Trogloneta yunnanensis in South China Karst. The results showed that there was high genetic divergence among six populations, and the divergence of these six populations can be traced back to the late Pleistocene. Our results suggested that isolation was a pivotal factor affecting the biodiversity of cave faunas, and the biodiversity of cave-dwelling faunas needs to be studied as soon as possible.The subterranean fauna is an important component of global biodiversity. However, research on the subterranean fauna of the Yunnan–Guizhou Plateau in Southwest China, one of three important karst landforms in the world, is limited. In this study, we performed a population genetic analysis and reconstructed the phylogenetic tree of six populations of Trogloneta yunnanensis  based on 73 spider samples from six underground populations in South China Karst. Population genetic structure analysis showed a clear divergence (FST > 0.9 and Nm < 0.05) among populations according to mitochondrial genes. The phylogenetic gene tree constructed by BI and ML methods recovered six geographic clades. Divergence time estimation indicated that the divergence of these six populations can be traced back to the late Pleistocene. We supposed that the geographic isolation led to the extreme population structure. According to this study and previous studies about troglobites living in this region, the subterranean habitats of the Yunnan-Guizhou Plateau may contain many organisms with similar genetic structures. The subterranean biodiversity in the karst area of Southern China needs to be re-evaluated and protected.Subterranean karst caves can contain unexpected biodiversity, but few studies related to spider population genetics have been conducted in the karst area of Southern China. In this study, we investigated the population genetic structure of  Compare of food . Culver  of food  estimate of food . Thus, t of food ,7,8. The of food .Many cave species have extremely small ranges, with a large part of troglobitic species, and subspecies limited to a single county, and many species are even known from a single cave . IsolatiThe Mountains of Southwest China are one of the 34 global biodiversity hotspots . The YunTrogloneta yunnanensis [Trogloneta yunnanensis has been showing some cave-adapting characteristics, such as lighter color skin with little pigmentation. The main aims of this study were: (1) to explore the genetic structure of Trogloeta yunnanensis populations; (2) to reconstruct phylogenetic relationships and estimate the divergence time of different populations.In this study, we investigated the population genetics of a tiny cave spider, nanensis , which bnanensis . TroglonT. yunnanensis outside the cave, and there were no surface records of T. yunnanensis individuals in previous studies [The samples of spiders were collected from six isolated caves in Southwestern China . The sam studies ,29.Trogloneta yuensis [Yamaneta kehen  and Yamaneta paquini  were selected as outgroups. Trogloneta yuensis is the most closely related species to T. yunnanensis, and two Yamaneta species are also cave-dwelling mysmenid spiders from the Mountains of Southwest China [cox1), 16S ribosomal RNA (rRNA), 28S rRNA, H3, and ITS-2. The primers were provided in Depending on the abundance of specimens, ten to fourteen individuals per cave were selected to extract DNA.  yuensis  YamanetaT. yunnanesis. DNAsp v6.0 [We used five genes to analyze the genetic structure of six populations of Asp v6.0  was usedAsp v6.0  were conAsp v6.0 . The F-sAsp v6.0 , and we Asp v6.0 .T. yunnanensis based on the concatenated genes (cox1 + 16S + H3 + 28S + ITS-2). We used PartitionFinder2 [To examine the monophyly and allow us to compare diversity between populations, we used the Bayesian Inference (BI) method to reconstruct the phylogenetic tree of nFinder2  to identnFinder2 , and fouThe maximum-likelihood phylogenetic analysis was conducted in IQ-TREE v1.6.12 . We usedT. yunnanensis, the species tree was constructed in BEAST v1.10.4 [To estimate the divergence time among six populations of  v1.10.4  under th v1.10.4 . TreeAnncox1 gene for divergence time estimation. Preliminary analyses using a lognormal relaxed clock for the cox1 gene showed that the posterior distribution of the ucld.mean parameter accreted to zero, and hence a strict clock was preferred. The prior rate parameter was set to normal distribution with mean ± SD = 0.0168 ± 0.0018 for cox1.Due to the lack of suitable calibration points, we used prior information on substitution rates of genes to estimate population divergence time based on available information for spiders ,47,48,49cox1, 420 bp 16S, 781 bp 28S, 312 bp H3, and 373 bp ITS-2) in 73 individuals of T. yunnanensis. However, several cox1, 28S, and ITS-2 sequences could not be recovered because of their high AT content. The details are provided in T. yunnanensis, we believe that these missing data did not affect our analyses. The cox1, 16S, H3, and ITS-2 datasets had 30, 10, 4, and 2 polymorphic sites, respectively. The genetic diversity parameters of these six populations are summarized in 16S and H3 genes, respectively  was attributable to interpopulation (within populations), and nearly 97.4% of the ulations .T. yunnanensis. The analysis of the cox1 gene showed the distinct geographic structure in T. yunnanensis. There were 12 haplotypes in 72 individuals from six populations, and six populations had no shared haplotypes  were clustered into the second one  sites ranged from 0 to 3. The genetic diversity varied significantly among geographical distinct populations of T. yunnanensis. The lack of shared haplotypes among these six populations  were observed occupying geographically adjacent areas, but there was no mixing, suggesting that migration was limited (T. yunnanensis is limited, and cave isolation has a significant impact on its genetic structure.We reconstructed the phylogenetic tree of  limited . The phyThe colonization and speciation of cave animals is generally explained by two hypotheses, one is the “climate-relict”, and the other is the “adaptive-shift” ,5. The fT. yunnanensis occurred in the middle-to-late Pleistocene. The results were similar to previous studies on Nesticella spiders in this region. Zhang and Li [Nesticella in the Yunnan-Guizhou Plateau originated in the Miocene, and most populations of different species formed in the Pleistocene. Further research by Ballarin and Li [Nesticella to take refuge in caves and to begin rapid differentiation in the 5.5 Ma. We suggest that T. yunnanensis, which is similar to the Nesticell in body size and habitat, may have experienced a similar evolutionary history. Because of the absence of surface populations and closest sister species in Yunnan-Guizhou Plateau, we could not determine the origin of T. yunnanensis. Ideally, if we can collect related species of T. yunnanensis on the surface, the origin could be inferred, and how the geological change impacted the organisms in the Yunnan-Guizhou Plateau can be further explored.The divergence time in six populations of g and Li  found thn and Li  found thThis study systematically explored the population genetics of a cave spider in Yunnan-Guizhou Plateau. Our results suggested that the isolation was a pivotal factor increasing biodiversity of cave faunas. We believe that cave faunas with similar body sizes and habitats also have similar genetic structures in the Yunnan-Guizhou Plateau. A correct understanding of biodiversity is fundamental to conservation, and karst areas need to be further studied, because there are thousands of caves which are unexplored. Our study provides new insights into the diversity of subsurface life in karst areas. Further research could use larger datasets, such as NGS data, and new analytical tools to explore genetic structures between and even within populations. More research on the underground fauna in karst areas will shed light on the formation pattern of biodiversity in this region.


Our results illustrate how shifting socio-cultural relationships between enslaved and non-enslaved communities during and after the TAST, shaped enslaved-African descendants’ genomic diversity and structure on both sides of the Atlantic.From the 15th to the 19th century, the Trans-Atlantic Slave-Trade (TAST) influenced the genetic and cultural diversity of numerous populations. We explore genomic and linguistic data from the nine islands of Cabo Verde, the earliest European colony of the era in Africa, a major Slave-Trade platform between the 16th and 19th centuries, and a previously uninhabited location ideal for investigating early admixture events between Europeans and Africans. Using local-ancestry inference approaches, we find that genetic admixture in Cabo Verde occurred primarily between Iberian and certain Senegambian populations, although forced and voluntary migrations to the archipelago involved numerous other populations. Inter-individual genetic and linguistic variation recapitulates the geographic distribution of individuals’ birth-places across Cabo Verdean islands, following an isolation-by-distance model with reduced genetic and linguistic effective dispersals within the archipelago, and suggesting that Kriolu language variants have developed together with genetic divergences at very reduced geographical scales. Furthermore, based on approximate bayesian computation inferences of highly complex admixture histories, we find that admixture occurred early on each island, long before the 18 Between the 15th and 19th centuries, European colonization and the Trans-Atlantic Slave-Trade (TAST) put into contact groups of individuals previously isolated genetically and culturally. These forced and voluntary migrations profoundly influenced the descent of numerous European, African, and American populations, creating new cultures, languages, and genetic patterns .Population geneticists have extensively described genetic admixture patterns in enslaved-African descendants in the Americas, and mapped their genomes for regions of ancestry recently shared with continental Africa and Europe . This ali) While the genetic history of enslaved-African descendants in the Americas has been extensively studied, the influence of the TAST on genetic admixture in Africa remains under-investigated. Studying these questions in Africa would provide invaluable information about the influence of the onset and early stages of the TAST and the subsequent expansion of European empires on genetic admixture patterns on both sides of the Atlantic. (ii) While admixture-LD inference methods have repeatedly brought novel insights into the admixture processes experienced by enslaved-African