Garcinia kola Heckel (Clusiaceae) are popular products in West and Central Africa. Despite the tree’s economic and cultural importance, little is known about its phenotypic and genotypic variation. This study characterised the morphological and genetic diversity of G. kola in South Cameroon, searching for traits and populations that might be used for domestication. Morphological assessment and amplified fragment length polymorphism (AFLP) markers were applied to characterise diversity among geographic populations from Central and South regions, and between managed and wild trees. AFLP-SURV and analysis of molecular variance results indicated that a major part of genetic diversity is harboured within populations rather than between them. Bayesian analysis, principal component analysis and t-SNE identified three clusters where Ebolowa emerged as the transition population combining features from both regions. Trees from the South demonstrated a higher incidence of domestication-related traits, showing higher genetic diversity compared to the Central region. This suggests that individuals from the South might be more suitable for selection as “elite trees” in future breeding strategies for the species. No significant differences in phenotype and genotype were revealed between wild and managed populations, suggesting G. kola is still in the early stages of its domestication process.Seeds and bark of  Garcinia kola Heckel (Clusiaceae), commonly referred to as bitter kola, is a multipurpose agroforestry tree species native to Africa’s Western and Central regions [ regions ,2,3. The regions ,5.Bitter kola is of compelling economic and folk medicinal value to rural communities, significantly contributing to households’ livelihoods. It is most valued for the medicinal properties of its seeds, bark and leaves ,7,8. TheG. kola seem to be declining, and the species is classified as “vulnerable” by The International Union for Conservation of Nature (IUCN) [G. kola is usually propagated by seedlings; however, due to seed dormancy systems, seed germination is known to be difficult [G. kola was prioritised in a Participatory Tree Domestication Programme by World Agroforestry (CIFOR-ICRAF) [G. kola is still poorly understood. Existing information gaps are limiting the potential improvement of the tree [G. kola [G. kola [However, the natural populations of e (IUCN) . This sie (IUCN) ,16,17. Gifficult . Studiesifficult ,20; noneR-ICRAF) ,22. Despthe tree . Only a the tree ,24 and g[G. kola ; however[G. kola ,27.G. kola by comparing different geographic populations and trees from wild and managed landscapes.Bitter kola has been recently described as an incipiently domesticated species, implying that the tree is still in the early stages of its domestication process . HoweverG. kola in Nigeria, suggesting that adaptation to local climatic factors has a significant role in the genetic diversity of the species. Similarly, Dadjo et al. [In farming systems, high genetic diversity could be the key to increasing crops’ resilience, helping to deal with emerging challenges such as climate change . The geno et al.  reportedG. kola populations in the Central and South regions of Cameroon using AFLP markers and morphological descriptions. The objectives were to (i) assess the species’ morphological and genetic diversity over various geographic populations; (ii) compare the morphological and genetic diversity between managed and wild populations; (iii) identify morphological traits and potential “elite trees/populations” to advance the domestication process.This study assessed the morphological and genetic variation of Based on means of our measurements, an average bitter kola tree is about 14.3 m high, with a trunk of 5.4 m, 60 cm in DBH (diameter at breast height) and 8.9 m in crown diameter. More than half of the trees had a pyramidal crown shape (54.2%), followed by oblong, elliptical and spherical types  . The pyrBased on farmers’ estimation, trees in the Centre region were much older than the ones in the South, being on average 49 years old compared to 34 years in the South . The bigOn average, a bitter kola fruit weighed 167.3 g, its diameter was 6.8 cm with a length of 8.6 cm, and it contained 2.5 seeds. The most common shape of the fruit was spherical, followed by ellipsoid and flattened shapes  . The othIn terms of diameter, the fruits were alike. The only differences appeared in the length of the fruits . Major dAn average bitter kola seed weighed 5.5 g, was 3.1 cm long and 1.6 cm wide. The most common seed shape was oblong-elongated, followed by ellipsoid and oblong  . The othMajor differences were detected in the weight of seeds . Lekie-ABased on the t-SNE analysis, Central and South regions can be well separated according to both trees’ quantitative and qualitative morphological features . The EboDividing the sampled trees based on their growing site (managed and wild stands), significant differences were discovered in tree DBH, tree height and trunk height . This deA total of 1299 loci were amplified with the four primer combinations , with thTotal gene diversity across all populations, according to AFLP-SURV, was moderately low , a model-free method to infer several clusters of genetically related individuals. Cross-validation retained 40 principal components for further analysis . AccordiThe plot based on principal component analysis (PCA) confirms a similar trend in clustering, with populations from the Central region clustering apart from the South populations, except for individuals from Ebolowa, scattered over the plot .Based on genetic diversity indices, growing site had only a small influence on the genetic makeup of the population, where managed trees showed slightly higher genetic diversity than wild trees  . Low valG. kola morphological and genetic variation in Cameroon.This study represents the first quantified description of G. kola from Benin also revealed low levels of genetic diversity [All populations exhibited moderately low levels of genetic diversity as expressed by percentage of polymorphic loci and Nei’s gene diversity . These viversity , which tiversity ,28. The iversity .G. kola individuals belong to three genetic clusters, differentiating South and Centre regions and converging in Ebolowa. This distribution was confirmed by t-SNE analysis of morphological characteristics  and genetic introgression between two different countries in Europe [Differences between the Centre and South regions seem to be influenced more by genotype than external conditions. Ebolowa, as a population manifesting both South and Centre morphological and genetic parameters, might be the result of a human-mediated gene flow. Ebolowa is the capital city of the South region, connected to the Cameroonian capital city Yaoundé by the main road. This motorway is a thoroughfare between the two cities and their markets. Bitter kola seeds are traded and distributed along the way as well as in the main city markets, which may explain why Ebolowa’s geographic population forms a kind of transition between the two South and Centre clusters. It is not uncommon for widely traded indigenous fruit trees to have high genetic diversity in urban centres, due to mixing planting materials from diverse regions . A similn Europe .Domestication of indigenous fruit trees is a multifaceted process based on a close interaction between people and the environment. Effective tree improvement requires an understanding of the morphological and genetic variation background of the species, which helps to select its human-desired characteristics ,37.G. kola utilisation—raw seeds consumption.Most trees (around 85%) in our study were sampled from agroforestry systems, i.e., plots with cocoa and oil palm or from homegardens. A higher proportion of trees in wild stands was found in the South region. However, this did not bring any morphological and/or genetic variation. Based on genetic diversity indices, the growing site factor had only a small influence on the genetic makeup of the population, and managed trees only showed slightly higher genetic diversity than wild trees . The onlOn the contrary, differences in these important domestication characteristics were revealed to occur between the Centre and South regions. Even though we did not identify major differences in fruit morphological traits between regions, trees from the South region proved to bear an increased number of seeds and have higher seed mass as well as seed mass ratio. Seeds were also heavier and greater in length compared to those of the Centre region. These results suggest that the trees from the South region might be more suitable for selection as “elite trees” in future breeding improvement of the species. Based on the genetic data analysis, the above-mentioned phenotypical differences are influenced by genotype more than by external factors.G. kola trees was locally detected, most trees are harvested from managed land use systems. Because no significant differences in phenotype and genotype between wild and managed populations were identified, we assume the domestication of bitter kola is still in its initial stage. However, its broad gene pool, not influenced by major human interference, is very promising for the future improvement of the species.Even though no major effort to select superior G. kola, further studies should expand on what is more favourable to the farmers and bitter kola consumers; is it a higher number of smaller seeds in a fruit or a smaller number of bigger seeds? What is more lucrative, and how does the price vary with the seasonality of the product? What is the consumers’ taste preference, and how does it differ on a socioeconomic and geographical level? For example, should we rather search for sweeter tastes or bitter varieties? The desired fruit ideotype has to reflect the specific market demand [To proceed in elite tree selection, a number of morphological discrimination criteria have to be defined first . In the t demand ,39.G. kola’s dioecious cross-pollinating nature [G. kola . Even though there are studies proving that bitter kola might be propagated by stem cuttings [Due to the g nature , there ag nature ,42. If ag nature . This dycuttings  and grafcuttings , these tAnother restricting