ACC) connects benthic populations by transporting larvae around the continent, but also isolates faunas north and south of the Antarctic Convergence. We test circumpolar panmixia and dispersal across the Antarctic Convergence barrier in the benthic sea star Glabraster antarctica.The Antarctic Circumpolar Current  gene (n = 285) and the internal transcribed spacer region 2  were sequenced. We calculated haplotype networks for each genetic marker and estimated population connectivity and the geographic distribution of genetic structure using ΦST for COI data.The cytochrome c oxidase subunit I , and some subpopulations are genetically isolated. Genetic breaks in the Magellanic region do not align with the Antarctic Convergence, in contrast with prior studies. Connectivity patterns in East Antarctic sites are not uniform, with some regional isolation and some surprising affinities to the distant Magellanic and Scotia Arc regions.Despite gene flow over extraordinary distances, there is strong phylogeographic structuring and genetic barriers evident between geographically proximate regions . Circumpolar panmixia is rejected, although some subpopulations show a circumpolar distribution. Stepping‐stone dispersal occurs within the Scotia Arc but does not appear to facilitate connectivity across the Antarctic Convergence. The patterns of genetic connectivity in Antarctica are complex and should be considered in protected area planning for Antarctica. Estimates of the timing of ACC formation range from 20 to 41 Ma, corresponding with the opening of Drake Passage and the Tasman Seaway , and is subject to high disturbance via seasonal ice scour and iceberg groundings , which represents at least 32 cryptic species  were sampled from 19 sites around the Antarctic continent . Voucher specimens and field photographs were examined qualitatively for variation in abactinal spines for each site to assess the validity of subspecific names. Tissue samples preserved in 95% ethanol were kept cold until DNA extraction. Voucher specimens are housed at the Scripps Institution of Oceanography's Benthic Invertebrate Collection (SIO‐BIC) in La Jolla, California  was amplified in PCR using the COIceF/COIceR primer set  was PCR‐amplified with difficulty using an annealing temperature of 42°C using the forward primer JW‐5.8SL , an Analysis of Molecular Variance (AMOVA) was performed in Arlequin 3.5  between 2 and 7 were analyzed, each with 100 repetitions and 10,000 iterations for the simulated annealing process and repeated to evaluate consistency. To identify the appropriate grouping scheme  for these data, the pseudo‐F criterion  and to test the Antarctic Convergence as a dispersal barrier (Hypothesis 3), a Spatial Analysis of Molecular Variance analysis (SAMOVA) of COI data was performed in SAMOVA v.2  recovered 17 haplotypes, of which 13 were private and 11 singletons , and 106 were singletons Figure . UncorreG. antarctica appears to constitute a single morphologically variable species.Although morphological variation in size and the presence of abactinal spines exists among the sampled areas Figure , these vCOI data formed a single, diffuse haplotype network, with some regional clustering of haplotypes Figure . There wThe ITS2 network mirrored the pattern in the COI data, forming a single, diffuse network Figure . A commoST = 0.52316, p < 0.000, Table ST comparisons show a strong affinity between Magellanic sites north of the Antarctic Convergence and Shag Rocks, and instances of affinity between distant sites  was driven by the affinity of Shag Rocks with the Magellanic sites  and a corresponding increase in genetic differentiation between regions .AMOVA tests of circumpolarity using COI data showed strong differentiation between sites overall  between pairwise geographic distances and Slatkin's linearized ΦST. A general isolation‐by‐distance model did not adequately explain the variation in pairwise ΦST among all samples, however, a linear regression of distance versus Slatkin's linearized ΦST shows that this correlation is stronger when comparisons are made within geographic regions (R2 = 0.476), but not when made across regions (R2 = 0.025). Thus, IBD is important to explaining genetic connectivity at relatively small spatial scales  but other factors influence connectivity over longer distances.The Mantel test of isolation‐by‐distance (IBD) indicated low correlation , which supports the hypothesis that connectivity is maintained across the Scotia Arc via “stepping‐stone” dispersal across shelf habitats. However, the strong genetic structure among the Magellanic, Scotia Arc, and Antarctic Peninsula regions in the AMOVA and SAMOVA analyses indicate that dispersal is limited at broader scales, particularly in the area of the Antarctic Convergence, and overall, we reject Hypothesis 3.The Mantel test of stepping‐stone dispersal along the Scotia Arc showed strong correlation between step distance and Slatkin's linearized ΦCT) for each K from 2 to 7 , and the population groupings for each K are given in Table K = 5, however, the rate of change between ΦCT values is maximized at K = 4 , and the Scotia Arc group includes West Ross Sea and Enderby (East Antarctica). These results support the hypothesis of circumpolar connectivity despite overall strong population structure. The inclusion of Shag Rocks and two other sites south of the Antarctic Convergence within the Magellanic group indicates that the Antarctic Convergence is an incomplete barrier to gene flow in this species.SAMOVA results show strong population structure . Magellanic specimens are bright red‐orange with distinct abactinal spination and correspond to the synonymized subspecies G. antarctica magellanica , these results demonstrate enough intermittent genetic connectivity to maintain a structured circumpolar species.4.2G. antarctica are highly buoyant, with a PLD of 60 days  of Antarctic Intermediate Water show a relatively strong southward flow of 20 cm/s  and KT459301‐KT459333 (ITS2). Additional specimen, collections, and locality data are given in Appendix DNA sequences: NCBI Genbank Accession numbers  Click here for additional data file.


Historical records and genetic analyses indicate that Latin Americans trace their ancestry mainly to the intermixing (admixture) of Native Americans, Europeans and Sub-Saharan Africans. Using novel haplotype-based methods, here we infer sub-continental ancestry in over 6,500 Latin Americans and evaluate the impact of regional ancestry variation on physical appearance. We find that Native American ancestry components in Latin Americans correspond geographically to the present-day genetic structure of Native groups, and that sources of non-Native ancestry, and admixture timings, match documented migratory flows. We also detect South/East Mediterranean ancestry across Latin America, probably stemming mostly from the clandestine colonial migration of Christian converts of non-European origin (Conversos). Furthermore, we find that ancestry related to highland  versus lowland (Mapuche) Natives is associated with variation in facial features, particularly nose morphology, and detect significant differences in allele frequencies between these groups at loci previously associated with nose morphology in this sample. Latin Americans trace their ancestry to the admixture of Native Americans, Europeans and Sub-Saharan Africans. Here, the authors develop a novel haplotype-based approach and analyse over 6,500 Latin Americans to infer the geographically-detailed genetic structure of this population. Most genetic studies carried out to date have examined this process mainly in relation to variation in overall Native American, European and Sub-Saharan African ancestry across regions and between individuals4; with small and geographically-restricted East Asian ancestry also reported7. In addition, some genetic analyses have sought to detect regional ancestry within the three major continental components, i.e. the sub-continental origins for individuals having contributed to admixture in Latin America. For instance, mtDNA and Y-chromosome data suggest that historical admixture in North West Colombia involved local Native women, and that some immigrant men carried haplogroups common in Jewish populations8. The inference that historical admixture of Latin Americans in specific regions involved Natives with a relatively close genetic affinity to those currently living in the same areas was subsequently supported using genome-wide autosomal data10. Recent genome-wide SNP studies (GWAS), partly implementing haplotype-based analyses, have further expanded the notion that the demographic shifts of the last few generations have not entirely erased signals of historical population structure in Latin America15. A finer characterization of the admixture history of Latin America would benefit from a more extensive sampling across the region, as well as from further methodological improvements  and a wider survey of reference population samples .The history of Latin America has involved extensive admixture between Native Americans and people arriving from other continents, particularly Europe and Africa16. Furthermore, studies of regional human genome diversity, and its bearing on phenotypic variation, have so far been strongly biased towards European-derived populations17. The study of populations with non-European ancestry is essential if we are to obtain a more complete picture of human diversity. Latin America represents an advantageous setting in which to examine regional genetic variation and its bearing on human phenotypic diversity18, considering that the extensive admixture resulted in a marked genetic and phenotypic heterogeneity19. Relative to disease phenotypes, the genetics of physical appearance can be viewed as a model setting with distinct advantages for analyzing patterns of genetic and phenotypic variation. Many physical features are relatively simple to evaluate, show substantial geographic diversity and are highly heritable. We have previously shown that variation at a range of physical features correlates with continental ancestry in Latin Americans19 and have identified genetic variants with specific effects for a number of features22.The broad significance of characterizing these fine-grained patterns of human genetic diversity in Latin America is emphasized by the realization that geographically-restricted genetic variation is potentially a key component of the genetic architecture of common human phenotypes, including disease23, for example recently enabling a fine-grained analysis of the population structure and demographic history of the British Isles24. We present a novel model-based technique for ancestry estimation with a substantial increase in accuracy compared to the technique used in the aforementioned study. We applied this technique to the largest Latin American sample available to date, and an extensive set of reference population samples, in order to delineate patterns of sub-continental genetic diversity across Latin America. Our results demonstrate a striking geographical correspondence between Native ancestry components in Latin Americans and the genetic structure of present-day Native groups. We also match non-Native ancestry components to precise regions of Europe at a sub-country level