Cephalorhynchus eutropia, has a continuous coastal distribution with strong genetic differentiation identified by nuclear DNA markers. We explored the historical dimension of this genetic differentiation between northern and southern populations to evaluate phylogeographic structure. Additionally, we conducted mtDNA and microsatellite analyses to detect past and recent demographic changes. The southern population was characterized by lower genetic diversity with a signal of population expansion, likely associated with ice retreat and habitat extension after the Last Glacial Maximum (LGM). In contrast, structure within the northern population was more consistent with stable historical population size. Approximate Bayesian Computation analyses suggested that during the LGM, C. eutropia persisted in the northern area; while the south was colonized by dispersal ~11,000 years ago followed by population expansion. This study shows that Chilean dolphin population structure is consistent with predictions from the Expansion-Contraction biogeographic model, with a poleward post-glacial shift revealed in current genetic structure. The results also confirm the validity of the population units previously identified, demonstrating their historical origin and highlighting the utility of integrating genetic markers with different temporal scale resolutions.The complementarity of historical and contemporary processes contributes to understanding the genetic structure of continuously distributed marine species with high dispersal capabilities.  Thunnus thynnus thynnusCarcharodon carcharias, in the Mediterranean SeaMegaptera novaeangliae, in the North PacificUnderstanding the population structure of marine species represents a challenge as their environments exhibit fewer obvious physical geographic barriers to gene flow than terrestrial onesa priori hypotheses regarding the placement of subpopulation boundaries or patterns of genetic structure may help to explain the observed patterns712Eleginops maclovinusGalaxias maculatusGenetic structure in marine populations commonly reflects a historical and contemporary interplay among a combination of ecological, demographic, behavioral, genetic, oceanographic, climatic and other factorsDelphinapterus leucas, in the western Nearctic occurred after the retreat of the Pleistocene ice cover, and the chronology of postglacial events is thought to have shaped the dispersal routes of this species from its glacial refugesLagenorhynchus albirostris) in the North Atlantic, demographic inference based on mitochondrial DNA suggests a population expansion in the Barents Sea off the Norwegian coast and geographic extension to the British Isles in the North Sea after the Pleistocene glacial eventsPhocoena phocoena, and minke whale, Balaenoptera acutorostrata, in the North Sea were mainly attributed to environmental requirements for feeding and breedingEschrichtius robustus, in the North Pacific were also modified by variations in sea level and the availability of environments not covered by ice in the late PleistoceneClimatic and oceanographic variations have also contributed substantially to the current distribution pattern, geographic variation and population structure of some species of the Order Cetacea. The population expansion of belugas, Cephalorhynchus eutropia), distributed along the Southeast Pacific coast between 30° and 56°S, represents an interesting model for understanding how a combination of historical and contemporary factors resulted in the current genetic structure of this continuously distributed marine species. Using 21 microsatellite markers, Pérez-Alvarez et al.ST = 0.15 and RST = 0.19) were higher than those reported among populations of most other dolphin species at largeThe coastal endemic Chilean dolphin  the northern area is located from 30°S to 42°S  characterized by an open and exposed coast with the presence of river runoff, where Chilean dolphins are associated with estuarine habitats29C. eutropia may have survived the Last Glacial Maximum (LGM) in low latitude areas, contracting its distribution to areas less impacted by ice, and then recolonizing higher latitudes through range expansion after the ice retreatC. eutropia to test whether population structure as influenced by the LGM is more consistent with the predictions of the expansion-contraction model under the “In situ refugia” or “Northern shift” scenarios.Consistent with the basic E-C model, Analyzing the microsatellite and mtDNA data simultaneously, the spatial model in GENELAND identified two genetic clusters with a boundary located between Maullín (41°36′S) and Aysén (46.36′S)  and 2. IST = 0.39, P < 0.0001 and FST = 0.19, P < 0.0001). Phylogeographic structure was indicated by the significantly greater value for ΦST compared to FST (P = 0.0004).A total of 14 haplotypes were defined by 15 polymorphic sites among the 64 individuals. Overall, mtDNA haplotype diversity (h) was 0.75 ± 0.053 and nucleotide diversity (π) was 0.038 ± 0.002 . The SouAnalysis of Bayesian skyline plots of each genetic group showed that the Northern population of Chilean dolphin  remainedC. eutropia along its distribution. The results supported the “Northern shift” scenario with the highest posterior probability 0.81 (CI 95% 0.801–0.825) and 0.99 (CI 95% 0.926–1.000) for mtDNA and microsatellites respectively. Additionally, model checking, direct approach and logistic regression also supported the Northern shift scenario to be the most likely (The ABC (Approximate Bayesian Computation) analysis discriminated between the two proposed scenarios based on geographic population structure and demographic inference of t likely . This suLeopardus guigna in southern South America, where geographical barriers such as the presence of the Andes Mountains and the Chacao Channel have shaped its current genetic population structureOrestias ascotanis, endemic to the Altiplano, where population structure reflects a long-term consequence of habitat fragmentationThe interpretation of the structure and distribution patterns of genetic diversity is one of the research objectives of fields including molecular ecology, population genetics and landscape geneticse.g. using microsatellite locie.g. using mtDNAHowever, as few absolute barriers to gene flow exist in oceans, even widely separated regions may be genetically connected and many marine species often exhibit low levels of genetic differentiation over large distances34Cephalorhynchus is an example of marine species with widespread but discontinuous distribution in the cool-temperate zone of the Southern HemisphereC. commersonii; Hector’s dolphin, C. hectori; and Chilean dolphin, C. eutropia) have demonstrated genetically divergent and structured populations. In the Commerson’s dolphin, genetic differentiation has been described for populations found along the southeastern coast of South America and around the Kerguelen Islands in the southern Indian Ocean  and the absence of a geographic gap or barrier separating the genetically distinct populations make its current genetic structure puzzling. Nevertheless, from a historical point of view, the genetic break between Northern and Southern populations appears to coincide with the boundary between two major marine biogeographic units31vs. non-affected areas during glacial cycles.The dolphin genus in situ refugiaST = 0.15 and RST = 0.19, Pérez-Alvarez et al.ST = 0.39, P < 0.0001 and FST = 0.19, P < 0.0001). The geographical boundary between these two population units coincides with the extension of the LGM ice sheetA signal of population structure is predicted, as recolonization from non-affected areas will likely produce genetic differentiation between areas affected and others not affected by the glacial cycles due to genetic drift or alternatively, long-term isolation of populations that persisted within Less genetic diversity is predicted in areas recolonized postglacially compared to populations that remained in refugia or at lower latitudes as consequences of the ice-free recolonization process, founder effect and genetic driftFinally, population growth that follows the recolonization process is predicted to generate a population expansion signal in deglaciated areas. For the Chilean dolphin, Northern and Southern populations exhibit different demographic histories. Demographic inference analyses , revealed a relatively stable population size for the last 40,000 years for the Northern population, after which a non-significant trend of population decrease is observed. This signal could reflect a current decrease in this population maybe related to human activities. In contrast, for the Southern population, Bayesian skyline plots as well as ABC estimated parameters, showed a population size increase from approximately 11,000 years ago. In both cases, coalescent method  tends toIn this context, the present study shows that the predictions of the Expansion-Contraction model are consistent with the population structure, genetic diversity and demographic history of the Chilean dolphin:Overall, our results suggest that the southern Chilean dolphin population went through an expansion process after LGM ice retreat by population expansion initiated about 11,000 years ago. This is consistent with the chronological dating information of glacial cycles reported for these latitudes, particularly during the LGM. The time period estimated for the glacial advance to the northern limit of Pacific Patagonia is approximately 17,900 years ago, and the deglaciation would have begun about 17,500 years ago, synchronously throughout Patagonia35in situ refugia” scenario involves the persistence of species in refugia within the glaciated area, followed by expansion within the higher-latitude area from those refugia. The “Northern shift” scenario involves a contraction in population size and displacement toward lower latitudes during periods of cooling, followed by recolonization of southern glaciated zones during postglacial warming from the northern area. Based on geographic population structure and demographic inferences, The Approximate Bayesian Computation approach supported the “Northern shift” as the more probable scenario. This scenario essentially amounts to restriction of the distribution of C. eutropia during the LGM to the area north of Chacao Channel. The exclusion of C. eutropia from the southern area during the LGM may be explained by decreased primary productivity, prey distribution and abundance decrease within the area affected by the LGM, and simple physical displacement from ice-covered areas. The absence of this species in areas directly affected by the glacial cycles is consistent with what is known