Vitis vinifera L. cv. Corvina, one of the major components of the Amarone musts, were dissected aiming at the identification of constant characteristics possibly influenced by the productive process. The fungal populations and metabolomic profiles were analyzed in three different vintages. 454-pyrosequencing on the ribosomal ITS1 region has been used to identify the fungal population present in Corvina grapes and fresh must. Samples were also subjected to metabolomics analysis measuring both free volatile compounds and glycosylated aroma precursors through an untargeted approach with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. Albeit strongly dependent on the climate, both the mycobiota and metabolome of Corvina grapes and fresh musts show some characteristics recursive in different vintages. Such persistent characteristics are likely determined by the method adopted to produce Amarone or other dry wines made from partially dried grapes. In particular, the harsh conditions imposed by the prolonged withering appear to contribute to the shaping of the fungal populations. The fungal genera and metabolites present in different vintages in V. vinifera L. cv. Corvina grapes and fresh musts represent core components of the peculiar technique of production of Amarone. Their identification allows the in-depth understanding and improved control of the process of production of this economically and culturally relevant wine.The composition and changes of the fungal population and of the metabolites present in grapes and in ferments of  Vitis vinifera L. cv. Corvina and V. vinifera L. cv. Rondinella withered red grapes  by the combination of V. vinifera L. cv. Corvina), Rondinella, and Molinara. Grapes and musts were sampled during three vintages (2013–2015). The time of harvesting was decided by the wine-makers according to the evaluated grade of grape maturation. After the harvesting in the vineyard “Costalunga” , grapes were subjected to withering in a dedicated warehouse located a few kilometers far from the vineyards (<5 Km). The warehouse was equipped with automatic systems able to control and modify the internal temperature and humidity. The duration of grape withering was defined according to the regional rules for Amarone production , where red wine is typically made from three grape varieties: Corvina  which could interfere with the DNA extraction. Musts were centrifuged 30 min at 14,000 g and at 4°C, and the pellet was dissolved in 2 ml TE buffer. Must was centrifuged again for 15 min at 14,000 g at 4°C, and the pellet was dissolved in 300 μl TE buffer. Extraction of DNA was then carried out with the FastDNA Spin Kit for Soil  following the manufacturer's instructions.To monitor the absolute amounts of microbes present in the samples, we carried out quantitative Real Time PCR (qRT-PCR) as previously described  sequences per sample. Sequences were then checked for the following criteria: (i) no more than one mismatch/deletion/insertion both in the bar code and in the primer, (ii) length of at least 150 nucleotides (barcodes and primers excluded) and (iii) no more than two undetermined bases (denoted by N). Data were submitted to the European Nucleotide Archive with accession number PRJEB15229 (http://www.ebi.ac.uk/ena/data/view/PRJEB15229). The correspondence between submitted data IDs and samples is shown in Table Meta-taxonomics analysis was carried out on samples collected in the 2013 and 2014 vintages. Library preparation, sequencing, and initial raw sequences processing were carried out by the Sequencing Platform at Fondazione E. Mach. Fungal genera identification was carried out by mean of 454-pyrosequencing of the ITS1 region. To avoid biases in abundance detection due to preferential amplifications of sequences showing different lengths (ITS1-5.8S-ITS2), we sequenced the ITS1 region which is not subjected to the wide length polymorphism affecting the entire ITS region : OTUs were assigned by clustering the sequences with a threshold of 97% pairwise identity, and their representative sequences were classified using blast against the “unite” database . Blasting results are shown in Supplementary Table t-statistics were computed using the function mt in the phyloseq library   was used as carrier gas at a constant flow of 1.2 mL min-1 The MS parameters included electron ionization at 70 eV with ion source temperature at 230°C, detector voltage of 1317 V, mass range of m/z 35–450 and acquisition rate of 200 spectra s−1. For GC × GC-MS data a LECO ChromaTOF (Version 4.22) software was used for acquisition control, and data processing. The identification of volatile compounds was done using NIST 2.0, Wiley 8, and the FFNSC 2 mass spectral library , with a library similarity match factor of 750. Raw metabolomic data were submitted to the MetaboLights database with accession number MTBLS392. Free and glycosylated aroma precursors volatile organic compounds were annotated by comparing their mass spectra to NIST 2.0, Wiley 8, and FFNSC 2 libraries, with a similarity match factor of 750. Compounds with a similarity match factor lower than 750 were classified as “unknown”. In the 2014 vintage samples, 93 glycosylated aroma precursors (89 tentatively identified + 4 unknown) and 728 free compounds (664 tentatively identified + 64 unknown) were measured. In the must samples collected in the 2015 vintage 947 free volatile compounds were measured. The response of internal standard 1-heptanol was used for normalization and to make a relative estimation of the identified compounds as commonly accepted in the analysis of aroma compounds. Wilcoxon-Mann-Withney test was carried out to evaluate the significance in the differences abundance of each chemical among either sample types (grapes or musts) or vintages. Pearson coefficients were calculated between chemical compound content and fungal genera relative abundances. All the calculated p-values were adjusted for multiple comparison (FDR)  volatile organic compounds (VOCs) were extracted following the SPE method reported in Vrhovsek et al.   Figure . Grapes ) Figure . In addi) Figure . To furt) Figure . Differe) Figure , highlig) Figure . In addi) Figure . The sam) Figure . No signt-test FDR > 0.05, Supplementary Figure t-test FDR < 0.05, Supplementary Figure Pithomyces, Phoma, Leptospherulina, Cryptococcus, and Alternaria were significantly more abundant in grape than in must samples , we calculated the Pearson coefficient between every possible couple of genera enriched in grapes and must samples. As expected, strong positive correlations were found among fungal genera that were enriched in the same matrix (grape or must)  Candida (must) and Leptosphaerulina (grape) and (iii) Cytospora (must) and Alternaria and Pithomyces (grape) , the grape samples collected at T0 and at T1 were substantially similar even when comparing the fungal genera relative abundances -3-hexenoic acid  among the fungal genera constantly present in the 2013 and 2014 vintages Figure . We carrd Figure . The funl Figure . In addiV. vinifera L. cv. Corvina both the climate and the duration of withering contribute in defining the microbial populations present on grapes both at the beginning and at the end of withering period. Several parameters indicate that the fungal populations present on grapes changes only after a 7–8 weeks withering. After this period, fungal genera, despite not changing in numerosity, change in relative abundances, suggesting a selection of genera able to survive to the harsh environmental conditions imposed by the long lasting withering.The composition of the fungal microbiota present on grape skins and maintained in early phases of must fermentation has been shown to be strongly influenced by environmental factors  or in grapes  were significantly different in the studied vintages. To note, two genera of the Botryosphaeriaceae family and known Vitis pathogens, Neofusicoccum and Botryosphaeria, were more abundant in 2014 than in 2013 must samples, indicating that the adverse environmental conditions could have favored their persistence on grapes even after a prolonged withering period. On the other hand, the genus Botrytis, found to be more abundant in 2013 than in 2014 must samples, is an example of the ability of the process to modify the environmental fungal populations. This genus is known to grow better in presence of high levels of humidity, thus potentially in the wet 2014 grapes  show a higher number of fungal genera compared to the “dry” vintage (2013), not corresponding to an increase of total amount of either bacteria or fungi. Similarly, the total amount of fungi quantified in recently harvested-grapes was higher (even if not significantly) in 2014 than in 2013. Despite the microbial quantification could be biased by the amplification of the DNA of dead cells  in the fermentation for the production of volatile aroma compounds  which we did not find in either grapes or fresh musts in the studied vintages  was positively correlated with compounds usually detected in traces in grapes and fresh fruits, 1- pentanol and 2,6-dimethoxyphenol  , contributes to the typicity of Amarone. The disclosure of these persistent microbial and chemical features helps in understanding the details of this production technology and will thus give pivotal insights to further optimize the process.As all the other fermentative processes, also Amarone production is strongly affected by the environmental conditions during grapes maturation, thus by the climate. Our findings highlight that, despite some changes that could be ascribed to the vintage, both microbial populations and chemical profile of DC, IS, and FM conceived and designed the study. IS, MP, and NT handled the samples and carried out DNA extractions, qRT-PCR, and PCR amplification before library preparation. AZ, ST, and AB were responsible for the processes conducted in the winery. SC, UV, NT, and FM extracted and analyzed the metabolites. PF supervised and carried out statistical analyses. CD and DA supervised and carried out metataxonomics analyses. All the authors discussed the results and contributed at writing the manuscript.This study was supported by Cantina Zenato.