and unearth ancestry related to present-day groups from the East/South Mediterranean, Africa and East Asia. We infer the timings of these genetic contributions and relate them to historically-attested migrations, for example providing compelling new evidence of widespread ancestry from undocumented migrants during the colonial era. We further show how differences in Native and European sub-continental ancestry components are associated with variation in physical appearance traits in Latin Americans, highlighting the impact of regional genetic variation on human phenotypic diversity.Here we apply fully haplotype-based methods that have been shown to provide higher resolution than allele-based approaches for examining patterns of human population sub-structure25 shows major limitations for characterising sub-continental ancestry , a sub-component related to Andean-Piedmont Natives (concentrating in Northern Peru) and a smaller Aymara sub-component (seen mostly in Southern Peruvians). In Chileans the predominant Native sub-component is most closely related to the Mapuche from Southern South America, while smaller components, related to those observed in Peruvians, are observed in Northern Chileans. In Colombians Native ancestry is most similar to Chibchan-Paezan Natives from Colombia and lower Central America, particularly in Northwestern Colombians. Other components are most closely related to the Central American Maya and, in Southern Colombians, to the Peruvian Andean Piedmont component. The overlap in Native Ancestry between Peru and neighboring Chile (to the south) and Colombia (to the north) is consistent with the high population density of the Central Andes in pre-Columbian America, possibly associated with major cultural developments in the region (at its peak the Inca Empire extended from southern Colombia to northern Chile29). Finally, Andean-Piedmont ancestry from North-eastern Peru represents the major Native American contribution in the Brazilian sample  and the lack of better surrogates for the Native American ancestors of current-day Brazilians . The distribution of European ancestry in the CANDELA sample shows a sharp differentiation between Brazil and the Spanish American countries Fig. . In Braztry Fig. . This pa27. Since admixture proportions in Latin Americans vary greatly, we analyzed each individual separately; simulations confirmed the accuracy of GLOBETROTTER in this setting  have a significant skew towards more recent dates , consistent with the relatively recent arrival of Germans and Italians30.To assess the time-frame of admixture between the ancestry components described above we used the program GLOBETROTTERtry Fig. , with sip-value > 0.1), consistent with most of this ancestry component being contributed simultaneously with the initial colonial immigrants. Jewish communities existed in Iberia (Sepharad) since roman times and much of the peninsula was ruled by Arabs and Berbers for most of the Middle Ages, by the end of which large Sephardic communities had developed33. Genetic studies have detected South and East Mediterranean ancestry in the current Spanish population, as well European admixture in the Sephardim36. The estimates of South/East Mediterranean ancestry in Latin Americans obtained here represent values over and above those present in the Iberian individuals we examined, suggesting colonial migration to Latin America involved people with relatively higher levels of South/East Mediterranean ancestry. Columbus’ arrival to the New World in the late 15th century coincided with the expulsion and forced conversion of Spanish Jews, with similar measures subsequently affecting Spanish Muslims. Although Christian converts were legally forbidden from migrating to the colonies, historical records (often from the Inquisition) document that some individuals made the journey33. Since this migration was mostly a clandestine process, its magnitude has been difficult to assess. Genetic studies have occasionally provided evidence that certain Latin American populations could have some Converso ancestry and this is at times supported by some historical evidence38. Our findings indicate that the signature of a colonial migration to Latin America of people with relatively high South/East Mediterranean ancestry is much more prevalent than suggested by these special cases, or by historical records.SOURCEFIND finds that Sephardic/East/South Mediterranean ancestry is detectable in each country’s samples: Brazil (1%), Chile (4%), Colombia (3%), Mexico (3%) and Peru (2%). Altogether, ~23% of the CANDELA individuals show >5% of such ancestry (an average of 12.2%) Fig.  and in t3. However, the average Sub-Saharan ancestry in the full CANDELA sample is relatively low (<4%), probably reflecting the fact that regions which historically received large numbers of slaves are under-represented in this sample 19. Altogether, ~22% of the individuals studied show more than 5% sub-Saharan African ancestry. SOURCEFIND infers a marked predominance of the West African sub-component, particularly in the Spanish American countries  while Spanish America altogether received about 1.5 millionred Fig. , possibl30. SOURCEFIND estimates East Asian ancestry in the CANDELA sample to be, on average, very low (<1%) in Brazil, Chile, Colombia, Mexico, and slightly higher in Peru (1.4%). In individuals with >5% East Asian ancestry, this component is inferred to be most closely related to the Chinese and to a lesser extent the Japanese, except in Brazil where the opposite is found  versus the relatively differentiated Mapuche component   and broader nose tip angle . This is consistent with physical anthropology studies indicating that the Mapuche have a flatter, wider nose than Central Andean populations43. In a recent GWAS for facial features in the CANDELA sample, most loci identified impacted on nose shape21. For each of the six index SNPs significantly associated with facial features in that GWAS, allele frequencies at haplotypes inferred to be of Central Andean ancestry were significantly different from allele frequencies at haplotypes inferred to be of Mapuche ancestry  or inferred Mapuche  haplotypes  ancestry than individuals migrating to the Americas during the colonial period, due to more recent admixture with other Europeans. In this case SOURCEFIND may overestimate the contributions from the non-European groups. Because of this, estimates for each of the East/South /Mediterranean and African components should be interpreted as values over and above those present in the present-day Spanish/Portuguese reference individuals examined. As noted above, the similarity in inferred dates for admixture involving East/South Mediterranean versus Iberian ancestry furthermore suggests that the individuals carrying this excess East/South Mediterranean ancestry migrated to Latin America during the colonial period.45. Our results underline the importance, for a fuller exploitation of genomic data, of a broader description of human genetic and phenotypic diversity than is currently available.In conclusion, the results presented here exemplify how historical events have finely structured the genetic make-up of Latin Americans, and provide insights into the complicated dynamics and timescales of intermixing among different continental groups from the colonial-period up until recently. Our findings illustrate how genetic analyses can contribute to building a fuller picture of human history. This is particularly the case for poorly documented events such as the clandestine migration of recent Christian Conversos, of East/South Mediterranean ancestry, to colonial Latin America. Furthermore, our analyses show how regional genetic variation, subtly shaped by history, can impact on the genetic architecture of complex phenotypes across major geographic regions. We demonstrate how this regional genetic diversity can be analyzed in admixed individuals with ancestry from various sources; an encouraging result given the ubiquity of recent admixture in world-wide populationshttp://www.ucl.ac.uk/candela) consists of genotypes from 6,852 individuals ascertained in five Latin American countries  . Informed consent was obtained from all participants, complying with all relevant ethical regulations as approved by Universidad Nacional Autónoma de México (México), Universidad de Antioquia (Colombia), Universidad Perúana Cayetano Heredia (Perú), Universidad de Tarapacá (Chile), Universidade Federal do Rio Grande do Sul (Brazil) and University College London (UK). A structured interview recorded the birthplace of volunteers and their ancestors (up to grandparents), as well as information on the language(s) spoken by them. We have previously reported genome-wide association studies based on Illumina HumanOmniExpress chip data obtained in these individuals22.The CANDELA dataset , but reference individuals with less than 95% Native American ancestry were only used for haplotype phase inference. In the case of East/South Mediterranean individuals, ADMIXTURE consistently inferred Sub-Saharan African ancestry. The estimated Sub-Saharan African ancestry proportions were found to be quite homogeneous across individuals, possibly indicating relatively old shared ancestry. Based on this assumption, we excluded only four individuals with admixture proportions deviating markedly from this observation in a manner suggestive of recent admixture .To perform ancestry analyses in the CANDELA individuals we collated a reference population dataset from regions having potentially contributed to admixture in Latin America. We combined publicly available dataere Fig. . AltogetAfter QC, the merged CANDELA plus reference population dataset comprised genotypes for 546,780 autosomal SNPs in 8,647 individuals . A global overview of our analysis strategy is provided in Supplementary Figure 53 using default parameters. Genetic distances used were obtained from the HapMap Phase II genetic map build GRCh3754. Missing genotypes for any SNP (<5% after the QC) were imputed during the phasing process.Phasing of the merged dataset was performed with SHAPEIT223 was used to infer haplotype similarity  and estimate the proportion of DNA shared between donor and recipient individuals, thus generating a coancestry matrix relating individuals. CHROMOPAINTER was setup so that the donors were exclusively reference population individuals, while recipients included CANDELA individuals as well as reference population individuals. Consideration of reference population individuals as recipients as well as donors enabled the analysis of genetic structure in the reference population samples (using fineSTRUCTURE as described below), while the analysis of coancestry between CANDELA recipients and reference population donors enabled the inference of ancestry profiles in the CANDELA individuals from donor clusters (using SOURCEFIND as described below).CHROMOPAINTERNe and the mutation parameter θ used by CHROMOPAINTER were jointly estimated for every individual in a subset of chromosomes  with 10 Expectation-Maximization steps, starting from default values defined by the software. The average Ne and θ values across chromosomes (weighted by chromosome size) were then used for subsequent CHROMOPAINTER runs on all autosomes (Ne = 290.83 and θ = 0.00038). Genetic distances