Choloepus hoffmanni) from four different geographic regions, we determined the presence of four potential genetic groups, three of them with minimal population structuring despite the limited dispersal ability and presence of physical barriers. Sloths from the North appear to represent a highly distinct population that we propose may require management as a discrete unit for conservation. We stress the need for additional analyses to better understand the genetic structure and diversity of North andWest regions and suggest that rescue facilities in Costa Rica should consider the genetic background of rehabilitated sloths when planning future reintroductions. Our results also highlight the threat posed by physical isolation due to widespread urbanization and agriculture expansion for a species with a weak dispersal ability.Sloths are notoriously slow and consequently have limited dispersal ability, which makes them particularly vulnerable to the effects of habitat fragmentation and degradation. Sloths in Costa Rica are considered of conservation concern due to habitat loss, livestock production and increasing urbanization. Reintroductions from rescue centres are commonplace across the country, yet their genetic diversity and population structure are unknown, and there is currently little consideration of the genetic background prior to intervention or releases. We used microsatellite analysis to undertake the first exploratory investigation into sloth population genetics in Costa Rica. Using data from 98 two‐fingered sloths ( All research was performed in accordance with relevant guidelines and regulations.2.2Choloepus hoffmanni) originating from numerous regions across Costa Rica  and 0.008 mg/kg of dexmedetomidine  were administered intramuscularly by a licensed veterinarian following standardized sanctuary procedures. The anaesthesia was reversed using 0.008 mg/kg of anti‐sedative . The hair samples were exported to Swansea University for analysis.All sampling was performed by a licensed veterinarian in the Sloth Sanctuary's veterinary clinic between September 2014 and November 2015. Between 30 and 50 hairs (including the follicular bulb) were collected from a total of 98 Hoffman's two‐fingered sloths (ca Table . All slo2.3DNA was extracted from between six and nine hair roots per sloth using 1‐Step DNA isolation kit (nexttec™) following the “Hair” protocol. The quality of extracted DNA was analysed using a Nanodrop 2000 (Thermo Fisher Scientific Inc.), and 10 μg was used for amplification. A total of 98 individuals were genotyped with 15 microsatellites Table  in two mConditions for amplification consisted of a single‐cycle initial activation step of 15 min at 95°C followed by a touchdown PCR of eight cycles with a 30‐s denaturation step at 94°C, a 90‐s annealing step starting at 64°C and descending in 2 cycle steps of 2°C  and 90‐s extension at 72°C. Following this, 24 additional cycles of PCR were run as above at an annealing temperature of 56°C followed by a single final extension cycle of 30 min at 60°C. Microsatellites were resolved on an Applied Biosystems ABI3130xl Genetic Analyser (Applied Biosystems), fragment length was determined using the GeneScan 500–LIZ size standard and scored using GeneMapper v45.0 (Applied Biosystems).2.4Choloepus hoffmanni individuals from 13 localities  and the populations’ expected (HE) and observed heterozygosities (HO). In addition, GENALEX was also used to perform a Mantel test for isolation by distance (IBD), using standard parameters, including raw genetic and geographic values, with geographic distance measured in kilometres. Conformity to Hardy–Weinberg expectations of genotype frequencies and tests for linkage disequilibrium was investigated using GENEPOP online v4.0.10  using the neighbour‐joining method with 999 bootstrap iterations in POPTREEW.Prior to assessing patterns of population structure, a phylogenetic tree was constructed based on the uncorrected Kmax from K = 2 to 13, following standard suggested parameters. The most likely number of clusters was assessed using the average deviance information criterion (DIC) of the lowest 10 DIC values for each Kmax. Results of most likely K were visualized using STRUCTURE . There was evidence of significant deviation from Hardy–Weinberg equilibrium (HWE) across various loci, though out of 60 chi‐squared tests only 11.6% of loci deviated significantly from HWE after Bonferroni corrections  varied over the four populations, with the South‐East (5.67) having the highest and the West (4.53) the lowest (Table HE) ranged from 0.62 to 0.69. The West population had the lowest mean number of effective alleles (NEF) of 2.95, whereas the South‐East had the highest NEF of 3.39. Results of a one‐way ANOVA revealed no significant difference in He  nor NEF  between populations.The allelic richness (st Table . Mean exNm) calculated between sites, ranging from 1.00 between the two Eastern groups and 0.161 between the South‐East and Northern groups (Table There was considerable variation in effective number of migrants (ps Table . Though ps Table .3.2FST (0.06) and pairwise FST values were significant , albeit very low in general, except for the pairwise comparisons between East and West (Table FST = 0.14), with the lowest divergence between West and East (FST = 0.02). Most populations displayed a high degree of admixture, with average Q values ranging between 2% and 20%, apart from the South‐East that had an average Q value of 73%. The AMOVA results for all populations suggested that the greatest degree of genetic differentiation was within individuals (75%), with differentiation among populations and among individuals accounting for 5% and 22%, respectively .Global st Table . The higKmax of 8; however, four of the groups only contributed 6.97% to the genetic background. The most likely number of genetic clusters (K) was 4  varied across the four groups, with the West having the highest degree of homozygosity (0.56) and the South‐East the lowest , between the West and South‐East populations .Homozygosity by locus , which flows into the Caribbean on the eastern coast , indicating higher levels of inbreeding (FIS = 0.27), which is expected as sloths in this area are limited to severely fragmented forest pockets within the highly urbanized San José region.Despite the presence of the Central mountain range, there was evidence of gene flow between the East and West groups, combined with a lack of significant population structuring in the West population  was more genetically diverse, with high levels of admixture and lack of population structuring. Located in the South‐East are wildlife rescue facilities, that release rehabilitated sloths, originating from across Costa Rica, into the local area. The reintroduction of many individuals (likely with different genetic origins) into the local area may have contributed to the nonexistent population structuring and higher levels of genetic diversity observed  may result in lack of sufficient genetic diversity to maintain viability of populations located in the North Lacy, . RemovinIt was not possible to determine population genetic structure prior to sloth reintroduction in Costa Rica. Therefore, it is difficult to determine whether the current genetic uniformity and lack of isolation by distance observed are largely natural or due to translocations of individuals; however, this study will act as a baseline for future research. Additionally, due to sample size limitations, our South‐East group spans sloths from many different habitat fragments within the region. It is possible that by combining these subclusters together as a single population, we could be masking the true levels of diversity in current analyses. This effect may not be present in other regions, or perhaps was not identified due to lower sample sizes. While our data do not allow to draw firm conclusions on the driving factors behind the high level of admixture observed across Costa Rica, future work could include the use of SNPs or other fine‐scale genomic analyses (Morin, Luikart, & Wayne, C. hoffmanni in Costa Rica.Anthropogenic pressures on sloth populations in Costa Rica are increasing (Neam & Lacher, R.N.C., R.P.W., J.A.A. and S.C. conceived and designed the study. R.N.C., S.J.K. and J.A.A. collected the samples. R.N.C., C.V.R. and B.A.W. carried out the genetic analysis. C.V.R. and B.A.W. carried out the statistical analysis. All contributed to writing and reviewing the manuscript. Authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.


