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Article

Colonization by Distinct Lineages, the Sundaland Barrier, and Historical Bottlenecks Shape the East–West Population Structure of Avicennia Mangroves Across the Indo-Pacific Interface

by
Poompat Phadphon
1,
Chutintorn Yundaeng
1,
Nattapol Narong
1,
Nukoon Jomchai
1,
Phakamas Phetchawang
1,
Nawin Phormsin
2,
Darunee Jiumjamrassil
2,
Sithichoke Tangphatsornruang
1 and
Wirulda Pootakham
1,*
1
Genomic Research Team, National Omics Center, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
2
Department of Marine and Coastal Resources, 120 The Government Complex, Thung Song Hong, Bangkok 10210, Thailand
*
Author to whom correspondence should be addressed.
Biology 2026, 15(5), 385; https://doi.org/10.3390/biology15050385
Submission received: 24 January 2026 / Revised: 20 February 2026 / Accepted: 24 February 2026 / Published: 26 February 2026
(This article belongs to the Section Conservation Biology and Biodiversity)
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Simple Summary

This genetic study of Avicennia marina, the most widely distributed mangrove species; Avicennia alba; and Avicennia officinalis across the Andaman Sea and the Gulf of Thailand suggests that colonization events by distinct ancestral lineages (Indian and West Pacific Ocean lineages), the Indo-Pacific Barrier (Sundaland), and Pleistocene sea-level fluctuations shaped the East–West population structure and contributed to low genetic diversity and high inbreeding. By highlighting Thailand as a contact zone between Indian and West Pacific Ocean lineages, this study recommends that Andaman and Thai Gulf populations be managed as separate evolutionarily significant units for conservation management.

Abstract

The emergence of Sundaland during the Pleistocene glaciation has played a crucial role, as the Indo-Pacific Barrier (IPB), in shaping the genetic structure of marine taxa and coastal flora, specifically mangroves. This study investigated the genetic diversity, population structure, demographic history and phylogeography of Avicennia marina and two other Indo-West Pacific (IWP) Avicennia species, Avicennia alba and Avicennia officinalis, across the Andaman Sea (Indian Ocean) and the Gulf of Thailand (Pacific Ocean). Using Restriction-site-Associated DNA sequencing (RADseq), we generated thousands of genome-wide SNPs for 362 Avicennia individuals and revealed a pronounced East–West genetic divergence, separating the Andaman and Gulf of Thailand populations. Phylogeographic and demographic analyses suggest that colonization events by distinct ancestral lineages (Indian and West Pacific Ocean lineages), the Indo-Pacific Barrier (Sundaland), and Pleistocene sea-level fluctuations shaped the population structure and contributed to low genetic diversity (Ho = 0.073–0.083) and high inbreeding coefficients (FIS = 0.169–0.501). This study highlights the importance of Thailand, as part of the Indo-Pacific interface, in harboring genetic resources from both Indian and West Pacific Ocean lineages, as exemplified in A. marina. Consequently, Andaman and Thai Gulf populations should be managed as distinct evolutionarily significant units (ESUs).

