Population genetic structure and biogeographic distribution of tropical Halodule uninervis in the Bohol Sea and adjacent waters in the Philippines

Population genetic structure and biogeographic distribution of tropical Halodule uninervis in the Bohol Sea and adjacent waters in the Philippines | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 March 2026 V1 Latest version Share on Population genetic structure and biogeographic distribution of tropical Halodule uninervis in the Bohol Sea and adjacent waters in the Philippines Authors : ANGELA GRACE SINGSON 0009-0003-3498-4394 [email protected] , Koji Takayama , Yoshihisa Suyama , Naoko Ishikawa , Shoki Murakami , Venus Leopardas , Nonillon Aspe , Wilfredo Uy , Lilibeth Coronel , Dan Arriesgado , and Ruby Gonzales Authors Info & Affiliations https://doi.org/10.22541/au.177381229.92108407/v1 171 views 92 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Halodule uninervis plays a critical role in the seagrass ecosystem with its opportunistic traits, which facilitate rapid expansion and persistence in disturbed and changing environmental conditions. Yet the population genetic dynamics and connectivity of this species, particularly in the Philippines, remain poorly understood. Using genome-wide single-nucleotide polymorphisms (SNPs) generated from MIG-seq data, we assessed patterns of clonal reproduction, genetic differentiation, isolation by distance, and recent migration across ten populations in a seascape characterized by complex oceanographic circulation linking the Visayas and Mindanao. Clone detection revealed pronounced spatial variation ranging from predominantly sexual populations with high genotypic richness to strongly clonal populations dominated by a few multilocus genotypes. Genetic diversity was generally low to moderate, consistent with seagrass life-history traits, but the outgroup population exhibited high nucleotide diversity and divergent genotypes, suggesting long-term persistence of distinct lineages. Population structure analyses showed weak but detectable genetic structuring, with overlapping genetic clusters and heterogeneous ancestry profiles indicating regional connectivity. Genetic differentiation among populations was low to moderate and not significantly associated with geographic distance, highlighting the importance of oceanographic processes over spatial proximity. Contemporary migration analyses revealed high self-recruitment across most populations, coupled with asymmetric gene flow, indicating that Mambajao is a regional convergence or sink population. These findings demonstrate that H. uninervis populations in Visayas and Mindanao form a semi-connected metapopulation influenced by clonal reproduction, selective dispersal, and complex circulation patterns. Incorporating genetic connectivity into a network-based restoration and management is essential for enhancing seagrass resilience under ongoing environmental change. Population genetic structure and biogeographic distribution of tropical Halodule uninervis in the Bohol Sea and adjacent waters in the Philippines Angela Grace E. Singson 1,2 *, Koji Takayama c , Yoshihisa Suyama 4 , Naoko Ishikawa 4 , Shoki Murakami 3 , Venus E. Leopardas 1 , Nonillon M. Aspe 1 , Wilfredo H. Uy 1 , Lilibeth P. Coronel 1 , Dan M. Arriesgado 1 , Ruby C. Gonzales 1 1 Mindanao State University at Naawan, Naawan, Misamis Oriental 9023, Philippines 2 Department of Fisheries, College of Agriculture and Forestry, Central Philippines State University, Ilog, Negros Occidental 6109, Philippines 3 Makino Herbarium, Department of Biological Science, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan 4 Kawatabi Field Science Center, Graduate School of Agricultural Science, Tohoku University, Osaki, Miyagi 989-6711, Japan *Corresponding author Angela Grace E. Singson [email protected] ABSTRACT: Halodule uninervis plays a critical role in the seagrass ecosystem with its opportunistic traits, which facilitate rapid expansion and persistence in disturbed and changing environmental conditions. Yet the population genetic dynamics and connectivity of this species, particularly in the Philippines, remain poorly understood. Using genome-wide single-nucleotide polymorphisms (SNPs) generated from MIG-seq data, we assessed patterns of clonal reproduction, genetic differentiation, isolation by distance, and recent migration across ten populations in a seascape characterized by complex oceanographic circulation linking the Visayas and Mindanao. Clone detection revealed pronounced spatial variation ranging from predominantly sexual populations with high genotypic richness to strongly clonal populations dominated by a few multilocus genotypes. Genetic diversity was generally low to moderate, consistent with seagrass life-history traits, but the outgroup population exhibited high nucleotide diversity and divergent genotypes, suggesting long-term persistence of distinct lineages. Population structure analyses showed weak but detectable genetic structuring, with overlapping genetic clusters and heterogeneous ancestry profiles indicating regional connectivity. Genetic differentiation among populations was low to moderate and not significantly associated with geographic distance, highlighting the importance of oceanographic processes over spatial proximity. Contemporary migration analyses revealed high self-recruitment across most populations, coupled with asymmetric gene flow, indicating that Mambajao is a regional convergence or sink population. These findings demonstrate that H. uninervis populations in Visayas and Mindanao form a semi-connected metapopulation influenced by clonal reproduction, selective dispersal, and complex circulation patterns. Incorporating genetic connectivity into a network-based restoration and management is essential for enhancing seagrass resilience under ongoing environmental change. KEY WORDS: Coastal management, Geographic connectivity, Population genetics, Restoration, Seagrass, Single-nucleotide polymorphism (SNP) 1. INTRODUCTION One of the diverse seagrass species belongs to the genus Halodule. Halodule species have less robust rhizomes and limited tolerance to prolonged low-light conditions due to relatively low carbohydrate reserves and weak clonal integration compared to other species, which makes them susceptible to physical disturbances, including strong currents and turbulence (Uy 2001, McKenzie et al. 2021). As opportunistic and fast-growing pioneer species, they are associated with seed production and clonal growth, which facilitate rapid colonization, population expansion, and persistence under disturbed and changing environmental conditions (Uy 2001, Kilminster et al. 2015). Moreover, their wide distribution across Pacific Island countries and territories highlights their adaptability, while their reproductive strategies significantly contribute to habitat recovery and resilience to environmental change (Kilminster et al. 2015, McKenzie et al. 2021). In the Philippines, population genetic studies have predominantly focused on the dominant species, including Cymodecea spp., Enhalus sp., Thalassia sp., and with limited inclusion of Syringodium spp. (e.g. Nakajima et al. 2014b, 2023, Arriesgado et al. 2016a,b, 2022, 2023, Kurokochi et al. 2016, Malanguis et al. 2023). A significant knowledge gap remains in the ecological and population genetics of less-studied seagrasses, including Halodule spp., Syringodium spp., and Halophila spp, as their population structure and genetic connectivity remain largely unexplored despite their wide distribution across Southeast Asia, particularly in diverse Philippine