factor is the role morphological and genetic markers play in order to find superior genotypes because they cannot reflect the social, ecological and economic value of the species ,45. To eG. kola. Both regions belong to the agroecological zone IV  and are dominated by hilly landscapes exceeding an average altitude of 600 m a.s.l. The climate is classified as tropical rainforest (Af) according to Köppen-Geiger [The study was conducted in the Central (Centre) and South regions of Cameroon, as a part of the Congo basin tropical forest, covering the zone of both natural distribution and intentional cultivation of n-Geiger , with ann-Geiger . Soils in-Geiger .G. kola fruits in 2018 and 2019. To unify the term referring to the sampling areas, we decided to use “geographic populations”. That means that within each region, there are four or five distinct geographic populations  [Adansonia digitata L.) [Individual trees were measured and described based on descriptors adapted from mangosteen (tana L.)  and baobtata L.) . Tree hetata L.) . Tree agtata L.) .Genomic DNA was extracted from the dried tissue using a modified CTAB method , followed by purification with 3 M sodium acetate and precipitation with absolute ethanol. The concentration of extracted DNA was measured using a NanoDrop 2000  spectrophotometer, and all samples were diluted to a final concentration of 500 ng/µL.EcoRI and MseI, and respective adaptors were ligated to the splicing sites with T4 ligase. The reaction mixture contained 500 ng of DNA, T4 ligase (67 U) and T4 ligase buffer, EcoRI and MseI , EcoRI and MseI adaptors  and H2O in a final volume of 20 µL. The mixture was incubated at 37 °C for 4 h, followed by 65 °C for 20 min and finally stored at 4 °C. The efficiency of the restriction reaction was tested by gel electrophoresis on a 2% agarose gel stained by Ethidium Bromide (EtBr) and run at 90 V for 1 h.AFLP analysis was performed following the methodology of Vos et al.  with somThe restriction–ligation (RL) product was diluted tenfold and used for preselective amplification using a pair of primers compatible with the adaptors with one selective nucleotide . The reaEcoRI primer was fluorescently labelled with 6-FAM. The PCR composition included the Qiagen Multiplex PCR Master Mix, two primers and the preselective amplification product. The cycler profile was identical to the one used for preselective amplification.After an initial screening of 24 primer combinations, four selective primer combinations were chosen for selective amplification. The preamplification products were again diluted tenfold and used for PCR amplification with four combinations of primers with three selective nucleotides each , whereinThe selective amplification products were separated by capillary electrophoresis on a 3500 Series Genetic Analyzer . The fragment analysis results were visualised using Geneious Prime 2020.1.1 software.t-test with a significance level α = 0.01 for each morphological value. Afterwards, t-distributed stochastic neighbour embedding (t-SNE) [The statistical importance of the factor “location” on the measured morphological values was evaluated in SPSS 23.0 employing one-way analysis of variances (ANOVA) and Tukey’s tests at a significance level α = 0.05 for each morphological value. All collected data were tested for normality and homogeneity of variance by Levene’s and Shapiro tests. The analysis of the difference of the mean values was performed in Wolfram Mathematica using the hypothesis testing package with the two-sided  (t-SNE)  was used (t-SNE) . Analysi (t-SNE) . To reve (t-SNE) , a crossG. kola from South Cameroon. All populations exhibited moderately low levels of genetic diversity, with most of the variation harboured within populations rather than between them. However, the two compared regions, Central and South, were clearly different in both morphological and genetic analyses. Trees from Ebolowa emerged as a transition population combining traits from both Centre and South clusters, which might result from a human-mediated gene flow.This study revealed differences and similarities in morphological and genetic diversity of The growing site factor had a small influence on the genetic makeup of the populations. The only significant morphological differences between wild and managed populations were related to tree habit rather than fruit productivity traits—seed number, seed mass and seed mass ratio. However, individuals from the South demonstrated a higher incidence of these domestication-related traits and higher genetic diversity compared to trees from the Centre region. These results suggest that the individuals from the geographic populations of the South region might be more suitable as “elite trees” for future breeding strategies.G. kola is still in its initial stage. However, its broad gene pool, not influenced by major human interference, is very promising for the future improvement of the species.The absence of significant differences in phenotype and genotype between wild and managed populations suggests that domestication of


Hakea (Proteaceae), and evaluated the extent to which genetic diversity was captured in seeds. We genotyped hundreds of seedlings and mother plants via DArTseq, and developed novel implementations of two approaches to inferring the mating system from SNP data. A striking contrast in patterns of genetic diversity between H. sericea and H. teretifolia was revealed, consistent with a contrast in their mating systems. While both species had mixed mating systems, H. sericea was found to be habitually selfing, while H. teretifolia more evenly employed both selfing and outcrossing. In both species, seed collection schemes maximised genetic diversity by increasing the number of maternal lines and sites sampled, but twice as many sites were needed for the selfing species to capture equivalent levels of genetic variation at a regional scale.Plant mating systems shape patterns of genetic diversity and impact the long-term success of populations. As such, they are relevant to the design of seed collections aiming to maximise genetic diversity . However, for most species, little is known empirically about how variation in mating systems and genetic diversity is distributed. We investigated the relationship between genetic diversity and mating systems in two functionally similar, co-occurring species of  In plant conservation and restoration, maximising genetic diversity in seed collections aims to support the persistence of re-established populations by promoting their adaptive potential ,2,3. In The mating system is likely to be a major determinant of the extent to which seed collected under different strategies reflects the genetic diversity of standing populations. When mating systems are more selfing (vs. more outcrossing), it has been suggested that more effort is required to reliably capture genetic diversity both within populations  and amonWhile landscape-scale genomic data are increasingly applied to inform ecological restoration and conservation practice ,21, therHakea sericea and Hakea teretifolia, common shrubs of temperate Eastern Australia. We took a two-tiered approach to sampling. First, we sampled adult populations throughout the distributions of the species to characterise patterns of genetic diversity in the landscape. Then, we sampled wild plants and their progeny at a subset of sites to explore relationships between the genetic diversity, mating system, and the genetic diversity captured using different seed collecting approaches within and among sites. Tools to infer the mating system in incompletely sampled families from large SNP datasets are still emerging, so we used two approaches for estimating selfing rates from mother–seedling progeny arrays. The first was a reimplementation of Ritland’s )Data were processed and downstream analyses were carried out using R. Prior to using the SNP data for downstream analysis, matrices were filtered based on several quality criteria using an in-house R package (see ). In sumIS) and SNPrelate [ST).Genetic diversity measures were calculated from the filtered SNP data using the R packages diveRsity  , using We aimed to investigate mating systems in the context of seed collections for restoration and conservation, specifically asking how many of the progeny collected from 10 known mothers per site were produced via selfing vs. outcrossing. Several software tools exist for investigating mating systems, including MLTR ; also se. Second,Statistical significance of differences in We used progeny array data to evaluate the range in genetic diversity captured by subsamples of seedlings according to different hypothetical sampling strategies . We resampled replicate subsets of seedlings for each species by randomly drawing seedlings among maternal lines and sites according to the sampling schemes described below (custom scripts).The within-site sampling schemes drew ten replicate subsamples of eight seedlings: one seedling from each of eight mother plants, or two seedlings from each of four mother plants, or four seedlings from each of two mother plants, or eight seedlings from a single mother plant. For each replicate subsample, pairwise Euclidean distance matrices were calculated and the mean of each matrix taken to express the genetic diversity captured per subsample per site. Euclidean distances were chosen to measure genetic diversity for their simplicity, lack of assumptions, and demonstrated concordance with other more complex metrics ; small wAmong-site sampling schemes drew 100 replicate seedling subsamples from among all seed collection sites per species. We compared the effect of sampling fewer vs. more sites with a scheme drawing one seedling from each of eight maternal lines per site, across increasing numbers of seed collection sites. For among-site subsamples, we evaluated allele capture by calculating whether both alleles at polymorphic loci in adult wild-sampled individuals were also present in each seedling subsample. We considered alleles occurring at ≥0.5%, 1%, 2%, and 5% frequency in mother plants (seed collection sites only), and all wild plants . Alleles rarer than 0.5% were not considered due to the possibility of genotyping error in this frequency range.