about genus Cephalorhynchus, which appears to be restricted to particular and limited habitats in the cool-temperate zoneThe population genetic diversity and structure of the Chilean dolphin can be analyzed relative to two alternative E-C historical scenarios. The “Lontra provocax)i.e. “in situ refugia”) as the most plausible scenario for that species during the LGM. This is mainly based on the absence of a star-shaped haplotype genealogy, no sign of population growth for the area, and high levels of genetic diversity observed in the area affected by the ice. The persistence of the southern river otter in the glaciated area would have been possible given the survival of other aquatic and marine species that serve as its food source and/or flexibility in its diet as an adaptation to the changing environment during the LGMIn contrast to the findings for the Chilean dolphin, a study on another aquatic mammal found in similar latitudes, the southern river otter  was amplified for 64 individuals using the primers M13 Dlp1.5 5′-TGTAAAACGACAGCCAGTTCACCCAAAGCTGRARTTCTA-3′ and 8G 5′GGAGTACTATGTCCTGTAACCASequences were edited and aligned in PROSEQ 2.91 and the species was confirmed using a BLAST search undertaken in GeneBank and by DNA Surveillance.et al.C. eutropia samples and 21 dinucleotide microsatellite loci: Ev1, EV14, EV37, EV94, EV104We used genotype data obtained by Pérez-Alvarez, To explore spatial boundaries we used GENELAND, a Bayesian model that uses genotypes and spatial coordinates of individuals to cluster them into populations at approximately Hardy-Weinberg equilibrium, considering linkage equilibrium between loci. For this analysis, both markers (mtDNA and 21 microsatellites loci) were used simultaneously. An allele frequency uncorrelated model was set, with 1,000,000 MCMC iterations and thinning of 100.ΦST  and FST (using haplotype frequencies) in ARLEQUIN v 3.5. Phylogeographic structure was evaluated using PERMUT Software by comparisons between the observed ΦST and FST values, using 10,000 permutations. Because FST is a genetic differentiation index between populations based on haplotype frequency and ΦST considers both haplotype frequencies and genetic distance between haplotypes, a higher value of ΦST compared to FST reveals the existence of a phylogeographic structure.To evaluate the existence of phylogeographic structure, control region sequences were divided into two groups based on results obtained from GENELAND. Genetic diversity at the haplotype (h) and nucleotide (π) levels was estimated for the complete study area and also for the Northern and Southern areas using ARLEQUIN v 3.5. Considering the differences in sample size between areas, values of haplotype richness were also adjusted to sample size using a rarefaction analysis in PAST software. Genealogical relationships of the haplotypes were investigated by constructing a median-joining network in NETWORK 4.5.1.0. Genetic differentiation between Northern and Southern populations was assessed by calculating To assess the patterns of demographic history (1) The past population dynamics (demographic history) of each population was reconstructed by a Bayesian Skyline-plot method implemented in BEAST 1.7, which is based on coalescent theory and quantifies the relationship between the genealogy of the sequences and the demographic history of the populationC. eutropia, we used the Approximate Bayesian Computation (ABC) method. This approach makes it possible to test complex population genetic models by simulating the data sets of predefined scenarios and comparing their summary statistics with the summary statistics of the observed datain situ refugia” scenario proposed that the population of C. eutropia present in the southern area of distribution (Chacao Channel to the South) experienced reduced population size and survival only in restricted areas during the Last Glacial Maximum (LGM). Subsequently, a population expansion would have occurred in this area after the retreat of the ice. The “Northern shift” scenario considered that during the LGM the distribution of C. eutropia was restricted to the area north of Chacao Channel. In this case, the Southern population was originated after the ice retreat through postglacial colonization from the Northern population, and was characterized by founder effects followed by rapid population expansion .


The Parsis are one of the smallest religious communities in the world. To understand the population structure and demographic history of this group in detail, we analyzed Indian and Pakistani Parsi populations using high-resolution genetic variation data on autosomal and uniparental loci . Additionally, we also assayed mitochondrial DNA polymorphisms among ancient Parsi DNA samples excavated from Sanjan, in present day Gujarat, the place of their original settlement in India.Among present-day populations, the Parsis are genetically closest to Iranian and the Caucasus populations rather than their South Asian neighbors. They also share the highest number of haplotypes with present-day Iranians and we estimate that the admixture of the Parsis with Indian populations occurred ~1,200 years ago. Enriched homozygosity in the Parsi reflects their recent isolation and inbreeding. We also observed 48% South-Asian-specific mitochondrial lineages among the ancient samples, which might have resulted from the assimilation of local females during the initial settlement. Finally, we show that Parsis are genetically closer to Neolithic Iranians than to modern Iranians, who have witnessed a more recent wave of admixture from the Near East.Our results are consistent with the historically-recorded migration of the Parsi populations to South Asia in the 7th century and in agreement with their assimilation into the Indian sub-continent's population and cultural milieu "like sugar in milk". Moreover, in a wider context our results support a major demographic transition in West Asia due to the Islamic conquest.The online version of this article (doi:10.1186/s13059-017-1244-9) contains supplementary material, which is available to authorized users. The Parsi (or Parsee) community of the Indian sub-continent are a group of Indo-European speakers and adherents of the Zoroastrian faith, one of the earliest monotheisms that flourished in pre-Islamic Persia (present-day Iran) , 2. ZoroQissa-e-Sanjaan is that an Indian ruler called Jadi Rana sent a glass full of milk to the Parsi group seeking asylum [The Parsi trace their ancestry to the ancestors of the Zoroastrians of modern Iran, who are followers of the Prophet Zoroaster or Zarathushtra . In the g asylum , 18. Hisg asylum , 18. In g asylum , 19–21.Previous genetic analyses of the Parsis have focussed mainly on low-resolution uni-parental markers, which have suggested their affinity with both West Eurasian and Indian populations –24. Autodokhama (tower of silence) District Valsad, Gujarat, were excavated in 2004. The accelerator mass spectrometry dating of human remains suggest that the dokhama belongs to the 14th to 15th century A.D. [These early studies of the Parsi populations relied mainly on low-resolution markers, limiting the power of the analyses , 23–25, ury A.D. .We investigated whether the current Parsi people living in India and Pakistan are genetically related amongst themselves and with the present-day Iranian population, and if their genetic composition has been affected by the neighboring Indian and Pakistani populations. We also examined runs of homozygosity (RoH) to study consanguinity. To address the extent to which the current Parsi populations assimilated local females during their long formation history, we compared their mtDNA haplogroup composition with ancient remains excavated in Sanjan, the initial settlement established by these migrants from Persia .FST Indian and Pakistani Parsi = 0.00033 ± 0.000025), followed by the differentiation of each from the Iranian population , suggesting a common stock for both the Indian and Pakistani Parsis with the closest interpopulation affinity with populations from their putative homeland, Iran .For autosomal analyses, we used Illumina HumanHap 650 K genotyping chips on 19 Indian Parsi samples collected from Mumbai, and Illumina 2.5 M genotyping chips for 24 Pakistani Parsi individuals from Karachi  ancestry component was significantly higher (two-tailed P < 0.0001) in the Parsis than in any other populations residing in South Asia that were examined. The present-day Iranian population exhibited a striking difference from the Parsis, mainly in carrying an additional European component (light blue) and substantially lower South Asian ancestry (dark green)   had significant levels of gene flow with each other, as well as with both of the Parsi populations when evaluated with respect to the present-day Iranian population . However, the number of these chunks were significantly lower than the chunk counts shared between any pair of South Asian populations (two-tailed P < 0.0001)  . The max . The maWe used ALDER, a method based on linkage disequilibrium , to estiTo investigate further the parental relatedness among Parsis , we analIQBC1, an IQ-motif-containing B1 gene that is highly expressed in Epstein–Barr virus-transformed B lymphocytes [To investigate how random genetic drift has shaped the functional genetic variation after admixture in the Parsis, we implemented the population branch statistic  using thphocytes .To obtain a detailed understanding of the sex-specific South Asian and Iranian ancestries, we examined maternally inherited mtDNA and paternally inherited Y chromosome biallelic polymorphisms in a larger sample in both the Indian and Pakistani Parsi populations Additional file 2: Table S3.FST values based on an autosomal data set for all the populations included in this study. (XLSX 120 kb)The population-wise


