The Inca Empire is claimed to have driven massive population movements in western South America, and to have spread Quechua, the most widely-spoken language family of the indigenous Americas. A test-case is the Chachapoyas region of northern Peru, reported as a focal point of Inca population displacements. Chachapoyas also spans the environmental, cultural and demographic divides between Amazonia and the Andes, and stands along the lowest-altitude corridor from the rainforest to the Pacific coast. Following a sampling strategy informed by linguistic data, we collected 119 samples, analysed for full mtDNA genomes and Y-chromosome STRs. We report a high indigenous component, which stands apart from the network of intense genetic exchange in the core central zone of Andean civilization, and is also distinct from neighbouring populations. This unique genetic profile challenges the routine assumption of large-scale population relocations by the Incas. Furthermore, speakers of Chachapoyas Quechua are found to share no particular genetic similarity or gene-flow with Quechua speakers elsewhere, suggesting that here the language spread primarily by cultural diffusion, not migration. Our results demonstrate how population genetics, when fully guided by the archaeological, historical and linguistic records, can inform multiple disciplines within anthropology. Genetic studies have begun to contribute significantly to our understanding of the pre-colonial history of the Americas, and are able to fill in some of the gaps in the archaeological and historical records. Archaeology faces preservation biases between the diverse environments of the desert along the Pacific coast, the Andean highlands and the Amazonian rainforest. Written history in the Andes begins only with the Spanish conquest in the 1530s, and these first chronicles are fragmentary and contradictory.3 and the broad patterns of diversity, such as the genetic contrast between the ecological domains of the Andes and Amazonia5. Studies focused on sub-regions, however, face significant limitations when attempting to evaluate past population movements and contacts on a much finer scale, often due to the low resolution of the genetic markers selected, or to poor sample coverage. This study therefore explicitly selects high-resolution markers, and to improve sampling follows a strategy fully informed by historical, archaeological and linguistic contexts.Genetic results have returned important insights particularly on the macro-scale: the initial colonization events6. Towards the eastern slopes, where the environmental transition to Amazonia begins, is the cloud forest region of Chachapoyas7. The archaeological record here attests to a rich diversity of regional cultures over time, up until Inca conquest in the 1470s. ‘Chachapoya culture’ serves as a collective term for a series of political entities, independent but sharing common architecture, art (ceramic style) and iconography9. In the earliest historical accounts, Spanish chronicles make extensive mention of Chachapoyas for its long resistance to Inca conquest — and then as a clear example of the Inca state policy of forced resettlements. Chachapoyas is taken as one of few regions where the recalcitrant local population was essentially completely removed and replaced10 — making this a test-case for the ability of population genetics to challenge or confirm the (proto-)historical record, of questionable veracity here10 (91–118).Our focus is on northern Peru, selected as a case-study for its significance in both environmental and historical terms. The Andes here are at their lowest elevation, and thus serve as a preferential corridor between Amazonia and the Pacific12. Quechua, meanwhile, never seems to have been dominantly established across northern Peru, and is spoken, in diverse local forms, only in sporadic pockets scattered across the region  is extinct, although a few of its characteristics can be inferred from surviving placenames and surnamesTo introduce a genetic perspective on these scenarios from archaeology, history and linguistics, this study focuses on uniparental markers: i.e. mtDNA and the non-recombinant portion of the Y chromosome.16). Beyond that, however, sequencing the hypervariable region alone is often insufficient to disentangle population relationships. This is a result of the Americas being settled only relatively recently, and through a bottleneck in Beringia2. Sequencing full mtDNA genomes gives an important gain in resolution, as demonstrated by recent studies that generate native mitogenomes for some parts of the Americas21. So by sequencing new sets of full mtDNA genomes, from regions of the continent selected for their distinctive local population prehistories, this study adds to our database for the Americas, and uncovers new patterns of genetic variation that bear directly on population prehistory here.For mitochondrial DNA, in the Americas data are relatively abundant for the hypervariable region only. At a broad level, it is possible to identify variation in the frequencies of the four haplogroups most heavily represented in Native Americans 24.For the Y chromosome, standardized Short Tandem Repeat (STR) sets have been successfully applied to explore genetic diversity in the paternal line in South America25. Finally, a recent study of 23 Y-chromosome STRs and the mtDNA HVSI has reported high genetic diversity in populations from Chachapoyas and surrounding areas27. It remains to be clarified, however, what the relationships are between Chachapoyas and other parts of the Andes, which we test here using more and higher-resolution genetic data from Chachapoyas.Beyond this nucleus of homogeneity in the Andes, occasional populations in the Amazonian lowlands also speak the ‘highland’ language Quechua. Genetically, however, these populations do not show any particular relationship to the central highlands that would parallel this linguistic link. Quechua speakers from Lamas likely acquired the language by processes of cultural diffusion, not by significant migration from the highlandsFor this study, we generated 23-STR profiles and full mtDNA genomes from Quechua-speaking populations of northern Peru, with the aim of exploring the impacts of the Inca period, the possible genetic inheritance from pre-Inca periods, and the dynamics behind the diffusion and differentiation of the multiple Quechua varieties scattered across the region. We sampled 119 individuals, with an explicit strategy targeting firstly the surviving Quechua-speaking pockets in Chachapoyas and Lamas. Secondly, we focused on surnames characteristic of putative “Chacha” linguistic origin (i.e. neither Spanish nor Quechua), to try to trace back to a possible genetic legacy of the Chachapoya population from before the Inca conquest  interaction in the south-central Andes. Our findings have multiple implications for Andean (pre)history. Firstly, we find evidence to challenge the presumption of major Inca impacts and resettlements affecting the Chachapoyas population. Secondly, we find no direct demographic connections between populations that speak varieties of the disputed QIIb clade of Quechua, and support a cultural rather than demographic model for its spread in northern Peru.Full mitochondrial genomes were successfully sequenced for 116 individuals (data deposited in GenBank under accession numbers MG571104 - MG571221). Of these, 113 could be assigned  to one of six broadly-defined geographical groups Fig. . Sample ST genetic distances  from 1000 to 35,000 individuals. BSPs for the individual population groups . The BSP for all the sequences generated in this study displays a steep curve indicative of an expansion starting ~17,000 years ago (kya): the High Probability Density (HPD) ranges from 15 to 22 kya Fig. , in lineups Fig.  do revea29 and a recent review19). Within A2, B2 and D1 we find 27 previously unreported branches with divergence times ≥15,000 years ago, i.e. possibly shortly after first human entry to the Americas. Only within C1 could all the lineages be assigned to already known sub-haplogroups: C1b, C1c and C1d.The annotated tree from the BEAST runs Fig.  gives agMale individuals were first genotyped for haplogroup assignment; only those belonging to haplogroup Q, the characteristic Native American marker of interest for our study, were then typed for STR haplotypes and included in the intra- and inter-population analyses. The other Native American haplogroup, C2-M217, was not found in our sample. The remaining samples belong to broad macro-haplogroups other than Q, as detailed in the Supplementary Text. Haplogroup frequencies for each population are included in Table et al.27. The haplotype sharing plot  with our Wayku.After controlling for non-significant Ret al. and Sandoval et al.) are those from Huancas, La Jalca and Wayku: their low variance is similar to that found in populations predominantly from Amazonia. Higher values for within population variance (but close to the average for the continent) are found for the Chachapoyas city, Luya and Utcubamba South groups. Haplotype diversity values follow the same trend.Between population comparisons at a continental scale are based on 17 or 15 loci. Supplementary Table To identify close connections between populations, identical haplotypes were annotated with two methods: first by a heatplot of sharing between relevant populations from Ecuador and Peru, based on 17 loci  until 10 generations split time and Ne > 1000. For more recent split times and Ne ≤ 1000, instead, haplotype sharing would be predominant Fig. , while fFirst we look at a continental scale, and the position of the population of Chachapoyas and Lamas (Wayku) within the Americas. On this broadest level, our high-resolution analysis of uniparental markers reveals a pronounced (high frequency of Native American haplogroups) and distinctive  native component. Our dataset of 114 Native American mtDNA genomes corresponds to a new hotspot of diversity, and suggests that other regions of South America may likewise harbour pockets of diversity that have so far remained undetected.29 and the latest publication by Brandini et al.19).Recent publications of mtDNA genomes have mostly focused on specific lineages of interest, misrepresenting