Lepus europaeus) to explore the effect of evolutionary events, such as the post-deglaciation translocations, spontaneous and human-mediated, local adaptation and the genetic drift in the shaping of the phylogeographic patterns of the species. For this purpose, we used molecular markers, both nuclear and mitochondrial, that are indicative for local adaptation as well as neutral markers to elucidate the patterns of population differentiation based on geographic isolation and the clade of origin. To broaden our analysis, we included data from our previous studies concerning mainland populations, to explore the genetic differentiation in the base of the geographic origin (mainland/island) of the populations.The aim of the study was to use hybrid populations as well as island populations of the European brown hare  or haplotypes in mtDNA . mtDNA variation was found to be in a higher level and was able to give a phylogeographic signal for the populations. Furthermore, the degree of variation was influenced not only by the geographic origin, but also by the clade of origin, since specific island populations of Anatolian origin showed a greater degree of variation compared to specific mainland populations of the European clade. Concerning the hybrid population, we confirmed the existence of both clades in the territory and we provided a possible explanation for the lack of introgression between the clades.Our results indicate that the Quaternary’s climatic oscillations played a major role in the shaping of the phylogeographic patterns of the species, by isolating populations in the distinct refugia, where they adapted and differentiate in allopatry, leading to genome incompatibilities observed nowadays.The online version of this article (10.1186/s12862-019-1354-y) contains supplementary material, which is available to authorized users. Chorthippus parallelus), the European green toad (Bufo viridis), the bicolored shrew (Crocidura leucodon), the crested newt (Triturus karelinii) and the European brown hare (Lepus europaeus) .Lepus europaeus, Pallas). Brown hare distribution covers Europe and Anatolia (which is a part of modern Turkey) and has been introduced in the past to South America, Australia and New Zealand [A model species to study the evolutionary forces acting on multiple levels of population dynamics is the European brown hare ). The major role of MHC is the presentation of processed antigens to T-cells (CD4 or CD8). MHC molecules are binding antigens using specific structural domains, called peptide-binding regions (PBRs) or binding pockets. A match between the three components, PBR, the antigen and receptors of T-cell are required to start the immune response of the organism [In the context of adaptive variation, one of the loci widely studied is the major histocompatibility complex (MHC). MHC is part of the adaptive immune system and its gene family is comprised by several genes, usually organized in clusters  virus [L. europaeus, the balance of the energy allocation to pathogen resistance and development is critical, due to their fast growth rate [Many associations between the variation of DQ genes  and susceptibility or resistance to disease have been conducted so far, reflecting the importance of maintaining high levels of variation at these particular genes. In the species of S) virus . FurtherS) virus –42. Espewth rate . The Eurwth rate , 45. Conwth rate .L. europaeus and compare it with the respective levels in mainland populations. For adaptive variation, we use a marker of the MHC gene family and more specifically the exon 2 of DQA gene (MHC class II), that has been proven to be useful for this kind of studies [The aim of this study is to address the contribution of adaptive selection and the genetic drift in the shaping of phylogeographic patterns and population structures observed in the species. For this purpose, we focused on the adaptive and neutral variations that are established in island populations of the species of  studies , 39 and All samples were assigned to the 3 major phylogeographic groups according to their restriction pattern of the partial CytB gene according to the method proposed by Stamatis and his colleagues . The samFst values of the population pairs range from 0.046  to 0.434   were detected combined to 21 different genotypes. Six alleles were discovered for the first time and were deposited in GenBank under the accession numbers MH029632-MH029637. Allele frequencies are presented in Table The translation of the sequences of the 12 alleles also revealed 12 different amino acid sequences, which means that each allele carries at least one different non-synonymous mutation compared to the rest. Specific positions of the amino acid sequence encode for partial regions of pocket 1, 6 and 9 according to Bondinas and his colleagues . In thisThe most frequent haplotype in both clades was found to be the YHEFWR/NETAN/YNILR with a significant difference in their frequency (92% vs 34%). Moreover, three unique combinations were revealed in the Anatolian group, two of them only present in Cypriot population (YHQFWA/NNTAG/YGIMR and YHQFWA/NNTEN/YNILR) and the third one (YHQFWA/NNTAN/YNILR) solely in Samos.A total of 49 MHC DQA exon 2 sequences  were used for the selection analyses. Since we used more than one methods for detecting selective pressures, we chose sites that were in accordance with at least two of them. Applying the above criteria, we found strong evidence for positive selection in codon 8 and six positions under negative selective pressure . Neither the positively selected site nor the negatively selected sites encode for the PBR regions that we mentioned above.After splitting the region in its counterparts, we detected 10 different haplotypes for the tRNAs genes, 23 for the partial CytB gene and 54 in the partial CR sequences (GenBank Accession Numbers: MH029638-MH029691). The outcome of the BlastClust program showed that the CytB and tRNAs regions fail to show geographic grouping of the sequences in finer scale (sequences from different populations were grouped together due to their 100% similarity) while CR grouping had a clear geographic signal following the distribution of the samples. The only exception in the CR grouping were three shared haplotypes between England and New Zealand (possibly due to their common origin), which led us to treat these two populations as one in the analysis. The pairwise genetic distances among the CR haplotypes are shown in Table The phylogenetic tree Fig.  revealedp-values using the sequential Bonferroni method, we detected population-locus specific deviations from the HWE , so we believe this is not a true signal of physical linkage of the loci. Based on these results, we used all the 6 markers for the downstream analysis.All 15 tests for LD failed to reject the null hypothesis of linkage equilibrium between the pairs of loci  calculated for each locus and population are shown in Additional file obs equal to 0.52 while the island populations had an average of 0.30. Continental populations are shown to contain approximately double numbers of alleles compared to the island populations . The number of alleles and the Hobs and Hexp for each locus and population are presented in Additional file The six microsatellite loci analyzed in this study were polymorphic with a mean number of alleles per locus equal to 15.3, ranging from 6 (Lsa6) to 30 (sol30). The number of alleles, the observed and the expected heterozygosities . Pairwise ed Table  for all obs and Hexp respectively) were calculated for each locus and population. The Hobs values ranged from 0.04 to 0.88  and Anatolian haplotypes, we would expect the fullsib or halfsib relationships to be limited between individuals from the different clades in the case of pre-zygotic reproductive isolation between them. However, we detected halfsib relationships from all the different combinations . Since these halfsibs do not share the maternal lineage, as they carry a different mtDNA haplotype, we can conclude that these are paternal halfsibs (i.e. they have the same father).Fst values do not follow the expected pattern of the phylogeographic status of the species  and the European woodmouse (Apodemus sylvaticus) [H.sapiens), Rodentia (M.musculus), Lagomorpha (O. cuniculus), Cetartiodactyla , Carnivora (C.familiaris)). These positions that are recognized in a variety of species as positively selected might have functional implication in antigen presenting process [The different challenges each population had to face during the isolation in the distinct refugia was the selective force that shaped the DQA variation among the clades. This is reflected by the diversity in allele distribution in DQA gene between the Anatolian and European populations; they had to challenge different pathogens in their refugia, so specific alleles were favored by selection in each population. The overlap of common alleles between the clades was found to be absent  or limitvaticus) , 52. Alsvaticus) . This hy process . The remobs and Hexp, we can notice the extremely low values of Hobs in all populations under study. These results are in contrast with the overdominance (the heterozygote advantage), which is believed to be one of the factors contributing to the high levels of MHC variation. This may be a result of a failure to amplify the second allele in the PCR reaction, so we get a pattern of homozygosity in SSCP pattern, or due to selecting only the exon 2 of the gene, where the samples appear to be homozygous, while they are heterozygous at other exonic regions. However, repeating the PCR/SSCP method or sub-cloning the PCR products and further sequencing them, did not change the results of the analysis.Comparing the values of Hobs values range from 0 (in Rodos population) to 0.33 in Mytilini population, with a weighted mean value of 0.18, while the continental populations from the study of Koutsogiannouli and her colleagues revealed a mean value of Hobs of 0.31 for the Greek haplogroup, 0.42 for the Central European and 0.42 for the Anatolian haplogroup. This reflects a signal of the founder effect: small island populations are created either by translocations  or by nZealand, , 20) andAnother example of adaptive variation is the genetic variance of CR under study among the different territories. The mitochondrial DNA, once considered neutral in terms of selection, is now believed to be under purifying selection : proteinIn our populations, there is a clear phylogeographic signal in the CR Fig. ; the EurConcluding, the CR is affected by high mutation rate and possibly by coevolution patterns governing the relations between the two separate genomes, leading to locally adaptive variants, specific for each clade of the species.It is noteworthy that CR variation is not only influenced by the population localization  but also by the clade of origin. In this study, we detected a higher rate of haplotypes per sample in mainland populations when compared to island populations , which may be a result of human-mediated translocations. However, when we studied variation in the level of mtDNA, these samples showed a clear signal of belonging to the Anatolian clade, as was revealed by the phylogenetic tree constructed by the CR as well as by the restriction pattern of the mitochondrial Cytb gene. We believe that these ambiguities are a result of human-mediated translocations of male individuals; human-mediated because there is no land bridge connecting the Central Europe to these islands and male individuals, since the mitochondrial clade remains of the Anatolian origin.Fis values. Furthermore, null or rare alleles may be responsible for the deviations. According to Hale and her colleagues [Tests for HWE revealed some specific population-locus combinations