D. b. longipes), declared extinct in 2011, extends into southern Kenya, where a handful of individuals survive in the Masai Mara. We also identify conservation units that will help maintain evolutionary potential. Our results suggest a complete re-evaluation of current conservation management paradigms for the black rhinoceros.The black rhinoceros is again on the verge of extinction due to unsustainable poaching in its native range. Despite a wide historic distribution, the black rhinoceros was traditionally thought of as depauperate in genetic variation, and with very little known about its evolutionary history. This knowledge gap has hampered conservation efforts because hunting has dramatically reduced the species’ once continuous distribution, leaving five surviving gene pools of unknown genetic affinity. Here we examined the range-wide genetic structure of historic and modern populations using the largest and most geographically representative sample of black rhinoceroses ever assembled. Using both mitochondrial and nuclear datasets, we described a staggering loss of 69% of the species’ mitochondrial genetic variation, including the most ancestral lineages that are now absent from modern populations. Genetically unique populations in countries such as Nigeria, Cameroon, Chad, Eritrea, Ethiopia, Somalia, Mozambique, Malawi and Angola no longer exist. We found that the historic range of the West African subspecies ( Diceros bicornis) populations, including the western subspecies (D. b. longipes) declared extinct in 2011, has raised fears that this species will disappear from the wild within the next two decadesth century, populations are thought to have declined by more than twenty-fold until the mid-1990s, when intensive protection led to a population recovery to just over 5,000 individuals by 2014. Despite a historic range that included much of sub-Saharan Africa The well documented poaching and subsequent demographic collapse of black rhinoceros , eastern (D. b. michaeli), south-central (D. b. minor) and south-western (D. b. bicornis) subspecies was adopted by the IUCN’s African Rhino Specialist Group (AfRSG) and is the prevailing basis for conservation management of this species , may have occurred even before populations were identified by the relevant conservation authorities. In addition, the populations of each of the five countries in which black rhinoceroses still persist are mostly the result of large scale consolidation of fragmented local populations by means of past translocations and it is unknown how these admixed stocks relate to their founding local populations.During the 20 extinct . One reaPrevious black rhinoceros genetic studies have largely been restricted, either in geographic scale, molecular coverage or sampling1617181924Molecular data is now routinely used to help define conservation units within threatened species that require management plans. The evolutionarily significant unit (ESU) and management unit (MU) concepts sought to conserve the evolutionary potential of a species by defining populations possessing unique evolutionary and adaptive variation on the basis of phylogenetic distinctiveness and/or differences in allele frequencies, respectively26272829We assessed the pre-decline genetic variation of the black rhinoceros across its known historic range using a comprehensive set of museum and modern samples. We used these data to first infer the evolutionary history of the species and then to document how hunting has compressed, partitioned and eroded genetic diversity within and among the species’ final strongholds. Finally, we spatially evaluated our molecular data to determine the geographic components of black rhinoceros molecular diversity, from which we define candidate genetic units for conservation.Mitochondrial DNA variation was found to be unevenly distributed across the species range. East Africa  harboured the highest levels of museum sample variation, followed by countries in southern Central Africa  could be detected in samples from extant populations  for these samples was the highest recorded for this species , decreases in HE and the mean number of alleles per locus (A) were detected when historic and modern samples were compared, but this trend was not as marked as for the mitochondrial DNA (mtDNA) dataset.Levels of nuclear diversity were also higher in historic (museum) samples. Since pre-existing data sets were incorporated into our study, data from all loci were not available for all samples . Althouges 0.75; . For couOnly nine of the described local populations had not been extirpated in their original habitat, and of these, six were classed as potentially genetically eroded while only two  were found not to have lost haplotypes . Howeverth century, then recovering to between 40–70% of their starting numbers. The highest number of pre-decline effective breeders was Kenya (77), followed by Tanzania (62), Zimbabwe (28), South Africa (11) and Namibia (10).We also investigated whether the entire surviving black rhinoceros population and the five remaining country populations have undergone demographic changes as a result of the local and genetic extinctions documented above. BEAST analysis of the mitochondrial control region data set showed clearly that the entire black rhinoceros population has undergone a collapse in effective population size, beginning approximately 200 years ago, and reaching its lowest point some 15 years ago before slightly recovering –f. Despith century declines. We identified 34 local populations and listed them geographically from West to North-East Africa and then to Central and southern Africa  criterionThis is the first study defining the range-wide geographic levels and structure of putatively neutral genetic variation in the black rhinoceros, a highly threatened species for which such an assessment has long been overdue1532This study emphasises the value of historical data in quantifying losses in genetic diversity and inferring effective population size changes across the range of a widely distributed species. The inclusion of historical material where possible can provide opportunities to investigate demographic histories (i.e. effective population size changes) that span large numbers of generationsth century (approximately 170 years ago) when European powers were competing for control of the continent’s resourcesK-selected species such as the high mortality of calves and relatively low reproductive ratesDespite this, the five countries with surviving populations of black rhinoceroses have undergone marked reductions in effective population size –e. Low ln = 5), because black rhinoceroses were already rare in this country by the mid 1800s when museum collecting became popular. Today, the remnant KwaZulu-Natal population of South Africa comprises over 2,000 individuals, all carrying a single mtDNA haplotypeBy this time, however, South Africa was already a thriving colony for 200 years with much of its wildlife already severely depleted. This is why the historic mtDNA sample from South Africa is old . The onset of these losses can be inferred to 100–150 years ago . ColoniaDividing the sample into respective local populations improved the resolution of geographic patterns of mtDNA loss. Highest haplotype diversity could be localised to the area comprising southern Kenya/northern Tanzania. East African populations also featured the highest levels of haplogroup diversity with three populations harbouring up to three mtDNA haplogroups and one, central Tanzania, up to four. The majority of black rhinoceros fossil data, including the oldest known fossil, also originates in this regionThe most severe form of global population extinction in our scheme could be inferred in just under a third of all black rhinoceros populations , compared to microsatellites (10%). This is exemplified by the haplogroup RU, where only mtDNA data were available for most of the historic specimens from the region. This may also help explain why the magnitude of erosion of genetic diversity was much greater for mtDNA than for microsatellites. Maternally inherited mtDNA haplotypes are four times more prone to extinction through genetic drift than nuclear alleles. A future examination of our extant and historic genetic data set using the greater number of molecular markers afforded by newer generation sequencing technologies and methods that enable reliable data generation from museum specimens  will undoubtedly shed more light on the structure of genetic variation in this species. This approach will also distinguish between neutral and adaptive genetic variation, aiding in the inference of, and mechanisms underpinning, local adaptaion, thus leading to a more holistic management of this species’ adaptive potential.D. b. minor), thought to inhabit a massive region including the KwaZulu-Natal province of South Africa in the south and Tanzania in the north, thus spanning the Zambezi. Here we show that this region was historically inhabited by two nDNA populations (SE and SN) to the south and three  to the north of the divide. The geographic isolation of the RU mtDNA haplogroup in the east of this region also suggests additional genetic structuring, however, RU may correspond to D. b. rovumae and D. b. nyasae from Zukowsky’s (1964)D. b. bicornis), also known as D. b. bicornis (Rookmaaker and Groves 1978)bicornis  the application of the name bicornis to the extant Namibian population is erroneous. Several taxonomists recognised the evolutionary distinctiveness of populations in North-eastern and North-western Africa, but only Spinage (1986)D. b. bruciiD. b. michaeliD. b. longipes, but here we show strong mtDNA discontinuity across the Shari-Logone River system, with haplogroup WW to the west and haplogroup CV to the east as far as East Africa. If this genetic discontinuity was taken to denote subspecies, then the name longipes would correspond to haplogroup CV because it includes a DNA sequence from the type location of this subspecies in Mogrum, which is on the eastern bank of the Shari in Chad.Taxonomic delineations based on a limited number of morphological characteristics are often not congruent with molecular genetic structure464748owsky’s 164D. b. bLimited resources necessitate prioritisation or ranking of management areas for conservation. For the black rhinoceros, even the most comprehensive previous system of priority rankingHere, we applied the genetic ESU/MU concept of Moritz (1994)The CV ESU was geographically distinct throughout much of its range, but overlapped in East Africa with CE and EA. It is also in East Africa, specifically in the Masai Mara Game Reserve, where the only extant individual in our data set (MA1516) known to bear the CV haplogroup was observed. The CV nDNA population is also found only in the Masai Mara Game Reserve, but was observed in six individuals . It is hin situ and ex situ Kenyan populations, so the management of this ESU could be straightforward. In contrast, the geographic distribution of the CE ESU suggests that it would be best managed by conserving what remains of the black rhinoceros population in Tanzania. Although the extant Tanzanian sample was small (n = 3), the larger museum sample shows that CE historically occurred at high frequencies in Tanzania and southern Kenya  were rehydrated for 48 hours in distilled water, changing the water once. Bone was ground into a powder. DNA was then extracted according to Rohland th and 20th century museum specimens, following Brown and Houlden (2000)et