1. Introduction

The Indo-Pacific Barrier (IPB) is a major biogeographic barrier that limits gene flow and restricts the distribution of marine lives, leading to genetic divergence and distinct species distribution across the Indian and Pacific Oceans [1]. This barrier is located around the Thai-Malay Peninsula and the Indo-Malay Archipelago. Its efficacy as a barrier was historically intensified by the exposure of the Sunda Shelf—a vast extension of the continental shelf underlying the Thai-Malay Peninsula and the Indo-Malay Archipelago [1,2]. During the Pleistocene, glacial cycles repeatedly recurred and became greater in amplitude and duration after the Mid-Pleistocene Transition, causing ~100 ka glacial cycles and the sea-level fluctuation of more than 100 m in the Late Pleistocene [3,4,5]. At the Last Glacial Maximum (26.5–19.0 kya), sea levels fell to approximately −130 m relative to the present-day level [5,6]. The area of the Sunda Shelf was exposed and formed the gigantic Sundaland. The land connected all the Greater Sunda Islands of Borneo, Java, and Sumatra with continental Asia and effectively reinforced the barrier between the Indian and Pacific Oceans [2,7].
The evolutionary impacts of the Indo-Pacific Barrier are prominent in low-dispersal invertebrates and reef fishes [1]. Phylogeographic patterns and genetic structuring have been reported in several Indo-Pacific marine taxa, such as the neritid gastropod, Nerita albicilla [8]; the cone snail, Conus litteratus [9]; the peacock grouper, Cephalopholis argus [10]; pomfrets, Pampus chinensis and Pampus cinereus [11]; macroalgae, Padina boryana [12]; and seagrass, Enhalus acoroides [13]. The legacy of the barrier is not limited only to marine taxa but also extends its influence to coastal flora, specifically mangroves. The effects of historical glacial vicariance on Indo-Pacific genetic differentiation have been observed in various mangrove species, e.g., Sonneratia alba [14], Heritiera littoralis [15], and Aegiceras corniculatum [16]. The great contraction of coastal mangrove forests resulting from the Pleistocene glaciation induced severe bottlenecks, magnified the effect of genetic drift, and greatly reduced genetic diversity, thereby lowering the adaptive potential of mangrove populations to changing environments under ongoing global climate change [14,15,16,17,18,19].
Avicennia, one of the most diverse mangrove genera, is a dominant genus, serving as a key pioneer colonizer essential for the development of new mangrove habitats. These mangroves provide vital economic services through coastal protection and support for local fisheries, yet they face significant contemporary threats from habitat fragmentation and land-use changes [20]. Avicennia comprises eight species. Five of them, A. marina, A. alba, A. officinalis, A. integra, and A. rumphiana, distribute across the Indo-West Pacific (IWP) region [21,22]. Based on morphological and molecular phylogenetic studies, these IWP Avicennia can be divided into three groups, namely, (1) A. alba and A. rumphiana, (2) A. officinalis and A. integra, and (3) A. marina, which can be differentiated using their floral structures [22]. Exhibiting the widest distribution of all mangroves, ranging from East Africa to the Western Pacific and from New Zealand to Japan, A. marina dominates mangrove forests and has been in the focus of studies compared with other IWP Avicennia species [22,23,24,25,26,27]. However, only a few genetic studies have included samples from both the Indian and Pacific Oceans [22,26,28]. The Thai-Malay Peninsula offers an important advantage in this regard, as it provides easy access to populations from both the Indian and Pacific Oceans.
Genetic studies of IWP Avicennia species across the Thai-Malay Peninsula have been conducted predominantly on Malaysian populations, with only limited sampling in Thailand [26,28]. In this study, we aim to evaluate the genetic diversity, genetic structure, and demographic history of three representative IWP Avicennia species, A. marina, A. alba, and A. officinalis, across the Indo-Pacific interface (the Andaman Sea and the Gulf of Thailand). The physical separation of the two ocean basins by the Indo-Pacific Barrier imposes a state of geographical isolation, which restricts gene flow and drives genetic divergence across the Indo-Pacific interface. We evaluated the correlation between genetic and geographic distances in the three Avicennia species and performed phylogeographic analysis in A. marina to determine its spatial genetic structure using single-nucleotide-polymorphism (SNP) markers. We employed Restriction-site-Associated DNA sequencing (RADseq) to generate thousands of genome-wide SNPs and demonstrated the influence of the Indo-Pacific Barrier, the Pleistocene sea-level fluctuations, and the colonization of distinct linages in shaping the population structure of Avicennia species across the Indo-Pacific interface.

2. Materials and Methods

2.1. Sample Collection and DNA Extraction

Leaf samples of three Avicennia species, A. marina, A. alba, and A. officinalis, were collected from 23 provinces, covering their distribution ranges on both Andaman and Gulf of Thailand (Figure 1, Table S1). The total number of samples of A. marina, A. alba, and A. officinalis were 129, 128, and 105, respectively (Table S1). Leaf samples were used to extract genomic DNA with either the standard CTAB (Cetyl Trimethly Ammonium Bromide) method or the DNeasy Plant Kit (Qiagen, Hilden, Germany). The isolated genomic DNA’s quality and quantity were evaluated using 0.8% agarose gel electrophoresis and the Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) with a Qubit dsDNA BR Assay kit (Invitrogen, Carlsbad, CA, USA).

2.2. DNA Sequencing and Variant Calling

The extracted DNA samples were used to prepare RADseq libraries with the MGIEasy RAD library preparation kit (MGI Tech, Shenzhen, China) following the manufacturer’s instructions. Briefly, genomic DNA was digested using TaqI restriction enzyme. The DNA fragments were ligated with unique barcoded adapters. The adapter-ligated DNA fragments were pooled, size-selected, and enriched via PCR amplification. The quality and quantity of the libraries were assessed using a Fragment Analyzer system (Agilent Technologies, Santa Clara, CA, USA) and sequenced with DNBSEQ-G400 (MGI Tech, Shenzhen, China) to generate 150 bp paired-end reads.
Read quality was evaluated with FastQC v0.12.1 [29]. Reads were then mapped to the reference genome of each species (A. marina: GCA_019155195.1, A. alba: GWHBCJG00000000, A. officinalis: GWHBCJF00000000 [30]) using Bowtie2 v2.5.1 [31]. All selected reference genomes exhibited high assembly quality, with BUSCO completeness exceeding 95% against the embryophyta_odb10 or viridiplantae_odb10 databases. The HaplotypeCaller tool in GATK v4.4 [32] was applied to identify variants with default parameters. Only SNP variants were selected and filtered with the VariantFiltration tool following the recommended hard-filtering criteria: QD < 2.0, QUAL < 30.0, SOR > 3.0, FS > 60.0, MQ < 40.0, MQRankSum < −12.5, ReadPosRankSum < −8.0. The datasets were further filtered using VCFtools v0.1.16 [33] to retain only biallelic loci (--max-alleles 2) with a minimum depth of 10 (--minDP 10) and a minor allele frequency of 0.01 (--maf 0.01). To balance analytical power and computational burden, the datasets were further refined using a stringent no-missing-data criterion (--max-missing 1). The final datasets comprised 7617 SNPs for A. marina, 7054 for A. alba, and 4741 for A. officinalis, which were used for downstream analyses (Table S2).