descendant populations, they could only explore historical models with one or two pulses of admixture, a methodological limitation (iii) Finally, while the comparison of genetic and linguistic diversities has been the focus of numerous evolutionary anthropology studies at large geographical scales (Numerous questions remain unsolved and novel interrogations have emerged concerning the history of admixture during and after the TAST. (mitation . Complexmitation . (iii) Fl scales , it has l scales .th century.Here, we propose to reconstruct the detailed genetic and linguistic admixture histories of Cabo Verde, as this archipelago represents an ideal case to address these three understudied aspects of TAST history. First, Cabo Verde is the first European settlement-colony in Sub-Saharan Africa, located 500 kms West of Senegal in Western Africa , and setPrevious genetic studies exploring, first, sex-specific genetic diversity, and, then, genome-wide markers from several islands of the archipelago , attesteBased on these previous studies, we propose to first determine which continental African and European populations in fact contributed to the genetic landscape of each Cabo Verdean island today. Indeed, which enslaved-African populations only briefly transited through the archipelago, and which remained for longer periods is largely debated by historians ; and, whMetHis-Approximate Bayesian Computation (ABC) approach  projection of individual pairwise allele sharing dissimilarities ASD , computeK, which allows to explore individual pairwise genetic resemblances for numerous major axes of variation at once. Extended descriptions of the results are presented in Appendix 3.Based on these results, we further investigated patterns of individual genetic structure among Cabo Verde-born individuals, ASW, and ACB populations with respect to European and Western, Central, South-Western, and Southern African populations , using AK=2, the orange genetic-cluster in st/Fstmax values (−8), with variability across islands ranging from a lowest value of 0.010 in individuals from Santo Antão to a highest value of 0.0519 in Santiago .At K=6, these two groups of islands are now clearly differentiated, as the novel green cluster is maximized in numerous individuals born on Sal, Boa Vista, São Nicolau, São Vicente, and Santo Antão, but represented to a much lesser extent in Santiago, Fogo, and Brava. Instead, individuals in these latter islands retain a majority of membership to the orange ‘West Western African’ cluster, and Maio-born individuals are now found intermediately between the two groups with relatively even memberships to the orange and green clusters respectively. Interestingly, this new green cluster appears to be specific to Cabo Verdean genetic variation, as it is virtually absent from other populations in our dataset except for a small proportion in certain Wolof individuals from West Western Africa.At K=10, the light-green cluster is maximized in Cabo Verdean individuals born on Maio, Boa Vista, Sal, and São Nicolau, distinct form the dark green cluster maximized in individuals born on Santo Antão and São Vicente, and hence producing three distinct ADMIXTURE patterns among Cabo Verdean birth-islands. Furthermore, an alternative mode at K=10 shows .Finally, we aim at describing potential genetic resemblances between the East Asian gene-pool, represented here by the Chinese CHB population, and the Cabo Verdean gene-pool, as a community from China is established in the archipelago since at least the 1950s. Note that for every value of entified , but is Altogether, these ADMIXTURE results, differentiating patterns of genetic resemblance across Cabo Verde and with respect to varied continental African and European populations, have been possible to uncover due to inclusion of varied reference populations from continental Atlantic Africa and Europe, treating all Cabo Verdean islands of birth as differentiated in the analyses .f3-admixture tests  and very reduced to no shared ancestries with other regions of Africa. More specifically, we find that the Mandinka from Senegal are virtually the sole African population to share haplotypic ancestry with Cabo Verdeans born on Brava, Fogo, Santiago, Maio, and Boa Vista, and the majority of the African shared ancestry for Sal, São Nicolau, São Vicente, and Santo Antão. In individuals from these four latter islands, we find shared haplotypic ancestry with the Wolof population ranging from 4%–5% for individuals born on Sal , up to 16–22% for Santo Antão. Finally, we find limited (1–6%) shared haplotypic ancestry with East Western  or South-West Central  African populations in all Cabo Verdean islands, except Fogo and Brava, and the specific populations identified and their relative proportions of shared haplotypic ancestries vary across analyses. Conversely, we find that the ASW and ACB populations share African haplotypic ancestries in majority with East Western African populations , and substantial shared ancestries with Senegambian populations (5–10%), the MSL from Sierra Leone in Central Western Africa (5–12%), and South-West Central African populations (3–18%), albeit variable depending on the number of putative sources considered.Runs of homozygosity (ROH) are Identical-By-Descent haplotypes resulting from recent parental relatedness and present in individuals as long stretches of homozygous genotypes. Their length and abundance can reflect demographic events, such as population bottlenecks and founder events, natural selection, and cultural preferences for endogamy ; and ROHρ=−0.06689, p=0.3179), European , or East Asian . Of all ROH identified, the mean proportion of ROH that were long ranged from 0.065 to 0.280 (We find higher levels of long ROH (≥1 cM) in Cabo Verdeans compared to most other analyzed populations, including ASW and ACB . We findto 0.280 .Material and Methods), indicating that the most recent common ancestor existed after the initial admixture event having generated local-ancestry patterns. Furthermore, we expected that these ‘spanning’ ROH would be among the longest ROH observed if admixture occurred only in the past few generations. We find that , in the same set of 225 Cabo Verde-born individuals. To do so, we used MDS and Mantel testing of correlations between, respectively, genetic and linguistic pairwise differentiation, and socio-cultural and geographical covariates including age, duration of academic education, residence locations, birth-places, and parental birth-places . This correlation increases when considering only within-islands pairwise comparisons and excluding all inter-island comparisons , thus illustrating the strong signal of genetic isolation-by-distance , as well as paternal and maternal birth-places respectively . Finally, while we might intuitively expect that academic education influences idiolects, we find instead that differences in education-duration do not correlate with Kriolu utterance-frequencies differences, whether correcting for residence or birth-places distances, or not , but not within-islands only . This shows that two individuals born on far-away islands are likely to differ more in African genetic admixture levels, than two individuals born on close-by islands, a form of isolation-by-distance pattern for genetic admixture across Cabo Verdean islands.Based on these results of genetic and linguistic diversity isolation-by-distance patterns anchored in individual’s birth-places, we aim at investigating whether individual genetic and/or linguistic admixture levels also exhibit isolation-by-distance patterns across and within islands, beyond the qualitative observation that genetic and linguistic admixture patterns vary across different islands of Cabo Verde obtained above and in previous results . InteresMaterial and Methods) providing an estimate of individual African linguistic-admixture scores , and even marginally significantly correlate with birth-places’ distances at short distances within birth-islands .We explored inter-individual variation in Kriolu utterance frequencies specifically for uttered items of clearly African and dual European-African origins  between genetic and linguistic admixture in Cabo Verde . These positive correlations between genetic and linguistic admixture generalize to the whole archipelago our previous results obtained in Santiago only  goodness-of-fit, visual inspection of summary-statistics PCA-projections, and each summary-statistic’s distribution, for each Cabo Verdean birth-island and for all Cabo Verde-born individuals grouped in a single population, separately. Prior-checks thus demonstrate that MetHis simulations are appropriate for further ABC scenario-choice and posterior parameter inferences using observed data in the African Mandinka and the European Iberian IBS source populations and each Cabo Verde islands separately or grouped altogether, as they allow to mimic the observed summary-statistics, despite the assumption that the European and African proxy source populations are at the drift-mutation equilibrium over the last 21 generations.We find that the summary-statistics calculated from the observed datasets fall well within the space of summary-statistics obtained from 10,000 simulated-datasets under each of the four competing scenarios –10, consMetHis-RF-ABC scenario-choices indicate that multiple pulses of admixture from the European and African source populations , best explain the genetic history of individuals born on six of nine Cabo Verdean islands. Furthermore, we find that even more complex scenarios involving a period of recurring admixture from either source best explain the history of the remaining three islands. Scenarios with periods of recurring admixture from both Africa and Europe  are the least favored across Cabo Verde.Overall , MetHis-MetHis-RF-ABC scenario-choice  contribute to the genetic makeup of Cabo Verde –3. OtherThis could be due to Cabo Verde being only a temporarily waypoint for these latter enslaved-African populations between Africa, the Americas, and Europe, but would also be consistent with additional socio-historical processes (see below). Interestingly, and further echoing these genetic results, the Cabo Verdean Kriolu language carries specific signatures mainly from the Mande language-family, and Wolof and Temne languages from Western Africa, and largely more limited signatures of Kikongo and Kimbundu Bantu languages from Central and South-Western Africa .These results contrast with the admixture patterns identified in other enslaved-African descendant populations in the Americas in our dataset  the linear increase in Santiago, the political and commercial center of