al.D. bicornis-specific microsatellite loci  data only, and extant data collected after population declines using Arlequinth century population declines. Since all historical material included collection dates, we used coalescent simulation to infer the demographic history of each surviving stock, in order to test whether recorded population reductions translated into a tangible reduction in effective population size over recent time. We ran simulations using a Bayesian extended skyline tree priorSurviving black rhinoceros populations were hypothesised to have undergone genetic erosion due to genetic drift and/or inbreeding and when local populations have received haplotypes not historically present via translocation. We assessed the level of genetic erosion in surviving stocks by comparing genetic diversity before and after the 20Rhino Resource Centre repositoryhttp://www.rhinoresourcecenter.com/th century population extinctions, a population was considered locally extinct if all its haplotypes could be detected in other extant populations, whereas it was considered globally extinct if none of its historical haplotypes was sampled among extant populations, that is, extirpation of both the population and all its haplotype variants. Since neither of the above definitions provided a measure of the loss of genetic uniqueness, we further considered an extirpated local population genetically extinct if its private haplotypes did not survive to the present day, even if the shared alleles may still be present in modern day country populations. Lastly, since these surviving stocks were mixed due to population consolidation, we used haplotype sharing data to match genetic persistence of local haplotypes in modern day population stocks.We used genetic information to compile an overview of the change in variation by generating a summary of local populations of black rhinoceroses extant in sub-Saharan Africa based on geographic data from museum specimens and their associated historic information sourced from the invaluable via Bayesian coalescent simulation using BEASTMitochondrial DNA control region variation was further explored by reconstructing an intraspecific phylogeny from black rhinoceros haplotypes recovered in our data set. Phylogenetic reconstruction was carried out K) across the species’ range, which in combination with significant allele frequency differences, as measured by population assignment, would in principle confer the inferred populations the status of at least a management unit (MU)We used Bayesian clustering implemented in STRUCTUREGeographic information helps define the spatial scale of genetic units for conservationWe used the criteria set out by Moritz (1994)http://dx.doi.org/10.5061/dryad.192f1. DNA sequence data available from GenBank (accession numbers KY472315 - KY472605).Microsatellite data available from the Dryad Digital Repository: How to cite this article: Moodley, Y. et al. Extinctions, genetic erosion and conservation options for the black rhinoceros (Diceros bicornis). Sci. Rep.7, 41417; doi: 10.1038/srep41417 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


DNA may provide contrasting insights into past events. Forest elephants (Loxodonta cyclotis) were impacted by climate and habitat change during the Pleistocene, which likely shaped phylogeographic patterns in mitochondrial (mt) DNA that have persisted due to limited female dispersal. By contrast, the nuclear (nu) DNA phylogeography of forest elephants in Central Africa has not been determined. We therefore examined the population structure of Central African forest elephants by genotyping 94 individuals from six localities at 21 microsatellite loci. Between forest elephants in western and eastern Congolian forests, there was only modest genetic differentiation, a pattern highly discordant with that of mtDNA. Nuclear genetic patterns are consistent with isolation by distance. Alternatively, male‐mediated gene flow may have reduced the previous regional differentiation in Central Africa suggested by mtDNA patterns, which likely reflect forest fragmentation during the Pleistocene. In species like elephants, male‐mediated gene flow erases the nuclear genetic signatures of past climate and habitat changes, but these continue to persist as patterns in mtDNA because females do not disperse. Conservation implications of these results are discussed.The past processes that have shaped geographic patterns of genetic diversity may be difficult to infer from current patterns. However, in species with sex differences in dispersal, differing phylogeographic patterns between mitochondrial (mt) and nuclear (nu)  Samples were collected in full compliance with required Convention on International Trade in Endangered Species of Wild Fauna and Flora and other institutional permits. Wild African forest elephants   Tables . As only) Tables . Most of) Tables .L. africana) were genotyped, one each from 15 localities . Savanna elephant samples were included in light of previous findings that forest and savanna elephants are genetically distinct species with a narrow region of hybridization  with 0.04 units/μl final concentration of AmpliTaq Gold DNA Polymerase (Life Technologies) along with 1.2 μl of the primer mix. For the DNA samples from BF that had been extracted from dung, 1 μg/μl final concentration of bovine serum albumin (New England BioLabs Inc.) was also included. The PCR cycling program consisted of an initial 95°C for 10 min; with cycles of 15 s denaturing at 95°C, followed by 30 s annealing at 60, 58, 56, 54, or 52°C (two cycles each temperature); or 50°C (last 30 cycles), followed by 45 s extension at 72°C; with a final extension of 30 min at 72°C. For locus Lcy‐M45, the PCR cycling program was modified as described previously  and observed heterozygosity (Ho). The software GenAlEx 6.5 was also used to calculate Shannon's diversity index (I) and to make allele frequency distribution histograms for each locus for each locality. Tests for Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were conducted on the forest elephant microsatellite data. A Markov chain algorithm was used to test for HWE using 10,000 dememorization steps, 1,000 batches, and 10,000 iterations per batch using the software Genepop 4.2  was conducted in Arlequin version 3.5.1.3  were implemented in GENETIX version 4.05 , and the pairwise distances between each locality were calculated using the coordinate calculators and distance tools in GPS Visualizer . To examine the relationship between genetic distances and geographic distances among forest elephants at the five localities in Central Africa, a spatial autocorrelation analysis was implemented in GenAlEx 6.5 . Mantel tests were run with 30,000 randomizations (one‐tailed test). For Slatkin's similarity index, we used the recommended log‐transformation of both M and geographic distance. As we had only one sample from West Africa , we excluded this sample from analyses.The coordinate information of each locality was estimated using the LatLong.net website , we also conducted additional analyses using only one sample from an alternative location (LO). The single LO sample was chosen randomly by RESEARCH RANDOMIZER (https://www.randomizer.org/). Three iterations were run in which a single sample from LO was chosen at random, and the NJ tree was reconstructed with the single LO sample.We inferred the phylogenetic relationships among localities using the neighbor–joining (NJ) method implemented in POPTREEW , and was monomorphic in three localities. The remaining 18 microsatellite loci  with significant deviation from HWE while FIS was 0.024 but did not deviate significantly from HWE. The value calculated for FST was low at 0.030, and this value was statistically significant (p < .001).Population genetic analyses involved only forest elephants, except as otherwise indicated. Many analyses were run separately for datasets that included or excluded elephants in GR that were identified as hybrids between forest and savanna elephants, although the outcomes of these analyses were not greatly affected by including or excluding hybrids. Analysis of molecular variance (AMOVA) found that only 3.04% of the variance was accounted for by differences among localities Table . FIT waFST was also calculated , although this value was statistically highly supported (p < .001). By contrast, FST values that had been previously estimated using only mitochondrial DNA  and the two localities in the eastern side of the Congolian forest block (BF and GR), although partitioning between west and east was incomplete . The STRUCTURE analyses excluding 5 hybrid GR elephants produced similar results  conducted using GenAlEx showed that 25.03% of the genetic variance was explained by coordinate 1, which revealed clear separation between savanna and forest elephant Figure a, exceptIn PCoAs using only forest elephant data, distinctiveness among localities was not evident when every individual elephant was plotted Figure b. HoweveDA, bias‐corrected DST, and bias‐corrected FST. The latter two methods correct for biases caused by sample size differences among localities. The analyses involving DA tended to show a longer branch for the localities with small sample sizes  formed a lineage distinct from forest elephants  analyses of forest elephant genotypes grouped by locality produced consistent topologies using three genetic distance calculations:  Figures b, S4b. T Figures b, S4b. Is Figure b, S4b. Tl Figure . Reduciny Figure . This wa3.1.2x‐axis in Figure y‐axis in Figure We examined the degree to which genetic differences among forest elephants at different localities varied with the geographic distances separating them. Geographic distances were computed between each pair of elephants, with the distances placed into quartiles  and for Rousset's distance     Figure . However44.1FST values were modest. All of these results point to only limited genetic differentiation between eastern and western localities within the Central African forests.Nuclear genetic differences were detected among Central African forest locations. Specifically, genetic partitioning by STRUCTURE identified that elephants in western  and in eastern (BF and GR) Congolian forests were detectably different, although partitioning was far from complete Figure . The samHybrids between forest and savanna elephants have been documented, but only within relatively narrow transition zones between forest and savanna habitats , and primer sequences are available in Gugala et al. (Gugala et al., The sequences of microsatellites used in this manuscript were established and deposited at NCBI GenBank ( Click here for additional data file.