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Machilus pauhoi Kanehira is an important timber species in China. A provenance trial was recently set up to evaluate the growth performance of trees from different localities, with the aim of designing seed transfer guidelines. Here, we tested twelve nuclear microsatellite markers derived from other species of the Lauraceae family and investigated population genetic structure in M. pauhoi. Both the number of observed alleles per locus (Na) and the polymorphic information content (PIC) significantly decreased against the latitude, but showed an insignificant decrease against the longitude. Heterozygosity (Ho) and gene diversity (h) exhibited a weak correlation with geographic location. Private alleles were present in multiple populations, and a moderate level of population genetic differentiation was detected (Gst = 0.1691). The joint pattern of genetic diversity  suggests that general northeastward dispersal led to the current distribution of M. pauhoi. Significant but weak effects of isolation-by-distance (IBD) occurred, implicating the mountain ranges as the major barrier to gene flow. Both STRUCTURE and hierarchical clustering analyses showed three distinct groups of populations related to the physical connectivity among mountain ranges. A priority in designing genetic conservation should be given to the populations at the southwest side of the species’ distribution. This conservation strategy can also be combined with the pattern of adaptive genetic variation from the provenance trial for comprehensive genetic resource management of native M. pauhoi. Machilus pauhoi Kanehira is an important long-lived and evergreen broad-leaved tree species. It is naturally distributed in the subtropical and tropical regions in China, covering Anhui, Zhejiang, Jiangxi, Hunan, Fujian, Guangdong, and Guangxi Provinces (Castanopsis eyrei (Champ.) Tutch, Schima superba Gardn. et Champ., and Prunus persica L. Populations of M. pauhoi in more distant sites may reside in communities that have independently experienced long-term community successions. The current populations of M. pauhoi are distributed in patches. Important biological characteristics of this species are that the young seedlings require shade and are adapted to both a fertile acidic soil and an environment of high humidity. However, adult trees show reduced shade requirement and can grow in open sunny habitats [rovinces  1]. It . It Machhabitats , 3]. Tr. TrMachihabitats . The spehabitats , 6]..MachilusM. pauhoi belongs to the Lauraceae family, one of the oldest families of flowering plants. Worldwide, the family includes approximately 50 genera and 2500 to 3000 species , a, aM. pau species . The proa tenera  and Neol.) Koidz . It is dM. pauhoi has not been investigated, but other species in the Lauraceae family, including Ocotea tenera , , . H. HM. thuhttp://data.cma.cn/data/index/0b9164954813c573.html, accessed on October 15, 2015. The longitude (N), latitude (E), and altitude data were recorded with hand held GPS equipment . The fruit samples were washed, and seeds were buried in the wet sand before sowing in soil. Plants were grown in an open experimental site of South China Agricultural University (E113°22’ N23°09’), Guangzhou, China. Annual rainfall is 1689-1888mm, with average relative humidity of 68%, and annual average temperature of 20–22°C according to CMDC records. After a few months, fresh leaves were collected from seedlings of about 40 centimeters in height for DNA extraction.Mature fruits were collected from about 16 maternal parents that were located at least 50m apart in each of 24 natural populations in July 2014 ; Fig 1. -1. Quantified DNA samples were stored at -20°C for PCR amplification.About 80mg fresh leaves were used for DNA extraction using an E.Z.N.A. high-performance DNA mini kit  following the manufacturer’s instructions. The quality of DNA was checked by 1.0% (w/v) agarose gel electrophoresis. The DNA concentration was measured with a Thermo Scientific NanoDrop 1000 spectrophotometer , and adjusted to 50 ng.μLM. thunbergii, M. pseudokobu, and Persea americana from the Lauraceae family . T. TM. thuTaq DNA polymerase , 1μL 20ng·μL-1 template DNA, 2μL 25 mmol·L-1MgCl2, 0.5μL 10mmol·L-1dNTP, 0.5μL10mmol·L-1 of each forward and reverse fluorescent primer, and 2.5μL10×PCR buffer . PCR amplification was conducted using the following cycle procedure: an initial 3 minutes of denaturing at 95°C, followed by 10 cycles of three steps . The next 20 cycles were then conducted with the following steps: 30 seconds of denaturing at 95°C, 30 seconds of annealing at 55°C, and 30 seconds of elongation at 72°C. A final elongation step was set at 72°C for 6 minutes. The PCR products were detected by capillary electrophoresis using an ABI 3730XL DNA analyzer after confirmation of PCR amplification on a 2% agarose gel.PCR amplification was carried out in a 25μL reaction volume that contained 1U Na) per locus, observed (Ho) heterozygosity, gene diversity (h), and polymorphic information content (PIC). Gene diversity (h) at a locus with k alleles was calculated as i = 1,..,k) , , , . I. IM. pauM. pauhoi is present in protected nature reserves. Most populations remain in a vulnerable status and are often faced with potential deforestation. Although M. pauhoi is more widely distributed than Persea chekiangensis C.B. and Persea bournei (Hemsl.) Yang in the Lauraceae family in South China, its genetic resource is rapidly eroded due to its extensive utility for timber. Our results indicate the potential evolutionary processes forming the present population genetic structure, and this provides useful reference for genetic resource management in M. pauhoi. The three groups of populations revealed by STRUCTURE and cluster analyses Click here for additional data file.


Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. ‘Animal models’, which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations.However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non‐randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology.We first illustrate how the occurrence of non‐random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce ‘genetic group’ methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents.r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods.We summarize the conceptual foundations of genetic group animal models and provide extensive, step‐by‐step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow.We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets. Adaptive evolution is a critical way by which populations can respond to environmental change and persist. Quantifying and predicting microevolutionary responses to environmental change in wild populations is consequently a major focus in biology  has revolutionized quantitative genetic studies of wild populations  and residual deviation (e)  as:In brief, an individual's phenotypic value for any polygenic trait is a sum of independent genetic and environmental factors  for each individual included in y are estimated as ‘random effects’, where the design matrix Z maps each individual's value in a to their phenotype in y.Factor levels for ‘fixed effects’ are grouped with the overall population mean (or intercept) in vector a potentially allow ecologists to quantify changes in population mean breeding value over time, and hence test for microevolutionary change, and to determine whether selection acts on genetic or environmental components of phenotypic variation , which quantifies the covariance in additive genetic effects among individuals. In diploid organisms, this covariance is proportional to twice the probability that two individuals inherited homologous alleles ‘identical‐by‐descent’ from common ancestors. The A matrix can be constructed from a pedigree following certain rules and assumptions . This property makes animal models very appealing for wild population studies, because A can prevent bias in estimates of σA2 due to selection, drift, assortative mating and inbreeding . Breeding values and environmental deviations, and thus phenotypes, of focal population founders were simulated from normal distributions with means of zero and variances of one. Mates were randomly assigned and offspring breeding values were calculated as the mean of their parents’ breeding values plus a Mendelian sampling deviation. The population was not subject to selection and is large enough to ensure that genetic drift is negligible. No change in mean breeding value or phenotype across generations is therefore expected. However, immigrants from a separate population were simulated with a mean breeding value three units greater , but the variance was also one . Therefore, immigrants have greater phenotypic values. Since natives and immigrants were paired with randomly assigned mates, the mean phenotypic value of all individuals within the focal population increased across generations solely due to gene flow stemming from immigration Fig. a.Since all immigrants have unknown parents, their phantom parents are by default assigned to an animal model base population, along with the phantom parents of the true focal population founders. This combined distribution of breeding values in the default base population is consequently heterogeneous, because the phantom parents of founders and immigrants have different mean values Fig. b,c. Two First, a basic animal model predicts breeding values assuming a mean of zero in the default base population Mrode , p. 40. Secondly, a basic animal model eqn  returns a priori as it depends on the mean and variance of true breeding values in the true base population relative to the means and variances of true breeding values for the different groups in the default base population. However, our simple simulation illustrates that breeding values defined within the context of a single population  quantify the average deviation from the population mean genotype attributed to the additive genetic effects of an individual's genotype  equals the expected average genetic effect in group j (gj) plus a deviation from the group mean caused by i's genotype . Because all breeding values are deviations from group means, all base population breeding values have an expectation of zero. Consequently, breeding values represent standardized measures of additive genetic effects that allow direct comparison across genetic groups of individuals’ additive genetic effects distinct from the mean additive genetic differences among groups.In an animal model with genetic groups, the ‘total additive genetic effect’ (Appendix S1) of an individual's genotype is separated into a genetic group effect and a breeding value. The genetic group effect measures the expected mean deviation from a reference attributed to a group's total additive genetic effects and the breeding value measures the average deviation from the genetic group mean caused by an individual's genotype . The expression for a quantitative trait phenotype ywhich expands to:i's genotype ui=∑j=1rqijgj+ai replaces the breeding value ai in a basic quantitative trait model without genetic groups. The jth group effect (gj), out of r groups in the base population, contributes to the total additive genetic effects of i in proportion to the expected fraction of i's genome derived from group j (qij). Each gj constitutes an element in the vector g containing all genetic group effects. The fraction qij can be calculated from qdj and qsj of i's parents d and s  and all genetic groups in the base population (r columns). Each row of Q, which lists the contributions of each genetic group to an individual, sums to one.Here, the total additive genetic effect of individual The collection of eqn u) are normally distributed with an expected covariance of σA2A . However, the expected mean of u is no longer zero, but Qg. Therefore, animal models that account for the contribution of genetic group effects to the total additive genetic effects of individuals do so by modelling the mean of u.The total additive genetic effects (anova contrasts) are estimable . However, because individuals inherit genetic group effects, estimating genetic group effects as separate fixed effects is more complicated than simply fitting categorical fixed effects of discrete group membership. An animal model that fits explicit genetic group effects is:a are breeding values of individuals with expected mean zero and covariance σA2A  is utilized as in a basic animal model without genetic groups  as the first r columns of the T matrix in Henderson's  is the sum of the genetic group effects, weighted by the contribution of each genetic group to that individual, plus the individual's breeding value (ai):Solutions to the model in eqn u) (Quaas & Pollak g) are inherited the same way as breeding values (a), as quantified by the A matrix. Therefore, the sum of their effects can be modelled by augmenting A−1 to implicitly incorporate the group effects into predictions of u , and estimates of the r group effects g. In contrast to the approach where genetic group effects are explicitly estimated as separate fixed regression coefficients, obtaining the predicted breeding value for an individual (ai) requires subtracting the sum of the genetic group effects, weighted by the contribution of