Teleogryllus oceanicus) in which ‘flatwing’ males that lack sound-producing wing structures recently arose and spread under selection from an acoustically-orienting parasitoid. Morphometric and genetic comparisons identify distinct flatwing phenotypes in populations on three islands, localized to different loci. Nevertheless, we detect strong, recent and ongoing gene flow among the populations. Using genome scans and gene expression analysis we find that parallel evolution of flatwing on different islands is associated with shared genomic hotspots of adaptation that contain the gene doublesex, but the form of selection differs among islands and corresponds to known flatwing demographics in the wild. We thus show how parallel adaptation can occur on contemporary timescales despite gene flow, indicating that it could be less constrained than previously appreciated.Gene flow is predicted to impede parallel adaptation via de novo mutation, because it can introduce pre-existing adaptive alleles from population to population. We test this using Hawaiian crickets ( Gene flow is classically thought to impede local adaptation via parallel evolution. However, a genomic study on Hawaiian crickets from different island populations finds evidence of parallel adaptation to the same lethal parasitoid in spite of strong ongoing gene flow. One way this can occur is through adaptive introgression, by repeatedly introducing the same pre-existing adaptive allele from one population into others, whereupon they are selected. In this scenario, gene flow is predicted to play an important role in moving the same allelic variants, which share a single mutational origin, among populations3. In contrast, parallel evolution occurs when traits that are functionally identical at the organismal level nonetheless have independent mutational origins at the genetic level4. In the latter case, uniquely arisen adaptive genetic variants are independently selected in different populations, and theory strongly predicts that high levels of gene flow will reduce the likelihood of parallel evolution10. For that reason, the most compelling examples of parallel evolution are those that have been documented in highly isolated populations, for example, the independent selection of different genetic variations that enable rabbits (Oryctolagus cuniculus) on different continents to survive myxoma virus11, and the repeated adaptive deletion of the Pitx1 gene, which causes stickleback (Gasterosteus aculeatus) pelvic reduction in different spatially isolated lake systems13. Exceptions to this general pattern are only predicted when selection is extreme or mutation rates are particularly high14, but these scenarios are difficult to empirically test in natural systems. Most adaptive novelties arose and spread in the distant past, necessitating the use of sophisticated techniques to distinguish processes such as incomplete lineage sorting and gene flow17. Dissecting the mutational origins of novel adaptive traits undergoing convergent phenotypic evolution, while simultaneously inferring gene flow, is complicated by these sorts of demographic developments occurring during and after episodes of adaptive evolution19. Thus a question at the centre of arguments about evolvability and adaptive potential remains largely unsolved: Can parallel evolution underpinned by independent mutations occur despite high levels of gene flow, and if so, how?Adaptive evolution can be surprisingly repeatable. Convergent phenotypic evolution occurs when similar selection pressures drive the spread of functionally identical traits at the organismal level but in different populationsTeleogryllus oceanicus) on the Hawaiian archipelago. Typically, male crickets attract females for mating by rubbing their wings together to produce a conspicuous, long-range acoustic signal. However, in approximately 2003, a variant segregating in the manner of a single locus on the X chromosome—flatwing—arose and spread in a population on the island of Kauai20. This adaptive allele erases sound-producing structures on males’ wings during development, causing them to resemble the undifferentiated wings of females , and evidence from the field shows that the flatwing morph is now fixed at 100% in the Kauai population  show the same mode of inheritance on the X chromosome yet are predominately associated with different restriction site associated DNA (RAD) markers, suggesting they are independently generated alleles23.We addressed this question using a unique system of rapidly evolving field crickets , which has been widely implicated in sex-specific adaptive traits in other insects, by quantifying gene expression in developing wingbuds of different male morphs on two islands where flatwing crickets exist in appreciable abundance (Kauai and Oahu). Our results reveal that parallel evolution can occur despite gene flow, indicate how this process occurs, and implicate the doublesex pathway in the repeated origins of an adaptive, male-feminising trait in wild insects.Here, we take advantage of the repeated evolutionary origin and spread of flatwing crickets in multiple Hawaiian island populations to test the expected trade-off between gene flow and rapid parallel adaptation via independent mutational events, which is predicted by standard evolutionary models26. Flatwing venation differs between Kauai and Oahu males and appears to be under independent genetic control23, so to test whether the wing venation of flatwing males is morphologically distinct among all three populations we studied here, we collected right forewings of 114 wild flatwing male crickets over multiple years from populations on the islands of Kauai, Oahu and Hilo , the degree of this reduction varied among populations  were also sequenced. Supplementary Table T. commodus was used as an outgroup  were derived from parasitized populations on each of three Hawaiian islands  Fig. . Seven nT. oceanicus reference genome21, confirming the quality of this re-sequencing dataset. We used a customised pipeline to account for potential biases caused by outgroups or sequencing errors to obtain a total of 107 million high-quality single nucleotide polymorphisms (SNPs) 30 using X chromosome data because we were specifically interested in understanding gene flow that could affect the movement of flatwing alleles among populations. This analysis revealed considerable gene flow among the three Hawaiian island populations , which is consistent with common observations that modest but significant gene flow can be detected among geographically distinct distant populations within the same species33. The key observation is that all Hawaii island scenarios showed significantly more extreme D-values compared to gene flow involving Australian populations  showed the strongest signals of gene flow, and the large majority of autosomal linkage groups (LGs) showed qualitative patterns consistent with the X  of autosomal SNPs implemented in Fastsimcoal236. The ancestral effective population size of Hawaiian crickets showed a peak about 20,000 years ago followed by a decline involving at least a twofold decrease in the effective population size coinciding with the last glacial maximum37, likely reflecting their putative ‘out-of-Australia’ origins and island-hopping across the Pacific39.The results indicate that gene flow among all three islands started recently, and occurred continuously until the present, rather than transiently. Almost all scenarios assuming recent gene flow were more probable than those without gene flow (Supplementary Data 21 using pseudomolecule coordinates (bp); top panel of Fig. D. melanogaster wing development or other relevant developmental processes , one for each island. Because of the extreme scarcity of Kauai normal-wing males and Hilo flatwing males, GWAS involving these two morphs used lab stocks established from the offspring of wild-caught individuals (see ‘Methods’ section). Our previous quantitative trait locus (QTL) mapping of flatwing in Kauai yielded a broad peak occupying approximately one-third of the X chromosome (redrawn from ref. flatwing mutation(s) being masked in our analysis by potential biases of performing three separate GWASs, for example, due to population genetic structure. To further evaluate the idea that parallel evolution occurred in genetic hotspots, we established maximum-likelihood phylogenetic trees using SNPs extracted from (1) flatwing-associated scaffolds shared by different populations, (2) GWAS candidate SNP sets from all populations combined, (3) each of the three population-specific GWAS candidate SNP sets separately, and (4) flanking regions of these flatwing-associated SNPs. Given our precise knowledge of how recently flatwing appeared in each population23, if a single flatwing allele had arisen and introgressed among populations, then trees built using restrictive sets of markers strongly linked to the flatwing phenotype should show elevated relatedness of all flatwing individuals regardless of their population origin compared to normal-wing individuals. None of the unrooted trees using Hawaiian individuals, nor rooted trees with Australian individuals as outgroups, supported such an adaptive introgression scenario: flatwing individuals from different populations did not all cluster together as would be expected if flatwing-associated regions were shared due to recent introgression across populations  in all three analyses. We therefore subsetted the Hilo and Kauai flatwing-associated SNPs according to the most conservative criterion based on the Oahu GWAS Fig. . This paombined,  each of ons Fig. –25. Becaflatwing introgressed very recently through these Hawaiian populations, a divergence of flatwing-associated allele(s) between population pairs should be lower than the divergence of other loci on the X chromosome42. Instead, we found that net nucleotide divergence (d) between Oahu and Kauai wild flatwing individuals at flatwing-associated regions was significantly greater than that for all other X chromosome regions  between flatwing-associated regions versus other X chromosome regions was similar  located in the X chromosome among Kauai, Oahu and Hilo populations  can have a strong influence on adaptive evolution in insectsFST) between wild flatwing Hawaiian crickets and normal-wing Australian crickets, which are not under selection pressure from O. ochracea  than normal-wing individuals in the flatwing hotspot scaffolds els Fig. . Consistion Fig. . This paion Fig. .prospero (pros) and doublesex (dsx), and three flatwing-associated SNPs supported in the selection analysis were directly located within introns of these genes , and expression analysis reinforced the importance of the genomic region containing dsx to the manifestation of independently derived flatwing phenotypes from different populations.Crickets are hemimetabolous and wing venation is established early during nymphal developmention Fig. . We collflatwing genotypes from Kauai vs. Oahu  in which dsx dysregulation affects the development of male and female horn length in opposing directions49. We found no evidence of sex- or morph-segregating dsx isoforms. We cannot exclude that these might be detected at a different developmental stage or in different tissues, but note with interest that there are now multiple exceptions to the expectation of sex-specific splicing in arthropods53. The expression patterns of the above two transcripts were further validated by re-analysing an independent, previously collected RNA-seq dataset (see ‘Methods’ section). Prospero was lowly expressed in wingbud tissues (mean count-per-million of 0.35) so did not pass expression filtering of transcripts, nor showed any evidence of differential expression between genotypes after relaxing the filtering threshold (unadjusted P > 0.88)  distinct morphometric patterns of flatwing venation on different