coastal habitats (Singson et al. 2025). Genetic diversity and population genetic structure are essential factors in understanding the resilience and adaptability of seagrass populations, particularly Halodule spp. (Arriesgado et al. 2016b, Schierenbeck 2017, Coates et al. 2018, Fortes et al. 2018). Genetic diversity is the variation of gene composition, which provides data on the ability to adapt and withstand environmental stressors and disturbances, while population structure provides insights into connectivity, gene flow, and how genetic variation is distributed across different populations (Pazzaglia et al. 2021, Hernawan et al. 2023, Hosokawa et al. 2025). Seagrass dispersal is influenced by ocean currents, reproductive strategies, and environmental conditions (Arriesgado et al. 2014, 2015, 2016b, 2023a, 2023b, Nakajima et al. 2014a, 2017, 2023, Kurokochi et al. 2016, McKenzie et al. 2021, Malanguis et al. 2023). Understanding these factors is crucial, particularly in the light of ongoing climate change and anthropogenic disturbances. Within this framework, Halodule uninervis is a widely distributed seagrass that plays an important role in sediment stabilization, habitat provisioning, and ecosystem recovery in dynamic coastal environments. In the Philippines, H. uninervis meadows contribute to coastal productivity within highly connected marine ecosystems. The Bohol Sea and adjacent regions, such as the Visayan Sea, form an ecologically linked seascape influenced by complex oceanographic circulation that may facilitate dispersal and genetic exchange among seagrass populations. Understanding the genetic structure of H. uninervis across these interconnected regions is therefore essential for informing seagrass conservation, restoration planning, and ecosystem-based coastal management. The Bohol Sea is a marginal sea, bounded by the Visayas and Mindanao islands in the southern Philippines (Cabrera et al. 2011). The complex oceanographic features, including the Bohol Jet and the circulation patterns of the Iligan Bay cyclonic eddy, significantly affect the genetic connectivity of the marine organisms (Cabrera et al. 2011, Gordon et al. 2011). Although no studies have yet conducted on the genetic connectivity in seagrass populations, specifically within the Bohol Sea region, related studies have been conducted in parts of Mindanao, which mainly focused on common species, including Cymodocea, Thalassia, and Enhalus, as reported in the recent review (Singson et al. 2025). Conversely, the genetic connectivity of H. uninervis remains largely unexplored in this region, which implies knowledge gaps in understanding its genetic diversity and population structure, despite its important role in restoration through habitat recovery as a pioneer species in coastal habitats. Furthermore, genetic information is rarely incorporated into seagrass restoration practices in the Philippines. Restoration studies on seagrass have been initiated with a focus mainly on the physicochemical compatibility of source and recipient sites, without considering the genetic makeup of the plant material (e.g. Creencia et al. 2023) Given the ecological and economic importance of Halodule species as well as the existing knowledge gaps, this study aims to investigate the large-scale geographical population structure and genetic diversity of H. uninervis . Specifically, it assesses the clonal, population genetic diversity, genetic structure and differentiation, isolation by distance, and historical migration across the region. This work provides valuable data for seagrass conservation and restoration, and develops management strategies to enhance and maintain the resilience of seagrass ecosystems in the Bohol Sea. 2. MATERIALS AND METHODS 2.1. Sampling Sites The study was conducted in Bohol and the Visayan Seas, including Surigao City, Nasipit, Mambajao, Laguindingan, Kauswagan, Plaridel, Maasin City, Jagna, and Maria. To provide a comprehensive comparison of inferences on regional genetic structuring, at least one outgroup population was recommended to serve as a reference population. A population located in Sagay in the Visayan Sea was used as an outgroup (Figure 1; Table 1). Figure 1. The nine study sites are within the Bohol Sea, and one is in the Visayan Sea. The map was generated using the Philippine base map in QGIS v3.14. The circulation figure of the Bohol Jet current passing through the Surigao Strait is shown for spatial reference, with basin boundaries defined according to the configuration described by Cabrera et al. (2011). Table 1. The study sites with their corresponding coordinates. Surigao City, Surigao del Norte 9.818196° N 125.451416° E Nasipit, Agusan del Norte 8.991126° N 125.346375° E Mambajao, Camiguin 9.253386° N 124.722617° E Laguindingan, Misamis Oriental 8.625000° N 124.465300° E Kauswagan, Lanao del Norte 8.220400° N 124.111944° E Plaridel, Misamis Occidental 8.605172° N 123.740517° E Maasin, Southern Leyte 10. 145564° N 124.769739° E Jagna, Bohol 9.643046° N 124.366302° E Maria, Siquijor 9.178706° N 123.661102° E Sagay City, Negros Occidental 11.051089° N 123.456253° E 2.2. Sampling Collection and DNA Extraction At each sampling site, 25 to 28 vegetative shoots were randomly collected, maintaining a 10-meter distance between each sample to avoid overestimating clonal diversity (Arriesgado et al. 2015, Nakajima et al. 2017, Wainwright et al. 2018). The collection area for each site ranged from 200 to 300 meters in length and 30 to 40 meters in width. Samples were put into tea bags and preserved in silica gel within zip-lock plastic bags at room temperature. Each silica-gel-dried leaf tissue sample (<10 mg) underwent total genomic DNA extraction using a modified cetyltrimethylammonium bromide (CTAB) protocol. 2.3. Multiplexed ISSR Genotyping by Sequencing (MIG-seq) Protocol and Procedures A multiplexed ISSR Genotyping by Sequencing (MIG-seq; Suyama & Matsuki,\ 2015) library was constructed following the protocol of Suyama et al. (2022), which includes two PCR steps. The first PCR used a Multiplex PCR Assay Kit Version 2 (Takara RR062) with MIG-seq first-PCR primers comprising eight pairs of universal ISSR multiplex primers. The PCR was performed in a 96-well Thermal Cycler with the PCR amplification conditions of 94°C for 1 minute and followed by 25 cycles of denaturation at 94°C for 30 seconds, annealing at 38°C for 1 minute, extension at 72°C for 1 minute, and a final extension at 72°C for 10 minutes. Each sample underwent an amplification check using the Microchip Electrophoresis System (MultiNA, Shimadzu), with the DNA-2500 Reagent Kit (Shimadzu). The first PCR products were purified, and the second PCR was performed with PrimeSTAR GXL DNA polymerase (Takara, RR050) reagent. The second PCR was performed in a 96-well Thermal Cycler using the following amplification profile: 98°C for 10 seconds for denaturation, 54°C for 15 seconds for annealing, and 68°C for 1 minute for extension, repeated in 12 cycles. The second PCR products from each sample were pooled in equal amounts and purified again, with a size-selection step using AMPure XP magnetic beads (Beckman Coulter) to remove fragments shorter than 250 bp, ensuring the library consists of appropriately sized DNA fragments. The pooled library was subjected to high-throughput sequencing on an Illumina NextSeq 1000 platform using NextSeq 1000/2000 P1 Reagents (300 cycles). This enables the generation of substantial amounts of sequence data across many genomic regions. 