each genetic group to that individual, from the predicted total genetic effects of the individual (ui) returned by the model:Solutions to the model in eqn g). The genetic group effects can be fitted either explicitly by including fixed covariate regressions on columns of Q, or implicitly by modelling total additive genetic effects using A*. Both models provide equivalent unbiased predictions of breeding values and individual total additive genetic effects as well as unbiased estimates of the additive genetic variance for the simulated data set , thereby extending and standardizing current software capabilities. We comprehensively demonstrate how to use these functions with MCMCglmm, asreml, WOMBAT and ASReml (Appendix S6.4) and model outputs have been deposited in the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.jf7cr .While the complexities of fitting genetic group animal models might seem intimidating, in fact there is no technical reason why such models cannot be fitted to appropriate wild population data using either residual maximum likelihood or Bayesian methods. Further, genetic group methods can be applied to multivariate and non‐Gaussian response variables. In Appendix S6.4, we provide extensive tutorials that demonstrate how to fit genetic groups either explicitly as separate fixed regressions or implicitly within the random effects structures that predict total additive genetic effects in animal models implemented in Hadfield  and asreHowever, as with any complex quantitative genetic analyses of wild population data, key decisions need to be carefully made before animal models with genetic groups can be fitted. Further, limitations of and constraints on the genetic group methods remain, meaning that estimated effects will need to be interpreted with due caution. In the following sections, we summarize some key decisions and suggest potential resolutions to current limitations.a vs. u), values are easily transformed from one to the other through simple mathematical formulae  means that the uncertainty in genetic group effects is included in the prediction of u, which can increase the prediction error variance of u. Unless uncertainty is incorporated into calculations transforming values from u and a, the accuracy of calculated breeding values . However, it is less clear how priors specified for additive genetic variances affect posterior inference on genetic group effects fitted implicitly (Appendix S6.4.2.3), particularly when variance component estimation is the aim of the analysis. Consequently, fitting genetic groups explicitly as separate fixed regressions may be the most straightforward approach when using Bayesian inference . However, evolutionary dynamics can cause the variance in true total additive genetic values within genetic groups to differ among groups, violating the assumption of homogeneous variances  assume that phantom parents are unrelated within and across groups and that no drift or inbreeding occurs within the base population . Since the consequences of missing pedigree information are likely to differ from the consequences of missing phenotypic information . We propose that such methods should be applied in animal model analyses of wild population data and provide Hadfield , it remaet al. et al. Genetic group methods are not a panacea; they cannot be expected to completely rescue analyses based on poor population pedigrees or fix all resulting problems. Missing pedigree information may still bias animal model parameter estimates even when genetic groups are modelled. Unknown parents decrease pedigree connectedness, potentially causing phenotypic variation to be attributed to environmental rather than genetic effects. Likewise, missing parent information can affect estimates of inbreeding and hence inbreeding depression (Pemberton It is equally important to realize the opportunities for conceptual advances that genetic group animal models afford, rather than solely viewing them as technical means to minimize bias. Genetic group methods can reveal the relative additive genetic values of natives and immigrants for any quantitative trait of interest, and thereby distinguish microevolutionary changes in population mean phenotypes arising through direct responses to local selection from changes caused by immigration and resulting gene flow. We can thereby quantify the degree to which immigrants introduce genetic effects that are congruent or counter to the direction of local selection, which is an essential step towards predicting adaptive responses to environmental change and explaining microevolutionary stasis (Merilä, Sheldon & Kruuk http://dx.doi.org/10.5061/dryad.jf7cr (Wolak & Reid Data associated with this paper are deposited in the Dryad Digital Repository Appendix S1. Glossary.Appendix S2. Collection of pedigrees from wild populations.Appendix S3. Breeding value prediction.Appendix S4. Simulating data with genetic group effects.Appendix S5. Development of genetic groups in agricultural science.Appendix S6. Construction and implementation of Q and A*.Appendix S7. Fuzzy classification of genetic groups.Click here for additional data file.


Caragana microphylla) important for restoration efforts on the Mongolian Plateau in China. This region houses several major bioclimatic gradients, and C. microphylla is an important restoration species because it stabilizes soils and prevents advancing desertification on the Inner Mongolia Plateau caused by ongoing climate change.Understanding how landscape factors, including suites of geographic and environmental variables, and both historical and contemporary ecological and evolutionary processes shape the distribution of genetic diversity is a primary goal of landscape and conservation genetics and may be particularly consequential for species involved in ecological restoration. In this study, we examine the factors that shape the distribution of genetic variation in a leguminous shrub  contains supplementary material, which is available to authorized users. Unraveling the factors that influence spatial genetic variation and population structure is one of the fundamental goals of ecological and landscape genetics . PatternCaragana microphylla, a perennial sandy grassland and desert deciduous shrub species belonging to the legume family (Fabaceae). Native to temperate Asia, including Siberia, Mongolia, and China [C. microphylla is a widely distributed shrub species in the northern steppe and agro-pastoral ecotone of China. On the high plain of the Inner Mongolia Plateau, C. microphylla is a key component of the shrub steppe landscape, and on the sandy land of the steppe it is a dominant species of vegetation [C. microphylla from the Inner Mongolia Plateau have been evaluated by different marker systems, including AFLPs, RAPDs, and microsatellites [One such species is nd China , C. micrgetation . The spegetation , and it getation . Genetictellites –17, but tellites .C. microphylla across its entire geographic range on the Inner Mongolia Plateau, using a set of 22 polymorphic microsatellite markers, four cpDNA sequences and 5788 SNPs generated through genotyping-by-sequencing (GBS). The major goals of this study were (i) to characterize genetic variation and population structure in C. microphylla, and (ii) to quantify the relative contributions of geographic factors (IBD) and environmental clines (IBE) to genetic differentiation in this important restoration species.The Inner Mongolia Plateau is characterized by pronounced biophysical gradients, presenting an opportunity to investigate the effects of multiple geographic and environmental factors on population connectivity. A temperature gradient runs roughly North-South, while a precipitation gradient runs from arid regions in the Southwest to wetter regions in the Northeast. An ecotone largely tracks the precipitation gradient, transitioning from desert in the Southwest to grassland (high meadow and steppe) in the central plateau and forest in the Northeast, with pockets of shrublands and sandy lands in the Southeast. Here, we evaluated population genetic divergence of C. microphylla at ten sites throughout the natural distribution of the species in the southern Inner Mongolia Plateau of China , central Inner Mongolia Plateau (XU and DU), and northeastern Inner Mongolia Plateau  . The forward primers were tagged with a fluorescent 6-FAM or HEX label to produce flourescent-labeled PCR amplified fragments. PCR was performed on a Mastercyler gradient thermocycler  using the following procedure: 10 min at 94 °C, followed by 10 touchdown cycles of 45 s at 94 °C, 60 s at 65 °C (−1 °C per cycle), and 60 s at 72 °C, then 35 cycles of 45 s at 94 °C, 60 s at 55 °C, and 60 s at 72 °C, and a 10 min final extension step at 72 °C. An ABI3730xl DNA Analyzer  was used to capture amplified products by a fluorescence detection system for SSR markers. Fragment sizes were determined using an internal size standard , and the output was analyzed using GeneMapper software (Applied Biosystems).We genotyped all samples at 22 microsatellite  markers . All cpDNA sequences were deposited in Genbank (accession numbers KU564257 to KU564268).Four cpDNA regions, including t et al. , and Shat et al. , respectApeKI according to the protocol in Elshire et al. [A total of 127 samples from ten populations were used to generate the genotyping by sequencing dataset , observed and expected heterozygosity (Ho and He), and global and pairwise FlEx v6.5 .We used the software STRUCTURE to infer the probability of assignment to distinct genetic clusters for all 221 individuals in the ten sampled populations . The anaHd) and nucleotide (π) diversities were calculated from aligned DNA sequences using DnaSP v5 [Medicago sativa as an outgroup.Chloroplast DNA (cpDNA) sequences were edited and assembled using SeqMan software . Multiple alignments of the DNA sequences were performed with Clustal X , with suDnaSP v5 . We condDnaSP v5 . A haploDnaSP v5  using Mewww.r-project.org). Both global and pairwise FST were calculated and tested for significance based on 9999 permutations using Genepop v4.0 [Observed and expected heterozygosities (Ho and He) were calculated using the package Adegenet v2.0.1.  