the diversity at the population level at the fullest resolution for this  marker. Our new dataset has not only been able to distinguish characteristic lineages for the population groups involved, but has also uncovered notable diversity within the four major Native American mtDNA haplogroups. In the BEAST tree reconstruction Fig. , 65 of tThis result entails two significant findings: a) Previous studies have underestimated the extent of diversity and geographical differentiation within Native American mtDNA lineages; and b) Despite the generally low diversity caused by the strong founding bottleneck in Beringia, the significantly higher resolution available from full mitogenome analysis over the HVSI does make it possible to reveal demographic dynamics at a regional scale. As illustrated in Fig. 6, and particularly in comparison to the conspicuous amount of sharing found in other populations, for example the Yanesha of central Peru, supposedly more isolated both geographically and culturally24. The high-altitude Yanesha, with a sample size of 55 individuals, share haplotypes with 20 other populations . While occasional other populations in the database display a unique genetic profile with no haplotype sharing  or an autochtonous one (see Supplementary Text). To explore these hypotheses, the present-day genetic data can be interrogated for any signals to support connections to other regions, and to assess whether the genetic make-up of Chachapoyas has or has not been relatively stable through time. Since mitogenome data from the Andes are still too scarce, we considered the Y-chromosome STR dataset (of 90 populations), with the caveat that it reflects only paternal population history. In principle, the high internal diversity of our sample could be compatible with populations relocated here from multiple, distinct and far-flung regions of the Inca empire or indeed from earlier highland polities. Our sample from the Chachapoyas region turns out not to be genetically close, however, to the core Andean exchange network in the highlands of southern Peru and Bolivia, sharing only four haplotypes with Utcubamba South Fig. ,S8,S9c. e of 500 or 1000  that they established among various tribes of the Marañón, Napo and Amazon rivers31. This finding is compatible with recent genetic analyses of the Wayku population as predominantly local Amazonian25. For Chachapoyas, however, there is little parallel documentary evidence for a missionary diffusion model, and the alternative explanation remains that Quechua arrived here with at least some population resettled here by the Incas, from some other part(s) of their Empire. But in either scenario, the genetic data offer no support for the existence of any putative single Proto-QIIb language, whose speakers could have been a common migration source for the populations that today speak the Quechua varieties of Ecuador, Colombia, Chachapoyas and Wayku.This rules out any strong demographic connection between speakers of the disparate varieties of Quechua all traditionally classified into the IIb clade, and bears on the open question of when and how Quechua came to be spoken in northern Peru, Ecuador and southern Colombia. In the case of Wayku, historical sources report how Jesuit and Franciscan missionaries from Quito purposely introduced Quechua as a Zooming out to the rest of northern Peru, there are only two other Quechua-speaking groups in the region, both traditionally classified within a hotly disputed ‘QIIa’ clade. Only for one of these — Cajamarca — does the genetic literature include samples that may represent speakers of the local Quechua (or their descendants). Again, however, our Chachapoyas sample shows no connections to the Cajamarca sample, which itself shows no connections to other Quechua-speakers in northern Peru Fig. , but onl13. That linguistic homeland falls within the network of very high homogeneity in the gene pool and high levels of haplotype sharing across the highlands of southern Peru and Bolivia, as already reported in the literature32 history of Andean civilization has left traces that can be recovered not only from a rich archaeological record and a complex linguistic panorama, but also from the details of the genetic makeup of Andean populations living there today. This precision also helps clarify models of the diffusion of the largest surviving language family of the entire Americas, Quechua. Clearly, those models need to combine two contrasting mechanisms: significant demographic exchange in the south, and language shift by predominantly cultural forces in the north. To further test this double scenario, and to gain a full insight into the genetic ancestry of the region, this study should ideally be complemented by autosomal genomic data. Finally, the full mtDNA genome dataset released here will stand as a valuable resource for broader, continent-wide comparisons, and for ancient DNA studies — for which the case-study region of Chachapoyas holds out particular potential.This work is the result of a fieldwork expedition conducted in 2015 to the regions of Amazonas and San Martín, with the support of local political authorities and cultural representatives. Our sampling strategy was guided by linguistic indicators that parallel, and can help tease apart, the three main layers of history in the region: the Chachapoya autochthonous substrate; the brief period of Inca influence and then dominance, from the 1470s to the 1530s, which presumably fostered the use of Quechua; and finally Spanish rule immediately thereafter.Linguistic research directed our sampling in two ways: firstly it enabled us to focus on the Native American genetic component, and secondly, within the native component, it allowed us to further distinguish between Chacha and Quechua, most plausibly corresponding to periods before and after Inca impacts here. The presence of Quechua, surviving in just a few villages scattered across Chachapoyas, enabled us to target sampling on those locations. In each one, we conducted a survey to assess how far Quechua was (or still is) spoken in each participant’s family, and to identify the birthplaces of parents and grandparents. We also conducted extensive archival work on historical patterns in local surname origins, following not just Quechua markers but trace indicators of the now extinct Chacha language, too. Supplementary Table Saliva samples were collected from healthy volunteers. Each participant signed a written consent form, after being fully informed of the purpose of the study, with the opportunity to ask questions for further clarification. The project and the informed consent were approved by the Ethics Committee of the University of San Martín de Porres, Lima  and by the Ethics Committee of the University of Jena . All methods were performed in accordance with the relevant guidelines and regulations. The sample analysed in this study represents only a small fraction of the population living in the provinces of Chachapoyas and Luya, and in Wayku, and so is only partially representative of the complex demographic history of these regions and of their inhabitants’ ancestors.For Y-chromosome and mitochondrial analysis, individuals were assigned to different regional groups according to their self-identified paternal or maternal ancestry (up to two generations): Huancas, La Jalca, Chachapoyas city, Utcubamba South, Luya. These locations are all in the Amazonas region; our final sample is from the region of San Martín, specifically from the Wayku neighbourhood in Lamas.  according to the manufacturer’s protocol.35. Libraries were pooled and sequenced in Illumina HiSeq. 2500 on two Rapid lanes with 100 + 7 + 100 + 7 cycles. The Kit for the chemistry was V2.Libraries were prepared with a multiplex protocol for the Illumina Genome Analyzer platform, and enriched for mtDNA with in-solution capture following standard protocols as performed in previous studies36, and reads were processed as follows38: read adaptors were trimmed, and reads were filtered for at most 5 bases with a quality score <15, and indexes for no bases with quality scores <10. Sequences were manually checked with Bioedit (www.mbio.nc-su.edu/BioEdit/bioedit.html). Average coverage was ~3000X, with a minimum of 58X and a maximum of 12,093X.Base-calling was performed using freeIbis39, were assembled with MAFFT v7.123b40.The two poly-C regions , prone to sequencing errors, were trimmed from the final alignment used in the analysis. Sequence alignments, including the RSRS sequence41 and Haplogrep 242, and manually confirmed by checking diagnostic positions as described in Phylotree v.1729. Haplogrep was also used to list the polymorphisms in each sample, as included in Table Haplogroup assignment was performed with Haplofind24 , and a dataset of full genome mtDNA sequences54. The comparative sequence dataset includes a total of 725 sequences from haplogroups A, B, C and D, from North America, Mesoamerica and South America, and was screened to minimize the number of missing sites. The maximum number of missing sites allowed per sequence was 2. The list of sequences included, with references to the corresponding publications, is available in Table Two sets of comparative data were collected from the literature: a dataset of haplogroup frequencies from across the Americas, adapted fromSNaPshot® multiplex, as described in55. The Q samples were also tested for markers downstream of M3: Y14998418(MG2), PV2, SA01, M19, M55756. Samples belonging to haplogroup Q were typed with the PowerPlex® Y23 System  as previously described57. The data were analyzed with GeneMapper® ID-X1.1.1. . Haplogroup nomenclature follows van Oven et al.58 where possible, with the name of the diagnostic marker following the conventional name of the haplogroup. Haplogroup affiliations and STR profiles are reported in Supplementary Table Eighty-eight male individuals were typed with a basal 27, which included other individuals from Chachapoyas  and Huancas, as well as neighbouring populations from Cajamarca in the highlands, and the Jívaro in Amazonia. The dataset from Guevara et al. was filtered for individuals assigned to haplogroup Q with more than 90% accuracy from the haplogroup predictor. The resulting dataset for 23 loci included a