that deviated from the expected equilibrium. These deviations may be explained by the homozygote excess, as revealed by the Wright’s lleagues , there iobs compared to island populations (0.55 vs 0.34 respectively), which is a result of the creation of island populations under the founder effect and the action of genetic drift. Also, it is notable that in all six loci, island populations appear to have alleles that are absent from the parental terrestrial populations. Random mutations may be the “creative force” for these alleles, which remain to the population and establish more easily than the newly created DQA alleles, because of the selective neutrality of these nuclear loci. Pairwise comparison of the 3 groups that were formed in our analysis with Structure showed that the groups that comprised the South-Eastern European samples and the Central European samples had the minimum difference (0.11), while the groups South-Eastern European versus Anatolian and the Central European versus Anatolian showed an elevated differentiation (0.13 and 0.19 respectively). Samples from the hybrid zone, despite carrying all the possible mitochondrial haplotypes, show the South-Eastern European pattern as the dominant pattern in their grouping, with some particular individuals to show an intermediate pattern among the Central European and the South-Eastern European. Anatolian pattern is almost absent from the profiling of the samples of the hybrid zone, so we can hypothesize that the hybrids are not F1 hybrids, where we would expect an intermediate pattern in the samples. The SE European haplotype is in geographical proximity with the territory, so we can assume that it is the prevalent one in the specific area through random dispersal of individuals. Mitochondrial haplotypes of the other two groups may be present through the maternal lineage for a prolonged period of time, but their correspondent nuclear loci seem to decrease over time. The decline in their frequency may be a result of their limited dispersal along with the limited fitness of hybrids carrying an admixture of different mitochondrial and nuclear counterparts. Furthermore, when performed kinship analysis, we detected halfsib relationships among individuals carrying different mitochondrial haplotypes , meaning that these individuals share the paternal lineage. Thus, this is another indication that the clades do not only mix geographically in this territory but they also reproduce and produce offspring. Considering the fact that the lineages meet and mate but there is no introgression between them, we can hypothesize that there is a genetic barrier in the gene flow since there are no geographical barriers that could halt the translocations. This hypothesis can also be supported by our previous results [Tigriopus californicus, [Drosophila, [The continental populations showed a higher level of H results , 26, wheornicus, , 66, Drosophila, ).L.europaeus among them [Another possible explanation for this pattern of introgression is the outcome of the meta-analysis of Petit and Excoffier , which cong them , 70 appeOur multilocus phylogeographic analysis of European brown hare populations from islands allowed us to test the effect of the founder effect on these populations in terms of genetic polymorphism, compared to mainland populations. We found evidence that island populations show a lower level of genetic polymorphism but the geographic origin is not the only decisive factor, since the clade of origin seems to contribute as well in the levels of the genetic polymorphism of the island populations. Concerning the hybrid population, we confirmed the mtDNA introgression in the hybrid zone which was not followed by the nuclear DNA introgression of the invasive haplotype (the Anatolian) and we provided a possible explanation based on previous studies of the species and in a theory developed by Petit and Excoffier  that conOne hundred ninety-two samples (192) were collected during 2000–2016 from hunting associations. Sampling areas are shown in Fig. The primers sets used for the analysis were: for DQA exon 2 DQAF:5′- TCATCAGCTGACCACGTTGG-3′ and DQAR:Thr and tRNAPro and the partial CR) [ο C for 10 min and immediately kept in ice to avoid re-hybridization of the clones. The samples were loaded in a 10% acrylamide gel and electrophoresed overnight at 220 V. The results were visualized using silver staining and the samples were grouped according to their electrophoretic profile. Representative samples of each profile were selected and cloned in a pGEM-T easy vector and transformed into E.coli DH5a competent cells. Using this approach, we could obtain both sequences from a heterozygote profile and a unique sequence from a homozygous profile. After blue/white screening, a number of clones were selected and grown in order to perform isolation of the plasmid DNA. The subsequent PCR-SSCP analysis provided all the alleles in hemizygous condition, so we could perform the sequencing analysis. Sequences were aligned in Bioedit 7.0 and refined by eye. Along with these data, we used data from our previous analysis [5’-GACAGCAGCAGTAGAGTTGG-3′ (a 231-bp long amplified region) , LepCyb2tial CR) . DQA samtial CR) . The hybtial CR)  so they analysis  that useFor the microsatellite analysis, we used 6 primer sets  –74. DNA Fst values among populations were estimated using the package adegenet which is implemented in R [p < 0.05. FEL and SLAC methods use Maximum Likelihood approaches to infer nonsynonumous and synonymous substitutions rates per base of a given alignment while REL tests for selection signatures for each branch of the phylogeny instead of testing for specific sites [Genotype and allele frequencies for each population as well as genotypic differentiation between island populations were measured using GenePop 4.6. Expected and observed heterozygosities and pairwise ted in R , 76, usited in R  under thic sites . To estiic sites  and recoic sites . The freic sites .Thr and tRNAPro region and finally the partial CR, by aligning it to the reference genome (Accession number: AJ421471). The total numbers of mutations in each of the three sub-divisions of the amplicon were calculated using MEGA 7. We used the BlastClust algorithm to extract the unique sequences among the samples (100% length coverage plus 100% similarity) and distributed each sample to the unique haplotypes derived from the program. The total number of mutations and haplotypes for each region is presented in Table The 1200 bp-long region was further split in the three different loci: the partial CytB region, the tRNAc while optimizing parameters a, b and c so that rounding errors are minimal when rounding transformed allele sizes to integers that fit the expected nucleotide repeat patterns [genepop.curtin.edu.au) [p-values of each test were compared to the corrected α for multiple testing using the sequential Bonferroni correction, according to Holm and Rice [p-values for the LD testing are shown in Additional file Genotype construction for each sample was extracted using GeneScan with default parameters, using LIZ 500 as a marker (ThermoFisher Scientific). The sizes of the peaks were estimated by comparing the samples’ peak distance from the peaks of the LIZ 500 marker, which are of known sizes (ranging from 35 to 500). In order to round all allele sizes in an equivalent way, the program tandem was used, which uses an exhaustive search using the power function  = a + b × patterns . The fir.edu.au) . We consand Rice , 84 to cHobsHexp .  and we calculated the allele frequencies, observed and expected heterozygosity for each population and each locus using the package adegenet, and the pairwise MSA 4.05 . In the MSA 4.05 . For theMSA 4.05 . The truMSA 4.05 , using aMSA 4.05 . By examAdditionally, to assess the deeper structure of the hybrid population and infer the relationships between individuals, we used the program Colony , which iAdditional file 1:Table S3. Pocket frequencies per population. (PDF 23 kb)Additional file 2:Table S1. Deviations from HQE and the respective Fis values. (PDF 21 kb)Additional file 3:Table S2. Pairwise test for LD among marker pairs. (PDF 18 kb)Additional file 4:Table S4. Number of alleles, observed and expected heterozygosities in six microsatellite markers. (PDF 74 kb)Additional file 5:Table S5. Observed and expected heterozygosities in six microsatellite markers. (PDF 19 kb)Additional file 6:Figure S1. The K-Δ(Κ) plot using the Evanno’s approach. The peak of the plot is for K = 3. (JPEG 32 kb)


Sinanodonta woodiana, which has been present in Europe since 1979 but which has expanded rapidly in the last decade. Data from a mitochondrial marker and nuclear microsatellites have suggested that all European populations of S. woodiana originate from the River Yangtze basin in China. Only a single haplotype was detected in Europe, in contrast to substantial mitochondrial diversity in native Asian populations. Analysis of microsatellite markers indicated intensive gene flow and confirmed a lower genetic diversity of European populations compared to those from the Yangtze basin, though that difference was not large. Using an Approximate Bayesian Modelling approach, we identified two areas as the probable source of the spread of S. woodiana in Europe, which matched historical records for its establishment. Their populations originated from a single colonization event. Our data do not support alternative explanations for the rapid recent spread of S. woodiana; recent arrival of a novel (cold‐tolerant) genotype or continuous propagule pressure. Instead, in situ adaptation, facilitated by repeated admixture, appears to drive the ongoing expansion of S. woodiana. We discuss management consequences of our results.Understanding the invasive potential of species outside their native range is one of the most pressing questions in applied evolutionary and ecological research. Admixture of genotypes of invasive species from multiple sources has been implicated in successful invasions, by generating novel genetic combinations that facilitate rapid adaptation to new environments. Alternatively, adaptive evolution on standing genetic variation, exposed by phenotypic plasticity and selected by genetic accommodation, can facilitate invasion success. We investigated the population genetic structure of an Asian freshwater mussel with a parasitic dispersal stage,  It is a benthic species associated with soft sediment and is known to tolerate low water quality in terms of organic and inorganic pollution  into the water column where they attach to a fish host to complete development and metamorphose into a free‐living juvenile mussel. Generation time of S. woodiana is 2–5 years , Indonesia (1969), Dominican Republic (1982), Costa Rica (1994), USA (2010), Myanmar (2016), and Siberia (2016)  rather than genetically determined variability  and a set of specifically designed nuclear microsatellite markers  to describe the genetic diversity and structure of invasive S. woodiana populations in Europe; (b) to identify the putative original European populations that served as sources for the subsequent invasion across the continent; (c) to model complex associations across the non‐native European range, including the level of admixture and its spatial structure, using Approximate Bayesian Computation; (d) to locate the potential sources of S. woodiana from its native range that were introduced to Europe, and (e) to consider the management implications of the results.In the present study, a set of 16 populations collected from the European non‐native range of 22.1S. woodiana were collected in Europe (non‐native range) and Asia (native range) between 2011 and 2014 by the authors and collaborators   was amplified using the primers LCO1490 and HCO2198   and