2.4. Data Analyses Clone Detection and Diversity Clonality can be biased in population genetics since it counts the same genotype multiple times (Arriesgado et al. 2015, Nakajima et al. 2017, Wainwright et al. 2018). The study used GenoDive v3.06 (Meirmans 2020) to identify multilocus genotypes (MLGs) and quantify the extent of clonal and sexual reproduction across all populations. SNP calling and genotyping were first performed for all samples using Stacks v2.2 following the standard de novo pipeline (Catchen et al. 2011). Loci were first assembled and cataloged using cstacks, allowing a maximum of two mismatches between catalog loci (n = 2). Individual samples were then matched to the catalog using sstacks. Conversion of loci data to BAM format was carried out using tsv2bam, followed by SNP calling and genotype likelihood estimation using gstacks (Catchen et al. 2011, Catchen et al. 2013). Genotype data generated using Stacks were exported to GenoDive and analyzed, following Meirmans & Tienderen (2004), where individuals sharing identical allelic profiles across all single-nucleotide polymorphism (SNP) loci were assigned to the same clone (ramets), and those with differing alleles were considered as unique genets. Clone assignment was based on exact multilocus matches using a retention threshold of 0.5. Clonal richness was calculated as R = (G − 1)/(N − 1), where G is the number of distinct MLGs, and N is the number of sampled individuals (Dorken & Eckert 2001). GenoDive computed standard clonal diversity indices, including the numbers of distinct genotypes, effective number of genotypes, Simpson’s clonal diversity, genotypic evenness, and Shannon’s diversity corrected and uncorrected. Population differences in clonal diversity were tested using 1000 bootstrap replicates, while the variability of diversity estimates was assessed through jackknifing over loci to obtain standard deviations. Statistical significance was evaluated at a = 0.05. For analyses sensitive to clonal replication, a clone-corrected dataset was generated by retaining one sample per clone to avoid bias from clonal pseudo-replication. All subsequent genetic analyses were performed using this clone-corrected dataset. Detailed information on a clone assignment and the number of genets was presented in Table 2. Population Genetic Diversity The geographical population genetic diversity was assessed using a clone-corrected dataset derived from GenoDive clone assignment. The dataset was then reprocessed using the same Stacks v2.2 pipeline to generate a clone-corrected SNP dataset. Diversity indices, including nucleotide diversity ( π ), heterozygosity ( H o ), and inbreeding coefficient ( F IS ), were extracted and summarized per population using a custom R bootstrap resampling pipeline to compute mean estimates and 95% confidence intervals (Coulon 2010). This resampling approach is particularly appropriate for datasets with modest sample sizes and has been shown to provide reliable estimates of genetic variability (Nazareno et al. 2017). Additionally, nucleotide diversity ( π ) and Tajima’s D were computed from the variant call format (VCF) files using VCFtools v0.1.14 to investigate patterns of neutral evolution and potential selection across populations (Danecek et al. 2011). Population Genetic Structure The genetic structure was analyzed using a clone-corrected SNP dataset through Principal Component Analysis (PCA) with the adegenet v2.1.1 package in R v4.3.1 through RStudio v2023.9.0.463, to detect genetic patterns (Jombart 2008, Jombart & Ahmed 2011, Posit team 2023). Analyses were performed on a clone-corrected SNP dataset comprising 156 MLGs representing 10 populations. PCA was conducted using the dudi.pca() function from the ade4 package, retaining the first 50 principal components (Bougeard & Dray 2018). Genetic structure among populations was visualized using scatterplots of the first two principal components (PC1 and PC2), which explain the largest proportions of genomic variance, with population clustering illustrated using 95% confidence ellipses generated in the ggplot2 package (Francis 2017) in R v4.3.1. In parallel, the population structure was inferred using the ADMIXTURE maximum-likelihood algorithm (Alexander et al. 2009). The analyses were performed for K = 1–10 with 20 independent replicates of each K under a 10-fold cross-validation scheme. Each replicate was initialized using distinct random seeds to test the stability of ancestry assignments. The optimal K was selected based on the lowest cross-validation error in combination with consistency among replicate runs and biological interpretability of the inferred structure. (Porras-Hurtado et al. 2013). Genetic Differentiation Between Populations The genetic differentiation between populations was analyzed using the R package mmod v1.3.3 (Winter 2012). Analyses were conducted on the clone-corrected dataset, retaining one representative per multilocus genotype per population to avoid bias from clonal replicates. The study calculated three complementary estimators, including Nei’s G ST as a frequency-based analogue of Wright’s F ST (Nei 1973), Hedrick’s standardized G ’ ST used to compare across loci with different levels of heterozygosity (Hedrick 2005), and Jost’s D used for the diversity-based measure of allelic differentiation (Jost 2008). Pairwise estimates were computed between all populations. Confidence intervals of 95% were generated by non-parametric bootstrapping over loci with 1,000 replicates (Winter 2012). For each replicate, loci were resampled with replacement, and all pairwise statistics were recalculated with 2.5% and 97.5% quantiles of the bootstrap distributions were used to derive confidence intervals for each population pair. Differentiation matrices were visualized as heatmaps for comparison of spatial clustering patterns and identification of highly differentiated populations Isolation by Distance The isolation by distance was analyzed by using the Mantel test in R v4.3.1, correlating pairwise genetic distance matrices derived from the clone-corrected dataset, including Nei’s G ST , Hedrick’s G ’ ST , and Jost’s D , with geographic distances among populations. Sample-level geographic coordinates were averaged to obtain representative latitude and longitude values for each population. Pairwise geographic distances were calculated using a planar distance approximation (Hijmans 2024). Mantel tests were conducted using Pearson’s correlation coefficient with 999 to assess the significance of the relationship between genetic and geographical distances (Jombart 2008, Jombart & Ahmed 2011). Recent Migration Inference Recent migration among populations was assessed using BayesAss v3.0.4 (Wilson & Rannala 2003) based on a clone-corrected dataset, which estimates contemporary gene flow rates occurring within the last 1 to 5 generations without assuming drift-migration equilibrium (Faubet et al. 2007, Mussmann et al. 2019). The SNP dataset was randomly reduced to 500 loci using a custom R script, retaining 156 cloned-corrected individuals across 10 populations. Three independent Markov Chain Monte Carlo (MCMC) runs were performed using 21,000,000 iterations. Convergence across runs was assessed by visual inspection of likelihood traces. Migration rates and flow were visualized using annotated heatmaps and geographic flow diagrams in RStudio v2023.9.0.463, facilitating comparison of relative migration intensity and direction of contemporary gene flow among populations (Wickham 2016). 