from the HapMap Phase II genetic map build GRCh37 were used in the CHROMOPAINTER runs. CANDELA individuals with >99% European ancestry  or with >95% Native American ancestry  were included amongst the donors as they may harbor ancestry components not present in our reference dataset. In Supplementary Note The recombination scaling constant 23, a program that defines homogeneous clusters of individuals based on the coancestry matrix produced by CHROMOPAINTER. We performed additional analyses on the clusters defined by fineSTRUCTURE in order to select a final subset of donor clusters to be used as surrogates for the unknown populations that historically contributed ancestry to Latin Americans (we refer to this subset as surrogate clusters or surrogates). These additional analyses aimed to: (i) reduce the number of clusters potentially representing sources of ancestry, (ii) avoid problems related to collinearity between the different sources when estimating ancestry, and (iii) facilitate interpretation of results. The end result was that 56 surrogate clusters were defined and subsequently used for sub-continental ancestry estimation in the CANDELA samples. The fineSTRUCTURE analyses and their subsequent refinement are described below and a diagram summarizing the overall strategy is shown in Supplementary Fig. To evaluate genetic structure in the reference populations independent of sample labels we used fineSTRUCTUREc) is initially calculated in order to account for the inaccurate assumption that the amount of DNA matching among individuals is independent. Using default CHROMOPAINTER settings to infer the adjustment factor, this was estimated as c = 0.236. Two MCMC runs were performed using 1,000,000 iterations . The first run was used as the baseline to define the clusters, while the second one was used to assess the variability of the cluster assignments between runs (described below). Following Leslie et al.24, for each run the sample with maximum posterior probability was selected and an additional 100,000 hill-climbing moves were then performed to search for merges or splits that further improve the overall model likelihood23. After this procedure, fineSTRUCTURE classified individuals into 129 clusters.First, the reference population individuals were clustered using fineSTRUCTURE. A likelihood adjustment factor  using NNLS  so as to identify the surrogate clusters detected in a computationally efficient manner. Based on this preliminary analysis, we outlined criteria to define certain surrogate clusters (as described below). Note that even though only surrogate clusters are used for estimating ancestry in CANDELA samples, our modelling requires use of the full coancestry matrix, implying that individuals from non-surrogate clusters are still included as donors in order to define the haplotype similarity profiles for both surrogates and admixed samples.Using the 129 clusters defined by fineSTRUCTURE, we performed a preliminary estimation of sub-continental ancestry in the CANDELA samples using a modification of the NNLS approach implemented by Hellenthal et al.24) and Tree distance values from their larger, related clusters.We checked the consistency of the assignments of individuals to clusters across samples in the 1,000,000 iterations of the two fineSTRUCTURE runs. We excluded individuals that were assigned to different clusters more than 10% of the time. This included five clusters consisting exclusively of individuals that were inconsistently assigned across samples. We also excluded: (i) 12 clusters consisting of only one sample, (ii) ten small clusters with low ancestry contributions to CANDELA samples , and (iii) 17 clusters that did not contribute to the CANDELA samples. Altogether, this led to 44 clusters being excluded as surrogates. Since these 44 clusters had no clearly defined structure, the individuals they included were reclassified, based on population sample labels, into 54 non-surrogate donor clusters. Furthermore, we merged 16 clusters with other clusters containing mainly individuals from the same reference population sample. NNLS usually randomly assigned ancestry to Latin Americans from these 16 clusters and the related, larger, clusters seven (7) clusters that contributed substantial ancestry to several other surrogate groups  or showed ancestry from more than one continent (e.g. a Turkey cluster was inferred to have >5% ancestry from East Asia and 5% from Europe). These seven clusters were excluded from the surrogates and subsequently considered as non-surrogate donor clusters.(ii)Six (6) Native American clusters  or had complex ancestry profiles complicating the interpretation of results. For this, the proportions of DNA that each individual from the surrogate clusters matches to each donor individual (estimated by CHROMOPAINTER) were summed across the 123 non-surrogate donor clusters defined above . We obtained the average copying vectors  for each of the 69 surrogate clusters. We then performed a NNLS regression with the average copying vector of a surrogate cluster as the response and the average copying vectors the other 68 clusters as predictors. These analyses detected:The end result was that the initial set of 69 surrogate clusters was reduced to 56, which was the final set of surrogates used for the ancestry analyses of the CANDELA individuals, and the total set of donors clusters (surrogates and non-surrogates) reduced to 117. The 56 surrogate clusters include a total of 1,444 reference population individuals. Supplementary Table 55 using packages rworldmap, maptools, and plotrix.The maps in Figs. 27. To do so, we introduce a model-based approach we term SOURCEFIND (see code availability), which outperformed the NNLS approach taken in Leslie et al.24 in simulations related to this study . Here for any r, C is equal to the total genome length of DNA (in cM), times two because we sum matching across a recipient’s two haploid genomes, and we further define r denote a CANDELA individual, and s denote a surrogate cluster. In the latter case, Let s ∈  = 56 represents a surrogate cluster used to describe the ancestry of group r. In practice, often all the donor clusters are used as surrogates, so that S = D. However, in our case the surrogates are a subset of the donors so that S < D.We assume that:βr, further assuming the following:We take a Bayesian approach to inferring λ = {λ1,...,λS}. For each recipient r, we wish to sample the mixing coefficients l ≡ {lr, l1,…,lS}.We do so using the following Markov Chain Monte Carlo (MCMC) technique. We start with an initial value of λ(0) = 0.5 and sample our initial values of λ(0),…,λ(0)). Then we perform the following for m ∈ , where M is the total number of MCMC iterations:where i.Y ~ Unif .Randomly sample ii.sx and set Randomly sample a surrogate sx.Repeat this for four additional randomly sampled (with replacement) surrogates iii.sx and set Randomly sample a surrogate sxRepeat this for four additional randomly sampled (with replacement) surrogates iv.s ∈ , excluding the randomly sampled set above, set For all other surrogates v.Re-scale vi.βr(m)with probability min, where:Accept Update λs(m) for s = 1,…,S using a M-H step:i.λs(m) from a Normal (λs(m − 1), sd = 0.2).Propose a new ii.λs(m) ∉ .Automatically reject if iii.λs(m) with probability min), where:Otherwise accept Update each M, this algorithm is guaranteed to converge to the true posterior distribution of the βr’s (e.g. Gamerman56). In practice, we used M = 200,000, sampling every 1000 iterations. Also, for each recipient individual r, we combined results across 50 independent runs of the above procedure, extracting the estimates with the highest posterior probability in each run and then taking a weighted (by posterior probability) average of these 50 estimates. We refer to the final estimates of r conditional on this set of S surrogates. This approach differs from the mixture model procedure applied in previous studies58 in that it assumes that lr is multinomial distributed and solves for βr using a Bayesian approach rather than a NNLS optimization. The model is similar to the one described by Broushaki et al.59, but introduces new improvements in the way that λ is estimated and in the MCMC proposal procedure.For large 27. GLOBETROTTER tests for evidence of one or more pulses of admixture between two or more ancestral groups, and dates these admixture events and infers the genetic make-up of the admixing groups involved. Due to the recent nature of intermixing in the Americas, admixture times and proportions may vary substantially across CANDELA individuals. Therefore we tested each individual separately, restricting this analysis to the 6,352 individuals inferred by SOURCEFIND to have ancestry from more than one surrogate cluster.The times and sources of major admixture events were inferred using the program GLOBETROTTERFor each haploid genome of each individual, we used ten random samples of genome-wide local matching to donor clusters per haploid as provided by the CHROMOPAINTER analysis described above. For each CANDELA individual, we ran GLOBETROTTER including as surrogates only the subset of ≤56 clusters that contributed >1% to that individual, as inferred by SOURCEFIND. For each CANDELA individual, GLOBETROTTER categorized admixture inference into one of three types: (i) one date of admixture involving two sources, (ii) one date of admixture involving more than two sources , and (iii) multiple dates of admixture between two or more sources (not necessarily the same two), suggesting a more complicated history but which GLOBETROTTER attempts to describe as two major pulses of admixture.Altogether, for 55.4% of the CANDELA individuals  GLOBETROTTER inferred a single admixture event between two source groups, while in 44.6% of the CANDELA individuals 2,833/6,352) a more complex admixture was inferred. This could consist of more than two groups admixing , where y is the year of admixture, 1990 is the mean birth year in CANDELA individuals, g the estimated admixture time (in generations), and taking 28 years as the generation time60.To convert the time estimates obtained by GLOBETROTTER (in generations) into years, we used the formula In Fig. Iberia: CanaryIslands, Portugal/WestSpain, CentralSouthSpain, CentralNorthSpain, Basque and Catalonia.NorthWestEurope & Italy: Italy1 and NorthWestEurope1.East Mediterranean & Sephardic: Sephardic1, EastMediterranean1 and EastMediterranean2.Sub Saharan Africa: WestAfrica1, WestAfrica2, WestAfrica3, EastAfrica1, EastAfrica2, Namibia, and SouthAfrica.East Asia: Japan, ChinaHan, China/Vietnam1, and China/Vietnam2.55 to perform a one-sided Wilcoxon rank-sum test  to test the alternative hypothesis that the distribution of admixture dates for each geographic label X = {East Asia, NorthWestEurope & Italy, East Mediterranean & Sephardic, SSA} is skewed towards more recent dates relative to the Iberia geographic label, versus the null hypothesis that distributions are the same. Though they may represent genuine admixture events, for these tests and the histograms of Fig. 27. For the Wilcoxon rank-sum test, we further excluded individuals with ≤5% ancestry from X and individuals with date ≥ 30 generations to avoid admixture events that occurred prior to colonial-era migrations. In addition, this analysis