Cneorum tricoccon throughout the majority of its western Mediterranean distribution range, and using nine polymorphic microsatellite markers, we examined the effects of periphery , population size , and seed disperser (introduced carnivores vs. endemic lizards) on the genetic diversity and population structure of the species. Contrasting genetic variation (HE: 0.04–0.476) was found across populations. Peripheral populations showed lower genetic diversity, but this was dependent on population size. Large peripheral populations showed high levels of genetic diversity, whereas small central populations were less diverse. Significant isolation by distance was detected, indicating that the effect of long‐distance gene flow is limited relative to that of genetic drift, probably due to high selfing rates (FIS = 0.155–0.887), restricted pollen flow, and ineffective seed dispersal. Bayesian clustering also supported the strong population differentiation and highly fragmented structure. Contrary to expectations, the type of disperser showed no significant effect on either population genetic diversity or structure. Our results challenge the idea of an effect of periphery per se that can be mainly explained by population size, drawing attention to the need of integrative approaches considering different determinants of genetic variation. Furthermore, the very low genetic diversity observed in several small populations and the strong among‐population differentiation highlight the conservation value of large populations throughout the species’ range, particularly in light of climate change and direct human threats.The effect of population size on population genetic diversity and structure has rarely been studied jointly with other factors such as the position of a population within the species’ distribution range or the presence of mutualistic partners influencing dispersal. Understanding these determining factors for genetic variation is critical for conservation of relict plants that are generally suffering from genetic deterioration. Working with 16 populations of the vulnerable relict shrub  It is an andromonoecious, insect‐pollinated, and self‐compatible species . However, introduced carnivorous mammals (approximately 250 B.C.), such as pine martens (M. martes) or genets (Genetta genetta L.), are currently the main dispersers in localities where lizards are extinct  were collected and stored in bags with silica gel.We sampled 21–30 indiscriminately selected individuals within areas of about 1 km2.2Total genomic DNA was extracted from 20 mg of dry leaf tissue using the DNeasy Plant Mini Kit  following the manufacturer's protocol. Its concentration and purity were quantified on a NanoDrop spectrophotometer .2, 0.25 μmol/L of each dNTP, 0.04 μmol/L of the forward primer with M13 tail, 0.16 μmol/L of the reverse primer and 0.16 μmol/L of the universal fluorescently labeled M13 primer , 0.5 U of Taq polymerase (Biotools), and 10 ng of template DNA. The PCR program consisted of one step of 4 min at 94°C followed by 30 cycles each of 30 s at 95°C, 45 s at 56°C, and 45 s at 72°C. Another eight cycles were then performed, consisting of 30 s at 95°C, 45 s at 53°C, and 45 s at 72°C, followed by a final step of 10 min at 72°C. A S1000 Thermal Cycler  was used. Each reaction was checked for successful amplification by running 3 μl of PCR product of six indiscriminately selected individuals per population and microsatellite locus on a 1% agarose gel stained with SYBR Safe . PCR products (1 μl) were diluted with 9.1 μl of a loading mixture containing 9 μl of HiDi Formamide and 0.1 μl GeneScan LIZ500 internal size standard  and analyzed on an automated DNA sequencer  in Unidad de Genómica, Parque Científico de Madrid, Spain. Amplified fragment lengths were scored with GENEMARKER v. 2.4.0 .We genotyped each sample at nine microsatellite loci known to be polymorphic across the species, described in Garcia‐Fernandez, Lazaro‐Nogal, Traveset, and Valladares : Ctric 02.32.3.1P, percentage of polymorphic loci; A, mean number of alleles per locus ; AE, mean number of effective alleles ; HO, observed heterozygosity ; HE, expected heterozygosity (1−Σpi2); FIS, fixation index (1 − (HO/HE)), and the number of private alleles, were estimated using GENALEX v. 6.41 . Deviation from the Hardy–Weinberg equilibrium (HW) was evaluated at each population with the MCMC approximation  of Fischer's test implemented in GENEPOP V. 4.1  distances was generated with PASSAGE v. 2 , FSC (among populations within regions), and FST . The significance of the variance components was tested using nonparametric permutation procedures, with 50,000 permutations.Population genetic structure in central and peripheral regions was examined by means of analysis of molecular variance , where each K is characterized by a set of allele frequencies at each locus. Individuals are probabilistically assigned to one or more clusters. Our analyses were based on an admixture ancestral model with correlated allele frequencies . The proportion of membership of each individual and population to the inferred K cluster was then calculated. We performed 12 independent runs for each K, using a burn‐in period and a run length for the MCMC of 5 × 105 and 106 iterations, respectively. We used HARVESTER  and the LargeKGreedy option (for K ≥ 10). Membership in clusters was graphically represented using DISTRUCT v. 1.1  based on FST pairwise values was performed in GENALEX to provide further insight into population differences.Genetic structure was also analyzed using Bayesian clustering methods implemented in STRUCTURE v. 2.3.1.  and differentiation (FST) among groups using FSTAT v. 2.9.3 3  was high in central populations with the exception of CA and ME, in which 89% of the loci were monomorphic. Conversely, P was low (33%–56%) in all periphery populations, except in FI, where all loci were polymorphic. The lowest mean number of alleles per locus (A) was found in CA and ME (1.11), whereas SA had the largest value (4.11). The mean number of effective alleles (AE) showed similar results, ranging from 1.0 (in CA) to 2.82 (in SA). Observed (HO) and expected (HE) heterozygosities tended to be higher in central populations than in peripheral populations , but differences were not significant (Table HO) ranged from 0.004 (CA) to 0.366 (SA), whereas expected heterozygosities (HE) ranged from 0.004 (CA) to 0.485 (LL). The inbreeding coefficient (FIS) varied among populations from 0.185 (SA) to 0.887 (CA) and also showed a (not‐significant) tendency to be higher in peripheral populations, with the exception of CA and ME, which showed the highest FIS values. We found a total of 20 private alleles, present in 10 of the 16 populations. The number of private alleles per population ranged from one  to five (DR). All populations showed significant departures from HW equilibrium toward heterozygote deficiency (p < .001 across loci and populations). No consistent LD was found between any pairwise comparisons across loci and populations (data not shown).The percentage of polymorphic loci . Pairwise FST values were higher than 0.5 in ≥70% of the comparisons. In general, FST values were lower between nearby populations . Accordingly, we detected significant IBD between populations, either using the matrix of linear Euclidean distances  or the logarithm of the distances  that were included in the same cluster  and higher inbreeding (FIS) than central populations, although differences were not significant. In contrast, population size was significantly associated with most genetic diversity indices. It is generally predicted that central populations are larger than peripheral ones  maintain high levels of genetic diversity, despite their geographic isolation. Similarly, small populations located in the center of the species distribution  are genetically depauperated, showing even lower genetic diversity than peripheral populations. Our results support the stronger role of population size in determining the genetic diversity of populations  could be more frequent than outcrossing in current conditions, likely due to the limited pollinator visits observed in many populations . Limited gene flow among populations is also in agreement with the strong genetic drift observed in certain populations , where many alleles were fixed. Finally, as has been documented in other long live Tertiary relicts, gene flow may have been interrupted during some historic periods, increasing isolation and therefore FST values  may replace the native lizards as seed dispersers  may account for the absence of significant differences in genetic diversity and population structure among populations with different dispersers. In other Tertiary relicts, where natural dispersers have disappeared, other species have occupied their role with important consequences for the species dispersion patterns . An experimental study with C. tricoccon adds support to this hypothesis, showing that dry‐edge populations  may be locally adapted, as they exhibited multiple functional traits that favored drought tolerance  can be considered for further investigations into their conservation value. Accordingly, the low genetic variation found in some populations  may compromise their long‐term viability. In particular, two populations (CA and ME) showed extremely low genetic variation, in terms of heterozygosity and polymorphism. Both populations are small and are geographically isolated in islands, where genetic drift can increase the fixation of deleterious alleles and outcrosses may be carried out between related parents. Therefore, reduced individual fitness and increased inbreeding depression can be expected in both populations (Kimura, Maruyama, & Crow, In conclusion, our results highlight that, despite geographic isolation and important gene flow limitations related to pollen and seed dispersal mechanisms, population size can override, at least partially, the effects of geographical periphery. Integrative approaches taking into account different factors that determine genetic diversity and population structure, such as the position of a population in the species’ distribution range, size, the presence of effective seed and pollen dispersers, and the stability of local adaptations, are necessary to develop effective conservation strategies.None declared.A. L‐N., FV, and AT conceived the ideas; A L‐N collected the data; A L‐N, A G‐F, and SM analyzed the data; and A L‐N, A G‐F, and SM led the writing with contributions from all authors. Click here for additional data file.


Aedes aegypti, commonly known as “the yellow fever mosquito”, is of great medical concern today primarily as the major vector of dengue, chikungunya and Zika viruses, although yellow fever remains a serious health concern in some regions. The history of Ae. aegypti in Brazil is of particular interest because the country was subjected to a well-documented eradication program during 1940s-1950s. After cessation of the campaign, the mosquito quickly re-established in the early 1970s with several dengue outbreaks reported during the last 30 years. Brazil can be considered the country suffering the most from the yellow fever mosquito, given the high number of dengue, chikungunya and Zika cases reported in the country, after having once been declared “free of Ae. aegypti”.Ae. aegypti populations, genetic variability, genetic affinities with neighboring geographic areas, and the timing of their arrival and spread. This enabled us to reconstruct their recent history and evaluate whether the reappearance in Brazil was the result of re-invasion from neighboring non-eradicated areas or re-emergence from local refugia surviving the eradication program. Our results indicate a genetic break separating the northern and southern Brazilian Ae. aegypti populations, with further genetic differentiation within each cluster, especially in southern Brazil.We used 12 microsatellite markers to infer the genetic structure of Brazilian Ae. aegypti in Brazil. While populations in the northern cluster are likely to have descended from Venezuela populations as early as the 1970s, southern populations seem to have derived more recently from northern Brazilian areas. Possible entry points are also revealed within both southern and northern clusters that could inform strategies to control and monitor this important arbovirus vector.Based on our results, re-invasions from non-eradicated regions are the most likely scenario for the reappearance