Plethodon shenandoah) is a federally endangered species known only from three mountaintops in Virginia, USA. To reconstruct the evolutionary history and population connectivity of this species, we generated both mitochondrial and nuclear data using sequence capture from individuals collected across all three mountaintops. Applying population and landscape genetic methods, we found strong population structure that was independent of geographic distance. Both the nuclear markers and mitochondrial genomes indicated a deep split between the most southern population and the genetically similar central and northern populations. Although there was some mitochondrial haplotype‐splitting between the central and northern populations, there was admixture in nuclear markers. This is indicative of either a recent split or current male‐biased dispersal among mountain isolates. Models of landscape resistance found that dispersal across north‐facing slopes at mid‐elevation levels best explain the observed genetic structure among populations. These unexpected results highlight the importance of incorporating landscape features in understanding and predicting the movement and fragmentation of this range‐restricted salamander species across space.Species with narrow environmental tolerances are often distributed within fragmented patches of suitable habitat, and dispersal among these subpopulations can be difficult to directly observe. Genetic data can help quantify gene flow between localities, which is especially important for vulnerable species with a disjunct range. The Shenandoah salamander ( MountaiPlethodon salamanders are associated with cool, moist microhabitats in mature, upland forests where temperature and moisture conditions allow for cutaneous respiration and successful foraging of invertebrates. Because Plethodon salamanders are lungless and rely on cutaneous respiration, individuals typically demonstrate avoidance of warmer temperatures and drier substrates  have small home ranges  is a montane‐adapted species that is federally endangered in the US and is considered Vulnerable by the International Union for Conservation of Nature  of the mitochondrial Cytochrome‐b gene, and failed to find population structure between mountains, indicating either contemporary gene flow or recent population fragmentation  were identified at both the species and population levels. We focused on developing a probe set appropriate for both species to facilitate future work. We targeted 875 loci based on the RADtag sequences, added 334 partial UCE's that had previously worked on Psuedotriton ruber  and 43 conserved loci that were found in publicly available transcriptome assemblies of Ambystoma mexicanum and Notophthalmus viridescens  to enrich our genomic libraries for a predetermined set of loci. As no genomic or transcriptomic data were available for P. shenandoah samples using the Qiagen DNeasy blood and tissue kit  following manufacturer's guidelines. Up to 2,000 ng of gDNA was sheared to an average size of ~400 bp using the Q800R sonicator  and subsequently prepared for Illumina sequencing using Nextera‐style adapters with dual‐indexed 8 bp barcodes. Libraries were quantified by Qubit 2.0 and samples of similar concentrations were pooled in groups of 6–10 samples for a total of either 500 ng or 1,000 ng in 3.4 μl of volume. Hybridization followed manufacturer's guidelines for MYcroarray's stringent protocol except for the increased hybridization time of 48 hr as well as an increased concentration of Cot‐1. Following sequence capture, the samples were re‐amplified for 16 cycles using Nextera primers, quantified using qPCR and sequenced with paired‐end reads of 178 bp and 151 bp on two lanes of HiSeq 2500.Genomic DNA was extracted from 85 2.3We demultiplexed sequences, removed adapters with Cutadapt 1.9 Martin, , and mapP. shenandoah and P. cinereus) together with Trinity v2.4 using default settings  and the large size of the salamander genome, using only the baits as a reference sequence may result in paralogs being combined as one locus, resulting in spurious SNPs. To create a reference of longer contigs that could distinguish between similar loci, we assembled all the reads that did not map to the mitogenome for three individuals of each species  and expected heterozygosity (He) for every SNP with plink v1.05  and P. petraeus (AY728222). For phylogenetic reconstruction, we removed all non‐coding sequences and partitioned the data by codon position for a total alignment of 11,310 bp. We estimated the phylogeny and divergence times using Bayesian inference in BEAST 2.5.0 , thus we did not expect that a simple linear response would adequately represent dispersal resistance across elevations. We, therefore, created multiple surfaces along nine different elevations from 800 to 1,200 m in 50 m intervals using a Gaussian transformation of the 15 m DEM using ArcGIS  can be used to rank the importance of resistance surfaces . The mitochondrial phylogenetic tree . Within P. shenandoah we recovered three lineages corresponding to the three mountains with an earlier divergence between the southern population and the other two  and more recent divergence between the northern and central populations . A total of 67 Shenandoah samples had over 90% of the selected mitochondrial genome covered and were included in the haplotype network (GenBank MK493161–MK493192). The haplotype network  and the Central mountain and the Northern mountain (the northern corridor) were almost equal, the mean resistance between populations differed for most of the landscape resistance surfaces we examined. In all cases, resistance was substantially higher when calculated directly between the Southern and Northern mountain (the complete corridor), making a stepwise model of dispersal more likely .The maximum elevation on Mountain N is approximately 1,100 m whereas the highest points on Mountains C and S are greater than 1,200 m. For this reason, the relative landscape resistance for the northern corridor was lower from 800–1,000 m and much higher between 1,050–1,200 m Figure . The low4P. shenandoah. Given similarity in geographic distances among the populations, we hypothesized that P. shenandoah populations would have a similar genetic distance among the three mountains. However, from both the mitochondrial and nuclear DNA results, we identified a strong split between the more similar northern and central populations and the distinct southern population.Despite the small geographic distances among mountains, we found a strong and consistent signal of genetic structure in both the nuclear and mitochondrial datasets for Our results contrast markedly with the only other population genetics study on the Shenandoah salamander, which found no mitochondrial genetic structure across the three mountains  indicating that the structural cause  is likely a common denominator. Expanses of south‐facing slopes along the southern corridor would also seem to favor dispersal of P. shenandoah if it is indeed more drought tolerant than P. cinereus (Jaeger, There are several other potential mechanisms that could have produced the observed genetic structure results that are independent of landscape characteristics or gradients. Competition with  Jaeger, .P. shenandoah(Grant, Brand, De Wekker, Lee, & Wofford, In addition, there are potential factors that we cannot currently test for that could be influencing genetic structure. Recent research finds that the cloud base height sets the lower limit of the distribution of 5P. shenandoah show that northern and central populations group together genetically whereas the southern population is distinct with limited to no gene flow with the other two populations. Phylogenetic reconstruction shows that the southern population likely has been separated from the other two populations since the late Pleistocene. The stronger signal of contemporary gene flow in the nuclear genome is likely a result of male sex‐biased dispersal that allows some mitochondrial haplotype structure across the landscape. Both elevation and north‐facing slopes are potential features that can generate this genetic structure by allowing for dispersal between the central and northern population. These results help us understand the evolutionary history of P. shenandoah, and can help guide potential conservation efforts for this endangered and range‐restricted species. Furthermore, our work identifies several landscape features that are likely to influence dispersal for low vagility species such as salamanders and demonstrates the importance of using both mitochondrial and nuclear markers to make population genetic inferences.Genetic data collected on three populations of The authors have no conflicts of interest or competing interests to disclose.EHCG, AB and RCF conceived and designed the study. AB and EHCG collected the samples and NCR and KPM conducted the labwork. KPM, AB, RCF and EHCG analyzed the data, and wrote the paper. All authors read and approved the final product. Click here for additional data file.