assumes each inferred event is an independent observation, even though some individuals have two inferred events. However, we note that conclusions and trends do not change if we restrict to one inferred event per individual, e.g. by excluding individuals who infer multiple dates of admixture (i.e. case (iii) described in the previous section) and only including the more strongly signaled event in individuals who infer more than two sources of admixture at the same time (i.e. case (ii) described in the previous section).We used wilcox.test in R22 and brief definitions are provided in Supplementary text We recorded 28 physical appearance traits, by physical examination of the volunteers and/or by examining facial photographs. These traits have been described in detail in previous studies14, but including a contrast between subcontinental ancestry components. To maximize power, we defined three criteria for making these contrasts: (i) each component tested should have at least 10% frequency in a country (ii) the two sub-continental ancestry components contrasted should add up to at least half of the total continental ancestry in a country, and (iii) the components contrasted should show a relatively high genetic differentiation.To evaluate the phenotypic effect of sub-continental ancestry components defined by SOURCEFIND we used linear regression. Since these components are (negatively) correlated with other major continental ancestries, using them directly would cause confounding in the linear model. We therefore performed linear regression analysis in a manner analogous to that in Moreno-Estrada et al.K = 7 , 101 YRI  and 125 Native American samples) were used for this purpose. RFMix assigns local continental ancestry to each allele of each CANDELA haplotype, allowing for errors in genotyping, slight admixture in the reference samples, etc. Thus, for each allele of each haplotype, it produces two files of relevance—the local ancestry at that site, and the putative allele at that site (after fixing any such errors).To do so, we used the software RFMixr2 > 0.99; the number of surrogate individuals with >99% Native ancestry for the Mapuche group wasn’t large enough for such a comparison).Using SOURCEFIND sub-continental ancestry proportions, two different sets of CANDELA individuals were selected to obtain allele frequencies for Central Andes and Mapuche groups. For each set, all individuals had >10% inferred ancestry from that Native group, with <1% combined inferred ancestry from all other Native groups and <1% inferred East Asian ancestry. For all individuals in a group, for each locus, all alleles that had local Native ancestry (as inferred by RFMix) were aggregated to estimate the allele frequency for that group. Allele frequencies thus obtained for Central Andes were very similar to the allele frequencies obtained from 49 surrogate individuals of the Central Andes group who were inferred to have >99% Native ancestry  was used to assess whether the allele frequencies were significantly different in Central Andes vs. Mapuche individuals. The FDR  procedure was used to control the Type-I error rate at 0.05 level. After the FDR procedure, all SNPs showed a significant difference in allele frequency between Central Andes & Mapuche (Supplementary Table Allele frequencies were thus obtained for the index SNPs (among the chip data) of all the six genomic regions identified in Adhikari et al.t-test to calculate a p-value testing the null hypothesis that the Central Andean and Mapuche allele frequencies were the same, taking the average −log p-value across all six SNPs in the set. We found the proportion of 10,000 such random samples of six SNPs with average −log p-value less than or equal to that of the six GWAS index SNPs, using this proportion as an empirical p-value testing whether the six GWAS index SNPs were more differentiated than usual. In order to match power between our six GWAS hit SNPs and each random set of six SNPs, we only randomly selected from SNPs matched to the GWAS index SNPs for both the number of observations and minor-allele-frequency. In particular, for each GWAS index SNP we generated two sets  of matching SNPs that (a) excluded the six GWAS index SNPs, (b) had number of inferred Central Andean and Mapuche haplotypes within 20 of that for the index SNP, and (c) had minor-allele-frequency within 1% of the index SNP among inferred Central Andean haplotypes (set I) or inferred Mapuche haplotypes (set II). The matching SNP counts for each GWAS index SNP in each of set I and set II, plus the empirical p-values, are provided in Supplementary Table We also assessed whether the allele frequencies at these six SNPs jointly were excessively differentiated between haplotypes inferred to be of Central Andean ancestry versus those inferred to be of Mapuche ancestry, potentially indicating selection. To do so, we randomly selected sets of six genome-wide SNPs. For each SNP in the set of six, we used the same Further information on research design is available in the www.paintmychromosomes.com.SOURCEFIND is available at Supplementary InformationPeer Review FileReporting Summary


Duma florulenta (lignum) is a keystone species that facilitates biodiversity in the floodplains and wetlands of Australia's dryland river systems. This paper explores spatial genetic structure of lignum and investigates factors influencing dispersal and gene flow within and among river catchments of the northern Murray–Darling Basin.The conservation of plant species biodiversity has been identified as a crucial factor for the resilience of dryland ecosystems in the face of climate change and desertification. Northern Murray–Darling Basin, eastern Australia.A total of 122 individual plants from subpopulations located on rivers in four adjacent catchments were genotyped using 10 microsatellite markers. Microsatellite data were then analyzed using population genetic techniques to evaluate levels of gene flow and genetic structure and identify factors influencing dispersal.R2 value of the former was only half that of the latter.Results suggest high levels of gene flow between lignum subpopulations of the northern Murray–Darling Basin. AMOVA revealed small but significant differences between subpopulations, and STRUCTURE analysis did not detect meaningful structure when sampling information was not provided. However, when sampling information was supplied using the LOCPRIOR model, three genetic clusters were identified. All Lower Balonne subpopulations were assigned to cluster 1 while a number of the other subpopulations showed mixed ancestry. Weak relationships were identified between pairwise genetic distance and geographic as well as river distance, although the Patterns of genetic variation suggest frequent long‐distance overland gene flow largely as a result of the movement of seeds via floodwater. Therefore, maintenance of natural variability in flow regime is key both to maintain conditions favorable to recruitment and to promote dispersal and gene flow across the landscape. However, given future climate change projections persistence may be more reliant on the species ability to endure long periods of drought between flood events. Shust (hereafter “lignum”) is a large diecious shrub that is a keystone species throughout the wetland ecosystems of Australian dryland rivers  was originally developed to describe patterns of genetic structure in desert fishes and describes a situation in which an organism is restricted to dispersal within the river corridor . In addition, we predicted that genetic diversity would increase downstream as a result of predominantly downstream dispersal.22.1The MDB is the largest river system in Australia draining over 14 percent of the continent and consisting of a network of approximately 77,000 km of river channel Figure . WetlandAverage annual rainfall in the MDB ranges from over 1,000 mm in the areas of the Great Dividing Range in the south and east of the catchment and to as low as 100 mm in the western margins. Annual discharge for the basin is generally low but highly variable with an average of approximately 32,500 gigaliters (GL). The northern MDB includes the Darling River and all catchments draining into it while the southern MDB includes the Murrumbidgee, the Murray, and all catchments these rivers drain. Flows in the southern part of the MDB are relatively less variable and more predictable as result of higher levels of regulation and a more seasonal high flow season stemming from winter rainfall and spring snowmelt. Flows in the northern MDB are more variable with the Darling River being ranked among the most hydrologically variable and unpredictable rivers in the world . Where possible, samples were collected at a distance of at least 20 m from each other in order to avoid the collection of clonal ramets with identical genotypes at a local scale. For each sample, 20 mg of silica‐dried leaf or stem tissue, depending on availability at time of sampling, was ground mechanically using a Mixer Mill MM 301 (Retsch GmbH & Co.) at 30 Hz for 2 min (leaf) to 4 min (stem). Genomic DNA was isolated using a Bioline ISOLATE II Plant DNA Kit (Bioline) according to the manufacturer's protocol. Following isolation, purity and concentration of DNA samples were determined using a NanoDrop 8000 Spectrophotometer (Thermo Fisher Scientific).2.4KX762272–KX762283). To reduce the costs by multiplexing, PCR products were labeled using M13 universal primers as outlined in Sheulke  and the New South Wales Water Information website (http://waterinfo.nsw.gov.au/)  were retrieved for 25 gauging stations in the study area from the Queensland Water Monitoring Information Portal  Figure . For eac) Figure . Given t2.6sibs. MicroChecker 2.2.3  was estimated using CERVUS   (α = 0.05). However, following Bonferroni correction significant Fis was restricted to Trilby, Culgoa, and Tinnenburra subpopulations  failed to amplify with 28% and 20% of individuals, respectively, so these data were not included in further analysis. Null alleles were detected at 3 , 2 , 2 , and 1 (Wanaaring) subpopulation for loci Df 80, Df 78, Df 40, and Df 3, respectively. At locus Df 87, however, null alleles were detected at all but four subpopulations . For this reason, Df 87 was excluded from further analysis while Df 80, Df 78, Df 40, and Df 3 were retained in order to maintain statistical robustness, given that null alleles were only detected in a small portion of subpopulations for these loci. A total of 122 individuals from 13 subpopulations were scored at the remaining nine microsatellite loci revealing 81 alleles, with alleles per locus ranging from 2 to 18. Linkage disequilibrium was not significant for all 36 pairwise locus combinations across all subpopulations. Exact tests indicated significant deviation from HWE for 12 of 117 locus–subpopulation combinations at an alpha of 0.05. This included 2  at locus Df 100, 3  at locus Df 3, 3  at locus Df 40, 2  at locus Df 78, and 2  at locus Df 80. Following sequential Bonferroni correction Rice, , 1 combi3.2In general, the Darling River was the least variable of the rivers in terms of flow with gauging stations recording the lowest proportions of no flow days and also comparatively low frequencies of 90th and 75th percentile flow events Table . The hig3.3FSR = 0.023, p ≤ 0.0001)  and between subpopulations overall were also significant     nor any of the flow variables  exhibited significant relationships with pairwise genetic distance as measured by FST. Partial Mantel analysis used to identify relationships that might arise from the effect of interactions between variables was also not significant when all but the 75th percentile independent variable were analyzed in unison .Hierarchical AMOVA indicated that the majority (~95%) of variation in lignum could be attributed to variation within rather than among subpopulations. Of the variation that could be attributed to differences among subpopulations, the majority was the result of differences between subpopulations within river systems . However, reproductive characteristics of the species would suggest that homozygote excess as observed here could be expected in the sampled subpopulations. In addition, although null alleles were detected they were only detected at a small proportion of the subpopulations for any one marker.Homozygote excess at the majority of subpopulations resulted in a number of deviations from HWE as evidenced by positive 4.2FST values indicate that the two subpopulations not genetically differentiated and located the furthest away from each other were located at a distance of 456 km  to the Hawaiian Islands, USA (Le Roux et al., Other more widely traveled animals that may play an important role as vectors of overland dispersal include waterbirds that travel long distances to take advantage of the boom in productivity that occurs in these areas during times of expansive flooding. Lignum is the preferred nesting habitat for many of these waterbird species (Kingsford, Thomas, & Curtin, While lignum does not have any specific adaptations to dispersal by birds, the frequent use of the plant for refuge and nesting habitat by waterbirds, as well as the small size of its seed, indicates that ectozoochoric dispersal of seeds and pollen that become stuck in feathers and other parts of the body may be an important and frequent means of long‐distance dispersal and gene flow in the species. In addition, the small size of the seeds might result in accidental ingestion of seeds and endozoochoric dispersal. However, the ability of lignum seeds survive gut passage is unknown. Feral goats were frequently observed grazing on lignum in the field and feral pigs are also known to use lignum stands for cover, suggesting that both feral and domestic ungulates may also be vectors of dispersal. The dispersal of stem fragments could also occur in the manner described here although multilocus genotypes were not recorded so vegetative dispersal could not be confirmed.FST confirmed the grouping of these sites with none of them being significantly differentiated from each other. While some of the Warrego and Paroo subpopulations show mixed ancestry in the STRUCTURE analysis, and overall, they are not as tightly grouped on the PCoA plot they also do not exhibit significant pairwise genetic differentiation. On the Darling River, however, the Trilby subpopulation differed from both the other two subpopulations.A less obvious but probably more plausible explanation for frequent overland dispersal and gene flow indicated by patterns of genetic variation in this study is dispersal via floodwater. Hydrochory is a dispersal mechanism frequently associated with long‐distance dispersal and gene flow in riparian plant species (Fér & Hroudová, The increased genetic similarity between Warrego and Paroo River subpopulations and subpopulations located on the rivers of the Lower Balonne could be a result of increased hydrochoric dispersal across shared floodplains. A similar pattern was identified for an endangered shrub in Western Australia, albeit over much smaller distances. Although unlike this study, Hevroy et al.  found gr5In conclusion, the results of this study indicate that gene flow is high between subpopulations for lignum in the northern MDB. Frequent long‐distance dispersal events are likely, as indicated by weak levels of spatial genetic structure. The exact mechanisms of long‐distance dispersal are not clear; however, the presence of greater genetic differentiation between subpopulations within river systems in comparison with subpopulations among river systems indicates nonconformity with the SHM and suggests that dispersal is not limited to the river corridor. Despite this, patterns of pairwise genetic differentiation and clustering according to STRUCTURE suggest a pattern of genetic variation best explained by floodplain dispersal via hydrochory. This is in agreement with experimental data finding that lignum seeds can germinate while floating and achieve optimal germination when inundation occurs for around 20 days before seeds are deposited on to wet soil (Chong & Walker, This study highlights the importance of maintaining natural flow variability in these systems as overbank flows are clearly integral to the persistence of this species not just for the provision of conditions that promote germination and establishment but also to facilitate gene flow and dispersal across the floodplain and between river channels. While rainfall predictions under future climate change scenarios are uncertain predictions for the MDB generally forecast an overall drying effect with an increase in the intensity of rainfall events and the prolonging of intervening drought events (James, Reid, & Capon, None declared.BM, MR, SC, and SW conceived the ideas; BM collected data; BM and SW performed laboratory analysis; BM and MR performed statistical analysis; BM led the writing; and MR, SC, and SW revised drafts for important intellectual content.


One of the main objectives of conservation programs is the maintenance of genetic diversity because this provides the adaptive potential of populations to face new environmental challenges. Genetic diversity is generally assessed by means of neutral molecular markers, and it is usually quantified by the expected heterozygosity under Hardy-Weinberg equilibrium and the number of alleles per locus or allelic diversity. These two measures of genetic diversity are complementary because whereas the former is directly related to genetic variance for quantitative traits and, therefore, to the short-term response to selection and adaptation, the latter is more sensitive to population bottlenecks and relates more strongly to the long-term capacity of adaptation of populations. In the context of structured populations undergoing conservation programs, it is important to decide the optimum management strategy to preserve as much genetic diversity as possible while avoiding inbreeding. Here we examine, through computer simulations, the consequences of choosing a conservation strategy based on maximizing either heterozygosity or allelic diversity of single-nucleotide polymorphism haplotypes in a subdivided population. Our results suggest that maximization of allelic diversity can be more efficient in maintaining the genetic diversity of subdivided populations than maximization of expected heterozygosity because the former maintains a larger number of alleles while making a better control of inbreeding. Thus, maximization of allelic diversity should be a recommended strategy in conservation programs for structured populations. Genetic diversity is the fuel for the adaptation of species to the environmental challenges and one of the main control variables to be assessed within the planetary boundaries framework . ConservThe consensus criterion for the maintenance of genetic diversity in conservation and animal breeding programs is the maximization of expected heterozygosity, which is equivalent to the minimization of mean weighted coancestry  and implConservation programs can be aimed at maximizing either expected heterozygosity or allelic diversity. The results from N = 2,000 and four of size N = 100 individuals, to obtain different degrees of variation within subpopulations, which were maintained independently for 25 generations of random mating. The software SLiM 3  was obtained and maintained with that size under a common conservation scheme based on maximization of either expected heterozygosity or allelic diversity for 25 generations with controlled migration between subpopulations. Marker loci to be used for analysis and management were assumed to be haplotypes of groups of five consecutive SNPs, such that the different haplotypic combinations of SNPs per locus were considered as different alleles, providing a maximum of 32 per locus. The total number of available loci was about 2,000, but the number of segregating loci available for analysis at the start of the conservation management process was approximately 1,200.The second step of the study was the conservation management of the structured population created from the previous simulation. From each of the five subpopulations, a sample of HT) is partitioned, following HS) and the average Nei’s minimum genetic distance between subpopulations, averaged over all possible pairs of subpopulations (DG). In an analogous way, allelic diversity is partitioned, following AS). The between-subpopulation component (DA) is calculated as the number of alleles present in a subpopulation and absent in other when subpopulations are compared in pairs and averaged over all possible pairs of subpopulations. Total allelic diversity is then defined as AT = AS + DA and represents the total number of alleles present in a given pair of subpopulations, averaged for all possible pairs.For conservation management, we used the software Metapop2 , which pT) or total allelic diversity (maxAT) is obtained by maximizing the functions HT = DG + λHS and AT = DA + λAS, respectively, where λ is the desired weight given to the within-subpopulation component. In addition, the program also maximizes the total allelic number in the whole population (maxK) by managing contributions from parents to progeny and migrations so that the global probability of alleles’ losses in the progeny is minimized  and recombination was free between them, which are reasonable assumptions given the short number of SNPs per locus and the scarcity of loci used along the sequence.Management was run assuming two different objectives: (1) Conservation of diversity for a particular set of loci for which one locus every 100 in the genome was used for management and genetic variation was measured directly on that set of loci. This refers to a scenario in which a few known loci or genomic regions of particular interest have to be managed, for example, for loci that are known to have an effect on a particular trait of interest, such as those affecting a productive trait, the immune system, and so on. (2) Conservation of diversity in the whole genome for which one locus every 10 was used for management and the results were analyzed for all genomic loci. This is a situation where a number of markers are used for conserving overall genetic diversity. For this latter case, we used a modification of the software Metapop2 . With thλ = 1, thus giving the same weight to within- and between-subpopulation components. However, other values of λ were also considered, including those for which all weight is given to between-subpopulation diversity (ခλ = 0), all weight is given to within-subpopulation