nucleotide diversity   Nei, , calculaes) Nei, . Sequences) Nei, . The numes) Nei, .2.4HO) and unbiased expected heterozygosity according to Nei (HE) in GENETIX 4.05.2  over loci was determined with the rarefaction procedure in FSTAT 2.9.3  implemented in FSTAT.Of the 17 originally developed loci, a high frequency of null alleles (mean >0.07 per population) was detected at seven loci and they were excluded from all subsequent analyses to avoid the biases caused by null alleles such as an increase in interpopulation divergence  in Goudet,  to estimK = 2, with no mixing between Chinese and European populations at higher K values , with different values of the Dirichlet α parameter for each assumed cluster. We used a model with correlated allele frequencies ) for each K value.Post‐processing of the STRUCTURE output was performed with the CLUMPAK software  or three  populations formed a single group, only one population was chosen as the representative sample. This resulted in the selection of 11 populations (sampling sites) for ABC analysis  framework , using DIYABC 2.0.4  within each pair of populations by building 55 independent “pairwise ABC comparisons,” an approach similar to Miller et al. . Resulting simulated genotypes (represented by their summary statistics) were compared to the sampled genotypes. Each event was linked to particular parameters: timing of the event (generations to the past), effective population size, or admixture rate . In addition, because we modelled invasion events, we always considered a potential bottleneck effect in each newly founded population (defined by the duration of bottleneck and limited effective population size during the time of the bottleneck). The definition of the 12 scenarios in the DIYABC is reported in −5 to 10−3. To estimate sensitivity to priors, we reran the same 55 pairwise comparisons with log‐uniform prior distributions (over the same range as for uniform priors) for effective population sizes and timing of events. For model choice inference, we generated a table of 106 simulated datasets for each of the 12 scenarios for each of the ABC pairwise comparisons.The DIYABC analyses were conducted with two groups of loci: The first group included five loci with a dinucleotide microsatellite motif  and the second group was composed of five loci with a trinucleotide motif . Generalized Stepwise Mutation models 2 distance  was summarized with the statistics equivalent to those traditionally used in population genetics); NAL: mean number of alleles, HET: mean genic diversity, N2P: mean number of alleles in two samples, FST, LIK: classification index, and DM2:  not overlapping with those for the other scenarios. Accordingly, a single or several scenarios could be selected for each “pairwise comparison.” We compared the outcome of the analyses with both prior settings and selected scenarios that were supported in both analyses. We assessed the goodness‐of‐fit of the best model by evaluating consistency of the observed data with the posterior predictive distribution of the model for the best scenarios. We conducted model checking for all 55 pairwise comparisons using all summary statistics, including those that had not been used in the initial ABC analyses for model selection   obtained from 78 individuals from Europe and 49 individuals from Asia  was also the most common Asian haplotype  varied from 3.97 in Italian population (ITPO) to 5.11 in a population from southern France (FR) and the pattern of allelic richness for AR17 was concordant. The expected unbiased heterozygosity over all loci (HE) ranged from 0.676 ± 0.200 (SD) to 0.795 ± 0.128 in ITPO and FR, respectively , lower than in Chinese populations . Standardized mean allelic richness . However, there was no significant difference between FST within European and Yangtze populations , with mean FST of 0.054 in Europe and 0.068 in the Yangtze basin. In Europe, mean pairwise genetic differentiation between populations ranged from 0.005 (FR vs. BGDA) to 0.144  did not differ between populations from Europe (0.736) and the Yangtze basin (0.726) (p = 0.677).Genetic diversity in six Yangtze basin populations was larger than in the introduced European populations . Bayesian clustering analysis revealed weak population genetic structure in our dataset but with populations of relatively consistent genetic background across an increasing number of putative clusters  Sampled “source” populations in Europe  of the best supported scenarios in the single‐winner pairwise comparisons involved an admixture event between another sampled population and an unsampled population , geographically concordant with the oldest European records, as the source of the other S. woodiana populations in Europe, a sets of derived populations (current invasion endpoints) and populations that were historically derived from other non‐native populations and subsequently served as a source of further invasion.We have documented largely depauperate mitochondrial diversity in Sinanodonta woodiana was first recorded in Europe in 1979 in western Romania (middle Danube basin) . Glochidia are released into the water column and attach to a fish host to complete development. Glochidia of S. woodiana remain attached to the gills or fins of the host fish for 5–20 days  to tropical southern China, with large genetic divergence among lineages , Central America (1982), the USA (2010), and Siberia (2016)  were imported from Taiwan  from Taiwan ), a generalist host of all European unionids, shows distinct responses to different S. woodiana populations in Europe. While populations from Central Poland were used by R. amarus for oviposition, with all bitterling embryos later ejected, S. woodiana populations from the southern Czech Republic were completely avoided by R. amarus, thereby mitigating the costs of failed development. Hence, the identity of S. woodiana populations determines the impact of S. woodiana on native R. amarus, from almost neutral to highly negative . This finding highlights that different S. woodiana populations can have different impacts on native communities, an increasingly recognized feature of many invasive species  were analyzed by A.K., with the help from O.P. The manuscript was drafted by A.K., O.P., and M.R., with significant contribution from all authors. We thank Jasna Lajtner, Teodora Trichkova, Zdravko Hubenov, Marianna Soroka, Nicoletta Riccardi, Irene Guarneri, Vincent Prié, Huanzhang Liu, and Wenjing Yi for providing samples and two anonymous referees for constructive comments that improved the ms.MG515731‐MG515742. Microsatellite data have been deposited in Figshare: https://doi.org/10.6084/m9.figshare.6895682.DNA sequences have been deposited in GenBank: Accession Numbers  Click here for additional data file.


The genetic data of 17 Y chromosome short tandem repeats in 146 unrelated donor residents in the provinces of Granada, Málaga, and Almería (GMA) were analyzed to determine the genetic legacy of the male inhabitants of the former Kingdom of Granada. A total of 139 unique haplotypes were identified. Observed allele frequencies and haplogroup frequencies were also analyzed. By AMOVA and STRUCTURE analysis, the populations of the 3 provinces could be treated genetically as a single population. The most frequent haplogroup was R1b1b2 (58.22%). By network analysis of all individuals, we observed a distribution according to haplogroup assignment. To improve the characterization of GMA population, it was compared with those of North Africa, the Iberian Peninsula, and southern Europe. In our analysis of allele frequencies and genetic distances, the GMA population lay within the Spanish population group. Further, in the STRUCTURE analysis, there was no African component in the GMA population, confirming that, based on our genetic markers, the GMA population does not reflect any male genetic influence of the North African people. The presence of African haplogroups in the GMA population is irrelevant when their frequency is compared with those in other European populations. The former Kingdom of Granada comprised the present-day provinces of Granada, Málaga, and Almería, in addition to parts of Cádiz, Jaén, Córdoba, and Sevilla, with Granada as the capital. During the 14th and 15th centuries, it was one of the most prosperous cities in Europe, with almost 165,000 inhabitants2.For the nearly 800 years of the Arab invasion of the Iberian Peninsula, North African groups spread throughout all the territory except for the Basque Country, Galicia, Cantabria, Asturias, and most of the Pyrenees; but, their influence was greater in the south of the PeninsulaIliberir. In 1013, the Zirid dynasty gained control of the region and established Ilbira in 1025. To prevent future invasions, the kingdom expanded and occupied the entire territory of the Kingdom of Granada4. With the expansion of the Kingdom in 1090, a large part of the Iberian Peninsula came under control of the Zirid dynasty, known as Al-Ándalus. After losing the battle of Navas de Tolosa, in 1212, Al-Ándalus became subdued and was reduced to the Nasrid Kingdom of Granada. The Nasrid dynasty was the longest surviving Muslim dynasty in the Iberian Peninsula. Ultimately, with the confiscation of the city of Granada by Los Reyes Católicos, Fernando II, and Isabel I in 1492, the Kingdom of Granada came to an end4.The first evidence of African invaders originated from 711, when Syrian Berbers entered the Iberian Peninsula and conquered the Granada region, known as 3. On the December 9, 1609, Felipe III signed the expulsion warrant of all Moriscos from Spain4. In 1833, after 314 years of existence and with the separation of the provinces of Almería and Málaga, the former Kingdom of Granada ended4.Although the Muslims signed capitulation agreements to follow the religion of the kingdom, they were forced to convert to Christianity or emigrate. Once the Morisco properties were expropriated and expelled, it became necessary to repopulate the region by sending new inhabitants from various regions of the Peninsula. The repopulation began in 1571 and lasted until 1595, by which time a total of 12,546 families repopulated 270 areasThus, during the establishment of the Kingdom of Granada and throughout its existence, people from diverse religions and regions coexisted. The coexistence of Visigoths, Syrians, Saharans, Moroccans, Jews, and Christians is reflected in the architecture, culture, and folklore of the present-day cities of Granada, Málaga, and Almería.7. Crossbreeding studies that are based on the characterization of ALU sequences have found traces of sub-Saharan genes in north Mediterranean populations, suggesting continuous contact between both coasts8. That genetic traits and certain specific haplotypes have been detected along the northern coast of the Mediterranean Sea confirms the hypothesis that gene flow in this region was linked to the first trans-Mediterranean sailings and remained homogeneous while the slave trade lasted, until the late 17th century, rather than reflecting Islamic expansion (S. VII to S. XV)8. Y chromosome studies have described the genetic structure in the Iberian Peninsula, calculating the various contributions of Muslims and Jews to the current population of the Peninsula6, focusing on the E3b2 haplogroup, which is common in northern Africa and present in 5.6% of the Peninsula9. The distribution of the Y chromosome haplogroup E-M81 in the Iberian Peninsula suggests genetic flow of North Africa during this period6. High levels of patrilineal descent from North Africa and Sephardic Jews in the current population of the Iberian Peninsula have been detected6 and contributed to the higher genetic diversity in southwestern Europe10.The study of mtDNA and Y chromosomes has identified geographic