Contarinia brassicola. Clustering analyses recovered little geographic structure across the main canola production region but differentiated several geographically disparate populations at edges of the agricultural zone. Given a lack of alternative hypotheses for this pattern, we suggest these data support alternative hosts for this species and thus our canola‐centric view of this midge as a pest has limited our understanding of its biology. These results speak to the need for increased surveying efforts across multiple habitats and other potential hosts within Brassicaceae to improve both our ecological and evolutionary knowledge of this species and contribute to effective management strategies. We additionally found that use of WGA prior to library preparation was an effective method for increasing DNA quantity of these small insects prior to restriction site‐associated DNA sequencing and had no discernible impact on genotyping consistency for population genetic analysis; WGA is therefore likely to be tractable for other similar studies that seek to randomly sample markers across the genome in small organisms.Population genomics is a useful tool to support integrated pest management as it can elucidate population dynamics, demography, and histories of invasion. Here, we use a restriction site‐associated DNA sequencing approach combined with whole‐genome amplification (WGA) to assess genomic population structure of a newly described pest of canola, the diminutive canola flower midge,  The canola flower midge is a newly discovered pest of canola from the Canadian Prairies. The population genetic analyses presented in this study recovered little population structure across its known range, consistent with high levels of gene flow in this region. However, a few geographically disparate populations were genetically distinct, and we suggest that these results reflect bleed‐over genetic structure from an unsampled and unknown alternative host range. Today, Barthet, .COI diversity, and relatively large range across the Canadian Prairies . Surveyors visited 546 fields from the northern limit of canola production to the southern limit of CFM range in Alberta and Saskatchewan. In Manitoba, the survey was mostly limited to the agricultural extent in the northwest of the province, with the exception of a single, additional site in Portage la Prairie. At each site, 100 canola racemes along the edge of each field were examined. All galled flowers found were collected and returned to the laboratory in a refrigerated container. In the laboratory, buds were dissected and larvae were placed into individual 2 ml tubes and frozen at −80°C. From all survey results, we subsampled sites for genetic analysis by selecting the sites that had the highest CFM densities, defined as any location where more than four larvae were sampled. Our genetic sampling also aimed to maximize the geographic scope across the range of CFM.Genomic DNA was extracted from whole specimens sampled at 16 localities Table  using a 2.2Given the small body size of CFM and the relatively high amount of input DNA required for GBS (200 ng per sample), consistently isolating enough DNA from each specimen was challenging. Recently developed WGA methods, such as the REPn = 48 libraries). Given preliminary results of these 48 libraries, the remaining 96 CFM samples collected in 2017 and 2018 underwent WGA prior to library preparation. GBS library preparation largely followed Poland et al.  was conducted at the National Research Council of Canada Laboratory .To test the effect of WGA on GBS sequencing of small insect samples, we created GBS libraries with and without WGA for 24 of the CFM samples collected in 2017 , specified a minimum minor allele frequency of 3%, limited the number of SNPs output per locus to one using the ‐‐write_random_snp option in the populations module of Stacks to reduce genomic linkage, and removed any individuals with more than 50% missing data. COI sequences for the same specimens were aligned and quality checked following Mori et al. . We tested K = 1–20 with 20 independent replicates per value of K. Each value of K ran for 400,000 MCMC reps with a burn‐in period of 200,000, and we averaged runs using CLUMPAK v1.1   and the program Structure 2.3.4  were calculated in dartR  Figure . These r3.23.2.1Samples treated with WGA prior to GBS library preparation had higher numbers of raw sequence reads relative to the non‐WGA samples; however, this read abundance was not evenly distributed across individuals Table . The WGAM and n using the 48 libraries in the WGA test dataset (24 with WGA and 24 without). Following Paris et al.  produced a total of 354.9 million sequence reads, which was reduced to 69.3 million after quality filtering; here, 70.7% of sequence reads were removed during quality filtering due to adapter contamination, and 1.1% were discarded due to low quality  of the 16 localities recovered two highly divergent populations from the eastern edge of the sampled region in Manitoba: Swan River and Portage la Prairie Figure . Two Alblocprior) did not produce substantial differences in cluster assignments when compared to the analyses that did not incorporate this information (nolocprior); thus, we focus only on the latter here. We found variable support for an optimal value of K: LnPr(X|K) displayed only a gradual plateau starting at K = 5 to 7, ΔK values were generally low (maximum ΔK = 21.8) but supported K = 2, 5, 7, and 9, and the Puechmaille statistics supported K = 5, 6, and 7  formed a distinct cluster, as was observed in the PCA  values for all 16 localities was highly significant . We note however that the North Battleford population had far higher levels of missing data than the other populations . Pairwise FST values ranged from 0 to 0.39 . The minimum spanning haplotype network depicted a single large haplogroup and nine additional smaller haplogroups  sequence at the 5ʹ and/or 3ʹ ends in 20 specimens  grown in a small plot on an AAFC research farm in Melfort, Saskatchewan . This locality is outside of the typical mustard growing region of southwestern Saskatchewan and inside the primary distribution of CFM. If alternative hosts do exist for this species, our canola‐centric sampling may have anthropogenically biased our assessments of population structure in two ways: (1) these geographically disparate, differentiated populations at the edge of the canola production region may represent bleed‐over genetic structure from an alternative and more geographically widespread host range, and (2) the lack of strong differentiation in the majority of our central localities may reflect a relatively recent bottleneck onto the anthropogenic host.Our surveying and sampling were limited to canola production regions across the Prairie provinces. Given the short history of widespread canola production in Canada (ca. 40 years), if CFM is native then it must have some native  hosts within or outside of this geographic region. Alternative host associations have yet to be thoroughly evaluated for this species, although CFM larvae and galls were found on mustard ; Formal analysis ; Investigation ; Writing‐original draft (lead); Writing‐review & editing . Julian R. Dupuis: Formal analysis ; Investigation ; Writing‐original draft (supporting); Writing‐review & editing . Jennifer Holowachuk: Investigation ; Methodology ; Writing‐review & editing (supporting). Shane Hladun: Investigation ; Methodology ; Writing‐review & editing (supporting). Meghan A. Vankosky: Conceptualization ; Funding acquisition ; Investigation ; Methodology ; Writing‐review & editing (supporting). Boyd A. Mori: Conceptualization ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Writing‐original draft (supporting); Writing‐review & editing .Figure S1Click here for additional data file.Table S1Click here for additional data file.Appendix S1Click here for additional data file.