in R , were included in the analysis  at multiple time periods to investigate whether current and past climate suitability is a relevant factor shaping observed patterns of genetic differentiation among populations of T v3.3.3 . A totalT v3.3.3 –16 and oC. microphylla at the Last Glacial Maximum (LGM), we projected the model obtained by our present-day species-climate analysis onto the LGM using data layers for past climate constructed under the commonly used Model for Interdisciplinary Research on Climate (MIROC v3.2) [C. microphylla under a future climate scenario, we acquired data layers (2.5 arc min resolution) predicted for the year 2080 under, again, the commonly used general circulation model MIROC from WorldClim. As with the LGM scenario, we projected the present-day ENM onto the future climate layers to explore how the predicted distribution of C. microphylla may be affected by ongoing global climate change. The performance of the model prediction was evaluated using the area under the (receiver operating characteristic) curve (AUC) calculated by MAXENT. Model predictions were visualized in ARCMAP v9.3 .To estimate the distribution of OC v3.2)  scaled dST / (1-FST) as recommended by Rousset [10) of geographic distances between all pairs of populations. For the environmental predictors, we downloaded the 19 bioclimate variables from the WorldClim database (www.worldclim.org) at 0.5 arc min resolution and then reduced covariance by performing spatial principal component analysis (PCA) analysis on the raster layers using the R package ‘RStoolbox’ [To investigate the roles of geographic and environmental factors in spatial genetic differentiation, we tested for isolation by distance (IBD) and isolation by environment (IBE) using all three marker types collected in our study. In all analyses, population pairwise genetic distances were represented by matrices of pairwise F Rousset . Geograptoolbox’ . We retatoolbox’ . We alsotoolbox’ . In eachTo further evaluate IBD and IBE, we also performed a complementary analysis, generalized dissimilarity modelling (GDM). GDM is a statistical technique that uses nonlinear matrix regression to model spatial patterns of biological dissimilarity, including genetic distance, between sampling sites against differences in geographic and environmental variables . GDM useST / (1-FST) matrices used in the MMRR analysis, and geographic distances were based on the geographic coordinates of each sampling site. For the environmental predictors, we used the same three PC rasters as in the MMRR analysis. After fitting the GDM model to these data, we visualized spatial patterns of genetic turnover by projecting the GDM model onto the PC rasters. This assigns a color value to each cell on the raster based on its predicted genetic composition, and greater differences in the colors between cells indicate greater predicted genetic differences. All of the GDM analyses were performed in the R package ‘gdm’ [Genetic distances for each model were the same Fge ‘gdm’ .C. microphylla  with only one haplotype (H8) observed in each population. The highest diversity was observed in population SZW  were identified based on 7 polymorphic sites detected in four cpDNA sequences  per population ranged from 0.196 in CB to 0.303 in XH, and mean expected heterozygosity (He) values ranged from 0.247in CB to 0.308 in XH  across all 10 populations ranged from 0.017 (ZXB vs. XH) to 0.139 (XBY vs. QYH)  and northeast  populations (mean FST = 0.071) nor between the central and southwest  populations (mean FST = 0.073), but did reveal higher differentiation between the northeast and the southwest populations (mean FST = 0.138). The population XBY showed the highest degree of genetic differentiation from other populations (mean FST = 0.171), followed by populations CB (mean FST = 0.168) and EWK (mean FST = 0.138).Global FST among all sites was 0.360. About 76% of pairwise FST estimates among C. microphylla population pairs were statistically significant , and northeast vs. central populations (mean FST = 0.17).For cpDNA, global FST among the nine sampling sites retained in this dataset was 0.246. Pairwise comparisons between regions revealed similar patterns compared to the microsatellite and cpDNA results , and the future (year 2080) Fig. . The modion Fig. . In totaeau Fig. . The ENMeau Fig. , resultiwww.worldclim.org), while PC2 was derived from precipitation variables . PC3 was driven by three variables – precipitation seasonality (Bio15), mean diurnal temperature range (Bio2), and temperature annual range (Bio7) – and therefore represents an environmental seasonality and range of variation axis.The spatial PCA that we performed on the 19 bioclimate data layers returned three PC rasters that explained >85% of the total bioclimatic variation. Factors loadings showed that PC1 was primarily described by temperature variables  and PC3  but showed no correlation with PC2 .Multiple matrix regression with randomization (MMRR) analysis suggested that genetic differentiation showed a significant pattern of both IBE and IBD for all three molecular datasets Table . For eacp < 0.01 for each model) and explained a large proportion of the deviance in the data structure, with 77.95% of deviance explained for the microsatellite dataset, 74.81% of deviance explained for the cpDNA dataset, and 89.18% of deviance explained for the GBS dataset. Deviance explained values for non-linear models are analogous to R2 values for linear models. Concordant with the results of the MMRR analysis, GDM revealed significant associations between genetic distances and PC3 distances for each dataset  analysis also revealed significant patterns of IBE and IBD in all three of our molecular datasets Table . OverallChina’s Inner Mongolia Plateau contains dramatic clines in several bioclimatic variables that are critical for plant growth and community assembly and also exhibits spatial heterogeneity in various soil properties and characteristics , making C. microphylla, an important species for ecological restoration efforts, on the Inner Mongolia Plateau. We found that genetic diversity  was distributed across the range of the species, with more centrally located populations typically showing higher levels of genetic diversity than populations nearer to range edges, particularly to the northern range edge of this species , it suggested that suitable habitat for C. microphylla was much more limited, compared to present day, and was primarily restricted to an area in the southwest corner of C. microphylla’s current range. This suggests that a northward expansion following glacial retreat after the LGM allowed C. microphylla to achieve the distribution it has today. Glacial refugia often harbor greater genetic diversity, particularly in plants [The exceptions to this general pattern in our study system were found in the southern populations that we sampled. Several of these populations  harbored among the highest levels of haplotype and nucleotide diversity (based on our cpDNA dataset), allelic diversity (based on our microsatellite dataset), and observed heterozygosity  [C. microphylla, even if their precise strengths are uncertain, and therefore geographic and environmental factors are both important contributors to the genetic structuring of populations in this system.To investigate how contemporary landscape factors affect patterns of genetic variation in n (MMRR)  and genen (MMRR)  revealedn (MMRR) , 46. WheIBE = 0.196 vs. βIBD = 0.631), but IBE and IBD were much more balanced in the cpDNA (βIBE = 0.494 vs. βIBD = 0.494) and GBS datasets (βIBE = 0.581 vs βIBD = 0.593). There is no clear reason why microsatellite markers would be expected to have lower IBD than cpDNA or GBS markers. There is an interesting possibility that chloroplast DNA, which is uni-parentally inherited through seeds, could show a different spatial pattern from nuclear DNA, which is bi-parentally inherited through seeds and pollen, if dispersal in seeds and pollen are subject to different controls. For instance, if different animals disperse seeds and pollen, which is commonly the case, or if pollen is wind dispersed while seeds fall onto the underlying terrain, then we would expect that patterns of spatial genetic variation in cpDNA and nuclear DNA could be very different [C. microphylla, which has pollen dispersed by wind and insect pollinators [There were also slight differences among our three genetic datasets. If we average the results for the MMRR and GDM analyses, we find that IBE played a considerably stronger role than IBD in structuring genetic variation in the microsatellite data  and explained a large proportion of genetic variation (β = 0.383 to 0.565) in all three of our genetic datasets in the results of both MMRR and GDM analysis . For instance, in this system, populations exhibit genetic differentiation between environments with different temperature regimes, and therefore restoration efforts should focus on transplanting between areas with similar environmental conditions. Plants adapted to different thermal climates may flower at the wrong time or out of sync with the local population, reducing the effectiveness of transplant efforts. Because C. microphylla also show geographically driven genetic differentiation, plants that are transplanted from distant sites that are environmentally similar could be effective at introducing genetic variation into struggling populations [Thus, overall, our study presents a strong case for a role of both historic and contemporary factors, including both geographic and environmental variables, in generating the currently observed patterns of spatial genetic variation in ulations . Hence,


The lions of North Africa were unique in ecological terms as well as from a human cultural perspective and were the definitive lions of Roman and Medieval Europe. Labelled “Barbary” lions, they were once numerous in North Africa but were exterminated by the mid-20th century. Despite subsequent degeneration of the Atlas Mountain ecosystem through human pressures, the feasibility of lion reintroduction has been debated since the 1970s. Research on the long-established captive lion collection traditionally kept by the sultans and kings of Morocco has enabled selective breeding coordinated across Moroccan and European zoos involving a significant number of animals. Molecular genetic research has recently provided insights into lion phylogeny which, despite previous suggestions that all lions share recent common ancestry, now indicates clear distinctions between lions in North, West, and Central Africa, the Middle East, and India versus those in Southern and Eastern Africa. A review of the evolutionary relevance of North African lions highlights the important challenges and opportunities in understanding relationships between Moroccan lions, extinct North African lions, and extant lion populations in India and West and Central Africa and the potential role for lions in ecosystem recovery in those regions. Panthera leo) formerly ranged throughout Africa, the Middle East, and southwestern Asia [P. l. persica) by the IUCN and CITES [Lions  in North Africa/Asia, West Africa, and Central Africa, which should be considered Panthera leo leo, whilst the second group in Southern Africa, East Africa, and North East Africa should be considered Panthera leo melanochaita [Lions finally disappeared from the Maghreb due to a combination of human pressures on the landscape and direct extirpation , a loss nochaita . At the nochaita , 12, 33. Panthera leo leo and within this context considered as an evolutionary significant unit (ESU) based on geographical location. In the absence of a wild population in North Africa today, contemporary conservationists argue that lions should be restored as an apex predator to balance the ecosystems of the region. Potential candidates for reintroduction would include either their nearest relatives from India [In general terms, historical populations of North Africa's “Barbary” lion would therefore be considered within the modern taxonomy forom India  or possiDespite the disappearance of animals in the wild, debate surrounding the possibility of extant representatives of North African lions in captive collections has continued for decades without resolution. In Morocco, a collection of lions, traditionally trapped and presented to the Sultan by the local Berber tribes who inhabited the surrounding region, have been held at the Royal Palaces for centuries . It is nPrevious attempts to address the issue of identification were led in the 1970s by Helmut Hemmer and Paul Leyhausen . In the In the late 1980s when genetic research appeared to suggest that all lions (including Asiatic lions) shared a common ancestor, many zoos stopped their involvement in captive conservation efforts with Moroccan lions  and seveThe most plausible prospect is that animals derived from the King's collection in Morocco are the most likely wild descendants from North Africa . Althoug Panthera leo leo within the IUCN's proposed classification [However, there is a possibility that some individuals in the original Moroccan collection bred with lions of non-North-African descent before the 1960s . Whilst this raises the chance of a watered-down genotype across the current captive