Pseudacris). Specifically, we estimated the frequency of hybridization among three taxa, assessed genetic structure within the focal species, P. feriarum, and ascertained the directionality of gene flow within P. feriarum across replicated contact zones via coalescent modeling. Through field observations and preliminary experimental crosses, we assessed whether hybridization is possible under natural and laboratory conditions. We found that hybridization occurs among P. feriarum and two conspecifics at a low rate in multiple contact zones, and that gene flow within the former species is unidirectional from allopatry into sympatry with these other species in three of four contact zones studied. We found evidence of substantial genetic structuring within P. feriarum including a divergent western allopatric cluster, a behaviorally‐distinct sympatric South Carolina cluster, and several genetically‐overlapping clusters from the remainder of the distribution. Furthermore, we found sub‐structuring between reinforced and nonreinforced populations in the two most intensely‐sampled contact zones. Our literature review indicated that P. feriarum hybridizes with at least five heterospecifics at the periphery of its range providing a mechanism for further intraspecific diversification. This work strengthens the evidence for cascade reinforcement in this clade, revealing the geographic and genetic landscape upon which this process can contribute to the proliferation of species.Species interactions drive diverse evolutionary outcomes. Speciation by cascade reinforcement represents one example of how species interactions can contribute to the proliferation of species. This process occurs when the divergence of mating traits in response to selection against interspecific hybridization incidentally leads to reproductive isolation among populations of the same species. Here, we investigated the population genetic outcomes of cascade reinforcement in North American chorus frogs (Hylidae:  Here, we investigated the population genetic outcomes of cascade reinforcement, a powerful mode of diversification, to provide the first insight into species‐wide patterns of population genetic differentiation and gene flow that lend support to theoretical predictions. This work advances our understanding of how complex species interactions contribute to the proliferation of species and the potential for rapid diversification in our geographically‐ and genetically‐unique study system. The preReinforcement, the evolution of premating isolating barriers in response to selection against hybridization  represent an ideal model to assess the population genetic predictions of cascade reinforcement. The Upland chorus frog (Pseudacris feriarum) diverged from its congener, P. nigrita, ~8 mya . These estimates of gene flow had substantial error margins, however, and a later estimate of hybridization in one contact zone suggested substantially lower levels of admixture  species can increase the power of cascade reinforcement to accelerate diversification and the evolution of reproductive isolation  What degree of genetic structure is present within the focal species P. feriarum?, (3) What is the directionality of gene flow across replicated hybrid zones between P. feriarum and P. nigrita?, and (4) Does the focal species P. feriarum hybridize with other congeneric taxa at the periphery of its range? We build upon Lemmon and Juenger's . AHE is a hybrid enrichment (sequence capture) approach for recovering hundreds of unique, genome‐wide orthologous loci for resolving evolutionary relationships within nonmodel systems on both shallow and deep scales  described by Banker et al. . Orthology was assessed using pairwise sequence distances as described by Hamilton et al.  a Florida data set of n = 285 individuals from the Florida contact zone and adjacent areas, identified in the field as P. feriarum (n = 207), and P. nigrita (n = 78), and (3) a South Carolina data set of n = 452 individuals from the South Carolina contact zone and adjacent areas, identified in the field as P. feriarum (n = 280), P. nigrita (n = 92), P. brimleyi (n = 73), and captive‐bred P. feriarum–P. brimleyi F1 hybrid (n = 7) individuals, for model validation  a Range‐wide data set of on Table . After ion Table  to remov−6, and 100 cross‐validation repetitions; default settings were employed for other parameters. Analyses were conducted assuming K = 1 to K = 8. The optimal K‐value was identified using the “fastChooseK.py” script implemented in Structure_threader. This approach generates a range of most likely K's bounded by the result of a maximum likelihood estimation and the result of a model complexity estimation , (5) South Carolina P. feriarum and P. nigrita samples (n = 259), and (6) South Carolina P. feriarum and P. brimleyi samples . DAPC is an unsupervised‐supervised clustering method that relies on the selection of different sets of alleles that best describe the genetic similarities among samples. The linear allele combinations are known as Discriminant Functions (DFs) that maximize the genetic variation among clusters while minimizing the diversity within clusters. The data set was formatted into a Structure file with PGDSpider v2.1.1.5 in order to analyze it in the R package “adegenet”  as a metric to select the most likely number of clusters, which we assessed from K = 1 to K = 20 via 1 × 106 replicates. For each of the most likely Ks, 100 cross‐validations were performed  to obtain the minimum number of principal components (PC) that best described the variation within the proposed clusters while reducing the chance of model overfitting. Finally, DFs were generated for each likely K in the data set (function “dapc”). Each individual was assigned to a cluster by selecting the two most informative DFs.Discriminant analysis of principal component  using the “keep” function in VCFtools  and Tajima's D were calculated and visualized using the main function “calcdiversity” in the package “SambaR”  and mutation‐scaled gene flow rates (M) among populations  Can the species produce viable hybrid offspring?, (2) Do hybrid crosses have lower hatching success (#tadpoles hatched/#eggs produced × 100) than pure parental species crosses? For this experiment, wild‐caught frogs were crossed under laboratory conditions described in Lemmon and Lemmon . Parents were collected from sympatry in Colleton and Dorchester counties, South Carolina , with only 2.2% ambiguous/missing characters. The average locus contained 930 sites.3.2P. feriarum, P. nigrita, and P. brimleyi. Analyses using fastSTRUCTURE identified low levels of admixture between P. feriarum and P. nigrita in all three data sets, and between P. feriarum and P. brimleyi in the South Carolina data set  in Florida and 1.2%  in South Carolina but estimated at 0.74% in South Carolina between P. feriarum and P. brimleyi  between P. feriarum and P. nigrita were present, as well as one F1 hybrid backcross to P. feriarum  were found between P. feriarum and P. brimleyi in addition to F1 P. feriarum and P. nigrita hybrid backcrosses to P. feriarum , sympatric and most allopatric P. feriarum formed a second cluster (green), and western allopatric P. feriarum formed a third (purple). The split of P. feriarum into two groups at K = 3 corresponds to a deep divergence at the base of the P. feriarum phylogeny between the western allopatric cluster and the remainder of the species. The other best model, K = 4, introduced an additional cluster supported at very low percentage (~0.024%) in all individuals, and thus did not contribute further to explaining population structure  and sympatric (lime green) clusters , both of the two best models of genetic structure , and sympatric and allopatric P. brimleyi formed a second group (light blue gray), but sympatric and allopatric P. feriarum were clustered into separate groups , the best model of the genetic structure reflects the significant genetic divergence between sympatric s Figure . Under ts Figure , sympatrP. feriarum samples (Range‐wide data set) reinforces the findings from the regional analyses of multiple species above and identifies significant genetic divergence among populations across the species range. The Range‐wide P. feriarum analysis found the highest support for two models , Edisto (SC), and James/Anna (VA) contact zones . The third cross produced seven tadpoles from 140 eggs and the fourth produced 36 tadpoles from 60 eggs. One of the two P. brimleyi male × P. feriarum female crosses produced no viable eggs, and the other produced two tadpoles from 60 eggs. Of the three pure P. brimleyi crosses, one produced no viable eggs, the second 3 tadpoles from 80 eggs, and the third produced 33 tadpoles from 70 eggs. Of the five pure P. feriarum crosses, all produced tadpoles . In the crosses that produced no tadpoles, no amplexus was observed, but instead, the female apparently released her eggs without fertilization (Table p = .195), likely as a consequence of low statistical power due to the small number of replicate crosses , and we did not detect advanced hybrids beyond the second generation. The focal species is structured genetically, containing multiple genetic clusters spanning contact zones with other species, with genetic sub‐structuring between reinforced and nonreinforced populations. We identified two highly divergent genetic clusters within P. feriarum, a phylogenetically‐ancestral western allopatric group