Greco, survived in the isolated Aspromonte mountain area of Southern Italy. To understand their genetic ancestry and explore possible effects of geographic and cultural isolation, we genome-wide genotyped a large set of South Italian samples including both communities that still speak Greco nowadays and those that lost the use of this language earlier in time. Comparisons with modern and ancient populations highlighted ancient, long-lasting genetic links with Eastern Mediterranean and Caucasian/Near-Eastern groups as ancestral sources of Southern Italians. Our results suggest that the Aspromonte communities might be interpreted as genetically drifted remnants that departed from such ancient genetic background as a consequence of long-term isolation. Specific patterns of population structuring and higher levels of genetic drift were indeed observed in these populations, reflecting geographic isolation amplified by cultural differences in the groups that still conserve the Greco language. Isolation and drift also affected the current genetic differentiation at specific gene pathways, prompting for future genome-wide association studies aimed at exploring trait-related loci that have drifted up in frequency in these isolated groups.Calabrian Greeks are an enigmatic population that have preserved and evolved a unique variety of language,  The genomic variability among present-day Italians is indeed due to the multi-layered mosaic of pre-historical and historical processes of migration and admixture that interested the continent throughout time, but also to its valuable diversity and richness in terms of ecological complexity and cultural heterogeneity. The existence of distinct environmental pressures and climate conditions were proved to have forced different patterns of local adaptations between the Northern and Southern Italy, contributing to the observed genetic structure3. In the same way, geographic constraints or cultural factors  were suggested to have additionally influenced human population variability along the Peninsula, being responsible for different paths of isolation and differentiation or population mobility at more fine-grained local levels6.The Italian Peninsula represents a key area of investigation to explore population and demographic processes that characterized the peopling history of Europe and the Mediterranean, and to reconstruct patterns of genetic diversity at different geographical scales10, autosomal SNP-chip data13 and, more recently, through one of the first study on whole genome sequencing3. In this context, the analysis of geographically isolated populations or of cultural enclaves and ethno-linguistic minorities can provide a simplified observatory for exploring population relationships within and among human groups. Indeed, the condition of isolation might have helped reducing the confounding effects of admixture15. Furthermore, the founding event and the limited external gene flow, ultimately resulting in smaller effective population size (Ne) and increased homozygosity and linkage disequilibrium (LD), may help the study of potentially trait-associated alleles found at higher frequency in these groups, thus making population isolates key models for genome-wide association studies20. In line with this viewpoint, some of the geographic and cultural isolates today settled in the Italian territory have long attracted the attention of population genetic studies based on both uniparental and autosomal markers26.The distinctive genomic variability of Italy has been largely investigated by using both uniparental markers27 brought specific interest on the enigmatic Calabrian Greek-speaking communities residing in the Aspromonte mountain area of Bovesia, in the territory of Reggio Calabria . These ethno-linguistic groups represent extant Hellenic islands in Southern Italy that still preserve a unique variety of Greek, also known as Greco or Calabrian Greek. Linguistic studies have supplied interesting information about the possible origins of these communities, historically counterpoising two main antithetic hypotheses. The first hypothesis leads back to the Medieval period and suggests that this language might derive from the descendants of Byzantines who settled Southern Italy between the fifth and eleventh centuries32. The second hypothesis instead brings to the Magna Graecia colonization of Southern Italy in the eighth century BC to trace the origin of the language. Thereafter, the Greek of Calabria would have been uninterruptedly spoken during the centuries until the present, with local developments35. More recently, reconciling scenarios tried to mitigate the initial controversy between the “Hellenic” vs. “Byzantine” dichotomy, mostly in virtue of two considerations: (i) the impact of intense Greek-Romance linguistic contacts into a dynamic model of coexistence and cohabitation between Greek-speaking Latins and Latin-speaking Greeks; and (ii) the implications of archaic lexical elements shared with peripheral Greek dialects such as those of Cyprus and the Dodecanese, to assess the importance of linguistic contributes from the Greek of different periods37. The new moderated hypotheses in fact consider the Greek of Magna Graecia to have been survived in diglossia with Latin during the Roman Empire and then to have been rekindled in the Byzantine era and subsequent periods. Importantly, multiple strata which repeatedly brought waves of Greek speakers onto the Calabrian coasts were suggested to have overall contributed to the Greek heritage of the region38. Historically, it is known that the Greek presence in Calabria was continuous since ancient times and that the area of Greek-influence in Southern Italy was originally more largely extended with respect to the enclaves present nowadays39. In fact, the number of Greco-speaking people is rather limited at present and today the language survives mainly in few communities residing in the Aspromonte mountain area of Reggio Calabria41.Recently, a genomic survey on patterns of ancient and recent admixture in Southern Italy and the Mediterranean27. In the present study, we further address issues of geographic and cultural isolation by using a higher in-depth level of analysis, which was achieved both by increasing the number of analyzed markers (720 K) and by genotyping also other communities from the same geographic area of Southern Italy  for a mixture between Sardinian and Caucasian or between Near-Eastern and continental European sources were obtained for the populations of Benevento, Castrovillari and Catanzaro (Suppl. Table Aspromonte” (ASPR) cluster, the Greco-speaking communities from Roghudi, Roccaforte del Greco, Condofuri and Gallicianò form a further sub-group that exhibits more remarkable signals of genetic drift  cluster and appear instead grouped to the other Southern Italian populations, namely to the cluster of villages from the province of Catanzaro (CZ) and then to a cluster indifferently encompassing all the individuals from both Benevento and Castrovillari (BN + CS)  Fig. .Greco language appear as the most differentiated . Similarly, ADMIXTURE results for the best value of K = 2  and accounts for 30–60% of ancestry also in the core of the other communities from the same Aspromonte group.With the aim of directly exploring local patterns of genetic differentiation, we focused more specifically on the genetic structure observed within Southern Italy. Consistently with the global analysis, the PCA applied exclusively on our Southern Italian “local” dataset replicates the distinctiveness observed for the Aspromonte group  and the genome-wide homozygosity (Fhom) indexes, and by analyzing the number and the extension of genomic runs of homozygosity (ROH). Both Fin and Fhom values are averagely higher for the populations from the Aspromonte area compared to the rest of Southern Italy (Suppl. Table Fhom) shows higher variance in inbreeding for the Aspromonte group than for the other Southern Italian populations  at different classes of length using the fastIBD pipeline implemented in the BEAGLE software. Overall, patterns of genetic relatedness across populations reveal higher within-population compared to between-population sharing for longer bin classes. In particular, for bins ≥ 27 cM almost all the connections are within-population and the few links between-populations appear almost exclusively limited to the communities belonging to the ASPR cluster, consistently with these groups being more closely related to each other. For shorter classes of length among-population connections then extend to all the rest of Southern Italy  cluster and the one grouping individuals from Catanzaro (CZ) as representative of the “not-isolated” Southern Italian background. We then computed pairwise FST values between the above-mentioned three clusters  for all the 621,755 SNPs included in our “local” dataset. Finally, we retained the 797 loci scoring in the top 1% of FST distribution in both ASPR vs. CZ and ASPR vs. BN + CS comparisons , Eastern European Hunter-Gatherers (EHG), Caucasian Hunter-Gatherers (CHG), Anatolian Neolithic farmers (AN) and Pontic-Steppe Yamnaya (EMBA). Overall Sardinia shows the highest levels of shared drift with samples of Neolithic-related ancestry compared to Northern and Southern Italy. Both Sardinians and Northern Italians show higher affinity to WHG than Southern Italians, who instead appear more affected by CHG-related groups. On the other hand, Yamnaya Steppe and EHG share more affinity to North Italy than to both Southern Italians and Sardinians  have been largely debated from a linguistic point of view. The first hypotheses defending the continuity of the language from the Magna Graecia or its Byzantine origin have been more recently reconciled into composite scenarios, in which the importance of longstanding contacts and multiple contributions from different periods have been reevaluated. As a matter of fact, the territory corresponding to present-day Calabria has been inhabited since prehistoric times and its centrality in the Mediterranean Sea is attested by the presence of artefacts from the most important Neolithic cultures of Southern Italy. Furthermore, its richness in mineral deposits testifies exchanges with the Aegean and Asia Minor civilizations that must have been intense during Metal Ages48. Accordingly, the extent of population and cultural interactions between South Italy and the southern part of the Balkan Peninsula including Greece, Crete and the Aegean islands has been confirmed by the presence of Mediterranean genetic links between these regions tracing back to Neolithic and post-Neolithic times27.The origins of the Greco-speaking groups in the context of the local Southern Italian genetic landscape and with respect to the temporal and spatial structuring of the Euro-Mediterranean genetic variation, with the aim to infer the main demographic processes that shaped the genetic heritage of these populations. To this end, we collected a large set of samples representative of the communities settled in the Aspromonte mountain area, including both those that conserved Greco up to the present and the ones that lost the use of this language earlier in time. Then, we compared them against the genomic patterns observed for other not-isolated populations from Southern Italy as well as to a wider reference panel composed of both modern and ancient samples.In this study, we analyzed the genetic variability of Calabrian 27. Inferences of ADMIXTURE proportions indeed revealed the ancestry of present-day Southern Italian populations, regardless of their linguistic affiliation, to be composed mainly by Sardinian-like and South-Eastern Mediterranean genetic