The material culture of the Late Chalcolithic period in the southern Levant (4500–3900/3800 BCE) is qualitatively distinct from previous and subsequent periods. Here, to test the hypothesis that the advent and decline of this culture was influenced by movements of people, we generated genome-wide ancient DNA from 22 individuals from Peqi’in Cave, Israel. These individuals were part of a homogeneous population that can be modeled as deriving ~57% of its ancestry from groups related to those of the local Levant Neolithic, ~17% from groups related to those of the Iran Chalcolithic, and ~26% from groups related to those of the Anatolian Neolithic. The Peqi’in population also appears to have contributed differently to later Bronze Age groups, one of which we show cannot plausibly have descended from the same population as that of Peqi’in Cave. These results provide an example of how population movements propelled cultural changes in the deep past. The Late Chalcolithic material culture in the southern Levant has unique attributes that suggest spread of people or culture. Here, the authors use genome-wide ancient DNA data from 22 individuals from a Chalcolithic site and show evidence of complex population movements and turnovers. The period’s impressive metal artifacts, which reflect the first known use of the “lost wax” technique for casting of copper, attest to the extraordinary technical skill of the people of this period11.The material culture of the Late Chalcolithic period in the southern Levant contrasts qualitatively with that of earlier and later periods in the same region. The Late Chalcolithic in the Levant is characterized by increases in the density of settlements, introduction of sanctuaries13. Others have suggested that the local populations of the Levant were entirely responsible for developing this culture, and that any similarities to material cultures to the north are due to borrowing of ideas and not to movements of people19.The distinctive cultural characteristics of the Late Chalcolithic period in the Levant  have few stylistic links to the earlier or later material cultures of the region, which has led to extensive debate about the origins of the people who made this material culture. One hypothesis is that the Chalcolithic culture in the region was spread in part by immigrants from the north , based on similarities in artistic designs21. It has been estimated that the burial cave contained up to 600 individuals22, making it the largest burial site ever identified from the Late Chalcolithic period in the Levant. Direct radiocarbon dating suggests that the cave was in use throughout the Late Chalcolithic (4500–3900 BCE), functioning as a central burial location for the region23.To explore these questions, we studied ancient DNA from a Chalcolithic site in Northern Israel, Peqi’in Fig. . This ca25. By the Bronze Age, however, expansion of different Near Eastern agriculturalist populations—Anatolian, Iranian, and Levantine—in all directions and admixture with each other substantially homogenized populations across the region, thereby contributing to the relatively low genetic differentiation that prevails today24. Lazaridis et al.24 showed that the Levant Bronze Age population from the site of 'Ain Ghazal, Jordan (2490–2300 BCE) could be fit statistically as a mixture of around 56% ancestry from a group related to Levantine Pre-Pottery Neolithic agriculturalists  and 44% related to populations of the Iranian Chalcolithic . Haber et al.26 suggested that the Canaanite Levant Bronze Age population from the site of Sidon, Lebanon (~1700 BCE) could be modeled as a mixture of the same two groups albeit in different proportions . However, the Neolithic and Bronze Age sites analyzed so far in the Levant are separated in time by more than three thousand years, making the study of samples that fill in this gap, such as those from Peqi’in, of critical importance.Previous genome-wide ancient DNA studies from the Near East have revealed that at the time when agriculture developed, populations from Anatolia, Iran, and the Levant were approximately as genetically differentiated from each other as present-day Europeans and East Asians are today27. We extracted DNA28 and built next-generation sequencing libraries to which we attached unique barcodes to minimize the possibility of contamination. We treated the libraries with Uracil–DNA glycosylase (UDG) to reduce characteristic ancient DNA damage at all but the first and last nucleotides29  a lack of variation at known polymorphic positions on chromosome X (point estimates of contamination of less than 2%)32. We also restricted to individuals with at least 5000 of the targeted SNPs covered at least once.In a dedicated clean room facility at Harvard Medical School, we obtained bone powder from 48 skeletal remains, of which 37 were petrous bones known for excellent DNA preservation26 to shed light on the history of the individuals buried in the Peqi’in cave site, and on the population dynamics of the Levant during the Late Chalcolithic period.This procedure produced genome-wide data from 22 ancient individuals from Peqi’in Cave , with the individuals having a median of 358,313 of the targeted SNPs covered at least once . The dataset is of exceptional quality given the typically poor preservation of DNA in the warm Near East, with a higher proportion of samples yielding appreciable coverage of ancient DNA than has previously been obtained from the region, likely reflecting the optimal sampling techniques we used and the favorable preservation conditions at the cave. We analyzed this dataset in conjunction with previously published datasets of ancient Near Eastern populations24, observing a median pairwise FST of 0.023 (range: 0.009–0.061) between the Peqi’in samples (abbreviation: Levant_ChL) and other West Eurasian Neolithic and Chalcolithic populations, relative to a previously reported median pairwise FST of 0.098 (range: 0.023–0.153) observed between populations in pre-Neolithic periods, 0.015 (range: 0.002–0.045) in the Bronze Age periods, and 0.011 (range: 0–0.046) in present-day West Eurasian populations24. Thus, the collapse to present-day levels of differentiation was largely complete by the Chalcolithic , and we used these as our analysis set. Taking advantage of the new data point added by the Peqi’in samples, we began by studying how genetic differentiation among Levantine populations changed over time. We replicate previous reports of dramatic decline in genetic differentiation over time in West EurasiaWe also observe an increase in genetic diversity over time in the Levant as measured by the rate of polymorphism between two random genome sequences at each SNP analyzed in our study. Specifically, the Levant_ChL population exhibits an intermediate level of heterozygosity relative to the earlier and later populations Fig. .Fig. 2GeST, are consistent with a model in which gene flow reduced differentiation across groups while increasing diversity within groups.Both the increasing genetic diversity over time, and the reduced differentiation between populations as measured via F33. In a plot of the first and second principal components nts Fig. , the sam34 produced results consistent with PCA in suggesting that individuals from the Levant_ChL population had a greater affinity on average to Iranian agriculturalist-related populations than was the case for earlier Levantine individuals. Figure K = 11 clusters 24. Like all Levantine populations, the primary ancestry component assigned to the Levant_ChL population, shown in blue, is maximized in earlier Levant_N and Natufian individuals. ADMIXTURE also assigns a component of ancestry in Levant_ChL, shown in green, to a population that is generally absent in the earlier Levant_N and Natufian populations, but is present in later Levant_BA_South and Levant_BA_North samples. This green component is also inferred in small proportions in several samples assigned to the Levant_N, but there is not a clear association to archaeological location or date, and these individuals are not significantly genetically distinct from the other individuals included in Levant_N by formal testing, and thus we pool all Levant_N for the primary analyses in this study  for all ancient test populations, A , we find that the statistic is often negative, with Near Eastern populations outside the Levant sharing more alleles with Levant_ChL than with Levant_N , which for some populations, A, is significantly negative indicating that allele frequencies in Levant_ChL tend to be intermediate between those in Levant_N and A—a pattern that can only arise if Levant_ChL is the product of admixture between groups related, perhaps distantly, to Levant_N and A35. The most negative f3- and f4-statistics are produced when A is a population from Iran or the Caucasus. This suggests that the Levant_ChL population is descended from a population related to Levant_N, but also harbors ancestry from non-Levantine populations related to those of Iran or the Caucasus that Levant_N does not share (or at least share to the same extent).To determine the relationship of the Levant_ChL population to other ancient Near Eastern populations, we used , A Fig. . Howevert_N Fig. . We conc36.We used qpAdm as our main tool for identifying plausible admixture models for the ancient populations for which we have data (see Supplementary Note N “Left” populations—including a “target” population (the population whose ancestry is being modeled) and a set of N − 1 additional populations—are consistent with being derived from mixtures in various proportions of N − 1 ancestral populations related differentially to a set of outgroup populations, referred to as “Right” populations. For all our analyses, we use a base set of 11 “Right” outgroups referred to collectively as “09NW”—Ust_Ishim, Kostenki14, MA1, Han, Papuan, Onge, Chukchi, Karitiana, Mbuti, Natufian, and WHG—whose value for disentangling divergent strains of ancestry present in ancient Near Easterners has been documented in Lazaridis et al.24 . To evaluate whether the “Left” populations are consistent with a hypothesis of being derived from N − 1 sources, qpAdm effectively computes all possible statistics of the form f4, for all possible pairs of populations in the proposed “Left” and “Right” sets. It then determines whether all the statistics can be written as a linear combination of f4-statistics corresponding to the differentiation patterns between the proposed N − 1 ancestral populations, appropriately accounting for the covariance of these statistics and computing a single p value for fit based on a Hotelling T-squared distribution36. For models that are consistent with the data (p > 0.05), qpAdm estimates proportions of admixture for the target population from sources related to the N − 1 ancestral populations (with standard errors). Crucially, qpAdm does not require specifying an explicit model for how the “Right” outgroup populations are related.The qpAdm method evaluates whether a tested set of p values for all possible Left populations were below 0.05  and European Early Neolithic (Europe_EN), we repeated the analysis including additional outgroup populations in the “Right” set that are sensitive to the European hunter-gatherer-related admixture present to a greater extent in Europe_EN than in Anatolia_N  Levant_N 2) either