2.3. Population Structure Analysis

To evaluate the population structure of each Avicennia species, principal component analysis (PCA) and the Bayesian clustering method in STRUCTURE v2.3.4 [34] were implemented. The principal component analysis was performed using the scikit-learn Python module v1.8.0 [35]. The first two principal components were presented. Regarding Bayesian clustering, STRUCTURE was run under an admixture model with K values ranging from 1 to 10. For each K value, ten independent runs were performed with 100,000 MCMC replicates after a burn-in period of 20,000 for each run. The ΔK [36] was applied to infer the most likely K for each species in the STRUCTURE HARVESTER [37]. The most likely K of each species was rerun with 1,000,000 MCMC replicates with a burn-in period of 200,000. It should be noted that LD pruning was not applied to the datasets. This could lead to the inclusion of linked SNPs and the violation of the assumption of independent loci, potentially resulting in the inflation of the clustering signal. Nonetheless, STRUCTURE v2 can handle weakly linked markers, which helps mitigate this concern [34].
The analysis of molecular variance (AMOVA) and the population pairwise FST were performed in Arlequin v3.5 [38] with 1000 permutations to evaluate genetic variation across hierarchical levels and to estimate the genetic distance between the clusters defined by the STRUCTURE analysis. Individuals with a membership coefficient (Q) lower than 0.8 were assigned to a new cluster, an admixture cluster. Gene flow among clusters was inferred from the number of migrants per generation (Nm = [(1/FST) − 1]/4) [39]. GenAlEx v6.51b2 [40] was used to estimate the observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (FIS).

2.4. Isolation-by-Distance Analysis

To evaluate the influence of geographic distance on the genetic structure of the three Avicennia species, an isolation-by-distance (IBD) analysis was implemented using the Mantel test in Arlequin 3.5. Populations from the Andaman Sea and the Gulf of Thailand were analyzed separately to account for regional differences. Any population represented by a single individual was excluded from the dataset to maintain statistical robustness. The analysis utilized Slatkin’s linearized FST [FST/(1 − FST)] as the genetic distance measure, with significance determined through 1000 permutations.

2.5. Demographic Inference

To infer the demographic history of each population (genetic cluster), the coalescent-based Stairway Plot 2 [41] was implemented using a folded site frequency spectrum (SFS). The folded SFS was estimated using ANGSD v0.940 [42]. Specifically, the site allele frequency likelihoods were calculated (-doSaf 1) with quality filters -GL 2, -minMapQ 30, -minQ 20, followed by SFS estimation using realSFS with parameters -fold 1, -maxiter 100. The Stairway Plot 2 was run under a mutation rate of 5.22 × 10−8 per site per generation and a generation time of 20 years [30]. Due to the limited resolution of the method for very recent demographic events, only demographic trajectories of time periods ≥ 1 kya were reported.

2.6. Phylogeographic Analysis of A. marina

To elucidate the ancestral lineage of A. marina in Thailand, available whole-genome sequencing (WGS) datasets from five localities across the Indo-West Pacific region, including Oman (2 sites), India (1 site), and China (2 sites), were retrieved and included in the analysis (Table S3). One sample of A. marina eucalyptfolia and two samples of A. marina australasica were included as outgroups. Variant calling for these WGS data followed the steps mentioned earlier with an additional process of marking duplicate reads, which could arise during library construction using PCR, with the MarkDuplicates tool in GATK. The 7617 SNPs identified in the RADseq analysis were extracted from the WGS datasets and filtered using VCFtools v0.1.16, following the criteria described in the previous section. The final combined dataset, comprising 138 samples and 6246 loci, was used for phylogenetic analysis.
The phylogenetic analysis was done with both maximum-likelihood and Bayesian inference approaches. ModelTest-NG v0.1.7 [43] was used to find the best-fit substitution model according to the BIC value. The maximum-likelihood (ML) tree was constructed using RAxML-NG v1.2.0 [44] with the HKY model and 1000 bootstrap replicates. Bayesian inference was run in MrBayes v3.2.7 [45] under GTR + G for 1,000,000 generations, sampling every 500 generations, and the first 25% of samples were discarded as burn-in. The trees were visualized with iTOL v7 [46].