islands, (2) entirely non-overlapping flatwing-associated SNPs among all three populations, (3) all phylogenetic trees for flatwing-associated genetic regions falsified a scenario of adaptive introgression (4) greater net nucleotide divergence at flatwing-associated regions (5) lack of shared flatwing-associated structural variants (6) long-range, complete linkage disequilibrium on flatwing-associated scaffolds in Kauai but not Oahu (7) little overall similarity in wingbud gene expression changes associated with flatwing genotypes from different islands. This parallel adaptation occurred despite considerable recent, ongoing gene flow among all three populations. T. oceanicus in the Hawaiian islands have, like many island species, lost long-distance flight capability, and migration appears to be maintained by human activities, which became more frequent as human settlement increased 300 years ago  and from Oahu (fwO) can be expressed in alternative genomic backgrounds27, but the fitness consequences of this are not known. Future work testing constraints on convergent evolution via adaptive introgression is necessary to evaluate this scenario.The tension between mutation and migration in shaping the dynamics of adaptive evolution is a central idea in evolutionary theory, which predicts that adaptive introgression should reduce the role of de novo mutations when selection is extreme and gene flow is high19, but the silent Hawaiian cricket system is uniquely suited to overcome this issue owing to the recent origin and spread of flatwing morphs in all three populations studied here22. If, as our results suggest, parallelism is less constrained by gene flow than previously appreciated, it is worth considering what factors favour such an outcome. The functional genetic basis of adaptive traits and the phenotypic nature of the adaptation may each have important roles in relaxing this constraint. In crickets and other ensiferan insects, forewing venation is a sexually dimorphic trait critical for sexual reproduction. Signals emitted by resonating membranes on the wings are major components of the mate recognition system, often the first long-range signal detected by receivers, and they have a well-known role in sexual selection, reproductive isolation and diversification62. Male forewings and the signals they produce are highly variable across ensiferan taxa65. Traits showing an obvious signature of extensive evolutionary variation have, by definition, arisen through the action of repeated genetic mutation, and could therefore be particularly disposed to recurrent parallel evolution. In addition, loss of an existing complex trait may be more evolvable than gain of a novel trait66, because disrupting the developmental genetic pathways underlying a complex structure such as a cricket wing resonator or a stickleback’s pelvic girdle can be achieved through simple genetic change affecting one of many loci. Involvement of the doublesex pathway in the evolution and development of flatwing male crickets is consistent with such an evolutionary mode of action. Dsx is an essential regulator of sexual dimorphism in insects, for example, controlling the development of sexually dimorphic horns in the dung beetle Onthophagus taurus67 and the Japanese rhinocerous beetle Trypoylus dichotomus68, female-limited wing mimicry in Papilio butterflies70, sexual morphology in Drosophila melanogaster49, and male scent organs in Bicyclus butterflies71.Empirical examples that violate this predictable trade-off are rare. Part of the reason for this may be the well-known challenges of disentangling demographic signal from introgression or independent mutational origins when convergent evolution is supposed to have occurred in the distant evolutionary past72. The repeated loss of song in Hawaiian crickets under pressure from eavesdropping parasitoids provides a compelling test case in a wild population undergoing ‘real-time’ evolutionary adaptation, showing that extremely rapid parallel adaptation can occur despite evolutionary forces traditionally thought to impede it.The emergence and spread of parallel adaptations in populations subject to gene flow are liable to be biased towards highly evolvable traits such as those functioning in sexual selection and towards genomic hotspots of adaptation where relatively simple changes result in functional losses. Given the increasing research focus on evolutionary rescue and rapid adaptation in wild populations, the speed of such adaptation, its repeatability, and the factors favouring it are important to dissect and translate to applied contexts, to mitigate resistance evolution in agricultural pests and human pathogens, and also to design effective interventions to cope with negative consequences of anthropogenic environmental change22. Slides were photographed using a Leica DFC295 digital camera mounted on a Leica M60 dissecting microscope. We performed a landmark-based geometric morphometric analysis of these photos27. Specifically, landmark data were generated by manually placing 16 landmarks on each wing photograph using the tpsDig2 (v2.31)73 as described in Fig. 23 and Bailey et al.27. Morphological comparisons were then conducted using MorphoJ (v1.07a), which extracts shape information by performing a Procrustes superimposition on all the landmark data74. A covariance matrix generated from Procrustes distances was then used to perform principal component analysis (PCA) without a priori information of group (i.e. island) identity. The PCA results and corresponding group information were then analysed using a multivariate analysis of variance (MANOVA) in R v. 3.5.2 to test whether flatwings from the three groups were morphologically different. They were, so we used a canonical variates analysis (CVA) to visualise the differences among islands. CVA is designed to maximise the separation of three populations, producing axes, which best explain inter-group variation23. A scatter diagram and a transformation grid plot were plotted to visualise the CVA results.Right forewings of wild flatwing male crickets were collected from three Hawaiian islands and mounted between two microscope slides  Edition, and then clustered onto a HiSeqX Flow cell v2.5 on cBot2s. The clustered flow cell was transferred to a HiSeqX for sequencing using a HiSeqX Ten Reagent kit v2.5.Seventy individuals were collected for whole-genome re-sequencing  reference were generated using bwa and samtools (v1.3.1)75. The filtered pair-end sequence reads were then mapped to the reference genome21 using BWA-MEM (v0.7.12) with default parameters76. Secondary alignments with flag ≥ 255 were discard using a customised Perl script33. Samtools (v1.3.1) was used to assess coverage individually. We used samtools and picard (2.14.1) to sort, format and index the resulting binary alignment map (BAM) files and remove PCR duplicates. To increase the accuracy of alignments around indel regions, we detected indels and re-aligned these regions using RealignerTargetCreator and IndelRealigner functions in GATK (v3.7.0)77. All following analyses were conducted based on these re-aligned BAM files. We detected SNPs and indels following GATK best practice workflows78. Briefly, raw SNPs and indels were first detected individually using HaplotypeCaller. CombineGVCFs was than applied to merge SNPs of different individuals from the same population. The VCF records of multiple populations were then re-genotyped and aggregated using GenotypeGVCFs. Following the authors’ recommendations, hard-filtering criteria  were decided manually based on density plots of the raw SNP dataset and applied using SelectVariants and VariantFiltration. To further improve the accuracy of genotyping and economise computing resources, SNPs of extremely short scaffolds (≤5 kb) were not called. In consideration of the fact that sex of crickets is determined by an XX/XO  system21, all males were fixed to have homozygous genotypes of X-linked loci79. Low-quality SNPs potentially caused by sequencing error were also discarded using a customised Perl script. Specifically, all SPNs with Phred-scaled quality score < 30 or with abnormal depth  were filtered33. The X scaffold was defined according to the previously published linkage map21. For SNPs located in X-linked scaffolds, the criteria of abnormal depth were set as the half that of the autosomes. Heterozygous calls of X-linked SNPs were fixed to homozygous based on the allele depth if the depth of one allele was greater than three times the other one. If none of these alleles were supported by the supermajority, the genotype was treated as unknown. Loci that had unknown genotypes in more than 25% of all individuals were also excluded. To avoid bias caused by outgroups (unrelated populations), multiple SNP datasets were generated independently and utilised accordingly for the following analyses , allowing 1 mismatch when assigning reads to barcodes. Adaptors were trimmed during the demultiplexing process. Raw reads that included ≥5% unidentified nucleotides (N) or more than 65% low-quality nucleotides  were discarded80 to calculate identity-by-state based on SNP datasets. The resulting distance matrix was then passed to PHYLIP (v3.696)81 to construct neighbour-joining phylogenetic trees 82 and FigTree (v1.4.4). We used the smartpca programme in EIGENSOFT (v7.2.1)83 to perform the principal component analysis (PCA) and visualised the result by plotting a scatter diagram using rgl and ggplot2 R packages84. We used ADMIXTURE (v1.3.0) to estimate population structure with the numbers of ancestral populations (K) from 2 to 7 after thinning the SNP set for linkage disequilibrium using PLINK with an r2 threshold of 0.185.We used PLINK (v1.90b6.8)ees Fig. , 3. T. c86 to calculate genetic diversity with a sliding-window size of 10 kb and a step size of 2.5 kb. To calculate linkage disequilibrium, the original SNP dataset was first divided into subsets of each population using vcftools. The split VCF files were then converted to haploview-recognised format using PLINK. Haploview (v4.2) calculated correlation coefficients (r2), which are used to infer pairwise linkage disequilibrium87. To visually depict LD patterns on scaffold 18404 404 Fig. , a reaso404 Fig. .D-statistic) test88 and multiple individuals’ D-statistic test34 to detect gene flow among different populations. Six different scenarios were statistically tested. To do so, BAM files of individuals from three Hawaiian populations, one Australian population and one outgroup population (T. commodus) were passed to ANGSD (v0.930), which counted the number of ABBA and BABA sites and calculated D-statistic metrics88. We inferred relatively late  demographic parameters including the divergence of three populations, bottlenecks and migrations from the site frequency spectrum (SFS) using Fastsimcoal35. To minimise bias caused by potential inbreeding, linkage and selection, the following criteria were applied to further filter SNP datasets before format conversion: (i) Only autosomal scaffolds were retained, based on previous linkage map, (ii) SNPs with an r2 > 0.3 were excluded using the sliding-window method, (iii) PLINK was used to exclude one of the individual pairs, which had a proportion identity-by-descent outlier, (iv) SNPs located inside genic regions were excluded, (v) Sites with unknown genotype were also filtered. The filtered SNP dataset was converted to Arlequin format using PGDSpider (v2.1.1.5)89. We used Arlequin (v3.5)90 to produce the minor allele SFS (folded SFS) and joint multidimensional SFS. Using Fastsimcoal (v2.6.0.3), we tested 62 models including effective population size changes, different divergence scenarios, presence or absence of gene flow, and permutations of three island populations. For each model, we performed 50 independent parameter inferences. For each run, 100,000 simulations per likelihood and 40 