This paper represents the first fundamental report of mtDNA diversity in Turkish indigenous donkey breeds and presents findings for the origin and genetic characterization of donkey populations dispersed in seven geographical regions in Turkey, and thus reveals insights into their genetic history. The median-joining network and phylogenetic tree exhibit two different maternal lineages of the 16 Turkish indigenous donkey populations.D) and nucleotide diversity (πD), for the Turkish donkey populations ranged from 0.533 ± 0.180  to 0.933 ± 0.122 , and from 0.01196 ± 0.0026  to 0.02101 ± 0.0041 , respectively. We observed moderate-to-high levels of haplotype diversity and moderate nucleotide diversity, indicating plentiful genetic diversity in all of the Turkish indigenous donkey populations. Phylogenetic analysis (NJT) and median-joining network analysis established that all haplotypes were distinctly grouped into two major haplogroups. The results of AMOVA analyses, based on geographic structuring of Turkish native donkey populations, highlighted that the majority of the observed variance is due to differences among samples within populations. The observed differences between groups were found to be statistically significant. Comparison among Turkish indigenous donkey mtDNA D-loop regions and haplotypes, and different countries’ donkey breeds and wild asses, identified two clades and which is named Somali (Clade IV) and Nubian (Clade V) lineages. The results can be used to understand the origin of Turkish donkey populations clearly, and to resolve the phylogenetic relationship among all of the different regions.In this study, to analyze the mtDNA D-loop region and the origin of the maternal lineages of 16 different donkey populations, and to assess the domestication of Turkish indigenous donkeys in seven geographical regions, we investigated the DNA sequences of the D-loop region of 315 indigenous donkeys from Turkey. A total of 54 haplotypes, resulting from 35 polymorphic regions (27 parsimoniously informative and 6 singleton sites), were defined. Twenty-eight of these haplotypes are unique (51.85%), and 26 are shared among different Turkish indigenous donkey populations. The most frequent haplotype was Hap 1 (45.71%), followed by two haplotypes . The breed genetic diversity, evaluated by the haplotype diversity (H Cyt-b) gene. In donkeys, the span of the control region of the mtDNA, except for the repetitive regions, is 959 base pairs long, it is also one less nucleotide than the horse’s mtDNA. Between the donkey and the horse mtDNA there are 108 variations (11.2%), in an alignment of the two sequences, there are 88 transitions, 16 transversions, and four insertion/deletions (indels) [Animal domestication performed a substantial role in the occurrence of modern life and urbaneness. The definition of genetic origin and phylogenetic relations between animals is crucial for comprehension of cultural alterations in these civilizations, and eventually has public and scientific desirability ,2. Genetriations .2%, in a(indels) ,13.Equus asinus) is especially interesting for reason that the donkey has been substantially used by humans to transport materials and people from up to as long as roughly 5000–7000 years ago [Equus africanus africanus) and Somalian  ass. Two mitochondrial haplogroups lineages, clade 1 and clade 2, were defined in analyses on modern domestic donkeys. Therefore, giving evidence for two unconnected domestication processes in donkeys [E. a. africanus) [The period of domestication and distribution of the donkey  ,18, wherricanus)  in recenricanus)  defined ricanus) .Donkeys are a former constituent of Turkey’s domestic animal community. They had a crucial role in the country’s agricultural economy for hundreds of years. Generally, as in most of the world in the 21st century, the donkey in Turkey has a depreciated status as a center of the rural economy, and has been supplanted by mechanical means of transport and power . In anciIn many developed countries the advent of “conservation” strategies has resulted in many native breeds of many domestic species being described genetically and morphologically ,21,22,23The objective of this study was: (i) to determine the genetic variability between seven different geographical regional donkey populations in Turkey, (ii) to investigate the genetic structure of Turkish native donkey populations and to explore the maternal origin, with mtDNA D-loop sequences, and explore the domestic history of Turkish indigenous donkey populations, (iii) to evaluate genetic breed sub-structuring, and (iv) the comparison of mitochondrial DNA sequences of Turkish populations with other countries’ domestic donkey breeds and wild asses. The results will be used to better understand the Turkish donkey population’s origin, and to resolve the phylogenetic relationship among all the different regions.According to the FAO database ; the estimated number of donkeys in Turkey is about 150,000. Three types of donkey are recognized in Turkey (FAO/DADIS 2012). The Anatolian is found all over the country, not just on the central plateau, and is usually grey or black in color. The Merzifon or Marsovan, from the town and district of the same name in Amasya province in the central Black Sea region, is at risk. The Karakaçan type, presumably named after the Karakaçan nomads of the Balkans and Thrace, is also considered at risk (FAO-DADIS 2012) [In this study a total of 315 blood samples were collected from Turkish indigenous donkeys raised in seven different geographical regions, from 16 different provinces of Turkey . These lFrom those donkey samples, 10 mL of whole blood was taken from the jugular vein with EDTA-coated vacutainer tubes. Genomic DNA was isolated by using a standard phenol-chloroform-isoamyl alcohol extraction method . The con2, 0.2 mM each of dNTP, 0.5 µM of each primer, 1 X PCR buffer, and 1 U of Taq DNA polymerase. Amplification was performed using a BIORAD MyCycler Thermal Cycler  with the following conditions: initial denaturation at 94 °C for 5 min followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 58 °C for 30 s, extension at 72 °C for 2 min, and final extension at 72 °C for 15 min. The amplified products were electrophoresed, visualized under UV radiations, and purified to be sequenced using a ABI prism 3500 genetic analyzer. PCR products were sequenced for both directions. The raw sequence trace files were checked for the presence of ambiguous bases using ChromasPro v. 1.7.4 software.The 383 bp segment, from the mitochondrial DNA control region (D-loop), from the donkey genome, was amplified by PCR, as described by Aranguren-Méndez et al. . The priH), number of private haplotypes (PH), haplotype diversity (HD), total polymorphic sites (S), parsimony informative sites (SPI), singleton site (SS), shared haplotype (SH), the nucleotide diversity (πD), and average number of nucleotide differences (k) were estimated using DNASP 6.12.03 ×64 software [ST) and the average number of pairwise differences within and between populations were calculated using Arlequin v.3.5.2.2 [The obtained sequences were controlled for the presence of ambiguous bases by using the software, ChromasPro v.1.7.4. For identifying the haplotypes (Hap), all sequences were aligned with the complete donkey mtDNA sequences, MK896308 , commonlsoftware . Pairwis.3.5.2.2 . In orde.3.5.2.2 . MEGA v..3.5.2.2 . The T-R.3.5.2.2 . Whole mThe mtDNA D-loop sequences were taken for all analyzed 315 samples from seven different regions, and from 16 different provinces of Turkey. In In D) and nucleotide diversity (πD), for the Turkish indigenous donkey populations ranged from 0.533 ± 0.180 (MAL)–0.933 ± 0.122 (AYD), and 0.01196 ± 0.0026 (ANT)–0.02101 ± 0.0041 (AYD), respectively. The overall values for haplotype and nucleotide diversities were 0.763 ± 0.023 and 0.01656 ± 0.00046, respectively , while the lowest number of polymorphic sites (S) was identified in MAL and KRM populations. In addition, the mean number of nucleotide differences (k) was the highest for AYD donkeys (7.333) followed by ISP donkeys (6.689), while ANT donkeys had the lowest mean number of nucleotide differences (4.172). The AYD breed observed five haplotypes, the highest π value (0.02101), the highest number of haplotype diversity (0.933), and the highest nucleotide diversity (k = 7.333). The high k value for the AYD breed is due to the high difference among the five haplotypes identified in the population. Despite the low number of samples, a high HD and (π) value, with five haplotypes  for the AYD was found  and k value (k = 4.172), but haplotypes have a high HD value (0.717).The 54 haplotypes were defined by 29 parsimony informative sites. Parsimony informative sites  are defias found . HoweverST (fixation index) value and DA distances value between Turkish indigenous donkey populations from seven geographical regions are shown in ST values range from 0.0302  to 0.2523 . The highest differences in FST values were found between the Marmara and Central Anatolia region populations , followed by between the Mediterranean and Central Anatolia region donkey populations . In contrast, there is little genetic differentiation between Southeastern Anatolia and Black Sea donkeys (FST:0.0004), followed by between Mediterranean and Black Sea (FST:0.0068). In the study, some FST values were observed to be negative  to 0.3446 (KUT-KRM). Out of 120 pairwise FST comparisons, 28 had FST between 0 and 0.05, showing little genetic variation, while 16 comparisons had FST between 0.05 and 0.15, revealing moderate genetic variation, 13 comparisons had FST between 0.15 and 0.25, indicating major genetic variation, and eight comparisons had FST greater than 0.25. Negative FST were registered in some comparisons and these equate to zero FST (ST (>0.25) values were seen between AER, MRM, MDR, BSR, and CAR region populations–KUT-KIR, KUT-CAT, KUT-KRM, KRM-AYD, KRM-KAS, KAS-CAT, KON-CAT, and KON-KRM. These populations showed values corresponding to very great genetic differentiation (The Fzero FST . The higntiation .ST: A Neighbor-joining tree (NJT) from Reynolds’ linearized genetic distances  for 315 The median-joining network of mtDNA D-loop region was described between Turkish donkey populations . The sizComparison of the 427 reference . The median joining network showed that there are five distinct lineages, as shown in the phylogenetic tree and the star-like phylogeny. Three samples, Hap 126–127 and Hap 63, were horses (E. cabullus) that grouped together as an outgroup to the donkeys in the network. Equus