total of 177 individuals. Continent-wide comparisons were performed with a subset of 17 loci, to include further data available from the literature, for a total of 90 populations61.Intra-population comparisons were performed on a set of 23 or 17 loci. Comparisons with 23 loci were performed with the analogous dataset fromST, haplogroup and haplotype comparisons, haplotype sharing and correlations between features of the dataset were calculated and plotted in R62 using the packages Pegas63, factoextra64, MASS65, vegan66, ape67, maps68, geosphere69, ggplot270 and ggmap71. Median-joining networks were calculated with Network 4.6.1.3  and plotted with Network Publisher. In the Network analysis of the STR haplotypes, weights were assigned to each individual STR locus in inverse proportion to the variance observed in our dataset. Individuals from the published dataset who were missing values for one or more loci were excluded from this analysis. Direct haplotype sharing was performed with the 23 and 17 loci datasets and visualized in heatplot maps. The maps showing amounts of shared identical and similar haplotypes were calculated for a subset of 15 loci (excluding unstable loci DSY385a and b), adjusted for the mutation rate for each locus (mutation rates from https://yhrd.org/). The similar profiles allow up to one mutation in one of five loci DYS389II, DYS439, DYS456, DYS458 and DYS635 with mutation rates higher than 0.003 mutations per year per generation.Values of diversity, ϕ72. BEAST runs were performed with full mtDNA genomes, for the entire dataset as well as for individual populations. The dataset was partitioned into the coding region, to which we assigned a rate of 1.708 × 10−8 substitutions per nucleotide per year, and the non-coding region, to which we assigned a rate of 9.883 × 10−8 substitutions per nucleotide per year73. The best substitution model for each partition and for each population subset was determined using jModelTest v2.1.774. In order to determine the best clock model and tree model, different runs were performed with BEAST and evaluated by a Bayes Factor (BF) analysis75. For the clock model, we compared a strict clock model, a relaxed exponential, an uncorrelated relaxed lognormal (ULN) clock model and a random clock.Phylogenetic trees and Bayesian Skyline Plots (BSPs) were generated with BEAST v1.876, and showed decisive support for a ULN clock for the coding region and a random clock for the D-Loop. The Bayesian Skyline tree model was chosen in order to display the BSP . A total of 50 million chains were executed for the entire sequence set, to ensure reliable ESS values. For the single populations set, we performed 10–20 million chains. Multiple runs were performed on each dataset, and combined using logCombiner. The maximum clade credibility was determined using TreeAnnotator and visualized with FigTree (http://tree.bio.ed.ac.uk/software/figtree/).The best substitution models determined by jModelTest for the whole dataset were TNI + Invariant Sites for the coding part of the alignment, and HKI + Invariant Sites + gamma for the non-coding part. Maximum likelihood estimates for the different combinations of clock models were evaluated by BF analysishttp://cmpg.unibe.ch/software/simcoal2/), with the mutation rates used above, a stepwise mutation model with a geometric parameter of 0.1, and a simple population history of two populations  coalescing at a given split time T1 with no growth rate. We performed a total of 800 runs of 100 simulations each with varying conditions: T1 between 10 and 200 generations, Ne for pop1 and pop2 between 500 and 5000, and symmetric mutation rate between pop1 and pop2 between 0 and 0.1. For each simulation, we recorded the presence of haplotype sharing events, to give a percentage of sharing events for each simulated condition.Coalescent simulations for 15 Y chromosome STRs were performed with Simcoal v.2.1.2 (The STR and mtDNA haplotypes generated during this study are included in the Supplementary Information files. The whole mtDNA sequences are available in GenBank under accession numbers MG571104 - MG571221.Supplementary MaterialSupplementary Tables


The survival of isolated small populations is threatened by both demographic and genetic factors. Large carnivores declined for centuries in most of Europe due to habitat changes, overhunting of their natural prey and direct persecution. However, the current rewilding trends are driving many carnivore populations to expand again, possibly reverting the erosion of their genetic diversity. In this study we reassessed the extent and origin of the genetic variation of the Italian wolf population, which is expanding after centuries of decline and isolation. We genotyped wolves from Italy and other nine populations at four mtDNA regions  and 39 autosomal microsatellites. Results of phylogenetic analyses and assignment procedures confirmed in the Italian wolves a second private mtDNA haplotype, which belongs to a haplogroup distributed mostly in southern Europe. Coalescent analyses showed that the unique mtDNA haplotypes in the Italian wolves likely originated during the late Pleistocene. ABC simulations concordantly showed that the extant wolf populations in Italy and in south-western Europe started to be isolated and declined right after the last glacial maximum. Thus, the standing genetic variation in the Italian wolves principally results from the historical isolation south of the Alps. Human activities have deeply shaped the structure of landscapes in continental Europe for millennia, often reducing the extension of pristine ecosystems to small isolated fragments . NowadayNe), which is usually much lower than the observed census population size (Nc) [Nc in natural populations is not simple, and estimating Ne is even more difficult due to the variability of its several determinants: reproductive success, sex-ratio, departures from random mating and variations of these factors from one generation to the next. Various simulation methods, including the Approximate Bayesian Computation (ABC) [Ne is not too small, the outcomes of genetic drift can be contrasted by balancing or frequency-dependent selection and by hitchhiking on functional gene complexes [Ne and low standing genetic variation are controversial, the assumption that adaptive potential and evolvability are positively correlated to heterozygosity is a precautionary principle widely accepted in conservation genetics [The genetic consequences of protracted population declines are theoretically well known but not easily predictable in empirical case-studies ,6. Accorize (Nc) . Estimaton (ABC) , have beon (ABC) . Howeveromplexes . Althouggenetics .Canis lupus) population that remained isolated and declined in peninsular Italy for centuries [c. 1500 (± 300) individuals and at least 320 documented packs [In this study, we reassessed the amount and origin of genetic variation in a wolf  [c. 100 years estimated from the observed isolation south of the Alps. Genetic analyses indicate that the autosomal genetic diversity in the Italian wolves is c. 30% lower than in other wolf populations in Europe [Regardless of the strength of the ongoing wolf expansion trend, the amount of standing genetic variability should have been largely determined by the duration of the last bottleneck, because there has not been enough time for mutations to reconstitute the lost variation . However values) , 3200–56 values) , or 2800 values) ), much ln Europe . The mtDn Europe , althougn Europe . Monitorn Europe . The uncx dog hybrids sampled from areas within the wolf distribution range in Italy and Estonia. Specifically, we aimed to: 1) confirm the attribution of the rare mtDNA CR haplotype W16 to the Italian wolf population [In this study we genotyped wolves sampled from the Italian and nine other populations in Europe at four mtDNA regions  and 39 autosomal microsatellites . Aiming pulation , increasNo animals were sacrificed for the only purposes of this study. In Italy, all samples of found-dead wolves were collected by specialised technician personnel on behalf of the Italian Ministry of Environment (MATTM) and Italian Institute for Environmental Protection and Research (ISPRA). Wolf stool samples from Czech Republic and Slovakia were collected by Friends of the Earth organisation (FoE CZ), which is monitoring the wolf population in the Carpathian Mountains. In Slovakia, FoE CZ has permission to collect non-invasive samples of wolves, issued by Regional Office Trenčín, Department of Environment, No. OU-TN-OSZP1-2014/49/3475. Carpathian wolf tissue samples were legally culled during the open hunting season in Slovakia within a quota set by the local authorities, in conformity with regulation No 344/2009 Coll. The wolves were shot during individual patrols or collective hunts. The use of poisoned bait or leg-hold traps is strictly forbidden according to hunting law. All hunters had permission for hunting, and we confirmed that the culls were reported before quota fulfilment. Croatian and Slovenian samples were obtained either from animals killed in traffic accidents or culled during regular hunting management according to quotas defined by the Croatian Commission for monitoring large carnivore populations and approved by the Croatian Ministry for Environmental and Nature Protection. Iberian wolf samples from Portugal were from found-dead individuals collected by specialised technician personnel on behalf of the Portuguese Institute for Nature Conservation (ICNF). Wolf samples from Spain were obtained from animals that were road-killed or culled during regular hunting management according to quotas defined by Principado de Asturias authorities. Samples from Estonia, Latvia, Finland, Poland, Greece, and Bulgaria were also collected from animals found dead or legally harvested by hunters for purposes other than this project. No ethics permit was required since the sample collection involved dead animals. All samples were collected by specialised technician personnel. No ethic permit is also required to collect stool samples in these countries. Dog blood samples were obtained by veterinaries with the assistance of the owners and all the possible efforts to minimize animal suffering. The owners of the dogs gave