diversity (␭λ = 1000), and λ = 0.5, a value suggested to maximize the total genetic variance of a hypothetical quantitative trait , allelic diversity measures , and the observed marker homozygosity of all individuals in the subpopulations  were obtained from the 12 managed markers for the scenario aimed at conserving diversity for a specific set of loci and from the whole sequence in the scenario aimed at conserving diversity for the whole genome.The optimization was carried out for 25 generations generally assuming a value of ve trait . A maximve trait . In all T, maxAT, and maxK), aimed at maximizing global heterozygosity HT, global allelic diversity AT, and the total number of alleles K, respectively. The evolution of these parameters and their within- and between-subpopulation components are shown in λ = 1). As expected, no management  led to a generalized loss of genetic diversity and to an increase in molecular inbreeding whereas any of the specific management methods increased diversity or restrained its loss through generations. The relative performance of the different optimization methods was very similar for scenarios aiming at the conservation of diversity for either a particular set of loci (Three optimization methods were compared (maxHT (blue lines) was the best method, preserving HT in the global population by means of an initial increase in the diversity between subpopulations (DG) while keeping or slightly decreasing that within subpopulations (HS). Method maxAT (green lines) produced the largest AT by increasing or keeping a high diversity within subpopulations (AS) and decreasing that between subpopulations (DA). Finally, maxK (red lines) maintained the largest number of alleles segregating in the whole population (K), although maxAT maintained only a little less or about the same number of alleles.As expected, each maximization method maintained higher levels of the corresponding measure of diversity. Thus, maxHF) was better restrained by maxAT, whereas maxK produced the highest molecular inbreeding levels, close to those yielded by RND. Method maxAT was also the optimizing method making a wider use of the individuals available for mating (nMates) and produced the lowest variance of contributions from females to progeny (VarContFem), thus approaching the equalization of contributions from parents to progeny. Method maxHT produced the highest variance of contributions from females to the progeny in the initial generations.The molecular inbreeding coefficient . Method maxK was producing the largest variation in allelic frequencies.As already observed in previous studies, maxHT was the optimizing procedure with the lowest variation in subpopulation migrations.All management methods involved an average number of one migrant per generation and subpopulation. However, there were differences in the variance of the number of migrants per subpopulation depending on the generations and methods (see graphs VarMigrants in λ) given to the within-subpopulation component. Method maxK and no management (RND) obviously were unaffected by the different weighting. Method maxHT maintained the highest HT for all λ values. This was attained by increasing DG when all weight is given to the between-subpopulation component (ခλ = 0) at the expense of decreasing the within-subpopulation component HS, or by increasing HS when all weight is given to the within-subpopulation component  at the expense of decreasing the between-subpopulation component DG. Method maxAT preserved better AT when some substantial weight was given to the within-subpopulation component . If all weight is given to the within-subpopulation component , maxHT would produce the lowest molecular inbreeding (F), as expected, but the number of alleles maintained would be lower than those obtained by the allelic optimization methods. For intermediate values of λ (0.5 or 1), maxAT seems to be the most robust method, producing the lowest inbreeding and a number of alleles almost as large as that maintained by maxK, although giving lower HT than that of maxHT.c = 10–6. c = 10–5). The results are, in general terms, also similar to those obtained before.Additional simulations regarding alternative parameter settings with respect to those considered above are given as Supplementary figures. First, the results shown in e.g.,T, can be recommended as the method of choice because it maintains a high allelic richness in the population (uniformly distributed across subpopulations) and controls inbreeding rather efficiently.Preservation of genetic diversity is one of the main objectives of conservation programs . BecauseWe considered two scenarios regarding the number of markers to be managed. One in which a specific set of loci is the target for conservation, as it could apply, for example, to specific loci of interest, such as those related to the immune system. In this case, because management is carried out on the specific loci of interest, the management methods are very effective in increasing genetic diversity Figure 2Drosophila melanogaster in which synthetic populations were built from a group of subpopulations by maximizing either the heterozygosity or the total number of alleles for nine microsatellite loci. Artificial selection for sternopleural bristle number during eight generations showed that the response to selection was larger (for both upward and downward number of bristles) for synthetic populations obtained by maximizing the number of marker alleles than for those obtained by maximizing marker heterozygosity. In addition, it has been observed in Arabidopsis halleri that genome-wide SNP diversity does not show a significant correlation with microsatellite heterozygosity based on 20 markers but is significantly correlated with microsatellite allelic richness  with a method aimed at maintaining the overall number of alleles in the population (maxK). Although the former can be used to control the distribution of allelic variants within and between subpopulations, the second is applied without such a control. A notable different outcome is observed with each method. Method maxK maximizes, as expected, the total number of alleles in the whole population, but alleles are distributed variably across subpopulations, as indicated by a high value of DA. In contrast, maxAT maintains almost as many alleles as maxK in the whole population but keeps them more homogeneously distributed over subpopulations, as indicated by a low value of DA. In conservation programs of structured populations, the objective may be to maintain reservoirs of variation such that there is little overlap between different subpopulations, for example, when there are local adaptations and a risk of outbreeding depression, in which case, a method such as maxK could be more appropriate. However, the loss of a subpopulation implies, in this case, the irreversible loss of allelic variation. If, on the contrary, allelic diversity is maintained uniformly in all subpopulations, as achieved by maxAT , the loss of a subpopulation does not imply a loss of allelic diversity because each subpopulation would provide a backup for the others. In a recent article, AT to prioritize different European cattle breeds for conservation.Regarding allelic diversity procedures, we have compared the allelic diversity partition suggested by T produces almost the same outcome in terms of total allelic number with the desirable addition of a better control of inbreeding.T, the maximization of the total allelic diversity (AT) following In summary, our results suggest that maxAhttps://gitlab.com/elcortegano/hadopt.The pipeline required to simulate all populations assayed as well as to run the corresponding population analyses and management simulations is available in AC, EL-C, and JF contributed to the conception and design of the study; EL-C and AP-F developed the required software and pipelines; EL-C, RP, and AL performed computer simulations; AC and EL-C wrote the manuscript. All authors contributed to manuscript revision and read and approved the submitted version.This work was supported by Agencia Estatal de Investigación (AEI) (CGL2016-75904-C2), Xunta de Galicia (ED431C 2016-037), and Fondos Feder: “Unha maneira de facer Europa.” UVigo Marine Research Centre (CIM-UVIGO) is funded by the “Excellence in Research (INUGA)” Program from the Regional Council of Culture, Education and Universities, with co-funding from the European Union through the ERDF Operational Program Galicia 2014-2020.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bemisia tabaci) have been associated with epidemics of two viral diseases in Eastern Africa. We investigated population dynamics and genetic patterns by comparing whiteflies collected on cassava in 1997, during the first whitefly upsurges in Uganda, with collections made in 2017 from the same locations. Nuclear markers and mtCOI barcoding sequences were used on 662 samples. The composition of the SSA1 population changed significantly over the 20-year period with the SSA1-SG2 percentage increasing from 0.9 to 48.6%. SSA1-SG1 and SSA1-SG2 clearly interbreed, confirming that they are a single biological species called SSA1. The whitefly species composition changed: in 1997, SSA1, SSA2 and B. afer were present; in 2017, no SSA2 was found. These data and those of other publications do not support the ‘invader’ hypothesis. Our evidence shows that no new species or new population were found in 20 years, instead, the distribution of already present genetic clusters composing SSA1 species have changed over time and that this may be in response to several factors including the introduction of new cassava varieties or climate changes. The practical implications are that cassava genotypes possessing both whitefly and disease resistances are needed urgently.High populations of African cassava whitefly ( Cassava (Manihot esculenta Crantz) is an important root crop, which is drought tolerant and able to grow under suboptimal conditions such as low soil fertility2. It provides food for about 800 million people worldwide3. Cassava has proven to be an invaluable food security crop, particularly to smallholder farmers in Sub-Saharan African countries4. Cassava production, however, has been decreasing, particularly in East Africa, despite the increasing area under cultivation5. The main cause of this trend is two major viral diseases, cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). These are both transmitted by their whitefly vector, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae)6.Crop protection involves practices to manage the plant diseases, weeds and pests that damage agricultural crops and forestry. It plays a key role in safeguarding global crop production against losses, thereby helping to meet the increasing demand for food caused by a growing human populationBegomovirus)8 and CBSD by two cassava brown streak ipomoviruses (CBSIs) (family Potyviridae: genus Ipomovirus)12. In addition to being transmitted by B. tabaci, both CMBs and CBSIs are spread by farmers, through the use of virus-infected stem cuttings. The two diseases can occur singly or in dual infections in cassava and the damage can be severe. Yield losses of about 47% from CMD infected fields were recorded in eastern and central African cassava-growing areas13, while