regions with a genetic influence of North Africans of 8% to 10%6. The main gradient of the frequencies of North African genes descend from west to east11. Furthermore, recent studies based on autosomal SNPs11 and Y-chromosome lineages12 reveal that Andalusian population does not specially cluster with North African populations more than other Iberian populations13. After the Reconquest, the Moors were distributed homogeneously throughout the Peninsula, but their final expulsion in 1609 was absolute in certain regions of Spain, Valencia, and western Andalusia, whereas in Galicia and Extremadura, the population dispersed and integrated into society6.However, contrary to what might be expected based on historical data that favor a gradient of North African genetic influence from south to north, most such influence has been found in Galicia and northern Castilla (>20%)−8. A total of 139 unique haplotypes were detected, 133 of which were described once, 5 that were described twice, and 1 that was described 3 times. The Y chromosome haplotype diversity was 0.9974, and the discriminatory capacity was 95.21% [(N haplotypes/n)·100].Allele frequencies and forensic summary statistics for the 16 loci are summarized in Table By AMOVA, there was no significant genetic substructure in the GMA population (p > 0.05). All variations were within populations (99.93%) rather than between populations (0.07%) Table .Table 2ABased on the 17 Y chromosome STR markers, most men in the GMA population carried the R1b1b2 haplogroup (58.22%). The second most frequent haplogroup was E1b1b1; many subhaplogroups were detected (11.64%), of which E1b1b1b was the most frequent (4.79%). J2a4 had a proportion of 6.16%. Other subhaplogroups from J included J1c3 (4.11%) and J1-M267, J2b1, and J2bf2 (0.68%). The G2a-P15 haplogroup was observed in 5.48% of samples  Fig.  and thosIndividuals were then distributed by haplogroup Fig. . The cenTwenty repeated surnames were identified among the 108 surnames of the 146 individuals , most of which were associated with the R1b1b2-M269 haplogroup (28/58). Haplogroups from lineages E , I (I2a-P37.2 and I2b1-M233), J , and G2a-P15 were also common between surnames. Table 15. However, in the GMA population, none of the individuals who shared haplotypes had the same surname. Further, except for Martín and Gomez, the remaining individuals who shared haplotypes had unique surnames. Six of the 108 different surnames in the GMA population were Arab in origin; only Silla was associated with the J1c3-P58 lineage, a characteristic of the haplogroup in the population of the Arabian Peninsula.Unrelated men who have the same surname are significantly more likely to share haplotypesY chromosome allele frequencies from the GMA population and published frequencies . Studies in the Libyan population support the importance of migratory movements that lead to admixture between the original Berber inhabitants and neighboring and more distant populations, despite a solid Berber genetic background remaining32. Berber populations inhabited the Iberian Peninsula for almost 250 years3 but were completely expelled, and very few genetic substrata can be detected in the GMA population, as evidenced by the high genetic differences between the GMA population and those with a significant Berber genetic influence.The genetic distances confirm the results of the genetic frequency analysis Fig. . When RsThe Rst pairwise distances between the GMA and European and African populations indicated that prior to Bonferroni adjustments; all pairwise comparisons generated statistically significant differences.34. However, studies that have compared different samples sizes have revealed that, after increasing sample size, newly detected alleles are rare34. In this case, nearly all alleles represented in the allelic ladder are represented in the population and all alleles have frequencies that are similar to those in nearby populations24.The populations of the provinces of Granada, Malaga, and Almeria behaved as a single population with regard to their genetic structure. These results are consistent with the historical and cultural expectations, because these 3 provinces share the same origin and proximity over a large area. We are aware that 146 individuals is a small sample size for 3 provinces. Reduced sample sizes in population and evolutionary studies may underscore the allelic frequencies of certain alleles, underestimating the number of alleles that aredetected35. The second most frequent haplogroup was E1b1b1, but many subhaplogroups were seen (11.64%), of which E1b1b1b was the most frequent (4.79%). The highest frequencies of this haplogroup were detected in the north of Africa, at >80% in the Moroccan Berbers36. However, this haplogroup is rare in Europe, except in the Iberian Peninsula 36. Studies based on whole Y-chromosome sequences attribute these frequencies to genetic drift acting on this low-frequency variant37. J2a4 had a proportion of 6.16%. The other subhaplogroups from J that were observed were J1c3 (4.11%), typically seen in Arabian Peninsula populations 38 and J1-M267, J2b1, J2bf2 (0.68%)—both lineages (J1 and J2) are detected primarily in southeast Europe. The G2a-P15 haplogroup was observed in 5.48% of samples , which is the most common European haplogroup, increasing in frequency east to west. This gradient indicates that this haplogroup spread throughout Europe from a single source in the Near East during the Neolithic period36. Recent studies based on the analysis of haplogroup E1b1b-M81 suggest that Andalusian Y chromosomes may derive from a single common ancestor imported from North Africa that originated in the Andalusian present-day haplotypes12. Analyzing the second most common Y chromosome haplogroup among North African populations (haplogroup J), the GMA population had high values of this haplogroup. However, a detailed evaluation of J subhaplogroups showed that the prevalent haplogroup in the GMA population was lineage J2a4, found along the northern coast of the Mediterranean and the Caucasus, whereas the predominant subhaplogroup in the North African populations were J1 lineages, existing at low frequencies in subpopulations of southern Europe.Based on the Y chromosome haplogroups, there was a large difference between the North African and Spanish populations—specifically the GMA population Fig. . The mai12.The analyses of allele frequencies and genetic distances confirm the results of the haplogroup analyses. Mediterranean populations were genetically closest to the GMA population, especially the Spanish populations. The genetic data revealed that no significant African component remained in the genetic legacy of the population of the southern Iberian Peninsula compared to other Iberian and European populations, despite North African people living in the region for almost 800 years. Similar results have been recently reported. An analysis of Y-chromosome lineages in the Andalusian population demonstrated that Andalusia and other Iberian populations are related to North African populations on a larger scale than other European regions but that if South European populations, which historically have been influenced by North African populations, are included in the analysis, this influence is not significant enough40. In most western societies, they are transmitted through the male line and inherited as alleles42, which is their transmission should closely match the inheritance of the Y chromosome. The spectrum of surnames in this study is supported by history. Most of the surnames originated in the north and center of the Peninsula but are more frequently observed in the South.Surnames are used to enhance the genetic signals of a population structure3. Although many of the Moriscos who inhabited the region were converted to Christianity and although mixed marriages might have formed, the Y chromosome lineages indicate that this phenomenon occurred at such a low frequency that its small influence prevented it from surviving the 600 years that have passed since the dissolution of the Kingdom of Granada.The analysis of Y chromosomes in the GMA population indicated that the male influence of the North African inhabitants that could have remained in the population in the southern peninsula did not influence the genetic legacy of the population in this region more intensely than in other Iberian populations. After the Reconquest of the region by the Catholic Monarchs, the region was repopulated with entire families from the rest of the peninsulaA total of 146 buccal cell swab samples from unrelated individuals were collected. Subjects were selected according to their self-declared affiliation to the provinces of Granada, Málaga, and Almería and their residence in the region for at least 3 generations  and quantified on an 0.8% agarose gel.Genomic DNA was extracted using phenol/chloroform/isoamyl alcohol and proteinase K. The DNA was purified on AmiconlSTR® Yfiler® PCR Amplification kit , per the manufacturer. Alleles were separated and detected on an Applied Biosystems ABI 310 genetic analyzer. Fragment sizes were analyzed using GeneMapper ID-X v1.1 . The alleles were named according to the number of repeated units, based on the sequenced allelic ladder (ISFG recommendations).One hundred forty-six samples were amplified using the AmpF44 (accession numbers YA004154–YA004156).All individuals with complete genotypes were deposited into the YHRD database45. Y chromosome haplotypes were calculated with Arlequin v3.5.1.3. Analysis of molecular variance (AMOVA) was performed with Arlequin v3.5.1.3 to determine any possible population substructure.Y STR allele frequencies, polymorphism information content (PIC), power of discrimination (PD), and matching probability (MP) were calculated for each locus using PowerStats v1247 and YPredictor by Vadim Urasin v15.0.Y chromosome haplogroups were determined with the 23-Haplogroup Beta program (online software)Network analyses were performed for individuals representing all haplogroups reducing the sample size to seventy nine individuals and individuals R1b1b2 (n = 85) to determine the most common ancestor.Correspondence analysis with Y-STR allele frequencies from published populations (Supplementary Table Supplementary Material