permission for their animals to be used in this study. Salivary samples were obtained through buccal swabs by specialised technicians.x dog hybrids from Italy and several other countries in Europe (n = 34), Spain and Portugal , Slovenia (20), Croatia (20), Greece (15), Bulgaria (17), Czech Republic and Slovakia (20), Poland (16), Estonia (10), Latvia (10), and Finland (9). The 69 village dogs were sampled in Italy from northern and central Apennine areas, had size and shapes similar to shepherd dogs, did not belong to certified breeds, and were selected independently of genotypic information. The known wolf x dog hybrids were sampled in Italy (68) and Estonia (6), and were previously identified by genetic or morphological analyses [EX . All the DNA samples used in previous studies were reanalyzed. Negative extraction controls (no DNA in the test tubes) were used to check for laboratory contaminations.We genotyped 190 wild-living wolves, 69 village dogs and 74 known wolf n Europe . Moreoven Europe . Wolf saanalyses . All wolanalyses  at -20°C™ Panel 1.1 multiplex kit . One of them, the Amelogenin, was used to sex the individuals. The microsatellites were amplified in eight PCR multiplexes using the Qiagen Multiplex PCR Kit . Negative (no DNA) and positive (samples with known genotypes) PCR controls were used to check for laboratory contaminations. To confirm allele calls, all samples were independently analysed twice to check for the occurrence of allelic dropout and false alleles, which were never observed. The amplicons were analysed in an ABI 3130XL automated sequencer  and allele sizes were estimated using the software Genemapper 4.0. Details on the selected markers, primers and PCR profiles are available in the We selected a panel of 39 canine autosomal microsatellites (seven tetranucleotides and 32 dinucleotides) that were used in some of the most recent studies on wolf population genetics and hybridization in Europe  and refe and wereAo and Ae), observed and expected heterozygosity (Ho and He), F-statistics [enAlEx 6.01 [enetix 4.05 [enAlEx); 2) a discriminant principal component analysis (DAPC in Adegenet) [tructure 2.3.4 [tructure to infer the optimal genetic partition of the sampled groups, assuming K from 1 to 15 with four independent runs for each K with 500 000 Monte Carlo Markov Chains (MCMC) steps and discarding the first 50 000 steps as burn-in, using the admixture and independent allele frequency models, and no prior information (option usepopinfo not activated). We used Clumpak (http://clumpak.tau.ac.it) to identify the highest rate of increase in the posterior probability LnP(D) of the clusters between each consecutive K [qi) from the four MCMC replicates [Observed and effective allele numbers  ; and 3) re 2.3.4 ,30. We ucutive K  and to aplicates .2+, 0.3 μM of primer mix (forward and reverse) and 0.25 units of Taq Polymerase . Amplifications were performed with an initial DNA denaturation step at 94°C for 2 minutes, followed by 45 cycles of denaturation at 94°C for 15 seconds, annealing at 55°C for 15 seconds, extension at 72°C for 30 seconds, and final extension at 72°C for 10 minutes. Amplicons were purified using ExoSAP-IT  and sequenced in both directions in an ABI automated DNA sequencer 3130XL (Applied Biosystems). Sequences were visually corrected in SeqScape 2.5 and aligned in Geneious 7.1 . Geneious was also used to fix alignment ambiguities, mainly caused by indels in the mtDNA CR. The four mtDNA regions were concatenated in a multi-fragment alignment of 2164 bp. Taking into account the presence of indels, identical haplotypes were collapsed using DnaSP 5.10.01 [H) and nucleotide (π) diversity [We amplified by Polymerase Chain Reaction (PCR) a fragment of the left peripheral and central domains of the mtDNA control-region (CR) using primers WDLOOPL and H519 , and fra 5.10.01 , that waiversity  for eachCanis latrans, GenBank access number DQ480509). NJ and ML phylogenetic analyses were done in Paup* 4.0 beta [rBayes 3.2 [modeltest option [faststep search in Paup. We identified the best-fit evolutionary model for Bayesian analyses using PartitionFinder [rBayes 3.2 was run for 2x106 generations, with a sampling frequency of 100 generations, and with one cold and three heated MCMC  [http://tree.bio.ed.ac.uk/software/tracer). We estimated in Geneious the frequency of invariable (I) sites, the parameters of the gamma (γ) distributions and the transition-transversion (Ti/Tv) ratios for the NJ, ML, and BT analyses.In addition to the new 210 mtDNA sequences , we down4.0 beta ; the BT ayes 3.2 . For thet option  and the t option . We obtat option  in NJ tronFinder . MrBayesergence) . To checeast 2.4.2 [coalescent extended bayesian skyline model [racer to check for MCMC convergence. When the two independent runs converged on the posterior distributions and reached stationarity, we combined the sampled trees into a single tree file with LogCombiner 2.4.2 (burn-in = 10%). With TreeAnnotator 2.4.2 we summarized information from a sample of trees into a single final tree visualized in FigTree 1.4.2 (http://tree.bio.ed.ac.uk/software/figtree/). LogCombiner and TreeAnnotator are part of the Beast package.We used Bst 2.4.2  to estimne model , and usiiyabc 2.1.0 [tructure.We used microsatellite data to run Approximate Bayesian Computation simulations (ABC)  implemenbc 2.1.0  to modelWe selected three wolf population samples for modelling full ABC simulation scenarios: WIT (pop1), WIBP (pop2) and WDIN (pop3), excluding any sample with possible traces of dog admixture . SamplesAccording to Pilot et al.  and Fan 6 simulations for each scenario using uniform prior distributions of the effective population size and time parameters with default mutation settings. We selected the following summary statistics for all the microsatellites: a) one sample: mean number of alleles, mean genetic diversity, mean size variance; b) two samples: mean number of alleles, mean genetic diversity, Fst, shared allele distance  and the highest in WBALT . These two populations also showed the lowest  and the highest  values of heterozygosity. Values of Ho were slightly smaller than expected in all the population clusters except WCARP that showed identical Ho and He values. Thus, the F-values within clusters were positive, indicating departures from HWE , and the lowest haplotype and nucleotide diversity (H and π) at each of the four mtDNA regions . Haplotype WH14 includes the shorter mtDNA CR haplotype W14 repeatedly described in the Italian wolf population [x dog hybrids sampled in Italy showed the same wolf haplotype WH14, suggesting a preferential wolf maternal-biased hybridization [We sequenced 498 bp of the mtDNA CR (GeneBank accession number KY549989-KY550013), 588 bp of the ATP6 (GeneBank accession no. KY549946-KY549953), 231 bp of the COIII (GeneBank accession no. KY549954-KY54960) and 847 bp of the ND4 (GeneBank accession no. KY549961-KY549974 for the first 414 bp and no. KY549975-KY549988 for the last 433 bp) mitochondrial genes in 210 canid samples  that clearly better performed than the other three .P-values for all the posterior parameters after Bonferroni correction . In conc. 1.9 and the other populations c. 3.0–4.4 times, coherently with inferences from genomic data [The phylogeographic patterns emerging from the mtDNA data are completed by multivariate and Bayesian analyses of the multilocus autosomal wolf genotypes. Results indicate that the main geographical populations in central and southern Europe are genetically distinct. The Italian and the Iberian wolves were the first ones to cluster separately in both multivariate and Bayesian clustering, confirming Lucchini et al. , Stronenmic data .c. 20 000–16 000 years ago). During this process, the Italian wolf population underwent a sharp demographic decline, which strongly reduced its standing genetic diversity. Thus, the result of the ancient isolation compromised the genetic pool of the Italian wolves long before the most recent anthropogenic bottleneck. However, these findings do not rule out the possibility that a recent demographic decline impoverished even more the genetic variation of this population.Despite the uncertainty that is usually associated to TMRCA estimates computed from limited number of markers (as in this study), or from limited number of samples Click here for additional data file.S2 FigDog and wolf population samples are shown in the same sequence as in (PDF)Click here for additional data file.S3 FigDetails of clade A are highlighted in the top -left figures.(PDF)Click here for additional data file.S4 FigCanis latrans (DQ480509) is used as an outgroup. Every node shows its posterior probability. Clade A, that includes the Italian wolf haplotypes WH14 and WH19, is highlighted at the top left of the figure.A homologous concatenated sequence of (PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 FigPre-evaluation of scenario-prior combinations; direct and logistic regression comparison methods of the estimated posterior probabilities among scenarios and fit of the selected best scenarios (Sc2 and Sc4) with the observed data. PCA I and II plotted using 10.000 data points.(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Tabletructure with K = 3, assuming that genotypes could have ancestry in a dog cluster (qd), an Italian wolf cluster (qwit), or un a third wolf cluster including all the other wolves (qweu).The individual Bayesian clustering assignments were computed using S(PDF)Click here for additional data file.S3 TableFor every sample is shown: the accession number, country of origin of the sequenced sample (if available), taxon, dog breed (if available), and clade of memberships , and haplotypes at different genic regions.(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 TableDetailed composition of the concatenated multifragment haplotypes.(PDF)Click here for additional data file.S6 TableCountry acronyms: IB = Iberian Peninsula; IT = Italy; HR = Croatia; SL = Slovenia; GR = Greece; BG = Bulgaria; PL = Poland; ES = Estonia; LA = Latvia; FI = Finland; UK = Ukraine; SW = Sweden; IR = Iran; OM = Oman; SA = Saudi Arabia; IS = Israel; IN = India; CH = China; AK = Alaska; CA = Canada; US = USA; MX = Mexico.(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.