population, this hypothesis is currently untested and the facts are therefore unknown. In the absence of definitive diagnostic molecular genetic techniques, including investigation of paternal bloodlines, neither the distinctiveness of Moroccan lions  nor their authenticity as true North African lions has been established . Neverthfication . Panthera leo leo , then as a captive group they become significant. The current known population for the P. l. leo group, accounting for known Asiatic lions in captivity  would represent a significant proportion (>5%) of the extant genome for the designated subspecies. This captive Moroccan lion group is also unusual since the original menagerie curators and subsequent zoos have kept the animals separate from generic zoo lions  for nearly 50 years [If Moroccan lions were genetically designated withinirca 100 ), plus 4irca 100 , with peirca 100 , 40 and irca 100 , 41, for50 years .Currently, the understanding of genetics in lions lags somewhat behind equivalent knowledge for leopards, jaguars, and tigers . HistoriThe past decade has seen increasing clarity on the relationships between lion populations through the application of molecular techniques. Initial progress was made by Barnett et al. –45 usingMicrosatellites  have also been used alongside mtDNA analysis in a number of studies to differentiate lion populations and infer evolutionary history of the species , 46, 47.Pleo) within host lion populations could only consider sub-Saharan African lions, since Asiatic lions were found not to have the virus and ancient samples from North Africa were not accessed.Improvements in molecular technologies have, however, encountered a number of practical limitations including a lack of reference samples or the necessity to sample captive animals of dubious provenance . First,  P. l. leo and P. l. melanochaita.The most recent studies have been able to move towards a clearer consensus, particularly that extant populations of lions from West and Central Africa (plus India) should be considered distinct from lions in Southern and East Africa , 14. DubBertola et al. , 14 cond P. l. leo  and a south group of P. l. melanochaita  [The pattern of clusters identified in these extensive population genetics studies suggests a possible ice age extinction of lions from West and Central Africa, followed by recolonisation of the region by lions from refugia in the Middle East , 21. In  Africa) , 32, 47.Previous research including genetic analysis infers that lions from the Rabat Zoo supposedly descending from Barbary lions have a complicated history including a suggestion that they originated in West or Central Africa instead , 28, 43.So whilst mitochondrial DNA studies on a small number of animals from the Moroccan Royal Lion collection have demonstrated that these individuals cluster with lions from Central Africa , 13, an The number of Moroccan lions  is under 100 animals, well below the 1000 or so individuals which is regarded as the lower limit for an effective, sustainable population. Moroccan lions as a group are therefore vulnerable to the normal threats associated with small populations , amplifiThe Moroccan lion group has likely undergone a genetic bottleneck, due to the establishment (or sometimes historic shrinkage) of the population to a small number of individuals from which the subsequent existing population is founded, largely imposed by the limitations of captive breeding. Within a bottlenecked population, inbreeding may have occurred which could skew genetic characteristics (“genetic drift”) due to limitations in parental heritage . This coP. l. persica) than Sub-Saharan lions. However, Yamaguchi [Burger and Hemmer  report aamaguchi  argues tamaguchi  did not Variation in cytochrome b (an mtDNA region of approximately 1300 base pairs) is very low across lion populations as would be expected, since the region codes for a protein necessary for electron transport within cells so variation over time (mutation rate) is very low. Cytochrome b is commonly used in phylogenetic reconstruction and is useful in initial matching of samples with lion mtDNA documented in GENBANK  and has The North African “Barbary” lion remains an enigma, recently extirpated, less accessible even to established ancient molecular genetic techniques , and tantalisingly out of reach to modern conservation interventions. Clearly, as apex predator, the species had an important role in North African ecosystems and its cultural importance within North African countries and their near neighbours around the Mediterranean suggests that the lion is a potential flagship for conservation of the Maghreb region. To date, the insufficient evidence to count or discount the relevance of putative descendants, particularly the captive Moroccan Royal Lions, does not change this expectation. Panthera leo leo) are either the few hundred Asiatic lions living wild in Gujarat or those captive in Asiatic lion studbook programmes or the few hundred wild animals dispersed at low densities across West and Central Africa. These highly threatened populations, particularly the micropopulations in West and Central Africa, form a group that is distinct from the bulk of global lion populations, found in Eastern and Southern Africa [The evolutionary importance of finding representatives who could form the basis of a recovered North African population is significant, since the only other representatives of the “northern” lion clade  between members of a species. SNV/SNP genotyping techniques may hold an important role in determining the lion genome to an extent where regional populations can be identified as has been demonstrated in other fields . ImportaAs Yamaguchi  points o ex situ conservation of the lion to support retention of its genetic diversity. For example, the mtDNA haplotype within cytochrome b of the Neuwied Moroccan Royal lion is not found in any wild lion population so far tested (only in one origin-unknown zoo lion). It is possible therefore that genetic characteristics lost from wild populations may continue to survive in captive animals. This increases the potential importance of restoring the zoo population of Moroccan lions and better understanding of their genetics relative to wild lion populations and the conservation management implications of that knowledge.In terms of the captive Moroccan lion population, to date, the findings from a small number of mtDNA isolates suggest that the genetic characteristics of Moroccan lions contrast with other lions of known origin , 50. Fur Panthera leo leo ESUs [ in situ and ex situ) is vitally important in the species' future conservation [In any event, lion populations are in dangerous decline across their range, with the IUCN suggesting a 43% population decline between 1993 and 2014  noticeabervation . On a prervation  and geneHowever, the potential restoration of an authentic North African “Barbary” bloodline as an ESU, through some form of backbreeding of Moroccan lions, might itself also be possible and is not entirely without precedent. The approach shows similarities to the process already used for Przewalski's horse . Recent If potential representatives of North Africa's “Barbary” lion still persist in zoo collections, then efforts to conserve its genotype are justified. Provisional research should focus initially on which origin-known haplotypes , 45 can  in situ conservation of lions in North Africa, it is also possible to consider [ Panthera leo.In terms ofconsider  whether


Sexually transmitted infections spread across contact networks. Partner elicitation and notification are commonly used public health tools to identify, notify, and offer testing to persons linked in these contact networks. For HIV-1, a rapidly evolving pathogen with low per-contact transmission rates, viral genetic sequences are an additional source of data that can be used to infer or refine transmission networks.The New York City Department of Health and Mental Hygiene interviews individuals newly diagnosed with HIV and elicits names of sexual and injection drug using partners. By law, the Department of Health also receives HIV sequences when these individuals enter healthcare and their physicians order resistance testing. Our study used both HIV sequence and partner naming data from 1342 HIV-infected persons in New York City between 2006 and 2012 to infer and compare sexual/drug-use named partner and genetic transmission networks. Using these networks, we determined a range of genetic distance thresholds suitable for identifying potential transmission partners. In 48% of cases, named partners were infected with genetically closely related viruses, compatible with but not necessarily representing or implying, direct transmission. Partner pairs linked through the genetic similarity of their HIV sequences were also linked by naming in 53% of cases. Persons who reported high-risk heterosexual contact were more likely to name at least one partner with a genetically similar virus than those reporting their risk as injection drug use or men who have sex with men.We analyzed an unprecedentedly large and detailed partner tracing and HIV sequence dataset and determined an empirically justified range of genetic distance thresholds for identifying potential transmission partners. We conclude that genetic linkage provides more reliable evidence for identifying potential transmission partners than partner naming, highlighting the importance and complementarity of both epidemiological and molecular genetic surveillance for characterizing regional HIV-1 epidemics. Understanding the path over which viruses such as HIV have been transmitted may be crucial for directing public health resources and guiding policy decisions. Contact tracing of named sexual and injection drug-use partners of people recently diagnosed with HIV is an indispensible tool for reconstructing this transmission network. Viral genetic sequence data—routinely collected by public health agencies—can also be used to infer the dynamics of HIV transmission. We analyzed partner naming and viral genetic sequence data in 1342 people living with HIV in New York City reported to the New York City Department of Health and Mental Hygiene between 2006 and 2012. Genetically linked partners were more likely to be named partners than named partners were to be genetically linked. This finding indicates that genetic sequence data are better than partner naming data for reconstructing this viral transmission network. Importantly, the success rate in naming a genetically linked partner varied by transmission risk category . This study validates the use viral genetic sequences in reconstructing these viral transmission networks in a public health surveillance setting. Analysis of pathogen genetics has shaped our understanding of the origin and spread of numerous infectious diseases, both viral and bacterial –5. Genetpol sequences  tend not diverge more than 0.01 substitutions/site from the baseline sequence in the first 10 years of infection  to curate problematic sequences, we excluded from the study 13 persons whose viral sequences contained ≥5% ambiguities.When calculating genetic distance between sequences, we resolved all IUPAC defined nucleotide ambiguities  to the corresponding nucleotide in the other sequences . Following the protocols established by the Los Alamos National Laboratory HIV Sequence Database [To investigate the distribution of genetic distances among named partners, a mixture distribution was defined as the weighted sum of a Gamma distribution (mean μ parameter and standard deviation σ), and a Gaussian  distribution (with mean μ and standard deviation σ), and the parameter p controlling the mixture weight . The MetThis study was a routine