Cabo Verde throughout the colonial history of the archipelago, and (ii) the relatively constant reduced reproductive sizes in Brava until today. Altogether, this result was expected as the dry Sahelian climate of Cabo Verde with scarcely accessible water resources, recurring famines and epidemic outbreaks, and the Portuguese crown maintaining a strong control over population movements within Cabo Verde, rendered difficult the perennial peopling of most islands (see Appendix 5). Furthermore, such demographic scenarios are also consistent with long-ROH patterns reflecting isolation on each Cabo Verdean islands, whereas elevated shorter ancestry-specific ROH patterns likely stemmed from admixture , also anchored in individual birth-places , Table 1Altogether, these joint genetic-linguistic patterns highlight the limited effective genetic and linguistic dispersal from one generation to the next within Cabo Verde, including for genetic and linguistic admixture levels, despite extended mobility of individuals within the archipelago. Both mechanisms may thus underline individual linguistic identity construction processes and the genetic relative isolation across and within Cabo Verdean islands.Importantly, we considered only random mating processes in our inferences and interpretations. However, the almost complete lack of identifiability of the admixture-history parameters obtained when considering Cabo Verde as a single random-mating population in our ABC inferences , and ourMetHis–ABC scenario-choices indicate that, qualitatively, these demographic migrations did not necessarily translate into clearly recurring gene-flow processes to shape genetic patterns in most of Cabo Verde islands (MetHis–ABC posterior parameter estimations further highlighted often largely differing admixture histories across Cabo Verde islands (Appendix 5).While we expected recurring African admixture processes due to the known history of regular forced migrations from Africa during most of Cabo Verde history , our Met islands . Indeed,th century, and had thus been relatively limited during most of the plantation economy era .GPS coordinates for each reported location were acquired on site during field-work, supplemented by paper maps and Google Earth for non-Caboverdean locations and islands that we did not visit . While participants’ birth-locations were often precise, increasing levels of uncertainty arose for the reported parental and grand-parental locations. We arbitrarily assigned the GPS coordinates of the main population center of an island when only the island of birth or residence could be assessed with some certainty by participants. All other uncertain locations where recorded as missing data. Figure maps were designed with the software The 261 participants each provided 2 mL saliva samples collected with DNAGenotek OG-250 kits, and complete DNA was extracted following manufacturer’s recommendations. DNA samples were genotyped using an Illumina HumanOmni2.5Million-BeadChip genotyping array following manufacturer’s instructions. We followed the quality-control procedures for genotypic data curation using Illumina’s GenomeStudio Genotyping Module v2.0 described in In summary, we extracted a preliminary dataset of 259 Cabo Verdean Kriolu-speaking individuals, including relatives, genotyped at 2,118,835 polymorphic di-allelic autosomal SNPs genome-wide. We then merged this dataset with 2504 worldwide samples from the 1000 Genomes Project Phase 3 –4 on average within Cabo Verdean islands of birth (SD = 3.0 × 10–4 across islands).After merging all datasets , we consThe Pear Story’  speech-less movie, ‘r Story’ , after wp.107: “a particular instance of actually-occurring language as it is pronounced, grammatically structured, and semantically interpreted in its context”. Transcripts were parsed together and revealed 4831 (L=4831) unique uttered items among the 92,432 uttered items transcribed in total from the 225 discourses from the genetically-unrelated Cabo Verde-born individuals. To obtain these counts, we considered phonetic, morphological, and syntactic variation of the same lexical root items that were uttered/pronounced differently, and we excluded from the utterance-counts onomatopoeia, interjections, and names. Note that we were here interested in the diversity of realizations in the Kriolu lexicon, including within the same individuals. In other words, we are interested in both between-speaker and within-speaker variation. Also note that a very limited number of English words were pronounced by particular individuals (10 utterances each occurring only once), and were kept in utterance-counts.Building on the approach of Croft, 1996i  and each unique uttered item l , i,ln is the absolute number of times individual i uttered the unique item l over her/his entire discourse, if being the vector i and j, The list of unique uttered items was then used to compute individual’s specific vectors of uttered items’ relative frequencies as, for each genetically unrelated Cabo Verde-born individual l-fj,l2, .We categorized each of the 4831 unique uttered items separately into five linguistic categories . Categori and the set of utterances in each category denoted Cat , CatL the number of uttered items in the corresponding category, and i,lf defined as previously.Following , we defiasd software .Recently admixed individuals are intuitively expected to be at intermediate distances between the clusters formed by their putative proxy source populations on ASD-MDS two-dimensional plots. A putative estimate of individual admixture rates can then be obtained by projecting admixed individuals orthogonally on the line joining the respective centroids of each proxy-source populations and, then, calculating the distance between the projected points and either centroid, scaled by the distance between the two centroids . We estiBased on ASD-MDS explorations, we focused on the genetic structure of individuals born in Cabo Verde compared to that of other admixed populations related to TAST migrations. Therefore, we conducted ADMIXTURE analyses  using 1,plink , with Symmetric Similarity Coefficient strictly above 99.9% for all pairs of runs within a mode, using CLUMPP  and one continental African population (Source 2), or the East Asian CHB (Source 1) and one continental African population (Source 2). For all tests, we used the same individuals, population groupings, and genotyping dataset as in the previous ADMIXTURE analyses , and using the same recombination map as for phasing, window sizes ranging from 30 to 90 SNPs in increments of 10 SNPs, 100 resampling to estimate allele frequencies, and all other GARLIC parameters set to default values. We only considered results obtained with a window size of 40 SNPs, which was the largest window size associated with a bimodal wLOD score distribution and a wLOD score cutoff between the two modes, for all populations.Considering the same sample and SNP set as in the above local-ancestry analyses , we callg=100/2l, where l is the ROH length in cM and g is the number of generations to the most recent common ancestor of the haplotypes  absent from our marker set. Therefore, when computing the total ancestry content of an ROH that overlaps one of these ancestry breakpoints, we assign half of the length of this gap to each ancestry classification, effectively extending each local ancestry segment to meet at the midpoint.We then plotted the length distribution of long ROH for each island and we break out the distributions by ancestral haplotype background: those with only African ancestry, those with only European ancestry, and those that span at least one ancestry breakpoint . We exclFinally, we explored how total African/European ancestry in long ROH varies between islands. For each individual, we summed the total amount of each ancestry in long-ROH and plot the distributions across islands . High leTo assess the significance of these deviations, we performed a non-parametric permutation test. For each individual in each island, we randomly permuted the location of all long ROH (ensuring that no permuted ROH overlap), re-computed the local AFR ancestry proportion falling within these permuted ROH, and then subtracted the global ancestry proportion. We then took the mean of this difference across all individuals for each island and repeated the process 10,000 times. As there is negligible ASN ancestry across these individuals, the AFR and EUR proportions essentially add to 1, and therefore we consider an over/under representation of AFR ancestry in long ROH to be equivalent to an under/over representation of EUR ancestry in long ROH. Permutation distributions with observed values are plotted in plink function --indep-pairwise for a 50 SNPs window moving in increments of 10 SNPs and a r2 threshold of 0.025 resulted in 85,425 SNPs for which the ASD matrix was highly correlated with the one using all SNPs . In parallel, we described the diversity of Kriolu idiolects  among the 225 genetically unrelated Cabo Verde-born individuals by projecting, with metric MDS, the matrix of pairwise Euclidean distances between vectors of individuals’ utterance-frequencies , considering the 4831 unique uttered items.We explored genetic pairwise levels of differentiation calculated with ASD as previously, considering the 1,899,878 non-monomorphic SNPs within Cabo Verde obtained after QC Stage 3 . We projpartial.mantel.test function in the R package ncf, with Spearman correlation and 10,000 Mantel permutations, to explore possible isolation-by-distance (i) absolute age differences, (ii) absolute differences in academic education duration, (iii) geographic distances between residence-locations , (iv) between birth-locations, (v) between mothers’ birth-locations, (vi) between fathers’ birth-locations .Following our descriptive analyses results, we considered scenarios with only one ‘European’ and one ‘African’ source population, and each Cabo Verde island, separately, as the ‘Admixed’ recipient population H. This corresponds to the ‘two-source’ version of the general admixture model from eviously .We thereMetHis performs simple Wright-Fisher , at each generation, t-Fisher  forward-N0 diploid individuals at generation 0, with N0 in , and N20 in  at generation 20, such that N0 < N20. In between these two values, we considered for the Ng values at each generation the discrete numerical solutions of an increasing rectangular hyperbola function of parameter uN in  , after founding at generation 0, we considered scenarios with two additional pulses of admixture (‘Source’–2Pulses scenarios). The two pulses occur, respectively, at generation tSource,p1 = tSource,p2 +1, and