Neophocaena asiaeorientalis asiaeorientalis). By analysing genetic variation at the mitochondrial and nuclear microsatellite loci for 148 individuals, we identified three populations along the Yangtze River, each one connected to a group of admixed ancestry. Each population displayed extremely low genetic diversity, consistent with extremely small effective size . Habitat degradation and distribution gaps correlated with highly asymmetric gene-flow that was inefficient in maintaining connectivity between populations. Genetic inferences of historical demography revealed that the populations in the Yangtze descended from a small number of founders colonizing the river from the sea during the last Ice Age. The colonization was followed by a rapid population split during the last millennium predating the Chinese Modern Economy Development. However, genetic diversity showed a clear footprint of population contraction over the last 50 years leaving only ~2% of the pre-collapsed size, consistent with the population collapses reported from field studies. This genetic perspective provides background information for devising mitigation strategies to prevent this species from extinction.Understanding demographic trends and patterns of gene flow in an endangered species is crucial for devising conservation strategies. Here, we examined the extent of population structure and recent evolution of the critically endangered Yangtze finless porpoise ( Persistence of wide-ranging animals occupying fragmented landscapes depends on the matrix quality of the habitat and the ability of individuals to move among habitat patches3, and corridors facilitating this movement across such landscape6. Along the Yangtze River 7. Today, the Yangtze finless porpoise  has become the only surviving freshwater cetacean now found in China and the world’s only freshwater porpoise species8.Dispersal and gene flow in a meta-population maintain local demographic and genetic variation, thus increasing the probability of species persistence9. The subspecies has occasionally been reported from some of the larger adjacent tributaries though this is now rare12. The amount of river and lake habitat available to this subspecies is relatively small compared to that available to marine populations of finless porpoises, which occur in coastal waters from Japan to the Arabian Sea8. However, YFP abundance has suffered from dramatic reductions from 2,500 individuals in 199110 to 1,800 in the end of 2006, as estimated by the Yangtze Freshwater Dolphin Expedition in 2006 (YFDE2006)13. More recent surveys conducted during the Yangtze Freshwater Dolphin Expedition conducted in 2012 (YFDE2012) reported that populations declined even further to ∼1,040 individuals, including ~500 porpoises in the Yangtze main stream, ~450 in PY and ~90 in DT14. With such rapid range contraction, Mei et al.15 estimated that the YFP may become extinct within the next 60 years or less14. The YFP was thus recently reclassified as a Critically Endangered sub-species on the IUCN Red List9. As a top predator, the survival of the finless porpoise depends heavily on habitat suitability, food availability and maintenance of corridors allowing dispersal between populations. However, with increasing underwater noise from boat traffic and incidental catches in fisheries, food and habitat resources for the species have become increasingly scattered and fragmented, and corridors across the landscape have been compromised by the booming of the Chinese economy over the last decades13. Despite these imminent threats, we still do not know how reduction in suitable habitats in the Yangtze River has reduced the number of breeding porpoises and how habitat fragmentation has impacted connectivity between populations and the population structure itself. This information is extremely difficult to quantify using direct observations. On the other hand, population genetic approaches can provide key insights about the current population structure and connectivity and historical population demography by leaving detectable footprints on the genetic diversity and its geographic structure17.Endemic to the Yangtze River drainage Fig. , the YFP19. A subsequent study within the Yangtze River by Chen et al.20 used mtDNA-CR and nuclear microsatellite loci and revealed evidence of genetic subdivisions within the YFP populations suggestive of population fragmentation. However, the fine scale population structure remained unclear and the processes shaping the genetic variation in the YFP unknown. Changes in connectivity between populations, dynamics of population expansion-contraction and demographic history are potent factors shaping the genetic variation in the YFP, but these were not investigated in further details so far.Previous phylogeographic analyses based on the non-coding mitochondrial Control Region (mtDNA-CR) of the finless porpoises from Chinese and Japanese waters documented evidence of a demographic expansion following the Last Glacial Maximum  and the colonization of Yangtze River from the Yellow Sea ~22,000 yrs BP agoet al.20 that combined fast evolving microsatellite loci together with more slowly evolving sequences from the mtDNA-CR. Specifically, in contrast with previous studies, we used a statistical population genetic framework in order to (i) resolve the fine-scale population structure using a combination of individual-based multivariate and Bayesian clustering approaches designed for weak genetic structure when it exists; (ii) estimate past and contemporaneous effective population sizes and connectivity among populations; and (iii) reconstruct the demographic history best fitting with the genetic diversity observed in the populations of the YFP using a quantitative model-based inferential population genetic framework relying on Approximate Bayesian Computation (ABC) approaches23. Determining and quantifying to which extent populations in the YFP are fragmenting, loosing connectivity, and the magnitudes of the demographic trends are critical knowledge for designing conservation and management plans. For example, we still do not understand whether population fragmentation and decline have been triggered only recently by human activities during the past 50 years or if these patterns were initiated earlier by more long-term ecological and evolutionary processes and exacerbated lately during the Anthropocene.In this study, we address these above questions by re-analysing the previously published data set of Chen STRUCTURE26 provided consistent results over 10 replicated runs performed for each number K of cluster tested  and Poyang Lake (PY), Fig. ted Fig. . The proted Fig. . At K > ted Fig. . A visuated Fig.  revealedted Fig. . The thrSTRUCTURE were further validated using two multivariate approaches that do not rely on model assumptions27: a Discriminant Analysis of Principal Components (DAPC)28 and a Principal Component Analysis (PCA)29. Both methods showed a genetic structure consistent with the results of STRUCTURE  of individuals to their original clusters was high , private allelic richness (pA) and expected heterozygosity (He) . The mitochondrial genetic diversity followed a similar trend with the admixed group showing the highest haplotype and nucleotide diversity followed by TL and PY (Table None of the identified populations displayed significant departures from Hardy–Weinberg expectations as shown by the es Table . Porpoise) Table . Only thPY Table . We founFST values ranging from 0.023 to 0.070 , while FST values were intermediate between the Admix group and each distinct population , and were especially strong between XCSS and all other groups, due to the fact that one haplotype is fixed in this population  in each population from the microsatellite data: NeEstimator30 based on linkage disequilibrium among loci within population and ONESAMP31 relying on an Approximate Bayesian Computation (ABC) approach. Both approaches provided comparable estimates of Ne for each population  between pairs of groups over the last few generations using BayesAss32. Out of the 12 repeat runs, 10 showed good mixing properties with Bayesian Deviance values33 close to each other  and convergent estimates for each parameter . We thus combined them all to estimate the parameter values (Table Ne × m) per generation over the last generations were obtained by combining Ne estimates from ONESAMP31 with m estimates of BayesAss32  – identify this scenario SC1 as the best one with a posterior probability respectively of 66.7% with a 95%CI not overlapping with any other scenarios and 55.8 ± 2.5% 38 of 38.6% (62.9% using the ABC-RF). Simulations under the nine other competing scenarios led to a Type-II error rate of <12.9% incorrect assignment to SC1  and a power (87.1%) to discriminate the best scenario from the others  during the divergence of the three populations under six nested competing scenarios  or simple decline or expansion in the ancestral or daughter populations, SC2, SC4-6), with a probability of 78% and no overlap in 95%CI for the ABC-LDA, and 69.8 ± 1.9% under the ABC-RF  could have produced a detectable genetic footprint. The scenario involving a recent collapse (SC2) in each population received a significantly higher probability  compared with the alternative scenario of constant Ne since the population split , and that a small fraction would have founded the Yangtze River populations ∼3,400 95%CI:  years ago (Texp2) and expanded to reach an effective size of about 5,660 individuals . The three daughter populations would have then split from each other ∼1,030 years ago , and reached an effective size of about 2,000 individuals after their split. Each of these daughter populations would have gone through a significant decline leaving less than 2% of their pre-collapse size during the last 50 years 41. These environmental changes might have promoted the split of the ancestral population after the colonization of the Yangtze River.Our study shows that the present-day genetic diversity of finless porpoise in the Yangtze River (YFP) has been strongly influenced by an initial founder event that took place several thousand years ago, followed by a relatively recent