and a recently‐derived sympatric South Carolina group. The latter group corresponds to populations that show strong behavioral reproductive isolation from the rest of the species . Gene flow is generally unidirectional within P. feriarum, moving downstream from allopatry into areas of sympatry with other species in replicate river drainages. Our field observations and laboratory experiments revealed that in sympatry, P. feriarum has frequent interactions with closely‐related species at the breeding sites and is capable of producing viable hybrid offspring with these taxa. In sum, our study indicates that although P. feriarum breeds syntopically with and can generate viable offspring by mating with closely‐related taxa, hybridization now occurs infrequently.Our work revealed that hybridization is uncommon but widespread among chorus frogs undergoing reinforcement. The focal species in our ongoing cascade reinforcement studies, 4.1P. feriarum and two congeners. With many divergent SNP markers, we have high power to accurately detect hybrids and substantially decrease the margin of error on hybridization estimates. Lemmon and Juenger  compared with geographically‐distant population pairs , interpopulation crosses  result in fewer viable offspring, which prevents alleles for coevolved, sympatric reproductive traits from spreading outside the contact zone , where gene flow occurs asymmetrically from one species (I. lacunosa) to a sympatric congener (I. cordatotriloba). Crucially, this asymmetry in interspecific gene flow contributes to the enhancement of reproductive barriers both between sympatric I. lacunosa and its sympatric congener and allopatric conspecifics  in silico estimates demonstrate that reproductive isolation with respect to other species increases during cascade reinforcement. This prediction is consistent with work involving two‐species interactions that drive cascade reinforcement in killifish, where sympatric females discriminate against both heterospecific males and foreign conspecific males  contributing to behavioral and genetic divergence of a reinforced population of a third species (P. feriarum).Theory predicts that a rapid species radiation can result from cascade reinforcement when conspecific populations experience different heterospecific assemblages , then reinforced traits are likely to reproductive isolation ; project administration ; supervision ; writing – original draft (lead); writing – review and editing . Oscar Ospina: Data curation ; formal analysis (lead); validation ; visualization (lead); writing – review and editing (supporting). Peter Beerli: Formal analysis (supporting); methodology ; software ; writing – review and editing (supporting). Alan Lemmon: Data curation ; formal analysis ; methodology ; software ; writing – review and editing (supporting). Sarah E Banker: Investigation ; resources (supporting); writing – review and editing (supporting). Alyssa Bigelow Hassinger: Investigation ; resources (supporting); writing – review and editing (supporting). Mysia Dye: Formal analysis (supporting); resources . Michelle L Kortyna: Investigation ; resources (lead). Emily Claire Moriarty Lemmon: Conceptualization (lead); funding acquisition (lead); project administration ; supervision ; writing – original draft (supporting); writing – review and editing .The authors declare no competing interests.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.Figure S5.Click here for additional data file.Figure S6.Click here for additional data file.Figure S7.Click here for additional data file.Tables S1–S36.Click here for additional data file.


Senegalia senegal, a savannah tree species in sub-Saharan Africa. Three phylogroups are identified corresponding to Sudano-Sahelian, Zambezian and Southern African biogeographic regions in West, East and Southern Africa. Genetic diversity was highest in Southern and Zambesian and lowest in the Sudano-Sahelian phylogroups. Using species distribution modeling, we infer highly divergent future distributions of the phylogroups under three climate change scenarios. Climate change will lead to severe reductions of distribution area of the genetically diverse Zambezian (− 41–− 54%) and Southern (− 63–− 82%) phylogroups, but to an increase for the genetically depauperate Sudano-Sahelian (+ 7– + 26%) phylogroups. This study improves our understanding of the impact of climate change on the future distribution of this species. This knowledge is particularly useful for biodiversity management as the conservation of genetic resources needs to be considered in complementary strategies of in-situ conservation and assisted migration.Global climate change is proceeding at an alarming rate with major ecological and genetic consequences for biodiversity, particularly in drylands. The response of species to climate change may differ between intraspecific genetic groups, with major implications for conservation. We used molecular data from 10 nuclear and two chloroplast genomes to identify phylogeographic groups within 746 individuals from 29 populations of  Major drivers of intraspecific genetic variation include climatic shifts during the Quaternary, ecological gradients, demographic processes and anthropogenic land-use change6. Changes in geographic ranges driven by fluctuations in climate have fragmented or reconnected populations of the same species7. This can lead to spatial structuring of genetic diversity in distinct gene pools2 where a phylogeographic discontinuity is interpreted as evidence for range fragmentation during past climatic changes8.Contemporary patterns of genetic variation have become a crucial aspect of research in biogeography, conservation and evolutionary biology10. There is sufficient evidence that modern climate change is reshuffling the geographic distributions of plant and animal species world-wide12. Populations or species react to climate change by either persisting, dispersing to suitable ecological conditions, or going extinct, which influences the amount and distribution of intraspecific genetic diversity13. Despite the increasing evidence for global climate warming, model predictions of how climate will change in the course of the twenty-first century is treated with some level of uncertainty14. This is addressed by assuming that emissions will follow one of several scenarios known as the Representative Concentration Pathways (RCPs)17. Global climate change projections show a strong warming trend over the twenty-first century and Africa has been identified as one of the regions of the world most vulnerable to climate change18. Particularly in sub-Saharan Africa, temperature is expected to rise by approximately + 2.0 to + 4.5 °C by 210020.Recent advances in phylogeography and landscape genetics have focused on identifying and understanding genetic structure, including the influence of geographic and environmental heterogeneity, on spatial genetic variation with possible implications for forecasting the distribution of gene pools under climate change22, resulting in major partitions into the Sudano-Sahelian , Zambezian (East Africa) and Southern African biogeographic regions23 for both plant and animal species22. Prominent historical barriers include the Mega Lake Chad, Dahomey Gap, East African Rift valley, Ethiopian highlands, Adamawa Highlands, Namib desert and Kalahari desert25, hindering dispersal and gene flow among regions. Documented evidence shows that tropical savannah and woodland trees potentially played an important role in forest assemblages during the Last Glacial Maxumum (LGM)22. Phylogeographic studies of these tree species have been instrumental in unravelling the role of past environmental changes in the structuring of genetic diversity found in contemporary populations22. Recently, new insights into the geographic distribution and range dynamics of many African savannah tree species have been provided by the analyses of ecological data, while additional insights come from analysing the pattern and distribution of genetic diversity within species27. In some species, intraspecific phylogeographic patterns mirror biogeographic patterns. For example, Prunus africana (Hook.f.) Kalkmanis genetically structures into Sudanian, Zambezian and Southern gene pools26, while genetic discontinuities in the form of parapatric genetic clusters were detected in the Sudanian savannah for Vitellaria paradoxa C.F. Gaertner and Parkia biglobosa (Jacq.) R.Br ex G. Don29. However, the absence of clear-cut genetic discontinuities over large distances has been reported for other species, including Adansonia digitata Linn.30, Khaya senegalensis (Desr.) A. Juss.22, Afzelia africana Sm. ex Pers. in the Sudanian, and Afzelia quanzensis Welw. in the Zambezian region8. Although some of these studies investigated the impact of historical perturbations on genetic patterns31, there is a lack of studies into the genetic consequences of climate change for the future distribution of species and their intraspecific groups.The savannah belt of sub-Saharan Africa is affected by significant climatic, ecological and geological barriersS. senegal (L.) Britton , commonly known as gum arabic, one of the most important tree species in the tropical woodlands of sub-Saharan Africa both economically and ecologically32. Senegalia senegal has an extensive geographic coverage on the African continent and is common to habitats that experienced past range contraction and expansions due to