analysis of surveillance, laboratory, and partner services data reported to the Department of Health as mandated by New York State Public Health Law. All patient and partner matching was performed by authorized surveillance personnel. Cases and partners were assigned identification numbers that were unique to this analysis and could only be linked back to the original data by the same authorized personnel, in essence de-identifying the analytic dataset. Consent was not required because these data were collected and analyzed in the course of routine public health surveillance. The Institutional Review Board (IRB) of the University of California, San Diego Human Research Protections Program reviewed this study and certified it exempt from IRB review, stating that this research involved “the study of existing data…and the information was provided in such a way that the subjects cannot not be identified, directly or through identifiers linked to the subjects.”pol sequence reported to the DOHMH. This study population was comprised mostly of individuals reporting MSM risk factor (44%), then heterosexual risk (32%) and injection drug use (8%). MSM index cases named more partners on average (p = 0.05). The mean number of named partners who were genotyped did not vary significantly by race/ethnicity.Between 2006 and 2012, the FSU identified 756 index cases who named 586 unique HIV-positive partners who also had an HIV-1  average  and wereThe distribution of genetic distances between viral sequences from index cases to all of their named partners was bimodal , with thWe performed our initial analyses using a genetic distance threshold of 0.0175 substitutions/site, because this distance identifies the maximum number of clusters in the genetic network . Above 0Determination of genetic linkage was robust to the inclusion or exclusion of sites associated with drug resistance , a possiWe constructed two networks in which nodes represent index cases and partners: a named partner network and a genetic linkage network . To inveOf the 651 recorded partner namings , the genetic data provide corroborating evidence for transmission along 48% (310/651) of these edges; the genetic data effectively rule out transmission along the remaining 52% (341/651) of edges. Importantly, these genetic distances that rule out transmission were not just slightly above the threshold, but overwhelmingly represented random within subtype-diversity , there were fewer genetic links (466 edges), and a greater proportion of them were supported by partner naming: 65% (304/466) of links ; Table 2As the genetic distance threshold increases, connections in the genetic network become less informative. Additional edges, rather than forming new clusters, tend to fill in already existing clusters and bridge distinct clusters, creating accreted dense clusters in which nearly all members are directly linked to each other, an epidemiologically uninformative scenario. This unwanted scenario occurred above the epidemiologically plausible range of 0.01 to 0.02 substitutions/site, thus providing additional evidence that a threshold within this range is ideal for identifying potential transmission partners and clusters. Importantly, even when the genetic distance threshold was extremely permissive  and the number of genetic links increased dramatically . Index cases who reported injection drug use were also less likely to be genetically linked to named partners . Although Black/African American index cases were significantly less likely to be genetically linked to a named partner than Hispanics or Whites/Other (p < 0.001 and p = 0.014), the magnitudes of the differences by race/ethnicity were smaller than those among risk groups  were genetically linked to at least one named partner at 0.0175 substitutions/site. The frequency at which an index case was genetically linked to one or more named partners varied by risk group . Heterosk groups . Furtherp = 0.040). It is unclear whether this difference was due to increased infectiousness or transmission risk during acute/early infection [There was an increased rate of genetic linkage to a named partner when the index case had been diagnosed during the acute or early stages of infection  and the univariate models . ForeignAs illustrated earlier , the genAcross the range of epidemiologically plausible distance thresholds , the proportion of index cases who were genetically linked to at least one named partner remained fairly consistent 46–60%; . More imp = 0.023; p < 0.001; Index cases who named more partners were slightly less likely to be genetically linked to any of these partners . Nearly 4 out of 5 (149/189) reciprocally named partner pairs were also genetically linked . The ratWe analyzed an unprecedentedly expansive and detailed partner tracing and HIV sequence dataset collected from 1342 HIV-infected persons in New York City between 2006 and 2012 and determined an empirically justified and epidemiological plausible range of genetic distance thresholds for identifying potential transmission partners. Over this range of genetic distance thresholds, if an index case named an identifiable partner who tested positive for HIV, a genetic link indicating transmission was absent more than 50% of the time. Therefore, according to the genetic data, at least half of named partners are not plausible transmission partners; their HIV-1 sequences are no more similar to the HIV-1 sequence isolated from the corresponding index case sequences than to a randomly chosen sequence of the same HIV-1 subtype. Encouragingly, if both partners named each other, the odds of genetic linkage increased significantly across all transmission risk factors.We found that despite naming more partners per index case than heterosexuals, MSM were less likely to name any partners with genetically linked viruses. Black/African American index cases, compared with Hispanic and White/Other index cases, were less likely to name a partner with genetically linked viruses. When an index case was reciprocally named by their named partner, the odds of infection with a genetically linked virus increased for all risk and race/ethnicity groups. This observation highlights the importance of reciprocal naming in identifying potential transmission partners.Although one should never expect perfect concordance between social and genetic networks, their relative overlap provides insight into their respective usefulness in guiding public health interventions. Genetic transmission links were supported between 43% and 65% of the time by partner tracing information, depending on the genetic distance threshold. Importantly, lack of naming does not contradict the genetic inference. Rather, it can be attributable to an absence of evidence, not evidence of absence. If two genetically linked individuals are not named partners, it may be due to incomplete partner enumeration, thus highlighting the difficulty in eliciting the names of transmission partners. The proportion of genetic links supported by partner naming data can be viewed as the lower bound on the proportion of genetic links that represent recent transmission events in the network. Therefore, over a range of epidemiologically plausible genetic distance thresholds, genetic data are at least as good, and almost certainly better, than partner tracing data for inferring HIV recent transmission partners, despite known issues in genetic sequence analysis . Furthermore, genetic data can help filter out reported at-risk contacts that did not lead to transmission, providing independent evidence for absence of a transmission event between named partners.We recommend using a genetic distance threshold between 0.01 and 0.02 substitutions/site for identifying potential transmission partners in a surveillance setting. Admittedly, the approach to validating this cutoff will be biased towards detecting more recent transmission partners; however, this bias can be advantageous from a public health perspective, where the goal may be to identify recent partners in a growing transmission cluster. It is nontrivial to define a specific threshold systematically, because threshold tuning is always an exercise in balancing sensitivity and specificity. For example, when using genetic distance comparisons in typical HIV surveillance datasets that are not restricted to named partners, we suggest using a more conservative genetic threshold  to identify partners with an epidemiologically meaningful relationship. Nevertheless, our findings confirm previous work showing that genetic distance information can be used to identify potential transmission partners in both early , 26 and Because financial and personnel resources needed for exhaustive partner tracing are not feasible, genetic transmission networks inferred from sequences collected during routine drug resistance screening represent a relatively easy and inexpensive method for reconstructing the transmission history of HIV. Nevertheless, our study shows that there is great value in collecting partner-tracing data. For example, many named partners who, according to genetic data collected after diagnosis, are unlikely to be direct transmission partners, are HIV-positive but undiagnosed/unaware until they are offered HIV testing through the partner services program offered by the FSU. Therefore, partner tracing discovers HIV-infected persons who may not have been previously known to public health officials, allowing these persons to enter care and expanding the scope of potential intervention across the transmission network. Identifying these persons, regardless of whether they were previously known to surveillance, allows field workers to contact them, ensure linkage or return to care, elicit and notify partners, and expand the network of persons in the city that are receiving public health services. For example, 15.3% (81/448) of named partners in the genetic network linked only to someone other than the index case who named them; therefore, it is possible that simply being a named partner indicates an elevated risk status and possibly increased importance in the network  , 33.To maximize the probability of finding transmission partners, the geographic scope of surveillance and comparison should be as broad as possible. In other studies of HIV transmission in citywide epidemics in Uganda, United Kingdom, and the United States , 47–49, In addition, index cases who are not genetically linked to any of their named partners may be attractive candidates for additional interviews to identify potential transmission partners and expand network surveillance. On the other hand, because persons who named more partners were less likely to be genetically linked to any of their named partners, additional follow-up interviews may fail to meaningfully expand the scope of the network. To determine the value of repeated interviews, it will be important to record whether their “second round” of named partners were HIV-positive, linked to care, and yielded a viral genotype.Identifying transmission partners is of critical importance in combating HIV, because HIV transmission networks tend to be best described by preferential attachment  models , 23, in S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 FigShaded nodes are genetically linked to at least one named partner. Bold edges indicate partner naming that is supported by genetic distance. Edges with arrows indicate direction of partner naming. Edges without arrows are links supported only by genetic distance.(EPS)Click here for additional data file.S2 Fig(A) Genetic data mapped onto named partner network. Edges indicate partner naming. (B) Partner naming data mapped onto genetic network. Edges indicate genetic linkage.(EPS)Click here for additional data file.