split into 3 populations , and a recent demographic collapse within the last 50 years. Indeed, consistent with previous studiesMGW statistic  are consistent with the very small contemporary effective population size (Ne) estimated from genetic data  in each population, with the XCSS population being the smallest of all three differentiated populations  in European waters (Ne ≤ 8045) or the coastal ecotype of bottlenose dolphins (Tursiops truncatus) in European waters (Ne ≤ 7746). However only highly endangered populations, such as the Borneo Orangutans (Pongo pygmaeus)47 or the Canadian woodland caribou (Rangifer tarandus)48, have Ne values as low as those observed in the XCSS population. Extremely low Ne clearly translate the very low genetic diversity observed in each population of the YFP and imply very low numbers of breeding individuals in each populations of the Yangtze River and the adjacent lakes49. However, drawing a more direct link between Ne and N is actually difficult considering our study design. Previous studies have shown that no direct relationship can be expected between Ne and N when generations are overlapping, sampling spans several years, includes multiple cohorts and age classes, and when immigration may occur52.These very small census populations sizes  were drawn from the caudal vein of live porpoises, immediately preserved in an Acid-Citrate-Dextrose solution, and stored in liquid nitrogen. Tissue samples  were obtained from dead porpoises and preserved in 80% alcohol. Samples from the Yangtze mainstream and Dongting Lake were collected from accidentally killed or stranded individuals over the past decade. Fifty-eight blood samples from PY were collected from live animals during three field surveys conducted in early spring of 2009 (n = 28), 2010 (n = 17) and 2011 (n = 13), under a special permit from the Poyang Lake Fishery and Fishing Administration Office of Jiangxi Province. Gender was identified by visual inspection of the genital parts. In total, 76 males and 77 females were sampled. The sampling was conducted in accordance with the Regulations of the People’s Republic of China for the Implementation of Wild Aquatic Animal Protection promulgated in 1993 by the Food and Agriculture Organization of the United Nations , adhering to all ethical guidelines and legal requirements in China.A total of 153 Yangtze finless porpoises were sampled between 1998 and 2011 across the distribution range  following the manufacturer’s instructions. For tissue samples, total genomic DNA was extracted using a standard proteinase K digestion and phenol/chloroform extraction protocolet al.20. All 153 porpoises were genotyped at 11 polymorphic microsatellite loci . Those filters excluded of 5 individuals, leading to a final data set of 148 individuals. The mtDNA data includes a 597 base-pairs fragment of the hyper-variable region 1 of the control region successfully sequenced for 129 individuals . The microsatellite and mtDNA-CR data are available in Supplementary Dataset Microsatellite and mitochondrial (mtDNA) data have been previously obtained and described by Chen STRUCTURE v2.3.426 to estimate the admixture proportions for each individual to each cluster identified in the microsatellite data. STRUCTURE cluster individual multilocus genotypes into K groups while minimizing departures from Hardy-Weinberg and Linkage Equilibria. We used the admixture Locprior model with correlated allele frequencies designed to detect weak signals of genetic structure without introducing bias or forcing the clustering26. We conducted a series of independent runs with different value for K from 1 to 5. Each run used 1 × 106 iterations after a burn-in of length 2 × 105. To assess that convergence of the Monte Carlo Markov Chains (MCMCs) had been reached, we performed 10 independent replicates for each K and checked the consistency of results using CLUMPAK59. We assessed which K value best fit with the data using (1) the likelihood of each K, following STRUCTURE’s user manual; (2) its rate of change with increasing K60; and (3) visual inspection of newly created clusters with increasing K61. Post-processing of the results, including generation of barplots, was conducted using CLUMPAK59. The geographic distribution of each group 29. These exploratory methods does not rely on any model assumptions and provides a complementary validation of the structure depicted by STRUCTURE64. These analysis were conducted using adegenet 2.0.1 package65 for R66 on centred genetic data , with missing data replaced by the mean, following the authors’ recommendations. The PCA was used to display the individual multilocus genotypes into a reduced multidimensional space defined by the first two principal components (PCs), colour-coding each individual according to the clustering identified by STRUCTURE, in order to assess the congruence. The DAPC method identifies genetic clusters by optimizing the difference between predefined groups and minimizing the variation within those groups28. It first reduces the number of variables using a PCA and then maximises the differences between groups using a Discriminant Analysis. The DAPC was performed with prior information on groups using the clusters defined by STRUCTURE. The number of PCs to retain and the reliability of the DAPC were determined using the cross-validation approach present in the adegenet 2.0.1 package65 for R66. As a result of this cross-validation step, a total of 60 PCs and 4 discriminant functions were retained to describe the relationship between the clusters. This number of PCs captured 80% of the total variation and provided the highest percent of correctly predicted subsamples with the lowest error. Finally, the score of each individual for the first two discriminant functions (DFs) were plotted as scatter plot in R and the memberships probability of each individual to the clusters defined by the DAPC were plotted as barplot in R.The genetic structure in the microsatellite data was further inspected using a Discriminant Analysis of Principal Components (DAPC)Ar) and private Ar (pAr) computed with ADZE67, and the observed and expected heterozygosity (Ho and He) computed with GenAlEx v6.568. Departures from Hardy-Weinberg were tested using 104 permutations with FSTAT v2.9.3.269 and quantified using FIS and FST70 in GenAlEx v6.568. We applied a Bonferonni correction to correct for multiple tests.We compared microsatellite genetic diversity between populations using the allelic richness (S), number of singletons and shared polymorphisms, number of haplotypes, haplotype diversity (Hd) and two estimators of population genetic diversity, π based on the average number of pairwise differences71 and θW based on the number of polymorphic sites72. All statistics were calculated using DNASP v5.10.0173. We used the FST statistics estimated from the average number of differences within and between populations74. Significance was tested with 1,000 permutations of Hudson’s nearest neighbour distance Snn statistics, which measures how often the nearest neighbour of a sequence (in sequence space) is from the same population75.Mitochondrial genetic diversity was estimated for each of the genetically distinct groups identified in the present study. Variation among sequences was measured using the number of segregating sites (NeEstimator v2.0130 as a first approach to estimate contemporary Ne based on linkage disequilibrium (LD) between loci, filtering out rare alleles with a frequency Pcrit ≤ 0.02 that could bias Ne estimate76. The second method implemented in ONESAMP v1.231 also use LD as summary statistics among others in an Approximate Bayesian Computation (ABC) to estimate Ne, considering uniform prior between 2 and 500 of Ne.We used m) between populations using BayesAss v.3.0.332. Preliminary runs were performed to adjust the mixing parameters of the MCMC and ensure proposal acceptance rates between 20% and 60% following authors’ recommendations. We then performed 12 independent runs with different seeds, a burn-in of 5 × 106 iterations followed by 2 × 108 iterations, and a sampling parameter values every 2000 iterations. Convergence of the MCMCs was checked by comparing the traces of each run using Tracer v1.577 and by evaluating the Effective Sample Sizes (ESSs) of each parameter, keeping only runs where ESS ≥ 200. Model fitting to the data was assessed using the Bayesian Deviance Index using the R-script of Meirmans33. Runs that converged were combined to estimate the mean, median and 95% Highest Probability Density interval for each parameter in Tracer v1.5. The effective number migrants (Ne × m) per generation between populations was obtained by combining Ne and m estimates.We estimated contemporary effective migration rate  with six nested competing scenarios, and (ii) then testing whether adding the known population decline observed in each population over the last 50 yrs in the best scenario improved significantly the model fit to the data  for each competing scenario and calculation of summary statistics (SS) describing the observed genetic variation for each POD; (2) select the best model by estimating the posterior probability (PPr) of each scenario using two approaches: the “standard” procedure relying on a logistic regression on 1% PODs producing SS values closest to the observed ones after a Linear Discriminant Analysis (ABC-LDA) as a pre-processing step36 and the recently introduced Random Forest (ABC-RF) procedure38; (3) evaluate the confidence in scenario choice by estimating the type-I and type-II error rates based on simulated PODs using the ABC-LDA, as well as the prior error rate from the of the ABC-LDA and ABC-RF38; (4) estimate the marginal posterior distribution of each parameter based on the best model including (among other) Ne and times of population size changes and splits (T); and finally, (5) evaluate the goodness-of-fit of the fitted model to the data. Details are provided in Fig. For each part Fig. , an ABC eps Fig. : (1) coaBC-RF38;  estimateAll data generated during this study are included in this article (and its Supplementary Information files).Supplementary informationSupplementary Dataset S1