Pleistocene climatic fluctuation , population sizes, structure of gene pools, and natural regeneration patterns of the species at regional scale39. Investigating the impact of climate change on GD of S. senegal will provide insights into population structure, and can further advance our understanding of the genetic consequences of post-glacial expansion processes and climate warming on the future distribution of genetic variation. We here use a comprehensive data set of nuclear and chloroplast microsatellite data and combine phylogeography, landscape genetics and distribution range modeling to investigate how future climate change will affect the distribution of phylogeographic groups (hereafter referred to as phylogroups) and their gene pools across Africa. Specifically, we address the following questions:40.How is phylogeographic structure reflected in levels of genetic variation within populations? Population expansion often leads to the loss of genetic variation due to genetic drift or bottleneck effects. We thus hypothesize that within-population GD declines with distance from the proposed origin of the range expansion towards West AfricaS. senegal correlate with genetic differentiation among populations? Landscape characteristics , past range changes and habitat fragmentation can influence population genetic structure10, and heterogeneity in landscape features has been shown to impact genetic variation in east African populations of Senegalia mellifera24. Thus, the highly contrasting ecological conditions across sub-Saharan Africa are expected to be reflected in patterns of genetic variation. We hypothesize that population genetic differentiation is correlated with geographic and environmental distance resulting in both patterns of isolation by distance (IBD) and isolation by environment (IBE).How do environmental conditions in the local habitat of S. senegal? Climatic changes may lead to conditions that fall outside the current environmental tolerances of populations and may trigger either a geographic range shift, ecological adaptation or extinction40. Assuming niche conservatism , populations will need to track their current environmental tolerances by geographic range shift. Depending on the overall magnitude, direction and rate of climate change, species may suffer potentially severe reduction of suitable habitat and in turn loss of population GD. We predict that the ongoing climate change will lead to a loss of GD due to an overall reduction of range size. We use a population genetic approach, with an improved marker resolution and larger sample size compared to previous attempts38 to refine the phylogeographic structure of African S. senegal. In addition, we model the potential impact of climate change on the distribution of phylogeographic groups of S. senegal using SDMs. This study will increase our understanding of the spatial structuring of genetic variation including how phylogeographic groups may respond under future environmental change scenarios.What are the genetic consequences of future climate change for Here, we assess the impact of climate change on intraspecific lineages of a savannah taxon using ion Fig. 33,34, anS. senegal were collected  loci yielded 20 haplotypes across the 29 populations from which samples of ted Fig. . BetweenA strong phylogeographic division between the Zambezian-Southern ranges (haplotypes H1-H11) and the Sudano-Sahelian range (haplotypes H15-H20) is visible in the haplotype network , whereas hierarchical AMOVAs, using the STRUCTURE clusters as groups (see below) found increasing variance among clusters when more clusters were considered, starting at 43% (ФRT = 0.427) at K = 2, and 48% (ФRT = 0.485) at K = 3, reaching 90% (ФRT = 0.901) at K = 7  and Sudanian (dark blue) biogeographic regions and the Southern African region separated into Namib (SOU1) and Kalahari (SOU2). At K = 6, the Sudano-Sahelian range was further divided in the western-most , the southern  and the central  populations. At K = 7, the Zambesian range split into two gene pools, representing north-western (ZAM1) and south-eastern (ZAM2) populations. At K = 7, the major mode comprised 67/90 (74%) of the STRUCTURE runs . Hierarchical AMOVAs revealed increasing variation among clusters with increasing K, ranging from 17% (FRT = 0.171) at K = 2, 20% (FRT = 0.201) at K = 3 up to 25%  significantly declined with distance from the assumed East African origin of the range expansion into West Africa . This pattern is consistent with range expansion from East to West with accompanying bottlenecks and genetic drift, while this was not the case for the Eastern and Southern ranges. The sub-range means for nuclear markers  indicate that the diversity of haplotypes was significantly higher in the Southern range, with a total of 11 haplotypes present compared to eight and six in the Sudano-Sahelian and Zambezian ranges  of variation  and for the combined Sudano-Sahelian and Zambezian subrange (r2 = 0.29), pointing to an older or more established relationship in Zambezian—Southern than in the Sudano-Sahelian—Zambezian ranges  but not significant in the Zambezian—Southern . However, our data did not show a significant correlation in the Zambezian—Southern range indicating that both parameters are acting in concert with the other in the Sudanian—Zambezian range but maybe independent in the Zambezian—Southern range  and isolation by environment (IBE) in the genetic structuring of S. senegal. Our SDM projections predict different impacts of climate change on the distribution of the phylogeographic groups under future environmental change scenarios with evolutionary older groups being most drastically affected.In this study, we used molecular markers to highlight the phylogeography and population-level genetic diversity in the African distribution range of He = 0.56) are very similar to levels obtained in previous analyses of microsatellite markers of S. senegal populations in western Africa (He = 0.54–0.56)44 but slightly lower than in the eastern range in Kenya (He = 0.617)6. This may be due to the use of slightly different sets of markers and differences in sample size. Within our study, genetic diversity estimates were highest in the Zambezian and the Southern ranges, the latter showing highest diversity of plastid markers among the subranges. This finding corroborates earlier analyses finding higher diversity in eastern6 and in southern Africa38 compared to Sudano-Sahelian populations in Western Africa. Genetic diversity in Southern Africa may even be underestimated as indicated by the presence of null-alleles in that region. A distance-dependent decline of diversity from Eastern to Western Africa indicates evolutionarily younger populations are affected by, e.g., bottlenecks and genetic drift during range expansion from East to West Africa38. In addition, increasingly unfavorable climatic conditions may have contributed to drift as indicated by the observed IBE. A similar pattern of an East-to-West decline of genetic diversity across Africa was found in P. africana26. However, contrasting patterns with higher diversity in the Sudanian are known for other tree species45. Thus, the relationships found across sub Saharan Africa corroborate a general pattern of diversity clines that are correlated with climate driven range changes, as e.g. observed in the Mediterranean46.Range-wide heterozygosity levels obtained from this study  to east (Ethiopia) sub-Saharan Africa27, our data show a strong genetic separation between West Africa, i.e. the Sudano-Sahelian biogeographic regions, and East and Southern Africa, i.e. Zambesian and Southern African biogeographic regions. Molecular data were largely congruent between nuclear and plastid genomes at the range wide scale, both showing similar regional genetic structuring and phylogeographic patterns.Although 38, and thus corroborate an evolutionary origin of the West African S. senegal in the eastern region. The scenario of colonization from East to West Africa is supported by the relatively small genetic distances (< 4 mutations) from the two dominant West African haplotypes , suggesting that these haplotypes have originated from H8 in the East. Assuming relatively stable environmental conditions during the time frame of range expansion, dispersal from East Africa likely has occurred via Ethiopia-Sudan-northeast Nigeria and westwards to the rest of the Sudano-Sahelian distribution range.Our population level GD data are in line with phylogenetic evidence of a sister group relationship between West- and East-AfricaPT = 0.733) and nuclear markers (FST = 0.287) and higher than previous values for regional assessments47 due to differentiation among phylogroups at the range level, in particular as we included the Namib region in our analysis (Table 39 and eastern (0.045)47 Africa respectively. The K = 3 partition in STRUCTURE largely reflected the phylogeographic regions of Sudano-Sahelian, Zambezian and Southern ranges. Pairwise FST revealed that the highest differentiations occur between populations from Sudano-Sahelian and Southern ranges, consistent with a range-wide pattern of isolation by distance. Population genetic differentiation can result from evolutionary processes of mutation and genetic drift, demography, anthropogenic disturbances, geographic isolation and limited or restricted gene flow among populations48. The high GD coupled with endemic gene pools in the Southern range may be