3. Results

3.1. Population Structure

The principal component and Bayesian clustering analyses consistently revealed genetic structures that were concordant with the geographic separation between the Andaman Sea and the Gulf of Thailand in A. marina and A. alba but not in A. officinalis (Figure 2). In A. marina and A. alba (Figure 2A,B), the first two principal components clearly separated individuals into two non-overlapping clusters corresponding to the Andaman Sea and the Gulf of Thailand, explaining 56.07% and 55.23% of the total variance, respectively. The STRUCTURE analysis with the maximum ΔK at K = 2 showed the same pattern. Populations from the Andaman Sea were assigned to one genetic cluster (Andaman cluster), whereas those from the Gulf of Thailand were assigned to another cluster (Thai Gulf cluster). The ΔK plot revealed a secondary peak at K = 4 in A. alba (Figure 2B). At this clustering level, the STRUCTURE analysis distinguished populations in the upper Gulf of Thailand from the remaining Thai Gulf populations (Figure S1B).
As for A. officinalis, the PCA did not clearly separate individuals from the Andaman Sea and the Gulf of Thailand (Figure 2C). Some individuals from the Andaman Sea were grouped with populations from the Gulf of Thailand (while grouping in the opposite direction was negligible), and several individuals were scattered between the two clusters. The STRUCTURE analysis (optimal K = 2) showed a consistent result. The Andaman Sea individuals that grouped with populations from the Gulf of Thailand in the PCA were assigned to the Thai Gulf cluster, whereas one individual from the Gulf of Thailand (Songkhla; SKA) was assigned to the Andaman cluster. Individuals scattered between the two clusters in the PCA plot exhibited varying levels of admixture. At K = 3, the Ranong (RNG) population formed a distinct genetic cluster, consistent with the subtle divergence of this population from the Andaman cluster observed in the PCA (Figure S1C). Spatial patterns of genetic structure across Avicennia species were visualized (Figure S2).
Supporting the PCA results and the genetic structuring of the STRUCTURE-defined populations, the AMOVA (Table 1) indicated that the majority of genetic variance occurred among clusters, accounting for 55.50, 59.07, and 79.70% of the total variance for A. marina, A. alba, and A. officinalis, respectively; consequently, the variances within clusters were 44.50, 40.93, and 21.30% for A. marina, A. alba, and A. officinalis, respectively. Significant genetic differentiation was observed between all cluster pairs across all species, as indicated by the pairwise FST values (p-value < 0.05; Table S4). Notably, the high FST values between the Andaman and Thai Gulf clusters (ranging from 0.56 in A. marina to 0.86 in A. officinalis) support the robust genetic structuring previously identified via STRUCTURE and PCA. The mean numbers of migrants per generation (Nm) between the Andaman and Thai Gulf clusters were low (Nm < 1) across three Avicennia species, ranging from 0.042 in A. officinalis to 0.192 in A. marina, which suggest limited gene flow (Table S4). The observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (FIS) of each genetic cluster of each species are summarized in Table 2. All genetic clusters across the three Avicennia species showed a low level of heterozygosity. The heterozygote deficits and positive FIS values were observed in both the Andaman and Thai Gulf clusters for all species. This pattern is consistent with inbreeding within clusters and limited gene flow among clusters.

3.2. Isolation-by-Distance Analysis

The Mantel test revealed the significantly positive correlation between genetic and geographic distances in almost all groups of populations across the three Avicennia species, except A. officinalis populations in the Gulf of Thailand (Figure 3). Strong significant correlation coefficients (r) between genetic and geographic distances were observed in Andaman (r = 0.770, p = 0.003) and Gulf of Thailand (r = 0.798, p = 0.000) populations of A. marina, as well as in Andaman populations of A. alba (r =0.981, p = 0.002). The coefficients of determination (R2 = 0.593 and 0.637 in A. marina, and 0.962 in A. alba) indicated that more than half of the observed variance in genetic distance of these populations could be explained by geographic distance. On the other hand, moderate R2 values in the Gulf of Thailand A. alba (0.397) and Andaman A. officinalis (0.453) suggest that additional factors beyond geographic distance influence their genetic differentiation. Although a high correlation coefficient was observed in Gulf of Thailand A. officinalis (r = 0.942), the relationship was not statistically significant (p = 0.366). This likely reflects the limitations of a small sample size; therefore, expanded sampling is required to effectively evaluate the influence of geographic distance on the genetic structure of this group.

3.3. Demographic History

Demographic trajectories for the Andaman and Thai Gulf cluster of each Avicennia species, inferred from folded SFS, revealed at least one sharp reduction in effective population size over the past 150,000 years (Figure 4). Most of these reductions broadly coincided with sea-level regressions during the last glacial period, prior to the Last Glacial Maximum. This sea-level regression exposed an extensive area of the Sunda Shelf and shifted the coastline seaward (Figure 4A), thereby altering the distribution of mangrove vegetation. The Andaman clusters of A. marina and A. alba exhibited population reduction during ~37–50 kya, when the sea level was around −75 to −100 m relative to the present-day level (Figure 3B), whereas the decline in the Andaman A. officinalis cluster occurred earlier at ~85–100 kya. In the Thai Gulf clusters, all species showed concordant declines in effective population size around ~60–70 kya, coinciding with the sea-level fall to approximately −65 to −85 m. The Thai Gulf cluster of A. officinalis showed a second decline at ~33–40 kya, when the sea level reached −100 m, while the second declines in Thai Gulf clusters of A. marina and A. alba were detected much more recently, within the last 2000 years.

3.4. Phylogeography of A. marina

The data availability of A. marina allowed us to investigate the genetic relationship of Thai populations and A. marina from other origins. The phylogenetic analysis not only revealed the genetic relationship but also highlighted the deep divergence between the Andaman and Thai Gulf clusters. The phylogenetic results from both Bayesian and maximum-likelihood approaches showed similar topologies separating individuals from the Indian Ocean and the West Pacific Ocean into two major clades, with high support values (posterior probability = 1, bootstrap = 100) (Figure 5 and Figure S3). The Andaman cluster was grouped with individuals from Oman and India in the Indian Ocean clade, while the Thai Gulf cluster was grouped with individuals from Hainan and Fujian, China, in the West Pacific Ocean clade.
Within the Indian Ocean clade, A. marina from Oman diverged first, followed by A. marina from India. The Andaman cluster of Thai A. marina exclusively formed a monophyletic clade (posterior probability = 1, bootstrap = 100). Within this clade, the earliest split separated individuals from Ranong (RNG) province, with subsequent divergences proceeding southward through Pang-Nga (PNG) and Phuket (PKT), Krabi (KBI), Trang (TRG) and Satun (STN). In contrast, the phylogenetic analyses could not clearly resolve Thai Gulf and Chinese populations within the West Pacific Ocean clade. Individuals from Hainan and Fujian, China, were clustered together within a subclade containing individuals from Prachuap Khiri Khan (PKN), Surat Thani (SNI), Nakhon Si Thammarat (NST), and Pattani (PTN) (posterior probability = 0.89, bootstrap = 22). Overall, the West Pacific Ocean clade showed polytomies, short branches, and low support values in many subclades. This pattern is consistent with rapid population expansion/divergence from a small ancestral population.