Oreothlypis celata) in western North America with particular focus on characterizing the divergence between O. celata populations on the mainland of southern California and on the Channel Islands. We obtained sequences of the mitochondrial gene ND2 and genotypes at ten microsatellite data for 192 O. celata from populations spanning all four recognized subspecies. We recovered shallow, but significant, levels of divergence among O. celata populations across the species range. Our results suggest that island isolation, subspecies , and isolation-by-distance, in that order, are the variables that best explain the geographic structure detected across the range of O. celata. Populations on the Channel Islands were genetically divergent from those on the mainland. We found evidence for greater gene flow from the Channel Islands population to mainland southern California than from the mainland to the islands. We discuss these data in the context of differentiation in phenotypic and ecological characters.We used molecular data to assess the degree of genetic divergence across the breeding range of the orange-crowned warbler ( Oceanic islands have served as a natural laboratory for evolutionary studies for decades . PatternAphelocoma californica and A. insularis  is currently divided into four subspecies that differ in plumage color  in their analyses, their data do not provide insight into broader patterns and processes of differentiation across the species, including between Channel Islands and mainland populations.In the only published genetic study of Oreothlypis celata. We assessed the relationship between Channel Island and mainland southern California populations and determined the relative rates of migration between these populations to test  to use as outgroups in our analyses. We obtained samples from museum tissue collections  and ATP Synthase subunit 6 (ATP6), both of which are commonly used in avian phylogeographic studies. We amplified a 1041 base pair (bp) fragment of the ND2 gene using the polymerase chain reaction (PCR) with primers L5204 and H6312 , 0.6 µm of each primer, 200 µm of each dNTP, 0.6 U of Taq and approximately 5–10 ng of genomic DNA. The PCR profile included an initial denaturation at 94 °C for 2 min; followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 53 °C for 30 s, and extension at 72 °C for 1 min; with a final extension at 72 °C for 10 min. We amplified a 704 bp fragment of the ATP6 gene by PCR using the primers a8PWL and C03HMH  following the Qiagen protocol for animal tissues. We sequenced the mitochondrial genes NADH subunit 2  and sequenced the purified products using Big Dye terminator chemistry v. 3.1 (Applied Biosystems) and an AB PRISM 3730 DNA Analyzer (Applied Biosystems). We analyzed only samples for which we obtained sequences of both the forward and reverse DNA strands. We aligned complementary DNA strands, edited all sequences, detected stop codons, and aligned consensus sequences using Sequencher version 4.7 . After obtaining 704 bp of O. celata , 0.6 µm of each primer, 200 µm of each dNTP, 0.6 U of Taq and approximately 5-10 ng of genomic DNA. The PCR conditions included one denaturation cycle at 94 °C for 2 min and 30 cycles consisting of 15 s of denaturation at 94 °C, 15 s of annealing at 50–55 °C, and 15 s of extension at 72 °C. We used T4 DNA polymerase  treatment to clean the PCR products of the Vce34, Vce50, Vce102, Vce103, Vce128, and Vce179 markers  developed for . celata . All ten markers . We mixeND2 sequences using maximum likelihood (ML), neighbor-joining (NJ), and maximum parsimony (MP) algorithms. We used RAxML BlackBox , using a non-parametric permutation approach to determine significance levels  and applying sequential Bonferroni corrections when evaluating significance (h) and nucleotide diversity (π)  , to calcsity (π)  and Fu’sy (π)  using SAMOVA 1.0  to assesized FCT .O) and expected (HE) heterozygosity values. We tested for Hardy–Weinberg equilibrium (HWE) and heterozygote deficiency using Genepop version 4.0.10  (s), which controls for sample size when comparing the number of alleles among populations  . In addirations) , applyinrations) . We examrations)  and usedrations)  to estimulations .FST. We calculated the significance levels for the AMOVA using a non-parametric permutation approach   and pairwise RST values using RSTCALC version 2.2  . We examsion 2.2 , applyinsion 2.2 .We tested the pairwise correlation between direct geographic and genetic  distanceK) based on the likelihood values for varying K values. We performed preliminary analyses without providing any information concerning population designations. After these initial analyses, we then designated eight populations in the input and used this information as a prior (LOCPRIOR)   in furthOCPRIOR) , examinen 0.6.94 , which un 0.6.94  with GNUn 0.6.94 . To align 0.6.94  and thenn 0.6.94  to plot K = 1 − 10. We then analyzed the seven remaining populations with the same parameters as above for K = 1 − 20.Based on the results of the Structure analysis described above, we ran two additional Structure analyses to check for the presence of substructure. We first analyzed the Channel Islands samples with the samples from Santa Cruz Island and Santa Catalina Island split into separate populations. We used the parameters as detailed above, including the LOCPRIOR for ND2 sequences and microsatellite genotypes and performed three separate runs each with 15 chains, 1,000,000 burnin steps, and 2,000,000 further steps following the burnin. We have provided further methodology details in ocwa-popgen version 1.0.0 on GitHub  between Oreothlypis celata sequences and two sequences of the closely related O. ruficapilla  of all eight populations, whereas the northern California population had the highest (3.7 × 10−3). The nucleotide diversity of the Haida Gwaii population (2.8 × 10−3) was substantially higher than that of the Channel Islands populations and equaled that of the southern California population (2.8 × 10−3). When grouped into northern and southern population clusters, the two groupings contained almost exactly the same nucleotide diversities .Variability in mtDNA sequences differed among populations . We founFST estimates from our AMOVA analysis of ND2 sequences were all highly significant (p < 0.01) for samples grouped into: (1) northern and southern clusters (0.191); (2) eight populations (0.202); (3) coastal and interior clusters (0.186); and (4) subspecies (0.195). Overall ΦST estimates were greater than the FST estimates for the different population data sets, and were also all highly significant with p < 0.01: (1) northern-southern (0.429); (2) eight-population (0.365); (3) coastal-interior (0.254); and (4) subspecies (0.299). The pairwise population FST values reflected patterns that were nearly congruent to the pairwise ΦST estimates, so we have chosen to present only the pairwise ΦST estimates  between Santa Cruz Island (northern Channel Islands) and Santa Catalina Island (southern Channel Islands) were not significant. However, pairwise ΦST estimates supported the collective Channel Islands as a distinct population. Pairwise ΦST values between the Channel Islands and every other population were significant, ranging from 0.245 to 0.809 with the samples grouped into eight populations . Of all of the pairwise comparisons involving the Channel Islands, we estimated the lowest ΦST between the Channel Islands and the northern and southern California populations .Pairwise population ulations  and fromulations . With thST estimates between Haida Gwaii and every other population within the set of eight populations were significant, except for those between Haida Gwaii and the northern and southern California populations. Of all of the Haida Gwaii pairwise comparisons, pairwise ΦST was highest (0.564) between the Haida Gwaii and Channel Islands populations. The pairwise ΦST estimate was significant between the northern and southern populations  and the highest between O. c. sordida and O. c. celata (0.786). Oreothlypis c. lutescens and O. c. orestera had the lowest pairwise ΦST value of all of the subspecies comparisons. All of the pairwise ΦST estimates were significant when we grouped the samples by subspecies  in our designated northern population. Grouping samples with K = 3 and K = 4 created partitions within the northern and southern populations but were not consistent with subspecies boundaries.As we found with our maximum parsimony and maximum likelihood analyses, our SAMOVA analyses indicated that deep genetic structure is not present in our mitochondrial sequence data set. We never obtained a maximized distance  and we rF  were all highly significant (p < 0.001). Overall RST estimates were also highly significant (p < 0.001), exhibiting the same pattern as the FST estimates and exceeded these for the northern-southern, eight-population, coastal-interior, and subspecies population sets .The overall FST and RST estimates from our microsatellite data displayed patterns almost congruent to the pairwise FST and ΦST estimates obtained from the mtDNA data. As with the pairwise FST and ΦST estimates for the mtDNA data, the pairwise population FST values were smaller than and showed patterns similar to the pairwise RST estimates, so we chose to present only pairwise RST estimates  were not statistically significant. Pairwise RST estimates between the Channel Islands population and the northern, southern, and Haida Gwaii populations were significant at 0.130, 0.091, and 0.178, respectively  was between the Channel Islands and Fairbanks populations. Of the pairwise comparisons amongst the set of eight populations that included the Channel Islands, the lowest pairwise RST estimate (0.027) was between the Channel Islands and southern California populations; the second-lowest estimate (0.094) was between the Channel Islands and Northern Rocky Mountains. Across all loci, we identified three private alleles in the Channel Islands and four in Haida Gwaii, whereas we found only two private alleles in the southern California population (RST estimates involving O. c. sordida (0.187) was between O. c. sordida and O. c. celata. The lowest of these estimates (0.105) was between O. c. sordida and O. c. orestera, but the estimate between O. c. sordida and O. c. lutescens (0.106) was very close  and Log(1+GGD) versus genetic distance (GD) resulted in weak, statistically significant, positive correlation between geographical distance of O. celata sampling localities and genetic distance measured at microsatellite loci  vs. GD). Our preliminary Structure analyses, in which we did not provide any a priori population information, suggested K = 1 as the optimal number of genetic clusters. When we grouped the samples into eight pre-designated populations, the mean ln Pr(X—K) and ΔK  was for K = 1, although the log probability for K = 2 was very similar. With K = 2, the southern California, Nevada, and Southern Rocky Mountains populations had high ancestry in one of the clusters and the northern California, Northern Rocky Mountains, Haida Gwaii, and Fairbanks populations had similarly high ancestry in the other genetic cluster  was for K = 1.Overall, the microsatellite data revealed little genetic structure and low divergence of populations among our ) and ΔK  suggesteclusters . All of  cluster . In our Our IMa2p analyses obtained an upper bound to the effective size of the Channel Islands population, but the analyses did not converge on an upper bound to the effective size of the mainland southern California population. This suggests that the effective size of the mainland population is likely much higher than that of the Channel Islands. Even though we were unable to accurately calculate migration rates scaled by population size, we were still able to assess the relative population sizes and rates of migration between the two populations. Across all three runs, we calculated a pairwise probability of 1.000 that the current effective population size of the southern California population is greater than that of the Channel Islands population. The probability that the current effective population size of the Channel Islands population is greater than that of the southern California population was <0.001. Our migration rate estimates were similar across our three IMa2p runs. Across all three runs, we estimated probabilities of 0.986 to 1.000 that the rate at which (looking forward in time) the southern California population receives genes from the Channel Islands population is greater than that of the reverse direction. Inversely, we calculated probabilities ranging from 0.000–0.013 that the rate at which (looking forward in time) the Channel Islands receives genes from southern California is greater than migration in the reverse direction.Oreothlypis celata revealed some structure in portions of the range and high levels of shared alleles across much of the mainland distribution of O. celata. The strongest result derived from our present dataset is that both the mitochondrial and microsatellite data suggested that the Channel Islands represent the most genetically distinct population included in our study. We found the highest genetic divergence between the Channel Islands and Fairbanks populations, the two most geographically