due to barriers to gene flow, triggered by the heterogeneous complex mosaic of landscapes38 among the surrounding populations. In addition to demographic history49, spatially heterogeneous landscapes in most cases support greater genetic and species diversity52. The presence of mountains  and inhospitable landscapes  within the sampled range in Namibia, might be acting as a barrier, thus limiting gene flow and facilitating differentiation in local populations. Greater genetic diversity increases the likelihood that appropriate adaptive variation will be available to facilitate adaptation to the new conditions53. Our results suggest gene flow limitation between regions resulting in considerable genetic differentiation among phylogeographic lineages. However, populations have remained connected within phylogroups throughout the large, continuous Sudano-Sahelian or Zambezian savannahs.Overall, population differentiation was high at both chloroplast  between populations of S. senegal. IBD patterns are commonly found in large scale analyses of widespread plant species55 unless either gene flow or drift are dominating57. Isolation by environment (IBE) patterns are caused by environmental heterogeneity and local adaptation related to strong divergent selection59. Although IBE patterns for S. senegal were generally weak on a range-wide scale, slightly stronger ecological isolation observed at the subrange level could be explained by either difference in the occupied environmental space, i.e., regional environmental space differentiation27, or selection and local adaptation61. Thus, populations that might have dispersed to other suitable ranges within Africa over time adapted to the prevailing local conditions through selection. This result suggests that IBE in S. senegal is primarily driven by differences in temperature during the wettest months in the species local habitat. In addition, different flowering times were observed among population during fieldwork to collect samples. It should be noted that differences in temperature regimes between populations might cause differences in phenology, with reduced overlap of flowering time potentially leading to partial reproductive isolation62. This pattern of reduced overlap in reproductive timing known as isolation by time63 may additionally be a contributing factor driving genetic structure in S. senegal.In deciphering the role of geography and environment as drivers of genetic structure, we found strong relationships between genetic differentiation and both geographic distance (IBD) and environmental distance (IBE), with IBD explaining the majority of genetic differentiation and IBE also contributing significantly. The IBD model assumes that genetic differentiation increases among populations with geographic distance due to limited gene flow and driftS. senegal, we quantified and mapped declining and expanding phylogroups that are projected as a result of the twenty-first century climate change. In principle, the projections across the three future climate scenarios present a complementary pattern of range gain for the Sudano-Sahelian phylogroup and range loss for the Zambezian and Southern phylogroups , represent areas where highest levels of genetic variation are to be expected. Anthropogenic climate warming is projected to cause the disappearance of phylogroups in these regions of overlap for the next 50-plus years across all scenarios considered in this study, consequently leading to the loss of GD . Generally, our results show evidence for severe future range loss for the evolutionary older populations (Zambezian and Southern), with a negative impact on unique genetic variation of these phylogroups in comparison to the evolutionary younger and genetically depauperate Sudano-Sahelian phylogroup11. The mid-portion of the currently occupied native range of the Southern phylogroup might be lost to the expanding Sudano-Sahelian phylogroup during the intermediate scenarios and even more severe range retraction during the RCP 8.5 scenario, leaving extant populations as fragments in the Namib region as well as in the Mediterranean scrub of the south-eastern tip of Africa. The extremely localized high-elevation populations that harbour highest levels of genetic diversity within the whole species range occur in the Namib region (Spitzkoppe and Brandberg mountains) and might be at risk. Overall, a range reduction of the Zambezian and Southern phylogroup and potential range extension of the Sudano-Sahelian phylogroup would result in a movement from North to South, in contrast to a South to North movement observed in the northern hemisphere66.The highly genetically diverse Southern populations of on Table , Fig. 5.68. Our model highlights that the Zambesian and Southern phylogroups of S. senegal will be at risk due to climate change. Three options may allow them to contend with rapidly changing environments: dispersal, phenotypic plasticity, or adaptation70. Although S. senegal is known to exhibit potential for long-distance dispersal38, it is uncertain whether phylogroups, e.g., populations from the Sudano-Sahelian phylogroup might be able to track a suitable habitat, predicted to occur outside of its currently occupied range in the far Southern range. However, high levels of GD observed for S. senegal in this study might ultimately determine the fate of the species through a rapid adaptive change should, for example, the species be incapable of a plastic response. Greater GD increases the likelihood that appropriate adaptive variation will be available for adaptation to the new conditions. Evolutionary adaptation, however, will need time, and large and reproductively active populations. Therefore, regions with highly diverse extant gene pools predicted to be extirpated or go extinct in the future (e.g. Southern phylogroup), should be high priority areas for both in situ and ex situ conservation. Our model predicted largely non-overlapping distribution areas for the phylogroups based on environmental suitability and thus may guide assisted migration programs. However, while our modeling provides predictions on where the phylgroups would find suitable climate in the future, we propose that common gardens should first be established across different biogeographic regions to prove adaptedness prior to the implementation of assisted migration using the most appropriate seed material72.There is growing concern over the global rate of environmental change. This situation has raised further concerns about whether organisms, especially plant species can keep track, by migration or evolution, with the predicted changing distribution and spatial arrangement of suitable habitat73. If the projected range loss of S. senegal leads to fragmentation, then these changes have severe implications for the species through genetic drift, gene flow and inbreeding depression75. This will require enhanced conservation efforts, including proactive and intensive management, to provide greater flexibility for the species to respond successfully and avoid extinction. We hope that our study will provide a clear directive on the genetic consequences of climate change on this economically and ecologically important savannah tree species and that the understanding from the findings will support the development or re-designing of effective conservation strategies in S. senegal, in particular considering intraspecific genetic groups and their individual predicted fate. Furthermore, several other tree species have similarly wide distributions across the drier parts of Africa77. Therefore, it would be interesting to investigate whether the phylogeographic patterns as well as the potential impact of future climate change observed for S. senegal can serve as a model for other tree species occupying the savannah type environments. Finally, we hope our study will be of interest to biodiversity stakeholders and can be integrated in the conservation programs of the United Nations Environment Programme (UNEP), United Nations Convention to Combat Desertification (UNCCD), Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), National Biodiversity Strategy and Action Plan (NBSAP) and important plant areas (IPAs).Additionally, forecasting large-scale species distributions is becoming a crucial component for conservation planning, especially for ecological and commercially relevant speciesS. senegal encompasses tropical woodland, open savannah and semi-desert steppe, encompassing biogeographically the Sudano-Sahelian region that extends from extreme western Africa to the Zambezian region in eastern Africa and then southwards up-to the Kalahari and Namib regions21. We did not consider the small Asian part of the species range. Fresh leaf samples were collected from 746 individuals of S. senegal across 29 locations covering most of its geographic range in Africa , i.e. maternally inherited, microsatellite markers as described previously39. The final data set consisted of 730 samples for nSSR and 746 for cpSSR. Genotypes of nSSR markers were tested for null alleles using the program FreeNA78 finding null allele frequencies > 0.2 in 4.8% of locus x population combinations, preferentially in Southern African populations. We did not correct for null alleles, as the number and identity of null alleles is unknown. However, the results obtained with null allele corrected data are qualitatively the same , the unbiased estimate of expected (He) and observed (Ho) heterozygosity using Arlequin v.3.5.1.379. The number of private alleles (Apriv) was estimated using GDA v.1.080. FSTAT v.1.281 was used to calculate the inbreeding coefficient (Fis) and rarified allelic richness (Ar), thus correcting for different sample sizes by rarefaction82. The package genepop v.1.283 was used to perform exact tests of Hardy–Weinberg equilibrium (HWE).We used ten diploid nuclear simple sequence repeat (nSSR), i.e. biparentally inherited, markers developed for 84. For each K ranging from one to 29 (the number of sampling sites), we performed 90 replicate runs with 100,000 steps after a burn-in period of 50,000 steps considering the model of correlated allele frequencies and admixture without prior population information85. The “Evanno approach”85 was used to identify the value of K for the uppermost hierarchical level using Structure Harvester86, but we also scrutinized whether results of other K allowed for a clear biologically interpretation, i.e. whether emerging clusters include several individuals who are strongly assigned to that cluster, as suggested in the STURCTURE manual87. Moreover, as different non-symmetric modes of model outcomes are possible in large and complex data sets87, we used CLUMPAK43 to sort out modes, align clusters across runs and calculate the consensus. We used STRUCTURE both for an analysis with only nSSR data , number of haplotypes detected per population (A), effective number of haplotypes (Ne), genetic diversity (D2sh), haplotype richness, correcting for sample size (Rh), the number of private haplotypes (P), gene diversity within and over all populations. A parsimony network illustrating genetic relationships among haplotypes was inferred using PopArt89, assuming single-step mutations between alleles. For both nSSR and cpSSR, we quantified genetic differentiation with analyses of molecular variance (AMOVA) using sampling sites and clusters suggested by STRUCTURE as hierarchical levels using GenAlex v.6.90.For the cpSSR data, we used HAPLOTYPE v.1.05r) with distance from the East African origin of the range expansion into West Africa, with a linear model. A hypothetical location at 0.57°N; 36.38°E between two eastern populations was chosen as the point of origin. We compared population level diversity estimates among the three phylogroups with ANOVA, followed by a TukeyHSD test.We tested for a decline of genetic variation 91 and soil pH92. To quantify IBD and IBE, we performed Multiple Matrix Regression with Randomization (MMRR) using the R function ‘MMRR’93. This analysis was also done for two subranges (Sudano-Sahelian + Zambezian and Zambezian + Southern) to assess whether the same parameters are relevant in different parts of the range.To elucidate the roles of geographic and environmental factors for genetic differentiation, we tested for isolation by distance (IBD) and isolation by environment (IBE). We generated matrices for genetic distance as pairwise S. senegal generated from a total of 1132 unique occurrence records and four environmental variables27. The occurrence records were assigned to three phylogroups at K = 3  while the future projections were assessed at a resolution of 10 min (18.6 × 18.6 km = 346 km2 at the equator) to three IPCC climate scenarios. We use the most recently updated scenarios based on different socioeconomic assumptions, also known as the “Shared Socioeconomic Pathways” (SSPs). The SSP2 4.5 (RCP 4.5) and SSP4 6.0 (RCP 6.0) are intermediate scenarios, while the SSP5 8.5 (RCP 8.5) is the high emission scenario. All projections were estimated by ensemble SDMs implemented in Biomod2. The model accuracy was evaluated with kappa, TSS and AUC for each phylogenetic group  for Limpopo; Permit number: MPB. 1371 (Issued date: 16.03.2018) for Mpumalanga both from South Africa. Permit number: RPIV00312018 (Issued date: 29.10.2018) for Namibia. Samples from Tanzania were received from the Herbarium, Department of Botany, University of Dar es Salam, Tanzania through collaboration . Leaf material from six populations sampled in Ethiopia, Sudan, Mali, Mauritania, Burkina Faso and Senegal were received from Odee et al. 2012 . Populations from Nigeria, Republic of Niger and Ghana were sampled under the coverage of the Economic Community of West African States (ECOWAS) protocol, in favor of the first author. Supplementary Information.


Rhizophora apiculata in Thailand. Using SNPs identified from the R. apiculata genome sequence, moderate genetic diversity and high genetic differentiation were observed among 82 R. apiculata accessions collected along the coasts of Thailand. Two subpopulations corresponding to the Gulf of Thailand and the Andaman Sea coasts were clustered and confirmed from three approaches: population structure, PCA, and phylogenetic analyses. The AMOVA result revealed that the percentage of variation within populations (76%) was higher than that among populations (24%). We utilized the 10× Genomics technology to obtain a reference whole-genome sequence for assessing the genetic diversity and population structure of Rhizophora apiculata is one of the most widespread and economically important mangrove trees in the Indo-West Pacific region. Knowledge of the genetic variation of R. apiculata in Thailand is limited. Here, we generated a whole-genome sequence of R. apiculata using the 10× Genomics technology. R. apiculata genome assembly was 230.47 Mb. Based on its genome, 2640 loci of high-quality biallelic SNPs were identified from 82 R. apiculata accessions collected from 17 natural mangrove forests in Thailand to assess the genetic diversity and population structure among them. A moderate level of genetic diversity of R. apiculata was observed. The average observed heterozygosity (Ho = 0.48) was higher than the average expected heterozygosity (He = 0.36). Two subpopulations were observed and confirmed from three approaches: population structure, PCA, and phylogenetic analyses. They corresponded to the Gulf of Thailand and the Andaman Sea separated by the Malay Peninsula. AMOVA analyses indicated that genetic variation was attributable to 76.22% within populations and 23.78% among populations. A high level of genetic differentiation between the two subpopulations  was observed. This study evaluated the genetic diversity and population structure of R. apiculata, providing useful information for sustainable mangrove management in Thailand. Mangroves generally grow in intertidal habitats, which are at the interface between land and sea in tropical and sub-tropical regions . They arRhizophora apiculata belongs to the family Rhizophoraceae and is a true mangrove that is one of the most widespread and economically important plant species in tropical regions [R. apiculata, the leaf shape is narrowly elliptic-oblong and the bark is dark grey and vertically fissured [Rhizophora species  for supporting respiration when their lower roots are submerged and elevating the plants above the water. [R. apiculata is one of the most regenerated mangrove species in Thailand because seeds are easy to plant in nurseries [ regions . It is d regions ,14. It i regions . In addi regions ,17,18. Bfissured . Remarkae water. ,20. Notaurseries ,21.R. apiculata populations using various molecular markers [R. apiculata individuals in three collection sites  in Thailand revealed low genetic variation and a significant genetic differentiation between populations based on five nuclear genes and two chloroplast DNA regions [R. apiculata populations based on 81 nuclear loci showed high genetic diversity and various levels of genetic differentiation among populations [R. apiculata populations were studied in several regions, such as the Greater Sunda Islands of Indonesia [R. apiculata was low in the IWP region and Malaysia but high in China [Molecular markers, such as amplified fragment length polymorphisms (AFLPs), nuclear and chloroplast DNA sequences, microsatellites, and single nucleotide polymorphisms (SNPs), have been used to study population genetics in mangroves ,30,31,32 markers ,27,28,29 regions . Populat regions . In contulations . They weulations . Using mndonesia , the IWPndonesia , Malaysiin China ,28,29. Hin China ,26,28. IR. apiculata across the range of Thailand coasts has not been evaluated. To obtain a whole-genome reference sequence R. apiculata in Thailand, the whole genome of R. apiculata was sequenced and assembled as a reference sequence for this study. Based on our R. apiculata reference genome, numerous variants were identified from 82 R. apiculata accessions collected from 17 natural mangrove forests in Thailand. The variants were filtered to identify high-quality biallelic SNPs that were used to assess the genetic diversity and population structure of R. apiculata in Thailand.The genetic diversity and population structure of R. apiculata individual was chosen as a representative species in this study. It is in the natural mangrove forest in the Ranong province (9°52′36.1″ N 98°36′11.5″ E) under the protection of the Department of Marine and Coastal Resources. The morphology of R. apiculata as a reference genome is presented in For reference genome sequencing, one R. apiculata accessions were collected from natural mangrove forests in 17 provinces of Thailand  method was used to extract genomic DNA from the R. apiculata leaves [R. apiculata accession as a reference sequence was sequenced using the 10× Genomics technology with linked-read sequencing, which