in other fields losses of up to 70% were reported due to CBSD14.CMD in Africa is caused by at least seven species of single-stranded DNA cassava mosaic begomoviruses (CMBs)  for their upsurges remain uncertain26.B. tabaci (described by their mtCO1 marker) have been found colonising cassava in sub-Saharan Africa (SSA) and these were named serially SSA1 to SSA528, with several sub-groups reported for some species. The SSA1 species is widely distributed in Africa; SSA2 is mostly found in the eastern, southern and central areas of Africa as well as in the south of Spain; while SSA3 and SSA4 have been reported in Cameroon and the Central African Republic; SSA5 has only been described in the Ivory Coast and South Africa29. SSA2 was hypothesised to be an invasive species associated with the CMD epidemic in Uganda in the 1990s, but has subsequently been rarely found31. In addition, phylogenetically distinct populations have been described within the SSA1 species, known as SSA1 sub-groups 1 and 2 , which were also associated with the CMD and CBSD epidemics32.Five putative species of B. tabaci species is of crucial importance as it can facilitate selection of appropriate management control measures33. Analysing the nuclear genetic diversity of whitefly populations had been performed in the past using several types of markers, among which neutral-codominant markers such as microsatellites gave reliable results. Those markers allowed to distinguish B tabaci species and populations within those species, including Med Q1 and ASl34, Med and MEAM137, Med Q1/Q238, or between a wide range of species worldwide39. Nevertheless, those markers had not been commonly used to untangle population structure among SSA species in Sub-Sahara Africa.Analysis of genotypes and genetic diversity of B. tabaci species. To meet these objectives, we estimated the genetic diversity and population structuring of the whitefly species by sequencing the partial mitochondrial cytochrome oxidase I (mtCOI) barcoding region and 13 nuclear markers from specimens collected in 1997 and 2017 from the same geographical location.The objectives of the current study, therefore, were to understand: (i) whitefly species’ distributions in cassava fields in Uganda in 1997 during the initial stages of the CMD epidemic and compare these with the high whitefly populations still present in 2017; (ii) the genetic pattern (diversity and genetic structuring) of population dynamics over two decades in rapidly evolving Whitefly abundance varied between fields from <10 to over 500 adults per plant. Fields with over 100 adult whiteflies per plant were considered superabundant populations. Based on this criterion, eight (61.5%) of the 13 fields visited in 2017 had superabundant whitefly populations, among which two fields did not show CMD and CBSD symptoms (Table n = 251), 657 bp (n = 219), 500 bp (n = 112) and 300 bp (n = 80) depending on the sequence quality obtained. Despite the shorter sizes of some sequences (300 bp), it was possible to differentiate between putative species and SSA1 sub-groups, as well as to use the shorter sequences as species tags in further analysis. To increase robustness of the phylogenetic tree, a total of 470 sequences that were at least 651 bp long, together with an additional 12 sequences from the GenBank, were used. Bayesian phylogenetic analysis used to generate the tree divided our sequences into four main clusters (SSA1(-SG1 -SG2 and -SG3), SSA2, Mediterranean (Med) and Bemisia afer) supported by high posterior probability values (>0.9) (data not shown). The specific group of haplotypes within SSA1 named SSA1- SG1 were found to be dominant with 183 individuals (84%), followed by SSA2 , whereas others belonged to the other haplotype groups named SSA1-SG2  and SSA1-SG3 , and to B. afer individuals  from the 1997 collection  and SSA1-SG2  as the dominant groups over all observed genetic clusters. The other species characterised were Med and B. afer, which together represented 1.2% of the total  and the remaining 446 were all adults from the 2017 collection. After manual checking and editing for errors, the mtCOI sequences were trimmed to different lengths: 700 bp (B. tabaci species within the combined dataset of longest sequences (651 bp), comprising 470 . Two haplotypes were observed for SSA1-SG2. Apart from SSA1, two haplotypes were found for SSA2  from 1997 collected samples. In 2017, five haplotypes were found from B. tabaci species, including one haplotype for SSA1-SG1  represented as P10G3 . One haplotype  of the Med species was found. Apart from 12 haplotypes of B. tabaci species, two other B. afer haplotypes were also observed. All the new haplotypes were submitted to GenBank and were assigned accession numbers from MK360160 to MK360177  were removed. The number of alleles per locus from the 13 microsatellite markers over the whole dataset ranged from 5 to 52. The highest polymorphism observed was for the P5 locus and the lowest was for the CIRSSA2 locus. The mean null allele frequency for all loci and populations was 0.128 but ranged from 0.01 to 0.56   were successfully genotyped. All loci were checked with Microcheckern = 14; 2017) to 6.66  with the highest mean values observed in the 1997 collection . The first two genetic clusters at K3, in Fig. B. tabaci samples. Individuals of the two mtCO1 sub-groups SSA1-SG1 and SSA1-SG2 were not differentiated and belonged to the same genetic clusters , another Bayesian analysis was run together with SSA1 individuals to understand the genetic pattern between the two-putative species. To decrease the effect of unbalanced samples, 102 samples of SSA1 together with 17 samples of SSA2 were randomly chosen, and similar results were obtained showing some level of shared genetic background, as expected for closely related species Fig. .K = 3, as the best K number of assumed populations. Accordingly, the DAPC spread the dataset into three clusters; two were dominated by 1997 collections, whereas the third cluster was represented by the 2017 samples (Data not shown).Individuals with 70% posterior probability from the Bayesian analysis dataset were selected and used to perform a principal component analysis (PCA), subsequently the analysis split the dataset into three clusters/ellipses similar to the previously identified genetic clusters of Bayesian analysis Fig. . The 201The AMOVA carried out on our SSA1 samples to test for population differentiation between years, using only sites that were sampled in 1997 and 2017 (ie. 6 sites were considered for each year) showed a very low, but significant variation Table .Table 4AFurther analysis of differentiation between groups was performed through analysis of the population pairwise matrix of genetic distances Fst between sites, subgroups and years. Results revealed a few significant differences between SSA1-SG1 and SSA1-SG2 of the 2017 samples for only 33 comparisons. Most of the populations of 1997 were significantly different from the ones of 2017, except for field 5  in the recent past with the SMM model  showed CMD symptoms and 50% showed symptoms of CBSD. The association between the two diseases and B. tabaci population on cassava has been reported by Colvin et al. and Legg et al.44. In the remaining five fields with <50 mean adult whiteflies per plant, up to 60% CMD and CBSD symptoms were observed. The improved cassava varieties TME14 and TME 204 were grown in these fields. Despite the relatively low number of whiteflies observed in the field located at Kynagolo (1–9 mean adult whiteflies per plant) planted with TME 14 and the field at Masaka (10–49 mean adult whiteflies per plant) planted with an unknown variety, both had average CMD and CBSD symptom severity , no CMD and CBSD symptoms were observed, which may be related to the type of variety grown .45 of 2–3 months. These observed discrepancies might be linked to the surveyed cassava varieties, which might be more whitefly susceptible in our case, or the very small amount of field samples in our study (n = 8). Furthermore, our study revealed whitefly abundance increased toward the southern part of Uganda, with a maximum population of >500 whiteflies per plant in 2017. This result corresponds to the previous studies conducted in Uganda during the 1990s47.The average age of cassava field with high whitefly abundance (100 and above) was 6 months, which contrasts with the age recently shown by Kalyebi48 in Uganda, most of the cassava varieties grown in the 2017 surveyed fields  were infected with both diseases. Development of whiteflies on cassava is the result of synergistic interaction of several factors including viruses, bacterial symbionts and cassava genotypes49, all of which require more research attention. In order to combat these problems effectively, research and development efforts need to be focussed on creating cassava varieties that combine both virus- and whitefly-resistance traits.Despite all efforts made to combat CMD and CBSD diseases since the first outbreaks of whitefly populations reported in the 1990s50.SSA1-SG1 was present at all sites in 1997 and 2017  in 1997 and we did not detect it at all in 2017. Legg 31. These results collectively indicate that SSA1-SG2 and SSA1-SG1 should not be considered as different entities, but only as different mitochondrial haplotypes within the SSA1 species.Analysis of mitochondrial DNA placed SSA1-SG1 and SSA1-SG2 in different haplotype groups within SSA1; however, nuclear analyses based on several methods revealed substantive gene flow between these two haplotypes. The ability of these two groups to interbreed and exchange genetic material resulted in there being no significant genetic differentiation between the individuals of both groups. The homogenisation of this group from potentially two different maternal lineages probably resulted in the maintenance and increase of SSA1-SG2 over the years. Similar results were observed from a genomic approach, showing no differences between SSA1-SG2 and SSA1-SG1 from Burundi, Tanzania, Rwanda or the Democratic Republic of Congoet al.31 and had been observed in La Réunion between the invasive Middle East Asia minor 1 (MEAM1) and indigenous Indian Ocean (IO) species53. High abundant SSA1 populations and low abundant SSA2 populations in sympatry might have created conditions favouring mating between those groups, which could have resulted in few cases of mating success between both cryptic species.Nuclear analyses from 1997 sampling also revealed that SSA2 individuals analysed were considered as a separate group to SSA1; however, with a signal of low shared background between both groups. This weak signal could be explain by the fact that both species are closely related ones. Similar results were seen in the recent genomic analysis by Wosula Our study also revealed no new populations between 1997 and 2017, but a significant genetic difference between the two collection periods. This is clearly shown in the Bayesian analysis structure Fig.  with the55 as well as whiteflies, where a specific bacterial community (Arsenophonus and/or Cardinium) had been partly implicated in manipulating reproduction of MEAM I and IO species56. SSA1 supports a high bacterial diversity57; however, no link has yet been established between the complex bacterial community and hybridisation