components, with a negligible contribution from a continental Eastern European ancestry instead higher in Northern Italy  specific cluster identified by FineSTRUCTURE  and/or in Byzantine and subsequent times. Therefore, the observed patterns could be linked to a tendency to mobility that has always characterized these populations, resulting in continuous cultural and genetic exchanges over time. That being so, the Calabrian Greek ethno-linguistic minorities of Southern Italy may be interpreted as the remnants of a wider area of Greek influence, that by virtue of their geographic isolation have preserved and evolved a unique variety of Greek which has survived through centuries in the mountains of the Aspromonte area. At this respect, the communities showing higher signatures of genetic isolation  have made efforts to identify loci or regions of the genome evolving in truly neutral vs. non-neutral manner to perform demographic inferences based on whole-sequencing data, also stressing how a-priori assumptions on the neutrality of great part of the genome may bias some resultant inferences . Therefore, even if the limited temporal depth and relatively micro-geographical setting of the present study should in some way prevent relevant biases, future researches in these directions may integrate and be compared to the present work in order to obtain more accurate demographic inferences.Incorporating in future studies the information provided by whole genome sequence data will be an additional value to comprehensively understand the interplaying impact of complex demographic history and evolutionary processes. Recent studies .Besides the importance in population history, ethnogenesis and linguistic variation, demographic processes of isolation might have also affected the genetic composition of present-day groups inhabiting these areas of Southern Italy. In fact, the GO analysis showed peculiar biological function of genes related to neurological pathways with higher level of differentiation in the Calabrian area , 4 villages from the province of Catanzaro , and to population samples from the provinces of Cosenza  and Benevento (Campania) Fig. .Saliva samples were collected with the Oragene-DNA Self Collection Kit OG-500  from unrelated volunteers, by focusing on subjects with a local genetic ancestry over at least three generations in their respective communities of origin, which were also surveyed for language affiliation.All donors provided a written informed consent to data treatment and project objectives, and all the procedures concerning this population genetics study was approved by the Bioethic Committee of the University of Bologna on 08/04/2013. The study was designed and conducted in agreement with relevant guidelines and regulations according to the ethical principles for research involving human subjects stated by the WMA Declaration of Helsinki.Genomic DNA was purified from Oragene-DNA collection kits following manufacturer’s recommendations and quantified with the Qubit dsDNA BR Assay Kit . DNA samples were then genotyped for the 713,014 SNPs implemented in the HumanOmniExpress BeadChip , by using the facilities available at the Center for Biomedical Research & Technologies of the Italian Auxologic Institute .60 after having excluded SNPs on the sex chromosomes. We removed all individuals with a genotyping success rate lower than 92%, variants with missing call rates exceeding 2%, SNPs with a minor allele frequency (MAF) lower than 1%, and markers showing significant deviations from the Hardy–Weinberg equilibrium. In addition, we estimated the degree of identity-by-descent (IBD) sharing and excluded one individual for each pair of samples with a kinship coefficient (PiHat) higher than 12.5%.Genotyping results were filtered using the PLINK software 1.9--indep-pairwise 50 10 0.1), obtaining a “pruned local” dataset consisting of 64,147 SNPs.After filtering procedures, we obtained a final “local” dataset composed by 141 individuals typed for 621,755 autosomal SNPs markers. The dataset was thinned for genotype-based analyses by removing SNPs in LD (r2 > 0.1) within a sliding window of 50 SNPs advanced by 10 SNPs at the time .In order to frame the variability of analyzed populations into the Euro-Mediterranean genetic landscape, we merged our Southern Italian “local” dataset with publicly available genome-wide data from Europe, Near East and the Caucasus, extracted from the Human Genome Diversity Project (HGDP)72 and genotyped on the 1240 K panel , finally obtaining a common “modern-plus-ancient” dataset of 326,832 SNPs. For the genotype-based analyses involving also ancient samples we applied a LD-pruning procedure by excluding one SNP for each pair of loci showing r2 values higher than 0.4 within a 200‐SNPs window, sliding 25 loci at the time (PLINK option --indep-pairwise 200 25 0.4), for a total of 286,656 SNPs left after pruning.To test temporal patterns of genetic relationships, we finally merged the “modern extended” dataset with genomic data for 1059 ancient samples  was performed on the “local” and “extended” datasets including modern populations by using the 74, by testing hypothetical ancestral populations (K) from 2 through 10. We performed ten independent runs with different random seeds for each given K and used those with the highest log-likelihood values for the final plot. Cross‐validation (CV) errors were also calculated for each run with the aim of identifying the number of K showing the best fit to the data.Inferences of ancestry proportions for the modern groups were estimated with the ADMIXTURE software75. We run Treemix including a North-African population (Mozabites) as root to build a phylogenetic tree without allowing for migration, and then we tested an increasing number of migratory events from m = 1 to m = 6.Genetic relationships and gene-flow patterns between modern populations were explored using the Treemix v1.12 softwareoutgroup-f3 statistics in the form of f3, by using the qp3pop function implemented in the ADMIXTOOLS package77. Furthermore, to test models of phylogenetic relationships between present-day and ancestral populations, we applied the modeling approach implemented in the qpGraph software of the ADMIXTOOLS v3.0 package77, relying on defined topologies with ancient West Eurasian groups78. In order to keep the models simple, we started with small, well-understood subgraph by adding additional sources one at a time and testing at each subsequent step of the analysis the fitting of present-day populations as being explained by a mixture of two possible ancestral populations. In particular, by starting with a skeleton tree including Mbuti, WHG and MA1, we consequently grafted onto this basic phylogeny additional putative ancient sources representative of Early farmers, Eastern Hunter Gatherers (EHG) and Caucasian Hunter Gatherers (CHG)78. Then, we tried to explore the fitting of the analyzed modern Italian populations  into the progressively considered qpGraph-based phylogenies, evaluating the fits to the models based on the maximum |Z|-score comparing predicted and observed values. Finally, to better characterize the ancestral composition of analyzed modern Italian groups, we exploited the modeling approach implemented in qpAdmix63 to quantitatively estimate the admixture profile of each modern Test population using a four-population model of ancient ancestral Source groups (“Left” populations) with respect to a specific set of Outgroups (“Right” populations). In details, we first checked whether the considered “Right” and “Left” populations were significantly distinguishable by using qpWave and the following set of outgroups  as defined previously79. Then, by using the same set of outgroups, we performed qpAdm runs to establish if the admixture profile of each target modern population was consisted with the selected set of ancient ancestral sources , inferring their relative admixture proportions. We considered a P-value threshold of 0.01 to assess the significance of tested models.To formally assess affinity patterns between modern groups and ancient individuals we computed 80. Samples were phased using SHAPEIT81 by applying default parameters and using HapMap phase 3 recombination maps. We run CHROMOPAINTER analysis on the 379 individuals of the “unpruned modern extended” dataset, by initially estimating the switch and mutation/emission rates on a subset of four chromosomes {4, 10, 15, 22} using 10 steps of Expectation–Maximisation (E-M). Then, we averaged the inferred values across these chromosomes, weighting by the number of markers and individuals, and we exploited the obtained parameters to re-run CHROMOPAINTER on all chromosomes using each individual both as “donor” and “recipient”. The obtained matrix of shared haplotype “chunk-counts”, combined across the 22 autosomes, was submitted to the fineSTRUCTURE clustering algorithm version fs2.180. We ran fineSTRUCTURE by setting 3,000,000 “burn-in” MCMC iterations, followed by 2,000,000 additional iterations where the inferred clustering patterns were sampled every 10,000 runs. Finally, we set 1,000,000 additional hill-climbing steps to improve posterior probability and merge clusters in a step-wise fashion until reaching the final configuration tree.To explore fine-grained patterns of population structure and define genetic clusters of homogeneous individuals, we exploited the haplotype-based approach implemented in CHROMOPAINTERv2/ fineSTRUCTURE83 and the Fin and Fhom indexes by using respectively the --homozyg and --het functions of PLINK software as described previously59.To explore patterns of within population genetic variation, we calculated the number and extension of ROH segmentsfastIBD method implemented in the BEAGLE 3.3 software84. Data were phased with SHAPEIT81 as specified above and fastIBD was run ten times for each chromosome using different random seeds. IBD blocks were called by post-processing the obtained results with the ‘plus-process-fibd.py’ pipeline85, setting the fastIBD threshold to 1e-10 and considering only blocks longer than 1 cM. We then explored the distribution of segments shared IBD between pairs of individuals both within- and among-populations for different bins of length (in cM) to approximate different degrees of relatedness86.Furthermore, we estimated patterns of IBD sharing within and among Southern Italian populations using the Aspromonte cluster from both Catanzaro and Benevento + Castrovillari comparison clusters. The list of genes encompassing the detected top 1% most differentiating SNPs was compared to the reference list of all genes covered by the Illumina HumanOmniExpress BeadChip and submitted to an Enrichment Analysis using the PANTHER Gene Ontology (GO) tool88 with the aim to pinpoint the most relevant pathways involved in the observed differentiation. Information about chromosome location, start and end positions of each gene, as well as its approved name and alternative nomenclature was built by cross-checking the Ensembl database gene list for the human reference genome hg37.p13  and the HUGO Gene Nomenclature Committee resource , with data recovered through the BioMart data mining tool in both cases91.Finally, to examine possible signals of genetic differentiation in the isolated groups of Aspromonte area with respect to the more general Southern Italian population, we compared allele frequencies between the population clusters identified by fineSTRUCTURE, computing single locus Weir and Cockerham Fst for each of the 621,755 SNPs included in the high-density “local” dataset. More precisely, we retained the top 1% of markers in the Fst distribution that differentiate the Supplementary Information 1.Supplementary Information 2.