Anatolia_N or Europe_EN and (3) either Iran_ChL, Iran_N, Iran_LN, Iran_HotuIIIb or Levant_BA_North. These are all very similar models, as Europe_EN  are known to be genetically primarily derived from Anatolian agriculturalists (Anatolia_N) either A Levant_N05 Table . Only th26. This suggests that the Levant_ChL population may not be directly ancestral to these later Bronze Age Levantine populations, because if it were, we would also expect to detect an Anatolia_N component of ancestry. In what follows, we treat Levant_BA_South and Levant_BA_North as separate populations for analysis, since the symmetry statistic f4 is significant for a number test populations A (|Z| ≥ 3)  and Sidon in present-day Lebanon (Levant_BA_North) can be modeled as two-way admixtures, without the Anatolia_N contribution that is required to model the Levant_ChL populationTo test the hypothesis that Levant_ChL may be directly ancestral to the Bronze Age Levantine populations, we attempted to model both Levant_BA_South and Levant_BA_North as two-way admixtures between Levant_ChL and every other ancient population in our dataset, using the base 09NW set of populations as the “Right” outgroups. We also compared these models to the previously published models that used the Levant_N and Iran_ChL populations as sources Table . In the 26 , although we find that the specific model involving Iran_HotuIIIb no longer works with this “Right” set of populations. Investigating this further, we find that the addition of Anatolia_N in the “Right” outgroup set excludes the model of Levant_N + Iran_ChL favored by Haber et al.26. These results imply that a population that harbored ancestry more closely related to Levant_ChL than to Levant_N contributed to the Levant_BA_North population, even if it did not contribute detectably to the Levant_BA_South population.We observe a qualitatively different pattern in the Levant_BA_North samples from Sidon, Lebanon, where models including Levant_ChL paired with either Iran_N, Iran_LN, or Iran_HotuIIIb populations appear to be a significantly better fit than those including Levant_N + Iran_ChL. We largely confirm this result using the “Right” population outgroups defined in Haber et al.” set of 11 outgroups. The addition of the Levant_ChL causes the model to fail, indicating that Levant_BA_South and Levant_ChL share ancestry following the separation of both of them from the ancestors of Levant_N and Iran_ChL. Thus, in the past there existed an unsampled population that contributed both to Levant_ChL and to Levant_BA_South, even though Levant_ChL cannot be the direct ancestor of Levant_BA_South because, as described above, it harbors Anatolia_N-related ancestry not present in Levant_BA_South.We obtained additional insight by running qpAdm with Levant_BA_South as a target of two-way admixture between Levant_N and Iran_ChL, but now adding Levant_ChL and Anatolia_N to the basic 09NW “Right38, which determines the minimum number of source populations required to model the relationship between “Left” populations relative to “Right” outgroup populations. Unlike qpAdm, qpWave does not require that populations closely related to the true source populations are available for analysis. Instead it treats all “Left” populations equally, and attempts to determine the minimum number of theoretical source populations required to model the “Left” population set, relative to the “Right” population outgroups. Therefore, we model the relationship between Levant_N, Levant_ChL, and Levant_BA_South as “Left” populations, relative to the 09NW “Right” outgroup populations . This may explain why Haber et al.26. Thus, the present study substantially increases the power to analyze the change in frequencies of alleles known to be biologically important.This study nearly doubles the number of individuals with genome-wide data from the ancient Levant. Measured in terms of the average coverage at SNPs, the increase is even more pronounced due to the higher quality of the data reported here than in previous studies of ancient Near Easterners39 , East Asian (EAS), and European (EUR) ancestry in the 1000 Genomes Project Phase 3 datasetrs12913832 near the OCA2 gene, with a proven association to blue eye color in individuals of European descent40, has an estimated alternative allele frequency of 49% in the Levant_ChL population, suggesting that the blue-eyed phenotype was common in the Levant_ChL.We highlight three findings of interest. First, an allele (G) at rs1426654 in the SLC24A5 gene which is one of the most important determinants of light pigmentation in West Eurasians41 is fixed for the derived allele (A) in the Levant_ChL population suggesting that a light skinned phenotype may have been common in this population, although any inferences about skin pigmentation based on allele frequencies observed at a single site need to be viewed with caution42.Second, an allele at G) at rs6903823 in the ZKSCAN3 and ZSCAN31 genes which is absent in all early agriculturalists reported to date  and that has been argued to have been under positive selection by Mathieson et al.31, occurs with an estimated frequency of 20% in the Levant_ChL, 17% in the Levant_BA_South, and 15% in the Iran_ChL populations, while it is absent in all other populations. This suggests that the allele was rising in frequency in Chalcolithic and Bronze Age Near Eastern populations at the same time as it was rising in frequency in Europe.Third, an allele  Levantine populations, which were dominated by haplogroup E24, and later Bronze Age individuals, all of whom belonged to haplogroup J26.We find that the individuals buried in Peqi’in Cave represent a relatively genetically homogenous population. This homogeneity is evident not only in the genome-wide analyses but also in the fact that most of the male individuals (nine out of ten) belong to the Y-chromosome haplogroup T (see Supplementary Table 24.Our finding that the Levant_ChL population can be well-modeled as a three-way admixture between Levant_N (57%), Anatolia_N (26%), and Iran_ChL (17%), while the Levant_BA_South can be modeled as a mixture of Levant_N (58%) and Iran_ChL 42%), but has little if any additional Anatolia_N-related ancestry,  can only be explained by multiple episodes of population movement. The presence of Iran_ChL-related ancestry in both populations – but not in the earlier Levant_N – suggests a history of spread into the Levant of peoples related to Iranian agriculturalists, which must have occurred at least by the time of the Chalcolithic. The Anatolian_N component present in the Levant_ChL but not in the Levant_BA_South sample suggests that there was also a separate spread of Anatolian-related people into the region. The Levant_BA_South population may thus represent a remnant of a population that formed after an initial spread of Iran_ChL-related ancestry into the Levant that was not affected by the spread of an Anatolia_N-related population, or perhaps a reintroduction of a population without Anatolia_N-related ancestry to the region. We additionally find that the Levant_ChL population does not serve as a likely source of the Levantine-related ancestry in present-day East African populations , 0.05% Proteinase K (Sigma)) to the bone powder and incubated in 2.0 mL tubes at 37 °C overnight, while rotating. Following incubation, we centrifuged the samples at maximum speed for 2 min, and added 13 mL of binding buffer (5 M GuHCl (Sigma), 40% Isopropanol (Sigma), 400 μg Sodium Acetate (Sigma), pH 5.2 (Sigma)) to the supernatant. We transferred the mixture to a High Pure Extender from a Viral Nucleic Acid Large Volume Kit (Roche) and centrifuged at 2000×g until all liquid disappeared from the funnel. We detached the silica column from the funnel, placed it in a fresh 2 mL collection tube, and spun for 1 min at 8000×g. We performed two washes by adding 700 μL PE buffer (Qiagen) to the columns, and spun at 8000×g for 30 s, replacing the collection tube after each wash. We performed a dry spin at maximum speed for 1 min, and then replaced the collection tube. We removed the DNA eluate from the column by adding 45 μL of TTE , 1 mM EDTA, pH 9.0 (BioExpress), 0.05% Tween-20 (Sigma)) to the silica matrix, incubating for 5 min, and then spun at maximum speed for 1 min. We repeated this step until we obtained a total volume of 90 μL. For reattempts of one of the samples, we washed the powder with 1 mL 0.5% bleach (incubating for 15 min), followed by three washes with 1 mL water (incubating 3 min), prior to DNA extraction as described in Korlević et al.65 , which we added to the silica membrane and allowed to sit for 5 min, followed by centrifuging for 1 min at maximum speed. We ligated unique adapters to the molecules in each sample by incubating the sample mixture in a ligation reaction mixture (1× T4 DNA ligase buffer (ThermoFisher), 5% PEG-4000 (ThermoFisher), 0.25 μM P5-adapter (see ref. 29 for suggested preparation information), 0.25 μM P7 adapter (see ref. 29 for suggested preparation information), 0.125 U/μL T4 DNA ligase (ThermoFisher)) for 30 min at room temperature. We cleaned up the ligation mixture using the clean-up procedure described above, eluting in 20 μL 10 mM Tris-HCl. We filled in the ligated adapters by adding a fill-in reaction mixture (1× ThermoPol buffer (NEB), 250 μM dNTP Mix (ThermoFisher), 0.4 U/μL Bst Polymerase, large fragment (NEB)) to the ligation product, and incubating at 37 °C for 20 min, followed by 80 °C for 20 min. Finally, we amplified the libraries via PCR by adding 39 μL of the fill-in reaction product to the PCR reaction mixture (1× Pfu Turbo Cx Reaction Buffer (Agilent Technologies), 0.4 μM PreHyb-F (5′-CTTTCCCTACACGACGCTCTTC-3′), 0.4 μM PreHyb-R (5′-GTGACTGGAGTTCAGACGTGTGCT-3′), 0.2 mM dNTP Mix (ThermoFisher), 5U Pfu Turbo Cx Hotstart DNA Polymerase (Agilent Technologies)). We divided each reaction into 50 μL aliquots and placed in a sealed PCR plate. We performed initial denaturation of the samples for 2 min at 95 °C, followed by 30 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 1 min, and performed a final extension at 72 °C for 10 min.Peqi’in burial Cave was excavated under the auspices of the Israel Antiquities Authority (Permit no. 2297/1995). We screened 46 human skeletal elements from Peqi’in Cave, of which 37 were petrous fragments of the temporal bone. We prepared between 15 and 114 mg of bone powder for each sample by drilling from a compact part of the sample after surface cleaning using a Dremel tool or by drilling into the inner ear part of the petrous portion of the temporal bone and then sequenced on a NextSeq500 sequencer using 75 base pair paired-end sequences. During computational processing, we initially stripped identifying oligonucleotide sequences and adapters, separating individual samples from pooled captures by their identifying 7 base pair indices at the 5′ and 3′ ends of reads, and requiring matches to sample-specific barcodes appended directly to the sequence fragments, allowing no more than one mismatch per index/barcode. We used SeqPrep67 to strip adapters and also to merge paired end reads into single sequences by requiring a minimum of 15 base pair overlap , using the highest quality base in the merged region where there was a conflict. We used samse in bwa (v0.6.1)68 to align reads. For the mitochondrial DNA enrichment experiment we aligned to the