barriers. Further studies should be conducted to better understand the roles of endosymbionts in different B. tabaci species.The Bayesian analyses, DAPC (BIC criteria) and PCA all showed structuring of SSA1 putative species into three genetic clusters that can interbreed. However, despite the gene flow patterns between several individuals, this structuring into three genetic clusters was stable between 1997 and 2017 and the individuals did not completely homogenise into a single population, with significant differentiation observed between populations of both years. Although the reasons for this are not entirely clear, mating preferences or other specific loose barriers to hybridisation may act to support this pattern. Presence of different symbiont communities could also be a factor. Indeed, some symbionts are known to play such a role in other insects25. Nevertheless, frequency of specific genetic clusters significantly changed over the studied 20-year period within the SSA1 species, with 2017 populations having a strong signature of a recent bottleneck event. It is possible that the most abundant genetic cluster comprising SSA1-SG1 and SSA1-SG2 might have overcome or displaced the previous most abundant genetic cluster. Correspondingly, three new hypotheses might be raised to explain the observed results: (i) the previous “old” dominant genetic cluster might be less fit for new cassava cultivars released in Uganda, (ii) environmental change occurred within the studied 20-year period and the SSA1-SG1 and SSA1-SG2 clusters were preferably adapted to it and (iii) the re-emergence of CBSD in Uganda in the early 2000s58. Confirming these hypotheses will require further experiments.Despite the moderate null allele frequencies detected in our dataset, our results remained consistent through the four different analyses. All analyses produced similar patterns, which indicates the robustness of our results. All results obtained here categorically reject the hypothesis that new outbreaks of whiteflies in Uganda in the 1990s were due to the arrival of a new population or species of whiteflies3 and many virus-resistant cassava varieties are highly susceptible to whiteflies59. The intensification of cassava production to meet the high demand for food under increasing human population in the era of climate change might be impossible without the concomitant control of B. tabaci populations and development of virus-resistant crop varieties. This can only be achieved by a better understanding of the main viral vectors, which will facilitate design and selection of appropriate disease management and control measures.The strategy of using disease-resistant cultivars has not proved effective in combating CMD and CBSD. Some ‘improved’ varieties such as TME 14 and TME 204 became susceptible in Ugandahttps://qgis.org) was used to map the site locations  in Uganda in February 2017 Table . The fieons Fig. . Whiteflet al.60 in the 2017 sampling. The number of whiteflies per plant was estimated according to the following system: “1” = 1–9 adults per plant, “10” = 10–49, “50” = 50–99, “100” = 100–499 and “500” = >500.The number of adult whiteflies on the top five leaves of five plants selected randomly in each cassava field was recorded as described by Sseruwagi et al.60, where 1 = no disease symptoms and 5 = the most severe symptoms. Five plants were randomly assessed in each field in 2017.The symptom severity for CMD and CBSD were recorded for each sampled field. The severity was assessed by using a disease scale 1–5 according to Sseruwagi et al.61 and the destructive method of Ghosh et al.51 for 1997 collections at Natural Resource Institute (NRI), University of Greenwich, England.Leica MZ8 stereomicroscope 100X  was used for selection, at most 35 adult female whiteflies were selected from each field. A total of 662 samples were successfully extracted for DNA in this study . Sex differentiation for eggs, nymphs and pupae was obtained indirectly, with the use of the microsatellite markers, whenever all loci were homozygotes for a given individual it was considered as a male. Two methods of DNA extraction were utilised. The non-destructive method was used for 2017 samples at 3P, CIRAD UMR PVBMT in Reunion Island, as described in Delatte et al.41 (2195Bt 5′-TGRTTTTTTGGTCATCCRGAAGT-3′ and C012/Bt-sh2 5′-TTTACTGCACTTTCTGCC-3′). The PCR reaction mixture was prepared with a final volume of 15 µl, containing 7.5 µl of Type-it (2x) PCR master mix , 4.1 µl of pure HPLC water CHROMASOLV , 0.8 µl of each primer and 1 µl of DNA template. Initial denaturation of DNA template occurred at 95 °C for 15 min followed by 40 cycles of denaturation at 95 °C for 30 s, primer annealing at 52 °C for 30 s, extension at 72 °C for 1 min and final extension at 72 °C for 10 min. Plates were sent to the Macrogen Europe laboratory for sequencing.A total of 662 individuals were successfully PCR-amplified and sequenced for mtCOI by using a primer pair designed by Mugerwa 62. The number and distribution of haplotypes within our sequences were achieved through DnaSP v.6 software63. The selected sequences together with reference sequences from the literature were aligned using ClustalW64 before being subjected to Jmodeltest 2.1.1065. The phylogenetic tree was computed using MrBayes66 at GTR + G (the closest to the selected model under MrBayes). Four Markov chains were conducted simultaneously for 1 100 000 generations starting from random initial trees, and sampled every 200 generations. Variation in the ML scores was examined graphically and 10% of the trees generated prior to stabilization of ML scores were discarded.Sequences were manually edited and aligned using the Geneious R10 softwareB. tabaci from laboratory colonies of SSA2, SSA1-SG1, SSA1-SG2, SSA1-SG3 and SSA3 were made and sent to GenoScreenVR . Each pool contained 10.2 ng of DNA. The company developed a microsatellite-enriched library using a 454GS-FLX Titanium pyrosequencing67 tool. The enriched library was then constructed as described by Atiama et al.68. Total DNA was enriched by probes with the following motifs: TG, TC, AAC, AGG, ACAT, ACG, AAG and ACTC. About 534,451 reads were obtained with average fragment length of 247 bp. A first filter of quality was applied to discard short fragments (<40 bp) and low-intensity fragments, which removed 38% of the sequences.Two pools of extracted DNA of 25 individuals (each tube) of 69 was run on the remaining sequences to identify microsatellite motifs in 73,060 raw sequences, among which 160 primers were designed. The objective was to obtain primers that would cross-amplify between all the pooled species and other whiteflies from the same complex of species with different sizes. From these, 41 primers were selected that could amplify various fragment lengths (100–260 bp) and had different repeat motifs (from di nucleotides to tetra nucleotide motifs). Those primers were tested by PCR individually, on four female specimens of the following species or whitefly genotype groups (-SG): SSA1-SG1, SSA1-SG2, SSA1-SG3, SSA2, SSA3, MEAM1, Med and IO. The amplified DNA was loaded on agarose gels and sorted. Among all those tested primers we kept nine primers that were (i) amplifying for all species or genotype groups with good signal intensity, and (ii) giving polymorphisms between individuals within species and between species/genotypes. These nine primers were fluorescently labelled  and tested in simplex and multiplex PCR mixes on several field samples of the different whitefly species named above. Only five of them were retained in the present study, the other four were discarded due to the high number of null alleles observed in different populations and species within the species complex tested.The software QDDB. tabaci complex.PCR for genotyping was conducted using 13 microsatellite loci, which were combined in three multiplex primer reactions. Five of the markers were newly developed for this study Table . All marA PCR mix of 15 µl was prepared with 7.5 µl of 2x multiplex PCR master mix , 4.5 µl of HPLC water and 0.1 µl of each primer followed by addition of 2 µl of template DNA. The volume was slightly changed in Mix 2, in which 0.2 µl of WF1GO3 and P5 primers were used. All PCR programs were as follows: initial denaturation 95 °C (15 min) followed by 40 cycles, 95 °C (30 s), 55 °C (180 s), 72 °C (1 min) and at 60 °C (15 min) for denaturation, primer annealing, extension and final extension, respectively, except for Mix 3 for which the annealing temperature was increased from 55 °C to 56 °C. Prior to genotyping, the amplified PCR products were diluted in different ratios according to the band intensity obtained for each mix. The final mix consisted of 10.8 µl of formamide, 0.2 µl of Applied Biosystems LIZ size marker and 1 µl of diluted amplified DNA. The mix was run in an Applied Biosystems 3130XL DNA sequencer machine. Genotypic data were retrieved visualised and scored manually using Gene mapper v.4.0 software.70 was used to assess genetic population structure between individuals. The method assigns individuals to different clusters (a series of K to be set). Each K is the number of estimated population clusters characterised by posterior probabilities. Structure 2.3.4 was set at 100,000 burn in length with run length of 1,000,000 MCMC, this step was repeated three times and K was set to range from 1 to 20. The dataset was arranged according to mtCOI results and field numbers. The best number of clusters (K) was estimated by means of ∆K as described by Evanno et al.43 using the online program Structure Harvester71. An online program CLUMPAK (Clustering Markov Packager Across)72 was used to summarise the best K posterior probabilities and to reconstruct the bar plots using Clumpp73 and Distruct74 software.The Bayesian cluster approach with Structure v.2.3.4et al.75. Both datasets were executed using burn-in length of 100,000 and MCMC run length of 1,000,000, repeated three times, and an assumed number of population (K) values between 1 and 20. Similar results were obtained from both analyses  and correlation within individuals following the method of Weir and Cockerham77. Deviation from Hardy-Weinberg equilibrium was tested using MCMC  implemented in Arlequin v.3.5.2.278 following the method utilized by Guo and Thomson79. GENEPOP v.4.280 was used to test genotypic disequilibrium by Fisher’s method81. The effect of null alleles on inferring population structure was studied, as described by Falush et al.75. Allelic richness using rarefaction was estimated by FSTAT v.2.9.3.282. Genetic differentiation among year, between populations within year was inferred by AMOVA by Arlequin. PCA and DAPC were also used to determine the genetic clusters among individuals using R software v. 3.4.283 with the Adegenet package84.The basic population parameters were analysed by using a set of programs within Genetix v.4.05.2, such as the number of alleles per population, expected heterozygosity and observed heterozygosity  and the two-phased model of mutation (TPM). One-tailed Wilcoxon sign-rank tests were used to determine whether a population exhibits significant heterozygosity deficit or excess.Recent genetic bottleneck signature was also tested in population of 2017 using the genetic software Bottelneck 1.2.02Supp data Fig 1, 2, Table 1-3