distant populations in our analyses. More generally, the mitochondrial data suggested higher pairwise divergences among populations than the microsatellite data. The mitochondrial, but not the microsatellite data, supported statistically significant divergence between northern and southern O. celata. The microsatellite data provided weak support for isolation-by-distance across the species range.Genetic analyses of population structure in Oreothlypis celata comprise a separate population that is distinct from the mainland population. Notable is the lack of ND2 haplotype diversity  in the Channel Islands. This is suggestive of a founder event followed by persistence of a relatively small population that has likely fluctuated in size over time or of strong selection among mitochondrial genotypes to favor one genotype. Our mismatch distribution plots, Tajima’s D and Fu’s FS results, are consistent with the population recently having expanded in size. The nucleotide diversity within all other populations was much higher than that of the Channel Islands. Within the Channel Islands, the northern and southern island populations  did not display divergence in pairwise FST or ΦST comparisons of the mtDNA gene ND2 or in pairwise FST or RST comparisons of microsatellite data. Sequences from both islands clustered in our phylogenetic trees and haplotype network, suggesting that O. c. sordida from the northern and southern Channel Islands constitute one large population. The O. c. sordida individuals from Santa Cruz Island all shared the same ND2 haplotype, which was also present on Santa Catalina Island. We identified three additional ND2 haplotypes unique to Santa Catalina Island. The difference in haplotype diversity could be merely a sampling artifact, but this is unlikely given our sample size of 15 individuals from each island. Although the northern and southern Channel Islands may, in fact, be two separate populations that are diverging, any divergence is likely too recent to be statistically detected with our genetic data, despite the high mutation rates of our markers.Both the mtDNA and microsatellite data suggested that the Channel Islands Oreothlypis celata, O. c. sordida from the Channel Islands do not undertake a lengthy migration, although individuals may move short distances outside of the breeding season . The institution and collection codes that precede the specimen number in each specimen identifier match those of the National Center for Biotechnology Information (NCBI) BioCollections database. We list the collections that archive each sample as well as additional information for each sample, including specific location, county, state, country, and date of collection. For the O. celata samples, we also provide a number to denote the ND2 haplotype in the column “ND2 Hap Num”, which matches the numbers used in the haplotype network figures. We list the subspecies of O. celata in the column “Ssp” and the population designation of the sample within the “8 populations” schema in the column “Population”. We provide the NCBI GenBank Accession numbers for all of the ND2 sequences we produced.We provide the identifying codes and taxon of each Click here for additional data file.10.7717/peerj.7388/supp-2Table S2ND2 mitochondrial DNA sequence  and microsatellite data . Values followed by asterisks are significant after applying a Bonferroni correction (p ≤ 0.008).This table presents divergence statistics for pairwise coastal and interior population comparisons calculated using Click here for additional data file.10.7717/peerj.7388/supp-3Table S3N” is the number of individuals genotyped. “A” is the number of alleles in the population followed by the number of private alleles in parentheses. ”R˙S” is allelic richness, ”H˙O” is observed heterozygosity, ”H˙E” is expected heterozygosity, and “p-val” is the p-value resulting from a test of Hardy-Weinberg Equilibrium. P-values followed by asterisks indicate significant difference between the observed and expected heterozygosities after applying a Bonferroni correction (p < 0.005). See This table presents the statistics regarding the variability of the microsatellite loci in each population. “Click here for additional data file.10.7717/peerj.7388/supp-4Table S4We present the genotype values of all successfully genotyped individuals. The first row gives the names of the loci. The first column gives the sample codes of the individuals (see Click here for additional data file.10.7717/peerj.7388/supp-5Figure S1Oreothlypis celata museum skins. From left to right, the specimens are MVZ:Bird:177025, MVZ:Bird:177011, MVZ:Bird:179458, MVZ:Bird:173505, and MVZ:Bird:173962. These represent the four recognized subspecies with representatives of O. c. sordida from both the southern and northern Channel Islands. From left to right, the specimens are an O. c. sordida from Santa Catalina Island; an O. c. sordida from Santa Cruz Island; an O. c. lutescens from interior northern California; an O. c. orestera from Nevada; and an O. c. celata from Fairbanks, Alaska. Image taken by Anand Varma, reproduced with permission.This is a ventral view of five Click here for additional data file.10.7717/peerj.7388/supp-6Figure S2This is a dorsal-view of the same five specimens seen in Click here for additional data file.10.7717/peerj.7388/supp-7Figure S3ND2 haplotype network colored by samples’ grouping into coast and interior populations. See This is the Click here for additional data file.10.7717/peerj.7388/supp-8Figure S4Oreothlypis celata. The height of each color in a given bar illustrates the proportion of ancestry derived from each genetic cluster for that individual.This figure depicts the ancestry of each individual in the two genetic clusters identified by Structure within the seven labeled populations after we excluded the Channel Islands population. Different colors represent the two genetic clusters identified by Structure. Each vertical bar represents an individual Click here for additional data file.


The Neolithic transition has led to marked increases in census population sizes across the world, as recorded by a rich archaeological record. However, previous attempts to detect such changes using genetic markers, especially mitochondrial DNA (mtDNA), have mostly been unsuccessful. We use complete mtDNA genomes from over 1700 individuals, from the 1000 Genomes Project Phase 3, to explore changes in populations sizes in five populations for each of four major geographical regions, using a sophisticated coalescent-based Bayesian method (extended Bayesian skyline plots) and mutation rates calibrated with ancient DNA. Despite the power and sophistication of our analysis, we fail to find size changes that correspond to the Neolithic transitions of the study populations. However, we do detect a number of size changes, which tend to be replicated in most populations within each region. These changes are mostly much older than the Neolithic transition and could reflect either population expansion or changes in population structure. Given the amount of migration and population mixing that occurred after these ancient signals were generated, we caution that modern populations will often carry ghost signals of demographic events that occurred far away from their current location. HoweveA major difficulty in interpreting these results is that genetic dating of events is a very challenging endeavour, as mutation rates  come with high levels of uncertainty . Over thHere, we take advantage of the Phase 3 data of the 1000 Genomes Project, which now includes over 2500 individuals from several major continental regions . We use 2.2.1.www.1000genomes.org/data, [The Phase 3 sequence data from 20 populations, comprising five populations for each of the four main geographical regions of Europe, East Asia, South Asia and Africa, were downloaded from the 1000 Genomes Project website , includrg/data, ,14.www.1000genomes.org).The European populations were as follows: Finnish sampled in Finland (FIN); European Caucasians resident in Utah, USA (CEU); British in England and Scotland (GBR); an Iberian population from Spain (IBS) and Toscani from Italy (TSI). Representing East Asia were the Han Chinese in Beijing (CHB); Southern Han Chinese (CHS); Dai Chinese from Xishuangbanna, China (CDX); Kinh population from Ho Chi Minh City, Vietnam (KHV) and Japanese from Tokyo (JPT). The South Asian populations were Punjabi Indians from Lahore, Pakistan (PJL); Gujarati Indians in Houston, USA (GIH) as well as Indian Telugu sampled in the UK (ITU); Bengali from Bangladesh (BEB) and Sri Lankan Tamil from the UK (STU). Finally, in Africa, we chose a population from the Western Division within The Gambia (GWD); Mende from Sierra Leone (MSL); the Yoruba from Nigeria (YRI); the Esan, also from Nigeria (ESN); as well as the Luhya from Webuye in Kenya (LWK). Full details of the populations and the original sampling and sequencing methods can be found on the 1000 Genomes Project website  with TN93 + I + G and 1.007 × 10−8 µ/Site/Year; protein coding positions at 1st and 2nd codon (PC1 + PC2) with TN93 + I + G and 0.756 × 10−8 µ/Site/Year; and protein coding positions at the 3rd codon (PC3) with TN93 + G and 3.323 × 10−8 µ/Site/Year. See electronic supplementary material, table S1.Mutation rates of mtDNA vary among bases according to region, codon position and depending on whether the region is genic or non-genic . We maxiet al. , who useet al.  on a laret al. , the par2.3.Ne [We analysed our mtDNA data with the extended Bayesian skyline plot (EBSP) method, a Bayesian, non-parametric technique for inferring past population size fluctuations from genetic data. Building on the previous Bayesian skyline plot (BSP) approach, EBSP uses a piecewise-linear model and Markov chain Monte Carlo (MCMC) methods to reconstruct a populations' demographic history  and is iNe . A linkeEach population was run separately with each run consisting of 100 million generations sampled every 10 000 steps and the first 10 million samples were discarded as burn-in . To maxin = 107 samples); CHS in East Asia (n = 105 samples); GIH in South Asia (n = 103 samples); GWD in Africa (n = 113 samples). The resulting profiles were essentially identical, though with somewhat narrower confidence intervals . Consequently, for maximum comparability, the results presented are for the sample size of 85 that could be achieved for all populations.To confirm that 85 samples provide adequate data for accurate population reconstruction, we re-ran the analyses using all available samples for the population from each major region with the maximum samples. Run length was extended to 200 million generations to account for increased sample size, while burn-in remained at 10%. For the four major regions, these largest samples were: IBS in Europe . At each of these 21 time-points, the size of each population was estimated using linear interpolation between the two immediately flanking values, and the total difference was calculated ass1 and s2 are the interpolated sizes in the two populations at bin i.Each BEAST analysis yields a profile comprising 85 paired size–time estimates that together describe the demographic history of that population. Unfortunately, different populations have different history lengths and the densities of the points vary along each profile. To attempt to obtain a fair estimate of similarity between any given pair of profiles, we used the following strategy. Comparisons were made based on 20 evenly spaced time intervals summing to the length of the shorter history  of relevant parameters was greater than 200, our criterion for convergence, for 19 populations. One South Asian population (BEB) failed to reach 200, so the results for this population should be treated with some