cabullus, Equus kiang, and Asiatic wild asses  shared the (I) and (II) lineage, and had their respective lineages. Lineage (I) (E. cabullus) has three haplotypes, lineage (II)  has eight haplotypes. Four haplotypes were determined in E. asinus somalicus, and were grouped in the same (III) lineage. Lineages IV and V consist of domestic donkey populations. In The MJN analysis detected 145 haplotypes from 742 sequences  Turkey’s indigenous donkey populations (n = 315), (2) European countries data: Balkan donkeys [ST values and genetic distances (Reynolds’ linearized genetic distances in Turkish donkey populations from different regions) were considered between Turkey’s domestic donkey populations. (1) CAR, AER, and SAR region donkeys; (2) MDR, MRM, and EAR region donkeys, and (3) BSR region donkeys. The AMOVA analysis is a useful tool to check how maternal genetic diversity is distributed within, and among, populations whose structure is quantified by F donkeys , Italian donkeys , Serbianons n = 3, (2) Eur donkeys ,30, (4)  donkeys , (5) wil donkeys ,39,40,41p < 0.05), and within populations (p < 0.001) were significant.Hypothesis 1; the AMOVA analyses results revealed that the variation among groups, among populations within groups, and within populations were 7.95%, 1.72%, and 90.34%, respectively. The findings showed that most of the obtained variance is due to differences within populations. Variance components among groups were nonsignificant, demonstrating non-significant geographical distribution in the analyzed donkey populations. Moreover, the variance component among populations, within groups (p < 0.05), which showed important geographical distribution in the analyzed Turkish donkey populations by using Reynolds’ linearized genetic distances. In addition, variance components among populations within groups were non-significant statistically, but were statistically significant within populations (p < 0.001).Hypothesis 2; the AMOVA analysis findings indicated that the differentiation among groups, among populations within groups, and within populations were 7.31%, 0.39%, and 92.29%, respectively. The findings revealed that most of the obtained variance is based on differences within populations. Variance components among groups were significant  and moderate nucleotide diversity (ranging from 0.01196 ± 0.0026 to 0.02101 ± 0.0041) indicating plentiful genetic diversity in all the Turkish donkey populations. A high level of genetic variability was observed in the AYD populations followed by KAS, MUG, ISP, KON, and KUT. The overall values for haplotype and nucleotide diversity among all donkey populations was  similar to that estimated in other donkeys, such as the Italian donkey population donkeys  [D: 0.1688 ± 0.0012), Cypriot donkey populations  [D: 0.737; πD: 0.0169) [D: 0.7417 + 0.0444) [Genetic diversity is commonly measured by haplotype (HD) and nucleotide diversity (πD), both of which reflect the magnitude of genetic variation at different scales ,47. The : 0.018) , Anatoli± 0.001) , AndaluzD: 0.903 ± 0.032; πD: 0.020 ± 0.003), reported by Kefena et al. [D: 0.982 ± 0.002; πD: 0.017 ± 0.009) reported by Pérez-Pardal et al. [D: 0.9055 ± 0.017–0.9778 ± 0.0540, πD: 0.02265 ± 0.00040–0.0285 ± 0.0160) reported by Chen et al. [D: 0.737 ± 0.028–0.910 ± 0.032; πD: 0.0058 ± 0.0008–0.0179 ± 0.0035) reported by Xia et al. [D: 0.849 ± 0.087; πD: 0.01549 ± 0.008) reported by Stanisic et al. [D: 0.421, πD: 0.0006) reported by Aranguren-Mendez et al. [When haplotype and nucleotide diversity values for D-loop were compared with different donkey breeds, Turkish donkey populations were found to be lesser than, Ethiopian donkeys  or moderate, but no significant differentiation . However, great differentiation was found between AER, MRM, MDR, BSR, and CAR region populations  (p < 0.01). Matrix of pairwise FST, Reynolds linearized pair-wise matrilineal genetic distances and DA genetic distances confirmed similar results between 16 Turkish native donkey populations. A Neighbor joining tree (NJT) from Reynolds’ linearized genetic distances clearly demonstrated three distinct clades in Turkish donkey populations from seven different regions; Clade I ; Clade II , Clade III (BSR). NJT reconstructed using D-loop sequence FST and DA genetic distance value estimates also generated the same topology (figure not shown) to the dendrogram tree constructed using Reynolds’ linearized pair-wise genetic distance, shown in ST values of Turkish donkey populations detected in this study were similar to the value of Mexican Creole donkeys [Pairwise F donkeys , and Spa donkeys , but low donkeys , Italian donkeys , and Chi FST > 0. . D-loop gene lineages in Turkish donkeys appear mixed with those of other donkeys.Equus cabullus, Equus kiang) as outgroups [The Turkish native donkey mtDNA sequences n = 315) were compared with 427 publicly available mtDNA D-loop sequences belonging to different countries’ donkey breeds, wild asses  were com,33,34,35utgroups ,30. The The haplotype and nucleotide diversity within Turkish native donkeys were (0.763 ± 0.023) and (0.01680 ± 0.00046), respectively. The haplotype and nucleotide diversity values of other countries’ donkeys ranged from (0.890 ± 0.013), (0.01759 ± 0.00053) in the Chinese donkeys to (0.983 ± 0.007), and (0.02843 ± 0.00195) in the African origin domestic donkeys, respectively. The haplotype and nucleotide diversity values of the Turkish donkey populations was found to be lower than that of the other donkey populations  shared (I) and (II) lineages and had their respective lineages. Lineage (I) (Equus cabullus) have three haplotypes, lineage (II)  has eight haplotypes. Four haplotypes were identified in E. asinus somalicus and were distributed into the same lineage (III). There were 130 haplotypes in domestic donkey populations representing two lineages . Out of 145 haplotypes, 54 (clade I) are referred to Somali lineages , whereas 75 haplotypes (clade II) belong to Nubian lineage (Equus africanus africanus). The clade I haplotypes were mainly derived from a single major haplotype (Hap 2), with a simple star-like shape, whereas the genetic architecture of the clade II haplotypes was more complicated, with more universally occurring haplotypes . This was consistent with the much higher genetic distance and nucleotide diversity within the clade II lineage than within the clade I lineage, implying that the clade II lineage involved many more individuals at the beginning of domestication compared with the clade I lineage. In the study twenty-four haplotypes were newly discovered in Turkish native donkeys . When we compared all sequences, twenty-five haplotypes were shared among the different countries’ donkey breeds and Turkish native donkey populations  . The MJ network analysis showed that both Somali and Nubian lineages had made a genetic contribution to Turkish indigenous donkey evolution, as 19 haplotypes of lineage Somali (n = 191 samples) and 30 haplotypes of lineage Nubian (n = 124) were identified in Turkish donkey populations. These findings showed that 16 different Turkish native donkey populations and other domestic donkey populations possessed abundant mtDNA diversity, and indicated two different maternal origins. Clade 1 includes 49.59% of sequences, whereas clade 2 includes 48.52% of sequences. Out of 145 haplotypes, 53 were referred to the Somali lineage (clade I), whereas 77 haplotypes belonged to Nubian lineage (clade II). As shown in the network, two lineages were clearly revealed, and showed a star like phylogenetic pattern, in which two large haplotypes, H2 and H5, are located in the center of the clade 1 and clade 2 lineages, respectively. The clade 1 lineage appeared to predominate slightly  in the domestic donkeys. The results showed that the Nubian (E. africanus africanus) and Somali  wild asses were the probable progenitor for ancient Turkish donkeys and domestic donkeys. These results support previous studies on the origins of the domestic donkey [The MJN analysis identified 145 haplotypes from 742 mtDNA D-loops (427 Genbank sequences and our 315 Turkish donkeys) based on 344 bp of the control region sequence, and all these haplotypes grouped into five lineages. p < 0.001, p < 0.05). In hypothesis 2; among groups and among individuals within populations there was also statistically significant variation . Our AMOVA analysis and phylogenetic analysis revealed low genetic variation between the groups that were defined geographically among Turkish donkey populations. It was observed that there is a small difference in the donkey population of the BSR compared to the other region’s populations. In order to explain the reason for this difference, it is considered appropriate to conduct new studies with more samples in BSR.In our study, to assess a hierarchical structure among Turkish native donkey populations and other countries’ donkeys  and wild asses, AMOVA analysis was performed under two different hypotheses, grouping populations in different clusters. In both hypotheses, the AMOVA analysis results indicated that the majority of the observed variance was due to differences among individuals within populations. The greater part of the variation was observed within individuals (90.34% hypothesis 1 and 92.29% hypothesis 2) whereas the differences among groups represented only the 7.95% (hypothesis 1) and 7.31% (hypothesis 2) of the variation, respectively. Our results are similar to other studies on the mtDNA D-loop region ,26,28,42The present study demonstrated the abundant mtDNA diversity existing in Turkish indigenous donkey populations. The detection of 54 haplotypes in 315 donkeys suggests that abundant genetic variety exists in Turkish donkey populations. We confirmed two different maternal lineages  of domestic donkeys reported by other researchers. No obvious geographical distribution was found among Turkish donkey populations. The study found high nucleotide and haplotype diversity values, no haplotypes clearly distinct from other populations, and no clear clustering on the median joining tree in Turkish donkeys. In summary, our results imply the moderately high genetic diversity of the 16 Turkish donkey populations in mtDNA D-loop region, and give an insight into the origin of the analyzed populations. Our results obviously exclude the Asian wild donkey as ancestors of Turkish indigenous donkey populations, and the two African wild donkeys  are the likely ancestors of Turkish indigenous donkeys. The results provided here could be regarded as a genetic structure of the maternal ancestors of Turkish donkey populations.