RSRS mitochondrial genome69. For the whole-genome enrichment experiment we aligned to the hg19 reference genome. We identified duplicate sequences as ones with the same start and end positions and orientation and also identical barcode pairs, and retained the highest quality sequence from each duplicate. We made pseudo-haploid SNP calls for each position using a randomly chosen sequence covering each targeted site, stripping the two bases at the ends of each sequence to remove deaminated mutations, and requiring a minimum mapping quality (MAPQ ≥ 10), and restricting to sites with a minimum base quality (≥20).We screened all libraries for authentic DNA by enriching for the mitochondrial genome and 50 nuclear target loci, followed by sequencing on an Illumina NextSeq500 instrument for 2 × 75 cycles and 2 × 7 cycles to read out the indices. We enriched promising libraries for approximately 1.2 M SNPs as described in refs. 30. Second, we restricted to samples in which the rate of C-to-T substitutions in terminal nucleotides was at least 3%, as expected for genuine ancient DNA using the partial UDG treatment protocol29. Finally, we used the ANGSD software to obtain a conservative estimate of contamination in the X-chromosome of individuals determined to be male based on the rate of polymorphism on X-chromosome sequences ; we excluded libraries with X-contamination estimates greater than 1.5%32. For samples where multiple libraries were produced for a single individual, we merged libraries that passed quality control, and obtained new pseudo-haploid SNP calls.We assessed the quality of each library at the screening stage using three standard methods for determining ancient DNA authenticity. First, we analyzed mitochondrial genome data to determine the rate of matching to the consensus sequence, using contamMix70, using a consensus sequence built from reads enriched for the mitochondrial genome, restricting to damaged reads using PMDtools71 (pmdscore ≥ 3), and trimming 5 bases from each end to greatly reduce the error rate due to deamination.We determined mitochondrial DNA haplogroups using the tool haplogrep272, which uses a breadth-first search to traverse the Y-chromosome tree. We made our calls on the ISOGG tree from 04.01.2016 [http://isogg.org], and modified the caller to output derived and ancestral allele calls for each informative position on the tree. We then assigned a score to each of the reference haplogroups by counting the number of mismatches in the number of observed derived alleles on that branch and down-weighted derived mutations that were transitions to 1/3 that of transversions to account for DNA damage related errors. We assigned the sample to the reference haplogroup with the closest match based on this score. While we endeavored to produce a call on each sample, we note that samples with fewer than 100,000 SNPs have too little data to confidently identify the correct haplogroup, and we encourage caution when interpreting these results.Ancient DNA presents challenges in the assignment of Y-chromosome haplogroups due to the chance that there may be contamination, DNA damage or missing data present in them. In order to assign Y haplogroups to our data, we used a modified version of the procedure used in the analysis of modern Y chromosomes in the 1000 Genomes Project73. Showed that they were first-degree relatives of higher coverage samples in the dataset. We restricted data from sample I1183 to include only sequences with evidence of C-to-T substitution in order to minimize contamination which was evident in the full data from these samples.The data from the 22 samples that passed contamination and quality control tests are reported in Supplementary Table 24 and Haber et al.26, using the mergeit program of EIGENSOFT33. The resulting datasets, referred to as HO + and HOIll+, contain the 20 new unrelated samples combined with HO and HOIll from Lazaridis et al.24 and 5 ancient samples from Sidon, Lebanon (population name: Levant_BA_North) from Haber et al.26, respectively. HO+ includes data from 2891 modern and ancient individuals at 591,642 SNPs, and HOIll+ includes data from 306 ancient individuals at 1,054,637 SNPs.We combined the newly reported data with existing data from Lazaridis et al.33. We used a total of 984 present-day individuals for PCA, and projected the 306 ancient samples. We used default parameters with lsqproject: YES and numoutlieriter: 0 settings. We estimated FST using smartpca for the 21 ancient Near Eastern populations made up of more than one individual and 8 modern populations using default parameters, with inbreed: YES and fstonly: YES , which we ran on the HO+ dataset.We computed 36. We tested both 2- and 3-way admixtures between ancient “Left” populations from the HOIll+ dataset. We used the 09NW populations defined in Lazaridis et al.24 as preliminary outgroups. We selected additional outgroups based on the statistics f4 and f4, and we repeated qpAdm with each additional outgroup added into the “Right” list until all but one admixture model was eliminated.We estimated proportions of ancestry in the Levant_ChL population using the qpAdm methodology, with parameters allsnps: YES and details:YES” populations as preliminary outgroup populations, and confirmed our findings for Levant_BA_North using the outgroups defined in Haber et al.26. We added additional outgroups to further differentiate between plausible models, and repeated qpAdm analysis until all but one candidate admixture model was eliminated.We used qpAdm to determine whether the Levant_BA_South and Levant_BA_North populations could be modeled using Levant_ChL as a source population. We tested 2-way admixtures between Levant_ChL and every other ancient “Left” population from the HOIll+ dataset. We also tested the “Left” populations Levant_N and Iran_ChL. We used the 09NW “Right38 methodology with parameter allsnps:YES.We computed the minimum number of streams of ancestry required to model two sets of three Levantine populations  using the qpWave31. For each population of size, N, we observe Ri sequences that possess the reference allele out of a total Ti sequences. The likelihood of the reference allele frequency, p, in each population given the data D = {X,N,Ri,Ti} is L = B = We examined the frequencies of SNPs associated with phenotypically important functions in the categories of metabolism, pigmentation, disease susceptibility, immunity, and inflammation in Levant_ChL in conjunction with the Levant_N, Levant_BA_North, Levant_BA_South, Anatolia_N and Iran_ChL populations, with allele frequencies for three pooled continental populations  in Phase 3 the 1000 Genomes Project reported where available. We computed allele frequencies at each site of interest by computing the likelihood of the population reference allele frequency given the data, using a method established in Mathieson et al.https://reich.hms.harvard.edu/datasets.The aligned sequences are available through the European Nucleotide Archive under accession number PRJEB27215. Genotype datasets used in analysis are available at Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8


Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) was officially declared as present in Brazil and, after two years, the species was detected in the Caribbean and North America. Information on genetic features and accurate distribution of pests is the basis for agricultural protection policies. Furthermore, such knowledge is imperative to develop control strategies, understand the geographical range, and genetic patterns of this species in the Americas. Here, we carried out the widest sampling of H. armigera in the South American continent and Puerto Rico, after we estimated the diversity, demographic parameters, and genetic structure. The Internal Transcribed Spacer 1 (ITS1) nuclear marker was used to investigate the presence of putative hybrids between H. armigera and H. zea, and they were observed at a frequency of 1.5%. An ABC analysis, based in COI gene fragment, suggested Europe as the origin of South America specimens of H. armigeraand following a movement northward through the Caribbean. Three mtDNA genes and three nDNA markers revealed high genetic diversity distributed without the defined population structure of H. armigera in South America. Most of the genetic variation is within populations with a multidirectional expansion of H. armigera among morphoclimatic regions. High genetic diversity, rapid population expansion, and hybridization have implications for pest management since they suggest that adaptive alleles are spread through wide areas in South America that favor rapid local adaptation of H. armigera to new and disturbed environments .In 2013,  NeverthH. armigera in Brazil in January 2013 in Goiás, Mato Grosso, and Bahia States  (Lepidoptera: Noctuidae). In larval stages, they are morphologically indistinguishable, which made data collection concerning geographical distribution and dispersion of this pest in Brazil difficult  and is one of the largest winter nursery breeding operations in the United States for major row crops   still not used to reveal the demographic events that shaped the H. armigera populations in South America, which are still suffering from significant data scarcity at the broader geographical level. Thus, we used seven different nuclear and mitochondrial molecular markers in order to (a) elucidate the invasion routes of H. armigera populations in South America, (b) assess the demographic parameters and population structure of H. armigera populations from Brazil, its bordering countries, and Puerto Rico, (c) assess the gene flow among H. armigera population collected in different Brazilian regions, and finally, (d) investigate the presence of putative hybrids between H. armigera and H. zea in Brazil. Such information is important in helping to define immediate control strategies and long‐term solutions for Helicoverpa management in South America and providing substantial information to support prevention and preparedness for the imminent arrival of the pest in the main North America agricultural regions.Here, we present the largest temporal and geographical sampling of 22.1Helicoverpaspp. individuals were collected at 69 geographical sites from Brazil, Paraguay, Argentina, and Puerto Rico (Caribbean) during the 2013–2016 harvests . Larvae collected directly from host plants and moths from delta sticky and bucket traps baited with pheromone lures were immediately preserved at −20°C in 100% ethanol until DNA extraction. Total DNA was extracted from the abdomen of the specimens using the standard phenol:chloroform method, adapted for microcentrifuge tubes  , cycle sequencing reactions were completed using the BigDye® Terminator v3.1 kit , and bidirectional sequenced by the ABI 3730xl DNA Analyzer sequencer .We used three partial sequences of mitochondrial genes: cytochrome www.ncbi.nlm.nih.gov/BLAST/) against sequences stored in NCBI GenBank database (http://www.ncbi.nlm.nih.gov/genbank/). In this step, COI also were used to H. armigera species identification.Geneious 6.0.6 software package  was used to assemble the nucleotide sequences into a contig for each specimen. The generated sequences were aligned employing the multiple sequences alignment algorithm implemented in Clustal Omega  that were used to generate a simulated data distribution