Expectation Conditional Maximisation (ECM) cycles were performed to estimate parameters with maximum-likelihood. We compared different models using the Akaike Information Criterion (AIC), and estimated the 95% confidence interval of the best model using 30 parametric bootstraps35. We used the Pairwise Sequentially Markovian Coalescent (PSMC) method to reconstruct the most recent common ancestor (TMRCA) distribution, infer the divergence time from now to the TMRCA, and finally infer the history of effective population size36. Since local density of heterozygotes information was used by PSMC, X-linked scaffolds were excluded from this analysis. To further improve the accuracy of this analysis, only wild individuals with the highest sequencing depth  were used. We used samtools to generate diploid consensus sequences based on individual BAM file. Loci with abnormal depth were masked by N. PSMC (v0.6.5-r67) was used to infer historical effective population size with a mutation rate of 3.5 × 10−9 and a generation time of 0.25 year92.To avoid bias caused by inbreeding, only wild-caught, unrelated samples were used to infer gene flow and demographic history of Hawaiian cricket populations. We used the ABBA–BABA . Thus for Hilo, we used 10 normal-wing wild males and 10 flatwing lab-reared males. For Kauai, because of the inbreeding of lab-reared normal-wing individuals , the association analysis was performed between 10 wild Kauai flatwing males and 30 normal-wing males from Kauai lab stock, wild Hilo, and wild Oahu populations. The standard case/control threshold for association significance was generated using Fisher’s exact test implemented in PLINK80. P-values were Bonferroni-adjusted in R to account for multiple testing. To ensure results were not affected by different numbers of individuals entering GWAS analyses for different islands, we performed another independent GWAS for the Oahu population using the same strategy as for Kauai . It generated qualitatively identical results  system, and the expression of 75. These extracted BAM files were then passed to bam2cfg to generate a per-invocation config file93. Based on this config file and the X-chromosomal BAM files, Breakdancer (1.3.6) was used to detect deletion, insertion, inversion, and translocation93. The SVs (i) smaller than 100 bp, or (ii) with a quantity score < 30, or (iii) supported by fewer than 2 reads were filtered94. For each island, SVs of flatwing vs. normal-wing morphs were compared. Flatwing-differentiated SVs were defined according to the association significance calculated using Fisher’s exact test. We used the read-depth method implemented in CNVnator (v0.3.3), which is a software combined mean-shift approach, multiple-bandwidth partitioning and GC correction, to detect copy number variants (CNVs)95. To maximise the accuracy and sensitivity, a bin size of 100 was used. We also filtered the detected CNVs with p > 0.05 or q0 > 50% to avoid potential bias caused by inequality of sequencing depth and the reads mapped to multiple genomic regions95. Considering the fact that all individuals used in this research were males, which have only one X chromosome, the copy number was normalised to one. Copy number variation regions (CNVRs) were defined as the genomic regions that showed copy number variations in multiple individuals (>3). The overlapped CNVs of different individuals were merged into CNVRs using the method, which defined the minimum and maximum boundaries of the CNVs as the boundaries of the CNVR97. CNVRs supported by fewer than three individuals were filtered to further reduce the false discovery rate97. To detect flatwing-differentiated CNVRs, both VST and student’s t-tests were used97. Here, VST = (VT − VS)/VT. VT is the total variance in copy number between the two groups. VS is the average of the variance within each group, weighted for its sample size99. A CNVR in the top 5% of all VST values plus a student’s t-test P-value < 0.05 was considered to be flatwing-differentiated CNVR97.We detected structural variants (SVs) across the X chromosome. The alignment results of reads mapped to the X chromosome were first extracted from BAM files using customised Perl script and SAMTOOLS82. Absolute divergence (dxy) and net nucleotide divergence (d) were used to estimate the amount of DNA divergence (nucleotide substitutions). For the latter, d = dxy − (dx + dy)/2, where dx and dy are per-site pairwise nucleotide differences within populations X and Y, respectively, and dxy represents pairwise nucleotide differences between populations X and Y101. Estimates of d and dxy were calculated using PopGenome (v2.6.1)102. The d and dxy values of different genomic regions in different populations were compared using sliding-window approaches, and statistical differences were tested using 1000 random simulations implemented with custom Perl and R scripts.Intersections of flatwing-associated regions of different populations were calculated and visualised using a Venn diagram tool . The SNPs of flatwing-associated regions were extracted and converted to MEGA-format using Perl scripts. We constructed maximum-likelihood (ML) phylogenetic trees for flatwing-associated SNPs and their flanking regions Fig. –25 usingπ) and Tajima’s D for different populations75. Fixation indices (FST) between different populations were also calculated. Specifically, VCF files of corresponding populations were analysed using a sliding-window approach with 10 kb windows and a 2.5 kb step size. Windows overstepping boundaries of scaffolds were filtered. Selection pressure reduces genetic diversity of the targeted region and enhances the degree of differentiation between populations under selection versus those not under selection45. Here, we compared nucleotide diversities of different populations and calculated their π log-ratios in Perl and tested the empirical significance of these measures in R using empirical percentiles. Genomic regions within the top 5% of FST values and top 5% of π log-ratios were identified as candidate selective-sweep regions45, and any adjacent (overlapped) candidate regions were merged in to a single candidate region.We used samtools to calculate nucleotide diversities (T. oceanicus21. Genes overlapping the corresponding candidate region (loci) were defined as candidate genes. Functional annotations of these genes were assigned based on Swissport, TEMBL and the NR database103.The gene content of candidate selective-sweep regions, significant GWAS loci, flatwing-differentiated SVs, and flatwing-differentiated CNVRs was obtained by comparing their coordinates with the official gene set for normal-wing and flatwing genotypes of both sexes, from Kauai and Oahu lab populations. There were 3 replicate pure-breeding lines per group. Populations used in the experiment were derived from eggs laid by wild-caught females from populations of Kauai and Oahu in 201452. Laboratory populations were reared in a growth chamber at 25 °C, under a 12-h L:D cycle. Kauai lines had been recently outcrossed by mixing all lines, and pure-breeding lines were reconstituted prior to the current experiment by performing genetic crosses across two generations to select only those families showing patterns of offspring morph ratios indicating they were derived from homozygous flatwing or normal-wing dams; males and females from these lines were, therefore, pure-breeding for the respective genotype, with males carrying one copy and females two. Kauai and Oahu lines were reared and maintained under identical conditions, in 20 L boxes with cardboard shelter, and food and water available ad libitum. Individuals were removed from stock boxes at their penultimate nymphal instar  was used for base calling and demultiplexing indexed reads. Adaptor sequences were trimmed from fastq files using Cutadapt v1.2.1106. Reads were aligned to the T. oceanicus genome using HiSat2 v2.1.0. A genome-guided transcriptome was then assembled from output files using StringTie v1.3.4, and gene expression values quantified for each of the samples. Differential expression analyses were performed at the ‘gene’ level. To retain in our transcriptome only transcripts with strong empirical support and which appear to be protein-coding, we filtered any without open reading frames of >100 amino acids, and those which were not expressed at >1 count per million in at least three samples from each population as well as a previously sequenced Kauai wingbud RNA-seq dataset (see below). The expression filter was relaxed to >0.1 count per million when investigating patterns of prospero expression , after which a single negative binomial GLM was fit incorporating all data, using per-transcript normalised expression values as the response variable. Differential expression (DE) analyses were performed using likelihood ratio tests for pairwise comparisons between morph genotypes and sexes. P-values were FDR-adjusted using the Benjamini–Hochberg procedure, and transcripts were considered DE between groups if FDR values were < 0.05. Statistical analyses were performed using R v3.4.1. DE transcripts associated with morph genotype in each population were highly correlated between sexes . Samples were also sequenced on a different platform (Illumina HiSeq 2000). Thus, overlap between datasets in transcripts identified as DE would be indicative of biologically meaningful and robust results. We aligned these samples to the transcriptome and performed DE analysis following the same procedure as above. The results showed that of the 30 transcripts identified as DE between Kauai male morphs in the current dataset, 10  were also significantly DE in the previously collected dataset, all in the same direction, with well-correlated log2-fold changes , supporting the validity of our results.As partial validation of our results, we compared the identity of transcripts DE between morph genotypes from Kauai with those found to be DE between male morphs in previously sequenced samples from male Kauai wingbudsFurther information on research design is available in the Supplementary informationPeer ReviewReporting summaryDescription of Additional Supplementary FilesSupplementary Data 1-4Supplementary Data 5Supplementary Data 6Supplementary Data 7-10