4. Discussion

Genetic studies of Avicennia species across the Thai-Malay Peninsula have been primarily conducted on Malaysian populations and have relied on a limited set (up to 13) of microsatellites [26,28]. We fulfilled the genetic investigation of three Avicennia species on the Thai-Malay Peninsula with extensive sampling populations throughout their distribution range in Thailand, covering both the Indian Ocean (Andaman Sea) and Pacific Ocean (the Gulf of Thailand) sides of the peninsula, and with the employment of thousands of genome-wide SNPs derived from the RADseq approach.
The East–West genetic differentiation of Avicennia in the Thai-Malay Peninsula was not limited to only the Malaysian populations [26,28]. The genetic investigation of A. marina, A. alba, and A. officinalis in Thailand revealed a similar pattern of genetic structures that distinguished individuals from the Andaman Sea and the Gulf of Thailand. A high pairwise FST and a high proportion of genetic variance among the Andaman and Thai Gulf clusters in AMOVA supported this genetic structuring pattern. While the genetic structuring of A. marina and A. alba was concordant with the sample localities, certain A. officinalis individuals from the Andaman Sea genetically clustered with the Thai Gulf cluster and exhibited admixed individuals in populations. Although the Strait of Malaca, a corridor of the Indian and Pacific Oceans, is considered as a filter rather than a barrier, the low dispersal ability of Avicennia propagules suggests that this phenomenon is unlikely to be natural but is the consequence of anthropogenic translocation such as reforestation [28].
Similar genetic structuring has been observed in several Indo-West Pacific mangroves, such as Bruguiera cylindrica [48], Bruguiera sexangular [49], Bruguiera gymnorhiza [50], and in Rhizophora apiculata [51,52], Lumnitzera racemosa [53], and Xylocarpus granatum [54]. This genetic structuring is hypothesized to be the legacy of Sundaland, which occurred during the Pleistocene glacial period. The emerged Sunda Shelf connected the Thai-Malay Peninsula and all the Greater Sunda Islands of Borneo, Java, and Sumatra [2,7], becoming the Indo-Pacific Barrier that isolated and hampered gene flow between populations across the Indian and Pacific Oceans.
The phylogeographic analysis of A. marina, as a representative of Avicennia, from Thailand and other localities of origin in the Indo-West Pacific region confirmed the two genetic clusters and further indicated the deep divergence of the Andaman and Thai Gulf clusters as well as revealed two major lineages of Indian and West Pacific A. marina. The earliest divergence of Oman A. marina within the Indian Ocean lineage, followed by Indian A. marina and the southward divergence of Thai populations, suggests a West-to-East historical migration route colonizing the Andaman Sea coast of Thailand. A similar phylogeographic pattern was observed in the analysis of 577 A. marina from 16 populations across the Indo-West Pacific region using 94 nuclear gene sequences [27]. Individuals from the Gulf of Thailand, China, the Philippines, Malaysia (Sabah), and Indonesia (Bali) formed a monophyletic clade, while individuals from Kenya, the Andaman Sea of Thailand, and Malaysia formed another clade, with the earliest divergence of the Kenya population. A genetic connectivity study of A. marina populations in Kenya and Tanzania (Western Indian Ocean) indicated a stepping-stone pattern of South-to-North migration [55], implying that the migration route of Indian Ocean A. marina might be South-to-North along the coast of West Africa and West-to-East to the Andaman Sea coast of Thailand. These findings suggest that the East–West genetic differentiation of A. marina in Thailand, if not in the whole Thai-Malay Peninsula, was potentially shaped by the two independent colonization events originating from distinct ancestral lineages of Indian and West Pacific A. marina. Nonetheless, the divergence time of the two lineages and the timing of the colonization should be further investigated.
This hypothesis does not rule out the influence of Sundaland. The emergence of Sundaland during the Pleistocene glaciation reinforced the genetic divergence of these two genetic clusters by limiting gene flow across the oceans. The Pleistocene sea-level fluctuations might also have promoted the genetic differentiation between populations across the Indian and Pacific Oceans through genetic drift in the bottleneck event [14,16]. During the Late Pleistocene, sea levels fluctuated by more than 100 m, dropping to as low as 130 m below the present level during the Last Glacial Maximum [5,6]. The coastline retreated seaward, and the mangrove forests were in a refugial stage that was restricted to a narrow belt along the outer margin of the Sunda Shelf [17]. Steep declines in effective population size observed in the demographic trajectories of the three Avicennia species suggest that these populations experienced bottlenecks. This finding suggests that the genetic differentiation between the Andaman and Thai Gulf clusters was potentially driven, at least in part, by genetic drift.
The low genetic diversity observed across the three Avicennia species, with observed heterozygosity (Ho) ranging from 0.073 in A. officinalis to 0.083 in A. marina, is a plausible result of the historical genetic drift. Recent bottlenecks, as detected in Thai Gulf clusters of A. marina and A. alba, together with the limited dispersal ability of Avicennia propagules that restricted gene flow and promoted inbreeding, reflected by low Nm values and positive inbreeding coefficients, could contribute to the low genetic diversity of the species. Polytomies, short branch lengths, and low support values within the West Pacific Ocean clade of A. marina, together with the nesting of Chinese populations within a Thai A. marina subclade and the lower genetic diversity in Thai Gulf clusters than that in Andaman clusters, suggest a rapid expansion and recolonization from a small founder population and also reflect the pronounced impact of sea-level fluctuation in the South China Sea, as observed in Sonneratia alba [14] and Aegiceras corniculatum [16].
Considering the fine-scale spatial genetic structure, the isolation-by-distance analysis indicated significantly high correlation coefficients between genetic and geographic distances in most of the Andaman and Gulf of Thailand populations across the three Avicennia species. This finding suggests that genetic differentiation between populations within each regional cluster is substantially influenced by the geographic distance among populations. This pattern is consistent with the stepping-stone model of gene flow and migration commonly reported in mangroves, especially those with low dispersal and short propagule viability [24,55]. The strong correlation between genetic and geographic distances, particularly within an approximately 500 km range in Andaman populations, suggests that gene flow is sensitive to geographic proximity and the degree of population connectivity. Habitat fragmentation can effectively disrupt population connectivity, which potentially restricts gene flow, accelerates genetic isolation and inbreeding, and finally diminishes the population’s adaptive potential.
The genetic sub-structuring revealed in Thai Gulf A. alba and Andaman A. officinalis at higher K values cannot be explained solely by the isolation by distance. The moderate coefficients of determination of these populations suggest that additional drivers such as environmental factors likely interact with geographic distance to shape the observed genetic structure. Given that mangrove dispersal relies on water-borne propagules, oceanic circulation is a primary factor underpinning population connectivity. The upper Gulf of Thailand is a semi-enclosed basin characterized by restricted water exchange, with water mass residence times ranging from 101 to 219 days depending on seasonal monsoons [56]. Such prolonged residence times likely facilitate hydrodynamic isolation by restricting gene flow between the upper Gulf and other regions, consequently leading to the population isolation observed in A. alba. Salinity also represents a critical variable. River discharge from the Chao Phraya, Mae Klong, Tha Chin, and Bang Pakong rivers into the upper Gulf of Thailand results in significantly lower salinity levels than in the rest of the gulf [57]. A similar condition occurs in Ranong (RNG) province, where the Kraburi river discharges. A genetic study of A. officinalis across the salinity gradient areas of the Sundarbans mangrove forest (21°30′–22°30′ N, 89°00′–89°55′ E), Bangladesh, revealed the genetic divergence of low salt-adapted A. officinalis from those inhabiting medium- and high-salinity zones [58]. This suggests that salinity can be a selective pressure promoting locally adapted populations. These distinct patterns of fine-scale population structure reflect the species-specific impacts of evolutionary/environmental challenges across the three Avicennia species. Nonetheless, the integration of spatial environmental data in future work would provide a more robust understanding of the environmental drivers shaping Avicennia genetic structure.