of  Aedes aegypti (“yellow fever mosquito”) is of great medical concern because it is the primary vector of important viruses causing dengue fever, chikungunya and Zika, as well as increasingly transmitting yellow fever once again. Due to the Zika outbreak, which started in Brazil in 2015 and rapidly spread with million cases reported in Central/South America and the Caribbean, this mosquito has attained special notoriety. Brazil is by far the most affected country not only by Zika, but also by dengue and chikungunya. Interestingly, in 1950s Brazil had been declared “free of Ae. aegypti” after a well-documented eradication program. However, during the last 45 years Ae. aegypti reappeared in Brazil causing several dengue outbreaks, with millions of reported cases. Here, we used genetic data to study the reappearance of Ae. aegypti in Brazil after the putative eradication event. Our results support re-invasion; mosquitoes from the non-eradicated areas in Venezuela invaded North Brazil, later expanding their distribution southwards. We also identify specific locations in Brazil, as possible entry points. This knowledge can inform strategies to control the spread of the vector and of the diseases it transmits. Aedes aegypti is one of the most successful worldwide invaders, spreading from its native Africa to most tropical and subtropical regions of the world [Ae. aegypti remains the major target for disease control. The species’ highly anthropophilic behavior, its ability to lay desiccation-resistant eggs, its high passive dispersal, and its tendency to develop pesticide-resistance [Ae. aegypti’s arrival in the Americas, shortly after Europeans in the 16th Century, many dengue outbreaks have been recorded, including several in Brazil and the Caribbean [Ae. aegypti free. However, after the relaxation of the eradication campaigns, Ae. aegypti reappeared in some of these regions. This could have happened either through re-invasion from non-eradicated regions, such as Venezuela, USA, and some Caribbean islands [he world ,2 and itsistance ,4, have aribbean . Brazil The study of the genetic structure and the identification of likely source populations or isolated Brazilian populations that may be efficiently targeted for control can contribute to develop better strategies to control the spread of this vector and of the diseases that it transmits. It can also provide insights on the roles of environmental and human induced factors that can contribute to vector or disease spread.Ae. aegypti from the east, southern and central Brazil are genetically close to populations from the island of Dominica in the Caribbean, whereas the populations from northern Brazil have genetic affinities primarily with northern South American countries  revealed moderate  to high ountries . This suAe. aegypti genetic diversity in Brazil by screening for genetic diversity at 12 microsatellite loci in 48 Brazilian and non-Brazilian populations, a much denser spatial sampling than in previous studies. We use these data to study the patterns of genetic differentiation among populations of Ae. aegypti in Brazil at a fine geographic scale, to describe their genetic affinities with populations from neighboring regions from both South and North/Central America and the Caribbean, and to reconstruct the history of the re-appearance of Ae. aegypti in Brazil.In this study we revisit the analysis of Aedes aegypti samples were collected from 46 locations  in America and Caribbean (Ae. aegypti (Morenaa Network) recommended procedures [Ae. aegypti mosquitoes in total) were used in the present study  according to the manufacturer instructions, with an additional RNAse A (Qiagen) step. DNA samples were stored at -20n et al.  for tweln et al. ,14 microormation .All microsatellite loci were analyzed for within-population deviations from Hardy-Weinberg equilibrium (HWE) using the exact HWE test in GENEPOP v.4.5.1 ,16. The a priori information of sampling location. We conducted different runs using different datasets , was determined by conducting 10 independent runs for each K = 1 to the maximum number of populations included in the analysis. Each run assumed an admixture model and independent allele frequencies (lambda set to one), using a burn-in value of 100,000 iterations followed by 500,000 repetitions. The optimal number of K clusters was determined using the Delta K method of Evanno et al. , using tTo complement the Bayesian analysis, we also performed a Discriminant Analysis of Principal Components (DAPC), using the “find.clusters” option of ADEGENET package  in R v.3Mantel tests to assess the significance of correlation between geographic , we useAe. aegypti populations [Evidence of population bottleneck events was tested using two methods as implemented in the widely used program BOTTLENECK . In the ulations –42 and aulations ,44 the mAe. aegypti in Brazil. For our analysis we used five regions . Different combinations of these five regions lead to 120 scenarios that could be tested. However, testing all the possible scenarios makes the estimation computationally challenging. Thus, usually [Ae. aegypti was recorded again in Brazil in the late 1970’s). A mutation rate ranging from 9x10-6 to 1x10-5 was used based on rates reported in the literature for other Diptera species [Bayesian computation methods (ABC)  as imple usually –49 a sma usually , (iii) t usually ) and  population-by-locus comparisons deviated significantly from Hardy-Weinberg equilibrium (HWE) after sequential Bonferroni correction , a levelPopulation genetic statistics for each population are provided in We first performed a preliminary STRUCTURE analysis  on the cWe then carried out analyses on each of the two clusters separately. For Cluster 2 the Evanno et al.  method aThe assignment of the samples from each population to each one of the three DAPC-groups  is preseMantel test on Cluster 1 dataset  supporteLevels of genetic differentiation  between all pairs of populations within Cluster 1 and Cluster 2 are presented in Pooling the samples within each genetic cluster or group of samples within a country , revealed high private allele (Np) richness, especially in Cluster 1 .2,38 = 12.5; p<0.001). Nevertheless, no significant difference was found in allelic richness levels  as three separate groups, ANOVA results  showed ts levels .Ae. aegypti from the non-eradicated populations from Venezuela invaded North Brazil (Cluster 2) and subsequently invaded the South of Brazil (Cluster 1). Except for scenario 2 (PP = 0.12), alternative scenarios tested were poorly supported  analysis provided strong support (PP = 0.845) for scenario 1 . In this Diptera ,51.Ae. aegypti populations (southern-Cluster 1 and northern-Cluster 2), that are roughly separated  is genetically close to Colombia, Venezuela, and Trinidad samples . The corispersal ,57 in waAe. aegypti populations from northern Brazil derive from Venezuela, where this species was never eradicated [Our data show high levels of gene flow between Venezuela and Brazilian Cluster 2 samples. This is supported by the high number of possible first generation migrants detected in Brazilian Cluster 2  and the adicated . Boa Visadicated . In 2000Ae. aegypti with close genetic affinities [Ae. aegypti in the Americas in agreement with previous findings [What were the origins of the two major genetic clusters in Brazil? Brazil, North America, and the Caribbean, have a long history of international trade and populations of finities ,7,59. Hefindings . For insAe. aegypti re-infestation in the 1970s [Ae. aegypti survived in residual small populations, a genetic signature should be detectable, such as lower genetic diversity than samples from non-eradicated areas and signs of recent bottlenecks. Our data do not support this hypothesis; Brazilian samples have levels of genetic diversity comparable with that found in samples from countries where eradication never occurred , and timing of he 1970s ,7, we teoccurred . As far occurred , this waAe. aegypti re-infesting northern Brazil in the 1970s [The ABC analyses , stronglhe 1970s  and sprehe 1970s , as sugghe 1970s . HoweverAe. aegypti populations in present day Brazil, a northeast set and south set  and Cluster 2 (B) populations.DAPC on the two genetic sub-clusters as predefined according to the STRUCTURE output for K = 2 for Cluster 1 ((TIF)Click here for additional data file.S2 FigA) and Cluster 2 (B). The graphs represent the individuals as dots and the groups as inertia ellipses. DA eigenvalues of the analysis are displayed in insets.DAPC scatterplots representing three groups of populations for each Cluster 1 ((TIF)Click here for additional data file.S3 FigA, B) and 2  and all populations pairs within the sub-cluster 1A , sub-cluster 1B  and sub-cluster 2A . Statistical significance was evaluated through Mantel test as implemented using the ade4 package in R. The original value of the correlation between the two matrices  is represented by a dot, while the histograms represent the permutated values assuming absence of spatial structure. Significant spatial structure result in the original value being out of the reference distribution. The correlation between geographic and genetic distance was plotted and the correlation coefficient (r) and as well as the R-squared, were estimated using the web version of IBD.Isolation-by-distance plots for all pairs of populations within the STRUCTURE defined Clusters 1 ((TIF)Click here for additional data file.S1 TableNumbers in brackets after the population name are as in (DOCX)Click here for additional data file.S2 TableAe. aegypti, using DIYABC software [Prior and posterior distribution of parameters of Approximate Bayesian Computation (ABC) analysis, to test hypothesis of re-colonization of Brazil by software . Priors software , mean si(DOCX)Click here for additional data file.S3 TableIS estimated values using the Genepop software [Ae. aegypti populations for each microsatellite locus. Values that remained significant  after sequential Bonferroni correction are indicated by bold characters. Abbreviations: NA: non available.Weir & Cockerham's  FIS estisoftware  for all (DOCX)Click here for additional data file.S4 TableNull allele frequencies for each of the 12 microsatellite markers and the populations with at least one possible null allele at a given locus as estimated using the Microchecker software .(DOCX)Click here for additional data file.S5 TableLocus-by-locus test for Linkage Disequilibrium (LD) for all 48 populations under study as implemented in Genepop software . Only ca(DOCX)Click here for additional data file.S6 TableAe. aegypti populations as estimated by FreeNA [Pairwise Fst values between all Brazilian y FreeNA . Fst val(XLSX)Click here for additional data file.S7 TableAe. aegypti samples to the genetic groups based on the Q-matrix retrieved from CLUMPP [Assignment of all m CLUMPP  for eachm CLUMPP  is prese(PDF)Click here for additional data file.S8 TableAe. aegypti populations pooled in geographic groups (1–8) or in STRUCTURE defined clusters (9–10). All Fst values were significant in Arlequin estimates (p<0.05).Fst values as estimated by FreeNA  and Number of private allele richness (Np)  as estimated by HPrare (assuming 100 genes) between (DOCX)Click here for additional data file.S9 TableF scores and p values of the One-way ANOVA applied for the Heterozygosity, Private alleles, number of alleles and Allelic richness. In cases of statistical significance (p<0.05) post-hoc Tukey and Bonferroni tests were applied. Statistical significant cases (p<0.05) are indicated by bold characters.(DOCX)Click here for additional data file.S10 TableThe percentage of within groups variation as estimated by AMOVA in the GenAlex software for different levels of grouping for the two Structure-defined Clusters 1 and 2 as in (DOCX)Click here for additional data file.S11 TableAe. aegypti populations between the major geographic regions of the study area and genetically defined clusters, as estimated by Geneclass2 [First generation migrants (Nmigrants) for neclass2  consider(DOCX)Click here for additional data file.S12 TableResults of the bottleneck tests for populations for which at least one method indicated significance population reductions. Results from both Wilcoxon test (TPM and SMM model) and the mode shift in allele frequency distributions are presented, as estimated by BOTTLENECK . Signifi(DOCX)Click here for additional data file.S1 Appendix(XLSX)Click here for additional data file.