FST) and quantitative genetic (QST) differentiation in multiple populations to assess their evolutionary signature and identify candidate traits involved with local adaptation. Investigating these signatures in closely related or recently diversified species has the potential to shed light on the divergence processes acting at the interspecific level. Here, we conducted this comparison in two subspecies of snapdragon plants (eight populations of Antirrhinum majus pseudomajus and five populations of A. m. striatum) in a common garden experiment. We also tested whether altitude was involved with population phenotypic divergence. Our results identified candidate phenological and morphological traits involved with local adaptation. Most of these traits were identified in one subspecies but not the other. Phenotypic divergence increased with altitude for a few biomass‐related traits, but only in A. m. striatum. These traits therefore potentially reflect A. m. striatum adaptation to altitude. Our findings imply that adaptive processes potentially differ at the scale of A. majus subspecies.Phenotypic divergence among natural populations can be explained by natural selection or by neutral processes such as drift. Many examples in the literature compare putatively neutral ( Some debate around the accuracy of QST–FST comparisons resulted in a variety of methodological adjustments  by using QST–FST comparisons estimated in a common garden experiment, and evaluate whether altitudinal gradients might play a role in the potential adaptive divergence of populations. We studied eight populations of magenta‐flowered A. m. pseudomajus and five populations of yellow‐flowered A. m. striatum sampled along altitudinal gradients. These two subspecies are interfertile , and quantitative genetic differentiation (QST) based on the partition of trait genetic variance and trait heritability (h2) in A. majus. Previous studies of genetic differentiation between populations and subspecies of A. majus at putatively neutral microsatellite markers showed that gene flow was limited between populations  is a hermaphroditic, self‐incompatible, short‐lived perennial species, characterized by a patchy distribution in southern Europe centred over the Pyrenees Mountains , which is one order of magnitude lower than the ca. 10% differentiation found among populations within subspecies  to represent the overall diversity of the species, with eight populations of A. m. pseudomajus and five populations of A. m. striatum included  filled with universal compost. Plants germinated and grew with no nutrient addition under an average temperature from 15°C to 28°C and weekly watering. Mature plants were hand‐pollinated during the summer 2012 to produce progeny from crosses within populations where mates from different families were assigned randomly. Seed of full sib families produced from these plants was stored at room temperature, in the dark, under dry conditions until they were used to produce the plants measured in our experiment. This intermediate generation of plants grown under controlled conditions allowed us to reduce potential maternal environmental effects that could have otherwise affected plants grown from seeds sampled in the wild.2.4−2) that allowed regulation of the moisture content of the compost. Plants were supplied with water in case of prolonged drought. Damage caused by herbivorous insects was contained by using a wintering veil. This veil also limited pollination.Nine to 42 seed families from each of the 13 study populations were grown outdoors in spring 2014 in a common garden at ENSFEA . Two plants per family were grown. Some plants died before measurements were made, which resulted in some families being represented by only one plant Table . Plants 2.5We investigated several vegetative traits on each individual: a phenological trait, a functional trait, a developmental trait, and four morphological traits. The functional trait (SLA) was calculated as the ratio between the cumulated area of five mature but nonsenescent fresh leaves and their oven‐dried mass  following the manufacturer's instructions. Individuals were genotyped for 23 putatively neutral microsatellite markers that were developed for population genetic studies  as a random effect. Estimates of marginal means for each trait in each population were extracted using the emmeans package  were estimated for each phenotypic trait across all populations using a model with population, family and plastic containers as random factors as h2 = 2Vw/(Vw + Vres), where Vw is the family variance component and Vres is the residual variance component corresponding to the within‐population variance component. We multiplied Vw by two in the calculation of h2 because we used a full‐sib crossing design  were generated among populations  and for each population pair (population pairwise QST) based on mixed model analyses. In these models, population, family and plastic containers were random factors. Variance components were extracted from these analyses for each trait and used for estimating QST using the following formula ) with bV being the trait genetic variance among populations. h2 was calculated based on all the families and populations by subspecies. Here, no environmental sources of phenotypic variance due to the ecological conditions of the location of origin of populations could in theory bias QST estimates because data were obtained from a common garden experiment , data were normalized by using a square root transformation. All variance components were estimated by using the linear mixed model approach implemented in the R package lme4 v. 1.1.17 , or whether it was more probably explained by directional selection  or by stabilizing selection . Comparisons between overall QST and FST values were performed for each trait based on two methods: (a) a comparison of confidence intervals (CIs), the QST is considered nonsignificantly different from neutral differentiation when the CI of the overall QST for a trait overlaps the mean FST value; and (b) a bootstrapping method developed by Whitlock and Guillaume , and quantitative genetic differentiation (population pairwise QST). They were run separately for each subspecies. First, a correlation test between population pairwise FST and population pairwise geographic distance matrices was performed to test for an isolation by distance relationship. Second, a correlation test between population pairwise FST and population pairwise QST was performed for each trait to test if neutral genetic differentiation explained divergence in quantitative traits. Third, a correlation test between population pairwise QST and population pairwise altitudinal differences was performed for each trait to test whether divergence in quantitative traits was related to altitudinal differences. Finally, we conducted partial Mantel tests to test for the association between population pairwise QST and population pairwise altitude differences, while controlling for neutral genetic differentiation (FST). All Mantel and partial Mantel tests were performed in R, with a significance threshold α = 0.05, using the vegan package  was lower than among populations , and ranged from 0.06 to 0.159 across population pairs (see Table FST among populations of A. m. striatum was 0.097 (p < .001), and ranged from 0.055 to 0.131  and the log of population pairwise geographic distance for either subspecies  vs. log distance: A. m. pseudomajus Mantel r = .04, p = .405, A. m. striatum Mantel r = −.18, p = .595). Similarly, there was no significant relationship between population pairwise FST and population pairwise altitude difference for either subspecies .Population neutral genetic differentiation was low but significant. Overall 31 Table . There ws Figure , althoug3.3A. m. striatum populations. Plants from populations at low altitude had more nodes and branches than plants from populations at high altitude for A. m. striatum  and altitude for two traits across m Figure . No phen3.4A. m. pseudomajus, and 0.01 and 0.89 for A. m. striatum (Table A. m. pseudomajus (0.83) and SLA in A. m. striatum (0.89). Several traits had similar heritabilities in each subspecies , as illustrated by their overlapping confidence intervals (CIs). However, this was not the case for other traits, i.e., there was no CI overlap , and for three traits in A.m. striatum , observed values of overall QST–FST differences were either in the tail of the expected probability distribution under the hypothesis of neutrality, or did not overlap with this distribution . This suggests that only traits with very high QST values could be tested significant for the QST–FST difference in our study because the confident intervals were very large for most overall QST estimates . This result is expected under the hypothesis that the divergence among populations of A. m. striatum in the number of nodes is a result of altitude‐mediated divergent selection. In contrast, none of the seven traits showed a significant correlation between population pairwise QST and population pairwise altitude difference in A. m. pseudomajus.Mantel tests showed a significant correlation between population pairwise es Table . For botT Figure . Partial4A. m. pseudomajus and A. m. striatum subspecies. We detected phenotypic differentiation in a common garden among populations of A. m pseudomajus, among populations of A. m striatum, and among subspecies. For both subspecies, local adaptation and neutral evolution explained the extent to which populations diverged over their geographic range, with slight differences between subspecies. Signatures of potential selection were found for few traits. Potential divergence along altitude was also detected, but only for one subspecies: A. m. striatum.Our results support the hypothesis of differential adaptation between QST–FST comparisons are a good first step for exploring the potential roles of divergent natural selection and neutral evolutionary processes in phenotypic divergence  was shaped by divergent selection between populations from different altitudes in 4.2A. m. striatum populations for three of the seven traits . They imply that adaptation to local sites of origin potentially shaped the phenotypic diversity of populations for A. m. striatum across their geographic range. We used classical overall QST–FST comparisons to detect potential adaptation to local sites conditions  was in fact more similar among populations than expected under neutrality in A. m. pseudomajus. A scenario of stabilizing selection is classically extrapolated in the case of similar results  has probably diverged among populations as a result of adaptation to local sites of origin. Divergence in the genetic variation underlying the shape and size of plants was previously found at the level of Antirrhinum species, though its adaptive significance was not tested (Langlade et al., In the absence of environmental measures included in the overall 4.4A. majus requires invoking a history of adaptation to local sites in a complex background of gene flow, ecological heterogeneity and reproductive isolation. The Pyrenees are widely acknowledged to constitute a heterogeneous landscape that promotes complex patterns of population connectivity and is prone to generate local adaptation (Alberto et al., QST–FST comparisons reflected a potential scenario of population divergent adaptation to contrasting environmental conditions between local sites of origins. Our findings also suggested that evolutionary signatures of local adaptation differed between A. m. pseudomajus and A. m striatum, as illustrated by the potential adaptation to altitude detected only for A. m. striatum. One might speculate that this divergence might be related to the distribution of A. m. striatum populations across a narrower range of climatic conditions, even if both subspecies share to a large extent the same ecological niche (Khimoun et al., Our findings imply that the most likely evolutionary scenario applying to A. majus populations and to some extent subspecies at the scale of their global geographic range. There is evidence for the genetic signature of restricted gene exchanges in A. majus (Pujol et al., A. majus subspecies' divergence might not be expected because both subspecies are interfertile (Andalo et al., Restricted gene flow or strong selection pressures are required for evolutionary divergence. Genetic drift, or foundation events by different gene pools, might have shaped differentially the genetic background of QST–FST approaches conducted in common garden experiments to explore potential adaptive evolutionary divergence among populations and between subspecies in plants. They also illustrate the limits of this approach that identifies traits that might be involved with local adaptation but which fails to provide direct evidence for their response to selection. Here, our common garden results for A. m. pseudomajus and A. m. striatum populations identified vegetative traits that might play a role in the local adaptation and the differential adaptation of A. m. pseudomajus and A. m. striatum along altitudinal gradients. They suggest that the adaptation to climate variables of these otherwise interfertile subspecies might differ as a result of reproductive isolation.In conclusion, our findings corroborate the utility of BP designed the research program. SM, JA, VB, ML carried out the experiments; AG and SM analysed the data; AG, SM and BP wrote the manuscript.Supplementary MaterialClick here for additional data file.