on which summary statistics were calculated. A rejection algorithm retained the statistics, among all the models, that have the smallest Euclidean distance to the empirical data, obtained from the COI dataset. Then, the model that best recovered the empirical data presented the highest Posterior Probability value (PP).In short, we used an Approximate Bayesian Computation (ABC) approach , 11 from Europe , and five from Africa . We chose not to use all sequences obtained in our work in order to have a similar number of sequences among the continents.We used 36 COI mtDNA sequences from the Americas (the first two sequences obtained in each Brazilian state and in each South American country), 40 from Asia  testing three similar models in each as shown in Figure ms 20161016 coalescent sampler ; 4.123 = Ne(7.199/106); Ne = 572,718.43, and tau was found as number of generations/2Ne; 3,750,000/1.145.436,86 = 3.274. The following observed Summary Statistics (SuSt) were calculated in DnaSp v5: nucleotide diversity (π), segregating sites (SS), Tajima's D (D), nucleotide diversity within each population (πw), and nucleotide diversity between each pair of populations (πb). For the simulated datasets, the same SuSt were calculated with the software Sample Stats .The ABC analyses were performed separately for each scenario in the  Hudson,  to constnd Nylin  detected Hudson,  and msSSgfitpca function to evaluate how well the simulated models fit the observed data. A good fit occurs when the observed SuSt is positioned within the simulated SuSt. Next, all possible combinations of two or more simulated SuSt were grouped into vectors. A rejection step was conducted with a Python script in the Euclidean distance C program msReject (http://msbayes.sourceforge.net/) using 10 simulations from the prior distribution of each model treated as empirical data to find the vector that best detects the true model , COII (554 pb), and Cyt b (434 pb) in a 1,646 bp‐long sequence for each of the 303 individuals, generating a total of 54 mtDNA haplotypes. The genetic diversity, demography, and population structure indices were estimated according to the morphoclimatic regions of the individuals  and haplotype diversity (Ĥ)  Nei, . The neu Tajima, , Fu's Fs Tajima,  and the  Tajima,  were per2.2.3b). A second haplotype network of COI mtDNA was estimated with the dataset used in the ABC analysis  with 230 other sequences from this study, 65 from Mastrangelo et al.  was estimated with the default 95% plausible connection limit using the TCS 1.2.1  of 2.3H. armigera individuals, representative of all geographical regions, were genotyped using three nDNA markers (Exon Primed Intron Crossing—EPIC): dopa decarboxylase , the ribosomal protein S6 , and the ribosomal protein L11   to evaluate size polymorphism: NED™ (yellow: DDC), VIC™ (green: RpS6), and 6‐FAM™ (blue: RpL11) , 92 µM of dNTPs, 1.7 mM of MgClFIS), pairwise FST matrix for all pairs of morphoclimatic regions, and observed (HO) and expected (HE) heterozygosities for each nDNA marker (and combination of them) were calculated using Arlequin v.3.5. The FST relations results were displayed as a heatmap using the pheatmap R package (https://cran.r-project.org/web/packages/pheatmap/index.html). Deviations from Hardy–Weinberg equilibrium (HWE) were tested in Genepop v.4.2 (Option 1)  algorithm for each locus in each population  region of the nuclear ribosomal DNA (rDNA) using a Helicoverpa spp. forward primer (3373Ha_Hz_ITS1‐F) and two species‐specific reverse primers: for the H. armigera (3374Ha_ITS1‐R) and for H. zea (3377Hz_ITS1‐R)  and H. zea (334 bp), allowing the identification of putative hybrids when checked in agarose gel electrophoresis   Figure . Model c) Figure ‐I, and t) Figure ‐II. ScenObserved SuSt appeared within the cloud of simulated SuSt in scenarios 2 and 3, which did not happen in scenario 1 and in the last run Figure  and C. O3.2b concatenated genes were Hd = 0.926 ± 0.006 and π = 0.002 ± 0.001 (Table n = 33) and the region AM a lower number of haplotypes (n = 12). However, the haplotype and nucleotide diversity indicate similar diversity among Brazilian regions. The variation in sample size influenced the amount of haplotypes found in the regions , while Fu's Fs assumed −25.5408 (p < 0.001), and both results showed a departure from mutation‐drift equilibrium, which usually indicates a sudden demographic expansion or purifying selection (Table r = 0.0235), which is to be expected in a population that has experienced recent expansion   Table . For thr4) Table .3.3n = 31; 57.4%). A haplogroup separated for 12 mutation steps is represented by haplotypes H49, H48, H46, H24, and H12. The Bayesian phylogenetic analyses show short branch and low supported nods . The remaining four samples, which had unspecific amplification for any nDNA marker, were discarded from the analysis. From those samples, 56 alleles were found in total for the three markers (Table HE) and observed (HO) heterozygosities . Most of the localities were in HWE, except DDC marker from CE/CA and RpS6 marker from AM/CE, CE, and AT. All of them had a p‐value < 0.05. When all samples were joined and treated as one locality, both DDC and RpS6 were not in HWE. On the other hand, when the three markers were treated together, AM/CE, CE/CA, and CE were significantly out of HWE. Except for RpL11 marker, the FIS values in each locality ranged widely from −0.5 (RpS6 in Puerto Rico) to 0.493 (RpS6 in AM/CE). The AMOVA showed that only 1.31% of variation occurred among sites' level, and 98.69% within sites' level . F statistics , FIS = 0.1343 (p < 0.001), and FIT = 0.1408 (p < 0.001), respectively.A total of 92 samples were successfully predicted to allele size for all three nDNA‐PCR markers  had a relatively high FST value but were not significant. All other comparisons had low FST values (−0.018 to 0.026) and were not significant showing greater homogeneity. The data also show, even subtly, that Puerto Rico is closer to the AM and AM/CE samples  than to other morphoclimatic regions.Pairwise 9 Figure , and fou3.5Helicoverpaspp. individuals tested, twelve samples  exhibited two bands on agarose gel electrophoresis (~147 bp and ~334 bp). Thus, they could be hybrids between H. armigera and H. zea because these species‐specific sequences were found in a single individual. After the sequencing, alignment, and GenBank conference, only six individuals had their 147 bp‐long amplicons matched >98% with ITS1 from H. armigera and its 334 bp‐long amplicons matched > 98% with ITS1 from H. zea  separated for a single mutation step suggests that H. armigera populations on Old World continents. This haplogroup divergence event among haplotypes is not expected for H. armigera in South America due to its recent invasion, population expansion, and absence of genetic structure on the continent  invasion events in South America. A more recent haplogroup was identified in the analysis, probably associated with a vicariance/sympatric event among 4.3H. armigera in the South America. The lack of genetic structure of H. armigera is consistent with traits of (a) a pest of recent invasion, (b) huge dispersion capacity of the moths carried by wind, and (c) a high polyphagia associated with uninterrupted and diversified crops during the entire year, stimulating a high reproductive rate of H. armigera in South America , which may rapidly modify the hybridization dynamic between H. armigera and H. zea. Thus, a constant monitoring of H. armigera and H. zea populations is recommended, since introgressions of adaptive genes/alleles between H. armigera and H. zea may have implications for the population management of these pests.We found a low number of putative hybrids between H. armigera in South America what could be supported by the history of invasion events between both continents. Like the European colonization after the great navigations, the invading H. armigera populations have established successfully on the South American continent, and their expansion north continues. The highly diverse genetic structure, recent expansion population, and multidirectional gene flow, plus the presence of putative hybrids, assumes a scenario of uncertainties for pest management of H. armigera. Alleles previously selected may spread long distances and, eventually, be transferred between the species, promoting a fitness increase in Helicoverpa local populations and the rapid evolution of resistance to pesticides and Bt crops in South America (Chakroun et al., In conclusion, we found demographic signals of European origin of We have no competing financial interests.RMG, TM, JCVR, DFP, and AMLAE conceived and designed the study. RMG collected the data. ASC, CO, and AMLAE provided reagents and analytical tools. RMG, TM, and ASC analyzed the data. RMG, TM, and ASC wrote the manuscript. All authors read, corrected, and approved the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Hyperoodon ampullatus has the lowest known mitochondrial diversity of any cetacean and was intensely whaled in the Northwest Atlantic over the last century, but whether exploitation imposed genetic risks that could limit recovery is unknown. We sequenced full mitogenomes and genotyped 37 novel microsatellites for 128 individuals from known areas of abundance in the Scotian Shelf, Northern and Southern Labrador, Davis Strait, and Iceland, and a newly discovered group off Newfoundland. Despite low diversity and shared haplotypes across all regions, both markers supported the Endangered Scotian Shelf population as distinct from the combined northern regions. The genetic affinity of Newfoundland was uncertain, suggesting an area of mixing with no clear population distinction for the region. Demographic reconstruction using mitogenomes suggests that the northern region underwent population expansion following the last glacial maximum, but for the peripheral Scotian Shelf population, a stable demographic trend was followed by a drastic decline over a temporal scale consistent with increasing human activity in the Northwest Atlantic. Low connectivity between the Scotian Shelf and the rest of the Atlantic likely compounded the impact of intensive whaling for this species, potentially imposing genetic risks affecting recovery of this population. We highlight how the combination of historical environmental conditions and modern exploitation of this species has had very different evolutionary impacts on structured populations of northern bottlenose whales across the western North Atlantic.Interpretation of conservation status should be informed by an appreciation of genetic diversity, past demography, and overall trends in population size, which contribute to a species' evolutionary potential and resilience to genetic risks. Low genetic diversity can be symptomatic of rapid demographic declines and impose genetic risks to populations, but can also be maintained by natural processes. The northern bottlenose whale  Hyperoodon ampullatus), we reconstructed the trajectories of two distinct populations that were heavily exploited by commercial whaling. We found that for a small population at their range edge, the impacts of whaling were more severe than for the larger more connected population, and combined with having the lowest range‐wide genetic variability of any cetacean, we suggest northern bottlenose whales