Sclerotinia sclerotiorum is a necrotrophic pathogen on over 400 known host plants, and is the causal agent of white mold on dry bean. Currently, there are no known cultivars of dry bean with complete resistance to white mold. For more than 20 years, bean breeders have been using white mold screening nurseries (wmn) with natural populations of S. sclerotiorum to screen new cultivars for resistance. It is thus important to know if the genetic diversity in populations of S. sclerotiorum within these nurseries (a) reflect the genetic diversity of the populations in the surrounding region and (b) are stable over time. Furthermore, previous studies have investigated the correlation between mycelial compatibility groups (MCG) and multilocus haplotypes (MLH), but none have formally tested these patterns. We genotyped 366 isolates of S. sclerotiorum from producer fields and wmn surveyed over 10 years in 2003–2012 representing 11 states in the United States of America, Australia, France, and Mexico at 11 microsatellite loci resulting in 165 MLHs. Populations were loosely structured over space and time based on analysis of molecular variance and discriminant analysis of principal components, but not by cultivar, aggressiveness, or field source. Of all the regions tested, only Mexico (n = 18) shared no MLHs with any other region. Using a bipartite network-based approach, we found no evidence that the MCGs accurately represent MLHs. Our study suggests that breeders should continue to test dry bean lines in several wmn across the United States to account for both the phenotypic and genotypic variation that exists across regions.The ascomycete pathogen  Sclerotinia sclerotiorum (Lib.) de Bary is an ascomycete plant pathogen with a worldwide distribution (Helianthus spp.), soybean (Glycine max L.), canola , and dry bean (Phaseolus vulgaris L.) (ribution . This isribution . It causribution  includinaris L.) .Sclerotinia sclerotiorum is the causal agent of white mold, a devastating disease that can be yield-limiting in temperate climates , Bunsi , and G122   . We addiSeveral (156) of the isolates used for this study were collected as reported in previous studies using the same methods . BroadlySampling was carried out by collecting sclerotia from diseased tissue in zigzag transects across field plots. Because sampling depended on disease incidence, the number of samples isolated varied from year to year. Although the nursery locations were the same over sampling years, sampling plots within a location varied for sampling years.S. sclerotiorum were collected over several years from grower fields and/or wmn in 11 states of the Australia, France, Mexico, and the United States , with four to five sclerotia of each isolate separated on each plate and stored on the counter top at room temperature for five to six days. An 8 mm plug from a 5- or 6-day-old culture was transferred from the advancing margin of the mycelia onto a plate of Difco potato dextrose agar (PDA at 39 g/l of ddH2O) (Sclerotia of d States . After cf ddH2O) . In combf ddH2O) .Mycelial compatibility groups was determined as described previously through co-culturing pairs of 2-day-old isolates 2.5 cm apart on Diana Sermons (DS) Medium  Cubeta,. IncompaAggressiveness of each isolate was assessed using a straw test as described in Prior to DNA extraction, isolates were grown on PDA and plugs were subsequently transferred to potato dextrose broth where they were grown until there was significant mycelial growth, but before the mycelial mat became solidified (four to five days). Each mycelial mat was collected in a filtered Büchner funnel, agar plugs removed, lyophilized and pulverized manually in Whirl-pak® HDPE sampling bags . Lyophilized mycelia was then stored in microcentrifuge tubes at −20 °C until needed for DNA extraction. DNA from 25 mg of pulverized mycelia was purified using a phenol–chloroform extraction method followed by alcohol precipitation and evaporation, suspending the DNA in 200 μl TE . SuspendS. sclerotiorum isolate using 16 microsatellite primer pairs developed previously  (5E) (G) (H) (E5), and the expected number of multilocus haplotypes via rarefaction (eMLH) with 10 samples (He (the exponentiation of H) are expected to be equal to the number of haplotypes, λ and 5E are expected to equal one, and eMLH is expected to be at its maximum value    and alle(h) (E5) . To avoi(E5) (G) , Shannon (G) (H) , Simpson (G) (H) , evennes samples . If all ase, 10) . To asseutations . Both has  was then performed to test for significance of the reduced model and marginal effects using 999 permutations. The varpart function of vegan was used to determine variation partitioning of explanatory variables.A distance-based redundancy analysis (dbRDA)  was perfon 2.4.4 . This meite data . BecauseHSD.test function in the package agricolae version 1.2.8  was significantly different with respect to Region, MCG, or MLH. To minimize complications due to small sample sizes, we chose the top 10 MCGs, representing 56.5% of the isolates collected, the 10 most abundant MLHs representing 26.7% of the isolates, and populations with more than five isolates. If ANOVA results were significantly different at α = 0.05, pairwise differences were assessed using Tukey’s HSD test (α = 0.05) using the on 1.2.8 .cluster_walktrap function in igraph version 1.1.2  of the data, trying to find the minimum number of principal components that maximizes the rate of successful group reassignment. To assess if cultivar had an influence on genetic diversity between wmn, we first subset the clone-corrected data to contain only samples from wmn and from the cultivars Beryl, Bunsi, and G122 and tested differentiation using AMOVA and DAPC as described above. We additionally assessed population stability over time by calculating DAPC over the combined groups of Region and Year as described above.We used analysis of molecular variance (AMOVA) with Bruvo’s genetic distance in ame year . To idenbability . This mebability . To avoipoppr function mlg.crosspop (igraph (dplyr version 0.7.4 (purrr version 0.2.4 (n*(n − 1))/2 edges where n represents the number of populations crossed. Edges were weighted by 1 − Psex, where Psex is the probability of encountering the same haplotype via two independent meiotic events  with the osspop . From th , ND (60), WA (59), NE (47). Four regions had fewer than 10 isolates, Australia (6), WI (2), NY (1), ID (1). We observed 87 MCGs, the most abundant of which (“MCG 5”) was represented by 73 isolates over 37 MLHs  from diseased dry bean plants in 11 states in the United States as well as Australia, France, and Mexico . With th 37 MLHs  and 1C.h (0.0533) and 5E (0.42), indicating that there was one dominant allele present. Analysis of the haplotype accumulation curve showed no clear plateau for 11 or 16 loci , WA was the most diverse population with both G (54.3) and He (55.3) being close to the observed number of MLHs (56). This indicated that there are few duplicated genotypes in WA  and Australia (P = 0.052). Both of these populations showed only moderate significance with After clone-correction on the hierarchy of Region/Source/Host/Year, a total of 48 isolates were removed from the data set, resulting in 318 isolates representing 165 MLHs that were used in subsequent analyses . The reses in WA . A more enotypes . Evaluatulations . When we linkage , which sR2 of 0.0675 (P = 0.001). Assessment of the marginal effects showed that all variables significantly explained genetic variation (P ≤ 0.007). We found that there was multicollinearity when MCG was combined with any other variable, so repeated the analysis, dropping MCG from the list of potential predictors. From these results, Year, Region, Host, and Aggressiveness were found to be optimal, accounting for 17.6% of the total variation. ANOVA revealed significant effects with an adjusted R2 of 0.0325 (P = 0.001). While the marginal effect assessment revealed that Year, Region, and Host significantly explained variation at P = 0.001, and Aggressiveness significantly explained variation at P = 0.039. Much of the variation appeared to be driven by isolates from Mexico and 2005 , with means that ranged from 5.8 (MN) to 4.0 (CA) (P < 0.001), with means that ranged from 6.0 (“MCG 44”) to 4.6 , with means that ranged from 6.0 (“MLH 78”) to 4.3 (“MLH 140”)  . MCGs alLH 140”) .In our analysis, we found 165 MLHs with 70 singletons and 87 MCGs with 43 singletons  and 1B wThe most abundant MLH was represented by 27 isolates  from fivPsex  separated the centroid for the Mexico isolates from the rest of the data, indicating strong differentiation . The seculations .Discriminant analysis of principal components grouping by cultivar used the first 20 principal components, representing 89% of the total variance. The first two discriminant axes (representing 100% of the discriminatory power) failed to separate any of the cultivars where the mean posterior assignment probabilities were 34% (G122), 35.9% (Beryl), and 30.1% (Bunsi). DAPC grouping by Region and Year used the first 15 principal components, representing 80.3% of the total variance. The North Central United States populations  did not appear to have any variation across time in contrast to WA, which showed a shift in population structure in the last year of sampling, 2008 . FurtherS. sclerotiorum from dry bean fields across the United States. Our results suggest that, broadly, populations from wmn reflect the populations of the surrounding regions, indicating that resistance screening may be successful within regions. We found significant population differentiation by geographic region and year, mainly differentiated into three broad North American groups based on shared haplotypes and posterior groupings, a Coastal Region, Midwestern Region, and Mexico. To date, with 366 isolates, this is the largest single population genetic study of S. sclerotiorum assessing population structure within managed and unmanaged agricultural environments. These findings indicate that the wmn can be effective at screening for potential resistant lines within growing regions.In this study, we characterized the diversity of S. sclerotiorum populations are spatially structured . While the evidence may suggest host as being a factor, previous studies have shown no significant differentiation across host species  is compatible with 57 other haplotypes through three MCG when the population structure of S. sclerotiorum is known to be clonal.The previous study of the wmn populations used MCGs to assess genotypic diversity . HistoriOver the past few years, researchers have noticed inconsistencies among the relationship between MCGs and MLHs . Either Neurospora crassa (S. homoeocarpa (Verticillium dahliae (S. sclerotiorum (S. sclerotiorum should not rely on MCG data derived from barrage reactions as an indicator for genetic diversity.One long-held assumption was that MCGs (as determined via barrage reaction) represent vegetative compatibility groups (VCGs) , which aa crassa , S. homooeocarpa , Vertici dahliae , and S. rotiorum —that barP. vulgaris as a host. It has been shown that S. sclerotiorum in the Midwestern United States does not have a particular preference for host (S. sclerotiorum is even across agricultural hosts in the United States, then our sample may yet be representative of the genetic pool present in other crops and weedy species. Additionally, while we found no significant association between genotype and aggressiveness, it is important to note that the straw test is only one measure of aggressiveness. Additional phenotypes for aggressiveness should be evaluated for future studies.One of the main limitations of this study is the focus on for host . If the S. sclerotiorum should employ techniques such as Genotyping-By-Sequencing (Another limitation was the microsatellite markers used for this particular study . The hapquencing , Sequencquencing , or WholS. sclerotiorum from the United States to date, giving us a unique insight to continent-wide population structure and relationships between phenotypic and genotypic variables. Populations in wmn appear to show no significant differentiation when compared to their production field counterparts, suggesting that the wmn populations of S. sclerotiorum may be considered representative of the surrounding regions. While we found no direct relationship between haplotype and severity, it is evident that there is a gradient of severity by region, further supporting the need for screening in multiple locations. Based on our analysis of the relationships between MCG and MLH, we found no clear evidence that the two are directly related, suggesting that MCG does not necessarily represent vegetative compatibility groups and thus should not be used as a proxy for identifying clones.This study represents the largest genetic analysis of 10.7717/peerj.4152/supp-1Supplemental Information 1Click here for additional data file.