caution. However, because replicate subsets all yield similar profiles and the average profile is similar to others from the same geographical region, we believe that the broadly correct demographic history has been recovered. A constant population size can be confidently rejected for all 20 profiles as the 95% highest posterior density for the number of population changes excludes 0 in every instance.et al. [In terms of population similarity, we used autosomal SNP data from the 1000 Genomes Phase 3 to calculate Fst between all population pairs, using the method of Hudson et al. . As expe3.1.3.1.1.Profiles for the five African populations are presented in 3.1.2.The five European profiles are presented in 3.1.3.The five profiles for South Asia are shown in 3.1.4.et al. [The five population profiles for East Asia are presented in et al. .Figure For a more objective depiction of the extent to which profiles are more similar between related populations, we plotted a measure of curve similarity (CS) against Fst . As CS c4.We used the Bayesian program BEAST to infer population histories for 20 global human populations using whole mitochondrial genome sequence data from Phase 3 of the 1000 Genomes Project. Our analysis builds on earlier studies using the Phase 1 data e.g. ,24. The et al. [Compared with previous studies, our analysis has been able to deploy larger sample sizes, a coalescent model and improved mutation rate estimates. The fact that we still fail to detect a clear signal from the Neolithic transition may suggest that even complete mtDNA genomes lack sufficient resolution to detect changes over this time scale. This conclusion agrees with simulations by Aimé & Austerlitz , who arget al.  analysedet al. . Here, tet al. [Ne.The possibility that different markers can tell different stories is emphasized by the work of Karmin et al. . Their rVerifying the ability of programs like BEAST to infer accurate population histories by simulation is difficult. Modern human populations have extremely complicated histories with changing levels of substructure, stratification by religion and politics and mixing through trade, wars and slavery –29. Yet,et al. who suggest that the expansion signal seen in their BSPs around 45–35 ka may be indicative of a secondary founder event in the region that obliterated more ancient signals.The reconstructed population profiles we have generated exhibit several features that appear consistent with known demographic events. Thus, the very early expansion observed in East and West Asian populations is compatible with the out-of-Africa bottleneck and subsequent expansion. Similarly, the timing of the expansion in Southern Europe could be seen as pointing to the beginning of the Neolithic Transition in the Near East, the source of farmers who later colonized the rest of Europe –32. In bWithin each major region, the profiles are generally rather similar, though interestingly there also appear to be east–west/north–south trends. Thus, in Africa, the two westernmost populations GWD and MSL both show an earlier but smaller expansion compared with the two Nigerian populations YRI and ESN. Similarly, among the East Asian populations, there is tendency for the most recent expansion to occur more recently in the more northern populations CDX, CHB and JPT. It is also notable that the more northern/eastern South Asian populations have profiles that are most similar to the more western East Asian populations, with PJL and ITU appearing most similar to CDX and KHV. These putative trends require a further increase in sample size to quantify but suggest that demographic change can, in principle, be tracked across both time and space.Ne which might have nothing to do with actual changes in the local census population. A single uniparental marker offers a powerful tool for investigating demographic histories, but interpretation must be done carefully with the understanding of what details might be missing, wiped out or swamped by a suite of different influential processes [The fact that the earliest signals are found in populations that are mostly far from where they were when the changes occurred raises an important cautionary note in interpreting these trajectories: such reconstructions are only valid under the assumption of a closed population ,34,35. Processes . This isrocesses . It is tIn conclusion, expansion of the analysis of the 1000 Genomes Project mitochondrial DNA data to Phase 3 allows novel comparisons both between and within four major geographical regions. Although it remains difficult to ground-truth the dates, the fact that clear geographical trends are apparent suggests that the relative size and timing of expansions found are probably reliable. However, naive interpretation of the data would imply that the populations studied all experienced expansions that initiated prior to the adoption of agriculture. It was previously suggested that such changes might be associated with changes in lifestyle, such as an increase in sedentarism, that occurred before the advent of food production in the Neolithic [


Coffea mauritiana represents an interesting model case for investigating patterns of within-island differentiation at small spatial scale. In this study, we examined the genetic diversity and population divergences of C. mauritiana using SNP markers obtained from 323 individuals across 34 locations in Reunion Island. Using redundancy analysis, we further evaluated the contribution of geographic and climatic factors to shaping genetic divergence among populations. Genetic diversity analyses revealed that accessions clustered according to the source population, with further grouping in regional clusters. Genetic relationships among the regional clusters underlined a recent process of expansion in the form of step-by-step colonization on both sides of the island. Divergence among source populations was mostly driven by the joint effect of geographic distance and climatic heterogeneity. The pattern of isolation-by-geography was in accordance with the dispersal characteristics of the species, while isolation-by-environment was mostly explained by the heterogeneous rainfall patterns, probably associated with an asynchronous flowering among populations. These findings advance our knowledge on the patterns of genetic diversity and factors of population differentiation of species native to Reunion Island, and will also usefully guide forest management for conservation.Oceanic islands are commonly considered as natural laboratories for studies on evolution and speciation. The evolutionary specificities of islands associated with species biology provide unique scenarios to study the role of geography and climate in driving population divergence. However, few studies have addressed this subject in small oceanic islands with heterogeneous climates. Being widely distributed in Reunion Island forest,  Oceanic islands are de novo islands that are usually result of volcanic activity and have never been connected to landmasses (García-Verdugo and Fay Reunion Island (2512 km²) is a young island belonging to the Mascarene archipelago  and isolation-by-environment (IBE)  comprises 124 species most of which are distributed in tropical Africa, Madagascar and islands in the western Indian Ocean, and when Psilanthus species are included, even in tropical Asia and Australia , all Coffea species are diploid (2n = 2 × = 22). Among the latter, coffee trees from the Mascarene Islands (Mauritius and Reunion Island) are of monophyletic origin . Several cases of natural hybridization between them were described, especially considering the crossing C. macrocarpa × C. mauritiana  in Reunion Island forest. C. mauritiana is present as small patches of individuals widely distributed throughout the rainforest (270–1500 m a.s.l.)  explore the genetic connectivity between C. mauritiana from Reunion Island and the Mauritian coffee species, (3) investigate the role of climate and geographic distance in shaping population divergences in Reunion Island, and (4) highlight historical migration and mixture events.This study focusses on genetic diversity and population divergence of an indigenous tree  and rainfall . Apart from lowlands, the original habitat of C. mauritiana in Reunion Island was almost well preserved from human disturbances. Collecting missions were organized mainly based on previous records available in the “Mascarine” database  developed by the Conservatoire Botanique National de Mascarin . Sampling was designed to cover the environmental range of habitats and the geographic distribution of the species in Reunion Island as well as possible. The number of samples per source population (~ 10) differed depending on accessibility and on the number of trees identified. Special care was taken to collect distantly located trees to minimize relatedness between the trees sampled. Nineteen additional C. mauritiana trees were sampled at the international Coffea collection , not only providing more individuals per source population but also individuals from two additional source populations . In all, a total of 326 C. mauritiana individuals from 34 source populations were collected   Table .Fig. 1Saw/v) agarose gel electrophoresis, in comparison with a standard series of lambda DNA/EcoRI + HindIII markers .Leaves were freeze-dried and stored at −80 °C until DNA extraction. Approximately 70 mg of lyophilized leaf material were used for genomic DNA extraction using an in-house MATAB based protocol. The DNA was treated with RNase A . DNA concentration was estimated by 1%  and contrasted annual rainfall regimes  were obtained from the METEOR meteorological data repository . One DNA sample was duplicated to be used as technical replicate. Samples were genotyped by Diversity Arrays Technology Pty Ltd using the DArTseq platform . Genotype data were generated using proprietary analytical pipelines (DArT Pty Ltd). Identical sequences were collapsed into “fastqcall files”. These files were then processed to produce “SNP” tables (codominant markers) and “silicoDArT” tables (dominant markers). Finally, the different sequences were blasted  against the publicly available C. canephora genome (http://coffee-genome.org/) to determine the corresponding genomic positions.Genomic DNA from each accession was standardized to a concentration of 50–100 ng μlC. mauritiana from Reunion Island, seven accessions were selected to cover the natural distribution of the species. Concerning the other DArTseq genotyped species, all accessions were included. The resulting dataset included 24 accessions. SNP showing missing values  higher than 8% in all Coffea accessions were discarded. SNP with minor allele frequency (MAF) lower than 0.02  among the seven C. mauritiana from Reunion Island were filtered out. Regarding the other Coffea accessions, no MAF filtering was performed.The DArTseq-derived SNP dataset was subset for phylogenetic analysis purposes. Regarding The genetic distance (simple matching) between accessions was calculated from the filtered SNPs using DARwin 6.0.14 software , and the observed heterozygosity (Ho) were calculated for the 34 source populations, using ARLEQUIN version 3.5.2 .The DArTseq-derived SNP data set corresponding to the C. canephora genome could not be determined. Individuals were also filtered to only retain those with NAs <5%.Otherwise, the SilicoDArT data set was filtered to avoid markers with NAs >5%; markers for which a restriction site was detected only once; markers whose location on A tree-based method was first performed using DARwin. Genetic distance was computed between each pair of individuals  using a simple matching distance and the corresponding neighbor-joining tree was computed.K), ranging from K = 1–30 (α = 1000). The cross-entropy criteria (CEC) helped to choose the optimal K value, a smaller value of the CEC generally indicating a better run  out of the 100 runs corresponding to the selected K was retained for the graphical representation of ancestry estimates.Next, the sNMF program  implemented in the LEA R package v. 2.0.0.  