Genetic considerations in ecosystem restoration using native tree species. For. Ecol. Manag. 2014, 333, 66–75. [Google Scholar] [CrossRef] [Green Version]
  55. Salas-Leiva, D.E.; Mayor-Durán, V.M.; Toro-Perea, N. Genetic diversity of the black mangrove (Avicennia germinans L.) in Colombia. Aquat. Bot. 2009, 91, 187–193. [Google Scholar] [CrossRef]
  56. Salas-Leiva, D.E.; Mayor-Durán, V.M.; Toro-Perea, N. Genetic diversity of black mangrove (Avicennia germinans) in natural and reforested areas of Salamanca Island Parkway, Colombian Caribbean. Hydrobiologia 2009, 620, 17–24. [Google Scholar] [CrossRef]
  57. Millán-Aguilar, O.; Manzano-Sarabia, M.; Nettel-Hernanz, A.; Dodd, R.S.; Hurtado-Oliva, M.Á.; Velázquez-Velázquez, E. Genetic diversity of the black mangrove Avicennia germinans (L.) Stearn in Northwestern Mexico. Forests 2016, 7, 197. [Google Scholar] [CrossRef] [Green Version]
  58. Yahya, A.F.; Hyun, J.O.; Lee, J.H.; Kim, Y.Y.; Lee, K.M.; Hong, K.N.; Kim, S.-C. Genetic variation and population genetic structure of Rhizophora apiculata (Rhizophoraceae) in the greater Sunda Islands, Indonesia using microsatellite markers. J. Plant Res. 2014, 127, 287–297. [Google Scholar] [CrossRef]
  59. Azman, A.; Ng, K.-K.-S.; Ng, C.-H.; Lee, C.-T.; Tnah, L.-H.; Zakaria, N.-F.; Mahruji, S.; Perdan, K.; Abdul-Kadir, M.-Z.; Cheng, A.; et al. Low genetic diversity indicating the threatened status of Rhizophora apiculata (Rhizophoraceae) in Malaysia: Declined evolution meets habitat destruction. Sci. Rep. 2020, 10, 19112. [Google Scholar] [CrossRef] [PubMed]
  60. Christensen, B.; Wium-Andersen, S. Seasonal growth of mangrove trees in southern Thailand. I. The phenology of Rhizophora apiculata Bl. Aquat. Bot. 1977, 3, 281–286. [Google Scholar] [CrossRef]
  61. Maguire, T.L.; Saenger, P.; Baverstock, P.; Henry, R. Microsatellite analysis of genetic structure in the mangrove species Avicennia marina (Forsk.) Vierh.(Avicenniaceae). Mol. Ecol. 2000, 9, 1853–1862. [Google Scholar] [CrossRef] [PubMed]
  62. Duke, N.C. Mangrove taxonomy, biogeography and evolution-Implications for conservation and management (An Indo West Pacific perspective). Excellence in Research Australia (ERA)-Collection Centre for Marine Studies Publication. In Proceedings of the International Conference and Exhibition on the Mangroves of Indian and Western Pacific Oceans, Kuala Lumpur, Malaysia, 21–24 August 2006; Hooi, T.K., Eong, O.J., Primavera, J.H., Eds.; Maritime Institute of Malaysia (MIMA): Kuala Lumpur, Malaysia, 2008. [Google Scholar]
  63. Arbeláez-Cortes, E.; Castillo-Cárdenas, M.F.; Toro-Perea, N.; Cárdenas-Henao, H. Genetic structure of the red mangrove (Rhizophora mangle L.) on the Colombian Pacific detected by microsatellite molecular markers. Hydrobiologia 2007, 583, 321–330. [Google Scholar] [CrossRef]
  64. Chen, X.-Y.; Lin, P.; Lin, Y.-M. Mating systems and spontaneous mutation rates for chlorophyll-deficiency in populations of the mangrove Kandelia candel. Hereditas 1996, 125, 47–52. [Google Scholar]
  65. Jump, A.S.; Peñuelas, J. Genetic effects of chronic habitat fragmentation in a wind-pollinated tree. Proc. Natl. Acad. Sci. USA 2006, 103, 8096–8100. [Google Scholar] [CrossRef] [Green Version]
  66. Ge, J.P.; Cai, B.; Ping, W.; Song, G.; Ling, H.; Lin, P. Mating system and population genetic structure of Bruguiera gymnorrhiza (Rhizophoraceae), a viviparous mangrove species in China. J. Exp. Mar. Biol. Ecol. 2005, 326, 48–55. [Google Scholar] [CrossRef]
  67. Hanson, T.R.; Brunsfeld, S.J.; Finegan, B.; Waits, L.P. Pollen dispersal and genetic structure of the tropical tree Dipteryx panamensis in a fragmented Costa Rican landscape. Mol. Ecol. 2008, 17, 2060–2073. [Google Scholar] [CrossRef]