PLOS Genetics publications that exemplify recent progress in human genetic admixture studies, and we discuss potential areas for future work.Throughout human history, large-scale migrations have facilitated the formation of populations with ancestry from multiple previously separated populations. This process leads to subsequent shuffling of genetic ancestry through recombination, producing variation in ancestry between populations, among individuals in a population, and along the genome within an individual. Recent methodological and empirical developments have elucidated the genomic signatures of this admixture process, bringing previously understudied admixed populations to the forefront of population and medical genetics. Under this theme, we present a collection of recent  One of the major insights from the modern genomic era is the ubiquity of migration and admixture throughout human history –7. Admix—the population origins of material within a genome. This is related to, but distinct, from genetic similarity and from genealogical ancestry, with the relationship further discussed by Mathieson and Scally [Human population movement frequently lacks historical records. Many migration events have occurred through colonization or forced displacement, and ancient admixture often predates historical records. Therefore, genetic studies provide an opportunity to understand population history and the forces generating variation. Recent empirical work has shown that studying the genetics of a wider set of human populations can yield historical insights as well as medically relevant information about health and phenotypes. The mosaic ancestry patterns of admixed populations can also be used to elucidate the mechanisms and timescales of evolution in humans more generally. For example, ancestry patterns in admixed populations have been used to infer recombination rates –15 and td Scally .PLOS Genetics publications. This compilation is a limited selection of work that exemplifies recent key advances and stimulates discussion about priorities for the future.Here, we highlight recent progress and discuss future directions for the study of admixed human populations . The terBroader genetic sampling of populations worldwide is increasingly being combined with advances in theory and computational methods to elucidate human history.Genetic ancestry often varies along a chromosome within an individual and between individuals within an admixed population . SummariClassic statistical methods in population genetics typically rely on allele frequencies, patterns of linkage disequilibrium (LD), and interpopulation sequence differences. However, the admixture process may distort patterns of LD, break up runs of homozygosity, and combine allele frequency distributions from distinct parental populations –29. Yet,PLOS Genetics that improve our understanding of the demographic processes shaping genetic variation in admixed populations and human evolutionary history. Ragsdale and colleagues (2019) generalize 1- and 2-locus genetic summary statistics in a computationally efficient algorithm able to explore complex demographic models [A number of major theoretical and methodological advances have recently been published in c models . Other rc models . Methodoc models . Identifc models –38. Browc models –41. A rec models .Intricate sociocultural practices such as marriage customs, colonization events, and phenotypic preferences direct how parental populations interact to form admixed human populations. Whereas computational methods are increasingly considering these intricacies, empirical studies of admixed populations have already made substantial progress on fine-scale demography providing insight into how sociocultural practices shape the admixture process and within-region variation.Using these new methods, empirical studies have brought to the forefront within-continent variation, particularly for historically excluded and under-sampled populations. Analyses of genomic ancestry within the context of geography have revealed between and within continent population structure . For exaEmpirical studies such as these highlight the role social processes play in shaping genetic variation; for example, male and female demographic histories can differ. Considering population structure, sex-biased admixture, and effective population size changes, Font-Porterias and colleagues (2019) clarified the dynamic demographic history of European Roma groups, including showing that they share a common South Asian origin but have complex contributions from West Eurasian groups . AnotherSelective pressures, such as those from environments and pathogens, play an important role in genetic variation and disease risk. Admixture both obscures genetic signatures of selection in source populations and provides new genetic material upon which selection can rapidly act.Despite major progress on theory and methods to study demography in admixed human populations, methodological advances to study other processes such as adaptation and phenotypic variation remains an open area with substantial room for growth. Recent admixture may obscure genetic signatures of selection in the source populations by distorting linkage, rapidly changing allele frequencies, and breaking up homozygosity ,56. TherDARC locus is protective against malaria-causing Plasmodium vivax infection, which is estimated to be one of the strongest selective pressures in recent human history. The Duffy-null allele is nearly fixed in sub-Saharan African populations and mostly absent in non-African populations, producing a classic signal in which the DARC locus is an outlier in its proportion of local African ancestry in multiple admixed populations [DARC locus is not typical of human adaptation. These local ancestry outlier approaches likely miss many loci under weaker or polygenic selection or those not highly differentiated in the sources. Additionally, outlier approaches generate false positives due to drift or long-range LD and discard other genetic information along the genome. For example, in Western African rainforest hunter–gatherer populations, Jarvis and colleagues (2012) found reduced levels of switching between ancestry types in a genomic region that may contribute to adaptive phenotypes such as short stature [A common approach to identify candidate regions under selection post-admixture looks for outliers in local ancestry –21,59,60ulations . However stature . DespiteDespite the challenges of identifying phenotypically important loci and regions under selection in admixed populations, recent empirical studies have highlighted the importance of using admixed populations to understand how selective pressures shape genetic variation and disease risk. Discovering phenotypically important loci in admixed populations requires careful consideration of the data, including strategies to integrate genotypes, local ancestry, and information from source populations. Once phenotypically important loci are identified, admixed populations can provide insight into how these loci interact with different ancestral backgrounds and how characteristics of the source populations shape post-admixture selection.ApoE is the strongest known risk gene for late-onset Alzheimer disease; Rajabli and colleagues (2018) showed that variation in risk for Alzheimer disease across populations may be at least partially explained by the ancestral background interacting with the risk allele [In addition to looking for correlations between genotype and phenotype, phenotypically important loci in admixed populations can be identified by looking for correlation between local ancestry and phenotype when the phenotype differs in the source populations . A combik allele . The relk allele . In Latik allele . Going fNotably, these types of studies of quantitative variation and disease risk in admixed populations rely on careful consideration of the genotype and local ancestry calls, including reference-bias and characteristics of the source populations. Identifying phenotypically important loci in admixed populations is challenging in the absence of appropriate reference panels and knowledge of the source populations. The growing availability of genetic data from diverse groups shows that the unique LD structure of admixed groups and limited availability of suitable reference panels can particularly impair variant mapping and detection for novel or rare alleles. For example, Kowalski and colleagues (2019) recently analyzed a large cohort of phased genomes from African and Hispanic/Latino individuals (NHLBI TOPMed), revealing significant phenotypic associations for rare variants that were not detected with imputation using 1000 Genomes reference data .Contributing source populations also have their own distinct selective and demographic histories. Although recent admixture obscures genetic signatures of selection in the source populations, consideration of past selective pressures can help identify important loci and clarify the genetic basis of disease risk. For example, Yao and colleagues (2018) found that in African ancestral backgrounds, past selection for the Duffy-null allele may also contribute to population differences in plasma levels of chemokines involved in a variety immune processes and diseases . In IndiThe set of papers highlighted here exemplifies recent advances and important areas for future work in the study of genetic admixture and its roles in human evolution. Recent theoretical and methodological advances have improved our understanding of the dynamic and complex demographic histories of modern human populations. New empirical insights will continue to emerge with the development of approaches that consider complex sociocultural variables, account for within-population heterogeneity, and avoid reference bias. With increasing genomic and phenotypic data available from populations around the world, we are only beginning to characterize genetic underpinnings of traits, the importance of their genetic background, and mechanisms of adaptation. Importantly, as we move forward, the field must emphasize the inclusion of local communities, ensure that people maintain agency over their genomic information, prioritize infrastructure for science, and improve scientific theories by collaborating across disciplinary knowledge –75.


Rhizophora mucronata across a > 3,000 km coastal stretch in the Western Indian Ocean (WIO) including WIO islands. Based on 359 trees from 13 populations and using 17 polymorphic microsatellite loci we detected genetic breaks between populations of the (1) East African coastline, (2) Mozambique Channel Area (3) granitic Seychelles, and (4) Aldabra and northern Madagascar. Genetic structure, diversity levels, and patterns of inferred connectivity, aligned with the directionality of major ocean currents, driven by bifurcation of the South Equatorial Current, northward into the East African Coastal Current and southward into the Mozambique Channel Area. A secondary genetic break between nearby populations in the Delagoa Bight coincided with high inbreeding levels and fixed loci. Results illustrate how oceanographic processes can connect and separate mangrove populations regardless of geographic distance.Estimates of population structure and gene flow allow exploring the historical and contemporary processes that determine a species’ biogeographic pattern. In mangroves, large-scale genetic studies to estimate gene flow have been conducted predominantly in the Indo-Pacific and Atlantic region. Here we examine the genetic diversity and connectivity of  Efforts to fill this gap are valuable, as connectivity determines the genetic diversity and structure of populations, increases the effective population size, while high connectivity can replenish degraded sites. Insights in connectivity are therefore essential for effective conservation management. In a broader perspective, a better qualitative and quantitative understanding of mangrove connectivity would provide an interesting biogeographic comparison with systems that have a circumtropical overlap in their range but show distinct differences in their dispersal traits .In coastal and marine systems, where ocean currents may transport