Cossypha caffra). We find evidence supporting a central role for aridland biomes in structuring populations across Southern Africa. Our findings support a scenario wherein populations were isolated in different biome refugia, due to separation by the exceptionally arid Nama Karoo biome. This biome barrier may have arisen through a combination of habitat instability and environmental unsuitability, and was highly unstable throughout the Plio‐Pleistocene. However, we also recovered a pattern of extensive contemporary gene flow and admixture across the Nama Karoo, potentially driven by the establishment of homesteads over the past 200 years. Thus, the barrier has become permeable, and populations are currently merging. This represents an instance where initial formation of a barrier to gene flow enabled population differentiation, with subsequent gene flow and the merging of populations after the barrier became permeable.Environments are heterogeneous in space and time, and the permeability of landscape and climatic barriers to gene flow may change over time. When barriers are present, they may start populations down the path toward speciation, but if they become permeable before the process of speciation is complete, populations may once more merge. In Southern Africa, aridland biomes play a central role in structuring the organization of biodiversity. These biomes were subject to substantial restructuring during Plio‐Pleistocene climatic fluctuations, and the imprint of this changing environment should leave genetic signatures on the species living there. Here, we investigate the role of adjacent aridland biome boundaries in structuring the genetic diversity within a widespread generalist bird, the Cape Robin‐chat  has made the barrier permeable enabling recurrent gene flow. There are two morphologically distinct subspecies in South Africa: Cossypha caffra caffra which is found across most of South Africa, and C. caffra namaquensis which has a more limited distribution in northwest South Africa and neighboring Namibia. Our goals are to characterize genetic diversity in South African populations of C. caffra, assess genetic discontinuities and connectivity across the landscape, and relate genetic diversity to contemporary and historical environmental features to better understand the processes that underlie patterns of genetic diversity. Although both historical and contemporary landscape structure can have a pronounced influence on population structure, disentangling the effects of historical versus contemporary features on population connectivity, dispersal and migration can be complicated, often requiring a multipronged approach to resolve the temporal effects of a changing landscape  to facilitate the generation of a suite of spatially explicit a priori hypotheses pertinent to the evolutionary history of We next built ENMs for each biome and hindcast them as above, except we used the full suite of 19 bioclimatic variables. We used the modern distribution of biomes as input to our models . We genotyped 14 highly polymorphic microsatellite loci for each individual using primers and protocols from Wogan, Feldheim, Voelker, and Bowie . Alleles2.3k‐values from 2 to 10 with 20 replicates at each k with a run length of 150,000 steps after a 10,000 step burn‐in. These parameters were selected after initial tests of chain convergence; further details are provided in the supplementary documents. To visualize the results, we created a genetic surface from the q‐values using IDW interpolation in ArcGIS  with a one km cell size, a search radius of 12, and a power function of 2  which relies on sequential C. caffra with respect to the two population clustering methods. We compared the partitioning of genetic variation for the populations delimited with DAPC and Structure. We used the sum of squared size differences (RST), and each analysis was run with 16,000 permutations to assess statistical significance of the fixation indices in Arlequin 3.5  tests to evaluate the partitioning of genetic variation in We calculated the number of haplotypes and a median‐joining haplotype network for our mtDNA marker using PopArt , and then used Genepop , FIS, and Fisher's exact test of genic differentiation. We estimated the mean expected and observed heterozygosity with 1,000 jackknife replicates using the R package PopGenKit (Paquette 2013). We tested for heterozygote deficiency using Genepop. Using the mtDNA data, we calculated the number of pairwise differences (π), nucleotide diversity, and θS in Arlequin  data, we assessed genetic diversity and differentiation among unique geographic localities by calculating Nei's Gst Nei, , G”st H and JostRousset,  and hierRousset,  to calcu7 with a 1*106 burn‐in and assessed convergence with Tracer v. 1.5  estimated in Migrate‐n by an estimated average microsatellite mutation rate of 5*10–4  in Arlequin. We estimated Bayesian skyline plots (BSP) using Beast v. 1.7.5  and then evaluated them in relation to contemporary genetic patterns among C. caffra populations. We also implemented an estimated effective migration approach to identify corridors and barriers to gene flow  to evaluate cryptic genetic diversity in the mitochondrial DNA. We then used the residuals from a linear regression of individual pairwise genetic distances against geographic distances to remove the effects of geographic distance on genetic distance . Analyses were carried out as detailed above.We implemented additional AMOVA to evaluate the partitioning of genetic variation with respect to different spatial and taxonomic hypothesis: (A) current biome boundaries; (B) biome stability (two groups: those within stable habitat refugia and those not within refugia); (C) species refugia (two groups: those within predicted species refugia and those not); (D) Geneland detected barrier (2 groups on either side of the putative barrier); and (E) subspecies taxonomy . There were 68 variable sites and 33 parsimony informative sites across 54 haplotypes.Microsatellite loci have a high mutation rate and capture contemporary processes, while mtDNA has a mutation rate that is roughly four orders of magnitude slower than that of microsatellites Wang, , and thu3.3k‐value of 3 for uncorrelated models and of 4 for correlated models. Visual inspection of combined‐replicate bar plots indicated that individual assignment probabilities become stable at a k‐value of 3 . The clusters recovered from Structure analyses consist of one widespread regional cluster spanning 46 unique localities found in the Grassland and Nama Karoo biomes and along the southern coast in the Fynbos biome (“grassland” population hereafter). The other two clusters are comparatively localized with a cluster restricted to two localities along the western coast in the Succulent Karoo biome , and a cluster found in the northwest near the Namibian border and extending along the Orange River and Vaal River drainages (“northwest” population)  Table ,d. DAPC n) Table . DAPC asn) Table . At manyn) Table .3.4AMOVA analyses revealed that the three recovered DAPC clusters explained just 0.34% of the variance in the dataset, whereas the genetic clusters recovered from Structure analyses explained almost 10% of the variance Table . Since t3.5C. caffra . The three population clusters are highly differentiated . Because the M‐ratio reaches equilibrium much slower than does heterozygosity following a bottleneck, in combination, these statistics have frequently been used to infer the relative timing of bottlenecks  and no significant negative eigenvalues  Figure . PC1 ideC. caffra populations from the remaining populations  and no significant negative eigenvalues  Figure . The sPCOf the spatial and taxonomic partitions we implemented in AMOVA, biome refugia explained the greatest amount of variance and maximized the variance among groups relative to variance among populations within groups Table . Samples4C. caffra corresponds to boundaries between historically stable aridland biome refugia and areas where habitats have been unstable. Some ecotones between biomes are marked by changes in elevation, for example, the uplifted escarpment separating much the Succulent and Nama Karoo, and very arid regions such as the Knersvlatke, and such physical barriers reinforce biome boundaries. These analyses support a central role for biome history in structuring genetic diversity in C. caffra.The Cape Robin‐chat is a highly mobile widespread species that inhabits a number of natural and modified habitat types, and is considered a habitat generalist. In South Africa, our data support the presence of three distinct genetic clusters, in contrast to the two named subspecies delineated from morphological data. We find evidence for a widespread group with an extensive range across the Grassland biome, and for two more localized groups: one found primarily in the dry northwest, and a second distributed along the western coastal regions in the Fynbos and Succulent Karoo biomes. The distribution of genetic diversity within C. caffra is similar to that recovered for other species in South Africa. For example, two major genetic clusters of Smith's Red Rock Rabbit (Pronolagus rupestris) exist: one in the northwest and the other in the southeast  suggests a northwest group that is distinct from a widespread group distributed throughout much of South Africa , and these admixed individuals are not confined to the contact zones, but instead are scattered among many South African populations. Of the 82 localities we sampled, there were only 14 that did not contain admixed individuals, and these were primarily confined to the grassland population within the Grassland biome, and the northwest. localities at the Nama Karoo and Grassland biome boundary have highly mixed composition and admixed individuals. We find a similar situation where the coastal Fynbos and the Grassland biome meet in the Cape region of South Africa . This finding supports spatial analyses suggesting that aridland biome refugia are important in structuring avian diversity in South Africa, and this is the first study to confirm this using genetic approaches.Voelker et al.  found thThe authors declare no competing financial interests in relation to this work.GOUW designed the study, collected the data, analyzed the data, and wrote and edited the manuscript. GV designed the study, collected the samples, and edited the manuscript. GO collected the samples and edited the manuscript. RCKB designed the study, collected the samples, and wrote and edited the manuscript.Supplementary MaterialClick here for additional data file.Appendix S1Click here for additional data file.