The hawksbill turtle is a broadly distributed, highly migratory and critically endangered sea turtle species. The paucity of studies restricts the comprehension of its behavior and life history. In this work, we performed a global phylogeographic analysis using a compilation of previously published mitochondrial haplotype data to understand the dynamics and diversity of hawksbill populations worldwide. Our results revealed a complex demographic pattern associated to hawksbill phylogeography since the Pliocene. Isolation by distance is not enough to explain distinct demographic units of hawksbill turtles, which are also influenced by other factors as oceanic currents, coral reef distribution and nesting timing. The foraging aggregations are typically mixed stocks of individuals originating from multiple nesting areas, but there is also a trend of foragers coming from nearby natal beaches. Phylogenetic analysis indicates two highly divergent major lineages split between Atlantic and Indo-Pacific rookeries, but there is also a more recent Atlantic Ocean colonization from the Indo-Pacific Ocean. Long-distance dispersal events are likely responsible for homogenization between distant populations within oceans. Our findings provided new insights about population connectivity, identified gaps that should be prioritized in future research and highlighted the need for international efforts aiming at hawksbill's conservation. Species that exhibit worldwide distribution and highly migratory behavior are rarely studied in a global context, covering all population diversity and connectivity. As a result, only fragmented information is generated on the evolutionary history of species. Sea turtles are cosmopolitan animals that can disperse among different ocean basins during their lifetime  markers to study the genetic diversity of sea turtles (Caretta caretta) and green turtles (Chelonia mydas), in a collaboration between research groups (http://accstr.ufl.edu/resources/mtdna-sequences). For C. mydas, a recent study investigated the global phylogeography using a compiled worldwide mtDNA dataset  is a critically endangered species that occurs in tropical and subtropical waters around the world  have been recognized as populations with a significant divergence of allele frequencies considering current population structure and management issues . Sea turThus, comparative studies exploring all population diversity and linking separated studies are essential to comprehend the biology, behavior and life history of hawksbill turtles. In this work, we compiled a complete dataset of all previously published mtDNA data with a standardized nomenclature to analyze the global phylogeography in order to investigate the hawksbill turtle dynamics and diversity worldwide.www.ncbi.nlm.nih.gov, accessed 11 February 2019) and from the Atlantic Ocean hawksbill haplotype database . According to the purpose of this work, only haplotypes with associated population frequencies data were recovered, including females nesting in rookeries and juveniles in foraging aggregations. Populations with less than three individuals analyzed were grouped with the closest population (<300 km). We analyzed control region haplotypes spanning 739 bp, aiming to increase detection of variation and population structure. We standardized the nomenclature to abbreviation EiA plus sequential number, when the haplotype was first detected in Atlantic Ocean, and EiIP plus sequential number, when the haplotype was first recorded in Indo-Pacific Ocean. The alignment of sequences was performed using ClustalX algorithm of the MEGA 7 software  under the Coalescent Bayesian Skyline Population tree model due to the intraspecific nature of the dataset . The timWe conducted three independent runs for 200,000,000 generations, sampled every 1000 generations. Trace files were checked for chain convergence and sufficient effective sample sizes (ESS) in Tracer v. 1.6 (ESS>200 were considered acceptable) , number of variable sites and average number of nucleotide differences using the program DnaSP v5 program . The parThe level of differentiation between populations was estimated using the population pairwise FST values based on haplotype frequencies and exact tests of population differentiation in the software Arlequin v3.5 Excoffier and Lischer, 2010). We also used the spatial analysis of molecular variance (SAMOVA) to find the most likely demographic groupings, whose approach is based on the maximization of the proportion of total genetic variance due to the differences between groups (FCT). The analysis was conducted for the complete dataset and separately for each oceanic basin in the program SAMOVA 2  and ambiguous names for haplotypes are available in Table S1.Haplotype frequencies were obtained from the literature for 18 rookeries and 23 foraging aggregations from Atlantic Ocean and for 22 rookeries and 17 foraging aggregations from Indo-Pacific Ocean. We analyzed a total of 1983 individuals from worldwide rookeries that exhibited 88 haplotypes and 1577 individuals from foraging aggregations that displayed 79 haplotypes. The global dataset including all mtDNA control region haplotypes previously recorded for hawksbill turtles is presented in Table S1). For example, haplotypes EiBR14 (Table S1).Several ambiguities in the nomenclature of mtDNA haplotypes were found , when the Atlantic and Indo-Pacific lineages diverged at the early Pliocene . The firTable S2. The diversity parameters are higher in the Indo-Pacific basin in relation to the Atlantic basin . The genetic variance of Indo-Pacific populations was maximally differentiated when they were clustered in five geographical groups, when 65.4% of the variation was found among the following groups: (1) Persian Gulf, Western Australia, Solomon Islands and Eastern Pacific , (2) Northern Territory and North Queensland, (3) Seychelles and Chagos Archipelago, (4) Peninsular Malaysia, and (5) East Malaysia (Table S5). However, the grouping of Atlantic populations was not consistent, suggesting different partitions for each K.The FST test (based on haplotype frequencies) indicated genetic differentiation among 22 groups of populations, while the exact test showed population differentiation among 25 rookery groups (Table S2).Based on exact tests of population differentiation, FST and SAMOVA analyses, we defined the most appropriated regional groups considering both genetic variation and geographical distribution. Considering that different groupings of Atlantic populations were proposed in different tests, we decided to treat each Atlantic population as independent, except for the two Tobago populations. For Indo-Pacific populations, a consistent grouping of 13 different regional groups was observed for all tests .The frequencies of control region 739 bp haplotypes in foraging aggregations are shown in Eastern Pacific presents all the haplotypes from clade Indo-Pacific II. Southwest Atlantic exhibits a prevalence of haplotypes from clade Atlantic II, while haplotypes of both clades are present in Northwest Atlantic. Foraging aggregations in East and Southwest Atlantic present haplotypes without known origin , which belong to the clade Indo-Pacific IIA according to phylogenetic analysis. They were typically found in Seychelles and Chagos rookeries and with one record in Malaysia East rookery. The natal origins of 48.1% of the haplotypes registered in foraging aggregations could not be identified, as they had not been registered in rookeries. These ‘orphan’ haplotypes were found in 76 individuals .Combining all the previously published mtDNA haplotypes, we were able to investigate the phylogeographic patterns of hawksbill turtles in a global context. We demonstrated that the mtDNA diversity among hawksbill turtle rookeries was higher in Indo-Pacific basin relative to Atlantic basin. Six out of the nine phylogenetic clades were found in Indo-Pacific rookeries, which correspond to 69.3% of all compiled haplotypes. It is important to note that only western Atlantic rookeries were analyzed, considering that the available data from eastern Atlantic rookeries comprises short sequences of 384 bp (EATL haplotype) preventing their inclusion in these analyses .However, mtDNA haplotypes were shared between distant intraoceanic rookeries. No significant genetic differentiation was observed in FST and exact tests between Colombia and United States Virgin Islands, located about 1000 km apart in the Atlantic Ocean. In the Indo-Pacific basin, widely distributed rookeries throughout the Persian Gulf and Eastern Pacific were clustered together in SAMOVA analysis even when a number of population groups of K=15 was simulated . Only over the last years, hawksbill rookeries from the Eastern Pacific were examined . They exThe star shaped network of the clades and the neutrality tests suggest that the groups have experienced a population expansion . SignalsIn a smaller geographical scale, significant genetic differentiation between nearby hawksbill's rookeries is also commonly observed. Two separate demographic units were recognized for Northern (Ceará and Rio Grande do Norte) and Southern (Bahia and Sergipe) Brazilian nesting areas  were never registered in rookeries. According to the phylogenetic analysis, they belong to clade Indo-Pacific IIA, which is present in rookeries from Seychelles and Chagos Islands and there is also a record of one individual in the nesting area of East Malaysia. Interestingly, considering the mtDNA data of shorter sequences (384 bp), the clade Indo-Pacific IIA is also found in the rookeries of East Atlantic. The foraging ground in Ascension Island is composed by sea turtles originated from Western and Eastern Atlantic rookeries. Simulations of physical transport using an ocean circulation model showed that passive dispersal influencing Ascension Island ground is primarily from the East, involving rookeries along Western Africa and, potentially, the Indian Ocean  have no known source rookery, which may suggest that there are many rookeries not yet studied. Further sampling of “genetically unknown” rookeries is needed to identify haplotype origins and improving mixed stock analysis (MSA) resolution of foraging grounds. For example, et al., 2007; Satellite telemetry data can provide fine-scale information about breeding, foraging and migration of sea turtles in different life stages  the Isthmus of Panama and (2) the South Africa, where the cold-water Benguela Current disrupts the flow between the Indian and Atlantic oceans .The divergence of Indo-Pacific lineages of hawksbill turtles occurred during or subsequent to the early Pliocene. The clade Indo-Pacific I, currently the most widely distributed lineage, is the early diverging lineage in the Indo-Pacific basin, separated from the remaining clades at approximately 3.77 mya. In late Pliocene or early Pleistocene, the split of other Indo-Pacific lineages , as well as the Atlantic clades (I and II), occurred in parallel with the glacial cycles. The climatic changes during this period affected the sea level and temperature, as well as the oceanic currents, which are expected to impact the gene flow patterns of hawksbill turtles and other marine species . Based on short mtDNA sequence data, the clade Indo-Pacific IIA is present in rookeries of Príncipe Island in Atlantic Ocean (As depicted in The global phylogeographic pattern of hawksbill turtles can be interpreted as two highly divergent major lineages split between Atlantic and Indo-Pacific oceans during the early Pliocene. Following, both major oceanic lineages experienced population contractions and expansions associated with the climatic oscillations and oceanic current changes during the Pliocene or early Pleistocene, which resulted in the separation of different mtDNA lineages with some intraoceanic geographic structure. Migrations and long-distance dispersal events were likely responsible for the partial homogenization of some distant rookeries. During the late Pleistocene, intraoceanic population expansions and a likely transoceanic dispersal from Indian to Atlantic Ocean were observed, establishing a recent separation between East Atlantic/Indo-Pacific and West Atlantic Ocean basins.This work demonstrated the importance of detailed analyses including the global distribution and diversity of mtDNA lineages of hawksbill turtles. Global demographic patterns were being missed out due to the use of non-representative data or duplicate haplotype names. The systematic exploration of published data allowed achieving relevant conclusions about hawksbill's demography, phylogeography and phylogeny. This shows that worldwide data compilation and standardization can be highly relevant for future studies and for species conservation. Furthermore, this work highlights the need to consolidate international efforts and cooperation to investigate the life history and demographic patterns of hawksbill turtles in a global perspective.