Anolis carolinensis) is a lizard widespread throughout the southeastern United States and is a model organism for the study of reproductive behavior, physiology, neural biology, and genomics. Previous phylogeographic studies of A. carolinensis using mitochondrial DNA and small numbers of nuclear loci identified conflicting and poorly supported relationships among geographically structured clades; these inconsistencies preclude confident use of A. carolinensis evolutionary history in association with morphological, physiological, or reproductive biology studies among sampling localities and necessitate increased effort to resolve evolutionary relationships among natural populations. Here, we used anchored hybrid enrichment of hundreds of genetic markers across the genome of A. carolinensis and identified five strongly supported phylogeographic groups. Using multiple analyses, we produced a fully resolved species tree, investigated relative support for each lineage across all gene trees, and identified mito‐nuclear discordance when comparing our results to previous studies. We found fixed differences in only one clade—southern Florida restricted to the Everglades region—while most polymorphisms were shared between lineages. The southern Florida group likely diverged from other populations during the Pliocene, with all other diversification during the Pleistocene. Multiple lines of support, including phylogenetic relationships, a latitudinal gradient in genetic diversity, and relatively more stable long‐term population sizes in southern phylogeographic groups, indicate that diversification in A. carolinensis occurred northward from southern Florida.The green anole ( This is despite the fact that with a natural range across the southeastern United States, A. carolinensis exhibits wide geographic variation in morphology  clarify the evolutionary relationships of previously identified clades in A. carolinensis, (2) explore patterns and trends of genetic diversity and differentiation within and among lineages, (3) elucidate the demographic history and timing of diversification within the species, and (4) compare the phylogeographic patterns found in A. carolinensis with other species from the southeastern United States.Recently developed methodologies such as restriction site‐associated DNA sequencing . Each genomic DNA sample was sonicated to a fragment size of ~300–800 bp using a Covaris E220 Focused‐ultrasonicator with Covaris microTUBES. Following a protocol modified from Meyer and Kircher  using Megablast  using the admixture model and correlated allele frequencies. We ran the analyses for a burn‐in of 50,000 steps followed by 50,000 MCMC iterations. Lastly, we used the ΔK method of Evanno, Regnaut, and Goudet , as gene trees based on little or no informative sites may negatively impact species tree analyses , a summary statistic of all gene trees, that is calculated using the weighted sum of the GSI values from each tree topology . From these loci, we created two datasets where we randomly selected one allele from each lineage of A. carolinensis . The α parameter represents the expected amount of discordance across loci.In addition to the GSI tests across loci, we also investigated phylogenetic discordance across gene trees using BUCKy v1.4.4  among marker types within populations, but relatively different estimates when extended to inferring ancestral population sizes. This comparison of RAD‐seq  and target capture (possibly linked to sites under purifying selection) suggests demographic results within a population or lineage, but not extending back to ancestral populations with multiple lineages, are robust when limited to comparing populations’ relative demographic estimates.Because the loci used here flank conserved regions across multiple vertebrate genomes , skipping 100 steps between samples, and removing the first 20% of iterations as burn‐in.To obtain a secondary estimate of effective population sizes, as well as identify relative divergence timing, we used G‐PhoCS , with generally strong genetic structure in Florida  but moderate values of GSIT (range: 0.206–0.322) when considering all analyzed gene trees  as monophyletic, with the other two paraphyletic Figure c, with GA. carolinensis groups  gene trees in three species tree analyses, we identified strongly supported relationships that are novel relative to previous work Figure d. The nos Figure d. TreeMis Figure , with ons Figure d.A. carolinensis lineages, and (2) the Carolinas and Gulf‐Atlantic clades were sister. The relationships of the eastern and northwestern Florida lineages varied across the data subsets. Even with the consistencies across BUCKy analyses, the ranges of concordance factors  suggest a general trend of incomplete lineage sorting across most loci. The inability to use all individuals in BUCKy—due to the massive increase in gene tree space as number of tips increases—potentially limited our results in this analysis and may have been compounded by the nature of the genetic markers used and the recent nature of divergence between the A. carolinensis lineages.In BUCKy, using different values of α resulted in the same primary concordance tree topologies within a dataset. However, using different subsamples of alleles from different lineages resulted in different primary concordance tree topologies . There were two consistencies across data subsets used in BUCKy: (1) The southern Florida group was basal to all other 3.3Based on EBSPs, all three Florida groups appear to have undergone steady population size increases over the last 0.25–1.5 million years . Population sizes estimated with different individuals’ data showed consistent estimates . Because G‐PhoCS outputs relative divergence times, we used multiple previous estimates of divergence dates between western A. porcatus and A. carolinensis to estimate timing of diversification within A. carolinensis. In other words, the root divergence between A. porcatus and A. carolinensis was calibrated using three previously published divergence dates between the two species, with all intraspecific splits estimated relative to the calibrated root divergence. First, we used 12.3 mya as the root divergence date, the central estimate of Campbell‐Staton et al. (A. porcatus and A. carolinensis ND2 sequences (0.087) from Glor et al.  Phylogenetic estimates  the Carolinas and Gulf‐Atlantic lineages as sister to each other. Although BUCKy analyses found inconsistent results for two of the lineages—eastern and northwestern Florida—the unvarying results are in discordance with previous mitochondrial studies. However, in agreement with the GSI results, the concordance factors had wide confidence intervals suggesting lack of complete lineage sorting across most loci. These patterns could simply be due to shared ancestral polymorphisms in the period of transition from polyphyly to paraphyly to monophyly of each independently evolving lineage. An alternative is that some level of gene flow is precluding complete lineage sorting across all loci, although not enough to diminish the overall signatures of genetic structure between the lineages. Whether the lack of signal per locus is due to ascertainment bias of targeted loci or is simply because of genome‐wide retained shared ancestral variation remains to be determined .In contrast, we found the same genetic clusters using nuclear DNA loci Figure  as those4.2A. carolinensis: A. c. seminolus in southwestern Florida and A. c. carolinensis throughout the rest of the range. While this initially appears to line up with the split between the southern Florida phylogeographic cluster and all other A. carolinensis phylogeographic groups, Vance  and the Appalachian Mountains  experienced isolation from peninsular Florida  species , including: (1) all trimmed sequence data alignments, (2) RAxML gene trees, multilocus bootstraps, and species trees, (3) concatenated sequence matrix and RAxML concatenated tree output, (4) TreeMix output tree and bootstraps, (5) SVDquartets input file and output bootstraps, (6) genealogical sorting index output, (7) extended Bayesian skyline plots xml input and EBSPAnalyser output, (8) G‐PhoCS input, control, and output files. Other outputs  are available in the online supplementary information.All data used for analyses are available in the Dryad Digital Repository (doi:  Click here for additional data file.