Lespedeza maritima, has been proposed as either a hybrid (L. cyrtobotrya × L. maximowiczii) or a synonym of L. thunbergii. A distinct taxon, L. uekii, has been proposed for inland populations. We investigated genetic diversity and structure in L. maritima and related taxa to resolve this. Genotypes of L. maritima  were determined using 12 microsatellite loci, then compared with those of related species. Genetic diversity within L. maritima was estimated, and Bayesian clustering analysis was used to represent its genetic structure and that of related taxa. Its distribution during the last glacial maximum (LGM) was predicted using ecological niche modelling (ENM). Neighbour-joining (NJ) analysis and principal coordinate analysis (PCoA) were used to investigate relationships among species. Bayesian tree based on nuclear ribosomal internal transcribed spacers (nrITS) was also reconstructed to show relationships and divergence time among species. Morphological features were examined using flower characteristics. In result, expected heterozygosity (HE) and allelic richness (AR) within L. maritima were higher in southern than northern populations. Bayesian clustering analysis largely assigned populations to two clusters (K = 2) (south vs. north). The ENM showed that L. maritima occurred around the East China Sea and Korean Strait land bridge during the LGM. Compared with other Lespedeza species, L. maritima was assigned to an independent cluster (K = 2–5), supported by the NJ, PCoA, Bayesian tree and morphological examination results. Lespedeza maritima and L. uekii were clustered to one clade on Bayesian tree. Given results, current L. maritima populations derive from post-LGM colonization away from southern refugia. The type L. uekii, which grows inland, is thought synonym of L. maritima. In addition, L. maritima is considered a distinct species, compared with related taxa.Various plant species are endemic to the Korean Peninsula, but their evolutionary divergence and establishment are poorly understood. One of these,  Lespedeza maritima is inferred in this study. The taxonomic entity of this species is also described with comparison of related species. The genetic diversity and structure of L. maritima based on microsatellite loci suggest that current populations derive from post-LGM colonization away from southern refugia. Ecological niche modelling supports this. Compared with other Lespedeza species, molecular analyses  and morphological examination indicate that L. maritima is a distinct species and L. uekii should be regarded as a synonym. This study could help to improve our understanding about the evolution of Korean endemic species.The establishment process of  The warm temperate forests of East Asia host many endemic species, making the region a global hotspot of species diversity . During Approximately 360 plant species have been reported as endemic to the Korean Peninsula , which wLespedeza maritima (Fabaceae) is a Korean endemic shrub that is primarily distributed in warm temperate forests along the southern and eastern coasts of the Korean Peninsula , at the western end of the South Sea of Korea, whereas L. uekii was described based on specimens sampled at Dongnae beach (Busan-si), which is situated at the opposite side of the South Sea. The former is smaller (ca. 0.4 m) than the latter (up to 2 m), and its upper parts are more branched  as a hybrid of L. cyrtobotrya and L. maximowiczii; the author furthermore suggested that L. maritima is closer to L. cyrtobotrya while L. uekii is closer to L. maximowiczii, based on morphology. In contrast, L. formosa subsp. velutina  from 11 populations of Korea, to represent entire distribution . Each populations included 12–32 individuals. In addition, we collected samples of L. cyrtobotrya (n = 42), L. maximowiczii (n = 41) and L. thunbergii (n = 22) to investigate the relationship with L. maritima. The sampling information of related species is provided in We sampled 244 individuals of ribution ; Table 1Taq MasterMix with Dye (MGmed). The mixtures also contained the appropriate 0.08 μM forward M13 (-21)-tagged primer, a 0.3 μM reverse primer and a 0.3 μM M13 (-21)-labelled fluorescent marker . The PCR reaction mixture for nrITS  included MG 2× Taq MasterMix with Dye (MGmed), 10 ng of DNA, 0.3 μM primers and distilled water. Conditions included initial denaturation at 94 °C for 3 min; then 30 cycles at 94 °C for 30 s, 53 °C for 45 s and 72 °C for 45 s (1 min for nrITS); with no final extension (7 min for nrITS). Afterward, the PCR products were visualized on 2 % agarose gels. Microsatellite products were resolved to genotype on an ABI 3730XL sequencer with GeneScan™ 500LIZ™ size standards (Applied Biosystems). The sizes of the alleles were determined with GENEMAPPER 3.7, and the genotype data are provided in MT919326–MT919335).Total genomic DNA was extracted from the silica-dried leaves of individuals with a MG Plant Genomic DNA Extraction SV miniprep . The process was as per the manufacturer’s manual. We used PCR amplification for 12 microsatellite loci and internal transcribed spacer of nuclear ribosomal DNA (nrITS), and the primers were employed as follows: LMS11, LMS18, LMS28, LMS33, LMS34, LMS39, LMS47, LMS53, LMS55, LMS58, LMS61 and LMS62 (NA), observed heterozygosity (HO), expected heterozygosity (HE) and Wright’s inbreeding coefficient (FIS), using GenAlEx version 6.503 (AR). Deviation from the Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) for each loci within populations was verified using the Hardy–Weinberg exact probability test and LD options provided in GenePop version 4.7 , employing 300 000 MCMC sampling runs after a burn-in period of 30 000 iterations and an admixture model under an assumption of correlated allele frequencies. Clustering Markov Packager Across K (CLUMPAK) software ) and geographic distances (km) between all population pairs (In order to test the presence of isolation by distance (IBD), we estimated the genetic (on pairs . CorrelaThe reduction in past population size was determined using BOTTLENECK version 1.2.02 (DA) (A neighbour-joining (NJ) tree was constructed with 1000 resampling and bootstrap values per locus, using POPTREE 2  at the Campylotropis/Kummerowia-plus-Lespedeza node; 9.2 mya (95 % HPD: 6.3–14.4) at the Kummerowia/Lespedeza node; and 8.2 mya (95 % HPD: 5.4–11.1) at the American/Asian Lespedeza node. The BEAST analyses were performed with a burn-in period of 10 % generations, using data collected for a 10 000 000 MCMC cycle sampling. A total of 1 000 000 burn-in samples and 1000 iterations were included per MCMC cycle. The phylogenic evolutionary trees were confirmed used Figtree version 1.4.0 (http://tree.bio.ed.ac.uk/software/figtree/). The statistical values for the results parameters were observed using Tracer version 1.7 . Two models were used for past climate data relating to the LGM: the Community Climate System Model . The following Maxent programme runs were performed with these settings: ‘create response curves’, ‘conduct jackknife tests’, ‘use 10 replicates of cross-validation’, ‘generate logistic output’, ‘select random seeds’ and ‘use 10 000 background points and 1000 iterations’.Predictions were made of the current and LGM geographic distribution of suitable habitat for L. maritima and related species  that are deposited in the Herbarium of Inha University (IUI). Additional morphological examination was conducted by observing specimen from the Korea National Arboretum (KH). All observations of morphological characters were made with a stereomicroscope , and criteria for floral measurements were mainly those stipulated by We examined the morphological characters of L. maritima across populations was evaluated based on 12 microsatellite loci  to 0.647 (CS) and, based on 12 individuals, AR was the highest in CS (AR = 5.646), but the lowest in DS (AR = 3.607). When comparing regions (south vs. north), south populations  showed higher genetic diversity than north populations . The inbreeding coefficient (FIS) varied from 0.019 (NS) to 0.321 (CS). Although FIS was higher in CS than in other populations , FIS values in the latter implied a low probability of inbreeding.The genetic diversity of ite loci . All theP > 0.05), but this was not the case for the other eight loci (P < 0.05). No LD was found in any of the locus pairs across all populations (P > 0.05). Using BOTTLENECK, all populations were shown to be at mutation-drift equilibrium on the basis of both TPM and SMM in a Wilcoxon’s test (P > 0.05) [see  ], i.e. there was no evidence for a recent bottleneck. The allele frequency distribution in all populations was represented by a normal L-shape, consistent with the results of the Wilcoxon’s test [see  ].According to a Hardy–Weinberg exact test, M, BS and JY deviated from the HWE, but the other eight populations were in agreement with it . At the L. maritima was determined using Bayesian clustering analysis (K = 1–16); the optimum cluster number was two (K = 2) [see  ]. Populations were mostly divided into two clusters by geographic region, i.e. south  versus north   [see  ].The genetic structure of analysis . The resuster 2) . A furthuster 2) . A stronL. maritima distribution modelling had a high value , based on nine environmental variables. Among these variables, the predicted current distribution [see  ] of L. maritima was sited along the southern coastline, including Jeju island, stretching towards a section of the eastern coastline, based on a high distribution suitability (>0.8). Although L. maritima is no longer found on Jeju island, this prediction was otherwise largely in agreement with the actual geographic distribution of L. maritima.The operating characteristic curve for the L. maritima during the LGM yielded significantly different results. Firstly, CCSM4 indicated that the area around the Korea/Tsushima Strait (KS) land bridge was a potential channel of L. maritima distribution (distribution suitability > 0.8) , and ECS (distribution suitability > 0.8) (Two models used to predict the distribution of y > 0.8) . In conty > 0.8) . This moLespedeza species and other Desmodieae were aligned; the length was 656 bp. Effective sample sizes for each taxon group exceeded 1000, indicating that the chains were well-mixed. The mutation rate was estimated to be 0.689 × 10–2. Divergence times along the phylogenetic tree  were estimated as follows: 15.535 mya (95 % HPD: 12.068–18.912), Hylodesmum from other genera; 11.116 mya (95 % HPD: 8.409–13.822), Campylotropis from Kummerowia/Lespedeza; 7.903 mya (95 % HPD: 5.814–10.179), Kummerowia from Lespedeza; 6.224 mya (95 % HPD: 4.365–8.320), Lespedeza species; and 1.299 mya (95 % HPD: 0.661–2.197), Macrolespedeza from Lespedeza. Although most Macrolespedeza species were clustered polyphyletic, L. maritima, formed a clade excluding one individual (voucher no. 159039), close to L. maximowiczii. So, when this individual was discounted, the age the L. maritima node was the same as for Lespedeza species, i.e. 0.274 mya (95 % HPD: 0.207–1.579).The nrITS sequences for tic tree  among geL. maritima and three related species  were compared using Bayesian clustering analysis (K = 2) [see  ]. Lespedeza maritima was assigned to cluster 2, while L. cyrtobotrya and L. maximowiczii were primarily assigned to cluster 1. Most L. thunbergii samples were showed an intermediate lineage, i.e. the posterior probability of a major cluster was below 0.8. Although ∆K values were low (since K = 3), more detailed divergences were observed among taxa. Divergence was found within L. maritima at K = 4 and 5 . Lespedeza cyrtobotrya (cluster 4) was separated from L. maximowiczii at K = 3. Lespedeza thunbergii could be distinguished from other taxa since K = 5 (cluster 5). The results of the PCoA determined genetic differences among the taxa, showing that L. maritima was distantly separated from other taxa .The genetic structures of analysis ; the opther taxa . IndividL. maritima, a Korean endemic warm temperate shrub, across its distribution range  were on average higher than those of northern populations   . The ‘soscenario .AR = 4.643–5.646, HE = 0.608–0.647), which were mainly allocated to cluster 1, harboured a higher genetic diversity than the other populations  indicated that the only refugium was the KS land bridge, but the other scenario (MPI-ESM-P) predicted that the ECS land bridge should also be included . Consideulations  and had ulations . Thus, wL. maritima in Kyushu (south Japan) and on Jeju island is confusing, due to its current absence . It is therefore possible that L. maritima was unable to thrive and survive in the harsher conditions of higher altitude environments. We excluded the possibility of the existence of refugia in China and Taiwan on the basis of the dispersal range of Lespedeza species . This distribution shift was a gradual process rather than rapid expansion, a conclusion that is supported by significant evidence of IBD  were each, respectively, assigned to two clusters using STRUCTURE, but the intermediate populations  harboured an admixed genetic structure  and GJ  had the lowest genetic diversity (P > 0.05) [see  ], suggesting they had been established for a long time; this was also inferred from the IBD results.Current populations of tructure . This griversity . A BOTTLL. maritima occurs from along the southern to eastern coastline. Taking note of this, we hypothesized why L. maritima occurs in this distribution pattern. During the LGM, L. maritima mainly occurred around