Aphrastura spinicauda), a passerine bird inhabiting the temperate forests of South America. We used 12 microsatellite loci to genotype 582 individuals from eight localities across a large latitudinal range (30°S–56°S). Using population structure metrics, multivariate analyses, clustering algorithms, and Bayesian methods, we found evidence for moderately low regional genetic structure and reduced gene flow towards the range margins. Genetic differentiation increased with geographic distance, particularly in the southern part of the species’ distribution where forests are continuously distributed. Populations in the north seem to experience limited gene flow likely due to forest discontinuity, and may comprise a demographically independent unit. The southernmost population, on the other hand, is genetically depauperate and different from all other populations. Different analytical approaches support the presence of three to five genetic clusters. We hypothesize that the genetic structure of the species follows a hierarchical clustered pattern.Understanding the population genetic consequences of habitat heterogeneity requires assessing whether patterns of gene flow correspond to landscape configuration. Studies of the genetic structure of populations are still scarce for Neotropical forest birds. We assessed range-wide genetic structure and contemporary gene flow in the thorn-tailed rayadito ( Studies of range-wide genetic structure not only provide information about dispersal rates and population connectivity, but also allow a better understanding of how contemporary population dynamics are linked to spatial and temporal environmental variation2. In species with restricted dispersal, genetic variation is often found to vary continuously across space, such that genetic differentiation increases with geographic distance –isolation by distance (IBD)5. However, distinct landscape features can act as barriers to dispersal and may have a profound impact on gene flow and population dynamics –isolation by resistance (IBR)6. As limited dispersal and physical and environmental barriers promote the isolation of local populations, they can ultimately lead to genetic differentiation and divergence or to local extinctions8.Investigating how genetic variation is distributed across a species’ geographic range is fundamental to identify the factors contributing to demographic and population structure9. The vagility of organisms like birds allows them to overcome many potential barriers to gene flow. Consequently, birds exhibit lower levels of genetic structure compared to other vertebrates10. On the other hand, behavioral barriers to dispersal such as philopatry11 can prevent gene flow and promote genetic differentiation12. In fact, genetic differentiation has been documented even at local scales in several bird species15. Furthermore, it has been shown that variation in bird dispersal capacity is associated with differences in ecological traits16. For instance, dispersal propensity is higher in frugivorous and canopy birds compared to insectivorous species or those inhabiting the forest understory17. Likewise, forest interior birds are less prone to cross open areas than those that prefer forest edges or early successional habitats18.The patterns of spatial distribution of neutral genetic variation vary among organisms depending on their dispersal capacity and their level of ecological specialization20. Among Neotropical species, for example, the demographic and genetic consequences of forest fragmentation appear to be more severe for forest specialists21. Given the accelerated rates of deforestation in the Neotropics and the lack of information on spatial patterns of genetic diversity for several species, investigating range-wide genetic structure is crucial to determine the factors affecting gene flow and to identify local populations with unique evolutionary trajectories22. This is essential to inform conservation planning and to predict the genetic consequences of rapid environmental change.Spatial patterns of genetic variation and divergence not only depend on ecological and life-history differences among species, but also on the way a species responds to landscape heterogeneity and habitat loss. Habitat specialization in birds seems to be a good predictor of genetic response to habitat loss and fragmentationAphrastura spinicauda), a secondary-cavity nester found in south-temperate forests distributed along a large latitudinal range (~30°S–56°S) in Chile and western Argentina23 . Quantitative information on dispersal rates is currently lacking, and it remains unclear whether contemporary gene flow is affected by forest fragmentation.Here we assessed spatial patterns of genetic diversity, range-wide genetic structure, and contemporary gene flow in a small forest passerine bird of South America. We studied the thorn-tailed rayadito  explore genetic diversity and range-wide spatial structure and (ii) quantify contemporary gene flow. To this end, we combined population structure metrics, multivariate analyses, clustering algorithms, and Bayesian methods. We describe how genetic diversity varies along the extensive latitudinal gradient, encompassing most of the species’ distribution, and relate the observed patterns to the availability of forested habitats. We also discuss some conservation and taxonomic implications of our results.29. Rayaditos also use second-growth forests and exotic pine plantations as breeding habitats when nestboxes are provided31. Nest-site supplementation experiments suggest that populations in these atypical habitats are limited by nest site availability32. Population responses to nest-site supplementation further suggest that rayaditos are sensitive to forest fragmentation33. Birds inhabiting fragmented landscapes seldom cross open areas between forest patches, and flights through non-forest habitats usually are <300 m34.The thorn-tailed rayadito is a forest specialist that breeds in tree cavities in old-growth forestsA. s. spinicauda, the most widespread form that is distributed across mainland Chile and Argentina and is also present on some islands along the Chilean coast; A. s. bullocki, endemic to Mocha Island; and A. s. fulva, restricted to Chiloé Island and the Chonos islands23. A population occupying the remote islands of the Diego Ramírez Archipelago has long been considered as A. s. spinicauda23, but behavioral, morphological and genetic evidence suggests that it might be a different taxon .Three subspecies of thorn-tailed rayadito are recognized based on morphological differences: A. s. spinicauda, A. s. fulva, and the population from Diego Ramírez , in the metropolitan district in central Chile, in which sclerophyllous forests are the main habitat of rayaditos24. The distribution of wooden habitats in this region is more continuous  using mist nets or mechanical traps inside nestboxes. Birds were marked using individual numbered aluminum bands and bled by brachial venipuncture. All birds were released near the capture site immediately after handling. Details on capture methods and field procedures are given in Supplementary Information Appendix 1. The methodology was approved by the Ethics Committee of the Sciences Faculty, Universidad de Chile following guidelines from Biosecurity Manual from CONICYT (version 2008) and Chilean law 20380 about animal protection. Full permission for sampling and animal ethics approval were granted by Servicio Agrícola y Ganadero  and Corporación Nacional Forestal (CONAF), Chile, and Administración de Parques Nacionales , Argentina.et al.25 . We determined the sex of each individual by amplification of the CHD locus using the primers P2/P840.We extracted DNA from blood samples and genotyped all individuals at 12 autosomal polymorphic microsatellite loci. Primer sequences and PCR conditions are described in Botero-Delgadillo 41 and poppr42 packages in the free software R 3.5.243. Only two loci in the CH population and one in MA deviated from HWE  and estimated the frequency of potential null alleles at each locus in each population. Additionally, we tested for linkage disequilibrium between all pairs of loci in each locality. These analyses were performed in the adegenet44 in R. We also estimated contemporary population effective sizes and tested for genetic signals of recent demographic changes based on allele frequency data. Estimation of effective sizes and demographic inference were implemented in the programs NeEstimator 2.145 and BOTTLENECK 1.2.0246, respectively. Parameter settings and models are specified in Supplementary Information Appendix 1.We assessed standard measures of genetic diversity for each population using the packages poppr and hierfstat47 to calculate the relationship between genetic and geographic distances using the adegenet package in R. We tested for IBD by comparing the observed correlation with a randomly generated distribution of simulated correlations under no IBD. For this, we first generated matrices of genetic  and geographic (Euclidean) distances, and then calculated Spearman rank correlations for 1000 matrix permutations49. We created plots of geographic vs. genetic distances and measured local density of points with a 2-dimensional kernel estimation, to determine whether the data are consistent with a single genetic cline or whether there is evidence for two or more regional clusters50.We performed a Mantel test51, estimating both the Nei´s standardized index (G’ST(Nei))52 and the Hedrick’s standardized index corrected for small samples (G”ST)53. Second, we conducted a Principal Component Analysis (PCA) as implemented in the adegenet package to assess genetic substructure55. Third, we used the snapclust clustering algorithm56, also available in adegenet, to infer the number of genetic clusters present throughout the species’ range. This method performs similarly to standard clustering methods, but rapidly converges to a maximum-likelihood solution by combining a geometric approach and the Expectation-Maximization algorithm56. We tested values of K –i.e. the number of genetically distinct clusters– between 1 and 10, and determined the optimal number of groups using the Akaike Information Criterion (AIC). Fourth, we implemented a Discriminant Analysis of Principal Components (DAPC57) as a complementary approach, given the potential bias introduced by IBD in the PCA and snapclust results58. The DAPC outperforms standard clustering algorithms when genetic structure is more or less continuous, making it ideal for assessing genetic structure under complex dispersal scenarios such as those represented by the stepping-stone and hierarchical stepping-stone models57. This method uses sequential K-means clustering and the Bayesian Information Criterion (BIC) to infer the number of genetic clusters. We selected the number of retained PCs for the DAPC to optimize the a-score , using the optim.a.score function in adegenet. Last, we performed Hierarchical Analyses of Molecular Variance (AMOVA) using the poppr package to quantify the partitioning of genetic variation at different levels. For this, we entered each genetic cluster identified by snapclust as ‘region’ and each sampled locality as ‘population’. We provide further details on these analyses in the Supplementary Information Appendix 1.We used five different approaches to evaluate range-wide genetic structure. First, we calculated G-statistics for all pairs of sampled localities in GenAlEx 6.559. This program calculates migration rates  over the last few generations and does not assume HWE or mutation-drift equilibrium59. We estimated dispersal rates (i) for all sampled populations and (ii) for the genetic clusters identified by snapclust. For each analysis, we used default parameters for allelic frequency (a), gene flow rate (m), and inbreeding (f) for a first run. In subsequent runs, we modified delta values –i.e. the maximum amount a parameter can change in each iteration– to ensure that proposed changes in parameters were between 20–40%59. Adjusted final delta values are provided in the Supplementary Information Appendix 1. Each analysis was run three additional times with 30 million iterations and different random seeds, a sampling frequency of 2000, and a burn-in of 10%. A visual inspection of the continuous parameters sampled from the Bayesian MCMC in the program Tracer 1.7.160 showed that the chains did converge for both analyses , we replicated analyses of genetic structure and dispersal rates using a reduced, relatively even, subset of data. Simulation studies recommend using 40–60 individuals per population for implementing Bayesian analyses60. Consequently, the reduced data set consisted of 40 randomly selected individuals from each of the populations with large sample sizes –i.e. FJ, MA, BA, CH, and NI–, and all individuals from the remaining localities. Analyses using the reduced data yielded similar results and are only briefly described in the Results.Sample sizes among populations were uneven (see Supplementary Information Appendix 1). To minimize potential biases in our results , although the southern populations (TF and NI) also showed high diversity Table . AllelicEstimates of effective population size were higher in those populations with the highest genetic diversity, except for CH Table . GeneticrS = 0.36, p = 0.001; Fig. rS = 0.33, p = 0.05; Supplementary Fig. rS = 0.64, p = 0.02; Supplementary Fig. The Mantel test performed on the matrices of genetic and geographic distances detected IBD (G’ST(Nei) index, and from 0.005 to 0.85 with the G”ST index  to 0.45 (between FJ and DR) when using the ex Table . Regardlex Table . Rayaditex Table . This waIn the PCA, we retained 46 dimensions that explained 80% of the total genetic variance. Although variation was more or less continuous, three to four groups could be discerned when plotting the first three components . The m = 0 in all cases). We therefore show results from analyses excluding this population. A first analysis estimates gene flow between the remaining populations , while a second analysis was based on the remaining genetic clusters –i.e. north, north-central, and south-central clusters . Reported mean dispersal rates were obtained from the MCMC run with the smallest value of -2 log Pr(X/K) and the largest effective sample size  shows asymmetric gene flow between geographically isolated populations and their neighboring localities –i.e. between FJ and SI, CH and BA, NI and TF, and NI and BA Fig. . As expeAlthough estimates of gene flow were similar for the reduced data set, values were in general slightly higher (Supplementary Information Appendix 4). However, changes in dispersal rates rarely exceeded 0.01 (4 out of 42 cases), and patterns of demographic interactions among populations/clusters remained as shown in Fig. Results from a variety of methods showed that populations from the edge of the breeding range of the thorn-tailed rayadito were more differentiated, with evidence for the existence of three to five genetic clusters. Analyses based on both the complete and the reduced data sets identified four genetic clusters that followed a clear geographic pattern: a northern cluster (FJ and SI), a cluster on the west side of the Andes (MA and CH), a cluster east of the Andes extending south to Patagonia , and the rayaditos from the Diego Ramírez Archipelago. We also found genetic evidence for gene flow across the species’ distribution. Below we discuss these findings starting with the demographic implications of the observed patterns of genetic diversity.62. The low number of private alleles and the moderately high levels of heterozygosity in FJ and SI  might keep up genetic variation, possibly counterbalancing the potential effect of genetic drift due to a reduced population size. Although we had no genetic evidence of a reduced population in SI, capture-mark-recapture data show that the breeding population is small . The high genetic diversity observed in the southern populations confirms findings from a previous study27, and supports the hypothesis of an austral paleorefugium in Tierra del Fuego from which rayaditos colonized different localities as the ice retreated and forests expanded during the Holocene. The severely reduced genetic diversity found in DR could be a combined effect of a recent founder event, genetic drift, inbreeding, and strong selection in the extreme environment of this isolated archipelago63.Patterns of allelic richness and heterozygosity are informative about the role that stochastic processes may have played in the recent dynamics of populations. During founder effects or population bottlenecks, rare alleles are readily lostSI Table  suggest 27 also reported low levels of among-population genetic variance (~11% of variance explained in an AMOVA).Genetic structure was pronounced at the between-region level, but not between populations within regions, at least along the more continuous part of the species’ range –i.e. from MA down to NI. The relatively low genetic structure revealed by the AMOVAs  likely reflects the interplay of moderate genetic connectivity and ongoing gene flow that might be limited by strong landscape resistance, particularly towards the range margins. A previous study using mitochondrial sequence data (Cytb) and inter-simple-sequence-repeats (ISSR) from 8 populations 64. This can be particularly problematic when spatial sampling is sparse65, as in this study. However, we suggest that our data support a more complex scenario in which range-wide genetic structure is not strictly continuous, but follows a hierarchical clustered pattern67. For instance, visual inspection of IBD suggests more than one cloud of points nts Fig. . More im57. Results from this approach were concordant with the possibility of clinal variation of genetic diversity in certain parts of the distribution of thorn-tailed rayadito, but also with the presence of distinct clusters. Environmental and physical barriers to dispersal may be responsible for this, but this needs to be further investigated.Although the DAPC is not immune to the potential biases related to spatially heterogeneous sampling, it provides robust results under different scenarios, including a hierarchical stepping-stone model68. The model also predicts that populations are more sparsely distributed towards the range edge69. Although this appears to be the case in the northern range margin, it is clearly due to the reduced availability of suitable habitat, whereas populations in the south exhibited high diversity and connectivity, as available habitat is widespread .Despite the fact that rayaditos are considered forest specialists whose local movements are restricted by landscape structure61. These analyses also showed that FJ and SI could be considered demographically independent from southerly populations. Care must be taken when interpreting these estimates, because these only represent the proportion of immigrants in a given population or cluster, and not the proportion of emigrants59. For instance, birds from FJ comprised a large proportion within the small sample from SI, but this does not reflect the fraction of actual emigration from FJ, which could be very low.Estimates of contemporary gene flow supported the genetic structure analyses and showed that there was no gene flow from or towards the population in DR. Bayesian analyses indicated that ‘continental’ populations and clusters from MA southwards had non-independent demographic dynamics –i.e. the 95% CI around the mean dispersal rates did not include zero68. The larger influx of rayaditos from CH and NI to the continent as compared to the island is harder to explain, but was also found by González and Wink27. Limited breeding space on these islands could force birds to move to the nearby continent.Asymmetric gene flow was more notable in the northern and southern part of the species’ breeding range. In the north, this may reflect source-sink population dynamics between FJ and SI. Although this requires further study, we hypothesize that demographic differences rather than landscape configuration are the main cause underlying this pattern. Because the landscape is highly fragmented and rayaditos moving between FJ and SI are likely to encounter the same resistance to dispersal, more individuals will move from the larger into the smaller population than vice versa61. However, both population- and cluster-based analyses indicated gene flow between populations from the eastern and western sides of the Andes and down to Patagonia.It is worth noting that the analyses based on the reduced data set yielded similar results to those with the complete data set. It is well known that individual assignments and immigration rates are more accurate when using genetic clusters previously identified instead of sampled populations, as clusters comprise more clear-cut groupset al.71). Nevertheless, the population does not seem affected by strong genetic drift in the recent past, but future studies should determine the level of inbreeding depression. The DR population is clearly genetically depauperate and different from other populations. Thus, conservation measures should be given high priority given the population’s potential value as a source for future speciation events . Whether this apparent genetic singularity also involves unique adaptive genetic variation requires further investigation.Based on our characterization of 12 putatively neutral genetic markers, we found that populations at the range margins were genetically differentiated from more central populations. Our results confirm the long-held assumption that the FJ population is relatively isolated from other populations of rayadito .The clear genetic structuring and restricted gene flow in the northern part of the species’ breeding range suggest that FJ and SI should be considered as a demographically independent unit.27 found evidence of genetic differentiation of the subspecies A. s. bullocki (not sampled here) relative to other subspecies, they observed weak differentiation between A. s. fulva and the nominate subspecies. In agreement with this, our results indicate gene flow between A. s. fulva and A. s. spinicauda. It has been suggested that the ochraceous underparts –typical of fulva but also present in bullocki to a lesser extent– may have appeared independently in insular populations near mainland Chile in response to the more humid conditions found on these islands –following Gloger’s Rule23. A phylogeographic study is needed to assess the validity of A. s. fulva as a subspecies.All subspecies of thorn-tailed rayadito are currently described based on morphology and plumage coloration. Although González and Wink27 suggested the potential presence of a non-described Austral subspecies. Although we here describe neutral genetic variation only, our data suggest that populations on the east side of the Andes (e.g. BA) and those further south down to Patagonia may constitute one highly diverse genetic cluster within which moderate levels of gene flow are maintained.González and WinkTo conclude, we summarize the main implications of our study for the current taxonomic treatment of the sampled populations and subspecies. Note that the case of the DR rayadito will be treated in detail elsewhere (Rozzi Supplementary Information.