is a microfluidics-based method to generate long-range information from short-read sequencing data ). The 10× genomics library was constructed from approximately 1 ng of high quality, high molecular weight DNA using the Chromium Genome Library Kit & Gel Bead Kit v2, the Chromium Genome Chip Kit v2 and the Chromium i7 Multiplex Kit according to the manufacturer’s instructions (10× Genomics) and was sequenced using the Illumina HiSeq X Ten (paired-end reads at 150 bp). Furthermore, the 82 R. apiculata accessions were sequenced using MGI technology with restriction site-associated DNA sequencing (RAD-seq), which is one of the most genomic library preparations to reduce representation sequencing [The fresh leaves of all a leaves . One R. quencing . For eacR. apiculata was assembled to link read data using SuperNova v2.1.1 with default settings (https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running (accessed on 10 January 2022); 10× Genomics; [R. apiculata genome (GCA_900174605.1 in the European Nucleotide Archive (ENA), [R. apiculata genome was assessed with the previous R. apiculata genome [R. apiculata has been deposited in the National Center for Biotechnology Information (NCBI) under the project accession number PRJNA846534.The whole genome of enomics; ). All coe (ENA), ) using Re (ENA), . Redundae (ENA), . Our R. a genome  using QUa genome ,41. The R. apiculata using BWA v0.7.17-r1188 (https://github.com/lh3/bwa (accessed on 2 February 2022)) with default settings. Sequence alignment map (SAM) files were converted to a binary format (BAM) that were sorted and indexed using samtools v1.9 [To identify single nucleotide polymorphisms (SNPs), paired-end reads of each accession were mapped to our genome sequences of ols v1.9 . Mappingols v1.9 . Individols v1.9 . SNPs weols v1.9  with a mols v1.9 . FinallyR. apiculata was examined using the STRUCTURE program v2.3.4 with a Bayesian clustering approach via the Markov Chain Monte Carlo (MCMC) estimation [The population structure analysis of timation ,48. To ptimation . Then, ttimation . The mostimation ,51. Basetimation .To determine the proportion of variation explained by each principal component, eigenvalues of the filtered SNPs were generated using PLINK v1.9 . PrincipNe), Shannon’s information index (I), observed (Ho) and expected (He) heterozygosity, the percentage of polymorphic loci (PPL), and the inbreeding coefficient (F), were generated using GenAlEx v6.502 [FST) was conducted using Arlequin v3.5.2.2 [FST value was greater than 0.15, indicating the genetic differentiation between populations [Gene diversity (heterozygosity), polymorphism information content, and minor allele frequency were carried out using PowerMarker v3.25 . Additiox v6.502 . The anav3.5.2.2 . An FST ulations .R. apiculata accessions, phylogenetic analysis was performed using a maximum likelihood (ML) method based on SNPs. The SNPs in all accessions were aligned using MUSCLE with default in MEGA X [To assess the phylogenetic relationships among the 82 n MEGA X . To be tn MEGA X .R. apiculata, a total of 110.04 Gb of 150 bp paired-end reads were obtained and assembled. The de novo assembly contained 22,267 contigs with the longest scaffold, N50 length, and N90 length of 1,917,847, 144,278, and 3785 bp, respectively  is similar to the size of the previously reported R. apiculata genomes (232 Mb) in China [R. apiculata genome based on flow cytometry (274 Mb) [R. apiculata genes  and minimum read depth < 10 in each accession  were removed. After filtering SNPs using the criteria mentioned in the Materials and Methods Section, 2640 high-quality biallelic SNPs were obtained.To generate SNPs data, 82 Thailand  were seqThailand . A totalR. apiculata accessions are shown in The distributions of values for genetic diversity, polymorphic information content (PIC), and minor allele frequency (MAF) of the 2640 SNPs estimated on the 82 R. apiculata in Thailand was performed  of erformed . The Bayerformed A, suggeserformed B consisterformed B consistcessions C where tNe), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), and the percentage of polymorphic loci (PPL) were estimated , indicating an excess of heterozygosity.At the population level, the average number of effective alleles (stimated . All divFST) was 0.24 (p < 0.001), suggesting significant high genetic differentiation between the subpopulations.To assess genetic differences, AMOVA  analyses of the accessions with the two subpopulations revealed that 23.78% of the total genetic variation was attributable to differences among subpopulations, and 76.22% of the total genetic variation was attributable to differences among accessions within populations . The fixR. apiculata accessions, a maximum likelihood (ML) tree was conducted  collected from 17 natural mangrove forests in Thailand were evaluated using SNP markers. Gene diversity (GD) and polymorphic information content (PIC) values have been used to evaluate the level of genetic variation in populations [R. apiculata. In addition, the PIC value is commonly used to measure the informativeness of genetic markers [Because of the loss of mangrove forest areas, an understanding of ulations . Based o markers . Followi markers , 77%  and Andaman Sea (Trang) [R. mucronata in Thailand, which is a sympatric mangrove species with R. apiculata, was not clustered into two groups between the Gulf of Thailand and Andaman Sea [R. apiculata in Thailand were shown to independently adapt to their own environments, probably due to the Malay Peninsula barrier that prevents the movement among R. apiculata subpopulations between the Gulf of Thailand and Andaman Sea coasts. Based on SNP markers, 63 accessions of Bruguiera parviflora (a mangrove species in the family Rhizophoraceae) were also clustered in two subpopulations, the Gulf of Thailand (Surat Thani and Trat) and the Andaman Sea  [Ceriops tagal (one of the mangrove species in the family Rhizophoraceae) was evaluated in Thailand and China and it was shown that populations from the eastern coastline of Thailand were more genetically similar to populations from the South China Sea coast than to populations from the western coastline of Thailand, using inter-simple sequence repeat [R. apiculata and other mangrove species in the family Rhizophoraceae is conceptually consistent with the land barrier hypothesis of the Malay Peninsula [To examine the population structure of 82  (Trang) . In contaman Sea . Hence, d Satun) . Furthere repeat . The popeninsula .R. apiculata in Thailand , in Indonesia, and in Malayia as well as the genetic admixture of relative Rhizophora species in some parts of the IWP region [R. apiculata in Thailand reflected higher levels of genetic diversity with admixed alleles from the two subpopulations.Interestingly, genetic admixture was found in several accessions in both subpopulations in the Gulf of Thailand  and Andaman Sea  B, which P region ,28. The R. apiculata population in Thailand were evaluated. Moderate genetic diversity (mean He = 0.39) was observed in the R. apiculata population along coastlines in Thailand. This is concordant with the study of R. apiculata populations in the Hainan Island, Gulf of Thailand, and the west coast of Thailand [R. apiculata by mixed mating or predominantly outcrossing, which maintains the level of genetic diversity [Rhizophora species [FST = 0.24, p < 0.001) was similarly observed in several studies of R. apiculata populations, such as in the Strait of Malacca between the east coast (Hainan Island and Gulf of Thailand) and the west coast (Gulf of Thailand) of the Indo-Malayan region (FST = 0.48 (SNPs)) [FST = 0.875 (five nuclear genes) and FST = 0.688 (two cpDNA regions)) [FST = 0.381 (five microsatellite markers)) [FST = 0.242–0.532 (SNPs)) [FST = 0.315 (three nuclear microsatellite markers)) [Genetic diversity and genetic differentiation in the Thailand  as well iversity . In geneiversity ,29. Low  species ,64,65,66 (SNPs)) , in Thaiegions)) , in the  (SNPs)) , and in arkers)) .R. apiculata accessions were grouped into two genetic clusters (the Gulf of Thailand and the Andaman Sea) corresponding to the two subpopulations of the STRUCTURE and PCA clustering. Our tree topology is consistent with others [R. apiculata between the western and eastern regions of Peninsular Malaysia based on microsatellite markers [R. apiculata populations in the Malay Peninsula regions showed two clusters, the west and south of the Malay Peninsula, and the east of the Malay Peninsula [R. apiculata population in Thailand that were geographically isolated on the Gulf of Thailand coast on the east and the Andaman Sea coast on the west. In addition, one accession (SNI 04) from Surat Thani province is separated between cluster I and II based on the ML tree. The STRUCTURE analysis has shown genetic admixture in this accession  along coastlines in Thailand based on SNP markers. Moderate genetic diversity and high genetic differentiation were observed. The results showed two subpopulation differentiations, indicating genetic discontinuity between the coast of the Gulf of Thailand and the Andaman Sea. An AMOVA indicating 76% of variation found within populations and 24% of variation found among populations corroborated these results. Genetic diversity after the divergence of the R. apiculata populations might have been caused by geographic isolation.This study was addressed to examine the genetic diversity and population structure of