3. RESULTS 3.1. Clone detection and diversity Out of 245 extracted samples, there are 1,118 loci detected from GenoDive analysis using a retention threshold of 0.5 across 10 populations. Clonal structure varied among populations (Table 2), revealing a total of 156 distinct MLGs (genets). Highly clonal populations such as Sagay (G = 2 of N = 25), Laguindingan (G = 2 of N = 24), and Mambajao (G = 6 of N = 25) were dominated by a small number of genets, resulting in very low clonal richness (R = 0.04, 0.21). In contrast, Nasipit and Plaridel exhibited no clonal repetition (G = N), indicating predominantly sexual recruitment. Intermediate levels of clonality were observed in Kauswagan (R = 0.71) and Surigao (R = 0.46). Clonal diversity indices further reflected these patterns (Table 3). The high clonal diversity was observed in Plaridel, Nasipit, Maasin, Jagna, and Maria, where nearly all sampled individuals possessed distinct MLGs. Moderate diversity was observed in Surigao and Kauswagan, having nearly half of the sampled individuals being distinct MLGs, while low diversity was characterized in Laguindingan, Sagay, and Mambajao, showing only a few MLGs dominating the population. A randomization test using the corrected Nei’s diversity index revealed significant deviations from random mating in most populations (p < 0.05). Observed clonal diversity was significantly lower than expected under random mating in most populations at p = 0.001, indicating widespread clonal structure. No significant differences were detected in Plaridel, Nasipit, and Sagay, where p = 1.000, which conformed to expectations of random mating (Table 4). Table 2. Clonal structure and richness of H. uninervis populations inferred using GenoDive v3.06 with a retention threshold of 0.5. Clone assignment was based on identical allelic profiles across all SNP loci. Surigao 25 12 13 0.458 Nasipit 21 21 0 1.000 Mambajao 25 6 19 0.208 Laguindingan 24 2 22 0.043 Kauswagan 22 16 6 0.714 Plaridel 24 24 0 1.000 Maasin 25 24 1 0.958 Jagna 27 23 4 0.846 Maria 27 26 1 0.962 Sagay 25 2 23 0.042 Table 3. The clonal diversity and indices of H. uninervis across all populations were generated by GenoDive v3.06, with a retention threshold of 0.5. Surigao 5.631 0.857 0.822 0.469 1.127 0.916 Nasipit 21.000 1.000 0.952 1.000 nan 1.322 Mambajao 1.543 0.367 0.352 0.257 0.554 0.357 Laguindingan 1.180 0.159 0.153 0.590 0.137 0.125 Kauswagan 8.963 0.931 0.888 0.560 1.547 1.103 Plaridel 24.000 1.000 0.958 1.000 nan 1.380 Maasin 23.148 0.997 0.957 0.965 2.479 1.374 Jagna 19.703 0.986 0.949 0.857 1.933 1.334 Maria 25.138 0.997 0.960 0.967 2.547 1.409 Sagay 1.083 0.080 0.077 0.542 0.120 0.073 Note. Shannon diversity (corrected for sample size) was undefined (nan) for Nasipit and Plaridel, as all individuals possessed distinct MLGs, resulting in maximal genotypic diversity. Values are color-scaled with dark colors representing the highest values and light colors representing the lowest values. Table 4. Comparison of observed and expected diversity under random mating for each population based on the corrected Nei’s diversity index, obtained using GenoDive v3.06. Significant p-values indicate deviation from random mating. Surigao 0.857 1.000 0.001* Nasipit 1.000 1.000 1.000 Mambajao 0.367 0.988 0.001* Laguindingan 0.159 0.805 0.001* Kauswagan 0.931 1.000 0.001* Plaridel 1.000 1.000 1.000 Maasin 0.997 1.000 0.001* Jagna 0.986 1.000 0.001* Maria 0.997 1.000 0.001* Sagay 0.080 0.003 1.000 Note. * p < 0.05 statistically significant 3.2. Population genetic diversity Genetic diversity analysis of clone-corrected datasets showed nucleotide diversity ( π ) ranging from 0.12 in Jagna to 0.37 in Sagay. Observed heterozygosity ( H ₒ) varied from 0.12 in Kauswagan to 0.37 in Sagay, while the inbreeding coefficient ( F IS ) ranged from –0.01 in Jagna to 0.20 in Kauswagan. The Sagay population, as an outgroup, exhibited the highest genetic variability, whereas Jagna had the lowest (Figure 2). Tajima’s D values calculated from 10 kb non-overlapping windows across all populations ranged from approximately −2 to +3 (Figure 3). The mean Tajima’s D per population ranged from 0.45 ± 0.08 in Surigao to 2.01 ± 0.10 in Sagay, with most populations exhibiting positive Tajima’s D values. The highest mean values were observed in the Laguindingan and Sagay populations, while the lowest were detected in Surigao (Figure 4). Figure 2. Mean nucleotide diversity ( π ), observed heterozygosity ( H ₒ), and inbreeding coefficient ( F IS ) were estimated from a clone-corrected SNP dataset generated using Stacks v2.2. Error bars represent 95% confidence interval obtained from bootstrap resampling across loci. Figure 3. Distribution of Tajima’s D values calculated from 10 kb non-overlapping genomic windows for each population using VCF tools v0.1.14. Boxplots summarize population-level variation in deviations from neutral expectations. SU = Surigao, N = Nasipit, MAM = Mambajao, L = Laguindingan, K = Kauswagan, P = Plaridel, MAA = Maasin, J = Jagna, MAR = Maria, and S = Sagay. Figure 4. Mean of Tajima’s D per population with standard error per population derived from 10 kb windows, showing the differences in allele frequency spectra among H. uninervis populations. SU = Surigao, N = Nasipit, MAM = Mambajao, L = Laguindingan, K = Kauswagan, P = Plaridel, MAA = Maasin, J = Jagna, MAR = Maria, and S = Sagay. 3.3. Population genetic structure Principal Component Analysis (PCA) based on 6,952 SNPs from the clone-corrected datasets revealed the genetic relationships across the 10 populations. The first two principal components (PC1 = 6.3 %, PC2 = 5.1 %) explained the majority of the observed variance. Populations from the Bohol Sea formed partially overlapping clusters, reflecting moderate differentiation and possible gene flow across sites (Figure 5). The Sagay population from the Visayan Sea showed a tight, homogeneous cluster but remained near the Bohol Sea groups in ordination space. The ADMIXTURE analysis showed a progressive decrease at K = 9 (CV = 0.357) with nine distinct ancestry components across 10 populations. It shows that the population from the Bohol Sea exhibited partially shared ancestry components, whereas the Sagay population formed a distinct cluster, supporting its role as an outgroup. Among populations, Maasin, Mambajao, Laguindingan, Kauswagan, and Sagay detected high admixture proportions. In contrast, Surigao, Nasipit, Plaridel, Maria, and Jagna showed homogeneous ancestry, dominated by clusters 9, 4, 8, and 6, respectively (Figure 6). Figure 5. The first two principal components (PC1 = 6.3 %, PC2 = 5.1 %) of H. uninervis populations. Note: Ellipses represent 95% confidence intervals for populations computed where n ≥ 3; Laguindingan and Sagay are shown as individual points due to small sample sizes. SU = Surigao, N = Nasipit, MAM = Mambajao, L = Laguindingan, K = Kauswagan, P = Plaridel, MAA = Maasin, J = Jagna, MAR = Maria, and S = Sagay. Figure 6. ADMIXTURE model of the ancestry components and proportions of H. uninervis across 10 populations (K = 9) at 0.357, with a maximum-likelihood algorithm with 20 replicate runs per K under a 10-fold cross-validation scheme, whereas SU = Surigao, N = Nasipit, MAM = Mambajao, L = Laguindingan, K = Kauswagan, P = Plaridel, MAA = Maasin, J = Jagna, MAR = Maria, and S = Sagay. 3.4. Genetic differentiation between populations Genetic differentiation analyses using Nei’s G ST , Hedrick’s G’ ST , and Jost’s D consistently revealed low to moderate genetic differentiation structure among the populations. All metric results showed the highest values for Laguindingan to Jagna, Jagna to Sagay, Surigao to Laguindingan, and Nasipit to Laguindingan. In contrast, the lowest differentiation values were detected in Sagay to Mambajao at 0.01 to 0.03 (Figure 7). Figure 7. The three complementary metrics of genetic differentiation values of H. uninervis across all populations were calculated from the clone-corrected SNP dataset, whereas darker shades indicate higher differentiation. SU = Surigao, N = Nasipit, MAM = Mambajao, L = Laguindingan, K = Kauswagan, P = Plaridel, MAA = Maasin, J = Jagna, MAR = Maria, and S = Sagay. 