Arctocephalus gazella), a species that was hunted to the brink of extinction by 18th and 19th century sealers. Molecular genetic data from over 2,000 individuals sampled from all eight major breeding locations across the species’ circumpolar geographic distribution, show that at least four relict populations around Antarctica survived commercial hunting. Coalescent simulations suggest that all of these populations experienced severe bottlenecks down to effective population sizes of around 150–200. Nevertheless, comparably high levels of neutral genetic variability were retained as these declines are unlikely to have been strong enough to deplete allelic richness by more than around 15%. These findings suggest that even dramatic short-term declines need not necessarily result in major losses of diversity, and explain the apparent contradiction between the high genetic diversity of this species and its extreme exploitation history.Understanding the effects of human exploitation on the genetic composition of wild populations is important for predicting species persistence and adaptive potential. We therefore investigated the genetic legacy of large-scale commercial harvesting by reconstructing, on a global scale, the recent demographic history of the Antarctic fur seal ( For example, hunting has decimated many terrestrial species from the plains buffalo to the passenger pigeon4, while over-fishing has resulted in the collapse of many fish and marine mammal stocks6, reducing the capacity of the world᾿s oceans to provide food and ecosystem services7. This depletion of natural capital has been accelerating in pace as human populations continue to grow2 while animal populations across the planet are declining and being driven to extinction at an unprecedented rate8.Anthropogenic exploitation is a major threat to global biodiversityNe) increase extinction risk not only because small populations are more sensitive to demographic and environmental stochasticity10 but also because the erosion of genetic diversity that accompanies severe demographic reductions can reduce population viability and adaptive potential12. However, the genetic consequences of anthropogenic exploitation are in general poorly understood13 with debate continuing over the contribution of genetic factors to extinction11. For example, comparative studies have shown that threatened species often have lower genetic diversity than non-threatened taxa14. However, this pattern does not necessarily imply a causal link between genetic diversity and viability, as many threatened species appear to have small ancestral population sizes, implying that they may not have been very abundant in the first place14.Theoretical and experimental studies suggest that severe declines in the effective population size 23.A compelling approach for investigating the genetic consequences of exploitation on natural populations is to use highly polymorphic genetic markers such as microsatellites to reconstruct the demographic histories of species that were heavily hunted in the past. In particular, recent severe demographic declines or bottlenecksArctocephalus gazella) provides an excellent case to evaluate the genetic consequences of exploitation. This pinniped species is polygynous24, highly site faithful26 and has a generation time of around ten years27. Despite breeding on all of the major sub-Antarctic islands  so that the first in the field at a new sealing ground was sure of an immense booty, and late-comers as likely as not would go empty away’30. Sealing reached its peak at South Georgia in 1800 when over 100,000 seals were harvested31. Shortly afterwards, with the discovery of the other sub-Antarctic islands, a wave of sealing spread across the entire sub-Antarctic region . The 39 microsatellite loci carried between 2 and 26 alleles (mean = 11.44) and none deviated significantly from Hardy-Weinberg equilibrium in more than two out of eight sampling localities after correction for multiple tests (see Supplementary methods and results). The genotyping error rate determined by re-genotyping 96 samples was low .k, which provides an indication of the uppermost hierarchical level of structure, peaked at k = four, while the average log likelihood of the data also gradually increased until k = four, after which a plateau was reached , standardised across species as the average number of alleles per ten individuals, was high relative to other otariids, with the Antarctic fur seal being ranked fourth highest out of 13 species  and the least diverse populations .Allelic richness  provides an indicator of recent bottlenecks because rare alleles are preferentially lost during bottlenecks but their loss has little impact on heterozygosity15. Recent bottlenecks therefore generate a transient excess of heterozygosity relative to a population at equilibrium with an equivalent number of alleles, which can be quantified through coalescent simulation. A significant excess of heterozygosity relative to expectations was detected in all five genetic populations identified by STRUCTURE , the prediction error will be below one. Under the bottleneck model, the prediction error was 0.457 for the bottleneck effective population size (Nebot), indicating that posterior estimates for this parameter contain information about the underlying true values , a plot of the cross-validation results  in the eastern population comprising Kerguelen, Heard and Macquarie Islands, whereas maximum densities were closer to ~200 elsewhere. Similar results were obtained when the sampling locations were analysed separately  only around 14% of alleles were lost. Furthermore, simulated bottlenecks of varying strength and duration, again with an Nehist of 10,000, suggest that only a combination of very strong (i.e. Nebot < 50) and long (i.e. 15–20 generations) bottlenecks would result in genetic diversity losses in excess of 50%. Consequently, the Antarctic fur seal is unlikely to have lost a substantial proportion of neutral genetic diversity despite an inferred steep decline in Ne due to overhunting.The expected loss of neutral genetic diversity caused by a bottleneck with an effective size 000 Fig. . Under tWe investigated the genetic consequences of historical commercial exploitation in the Antarctic fur seal. Information on the number of sealing vessels recorded as having visited each of the main sealing grounds allowed us both to formulate realistic priors for our ABC analysis and to interpret our results in the light of historical evidence. An innovation of this study was to use a comparative approach to shed light on the genetic diversity and recent demographic histories of multiple populations within a single species.54. However, our larger sample size of individuals and exhaustive geographical coverage yields a more comprehensive global picture. On the one hand, the discovery of four genetically distinct clusters builds upon recent studies suggesting that relict populations likely persisted in localities other than South Georgia50. This is in contrast to the previously held belief that Antarctic fur seals only survived in one relict population off South Georgia46 and suggests that, at least in some regards, the species as a whole may have been more resilient to commercial exploitation than previously believed. Nevertheless, our data also suggest that at least one and possibly several populations may have gone locally extinct. In particular, Macquarie, Heard and Kerguelen Islands showed little in the way of population structure, consistent with the suggestion that this species was locally extirpated at Macquarie and Heard Islands, leaving these sites to be subsequently recolonised by individuals mainly from the Kerguelen Archipelago55. In addition, a strong signal of admixture at Marion and Crozet Islands implies that these two populations may have either gone extinct or been reduced to sufficiently small numbers for the local gene pool to be swamped by emigrants from nearby Bouvetøya and the Kerguelen Islands. This is supported by anecdotal evidence suggesting that Antarctic fur seals were not present after sealing ceased at Marion Island in the early 1930s56.Consistent with previous studies of Antarctic fur seals, we found evidence for strong population structurerelative strength of population decline, whereas our ABC analysis specifically tested for an absolute reduction in Ne (i.e. to below 600). By focusing on time priors that reflected the known sealing history, the ABC analysis also evaluated a clear hypothesis‒that 18th and 19th century commercial sealing resulted in strong bottlenecks. Although in principle, similar patterns of genetic diversity might be produced by more ancient bottlenecks, these are unlikely to be detected reliably using microsatellite data when subsequent recovery occurred57. This is in keeping with a previous study in which a simple model incorporating a recent bottleneck was better supported than a more complex model that also included a small population size during the last glacial maximum followed by expansion20.Two approaches were used to infer the strength of recent population declines. Heterozygosity excess provides a measure of the Nebot estimates from the ABC analysis were also quite similar across populations, although the mode of the posterior distribution was slightly lower for the easternmost population. This subtle difference in inferred bottleneck intensity is a reflection of the lower genetic diversity of the Kerguelen Islands, Heard and Macquarie Island populations. It is furthermore consistent with the historical sealing data , we additionally