individuals over 100–1000 s of kilometres, population genetic approaches strongly promoted connectivity research during the last three decades. Interestingly, a recent quantitative assessment of this research revealed important taxonomic and geographical biases, with mangrove population connectivity having received minimal attention compared to for example fish, seagrasses and corals2. The global spatial coverage of mangroves is estimated at ~ 137,000 km2, spanning the territories of more than 100 countries3. Nevertheless, the consequences of climate change such as accelerated sea-level rise, storm intensification, and changes in temperature and precipitation regimes4, as well as human activities, such as urbanization, aquaculture and agriculture5, alter the distributional range of mangrove forests.Mangroves consist of woody salt-tolerant plants that are morphologically and eco-physiologically adapted to the dynamic conditions of the intertidal zone. They are found on coastal, estuarine and deltaic tidal flats, predominantly at tropical and subtropical latitudes7 and the dispersal of water-buoyant propagules  via near-shore, coastal, and open-ocean surface currents8. While pollination operates at local scales, propagule floating and viability periods of several months9 allow for local-to-global scale dispersal and connectivity12. The spatial scale of connectivity ultimately depends on the additive effect of different factors, such as propagule traits, the timing of propagule release, physical barriers, the availability of suitable habitats, and the spatiotemporal characteristics of the dispersal vectors13. Oceanographic features such as eddies and regions of convergence may constrain, delay or prevent gene flow between nearby populations, rendering the isolation-by-distance (IBD) model too simplistic to explain observed genetic differentiation14. Using nuclear microsatellite markers and a Lagrangian particle-tracking model, a strong genetic discontinuity was found between Rhizophora racemosa G. Mey populations at both sides of an oceanic convergence zone in the Gulf of Guinea15. Similarly, genetic structure of Rhizophora mucronata Lam. populations along the Malay Peninsula was explained by prevailing ocean currents and the species’ dispersal potential, and not by geographical distance17. Indeed, as more species are being studied, biogeographical “barriers” seem more permeable than previously thought and act as filters rather than impenetrable barriers18.Gene flow in mangroves is determined by insect, wind, and bird pollinationAvicennia and Rhizophora species in Southeast Asia24, Central and South America29, and East Atlantic31, with only limited attention for mangrove populations in the Western Indian Ocean (WIO) in general (but see32). Several large-scale mangrove genetic studies have considered samples from the WIO region21, but the majority of locations and the focus in these studies were centered on the Eastern Indian Ocean (EIO) region.Although the amount of mangrove genetic and phylogeographic data is increasing, most studies have focused on populations of Rhizophora mucronata has a distributional range stretching across mangroves of the Indo-West Pacific (IWP) region, from the East African shorelines to the Western Pacific33, with its southern limit in Southeast Africa and its northern limit in the Persian Gulf34  in the Western Pacific. It is the only Rhizophora species in the WIO33 and has one of the widest ranges of all mangrove species. Even-though its global conservation status currently is of ‘least concern’, its global population is decreasing due to residential and commercial development, logging and wood harvesting, agriculture and aquaculture, and potentially climate-induced habitat alteration36.f34 Fig. a. The noR. mucronata in the WIO region. This region is characterised by the bifurcation of the South Equatorial Current (SEC) and the complex circulation system of the Mozambique Channel that is dominated by energetic southward moving eddies38. As a null-hypothesis, we considered that over a broad geographical scale, R. mucronata populations in the WIO would comprise a single evolutionarily significant unit and hypothesise that (1) highest genetic diversity levels will be found for populations located in the core region of the species’ distribution, whereas lower levels of genetic diversity will be found for populations on remote small islands; (2) spatial patterns of genetic differentiation will align with patterns of dominant contemporary ocean currents. For this purpose, we sampled 13 R. mucronata populations distributed across the western extent of its range  to 0.416 (KEN) , the effective number of alleles was 1.5 (1.3–2), whereas the allelic richness ranged from 1.7 to 2.8 (at k = 34 gene copies) for comparison of allele diversity between populations . Within population FIS ranged from 0.381 (MOZ2) up to 0.690 (MOZ5) with all populations significantly inbred (Table R. mucronata were significantly high in most populations (S = 0.45–0.89), except for MOZ1 and ALD2 (Table FIJ of 0.271.The within population inbreeding coefficient 0.381 MOZ up to 0.0.381 MOZ up to 0.FIT of 0.609 (p < 0.001) and a population differentiation AMOVA-FST of 0.281 (p < 0.001)  for the grouping of the Aldabra-Madagascar populations  versus all other populations. When considering a hierarchy of three regions, highest values  were obtained when grouping East Africa , versus Aldabra and Madagascar, versus all other populations . A hierarchical AMOVA considering four regions (Table FRT = 0.185 (p < 0.001). Overall connectivity between populations is very low with an estimated Nm less than 1. Pairwise FST values between populations varied between 0.002 and 0.529 . A Mantel test for all 13 populations suggested isolation by distance , but with a low predictability for Euclidean distances (r2 = 0.24). A more precise test of IBD was performed at the level of individual pairwise kinship coefficients using four classes of increasing geographic distances. Significantly higher than average kinship values were found for the first distance class up to 260 km—average 136 km—, and the second distance class up to 500 km—average 385 km—. The ln-transformed b-slope was − 0.078 (p < 0.001) over a full range of 3008 km.Global inbreeding reached an AMOVA-1) Table . Global ns Table , namely Rhizophora mucronata trees from the Seychelles showed intermediate allele diversity, only few fixed loci, several nearly private alleles at high frequencies, and hence, did not separate as a group, unless along the third axis (11%). A population assignment test gave an average of 71% of the individuals assigned to their own population. However, MOZ1 and MOZ2 individuals could be assigned largely to other populations, whereas individuals from SEY were assigned solely to their own population.A PCoA of individual genotypic distances had both axes explaining a nearly similar amount of variance Fig. . A firsttructure analysis according to the Delta K estimation supported K = 3 clusters (Mean LnP(K) = − 7390, Delta K = 54) or nearly as likely also four clusters (Mean LnP(K) = − 6990, Delta K = 42), the latter with more admixed individuals  appeared less differentiated from those of the African coastline. K = 11 subdivided up to population level, except for East Africa , an admixture in Mozambique (MOZ1 to MOZ4) and on Aldabra . When testing for diversity differences between the K3 clusters, a higher allele diversity (p = 0.011) and gene diversity (p = 0.002) was found in the two East African sites (Ar = 2.8 and He = 0.410) than in the sites of the Mozambique Channel and SEY (Ar = 2.3 and He = 0.280) and Aldabra/Madagascar (Ar = 2.1 and He = 0.200). When considering K4, there were only few individuals (N = 29 out of 359) containing but a limited number of private alleles (PA = 24), all at lowest frequencies such that these private alleles do not explain much of the obtained genetic structure.A Sigrate-n was used to estimate the migration pattern along the Mozambique Channel which was best supported by a unidirectional stepping-stone model (Table Nem = 10.89) and MOZ2 to MOZ1 (Nem = 11.51). All other Nem estimates of more distant populations were below 1. Further specific testing on directionality according to the SEC and on a most likely historical source of the Aldabra population and mainland African coast for migration between the Seychelles (SEY); northern Madagascar (MAD2), Aldabra (ALD1) and the African coast (MOZ2) gave the best support for a stepping-stone model (Table Nem = 0.81) and from Madagascar towards Aldabra (Nem = 0.78). Historical connectivity between Aldabra and the African coastline reached Nem = 0.65.Mel Table . The direl Table , with hiFST matrices), and to a lesser extent (ii) the Seychelles from Aldabra with seven counts, the Seychelles from East Africa (KEN) with six barrier counts, Aldabra from northern Mozambique (MOZ1) with six barrier counts, and the Southern Mozambican population MOZ4 from MOZ5 with 4 barrier counts. Contemporary kinship values of individual R. mucronata trees among populations reached an overall FIJ = 0.102, though with large variation between pairs, namely with high values for most pairs of neighbouring populations (FIJ ranging from 0.111 to 0.289) except for those pairs where a genetic break was observed (FIJ ranging from zero to 0.051). The latter breaks along the East African coastline were between TAN and MOZ1, MOZ4 and MOZ5, MOZ5 and MOZ6. The Seychelles population had zero kinship with Aldabra and Northern Madagascar.The most relevant genetic barriers Fig. , betweenR. mucronata populations of East Africa as more recent than those of Madagascar and the Seychelles. The estimated divergence time of R. mucronata populations of the eastern WIO island populations (SEY and MAD2) appears approximately twice as old. Considering a generation time of about 20 years for R. mucronata, albeit with much potential overlap between generations, the divergence times along the East African coastline could be rather recent and roughly within the Holocene.Comparing the five scenarios of the ABC approach, the highest value of posterior probability (0.338) and 95% confidence interval (CI 0.329–0.346) was obtained for scenario 3 Fig. . This inRhizophora mucronata from the Western Indian Ocean (WIO) showed a clear genetic structure, largely corresponding to the dominant ocean currents in the region. A major genetic break was apparent between populations on the islands of Aldabra and northern Madagascar, and those on the mainland African coast, whereas the granitic Seychelles (located > 1100 km northeast of Aldabra) showed a slightly separate position. Genetic connectivity usually can be expected up to a few 100 s of km. However, genetic breaks were also found on a smaller spatial scale near the bifurcation of the South Equatorial Current (SEC) and between southernmost populations of the Mozambique Channel. The highest levels of genetic diversity are found along the East African coastline where a bifurcated SEC reaches the continent, whereas the lowest allelic richness was found for populations on remote small islands and in peripheral populations in the southernmost region of the Mozambique Channel. High levels of inbreeding were noticed within most sites, as a result of a mixed mating system of the species.Information retrieved from nuclear microsatellites in R. mucronata populations in the WIO would comprise a single evolutionarily significant unit, brought along with the SEC, and originating in a transoceanic manner from either nowadays well-differentiated Southeast Asian21 or Australian sources12. Combined conservative chloroplast DNA and nuclear ITS data indicated a deeper Australian origin12, whereas microsatellite markers reflected contemporary genetic structures21. Within the WIO we found no evolutionary signals over a 3000-km distance, neither in the microsatellite allele sizes (RST = zero) nor in private allele frequencies, which indicates more recent colonization compared to the Eastern Indian Ocean (EIO) and expansion of R. mucronata in the region. The share of a large number of common alleles could eventually suggest a stronger influence of environmental conditions during the Last Glacial Maximum  