3.5. Isolation by distance The pattern detected using Mantel tests with 999 permutations revealed no significant association between geographic distance and genetic differentiation among populations. Nei’s G ST and Hedrick’s G’ ST exhibited no significant correlations at r = 0.000005, p = 0.466, and r = 0.0225, p = 0.427, respectively. Thus, Jost’s D had a weak positive correlation, but this relationship was not statistically significant at p = 0.269 (Figure 8, Table 5). Figure 8. The relationship of pairwise geographic distance (km) and genetic differentiation metrics (Jost’s D , Hedrick’s G ′ ST , and Nei’s G ST ). Mantel tests based on 999 permutations revealed no significant correlations, indicating the absence of an IBD pattern among populations. Table 5. Mantel test results of H. uninervis across populations assessing IBD using Jost’s D , Hedrick’s G ′ ST , and Nei’s G ST based on 999 permutations . Jost’s D 0.1509 0.269 Hedrick’s G′ ST 0.0225 0.427 Nei’s G ST 0.000005 0.466 Note. p < 0.05 statistically significant. No significant correlation detected. 3.6. Recent migration inference The recent migration inference, which estimates contemporary gene flow occurring approximately one to five generations, revealed that Nasipit has the highest self-recruitment rate at 0.903, followed by Maria, Jagna, Maasin, Plaridel, Kauswagan, and Surigao, which ranged from 0.847 to 0.899. Moderately high self-recruitment was observed at Sagay, Laguindingan, and Mambajao, ranging from 0.749 to 0.758. The highest off-diagonal migration rate was observed from Plaridel to Mambajao at 0.042, followed by Maria to Mambajao, Sagay to Mambajao, and Maria to Surigao at 0.039, 0.036, and 0.030, respectively. Laguindingan and Sagay showed receiving approximately 0.030 migration rates from all populations. In contrast, Nasipit, Kauswagan, Plaridel, Maasin, Jagna, and Maria exhibited low incoming and outgoing migration rates, while Surigao and Mambajao received moderate migration rates from all populations (Figure 9). Figure 9. Heatmap (left) and migration network (right) of recent gene flow inferred by BayesAss. The heatmap illustrates recent migration rates, where darker blue shading indicates higher migration probabilities and gray shade represent self-recruitment, while the migration network with only migration rates ≥ 0.025 and ≤ 0.040 is shown to highlight relatively strong network migration pathways. SU = Surigao, N = Nasipit, MAM = Mambajao, L = Laguindingan, K = Kauswagan, P = Plaridel, MAA = Maasin, J = Jagna, MAR = Maria, and S = Sagay. 4. DISCUSSIONS 4.1. Clone detection and diversity The reproductive strategies of H. uninervis populations exhibit a clear spatial variation, ranging from predominantly sexual reproduction to strongly clonal propagation. Populations such as Plaridel, Nasipit, Maasin, Jagna, and Maria showed high clonal richness and clonal diversity values, indicating frequent sexual reproduction and regular recruitment of genetically distinct individuals (Arriesgado et al. 2023a). The absence of significant deviation from random mating in Plaridel and Nasipit further supports this interpretation, suggesting that clonal propagation plays a limited role in structuring genetic diversity in these populations (Arriesgado et al. 2023b, Malanguis et al. 2023). In contrast, low clonal richness and diversity values, such as population in Laguindingan, Sagay, and Mambajao, reflect strong clonal propagation and dominance of a few genets (Litsi‐Mizan et al. 2023). Although Sagay showed no significant difference between observed and expected diversity under random mating, this result reflects the similarity of the low values of both observed and expected diversity. This emphasizes the importance of interpreting random mating tests with absolute diversity metrics, as conformity to expectations does not necessarily imply high genetic diversity (Arnaud-Haond et al. 2019). Moderate clonal richness was observed in Surigao and Kauswagan, suggesting a mixed reproductive strategy in which both clonal and sexual recruitment contribute to the population structure (Arriesgado et al. 2023a). Differences in clonal structure among populations may represent adaptation to site-specific environmental conditions, disturbances, and connectivity patterns. High clonal richness populations, such as Plaridel, Nasipit, Maasin, Jagna, and Maria, are located in a hydrodynamically connected coastal environment with minimal disturbance, which may favor seed and pollen dispersal and promote recurrent recruitment (Kendrick et al. 2012b, Evans et al. 2021). In contrast, low clonal richness populations, such as Laguindingan, Sagay, and Mambajao, may experience localized habitat disturbances, which limit seedling establishment and favor the clonal expansion of a limited number of genets (Kendrick et al. 2017b, McMahon et al. 2017, Dierick et al. 2021), while moderate clonal richness populations, such as Surigao and Kauswagan, are likely associated with intermediate levels of disturbances that support both clonal and periodic sexual recruitment (McMahon et al. 2017). Similar patterns have been commonly reported in seagrass ecosystems, where reproductive structure is influenced by the interaction of local environmental conditions, disturbance regimes, and hydrodynamic processes (e.g. Paolo et al. 2019, Xu et al. 2019, Jiang et al. 2020, Johnson et al. 2020, Arriesgado et al. 2023a). Intermediate disturbances, including grazing and moderate physical disturbance, have been associated with clonal richness, creating a balance between clonal propagation and sexual recruitment, in which established genets can persist through vegetative growth and seedling establishment, facilitating recruitment of new genotypes (McMahon et al. 2017). In contrast, frequent and intense physical disturbance may act as a recruitment bottleneck by reducing seedling survival and dominating the long-term survival of established clones (Dierick et al. 2021). Reviews on seagrass connectivity further emphasize that favorable environmental conditions facilitate successful sexual recruitment, which subsequently interacts with ongoing local expansion to produce an intermediate level of genetic diversity and uneven genotype frequencies (Kendrick et al. 2017b). Moreover, prevailing ocean currents and seasonally driven circulation patterns further influence pollen and seed dispersal, sustaining contemporary connectivity among populations, while local circulation and environmental conditions modulate settlement and recruitment success (Kendrick et al. 2017b, Evans et al. 2021, Hernawan et al. 2023). These factors influenced spatially heterogeneous reproductive strategies, consistent with the mixed clonal-sexual patterns observed in the study. The observed clonal structure reflects a biological pattern including population resilience, reproductive strategy, and local adaptation. Consequently, the clone-corrected datasets are essential to avoid pseudo-replication and minimize analytical bias, while the original clonal patterns provide valuable ecological and evolutionary insights. 