Bombax ceiba tree in southwest China. We sampled 224 individuals from 17 populations throughout the dry‐hot valley regions. Six polymorphic expressed sequence tag–simple sequence repeat primers were employed to sequence the PCR products using the first‐generation Sanger technique. The analysis based on population genetics suggested that B. ceiba exhibited a high level of gene diversity . The 17 populations were divided into two groups by cluster analysis, which corresponded to geographic characters on each side of the Tanaka Line. In addition, a Mantel test indicated that the phylogeographic structure among the populations could be fitted to the isolation‐by‐distance model . A barrier test indicated that there were obstacles among populations and between the two groups due to complex terrain isolation and geographic heterogeneity. We inferred that the Tanaka Line might have promoted the intraspecific phylogeographic subdivision and divergence of B. ceiba. These results provide new insights into the effects of the Tanaka Line on genetic isolation and population differentiation of plant species in southwest China.Southwest China is an important biodiversity hotspot. The interactions among the complex topography, climate change, and ecological factors in the dry‐hot valley areas in southwest China may have profoundly affected the genetic structure of plant species in this region. In this study, we determined the effects of the Tanaka Line on genetic variation in the wild  Moreover, the PCR products were subjected to Sanger sequencing to make the results more accurate and reliable  according to the manufacturer's protocol. The concentration and quality of the DNA were detected using a spectrophotometer. Six highly variable pairs of EST‐SSR primers Appendix  were useand Tian . All of 2.3A), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), and polymorphism information content (PIC) were calculated for each locus using GenAlEx version 6.501  with 50,000 burn‐in and 100,000 Markov Chain Monte Carlo iterations. A suitable number of clusters (K) were selected as the largest rate of change in the log probability of data between successive K values . Admixture proportions obtained from replicate simulations at the optimal K were averaged using CLUMPP version 1.1.2  ranged from two to seven  and 0.2424 to 0.6085 , respectively  and Shannon's index (I) ranged from 0.2377 to 0.4775 and 0.3997 to 0.7848, respectively (Table The population genetic diversity results obtained at the population level are listed in Table 3.2B. ceiba samples clustered into two groups (cluster 1 and cluster 2). Cluster 1 comprised the BN, HH, YJ, YY, YB, BC, ET, YM, and RH populations. The LC, PE, YD, GM, SD, LJ, LSH, and LL populations were grouped in cluster 2  .We defined two clusters based on the STRUCTURE results in order to evaluate the direction of historical gene flow among the 17 populations. The gene pool in cluster 1 was mostly attributed to the northeast group and that in cluster 2 was attributed to the southwest group. We performed maximum likelihood analyses with MIGRATE‐N using 10 short chains  and three long chains , where 10,000 trees were discarded as a burn‐in. Interesting patterns of historical gene flow were determined between the two groups where these patterns were relatively symmetrical with slight differences   Table . Hence, 8) Table . The natcies Li,  in these4.2B. ceiba populations could be divided into two genetically divergent clusters  (Figure B. ceiba population, while habitat fragmentation may have been responsible for the second level of hierarchical variation. The isolation between populations is due to physical barriers in the form of complex terrain with mountains and rivers in southwest China. The genetic structure is expected to be congruent with the geographic arrangement of the mountains and river systems. The genetic distance was relatively large even with a close geographic distance, and genetic discontinuities between the two nearby territories were also identified by BARRIER (Figure In addition, the ty Table , the difB. ceiba natural populations into two parts. Indeed, previous studies have demonstrated that the Tanaka Line currently plays a key role in shaping plant dispersal and it is a habitat–heterogeneity boundary in southwest China (Tanaka, Sophora davidii found obvious differences in the population structure on both sides of the Tanaka–Kaiyong Line (Fan et al., B. ceiba populations associated with an ecological transition. This major form of isolation may hinder the gene exchange via birds but not pollen dispersal. Hence, this pattern may weaken the specific population structure of B. ceiba on either side of the Tanaka Line. Natural adaptation and physical barriers could explain the divergence among the two subpopulations. Overall, our findings support a hypothesis that the Tanaka Line has contributed to the intraspecific divergence pattern in this species, thereby facilitating the protection and exploitation of wild B. ceiba population resources.BARRIER analysis based on microsatellite data showed that, in recent times, variations in the topography and climate have contributed to the high endemic biodiversity in southwest China (Myers, Mittermeier, Mittermeier, Da, & Kent,  Tanaka, . The hetNone declared.BT contributed to the conception of the study. BT and YF collected the materials. ZHL and MMJ contributed significantly to analysis and manuscript preparation. MMJ performed the data analyses and wrote the manuscript. CZH contributed the reagents/materials/analysis tools. BT, ZHL, and GFZ helped perform the analysis with constructive discussions. All authors contributed critically to the drafts and gave final approval for publication.


This study describes the genetic diversity and population structure of 194 native maize populations from 23 countries of Latin America and the Caribbean. The germplasm, representing 131 distinct landraces, was genetically characterized as population bulks using 28 SSR markers. Three main groups of maize germplasm were identified. The first, the Mexico and Southern Andes group, highlights the Pre-Columbian and modern exchange of germplasm between North and South America. The second group, Mesoamerica lowland, supports the hypothesis that two separate human migration events could have contributed to Caribbean maize germplasm. The third, the Andean group, displayed early introduction of maize into the Andes, with little mixing since then, other than a regional interchange zone active in the past. Events and activities in the pre- and post-Columbian Americas including the development and expansion of pre-Columbian cultures and the arrival of Europeans to the Americas are discussed in relation to the history of maize migration from its point of domestication in Mesoamerica to South America and the Caribbean through sea and land routes. Zea mays ssp. parviglumis, in the Balsas River region in western Mexico [Maize was domesticated about 9000 years ago in Mexico from tropical teosinte, n Mexico ,2. The un Mexico . The disMany archaeological sites in the Americas have evidence of sedentary communities linked to maize cultivation, some in Mesoamerica as early as nearly 9000 years ago . The AmeAn intermediate cultural region existed between Mesoamerica and northern South America, in which the Valdivia and Chorrera cultures (among others) were established. Both these cultures are associated with early cultivation of maize (3500–300 AD), geographically far from maize’s domestication origin. In South America, several cultural regions can be identified, including the Amazonian and Chaco regions. The central Andes were the site of the emergence of the Chavin, Vicus, Moche, Tiwanaco, Nazca, and Inca cultures (1000–1532 AD). The southern Andes encompass southern Bolivia, northwest Argentina, and the southern section of the Andes along Chile; cultures including the Aconcagua, Diaguita, and Tafi (300–1532 AD) can be linked to this region. The Caribbean and northern Venezuela are principally linked with the Taino-Arawak culture (450–1500 AD) .Investigation of the spread of maize from its center of domestication has been extensive, based on cytogenetic data , historiArcheological studies include the study and dating of macrobotanical remains, phytoliths , and pollen samples recovered from sediments in lakes, wetlands, and archaeological deposits ,6,7. KeyQuestions remain regarding the domestication event itself. Evidence  of the intentional human selection for increased cob and grain size in maize  indicateConsidering that Mexico is the center of origin and one main center of diversity of maize, and further considering the cultural importance of maize, genetic variability in Mexico has been thoroughly studied since 1913 ,24. The Understanding evolutionary history, genetic diversity within and among indigenous races, and relationships between the many traditional Latin American races of maize are all critical for fundamental research, conservation, and utilization of these genetic resources for maize breeding. Here, we hypothesize that morphological and genetic variation present in modern maize populations were influenced by geographical factors and reflect the distribution of human historical and cultural events in the New World. To this end, we performed genetic characterization using SSR markers of native Latin American maize populations, in order to study diversity and population structure and argue that they relate to the migration of maize from its center of origin in Mesoamerica towards South America and the Caribbean. We first analyzed SSR data from Mexican maize landraces alone, to validate the genetic data in germplasm for which very good historical and anthropological evidence are available. This was followed by an analysis of 194 maize landraces from all over Latin America, to clarify some of the gaps and disputes concerning early maize diffusion.For this study, 194 native Latin American maize populations were selected from the CIMMYT Maize Germplasm Bank based on eco-geographical data to represent 131 classified landraces from 23 countries . A subseA population-level genotyping strategy was chosen to characterize the range