  68. Ha, H.T.; Duarte, C.M.; Tri, N.H.; Terrados, J.; Borum, J. Growth and population dynamics during early stages of the mangrove Kandelia candel in Halong Bay, North Viet Nam. Estuar. Coast. Shelf Sci. 2003, 58, 435–444. [Google Scholar]
  69. Kettenring, K.M.; Mercer, K.L.; Reinhardt Adams, C.; Hines, J. EDITOR’S CHOICE: Application of genetic diversity-ecosystem function research to ecological restoration. J. Appl. Ecol. 2014, 51, 339–348. [Google Scholar] [CrossRef]
Diversity 14 00115 g001 550
Figure 1. Images of Rhizophora apiculata and Avicennia marina in Tielu Harbor Mangrove Nature Reserve. Individuals of R. apiculata on land (a) and near the sea (b) and flowers of R. apiculata (c). Individuals of A. marina on land (d) and near the sea (e) and flowers of A. marina (f). The red arrows indicate pieces of garbage in the mangroves.
Figure 1. Images of Rhizophora apiculata and Avicennia marina in Tielu Harbor Mangrove Nature Reserve. Individuals of R. apiculata on land (a) and near the sea (b) and flowers of R. apiculata (c). Individuals of A. marina on land (d) and near the sea (e) and flowers of A. marina (f). The red arrows indicate pieces of garbage in the mangroves.
Diversity 14 00115 g001
Diversity 14 00115 g002 550
Figure 2. Map of Rhizophora apiculata (circles) and Avicennia marina (squares) individuals in quadrats.
Figure 2. Map of Rhizophora apiculata (circles) and Avicennia marina (squares) individuals in quadrats.
Diversity 14 00115 g002
Diversity 14 00115 g003 550
Figure 3. Kinship coefficients (Fij) in eight distance classes for all individuals (a) and adults (b) of the R. apiculata population and all individuals (c) and adults (d) of the A. marina population. The dashed lines indicate 95% confidence limits.
Figure 3. Kinship coefficients (Fij) in eight distance classes for all individuals (a) and adults (b) of the R. apiculata population and all individuals (c) and adults (d) of the A. marina population. The dashed lines indicate 95% confidence limits.
Diversity 14 00115 g003
Table
Table 1. Genetic diversity of the selected nSSR primer pairs in all individuals of R. apiculata and A. marina.
Table 1. Genetic diversity of the selected nSSR primer pairs in all individuals of R. apiculata and A. marina.
LocusnNaNeHoHeuHeIPr(Ex1)Pr(Ex2)
R. apiculata
Rhst0116721.5190.0180.3420.3430.5250.9620.880
Rhst0216741.8770.6650.4670.4690.7470.8710.755
Rhst1116721.9160.3470.4780.4790.6710.9030.828
Rhst1316772.0650.4130.5160.5170.9200.8610.743
Rhst2016784.3220.3110.7690.7711.5890.6300.452
RM11316752.4530.5930.5920.5941.0560.8140.671
RM11416762.2070.4670.5470.5480.9990.8390.692
RM11616731.0680.0420.0640.0640.1550.9970.960
Mean1674.62.1780.3570.4720.4730.833//
A. marina
Avma0121031.8400.2620.4570.4580.6620.8810.813
Avma0221051.4790.2480.3240.3250.5960.9300.823
Avma1621041.9750.2380.4940.4950.7220.8740.804
Avma1721052.7950.4760.6420.6441.2010.7970.634
Am321021.3790.1570.2750.2750.4470.9750.901
Am3221031.1000.0100.0910.0910.2120.9990.976
Am4021041.7760.1520.4370.4380.6710.9030.819
Am8121041.5470.0140.3540.3540.5940.9080.818
Mean2103.51.7500.1970.3870.3890636//
Note: Genetic diversity parameters were estimated for combined adult and seedling plants; Abbreviations: n, number of individuals; Na, number of alleles; Ne, effective number of alleles; Ho, observed heterozygosity; He, expected heterozygosity; uHe, unbiased expected heterozygosity; I, Shannon’s index of diversity; [Pr(Ex1)] and [Pr(Ex2)], exclusion probability of the first and second parent, respectively.
Table