5. Conclusions

This study highlights the importance of Thailand as part of the Indo-Pacific interface, which harbors genetic resources from both Indian and West Pacific Ocean lineages, as exemplified in A. marina. The pronounced East–West genetic structure is not just a legacy of the Indo-Pacific Barrier (Sundaland) and sea-level fluctuation during the Pleistocene glacial period but also the outcome of two independent colonization events by distinct ancestral lineages. Although the evidence for distinct ancestral lineages is most robustly established for A. marina, these results provide a framework for evaluating the genetic structuring of other IWP mangroves across the Indo-Pacific interface, including A. alba and A. officinalis. To assess the extent to which these trends extend across the broader IWP mangrove community, further species-specific data are required. The recent severe bottlenecks, low genetic diversity, and high isolation by distance at a small scale observed in the three Avicennia species raise concerns regarding the vulnerability of these populations. However, for conservation management to be effective, it is recommended to treat the Andaman and Thai Gulf populations as distinct evolutionarily significant units (ESUs) and to avoid a one-size-fits-all strategy that ignores regional genetic distinctiveness. Management efforts should prioritize safeguarding both distinct lineages, ensuring the unique genetic identities and mitigating habitat fragmentation to maintain population connectivity within clusters. Furthermore, future investigations of how fine-scale environmental factors such as local hydrology and salinity shape population structure and drive local adaptation will be essential for refining management zones and ensuring more-effective mangrove conservation.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology15050385/s1: Figure S1: The STRUCTURE bar plots of A. marina (A), A. alba (B), and A. officinalis (C) at K = 3 and 4. Figure S2: Spatial patterns of genetic structure of A. marina (A), A. alba (B), and A. officinalis (C) based on STRUCTURE analysis. Pie charts represent the mean membership coefficient for each population at K = 2, 3, and 4. Size of each pie chart is proportional to the sample size of the population. Figure S3: The maximum likelihood tree of Thai A. marina populations and representative populations from the Indian Ocean and West Pacific Ocean coasts. A. marina eucalyptifolia and A. marina australasica were used as outgroups. The numbers at nodes are bootstrap values. Table S1: The sample locations of 129 A. marina, 128 A. alba, and 105 A. officinalis. Table S2: The numbers of SNPs before and after filtering. Table S3: Origins of A. marina accessions included in the phylogenetic analysis. Table S4: Pairwise FST (below diagonal) and the mean number of migrant per generation (Nm, above diagonal) among genetic clusters of A. marina, A. alba, and A. officinalis.