4.2. Population genetic diversity The genetic diversity of H. uninervis populations generally exhibits low to moderate variability, as reflected by relatively low nucleotide diversity ( π ) and observed heterozygosity ( H ₒ) at most sites. These patterns were consistent with those reported by Evans et al. (2021) and Hernawan et al. (2023) for the life-history strategy of seagrasses, in which extensive propagation often dominates local population structure and sexual recruitment via seeds occurs unevenly across space. Among the populations, Sagay displayed the highest genetic variability, and as an outgroup population, it may represent a genetically distinct lineage or a population with a different demographic history, potentially shaped by long-term isolation, historical stability, and retention of divergent haplotypes (Nguyen et al. 2014, Nguyen et al. 2021, Alias et al. 2024). Conversely, Jagna consistently exhibited the lowest nucleotide diversity ( π ), indicating limited genetic variation, possibly due to restricted gene flow or historical bottlenecks (Xu et al. 2019, Evans et al. 2021). Despite its low nucleotide diversity ( π ), Jagna showed a slightly negative inbreeding coefficient ( F IS ). However, this pattern does not imply enhanced sexual reproduction or the absence of inbreeding (Xu et al. 2019, Hernawan et al. 2023). Thus, it reflects heterozygote excess at a small number of polymorphic loci, a statistical outcome that can arise when few remaining genets are heterozygous and overall allelic variation is low, which is also similar to that of Halophila ovalis reported by Xu et al. (2019). High F IS values observed in most populations indicate heterozygote deficiency, attributed to clonal growth, localized dispersal, and Wahlund effects (Evans et al. 2021, Hernawan et al. 2023). Notably, most populations exhibited positive mean Tajima’s D values, indicating an excess of intermediate-frequency alleles, which are commonly associated with balancing selection, population subdivision, and historical population contraction, rather than strict neutral evolution (Schmidt & Pool 2002). Populations with the highest mean Tajima’s values, such as Laguindingan and Sagay, may have experienced long-term demographic stability or historical bottlenecks, allowing divergent haplotypes to persist within the populations (Hernawan et al. 2023). However, the underlying mechanism differs between the two populations. In Sagay, high genetic variability and a strong positive Tajima’s D suggest an excess of intermediate-frequency alleles and retention of multiple genetic lineages associated with long-term demographic stability or historical population bottlenecks followed by limited recovery (Kendrick et al. 2017b, Hernawan et al. 2023). In stable or long-persistent populations, multiple haplotypes can be maintained over extended periods, allowing divergent genetic lineages to accumulate and persist (Arnaud-Haond et al. 2019). Similarly, following a population contraction and demographic stability, rare alleles are often lost, while surviving haplotypes increase in frequency, which results in elevated Tajima’s D values (Flanagan et al. 2021, Stoeckel et al. 2021). This pattern is further supported by the concurrence of high nucleotide diversity, high observed heterozygosity, and low genotypic richness, suggesting the dominance of a limited number of highly divergent genets that have expanded clonally and persisted over time (Arnaud-Haond et al. 2019). These genetically distinct lineages likely represent historical recruitment events that have been retained through long-term vegetative propagation (Dierick et al. 2021). Limited contemporary gene flow with the neighboring population may further reinforce this pattern by restricting the introduction of new rare alleles and preserving intermediate frequency variants within the population (Evans et al. 2021, Hernawan et al. 2023). With these, the Sagay population reflects the long-term persistence of multiple genetic lineages influenced by historical demography and constrained connectivity, rather than population expansion. In contrast, Laguindingan exhibited lower genetic diversity with positive Tajima’s D , indicating the persistence of a more restricted set of haplotypes at intermediate frequencies. This pattern is consistent with historical population contraction or prolonged demographic stability under strong clonal dominance, where limited sexual recruitment has slowed allele turnover (Arnaud-Haond et al. 2019). Furthermore, Surigao exhibited the lowest mean Tajima’s D , closer to neutral expectations, which may reflect recent population expansion or the recovering stage after disturbance (Kendrick et al. 2017a). These contrasting Tajima’s D signals among populations indicate heterogeneous evolutionary histories, likely influenced by local environmental conditions, historical connectivity, and differences in reproductive dynamics (Kendrick et al. 2017a, Kendrick et al. 2017b, Pazzaglia et al. 2021, Hernawan et al. 2023). 4.3. Population genetic structure The genetic structure of H. uninervis across the regions revealed weak genetic structuring, consistent with high connectivity and ongoing gene flow. Similar patterns were reported in sea cucumbers, cod, and macroalgae by Xuereb et al. (2018), Breistein et al. (2022), Legrand et al. (2024), and Song et al. (2025). Low percentages (PC1 = 6.3%, PC2 = 5.1%) are typical of marine foundation species with large effective population sizes and extensive dispersal, whereas genetic variation is distributed across many loci rather than concentrated along a few major components (Hernawan et al. 2017, Hosokawa et al. 2025). Most populations within Bohol Sea formed partially overlapping clusters in principal component ordination, indicating moderate differentiation accompanied by shared genetic backgrounds (Jahnke et al. 2019, Hernawan et al. 2023). It has a similar pattern to other seagrass species, including Thalassia hemprichii, Zostera marina, Zostera muelleri, and Syringodium filiforme from the Indo-Australian Archipelago (Hernawan et al. 2017), the Florida Keys and subtropical Atlantic region (Bijak et al. 2018), and the Skagerrak–Kattegat region of the eastern North Sea (Jahnke et al. 2018). This pattern suggests that populations are weakly isolated but experience historical and contemporary gene flow across sites (Hernawan et al. 2017, Hernawan et al. 2023). The lack of clear genetic separation among most populations supports the presence of a regional metapopulation structure, where genetic exchange occurs across multiple spatial scales (Kendrick et al. 2017b, Pazzaglia et al. 2021). Sagay, as an outgroup population from the Visayan Sea, exhibited a tight and homogeneous cluster, reflecting lower within-population genetic variation relative to populations in the Bohol Sea (Hosokawa et al. 2025). It also remained near the Bohol Sea populations in principal component ordination, indicating genetic affinity, suggesting weak genetic isolation due to historical connectivity and episodic long-distance dispersal (Jahnke et al. 2018, Hosokawa et al. 2025). ADMIXTURE analysis further supported these observations by revealing heterogeneous ancestry compositions across populations at K = 9 (CV = 0.357), exhibiting partially shared ancestry, suggesting historical and ongoing connectivity. The partial differentiation of Sagay reflects its geographic separation in the Visayan Sea and reduced connectivity with Bohol Sea populations, rather than complete genetic isolation. Populations such as Maasin, Mambajao, Laguindingan, Kauswagan, and Sagay showed high admixture proportions, indicating contribution of multiple ancestral sources, while Surigao, Nasipit, Plaridel, Maria, and Jagna exhibited more homogenous ancestry profiles (Janhke et al. 2018, Jahnke et al. 2019). These results indicate that population genetic structure in H. uninervis is shaped by a balance between shared ancestry, localized recruitment, and regional connectivity (Jahnke et al. 2018, Jahnke et al. 2019, Hernawan et al. 2023, Hosokawa et al. 2025). 