the ECS and KS land bridge (see above). As the post-LGM climate warmed, sea level began to rise  show different characteristics to southern populations ; northern individuals are usually taller (up to 2 m) and with acute and narrow leaflets, compared with the height (ca. 40 cm) and obtuse leaflet shape of southern individuals . So, we examined these taxonomic views.The taxonomic position of f debate . It was tobotrya , but AkiL. maritima was not the result of hybridization between putative parents (L. cyrtobotrya and L. maximowiczii) . The shiny and thick leaflets and long flowers (ca. 12 mm) of L. maritima is also distinguishable from the putative parents  than other species in section Macrolespedeza (L. maritima (shrub) is woodier than L. thunbergii (subshrub), and the leaflet characters of the two are clearly different  ; if this parents . Therefomaritima . The taxom K = 3 . Further results ; this isespedeza , and bloespedeza . Howeverifferent . TherefoL. thunbergii mainly grows at the foot of mountains, forest roads and roadsides, where it is prone to anthropogenic disturbance. Although L. maritima is distributed only in southern regions of Korea, this species often occupies habitats that L. thunbergii may prefer. Given their similar niche, we hypothesized that L. maritima and L. thunbergii have a shared most recent common ancestor (MRCA) compared with other related species. This assumption is supported by the PCoA (L. maritima are more closely related to L. thunbergii than to L. maximowiczii and L. cyrtobotyra. The time to the MRCA of L. maritima and L. thunbergii is dated to the Early Pleistocene across East Asia (1.299 mya). Subsequently, the sea level rise during the Early to Middle Pleistocene (1.0–0.2 mya) caused the land bridge to become submerged, therefore limiting interregional migrations  and stepwise mutation model (SMM).Lespedeza maritima. (A) Habit. (B) Upper surface of leaflet. (C) Inflorescence. (D) Flower.K (y-axis) according to number of clusters (K) (x-axis) as calculated based on Bayesian clustering analysis. (A) Result of analysis for 11 populations of Lespedeza maritima (L. maritima and related taxa (maritima . (B) Rested taxa .FST/[1 − FST]) (x-axis) and geographic distance (km) (y-axis) of Lespedeza maritima populations. A positive correlation was significant between two distances .Lespedeza maritima during the current, inferred using ecological niche modelling.Lespedeza maritima and its related based on eight microsatellite loci. One thousand bootstrap matrices of Nei’s genetic distance (DA) . The GenBank accession numbers are shown in The genotype data of


Many native populations in South America have been severely impacted by two relatively recent historical events, the Inca and the Spanish conquest. However decisive these disruptive events may have been, the populations and their gene pools have been shaped markedly also by the history prior to the conquests. This study focuses mainly on the Chachapoya peoples that inhabit the montane forests on the eastern slopes of the northern Peruvian Andes, but also includes three distinct neighboring populations . By assessing mitochondrial, Y-chromosomal and autosomal diversity in the region, we explore questions that have emerged from archaeological and historical studies of the regional culture (s). These studies have shown, among others, that Chachapoyas was a crossroads for Coast-Andes-Amazon interactions since very early times. In this study, we examine the following questions: 1) was there pre-Hispanic genetic population substructure in the Chachapoyas sample? 2) did the Spanish conquest cause a more severe population decline on Chachapoyan males than on females? 3) can we detect different patterns of European gene flow in the Chachapoyas region? and, 4) did the demographic history in the Chachapoyas resemble the one from the Andean area? Despite cultural differences within the Chachapoyas region as shown by archaeological and ethnohistorical research, genetic markers show no significant evidence for past or current population substructure, although an Amazonian gene flow dynamic in the northern part of this territory is suggested. The data also indicates a bottleneck c. 25 generations ago that was more severe among males than females, as well as divergent population histories for populations in the Andean and Amazonian regions. In line with previous studies, we observe high genetic diversity in the Chachapoyas, despite the documented dramatic population declines. The diverse topography and great biodiversity of the northeastern Peruvian montane forests are potential contributing agents in shaping and maintaining the high genetic diversity in the Chachapoyas region. Since the Chachapoyas region has been an important area even before the Late intermediate Period (1000–1475 CE), it has been a target of various archaeological, ethnohistoric, and genetic studies. Previous genetic assessments have demonstrated that the Chachapoyas region today harbors, unlike many other regions inhabited by Native South Americans, high levels of genetic diversity [th and 16th century and include a long-standing question regarding population substructure within Chachapoyas, as well as the sex-specific demographic impacts of the conquests. At a somewhat broader scale, we consider the issue of Chachapoyan affinities to human groups in the highland Andes and in lowland Amazonia.Chachapoya culture developed in a singular and biodiverse zone between the Andean mountain range and the Amazonian rainforests from northeastern Peru, and fluoresced around 900 CE (Common Era). Populations thrived in the area until conquered by the Incas around 1475 CE) and Spaniards  was there pre-Hispanic genetic population substructure in the Chachapoyas sample? 2) did the Spanish conquest cause a more severe population decline on Chachapoyan males than on females? 3) can we detect different patterns of European gene flow in the Chachapoyas region? and 4) did the demographic history in the Chachapoyas resemble the one from the Andean area?N = 172), Y-chromosomal (N = 190) and autosomal markers (N = 246).This study followed ethical guidelines and standards (Helsinki declaration) and was approved by Helsinki University Hospital Ethical Committee (Decision #329/13/03/00/13) as previously described in [For the analyses of substructure within Chachapoyas, the samples were divided into seven subgroups: Pomacochas, Chillao, Corobamba, Rodríguez de Mendoza, Chachapoya, La Jalca and Leymebamba. The assignment of individuals into a given subgroup was based on the place of birth up to the grandparents’ generation and followed the strategy outlined in . BrieflyDNA extractions and Y chromosome short tandem repeat (STR) genotyping have been described in . Full miFor the Y single nucleotide polymorphisms (Y-SNPs) analyses eight multiplex assays were used: MI and MII ; SpecE, ™ DNA Signature Prep Kit  as described elsewhere [™ Universal Analysis Software (UAS) pipeline were processed with STRait Razor v2s [Autosomal genotyping was carried out using the ForenSeqlsewhere . Output azor v2s  to obtaiAfter data curation and filtering, 164 mitochondrial genomes (GenBank: MN857248–MN857411), 177 Y-chromosome (new Y-SNPs data) and 241 autosomal  profiles from four Peruvian populations  were used in downstream analyses.Haplogroup composition, basic statistics and genetic distances. For the analyses, the newly produced mitochondrial genomes were merged with data from [ata from . AdditioN haplotypes, with N corresponding to the sample size in each subpopulation. In the permutations, the success of drawing a certain haplotype was determined by its frequency in the modern Peruvian reference population (N = 442).Haplogroup assignments were obtained from HaploGrep v2.2 based on Phylotree 17 , 24. Haph), nucleotide diversity (π), mean number of pairwise differences (MNPD), and pairwise genetic distances (ΦST) were estimated using Arlequin ver. 3.11 [cmdscale.Summary statistics, e.g. haplotype diversity (er. 3.11 . GeneticBayesian Skyline Plots (BSPs). Maternal demographic histories were explored with BSPs constructed using mitochondrial coding region data (nucleotides 577–16023) at three levels: 1) local (Chachapoyan subgroups), 2) regional , 3) subcontinental (Andean and Amazonian macro-regions). The best-fit substitution model for each population was determined with bModelTest [μ = 1.546 x 10−8 ± 3.675 x 10−9 (SD) substitutions/pair/year. For each population, two molecular clocks were tested: strict and log normal relaxed molecular clock and the best-fit molecular clock was determined with Akaike information criterion method (AICM) implemented in Tracer [odelTest ; the subodelTest  assumingodelTest –33. The n Tracer . The resn Tracer  for eachHaplogroup composition, basic statistics and genetic distances. Similar to the mitochondrial DNA data, our Chachapoya Y-chromosomal dataset was merged with the data from [http://www.hprg.com/hapest5/). Summary statistics were calculated in a similar fashion as the mitochondrial data using Arlequin ver.3.11.ata from . In addiBayesian skyline plots. Demographic histories were assessed by constructing BSPs for the Chachapoyas pooled, as well as for one Andean and one Amazonian set. The male demographic history was explored by reconstructing Bayesian skyline plots with BEASTvntr package [−4 ± 5.7 x 10−4 mutations/locus/generation calculated based on loci specific estimates presented in [ package  implemen package  with comented in . A node ented in ) were inented in . We assuented in  (mean = p < 0.05) from Hardy-Weinberg Equilibrium (HWE) and Linkage disequilibrium (LD) using Arlequin ver. 3.11.In case of the autosomal data, the Chachapoyan samples were allocated to subgroups in a slightly different manner than with the lineage markers. The Kuelap in Chachapoya, Uchucmarca in Leymebamba and the original Jivaroan sample collection groups Awajún and Wampís were analyzed as separate units. Additionally, 31 reference populations were used . For fivGenetic distances and population structure. Pairwise genetic distances (FST) between populations were calculated in Arlequin ver. 3.11. Genetic substructure and admixture proportions were calculated at three different hierarchical levels: 1) locally within Chachapoyas, 2) intra-continentally (Americas) and 3) inter-continentally, using STRUCTURE v 2.3.4 [ v 2.3.4 –43. For  v 2.3.4  were incλ) was estimated for our whole dataset and the resulting value (λ = 0.5) was fixed in all runs. The parameter alpha (α) was estimated independently for each population with an initial value of one. The admixture model chosen was correlated alleles and 8–12 iterations were calculated in each run. Convergence in the hyperparameters such as alpha and F was observed after c. 60,000 steps in both burn-in and run length. Number of ancestry components, K, was chosen based on two criteria, the estimated log probability of the data (lnP (X|K) [K) [K from 1 to 7 were carried out with CLUMPAK main pipeline [For the STRUCTURE analyses, the parameter lambda (nP (X|K)  and deltX|K) [K) . Clusterpipeline . An altepipeline  and PASTpipeline  were usepipeline .Among the newly produced data three mitochondrial lineages previously unreported in Phylotree17 were observed. Roughly 97% of the samples belonged to Native American haplogroups A2, B2, C1 and D . The nonAmong the seven Chachapoyan subgroups, differences in haplogroup distribution were apparent, e.g. Chillao lacks haplogroup D, whereas the Rodriguez de Mendoza lacks haplogroup A2 . In ordeh, π) were observed in all study populations , high levels of genetic diversity (e.g. ulations  even wheThe Chachapoyas pooled had genetic affinities (non-significant) with the Cajamarca but remained distant to the Jívaro and the Huancas. Genetic distances in the context of the Americas are summarized in an MDS plot where the clumped Chachapoyas set is positioned near the centroid. The Pomacochas falls in a group that includes the Amazonian Jívaro and populations from Ecuador. The Rodríguez de Mendoza set situates instead close to ancient Peruvian