sampled 91 pups from a reference population of A. tropicalis at Macquarie Island in order to check the full A. gazella dataset for individuals with a high proportion of A. tropicalis ancestry (see Supplementary methods and results). We found that 49 of the Antarctic fur seal individuals were inadvertently pure A. tropicalis, while five of the A. tropicalis reference individuals were hybrids  and 10 iterations for each k. The most likely number of clusters was evaluated using the maximal average value of Ln P(D) as well as the Evanno method78. Individual admixture proportions were integrated over all simulations using pophelper79. For comparison, we also implemented principle component analysis (PCA) using the R-package adegenet80. Genetic diversity was quantified as allelic richness (Ar) within hierfstat81. Observed heterozygosity (Ho) was calculated with adegenet80, the M-ratio was calculated with StrataG82, the inbreeding coefficient Fis was calculated with diveRsity83 and the number of private alleles was calculated with poppr84. Pairwise Fst values together with their 95% confidence intervals were obtained using hierfstat81.For the full dataset of 2,000 Antarctic fur seals, we evaluated whether population structure could be detected without prior knowledge of sampling locations using STRUCTURE version 2.3.385. This analysis was based on the two-phase model with 80% single-step mutations (TPM80), consistent with recent estimates from multiple pinniped species20. A total of 1,000 iterations were run for each population and all other parameters were left at default values. Statistical significance was evaluated using sign, standardized differences and Wilcoxon signed ranks tests.We used two coalescent-based approaches to infer recent demographic histories. First, we quantified the proportion of loci in heterozygosity-excess using the program BOTTLENECK v. 1.2.02th to 20th century commercial exploitation caused severe demographic reductions, we defined a “bottleneck model”, which incorporated a strong decline in population size within strictly bound time priors that fit the time period hunting took place. The priors for pre- and post-bottleneck effective population size were drawn independently from the same distribution to allow the model not only to incorporate a bottleneck, but also longer-term reductions or expansions within realistic bounds. For comparison, we defined a “non-bottleneck model” which was identical to the bottleneck model with the exception of a bottleneck.Second, we tested for signatures of recent population bottlenecks using a coalescent-based approximate Bayesian computation (ABC) framework. Support was evaluated for two alternative demographic models  and historical pre-bottleneck effective population size (Nehist) were drawn from a log-normal distribution in order to encompass a wide range of values while mainly sampling from the most likely ones. We specified Ne and Nehist ~ lognorm, which concentrated around 90% of sampling in the range between 3,000 and 15,000 while also occasionally simulating much larger Ne values. This prior range encompassed the estimated current census population sizes across all islands, i.e. from 3,600 at Marion Island86 to 5 × 106 at South Georgia38. For the bottleneck model, the effective population size during the bottleneck (Nebot) was drawn from a uniform distribution between 1 and 600 . Start and end time for the bottleneck were defined by tbotstart and tbotend and drawn from uniform distributions between ten and 40  and one and 20  generations ago respectively. This places the bottleneck in the last one to four centuries, as the generation time of the Antarctic fur seal is estimated at around nine years87. For the neutral model, the time parameter corresponding to the historical population size (thist) was drawn from a uniform distribution ranging between 10 and 40 generations ago . The microsatellite mutation rate (µ) was drawn from a uniform prior with µ ~ U. A stepwise mutation model was used with the geometric parameter GSMpar reflecting the proportion of multistep mutations, uniformly distributed from GSMpar ~U.For both models, priors for current effective populations size  for the simulated data: (1) allelic richness; (2) the proportion of low frequency (<5%) alleles; and (3) the M-ratio88, and retained the 5,000 simulations with summary statistics closest to the empirical data using a tolerance threshold of 5 × 10−4.Genetic data were simulated under each demographic model for 181 individuals, reflecting the smallest genetic population (as determined by STRUCTURE) in the empirical dataset, and 39 microsatellites using fastsimcoal2cv4postpr function of the abc R-package89. Here, simulations are selected at random and their summary statistics are used as pseudo-observed data. The remaining simulations are then used to classify the data into the bottleneck or the neutral model. If ABC is capable of distinguishing between the two models, a large posterior probability will be assigned to the same model under which the pseudo-observed data were generated. This procedure was repeated 100 times and the posterior probabilities for a given model were averaged to derive the rate of misclassification. Furthermore, posterior probabilities were calculated for each model using the rejection method of the postpr function and the fit of the preferred model to the empirical data was evaluated using a formal hypothesis testing procedure implemented by the gfit function of the abc R-package.To evaluate model specification and fit, we implemented leave-one-out cross-validation using the cv4abc function. For a randomly selected pseudo-observed dataset, parameters were estimated using all remaining simulations via ABC using the rejection method. This procedure was repeated 1,000 times and the mean prediction error was calculated. The smaller the prediction error, the better the fit to the true parameter value. After performing the four checks, we constructed posterior distributions of the parameters using a simple rejection algorithm with the abc function of the abc package.Next, we tested the accuracy of the ABC parameter estimates using leave-one-out cross validation in the 52. After generating posterior distributions for each parameter, 1,000 multivariate parameters were sampled from their respective posterior distributions and used to simulate summary statistics a posteriori based on the preferred model. Those summary statistics were then plotted as histograms and superimposed over the observed summary statistics23.To provide a measure of the fit of the preferred models to the empirical data, we conducted posterior predictive checks53 to estimate the expected loss of microsatellite alleles for a bottleneck effective population size (NeBot) of 200 lasting for ten generations. As the historical effective population size Nehist is unknown but will be an important determinant of the overall level of genetic diversity, we simulated three different scenarios with Nehist values of 1,000, 10,000 and 50,000. The post-bottleneck Ne is unlikely to have a major effect on diversity, as only a few generations have passed since the bottleneck, and was thus simulated equal to the Nehist within each scenario. All other model parameters were the same as in the models above. We then compared the resulting allelic richness to the allelic richness generated in equivalent models with constant effective population sizes of 1,000, 10,000 and 50,000 respectively to estimate the proportional loss of alleles due to the bottleneck. To account for uncertainty in building coalescent trees, we simulated 100,000 datasets under both the bottleneck and constant size model and treated the difference in mean allelic richness between the distributions as the average loss of alleles. To further explore the effects of varying bottleneck strength and duration, we conducted a final round of simulations based on the scenario that provided the best fit to the empirical data . Specifically, we quantified the expected loss of alleles for Nebot ranging 25 to 600 in steps of 25 and for bottleneck duration ranging from one to 20 generations in steps of one. As hunting ceased by the turn of the 20th century, we fixed tbotend at ten generations ago. 1,000 simulations were conducted for each combination of parameter values.Finally, we used neutral coalescent simulations in fastsimcoal2Samples were collected from the South Shetlands under Marine Mammal Protection Act Permit no. 774-1847-04 granted by the Office of Protected Resources, National Marine Fisheries Service. Sampling and procedures used on South Georgia were approved by the British Antarctic Survey Animal Welfare and Ethics Review Body (reference no. PEA6) and collected as part of the Polar Science for Planet Earth programme of the British Antarctic Survey. Samples from Bouvetøya were obtained under Permit no. 7001 issued by the Norwegian Department of Plants, Fish, Animals and Food. Samples were collected on Marion Island, Crozet Islands, Kerguelen Islands, Heard Island and Macquarie Island with approval from the Animal Ethics Committee of the Faculty of Veterinary Science, University of Pretoria, South Africa , the Prince Edward Islands Management Committee and Department of Environmental Affairs, the Territory of Heard Island and McDonald Islands Environment Protection and Management Ordinance 1987 (Permit no. 00/18) and the Parks and Wildlife Service, Tasmania (Scientific Collecting Permit no. FA 99167). All sampling and procedures were performed in accordance with the relevant guidelines and regulations.Supplementary information.