4.4. Genetic differentiation between populations Genetic differentiation analyses consistently indicated low to moderate levels of genetic differentiation among H. uninervis populations, which strengthens the inference that population structure is present but relatively weak, reflecting ongoing gene flow among populations (Bijak et al., 2018). The highest differentiation values were observed between Laguindingan and Jagna (~114 km), Jagna and Sagay (~186 km), Surigao and Laguindingan, and Nasipit and Laguindingan (~105 km). This suggests that Laguindingan is comparatively differentiated from several populations, potentially due to localized environmental conditions, habitat discontinuity, or reduced effective connectivity, which may reflect site-specific demographic or ecological processes rather than complete genetic isolation (Jackson et al. 2020, Hosokawa et al. 2025). In contrast, the lowest differentiation values were detected between Sagay and Mambajao, despite these populations being separated by approximately 243 km. This high genetic similarity may be influenced by recent shared ancestry and sustained gene flow mediated by the ocean current, seasonal monsoon-driven circulation, and episodic dispersal events that promote the transport of propagules and vegetative fragments across large spatial scales (Hernawan et al. 2023). Moreover, the presence of intermediate habitats may enable stepping-stone dispersal, allowing gradual genetic exchange through multiple short-distance movements, which contribute to genetic homogenization between distant populations and result in low differentiation (e.g. Kendrick et al. 2017, Jahnke et al. 2018, Jahnke & Jonsson 2022). This pattern highlights that genetic structure in marine seagrass systems is influenced strongly by hydrodynamic connectivity, habitat configuration, and historical dispersal pathways, rather than geographic distance alone (Jahnke et al. 2019, Hernawan et al. 2023, Nakajima et al. 2023, Hosokawa et al. 2025). 4.5. Isolation by distance Mantel test analyses revealed no significant association between geographic distance and genetic differentiation across all the metrics, including Nei’s G ST , Hedrick’s G’ ST , and Jost’s D. Although Jost’s D showed a weak positive correlation with geographic distance, this relationship was not statistically significant, indicating an overall absence of isolation-by-distance (IBD) in H. uninervis populations within the Bohol Sea and adjacent waters (e.g. Xuereb et al. 2018, Jahnke & Jonsson 2022, Hosokawa et al. 2025). This pattern suggests that geographic distance alone does not explain genetic differentiation in this system ( Jahnke et al. 2019 , Hernawan et al. 2023). The absence of the IBD pattern is consistently observed in many marine organisms, whose dispersal and connectivity are strongly influenced by oceanographic processes (e.g. Jahnke & Jonsson 2022, Hernawan et al. 2023, Legrand et al. 2024, Hosokawa et al. 2025). In the Bohol Sea, prevailing ocean currents and seasonal circulation, including the Bohol Jet, may facilitate the transport of seagrass propagules and fragments. Given the fact that H. uninervis reproduces both sexually and asexually, floating fragments and detached shoots may remain viable during transport, which allows successful colonization and recruitment in distant places, thus maintaining genetic connectivity (Evans et al. 2021). These current-mediated dispersal mechanisms can homogenize genetic variation among populations and weaken isolation-by-distance patterns (Kendrick et al. 2017, Jahnke et al. 2018, 2019, Evans et al. 2021, Jahnke & Jonsson 2022, Hernawan et al. 2023, Legrand et al. 2024, Hosokawa et al. 2025). These biological traits and oceanographic processes likely contribute to sustained gene flow among populations, which weakens and non-significant IBD pattern observed in this study. 4.6. Recent migration and connectivity BayesAss revealed high self-recruitment rates across most populations, indicating strong local retention of individuals. Nasipit showed the highest self-recruitment, followed by several populations with a similar pattern reported for corals and reef fishes, suggesting that local reproduction plays a major role in maintaining population genetic composition (López-Márquez et al. 2019, Swearer et al. 2020, Afiq-Rosli et al. 2021). Despite high self-recruitment, asymmetric gene flow was detected among certain population pairs. In particular, Mambajao received the highest incoming migration from multiple populations, including Plaridel (~130 km), Maria (~117 km), and Sagay (~243 km), suggesting that it may function as a regional sink or convergence zone (Jahnke et al. 2018, López-Márquez et al. 2019, Afiq-Rosli et al. 2021). Moderate migration rates into Surigao and Mambajao, with an approximate distance of 105 km, further indicate that some populations serve as important recipients of genetic input, structuring the metapopulation (Jahnke et al. 2018). In contrast, populations such as Nasipit, Kauswagan, Plaridel, Maasin, Jagna, and Maria exhibited relatively low incoming and outgoing migration rates, suggesting more localized dynamics and demographic stability (López-Márquez et al. 2019, Swearer et al. 2020). The combination of high self-recruitment and selective connectivity supports a model of semi-connected populations, where gene flow occurs through specific pathways, which are widely recognized for seagrasses and reef organisms under the combined influence of ocean currents, habitat distribution, and stochastic dispersal (Kendrick et al. 2017b, Jahnke et al. 2018, López-Márquez et al. 2019, Jahnke et al. 2019, Swearer et al. 2020, Afiq-Rosli et al. 2021, Legrand et al. 2022). The pattern in the Bohol Sea is likely influenced by the dominant oceanographic circulation, particularly the Bohol Jet emerging from the Surigao Strait. The Bohol Jet is a strong subsurface current that transports water masses westward, facilitating episodic long-distance dispersal of biological particles (Bernardo 2011). Its interaction with mesoscale circulation features, including the cyclonic eddy system in Iligan Bay, can promote asymmetric and directional gene flow, consistent with the observed convergence of migrant at Mambajao and absence of a significant IBD pattern. Similar circulation-driven connectivity mechanisms have been documented in the Bohol Sea, where the Bohol Jet enhances biological exchange across seascapes despite substantial geographic separation (Bernardo 2011, Cabrera et al. 2011, Gordon et al. 20211). In conclusion, this paper provides a comprehensive geographic population genetic assessment of Halodule uninervis in the Bohol Sea and adjacent waters in the Philippines. It established a high-resolution baseline of understanding clonal structure, genetic diversity, connectivity, and migration dynamics of the species. The results revealed a pronounced spatial variation in reproductive strategies from predominantly sexual populations, including Plaridel, Nasipit, Maasin, Jagna, and Maria, to strongly clonal populations such as Laguindingan and Sagay, whereas Surigao, Kauswagan, and Mambajao were the intermediate populations that exhibit mixed reproductive strategies. Notably, although Sagay is strongly clonal, it exhibited the highest genetic diversity, indicating its highly divergent genotypes, which reflect its outgroup status and long-term persistence of genetically distinct