populations .Ne starting at around 125 generations ago (~3 kya) and persisting forward in time. The observed pattern resembles the one observed for the Andean region, but differs markedly from the Amazonian one which do not show prominent growth at any stage in the past  when using Y-STR markers  from most of the other subgroups  only with the Huancas and remained distant to both the Jívaro and the Cajamarca. The Jivaroan set did not have any affinities with study or reference populations. In the MDS plot, the Chachapoyas pooled set falls in between Andean and Amazonian clusters very close to the Huancas. The subgroups Pomacochas and La Jalca are relatively close to each other but slightly distant to the pooled Chachapoyas. The Jívaro situates in the periphery of the plot, similar to other Amazonian groups, while the Cajamarca falls within an Andean cluster , all study populations exhibit high levels of diversity . Population decline for this set starts at around 200 generations and persists until it reaches its lowest point about 25 generations ago (c. 625 years ago assuming the mutation rate above) . After this event, there is a continuous increase of the population size of more than 20% since reaching the lowest point, which is absent in the BSPs for mitochondrial data. Among the reference sets, the Amazonian one shows a very modest population increase starting at 800 generations ago (20 kya), which reaches a maximum height at 585 generations ago (~15 kya). After this, the population size declines slowly until reaching its lowest point at 110 generations ago (~2.8 kya). Following this recent population contraction, a rebound in Ne similar to the one observed in the Chachapoyas is detected for the Amazonian set  . This denian set . The BSPp < 0.0001), two pairs of loci showed departures from expectations, one in the Chachapoyas (rs13182883–rs338882), and one pair in the Cajamarca (rs6955448–rs917118). Genetic distances showed affinities  among all study populations and the Hispanic set (HIS). The Caucasian dataset (CAU) in turn exhibited short genetic distances  only to the Chachapoyas, the Awajún and the Cajamarca.All loci were in Hardy-Weinberg equilibrium in all populations with the exception of locus D1S1656 in the Chachapoyas. Linkage disequilibrium (LD), was assessed for 27 aSTRs and 94 aSNPs in each study population. After correction for multiple testing (P (X|K) and ΔK showed four (K = 4) well defined clusters represented by Africans (AFA), Europeans (CAU), Asians (ASN) and Native Americans (HIS and study populations) . This finding was supported by both ln P (X|K) and ΔK cross validation errors for K = 3  . When onlations) . Additiodatasets . Furtherdatasets , showed or K = 3 . Zoomingor K = 3 . The Natrd unknown component” fall close to each other (black dots) in a cline towards the Native American cluster.When performing a PCoA including only the reference set having European ancestry (CAU) and the study populations , it is prd unknown component”, the genotypes of individuals harboring more than 60% of this component were placed in a new synthetic “population” and compared to other populations worldwide. These comparisons were carried out both with a larger set of markers 116 loci  with the Hispanic reference population from the US (HIS), urban populations from Mexico (Tijuana and Sonora), Chile  and a Mestizo sample from Nicaragua. On the other hand, the “3rd unknown component” remained distant  to the reference European populations, CAU from the US and Huelva from Spain. In the MDS plot with the full set of markers (116 loci), the “3rd unknown component” remains distant to all study and reference populations. With a reduced set of markers (10 aSTR loci) and more reference populations from Peru, the “3rd unknown component” falls in the periphery of the plot close to the Amazonian Cashibo (To resolve the origins of the “3116 loci  and with116 loci . With th Cashibo .FCT (among groups) and FSC (among populations within groups) values were non-significant and rather very small , AMOVA for autosomal data did not uncover population substructure within Chachapoyas, both FCT and minimizing FSC values mirrored the patterns suggested by ΦST values (haplotypic AMOVA). The mtDNA distances indicate a grouping placing the Jívaro and the Pomacochas together and the remaining Chachapoya subsets in a different group, with the exception of Rodríguez de Mendoza which remains separate like the Huancas and the Cajamarca. For this scheme, 7.21% of the total variation resides between groups (p < 0.001) and 1.68% among populations within groups (p < 0.05)  and FSC  values  values .The Chachapoya peoples flourished in the northeastern Peruvian montane forests until their encounter with the Inca and Spanish led gradually to the dissolution of the culture. Here we have assessed past population signals of the Chachapoyas persisting in the modern Peruvian genetic diversity. Despite cultural diversity among the Chachapoyas, we could not find clear signs of genetic substructure in this region. However, the high native genetic diversity observed sheds light on the complex, gender and region-specific population history of the Chachapoyas. Obviously, these inferences are to some extent limited by the contemporary data used, as the modern diversity has been shaped by the entire history of the target populations. Ancient DNA data could provide additional information, but only if sample sets representative in space and time could be obtained. Furthermore, one could also argue that the autosomal markers used in the ForenSeq kit, geared for forensic purposes, are not ideal for detecting substructure. This could stem from the relatively limited number of loci as well as from ascertainment bias, selective inclusion of loci in the forensics-aimed kits that maximize the diversity and power for identification of individuals . Larger, genome-wide data sets would obviously offer better resolution for substructure detection, but the impact of the latter, ascertainment bias, is difficult to quantify for our data. Previous studies  have shoIt is becoming increasingly clear through recent ethnohistorical and archaeological research that the Chachapoyas region has hosted considerable social, political, and cultural diversity before the Late Horizon or Inca period (1475–1532 CE) , 50. WitDespite these cultural clues from the past, signs of genetic subdivision in the region are weak. Although genetic distances and allele (haplotype) frequencies point to genetic differentiation of some Chachapoya subgroups, Y-chromosomal, mitochondrial and autosomal markers all convey conflicting pictures, thus evincing for rather insubstantial substructure .ΦST > 0.070). On the other hand, admixture analyses based on autosomal markers showed again no detectable population substructure within Chachapoyas. Here, regardless of the hierarchical setting , the Chachapoya samples clustered together  was part of the Chachapoyas realm but potential historical connections between these two areas cannot be completely ruled out. It has been suggested that the historical Jívaro and the ancient Chachapoya peoples shared some cultural features , 58, howrd component group” showed no obvious affinity to reference datasets of African, European or Asian origin, nor to the populations included from the Americas. In analyses with more reference populations but less data (10 aSTRs), the group remained distant to all the other populations except the Amazonian Cashibo from Peru. The exact origin of this variation component remains thus elusive; it could derive from a yet unsampled ancient or modern population, for example representing assemblages that have developed locally in semi-isolated populations with no distinctive cultural features. On this note, a recent study [rd unknown component among the Amazonian Jívaro, we could suggest ties to the second component, although this cannot be tested with our current resolution.Among the Chachapoyas and the Jívaro, admixture analyses revealed- in addition to Native American and Caucasian components—a third autosomal component that could not be readily associated to any modern source population. Within Chachapoyas, inter-subgroup differences in the commonness of this component was observed especially in Rodríguez de Mendoza, but nearly absent in e.g. Chillao, La Jalca and Uchucmarca. On the regional level, the component was only present among the Chachapoyas and the Jivaroan Awajún and Wampís. In order to understand the origin of this variation, individuals harboring ≥ 60% of this third component were regrouped and genetic distances between this group and reference populations from the Americas were estimated. These comparisons, however, were unable to shed light on the origin: the synthetic “3nt study  of SouthAs in several previous studies on South American populations , 64, we th century [During the conquest, the Spanish formed close alliances with the Chachapoyas against the Inca, and established the city of Chachapoyas as capital of the region. As the center of Spanish rule, and currently the largest town in the region, the Chachapoya subgroup  would be expected to host a higher proportion of European alleles but the highest European contribution (autosomes > 50%) was found instead in Kuelap, Leymebamba, and Rodríguez de Mendoza. In fact, the proportion of non-Native American haplotypes peaked in Leymebamba and Rodríquez de Mendoza (≥ 58%). Of these three areas, high European contribution in Leymebamba could be linked to its role as a gatekeeper to the Chachapoyas region, since it is one of the most important settlements on the eastern side of the mountains along the only road in the region that crosses the Andes and the Marañon River. This road system connected the Chachapoya and the Cajamarca regions, as well as Uchumarca in the south, already before the Spanish conquest , which m century , 67. TheFor mitochondrial DNA, coalescent-based BSPs are extensively used methods to make inferences of the changes in the past female population size . HoweverThus, demographic histories in northeast Peru were explored with BSPs, using both mitochondrial sequences and Y-chromosomal STR haplotypes among the study populations. While the effective population sizes reconstructed by the BSPs are largely conjectural, the overall trends are revealing. The BSP patterns obtained from our data differed both, between sexes (markers), and regions (Andean/Amazonian).Poaceae pollen, decrease of arboreal pollen and appearance of maize in the sedimentary record around this time all point to the clearing of forests for maize cultivation [In general, all BSP plots showed an ancient population expansion period that began at 625–500 generations ago i.e. 15.6–12.5 kya , a range that encompasses the earliest evidence of human presence in the continent , 71. Thetivation .The effective population size trajectories differ between sexes for the last 125 generations i.e. 3 kya. In the recent past, BSPs based on mitochondrial sequences show a steep but a relatively continuous decline which becomes severe over time but does not show a clear low point of population contraction. In contrast, the Y-chromosome BSP for the Chachapoyas sample set registers a steep population decline that reaches the lowest point c. 25 generations ago. This bottleneck would match the Inca or the Spanish conquest if a generation interval of 23 or 20 years, respectively, is assumed, which seem at least marginally realistic. These generation interval estimates would date the start of the general decline at 125 generations ago (above) to c. 2 850 and 2 500 BP.th-17th centuries. It is tempting to associate demographic events as drastic as this with the Inca and Spanish conquests. During the Late Horizon (1475–1532 CE), the relocation policies applied by the Incas are recognized as important contributing factors to the depopulation in the Chachapoya region but these are not described as severe as the demographic collapse during and after European contact  [The BSPs also suggest that the Chachapoya male population was nearly