may face genetic limitations to the recovery of their populations.Distinct populations may respond differently to range‐wide exploitation, resulting in evolutionary impacts and reduced genetic diversity that can limit recovery and resilience of remnant populations. Sequencing the full mitogenomes and 37 novel microsatellites for 127 specimens of northern bottlenose whales ( TissuesNorthern Labrador samples were obtained from dried gum tissue collected from the teeth of 80 whales killed by whalers in the Northern Labrador Sea in 1971  to identify microsatellites containing sequences and design PCR primers. Search criteria included a minimum of 10 repeats for trinucleotide motifs and a minimum of 20 for dinucleotide repeats with target amplicon lengths between 70 and 130 bp. This amplicon size allows us to sequence the PCR products using Illumina MiSeq Reagent v3 (150‐cycle) kits in a single 150 base read length. While longer reads lengths are possible, the 150‐base read length was cost‐effective, so we designed amplicons with this in mind.n = 153) to further evaluate their performance, dropping loci which proved invariant or amplified unreliably. We retained 49 loci for data collection and analyses  sequences, allowing each individual to be demultiplexed from the sequence output, the only limitation being the number of unique index combinations available and the desired depth of sequencing per sample. Following the index PCR, samples were pooled into libraries (1 μl of PCR product per sample) for sequencing. The resulting libraries were sequenced using Illumina MiSeq Reagent Kit v3 (150‐cycle).Libraries were loaded with a target depth of 500 reads per sample per locus. We chose this target as a trade‐off between exceeding a minimum depth threshold of 50 reads (per sample per locus) in the weakly amplifying loci versus total sequencing cost per sample. We used Megasat  using the Bowtie2 v2.3.4.2 aligner . We tested for linkage equilibrium  for microsatellites and Kimura 2‐parameter corrected distances (ΦST) for mitogenomes  with samples stratified by region and year collected using Ade4 v1.7‐13 , and within Scotian Shelf (p = .004) and Northern Labrador (p = .005) regions, exhibiting homozygote excess. Across all samples, there was no evidence of linkage disequilibrium between pairs of microsatellite loci. When subdivided by region, samples from the Scotian Shelf significantly deviated from independent assortment . This was due to a distribution of dr with higher than expected values across all pairs of loci rather than strong association between a small number of loci, suggesting that deviation from independent assortment results from demographic processes such as inbreeding, restricted connectivity, or genetic bottlenecks, rather than physical linkage among microsatellite loci  deviated from Hardy–Weinberg equilibrium across the entire dataset (r¯w = −0.0304) was normally distributed and not significantly different from zero, with the mean range of 95% confidence intervals (r¯w-hight-r¯w-low = 0.7267) spanning values expected for both kin and unrelated pairs. While it is likely that we sampled related pairs of individuals on the Scotian Shelf, the wide distributions of relatedness estimate confidence intervals indicate that these microsatellite data have insufficient power to resolve close kin relationships among individuals.The mean of all comparisons for relatedness (FIS) did not differ significantly from zero, and maximum likelihood estimates of inbreeding did not differ significantly among populations. Simpson's diversity index ranged from 0.35 (North Iceland) to 0.39 (Newfoundland), and observed heterozygosity (Ho) ranged from 0.34 (North Iceland) to 0.40 (Davis Strait). Out of 124 alleles found across all microsatellite loci, only 14 were private or found in only one region. Most of these were found in Northern Labrador  or Scotian Shelf . All private alleles were rare .Levels of microsatellite diversity were similar for each region. For each population and over all data, inbreeding coefficients . Regionally, nucleotide diversity was lowest in the Scotian Shelf (π = 0.00058), with only 15 unique mitogenome sequences recovered from 47 individuals. Northern Labrador (n = 53) had the most haplotypes not found in any other region (NP = 26) and had 60% (N = 36/60) of the unique mitogenome sequences found in this study. Every mitogenome sequence from Newfoundland was distinct , and 60% of these were not found in other regions (NP = 6). A bootstrapped phylogenetic tree of mitogenomes resolved several major branches within H. ampullatus that were represented in all sampling regions. Out of the 60 mitogenome haplotypes, 14 were found in at least two regions  and was not significant in any individual sampling region.Range‐wide mitogenome nucleotide diversity was low . Variation among regions  for microsatellites and for mitogenomes  was higher than expected at random   and no significant pairwise ΦST (based on Kimura 2‐parameter distances) for mitogenomes detected in any pairwise comparisons of regions. However, AMOVA of Scotian Shelf against all other individuals grouped together showed significant regional structure for both microsatellites  and mitogenome data .Analyses of molecular variance (AMOVA) revealed low but significant population structure for microsatellites (om Table . Pairwis3.3.2k) estimates across 10 replicate runs for each value of k from 1 to 5 were at k = 2, and the ΔK method identified the highest rate of change in –log Pr(X|k) estimates for k = 2. Assignment probabilities of individuals separated genetic clusters entirely by sample location rather than year  had a lower assignment coefficient to the Scotian Shelf cluster  than for the other cluster , suggesting they may be, or descend from, recent migrants into the Scotian Shelf from another region. Both samples from stranded individuals in the Gulf of St. Lawrence had ambiguous clustering results and could not be reliably assigned to a source location from the STRUCTURE results.To assess population structure while accounting for potential differences between both the locations and years that samples were collected in, we performed Bayesian clustering of microsatellite data in Structure with sampling units defined by location–year. The highest –log Pr or males . STRUCTURE assignment indices identified geographic structure between Scotian Shelf and all other regions in males but not females. This suggests that female northern bottlenose whales may be more dispersive, in contrast to the general male bias pattern of mammals  than the more philopatric sex. We did not find significant differences between the distributions of estimates of linkage disequilibrium among microsatellites in females  in each region had infinite upper bounds of 95% confidence intervals for all regions except Scotian Shelf , reflecting limited statistical power to estimate upper bounds. The lower 95% CI bounds for Northern Labrador  were higher than the upper bound for Scotian Shelf. Estimates for Newfoundland , Davis Strait , and Iceland  overlapped with estimates for the Scotian Shelf. Due to the low statistical power caused by having only three samples from Southern Labrador, all estimates of eN for this region were infinite. Estimates for Davis Strait, Northern Labrador, Southern Labrador, and Newfoundland combined  were higher than for each region separately.Estimates of effective population size . Because neither group is monophyletic, both are expected to have similar ranges for their time to most recent common ancestor (TMRCA). Consistent with this expectation, the estimated TMRCA for the Northern region was 47.4 kya : 32.8–61.5 kya), and the estimated TMRCA for Scotian Shelf was 46.6 kya (95% HPDI: 34.1–61.3 kya). The skyline analysis for the Northern region shows an increasing trend from the estimated time of the last glacial maximum (19.0–26.5 kya) to present times  is well established , the lowest found for any cetacean; the next lowest is sperm whales   and Commerson's dolphin , to evolutionarily significant units (ESUs), to subspecies , suggesting that we may not yet have the full picture of contemporary connectivity. Ongoing genetic monitoring is required to resolve whether genetic connectivity is the result of recent historical or contemporary migration or incomplete lineage sorting from a common ancestral population. Previous studies have suggested that genetic separation of the Scotian Shelf from other regions likely predates human exploitation and is not the result of a decline in population size , suggesting either a population expansion or selective sweep occurred. Population expansions following the LGM have been detected in other cetacean species in the North Atlantic  of whaling era samples in our analysis and low genetic diversity, which is reflected by increasing uncertainty of demographic reconstructions over the last two centuries. Comparing contemporary samples from areas in the Northern region will help validate the lack of a recent bottleneck outside the Scotian Shelf. Stable trends in the effective population size of whales in the Northern region and the new aggregation of whales observed in Newfoundland suggest that in core population centers, the species may be recovering from historical whaling. Comparisons with northern bottlenose whale populations in the eastern North Atlantic may provide additional context for these demographic trends and resolve the phylogeographic history of this species.5H. ampullatus is likely naturally occurring, but further population declines or reductions in connectivity could compromise the evolutionary potential of the species and risk the recovery of the more depleted Scotian Shelf population. The genetic risks imposed by harvesting and the slow recovery for the Scotian Shelf population identify a number of considerations that are broadly relevant to the assessment of genetic impacts on commercially exploited species. Distinct populations can respond differently to human exploitation, and determining risk requires an assessment of range‐wide population subdivision and historical trends. We highlight that understanding the evolutionary context and demographic trajectories of distinct populations, using techniques such as Bayesian skyline analysis, can reveal potential genetic risks that can help inform species conservation and management priorities. Population structure may be cryptic and require high‐resolution markers with the power to detect variability, particularly in species with low genetic diversity, which is important to consider when reconstructing historical demography to assess recent human impacts such as exploitation and the recovery of a species across their range.Low diversity in None declared.Laura Joan Feyrer conducted the field work, including biopsy collection from 2015 to 2017, designed the study, and wrote the manuscript. Tony Einfeldt designed and conducted the genetic analyses and contributed to the interpretation of results and writing of the manuscript. Ian Paterson designed the microsatellite primers and conducted most of the laboratory analyses. Paul Bentzen contributed to the analytical design and interpretation of results and revised the manuscript. Hal Whitehead designed the study, contributed to field data collection, and revised the manuscript. Click here for additional data file.