Human papillomaviruses (HPV) activate a number of host factors to control their differentiation-dependent life cycles. The transcription factor signal transducer and activator of transcription (STAT)-3 is important for cell cycle progression and cell survival in response to cytokines and growth factors. STAT3 requires phosphorylation on Ser727, in addition to phosphorylation on Tyr705 to be transcriptionally active. In this study, we show that STAT3 is essential for the HPV life cycle in undifferentiated and differentiated keratinocytes. Primary human keratinocytes containing high-risk HPV18 genomes display enhanced STAT3 phosphorylation compared to normal keratinocytes. Expression of the E6 oncoprotein is sufficient to induce the dual phosphorylation of STAT3 at Ser727 and Tyr705 by a mechanism requiring Janus kinases and members of the MAPK family. E6-mediated activation of STAT3 induces the transcription of STAT3 responsive genes including cyclin D1 and Bcl-xL. Silencing of STAT3 protein expression by siRNA or inhibition of STAT3 activation by small molecule inhibitors, or by expression of dominant negative STAT3 phosphorylation site mutants, results in blockade of cell cycle progression. Loss of active STAT3 impairs HPV gene expression and prevents episome maintenance in undifferentiated keratinocytes and upon differentiation, lack of active STAT3 abolishes virus genome amplification and late gene expression. Organotypic raft cultures of HPV18 containing keratinocytes expressing a phosphorylation site STAT3 mutant display a profound reduction in suprabasal hyperplasia, which correlates with a loss of cyclin B1 expression and increased differentiation. Finally, increased STAT3 expression and phosphorylation is observed in HPV positive cervical disease biopsies compared to control samples, highlighting a role for STAT3 activation in cervical carcinogenesis. In summary, our data provides evidence of a critical role for STAT3 in the HPV18 life cycle. Human papillomaviruses (HPV) are the leading cause of viral induced cancers worldwide. HPV are the causative agents of cervical cancers and an increasing number of head and neck cancers. HPV infections are dependant on the manipulation of the host cell for their replication and this may result in diseases such as cancer. The STAT3 transcription factor, a known driver of cancer progression, is often over active in HPV-associated cancers; however, its role in the life cycle of HPV has not been studied. Using primary cell culture models we provide the first evidence demonstrating that HPV increases both the phosphorylation and activity of STAT3 and that this is required for viral gene expression and replication. Importantly, inhibition of STAT3 by small molecule inhibitors or expression of STAT3 mutants that cannot be phosphorylated impairs the HPV life cycle. Finally, we demonstrate that STAT3 phosphorylation is increased during cervical disease progression, highlighting the potential of STAT3 as a novel therapeutic target in HPV-associated cancers. High-risk HPVs are responsible for >99% of cervical cancer cases and a growing number of oropharyngeal carcinomas -CTP. The membrane was incubated with this radiolabelled linear probe at 42°C overnight. Following washing the membrane was exposed to auto-radiograph film  ATP for 30 minutes at 30°C. The reaction was terminated by the addition of 5x SDS sample buffer  SDS, 40% (v/v) glycerol, 5% (v/v) 2-mercaptoethanol). Incorporation of phosphate was determined following SDS-PAGE and autoradiography or immunoblotting with pS727 STAT3 antibody. For autoradiography, the gel was stained with Instant Blue (Expedeon) for 1h at room temperature, destained in water and exposed to film at -80°C. Protein bands were excised from the gel and γ32P measured by Cerenkov counting in a liquid scintillation counter. The pSTAT3 western blot and the Coomassie stained gel were imaged using a LiCor Odyssey and quantified using Image Studio software.Human recombinant STAT3 (Sigma SRP2062) was incubated with 10 mU active JNK1, ERK2 or p38α in 50 mM Tris-HCl pH 7.5, 0.1 mM EGTA, 10 mM magnesium acetate, 0.1 mM NaTotal protein was resolved by SDS-PAGE (10–15% Tris-Glycine), transferred onto Hybond nitrocellulose membrane (Amersham biosciences) and probed with antibodies specific for Phospho-STAT3 (S727) , Phospho-STAT3 (Y705) ), Total STAT3 , Phospho-STAT5 (Y694) , Phospho-JAK2 (Y1007/1008) , Total JAK2 , involucrin , HPV18 E6 , HPV18 E7 , HPV 16/18 E6 , HPV 16 E7 , Phospho-ERK1/2 (Thr202/Tyr204) , Phospho-JNK (Thr183/Tyr185) 4668, CST), Phospho-p38 (Thr180/Tyr182) , Bcl xL , Cyclin D1  p53 , p21 , FLAG , GFP  and GAPDH . Western blots were visualized with species-specific HRP conjugated secondary antibodies (Sigma) and ECL (Thermo/Pierce).Keratinocytes were incubated for 48 hours with STAT3 inhibitors or transfected with STAT3 siRNA, harvested and fixed in 70% ethanol overnight. The ethanol was removed and cells washed with PBS containing 0.5% BSA. Cells were stained with PBS containing 0.5% BSA, 50 μg/ml propidium iodide (Sigma) and 5 μg/ml RNase (Sigma) and incubated in this solution for 30 minutes at room temperature. Samples were processed on an LSRFortessa cell analyzer (BD) and the PI histograms analyzed on modifit software.Lentivirus plasmids were transfected into HEK 293TT cells with envelope and GAG/polymerase plasmids  using PEI transfection reagent. After 48 hours the medium was removed from the HEK 293TT cells and added to keratinocytes for 3 hours. After this time, the complete E medium was replaced and the cells incubated for 48 hours.Total RNA was extracted from NHK using the E.Z.N.A. Total RNA Kit I (Omega Bio- Tek) according to the manufacture’s protocol. One μg of total RNA was DNase treated following the RQ1 RNase-Free DNase protocol (Promega) and then reverse transcribed with a mixture of random primers and oligo(dT) primers using the qScript cDNA SuperMix (Quanta Biosciences) according to instructions. qRT- PCR was performed using the QuantiFast SYBR Green PCR kit (Qiagen). The PCR reaction was conducted on a Corbett Rotor-Gene 6000 (Qiagen) as follows: initial activation step for 10 min at 95°C and a three-step cycle of denaturation (10 sec at 95°C), annealing (15 sec at 60°C) and extension (20 sec at 72°C) which was repeated 40 times and concluded by melting curve analysis. The data obtained was analysed according to the ΔΔCt method using the Rotor-Gene 6000 software . SpecifiChIP experiments were carried out in primary foreskin keratinocytes containing episomal HPV18 genomes as previously described . ChIP efWhere indicated, data was analyzed using a two-tailed, unpaired Student’s t-test.S1 FigA) Representative western blots of HPV18-containing keratinocytes transfected with a pool of two HPV18 E6 specific siRNA and analysed with antibodies specific for phosphorylated and total STAT3, HPV18 E6 and E7 and p53. GAPDH expression was used as a loading control. B) Representative western blots of HPV18-containing keratinocytes transfected with a HPV18 E7 specific siRNA and analysed with antibodies specific for phosphorylated and total STAT3, HPV18 E6 and E7. GAPDH expression was used as a loading control. C) Representative western blots of HPV18-containing keratinocytes and HPV18 E5KO-containing keratinocytes subjected to high calcium differentiation and analysed for phosphorylated and total STAT3. GAPDH serves as a loading control. D) Representative sections of organotypic raft cultures from HPV18 wild type and HPV18 E5KO-containing keratinocytes stained with antibodies specific for pS727 STAT3 and counterstained with DAPI to highlight the nuclei (blue in the merged panels). Images were acquired using identical exposure times. Scale bar, 20 μm. White dotted lines indicate the basal cell layer.(TIFF)Click here for additional data file.S2 FigA) Representative western blot of HPV18-containing keratinocytes differentiated in high calcium media for 48 h and untreated or treated with 10 μM cryptotanshinone analysed with an antibody specific for phosphorylated STAT5. B) Representative western blot of HPV18-containing keratinocytes treated with 4 individual STAT3 specific siRNAs or a scramble control and analysed with an antibody specific for phosphorylated STAT5. C) Representative western blot of HPV18-containing keratinocytes transduced with a lentivirus encoding a STAT3 Y705F mutant or transiently transfected with a STAT3 S727A expression plasmid and analysed with an antibody specific for phosphorylated STAT5. GAPDH expression was used as a loading control in all western blots. All experiments were performed independently at least three times.(TIFF)Click here for additional data file.S3 Figin vitro kinase reactions with recombinant MSK1, JNK1, ERK2 and p38α as described in materials and methods. Proteins were analysed by SDS PAGE and protein bands excised from the gel and γ32P measured by Cerenkov counting in a liquid scintillation counter. Data are represented relative to a no kinase control.Recombinant STAT3 was incubated in (TIFF)Click here for additional data file.S4 FigA) HPV18-containing primary keratinocytes treated with increasing doses of cryptotanshinone and analyzed for cell viability by MTT assay. Bars represent the means ± standard deviation of at least three independent experiments.(TIFF)Click here for additional data file.S5 FigA) Representative images of H&E stained organotypic raft cultures of NHK and HPV18-containing keratinocytes transduced with empty lentivirus or lentivirus expressing Y705F STAT3 and imaged at 40x magnification. Organotypic raft cultures of NHKs were stained with antibodies specific for B) cyclin B1 and C) involucrin. Nuclei are visualised with DAPI (blue) and white dotted lines indicate the basal cell layer. D) Representative sections from HPV18-containing raft cultures transduced with empty lentivirus or lentivirus expressing Y705F STAT3 and stained with an antibody specific for E1^E4. DAPI stained nuclei (Blue) and dotted white lines indicate basal layer. Widefield image 40x magnification.(TIFF)Click here for additional data file.S1 TableThe table includes gene name and sequences of forward and reverse primers.(TIFF)Click here for additional data file.