of allelic diversity among and within populations . Thirty For genetic analysis, 28 SSRs that had been optimized previously to work in pooled samples of 15 individuals were selected . These SR, an open-source computer program and language for data analysis (http://cran.r-project.org), using the program Freqs-R [R program FtoL-R [Frequency of each allele in the bulk was calculated from peak height (intensity) in  Freqs-R . This prm FtoL-R  was usedm FtoL-R , for eacm FtoL-R  was usedK populations/groups were characterized by a set of allele frequencies at each locus. Populations were not assigned to any group a priori, and individual simulations of each population were allowed to vary. After the first analysis including all populations, additional Structure analyses were performed in order to reveal possible sub-structure within each of the main clusters detected. Linked to the Structure software package an additional calculation was used to determine the most suitable number of clusters or subpopulations taking into account the values obtained forΔK .The model-based clustering method, Structure 2.2  was usedK were calculated from 1 to 8, and for the entire data set, including all populations, from 1 to 15. Calculations were performed using 1,000,000 replications after a burn in period of 500,000 iterations, and the procedure was repeated five times for each K value. Populations were then assigned to each group for which they had an ancestry proportion Qjk greater than 51.0%; if a population did not show an ancestry proportion higher than this value, it was assigned to the mixed group.Preliminary classification of the Mexican landraces was compared to reported classification studies available for these landraces , based oThe relationships between the Mexican maize races can be seen in the cluster analysis in ΔK  found in cluster analysis, plus one additional group, a Chapalote related complex . Similarly, the races of western and northwestern Mexico were found to belong to a related group, and the populations in the Chapalote complex  were highly interrelated [K was set to six, the structure was more specific and corresponded better with phenotype.Four clusters in Structure were assumed based on ΔK , and theΔK . At K = rrelated . In agreIn general, the same clusters were seen, except that the Sierra Madre Occidental cluster divided into populations from the eastern slope (ESMO) and western slope (WSMO). The ESMO landraces are limited to the northern and westernhighlands of Mexico  in small valleys from altitudes from 2000 to 2600 masl. The WSMO landraces are comprised of mostly eight-row landraces  distributed in low elevations in the west and northwest of Mexico . The SouCluster analysis of all populations in the study identified four groups loosely based on geography , includiThe Eastern South American group found in the cluster analysis contained some populations adapted to a range of altitudes from Central America, and is similar in constitution to the group named "Northern Territory" in a previous classification . In the The cluster containing the 79 South American populations  included the rest of the South American populations and two populations from Guatemala. The South America–Andean region cluster (G3) encompassed nine sub-clusters and populations that did not group at this level: g8—mainly formed by landraces from Bolivian highlands but extended to Chile and Peru; g9 –lowland landraces from Bolivia; g10—Columbian sub-cluster; g11—second Columbian sub-cluster formed by Columbian, Brazilian and Uruguayan tropical populations; g12 –highland landraces from the Andes; g13 –landraces from the central highland Andes; g14 –typical landraces from the Ecuadorian highlands; g15 –northern tropical lowlands; and g16—Avati’ sub-cluster.Genetic diversity values, average number of alleles per locus, and unique alleles found between the three main groups and sub-groups identified by Structure and sub-structure analyses for the 194 maize populations are presented in In the latest published classification , 59 distThe distinct racial complexes obtained for Palomero Toluqueño and Arrocillo Amarillo , Chapalote , and Nal-Tel (Southern and Southwestern) are consistent with differences between the Ancient Indigenous landraces as a consequence of their independent development in different locations and environments. These ancient landraces are believed to have originated in Mexico from tunicate primitive maize, and have been in existence for a very long time . In contGenetic diversity levels in populations from the maize center of origin were high, and decreased as distance from the center of origin grew, reaching a low in South American populations, in agreement with a previous study . High ovIn spite of the differences in methodology used for determining the patterns of population structure for the most representative accessions from the Americas in a previous study that included a subset of the data from the current study , the twoMaize spread into the southwestern US from northern Mexico, and finally into the northern US and Canada from the southwestern US . In agreThe Mesoamerica lowland group (G2) sheds light on the origin of Caribbean maize, supporting the theory that maize was introduced into the Caribbean twice : first fThe South America–Andean group G3) displayed an overlapping area of different sub-clusters especially in the Central Andes, supporting the hypothesis that this region was an important center of maize interchange or meeting ground  displaye. HoweverAlthough mutation and selection over such long time periods as those suggested for domesticated maize evolution  make it Maize has been a cornerstone in past and current cultures throughout the Americas, which has led to the development and continuous improvement of many landraces. Based on the association relationships found across and within groups of the accessions evaluated in this study, it is evident that the structure of Latin American maize genetic pools are dynamic and influenced by discrete micro- and macro-environmental zones, human migrations and trade, as well as landrace selection and conservation by indigenous communities. Our results have a practical application, for example facilitating the identification of gene bank pools and accessions containing valuable, unique alleles for breeding, providing access to allelic diversity that has been eliminated locally due to selection pressure and evolution of populations in the target environments inherent to human migration and settlement.S1 FigLandraces within big cells correspond to the definitive racial complex system of classification of Goodman and Brown ; the lan(DOCX)Click here for additional data file.S2 Fig(a) and ΔK(b) for 38 Mexican accessions from structure analysis. For the log likelihood plots and the calculation of ΔK, the average log likelihood from among the five replicate runs performed at each K is plotted . The high values of ΔK  are labeled with red. K = 4 was selected like the optimal structure model.Plots of the log likelihood (DOCX)Click here for additional data file.S3 Fig(a) and ΔK(b) for 194 Latin America and the Caribbean accessions, including Mexican accessions from structure analysis. For the log likelihood plots and the calculation of ΔK, the average log likelihood from among the five replicate runs performed at each K is plotted . The high values of ΔK (2 and 3) are labeled with red.Plots of the log likelihood (DOCX)Click here for additional data file.S4 Fig(a) and ΔK(b) for 48 Mexico and southern Andes accessions from substructure analyses. For the log likelihood plots and the calculation of ΔK, the average log likelihood from among the five replicate runs performed at each K is plotted . The high values of ΔK (2 and 3) are labeled with red. The K = 3 was selected like the optimal substructure model.Plots of the log likelihood (DOCX)Click here for additional data file.S5 Fig(a) and ΔK(b) for 58 accessions from Mexico, Central America, the Caribbean and northeastern of South America. For the log likelihood plots and the calculation of ΔK, the average log likelihood from among the five replicate runs performed at each K is plotted . The high values of ΔK (2 and 4) are labeled with red. The K = 4 was selected like the optimal substructure model.Plots of the log likelihood (DOCX)Click here for additional data file.S6 Fig(a) and ΔK(b) for 64 accessions from South America- Andean region. For the log likelihood plots and the calculation of ΔK, the average log likelihood from among the five replicate runs performed at each K is plotted . The high values of ΔK (2 and 9) are labeled with red. The K = 9 was selected like the optimal substructure model.Plots of the log likelihood (DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Tablea SSR location in the genome. b SSR repeat unit. c Allele size range in bp over the whole dataset. Loci§ in common with global diffusion of maize study [ze study .(DOCX)Click here for additional data file.S3 TableClassification of accessions according to 5 previous studies. ᵃResults of this study based on the structure analysis for K = 4, group 1 corresponds to maize accessions from Sierra Madre Occidental, group 2 to Southern Mexico, group 3 to Central Valleys, and group 4 to Chapalote Complex; ᵇResults of this study based on Neighbor Joining (NJ) cluster analysis; ᶜRacial Complexes, classification based on a combination of morphological, cytological and isozyme data [yme data ; ᵉClassiyme data ; ᶠClassiyme data ; ᶢClassiyme data ; ng: rac(DOCX)Click here for additional data file.S4 TableCodes used and correspondences between clusters inferred with the most representative accessions with the study mentioned, and structure and sub-structure patterns in the present study, ng populations not grouped at structure or sub-structure level.(DOCX)Click here for additional data file.S5 TableFreqsbulk_194pops data correspond to allelic frequency of each allele in the bulks, bulk number correspond in the same order to accesion number on the passport data information provided. Freqsindivi_2010 sheet correspond to the simulated individual alleles for each bulk.(XLSX)Click here for additional data file.