Araucaria angustifolia aiming to (i) assess the species genetic structure within its main range of occurrence, (ii) infer its population demographic history, looking for evidence of southward expansion, (iii) search for evidence of glacial refugia within the species distribution area and (iv) discuss some conservation and management strategies for this species. Twenty haplotypes were identified, revealing the presence of three distinct genetic groups across the geographic range of the species and structuring the populations into Northern, Central and Southern groups. Our results suggest the occurrence of post-glacial expansion of A. angustifolia towards the south, as well as the existence of at least three refugia within the species occurrence area. Testing the occurrence of historical demographic expansion, we suggest that genetic groups experienced fluctuations in effective size, associated to a structured distribution of populations. The identification of three genetic groups in this study corroborates the proposition of using the geographic distribution of A. angustifolia for selecting in situ conservation areas, for planning seed collection for ex situ conservation, as well as for the delineation of seed zones.Despite the high diversity of the Southern South American environments, the patterns and processes driving both their species diversity and demographic history are still poorly known and are a challenging task. In this study, we evaluate plastid DNA sequences of the conifer species  Because habitats are often megadiverse5. During the Pleistocene glaciations, many species moved towards lower latitudes, whereas other species migrated to refugia, which fragmented a once relatively contiguous distribution into multiple patchily distributed populations6. According to a paleomodel proposed by Carnaval & Moritz7, in the Atlantic Forest these refugia were restricted to the northern limits of the biome, mainly in coastal areas, while severe forest contractions nearly eliminated forests at higher latitudes. The southern Atlantic Forest would subsequently have been colonized from refugia in the north during the Holocene3, through southward expansion. However, a most recent study on the environmental drivers of diversity in Brazilian subtropical highlands8 suggests the existence of areas suitable for forest species occurrence at higher southern latitudes during the Last Glacial Maximum.Quaternary climatic shifts altered the spatial distribution of species and environments worldwide, acting as a main diversification driver in both temperate and tropical regionsAraucaria angustifolia populations12 and the latitudinal discontinuous distribution of the forests with araucaria suggest, in addition to southward expansion, the existence of further refugia at higher southern latitudes. Several phylogeographic studies in South America have identified multiple refugia13, even at high latitudes, as evidenced by plastid DNA markers of Nothofagus nervosa14.As well, the phylogeographic pattern of genetic structuring previously described for 13, particularly in the southernmost portion. The southern Atlantic Forest has a subtropical climate, mainly in the highlands, and holds predominantly montane grasslands and subtropical forests with araucaria. Araucaria angustifolia (Bert.) O. Ktze. is a long-lived dioecious conifer species endemic to the subtropical Atlantic Forest in the highlands of Brazil and small patches of forest in Argentina15 and Paraguay16. This species presents a latitudinal discontinuous distribution with a large gap dividing the occurrence area of the species in two main geographical zones in Brazil: (i) a northernmost zone that is formed by scattered medium to large forest formations at high altitudes in the Southeastern region of the country and (ii) a southernmost zone, which is composed by a much wider area originally formed by large continuous forest formations in the Southern region of Brazil. This zone includes the Argentinian and Paraguayan patches of forest with araucaria. The gap area between the northern and southern occurrence areas of A. angustifolia lacks register of recent population presence and only ecological simulations suggest the possibility of occurrence of the species in this area.Despite the diversity richness and importance of the Atlantic Forest, as well as the advances in methodological and statistical analyses, the patterns and processes driving its species diversity are still poorly known9 and nuclear microsatellite markers11, linked to palynological data proposed that this species has a strong phylogeographic pattern of genetic structuring that may be related to Quaternary climatic shifts and recent population expansion19. Based on the distribution of alleles and genotypes from nuclear microsatellite markers, a clear genetic structure is observed, revealing the presence of distinct genetic groups in a latitudinal pattern. Samples from the northernmost zone of the species occurrence are significantly distinguished from samples from the southern zone of occurrence. On the other hand, populations collected within the southernmost zone are less differentiated from each other. This pattern of population differentiation suggested the presence of two or three distinct genetic groups of A. angustifolia10. These outcomes allow to elaborate two main hypotheses about the current distribution of genetic groups of this species. One hypothesis concerns the southward expansion of A. angustifolia populations from a northern genetic group originated from a northern refuge , following the retraction of the glaciated areas. Another hypothesis is related to the existence of southern glacial refugia, which originated the southernmost genetic groups, independent of the northern genetic groups. The occurrence of anthropic activities driving the geographic expansion of A. angustifolia within Southern Brazil was recently proposed based on archeological, genetic and ecological data20. Since these studies are based only on data from southernmost populations, without inclusion of samples representing the northern genetic group and weakly representing the most central populations in the Paraná State, those hypotheses were not tested.Previous genetic studies based on isozymesA. angustifolia across most of the species range, and to infer its demographic history, we conducted a large-scale genetic analysis based on the sequence variation of three intergenic regions of plastid DNA (ptDNA). Overall, this study aimed to test the above presented hypotheses about the current distribution of A. angustifolia genetic groups by exploring these ptDNA sequences. Signatures of recent demographic expansion , genetic divergence among populations associated to geographic distances, and haplotype network presenting a star-like form  would suggest that the current distribution of this species can be accepted as the result of southward expansion. On the other hand, a strong genetic structure among genetic groups, and a haplotype network containing more than one putative ancestor haplotype would imply that the current distribution of A. angustifolia genetic groups is a consequence of the expansion of populations from different glacial refugia in the northern and in the southern zones of the species occurrence.Aiming to evaluate distribution patterns of genetic variation among natural populations of A. angustifolia influenced by Quaternary shifts on the distribution of the genetic variants, this study intended to (i) evaluate the genetic structure of A. angustifolia within its range of occurrence; (ii) infer the population demographic history of this species, looking for evidence of southward expansion; (iii) search for evidence of glacial refugia within the species distribution area of A. angustifolia; and (iv) discuss some conservation and management strategies for this species, based on its current genetic structure and inferred evolutionary history.Following this logic and considering the results of previous studies that suggest geographic patterns of genetic differentiation in populations of The alignment of the three intergenic plastid regions generated in the present study encompasses 2,382 bp, with 16 variable sites and two indels, revealing low variation . The Northern group comprises seven populations and has eight haplotypes, all exclusive to this geographic region  and continuously increased from K = 2 (FCT = 0.620) up to K = 15 (FCT = 0.690)  clustered together, as did a group of five Northern populations . The other two northern populations (BAR and CDJ) grouped with Central populations or formed a single group, but never grouped with Southernmost populations. Estimations of FCT and FST showed a pattern of stabilization after K = 3 . The differences between the Northern and Central/Southern groups, Northern and Central groups and Northern and Southern groups were also high and statistically significant , while the differences between the Central and Southern groups were moderate .The hierarchical AMOVA analysis Table  revealedrg and SSD statistics for the Northern group revealed low and statistically non-significant values  rg and SSD estimations  negative values for the Northern and Southern groups (D(N) = −0.04, FS(N) = −0.19 and D(S) = −0.08, FS(S) = −3.40) and positive values for the Central group (D(C) = 1.02 and FS(C) = 1.75).Tajima’s ts Table  presente(N) = 3.28), than for Central and Southern groups (τ(C) = 2.04 and τ(S) = 1.00). The similar estimations of θ0 for all genetic groups suggest a comparable intensity of one possible genetic bottleneck in the past  just after the coalescence. Contrarily, the Central genetic group experienced a small decline in Ne just after the coalescence, while no significant changes in Ne is observed in the Southern group.Opposing the mismatch distribution, ime Fig. . PopulatA. angustifolia at 20,000 years before present  is typically stronger with uniparental than with biparental inherited markers13. Araucaria angustifolia revealed the same pattern in the analysis of uniparental ptDNA markers and biparental nuclear markers10, suggesting the correspondence of the current marker phylogeographic signature to the actual species phylogeography, with three