simulations with either sSource,tSource,p1 or sSource,tSource,p2 close to 0, may strongly resemble those expected under scenarios with only one pulse of admixture after the founding of the admixed population H.Note that simulations considering values of parameters tSource,t1 and tSource,t2 in  with tSource,t2 ≥tSource,t1 +1. The beginning of the admixture period at tSource,t1 is associated with admixture intensity sSource,tSource,t1 in . The end of the admixture period at tSource,t2 is associated with intensity sSource,tSource,t2 in  such that sSource,tSource,t1 ≥sSource,tSource,t2. In between, the admixture intensity values at each generation of the admixture period are the discrete numerical solutions of a decreasing rectangular hyperbola function of parameter uSource in  . The period of admixture occurs between times   .u parameter value close to 0 corresponds to a sharp pulse of admixture occurring at the beginning of the admixture period of intensity sSource,tSource,t1, followed at the next generation by constant recurring admixture of intensity sSource,tSource,t2 until the end of the admixture period. Alternatively, a u parameter value close to 1/2 corresponds to a linearly decreasing admixture at each generation of the admixture period, from sSource,tSource,t1 to sSource,tSource,t2.Intuitively, a sSource,tSource,t1 ~sSource,tSource,t2, Recurring scenarios correspond to constant recurring admixture of that intensity. Furthermore, simulations with u and sSource,tSource,t2 parameter values both close to 0 correspond to scenarios with a single pulse of admixture from a given source after the founding pulse, occurring at time tSource,t1 and with intensity sSource,tSource,t1.Note that in the limit when g between 1 and 20, sAfr,g + sEur,g=1 – hg, where hg is the contribution of the admixed population H to itself at the following generation in .Finally, we combined the 2Pulses and Recurring scenarios from either the African and European Source populations in order to produce four competing scenarios of admixture for the genetic history of Cabo Verde , with thAs introduced previously, our results showed that the Mandinka from West Western Africa and the Iberian IBS from South West Europe were reasonable proxies of the main source populations for the gene-pool of Cabo Verde, at least when considering a relatively small number of independent autosomal SNPs. We decided to consider both populations as very large and at the drift-mutation equilibrium since the 1460s and the initial founding of Cabo Verde. We thus chose not to explicitly simulate the evolutionary history of the two European and African populations at the source of the genetic history of the Cabo Verde islands.Instead, we first randomly drew 60,000 independent SNPs, avoiding singletons, from the LD-pruned 102,543 independent SNPs employed for the ADMIXTURE analyses. We then built a single reservoir of ‘African’ gametes comprising 20,000 haploid genomes of 60,000 independent SNP-sites each, where each allele at each site of a gamete was randomly drawn in the site frequency spectrum observed for the corresponding SNP in the Mandinka proxy source population. Separately, we proceeded similarly to build a reservoir of 20,000 European gametes matching instead the site-frequency spectrum observed in the Iberian IBS at the 60,000 SNPs.sAfr,0 and sEur,0, each in  such that sAfr,0 + sEur,0 = 1, and a given value  of N0 diploid individuals in the admixed population H, MetHis randomly draws two different gametes in the African gamete-reservoir and randomly pairs them to produce one parent from the African source and repeats the process sAfr,0 x 2N0 times to obtain that number of African parents. In parallel and using the same procedure, MetHis randomly builds sEur,0 x 2N0 parents from the European gamete-reservoirs. For each of N0 individuals in the admixed population separately, MetHis then draws randomly a pair of parents among the European and African parents hence obtained, and for each parent separately, builds one haploid gamete by randomly drawing one allele for each 60,000 genotypes. Finally, MetHis pairs both hence obtained gametes to create the novel individual in the admixed population at the following generation, and repeats the procedure  for each of N0 individuals separately.For a given simulation with, at generation 0, given parameter values g  associated with a given intensity ssource,g, keeping in mind that at all g in , sAfr,g + sEur,g + hg = 1. MetHis then builds anew ssource,g x 2Ng reproductive parents from this source population’s gamete-reservoir as previously, and in parallel randomly draws hg x 2Ng parents from the admixed population H itself at the previous generation. Then, for each of Ng individuals in the admixed population separately, MetHis randomly draws a pair of different parents , randomly builds haploid gametes from each parent, and pairs them similarly as previously to obtain a new individual at the following generation.Then, for the same given simulation, admixture from a source population is set to occur at a given generation MetHis simulations as all SNPs are considered statistically independent.Thus, note that while our source-populations’ gamete reservoirs were fixed during the admixture process, the African or the European diploid parents possibly contributing to the gene-pool of the admixed population are randomly built anew, and each produce novel gametes, at each generation and in each simulation separately. Importantly, note that recombination is thus not a parameter in MetHis by explicit genealogical flagging of individuals during the last two generations of the admixture process, hence mimicking our observed family unrelated dataset within Cabo Verde.At the end of each simulation, we randomly drew individual samples from each source and the admixed population H matching observed sample sizes. We randomly sampled 60 individuals in the African source gamete reservoirs, 60 individuals in the European source, and the number of admixed individuals corresponding to the sample size of each Cabo Verde island of birth of individuals or to all 225 Cabo Verde-born individuals grouped in a single random mating population, in turn . We sampMetHis simulations under each of four admixture scenarios for each of the nine birth-islands of Cabo Verde and for all Cabo Verde-born individuals grouped in a single population, separately. Each simulation was performed under a vector of scenario parameters drawn from prior distributions described above and in MetHis parameter generator tools. Separately for each island and for Cabo Verde as a single population, we used this set of simulations to determine which scenario best explained the observed data using Random-Forest ABC (see below). Under the winning scenario for each birth-island and separately for Cabo Verde as a whole, we then performed an additional 90,000 simulations each corresponding to a different vector of parameter values drawn randomly from the priors set by the user for this scenario, to produce a total of 100,000 simulations to be used for Neural-Network ABC posterior parameter estimation, for each ten – 9 islands and Cabo Verde as a whole, separately – analysis separately.We performed 10,000 such MetHis as described above under the four competing admixture history scenarios. We performed Random-Forest ABC scenario-choice  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, including Emilia Huerta-Sanchez as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Molly Przeworski as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Xinjun Zhang (Reviewer #2).Thank you for submitting your article "The admixture histories of Cabo Verde" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Both reviewers enjoyed reading the paper and found the results of this study to be important as it provides insights into how admixture shapes patterns of genetic variation. Both reviewers would like to see a revised version of the manuscript. There were some clarifications and a few additional analyses that the reviewers feel could strengthen the paper. These include:1) The authors infer demographic history using a previously developed method (meTHIS), and it would be great to see the model robustness when fixed parameters are misspecified: the ancestral population sizes, recent gene flow between Europeans and Africans (eg. back-to-Africa migrations), and the possibility of population size fluctuation in Cabo Verde in recent history .2) The authors suggest that longer ROH in one ancestry implies more admixture; is a higher proportion of admixture the only way to generate these ROH patterns ?3) Revealing that different island populations in Cabo Verde have different admixture histories provides support for variation in the way in which these islands were founded. The authors propose a set of interesting hypotheses for how the different islands were founded; could the authors use simulations to see if there is some support for these hypotheses?4) The authors could motivate why they chose the methods applied to the data.5) Please review and respond to the individual reviewer comments as well.Reviewer #1 (Recommendations for the authors):Line 333: "thus illustrating the strong signal of genetic isolation-by-distance (51) within Cabo Verde at very reduced geographical scales." Is this consistent/similar with what is observed for other island populations?Line 335: It will be helpful for readers to include the definition of "utterance" in the main text.Figure F6: What is h?Methods:Line 856: "We collapsed the local ancestry assignments for each SNP in each Cabo Verdean individuals hence obtained into" I didn't get what "collapsed" means in this context. Also, I thought that RFmix has a phasing step, so the phase that is given as input may be different from the phase it infers. So how do the authors deal with regions that are heterozygous in ancestry (e.g. one chromosome is African and the other is European) with their calls of runs of homozygosity for their ancestry-specific ROH sizes?Line: 916: "we first design MetHis v1.0 (37) forward-in-time simulations of four competing" Could the authors say more about their forward-in-time simulations? Are these simulations created by the authors? It seems the authors are using real data. How did the authors make 20000 haploid genomes from the Mandinka and IBS who have really small sample sizes? Could they say more here to explain how these forwards in time simulations are different than what we typically use (e.g. SLIM)? The real data is not sequencing data, so how did the authors recreate that in the simulations? I assume that might affect their ROH calling and ancestry calling?Reviewer #2 (Recommendations for the authors):There are a number of areas where the paper can improve and strengthen.Specific revision suggestions:1) Title: the current title "the admixture histories of Cabo Verde" is underwhelming and does not capture the main findings of the paper. The authors can consider a different title that reflects the admixture history reconstructed by revealing the co-shifting of genetic and sociocultural diversity, which is storytelling about TAST.2) The genetic relationship analysesa. the authors used MDS to draw relationships between Cabo Verdeans and other European and African continental populations. I am not an MDS expert , and it is not clear to me why ADS-MDS is used here instead of a traditional PCA in pair with classic admixture statistical tests such as F statistics. Do we learn anything new from ADS-MDS that PCA and F stats can't tell us? Would like to see more explanation and justification in the text. It would also be helpful to include F statistic tests and how they compare to the MDS results, at least in the Supplementary Figures.b. Could the authors comment on the proportion of missing data in the genotypes, and whether the removal of them is expected to change MDS results in any way?c. Furthermore, I find Figure F2 very hard to read as I need to constantly scroll back to Figure F1 for color legends – the authors should consider either (1) adding legends to Figure F2, or (2) merging Figure F1 and F2 as the same figure, highlighting the current Figure F2 panel A-C in the context of geographical locations in Figure F1, and move the other panels to Supplementary Figures.3) Admixture analysis:a. In Verdu et al. 2017 Current Biology, the authors performed similar MDS and ADMIXTURE analyses and made similar conclusions on Cabo Verdeans being related to Senegambians and Southern Europeans. What new information are we learning from here? Are we only confirming the previous observations on all CV islands? In any case, if there are novel discoveries from here that I am missing from reading the text, the authors should at least consider clarifying and re-emphasizing the messages.b. For the ADMIXTURE analysis, could the authors explain the justification for choosing the maximum value of K=10? What would the plot look like when K is larger?c. The authors mentioned that Asian populations are being compared with Cabo Verdeans in the admixture analyses, but I don't see them being displayed on the plot  – in general, is there any evidence there's ancestry contribution in CV from populations outside of Europe and Africa? How would the admixture plot look like when other worldwide populations are included?4) ROH and local ancestry inferencea. What are the proportions of long ROHs among all ROHs in these island populations?b. The authors interpreted the finding of more long-ROHs exclusively in one ancestry as essentially an increase in admixture proportion from the respective source population, either through recurring admixtures or relatively recent admixture events. However, I wonder if two source populations contributed equally to the admixture, but one source experienced more bottleneck themselves and therefore carry more long-ROH in the first place, would that lead to the enrichment of long ROHs in that ancestry? Could the authors run some simulations to test whether alternative model(s)  could lead to a similar distribution of long ROH and their overlapping with respective ancestry?c. Additionally, I wonder if the long ROHs in these island populations are enriched in low recombination regions or functionally important regions. And if so, would the authors expect any of such factors could affect the distribution of ROHs and the interpretations thereafter? Furthermore, how would the authors expect the difference in fine-scale recombination rate (and mutation rates) between Africans and Europeans possibly affect the ROH and ancestry distributions?d. For Figure F4D – it's a bit hard to read the differences here as these violin plots appear uniformly distributed across populations despite slight differences. For example, is the overrepresentation of ancestries in certain populations significant, such as European contribution in Fogo and Brava and African contribution in Sal and Sao Vicente? Could the authors provide some p-values here?e. Line 358-359: why is it surprising to find that individuals from distant islands differ in African admixture level if we already see that their ADMIXTURE-revealed ancestry components differ between islands?5) Estimation of admixture historya. From what I see, the RF model considers many admixture-related parameters but doesn't consider the evolutionary history in the source populations before 20 generations ago. Can the authors comment on how robust the RF model is to demographic model misspecifications, especially related to the ancestral population size in the source populations? Most importantly, are the ABC posteriors sensitive to the demographic parameters that are fixed in the simulations?b. For the recipient population, the population sizes are projected to be increasing over the 20 generations in all 4 admixture scenarios – is that realistic according to the historical record? Was there any reduction in Ne in Cabo Verde islands during the TAST? How is the inference robust to alternative models where the admixed population's size fluctuates over time?eLife; Botigue et al. 2013 PNAS) confound the inference of admixture history?c. Also, would the historical gene flows between Southern Europeans and Northern Africans and within Africa  The authors infer demographic history using a previously developed method (meTHIS), and it would be great to see the model robustness when fixed parameters are misspecified: the ancestral population sizes, recent gene flow between Europeans and Africans (eg. back-to-Africa migrations), and the possibility of population size fluctuation in Cabo Verde in recent history .We thank both reviewers and the editor for pointing out that these much-needed aspects were lacking in the previous version of our manuscript. To address these points, we deeply modified our manuscript, in particular in the material and methods, results, and Discussion sections, and added a novel MetHis-ABC analysis. Detailed modifications are presented below and in the detailed answers to each reviewer specifically. Altogether, we believe our manuscript strongly benefited from these major changes.Note that all the sections and line-references of the changes brought to our initial version indicated in the detailed answers below refer to the FULL Article version  of our re-submission.1. We clarified our methods’ design and discussed extensively its limitations with respect to ancestral populations’ sizes mis-specifications. Indeed, ancestral source population sizes are not modelized in our MetHis-ABC approach. Instead, we consider that the observed proxy source populations from Africa and Europe are at the drift-mutation equilibrium and are large since the initial and recent founding of Cabo Verde in the 1460’s. We thus use observed genetic variation patterns in these populations to build virtual gamete reservoirs for the admixture history of Cabo Verde with the MetHis-ABC framework. Therefore, while we cannot evaluate explicitly the influence of ancestral source population sizes differences on our inferences in Cabo Verde, as we now state in the revised version of our manuscript: “we nevertheless implicitly take the real demographic histories of these source populations into account in our simulations, as we use observed genetic patterns themselves the product of this demographic history to create the virtual source populations at the root of the admixture history of each Cabo Verdean island.”. We then discuss the outcome of such an approach which mimics satisfactorily the real data for ABC inference. See in particular the revised versions of the Material and Methods L1454-1491 novel section “Simulating the admixed population from source-populations for 60,000 independent SNPs with MetHis”, and Results L637-649.2. Concerning the possibilities for population-size changes in the admixed population in our simulations and ABC inferences, we clarified our Material and Methods and explanations of our Results to better show that we readily consider various possible scenarios (for each island separately). Indeed, with our MetHis simulation design, given values of model-parameters correspond either to a constant, a linearly increase, or a hyperbolic increase in reproductive size in the admixed population over time. We further clarified our Results and Discussion pointing out that we find, a posteriori, indeed, different demographic regimes among islands.Nevertheless, reviewers are right that we did not test the possibility for bottlenecks. We thus substantially expanded the Results and Discussion sections in multiple locations to highlight this limitation and the challenges involved in overcoming it in future work. See in particular Material and Methods L1386-1404 section “Hyperbolic increase, linear increase, or constant reproductive population size in the admixed population”, Results L739-742, and Discussion L934-941, and Perspectives.3. In the revised version of the manuscript, we now extensively discuss the important point raised by the reviewers concerning possible recent gene-flow between Europe and Africa which may have influenced genetic patterns among the proxy populations at the source of admixture in Cabo Verde, and how they may affect our inferences. See in particular Discussion L903-916.4. Finally, in order to evaluate the robustness of our inferences, we conducted a novel MetHis-ABC inference considering all Cabo Verde-born individuals in a single random-mating population in addition to the analyses conducted separately for each island of birth. Most interestingly, we find that our ABC inferences fail to accurately reconstruct the detailed admixture history of Cabo Verde when considered as a whole instead of per each island of birth separately. This is due to admixture histories substantially differing across islands of birth of individuals, also consistent with the significantly differentiated genetic patterns within Cabo Verde obtained from ADMIXTURE, local-ancestry inferences, ROH, and isolation-by-distance analyses. These results are now implemented throughout the revised version of the manuscript and in supplementary figures and tables. See in particular Results L758-769, and Appendix1-figures and tables, Figure7—figure supplement 1-3, and Appendix 5-table 10.