halved sometime between the 15d labor) , 77. Howd labor) . This woIn addition to the sex-specific differences, there are notable dissimilarities in the shape of BSPs between Andean and Amazonian reference populations. In general, the Andean male and female populations show higher effective population sizes for the last 250 generations, and clear signs of population growth in the past. The Amazonian populations exhibit a starkly contrasting pattern with smaller overall sizes and very moderate or non-existent population growth. This contrasting Andes/Amazon demographic pattern could reflect different levels of isolation and resource availability. This kind of regional dynamics has earlier been suggested for South America, based on Y-chromosomal data , and hasin silico analyses of species richness in South America from Late Quaternary until present have identified this region in northeastern Peru and southwestern Ecuador as one of the hotspots of biodiversity, also confirmed by empirical observations [The growing effective population sizes in the Andean populations and in the Chachapoyas, as well as the overall high diversity in the Chachapoyas region, differ markedly from other South American Native populations. Interestingly, rvations . Here, brvations . These rrvations –83. Thisrvations .To the extent of our statistical resolution, we did not detect population substructure in the Chachapoyas sample, which accounts for the notion that genetic subdivision does not necessarily accompany cultural differentiation. In other regions of South America, population substructure has also been found to be negligible in groups from the same and different language stock –87 In liEuropean admixture impacted the indigenous genetic variation at various scales, from macro-regions  to populations , 96. In The female demographic signature of the Chachapoyas as a whole and the Jivaroan set resemble the one from the Andean area whereas the male population history in the Chachapoyas is more similar to the Amazonian one. Larger long-term effective population sizes have been previously characterized for the populations from the Andean area which contrasts with other regions of South America , 97. MorS1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Figrd unknown component”.The circle indicates Individuals harboring > 60% of the “3(TIF)Click here for additional data file.S8 FigStudy populations and Chachapoyan subgroups in black, m: mestizo. (A) 116 loci. (B) 10 loci.(TIF)Click here for additional data file.S1 Table(A) Genotyping. (B) Primer information grouped by multiplexes.(XLSX)Click here for additional data file.S2 Table(A) mitochondrial DNA. (B) Y-Chromosome. (C) autosomal data.(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 TableFrequencies only from our datasets.(XLSX)Click here for additional data file.S6 Table(A) Non-Native American haplotypes are included in the study populations. (B) Non-Native American haplotypes are excluded in the study populations.(XLSX)Click here for additional data file.S7 Table(A) Non-Native American haplotypes are included in the study populations. (B) Non-Native American haplotypes are excluded in the study populations.(XLSX)Click here for additional data file.S8 TableNon-Native American haplotypes are excluded in the study populations.(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 TableFrequencies only from our datasets.(XLSX)Click here for additional data file.S11 Table(A) 23 Y-STRs. (B) 17 Y-STRs.(XLSX)Click here for additional data file.S12 Table(A) 23 Y-STRs when the Chachapoyan subgroups are not pooled. (B) 17 Y-STRs when the Chachapoyan subgroups are pooled.(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Several recent studies detected fine-scale genetic structure in human populations. Hence, groups conventionally treated as single populations harbour significant variation in terms of allele frequencies and patterns of haplotype sharing. It has been shown that these findings should be considered when performing studies of genetic associations and natural selection, especially when dealing with polygenic phenotypes. However, there is little understanding of the practical effects of such genetic structure on demography reconstructions and selection scans when focusing on recent population history. Here we tested the impact of population structure on such inferences using high-coverage (~30×) genome sequences of 2305 Estonians. We show that different regions of Estonia differ in both effective population size dynamics and signatures of natural selection. By analyzing identity-by-descent segments we also reveal that some Estonian regions exhibit evidence of a bottleneck 10–15 generations ago reflecting sequential episodes of wars, plague and famine, although this signal is virtually undetected when treating Estonia as a single population. Besides that, we provide a framework for relating effective population size estimated from genetic data to actual census size and validate it on the Estonian population. This approach may be widely used both to cross-check estimates based on historical sources as well as to get insight into times and/or regions with no other information available. Our results suggest that the history of human populations within the last few millennia can be highly region specific and cannot be properly studied without taking local genetic structure into account. With more and more datasets including genetic data from hundreds and thousands individuals now available it becomes apparent that most if not all human populations exhibit at least some degree of geography-driven genetic structure even at small scales (for some examples see –5). SuchIn addressing this question we make use of high-coverage whole genome sequences from more than 2300 Estonian Biobank donors generated as a part of a study by Kals et al. . PreviouTo get a first glance at the Estonian population structure we performed principal component analysis (PCA) both using only the Estonian samples Fig.  and by pIBDseq [Next, to zoom-in into the fine-scale structure in Estonia we used a subset of 468 individuals sampled in rural areas at the age of 50 or more, as this cohort is expected to be the least affected by recent migrations. We refer to this subset as “R50+” throughout the text (Methods). We used total genetic length of shared IBD segments detected with IBDseq  as inputIBDseq  clusteriIBDseq ) allows IBDseq  clusteriIBD-based analysis Fig.  reinforcIn order to understand how gene flow barriers and/or differences in local population density shaped the IBD-sharing pattern in the R50+ dataset, we inferred migration surfaces using MAPS . We usedAs local differences in admixture with external populations may have played a role in creating the observed genetic structure within Estonia we looked at patterns of haplotype sharing between R50+ Estonians and different non-Estonian populations Table . Here weAdmixture signals in Fig. IBDNe, which estimates effective population size (Ne) in past generations [We show that, despite the small territory it occupies, the Estonian population is structured Figs.  and S2.4erations , and sinerations , as bothIBDNe [We ran IBDNe  on the fBased on MAPS results, we propose that most of the differences in Ne dynamics between Estonian subpopulations may be attributed to different patterns of gene flow and external admixture. South-West and North-West Estonia are characterized by an overall high level of gene flow  of the ancestors of contemporary Estonians, adapting previous theoretical work [IBDNe estimates are extrapolated from preceding time points [Given our understanding of confounders of the observed regional Ne patterns, we exploited the fine-grained temporal resolution enabled by cal work  to empire points . Howevere points , providee points , which aAll the analyses performed so far speak for South-East Estonia showing relatively strong genetic differentiation from the rest of the country and having a partially independent demographic history. So we went on to look into signals of recent action of natural selection with a specific focus on whether treating South-East Estonia independently can reveal any additional insights. In order to do so we applied SDS  to the ep values in the three datasets  between Estonian and the UK dataset compared to the study by Field et al.  values in the two subsets Fig.  with moser Table . Though on Table . One of p value of 0.0505 even after removing linked SNPs and those in the HLA locus thus indicating a suggestive instance of polygenic selection specific to South-East Estonia. On the other hand, gene enrichment results show broadly the same results in all the datasets.Next, we looked for possible signals of polygenic selection both in the entire dataset as well as in SE and nonSE by (1) focusing on SNPs and testing if any GWAS category was enriched in SNPs with high absolute sSDS scores and if there was any correlation between the absolute sSDS and absolute GWAS betas  phasing and imputation was done using Eagle v2.3  on the dAll Estonian Biobank participants have signed a broad informed consent which allows research in the fields of genetic epidemiology, disease risk factors and population history. All work at Estonian Biobank is conducted according to the Estonian Human Gene Research Act. The original study generating the WGS data  was apprAs information on parents’ and grandparents’ birthplace is mostly unavailable for the samples used here, we subset the 2305 dataset for individuals born in rural areas and sampled at the age of 50 or older as we expect this cohort to be the least affected by recent migration. This resulted in a dataset of 474 individuals which we further pruned for PCA outliers (see below) and samples with more than 10,000 singletons  and “long” (more than 6 cM), and performed two independent MAPS runs for each length class to assess convergence. Estonian territory was modelled as having a total of 200 demes. Each run had 5 million iterations thinned every 10,000 and preceded by a burn-in of 2 million discarded cycles. The obtained migration surfaces were subsequently plotted using the plotmaps R package [In order to evaluate the extent of gene flow across the whole country together with local population densities, we estimated migration surfaces using MAPS , which h package . We repe between  and 6 cMWe used total genetic length of IBD segments longer than 2 cM as a measure of genetic similarity between pairs of individuals as described in Supplementary text We applied this approach to the R50+ dataset (468 samples) and the entire dataset (2305 samples). In both cases FS was run twice to assess convergence  and removing sites with MAF < 0.05 and missing rate > 0.1. Per-site Weir and Cockerham [Fst between Estonian clusters was calculated using smartpca from the EIGENSOFT package v7.2.0  after LDockerham  Fst estiockerham  after fihttps://geoportaal.maaamet.ee/eng/Spatial-Data/Administrative-and-Settlement-Division-p312.html). Geographic data were visualized in R [Geographic coordinates of the corresponding birth town/parish were assigned to each sample with birthplace information available (2168 out of 2305 samples). For MAPS these coordinates were used directly. When plotting IBD/FS and NNLS results for the R50+ panel, coordinates of the samples were changed manually to avoid overplotting. When plotting samples from the entire dataset jittering were used for the same purpose. Shp objects used to plot maps of Estonia with parish and county borders were retrieved from the Estonian Land Board website  and excess in variance of reproductive success compared to the Poisson distribution (DV):Several lines of evidence, based both on theoretical reasoning  and empim = 0.5 and DV = −1) and 3.53 (with m = 0.1 or 0.9 and DV = 3), respectively  database. Details of SNP annotation and enrichment analyses are specified in Supplementary text section Predicted functional effect of the test SNPs was assessed using CADD tool . In addias/home; ). Candidas/home;  (http://Version with tracked changesSupplementary tableSupplementary text