main genetic groups. The origin of these three genetic groups, defined by the configurations of haplotype distribution, genetic diversity and genetic structure, can be explained by both the occurrence of southward expansion and the existence of further unidentified glacial refugia.Our phylogeographic survey based on ptDNA sequences revealed genetic structuring across the full distribution range of lia Fig. , with a 25 and animal 26 groups, suggesting that this might be a general pattern. During the Last Glacial Maximum, Australia and South America had their climates significantly modified, but were not subject to extensive glaciation27. Considering the substantial need for humidity by A. angustifolia , glacial refugia for this species in South America were related to localities with enough moisture for survival. Glacial refugia in Brazil have been proposed based on the distribution of woody angiosperm families22 and through species niche modeling of the Atlantic Forest7. Both strategies propose the existence of a refuge situated in the region of the states of Rio de Janeiro and Espírito Santo ups Fig.  revealedups Fig. .12 reported a star-like haplotype network and negative estimations of Tajima’s D and Fu’s FS neutrality tests, suggesting rapid and recent expansion of such populations from a single southern refuge.Likewise, populations from the Southern group can have been originated from a second refuge that coined haplotype H4 see Fig. , which i29. Estimations of ΦCT (differentiation between groups) and ΦST (differentiation among populations) are lower in the comparison between Central and Southern groups than estimations that include the Northern group  of the Central and Southern groups indicate a more recent origin in comparison to the Northern group  declined in a north-to-south gradient (Table θS (the number of polymorphic sites) were higher for the Northern group, followed by the Southern and Central groups  is the most appropriate sampling strategy for analyzing structured populations33. Even though, the high mean FST estimations obtained from AMOVA analysis when the whole data is partitioned in three genetic groups  analysis that revealed mainly constant size for all three genetic groups. These contrasting results may be effect of pooled sampling from a structured population that violates the assumption of panmixia in this analysisD, Fu’s FS and BSP analyses), the demographic history of A. angustifolia have to be interpreted with clue off all pertinent approaches, including SAMOVA, AMOVA, rg, SSD and mismatch distribution analyses. Taking all analysis in account, we suggest that A. angustifolia genetic groups experienced fluctuations in effective size, including recent demographic expansions, also suggested in other studies19, allied to a structured distribution of populations.Since population structure is a general problem that affects all methods that do not explicitly take subdivision into account , psbC-trnS  and trnS-trnfM  were sequenced using universal primers40. Fragments were amplified using the following PCR conditions: 20 ng of template DNA, 0.2 μM of each primer, 2.5 mM of MgCl2, 1.0 U Taq polymerase, 1X PCR buffer, 0.3 mM of each dNTP, for a final volume of 20.0 μL. PCR cycles were performed as described by Demesure et al.40. PCR products were purified by precipitation using a 20% Polyethylene Glycol 8000 + 2.5 mM NaCl solution followed by washing in 80% ethanol and checking under UV in a 1% agarose gel stained with GelRed® (BiotiumTM). Sequencing reactions consisted of 1.0 μL of PCR product (100–200 ng μL−1), 0.35 μL of primer (10.0 μM) and 4.0 μL of DYEnamicTM ET dye terminator® mix , for a final volume of 10 μL. Alternatively, some sequencing reactions were performed using 1.0 μL of PCR product (100–200 ng μL−1), 0.35 μL of primer (10.0 μM), 3.5 μL of 5X Sequencing Buffer and 1.0 μL of BigDye TerminatorTM v3.1 , for a final volume of 20 μL. Each purified PCR product was subjected to DNA sequencing by capillary electrophoresis (CE) from both the forward and reverse directions using a MegaBACE 1000 DNA Sequencing System , and an ABI3500XL Genetic Analyzer (Applied Biosystems), following the manufacturer’s instructions. Sequences were extracted using the programs Sequence Analyzer v. 4.0  and Sequencing Analysis v5.4 (Applied Biosystems). Alignments and final concatenation were performed using the program CLC Genomics Workbench v.8 . DNA sequences were deposited in the NCBI database under ID numbers MH223675 – MH225411.Sampling for this study included 580 adult individuals from 39 populations, distributed across most of the distribution range of the species, including Brazil and Argentina Fig. . Leaf ti41 model of substitution, determined as the best model through the BIC scores (Bayesian Information Criterion) computed with the software Mega 6.042. Evolutionary relationships among ptDNA sequences were evaluated by a median-joining43 using the software Network 5.0 (http://www.fluxus-engineering.com).Since the loci are linked in the plastid chromosome, all analyzes were performed with the concatenated sequences. All genetic parameters were estimated using the Tamura10 suggest the presence of three main genetic groups for populations of A. angustifolia across its distribution range in Brazil. A similar trend was observed in the distribution of plastid haplotypes  by maximizing FCT value between K groups of geographically adjacent populations. The number of geographic groups K was set from 2 to 15 and estimations were performed using the Tamura 2-parameters model.Previous AMOVA studies based on nuclear SSR and AFLP markersvs. Central group vs. Southern group, (ii) Northern group vs. Southern and Central groups clustered, (iii) Central group vs. Southern group, (iv) Northern group vs. Central group, and (v) Northern group vs. Southern group. Computations were performed using the software Arlequin 3.544.Population genetic structure was then evaluated by means of a hierarchical AMOVA analysis considering the three genetic groups suggested by the haplotype distribution and SAMOVA analysis. A total of five AMOVA tests were performed: (i) Northern group 45. A run length of 2.5 × 106 generations was used under the HKY nucleotide substitution model, sample and print frequencies of 500, and diagnostic frequency of 5,000.Phylogenetic relationships among the 580 samples were estimated using a Bayesian Markov chain Monte Carlo approach as implemented in the software MrBayes 3.1θπ46 and (iv) nucleotide diversity θS47. All estimations were calculated overall populations and for each defined genetic group . Genetic groups were defined based on the haplotype distribution  the total number of base mutations (SNPs) and indels, (ii) the total number of haplotypes, (iii) average number of pairwise differences see Fig. , SAMOVA see Fig.  and prevFS statistic is more powerful for capturing molecular signatures of population expansion, while Tajima’s D and the raggedness index are more conservative48), different tests were employed.Based on the revealed genetic structure, evidence of demographic expansion/decline was assessed for each group . Since contrasting results among different tests can occur  and the sum of squared differences (SSD). Significance of rg and SSD were assessed through 1,000 bootstrap replicates. Statistically significant values were taken as evidence of departure from the null hypothesis of no ancient expansion. Computations were performed using Arlequin 3.544.The validity of the estimated demographic model was evaluated by computing the Harpending’s raggedness indexD46, which is based on the difference between estimations of θπ and θS; and (ii) Fu’s FS52, which uses information from the haplotype distribution. Statistical significance of the analyses was determined through 10,000 simulations. The relative measure of time since population expansion in generations (τ) and the expected pairwise differences before and after change in population size  were also estimated. All computations were performed using Arlequin 3.544.Additionally, the hypothesis of population growth against the null hypothesis of a constant size population under the neutral model was tested using two complementary approaches: (i) Tajima’s Ne) through time was estimated for each genetic group using a non-parametric analysis based on the coalescence theory. The Bayesian skyline plot (BSP) approach was employed with the HKY substitution site model using a strict molecular clock with rate 1.0 and running 1.0 × 108 Markov Chain Monte Carlo (MCMC) simulations in the software BEAST 2.053. The convergence of the MCMC runs was evaluated by means of effective sample size (ESS) values for the parameters. Conditions of the MCMC runs were adjusted for reaching values ESS > 200 for all parameters (Supplementary File http://tree.bio.ed.ac.uk/software/tracer/).Further, the effective breeding population size  was used to compile the MaxEnt results, interpolating climatic surfaces for global land areas in 2.5 arc minute maps (about 5 km2 resolution). Model validation was determined by the partial ROC  with values of pROC > 0.90 considered as excellent. A Jackknife test was employed to determine the prediction power of each variable by setting the variable aside from the test and generating its percentage of contribution to the model. All employed parameters were setting as follow: convergence threshold = 10−5; maximum iterations = 500; regularization multiplier = 1; application of a random seed, duplicate presence records removal and logistic probabilities used for the output. The model training was performed using 80% of species records and 20% was used to test the model.Nineteen bioclimatic variables available in the WorldClim databaseA. angustifolia populations at the end of the Last Glacial Maximum , the paleodistribution of the species was also predicted, using the bioclimatic variables from ECHAM358. Data files were downloaded at their original 2.8125° resolution and resampled to 30” resolution via bilinear interpolation. Species paleodistribution modeling was conducted using the same parameters used for modeling current species distribution.In order to determine putative refugia for Supplementary Dataset 1