were performed using ARLEQUIN. The tested model was the partitioning of total variance among the inferred genetic clusters from the sNMF analysis, among source populations within clusters, among individuals within source populations and within all individuals. When individuals from the same source population were distributed in several clusters, or showed more than 30% admixture, these individuals were discarded from the analysis. Weir and Cockerham’s FC. mauritiana were assessed by Treemix v. 1. 12 . Each Treemix analysis was computed three times to ensure consistency of the results. As all three runs were consistent, only one run is presented in the results.The dataset was divided into windows of 20 SNPs to account for potential remaining linkage disequilibrium by jackknifing. Up to three migration edges were tested  included in the TreeMix package was performed to test for mixture events for all combinations of three points out of the K clusters of C. mauritiana samples. A window of 20 SNPs was used to account for linkage disequilibrium by jackknifing. A negative f3 statistic provided evidence that X originated from a mixture between Y and W. According to Reich et al.  and a second matrix of explanatory variables  where collinearity among variables must be minimized (Legendre and Legendre ST were set as dependent variable. The explanatory variables were set as follows: (i) Geographic variables. Principal coordinates of neighborhood matrix (PCNM) was used to transform the original geographical matrix of Euclidean distances into uncorrelated vectors. As positive eigenvectors better describe the geographical distance  was used to estimate the relative contribution of IBD and IBE to the differentiation of capscale in the vegan R package v. 2.4-2  and IBE . Finally, the relative contribution of both IBD and IBE and their intersection was estimated by variance partitioning.dbRDA was performed using the function A total of 31,398 codominant SNPs and 16,775 SilicoDArTs were obtained from the DArTseq analysis of the overall coffee accessions. In the analysis, special attention was paid to the SNP data. The average genotype call rate was of 83.2%, with SNP scoring reproducibility of 99.7%. The rate of SNP genotyping error was 1.7%.C. myrtifolia appeared to be the most divergent, while C. macrocarpa, C. mauritiana, and C. bernardiniana were more closely related. Accessions of C. mauritiana from Reunion Island formed a distinct cluster that is divergent from the Mauritian accessions. This cluster was supported by high bootstrap values. Finally, C. mauritiana from Reunion Island was as distant from C. bernardiana representatives, C. macrocarpa and C. mauritiana from Mauritius.Genetic distances among the 24 selected coffee accessions were calculated from the 2251 filtered SNPs. The resulting neighbor-joining tree showed that accessions were grouped according to their geographic origin  and observed heterozygosity (Ho) among the 34 C. mauritiana source populations collected in Reunion Island were 0.14 and 0.18, respectively  tree Fig. . IndividThe overall observation of individual clustering according to source population affiliation was confirmed by the NJ tree using filtered SilicoDArT markers Fig. . The tenC. mauritiana individuals in K clusters and to infer individual ancestry coefficients. The K values ranging from 5 to 8 minimized the cross-entropy criteria (CEC)  Fig. . An overEC) Fig.  led us tion Fig. . The barion Fig. . IndividST values were computed among the source populations comprising more than five individuals (e.g. 32 source populations). The FST values ranged from 0.014–0.430, with an average genetic differentiation of 0.168 . The FST values for source populations belonging to the same regional cluster were low whereas higher FST values were observed between source populations from different regional clusters. Pairwise FST was also computed between the seven regional clusters  and west , respectively. The highest differentiation was observed between clusters from the east and west coast, the maximum FST value was recorded for the north–western and eastern pair (FST = 0.26), while the minimum FST value was recorded for the north–eastern and eastern pair (FST = 0.04). A low FST value was also observed between southern clusters (FST = 0.08)Pairwise F68 Table . All FSTFST, with 15.2% of the global variation between regional clusters, and 5.0% between source populations belonging to the same genetic cluster.The results from the AMOVA were consistent with the differentiation depicted by the pairwise C. mauritiana, the three C. myrtifolia and the three C. bernardiniana samples were retained. A Treemix analysis was conducted to examine the topology of relationships and migration history among the seven regional clusters. With C. myrtifolia and C. bernardiniana individuals used as outgroups, the ML tree showed a split between regional clusters from the east coast (north–eastern and eastern clusters) and the other clusters from the south, the center and the west coast , indicating the highest divergence from a set of ancestral allele frequencies. The tree representation captured the total variation (98.9%) well. The residual heatmap revealed that the north–western and the central clusters were the worst represented, with a standard error of 3.7 .After filtering for quality criteria and linkage disequilibrium, a total of 1280 SNPs for the 323 ast Fig. . The dri3.7 Fig. . Migrati3.7 Fig. . This hi3.7 Fig. . Adding f3-statistics test (three-population test) was applied to combinations of three regional groups to further investigate their mixture history. The results presented in Table Z-score: −2.12). In addition, a complex history was obtained for the south-western cluster: significant evidence was found for a mixture between western and eastern clusters (Z-score: −2.98), and between western and south-eastern clusters (Z-score: −3.29). Weak evidence was also found for a mixed origin of the central cluster from the north-eastern and the north-western clusters (Z-score: −1.95).An exhaustive ST) explained by uncorrelated geographic vectors and climatic factors. The optimized model  constrained 88.4% of total inertia, and retained seven variables: the four vectors of geographical distance, CDD (mean number of consecutive dry days), PET  and Tmean (mean of average daily temperature)  was used to assess the amount of genetic divergence among source populations (pairwise Fe) Table , Fig. S4A first variance partitioning test was then conducted to assess the relative contribution of IBD, IBE and their intersection in explaining the genetic differentiation. The largest contributor to the genetic divergence was the intersection IBD ∩ IBE explaining 56.4% Fig. . A seconP-value = 0.002, R²adj: 49.43%). These results confirmed the role of climate in explaining genetic divergence at reduced spatial scale and more precisely, the influence of the rainfall pattern.Even if we attempted to control for collinearity in data processing, this result highlighted the difficulty of disentangling the effect of geography and climate in Reunion Island, since geographically distant sites also tend to differ environmentally. To test for climatic effects while controlling for geographic distance, a dbRDA analysis was again performed on a smaller region of the island. The southern region was chosen as it contained sufficient source populations (10) to perform the analysis, with different cluster affiliation and a context of climatic heterogeneity at small spatial scale. Among the five climatic variables and the five geographic vectors, the optimized model only retained CDD and geo2. As the marginal effect of geo2 was not significant in the ANOVA table, it was removed from the explanatory variables, leading to a model with a single explanatory variable (CDD) that explained 49.4% of overall genetic differentiation , than from C. bernardiniana representatives (Mauritius) and C. macrocarpa (Mauritius). Thus, the ancestors of Reunionnese C. mauritiana most likely diverged from the ancestral lineage at the origin of Mauritian C. mauritiana, C. bernardiniana and C. macrocarpa species. Otherwise, the cluster formed by C. mauritiana accessions from Reunion Island was clearly divergent from the Mauritian species, arguing for genetic isolation after the ancestral events of colonization. So, Reunion Island colonization would result from few ancient events of long distance dispersal from Mauritius.The origin of C. mauritiana in Reunion Island and Mauritius were also reported to be distinct for fruits and leaves phenotypes , indices can be compared to those obtained when considering a continental coffee species. Since C. canephora is an allogamous species and also presents the widest geographic distribution among Coffea species in Africa, it was chosen for comparison. Bearing C. canephora evolutionary history in mind  is quite ancient  revealing higher differentiation between distant source populations. These elements suggest a pattern of local dispersal with isolation-by-distance (IBD). Indeed, IBD was of considerable importance in explaining divergence between source populations (21.4% of genetic differentiation was explained by geographic distance). Pollen and seed dispersal are major determinants of spatial genetic structure. Compared to pollen transport, seed dissemination appears as marginal in several species of coffee trees. Berries seem mainly dispersed by gravity to a few meters from the mother tree and possibly transported by run-off water . Whether considering all source populations or focusing on the southern ones, the indices for dry period duration, CDD, appeared as the main climatic factor associated with differentiation. In coffee species, floral bud morphogenesis is induced by a dryness period and synchronized flowering occurs at the first rain showers of the rainy season DaMatta . BecauseC. mauritiana and population admixture. Overall, the low level of divergence between clusters suggests recent differentiation. The architecture of the TreeMix-derived ML tree led us to hypothesize distinct evolutionary pathways for regional groups from the eastern and western coasts, which is also supported by higher differentiation in pairwise FST. The central montane barrier must have been a major barrier to gene flow, separating the two sides of the island (east coast versus west coast). Moreover, results suggest the recent expansion of the species through a step-by-step colonization on both sides of the island. Along the western coast, the colonization front apparently proceeded from south to north. The north–western cluster appeared to be the most differentiated (higher drift parameter) and the lowest levels of genetic diversity were also found in this regional group. Apart from separate evolution on the two sides of the island, a few ancestral mixtures were identified in the south–western and south–eastern clusters, attesting to a more complex history of population establishment in the southern part of the island. Due to proximity to the younger volcano (Piton de la Fournaise), the establishment of C. mauritiana in the south may have been locally impacted by several geologic events linked with volcanic activity  have been reported in Mauritius in association with climate change (Van Der Plas et al. In conclusion, our study provides a comprehensive picture of the genetic diversity and structure of DArTseq genotype data available from the Dryad Digital Repository: 10.5061/dryad.4vc0rg0Supplementary data