Ursus americanus) are continuously distributed, genetically diverse, and exhibit an isolation-by-distance structuring pattern, except on the Bruce Peninsula (BP). To identify the processes that led to the genetic isolation of BP black bears, we modelled various levels of historical and contemporary migration and population size reductions using forward simulations. We compared simulation results with empirical genetic indices from Ontario black bear populations under different levels of geographic isolation, and conducted additional simulations to determine if translocations could help achieve genetic restoration. From a genetic standpoint, conservation concerns for BP black bears are warranted because our results show that: i) a recent demographic bottleneck associated with recently reduced migration best explains the low genetic diversity on the BP; and ii) under sustained isolation, BP black bears could lose between 70% and 80% of their rare alleles within 100 years. Although restoring migration corridors would be the most effective method to enhance long-term genetic diversity and prevent inbreeding, it is unrealistic to expect connectivity to be re-established. Current levels of genetic diversity could be maintained by successfully translocating 10 bears onto the peninsula every 5 years. Such regular translocations may be more practical than landscape restoration, because areas connecting the peninsula to nearby mainland black bear populations have been irreversibly modified by humans, and form strong barriers to movement.The processes leading to genetic isolation influence a population’s local extinction risk, and should thus be identified before conservation actions are implemented. Natural or human-induced circumstances can result in historical or contemporary barriers to gene flow and/or demographic bottlenecks. Distinguishing between these hypotheses can be achieved by comparing genetic diversity and differentiation in isolated vs. continuous neighboring populations. In Ontario, American black bears ( Geographically isolated populations have higher extirpation risks than their contiguous counterparts because they are more likely to experience population size reduction and genetic drift –4. GenetTwo elements are needed when establishing whether genetic isolation actually warrants conservation concerns. The first is to estimate baseline genetic diversity and differentiation indices from contiguous populations. The second is to identify drivers of genetic isolation, as some species can survive at extremely low levels of genetic diversity if reduced variability predates demographic bottlenecks –21.Population genetic simulations are powerful tools to identify the processes that lead to genetic isolation and decreased diversity –26. AlthDemographic bottlenecks, which may lead to higher local extirpation risks, can be detected via genetic signals such as a heterozygosity excess at polymorphic loci , 30, or E < 0.56 /8μ [We estimated long-term effective population size (Neion rate . Under t)2–1]/8μ .C) with a single temporal sample through: i) the linkage disequilibrium method with jacknifing implemented in LDNe 1.31 for a minimum allele frequency of 0.05 [C obtained with the first method is based on the expectation that small populations accumulate more disequilibrium over time [C is precise [C [C obtained with the second method relies on the estimation of the size of a Wright-Fisher population with no migration or selection based on summary statistics calculated from the empirical genotypic data. The program simulated 50,000 populations with the lower and upper limits of NeC set at 2 and 150, respectively. Priors of 10–150, 2–100, and 10–100 were also tested to assess the robustness of the results.We also estimated contemporary effective population size  of 0.05 . The estver time –64. Unde precise , and, as precise , 67. The precise , and theecise [C , 70. TheC over one black bear generation (assumed to be 10 years) through 2 temporal methods implemented in NeEstimator [C based on changes in allele frequencies across generations [For comparison purposes, we calculated Nestimator . Individerations . The secerations .O) with the expected heterozygosity at mutation-drift equilibrium (HEQ). If there was a recent reduction in effective population size, HO should be significantly larger than HEQ, because bottlenecks induce a faster reduction in allelic diversity than heterozygosity. To incorporate uncertainties in the mutation process [pg) (see [δg). We ran the analysis for 10,000 replications, and assessed bottleneck significance via the Wilcoxon signed rank test [We used the heterozygosity excess and the M-ratio tests to assess the presence of a bottleneck in BP black bears , 31. We  process , we used process ) that inpg) see , 73). We. WeO) wiank test .−4. We used the NBP and NeC values from the linkage disequilibrium and temporal methods to seed our M-ratio test. NBP represented a realistic upper bound of the effective population size (Ne), as it incorporates individuals of any age and sex category, whereas the Ne statistics only includes reproductive individuals. We used 10,000 replications, with δg = 3.1 and 3.5 and pg = 0.1 and 0.2, as suggested in Piry et al. [The M-ratio test supposes that bottlenecked populations experience a faster reduction in the number of alleles (k) than in the range of allele size  due to a loss of rare alleles from genetic drift. The M-ratio is then calculated as M = k/r. For a bottleneck to be identified, M should fall under a critical value, Mc , in 95% of the cases or above. The M-ratio is generally expected to be lower than 0.7 for bottlenecked populations, and higher than 0.8 for the others . To obtay et al.  and Peery et al. .SE-ON) and start (NBP) populations, respectively. The goal was to determine if the current BP population could maintain its genetic diversity over time under sustained isolation from SE-ON.We used the R package AlleleRetain  to deterFor all simulations, we considered rare alleles to be those present in 5% or less of the source population. Other simulation parameters were based on information on the demography of Ontario black bears , 77, andST in q = 0.9).The genetic differentiation levels observed between BP and SE-ON and between Cockburn Island and SE-ON were equivalent (see overlap of 95% CIs of Jost D and Fs) was also higher on Cockburn Island than on the BP .BP black bears had a lower allelic richness and a lower observed and expected heterozygosity than Cockburn Island individuals . Two mtDFor Scenarios 1  and 2a , all simulated situations showed higher levels of diversity and lower differentiation than our empirical data, except in the absence of migration, for which lower levels of diversity and higher differentiation were detected compared to what was observed on the BP .BP = 220; NSE-ON = 2,200; number of generations = 1,200 –Under Scenario 2b , simulations that included 0.1 to 0.2 migrants per generation provided lower levels of diversity than on the BP . SimulatResults from Scenarios 3 (recent bottleneck) and 4 (recently reduced migration) showed higher levels of diversity than our empirical BP data in all situations, rejecting these hypotheses as the sole factor causing low diversity in BP black bears .BP = 220, and NSE-ON = 2,200. The associated bottleneck included a 40% initial mortality drop, followed by 90% mortality, and a 5% growth rate (BottleSim hypothesis 3a –The supported scenario 5 simulations (combination of Scenarios 3 and 4) corresponded to a range of 0 to 0.5 migrants per generation over 40 generations, NResults for all simulations (supported and unsupported) are provided in −4, NeL for the BP population ranged from 1,527 under the IAM, to 2,461 under the SMM. NeC-LD was estimated at 15.4  and 10–150 .Based on a mutation rate of 2*10C similar to NeC-10-150 , whereas the estimates from the Bayesian coalescence approach , were similar to NeC-2-150 , BP black bears could lose 70 to 80% of their rare alleles over the next 100 years under sustained isolation ; however, considering the dispersal abilities of black bears , itIn addition to reduced gene flow, rapid declines in population size decrease genetic diversity, which may hinder population viability  seems the most plausible to explain the reduced diversity of the BP population.ST overlapped. These inherent differences in how each statistic responds to specific demographic processes likely explain why our supported scenarios only matched with two or three of the eight indices used to assess fit to the empirical data  were lower than expected considering the estimated black bear abundance on the BP . The valso high , and evelso high , 45. TheE = 0.27 [Based on the relationship between inbreeding coefficient and effective population size, we calculated that inbreeding could increase from 1 to 3% per generation in BP black bears. The deleterious effects of inbreeding on population persistence , 91. ForE = 0.27 ). In FloWhen only taking distance into account, the SE-ON and BP populations could be considered as having the ability to reconnect. Indeed, several records of black bears travelling over much longer distances than 100–150 km exist , 93, 94.Our results show that successfully translocating 5 to 10 bears every 5 years would help to retain allelic diversity. However, survival of translocated bears can range from 0.2 to 0.624 the year post-release –98. BaseOur genetic results further suggest that allowing for the possibility of a continuous intake of migrants spread over several years would enable the population to better retain diversity than if the population was to remain isolated and received translocated individuals. Indeed, under isolation, the diversity brought in by the new migrants could be lost after a short period of time . ConsideTo carry out a successful translocation program, it will be critical to select conditions that will maximize the survival probability and breeding opportunities of translocated bears. These conditions integrate choice of habitat, individuals to translocate, capture and release technique, public acceptance of a translocation program, and follow-up monitoring. As such, we recommend:For translocation logistics:Because causes of human–bear conflict are controversial , consultFavoring soft winter release over summer release, as survival of translocated individuals is significantly higher for soft winter releases (0.88 vs. 0.2 ).Conducting a larger translocation event followed by subsequent smaller translocations to boost and subsequently retain genetic diversity.Favoring the frequency of translocations over the number of individuals translocated.Avoiding selecting the lowest possible number of individuals to translocate in the event that these individuals would have difficulty surviving and reproducing.For habitat and landscape management:Ensuring that bears are released in good quality habitat, as food shortages have been suggested to be associated with long distance movements .Protecting bear habitat on the BP, particularly outside of the National Park .For the choice of individuals:Choosing individuals that are less likely to travel back to their capture site, such as subadults and encumbered females .Avoiding the release of food conditioned individuals , 103.For follow-up management:nd year post-release [Lowering harvest rates the first year post-release to allow enough time for individuals to establish themselves and reach a high survival rate, which can be as high as 0.9 the 2-release .Conducting other programs aiming at reducing sources of non-natural mortality.Our results provide additional information to the demographic data that suggested that BP black bears were of conservation concern . Here, wIn North America, anthropogenic activities inducing habitat loss and fragmentation are increasing, and moving northward , 17, 104Translocation efforts to boost genetic diversity have been successful in Greater prairie chickens  and FlorIf public acceptance is obtained, the proposed translocation program will require a well thought-out choice of capture technique, individuals to translocate, and release site, as well as a close monitoring of the translocated bears to evaluate success and avoid potential human-bear conflicts stemming from future translocations , 106.Overall, our study enabled us to estimate the migration rates and timeframes necessary for populations to go from high to intermediate levels of diversity. Our results also enabled us to make recommendations to enhance the genetic diversity of BP black bears. Our approach can be applied to other vagile species to determine at which point concerns regarding the persistence of isolated populations should be raised, and thus inform long-term and large-scale management plans for populations that are becoming increasingly small and fragmented.S1 Filewww.sharcnet.ca). Results for all simulations are provided in Simulations were performed in EASYPOP  and in BottleSim (Kuo and Janzen 2003), via the high-performance computer clusters offered on SHARCNET ((DOCX)Click here for additional data file.S1 TableThis file contains the multilocus genotypes for the 842 American black bears used in this study. The data are formatted with two columns per locus.(XLSX)Click here for additional data file.S2 TableThis file contains the results of all of the simulations conducted in this study, and provides the parameters used in EASYPOP . Results include genetic diversity, differentiation and inbreeding statistics, as well as a relatedness statistic. Supported simulations are indicated in bold, and alternative scenarios are color coded.(XLSX)Click here for additional data file.