(2) The authors suggest that longer ROH in one ancestry implies more admixture; is a higher proportion of admixture the only way to generate these ROH patterns ?We thank the reviewer for this comment and clarified our text to avoid such suggestion that we did not intend originally. In particular, see discussion of ROH patterns: Discussion Lines 1020-1024.(3) Revealing that different island populations in Cabo Verde have different admixture histories provides support for variation in the way in which these islands were founded. The authors propose a set of interesting hypotheses for how the different islands were founded; could the authors use simulations to see if there is some support for these hypotheses?The reviewers are perfectly right that formally testing the serial founder model here hypothesized is of major interest, and we gratefully thank them for pointing the lack of such discussion in the previous version of our manuscript. This is a challenging task due to the vast amounts of models that would need to be formally tested for each possible routes of series of founding events for each island in the archipelago. We now explicitly discuss this important point and refer to future work in the revised version of our manuscript; in particular in Discussion L942-953 and in Perspectives L1066-1072, as we are currently preparing a novel investigation dedicated specifically to this task.(4) The authors could motivate why they chose the methods applied to the data.We extensively modified our manuscript in both Results and Material and Methods, to clarify and justify our reasoning for each one of the analyses conducted, and to discuss pros and cons of the methods used. We thank the reviewers for this request, as we believe it allowed us to strongly improve the accessibility of our work in particular for the less specialized audience, as well as equally crucially improve replicability of our work for specialists. See in particular Results L185-193, L245-250, L368-371, L380-386, L495—511, L567-571, L606-621, and the corresponding Material and Methods sections.(5) Please review and respond to the individual reviewer comments as well.We thank the reviewers for their detailed comments and suggestions, and respond to them individually and in details below.Reviewer #1 (Recommendations for the authors):Line 333: "thus illustrating the strong signal of genetic isolation-by-distance (51) within Cabo Verde at very reduced geographical scales." Is this consistent/similar with what is observed for other island populations?The reviewer is right that this original result of our article was not sufficiently contextualized nor discussed in the previous version of the manuscript. We developed these aspects throughout the revised version of the manuscript. See Abstract, Introduction L145-153, Results L495-511, and the corresponding Discussion section.Line 335: It will be helpful for readers to include the definition of "utterance" in the main text.We clarified the revised version of our manuscript according to the reviewer’s comment in particular in the Results L506-508.Figure F6: What is h?hg is the proportion of parents of individuals in the admixed population at generation g coming from the admixed population itself at the previous generation. We clarified this point directly in the legend of Figure 6 as well as in Material and Methods.Methods:Line 856: "We collapsed the local ancestry assignments for each SNP in each Cabo Verdean individuals hence obtained into" I didn't get what "collapsed" means in this context.Any ancestry call that was assigned a population from the African continent was assigned a category of “AFR”. Any ancestry call that was assigned a population from the European continent was assigned a category of “EUR”. And, finally, any ancestry call that was assigned a population from the Asian continent was assigned a category of “ASN”. We have clarified this in different parts of the text, and in particular in Material and Methods L1282-1285.Also, I thought that RFmix has a phasing step, so the phase that is given as input may be different from the phase it infers. So how do the authors deal with regions that are heterozygous in ancestry (e.g. one chromosome is African and the other is European) with their calls of runs of homozygosity for their ancestry-specific ROH sizes?Regions of heterozygous ancestry within an ROH were rare. We provide this result in Figure4-source data 3, which we comment in the Material and Methods of the main text L1297-1298.For Figure 4C, these regions are excluded from the plot. For Figure 4D, these regions were counted as fractionally contributing to either AFR or EUR totals. We have added text to clarify this in Material and Methods L1308-1316 in addition to the novel supplementary source-data tables.Line: 916: "we first design MetHis v1.0 (37) forward-in-time simulations of four competing" Could the authors say more about their forward-in-time simulations? Are these simulations created by the authors? It seems the authors are using real data. How did the authors make 20000 haploid genomes from the Mandinka and IBS who have really small sample sizes? Could they say more here to explain how these forwards in time simulations are different than what we typically use (e.g. SLIM)? The real data is not sequencing data, so how did the authors recreate that in the simulations? I assume that might affect their ROH calling and ancestry calling?MetHis L1454-1486, which shows that our simulation algorithm is equivalent to what is implemented in SLIM and other forward-in-time simulators, as shown in the original MetHis paper .We have considerably clarified the revised version of our Material and Methods as well as Results to address the points raised by the reviewer. Indeed, we now thoroughly explain how the forward-in-time simulations individual-centered are conducted with Next, we clarified in the revised version of the manuscript that we simulate only independent genotype data, and that all summary statistics are based on these data, and thus do not include ROH calculations for the ABC inference. In particular we moved the summary-statistics Table originally presented in the Supplementary Material into the main text (Table 3 in the revised version) of our paper to further avoid the legitimate confusion raised by the reviewer.Reviewer #2 (Recommendations for the authors):There are a number of areas where the paper can improve and strengthen.Specific revision suggestions:(1) Title: the current title "the admixture histories of Cabo Verde" is underwhelming and does not capture the main findings of the paper. The authors can consider a different title that reflects the admixture history reconstructed by revealing the co-shifting of genetic and sociocultural diversity, which is storytelling about TAST.We now propose for title:A genetic and linguistic analysis of the admixture histories of the islands of Cabo Verde(2) The genetic relationship analysesa. the authors used MDS to draw relationships between Cabo Verdeans and other European and African continental populations. I am not an MDS expert , and it is not clear to me why ADS-MDS is used here instead of a traditional PCA in pair with classic admixture statistical tests such as F statistics. Do we learn anything new from ADS-MDS that PCA and F stats can't tell us? Would like to see more explanation and justification in the text. It would also be helpful to include F statistic tests and how they compare to the MDS results, at least in the Supplementary Figures.As stated above, concerning ASD-MDS versus classical PCA, we clarified our choice in the main text of the article in Results L185-192.Furthermore, as recommended by the reviewer, we now explain and discuss Patterson’s f3 methods and provide novel results in Results L376-386 and corresponding Figure 3—figure supplement 2.b. Could the authors comment on the proportion of missing data in the genotypes, and whether the removal of them is expected to change MDS results in any way?-4 missing genotypes on average per individual within island of birth in Cabo Verde (SD=3.0x10-4 across islands), among the 455,705 autosomal SNPs in our working dataset used for the ASD-MDS analysis, which should not affect our results. Indeed, even if missing data do not overlap the same SNPs for a given pair of individual, they should represent, on average per individual pairwise comparison, in the order of 1000 un-used SNPs per ASD calculus . In-turn, such very low levels of missingness will not be expected to have a major effect on the final MDS projection of the ASD matrix. We have added a sentence to clarify this point in the Material and Methods L1120-1121.We found a fraction of 7.0x10c. Furthermore, I find Figure F2 very hard to read as I need to constantly scroll back to Figure F1 for color legends – the authors should consider either (1) adding legends to Figure F2, or (2) merging Figure F1 and F2 as the same figure, highlighting the current Figure F2 panel A-C in the context of geographical locations in Figure F1, and move the other panels to Supplementary Figures.According to the reviewer’s legitimate concern, we modified Figure 2 to show only Cabo Verdean panels alongside the legend for population locations and symbols, and moved other panels concerning ASW, ACB and PUR, to a novel Figure 2—figure supplement 1. We updated the former Table ST1  information for population inclusion in each analysis accordingly.