Author Contributions

Conceptualization—P.P. (Poompat Phadphon), W.P., S.T.; Data Acquisition—C.Y., N.N., N.J., P.P. (Phakamas Phetchawang), N.P., D.J.; Analysis, Interpretation, Manuscript Writing—P.P. (Poompat Phadphon), W.P.; Visualization—P.P. (Poompat Phadphon); Revision and Editing—W.P.; Supervision—W.P., S.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science and Technology Development Agency (NSTDA), Thailand, grant number P2251306.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are openly available on FigShare at 10.6084/m9.figshare.31130533.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
IPBIndo-Pacific Barrier
IWPIndo-West Pacific
RADseqRestriction-site-Associated DNA sequencing
ESUsEvolutionarily significant units

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Figure 1. Sampling locations of three Avicennia species in Thailand. Left, middle, and right panels are sampling locations of A. marina, A. alba, and A. officinalis, respectively. Three-letter codes denote province locations corresponding to Table S1. Numbers in parentheses indicate sample sizes for each location.
Figure 1. Sampling locations of three Avicennia species in Thailand. Left, middle, and right panels are sampling locations of A. marina, A. alba, and A. officinalis, respectively. Three-letter codes denote province locations corresponding to Table S1. Numbers in parentheses indicate sample sizes for each location.
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Figure 2. Genetic structure of three Avicennia species across the Andaman Sea and Gulf of Thailand. Left, middle, and right panels are the principal component analysis, STRUCTURE bar plot, and ΔK plot of A. marina (A), A. alba (B), and A. officinalis (C), respectively.
Figure 2. Genetic structure of three Avicennia species across the Andaman Sea and Gulf of Thailand. Left, middle, and right panels are the principal component analysis, STRUCTURE bar plot, and ΔK plot of A. marina (A), A. alba (B), and A. officinalis (C), respectively.
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Figure 3. Isolation-by-distance analysis for three Avicennia species. Scatter plots illustrate the correlation between geographic and genetic distances for (A) Andaman and (B) Gulf of Thailand A. marina; (C) Andaman and (D) Gulf of Thailand A. alba; and (E) Andaman and (F) Gulf of Thailand A. officinalis. Statistical parameters include the correlation coefficient (r), coefficient of determination (R2), and permutation-based p-value (p).
Figure 3. Isolation-by-distance analysis for three Avicennia species. Scatter plots illustrate the correlation between geographic and genetic distances for (A) Andaman and (B) Gulf of Thailand A. marina; (C) Andaman and (D) Gulf of Thailand A. alba; and (E) Andaman and (F) Gulf of Thailand A. officinalis. Statistical parameters include the correlation coefficient (r), coefficient of determination (R2), and permutation-based p-value (p).
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Figure 4. Changes in Sunda Shelf exposure and demographic trajectories of Andaman and Thai Gulf clusters of three Avicennia species. (A) Sunda Shelf exposure and coastline position at different sea levels relative to the present-day level. The figure was generated by integrating the GEBCO gridded bathymetry data [47] with Pleistocene sea-level reconstructions [5]. (B) Demographic trajectories of Andaman (upper panels) and Thai Gulf (lower panels) clusters of three Avicennia species alongside sea-level changes over the past 150 kya [5]. Solid lines show median effective population size (Ne). Dark and light shaded areas indicate 75% and 95% confidence intervals, respectively. Time (kya) on the y-axis and effective population size (1 k individuals) on the x-axis are on logarithmic scales. Note: Interpretation of demographic trajectories should focus on broad historical trends, as fine-scale fluctuations may reflect modeling noise rather than definitive biological events.
Figure 4. Changes in Sunda Shelf exposure and demographic trajectories of Andaman and Thai Gulf clusters of three Avicennia species. (A) Sunda Shelf exposure and coastline position at different sea levels relative to the present-day level. The figure was generated by integrating the GEBCO gridded bathymetry data [47] with Pleistocene sea-level reconstructions [5]. (B) Demographic trajectories of Andaman (upper panels) and Thai Gulf (lower panels) clusters of three Avicennia species alongside sea-level changes over the past 150 kya [5]. Solid lines show median effective population size (Ne). Dark and light shaded areas indicate 75% and 95% confidence intervals, respectively. Time (kya) on the y-axis and effective population size (1 k individuals) on the x-axis are on logarithmic scales. Note: Interpretation of demographic trajectories should focus on broad historical trends, as fine-scale fluctuations may reflect modeling noise rather than definitive biological events.
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Figure 5. Phylogeography of Thai A. marina populations and representative populations from the Indian Ocean and West Pacific Ocean. (A) Geographical distribution of the samples included in this analysis. Dots indicate sample localities, with colors corresponding to those used in the phylogenetic tree. The localities of A. marina eucalyptifolia and A. marina australasica are not shown. (B) The phylogenetic tree inferred based on Bayesian approach. The tree showed two major clades of individuals from the Indian Ocean and the West Pacific Ocean, with the deep divergence of Andaman and Thai Gulf clusters. A. marina eucalyptifolia and A. marina australasica were used as outgroups. The numbers at nodes are posterior probabilities.
Figure 5. Phylogeography of Thai A. marina populations and representative populations from the Indian Ocean and West Pacific Ocean. (A) Geographical distribution of the samples included in this analysis. Dots indicate sample localities, with colors corresponding to those used in the phylogenetic tree. The localities of A. marina eucalyptifolia and A. marina australasica are not shown. (B) The phylogenetic tree inferred based on Bayesian approach. The tree showed two major clades of individuals from the Indian Ocean and the West Pacific Ocean, with the deep divergence of Andaman and Thai Gulf clusters. A. marina eucalyptifolia and A. marina australasica were used as outgroups. The numbers at nodes are posterior probabilities.
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Table 1. Analysis of molecular variance (AMOVA) for A. marina, A. alba, and A. officinalis. Populations defined based on STRUCTURE analysis.
Table 1. Analysis of molecular variance (AMOVA) for A. marina, A. alba, and A. officinalis. Populations defined based on STRUCTURE analysis.
Source of Variationd.f.Sum of SquaresVariance
Components		Percentage of
Variation
A. marina
  Among populations264,197.62501.5855.50
  Within populations255102,588.29402.3144.50
  Total257166,785.91903.88
A. alba
  Among populations166,962.14564.7659.07
  Within populations25499,413.25391.3940.93
  Total255166,375.39956.15
A. officinalis
  Among populations2112,444.71940.2179.70
  Within populations20749,581.22239.5220.30
  Total209162,025.931179.73
Note: d.f.: degree of freedom.
Table 2. Summary of observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (FIS) in each genetic cluster of three Avicennia species.
Table 2. Summary of observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (FIS) in each genetic cluster of three Avicennia species.
SpeciesNHoHeFIS
A. marina
  Andaman490.106 ± 0.0010.172 ± 0.0020.203 ± 0.004
  Thai Gulf780.091 ± 0.0010.166 ± 0.0020.183 ± 0.004
  Admixture20.050 ± 0.0020.038 ± 0.001−0.322 ± 0.006
  Total 1290.083 ± 0.0010.125 ± 0.0010.169 ± 0.002
A. alba
  Andaman460.085 ± 0.0010.160 ± 0.0020.286 ± 0.005
  Thai Gulf820.079 ± 0.0010.189 ± 0.0020.324 ± 0.004
  Total1280.082 ± 0.0010.174 ± 0.0010.306 ± 0.003
A. officinalis
  Andaman630.094 ± 0.0010.328 ± 0.0030.536 ± 0.006
  Thai Gulf340.115 ± 0.0040.313 ± 0.0030.486 ± 0.005
  Admixture80.010 ± 0.0080.008 ± 0.001−0.194 ± 0.007
  Total1050.073 ± 0.0010.216 ± 0.0020.501 ± 0.003
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Phadphon, P.; Yundaeng, C.; Narong, N.; Jomchai, N.; Phetchawang, P.; Phormsin, N.; Jiumjamrassil, D.; Tangphatsornruang, S.; Pootakham, W. Colonization by Distinct Lineages, the Sundaland Barrier, and Historical Bottlenecks Shape the East–West Population Structure of Avicennia Mangroves Across the Indo-Pacific Interface. Biology 2026, 15, 385. https://doi.org/10.3390/biology15050385