lineages. Genetic diversity across all populations was low to moderate, consistent with life-history traits, and population-specific signals of demographic stability, historical bottlenecks, and recovery were reflected in heterozygosity patterns and Tajima’s D values. Population structure analyses consistently showed weak but detectable genetic structuring through overlapping principal component clusters and heterogeneous ancestry profiles, suggesting a semi-connected regional metapopulation. Genetic differentiation between populations was generally low to moderate, with no significant isolation-by-distance, suggesting that the connectivity of H. uninervis is not governed by geographic proximity. Instead, contemporary and historical gene flow is mediated by complex oceanographic circulation. Recent migration analyses further revealed high self-recruitment across populations, with asymmetric and selective gene flow pathways. Notably, Mambajao was observed as an emerging regional convergence or sink population receiving the highest migrants from multiple geographically distant populations. The outgroup population, which is located in the Visayan Sea exhibit genetic affinity with Bohol Sea populations, indicating connectivity and episodic long-distance dispersal across seascapes. These findings demonstrate that the H. uninervis populations in the Bohol Sea are genetically connected through a complex three-dimensional circulation pattern, such as the Bohol Jet through the Surigao Strait, reinforcing the role of oceanographic processes in structuring the H. uninervis metapopulation. However, the coexistence of strong local retention and selective regional connectivity indicates that population recovery may be slow, particularly in highly clonal and low genetic diversity populations. Thus, genetically diverse and sexually reproducing populations act as a critical reservoir of adaptive potential, such as Maasin, Plaridel, Nasipit, and Maria, while the sink populations, such as Mambajao plays a central role in maintaining regional connectivity. Mambajao may represent a potential donor population for seagrass restoration initiatives within the Bohol Sea. Its high admixture levels indicate contributions from multiple genetic sources, reflecting broad genetic representation. Proven connectivity with several populations suggests its capacity to supply propagules across the region, while its role as a sink or transitional population highlights its integration within regional dispersal pathways. In addition, moderate self-recruitment indicates population stability without strong isolation, supporting its use as a donor without substantially compromising local persistence. Accordingly, a network-based management approach that incorporates genetic connectivity into restoration planning and marine spatial management is recommended, supported by continued genetic monitoring to ensure long-term resilience. Acknowledgement This study was supported by the Department of Science and Technology – Science, Technology, and Research for National Development (DOST–STRAND). Additional research support was provided by the Nagao Natural Environment Foundation (FY 2025–2028), under the leadership of Katsuyuki Eguchi, and by the Kawatabi Field Science Center, Graduate School of Agricultural Science, Tohoku University. The authors gratefully acknowledge these institutions for their financial, logistical, and institutional support. The authors also extend their sincere appreciation to Melanie Cayacay, Ronnel Alingasa, and other colleagues and friends for their invaluable assistance during field sampling. We further acknowledge the support of the local government units for granting access and permission to conduct the study, as well as the Bureau of Fisheries and Aquatic Resources (BFAR) and the Department of Environment and Natural Resources (DENR) for their assistance in the processing of research permits. Author Contribution Angela Grace E. Singson : Conceptualization (lead); Methodology (supporting); Investigation (lead); Formal Analysis (supporting); Visualization (lead); Writing – Original Draft Preparation (lead); Writing – Review & Editing (equal). Koji Takayama : Methodology (supporting); Resources (lead); Validation (supporting); Writing – Review & Editing (equal). Yoshihisa Suyama : Formal Analysis (supporting); Methodology (lead); Resources (supporting); Validation (lead); Writing – Review & Editing (equal). Naoko Ishikawa : Data Curation (lead); Formal Analysis (supporting); Methodology (supporting); Resources (supporting); Writing – Review & Editing (equal). Shoki Murakami : Investigation (supporting); Resources (supporting); Writing – Review & Editing (equal). Lilibeth P. Coronel : Data Curation (supporting); Formal Analysis (lead); Resources (supporting); Visualization (supporting); Writing – Review & Editing (equal). Venus E. Leopardas : Conceptualization (supporting); Writing – Review & Editing (equal). Nonillon M. Aspe : Resources (supporting); Conceptualization (supporting); Writing – Review & Editing (equal). Wilfredo H. Uy : Conceptualization (supporting); Writing – Review & Editing (equal). Dan M. Arriesgado : Conceptualization (supporting); Writing – Review & Editing (equal). Ruby C. Gonzales : Conceptualization (supporting); Writing – Review & Editing (equal). Competing Interest The authors declare that they have no competing interests. Data Accessibility Statement Raw sequencing reads generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1438458 [https://dataview.ncbi.nlm.nih.gov/object/PRJNA1438458?reviewer=p352mt27fg0qu7p6pp61hm1odb]. The code snippets used for data processing and statistical analyses are publicly available in the GitHub repository [https://github.com/angelagracesingson-coder/Population-genetics]. LITERATURE CITED Afiq-Rosli L, Wainwright B, Gajanur A, Lee A, Ooi S, Chou L, Huang D (2021) Barriers and corridors of gene flow in an urbanized tropical reef system. Evolutionary Applications 14:2502–2515. https://doi.org/10.1111/eva.13276 Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. 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Keywords ecosystem marine molecular genetics plants population ecology Authors Affiliations ANGELA GRACE SINGSON 0009-0003-3498-4394 [email protected] Mindanao State University at Naawan View all articles by this author Koji Takayama Tokyo Metropolitan University View all articles by this author Yoshihisa Suyama Tohoku University View all articles by this author Naoko Ishikawa Tohoku University View all articles by this author Shoki Murakami Tokyo Metropolitan University View all articles by this author Venus Leopardas Mindanao State University at Naawan View all articles by this author Nonillon Aspe Mindanao State University at Naawan View all articles by this author Wilfredo Uy Mindanao State University at Naawan View all articles by this author Lilibeth Coronel Mindanao State University at Naawan View all articles by this author Dan Arriesgado Mindanao State University at Naawan View all articles by this author Ruby Gonzales Mindanao State University at Naawan View all articles by this author Metrics & Citations Metrics Article Usage 171 views 92 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation ANGELA GRACE SINGSON, Koji Takayama, Yoshihisa Suyama, et al. Population genetic structure and biogeographic distribution of tropical Halodule uninervis in the Bohol Sea and adjacent waters in the Philippines. Authorea . 18 March 2026. DOI: https://doi.org/10.22541/au.177381229.92108407/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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