(3) Admixture analysis:a. In Verdu et al. 2017 Current Biology, the authors performed similar MDS and ADMIXTURE analyses and made similar conclusions on Cabo Verdeans being related to Senegambians and Southern Europeans. What new information are we learning from here? Are we only confirming the previous observations on all CV islands? In any case, if there are novel discoveries from here that I am missing from reading the text, the authors should at least consider clarifying and re-emphasizing the messages.We substantially modified this section of the previous version of the manuscript to better emphasize the novelty of our findings for the ADMIXTURE analyses with respect to previous results, in order to clarify the reviewer’s legitimate concern (in particular at L266-281).Furthermore, we also clarified the novelty of our approach in the following local-ancestry inference section, as such approach was not endeavored in previous studies across Cabo Verdean islands. Indeed, note that this LAI approach formally tests and identifies the respective genetic contribution from varied possible source populations to the gene-pool of admixed individuals, which is not the case of ASD-MDS nor ADMIXTURE analyses, a point that we further clarified in the revised version of the manuscript such as in Results L368-386.b. For the ADMIXTURE analysis, could the authors explain the justification for choosing the maximum value of K=10? What would the plot look like when K is larger?We thank the reviewer for this suggestion. We now provide ADMIXTURE results until K=15 and describe the obtained results in the corresponding Result L289-314. Furthermore, we extended the text in corresponding Appendix 3 and Appendix 3-figure 1 to describe all alternative ADMIXTURE solutions identified until K=15.In short here, this extended ADMIXTURE analysis allowed us to identify additional genetic sub-structures within Cabo Verde, differentiating five genetic clusters specific to different groups of islands of birth within the archipelago. These patterns are further consistent with our next results concerning Isolation-By-Distance across Cabo Verdean islands, both results which were largely unsuspected from previous studies, including ours.c. The authors mentioned that Asian populations are being compared with Cabo Verdeans in the admixture analyses, but I don't see them being displayed on the plot  – in general, is there any evidence there's ancestry contribution in CV from populations outside of Europe and Africa?The Chinese CHB sample can be found at the extreme left of the ADMIXTURE analyses in Figure 3. We moved the previous supplementary text concerning the interpretations of this result to the main text Results L305-310 to address the legitimate concern of the reviewer.How would the admixture plot look like when other worldwide populations are included?K=5 would mainly differentiate African, European, Asian, American, and Oceanian individuals, and recently admixed populations would be found with intermediate membership proportions’ patterns between their historical sources. However, conducting such analysis is computationally intensive for very large worldwide datasets and has already been conducted in previous studies, we prefer not to include them in the current article as we believe such analysis is outside our specific focus of interest.ADMIXTURE analyses capture the same information and recapitulate higher-order of pairwise individual variation from ASD-MDS or PCA analyses, as we now clarified in particular in Results L245-250. Therefore, to answer the reviewer’s interesting comment we expect the ADMIXTURE plots for a worldwide dataset to resemble, for lower values of K, what we identified here in the worldwide ASD-MDS analyses presented in Appendix 2 figures 1-4: (4) ROH and local ancestry inferencea. What are the proportions of long ROHs among all ROHs in these island populations?We now report the mean proportion of total long ROH among all called ROH. See Results L422-423 and a novel supplemental Figure 4-source data 1.b. The authors interpreted the finding of more long-ROHs exclusively in one ancestry as essentially an increase in admixture proportion from the respective source population, either through recurring admixtures or relatively recent admixture events. However, I wonder if two source populations contributed equally to the admixture, but one source experienced more bottleneck themselves and therefore carry more long-ROH in the first place, would that lead to the enrichment of long ROHs in that ancestry? Could the authors run some simulations to test whether alternative model(s)  could lead to a similar distribution of long ROH and their overlapping with respective ancestry?We thank the reviewer for this comment and clarified our text to avoid such suggestion that we did not intend originally. We have thus added more nuanced language to our discussion of ROH patterns, Discussion L1020-1024.c. Additionally, I wonder if the long ROHs in these island populations are enriched in low recombination regions or functionally important regions. And if so, would the authors expect any of such factors could affect the distribution of ROHs and the interpretations thereafter? Furthermore, how would the authors expect the difference in fine-scale recombination rate (and mutation rates) between Africans and Europeans possibly affect the ROH and ancestry distributions?As we mention in the answer to the reviewers above, we note that in Figure 8 Pemberton 2012 (AJHG 91:275-292) shows that occurrence of long ROH at the same genomic location across individuals is correlated with low recombination rates, although the effect is relatively weak unless in extreme recombination cold spots. Unless there were many extreme recombination cold spots that were different among the islands or ancestral populations, we anticipate fine-scale recombination rate differences not to matter very much for total ROH levels in these data. Similarly, we do not expect large genome-wide differences in mutation rate, and therefore we don’t anticipate minor local variation in mutation rates to make a systematic difference in total ROH levels. We briefly clarified these important points in the revised version of the manuscript in particular in Results L414-415.d. For Figure F4D – it's a bit hard to read the differences here as these violin plots appear uniformly distributed across populations despite slight differences. For example, is the overrepresentation of ancestries in certain populations significant, such as European contribution in Fogo and Brava and African contribution in Sal and Sao Vicente? Could the authors provide some p-values here?We have now performed a permutation test to assess which of these patterns among the islands are significant. For each individual in each island, we randomly permuted the location of all long ROH (ensuring that no permuted ROH overlap), re-computed the local AFR ancestry proportion falling within these permuted ROH, and then subtracted the global ancestry proportion. We then take the mean of this difference across all individuals for each island and repeat the process 10,000 times. As there is negligible ASN ancestry across these individuals, the AFR and EUR proportions essentially add to 1, and therefore we consider an over/under representation of AFR ancestry in long ROH to be equivalent to an under/over representation of EUR ancestry in long ROH, respectively. This permutation test shows that AFR ancestry in long ROH is under represented (and therefore EUR ancestry in long ROH is over represented) on Fogo, whereas on Sao Vicente and Santo Antão there is an over representation of AFR ancestry in long ROH (and therefore an under representation of EUR ancestry in long ROH). We have added this information to the manuscript, in Results L432-441 and Material and Methods L1308-1316. We have also updated Figure 4D with the significance information added in detail in Figure 4-source data 2.e. Line 358-359: why is it surprising to find that individuals from distant islands differ in African admixture level if we already see that their ADMIXTURE-revealed ancestry components differ between islands?We thank the reviewer for pointing out this unclear passage. We reshaped the introduction to this section, as well as the Results in multiple locations to clarify the importance and originality of these findings. We have thus profoundly reshaped large parts of our text as can be seen in the revised version of our manuscript in the Results sections 5 and 6 starting L495 and L567 respectively, and in Discussion L942-L953.(5) Estimation of admixture historya. From what I see, the RF model considers many admixture-related parameters but doesn't consider the evolutionary history in the source populations before 20 generations ago. Can the authors comment on how robust the RF model is to demographic model misspecifications, especially related to the ancestral population size in the source populations? Most importantly, are the ABC posteriors sensitive to the demographic parameters that are fixed in the simulations?As mentioned above in the answers to reviewers:1. We clarified our methods’ design and discussed extensively its limitations with respect to ancestral populations’ sizes mis-specifications. Indeed, ancestral source population sizes are not modelized in our MetHis-ABC approach. Instead, we consider that the observed proxy source populations from Africa and Europe are at the drift-mutation equilibrium and are large since the initial and recent founding of Cabo Verde in the 1460’s, and thus use observed genetic variation patterns in these populations to build virtual gamete reservoirs for the admixture history of Cabo Verde with the MetHis-ABC framework. Therefore, while we cannot evaluate explicitly the influence of ancestral source population sizes differences on our inferences in Cabo Verde, as we now state in the revised version of our manuscript: “we nevertheless implicitly take the real demographic histories of these source populations into account in our simulations, as we use observed genetic patterns themselves the product of this demographic history to create the virtual source populations at the root of the admixture history of each Cabo Verdean island.”. We then discuss the outcome of such an approach which mimics satisfactorily the real data for ABC inference. See in particular the revised versions of the Material and Methods L1454-1491 novel section “Simulating the admixed population from source-populations for 60,000 independent SNPs with MetHis”, and Results L637-649.2. Then, we now extensively discussed the important point raised by the reviewer about the possible admixture history in the African and European populations at the historical source of Cabo Verde, having possibly occurred before the colonization of the archipelago, or even after that. See in particular Discussion L903-916.3. Finally, in order to evaluate the robustness of our inferences, we conducted a novel MetHis-ABC inference considering all Cabo Verde-born individuals in a single random-mating population in addition to the analyses conducted separately for each island of birth. Most interestingly, we find that our ABC inferences fail to accurately reconstruct the detailed admixture history of Cabo Verde when considered as a whole instead of per each island of birth separately. This is due to admixture histories substantially differing across islands of birth of individuals, also consistent with the significantly differentiated genetic patterns within Cabo Verde obtained from ADMIXTURE, local-ancestry inferences, ROH, and isolation-by-distance analyses. These results are now implemented throughout the revised version of the manuscript and in supplementary figures and tables. See in particular Results L758-769, and Appendix1-figures and tables, Figure7—figure supplement 1-3, and Appendix 5-table 10.b. For the recipient population, the population sizes are projected to be increasing over the 20 generations in all 4 admixture scenarios – is that realistic