on the genetic structure within the WIO. The demographic evolution scenarios that we tested were from diverse locations featured by separate gene pools as shown in our Structure and Barrier analysis. ABC tests indicate that the Seychelles and Madagascar (including Aldabra) gene pool, within the context of the currently studied populations, most likely resulted from ancestral migration events, relatively, about twice as old when compared to the gene pool of more westerly located populations on the East African coastline. All events were estimated at only a few thousand generations ago, hypothesised approximately a timeframe within the Holocene, though the latter needs to be thoroughly tested using large-scale phylogeographic studies and preferably based on maternally inherited chloroplast genome sequences information. We assume that population expansion history of R. mucronata in the WIO is characterised by at least two separate ancestral gene pools, namely on the granitic Seychelles and on Madagascar. More recent gene pools may account for the detected patterns along the East African coastline.Prior to our study we considered the null-hypothesis that over a broad geographical scale, AR, HS, FST, Dest) and structure variables  consistently evidenced a contemporary genetic break between mangrove populations of northern Madagascar and Aldabra versus all other populations, as well as the Seychelles granitic islands, reflecting a complex colonization history of the islands. Coastal areas in northern Madagascar may have been populated by sea-faring propagules transported from the east, via the SEC. Despite the westward directionality of the SEC, broadly along these Madagascan populations, this would require dispersal over 6000 + kilometres, as the most proximate large mangrove populations are situated in Southeast Asia or northern Australia. However, direct and reciprocal dispersal across the Indian Ocean may occur via the SEC and seasonally reversing monsoon currents, particularly for propagules with longer floating and viability periods40, such as found in R. mucronata9. The latter hypothesis of reciprocal dispersal should be tested with materials from the northern part of the WIO. When approaching the central eastern coast of Madagascar, the SEC branches into the North-East Madagascar Current (NEMC) and the South-East Madagascar Current (SEMC)  4; Fig. 1.R. mucronata populations in Aldabra and northern Madagascar strongly suggest that the Aldabra Atoll was populated from nearby sources on northern Madagascar. Aldabra populations are extremely isolated by their position on a small and remote island, far away from more extensive and continuous mangrove stands. Moreover, mangroves occur within the lagoon where conditions are more favourable for establishment34. The unfavourable conditions of the atoll’s outer rim could drastically reduce the establishment of propagules from source locations such as northern Madagascar, located about 420 km southeast of the Aldabra Atoll. Hence, any input of genetic material from other populations via long-distance dispersal is assumed to be seldom throughout its recent history. Allelic data shows that colonisation occurred on more than one occasion, as suggested also for terrestrial vertebrates such as giant tortoises42. These cycles of recolonization were interrupted by variations in sea level, which have strongly affected the atoll’s land area and environment, with a complete submergence during the last interglacial period (ca. 125 ka BP)43.The characteristics of these currents, the genetic similarities and migration scenario’s between R. mucronata populations contain more than two alleles for most polymorphic loci, thus beyond the maximum allele diversity that a single diploid founder may bring along. Allele size differences within the Seychelles were not larger than for other WIO populations, indicating either an admixture of populations or in-situ mutations to explain their multiple allele status. While the SEC provides an important vector for the transoceanic transport of propagules, previous studies reported that the zonal flow of the SEC creates a barrier for connectivity between the granitic Seychelles and more southerly positioned populations44. This is consistent with a genetic “isolation” of the granitic Seychelles from Aldabra and Northern Madagascar, although various migration models indicated a historical role of the Seychelles in the connectivity between islands. Moreover, the ABC analysis supported both the Seychelles and Madagascar as an ancestral divergence, which might explain the support of a unidirectional model in Migrate-n albeit with very low gene flow values. Mangrove populations on the Seychelles are likely maintained by propagules from sources of the northern part of the WIO, via the SECC. During the winter monsoon, the Somali Current flows southward, meets the northward flowing EACC, supplying water for the eastward flowing SECC41. This confluence and eastward flow occur at latitudes of the granitic Seychelles and enables propagule transport from mainland mangrove populations to these islands. This could not be detected from nuclear microsatellite markers and additional chloroplast sequence information could provide valuable information for unravelling patterns of connectivity between the Seychelles, Madagascar and other African populations. Nuclear genes and chloroplast sequences of Bruguiera gymnorrhiza (L.) Lamk. indicated a relatedness of Madagascar populations to those of the EIO45 and cpDNA of the widespread Xylocarpus granatum J. Koenig showed a most common South China Sea haplotype in several WIO populations46.At least more than a single propagule reached the granitic Seychelles or, alternatively, different cycles of colonisation may have occurred, because there, 40. However, the spatial density and spread of our sample sites did not allow us to test the latter scenario.A north-to-south gradient of allelic richness characterised the African mainland sites with highest diversity values in Kenya and Tanzania, north of the SEC bifurcation. The mangrove sites influenced by the EACC appear to have received and sustained more migrants than other areas although, based on our ABC analysis, this appeared to have occurred during more recent rather than ancestral times. The SEC very likely sustained these populations with a historically accumulated input of migrants from remote easterly mangrove populations, located within or beyond the WIO. While this scenario could suggest the expectation to observe the highest genetic diversity in (northern) East Madagascar, spatial differences in the availability of suitable geomorphic and sedimentary settings for mangrove establishment may present higher chances of opportunistic colonization on the East African coast compared to the higher-energy coast of East Madagascar. Importantly, migrants from eastern populations may have also arrived via populations in the Arabian Sea and Gulf of Bengal, as suggested in previous studiesR. mucronata populations with the highest kinship values, and even appeared as a single gene pool from the Structure outcome at K11. Similar evidence of high connectivity alongside this part of the East African coast was also observed in other taxa, such as the stony coral Acropora tenuis47 and the seagrass Thalassia hemprichii48. Likewise, a higher genetic diversity and stronger connectivity was observed for the populations of northern Mozambique (MOZ1 and MOZ2) as compared to the more distant southernmost populations in the Mozambique Channel. Overall, the core region of mangrove populations, situated at latitudes of the SEC , seems to harbour the most diverse R. mucronata populations. Such enhanced diversity levels in a zone of largest and most direct—perpendicular—SEC influence could become a more general hypothesis because Avicennia marina mangroves also showed highest allele diversity in Kenya and Tanzania in comparison to South Africa32. Strong genetic differentiation between populations along the northern East African coast (TAN and KEN) and populations located in the Mozambique Channel  corresponds with the SEC bifurcation. These distinct gene pools most evidently originate from a historically different accumulation of migrants and the opposite directionality of ocean currents hampering mutual propagule exchange50. One may assume that besides the SEC sorting migrants at bifurcation, subsequent differentiation and sorting in a stepping-stone manner may have occurred northwards and southwards along the East African coastline.Over a 225 km distance, KEN and TAN were the most connected igrate-n analysis indicated MOZ2 as a population with detectable Nem input. Experimental and modelling studies in the Mozambique Channel have demonstrated the capability of cross-channel transport between Mozambique and Madagascar within 19–30 days, likely facilitated by the frontal zones between eddies and the associated interstitial waters of the turbulence field50. This potential for cross-channel transport was also demonstrated in a large-scale WIO population genetic study of the seagrass T. hemprichii, suggesting Madagascar as a potential source for main African populations48. The remote Seychelles population could be occasionally connected to northern Madagascar and to the coast of the African mainland, though the input source of migrants will be far too limited to leave a detectable trace in a vast area of existing mangrove forests. Whereas it appears that the Seychelles would share a common gene pool with MOZ1 and MOZ2 for K3, all SEY individuals were 100% assigned to their own population.The geographical position of MOZ2 at the southern bifurcation of the SEC and starting point of eddies in the Mozambique Channel could be hypothesised as a crossroad. A genetic connectivity from the eastern part of the Mozambique Channel (northern Madagascar or Aldabra) towards MOZ1 and MOZ2 could not be detected with certainty, but only indirectly inferred; the MR. mucronata populations are poorly differentiated and all share a common genetic cluster at K3. This gradient and connectivity is likely to be maintained by the generally southward flowing current along the Mozambique coast51 and the anti-cyclonic circulation pattern in the region, which is believed to facilitate an almost random dispersal and homogenise populations along the East African coast54. Despite the dominance of southward flow in the Mozambique Channel, propagules might be transported both southward and northward through the Channel within periods of 51–207 days, via the frontal flow field between eddies51. These time spans are within the range of buoyancy and viability periods observed in R. mucronata propagules9. Along the southward component of the SEC, and further south along the Mozambique Channel, a gradient was apparent in R. mucronata populations for their lowered allelic richness and gene diversities, increased inbreeding levels, number of fixed loci and an admixed assignment of individuals for genetic clustering (cf. K4 and K11). The connectivity between neighbouring populations decreased  and kinship values dropped to zero between MOZ4 and MOZ5, also indicated by low gene flow estimates and a genetic break situated on the Delagoa Bight. An extensive chain of mangrove forest can be found from approximately the northern Kenyan border further southwards up to about 50 km south from the mouth of River Save on the Sofala bank in Mozambique34. However, beyond this point, mangrove forests line the coasts in a less continuous manner and are mostly found in more sheltered inlets34. The populations MOZ3 to MOZ6 are situated in this area of non-contiguous mangrove forests. MOZ3 lies only 100 km south of a 1900 km2 mangrove stand fringing the Sofala Bank, which is considered the largest mangrove area in the region55 and hence might provide a higher number of propagules for dispersal. MOZ4, MOZ5 and MOZ6 are located ca. 300–800 km from the extensive Sofala Bank mangrove stand. Therefore, we hypothesise that migrants from the Sofala Bank favour higher diversity in MOZ3 and MOZ4 when compared to the Delagoa Bight populations, MOZ5 and MOZ6.Within the Mozambique Channel, 51, flowing southward without entering the Delagoa