Table 2. Genetic diversity parameters for adults and seedlings of R. apiculata and A. marina.
Table 2. Genetic diversity parameters for adults and seedlings of R. apiculata and A. marina.
SpeciesnNaNeHoHeuHeIFisARPAR
R. apiculata
Adults1394.6252.1650.3660.4640.4660.8280.2153.0460.433
(0.800)(0.365)(0.084)(0.073)(0.074)(0.152)/(0.441)(0.130)
Seedlings283.3752.1690.3130.4860.4950.8130.373 *2.9410.327
(0.460)(0.262)(0.081)(0.071)(0.072)(0.135)/(0.402)(0.155)
Total1674.6252.1780.3570.4720.4730.8330.246//
(0.800)(0.341)(0.083)(0.072)(0.073)(0.149)///
A. marina
Adults1523.5001.7500.1970.3870.3890.6360.497 *2.6230.277
(0.267)(0.181)(0.056)(0.060)(0.061)(0.102)/(0.233)(0.111)
Seedlings582.8751.6990.1900.3720.3760.6250.497 *2.7040.358
(0.389)(0.181)(0.049)(0.055)(0.055)(0.097)/(0.329)(0.143)
Total2103.7501.7360.1950.3840.3850.6380.497 *//
(0.366)(0.180)(0.053)(0.058)(0.058)(0.099)///
Note: The standard error of the corresponding parameter is shown in parentheses; * Significant at the 95% confidence level. Abbreviations: n, number of individuals; Na, number of alleles; Ne, effective number of alleles; Ho, observed heterozygosity; He, expected heterozygosity; uHe, unbiased expected heterozygosity; I, Shannon’s index of diversity; Fis, coefficient of local inbreeding; AR, allele abundance; PAR, private allele abundance.
Table
Table 3. The probability of population bottlenecks of R. apiculata and A. marina under three models.
Table 3. The probability of population bottlenecks of R. apiculata and A. marina under three models.
ModelR. apiculataA. marina
AdultsSeedlingsTotalAdultsSeedlingsTotal
Sign testTPM0.4500.2000.4300.5310.1420.534
IAA0.1540.0420.1450.1300.0960.138
SMM0.0860.2690.0830.2250.5930.069
mean0.2300.1700.2190.2950.2770.247
One-tailed Wilcoxon testTPM0.4220.1250.4220.5270.2300.578
IAA0.0980.0200.0270.0980.1250.125
SMM0.9630.4730.9020.9730.5270.980
mean0.4940.2060.4510.5330.2940.561
Note: The one-tailed Wilcoxon sign-rank test was only used to determine heterozygosity excess. Values indicating significant bottlenecks are in bold (p < 0.05). Abbreviations: TPM, two-phase model; IAA, infinite allele model; SMM, stepwise mutation model.
Table
Table 4. Mating system parameters for each mother tree of R. apiculata and A. marina.
Table 4. Mating system parameters for each mother tree of R. apiculata and A. marina.
Mother TreePropagulestm (SE)ts (SE)tm−ts
R. apiculata
Z1261.001 (0.001)1.138 (0.053)−0.137
Z85111.116 (0.041)1.551 (0.165)−0.435
Z11061.004 (0.001)1.082 (0.112)−0.078
A. marina
A1170.822 (0.209)1.027 (0.203)−0.205
A2100.186 (0.151)0.094 (0.073)0.092
A2190.755 (0.170)0.691 (0.199)0.064
A4891.024 (0.001)0.589 (0.079)0.435
A12660.849 (0.162)0.818 (0.228)0.031
Abbreviations: tm, multilocus outcrossing rates; ts, single-locus outcrossing rates; tm−ts, biparental inbreeding rates.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Lu, W.; Zou, Z.; Hu, X.; Yang, S. Genetic Diversity and Mating System of Two Mangrove Species (Rhizophora apiculata and Avicennia marina) in a Heavily Disturbed Area of China. Diversity 2022, 14, 115. https://doi.org/10.3390/d14020115

AMA Style

Lu W, Zou Z, Hu X, Yang S. Genetic Diversity and Mating System of Two Mangrove Species (Rhizophora apiculata and Avicennia marina) in a Heavily Disturbed Area of China. Diversity. 2022; 14(2):115. https://doi.org/10.3390/d14020115

Chicago/Turabian Style

Lu, Wenxun, Zhen Zou, Xueying Hu, and Shengchang Yang. 2022. "Genetic Diversity and Mating System of Two Mangrove Species (Rhizophora apiculata and Avicennia marina) in a Heavily Disturbed Area of China" Diversity 14, no. 2: 115. https://doi.org/10.3390/d14020115

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