AMA Style

Phadphon P, Yundaeng C, Narong N, Jomchai N, Phetchawang P, Phormsin N, Jiumjamrassil D, Tangphatsornruang S, Pootakham W. Colonization by Distinct Lineages, the Sundaland Barrier, and Historical Bottlenecks Shape the East–West Population Structure of Avicennia Mangroves Across the Indo-Pacific Interface. Biology. 2026; 15(5):385. https://doi.org/10.3390/biology15050385

Chicago/Turabian Style

Phadphon, Poompat, Chutintorn Yundaeng, Nattapol Narong, Nukoon Jomchai, Phakamas Phetchawang, Nawin Phormsin, Darunee Jiumjamrassil, Sithichoke Tangphatsornruang, and Wirulda Pootakham. 2026. "Colonization by Distinct Lineages, the Sundaland Barrier, and Historical Bottlenecks Shape the East–West Population Structure of Avicennia Mangroves Across the Indo-Pacific Interface" Biology 15, no. 5: 385. https://doi.org/10.3390/biology15050385

APA Style

Phadphon, P., Yundaeng, C., Narong, N., Jomchai, N., Phetchawang, P., Phormsin, N., Jiumjamrassil, D., Tangphatsornruang, S., & Pootakham, W. (2026). Colonization by Distinct Lineages, the Sundaland Barrier, and Historical Bottlenecks Shape the East–West Population Structure of Avicennia Mangroves Across the Indo-Pacific Interface. Biology, 15(5), 385. https://doi.org/10.3390/biology15050385

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