Bight. Additionally, the Delagoa Bight is characterised by mesoscale features such as countercurrents and eddies56 and consists of a large extended shelf area where lies a quasi-permanent lee eddy57. These features may retain propagules from populations in this area (MOZ5 and MOZ6) and prevent dispersal offshore58. A sharp gradient in genetic diversity was also observed in the spiny lobster Panulirus homarus and separated even evolutionary units of subspecies at the Delagoa Bight59. It is most probable that both the lee eddy of the Delagoa Bight and the jet current prevent propagules from northward populations to strand in the Delagoa Bight. Subsequently, the mixed mating system of R. mucronata may promote high inbreeding values and many fixed loci as were noticed in the well-connected populations MOZ5 and MOZ6.The genetic break between MOZ4 and MOZ5, despite their geographic proximity, can be explained by the dynamic hydrography near the Delagoa Bight area. MOZ3 and MOZ4 are located on a protruding land area, where the continental shelf is practically inexistent. A jet current  passes just along MOZ4R. mucronata site is most likely the result of geitonogamy as in R. racemosa60. This can blur the determination of a genetic structure at the population level because occasional fixation of private alleles might lead to enforced clusters of gene pools. However, this situation was not observed in the WIO because R. mucronata populations shared common alleles throughout the region, such that allelic richness (AR) and gene diversities (HE and Dest) determined the detectable patterns of connectivity at population level. High within-site kinship values and estimated selfing rates can be explained from their high frequencies of most common alleles. Rhizophora mucronata is a self-compatible species61. Similar inbreeding levels for R. mucronata have been reported in Southeast Asia and were attributed to the reproductive biology and dispersal strategies of the species16. Interestingly, lower inbreeding values were found in populations of R. racemosa in West Africa15 than in R. mucronata. Rhizophora racemosa’s fruits resemble those of R. mucronata, which suggests that besides reproductive strategy, propagule morphological traits, and dispersal strategies, other factors such as local topography and species zonation (landward/seaward) may influence the local inbreeding level of these coastal populations13.Inbreeding within nearly each R. mucronata of the WIO should be considered as a recent colonization (in comparison to the EIO) with an expansion zone that has particularly led to areas of unicity, of higher diversity, of inbreeding, and of contrasting connectivity even over short distances, which all are relevant in a transboundary context. Anticipating the ever-increasing need for international cooperation regarding the conservation and management of dispersive coastal and marine species, we encourage the integration of available genetic data and models to further increase our understanding of the interplay between the ecology and evolution of mangroves, large-scale  connectivity, and aspects such as species distributions and long-term persistence.In conclusion, our study highlighted that Rhizophora mucronata samples were collected in 13 locations  were performed with the QIAGEN Multiplex PCR kit and consisted of 12.5 μL reactions with 2.5 μL H2O, 6.25 μL of Qiagen Multiplex PCR Master Mix, 1.25 μL primer mix and 2.5 μL DNA. The primer mix consisted of 2 μM of each fluorescence-labelled  microsatellite primer . PCRs were carried out in a BIO-RAD T100 thermal cycler , starting with an initial activation step of 15 min at 95 °C, followed by 35 cycles of 30 s at 94 °C, 90 s at 57 °C and 90 s at 72 °C, and ended with a final extension of 10 min at 68 °C. Fragment analysis was done by Macrogen Inc., after which allele sizes were assessed with the software Genemarker 2.4.0 .Total genomic DNA extraction was performed on 30 mg of dried leaf material using the E.Z.N.A. SP Plant DNA kit  for subsequent microsatellite analysis. A total of 50 previously published microsatellite primer pairs of 68 and no significant genetic linkage (p < 0.05) was found. No scoring errors or large allele size dropouts were indicated using Micro-Checker69, whereas a general excess of homozygotes for many loci suggests the presence of null alleles. This test, however, does not consider the effect of inbreeding situations and therefore INEST 2.270 was applied as a Bayesian approach for estimating both null alleles and inbreeding simultaneously71. The model was run with 50,000 burn-ins and 500,000 cycles and supported for each population a model including inbreeding rather than a null allele model. Three parameters were used for the model comparison: n, null alleles; f, inbreeding; b, genotyping failure. With these three parameters, six models are composed: n, b, nf, nb, bf, nfb . The six models were run with 50,000 burn-ins and 500,000 cycles for each population. A comparison of these models with the lowest Deviance Information Criterion (DIC) outperforming the other models, selected nf (in 8 populations), nbf (in 4 populations) and bf (in 1 population) as best fit, hence putting inbreeding instead of null alleles as a most likely explanation for the lack of heterozygotes. The probability of identity, using Genalex 6.572, was checked for a combination of all loci and ranged from 5 × 10–3 to 9 × 10–8. Multilocus repeats were seldom, namely 16 repeats for 9 MLG’s out of 359 samples. All repeated MLG’s were kept for further analysis, considering the INEST indications of inbred situations.Prior to full data analysis, all pairs of microsatellite loci were tested for linkage disequilibrium with FSTAT 2.9.3 softwareAM), effective number of alleles (AE), observed heterozygosity (HO) and unbiased expected heterozygosity (HE) were calculated using Genalex. Total number of alleles (A), allelic richness (AR), deviation from the Hardy–Weinberg Equilibrium and the within population inbreeding coefficient (FIS) per population were obtained with FSTAT. Deficiency of heterozygotes was assessed after Bonferroni correction using FIS values with 221,000 randomisations. The selfing rate was estimated and based on a standardised identity disequilibrium, assuming a mixed mating model using spagedi 1.573. A population assignment (with leave-one-out option) was done for their proportion of 'self' or 'other' populations with Genalex. The overall pairwise kinship coefficient (FIJ)74 was estimated for all comparisons at the ‘within population level’ using spagedi. To strengthen the evidence at both large and small geographical scales, we intentionally conducted most approaches at the individual level, because population level analysis might become skewed in case of strong inbreeding.The percentage of polymorphic loci per population, mean number of alleles (FST) was performed with Genalex. Additionally, all combinations within a hierarchy of 2, 3 and 4 groups were tested for highest % variance among regions (FRT). To estimate the effects of microsatellite allele sizes, an AMOVA-RST was compared to AMOVA-FST. Pairwise genetic differentiation (FST) and allelic differentiation (Dest) between all populations were estimated and tested for significant differences (999 permutations) using Genalex. A principal coordinate analysis (PCoA) at individual level based on genotypic distances was performed in Genalex. The underlying pattern of genetic structure was determined with Structure 2.3.275, using the admixture model with correlated allele frequencies without prior population information. We carried out 10 replications for 1 ≤ K ≤ 15 with 1 × 105 Markov Chain Monte Carlo (MCMC) iterations and 1 × 104 burn-in. The optimal K was inferred with the ΔK statistic76 and LnPK using Structure Harvester77 calculated with Structureselector78. Two-sided tests for comparisons among three groups  were performed (1000 permutations in FSTAT) to test for differences in their allelic richness, heterozygosity, inbreeding and differentiation. Private alleles were checked for the K = 3 and K = 4 clusters in Genalex.An analysis of Molecular Variance  was used for the genetic distance matrix. To test for genetic IBD at the individual level, an analysis of the pairwise kinship coefficients (FIJ)74 was done using spagedi, namely a spatial autocorrelation for different classes of geographic distances  with estimation of an ln-transformed b-slope over the full range of 3008 km. The overall pairwise kinship coefficient was estimated for all comparisons at the ‘among population level’. The software Barrier 2.279 was used to detect the location of sharp genetic changes between neighbouring populations on basis of one overall pairwise FST matrix and 17 pairwise FST matrices of every microsatellite locus allowing a maximum of two barriers per matrix. To test for these potential genetic breaks specifically between neighbouring populations, an analysis of the pairwise kinship coefficients (FIJ)74, was done for each case of only among population’ pairwise estimation  using spagedi.To detect whether there is a decrease in connectivity among populations with increasing geographic distance, a Mantel test was performed  in Gigrate-n81 was used to estimate the mutation-scaled population sizes (Theta) and immigration rates (M). The Brownian model was tested locus by locus along with the product of distributions of all loci for all individuals in a population. Uniform prior distribution settings  were Theta = 0.0, 10.0, 0.1 and for M = 0.0, 100, 10.0. The number of recorded steps was 106 at a sampling frequency of 103 after an initial burn-in, computing two replicate chains, and using the Bezier thermodynamic integration81 for calculation of the Bayes factors from marginal likelihoods giving model probabilities. The effective number of immigrants per generation (Nem) was calculated as [Theta × M]/428. Panmixia, bidirectional and unidirectional stepping-stone historical migration/expansion models were used for testing the hypothesis of a North to South migration in the Mozambique Channel (MOZ1 to MOZ6). Panmixia, source-sink or stepping-stone models were considered to test a specific hypothesis on directionality for migration among the island populations Seychelles (SEY), northern Madagascar (MAD2) and Aldabra (ALD1), and towards the African mainland (MOZ2).MR. mucronata populations of the WIO was carried out on seventeen microsatellites considered as a single group with dinucleotide repeats, using the Approximate Bayesian Computation (ABC) approach implemented in DIYABC ver. 2.082. The aim of this analysis was to answer a main phylogeographic question that was raised from the genetic Structure and the Migrate-n outcome, namely about the ancestral origin of eastern WIO island populations  and of coastal African populations (East to Southern Africa). Therefore, we considered representative allelic rich R. mucronata populations within each of the of five regions, namely KEN, MOZ2, ALD2, MAD2 and SEY to conduct a model where we specifically conceptualised five scenarios of demographic history 2 distance) were considered for each population or pairwise comparison of samples. Five million simulation data sets were run for the prior distribution and the most-likely scenario was obtained from a comparative assessment of their posterior probabilities. One million simulated data sets were used to compute posterior, of which 10,000 were used in the local regression. From the posterior 1,000 data sets were simulated. The goodness-of-fit was checked through principal component analysis (PCA) using the ‘model checking’ option. The posterior distribution of parameters (N# and t#) was estimated.In all scenarios, t# represents the time scale measured in number of generations (largest numbers refer to oldest events) and N# represents the effective size of the corresponding populations during the indicated time period. We used default prior values for all parameters, except for the maximum population size and maximum values of time scale  based on the outcome of the preliminary test runs of prior distributions. Summary statistics of 65 variables (mean number of alleles, mean gene diversity, mean allele size variance, Supplementary information.