according to the historical record? Was there any reduction in Ne in Cabo Verde islands during the TAST? How is the inference robust to alternative models where the admixed population's size fluctuates over time?As mentioned above in response to the reviewer’s legitimate and accurate comment, concerning the possibilities for population-size changes in the admixed population in our simulations and ABC inferences, we clarified our Material and Methods and explanations of our Results to better show that we readily consider various possible scenarios (for each island separately). Indeed, with our MetHis simulation design, given values of model-parameters correspond either to a constant, a linearly increasing, or a hyperbolic increase in reproductive size in the admixed population over time. We further clarified our Results and Discussion pointing out that we find, a posteriori, indeed, different demographic regimes among islands.th century.Concerning the reduction of Ne in the history of Cabo Verde, historical data are scarce for detailed censuses before the 1860’s . While there have unquestionably been some events that likely impacted census sizes before then, such as epidemics or hunger, it is not obvious how they might have impacted effective sizes as population sizes were altogether small until very recently. Our historical table in Figure 7-source data 1 provides details of the known changes in census sizes over the entire history of Cabo Verde and the only things that are precisely known are the very strong very recent increase in census in Cabo Verde mostly during the 20Nevertheless, reviewers are right that we did not test the possibility for bottlenecks. We thus substantially expanded the Results and Discussion sections in multiple locations to highlight this limitation and the challenges involved in overcoming it in future work. See in particular Material and Methods L1386-1404 section “Hyperbolic increase, linear increase, or constant reproductive population size in the admixed population”, Results L739-742, and Discussion L934-941, and Perspectives.c. Also, would the historical gene flows between Southern Europeans and Northern Africans and within Africa  confound the inference of admixture history?We thank the reviewer for this interesting discussion point that we overlooked in the previous version of our manuscript. In our revised version, we now explicitly discuss it in a novel paragraph Discussion L903-L916, and in Appendix 2.


Osyris lanceolata (Hochst. & Steud.)  is a multipurpose plant highly valued culturally and economically in Africa. However, O. lanceolata populations have rapidly dwindled in East Africa due to overexploitation and this is believed to cause further consequences on the species’ genetic diversity and structure within the region. Information regarding a species’ genetic diversity and structure is necessary for conservation but this is currently lacking for O. lanceolata in Uganda and Kenya. Lack of adequate scientific data hinders conservation efforts hence threatening the species survival and livelihoods. This study investigated patterns in genetic diversity and structure of O. lanceolata in Uganda and Kenya. Ten polymorphic microsatellite loci were used to genotype 210 individuals: 96 from Ugandan and 114 from Kenyan populations.All populations were highly polymorphic (80–100% polymorphism). A genetic differentiation was found between Kenyan and Ugandan populations. The highest genetic differentiation was among individuals and the least among populations. The Kenyan populations showed higher genetic diversity than Ugandan populations. The Ugandan populations showed more marker deviations from Hardy-Weinberg equilibrium and inbreeding coefficient. Two populations showed evidence of going through a recent bottleneck. There was significant genetic differentiation and structuring at higher K values into larger clusters and observed admixture between populations. The populations were significantly isolated by altitude as opposed to distance and climatic variables. Main barriers were associated with altitude differences. The data supports the idea of long-distance gene-flow between high altitude populations in both countries.O. lanceolata which are characteristic to lower altitudes and higher altitudes including most Kenyan populations with divergent evolutionary patterns. Geographical barriers and environmental gradients could have influenced this genetic divergence, and such patterns may escalate the species microevolutionary processes into full allopatric speciation. Further investigations into the species’ genetic admixture and emerging taxonomic units are necessary to guide conservation strategies in the region.The divergence in genetic structure suggests unrecognised taxonomic units within The online version contains supplementary material available at 10.1186/s12862-023-02182-2. Osyris lanceolata, Hochst. & Steud., Santalaceae) is a valuable commercial species because of its aromatic wood and essential oils used in perfumery and pharmaceutical industries 24] despiulations . This miulations , 49. TheO. lanceolata populations in Uganda and Kenya have experienced higher exploitation levels that have led to significant declines in species abundance, density [O. lanceolata trees , habitat clearing, destruction of host species for charcoal burning, timber, and fuel wood especially in Uganda [O. lanceolata in both countries contributing to the loss of genetic diversity. At long term this might have particularly drastic consequences to the species populations in Uganda where the control measures for harvesting of O. lanceolata resources are limited.For the past ten years,  density , 12, 18 n Uganda , 12. SucO. lanceolata was detected earlier in Kenya and measures were established in 2007 to protect the resource base through a presidential decree that banned harvesting and trade in O. lanceolata resources [O. lanceolata resource utilisation in Kenya shifted pressure to Ugandan populations to meet the illegal market demand for the species resources [O. lanceolata individuals could have been higher in community habitats compared to habitats in protected areas such as Mt. Elgon in Kenya and Mt. Moroto and this supported by the lower genetic diversity in populations on communal habitats such as Nakapiripirit and Amudat than those in protected areas of Mt. Moroto which may explain the differing patterns in genetic diversity and deviations from HWE in these populations.The overexploitation of esources . Most liesources , 18. UnfST values between some populations of different countries, and the fact that AMOVA showed that groupings based on country of origin explained a higher proportion of the variation than those based on locality. The genetic structuring between Kenya and Uganda O. lanceolata populations could be attributed to ecological, evolutionary, and anthropogenic factors.The seven populations were mainly structured into two major genetic clusters in relation to altitude gradients and some populations showed genetic admixture (Baringo and Moroto) between Uganda and Kenya. This was further supported by the PCoA clustering pattern along the first coordinate and high FO. lanceolata individuals in the two regions. Second, the O. lanceolata habitats in Uganda and Kenya are highly fragmented by geographical barriers, leading to continued inbreeding that is reflected in the high levels of fixation indices among the Kenyan and Ugandan populations. For instance, Mt. Kadam separates Nakapiripirit population from Amudat, Mt. Moroto, separates Moroto populations from Amudat and Nakapiripirit, and the long distance between Moroto and other populations in Kenya such as Baringo, Mau and Laikipia are barriers that obstruct continuous dispersal and flow of genes among the populations, hence leading to gradual differentiation, in absence of long-distance dispersal and geneflow. In addition, most Ugandan populations (Amudat and Nakapiripirit) occurred in lower altitude areas (≤ 1500 m.a.s.l.) compared to Moroto (≥ 1700 m.a.s.l.), and Kenyan populations found at higher altitudes > 2000 m.a.s.l.)  with a higher genetic potential for adaptation in a wide range of semiarid habitats in Africa and beyond.Finally, the findings inferred evolutionary, anthropogenic and ecophysiological impacts on population genetic diversity and structure patterns of O. lanceolata across seven populations in Uganda and Kenya. There was evidence that Ugandan populations were more threatened than Kenyan populations due to genetic diversity loss. Population bottlenecks, evolutionary processes within the species, and environmental gradients might be key drivers of species genetic structuring between Uganda and Kenya rather than isolation by distance. The observed evidence of genetic admixture and the causes and consequences of species fitness should be further investigated. Additionally, the large genetic divergence between O. lanceolata populations in both countries should present an opportunity for conservation of a wide range of the species gene pool. These results are important to guide conservation efforts. For example, priority in restoration efforts might be given to populations that have experienced recent bottlenecks, and ex-situ programs should consider the different genetic units found and their potentially divergent adaptive ability.This study investigated the genetic diversity and structure patterns of Below is the link to the electronic supplementary material.Supplementary Materials (Appendices): Appendix 1: Table 1: Herbarium accession numbers and voucher specimens for Osyris lanceolata; Appendix 2: Patterns of allele frequencies among sampled populations in Uganda and Kenya; Appendix 3 Table 3: Probability values for Hardy Weinberg equilibrium (HWE) tests in populations; Appendix 4: Table 4 p values for genotypic linkage disequilibrium for each pair of loci across all populations; Appendix 5 Table 5 Chi-square tests for Hardy-Weinberg equilibrium per loci per population; Appendix 6 Table 6: Null alleles estimation among populations; Appendix 7: Table 7: Genetic diversity indices for the 10 loci over the seven studied populations; Appendix 8: Table 8 F statistics results; Appendix 9 Table 9: Pairwise FST and Nm values across populations in Uganda and Kenya; Appendix 10: Table 10 Delta K values for the seven K populations proposed; Appendix 11 Criteria for detecting the optimum K values by Delta K values; Appendix 12: Results of Mantel Tests for isolation by distance, temperature, rainfall and altitude; Appendix 13: Barriers identified among sampled populations; Appendix 14: Migration rates across sink and source populations in Uganda and Kenya